From 854e7d2fa0065eb969b708111ed8b1beead3a2cd Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 12 Oct 2026 15:01:29 +0000 Subject: [PATCH 01/65] Chapter 1 introduction and 1.1: openers, mechanism, one currency, "In brief"; researcher's style recorded in handoff Applied after a before/after preview the researcher approved: - Paragraphs 2-5 open on their own subject instead of a back-reference; one sentence added to paragraph 2 so "the same split" in paragraph 3 has a referent. - Paragraph 4 now says how the movement happened (a platform's share can change; Tokopedia: about 60 percent of the rise in revenue reflected smaller discounts and incentives). - Paragraph 7 in US$ at the World Bank 2024 rate (Rp15,855 per US$). - "In brief": both figures overstated growth; H2 stated plainly; approved wording "not explained by first-time entry". - The 1.1 ambition paragraph is deliberately unchanged. Handoff: "Readability before safety", the before/after review loop, one currency and mechanism rules added to the thesis README and HANDOFF.md (sections 9-11); rounds 15-19 and two researcher concerns logged in EDIT_HISTORY.md. Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- HANDOFF.md | 5 +++++ papers/thesis_2026-10/EDIT_HISTORY.md | 9 ++++++++- papers/thesis_2026-10/README.md | 4 ++++ papers/thesis_2026-10/ch1_introduction.md | 12 ++++++------ 4 files changed, 23 insertions(+), 7 deletions(-) diff --git a/HANDOFF.md b/HANDOFF.md index 513f41e..8773c07 100644 --- a/HANDOFF.md +++ b/HANDOFF.md @@ -109,6 +109,9 @@ Frozen wording of the pitch: `research/option_space_2026-10-01/GOLDEN_PITCH.md` - Wants honest assessments and pushback when a request would weaken the thesis; does not want reassurance. - Judges the writing by ear: expect to be corrected on flow and tone, and record each round in `EDIT_HISTORY.md`. - Cheap checks on existing data are always worth running; new analysis is not wanted now. +- **Readability comes before safety.** On 12 Oct (evening) the researcher said he does not want flags on the ambition paragraph ("technically no evidence, it is an educated guess, I am not going for safer options"), and that a safer claim that makes a sentence convoluted is the very thing Prof. Kong and Prof. Thomas complained about. So: the ambition and framing stay bold and are not flagged; support goes in notes or later chapters, never into hedged prose; flag a sentence only when it makes a different claim, once. This sits beside section 8: statements of what the data show keep their evidence strength; the framing is bold by design. +- **He reviews before and after.** Show the change as before and after in chat, apply only what he approves, commit once per round, log it in `EDIT_HISTORY.md`. To assess a passage: print the full text with numbered paragraphs, say what each paragraph does and whether it hands over to the next, then list only reading problems, ranked. +- **Style settled in this round** (full list in the thesis README, Writing rules): paragraph openers name their subject; a movement gets its mechanism in the paragraph that explains it; one currency (US$, rounded, World Bank rate for the year); no bare rupiah in the narrative. ## 10. Open items @@ -116,6 +119,7 @@ Frozen wording of the pitch: `research/option_space_2026-10-01/GOLDEN_PITCH.md` - Section 1.1 should be "brief, yet impactful" as standard prose; two attempts were rejected (`EDIT_HISTORY.md`, Part D). - Citations are not yet in the chapter text. - The YZU template is not yet in the repository. +- Chapter 1: the introduction and 1.1 were reviewed and revised on 12 Oct (evening); the researcher has not yet reached 1.2 or 1.3, and 1.2 is still to be rechecked against the ambition paragraph. - Root-level files not part of the thesis are untracked and should be left alone (`side-academic-papers-*.zip`, `ssrn-7514022.pdf`, `Attraction-json_2026-10-09.zip`, a Chinese-named PDF). ## 11. Notes for a cloud or fresh session (added 12 Oct after a cloud session's first read) @@ -125,6 +129,7 @@ Frozen wording of the pitch: `research/option_space_2026-10-01/GOLDEN_PITCH.md` - **Numbers.** Take them only from `research/option_space_2026-10-01/FINDINGS.md`, the Part 1-7 files with their dated notes, and section 4 above. H1 now rests on 5 Indonesian and regional platforms against 27 abroad (p = 0.004); the proposal's 8-platform comparison is the earlier version. Anything from the 24 Sep proposal, the September manuscripts or `main`'s routing documents is out of date. - **Two notes exist only on `main`:** `docs/MEASUREMENT_PROXY_FRAMEWORK_2026-09-23.md` and `docs/PAPER_COMPARISON_BASELINE_2026-09-23.md`. Both predate the 27 Sep proposal: the first is the "adequate proxy" framing of the 24 Sep lineage (superseded by the stand-in conditions in Part 2), the second ranks the researcher's wider set of papers. Neither is needed for the thesis; leave them on `main`. - **Do not run or trust the old generators** (`scripts/build_thesis_manuscript.py`, proposal surgery scripts). The thesis is written by hand in `papers/thesis_2026-10/`. +- **Working with two sessions.** A cloud session works on its own branch (`claude/happy-pascal-uc2eya` this time), kept at the tip of the integrated branch. It sends changes back by pull request into the integrated branch when the researcher asks, never into `main`. Only one session edits the thesis files at a time; fetch before editing. - **First task:** section 6, step 1 (recheck section 1.2 of chapter 1), then chapters 3 and 4. Log each round of the researcher's feedback in `papers/thesis_2026-10/EDIT_HISTORY.md`. ## 12. Timeline diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 406f1ee..4bdc153 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -41,6 +41,8 @@ One place for every concern raised about this thesis and what was done about it: | 12 Oct | A findings section in chapter 1 "gives spoilers" | Removed; short declaration of the answer kept | Addressed | | 12 Oct | The first paragraph of 1.1 must be the grand ambition; the question is the step toward it | Done | Addressed | | 12 Oct | Section 1.1 should be "brief, yet impactful" | Two attempts rejected (see Part D) | **OPEN** | +| 12 Oct, evening | On the ambition paragraph of 1.1: "technically no evidence, it is an educated guess, I do not want the flag"; "if getting a safer claim means convoluting, fuck safety"; "what we really need is readability while still maintaining quality". This is the line Prof. Kong and Prof. Thomas complained about | Ambition claims are not flagged. Rule recorded as "Readability before safety" in the thesis README and in `HANDOFF.md` section 9 | Addressed | +| 12 Oct, evening | Wants to see before and after, so he can check immediately | Preview-then-apply loop recorded in the thesis README and `HANDOFF.md` section 9 | Addressed | Standing constraints set by the researcher: no contact with BPS by email; BPS microdata and LSEG data are never shared or committed, only aggregates; payments and credentials are handled by the researcher. @@ -82,7 +84,12 @@ Standing constraints set by the researcher: no contact with BPS by email; BPS mi | 12 | "That aim cannot be reached directly…" then "For that, one question…" | Weird flow; then "For that" has no referent | "Measuring what the current figures miss begins with one question:" | | 13 | Section 1.1 cut from 406 to 257 words | Not what was meant: shorter paragraphs, not a shorter section | Reverted | | 14 | Section 1.1 as short paragraphs and bullet lists | Too non-standard for a thesis | Reverted to the five-paragraph prose version (commit `de9ca7d`) | +| 15 | Introduction paragraphs 2 to 5 still opened on back-references ("The difference…", "The same divergence…", "These figures…", "The difference matters…") although the openers rule was applied earlier | Preview shown as before and after; "all improvements, implement directly" | Openers now name their subject: "In 2023, traders…", "Tokopedia… shows the same split…", "Revenue and transaction value differ…", "Money and public decisions rest on figures like these…". One sentence added at the end of paragraph 2 ("The two official figures moved in opposite directions.") so "the same split" has a referent | +| 16 | Paragraph 4 explained why the levels differ, but paragraphs 2 and 3 had raised a movement (revenue up, sales down) | Approved | Mechanism added: a platform keeps only part of each sale and that part can change; at Tokopedia about 60 percent of the rise in revenue reflected smaller discounts and incentives (60.6%, Part 3) | +| 17 | Paragraph 7 used rupiah where paragraph 5 used US$ (Prof. Kong: one currency) | Approved | US$81 billion and US$31 billion; levy US$60 million and US$300 million. World Bank 2024 rate Rp15,855 per US$ (exact: US$81.3bn, US$30.7bn, US$64m, US$310m) | +| 18 | "In brief" said both figures "stopped moving with the activity", which can be read as H2 failing | Approved | "Each overstated growth" (Part 7's own phrase); H2 stated plainly ("National growth came mainly from more sellers, as H2 states, but about half of the 2023 rise in the seller count is not explained by first-time entry") | +| 19 | Claim flags on the ambition paragraph (trillions, "outside those figures altogether", "most is missing") | "I really do not want to hear or care about" those | Dropped. Ambition paragraph left exactly as it was | **OPEN: section 1.1 should be brief yet impactful.** The researcher's concern, in their words: the research question and objectives "should be brief, yet impactful", "not much reading". Two fixes failed (a shorter section; bullet points). The five-paragraph prose version stands as "fine enough". To revisit: how to make each paragraph of 1.1 land quickly while staying standard prose. Candidates not yet tried: tighter sentences inside the same five paragraphs; moving the two-halves explanation to chapter 3; splitting the long ambition paragraph in two. -**Also open in chapter 1:** the objectives keep the proposal's formal wording (the stiffest sentences in the section); citations not yet inserted; section 1.2 written before the ambition paragraph existed and not yet rechecked against it. +**Also open in chapter 1:** the objectives keep the proposal's formal wording (the stiffest sentences in the section); citations not yet inserted; section 1.2 written before the ambition paragraph existed and not yet rechecked against it (the researcher is going through the introduction and 1.1 first and has not reached 1.2). diff --git a/papers/thesis_2026-10/README.md b/papers/thesis_2026-10/README.md index b831e69..7f6e1ff 100644 --- a/papers/thesis_2026-10/README.md +++ b/papers/thesis_2026-10/README.md @@ -20,6 +20,10 @@ The thesis written in full. Same title, research question, hypotheses and measur - **Chapter 1.** No "main findings" section (it spoils the tension); a three-sentence declaration of the answer is enough. The first paragraph of 1.1 is the grand ambition, stated as boldly as the subject allows; the research question follows as the step needed to reach it. Contributions are tied to the three groups who rely on the figures. - **Claims.** Each claim at its evidence strength. Never write that the extra 2023 sellers "were there all along" or "were only counted that year", that the gap is "too large to be chance", or that returning sellers are ruled out. Say "about half of the increase is not explained by first-time entry" and "the additional count was concentrated among older, long-established small businesses". The growth identity is D = (1 + gV)(m1 - m0)/m0. Do not mention "the proposal" in thesis prose. - **Terms.** Define each once, in plain words (transaction value, revenue, take rate, invisible wedge, ecosystem ratio, growth divergence). Avoid "indicator", "Overall,", "therefore", "rather than". Do not use the sentence "of every 100 rupiah, about 7 appear as revenue". +- **Readability before safety** (12 Oct, evening). The test for a sentence is how easy it is to read, then whether it is right. A safer wording that makes a sentence heavier is rejected: that is what Prof. Kong and Prof. Thomas complained about. The ambition and the framing are bold by design and are not flagged for lacking proof. Back them with a source in a note or a later chapter when that is cheap, never by hedging the prose. Statements of what the data show keep their evidence strength (the Claims rule above). Flag a sentence only when it makes a different claim; say so once and offer the closest supportable version. +- **A movement gets its mechanism.** When a paragraph shows a movement (revenue up while sales fell), the paragraph that explains it says how it happened (the platform kept more of each sale; at Tokopedia, smaller discounts and incentives), not only that the measures differ. +- **One currency.** Running text uses US$, rounded, converted at the World Bank official rate for the year (`research/option_space_2026-10-01/tables/src/worldbank_fx_2022_2024.json`; 2024: Rp15,855 per US$). No bare rupiah in the narrative. +- **Show before you change.** Preview the change as before and after in chat; apply only what the researcher approves; then one commit, and a row in `EDIT_HISTORY.md`. When asked to assess a passage, print the full text first with numbered paragraphs, then say what each paragraph does and whether it hands over to the next, then list only reading problems (flow, openers, number density, currency), ranked. - **The committee's points** and where each is answered: see Part A of `EDIT_HISTORY.md`. `EDIT_HISTORY.md` records each round of edits, the researcher's response and any open concern; read it before revising a chapter. diff --git a/papers/thesis_2026-10/ch1_introduction.md b/papers/thesis_2026-10/ch1_introduction.md index 1965b96..313f82b 100644 --- a/papers/thesis_2026-10/ch1_introduction.md +++ b/papers/thesis_2026-10/ch1_introduction.md @@ -2,17 +2,17 @@ Digital commerce has changed how goods and services are bought and sold in Indonesia. Sellers in small towns reach buyers across the country, businesses operate from mobile phones, and payments are made by QR code. The figures used to measure this activity, however, differ on both its size and its growth. -The difference was visible in 2023. Traders at Tanah Abang, a major wholesale market in Jakarta, reported that their customers had moved to sellers on TikTok Shop. The government then banned transactions on social media platforms, and TikTok Shop closed in Indonesia shortly afterwards. The national statistics office, BPS, later reported that online sales rose by about 41 percent that year. The central bank's measure of online shopping fell by about 5 percent over the same period. +In 2023, traders at Tanah Abang, a major wholesale market in Jakarta, reported that their customers had moved to sellers on TikTok Shop. The government then banned transactions on social media platforms, and TikTok Shop closed in Indonesia shortly afterwards. The national statistics office, BPS, later reported that online sales rose by about 41 percent that year. The central bank's measure of online shopping fell by about 5 percent over the same period. The two official figures moved in opposite directions. -The same divergence appears within a single company. Between 2022 and 2023, Tokopedia's revenue rose by 53 percent while the value of transactions on the platform fell by 9 percent. Both figures come from the company's own reports. Measured by revenue, the platform grew; measured by transaction value, it contracted. +Tokopedia, an Indonesian online marketplace, shows the same split inside a single company. Between 2022 and 2023, its revenue rose by 53 percent while the value of transactions on the platform fell by 9 percent. Both figures come from the company's own reports. Measured by revenue, the platform grew; measured by transaction value, it contracted. -These figures differ because they measure different things. A platform that handles 100 units of sales may keep only a small part as its own revenue, in the form of commissions and fees. The rest belongs to the sale itself: the seller's receipts, the driver's fare and other amounts that pass through. An analyst reading the platform's accounts sees the small part, while one measuring the commerce it carries sees the whole. This thesis calls the difference the invisible wedge. The term refers to what a revenue-only measure leaves out, not to activity that goes unrecorded. +Revenue and transaction value differ because a platform keeps only part of each sale, and that part can change. A platform that handles 100 units of sales may keep only a small part as its own revenue, in the form of commissions and fees. The rest belongs to the sale itself: the seller's receipts, the driver's fare and other amounts that pass through. When the platform's share rises, revenue can grow even while sales fall. At Tokopedia, about 60 percent of the rise in revenue reflected smaller discounts and incentives. An analyst reading the platform's accounts sees the small part, while one measuring the commerce it carries sees the whole. This thesis calls the difference the invisible wedge. The term refers to what a revenue-only measure leaves out, not to activity that goes unrecorded. -The difference matters because money and public decisions depend on these figures, and not only in Indonesia. Businesses in 43 economies sold almost US$27 trillion online in 2022, by the United Nations' estimate, and platforms carry a growing part of that trade. The companies that run them are among the most valuable in the world, and they are valued largely on the growth they report. If part of that growth comes from a platform keeping more of each sale, and not from more commerce, then part of what investors pay for is a pricing decision. Governments face the same uncertainty. They use these figures to judge how large their digital economies are, and a growing number now ask platforms to report what their sellers earn, or to collect tax from them. A rule built on one figure reaches only what that figure covers. Each of these readers is acting on a number without knowing how far it can be relied on. +Money and public decisions rest on figures like these, and not only in Indonesia. Businesses in 43 economies sold almost US$27 trillion online in 2022, by the United Nations' estimate, and platforms carry a growing part of that trade. The companies that run them are among the most valuable in the world, and they are valued largely on the growth they report. If part of that growth comes from a platform keeping more of each sale, and not from more commerce, then part of what investors pay for is a pricing decision. Governments face the same uncertainty. They use these figures to judge how large their digital economies are, and a growing number now ask platforms to report what their sellers earn, or to collect tax from them. A rule built on one figure reaches only what that figure covers. Each of these readers is acting on a number without knowing how far it can be relied on. Existing research gives investors and governments limited help in checking the figures they use. It explains why the figures differ: platforms set prices on both sides of a market, accounting rules decide how much of each sale counts as revenue, and national accounts separate a platform's own service from the commerce it carries. Statistical agencies have also begun to set business surveys beside platform transaction values. What this work does not show is when a figure stops moving with the activity it is used to represent, or by how much. Answering that requires two things that are rarely available together: a platform's revenue and transaction value reported over several years, and a national count of sellers that can be examined from the inside. -Indonesia is one of the few markets where both kinds of evidence are available. Several of its platforms report their transaction value and their revenue each year, and BPS asks online sellers when they began selling and how their sales changed. The stakes are also concrete there. For 2024, the state's two main estimates of online sales are about Rp1,290 trillion and about Rp490 trillion, so the size of the market is uncertain by a factor of more than two. The government is nevertheless writing rules for that market, on platform commissions, on discounts and on tax. Its new levy on marketplace sellers would raise about Rp1 trillion a year on one measure of the market and about Rp5 trillion on another, before exemptions. Indonesia is also a market where platform figures and survey figures are likely to part, because most of its online sellers work outside the marketplaces, through messaging and social media applications. The events of 2023 suggest that they did part. +Indonesia is one of the few markets where both kinds of evidence are available. Several of its platforms report their transaction value and their revenue each year, and BPS asks online sellers when they began selling and how their sales changed. The stakes are also concrete there. For 2024, the state's two main estimates of online sales are about US$81 billion and about US$31 billion, so the size of the market is uncertain by a factor of more than two. The government is nevertheless writing rules for that market, on platform commissions, on discounts and on tax. Its new levy on marketplace sellers would raise about US$60 million a year on one measure of the market and about US$300 million on another, before exemptions. Indonesia is also a market where platform figures and survey figures are likely to part, because most of its online sellers work outside the marketplaces, through messaging and social media applications. The events of 2023 suggest that they did part. ## 1.1 Research question and objectives @@ -24,7 +24,7 @@ The research question is addressed through three objectives. The first is to mea Two hypotheses test whether revenue and the national figures move with the activity behind them. H1 states that a platform's revenue grows in proportion to its transaction value. H2 states that national e-commerce growth comes mainly from more businesses selling online, and less from higher sales per business. Chapter 3 sets out both in full. A further analysis estimates how many sellers a rule based on platform records can reach. -In brief, the thesis finds that platform revenue and the official count of sellers both stopped moving with the activity behind them in the period studied. Platform revenue rose mainly because platforms kept a larger share of each sale, and about half of the 2023 increase in the official count of sellers was not first-time entry. Chapters 5 and 6 establish how large these effects were and what follows from them. +In brief, the thesis finds that platform revenue and the official count of sellers each overstated growth in the period studied. Platform revenue rose mainly because platforms kept a larger share of each sale, not because buyers bought more. National growth came mainly from more sellers, as H2 states, but about half of the 2023 rise in the seller count is not explained by first-time entry. Chapters 5 and 6 establish how large these effects were and what follows from them. ## 1.2 Contributions From bc74c570eae261ad35d157a17cbc826a322ad008 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 12 Oct 2026 15:26:10 +0000 Subject: [PATCH 02/65] Chapter 3 first draft: framework, measures, hypotheses and the entry test Built on the proposal's and the oral deck's order (deck slides 8-9). Terms in one table, the identity D = (1 + g(V)) x (m1 - m0) / m0 with the Tokopedia example, the two jobs of the wedge, H1, H2, the entry test as a test of what "more businesses" means, further analyses, and a tests table. Status row added to the thesis README and a Chapter 3 section to EDIT_HISTORY.md. Awaiting the researcher's reading. Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 12 ++++ papers/thesis_2026-10/README.md | 3 +- papers/thesis_2026-10/ch3_framework.md | 88 ++++++++++++++++++++++++++ 3 files changed, 102 insertions(+), 1 deletion(-) create mode 100644 papers/thesis_2026-10/ch3_framework.md diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 4bdc153..2e78581 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -93,3 +93,15 @@ Standing constraints set by the researcher: no contact with BPS by email; BPS mi **OPEN: section 1.1 should be brief yet impactful.** The researcher's concern, in their words: the research question and objectives "should be brief, yet impactful", "not much reading". Two fixes failed (a shorter section; bullet points). The five-paragraph prose version stands as "fine enough". To revisit: how to make each paragraph of 1.1 land quickly while staying standard prose. Candidates not yet tried: tighter sentences inside the same five paragraphs; moving the two-halves explanation to chapter 3; splitting the long ambition paragraph in two. **Also open in chapter 1:** the objectives keep the proposal's formal wording (the stiffest sentences in the section); citations not yet inserted; section 1.2 written before the ambition paragraph existed and not yet rechecked against it (the researcher is going through the introduction and 1.1 first and has not reached 1.2). + +### Chapter 3 (12 Oct 2026, evening) + +| Round | What was tried | Researcher's response | Outcome | +|---|---|---|---| +| 1 | First full draft, on the proposal's and deck's order (deck slides 8 and 9: question and hypotheses, then "revenue = share kept x transaction value", then why D and not E). Opens on the chain of stand-ins; terms in one table; the identity D = (1 + g(V)) x (m1 - m0) / m0 with the Tokopedia example (take rate +68%, transaction value -9%, revenue +53%, D = 62 points); the wedge's two jobs (it follows the commerce; its size next to revenue sets how far a take-rate change swings growth), with a 2% and 17% illustration; H1 and H2; the entry test as a test of what "more businesses" means; further analyses; tests table | Awaiting | Drafted. Checked against the writing rules before sending: no "therefore", "indicator" or "rather than"; no bare rupiah; no mention of the proposal; openers name their subject; prose sentences average 15 words, longest 29 (a figure placeholder). Numbers recomputed: 1.682 x 0.911 - 1 = 53.2%; D = 62.1; 27.4 / 127.4 = 21.5%; 1/0.02 = 50 and 1/0.17 = 5.9 | + +Choices made that the researcher may want to change: +- H1 and H2 are stated in the plain wording already used in section 1.1, which keeps the meaning of the originals ("grow in proportion"; "comes mainly from more businesses selling online, and less from higher sales per business"). +- The 2% and 17% platforms are round numbers near the medians that chapter 5 reports (1.6% and 16.9%); they are labelled as an illustration, so the medians keep their home in chapter 5. +- The Figure 3.1 placeholder stands for the three nested circles of the oral deck (slide 8); the figure is not yet drawn. +- No citations yet (they are inserted after the chapters are written). diff --git a/papers/thesis_2026-10/README.md b/papers/thesis_2026-10/README.md index 7f6e1ff..dda7d4b 100644 --- a/papers/thesis_2026-10/README.md +++ b/papers/thesis_2026-10/README.md @@ -32,4 +32,5 @@ The thesis written in full. Same title, research question, hypotheses and measur |---|---|---| | `ch1_introduction.md` | 1 Introduction | drafted 12 Oct on the proposal's and deck's flow (opening, 1.1 question and objectives, 1.2 contributions, 1.3 structure); open concern on 1.1 in `EDIT_HISTORY.md` | | `ch5_results.md` | 5 Results | early draft of 5.1-5.5 in the older report style; to be rewritten to the v3 structure (ten question sections, `research/option_space_2026-10-01/DRAFT_STORY_v2.md`) | -| ch2-ch4, ch6, ch7 | | not started | +| `ch3_framework.md` | 3 Framework and Hypotheses | first full draft 12 Oct (evening), awaiting the researcher's reading: the chain of stand-ins, terms box (Table 3.1), the measures and the identity, H1, H2, the entry test, further analyses, tests table (Table 3.2); Figure 3.1 still to be drawn | +| ch2, ch4, ch6, ch7 | | not started | diff --git a/papers/thesis_2026-10/ch3_framework.md b/papers/thesis_2026-10/ch3_framework.md new file mode 100644 index 0000000..004c51b --- /dev/null +++ b/papers/thesis_2026-10/ch3_framework.md @@ -0,0 +1,88 @@ +# Chapter 3. Framework and Hypotheses + +Three numbers sit in a chain. A platform's revenue is the smallest, the commerce the platform carries is larger, and the online commerce of the whole country is larger still. Each number is used as a stand-in for the next one out. Investors read revenue as a sign of the commerce a platform carries. Platform figures are then read as a sign of what is happening in the national total. + +[Figure 3.1 to be drawn: three nested circles, with revenue inside platform commerce inside all online commerce, H1 on the inner link and H2 on the outer link.] + +A stand-in works only while a condition holds. Revenue follows the commerce a platform carries while the share of each sale that the platform keeps stays steady. A count of online sellers follows the arrival of new sellers while the survey keeps reaching the same kinds of seller. This chapter defines the terms, builds the measures, and states the two hypotheses and the entry test that put each condition to the test. + +## 3.1 Terms and measures + +Table 3.1 lists the terms used throughout the thesis, each defined once, with the symbol used in the formulas. The first rows describe a single platform, and the last rows describe the national survey. + +**Table 3.1. Terms and symbols** + +| Term | Symbol | Meaning | +|---|---|---| +| Transaction value | V | Everything bought through a platform in a period. Companies call it GMV, GTV or TPV; the thesis treats these as one measure and notes where a company's scope differs. | +| Revenue | R | What the platform recognizes as its own income, after the discounts and incentives it pays to customers are deducted. | +| Take rate | m | Revenue divided by transaction value: the share of each sale that the platform keeps. | +| Customer incentives | | Discounts, vouchers and other promotions that a platform pays for. Accounting rules deduct them from revenue. | +| Invisible wedge | W | Transaction value minus revenue: what a revenue figure leaves out. It is defined for one platform; no national total is computed. | +| Ecosystem ratio | E | The wedge divided by revenue. It equals one divided by the take rate, minus one. This is the author's own measure, built for this thesis. | +| Growth | g | The percentage change from one year to the next. | +| Growth divergence | D | Growth of revenue minus growth of transaction value in the same year, in percentage points. | +| Online seller | | A business that the national statistics office, BPS, counts as selling online. | +| First-time entry | | A seller that began selling online in the year being measured. | + +Three measures describe a platform. The invisible wedge gives the size of what revenue leaves out, in currency. The ecosystem ratio gives the same size relative to revenue, so that large and small platforms can be compared. Growth divergence shows whether revenue still follows the commerce from one year to the next. The wedge and growth divergence are plain arithmetic on reported figures. The ecosystem ratio restates the take rate, as the formulas show. + +``` +W = V − R +E = W / R = 1/m − 1 +D = g(R) − g(V) +``` + +Revenue equals the take rate times transaction value, so growth divergence can be written with the take rate alone: + +``` +D = (1 + g(V)) × (m1 − m0) / m0 +``` + +Here m0 and m1 are the take rate in the first and the second year. When the take rate is steady, D is zero and revenue grows exactly as fast as the commerce. When the take rate rises, D is positive and revenue grows faster than the commerce. + +Tokopedia shows how this works. Between 2022 and 2023 its take rate rose by about 68 percent while its transaction value fell by about 9 percent. Its revenue grew by about 53 percent even though sales fell, and growth divergence was 62 points. + +The wedge and growth divergence do different jobs. The wedge follows the commerce. Transaction value is far larger than revenue, so the wedge is almost all of transaction value and moves with it. Growth divergence follows the take rate and shows whether revenue remains a fair guide. The wedge has a second use. Its size next to revenue decides how far a change in the take rate swings revenue growth. Take a platform that keeps 2 percent of each sale, with an ecosystem ratio of 49. If transaction value is flat, a one-point rise in its take rate lifts revenue growth by about 50 points. A platform that keeps 17 percent has an ecosystem ratio near 5, and the same one-point rise lifts its revenue growth by about 6 points. A large wedge means more than a large gap in level. It also means that ordinary changes in fees or discounts can swing reported growth. + +## 3.2 Hypotheses + +Two hypotheses put the two links of the chain to the test. H1 concerns the inner link, between revenue and the commerce a platform carries. H2 concerns the outer link, between that commerce and national online commerce. + +Revenue depends on the take rate, and the take rate depends on fees, on discounts and on how the platform's accounts treat each sale. A platform that cuts its discounts keeps more of each sale and reports more revenue without any more commerce. Revenue is a fair stand-in for the commerce only while the take rate is steady, which means that growth divergence stays close to zero. The first hypothesis states that case. + +> **H1.** Transaction value and revenue recognized by the same platform grow in proportion over time. + +H1 is tested by comparing growth divergence among Indonesian platforms with growth divergence among listed platforms abroad, where revenue and transaction value broadly move together. Chapter 4 describes both groups. + +National online sales can grow in two ways. Existing businesses can sell more, or more businesses can start selling online. BPS reports both the number of businesses selling online and their total online sales, so national growth can be divided between the two. Growth from new sellers can take place in channels that platform records do not cover, such as messaging and social media, which most sellers use. Platform figures can miss that growth even when every platform reports accurately. + +> **H2.** The growth of national e-commerce value comes mainly from more businesses selling online, and less from higher sales per business. + +The H2 split is arithmetic. It does not say which channels the new businesses use, or whether they are new. + +## 3.3 The entry test + +The split in H2 rests on BPS's count of online sellers. A count can rise for two reasons: more businesses start selling, or the survey reaches businesses it did not reach before. The published figures cannot tell these apart. The survey itself can, because it asks every seller in which year it began selling online. + +If a rise in the count comes from first-time entry, a large enough share of sellers must have started in the year of the rise. For 2023 the published count rose by 27.4 percent. Even if no seller had stopped, new sellers would then have to make up at least 21.5 percent of all sellers. The test was written down before the survey files were opened, and Chapter 4 gives the rule. It does not test H2 itself. It tests what "more businesses" means. + +If the share of new sellers falls short, part of the rise is something other than first-time entry. Two readings are open: the survey reached more of the sellers who were already there, or sellers came back after a pause. Chapter 5 weighs them. + +## 3.4 Further analyses + +Two further analyses support the hypotheses without forming hypotheses of their own. The first divides national growth by sales channel, to show how much of it took place inside the marketplace channel. The second compares marketplace sellers with other sellers on whether they keep financial statements, which bears on what platform records can add to tax administration. A third analysis, developed in Chapter 6, estimates how many sellers a rule based on platform records can reach. + +## 3.5 The tests together + +Table 3.2 pairs each test with the result that would count against it. + +**Table 3.2. Tests and what would count against them** + +| Test | What it asks | What would count against it | +|---|---|---| +| H1: growth divergence | Do revenue and transaction value grow together within a platform? | Growth divergence in Indonesia far larger than among listed platforms abroad | +| H2: split of national growth | Does national e-commerce value grow because of more businesses or because of higher sales per business? | Higher sales per business accounting for most of the growth | +| Entry test | Does a rise in the seller count come from first-time entry? | Fewer new sellers than the rise requires, so that the count reflects more than first-time entry | + +The first two rows test the hypotheses. The third row sits beside H2, because it tests what the count of businesses in H2 represents. Chapter 4 describes the data behind each test. From c295da45d769132868471b1f5c53b3b66233f915 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 12 Oct 2026 15:26:30 +0000 Subject: [PATCH 03/65] Handoff: Chapter 3 now has a first draft; next steps updated Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- HANDOFF.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/HANDOFF.md b/HANDOFF.md index 8773c07..b6d992b 100644 --- a/HANDOFF.md +++ b/HANDOFF.md @@ -35,7 +35,7 @@ On 12 October the advisor wrote: focus on the issues she and Prof. Thomas raised |---|---| | Research and evidence | **Complete.** Every number is rebuilt by `research/option_space_2026-10-01/rebuild_all.sh` (272 ledger checks pass). No further analysis is planned. | | Story | **Settled.** Golden pitch and five-part arc frozen (tags `golden-pitch-2026-10-08`, `narrative-arc-2026-10-09`). | -| Thesis text | **Started.** Chapter 1 drafted and revised over many rounds. Chapter 5 has an older draft to be rewritten. Chapters 2, 3, 4, 6, 7 not started. | +| Thesis text | **Started.** Chapter 1 drafted and revised over many rounds (introduction and 1.1 reviewed; 1.2 and 1.3 next). Chapter 3 has a first full draft (12 Oct, evening), awaiting the researcher's reading. Chapter 5 has an older draft to be rewritten. Chapters 2, 4, 6, 7 not started. | | Advisor | Progress deck and data package ready (`papers/IL_Advisor_Package_2026-10-12.zip`); the researcher uploads it. The advisor has **not yet seen** the change in what H2 means. | | Branch | All work is on `proposal/integrated-2026-09-16`, about 140 commits ahead of `main`. **`main` is stale** (it stops on 1 Oct, when PR #9 merged). | @@ -78,7 +78,7 @@ Frozen wording of the pitch: `research/option_space_2026-10-01/GOLDEN_PITCH.md` ## 6. Next steps, in order 1. Recheck section 1.2 (contributions) of chapter 1 against the ambition paragraph in 1.1. -2. Write chapter 3 (terms in one box, the growth formula, H1, H2, the entry test) and chapter 4 (data and tests). +2. Chapter 3 (terms in one box, the growth formula, H1, H2, the entry test) has a first draft in `papers/thesis_2026-10/ch3_framework.md`; revise it from the researcher's reading, then write chapter 4 (data and tests). 3. Rewrite chapter 5 as ten sections, each answering one question, using `DRAFT_STORY_v2.md` for content and the writing rules for form. 4. Write chapters 2, 6 and 7. Chapter 6 waits for the scheduled results below. 5. Insert citations; format to the YZU template (the researcher obtains it). From b9dd114f3d5441c49a67c1300d474c075d1e0d75 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 12 Oct 2026 15:41:54 +0000 Subject: [PATCH 04/65] Chapter 2 first draft (literature review); accuracy fix to the Chapter 3 terms table Written on the proposal's four sections as the spine, lightened, with the survey-count strand the entry test needs. Every citation traced to REFERENCES_WORKING.md and used within its use limit. Status rows, a Chapter 2 section in EDIT_HISTORY.md and the handoff status updated. In Chapter 3 the terms table no longer states that accounting rules always deduct incentives from revenue. Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- HANDOFF.md | 2 +- papers/thesis_2026-10/EDIT_HISTORY.md | 15 ++++++++++ papers/thesis_2026-10/README.md | 3 +- papers/thesis_2026-10/ch2_literature.md | 37 +++++++++++++++++++++++++ papers/thesis_2026-10/ch3_framework.md | 4 +-- 5 files changed, 57 insertions(+), 4 deletions(-) create mode 100644 papers/thesis_2026-10/ch2_literature.md diff --git a/HANDOFF.md b/HANDOFF.md index b6d992b..e7d9efa 100644 --- a/HANDOFF.md +++ b/HANDOFF.md @@ -35,7 +35,7 @@ On 12 October the advisor wrote: focus on the issues she and Prof. Thomas raised |---|---| | Research and evidence | **Complete.** Every number is rebuilt by `research/option_space_2026-10-01/rebuild_all.sh` (272 ledger checks pass). No further analysis is planned. | | Story | **Settled.** Golden pitch and five-part arc frozen (tags `golden-pitch-2026-10-08`, `narrative-arc-2026-10-09`). | -| Thesis text | **Started.** Chapter 1 drafted and revised over many rounds (introduction and 1.1 reviewed; 1.2 and 1.3 next). Chapter 3 has a first full draft (12 Oct, evening), awaiting the researcher's reading. Chapter 5 has an older draft to be rewritten. Chapters 2, 4, 6, 7 not started. | +| Thesis text | **Started.** Chapter 1 drafted and revised over many rounds (introduction and 1.1 reviewed; 1.2 and 1.3 next). Chapters 2 and 3 have first full drafts (12 Oct, evening), awaiting the researcher's reading. Chapter 5 has an older draft to be rewritten. Chapters 4, 6, 7 not started. | | Advisor | Progress deck and data package ready (`papers/IL_Advisor_Package_2026-10-12.zip`); the researcher uploads it. The advisor has **not yet seen** the change in what H2 means. | | Branch | All work is on `proposal/integrated-2026-09-16`, about 140 commits ahead of `main`. **`main` is stale** (it stops on 1 Oct, when PR #9 merged). | diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 2e78581..7d5a920 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -105,3 +105,18 @@ Choices made that the researcher may want to change: - The 2% and 17% platforms are round numbers near the medians that chapter 5 reports (1.6% and 16.9%); they are labelled as an illustration, so the medians keep their home in chapter 5. - The Figure 3.1 placeholder stands for the three nested circles of the oral deck (slide 8); the figure is not yet drawn. - No citations yet (they are inserted after the chapters are written). + +### Chapter 2 (12 Oct 2026, evening) + +| Round | What was tried | Researcher's response | Outcome | +|---|---|---|---| +| 1 | The researcher asked whether Chapter 2 was being left alone. It was not: it had been put last in the plan because of citation risk. Written now, on the proposal's four sections (2.1 to 2.4) as the spine, lightened. Added the survey-count strand that the entry test needs (survey breaks, recall, small-firm exit, margins). Ends on the three open questions and hands over to Chapter 3 | Awaiting | Drafted. Checked before sending: no "therefore", "indicator", "Overall" or "rather than"; no regulation numbers or dates; institutions named with context (OECD, UNCTAD); sentences average 16 words without citations; 40 citations, each traced to `REFERENCES_WORKING.md` and used within its use limit | + +Choices made that the researcher may want to change: +- Dropped from the proposal's version: Medina and Schneider (2019) (reserve in the working list; the thesis makes no shadow-economy claim) and Momentum Works (a data source, cited in the data chapter). +- Kept despite being marked "reserve" in the working list: Barrios et al. (2022) and Denes et al. (2025), both Journal of Financial Economics, because Prof. Kong asked for finance citations. Cut them if the gig-economy angle feels off-topic. +- Added since the proposal: UNCTAD (2023, 2024), van den Brakel et al. (2008, 2020), Neter and Waksberg (1964), McKenzie and Paffhausen (2019), Bowen, Davis and Rajgopal (2002), Trueman, Wong and Zhang (2000), Brockmeyer and Hernandez (2016). +- Use limits respected: Hagiu and Wright is cited for platform versus reseller models, not for the IFRS principal and agent test; Givoly et al. for a general principle; Hummels and Klenow as trade terms; the OECD handbook and Ahmad and Schreyer for the separation of a platform's service from the sale, with no claim that GDP counts only platform revenue. +- Still open before submission (from the working list): check every citation against what the work supports, format to the YZU style, build the bibliography. + +Accuracy fix in Chapter 3 made in the same round: the terms table no longer says accounting rules always deduct incentives from revenue (IFRS 15 has an exception for payments for distinct goods or services). It now says "normally", and that Tokopedia reports the deduction as a separate line. diff --git a/papers/thesis_2026-10/README.md b/papers/thesis_2026-10/README.md index dda7d4b..8135018 100644 --- a/papers/thesis_2026-10/README.md +++ b/papers/thesis_2026-10/README.md @@ -33,4 +33,5 @@ The thesis written in full. Same title, research question, hypotheses and measur | `ch1_introduction.md` | 1 Introduction | drafted 12 Oct on the proposal's and deck's flow (opening, 1.1 question and objectives, 1.2 contributions, 1.3 structure); open concern on 1.1 in `EDIT_HISTORY.md` | | `ch5_results.md` | 5 Results | early draft of 5.1-5.5 in the older report style; to be rewritten to the v3 structure (ten question sections, `research/option_space_2026-10-01/DRAFT_STORY_v2.md`) | | `ch3_framework.md` | 3 Framework and Hypotheses | first full draft 12 Oct (evening), awaiting the researcher's reading: the chain of stand-ins, terms box (Table 3.1), the measures and the identity, H1, H2, the entry test, further analyses, tests table (Table 3.2); Figure 3.1 still to be drawn | -| ch2, ch4, ch6, ch7 | | not started | +| `ch2_literature.md` | 2 Literature Review | first full draft 12 Oct (evening), awaiting the researcher's reading: on the proposal's four sections (2.1 pricing and revenue recognition, 2.2 measuring the online economy, 2.3 digital records and third-party information, 2.4 the gap), with the survey-count strand added (breaks, recall, exit, margins). Citations are author-year, every one traced to `REFERENCES_WORKING.md`; the bibliography itself is not yet built | +| ch4, ch6, ch7 | | not started | diff --git a/papers/thesis_2026-10/ch2_literature.md b/papers/thesis_2026-10/ch2_literature.md new file mode 100644 index 0000000..e06ee37 --- /dev/null +++ b/papers/thesis_2026-10/ch2_literature.md @@ -0,0 +1,37 @@ +# Chapter 2. Literature Review + +Platforms sit between buyers and sellers and the figures used to measure their activity. They carry transactions between buyers and sellers, record only part of the value as their own revenue, and keep records that other systems may also hold. Research explains each part of this chain from a different angle. This chapter follows the chain in four steps: how platforms earn revenue, how the online economy is measured, what platform records can show, and what remains unanswered. + +## 2.1 Platform pricing and revenue recognition + +Platform economics explains why the commerce a platform carries can be much larger than its revenue. Platforms connect buyers with sellers or service providers and earn commissions, fees, advertising or logistics income. They do not earn the full value of each transaction. Because a platform must attract both sides of a market, it sets prices on each side, and its revenue need not follow the commerce it coordinates (Rochet and Tirole 2003; Caillaud and Jullien 2003; Parker and Van Alstyne 2005; Armstrong 2006; Evans and Schmalensee 2016). The business model matters too. A platform that connects buyers and sellers records a fee, while a reseller records the whole sale (Hagiu and Wright 2015). + +Accounting rules decide how much of each sale becomes revenue. IFRS 15 is the international standard for revenue. Under it, a company that acts as a principal records the full payment from the customer, while one that acts as an agent records only what it earns from arranging the sale (International Accounting Standards Board 2014). Payments that a company makes to its own customers, such as discounts and vouchers, can reduce the revenue it reports. Studies of internet firms show that firms chose different ways of reporting revenue, including whether to report sales gross (Bowen, Davis and Rajgopal 2002). Customer incentives, service mix, acquisitions and changes in business scope can all move reported revenue without an equal change in commerce. + +The usefulness of a measure also depends on how it is computed. Comparable accounting information helps analysts use financial statements (De Franco, Kothari and Verdi 2011). Operating measures that companies report themselves, such as transaction value or active users, lose information when the way they are computed changes or goes unreported (Givoly, Li, Lourie and Nekrasov 2019). Investors look past revenue as well. In the early years of the internet, measures of activity carried information about a firm's value and future revenue beyond what its reported revenue showed (Trueman, Wong and Zhang 2000, 2001; Rajgopal, Venkatachalam and Kotha 2003). + +This work explains why the gap between transaction value and revenue exists and can move. It does not show how large the gap becomes in practice. + +## 2.2 Measuring the online economy + +Official frameworks already separate the commerce a platform carries from the service the platform provides. National accounting guidance treats the sale between buyer and seller as distinct from the platform's intermediation service. It does not count the full transaction value as the platform's output (Ahmad and Schreyer 2016; International Monetary Fund 2018; OECD 2023; United Nations et al. 2025). + +In practice, different producers publish different quantities. Industry reports size the regional digital economy by transaction value (Google, Temasek and Bain 2024). Statistics offices measure the online sales of surveyed businesses (BPS 2025). Listed platforms report the revenue they recognize, and central banks report the value of payments (Bank Indonesia 2025). The United Nations Conference on Trade and Development (UNCTAD) has shown that e-commerce values change with the definition used. It has also set business e-commerce sales beside the transaction values of online platforms (UNCTAD 2023, 2024). A difference between these measures is harmless while they move together. It matters when they part, because the choice of measure can then change the conclusion about scale and growth. Research on night-time lights treats such a choice as a question to test: a measure should be checked against the activity it stands for (Henderson, Storeygard and Weil 2012). + +A survey's count of sellers has a further weakness. The count depends on how the survey finds sellers, and a change in design can break a series in a way that looks like a change in the economy (van den Brakel, Smith and Compton 2008; van den Brakel, Zhang and Tam 2020). Respondents also misdate events, and they tend to place them closer to the interview than they were (Neter and Waksberg 1964). Exit among small firms is more common for young firms and young owners (McKenzie and Paffhausen 2019), which matters when a count mixes new and long-established businesses. + +National online sales can grow because existing sellers sell more or because more sellers enter. Trade economists call these the intensive and extensive margins and use them to explain the growth of exports (Hummels and Klenow 2005). + +## 2.3 Digital records and third-party information + +Transactions that do not enter a platform's revenue still leave detailed records in its system. Sellers and service providers may be small, self-employed or weakly represented in conventional financial records, yet the platform records what they sell. This differs from the shadow-economy setting, where reliable records may be sparse (La Porta and Shleifer 2014; Ulyssea 2018). Digital records can carry information that conventional records lack (Berg, Burg, Gombovic and Puri 2020), and platform-mediated work leaves traces in financial and administrative data (Barrios, Hochberg and Yi 2022; Denes, Lagaras and Tsoutsoura 2025). + +Information from a third party changes how people comply with tax. Audit experiments in Denmark, and studies of the value-added tax and of receipt lotteries, show that reports from third parties help compliance (Kleven et al. 2011; Pomeranz 2015; Naritomi 2019; Kleven, Kreiner and Saez 2016; Slemrod 2019). Tax withheld through a third party rests on the same information (Brockmeyer and Hernandez 2016). + +Rules for platforms apply this idea. The Organisation for Economic Co-operation and Development (OECD), a group of mostly high-income countries, published model rules under which platforms report what their sellers earn (OECD 2020). The European Union's DAC7 directive applies them (European Union 2021). Indonesia's marketplace tax rule also makes marketplaces collect tax from their sellers, as well as report on them (Ministry of Finance of the Republic of Indonesia 2025). What a rule can show depends on the records it uses. + +## 2.4 The research gap + +Existing research explains the pieces of this chain separately. Platform economics explains why transaction value can exceed revenue. Accounting explains why the gap can move. Official frameworks explain why the two should not be treated as the same quantity. Research on digital records explains why some activity outside revenue stays observable in platform records. Statistical agencies and UNCTAD have begun to set business surveys beside platform transaction values. + +Three questions remain open. The first is when a figure stops moving with the activity it is used to represent, and by how much. The second is whether a national count of sellers keeps pace with entry, given that survey counts can break when the way they are collected changes. The third is how far rules built on platform records reach. Answering them requires a platform's revenue and transaction value over several years, and a national survey that can be examined from the inside. Indonesia offers both. Chapter 3 turns the questions into measures and hypotheses. diff --git a/papers/thesis_2026-10/ch3_framework.md b/papers/thesis_2026-10/ch3_framework.md index 004c51b..efdfd81 100644 --- a/papers/thesis_2026-10/ch3_framework.md +++ b/papers/thesis_2026-10/ch3_framework.md @@ -15,9 +15,9 @@ Table 3.1 lists the terms used throughout the thesis, each defined once, with th | Term | Symbol | Meaning | |---|---|---| | Transaction value | V | Everything bought through a platform in a period. Companies call it GMV, GTV or TPV; the thesis treats these as one measure and notes where a company's scope differs. | -| Revenue | R | What the platform recognizes as its own income, after the discounts and incentives it pays to customers are deducted. | +| Revenue | R | What the platform recognizes as its own income in its accounts. | | Take rate | m | Revenue divided by transaction value: the share of each sale that the platform keeps. | -| Customer incentives | | Discounts, vouchers and other promotions that a platform pays for. Accounting rules deduct them from revenue. | +| Customer incentives | | Discounts, vouchers and other promotions that a platform pays for. Accounting rules normally deduct them from revenue, and Tokopedia reports the deduction as a separate line. | | Invisible wedge | W | Transaction value minus revenue: what a revenue figure leaves out. It is defined for one platform; no national total is computed. | | Ecosystem ratio | E | The wedge divided by revenue. It equals one divided by the take rate, minus one. This is the author's own measure, built for this thesis. | | Growth | g | The percentage change from one year to the next. | From fb5b22e04559ab3f49fb71103850b6cbba2f57b5 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 12 Oct 2026 17:24:35 +0000 Subject: [PATCH 05/65] Seam pass across Chapters 1 to 3: vocabulary, the H2 and entry-test spine, order, numbers Applied as clusters, each rebuilt as whole paragraphs and re-read in context, because the line-level edits collided (entry-test naming, the H2 wording against the Chapter 3 opening, bridges that depended on a paragraph swap, wedge against gap, section 1.1 length). - Vocabulary fixed in the thesis README (wedge, transaction value, figure and measure, count of online sellers, entry test, chain only in Chapter 3). - Chapter 2 open questions now include H2's; Chapter 3 opens from them and gives each link of the chain a condition; the entry test is named in 1.1 and 1.3. - Order: Chapter 1 paragraph 4, Chapter 2 section 2.2, Chapter 3 wedge paragraphs. - One set of numbers (US$ at the World Bank 2024 rate; Tokopedia 53, 9, 60, 68, 62; 127.4 step). Repeats removed. Objectives 2 and 3 given a stated home in Chapter 4. - "In brief" now answers the size half of the question. 1.2 and 1.3 rebuilt to match. - Records: EDIT_HISTORY section on the pass, HANDOFF status and Chapter 4 obligations. Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- HANDOFF.md | 8 +++-- papers/thesis_2026-10/EDIT_HISTORY.md | 25 ++++++++++++++++ papers/thesis_2026-10/README.md | 8 +++++ papers/thesis_2026-10/ch1_introduction.md | 28 +++++++++--------- papers/thesis_2026-10/ch2_literature.md | 26 ++++++++-------- papers/thesis_2026-10/ch3_framework.md | 36 ++++++++++++----------- 6 files changed, 84 insertions(+), 47 deletions(-) diff --git a/HANDOFF.md b/HANDOFF.md index e7d9efa..b6acce3 100644 --- a/HANDOFF.md +++ b/HANDOFF.md @@ -35,7 +35,7 @@ On 12 October the advisor wrote: focus on the issues she and Prof. Thomas raised |---|---| | Research and evidence | **Complete.** Every number is rebuilt by `research/option_space_2026-10-01/rebuild_all.sh` (272 ledger checks pass). No further analysis is planned. | | Story | **Settled.** Golden pitch and five-part arc frozen (tags `golden-pitch-2026-10-08`, `narrative-arc-2026-10-09`). | -| Thesis text | **Started.** Chapter 1 drafted and revised over many rounds (introduction and 1.1 reviewed; 1.2 and 1.3 next). Chapters 2 and 3 have first full drafts (12 Oct, evening), awaiting the researcher's reading. Chapter 5 has an older draft to be rewritten. Chapters 4, 6, 7 not started. | +| Thesis text | **Started.** Chapter 1 drafted and revised over many rounds (introduction and 1.1 reviewed; 1.2 and 1.3 next). Chapters 2 and 3 have first full drafts (12 Oct, evening), awaiting the researcher's reading; a seam pass then aligned Chapters 1 to 3 with each other (vocabulary, the H2 and entry-test spine, order, numbers; see `EDIT_HISTORY.md`, last section). Chapter 5 has an older draft to be rewritten. Chapters 4, 6, 7 not started. | | Advisor | Progress deck and data package ready (`papers/IL_Advisor_Package_2026-10-12.zip`); the researcher uploads it. The advisor has **not yet seen** the change in what H2 means. | | Branch | All work is on `proposal/integrated-2026-09-16`, about 140 commits ahead of `main`. **`main` is stale** (it stops on 1 Oct, when PR #9 merged). | @@ -78,7 +78,7 @@ Frozen wording of the pitch: `research/option_space_2026-10-01/GOLDEN_PITCH.md` ## 6. Next steps, in order 1. Recheck section 1.2 (contributions) of chapter 1 against the ambition paragraph in 1.1. -2. Chapter 3 (terms in one box, the growth formula, H1, H2, the entry test) has a first draft in `papers/thesis_2026-10/ch3_framework.md`; revise it from the researcher's reading, then write chapter 4 (data and tests). +2. Chapters 2 and 3 have first drafts (`ch2_literature.md`, `ch3_framework.md`) and Chapters 1 to 3 have had a seam pass; revise from the researcher's reading, then write chapter 4. **Chapter 4 owes the reader**: both groups for the H1 test (Indonesian platforms and the foreign benchmark); the entry-test rule; the data behind each test; how revenue movements are traced to fees and discounts (Objective 2); how the platform evidence is compared with national and payment figures (Objective 3). 3. Rewrite chapter 5 as ten sections, each answering one question, using `DRAFT_STORY_v2.md` for content and the writing rules for form. 4. Write chapters 2, 6 and 7. Chapter 6 waits for the scheduled results below. 5. Insert citations; format to the YZU template (the researcher obtains it). @@ -111,6 +111,7 @@ Frozen wording of the pitch: `research/option_space_2026-10-01/GOLDEN_PITCH.md` - Cheap checks on existing data are always worth running; new analysis is not wanted now. - **Readability comes before safety.** On 12 Oct (evening) the researcher said he does not want flags on the ambition paragraph ("technically no evidence, it is an educated guess, I am not going for safer options"), and that a safer claim that makes a sentence convoluted is the very thing Prof. Kong and Prof. Thomas complained about. So: the ambition and framing stay bold and are not flagged; support goes in notes or later chapters, never into hedged prose; flag a sentence only when it makes a different claim, once. This sits beside section 8: statements of what the data show keep their evidence strength; the framing is bold by design. - **He reviews before and after.** Show the change as before and after in chat, apply only what he approves, commit once per round, log it in `EDIT_HISTORY.md`. To assess a passage: print the full text with numbered paragraphs, say what each paragraph does and whether it hands over to the next, then list only reading problems, ranked. +- **Vocabulary is fixed** (thesis README, Vocabulary rule): wedge, not gap; transaction value, with "the commerce a platform carries" as its plain companion; figure for a published number, measure for a yardstick; count of online sellers; entry test. New chapters use these words; run a sweep for the old ones before sending a chapter. - **Style settled in this round** (full list in the thesis README, Writing rules): paragraph openers name their subject; a movement gets its mechanism in the paragraph that explains it; one currency (US$, rounded, World Bank rate for the year); no bare rupiah in the narrative. ## 10. Open items @@ -119,7 +120,8 @@ Frozen wording of the pitch: `research/option_space_2026-10-01/GOLDEN_PITCH.md` - Section 1.1 should be "brief, yet impactful" as standard prose; two attempts were rejected (`EDIT_HISTORY.md`, Part D). - Citations are not yet in the chapter text. - The YZU template is not yet in the repository. -- Chapter 1: the introduction and 1.1 were reviewed and revised on 12 Oct (evening); the researcher has not yet reached 1.2 or 1.3, and 1.2 is still to be rechecked against the ambition paragraph. +- Chapter 1: the introduction and 1.1 were reviewed and revised on 12 Oct (evening); 1.2 and 1.3 were then rebuilt in the seam pass and have not yet been read by the researcher. Section 1.1 is 28 words longer than before (+7 percent) against the open wish for it to be brief; cut candidates are in `EDIT_HISTORY.md`. +- Chapter 3 now gives each link of the chain its own condition (inner: take rate steady; outer: national growth comes from sellers the platforms record; the count: survey keeps reaching the same sellers). The advisor has not yet seen how H2 is framed, so confirm this wording with the researcher before it goes to her. - Root-level files not part of the thesis are untracked and should be left alone (`side-academic-papers-*.zip`, `ssrn-7514022.pdf`, `Attraction-json_2026-10-09.zip`, a Chinese-named PDF). ## 11. Notes for a cloud or fresh session (added 12 Oct after a cloud session's first read) diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 7d5a920..4e0c300 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -120,3 +120,28 @@ Choices made that the researcher may want to change: - Still open before submission (from the working list): check every citation against what the work supports, format to the YZU style, build the bibliography. Accuracy fix in Chapter 3 made in the same round: the terms table no longer says accounting rules always deduct incentives from revenue (IFRS 15 has an exception for payments for distinct goods or services). It now says "normally", and that Tokopedia reports the deduction as a separate line. + +### Seam pass across Chapters 1 to 3 (12 Oct 2026, evening) + +Why: the line-by-line read of Chapters 2 and 3 (sentence, paragraph, section, in order) and a check of how they line up against Chapter 1 found breaks that no single-line edit would fix, and showed that individual fixes would collide with each other. So the fixes were applied as clusters, each rewritten as whole paragraphs, and then re-read in context. Baseline: commit `b9dd114`. + +| Cluster | What was done | Where | +|---|---|---| +| Vocabulary | One word for one thing (see the Vocabulary rule in the thesis README): wedge, not gap; transaction value with "the commerce a platform carries" tied once; figure for a published number and measure for a yardstick; count of online sellers; entry test; chain only in Chapter 3; report for accounts and records for data | All three chapters; README | +| H2 and the entry test | The entry test is named in the first sentence of 1.1's design paragraph and in 1.3. Chapter 2's open questions now include H2's (do platform figures follow national growth). Chapter 3 opens from those questions and gives each link of the chain its condition: inner (take rate steady), outer (national growth comes from sellers the platforms record), and the count (survey keeps reaching the same sellers) | ch1 1.1 and 1.3; ch2 2.4; ch3 opening, 3.2, 3.3 | +| Order and structure | Chapter 1 paragraph 4 re-ordered (wedge named first, movement and Tokopedia last); Chapter 2 section 2.2 re-ordered (growth split before the survey weakness) with bridges into 2.2 and 2.3; Chapter 3 paragraph on the wedge split in two | ch1, ch2, ch3 | +| Numbers | One set across chapters: US$ at the World Bank 2024 rate; Tokopedia 53, 9, 60, 68 and 62 (0.91 x 0.68 = 0.62); growth defined as a fraction so the formula holds; the 127.4 step; 2 to 3 and 17 to 18 percent examples | ch1, ch3 | +| Repeats removed | UNCTAD "closest work" sentence four appearances down to two; the "cutting discounts" mechanism not repeated a third time in Chapter 3; Chapter 3's "two ways to grow" sentence no longer duplicates Chapter 2's | ch2, ch3 | +| Promises and homes | Objectives 2 and 3 now have a stated home (Chapter 4), named in 1.3 and 3.4; "payment indicators" became "payment figures"; "In brief" now answers the size half of the question | ch1, ch3 | +| Single lines | IFRS and GMV, GTV, TPV spelled out; a missing citation; "a study" for one study; the region named; the night-lights analogy explained; "from the inside" replaced by "whose underlying answers can be examined"; the wedge scope fixed ("at the marketplaces studied in Indonesia") | ch1, ch2, ch3 | + +Decisions taken on the researcher's "try it out", to be confirmed or reversed: +- The chain keeps the proposal's Figure 1 and each link has its own condition (the recommended option). The alternative, two stand-ins and no chain, would have changed how H2 reads. +- The size sentence in "In brief" is in. It adds length to 1.1 (see below). +- "Indicators" in Objective 3 became "figures" (it is a banned word, and Chapter 2 uses "figures"). + +Checks run after the rebuild: banned words none; no paragraph opens on a back-reference; the vocabulary sweep clean (the only "gap" left is the "research gap" heading); 41 citations in Chapter 2 each traced to `REFERENCES_WORKING.md`; every number recomputed; repeated phrases across chapters listed (only intended ones remain); sentence length (prose, citations excluded) averages 18.8, 15.2 and 15.3 words in Chapters 1, 2 and 3, with no sentence over 30 in Chapters 2 and 3. + +Size against the baseline: Chapter 1 +9 percent, Chapter 2 +8, Chapter 3 +12. **Section 1.1 grew by 28 words (+7 percent)**, against the researcher's open wish for it to be brief. Candidates to cut if wanted: the "A further analysis estimates…" sentence in the design paragraph (it is covered in 1.2 and 1.3), or the second sentence of "In brief". + +Chapter 4 now owes the reader (promised in 1.3, 3.2, 3.3 or 3.4): both groups for the H1 test (Indonesian platforms and the foreign benchmark); the entry-test rule; the data behind each test; how revenue movements are traced to fees and discounts (Objective 2); how the platform evidence is compared with national and payment figures (Objective 3). diff --git a/papers/thesis_2026-10/README.md b/papers/thesis_2026-10/README.md index 8135018..0a7dcfa 100644 --- a/papers/thesis_2026-10/README.md +++ b/papers/thesis_2026-10/README.md @@ -24,6 +24,14 @@ The thesis written in full. Same title, research question, hypotheses and measur - **A movement gets its mechanism.** When a paragraph shows a movement (revenue up while sales fell), the paragraph that explains it says how it happened (the platform kept more of each sale; at Tokopedia, smaller discounts and incentives), not only that the measures differ. - **One currency.** Running text uses US$, rounded, converted at the World Bank official rate for the year (`research/option_space_2026-10-01/tables/src/worldbank_fx_2022_2024.json`; 2024: Rp15,855 per US$). No bare rupiah in the narrative. - **Show before you change.** Preview the change as before and after in chat; apply only what the researcher approves; then one commit, and a row in `EDIT_HISTORY.md`. When asked to assess a passage, print the full text first with numbered paragraphs, then say what each paragraph does and whether it hands over to the next, then list only reading problems (flow, openers, number density, currency), ranked. +- **Vocabulary** (settled in the 12 Oct seam pass; one word for one thing, in every chapter): + - *transaction value* is the term for what is bought through a platform; "the commerce a platform carries" is its plain companion (tied once, in Table 3.1). "Sales" means a single sale, or BPS's national online sales, never a platform's transaction value. + - *invisible wedge* or *the wedge* is transaction value minus revenue. Never "gap" for that quantity ("research gap" is a different thing). + - *figure* is a published or reported number; *measure* is a yardstick, or one of the three the thesis builds (wedge, ecosystem ratio, growth divergence). + - *count of online sellers*, then *the count*; *entry test*; *first-time entry*; *H1*, *H2*. + - *chain* is used only in Chapter 3, for revenue, platform commerce and national commerce. + - *report* for what an accounting statement shows; *records* for a platform's data on sales. + - *take rate* for the share of each sale a platform keeps; *US$* for money in running text. - **The committee's points** and where each is answered: see Part A of `EDIT_HISTORY.md`. `EDIT_HISTORY.md` records each round of edits, the researcher's response and any open concern; read it before revising a chapter. diff --git a/papers/thesis_2026-10/ch1_introduction.md b/papers/thesis_2026-10/ch1_introduction.md index 313f82b..29e9cb4 100644 --- a/papers/thesis_2026-10/ch1_introduction.md +++ b/papers/thesis_2026-10/ch1_introduction.md @@ -6,38 +6,38 @@ In 2023, traders at Tanah Abang, a major wholesale market in Jakarta, reported t Tokopedia, an Indonesian online marketplace, shows the same split inside a single company. Between 2022 and 2023, its revenue rose by 53 percent while the value of transactions on the platform fell by 9 percent. Both figures come from the company's own reports. Measured by revenue, the platform grew; measured by transaction value, it contracted. -Revenue and transaction value differ because a platform keeps only part of each sale, and that part can change. A platform that handles 100 units of sales may keep only a small part as its own revenue, in the form of commissions and fees. The rest belongs to the sale itself: the seller's receipts, the driver's fare and other amounts that pass through. When the platform's share rises, revenue can grow even while sales fall. At Tokopedia, about 60 percent of the rise in revenue reflected smaller discounts and incentives. An analyst reading the platform's accounts sees the small part, while one measuring the commerce it carries sees the whole. This thesis calls the difference the invisible wedge. The term refers to what a revenue-only measure leaves out, not to activity that goes unrecorded. +Revenue and transaction value differ because a platform keeps only part of each sale, and that part can change. A platform that carries 100 units of transaction value may keep only a small part as its own revenue, in the form of commissions and fees. The rest belongs to the sale itself: the seller's receipts, the driver's fare and other amounts that pass through. An analyst reading the platform's accounts sees the small part, while one measuring the commerce it carries sees the whole. This thesis calls the difference the invisible wedge. The term refers to what a revenue-only measure leaves out, not to activity that goes unrecorded. The platform's share is not fixed. When it rises, revenue can grow even while transaction value falls. At Tokopedia, about 60 percent of the rise in revenue reflected smaller discounts and incentives. -Money and public decisions rest on figures like these, and not only in Indonesia. Businesses in 43 economies sold almost US$27 trillion online in 2022, by the United Nations' estimate, and platforms carry a growing part of that trade. The companies that run them are among the most valuable in the world, and they are valued largely on the growth they report. If part of that growth comes from a platform keeping more of each sale, and not from more commerce, then part of what investors pay for is a pricing decision. Governments face the same uncertainty. They use these figures to judge how large their digital economies are, and a growing number now ask platforms to report what their sellers earn, or to collect tax from them. A rule built on one figure reaches only what that figure covers. Each of these readers is acting on a number without knowing how far it can be relied on. +Money and public decisions rest on figures like these, and not only in Indonesia. Businesses in 43 economies sold almost US$27 trillion online in 2022, by the United Nations' estimate, and platforms carry a growing part of that trade. The companies that run them are among the most valuable in the world, and they are valued largely on the growth they report. If part of that growth comes from a platform keeping more of each sale, and not from more commerce, then part of what investors pay for is a pricing decision. Governments face the same uncertainty. They use these figures to judge how large their digital economies are, and a growing number now ask platforms to report what their sellers earn, or to collect tax from them. A rule built on platform records, for instance, reaches only the sellers who use platforms. Each of these readers is acting on a number without knowing how far it can be relied on. -Existing research gives investors and governments limited help in checking the figures they use. It explains why the figures differ: platforms set prices on both sides of a market, accounting rules decide how much of each sale counts as revenue, and national accounts separate a platform's own service from the commerce it carries. Statistical agencies have also begun to set business surveys beside platform transaction values. What this work does not show is when a figure stops moving with the activity it is used to represent, or by how much. Answering that requires two things that are rarely available together: a platform's revenue and transaction value reported over several years, and a national count of sellers that can be examined from the inside. +Existing research gives investors and governments limited help in checking the figures they use. It explains why the figures differ: platforms set prices on both sides of a market, accounting rules decide how much of each sale counts as revenue, and national accounts separate a platform's own service from the commerce it carries. Statistical agencies have also begun to set business surveys beside platform transaction values. What that research does not show is when a figure stops moving with the activity it is used to represent, or by how much. Answering that requires two things that are rarely available together: a platform's revenue and transaction value reported over several years, and a national count of online sellers whose underlying answers can be examined. -Indonesia is one of the few markets where both kinds of evidence are available. Several of its platforms report their transaction value and their revenue each year, and BPS asks online sellers when they began selling and how their sales changed. The stakes are also concrete there. For 2024, the state's two main estimates of online sales are about US$81 billion and about US$31 billion, so the size of the market is uncertain by a factor of more than two. The government is nevertheless writing rules for that market, on platform commissions, on discounts and on tax. Its new levy on marketplace sellers would raise about US$60 million a year on one measure of the market and about US$300 million on another, before exemptions. Indonesia is also a market where platform figures and survey figures are likely to part, because most of its online sellers work outside the marketplaces, through messaging and social media applications. The events of 2023 suggest that they did part. +Indonesia is one of the few markets where both kinds of evidence are available. Several of its platforms report their transaction value and their revenue each year, and BPS asks online sellers when they began selling and how their sales changed. The stakes are also concrete there. For 2024, BPS and the central bank give the two main official estimates of online sales, about US$81 billion and about US$31 billion. The size of the market is uncertain by a factor of more than two. The government is nevertheless writing rules for that market, on platform commissions, on discounts and on tax. Its new levy on marketplace sellers would raise about US$60 million a year on one measure of the market and about US$300 million on another, before exemptions. Indonesia is also a market where platform figures and survey figures are likely to part, because most of its online sellers work outside the marketplaces, through messaging and social media applications. The events of 2023 suggest that they did part. ## 1.1 Research question and objectives This thesis sets out to change how the digital economy is measured. Most online commerce is missing from the platform figures on which companies are valued and sellers are taxed, and millions of the people who sell online are outside those figures altogether. Decisions worth trillions of dollars rest on numbers that show a fraction of the activity they are taken to describe. The ambition of this thesis is to bring the rest into view: to show how much of the online economy the current figures miss, where it is, and who is in it. -Measuring what the current figures miss begins with one question: how much economic activity remains invisible when digital platforms are measured through their reported revenue, and when does that choice change the conclusion about growth? The question has two halves. The first concerns size, which is how much of the activity lies outside the figure. The second concerns growth, which is whether the figure still moves with the activity once prices or coverage change. A large gap would matter less if it stayed in proportion; the second half asks whether it did. +Measuring what the current figures miss begins with one question: how much economic activity remains invisible when digital platforms are measured through their reported revenue, and when does that choice change the conclusion about growth? The question has two halves. The first concerns size, which is how much of the activity lies outside the figure. The second concerns growth, which is whether the figure still moves with the activity once prices or coverage change. A large wedge would matter less if it stayed in proportion; the second half asks whether it did. -The research question is addressed through three objectives. The first is to measure the invisible wedge across the platform histories available from 2020 to 2025. The second is to reconcile selected large movements with disclosed changes in monetization, customer incentives, revenue recognition and reporting scope. The third is to compare the platform evidence with national e-commerce and payment indicators, to test whether they give the same account of economic scale and growth. +The research question is addressed through three objectives. The first is to measure the invisible wedge across the platform histories available from 2020 to 2025. The second is to reconcile selected large movements with disclosed changes in monetization, customer incentives, revenue recognition and reporting scope. The third is to compare the platform evidence with national e-commerce and payment figures, to test whether they give the same account of economic scale and growth. -Two hypotheses test whether revenue and the national figures move with the activity behind them. H1 states that a platform's revenue grows in proportion to its transaction value. H2 states that national e-commerce growth comes mainly from more businesses selling online, and less from higher sales per business. Chapter 3 sets out both in full. A further analysis estimates how many sellers a rule based on platform records can reach. +Two hypotheses and an entry test ask whether revenue and the national figures move with the activity behind them. H1 states that a platform's revenue grows in proportion to its transaction value. H2 states that national e-commerce growth comes mainly from more businesses selling online, and less from higher sales per business. The entry test asks whether a rise in the count of online sellers is first-time entry. Chapter 3 sets out all three in full. A further analysis estimates how many sellers a rule based on platform records can reach. -In brief, the thesis finds that platform revenue and the official count of sellers each overstated growth in the period studied. Platform revenue rose mainly because platforms kept a larger share of each sale, not because buyers bought more. National growth came mainly from more sellers, as H2 states, but about half of the 2023 rise in the seller count is not explained by first-time entry. Chapters 5 and 6 establish how large these effects were and what follows from them. +In brief, the thesis finds that revenue shows only a small part of the commerce platforms carry, and that platforms carry only about a fifth of online sales. Both revenue and the official count of online sellers also overstated growth in the period studied. Platform revenue rose mainly because platforms kept a larger share of each sale, not because buyers bought more. National growth came mainly from more sellers, as H2 states, but about half of the 2023 rise in the count is not explained by first-time entry. ## 1.2 Contributions -The thesis makes three contributions, one for each group that relies on these figures: investors, statistics offices, and the authorities that tax and regulate online selling. +The thesis makes three contributions, one for each group that relies on these figures: investors, statistics offices, and the authorities that tax and regulate online selling. Together they show how much the figures miss, where it sits, and who is in it. -For those who value platforms, it shows why the size of the invisible wedge matters for growth and not only for scale. The larger the wedge is relative to revenue, the more a given change in fees or discounts moves revenue growth. This gives investors a direct check on reported growth: whether it came from more commerce or from the platform keeping a larger share of each sale. +For those who value platforms, it shows how small a part of the commerce a platform's revenue records, and why that matters for growth and not only for scale. The larger the wedge is relative to revenue, the more a given change in fees or discounts moves revenue growth. It also traces why revenue moved, mostly to smaller discounts and higher fees. This gives investors a direct check on reported growth: whether it came from more commerce or from the platform keeping a larger share of each sale. -For those who measure the economy, it provides a test of what an increase in a national count of online sellers represents. Each seller in the survey reports the year they began selling online, so the survey itself shows how much of an increase is first-time entry. The test was specified before the survey files were opened. It bears directly on a market whose official size estimates differ by a factor of more than two. +For those who measure the economy, it shows where the commerce outside the platforms sits and tests what a rise in the count of online sellers represents. Most online sellers work through messaging and social media, and most are small businesses that keep no financial statements. Each seller in the survey reports the year they began selling online, so the survey itself shows how much of a rise is first-time entry. The test was specified before the survey files were opened. -For those who design tax and regulation, it measures how far rules based on platform records reach. Where most sellers work outside the platforms, such rules cover a minority of them, and the revenue they can raise depends on which measure of the market is correct. Because several countries have adopted similar rules, the result is relevant beyond Indonesia. +For those who design tax and regulation, it measures how far rules based on platform records reach. It also sets each recent rule, from the cap on ride-hailing commissions to the limit on discounts, against the figure it acts on. Where most sellers work outside the platforms, such rules cover a minority of them, and the revenue they can raise depends on which measure of the market is correct. Several other countries have adopted similar rules, so the same question applies there. -Earlier work has already compared platform transaction values with national e-commerce statistics. The contribution of this thesis is the connected evidence for one country, drawn from the platforms' accounts, the sellers' survey responses and the rules built on both. +Earlier work has already compared platform transaction values with national e-commerce statistics. The contribution of this thesis is the connected evidence for one country, drawn from the platforms' accounts, the sellers' own survey responses and the rules that act on those figures. ## 1.3 Structure of the thesis -Chapter 2 reviews the literature on platform revenue, the measurement of online selling and the use of platform records for tax. Chapter 3 defines the measures and develops the hypotheses. Chapter 4 describes the data and the tests. Chapter 5 presents the results, beginning with the platforms and moving to the national figures. Chapter 6 examines how far Indonesia's recent rules reach and what the findings imply for other countries. Chapter 7 concludes and states the limitations. +Chapter 2 reviews the literature on platform revenue, the measurement of online selling and the use of platform records for tax. Chapter 3 defines the measures, develops the hypotheses and sets out the entry test. Chapter 4 describes the data and the tests, including how revenue movements are traced and how the platform evidence is compared with national figures. Chapter 5 presents the results, beginning with the platforms and moving to the national figures. Chapter 6 examines how far Indonesia's recent rules reach and what the findings imply for other countries. Chapter 7 concludes and states the limitations. diff --git a/papers/thesis_2026-10/ch2_literature.md b/papers/thesis_2026-10/ch2_literature.md index e06ee37..f87ab16 100644 --- a/papers/thesis_2026-10/ch2_literature.md +++ b/papers/thesis_2026-10/ch2_literature.md @@ -1,37 +1,37 @@ # Chapter 2. Literature Review -Platforms sit between buyers and sellers and the figures used to measure their activity. They carry transactions between buyers and sellers, record only part of the value as their own revenue, and keep records that other systems may also hold. Research explains each part of this chain from a different angle. This chapter follows the chain in four steps: how platforms earn revenue, how the online economy is measured, what platform records can show, and what remains unanswered. +Platforms sit between the people who trade and the figures that measure their trade. They carry transactions between buyers and sellers and report only part of the value as their own revenue. They also keep records of every sale, which payment, statistical and administrative systems may hold too. Research explains each of these parts from a different angle. This chapter follows them in four steps: how platforms earn revenue, how the online economy is measured, what platform records can show, and what remains unanswered. ## 2.1 Platform pricing and revenue recognition -Platform economics explains why the commerce a platform carries can be much larger than its revenue. Platforms connect buyers with sellers or service providers and earn commissions, fees, advertising or logistics income. They do not earn the full value of each transaction. Because a platform must attract both sides of a market, it sets prices on each side, and its revenue need not follow the commerce it coordinates (Rochet and Tirole 2003; Caillaud and Jullien 2003; Parker and Van Alstyne 2005; Armstrong 2006; Evans and Schmalensee 2016). The business model matters too. A platform that connects buyers and sellers records a fee, while a reseller records the whole sale (Hagiu and Wright 2015). +Platform economics explains why the commerce a platform carries can be much larger than its revenue. Platforms connect buyers with sellers or service providers and earn commissions, fees, advertising or logistics income. They do not earn the full value of each transaction. Because a platform must attract both sides of a market, it sets prices on each side, and it may charge one side little or nothing. Its revenue then need not follow the commerce it coordinates (Rochet and Tirole 2003; Caillaud and Jullien 2003; Parker and Van Alstyne 2005; Armstrong 2006; Evans and Schmalensee 2016). The business model matters as well: a platform that connects buyers and sellers reports a fee as revenue, while a reseller reports the whole sale (Hagiu and Wright 2015). -Accounting rules decide how much of each sale becomes revenue. IFRS 15 is the international standard for revenue. Under it, a company that acts as a principal records the full payment from the customer, while one that acts as an agent records only what it earns from arranging the sale (International Accounting Standards Board 2014). Payments that a company makes to its own customers, such as discounts and vouchers, can reduce the revenue it reports. Studies of internet firms show that firms chose different ways of reporting revenue, including whether to report sales gross (Bowen, Davis and Rajgopal 2002). Customer incentives, service mix, acquisitions and changes in business scope can all move reported revenue without an equal change in commerce. +Accounting rules decide how much of each sale becomes revenue. International Financial Reporting Standard 15 (IFRS 15) is the international standard for revenue. Under it, a company that acts as a principal reports the full payment from the customer as revenue. One that acts as an agent reports only what it earns from arranging the sale (International Accounting Standards Board 2014). Payments that a company makes to its own customers, such as discounts and vouchers, can reduce the revenue it reports (International Accounting Standards Board 2014). A study of internet firms shows that firms chose different ways of reporting revenue, including whether to report sales gross (Bowen, Davis and Rajgopal 2002). Fees, customer incentives, revenue recognition, service mix, acquisitions and changes in business scope can all move reported revenue without an equal change in commerce. The usefulness of a measure also depends on how it is computed. Comparable accounting information helps analysts use financial statements (De Franco, Kothari and Verdi 2011). Operating measures that companies report themselves, such as transaction value or active users, lose information when the way they are computed changes or goes unreported (Givoly, Li, Lourie and Nekrasov 2019). Investors look past revenue as well. In the early years of the internet, measures of activity carried information about a firm's value and future revenue beyond what its reported revenue showed (Trueman, Wong and Zhang 2000, 2001; Rajgopal, Venkatachalam and Kotha 2003). -This work explains why the gap between transaction value and revenue exists and can move. It does not show how large the gap becomes in practice. +Together, these studies explain why the invisible wedge, the difference between transaction value and revenue, exists and can move. They do not show how large the wedge becomes in practice. ## 2.2 Measuring the online economy -Official frameworks already separate the commerce a platform carries from the service the platform provides. National accounting guidance treats the sale between buyer and seller as distinct from the platform's intermediation service. It does not count the full transaction value as the platform's output (Ahmad and Schreyer 2016; International Monetary Fund 2018; OECD 2023; United Nations et al. 2025). +How large the wedge becomes is a question of measurement. Official frameworks already separate the commerce a platform carries from the service the platform provides. National accounting guidance treats the sale between buyer and seller as distinct from the platform's intermediation service. It does not count the full transaction value as the platform's output (Ahmad and Schreyer 2016; International Monetary Fund 2018; OECD 2023; United Nations et al. 2025). -In practice, different producers publish different quantities. Industry reports size the regional digital economy by transaction value (Google, Temasek and Bain 2024). Statistics offices measure the online sales of surveyed businesses (BPS 2025). Listed platforms report the revenue they recognize, and central banks report the value of payments (Bank Indonesia 2025). The United Nations Conference on Trade and Development (UNCTAD) has shown that e-commerce values change with the definition used. It has also set business e-commerce sales beside the transaction values of online platforms (UNCTAD 2023, 2024). A difference between these measures is harmless while they move together. It matters when they part, because the choice of measure can then change the conclusion about scale and growth. Research on night-time lights treats such a choice as a question to test: a measure should be checked against the activity it stands for (Henderson, Storeygard and Weil 2012). +In practice, different producers publish different quantities. Industry reports size the digital economy of Southeast Asia by transaction value (Google, Temasek and Bain 2024). Statistics offices measure the online sales of surveyed businesses (BPS 2025). Listed platforms report the revenue they recognize, and central banks report the value of payments (Bank Indonesia 2025). The United Nations Conference on Trade and Development (UNCTAD) has shown that e-commerce values change with the definition used. It has also set business e-commerce sales beside the transaction values of online platforms (UNCTAD 2023, 2024). A difference between these figures is harmless while they move together. It matters when they part, because the choice of figure can then change the conclusion about scale and growth. Economists who check national income figures against satellite images of night-time lights follow the same logic: a figure should be tested against the activity it stands for (Henderson, Storeygard and Weil 2012). -A survey's count of sellers has a further weakness. The count depends on how the survey finds sellers, and a change in design can break a series in a way that looks like a change in the economy (van den Brakel, Smith and Compton 2008; van den Brakel, Zhang and Tam 2020). Respondents also misdate events, and they tend to place them closer to the interview than they were (Neter and Waksberg 1964). Exit among small firms is more common for young firms and young owners (McKenzie and Paffhausen 2019), which matters when a count mixes new and long-established businesses. +One national figure needs this test in particular: the growth of online sales. It can come from existing sellers selling more or from more sellers entering, and so it can be split between the two. Trade economists divide the growth of exports in the same way (Hummels and Klenow 2005). -National online sales can grow because existing sellers sell more or because more sellers enter. Trade economists call these the intensive and extensive margins and use them to explain the growth of exports (Hummels and Klenow 2005). +The split of national growth rests on a count of online sellers, and that count has a weakness of its own. It depends on how the survey finds sellers, and a change in design can break a series in a way that looks like a change in the economy (van den Brakel, Smith and Compton 2008; van den Brakel, Zhang and Tam 2020). Respondents also misdate events, and they tend to place them closer to the interview than they were (Neter and Waksberg 1964). Exit among small firms is more common for young firms and young owners (McKenzie and Paffhausen 2019), which matters when a count has to be corrected for sellers who stopped. ## 2.3 Digital records and third-party information -Transactions that do not enter a platform's revenue still leave detailed records in its system. Sellers and service providers may be small, self-employed or weakly represented in conventional financial records, yet the platform records what they sell. This differs from the shadow-economy setting, where reliable records may be sparse (La Porta and Shleifer 2014; Ulyssea 2018). Digital records can carry information that conventional records lack (Berg, Burg, Gombovic and Puri 2020), and platform-mediated work leaves traces in financial and administrative data (Barrios, Hochberg and Yi 2022; Denes, Lagaras and Tsoutsoura 2025). +Surveys are not the only source on sellers. Transactions that do not enter a platform's revenue still leave detailed records in its system. Sellers and service providers may be small, self-employed or weakly represented in conventional financial records, yet the platform records what they sell. This differs from the shadow-economy setting, where reliable records may be sparse (La Porta and Shleifer 2014; Ulyssea 2018). Digital records can carry information that conventional records lack (Berg, Burg, Gombovic and Puri 2020), and platform-mediated work leaves traces in financial and administrative data (Barrios, Hochberg and Yi 2022; Denes, Lagaras and Tsoutsoura 2025). -Information from a third party changes how people comply with tax. Audit experiments in Denmark, and studies of the value-added tax and of receipt lotteries, show that reports from third parties help compliance (Kleven et al. 2011; Pomeranz 2015; Naritomi 2019; Kleven, Kreiner and Saez 2016; Slemrod 2019). Tax withheld through a third party rests on the same information (Brockmeyer and Hernandez 2016). +A platform is a third party to its sellers' tax affairs, and information from a third party changes how people comply with tax. Audit experiments in Denmark, and studies of the value-added tax and of receipt lotteries, show that reports from third parties help compliance (Kleven et al. 2011; Pomeranz 2015; Naritomi 2019; Kleven, Kreiner and Saez 2016; Slemrod 2019). Tax withheld through a third party rests on the same information (Brockmeyer and Hernandez 2016). -Rules for platforms apply this idea. The Organisation for Economic Co-operation and Development (OECD), a group of mostly high-income countries, published model rules under which platforms report what their sellers earn (OECD 2020). The European Union's DAC7 directive applies them (European Union 2021). Indonesia's marketplace tax rule also makes marketplaces collect tax from their sellers, as well as report on them (Ministry of Finance of the Republic of Indonesia 2025). What a rule can show depends on the records it uses. +Rules for platforms apply this idea. The Organisation for Economic Co-operation and Development (OECD), a group of mostly high-income countries, published model rules under which platforms report what their sellers earn (OECD 2020). The European Union's DAC7 directive applies them (European Union 2021). Indonesia's marketplace tax rule goes further: it makes marketplaces collect tax from their sellers, not only report on them (Ministry of Finance of the Republic of Indonesia 2025). What a rule can reach depends on the records it uses. ## 2.4 The research gap -Existing research explains the pieces of this chain separately. Platform economics explains why transaction value can exceed revenue. Accounting explains why the gap can move. Official frameworks explain why the two should not be treated as the same quantity. Research on digital records explains why some activity outside revenue stays observable in platform records. Statistical agencies and UNCTAD have begun to set business surveys beside platform transaction values. +Existing research explains these pieces separately. Platform economics explains why transaction value can exceed revenue. Accounting explains why the wedge can move. Official frameworks explain why the two should not be treated as the same quantity. Research on digital records explains why some activity outside revenue stays observable in platform records. -Three questions remain open. The first is when a figure stops moving with the activity it is used to represent, and by how much. The second is whether a national count of sellers keeps pace with entry, given that survey counts can break when the way they are collected changes. The third is how far rules built on platform records reach. Answering them requires a platform's revenue and transaction value over several years, and a national survey that can be examined from the inside. Indonesia offers both. Chapter 3 turns the questions into measures and hypotheses. +Three questions remain open. The first is when a figure stops moving with the activity it is used to represent, and by how much. The second is whether platform figures follow national growth. That depends on where the growth comes from, and on whether a national count of online sellers keeps pace with entry. Survey counts can break when the way they are collected changes. The third is how far rules built on platform records reach. Answering them requires a platform's revenue and transaction value over several years, and a national survey whose underlying answers can be examined. Indonesia offers both. Chapter 3 turns the questions into measures and hypotheses. diff --git a/papers/thesis_2026-10/ch3_framework.md b/papers/thesis_2026-10/ch3_framework.md index efdfd81..bd07435 100644 --- a/papers/thesis_2026-10/ch3_framework.md +++ b/papers/thesis_2026-10/ch3_framework.md @@ -1,10 +1,10 @@ # Chapter 3. Framework and Hypotheses -Three numbers sit in a chain. A platform's revenue is the smallest, the commerce the platform carries is larger, and the online commerce of the whole country is larger still. Each number is used as a stand-in for the next one out. Investors read revenue as a sign of the commerce a platform carries. Platform figures are then read as a sign of what is happening in the national total. +Chapter 2 left three questions open. The first two rest on one idea: a figure is used as a stand-in for the activity behind it. Three figures sit in a chain. A platform's revenue is the smallest, the commerce the platform carries is larger, and the online commerce of the whole country is larger still. Investors read revenue as a sign of the commerce a platform carries. Platform figures are then read as a sign of what is happening in the national total. [Figure 3.1 to be drawn: three nested circles, with revenue inside platform commerce inside all online commerce, H1 on the inner link and H2 on the outer link.] -A stand-in works only while a condition holds. Revenue follows the commerce a platform carries while the share of each sale that the platform keeps stays steady. A count of online sellers follows the arrival of new sellers while the survey keeps reaching the same kinds of seller. This chapter defines the terms, builds the measures, and states the two hypotheses and the entry test that put each condition to the test. +A stand-in works only while a condition holds. For the inner link, the condition is that the share of each sale that the platform keeps stays steady, because then revenue follows the commerce. For the outer link, the condition is that national growth comes from sellers the platforms record. National growth is split into new and existing sellers using a count of online sellers. The count follows new selling only while the survey keeps reaching the same kinds of seller. This chapter defines the terms and measures, states a hypothesis for each link, and sets out the entry test of the count. ## 3.1 Terms and measures @@ -14,15 +14,15 @@ Table 3.1 lists the terms used throughout the thesis, each defined once, with th | Term | Symbol | Meaning | |---|---|---| -| Transaction value | V | Everything bought through a platform in a period. Companies call it GMV, GTV or TPV; the thesis treats these as one measure and notes where a company's scope differs. | +| Transaction value | V | Everything bought through a platform in a period, also called the commerce the platform carries. Companies call it gross merchandise value (GMV), gross transaction value (GTV) or total payment volume (TPV); the thesis treats these as one measure and notes where a company's scope differs. | | Revenue | R | What the platform recognizes as its own income in its accounts. | | Take rate | m | Revenue divided by transaction value: the share of each sale that the platform keeps. | | Customer incentives | | Discounts, vouchers and other promotions that a platform pays for. Accounting rules normally deduct them from revenue, and Tokopedia reports the deduction as a separate line. | | Invisible wedge | W | Transaction value minus revenue: what a revenue figure leaves out. It is defined for one platform; no national total is computed. | | Ecosystem ratio | E | The wedge divided by revenue. It equals one divided by the take rate, minus one. This is the author's own measure, built for this thesis. | -| Growth | g | The percentage change from one year to the next. | -| Growth divergence | D | Growth of revenue minus growth of transaction value in the same year, in percentage points. | -| Online seller | | A business that the national statistics office, BPS, counts as selling online. | +| Growth | g | The change from one year to the next as a share of the first year: 0.09 means 9 percent. | +| Growth divergence | D | Growth of revenue minus growth of transaction value in the same year: 0.62 means 62 percentage points. | +| Online seller | | A business that the national statistics office, BPS, counts as selling online; the text also calls it a seller or a business. The count of online sellers is BPS's estimate of how many there are in a year. | | First-time entry | | A seller that began selling online in the year being measured. | Three measures describe a platform. The invisible wedge gives the size of what revenue leaves out, in currency. The ecosystem ratio gives the same size relative to revenue, so that large and small platforms can be compared. Growth divergence shows whether revenue still follows the commerce from one year to the next. The wedge and growth divergence are plain arithmetic on reported figures. The ecosystem ratio restates the take rate, as the formulas show. @@ -33,29 +33,31 @@ E = W / R = 1/m − 1 D = g(R) − g(V) ``` -Revenue equals the take rate times transaction value, so growth divergence can be written with the take rate alone: +Let m0 and m1 be the take rate in the first and the second year. Revenue equals the take rate times transaction value, so revenue in the second year is (1 + g(V)) × m1 / m0 times revenue in the first year. Growth divergence can then be written with the take rate alone: ``` D = (1 + g(V)) × (m1 − m0) / m0 ``` -Here m0 and m1 are the take rate in the first and the second year. When the take rate is steady, D is zero and revenue grows exactly as fast as the commerce. When the take rate rises, D is positive and revenue grows faster than the commerce. +When the take rate is steady, D is zero and revenue grows exactly as fast as the commerce. When the take rate rises, D is positive and revenue grows faster than the commerce. -Tokopedia shows how this works. Between 2022 and 2023 its take rate rose by about 68 percent while its transaction value fell by about 9 percent. Its revenue grew by about 53 percent even though sales fell, and growth divergence was 62 points. +Tokopedia shows how this works. Between 2022 and 2023 its take rate rose by about 68 percent, so that it kept about two thirds more of each sale. Its transaction value fell by about 9 percent, yet its revenue grew by about 53 percent. Growth divergence was 62 points, which is 0.91 × 0.68 = 0.62. -The wedge and growth divergence do different jobs. The wedge follows the commerce. Transaction value is far larger than revenue, so the wedge is almost all of transaction value and moves with it. Growth divergence follows the take rate and shows whether revenue remains a fair guide. The wedge has a second use. Its size next to revenue decides how far a change in the take rate swings revenue growth. Take a platform that keeps 2 percent of each sale, with an ecosystem ratio of 49. If transaction value is flat, a one-point rise in its take rate lifts revenue growth by about 50 points. A platform that keeps 17 percent has an ecosystem ratio near 5, and the same one-point rise lifts its revenue growth by about 6 points. A large wedge means more than a large gap in level. It also means that ordinary changes in fees or discounts can swing reported growth. +The wedge and growth divergence do different jobs. The wedge follows the commerce. At the marketplaces studied in Indonesia, revenue is a few percent of transaction value, so the wedge is almost all of transaction value and moves with it. Growth divergence follows the take rate and shows whether revenue remains a fair guide. + +The wedge has a second use. Its size next to revenue decides how far a change in the take rate swings revenue growth. Take a platform that keeps 2 percent of each sale, with an ecosystem ratio of 49. If transaction value is flat, a rise in its take rate from 2 to 3 percent lifts revenue growth by about 50 points. A platform that keeps 17 percent has an ecosystem ratio near 5, and a rise from 17 to 18 percent lifts its revenue growth by about 6 points. A wedge that is large next to revenue means more than a big number. It also means that ordinary changes in fees or discounts can swing reported growth. ## 3.2 Hypotheses Two hypotheses put the two links of the chain to the test. H1 concerns the inner link, between revenue and the commerce a platform carries. H2 concerns the outer link, between that commerce and national online commerce. -Revenue depends on the take rate, and the take rate depends on fees, on discounts and on how the platform's accounts treat each sale. A platform that cuts its discounts keeps more of each sale and reports more revenue without any more commerce. Revenue is a fair stand-in for the commerce only while the take rate is steady, which means that growth divergence stays close to zero. The first hypothesis states that case. +Revenue depends on the take rate, and the take rate depends on fees, on discounts and on how the platform's accounts treat each sale. Revenue is a fair stand-in for the commerce only while the take rate is steady, which means that growth divergence stays close to zero. The first hypothesis states that case. > **H1.** Transaction value and revenue recognized by the same platform grow in proportion over time. -H1 is tested by comparing growth divergence among Indonesian platforms with growth divergence among listed platforms abroad, where revenue and transaction value broadly move together. Chapter 4 describes both groups. +Growth rates never match exactly, so H1 is tested by asking whether growth divergence among Indonesian platforms is unusually large. The yardstick is growth divergence among listed platforms abroad, where revenue and transaction value broadly move together. Chapter 4 describes both groups. -National online sales can grow in two ways. Existing businesses can sell more, or more businesses can start selling online. BPS reports both the number of businesses selling online and their total online sales, so national growth can be divided between the two. Growth from new sellers can take place in channels that platform records do not cover, such as messaging and social media, which most sellers use. Platform figures can miss that growth even when every platform reports accurately. +H2 splits national growth into the part that comes from existing sellers selling more and the part that comes from more sellers entering. BPS reports both the number of businesses selling online and their total online sales, so the split can be made from published figures. Growth from new sellers can take place in channels that platform records do not cover, such as messaging and social media, which most sellers use. Platform figures can miss that growth even when every platform reports accurately. > **H2.** The growth of national e-commerce value comes mainly from more businesses selling online, and less from higher sales per business. @@ -65,13 +67,13 @@ The H2 split is arithmetic. It does not say which channels the new businesses us The split in H2 rests on BPS's count of online sellers. A count can rise for two reasons: more businesses start selling, or the survey reaches businesses it did not reach before. The published figures cannot tell these apart. The survey itself can, because it asks every seller in which year it began selling online. -If a rise in the count comes from first-time entry, a large enough share of sellers must have started in the year of the rise. For 2023 the published count rose by 27.4 percent. Even if no seller had stopped, new sellers would then have to make up at least 21.5 percent of all sellers. The test was written down before the survey files were opened, and Chapter 4 gives the rule. It does not test H2 itself. It tests what "more businesses" means. +If a rise in the count comes from first-time entry, a large enough share of sellers must have started in the year of the rise. For 2023 the published count rose by 27.4 percent. Even if no seller had stopped, there would then be 127.4 sellers for every 100 a year earlier, and the 27.4 extra would have to be new. New sellers would have to make up at least 21.5 percent of all sellers. The test was written down before the survey files were opened, and Chapter 4 gives the rule. It does not test H2 itself. It tests what "more businesses" means. If the share of new sellers falls short, part of the rise is something other than first-time entry. Two readings are open: the survey reached more of the sellers who were already there, or sellers came back after a pause. Chapter 5 weighs them. ## 3.4 Further analyses -Two further analyses support the hypotheses without forming hypotheses of their own. The first divides national growth by sales channel, to show how much of it took place inside the marketplace channel. The second compares marketplace sellers with other sellers on whether they keep financial statements, which bears on what platform records can add to tax administration. A third analysis, developed in Chapter 6, estimates how many sellers a rule based on platform records can reach. +Two further analyses support the hypotheses without forming hypotheses of their own. The first divides national growth by sales channel, to show how much of it took place inside the marketplace channel. The second compares marketplace sellers with other sellers on whether they keep financial statements, which bears on what platform records can add to tax administration. A separate analysis, developed in Chapter 6, estimates how many sellers a rule based on platform records can reach. Chapter 4 also describes how revenue movements are traced to fees and discounts, and how the platform evidence is compared with national and payment figures. ## 3.5 The tests together @@ -83,6 +85,6 @@ Table 3.2 pairs each test with the result that would count against it. |---|---|---| | H1: growth divergence | Do revenue and transaction value grow together within a platform? | Growth divergence in Indonesia far larger than among listed platforms abroad | | H2: split of national growth | Does national e-commerce value grow because of more businesses or because of higher sales per business? | Higher sales per business accounting for most of the growth | -| Entry test | Does a rise in the seller count come from first-time entry? | Fewer new sellers than the rise requires, so that the count reflects more than first-time entry | +| Entry test | Does a rise in the count of online sellers come from first-time entry? | Fewer new sellers than the rise requires, so that the count reflects more than first-time entry | -The first two rows test the hypotheses. The third row sits beside H2, because it tests what the count of businesses in H2 represents. Chapter 4 describes the data behind each test. +The first two rows test the hypotheses. The third row sits beside H2, because it tests what the count of online sellers behind H2 represents. Chapter 4 describes the data behind each test. From 2f6b0593402e269712dd23fd16755b0cd952423f Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 12 Oct 2026 17:33:38 +0000 Subject: [PATCH 06/65] Second line-by-line read: eleven fixes across Chapters 1 to 3 Applied together and re-read with neighbouring paragraphs. Two of the problems had been introduced by the seam pass (the connective before the count of online sellers in the Chapter 3 opening, and the dropped stand-in line). Others: the entry-test wording in 1.1, the wedge paragraph split into level and movement, the driver's fare example, the reason smaller discounts raise the share, 1.2 pronouns, the join into 2.1's third paragraph, the 2.2 opening sentence, the 2.4 summary, and the labelled arithmetic in the Tokopedia example. Section 1.1 is now 25 words over its baseline (down from 28). Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- HANDOFF.md | 2 +- papers/thesis_2026-10/EDIT_HISTORY.md | 18 ++++++++++++++++++ papers/thesis_2026-10/ch1_introduction.md | 12 +++++++----- papers/thesis_2026-10/ch2_literature.md | 8 ++++---- papers/thesis_2026-10/ch3_framework.md | 6 +++--- 5 files changed, 33 insertions(+), 13 deletions(-) diff --git a/HANDOFF.md b/HANDOFF.md index b6acce3..164fa72 100644 --- a/HANDOFF.md +++ b/HANDOFF.md @@ -120,7 +120,7 @@ Frozen wording of the pitch: `research/option_space_2026-10-01/GOLDEN_PITCH.md` - Section 1.1 should be "brief, yet impactful" as standard prose; two attempts were rejected (`EDIT_HISTORY.md`, Part D). - Citations are not yet in the chapter text. - The YZU template is not yet in the repository. -- Chapter 1: the introduction and 1.1 were reviewed and revised on 12 Oct (evening); 1.2 and 1.3 were then rebuilt in the seam pass and have not yet been read by the researcher. Section 1.1 is 28 words longer than before (+7 percent) against the open wish for it to be brief; cut candidates are in `EDIT_HISTORY.md`. +- Chapter 1: the introduction and 1.1 were reviewed and revised on 12 Oct (evening); 1.2 and 1.3 were then rebuilt in the seam pass and have not yet been read by the researcher. Section 1.1 is 25 words longer than before (+6 percent) against the open wish for it to be brief; cut candidates are in `EDIT_HISTORY.md`. - Chapter 3 now gives each link of the chain its own condition (inner: take rate steady; outer: national growth comes from sellers the platforms record; the count: survey keeps reaching the same sellers). The advisor has not yet seen how H2 is framed, so confirm this wording with the researcher before it goes to her. - Root-level files not part of the thesis are untracked and should be left alone (`side-academic-papers-*.zip`, `ssrn-7514022.pdf`, `Attraction-json_2026-10-09.zip`, a Chinese-named PDF). diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 4e0c300..32e09b6 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -145,3 +145,21 @@ Checks run after the rebuild: banned words none; no paragraph opens on a back-re Size against the baseline: Chapter 1 +9 percent, Chapter 2 +8, Chapter 3 +12. **Section 1.1 grew by 28 words (+7 percent)**, against the researcher's open wish for it to be brief. Candidates to cut if wanted: the "A further analysis estimates…" sentence in the design paragraph (it is covered in 1.2 and 1.3), or the second sentence of "In brief". Chapter 4 now owes the reader (promised in 1.3, 3.2, 3.3 or 3.4): both groups for the H1 test (Indonesian platforms and the foreign benchmark); the entry-test rule; the data behind each test; how revenue movements are traced to fees and discounts (Objective 2); how the platform evidence is compared with national and payment figures (Objective 3). + +### Follow-up round after the second line-by-line read (12 Oct 2026, evening) + +The second sentence-by-sentence read (on the files after the seam pass) found 11 items, two of them introduced by the pass itself. All were fixed in one round and the touched paragraphs re-read with their neighbours. + +| Where | Problem found | Fix | +|---|---|---| +| ch3 opening, second paragraph | The sentence that brings in the count of online sellers started a new topic with no link to the outer-link condition before it (introduced by the seam pass) | "To check this, national growth is split into…" | +| ch3 opening, first paragraph | The seam pass dropped the line saying each figure stands in for the next (introduced by the seam pass) | "Each figure stands in for the next one out." restored | +| ch1 1.1 design paragraph | "is first-time entry" read awkwardly; the pointer to Chapter 3 sat before the further analysis | "comes from sellers entering for the first time"; the pointer dropped (1.3 already gives it), which also brings 1.1 back toward its old length | +| ch1 intro, paragraph on the wedge | "the driver's fare" arrived before ride-hailing was in view; the reason smaller discounts raise the share was missing; the paragraph was long | "the seller's receipts on a marketplace, the driver's fare on a ride-hailing app"; "which a platform pays for out of what it earns"; the paragraph split into the level (the wedge) and the movement (the share is not fixed) | +| ch1 1.2 | "it shows" followed a sentence whose subject was "they" | "the thesis shows / measures" | +| ch2 2.1, third paragraph | Weak join from revenue recognition to measures | "Revenue is not the only reported figure whose meaning depends on how it is computed." | +| ch2 2.2, first sentence | Promised "how large" but the section is about definitions and sources | "Measuring the wedge is a question of definitions and sources." | +| ch2 2.4 | The summary left out the survey-methods strand; "pieces" against "parts"; an orphan sentence on survey counts | Survey-methods sentence added to the summary and the orphan removed; "parts" | +| ch3 Tokopedia example | "0.91 x 0.68" did not show which factor was which | "(1 - 0.09) x 0.68 = 0.62" | + +After this round: Chapter 1 1,667 words, Chapter 2 1,280, Chapter 3 1,244 (baseline 1,520, 1,177, 1,095). **Section 1.1 is now 435 words, 25 more than the baseline (+6 percent)**, down from +28. No banned words; no sentence over 30 words in Chapters 2 and 3. diff --git a/papers/thesis_2026-10/ch1_introduction.md b/papers/thesis_2026-10/ch1_introduction.md index 29e9cb4..97e55f3 100644 --- a/papers/thesis_2026-10/ch1_introduction.md +++ b/papers/thesis_2026-10/ch1_introduction.md @@ -6,7 +6,9 @@ In 2023, traders at Tanah Abang, a major wholesale market in Jakarta, reported t Tokopedia, an Indonesian online marketplace, shows the same split inside a single company. Between 2022 and 2023, its revenue rose by 53 percent while the value of transactions on the platform fell by 9 percent. Both figures come from the company's own reports. Measured by revenue, the platform grew; measured by transaction value, it contracted. -Revenue and transaction value differ because a platform keeps only part of each sale, and that part can change. A platform that carries 100 units of transaction value may keep only a small part as its own revenue, in the form of commissions and fees. The rest belongs to the sale itself: the seller's receipts, the driver's fare and other amounts that pass through. An analyst reading the platform's accounts sees the small part, while one measuring the commerce it carries sees the whole. This thesis calls the difference the invisible wedge. The term refers to what a revenue-only measure leaves out, not to activity that goes unrecorded. The platform's share is not fixed. When it rises, revenue can grow even while transaction value falls. At Tokopedia, about 60 percent of the rise in revenue reflected smaller discounts and incentives. +Revenue and transaction value differ because a platform keeps only part of each sale, and that part can change. A platform that carries 100 units of transaction value may keep only a small part as its own revenue, in the form of commissions and fees. The rest belongs to the sale itself: the seller's receipts on a marketplace, the driver's fare on a ride-hailing app, and other amounts that pass through. An analyst reading the platform's accounts sees the small part, while one measuring the commerce it carries sees the whole. This thesis calls the difference the invisible wedge. The term refers to what a revenue-only measure leaves out, not to activity that goes unrecorded. + +The share of each sale that a platform keeps is not fixed. When it rises, revenue can grow even while transaction value falls. At Tokopedia, about 60 percent of the rise in revenue reflected smaller discounts and incentives, which a platform pays for out of what it earns. Money and public decisions rest on figures like these, and not only in Indonesia. Businesses in 43 economies sold almost US$27 trillion online in 2022, by the United Nations' estimate, and platforms carry a growing part of that trade. The companies that run them are among the most valuable in the world, and they are valued largely on the growth they report. If part of that growth comes from a platform keeping more of each sale, and not from more commerce, then part of what investors pay for is a pricing decision. Governments face the same uncertainty. They use these figures to judge how large their digital economies are, and a growing number now ask platforms to report what their sellers earn, or to collect tax from them. A rule built on platform records, for instance, reaches only the sellers who use platforms. Each of these readers is acting on a number without knowing how far it can be relied on. @@ -22,7 +24,7 @@ Measuring what the current figures miss begins with one question: how much econo The research question is addressed through three objectives. The first is to measure the invisible wedge across the platform histories available from 2020 to 2025. The second is to reconcile selected large movements with disclosed changes in monetization, customer incentives, revenue recognition and reporting scope. The third is to compare the platform evidence with national e-commerce and payment figures, to test whether they give the same account of economic scale and growth. -Two hypotheses and an entry test ask whether revenue and the national figures move with the activity behind them. H1 states that a platform's revenue grows in proportion to its transaction value. H2 states that national e-commerce growth comes mainly from more businesses selling online, and less from higher sales per business. The entry test asks whether a rise in the count of online sellers is first-time entry. Chapter 3 sets out all three in full. A further analysis estimates how many sellers a rule based on platform records can reach. +Two hypotheses and an entry test ask whether revenue and the national figures move with the activity behind them. H1 states that a platform's revenue grows in proportion to its transaction value. H2 states that national e-commerce growth comes mainly from more businesses selling online, and less from higher sales per business. The entry test asks whether a rise in the count of online sellers comes from sellers entering for the first time. A further analysis estimates how many sellers a rule based on platform records can reach. In brief, the thesis finds that revenue shows only a small part of the commerce platforms carry, and that platforms carry only about a fifth of online sales. Both revenue and the official count of online sellers also overstated growth in the period studied. Platform revenue rose mainly because platforms kept a larger share of each sale, not because buyers bought more. National growth came mainly from more sellers, as H2 states, but about half of the 2023 rise in the count is not explained by first-time entry. @@ -30,11 +32,11 @@ In brief, the thesis finds that revenue shows only a small part of the commerce The thesis makes three contributions, one for each group that relies on these figures: investors, statistics offices, and the authorities that tax and regulate online selling. Together they show how much the figures miss, where it sits, and who is in it. -For those who value platforms, it shows how small a part of the commerce a platform's revenue records, and why that matters for growth and not only for scale. The larger the wedge is relative to revenue, the more a given change in fees or discounts moves revenue growth. It also traces why revenue moved, mostly to smaller discounts and higher fees. This gives investors a direct check on reported growth: whether it came from more commerce or from the platform keeping a larger share of each sale. +For those who value platforms, the thesis shows how small a part of the commerce a platform's revenue records, and why that matters for growth and not only for scale. The larger the wedge is relative to revenue, the more a given change in fees or discounts moves revenue growth. It also traces why revenue moved, mostly to smaller discounts and higher fees. This gives investors a direct check on reported growth: whether it came from more commerce or from the platform keeping a larger share of each sale. -For those who measure the economy, it shows where the commerce outside the platforms sits and tests what a rise in the count of online sellers represents. Most online sellers work through messaging and social media, and most are small businesses that keep no financial statements. Each seller in the survey reports the year they began selling online, so the survey itself shows how much of a rise is first-time entry. The test was specified before the survey files were opened. +For those who measure the economy, the thesis shows where the commerce outside the platforms sits and tests what a rise in the count of online sellers represents. Most online sellers work through messaging and social media, and most are small businesses that keep no financial statements. Each seller in the survey reports the year they began selling online, so the survey itself shows how much of a rise is first-time entry. The test was specified before the survey files were opened. -For those who design tax and regulation, it measures how far rules based on platform records reach. It also sets each recent rule, from the cap on ride-hailing commissions to the limit on discounts, against the figure it acts on. Where most sellers work outside the platforms, such rules cover a minority of them, and the revenue they can raise depends on which measure of the market is correct. Several other countries have adopted similar rules, so the same question applies there. +For those who design tax and regulation, the thesis measures how far rules based on platform records reach. It also sets each recent rule, from the cap on ride-hailing commissions to the limit on discounts, against the figure it acts on. Where most sellers work outside the platforms, such rules cover a minority of them, and the revenue they can raise depends on which measure of the market is correct. Several other countries have adopted similar rules, so the same question applies there. Earlier work has already compared platform transaction values with national e-commerce statistics. The contribution of this thesis is the connected evidence for one country, drawn from the platforms' accounts, the sellers' own survey responses and the rules that act on those figures. diff --git a/papers/thesis_2026-10/ch2_literature.md b/papers/thesis_2026-10/ch2_literature.md index f87ab16..b042c95 100644 --- a/papers/thesis_2026-10/ch2_literature.md +++ b/papers/thesis_2026-10/ch2_literature.md @@ -8,13 +8,13 @@ Platform economics explains why the commerce a platform carries can be much larg Accounting rules decide how much of each sale becomes revenue. International Financial Reporting Standard 15 (IFRS 15) is the international standard for revenue. Under it, a company that acts as a principal reports the full payment from the customer as revenue. One that acts as an agent reports only what it earns from arranging the sale (International Accounting Standards Board 2014). Payments that a company makes to its own customers, such as discounts and vouchers, can reduce the revenue it reports (International Accounting Standards Board 2014). A study of internet firms shows that firms chose different ways of reporting revenue, including whether to report sales gross (Bowen, Davis and Rajgopal 2002). Fees, customer incentives, revenue recognition, service mix, acquisitions and changes in business scope can all move reported revenue without an equal change in commerce. -The usefulness of a measure also depends on how it is computed. Comparable accounting information helps analysts use financial statements (De Franco, Kothari and Verdi 2011). Operating measures that companies report themselves, such as transaction value or active users, lose information when the way they are computed changes or goes unreported (Givoly, Li, Lourie and Nekrasov 2019). Investors look past revenue as well. In the early years of the internet, measures of activity carried information about a firm's value and future revenue beyond what its reported revenue showed (Trueman, Wong and Zhang 2000, 2001; Rajgopal, Venkatachalam and Kotha 2003). +Revenue is not the only reported figure whose meaning depends on how it is computed. Comparable accounting information helps analysts use financial statements (De Franco, Kothari and Verdi 2011). Operating measures that companies report themselves, such as transaction value or active users, lose information when the way they are computed changes or goes unreported (Givoly, Li, Lourie and Nekrasov 2019). Investors look past revenue as well. In the early years of the internet, measures of activity carried information about a firm's value and future revenue beyond what its reported revenue showed (Trueman, Wong and Zhang 2000, 2001; Rajgopal, Venkatachalam and Kotha 2003). Together, these studies explain why the invisible wedge, the difference between transaction value and revenue, exists and can move. They do not show how large the wedge becomes in practice. ## 2.2 Measuring the online economy -How large the wedge becomes is a question of measurement. Official frameworks already separate the commerce a platform carries from the service the platform provides. National accounting guidance treats the sale between buyer and seller as distinct from the platform's intermediation service. It does not count the full transaction value as the platform's output (Ahmad and Schreyer 2016; International Monetary Fund 2018; OECD 2023; United Nations et al. 2025). +Measuring the wedge is a question of definitions and sources. Official frameworks already separate the commerce a platform carries from the service the platform provides. National accounting guidance treats the sale between buyer and seller as distinct from the platform's intermediation service. It does not count the full transaction value as the platform's output (Ahmad and Schreyer 2016; International Monetary Fund 2018; OECD 2023; United Nations et al. 2025). In practice, different producers publish different quantities. Industry reports size the digital economy of Southeast Asia by transaction value (Google, Temasek and Bain 2024). Statistics offices measure the online sales of surveyed businesses (BPS 2025). Listed platforms report the revenue they recognize, and central banks report the value of payments (Bank Indonesia 2025). The United Nations Conference on Trade and Development (UNCTAD) has shown that e-commerce values change with the definition used. It has also set business e-commerce sales beside the transaction values of online platforms (UNCTAD 2023, 2024). A difference between these figures is harmless while they move together. It matters when they part, because the choice of figure can then change the conclusion about scale and growth. Economists who check national income figures against satellite images of night-time lights follow the same logic: a figure should be tested against the activity it stands for (Henderson, Storeygard and Weil 2012). @@ -32,6 +32,6 @@ Rules for platforms apply this idea. The Organisation for Economic Co-operation ## 2.4 The research gap -Existing research explains these pieces separately. Platform economics explains why transaction value can exceed revenue. Accounting explains why the wedge can move. Official frameworks explain why the two should not be treated as the same quantity. Research on digital records explains why some activity outside revenue stays observable in platform records. +Existing research explains these parts separately. Platform economics explains why transaction value can exceed revenue. Accounting explains why the wedge can move. Official frameworks explain why the two should not be treated as the same quantity. Survey methods explain why a count of sellers can break when its design changes. Research on digital records explains why some activity outside revenue stays observable in platform records. -Three questions remain open. The first is when a figure stops moving with the activity it is used to represent, and by how much. The second is whether platform figures follow national growth. That depends on where the growth comes from, and on whether a national count of online sellers keeps pace with entry. Survey counts can break when the way they are collected changes. The third is how far rules built on platform records reach. Answering them requires a platform's revenue and transaction value over several years, and a national survey whose underlying answers can be examined. Indonesia offers both. Chapter 3 turns the questions into measures and hypotheses. +Three questions remain open. The first is when a figure stops moving with the activity it is used to represent, and by how much. The second is whether platform figures follow national growth. That depends on where the growth comes from, and on whether a national count of online sellers keeps pace with entry. The third is how far rules built on platform records reach. Answering them requires a platform's revenue and transaction value over several years, and a national survey whose underlying answers can be examined. Indonesia offers both. Chapter 3 turns the questions into measures and hypotheses. diff --git a/papers/thesis_2026-10/ch3_framework.md b/papers/thesis_2026-10/ch3_framework.md index bd07435..3e29a15 100644 --- a/papers/thesis_2026-10/ch3_framework.md +++ b/papers/thesis_2026-10/ch3_framework.md @@ -1,10 +1,10 @@ # Chapter 3. Framework and Hypotheses -Chapter 2 left three questions open. The first two rest on one idea: a figure is used as a stand-in for the activity behind it. Three figures sit in a chain. A platform's revenue is the smallest, the commerce the platform carries is larger, and the online commerce of the whole country is larger still. Investors read revenue as a sign of the commerce a platform carries. Platform figures are then read as a sign of what is happening in the national total. +Chapter 2 left three questions open. The first two rest on one idea: a figure is used as a stand-in for the activity behind it. Three figures sit in a chain. A platform's revenue is the smallest, the commerce the platform carries is larger, and the online commerce of the whole country is larger still. Each figure stands in for the next one out. Investors read revenue as a sign of the commerce a platform carries. Platform figures are then read as a sign of what is happening in the national total. [Figure 3.1 to be drawn: three nested circles, with revenue inside platform commerce inside all online commerce, H1 on the inner link and H2 on the outer link.] -A stand-in works only while a condition holds. For the inner link, the condition is that the share of each sale that the platform keeps stays steady, because then revenue follows the commerce. For the outer link, the condition is that national growth comes from sellers the platforms record. National growth is split into new and existing sellers using a count of online sellers. The count follows new selling only while the survey keeps reaching the same kinds of seller. This chapter defines the terms and measures, states a hypothesis for each link, and sets out the entry test of the count. +A stand-in works only while a condition holds. For the inner link, the condition is that the share of each sale that the platform keeps stays steady, because then revenue follows the commerce. For the outer link, the condition is that national growth comes from sellers the platforms record. To check this, national growth is split into new and existing sellers using a count of online sellers. The count follows new selling only while the survey keeps reaching the same kinds of seller. This chapter defines the terms and measures, states a hypothesis for each link, and sets out the entry test of the count. ## 3.1 Terms and measures @@ -41,7 +41,7 @@ D = (1 + g(V)) × (m1 − m0) / m0 When the take rate is steady, D is zero and revenue grows exactly as fast as the commerce. When the take rate rises, D is positive and revenue grows faster than the commerce. -Tokopedia shows how this works. Between 2022 and 2023 its take rate rose by about 68 percent, so that it kept about two thirds more of each sale. Its transaction value fell by about 9 percent, yet its revenue grew by about 53 percent. Growth divergence was 62 points, which is 0.91 × 0.68 = 0.62. +Tokopedia shows how this works. Between 2022 and 2023 its take rate rose by about 68 percent, so that it kept about two thirds more of each sale. Its transaction value fell by about 9 percent, yet its revenue grew by about 53 percent. Growth divergence was 62 points, which is (1 − 0.09) × 0.68 = 0.62. The wedge and growth divergence do different jobs. The wedge follows the commerce. At the marketplaces studied in Indonesia, revenue is a few percent of transaction value, so the wedge is almost all of transaction value and moves with it. Growth divergence follows the take rate and shows whether revenue remains a fair guide. From 66a95778e40ca74a0e0e6ad7b9bdb58ace55782e Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 12 Oct 2026 17:58:14 +0000 Subject: [PATCH 07/65] Thesis: first draft of Chapter 4 (Data and Methods); log, README and handoff updated Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- HANDOFF.md | 3 +- papers/thesis_2026-10/EDIT_HISTORY.md | 12 +++ papers/thesis_2026-10/README.md | 3 +- papers/thesis_2026-10/ch4_data_methods.md | 125 ++++++++++++++++++++++ 4 files changed, 141 insertions(+), 2 deletions(-) create mode 100644 papers/thesis_2026-10/ch4_data_methods.md diff --git a/HANDOFF.md b/HANDOFF.md index 164fa72..c992a85 100644 --- a/HANDOFF.md +++ b/HANDOFF.md @@ -78,7 +78,8 @@ Frozen wording of the pitch: `research/option_space_2026-10-01/GOLDEN_PITCH.md` ## 6. Next steps, in order 1. Recheck section 1.2 (contributions) of chapter 1 against the ambition paragraph in 1.1. -2. Chapters 2 and 3 have first drafts (`ch2_literature.md`, `ch3_framework.md`) and Chapters 1 to 3 have had a seam pass; revise from the researcher's reading, then write chapter 4. **Chapter 4 owes the reader**: both groups for the H1 test (Indonesian platforms and the foreign benchmark); the entry-test rule; the data behind each test; how revenue movements are traced to fees and discounts (Objective 2); how the platform evidence is compared with national and payment figures (Objective 3). +2. Chapters 2 and 3 have first drafts (`ch2_literature.md`, `ch3_framework.md`) and Chapters 1 to 3 have had a seam pass; revise from the researcher's reading. **Chapter 4 has a first draft** (`ch4_data_methods.md`, 12 Oct night); it covers what it owed the reader: both groups for the H1 test (Indonesian platforms and the foreign benchmark); the entry-test rule; the data behind each test; how revenue movements are traced to fees and discounts (Objective 2); how the platform evidence is compared with national and payment figures (Objective 3). +2b. Chapter 4 open points: market values (4.7) are not promised in Chapters 1 or 3, so either add one clause to 3.4 and 1.3 or cut 4.7; the appendix it points to (platform list, seven traced windows, registered tests with dates) must be built; Table 4.1 labels Blibli's and Bukalapak's scope as the sources describe it, to be rechecked against the filings; household spending needs a reference. 3. Rewrite chapter 5 as ten sections, each answering one question, using `DRAFT_STORY_v2.md` for content and the writing rules for form. 4. Write chapters 2, 6 and 7. Chapter 6 waits for the scheduled results below. 5. Insert citations; format to the YZU template (the researcher obtains it). diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 32e09b6..7ce8874 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -163,3 +163,15 @@ The second sentence-by-sentence read (on the files after the seam pass) found 11 | ch3 Tokopedia example | "0.91 x 0.68" did not show which factor was which | "(1 - 0.09) x 0.68 = 0.62" | After this round: Chapter 1 1,667 words, Chapter 2 1,280, Chapter 3 1,244 (baseline 1,520, 1,177, 1,095). **Section 1.1 is now 435 words, 25 more than the baseline (+6 percent)**, down from +28. No banned words; no sentence over 30 words in Chapters 2 and 3. + +### Chapter 4 first draft (12 Oct 2026, night) + +New file `ch4_data_methods.md`, about 3,100 words, eight sections: 4.1 platform data (two groups, Table 4.1 with the scope of every series), 4.2 the H1 test, 4.3 tracing revenue movements (Objective 2), 4.4 the BPS files and the entry test (Table 4.2), 4.5 channels, records and reach, 4.6 comparison with national and payment figures (Objective 3, Table 4.3), 4.7 market values, 4.8 traceability. + +Written from the repository records only: `h1_extended.py` and its tables (27 foreign platforms, 158 pairs; 5 Indonesian platforms, 15 pairs), `PREREGISTRATION.md` (entry-test rule, data checks, scenarios), `LICENSED_DATA.md` (rows, weights, checksums), `tokopedia_segment.py`, `wedge_split.py`, `m1_market_value.py`, Parts 3 to 6. No result is stated in the chapter; results stay in Chapter 5. + +Obligations checked against the promises in 1.3, 3.2, 3.3, 3.4 and 3.5: both H1 groups (4.1, 4.2); the entry-test rule with its 127.4 step and the cohort check (4.4); the data behind each test (Tables 4.1 to 4.3); tracing to fees and discounts (4.3); comparison with national and payment figures (4.6). Kong's asks: scope stated for every series, each source explained, the author's own measures marked, one currency. + +Checks: banned words none; no sentence over 33 words (two between 30 and 33 were split); every number traced to the table or script named above (pair counts, 21.5 and 23.9 percent thresholds, weights, 9,999 shuffles, 0.52 log change = 68 percent, Rp783 trillion about US$53 billion at the 2022 World Bank rate). + +Decisions for the researcher: (1) section 4.7 (market values) is outside what Chapters 1 and 3 promise; keep it with one added clause in 3.4, or cut it. (2) Length: Chapter 4 is longer than Chapter 3 by about 1,900 words; trimming candidates are 4.5 (World Bank paragraph) and the fourth H1 check in 4.2. (3) Spin-off tests, listing timing and the market-income pattern are left out, as the cut list puts them in reserve. diff --git a/papers/thesis_2026-10/README.md b/papers/thesis_2026-10/README.md index 0a7dcfa..635bb8f 100644 --- a/papers/thesis_2026-10/README.md +++ b/papers/thesis_2026-10/README.md @@ -42,4 +42,5 @@ The thesis written in full. Same title, research question, hypotheses and measur | `ch5_results.md` | 5 Results | early draft of 5.1-5.5 in the older report style; to be rewritten to the v3 structure (ten question sections, `research/option_space_2026-10-01/DRAFT_STORY_v2.md`) | | `ch3_framework.md` | 3 Framework and Hypotheses | first full draft 12 Oct (evening), awaiting the researcher's reading: the chain of stand-ins, terms box (Table 3.1), the measures and the identity, H1, H2, the entry test, further analyses, tests table (Table 3.2); Figure 3.1 still to be drawn | | `ch2_literature.md` | 2 Literature Review | first full draft 12 Oct (evening), awaiting the researcher's reading: on the proposal's four sections (2.1 pricing and revenue recognition, 2.2 measuring the online economy, 2.3 digital records and third-party information, 2.4 the gap), with the survey-count strand added (breaks, recall, exit, margins). Citations are author-year, every one traced to `REFERENCES_WORKING.md`; the bibliography itself is not yet built | -| ch4, ch6, ch7 | | not started | +| `ch4_data_methods.md` | 4 Data and Methods | first full draft 12 Oct (night), awaiting the researcher's reading: platform data and the two groups (Table 4.1), the H1 test, tracing revenue movements, the BPS files and the entry test (Table 4.2), channels, records and reach, the comparison with national and payment figures (Table 4.3), market values, traceability. The appendix it points to (platform list, seven traced windows, registered tests) is not built | +| ch6, ch7 | | not started | diff --git a/papers/thesis_2026-10/ch4_data_methods.md b/papers/thesis_2026-10/ch4_data_methods.md new file mode 100644 index 0000000..2732bb6 --- /dev/null +++ b/papers/thesis_2026-10/ch4_data_methods.md @@ -0,0 +1,125 @@ +# Chapter 4. Data and Methods + +This chapter describes the data and the tests behind the results in Chapter 5. It starts with the platform data and the test of H1. It then shows how revenue movements are traced to fees and discounts. After that it turns to the national survey, the entry test and the further analyses. The last parts cover the comparison with national and payment figures, the market values, and the rules that keep every number traceable. + +## 4.1 Platform data + +A platform pair is one platform's revenue and transaction value in the same year. Both come from the company's own reports. Revenue is the amount in its audited accounts, and transaction value is the figure it discloses in annual reports and earnings releases. Companies define transaction value in different ways. Some include shipping charges, digital goods or travel. The thesis keeps each company's own definition and records it in a definition map. Each take rate is built within one company, so the company's own definition cancels out of its yearly changes unless the company changes it. + +Two groups of platforms are compared. The Indonesian group has five platforms, listed in Table 4.1. Two of them, Grab and GoTo, are ride-hailing and delivery businesses, which the thesis calls on-demand. The foreign group has 27 listed platforms. Eight form the first benchmark: Etsy, eBay, Jumia, Mercado Libre, Rakuten, Sea, Shopify and Zalando. The other 19 were added to make the comparison larger. They include online marketplaces, travel and ticketing sites, freelance and resale platforms, and food delivery. Sea sits in the group although Indonesia is one of its main markets, so Chapter 5 reports the test with and without it. The appendix lists every platform and year. + +**Table 4.1. The Indonesian group of platforms** + +| Platform series | What it covers | Years | Yearly pairs | How it is labelled | +|---|---|---|---|---| +| Tokopedia | The e-commerce segment of GoTo, an Indonesian marketplace | 2022–2023 | 1 | Direct segment disclosure | +| Blibli | Blibli's third-party retail segment, which includes online travel | 2021–2025 | 4 | Direct segment disclosure | +| Bukalapak | The whole Bukalapak group, including Mitra, its offline business | 2020–2023 | 3 | Direct company disclosure | +| Grab on-demand | Grab's rides and deliveries across Southeast Asia, not Indonesia alone | 2020–2024 | 4 | Direct segment disclosure | +| GoTo on-demand | GoTo's rides and deliveries segment | 2022–2025 | 3 | Direct segment disclosure | + +Table 4.1 states the scope of each series because the series differ. Only Tokopedia is an Indonesian segment alone. Grab covers the region, and Bukalapak covers a whole group. The thesis never adds the series together. Each platform enters the tests on its own. + +Three rules govern the pairs. Only two consecutive years form a pair. A missing year stays missing and is never filled in. Annual and quarterly pairs are never pooled. Each pair uses the company's own financial year. A take rate is a ratio of two amounts in one currency, so the tests need no exchange rate. Running text gives amounts in US dollars, converted at the World Bank's official average rate for the year. + +Two extra rules apply to the foreign group. A company that sells goods itself reports the whole sale as revenue, and its take rate then says little about a platform. For the 19 added platforms, the thesis drops any year in which the company's own goods make up more than half of its revenue. It keeps years in which they make up between a fifth and a half, and marks them. For the eight first platforms, a pair counts only if the company reported both amounts on the same scope in both years. After these rules, the foreign group has 158 yearly pairs, and the Indonesian group has 15. + +## 4.2 The test of H1 + +H1 says that revenue and transaction value grow in proportion. If they do, the take rate stays close to steady. The test measures how far each platform's take rate moves from one year to the next. + +For each pair of years, the thesis takes the change in the logarithm of the take rate. A log change measures growth in a way that gives a rise by some factor and a fall by the same factor the same size. It equals the log growth of revenue minus the log growth of transaction value. This makes it growth divergence in a form that does not depend on how fast transaction value grew. Tokopedia's log change in 2023 is 0.52, which means its take rate rose by a factor of 1.68, or 68 percent. + +H1 is about proportion, so a rise and a fall both count against it. The test uses the size of each change and ignores its direction. + +Each platform then gets one number: the median size of its yearly changes. The platform is the unit of the test, not the pair, because the pairs of one platform are not independent of one another. The test compares 5 numbers for the Indonesian group with 27 for the foreign group. + +Two tests compare the groups. The first is a one-sided Mann–Whitney test. It ranks all 32 platforms by their number and asks whether the Indonesian platforms rank higher than chance would give. It is one-sided because H1 is rejected only if Indonesian platforms move more than the others, and not if they move less. The second is a shuffle test. It assigns the group labels to the 32 platforms at random 9,999 times. It then counts how often the Indonesian group's median is at least as far above the foreign median as in the real data. + +Four checks follow, and Chapter 5 reports each. The first drops each Indonesian platform in turn. The second treats Tokopedia and GoTo on-demand as one company, since GoTo owns both. The third repeats the test with growth divergence in points, as defined in Chapter 3. The fourth is a narrower version that uses only the three Indonesian marketplaces and the eight earlier foreign platforms. It uses a different measure and a smaller benchmark, so it is not a stricter form of the main test. A last description counts the pairs in which the take rate moved by 0.10 or less in log terms, which the thesis calls tracking. + +A quarterly version of the test is run where companies report quarterly pairs. It uses its own sample and is never mixed with the annual one. + +## 4.3 Tracing revenue movements + +The second objective asks where large movements in revenue came from. The thesis answers in two steps. + +The first step divides each movement into volume and take rate. In log terms, revenue growth equals transaction value growth plus the change in the take rate. For each pair, the take-rate share is the size of the take-rate change divided by the sum of the sizes of both changes. The same split is made for the wedge, which equals transaction value minus revenue. The share is computed for the three Indonesian marketplaces (8 pairs) and the eight earlier foreign platforms (29 pairs), and Chapter 5 compares their medians. + +The second step finds the cause of a take-rate change. A platform's take rate equals the rate of its gross fees minus the rate of its incentives, each measured against transaction value. A rise comes from higher fees, from lower incentives, or from a change in the mix of businesses. + +The thesis divides the change in net revenue between fees and incentives wherever a company discloses both. Tokopedia's audited segment note gives gross revenue, incentives and net revenue directly, and the thesis checks them against the audited consolidated statements. For Grab, GoTo and Blibli, the thesis uses the company's releases and the split between rides, deliveries and other businesses. Seven windows are traced in all, and the appendix lists them. Where a company does not separate the parts, the movement is left untraced and the table says so. The thesis reads company explanations as reports and does not test them. + +## 4.4 The national survey and the entry test + +The national data come from the survey of online sellers run by BPS, the national statistics office. BPS publishes the number of businesses selling online, their total online sales and the shares by channel (BPS 2025). The survey rounds carry the year of publication in their names, but their questions refer to the year before. The thesis uses the year of activity throughout. The round named 2023 describes 2022. + +H2 uses the published figures. National online sales value equals the number of businesses times the sales per business. In log terms, growth in value is growth in the number of businesses plus growth in sales per business. + +``` +share from more businesses = Δln(number of businesses) / Δln(value) +``` + +H2 holds on the published figures if this share is above one half. BPS prints two counts of businesses for 2023. The thesis computes the split with both and reports both. It never replaces one with the other. + +The entry test needs the survey files themselves. BPS sold them under a data-use agreement, and Table 4.2 lists them. Each row is one business that sold online in the activity year, and each business has a survey weight. The weight shows how many businesses in the country the row represents, so the weights add up to the national count. The files carry no business identifier across rounds. They carry no area variable below the province. The rounds compare groups of sellers, not the same sellers over time. + +**Table 4.2. The three BPS survey files** + +| Survey round | Activity year | Rows | Weighted total | Count published by BPS | +|---|---|---|---|---| +| E-Commerce 2021 | 2020 | 11,848 | 2,361,423 | 2,361,423 | +| E-Commerce 2023 | 2022 | 31,724 | 2,995,986 | 2,995,879 | +| E-Commerce 2024 | 2023 | 39,736 | 3,814,799 | 3,816,750 | + +Two checks come before any test. The weighted total of each file must fall within 1 percent of the published count, and Table 4.2 shows that all three do. The 2024 file must also reproduce two published shares: 28.63 percent of marketplace sellers and 12.25 percent of other sellers keep complete financial statements. It does so to two decimals. The check shows that the thesis reads the files correctly. + +The entry test follows the rule written in Chapter 3. In the 2024 file, the weighted share of businesses that began selling online in 2023 must be at least 21.5 percent. The threshold comes from the published rise of 27.4 percent: 27.4 divided by 127.4 is 21.5 percent. If the weights had matched BPS's other count for 2023, the threshold would have been 23.9 percent. They did not, so 21.5 percent stands. The test was written down and dated in the project repository before the files were opened. + +If the test fails, a second question follows: how large is the part of the rise that first-time entry does not explain? The thesis answers with a cohort check. A cohort is the group of sellers that began selling online by a given year. If two surveys count the same population, a cohort can only shrink between them, because sellers stop and no new seller can join it. A cohort that grows means the later survey counted sellers the earlier one missed, or that sellers returned after a pause. + +The thesis compares the 2023 survey's count of sellers who began before 2023 with the whole 2022 count. It makes the comparison twice. The first assumes that no seller stopped. The second assumes that sellers stopped at about 5 percent a year, the rate at which one cohort shrank between the 2020 and 2022 surveys. The gap is then stated as a share of the published rise. Chapter 5 reports both. + +Four further checks were designed after the entry test failed, so Chapter 5 labels them exploratory. The first compares count growth with sample size and with the share of new sellers across the 34 provinces. The second splits the extra sellers by the channels they use. The third uses what sellers report about their own sales. The fourth asks whether sampling error could explain the gap, using the relative standard errors that BPS publishes. A seller who stopped and returned keeps an old start year, so the files cannot rule out returners. Chapter 5 weighs that reading against the others. + +## 4.5 Channels, records and reach + +Three further analyses use the same files. The first asks which channels sellers use and how sales divide between them. The questions list websites, e-mail, chat, social media and marketplaces, and they ask what share of sales is offline, on marketplaces or elsewhere online. The published tables also split the 2023 to 2024 rise in value between marketplaces and other channels. BPS counts food and ride apps in its marketplace category, so the thesis reports the shares as BPS defines them. + +Value by channel needs more care. The 2021 and 2023 files give each business's revenue in four brackets, and the 2024 file gives none. The thesis assigns each bracket a low, a middle and a high amount, and it reports all three scenarios. The scenario amounts were fixed before the file was opened. A validation rule applies first. The middle scenario's total for 2022, from the 2023 file, must lie between half and double BPS's published total of about US$53 billion. If it does not, the thesis reports only the range between the low and the high scenario. + +The second analysis asks whether a seller keeps complete financial statements. This bears on what platform records can add, because a seller with no statements leaves few other records. The thesis reports the share of sellers with statements, and the share of online value they hold, in each round. + +The third analysis, developed in Chapter 6, counts how many sellers a rule based on platform records can reach. It counts marketplace sellers whose revenue is above a bracket line and expresses the count as a share of all online sellers. BPS's brackets divide at about US$20,000 a year, and the marketplace tax exempts individual sellers up to about US$33,000. So the count above BPS's line is an upper limit on the sellers the tax can reach. + +World Bank Enterprise Surveys add a check on records. They cover informal sellers in six Indonesian cities and formal firms in seven countries, and the appendix describes them. + +## 4.6 Comparing platform evidence with national and payment figures + +The third objective compares the platform evidence with national and payment figures. Table 4.3 lists the sources and what each one counts. The sources differ in what they cover, so the comparison follows three rules. + +**Table 4.3. Sources compared in Objective 3** + +| Source | What it covers | How it counts | +|---|---|---| +| Platform reports | Everything bought through one platform | The platform's own records | +| BPS online-seller survey | Every business that sold online, including chat and social media | Listing in sampled areas, scaled up by survey weights | +| Bank Indonesia e-commerce statistics | Transactions of large e-commerce firms (Bank Indonesia 2025) | Data sent to the central bank; the method is not public | +| Industry estimates | Shopping marketplaces in Indonesia or Southeast Asia (Momentum Works 2024; Google, Temasek and Bain 2024) | Outside estimates | +| Household spending | All household purchases | BPS national accounts | + +First, the thesis compares growth rates across sources and keeps each source in its own unit. Second, it compares levels only after reconciling the definitions, using the definition map from section 4.1. Third, it compares sources most closely where their coverage overlaps. The 2023 file lets the thesis separate the marketplace part of BPS sales. It compares that part with Bank Indonesia's figure for the same year, in a test written down before the file was opened. The thesis also sets quarterly platform growth beside quarterly household spending growth. It treats payment figures as context. They show how people pay, and not what they buy online. + +## 4.7 Market values + +The last analysis asks whether markets value a platform's growth in transaction value differently from growth in its take rate. The data are year-end market values from the financial data service LSEG, under the university licence. The main sample has 28 listed platforms and 138 platform-years: Bukalapak and 27 platforms abroad. It overlaps with the groups in section 4.1 but is not the same, and it uses whole-company figures only. A regression relates the yearly change in log market value to the changes in log transaction value and log take rate. It gives one coefficient for each, adds a fixed effect for each year and clusters standard errors by company. + +Two cautions come with the design. The result is an association. Market value also changes when a company issues shares, so the thesis does not call it a stock return. A second run adds the segment series. Segment figures do not match a whole company's market value, so that run is a robustness check and not the main test. The test was written down before the market values were pulled. + +## 4.8 Keeping every number traceable + +Every number in the thesis follows one path: source document, extract, analytic row, result. Each extract keeps the company's currency and units. Each value carries a label: direct company disclosure, direct segment disclosure, external estimate, derived value, conversion or scenario. The thesis never mixes the labels in one sample. It reports a restatement or a conflict between two reports and never overwrites one with the other. A single script rebuilds every table that depends only on files in the repository. In addition, 272 automatic checks confirm that each recorded result still matches its table. + +The thesis also separates tests by when they were written. A registered test was written down, dated and stored in the repository before the data were opened. This applies to the entry test, the scenario rules for value, the comparison with Bank Indonesia and the market-value test. An exploratory test was designed after some results were seen. Chapter 5 marks each result as registered or exploratory, and the appendix lists the registered tests with their dates. + +Three sources are licensed and are not in the repository: the BPS survey files, the LSEG market values and the World Bank survey files. The repository records the size and checksum of each file, so anyone who obtains the same files can check them. It holds the code that reads them and the national totals that the code produces, and no business-level record. BPS is cited as the source of every survey figure, in line with the data-use agreement. Chapter 7 states what these data cannot show. From 60c91304865d54bd81243ea749ade686a0f7d0de Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 12 Oct 2026 17:58:30 +0000 Subject: [PATCH 08/65] Thesis ch4: define the repository at first use Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/ch4_data_methods.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/papers/thesis_2026-10/ch4_data_methods.md b/papers/thesis_2026-10/ch4_data_methods.md index 2732bb6..12aadff 100644 --- a/papers/thesis_2026-10/ch4_data_methods.md +++ b/papers/thesis_2026-10/ch4_data_methods.md @@ -74,7 +74,7 @@ The entry test needs the survey files themselves. BPS sold them under a data-use Two checks come before any test. The weighted total of each file must fall within 1 percent of the published count, and Table 4.2 shows that all three do. The 2024 file must also reproduce two published shares: 28.63 percent of marketplace sellers and 12.25 percent of other sellers keep complete financial statements. It does so to two decimals. The check shows that the thesis reads the files correctly. -The entry test follows the rule written in Chapter 3. In the 2024 file, the weighted share of businesses that began selling online in 2023 must be at least 21.5 percent. The threshold comes from the published rise of 27.4 percent: 27.4 divided by 127.4 is 21.5 percent. If the weights had matched BPS's other count for 2023, the threshold would have been 23.9 percent. They did not, so 21.5 percent stands. The test was written down and dated in the project repository before the files were opened. +The entry test follows the rule written in Chapter 3. In the 2024 file, the weighted share of businesses that began selling online in 2023 must be at least 21.5 percent. The threshold comes from the published rise of 27.4 percent: 27.4 divided by 127.4 is 21.5 percent. If the weights had matched BPS's other count for 2023, the threshold would have been 23.9 percent. They did not, so 21.5 percent stands. The test was written down and dated in the project's code archive, called the repository, before the files were opened. If the test fails, a second question follows: how large is the part of the rise that first-time entry does not explain? The thesis answers with a cohort check. A cohort is the group of sellers that began selling online by a given year. If two surveys count the same population, a cohort can only shrink between them, because sellers stop and no new seller can join it. A cohort that grows means the later survey counted sellers the earlier one missed, or that sellers returned after a pause. From 775c8467583301b773c6d1baa5122cb9f0653393 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 12 Oct 2026 18:13:31 +0000 Subject: [PATCH 09/65] Thesis: Chapter 5 first draft (results) after linear read; Chapter 4 consistency fixes; logs updated Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- HANDOFF.md | 2 +- papers/thesis_2026-10/EDIT_HISTORY.md | 22 +++ papers/thesis_2026-10/README.md | 2 +- papers/thesis_2026-10/ch4_data_methods.md | 16 +-- papers/thesis_2026-10/ch5_results.md | 166 ++++++++++++++++------ 5 files changed, 156 insertions(+), 52 deletions(-) diff --git a/HANDOFF.md b/HANDOFF.md index c992a85..70a9f22 100644 --- a/HANDOFF.md +++ b/HANDOFF.md @@ -80,7 +80,7 @@ Frozen wording of the pitch: `research/option_space_2026-10-01/GOLDEN_PITCH.md` 1. Recheck section 1.2 (contributions) of chapter 1 against the ambition paragraph in 1.1. 2. Chapters 2 and 3 have first drafts (`ch2_literature.md`, `ch3_framework.md`) and Chapters 1 to 3 have had a seam pass; revise from the researcher's reading. **Chapter 4 has a first draft** (`ch4_data_methods.md`, 12 Oct night); it covers what it owed the reader: both groups for the H1 test (Indonesian platforms and the foreign benchmark); the entry-test rule; the data behind each test; how revenue movements are traced to fees and discounts (Objective 2); how the platform evidence is compared with national and payment figures (Objective 3). 2b. Chapter 4 open points: market values (4.7) are not promised in Chapters 1 or 3, so either add one clause to 3.4 and 1.3 or cut 4.7; the appendix it points to (platform list, seven traced windows, registered tests with dates) must be built; Table 4.1 labels Blibli's and Bukalapak's scope as the sources describe it, to be rechecked against the filings; household spending needs a reference. -3. Rewrite chapter 5 as ten sections, each answering one question, using `DRAFT_STORY_v2.md` for content and the writing rules for form. +3. Chapter 5 has a rewritten first draft (`ch5_results.md`, 13 Oct) with 11 sections; figures (exhibits) are not yet placed; Chapter 6 (rules and reach) is next. Original instruction for Chapter 5 kept for reference: rewrite as ten sections, each answering one question, using `DRAFT_STORY_v2.md` for content and the writing rules for form. 4. Write chapters 2, 6 and 7. Chapter 6 waits for the scheduled results below. 5. Insert citations; format to the YZU template (the researcher obtains it). diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 7ce8874..1a9bf66 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -175,3 +175,25 @@ Obligations checked against the promises in 1.3, 3.2, 3.3, 3.4 and 3.5: both H1 Checks: banned words none; no sentence over 33 words (two between 30 and 33 were split); every number traced to the table or script named above (pair counts, 21.5 and 23.9 percent thresholds, weights, 9,999 shuffles, 0.52 log change = 68 percent, Rp783 trillion about US$53 billion at the 2022 World Bank rate). Decisions for the researcher: (1) section 4.7 (market values) is outside what Chapters 1 and 3 promise; keep it with one added clause in 3.4, or cut it. (2) Length: Chapter 4 is longer than Chapter 3 by about 1,900 words; trimming candidates are 4.5 (World Bank paragraph) and the fourth H1 check in 4.2. (3) Spin-off tests, listing timing and the market-income pattern are left out, as the cut list puts them in reserve. + +### Chapter 5 first draft and a linear read of Chapters 4 and 5 (13 Oct 2026) + +`ch5_results.md` was replaced by a new draft (the older report-style draft stays in git history). It follows the story order of `DRAFT_STORY_v2.md` as eleven sections, with thesis prose instead of labelled beats: 5.1 wedge (Table 5.1), 5.2 H1 (Table 5.2), 5.3 tracing (Table 5.3), 5.4 market values, 5.5 the platforms in 2023 (Table 5.4), 5.6 what sellers reported, 5.7 count and entry test (Table 5.5), 5.8 who the extra sellers were (Table 5.6), 5.9 returners, 5.10 channels and records (Table 5.7), 5.11 why the national figures disagree. The 2026 rules and tax reach go to Chapter 6. Each result is marked registered or exploratory; every number comes from FINDINGS.md, Parts 3 to 6, the committed tables or `PREREGISTRATION.md`. + +The linear read (sentence, paragraph, section, in order; then against Chapters 1, 3 and 4) found these, all fixed: + +| Where | Problem | Fix | +|---|---|---| +| 5.1 | "the three largest platforms" is not shown; a summed wedge sat against Chapter 3's "defined for one platform"; "does not depend on the assumptions" overclaimed; the "three quarters" sentence was unclear | "three major platforms", "the sum of their invisible wedges", "changes little", sentence rewritten | +| 5.2 | the 5-v-27 test is not registered but read as if it were; the quarterly sentence contradicted itself on dates; "points of transaction value" confused | stated as not registered; dates fixed; "percentage points of the take rate" | +| 5.3 | "the other part" vague; Grab 2022-23 looked like an eighth window; no "so what" | volume part named; wording tied to the take-rate rise; closing sentence added | +| 5.4 | "second prediction" had no first | both predictions stated; handover to 5.5 added | +| 5.6 | marketplace-slice sentence belonged to 5.11; no handover | moved to 5.11; handover to 5.7 | +| 5.7 | bullet list in running text; "fourth check" appeared after three unlabelled ones; "cohorts" and "the count" confused; 5.8/5.9 order in the handover | prose; the four checks named in order; handover names both sections | +| 5.8 | inferred education codes mentioned (a limit) | removed; goes to Chapter 7 | +| 5.9 | the cited study is about exit, not restart | wording changed to exit | +| 5.10 | records paragraph contradicted itself on coverage; E7 prediction not stated | rewritten with the prediction and the 5.8-point fall | +| 5.11 | one 130-word paragraph; "4.4 times smaller" | split; "less than a quarter" | +| ch4 | no mention of the points comparison, the monthly-versus-annual reading or the overshoot rule, all used in Chapter 5; "earlier foreign platforms" and "first benchmark" both used; "the table says so" unclear | all added or unified | + +Decisions for the researcher: (1) 5.2 says plainly that the larger H1 test is not registered. (2) Chapter 7 must carry: inferred education codes in the 2023 file, the 19 sample rows in the top bracket, Bank Indonesia's method not public, the exploratory status of the 2023 accounting. (3) Figures: six exhibits exist (`exhibits/v2`) and none is placed. diff --git a/papers/thesis_2026-10/README.md b/papers/thesis_2026-10/README.md index 635bb8f..d6e1157 100644 --- a/papers/thesis_2026-10/README.md +++ b/papers/thesis_2026-10/README.md @@ -39,7 +39,7 @@ The thesis written in full. Same title, research question, hypotheses and measur | File | Chapter | Status | |---|---|---| | `ch1_introduction.md` | 1 Introduction | drafted 12 Oct on the proposal's and deck's flow (opening, 1.1 question and objectives, 1.2 contributions, 1.3 structure); open concern on 1.1 in `EDIT_HISTORY.md` | -| `ch5_results.md` | 5 Results | early draft of 5.1-5.5 in the older report style; to be rewritten to the v3 structure (ten question sections, `research/option_space_2026-10-01/DRAFT_STORY_v2.md`) | +| `ch5_results.md` | 5 Results | rewritten 13 Oct as 11 sections (5.1 wedge, 5.2 H1, 5.3 tracing, 5.4 market values, 5.5 the platforms in 2023, 5.6 what sellers reported, 5.7 count and entry test, 5.8 who the extra sellers were, 5.9 returners, 5.10 channels and records, 5.11 why the national figures disagree), awaiting the researcher's reading. Rules and reach of the 2026 rules go to Chapter 6. Figures not yet placed; exhibits in `research/option_space_2026-10-01/exhibits/` | | `ch3_framework.md` | 3 Framework and Hypotheses | first full draft 12 Oct (evening), awaiting the researcher's reading: the chain of stand-ins, terms box (Table 3.1), the measures and the identity, H1, H2, the entry test, further analyses, tests table (Table 3.2); Figure 3.1 still to be drawn | | `ch2_literature.md` | 2 Literature Review | first full draft 12 Oct (evening), awaiting the researcher's reading: on the proposal's four sections (2.1 pricing and revenue recognition, 2.2 measuring the online economy, 2.3 digital records and third-party information, 2.4 the gap), with the survey-count strand added (breaks, recall, exit, margins). Citations are author-year, every one traced to `REFERENCES_WORKING.md`; the bibliography itself is not yet built | | `ch4_data_methods.md` | 4 Data and Methods | first full draft 12 Oct (night), awaiting the researcher's reading: platform data and the two groups (Table 4.1), the H1 test, tracing revenue movements, the BPS files and the entry test (Table 4.2), channels, records and reach, the comparison with national and payment figures (Table 4.3), market values, traceability. The appendix it points to (platform list, seven traced windows, registered tests) is not built | diff --git a/papers/thesis_2026-10/ch4_data_methods.md b/papers/thesis_2026-10/ch4_data_methods.md index 12aadff..466e07a 100644 --- a/papers/thesis_2026-10/ch4_data_methods.md +++ b/papers/thesis_2026-10/ch4_data_methods.md @@ -6,7 +6,7 @@ This chapter describes the data and the tests behind the results in Chapter 5. I A platform pair is one platform's revenue and transaction value in the same year. Both come from the company's own reports. Revenue is the amount in its audited accounts, and transaction value is the figure it discloses in annual reports and earnings releases. Companies define transaction value in different ways. Some include shipping charges, digital goods or travel. The thesis keeps each company's own definition and records it in a definition map. Each take rate is built within one company, so the company's own definition cancels out of its yearly changes unless the company changes it. -Two groups of platforms are compared. The Indonesian group has five platforms, listed in Table 4.1. Two of them, Grab and GoTo, are ride-hailing and delivery businesses, which the thesis calls on-demand. The foreign group has 27 listed platforms. Eight form the first benchmark: Etsy, eBay, Jumia, Mercado Libre, Rakuten, Sea, Shopify and Zalando. The other 19 were added to make the comparison larger. They include online marketplaces, travel and ticketing sites, freelance and resale platforms, and food delivery. Sea sits in the group although Indonesia is one of its main markets, so Chapter 5 reports the test with and without it. The appendix lists every platform and year. +Two groups of platforms are compared. The Indonesian group has five platforms, listed in Table 4.1. Two of them, Grab and GoTo, are ride-hailing and delivery businesses, which the thesis calls on-demand. The foreign group has 27 listed platforms. Eight form the first benchmark: Etsy, eBay, Jumia, Mercado Libre, Rakuten, Sea, Shopify and Zalando. The other 19 were added to make the comparison larger. They include online marketplaces, travel and ticketing sites, freelance and resale platforms, and food delivery. Sea sits in the group although Indonesia is one of its main markets, so Chapter 5 reports the narrower test of section 4.2 with and without it. The appendix lists every platform and year. **Table 4.1. The Indonesian group of platforms** @@ -28,7 +28,7 @@ Two extra rules apply to the foreign group. A company that sells goods itself re H1 says that revenue and transaction value grow in proportion. If they do, the take rate stays close to steady. The test measures how far each platform's take rate moves from one year to the next. -For each pair of years, the thesis takes the change in the logarithm of the take rate. A log change measures growth in a way that gives a rise by some factor and a fall by the same factor the same size. It equals the log growth of revenue minus the log growth of transaction value. This makes it growth divergence in a form that does not depend on how fast transaction value grew. Tokopedia's log change in 2023 is 0.52, which means its take rate rose by a factor of 1.68, or 68 percent. +For each pair of years, the thesis takes the change in the logarithm of the take rate. A log change treats a rise by some factor and a fall by the same factor as equal in size. It equals the log growth of revenue minus the log growth of transaction value. This makes it growth divergence in a form that does not depend on how fast transaction value grew. Tokopedia's log change in 2023 is 0.52, which means its take rate rose by a factor of 1.68, or 68 percent. H1 is about proportion, so a rise and a fall both count against it. The test uses the size of each change and ignores its direction. @@ -36,7 +36,7 @@ Each platform then gets one number: the median size of its yearly changes. The p Two tests compare the groups. The first is a one-sided Mann–Whitney test. It ranks all 32 platforms by their number and asks whether the Indonesian platforms rank higher than chance would give. It is one-sided because H1 is rejected only if Indonesian platforms move more than the others, and not if they move less. The second is a shuffle test. It assigns the group labels to the 32 platforms at random 9,999 times. It then counts how often the Indonesian group's median is at least as far above the foreign median as in the real data. -Four checks follow, and Chapter 5 reports each. The first drops each Indonesian platform in turn. The second treats Tokopedia and GoTo on-demand as one company, since GoTo owns both. The third repeats the test with growth divergence in points, as defined in Chapter 3. The fourth is a narrower version that uses only the three Indonesian marketplaces and the eight earlier foreign platforms. It uses a different measure and a smaller benchmark, so it is not a stricter form of the main test. A last description counts the pairs in which the take rate moved by 0.10 or less in log terms, which the thesis calls tracking. +Four checks follow, and Chapter 5 reports each. The first drops each Indonesian platform in turn. The second treats Tokopedia and GoTo on-demand as one company, since GoTo owns both. The third repeats the test with growth divergence in points, as defined in Chapter 3. The fourth is a narrower version that uses only the three Indonesian marketplaces and the eight platforms of the first foreign benchmark. It uses a different measure and a smaller benchmark, so it is not a stricter form of the main test. A description counts the pairs in which the take rate moved by 0.10 or less in log terms, which the thesis calls tracking. An exploratory comparison, designed after the main result, sets the yearly change in points of transaction value beside the share each platform keeps. A quarterly version of the test is run where companies report quarterly pairs. It uses its own sample and is never mixed with the annual one. @@ -44,11 +44,11 @@ A quarterly version of the test is run where companies report quarterly pairs. I The second objective asks where large movements in revenue came from. The thesis answers in two steps. -The first step divides each movement into volume and take rate. In log terms, revenue growth equals transaction value growth plus the change in the take rate. For each pair, the take-rate share is the size of the take-rate change divided by the sum of the sizes of both changes. The same split is made for the wedge, which equals transaction value minus revenue. The share is computed for the three Indonesian marketplaces (8 pairs) and the eight earlier foreign platforms (29 pairs), and Chapter 5 compares their medians. +The first step divides each movement into volume and take rate. In log terms, revenue growth equals transaction value growth plus the change in the take rate. For each pair, the take-rate share is the size of the take-rate change divided by the sum of the sizes of both changes. The same split is made for the wedge, which equals transaction value minus revenue. The share is computed for the three Indonesian marketplaces (8 pairs) and the eight platforms of the first foreign benchmark (29 pairs), and Chapter 5 compares their medians. The second step finds the cause of a take-rate change. A platform's take rate equals the rate of its gross fees minus the rate of its incentives, each measured against transaction value. A rise comes from higher fees, from lower incentives, or from a change in the mix of businesses. -The thesis divides the change in net revenue between fees and incentives wherever a company discloses both. Tokopedia's audited segment note gives gross revenue, incentives and net revenue directly, and the thesis checks them against the audited consolidated statements. For Grab, GoTo and Blibli, the thesis uses the company's releases and the split between rides, deliveries and other businesses. Seven windows are traced in all, and the appendix lists them. Where a company does not separate the parts, the movement is left untraced and the table says so. The thesis reads company explanations as reports and does not test them. +The thesis divides the change in net revenue between fees and incentives wherever a company discloses both. Tokopedia's audited segment note gives gross revenue, incentives and net revenue directly, and the thesis checks them against the audited consolidated statements. For Grab, GoTo and Blibli, the thesis uses the company's releases and the split between rides, deliveries and other businesses. Seven windows are traced in all, and the appendix lists them. Where a company does not separate the parts, the movement is left untraced, and the appendix table says so. The thesis reads company explanations as reports and does not test them. ## 4.4 The national survey and the entry test @@ -72,7 +72,7 @@ The entry test needs the survey files themselves. BPS sold them under a data-use | E-Commerce 2023 | 2022 | 31,724 | 2,995,986 | 2,995,879 | | E-Commerce 2024 | 2023 | 39,736 | 3,814,799 | 3,816,750 | -Two checks come before any test. The weighted total of each file must fall within 1 percent of the published count, and Table 4.2 shows that all three do. The 2024 file must also reproduce two published shares: 28.63 percent of marketplace sellers and 12.25 percent of other sellers keep complete financial statements. It does so to two decimals. The check shows that the thesis reads the files correctly. +Two checks come before any test. The weighted total of each file must fall within 1 percent of the published count, and Table 4.2 shows that all three do. The 2024 file must also reproduce two published shares: 28.63 percent of marketplace sellers and 12.25 percent of other sellers keep complete financial statements. It does so to two decimals. The check confirms that the thesis reads the files as BPS does. The entry test follows the rule written in Chapter 3. In the 2024 file, the weighted share of businesses that began selling online in 2023 must be at least 21.5 percent. The threshold comes from the published rise of 27.4 percent: 27.4 divided by 127.4 is 21.5 percent. If the weights had matched BPS's other count for 2023, the threshold would have been 23.9 percent. They did not, so 21.5 percent stands. The test was written down and dated in the project's code archive, called the repository, before the files were opened. @@ -80,13 +80,13 @@ If the test fails, a second question follows: how large is the part of the rise The thesis compares the 2023 survey's count of sellers who began before 2023 with the whole 2022 count. It makes the comparison twice. The first assumes that no seller stopped. The second assumes that sellers stopped at about 5 percent a year, the rate at which one cohort shrank between the 2020 and 2022 surveys. The gap is then stated as a share of the published rise. Chapter 5 reports both. -Four further checks were designed after the entry test failed, so Chapter 5 labels them exploratory. The first compares count growth with sample size and with the share of new sellers across the 34 provinces. The second splits the extra sellers by the channels they use. The third uses what sellers report about their own sales. The fourth asks whether sampling error could explain the gap, using the relative standard errors that BPS publishes. A seller who stopped and returned keeps an old start year, so the files cannot rule out returners. Chapter 5 weighs that reading against the others. +The cohort check and four further checks were designed after the entry test failed, so Chapter 5 labels them exploratory. The first compares count growth with sample size and with the share of new sellers across the 34 provinces. The second splits the extra sellers by the channels they use. The third uses what sellers report about their own sales. The fourth asks whether sampling error could explain the gap, using the relative standard errors that BPS publishes. A seller who stopped and returned keeps an old start year, so the files cannot rule out returners. Chapter 5 weighs that reading against the others. ## 4.5 Channels, records and reach Three further analyses use the same files. The first asks which channels sellers use and how sales divide between them. The questions list websites, e-mail, chat, social media and marketplaces, and they ask what share of sales is offline, on marketplaces or elsewhere online. The published tables also split the 2023 to 2024 rise in value between marketplaces and other channels. BPS counts food and ride apps in its marketplace category, so the thesis reports the shares as BPS defines them. -Value by channel needs more care. The 2021 and 2023 files give each business's revenue in four brackets, and the 2024 file gives none. The thesis assigns each bracket a low, a middle and a high amount, and it reports all three scenarios. The scenario amounts were fixed before the file was opened. A validation rule applies first. The middle scenario's total for 2022, from the 2023 file, must lie between half and double BPS's published total of about US$53 billion. If it does not, the thesis reports only the range between the low and the high scenario. +Value by channel needs more care. The 2021 and 2023 files give each business's revenue in four brackets, and the 2024 file gives none. The thesis assigns each bracket a low, a middle and a high amount, and it reports all three scenarios. The scenario amounts were fixed before the file was opened. The 2023 questionnaire asks for monthly revenue, but the bracket limits are annual size classes. The thesis computes both readings and keeps the one that passes a validation rule. The middle scenario's total for 2022, from the 2023 file, must lie between half and double BPS's published total of about US$53 billion. A second rule applies if the lower brackets alone already exceed that total: the thesis then reports value shares as ranges only. If validation fails, it reports only the range between the low and the high scenario. The second analysis asks whether a seller keeps complete financial statements. This bears on what platform records can add, because a seller with no statements leaves few other records. The thesis reports the share of sellers with statements, and the share of online value they hold, in each round. diff --git a/papers/thesis_2026-10/ch5_results.md b/papers/thesis_2026-10/ch5_results.md index 7233383..1539d3f 100644 --- a/papers/thesis_2026-10/ch5_results.md +++ b/papers/thesis_2026-10/ch5_results.md @@ -1,82 +1,164 @@ # Chapter 5. Results -This chapter follows the numbers from the platforms outward. It starts with what a platform keeps from each sale, then asks whether its revenue followed its sales (H1). It then turns to 2023, the year the headline figures described a boom, and to what sellers themselves reported. The national count of sellers (H2) follows in section 5.6. +This chapter reports the results in the order of the thesis. It starts with the platforms, then moves to the national figures. Each section marks its results as registered or exploratory, as section 4.8 defines. ## 5.1 How much revenue leaves out -A platform keeps only a small part of what passes through it. In 2023, Tokopedia, Shopee and Grab together carried US$43.23 billion of transaction value. Their combined revenue was US$3.16 billion. The invisible wedge, transaction value minus revenue, was therefore US$40.07 billion. Revenue showed about 7 rupiah of every 100 that buyers spent. +In 2023, three major platforms in Indonesia carried US$43.2 billion of transaction value between them. They kept US$3.2 billion as revenue. The sum of their invisible wedges was US$40.1 billion. Revenue was about 7 percent of what passed through them. Their ecosystem ratio, the wedge divided by revenue, was 12.7. Table 5.1 shows the three platforms. Only Tokopedia's amounts are a direct disclosure. The Shopee and Grab amounts for Indonesia are the author's own derived estimates from company-wide disclosures. -The size of the wedge does not depend on any one assumption. Shopee's Indonesian revenue rests on an assumed take rate of 9 to 11 percent. Across that range, the wedge moves only between US$39.86 billion and US$40.29 billion. Dropping any one platform still leaves a wedge of at least US$20.70 billion. +**Table 5.1. The invisible wedge of three platforms, 2023 (US$ billion)** -The wedge is also stable as a share of what the platforms carry. For the three platforms that report both numbers themselves, it was 97 to 99 percent of transaction value in every year. It therefore moves almost one for one with transaction value. Tokopedia in 2023 shows this clearly: transaction value fell 9 percent and the wedge fell 10 percent. +| Platform | Transaction value | Revenue | Wedge | How it is labelled | +|---|---|---|---|---| +| Tokopedia | 16.33 | 0.41 | 15.92 | Direct segment disclosure | +| Shopee, Indonesia | 21.52 | 2.15 | 19.37 | Derived, at an assumed take rate of 10 percent | +| Grab, Indonesia | 5.38 | 0.61 | 4.78 | Derived | +| Total | 43.23 | 3.16 | 40.07 | | -Revenue is the small part left over, and it can move on its own. In the same year, Tokopedia's revenue rose 53 percent. +The size of the wedge changes little with the assumptions. Assume instead that Shopee keeps 9 or 11 percent, and the total wedge stays between US$39.9 billion and US$40.3 billion. Drop any one platform, and the wedge is still at least US$20.7 billion. For the platforms that report both amounts directly, the wedge is 97 to 99 percent of transaction value in every year. It moves almost one for one with transaction value. The appendix gives the wedge for each platform and year from 2020 to 2025. -The platforms themselves carry only part of online selling. In BPS's own survey of online businesses, sales through marketplaces and ride-hailing apps were about 16 to 26 percent of online sales value. The rest moved through chat apps, social media and sellers' own websites. A measure built on the platforms therefore sees a thin slice of a minority of online selling. +The platforms are only part of online selling. In the BPS survey, the marketplace channel carries about a fifth of online sales value: between 16 and 26 percent, depending on the year and the scenario. BPS counts food and ride apps in this channel. So a measure built on platforms misses about three quarters of online sales value. Revenue, a few percent of what the platforms carry, shows much less still. -So the first half of the research question has a plain answer. Revenue shows about 7 percent of what passes through the platforms, and the platforms carry about a fifth of online sales. The next question is whether that thin slice still tracks growth. +## 5.2 Do revenue and transaction value grow together? -## 5.2 Did revenue grow with transaction value? (H1) +H1 is rejected. In Indonesia, revenue did not grow in proportion to transaction value. Table 5.2 gives the tests, which compare five Indonesian platforms with 27 foreign ones. The typical Indonesian platform changed its take rate by about 22 percent in a year, up or down. The typical foreign platform changed it by about 6 percent. The difference is about threefold. The first version of the test used only the three marketplaces. The larger groups were set up after that result was known, so this is not a registered test. -H1 states that transaction value and revenue grow in proportion over time. If it holds, revenue is a fair guide to the commerce a platform carries. The test compares how much the take rate moves in Indonesia with how much it moves at platforms abroad. The take rate is revenue divided by transaction value. +**Table 5.2. The test of H1** -H1 is rejected. Five Indonesian and regional platforms were compared with 27 listed platforms abroad, one result per firm. In a typical year, the Indonesian take rate moved by about 20 percent of its own level. Abroad, it moved by about 6 percent. The difference is significant at the firm level (Mann-Whitney, p = 0.004). +| Test | Indonesian group (median) | Foreign group (median) | p-value | +|---|---|---|---| +| Typical yearly change in log take rate, 5 platforms against 27 (Mann–Whitney) | 0.20 | 0.06 | 0.004 | +| The same, by shuffling the group labels | 0.20 | 0.06 | 0.022 | +| Growth divergence in points, 5 against 26 | 27.0 | 6.5 | 0.005 | +| Drop each Indonesian platform in turn | 0.17 to 0.23 | 0.06 | 0.003 to 0.016 | +| Tokopedia and GoTo on-demand as one company, 4 against 27 | — | — | 0.003 | +| Three marketplaces against eight foreign platforms, growth divergence | 38.9 | 7.1 | 0.06 | +| The same, without Sea | 38.9 | 4.2 | 0.008 | +| Quarterly pairs, 3 against 8 | 0.071 | 0.049 | 0.19 | -No single platform drives the result. Dropping each Indonesian platform in turn leaves p at or below 0.016. The proposal's own measure, growth divergence, gives the same answer: 27.0 percentage points in Indonesia against 6.5 abroad (p = 0.005). Treating GoTo's two segments as one company leaves p = 0.003. +Table 5.2 reads as follows. The first two rows are the main test. The second row replaces the Mann–Whitney p-value with the shuffle p-value, and the group medians stay the same. The third row repeats the test with growth divergence in points, as Chapter 3 defines it; one foreign platform has no pair with both amounts in consecutive years. The dropping and grouping rows show that no single platform carries the result. The narrower test of the three marketplaces uses the shuffle p-value. It gives 0.06 on growth divergence and 0.03 on the take-rate change. It uses a smaller benchmark and a different measure, so it is not a stricter form of the main test. Removing Sea from its benchmark lowers these p-values to 0.008 and 0.017. -The size of the gap shows how far revenue and sales parted. For the three platforms in the main sample, the typical absolute growth divergence was 38.9 percentage points. Abroad, it was 7.1 points. Revenue stayed close to transaction value in only 12.5 percent of Indonesian comparisons, against 69 percent abroad. +A second feature separates the groups. In one third of Indonesian pairs, the take rate stayed within about 10 percent. Abroad, it did so in about two thirds. The quarterly version of the test does not reject H1 (Table 5.2). Its Indonesian pairs mostly cover 2023 to 2026, after most of the repricing. The large moves came in 2022 to 2024, so the rejection of H1 describes that period. -The main sample on its own, with eight comparisons, gives p = 0.06 on a firm-level test. That test uses growth divergence against the proposal's eight foreign platforms; it is a different test, not a stricter one. One registered check did not hold. With quarterly data, the difference shrinks and is not significant (p = 0.19). The quarterly series mostly cover 2023 to 2026, after the largest price changes had happened. +The reason for the threefold difference is the size of the slice. This part is exploratory. Measured in percentage points of the take rate, Indonesian platforms changed it by about as much as foreign ones. The change was about 1 point a year in both groups, with a p-value of 0.65. But the typical Indonesian platform keeps only 1.6 percent of each sale, against 16.9 percent abroad. A rise of 1 point lifts the revenue of the first by more than half. The same rise lifts the revenue of the second by about 6 percent. This is the wedge at work. The larger it is next to revenue, the more a small price change moves revenue growth. -So revenue did not grow in proportion to transaction value in Indonesia. Most of the separation happened in 2022 to 2024. The next section asks why. +## 5.3 Why the take rate moved -## 5.3 Why revenue moved: similar price changes, a much thinner slice +Revenue moved mainly because platforms changed what they keep. Two results show this. First, the thesis divides each movement in revenue into volume and take rate, as section 4.3 describes. For the three Indonesian marketplaces, the take rate accounts for two thirds of the movement at the median (66.5 percent). For the eight platforms of the first foreign benchmark, it accounts for about a quarter (26.7 percent). The wedge follows the volume part. About 96 percent of each change in the wedge comes from the change in transaction value, in both groups. Second, Table 5.3 traces the large moves to fees and discounts. -The obvious explanation is that Indonesian platforms changed their prices far more than platforms abroad. The data do not show that. Measured in rupiah per 100 of transaction value, the typical yearly change in what a platform keeps was about one rupiah. That held in Indonesia and abroad alike. +**Table 5.3. What moved revenue in four Indonesian windows** -The difference lies in the slice itself. The Indonesian marketplaces keep under 2 rupiah of every 100 sold. A typical platform abroad keeps about 17. A change of one rupiah is small against a slice of 17 and large against a slice of 2. The same price move therefore shifts Indonesian revenue growth about three times as much (Figure 5.1). +| Platform and window | Change in revenue | Main source of the rise | +|---|---|---| +| Tokopedia, 2022–23 | +53% (transaction value −9%) | Lower incentives, 60% of the extra revenue; higher fees, 40% | +| Blibli third-party retail, 2022–23 | +465% | Lower incentives, 63%; part of the rest is a shift toward travel | +| Grab on-demand, 2021–24 | +321% | About one third each from volume, fees and lower incentives | +| GoTo on-demand, 2023–25 | +114% | Lower incentives, 67%; volume, 27% | -This gives the wedge its job in the thesis. Growth divergence follows directly from the change in the take rate: +Table 5.3 lists four of the seven windows traced. In Tokopedia's accounts, the discounts it pays for are deducted from revenue. They fell by about one third in 2023. Revenue rose by 53 percent while the commerce it carried shrank. Across the seven windows, lower incentives were the larger part in four and higher fees in two. In the seventh, the parts were about equal. Grab's take rate rose by 3.9 points in 2022–23, and 3.5 of them came from pricing within rides and deliveries. For Bukalapak, the reports do not allow the parts to be separated. So in 2022 to 2024, revenue was not a fair guide to the commerce Indonesian platforms carried. The take rate moved a lot, mostly through smaller discounts and higher fees. -> D = (1 + gV) × (m1 − m0) / m0 +## 5.4 What markets valued -Here m0 and m1 are the take rates in the two years and gV is growth in transaction value. The term 1 / m0 equals 1 + E0, one plus the ecosystem ratio. The larger the wedge is next to revenue, the more an ordinary price change moves revenue growth. A thin slice helps explain why Indonesian revenue swung so much. This comparison was made after the H1 results and is exploratory. +The market-value results are registered. Across 28 listed platforms and 138 platform-years, market value moved with growth in transaction value. That was the first prediction. A 1 percent rise in transaction value goes with a 0.6 percent rise in market value, with a p-value of 0.002. The second prediction was that markets value growth from a higher take rate less. It did not hold. A 1 percent rise in the take rate goes with a rise of only 0.3 percent, but the difference between the two is not significant (p = 0.20). It becomes significant only when segment series are added, and the thesis does not rely on that run. Both results are associations. Market value also changes when a company issues shares, so neither is a stock return. Market value thus moved with the buying underneath revenue, and the next section asks what that buying did in 2023. -The companies' own accounts show what the price changes were. Between 2022 and 2024, the platforms paid for fewer discounts and charged higher fees. Two forces move revenue: how much people buy, and how much of each sale the platform keeps. In a typical Indonesian year, the second was about two thirds of the movement. Abroad, it was about a quarter. +## 5.5 The platforms in 2023 -The cases differ in their mix: +Buying through platforms did not boom in 2023. Table 5.4 sets the Indonesian measures against household spending, which rose by 9.4 percent in current prices. Of six measures of buying through Indonesian platforms, five grew more slowly, and three fell. Blibli is the exception. -- **Tokopedia, 2022 to 2023.** Transaction value fell 8.9 percent and net revenue rose 53.2 percent. Customer incentives fell from Rp4.11 trillion to Rp2.81 trillion. Lower incentives account for 60.6 percent of the rise in net revenue (Figure 5.2). -- **Grab, 2022 to 2023.** Its take rate rose 3.9 points; repricing accounts for 3.5 of them. -- **GoTo's ride and delivery business, 2023 to 2025.** Revenue rose 114 percent; fewer discounts account for 67 percent of that rise. -- **Blibli, 2022 to 2023.** Revenue rose 465 percent; fewer discounts account for 63 percent, and part of the rest is a shift toward travel. +**Table 5.4. Growth in 2023 (percent)** -So in 2022 to 2024, Indonesian platform revenue mostly reported pricing decisions. It did not mainly report more buying. +| Measure | Growth | Unit | +|---|---|---| +| Household spending | +9.4 | Rupiah, current prices | +| Tokopedia transaction value | −8.9 | Rupiah | +| GoTo on-demand transaction value | −10.5 | Rupiah | +| Bank Indonesia e-commerce | −4.7 | Rupiah | +| Momentum Works marketplace estimate | +3.5 | Estimate | +| Bukalapak transaction value | +6.9 | Rupiah | +| Blibli third-party retail transaction value | +34.7 | Rupiah | +| Tokopedia revenue | +53.2 | Rupiah | +| BPS e-commerce value | +40.6 | Rupiah | +| BPS online sellers | +27.4 | Number | -## 5.4 What market value followed +Each row of Table 5.4 is in its own unit, and the thesis compares only the rates. Rows two to seven show the buying. The last three rows show the figures that described a boom. The platforms' revenue and BPS's value rose strongly, while the buying through platforms did not. Section 5.3 explains the revenue. The next sections ask where BPS's growth came from. -Revenue that grows through pricing could still matter to investors. I tested this before collecting the market data, with two predictions registered in advance. Across 28 listed platforms and 138 firm-years, changes in market value moved with growth in transaction value (b = 0.60, p = 0.002). This is an association, not a measure of how investors decide. +## 5.6 What sellers reported -The second prediction did not hold in the main sample. Revenue growth that came from a higher take rate had a smaller estimated effect (b = 0.33). The difference from volume growth is not significant (p = 0.20). It becomes significant only when segment series are added, which mixes segment figures with group market values. +Sellers did not describe a boom in 2023. This section is descriptive. BPS asks each business how its online sales changed. Among businesses already selling, 24 percent reported a rise and 32 percent a fall, and 44 percent saw no change. In 2022, the balance was the other way: 37 percent reported a rise and 24 percent a fall. The mean reported change in 2023 was −4.3 percent. Lack of demand became the main obstacle, named by 41 percent of sellers in 2023 and 35 percent in 2022. Yet the published count of online sellers rose by 27 percent in the same year. Section 5.7 asks where that rise came from. -So market value moved with buying. Whether investors priced revenue from higher fees differently is not settled. +## 5.7 The count of online sellers and the entry test -## 5.5 2023: the headline figures and the sellers +On BPS's published figures, H2 holds. More businesses account for 71 percent of the growth in online sales value from 2022 to 2023. They account for 90 percent from 2023 to 2024, or 71 percent if BPS's other count for 2023 is used. The split is arithmetic, and it cannot tell a new seller from one counted for the first time. -The platform results meet the national figures in 2023. That year, the headline figures described a boom. Tokopedia's revenue rose 53.2 percent. BPS later reported that the value of online sales grew 40.6 percent. It also reported 27.4 percent more online sellers, about 819,000 more (Figure 5.3). +The entry test can. This result is registered. In the 2024 file, 13.45 percent of sellers began selling online in 2023. The rule required at least 21.5 percent. The entry test fails. Even if no seller stopped, entry on that scale caps the growth of the count at 15.5 percent, against the published 27.4 percent. -Measures of buying through the platforms told a different story. Household spending grew 9.4 percent in nominal terms. Five of six Indonesia-only measures of buying through platforms grew more slowly than that. Three of them fell: +**Table 5.5. The 2023 rise in the count of online sellers (thousands)** -- Tokopedia's transaction value fell 8.9 percent. -- GoTo's ride and delivery transaction value fell 10.5 percent. -- Bank Indonesia's e-commerce figure fell 4.7 percent. +| Part of the rise | Sellers | Share of the rise | +|---|---|---| +| Published rise, 2022 to 2023 | 819 | 100 | +| Sellers who began selling online in 2023 | 513 | 63 | +| Earlier sellers counted above the whole 2022 count, if no seller stopped | 306 | 37 | +| The same, if sellers stopped at 5 percent a year | 458 | 56 | -The exception was Blibli, whose transaction value grew 34.7 percent. +Table 5.5 shows the accounting. The 2023 survey counted 306 thousand more sellers who began before 2023 than the whole 2022 count held. This is 37 percent of the rise if no seller stopped. If sellers stopped at 5 percent a year, it is 56 percent. So about half of the rise is not explained by first-time entry. This accounting is exploratory, like the cohort check and the four further checks, and they point the same way. -Sellers described a hard year. BPS asks each business how its online sales changed. Among businesses already selling online before 2023, 24 percent said up, 44 percent said about the same, and 32 percent said down. Their average reported change was a fall of 4.3 percent. Asked about their main problem, 41 percent named a lack of customers, up from 35 percent the year before. +The cohort check compares groups of sellers across surveys. Sellers who began selling by 2020 numbered 9.9 percent fewer in the 2022 survey than in the 2020 survey, as a cohort should. In the 2023 survey they numbered 6.0 percent more than in the 2022 one. From 2023 to 2024, the cohorts behave as they should again. Sellers who began before 2020 fell by 10.2 percent, and at least 754 thousand new sellers appeared, against a rise of 584 thousand. So 2023 stands out as the break. -Where two measures look at the same place, they agree. BPS's own estimate of sales through marketplaces grew little in 2023: 0.2 percent in the middle scenario, at most 13 percent. That is in line with the platforms. The national boom sits outside the marketplaces. If online selling grew strongly that year, it grew among sellers who use chat apps and social media. The official count of those sellers is the subject of H2. +The four further checks ask where the break sits and how large it is. The first compares the 34 provinces. Count growth did not follow the share of new sellers (rank correlation 0.10, p = 0.59). It did follow growth in sample size (0.35, p = 0.04). In 23 provinces, growth exceeded what entry can explain, and these carry 98.5 percent of the increase. The second check splits sellers by channel. Among sellers who began by 2022, those using only chat or social media rose by 13 percent, and marketplace users rose by 0.3 percent. ---- +The third check asks about sampling error. BPS's published error rates imply a standard error of about 1.4 percent for the national count. The 306 thousand sellers are about 4.6 times the standard error of the change. They would be 2.3 times it if the true error were twice as large. The fourth check is the sellers' own reports in section 5.6, which do not describe a rise in sales. -*Figures for this part: 5.1 `exhibits/v2/1_thin_slice.png`; 5.2 `exhibits/v2/2_tokopedia_2023.png`; 5.3 `exhibits/6_verdict_2023.png` (paths under `research/option_space_2026-10-01/`). Sources: `FINDINGS.md` sections 1-3 and the 2023 verdict; Part 3 with its dated notes; `h1_extended.py`, `h1_points_vs_log.py`, `wedge_split.py`, `growth_decomposition.py`, `m1_market_value.py`, `gel_checks.py`; GoTo annual report 2023, Note 29.* +The value side tells a similar story. BPS's value rose by about US$21 billion. Sellers not explained by entry account for 15 to 17 percent of this, and entrants for 25 to 30 percent. The remaining 53 to 59 percent cannot be traced to existing sellers' answers, which show a mean change of −4.3 percent. The channel and province checks point to wider survey coverage of chat and social sellers. The data cannot separate that from sellers returning after a pause. Section 5.8 asks who the extra sellers were, and section 5.9 asks whether they returned. + +## 5.8 Who the extra sellers were + +The comparison of who the extra sellers were is exploratory. The surveys do not follow the same businesses, so the thesis compares groups. For each group, it takes the 2023 count above what the group's normal change in 2020 to 2022 would give. Table 5.6 gives the share of this extra count held by each group, next to the group's share of the 2022 count. + +**Table 5.6. Who the extra sellers were (percent)** + +| Group | Share of the 2022 count | Share of the extra count | +|---|---|---| +| Business opened before 2010 | 18 | 41 | +| Owner aged 50 or over | 29 | 41 | +| Outside Java | 24 | 36 | +| Food, drink or making goods | 39 | 53 | + +The extra count leaned toward long-running small businesses, older owners and places outside Java. The lean toward men in the raw data disappears once normal change is taken out. The extra count does not look like the young start-ups of the usual growth story. It raises one question: could these be established sellers who paused and came back? + +## 5.9 Could they be sellers who returned? + +A seller returning from a break usually sells for part of the year. Part-year selling fell in 2023. Among sellers who began by 2021, the number selling for part of the year fell from 472 thousand to 318 thousand. It fell in every start-year group. Small firms that stop are more often young, with young owners (McKenzie and Paffhausen 2019). If returners were mostly such firms, the extra count would lean young. It leaned old. Returners equal to 10 to 15 percent of the 2022 count would remove the unexplained part. This is a scenario, not an estimate. The check cannot rule out returners: the files carry no linked histories, and exits or a move to full-year selling could offset them. + +## 5.10 Channels and records + +Two additional analyses, on channels and on records, are reported here. The path of marketplace use and the result on the value held by sellers without statements were registered. The comparison within the pre-2020 group is exploratory. + +Most online sellers do not use a marketplace. About 96 percent use chat or social media. Use of marketplaces fell every time it was measured, from 21.6 percent of sellers in 2020 to 17.2 percent in 2024. From 2023 to 2024, BPS's marketplace value grew by 1.45 percent and other channels by 20.57 percent, so 98.5 percent of the increase came from outside the marketplaces. BPS's marketplace category includes many food and ride merchants: 43 percent of its sellers use only Gojek or Grab, and 47 percent use a shopping marketplace. + +**Table 5.7. Marketplace use and financial statements (percent of sellers)** + +| Year | Use a marketplace | Keep complete financial statements | +|---|---|---| +| 2020 | 21.6 | 23.5 | +| 2022 | 19.8 | 20.7 | +| 2023 | 17.8 | 15.2 | +| 2024 | 17.2 | | + +Records are thinner outside the marketplaces. In 2023, 28.6 percent of marketplace sellers kept complete financial statements, against 12.3 percent of other sellers. Table 5.7 shows that the share of all sellers who keep them fell. The fall from 2020 to 2022 came while coverage was stable. The fall in 2023 partly reflects the chat-only sellers added to the count. Even among sellers who began before 2020, the share fell from 23.4 to 16.8 percent between the two surveys. Sellers without statements held between 26 and 40 percent of online value in 2022, depending on the scenario, against 79 percent of the count. The registered prediction was that their share of value would not fall by more than 5 points between 2020 and 2022. It fell by 5.8 points, so the prediction failed, narrowly. + +The World Bank surveys point the same way. In six Indonesian cities, 27 percent of unregistered firms sell through social media. Their median sales are about US$4,000 a year, and 70 percent keep no written records. Among formal firms, 39.9 percent in Indonesia have neither a website nor online tax filing, the highest of seven countries. Malaysia has 9.1 percent and Thailand 8.7 percent. + +## 5.11 Why the national figures disagree + +The third objective asks whether platform, national and payment figures give the same account. They do not give the same account of scale. They give the same account of growth only where they cover the same sellers. + +In 2023, Bank Indonesia's figure fell by 4.7 percent and BPS's rose by 40.6 percent. Where the two cover the same ground, they agree on slow growth. BPS's own marketplace slice grew by 0.2 percent from 2022 to 2023, and between 0 and 13 percent across the scenarios. It grew by 1.45 percent from 2023 to 2024. A registered test also holds: BPS's marketplace slice for 2022 is between 0.16 and 0.68 times Bank Indonesia's figure. So the disagreement sits outside the marketplaces, among the chat and social sellers that only BPS reaches. + +The 2024 figures show the same split. Those that cover mainly platforms grew by 5 to 7 percent, and BPS's grew by 17.1 percent. Adjacent figures point the same way as BPS, but they do not measure Indonesian e-commerce. Southeast Asian parcels grew by 25.2 percent, and the tax on foreign digital services by 24.9 percent. + +Sizes cannot be reconciled. BPS's marketplace slice, about US$13 billion in 2024, is less than a quarter of the marketplace estimate of Momentum Works, about US$57 billion. Platform transaction value counts shipping, digital goods, travel and offline sales that a seller survey does not count in the same way. Sellers with revenue above about US$3 million a year are 0.5 percent of BPS's marketplace sellers but hold between 18 and 39 percent of the slice's value. Only 19 sample rows lie in that bracket. Counts also differ. Tokopedia alone said it had about 15 million merchants in 2022, while BPS counts about 0.6 million marketplace sellers. + +Platform sales also trail household spending. GoTo's on-demand transaction value grew more slowly than household spending in each of the four quarters from the third quarter of 2025 to the second quarter of 2026. In the last of them it grew by 2.0 percent against 8.3 percent, while its revenue grew by 20.5 percent. Payment figures such as QRIS are context only. They include in-person payments and the switch from cash. + +Chapter 6 asks what these results mean for the rules that Indonesia is writing. From 748c5b49efbce641aaaf5a5362b35c3290c3c894 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 12 Oct 2026 18:26:54 +0000 Subject: [PATCH 10/65] Thesis ch4/ch5: reconcile Tokopedia's two splits, add seven-window table, fix scope and wording Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/ch4_data_methods.md | 12 +++--- papers/thesis_2026-10/ch5_results.md | 47 ++++++++++++++--------- 2 files changed, 34 insertions(+), 25 deletions(-) diff --git a/papers/thesis_2026-10/ch4_data_methods.md b/papers/thesis_2026-10/ch4_data_methods.md index 466e07a..26a18ff 100644 --- a/papers/thesis_2026-10/ch4_data_methods.md +++ b/papers/thesis_2026-10/ch4_data_methods.md @@ -4,9 +4,9 @@ This chapter describes the data and the tests behind the results in Chapter 5. I ## 4.1 Platform data -A platform pair is one platform's revenue and transaction value in the same year. Both come from the company's own reports. Revenue is the amount in its audited accounts, and transaction value is the figure it discloses in annual reports and earnings releases. Companies define transaction value in different ways. Some include shipping charges, digital goods or travel. The thesis keeps each company's own definition and records it in a definition map. Each take rate is built within one company, so the company's own definition cancels out of its yearly changes unless the company changes it. +A platform pair is one platform's revenue and transaction value in the same year. Both come from the company's own reports. Revenue is the amount in its financial statements, and transaction value is the figure it discloses in annual reports and earnings releases. Companies define transaction value in different ways. Some include shipping charges, digital goods or travel. The thesis keeps each company's own definition and records it in a definition map. Each take rate is built within one company, so the company's own definition cancels out of its yearly changes unless the company changes it. -Two groups of platforms are compared. The Indonesian group has five platforms, listed in Table 4.1. Two of them, Grab and GoTo, are ride-hailing and delivery businesses, which the thesis calls on-demand. The foreign group has 27 listed platforms. Eight form the first benchmark: Etsy, eBay, Jumia, Mercado Libre, Rakuten, Sea, Shopify and Zalando. The other 19 were added to make the comparison larger. They include online marketplaces, travel and ticketing sites, freelance and resale platforms, and food delivery. Sea sits in the group although Indonesia is one of its main markets, so Chapter 5 reports the narrower test of section 4.2 with and without it. The appendix lists every platform and year. +Two groups of platforms are compared. The Indonesian group has five platforms, listed in Table 4.1. Two of them, Grab and GoTo, are ride-hailing and delivery businesses, which the thesis calls on-demand. The foreign group has 27 listed platforms. Eight form the first benchmark: Etsy, eBay, Jumia, Mercado Libre, Rakuten, Sea, Shopify and Zalando. The other 19 were added to make the comparison larger. They include online marketplaces, travel and ticketing sites, freelance and resale platforms, and food delivery. Ten more were examined and left out: six sell mostly their own goods, and four do not disclose transaction value. Sea sits in the group although Indonesia is one of its main markets, so Chapter 5 reports the narrower test of section 4.2 with and without it. The appendix lists every platform and year. **Table 4.1. The Indonesian group of platforms** @@ -14,7 +14,7 @@ Two groups of platforms are compared. The Indonesian group has five platforms, l |---|---|---|---|---| | Tokopedia | The e-commerce segment of GoTo, an Indonesian marketplace | 2022–2023 | 1 | Direct segment disclosure | | Blibli | Blibli's third-party retail segment, which includes online travel | 2021–2025 | 4 | Direct segment disclosure | -| Bukalapak | The whole Bukalapak group, including Mitra, its offline business | 2020–2023 | 3 | Direct company disclosure | +| Bukalapak | The whole Bukalapak group, mainly in Indonesia, including Mitra, its online-to-offline business | 2020–2023 | 3 | Direct company disclosure | | Grab on-demand | Grab's rides and deliveries across Southeast Asia, not Indonesia alone | 2020–2024 | 4 | Direct segment disclosure | | GoTo on-demand | GoTo's rides and deliveries segment | 2022–2025 | 3 | Direct segment disclosure | @@ -36,7 +36,7 @@ Each platform then gets one number: the median size of its yearly changes. The p Two tests compare the groups. The first is a one-sided Mann–Whitney test. It ranks all 32 platforms by their number and asks whether the Indonesian platforms rank higher than chance would give. It is one-sided because H1 is rejected only if Indonesian platforms move more than the others, and not if they move less. The second is a shuffle test. It assigns the group labels to the 32 platforms at random 9,999 times. It then counts how often the Indonesian group's median is at least as far above the foreign median as in the real data. -Four checks follow, and Chapter 5 reports each. The first drops each Indonesian platform in turn. The second treats Tokopedia and GoTo on-demand as one company, since GoTo owns both. The third repeats the test with growth divergence in points, as defined in Chapter 3. The fourth is a narrower version that uses only the three Indonesian marketplaces and the eight platforms of the first foreign benchmark. It uses a different measure and a smaller benchmark, so it is not a stricter form of the main test. A description counts the pairs in which the take rate moved by 0.10 or less in log terms, which the thesis calls tracking. An exploratory comparison, designed after the main result, sets the yearly change in points of transaction value beside the share each platform keeps. +Four checks follow, and Chapter 5 reports each. The first drops each Indonesian platform in turn. The second treats Tokopedia and GoTo on-demand as one company, since GoTo owns both. The third repeats the test with growth divergence in points, as defined in Chapter 3. The fourth is a narrower version that uses only the three Indonesian marketplaces and the eight platforms of the first foreign benchmark. It uses a different measure and a smaller benchmark, so it is not a stricter form of the main test. A description counts the pairs in which the take rate moved by 0.10 or less in log terms, which the thesis calls tracking. An exploratory comparison, designed after the main result, sets the yearly change in the take rate, in percentage points, beside the share of each sale that each platform keeps. A quarterly version of the test is run where companies report quarterly pairs. It uses its own sample and is never mixed with the annual one. @@ -46,9 +46,9 @@ The second objective asks where large movements in revenue came from. The thesis The first step divides each movement into volume and take rate. In log terms, revenue growth equals transaction value growth plus the change in the take rate. For each pair, the take-rate share is the size of the take-rate change divided by the sum of the sizes of both changes. The same split is made for the wedge, which equals transaction value minus revenue. The share is computed for the three Indonesian marketplaces (8 pairs) and the eight platforms of the first foreign benchmark (29 pairs), and Chapter 5 compares their medians. -The second step finds the cause of a take-rate change. A platform's take rate equals the rate of its gross fees minus the rate of its incentives, each measured against transaction value. A rise comes from higher fees, from lower incentives, or from a change in the mix of businesses. +The second step finds the cause of a take-rate change. A platform's take rate equals the rate of its gross fees minus the rate of its incentives, each measured against transaction value. A rise comes from higher fees, from lower incentives, or from a change in the mix of businesses. The thesis splits the growth of revenue over a window into three parts: more commerce, a change in the fee rate and a change in the incentive rate. -The thesis divides the change in net revenue between fees and incentives wherever a company discloses both. Tokopedia's audited segment note gives gross revenue, incentives and net revenue directly, and the thesis checks them against the audited consolidated statements. For Grab, GoTo and Blibli, the thesis uses the company's releases and the split between rides, deliveries and other businesses. Seven windows are traced in all, and the appendix lists them. Where a company does not separate the parts, the movement is left untraced, and the appendix table says so. The thesis reads company explanations as reports and does not test them. +It makes this split wherever a company discloses both its fees and its incentives. Tokopedia's audited segment note gives gross revenue, incentives and net revenue directly, and the thesis checks them against the audited consolidated statements. For Tokopedia it also reports the change line by line in rupiah, which is the usual way to read accounts, and it says where the two views differ. For Grab, GoTo and Blibli, the thesis uses the company's releases and the split between rides, deliveries and other businesses. Seven windows are traced in all, and the appendix lists them. Where a company does not separate the parts, the movement is left untraced, and the appendix table says so. The thesis reads company explanations as reports and does not test them. ## 4.4 The national survey and the entry test diff --git a/papers/thesis_2026-10/ch5_results.md b/papers/thesis_2026-10/ch5_results.md index 1539d3f..59d88d8 100644 --- a/papers/thesis_2026-10/ch5_results.md +++ b/papers/thesis_2026-10/ch5_results.md @@ -15,13 +15,13 @@ In 2023, three major platforms in Indonesia carried US$43.2 billion of transacti | Grab, Indonesia | 5.38 | 0.61 | 4.78 | Derived | | Total | 43.23 | 3.16 | 40.07 | | -The size of the wedge changes little with the assumptions. Assume instead that Shopee keeps 9 or 11 percent, and the total wedge stays between US$39.9 billion and US$40.3 billion. Drop any one platform, and the wedge is still at least US$20.7 billion. For the platforms that report both amounts directly, the wedge is 97 to 99 percent of transaction value in every year. It moves almost one for one with transaction value. The appendix gives the wedge for each platform and year from 2020 to 2025. +The size of the wedge changes little with the assumptions. Assume instead that Shopee keeps 9 or 11 percent, and the total wedge stays between US$39.9 billion and US$40.3 billion. Drop any one platform, and the wedge is still at least US$20.7 billion. For the platforms that report both amounts directly, the wedge is 97 to 99 percent of transaction value in every year. It moves almost one for one with transaction value. The appendix gives the wedge for each platform and each year available between 2020 and 2025. -The platforms are only part of online selling. In the BPS survey, the marketplace channel carries about a fifth of online sales value: between 16 and 26 percent, depending on the year and the scenario. BPS counts food and ride apps in this channel. So a measure built on platforms misses about three quarters of online sales value. Revenue, a few percent of what the platforms carry, shows much less still. +The platforms are only part of online selling. In the BPS survey, the marketplace channel carries about a fifth of online sales value: between 16 and 26 percent, depending on the year and the scenario. BPS counts food and ride apps in this channel. So a measure built on platforms misses between three quarters and four fifths of online sales value. Revenue, which is about 7 percent of what the platforms carry, shows much less still. ## 5.2 Do revenue and transaction value grow together? -H1 is rejected. In Indonesia, revenue did not grow in proportion to transaction value. Table 5.2 gives the tests, which compare five Indonesian platforms with 27 foreign ones. The typical Indonesian platform changed its take rate by about 22 percent in a year, up or down. The typical foreign platform changed it by about 6 percent. The difference is about threefold. The first version of the test used only the three marketplaces. The larger groups were set up after that result was known, so this is not a registered test. +H1 is rejected. In Indonesia, revenue did not grow in proportion to transaction value. Table 5.2 gives the tests, which compare five Indonesian platforms with 27 foreign ones. The typical Indonesian platform changed its take rate by about 20 percent in a year, up or down. The typical foreign platform changed it by about 6 percent. The difference is about threefold. The first version of the test used only the three marketplaces. The larger groups were set up after that result was known, so this is not a registered test. **Table 5.2. The test of H1** @@ -44,22 +44,31 @@ The reason for the threefold difference is the size of the slice. This part is e ## 5.3 Why the take rate moved -Revenue moved mainly because platforms changed what they keep. Two results show this. First, the thesis divides each movement in revenue into volume and take rate, as section 4.3 describes. For the three Indonesian marketplaces, the take rate accounts for two thirds of the movement at the median (66.5 percent). For the eight platforms of the first foreign benchmark, it accounts for about a quarter (26.7 percent). The wedge follows the volume part. About 96 percent of each change in the wedge comes from the change in transaction value, in both groups. Second, Table 5.3 traces the large moves to fees and discounts. +Revenue moved mainly because platforms changed what they keep. Two results show this. First, the thesis divides each movement in revenue into volume and take rate, as section 4.3 describes. For the three Indonesian marketplaces, the take rate accounts for two thirds of the movement at the median (66.5 percent). For the eight platforms of the first foreign benchmark, it accounts for about a quarter (26.7 percent). The wedge follows the volume part. About 96 percent of each change in the wedge comes from the change in transaction value, in both groups. -**Table 5.3. What moved revenue in four Indonesian windows** +Second, Table 5.3 splits the growth of revenue into three parts: more commerce, higher fees and smaller discounts. Fees and discounts are measured per unit of transaction value. A negative share means that the part pulled revenue down. Windows of one platform overlap, so the seven windows are not seven independent cases. -| Platform and window | Change in revenue | Main source of the rise | -|---|---|---| -| Tokopedia, 2022–23 | +53% (transaction value −9%) | Lower incentives, 60% of the extra revenue; higher fees, 40% | -| Blibli third-party retail, 2022–23 | +465% | Lower incentives, 63%; part of the rest is a shift toward travel | -| Grab on-demand, 2021–24 | +321% | About one third each from volume, fees and lower incentives | -| GoTo on-demand, 2023–25 | +114% | Lower incentives, 67%; volume, 27% | +**Table 5.3. What moved revenue in seven Indonesian windows (share of the growth of revenue, percent)** + +| Platform and window | Growth of revenue | More commerce | Higher fees | Smaller discounts | +|---|---|---|---|---| +| Tokopedia, 2022–23 | +53% | −22 | 77 | 45 | +| Blibli third-party retail, 2022–23 | +465% | 17 | 20 | 63 | +| Blibli third-party retail, 2022–25 | +679% | 18 | 63 | 20 | +| Grab on-demand, 2021–24 | +321% | 34 | 34 | 33 | +| Grab on-demand, 2022–24 | +86% | 43 | 11 | 47 | +| GoTo on-demand, 2022–25 | +111% | 12 | 36 | 52 | +| GoTo on-demand, 2023–25 | +114% | 27 | 6 | 67 | + +Table 5.3 gives rounded shares, so a row can add to 101. Smaller discounts were the larger of the two take-rate parts in four windows, and higher fees in two. In the seventh, Grab from 2021 to 2024, the two were equal. Blibli's releases credit part of its 2022–23 rise to travel and digital products inside the segment, so some of its fee and discount shares reflect a change in mix, which the reports do not let us separate. For Bukalapak, the reports do not allow the parts to be separated. + +Tokopedia needs two views. Its incentives, which its accounts deduct from revenue, fell by about one third in 2023, while its transaction value fell by 9 percent and its revenue rose by 53 percent. Counting the lines of its accounts, 60 percent of the extra net revenue came from lower incentives and 40 percent from higher gross fees. That count leaves the fall in volume inside both lines. Table 5.3 takes it out and compares fees and discounts per unit of transaction value. On that basis, higher fees account for more of the rise in the take rate than smaller discounts. Both views agree that the rise came from pricing, and not from more commerce. -Table 5.3 lists four of the seven windows traced. In Tokopedia's accounts, the discounts it pays for are deducted from revenue. They fell by about one third in 2023. Revenue rose by 53 percent while the commerce it carried shrank. Across the seven windows, lower incentives were the larger part in four and higher fees in two. In the seventh, the parts were about equal. Grab's take rate rose by 3.9 points in 2022–23, and 3.5 of them came from pricing within rides and deliveries. For Bukalapak, the reports do not allow the parts to be separated. So in 2022 to 2024, revenue was not a fair guide to the commerce Indonesian platforms carried. The take rate moved a lot, mostly through smaller discounts and higher fees. +Grab's take rate rose by 3.9 points in 2022–23, and 3.5 of them came from pricing within rides and deliveries. Across the windows, then, revenue in 2022 to 2024 was not a fair guide to the commerce Indonesian platforms carried. The take rate moved a lot, mostly through smaller discounts and higher fees. ## 5.4 What markets valued -The market-value results are registered. Across 28 listed platforms and 138 platform-years, market value moved with growth in transaction value. That was the first prediction. A 1 percent rise in transaction value goes with a 0.6 percent rise in market value, with a p-value of 0.002. The second prediction was that markets value growth from a higher take rate less. It did not hold. A 1 percent rise in the take rate goes with a rise of only 0.3 percent, but the difference between the two is not significant (p = 0.20). It becomes significant only when segment series are added, and the thesis does not rely on that run. Both results are associations. Market value also changes when a company issues shares, so neither is a stock return. Market value thus moved with the buying underneath revenue, and the next section asks what that buying did in 2023. +The market-value results are registered. One prediction was that market value moves with transaction value. It held. Across 28 listed platforms and 138 platform-years, a 1 percent rise in transaction value goes with a 0.6 percent rise in market value, with a p-value of 0.002. The other prediction was that markets value growth from a higher take rate less than growth in transaction value. It did not hold. A 1 percent rise in the take rate goes with a rise of only 0.3 percent, but the difference between the two is not significant (p = 0.20). It becomes significant only when segment series are added, and the thesis does not rely on that run. Both results are associations. Market value also changes when a company issues shares, so neither is a stock return. Market value thus moved with the buying underneath revenue, and the next section asks what that buying did in 2023. ## 5.5 The platforms in 2023 @@ -73,7 +82,7 @@ Buying through platforms did not boom in 2023. Table 5.4 sets the Indonesian mea | Tokopedia transaction value | −8.9 | Rupiah | | GoTo on-demand transaction value | −10.5 | Rupiah | | Bank Indonesia e-commerce | −4.7 | Rupiah | -| Momentum Works marketplace estimate | +3.5 | Estimate | +| Momentum Works marketplace estimate | +3.5 | Outside estimate | | Bukalapak transaction value | +6.9 | Rupiah | | Blibli third-party retail transaction value | +34.7 | Rupiah | | Tokopedia revenue | +53.2 | Rupiah | @@ -119,16 +128,16 @@ The comparison of who the extra sellers were is exploratory. The surveys do not | Group | Share of the 2022 count | Share of the extra count | |---|---|---| -| Business opened before 2010 | 18 | 41 | -| Owner aged 50 or over | 29 | 41 | -| Outside Java | 24 | 36 | -| Food, drink or making goods | 39 | 53 | +| Business opened before 2010 | 18.3 | 40.5 | +| Owner aged 50 or over | 28.9 | 41.2 | +| Outside Java | 23.6 | 36.3 | +| Food and lodging, or making goods | 38.5 | 53.0 | The extra count leaned toward long-running small businesses, older owners and places outside Java. The lean toward men in the raw data disappears once normal change is taken out. The extra count does not look like the young start-ups of the usual growth story. It raises one question: could these be established sellers who paused and came back? ## 5.9 Could they be sellers who returned? -A seller returning from a break usually sells for part of the year. Part-year selling fell in 2023. Among sellers who began by 2021, the number selling for part of the year fell from 472 thousand to 318 thousand. It fell in every start-year group. Small firms that stop are more often young, with young owners (McKenzie and Paffhausen 2019). If returners were mostly such firms, the extra count would lean young. It leaned old. Returners equal to 10 to 15 percent of the 2022 count would remove the unexplained part. This is a scenario, not an estimate. The check cannot rule out returners: the files carry no linked histories, and exits or a move to full-year selling could offset them. +A seller returning from a break would usually sell for only part of the year. Part-year selling fell in 2023. Among sellers who began by 2021, the number selling for part of the year fell from 472 thousand to 318 thousand. It fell in every start-year group. Small firms that stop are more often young, with young owners (McKenzie and Paffhausen 2019). If returners were mostly such firms, the extra count would lean young. It leaned old. Returners equal to 10 to 15 percent of the 2022 count would remove the unexplained part. This is a scenario, not an estimate. The check cannot rule out returners: the files carry no linked histories, and exits or a move to full-year selling could offset them. ## 5.10 Channels and records From 3a6a15e51c698847fa57953e14c9bd8152d63bcc Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 12 Oct 2026 18:43:58 +0000 Subject: [PATCH 11/65] Thesis ch4/ch5: cleanup after independent audit (provenance labels, Tokopedia two views, registered/exploratory labels, missing methods) Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- HANDOFF.md | 1 + papers/thesis_2026-10/EDIT_HISTORY.md | 27 ++++++ papers/thesis_2026-10/ch4_data_methods.md | 67 +++++++------- papers/thesis_2026-10/ch5_results.md | 103 +++++++++++----------- 4 files changed, 116 insertions(+), 82 deletions(-) diff --git a/HANDOFF.md b/HANDOFF.md index 70a9f22..4eea921 100644 --- a/HANDOFF.md +++ b/HANDOFF.md @@ -79,6 +79,7 @@ Frozen wording of the pitch: `research/option_space_2026-10-01/GOLDEN_PITCH.md` 1. Recheck section 1.2 (contributions) of chapter 1 against the ambition paragraph in 1.1. 2. Chapters 2 and 3 have first drafts (`ch2_literature.md`, `ch3_framework.md`) and Chapters 1 to 3 have had a seam pass; revise from the researcher's reading. **Chapter 4 has a first draft** (`ch4_data_methods.md`, 12 Oct night); it covers what it owed the reader: both groups for the H1 test (Indonesian platforms and the foreign benchmark); the entry-test rule; the data behind each test; how revenue movements are traced to fees and discounts (Objective 2); how the platform evidence is compared with national and payment figures (Objective 3). +2a. Chapters 4 and 5 were audited by an independent agent and cleaned on 13 Oct (see the last section of `EDIT_HISTORY.md`); it lists three decisions that touch Chapters 1 and 3 and the list Chapter 7 must carry. 2b. Chapter 4 open points: market values (4.7) are not promised in Chapters 1 or 3, so either add one clause to 3.4 and 1.3 or cut 4.7; the appendix it points to (platform list, seven traced windows, registered tests with dates) must be built; Table 4.1 labels Blibli's and Bukalapak's scope as the sources describe it, to be rechecked against the filings; household spending needs a reference. 3. Chapter 5 has a rewritten first draft (`ch5_results.md`, 13 Oct) with 11 sections; figures (exhibits) are not yet placed; Chapter 6 (rules and reach) is next. Original instruction for Chapter 5 kept for reference: rewrite as ten sections, each answering one question, using `DRAFT_STORY_v2.md` for content and the writing rules for form. 4. Write chapters 2, 6 and 7. Chapter 6 waits for the scheduled results below. diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 1a9bf66..d946559 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -197,3 +197,30 @@ The linear read (sentence, paragraph, section, in order; then against Chapters 1 | ch4 | no mention of the points comparison, the monthly-versus-annual reading or the overshoot rule, all used in Chapter 5; "earlier foreign platforms" and "first benchmark" both used; "the table says so" unclear | all added or unified | Decisions for the researcher: (1) 5.2 says plainly that the larger H1 test is not registered. (2) Chapter 7 must carry: inferred education codes in the 2023 file, the 19 sample rows in the top bracket, Bank Indonesia's method not public, the exploratory status of the 2023 accounting. (3) Figures: six exhibits exist (`exhibits/v2`) and none is placed. + +### Cleanup of Chapters 4 and 5 after an independent audit (13 Oct 2026) + +Why: the researcher asked for both chapters to reach Chapter 1's standard with no open problems. A fresh agent that had not seen my conclusions audited every number and claim in both chapters against the repository sources (53 findings; 13 categories of items verified clean, including all table cells in 5.2, 5.3, 5.4, 5.6 and 5.7). I checked its main claims against the sources myself (Shopee's transaction value is a Momentum Works estimate; GoTo's 2022 transaction value is an estimate; the 0.16 to 0.68 range is the final-consumer slice; Blibli's incentives are a proxy; the Tokopedia 60.6 percent and the per-unit split disagree) and all held. + +Substantive fixes: +| Where | Problem | Fix | +|---|---|---| +| 5.3 | Table 5.3 said Tokopedia was incentive-led while the text counted it as fee-led; the repo's two Tokopedia splits (60 percent from lower incentives by account lines; fees larger per unit of transaction value) disagree | One table of all seven windows on the per-unit basis; both Tokopedia views stated; "both agree on pricing, differ on which lever" | +| 5.1 | Table 5.1 labelled Shopee and Grab as the author's derived estimates; Shopee's value is Momentum Works; Grab's revenue is a direct country disclosure; 15.92 should be 15.93; "three quarters to four fifths" understated 84 percent | Labels, figure and range fixed; Sea's 11.5 percent rate added to the sensitivity | +| 5.11 | The 0.16 to 0.68 band was called the whole marketplace slice | Now the final-consumer slice, with low, middle and high (0.16, 0.68, 1.62); 0 to 13 percent growth range corrected to 0.2 to 13.2 | +| 5.7 | Table 5.5 mixed two exit scenarios; "published rise" was the weighted-file gap (819 vs 821 published); two different "caps"; US$ conversion broke the one-currency rule | Two-column table, labelled; 15.5 percent explained; US$53 to US$72 billion | +| 4.1, 5.2 | GoTo on-demand 2022 value is an estimate and the segment includes Vietnam to 2024; Grab is regional; Ozon's pair rule | Stated in Table 4.1 and under Tables 5.3, 5.4; the group is named "Indonesian and regional"; pair rule fixed | +| 5.2 | Table 5.2 mixed Mann-Whitney and shuffle p-values without saying so; "the reason is the slice" too strong | Method column; "most of the difference comes from the slice" | +| 5.10 | World Bank mean-sales failure (W3) omitted; second E7 version omitted; question-wording change and stable-coverage statements stated as fact; validation outcome never reported | All added or hedged | +| 4 and 5 | Method statements missing from Chapter 4 for Objective 1, the quarterly sample, the value side, the profile control, part-year selling, the returners scenario | Added to 4.1, 4.2 and 4.4 | +| labels | Several sections had no status label; "registered" list incomplete | Every section now marked registered, exploratory or descriptive | + +Also fixed: terms (incentives, platform-year, file versus survey year), the p-value and rank correlation defined at first use, rupiah removed from the narrative, sentences over 26 words split, "therefore" removed. + +Open for the researcher (these touch other chapters, so nothing was changed there): +1. **Chapter 1, paragraph 4:** "about 60 percent of the rise in revenue reflected smaller discounts and incentives" is the account-lines view. Per unit of transaction value, higher fees account for more. Proposed: "…about 60 percent of the extra net revenue came from smaller discounts and incentives, counting the lines of its accounts." +2. **Chapter 1, "In brief":** "the official count of online sellers also overstated growth" is stronger than Chapter 5, which cannot separate wider coverage from returning sellers. Proposed: "the growth in the count of online sellers is only partly explained by first-time entry." +3. **Chapter 3, 3.2:** H1 is "tested by asking whether growth divergence … is unusually large". The main test uses the log form. Proposed: add "measured in logs" and note the points form is a check. +4. **Chapter 7 must carry:** inferred education codes (2023 file); 19 sample rows in the top bracket; Bank Indonesia's method; the exploratory status of the 2023 accounting; the month question changing format; Grab's two vintages; the one-sided tests. +5. **Appendix still to build:** platform list; seven windows with the Blibli proxy; wedge by platform and year; registered tests with dates; World Bank description. +6. **Length:** Chapter 4 is now about 3,900 words and Chapter 5 about 4,200 including tables. Cut candidates are 4.5 (World Bank), 5.7's value paragraph, and the Table 5.2 narrower-test rows. diff --git a/papers/thesis_2026-10/ch4_data_methods.md b/papers/thesis_2026-10/ch4_data_methods.md index 26a18ff..d6c2287 100644 --- a/papers/thesis_2026-10/ch4_data_methods.md +++ b/papers/thesis_2026-10/ch4_data_methods.md @@ -1,14 +1,14 @@ # Chapter 4. Data and Methods -This chapter describes the data and the tests behind the results in Chapter 5. It starts with the platform data and the test of H1. It then shows how revenue movements are traced to fees and discounts. After that it turns to the national survey, the entry test and the further analyses. The last parts cover the comparison with national and payment figures, the market values, and the rules that keep every number traceable. +This chapter describes the data and the tests behind the results in Chapter 5. It starts with the platform data and the test of H1. It then shows how revenue movements are traced to fees and incentives. After that it turns to the national survey, the entry test and the further analyses. The last parts cover the comparison with national and payment figures, the market values, and the rules that keep every number traceable. ## 4.1 Platform data -A platform pair is one platform's revenue and transaction value in the same year. Both come from the company's own reports. Revenue is the amount in its financial statements, and transaction value is the figure it discloses in annual reports and earnings releases. Companies define transaction value in different ways. Some include shipping charges, digital goods or travel. The thesis keeps each company's own definition and records it in a definition map. Each take rate is built within one company, so the company's own definition cancels out of its yearly changes unless the company changes it. +A platform-year is one platform's revenue and transaction value in one year. Both come from the company's own reports. Revenue is the amount in its financial statements, and transaction value is the figure it discloses in annual reports and earnings releases. Companies define transaction value in different ways. Some include shipping charges, digital goods or travel. The thesis keeps each company's own definition and records it in a definition map. Each take rate is built within one company, so the company's own definition cancels out of its yearly changes unless the company changes it. -Two groups of platforms are compared. The Indonesian group has five platforms, listed in Table 4.1. Two of them, Grab and GoTo, are ride-hailing and delivery businesses, which the thesis calls on-demand. The foreign group has 27 listed platforms. Eight form the first benchmark: Etsy, eBay, Jumia, Mercado Libre, Rakuten, Sea, Shopify and Zalando. The other 19 were added to make the comparison larger. They include online marketplaces, travel and ticketing sites, freelance and resale platforms, and food delivery. Ten more were examined and left out: six sell mostly their own goods, and four do not disclose transaction value. Sea sits in the group although Indonesia is one of its main markets, so Chapter 5 reports the narrower test of section 4.2 with and without it. The appendix lists every platform and year. +Two groups of platforms are compared. The Indonesian and regional group has five platforms, listed in Table 4.1. Two of them, Grab and GoTo, are ride-hailing and delivery businesses, which the thesis calls on-demand. The foreign group has 27 listed platforms. Eight form the first benchmark: Etsy, eBay, Jumia, Mercado Libre, Rakuten, Sea, Shopify and Zalando. The other 19 were added to make the comparison larger. They include online marketplaces, travel and ticketing sites, freelance and resale platforms, and food delivery. Ten more were examined and left out: six sell mostly their own goods, and four do not disclose transaction value. Sea sits in the group although Indonesia is one of its main markets. Chapter 5 reports the narrower test of section 4.2 with and without it. The appendix lists every platform and year. -**Table 4.1. The Indonesian group of platforms** +**Table 4.1. The Indonesian and regional group of platforms** | Platform series | What it covers | Years | Yearly pairs | How it is labelled | |---|---|---|---|---| @@ -16,43 +16,45 @@ Two groups of platforms are compared. The Indonesian group has five platforms, l | Blibli | Blibli's third-party retail segment, which includes online travel | 2021–2025 | 4 | Direct segment disclosure | | Bukalapak | The whole Bukalapak group, mainly in Indonesia, including Mitra, its online-to-offline business | 2020–2023 | 3 | Direct company disclosure | | Grab on-demand | Grab's rides and deliveries across Southeast Asia, not Indonesia alone | 2020–2024 | 4 | Direct segment disclosure | -| GoTo on-demand | GoTo's rides and deliveries segment | 2022–2025 | 3 | Direct segment disclosure | +| GoTo on-demand | GoTo's rides and deliveries segment, in Indonesia and Vietnam to 2024 | 2022–2025 | 3 | Direct segment disclosure; the 2022 transaction value is an estimate | -Table 4.1 states the scope of each series because the series differ. Only Tokopedia is an Indonesian segment alone. Grab covers the region, and Bukalapak covers a whole group. The thesis never adds the series together. Each platform enters the tests on its own. +Table 4.1 states the scope of each series because the series differ. Only Tokopedia is an Indonesian segment alone. Grab covers the region, GoTo's on-demand segment included Vietnam until 2024, and Bukalapak covers a whole group. The thesis never adds the series together. Each platform enters the tests on its own. -Three rules govern the pairs. Only two consecutive years form a pair. A missing year stays missing and is never filled in. Annual and quarterly pairs are never pooled. Each pair uses the company's own financial year. A take rate is a ratio of two amounts in one currency, so the tests need no exchange rate. Running text gives amounts in US dollars, converted at the World Bank's official average rate for the year. +Three rules govern the pairs. Two consecutive platform-years form a pair, and nothing else does. A missing year stays missing and is never filled in. Annual and quarterly pairs are never pooled. Each pair uses the company's own financial year. A take rate is a ratio of two amounts in one currency, so the tests need no exchange rate. Running text gives amounts in US dollars, converted at the World Bank's official average rate for the year. -Two extra rules apply to the foreign group. A company that sells goods itself reports the whole sale as revenue, and its take rate then says little about a platform. For the 19 added platforms, the thesis drops any year in which the company's own goods make up more than half of its revenue. It keeps years in which they make up between a fifth and a half, and marks them. For the eight first platforms, a pair counts only if the company reported both amounts on the same scope in both years. After these rules, the foreign group has 158 yearly pairs, and the Indonesian group has 15. +Two extra rules apply to the foreign group. A company that sells goods itself reports the whole sale as revenue, and its take rate then says little about a platform. For the 19 added platforms, the thesis drops any year in which the company's own goods make up more than half of its revenue. A pair is kept when its second year passes this rule. Years in which own goods make up between a fifth and a half are kept and marked. For the eight first platforms, a pair counts only if the company reported both amounts on the same scope in both years. After these rules, the foreign group has 158 yearly pairs, and the Indonesian group has 15. + +The first objective uses a different set of amounts. It sums the transaction value and revenue of three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. It then computes the wedge and the ecosystem ratio from the sums. Tokopedia reports both amounts for its segment. Shopee's transaction value is an outside estimate by Momentum Works. Its revenue assumes a take rate of 10 percent, which the thesis varies from 9 to 11 percent. Grab reports its Indonesian revenue, and the thesis derives its Indonesian transaction value from Grab's company-wide ratio of the two amounts. The thesis also drops each platform in turn. ## 4.2 The test of H1 H1 says that revenue and transaction value grow in proportion. If they do, the take rate stays close to steady. The test measures how far each platform's take rate moves from one year to the next. -For each pair of years, the thesis takes the change in the logarithm of the take rate. A log change treats a rise by some factor and a fall by the same factor as equal in size. It equals the log growth of revenue minus the log growth of transaction value. This makes it growth divergence in a form that does not depend on how fast transaction value grew. Tokopedia's log change in 2023 is 0.52, which means its take rate rose by a factor of 1.68, or 68 percent. +For each pair of years, the thesis takes the change in the logarithm of the take rate. A log change treats a rise by some factor and a fall by the same factor as equal in size. It equals the log growth of revenue minus the log growth of transaction value. This makes it growth divergence measured in logs. Chapter 3 defines the same idea in points of growth, and the third check below uses that form. The log form does not depend on how fast transaction value grew. Tokopedia's log change in 2023 is 0.52, which means its take rate rose by a factor of 1.68, or 68 percent. H1 is about proportion, so a rise and a fall both count against it. The test uses the size of each change and ignores its direction. -Each platform then gets one number: the median size of its yearly changes. The platform is the unit of the test, not the pair, because the pairs of one platform are not independent of one another. The test compares 5 numbers for the Indonesian group with 27 for the foreign group. +Each platform then gets one number: the median size of its yearly changes. The platform is the unit of the test, not the pair, because the pairs of one platform are not independent of one another. The test compares 5 numbers for the Indonesian and regional group with 27 for the foreign group. -Two tests compare the groups. The first is a one-sided Mann–Whitney test. It ranks all 32 platforms by their number and asks whether the Indonesian platforms rank higher than chance would give. It is one-sided because H1 is rejected only if Indonesian platforms move more than the others, and not if they move less. The second is a shuffle test. It assigns the group labels to the 32 platforms at random 9,999 times. It then counts how often the Indonesian group's median is at least as far above the foreign median as in the real data. +Two tests compare the groups. The first is a one-sided Mann–Whitney test. It ranks all 32 platforms by their number and asks whether the Indonesian and regional platforms rank higher than chance would give. It is one-sided because H1 is rejected only if these platforms move more than the others, and not if they move less. The p-value is the chance of a ranking at least this lopsided if the groups did not differ. A small p-value counts against that. The second is a shuffle test. It assigns the group labels to the 32 platforms at random 9,999 times. It then counts how often the gap between the two group medians is at least as large as the real gap. -Four checks follow, and Chapter 5 reports each. The first drops each Indonesian platform in turn. The second treats Tokopedia and GoTo on-demand as one company, since GoTo owns both. The third repeats the test with growth divergence in points, as defined in Chapter 3. The fourth is a narrower version that uses only the three Indonesian marketplaces and the eight platforms of the first foreign benchmark. It uses a different measure and a smaller benchmark, so it is not a stricter form of the main test. A description counts the pairs in which the take rate moved by 0.10 or less in log terms, which the thesis calls tracking. An exploratory comparison, designed after the main result, sets the yearly change in the take rate, in percentage points, beside the share of each sale that each platform keeps. +Four checks follow, and Chapter 5 reports each. The first drops each Indonesian platform in turn. The second treats Tokopedia and GoTo on-demand as one company, since GoTo owned both in 2022 and 2023. It combines their yearly changes in two ways: the median of all of them, and the average of the two platform medians. The third repeats the test with growth divergence in points, as defined in Chapter 3. The fourth is a narrower version that uses only the three Indonesian marketplaces and the eight platforms of the first foreign benchmark. It uses a different measure and a smaller benchmark, so it is not a stricter form of the main test. A description counts the pairs in which the take rate moved by 0.10 or less in log terms. An exploratory comparison, designed after the main result, sets two things side by side. One is the yearly change in the take rate, in percentage points. The other is the share of each sale that each platform keeps. -A quarterly version of the test is run where companies report quarterly pairs. It uses its own sample and is never mixed with the annual one. +A quarterly version of the test is run where companies report quarterly pairs. It compares each quarter with the same quarter a year earlier, which removes seasonal swings. Its Indonesian and regional group is Grab on-demand, GoTo on-demand and Blibli. Its foreign group is eight listed platforms: Sea, Airbnb, Booking, DoorDash, eBay, Etsy, Lyft and Uber. It uses its own sample and is never mixed with the annual one. ## 4.3 Tracing revenue movements The second objective asks where large movements in revenue came from. The thesis answers in two steps. -The first step divides each movement into volume and take rate. In log terms, revenue growth equals transaction value growth plus the change in the take rate. For each pair, the take-rate share is the size of the take-rate change divided by the sum of the sizes of both changes. The same split is made for the wedge, which equals transaction value minus revenue. The share is computed for the three Indonesian marketplaces (8 pairs) and the eight platforms of the first foreign benchmark (29 pairs), and Chapter 5 compares their medians. +The first step divides each movement into volume and take rate. In log terms, revenue growth equals transaction value growth plus the change in the take rate. For each pair, the take-rate share is the size of the take-rate change divided by the sum of the sizes of both changes. The same split is made for the wedge, which equals transaction value minus revenue. The share is computed for the three Indonesian marketplaces (8 pairs) and the eight platforms of the first foreign benchmark (29 pairs). Chapter 5 compares their medians. -The second step finds the cause of a take-rate change. A platform's take rate equals the rate of its gross fees minus the rate of its incentives, each measured against transaction value. A rise comes from higher fees, from lower incentives, or from a change in the mix of businesses. The thesis splits the growth of revenue over a window into three parts: more commerce, a change in the fee rate and a change in the incentive rate. +The second step finds the cause of a take-rate change. A platform's take rate equals the rate of its gross fees minus the rate of its incentives, each measured against transaction value. A rise comes from higher fees, from lower incentives, or from a change in the mix of businesses. The thesis splits the growth of revenue over a window into three parts. They are more commerce, a change in the fee rate and a change in the incentive rate. -It makes this split wherever a company discloses both its fees and its incentives. Tokopedia's audited segment note gives gross revenue, incentives and net revenue directly, and the thesis checks them against the audited consolidated statements. For Tokopedia it also reports the change line by line in rupiah, which is the usual way to read accounts, and it says where the two views differ. For Grab, GoTo and Blibli, the thesis uses the company's releases and the split between rides, deliveries and other businesses. Seven windows are traced in all, and the appendix lists them. Where a company does not separate the parts, the movement is left untraced, and the appendix table says so. The thesis reads company explanations as reports and does not test them. +It makes this split wherever a company discloses both its fees and its incentives. Tokopedia's audited segment note gives gross revenue, incentives and net revenue directly, and the thesis checks them against the audited consolidated statements. For Tokopedia it also reports the change line by line in rupiah, which is the usual way to read accounts. It says where the two views differ. For Grab it uses the annual filings, and for GoTo the audited statements. Blibli does not report incentives, so the thesis uses a proxy built from its releases, and the appendix shows how. Seven windows are traced in all, and the appendix lists them. Where a company does not separate the parts, the movement is left untraced, and the appendix table says so. The thesis reads company explanations as reports and does not test them. ## 4.4 The national survey and the entry test -The national data come from the survey of online sellers run by BPS, the national statistics office. BPS publishes the number of businesses selling online, their total online sales and the shares by channel (BPS 2025). The survey rounds carry the year of publication in their names, but their questions refer to the year before. The thesis uses the year of activity throughout. The round named 2023 describes 2022. +The national data come from the survey of online sellers run by BPS, the national statistics office. BPS publishes the number of businesses selling online, their total online sales and the shares by channel (BPS 2025). The survey rounds are named for the survey year, but their questions refer to the year before. The thesis names a file by its round and a year by the year of activity. So the file for the 2024 round describes 2023. H2 uses the published figures. National online sales value equals the number of businesses times the sales per business. In log terms, growth in value is growth in the number of businesses plus growth in sales per business. @@ -66,13 +68,15 @@ The entry test needs the survey files themselves. BPS sold them under a data-use **Table 4.2. The three BPS survey files** -| Survey round | Activity year | Rows | Weighted total | Count published by BPS | +| Survey round | Activity year | Rows | Weighted total | BPS count | |---|---|---|---|---| | E-Commerce 2021 | 2020 | 11,848 | 2,361,423 | 2,361,423 | -| E-Commerce 2023 | 2022 | 31,724 | 2,995,986 | 2,995,879 | +| E-Commerce 2023 | 2022 | 31,724 | 2,995,986 | 2,995,879* | | E-Commerce 2024 | 2023 | 39,736 | 3,814,799 | 3,816,750 | -Two checks come before any test. The weighted total of each file must fall within 1 percent of the published count, and Table 4.2 shows that all three do. The 2024 file must also reproduce two published shares: 28.63 percent of marketplace sellers and 12.25 percent of other sellers keep complete financial statements. It does so to two decimals. The check confirms that the thesis reads the files as BPS does. +*Implied by the published count for 2023 and its published growth of 27.4 percent. + +Two checks come before any test. The weighted total of each file must fall within 1 percent of the BPS count, and Table 4.2 shows that all three do. The 2024 file must also reproduce two published shares: 28.63 percent of marketplace sellers and 12.25 percent of other sellers keep complete financial statements. It does so to two decimals. The check confirms that the thesis reads the files as BPS does. The entry test follows the rule written in Chapter 3. In the 2024 file, the weighted share of businesses that began selling online in 2023 must be at least 21.5 percent. The threshold comes from the published rise of 27.4 percent: 27.4 divided by 127.4 is 21.5 percent. If the weights had matched BPS's other count for 2023, the threshold would have been 23.9 percent. They did not, so 21.5 percent stands. The test was written down and dated in the project's code archive, called the repository, before the files were opened. @@ -80,7 +84,9 @@ If the test fails, a second question follows: how large is the part of the rise The thesis compares the 2023 survey's count of sellers who began before 2023 with the whole 2022 count. It makes the comparison twice. The first assumes that no seller stopped. The second assumes that sellers stopped at about 5 percent a year, the rate at which one cohort shrank between the 2020 and 2022 surveys. The gap is then stated as a share of the published rise. Chapter 5 reports both. -The cohort check and four further checks were designed after the entry test failed, so Chapter 5 labels them exploratory. The first compares count growth with sample size and with the share of new sellers across the 34 provinces. The second splits the extra sellers by the channels they use. The third uses what sellers report about their own sales. The fourth asks whether sampling error could explain the gap, using the relative standard errors that BPS publishes. A seller who stopped and returned keeps an old start year, so the files cannot rule out returners. Chapter 5 weighs that reading against the others. +The rest of the national analysis adds a value side, a profile of the extra sellers, a check on part-year selling and a returners scenario. The value side asks how much of the rise in BPS's online sales value the newly counted sellers and the entrants could account for. It values each group at the 2022 average value per seller. For sellers who use only chat or social media, the value is scaled down. It repeats this under each of the three bracket scenarios of section 4.5. What is left over would have to come from sellers who were already counted. The profile takes each group's count in the 2023 survey above what the group's yearly change between the 2020 and 2022 surveys would give. It reports each group's share of that excess. The part-year check uses the months in which each seller sold. It asks whether sellers who returned after a pause could explain the excess. The scenario asks how many returners would remove the unexplained part. + +The cohort check and four further checks were designed after the entry test failed, so Chapter 5 labels them exploratory. The first compares count growth with sample size and with the share of new sellers across the 34 provinces. The second splits the extra sellers by the channels they use. The third uses what sellers report about their own sales; its median result was registered, and the rest is exploratory. The fourth asks whether sampling error could explain the gap, using the relative standard errors that BPS publishes. A seller who stopped and returned keeps an old start year, so the files cannot rule out returners. Chapter 5 weighs that reading against the others. ## 4.5 Channels, records and reach @@ -88,11 +94,11 @@ Three further analyses use the same files. The first asks which channels sellers Value by channel needs more care. The 2021 and 2023 files give each business's revenue in four brackets, and the 2024 file gives none. The thesis assigns each bracket a low, a middle and a high amount, and it reports all three scenarios. The scenario amounts were fixed before the file was opened. The 2023 questionnaire asks for monthly revenue, but the bracket limits are annual size classes. The thesis computes both readings and keeps the one that passes a validation rule. The middle scenario's total for 2022, from the 2023 file, must lie between half and double BPS's published total of about US$53 billion. A second rule applies if the lower brackets alone already exceed that total: the thesis then reports value shares as ranges only. If validation fails, it reports only the range between the low and the high scenario. -The second analysis asks whether a seller keeps complete financial statements. This bears on what platform records can add, because a seller with no statements leaves few other records. The thesis reports the share of sellers with statements, and the share of online value they hold, in each round. +The second analysis asks whether a seller keeps complete financial statements. This bears on what platform records can add, because a seller with no statements leaves fewer formal records. The thesis reports the share of sellers with statements in each round. It reports the share of online value held by sellers without statements for 2020 and 2022, the two rounds with revenue brackets. -The third analysis, developed in Chapter 6, counts how many sellers a rule based on platform records can reach. It counts marketplace sellers whose revenue is above a bracket line and expresses the count as a share of all online sellers. BPS's brackets divide at about US$20,000 a year, and the marketplace tax exempts individual sellers up to about US$33,000. So the count above BPS's line is an upper limit on the sellers the tax can reach. +The third analysis, developed in Chapter 6, counts how many sellers a rule based on platform records can reach. It counts marketplace sellers whose revenue is above a bracket line and expresses the count as a share of all online sellers. At the 2022 rate, BPS's brackets divide at about US$20,000 a year, and the marketplace tax exempts individual sellers up to about US$34,000. So the count above BPS's line is an upper limit on the sellers the tax can reach. -World Bank Enterprise Surveys add a check on records. They cover informal sellers in six Indonesian cities and formal firms in seven countries, and the appendix describes them. +World Bank Enterprise Surveys add a check on records. They cover unregistered firms in six Indonesian cities in 2023, and formal firms with five or more employees in seven countries. The appendix describes them. ## 4.6 Comparing platform evidence with national and payment figures @@ -105,21 +111,22 @@ The third objective compares the platform evidence with national and payment fig | Platform reports | Everything bought through one platform | The platform's own records | | BPS online-seller survey | Every business that sold online, including chat and social media | Listing in sampled areas, scaled up by survey weights | | Bank Indonesia e-commerce statistics | Transactions of large e-commerce firms (Bank Indonesia 2025) | Data sent to the central bank; the method is not public | -| Industry estimates | Shopping marketplaces in Indonesia or Southeast Asia (Momentum Works 2024; Google, Temasek and Bain 2024) | Outside estimates | +| Industry estimates | Shopping marketplaces in Indonesia or Southeast Asia, from the research firm Momentum Works (2024) and the e-Conomy report (Google, Temasek and Bain 2024) | Outside estimates | | Household spending | All household purchases | BPS national accounts | +| Payment figures | Payments made through systems such as QRIS, the national QR-code system | Payment records, in person and online | -First, the thesis compares growth rates across sources and keeps each source in its own unit. Second, it compares levels only after reconciling the definitions, using the definition map from section 4.1. Third, it compares sources most closely where their coverage overlaps. The 2023 file lets the thesis separate the marketplace part of BPS sales. It compares that part with Bank Indonesia's figure for the same year, in a test written down before the file was opened. The thesis also sets quarterly platform growth beside quarterly household spending growth. It treats payment figures as context. They show how people pay, and not what they buy online. +First, the thesis compares growth rates across sources and keeps each source in its own unit. Second, it compares levels only after reconciling the definitions, using the definition map from section 4.1. Third, it compares sources most closely where their coverage overlaps. The 2023 file lets the thesis separate the part of BPS's marketplace sales that went to final consumers. It compares that part with Bank Indonesia's figure for the same year, in a test written down before the file was opened. The thesis also sets quarterly platform growth beside quarterly household spending growth. It treats payment figures as context. They show how people pay, and not what they buy online. ## 4.7 Market values -The last analysis asks whether markets value a platform's growth in transaction value differently from growth in its take rate. The data are year-end market values from the financial data service LSEG, under the university licence. The main sample has 28 listed platforms and 138 platform-years: Bukalapak and 27 platforms abroad. It overlaps with the groups in section 4.1 but is not the same, and it uses whole-company figures only. A regression relates the yearly change in log market value to the changes in log transaction value and log take rate. It gives one coefficient for each, adds a fixed effect for each year and clusters standard errors by company. +The last analysis asks whether markets value a platform's growth in transaction value differently from growth in its take rate. The data are year-end market values from the financial data service LSEG, under the university licence. The main sample has 28 listed platforms and 138 platform-years: Bukalapak and 27 platforms abroad. It overlaps with the groups in section 4.1 but is not the same, and it uses whole-company figures only. A regression relates the yearly change in log market value to the changes in log transaction value and log take rate. It gives one coefficient for each, adds a fixed effect for each year and clusters standard errors by company. Market values are taken at calendar year-end and matched to each company's financial year. Each prediction names a direction, so both tests are one-sided. -Two cautions come with the design. The result is an association. Market value also changes when a company issues shares, so the thesis does not call it a stock return. A second run adds the segment series. Segment figures do not match a whole company's market value, so that run is a robustness check and not the main test. The test was written down before the market values were pulled. +Two cautions come with the design. The result is an association. Market value also changes when a company issues shares, so the thesis does not call it a stock return. A second run adds the segment series of Blibli, GoTo and Grab and the whole-company figures of Mercado Libre, Sea and Rakuten. Segment figures do not match a whole company's market value, so that run is a robustness check and not the main test. The test was written down before the market values were pulled. ## 4.8 Keeping every number traceable -Every number in the thesis follows one path: source document, extract, analytic row, result. Each extract keeps the company's currency and units. Each value carries a label: direct company disclosure, direct segment disclosure, external estimate, derived value, conversion or scenario. The thesis never mixes the labels in one sample. It reports a restatement or a conflict between two reports and never overwrites one with the other. A single script rebuilds every table that depends only on files in the repository. In addition, 272 automatic checks confirm that each recorded result still matches its table. +Every number in the thesis follows one path: source document, extract, analytic row, result. Each extract keeps the company's currency and units. Each value carries a label: direct company disclosure, direct segment disclosure, external estimate, derived value, conversion or scenario. The thesis never mixes the labels in one sample. It reports a restatement or a conflict between two reports and never overwrites one with the other. A single script rebuilds every table that depends only on committed files, apart from a few built once from local documents, which the script lists. In addition, 272 automatic checks confirm that each recorded result still matches its table. -The thesis also separates tests by when they were written. A registered test was written down, dated and stored in the repository before the data were opened. This applies to the entry test, the scenario rules for value, the comparison with Bank Indonesia and the market-value test. An exploratory test was designed after some results were seen. Chapter 5 marks each result as registered or exploratory, and the appendix lists the registered tests with their dates. +The thesis also separates tests by when they were written. A registered test was written down, dated and stored in the repository before the data were opened. This applies, among others, to the entry test, the scenario rules for value, the comparison with Bank Indonesia and the market-value test. An exploratory test was designed after some results were seen. Chapter 5 marks each result as registered or exploratory, and the appendix lists the registered tests with their dates. Three sources are licensed and are not in the repository: the BPS survey files, the LSEG market values and the World Bank survey files. The repository records the size and checksum of each file, so anyone who obtains the same files can check them. It holds the code that reads them and the national totals that the code produces, and no business-level record. BPS is cited as the source of every survey figure, in line with the data-use agreement. Chapter 7 states what these data cannot show. diff --git a/papers/thesis_2026-10/ch5_results.md b/papers/thesis_2026-10/ch5_results.md index 59d88d8..b336557 100644 --- a/papers/thesis_2026-10/ch5_results.md +++ b/papers/thesis_2026-10/ch5_results.md @@ -1,56 +1,56 @@ # Chapter 5. Results -This chapter reports the results in the order of the thesis. It starts with the platforms, then moves to the national figures. Each section marks its results as registered or exploratory, as section 4.8 defines. +This chapter reports the results in the order of the thesis. It starts with the platforms, then moves to the national figures. Each result is marked registered, exploratory or descriptive. Section 4.8 defines the first two. A descriptive result reports numbers without a test. ## 5.1 How much revenue leaves out -In 2023, three major platforms in Indonesia carried US$43.2 billion of transaction value between them. They kept US$3.2 billion as revenue. The sum of their invisible wedges was US$40.1 billion. Revenue was about 7 percent of what passed through them. Their ecosystem ratio, the wedge divided by revenue, was 12.7. Table 5.1 shows the three platforms. Only Tokopedia's amounts are a direct disclosure. The Shopee and Grab amounts for Indonesia are the author's own derived estimates from company-wide disclosures. +In 2023, three major platforms in Indonesia carried US$43.2 billion of transaction value between them. They kept US$3.2 billion as revenue. The sum of their invisible wedges was US$40.1 billion. Revenue was about 7 percent of what passed through them. Their ecosystem ratio, the wedge divided by revenue, was 12.7. The ratio is the author's own measure. These results are descriptive. Table 5.1 shows the three platforms and how each amount was obtained. **Table 5.1. The invisible wedge of three platforms, 2023 (US$ billion)** | Platform | Transaction value | Revenue | Wedge | How it is labelled | |---|---|---|---|---| -| Tokopedia | 16.33 | 0.41 | 15.92 | Direct segment disclosure | -| Shopee, Indonesia | 21.52 | 2.15 | 19.37 | Derived, at an assumed take rate of 10 percent | -| Grab, Indonesia | 5.38 | 0.61 | 4.78 | Derived | -| Total | 43.23 | 3.16 | 40.07 | | +| Tokopedia | 16.33 | 0.41 | 15.93 | Both direct segment disclosure | +| Shopee, Indonesia | 21.52 | 2.15 | 19.37 | Transaction value is an outside estimate (Momentum Works); revenue is derived at an assumed take rate of 10 percent | +| Grab, Indonesia | 5.38 | 0.61 | 4.78 | Revenue is a direct country disclosure; transaction value is derived from Grab's company-wide ratio | +| Total | 43.23 | 3.16 | 40.07 | Rounded amounts may not add | -The size of the wedge changes little with the assumptions. Assume instead that Shopee keeps 9 or 11 percent, and the total wedge stays between US$39.9 billion and US$40.3 billion. Drop any one platform, and the wedge is still at least US$20.7 billion. For the platforms that report both amounts directly, the wedge is 97 to 99 percent of transaction value in every year. It moves almost one for one with transaction value. The appendix gives the wedge for each platform and each year available between 2020 and 2025. +The size of the wedge changes little with the assumptions. Assume instead that Shopee keeps 9 or 11 percent, and the total wedge stays between US$39.9 billion and US$40.3 billion. At Sea's company-wide rate of 11.5 percent in 2023, it would be US$39.8 billion. Drop any one platform, and the wedge is still at least US$20.7 billion. For the platforms that report both amounts directly, the wedge is 97 to 99 percent of transaction value in every year. It moves almost one for one with transaction value. The appendix gives the wedge for each platform and each year available between 2020 and 2025. -The platforms are only part of online selling. In the BPS survey, the marketplace channel carries about a fifth of online sales value: between 16 and 26 percent, depending on the year and the scenario. BPS counts food and ride apps in this channel. So a measure built on platforms misses between three quarters and four fifths of online sales value. Revenue, which is about 7 percent of what the platforms carry, shows much less still. +The platforms are only part of online selling. In the BPS survey, the marketplace channel carries about a fifth of online sales value. The share is between 16 and 26 percent, depending on the year and the scenario. BPS counts food and ride apps in this channel. So a measure built on platforms misses between about 74 and 84 percent of online sales value. Revenue, which is about 7 percent of what the platforms carry, shows much less still. ## 5.2 Do revenue and transaction value grow together? -H1 is rejected. In Indonesia, revenue did not grow in proportion to transaction value. Table 5.2 gives the tests, which compare five Indonesian platforms with 27 foreign ones. The typical Indonesian platform changed its take rate by about 20 percent in a year, up or down. The typical foreign platform changed it by about 6 percent. The difference is about threefold. The first version of the test used only the three marketplaces. The larger groups were set up after that result was known, so this is not a registered test. +H1 is rejected. Among Indonesian and regional platforms, revenue did not grow in proportion to transaction value. Table 5.2 gives the tests, which compare five such platforms with 27 foreign ones. Three are Indonesian marketplaces, and two are the on-demand segments of Grab and GoTo, which cover more than Indonesia. The typical platform in the Indonesian and regional group changed its take rate by about 20 percent in a year, up or down. The typical foreign platform changed it by about 6 percent. The difference is about threefold. The first version of the test used only the three marketplaces. The larger groups were set up after that result was known, so the main test is not registered. **Table 5.2. The test of H1** -| Test | Indonesian group (median) | Foreign group (median) | p-value | -|---|---|---|---| -| Typical yearly change in log take rate, 5 platforms against 27 (Mann–Whitney) | 0.20 | 0.06 | 0.004 | -| The same, by shuffling the group labels | 0.20 | 0.06 | 0.022 | -| Growth divergence in points, 5 against 26 | 27.0 | 6.5 | 0.005 | -| Drop each Indonesian platform in turn | 0.17 to 0.23 | 0.06 | 0.003 to 0.016 | -| Tokopedia and GoTo on-demand as one company, 4 against 27 | — | — | 0.003 | -| Three marketplaces against eight foreign platforms, growth divergence | 38.9 | 7.1 | 0.06 | -| The same, without Sea | 38.9 | 4.2 | 0.008 | -| Quarterly pairs, 3 against 8 | 0.071 | 0.049 | 0.19 | +| Test | Method | Indonesian and regional group | Foreign group | p-value | +|---|---|---|---|---| +| Yearly change in log take rate, 5 platforms against 27 | Mann–Whitney | 0.20 | 0.06 | 0.004 | +| The same | Shuffle | 0.20 | 0.06 | 0.022 | +| Growth divergence in points, 5 platforms against 26 | Mann–Whitney | 27.0 | 6.5 | 0.005 | +| Drop each Indonesian platform in turn | Mann–Whitney | 0.17 to 0.23 | 0.06 | 0.003 to 0.016 | +| Tokopedia and GoTo on-demand as one company, 4 against 27 | Mann–Whitney | — | — | 0.003 | +| Three marketplaces against eight foreign platforms, growth divergence | Shuffle | 38.9 | 7.1 | 0.06 | +| The same, without Sea | Shuffle | 38.9 | 4.2 | 0.008 | +| Quarterly pairs, 3 platforms against 8 | Mann–Whitney | 0.071 | 0.049 | 0.19 | -Table 5.2 reads as follows. The first two rows are the main test. The second row replaces the Mann–Whitney p-value with the shuffle p-value, and the group medians stay the same. The third row repeats the test with growth divergence in points, as Chapter 3 defines it; one foreign platform has no pair with both amounts in consecutive years. The dropping and grouping rows show that no single platform carries the result. The narrower test of the three marketplaces uses the shuffle p-value. It gives 0.06 on growth divergence and 0.03 on the take-rate change. It uses a smaller benchmark and a different measure, so it is not a stricter form of the main test. Removing Sea from its benchmark lowers these p-values to 0.008 and 0.017. +The first two rows are the main test, with the two methods of Chapter 4. In rows one to five, the group figures are medians of platform medians. The third row repeats the test with growth divergence in points, as Chapter 3 defines it. Ozon has no pair in which both years pass the own-goods rule, so that row has 26 foreign platforms. The dropping and grouping rows show that no single platform carries the result. The two marketplace rows use pair-level medians. This narrower test gives 0.06 on growth divergence and 0.03 on the take-rate change. It uses a smaller benchmark and a different measure, so it is not a stricter form of the main test. Removing Sea from its benchmark lowers these p-values to 0.008 and 0.017. The quarterly test was registered before it was run, but after the annual result was known. A second feature separates the groups. In one third of Indonesian pairs, the take rate stayed within about 10 percent. Abroad, it did so in about two thirds. The quarterly version of the test does not reject H1 (Table 5.2). Its Indonesian pairs mostly cover 2023 to 2026, after most of the repricing. The large moves came in 2022 to 2024, so the rejection of H1 describes that period. -The reason for the threefold difference is the size of the slice. This part is exploratory. Measured in percentage points of the take rate, Indonesian platforms changed it by about as much as foreign ones. The change was about 1 point a year in both groups, with a p-value of 0.65. But the typical Indonesian platform keeps only 1.6 percent of each sale, against 16.9 percent abroad. A rise of 1 point lifts the revenue of the first by more than half. The same rise lifts the revenue of the second by about 6 percent. This is the wedge at work. The larger it is next to revenue, the more a small price change moves revenue growth. +Most of the threefold difference comes from the size of the slice. This part is exploratory. Measured in percentage points of the take rate, Indonesian and regional platforms changed it by about as much as foreign ones. The change was about 1 point a year in both groups, with a p-value of 0.65. But the typical Indonesian and regional platform keeps only 1.6 percent of each sale, against 16.9 percent abroad. If transaction value is flat, a rise of 1 point lifts the revenue of the first by more than half. The same rise lifts the revenue of a typical foreign platform by about 6 percent. The larger the wedge is next to revenue, the more a small price change moves revenue growth. ## 5.3 Why the take rate moved -Revenue moved mainly because platforms changed what they keep. Two results show this. First, the thesis divides each movement in revenue into volume and take rate, as section 4.3 describes. For the three Indonesian marketplaces, the take rate accounts for two thirds of the movement at the median (66.5 percent). For the eight platforms of the first foreign benchmark, it accounts for about a quarter (26.7 percent). The wedge follows the volume part. About 96 percent of each change in the wedge comes from the change in transaction value, in both groups. +Revenue moved mainly because platforms changed what they keep. Two results show this. First, the thesis divides each movement in revenue into volume and take rate, as section 4.3 describes. For the three Indonesian marketplaces, the take rate accounts for two thirds of the movement at the median (66.5 percent). For the eight platforms of the first foreign benchmark, it accounts for about a quarter (26.7 percent). The wedge follows the volume part. At the median, about 96 percent of a change in the wedge comes from the change in transaction value, in both groups. These results are descriptive. -Second, Table 5.3 splits the growth of revenue into three parts: more commerce, higher fees and smaller discounts. Fees and discounts are measured per unit of transaction value. A negative share means that the part pulled revenue down. Windows of one platform overlap, so the seven windows are not seven independent cases. +Second, Table 5.3 splits the growth of revenue into three parts. They are more commerce, higher fees and lower incentives, which are the discounts and vouchers a platform pays for. Fees and incentives are measured per unit of transaction value. A negative share means that the part pulled revenue down. Windows of one platform overlap, so the seven windows are not seven independent cases. **Table 5.3. What moved revenue in seven Indonesian windows (share of the growth of revenue, percent)** -| Platform and window | Growth of revenue | More commerce | Higher fees | Smaller discounts | +| Platform and window | Growth of revenue | More commerce | Higher fees | Lower incentives | |---|---|---|---|---| | Tokopedia, 2022–23 | +53% | −22 | 77 | 45 | | Blibli third-party retail, 2022–23 | +465% | 17 | 20 | 63 | @@ -60,19 +60,19 @@ Second, Table 5.3 splits the growth of revenue into three parts: more commerce, | GoTo on-demand, 2022–25 | +111% | 12 | 36 | 52 | | GoTo on-demand, 2023–25 | +114% | 27 | 6 | 67 | -Table 5.3 gives rounded shares, so a row can add to 101. Smaller discounts were the larger of the two take-rate parts in four windows, and higher fees in two. In the seventh, Grab from 2021 to 2024, the two were equal. Blibli's releases credit part of its 2022–23 rise to travel and digital products inside the segment, so some of its fee and discount shares reflect a change in mix, which the reports do not let us separate. For Bukalapak, the reports do not allow the parts to be separated. +Table 5.3 gives rounded shares, so a row can add to 101. Lower incentives were the larger of the two take-rate parts in four windows, and higher fees in two. In the seventh, Grab from 2021 to 2024, the two were nearly equal (34 and 33 percent). Grab's figures come from its annual filings, and Tokopedia's and GoTo's from audited statements. GoTo's 2022 transaction value is an estimate. Blibli does not report incentives, so its split rests on a proxy. Blibli's releases credit part of its 2022–23 rise to travel and digital products inside the segment. So some of its fee and incentive shares reflect a change in mix, which the reports do not let us separate. For Bukalapak, the reports do not allow the parts to be separated. -Tokopedia needs two views. Its incentives, which its accounts deduct from revenue, fell by about one third in 2023, while its transaction value fell by 9 percent and its revenue rose by 53 percent. Counting the lines of its accounts, 60 percent of the extra net revenue came from lower incentives and 40 percent from higher gross fees. That count leaves the fall in volume inside both lines. Table 5.3 takes it out and compares fees and discounts per unit of transaction value. On that basis, higher fees account for more of the rise in the take rate than smaller discounts. Both views agree that the rise came from pricing, and not from more commerce. +Tokopedia needs two views. Its accounts deduct incentives from revenue, and they fell by about one third in 2023. In the same year its transaction value fell by 9 percent and its revenue rose by 53 percent. Counting the lines of its accounts, 60 percent of the extra net revenue came from lower incentives and 40 percent from higher gross fees. That count leaves the fall in volume inside both lines. Table 5.3 takes it out and compares fees and incentives per unit of transaction value. On that basis, higher fees account for more of the rise in the take rate than lower incentives. Both views agree that pricing, and not more commerce, drove the rise. They differ on which price lever led. -Grab's take rate rose by 3.9 points in 2022–23, and 3.5 of them came from pricing within rides and deliveries. Across the windows, then, revenue in 2022 to 2024 was not a fair guide to the commerce Indonesian platforms carried. The take rate moved a lot, mostly through smaller discounts and higher fees. +On Grab's quarterly figures, its Southeast Asian on-demand take rate rose by 3.9 points in 2022–23. Of these, 3.5 came from pricing within rides and deliveries. Across the windows, revenue in 2022 to 2024 was not a fair guide to the commerce these platforms carried. The take rate moved a lot, mostly through lower incentives and higher fees. ## 5.4 What markets valued -The market-value results are registered. One prediction was that market value moves with transaction value. It held. Across 28 listed platforms and 138 platform-years, a 1 percent rise in transaction value goes with a 0.6 percent rise in market value, with a p-value of 0.002. The other prediction was that markets value growth from a higher take rate less than growth in transaction value. It did not hold. A 1 percent rise in the take rate goes with a rise of only 0.3 percent, but the difference between the two is not significant (p = 0.20). It becomes significant only when segment series are added, and the thesis does not rely on that run. Both results are associations. Market value also changes when a company issues shares, so neither is a stock return. Market value thus moved with the buying underneath revenue, and the next section asks what that buying did in 2023. +The market-value results are registered and one-sided. One prediction was that market value moves with transaction value. It held. Across 28 listed platforms and 138 platform-years, a 1 percent rise in transaction value goes with a 0.6 percent rise in market value. The p-value is 0.002. The other prediction was that markets value growth from a higher take rate less than growth in transaction value. It did not hold. A 1 percent rise in the take rate goes with a rise of 0.3 percent. Its standard error is 0.37, too wide to tell it from zero. The difference between the two is not large enough to count (p = 0.20). The difference counts only when segment series and three more whole companies are added, and the thesis does not rely on that run. Both results are associations. Market value also changes when a company issues shares, so neither is a stock return. Market value thus moved with the buying underneath revenue, and the next section asks what that buying did in 2023. ## 5.5 The platforms in 2023 -Buying through platforms did not boom in 2023. Table 5.4 sets the Indonesian measures against household spending, which rose by 9.4 percent in current prices. Of six measures of buying through Indonesian platforms, five grew more slowly, and three fell. Blibli is the exception. +These results are descriptive. Buying through platforms did not boom in 2023. Table 5.4 sets the Indonesian measures against household spending, which rose by 9.4 percent in current prices. Of six measures of buying through Indonesian platforms, five grew more slowly, and three fell. Blibli is the exception. **Table 5.4. Growth in 2023 (percent)** @@ -89,40 +89,39 @@ Buying through platforms did not boom in 2023. Table 5.4 sets the Indonesian mea | BPS e-commerce value | +40.6 | Rupiah | | BPS online sellers | +27.4 | Number | -Each row of Table 5.4 is in its own unit, and the thesis compares only the rates. Rows two to seven show the buying. The last three rows show the figures that described a boom. The platforms' revenue and BPS's value rose strongly, while the buying through platforms did not. Section 5.3 explains the revenue. The next sections ask where BPS's growth came from. +Each row of Table 5.4 is in its own unit, and the thesis compares only the rates. GoTo's change rests on an estimated 2022 value. Rows two to seven show the buying. The last three rows show the figures that described a boom. The platforms' revenue and BPS's value rose strongly, while the buying through platforms did not. Section 5.3 explains the revenue. The next sections ask where BPS's growth came from. ## 5.6 What sellers reported -Sellers did not describe a boom in 2023. This section is descriptive. BPS asks each business how its online sales changed. Among businesses already selling, 24 percent reported a rise and 32 percent a fall, and 44 percent saw no change. In 2022, the balance was the other way: 37 percent reported a rise and 24 percent a fall. The mean reported change in 2023 was −4.3 percent. Lack of demand became the main obstacle, named by 41 percent of sellers in 2023 and 35 percent in 2022. Yet the published count of online sellers rose by 27 percent in the same year. Section 5.7 asks where that rise came from. +Sellers did not describe a boom in 2023. BPS asks each business how its online sales changed. Among businesses already selling, the median change was zero, as a registered prediction expected. The other figures in this section are descriptive. A share of 24 percent reported a rise, 32 percent a fall and 44 percent no change. In 2022, the balance was the other way: 37 percent reported a rise and 24 percent a fall. The mean reported change in 2023 was −4.3 percent. Lack of demand became the main obstacle, named by 41 percent of sellers in 2023 and 35 percent in 2022. Yet the published count of online sellers rose by 27 percent in the same year. Section 5.7 asks where that rise came from. ## 5.7 The count of online sellers and the entry test On BPS's published figures, H2 holds. More businesses account for 71 percent of the growth in online sales value from 2022 to 2023. They account for 90 percent from 2023 to 2024, or 71 percent if BPS's other count for 2023 is used. The split is arithmetic, and it cannot tell a new seller from one counted for the first time. -The entry test can. This result is registered. In the 2024 file, 13.45 percent of sellers began selling online in 2023. The rule required at least 21.5 percent. The entry test fails. Even if no seller stopped, entry on that scale caps the growth of the count at 15.5 percent, against the published 27.4 percent. +The entry test can. This result is registered. In the 2024 file, 13.45 percent of sellers began selling online in 2023. The rule required at least 21.5 percent. The entry test fails. Even if no seller stopped, 13.45 percent new sellers means the count could have grown by at most 15.5 percent over the sellers already there. The published growth is 27.4 percent. **Table 5.5. The 2023 rise in the count of online sellers (thousands)** -| Part of the rise | Sellers | Share of the rise | +| Part of the rise | If no seller stopped | If sellers stopped at 5 percent a year | |---|---|---| -| Published rise, 2022 to 2023 | 819 | 100 | -| Sellers who began selling online in 2023 | 513 | 63 | -| Earlier sellers counted above the whole 2022 count, if no seller stopped | 306 | 37 | -| The same, if sellers stopped at 5 percent a year | 458 | 56 | +| Rise between the weighted totals of the two files | 819 (100%) | 819 (100%) | +| Net entry: sellers who began in 2023, less those who stopped | 513 (63%) | 360 (44%) | +| Earlier sellers newly counted above the whole 2022 count | 306 (37%) | 458 (56%) | -Table 5.5 shows the accounting. The 2023 survey counted 306 thousand more sellers who began before 2023 than the whole 2022 count held. This is 37 percent of the rise if no seller stopped. If sellers stopped at 5 percent a year, it is 56 percent. So about half of the rise is not explained by first-time entry. This accounting is exploratory, like the cohort check and the four further checks, and they point the same way. +Table 5.5 shows the accounting. The rise of 819 thousand is the gap between the weighted totals of the two files, and BPS's own published counts give 821 thousand. The 2023 survey counted 306 thousand more sellers who began before 2023 than the whole 2022 count held. This is 37 percent of the rise if no seller stopped. If sellers stopped at 5 percent a year, it is 56 percent. So about half of the rise is not explained by first-time entry. This accounting is exploratory, like the cohort check and the four further checks, and they point the same way. -The cohort check compares groups of sellers across surveys. Sellers who began selling by 2020 numbered 9.9 percent fewer in the 2022 survey than in the 2020 survey, as a cohort should. In the 2023 survey they numbered 6.0 percent more than in the 2022 one. From 2023 to 2024, the cohorts behave as they should again. Sellers who began before 2020 fell by 10.2 percent, and at least 754 thousand new sellers appeared, against a rise of 584 thousand. So 2023 stands out as the break. +The cohort check compares groups of sellers across surveys. Sellers who began selling by 2020 numbered 9.9 percent fewer in the 2022 survey than in the 2020 survey, as a cohort should. That fall took two years, or about 5 percent a year. In the 2023 survey they numbered 6.0 percent more than in the 2022 one. From 2023 to 2024, the cohorts shrink again. Sellers who began before 2020 fell by 10.2 percent in that one year. At least 754 thousand new sellers appeared, against a rise of 584 thousand. So 2023 stands out as the break. -The four further checks ask where the break sits and how large it is. The first compares the 34 provinces. Count growth did not follow the share of new sellers (rank correlation 0.10, p = 0.59). It did follow growth in sample size (0.35, p = 0.04). In 23 provinces, growth exceeded what entry can explain, and these carry 98.5 percent of the increase. The second check splits sellers by channel. Among sellers who began by 2022, those using only chat or social media rose by 13 percent, and marketplace users rose by 0.3 percent. +The four further checks ask where the break sits and how large it is. The first compares the 34 provinces. A rank correlation of 1 means that two measures rank the provinces alike, and 0 means no link. Count growth had almost no link to the share of new sellers (0.10, p = 0.59). It had a weak link to growth in sample size (0.35, p = 0.04). In 23 provinces, growth exceeded what entry can explain, and these carry 98.5 percent of the increase. The second check splits sellers by channel. Among sellers who began by 2022, those using only chat or social media rose by 13 percent, and marketplace users rose by 0.3 percent. -The third check asks about sampling error. BPS's published error rates imply a standard error of about 1.4 percent for the national count. The 306 thousand sellers are about 4.6 times the standard error of the change. They would be 2.3 times it if the true error were twice as large. The fourth check is the sellers' own reports in section 5.6, which do not describe a rise in sales. +The third check asks about sampling error. BPS's published error rates imply a standard error of about 1.4 percent for the national count. This is the size of error that sampling alone would typically cause. The 306 thousand sellers are about 4.6 times the standard error of the change. They would be 2.3 times it if the true error were twice as large. The fourth check is the sellers' own reports in section 5.6, which do not describe a rise in sales. -The value side tells a similar story. BPS's value rose by about US$21 billion. Sellers not explained by entry account for 15 to 17 percent of this, and entrants for 25 to 30 percent. The remaining 53 to 59 percent cannot be traced to existing sellers' answers, which show a mean change of −4.3 percent. The channel and province checks point to wider survey coverage of chat and social sellers. The data cannot separate that from sellers returning after a pause. Section 5.8 asks who the extra sellers were, and section 5.9 asks whether they returned. +The value side tells a similar story. BPS's value rose from about US$53 billion to about US$72 billion. Sellers not explained by entry account for 15 to 17 percent of this, and entrants for 25 to 30 percent. The remaining 53 to 59 percent cannot be traced to existing sellers' answers, which show a mean change of −4.3 percent. The channel and province checks point to wider survey coverage of chat and social sellers. The data cannot separate that from sellers returning after a pause. Section 5.8 asks who the extra sellers were, and section 5.9 asks whether they returned. ## 5.8 Who the extra sellers were -The comparison of who the extra sellers were is exploratory. The surveys do not follow the same businesses, so the thesis compares groups. For each group, it takes the 2023 count above what the group's normal change in 2020 to 2022 would give. Table 5.6 gives the share of this extra count held by each group, next to the group's share of the 2022 count. +The comparison of who the extra sellers were is exploratory. The surveys do not follow the same businesses, so the thesis compares groups. For each group, it takes the 2023 count above what the group's normal yearly change in 2020 to 2022 would give. These extra sellers total about 450 thousand, close to the 458 thousand of the second column in Table 5.5. Table 5.6 gives the share of this extra count held by each group, next to the group's share of the 2022 count. **Table 5.6. Who the extra sellers were (percent)** @@ -133,17 +132,17 @@ The comparison of who the extra sellers were is exploratory. The surveys do not | Outside Java | 23.6 | 36.3 | | Food and lodging, or making goods | 38.5 | 53.0 | -The extra count leaned toward long-running small businesses, older owners and places outside Java. The lean toward men in the raw data disappears once normal change is taken out. The extra count does not look like the young start-ups of the usual growth story. It raises one question: could these be established sellers who paused and came back? +The extra count leaned toward long-running businesses, older owners and places outside Java. The lean toward men in the raw data disappears once normal change is taken out. The extra count does not look like the young start-ups of the usual growth story. It raises one question: could these be established sellers who paused and came back? ## 5.9 Could they be sellers who returned? -A seller returning from a break would usually sell for only part of the year. Part-year selling fell in 2023. Among sellers who began by 2021, the number selling for part of the year fell from 472 thousand to 318 thousand. It fell in every start-year group. Small firms that stop are more often young, with young owners (McKenzie and Paffhausen 2019). If returners were mostly such firms, the extra count would lean young. It leaned old. Returners equal to 10 to 15 percent of the 2022 count would remove the unexplained part. This is a scenario, not an estimate. The check cannot rule out returners: the files carry no linked histories, and exits or a move to full-year selling could offset them. +A seller returning from a break would usually sell for only part of the year, unless the seller restarts in January. The exploratory check looks at part-year selling, which fell in 2023. BPS changed how it asks about months between the two rounds. Among sellers who began by 2021, the number selling for part of the year fell from 472 thousand to 318 thousand. It fell in every start-year group. Small firms that stop are more often young, with young owners (McKenzie and Paffhausen 2019), and sellers who pause are likely to be such firms. If returners were mostly such firms, the extra count would lean young. It leaned old. Returners equal to 10 to 15 percent of the 2022 count would remove the unexplained part. This is a scenario, not an estimate. The check cannot rule out returners: the files carry no linked histories, and exits or a move to full-year selling could offset them. ## 5.10 Channels and records -Two additional analyses, on channels and on records, are reported here. The path of marketplace use and the result on the value held by sellers without statements were registered. The comparison within the pre-2020 group is exploratory. +Two additional analyses, on channels and on records, are reported here. The path of marketplace use, the value held by sellers without statements and the World Bank results were registered. The comparison within the pre-2020 group is exploratory. The value shares come from bracket scenarios. The annual reading of the brackets passes the validation rule, because the middle scenario gives 1.95 times BPS's total for 2022. The monthly reading gives 23 times and fails. The lower brackets alone already exceed BPS's total, so the thesis reports value shares as ranges. -Most online sellers do not use a marketplace. About 96 percent use chat or social media. Use of marketplaces fell every time it was measured, from 21.6 percent of sellers in 2020 to 17.2 percent in 2024. From 2023 to 2024, BPS's marketplace value grew by 1.45 percent and other channels by 20.57 percent, so 98.5 percent of the increase came from outside the marketplaces. BPS's marketplace category includes many food and ride merchants: 43 percent of its sellers use only Gojek or Grab, and 47 percent use a shopping marketplace. +Most online sellers do not use a marketplace. About 96 percent use chat or social media. Use of marketplaces fell every time it was measured, from 21.6 percent of sellers in 2020 to 17.2 percent in 2024. BPS worded the question slightly differently in the 2023 and 2024 rounds. From 2023 to 2024, BPS's marketplace value grew by 1.45 percent and other channels by 20.57 percent. So 98.5 percent of the increase came from outside the marketplaces. BPS's marketplace category includes many food and ride merchants: 43 percent of its sellers use only Gojek or Grab, and 47 percent use a shopping marketplace. **Table 5.7. Marketplace use and financial statements (percent of sellers)** @@ -154,20 +153,20 @@ Most online sellers do not use a marketplace. About 96 percent use chat or socia | 2023 | 17.8 | 15.2 | | 2024 | 17.2 | | -Records are thinner outside the marketplaces. In 2023, 28.6 percent of marketplace sellers kept complete financial statements, against 12.3 percent of other sellers. Table 5.7 shows that the share of all sellers who keep them fell. The fall from 2020 to 2022 came while coverage was stable. The fall in 2023 partly reflects the chat-only sellers added to the count. Even among sellers who began before 2020, the share fell from 23.4 to 16.8 percent between the two surveys. Sellers without statements held between 26 and 40 percent of online value in 2022, depending on the scenario, against 79 percent of the count. The registered prediction was that their share of value would not fall by more than 5 points between 2020 and 2022. It fell by 5.8 points, so the prediction failed, narrowly. +Records are thinner outside the marketplaces. In 2023, 28.6 percent of marketplace sellers kept complete financial statements, against 12.3 percent of other sellers. Table 5.7 shows that the share of all sellers who keep them fell. The fall from 2020 to 2022 came while the survey appears to have counted the same kinds of seller. The fall in 2023 may partly reflect the chat-only sellers added to the count. Even among sellers who began before 2020, the share fell from 23.4 to 16.8 percent between the two surveys. Sellers without statements held between 26 and 40 percent of online value in 2022, depending on the scenario, against 79 percent of the count. The registered prediction was that their share of value would not fall by more than 5 points between 2020 and 2022. It fell by 5.8 points, so the prediction failed, narrowly. A second registered version of the test gives a fall of 3.3 points. -The World Bank surveys point the same way. In six Indonesian cities, 27 percent of unregistered firms sell through social media. Their median sales are about US$4,000 a year, and 70 percent keep no written records. Among formal firms, 39.9 percent in Indonesia have neither a website nor online tax filing, the highest of seven countries. Malaysia has 9.1 percent and Thailand 8.7 percent. +The World Bank surveys point the same way. In six Indonesian cities, 27 percent of unregistered firms sell through social media, and 70 percent keep no written records. Their median sales are about US$4,000 a year. The registered prediction on their mean sales failed: it was below about US$6,600, and the mean is about US$7,300. Among formal firms, 39.9 percent in Indonesia have neither a website nor online tax filing, the highest of seven countries. Malaysia has 9.1 percent and Thailand 8.7 percent. ## 5.11 Why the national figures disagree The third objective asks whether platform, national and payment figures give the same account. They do not give the same account of scale. They give the same account of growth only where they cover the same sellers. -In 2023, Bank Indonesia's figure fell by 4.7 percent and BPS's rose by 40.6 percent. Where the two cover the same ground, they agree on slow growth. BPS's own marketplace slice grew by 0.2 percent from 2022 to 2023, and between 0 and 13 percent across the scenarios. It grew by 1.45 percent from 2023 to 2024. A registered test also holds: BPS's marketplace slice for 2022 is between 0.16 and 0.68 times Bank Indonesia's figure. So the disagreement sits outside the marketplaces, among the chat and social sellers that only BPS reaches. +The comparison with Bank Indonesia is a registered test, and the other comparisons are descriptive. In 2023, Bank Indonesia's figure fell by 4.7 percent and BPS's rose by 40.6 percent. Where the two cover the same ground, they agree on slow growth. BPS's own marketplace slice grew by 0.2 percent from 2022 to 2023 in the middle scenario, and by 0.2 to 13.2 percent across the scenarios. It grew by 1.45 percent from 2023 to 2024. The registered test holds. The part of BPS's marketplace sales that went to final consumers in 2022 is 0.68 times Bank Indonesia's figure in the middle scenario. It is 0.16 times in the low scenario and 1.62 times in the high one. So the disagreement sits outside the marketplaces, in the count of sellers that only BPS reaches. The 2024 figures show the same split. Those that cover mainly platforms grew by 5 to 7 percent, and BPS's grew by 17.1 percent. Adjacent figures point the same way as BPS, but they do not measure Indonesian e-commerce. Southeast Asian parcels grew by 25.2 percent, and the tax on foreign digital services by 24.9 percent. -Sizes cannot be reconciled. BPS's marketplace slice, about US$13 billion in 2024, is less than a quarter of the marketplace estimate of Momentum Works, about US$57 billion. Platform transaction value counts shipping, digital goods, travel and offline sales that a seller survey does not count in the same way. Sellers with revenue above about US$3 million a year are 0.5 percent of BPS's marketplace sellers but hold between 18 and 39 percent of the slice's value. Only 19 sample rows lie in that bracket. Counts also differ. Tokopedia alone said it had about 15 million merchants in 2022, while BPS counts about 0.6 million marketplace sellers. +Sizes cannot be reconciled. BPS's marketplace slice, about US$13 billion in 2024, is less than a quarter of the marketplace estimate of Momentum Works, about US$56.5 billion. Platform transaction value counts shipping, digital goods, travel and offline sales that a seller survey does not count in the same way. In 2022, sellers with revenue above about US$3.4 million a year were 0.5 percent of BPS's marketplace sellers. They held between 18 and 39 percent of the slice's value. Only 19 sample rows lie in that bracket. Counts also differ. Tokopedia alone said it had about 15 million merchants in 2022, while BPS counts about 0.6 million marketplace sellers. -Platform sales also trail household spending. GoTo's on-demand transaction value grew more slowly than household spending in each of the four quarters from the third quarter of 2025 to the second quarter of 2026. In the last of them it grew by 2.0 percent against 8.3 percent, while its revenue grew by 20.5 percent. Payment figures such as QRIS are context only. They include in-person payments and the switch from cash. +Platform sales do not always keep pace with household spending. GoTo's on-demand transaction value grew more slowly than household spending in each of four quarters. They run from the third quarter of 2025 to the second quarter of 2026. In the last of them it grew by 2.0 percent against 8.3 percent, while its revenue grew by 20.5 percent. Grab and Shopee grew faster, but their figures cover the region. Payment figures such as QRIS are context only. They include in-person payments and the switch from cash. Chapter 6 asks what these results mean for the rules that Indonesia is writing. From fbafb6c75507c029bce3239610b0c08779ac9a51 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 12 Oct 2026 18:54:02 +0000 Subject: [PATCH 12/65] Thesis ch4: align with Chapters 1 and 3 (Objective 1 histories, Objective 2 recognition and scope, two further analyses plus reach) Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/ch4_data_methods.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/papers/thesis_2026-10/ch4_data_methods.md b/papers/thesis_2026-10/ch4_data_methods.md index d6c2287..2713667 100644 --- a/papers/thesis_2026-10/ch4_data_methods.md +++ b/papers/thesis_2026-10/ch4_data_methods.md @@ -1,6 +1,6 @@ # Chapter 4. Data and Methods -This chapter describes the data and the tests behind the results in Chapter 5. It starts with the platform data and the test of H1. It then shows how revenue movements are traced to fees and incentives. After that it turns to the national survey, the entry test and the further analyses. The last parts cover the comparison with national and payment figures, the market values, and the rules that keep every number traceable. +This chapter describes the data and the tests behind the results in Chapter 5. It starts with the platform data, which serve the measure of the wedge and the test of H1. It then shows how revenue movements are traced to fees and incentives. After that it turns to the national survey, the entry test and the further analyses. The last parts cover the comparison with national and payment figures, the market values, and the rules that keep every number traceable. ## 4.1 Platform data @@ -24,7 +24,7 @@ Three rules govern the pairs. Two consecutive platform-years form a pair, and no Two extra rules apply to the foreign group. A company that sells goods itself reports the whole sale as revenue, and its take rate then says little about a platform. For the 19 added platforms, the thesis drops any year in which the company's own goods make up more than half of its revenue. A pair is kept when its second year passes this rule. Years in which own goods make up between a fifth and a half are kept and marked. For the eight first platforms, a pair counts only if the company reported both amounts on the same scope in both years. After these rules, the foreign group has 158 yearly pairs, and the Indonesian group has 15. -The first objective uses a different set of amounts. It sums the transaction value and revenue of three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. It then computes the wedge and the ecosystem ratio from the sums. Tokopedia reports both amounts for its segment. Shopee's transaction value is an outside estimate by Momentum Works. Its revenue assumes a take rate of 10 percent, which the thesis varies from 9 to 11 percent. Grab reports its Indonesian revenue, and the thesis derives its Indonesian transaction value from Grab's company-wide ratio of the two amounts. The thesis also drops each platform in turn. +The first objective uses a different set of amounts. It sums the transaction value and revenue of three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. It then computes the wedge and the ecosystem ratio from the sums. For each platform, it also computes them for every year available between 2020 and 2025, and the appendix gives the results. Tokopedia reports both amounts for its segment. Shopee's transaction value is an outside estimate by Momentum Works. Its revenue assumes a take rate of 10 percent, which the thesis varies from 9 to 11 percent. Grab reports its Indonesian revenue, and the thesis derives its Indonesian transaction value from Grab's company-wide ratio of the two amounts. The thesis also drops each platform in turn. ## 4.2 The test of H1 @@ -48,7 +48,7 @@ The second objective asks where large movements in revenue came from. The thesis The first step divides each movement into volume and take rate. In log terms, revenue growth equals transaction value growth plus the change in the take rate. For each pair, the take-rate share is the size of the take-rate change divided by the sum of the sizes of both changes. The same split is made for the wedge, which equals transaction value minus revenue. The share is computed for the three Indonesian marketplaces (8 pairs) and the eight platforms of the first foreign benchmark (29 pairs). Chapter 5 compares their medians. -The second step finds the cause of a take-rate change. A platform's take rate equals the rate of its gross fees minus the rate of its incentives, each measured against transaction value. A rise comes from higher fees, from lower incentives, or from a change in the mix of businesses. The thesis splits the growth of revenue over a window into three parts. They are more commerce, a change in the fee rate and a change in the incentive rate. +The second step finds the cause of a take-rate change. A platform's take rate equals the rate of its gross fees minus the rate of its incentives, each measured against transaction value. A rise comes from higher fees, from lower incentives, or from a change in the mix of businesses. The thesis also records where a company's accounting treatment or reporting scope matters, such as the deduction of incentives from revenue and the travel business inside Blibli's retail segment. The thesis splits the growth of revenue over a window into three parts. They are more commerce, a change in the fee rate and a change in the incentive rate. It makes this split wherever a company discloses both its fees and its incentives. Tokopedia's audited segment note gives gross revenue, incentives and net revenue directly, and the thesis checks them against the audited consolidated statements. For Tokopedia it also reports the change line by line in rupiah, which is the usual way to read accounts. It says where the two views differ. For Grab it uses the annual filings, and for GoTo the audited statements. Blibli does not report incentives, so the thesis uses a proxy built from its releases, and the appendix shows how. Seven windows are traced in all, and the appendix lists them. Where a company does not separate the parts, the movement is left untraced, and the appendix table says so. The thesis reads company explanations as reports and does not test them. @@ -90,13 +90,13 @@ The cohort check and four further checks were designed after the entry test fail ## 4.5 Channels, records and reach -Three further analyses use the same files. The first asks which channels sellers use and how sales divide between them. The questions list websites, e-mail, chat, social media and marketplaces, and they ask what share of sales is offline, on marketplaces or elsewhere online. The published tables also split the 2023 to 2024 rise in value between marketplaces and other channels. BPS counts food and ride apps in its marketplace category, so the thesis reports the shares as BPS defines them. +Two further analyses use the same files, and a third builds on them. The first asks which channels sellers use and how sales divide between them. The questions list websites, e-mail, chat, social media and marketplaces, and they ask what share of sales is offline, on marketplaces or elsewhere online. The published tables also split the 2023 to 2024 rise in value between marketplaces and other channels. BPS counts food and ride apps in its marketplace category, so the thesis reports the shares as BPS defines them. Value by channel needs more care. The 2021 and 2023 files give each business's revenue in four brackets, and the 2024 file gives none. The thesis assigns each bracket a low, a middle and a high amount, and it reports all three scenarios. The scenario amounts were fixed before the file was opened. The 2023 questionnaire asks for monthly revenue, but the bracket limits are annual size classes. The thesis computes both readings and keeps the one that passes a validation rule. The middle scenario's total for 2022, from the 2023 file, must lie between half and double BPS's published total of about US$53 billion. A second rule applies if the lower brackets alone already exceed that total: the thesis then reports value shares as ranges only. If validation fails, it reports only the range between the low and the high scenario. -The second analysis asks whether a seller keeps complete financial statements. This bears on what platform records can add, because a seller with no statements leaves fewer formal records. The thesis reports the share of sellers with statements in each round. It reports the share of online value held by sellers without statements for 2020 and 2022, the two rounds with revenue brackets. +The second asks whether a seller keeps complete financial statements. This bears on what platform records can add, because a seller with no statements leaves fewer formal records. The thesis reports the share of sellers with statements in each round. It reports the share of online value held by sellers without statements for 2020 and 2022, the two rounds with revenue brackets. -The third analysis, developed in Chapter 6, counts how many sellers a rule based on platform records can reach. It counts marketplace sellers whose revenue is above a bracket line and expresses the count as a share of all online sellers. At the 2022 rate, BPS's brackets divide at about US$20,000 a year, and the marketplace tax exempts individual sellers up to about US$34,000. So the count above BPS's line is an upper limit on the sellers the tax can reach. +The third, developed in Chapter 6, counts how many sellers a rule based on platform records can reach. It counts marketplace sellers whose revenue is above a bracket line and expresses the count as a share of all online sellers. At the 2022 rate, BPS's brackets divide at about US$20,000 a year, and the marketplace tax exempts individual sellers up to about US$34,000. So the count above BPS's line is an upper limit on the sellers the tax can reach. World Bank Enterprise Surveys add a check on records. They cover unregistered firms in six Indonesian cities in 2023, and formal firms with five or more employees in seven countries. The appendix describes them. From cd12ad95b9694ef62c89800802e061fd6d2833c1 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 12 Oct 2026 18:54:10 +0000 Subject: [PATCH 13/65] Thesis ch4: split one long sentence Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/ch4_data_methods.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/papers/thesis_2026-10/ch4_data_methods.md b/papers/thesis_2026-10/ch4_data_methods.md index 2713667..406d0c5 100644 --- a/papers/thesis_2026-10/ch4_data_methods.md +++ b/papers/thesis_2026-10/ch4_data_methods.md @@ -48,7 +48,7 @@ The second objective asks where large movements in revenue came from. The thesis The first step divides each movement into volume and take rate. In log terms, revenue growth equals transaction value growth plus the change in the take rate. For each pair, the take-rate share is the size of the take-rate change divided by the sum of the sizes of both changes. The same split is made for the wedge, which equals transaction value minus revenue. The share is computed for the three Indonesian marketplaces (8 pairs) and the eight platforms of the first foreign benchmark (29 pairs). Chapter 5 compares their medians. -The second step finds the cause of a take-rate change. A platform's take rate equals the rate of its gross fees minus the rate of its incentives, each measured against transaction value. A rise comes from higher fees, from lower incentives, or from a change in the mix of businesses. The thesis also records where a company's accounting treatment or reporting scope matters, such as the deduction of incentives from revenue and the travel business inside Blibli's retail segment. The thesis splits the growth of revenue over a window into three parts. They are more commerce, a change in the fee rate and a change in the incentive rate. +The second step finds the cause of a take-rate change. A platform's take rate equals the rate of its gross fees minus the rate of its incentives, each measured against transaction value. A rise comes from higher fees, from lower incentives, or from a change in the mix of businesses. The thesis also records where accounting treatment or reporting scope matters. Examples are the deduction of incentives from revenue and the travel business inside Blibli's retail segment. The thesis splits the growth of revenue over a window into three parts. They are more commerce, a change in the fee rate and a change in the incentive rate. It makes this split wherever a company discloses both its fees and its incentives. Tokopedia's audited segment note gives gross revenue, incentives and net revenue directly, and the thesis checks them against the audited consolidated statements. For Tokopedia it also reports the change line by line in rupiah, which is the usual way to read accounts. It says where the two views differ. For Grab it uses the annual filings, and for GoTo the audited statements. Blibli does not report incentives, so the thesis uses a proxy built from its releases, and the appendix shows how. Seven windows are traced in all, and the appendix lists them. Where a company does not separate the parts, the movement is left untraced, and the appendix table says so. The thesis reads company explanations as reports and does not test them. From 5df7f014719d371a8e4e77e2b8d9b3418440d3c1 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 12 Oct 2026 19:07:39 +0000 Subject: [PATCH 14/65] Thesis: Chapters 4 and 5 rewritten in the voice of Chapters 1 and 2; appendix notes added; logs updated Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 14 +++ papers/thesis_2026-10/README.md | 1 + papers/thesis_2026-10/appendix_notes.md | 145 ++++++++++++++++++++++ papers/thesis_2026-10/ch4_data_methods.md | 98 +++++---------- papers/thesis_2026-10/ch5_results.md | 90 ++++++-------- 5 files changed, 224 insertions(+), 124 deletions(-) create mode 100644 papers/thesis_2026-10/appendix_notes.md diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index d946559..8c8feb3 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -224,3 +224,17 @@ Open for the researcher (these touch other chapters, so nothing was changed ther 4. **Chapter 7 must carry:** inferred education codes (2023 file); 19 sample rows in the top bracket; Bank Indonesia's method; the exploratory status of the 2023 accounting; the month question changing format; Grab's two vintages; the one-sided tests. 5. **Appendix still to build:** platform list; seven windows with the Blibli proxy; wedge by platform and year; registered tests with dates; World Bank description. 6. **Length:** Chapter 4 is now about 3,900 words and Chapter 5 about 4,200 including tables. Cut candidates are 4.5 (World Bank), 5.7's value paragraph, and the Table 5.2 narrower-test rows. + +### Chapters 4 and 5 rewritten in the voice of Chapters 1 and 2 (13 Oct 2026) + +The researcher's direction: the 27 September proposal is frozen, and Chapters 4 and 5 are where the post-proposal data and findings go, so they should read like the approved Chapters 1 and 2 (flowing paragraphs, each answering the last, plain words, numbers rounded in prose, exact values in tables). The audit-driven version had become a list of checks and caveats, which cut against "readability before safety". + +What changed: +- Chapter 4 follows the proposal's own spine (4.1 selecting the platform evidence, 4.2 comparing platform activity through time, 4.3 direct and constructed Indonesia measures, 4.4 evidence beyond platform accounts, 4.5 external evidence and comparisons) and adds 4.6 on labels, traceability and licensed data. It opens on the "two sides" idea from Chapter 1. +- Chapter 5 is nine sections, each opening on the finding and ending on the question the next one answers (wedge; H1; what moved the take rate; what markets valued; the platforms and the sellers in 2023; where the extra sellers came from; who they were and whether they returned; channels and records; why the national figures disagree). +- Proposal-era wording is gone from the prose ("the first version of the test" survives only as one status sentence; "first benchmark" became "chosen first"). +- The detail moved to `appendix_notes.md`: the 27-platform list, the seven windows in full, wedge by platform-year, every check on the test of H1 with both p-values, the quarterly sample, and all registered tests with outcomes. The audit's corrections stay in the main text where they change a claim (provenance labels, the two Tokopedia views, the GoTo estimate, the final-consumer slice). +- A mistake of mine found on the way: the World Bank 70 percent without written records belongs to unregistered firms that use social media with customers (29.9 percent keep records), not to all unregistered firms. Fixed in 5.8. +- Sentences split to about 15 to 18 words; Chapter 4 is about 3,000 words and Chapter 5 about 3,900 including tables (about 3,200 of prose). + +Still open from the audit: the two Chapter 1 sentences, the Chapter 3 wording of H1, "OECD" at first use in Chapter 2, market values in 1.3 and 3.4, and the list Chapter 7 must carry. diff --git a/papers/thesis_2026-10/README.md b/papers/thesis_2026-10/README.md index d6e1157..dc30b84 100644 --- a/papers/thesis_2026-10/README.md +++ b/papers/thesis_2026-10/README.md @@ -43,4 +43,5 @@ The thesis written in full. Same title, research question, hypotheses and measur | `ch3_framework.md` | 3 Framework and Hypotheses | first full draft 12 Oct (evening), awaiting the researcher's reading: the chain of stand-ins, terms box (Table 3.1), the measures and the identity, H1, H2, the entry test, further analyses, tests table (Table 3.2); Figure 3.1 still to be drawn | | `ch2_literature.md` | 2 Literature Review | first full draft 12 Oct (evening), awaiting the researcher's reading: on the proposal's four sections (2.1 pricing and revenue recognition, 2.2 measuring the online economy, 2.3 digital records and third-party information, 2.4 the gap), with the survey-count strand added (breaks, recall, exit, margins). Citations are author-year, every one traced to `REFERENCES_WORKING.md`; the bibliography itself is not yet built | | `ch4_data_methods.md` | 4 Data and Methods | first full draft 12 Oct (night), awaiting the researcher's reading: platform data and the two groups (Table 4.1), the H1 test, tracing revenue movements, the BPS files and the entry test (Table 4.2), channels, records and reach, the comparison with national and payment figures (Table 4.3), market values, traceability. The appendix it points to (platform list, seven traced windows, registered tests) is not built | +| `appendix_notes.md` | Appendices A to C | working draft 13 Oct: platform lists, the seven windows, wedge by platform-year, every check on the test of H1, registered tests with outcomes; the detail that Chapters 4 and 5 point to | | ch6, ch7 | | not started | diff --git a/papers/thesis_2026-10/appendix_notes.md b/papers/thesis_2026-10/appendix_notes.md new file mode 100644 index 0000000..30d28f4 --- /dev/null +++ b/papers/thesis_2026-10/appendix_notes.md @@ -0,0 +1,145 @@ +# Appendices to Chapters 4 and 5 (working draft, 13 Oct 2026) + +Holds the detail that the main text points to. Every number comes from a committed table named in the notes. Nothing here changes a result in Chapter 5. + +## Appendix A. Platforms, windows and the wedge by year + +### A.1 The foreign group (27 platforms) + +Pairs are consecutive years that pass the own-goods rule (Chapter 4, section 4.1). The median is the platform's median size of yearly change in log take rate. Platforms are shown by the ticker or name used in the panel (`tables/spinoff_panel.csv`, `tables/transitions_master.csv`); full company names are to be added. + +| Platform | Chosen first or added | Pairs | Median size of yearly change in log take rate | +|---|---|---|---| +| ACVA | Added | 5 | 0.15 | +| ASAPQ | Added | 6 | 0.10 | +| BABA | Added | 7 | 0.21 | +| DESP | Added | 9 | 0.07 | +| DIBS | Added | 6 | 0.03 | +| EXPE | Added | 11 | 0.03 | +| FVRR | Added | 7 | 0.05 | +| GrubHub | Added | 6 | 0.09 | +| HEPS | Added | 5 | 0.03 | +| KSPI | Added | 4 | 0.08 | +| MOGU | Added | 9 | 0.21 | +| Ozon | Added | 1 | 0.18 | +| PDD | Added | 4 | 0.13 | +| RBA | Added | 11 | 0.06 | +| REAL | Added | 8 | 0.04 | +| SEAT | Added | 6 | 0.07 | +| UPWK | Added | 8 | 0.04 | +| VCSA | Added | 5 | 0.03 | +| YTRA | Added | 11 | 0.03 | +| Etsy | Chosen first | 3 | 0.07 | +| Jumia | Chosen first | 3 | 0.16 | +| Mercado Libre | Chosen first | 2 | 0.06 | +| Rakuten | Chosen first | 1 | 0.02 | +| Sea / Shopee | Chosen first | 7 | 0.25 | +| Shopify | Chosen first | 5 | 0.05 | +| Zalando | Chosen first | 4 | 0.02 | +| eBay | Chosen first | 4 | 0.03 | + +Left out after examination: DDL, JD, Jumei, LQDT, NEGG and VIPS (own goods are most of revenue in every year); NBIS, TCOM, TOUR and UXIN (no transaction value disclosed). + +### A.2 The Indonesian and regional group + +| Platform | Pairs | Median size of yearly change in log take rate | Share of pairs with a change of 0.10 or less | +|---|---|---|---| +| Blibli 3P Retail | 4 | 0.25 | 0.00 | +| Bukalapak Group | 3 | 0.14 | 0.33 | +| GoTo on-demand | 3 | 0.09 | 0.67 | +| Grab on-demand | 4 | 0.20 | 0.50 | +| Tokopedia e-commerce segment | 1 | 0.52 | 0.00 | + +### A.3 The seven windows traced (Table 5.3 in full) + +Shares are of the log growth of revenue. Source: `tables/growth_decomposition.csv`, built from `tables/l_panel.csv` by `growth_decomposition.py`. Tokopedia, GoTo: audited statements. Grab: annual filings, summed over mobility and deliveries. Blibli: incentives are a proxy (gross profit before discounts less net revenue) and are marked `out` of the main sample in the panel. GoTo's 2022 transaction value is an estimate (2022 first and second quarters backed out of 2023 year-on-year comparisons). + +| Platform and window | Growth of revenue (%) | Growth of transaction value (%) | Take rate, start (%) | Take rate, end (%) | More commerce | Higher fees | Lower incentives | +|---|---|---|---|---|---|---|---| +| Grab On-demand, 2021-2024 | 320.5 | 62.3 | 5.34 | 13.83 | 33.7 | 33.8 | 32.5 | +| Grab On-demand, 2022-2024 | 85.8 | 30.1 | 9.69 | 13.83 | 42.5 | 10.6 | 46.9 | +| GoTo On-demand, 2023-2025 | 114.2 | 22.4 | 10.62 | 18.58 | 26.6 | 6.1 | 67.3 | +| GoTo On-demand, 2022-2025 | 110.7 | 9.6 | 9.66 | 18.58 | 12.3 | 36.1 | 51.7 | +| Tokopedia E-commerce, 2022-2023 | 53.2 | -8.9 | 1.48 | 2.48 | -21.9 | 76.5 | 45.4 | +| Blibli 3P Retail, 2022-2025 | 679.4 | 43.6 | 0.54 | 2.92 | 17.6 | 62.6 | 19.8 | +| Blibli 3P Retail, 2022-2023 | 465.3 | 34.7 | 0.54 | 2.25 | 17.2 | 20.1 | 62.7 | + +Tokopedia, 2022–23, line by line (Rp million, `tables/tokopedia_segment.csv`): gross revenue 8,143,239 to 8,988,909; incentives 4,112,320 to 2,813,719; net revenue 4,030,919 to 6,175,190. Net revenue rose by 2,144,271, of which lower incentives account for 60.6 percent and higher gross revenue for 39.4 percent. + +### A.4 Wedge and ecosystem ratio by platform-year, Indonesian series + +Original currency and units, as in the filings (Rp million for Tokopedia, Blibli and Bukalapak; US$ billion for Grab and Shopee, whose Indonesian amounts are derived). Take rate and ecosystem ratio carry no currency. Source: `tables/take_rate_levels.csv`. + +| Series | Year | Transaction value | Revenue | Wedge | Take rate (%) | Ecosystem ratio | Label | +|---|---|---|---|---|---|---|---| +| Grab | 2021 | 1.88 | 0.08 | 1.80 | 4.20 | 22.8 | conditional country reconstruction | +| Grab | 2022 | 3.83 | 0.28 | 3.55 | 7.19 | 12.9 | conditional country reconstruction | +| Grab | 2023 | 5.38 | 0.60 | 4.78 | 11.24 | 7.9 | conditional country reconstruction | +| Shopee | 2022 | 18.68 | 1.57 | 17.11 | 8.40 | 10.9 | conditional country reconstruction | +| Shopee | 2023 | 21.52 | 2.15 | 19.37 | 10.00 | 9.0 | conditional country reconstruction | +| Shopee | 2024 | 25.99 | 2.81 | 23.18 | 10.80 | 8.3 | conditional country reconstruction | +| Tokopedia e-commerce segment | 2022 | 273,146,166.00 | 4,030,919.00 | 269,115,247.00 | 1.48 | 66.8 | direct indonesia aligned segment | +| Tokopedia e-commerce segment | 2023 | 248,836,156.00 | 6,175,190.00 | 242,660,966.00 | 2.48 | 39.3 | direct indonesia aligned segment | +| Blibli 3P Retail | 2021 | 15,746.00 | 122.00 | 15,624.00 | 0.77 | 128.1 | direct issuer scope pending | +| Blibli 3P Retail | 2022 | 37,050.00 | 199.00 | 36,851.00 | 0.54 | 185.2 | direct issuer scope pending | +| Blibli 3P Retail | 2023 | 49,912.00 | 1,125.00 | 48,787.00 | 2.25 | 43.4 | direct issuer scope pending | +| Blibli 3P Retail | 2024 | 54,210.00 | 1,384.00 | 52,826.00 | 2.55 | 38.2 | direct issuer scope pending | +| Blibli 3P Retail | 2025 | 53,187.00 | 1,551.00 | 51,636.00 | 2.92 | 33.3 | direct issuer scope pending | +| Bukalapak Group | 2020 | 85,081,843.00 | 1,351,664.00 | 83,730,179.00 | 1.59 | 61.9 | direct issuer scope pending | +| Bukalapak Group | 2021 | 122,618,794.00 | 1,869,122.00 | 120,749,672.00 | 1.52 | 64.6 | direct issuer scope pending | +| Bukalapak Group | 2022 | 153,738,958.00 | 3,618,366.00 | 150,120,592.00 | 2.35 | 41.5 | direct issuer scope pending | +| Bukalapak Group | 2023 | 164,380,065.00 | 4,438,269.00 | 159,941,796.00 | 2.70 | 36.0 | direct issuer scope pending | + +## Appendix B. The checks on the test of H1 + +The main test is in Table 5.2. This appendix lists each check with both p-values where both exist. Sources: `h1_extended.py` (`tables/h1_extended_results.json`), `tables/test_battery.csv`, `tables/h1_quarterly_results.json`, `tables/h1_points_vs_log.json`, `../independent_review_2026-10-08/review_checks.py`. + +| Check | Indonesian and regional (median) | Foreign (median) | Mann–Whitney p | Shuffle p | +|---|---|---|---|---| +| Main test, 5 against 27 | 0.201 | 0.062 | 0.0038 | 0.0221 | +| 5 against the 8 chosen first | 0.201 | 0.055 | 0.0225 | 0.0497 | +| 5 against the 19 added | 0.201 | 0.070 | 0.0046 | 0.0175 | +| Growth divergence in points, 5 against 26 | 27.0 | 6.5 | 0.0055 | | +| Without Blibli 3P Retail | 0.169 | | 0.0137 | 0.0364 | +| Without Bukalapak Group | 0.225 | | 0.0049 | 0.0070 | +| Without GoTo on-demand | 0.225 | | 0.0030 | 0.0071 | +| Without Grab on-demand | 0.194 | | 0.0094 | 0.0166 | +| Without Tokopedia e-commerce segment | 0.169 | | 0.0164 | 0.0409 | +| Three marketplaces against the 8 chosen first, pair-level, absolute growth divergence in points | 38.917 | 7.105 | 0.0022 | 0.0606 | +| Three marketplaces against the 8 chosen first, pair-level, absolute growth divergence in points (without Sea) | 38.917 | 4.192 | 0.0000 | 0.0083 | +| Three marketplaces against the 8 chosen first, pair-level, absolute change in log take rate | 0.252 | 0.055 | 0.0037 | 0.0303 | +| Three marketplaces against the 8 chosen first, pair-level, absolute change in log take rate (without Sea) | 0.252 | 0.033 | 0.0001 | 0.0167 | +| Tokopedia and GoTo on-demand as one company, 4 against 27 | | | 0.003 | 0.007 | +| Quarterly pairs, 3 against 8 (same quarter a year earlier) | 0.071 | 0.049 | 0.19 | 0.33 | + +The quarterly sample is Grab on-demand, GoTo on-demand and Blibli against Sea, Airbnb, Booking, DoorDash, eBay, Etsy, Lyft and Uber. Airbnb and Booking use trailing four-quarter sums because they book revenue at check-in. DoorDash pairs ending in 2025Q4 or later are dropped because of an acquisition. + +The comparison in points of the take rate (exploratory, `h1_points_vs_log.py`): median yearly change 1.01 points in the Indonesian and regional group and 0.98 points in the foreign group (26 platforms), Mann–Whitney p = 0.65; median share kept 1.59 percent against 16.93 percent. + + +## Appendix C. Registered tests and their outcomes + +A registered test was written down, dated and stored in the repository before the data it concerns were opened. The dates are those of the commits in `research/option_space_2026-10-01/PREREGISTRATION.md`. Exact wording and thresholds are in that file. + +| Test | What it predicted | Registered | Outcome | +|---|---|---|---| +| E1 | Sellers without financial statements hold a smaller share of online value than of the count | 2 Oct 2026 | Holds, as a range (25.7 to 40.4 percent of value against 79.3 percent of the count) | +| E2 | The marketplace share of online value in 2022 is above the published 2023 figure | 2 Oct 2026 | Holds, as a range (22.6 to 25.6 percent) | +| E2b, E2c | Marketplace use did not rise between 2020 and 2024 | 2 Oct and 3 Oct 2026 | Hold (21.6, 19.8, 17.8 and 17.2 percent) | +| E3 | Shopee reaches at least as many sellers as Tokopedia | 2 Oct 2026 | Holds (7.5 against 2.2 percent of all sellers) | +| E4 | Entrants use chat or social media at least as often as earlier sellers | 2 Oct 2026 | Holds, with no power (nearly everyone uses them) | +| E5 | At least 21.5 percent of 2023 sellers began in 2023 (the entry test) | 2 Oct 2026 | Falsified (13.45 percent) | +| E6 | The median change in sales of sellers already online is at most 10.36 percent | 2 Oct 2026 | Holds (median 0 percent) | +| E7 | The share of value held by sellers without statements fell by at most 5 points from 2020 to 2022 | 3 Oct 2026 | Falsified, narrowly (5.8 points; 3.3 on the second version) | +| E8 | Sellers with Rp300 million or more hold over half of marketplace value | 3 Oct 2026 | Holds (92.2 percent, middle scenario, an upper bound) | +| RBI | The marketplace part sold to final consumers is below Bank Indonesia's 2022 figure | 3 Oct 2026 | Holds in the middle scenario (0.68); 0.16 low, 1.62 high | +| W1, W2 | Unregistered firms using social media keep written records and take digital payments more often | 3 Oct 2026 | Hold (29.9 against 12.3 percent; 11.5 against 2.7 percent) | +| W3 | Their mean annual sales are below Rp100 million | 3 Oct 2026 | Falsified (mean Rp111 million; median Rp60 million) | +| F1, F2 | Formal firms with a website take more e-payments and file taxes online more often | 3 Oct 2026 | Hold | +| F3, X3 | More than one third of formal firms have neither a website nor online tax filing, and Indonesia's share is the highest of the countries compared | 3 Oct 2026 | Hold (39.9 percent; Malaysia 9.1, Thailand 8.7) | +| X2 | Firms with a website file taxes online more often, in at least 3 of 4 countries | 3 Oct 2026 | Holds in 4 of 4 | +| Q1 | The test of H1 with quarterly pairs rejects H1 | 7 Oct 2026, before it was run | Falsified (p = 0.19) | +| M1 | Markets value growth from more transactions more than growth from a higher take rate | 8 Oct 2026, before the market values were pulled | Falsified in the main sample (p = 0.20) | +| M2 | Market value moves with transaction value | 8 Oct 2026 | Holds (p = 0.002) | +| P1 to P5, R1 to R3 | Predictions on the third-quarter 2026 results of GoTo, Grab and Sea | 2 and 3 Oct 2026 | Not yet scored; first results are due on 27 Oct 2026 | +| Marketplace tax start; BPS marketplace share | The tax start slips again; the next published marketplace share is at most 15.79 percent | 2 Oct 2026 | Not yet scored | diff --git a/papers/thesis_2026-10/ch4_data_methods.md b/papers/thesis_2026-10/ch4_data_methods.md index 406d0c5..4f95a7b 100644 --- a/papers/thesis_2026-10/ch4_data_methods.md +++ b/papers/thesis_2026-10/ch4_data_methods.md @@ -1,12 +1,12 @@ # Chapter 4. Data and Methods -This chapter describes the data and the tests behind the results in Chapter 5. It starts with the platform data, which serve the measure of the wedge and the test of H1. It then shows how revenue movements are traced to fees and incentives. After that it turns to the national survey, the entry test and the further analyses. The last parts cover the comparison with national and payment figures, the market values, and the rules that keep every number traceable. +Indonesia lets this thesis look at the same online economy from two sides. Platforms report what passes through them and what they keep. BPS, the national statistics office, asks the sellers themselves. This chapter describes the evidence on each side and the tests that use it. It starts with the platforms, where H1 is tested and revenue movements are traced. It then turns to the survey, where H2 and the entry test are examined. It ends with the comparisons and market values that bring the two sides together. -## 4.1 Platform data +## 4.1 Selecting the platform evidence -A platform-year is one platform's revenue and transaction value in one year. Both come from the company's own reports. Revenue is the amount in its financial statements, and transaction value is the figure it discloses in annual reports and earnings releases. Companies define transaction value in different ways. Some include shipping charges, digital goods or travel. The thesis keeps each company's own definition and records it in a definition map. Each take rate is built within one company, so the company's own definition cancels out of its yearly changes unless the company changes it. +The basic observation is one platform in one year, which this thesis calls a platform-year. Revenue and transaction value come from the company's own reports, and they refer to the same year and, as closely as the filings allow, the same business. Revenue is the amount in the financial statements. Transaction value is the figure the company discloses in its annual report or earnings release. Companies define it in different ways, and some include shipping charges, digital goods or travel. The thesis keeps each company's own definition and records it. A take rate is built inside one company, so the definition cancels out of its yearly changes unless the company changes it. -Two groups of platforms are compared. The Indonesian and regional group has five platforms, listed in Table 4.1. Two of them, Grab and GoTo, are ride-hailing and delivery businesses, which the thesis calls on-demand. The foreign group has 27 listed platforms. Eight form the first benchmark: Etsy, eBay, Jumia, Mercado Libre, Rakuten, Sea, Shopify and Zalando. The other 19 were added to make the comparison larger. They include online marketplaces, travel and ticketing sites, freelance and resale platforms, and food delivery. Ten more were examined and left out: six sell mostly their own goods, and four do not disclose transaction value. Sea sits in the group although Indonesia is one of its main markets. Chapter 5 reports the narrower test of section 4.2 with and without it. The appendix lists every platform and year. +Two groups of platforms are compared. The Indonesian and regional group has five platforms, listed in Table 4.1. Three are Indonesian marketplaces, and two are the on-demand businesses of Grab and GoTo, which cover rides and deliveries. The foreign group has 27 listed platforms in marketplaces, travel and ticketing, freelance work, resale and food delivery. Eight were chosen first, and 19 were added to widen the comparison. Ten more were examined and left out, six because they sell mostly their own goods and four because they do not disclose transaction value. **Table 4.1. The Indonesian and regional group of platforms** @@ -18,53 +18,31 @@ Two groups of platforms are compared. The Indonesian and regional group has five | Grab on-demand | Grab's rides and deliveries across Southeast Asia, not Indonesia alone | 2020–2024 | 4 | Direct segment disclosure | | GoTo on-demand | GoTo's rides and deliveries segment, in Indonesia and Vietnam to 2024 | 2022–2025 | 3 | Direct segment disclosure; the 2022 transaction value is an estimate | -Table 4.1 states the scope of each series because the series differ. Only Tokopedia is an Indonesian segment alone. Grab covers the region, GoTo's on-demand segment included Vietnam until 2024, and Bukalapak covers a whole group. The thesis never adds the series together. Each platform enters the tests on its own. +Table 4.1 states the scope of each series because the series differ. Only Tokopedia is an Indonesian segment alone. Grab covers Southeast Asia, GoTo's on-demand segment included Vietnam until 2024, and Bukalapak covers a whole group. The thesis never adds the series together, and each platform enters the tests on its own. Sea, whose Shopee business has Indonesia as a main market, sits in the foreign group. -Three rules govern the pairs. Two consecutive platform-years form a pair, and nothing else does. A missing year stays missing and is never filled in. Annual and quarterly pairs are never pooled. Each pair uses the company's own financial year. A take rate is a ratio of two amounts in one currency, so the tests need no exchange rate. Running text gives amounts in US dollars, converted at the World Bank's official average rate for the year. +Three rules keep the pairs comparable. Two consecutive platform-years form a pair, and nothing else does. A missing year stays missing and is never filled in. Annual and quarterly pairs are never pooled. A take rate is a ratio of two amounts in one currency, so the tests need no exchange rate. Amounts in running text are in US dollars at the World Bank's average rate for the year. In the foreign group, a company that sells goods itself reports the whole sale as revenue, so its take rate says little about a platform. The thesis drops years in which own goods exceed half of revenue and marks years between a fifth and a half. The result is 15 pairs for the Indonesian and regional group and 158 for the foreign group. -Two extra rules apply to the foreign group. A company that sells goods itself reports the whole sale as revenue, and its take rate then says little about a platform. For the 19 added platforms, the thesis drops any year in which the company's own goods make up more than half of its revenue. A pair is kept when its second year passes this rule. Years in which own goods make up between a fifth and a half are kept and marked. For the eight first platforms, a pair counts only if the company reported both amounts on the same scope in both years. After these rules, the foreign group has 158 yearly pairs, and the Indonesian group has 15. +## 4.2 Comparing platform activity through time -The first objective uses a different set of amounts. It sums the transaction value and revenue of three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. It then computes the wedge and the ecosystem ratio from the sums. For each platform, it also computes them for every year available between 2020 and 2025, and the appendix gives the results. Tokopedia reports both amounts for its segment. Shopee's transaction value is an outside estimate by Momentum Works. Its revenue assumes a take rate of 10 percent, which the thesis varies from 9 to 11 percent. Grab reports its Indonesian revenue, and the thesis derives its Indonesian transaction value from Grab's company-wide ratio of the two amounts. The thesis also drops each platform in turn. +H1 says that revenue and transaction value grow in proportion, which means that the take rate stays close to steady. The test asks how far each platform's take rate moves from one year to the next. For each pair of years it takes the change in the logarithm of the take rate. A log change treats a rise by some factor and a fall by the same factor as equal in size. It equals the log growth of revenue minus the log growth of transaction value. This is growth divergence measured in logs, and it does not depend on how fast transaction value grew. Tokopedia's log change in 2023 is 0.52, which is a rise by a factor of 1.68, or 68 percent. -## 4.2 The test of H1 +A rise and a fall both count against H1, so the test uses the size of each change. Each platform gets one number, the median size of its yearly changes, because the pairs of one platform are not independent of one another. The test compares five numbers for the Indonesian and regional group with 27 for the foreign group. It asks whether the first group ranks higher than chance would give, using a one-sided Mann–Whitney test. It repeats the question by shuffling the group labels 9,999 times. The p-value is the chance of a result at least this lopsided if the groups did not differ. Appendix B lists the checks that follow. They drop each platform in turn, treat Tokopedia and GoTo as one company, use growth divergence in points as Chapter 3 defines it, narrow the test to the three marketplaces, and repeat it with quarterly pairs. -H1 says that revenue and transaction value grow in proportion. If they do, the take rate stays close to steady. The test measures how far each platform's take rate moves from one year to the next. +The second objective asks where large movements in revenue came from, and the thesis answers in two steps. The first divides each movement into volume and take rate. In log terms, revenue growth equals transaction value growth plus the change in the take rate. The take-rate share is the size of the take-rate change divided by the sum of the sizes of both changes. The same split is made for the wedge, which equals transaction value minus revenue. The second step finds the cause of a take-rate change. A platform's take rate equals the rate of its gross fees minus the rate of its incentives, each measured against transaction value. So a rise comes from higher fees, lower incentives or a change in the mix of businesses. Wherever a company discloses both, the thesis splits the growth of revenue over a window into three parts. They are more commerce, a change in the fee rate and a change in the incentive rate. -For each pair of years, the thesis takes the change in the logarithm of the take rate. A log change treats a rise by some factor and a fall by the same factor as equal in size. It equals the log growth of revenue minus the log growth of transaction value. This makes it growth divergence measured in logs. Chapter 3 defines the same idea in points of growth, and the third check below uses that form. The log form does not depend on how fast transaction value grew. Tokopedia's log change in 2023 is 0.52, which means its take rate rose by a factor of 1.68, or 68 percent. +The sources differ by company. Tokopedia's audited segment note gives gross revenue, incentives and net revenue, and the thesis checks them against the audited consolidated statements. For Tokopedia it also reports the change line by line in rupiah, which is the usual way to read accounts, and it says where the two views differ. Grab is read from its annual filings and GoTo from its audited statements. Blibli does not report incentives, so its split rests on a proxy built from its releases. Seven windows are traced, and Appendix A lists them. The thesis also records where accounting treatment or reporting scope matters, such as the deduction of incentives from revenue and the travel business inside Blibli's retail segment. Company explanations are read as reports and are not tested. -H1 is about proportion, so a rise and a fall both count against it. The test uses the size of each change and ignores its direction. +## 4.3 Direct and constructed Indonesia measures -Each platform then gets one number: the median size of its yearly changes. The platform is the unit of the test, not the pair, because the pairs of one platform are not independent of one another. The test compares 5 numbers for the Indonesian and regional group with 27 for the foreign group. +The first objective needs a different set of amounts. It sums the transaction value and revenue of three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. Not every company reports Indonesia on its own. Tokopedia reports both amounts for its segment. Shopee's transaction value is an outside estimate by Momentum Works. Its revenue assumes a take rate of 10 percent, which the thesis varies from 9 to 11 percent. Grab reports its Indonesian revenue, and the thesis derives its Indonesian transaction value from Grab's company-wide ratio of the two amounts. The wedge and the ecosystem ratio are then computed from the sums. The thesis drops each platform in turn, and it repeats the exercise for every platform-year between 2020 and 2025 that the filings allow. -Two tests compare the groups. The first is a one-sided Mann–Whitney test. It ranks all 32 platforms by their number and asks whether the Indonesian and regional platforms rank higher than chance would give. It is one-sided because H1 is rejected only if these platforms move more than the others, and not if they move less. The p-value is the chance of a ranking at least this lopsided if the groups did not differ. A small p-value counts against that. The second is a shuffle test. It assigns the group labels to the 32 platforms at random 9,999 times. It then counts how often the gap between the two group medians is at least as large as the real gap. +## 4.4 Evidence beyond platform accounts -Four checks follow, and Chapter 5 reports each. The first drops each Indonesian platform in turn. The second treats Tokopedia and GoTo on-demand as one company, since GoTo owned both in 2022 and 2023. It combines their yearly changes in two ways: the median of all of them, and the average of the two platform medians. The third repeats the test with growth divergence in points, as defined in Chapter 3. The fourth is a narrower version that uses only the three Indonesian marketplaces and the eight platforms of the first foreign benchmark. It uses a different measure and a smaller benchmark, so it is not a stricter form of the main test. A description counts the pairs in which the take rate moved by 0.10 or less in log terms. An exploratory comparison, designed after the main result, sets two things side by side. One is the yearly change in the take rate, in percentage points. The other is the share of each sale that each platform keeps. +The national evidence comes from the survey of online sellers that BPS runs. BPS publishes the number of businesses selling online, their total online sales and the shares by channel (BPS 2025). The survey rounds are named for the survey year, but their questions refer to the year before. The thesis names a file by its round and a year by the year of activity, so the file for the 2024 round describes 2023. -A quarterly version of the test is run where companies report quarterly pairs. It compares each quarter with the same quarter a year earlier, which removes seasonal swings. Its Indonesian and regional group is Grab on-demand, GoTo on-demand and Blibli. Its foreign group is eight listed platforms: Sea, Airbnb, Booking, DoorDash, eBay, Etsy, Lyft and Uber. It uses its own sample and is never mixed with the annual one. +H2 uses the published figures. National online sales value equals the number of businesses times the sales per business. In log terms, growth in value is growth in the number of businesses plus growth in sales per business. H2 holds if the first part is more than half of the total. BPS prints two counts of businesses for 2023, so the thesis computes the split with both and reports both. -## 4.3 Tracing revenue movements - -The second objective asks where large movements in revenue came from. The thesis answers in two steps. - -The first step divides each movement into volume and take rate. In log terms, revenue growth equals transaction value growth plus the change in the take rate. For each pair, the take-rate share is the size of the take-rate change divided by the sum of the sizes of both changes. The same split is made for the wedge, which equals transaction value minus revenue. The share is computed for the three Indonesian marketplaces (8 pairs) and the eight platforms of the first foreign benchmark (29 pairs). Chapter 5 compares their medians. - -The second step finds the cause of a take-rate change. A platform's take rate equals the rate of its gross fees minus the rate of its incentives, each measured against transaction value. A rise comes from higher fees, from lower incentives, or from a change in the mix of businesses. The thesis also records where accounting treatment or reporting scope matters. Examples are the deduction of incentives from revenue and the travel business inside Blibli's retail segment. The thesis splits the growth of revenue over a window into three parts. They are more commerce, a change in the fee rate and a change in the incentive rate. - -It makes this split wherever a company discloses both its fees and its incentives. Tokopedia's audited segment note gives gross revenue, incentives and net revenue directly, and the thesis checks them against the audited consolidated statements. For Tokopedia it also reports the change line by line in rupiah, which is the usual way to read accounts. It says where the two views differ. For Grab it uses the annual filings, and for GoTo the audited statements. Blibli does not report incentives, so the thesis uses a proxy built from its releases, and the appendix shows how. Seven windows are traced in all, and the appendix lists them. Where a company does not separate the parts, the movement is left untraced, and the appendix table says so. The thesis reads company explanations as reports and does not test them. - -## 4.4 The national survey and the entry test - -The national data come from the survey of online sellers run by BPS, the national statistics office. BPS publishes the number of businesses selling online, their total online sales and the shares by channel (BPS 2025). The survey rounds are named for the survey year, but their questions refer to the year before. The thesis names a file by its round and a year by the year of activity. So the file for the 2024 round describes 2023. - -H2 uses the published figures. National online sales value equals the number of businesses times the sales per business. In log terms, growth in value is growth in the number of businesses plus growth in sales per business. - -``` -share from more businesses = Δln(number of businesses) / Δln(value) -``` - -H2 holds on the published figures if this share is above one half. BPS prints two counts of businesses for 2023. The thesis computes the split with both and reports both. It never replaces one with the other. - -The entry test needs the survey files themselves. BPS sold them under a data-use agreement, and Table 4.2 lists them. Each row is one business that sold online in the activity year, and each business has a survey weight. The weight shows how many businesses in the country the row represents, so the weights add up to the national count. The files carry no business identifier across rounds. They carry no area variable below the province. The rounds compare groups of sellers, not the same sellers over time. +The published figures cannot tell a new seller from one counted for the first time, but the survey files can. BPS sold them under a data-use agreement, and Table 4.2 lists them. Each row is a business that sold online in the activity year. Its survey weight shows how many businesses it represents, so the weights add up to the national count. Two checks come before any test. The weighted total of each file must fall within 1 percent of the BPS count. The 2024 file must also reproduce two published shares of sellers who keep complete financial statements, 28.63 and 12.25 percent. All three files pass. The files carry no business identifier across rounds and no area variable below the province. So the rounds compare groups of sellers and not the same sellers over time. **Table 4.2. The three BPS survey files** @@ -76,33 +54,19 @@ The entry test needs the survey files themselves. BPS sold them under a data-use *Implied by the published count for 2023 and its published growth of 27.4 percent. -Two checks come before any test. The weighted total of each file must fall within 1 percent of the BPS count, and Table 4.2 shows that all three do. The 2024 file must also reproduce two published shares: 28.63 percent of marketplace sellers and 12.25 percent of other sellers keep complete financial statements. It does so to two decimals. The check confirms that the thesis reads the files as BPS does. +The entry test follows the rule in Chapter 3. In the 2024 file, the weighted share of businesses that began selling online in 2023 must be at least 21.5 percent. That is 27.4 divided by 127.4, from the published rise of 27.4 percent. If the weights had matched BPS's other count for 2023, the threshold would have been 23.9 percent, but they did not. The test was written down and dated in the project's code archive, called the repository, before the files were opened. -The entry test follows the rule written in Chapter 3. In the 2024 file, the weighted share of businesses that began selling online in 2023 must be at least 21.5 percent. The threshold comes from the published rise of 27.4 percent: 27.4 divided by 127.4 is 21.5 percent. If the weights had matched BPS's other count for 2023, the threshold would have been 23.9 percent. They did not, so 21.5 percent stands. The test was written down and dated in the project's code archive, called the repository, before the files were opened. +If the test fails, a second question follows: how much of the rise does first-time entry not explain? A cohort is the group of sellers that began selling online by a given year. If two surveys count the same population, a cohort can only shrink between them, because sellers stop and no new seller can join it. The thesis compares the 2023 survey's count of sellers who began before 2023 with the whole 2022 count. It does so twice. The first time it assumes that no seller stopped. The second time it assumes that sellers stopped at about 5 percent a year, the rate at which one cohort shrank between the 2020 and 2022 surveys. -If the test fails, a second question follows: how large is the part of the rise that first-time entry does not explain? The thesis answers with a cohort check. A cohort is the group of sellers that began selling online by a given year. If two surveys count the same population, a cohort can only shrink between them, because sellers stop and no new seller can join it. A cohort that grows means the later survey counted sellers the earlier one missed, or that sellers returned after a pause. +Further checks ask where the extra sellers sit. They look across the 34 provinces, across channels, at sellers' own reports of their sales and at the sampling error that BPS publishes. They also profile who the extra sellers are, look at part-year selling, and test a scenario of returners. The cohort check and these further checks were designed after the entry test failed, so Chapter 5 labels them exploratory. The exception is the median change in sellers' sales, which was registered. A seller who stopped and returned keeps an old start year, so the files cannot rule out returners. -The thesis compares the 2023 survey's count of sellers who began before 2023 with the whole 2022 count. It makes the comparison twice. The first assumes that no seller stopped. The second assumes that sellers stopped at about 5 percent a year, the rate at which one cohort shrank between the 2020 and 2022 surveys. The gap is then stated as a share of the published rise. Chapter 5 reports both. +Three further analyses use the same files. The first asks which channels sellers use and how sales divide between them. The published tables split the 2023 to 2024 rise in value between marketplaces and other channels, and BPS counts food and ride apps in its marketplace category. Value by channel rests on revenue brackets, which only the 2021 and 2023 files give. The thesis assigns each bracket a low, a middle and a high amount. The amounts were fixed before the file was opened. A validation rule applies: the middle scenario's 2022 total must lie between half and double BPS's published total of about US$53 billion. The 2023 questionnaire asks for monthly revenue but the bracket limits are annual size classes, so both readings are computed and the one that passes is kept. If the lower brackets alone already exceed the total, value shares are reported as ranges. -The rest of the national analysis adds a value side, a profile of the extra sellers, a check on part-year selling and a returners scenario. The value side asks how much of the rise in BPS's online sales value the newly counted sellers and the entrants could account for. It values each group at the 2022 average value per seller. For sellers who use only chat or social media, the value is scaled down. It repeats this under each of the three bracket scenarios of section 4.5. What is left over would have to come from sellers who were already counted. The profile takes each group's count in the 2023 survey above what the group's yearly change between the 2020 and 2022 surveys would give. It reports each group's share of that excess. The part-year check uses the months in which each seller sold. It asks whether sellers who returned after a pause could explain the excess. The scenario asks how many returners would remove the unexplained part. +The second analysis asks whether a seller keeps complete financial statements. This bears on what platform records can add, because a seller with no statements leaves fewer formal records. The third builds on the first two and is developed in Chapter 6. It counts the marketplace sellers whose revenue is above a bracket line and expresses the count as a share of all online sellers. At the 2022 rate, BPS's brackets divide at about US$20,000 a year. The marketplace tax exempts individual sellers up to about US$34,000. So the count above BPS's line is an upper limit on the sellers the tax can reach. World Bank Enterprise Surveys add a check on records. They cover unregistered firms in six Indonesian cities in 2023, and formal firms with five or more employees in seven countries. -The cohort check and four further checks were designed after the entry test failed, so Chapter 5 labels them exploratory. The first compares count growth with sample size and with the share of new sellers across the 34 provinces. The second splits the extra sellers by the channels they use. The third uses what sellers report about their own sales; its median result was registered, and the rest is exploratory. The fourth asks whether sampling error could explain the gap, using the relative standard errors that BPS publishes. A seller who stopped and returned keeps an old start year, so the files cannot rule out returners. Chapter 5 weighs that reading against the others. +## 4.5 External evidence and comparisons -## 4.5 Channels, records and reach - -Two further analyses use the same files, and a third builds on them. The first asks which channels sellers use and how sales divide between them. The questions list websites, e-mail, chat, social media and marketplaces, and they ask what share of sales is offline, on marketplaces or elsewhere online. The published tables also split the 2023 to 2024 rise in value between marketplaces and other channels. BPS counts food and ride apps in its marketplace category, so the thesis reports the shares as BPS defines them. - -Value by channel needs more care. The 2021 and 2023 files give each business's revenue in four brackets, and the 2024 file gives none. The thesis assigns each bracket a low, a middle and a high amount, and it reports all three scenarios. The scenario amounts were fixed before the file was opened. The 2023 questionnaire asks for monthly revenue, but the bracket limits are annual size classes. The thesis computes both readings and keeps the one that passes a validation rule. The middle scenario's total for 2022, from the 2023 file, must lie between half and double BPS's published total of about US$53 billion. A second rule applies if the lower brackets alone already exceed that total: the thesis then reports value shares as ranges only. If validation fails, it reports only the range between the low and the high scenario. - -The second asks whether a seller keeps complete financial statements. This bears on what platform records can add, because a seller with no statements leaves fewer formal records. The thesis reports the share of sellers with statements in each round. It reports the share of online value held by sellers without statements for 2020 and 2022, the two rounds with revenue brackets. - -The third, developed in Chapter 6, counts how many sellers a rule based on platform records can reach. It counts marketplace sellers whose revenue is above a bracket line and expresses the count as a share of all online sellers. At the 2022 rate, BPS's brackets divide at about US$20,000 a year, and the marketplace tax exempts individual sellers up to about US$34,000. So the count above BPS's line is an upper limit on the sellers the tax can reach. - -World Bank Enterprise Surveys add a check on records. They cover unregistered firms in six Indonesian cities in 2023, and formal firms with five or more employees in seven countries. The appendix describes them. - -## 4.6 Comparing platform evidence with national and payment figures - -The third objective compares the platform evidence with national and payment figures. Table 4.3 lists the sources and what each one counts. The sources differ in what they cover, so the comparison follows three rules. +The third objective compares the platform evidence with national and payment figures. Table 4.3 lists the sources and what each one counts. The sources differ in what they cover, so the comparison follows three rules. It compares growth rates across sources and keeps each source in its own unit. It compares levels only after reconciling the definitions. And it compares sources most closely where their coverage overlaps. The 2023 file lets the thesis separate the part of BPS's marketplace sales that went to final consumers. It compares that part with Bank Indonesia's figure for the same year, in a test written down before the file was opened. The thesis also sets quarterly platform growth beside quarterly household spending growth. It treats payment figures as context, because they show how people pay and not what they buy online. **Table 4.3. Sources compared in Objective 3** @@ -115,18 +79,12 @@ The third objective compares the platform evidence with national and payment fig | Household spending | All household purchases | BPS national accounts | | Payment figures | Payments made through systems such as QRIS, the national QR-code system | Payment records, in person and online | -First, the thesis compares growth rates across sources and keeps each source in its own unit. Second, it compares levels only after reconciling the definitions, using the definition map from section 4.1. Third, it compares sources most closely where their coverage overlaps. The 2023 file lets the thesis separate the part of BPS's marketplace sales that went to final consumers. It compares that part with Bank Indonesia's figure for the same year, in a test written down before the file was opened. The thesis also sets quarterly platform growth beside quarterly household spending growth. It treats payment figures as context. They show how people pay, and not what they buy online. - -## 4.7 Market values - -The last analysis asks whether markets value a platform's growth in transaction value differently from growth in its take rate. The data are year-end market values from the financial data service LSEG, under the university licence. The main sample has 28 listed platforms and 138 platform-years: Bukalapak and 27 platforms abroad. It overlaps with the groups in section 4.1 but is not the same, and it uses whole-company figures only. A regression relates the yearly change in log market value to the changes in log transaction value and log take rate. It gives one coefficient for each, adds a fixed effect for each year and clusters standard errors by company. Market values are taken at calendar year-end and matched to each company's financial year. Each prediction names a direction, so both tests are one-sided. - -Two cautions come with the design. The result is an association. Market value also changes when a company issues shares, so the thesis does not call it a stock return. A second run adds the segment series of Blibli, GoTo and Grab and the whole-company figures of Mercado Libre, Sea and Rakuten. Segment figures do not match a whole company's market value, so that run is a robustness check and not the main test. The test was written down before the market values were pulled. +One last analysis asks whether markets value a platform's growth in transaction value differently from growth in its take rate. The data are year-end market values from the financial data service LSEG, under the university licence. The main sample has 28 listed platforms and 138 platform-years, made up of Bukalapak and 27 platforms abroad, and it uses whole-company figures only. A regression relates the yearly change in log market value to the changes in log transaction value and log take rate. It adds a fixed effect for each year and clusters standard errors by company. Each prediction names a direction, so both tests are one-sided. The result is an association, and market value also changes when a company issues shares, so the thesis does not call it a stock return. A second run adds segment series and three more whole companies as a robustness check. The test was written down before the market values were pulled. -## 4.8 Keeping every number traceable +## 4.6 Labels, traceability and licensed data -Every number in the thesis follows one path: source document, extract, analytic row, result. Each extract keeps the company's currency and units. Each value carries a label: direct company disclosure, direct segment disclosure, external estimate, derived value, conversion or scenario. The thesis never mixes the labels in one sample. It reports a restatement or a conflict between two reports and never overwrites one with the other. A single script rebuilds every table that depends only on committed files, apart from a few built once from local documents, which the script lists. In addition, 272 automatic checks confirm that each recorded result still matches its table. +Every number in the thesis follows one path: source document, extract, analytic row, result. Each extract keeps the company's currency and units, and each value carries a label: direct company disclosure, direct segment disclosure, external estimate, derived value, conversion or scenario. The labels are never mixed in one sample. A restatement or a conflict between two reports is reported and never overwritten. A single script rebuilds every table that depends only on committed files, and 272 automatic checks confirm that each recorded result still matches its table. -The thesis also separates tests by when they were written. A registered test was written down, dated and stored in the repository before the data were opened. This applies, among others, to the entry test, the scenario rules for value, the comparison with Bank Indonesia and the market-value test. An exploratory test was designed after some results were seen. Chapter 5 marks each result as registered or exploratory, and the appendix lists the registered tests with their dates. +The thesis also separates tests by when they were written. A registered test was written down, dated and stored in the repository before the data were opened. This applies to the entry test, the scenario rules for value, the comparison with Bank Indonesia and the market-value test, among others. Appendix C lists them with their dates. An exploratory test was designed after some results were seen. The test with the larger groups in Section 4.2 belongs here, because it was set up after a first test on the three marketplaces. A descriptive result reports numbers without a test. Three sources are licensed and are not in the repository: the BPS survey files, the LSEG market values and the World Bank survey files. The repository records the size and checksum of each file, so anyone who obtains the same files can check them. It holds the code that reads them and the national totals that the code produces, and no business-level record. BPS is cited as the source of every survey figure, in line with the data-use agreement. Chapter 7 states what these data cannot show. diff --git a/papers/thesis_2026-10/ch5_results.md b/papers/thesis_2026-10/ch5_results.md index b336557..376a8ea 100644 --- a/papers/thesis_2026-10/ch5_results.md +++ b/papers/thesis_2026-10/ch5_results.md @@ -1,10 +1,10 @@ # Chapter 5. Results -This chapter reports the results in the order of the thesis. It starts with the platforms, then moves to the national figures. Each result is marked registered, exploratory or descriptive. Section 4.8 defines the first two. A descriptive result reports numbers without a test. +This chapter follows the thesis from the platforms to the national figures. It first asks how much of the commerce a platform's revenue shows, and whether revenue keeps pace with it. It then asks why the two parted in Indonesia and what that meant for 2023. The second half turns to the survey: where the growth in sellers came from, who the extra sellers were, and why the national figures disagree. Each result is marked registered when its test was written down before the data were opened, and exploratory when the check was designed afterwards, as section 4.6 defines. A descriptive result reports numbers without a test. ## 5.1 How much revenue leaves out -In 2023, three major platforms in Indonesia carried US$43.2 billion of transaction value between them. They kept US$3.2 billion as revenue. The sum of their invisible wedges was US$40.1 billion. Revenue was about 7 percent of what passed through them. Their ecosystem ratio, the wedge divided by revenue, was 12.7. The ratio is the author's own measure. These results are descriptive. Table 5.1 shows the three platforms and how each amount was obtained. +Take three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. Together they carried US$43 billion of transaction value and kept US$3 billion as revenue. The difference, the invisible wedge, was US$40 billion. Revenue was about 7 percent of what passed through them, and their ecosystem ratio, the wedge divided by revenue, was 12.7. The ratio is the author's own measure. These results are descriptive, and Table 5.1 shows how each amount was obtained. **Table 5.1. The invisible wedge of three platforms, 2023 (US$ billion)** @@ -15,13 +15,13 @@ In 2023, three major platforms in Indonesia carried US$43.2 billion of transacti | Grab, Indonesia | 5.38 | 0.61 | 4.78 | Revenue is a direct country disclosure; transaction value is derived from Grab's company-wide ratio | | Total | 43.23 | 3.16 | 40.07 | Rounded amounts may not add | -The size of the wedge changes little with the assumptions. Assume instead that Shopee keeps 9 or 11 percent, and the total wedge stays between US$39.9 billion and US$40.3 billion. At Sea's company-wide rate of 11.5 percent in 2023, it would be US$39.8 billion. Drop any one platform, and the wedge is still at least US$20.7 billion. For the platforms that report both amounts directly, the wedge is 97 to 99 percent of transaction value in every year. It moves almost one for one with transaction value. The appendix gives the wedge for each platform and each year available between 2020 and 2025. +The size of the wedge hardly depends on the assumptions. If Shopee kept 9 or 11 percent instead of 10, the total would stay between US$39.9 and US$40.3 billion. At Sea's company-wide rate of 11.5 percent it would be US$39.8 billion, and dropping any one platform leaves at least US$20.7 billion. For platforms that report both amounts directly, the wedge is 97 to 99 percent of transaction value in every year. It moves almost one for one with it. Appendix A gives the wedge for each platform and year. -The platforms are only part of online selling. In the BPS survey, the marketplace channel carries about a fifth of online sales value. The share is between 16 and 26 percent, depending on the year and the scenario. BPS counts food and ride apps in this channel. So a measure built on platforms misses between about 74 and 84 percent of online sales value. Revenue, which is about 7 percent of what the platforms carry, shows much less still. +Platforms are only part of online selling. In the BPS survey, the marketplace channel carries between 16 and 26 percent of online sales value, depending on the year and the scenario. BPS counts food and ride apps in that channel. So a measure built on platforms misses between about 74 and 84 percent of online sales value, and revenue shows much less still. A small slice can still be a fair guide if it keeps its size. The next question is whether it does. ## 5.2 Do revenue and transaction value grow together? -H1 is rejected. Among Indonesian and regional platforms, revenue did not grow in proportion to transaction value. Table 5.2 gives the tests, which compare five such platforms with 27 foreign ones. Three are Indonesian marketplaces, and two are the on-demand segments of Grab and GoTo, which cover more than Indonesia. The typical platform in the Indonesian and regional group changed its take rate by about 20 percent in a year, up or down. The typical foreign platform changed it by about 6 percent. The difference is about threefold. The first version of the test used only the three marketplaces. The larger groups were set up after that result was known, so the main test is not registered. +In Indonesia they do not, and H1 is rejected. Among the Indonesian and regional platforms, the typical take rate moved by about 20 percent in a year, up or down. At the typical foreign platform it moved by about 6 percent. The difference is about threefold. The take rate stayed within about 10 percent in one third of the Indonesian and regional pairs, and in about two thirds of the foreign pairs. Table 5.2 gives the main test and its checks, and Appendix B gives the rest. **Table 5.2. The test of H1** @@ -30,23 +30,21 @@ H1 is rejected. Among Indonesian and regional platforms, revenue did not grow in | Yearly change in log take rate, 5 platforms against 27 | Mann–Whitney | 0.20 | 0.06 | 0.004 | | The same | Shuffle | 0.20 | 0.06 | 0.022 | | Growth divergence in points, 5 platforms against 26 | Mann–Whitney | 27.0 | 6.5 | 0.005 | -| Drop each Indonesian platform in turn | Mann–Whitney | 0.17 to 0.23 | 0.06 | 0.003 to 0.016 | -| Tokopedia and GoTo on-demand as one company, 4 against 27 | Mann–Whitney | — | — | 0.003 | +| Drop each platform in turn, or treat Tokopedia and GoTo as one company | Mann–Whitney | 0.17 to 0.23 | 0.06 | 0.003 to 0.016 | | Three marketplaces against eight foreign platforms, growth divergence | Shuffle | 38.9 | 7.1 | 0.06 | -| The same, without Sea | Shuffle | 38.9 | 4.2 | 0.008 | | Quarterly pairs, 3 platforms against 8 | Mann–Whitney | 0.071 | 0.049 | 0.19 | -The first two rows are the main test, with the two methods of Chapter 4. In rows one to five, the group figures are medians of platform medians. The third row repeats the test with growth divergence in points, as Chapter 3 defines it. Ozon has no pair in which both years pass the own-goods rule, so that row has 26 foreign platforms. The dropping and grouping rows show that no single platform carries the result. The two marketplace rows use pair-level medians. This narrower test gives 0.06 on growth divergence and 0.03 on the take-rate change. It uses a smaller benchmark and a different measure, so it is not a stricter form of the main test. Removing Sea from its benchmark lowers these p-values to 0.008 and 0.017. The quarterly test was registered before it was run, but after the annual result was known. +The first two rows of Table 5.2 are the main test, and the group figures in rows one to four are medians of platform medians. The third row repeats the test with growth divergence in points. One foreign platform drops out because it has no pair in which both years pass the own-goods rule. The fourth row shows that no single platform carries the result. The fifth row is the narrower test of the three marketplaces, whose p-value is 0.008 once Sea leaves the benchmark. The main test is exploratory, because the larger groups were set up after that first test. -A second feature separates the groups. In one third of Indonesian pairs, the take rate stayed within about 10 percent. Abroad, it did so in about two thirds. The quarterly version of the test does not reject H1 (Table 5.2). Its Indonesian pairs mostly cover 2023 to 2026, after most of the repricing. The large moves came in 2022 to 2024, so the rejection of H1 describes that period. +The quarterly version does not reject H1. Its Indonesian pairs mostly cover 2023 to 2026, after most of the repricing. The large moves came in 2022 to 2024, so the rejection of H1 describes that period. -Most of the threefold difference comes from the size of the slice. This part is exploratory. Measured in percentage points of the take rate, Indonesian and regional platforms changed it by about as much as foreign ones. The change was about 1 point a year in both groups, with a p-value of 0.65. But the typical Indonesian and regional platform keeps only 1.6 percent of each sale, against 16.9 percent abroad. If transaction value is flat, a rise of 1 point lifts the revenue of the first by more than half. The same rise lifts the revenue of a typical foreign platform by about 6 percent. The larger the wedge is next to revenue, the more a small price change moves revenue growth. +Most of the threefold difference comes from the size of the slice, which is an exploratory finding. Measured in percentage points of the take rate, Indonesian and regional platforms changed it by about as much as foreign ones. The change was about 1 point a year in both groups (p = 0.65). But the typical Indonesian and regional platform keeps only 1.6 percent of each sale, against 16.9 percent abroad. If transaction value is flat, a rise of 1 point lifts the revenue of the first by more than half. It lifts the revenue of a typical foreign platform by about 6 percent. The larger the wedge is next to revenue, the more a small price change moves revenue growth. What the platforms actually changed is the next question. -## 5.3 Why the take rate moved +## 5.3 What moved the take rate -Revenue moved mainly because platforms changed what they keep. Two results show this. First, the thesis divides each movement in revenue into volume and take rate, as section 4.3 describes. For the three Indonesian marketplaces, the take rate accounts for two thirds of the movement at the median (66.5 percent). For the eight platforms of the first foreign benchmark, it accounts for about a quarter (26.7 percent). The wedge follows the volume part. At the median, about 96 percent of a change in the wedge comes from the change in transaction value, in both groups. These results are descriptive. +Tokopedia shows it best. In 2023 its transaction value fell by 9 percent while its revenue rose by 53 percent. Across the three Indonesian marketplaces, the take rate accounts for two thirds of the movement in revenue at the median (66.5 percent). At the eight foreign platforms chosen first, it accounts for about a quarter (26.7 percent). The wedge follows the other part. At the median, about 96 percent of a change in the wedge comes from the change in transaction value, in both groups. These results are descriptive. -Second, Table 5.3 splits the growth of revenue into three parts. They are more commerce, higher fees and lower incentives, which are the discounts and vouchers a platform pays for. Fees and incentives are measured per unit of transaction value. A negative share means that the part pulled revenue down. Windows of one platform overlap, so the seven windows are not seven independent cases. +Table 5.3 then splits the growth of revenue into three parts: more commerce, higher fees and lower incentives, which are the discounts and vouchers a platform pays for. Fees and incentives are measured per unit of transaction value. A negative share means that the part pulled revenue down, and windows of one platform overlap, so the seven windows are not seven independent cases. **Table 5.3. What moved revenue in seven Indonesian windows (share of the growth of revenue, percent)** @@ -60,19 +58,17 @@ Second, Table 5.3 splits the growth of revenue into three parts. They are more c | GoTo on-demand, 2022–25 | +111% | 12 | 36 | 52 | | GoTo on-demand, 2023–25 | +114% | 27 | 6 | 67 | -Table 5.3 gives rounded shares, so a row can add to 101. Lower incentives were the larger of the two take-rate parts in four windows, and higher fees in two. In the seventh, Grab from 2021 to 2024, the two were nearly equal (34 and 33 percent). Grab's figures come from its annual filings, and Tokopedia's and GoTo's from audited statements. GoTo's 2022 transaction value is an estimate. Blibli does not report incentives, so its split rests on a proxy. Blibli's releases credit part of its 2022–23 rise to travel and digital products inside the segment. So some of its fee and incentive shares reflect a change in mix, which the reports do not let us separate. For Bukalapak, the reports do not allow the parts to be separated. +Lower incentives were the larger of the two take-rate parts in four windows, and higher fees in two. In the seventh, Grab from 2021 to 2024, the two were nearly equal. Shares are rounded, so a row can add to 101. Grab's figures come from its annual filings, and Tokopedia's and GoTo's from audited statements. GoTo's 2022 transaction value is an estimate, and Blibli does not report incentives, so its split rests on a proxy. Blibli's releases also credit part of its 2022–23 rise to travel and digital products inside the segment, so some of its shares reflect a change in mix. For Bukalapak, the reports do not allow the parts to be separated. -Tokopedia needs two views. Its accounts deduct incentives from revenue, and they fell by about one third in 2023. In the same year its transaction value fell by 9 percent and its revenue rose by 53 percent. Counting the lines of its accounts, 60 percent of the extra net revenue came from lower incentives and 40 percent from higher gross fees. That count leaves the fall in volume inside both lines. Table 5.3 takes it out and compares fees and incentives per unit of transaction value. On that basis, higher fees account for more of the rise in the take rate than lower incentives. Both views agree that pricing, and not more commerce, drove the rise. They differ on which price lever led. - -On Grab's quarterly figures, its Southeast Asian on-demand take rate rose by 3.9 points in 2022–23. Of these, 3.5 came from pricing within rides and deliveries. Across the windows, revenue in 2022 to 2024 was not a fair guide to the commerce these platforms carried. The take rate moved a lot, mostly through lower incentives and higher fees. +Tokopedia needs two views. Its accounts deduct incentives from revenue, and they fell by about one third in 2023. Counting the lines of its accounts, 60 percent of the extra net revenue came from lower incentives and 40 percent from higher gross fees. That count leaves the fall in volume inside both lines. Table 5.3 takes it out and compares fees and incentives per unit of transaction value. On that basis, higher fees account for more of the rise in the take rate than lower incentives. Both views agree that pricing, and not more commerce, drove the rise. They differ on which price lever led. Across the windows, revenue in 2022 to 2024 was not a fair guide to the commerce these platforms carried. The take rate moved a lot, mostly through lower incentives and higher fees. Did investors see through it? ## 5.4 What markets valued -The market-value results are registered and one-sided. One prediction was that market value moves with transaction value. It held. Across 28 listed platforms and 138 platform-years, a 1 percent rise in transaction value goes with a 0.6 percent rise in market value. The p-value is 0.002. The other prediction was that markets value growth from a higher take rate less than growth in transaction value. It did not hold. A 1 percent rise in the take rate goes with a rise of 0.3 percent. Its standard error is 0.37, too wide to tell it from zero. The difference between the two is not large enough to count (p = 0.20). The difference counts only when segment series and three more whole companies are added, and the thesis does not rely on that run. Both results are associations. Market value also changes when a company issues shares, so neither is a stock return. Market value thus moved with the buying underneath revenue, and the next section asks what that buying did in 2023. +Market value moved with transaction value. This result is registered. Across 28 listed platforms and 138 platform-years, a 1 percent rise in transaction value goes with a 0.6 percent rise in market value (p = 0.002). A second prediction, that markets value growth from a higher take rate less, did not hold. A 1 percent rise in the take rate goes with a rise of 0.3 percent. Its standard error is 0.37, too wide to tell it from zero. The difference between the two is not large enough to count (p = 0.20). Both results are associations, and market value also changes when a company issues shares, so neither is a stock return. Market value thus moved with the buying underneath revenue. The next section asks what that buying did in 2023. -## 5.5 The platforms in 2023 +## 5.5 The platforms and the sellers in 2023 -These results are descriptive. Buying through platforms did not boom in 2023. Table 5.4 sets the Indonesian measures against household spending, which rose by 9.4 percent in current prices. Of six measures of buying through Indonesian platforms, five grew more slowly, and three fell. Blibli is the exception. +Buying through platforms did not boom in 2023. Table 5.4 sets six measures of it against household spending, which rose by 9.4 percent in current prices. Five of the six grew more slowly, and three fell. Blibli is the exception. These results are descriptive. **Table 5.4. Growth in 2023 (percent)** @@ -89,17 +85,13 @@ These results are descriptive. Buying through platforms did not boom in 2023. Ta | BPS e-commerce value | +40.6 | Rupiah | | BPS online sellers | +27.4 | Number | -Each row of Table 5.4 is in its own unit, and the thesis compares only the rates. GoTo's change rests on an estimated 2022 value. Rows two to seven show the buying. The last three rows show the figures that described a boom. The platforms' revenue and BPS's value rose strongly, while the buying through platforms did not. Section 5.3 explains the revenue. The next sections ask where BPS's growth came from. - -## 5.6 What sellers reported +Each row of Table 5.4 is in its own unit, so the thesis compares only the rates, and GoTo's change rests on an estimated 2022 value. The first seven rows show the buying, and the last three show the figures that described a boom. Revenue and BPS's value rose strongly while the buying through platforms did not. -Sellers did not describe a boom in 2023. BPS asks each business how its online sales changed. Among businesses already selling, the median change was zero, as a registered prediction expected. The other figures in this section are descriptive. A share of 24 percent reported a rise, 32 percent a fall and 44 percent no change. In 2022, the balance was the other way: 37 percent reported a rise and 24 percent a fall. The mean reported change in 2023 was −4.3 percent. Lack of demand became the main obstacle, named by 41 percent of sellers in 2023 and 35 percent in 2022. Yet the published count of online sellers rose by 27 percent in the same year. Section 5.7 asks where that rise came from. +Sellers did not describe a boom either. BPS asks each business how its online sales changed. Among businesses already selling, the median change was zero, as a registered prediction expected. The other figures here are descriptive. A share of 24 percent reported a rise, 32 percent a fall and 44 percent no change. In 2022 the balance was the other way, with 37 percent reporting a rise and 24 percent a fall. The mean reported change in 2023 was −4.3 percent. Lack of demand became the main obstacle, named by 41 percent of sellers against 35 percent a year earlier. Yet the published count of online sellers rose by 27 percent that year. Where did those sellers come from? -## 5.7 The count of online sellers and the entry test +## 5.6 Where the extra sellers came from -On BPS's published figures, H2 holds. More businesses account for 71 percent of the growth in online sales value from 2022 to 2023. They account for 90 percent from 2023 to 2024, or 71 percent if BPS's other count for 2023 is used. The split is arithmetic, and it cannot tell a new seller from one counted for the first time. - -The entry test can. This result is registered. In the 2024 file, 13.45 percent of sellers began selling online in 2023. The rule required at least 21.5 percent. The entry test fails. Even if no seller stopped, 13.45 percent new sellers means the count could have grown by at most 15.5 percent over the sellers already there. The published growth is 27.4 percent. +On BPS's published figures, H2 holds. More businesses account for 71 percent of the growth in online sales value from 2022 to 2023. They account for 90 percent from 2023 to 2024, or 71 percent if BPS's other count for 2023 is used. The split is arithmetic, and it cannot tell a new seller from one counted for the first time. The entry test can, and its result is registered. In the 2024 file, 13.45 percent of sellers began selling online in 2023, and the rule required at least 21.5 percent. The entry test fails. Even if no seller stopped, 13.45 percent new sellers means the count could have grown by at most 15.5 percent over the sellers already there. The published growth is 27.4 percent. **Table 5.5. The 2023 rise in the count of online sellers (thousands)** @@ -109,19 +101,15 @@ The entry test can. This result is registered. In the 2024 file, 13.45 percent o | Net entry: sellers who began in 2023, less those who stopped | 513 (63%) | 360 (44%) | | Earlier sellers newly counted above the whole 2022 count | 306 (37%) | 458 (56%) | -Table 5.5 shows the accounting. The rise of 819 thousand is the gap between the weighted totals of the two files, and BPS's own published counts give 821 thousand. The 2023 survey counted 306 thousand more sellers who began before 2023 than the whole 2022 count held. This is 37 percent of the rise if no seller stopped. If sellers stopped at 5 percent a year, it is 56 percent. So about half of the rise is not explained by first-time entry. This accounting is exploratory, like the cohort check and the four further checks, and they point the same way. - -The cohort check compares groups of sellers across surveys. Sellers who began selling by 2020 numbered 9.9 percent fewer in the 2022 survey than in the 2020 survey, as a cohort should. That fall took two years, or about 5 percent a year. In the 2023 survey they numbered 6.0 percent more than in the 2022 one. From 2023 to 2024, the cohorts shrink again. Sellers who began before 2020 fell by 10.2 percent in that one year. At least 754 thousand new sellers appeared, against a rise of 584 thousand. So 2023 stands out as the break. +Table 5.5 shows the accounting. The rise of 819 thousand is the gap between the weighted totals of the two files, and BPS's own published counts give 821 thousand. The 2023 survey counted 306 thousand more sellers who began before 2023 than the whole 2022 count held. That is 37 percent of the rise if no seller stopped and 56 percent if sellers stopped at 5 percent a year. So about half of the rise is not explained by first-time entry. This accounting is exploratory, like the checks that follow, and they point the same way. -The four further checks ask where the break sits and how large it is. The first compares the 34 provinces. A rank correlation of 1 means that two measures rank the provinces alike, and 0 means no link. Count growth had almost no link to the share of new sellers (0.10, p = 0.59). It had a weak link to growth in sample size (0.35, p = 0.04). In 23 provinces, growth exceeded what entry can explain, and these carry 98.5 percent of the increase. The second check splits sellers by channel. Among sellers who began by 2022, those using only chat or social media rose by 13 percent, and marketplace users rose by 0.3 percent. +Sellers who began by 2020 numbered 9.9 percent fewer in the 2022 survey than in the 2020 survey, as a cohort should, and that fall took two years. In the 2023 survey they numbered 6.0 percent more than in the 2022 one. Between the 2023 and 2024 surveys, the cohort of sellers who began before 2020 shrank by 10.2 percent. At least 754 thousand new sellers appeared, against a rise of 584 thousand. So 2023 stands out as the break. -The third check asks about sampling error. BPS's published error rates imply a standard error of about 1.4 percent for the national count. This is the size of error that sampling alone would typically cause. The 306 thousand sellers are about 4.6 times the standard error of the change. They would be 2.3 times it if the true error were twice as large. The fourth check is the sellers' own reports in section 5.6, which do not describe a rise in sales. +Three more checks locate the break. Across the 34 provinces, count growth had almost no link to the share of new sellers (rank correlation 0.10). It had a weak link to growth in sample size (0.35). In 23 provinces growth exceeded what entry can explain, and they carry 98.5 percent of the increase. By channel, sellers who began by 2022 and use only chat or social media rose by 13 percent, while marketplace users rose by 0.3 percent. BPS's published error rates imply a standard error of about 1.4 percent for the national count. The 306 thousand sellers are about 4.6 times the standard error of the change. The value side tells a similar story. BPS's value rose from about US$53 billion to about US$72 billion. Sellers not explained by entry account for 15 to 17 percent of this, and entrants for 25 to 30 percent. The remaining 53 to 59 percent cannot be traced to existing sellers' answers, which show a mean change of −4.3 percent. These checks point to wider survey coverage of chat and social sellers, and the data cannot separate that from sellers returning after a pause. Who were the extra sellers? -The value side tells a similar story. BPS's value rose from about US$53 billion to about US$72 billion. Sellers not explained by entry account for 15 to 17 percent of this, and entrants for 25 to 30 percent. The remaining 53 to 59 percent cannot be traced to existing sellers' answers, which show a mean change of −4.3 percent. The channel and province checks point to wider survey coverage of chat and social sellers. The data cannot separate that from sellers returning after a pause. Section 5.8 asks who the extra sellers were, and section 5.9 asks whether they returned. +## 5.7 Who the extra sellers were, and whether they returned -## 5.8 Who the extra sellers were - -The comparison of who the extra sellers were is exploratory. The surveys do not follow the same businesses, so the thesis compares groups. For each group, it takes the 2023 count above what the group's normal yearly change in 2020 to 2022 would give. These extra sellers total about 450 thousand, close to the 458 thousand of the second column in Table 5.5. Table 5.6 gives the share of this extra count held by each group, next to the group's share of the 2022 count. +The comparison of who they were is exploratory. The surveys do not follow the same businesses, so the thesis compares groups. For each group it takes the 2023 count above what the group's normal yearly change in 2020 to 2022 would give. These extra sellers total about 450 thousand, close to the 458 thousand of the second column in Table 5.5. Table 5.6 gives each group's share of this extra count next to its share of the 2022 count. **Table 5.6. Who the extra sellers were (percent)** @@ -132,17 +120,15 @@ The comparison of who the extra sellers were is exploratory. The surveys do not | Outside Java | 23.6 | 36.3 | | Food and lodging, or making goods | 38.5 | 53.0 | -The extra count leaned toward long-running businesses, older owners and places outside Java. The lean toward men in the raw data disappears once normal change is taken out. The extra count does not look like the young start-ups of the usual growth story. It raises one question: could these be established sellers who paused and came back? - -## 5.9 Could they be sellers who returned? +The extra count leaned toward long-running businesses, older owners and places outside Java. The lean toward men in the raw data disappears once normal change is taken out. The extra count does not look like the young start-ups of the usual growth story. That raises one question: could these be established sellers who paused and came back? -A seller returning from a break would usually sell for only part of the year, unless the seller restarts in January. The exploratory check looks at part-year selling, which fell in 2023. BPS changed how it asks about months between the two rounds. Among sellers who began by 2021, the number selling for part of the year fell from 472 thousand to 318 thousand. It fell in every start-year group. Small firms that stop are more often young, with young owners (McKenzie and Paffhausen 2019), and sellers who pause are likely to be such firms. If returners were mostly such firms, the extra count would lean young. It leaned old. Returners equal to 10 to 15 percent of the 2022 count would remove the unexplained part. This is a scenario, not an estimate. The check cannot rule out returners: the files carry no linked histories, and exits or a move to full-year selling could offset them. +A seller returning from a break would usually sell for only part of the year, unless the seller restarts in January, and part-year selling fell in 2023. Among sellers who began by 2021, the number selling for part of the year fell from 472 thousand to 318 thousand, and it fell in every start-year group. This exploratory check has a limit: BPS changed how it asks about months between the two rounds. Small firms that stop are more often young, with young owners (McKenzie and Paffhausen 2019), and sellers who pause are likely to be such firms. If returners were mostly such firms, the extra count would lean young. It leaned old. Returners equal to 10 to 15 percent of the 2022 count would remove the unexplained part, which is a scenario and not an estimate. The check cannot rule out returners, because the files carry no linked histories, and exits or a move to full-year selling could offset them. -## 5.10 Channels and records +## 5.8 Channels and records -Two additional analyses, on channels and on records, are reported here. The path of marketplace use, the value held by sellers without statements and the World Bank results were registered. The comparison within the pre-2020 group is exploratory. The value shares come from bracket scenarios. The annual reading of the brackets passes the validation rule, because the middle scenario gives 1.95 times BPS's total for 2022. The monthly reading gives 23 times and fails. The lower brackets alone already exceed BPS's total, so the thesis reports value shares as ranges. +Two additional analyses, on channels and on records, show where these sellers sit. The path of marketplace use, the value held by sellers without statements and the World Bank results were registered, and the comparison within the pre-2020 group is exploratory. -Most online sellers do not use a marketplace. About 96 percent use chat or social media. Use of marketplaces fell every time it was measured, from 21.6 percent of sellers in 2020 to 17.2 percent in 2024. BPS worded the question slightly differently in the 2023 and 2024 rounds. From 2023 to 2024, BPS's marketplace value grew by 1.45 percent and other channels by 20.57 percent. So 98.5 percent of the increase came from outside the marketplaces. BPS's marketplace category includes many food and ride merchants: 43 percent of its sellers use only Gojek or Grab, and 47 percent use a shopping marketplace. +Most online sellers do not use a marketplace. About 96 percent use chat or social media. Use of marketplaces fell every time it was measured, from 21.6 percent of sellers in 2020 to 17.2 percent in 2024. BPS worded the question slightly differently in the 2023 and 2024 rounds. From 2023 to 2024, BPS's marketplace value grew by 1.45 percent and other channels by 20.57 percent, so 98.5 percent of the increase came from outside the marketplaces. BPS's marketplace category includes many food and ride merchants: 43 percent of its sellers use only Gojek or Grab, and 47 percent use a shopping marketplace. **Table 5.7. Marketplace use and financial statements (percent of sellers)** @@ -153,20 +139,16 @@ Most online sellers do not use a marketplace. About 96 percent use chat or socia | 2023 | 17.8 | 15.2 | | 2024 | 17.2 | | -Records are thinner outside the marketplaces. In 2023, 28.6 percent of marketplace sellers kept complete financial statements, against 12.3 percent of other sellers. Table 5.7 shows that the share of all sellers who keep them fell. The fall from 2020 to 2022 came while the survey appears to have counted the same kinds of seller. The fall in 2023 may partly reflect the chat-only sellers added to the count. Even among sellers who began before 2020, the share fell from 23.4 to 16.8 percent between the two surveys. Sellers without statements held between 26 and 40 percent of online value in 2022, depending on the scenario, against 79 percent of the count. The registered prediction was that their share of value would not fall by more than 5 points between 2020 and 2022. It fell by 5.8 points, so the prediction failed, narrowly. A second registered version of the test gives a fall of 3.3 points. +Records are thinner outside the marketplaces. In 2023, 28.6 percent of marketplace sellers kept complete financial statements, against 12.3 percent of other sellers. Table 5.7 shows that the share of all sellers who keep them fell. The fall from 2020 to 2022 came while the survey appears to have counted the same kinds of seller. The fall in 2023 may partly reflect the chat-only sellers added to the count. Even among sellers who began before 2020, the share fell from 23.4 to 16.8 percent between the two surveys. The value shares come from bracket scenarios. The annual reading of the brackets passes the validation rule, because the middle scenario gives 1.95 times BPS's total for 2022. The monthly reading gives 23 times and fails. The lower brackets alone already exceed BPS's total, so value shares are ranges. Sellers without statements held between 26 and 40 percent of online value in 2022, against 79 percent of the count. The registered prediction was that their share of value would not fall by more than 5 points between 2020 and 2022. It fell by 5.8 points, so the prediction failed, narrowly. A second registered version of the test gives a fall of 3.3 points. -The World Bank surveys point the same way. In six Indonesian cities, 27 percent of unregistered firms sell through social media, and 70 percent keep no written records. Their median sales are about US$4,000 a year. The registered prediction on their mean sales failed: it was below about US$6,600, and the mean is about US$7,300. Among formal firms, 39.9 percent in Indonesia have neither a website nor online tax filing, the highest of seven countries. Malaysia has 9.1 percent and Thailand 8.7 percent. +The World Bank surveys point the same way. In six Indonesian cities, 27 percent of unregistered firms use social media with their customers. Among those, 70 percent keep no written records, and their median sales are about US$4,000 a year. The registered prediction on their mean sales failed: it was below about US$6,600, and the mean is about US$7,300. Among formal firms, 39.9 percent in Indonesia have neither a website nor online tax filing, the highest of seven countries. Malaysia has 9.1 percent and Thailand 8.7 percent. -## 5.11 Why the national figures disagree +## 5.9 Why the national figures disagree The third objective asks whether platform, national and payment figures give the same account. They do not give the same account of scale. They give the same account of growth only where they cover the same sellers. -The comparison with Bank Indonesia is a registered test, and the other comparisons are descriptive. In 2023, Bank Indonesia's figure fell by 4.7 percent and BPS's rose by 40.6 percent. Where the two cover the same ground, they agree on slow growth. BPS's own marketplace slice grew by 0.2 percent from 2022 to 2023 in the middle scenario, and by 0.2 to 13.2 percent across the scenarios. It grew by 1.45 percent from 2023 to 2024. The registered test holds. The part of BPS's marketplace sales that went to final consumers in 2022 is 0.68 times Bank Indonesia's figure in the middle scenario. It is 0.16 times in the low scenario and 1.62 times in the high one. So the disagreement sits outside the marketplaces, in the count of sellers that only BPS reaches. - -The 2024 figures show the same split. Those that cover mainly platforms grew by 5 to 7 percent, and BPS's grew by 17.1 percent. Adjacent figures point the same way as BPS, but they do not measure Indonesian e-commerce. Southeast Asian parcels grew by 25.2 percent, and the tax on foreign digital services by 24.9 percent. - -Sizes cannot be reconciled. BPS's marketplace slice, about US$13 billion in 2024, is less than a quarter of the marketplace estimate of Momentum Works, about US$56.5 billion. Platform transaction value counts shipping, digital goods, travel and offline sales that a seller survey does not count in the same way. In 2022, sellers with revenue above about US$3.4 million a year were 0.5 percent of BPS's marketplace sellers. They held between 18 and 39 percent of the slice's value. Only 19 sample rows lie in that bracket. Counts also differ. Tokopedia alone said it had about 15 million merchants in 2022, while BPS counts about 0.6 million marketplace sellers. +In 2023, Bank Indonesia's figure fell by 4.7 percent and BPS's rose by 40.6 percent. Where the two cover the same ground, they agree on slow growth. BPS's own marketplace slice grew by 0.2 percent from 2022 to 2023 in the middle scenario, and by 0.2 to 13.2 percent across the scenarios. It grew by 1.45 percent from 2023 to 2024. A registered test also holds. The part of BPS's marketplace sales that went to final consumers in 2022 is 0.68 times Bank Indonesia's figure in the middle scenario. It is 0.16 times in the low scenario and 1.62 times in the high one. So the disagreement sits outside the marketplaces, in the count of sellers that only BPS reaches. The 2024 figures show the same split. Those that cover mainly platforms grew by 5 to 7 percent, and BPS's grew by 17.1 percent. Adjacent figures point the same way as BPS, but they do not measure Indonesian e-commerce. Southeast Asian parcels grew by 25.2 percent, and the tax on foreign digital services by 24.9 percent. -Platform sales do not always keep pace with household spending. GoTo's on-demand transaction value grew more slowly than household spending in each of four quarters. They run from the third quarter of 2025 to the second quarter of 2026. In the last of them it grew by 2.0 percent against 8.3 percent, while its revenue grew by 20.5 percent. Grab and Shopee grew faster, but their figures cover the region. Payment figures such as QRIS are context only. They include in-person payments and the switch from cash. +Sizes cannot be reconciled. BPS's marketplace slice, about US$13 billion in 2024, is less than a quarter of the marketplace estimate of Momentum Works, about US$56.5 billion. Platform transaction value counts shipping, digital goods, travel and offline sales that a seller survey does not count in the same way. In 2022, sellers with revenue above about US$3.4 million a year were 0.5 percent of BPS's marketplace sellers. They held between 18 and 39 percent of the slice's value, and only 19 sample rows lie in that bracket. Counts also differ. Tokopedia alone said it had about 15 million merchants in 2022, while BPS counts about 0.6 million marketplace sellers. Platform sales do not always keep pace with household spending either. GoTo's on-demand transaction value grew more slowly than household spending in each of four quarters, from the third quarter of 2025 to the second quarter of 2026. In the last it grew by 2.0 percent against 8.3 percent, while its revenue grew by 20.5 percent. Grab and Shopee grew faster, but their figures cover the region. Payment figures such as QRIS are context only, because they include in-person payments and the switch from cash. Chapter 6 asks what these results mean for the rules that Indonesia is writing. From dea218f7ce75322b17c0d512db56db69ef288926 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 12 Oct 2026 19:10:32 +0000 Subject: [PATCH 15/65] Thesis ch5/appendix: carry over proposal content that was missing (Blibli and Bukalapak wedges, GDP magnitude, 8-against-29 pair test, correlation, 2023 size comparison, H2 inputs) Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/appendix_notes.md | 6 ++++++ papers/thesis_2026-10/ch5_results.md | 13 ++++++++----- 2 files changed, 14 insertions(+), 5 deletions(-) diff --git a/papers/thesis_2026-10/appendix_notes.md b/papers/thesis_2026-10/appendix_notes.md index 30d28f4..fa13c7a 100644 --- a/papers/thesis_2026-10/appendix_notes.md +++ b/papers/thesis_2026-10/appendix_notes.md @@ -109,9 +109,15 @@ The main test is in Table 5.2. This appendix lists each check with both p-values | Three marketplaces against the 8 chosen first, pair-level, absolute growth divergence in points (without Sea) | 38.917 | 4.192 | 0.0000 | 0.0083 | | Three marketplaces against the 8 chosen first, pair-level, absolute change in log take rate | 0.252 | 0.055 | 0.0037 | 0.0303 | | Three marketplaces against the 8 chosen first, pair-level, absolute change in log take rate (without Sea) | 0.252 | 0.033 | 0.0001 | 0.0167 | +| Three marketplaces, dropping Tokopedia's 2022–23 comparison (5 pairs), absolute growth divergence in points | 14.411 | 7.105 | 0.0248 | 0.2222 | +| Three marketplaces, dropping the largest comparison (7 pairs), absolute growth divergence in points | 15.738 | 7.105 | 0.0061 | 0.0606 | +| Three marketplaces against all 40 foreign pairs, absolute growth divergence in points | 38.917 | 7.373 | 0.0014 | 0.0121 | +| Five Indonesian and regional series including constructed Grab and Shopee (12 pairs) against the 8 chosen first, absolute growth divergence in points | 42.017 | 7.105 | 0.0006 | 0.0225 | | Tokopedia and GoTo on-demand as one company, 4 against 27 | | | 0.003 | 0.007 | | Quarterly pairs, 3 against 8 (same quarter a year earlier) | 0.071 | 0.049 | 0.19 | 0.33 | +Growth in revenue and growth in transaction value had a correlation of 0.10 in the Indonesian main sample and 0.98 in the foreign sample excluding Sea (`tables/claims_ledger.csv`, C48 and C49). + The quarterly sample is Grab on-demand, GoTo on-demand and Blibli against Sea, Airbnb, Booking, DoorDash, eBay, Etsy, Lyft and Uber. Airbnb and Booking use trailing four-quarter sums because they book revenue at check-in. DoorDash pairs ending in 2025Q4 or later are dropped because of an acquisition. The comparison in points of the take rate (exploratory, `h1_points_vs_log.py`): median yearly change 1.01 points in the Indonesian and regional group and 0.98 points in the foreign group (26 platforms), Mann–Whitney p = 0.65; median share kept 1.59 percent against 16.93 percent. diff --git a/papers/thesis_2026-10/ch5_results.md b/papers/thesis_2026-10/ch5_results.md index 376a8ea..f992da5 100644 --- a/papers/thesis_2026-10/ch5_results.md +++ b/papers/thesis_2026-10/ch5_results.md @@ -15,6 +15,8 @@ Take three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. Tog | Grab, Indonesia | 5.38 | 0.61 | 4.78 | Revenue is a direct country disclosure; transaction value is derived from Grab's company-wide ratio | | Total | 43.23 | 3.16 | 40.07 | Rounded amounts may not add | +In size, that is about 3 percent of Indonesia's 2023 gross domestic product (GDP), which is the value of everything the country produces in a year. The comparison is one of magnitude only, because the wedge is transaction value and not value added. Blibli's and Bukalapak's wedges, US$3.2 and US$10.5 billion, are left out of the total because Blibli's measure includes online travel and Bukalapak reports for its whole group. + The size of the wedge hardly depends on the assumptions. If Shopee kept 9 or 11 percent instead of 10, the total would stay between US$39.9 and US$40.3 billion. At Sea's company-wide rate of 11.5 percent it would be US$39.8 billion, and dropping any one platform leaves at least US$20.7 billion. For platforms that report both amounts directly, the wedge is 97 to 99 percent of transaction value in every year. It moves almost one for one with it. Appendix A gives the wedge for each platform and year. Platforms are only part of online selling. In the BPS survey, the marketplace channel carries between 16 and 26 percent of online sales value, depending on the year and the scenario. BPS counts food and ride apps in that channel. So a measure built on platforms misses between about 74 and 84 percent of online sales value, and revenue shows much less still. A small slice can still be a fair guide if it keeps its size. The next question is whether it does. @@ -31,14 +33,15 @@ In Indonesia they do not, and H1 is rejected. Among the Indonesian and regional | The same | Shuffle | 0.20 | 0.06 | 0.022 | | Growth divergence in points, 5 platforms against 26 | Mann–Whitney | 27.0 | 6.5 | 0.005 | | Drop each platform in turn, or treat Tokopedia and GoTo as one company | Mann–Whitney | 0.17 to 0.23 | 0.06 | 0.003 to 0.016 | -| Three marketplaces against eight foreign platforms, growth divergence | Shuffle | 38.9 | 7.1 | 0.06 | +| Three marketplaces against eight foreign platforms, growth divergence, 8 against 29 pairs | Mann–Whitney | 38.9 | 7.1 | 0.002 | +| The same, one value per platform | Shuffle | 38.9 | 7.1 | 0.06 | | Quarterly pairs, 3 platforms against 8 | Mann–Whitney | 0.071 | 0.049 | 0.19 | -The first two rows of Table 5.2 are the main test, and the group figures in rows one to four are medians of platform medians. The third row repeats the test with growth divergence in points. One foreign platform drops out because it has no pair in which both years pass the own-goods rule. The fourth row shows that no single platform carries the result. The fifth row is the narrower test of the three marketplaces, whose p-value is 0.008 once Sea leaves the benchmark. The main test is exploratory, because the larger groups were set up after that first test. +The first two rows of Table 5.2 are the main test, and the group figures in rows one to four are medians of platform medians. The third row repeats the test with growth divergence in points. One foreign platform drops out because it has no pair in which both years pass the own-goods rule. The fourth row shows that no single platform carries the result. The fifth and sixth rows are the narrower test of the three marketplaces. It compares pairs in the fifth row and platforms in the sixth, because pairs from one platform are not independent. Its p-value is 0.008 once Sea leaves the benchmark. In it, revenue growth and transaction value growth were barely related in Indonesia (correlation 0.10) and closely related abroad (0.98, excluding Sea). The main test is exploratory, because the larger groups were set up after that first test. The quarterly version does not reject H1. Its Indonesian pairs mostly cover 2023 to 2026, after most of the repricing. The large moves came in 2022 to 2024, so the rejection of H1 describes that period. -Most of the threefold difference comes from the size of the slice, which is an exploratory finding. Measured in percentage points of the take rate, Indonesian and regional platforms changed it by about as much as foreign ones. The change was about 1 point a year in both groups (p = 0.65). But the typical Indonesian and regional platform keeps only 1.6 percent of each sale, against 16.9 percent abroad. If transaction value is flat, a rise of 1 point lifts the revenue of the first by more than half. It lifts the revenue of a typical foreign platform by about 6 percent. The larger the wedge is next to revenue, the more a small price change moves revenue growth. What the platforms actually changed is the next question. +Most of the threefold difference comes from the size of the slice, which is an exploratory finding. Measured in percentage points of the take rate, Indonesian and regional platforms changed it by about as much as foreign ones. The change was about 1 point a year in both groups (p = 0.65). But the typical Indonesian and regional platform keeps only 1.6 percent of each sale, against 16.9 percent abroad. The share abroad varies with the business model, from about 3 percent at Shopify to about 25 percent at Mercado Libre in 2025. If transaction value is flat, a rise of 1 point lifts the revenue of the first by more than half. It lifts the revenue of a typical foreign platform by about 6 percent. The larger the wedge is next to revenue, the more a small price change moves revenue growth. What the platforms actually changed is the next question. ## 5.3 What moved the take rate @@ -91,7 +94,7 @@ Sellers did not describe a boom either. BPS asks each business how its online sa ## 5.6 Where the extra sellers came from -On BPS's published figures, H2 holds. More businesses account for 71 percent of the growth in online sales value from 2022 to 2023. They account for 90 percent from 2023 to 2024, or 71 percent if BPS's other count for 2023 is used. The split is arithmetic, and it cannot tell a new seller from one counted for the first time. The entry test can, and its result is registered. In the 2024 file, 13.45 percent of sellers began selling online in 2023, and the rule required at least 21.5 percent. The entry test fails. Even if no seller stopped, 13.45 percent new sellers means the count could have grown by at most 15.5 percent over the sellers already there. The published growth is 27.4 percent. +On BPS's published figures, H2 holds. From 2022 to 2023, online sales value grew by 40.6 percent. The number of businesses grew by 27.4 percent and sales per business by 10.4 percent, so more businesses account for 71 percent of the growth. From 2023 to 2024 the figures were 17.1, 15.3 and 1.5 percent. More businesses then account for 90 percent of the growth, or 71 percent if BPS's other count for 2023 is used. The split is arithmetic, and it cannot tell a new seller from one counted for the first time. The entry test can, and its result is registered. In the 2024 file, 13.45 percent of sellers began selling online in 2023, and the rule required at least 21.5 percent. The entry test fails. Even if no seller stopped, 13.45 percent new sellers means the count could have grown by at most 15.5 percent over the sellers already there. The published growth is 27.4 percent. **Table 5.5. The 2023 rise in the count of online sellers (thousands)** @@ -149,6 +152,6 @@ The third objective asks whether platform, national and payment figures give the In 2023, Bank Indonesia's figure fell by 4.7 percent and BPS's rose by 40.6 percent. Where the two cover the same ground, they agree on slow growth. BPS's own marketplace slice grew by 0.2 percent from 2022 to 2023 in the middle scenario, and by 0.2 to 13.2 percent across the scenarios. It grew by 1.45 percent from 2023 to 2024. A registered test also holds. The part of BPS's marketplace sales that went to final consumers in 2022 is 0.68 times Bank Indonesia's figure in the middle scenario. It is 0.16 times in the low scenario and 1.62 times in the high one. So the disagreement sits outside the marketplaces, in the count of sellers that only BPS reaches. The 2024 figures show the same split. Those that cover mainly platforms grew by 5 to 7 percent, and BPS's grew by 17.1 percent. Adjacent figures point the same way as BPS, but they do not measure Indonesian e-commerce. Southeast Asian parcels grew by 25.2 percent, and the tax on foreign digital services by 24.9 percent. -Sizes cannot be reconciled. BPS's marketplace slice, about US$13 billion in 2024, is less than a quarter of the marketplace estimate of Momentum Works, about US$56.5 billion. Platform transaction value counts shipping, digital goods, travel and offline sales that a seller survey does not count in the same way. In 2022, sellers with revenue above about US$3.4 million a year were 0.5 percent of BPS's marketplace sellers. They held between 18 and 39 percent of the slice's value, and only 19 sample rows lie in that bracket. Counts also differ. Tokopedia alone said it had about 15 million merchants in 2022, while BPS counts about 0.6 million marketplace sellers. Platform sales do not always keep pace with household spending either. GoTo's on-demand transaction value grew more slowly than household spending in each of four quarters, from the third quarter of 2025 to the second quarter of 2026. In the last it grew by 2.0 percent against 8.3 percent, while its revenue grew by 20.5 percent. Grab and Shopee grew faster, but their figures cover the region. Payment figures such as QRIS are context only, because they include in-person payments and the switch from cash. +Sizes cannot be reconciled. In 2023, BPS's marketplace slice was about US$13 billion, against US$38 billion for Tokopedia and the Shopee estimate together. In 2024 it was less than a quarter of the marketplace estimate of Momentum Works, about US$56.5 billion. Platform transaction value counts shipping, digital goods, travel and offline sales that a seller survey does not count in the same way. In 2022, sellers with revenue above about US$3.4 million a year were 0.5 percent of BPS's marketplace sellers. They held between 18 and 39 percent of the slice's value, and only 19 sample rows lie in that bracket. Counts also differ. Tokopedia alone said it had about 15 million merchants in 2022, while BPS counts about 0.6 million marketplace sellers. Platform sales do not always keep pace with household spending either. GoTo's on-demand transaction value grew more slowly than household spending in each of four quarters, from the third quarter of 2025 to the second quarter of 2026. In the last it grew by 2.0 percent against 8.3 percent, while its revenue grew by 20.5 percent. Grab and Shopee grew faster, but their figures cover the region. Payment figures such as QRIS are context only, because they include in-person payments and the switch from cash. Chapter 6 asks what these results mean for the rules that Indonesia is writing. From 7e2d519a6a6bfce65061cc5d6d6c13421630be55 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 12 Oct 2026 19:14:53 +0000 Subject: [PATCH 16/65] Thesis ch5: main H1 table carries the framing we now stand behind; narrower test moved to one paragraph and Appendix B; GDP sentence dropped Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/ch5_results.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/papers/thesis_2026-10/ch5_results.md b/papers/thesis_2026-10/ch5_results.md index f992da5..f9199c6 100644 --- a/papers/thesis_2026-10/ch5_results.md +++ b/papers/thesis_2026-10/ch5_results.md @@ -15,7 +15,7 @@ Take three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. Tog | Grab, Indonesia | 5.38 | 0.61 | 4.78 | Revenue is a direct country disclosure; transaction value is derived from Grab's company-wide ratio | | Total | 43.23 | 3.16 | 40.07 | Rounded amounts may not add | -In size, that is about 3 percent of Indonesia's 2023 gross domestic product (GDP), which is the value of everything the country produces in a year. The comparison is one of magnitude only, because the wedge is transaction value and not value added. Blibli's and Bukalapak's wedges, US$3.2 and US$10.5 billion, are left out of the total because Blibli's measure includes online travel and Bukalapak reports for its whole group. +Blibli's and Bukalapak's wedges, US$3.2 and US$10.5 billion, are left out of the total because Blibli's measure includes online travel and Bukalapak reports for its whole group. The size of the wedge hardly depends on the assumptions. If Shopee kept 9 or 11 percent instead of 10, the total would stay between US$39.9 and US$40.3 billion. At Sea's company-wide rate of 11.5 percent it would be US$39.8 billion, and dropping any one platform leaves at least US$20.7 billion. For platforms that report both amounts directly, the wedge is 97 to 99 percent of transaction value in every year. It moves almost one for one with it. Appendix A gives the wedge for each platform and year. @@ -33,13 +33,13 @@ In Indonesia they do not, and H1 is rejected. Among the Indonesian and regional | The same | Shuffle | 0.20 | 0.06 | 0.022 | | Growth divergence in points, 5 platforms against 26 | Mann–Whitney | 27.0 | 6.5 | 0.005 | | Drop each platform in turn, or treat Tokopedia and GoTo as one company | Mann–Whitney | 0.17 to 0.23 | 0.06 | 0.003 to 0.016 | -| Three marketplaces against eight foreign platforms, growth divergence, 8 against 29 pairs | Mann–Whitney | 38.9 | 7.1 | 0.002 | -| The same, one value per platform | Shuffle | 38.9 | 7.1 | 0.06 | | Quarterly pairs, 3 platforms against 8 | Mann–Whitney | 0.071 | 0.049 | 0.19 | -The first two rows of Table 5.2 are the main test, and the group figures in rows one to four are medians of platform medians. The third row repeats the test with growth divergence in points. One foreign platform drops out because it has no pair in which both years pass the own-goods rule. The fourth row shows that no single platform carries the result. The fifth and sixth rows are the narrower test of the three marketplaces. It compares pairs in the fifth row and platforms in the sixth, because pairs from one platform are not independent. Its p-value is 0.008 once Sea leaves the benchmark. In it, revenue growth and transaction value growth were barely related in Indonesia (correlation 0.10) and closely related abroad (0.98, excluding Sea). The main test is exploratory, because the larger groups were set up after that first test. +The first two rows of Table 5.2 are the main test, and the group figures in rows one to four are medians of platform medians. The third row repeats the test with growth divergence in points. One foreign platform drops out because it has no pair in which both years pass the own-goods rule. The fourth row shows that no single platform carries the result. The fifth row is the quarterly test. -The quarterly version does not reject H1. Its Indonesian pairs mostly cover 2023 to 2026, after most of the repricing. The large moves came in 2022 to 2024, so the rejection of H1 describes that period. +A narrower test of the three Indonesian marketplaces against eight foreign platforms agrees (Appendix B). It gives p = 0.002 when it compares pairs and 0.06 when it compares platforms, because pairs from one platform are not independent. In it, revenue growth and transaction value growth were barely related in Indonesia (correlation 0.10) and closely related abroad (0.98, excluding Sea). The larger groups were set up after that test, so the main test is exploratory. + +The quarterly version does not reject H1, as Table 5.2 shows. Its Indonesian pairs mostly cover 2023 to 2026, after most of the repricing. The large moves came in 2022 to 2024, so the rejection of H1 describes that period. Most of the threefold difference comes from the size of the slice, which is an exploratory finding. Measured in percentage points of the take rate, Indonesian and regional platforms changed it by about as much as foreign ones. The change was about 1 point a year in both groups (p = 0.65). But the typical Indonesian and regional platform keeps only 1.6 percent of each sale, against 16.9 percent abroad. The share abroad varies with the business model, from about 3 percent at Shopify to about 25 percent at Mercado Libre in 2025. If transaction value is flat, a rise of 1 point lifts the revenue of the first by more than half. It lifts the revenue of a typical foreign platform by about 6 percent. The larger the wedge is next to revenue, the more a small price change moves revenue growth. What the platforms actually changed is the next question. From 02b5b6430daad93b66b6effcd801f70582afe6ab Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 12 Oct 2026 19:14:53 +0000 Subject: [PATCH 17/65] Thesis ch4: section headings renamed in the thesis's own voice (trial; revert this commit to restore the proposal's titles) Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/ch4_data_methods.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/papers/thesis_2026-10/ch4_data_methods.md b/papers/thesis_2026-10/ch4_data_methods.md index 4f95a7b..57cce83 100644 --- a/papers/thesis_2026-10/ch4_data_methods.md +++ b/papers/thesis_2026-10/ch4_data_methods.md @@ -2,7 +2,7 @@ Indonesia lets this thesis look at the same online economy from two sides. Platforms report what passes through them and what they keep. BPS, the national statistics office, asks the sellers themselves. This chapter describes the evidence on each side and the tests that use it. It starts with the platforms, where H1 is tested and revenue movements are traced. It then turns to the survey, where H2 and the entry test are examined. It ends with the comparisons and market values that bring the two sides together. -## 4.1 Selecting the platform evidence +## 4.1 Choosing the platforms The basic observation is one platform in one year, which this thesis calls a platform-year. Revenue and transaction value come from the company's own reports, and they refer to the same year and, as closely as the filings allow, the same business. Revenue is the amount in the financial statements. Transaction value is the figure the company discloses in its annual report or earnings release. Companies define it in different ways, and some include shipping charges, digital goods or travel. The thesis keeps each company's own definition and records it. A take rate is built inside one company, so the definition cancels out of its yearly changes unless the company changes it. @@ -22,7 +22,7 @@ Table 4.1 states the scope of each series because the series differ. Only Tokope Three rules keep the pairs comparable. Two consecutive platform-years form a pair, and nothing else does. A missing year stays missing and is never filled in. Annual and quarterly pairs are never pooled. A take rate is a ratio of two amounts in one currency, so the tests need no exchange rate. Amounts in running text are in US dollars at the World Bank's average rate for the year. In the foreign group, a company that sells goods itself reports the whole sale as revenue, so its take rate says little about a platform. The thesis drops years in which own goods exceed half of revenue and marks years between a fifth and a half. The result is 15 pairs for the Indonesian and regional group and 158 for the foreign group. -## 4.2 Comparing platform activity through time +## 4.2 Testing H1 and tracing the moves H1 says that revenue and transaction value grow in proportion, which means that the take rate stays close to steady. The test asks how far each platform's take rate moves from one year to the next. For each pair of years it takes the change in the logarithm of the take rate. A log change treats a rise by some factor and a fall by the same factor as equal in size. It equals the log growth of revenue minus the log growth of transaction value. This is growth divergence measured in logs, and it does not depend on how fast transaction value grew. Tokopedia's log change in 2023 is 0.52, which is a rise by a factor of 1.68, or 68 percent. @@ -32,11 +32,11 @@ The second objective asks where large movements in revenue came from, and the th The sources differ by company. Tokopedia's audited segment note gives gross revenue, incentives and net revenue, and the thesis checks them against the audited consolidated statements. For Tokopedia it also reports the change line by line in rupiah, which is the usual way to read accounts, and it says where the two views differ. Grab is read from its annual filings and GoTo from its audited statements. Blibli does not report incentives, so its split rests on a proxy built from its releases. Seven windows are traced, and Appendix A lists them. The thesis also records where accounting treatment or reporting scope matters, such as the deduction of incentives from revenue and the travel business inside Blibli's retail segment. Company explanations are read as reports and are not tested. -## 4.3 Direct and constructed Indonesia measures +## 4.3 Measuring the wedge in Indonesia The first objective needs a different set of amounts. It sums the transaction value and revenue of three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. Not every company reports Indonesia on its own. Tokopedia reports both amounts for its segment. Shopee's transaction value is an outside estimate by Momentum Works. Its revenue assumes a take rate of 10 percent, which the thesis varies from 9 to 11 percent. Grab reports its Indonesian revenue, and the thesis derives its Indonesian transaction value from Grab's company-wide ratio of the two amounts. The wedge and the ecosystem ratio are then computed from the sums. The thesis drops each platform in turn, and it repeats the exercise for every platform-year between 2020 and 2025 that the filings allow. -## 4.4 Evidence beyond platform accounts +## 4.4 Evidence from the sellers The national evidence comes from the survey of online sellers that BPS runs. BPS publishes the number of businesses selling online, their total online sales and the shares by channel (BPS 2025). The survey rounds are named for the survey year, but their questions refer to the year before. The thesis names a file by its round and a year by the year of activity, so the file for the 2024 round describes 2023. @@ -64,7 +64,7 @@ Three further analyses use the same files. The first asks which channels sellers The second analysis asks whether a seller keeps complete financial statements. This bears on what platform records can add, because a seller with no statements leaves fewer formal records. The third builds on the first two and is developed in Chapter 6. It counts the marketplace sellers whose revenue is above a bracket line and expresses the count as a share of all online sellers. At the 2022 rate, BPS's brackets divide at about US$20,000 a year. The marketplace tax exempts individual sellers up to about US$34,000. So the count above BPS's line is an upper limit on the sellers the tax can reach. World Bank Enterprise Surveys add a check on records. They cover unregistered firms in six Indonesian cities in 2023, and formal firms with five or more employees in seven countries. -## 4.5 External evidence and comparisons +## 4.5 Bringing the two sides together The third objective compares the platform evidence with national and payment figures. Table 4.3 lists the sources and what each one counts. The sources differ in what they cover, so the comparison follows three rules. It compares growth rates across sources and keeps each source in its own unit. It compares levels only after reconciling the definitions. And it compares sources most closely where their coverage overlaps. The 2023 file lets the thesis separate the part of BPS's marketplace sales that went to final consumers. It compares that part with Bank Indonesia's figure for the same year, in a test written down before the file was opened. The thesis also sets quarterly platform growth beside quarterly household spending growth. It treats payment figures as context, because they show how people pay and not what they buy online. From bed2edac72db32a9a84db1f50536c92e83cd075d Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 12 Oct 2026 19:17:51 +0000 Subject: [PATCH 18/65] Thesis ch5: fix two rounding errors in Table 5.3; log the check run Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 4 ++++ papers/thesis_2026-10/ch5_results.md | 6 +++--- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 8c8feb3..053b4f8 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -238,3 +238,7 @@ What changed: - Sentences split to about 15 to 18 words; Chapter 4 is about 3,000 words and Chapter 5 about 3,900 including tables (about 3,200 of prose). Still open from the audit: the two Chapter 1 sentences, the Chapter 3 wording of H1, "OECD" at first use in Chapter 2, market values in 1.3 and 3.4, and the list Chapter 7 must carry. + +### Check run after the Table 5.2 and heading changes (13 Oct 2026) + +Banned words, openers, sentence length, cross-references (sections, tables, appendices) and 22 number checks against the committed tables all pass, apart from two cells in Table 5.3 that I had rounded from already-rounded shares: Tokopedia's higher-fee share is 76 percent (not 77) and Grab 2022–24's commerce share is 42 percent (not 43). Fixed; the note under the table now says a row can add to 99 or 101. The earlier audit had matched these two cells at one decimal, so a check at whole-number rounding was the one that caught them. diff --git a/papers/thesis_2026-10/ch5_results.md b/papers/thesis_2026-10/ch5_results.md index f9199c6..c2d7473 100644 --- a/papers/thesis_2026-10/ch5_results.md +++ b/papers/thesis_2026-10/ch5_results.md @@ -53,15 +53,15 @@ Table 5.3 then splits the growth of revenue into three parts: more commerce, hig | Platform and window | Growth of revenue | More commerce | Higher fees | Lower incentives | |---|---|---|---|---| -| Tokopedia, 2022–23 | +53% | −22 | 77 | 45 | +| Tokopedia, 2022–23 | +53% | −22 | 76 | 45 | | Blibli third-party retail, 2022–23 | +465% | 17 | 20 | 63 | | Blibli third-party retail, 2022–25 | +679% | 18 | 63 | 20 | | Grab on-demand, 2021–24 | +321% | 34 | 34 | 33 | -| Grab on-demand, 2022–24 | +86% | 43 | 11 | 47 | +| Grab on-demand, 2022–24 | +86% | 42 | 11 | 47 | | GoTo on-demand, 2022–25 | +111% | 12 | 36 | 52 | | GoTo on-demand, 2023–25 | +114% | 27 | 6 | 67 | -Lower incentives were the larger of the two take-rate parts in four windows, and higher fees in two. In the seventh, Grab from 2021 to 2024, the two were nearly equal. Shares are rounded, so a row can add to 101. Grab's figures come from its annual filings, and Tokopedia's and GoTo's from audited statements. GoTo's 2022 transaction value is an estimate, and Blibli does not report incentives, so its split rests on a proxy. Blibli's releases also credit part of its 2022–23 rise to travel and digital products inside the segment, so some of its shares reflect a change in mix. For Bukalapak, the reports do not allow the parts to be separated. +Lower incentives were the larger of the two take-rate parts in four windows, and higher fees in two. In the seventh, Grab from 2021 to 2024, the two were nearly equal. Shares are rounded, so a row can add to 99 or 101. Grab's figures come from its annual filings, and Tokopedia's and GoTo's from audited statements. GoTo's 2022 transaction value is an estimate, and Blibli does not report incentives, so its split rests on a proxy. Blibli's releases also credit part of its 2022–23 rise to travel and digital products inside the segment, so some of its shares reflect a change in mix. For Bukalapak, the reports do not allow the parts to be separated. Tokopedia needs two views. Its accounts deduct incentives from revenue, and they fell by about one third in 2023. Counting the lines of its accounts, 60 percent of the extra net revenue came from lower incentives and 40 percent from higher gross fees. That count leaves the fall in volume inside both lines. Table 5.3 takes it out and compares fees and incentives per unit of transaction value. On that basis, higher fees account for more of the rise in the take rate than lower incentives. Both views agree that pricing, and not more commerce, drove the rise. They differ on which price lever led. Across the windows, revenue in 2022 to 2024 was not a fair guide to the commerce these platforms carried. The take rate moved a lot, mostly through lower incentives and higher fees. Did investors see through it? From 4b561edc008ada5e77b76bc3badb8daa5e50240a Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 13 Oct 2026 08:40:53 +0000 Subject: [PATCH 19/65] Thesis ch1-3: fix two claims that ran ahead of the evidence, add citations to Chapter 1, define OECD at first use, name the H1 measure and market values Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/ch1_introduction.md | 14 +++++++------- papers/thesis_2026-10/ch2_literature.md | 4 ++-- papers/thesis_2026-10/ch3_framework.md | 4 ++-- 3 files changed, 11 insertions(+), 11 deletions(-) diff --git a/papers/thesis_2026-10/ch1_introduction.md b/papers/thesis_2026-10/ch1_introduction.md index 97e55f3..6fb6d8f 100644 --- a/papers/thesis_2026-10/ch1_introduction.md +++ b/papers/thesis_2026-10/ch1_introduction.md @@ -2,19 +2,19 @@ Digital commerce has changed how goods and services are bought and sold in Indonesia. Sellers in small towns reach buyers across the country, businesses operate from mobile phones, and payments are made by QR code. The figures used to measure this activity, however, differ on both its size and its growth. -In 2023, traders at Tanah Abang, a major wholesale market in Jakarta, reported that their customers had moved to sellers on TikTok Shop. The government then banned transactions on social media platforms, and TikTok Shop closed in Indonesia shortly afterwards. The national statistics office, BPS, later reported that online sales rose by about 41 percent that year. The central bank's measure of online shopping fell by about 5 percent over the same period. The two official figures moved in opposite directions. +In 2023, traders at Tanah Abang, a major wholesale market in Jakarta, reported that their customers had moved to sellers on TikTok Shop (AFP 2023). The government then banned transactions on social media platforms, and TikTok Shop closed in Indonesia shortly afterwards. The national statistics office, BPS, later reported that online sales rose by about 41 percent that year (BPS 2025). The central bank's measure of online shopping fell by about 5 percent over the same period (Bank Indonesia 2025). The two official figures moved in opposite directions. -Tokopedia, an Indonesian online marketplace, shows the same split inside a single company. Between 2022 and 2023, its revenue rose by 53 percent while the value of transactions on the platform fell by 9 percent. Both figures come from the company's own reports. Measured by revenue, the platform grew; measured by transaction value, it contracted. +Tokopedia, an Indonesian online marketplace, shows the same split inside a single company. Between 2022 and 2023, its revenue rose by 53 percent while the value of transactions on the platform fell by 9 percent. Both figures come from the company's own reports (GoTo Gojek Tokopedia 2024). Measured by revenue, the platform grew; measured by transaction value, it contracted. Revenue and transaction value differ because a platform keeps only part of each sale, and that part can change. A platform that carries 100 units of transaction value may keep only a small part as its own revenue, in the form of commissions and fees. The rest belongs to the sale itself: the seller's receipts on a marketplace, the driver's fare on a ride-hailing app, and other amounts that pass through. An analyst reading the platform's accounts sees the small part, while one measuring the commerce it carries sees the whole. This thesis calls the difference the invisible wedge. The term refers to what a revenue-only measure leaves out, not to activity that goes unrecorded. -The share of each sale that a platform keeps is not fixed. When it rises, revenue can grow even while transaction value falls. At Tokopedia, about 60 percent of the rise in revenue reflected smaller discounts and incentives, which a platform pays for out of what it earns. +The share of each sale that a platform keeps is not fixed. When it rises, revenue can grow even while transaction value falls. In Tokopedia's accounts, about 60 percent of the extra revenue came from smaller discounts and incentives, which a platform pays for out of what it earns. -Money and public decisions rest on figures like these, and not only in Indonesia. Businesses in 43 economies sold almost US$27 trillion online in 2022, by the United Nations' estimate, and platforms carry a growing part of that trade. The companies that run them are among the most valuable in the world, and they are valued largely on the growth they report. If part of that growth comes from a platform keeping more of each sale, and not from more commerce, then part of what investors pay for is a pricing decision. Governments face the same uncertainty. They use these figures to judge how large their digital economies are, and a growing number now ask platforms to report what their sellers earn, or to collect tax from them. A rule built on platform records, for instance, reaches only the sellers who use platforms. Each of these readers is acting on a number without knowing how far it can be relied on. +Money and public decisions rest on figures like these, and not only in Indonesia. Businesses in 43 economies sold almost US$27 trillion online in 2022, by the United Nations' estimate (UNCTAD 2024), and platforms carry a growing part of that trade. The companies that run them are among the most valuable in the world, and they are valued largely on the growth they report. If part of that growth comes from a platform keeping more of each sale, and not from more commerce, then part of what investors pay for is a pricing decision. Governments face the same uncertainty. They use these figures to judge how large their digital economies are, and a growing number now ask platforms to report what their sellers earn, or to collect tax from them. A rule built on platform records, for instance, reaches only the sellers who use platforms. Each of these readers is acting on a number without knowing how far it can be relied on. Existing research gives investors and governments limited help in checking the figures they use. It explains why the figures differ: platforms set prices on both sides of a market, accounting rules decide how much of each sale counts as revenue, and national accounts separate a platform's own service from the commerce it carries. Statistical agencies have also begun to set business surveys beside platform transaction values. What that research does not show is when a figure stops moving with the activity it is used to represent, or by how much. Answering that requires two things that are rarely available together: a platform's revenue and transaction value reported over several years, and a national count of online sellers whose underlying answers can be examined. -Indonesia is one of the few markets where both kinds of evidence are available. Several of its platforms report their transaction value and their revenue each year, and BPS asks online sellers when they began selling and how their sales changed. The stakes are also concrete there. For 2024, BPS and the central bank give the two main official estimates of online sales, about US$81 billion and about US$31 billion. The size of the market is uncertain by a factor of more than two. The government is nevertheless writing rules for that market, on platform commissions, on discounts and on tax. Its new levy on marketplace sellers would raise about US$60 million a year on one measure of the market and about US$300 million on another, before exemptions. Indonesia is also a market where platform figures and survey figures are likely to part, because most of its online sellers work outside the marketplaces, through messaging and social media applications. The events of 2023 suggest that they did part. +Indonesia is one of the few markets where both kinds of evidence are available. Several of its platforms report their transaction value and their revenue each year, and BPS asks online sellers when they began selling and how their sales changed. The stakes are also concrete there. For 2024, BPS and the central bank give the two main official estimates of online sales, about US$81 billion and about US$31 billion (BPS 2025; Bank Indonesia 2025). The size of the market is uncertain by a factor of more than two. The government is nevertheless writing rules for that market, on platform commissions, on discounts and on tax. Its new levy on marketplace sellers would raise about US$60 million a year on one measure of the market and about US$300 million on another, before exemptions (Ministry of Finance of the Republic of Indonesia 2025; author's calculation). Indonesia is also a market where platform figures and survey figures are likely to part, because most of its online sellers work outside the marketplaces, through messaging and social media applications. The events of 2023 suggest that they did part. ## 1.1 Research question and objectives @@ -26,7 +26,7 @@ The research question is addressed through three objectives. The first is to mea Two hypotheses and an entry test ask whether revenue and the national figures move with the activity behind them. H1 states that a platform's revenue grows in proportion to its transaction value. H2 states that national e-commerce growth comes mainly from more businesses selling online, and less from higher sales per business. The entry test asks whether a rise in the count of online sellers comes from sellers entering for the first time. A further analysis estimates how many sellers a rule based on platform records can reach. -In brief, the thesis finds that revenue shows only a small part of the commerce platforms carry, and that platforms carry only about a fifth of online sales. Both revenue and the official count of online sellers also overstated growth in the period studied. Platform revenue rose mainly because platforms kept a larger share of each sale, not because buyers bought more. National growth came mainly from more sellers, as H2 states, but about half of the 2023 rise in the count is not explained by first-time entry. +In brief, the thesis finds that revenue shows only a small part of the commerce platforms carry, and that platforms carry only about a fifth of online sales. Platform revenue also grew faster than the commerce behind it, mainly because platforms kept a larger share of each sale and not because buyers bought more. National growth came mainly from more sellers, as H2 states, but about half of the 2023 rise in the count is not explained by first-time entry. ## 1.2 Contributions @@ -42,4 +42,4 @@ Earlier work has already compared platform transaction values with national e-co ## 1.3 Structure of the thesis -Chapter 2 reviews the literature on platform revenue, the measurement of online selling and the use of platform records for tax. Chapter 3 defines the measures, develops the hypotheses and sets out the entry test. Chapter 4 describes the data and the tests, including how revenue movements are traced and how the platform evidence is compared with national figures. Chapter 5 presents the results, beginning with the platforms and moving to the national figures. Chapter 6 examines how far Indonesia's recent rules reach and what the findings imply for other countries. Chapter 7 concludes and states the limitations. +Chapter 2 reviews the literature on platform revenue, the measurement of online selling and the use of platform records for tax. Chapter 3 defines the measures, develops the hypotheses and sets out the entry test. Chapter 4 describes the data and the tests, including how revenue movements are traced, how the platform evidence is compared with national figures, and how markets value platform growth. Chapter 5 presents the results, beginning with the platforms and moving to the national figures. Chapter 6 examines how far Indonesia's recent rules reach and what the findings imply for other countries. Chapter 7 concludes and states the limitations. diff --git a/papers/thesis_2026-10/ch2_literature.md b/papers/thesis_2026-10/ch2_literature.md index b042c95..a245cef 100644 --- a/papers/thesis_2026-10/ch2_literature.md +++ b/papers/thesis_2026-10/ch2_literature.md @@ -14,7 +14,7 @@ Together, these studies explain why the invisible wedge, the difference between ## 2.2 Measuring the online economy -Measuring the wedge is a question of definitions and sources. Official frameworks already separate the commerce a platform carries from the service the platform provides. National accounting guidance treats the sale between buyer and seller as distinct from the platform's intermediation service. It does not count the full transaction value as the platform's output (Ahmad and Schreyer 2016; International Monetary Fund 2018; OECD 2023; United Nations et al. 2025). +Measuring the wedge is a question of definitions and sources. Official frameworks already separate the commerce a platform carries from the service the platform provides. National accounting guidance treats the sale between buyer and seller as distinct from the platform's intermediation service. It does not count the full transaction value as the platform's output (Ahmad and Schreyer 2016; International Monetary Fund 2018; Organisation for Economic Co-operation and Development [OECD] 2023; United Nations et al. 2025). In practice, different producers publish different quantities. Industry reports size the digital economy of Southeast Asia by transaction value (Google, Temasek and Bain 2024). Statistics offices measure the online sales of surveyed businesses (BPS 2025). Listed platforms report the revenue they recognize, and central banks report the value of payments (Bank Indonesia 2025). The United Nations Conference on Trade and Development (UNCTAD) has shown that e-commerce values change with the definition used. It has also set business e-commerce sales beside the transaction values of online platforms (UNCTAD 2023, 2024). A difference between these figures is harmless while they move together. It matters when they part, because the choice of figure can then change the conclusion about scale and growth. Economists who check national income figures against satellite images of night-time lights follow the same logic: a figure should be tested against the activity it stands for (Henderson, Storeygard and Weil 2012). @@ -28,7 +28,7 @@ Surveys are not the only source on sellers. Transactions that do not enter a pla A platform is a third party to its sellers' tax affairs, and information from a third party changes how people comply with tax. Audit experiments in Denmark, and studies of the value-added tax and of receipt lotteries, show that reports from third parties help compliance (Kleven et al. 2011; Pomeranz 2015; Naritomi 2019; Kleven, Kreiner and Saez 2016; Slemrod 2019). Tax withheld through a third party rests on the same information (Brockmeyer and Hernandez 2016). -Rules for platforms apply this idea. The Organisation for Economic Co-operation and Development (OECD), a group of mostly high-income countries, published model rules under which platforms report what their sellers earn (OECD 2020). The European Union's DAC7 directive applies them (European Union 2021). Indonesia's marketplace tax rule goes further: it makes marketplaces collect tax from their sellers, not only report on them (Ministry of Finance of the Republic of Indonesia 2025). What a rule can reach depends on the records it uses. +Rules for platforms apply this idea. The OECD, a group of mostly high-income countries, published model rules under which platforms report what their sellers earn (OECD 2020). The European Union's DAC7 directive applies them (European Union 2021). Indonesia's marketplace tax rule goes further: it makes marketplaces collect tax from their sellers, not only report on them (Ministry of Finance of the Republic of Indonesia 2025). What a rule can reach depends on the records it uses. ## 2.4 The research gap diff --git a/papers/thesis_2026-10/ch3_framework.md b/papers/thesis_2026-10/ch3_framework.md index 3e29a15..a918211 100644 --- a/papers/thesis_2026-10/ch3_framework.md +++ b/papers/thesis_2026-10/ch3_framework.md @@ -55,7 +55,7 @@ Revenue depends on the take rate, and the take rate depends on fees, on discount > **H1.** Transaction value and revenue recognized by the same platform grow in proportion over time. -Growth rates never match exactly, so H1 is tested by asking whether growth divergence among Indonesian platforms is unusually large. The yardstick is growth divergence among listed platforms abroad, where revenue and transaction value broadly move together. Chapter 4 describes both groups. +Growth rates never match exactly, so H1 is tested by asking whether growth divergence among Indonesian and regional platforms, measured in logs, is unusually large. The yardstick is growth divergence among listed platforms abroad, where revenue and transaction value broadly move together. Chapter 4 describes both groups. H2 splits national growth into the part that comes from existing sellers selling more and the part that comes from more sellers entering. BPS reports both the number of businesses selling online and their total online sales, so the split can be made from published figures. Growth from new sellers can take place in channels that platform records do not cover, such as messaging and social media, which most sellers use. Platform figures can miss that growth even when every platform reports accurately. @@ -73,7 +73,7 @@ If the share of new sellers falls short, part of the rise is something other tha ## 3.4 Further analyses -Two further analyses support the hypotheses without forming hypotheses of their own. The first divides national growth by sales channel, to show how much of it took place inside the marketplace channel. The second compares marketplace sellers with other sellers on whether they keep financial statements, which bears on what platform records can add to tax administration. A separate analysis, developed in Chapter 6, estimates how many sellers a rule based on platform records can reach. Chapter 4 also describes how revenue movements are traced to fees and discounts, and how the platform evidence is compared with national and payment figures. +Two further analyses support the hypotheses without forming hypotheses of their own. The first divides national growth by sales channel, to show how much of it took place inside the marketplace channel. The second compares marketplace sellers with other sellers on whether they keep financial statements, which bears on what platform records can add to tax administration. A separate analysis, developed in Chapter 6, estimates how many sellers a rule based on platform records can reach. Chapter 4 also describes how revenue movements are traced to fees and discounts, how the platform evidence is compared with national and payment figures, and how markets value platform growth. ## 3.5 The tests together From 952cd54269f795b521d173622021a9da97dd33ef Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 13 Oct 2026 08:42:30 +0000 Subject: [PATCH 20/65] Thesis ch4/ch5: tighten, split long paragraphs, ground the thin-slice explanation in arithmetic, move secondary detail to the appendix; log Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 10 ++++ papers/thesis_2026-10/appendix_notes.md | 8 +++ papers/thesis_2026-10/ch4_data_methods.md | 6 +- papers/thesis_2026-10/ch5_results.md | 70 ++++++++++++----------- 4 files changed, 57 insertions(+), 37 deletions(-) diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 053b4f8..11554c1 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -242,3 +242,13 @@ Still open from the audit: the two Chapter 1 sentences, the Chapter 3 wording of ### Check run after the Table 5.2 and heading changes (13 Oct 2026) Banned words, openers, sentence length, cross-references (sections, tables, appendices) and 22 number checks against the committed tables all pass, apart from two cells in Table 5.3 that I had rounded from already-rounded shares: Tokopedia's higher-fee share is 76 percent (not 77) and Grab 2022–24's commerce share is 42 percent (not 43). Fixed; the note under the table now says a row can add to 99 or 101. The earlier audit had matched these two cells at one decimal, so a check at whole-number rounding was the one that caught them. + +### Fixes after the assessment (13 Oct 2026) + +Done, at the researcher's request ("fix those", skipping what is not important): +- Chapter 1: "overstated growth" replaced (revenue grew faster than the commerce behind it; the seller-count finding stays in its own sentence); the Tokopedia 60 percent now reads "in Tokopedia's accounts"; citations added for the figures (AFP, BPS, Bank Indonesia, GoTo, UNCTAD, Ministry of Finance); 1.3 now mentions market values. +- Chapter 2: OECD spelled out at first use. Chapter 3: H1 sentence names the group (Indonesian and regional) and the log measure; 3.4 mentions market values. +- Chapter 4: the entry-test rule no longer repeats Chapter 3's arithmetic; the list of further checks is shorter. +- Chapter 5: about 3,700 words (from 4,000); paragraphs split, status tags kept only where a result is registered or exploratory ("unmarked results are descriptive"); the first line of 5.2 names the group; the thin-slice explanation now rests on arithmetic (log change is roughly the change in points over the take rate) and not on a non-significant test; 5.4 says what the data show and do not show about investors; the top-bracket detail, the provinces and the one-third/two-thirds figure moved to Appendix B. + +Not done (not judged important now): figures, bibliography, YZU template, Chapters 6 and 7. diff --git a/papers/thesis_2026-10/appendix_notes.md b/papers/thesis_2026-10/appendix_notes.md index fa13c7a..c52106b 100644 --- a/papers/thesis_2026-10/appendix_notes.md +++ b/papers/thesis_2026-10/appendix_notes.md @@ -120,9 +120,17 @@ Growth in revenue and growth in transaction value had a correlation of 0.10 in t The quarterly sample is Grab on-demand, GoTo on-demand and Blibli against Sea, Airbnb, Booking, DoorDash, eBay, Etsy, Lyft and Uber. Airbnb and Booking use trailing four-quarter sums because they book revenue at check-in. DoorDash pairs ending in 2025Q4 or later are dropped because of an acquisition. +Details moved from Chapter 5. In the pairs of the Indonesian and regional group, the take rate stayed within about 10 percent (0.10 in log terms) in one third of pairs, against 67 to 69 percent in the foreign groups. The version of the test with growth divergence in points has 26 foreign platforms because Ozon has no pair in which both years pass the own-goods rule. + The comparison in points of the take rate (exploratory, `h1_points_vs_log.py`): median yearly change 1.01 points in the Indonesian and regional group and 0.98 points in the foreign group (26 platforms), Mann–Whitney p = 0.65; median share kept 1.59 percent against 16.93 percent. +### B.2 Where the 2023 break sits (exploratory) + +At the top of the size distribution, sellers with revenue above Rp50 billion a year (about US$3.4 million at the 2022 rate) were 0.5 percent of BPS's marketplace sellers in 2022 and held between 18 and 39 percent of the marketplace slice's value, resting on 19 sample rows (`tables/part5_checks.json`). + +Across the 34 provinces, count growth had a rank correlation of 0.10 (p = 0.59) with the share of new sellers and 0.35 (p = 0.04) with growth in sample size. In 23 provinces count growth exceeded what entry can explain, and these provinces carry 98.5 percent of the increase. The standard error of the national count implied by BPS's published relative standard errors is about 1.4 percent. The 306 thousand newly counted sellers are about 4.6 times the standard error of the change, and 2.3 times if the true error were twice as large (`tables/bps_frame_check.json`, `tables/rq_closing_checks.json`). + ## Appendix C. Registered tests and their outcomes A registered test was written down, dated and stored in the repository before the data it concerns were opened. The dates are those of the commits in `research/option_space_2026-10-01/PREREGISTRATION.md`. Exact wording and thresholds are in that file. diff --git a/papers/thesis_2026-10/ch4_data_methods.md b/papers/thesis_2026-10/ch4_data_methods.md index 57cce83..8518306 100644 --- a/papers/thesis_2026-10/ch4_data_methods.md +++ b/papers/thesis_2026-10/ch4_data_methods.md @@ -26,7 +26,7 @@ Three rules keep the pairs comparable. Two consecutive platform-years form a pai H1 says that revenue and transaction value grow in proportion, which means that the take rate stays close to steady. The test asks how far each platform's take rate moves from one year to the next. For each pair of years it takes the change in the logarithm of the take rate. A log change treats a rise by some factor and a fall by the same factor as equal in size. It equals the log growth of revenue minus the log growth of transaction value. This is growth divergence measured in logs, and it does not depend on how fast transaction value grew. Tokopedia's log change in 2023 is 0.52, which is a rise by a factor of 1.68, or 68 percent. -A rise and a fall both count against H1, so the test uses the size of each change. Each platform gets one number, the median size of its yearly changes, because the pairs of one platform are not independent of one another. The test compares five numbers for the Indonesian and regional group with 27 for the foreign group. It asks whether the first group ranks higher than chance would give, using a one-sided Mann–Whitney test. It repeats the question by shuffling the group labels 9,999 times. The p-value is the chance of a result at least this lopsided if the groups did not differ. Appendix B lists the checks that follow. They drop each platform in turn, treat Tokopedia and GoTo as one company, use growth divergence in points as Chapter 3 defines it, narrow the test to the three marketplaces, and repeat it with quarterly pairs. +A rise and a fall both count against H1, so the test uses the size of each change. Each platform gets one number, the median size of its yearly changes, because the pairs of one platform are not independent of one another. The test compares five numbers for the Indonesian and regional group with 27 for the foreign group. It asks whether the first group ranks higher than chance would give, using a one-sided Mann–Whitney test. It repeats the question by shuffling the group labels 9,999 times. The p-value is the chance of a result at least this lopsided if the groups did not differ. Appendix B lists the checks that follow. They drop each platform in turn and treat Tokopedia and GoTo as one company. They use growth divergence in points as Chapter 3 defines it, narrow the test to the three marketplaces, and repeat it with quarterly pairs. The second objective asks where large movements in revenue came from, and the thesis answers in two steps. The first divides each movement into volume and take rate. In log terms, revenue growth equals transaction value growth plus the change in the take rate. The take-rate share is the size of the take-rate change divided by the sum of the sizes of both changes. The same split is made for the wedge, which equals transaction value minus revenue. The second step finds the cause of a take-rate change. A platform's take rate equals the rate of its gross fees minus the rate of its incentives, each measured against transaction value. So a rise comes from higher fees, lower incentives or a change in the mix of businesses. Wherever a company discloses both, the thesis splits the growth of revenue over a window into three parts. They are more commerce, a change in the fee rate and a change in the incentive rate. @@ -54,11 +54,11 @@ The published figures cannot tell a new seller from one counted for the first ti *Implied by the published count for 2023 and its published growth of 27.4 percent. -The entry test follows the rule in Chapter 3. In the 2024 file, the weighted share of businesses that began selling online in 2023 must be at least 21.5 percent. That is 27.4 divided by 127.4, from the published rise of 27.4 percent. If the weights had matched BPS's other count for 2023, the threshold would have been 23.9 percent, but they did not. The test was written down and dated in the project's code archive, called the repository, before the files were opened. +The entry test follows the rule in Chapter 3: in the 2024 file, at least 21.5 percent of 2023 sellers must have begun selling online in 2023. If the weights had matched BPS's other count for 2023, the threshold would have been 23.9 percent, but they did not. The test was written down and dated in the project's code archive, called the repository, before the files were opened. If the test fails, a second question follows: how much of the rise does first-time entry not explain? A cohort is the group of sellers that began selling online by a given year. If two surveys count the same population, a cohort can only shrink between them, because sellers stop and no new seller can join it. The thesis compares the 2023 survey's count of sellers who began before 2023 with the whole 2022 count. It does so twice. The first time it assumes that no seller stopped. The second time it assumes that sellers stopped at about 5 percent a year, the rate at which one cohort shrank between the 2020 and 2022 surveys. -Further checks ask where the extra sellers sit. They look across the 34 provinces, across channels, at sellers' own reports of their sales and at the sampling error that BPS publishes. They also profile who the extra sellers are, look at part-year selling, and test a scenario of returners. The cohort check and these further checks were designed after the entry test failed, so Chapter 5 labels them exploratory. The exception is the median change in sellers' sales, which was registered. A seller who stopped and returned keeps an old start year, so the files cannot rule out returners. +Further checks ask where the extra sellers sit. They look across provinces and channels and at sellers' own reports of their sales. They use the sampling error that BPS publishes, profile who the extra sellers are, look at part-year selling, and test a scenario of returners. The cohort check and these further checks were designed after the entry test failed, so Chapter 5 labels them exploratory. The exception is the median change in sellers' sales, which was registered. The files cannot rule out returners, because a seller who stopped and returned keeps an old start year. Three further analyses use the same files. The first asks which channels sellers use and how sales divide between them. The published tables split the 2023 to 2024 rise in value between marketplaces and other channels, and BPS counts food and ride apps in its marketplace category. Value by channel rests on revenue brackets, which only the 2021 and 2023 files give. The thesis assigns each bracket a low, a middle and a high amount. The amounts were fixed before the file was opened. A validation rule applies: the middle scenario's 2022 total must lie between half and double BPS's published total of about US$53 billion. The 2023 questionnaire asks for monthly revenue but the bracket limits are annual size classes, so both readings are computed and the one that passes is kept. If the lower brackets alone already exceed the total, value shares are reported as ranges. diff --git a/papers/thesis_2026-10/ch5_results.md b/papers/thesis_2026-10/ch5_results.md index c2d7473..1e88c60 100644 --- a/papers/thesis_2026-10/ch5_results.md +++ b/papers/thesis_2026-10/ch5_results.md @@ -1,10 +1,10 @@ # Chapter 5. Results -This chapter follows the thesis from the platforms to the national figures. It first asks how much of the commerce a platform's revenue shows, and whether revenue keeps pace with it. It then asks why the two parted in Indonesia and what that meant for 2023. The second half turns to the survey: where the growth in sellers came from, who the extra sellers were, and why the national figures disagree. Each result is marked registered when its test was written down before the data were opened, and exploratory when the check was designed afterwards, as section 4.6 defines. A descriptive result reports numbers without a test. +This chapter follows the thesis from the platforms to the national figures. It first asks how much of the commerce a platform's revenue shows and whether revenue keeps pace with it. It then asks why the two parted in Indonesia and what that meant for 2023. The second half turns to the survey: where the growth in sellers came from, who the extra sellers were, and why the national figures disagree. Results marked registered were written down before the data were opened, and those marked exploratory were designed afterwards, as section 4.6 defines. Unmarked results are descriptive. ## 5.1 How much revenue leaves out -Take three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. Together they carried US$43 billion of transaction value and kept US$3 billion as revenue. The difference, the invisible wedge, was US$40 billion. Revenue was about 7 percent of what passed through them, and their ecosystem ratio, the wedge divided by revenue, was 12.7. The ratio is the author's own measure. These results are descriptive, and Table 5.1 shows how each amount was obtained. +Take three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. Together they carried US$43 billion of transaction value and kept US$3 billion as revenue. The difference, the invisible wedge, was US$40 billion. Revenue was about 7 percent of what passed through them, and their ecosystem ratio, the wedge divided by revenue and the author's own measure, was 12.7. Table 5.1 shows how each amount was obtained. **Table 5.1. The invisible wedge of three platforms, 2023 (US$ billion)** @@ -15,15 +15,13 @@ Take three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. Tog | Grab, Indonesia | 5.38 | 0.61 | 4.78 | Revenue is a direct country disclosure; transaction value is derived from Grab's company-wide ratio | | Total | 43.23 | 3.16 | 40.07 | Rounded amounts may not add | -Blibli's and Bukalapak's wedges, US$3.2 and US$10.5 billion, are left out of the total because Blibli's measure includes online travel and Bukalapak reports for its whole group. +Little of this depends on the assumptions. If Shopee kept 9 or 11 percent instead of 10, the total would stay between US$39.9 and US$40.3 billion. At Sea's company-wide rate of 11.5 percent it would be US$39.8 billion. Dropping any one platform leaves at least US$20.7 billion. Blibli's and Bukalapak's wedges, US$3.2 and US$10.5 billion, are left out because Blibli's measure includes online travel and Bukalapak reports for its whole group. For platforms that report both amounts directly, the wedge is 97 to 99 percent of transaction value in every year, so it moves almost one for one with it. Appendix A gives the wedge by platform and year. -The size of the wedge hardly depends on the assumptions. If Shopee kept 9 or 11 percent instead of 10, the total would stay between US$39.9 and US$40.3 billion. At Sea's company-wide rate of 11.5 percent it would be US$39.8 billion, and dropping any one platform leaves at least US$20.7 billion. For platforms that report both amounts directly, the wedge is 97 to 99 percent of transaction value in every year. It moves almost one for one with it. Appendix A gives the wedge for each platform and year. - -Platforms are only part of online selling. In the BPS survey, the marketplace channel carries between 16 and 26 percent of online sales value, depending on the year and the scenario. BPS counts food and ride apps in that channel. So a measure built on platforms misses between about 74 and 84 percent of online sales value, and revenue shows much less still. A small slice can still be a fair guide if it keeps its size. The next question is whether it does. +Platforms are only part of online selling. In the BPS survey, the marketplace channel carries between 16 and 26 percent of online sales value, and BPS counts food and ride apps in that channel. So a measure built on platforms misses between about 74 and 84 percent of online sales value, and revenue shows much less still. A small slice can still be a fair guide if it keeps its size. The next question is whether it does. ## 5.2 Do revenue and transaction value grow together? -In Indonesia they do not, and H1 is rejected. Among the Indonesian and regional platforms, the typical take rate moved by about 20 percent in a year, up or down. At the typical foreign platform it moved by about 6 percent. The difference is about threefold. The take rate stayed within about 10 percent in one third of the Indonesian and regional pairs, and in about two thirds of the foreign pairs. Table 5.2 gives the main test and its checks, and Appendix B gives the rest. +Among Indonesian and regional platforms they do not, and H1 is rejected. The typical take rate moved by about 20 percent in a year, up or down. At the typical foreign platform it moved by about 6 percent, so the difference is about threefold. Table 5.2 gives the main test and its checks. **Table 5.2. The test of H1** @@ -35,19 +33,15 @@ In Indonesia they do not, and H1 is rejected. Among the Indonesian and regional | Drop each platform in turn, or treat Tokopedia and GoTo as one company | Mann–Whitney | 0.17 to 0.23 | 0.06 | 0.003 to 0.016 | | Quarterly pairs, 3 platforms against 8 | Mann–Whitney | 0.071 | 0.049 | 0.19 | -The first two rows of Table 5.2 are the main test, and the group figures in rows one to four are medians of platform medians. The third row repeats the test with growth divergence in points. One foreign platform drops out because it has no pair in which both years pass the own-goods rule. The fourth row shows that no single platform carries the result. The fifth row is the quarterly test. - -A narrower test of the three Indonesian marketplaces against eight foreign platforms agrees (Appendix B). It gives p = 0.002 when it compares pairs and 0.06 when it compares platforms, because pairs from one platform are not independent. In it, revenue growth and transaction value growth were barely related in Indonesia (correlation 0.10) and closely related abroad (0.98, excluding Sea). The larger groups were set up after that test, so the main test is exploratory. +The first two rows are the main test, using the two methods of Chapter 4. The third row repeats it with growth divergence in points, and the fourth shows that no single platform carries the result. The main test is exploratory, because the larger groups were set up after a first test on the three marketplaces. That narrower test agrees, with p = 0.002 when it compares pairs and 0.06 when it compares platforms. In it, revenue growth and transaction value growth had a correlation of 0.10 in Indonesia and 0.98 abroad, excluding Sea (Appendix B). The quarterly test in the last row does not reject H1. Its Indonesian pairs mostly cover 2023 to 2026, after most of the repricing, and the large moves came in 2022 to 2024. So the rejection describes that period. -The quarterly version does not reject H1, as Table 5.2 shows. Its Indonesian pairs mostly cover 2023 to 2026, after most of the repricing. The large moves came in 2022 to 2024, so the rejection of H1 describes that period. - -Most of the threefold difference comes from the size of the slice, which is an exploratory finding. Measured in percentage points of the take rate, Indonesian and regional platforms changed it by about as much as foreign ones. The change was about 1 point a year in both groups (p = 0.65). But the typical Indonesian and regional platform keeps only 1.6 percent of each sale, against 16.9 percent abroad. The share abroad varies with the business model, from about 3 percent at Shopify to about 25 percent at Mercado Libre in 2025. If transaction value is flat, a rise of 1 point lifts the revenue of the first by more than half. It lifts the revenue of a typical foreign platform by about 6 percent. The larger the wedge is next to revenue, the more a small price change moves revenue growth. What the platforms actually changed is the next question. +The thin slice explains much of the threefold difference, which is an exploratory finding. A log change in the take rate is roughly the change in points divided by the take rate itself. Indonesian and regional platforms changed their take rates by about 1 point a year, as foreign platforms did. But the typical Indonesian and regional platform keeps only 1.6 percent of each sale, against 16.9 percent abroad. Abroad the share runs from about 3 percent at Shopify to about 25 percent at Mercado Libre. If transaction value is flat, a rise of 1 point lifts the revenue of the first by more than half. It lifts the revenue of a typical foreign platform by about 6 percent. The larger the wedge is next to revenue, the more a small price change moves revenue growth. What the platforms actually changed is the next question. ## 5.3 What moved the take rate -Tokopedia shows it best. In 2023 its transaction value fell by 9 percent while its revenue rose by 53 percent. Across the three Indonesian marketplaces, the take rate accounts for two thirds of the movement in revenue at the median (66.5 percent). At the eight foreign platforms chosen first, it accounts for about a quarter (26.7 percent). The wedge follows the other part. At the median, about 96 percent of a change in the wedge comes from the change in transaction value, in both groups. These results are descriptive. +Tokopedia shows it best. In 2023 its transaction value fell by 9 percent while its revenue rose by 53 percent. Across the three Indonesian marketplaces, the take rate accounts for two thirds of the movement in revenue at the median (66.5 percent). At the eight foreign platforms chosen first, it accounts for about a quarter (26.7 percent). The wedge follows the volume instead: at the median, about 96 percent of a change in the wedge comes from the change in transaction value, in both groups. -Table 5.3 then splits the growth of revenue into three parts: more commerce, higher fees and lower incentives, which are the discounts and vouchers a platform pays for. Fees and incentives are measured per unit of transaction value. A negative share means that the part pulled revenue down, and windows of one platform overlap, so the seven windows are not seven independent cases. +Table 5.3 splits the growth of revenue into more commerce, higher fees and lower incentives, which are the discounts and vouchers a platform pays for. Fees and incentives are measured per unit of transaction value. A negative share means that the part pulled revenue down. The windows of one platform overlap, so they are not seven independent cases. **Table 5.3. What moved revenue in seven Indonesian windows (share of the growth of revenue, percent)** @@ -61,17 +55,17 @@ Table 5.3 then splits the growth of revenue into three parts: more commerce, hig | GoTo on-demand, 2022–25 | +111% | 12 | 36 | 52 | | GoTo on-demand, 2023–25 | +114% | 27 | 6 | 67 | -Lower incentives were the larger of the two take-rate parts in four windows, and higher fees in two. In the seventh, Grab from 2021 to 2024, the two were nearly equal. Shares are rounded, so a row can add to 99 or 101. Grab's figures come from its annual filings, and Tokopedia's and GoTo's from audited statements. GoTo's 2022 transaction value is an estimate, and Blibli does not report incentives, so its split rests on a proxy. Blibli's releases also credit part of its 2022–23 rise to travel and digital products inside the segment, so some of its shares reflect a change in mix. For Bukalapak, the reports do not allow the parts to be separated. +Lower incentives were the larger of the two take-rate parts in four windows and higher fees in two. In the seventh, Grab from 2021 to 2024, the two were nearly equal. Shares are rounded, so a row can add to 99 or 101. Two series need a note. GoTo's 2022 transaction value is an estimate. Blibli does not report incentives, so its split rests on a proxy, and part of its 2022–23 rise comes from travel and digital products. Bukalapak's reports do not allow the parts to be separated. -Tokopedia needs two views. Its accounts deduct incentives from revenue, and they fell by about one third in 2023. Counting the lines of its accounts, 60 percent of the extra net revenue came from lower incentives and 40 percent from higher gross fees. That count leaves the fall in volume inside both lines. Table 5.3 takes it out and compares fees and incentives per unit of transaction value. On that basis, higher fees account for more of the rise in the take rate than lower incentives. Both views agree that pricing, and not more commerce, drove the rise. They differ on which price lever led. Across the windows, revenue in 2022 to 2024 was not a fair guide to the commerce these platforms carried. The take rate moved a lot, mostly through lower incentives and higher fees. Did investors see through it? +Tokopedia needs two views. Its accounts deduct incentives from revenue, and they fell by about one third in 2023. Counting the lines of its accounts, 60 percent of the extra net revenue came from lower incentives and 40 percent from higher gross fees. That count leaves the fall in volume inside both lines. Table 5.3 takes it out, and on that per-unit basis higher fees account for more of the rise in the take rate than lower incentives. Both views agree that pricing and not more commerce drove the rise, and they differ only on which lever led. Across the windows, revenue in 2022 to 2024 was not a fair guide to the commerce these platforms carried. Did investors see through it? ## 5.4 What markets valued -Market value moved with transaction value. This result is registered. Across 28 listed platforms and 138 platform-years, a 1 percent rise in transaction value goes with a 0.6 percent rise in market value (p = 0.002). A second prediction, that markets value growth from a higher take rate less, did not hold. A 1 percent rise in the take rate goes with a rise of 0.3 percent. Its standard error is 0.37, too wide to tell it from zero. The difference between the two is not large enough to count (p = 0.20). Both results are associations, and market value also changes when a company issues shares, so neither is a stock return. Market value thus moved with the buying underneath revenue. The next section asks what that buying did in 2023. +Market value moved with transaction value, a registered result. Across 28 listed platforms and 138 platform-years, a 1 percent rise in transaction value goes with a 0.6 percent rise in market value (p = 0.002). The second prediction, that markets value growth from a higher take rate less, did not hold. A 1 percent rise in the take rate goes with a rise of 0.3 percent, but that estimate is too uncertain to tell from zero. The difference between the two is not large enough to count (p = 0.20). So the data show that market value follows the commerce a platform carries. They do not show that markets saw through the pricing effect. Both results are associations and not stock returns, because market value also changes when a company issues shares. The next section asks what the commerce did in 2023. ## 5.5 The platforms and the sellers in 2023 -Buying through platforms did not boom in 2023. Table 5.4 sets six measures of it against household spending, which rose by 9.4 percent in current prices. Five of the six grew more slowly, and three fell. Blibli is the exception. These results are descriptive. +Buying through platforms did not boom in 2023. Table 5.4 sets six measures of it against household spending, which rose by 9.4 percent in current prices. Five of the six grew more slowly, and three fell. Blibli is the exception. **Table 5.4. Growth in 2023 (percent)** @@ -88,13 +82,15 @@ Buying through platforms did not boom in 2023. Table 5.4 sets six measures of it | BPS e-commerce value | +40.6 | Rupiah | | BPS online sellers | +27.4 | Number | -Each row of Table 5.4 is in its own unit, so the thesis compares only the rates, and GoTo's change rests on an estimated 2022 value. The first seven rows show the buying, and the last three show the figures that described a boom. Revenue and BPS's value rose strongly while the buying through platforms did not. +The rows are in different units, so the thesis compares only the rates, and GoTo's change rests on an estimated 2022 value. The first seven rows show the buying, and the last three show the figures that described a boom. Revenue and BPS's value rose strongly while the buying through platforms did not. -Sellers did not describe a boom either. BPS asks each business how its online sales changed. Among businesses already selling, the median change was zero, as a registered prediction expected. The other figures here are descriptive. A share of 24 percent reported a rise, 32 percent a fall and 44 percent no change. In 2022 the balance was the other way, with 37 percent reporting a rise and 24 percent a fall. The mean reported change in 2023 was −4.3 percent. Lack of demand became the main obstacle, named by 41 percent of sellers against 35 percent a year earlier. Yet the published count of online sellers rose by 27 percent that year. Where did those sellers come from? +Sellers did not describe a boom either. BPS asks each business how its online sales changed. Among businesses already selling, the median change was zero, as a registered prediction expected. A share of 24 percent reported a rise, 32 percent a fall and 44 percent no change, against 37 percent up and 24 percent down a year earlier. The mean change was −4.3 percent, and lack of demand became the main obstacle, named by 41 percent of sellers against 35 percent in 2022. Yet the published count of online sellers rose by 27 percent that year. Where did those sellers come from? ## 5.6 Where the extra sellers came from -On BPS's published figures, H2 holds. From 2022 to 2023, online sales value grew by 40.6 percent. The number of businesses grew by 27.4 percent and sales per business by 10.4 percent, so more businesses account for 71 percent of the growth. From 2023 to 2024 the figures were 17.1, 15.3 and 1.5 percent. More businesses then account for 90 percent of the growth, or 71 percent if BPS's other count for 2023 is used. The split is arithmetic, and it cannot tell a new seller from one counted for the first time. The entry test can, and its result is registered. In the 2024 file, 13.45 percent of sellers began selling online in 2023, and the rule required at least 21.5 percent. The entry test fails. Even if no seller stopped, 13.45 percent new sellers means the count could have grown by at most 15.5 percent over the sellers already there. The published growth is 27.4 percent. +On BPS's published figures, H2 holds. From 2022 to 2023, online sales value grew by 40.6 percent, the number of businesses by 27.4 percent and sales per business by 10.4 percent. So more businesses account for 71 percent of the growth. From 2023 to 2024 the figures were 17.1, 15.3 and 1.5 percent, and more businesses account for 90 percent, or 71 percent on BPS's other count for 2023. The split is arithmetic, and it cannot tell a new seller from one counted for the first time. + +The entry test can, and its result is registered. In the 2024 file, 13.45 percent of sellers began selling online in 2023, and the rule required at least 21.5 percent. The test fails. Even if no seller stopped, that many new sellers lets the count grow by at most 15.5 percent over the sellers already there, against the published 27.4 percent. **Table 5.5. The 2023 rise in the count of online sellers (thousands)** @@ -104,15 +100,15 @@ On BPS's published figures, H2 holds. From 2022 to 2023, online sales value grew | Net entry: sellers who began in 2023, less those who stopped | 513 (63%) | 360 (44%) | | Earlier sellers newly counted above the whole 2022 count | 306 (37%) | 458 (56%) | -Table 5.5 shows the accounting. The rise of 819 thousand is the gap between the weighted totals of the two files, and BPS's own published counts give 821 thousand. The 2023 survey counted 306 thousand more sellers who began before 2023 than the whole 2022 count held. That is 37 percent of the rise if no seller stopped and 56 percent if sellers stopped at 5 percent a year. So about half of the rise is not explained by first-time entry. This accounting is exploratory, like the checks that follow, and they point the same way. +Table 5.5 shows the accounting. The rise of 819 thousand is the gap between the weighted totals of the two files, and BPS's own published counts give 821 thousand. The 2023 survey counted 306 thousand more sellers who began before 2023 than the whole 2022 count held. That is 37 percent of the rise if no seller stopped and 56 percent if sellers stopped at 5 percent a year. So about half of the rise is not explained by first-time entry. This accounting and the checks below are exploratory, and they point the same way. -Sellers who began by 2020 numbered 9.9 percent fewer in the 2022 survey than in the 2020 survey, as a cohort should, and that fall took two years. In the 2023 survey they numbered 6.0 percent more than in the 2022 one. Between the 2023 and 2024 surveys, the cohort of sellers who began before 2020 shrank by 10.2 percent. At least 754 thousand new sellers appeared, against a rise of 584 thousand. So 2023 stands out as the break. +Cohorts behave as they should, except in 2023. Sellers who began by 2020 numbered 9.9 percent fewer in the 2022 survey than in the 2020 one, over two years. In the 2023 survey they numbered 6.0 percent more than in the 2022 one. Between the 2023 and 2024 surveys, the cohort that began before 2020 shrank by 10.2 percent, and at least 754 thousand new sellers appeared against a rise of 584 thousand. The break is also uneven. Across the 34 provinces, count growth had almost no link to the share of new sellers (rank correlation 0.10) and a weak link to growth in sample size (0.35). Sellers who began by 2022 and use only chat or social media rose by 13 percent, while marketplace users rose by 0.3 percent. BPS's published error rates put the standard error of the national count at about 1.4 percent. The 306 thousand sellers are about 4.6 times the standard error of the change. -Three more checks locate the break. Across the 34 provinces, count growth had almost no link to the share of new sellers (rank correlation 0.10). It had a weak link to growth in sample size (0.35). In 23 provinces growth exceeded what entry can explain, and they carry 98.5 percent of the increase. By channel, sellers who began by 2022 and use only chat or social media rose by 13 percent, while marketplace users rose by 0.3 percent. BPS's published error rates imply a standard error of about 1.4 percent for the national count. The 306 thousand sellers are about 4.6 times the standard error of the change. The value side tells a similar story. BPS's value rose from about US$53 billion to about US$72 billion. Sellers not explained by entry account for 15 to 17 percent of this, and entrants for 25 to 30 percent. The remaining 53 to 59 percent cannot be traced to existing sellers' answers, which show a mean change of −4.3 percent. These checks point to wider survey coverage of chat and social sellers, and the data cannot separate that from sellers returning after a pause. Who were the extra sellers? +The value side tells a similar story. BPS's value rose from about US$53 billion to about US$72 billion. Sellers not explained by entry account for 15 to 17 percent of this, and entrants for 25 to 30 percent. The remaining 53 to 59 percent cannot be traced to existing sellers' answers, which show a mean change of −4.3 percent. These checks point to wider survey coverage of chat and social sellers, and the data cannot separate that from sellers returning after a pause. Who were the extra sellers? ## 5.7 Who the extra sellers were, and whether they returned -The comparison of who they were is exploratory. The surveys do not follow the same businesses, so the thesis compares groups. For each group it takes the 2023 count above what the group's normal yearly change in 2020 to 2022 would give. These extra sellers total about 450 thousand, close to the 458 thousand of the second column in Table 5.5. Table 5.6 gives each group's share of this extra count next to its share of the 2022 count. +The comparison of who they were is exploratory. The surveys do not follow the same businesses, so the thesis compares groups. For each group it takes the 2023 count above what its normal yearly change in 2020 to 2022 would give. These extra sellers total about 450 thousand, close to the 458 thousand in Table 5.5. Table 5.6 gives each group's share of this extra count next to its share of the 2022 count. **Table 5.6. Who the extra sellers were (percent)** @@ -123,15 +119,15 @@ The comparison of who they were is exploratory. The surveys do not follow the sa | Outside Java | 23.6 | 36.3 | | Food and lodging, or making goods | 38.5 | 53.0 | -The extra count leaned toward long-running businesses, older owners and places outside Java. The lean toward men in the raw data disappears once normal change is taken out. The extra count does not look like the young start-ups of the usual growth story. That raises one question: could these be established sellers who paused and came back? +The extra count leaned toward long-running businesses, older owners and places outside Java, and the lean toward men in the raw data disappears once normal change is taken out. It does not look like the young start-ups of the usual growth story. That raises one question: could these be established sellers who paused and came back? -A seller returning from a break would usually sell for only part of the year, unless the seller restarts in January, and part-year selling fell in 2023. Among sellers who began by 2021, the number selling for part of the year fell from 472 thousand to 318 thousand, and it fell in every start-year group. This exploratory check has a limit: BPS changed how it asks about months between the two rounds. Small firms that stop are more often young, with young owners (McKenzie and Paffhausen 2019), and sellers who pause are likely to be such firms. If returners were mostly such firms, the extra count would lean young. It leaned old. Returners equal to 10 to 15 percent of the 2022 count would remove the unexplained part, which is a scenario and not an estimate. The check cannot rule out returners, because the files carry no linked histories, and exits or a move to full-year selling could offset them. +A returning seller would usually sell for only part of the year, unless the seller restarts in January, and part-year selling fell in 2023. Among sellers who began by 2021, the number selling for part of the year fell from 472 thousand to 318 thousand, and it fell in every start-year group. Small firms that stop are more often young, with young owners (McKenzie and Paffhausen 2019). If returners were mostly such firms, the extra count would lean young. It leaned old. Returners equal to 10 to 15 percent of the 2022 count would remove the unexplained part, which is a scenario and not an estimate. The check cannot rule out returners. The files carry no linked histories, BPS changed how it asks about months between the rounds, and exits or a move to full-year selling could offset them. ## 5.8 Channels and records -Two additional analyses, on channels and on records, show where these sellers sit. The path of marketplace use, the value held by sellers without statements and the World Bank results were registered, and the comparison within the pre-2020 group is exploratory. +Two additional analyses show where these sellers sit. The path of marketplace use, the value held by sellers without statements and the World Bank results were registered. The comparison within the pre-2020 group is exploratory. -Most online sellers do not use a marketplace. About 96 percent use chat or social media. Use of marketplaces fell every time it was measured, from 21.6 percent of sellers in 2020 to 17.2 percent in 2024. BPS worded the question slightly differently in the 2023 and 2024 rounds. From 2023 to 2024, BPS's marketplace value grew by 1.45 percent and other channels by 20.57 percent, so 98.5 percent of the increase came from outside the marketplaces. BPS's marketplace category includes many food and ride merchants: 43 percent of its sellers use only Gojek or Grab, and 47 percent use a shopping marketplace. +Most online sellers do not use a marketplace, and about 96 percent use chat or social media. Use of marketplaces fell every time it was measured, from 21.6 percent of sellers in 2020 to 17.2 percent in 2024. BPS worded the question slightly differently in the last two rounds. From 2023 to 2024, BPS's marketplace value grew by 1.45 percent and other channels by 20.57 percent, so 98.5 percent of the increase came from outside the marketplaces. BPS's marketplace category includes many food and ride merchants: 43 percent of its sellers use only Gojek or Grab, and 47 percent use a shopping marketplace. **Table 5.7. Marketplace use and financial statements (percent of sellers)** @@ -142,16 +138,22 @@ Most online sellers do not use a marketplace. About 96 percent use chat or socia | 2023 | 17.8 | 15.2 | | 2024 | 17.2 | | -Records are thinner outside the marketplaces. In 2023, 28.6 percent of marketplace sellers kept complete financial statements, against 12.3 percent of other sellers. Table 5.7 shows that the share of all sellers who keep them fell. The fall from 2020 to 2022 came while the survey appears to have counted the same kinds of seller. The fall in 2023 may partly reflect the chat-only sellers added to the count. Even among sellers who began before 2020, the share fell from 23.4 to 16.8 percent between the two surveys. The value shares come from bracket scenarios. The annual reading of the brackets passes the validation rule, because the middle scenario gives 1.95 times BPS's total for 2022. The monthly reading gives 23 times and fails. The lower brackets alone already exceed BPS's total, so value shares are ranges. Sellers without statements held between 26 and 40 percent of online value in 2022, against 79 percent of the count. The registered prediction was that their share of value would not fall by more than 5 points between 2020 and 2022. It fell by 5.8 points, so the prediction failed, narrowly. A second registered version of the test gives a fall of 3.3 points. +Records are thinner outside the marketplaces. In 2023, 28.6 percent of marketplace sellers kept complete financial statements, against 12.3 percent of other sellers, and Table 5.7 shows the share of all sellers who keep them falling. The fall from 2020 to 2022 came while the survey appears to have counted the same kinds of seller. The fall in 2023 may partly reflect the chat-only sellers added to the count. Even among sellers who began before 2020, the share fell from 23.4 to 16.8 percent between the two surveys. -The World Bank surveys point the same way. In six Indonesian cities, 27 percent of unregistered firms use social media with their customers. Among those, 70 percent keep no written records, and their median sales are about US$4,000 a year. The registered prediction on their mean sales failed: it was below about US$6,600, and the mean is about US$7,300. Among formal firms, 39.9 percent in Indonesia have neither a website nor online tax filing, the highest of seven countries. Malaysia has 9.1 percent and Thailand 8.7 percent. +Value shares rest on the bracket scenarios of section 4.4. The annual reading passes the validation rule, because the middle scenario gives 1.95 times BPS's total for 2022, and the monthly reading fails at 23 times. The lower brackets alone already exceed BPS's total, so the shares are ranges. Sellers without statements held between 26 and 40 percent of online value in 2022, against 79 percent of the count. The registered prediction was that their share of value would not fall by more than 5 points between 2020 and 2022. It fell by 5.8 points, so the prediction failed, narrowly, and a second registered version gives a fall of 3.3 points. + +The World Bank surveys point the same way. In six Indonesian cities, 27 percent of unregistered firms use social media with their customers. Among those, 70 percent keep no written records, and their median sales are about US$4,000 a year. The registered prediction on their mean sales failed: it was below about US$6,600, and the mean is about US$7,300. Among formal firms, 39.9 percent in Indonesia have neither a website nor online tax filing, the highest of seven countries, against 9.1 percent in Malaysia and 8.7 percent in Thailand. ## 5.9 Why the national figures disagree The third objective asks whether platform, national and payment figures give the same account. They do not give the same account of scale. They give the same account of growth only where they cover the same sellers. -In 2023, Bank Indonesia's figure fell by 4.7 percent and BPS's rose by 40.6 percent. Where the two cover the same ground, they agree on slow growth. BPS's own marketplace slice grew by 0.2 percent from 2022 to 2023 in the middle scenario, and by 0.2 to 13.2 percent across the scenarios. It grew by 1.45 percent from 2023 to 2024. A registered test also holds. The part of BPS's marketplace sales that went to final consumers in 2022 is 0.68 times Bank Indonesia's figure in the middle scenario. It is 0.16 times in the low scenario and 1.62 times in the high one. So the disagreement sits outside the marketplaces, in the count of sellers that only BPS reaches. The 2024 figures show the same split. Those that cover mainly platforms grew by 5 to 7 percent, and BPS's grew by 17.1 percent. Adjacent figures point the same way as BPS, but they do not measure Indonesian e-commerce. Southeast Asian parcels grew by 25.2 percent, and the tax on foreign digital services by 24.9 percent. +In 2023, Bank Indonesia's figure fell by 4.7 percent and BPS's rose by 40.6 percent. Where the two cover the same ground, they agree on slow growth. BPS's own marketplace slice grew by 0.2 percent from 2022 to 2023 in the middle scenario, and by 0.2 to 13.2 percent across the scenarios. It grew by 1.45 percent from 2023 to 2024. A registered test also holds. The part of BPS's marketplace sales that went to final consumers in 2022 is 0.68 times Bank Indonesia's figure in the middle scenario. It is 0.16 times in the low scenario and 1.62 times in the high one. So the disagreement sits outside the marketplaces, in the count of sellers that only BPS reaches. + +The 2024 figures show the same split. Those that cover mainly platforms grew by 5 to 7 percent, and BPS's grew by 17.1 percent. Adjacent figures point the same way as BPS but do not measure Indonesian e-commerce. Southeast Asian parcels grew by 25.2 percent, and the tax on foreign digital services by 24.9 percent. + +Sizes cannot be reconciled. In 2023, BPS's marketplace slice was about US$13 billion, against US$38 billion for Tokopedia and the Shopee estimate together. In 2024 it was less than a quarter of Momentum Works' marketplace estimate of about US$56.5 billion. Platform transaction value counts shipping, digital goods, travel and offline sales that a seller survey does not count in the same way. Counts differ too: Tokopedia alone said it had about 15 million merchants in 2022, while BPS counts about 0.6 million marketplace sellers. -Sizes cannot be reconciled. In 2023, BPS's marketplace slice was about US$13 billion, against US$38 billion for Tokopedia and the Shopee estimate together. In 2024 it was less than a quarter of the marketplace estimate of Momentum Works, about US$56.5 billion. Platform transaction value counts shipping, digital goods, travel and offline sales that a seller survey does not count in the same way. In 2022, sellers with revenue above about US$3.4 million a year were 0.5 percent of BPS's marketplace sellers. They held between 18 and 39 percent of the slice's value, and only 19 sample rows lie in that bracket. Counts also differ. Tokopedia alone said it had about 15 million merchants in 2022, while BPS counts about 0.6 million marketplace sellers. Platform sales do not always keep pace with household spending either. GoTo's on-demand transaction value grew more slowly than household spending in each of four quarters, from the third quarter of 2025 to the second quarter of 2026. In the last it grew by 2.0 percent against 8.3 percent, while its revenue grew by 20.5 percent. Grab and Shopee grew faster, but their figures cover the region. Payment figures such as QRIS are context only, because they include in-person payments and the switch from cash. +Platform sales do not always keep pace with household spending either. GoTo's on-demand transaction value grew more slowly than household spending in each of four quarters, from the third quarter of 2025 to the second quarter of 2026. In the last it grew by 2.0 percent against 8.3 percent, while its revenue grew by 20.5 percent. Grab and Shopee grew faster, but their figures cover the region. Payment figures such as QRIS are context only, because they include in-person payments and the switch from cash. Chapter 6 asks what these results mean for the rules that Indonesia is writing. From f49087af867e605947b4526fededa471bb8c4f9c Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 13 Oct 2026 08:47:55 +0000 Subject: [PATCH 21/65] Thesis ch4/ch5: five figures replace three tables, literature tied into the results, scale and synthesis added; appendix D Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/appendix_notes.md | 10 ++ papers/thesis_2026-10/ch4_data_methods.md | 12 +- papers/thesis_2026-10/ch5_results.md | 71 +++++------ .../figures/fig5_1_thin_slice.png | Bin 0 -> 137429 bytes .../figures/fig5_2_tokopedia_two_views.png | Bin 0 -> 113938 bytes .../figures/fig5_3_growth_2023.png | Bin 0 -> 211503 bytes .../figures/fig5_4_extra_sellers.png | Bin 0 -> 127360 bytes .../fig5_5_marketplace_and_records.png | Bin 0 -> 112162 bytes papers/thesis_2026-10/figures/make_figures.py | 115 ++++++++++++++++++ 9 files changed, 160 insertions(+), 48 deletions(-) create mode 100644 papers/thesis_2026-10/figures/fig5_1_thin_slice.png create mode 100644 papers/thesis_2026-10/figures/fig5_2_tokopedia_two_views.png create mode 100644 papers/thesis_2026-10/figures/fig5_3_growth_2023.png create mode 100644 papers/thesis_2026-10/figures/fig5_4_extra_sellers.png create mode 100644 papers/thesis_2026-10/figures/fig5_5_marketplace_and_records.png create mode 100644 papers/thesis_2026-10/figures/make_figures.py diff --git a/papers/thesis_2026-10/appendix_notes.md b/papers/thesis_2026-10/appendix_notes.md index c52106b..1ef44b0 100644 --- a/papers/thesis_2026-10/appendix_notes.md +++ b/papers/thesis_2026-10/appendix_notes.md @@ -157,3 +157,13 @@ A registered test was written down, dated and stored in the repository before th | M2 | Market value moves with transaction value | 8 Oct 2026 | Holds (p = 0.002) | | P1 to P5, R1 to R3 | Predictions on the third-quarter 2026 results of GoTo, Grab and Sea | 2 and 3 Oct 2026 | Not yet scored; first results are due on 27 Oct 2026 | | Marketplace tax start; BPS marketplace share | The tax start slips again; the next published marketplace share is at most 15.79 percent | 2 Oct 2026 | Not yet scored | + +## Appendix D. The values behind Figures 5.3 to 5.5 + +**Growth in 2023, percent (Figure 5.3).** Household spending +9.4; Tokopedia transaction value −8.9; GoTo on-demand transaction value −10.5 (rests on an estimated 2022 value); Bank Indonesia e-commerce −4.7; Momentum Works marketplace estimate +3.5; Bukalapak transaction value +6.9; Blibli third-party retail transaction value +34.7; Tokopedia revenue +53.2; BPS e-commerce value +40.6; BPS online sellers +27.4. Sources: `FINDINGS.md`, `tables/yardsticks.csv`, company filings. + +**Who the extra sellers were, percent (Figure 5.4).** Share of the 2022 count and share of the extra count: business opened before 2010, 18.3 and 40.5; owner aged 50 or over, 28.9 and 41.2; outside Java, 23.6 and 36.3; food and lodging or making goods, 38.5 and 53.0. Source: `tables/bps_newly_counted_profile.json`. + +**Marketplace use and financial statements, percent of sellers (Figure 5.5).** Marketplace use: 21.6 (2020), 19.8 (2022), 17.8 (2023), 17.2 (2024, published). Complete financial statements: 23.5 (2020), 20.7 (2022), 15.2 (2023). Source: `tables/bps_descriptives.json`. + +The figures are drawn by `papers/thesis_2026-10/figures/make_figures.py` from committed tables only. The palette is the reference categorical palette (blue and orange), validated for colour-blind readers with `scripts/validate_palette.js`. diff --git a/papers/thesis_2026-10/ch4_data_methods.md b/papers/thesis_2026-10/ch4_data_methods.md index 8518306..620feea 100644 --- a/papers/thesis_2026-10/ch4_data_methods.md +++ b/papers/thesis_2026-10/ch4_data_methods.md @@ -20,15 +20,15 @@ Two groups of platforms are compared. The Indonesian and regional group has five Table 4.1 states the scope of each series because the series differ. Only Tokopedia is an Indonesian segment alone. Grab covers Southeast Asia, GoTo's on-demand segment included Vietnam until 2024, and Bukalapak covers a whole group. The thesis never adds the series together, and each platform enters the tests on its own. Sea, whose Shopee business has Indonesia as a main market, sits in the foreign group. -Three rules keep the pairs comparable. Two consecutive platform-years form a pair, and nothing else does. A missing year stays missing and is never filled in. Annual and quarterly pairs are never pooled. A take rate is a ratio of two amounts in one currency, so the tests need no exchange rate. Amounts in running text are in US dollars at the World Bank's average rate for the year. In the foreign group, a company that sells goods itself reports the whole sale as revenue, so its take rate says little about a platform. The thesis drops years in which own goods exceed half of revenue and marks years between a fifth and a half. The result is 15 pairs for the Indonesian and regional group and 158 for the foreign group. +Three rules keep the pairs comparable. Two consecutive platform-years form a pair, and nothing else does. A missing year stays missing and is never filled in. Annual and quarterly pairs are never pooled. A take rate is a ratio of two amounts in one currency, so the tests need no exchange rate. Amounts in running text are in US dollars at the World Bank's average rate for the year. In the foreign group, a company that sells goods itself reports the whole sale as revenue, so its take rate says little about a platform (Hagiu and Wright 2015). The thesis drops years in which own goods exceed half of revenue and marks years between a fifth and a half. The result is 15 pairs for the Indonesian and regional group and 158 for the foreign group. ## 4.2 Testing H1 and tracing the moves H1 says that revenue and transaction value grow in proportion, which means that the take rate stays close to steady. The test asks how far each platform's take rate moves from one year to the next. For each pair of years it takes the change in the logarithm of the take rate. A log change treats a rise by some factor and a fall by the same factor as equal in size. It equals the log growth of revenue minus the log growth of transaction value. This is growth divergence measured in logs, and it does not depend on how fast transaction value grew. Tokopedia's log change in 2023 is 0.52, which is a rise by a factor of 1.68, or 68 percent. -A rise and a fall both count against H1, so the test uses the size of each change. Each platform gets one number, the median size of its yearly changes, because the pairs of one platform are not independent of one another. The test compares five numbers for the Indonesian and regional group with 27 for the foreign group. It asks whether the first group ranks higher than chance would give, using a one-sided Mann–Whitney test. It repeats the question by shuffling the group labels 9,999 times. The p-value is the chance of a result at least this lopsided if the groups did not differ. Appendix B lists the checks that follow. They drop each platform in turn and treat Tokopedia and GoTo as one company. They use growth divergence in points as Chapter 3 defines it, narrow the test to the three marketplaces, and repeat it with quarterly pairs. +A rise and a fall both count against H1, so the test uses the size of each change. Each platform gets one number, the median size of its yearly changes, because the pairs of one platform are not independent of one another. The test compares five numbers for the Indonesian and regional group with 27 for the foreign group. It asks whether the first group ranks higher than chance would give, using a one-sided Mann–Whitney test (Mann and Whitney 1947). It repeats the question by shuffling the group labels 9,999 times. The p-value is the chance of a result at least this lopsided if the groups did not differ. Appendix B lists the checks that follow. They drop each platform in turn and treat Tokopedia and GoTo as one company. They use growth divergence in points as Chapter 3 defines it, narrow the test to the three marketplaces, and repeat it with quarterly pairs. -The second objective asks where large movements in revenue came from, and the thesis answers in two steps. The first divides each movement into volume and take rate. In log terms, revenue growth equals transaction value growth plus the change in the take rate. The take-rate share is the size of the take-rate change divided by the sum of the sizes of both changes. The same split is made for the wedge, which equals transaction value minus revenue. The second step finds the cause of a take-rate change. A platform's take rate equals the rate of its gross fees minus the rate of its incentives, each measured against transaction value. So a rise comes from higher fees, lower incentives or a change in the mix of businesses. Wherever a company discloses both, the thesis splits the growth of revenue over a window into three parts. They are more commerce, a change in the fee rate and a change in the incentive rate. +The second objective asks where large movements in revenue came from, and the thesis answers in two steps. The first divides each movement into volume and take rate. In log terms, revenue growth equals transaction value growth plus the change in the take rate. The take-rate share is the size of the take-rate change divided by the sum of the sizes of both changes. The same split is made for the wedge, which equals transaction value minus revenue. The second step finds the cause of a take-rate change. A platform's take rate equals the rate of its gross fees minus the rate of its incentives, each measured against transaction value. Accounting rules deduct such incentives from revenue (International Accounting Standards Board 2014). So a rise comes from higher fees, lower incentives or a change in the mix of businesses. Wherever a company discloses both, the thesis splits the growth of revenue over a window into three parts. They are more commerce, a change in the fee rate and a change in the incentive rate. The sources differ by company. Tokopedia's audited segment note gives gross revenue, incentives and net revenue, and the thesis checks them against the audited consolidated statements. For Tokopedia it also reports the change line by line in rupiah, which is the usual way to read accounts, and it says where the two views differ. Grab is read from its annual filings and GoTo from its audited statements. Blibli does not report incentives, so its split rests on a proxy built from its releases. Seven windows are traced, and Appendix A lists them. The thesis also records where accounting treatment or reporting scope matters, such as the deduction of incentives from revenue and the travel business inside Blibli's retail segment. Company explanations are read as reports and are not tested. @@ -38,9 +38,9 @@ The first objective needs a different set of amounts. It sums the transaction va ## 4.4 Evidence from the sellers -The national evidence comes from the survey of online sellers that BPS runs. BPS publishes the number of businesses selling online, their total online sales and the shares by channel (BPS 2025). The survey rounds are named for the survey year, but their questions refer to the year before. The thesis names a file by its round and a year by the year of activity, so the file for the 2024 round describes 2023. +The national evidence comes from the survey of online sellers that BPS runs. BPS publishes the number of businesses selling online, their total online sales and the shares by channel (BPS 2025). The survey rounds are named for the survey year, but their questions refer to the year before. The thesis names a file by its round and a year by the year of activity, so the file for the 2024 round describes 2023. Each round lists businesses in sampled areas and updates its sampling frame, so a change in who is found can change the count (van den Brakel, Smith and Compton 2008). -H2 uses the published figures. National online sales value equals the number of businesses times the sales per business. In log terms, growth in value is growth in the number of businesses plus growth in sales per business. H2 holds if the first part is more than half of the total. BPS prints two counts of businesses for 2023, so the thesis computes the split with both and reports both. +H2 uses the published figures. National online sales value equals the number of businesses times the sales per business. In log terms, growth in value is growth in the number of businesses plus growth in sales per business. Trade economists split the growth of exports in the same way, into an extensive and an intensive margin (Hummels and Klenow 2005). H2 holds if the first part is more than half of the total. BPS prints two counts of businesses for 2023, so the thesis computes the split with both and reports both. The published figures cannot tell a new seller from one counted for the first time, but the survey files can. BPS sold them under a data-use agreement, and Table 4.2 lists them. Each row is a business that sold online in the activity year. Its survey weight shows how many businesses it represents, so the weights add up to the national count. Two checks come before any test. The weighted total of each file must fall within 1 percent of the BPS count. The 2024 file must also reproduce two published shares of sellers who keep complete financial statements, 28.63 and 12.25 percent. All three files pass. The files carry no business identifier across rounds and no area variable below the province. So the rounds compare groups of sellers and not the same sellers over time. @@ -79,7 +79,7 @@ The third objective compares the platform evidence with national and payment fig | Household spending | All household purchases | BPS national accounts | | Payment figures | Payments made through systems such as QRIS, the national QR-code system | Payment records, in person and online | -One last analysis asks whether markets value a platform's growth in transaction value differently from growth in its take rate. The data are year-end market values from the financial data service LSEG, under the university licence. The main sample has 28 listed platforms and 138 platform-years, made up of Bukalapak and 27 platforms abroad, and it uses whole-company figures only. A regression relates the yearly change in log market value to the changes in log transaction value and log take rate. It adds a fixed effect for each year and clusters standard errors by company. Each prediction names a direction, so both tests are one-sided. The result is an association, and market value also changes when a company issues shares, so the thesis does not call it a stock return. A second run adds segment series and three more whole companies as a robustness check. The test was written down before the market values were pulled. +One last analysis asks whether markets value a platform's growth in transaction value differently from growth in its take rate. The data are year-end market values from the financial data service LSEG, under the university licence. The main sample has 28 listed platforms and 138 platform-years, made up of Bukalapak and 27 platforms abroad, and it uses whole-company figures only. A regression relates the yearly change in log market value to the changes in log transaction value and log take rate. It adds a fixed effect for each year and clusters standard errors by company. Each prediction names a direction, so both tests are one-sided. The question follows early evidence that markets price measures of activity beyond reported revenue (Trueman, Wong and Zhang 2000). The result is an association, and market value also changes when a company issues shares, so the thesis does not call it a stock return. A second run adds segment series and three more whole companies as a robustness check. The test was written down before the market values were pulled. ## 4.6 Labels, traceability and licensed data diff --git a/papers/thesis_2026-10/ch5_results.md b/papers/thesis_2026-10/ch5_results.md index 1e88c60..c151858 100644 --- a/papers/thesis_2026-10/ch5_results.md +++ b/papers/thesis_2026-10/ch5_results.md @@ -4,7 +4,7 @@ This chapter follows the thesis from the platforms to the national figures. It f ## 5.1 How much revenue leaves out -Take three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. Together they carried US$43 billion of transaction value and kept US$3 billion as revenue. The difference, the invisible wedge, was US$40 billion. Revenue was about 7 percent of what passed through them, and their ecosystem ratio, the wedge divided by revenue and the author's own measure, was 12.7. Table 5.1 shows how each amount was obtained. +Take three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. Together they carried US$43 billion of transaction value and kept US$3 billion as revenue. The difference, the invisible wedge, was US$40 billion. Revenue was about 7 percent of what passed through them, and their ecosystem ratio, the wedge divided by revenue and the author's own measure, was 12.7. For every dollar of revenue, almost 13 dollars of commerce passed through the platforms without counting as revenue. A thin slice is what two-sided pricing and agent accounting lead one to expect (Rochet and Tirole 2003; International Accounting Standards Board 2014). Table 5.1 shows how each amount was obtained. **Table 5.1. The invisible wedge of three platforms, 2023 (US$ billion)** @@ -35,7 +35,11 @@ Among Indonesian and regional platforms they do not, and H1 is rejected. The typ The first two rows are the main test, using the two methods of Chapter 4. The third row repeats it with growth divergence in points, and the fourth shows that no single platform carries the result. The main test is exploratory, because the larger groups were set up after a first test on the three marketplaces. That narrower test agrees, with p = 0.002 when it compares pairs and 0.06 when it compares platforms. In it, revenue growth and transaction value growth had a correlation of 0.10 in Indonesia and 0.98 abroad, excluding Sea (Appendix B). The quarterly test in the last row does not reject H1. Its Indonesian pairs mostly cover 2023 to 2026, after most of the repricing, and the large moves came in 2022 to 2024. So the rejection describes that period. -The thin slice explains much of the threefold difference, which is an exploratory finding. A log change in the take rate is roughly the change in points divided by the take rate itself. Indonesian and regional platforms changed their take rates by about 1 point a year, as foreign platforms did. But the typical Indonesian and regional platform keeps only 1.6 percent of each sale, against 16.9 percent abroad. Abroad the share runs from about 3 percent at Shopify to about 25 percent at Mercado Libre. If transaction value is flat, a rise of 1 point lifts the revenue of the first by more than half. It lifts the revenue of a typical foreign platform by about 6 percent. The larger the wedge is next to revenue, the more a small price change moves revenue growth. What the platforms actually changed is the next question. +The thin slice explains much of the threefold difference, which is an exploratory finding. A log change in the take rate is roughly the change in points divided by the take rate itself. Indonesian and regional platforms changed their take rates by about 1 point a year, as foreign platforms did. But the typical Indonesian and regional platform keeps only 1.6 percent of each sale, against 16.9 percent abroad. Abroad the share runs from about 3 percent at Shopify to about 25 percent at Mercado Libre. If transaction value is flat, a rise of 1 point lifts the revenue of the first by more than half. It lifts the revenue of a typical foreign platform by about 6 percent. The larger the wedge is next to revenue, the more a small price change moves revenue growth. Figure 5.1 shows the three measures side by side. What the platforms actually changed is the next question. + +![Figure 5.1](figures/fig5_1_thin_slice.png) + +**Figure 5.1. The thin slice.** Each dot is one platform's median over its years. Left: the share of each sale that the platform keeps, on a log scale. Middle: the yearly change in that share, in percentage points. Right: the same change relative to the share itself, as a log change in percent. Black bars mark group medians. The foreign group has 26 platforms, those with usable pairs in both years. Source: company filings; author's calculations. Exploratory. ## 5.3 What moved the take rate @@ -57,32 +61,25 @@ Table 5.3 splits the growth of revenue into more commerce, higher fees and lower Lower incentives were the larger of the two take-rate parts in four windows and higher fees in two. In the seventh, Grab from 2021 to 2024, the two were nearly equal. Shares are rounded, so a row can add to 99 or 101. Two series need a note. GoTo's 2022 transaction value is an estimate. Blibli does not report incentives, so its split rests on a proxy, and part of its 2022–23 rise comes from travel and digital products. Bukalapak's reports do not allow the parts to be separated. -Tokopedia needs two views. Its accounts deduct incentives from revenue, and they fell by about one third in 2023. Counting the lines of its accounts, 60 percent of the extra net revenue came from lower incentives and 40 percent from higher gross fees. That count leaves the fall in volume inside both lines. Table 5.3 takes it out, and on that per-unit basis higher fees account for more of the rise in the take rate than lower incentives. Both views agree that pricing and not more commerce drove the rise, and they differ only on which lever led. Across the windows, revenue in 2022 to 2024 was not a fair guide to the commerce these platforms carried. Did investors see through it? +Tokopedia needs two views. Accounting rules deduct what a company pays its customers from its revenue (International Accounting Standards Board 2014), and Tokopedia's incentives fell by about one third in 2023. Counting the lines of its accounts, 60 percent of the extra net revenue came from lower incentives and 40 percent from higher gross fees. That count leaves the fall in volume inside both lines. Table 5.3 takes it out, and on that per-unit basis higher fees account for more of the rise in the take rate than lower incentives. Both views agree that pricing and not more commerce drove the rise, and they differ only on which lever led (Figure 5.2). Across the windows, revenue in 2022 to 2024 was not a fair guide to the commerce these platforms carried. Did investors see through it? + +![Figure 5.2](figures/fig5_2_tokopedia_two_views.png) + +**Figure 5.2. Tokopedia, 2022 to 2023: two ways to split the rise in revenue.** Left: the change in net revenue split by the lines of the accounts. Right: the growth of revenue split per unit of transaction value, where a negative share pulled revenue down. Source: GoTo annual report 2023, segment note; author's calculations. ## 5.4 What markets valued -Market value moved with transaction value, a registered result. Across 28 listed platforms and 138 platform-years, a 1 percent rise in transaction value goes with a 0.6 percent rise in market value (p = 0.002). The second prediction, that markets value growth from a higher take rate less, did not hold. A 1 percent rise in the take rate goes with a rise of 0.3 percent, but that estimate is too uncertain to tell from zero. The difference between the two is not large enough to count (p = 0.20). So the data show that market value follows the commerce a platform carries. They do not show that markets saw through the pricing effect. Both results are associations and not stock returns, because market value also changes when a company issues shares. The next section asks what the commerce did in 2023. +Market value moved with transaction value, a registered result. Across 28 listed platforms and 138 platform-years, a 1 percent rise in transaction value goes with a 0.6 percent rise in market value (p = 0.002). The second prediction, that markets value growth from a higher take rate less, did not hold. A 1 percent rise in the take rate goes with a rise of 0.3 percent, but that estimate is too uncertain to tell from zero. The difference between the two is not large enough to count (p = 0.20). So the data show that market value follows the commerce a platform carries. Early evidence on markets pricing measures of activity points the same way (Trueman, Wong and Zhang 2000; Rajgopal, Venkatachalam and Kotha 2003). The data do not show that markets saw through the pricing effect. Both results are associations and not stock returns, because market value also changes when a company issues shares. The next section asks what the commerce did in 2023. ## 5.5 The platforms and the sellers in 2023 -Buying through platforms did not boom in 2023. Table 5.4 sets six measures of it against household spending, which rose by 9.4 percent in current prices. Five of the six grew more slowly, and three fell. Blibli is the exception. +Buying through platforms did not boom in 2023. Figure 5.3 sets six measures of it against household spending, which rose by 9.4 percent in current prices. Five of the six grew more slowly, and three fell. Blibli is the exception. -**Table 5.4. Growth in 2023 (percent)** +![Figure 5.3](figures/fig5_3_growth_2023.png) -| Measure | Growth | Unit | -|---|---|---| -| Household spending | +9.4 | Rupiah, current prices | -| Tokopedia transaction value | −8.9 | Rupiah | -| GoTo on-demand transaction value | −10.5 | Rupiah | -| Bank Indonesia e-commerce | −4.7 | Rupiah | -| Momentum Works marketplace estimate | +3.5 | Outside estimate | -| Bukalapak transaction value | +6.9 | Rupiah | -| Blibli third-party retail transaction value | +34.7 | Rupiah | -| Tokopedia revenue | +53.2 | Rupiah | -| BPS e-commerce value | +40.6 | Rupiah | -| BPS online sellers | +27.4 | Number | - -The rows are in different units, so the thesis compares only the rates, and GoTo's change rests on an estimated 2022 value. The first seven rows show the buying, and the last three show the figures that described a boom. Revenue and BPS's value rose strongly while the buying through platforms did not. +**Figure 5.3. Growth in 2023 (percent).** Each measure is in its own unit. Sources: company reports, Bank Indonesia, Momentum Works and BPS; household spending from national accounts. GoTo's change rests on an estimated 2022 value. Appendix D gives the values. + +Because the measures are in different units, the thesis compares only their rates. The blue bars show the buying, and the orange bars show the figures that described a boom. Revenue and BPS's value rose strongly while the buying through platforms did not. Sellers did not describe a boom either. BPS asks each business how its online sales changed. Among businesses already selling, the median change was zero, as a registered prediction expected. A share of 24 percent reported a rise, 32 percent a fall and 44 percent no change, against 37 percent up and 24 percent down a year earlier. The mean change was −4.3 percent, and lack of demand became the main obstacle, named by 41 percent of sellers against 35 percent in 2022. Yet the published count of online sellers rose by 27 percent that year. Where did those sellers come from? @@ -90,9 +87,9 @@ Sellers did not describe a boom either. BPS asks each business how its online sa On BPS's published figures, H2 holds. From 2022 to 2023, online sales value grew by 40.6 percent, the number of businesses by 27.4 percent and sales per business by 10.4 percent. So more businesses account for 71 percent of the growth. From 2023 to 2024 the figures were 17.1, 15.3 and 1.5 percent, and more businesses account for 90 percent, or 71 percent on BPS's other count for 2023. The split is arithmetic, and it cannot tell a new seller from one counted for the first time. -The entry test can, and its result is registered. In the 2024 file, 13.45 percent of sellers began selling online in 2023, and the rule required at least 21.5 percent. The test fails. Even if no seller stopped, that many new sellers lets the count grow by at most 15.5 percent over the sellers already there, against the published 27.4 percent. +The entry test can, and its result is registered. In the 2024 file, 13.45 percent of sellers began selling online in 2023, and the rule required at least 21.5 percent. The test fails. Even if no seller stopped, that many new sellers lets the count grow by at most 15.5 percent over the sellers already there, against the published 27.4 percent. Respondents tend to date events closer to the interview than they were (Neter and Waksberg 1964). That would make new sellers look more numerous, so the shortfall is if anything understated. -**Table 5.5. The 2023 rise in the count of online sellers (thousands)** +**Table 5.4. The 2023 rise in the count of online sellers (thousands)** | Part of the rise | If no seller stopped | If sellers stopped at 5 percent a year | |---|---|---| @@ -100,24 +97,19 @@ The entry test can, and its result is registered. In the 2024 file, 13.45 percen | Net entry: sellers who began in 2023, less those who stopped | 513 (63%) | 360 (44%) | | Earlier sellers newly counted above the whole 2022 count | 306 (37%) | 458 (56%) | -Table 5.5 shows the accounting. The rise of 819 thousand is the gap between the weighted totals of the two files, and BPS's own published counts give 821 thousand. The 2023 survey counted 306 thousand more sellers who began before 2023 than the whole 2022 count held. That is 37 percent of the rise if no seller stopped and 56 percent if sellers stopped at 5 percent a year. So about half of the rise is not explained by first-time entry. This accounting and the checks below are exploratory, and they point the same way. +Table 5.4 shows the accounting. The rise of 819 thousand is the gap between the weighted totals of the two files, and BPS's own published counts give 821 thousand. The 2023 survey counted 306 thousand more sellers who began before 2023 than the whole 2022 count held. That is 37 percent of the rise if no seller stopped and 56 percent if sellers stopped at 5 percent a year. So about half of the rise is not explained by first-time entry. This accounting and the checks below are exploratory, and they point the same way. Cohorts behave as they should, except in 2023. Sellers who began by 2020 numbered 9.9 percent fewer in the 2022 survey than in the 2020 one, over two years. In the 2023 survey they numbered 6.0 percent more than in the 2022 one. Between the 2023 and 2024 surveys, the cohort that began before 2020 shrank by 10.2 percent, and at least 754 thousand new sellers appeared against a rise of 584 thousand. The break is also uneven. Across the 34 provinces, count growth had almost no link to the share of new sellers (rank correlation 0.10) and a weak link to growth in sample size (0.35). Sellers who began by 2022 and use only chat or social media rose by 13 percent, while marketplace users rose by 0.3 percent. BPS's published error rates put the standard error of the national count at about 1.4 percent. The 306 thousand sellers are about 4.6 times the standard error of the change. -The value side tells a similar story. BPS's value rose from about US$53 billion to about US$72 billion. Sellers not explained by entry account for 15 to 17 percent of this, and entrants for 25 to 30 percent. The remaining 53 to 59 percent cannot be traced to existing sellers' answers, which show a mean change of −4.3 percent. These checks point to wider survey coverage of chat and social sellers, and the data cannot separate that from sellers returning after a pause. Who were the extra sellers? +The value side tells a similar story. BPS's value rose from about US$53 billion to about US$72 billion. Sellers not explained by entry account for 15 to 17 percent of this, and entrants for 25 to 30 percent. The remaining 53 to 59 percent, about US$11 to 12 billion, cannot be traced to existing sellers' answers, which show a mean change of −4.3 percent. These checks point to wider survey coverage of chat and social sellers. That is the pattern survey research warns about, because a change in how a survey finds respondents can break a series (van den Brakel, Smith and Compton 2008). The data cannot separate it from sellers returning after a pause. Who were the extra sellers? ## 5.7 Who the extra sellers were, and whether they returned -The comparison of who they were is exploratory. The surveys do not follow the same businesses, so the thesis compares groups. For each group it takes the 2023 count above what its normal yearly change in 2020 to 2022 would give. These extra sellers total about 450 thousand, close to the 458 thousand in Table 5.5. Table 5.6 gives each group's share of this extra count next to its share of the 2022 count. +The comparison of who they were is exploratory. The surveys do not follow the same businesses, so the thesis compares groups. For each group it takes the 2023 count above what its normal yearly change in 2020 to 2022 would give. These extra sellers total about 450 thousand, close to the 458 thousand in Table 5.4. Figure 5.4 gives each group's share of this extra count next to its share of the 2022 count. -**Table 5.6. Who the extra sellers were (percent)** +![Figure 5.4](figures/fig5_4_extra_sellers.png) -| Group | Share of the 2022 count | Share of the extra count | -|---|---|---| -| Business opened before 2010 | 18.3 | 40.5 | -| Owner aged 50 or over | 28.9 | 41.2 | -| Outside Java | 23.6 | 36.3 | -| Food and lodging, or making goods | 38.5 | 53.0 | +**Figure 5.4. Who the extra sellers were.** The extra sellers are the 2023 count above what each group's normal yearly change in 2020 to 2022 would give, about 450 thousand in total. Source: BPS survey files, weighted; author's calculations. Exploratory. The extra count leaned toward long-running businesses, older owners and places outside Java, and the lean toward men in the raw data disappears once normal change is taken out. It does not look like the young start-ups of the usual growth story. That raises one question: could these be established sellers who paused and came back? @@ -129,16 +121,11 @@ Two additional analyses show where these sellers sit. The path of marketplace us Most online sellers do not use a marketplace, and about 96 percent use chat or social media. Use of marketplaces fell every time it was measured, from 21.6 percent of sellers in 2020 to 17.2 percent in 2024. BPS worded the question slightly differently in the last two rounds. From 2023 to 2024, BPS's marketplace value grew by 1.45 percent and other channels by 20.57 percent, so 98.5 percent of the increase came from outside the marketplaces. BPS's marketplace category includes many food and ride merchants: 43 percent of its sellers use only Gojek or Grab, and 47 percent use a shopping marketplace. -**Table 5.7. Marketplace use and financial statements (percent of sellers)** +![Figure 5.5](figures/fig5_5_marketplace_and_records.png) -| Year | Use a marketplace | Keep complete financial statements | -|---|---|---| -| 2020 | 21.6 | 23.5 | -| 2022 | 19.8 | 20.7 | -| 2023 | 17.8 | 15.2 | -| 2024 | 17.2 | | +**Figure 5.5. Marketplace use and financial statements, 2020 to 2024.** Marketplace use for 2020, 2022 and 2023 comes from the survey files and for 2024 from BPS's published figure. Financial statements come from the survey files. BPS worded the marketplace question slightly differently in the last two rounds. Source: BPS; author's calculations. -Records are thinner outside the marketplaces. In 2023, 28.6 percent of marketplace sellers kept complete financial statements, against 12.3 percent of other sellers, and Table 5.7 shows the share of all sellers who keep them falling. The fall from 2020 to 2022 came while the survey appears to have counted the same kinds of seller. The fall in 2023 may partly reflect the chat-only sellers added to the count. Even among sellers who began before 2020, the share fell from 23.4 to 16.8 percent between the two surveys. +Records are thinner outside the marketplaces. In 2023, 28.6 percent of marketplace sellers kept complete financial statements, against 12.3 percent of other sellers, and Figure 5.5 shows the share of all sellers who keep them falling. The fall from 2020 to 2022 came while the survey appears to have counted the same kinds of seller. The fall in 2023 may partly reflect the chat-only sellers added to the count. Even among sellers who began before 2020, the share fell from 23.4 to 16.8 percent between the two surveys. Value shares rest on the bracket scenarios of section 4.4. The annual reading passes the validation rule, because the middle scenario gives 1.95 times BPS's total for 2022, and the monthly reading fails at 23 times. The lower brackets alone already exceed BPS's total, so the shares are ranges. Sellers without statements held between 26 and 40 percent of online value in 2022, against 79 percent of the count. The registered prediction was that their share of value would not fall by more than 5 points between 2020 and 2022. It fell by 5.8 points, so the prediction failed, narrowly, and a second registered version gives a fall of 3.3 points. @@ -146,7 +133,7 @@ The World Bank surveys point the same way. In six Indonesian cities, 27 percent ## 5.9 Why the national figures disagree -The third objective asks whether platform, national and payment figures give the same account. They do not give the same account of scale. They give the same account of growth only where they cover the same sellers. +Chapter 1 began with two official figures that moved in opposite directions in 2023. The third objective asks whether platform, national and payment figures give the same account. They do not give the same account of scale. They give the same account of growth only where they cover the same sellers. In 2023, Bank Indonesia's figure fell by 4.7 percent and BPS's rose by 40.6 percent. Where the two cover the same ground, they agree on slow growth. BPS's own marketplace slice grew by 0.2 percent from 2022 to 2023 in the middle scenario, and by 0.2 to 13.2 percent across the scenarios. It grew by 1.45 percent from 2023 to 2024. A registered test also holds. The part of BPS's marketplace sales that went to final consumers in 2022 is 0.68 times Bank Indonesia's figure in the middle scenario. It is 0.16 times in the low scenario and 1.62 times in the high one. So the disagreement sits outside the marketplaces, in the count of sellers that only BPS reaches. @@ -156,4 +143,4 @@ Sizes cannot be reconciled. In 2023, BPS's marketplace slice was about US$13 bil Platform sales do not always keep pace with household spending either. GoTo's on-demand transaction value grew more slowly than household spending in each of four quarters, from the third quarter of 2025 to the second quarter of 2026. In the last it grew by 2.0 percent against 8.3 percent, while its revenue grew by 20.5 percent. Grab and Shopee grew faster, but their figures cover the region. Payment figures such as QRIS are context only, because they include in-person payments and the switch from cash. -Chapter 6 asks what these results mean for the rules that Indonesia is writing. +Taken together, the results answer the research question at two levels. Revenue showed a small part of the commerce the platforms carried, and in 2022 to 2024 it was not a fair guide to that commerce, because the take rate moved. The official count of online sellers followed first-time entry only in part in 2023, and the platforms and the survey disagree most where only the survey looks. 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Colour follows the entity in every figure: blue = what the Indonesian-side comparison group or fees stand for is NOT fixed by colour alone; +every mark is also named by a label or legend. Text is neutral ink. Run from the repository root: python3 papers/thesis_2026-10/figures/make_figures.py""" +import json, pathlib +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +ROOT = pathlib.Path(__file__).resolve().parents[3] +T = ROOT / "research/option_space_2026-10-01/tables" +OUT = pathlib.Path(__file__).resolve().parent +BLUE, ORANGE, GREY, INK, INK2, GRID = "#2a78d6", "#eb6834", "#a3a29d", "#0b0b0b", "#52514e", "#e4e3df" +plt.rcParams.update({"font.size": 9, "font.family": "DejaVu Sans", "axes.edgecolor": GRID, "axes.labelcolor": INK2, "xtick.color": INK2, + "ytick.color": INK, "axes.spines.top": False, "axes.spines.right": False, "axes.spines.left": False, + "figure.facecolor": "white", "axes.facecolor": "white", "axes.grid": True, "grid.color": GRID, "grid.linewidth": 0.6, + "axes.axisbelow": True, "savefig.dpi": 300}) + +def save(fig, name): + fig.savefig(OUT / name, bbox_inches="tight", facecolor="white"); plt.close(fig) + +# ---- Figure 5.1: the thin slice ------------------------------------------------------------------------------------------------------- +f = pd.read_csv(T / "h1_points_vs_log_firms.csv") +f["change_pct"] = 100 * f.change_log +rng = np.random.default_rng(7) +fig, axes = plt.subplots(1, 3, figsize=(7.2, 2.7), sharey=True) +panels = [("share_kept_pct", "What the platform keeps\n(percent of each sale)", True), ("change_points", "Yearly change in what it keeps\n(percentage points)", False), + ("change_pct", "Yearly change relative to\nwhat it keeps (percent)", False)] +rows = [("Indonesian and regional (5)", f[f.group == "Indonesia"], ORANGE, 1), ("Foreign (26)", f[f.group == "Foreign"], BLUE, 0)] +for ax, (col, lab, logx) in zip(axes, panels): + for name, d, c, y in rows: + jit = y + rng.uniform(-0.16, 0.16, len(d)) + ax.scatter(d[col], jit, s=22, color=c, edgecolor="white", linewidth=0.8, zorder=3) + m = d[col].median() + ax.plot([m, m], [y - 0.32, y + 0.32], color=INK, lw=2, zorder=4) + ax.text(m, y + 0.38, f"median {m:.1f}", ha="center", va="bottom", fontsize=8, color=INK) + if logx: ax.set_xscale("log"); ax.set_xticks([1, 3, 10, 30, 100]); ax.set_xticklabels(["1", "3", "10", "30", "100"]) + ax.set_xlabel(lab); ax.set_ylim(-0.6, 1.8); ax.set_yticks([0, 1]); ax.tick_params(axis="y", length=0); ax.grid(axis="y", visible=False) +axes[0].set_yticklabels(["Foreign (26)", "Indonesian and\nregional (5)"]) +fig.tight_layout(); save(fig, "fig5_1_thin_slice.png") + +# ---- Figure 5.2: Tokopedia, two ways to split the rise --------------------------------------------------------------------------------- +s = pd.read_csv(T / "tokopedia_segment.csv", index_col=0) +dg = s.loc[2023] - s.loc[2022] +inc_share, gross_share = -dg.incentives / dg.net * 100, dg.gross / dg.net * 100 +g = pd.read_csv(T / "growth_decomposition.csv"); g = g[(g.firm == "Tokopedia")].iloc[0] +vol, fee, dis = 100 * g.share_from_volume, 100 * g.share_from_fee, 100 * g.share_from_discount_cut +fig, (a, b) = plt.subplots(1, 2, figsize=(7.2, 2.5), gridspec_kw={"width_ratios": [1, 1.25]}) +a.barh([1, 0], [inc_share, gross_share], color=[ORANGE, BLUE], height=0.5, zorder=3) +for y, v in zip([1, 0], [inc_share, gross_share]): a.text(v + 1.5, y, f"{v:.1f}", va="center", color=INK) +a.set_yticks([1, 0]); a.set_yticklabels(["Lower incentives", "Higher gross fees"]); a.set_xlim(0, 80); a.set_xlabel("Share of the extra net revenue (percent)") +a.set_title("By lines of the accounts", fontsize=9, color=INK, loc="left", fontweight="bold") +cols = [GREY, BLUE, ORANGE]; vals = [vol, fee, dis]; labs = ["More commerce", "Higher fees", "Lower incentives"] +b.barh([2, 1, 0], vals, color=cols, height=0.5, zorder=3) +for y, v in zip([2, 1, 0], vals): b.text(v + (2 if v > 0 else -2), y, f"{v:.0f}".replace("-", "−"), va="center", ha="left" if v > 0 else "right", color=INK) +b.axvline(0, color=INK2, lw=0.8, zorder=4); b.set_yticks([2, 1, 0]); b.set_yticklabels(labs); b.set_xlim(-40, 95) +b.set_xlabel("Share of the growth of revenue (percent)"); b.set_title("Per unit of transaction value", fontsize=9, color=INK, loc="left", fontweight="bold") +for ax in (a, b): ax.grid(axis="y", visible=False); ax.tick_params(axis="y", length=0) +fig.tight_layout(); save(fig, "fig5_2_tokopedia_two_views.png") + +# ---- Figure 5.3: 2023 growth, each measure in its own unit ---------------------------------------------------------------------------- +items = [("Household spending", 9.4, GREY, "Reference"), ("Tokopedia transaction value", -8.9, BLUE, "Buying through platforms"), + ("GoTo on-demand transaction value", -10.5, BLUE, "Buying through platforms"), ("Bank Indonesia e-commerce", -4.7, BLUE, "Buying through platforms"), + ("Momentum Works marketplace estimate", 3.5, BLUE, "Buying through platforms"), ("Bukalapak transaction value", 6.9, BLUE, "Buying through platforms"), + ("Blibli third-party retail transaction value", 34.7, BLUE, "Buying through platforms"), ("Tokopedia revenue", 53.2, ORANGE, "Figures that described a boom"), + ("BPS e-commerce value", 40.6, ORANGE, "Figures that described a boom"), ("BPS online sellers (count)", 27.4, ORANGE, "Figures that described a boom")] +order = [i for i in items if i[3] == "Figures that described a boom"] + [items[0]] + sorted([i for i in items if i[3] == "Buying through platforms"], key=lambda t: -t[1]) +order = sorted(order[:3], key=lambda t: -t[1]) + order[3:] +fig, ax = plt.subplots(figsize=(7.2, 3.9)) +ys = np.arange(len(order))[::-1] +ax.barh(ys, [o[1] for o in order], color=[o[2] for o in order], height=0.62, zorder=3) +for y, o in zip(ys, order): ax.text(o[1] + (1.0 if o[1] >= 0 else -1.0), y, f"{o[1]:+.1f}".replace("-", "−"), va="center", ha="left" if o[1] >= 0 else "right", color=INK, fontsize=8.5) +ax.axvline(0, color=INK2, lw=0.9, zorder=4); ax.set_yticks(ys); ax.set_yticklabels([o[0] for o in order]); ax.tick_params(axis="y", length=0) +ax.set_xlim(-24, 62); ax.set_xlabel("Growth in 2023 (percent); each measure in its own unit"); ax.grid(axis="y", visible=False) +from matplotlib.patches import Patch +ax.legend(handles=[Patch(color=ORANGE, label="Figures that described a boom"), Patch(color=GREY, label="Reference"), Patch(color=BLUE, label="Buying through platforms")], + loc="lower right", frameon=False, fontsize=8.5) +fig.tight_layout(); save(fig, "fig5_3_growth_2023.png") + +# ---- Figure 5.4: who the extra sellers were ------------------------------------------------------------------------------------------ +p = json.load(open(T / "bps_newly_counted_profile.json"))["profile"] +sec = p["sector"] +groups = [("Business opened before 2010", p["business opened"]["before 2010"]["share_of_2022_count_pct"], p["business opened"]["before 2010"]["share_of_beyond_normal_pct"]), + ("Owner aged 50 or over", p["owner age"]["50 and over"]["share_of_2022_count_pct"], p["owner age"]["50 and over"]["share_of_beyond_normal_pct"]), + ("Outside Java", p["island"]["outside Java"]["share_of_2022_count_pct"], p["island"]["outside Java"]["share_of_beyond_normal_pct"]), + ("Food and lodging, or making goods", sec["food and lodging"]["share_of_2022_count_pct"] + sec["making goods"]["share_of_2022_count_pct"], + sec["food and lodging"]["share_of_beyond_normal_pct"] + sec["making goods"]["share_of_beyond_normal_pct"])] +fig, ax = plt.subplots(figsize=(7.2, 2.9)); y0 = np.arange(len(groups))[::-1]; h = 0.34 +ax.barh(y0 + h / 2, [g_[2] for g_ in groups], height=h, color=ORANGE, zorder=3, label="Share of the extra sellers counted in 2023") +ax.barh(y0 - h / 2, [g_[1] for g_ in groups], height=h, color=BLUE, zorder=3, label="Share of all sellers in the 2022 count") +for y, g_ in zip(y0, groups): + ax.text(g_[2] + 1, y + h / 2, f"{g_[2]:.1f}", va="center", color=INK, fontsize=8.5); ax.text(g_[1] + 1, y - h / 2, f"{g_[1]:.1f}", va="center", color=INK, fontsize=8.5) +ax.set_yticks(y0); ax.set_yticklabels([g_[0] for g_ in groups]); ax.tick_params(axis="y", length=0); ax.grid(axis="y", visible=False) +ax.set_xlim(0, 70); ax.set_xlabel("Percent of each group of sellers"); ax.legend(loc="lower left", bbox_to_anchor=(-0.02, 1.0), ncol=1, frameon=False, fontsize=8.5) +fig.tight_layout(); save(fig, "fig5_4_extra_sellers.png") + +# ---- Figure 5.5: marketplace use and financial statements -------------------------------------------------------------------------------- +d = json.load(open(T / "bps_descriptives.json")) +yrs_m = [2020, 2022, 2023, 2024]; mk = [d["marketplace_share"]["2020"], d["marketplace_share"]["2022"], d["marketplace_share"]["2023"], d["marketplace_share"]["2024_published"]] +yrs_b = [2020, 2022, 2023]; bk = [d["books_share"]["2020"], d["books_share"]["2022"], d["books_share"]["2023"]] +fig, ax = plt.subplots(figsize=(6.2, 2.9)) +ax.plot(yrs_m, mk, color=BLUE, lw=2, marker="o", ms=7, mec="white", mew=1.5, zorder=3, label="Sellers who use a marketplace") +ax.plot(yrs_b, bk, color=ORANGE, lw=2, marker="o", ms=7, mec="white", mew=1.5, zorder=3, label="Sellers who keep complete financial statements") +for x, v in zip(yrs_m, mk): + up = x == 2023 + ax.text(x, v + (0.8 if up else -0.8), f"{v:.1f}", ha="center", va="bottom" if up else "top", color=INK, fontsize=8.5) +for x, v in zip(yrs_b, bk): + down = x == 2023 + ax.text(x, v + (-0.8 if down else 0.8), f"{v:.1f}", ha="center", va="top" if down else "bottom", color=INK, fontsize=8.5) +ax.set_xticks(yrs_m); ax.set_ylim(10, 28); ax.set_ylabel("Percent of online sellers"); ax.legend(loc="upper right", frameon=False, fontsize=8.5) +ax.grid(axis="x", visible=False) +fig.tight_layout(); save(fig, "fig5_5_marketplace_and_records.png") +print("ok") From 16b0381e404db7c6ff7f7f238410ce32ad164b15 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 13 Oct 2026 09:09:28 +0000 Subject: [PATCH 22/65] Thesis ch4/ch5: act on second audit (rule wording, Ozon and Zalando checks, claim strength, citations, figure and appendix fixes) Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 11 ++++ papers/thesis_2026-10/appendix_notes.md | 37 ++++++------ papers/thesis_2026-10/ch3_framework.md | 2 +- papers/thesis_2026-10/ch4_data_methods.md | 15 ++--- papers/thesis_2026-10/ch5_results.md | 54 ++++++++++-------- .../figures/fig5_2_tokopedia_two_views.png | Bin 113938 -> 111976 bytes .../figures/fig5_3_growth_2023.png | Bin 211503 -> 211560 bytes .../figures/fig5_4_extra_sellers.png | Bin 127360 -> 117779 bytes papers/thesis_2026-10/figures/make_figures.py | 28 ++++++--- .../h1_thesis_drops.py | 22 +++++++ .../option_space_2026-10-01/rebuild_all.sh | 1 + .../tables/h1_thesis_drops.json | 30 ++++++++++ 12 files changed, 142 insertions(+), 58 deletions(-) create mode 100644 research/option_space_2026-10-01/h1_thesis_drops.py create mode 100644 research/option_space_2026-10-01/tables/h1_thesis_drops.json diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 11554c1..a566ff6 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -252,3 +252,14 @@ Done, at the researcher's request ("fix those", skipping what is not important): - Chapter 5: about 3,700 words (from 4,000); paragraphs split, status tags kept only where a result is registered or exploratory ("unmarked results are descriptive"); the first line of 5.2 names the group; the thin-slice explanation now rests on arithmetic (log change is roughly the change in points over the take rate) and not on a non-significant test; 5.4 says what the data show and do not show about investors; the top-bracket detail, the provinces and the one-third/two-thirds figure moved to Appendix B. Not done (not judged important now): figures, bibliography, YZU template, Chapters 6 and 7. + +### Second independent audit of Chapters 4 and 5 (13 Oct 2026) + +A fresh reviewer re-checked every number and citation (21 checks in Chapter 4, 118 in Chapter 5, 187 appendix cells; all five figures reproduce). It found 41 problems. Fixed: +- **Wrong:** the foreign range of share kept (2.6 percent at Shopify to 71 percent at Zalando, not "25 percent at Mercado Libre"); Table 5.4 net entry 361, not 360; appendix units (Blibli in Rp billion; Shopee's assumed take rates 8.4, 10 and 10.8 percent); file paths in Appendix D; the claim about a palette script that is not in the repository. +- **Own-goods rule:** it applies to the 19 added platforms only. The eight chosen first are filtered on unchanged reporting scope. Ozon's one pair starts in a dropped year. New check `h1_thesis_drops.py`: without Ozon p = 0.003 (shuffle 0.022), without Zalando 0.005, without both 0.004. Disclosed in 4.1 and Appendix B; headline 5 against 27 unchanged. +- **Claims tied to evidence:** 5.2 now says the planned three-marketplace test gives p = 0.002 by pairs and 0.06 by platforms, and that the larger test is exploratory; the Bank Indonesia reconciliation holds in the low and middle scenarios and fails in the high one; the marketplace slices "agree on slow growth" became "the growth is small"; the second market-value run (34 platforms, p = 0.004, mixes segment and whole-company values) is reported next to the main run, and the market sample is said to differ from H1's 27. +- **Citations:** IFRS 15 now carries the agent and incentive claims ("normally deduct"); Hagiu and Wright only the marketplace–reseller line; Trueman et al. and Rajgopal et al. described as web-traffic studies; Rochet and Tirole no longer used to predict a thin take rate; BPS cited for the 819 thousand rise. +- **Smaller:** Table 5.3 caption, 61/39 percent for Tokopedia's account lines, Grab revenue 0.60, Momentum Works 3.5 labelled as calculated, cohort groups described as cumulative, E6 threshold stated, E7 second version inside the limit, rank correlation and standard error defined, "about 750 thousand" for the cohort check, Table 4.3 gained a row for adjacent figures, checksum claim narrowed, Chapter 3's "no national total" reconciled with the three-platform sum, figure axis and caption fixes, Figure 5.3 now reads its values from tables. + +Not changed, for the researcher: the second-run market result stays secondary; Tokopedia's "15 million merchants" is a company statement not checked in the repository (said so in 5.9); full company names for the tickers in A.1 are still to be added (ASAPQ not identified from the repository). diff --git a/papers/thesis_2026-10/appendix_notes.md b/papers/thesis_2026-10/appendix_notes.md index 1ef44b0..5b54e3e 100644 --- a/papers/thesis_2026-10/appendix_notes.md +++ b/papers/thesis_2026-10/appendix_notes.md @@ -6,7 +6,7 @@ Holds the detail that the main text points to. Every number comes from a committ ### A.1 The foreign group (27 platforms) -Pairs are consecutive years that pass the own-goods rule (Chapter 4, section 4.1). The median is the platform's median size of yearly change in log take rate. Platforms are shown by the ticker or name used in the panel (`tables/spinoff_panel.csv`, `tables/transitions_master.csv`); full company names are to be added. +Pairs are consecutive years that pass the rules of Chapter 4, section 4.1: for the platforms added, the later year is not one in which own goods exceed half of revenue; for the eight chosen first, the reporting scope did not change. Ozon's one pair starts in a year that fails the own-goods rule (Appendix B). The median is the platform's median size of yearly change in log take rate. Platforms are shown by the ticker or name used in the panel (`tables/spinoff_panel.csv`, `tables/transitions_master.csv`); full company names are to be added. | Platform | Chosen first or added | Pairs | Median size of yearly change in log take rate | |---|---|---|---| @@ -64,11 +64,11 @@ Shares are of the log growth of revenue. Source: `tables/growth_decomposition.cs | Blibli 3P Retail, 2022-2025 | 679.4 | 43.6 | 0.54 | 2.92 | 17.6 | 62.6 | 19.8 | | Blibli 3P Retail, 2022-2023 | 465.3 | 34.7 | 0.54 | 2.25 | 17.2 | 20.1 | 62.7 | -Tokopedia, 2022–23, line by line (Rp million, `tables/tokopedia_segment.csv`): gross revenue 8,143,239 to 8,988,909; incentives 4,112,320 to 2,813,719; net revenue 4,030,919 to 6,175,190. Net revenue rose by 2,144,271, of which lower incentives account for 60.6 percent and higher gross revenue for 39.4 percent. +Tokopedia, 2022–23, line by line (Rp million, `tables/tokopedia_segment.csv`): gross revenue 8,143,239 to 8,988,909; incentives 4,112,320 to 2,813,719; net revenue 4,030,919 to 6,175,190. Net revenue rose by 2,144,271, of which lower incentives account for 60.6 percent and higher gross revenue for 39.4 percent (61 and 39 percent in the text). ### A.4 Wedge and ecosystem ratio by platform-year, Indonesian series -Original currency and units, as in the filings (Rp million for Tokopedia, Blibli and Bukalapak; US$ billion for Grab and Shopee, whose Indonesian amounts are derived). Take rate and ecosystem ratio carry no currency. Source: `tables/take_rate_levels.csv`. +Original currency and units, as in the filings: Rp million for Tokopedia and Bukalapak, Rp billion for Blibli, and US$ billion for Grab and Shopee, whose Indonesian amounts are derived or estimated. Shopee's revenue assumes a take rate of 8.4 percent in 2022, 10 percent in 2023 and 10.8 percent in 2024. Table 5.1 converts Tokopedia's 2023 rupiah amounts to US dollars. Take rate and ecosystem ratio carry no currency. Source: `tables/take_rate_levels.csv`. | Series | Year | Transaction value | Revenue | Wedge | Take rate (%) | Ecosystem ratio | Label | |---|---|---|---|---|---|---|---| @@ -92,7 +92,9 @@ Original currency and units, as in the filings (Rp million for Tokopedia, Blibli ## Appendix B. The checks on the test of H1 -The main test is in Table 5.2. This appendix lists each check with both p-values where both exist. Sources: `h1_extended.py` (`tables/h1_extended_results.json`), `tables/test_battery.csv`, `tables/h1_quarterly_results.json`, `tables/h1_points_vs_log.json`, `../independent_review_2026-10-08/review_checks.py`. +### B.1 The checks + +The main test is in Table 5.2. This appendix lists each check with both p-values where both exist. Sources: `h1_extended.py` (`tables/h1_extended_results.json`), `tables/test_battery.csv`, `tables/h1_quarterly_results.json`, `tables/h1_points_vs_log.json`, `tables/h1_thesis_drops.json`. | Check | Indonesian and regional (median) | Foreign (median) | Mann–Whitney p | Shuffle p | |---|---|---|---|---| @@ -100,15 +102,18 @@ The main test is in Table 5.2. This appendix lists each check with both p-values | 5 against the 8 chosen first | 0.201 | 0.055 | 0.0225 | 0.0497 | | 5 against the 19 added | 0.201 | 0.070 | 0.0046 | 0.0175 | | Growth divergence in points, 5 against 26 | 27.0 | 6.5 | 0.0055 | | +| Without Ozon, 26 foreign platforms | 0.201 | 0.060 | 0.0030 | 0.0215 | +| Without Zalando, 26 foreign platforms | 0.201 | 0.064 | 0.0045 | 0.0238 | +| Without Ozon and Zalando, 25 foreign platforms | 0.201 | 0.062 | 0.0036 | 0.0230 | | Without Blibli 3P Retail | 0.169 | | 0.0137 | 0.0364 | | Without Bukalapak Group | 0.225 | | 0.0049 | 0.0070 | | Without GoTo on-demand | 0.225 | | 0.0030 | 0.0071 | | Without Grab on-demand | 0.194 | | 0.0094 | 0.0166 | | Without Tokopedia e-commerce segment | 0.169 | | 0.0164 | 0.0409 | -| Three marketplaces against the 8 chosen first, pair-level, absolute growth divergence in points | 38.917 | 7.105 | 0.0022 | 0.0606 | -| Three marketplaces against the 8 chosen first, pair-level, absolute growth divergence in points (without Sea) | 38.917 | 4.192 | 0.0000 | 0.0083 | -| Three marketplaces against the 8 chosen first, pair-level, absolute change in log take rate | 0.252 | 0.055 | 0.0037 | 0.0303 | -| Three marketplaces against the 8 chosen first, pair-level, absolute change in log take rate (without Sea) | 0.252 | 0.033 | 0.0001 | 0.0167 | +| Three marketplaces against the 8 chosen first, comparing pairs of years, absolute growth divergence in points | 38.917 | 7.105 | 0.0022 | 0.0606 | +| Three marketplaces against the 8 chosen first, comparing pairs of years, absolute growth divergence in points (without Sea) | 38.917 | 4.192 | <0.0001 | 0.0083 | +| Three marketplaces against the 8 chosen first, comparing pairs of years, absolute change in log take rate | 0.252 | 0.055 | 0.0037 | 0.0303 | +| Three marketplaces against the 8 chosen first, comparing pairs of years, absolute change in log take rate (without Sea) | 0.252 | 0.033 | 0.0001 | 0.0167 | | Three marketplaces, dropping Tokopedia's 2022–23 comparison (5 pairs), absolute growth divergence in points | 14.411 | 7.105 | 0.0248 | 0.2222 | | Three marketplaces, dropping the largest comparison (7 pairs), absolute growth divergence in points | 15.738 | 7.105 | 0.0061 | 0.0606 | | Three marketplaces against all 40 foreign pairs, absolute growth divergence in points | 38.917 | 7.373 | 0.0014 | 0.0121 | @@ -120,7 +125,7 @@ Growth in revenue and growth in transaction value had a correlation of 0.10 in t The quarterly sample is Grab on-demand, GoTo on-demand and Blibli against Sea, Airbnb, Booking, DoorDash, eBay, Etsy, Lyft and Uber. Airbnb and Booking use trailing four-quarter sums because they book revenue at check-in. DoorDash pairs ending in 2025Q4 or later are dropped because of an acquisition. -Details moved from Chapter 5. In the pairs of the Indonesian and regional group, the take rate stayed within about 10 percent (0.10 in log terms) in one third of pairs, against 67 to 69 percent in the foreign groups. The version of the test with growth divergence in points has 26 foreign platforms because Ozon has no pair in which both years pass the own-goods rule. +Details moved from Chapter 5. In the pairs of the Indonesian and regional group, the take rate stayed within about 10 percent (0.10 in log terms) in one third of pairs, against 67 to 69 percent in the foreign groups. The version of the test with growth divergence in points has 26 foreign platforms because Ozon has no pair in which both years pass the own-goods rule. The main test keeps Ozon's pair, whose later year passes, and the first rows above show that dropping it, or Zalando, leaves the result in place. The comparison in points of the take rate (exploratory, `h1_points_vs_log.py`): median yearly change 1.01 points in the Indonesian and regional group and 0.98 points in the foreign group (26 platforms), Mann–Whitney p = 0.65; median share kept 1.59 percent against 16.93 percent. @@ -145,25 +150,25 @@ A registered test was written down, dated and stored in the repository before th | E5 | At least 21.5 percent of 2023 sellers began in 2023 (the entry test) | 2 Oct 2026 | Falsified (13.45 percent) | | E6 | The median change in sales of sellers already online is at most 10.36 percent | 2 Oct 2026 | Holds (median 0 percent) | | E7 | The share of value held by sellers without statements fell by at most 5 points from 2020 to 2022 | 3 Oct 2026 | Falsified, narrowly (5.8 points; 3.3 on the second version) | -| E8 | Sellers with Rp300 million or more hold over half of marketplace value | 3 Oct 2026 | Holds (92.2 percent, middle scenario, an upper bound) | -| RBI | The marketplace part sold to final consumers is below Bank Indonesia's 2022 figure | 3 Oct 2026 | Holds in the middle scenario (0.68); 0.16 low, 1.62 high | +| E8 | Sellers with revenue of Rp300 million a year or more hold over half of marketplace value | 3 Oct 2026 | Holds (92.2 percent, middle scenario, an upper bound) | +| RBI | The marketplace part sold to final consumers is below Bank Indonesia's 2022 figure | 3 Oct 2026 | Holds in the low (0.16) and middle (0.68) scenarios; fails in the high one (1.62) | | W1, W2 | Unregistered firms using social media keep written records and take digital payments more often | 3 Oct 2026 | Hold (29.9 against 12.3 percent; 11.5 against 2.7 percent) | -| W3 | Their mean annual sales are below Rp100 million | 3 Oct 2026 | Falsified (mean Rp111 million; median Rp60 million) | +| W3 | Their mean annual sales are below Rp100 million (about US$6,600) | 3 Oct 2026 | Falsified (mean Rp111 million; median Rp60 million) | | F1, F2 | Formal firms with a website take more e-payments and file taxes online more often | 3 Oct 2026 | Hold | | F3, X3 | More than one third of formal firms have neither a website nor online tax filing, and Indonesia's share is the highest of the countries compared | 3 Oct 2026 | Hold (39.9 percent; Malaysia 9.1, Thailand 8.7) | | X2 | Firms with a website file taxes online more often, in at least 3 of 4 countries | 3 Oct 2026 | Holds in 4 of 4 | -| Q1 | The test of H1 with quarterly pairs rejects H1 | 7 Oct 2026, before it was run | Falsified (p = 0.19) | -| M1 | Markets value growth from more transactions more than growth from a higher take rate | 8 Oct 2026, before the market values were pulled | Falsified in the main sample (p = 0.20) | +| Q1 | The test of H1 with quarterly pairs rejects H1 | 7 Oct 2026, before it was run, but after the quarterly series had been built and the annual result was known, so it is a robustness check | Falsified (p = 0.19) | +| M1 | Markets value growth from more transactions more than growth from a higher take rate | 8 Oct 2026, before the market values were pulled | Falsified in the main sample (p = 0.20) and when trimmed (p = 0.16); holds in the second run of 34 platforms (p = 0.004), which mixes segment figures with whole-company values | | M2 | Market value moves with transaction value | 8 Oct 2026 | Holds (p = 0.002) | | P1 to P5, R1 to R3 | Predictions on the third-quarter 2026 results of GoTo, Grab and Sea | 2 and 3 Oct 2026 | Not yet scored; first results are due on 27 Oct 2026 | | Marketplace tax start; BPS marketplace share | The tax start slips again; the next published marketplace share is at most 15.79 percent | 2 Oct 2026 | Not yet scored | ## Appendix D. The values behind Figures 5.3 to 5.5 -**Growth in 2023, percent (Figure 5.3).** Household spending +9.4; Tokopedia transaction value −8.9; GoTo on-demand transaction value −10.5 (rests on an estimated 2022 value); Bank Indonesia e-commerce −4.7; Momentum Works marketplace estimate +3.5; Bukalapak transaction value +6.9; Blibli third-party retail transaction value +34.7; Tokopedia revenue +53.2; BPS e-commerce value +40.6; BPS online sellers +27.4. Sources: `FINDINGS.md`, `tables/yardsticks.csv`, company filings. +**Growth in 2023, percent (Figure 5.3).** Household spending +9.4; Tokopedia transaction value −8.9; GoTo on-demand transaction value −10.5 (rests on an estimated 2022 value); Bank Indonesia e-commerce −4.7; Momentum Works marketplace estimate +3.5 (calculated from its published levels; its 2024 release states 3.7); Bukalapak transaction value +6.9; Blibli third-party retail transaction value +34.7; Tokopedia revenue +53.2; BPS e-commerce value +40.6; BPS online sellers +27.4. Sources: `tables/consistency_grid_growth.csv`, `tables/yardsticks_bi_bps.csv`, `tables/wedge_over_time.csv`, `tables/h1_groundwork.json`, company filings. **Who the extra sellers were, percent (Figure 5.4).** Share of the 2022 count and share of the extra count: business opened before 2010, 18.3 and 40.5; owner aged 50 or over, 28.9 and 41.2; outside Java, 23.6 and 36.3; food and lodging or making goods, 38.5 and 53.0. Source: `tables/bps_newly_counted_profile.json`. **Marketplace use and financial statements, percent of sellers (Figure 5.5).** Marketplace use: 21.6 (2020), 19.8 (2022), 17.8 (2023), 17.2 (2024, published). Complete financial statements: 23.5 (2020), 20.7 (2022), 15.2 (2023). Source: `tables/bps_descriptives.json`. -The figures are drawn by `papers/thesis_2026-10/figures/make_figures.py` from committed tables only. The palette is the reference categorical palette (blue and orange), validated for colour-blind readers with `scripts/validate_palette.js`. +The figures are drawn by `papers/thesis_2026-10/figures/make_figures.py` from committed tables only. The palette is the reference categorical palette (blue and orange). Colour carries the same meaning in each figure only where a legend or label says so. diff --git a/papers/thesis_2026-10/ch3_framework.md b/papers/thesis_2026-10/ch3_framework.md index a918211..a464f08 100644 --- a/papers/thesis_2026-10/ch3_framework.md +++ b/papers/thesis_2026-10/ch3_framework.md @@ -18,7 +18,7 @@ Table 3.1 lists the terms used throughout the thesis, each defined once, with th | Revenue | R | What the platform recognizes as its own income in its accounts. | | Take rate | m | Revenue divided by transaction value: the share of each sale that the platform keeps. | | Customer incentives | | Discounts, vouchers and other promotions that a platform pays for. Accounting rules normally deduct them from revenue, and Tokopedia reports the deduction as a separate line. | -| Invisible wedge | W | Transaction value minus revenue: what a revenue figure leaves out. It is defined for one platform; no national total is computed. | +| Invisible wedge | W | Transaction value minus revenue: what a revenue figure leaves out. It is defined for one platform. The thesis sums it over three platforms in Indonesia for 2023 and computes no national total. | | Ecosystem ratio | E | The wedge divided by revenue. It equals one divided by the take rate, minus one. This is the author's own measure, built for this thesis. | | Growth | g | The change from one year to the next as a share of the first year: 0.09 means 9 percent. | | Growth divergence | D | Growth of revenue minus growth of transaction value in the same year: 0.62 means 62 percentage points. | diff --git a/papers/thesis_2026-10/ch4_data_methods.md b/papers/thesis_2026-10/ch4_data_methods.md index 620feea..cb5017c 100644 --- a/papers/thesis_2026-10/ch4_data_methods.md +++ b/papers/thesis_2026-10/ch4_data_methods.md @@ -4,7 +4,7 @@ Indonesia lets this thesis look at the same online economy from two sides. Platf ## 4.1 Choosing the platforms -The basic observation is one platform in one year, which this thesis calls a platform-year. Revenue and transaction value come from the company's own reports, and they refer to the same year and, as closely as the filings allow, the same business. Revenue is the amount in the financial statements. Transaction value is the figure the company discloses in its annual report or earnings release. Companies define it in different ways, and some include shipping charges, digital goods or travel. The thesis keeps each company's own definition and records it. A take rate is built inside one company, so the definition cancels out of its yearly changes unless the company changes it. +The basic observation is one platform in one year, which this thesis calls a platform-year. Revenue and transaction value come from the company's own reports, and they refer to the same year and, as closely as the filings allow, the same business. Revenue is the amount in the financial statements. Transaction value is the figure the company discloses in its annual report or earnings release. Companies define it in different ways, and some include shipping charges, digital goods or travel. The thesis keeps each company's own definition and records it. A take rate is built inside one company, so the definition cancels out of its yearly changes, as long as the company keeps the definition and the mix of its businesses. Two groups of platforms are compared. The Indonesian and regional group has five platforms, listed in Table 4.1. Three are Indonesian marketplaces, and two are the on-demand businesses of Grab and GoTo, which cover rides and deliveries. The foreign group has 27 listed platforms in marketplaces, travel and ticketing, freelance work, resale and food delivery. Eight were chosen first, and 19 were added to widen the comparison. Ten more were examined and left out, six because they sell mostly their own goods and four because they do not disclose transaction value. @@ -20,15 +20,15 @@ Two groups of platforms are compared. The Indonesian and regional group has five Table 4.1 states the scope of each series because the series differ. Only Tokopedia is an Indonesian segment alone. Grab covers Southeast Asia, GoTo's on-demand segment included Vietnam until 2024, and Bukalapak covers a whole group. The thesis never adds the series together, and each platform enters the tests on its own. Sea, whose Shopee business has Indonesia as a main market, sits in the foreign group. -Three rules keep the pairs comparable. Two consecutive platform-years form a pair, and nothing else does. A missing year stays missing and is never filled in. Annual and quarterly pairs are never pooled. A take rate is a ratio of two amounts in one currency, so the tests need no exchange rate. Amounts in running text are in US dollars at the World Bank's average rate for the year. In the foreign group, a company that sells goods itself reports the whole sale as revenue, so its take rate says little about a platform (Hagiu and Wright 2015). The thesis drops years in which own goods exceed half of revenue and marks years between a fifth and a half. The result is 15 pairs for the Indonesian and regional group and 158 for the foreign group. +Three rules keep the pairs comparable. Two consecutive platform-years form a pair, and nothing else does. A missing year stays missing and is never filled in. Annual and quarterly pairs are never pooled. A take rate is a ratio of two amounts in one currency, so the tests need no exchange rate. Amounts in running text are in US dollars at the World Bank's average rate for the year. In the foreign group, a company that sells goods itself reports the whole sale as revenue, while an agent reports only its commission (International Accounting Standards Board 2014). The take rate of a seller of goods then says little about a platform, and Hagiu and Wright (2015) draw the same line between a marketplace and a reseller. For the 19 platforms added, the thesis drops any year in which own goods exceed half of revenue. For the eight chosen first, it keeps a pair only when the company's reporting scope did not change between the two years. Zalando is one of the eight and sells part of what it carries, so Appendix B repeats the test without it. One added pair, Ozon from 2021 to 2022, ends in a kept year but starts in a dropped one. Appendix B repeats the test without Ozon as well. The result is 15 pairs for the Indonesian and regional group and 158 for the foreign group. ## 4.2 Testing H1 and tracing the moves H1 says that revenue and transaction value grow in proportion, which means that the take rate stays close to steady. The test asks how far each platform's take rate moves from one year to the next. For each pair of years it takes the change in the logarithm of the take rate. A log change treats a rise by some factor and a fall by the same factor as equal in size. It equals the log growth of revenue minus the log growth of transaction value. This is growth divergence measured in logs, and it does not depend on how fast transaction value grew. Tokopedia's log change in 2023 is 0.52, which is a rise by a factor of 1.68, or 68 percent. -A rise and a fall both count against H1, so the test uses the size of each change. Each platform gets one number, the median size of its yearly changes, because the pairs of one platform are not independent of one another. The test compares five numbers for the Indonesian and regional group with 27 for the foreign group. It asks whether the first group ranks higher than chance would give, using a one-sided Mann–Whitney test (Mann and Whitney 1947). It repeats the question by shuffling the group labels 9,999 times. The p-value is the chance of a result at least this lopsided if the groups did not differ. Appendix B lists the checks that follow. They drop each platform in turn and treat Tokopedia and GoTo as one company. They use growth divergence in points as Chapter 3 defines it, narrow the test to the three marketplaces, and repeat it with quarterly pairs. +A rise and a fall both count against H1, so the test uses the size of each change. Each platform gets one number, the median size of its yearly changes, because the pairs of one platform are not independent of one another. The test compares five numbers for the Indonesian and regional group with 27 for the foreign group. It asks whether the first group ranks higher than chance would give, using a one-sided Mann–Whitney test (Mann and Whitney 1947). It repeats the question by shuffling the group labels 9,999 times. The p-value is the chance of a result at least this lopsided if the groups did not differ. Appendix B lists the checks that follow. They drop each platform in turn and treat Tokopedia and GoTo as one company. They use growth divergence in points as Chapter 3 defines it, and they compare the change in the take rate in percentage points, which tests the thin-slice explanation of Chapter 5. They narrow the test to the three marketplaces, correlate revenue growth with transaction value growth, and repeat the test with quarterly pairs. -The second objective asks where large movements in revenue came from, and the thesis answers in two steps. The first divides each movement into volume and take rate. In log terms, revenue growth equals transaction value growth plus the change in the take rate. The take-rate share is the size of the take-rate change divided by the sum of the sizes of both changes. The same split is made for the wedge, which equals transaction value minus revenue. The second step finds the cause of a take-rate change. A platform's take rate equals the rate of its gross fees minus the rate of its incentives, each measured against transaction value. Accounting rules deduct such incentives from revenue (International Accounting Standards Board 2014). So a rise comes from higher fees, lower incentives or a change in the mix of businesses. Wherever a company discloses both, the thesis splits the growth of revenue over a window into three parts. They are more commerce, a change in the fee rate and a change in the incentive rate. +The second objective asks where large movements in revenue came from, and the thesis answers in two steps. The first divides each movement into volume and take rate. In log terms, revenue growth equals transaction value growth plus the change in the take rate. The take-rate share is the size of the take-rate change divided by the sum of the sizes of both changes. The same split is made for the wedge, which equals transaction value minus revenue. The second step finds the cause of a take-rate change. A platform's take rate equals the rate of its gross fees minus the rate of its incentives, each measured against transaction value. Accounting rules normally deduct such incentives from revenue (International Accounting Standards Board 2014). So a rise comes from higher fees, lower incentives or a change in the mix of businesses. Wherever a company discloses both, the thesis splits the growth of revenue over a window into three parts. They are more commerce, a change in the fee rate and a change in the incentive rate. The sources differ by company. Tokopedia's audited segment note gives gross revenue, incentives and net revenue, and the thesis checks them against the audited consolidated statements. For Tokopedia it also reports the change line by line in rupiah, which is the usual way to read accounts, and it says where the two views differ. Grab is read from its annual filings and GoTo from its audited statements. Blibli does not report incentives, so its split rests on a proxy built from its releases. Seven windows are traced, and Appendix A lists them. The thesis also records where accounting treatment or reporting scope matters, such as the deduction of incentives from revenue and the travel business inside Blibli's retail segment. Company explanations are read as reports and are not tested. @@ -58,7 +58,7 @@ The entry test follows the rule in Chapter 3: in the 2024 file, at least 21.5 pe If the test fails, a second question follows: how much of the rise does first-time entry not explain? A cohort is the group of sellers that began selling online by a given year. If two surveys count the same population, a cohort can only shrink between them, because sellers stop and no new seller can join it. The thesis compares the 2023 survey's count of sellers who began before 2023 with the whole 2022 count. It does so twice. The first time it assumes that no seller stopped. The second time it assumes that sellers stopped at about 5 percent a year, the rate at which one cohort shrank between the 2020 and 2022 surveys. -Further checks ask where the extra sellers sit. They look across provinces and channels and at sellers' own reports of their sales. They use the sampling error that BPS publishes, profile who the extra sellers are, look at part-year selling, and test a scenario of returners. The cohort check and these further checks were designed after the entry test failed, so Chapter 5 labels them exploratory. The exception is the median change in sellers' sales, which was registered. The files cannot rule out returners, because a seller who stopped and returned keeps an old start year. +Further checks ask where the extra sellers sit. They look across provinces and channels, using rank correlations, and at sellers' own reports of their sales. They use the sampling error that BPS publishes. They profile who the extra sellers are, with each group's count in 2023 compared with what its own yearly change in 2020 to 2022 would give. They look at part-year selling and test a scenario of returners. The cohort check and these further checks were designed after the entry test failed, so Chapter 5 labels them exploratory. The exception is the median change in sellers' sales, which was registered. The files cannot rule out returners, because a seller who stopped and returned keeps an old start year. Three further analyses use the same files. The first asks which channels sellers use and how sales divide between them. The published tables split the 2023 to 2024 rise in value between marketplaces and other channels, and BPS counts food and ride apps in its marketplace category. Value by channel rests on revenue brackets, which only the 2021 and 2023 files give. The thesis assigns each bracket a low, a middle and a high amount. The amounts were fixed before the file was opened. A validation rule applies: the middle scenario's 2022 total must lie between half and double BPS's published total of about US$53 billion. The 2023 questionnaire asks for monthly revenue but the bracket limits are annual size classes, so both readings are computed and the one that passes is kept. If the lower brackets alone already exceed the total, value shares are reported as ranges. @@ -78,8 +78,9 @@ The third objective compares the platform evidence with national and payment fig | Industry estimates | Shopping marketplaces in Indonesia or Southeast Asia, from the research firm Momentum Works (2024) and the e-Conomy report (Google, Temasek and Bain 2024) | Outside estimates | | Household spending | All household purchases | BPS national accounts | | Payment figures | Payments made through systems such as QRIS, the national QR-code system | Payment records, in person and online | +| Adjacent figures | Parcels shipped in Southeast Asia, the value-added tax collected on foreign digital services (Ministry of Finance 2025), and the number of merchants a company reports | Used as context only, since none measures Indonesian e-commerce | -One last analysis asks whether markets value a platform's growth in transaction value differently from growth in its take rate. The data are year-end market values from the financial data service LSEG, under the university licence. The main sample has 28 listed platforms and 138 platform-years, made up of Bukalapak and 27 platforms abroad, and it uses whole-company figures only. A regression relates the yearly change in log market value to the changes in log transaction value and log take rate. It adds a fixed effect for each year and clusters standard errors by company. Each prediction names a direction, so both tests are one-sided. The question follows early evidence that markets price measures of activity beyond reported revenue (Trueman, Wong and Zhang 2000). The result is an association, and market value also changes when a company issues shares, so the thesis does not call it a stock return. A second run adds segment series and three more whole companies as a robustness check. The test was written down before the market values were pulled. +One last analysis asks whether markets value a platform's growth in transaction value differently from growth in its take rate. The data are year-end market values from the financial data service LSEG, under the university licence. The main sample has 28 listed platforms and 138 platform-years, made up of Bukalapak and 27 platforms abroad, and it uses whole-company figures only. It is not the list of 27 used for H1. It adds Airbnb, Booking, DoorDash, Lyft and Uber, and it lacks Despegar, Mercado Libre, Ozon, Rakuten and Sea. A regression relates the yearly change in log market value to the changes in log transaction value and log take rate. It adds a fixed effect for each year and clusters standard errors by company. Each prediction names a direction, so both tests are one-sided. The question follows early work on internet companies, which found that markets price web-traffic measures beyond reported revenue (Trueman, Wong and Zhang 2000). The result is an association, and market value also changes when a company issues shares, so the thesis does not call it a stock return. A second run adds three segment series and three more whole companies as a robustness check. The test was written down before the market values were pulled. ## 4.6 Labels, traceability and licensed data @@ -87,4 +88,4 @@ Every number in the thesis follows one path: source document, extract, analytic The thesis also separates tests by when they were written. A registered test was written down, dated and stored in the repository before the data were opened. This applies to the entry test, the scenario rules for value, the comparison with Bank Indonesia and the market-value test, among others. Appendix C lists them with their dates. An exploratory test was designed after some results were seen. The test with the larger groups in Section 4.2 belongs here, because it was set up after a first test on the three marketplaces. A descriptive result reports numbers without a test. -Three sources are licensed and are not in the repository: the BPS survey files, the LSEG market values and the World Bank survey files. The repository records the size and checksum of each file, so anyone who obtains the same files can check them. It holds the code that reads them and the national totals that the code produces, and no business-level record. BPS is cited as the source of every survey figure, in line with the data-use agreement. Chapter 7 states what these data cannot show. +Three sources are licensed and are not in the repository: the BPS survey files, the LSEG market values and the World Bank survey files. The repository records the size and checksum of each BPS and LSEG file and of the World Bank cross-country query, so anyone who obtains the same files can check them. It holds the code that reads them and the national totals that the code produces, and no business-level record. BPS is cited as the source of every survey figure, in line with the data-use agreement. Chapter 7 states what these data cannot show. diff --git a/papers/thesis_2026-10/ch5_results.md b/papers/thesis_2026-10/ch5_results.md index c151858..957f4bb 100644 --- a/papers/thesis_2026-10/ch5_results.md +++ b/papers/thesis_2026-10/ch5_results.md @@ -4,7 +4,7 @@ This chapter follows the thesis from the platforms to the national figures. It f ## 5.1 How much revenue leaves out -Take three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. Together they carried US$43 billion of transaction value and kept US$3 billion as revenue. The difference, the invisible wedge, was US$40 billion. Revenue was about 7 percent of what passed through them, and their ecosystem ratio, the wedge divided by revenue and the author's own measure, was 12.7. For every dollar of revenue, almost 13 dollars of commerce passed through the platforms without counting as revenue. A thin slice is what two-sided pricing and agent accounting lead one to expect (Rochet and Tirole 2003; International Accounting Standards Board 2014). Table 5.1 shows how each amount was obtained. +Take three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. Together they carried US$43 billion of transaction value and kept US$3 billion as revenue. The difference, the invisible wedge, was US$40 billion. Revenue was about 7 percent of what passed through them, and their ecosystem ratio, the wedge divided by revenue and the author's own measure, was 12.7. For every dollar of revenue, almost 13 dollars of commerce passed through the platforms without counting as revenue. A thin slice fits how platforms are paid and how they book revenue. A platform that charges both sides can charge little on one of them (Rochet and Tirole 2003). Accounting rules also let an agent record only its own commission (International Accounting Standards Board 2014). Table 5.1 shows how each amount was obtained. **Table 5.1. The invisible wedge of three platforms, 2023 (US$ billion)** @@ -12,16 +12,16 @@ Take three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. Tog |---|---|---|---|---| | Tokopedia | 16.33 | 0.41 | 15.93 | Both direct segment disclosure | | Shopee, Indonesia | 21.52 | 2.15 | 19.37 | Transaction value is an outside estimate (Momentum Works); revenue is derived at an assumed take rate of 10 percent | -| Grab, Indonesia | 5.38 | 0.61 | 4.78 | Revenue is a direct country disclosure; transaction value is derived from Grab's company-wide ratio | +| Grab, Indonesia | 5.38 | 0.60 | 4.78 | Revenue is a direct country disclosure; transaction value is derived from Grab's company-wide ratio | | Total | 43.23 | 3.16 | 40.07 | Rounded amounts may not add | Little of this depends on the assumptions. If Shopee kept 9 or 11 percent instead of 10, the total would stay between US$39.9 and US$40.3 billion. At Sea's company-wide rate of 11.5 percent it would be US$39.8 billion. Dropping any one platform leaves at least US$20.7 billion. Blibli's and Bukalapak's wedges, US$3.2 and US$10.5 billion, are left out because Blibli's measure includes online travel and Bukalapak reports for its whole group. For platforms that report both amounts directly, the wedge is 97 to 99 percent of transaction value in every year, so it moves almost one for one with it. Appendix A gives the wedge by platform and year. -Platforms are only part of online selling. In the BPS survey, the marketplace channel carries between 16 and 26 percent of online sales value, and BPS counts food and ride apps in that channel. So a measure built on platforms misses between about 74 and 84 percent of online sales value, and revenue shows much less still. A small slice can still be a fair guide if it keeps its size. The next question is whether it does. +Platforms are only part of online selling. In the BPS survey, the marketplace channel carries between 16 and 26 percent of online sales value, and BPS counts food and ride apps in that channel. So BPS places most online sales value outside the marketplace channel, and platform revenue shows less still. Section 5.9 sets the platform totals beside BPS's. A small slice can still be a fair guide if it keeps its size. The next question is whether it does. ## 5.2 Do revenue and transaction value grow together? -Among Indonesian and regional platforms they do not, and H1 is rejected. The typical take rate moved by about 20 percent in a year, up or down. At the typical foreign platform it moved by about 6 percent, so the difference is about threefold. Table 5.2 gives the main test and its checks. +Among Indonesian and regional platforms they do not, and the thesis rejects H1. The typical take rate moved by about 20 percent in a year, up or down. At the typical foreign platform it moved by about 6 percent, so the difference is about threefold. Table 5.2 gives the main test, which is exploratory, and its checks. **Table 5.2. The test of H1** @@ -33,13 +33,13 @@ Among Indonesian and regional platforms they do not, and H1 is rejected. The typ | Drop each platform in turn, or treat Tokopedia and GoTo as one company | Mann–Whitney | 0.17 to 0.23 | 0.06 | 0.003 to 0.016 | | Quarterly pairs, 3 platforms against 8 | Mann–Whitney | 0.071 | 0.049 | 0.19 | -The first two rows are the main test, using the two methods of Chapter 4. The third row repeats it with growth divergence in points, and the fourth shows that no single platform carries the result. The main test is exploratory, because the larger groups were set up after a first test on the three marketplaces. That narrower test agrees, with p = 0.002 when it compares pairs and 0.06 when it compares platforms. In it, revenue growth and transaction value growth had a correlation of 0.10 in Indonesia and 0.98 abroad, excluding Sea (Appendix B). The quarterly test in the last row does not reject H1. Its Indonesian pairs mostly cover 2023 to 2026, after most of the repricing, and the large moves came in 2022 to 2024. So the rejection describes that period. +The first two rows are the main test, using the two methods of Chapter 4. The third row repeats it with growth divergence in points, and the fourth shows that no single platform carries the result. The planned test was narrower: the three Indonesian marketplaces against the eight foreign platforms chosen first. It rejects H1 when it compares pairs of years (p = 0.002), but pairs from one platform are not independent. When it compares platforms, which is the fairer count, it comes close without passing (p = 0.06), because three platforms give little power. The larger groups were set up after that first result, which is why the main test is exploratory. In the narrower test, revenue growth and transaction value growth had a correlation of 0.10 in Indonesia and 0.98 abroad, excluding Sea (Appendix B). The quarterly test in the last row does not reject H1. Its Indonesian pairs mostly cover 2023 to 2026, after most of the repricing, and the large moves came between 2021 and 2024. So the rejection describes that period. -The thin slice explains much of the threefold difference, which is an exploratory finding. A log change in the take rate is roughly the change in points divided by the take rate itself. Indonesian and regional platforms changed their take rates by about 1 point a year, as foreign platforms did. But the typical Indonesian and regional platform keeps only 1.6 percent of each sale, against 16.9 percent abroad. Abroad the share runs from about 3 percent at Shopify to about 25 percent at Mercado Libre. If transaction value is flat, a rise of 1 point lifts the revenue of the first by more than half. It lifts the revenue of a typical foreign platform by about 6 percent. The larger the wedge is next to revenue, the more a small price change moves revenue growth. Figure 5.1 shows the three measures side by side. What the platforms actually changed is the next question. +The thin slice explains much of the threefold difference, which is an exploratory finding. A log change in the take rate is roughly the change in points divided by the take rate itself. Indonesian and regional platforms changed their take rates by about 1 point a year, as foreign platforms did. But the typical Indonesian and regional platform keeps only 1.6 percent of each sale, against 16.9 percent abroad. Among the foreign platforms the share runs from 2.6 percent at Shopify to 71 percent at Zalando, which also sells goods itself. If transaction value is flat, a rise of 1 point lifts revenue by more than half at a platform that keeps 1.6 percent. It lifts revenue by about 6 percent at one that keeps 16.9 percent. The larger the wedge is next to revenue, the more a small price change moves revenue growth. Figure 5.1 shows the three measures side by side. What the platforms actually changed is the next question. ![Figure 5.1](figures/fig5_1_thin_slice.png) -**Figure 5.1. The thin slice.** Each dot is one platform's median over its years. Left: the share of each sale that the platform keeps, on a log scale. Middle: the yearly change in that share, in percentage points. Right: the same change relative to the share itself, as a log change in percent. Black bars mark group medians. The foreign group has 26 platforms, those with usable pairs in both years. Source: company filings; author's calculations. Exploratory. +**Figure 5.1. The thin slice.** Each dot is one platform's median over its years. Left: the share of each sale that the platform keeps, on a log scale. Middle: the yearly change in that share, in percentage points. Right: the same change relative to the share itself, as a log change in percent. Black bars mark group medians. The foreign group has 26 platforms here and 27 in the main test, because one platform has no pair of usable years for the change in points. Source: company filings; author's calculations. Exploratory. ## 5.3 What moved the take rate @@ -47,7 +47,7 @@ Tokopedia shows it best. In 2023 its transaction value fell by 9 percent while i Table 5.3 splits the growth of revenue into more commerce, higher fees and lower incentives, which are the discounts and vouchers a platform pays for. Fees and incentives are measured per unit of transaction value. A negative share means that the part pulled revenue down. The windows of one platform overlap, so they are not seven independent cases. -**Table 5.3. What moved revenue in seven Indonesian windows (share of the growth of revenue, percent)** +**Table 5.3. What moved revenue in seven Indonesian and regional windows (share of the growth of revenue, percent)** | Platform and window | Growth of revenue | More commerce | Higher fees | Lower incentives | |---|---|---|---|---| @@ -61,7 +61,7 @@ Table 5.3 splits the growth of revenue into more commerce, higher fees and lower Lower incentives were the larger of the two take-rate parts in four windows and higher fees in two. In the seventh, Grab from 2021 to 2024, the two were nearly equal. Shares are rounded, so a row can add to 99 or 101. Two series need a note. GoTo's 2022 transaction value is an estimate. Blibli does not report incentives, so its split rests on a proxy, and part of its 2022–23 rise comes from travel and digital products. Bukalapak's reports do not allow the parts to be separated. -Tokopedia needs two views. Accounting rules deduct what a company pays its customers from its revenue (International Accounting Standards Board 2014), and Tokopedia's incentives fell by about one third in 2023. Counting the lines of its accounts, 60 percent of the extra net revenue came from lower incentives and 40 percent from higher gross fees. That count leaves the fall in volume inside both lines. Table 5.3 takes it out, and on that per-unit basis higher fees account for more of the rise in the take rate than lower incentives. Both views agree that pricing and not more commerce drove the rise, and they differ only on which lever led (Figure 5.2). Across the windows, revenue in 2022 to 2024 was not a fair guide to the commerce these platforms carried. Did investors see through it? +Tokopedia needs two views. Accounting rules normally deduct what a company pays its customers from its revenue (International Accounting Standards Board 2014), and Tokopedia's incentives fell by about one third in 2023. Counting the lines of its accounts, 61 percent of the extra net revenue came from lower incentives and 39 percent from higher gross fees. That count leaves the fall in volume inside both lines. Table 5.3 takes it out, and on that per-unit basis higher fees account for more of the rise in the take rate than lower incentives. Both views agree that pricing and not more commerce drove the rise, and they differ only on which lever led (Figure 5.2). Across these windows, revenue growth was not a fair guide to the commerce the platforms carried. Did investors see through it? ![Figure 5.2](figures/fig5_2_tokopedia_two_views.png) @@ -69,7 +69,9 @@ Tokopedia needs two views. Accounting rules deduct what a company pays its custo ## 5.4 What markets valued -Market value moved with transaction value, a registered result. Across 28 listed platforms and 138 platform-years, a 1 percent rise in transaction value goes with a 0.6 percent rise in market value (p = 0.002). The second prediction, that markets value growth from a higher take rate less, did not hold. A 1 percent rise in the take rate goes with a rise of 0.3 percent, but that estimate is too uncertain to tell from zero. The difference between the two is not large enough to count (p = 0.20). So the data show that market value follows the commerce a platform carries. Early evidence on markets pricing measures of activity points the same way (Trueman, Wong and Zhang 2000; Rajgopal, Venkatachalam and Kotha 2003). The data do not show that markets saw through the pricing effect. Both results are associations and not stock returns, because market value also changes when a company issues shares. The next section asks what the commerce did in 2023. +Market value moved with transaction value, a registered result. Across 28 listed platforms and 138 platform-years, a 1 percent rise in transaction value goes with a 0.6 percent rise in market value (p = 0.002). The second prediction was that markets value growth from a higher take rate less. It did not hold in the main sample. A 1 percent rise in the take rate goes with a rise of 0.3 percent. That estimate is too uncertain to tell from zero. The difference between the two is not large enough to count (p = 0.20). Trimming extreme values leaves the difference at p = 0.16. A second run adds six series, three segments and three more whole companies, and gives 0.63 for transaction value and 0.07 for the take rate. There the difference counts (p = 0.004). That run mixes segment figures with whole-company market values, as the registration warned, so the thesis does not lean on it. The answer depends on which platforms are in the sample. + +The sample differs from the 27 foreign platforms of H1. It adds Airbnb, Booking, DoorDash, Lyft and Uber, and it lacks Despegar, Mercado Libre, Ozon, Rakuten and Sea, which enter only the second run or not at all. So the data show that market value follows the commerce a platform carries. Early studies of internet companies found that markets price web-traffic measures too, which points the same way (Trueman, Wong and Zhang 2000; Rajgopal, Venkatachalam and Kotha 2003). The data do not show that markets saw through the pricing effect. Both results are associations and not stock returns, because market value also changes when a company issues shares. The next section asks what the commerce did in 2023. ## 5.5 The platforms and the sellers in 2023 @@ -77,31 +79,33 @@ Buying through platforms did not boom in 2023. Figure 5.3 sets six measures of i ![Figure 5.3](figures/fig5_3_growth_2023.png) -**Figure 5.3. Growth in 2023 (percent).** Each measure is in its own unit. Sources: company reports, Bank Indonesia, Momentum Works and BPS; household spending from national accounts. GoTo's change rests on an estimated 2022 value. Appendix D gives the values. +**Figure 5.3. Growth in 2023 (percent).** Each measure is in its own unit. Sources: company reports, Bank Indonesia, Momentum Works and BPS; household spending from national accounts. GoTo's change rests on an estimated 2022 value, and Momentum Works' 3.5 is the author's calculation from its published levels (its 2024 release states 3.7). Appendix D gives the values. Because the measures are in different units, the thesis compares only their rates. The blue bars show the buying, and the orange bars show the figures that described a boom. Revenue and BPS's value rose strongly while the buying through platforms did not. -Sellers did not describe a boom either. BPS asks each business how its online sales changed. Among businesses already selling, the median change was zero, as a registered prediction expected. A share of 24 percent reported a rise, 32 percent a fall and 44 percent no change, against 37 percent up and 24 percent down a year earlier. The mean change was −4.3 percent, and lack of demand became the main obstacle, named by 41 percent of sellers against 35 percent in 2022. Yet the published count of online sellers rose by 27 percent that year. Where did those sellers come from? +Sellers did not describe a boom either. BPS asks each business how its online sales changed. Among businesses already selling, the median change was zero. A registered prediction had set the limit at 10.4 percent, the published growth in sales per business. A share of 24 percent reported a rise, 32 percent a fall and 44 percent no change, against 37 percent up and 24 percent down a year earlier. The mean change was −4.3 percent, and lack of demand became the main obstacle, named by 41 percent of sellers against 35 percent in 2022. Yet the published count of online sellers rose by 27 percent that year. Where did those sellers come from? ## 5.6 Where the extra sellers came from On BPS's published figures, H2 holds. From 2022 to 2023, online sales value grew by 40.6 percent, the number of businesses by 27.4 percent and sales per business by 10.4 percent. So more businesses account for 71 percent of the growth. From 2023 to 2024 the figures were 17.1, 15.3 and 1.5 percent, and more businesses account for 90 percent, or 71 percent on BPS's other count for 2023. The split is arithmetic, and it cannot tell a new seller from one counted for the first time. -The entry test can, and its result is registered. In the 2024 file, 13.45 percent of sellers began selling online in 2023, and the rule required at least 21.5 percent. The test fails. Even if no seller stopped, that many new sellers lets the count grow by at most 15.5 percent over the sellers already there, against the published 27.4 percent. Respondents tend to date events closer to the interview than they were (Neter and Waksberg 1964). That would make new sellers look more numerous, so the shortfall is if anything understated. +The entry test can, and its result is registered. In the 2024 file, 13.45 percent of sellers began selling online in 2023, and the rule required at least 21.5 percent. The test fails. Even if no seller stopped, that many new sellers lets the count grow by at most 15.5 percent over the sellers already there, against the published 27.4 percent. Respondents often place past events nearer to the interview than they were (Neter and Waksberg 1964). If sellers who began earlier reported 2023, that would make new sellers look more numerous, so the shortfall is if anything understated. **Table 5.4. The 2023 rise in the count of online sellers (thousands)** | Part of the rise | If no seller stopped | If sellers stopped at 5 percent a year | |---|---|---| | Rise between the weighted totals of the two files | 819 (100%) | 819 (100%) | -| Net entry: sellers who began in 2023, less those who stopped | 513 (63%) | 360 (44%) | +| Net entry: sellers who began in 2023, less those who stopped | 513 (63%) | 361 (44%) | | Earlier sellers newly counted above the whole 2022 count | 306 (37%) | 458 (56%) | -Table 5.4 shows the accounting. The rise of 819 thousand is the gap between the weighted totals of the two files, and BPS's own published counts give 821 thousand. The 2023 survey counted 306 thousand more sellers who began before 2023 than the whole 2022 count held. That is 37 percent of the rise if no seller stopped and 56 percent if sellers stopped at 5 percent a year. So about half of the rise is not explained by first-time entry. This accounting and the checks below are exploratory, and they point the same way. +Source: BPS survey files, weighted; author's calculations. + +Table 5.4 shows the accounting. The rise of 819 thousand is the gap between the weighted totals of the two files, and BPS's own published counts give 821 thousand (BPS 2025). The 2023 survey counted 306 thousand more sellers who began before 2023 than the whole 2022 count held. That is 37 percent of the rise if no seller stopped and 56 percent if sellers stopped at 5 percent a year. So about half of the rise is not explained by first-time entry. This accounting and the checks below are exploratory, and they point the same way. -Cohorts behave as they should, except in 2023. Sellers who began by 2020 numbered 9.9 percent fewer in the 2022 survey than in the 2020 one, over two years. In the 2023 survey they numbered 6.0 percent more than in the 2022 one. Between the 2023 and 2024 surveys, the cohort that began before 2020 shrank by 10.2 percent, and at least 754 thousand new sellers appeared against a rise of 584 thousand. The break is also uneven. Across the 34 provinces, count growth had almost no link to the share of new sellers (rank correlation 0.10) and a weak link to growth in sample size (0.35). Sellers who began by 2022 and use only chat or social media rose by 13 percent, while marketplace users rose by 0.3 percent. BPS's published error rates put the standard error of the national count at about 1.4 percent. The 306 thousand sellers are about 4.6 times the standard error of the change. +Cohorts behave as they should, except in 2023. Sellers who began by 2020 numbered 9.9 percent fewer in the 2022 survey than in the 2020 one, over two years. In the 2023 survey they numbered 6.0 percent more than in the 2022 one. Between the 2023 and 2024 surveys, the cohort that began before 2020 shrank by 10.2 percent, and about 750 thousand new sellers appeared against a rise of 584 thousand. The break is also uneven. Across the 34 provinces, count growth had almost no link to the share of new sellers (0.10) and a weak link to growth in sample size (0.35). These are rank correlations, which compare the order of provinces on two measures and run from −1 to 1. Sellers who began by 2022 and use only chat or social media rose by 13 percent, while marketplace users rose by 0.3 percent. BPS's published error rates put the standard error of the national count, the usual size of its sampling noise, at about 1.4 percent. The 306 thousand sellers are about 4.6 times the standard error of the change. That calculation applies the 2024 edition's error rates to both years and treats the two surveys as independent. -The value side tells a similar story. BPS's value rose from about US$53 billion to about US$72 billion. Sellers not explained by entry account for 15 to 17 percent of this, and entrants for 25 to 30 percent. The remaining 53 to 59 percent, about US$11 to 12 billion, cannot be traced to existing sellers' answers, which show a mean change of −4.3 percent. These checks point to wider survey coverage of chat and social sellers. That is the pattern survey research warns about, because a change in how a survey finds respondents can break a series (van den Brakel, Smith and Compton 2008). The data cannot separate it from sellers returning after a pause. Who were the extra sellers? +The value side tells a similar story. BPS's value rose from about US$53 billion to about US$72 billion. Sellers not explained by entry account for 15 to 17 percent of this, and entrants for 25 to 30 percent. The remaining 53 to 59 percent, about US$11 to 12 billion at the 2023 exchange rate, cannot be traced to existing sellers' answers, which show a mean change of −4.3 percent. These checks point to wider survey coverage of chat and social sellers. That is the pattern survey research warns about, because a change in how a survey finds respondents can break a series (van den Brakel, Smith and Compton 2008). The data cannot separate it from sellers returning after a pause. Who were the extra sellers? ## 5.7 Who the extra sellers were, and whether they returned @@ -113,21 +117,21 @@ The comparison of who they were is exploratory. The surveys do not follow the sa The extra count leaned toward long-running businesses, older owners and places outside Java, and the lean toward men in the raw data disappears once normal change is taken out. It does not look like the young start-ups of the usual growth story. That raises one question: could these be established sellers who paused and came back? -A returning seller would usually sell for only part of the year, unless the seller restarts in January, and part-year selling fell in 2023. Among sellers who began by 2021, the number selling for part of the year fell from 472 thousand to 318 thousand, and it fell in every start-year group. Small firms that stop are more often young, with young owners (McKenzie and Paffhausen 2019). If returners were mostly such firms, the extra count would lean young. It leaned old. Returners equal to 10 to 15 percent of the 2022 count would remove the unexplained part, which is a scenario and not an estimate. The check cannot rule out returners. The files carry no linked histories, BPS changed how it asks about months between the rounds, and exits or a move to full-year selling could offset them. +A returning seller would usually sell for only part of the year, unless the seller restarts in January, and part-year selling fell in 2023. Among sellers who began by 2021, the number selling for part of the year fell from 472 thousand to 318 thousand. It fell for the groups who began by 2020 and by 2022 as well. Small firms that stop are more often young, with young owners (McKenzie and Paffhausen 2019). If returners were mostly such firms, the extra count would lean young. It leaned old. Returners equal to 10 to 15 percent of the 2022 count would remove the unexplained part, which is a scenario and not an estimate. The check cannot rule out returners. The files carry no linked histories, BPS changed how it asks about months between the rounds, and exits or a move to full-year selling could offset them. ## 5.8 Channels and records Two additional analyses show where these sellers sit. The path of marketplace use, the value held by sellers without statements and the World Bank results were registered. The comparison within the pre-2020 group is exploratory. -Most online sellers do not use a marketplace, and about 96 percent use chat or social media. Use of marketplaces fell every time it was measured, from 21.6 percent of sellers in 2020 to 17.2 percent in 2024. BPS worded the question slightly differently in the last two rounds. From 2023 to 2024, BPS's marketplace value grew by 1.45 percent and other channels by 20.57 percent, so 98.5 percent of the increase came from outside the marketplaces. BPS's marketplace category includes many food and ride merchants: 43 percent of its sellers use only Gojek or Grab, and 47 percent use a shopping marketplace. +Most online sellers do not use a marketplace, and about 96 percent use chat or social media. Use of marketplaces fell every time it was measured, from 21.6 percent of sellers in 2020 to 17.2 percent in 2024. BPS worded the question slightly differently for 2023 and 2024 than for the earlier years. From 2023 to 2024, BPS's marketplace value grew by 1.45 percent and other channels by 20.57 percent, so 98.5 percent of the increase came from outside the marketplaces. BPS's marketplace category includes many food and ride merchants: 43 percent of its sellers use only Gojek or Grab, and 47 percent use a shopping marketplace. ![Figure 5.5](figures/fig5_5_marketplace_and_records.png) -**Figure 5.5. Marketplace use and financial statements, 2020 to 2024.** Marketplace use for 2020, 2022 and 2023 comes from the survey files and for 2024 from BPS's published figure. Financial statements come from the survey files. BPS worded the marketplace question slightly differently in the last two rounds. Source: BPS; author's calculations. +**Figure 5.5. Marketplace use and financial statements, 2020 to 2024.** Marketplace use for 2020, 2022 and 2023 comes from the survey files and for 2024 from BPS's published figure. Financial statements come from the survey files. BPS worded the marketplace question slightly differently for 2023 and 2024, and the survey has no point for 2021. Source: BPS; author's calculations. -Records are thinner outside the marketplaces. In 2023, 28.6 percent of marketplace sellers kept complete financial statements, against 12.3 percent of other sellers, and Figure 5.5 shows the share of all sellers who keep them falling. The fall from 2020 to 2022 came while the survey appears to have counted the same kinds of seller. The fall in 2023 may partly reflect the chat-only sellers added to the count. Even among sellers who began before 2020, the share fell from 23.4 to 16.8 percent between the two surveys. +Records are thinner outside the marketplaces. In 2023, 28.6 percent of marketplace sellers kept complete financial statements, against 12.3 percent of other sellers, and Figure 5.5 shows the share of all sellers who keep them falling. The fall from 2020 to 2022 came while the survey appears to have counted the same kinds of seller. The fall in 2023 may partly reflect the chat-only sellers added to the count. Even among sellers who began before 2020, the share fell from 23.4 percent in 2022 to 16.8 percent in 2023. -Value shares rest on the bracket scenarios of section 4.4. The annual reading passes the validation rule, because the middle scenario gives 1.95 times BPS's total for 2022, and the monthly reading fails at 23 times. The lower brackets alone already exceed BPS's total, so the shares are ranges. Sellers without statements held between 26 and 40 percent of online value in 2022, against 79 percent of the count. The registered prediction was that their share of value would not fall by more than 5 points between 2020 and 2022. It fell by 5.8 points, so the prediction failed, narrowly, and a second registered version gives a fall of 3.3 points. +Value shares rest on the bracket scenarios of section 4.4. The annual reading passes the validation rule, because the middle scenario gives 1.95 times BPS's total for 2022, and the monthly reading fails at 23 times. The lower brackets alone already exceed BPS's total, so the shares are ranges. Sellers without statements held between 26 and 40 percent of online value in 2022, against 79 percent of the count. The registered prediction was that their share of value would not fall by more than 5 points between 2020 and 2022. It fell by 5.8 points, so the prediction failed, narrowly. A second registered version gives a fall of 3.3 points, which is inside the limit. The World Bank surveys point the same way. In six Indonesian cities, 27 percent of unregistered firms use social media with their customers. Among those, 70 percent keep no written records, and their median sales are about US$4,000 a year. The registered prediction on their mean sales failed: it was below about US$6,600, and the mean is about US$7,300. Among formal firms, 39.9 percent in Indonesia have neither a website nor online tax filing, the highest of seven countries, against 9.1 percent in Malaysia and 8.7 percent in Thailand. @@ -135,12 +139,12 @@ The World Bank surveys point the same way. In six Indonesian cities, 27 percent Chapter 1 began with two official figures that moved in opposite directions in 2023. The third objective asks whether platform, national and payment figures give the same account. They do not give the same account of scale. They give the same account of growth only where they cover the same sellers. -In 2023, Bank Indonesia's figure fell by 4.7 percent and BPS's rose by 40.6 percent. Where the two cover the same ground, they agree on slow growth. BPS's own marketplace slice grew by 0.2 percent from 2022 to 2023 in the middle scenario, and by 0.2 to 13.2 percent across the scenarios. It grew by 1.45 percent from 2023 to 2024. A registered test also holds. The part of BPS's marketplace sales that went to final consumers in 2022 is 0.68 times Bank Indonesia's figure in the middle scenario. It is 0.16 times in the low scenario and 1.62 times in the high one. So the disagreement sits outside the marketplaces, in the count of sellers that only BPS reaches. +In 2023, Bank Indonesia's figure fell by 4.7 percent and BPS's rose by 40.6 percent. Where the two cover the same ground, the growth is small. BPS's own marketplace slice grew by 0.2 percent from 2022 to 2023 in the middle scenario, and by 0.2 to 13.2 percent across the scenarios. It grew by 1.45 percent from 2023 to 2024. A registered test gives a mixed answer on size. The part of BPS's marketplace sales that went to final consumers in 2022 is 0.68 times Bank Indonesia's figure in the middle scenario and 0.16 times in the low one. In the high scenario it is 1.62 times. The prediction was that it would fall below Bank Indonesia's figure, so it holds in the first two scenarios and fails in the third. Most of the disagreement then sits outside the marketplaces, in the count of sellers that only BPS reaches. The 2024 figures show the same split. Those that cover mainly platforms grew by 5 to 7 percent, and BPS's grew by 17.1 percent. Adjacent figures point the same way as BPS but do not measure Indonesian e-commerce. Southeast Asian parcels grew by 25.2 percent, and the tax on foreign digital services by 24.9 percent. -Sizes cannot be reconciled. In 2023, BPS's marketplace slice was about US$13 billion, against US$38 billion for Tokopedia and the Shopee estimate together. In 2024 it was less than a quarter of Momentum Works' marketplace estimate of about US$56.5 billion. Platform transaction value counts shipping, digital goods, travel and offline sales that a seller survey does not count in the same way. Counts differ too: Tokopedia alone said it had about 15 million merchants in 2022, while BPS counts about 0.6 million marketplace sellers. +Sizes cannot be reconciled. In 2023, BPS's marketplace slice was about US$13 billion, against US$38 billion for Tokopedia and the Shopee estimate together. BPS's whole total for 2023, US$72 billion, lies above the three platforms' US$43 billion in Table 5.1, but the two count different things. In 2024 it was less than a quarter of Momentum Works' marketplace estimate of about US$56.5 billion. Platform transaction value counts shipping, digital goods, travel and offline sales that a seller survey does not count in the same way. Counts differ too: Tokopedia alone said in a company statement that it had about 15 million merchants in 2022, while BPS counts about 0.6 million marketplace sellers. Platform sales do not always keep pace with household spending either. GoTo's on-demand transaction value grew more slowly than household spending in each of four quarters, from the third quarter of 2025 to the second quarter of 2026. In the last it grew by 2.0 percent against 8.3 percent, while its revenue grew by 20.5 percent. Grab and Shopee grew faster, but their figures cover the region. Payment figures such as QRIS are context only, because they include in-person payments and the switch from cash. -Taken together, the results answer the research question at two levels. Revenue showed a small part of the commerce the platforms carried, and in 2022 to 2024 it was not a fair guide to that commerce, because the take rate moved. The official count of online sellers followed first-time entry only in part in 2023, and the platforms and the survey disagree most where only the survey looks. Chapter 6 asks what this means for the rules that Indonesia is writing. +Taken together, the results answer the research question at two levels. Revenue showed a small part of the commerce the platforms carried, and in the windows traced it was not a fair guide to that commerce, because the take rate moved. The official count of online sellers followed first-time entry only in part in 2023, and the platforms and the survey disagree most where only the survey looks. 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Built only from committed tables; no licensed data are read. Palette: reference categorical slots 1 (blue #2a78d6) and 2 (orange #eb6834), validated on a white surface (CVD dE 24.7, normal 33.6, contrast >= 3:1), -plus neutral grey. Colour follows the entity in every figure: blue = what the Indonesian-side comparison group or fees stand for is NOT fixed by colour alone; -every mark is also named by a label or legend. Text is neutral ink. Run from the repository root: python3 papers/thesis_2026-10/figures/make_figures.py""" +plus neutral grey. Colour is not fixed to one entity across figures; inside each figure a legend or a label names every mark, so colour is never the only +cue. Text is neutral ink. Run from the repository root: python3 papers/thesis_2026-10/figures/make_figures.py""" import json, pathlib import matplotlib matplotlib.use("Agg") @@ -49,7 +49,7 @@ def save(fig, name): vol, fee, dis = 100 * g.share_from_volume, 100 * g.share_from_fee, 100 * g.share_from_discount_cut fig, (a, b) = plt.subplots(1, 2, figsize=(7.2, 2.5), gridspec_kw={"width_ratios": [1, 1.25]}) a.barh([1, 0], [inc_share, gross_share], color=[ORANGE, BLUE], height=0.5, zorder=3) -for y, v in zip([1, 0], [inc_share, gross_share]): a.text(v + 1.5, y, f"{v:.1f}", va="center", color=INK) +for y, v in zip([1, 0], [inc_share, gross_share]): a.text(v + 1.5, y, f"{v:.0f}", va="center", color=INK) a.set_yticks([1, 0]); a.set_yticklabels(["Lower incentives", "Higher gross fees"]); a.set_xlim(0, 80); a.set_xlabel("Share of the extra net revenue (percent)") a.set_title("By lines of the accounts", fontsize=9, color=INK, loc="left", fontweight="bold") cols = [GREY, BLUE, ORANGE]; vals = [vol, fee, dis]; labs = ["More commerce", "Higher fees", "Lower incentives"] @@ -61,11 +61,21 @@ def save(fig, name): fig.tight_layout(); save(fig, "fig5_2_tokopedia_two_views.png") # ---- Figure 5.3: 2023 growth, each measure in its own unit ---------------------------------------------------------------------------- -items = [("Household spending", 9.4, GREY, "Reference"), ("Tokopedia transaction value", -8.9, BLUE, "Buying through platforms"), - ("GoTo on-demand transaction value", -10.5, BLUE, "Buying through platforms"), ("Bank Indonesia e-commerce", -4.7, BLUE, "Buying through platforms"), - ("Momentum Works marketplace estimate", 3.5, BLUE, "Buying through platforms"), ("Bukalapak transaction value", 6.9, BLUE, "Buying through platforms"), - ("Blibli third-party retail transaction value", 34.7, BLUE, "Buying through platforms"), ("Tokopedia revenue", 53.2, ORANGE, "Figures that described a boom"), - ("BPS e-commerce value", 40.6, ORANGE, "Figures that described a boom"), ("BPS online sellers (count)", 27.4, ORANGE, "Figures that described a boom")] +# Values come from committed tables: consistency_grid_growth.csv (BPS, Bank Indonesia, GoTo, Momentum Works, household spending), wedge_over_time.csv +# (Tokopedia, Bukalapak, Blibli) and bps_descriptives.json (published seller count). Momentum Works' 3.5 is the grid's calculation from published levels. +grid = pd.read_csv(T / "consistency_grid_growth.csv", index_col=0)["2023"] +wot = pd.read_csv(T / "wedge_over_time.csv"); wot = wot[wot.year == 2023].set_index("firm") +sellers = json.load(open(T / "bps_descriptives.json"))["published_count_growth_pct"] +items = [("Household spending", grid["Official household spending (nominal)"], GREY, "Reference"), + ("Tokopedia transaction value", wot.loc["Tokopedia e-commerce segment", "g_tv_pct"], BLUE, "Buying through platforms"), + ("GoTo on-demand transaction value", grid["GoTo on-demand GTV"], BLUE, "Buying through platforms"), + ("Bank Indonesia e-commerce", grid["Bank Indonesia e-commerce"], BLUE, "Buying through platforms"), + ("Momentum Works marketplace estimate", grid["Momentum Works Indonesia GMV"], BLUE, "Buying through platforms"), + ("Bukalapak transaction value", wot.loc["Bukalapak Group", "g_tv_pct"], BLUE, "Buying through platforms"), + ("Blibli third-party retail transaction value", wot.loc["Blibli 3P Retail", "g_tv_pct"], BLUE, "Buying through platforms"), + ("Tokopedia revenue", wot.loc["Tokopedia e-commerce segment", "g_revenue_pct"], ORANGE, "Figures that described a boom"), + ("BPS e-commerce value", grid["BPS e-commerce"], ORANGE, "Figures that described a boom"), + ("BPS online sellers (count)", sellers, ORANGE, "Figures that described a boom")] order = [i for i in items if i[3] == "Figures that described a boom"] + [items[0]] + sorted([i for i in items if i[3] == "Buying through platforms"], key=lambda t: -t[1]) order = sorted(order[:3], key=lambda t: -t[1]) + order[3:] fig, ax = plt.subplots(figsize=(7.2, 3.9)) @@ -93,7 +103,7 @@ def save(fig, name): for y, g_ in zip(y0, groups): ax.text(g_[2] + 1, y + h / 2, f"{g_[2]:.1f}", va="center", color=INK, fontsize=8.5); ax.text(g_[1] + 1, y - h / 2, f"{g_[1]:.1f}", va="center", color=INK, fontsize=8.5) ax.set_yticks(y0); ax.set_yticklabels([g_[0] for g_ in groups]); ax.tick_params(axis="y", length=0); ax.grid(axis="y", visible=False) -ax.set_xlim(0, 70); ax.set_xlabel("Percent of each group of sellers"); ax.legend(loc="lower left", bbox_to_anchor=(-0.02, 1.0), ncol=1, frameon=False, fontsize=8.5) +ax.set_xlim(0, 70); ax.set_xlabel("Percent"); ax.legend(loc="lower left", bbox_to_anchor=(-0.02, 1.0), ncol=1, frameon=False, fontsize=8.5) fig.tight_layout(); save(fig, "fig5_4_extra_sellers.png") # ---- Figure 5.5: marketplace use and financial statements -------------------------------------------------------------------------------- diff --git a/research/option_space_2026-10-01/h1_thesis_drops.py b/research/option_space_2026-10-01/h1_thesis_drops.py new file mode 100644 index 0000000..9a51e08 --- /dev/null +++ b/research/option_space_2026-10-01/h1_thesis_drops.py @@ -0,0 +1,22 @@ +"""Two checks added for the thesis (13 Oct 2026, exploratory; formed after the main H1 result, in answer to the second audit). +Does the 5-against-27 test survive without Ozon (its only pair starts in a year where own goods exceed half of revenue) and without Zalando +(a retailer that also sells goods itself)? Same statistic as h1_extended.py: one median size of yearly change in log take rate per platform, +one-sided Mann-Whitney and a 9,999-label shuffle of platform medians. +Run from this folder: python3 h1_thesis_drops.py -> tables/h1_thesis_drops.json""" +import json +import numpy as np, pandas as pd +from scipy import stats + +rng = np.random.default_rng(20261013) +f = pd.read_csv("tables/h1_extended_firms.csv") +idn = f[f.group == "Indonesia"].median_abs.values +out = {} +for label, drop in [("all 27", []), ("without Ozon", ["Ozon"]), ("without Zalando", ["Zalando"]), ("without Ozon and Zalando", ["Ozon", "Zalando"])]: + fo = f[(f.group != "Indonesia") & (~f.firm.isin(drop))].median_abs.values + obs = np.median(idn) - np.median(fo); pool = np.concatenate([idn, fo]); hits = 0 + for _ in range(9999): + rng.shuffle(pool); hits += (np.median(pool[:5]) - np.median(pool[5:])) >= obs + out[label] = {"foreign_platforms": len(fo), "median_indonesia": round(float(np.median(idn)), 4), "median_foreign": round(float(np.median(fo)), 4), + "mannwhitney_p": round(float(stats.mannwhitneyu(idn, fo, alternative="greater").pvalue), 4), "shuffle_p": round((hits + 1) / 10000, 4)} +json.dump(out, open("tables/h1_thesis_drops.json", "w"), indent=1) +print(json.dumps(out, indent=1)) diff --git a/research/option_space_2026-10-01/rebuild_all.sh b/research/option_space_2026-10-01/rebuild_all.sh index 48ce91e..0fe8cdd 100755 --- a/research/option_space_2026-10-01/rebuild_all.sh +++ b/research/option_space_2026-10-01/rebuild_all.sh @@ -32,6 +32,7 @@ step tax_base_rulers.py # marketplace tax base under four rulers step claims_ledger_v3.py # recomputes the 3 Oct numbers step publishable_track/spinoff_tests.py # 29-firm panel (quote-checked extractions) and the spin-off tests step h1_extended.py # H1 vs 27 foreign firms, D measure, leave-one-out, exploratory market-income pattern +step h1_thesis_drops.py # H1 without Ozon and without Zalando (exploratory, for the thesis appendix) step h1_points_vs_log.py # take-rate changes in points vs logs (exploratory, 9 Oct) step consistency_grid.py # eleven rulers of the online economy step exhibits.py # draft exhibits, one per story layer (exhibits/*.png) diff --git a/research/option_space_2026-10-01/tables/h1_thesis_drops.json b/research/option_space_2026-10-01/tables/h1_thesis_drops.json new file mode 100644 index 0000000..61d58c8 --- /dev/null +++ b/research/option_space_2026-10-01/tables/h1_thesis_drops.json @@ -0,0 +1,30 @@ +{ + "all 27": { + "foreign_platforms": 27, + "median_indonesia": 0.2011, + "median_foreign": 0.0615, + "mannwhitney_p": 0.0038, + "shuffle_p": 0.0212 + }, + "without Ozon": { + "foreign_platforms": 26, + "median_indonesia": 0.2011, + "median_foreign": 0.0597, + "mannwhitney_p": 0.003, + "shuffle_p": 0.0215 + }, + "without Zalando": { + "foreign_platforms": 26, + "median_indonesia": 0.2011, + "median_foreign": 0.0638, + "mannwhitney_p": 0.0045, + "shuffle_p": 0.0238 + }, + "without Ozon and Zalando": { + "foreign_platforms": 25, + "median_indonesia": 0.2011, + "median_foreign": 0.0615, + "mannwhitney_p": 0.0036, + "shuffle_p": 0.023 + } +} \ No newline at end of file From 03592426cf299b70c52375b8a80e522c5641aa7f Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 13 Oct 2026 09:31:19 +0000 Subject: [PATCH 23/65] Thesis ch1-4: trim Chapter 1 introduction, plainer 1.1 and 1.3, Chapter 2 basics and says/not-says per section, Chapter 3 opens fresh, Chapter 4 opens with the state of the data Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 9 ++++ papers/thesis_2026-10/ch1_introduction.md | 22 +++++---- papers/thesis_2026-10/ch2_literature.md | 54 ++++++++++++++++------- papers/thesis_2026-10/ch3_framework.md | 10 ++++- papers/thesis_2026-10/ch4_data_methods.md | 8 +++- 5 files changed, 72 insertions(+), 31 deletions(-) diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index a566ff6..dcb81e7 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -263,3 +263,12 @@ A fresh reviewer re-checked every number and citation (21 checks in Chapter 4, 1 - **Smaller:** Table 5.3 caption, 61/39 percent for Tokopedia's account lines, Grab revenue 0.60, Momentum Works 3.5 labelled as calculated, cohort groups described as cumulative, E6 threshold stated, E7 second version inside the limit, rank correlation and standard error defined, "about 750 thousand" for the cohort check, Table 4.3 gained a row for adjacent figures, checksum claim narrowed, Chapter 3's "no national total" reconciled with the three-platform sum, figure axis and caption fixes, Figure 5.3 now reads its values from tables. Not changed, for the researcher: the second-run market result stays secondary; Tokopedia's "15 million merchants" is a company statement not checked in the repository (said so in 5.9); full company names for the tickers in A.1 are still to be added (ASAPQ not identified from the repository). + +### Chapters 1 to 4: openings rebuilt so each chapter stands alone (13 Oct 2026) + +The researcher's reading: Chapter 1 before 1.1 is long and repeats itself; paragraphs 12 and 13 should be plainer; Chapter 2 should begin with basics and justify its sections; each section should say what it shows and does not show; Chapter 3 should not start from the tail of Chapter 2; Chapter 4 should open with the reality of the data. +- **Chapter 1:** the text before 1.1 went from about 840 to about 620 words (wedge and Tokopedia merged; the stakes paragraph and the gap paragraph shortened; one sentence on statistical agencies dropped). Paragraph 12 now reads as a list of the tests in plain words; paragraph 13 leads with the finding. 1.3 is a roadmap that follows the argument (ideas, evidence, implications), not a bare list. +- **Chapter 2:** a three-paragraph opening says what is being studied and what knowledge is needed (why figures differ; how else the activity is measured; what the records are used for), which justifies 2.1 to 2.3. 2.1 now defines platform, platform economics, transaction value, revenue and revenue recognition in plain words before the literature. 2.2 and 2.3 open with basic definitions. Each section ends with what it shows and what it leaves out. 2.4 states the three open questions as a numbered list, each tied to the section that left it open. +- **Chapter 3:** opens fresh, with the three figures, the stand-in idea and the three questions restated in full and mapped to measures, hypotheses and sections. No sentence depends on having read Chapter 2. +- **Chapter 4:** opens with the state of the evidence (Shopee, Grab and Tokopedia disclosure; Blibli and Bukalapak incentives; BPS files, three rounds, no identifier, province only; the central bank's method not public), then why Indonesia still allows the tests, then the chapter map. "Sets the bar low on purpose" is stated. +- **Open for the researcher:** the YZU template has not been seen, so whether 1.3 and the heading pattern match it is unconfirmed. Section 2.4 and 3 now use a numbered list of three questions; say if you want prose instead. diff --git a/papers/thesis_2026-10/ch1_introduction.md b/papers/thesis_2026-10/ch1_introduction.md index 6fb6d8f..0a7e158 100644 --- a/papers/thesis_2026-10/ch1_introduction.md +++ b/papers/thesis_2026-10/ch1_introduction.md @@ -1,20 +1,18 @@ # Chapter 1. Introduction -Digital commerce has changed how goods and services are bought and sold in Indonesia. Sellers in small towns reach buyers across the country, businesses operate from mobile phones, and payments are made by QR code. The figures used to measure this activity, however, differ on both its size and its growth. +Digital commerce has changed how goods and services are bought and sold in Indonesia. Sellers in small towns reach buyers across the country, businesses run from mobile phones, and customers pay by QR code. The figures that measure this activity disagree on both its size and its growth. -In 2023, traders at Tanah Abang, a major wholesale market in Jakarta, reported that their customers had moved to sellers on TikTok Shop (AFP 2023). The government then banned transactions on social media platforms, and TikTok Shop closed in Indonesia shortly afterwards. The national statistics office, BPS, later reported that online sales rose by about 41 percent that year (BPS 2025). The central bank's measure of online shopping fell by about 5 percent over the same period (Bank Indonesia 2025). The two official figures moved in opposite directions. +In 2023, traders at Tanah Abang, a major wholesale market in Jakarta, said their customers had moved to sellers on TikTok Shop (AFP 2023). The government then banned transactions on social media platforms, and TikTok Shop closed in Indonesia soon after. That year the national statistics office, BPS, reported that online sales rose by about 41 percent (BPS 2025). The central bank's measure of online shopping fell by about 5 percent (Bank Indonesia 2025). The two official figures moved in opposite directions. -Tokopedia, an Indonesian online marketplace, shows the same split inside a single company. Between 2022 and 2023, its revenue rose by 53 percent while the value of transactions on the platform fell by 9 percent. Both figures come from the company's own reports (GoTo Gojek Tokopedia 2024). Measured by revenue, the platform grew; measured by transaction value, it contracted. +Tokopedia, an Indonesian online marketplace, shows the same split inside one company. Between 2022 and 2023, its revenue rose by 53 percent while the value of transactions on the platform, its transaction value, fell by 9 percent (GoTo Gojek Tokopedia 2024). The two differ because a platform keeps only part of each sale, as commissions and fees. The rest is the seller's receipts, the driver's fare or another amount that passes through. This thesis calls the difference the invisible wedge. The term means what a revenue-only measure leaves out. It does not mean activity that goes unrecorded. -Revenue and transaction value differ because a platform keeps only part of each sale, and that part can change. A platform that carries 100 units of transaction value may keep only a small part as its own revenue, in the form of commissions and fees. The rest belongs to the sale itself: the seller's receipts on a marketplace, the driver's fare on a ride-hailing app, and other amounts that pass through. An analyst reading the platform's accounts sees the small part, while one measuring the commerce it carries sees the whole. This thesis calls the difference the invisible wedge. The term refers to what a revenue-only measure leaves out, not to activity that goes unrecorded. +The share a platform keeps is not fixed. When it rises, revenue can grow even while transaction value falls. In Tokopedia's accounts, about 60 percent of the extra revenue came from smaller discounts and incentives, which a platform pays for out of what it earns. -The share of each sale that a platform keeps is not fixed. When it rises, revenue can grow even while transaction value falls. In Tokopedia's accounts, about 60 percent of the extra revenue came from smaller discounts and incentives, which a platform pays for out of what it earns. +These figures carry weight beyond Indonesia. Businesses in 43 economies sold almost US$27 trillion online in 2022, by the United Nations' estimate (UNCTAD 2024), and platforms carry a growing part of that trade. Investors value the companies that run them largely on reported growth. If part of that growth is a platform keeping more of each sale, then part of the price is a pricing decision. Governments use the same figures to judge the size of their digital economies. A growing number now ask platforms to report what their sellers earn, or to collect tax from them. A rule built on platform records reaches only the sellers who use platforms. -Money and public decisions rest on figures like these, and not only in Indonesia. Businesses in 43 economies sold almost US$27 trillion online in 2022, by the United Nations' estimate (UNCTAD 2024), and platforms carry a growing part of that trade. The companies that run them are among the most valuable in the world, and they are valued largely on the growth they report. If part of that growth comes from a platform keeping more of each sale, and not from more commerce, then part of what investors pay for is a pricing decision. Governments face the same uncertainty. They use these figures to judge how large their digital economies are, and a growing number now ask platforms to report what their sellers earn, or to collect tax from them. A rule built on platform records, for instance, reaches only the sellers who use platforms. Each of these readers is acting on a number without knowing how far it can be relied on. +Research explains why the figures differ. Platforms set prices on both sides of a market. Accounting rules decide how much of each sale counts as revenue. National accounts separate a platform's own service from the commerce it carries. What the research does not show is when a figure stops moving with the activity it represents, or by how much. Answering that takes two things that are rarely found together: a platform's revenue and transaction value over several years, and a national count of online sellers whose underlying answers can be examined. -Existing research gives investors and governments limited help in checking the figures they use. It explains why the figures differ: platforms set prices on both sides of a market, accounting rules decide how much of each sale counts as revenue, and national accounts separate a platform's own service from the commerce it carries. Statistical agencies have also begun to set business surveys beside platform transaction values. What that research does not show is when a figure stops moving with the activity it is used to represent, or by how much. Answering that requires two things that are rarely available together: a platform's revenue and transaction value reported over several years, and a national count of online sellers whose underlying answers can be examined. - -Indonesia is one of the few markets where both kinds of evidence are available. Several of its platforms report their transaction value and their revenue each year, and BPS asks online sellers when they began selling and how their sales changed. The stakes are also concrete there. For 2024, BPS and the central bank give the two main official estimates of online sales, about US$81 billion and about US$31 billion (BPS 2025; Bank Indonesia 2025). The size of the market is uncertain by a factor of more than two. The government is nevertheless writing rules for that market, on platform commissions, on discounts and on tax. Its new levy on marketplace sellers would raise about US$60 million a year on one measure of the market and about US$300 million on another, before exemptions (Ministry of Finance of the Republic of Indonesia 2025; author's calculation). Indonesia is also a market where platform figures and survey figures are likely to part, because most of its online sellers work outside the marketplaces, through messaging and social media applications. The events of 2023 suggest that they did part. +Indonesia offers both. Several of its platforms report revenue and transaction value each year, and BPS asks online sellers when they began and how their sales changed. The stakes are concrete. For 2024, BPS and the central bank give the two main official estimates of online sales, about US$81 billion and about US$31 billion (BPS 2025; Bank Indonesia 2025). That is a gap of more than two to one. The government is writing rules for this market on platform commissions, discounts and tax. Its levy on marketplace sellers would raise about US$60 million a year on one measure of the market and about US$300 million on another, before exemptions (Ministry of Finance of the Republic of Indonesia 2025; author's calculation). Most online sellers work outside the marketplaces, through messaging and social media applications, so platform figures and survey figures are likely to part. The events of 2023 suggest that they did. ## 1.1 Research question and objectives @@ -24,9 +22,9 @@ Measuring what the current figures miss begins with one question: how much econo The research question is addressed through three objectives. The first is to measure the invisible wedge across the platform histories available from 2020 to 2025. The second is to reconcile selected large movements with disclosed changes in monetization, customer incentives, revenue recognition and reporting scope. The third is to compare the platform evidence with national e-commerce and payment figures, to test whether they give the same account of economic scale and growth. -Two hypotheses and an entry test ask whether revenue and the national figures move with the activity behind them. H1 states that a platform's revenue grows in proportion to its transaction value. H2 states that national e-commerce growth comes mainly from more businesses selling online, and less from higher sales per business. The entry test asks whether a rise in the count of online sellers comes from sellers entering for the first time. A further analysis estimates how many sellers a rule based on platform records can reach. +The thesis tests two hypotheses. H1 says that a platform's revenue grows in proportion to its transaction value. H2 says that national e-commerce growth comes mainly from more businesses selling online, and less from higher sales per business. An entry test then asks whether a rise in the count of online sellers comes from sellers entering for the first time. A further analysis estimates how many sellers a rule built on platform records can reach. -In brief, the thesis finds that revenue shows only a small part of the commerce platforms carry, and that platforms carry only about a fifth of online sales. Platform revenue also grew faster than the commerce behind it, mainly because platforms kept a larger share of each sale and not because buyers bought more. National growth came mainly from more sellers, as H2 states, but about half of the 2023 rise in the count is not explained by first-time entry. +In brief, revenue shows only a small part of the commerce that platforms carry, and in BPS's survey the marketplaces carry only about a fifth of online sales. Platform revenue also grew faster than the commerce behind it, mainly because platforms kept a larger share of each sale and not because buyers bought more. National growth came mainly from more sellers, as H2 states. But about half of the 2023 rise in the count of sellers is not explained by first-time entry. ## 1.2 Contributions @@ -42,4 +40,4 @@ Earlier work has already compared platform transaction values with national e-co ## 1.3 Structure of the thesis -Chapter 2 reviews the literature on platform revenue, the measurement of online selling and the use of platform records for tax. Chapter 3 defines the measures, develops the hypotheses and sets out the entry test. Chapter 4 describes the data and the tests, including how revenue movements are traced, how the platform evidence is compared with national figures, and how markets value platform growth. Chapter 5 presents the results, beginning with the platforms and moving to the national figures. Chapter 6 examines how far Indonesia's recent rules reach and what the findings imply for other countries. Chapter 7 concludes and states the limitations. +The thesis moves from ideas to evidence and then to what follows from it. Chapter 2 reviews why platform revenue differs from the commerce behind it, how online selling is measured, and what platform records can show. It ends with the questions the literature leaves open. Chapter 3 turns those questions into measures, two hypotheses and the entry test. Chapter 4 describes the data, which come from platform reports, BPS survey files and national and market figures, and the tests that use them. Chapter 5 gives the results, first for the platforms and then for the national figures. Chapter 6 asks how far Indonesia's recent rules reach and what the findings mean for other countries. Chapter 7 concludes and states the limits of the evidence. diff --git a/papers/thesis_2026-10/ch2_literature.md b/papers/thesis_2026-10/ch2_literature.md index a245cef..683401a 100644 --- a/papers/thesis_2026-10/ch2_literature.md +++ b/papers/thesis_2026-10/ch2_literature.md @@ -1,37 +1,59 @@ # Chapter 2. Literature Review -Platforms sit between the people who trade and the figures that measure their trade. They carry transactions between buyers and sellers and report only part of the value as their own revenue. They also keep records of every sale, which payment, statistical and administrative systems may hold too. Research explains each of these parts from a different angle. This chapter follows them in four steps: how platforms earn revenue, how the online economy is measured, what platform records can show, and what remains unanswered. +This thesis asks when a figure stops following the activity it is meant to describe. A platform's revenue is one such figure, and it is meant to describe the commerce the platform carries. A national count of online sellers is another, and it is meant to describe how many businesses sell online. Judging such figures takes three kinds of knowledge, and each comes from a different part of the literature. -## 2.1 Platform pricing and revenue recognition +The first is why a figure and its activity can differ at all. For platform revenue, the answer lies in how platforms set prices and how accountants record sales. The second is how else the activity can be measured, so that a figure has something to be checked against. Here the literature covers national accounts, business surveys and the methods behind them. The third is what the records and figures are used for. Governments now write tax rules on platform records, and what a rule can reach depends on what the records hold. -Platform economics explains why the commerce a platform carries can be much larger than its revenue. Platforms connect buyers with sellers or service providers and earn commissions, fees, advertising or logistics income. They do not earn the full value of each transaction. Because a platform must attract both sides of a market, it sets prices on each side, and it may charge one side little or nothing. Its revenue then need not follow the commerce it coordinates (Rochet and Tirole 2003; Caillaud and Jullien 2003; Parker and Van Alstyne 2005; Armstrong 2006; Evans and Schmalensee 2016). The business model matters as well: a platform that connects buyers and sellers reports a fee as revenue, while a reseller reports the whole sale (Hagiu and Wright 2015). +The chapter follows these three parts. Section 2.1 explains what a platform is and how it earns and reports revenue. Section 2.2 explains how online selling is measured and what can go wrong in the measure. Section 2.3 explains how platform records and information from third parties are used in tax. Each section ends by stating what it shows and what it leaves out. Section 2.4 collects what is left out into the questions this thesis answers. -Accounting rules decide how much of each sale becomes revenue. International Financial Reporting Standard 15 (IFRS 15) is the international standard for revenue. Under it, a company that acts as a principal reports the full payment from the customer as revenue. One that acts as an agent reports only what it earns from arranging the sale (International Accounting Standards Board 2014). Payments that a company makes to its own customers, such as discounts and vouchers, can reduce the revenue it reports (International Accounting Standards Board 2014). A study of internet firms shows that firms chose different ways of reporting revenue, including whether to report sales gross (Bowen, Davis and Rajgopal 2002). Fees, customer incentives, revenue recognition, service mix, acquisitions and changes in business scope can all move reported revenue without an equal change in commerce. +## 2.1 What a platform is and how it earns revenue -Revenue is not the only reported figure whose meaning depends on how it is computed. Comparable accounting information helps analysts use financial statements (De Franco, Kothari and Verdi 2011). Operating measures that companies report themselves, such as transaction value or active users, lose information when the way they are computed changes or goes unreported (Givoly, Li, Lourie and Nekrasov 2019). Investors look past revenue as well. In the early years of the internet, measures of activity carried information about a firm's value and future revenue beyond what its reported revenue showed (Trueman, Wong and Zhang 2000, 2001; Rajgopal, Venkatachalam and Kotha 2003). +A platform is a business that connects two groups and helps them deal with each other. Tokopedia connects shoppers with merchants, and Grab connects riders with drivers. Most platforms do not make or own what is sold. Platform economics is the field that studies how such businesses set prices and earn money. Its starting point is that a platform serves two groups at once, so it must price them together. -Together, these studies explain why the invisible wedge, the difference between transaction value and revenue, exists and can move. They do not show how large the wedge becomes in practice. +Two amounts describe a platform. Transaction value is the total that buyers spend through it. Revenue is the part the platform records as its own income. The difference between them is what Chapter 1 called the invisible wedge. -## 2.2 Measuring the online economy +Platform pricing explains why the difference can be large. A platform needs both groups to join. If few sellers join, buyers have little reason to come, and the same holds the other way round. So a platform may charge one group little or nothing and earn from the other. Its revenue then need not follow the commerce it carries (Rochet and Tirole 2003; Caillaud and Jullien 2003; Parker and Van Alstyne 2005; Armstrong 2006; Evans and Schmalensee 2016). The business model matters as well. A marketplace earns a fee from each sale, while a reseller buys goods and sells them on (Hagiu and Wright 2015). The reseller's revenue is the whole sale, and the marketplace's is the fee. -Measuring the wedge is a question of definitions and sources. Official frameworks already separate the commerce a platform carries from the service the platform provides. National accounting guidance treats the sale between buyer and seller as distinct from the platform's intermediation service. It does not count the full transaction value as the platform's output (Ahmad and Schreyer 2016; International Monetary Fund 2018; Organisation for Economic Co-operation and Development [OECD] 2023; United Nations et al. 2025). +Accounting rules also decide how much of each sale is revenue. Revenue recognition is the name for these rules. International Financial Reporting Standard 15 (IFRS 15) is the international standard for it. Under IFRS 15, a company acting as a principal reports the full payment from the customer as revenue. A company acting as an agent reports only what it earns from arranging the sale (International Accounting Standards Board 2014). Payments that a company makes to its own customers, such as discounts and vouchers, can reduce its reported revenue (International Accounting Standards Board 2014). A study of internet firms found that they chose different ways of reporting revenue, including whether to report sales gross (Bowen, Davis and Rajgopal 2002). So fees, customer incentives, accounting choices, service mix, acquisitions and changes in business scope can all move reported revenue without an equal change in commerce. -In practice, different producers publish different quantities. Industry reports size the digital economy of Southeast Asia by transaction value (Google, Temasek and Bain 2024). Statistics offices measure the online sales of surveyed businesses (BPS 2025). Listed platforms report the revenue they recognize, and central banks report the value of payments (Bank Indonesia 2025). The United Nations Conference on Trade and Development (UNCTAD) has shown that e-commerce values change with the definition used. It has also set business e-commerce sales beside the transaction values of online platforms (UNCTAD 2023, 2024). A difference between these figures is harmless while they move together. It matters when they part, because the choice of figure can then change the conclusion about scale and growth. Economists who check national income figures against satellite images of night-time lights follow the same logic: a figure should be tested against the activity it stands for (Henderson, Storeygard and Weil 2012). +Revenue is not the only reported figure whose meaning depends on how it is computed. Analysts use financial statements more easily when the accounting is comparable (De Franco, Kothari and Verdi 2011). Measures that companies choose to report, such as transaction value or active users, lose information when their definition changes or goes unreported (Givoly, Li, Lourie and Nekrasov 2019). Investors also look beyond revenue. In the early years of the internet, activity measures such as web traffic carried information about a firm's value and future revenue that reported revenue did not show (Trueman, Wong and Zhang 2000, 2001; Rajgopal, Venkatachalam and Kotha 2003). + +This literature shows why transaction value can exceed revenue and why the difference can move. It does not show how large the difference is for platforms in a market such as Indonesia. It also does not show whether the difference stays steady from year to year. + +## 2.2 How online selling is measured + +E-commerce means selling goods or services over the internet. No single number measures it, because different groups count different things. Listed platforms report the revenue they record and often their transaction value. Statistics offices survey businesses and report their online sales. Central banks report the value of payments. Industry reports estimate the size of whole markets from platform transaction values (Google, Temasek and Bain 2024; BPS 2025; Bank Indonesia 2025). Chapter 4 describes each source for Indonesia. + +Official frameworks already separate the commerce a platform carries from the service the platform provides. National accounts, the statistics that add up a country's production, income and spending, treat the sale between buyer and seller as distinct from the platform's intermediation service. They do not count the full transaction value as the platform's output (Ahmad and Schreyer 2016; International Monetary Fund 2018; Organisation for Economic Co-operation and Development [OECD] 2023; United Nations et al. 2025). + +The sources do not give the same number. The United Nations Conference on Trade and Development (UNCTAD) has shown that e-commerce values change with the definition used. It has also set business e-commerce sales beside the transaction values of online platforms (UNCTAD 2023, 2024). A difference between figures is harmless while they move together. It matters when they part, because the choice of figure can then change the conclusion about scale and growth. Economists who check national income figures against satellite images of night-time lights follow the same logic: a figure should be tested against the activity it stands for (Henderson, Storeygard and Weil 2012). One national figure needs this test in particular: the growth of online sales. It can come from existing sellers selling more or from more sellers entering, and so it can be split between the two. Trade economists divide the growth of exports in the same way (Hummels and Klenow 2005). -The split of national growth rests on a count of online sellers, and that count has a weakness of its own. It depends on how the survey finds sellers, and a change in design can break a series in a way that looks like a change in the economy (van den Brakel, Smith and Compton 2008; van den Brakel, Zhang and Tam 2020). Respondents also misdate events, and they tend to place them closer to the interview than they were (Neter and Waksberg 1964). Exit among small firms is more common for young firms and young owners (McKenzie and Paffhausen 2019), which matters when a count has to be corrected for sellers who stopped. +The split of national growth rests on a count of online sellers, and that count has a weakness of its own. It depends on how the survey finds sellers. A change in design can break a series in a way that looks like a change in the economy (van den Brakel, Smith and Compton 2008; van den Brakel, Zhang and Tam 2020). Respondents also misdate events, and they tend to place them closer to the interview than they were (Neter and Waksberg 1964). Exit among small firms is more common for young firms and young owners (McKenzie and Paffhausen 2019), which matters when a count has to be corrected for sellers who stopped. -## 2.3 Digital records and third-party information +This literature shows that official frameworks treat commerce and platform service as different things. It shows that figures from different sources need not agree, and that a seller count can break when a survey changes. It does not show whether platform figures and national figures move together in a given country. It also does not show what a rise in a seller count contains. -Surveys are not the only source on sellers. Transactions that do not enter a platform's revenue still leave detailed records in its system. Sellers and service providers may be small, self-employed or weakly represented in conventional financial records, yet the platform records what they sell. This differs from the shadow-economy setting, where reliable records may be sparse (La Porta and Shleifer 2014; Ulyssea 2018). Digital records can carry information that conventional records lack (Berg, Burg, Gombovic and Puri 2020), and platform-mediated work leaves traces in financial and administrative data (Barrios, Hochberg and Yi 2022; Denes, Lagaras and Tsoutsoura 2025). +## 2.3 Platform records and third-party information -A platform is a third party to its sellers' tax affairs, and information from a third party changes how people comply with tax. Audit experiments in Denmark, and studies of the value-added tax and of receipt lotteries, show that reports from third parties help compliance (Kleven et al. 2011; Pomeranz 2015; Naritomi 2019; Kleven, Kreiner and Saez 2016; Slemrod 2019). Tax withheld through a third party rests on the same information (Brockmeyer and Hernandez 2016). +Surveys are not the only source on sellers. A platform keeps a record of every sale: who sold, what, for how much and when. Third-party information is a report about a taxpayer made by someone else, such as an employer, a bank or a platform. Tax authorities value it because it does not depend on the taxpayer's own account. -Rules for platforms apply this idea. The OECD, a group of mostly high-income countries, published model rules under which platforms report what their sellers earn (OECD 2020). The European Union's DAC7 directive applies them (European Union 2021). Indonesia's marketplace tax rule goes further: it makes marketplaces collect tax from their sellers, not only report on them (Ministry of Finance of the Republic of Indonesia 2025). What a rule can reach depends on the records it uses. +Platform records matter most where other records are thin. Sellers and service providers may be small, self-employed or weakly represented in conventional financial records, yet the platform records what they sell. This differs from the shadow-economy setting, where reliable records may be sparse (La Porta and Shleifer 2014; Ulyssea 2018). Digital records can carry information that conventional records lack (Berg, Burg, Gombovic and Puri 2020), and platform-mediated work leaves traces in financial and administrative data (Barrios, Hochberg and Yi 2022; Denes, Lagaras and Tsoutsoura 2025). + +Information from a third party also changes how people comply with tax. Audit experiments in Denmark, and studies of the value-added tax and of receipt lotteries, show that reports from third parties help compliance (Kleven et al. 2011; Pomeranz 2015; Naritomi 2019; Kleven, Kreiner and Saez 2016; Slemrod 2019). Tax withheld through a third party rests on the same information (Brockmeyer and Hernandez 2016). + +Rules for platforms apply this idea. The OECD, a group of mostly high-income countries, published model rules under which platforms report what their sellers earn (OECD 2020). The European Union's DAC7 directive applies them (European Union 2021). Indonesia's marketplace tax rule goes further: it makes marketplaces collect tax from their sellers, not only report on them (Ministry of Finance of the Republic of Indonesia 2025). + +This literature shows that third-party reports help compliance and that platform rules apply the idea. It does not show how many of a country's online sellers such a rule can reach. It also does not show how much of the market the platform records cover. ## 2.4 The research gap -Existing research explains these parts separately. Platform economics explains why transaction value can exceed revenue. Accounting explains why the wedge can move. Official frameworks explain why the two should not be treated as the same quantity. Survey methods explain why a count of sellers can break when its design changes. Research on digital records explains why some activity outside revenue stays observable in platform records. +Section 2.1 explained why transaction value can exceed revenue and why accounting can move the difference. Section 2.2 explained how online selling is measured and how a count of sellers can break. Section 2.3 explained how third-party records are used in tax. Each field answers its own question. None measures how closely platform figures follow the activity behind them, or whether national figures do. + +Three questions remain open. + +1. **How far does revenue follow commerce?** Section 2.1 gives the reasons why a platform's revenue can part from the commerce it carries. It does not say when this happens or by how much. +2. **Do platform figures follow national growth?** Section 2.2 shows that figures can differ. Whether platform growth tracks national growth depends on where national growth comes from, and on whether a count of online sellers keeps pace with entry. +3. **How far do rules built on platform records reach?** Section 2.3 shows that platform records can support tax rules. It does not say how much of the online economy those records cover. -Three questions remain open. The first is when a figure stops moving with the activity it is used to represent, and by how much. The second is whether platform figures follow national growth. That depends on where the growth comes from, and on whether a national count of online sellers keeps pace with entry. The third is how far rules built on platform records reach. Answering them requires a platform's revenue and transaction value over several years, and a national survey whose underlying answers can be examined. Indonesia offers both. Chapter 3 turns the questions into measures and hypotheses. +Answering them takes a platform's revenue and transaction value over several years, and a national survey whose underlying answers can be examined. Indonesia offers both. Chapter 3 turns the three questions into measures and hypotheses. diff --git a/papers/thesis_2026-10/ch3_framework.md b/papers/thesis_2026-10/ch3_framework.md index a464f08..6317a5a 100644 --- a/papers/thesis_2026-10/ch3_framework.md +++ b/papers/thesis_2026-10/ch3_framework.md @@ -1,10 +1,16 @@ # Chapter 3. Framework and Hypotheses -Chapter 2 left three questions open. The first two rest on one idea: a figure is used as a stand-in for the activity behind it. Three figures sit in a chain. A platform's revenue is the smallest, the commerce the platform carries is larger, and the online commerce of the whole country is larger still. Each figure stands in for the next one out. Investors read revenue as a sign of the commerce a platform carries. Platform figures are then read as a sign of what is happening in the national total. +Three figures describe the same online economy at three scales. A platform's revenue is what it keeps from the sales it handles. Its transaction value is the total of those sales. National online commerce is the total across all sellers, whether or not they use a platform. Each figure is used as a stand-in for the next one out. Investors read revenue as a sign of the commerce a platform carries. Platform figures are then read as a sign of what is happening in the national total. [Figure 3.1 to be drawn: three nested circles, with revenue inside platform commerce inside all online commerce, H1 on the inner link and H2 on the outer link.] -A stand-in works only while a condition holds. For the inner link, the condition is that the share of each sale that the platform keeps stays steady, because then revenue follows the commerce. For the outer link, the condition is that national growth comes from sellers the platforms record. To check this, national growth is split into new and existing sellers using a count of online sellers. The count follows new selling only while the survey keeps reaching the same kinds of seller. This chapter defines the terms and measures, states a hypothesis for each link, and sets out the entry test of the count. +A stand-in works only while a condition holds. For the inner link, the condition is that the share of each sale that the platform keeps stays steady, because then revenue follows the commerce. For the outer link, the condition is that national growth comes from sellers the platforms record. To check this, national growth is split into new and existing sellers using a count of online sellers. The count follows new selling only while the survey keeps reaching the same kinds of seller. + +The thesis asks three questions about these links. This chapter turns each into a measure, a test or both. + +1. **How far does revenue follow commerce?** This is the inner link. It asks how much of the commerce a platform's revenue leaves out, and whether revenue keeps pace with the commerce from year to year. Section 3.1 defines the measures, and Section 3.2 states the hypothesis, H1. +2. **Do platform figures follow national growth?** This is the outer link. It asks whether national online growth comes mainly from more businesses selling or from higher sales per business, and whether a rise in the count of sellers means first-time entry. Section 3.2 states the hypothesis, H2, and Section 3.3 sets out the entry test. +3. **How far do rules built on platform records reach?** Governments use platform records to tax and regulate online selling, and a rule reaches only the sellers whom the records cover. Section 3.4 introduces the analysis, and Chapter 6 develops it. ## 3.1 Terms and measures diff --git a/papers/thesis_2026-10/ch4_data_methods.md b/papers/thesis_2026-10/ch4_data_methods.md index cb5017c..1e0569b 100644 --- a/papers/thesis_2026-10/ch4_data_methods.md +++ b/papers/thesis_2026-10/ch4_data_methods.md @@ -1,6 +1,12 @@ # Chapter 4. Data and Methods -Indonesia lets this thesis look at the same online economy from two sides. Platforms report what passes through them and what they keep. BPS, the national statistics office, asks the sellers themselves. This chapter describes the evidence on each side and the tests that use it. It starts with the platforms, where H1 is tested and revenue movements are traced. It then turns to the survey, where H2 and the entry test are examined. It ends with the comparisons and market values that bring the two sides together. +The evidence for this thesis is thinner than the question deserves, and it helps to say so first. No source shows the whole picture. Most platforms do not report the commerce they carry in Indonesia alone. Shopee, the largest, reports it only for all its markets together, so the Indonesian value used here is an outside estimate. Grab reports its Indonesian revenue but not its Indonesian transaction value. Only Tokopedia gives both for Indonesia, through the audited segment note of its parent company GoTo, and only for two years. Companies define transaction value in their own ways. Not all say what they pay out in discounts: Blibli does not, and Bukalapak's reports do not allow the parts to be separated. + +The national evidence is thin in the same way. BPS publishes a few totals, and the central bank does not publish its method. The survey files that show when each seller began selling were bought under a data-use agreement. They cover three rounds, carry no business identifier across rounds, and place sellers no more finely than the province. Nobody follows the same seller from year to year. + +Indonesia still allows what few markets do. Five platform series report revenue and transaction value in the same year, and three of them report what they pay out in incentives. BPS also asks each online seller when it began, so a rise in the seller count can be checked against entry. Most online selling there runs through messaging and social media, which makes the gap between platform figures and survey figures large enough to see. The tests are built to ask only what these sources can answer, and each limit is stated where it applies. That sets the bar low on purpose. The thesis cannot show everything, and whatever it does show is more than the published figures give. + +The chapter describes the evidence on each side and the tests that use it. It starts with the platforms, where H1 is tested and revenue movements are traced. It then turns to the survey, where H2 and the entry test are examined. It ends with the comparisons and market values that bring the two sides together. ## 4.1 Choosing the platforms From 961ecd3882ece21f61b7bf8a0d588719c6c458a1 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 13 Oct 2026 09:37:33 +0000 Subject: [PATCH 24/65] Thesis ch1-4: second pass on the openings after a linear reading (claims corrected, repetition removed) Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 10 ++++++++++ papers/thesis_2026-10/ch1_introduction.md | 8 ++++---- papers/thesis_2026-10/ch2_literature.md | 16 ++++++++-------- papers/thesis_2026-10/ch3_framework.md | 6 +++--- papers/thesis_2026-10/ch4_data_methods.md | 6 +++--- 5 files changed, 28 insertions(+), 18 deletions(-) diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index dcb81e7..a2057fb 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -272,3 +272,13 @@ The researcher's reading: Chapter 1 before 1.1 is long and repeats itself; parag - **Chapter 3:** opens fresh, with the three figures, the stand-in idea and the three questions restated in full and mapped to measures, hypotheses and sections. No sentence depends on having read Chapter 2. - **Chapter 4:** opens with the state of the evidence (Shopee, Grab and Tokopedia disclosure; Blibli and Bukalapak incentives; BPS files, three rounds, no identifier, province only; the central bank's method not public), then why Indonesia still allows the tests, then the chapter map. "Sets the bar low on purpose" is stated. - **Open for the researcher:** the YZU template has not been seen, so whether 1.3 and the heading pattern match it is unconfirmed. Section 2.4 and 3 now use a numbered list of three questions; say if you want prose instead. + +### Second pass on the openings, after the researcher asked for reasoning before editing (13 Oct 2026) + +The first pass ran mechanical checks only. A linear reading found: +- **Chapter 1 paragraph 3:** "The two differ because…" followed the 53 percent against minus 9 percent split and so seemed to explain the opposite movement. It explained only that the two are different amounts. Reworded; the movement is explained in the next paragraph. +- **Chapter 1:** paragraphs 7 and 8 and the 1.1 opening both said decisions rest on the figures. The government and rules material now sits once in paragraph 7 (money and rules) and once, concretely, in paragraph 8 (Indonesia, size and the levy). Paragraph 13 now says "in Indonesia" for the revenue finding, since it was not tested everywhere. +- **Chapter 2:** "explains why the difference can be large" was wrong (a fee share is large by definition); it now says revenue need not follow commerce, because pricing structure also matters. "Most platforms do not make or own what is sold" was false for resellers; fixed. The invisible wedge is no longer attributed to Chapter 1. "None shows" became "none of the studies reviewed here shows". The third kind of knowledge is now "how the records are used". +- **Chapter 3:** "the survey" was used before it was named (BPS). Question 2's elaboration did not match its heading; it now says why new sellers outside platform records matter. +- **Chapter 4:** "only Tokopedia gives both for Indonesia" was wrong for Blibli and Bukalapak, which are Indonesian companies; the opening now lists each series' scope. "Only one reports incentives" was wrong (Tokopedia, Grab and GoTo on-demand do; Blibli and Bukalapak do not). "Nobody follows the same seller" became "the files cannot". One sentence I wrote ("no database holds these figures") could not be checked and was removed. +- **Structure:** the proposal's Chapter 1 had only 1.1 and 1.2. Section 1.3 is my addition for the thesis, so its form is unconfirmed against the YZU template. The proposal's research gap named three things (how large, whether it persists, whether the choice changes the conclusion), not the three questions now in 2.4 and Chapter 3; Question 3 (rules) comes from the thesis extension. Flagged to the researcher. diff --git a/papers/thesis_2026-10/ch1_introduction.md b/papers/thesis_2026-10/ch1_introduction.md index 0a7e158..5b6b3a9 100644 --- a/papers/thesis_2026-10/ch1_introduction.md +++ b/papers/thesis_2026-10/ch1_introduction.md @@ -4,15 +4,15 @@ Digital commerce has changed how goods and services are bought and sold in Indon In 2023, traders at Tanah Abang, a major wholesale market in Jakarta, said their customers had moved to sellers on TikTok Shop (AFP 2023). The government then banned transactions on social media platforms, and TikTok Shop closed in Indonesia soon after. That year the national statistics office, BPS, reported that online sales rose by about 41 percent (BPS 2025). The central bank's measure of online shopping fell by about 5 percent (Bank Indonesia 2025). The two official figures moved in opposite directions. -Tokopedia, an Indonesian online marketplace, shows the same split inside one company. Between 2022 and 2023, its revenue rose by 53 percent while the value of transactions on the platform, its transaction value, fell by 9 percent (GoTo Gojek Tokopedia 2024). The two differ because a platform keeps only part of each sale, as commissions and fees. The rest is the seller's receipts, the driver's fare or another amount that passes through. This thesis calls the difference the invisible wedge. The term means what a revenue-only measure leaves out. It does not mean activity that goes unrecorded. +Tokopedia, an Indonesian online marketplace, shows the same split inside one company. Between 2022 and 2023, its revenue rose by 53 percent while the value of transactions on the platform, its transaction value, fell by 9 percent (GoTo Gojek Tokopedia 2024). The two are different amounts. A platform keeps only part of each sale, as commissions and fees. The rest is the seller's receipts, the driver's fare or another amount that passes through. This thesis calls the difference the invisible wedge. The term means what a revenue-only measure leaves out. It does not mean activity that goes unrecorded. The share a platform keeps is not fixed. When it rises, revenue can grow even while transaction value falls. In Tokopedia's accounts, about 60 percent of the extra revenue came from smaller discounts and incentives, which a platform pays for out of what it earns. -These figures carry weight beyond Indonesia. Businesses in 43 economies sold almost US$27 trillion online in 2022, by the United Nations' estimate (UNCTAD 2024), and platforms carry a growing part of that trade. Investors value the companies that run them largely on reported growth. If part of that growth is a platform keeping more of each sale, then part of the price is a pricing decision. Governments use the same figures to judge the size of their digital economies. A growing number now ask platforms to report what their sellers earn, or to collect tax from them. A rule built on platform records reaches only the sellers who use platforms. +Money and rules rest on these figures, and not only in Indonesia. Businesses in 43 economies sold almost US$27 trillion online in 2022, by the United Nations' estimate (UNCTAD 2024), and platforms carry a growing part of that trade. Investors value the companies that run them largely on reported growth. If part of that growth is a platform keeping more of each sale, then part of the price is a pricing decision. Governments use the same figures to size their digital economies and to tax online sellers. A rule built on platform records reaches only the sellers who use platforms. Research explains why the figures differ. Platforms set prices on both sides of a market. Accounting rules decide how much of each sale counts as revenue. National accounts separate a platform's own service from the commerce it carries. What the research does not show is when a figure stops moving with the activity it represents, or by how much. Answering that takes two things that are rarely found together: a platform's revenue and transaction value over several years, and a national count of online sellers whose underlying answers can be examined. -Indonesia offers both. Several of its platforms report revenue and transaction value each year, and BPS asks online sellers when they began and how their sales changed. The stakes are concrete. For 2024, BPS and the central bank give the two main official estimates of online sales, about US$81 billion and about US$31 billion (BPS 2025; Bank Indonesia 2025). That is a gap of more than two to one. The government is writing rules for this market on platform commissions, discounts and tax. Its levy on marketplace sellers would raise about US$60 million a year on one measure of the market and about US$300 million on another, before exemptions (Ministry of Finance of the Republic of Indonesia 2025; author's calculation). Most online sellers work outside the marketplaces, through messaging and social media applications, so platform figures and survey figures are likely to part. The events of 2023 suggest that they did. +Indonesia offers both. Several of its platforms report revenue and transaction value each year, and BPS asks online sellers when they began and how their sales changed. The stakes are concrete. On size, the same two official sources differ as much as they do on growth. For 2024, BPS and the central bank give about US$81 billion and about US$31 billion for online sales (BPS 2025; Bank Indonesia 2025), a difference of more than two to one. The government is writing rules for this market on platform commissions, discounts and tax. Its levy on marketplace sellers would raise about US$60 million a year on one measure of the market and about US$300 million on another, before exemptions (Ministry of Finance of the Republic of Indonesia 2025; author's calculation). Most online sellers work outside the marketplaces, through messaging and social media applications, so platform figures and survey figures are likely to part. The events of 2023 suggest that they did. ## 1.1 Research question and objectives @@ -24,7 +24,7 @@ The research question is addressed through three objectives. The first is to mea The thesis tests two hypotheses. H1 says that a platform's revenue grows in proportion to its transaction value. H2 says that national e-commerce growth comes mainly from more businesses selling online, and less from higher sales per business. An entry test then asks whether a rise in the count of online sellers comes from sellers entering for the first time. A further analysis estimates how many sellers a rule built on platform records can reach. -In brief, revenue shows only a small part of the commerce that platforms carry, and in BPS's survey the marketplaces carry only about a fifth of online sales. Platform revenue also grew faster than the commerce behind it, mainly because platforms kept a larger share of each sale and not because buyers bought more. National growth came mainly from more sellers, as H2 states. But about half of the 2023 rise in the count of sellers is not explained by first-time entry. +In brief, revenue shows only a small part of the commerce that platforms carry, and in BPS's survey the marketplaces carry only about a fifth of online sales value. In Indonesia, platform revenue also grew faster than the commerce behind it, mainly because platforms kept a larger share of each sale and not because buyers bought more. National growth came mainly from more sellers, as H2 states. But about half of the 2023 rise in the count of sellers is not explained by first-time entry. ## 1.2 Contributions diff --git a/papers/thesis_2026-10/ch2_literature.md b/papers/thesis_2026-10/ch2_literature.md index 683401a..73ba39a 100644 --- a/papers/thesis_2026-10/ch2_literature.md +++ b/papers/thesis_2026-10/ch2_literature.md @@ -1,24 +1,24 @@ # Chapter 2. Literature Review -This thesis asks when a figure stops following the activity it is meant to describe. A platform's revenue is one such figure, and it is meant to describe the commerce the platform carries. A national count of online sellers is another, and it is meant to describe how many businesses sell online. Judging such figures takes three kinds of knowledge, and each comes from a different part of the literature. +This thesis asks when a figure stops following the activity it is meant to describe. A platform's revenue is one such figure, and it is meant to describe the commerce the platform carries. A national count of online sellers is another, and it is meant to describe how many businesses sell online. Judging such figures, and what follows from them, takes three kinds of knowledge. Each comes from a different part of the literature. -The first is why a figure and its activity can differ at all. For platform revenue, the answer lies in how platforms set prices and how accountants record sales. The second is how else the activity can be measured, so that a figure has something to be checked against. Here the literature covers national accounts, business surveys and the methods behind them. The third is what the records and figures are used for. Governments now write tax rules on platform records, and what a rule can reach depends on what the records hold. +The first is why a figure and its activity can differ at all. For platform revenue, the answer lies in how platforms set prices and how accountants record sales. The second is how else the activity can be measured, so that a figure has something to be checked against. Here the literature covers national accounts, business surveys and the methods behind them. The third is how the records are used. Governments now write tax rules on platform records, and what a rule can reach depends on what the records hold. The chapter follows these three parts. Section 2.1 explains what a platform is and how it earns and reports revenue. Section 2.2 explains how online selling is measured and what can go wrong in the measure. Section 2.3 explains how platform records and information from third parties are used in tax. Each section ends by stating what it shows and what it leaves out. Section 2.4 collects what is left out into the questions this thesis answers. ## 2.1 What a platform is and how it earns revenue -A platform is a business that connects two groups and helps them deal with each other. Tokopedia connects shoppers with merchants, and Grab connects riders with drivers. Most platforms do not make or own what is sold. Platform economics is the field that studies how such businesses set prices and earn money. Its starting point is that a platform serves two groups at once, so it must price them together. +A platform is a business that connects two groups and helps them deal with each other. Tokopedia connects shoppers with merchants, and Grab connects riders with drivers. Platforms such as these do not make or own what is sold. Platform economics is the field that studies how such businesses set prices and earn money. Its starting point is that a platform serves two groups at once, so it must price them together (Rochet and Tirole 2003; Evans and Schmalensee 2016). -Two amounts describe a platform. Transaction value is the total that buyers spend through it. Revenue is the part the platform records as its own income. The difference between them is what Chapter 1 called the invisible wedge. +Two amounts describe a platform. Transaction value is the total that buyers spend through it. Revenue is the part the platform records as its own income. The difference between them is what this thesis calls the invisible wedge. -Platform pricing explains why the difference can be large. A platform needs both groups to join. If few sellers join, buyers have little reason to come, and the same holds the other way round. So a platform may charge one group little or nothing and earn from the other. Its revenue then need not follow the commerce it carries (Rochet and Tirole 2003; Caillaud and Jullien 2003; Parker and Van Alstyne 2005; Armstrong 2006; Evans and Schmalensee 2016). The business model matters as well. A marketplace earns a fee from each sale, while a reseller buys goods and sells them on (Hagiu and Wright 2015). The reseller's revenue is the whole sale, and the marketplace's is the fee. +Platform pricing explains why revenue need not follow the commerce a platform carries. A platform needs both groups to join. If few sellers join, buyers have little reason to come, and the same holds the other way round. So a platform may charge one group little or nothing and earn from the other. How much it earns then depends on its price structure as well as on the commerce it carries (Rochet and Tirole 2003; Caillaud and Jullien 2003; Parker and Van Alstyne 2005; Armstrong 2006; Evans and Schmalensee 2016). The business model matters as well. A marketplace earns a fee from each sale, while a reseller buys goods and sells them on (Hagiu and Wright 2015). The reseller's revenue is the whole sale, and the marketplace's is the fee. Accounting rules also decide how much of each sale is revenue. Revenue recognition is the name for these rules. International Financial Reporting Standard 15 (IFRS 15) is the international standard for it. Under IFRS 15, a company acting as a principal reports the full payment from the customer as revenue. A company acting as an agent reports only what it earns from arranging the sale (International Accounting Standards Board 2014). Payments that a company makes to its own customers, such as discounts and vouchers, can reduce its reported revenue (International Accounting Standards Board 2014). A study of internet firms found that they chose different ways of reporting revenue, including whether to report sales gross (Bowen, Davis and Rajgopal 2002). So fees, customer incentives, accounting choices, service mix, acquisitions and changes in business scope can all move reported revenue without an equal change in commerce. Revenue is not the only reported figure whose meaning depends on how it is computed. Analysts use financial statements more easily when the accounting is comparable (De Franco, Kothari and Verdi 2011). Measures that companies choose to report, such as transaction value or active users, lose information when their definition changes or goes unreported (Givoly, Li, Lourie and Nekrasov 2019). Investors also look beyond revenue. In the early years of the internet, activity measures such as web traffic carried information about a firm's value and future revenue that reported revenue did not show (Trueman, Wong and Zhang 2000, 2001; Rajgopal, Venkatachalam and Kotha 2003). -This literature shows why transaction value can exceed revenue and why the difference can move. It does not show how large the difference is for platforms in a market such as Indonesia. It also does not show whether the difference stays steady from year to year. +The studies reviewed here explain why transaction value can exceed revenue and why the difference can move. They do not measure how large the difference is for platforms in a market such as Indonesia. They also do not show whether it stays steady from year to year. ## 2.2 How online selling is measured @@ -38,7 +38,7 @@ This literature shows that official frameworks treat commerce and platform servi Surveys are not the only source on sellers. A platform keeps a record of every sale: who sold, what, for how much and when. Third-party information is a report about a taxpayer made by someone else, such as an employer, a bank or a platform. Tax authorities value it because it does not depend on the taxpayer's own account. -Platform records matter most where other records are thin. Sellers and service providers may be small, self-employed or weakly represented in conventional financial records, yet the platform records what they sell. This differs from the shadow-economy setting, where reliable records may be sparse (La Porta and Shleifer 2014; Ulyssea 2018). Digital records can carry information that conventional records lack (Berg, Burg, Gombovic and Puri 2020), and platform-mediated work leaves traces in financial and administrative data (Barrios, Hochberg and Yi 2022; Denes, Lagaras and Tsoutsoura 2025). +Platform records can help where other records are thin. Sellers and service providers may be small, self-employed or weakly represented in conventional financial records, yet the platform records what they sell. This differs from the shadow-economy setting, where reliable records may be sparse (La Porta and Shleifer 2014; Ulyssea 2018). Digital records can carry information that conventional records lack (Berg, Burg, Gombovic and Puri 2020), and platform-mediated work leaves traces in financial and administrative data (Barrios, Hochberg and Yi 2022; Denes, Lagaras and Tsoutsoura 2025). Information from a third party also changes how people comply with tax. Audit experiments in Denmark, and studies of the value-added tax and of receipt lotteries, show that reports from third parties help compliance (Kleven et al. 2011; Pomeranz 2015; Naritomi 2019; Kleven, Kreiner and Saez 2016; Slemrod 2019). Tax withheld through a third party rests on the same information (Brockmeyer and Hernandez 2016). @@ -48,7 +48,7 @@ This literature shows that third-party reports help compliance and that platform ## 2.4 The research gap -Section 2.1 explained why transaction value can exceed revenue and why accounting can move the difference. Section 2.2 explained how online selling is measured and how a count of sellers can break. Section 2.3 explained how third-party records are used in tax. Each field answers its own question. None measures how closely platform figures follow the activity behind them, or whether national figures do. +Section 2.1 explained why transaction value can exceed revenue and why accounting can move the difference. Section 2.2 explained how online selling is measured and how a count of sellers can break. Section 2.3 explained how third-party records are used in tax. Each field answers its own question. None of the studies reviewed here measures how closely platform figures follow the activity behind them, or whether national figures do. Three questions remain open. diff --git a/papers/thesis_2026-10/ch3_framework.md b/papers/thesis_2026-10/ch3_framework.md index 6317a5a..5203d4d 100644 --- a/papers/thesis_2026-10/ch3_framework.md +++ b/papers/thesis_2026-10/ch3_framework.md @@ -4,13 +4,13 @@ Three figures describe the same online economy at three scales. A platform's rev [Figure 3.1 to be drawn: three nested circles, with revenue inside platform commerce inside all online commerce, H1 on the inner link and H2 on the outer link.] -A stand-in works only while a condition holds. For the inner link, the condition is that the share of each sale that the platform keeps stays steady, because then revenue follows the commerce. For the outer link, the condition is that national growth comes from sellers the platforms record. To check this, national growth is split into new and existing sellers using a count of online sellers. The count follows new selling only while the survey keeps reaching the same kinds of seller. +A stand-in works only while a condition holds. For the inner link, the condition is that the share of each sale that the platform keeps stays steady, because then revenue follows the commerce. For the outer link, the condition is that national growth comes from sellers the platforms record. To check this, national growth is split into new and existing sellers, using the count of online sellers from BPS, Indonesia's national statistics office. The count follows new selling only while BPS's survey keeps reaching the same kinds of seller. The thesis asks three questions about these links. This chapter turns each into a measure, a test or both. 1. **How far does revenue follow commerce?** This is the inner link. It asks how much of the commerce a platform's revenue leaves out, and whether revenue keeps pace with the commerce from year to year. Section 3.1 defines the measures, and Section 3.2 states the hypothesis, H1. -2. **Do platform figures follow national growth?** This is the outer link. It asks whether national online growth comes mainly from more businesses selling or from higher sales per business, and whether a rise in the count of sellers means first-time entry. Section 3.2 states the hypothesis, H2, and Section 3.3 sets out the entry test. -3. **How far do rules built on platform records reach?** Governments use platform records to tax and regulate online selling, and a rule reaches only the sellers whom the records cover. Section 3.4 introduces the analysis, and Chapter 6 develops it. +2. **Do platform figures follow national growth?** This is the outer link. National online growth can come from existing sellers selling more or from new sellers entering, and new sellers often work in channels that platform records do not cover. The question asks which kind of growth dominates, and whether a rise in the count of sellers really means first-time entry. Section 3.2 states the hypothesis, H2, and Section 3.3 sets out the entry test. +3. **How far do rules built on platform records reach?** Governments use platform records to tax and regulate online selling, and a rule reaches only the sellers that the records cover. Section 3.4 introduces the analysis, and Chapter 6 develops it. ## 3.1 Terms and measures diff --git a/papers/thesis_2026-10/ch4_data_methods.md b/papers/thesis_2026-10/ch4_data_methods.md index 1e0569b..f42e512 100644 --- a/papers/thesis_2026-10/ch4_data_methods.md +++ b/papers/thesis_2026-10/ch4_data_methods.md @@ -1,10 +1,10 @@ # Chapter 4. Data and Methods -The evidence for this thesis is thinner than the question deserves, and it helps to say so first. No source shows the whole picture. Most platforms do not report the commerce they carry in Indonesia alone. Shopee, the largest, reports it only for all its markets together, so the Indonesian value used here is an outside estimate. Grab reports its Indonesian revenue but not its Indonesian transaction value. Only Tokopedia gives both for Indonesia, through the audited segment note of its parent company GoTo, and only for two years. Companies define transaction value in their own ways. Not all say what they pay out in discounts: Blibli does not, and Bukalapak's reports do not allow the parts to be separated. +The evidence for this thesis is thinner than the question deserves, and it helps to say so first. No source shows the whole picture, and each platform reports in its own way. Tokopedia reports an Indonesian segment, but only for two years. Blibli's retail segment includes online travel. Bukalapak reports for its whole group, and GoTo's on-demand segment included Vietnam until 2024. Grab reports its Indonesian revenue but not its Indonesian transaction value. Shopee, the largest, reports transaction value only for all its markets together, so the Indonesian value used here is an outside estimate. Companies define transaction value in their own ways, and they differ on discounts: Blibli reports nothing on incentives, and Bukalapak's reports do not allow the parts to be separated. Each figure was read from a filing or release, and the extraction tables keep the source file and the quoted passage beside every value. -The national evidence is thin in the same way. BPS publishes a few totals, and the central bank does not publish its method. The survey files that show when each seller began selling were bought under a data-use agreement. They cover three rounds, carry no business identifier across rounds, and place sellers no more finely than the province. Nobody follows the same seller from year to year. +The national evidence is thin in the same way. BPS publishes a few totals, and the central bank does not publish its method. The survey files that show when each seller began selling were bought from BPS under a data-use agreement. They cover three rounds, carry no business identifier across rounds, and place sellers no more finely than the province. The files cannot follow the same seller from one round to the next. -Indonesia still allows what few markets do. Five platform series report revenue and transaction value in the same year, and three of them report what they pay out in incentives. BPS also asks each online seller when it began, so a rise in the seller count can be checked against entry. Most online selling there runs through messaging and social media, which makes the gap between platform figures and survey figures large enough to see. The tests are built to ask only what these sources can answer, and each limit is stated where it applies. That sets the bar low on purpose. The thesis cannot show everything, and whatever it does show is more than the published figures give. +Indonesia still allows what few markets do. Five platform series report revenue and transaction value in the same year, and three of them report what they pay out in incentives. BPS also asks each online seller when it began, so a rise in the seller count can be checked against entry. Most online selling there runs through messaging and social media, which makes the difference between platform figures and survey figures large enough to see. The tests are built to ask only what these sources can answer, and each limit is stated where it applies. That sets the bar low on purpose. The thesis cannot show everything, and what it does show adds to what the published figures give. The chapter describes the evidence on each side and the tests that use it. It starts with the platforms, where H1 is tested and revenue movements are traced. It then turns to the survey, where H2 and the entry test are examined. It ends with the comparisons and market values that bring the two sides together. From 33a1c54eb136811bd0453daaf9698003c23bafdf Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 13 Oct 2026 09:41:27 +0000 Subject: [PATCH 25/65] Thesis ch1-4: cut repeated main points across paragraphs and chapters Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 9 +++++++++ papers/thesis_2026-10/ch1_introduction.md | 14 +++++++------- papers/thesis_2026-10/ch2_literature.md | 10 ++++------ papers/thesis_2026-10/ch3_framework.md | 4 ++-- papers/thesis_2026-10/ch4_data_methods.md | 8 ++++---- 5 files changed, 26 insertions(+), 19 deletions(-) diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index a2057fb..e1ff253 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -282,3 +282,12 @@ The first pass ran mechanical checks only. A linear reading found: - **Chapter 3:** "the survey" was used before it was named (BPS). Question 2's elaboration did not match its heading; it now says why new sellers outside platform records matter. - **Chapter 4:** "only Tokopedia gives both for Indonesia" was wrong for Blibli and Bukalapak, which are Indonesian companies; the opening now lists each series' scope. "Only one reports incentives" was wrong (Tokopedia, Grab and GoTo on-demand do; Blibli and Bukalapak do not). "Nobody follows the same seller" became "the files cannot". One sentence I wrote ("no database holds these figures") could not be checked and was removed. - **Structure:** the proposal's Chapter 1 had only 1.1 and 1.2. Section 1.3 is my addition for the thesis, so its form is unconfirmed against the YZU template. The proposal's research gap named three things (how large, whether it persists, whether the choice changes the conclusion), not the three questions now in 2.4 and Chapter 3; Question 3 (rules) comes from the thesis extension. Flagged to the researcher. + +### Repetition cut in Chapters 1 to 4 (13 Oct 2026) + +Main points that appeared more than once, and where each now lives: +- **Chapter 1:** the "figures differ" point is in paragraph 2 (growth) and paragraph 7 (size) only. The investor and government stakes are in paragraph 5 once; the "rule reaches only platform sellers" point moved to 1.2, where it belongs to the tax contribution. Paragraph 6 lost its three-item summary of the literature (Chapter 2's job). 1.2 lost its closing sentence (the 1.1 ambition) and its repeated "why revenue moved" and "revenue depends on the measure" sentences. The text before 1.1 is now about 560 words (from 840). +- **Chapter 2:** 2.4 no longer re-summarises 2.1 to 2.3, because each section already ends with what it shows and leaves out; the list items are one sentence each. +- **Chapter 3:** list items shortened. +- **Chapter 4:** the scope of each series is stated once, in Table 4.1; the opening gives the headline and the paragraph after the table no longer repeats it. The file limits are stated in the opening only as "cannot follow the same seller"; 4.4 keeps the detail. +- **Kept on purpose:** the short definitions of transaction value and revenue in Chapters 2 and 3 (each chapter must stand alone, which conflicts with "define once"; the researcher to decide), and the three questions in both 2.4 and Chapter 3. diff --git a/papers/thesis_2026-10/ch1_introduction.md b/papers/thesis_2026-10/ch1_introduction.md index 5b6b3a9..4c82567 100644 --- a/papers/thesis_2026-10/ch1_introduction.md +++ b/papers/thesis_2026-10/ch1_introduction.md @@ -8,11 +8,11 @@ Tokopedia, an Indonesian online marketplace, shows the same split inside one com The share a platform keeps is not fixed. When it rises, revenue can grow even while transaction value falls. In Tokopedia's accounts, about 60 percent of the extra revenue came from smaller discounts and incentives, which a platform pays for out of what it earns. -Money and rules rest on these figures, and not only in Indonesia. Businesses in 43 economies sold almost US$27 trillion online in 2022, by the United Nations' estimate (UNCTAD 2024), and platforms carry a growing part of that trade. Investors value the companies that run them largely on reported growth. If part of that growth is a platform keeping more of each sale, then part of the price is a pricing decision. Governments use the same figures to size their digital economies and to tax online sellers. A rule built on platform records reaches only the sellers who use platforms. +Money and rules rest on these figures, and not only in Indonesia. Businesses in 43 economies sold almost US$27 trillion online in 2022, by the United Nations' estimate (UNCTAD 2024), and platforms carry a growing part of that trade. Investors value the companies that run them largely on reported growth. If part of that growth is a platform keeping more of each sale, then part of the price is a pricing decision. Governments use the same figures to size their digital economies and to tax online sellers. -Research explains why the figures differ. Platforms set prices on both sides of a market. Accounting rules decide how much of each sale counts as revenue. National accounts separate a platform's own service from the commerce it carries. What the research does not show is when a figure stops moving with the activity it represents, or by how much. Answering that takes two things that are rarely found together: a platform's revenue and transaction value over several years, and a national count of online sellers whose underlying answers can be examined. +Research explains why the figures can differ, but not when a figure stops moving with the activity it represents, or by how much. Answering that takes two things that are rarely found together: a platform's revenue and transaction value over several years, and a national count of online sellers whose underlying answers can be examined. -Indonesia offers both. Several of its platforms report revenue and transaction value each year, and BPS asks online sellers when they began and how their sales changed. The stakes are concrete. On size, the same two official sources differ as much as they do on growth. For 2024, BPS and the central bank give about US$81 billion and about US$31 billion for online sales (BPS 2025; Bank Indonesia 2025), a difference of more than two to one. The government is writing rules for this market on platform commissions, discounts and tax. Its levy on marketplace sellers would raise about US$60 million a year on one measure of the market and about US$300 million on another, before exemptions (Ministry of Finance of the Republic of Indonesia 2025; author's calculation). Most online sellers work outside the marketplaces, through messaging and social media applications, so platform figures and survey figures are likely to part. The events of 2023 suggest that they did. +Indonesia offers both. Several of its platforms report revenue and transaction value each year, and BPS asks online sellers when they began and how their sales changed. The stakes are concrete. For 2024, BPS and the central bank give about US$81 billion and about US$31 billion for online sales (BPS 2025; Bank Indonesia 2025), a difference of more than two to one. The government is writing rules on platform commissions, discounts and tax. Its levy on marketplace sellers would raise about US$60 million a year on one measure of the market and about US$300 million on another, before exemptions (Ministry of Finance of the Republic of Indonesia 2025; author's calculation). Most online sellers work through messaging and social media, outside the marketplaces, so platform and survey figures are likely to part. The events of 2023 suggest that they did. ## 1.1 Research question and objectives @@ -28,13 +28,13 @@ In brief, revenue shows only a small part of the commerce that platforms carry, ## 1.2 Contributions -The thesis makes three contributions, one for each group that relies on these figures: investors, statistics offices, and the authorities that tax and regulate online selling. Together they show how much the figures miss, where it sits, and who is in it. +The thesis makes three contributions, one for each group that relies on these figures: investors, statistics offices, and the authorities that tax and regulate online selling. -For those who value platforms, the thesis shows how small a part of the commerce a platform's revenue records, and why that matters for growth and not only for scale. The larger the wedge is relative to revenue, the more a given change in fees or discounts moves revenue growth. It also traces why revenue moved, mostly to smaller discounts and higher fees. This gives investors a direct check on reported growth: whether it came from more commerce or from the platform keeping a larger share of each sale. +For those who value platforms, the thesis shows how small a part of the commerce a platform's revenue records. It shows why this matters for growth as well as scale: the larger the wedge is next to revenue, the more a small change in fees or discounts moves revenue growth. It also traces why revenue moved, which gives investors a check on whether reported growth came from more commerce or from a larger share of each sale. -For those who measure the economy, the thesis shows where the commerce outside the platforms sits and tests what a rise in the count of online sellers represents. Most online sellers work through messaging and social media, and most are small businesses that keep no financial statements. Each seller in the survey reports the year they began selling online, so the survey itself shows how much of a rise is first-time entry. The test was specified before the survey files were opened. +For those who measure the economy, the thesis shows where the commerce outside the platforms sits and tests what a rise in the count of online sellers represents. Most online sellers are small businesses that keep no financial statements. Each reports the year it began selling online, so the survey shows how much of a rise is first-time entry. The test was specified before the survey files were opened. -For those who design tax and regulation, the thesis measures how far rules based on platform records reach. It also sets each recent rule, from the cap on ride-hailing commissions to the limit on discounts, against the figure it acts on. Where most sellers work outside the platforms, such rules cover a minority of them, and the revenue they can raise depends on which measure of the market is correct. Several other countries have adopted similar rules, so the same question applies there. +For those who design tax and regulation, the thesis measures how far rules based on platform records reach. A rule built on platform records reaches only the sellers who use platforms, so where most sellers work outside them, such rules cover a minority. The thesis also sets each recent rule, from the cap on ride-hailing commissions to the limit on discounts, against the figure it acts on. Several other countries have adopted similar rules, so the same question applies there. Earlier work has already compared platform transaction values with national e-commerce statistics. The contribution of this thesis is the connected evidence for one country, drawn from the platforms' accounts, the sellers' own survey responses and the rules that act on those figures. diff --git a/papers/thesis_2026-10/ch2_literature.md b/papers/thesis_2026-10/ch2_literature.md index 73ba39a..ae81deb 100644 --- a/papers/thesis_2026-10/ch2_literature.md +++ b/papers/thesis_2026-10/ch2_literature.md @@ -48,12 +48,10 @@ This literature shows that third-party reports help compliance and that platform ## 2.4 The research gap -Section 2.1 explained why transaction value can exceed revenue and why accounting can move the difference. Section 2.2 explained how online selling is measured and how a count of sellers can break. Section 2.3 explained how third-party records are used in tax. Each field answers its own question. None of the studies reviewed here measures how closely platform figures follow the activity behind them, or whether national figures do. +Each field reviewed here answers its own question, and none measures how closely platform figures follow the activity behind them, or whether national figures do. Three questions remain open. -Three questions remain open. - -1. **How far does revenue follow commerce?** Section 2.1 gives the reasons why a platform's revenue can part from the commerce it carries. It does not say when this happens or by how much. -2. **Do platform figures follow national growth?** Section 2.2 shows that figures can differ. Whether platform growth tracks national growth depends on where national growth comes from, and on whether a count of online sellers keeps pace with entry. -3. **How far do rules built on platform records reach?** Section 2.3 shows that platform records can support tax rules. It does not say how much of the online economy those records cover. +1. **How far does revenue follow commerce?** Section 2.1 gives reasons why revenue can part from commerce, but not when or by how much. +2. **Do platform figures follow national growth?** Section 2.2 shows that figures can differ. Whether platform growth tracks national growth depends on where the growth comes from, and on whether a count of sellers keeps pace with entry. +3. **How far do rules built on platform records reach?** Section 2.3 shows that platform records can support tax rules, but not how much of the online economy they cover. Answering them takes a platform's revenue and transaction value over several years, and a national survey whose underlying answers can be examined. Indonesia offers both. Chapter 3 turns the three questions into measures and hypotheses. diff --git a/papers/thesis_2026-10/ch3_framework.md b/papers/thesis_2026-10/ch3_framework.md index 5203d4d..ce3746e 100644 --- a/papers/thesis_2026-10/ch3_framework.md +++ b/papers/thesis_2026-10/ch3_framework.md @@ -8,8 +8,8 @@ A stand-in works only while a condition holds. For the inner link, the condition The thesis asks three questions about these links. This chapter turns each into a measure, a test or both. -1. **How far does revenue follow commerce?** This is the inner link. It asks how much of the commerce a platform's revenue leaves out, and whether revenue keeps pace with the commerce from year to year. Section 3.1 defines the measures, and Section 3.2 states the hypothesis, H1. -2. **Do platform figures follow national growth?** This is the outer link. National online growth can come from existing sellers selling more or from new sellers entering, and new sellers often work in channels that platform records do not cover. The question asks which kind of growth dominates, and whether a rise in the count of sellers really means first-time entry. Section 3.2 states the hypothesis, H2, and Section 3.3 sets out the entry test. +1. **How far does revenue follow commerce?** This is the inner link: how much commerce revenue leaves out, and whether revenue keeps pace with commerce from year to year. Section 3.1 defines the measures, and Section 3.2 states H1. +2. **Do platform figures follow national growth?** This is the outer link. National online growth can come from existing sellers selling more or from new sellers entering, and new sellers often work in channels that platform records do not cover. The question asks which kind of growth dominates, and whether a rise in the count of sellers really means first-time entry. Section 3.2 states H2, and Section 3.3 sets out the entry test. 3. **How far do rules built on platform records reach?** Governments use platform records to tax and regulate online selling, and a rule reaches only the sellers that the records cover. Section 3.4 introduces the analysis, and Chapter 6 develops it. ## 3.1 Terms and measures diff --git a/papers/thesis_2026-10/ch4_data_methods.md b/papers/thesis_2026-10/ch4_data_methods.md index f42e512..8257679 100644 --- a/papers/thesis_2026-10/ch4_data_methods.md +++ b/papers/thesis_2026-10/ch4_data_methods.md @@ -1,10 +1,10 @@ # Chapter 4. Data and Methods -The evidence for this thesis is thinner than the question deserves, and it helps to say so first. No source shows the whole picture, and each platform reports in its own way. Tokopedia reports an Indonesian segment, but only for two years. Blibli's retail segment includes online travel. Bukalapak reports for its whole group, and GoTo's on-demand segment included Vietnam until 2024. Grab reports its Indonesian revenue but not its Indonesian transaction value. Shopee, the largest, reports transaction value only for all its markets together, so the Indonesian value used here is an outside estimate. Companies define transaction value in their own ways, and they differ on discounts: Blibli reports nothing on incentives, and Bukalapak's reports do not allow the parts to be separated. Each figure was read from a filing or release, and the extraction tables keep the source file and the quoted passage beside every value. +The evidence for this thesis is thinner than the question deserves, and it helps to say so first. No source shows the whole picture, and each platform reports in its own way. Tokopedia reports an Indonesian segment, but only for two years. Others report for a group, a region or a business that includes travel, and Table 4.1 gives each scope. Grab reports its Indonesian revenue but not its Indonesian transaction value. Shopee, the largest, reports transaction value only for all its markets together, so the Indonesian value used here is an outside estimate. Companies define transaction value in their own ways, and two of the five series say nothing usable about discounts. Each figure was read from a filing or release, and the extraction tables keep the source file and the quoted passage beside every value. -The national evidence is thin in the same way. BPS publishes a few totals, and the central bank does not publish its method. The survey files that show when each seller began selling were bought from BPS under a data-use agreement. They cover three rounds, carry no business identifier across rounds, and place sellers no more finely than the province. The files cannot follow the same seller from one round to the next. +The national evidence is thin in the same way. BPS publishes a few totals, and the central bank does not publish its method. The survey files that show when each seller began selling were bought from BPS under a data-use agreement. They cover three rounds and cannot follow the same seller from one round to the next. -Indonesia still allows what few markets do. Five platform series report revenue and transaction value in the same year, and three of them report what they pay out in incentives. BPS also asks each online seller when it began, so a rise in the seller count can be checked against entry. Most online selling there runs through messaging and social media, which makes the difference between platform figures and survey figures large enough to see. The tests are built to ask only what these sources can answer, and each limit is stated where it applies. That sets the bar low on purpose. The thesis cannot show everything, and what it does show adds to what the published figures give. +Indonesia still allows what few markets do. Five platform series report revenue and transaction value in the same year, and three of them report what they pay out in incentives. BPS also asks each online seller when it began, so a rise in the seller count can be checked against entry. Most online selling there runs outside the marketplaces, which makes the difference between platform and survey figures large enough to see. The tests are built to ask only what these sources can answer, and each limit is stated where it applies. That sets the bar low on purpose. The thesis cannot show everything, and what it does show adds to what the published figures give. The chapter describes the evidence on each side and the tests that use it. It starts with the platforms, where H1 is tested and revenue movements are traced. It then turns to the survey, where H2 and the entry test are examined. It ends with the comparisons and market values that bring the two sides together. @@ -24,7 +24,7 @@ Two groups of platforms are compared. The Indonesian and regional group has five | Grab on-demand | Grab's rides and deliveries across Southeast Asia, not Indonesia alone | 2020–2024 | 4 | Direct segment disclosure | | GoTo on-demand | GoTo's rides and deliveries segment, in Indonesia and Vietnam to 2024 | 2022–2025 | 3 | Direct segment disclosure; the 2022 transaction value is an estimate | -Table 4.1 states the scope of each series because the series differ. Only Tokopedia is an Indonesian segment alone. Grab covers Southeast Asia, GoTo's on-demand segment included Vietnam until 2024, and Bukalapak covers a whole group. The thesis never adds the series together, and each platform enters the tests on its own. Sea, whose Shopee business has Indonesia as a main market, sits in the foreign group. +Table 4.1 states the scope of each series because the series differ, and only Tokopedia is an Indonesian segment alone. The thesis never adds the series together, and each platform enters the tests on its own. Sea, whose Shopee business has Indonesia as a main market, sits in the foreign group. Three rules keep the pairs comparable. Two consecutive platform-years form a pair, and nothing else does. A missing year stays missing and is never filled in. Annual and quarterly pairs are never pooled. A take rate is a ratio of two amounts in one currency, so the tests need no exchange rate. Amounts in running text are in US dollars at the World Bank's average rate for the year. In the foreign group, a company that sells goods itself reports the whole sale as revenue, while an agent reports only its commission (International Accounting Standards Board 2014). The take rate of a seller of goods then says little about a platform, and Hagiu and Wright (2015) draw the same line between a marketplace and a reseller. For the 19 platforms added, the thesis drops any year in which own goods exceed half of revenue. For the eight chosen first, it keeps a pair only when the company's reporting scope did not change between the two years. Zalando is one of the eight and sells part of what it carries, so Appendix B repeats the test without it. One added pair, Ozon from 2021 to 2022, ends in a kept year but starts in a dropped one. Appendix B repeats the test without Ozon as well. The result is 15 pairs for the Indonesian and regional group and 158 for the foreign group. From c5e26c00279c5b7a8d01fa62619dc7c00daba192 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 13 Oct 2026 09:48:03 +0000 Subject: [PATCH 26/65] Thesis ch2-3: stop restating definitions and reasons; each opening keeps only its own role Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 4 ++++ papers/thesis_2026-10/ch2_literature.md | 4 +--- papers/thesis_2026-10/ch3_framework.md | 8 ++++---- 3 files changed, 9 insertions(+), 7 deletions(-) diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index e1ff253..9316d62 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -291,3 +291,7 @@ Main points that appeared more than once, and where each now lives: - **Chapter 3:** list items shortened. - **Chapter 4:** the scope of each series is stated once, in Table 4.1; the opening gives the headline and the paragraph after the table no longer repeats it. The file limits are stated in the opening only as "cannot follow the same seller"; 4.4 keeps the detail. - **Kept on purpose:** the short definitions of transaction value and revenue in Chapters 2 and 3 (each chapter must stand alone, which conflicts with "define once"; the researcher to decide), and the three questions in both 2.4 and Chapter 3. + +### Independence read correctly (13 Oct 2026) + +The researcher's clarification: a chapter or paragraph standing on its own means it does its own job without leaning on a neighbour, not that it repeats what came before. I had read it as "restate the terms". Reversed: Chapter 2 no longer re-defines transaction value, revenue and the wedge (Chapter 1 introduces them and Table 3.1 defines them); Chapter 3's opening names the three figures in one line and leaves definitions to Table 3.1; the three questions in Chapter 3 now carry only their link and the section that answers them, while 2.4 carries the reasons. The earlier "kept on purpose" note on repeated definitions is withdrawn. diff --git a/papers/thesis_2026-10/ch2_literature.md b/papers/thesis_2026-10/ch2_literature.md index ae81deb..1209118 100644 --- a/papers/thesis_2026-10/ch2_literature.md +++ b/papers/thesis_2026-10/ch2_literature.md @@ -10,9 +10,7 @@ The chapter follows these three parts. Section 2.1 explains what a platform is a A platform is a business that connects two groups and helps them deal with each other. Tokopedia connects shoppers with merchants, and Grab connects riders with drivers. Platforms such as these do not make or own what is sold. Platform economics is the field that studies how such businesses set prices and earn money. Its starting point is that a platform serves two groups at once, so it must price them together (Rochet and Tirole 2003; Evans and Schmalensee 2016). -Two amounts describe a platform. Transaction value is the total that buyers spend through it. Revenue is the part the platform records as its own income. The difference between them is what this thesis calls the invisible wedge. - -Platform pricing explains why revenue need not follow the commerce a platform carries. A platform needs both groups to join. If few sellers join, buyers have little reason to come, and the same holds the other way round. So a platform may charge one group little or nothing and earn from the other. How much it earns then depends on its price structure as well as on the commerce it carries (Rochet and Tirole 2003; Caillaud and Jullien 2003; Parker and Van Alstyne 2005; Armstrong 2006; Evans and Schmalensee 2016). The business model matters as well. A marketplace earns a fee from each sale, while a reseller buys goods and sells them on (Hagiu and Wright 2015). The reseller's revenue is the whole sale, and the marketplace's is the fee. +Platform pricing explains why a platform's revenue need not follow the commerce it carries. A platform needs both groups to join. If few sellers join, buyers have little reason to come, and the same holds the other way round. So a platform may charge one group little or nothing and earn from the other. How much it earns then depends on its price structure as well as on the commerce it carries (Rochet and Tirole 2003; Caillaud and Jullien 2003; Parker and Van Alstyne 2005; Armstrong 2006; Evans and Schmalensee 2016). The business model matters as well. A marketplace earns a fee from each sale, while a reseller buys goods and sells them on (Hagiu and Wright 2015). The reseller's revenue is the whole sale, and the marketplace's is the fee. Accounting rules also decide how much of each sale is revenue. Revenue recognition is the name for these rules. International Financial Reporting Standard 15 (IFRS 15) is the international standard for it. Under IFRS 15, a company acting as a principal reports the full payment from the customer as revenue. A company acting as an agent reports only what it earns from arranging the sale (International Accounting Standards Board 2014). Payments that a company makes to its own customers, such as discounts and vouchers, can reduce its reported revenue (International Accounting Standards Board 2014). A study of internet firms found that they chose different ways of reporting revenue, including whether to report sales gross (Bowen, Davis and Rajgopal 2002). So fees, customer incentives, accounting choices, service mix, acquisitions and changes in business scope can all move reported revenue without an equal change in commerce. diff --git a/papers/thesis_2026-10/ch3_framework.md b/papers/thesis_2026-10/ch3_framework.md index ce3746e..826c638 100644 --- a/papers/thesis_2026-10/ch3_framework.md +++ b/papers/thesis_2026-10/ch3_framework.md @@ -1,6 +1,6 @@ # Chapter 3. Framework and Hypotheses -Three figures describe the same online economy at three scales. A platform's revenue is what it keeps from the sales it handles. Its transaction value is the total of those sales. National online commerce is the total across all sellers, whether or not they use a platform. Each figure is used as a stand-in for the next one out. Investors read revenue as a sign of the commerce a platform carries. Platform figures are then read as a sign of what is happening in the national total. +Three figures describe the same online economy at three scales: a platform's revenue, the transaction value of the sales it handles, and national online commerce. Each is used as a stand-in for the next one out. Investors read revenue as a sign of the commerce a platform carries. Platform figures are then read as a sign of what is happening in the national total. [Figure 3.1 to be drawn: three nested circles, with revenue inside platform commerce inside all online commerce, H1 on the inner link and H2 on the outer link.] @@ -8,9 +8,9 @@ A stand-in works only while a condition holds. For the inner link, the condition The thesis asks three questions about these links. This chapter turns each into a measure, a test or both. -1. **How far does revenue follow commerce?** This is the inner link: how much commerce revenue leaves out, and whether revenue keeps pace with commerce from year to year. Section 3.1 defines the measures, and Section 3.2 states H1. -2. **Do platform figures follow national growth?** This is the outer link. National online growth can come from existing sellers selling more or from new sellers entering, and new sellers often work in channels that platform records do not cover. The question asks which kind of growth dominates, and whether a rise in the count of sellers really means first-time entry. Section 3.2 states H2, and Section 3.3 sets out the entry test. -3. **How far do rules built on platform records reach?** Governments use platform records to tax and regulate online selling, and a rule reaches only the sellers that the records cover. Section 3.4 introduces the analysis, and Chapter 6 develops it. +1. **How far does revenue follow commerce?** This is the inner link. Section 3.1 defines the measures, and Section 3.2 states H1. +2. **Do platform figures follow national growth?** This is the outer link. Section 3.2 states H2, and Section 3.3 sets out the entry test of the seller count behind it. +3. **How far do rules built on platform records reach?** This sits beside the chain, because a rule reaches only what the records cover. Section 3.4 introduces the analysis, and Chapter 6 develops it. ## 3.1 Terms and measures From 9ec2d6d86a15d2bcdb03f0745c0340e0635c9dc8 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 13 Oct 2026 09:50:52 +0000 Subject: [PATCH 27/65] Thesis ch1-3: apply the agreed writing rules (no lists in running text, openers name their subject, three-sentence declaration) Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 8 ++++++++ papers/thesis_2026-10/ch1_introduction.md | 2 +- papers/thesis_2026-10/ch2_literature.md | 14 +++++--------- papers/thesis_2026-10/ch3_framework.md | 6 +----- 4 files changed, 15 insertions(+), 15 deletions(-) diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 9316d62..60c7004 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -295,3 +295,11 @@ Main points that appeared more than once, and where each now lives: ### Independence read correctly (13 Oct 2026) The researcher's clarification: a chapter or paragraph standing on its own means it does its own job without leaning on a neighbour, not that it repeats what came before. I had read it as "restate the terms". Reversed: Chapter 2 no longer re-defines transaction value, revenue and the wedge (Chapter 1 introduces them and Table 3.1 defines them); Chapter 3's opening names the three figures in one line and leaves definitions to Table 3.1; the three questions in Chapter 3 now carry only their link and the section that answers them, while 2.4 carries the reasons. The earlier "kept on purpose" note on repeated definitions is withdrawn. + +### Checked against the agreed writing rules before editing (13 Oct 2026) + +The researcher asked for confirmation, not acceptance. Checked against `README.md` (rules agreed with the researcher) and the repository: +- "Stand alone" in the README means openers name their subject and do not lean on the previous paragraph ("these", "this literature", "both"); terms are defined once. It does not mean restating. This confirms the reading reached in the previous round. +- Violations found in my own drafting and fixed: bold-labelled numbered lists of the three questions in 2.4 and Chapter 3 (the README bars bullet lists and labelled beats in running text; they are now prose with "first, second, third"); three section endings in Chapter 2 opened with "This literature" or "The studies reviewed here"; 2.4's first sentence leaned on "each field reviewed here"; the "in brief" paragraph in 1.1 had grown to four sentences (the rule is a three-sentence declaration). +- Facts confirmed in the repository: Grab, GoTo on-demand and Tokopedia disclose incentives and Blibli does not (the panel and extraction notes); Sea's extraction carries no Indonesia split; no chapter mentions "the proposal". +- Open: the researcher once said Chapter 2's questions were "not in a list". The README rule and that comment pull apart; the questions are enumerated in prose for now. diff --git a/papers/thesis_2026-10/ch1_introduction.md b/papers/thesis_2026-10/ch1_introduction.md index 4c82567..5f04ef4 100644 --- a/papers/thesis_2026-10/ch1_introduction.md +++ b/papers/thesis_2026-10/ch1_introduction.md @@ -24,7 +24,7 @@ The research question is addressed through three objectives. The first is to mea The thesis tests two hypotheses. H1 says that a platform's revenue grows in proportion to its transaction value. H2 says that national e-commerce growth comes mainly from more businesses selling online, and less from higher sales per business. An entry test then asks whether a rise in the count of online sellers comes from sellers entering for the first time. A further analysis estimates how many sellers a rule built on platform records can reach. -In brief, revenue shows only a small part of the commerce that platforms carry, and in BPS's survey the marketplaces carry only about a fifth of online sales value. In Indonesia, platform revenue also grew faster than the commerce behind it, mainly because platforms kept a larger share of each sale and not because buyers bought more. National growth came mainly from more sellers, as H2 states. But about half of the 2023 rise in the count of sellers is not explained by first-time entry. +In brief, revenue shows only a small part of the commerce that platforms carry, and in BPS's survey the marketplaces carry only about a fifth of online sales value. In Indonesia, platform revenue also grew faster than the commerce behind it, mainly because platforms kept a larger share of each sale and not because buyers bought more. National growth came mainly from more sellers, as H2 states, but about half of the 2023 rise in the count of sellers is not explained by first-time entry. ## 1.2 Contributions diff --git a/papers/thesis_2026-10/ch2_literature.md b/papers/thesis_2026-10/ch2_literature.md index 1209118..0cba553 100644 --- a/papers/thesis_2026-10/ch2_literature.md +++ b/papers/thesis_2026-10/ch2_literature.md @@ -16,7 +16,7 @@ Accounting rules also decide how much of each sale is revenue. Revenue recogniti Revenue is not the only reported figure whose meaning depends on how it is computed. Analysts use financial statements more easily when the accounting is comparable (De Franco, Kothari and Verdi 2011). Measures that companies choose to report, such as transaction value or active users, lose information when their definition changes or goes unreported (Givoly, Li, Lourie and Nekrasov 2019). Investors also look beyond revenue. In the early years of the internet, activity measures such as web traffic carried information about a firm's value and future revenue that reported revenue did not show (Trueman, Wong and Zhang 2000, 2001; Rajgopal, Venkatachalam and Kotha 2003). -The studies reviewed here explain why transaction value can exceed revenue and why the difference can move. They do not measure how large the difference is for platforms in a market such as Indonesia. They also do not show whether it stays steady from year to year. +Platform economics and accounting research explain why transaction value can exceed revenue and why that difference can move. They do not measure how large it is for platforms in a market such as Indonesia, or whether it stays steady from year to year. ## 2.2 How online selling is measured @@ -30,7 +30,7 @@ One national figure needs this test in particular: the growth of online sales. I The split of national growth rests on a count of online sellers, and that count has a weakness of its own. It depends on how the survey finds sellers. A change in design can break a series in a way that looks like a change in the economy (van den Brakel, Smith and Compton 2008; van den Brakel, Zhang and Tam 2020). Respondents also misdate events, and they tend to place them closer to the interview than they were (Neter and Waksberg 1964). Exit among small firms is more common for young firms and young owners (McKenzie and Paffhausen 2019), which matters when a count has to be corrected for sellers who stopped. -This literature shows that official frameworks treat commerce and platform service as different things. It shows that figures from different sources need not agree, and that a seller count can break when a survey changes. It does not show whether platform figures and national figures move together in a given country. It also does not show what a rise in a seller count contains. +Research on measurement shows that official frameworks treat commerce and platform service as different things. It shows that figures from different sources need not agree, and that a seller count can break when a survey changes. It does not show whether platform and national figures move together in a given country, or what a rise in a seller count contains. ## 2.3 Platform records and third-party information @@ -42,14 +42,10 @@ Information from a third party also changes how people comply with tax. Audit ex Rules for platforms apply this idea. The OECD, a group of mostly high-income countries, published model rules under which platforms report what their sellers earn (OECD 2020). The European Union's DAC7 directive applies them (European Union 2021). Indonesia's marketplace tax rule goes further: it makes marketplaces collect tax from their sellers, not only report on them (Ministry of Finance of the Republic of Indonesia 2025). -This literature shows that third-party reports help compliance and that platform rules apply the idea. It does not show how many of a country's online sellers such a rule can reach. It also does not show how much of the market the platform records cover. +Research on third-party information shows that reports from third parties help compliance, and that platform rules apply the idea. It does not show how many of a country's online sellers such a rule can reach, or how much of the market platform records cover. ## 2.4 The research gap -Each field reviewed here answers its own question, and none measures how closely platform figures follow the activity behind them, or whether national figures do. Three questions remain open. +Platform economics, accounting, measurement and tax research each answer their own question. None measures how closely platform figures follow the activity behind them, or whether national figures do. Three questions remain. The first is how far a platform's revenue follows the commerce it carries. Section 2.1 gives reasons why the two can part, but not when or by how much. The second is whether platform figures follow national growth. Section 2.2 shows that figures can differ, but the answer depends on where national growth comes from and on whether a count of sellers keeps pace with entry. The third is how far rules built on platform records reach. Section 2.3 shows that such records can support tax rules, but not how much of the online economy they cover. -1. **How far does revenue follow commerce?** Section 2.1 gives reasons why revenue can part from commerce, but not when or by how much. -2. **Do platform figures follow national growth?** Section 2.2 shows that figures can differ. Whether platform growth tracks national growth depends on where the growth comes from, and on whether a count of sellers keeps pace with entry. -3. **How far do rules built on platform records reach?** Section 2.3 shows that platform records can support tax rules, but not how much of the online economy they cover. - -Answering them takes a platform's revenue and transaction value over several years, and a national survey whose underlying answers can be examined. Indonesia offers both. Chapter 3 turns the three questions into measures and hypotheses. +Answering these questions takes a platform's revenue and transaction value over several years, and a national survey whose underlying answers can be examined. Indonesia offers both. Chapter 3 turns the questions into measures and hypotheses. diff --git a/papers/thesis_2026-10/ch3_framework.md b/papers/thesis_2026-10/ch3_framework.md index 826c638..ca8a6d9 100644 --- a/papers/thesis_2026-10/ch3_framework.md +++ b/papers/thesis_2026-10/ch3_framework.md @@ -6,11 +6,7 @@ Three figures describe the same online economy at three scales: a platform's rev A stand-in works only while a condition holds. For the inner link, the condition is that the share of each sale that the platform keeps stays steady, because then revenue follows the commerce. For the outer link, the condition is that national growth comes from sellers the platforms record. To check this, national growth is split into new and existing sellers, using the count of online sellers from BPS, Indonesia's national statistics office. The count follows new selling only while BPS's survey keeps reaching the same kinds of seller. -The thesis asks three questions about these links. This chapter turns each into a measure, a test or both. - -1. **How far does revenue follow commerce?** This is the inner link. Section 3.1 defines the measures, and Section 3.2 states H1. -2. **Do platform figures follow national growth?** This is the outer link. Section 3.2 states H2, and Section 3.3 sets out the entry test of the seller count behind it. -3. **How far do rules built on platform records reach?** This sits beside the chain, because a rule reaches only what the records cover. Section 3.4 introduces the analysis, and Chapter 6 develops it. +The thesis asks three questions about these links, and this chapter turns each into a measure, a test or both. The first is how far revenue follows commerce, which is the inner link. Section 3.1 defines the measures, and Section 3.2 states H1. The second is whether platform figures follow national growth, which is the outer link. Section 3.2 states H2, and Section 3.3 sets out the entry test of the seller count behind it. The third is how far rules built on platform records reach. It sits beside the chain, because a rule reaches only what the records cover. Section 3.4 introduces the analysis, and Chapter 6 develops it. ## 3.1 Terms and measures From 17c791747d02d27fd524d04bd58452c24f746fe7 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 13 Oct 2026 09:56:38 +0000 Subject: [PATCH 28/65] Thesis ch2-3: restore the numbered list of the three questions (2.4 and Chapter 3 versions differ in role); README exception Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 4 ++++ papers/thesis_2026-10/README.md | 2 +- papers/thesis_2026-10/ch2_literature.md | 6 +++++- papers/thesis_2026-10/ch3_framework.md | 6 +++++- 4 files changed, 15 insertions(+), 3 deletions(-) diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 60c7004..9fadbdc 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -303,3 +303,7 @@ The researcher asked for confirmation, not acceptance. Checked against `README.m - Violations found in my own drafting and fixed: bold-labelled numbered lists of the three questions in 2.4 and Chapter 3 (the README bars bullet lists and labelled beats in running text; they are now prose with "first, second, third"); three section endings in Chapter 2 opened with "This literature" or "The studies reviewed here"; 2.4's first sentence leaned on "each field reviewed here"; the "in brief" paragraph in 1.1 had grown to four sentences (the rule is a three-sentence declaration). - Facts confirmed in the repository: Grab, GoTo on-demand and Tokopedia disclose incentives and Blibli does not (the panel and extraction notes); Sea's extraction carries no Indonesia split; no chapter mentions "the proposal". - Open: the researcher once said Chapter 2's questions were "not in a list". The README rule and that comment pull apart; the questions are enumerated in prose for now. + +### The three questions as a list again (13 Oct 2026) + +The researcher preferred the list and thought the README rule had been read too widely. Checked: the rule's origin is the 1.1 bullet attempt ("too non-standard for a thesis") and the labelled beats of the story drafts, both argument text. A short numbered list of research questions is ordinary thesis practice and the later chapters refer back to the three questions, so the list is better there. Restored in 2.4 (question plus what the section leaves open) and Chapter 3 (question plus link and sections, so it does not repeat 2.4's reasons). README exception added. Prose stays for everything else. diff --git a/papers/thesis_2026-10/README.md b/papers/thesis_2026-10/README.md index dc30b84..733ef3b 100644 --- a/papers/thesis_2026-10/README.md +++ b/papers/thesis_2026-10/README.md @@ -16,7 +16,7 @@ The thesis written in full. Same title, research question, hypotheses and measur - **Write linearly.** Each paragraph answers what the previous one left open. Test for every paragraph: does it make the next one necessary, and does it add to the stakes? A paragraph that only states what exists is "almost duh" and must be rewritten or cut. Deliver whole chapters; do not spend a turn per paragraph. - **Zoom in and out** to build stakes: a concrete Indonesian scene, then the general point, then global stakes with amounts and the cost of being wrong, then Indonesia with its own amounts. - **Openers stand alone.** No paragraph or section opens on a word that leans on the previous one ("both", "these readers", "these figures", "described above", "for that"). Name the subject. -- **Register.** Statements of fact in a neutral academic tone; no rhetorical lines, no labelled beats, no bullet lists in the running text of a section. Rounded figures in the prose (41%, 5%, 53%, 9%); exact values in tables and notes. No regulation numbers or exact dates in the narrative. Sentences of about 15 to 18 words with connecting words; do not chop to 11 words. +- **Register.** Statements of fact in a neutral academic tone; no rhetorical lines, no labelled beats, no bullet lists in the running text of a section. One exception, agreed 13 Oct 2026: the three research questions may appear as a short numbered list in 2.4 and at the start of Chapter 3, because later chapters refer back to them. Rounded figures in the prose (41%, 5%, 53%, 9%); exact values in tables and notes. No regulation numbers or exact dates in the narrative. Sentences of about 15 to 18 words with connecting words; do not chop to 11 words. - **Chapter 1.** No "main findings" section (it spoils the tension); a three-sentence declaration of the answer is enough. The first paragraph of 1.1 is the grand ambition, stated as boldly as the subject allows; the research question follows as the step needed to reach it. Contributions are tied to the three groups who rely on the figures. - **Claims.** Each claim at its evidence strength. Never write that the extra 2023 sellers "were there all along" or "were only counted that year", that the gap is "too large to be chance", or that returning sellers are ruled out. Say "about half of the increase is not explained by first-time entry" and "the additional count was concentrated among older, long-established small businesses". The growth identity is D = (1 + gV)(m1 - m0)/m0. Do not mention "the proposal" in thesis prose. - **Terms.** Define each once, in plain words (transaction value, revenue, take rate, invisible wedge, ecosystem ratio, growth divergence). Avoid "indicator", "Overall,", "therefore", "rather than". Do not use the sentence "of every 100 rupiah, about 7 appear as revenue". diff --git a/papers/thesis_2026-10/ch2_literature.md b/papers/thesis_2026-10/ch2_literature.md index 0cba553..9a62d68 100644 --- a/papers/thesis_2026-10/ch2_literature.md +++ b/papers/thesis_2026-10/ch2_literature.md @@ -46,6 +46,10 @@ Research on third-party information shows that reports from third parties help c ## 2.4 The research gap -Platform economics, accounting, measurement and tax research each answer their own question. None measures how closely platform figures follow the activity behind them, or whether national figures do. Three questions remain. The first is how far a platform's revenue follows the commerce it carries. Section 2.1 gives reasons why the two can part, but not when or by how much. The second is whether platform figures follow national growth. Section 2.2 shows that figures can differ, but the answer depends on where national growth comes from and on whether a count of sellers keeps pace with entry. The third is how far rules built on platform records reach. Section 2.3 shows that such records can support tax rules, but not how much of the online economy they cover. +Platform economics, accounting, measurement and tax research each answer their own question. None measures how closely platform figures follow the activity behind them, or whether national figures do. Three questions remain. + +1. **How far does a platform's revenue follow the commerce it carries?** Section 2.1 gives reasons why the two can part, but not when or by how much. +2. **Do platform figures follow national growth?** Section 2.2 shows that figures can differ. The answer depends on where national growth comes from and on whether a count of sellers keeps pace with entry. +3. **How far do rules built on platform records reach?** Section 2.3 shows that such records can support tax rules, but not how much of the online economy they cover. Answering these questions takes a platform's revenue and transaction value over several years, and a national survey whose underlying answers can be examined. Indonesia offers both. Chapter 3 turns the questions into measures and hypotheses. diff --git a/papers/thesis_2026-10/ch3_framework.md b/papers/thesis_2026-10/ch3_framework.md index ca8a6d9..dd65f2a 100644 --- a/papers/thesis_2026-10/ch3_framework.md +++ b/papers/thesis_2026-10/ch3_framework.md @@ -6,7 +6,11 @@ Three figures describe the same online economy at three scales: a platform's rev A stand-in works only while a condition holds. For the inner link, the condition is that the share of each sale that the platform keeps stays steady, because then revenue follows the commerce. For the outer link, the condition is that national growth comes from sellers the platforms record. To check this, national growth is split into new and existing sellers, using the count of online sellers from BPS, Indonesia's national statistics office. The count follows new selling only while BPS's survey keeps reaching the same kinds of seller. -The thesis asks three questions about these links, and this chapter turns each into a measure, a test or both. The first is how far revenue follows commerce, which is the inner link. Section 3.1 defines the measures, and Section 3.2 states H1. The second is whether platform figures follow national growth, which is the outer link. Section 3.2 states H2, and Section 3.3 sets out the entry test of the seller count behind it. The third is how far rules built on platform records reach. It sits beside the chain, because a rule reaches only what the records cover. Section 3.4 introduces the analysis, and Chapter 6 develops it. +The thesis asks three questions about these links, and this chapter turns each into a measure, a test or both. + +1. **How far does revenue follow commerce?** This is the inner link. Section 3.1 defines the measures, and Section 3.2 states H1. +2. **Do platform figures follow national growth?** This is the outer link. Section 3.2 states H2, and Section 3.3 sets out the entry test of the seller count behind it. +3. **How far do rules built on platform records reach?** This sits beside the chain, because a rule reaches only what the records cover. Section 3.4 introduces the analysis, and Chapter 6 develops it. ## 3.1 Terms and measures From 4fc1e0d52833ee79f7e081145bba251f92dd00c6 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 13 Oct 2026 09:59:16 +0000 Subject: [PATCH 29/65] Thesis README: state the bullet rule's real scope (no all-bullet sections; short numbered lists of parallel items allowed) Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 2 ++ papers/thesis_2026-10/README.md | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 9fadbdc..76a4b67 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -307,3 +307,5 @@ The researcher asked for confirmation, not acceptance. Checked against `README.m ### The three questions as a list again (13 Oct 2026) The researcher preferred the list and thought the README rule had been read too widely. Checked: the rule's origin is the 1.1 bullet attempt ("too non-standard for a thesis") and the labelled beats of the story drafts, both argument text. A short numbered list of research questions is ordinary thesis practice and the later chapters refer back to the three questions, so the list is better there. Restored in 2.4 (question plus what the section leaves open) and Chapter 3 (question plus link and sections, so it does not repeat 2.4's reasons). README exception added. Prose stays for everything else. + +Note, same day: the researcher says the README rule was written because an early Chapter 2 or 1.2 draft was all bullets with no paragraphs, and that the restored list version is the one they wanted from the start. The repository records only the 1.1 bullet attempt (row 14), not the Chapter 2 or 1.2 one, so this origin is taken from the researcher. README wording changed to match. diff --git a/papers/thesis_2026-10/README.md b/papers/thesis_2026-10/README.md index 733ef3b..1b7d29c 100644 --- a/papers/thesis_2026-10/README.md +++ b/papers/thesis_2026-10/README.md @@ -16,7 +16,7 @@ The thesis written in full. Same title, research question, hypotheses and measur - **Write linearly.** Each paragraph answers what the previous one left open. Test for every paragraph: does it make the next one necessary, and does it add to the stakes? A paragraph that only states what exists is "almost duh" and must be rewritten or cut. Deliver whole chapters; do not spend a turn per paragraph. - **Zoom in and out** to build stakes: a concrete Indonesian scene, then the general point, then global stakes with amounts and the cost of being wrong, then Indonesia with its own amounts. - **Openers stand alone.** No paragraph or section opens on a word that leans on the previous one ("both", "these readers", "these figures", "described above", "for that"). Name the subject. -- **Register.** Statements of fact in a neutral academic tone; no rhetorical lines, no labelled beats, no bullet lists in the running text of a section. One exception, agreed 13 Oct 2026: the three research questions may appear as a short numbered list in 2.4 and at the start of Chapter 3, because later chapters refer back to them. Rounded figures in the prose (41%, 5%, 53%, 9%); exact values in tables and notes. No regulation numbers or exact dates in the narrative. Sentences of about 15 to 18 words with connecting words; do not chop to 11 words. +- **Register.** Statements of fact in a neutral academic tone; no rhetorical lines, no labelled beats, no section written as bullets in place of paragraphs (an early Chapter 2 or 1.2 draft was all bullets, which is why this rule exists, per the researcher, 13 Oct 2026). Prose carries the argument. A short numbered list is fine where items are parallel and later chapters refer back to them, such as the three research questions in 2.4 and at the start of Chapter 3. Rounded figures in the prose (41%, 5%, 53%, 9%); exact values in tables and notes. No regulation numbers or exact dates in the narrative. Sentences of about 15 to 18 words with connecting words; do not chop to 11 words. - **Chapter 1.** No "main findings" section (it spoils the tension); a three-sentence declaration of the answer is enough. The first paragraph of 1.1 is the grand ambition, stated as boldly as the subject allows; the research question follows as the step needed to reach it. Contributions are tied to the three groups who rely on the figures. - **Claims.** Each claim at its evidence strength. Never write that the extra 2023 sellers "were there all along" or "were only counted that year", that the gap is "too large to be chance", or that returning sellers are ruled out. Say "about half of the increase is not explained by first-time entry" and "the additional count was concentrated among older, long-established small businesses". The growth identity is D = (1 + gV)(m1 - m0)/m0. Do not mention "the proposal" in thesis prose. - **Terms.** Define each once, in plain words (transaction value, revenue, take rate, invisible wedge, ecosystem ratio, growth divergence). Avoid "indicator", "Overall,", "therefore", "rather than". Do not use the sentence "of every 100 rupiah, about 7 appear as revenue". From 1d8166b9f1587135a5b66e10c11ec4e51d285f4c Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 13 Oct 2026 10:09:36 +0000 Subject: [PATCH 30/65] Thesis ch1-4: third pass, openers stand alone, Chapter 2 closing trimmed Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 4 ++++ papers/thesis_2026-10/ch1_introduction.md | 4 ++-- papers/thesis_2026-10/ch2_literature.md | 12 ++++++------ papers/thesis_2026-10/ch4_data_methods.md | 2 +- 4 files changed, 13 insertions(+), 9 deletions(-) diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 76a4b67..64fbf3f 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -309,3 +309,7 @@ The researcher asked for confirmation, not acceptance. Checked against `README.m The researcher preferred the list and thought the README rule had been read too widely. Checked: the rule's origin is the 1.1 bullet attempt ("too non-standard for a thesis") and the labelled beats of the story drafts, both argument text. A short numbered list of research questions is ordinary thesis practice and the later chapters refer back to the three questions, so the list is better there. Restored in 2.4 (question plus what the section leaves open) and Chapter 3 (question plus link and sections, so it does not repeat 2.4's reasons). README exception added. Prose stays for everything else. Note, same day: the researcher says the README rule was written because an early Chapter 2 or 1.2 draft was all bullets with no paragraphs, and that the restored list version is the one they wanted from the start. The repository records only the 1.1 bullet attempt (row 14), not the Chapter 2 or 1.2 one, so this origin is taken from the researcher. README wording changed to match. + +### Linear read of Chapters 1 to 4 openings, third pass (13 Oct 2026) + +Read sentence by sentence, then paragraph, section and chapter, against the README rules. Found and fixed: openers that leaned on the previous paragraph (Chapter 1 paragraph 5 "these figures" and paragraph 7 "both"; Chapter 2 "the chapter follows these three parts", "this test", "this idea", "Answering these questions", and the opening of 2.3 that leaned on 2.2); Chapter 2's closing paragraph repeated Chapter 1's data point and is now one handover sentence; "each figure" in Chapter 4 overstated (Shopee's value is an outside estimate), now "each company figure". Checked and left: the order of paragraphs in 2.2 (definitions differ, then numbers differ), and the Chapter 3 opening, which stands on its own. diff --git a/papers/thesis_2026-10/ch1_introduction.md b/papers/thesis_2026-10/ch1_introduction.md index 5f04ef4..64299f6 100644 --- a/papers/thesis_2026-10/ch1_introduction.md +++ b/papers/thesis_2026-10/ch1_introduction.md @@ -8,11 +8,11 @@ Tokopedia, an Indonesian online marketplace, shows the same split inside one com The share a platform keeps is not fixed. When it rises, revenue can grow even while transaction value falls. In Tokopedia's accounts, about 60 percent of the extra revenue came from smaller discounts and incentives, which a platform pays for out of what it earns. -Money and rules rest on these figures, and not only in Indonesia. Businesses in 43 economies sold almost US$27 trillion online in 2022, by the United Nations' estimate (UNCTAD 2024), and platforms carry a growing part of that trade. Investors value the companies that run them largely on reported growth. If part of that growth is a platform keeping more of each sale, then part of the price is a pricing decision. Governments use the same figures to size their digital economies and to tax online sellers. +Platform and online sales figures carry weight well beyond Indonesia. Businesses in 43 economies sold almost US$27 trillion online in 2022, by the United Nations' estimate (UNCTAD 2024), and platforms carry a growing part of that trade. Investors value the companies that run them largely on reported growth. If part of that growth is a platform keeping more of each sale, then part of the price is a pricing decision. Governments use the same figures to size their digital economies and to tax online sellers. Research explains why the figures can differ, but not when a figure stops moving with the activity it represents, or by how much. Answering that takes two things that are rarely found together: a platform's revenue and transaction value over several years, and a national count of online sellers whose underlying answers can be examined. -Indonesia offers both. Several of its platforms report revenue and transaction value each year, and BPS asks online sellers when they began and how their sales changed. The stakes are concrete. For 2024, BPS and the central bank give about US$81 billion and about US$31 billion for online sales (BPS 2025; Bank Indonesia 2025), a difference of more than two to one. The government is writing rules on platform commissions, discounts and tax. Its levy on marketplace sellers would raise about US$60 million a year on one measure of the market and about US$300 million on another, before exemptions (Ministry of Finance of the Republic of Indonesia 2025; author's calculation). Most online sellers work through messaging and social media, outside the marketplaces, so platform and survey figures are likely to part. The events of 2023 suggest that they did. +Indonesia has the platform histories and the seller survey that the question needs. Several of its platforms report revenue and transaction value each year, and BPS asks online sellers when they began and how their sales changed. The stakes are concrete. For 2024, BPS and the central bank give about US$81 billion and about US$31 billion for online sales (BPS 2025; Bank Indonesia 2025), a difference of more than two to one. The government is writing rules on platform commissions, discounts and tax. Its levy on marketplace sellers would raise about US$60 million a year on one measure of the market and about US$300 million on another, before exemptions (Ministry of Finance of the Republic of Indonesia 2025; author's calculation). Most online sellers work through messaging and social media, outside the marketplaces, so platform and survey figures are likely to part. The events of 2023 suggest that they did. ## 1.1 Research question and objectives diff --git a/papers/thesis_2026-10/ch2_literature.md b/papers/thesis_2026-10/ch2_literature.md index 9a62d68..ede577f 100644 --- a/papers/thesis_2026-10/ch2_literature.md +++ b/papers/thesis_2026-10/ch2_literature.md @@ -4,7 +4,7 @@ This thesis asks when a figure stops following the activity it is meant to descr The first is why a figure and its activity can differ at all. For platform revenue, the answer lies in how platforms set prices and how accountants record sales. The second is how else the activity can be measured, so that a figure has something to be checked against. Here the literature covers national accounts, business surveys and the methods behind them. The third is how the records are used. Governments now write tax rules on platform records, and what a rule can reach depends on what the records hold. -The chapter follows these three parts. Section 2.1 explains what a platform is and how it earns and reports revenue. Section 2.2 explains how online selling is measured and what can go wrong in the measure. Section 2.3 explains how platform records and information from third parties are used in tax. Each section ends by stating what it shows and what it leaves out. Section 2.4 collects what is left out into the questions this thesis answers. +The chapter has one section for each part. Section 2.1 explains what a platform is and how it earns and reports revenue. Section 2.2 explains how online selling is measured and what can go wrong in the measure. Section 2.3 explains how platform records and information from third parties are used in tax. Each section ends by stating what it shows and what it leaves out. Section 2.4 collects what is left out into the questions this thesis answers. ## 2.1 What a platform is and how it earns revenue @@ -12,7 +12,7 @@ A platform is a business that connects two groups and helps them deal with each Platform pricing explains why a platform's revenue need not follow the commerce it carries. A platform needs both groups to join. If few sellers join, buyers have little reason to come, and the same holds the other way round. So a platform may charge one group little or nothing and earn from the other. How much it earns then depends on its price structure as well as on the commerce it carries (Rochet and Tirole 2003; Caillaud and Jullien 2003; Parker and Van Alstyne 2005; Armstrong 2006; Evans and Schmalensee 2016). The business model matters as well. A marketplace earns a fee from each sale, while a reseller buys goods and sells them on (Hagiu and Wright 2015). The reseller's revenue is the whole sale, and the marketplace's is the fee. -Accounting rules also decide how much of each sale is revenue. Revenue recognition is the name for these rules. International Financial Reporting Standard 15 (IFRS 15) is the international standard for it. Under IFRS 15, a company acting as a principal reports the full payment from the customer as revenue. A company acting as an agent reports only what it earns from arranging the sale (International Accounting Standards Board 2014). Payments that a company makes to its own customers, such as discounts and vouchers, can reduce its reported revenue (International Accounting Standards Board 2014). A study of internet firms found that they chose different ways of reporting revenue, including whether to report sales gross (Bowen, Davis and Rajgopal 2002). So fees, customer incentives, accounting choices, service mix, acquisitions and changes in business scope can all move reported revenue without an equal change in commerce. +Accounting rules decide how much of each sale becomes revenue. Revenue recognition is the name for these rules. International Financial Reporting Standard 15 (IFRS 15) is the international standard for it. Under IFRS 15, a company acting as a principal reports the full payment from the customer as revenue. A company acting as an agent reports only what it earns from arranging the sale (International Accounting Standards Board 2014). Payments that a company makes to its own customers, such as discounts and vouchers, can reduce its reported revenue (International Accounting Standards Board 2014). A study of internet firms found that they chose different ways of reporting revenue, including whether to report sales gross (Bowen, Davis and Rajgopal 2002). So fees, customer incentives, accounting choices, service mix, acquisitions and changes in business scope can all move reported revenue without an equal change in commerce. Revenue is not the only reported figure whose meaning depends on how it is computed. Analysts use financial statements more easily when the accounting is comparable (De Franco, Kothari and Verdi 2011). Measures that companies choose to report, such as transaction value or active users, lose information when their definition changes or goes unreported (Givoly, Li, Lourie and Nekrasov 2019). Investors also look beyond revenue. In the early years of the internet, activity measures such as web traffic carried information about a firm's value and future revenue that reported revenue did not show (Trueman, Wong and Zhang 2000, 2001; Rajgopal, Venkatachalam and Kotha 2003). @@ -26,7 +26,7 @@ Official frameworks already separate the commerce a platform carries from the se The sources do not give the same number. The United Nations Conference on Trade and Development (UNCTAD) has shown that e-commerce values change with the definition used. It has also set business e-commerce sales beside the transaction values of online platforms (UNCTAD 2023, 2024). A difference between figures is harmless while they move together. It matters when they part, because the choice of figure can then change the conclusion about scale and growth. Economists who check national income figures against satellite images of night-time lights follow the same logic: a figure should be tested against the activity it stands for (Henderson, Storeygard and Weil 2012). -One national figure needs this test in particular: the growth of online sales. It can come from existing sellers selling more or from more sellers entering, and so it can be split between the two. Trade economists divide the growth of exports in the same way (Hummels and Klenow 2005). +The growth of national online sales has two possible sources, which makes it a figure worth testing. It can come from existing sellers selling more or from more sellers entering, and so it can be split between the two. Trade economists divide the growth of exports in the same way (Hummels and Klenow 2005). The split of national growth rests on a count of online sellers, and that count has a weakness of its own. It depends on how the survey finds sellers. A change in design can break a series in a way that looks like a change in the economy (van den Brakel, Smith and Compton 2008; van den Brakel, Zhang and Tam 2020). Respondents also misdate events, and they tend to place them closer to the interview than they were (Neter and Waksberg 1964). Exit among small firms is more common for young firms and young owners (McKenzie and Paffhausen 2019), which matters when a count has to be corrected for sellers who stopped. @@ -34,13 +34,13 @@ Research on measurement shows that official frameworks treat commerce and platfo ## 2.3 Platform records and third-party information -Surveys are not the only source on sellers. A platform keeps a record of every sale: who sold, what, for how much and when. Third-party information is a report about a taxpayer made by someone else, such as an employer, a bank or a platform. Tax authorities value it because it does not depend on the taxpayer's own account. +A platform keeps a record of every sale: who sold, what, for how much and when. Third-party information is a report about a taxpayer made by someone else, such as an employer, a bank or a platform. Tax authorities value it because it does not depend on the taxpayer's own account. Platform records can help where other records are thin. Sellers and service providers may be small, self-employed or weakly represented in conventional financial records, yet the platform records what they sell. This differs from the shadow-economy setting, where reliable records may be sparse (La Porta and Shleifer 2014; Ulyssea 2018). Digital records can carry information that conventional records lack (Berg, Burg, Gombovic and Puri 2020), and platform-mediated work leaves traces in financial and administrative data (Barrios, Hochberg and Yi 2022; Denes, Lagaras and Tsoutsoura 2025). Information from a third party also changes how people comply with tax. Audit experiments in Denmark, and studies of the value-added tax and of receipt lotteries, show that reports from third parties help compliance (Kleven et al. 2011; Pomeranz 2015; Naritomi 2019; Kleven, Kreiner and Saez 2016; Slemrod 2019). Tax withheld through a third party rests on the same information (Brockmeyer and Hernandez 2016). -Rules for platforms apply this idea. The OECD, a group of mostly high-income countries, published model rules under which platforms report what their sellers earn (OECD 2020). The European Union's DAC7 directive applies them (European Union 2021). Indonesia's marketplace tax rule goes further: it makes marketplaces collect tax from their sellers, not only report on them (Ministry of Finance of the Republic of Indonesia 2025). +Several rules now apply third-party reporting to platforms. The OECD, a group of mostly high-income countries, published model rules under which platforms report what their sellers earn (OECD 2020). The European Union's DAC7 directive applies them (European Union 2021). Indonesia's marketplace tax rule goes further: it makes marketplaces collect tax from their sellers, not only report on them (Ministry of Finance of the Republic of Indonesia 2025). Research on third-party information shows that reports from third parties help compliance, and that platform rules apply the idea. It does not show how many of a country's online sellers such a rule can reach, or how much of the market platform records cover. @@ -52,4 +52,4 @@ Platform economics, accounting, measurement and tax research each answer their o 2. **Do platform figures follow national growth?** Section 2.2 shows that figures can differ. The answer depends on where national growth comes from and on whether a count of sellers keeps pace with entry. 3. **How far do rules built on platform records reach?** Section 2.3 shows that such records can support tax rules, but not how much of the online economy they cover. -Answering these questions takes a platform's revenue and transaction value over several years, and a national survey whose underlying answers can be examined. Indonesia offers both. Chapter 3 turns the questions into measures and hypotheses. +Chapter 3 turns the three questions into measures and hypotheses. diff --git a/papers/thesis_2026-10/ch4_data_methods.md b/papers/thesis_2026-10/ch4_data_methods.md index 8257679..cece7b4 100644 --- a/papers/thesis_2026-10/ch4_data_methods.md +++ b/papers/thesis_2026-10/ch4_data_methods.md @@ -1,6 +1,6 @@ # Chapter 4. Data and Methods -The evidence for this thesis is thinner than the question deserves, and it helps to say so first. No source shows the whole picture, and each platform reports in its own way. Tokopedia reports an Indonesian segment, but only for two years. Others report for a group, a region or a business that includes travel, and Table 4.1 gives each scope. Grab reports its Indonesian revenue but not its Indonesian transaction value. Shopee, the largest, reports transaction value only for all its markets together, so the Indonesian value used here is an outside estimate. Companies define transaction value in their own ways, and two of the five series say nothing usable about discounts. Each figure was read from a filing or release, and the extraction tables keep the source file and the quoted passage beside every value. +The evidence for this thesis is thinner than the question deserves, and it helps to say so first. No source shows the whole picture, and each platform reports in its own way. Tokopedia reports an Indonesian segment, but only for two years. Others report for a group, a region or a business that includes travel, and Table 4.1 gives each scope. Grab reports its Indonesian revenue but not its Indonesian transaction value. Shopee, the largest, reports transaction value only for all its markets together, so the Indonesian value used here is an outside estimate. Companies define transaction value in their own ways, and two of the five series say nothing usable about discounts. Each company figure was read from a filing or release, and the extraction tables keep the source file and the quoted passage beside every value. The national evidence is thin in the same way. BPS publishes a few totals, and the central bank does not publish its method. The survey files that show when each seller began selling were bought from BPS under a data-use agreement. They cover three rounds and cannot follow the same seller from one round to the next. From f2580e7019243ba95f030f8b15e166b966a13388 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 13 Oct 2026 10:15:36 +0000 Subject: [PATCH 31/65] Thesis ch2-5: one standard (openers, repetition, paragraph splits, plain-word definitions, Chapter 2/3 handover checked) Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 9 ++++++++ papers/thesis_2026-10/ch2_literature.md | 4 +++- papers/thesis_2026-10/ch3_framework.md | 8 +++---- papers/thesis_2026-10/ch4_data_methods.md | 28 +++++++++++++++-------- papers/thesis_2026-10/ch5_results.md | 26 ++++++++++++--------- 5 files changed, 50 insertions(+), 25 deletions(-) diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 64fbf3f..5206e93 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -313,3 +313,12 @@ Note, same day: the researcher says the README rule was written because an early ### Linear read of Chapters 1 to 4 openings, third pass (13 Oct 2026) Read sentence by sentence, then paragraph, section and chapter, against the README rules. Found and fixed: openers that leaned on the previous paragraph (Chapter 1 paragraph 5 "these figures" and paragraph 7 "both"; Chapter 2 "the chapter follows these three parts", "this test", "this idea", "Answering these questions", and the opening of 2.3 that leaned on 2.2); Chapter 2's closing paragraph repeated Chapter 1's data point and is now one handover sentence; "each figure" in Chapter 4 overstated (Shopee's value is an outside estimate), now "each company figure". Checked and left: the order of paragraphs in 2.2 (definitions differ, then numbers differ), and the Chapter 3 opening, which stands on its own. + +### Chapters 2 to 5 brought to one standard (13 Oct 2026) + +Checked and fixed against the README rules, in the order Chapter 2, 3, 4, 5: +- **Chapter 2:** the 2.1 paragraph on reported figures is split in two (comparable accounting and companies' own measures; investors looking past revenue), each with its own job. +- **Chapter 3:** the opening no longer explains the entry-test logic (3.2 and 3.3 do), and no longer defines BPS twice (Table 3.1 does). 3.2 states the take-rate condition once with the link to growth divergence. 3.3's "Chapter 4 gives the rule" and Chapter 4's "follows the rule in Chapter 3" no longer point at each other. 3.4 now states the market-value question, which Chapter 3 had left out. The 3.1 arithmetic on the wedge's second use stays; Chapter 5 now applies it rather than re-explaining it. +- **Chapter 4:** 4.1's first paragraph no longer re-defines revenue and transaction value; the own-goods rules are a paragraph of their own; 4.2's checks are a separate paragraph, and the unexplained "thin-slice explanation of Chapter 5" now reads "to see whether a thin take rate explains the result"; "it also reports" (ambiguous subject) fixed; 4.3 lost its repeat of the opening; the data-use sentence in 4.4 is said once; the World Bank sentence and the regression paragraph are separated; "fixed effect" and "clusters" are explained in plain words. +- **Chapter 5:** openers that leaned ("Little of this", "The entry test can", "The value side tells a similar story", "these sellers", "The World Bank surveys point the same way", "The 2024 figures show the same split") now name their subjects; the literature restatement in 5.1 is one clause with the citations; 5.2's long paragraph is split and its repeat of Chapter 3's wedge argument is cut; two paragraphs over 170 words are split. +- **Checked and left:** how Chapter 2 hands to Chapter 3 (three questions to the chain and its two links) and Chapter 3 to 4 (each test's data and rule) works. diff --git a/papers/thesis_2026-10/ch2_literature.md b/papers/thesis_2026-10/ch2_literature.md index ede577f..1ea3a7f 100644 --- a/papers/thesis_2026-10/ch2_literature.md +++ b/papers/thesis_2026-10/ch2_literature.md @@ -14,7 +14,9 @@ Platform pricing explains why a platform's revenue need not follow the commerce Accounting rules decide how much of each sale becomes revenue. Revenue recognition is the name for these rules. International Financial Reporting Standard 15 (IFRS 15) is the international standard for it. Under IFRS 15, a company acting as a principal reports the full payment from the customer as revenue. A company acting as an agent reports only what it earns from arranging the sale (International Accounting Standards Board 2014). Payments that a company makes to its own customers, such as discounts and vouchers, can reduce its reported revenue (International Accounting Standards Board 2014). A study of internet firms found that they chose different ways of reporting revenue, including whether to report sales gross (Bowen, Davis and Rajgopal 2002). So fees, customer incentives, accounting choices, service mix, acquisitions and changes in business scope can all move reported revenue without an equal change in commerce. -Revenue is not the only reported figure whose meaning depends on how it is computed. Analysts use financial statements more easily when the accounting is comparable (De Franco, Kothari and Verdi 2011). Measures that companies choose to report, such as transaction value or active users, lose information when their definition changes or goes unreported (Givoly, Li, Lourie and Nekrasov 2019). Investors also look beyond revenue. In the early years of the internet, activity measures such as web traffic carried information about a firm's value and future revenue that reported revenue did not show (Trueman, Wong and Zhang 2000, 2001; Rajgopal, Venkatachalam and Kotha 2003). +Revenue is not the only reported figure whose meaning depends on how it is computed. Analysts use financial statements more easily when the accounting is comparable (De Franco, Kothari and Verdi 2011). Measures that companies choose to report, such as transaction value or active users, lose information when their definition changes or goes unreported (Givoly, Li, Lourie and Nekrasov 2019). + +Investors also look beyond revenue. In the early years of the internet, activity measures such as web traffic carried information about a firm's value and future revenue that reported revenue did not show (Trueman, Wong and Zhang 2000, 2001; Rajgopal, Venkatachalam and Kotha 2003). Platform economics and accounting research explain why transaction value can exceed revenue and why that difference can move. They do not measure how large it is for platforms in a market such as Indonesia, or whether it stays steady from year to year. diff --git a/papers/thesis_2026-10/ch3_framework.md b/papers/thesis_2026-10/ch3_framework.md index dd65f2a..ee87f79 100644 --- a/papers/thesis_2026-10/ch3_framework.md +++ b/papers/thesis_2026-10/ch3_framework.md @@ -4,7 +4,7 @@ Three figures describe the same online economy at three scales: a platform's rev [Figure 3.1 to be drawn: three nested circles, with revenue inside platform commerce inside all online commerce, H1 on the inner link and H2 on the outer link.] -A stand-in works only while a condition holds. For the inner link, the condition is that the share of each sale that the platform keeps stays steady, because then revenue follows the commerce. For the outer link, the condition is that national growth comes from sellers the platforms record. To check this, national growth is split into new and existing sellers, using the count of online sellers from BPS, Indonesia's national statistics office. The count follows new selling only while BPS's survey keeps reaching the same kinds of seller. +A stand-in works only while a condition holds. For the inner link, the condition is that the share of each sale that the platform keeps stays steady, because then revenue follows the commerce. For the outer link, the condition is that national growth comes from sellers the platforms record. The thesis asks three questions about these links, and this chapter turns each into a measure, a test or both. @@ -57,7 +57,7 @@ The wedge has a second use. Its size next to revenue decides how far a change in Two hypotheses put the two links of the chain to the test. H1 concerns the inner link, between revenue and the commerce a platform carries. H2 concerns the outer link, between that commerce and national online commerce. -Revenue depends on the take rate, and the take rate depends on fees, on discounts and on how the platform's accounts treat each sale. Revenue is a fair stand-in for the commerce only while the take rate is steady, which means that growth divergence stays close to zero. The first hypothesis states that case. +The take rate depends on fees, on discounts and on how the platform's accounts treat each sale. Revenue stays a fair stand-in for the commerce only while the take rate is steady, which means that growth divergence stays close to zero. The first hypothesis states that case. > **H1.** Transaction value and revenue recognized by the same platform grow in proportion over time. @@ -73,13 +73,13 @@ The H2 split is arithmetic. It does not say which channels the new businesses us The split in H2 rests on BPS's count of online sellers. A count can rise for two reasons: more businesses start selling, or the survey reaches businesses it did not reach before. The published figures cannot tell these apart. The survey itself can, because it asks every seller in which year it began selling online. -If a rise in the count comes from first-time entry, a large enough share of sellers must have started in the year of the rise. For 2023 the published count rose by 27.4 percent. Even if no seller had stopped, there would then be 127.4 sellers for every 100 a year earlier, and the 27.4 extra would have to be new. New sellers would have to make up at least 21.5 percent of all sellers. The test was written down before the survey files were opened, and Chapter 4 gives the rule. It does not test H2 itself. It tests what "more businesses" means. +If a rise in the count comes from first-time entry, a large enough share of sellers must have started in the year of the rise. For 2023 the published count rose by 27.4 percent. Even if no seller had stopped, there would then be 127.4 sellers for every 100 a year earlier, and the 27.4 extra would have to be new. New sellers would have to make up at least 21.5 percent of all sellers. The test was written down before the survey files were opened, as Chapter 4 describes. It does not test H2 itself. It tests what "more businesses" means. If the share of new sellers falls short, part of the rise is something other than first-time entry. Two readings are open: the survey reached more of the sellers who were already there, or sellers came back after a pause. Chapter 5 weighs them. ## 3.4 Further analyses -Two further analyses support the hypotheses without forming hypotheses of their own. The first divides national growth by sales channel, to show how much of it took place inside the marketplace channel. The second compares marketplace sellers with other sellers on whether they keep financial statements, which bears on what platform records can add to tax administration. A separate analysis, developed in Chapter 6, estimates how many sellers a rule based on platform records can reach. Chapter 4 also describes how revenue movements are traced to fees and discounts, how the platform evidence is compared with national and payment figures, and how markets value platform growth. +Two further analyses support the hypotheses without forming hypotheses of their own. The first divides national growth by sales channel, to show how much of it took place inside the marketplace channel. The second compares marketplace sellers with other sellers on whether they keep financial statements, which bears on what platform records can add to tax administration. A separate analysis, developed in Chapter 6, estimates how many sellers a rule based on platform records can reach. Chapter 4 also describes how revenue movements are traced to fees and discounts and how platform evidence is compared with national and payment figures. A last analysis asks whether markets value growth in transaction value differently from growth in the take rate. ## 3.5 The tests together diff --git a/papers/thesis_2026-10/ch4_data_methods.md b/papers/thesis_2026-10/ch4_data_methods.md index cece7b4..14301dc 100644 --- a/papers/thesis_2026-10/ch4_data_methods.md +++ b/papers/thesis_2026-10/ch4_data_methods.md @@ -10,7 +10,7 @@ The chapter describes the evidence on each side and the tests that use it. It st ## 4.1 Choosing the platforms -The basic observation is one platform in one year, which this thesis calls a platform-year. Revenue and transaction value come from the company's own reports, and they refer to the same year and, as closely as the filings allow, the same business. Revenue is the amount in the financial statements. Transaction value is the figure the company discloses in its annual report or earnings release. Companies define it in different ways, and some include shipping charges, digital goods or travel. The thesis keeps each company's own definition and records it. A take rate is built inside one company, so the definition cancels out of its yearly changes, as long as the company keeps the definition and the mix of its businesses. +The basic observation is one platform in one year, which this thesis calls a platform-year. Revenue comes from the company's financial statements and transaction value from its annual report or earnings release. Both refer to the same year and, as closely as the filings allow, the same business. Companies define transaction value in different ways, and some include shipping charges, digital goods or travel. The thesis keeps each company's own definition and records it. A take rate is built inside one company, so the definition cancels out of its yearly changes, as long as the company keeps the definition and the mix of its businesses. Two groups of platforms are compared. The Indonesian and regional group has five platforms, listed in Table 4.1. Three are Indonesian marketplaces, and two are the on-demand businesses of Grab and GoTo, which cover rides and deliveries. The foreign group has 27 listed platforms in marketplaces, travel and ticketing, freelance work, resale and food delivery. Eight were chosen first, and 19 were added to widen the comparison. Ten more were examined and left out, six because they sell mostly their own goods and four because they do not disclose transaction value. @@ -26,21 +26,27 @@ Two groups of platforms are compared. The Indonesian and regional group has five Table 4.1 states the scope of each series because the series differ, and only Tokopedia is an Indonesian segment alone. The thesis never adds the series together, and each platform enters the tests on its own. Sea, whose Shopee business has Indonesia as a main market, sits in the foreign group. -Three rules keep the pairs comparable. Two consecutive platform-years form a pair, and nothing else does. A missing year stays missing and is never filled in. Annual and quarterly pairs are never pooled. A take rate is a ratio of two amounts in one currency, so the tests need no exchange rate. Amounts in running text are in US dollars at the World Bank's average rate for the year. In the foreign group, a company that sells goods itself reports the whole sale as revenue, while an agent reports only its commission (International Accounting Standards Board 2014). The take rate of a seller of goods then says little about a platform, and Hagiu and Wright (2015) draw the same line between a marketplace and a reseller. For the 19 platforms added, the thesis drops any year in which own goods exceed half of revenue. For the eight chosen first, it keeps a pair only when the company's reporting scope did not change between the two years. Zalando is one of the eight and sells part of what it carries, so Appendix B repeats the test without it. One added pair, Ozon from 2021 to 2022, ends in a kept year but starts in a dropped one. Appendix B repeats the test without Ozon as well. The result is 15 pairs for the Indonesian and regional group and 158 for the foreign group. +Three rules keep the pairs comparable. Two consecutive platform-years form a pair, and nothing else does. A missing year stays missing and is never filled in. Annual and quarterly pairs are never pooled. A take rate is a ratio of two amounts in one currency, so the tests need no exchange rate. Amounts in running text are in US dollars at the World Bank's average rate for the year. + +Own goods need a separate rule. In the foreign group, a company that sells goods itself reports the whole sale as revenue, while an agent reports only its commission (International Accounting Standards Board 2014). The take rate of a seller of goods then says little about a platform, and Hagiu and Wright (2015) draw the same line between a marketplace and a reseller. For the 19 platforms added, the thesis drops any year in which own goods exceed half of revenue. For the eight chosen first, it keeps a pair only when the company's reporting scope did not change between the two years. Zalando is one of the eight and sells part of what it carries, so Appendix B repeats the test without it. One added pair, Ozon from 2021 to 2022, ends in a kept year but starts in a dropped one. Appendix B repeats the test without Ozon as well. The result is 15 pairs for the Indonesian and regional group and 158 for the foreign group. ## 4.2 Testing H1 and tracing the moves H1 says that revenue and transaction value grow in proportion, which means that the take rate stays close to steady. The test asks how far each platform's take rate moves from one year to the next. For each pair of years it takes the change in the logarithm of the take rate. A log change treats a rise by some factor and a fall by the same factor as equal in size. It equals the log growth of revenue minus the log growth of transaction value. This is growth divergence measured in logs, and it does not depend on how fast transaction value grew. Tokopedia's log change in 2023 is 0.52, which is a rise by a factor of 1.68, or 68 percent. -A rise and a fall both count against H1, so the test uses the size of each change. Each platform gets one number, the median size of its yearly changes, because the pairs of one platform are not independent of one another. The test compares five numbers for the Indonesian and regional group with 27 for the foreign group. It asks whether the first group ranks higher than chance would give, using a one-sided Mann–Whitney test (Mann and Whitney 1947). It repeats the question by shuffling the group labels 9,999 times. The p-value is the chance of a result at least this lopsided if the groups did not differ. Appendix B lists the checks that follow. They drop each platform in turn and treat Tokopedia and GoTo as one company. They use growth divergence in points as Chapter 3 defines it, and they compare the change in the take rate in percentage points, which tests the thin-slice explanation of Chapter 5. They narrow the test to the three marketplaces, correlate revenue growth with transaction value growth, and repeat the test with quarterly pairs. +A rise and a fall both count against H1, so the test uses the size of each change. Each platform gets one number, the median size of its yearly changes, because the pairs of one platform are not independent of one another. The test compares five numbers for the Indonesian and regional group with 27 for the foreign group. It asks whether the first group ranks higher than chance would give, using a one-sided Mann–Whitney test (Mann and Whitney 1947). It repeats the question by shuffling the group labels 9,999 times. The p-value is the chance of a result at least this lopsided if the groups did not differ. + +Appendix B lists the checks that follow. They drop each platform in turn and treat Tokopedia and GoTo as one company. They repeat the test with growth divergence in points as Chapter 3 defines it. They compare the change in the take rate in percentage points, to see whether a thin take rate explains the result. They narrow the test to the three marketplaces, correlate revenue growth with transaction value growth, and repeat the test with quarterly pairs. + +The second objective asks where large movements in revenue came from, and the thesis answers in two steps. The first divides each movement into volume and take rate. In log terms, revenue growth equals transaction value growth plus the change in the take rate. The take-rate share is the size of the take-rate change divided by the sum of the sizes of both changes. The same split is made for the wedge, which equals transaction value minus revenue. -The second objective asks where large movements in revenue came from, and the thesis answers in two steps. The first divides each movement into volume and take rate. In log terms, revenue growth equals transaction value growth plus the change in the take rate. The take-rate share is the size of the take-rate change divided by the sum of the sizes of both changes. The same split is made for the wedge, which equals transaction value minus revenue. The second step finds the cause of a take-rate change. A platform's take rate equals the rate of its gross fees minus the rate of its incentives, each measured against transaction value. Accounting rules normally deduct such incentives from revenue (International Accounting Standards Board 2014). So a rise comes from higher fees, lower incentives or a change in the mix of businesses. Wherever a company discloses both, the thesis splits the growth of revenue over a window into three parts. They are more commerce, a change in the fee rate and a change in the incentive rate. +The second step finds the cause of a take-rate change. A platform's take rate equals the rate of its gross fees minus the rate of its incentives, each measured against transaction value. Accounting rules normally deduct such incentives from revenue (International Accounting Standards Board 2014). So a rise comes from higher fees, lower incentives or a change in the mix of businesses. Wherever a company discloses both, the thesis splits the growth of revenue over a window into three parts. They are more commerce, a change in the fee rate and a change in the incentive rate. -The sources differ by company. Tokopedia's audited segment note gives gross revenue, incentives and net revenue, and the thesis checks them against the audited consolidated statements. For Tokopedia it also reports the change line by line in rupiah, which is the usual way to read accounts, and it says where the two views differ. Grab is read from its annual filings and GoTo from its audited statements. Blibli does not report incentives, so its split rests on a proxy built from its releases. Seven windows are traced, and Appendix A lists them. The thesis also records where accounting treatment or reporting scope matters, such as the deduction of incentives from revenue and the travel business inside Blibli's retail segment. Company explanations are read as reports and are not tested. +The sources differ by company. Tokopedia's audited segment note gives gross revenue, incentives and net revenue, and the thesis checks them against the audited consolidated statements. For Tokopedia the thesis also reports the change line by line in rupiah, the usual way to read accounts, and says where the two views differ. Grab is read from its annual filings and GoTo from its audited statements. Blibli does not report incentives, so its split rests on a proxy built from its releases. Seven windows are traced, and Appendix A lists them. The thesis also records where accounting treatment or reporting scope matters, such as the deduction of incentives from revenue and the travel business inside Blibli's retail segment. Company explanations are read as reports and are not tested. ## 4.3 Measuring the wedge in Indonesia -The first objective needs a different set of amounts. It sums the transaction value and revenue of three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. Not every company reports Indonesia on its own. Tokopedia reports both amounts for its segment. Shopee's transaction value is an outside estimate by Momentum Works. Its revenue assumes a take rate of 10 percent, which the thesis varies from 9 to 11 percent. Grab reports its Indonesian revenue, and the thesis derives its Indonesian transaction value from Grab's company-wide ratio of the two amounts. The wedge and the ecosystem ratio are then computed from the sums. The thesis drops each platform in turn, and it repeats the exercise for every platform-year between 2020 and 2025 that the filings allow. +The first objective needs a different set of amounts. It sums the transaction value and revenue of three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. Tokopedia reports both amounts for its segment. Shopee's transaction value is an outside estimate by Momentum Works. Its revenue assumes a take rate of 10 percent, which the thesis varies from 9 to 11 percent. Grab reports its Indonesian revenue, and the thesis derives its Indonesian transaction value from Grab's company-wide ratio of the two amounts. The wedge and the ecosystem ratio are then computed from the sums. The thesis drops each platform in turn, and it repeats the exercise for every platform-year between 2020 and 2025 that the filings allow. ## 4.4 Evidence from the sellers @@ -48,7 +54,7 @@ The national evidence comes from the survey of online sellers that BPS runs. BPS H2 uses the published figures. National online sales value equals the number of businesses times the sales per business. In log terms, growth in value is growth in the number of businesses plus growth in sales per business. Trade economists split the growth of exports in the same way, into an extensive and an intensive margin (Hummels and Klenow 2005). H2 holds if the first part is more than half of the total. BPS prints two counts of businesses for 2023, so the thesis computes the split with both and reports both. -The published figures cannot tell a new seller from one counted for the first time, but the survey files can. BPS sold them under a data-use agreement, and Table 4.2 lists them. Each row is a business that sold online in the activity year. Its survey weight shows how many businesses it represents, so the weights add up to the national count. Two checks come before any test. The weighted total of each file must fall within 1 percent of the BPS count. The 2024 file must also reproduce two published shares of sellers who keep complete financial statements, 28.63 and 12.25 percent. All three files pass. The files carry no business identifier across rounds and no area variable below the province. So the rounds compare groups of sellers and not the same sellers over time. +The published figures cannot tell a new seller from one counted for the first time, but the survey files can. Table 4.2 lists them. Each row is a business that sold online in the activity year. Its survey weight shows how many businesses it represents, so the weights add up to the national count. Two checks come before any test. The weighted total of each file must fall within 1 percent of the BPS count. The 2024 file must also reproduce two published shares of sellers who keep complete financial statements, 28.63 and 12.25 percent. All three files pass. The files carry no business identifier across rounds and no area variable below the province. So the rounds compare groups of sellers and not the same sellers over time. **Table 4.2. The three BPS survey files** @@ -68,7 +74,9 @@ Further checks ask where the extra sellers sit. They look across provinces and c Three further analyses use the same files. The first asks which channels sellers use and how sales divide between them. The published tables split the 2023 to 2024 rise in value between marketplaces and other channels, and BPS counts food and ride apps in its marketplace category. Value by channel rests on revenue brackets, which only the 2021 and 2023 files give. The thesis assigns each bracket a low, a middle and a high amount. The amounts were fixed before the file was opened. A validation rule applies: the middle scenario's 2022 total must lie between half and double BPS's published total of about US$53 billion. The 2023 questionnaire asks for monthly revenue but the bracket limits are annual size classes, so both readings are computed and the one that passes is kept. If the lower brackets alone already exceed the total, value shares are reported as ranges. -The second analysis asks whether a seller keeps complete financial statements. This bears on what platform records can add, because a seller with no statements leaves fewer formal records. The third builds on the first two and is developed in Chapter 6. It counts the marketplace sellers whose revenue is above a bracket line and expresses the count as a share of all online sellers. At the 2022 rate, BPS's brackets divide at about US$20,000 a year. The marketplace tax exempts individual sellers up to about US$34,000. So the count above BPS's line is an upper limit on the sellers the tax can reach. World Bank Enterprise Surveys add a check on records. They cover unregistered firms in six Indonesian cities in 2023, and formal firms with five or more employees in seven countries. +The second analysis asks whether a seller keeps complete financial statements. This bears on what platform records can add, because a seller with no statements leaves fewer formal records. The third builds on the first two and is developed in Chapter 6. It counts the marketplace sellers whose revenue is above a bracket line and expresses the count as a share of all online sellers. At the 2022 rate, BPS's brackets divide at about US$20,000 a year. The marketplace tax exempts individual sellers up to about US$34,000. So the count above BPS's line is an upper limit on the sellers the tax can reach. + +World Bank Enterprise Surveys add a check on records. They cover unregistered firms in six Indonesian cities in 2023, and formal firms with five or more employees in seven countries. ## 4.5 Bringing the two sides together @@ -86,7 +94,9 @@ The third objective compares the platform evidence with national and payment fig | Payment figures | Payments made through systems such as QRIS, the national QR-code system | Payment records, in person and online | | Adjacent figures | Parcels shipped in Southeast Asia, the value-added tax collected on foreign digital services (Ministry of Finance 2025), and the number of merchants a company reports | Used as context only, since none measures Indonesian e-commerce | -One last analysis asks whether markets value a platform's growth in transaction value differently from growth in its take rate. The data are year-end market values from the financial data service LSEG, under the university licence. The main sample has 28 listed platforms and 138 platform-years, made up of Bukalapak and 27 platforms abroad, and it uses whole-company figures only. It is not the list of 27 used for H1. It adds Airbnb, Booking, DoorDash, Lyft and Uber, and it lacks Despegar, Mercado Libre, Ozon, Rakuten and Sea. A regression relates the yearly change in log market value to the changes in log transaction value and log take rate. It adds a fixed effect for each year and clusters standard errors by company. Each prediction names a direction, so both tests are one-sided. The question follows early work on internet companies, which found that markets price web-traffic measures beyond reported revenue (Trueman, Wong and Zhang 2000). The result is an association, and market value also changes when a company issues shares, so the thesis does not call it a stock return. A second run adds three segment series and three more whole companies as a robustness check. The test was written down before the market values were pulled. +One last analysis asks whether markets value a platform's growth in transaction value differently from growth in its take rate. The data are year-end market values from the financial data service LSEG, under the university licence. The main sample has 28 listed platforms and 138 platform-years, made up of Bukalapak and 27 platforms abroad, and it uses whole-company figures only. It is not the list of 27 used for H1. It adds Airbnb, Booking, DoorDash, Lyft and Uber, and it lacks Despegar, Mercado Libre, Ozon, Rakuten and Sea. + +A regression relates the yearly change in log market value to the changes in log transaction value and log take rate. It adds a fixed effect for each year, which removes what all platforms share in that year. It clusters standard errors by company, which allows for the repeated observations of one company. Each prediction names a direction, so both tests are one-sided. The question follows early work on internet companies, which found that markets price web-traffic measures beyond reported revenue (Trueman, Wong and Zhang 2000). The result is an association, and market value also changes when a company issues shares, so the thesis does not call it a stock return. A second run adds three segment series and three more whole companies as a robustness check. The test was written down before the market values were pulled. ## 4.6 Labels, traceability and licensed data diff --git a/papers/thesis_2026-10/ch5_results.md b/papers/thesis_2026-10/ch5_results.md index 957f4bb..dc12806 100644 --- a/papers/thesis_2026-10/ch5_results.md +++ b/papers/thesis_2026-10/ch5_results.md @@ -1,10 +1,10 @@ # Chapter 5. Results -This chapter follows the thesis from the platforms to the national figures. It first asks how much of the commerce a platform's revenue shows and whether revenue keeps pace with it. It then asks why the two parted in Indonesia and what that meant for 2023. The second half turns to the survey: where the growth in sellers came from, who the extra sellers were, and why the national figures disagree. Results marked registered were written down before the data were opened, and those marked exploratory were designed afterwards, as section 4.6 defines. Unmarked results are descriptive. +This chapter follows the thesis from the platforms to the national figures. It first asks how much of the commerce a platform's revenue shows and whether revenue keeps pace with it. It then asks why the two parted in Indonesia and what that meant for 2023. The second half turns to the survey: where the growth in sellers came from, who the extra sellers were, and why the national figures disagree. Each result is marked registered, exploratory or descriptive, as section 4.6 defines. ## 5.1 How much revenue leaves out -Take three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. Together they carried US$43 billion of transaction value and kept US$3 billion as revenue. The difference, the invisible wedge, was US$40 billion. Revenue was about 7 percent of what passed through them, and their ecosystem ratio, the wedge divided by revenue and the author's own measure, was 12.7. For every dollar of revenue, almost 13 dollars of commerce passed through the platforms without counting as revenue. A thin slice fits how platforms are paid and how they book revenue. A platform that charges both sides can charge little on one of them (Rochet and Tirole 2003). Accounting rules also let an agent record only its own commission (International Accounting Standards Board 2014). Table 5.1 shows how each amount was obtained. +Take three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. Together they carried US$43 billion of transaction value and kept US$3 billion as revenue. The difference, the invisible wedge, was US$40 billion. Revenue was about 7 percent of what passed through them, and their ecosystem ratio, the wedge divided by revenue and the author's own measure, was 12.7. For every dollar of revenue, almost 13 dollars of commerce passed through the platforms without counting as revenue. A thin slice fits how platforms are paid and how agents book revenue (Rochet and Tirole 2003; International Accounting Standards Board 2014). Table 5.1 shows how each amount was obtained. **Table 5.1. The invisible wedge of three platforms, 2023 (US$ billion)** @@ -15,7 +15,7 @@ Take three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. Tog | Grab, Indonesia | 5.38 | 0.60 | 4.78 | Revenue is a direct country disclosure; transaction value is derived from Grab's company-wide ratio | | Total | 43.23 | 3.16 | 40.07 | Rounded amounts may not add | -Little of this depends on the assumptions. If Shopee kept 9 or 11 percent instead of 10, the total would stay between US$39.9 and US$40.3 billion. At Sea's company-wide rate of 11.5 percent it would be US$39.8 billion. Dropping any one platform leaves at least US$20.7 billion. Blibli's and Bukalapak's wedges, US$3.2 and US$10.5 billion, are left out because Blibli's measure includes online travel and Bukalapak reports for its whole group. For platforms that report both amounts directly, the wedge is 97 to 99 percent of transaction value in every year, so it moves almost one for one with it. Appendix A gives the wedge by platform and year. +The wedge does not depend much on the assumptions. If Shopee kept 9 or 11 percent instead of 10, the total would stay between US$39.9 and US$40.3 billion. At Sea's company-wide rate of 11.5 percent it would be US$39.8 billion. Dropping any one platform leaves at least US$20.7 billion. Blibli's and Bukalapak's wedges, US$3.2 and US$10.5 billion, are left out because Blibli's measure includes online travel and Bukalapak reports for its whole group. For platforms that report both amounts directly, the wedge is 97 to 99 percent of transaction value in every year, so it moves almost one for one with it. Appendix A gives the wedge by platform and year. Platforms are only part of online selling. In the BPS survey, the marketplace channel carries between 16 and 26 percent of online sales value, and BPS counts food and ride apps in that channel. So BPS places most online sales value outside the marketplace channel, and platform revenue shows less still. Section 5.9 sets the platform totals beside BPS's. A small slice can still be a fair guide if it keeps its size. The next question is whether it does. @@ -33,9 +33,11 @@ Among Indonesian and regional platforms they do not, and the thesis rejects H1. | Drop each platform in turn, or treat Tokopedia and GoTo as one company | Mann–Whitney | 0.17 to 0.23 | 0.06 | 0.003 to 0.016 | | Quarterly pairs, 3 platforms against 8 | Mann–Whitney | 0.071 | 0.049 | 0.19 | -The first two rows are the main test, using the two methods of Chapter 4. The third row repeats it with growth divergence in points, and the fourth shows that no single platform carries the result. The planned test was narrower: the three Indonesian marketplaces against the eight foreign platforms chosen first. It rejects H1 when it compares pairs of years (p = 0.002), but pairs from one platform are not independent. When it compares platforms, which is the fairer count, it comes close without passing (p = 0.06), because three platforms give little power. The larger groups were set up after that first result, which is why the main test is exploratory. In the narrower test, revenue growth and transaction value growth had a correlation of 0.10 in Indonesia and 0.98 abroad, excluding Sea (Appendix B). The quarterly test in the last row does not reject H1. Its Indonesian pairs mostly cover 2023 to 2026, after most of the repricing, and the large moves came between 2021 and 2024. So the rejection describes that period. +In Table 5.2, the first two rows are the main test, using the two methods of Chapter 4. The third row repeats it with growth divergence in points, and the fourth shows that no single platform carries the result. -The thin slice explains much of the threefold difference, which is an exploratory finding. A log change in the take rate is roughly the change in points divided by the take rate itself. Indonesian and regional platforms changed their take rates by about 1 point a year, as foreign platforms did. But the typical Indonesian and regional platform keeps only 1.6 percent of each sale, against 16.9 percent abroad. Among the foreign platforms the share runs from 2.6 percent at Shopify to 71 percent at Zalando, which also sells goods itself. If transaction value is flat, a rise of 1 point lifts revenue by more than half at a platform that keeps 1.6 percent. It lifts revenue by about 6 percent at one that keeps 16.9 percent. The larger the wedge is next to revenue, the more a small price change moves revenue growth. Figure 5.1 shows the three measures side by side. What the platforms actually changed is the next question. +The planned test was narrower: the three Indonesian marketplaces against the eight foreign platforms chosen first. It rejects H1 when it compares pairs of years (p = 0.002), but pairs from one platform are not independent. When it compares platforms, which is the fairer count, it comes close without passing (p = 0.06), because three platforms give little power. The larger groups were set up after that first result, which is why the main test is exploratory. In the narrower test, revenue growth and transaction value growth had a correlation of 0.10 in Indonesia and 0.98 abroad, excluding Sea (Appendix B). The quarterly test in the last row does not reject H1. Its Indonesian pairs mostly cover 2023 to 2026, after most of the repricing, and the large moves came between 2021 and 2024. So the rejection describes that period. + +The thin slice explains much of the threefold difference, which is an exploratory finding. A log change in the take rate is roughly the change in points divided by the take rate itself. Indonesian and regional platforms changed their take rates by about 1 point a year, as foreign platforms did. But the typical Indonesian and regional platform keeps only 1.6 percent of each sale, against 16.9 percent abroad. Among the foreign platforms the share runs from 2.6 percent at Shopify to 71 percent at Zalando, which also sells goods itself. If transaction value is flat, a rise of 1 point lifts revenue by more than half at a platform that keeps 1.6 percent. It lifts revenue by about 6 percent at one that keeps 16.9 percent. Figure 5.1 shows the three measures side by side. What the platforms actually changed is the next question. ![Figure 5.1](figures/fig5_1_thin_slice.png) @@ -89,7 +91,7 @@ Sellers did not describe a boom either. BPS asks each business how its online sa On BPS's published figures, H2 holds. From 2022 to 2023, online sales value grew by 40.6 percent, the number of businesses by 27.4 percent and sales per business by 10.4 percent. So more businesses account for 71 percent of the growth. From 2023 to 2024 the figures were 17.1, 15.3 and 1.5 percent, and more businesses account for 90 percent, or 71 percent on BPS's other count for 2023. The split is arithmetic, and it cannot tell a new seller from one counted for the first time. -The entry test can, and its result is registered. In the 2024 file, 13.45 percent of sellers began selling online in 2023, and the rule required at least 21.5 percent. The test fails. Even if no seller stopped, that many new sellers lets the count grow by at most 15.5 percent over the sellers already there, against the published 27.4 percent. Respondents often place past events nearer to the interview than they were (Neter and Waksberg 1964). If sellers who began earlier reported 2023, that would make new sellers look more numerous, so the shortfall is if anything understated. +The entry test separates the two, and its result is registered. In the 2024 file, 13.45 percent of sellers began selling online in 2023, and the rule required at least 21.5 percent. The test fails. Even if no seller stopped, that many new sellers lets the count grow by at most 15.5 percent over the sellers already there, against the published 27.4 percent. Respondents often place past events nearer to the interview than they were (Neter and Waksberg 1964). If sellers who began earlier reported 2023, that would make new sellers look more numerous, so the shortfall is if anything understated. **Table 5.4. The 2023 rise in the count of online sellers (thousands)** @@ -103,9 +105,11 @@ Source: BPS survey files, weighted; author's calculations. Table 5.4 shows the accounting. The rise of 819 thousand is the gap between the weighted totals of the two files, and BPS's own published counts give 821 thousand (BPS 2025). The 2023 survey counted 306 thousand more sellers who began before 2023 than the whole 2022 count held. That is 37 percent of the rise if no seller stopped and 56 percent if sellers stopped at 5 percent a year. So about half of the rise is not explained by first-time entry. This accounting and the checks below are exploratory, and they point the same way. -Cohorts behave as they should, except in 2023. Sellers who began by 2020 numbered 9.9 percent fewer in the 2022 survey than in the 2020 one, over two years. In the 2023 survey they numbered 6.0 percent more than in the 2022 one. Between the 2023 and 2024 surveys, the cohort that began before 2020 shrank by 10.2 percent, and about 750 thousand new sellers appeared against a rise of 584 thousand. The break is also uneven. Across the 34 provinces, count growth had almost no link to the share of new sellers (0.10) and a weak link to growth in sample size (0.35). These are rank correlations, which compare the order of provinces on two measures and run from −1 to 1. Sellers who began by 2022 and use only chat or social media rose by 13 percent, while marketplace users rose by 0.3 percent. BPS's published error rates put the standard error of the national count, the usual size of its sampling noise, at about 1.4 percent. The 306 thousand sellers are about 4.6 times the standard error of the change. That calculation applies the 2024 edition's error rates to both years and treats the two surveys as independent. +Cohorts behave as they should, except in 2023. Sellers who began by 2020 numbered 9.9 percent fewer in the 2022 survey than in the 2020 one, over two years. In the 2023 survey they numbered 6.0 percent more than in the 2022 one. Between the 2023 and 2024 surveys, the cohort that began before 2020 shrank by 10.2 percent, and about 750 thousand new sellers appeared against a rise of 584 thousand. + +The break is also uneven. Across the 34 provinces, count growth had almost no link to the share of new sellers (0.10) and a weak link to growth in sample size (0.35). These are rank correlations, which compare the order of provinces on two measures and run from −1 to 1. Sellers who began by 2022 and use only chat or social media rose by 13 percent, while marketplace users rose by 0.3 percent. BPS's published error rates put the standard error of the national count, the usual size of its sampling noise, at about 1.4 percent. The 306 thousand sellers are about 4.6 times the standard error of the change. That calculation applies the 2024 edition's error rates to both years and treats the two surveys as independent. -The value side tells a similar story. BPS's value rose from about US$53 billion to about US$72 billion. Sellers not explained by entry account for 15 to 17 percent of this, and entrants for 25 to 30 percent. The remaining 53 to 59 percent, about US$11 to 12 billion at the 2023 exchange rate, cannot be traced to existing sellers' answers, which show a mean change of −4.3 percent. These checks point to wider survey coverage of chat and social sellers. That is the pattern survey research warns about, because a change in how a survey finds respondents can break a series (van den Brakel, Smith and Compton 2008). The data cannot separate it from sellers returning after a pause. Who were the extra sellers? +On the value side, BPS's measure rose from about US$53 billion to about US$72 billion. Sellers not explained by entry account for 15 to 17 percent of this, and entrants for 25 to 30 percent. The remaining 53 to 59 percent, about US$11 to 12 billion at the 2023 exchange rate, cannot be traced to existing sellers' answers, which show a mean change of −4.3 percent. These checks point to wider survey coverage of chat and social sellers. That is the pattern survey research warns about, because a change in how a survey finds respondents can break a series (van den Brakel, Smith and Compton 2008). The data cannot separate it from sellers returning after a pause. Who were the extra sellers? ## 5.7 Who the extra sellers were, and whether they returned @@ -121,7 +125,7 @@ A returning seller would usually sell for only part of the year, unless the sell ## 5.8 Channels and records -Two additional analyses show where these sellers sit. The path of marketplace use, the value held by sellers without statements and the World Bank results were registered. The comparison within the pre-2020 group is exploratory. +Two additional analyses show where online sellers sit. The path of marketplace use, the value held by sellers without statements and the World Bank results were registered. The comparison within the pre-2020 group is exploratory. Most online sellers do not use a marketplace, and about 96 percent use chat or social media. Use of marketplaces fell every time it was measured, from 21.6 percent of sellers in 2020 to 17.2 percent in 2024. BPS worded the question slightly differently for 2023 and 2024 than for the earlier years. From 2023 to 2024, BPS's marketplace value grew by 1.45 percent and other channels by 20.57 percent, so 98.5 percent of the increase came from outside the marketplaces. BPS's marketplace category includes many food and ride merchants: 43 percent of its sellers use only Gojek or Grab, and 47 percent use a shopping marketplace. @@ -133,7 +137,7 @@ Records are thinner outside the marketplaces. In 2023, 28.6 percent of marketpla Value shares rest on the bracket scenarios of section 4.4. The annual reading passes the validation rule, because the middle scenario gives 1.95 times BPS's total for 2022, and the monthly reading fails at 23 times. The lower brackets alone already exceed BPS's total, so the shares are ranges. Sellers without statements held between 26 and 40 percent of online value in 2022, against 79 percent of the count. The registered prediction was that their share of value would not fall by more than 5 points between 2020 and 2022. It fell by 5.8 points, so the prediction failed, narrowly. A second registered version gives a fall of 3.3 points, which is inside the limit. -The World Bank surveys point the same way. In six Indonesian cities, 27 percent of unregistered firms use social media with their customers. Among those, 70 percent keep no written records, and their median sales are about US$4,000 a year. The registered prediction on their mean sales failed: it was below about US$6,600, and the mean is about US$7,300. Among formal firms, 39.9 percent in Indonesia have neither a website nor online tax filing, the highest of seven countries, against 9.1 percent in Malaysia and 8.7 percent in Thailand. +World Bank surveys of Indonesian firms add evidence on records and online presence. In six Indonesian cities, 27 percent of unregistered firms use social media with their customers. Among those, 70 percent keep no written records, and their median sales are about US$4,000 a year. The registered prediction on their mean sales failed: it was below about US$6,600, and the mean is about US$7,300. Among formal firms, 39.9 percent in Indonesia have neither a website nor online tax filing, the highest of seven countries, against 9.1 percent in Malaysia and 8.7 percent in Thailand. ## 5.9 Why the national figures disagree @@ -141,7 +145,7 @@ Chapter 1 began with two official figures that moved in opposite directions in 2 In 2023, Bank Indonesia's figure fell by 4.7 percent and BPS's rose by 40.6 percent. Where the two cover the same ground, the growth is small. BPS's own marketplace slice grew by 0.2 percent from 2022 to 2023 in the middle scenario, and by 0.2 to 13.2 percent across the scenarios. It grew by 1.45 percent from 2023 to 2024. A registered test gives a mixed answer on size. The part of BPS's marketplace sales that went to final consumers in 2022 is 0.68 times Bank Indonesia's figure in the middle scenario and 0.16 times in the low one. In the high scenario it is 1.62 times. The prediction was that it would fall below Bank Indonesia's figure, so it holds in the first two scenarios and fails in the third. Most of the disagreement then sits outside the marketplaces, in the count of sellers that only BPS reaches. -The 2024 figures show the same split. Those that cover mainly platforms grew by 5 to 7 percent, and BPS's grew by 17.1 percent. Adjacent figures point the same way as BPS but do not measure Indonesian e-commerce. Southeast Asian parcels grew by 25.2 percent, and the tax on foreign digital services by 24.9 percent. +In 2024, the figures again divide by coverage. Those that cover mainly platforms grew by 5 to 7 percent, and BPS's grew by 17.1 percent. Adjacent figures point the same way as BPS but do not measure Indonesian e-commerce. Southeast Asian parcels grew by 25.2 percent, and the tax on foreign digital services by 24.9 percent. Sizes cannot be reconciled. In 2023, BPS's marketplace slice was about US$13 billion, against US$38 billion for Tokopedia and the Shopee estimate together. BPS's whole total for 2023, US$72 billion, lies above the three platforms' US$43 billion in Table 5.1, but the two count different things. In 2024 it was less than a quarter of Momentum Works' marketplace estimate of about US$56.5 billion. Platform transaction value counts shipping, digital goods, travel and offline sales that a seller survey does not count in the same way. Counts differ too: Tokopedia alone said in a company statement that it had about 15 million merchants in 2022, while BPS counts about 0.6 million marketplace sellers. From 272cc24079bb970d80f4aa801442b1e600706449 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 13 Oct 2026 11:34:20 +0000 Subject: [PATCH 32/65] Thesis ch2-3: Chapter 2 rebuilt around the answers the literature gives and where they stop; Chapter 3 opens with its logic, formulas first, entry test first Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 7 ++++ papers/thesis_2026-10/ch2_literature.md | 50 +++++++++++++------------ papers/thesis_2026-10/ch3_framework.md | 42 ++++++++++++--------- 3 files changed, 57 insertions(+), 42 deletions(-) diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 5206e93..673c2db 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -322,3 +322,10 @@ Checked and fixed against the README rules, in the order Chapter 2, 3, 4, 5: - **Chapter 4:** 4.1's first paragraph no longer re-defines revenue and transaction value; the own-goods rules are a paragraph of their own; 4.2's checks are a separate paragraph, and the unexplained "thin-slice explanation of Chapter 5" now reads "to see whether a thin take rate explains the result"; "it also reports" (ambiguous subject) fixed; 4.3 lost its repeat of the opening; the data-use sentence in 4.4 is said once; the World Bank sentence and the regression paragraph are separated; "fixed effect" and "clusters" are explained in plain words. - **Chapter 5:** openers that leaned ("Little of this", "The entry test can", "The value side tells a similar story", "these sellers", "The World Bank surveys point the same way", "The 2024 figures show the same split") now name their subjects; the literature restatement in 5.1 is one clause with the citations; 5.2's long paragraph is split and its repeat of Chapter 3's wedge argument is cut; two paragraphs over 170 words are split. - **Checked and left:** how Chapter 2 hands to Chapter 3 (three questions to the chain and its two links) and Chapter 3 to 4 (each test's data and rule) works. + +### Chapters 2 and 3 rebuilt on the researcher's reading (13 Oct 2026) + +The researcher's verdict on the previous version: Chapter 2 did not do its job. Chapter 1 already sets the question, so Chapter 2 should give the current academic consensus and several perspectives that try to answer it with what existed before this research, show where each answer fails, and let the gap follow from those failures. Chapter 3 should open with a declaration of its logic and anchor it (the figure first), and no paragraph may open on a term the reader has not met. +- **Chapter 2** is now organised by attempts to answer the thesis question: pricing theory and accounting (2.1), national statistics and survey methods (2.2), tax research on platform records (2.3). It opens by naming the four fields and testing each against one case (Tokopedia 2023). Each section states the field's consensus, develops it in full paragraphs, and ends with where its answer stops. 2.4 builds the three open questions from those stopping points. The two-group idea is introduced with examples (shoppers and merchants; riders and drivers) before it is used; the accounting passage says in plain words what a principal and an agent record and that discounts reduce revenue. Use limits from `REFERENCES_WORKING.md` respected (Hagiu and Wright as business model, not an accounting test; IFRS 15 "normally"; web-traffic studies named as such). A claim about UNCTAD comparing levels at one point in time was dropped because the audit says its full report is unchecked. +- **Chapter 3** opens with the declaration that a measure is a stand-in and the framework tests the links; the figure placeholder follows at once, then the conditions, then how the two links answer the research question, then the three questions with what measures each. 3.1 puts the formulas first and names each measure at its first use; m0 and m1 are defined before the formula that uses them; the Tokopedia example is followed by what each measure records before the wedge and growth divergence are contrasted. 3.2 opens by saying the two hypotheses test the two links. 3.3 states the entry test first as a block, then the arithmetic. 3.4 is four questions instead of a repeated list. 3.5 is unchanged (the researcher said it reads clearly). +- **Not yet done:** the Chapter 4 opening still reads as complaint ("thinner than the question deserves"); the researcher wants a disclaimer that is presented tactically. Chapters 4 and 5 are on hold until 2 and 3 are accepted. One known slip in 5.9 ("In 2024 it was less than a quarter…" with an unclear "it"). diff --git a/papers/thesis_2026-10/ch2_literature.md b/papers/thesis_2026-10/ch2_literature.md index 1ea3a7f..e266857 100644 --- a/papers/thesis_2026-10/ch2_literature.md +++ b/papers/thesis_2026-10/ch2_literature.md @@ -1,42 +1,44 @@ # Chapter 2. Literature Review -This thesis asks when a figure stops following the activity it is meant to describe. A platform's revenue is one such figure, and it is meant to describe the commerce the platform carries. A national count of online sellers is another, and it is meant to describe how many businesses sell online. Judging such figures, and what follows from them, takes three kinds of knowledge. Each comes from a different part of the literature. +The question of how much of an online economy a platform's revenue leaves out, and when that matters for growth, has not been answered in one place. Four bodies of research have each answered part of it, from different directions. Platform economics explains why a platform keeps only a share of what its users trade. Accounting explains what a company may record as revenue. National statistics explain how the commerce of a country is counted. Tax research explains what the records of a platform can be used for. -The first is why a figure and its activity can differ at all. For platform revenue, the answer lies in how platforms set prices and how accountants record sales. The second is how else the activity can be measured, so that a figure has something to be checked against. Here the literature covers national accounts, business surveys and the methods behind them. The third is how the records are used. Governments now write tax rules on platform records, and what a rule can reach depends on what the records hold. +A case from Indonesia shows what the four fields can and cannot do. In 2023 Tokopedia's revenue rose by 53 percent while the value of the sales it carried fell by 9 percent. Platform economics would look for a change in how the platform prices the two groups it serves, shoppers and merchants. Accounting would look for a change in what the company records as revenue, such as smaller discounts. National statistics would ask whether the sales of all sellers, on platforms and off them, moved with either figure. Tax research would ask what the platform's records show about the sellers behind the sales. Each reading is sound. None says how large such a difference is, how often it occurs, or whether it changes the story of growth. -The chapter has one section for each part. Section 2.1 explains what a platform is and how it earns and reports revenue. Section 2.2 explains how online selling is measured and what can go wrong in the measure. Section 2.3 explains how platform records and information from third parties are used in tax. Each section ends by stating what it shows and what it leaves out. Section 2.4 collects what is left out into the questions this thesis answers. +This chapter takes the four fields in turn. For each, it sets out what the field has established, what answer that gives to the thesis question, and where the answer stops. Section 2.4 gathers the stopping points into the gap that the thesis addresses. -## 2.1 What a platform is and how it earns revenue +## 2.1 Why revenue differs from commerce: pricing and accounting -A platform is a business that connects two groups and helps them deal with each other. Tokopedia connects shoppers with merchants, and Grab connects riders with drivers. Platforms such as these do not make or own what is sold. Platform economics is the field that studies how such businesses set prices and earn money. Its starting point is that a platform serves two groups at once, so it must price them together (Rochet and Tirole 2003; Evans and Schmalensee 2016). +Economic theory treats a platform's revenue as the price of an introduction and not as the value of what is traded. A platform brings two groups together: buyers and sellers on a marketplace, riders and drivers on a ride-hailing app. Each group joins mainly because the other is already there, so the platform has to attract both before it can earn from either (Rochet and Tirole 2003; Caillaud and Jullien 2003; Evans and Schmalensee 2016). This is the central result of the field, and it fits a platform whose revenue is a small share of the commerce that passes through it. -Platform pricing explains why a platform's revenue need not follow the commerce it carries. A platform needs both groups to join. If few sellers join, buyers have little reason to come, and the same holds the other way round. So a platform may charge one group little or nothing and earn from the other. How much it earns then depends on its price structure as well as on the commerce it carries (Rochet and Tirole 2003; Caillaud and Jullien 2003; Parker and Van Alstyne 2005; Armstrong 2006; Evans and Schmalensee 2016). The business model matters as well. A marketplace earns a fee from each sale, while a reseller buys goods and sells them on (Hagiu and Wright 2015). The reseller's revenue is the whole sale, and the marketplace's is the fee. +Attracting two groups also shapes the price. A platform may charge one group little or nothing and earn from the other, because the presence of the free group makes the platform worth more to the paying group. The total fee and the way it is split between the groups are both choices, and the split affects how many transactions take place (Rochet and Tirole 2003; Parker and Van Alstyne 2005; Armstrong 2006). So a platform can change how much it earns from a given volume of commerce by changing its prices. The business model sets the starting point. A marketplace earns a fee on each sale between others, while a reseller buys goods and sells them itself (Hagiu and Wright 2015). The fee is a fraction of the sale, and the reseller's takings are the whole sale. -Accounting rules decide how much of each sale becomes revenue. Revenue recognition is the name for these rules. International Financial Reporting Standard 15 (IFRS 15) is the international standard for it. Under IFRS 15, a company acting as a principal reports the full payment from the customer as revenue. A company acting as an agent reports only what it earns from arranging the sale (International Accounting Standards Board 2014). Payments that a company makes to its own customers, such as discounts and vouchers, can reduce its reported revenue (International Accounting Standards Board 2014). A study of internet firms found that they chose different ways of reporting revenue, including whether to report sales gross (Bowen, Davis and Rajgopal 2002). So fees, customer incentives, accounting choices, service mix, acquisitions and changes in business scope can all move reported revenue without an equal change in commerce. +Accounting rules add a second reason for the difference. They decide how much of a sale a company may record as its own revenue, and the international rule is International Financial Reporting Standard 15 (IFRS 15). It asks whether a company acts as a principal or as an agent. A principal controls the goods or service before they reach the customer and records the whole price as revenue. An agent only arranges the sale and records its commission. The standard also normally treats discounts and vouchers that a company gives its customers as a cut in the price, so they reduce its revenue (International Accounting Standards Board 2014). -Revenue is not the only reported figure whose meaning depends on how it is computed. Analysts use financial statements more easily when the accounting is comparable (De Franco, Kothari and Verdi 2011). Measures that companies choose to report, such as transaction value or active users, lose information when their definition changes or goes unreported (Givoly, Li, Lourie and Nekrasov 2019). +Companies have some room to choose how they record revenue. A study of internet firms in the early 2000s found that they reported the same kind of sale in different ways, including whether to report it gross (Bowen, Davis and Rajgopal 2002). For a platform, then, reported revenue can rise because fees rise, because discounts shrink, or because the company changes how it records a sale. None of the three needs a change in what customers buy. -Investors also look beyond revenue. In the early years of the internet, activity measures such as web traffic carried information about a firm's value and future revenue that reported revenue did not show (Trueman, Wong and Zhang 2000, 2001; Rajgopal, Venkatachalam and Kotha 2003). +Companies also report operating measures of their own, such as transaction value or active users. Analysts use financial statements more easily when the accounting is comparable (De Franco, Kothari and Verdi 2011). Operating measures lose information when the company changes its definition or stops reporting them (Givoly, Li, Lourie and Nekrasov 2019). Transaction value, the figure on which this thesis relies, is such a measure. -Platform economics and accounting research explain why transaction value can exceed revenue and why that difference can move. They do not measure how large it is for platforms in a market such as Indonesia, or whether it stays steady from year to year. +Investors do not rely on revenue alone. In the early years of the internet, activity measures such as web traffic carried information about a firm's value and future revenue that reported revenue did not show (Trueman, Wong and Zhang 2000, 2001; Rajgopal, Venkatachalam and Kotha 2003). -## 2.2 How online selling is measured +Pricing theory and accounting together answer why revenue and commerce differ, and the answer fits a case like Tokopedia's. They do not answer how much. The theory explains how prices are set, but it does not say how large a platform's share should be or how steady it should stay. The accounting evidence concerns the rules and the reporting choices of internet firms in the early 2000s. The investor evidence rests on web traffic, which is not the transaction value of a modern platform. None of it follows the revenue and the transaction value of the same platforms through several years, and none covers a market such as Indonesia. -E-commerce means selling goods or services over the internet. No single number measures it, because different groups count different things. Listed platforms report the revenue they record and often their transaction value. Statistics offices survey businesses and report their online sales. Central banks report the value of payments. Industry reports estimate the size of whole markets from platform transaction values (Google, Temasek and Bain 2024; BPS 2025; Bank Indonesia 2025). Chapter 4 describes each source for Indonesia. +## 2.2 How the online economy is counted -Official frameworks already separate the commerce a platform carries from the service the platform provides. National accounts, the statistics that add up a country's production, income and spending, treat the sale between buyer and seller as distinct from the platform's intermediation service. They do not count the full transaction value as the platform's output (Ahmad and Schreyer 2016; International Monetary Fund 2018; Organisation for Economic Co-operation and Development [OECD] 2023; United Nations et al. 2025). +Official statistics give a firm answer on what a platform's commerce is not: it is not the platform's own output. National accounts add up a country's production, income and spending. They treat the sale between a buyer and a seller as one thing and the platform's service of bringing them together as another. So the full transaction value does not count as the platform's output (Ahmad and Schreyer 2016; International Monetary Fund 2018; Organisation for Economic Co-operation and Development [OECD] 2023; United Nations et al. 2025). -The sources do not give the same number. The United Nations Conference on Trade and Development (UNCTAD) has shown that e-commerce values change with the definition used. It has also set business e-commerce sales beside the transaction values of online platforms (UNCTAD 2023, 2024). A difference between figures is harmless while they move together. It matters when they part, because the choice of figure can then change the conclusion about scale and growth. Economists who check national income figures against satellite images of night-time lights follow the same logic: a figure should be tested against the activity it stands for (Henderson, Storeygard and Weil 2012). +Counting the commerce itself is harder, because each producer of figures counts something different. Listed platforms report the revenue they record and often their transaction value. Statistics offices survey businesses about their online sales. Central banks report the value of payments. Industry reports estimate the size of whole markets from platform transaction values (Google, Temasek and Bain 2024; BPS 2025; Bank Indonesia 2025). The United Nations Conference on Trade and Development (UNCTAD) has shown that e-commerce values change with the definition used, and it has set business e-commerce sales beside the transaction values of online platforms (UNCTAD 2023, 2024). -The growth of national online sales has two possible sources, which makes it a figure worth testing. It can come from existing sellers selling more or from more sellers entering, and so it can be split between the two. Trade economists divide the growth of exports in the same way (Hummels and Klenow 2005). +A difference between two figures for the same activity does no harm while they move together. It matters when they part, because the choice of figure then changes the conclusion about size and growth. Economists have a method for this problem. They test a figure against the activity it stands for, as researchers do when they check national income figures against satellite images of night-time lights (Henderson, Storeygard and Weil 2012). + +The growth of national online sales can be tested in the same spirit, because it has two possible sources. Existing sellers can sell more, or more sellers can enter. Trade economists divide the growth of exports into the same two kinds of margin (Hummels and Klenow 2005). The split of national growth rests on a count of online sellers, and that count has a weakness of its own. It depends on how the survey finds sellers. A change in design can break a series in a way that looks like a change in the economy (van den Brakel, Smith and Compton 2008; van den Brakel, Zhang and Tam 2020). Respondents also misdate events, and they tend to place them closer to the interview than they were (Neter and Waksberg 1964). Exit among small firms is more common for young firms and young owners (McKenzie and Paffhausen 2019), which matters when a count has to be corrected for sellers who stopped. -Research on measurement shows that official frameworks treat commerce and platform service as different things. It shows that figures from different sources need not agree, and that a seller count can break when a survey changes. It does not show whether platform and national figures move together in a given country, or what a rise in a seller count contains. +Official frameworks and survey methods say what should be counted and how the figures can mislead. They do not say whether platform figures and national figures grow for the same reasons, and the studies reviewed here do not test it for one country over time. They also say little about what a rise in a count of online sellers contains. -## 2.3 Platform records and third-party information +## 2.3 What platform records can show -A platform keeps a record of every sale: who sold, what, for how much and when. Third-party information is a report about a taxpayer made by someone else, such as an employer, a bank or a platform. Tax authorities value it because it does not depend on the taxpayer's own account. +Tax research offers a different answer to the question. If revenue shows little of the commerce, the commerce can still be seen in the platform's records, because a platform records every sale: who sold, what, for how much and when. A report about a taxpayer made by someone else, such as an employer, a bank or a platform, is third-party information. Tax authorities value it because it does not depend on the taxpayer's own account. Platform records can help where other records are thin. Sellers and service providers may be small, self-employed or weakly represented in conventional financial records, yet the platform records what they sell. This differs from the shadow-economy setting, where reliable records may be sparse (La Porta and Shleifer 2014; Ulyssea 2018). Digital records can carry information that conventional records lack (Berg, Burg, Gombovic and Puri 2020), and platform-mediated work leaves traces in financial and administrative data (Barrios, Hochberg and Yi 2022; Denes, Lagaras and Tsoutsoura 2025). @@ -44,14 +46,14 @@ Information from a third party also changes how people comply with tax. Audit ex Several rules now apply third-party reporting to platforms. The OECD, a group of mostly high-income countries, published model rules under which platforms report what their sellers earn (OECD 2020). The European Union's DAC7 directive applies them (European Union 2021). Indonesia's marketplace tax rule goes further: it makes marketplaces collect tax from their sellers, not only report on them (Ministry of Finance of the Republic of Indonesia 2025). -Research on third-party information shows that reports from third parties help compliance, and that platform rules apply the idea. It does not show how many of a country's online sellers such a rule can reach, or how much of the market platform records cover. +The consensus on platform records is that they are useful and that rules can rest on them. The literature does not say how many of a country's online sellers such a rule can reach. The answer depends on how much of the selling happens on platforms, which is the very point that Section 2.2 left open. The studies reviewed here do not estimate it for Indonesia. ## 2.4 The research gap -Platform economics, accounting, measurement and tax research each answer their own question. None measures how closely platform figures follow the activity behind them, or whether national figures do. Three questions remain. +Read together, the four answers stop at the same place. Each explains why figures can differ, and none measures how far they differ, how steadily, or with what effect on growth. Three questions remain open. -1. **How far does a platform's revenue follow the commerce it carries?** Section 2.1 gives reasons why the two can part, but not when or by how much. -2. **Do platform figures follow national growth?** Section 2.2 shows that figures can differ. The answer depends on where national growth comes from and on whether a count of sellers keeps pace with entry. -3. **How far do rules built on platform records reach?** Section 2.3 shows that such records can support tax rules, but not how much of the online economy they cover. +1. **How far does a platform's revenue follow the commerce it carries?** Pricing theory and accounting give reasons why the two part (Section 2.1). No study reviewed here follows the revenue and the transaction value of the same platforms over several years to measure how much revenue leaves out and how steady that share is. +2. **Do platform figures follow national growth?** Official frameworks show that figures differ (Section 2.2). Whether platform growth tracks national growth depends on where national growth comes from and on whether the count of sellers behind it keeps pace with entry. The studies reviewed here test neither. +3. **How far do rules built on platform records reach?** Tax research shows that platform records can support rules (Section 2.3). It does not show how much of the online economy those records cover. -Chapter 3 turns the three questions into measures and hypotheses. +Chapter 3 builds the framework that turns these questions into measures and hypotheses. diff --git a/papers/thesis_2026-10/ch3_framework.md b/papers/thesis_2026-10/ch3_framework.md index ee87f79..c89f2d1 100644 --- a/papers/thesis_2026-10/ch3_framework.md +++ b/papers/thesis_2026-10/ch3_framework.md @@ -1,16 +1,18 @@ # Chapter 3. Framework and Hypotheses -Three figures describe the same online economy at three scales: a platform's revenue, the transaction value of the sales it handles, and national online commerce. Each is used as a stand-in for the next one out. Investors read revenue as a sign of the commerce a platform carries. Platform figures are then read as a sign of what is happening in the national total. +A measure of the digital economy is a stand-in. Revenue stands in for the commerce a platform carries, and the figures of platforms stand in for the online economy of a country. A stand-in is only as good as its link to the thing it represents, so this thesis builds its framework around the links. It treats each link as a claim that can fail, measures how far each claim holds, and states in advance what result would show that it does not. + +Figure 3.1 shows the structure. Three circles sit inside one another. The smallest is a platform's revenue, the part of each sale that it keeps. Around it is platform commerce, the full value of the sales the platform handles. The largest is all online commerce in a country, which also includes sellers who never use a platform. The inner link joins revenue to platform commerce, and the outer link joins platform commerce to all online commerce. [Figure 3.1 to be drawn: three nested circles, with revenue inside platform commerce inside all online commerce, H1 on the inner link and H2 on the outer link.] -A stand-in works only while a condition holds. For the inner link, the condition is that the share of each sale that the platform keeps stays steady, because then revenue follows the commerce. For the outer link, the condition is that national growth comes from sellers the platforms record. +Each link holds only under a condition. The inner link holds while the share of each sale that the platform keeps stays steady, because then revenue rises and falls with the commerce. The outer link holds while national growth comes from the sellers that platforms record, because then platform figures show what the whole economy is doing. When a condition fails, the stand-in misleads. Revenue can rise while commerce falls, and platform growth can miss growth that happens elsewhere. -The thesis asks three questions about these links, and this chapter turns each into a measure, a test or both. +The two links answer the research question together. The inner link measures how much revenue leaves out and whether the share it keeps stays steady. The outer link asks whether the commerce that platforms carry is where the growth of the whole online economy happens. A third question sits beside them, because governments now write rules on platform records, and a rule can reach only what the records cover. So the framework asks three questions, and each becomes a measure, a test or both. -1. **How far does revenue follow commerce?** This is the inner link. Section 3.1 defines the measures, and Section 3.2 states H1. -2. **Do platform figures follow national growth?** This is the outer link. Section 3.2 states H2, and Section 3.3 sets out the entry test of the seller count behind it. -3. **How far do rules built on platform records reach?** This sits beside the chain, because a rule reaches only what the records cover. Section 3.4 introduces the analysis, and Chapter 6 develops it. +1. **How far does revenue follow commerce?** The invisible wedge, the part of transaction value that revenue leaves out, measures how much is missing. Growth divergence, the difference between the growth of revenue and the growth of transaction value, measures whether revenue still follows commerce from year to year. Hypothesis H1 states the expected case (Sections 3.1 and 3.2). +2. **Do platform figures follow national growth?** Hypothesis H2 asks whether national growth comes mainly from more businesses selling online or from higher sales per business. An entry test checks what a rise in the count of businesses contains (Sections 3.2 and 3.3). +3. **How far do rules built on platform records reach?** A separate analysis counts the sellers that such a rule can reach (Section 3.4 and Chapter 6). ## 3.1 Terms and measures @@ -31,7 +33,7 @@ Table 3.1 lists the terms used throughout the thesis, each defined once, with th | Online seller | | A business that the national statistics office, BPS, counts as selling online; the text also calls it a seller or a business. The count of online sellers is BPS's estimate of how many there are in a year. | | First-time entry | | A seller that began selling online in the year being measured. | -Three measures describe a platform. The invisible wedge gives the size of what revenue leaves out, in currency. The ecosystem ratio gives the same size relative to revenue, so that large and small platforms can be compared. Growth divergence shows whether revenue still follows the commerce from one year to the next. The wedge and growth divergence are plain arithmetic on reported figures. The ecosystem ratio restates the take rate, as the formulas show. +The framework builds three measures from a platform's revenue (R) and transaction value (V). Each is a formula: ``` W = V − R @@ -39,31 +41,33 @@ E = W / R = 1/m − 1 D = g(R) − g(V) ``` -Let m0 and m1 be the take rate in the first and the second year. Revenue equals the take rate times transaction value, so revenue in the second year is (1 + g(V)) × m1 / m0 times revenue in the first year. Growth divergence can then be written with the take rate alone: +The invisible wedge W is the part of transaction value that revenue leaves out, in currency. The ecosystem ratio E is the same part relative to revenue, so that large and small platforms can be compared. It restates the take rate. Growth divergence D shows whether revenue still follows the commerce from one year to the next. The wedge and growth divergence are plain arithmetic on reported figures. + +Growth divergence can also be written with the take rate alone. Let m0 be a platform's take rate in the first year and m1 its take rate in the second year. Revenue is the take rate times transaction value in each year, so revenue in the second year equals (1 + g(V)) × m1 / m0 times revenue in the first year. Subtracting the growth of transaction value gives: ``` D = (1 + g(V)) × (m1 − m0) / m0 ``` -When the take rate is steady, D is zero and revenue grows exactly as fast as the commerce. When the take rate rises, D is positive and revenue grows faster than the commerce. +When the take rate is steady, m1 equals m0, D is zero, and revenue grows exactly as fast as the commerce. When the take rate rises, D is positive, and revenue grows faster than the commerce. -Tokopedia shows how this works. Between 2022 and 2023 its take rate rose by about 68 percent, so that it kept about two thirds more of each sale. Its transaction value fell by about 9 percent, yet its revenue grew by about 53 percent. Growth divergence was 62 points, which is (1 − 0.09) × 0.68 = 0.62. +Tokopedia between 2022 and 2023 shows the formula at work. Its take rate rose by about 68 percent, so that it kept about two thirds more of each sale. Its transaction value fell by about 9 percent, yet its revenue grew by about 53 percent. Growth divergence was 62 points, which is (1 − 0.09) × 0.68 = 0.62. -The wedge and growth divergence do different jobs. The wedge follows the commerce. At the marketplaces studied in Indonesia, revenue is a few percent of transaction value, so the wedge is almost all of transaction value and moves with it. Growth divergence follows the take rate and shows whether revenue remains a fair guide. +Tokopedia's case also shows what each measure records. Its wedge was about 98 percent of its transaction value in both years, so the wedge moved with the commerce and fell by about 10 percent along with it. Its growth divergence was 62 points, because revenue followed the take rate and not the commerce. The two measures answer different halves of the research question. The wedge answers the question of size, and at the marketplaces studied in Indonesia it is almost all of transaction value. Growth divergence answers the question of growth, because it follows the take rate and shows whether revenue remains a fair guide. -The wedge has a second use. Its size next to revenue decides how far a change in the take rate swings revenue growth. Take a platform that keeps 2 percent of each sale, with an ecosystem ratio of 49. If transaction value is flat, a rise in its take rate from 2 to 3 percent lifts revenue growth by about 50 points. A platform that keeps 17 percent has an ecosystem ratio near 5, and a rise from 17 to 18 percent lifts its revenue growth by about 6 points. A wedge that is large next to revenue means more than a big number. It also means that ordinary changes in fees or discounts can swing reported growth. +The size of the wedge next to revenue also decides how far a change in the take rate swings revenue growth. Take a platform that keeps 2 percent of each sale, with an ecosystem ratio of 49. If transaction value is flat, a rise in its take rate from 2 to 3 percent lifts revenue growth by about 50 points. A platform that keeps 17 percent has an ecosystem ratio near 5, and a rise from 17 to 18 percent lifts its revenue growth by about 6 points. A wedge that is large next to revenue means more than a big number. It also means that ordinary changes in fees or discounts can swing reported growth. ## 3.2 Hypotheses -Two hypotheses put the two links of the chain to the test. H1 concerns the inner link, between revenue and the commerce a platform carries. H2 concerns the outer link, between that commerce and national online commerce. +The framework tests the two links with two hypotheses. H1 asks whether revenue follows commerce within a platform. H2 asks where the growth of national online commerce comes from, which decides whether platform figures can speak for the whole economy. -The take rate depends on fees, on discounts and on how the platform's accounts treat each sale. Revenue stays a fair stand-in for the commerce only while the take rate is steady, which means that growth divergence stays close to zero. The first hypothesis states that case. +The take rate depends on fees, on discounts and on how the platform's accounts treat each sale. Revenue stays a fair stand-in for the commerce only while the take rate is steady, which means that growth divergence stays close to zero. H1 states that case. > **H1.** Transaction value and revenue recognized by the same platform grow in proportion over time. Growth rates never match exactly, so H1 is tested by asking whether growth divergence among Indonesian and regional platforms, measured in logs, is unusually large. The yardstick is growth divergence among listed platforms abroad, where revenue and transaction value broadly move together. Chapter 4 describes both groups. -H2 splits national growth into the part that comes from existing sellers selling more and the part that comes from more sellers entering. BPS reports both the number of businesses selling online and their total online sales, so the split can be made from published figures. Growth from new sellers can take place in channels that platform records do not cover, such as messaging and social media, which most sellers use. Platform figures can miss that growth even when every platform reports accurately. +National online sales can grow because existing sellers sell more or because more businesses sell online. BPS publishes both the number of businesses selling online and their total online sales, so the split can be made from published figures. The split matters for platform figures because new businesses often start in channels that platform records do not cover, such as messaging and social media, which most sellers use. If growth comes mainly from more businesses, platform figures can miss it even when every platform reports accurately. H2 states that case. > **H2.** The growth of national e-commerce value comes mainly from more businesses selling online, and less from higher sales per business. @@ -71,15 +75,17 @@ The H2 split is arithmetic. It does not say which channels the new businesses us ## 3.3 The entry test -The split in H2 rests on BPS's count of online sellers. A count can rise for two reasons: more businesses start selling, or the survey reaches businesses it did not reach before. The published figures cannot tell these apart. The survey itself can, because it asks every seller in which year it began selling online. +H2 rests on a count: the number of businesses selling online. A rise in that count can mean two different things, so the thesis checks what it contains before relying on it. Either more businesses started selling, or the survey reached businesses that were already selling. The published figures cannot tell the two apart. The survey can, because it asks every seller in which year it began selling online. + +> **Entry test.** In the survey file for 2023, at least 21.5 percent of online sellers must report that they began selling online in 2023. -If a rise in the count comes from first-time entry, a large enough share of sellers must have started in the year of the rise. For 2023 the published count rose by 27.4 percent. Even if no seller had stopped, there would then be 127.4 sellers for every 100 a year earlier, and the 27.4 extra would have to be new. New sellers would have to make up at least 21.5 percent of all sellers. The test was written down before the survey files were opened, as Chapter 4 describes. It does not test H2 itself. It tests what "more businesses" means. +The threshold is arithmetic. The published count of online sellers rose by 27.4 percent in 2023. Suppose every extra seller was new and no seller stopped. Then for every 100 sellers a year earlier there were 127.4, and 27.4 of those were new. New sellers would make up 21.5 percent of all sellers, which is 27.4 divided by 127.4. A smaller share means that part of the rise came from somewhere else. The test was written down before the survey files were opened, as Chapter 4 describes. It does not test H2 itself. It tests what "more businesses" means. If the share of new sellers falls short, part of the rise is something other than first-time entry. Two readings are open: the survey reached more of the sellers who were already there, or sellers came back after a pause. Chapter 5 weighs them. ## 3.4 Further analyses -Two further analyses support the hypotheses without forming hypotheses of their own. The first divides national growth by sales channel, to show how much of it took place inside the marketplace channel. The second compares marketplace sellers with other sellers on whether they keep financial statements, which bears on what platform records can add to tax administration. A separate analysis, developed in Chapter 6, estimates how many sellers a rule based on platform records can reach. Chapter 4 also describes how revenue movements are traced to fees and discounts and how platform evidence is compared with national and payment figures. A last analysis asks whether markets value growth in transaction value differently from growth in the take rate. +Four further questions use the same measures but carry no hypothesis. Where did large movements in revenue come from: more commerce, higher fees or smaller discounts? Do platform, national and payment figures tell the same story where they cover the same sellers? Which channels do online sellers use, and do they keep financial statements? And do investors value a platform's growth in transaction value differently from growth in its take rate? Chapter 4 describes how each is answered. Chapter 6 uses the answers to ask how many sellers a rule built on platform records can reach. ## 3.5 The tests together From 8e21794963a25dedd1e2d10e3f14d83835f28a5f Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 13 Oct 2026 12:27:59 +0000 Subject: [PATCH 33/65] Thesis ch2-3: second attempt from the failure analysis (perspectives with consensus, application and breaking point; Chapter 3 opens with its logic) Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 8 ++++ papers/thesis_2026-10/ch2_literature.md | 60 +++++++++++++------------ papers/thesis_2026-10/ch3_framework.md | 10 ++--- 3 files changed, 45 insertions(+), 33 deletions(-) diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 673c2db..62986f1 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -329,3 +329,11 @@ The researcher's verdict on the previous version: Chapter 2 did not do its job. - **Chapter 2** is now organised by attempts to answer the thesis question: pricing theory and accounting (2.1), national statistics and survey methods (2.2), tax research on platform records (2.3). It opens by naming the four fields and testing each against one case (Tokopedia 2023). Each section states the field's consensus, develops it in full paragraphs, and ends with where its answer stops. 2.4 builds the three open questions from those stopping points. The two-group idea is introduced with examples (shoppers and merchants; riders and drivers) before it is used; the accounting passage says in plain words what a principal and an agent record and that discounts reduce revenue. Use limits from `REFERENCES_WORKING.md` respected (Hagiu and Wright as business model, not an accounting test; IFRS 15 "normally"; web-traffic studies named as such). A claim about UNCTAD comparing levels at one point in time was dropped because the audit says its full report is unchecked. - **Chapter 3** opens with the declaration that a measure is a stand-in and the framework tests the links; the figure placeholder follows at once, then the conditions, then how the two links answer the research question, then the three questions with what measures each. 3.1 puts the formulas first and names each measure at its first use; m0 and m1 are defined before the formula that uses them; the Tokopedia example is followed by what each measure records before the wedge and growth divergence are contrasted. 3.2 opens by saying the two hypotheses test the two links. 3.3 states the entry test first as a block, then the arithmetic. 3.4 is four questions instead of a repeated list. 3.5 is unchanged (the researcher said it reads clearly). - **Not yet done:** the Chapter 4 opening still reads as complaint ("thinner than the question deserves"); the researcher wants a disclaimer that is presented tactically. Chapters 4 and 5 are on hold until 2 and 3 are accepted. One known slip in 5.9 ("In 2024 it was less than a quarter…" with an unclear "it"). + +### Chapters 2 and 3, second attempt after the failure analysis (13 Oct 2026) + +Worked from the diagnosis, not from line fixes. Jobs fixed first. Chapter 2: give the best available answers to the thesis question, field by field, and show where each fails, so that the gap is a result. Chapter 3: declare the logic and its reason, anchor it with the figure, then measures, hypotheses and tests that answer Chapter 2's three questions. +- **Chapter 2** has four perspectives, one section each (2.1 pricing theory, 2.2 accounting, 2.3 national statistics, 2.4 platform records and tax), then 2.5 the gap. Each opens with the field's consensus, develops it in paragraphs of its own (the two groups introduced with examples, the mechanism of price structure, the principal and agent distinction with a worked ride of 100 and a driver's 80, the voucher rule explained), then states what the field says about the thesis question and where it breaks down. The three open questions are built from those breaking points. +- **Chapter 3** opens with the stand-in idea and why it matters (investors, governments and analysts read the figures as signs, the link is rarely tested). The figure paragraph names H1 and H2 on the links. 3.2 opens by naming the two checks and the hypothesis that states each. 3.3 opens with why the count has to be checked. +- **Cold-reader check run:** first use of each term compared with where it is introduced (script run in session, not kept). Fixed: the figure labels H1 and H2 before the hypotheses existed; transaction value in Chapter 2 now glossed at first use. +- **Known weak points:** 3.4 may still repeat Chapter 1 and Chapter 4 and could be cut; Chapter 2 has grown to about 2,300 words; the Tokopedia readings in the Chapter 2 opening are the author's application of each field and not quotations. diff --git a/papers/thesis_2026-10/ch2_literature.md b/papers/thesis_2026-10/ch2_literature.md index e266857..57a184c 100644 --- a/papers/thesis_2026-10/ch2_literature.md +++ b/papers/thesis_2026-10/ch2_literature.md @@ -1,59 +1,63 @@ # Chapter 2. Literature Review -The question of how much of an online economy a platform's revenue leaves out, and when that matters for growth, has not been answered in one place. Four bodies of research have each answered part of it, from different directions. Platform economics explains why a platform keeps only a share of what its users trade. Accounting explains what a company may record as revenue. National statistics explain how the commerce of a country is counted. Tax research explains what the records of a platform can be used for. +Chapter 1 asked how much of an online economy is missing when a platform is measured by its revenue, and when that changes the story about growth. No single study answers it, but four lines of research each answer a piece. Platform economics explains why a platform keeps only a share of what its users trade. Accounting explains what a company may record as revenue. National statistics explain how the commerce of a whole country is counted. Tax research explains what a platform's own records can show. This chapter sets out what each line concludes, uses it to answer the question as well as it can, and shows where the answer breaks down. The breaking points are the gap that this thesis fills. -A case from Indonesia shows what the four fields can and cannot do. In 2023 Tokopedia's revenue rose by 53 percent while the value of the sales it carried fell by 9 percent. Platform economics would look for a change in how the platform prices the two groups it serves, shoppers and merchants. Accounting would look for a change in what the company records as revenue, such as smaller discounts. National statistics would ask whether the sales of all sellers, on platforms and off them, moved with either figure. Tax research would ask what the platform's records show about the sellers behind the sales. Each reading is sound. None says how large such a difference is, how often it occurs, or whether it changes the story of growth. +A case from Indonesia shows how the four lines differ. In 2023 Tokopedia's revenue rose by 53 percent while the value of the sales it carried fell by 9 percent. Platform economics would look at how the platform prices the two groups it serves, shoppers and merchants, and ask whether it raised its commission. Accounting would look at what the company records as revenue, and ask whether smaller discounts or a change in bookkeeping lifted the figure. National statistics would ask whether the sales of all sellers, on platforms and off them, moved with either number. Tax research would ask what the platform's records show about the sellers behind the sales. Each reading is sound, and each is partial. None of them says how large such a difference is, how often it happens, or whether it changes the story of growth. -This chapter takes the four fields in turn. For each, it sets out what the field has established, what answer that gives to the thesis question, and where the answer stops. Section 2.4 gathers the stopping points into the gap that the thesis addresses. +## 2.1 Platform pricing: why revenue is only part of the commerce -## 2.1 Why revenue differs from commerce: pricing and accounting +Platform economics holds that a platform's revenue is a price, and that the price is set to keep two groups on board and not to collect a share of the value traded. A marketplace needs shoppers and merchants. A ride-hailing app needs riders and drivers. A shopper has little reason to visit a marketplace with no merchants, and a merchant has little reason to list on one with no shoppers, so each group is worth more to the platform when the other is present. Economists call these cross-side effects, and they are the starting point of the field (Rochet and Tirole 2003; Caillaud and Jullien 2003; Evans and Schmalensee 2016). -Economic theory treats a platform's revenue as the price of an introduction and not as the value of what is traded. A platform brings two groups together: buyers and sellers on a marketplace, riders and drivers on a ride-hailing app. Each group joins mainly because the other is already there, so the platform has to attract both before it can earn from either (Rochet and Tirole 2003; Caillaud and Jullien 2003; Evans and Schmalensee 2016). This is the central result of the field, and it fits a platform whose revenue is a small share of the commerce that passes through it. +The result is that a platform does not price each group the way an ordinary seller prices a product. Because one group attracts the other, it can pay to charge one group little or nothing and to earn from the other. A delivery app might keep fees low for customers and take a larger commission from restaurants, or the reverse, depending on which group is harder to attract and which cares less about price. The split between the groups matters as well as the total, because the split decides how many people join and how many transactions follow (Rochet and Tirole 2003; Parker and Van Alstyne 2005; Armstrong 2006). So a commission is a choice that the platform can change, and a change in it moves revenue without any change in what people buy. -Attracting two groups also shapes the price. A platform may charge one group little or nothing and earn from the other, because the presence of the free group makes the platform worth more to the paying group. The total fee and the way it is split between the groups are both choices, and the split affects how many transactions take place (Rochet and Tirole 2003; Parker and Van Alstyne 2005; Armstrong 2006). So a platform can change how much it earns from a given volume of commerce by changing its prices. The business model sets the starting point. A marketplace earns a fee on each sale between others, while a reseller buys goods and sells them itself (Hagiu and Wright 2015). The fee is a fraction of the sale, and the reseller's takings are the whole sale. +The business model sets the starting point for how large the platform's share is. A marketplace earns a fee on sales between others, so its revenue is a fraction of the sales it carries. A reseller buys goods and sells them itself, so its takings are the whole sale (Hagiu and Wright 2015). Many companies mix the two, which is one reason why the share of each sale that a platform keeps differs so much from one platform to the next. -Accounting rules add a second reason for the difference. They decide how much of a sale a company may record as its own revenue, and the international rule is International Financial Reporting Standard 15 (IFRS 15). It asks whether a company acts as a principal or as an agent. A principal controls the goods or service before they reach the customer and records the whole price as revenue. An agent only arranges the sale and records its commission. The standard also normally treats discounts and vouchers that a company gives its customers as a cut in the price, so they reduce its revenue (International Accounting Standards Board 2014). +Applied to the thesis question, the theory gives a firm first answer. It says that revenue will be a small share of the commerce, that the platform's pricing sets the share, and that the platform can change it. So revenue and commerce need not move together. The theory does not say how small the share is in practice, how often platforms change it, or by how much. It is a theory of how prices are set, and it leaves the size and the steadiness of the share to measurement. -Companies have some room to choose how they record revenue. A study of internet firms in the early 2000s found that they reported the same kind of sale in different ways, including whether to report it gross (Bowen, Davis and Rajgopal 2002). For a platform, then, reported revenue can rise because fees rise, because discounts shrink, or because the company changes how it records a sale. None of the three needs a change in what customers buy. +## 2.2 Accounting: what revenue records -Companies also report operating measures of their own, such as transaction value or active users. Analysts use financial statements more easily when the accounting is comparable (De Franco, Kothari and Verdi 2011). Operating measures lose information when the company changes its definition or stops reporting them (Givoly, Li, Lourie and Nekrasov 2019). Transaction value, the figure on which this thesis relies, is such a measure. +Accounting holds that revenue is not a measure of how much was sold. It is the amount a company is entitled to record as its own income, and the rules for that decision turn on one question: is the company selling the thing itself, or arranging a sale for someone else? Suppose a ride costs the rider 100 and the driver keeps 80. A company that controls the ride, as a principal, records 100 as revenue and the driver's 80 as a cost. A company that only arranges the ride, as an agent, records its commission of 20 and never counts the driver's 80 as its own. International Financial Reporting Standard 15 (IFRS 15), the international rule on revenue, draws this line (International Accounting Standards Board 2014). -Investors do not rely on revenue alone. In the early years of the internet, activity measures such as web traffic carried information about a firm's value and future revenue that reported revenue did not show (Trueman, Wong and Zhang 2000, 2001; Rajgopal, Venkatachalam and Kotha 2003). +The same standard treats discounts and vouchers in a way that matters for platforms. When a company pays its customers, for example by funding a voucher, it normally treats the payment as a cut in the price. The voucher then reduces revenue and does not appear as a cost (International Accounting Standards Board 2014). So a platform that spends heavily on vouchers reports lower revenue than one that spends little on the same sales, and its revenue rises when it cuts them. -Pricing theory and accounting together answer why revenue and commerce differ, and the answer fits a case like Tokopedia's. They do not answer how much. The theory explains how prices are set, but it does not say how large a platform's share should be or how steady it should stay. The accounting evidence concerns the rules and the reporting choices of internet firms in the early 2000s. The investor evidence rests on web traffic, which is not the transaction value of a modern platform. None of it follows the revenue and the transaction value of the same platforms through several years, and none covers a market such as Indonesia. +These rules leave room for judgment, and companies have used it. A study of internet firms in the early 2000s found that they chose between reporting sales gross and reporting them net, and that the choices differed across firms (Bowen, Davis and Rajgopal 2002). So the reported revenue of a platform can rise for three reasons that have nothing to do with buying: its fees rise, its discounts shrink, or it changes how it records a sale. -## 2.2 How the online economy is counted +Companies also publish operating figures that no standard governs, and transaction value, the total value of everything bought through the platform, is one of them. Analysts read accounts more easily when the accounting is comparable across firms (De Franco, Kothari and Verdi 2011). A measure that a company chooses to report, such as transaction value or active users, loses information when the company changes its definition or stops reporting it (Givoly, Li, Lourie and Nekrasov 2019). This matters for the thesis, because transaction value is the figure against which revenue is compared. -Official statistics give a firm answer on what a platform's commerce is not: it is not the platform's own output. National accounts add up a country's production, income and spending. They treat the sale between a buyer and a seller as one thing and the platform's service of bringing them together as another. So the full transaction value does not count as the platform's output (Ahmad and Schreyer 2016; International Monetary Fund 2018; Organisation for Economic Co-operation and Development [OECD] 2023; United Nations et al. 2025). +Investors know the limits of revenue, and finance research has shown how they respond. In the early years of the internet, activity measures such as web traffic carried information about a firm's value and future revenue that reported revenue did not show (Trueman, Wong and Zhang 2000, 2001; Rajgopal, Venkatachalam and Kotha 2003). -Counting the commerce itself is harder, because each producer of figures counts something different. Listed platforms report the revenue they record and often their transaction value. Statistics offices survey businesses about their online sales. Central banks report the value of payments. Industry reports estimate the size of whole markets from platform transaction values (Google, Temasek and Bain 2024; BPS 2025; Bank Indonesia 2025). The United Nations Conference on Trade and Development (UNCTAD) has shown that e-commerce values change with the definition used, and it has set business e-commerce sales beside the transaction values of online platforms (UNCTAD 2023, 2024). +Applied to the thesis question, accounting gives a second firm answer. Revenue is a convention, and a platform can move it by changing its commission, its discounts or its bookkeeping. The evidence does not say how large these moves are for real platforms. The studies of reporting choices describe internet firms of the early 2000s, and the studies of investors rest on web traffic and not on the transaction value of a modern platform. None of them follows the revenue and the transaction value of the same platform through several years, and none covers a market such as Indonesia. -A difference between two figures for the same activity does no harm while they move together. It matters when they part, because the choice of figure then changes the conclusion about size and growth. Economists have a method for this problem. They test a figure against the activity it stands for, as researchers do when they check national income figures against satellite images of night-time lights (Henderson, Storeygard and Weil 2012). +## 2.3 National statistics: what the whole online economy counts -The growth of national online sales can be tested in the same spirit, because it has two possible sources. Existing sellers can sell more, or more sellers can enter. Trade economists divide the growth of exports into the same two kinds of margin (Hummels and Klenow 2005). +National statistics hold that the commerce a platform carries is not the platform's output. A country's accounts add up the goods and services it produces. They record the sale between a buyer and a seller separately from the platform's service of bringing the two together, and only the service counts as the platform's output (Ahmad and Schreyer 2016; International Monetary Fund 2018; Organisation for Economic Co-operation and Development [OECD] 2023; United Nations et al. 2025). The official view agrees with the pricing and accounting views: the commerce and the revenue are different amounts, and the commerce is the larger. -The split of national growth rests on a count of online sellers, and that count has a weakness of its own. It depends on how the survey finds sellers. A change in design can break a series in a way that looks like a change in the economy (van den Brakel, Smith and Compton 2008; van den Brakel, Zhang and Tam 2020). Respondents also misdate events, and they tend to place them closer to the interview than they were (Neter and Waksberg 1964). Exit among small firms is more common for young firms and young owners (McKenzie and Paffhausen 2019), which matters when a count has to be corrected for sellers who stopped. +Counting the commerce itself is harder, because each source counts something different. Listed platforms report the revenue they record and often their transaction value. Statistics offices ask businesses about their online sales. Central banks report the value of payments. Industry reports estimate the size of whole markets from platform transaction values (Google, Temasek and Bain 2024; BPS 2025; Bank Indonesia 2025). The United Nations Conference on Trade and Development (UNCTAD) has shown that e-commerce values change with the definition used, and it has set business e-commerce sales beside the transaction values of online platforms (UNCTAD 2023, 2024). -Official frameworks and survey methods say what should be counted and how the figures can mislead. They do not say whether platform figures and national figures grow for the same reasons, and the studies reviewed here do not test it for one country over time. They also say little about what a rise in a count of online sellers contains. +A difference between two figures for the same activity does no harm while they move together. It matters when they part, because the choice of figure then changes the conclusion about size and growth. Economists have a standard answer to this problem: test each figure against the activity it stands for. Researchers do this when they check national income figures against satellite images of night-time lights (Henderson, Storeygard and Weil 2012). -## 2.3 What platform records can show +The growth of national online sales has two possible sources. Existing sellers can sell more, or more sellers can enter. Trade economists split the growth of exports into the same two margins (Hummels and Klenow 2005). The split needs a count of sellers, and a count has weaknesses of its own. It depends on how the survey finds sellers, and a change in design can break a series in a way that looks like a change in the economy (van den Brakel, Smith and Compton 2008; van den Brakel, Zhang and Tam 2020). Respondents misdate events, and they tend to place them nearer to the interview than they were (Neter and Waksberg 1964). Small firms that stop selling are more often young firms with young owners (McKenzie and Paffhausen 2019), which matters when a count has to allow for sellers who stopped. -Tax research offers a different answer to the question. If revenue shows little of the commerce, the commerce can still be seen in the platform's records, because a platform records every sale: who sold, what, for how much and when. A report about a taxpayer made by someone else, such as an employer, a bank or a platform, is third-party information. Tax authorities value it because it does not depend on the taxpayer's own account. +Applied to the thesis question, national statistics answer one part of it. They say what the whole should be measured as, and they warn that the sources will differ and that a count can break. They do not say whether platform figures and national figures grow for the same reasons, and the studies reviewed here do not test it for one country over time. They also say little about what a rise in a count of online sellers contains: more sellers, or a better count of the sellers already there. + +## 2.4 Platform records and tax: seeing the commerce directly + +Tax research holds that the commerce missing from a platform's revenue is not lost, because the platform sees it. A platform records every sale: who sold, what, for how much and when. A report about a taxpayer made by someone else, such as an employer, a bank or a platform, is third-party information. Tax authorities value it because it does not depend on the taxpayer's own account. Platform records can help where other records are thin. Sellers and service providers may be small, self-employed or weakly represented in conventional financial records, yet the platform records what they sell. This differs from the shadow-economy setting, where reliable records may be sparse (La Porta and Shleifer 2014; Ulyssea 2018). Digital records can carry information that conventional records lack (Berg, Burg, Gombovic and Puri 2020), and platform-mediated work leaves traces in financial and administrative data (Barrios, Hochberg and Yi 2022; Denes, Lagaras and Tsoutsoura 2025). Information from a third party also changes how people comply with tax. Audit experiments in Denmark, and studies of the value-added tax and of receipt lotteries, show that reports from third parties help compliance (Kleven et al. 2011; Pomeranz 2015; Naritomi 2019; Kleven, Kreiner and Saez 2016; Slemrod 2019). Tax withheld through a third party rests on the same information (Brockmeyer and Hernandez 2016). -Several rules now apply third-party reporting to platforms. The OECD, a group of mostly high-income countries, published model rules under which platforms report what their sellers earn (OECD 2020). The European Union's DAC7 directive applies them (European Union 2021). Indonesia's marketplace tax rule goes further: it makes marketplaces collect tax from their sellers, not only report on them (Ministry of Finance of the Republic of Indonesia 2025). +Several rules now apply this idea to platforms. The OECD, a group of mostly high-income countries, published model rules under which platforms report what their sellers earn (OECD 2020). The European Union's DAC7 directive applies them (European Union 2021). Indonesia's marketplace tax rule goes further: it makes marketplaces collect tax from their sellers, not only report on them (Ministry of Finance of the Republic of Indonesia 2025). -The consensus on platform records is that they are useful and that rules can rest on them. The literature does not say how many of a country's online sellers such a rule can reach. The answer depends on how much of the selling happens on platforms, which is the very point that Section 2.2 left open. The studies reviewed here do not estimate it for Indonesia. +Applied to the thesis question, tax research offers a way round revenue: read the records. The consensus is that platform records are useful and that rules can rest on them. The literature does not say how much of the online economy those records cover. The answer depends on how much selling happens on platforms, which is the point that national statistics left open, and the studies reviewed here do not estimate it for Indonesia. -## 2.4 The research gap +## 2.5 The research gap -Read together, the four answers stop at the same place. Each explains why figures can differ, and none measures how far they differ, how steadily, or with what effect on growth. Three questions remain open. +Each of the four answers is sound, and each breaks down at the same kind of point. Pricing theory and accounting explain why revenue and commerce differ, but they do not measure how far or how steadily. National statistics explain how the whole should be counted, but they do not test whether platform figures follow it. Tax research shows that records can reach the commerce, but not how much of it they reach. Three questions remain open. -1. **How far does a platform's revenue follow the commerce it carries?** Pricing theory and accounting give reasons why the two part (Section 2.1). No study reviewed here follows the revenue and the transaction value of the same platforms over several years to measure how much revenue leaves out and how steady that share is. -2. **Do platform figures follow national growth?** Official frameworks show that figures differ (Section 2.2). Whether platform growth tracks national growth depends on where national growth comes from and on whether the count of sellers behind it keeps pace with entry. The studies reviewed here test neither. -3. **How far do rules built on platform records reach?** Tax research shows that platform records can support rules (Section 2.3). It does not show how much of the online economy those records cover. +1. **How far does a platform's revenue follow the commerce it carries?** The reasons for a difference are known (Sections 2.1 and 2.2). The size of the difference, and how steady it stays over several years, have not been measured for the same platforms. +2. **Do platform figures follow national growth?** Official frameworks show that the figures differ (Section 2.3). Whether platform growth tracks national growth depends on where national growth comes from, and on whether the count of sellers behind it keeps pace with entry. The studies reviewed here test neither. +3. **How far do rules built on platform records reach?** Platform records can support tax rules (Section 2.4). How much of the online economy they cover is not known. -Chapter 3 builds the framework that turns these questions into measures and hypotheses. +Chapter 3 builds a framework that turns these three questions into measures and hypotheses. diff --git a/papers/thesis_2026-10/ch3_framework.md b/papers/thesis_2026-10/ch3_framework.md index c89f2d1..737093a 100644 --- a/papers/thesis_2026-10/ch3_framework.md +++ b/papers/thesis_2026-10/ch3_framework.md @@ -1,8 +1,8 @@ # Chapter 3. Framework and Hypotheses -A measure of the digital economy is a stand-in. Revenue stands in for the commerce a platform carries, and the figures of platforms stand in for the online economy of a country. A stand-in is only as good as its link to the thing it represents, so this thesis builds its framework around the links. It treats each link as a claim that can fail, measures how far each claim holds, and states in advance what result would show that it does not. +This thesis builds its framework on one idea: a measure is a stand-in, and a stand-in is only as good as its link to what it stands for. Investors read a platform's revenue as a sign of the commerce the platform carries. Governments and analysts read platform figures as a sign of how fast online commerce is growing in a country. Each reading assumes a link, and the assumption is rarely tested. The framework turns each assumption into a claim that can be measured, so that the thesis can say how far the claim holds and what result would show that it does not. -Figure 3.1 shows the structure. Three circles sit inside one another. The smallest is a platform's revenue, the part of each sale that it keeps. Around it is platform commerce, the full value of the sales the platform handles. The largest is all online commerce in a country, which also includes sellers who never use a platform. The inner link joins revenue to platform commerce, and the outer link joins platform commerce to all online commerce. +Figure 3.1 shows the structure. Three circles sit inside one another. The smallest is a platform's revenue, the part of each sale that it keeps. Around it is platform commerce, the full value of the sales the platform handles. The largest is all online commerce in a country, which also includes sellers who never use a platform. The inner link joins revenue to platform commerce, and the outer link joins platform commerce to all online commerce. The first hypothesis, H1, tests the inner link, and the second, H2, tests the outer link (Section 3.2). [Figure 3.1 to be drawn: three nested circles, with revenue inside platform commerce inside all online commerce, H1 on the inner link and H2 on the outer link.] @@ -59,7 +59,7 @@ The size of the wedge next to revenue also decides how far a change in the take ## 3.2 Hypotheses -The framework tests the two links with two hypotheses. H1 asks whether revenue follows commerce within a platform. H2 asks where the growth of national online commerce comes from, which decides whether platform figures can speak for the whole economy. +Answering the research question takes two checks, and each is stated as a hypothesis. The first is whether, within a platform, revenue grows in proportion to the commerce it comes from. The second is whether the growth of national online commerce comes mainly from more businesses selling online. H1 states the first check, and H2 states the second. The take rate depends on fees, on discounts and on how the platform's accounts treat each sale. Revenue stays a fair stand-in for the commerce only while the take rate is steady, which means that growth divergence stays close to zero. H1 states that case. @@ -75,11 +75,11 @@ The H2 split is arithmetic. It does not say which channels the new businesses us ## 3.3 The entry test -H2 rests on a count: the number of businesses selling online. A rise in that count can mean two different things, so the thesis checks what it contains before relying on it. Either more businesses started selling, or the survey reached businesses that were already selling. The published figures cannot tell the two apart. The survey can, because it asks every seller in which year it began selling online. +H2 says that national growth comes mainly from more businesses selling online. That conclusion holds only if the count of businesses reflects real entry. A rise in the count can mean two different things: more businesses started selling, or the survey reached businesses that were already selling. The published figures cannot tell the two apart. The survey can, because it asks every seller in which year it began selling online. The thesis checks what the count contains, with a test that was written down before the survey files were opened. > **Entry test.** In the survey file for 2023, at least 21.5 percent of online sellers must report that they began selling online in 2023. -The threshold is arithmetic. The published count of online sellers rose by 27.4 percent in 2023. Suppose every extra seller was new and no seller stopped. Then for every 100 sellers a year earlier there were 127.4, and 27.4 of those were new. New sellers would make up 21.5 percent of all sellers, which is 27.4 divided by 127.4. A smaller share means that part of the rise came from somewhere else. The test was written down before the survey files were opened, as Chapter 4 describes. It does not test H2 itself. It tests what "more businesses" means. +The threshold comes from arithmetic. The published count of online sellers rose by 27.4 percent in 2023. Suppose every extra seller was new and no seller stopped. Then for every 100 sellers a year earlier there were 127.4, and 27.4 of those were new. New sellers would make up 21.5 percent of all sellers, which is 27.4 divided by 127.4. A smaller share means that part of the rise came from somewhere else. The test does not test H2 itself. It tests what "more businesses" means, and Chapter 4 describes how it was registered. If the share of new sellers falls short, part of the rise is something other than first-time entry. Two readings are open: the survey reached more of the sellers who were already there, or sellers came back after a pause. Chapter 5 weighs them. From c670a51242997217336906523564bb62d48af6aa Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 13 Oct 2026 12:47:42 +0000 Subject: [PATCH 34/65] Thesis ch1-5: independence audit, openers name their subjects, Chapter 4 opening reframed Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 11 +++++++ papers/thesis_2026-10/ch1_introduction.md | 6 ++-- papers/thesis_2026-10/ch2_literature.md | 30 +++++++++---------- papers/thesis_2026-10/ch3_framework.md | 14 ++++----- papers/thesis_2026-10/ch4_data_methods.md | 30 +++++++++---------- papers/thesis_2026-10/ch5_results.md | 36 +++++++++++------------ 6 files changed, 69 insertions(+), 58 deletions(-) diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 62986f1..067aee5 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -337,3 +337,14 @@ Worked from the diagnosis, not from line fixes. Jobs fixed first. Chapter 2: giv - **Chapter 3** opens with the stand-in idea and why it matters (investors, governments and analysts read the figures as signs, the link is rarely tested). The figure paragraph names H1 and H2 on the links. 3.2 opens by naming the two checks and the hypothesis that states each. 3.3 opens with why the count has to be checked. - **Cold-reader check run:** first use of each term compared with where it is introduced (script run in session, not kept). Fixed: the figure labels H1 and H2 before the hypotheses existed; transaction value in Chapter 2 now glossed at first use. - **Known weak points:** 3.4 may still repeat Chapter 1 and Chapter 4 and could be cut; Chapter 2 has grown to about 2,300 words; the Tokopedia readings in the Chapter 2 opening are the author's application of each field and not quotations. + +### Independence audit of Chapters 1 to 5 (13 Oct 2026) + +Rule, as the researcher states it: the idea runs linearly, but every chapter, section and paragraph opening must stand on its own, naming its subject, never opening on the previous text. Links between chapters live at the end of the earlier chapter, inside a paragraph that does other work. + +Audit method: read the opening sentence of every paragraph in Chapters 1 to 5 as a cold reader; flag back-references to chapters, sections and earlier text, leaning starters (this, these, both, each, the first, the same, because, also, either, again), terms used before they are introduced, and answers to a heading phrased as a question. Fixed: +- **Chapter 2:** the chapter no longer opens with "Chapter 1 asked"; it states the question as its own subject. Section openers that leaned ("The result is that…", "The same standard…", "These rules…", "Applied to the thesis question…") now name their subject. The closing one-line pointer to Chapter 3 is a full paragraph. +- **Chapter 3:** "the research question" was used without being stated in the chapter; it is now stated before use. "Each link", "The threshold" and "Four further questions" now name their subjects. +- **Chapter 4:** the opening is rewritten as a statement of what the evidence takes and why Indonesia allows the tests (no complaint tone; no "thinner than the question deserves"); "the pairs", "own goods", "the second step", "the extra sellers", "the first objective" and "the third objective" are all introduced where first used. +- **Chapter 5:** headings phrased as questions were answered with "they" or "it" (5.2, 5.3, 5.7); fixed. 5.9 no longer opens "Chapter 1 began"; the unclear "it" in the 2024 sentence is fixed; "again" and "either" openers fixed. +- **Chapter 1:** "the same split", "the figures", "the question" at the starts of three paragraphs now name their subjects. Left for the researcher: the opening of the research-question paragraph in 1.1 ("Measuring what the current figures miss…"), which deliberately bridges from the ambition paragraph the researcher approved. diff --git a/papers/thesis_2026-10/ch1_introduction.md b/papers/thesis_2026-10/ch1_introduction.md index 64299f6..74eeca2 100644 --- a/papers/thesis_2026-10/ch1_introduction.md +++ b/papers/thesis_2026-10/ch1_introduction.md @@ -4,15 +4,15 @@ Digital commerce has changed how goods and services are bought and sold in Indon In 2023, traders at Tanah Abang, a major wholesale market in Jakarta, said their customers had moved to sellers on TikTok Shop (AFP 2023). The government then banned transactions on social media platforms, and TikTok Shop closed in Indonesia soon after. That year the national statistics office, BPS, reported that online sales rose by about 41 percent (BPS 2025). The central bank's measure of online shopping fell by about 5 percent (Bank Indonesia 2025). The two official figures moved in opposite directions. -Tokopedia, an Indonesian online marketplace, shows the same split inside one company. Between 2022 and 2023, its revenue rose by 53 percent while the value of transactions on the platform, its transaction value, fell by 9 percent (GoTo Gojek Tokopedia 2024). The two are different amounts. A platform keeps only part of each sale, as commissions and fees. The rest is the seller's receipts, the driver's fare or another amount that passes through. This thesis calls the difference the invisible wedge. The term means what a revenue-only measure leaves out. It does not mean activity that goes unrecorded. +Tokopedia, an Indonesian online marketplace, shows revenue and sales moving apart inside one company. Between 2022 and 2023, its revenue rose by 53 percent while the value of transactions on the platform, its transaction value, fell by 9 percent (GoTo Gojek Tokopedia 2024). The two are different amounts. A platform keeps only part of each sale, as commissions and fees. The rest is the seller's receipts, the driver's fare or another amount that passes through. This thesis calls the difference the invisible wedge. The term means what a revenue-only measure leaves out. It does not mean activity that goes unrecorded. The share a platform keeps is not fixed. When it rises, revenue can grow even while transaction value falls. In Tokopedia's accounts, about 60 percent of the extra revenue came from smaller discounts and incentives, which a platform pays for out of what it earns. Platform and online sales figures carry weight well beyond Indonesia. Businesses in 43 economies sold almost US$27 trillion online in 2022, by the United Nations' estimate (UNCTAD 2024), and platforms carry a growing part of that trade. Investors value the companies that run them largely on reported growth. If part of that growth is a platform keeping more of each sale, then part of the price is a pricing decision. Governments use the same figures to size their digital economies and to tax online sellers. -Research explains why the figures can differ, but not when a figure stops moving with the activity it represents, or by how much. Answering that takes two things that are rarely found together: a platform's revenue and transaction value over several years, and a national count of online sellers whose underlying answers can be examined. +Research explains why platform revenue and national sales figures can differ, but not when a figure stops moving with the activity it represents, or by how much. Answering that takes two things that are rarely found together: a platform's revenue and transaction value over several years, and a national count of online sellers whose underlying answers can be examined. -Indonesia has the platform histories and the seller survey that the question needs. Several of its platforms report revenue and transaction value each year, and BPS asks online sellers when they began and how their sales changed. The stakes are concrete. For 2024, BPS and the central bank give about US$81 billion and about US$31 billion for online sales (BPS 2025; Bank Indonesia 2025), a difference of more than two to one. The government is writing rules on platform commissions, discounts and tax. Its levy on marketplace sellers would raise about US$60 million a year on one measure of the market and about US$300 million on another, before exemptions (Ministry of Finance of the Republic of Indonesia 2025; author's calculation). Most online sellers work through messaging and social media, outside the marketplaces, so platform and survey figures are likely to part. The events of 2023 suggest that they did. +In Indonesia, several platforms report revenue and transaction value each year, and BPS asks online sellers when they began and how their sales changed. The stakes are concrete. For 2024, BPS and the central bank give about US$81 billion and about US$31 billion for online sales (BPS 2025; Bank Indonesia 2025), a difference of more than two to one. The government is writing rules on platform commissions, discounts and tax. Its levy on marketplace sellers would raise about US$60 million a year on one measure of the market and about US$300 million on another, before exemptions (Ministry of Finance of the Republic of Indonesia 2025; author's calculation). Most online sellers work through messaging and social media, outside the marketplaces, so platform and survey figures are likely to part. The events of 2023 suggest that they did. ## 1.1 Research question and objectives diff --git a/papers/thesis_2026-10/ch2_literature.md b/papers/thesis_2026-10/ch2_literature.md index 57a184c..ebf42dc 100644 --- a/papers/thesis_2026-10/ch2_literature.md +++ b/papers/thesis_2026-10/ch2_literature.md @@ -1,44 +1,44 @@ # Chapter 2. Literature Review -Chapter 1 asked how much of an online economy is missing when a platform is measured by its revenue, and when that changes the story about growth. No single study answers it, but four lines of research each answer a piece. Platform economics explains why a platform keeps only a share of what its users trade. Accounting explains what a company may record as revenue. National statistics explain how the commerce of a whole country is counted. Tax research explains what a platform's own records can show. This chapter sets out what each line concludes, uses it to answer the question as well as it can, and shows where the answer breaks down. The breaking points are the gap that this thesis fills. +How much of an online economy is missing when a platform is measured by its revenue, and when does that change the story about growth? No single study answers this, but four lines of research each answer a piece. Platform economics explains why a platform keeps only a share of what its users trade. Accounting explains what a company may record as revenue. National statistics explain how the commerce of a whole country is counted. Tax research explains what a platform's own records can show. This chapter sets out what each line concludes, uses it to answer the question as well as it can, and shows where the answer breaks down. The breaking points are the gap that this thesis fills. -A case from Indonesia shows how the four lines differ. In 2023 Tokopedia's revenue rose by 53 percent while the value of the sales it carried fell by 9 percent. Platform economics would look at how the platform prices the two groups it serves, shoppers and merchants, and ask whether it raised its commission. Accounting would look at what the company records as revenue, and ask whether smaller discounts or a change in bookkeeping lifted the figure. National statistics would ask whether the sales of all sellers, on platforms and off them, moved with either number. Tax research would ask what the platform's records show about the sellers behind the sales. Each reading is sound, and each is partial. None of them says how large such a difference is, how often it happens, or whether it changes the story of growth. +Tokopedia's 2023 shows how differently the four lines read the same facts. That year its revenue rose by 53 percent while the value of the sales it carried fell by 9 percent. Platform economics would look at how the platform prices the two groups it serves, shoppers and merchants, and ask whether it raised its commission. Accounting would look at what the company records as revenue, and ask whether smaller discounts or a change in bookkeeping lifted the figure. National statistics would ask whether the sales of all sellers, on platforms and off them, moved with either number. Tax research would ask what the platform's records show about the sellers behind the sales. Each reading is sound, and each is partial. None of them says how large such a difference is, how often it happens, or whether it changes the story of growth. ## 2.1 Platform pricing: why revenue is only part of the commerce -Platform economics holds that a platform's revenue is a price, and that the price is set to keep two groups on board and not to collect a share of the value traded. A marketplace needs shoppers and merchants. A ride-hailing app needs riders and drivers. A shopper has little reason to visit a marketplace with no merchants, and a merchant has little reason to list on one with no shoppers, so each group is worth more to the platform when the other is present. Economists call these cross-side effects, and they are the starting point of the field (Rochet and Tirole 2003; Caillaud and Jullien 2003; Evans and Schmalensee 2016). +Platform economics holds that a platform's revenue is a price, and that the price is set to keep two groups on board and not to collect a share of the value traded. A marketplace needs shoppers and merchants. A ride-hailing app needs riders and drivers. A shopper has little reason to visit a marketplace with no merchants, and a merchant has little reason to list on one with no shoppers. So each group is worth more to the platform when the other is present. Economists call these cross-side effects, and they are the starting point of the field (Rochet and Tirole 2003; Caillaud and Jullien 2003; Evans and Schmalensee 2016). -The result is that a platform does not price each group the way an ordinary seller prices a product. Because one group attracts the other, it can pay to charge one group little or nothing and to earn from the other. A delivery app might keep fees low for customers and take a larger commission from restaurants, or the reverse, depending on which group is harder to attract and which cares less about price. The split between the groups matters as well as the total, because the split decides how many people join and how many transactions follow (Rochet and Tirole 2003; Parker and Van Alstyne 2005; Armstrong 2006). So a commission is a choice that the platform can change, and a change in it moves revenue without any change in what people buy. +A platform with two groups to attract does not price them the way an ordinary seller prices a product. Because each group draws the other, it can pay to charge one group little or nothing and to earn from the other. A delivery app might keep fees low for customers and take a larger commission from restaurants, or the reverse, depending on which group is harder to attract and which cares less about price. The split between the groups matters as well as the total, because the split decides how many people join and how many transactions follow (Rochet and Tirole 2003; Parker and Van Alstyne 2005; Armstrong 2006). So a commission is a choice that the platform can change, and a change in it moves revenue without any change in what people buy. The business model sets the starting point for how large the platform's share is. A marketplace earns a fee on sales between others, so its revenue is a fraction of the sales it carries. A reseller buys goods and sells them itself, so its takings are the whole sale (Hagiu and Wright 2015). Many companies mix the two, which is one reason why the share of each sale that a platform keeps differs so much from one platform to the next. -Applied to the thesis question, the theory gives a firm first answer. It says that revenue will be a small share of the commerce, that the platform's pricing sets the share, and that the platform can change it. So revenue and commerce need not move together. The theory does not say how small the share is in practice, how often platforms change it, or by how much. It is a theory of how prices are set, and it leaves the size and the steadiness of the share to measurement. +Pricing theory answers the thesis question in part. It says that revenue will be a small share of the commerce, that the platform's pricing sets the share, and that the platform can change it. So revenue and commerce need not move together. The theory does not say how small the share is in practice, how often platforms change it, or by how much. It is a theory of how prices are set, and it leaves the size and the steadiness of the share to measurement. ## 2.2 Accounting: what revenue records -Accounting holds that revenue is not a measure of how much was sold. It is the amount a company is entitled to record as its own income, and the rules for that decision turn on one question: is the company selling the thing itself, or arranging a sale for someone else? Suppose a ride costs the rider 100 and the driver keeps 80. A company that controls the ride, as a principal, records 100 as revenue and the driver's 80 as a cost. A company that only arranges the ride, as an agent, records its commission of 20 and never counts the driver's 80 as its own. International Financial Reporting Standard 15 (IFRS 15), the international rule on revenue, draws this line (International Accounting Standards Board 2014). +Accounting holds that revenue is not a measure of how much was sold. It is the amount a company is entitled to record as its own income. The rules for that decision turn on one question: is the company selling the thing itself, or arranging a sale for someone else? Suppose a ride costs the rider 100 and the driver keeps 80. A company that controls the ride, as a principal, records 100 as revenue and the driver's 80 as a cost. A company that only arranges the ride, as an agent, records its commission of 20 and never counts the driver's 80 as its own. International Financial Reporting Standard 15 (IFRS 15), the international rule on revenue, draws this line (International Accounting Standards Board 2014). -The same standard treats discounts and vouchers in a way that matters for platforms. When a company pays its customers, for example by funding a voucher, it normally treats the payment as a cut in the price. The voucher then reduces revenue and does not appear as a cost (International Accounting Standards Board 2014). So a platform that spends heavily on vouchers reports lower revenue than one that spends little on the same sales, and its revenue rises when it cuts them. +IFRS 15 also treats discounts and vouchers in a way that matters for platforms. When a company pays its customers, for example by funding a voucher, it normally treats the payment as a cut in the price. The voucher then reduces revenue and does not appear as a cost (International Accounting Standards Board 2014). So a platform that spends heavily on vouchers reports lower revenue than one that spends little on the same sales, and its revenue rises when it cuts them. -These rules leave room for judgment, and companies have used it. A study of internet firms in the early 2000s found that they chose between reporting sales gross and reporting them net, and that the choices differed across firms (Bowen, Davis and Rajgopal 2002). So the reported revenue of a platform can rise for three reasons that have nothing to do with buying: its fees rise, its discounts shrink, or it changes how it records a sale. +Accounting rules leave room for judgment, and companies have used it. A study of internet firms in the early 2000s found that they chose between reporting sales gross and reporting them net, and that the choices differed across firms (Bowen, Davis and Rajgopal 2002). So the reported revenue of a platform can rise for three reasons that have nothing to do with buying: its fees rise, its discounts shrink, or it changes how it records a sale. Companies also publish operating figures that no standard governs, and transaction value, the total value of everything bought through the platform, is one of them. Analysts read accounts more easily when the accounting is comparable across firms (De Franco, Kothari and Verdi 2011). A measure that a company chooses to report, such as transaction value or active users, loses information when the company changes its definition or stops reporting it (Givoly, Li, Lourie and Nekrasov 2019). This matters for the thesis, because transaction value is the figure against which revenue is compared. Investors know the limits of revenue, and finance research has shown how they respond. In the early years of the internet, activity measures such as web traffic carried information about a firm's value and future revenue that reported revenue did not show (Trueman, Wong and Zhang 2000, 2001; Rajgopal, Venkatachalam and Kotha 2003). -Applied to the thesis question, accounting gives a second firm answer. Revenue is a convention, and a platform can move it by changing its commission, its discounts or its bookkeeping. The evidence does not say how large these moves are for real platforms. The studies of reporting choices describe internet firms of the early 2000s, and the studies of investors rest on web traffic and not on the transaction value of a modern platform. None of them follows the revenue and the transaction value of the same platform through several years, and none covers a market such as Indonesia. +Accounting answers the thesis question in part. Revenue is a convention, and a platform can move it by changing its commission, its discounts or its bookkeeping. The evidence does not say how large these moves are for real platforms. The studies of reporting choices describe internet firms of the early 2000s, and the studies of investors rest on web traffic and not on the transaction value of a modern platform. None of them follows the revenue and the transaction value of the same platform through several years, and none covers a market such as Indonesia. ## 2.3 National statistics: what the whole online economy counts National statistics hold that the commerce a platform carries is not the platform's output. A country's accounts add up the goods and services it produces. They record the sale between a buyer and a seller separately from the platform's service of bringing the two together, and only the service counts as the platform's output (Ahmad and Schreyer 2016; International Monetary Fund 2018; Organisation for Economic Co-operation and Development [OECD] 2023; United Nations et al. 2025). The official view agrees with the pricing and accounting views: the commerce and the revenue are different amounts, and the commerce is the larger. -Counting the commerce itself is harder, because each source counts something different. Listed platforms report the revenue they record and often their transaction value. Statistics offices ask businesses about their online sales. Central banks report the value of payments. Industry reports estimate the size of whole markets from platform transaction values (Google, Temasek and Bain 2024; BPS 2025; Bank Indonesia 2025). The United Nations Conference on Trade and Development (UNCTAD) has shown that e-commerce values change with the definition used, and it has set business e-commerce sales beside the transaction values of online platforms (UNCTAD 2023, 2024). +Counting online commerce itself is harder, because the sources that report it count different things. Listed platforms report the revenue they record and often their transaction value. Statistics offices ask businesses about their online sales. Central banks report the value of payments. Industry reports estimate the size of whole markets from platform transaction values (Google, Temasek and Bain 2024; BPS 2025; Bank Indonesia 2025). The United Nations Conference on Trade and Development (UNCTAD) has shown that e-commerce values change with the definition used, and it has set business e-commerce sales beside the transaction values of online platforms (UNCTAD 2023, 2024). A difference between two figures for the same activity does no harm while they move together. It matters when they part, because the choice of figure then changes the conclusion about size and growth. Economists have a standard answer to this problem: test each figure against the activity it stands for. Researchers do this when they check national income figures against satellite images of night-time lights (Henderson, Storeygard and Weil 2012). The growth of national online sales has two possible sources. Existing sellers can sell more, or more sellers can enter. Trade economists split the growth of exports into the same two margins (Hummels and Klenow 2005). The split needs a count of sellers, and a count has weaknesses of its own. It depends on how the survey finds sellers, and a change in design can break a series in a way that looks like a change in the economy (van den Brakel, Smith and Compton 2008; van den Brakel, Zhang and Tam 2020). Respondents misdate events, and they tend to place them nearer to the interview than they were (Neter and Waksberg 1964). Small firms that stop selling are more often young firms with young owners (McKenzie and Paffhausen 2019), which matters when a count has to allow for sellers who stopped. -Applied to the thesis question, national statistics answer one part of it. They say what the whole should be measured as, and they warn that the sources will differ and that a count can break. They do not say whether platform figures and national figures grow for the same reasons, and the studies reviewed here do not test it for one country over time. They also say little about what a rise in a count of online sellers contains: more sellers, or a better count of the sellers already there. +National statistics answer the thesis question in part. They say what the whole should be measured as, and they warn that the sources will differ and that a count can break. They do not say whether platform figures and national figures grow for the same reasons, and the studies reviewed here do not test it for one country over time. They also say little about what a rise in a count of online sellers contains: more sellers, or a better count of the sellers already there. ## 2.4 Platform records and tax: seeing the commerce directly @@ -48,16 +48,16 @@ Platform records can help where other records are thin. Sellers and service prov Information from a third party also changes how people comply with tax. Audit experiments in Denmark, and studies of the value-added tax and of receipt lotteries, show that reports from third parties help compliance (Kleven et al. 2011; Pomeranz 2015; Naritomi 2019; Kleven, Kreiner and Saez 2016; Slemrod 2019). Tax withheld through a third party rests on the same information (Brockmeyer and Hernandez 2016). -Several rules now apply this idea to platforms. The OECD, a group of mostly high-income countries, published model rules under which platforms report what their sellers earn (OECD 2020). The European Union's DAC7 directive applies them (European Union 2021). Indonesia's marketplace tax rule goes further: it makes marketplaces collect tax from their sellers, not only report on them (Ministry of Finance of the Republic of Indonesia 2025). +Several rules now build third-party reporting into the law for platforms. The OECD, a group of mostly high-income countries, published model rules under which platforms report what their sellers earn (OECD 2020). The European Union's DAC7 directive applies them (European Union 2021). Indonesia's marketplace tax rule goes further: it makes marketplaces collect tax from their sellers, not only report on them (Ministry of Finance of the Republic of Indonesia 2025). -Applied to the thesis question, tax research offers a way round revenue: read the records. The consensus is that platform records are useful and that rules can rest on them. The literature does not say how much of the online economy those records cover. The answer depends on how much selling happens on platforms, which is the point that national statistics left open, and the studies reviewed here do not estimate it for Indonesia. +Tax research answers the thesis question in part, by offering a way round revenue: read the records. The consensus is that platform records are useful and that rules can rest on them. The literature does not say how much of the online economy those records cover. The answer depends on how much selling happens on platforms, which is the point that national statistics left open, and the studies reviewed here do not estimate it for Indonesia. ## 2.5 The research gap -Each of the four answers is sound, and each breaks down at the same kind of point. Pricing theory and accounting explain why revenue and commerce differ, but they do not measure how far or how steadily. National statistics explain how the whole should be counted, but they do not test whether platform figures follow it. Tax research shows that records can reach the commerce, but not how much of it they reach. Three questions remain open. +Pricing theory, accounting, national statistics and tax research each give a sound answer, and each breaks down at the same kind of point. Pricing theory and accounting explain why revenue and commerce differ, but they do not measure how far or how steadily. National statistics explain how the whole should be counted, but they do not test whether platform figures follow it. Tax research shows that records can reach the commerce, but not how much of it they reach. Three questions remain open. 1. **How far does a platform's revenue follow the commerce it carries?** The reasons for a difference are known (Sections 2.1 and 2.2). The size of the difference, and how steady it stays over several years, have not been measured for the same platforms. 2. **Do platform figures follow national growth?** Official frameworks show that the figures differ (Section 2.3). Whether platform growth tracks national growth depends on where national growth comes from, and on whether the count of sellers behind it keeps pace with entry. The studies reviewed here test neither. 3. **How far do rules built on platform records reach?** Platform records can support tax rules (Section 2.4). How much of the online economy they cover is not known. -Chapter 3 builds a framework that turns these three questions into measures and hypotheses. +The three questions set the agenda for the rest of the thesis. The framework in Chapter 3 turns them into measures and hypotheses, and the evidence in Chapters 4 and 5 answers them. diff --git a/papers/thesis_2026-10/ch3_framework.md b/papers/thesis_2026-10/ch3_framework.md index 737093a..01aa7a3 100644 --- a/papers/thesis_2026-10/ch3_framework.md +++ b/papers/thesis_2026-10/ch3_framework.md @@ -2,13 +2,13 @@ This thesis builds its framework on one idea: a measure is a stand-in, and a stand-in is only as good as its link to what it stands for. Investors read a platform's revenue as a sign of the commerce the platform carries. Governments and analysts read platform figures as a sign of how fast online commerce is growing in a country. Each reading assumes a link, and the assumption is rarely tested. The framework turns each assumption into a claim that can be measured, so that the thesis can say how far the claim holds and what result would show that it does not. -Figure 3.1 shows the structure. Three circles sit inside one another. The smallest is a platform's revenue, the part of each sale that it keeps. Around it is platform commerce, the full value of the sales the platform handles. The largest is all online commerce in a country, which also includes sellers who never use a platform. The inner link joins revenue to platform commerce, and the outer link joins platform commerce to all online commerce. The first hypothesis, H1, tests the inner link, and the second, H2, tests the outer link (Section 3.2). +Figure 3.1 shows the structure of the framework. Three circles sit inside one another. The smallest is a platform's revenue, the part of each sale that it keeps. Around it is platform commerce, the full value of the sales the platform handles. The largest is all online commerce in a country, which also includes sellers who never use a platform. The inner link joins revenue to platform commerce, and the outer link joins platform commerce to all online commerce. The first hypothesis, H1, tests the inner link, and the second, H2, tests the outer link (Section 3.2). [Figure 3.1 to be drawn: three nested circles, with revenue inside platform commerce inside all online commerce, H1 on the inner link and H2 on the outer link.] -Each link holds only under a condition. The inner link holds while the share of each sale that the platform keeps stays steady, because then revenue rises and falls with the commerce. The outer link holds while national growth comes from the sellers that platforms record, because then platform figures show what the whole economy is doing. When a condition fails, the stand-in misleads. Revenue can rise while commerce falls, and platform growth can miss growth that happens elsewhere. +The inner link and the outer link each hold only under a condition. The inner link holds while the share of each sale that the platform keeps stays steady, because then revenue rises and falls with the commerce. The outer link holds while national growth comes from the sellers that platforms record, because then platform figures show what the whole economy is doing. When a condition fails, the stand-in misleads. Revenue can rise while commerce falls, and platform growth can miss growth that happens elsewhere. -The two links answer the research question together. The inner link measures how much revenue leaves out and whether the share it keeps stays steady. The outer link asks whether the commerce that platforms carry is where the growth of the whole online economy happens. A third question sits beside them, because governments now write rules on platform records, and a rule can reach only what the records cover. So the framework asks three questions, and each becomes a measure, a test or both. +The thesis asks how much of an online economy a platform's revenue leaves out, and when that changes the conclusion about growth. The two links answer it together. The inner link measures how much revenue leaves out and whether the share it keeps stays steady. The outer link asks whether the commerce that platforms carry is where the growth of the whole online economy happens. A third question sits beside them, because governments now write rules on platform records, and a rule can reach only what the records cover. So the framework asks three questions, and each becomes a measure, a test or both. 1. **How far does revenue follow commerce?** The invisible wedge, the part of transaction value that revenue leaves out, measures how much is missing. Growth divergence, the difference between the growth of revenue and the growth of transaction value, measures whether revenue still follows commerce from year to year. Hypothesis H1 states the expected case (Sections 3.1 and 3.2). 2. **Do platform figures follow national growth?** Hypothesis H2 asks whether national growth comes mainly from more businesses selling online or from higher sales per business. An entry test checks what a rise in the count of businesses contains (Sections 3.2 and 3.3). @@ -53,13 +53,13 @@ When the take rate is steady, m1 equals m0, D is zero, and revenue grows exactly Tokopedia between 2022 and 2023 shows the formula at work. Its take rate rose by about 68 percent, so that it kept about two thirds more of each sale. Its transaction value fell by about 9 percent, yet its revenue grew by about 53 percent. Growth divergence was 62 points, which is (1 − 0.09) × 0.68 = 0.62. -Tokopedia's case also shows what each measure records. Its wedge was about 98 percent of its transaction value in both years, so the wedge moved with the commerce and fell by about 10 percent along with it. Its growth divergence was 62 points, because revenue followed the take rate and not the commerce. The two measures answer different halves of the research question. The wedge answers the question of size, and at the marketplaces studied in Indonesia it is almost all of transaction value. Growth divergence answers the question of growth, because it follows the take rate and shows whether revenue remains a fair guide. +Tokopedia's case also shows what each measure records. Its wedge was about 98 percent of its transaction value in both years, so the wedge moved with the commerce and fell by about 10 percent along with it. Its growth divergence was 62 points, because revenue followed the take rate and not the commerce. The two measures answer different halves of the thesis question. The wedge answers the question of size, and at the marketplaces studied in Indonesia it is almost all of transaction value. Growth divergence answers the question of growth, because it follows the take rate and shows whether revenue remains a fair guide. The size of the wedge next to revenue also decides how far a change in the take rate swings revenue growth. Take a platform that keeps 2 percent of each sale, with an ecosystem ratio of 49. If transaction value is flat, a rise in its take rate from 2 to 3 percent lifts revenue growth by about 50 points. A platform that keeps 17 percent has an ecosystem ratio near 5, and a rise from 17 to 18 percent lifts its revenue growth by about 6 points. A wedge that is large next to revenue means more than a big number. It also means that ordinary changes in fees or discounts can swing reported growth. ## 3.2 Hypotheses -Answering the research question takes two checks, and each is stated as a hypothesis. The first is whether, within a platform, revenue grows in proportion to the commerce it comes from. The second is whether the growth of national online commerce comes mainly from more businesses selling online. H1 states the first check, and H2 states the second. +The thesis answers its question through two checks, and each is stated as a hypothesis. The first is whether, within a platform, revenue grows in proportion to the commerce it comes from. The second is whether the growth of national online commerce comes mainly from more businesses selling online. H1 states the first check, and H2 states the second. The take rate depends on fees, on discounts and on how the platform's accounts treat each sale. Revenue stays a fair stand-in for the commerce only while the take rate is steady, which means that growth divergence stays close to zero. H1 states that case. @@ -79,13 +79,13 @@ H2 says that national growth comes mainly from more businesses selling online. T > **Entry test.** In the survey file for 2023, at least 21.5 percent of online sellers must report that they began selling online in 2023. -The threshold comes from arithmetic. The published count of online sellers rose by 27.4 percent in 2023. Suppose every extra seller was new and no seller stopped. Then for every 100 sellers a year earlier there were 127.4, and 27.4 of those were new. New sellers would make up 21.5 percent of all sellers, which is 27.4 divided by 127.4. A smaller share means that part of the rise came from somewhere else. The test does not test H2 itself. It tests what "more businesses" means, and Chapter 4 describes how it was registered. +The 21.5 percent threshold comes from arithmetic. The published count of online sellers rose by 27.4 percent in 2023. Suppose every extra seller was new and no seller stopped. Then for every 100 sellers a year earlier there were 127.4, and 27.4 of those were new. New sellers would make up 21.5 percent of all sellers, which is 27.4 divided by 127.4. A smaller share means that part of the rise came from somewhere else. The test does not test H2 itself. It tests what "more businesses" means, and Chapter 4 describes how it was registered. If the share of new sellers falls short, part of the rise is something other than first-time entry. Two readings are open: the survey reached more of the sellers who were already there, or sellers came back after a pause. Chapter 5 weighs them. ## 3.4 Further analyses -Four further questions use the same measures but carry no hypothesis. Where did large movements in revenue come from: more commerce, higher fees or smaller discounts? Do platform, national and payment figures tell the same story where they cover the same sellers? Which channels do online sellers use, and do they keep financial statements? And do investors value a platform's growth in transaction value differently from growth in its take rate? Chapter 4 describes how each is answered. Chapter 6 uses the answers to ask how many sellers a rule built on platform records can reach. +Four questions use the framework's measures but carry no hypothesis. Where did large movements in revenue come from: more commerce, higher fees or smaller discounts? Do platform, national and payment figures tell the same story where they cover the same sellers? Which channels do online sellers use, and do they keep financial statements? And do investors value a platform's growth in transaction value differently from growth in its take rate? Chapter 4 describes how each is answered. Chapter 6 uses the answers to ask how many sellers a rule built on platform records can reach. ## 3.5 The tests together diff --git a/papers/thesis_2026-10/ch4_data_methods.md b/papers/thesis_2026-10/ch4_data_methods.md index 14301dc..95daf21 100644 --- a/papers/thesis_2026-10/ch4_data_methods.md +++ b/papers/thesis_2026-10/ch4_data_methods.md @@ -1,10 +1,10 @@ # Chapter 4. Data and Methods -The evidence for this thesis is thinner than the question deserves, and it helps to say so first. No source shows the whole picture, and each platform reports in its own way. Tokopedia reports an Indonesian segment, but only for two years. Others report for a group, a region or a business that includes travel, and Table 4.1 gives each scope. Grab reports its Indonesian revenue but not its Indonesian transaction value. Shopee, the largest, reports transaction value only for all its markets together, so the Indonesian value used here is an outside estimate. Companies define transaction value in their own ways, and two of the five series say nothing usable about discounts. Each company figure was read from a filing or release, and the extraction tables keep the source file and the quoted passage beside every value. +No ready-made dataset records both what a platform keeps and what passes through it, so this thesis builds one. Each platform reports in its own format. The thesis reads revenue and transaction value from annual reports, audited statements and earnings releases, and the extraction tables keep the source file and the quoted passage beside every value. It keeps each company's own definition and compares a platform only with itself from one year to the next, so that differences in definition cancel. Where a platform does not report Indonesia alone, as with Shopee and Grab, the thesis states what it derives or borrows and labels it. -The national evidence is thin in the same way. BPS publishes a few totals, and the central bank does not publish its method. The survey files that show when each seller began selling were bought from BPS under a data-use agreement. They cover three rounds and cannot follow the same seller from one round to the next. +The national side rests on a survey that BPS, the national statistics office, runs among online sellers. BPS publishes a few totals, and the answers of individual sellers behind them are released only under a data-use agreement. The thesis obtained those answers for three survey rounds, which show when each seller began selling online and how much it sold. The central bank's own figure has no published method, so it serves only as a point of comparison. -Indonesia still allows what few markets do. Five platform series report revenue and transaction value in the same year, and three of them report what they pay out in incentives. BPS also asks each online seller when it began, so a rise in the seller count can be checked against entry. Most online selling there runs outside the marketplaces, which makes the difference between platform and survey figures large enough to see. The tests are built to ask only what these sources can answer, and each limit is stated where it applies. That sets the bar low on purpose. The thesis cannot show everything, and what it does show adds to what the published figures give. +Setting the two sides against each other for one country is what makes the tests possible. Indonesia allows it. Five platform series report revenue and transaction value in the same year, three of them report what they pay out in incentives, and the survey asks every online seller when it began. The tests are built to ask only what these sources can answer, and each limit is stated where it applies. The aim is modest, and what the tests show adds to what the published figures give. The chapter describes the evidence on each side and the tests that use it. It starts with the platforms, where H1 is tested and revenue movements are traced. It then turns to the survey, where H2 and the entry test are examined. It ends with the comparisons and market values that bring the two sides together. @@ -26,27 +26,27 @@ Two groups of platforms are compared. The Indonesian and regional group has five Table 4.1 states the scope of each series because the series differ, and only Tokopedia is an Indonesian segment alone. The thesis never adds the series together, and each platform enters the tests on its own. Sea, whose Shopee business has Indonesia as a main market, sits in the foreign group. -Three rules keep the pairs comparable. Two consecutive platform-years form a pair, and nothing else does. A missing year stays missing and is never filled in. Annual and quarterly pairs are never pooled. A take rate is a ratio of two amounts in one currency, so the tests need no exchange rate. Amounts in running text are in US dollars at the World Bank's average rate for the year. +Platform-years are compared in pairs, and three rules keep the pairs comparable. Two consecutive platform-years form a pair, and nothing else does. A missing year stays missing and is never filled in. Annual and quarterly pairs are never pooled. A take rate is a ratio of two amounts in one currency, so the tests need no exchange rate. Amounts in running text are in US dollars at the World Bank's average rate for the year. -Own goods need a separate rule. In the foreign group, a company that sells goods itself reports the whole sale as revenue, while an agent reports only its commission (International Accounting Standards Board 2014). The take rate of a seller of goods then says little about a platform, and Hagiu and Wright (2015) draw the same line between a marketplace and a reseller. For the 19 platforms added, the thesis drops any year in which own goods exceed half of revenue. For the eight chosen first, it keeps a pair only when the company's reporting scope did not change between the two years. Zalando is one of the eight and sells part of what it carries, so Appendix B repeats the test without it. One added pair, Ozon from 2021 to 2022, ends in a kept year but starts in a dropped one. Appendix B repeats the test without Ozon as well. The result is 15 pairs for the Indonesian and regional group and 158 for the foreign group. +Platforms that sell goods themselves need a separate rule. In the foreign group, a company that sells goods itself reports the whole sale as revenue, while an agent reports only its commission (International Accounting Standards Board 2014). The take rate of a seller of goods then says little about a platform, and Hagiu and Wright (2015) draw the same line between a marketplace and a reseller. For the 19 platforms added, the thesis drops any year in which own goods exceed half of revenue. For the eight chosen first, it keeps a pair only when the company's reporting scope did not change between the two years. Zalando is one of the eight and sells part of what it carries, so Appendix B repeats the test without it. One added pair, Ozon from 2021 to 2022, ends in a kept year but starts in a dropped one. Appendix B repeats the test without Ozon as well. The result is 15 pairs for the Indonesian and regional group and 158 for the foreign group. ## 4.2 Testing H1 and tracing the moves H1 says that revenue and transaction value grow in proportion, which means that the take rate stays close to steady. The test asks how far each platform's take rate moves from one year to the next. For each pair of years it takes the change in the logarithm of the take rate. A log change treats a rise by some factor and a fall by the same factor as equal in size. It equals the log growth of revenue minus the log growth of transaction value. This is growth divergence measured in logs, and it does not depend on how fast transaction value grew. Tokopedia's log change in 2023 is 0.52, which is a rise by a factor of 1.68, or 68 percent. -A rise and a fall both count against H1, so the test uses the size of each change. Each platform gets one number, the median size of its yearly changes, because the pairs of one platform are not independent of one another. The test compares five numbers for the Indonesian and regional group with 27 for the foreign group. It asks whether the first group ranks higher than chance would give, using a one-sided Mann–Whitney test (Mann and Whitney 1947). It repeats the question by shuffling the group labels 9,999 times. The p-value is the chance of a result at least this lopsided if the groups did not differ. +A move in either direction counts against H1, so the test uses the size of each yearly change in the take rate. Each platform gets one number, the median size of its yearly changes, because the pairs of one platform are not independent of one another. The test compares five numbers for the Indonesian and regional group with 27 for the foreign group. It asks whether the first group ranks higher than chance would give, using a one-sided Mann–Whitney test (Mann and Whitney 1947). It repeats the question by shuffling the group labels 9,999 times. The p-value is the chance of a result at least this lopsided if the groups did not differ. -Appendix B lists the checks that follow. They drop each platform in turn and treat Tokopedia and GoTo as one company. They repeat the test with growth divergence in points as Chapter 3 defines it. They compare the change in the take rate in percentage points, to see whether a thin take rate explains the result. They narrow the test to the three marketplaces, correlate revenue growth with transaction value growth, and repeat the test with quarterly pairs. +Appendix B lists the checks on this test. They drop each platform in turn and treat Tokopedia and GoTo as one company. They repeat the test with growth divergence in points as Chapter 3 defines it. They compare the change in the take rate in percentage points, to see whether a thin take rate explains the result. They narrow the test to the three marketplaces, correlate revenue growth with transaction value growth, and repeat the test with quarterly pairs. -The second objective asks where large movements in revenue came from, and the thesis answers in two steps. The first divides each movement into volume and take rate. In log terms, revenue growth equals transaction value growth plus the change in the take rate. The take-rate share is the size of the take-rate change divided by the sum of the sizes of both changes. The same split is made for the wedge, which equals transaction value minus revenue. +The second objective, to reconcile large movements in revenue with disclosed changes in fees, discounts and scope, is answered in two steps. The first divides each movement into volume and take rate. In log terms, revenue growth equals transaction value growth plus the change in the take rate. The take-rate share is the size of the take-rate change divided by the sum of the sizes of both changes. The same split is made for the wedge, which equals transaction value minus revenue. -The second step finds the cause of a take-rate change. A platform's take rate equals the rate of its gross fees minus the rate of its incentives, each measured against transaction value. Accounting rules normally deduct such incentives from revenue (International Accounting Standards Board 2014). So a rise comes from higher fees, lower incentives or a change in the mix of businesses. Wherever a company discloses both, the thesis splits the growth of revenue over a window into three parts. They are more commerce, a change in the fee rate and a change in the incentive rate. +A take-rate change has three possible causes, and the thesis separates them. A platform's take rate equals the rate of its gross fees minus the rate of its incentives, each measured against transaction value. Accounting rules normally deduct such incentives from revenue (International Accounting Standards Board 2014). So a rise comes from higher fees, lower incentives or a change in the mix of businesses. Wherever a company discloses both, the thesis splits the growth of revenue over a window into three parts. They are more commerce, a change in the fee rate and a change in the incentive rate. The sources differ by company. Tokopedia's audited segment note gives gross revenue, incentives and net revenue, and the thesis checks them against the audited consolidated statements. For Tokopedia the thesis also reports the change line by line in rupiah, the usual way to read accounts, and says where the two views differ. Grab is read from its annual filings and GoTo from its audited statements. Blibli does not report incentives, so its split rests on a proxy built from its releases. Seven windows are traced, and Appendix A lists them. The thesis also records where accounting treatment or reporting scope matters, such as the deduction of incentives from revenue and the travel business inside Blibli's retail segment. Company explanations are read as reports and are not tested. ## 4.3 Measuring the wedge in Indonesia -The first objective needs a different set of amounts. It sums the transaction value and revenue of three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. Tokopedia reports both amounts for its segment. Shopee's transaction value is an outside estimate by Momentum Works. Its revenue assumes a take rate of 10 percent, which the thesis varies from 9 to 11 percent. Grab reports its Indonesian revenue, and the thesis derives its Indonesian transaction value from Grab's company-wide ratio of the two amounts. The wedge and the ecosystem ratio are then computed from the sums. The thesis drops each platform in turn, and it repeats the exercise for every platform-year between 2020 and 2025 that the filings allow. +The first objective, the size of the invisible wedge, needs amounts for Indonesia alone. It sums the transaction value and revenue of three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. Tokopedia reports both amounts for its segment. Shopee's transaction value is an outside estimate by Momentum Works. Its revenue assumes a take rate of 10 percent, which the thesis varies from 9 to 11 percent. Grab reports its Indonesian revenue, and the thesis derives its Indonesian transaction value from Grab's company-wide ratio of the two amounts. The wedge and the ecosystem ratio are then computed from the sums. The thesis drops each platform in turn, and it repeats the exercise for every platform-year between 2020 and 2025 that the filings allow. ## 4.4 Evidence from the sellers @@ -66,21 +66,21 @@ The published figures cannot tell a new seller from one counted for the first ti *Implied by the published count for 2023 and its published growth of 27.4 percent. -The entry test follows the rule in Chapter 3: in the 2024 file, at least 21.5 percent of 2023 sellers must have begun selling online in 2023. If the weights had matched BPS's other count for 2023, the threshold would have been 23.9 percent, but they did not. The test was written down and dated in the project's code archive, called the repository, before the files were opened. +The entry test asks whether at least 21.5 percent of the 2023 sellers in the 2024 file began selling online in 2023, a threshold that Chapter 3 derives. If the weights had matched BPS's other count for 2023, the threshold would have been 23.9 percent, but they did not. The test was written down and dated in the project's code archive, called the repository, before the files were opened. -If the test fails, a second question follows: how much of the rise does first-time entry not explain? A cohort is the group of sellers that began selling online by a given year. If two surveys count the same population, a cohort can only shrink between them, because sellers stop and no new seller can join it. The thesis compares the 2023 survey's count of sellers who began before 2023 with the whole 2022 count. It does so twice. The first time it assumes that no seller stopped. The second time it assumes that sellers stopped at about 5 percent a year, the rate at which one cohort shrank between the 2020 and 2022 surveys. +A failed entry test raises a second question: how much of the rise does first-time entry not explain? The sellers behind that part are called the extra sellers. A cohort is the group of sellers that began selling online by a given year. If two surveys count the same population, a cohort can only shrink between them, because sellers stop and no new seller can join it. The thesis compares the 2023 survey's count of sellers who began before 2023 with the whole 2022 count. It does so twice. The first time it assumes that no seller stopped. The second time it assumes that sellers stopped at about 5 percent a year, the rate at which one cohort shrank between the 2020 and 2022 surveys. Further checks ask where the extra sellers sit. They look across provinces and channels, using rank correlations, and at sellers' own reports of their sales. They use the sampling error that BPS publishes. They profile who the extra sellers are, with each group's count in 2023 compared with what its own yearly change in 2020 to 2022 would give. They look at part-year selling and test a scenario of returners. The cohort check and these further checks were designed after the entry test failed, so Chapter 5 labels them exploratory. The exception is the median change in sellers' sales, which was registered. The files cannot rule out returners, because a seller who stopped and returned keeps an old start year. -Three further analyses use the same files. The first asks which channels sellers use and how sales divide between them. The published tables split the 2023 to 2024 rise in value between marketplaces and other channels, and BPS counts food and ride apps in its marketplace category. Value by channel rests on revenue brackets, which only the 2021 and 2023 files give. The thesis assigns each bracket a low, a middle and a high amount. The amounts were fixed before the file was opened. A validation rule applies: the middle scenario's 2022 total must lie between half and double BPS's published total of about US$53 billion. The 2023 questionnaire asks for monthly revenue but the bracket limits are annual size classes, so both readings are computed and the one that passes is kept. If the lower brackets alone already exceed the total, value shares are reported as ranges. +Three analyses beyond the entry test also use the survey files. One asks which channels sellers use and how sales divide between them. The published tables split the 2023 to 2024 rise in value between marketplaces and other channels, and BPS counts food and ride apps in its marketplace category. Value by channel rests on revenue brackets, which only the 2021 and 2023 files give. The thesis assigns each bracket a low, a middle and a high amount. The amounts were fixed before the file was opened. A validation rule applies: the middle scenario's 2022 total must lie between half and double BPS's published total of about US$53 billion. The 2023 questionnaire asks for monthly revenue but the bracket limits are annual size classes, so both readings are computed and the one that passes is kept. If the lower brackets alone already exceed the total, value shares are reported as ranges. -The second analysis asks whether a seller keeps complete financial statements. This bears on what platform records can add, because a seller with no statements leaves fewer formal records. The third builds on the first two and is developed in Chapter 6. It counts the marketplace sellers whose revenue is above a bracket line and expresses the count as a share of all online sellers. At the 2022 rate, BPS's brackets divide at about US$20,000 a year. The marketplace tax exempts individual sellers up to about US$34,000. So the count above BPS's line is an upper limit on the sellers the tax can reach. +Whether a seller keeps complete financial statements is the subject of a second analysis. It bears on what platform records can add, because a seller with no statements leaves fewer formal records. A third builds on the first two and is developed in Chapter 6. It counts the marketplace sellers whose revenue is above a bracket line and expresses the count as a share of all online sellers. At the 2022 rate, BPS's brackets divide at about US$20,000 a year. The marketplace tax exempts individual sellers up to about US$34,000. So the count above BPS's line is an upper limit on the sellers the tax can reach. World Bank Enterprise Surveys add a check on records. They cover unregistered firms in six Indonesian cities in 2023, and formal firms with five or more employees in seven countries. ## 4.5 Bringing the two sides together -The third objective compares the platform evidence with national and payment figures. Table 4.3 lists the sources and what each one counts. The sources differ in what they cover, so the comparison follows three rules. It compares growth rates across sources and keeps each source in its own unit. It compares levels only after reconciling the definitions. And it compares sources most closely where their coverage overlaps. The 2023 file lets the thesis separate the part of BPS's marketplace sales that went to final consumers. It compares that part with Bank Indonesia's figure for the same year, in a test written down before the file was opened. The thesis also sets quarterly platform growth beside quarterly household spending growth. It treats payment figures as context, because they show how people pay and not what they buy online. +The third objective, to compare the platform evidence with national and payment figures, rests on the sources that Table 4.3 lists, with what each one counts. The sources differ in what they cover, so the comparison follows three rules. It compares growth rates across sources and keeps each source in its own unit. It compares levels only after reconciling the definitions. And it compares sources most closely where their coverage overlaps. The 2023 file lets the thesis separate the part of BPS's marketplace sales that went to final consumers. It compares that part with Bank Indonesia's figure for the same year, in a test written down before the file was opened. The thesis also sets quarterly platform growth beside quarterly household spending growth. It treats payment figures as context, because they show how people pay and not what they buy online. **Table 4.3. Sources compared in Objective 3** diff --git a/papers/thesis_2026-10/ch5_results.md b/papers/thesis_2026-10/ch5_results.md index dc12806..320313e 100644 --- a/papers/thesis_2026-10/ch5_results.md +++ b/papers/thesis_2026-10/ch5_results.md @@ -21,7 +21,7 @@ Platforms are only part of online selling. In the BPS survey, the marketplace ch ## 5.2 Do revenue and transaction value grow together? -Among Indonesian and regional platforms they do not, and the thesis rejects H1. The typical take rate moved by about 20 percent in a year, up or down. At the typical foreign platform it moved by about 6 percent, so the difference is about threefold. Table 5.2 gives the main test, which is exploratory, and its checks. +Revenue and transaction value do not grow together among Indonesian and regional platforms, and the thesis rejects H1. The typical take rate moved by about 20 percent in a year, up or down. At the typical foreign platform it moved by about 6 percent, so the difference is about threefold. Table 5.2 gives the main test, which is exploratory, and its checks. **Table 5.2. The test of H1** @@ -33,11 +33,11 @@ Among Indonesian and regional platforms they do not, and the thesis rejects H1. | Drop each platform in turn, or treat Tokopedia and GoTo as one company | Mann–Whitney | 0.17 to 0.23 | 0.06 | 0.003 to 0.016 | | Quarterly pairs, 3 platforms against 8 | Mann–Whitney | 0.071 | 0.049 | 0.19 | -In Table 5.2, the first two rows are the main test, using the two methods of Chapter 4. The third row repeats it with growth divergence in points, and the fourth shows that no single platform carries the result. +Table 5.2's first two rows are the main test, using the two methods of Chapter 4. The third row repeats it with growth divergence in points, and the fourth shows that no single platform carries the result. -The planned test was narrower: the three Indonesian marketplaces against the eight foreign platforms chosen first. It rejects H1 when it compares pairs of years (p = 0.002), but pairs from one platform are not independent. When it compares platforms, which is the fairer count, it comes close without passing (p = 0.06), because three platforms give little power. The larger groups were set up after that first result, which is why the main test is exploratory. In the narrower test, revenue growth and transaction value growth had a correlation of 0.10 in Indonesia and 0.98 abroad, excluding Sea (Appendix B). The quarterly test in the last row does not reject H1. Its Indonesian pairs mostly cover 2023 to 2026, after most of the repricing, and the large moves came between 2021 and 2024. So the rejection describes that period. +The test planned in advance was narrower than the main test: the three Indonesian marketplaces against the eight foreign platforms chosen first. It rejects H1 when it compares pairs of years (p = 0.002), but pairs from one platform are not independent. When it compares platforms, which is the fairer count, it comes close without passing (p = 0.06), because three platforms give little power. The larger groups were set up after that first result, which is why the main test is exploratory. In the narrower test, revenue growth and transaction value growth had a correlation of 0.10 in Indonesia and 0.98 abroad, excluding Sea (Appendix B). The quarterly test in the last row does not reject H1. Its Indonesian pairs mostly cover 2023 to 2026, after most of the repricing, and the large moves came between 2021 and 2024. So the rejection describes that period. -The thin slice explains much of the threefold difference, which is an exploratory finding. A log change in the take rate is roughly the change in points divided by the take rate itself. Indonesian and regional platforms changed their take rates by about 1 point a year, as foreign platforms did. But the typical Indonesian and regional platform keeps only 1.6 percent of each sale, against 16.9 percent abroad. Among the foreign platforms the share runs from 2.6 percent at Shopify to 71 percent at Zalando, which also sells goods itself. If transaction value is flat, a rise of 1 point lifts revenue by more than half at a platform that keeps 1.6 percent. It lifts revenue by about 6 percent at one that keeps 16.9 percent. Figure 5.1 shows the three measures side by side. What the platforms actually changed is the next question. +A thin slice, the small share of each sale that a platform keeps, explains much of the threefold difference, which is an exploratory finding. A log change in the take rate is roughly the change in points divided by the take rate itself. Indonesian and regional platforms changed their take rates by about 1 point a year, as foreign platforms did. But the typical Indonesian and regional platform keeps only 1.6 percent of each sale, against 16.9 percent abroad. Among the foreign platforms the share runs from 2.6 percent at Shopify to 71 percent at Zalando, which also sells goods itself. If transaction value is flat, a rise of 1 point lifts revenue by more than half at a platform that keeps 1.6 percent. It lifts revenue by about 6 percent at one that keeps 16.9 percent. Figure 5.1 shows the three measures side by side. What the platforms actually changed is the next question. ![Figure 5.1](figures/fig5_1_thin_slice.png) @@ -45,7 +45,7 @@ The thin slice explains much of the threefold difference, which is an explorator ## 5.3 What moved the take rate -Tokopedia shows it best. In 2023 its transaction value fell by 9 percent while its revenue rose by 53 percent. Across the three Indonesian marketplaces, the take rate accounts for two thirds of the movement in revenue at the median (66.5 percent). At the eight foreign platforms chosen first, it accounts for about a quarter (26.7 percent). The wedge follows the volume instead: at the median, about 96 percent of a change in the wedge comes from the change in transaction value, in both groups. +Tokopedia shows best how a take rate can move revenue. In 2023 its transaction value fell by 9 percent while its revenue rose by 53 percent. Across the three Indonesian marketplaces, the take rate accounts for two thirds of the movement in revenue at the median (66.5 percent). At the eight foreign platforms chosen first, it accounts for about a quarter (26.7 percent). The wedge follows the volume instead: at the median, about 96 percent of a change in the wedge comes from the change in transaction value, in both groups. Table 5.3 splits the growth of revenue into more commerce, higher fees and lower incentives, which are the discounts and vouchers a platform pays for. Fees and incentives are measured per unit of transaction value. A negative share means that the part pulled revenue down. The windows of one platform overlap, so they are not seven independent cases. @@ -73,7 +73,7 @@ Tokopedia needs two views. Accounting rules normally deduct what a company pays Market value moved with transaction value, a registered result. Across 28 listed platforms and 138 platform-years, a 1 percent rise in transaction value goes with a 0.6 percent rise in market value (p = 0.002). The second prediction was that markets value growth from a higher take rate less. It did not hold in the main sample. A 1 percent rise in the take rate goes with a rise of 0.3 percent. That estimate is too uncertain to tell from zero. The difference between the two is not large enough to count (p = 0.20). Trimming extreme values leaves the difference at p = 0.16. A second run adds six series, three segments and three more whole companies, and gives 0.63 for transaction value and 0.07 for the take rate. There the difference counts (p = 0.004). That run mixes segment figures with whole-company market values, as the registration warned, so the thesis does not lean on it. The answer depends on which platforms are in the sample. -The sample differs from the 27 foreign platforms of H1. It adds Airbnb, Booking, DoorDash, Lyft and Uber, and it lacks Despegar, Mercado Libre, Ozon, Rakuten and Sea, which enter only the second run or not at all. So the data show that market value follows the commerce a platform carries. Early studies of internet companies found that markets price web-traffic measures too, which points the same way (Trueman, Wong and Zhang 2000; Rajgopal, Venkatachalam and Kotha 2003). The data do not show that markets saw through the pricing effect. Both results are associations and not stock returns, because market value also changes when a company issues shares. The next section asks what the commerce did in 2023. +The 28 platforms of the market-value sample differ from the 27 foreign platforms of H1. It adds Airbnb, Booking, DoorDash, Lyft and Uber, and it lacks Despegar, Mercado Libre, Ozon, Rakuten and Sea, which enter only the second run or not at all. So the data show that market value follows the commerce a platform carries. Early studies of internet companies found that markets price web-traffic measures too, which points the same way (Trueman, Wong and Zhang 2000; Rajgopal, Venkatachalam and Kotha 2003). The data do not show that markets saw through the pricing effect. Both results are associations and not stock returns, because market value also changes when a company issues shares. The next section asks what the commerce did in 2023. ## 5.5 The platforms and the sellers in 2023 @@ -83,15 +83,15 @@ Buying through platforms did not boom in 2023. Figure 5.3 sets six measures of i **Figure 5.3. Growth in 2023 (percent).** Each measure is in its own unit. Sources: company reports, Bank Indonesia, Momentum Works and BPS; household spending from national accounts. GoTo's change rests on an estimated 2022 value, and Momentum Works' 3.5 is the author's calculation from its published levels (its 2024 release states 3.7). Appendix D gives the values. -Because the measures are in different units, the thesis compares only their rates. The blue bars show the buying, and the orange bars show the figures that described a boom. Revenue and BPS's value rose strongly while the buying through platforms did not. +Figure 5.3 compares only growth rates, because the measures are in different units. The blue bars show the buying, and the orange bars show the figures that described a boom. Revenue and BPS's value rose strongly while the buying through platforms did not. -Sellers did not describe a boom either. BPS asks each business how its online sales changed. Among businesses already selling, the median change was zero. A registered prediction had set the limit at 10.4 percent, the published growth in sales per business. A share of 24 percent reported a rise, 32 percent a fall and 44 percent no change, against 37 percent up and 24 percent down a year earlier. The mean change was −4.3 percent, and lack of demand became the main obstacle, named by 41 percent of sellers against 35 percent in 2022. Yet the published count of online sellers rose by 27 percent that year. Where did those sellers come from? +Sellers' own accounts of their sales show no boom. BPS asks each business how its online sales changed. Among businesses already selling, the median change was zero. A registered prediction had set the limit at 10.4 percent, the published growth in sales per business. A share of 24 percent reported a rise, 32 percent a fall and 44 percent no change, against 37 percent up and 24 percent down a year earlier. The mean change was −4.3 percent, and lack of demand became the main obstacle, named by 41 percent of sellers against 35 percent in 2022. Yet the published count of online sellers rose by 27 percent that year. Where did those sellers come from? ## 5.6 Where the extra sellers came from On BPS's published figures, H2 holds. From 2022 to 2023, online sales value grew by 40.6 percent, the number of businesses by 27.4 percent and sales per business by 10.4 percent. So more businesses account for 71 percent of the growth. From 2023 to 2024 the figures were 17.1, 15.3 and 1.5 percent, and more businesses account for 90 percent, or 71 percent on BPS's other count for 2023. The split is arithmetic, and it cannot tell a new seller from one counted for the first time. -The entry test separates the two, and its result is registered. In the 2024 file, 13.45 percent of sellers began selling online in 2023, and the rule required at least 21.5 percent. The test fails. Even if no seller stopped, that many new sellers lets the count grow by at most 15.5 percent over the sellers already there, against the published 27.4 percent. Respondents often place past events nearer to the interview than they were (Neter and Waksberg 1964). If sellers who began earlier reported 2023, that would make new sellers look more numerous, so the shortfall is if anything understated. +The entry test separates new sellers from sellers counted for the first time, and its result is registered. In the 2024 file, 13.45 percent of sellers began selling online in 2023, and the rule required at least 21.5 percent. The test fails. Even if no seller stopped, that many new sellers lets the count grow by at most 15.5 percent over the sellers already there, against the published 27.4 percent. Respondents often place past events nearer to the interview than they were (Neter and Waksberg 1964). If sellers who began earlier reported 2023, that would make new sellers look more numerous, so the shortfall is if anything understated. **Table 5.4. The 2023 rise in the count of online sellers (thousands)** @@ -105,15 +105,15 @@ Source: BPS survey files, weighted; author's calculations. Table 5.4 shows the accounting. The rise of 819 thousand is the gap between the weighted totals of the two files, and BPS's own published counts give 821 thousand (BPS 2025). The 2023 survey counted 306 thousand more sellers who began before 2023 than the whole 2022 count held. That is 37 percent of the rise if no seller stopped and 56 percent if sellers stopped at 5 percent a year. So about half of the rise is not explained by first-time entry. This accounting and the checks below are exploratory, and they point the same way. -Cohorts behave as they should, except in 2023. Sellers who began by 2020 numbered 9.9 percent fewer in the 2022 survey than in the 2020 one, over two years. In the 2023 survey they numbered 6.0 percent more than in the 2022 one. Between the 2023 and 2024 surveys, the cohort that began before 2020 shrank by 10.2 percent, and about 750 thousand new sellers appeared against a rise of 584 thousand. +A cohort is the group of sellers who began by a given year, and a cohort should only shrink between surveys. Cohorts do, except in 2023. Sellers who began by 2020 numbered 9.9 percent fewer in the 2022 survey than in the 2020 one, over two years. In the 2023 survey they numbered 6.0 percent more than in the 2022 one. Between the 2023 and 2024 surveys, the cohort that began before 2020 shrank by 10.2 percent, and about 750 thousand new sellers appeared against a rise of 584 thousand. -The break is also uneven. Across the 34 provinces, count growth had almost no link to the share of new sellers (0.10) and a weak link to growth in sample size (0.35). These are rank correlations, which compare the order of provinces on two measures and run from −1 to 1. Sellers who began by 2022 and use only chat or social media rose by 13 percent, while marketplace users rose by 0.3 percent. BPS's published error rates put the standard error of the national count, the usual size of its sampling noise, at about 1.4 percent. The 306 thousand sellers are about 4.6 times the standard error of the change. That calculation applies the 2024 edition's error rates to both years and treats the two surveys as independent. +The 2023 break in the cohort counts is also uneven. Across the 34 provinces, count growth had almost no link to the share of new sellers (0.10) and a weak link to growth in sample size (0.35). These are rank correlations, which compare the order of provinces on two measures and run from −1 to 1. Sellers who began by 2022 and use only chat or social media rose by 13 percent, while marketplace users rose by 0.3 percent. BPS's published error rates put the standard error of the national count, the usual size of its sampling noise, at about 1.4 percent. The 306 thousand sellers are about 4.6 times the standard error of the change. That calculation applies the 2024 edition's error rates to both years and treats the two surveys as independent. On the value side, BPS's measure rose from about US$53 billion to about US$72 billion. Sellers not explained by entry account for 15 to 17 percent of this, and entrants for 25 to 30 percent. The remaining 53 to 59 percent, about US$11 to 12 billion at the 2023 exchange rate, cannot be traced to existing sellers' answers, which show a mean change of −4.3 percent. These checks point to wider survey coverage of chat and social sellers. That is the pattern survey research warns about, because a change in how a survey finds respondents can break a series (van den Brakel, Smith and Compton 2008). The data cannot separate it from sellers returning after a pause. Who were the extra sellers? ## 5.7 Who the extra sellers were, and whether they returned -The comparison of who they were is exploratory. The surveys do not follow the same businesses, so the thesis compares groups. For each group it takes the 2023 count above what its normal yearly change in 2020 to 2022 would give. These extra sellers total about 450 thousand, close to the 458 thousand in Table 5.4. Figure 5.4 gives each group's share of this extra count next to its share of the 2022 count. +Comparing who the extra sellers were is exploratory. The surveys do not follow the same businesses, so the thesis compares groups. For each group it takes the 2023 count above what its normal yearly change in 2020 to 2022 would give. These extra sellers total about 450 thousand, close to the 458 thousand in Table 5.4. Figure 5.4 gives each group's share of this extra count next to its share of the 2022 count. ![Figure 5.4](figures/fig5_4_extra_sellers.png) @@ -135,20 +135,20 @@ Most online sellers do not use a marketplace, and about 96 percent use chat or s Records are thinner outside the marketplaces. In 2023, 28.6 percent of marketplace sellers kept complete financial statements, against 12.3 percent of other sellers, and Figure 5.5 shows the share of all sellers who keep them falling. The fall from 2020 to 2022 came while the survey appears to have counted the same kinds of seller. The fall in 2023 may partly reflect the chat-only sellers added to the count. Even among sellers who began before 2020, the share fell from 23.4 percent in 2022 to 16.8 percent in 2023. -Value shares rest on the bracket scenarios of section 4.4. The annual reading passes the validation rule, because the middle scenario gives 1.95 times BPS's total for 2022, and the monthly reading fails at 23 times. The lower brackets alone already exceed BPS's total, so the shares are ranges. Sellers without statements held between 26 and 40 percent of online value in 2022, against 79 percent of the count. The registered prediction was that their share of value would not fall by more than 5 points between 2020 and 2022. It fell by 5.8 points, so the prediction failed, narrowly. A second registered version gives a fall of 3.3 points, which is inside the limit. +The value held by each kind of seller rests on the bracket scenarios that section 4.4 describes. The annual reading passes the validation rule, because the middle scenario gives 1.95 times BPS's total for 2022, and the monthly reading fails at 23 times. The lower brackets alone already exceed BPS's total, so the shares are ranges. Sellers without statements held between 26 and 40 percent of online value in 2022, against 79 percent of the count. The registered prediction was that their share of value would not fall by more than 5 points between 2020 and 2022. It fell by 5.8 points, so the prediction failed, narrowly. A second registered version gives a fall of 3.3 points, which is inside the limit. World Bank surveys of Indonesian firms add evidence on records and online presence. In six Indonesian cities, 27 percent of unregistered firms use social media with their customers. Among those, 70 percent keep no written records, and their median sales are about US$4,000 a year. The registered prediction on their mean sales failed: it was below about US$6,600, and the mean is about US$7,300. Among formal firms, 39.9 percent in Indonesia have neither a website nor online tax filing, the highest of seven countries, against 9.1 percent in Malaysia and 8.7 percent in Thailand. ## 5.9 Why the national figures disagree -Chapter 1 began with two official figures that moved in opposite directions in 2023. The third objective asks whether platform, national and payment figures give the same account. They do not give the same account of scale. They give the same account of growth only where they cover the same sellers. +Two official figures moved in opposite directions in 2023: Bank Indonesia's fell by 4.7 percent and BPS's rose by 40.6 percent. The third objective asks whether platform, national and payment figures give the same account. They do not give the same account of scale. They give the same account of growth only where they cover the same sellers. -In 2023, Bank Indonesia's figure fell by 4.7 percent and BPS's rose by 40.6 percent. Where the two cover the same ground, the growth is small. BPS's own marketplace slice grew by 0.2 percent from 2022 to 2023 in the middle scenario, and by 0.2 to 13.2 percent across the scenarios. It grew by 1.45 percent from 2023 to 2024. A registered test gives a mixed answer on size. The part of BPS's marketplace sales that went to final consumers in 2022 is 0.68 times Bank Indonesia's figure in the middle scenario and 0.16 times in the low one. In the high scenario it is 1.62 times. The prediction was that it would fall below Bank Indonesia's figure, so it holds in the first two scenarios and fails in the third. Most of the disagreement then sits outside the marketplaces, in the count of sellers that only BPS reaches. +Where Bank Indonesia's figure and BPS's cover the same ground, the growth is small. BPS's own marketplace slice grew by 0.2 percent from 2022 to 2023 in the middle scenario, and by 0.2 to 13.2 percent across the scenarios. It grew by 1.45 percent from 2023 to 2024. A registered test gives a mixed answer on size. The part of BPS's marketplace sales that went to final consumers in 2022 is 0.68 times Bank Indonesia's figure in the middle scenario and 0.16 times in the low one. In the high scenario it is 1.62 times. The prediction was that it would fall below Bank Indonesia's figure, so it holds in the first two scenarios and fails in the third. Most of the disagreement then sits outside the marketplaces, in the count of sellers that only BPS reaches. -In 2024, the figures again divide by coverage. Those that cover mainly platforms grew by 5 to 7 percent, and BPS's grew by 17.1 percent. Adjacent figures point the same way as BPS but do not measure Indonesian e-commerce. Southeast Asian parcels grew by 25.2 percent, and the tax on foreign digital services by 24.9 percent. +In 2024, the figures divide by coverage. Those that cover mainly platforms grew by 5 to 7 percent, and BPS's grew by 17.1 percent. Adjacent figures point the same way as BPS but do not measure Indonesian e-commerce. Southeast Asian parcels grew by 25.2 percent, and the tax on foreign digital services by 24.9 percent. -Sizes cannot be reconciled. In 2023, BPS's marketplace slice was about US$13 billion, against US$38 billion for Tokopedia and the Shopee estimate together. BPS's whole total for 2023, US$72 billion, lies above the three platforms' US$43 billion in Table 5.1, but the two count different things. In 2024 it was less than a quarter of Momentum Works' marketplace estimate of about US$56.5 billion. Platform transaction value counts shipping, digital goods, travel and offline sales that a seller survey does not count in the same way. Counts differ too: Tokopedia alone said in a company statement that it had about 15 million merchants in 2022, while BPS counts about 0.6 million marketplace sellers. +Sizes cannot be reconciled. In 2023, BPS's marketplace slice was about US$13 billion, against US$38 billion for Tokopedia and the Shopee estimate together. BPS's whole total for 2023, US$72 billion, lies above the three platforms' US$43 billion in Table 5.1, but the two count different things. In 2024, BPS's marketplace slice was less than a quarter of Momentum Works' marketplace estimate of about US$56.5 billion. Platform transaction value counts shipping, digital goods, travel and offline sales that a seller survey does not count in the same way. Counts differ too: Tokopedia alone said in a company statement that it had about 15 million merchants in 2022, while BPS counts about 0.6 million marketplace sellers. -Platform sales do not always keep pace with household spending either. GoTo's on-demand transaction value grew more slowly than household spending in each of four quarters, from the third quarter of 2025 to the second quarter of 2026. In the last it grew by 2.0 percent against 8.3 percent, while its revenue grew by 20.5 percent. Grab and Shopee grew faster, but their figures cover the region. Payment figures such as QRIS are context only, because they include in-person payments and the switch from cash. +Platform sales do not always keep pace with household spending. GoTo's on-demand transaction value grew more slowly than household spending in each of four quarters, from the third quarter of 2025 to the second quarter of 2026. In the last it grew by 2.0 percent against 8.3 percent, while its revenue grew by 20.5 percent. Grab and Shopee grew faster, but their figures cover the region. Payment figures such as QRIS are context only, because they include in-person payments and the switch from cash. Taken together, the results answer the research question at two levels. Revenue showed a small part of the commerce the platforms carried, and in the windows traced it was not a fair guide to that commerce, because the take rate moved. The official count of online sellers followed first-time entry only in part in 2023, and the platforms and the survey disagree most where only the survey looks. Chapter 6 asks what this means for the rules that Indonesia is writing. From a174820fdfb4a3a1476f57c2ff7c9f5b4335d979 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 13 Oct 2026 13:11:20 +0000 Subject: [PATCH 35/65] Thesis ch2-3: rebuilt on the proposal's spine with expansion (anatomy-ordered review, gap from documented limits, framework declared first, formulas with definitions, hypotheses reasoned from the literature) Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 7 ++ papers/thesis_2026-10/ch2_literature.md | 64 ++++++++--------- papers/thesis_2026-10/ch3_framework.md | 92 ++++++++++++------------- 3 files changed, 81 insertions(+), 82 deletions(-) diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 067aee5..d5c0a8b 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -348,3 +348,10 @@ Audit method: read the opening sentence of every paragraph in Chapters 1 to 5 as - **Chapter 4:** the opening is rewritten as a statement of what the evidence takes and why Indonesia allows the tests (no complaint tone; no "thinner than the question deserves"); "the pairs", "own goods", "the second step", "the extra sellers", "the first objective" and "the third objective" are all introduced where first used. - **Chapter 5:** headings phrased as questions were answered with "they" or "it" (5.2, 5.3, 5.7); fixed. 5.9 no longer opens "Chapter 1 began"; the unclear "it" in the 2024 sentence is fixed; "again" and "either" openers fixed. - **Chapter 1:** "the same split", "the figures", "the question" at the starts of three paragraphs now name their subjects. Left for the researcher: the opening of the research-question paragraph in 1.1 ("Measuring what the current figures miss…"), which deliberately bridges from the ambition paragraph the researcher approved. + +### Chapters 2 and 3 rebuilt on the proposal's spine (13 Oct 2026) + +The researcher asked whether the proposal had been checked. It had not, properly, and the thesis README's own method (start from the proposal's paragraph order, lighten, insert new material where the line reaches it) had been broken by three rebuilds with invented frames. The proposal's Chapters 2 and 3 already had the structure the researcher kept asking for: sections that follow the platform's anatomy (it carries a transaction, records part as revenue, keeps a record of the whole), section endings on what the literature leaves unmeasured, a gap that maps onto the research question (how large, whether it persists, whether the choice of figure changes the conclusion), a Chapter 3 that opens by declaring the framework's logic before the figure, numbered formulas with "where" definitions, measures grounded in the literature, and hypothesis development that reasons from the literature to each hypothesis. Its fault was readability (Prof. Kong) and confusing terms (Prof. Thomas). +- **Chapter 2** keeps the proposal's order and section titles and expands each step: what platform economics studies and its central finding, the price-structure result with an example, the platform–reseller distinction, revenue recognition with a worked ride example and the voucher rule with its exception, reporting choices, comparability and operating measures, investor evidence; official statistics, sources, night lights, margins, survey breaks, recall error and exit; digital records, third-party compliance evidence with what each study found, and the platform rules. Each section closes on what remains unmeasured. 2.4 keeps the proposal's three-part gap; the reach of rules is stated as a further question that extends the thesis. +- **Chapter 3** keeps the proposal's declaration, figure note, pairing rule, equations (1) to (4) with "where" definitions, the E example, D as the central test, and hypothesis development from the literature. Added where the line reaches them: the terms table (Prof. Thomas), the take-rate form of D and the Tokopedia illustration, the thin-slice paragraph (Prof. Thomas's "just subtraction"), Table 3.2 placed before the hypotheses so they are anchored, and the entry test after H2's stated limit. The further analyses close the chapter in one paragraph. +- Register: no rhetorical questions, vignettes, invented frames or announcements; studies are reported by what they find; openers name their subject; "indicator", "therefore", "rather than" and "Overall" avoided. diff --git a/papers/thesis_2026-10/ch2_literature.md b/papers/thesis_2026-10/ch2_literature.md index ebf42dc..34bead7 100644 --- a/papers/thesis_2026-10/ch2_literature.md +++ b/papers/thesis_2026-10/ch2_literature.md @@ -1,63 +1,55 @@ # Chapter 2. Literature Review -How much of an online economy is missing when a platform is measured by its revenue, and when does that change the story about growth? No single study answers this, but four lines of research each answer a piece. Platform economics explains why a platform keeps only a share of what its users trade. Accounting explains what a company may record as revenue. National statistics explain how the commerce of a whole country is counted. Tax research explains what a platform's own records can show. This chapter sets out what each line concludes, uses it to answer the question as well as it can, and shows where the answer breaks down. The breaking points are the gap that this thesis fills. +Digital platforms sit between economic activity and the figures used to measure it. A platform handles a transaction between a buyer and a seller, records part of the payment as its own revenue, and keeps a record of the whole sale. Each of these steps is studied by a different body of research. Platform economics and accounting explain why a platform records only part of each payment as revenue (Section 2.1). Official statistics and survey methods explain how the transactions themselves are measured across an economy (Section 2.2). Research on third-party information explains what a platform's records of each sale can show to others, such as tax authorities (Section 2.3). Each section sets out the main findings and the evidence behind them, and it closes with what the findings leave unmeasured. Section 2.4 brings these points together as the research gap. -Tokopedia's 2023 shows how differently the four lines read the same facts. That year its revenue rose by 53 percent while the value of the sales it carried fell by 9 percent. Platform economics would look at how the platform prices the two groups it serves, shoppers and merchants, and ask whether it raised its commission. Accounting would look at what the company records as revenue, and ask whether smaller discounts or a change in bookkeeping lifted the figure. National statistics would ask whether the sales of all sellers, on platforms and off them, moved with either number. Tax research would ask what the platform's records show about the sellers behind the sales. Each reading is sound, and each is partial. None of them says how large such a difference is, how often it happens, or whether it changes the story of growth. +## 2.1 Platform economics and revenue recognition -## 2.1 Platform pricing: why revenue is only part of the commerce +Platform economics studies firms that bring together two groups of users who benefit from each other's presence, such as buyers and sellers on a marketplace or riders and drivers on a ride-hailing service. Its central finding is that such a firm sets its prices for the two groups jointly, because the value of joining for one group depends on how many members of the other group join (Rochet and Tirole 2003; Caillaud and Jullien 2003; Armstrong 2006). The firm earns commissions, fees, advertising income or delivery charges for arranging transactions, and it does not take the full value of each sale (Evans and Schmalensee 2016). So the value of the commerce that passes through a platform can be much larger than the revenue the platform records. -Platform economics holds that a platform's revenue is a price, and that the price is set to keep two groups on board and not to collect a share of the value traded. A marketplace needs shoppers and merchants. A ride-hailing app needs riders and drivers. A shopper has little reason to visit a marketplace with no merchants, and a merchant has little reason to list on one with no shoppers. So each group is worth more to the platform when the other is present. Economists call these cross-side effects, and they are the starting point of the field (Rochet and Tirole 2003; Caillaud and Jullien 2003; Evans and Schmalensee 2016). +The way a platform divides its charges between its two groups of users also matters. Rochet and Tirole (2003) show that the number of transactions on a platform depends on how the total charge is split between the two sides, and not only on its level. A platform may charge one side little or nothing and recover its costs from the other, as when a marketplace lets shoppers browse for free and charges merchants a commission on each sale (Caillaud and Jullien 2003; Parker and Van Alstyne 2005; Armstrong 2006). Because both the level and the split are choices, a platform can change how much revenue it takes from the same volume of sales. -A platform with two groups to attract does not price them the way an ordinary seller prices a product. Because each group draws the other, it can pay to charge one group little or nothing and to earn from the other. A delivery app might keep fees low for customers and take a larger commission from restaurants, or the reverse, depending on which group is harder to attract and which cares less about price. The split between the groups matters as well as the total, because the split decides how many people join and how many transactions follow (Rochet and Tirole 2003; Parker and Van Alstyne 2005; Armstrong 2006). So a commission is a choice that the platform can change, and a change in it moves revenue without any change in what people buy. +The business model sets how much of each sale can count as revenue in the first place. Hagiu and Wright (2015) distinguish a multi-sided platform, which lets sellers deal directly with buyers, from a reseller, which buys goods and sells them on its own account. A reseller's revenue includes the full price of what it sells. A platform's revenue is the charge it makes for the interaction, which is a fraction of the price. -The business model sets the starting point for how large the platform's share is. A marketplace earns a fee on sales between others, so its revenue is a fraction of the sales it carries. A reseller buys goods and sells them itself, so its takings are the whole sale (Hagiu and Wright 2015). Many companies mix the two, which is one reason why the share of each sale that a platform keeps differs so much from one platform to the next. +Accounting rules decide how much of each payment a company records as revenue. These rules are known as revenue recognition, and International Financial Reporting Standard 15 (IFRS 15) is the international standard for them (International Accounting Standards Board 2014). The rules turn on whether the company is a principal, which controls the good or service before it reaches the customer, or an agent, which arranges for another party to provide it. A principal records the full payment as revenue, and an agent records only its fee or commission. If a rider pays 100 for a trip and the driver keeps 80, a ride-hailing company acting as an agent records 20 as revenue. -Pricing theory answers the thesis question in part. It says that revenue will be a small share of the commerce, that the platform's pricing sets the share, and that the platform can change it. So revenue and commerce need not move together. The theory does not say how small the share is in practice, how often platforms change it, or by how much. It is a theory of how prices are set, and it leaves the size and the steadiness of the share to measurement. +IFRS 15 also treats payments that a company makes to its own customers, such as discounts and vouchers, as a reduction of revenue, unless the payment buys a distinct good or service from the customer (International Accounting Standards Board 2014). A platform that funds vouchers for shoppers reports less revenue on the same sales, and more when it cuts the vouchers. Changes in fees, customer incentives, the mix of services, acquisitions and the scope of the reported business can all move revenue without an equal change in the commerce behind it. -## 2.2 Accounting: what revenue records +Firms have used the room that revenue recognition rules leave. Bowen, Davis and Rajgopal (2002) examine how internet firms reported revenue around 2000 and find that the firms differed in whether they reported the full value of the sales they arranged, a practice known as reporting revenue gross. Comparability matters when such figures are read across firms: De Franco, Kothari and Verdi (2011) find that analysts forecast more accurately when a firm's accounting is comparable with that of its peers. Operating measures that firms disclose voluntarily, of which transaction value is one, raise a further problem. Givoly, Li, Lourie and Nekrasov (2019) find that such a measure loses information when the way it is computed changes. -Accounting holds that revenue is not a measure of how much was sold. It is the amount a company is entitled to record as its own income. The rules for that decision turn on one question: is the company selling the thing itself, or arranging a sale for someone else? Suppose a ride costs the rider 100 and the driver keeps 80. A company that controls the ride, as a principal, records 100 as revenue and the driver's 80 as a cost. A company that only arranges the ride, as an agent, records its commission of 20 and never counts the driver's 80 as its own. International Financial Reporting Standard 15 (IFRS 15), the international rule on revenue, draws this line (International Accounting Standards Board 2014). +Investors use operating measures alongside revenue. Trueman, Wong and Zhang (2000) find that measures of web traffic help explain the market values of internet firms beyond their financial statements, and Trueman, Wong and Zhang (2001) find that web traffic helps forecast those firms' revenue. Rajgopal, Venkatachalam and Kotha (2003) report similar results for measures of network advantage among e-commerce firms. -IFRS 15 also treats discounts and vouchers in a way that matters for platforms. When a company pays its customers, for example by funding a voucher, it normally treats the payment as a cut in the price. The voucher then reduces revenue and does not appear as a cost (International Accounting Standards Board 2014). So a platform that spends heavily on vouchers reports lower revenue than one that spends little on the same sales, and its revenue rises when it cuts them. +Platform economics and accounting research explain why a platform's revenue is smaller than the commerce it carries and why the share it keeps can change. It does not measure how large the difference is for actual platforms, or how much the share changes from one year to the next. The pricing models describe how charges are set, not how they move in a particular market. The accounting evidence comes from internet firms around 2000, and the investor evidence rests on web traffic, not on the transaction value of present-day platforms. None of the studies reviewed here follows the revenue and the transaction value of the same platforms over several years. -Accounting rules leave room for judgment, and companies have used it. A study of internet firms in the early 2000s found that they chose between reporting sales gross and reporting them net, and that the choices differed across firms (Bowen, Davis and Rajgopal 2002). So the reported revenue of a platform can rise for three reasons that have nothing to do with buying: its fees rise, its discounts shrink, or it changes how it records a sale. +## 2.2 Measuring the digital economy -Companies also publish operating figures that no standard governs, and transaction value, the total value of everything bought through the platform, is one of them. Analysts read accounts more easily when the accounting is comparable across firms (De Franco, Kothari and Verdi 2011). A measure that a company chooses to report, such as transaction value or active users, loses information when the company changes its definition or stops reporting it (Givoly, Li, Lourie and Nekrasov 2019). This matters for the thesis, because transaction value is the figure against which revenue is compared. +Official statistics distinguish the commerce that passes through a platform from the service the platform provides. National accounts, which measure a country's production and income, record a sale between a buyer and a seller as the seller's output. They record the platform's charge for arranging the sale as the platform's output, and they do not treat the full transaction value as platform output (Ahmad and Schreyer 2016; International Monetary Fund 2018; Organisation for Economic Co-operation and Development [OECD] 2023; United Nations et al. 2025). -Investors know the limits of revenue, and finance research has shown how they respond. In the early years of the internet, activity measures such as web traffic carried information about a firm's value and future revenue that reported revenue did not show (Trueman, Wong and Zhang 2000, 2001; Rajgopal, Venkatachalam and Kotha 2003). +In practice, different producers publish different quantities. Industry reports size the digital economy of Southeast Asia by gross merchandise value, the value of goods sold through platforms (Google, Temasek and Bain 2024). National statistics offices survey businesses about their online sales, as BPS, the Indonesian statistics office, does (BPS 2025). Listed platforms report the revenue they recognize under IFRS 15, and central banks report the value of electronic payments (Bank Indonesia 2025). The United Nations Conference on Trade and Development (UNCTAD) documents how far e-commerce values depend on the definition used, and it compares business e-commerce sales with the transaction values of large online platforms (UNCTAD 2023, 2024). -Accounting answers the thesis question in part. Revenue is a convention, and a platform can move it by changing its commission, its discounts or its bookkeeping. The evidence does not say how large these moves are for real platforms. The studies of reporting choices describe internet firms of the early 2000s, and the studies of investors rest on web traffic and not on the transaction value of a modern platform. None of them follows the revenue and the transaction value of the same platform through several years, and none covers a market such as Indonesia. +A difference between two figures for the same activity is harmless while they move together. It matters when they part, because the choice of figure can then change the conclusion about size and growth. Research on night-time lights treats this as a question to be tested. Henderson, Storeygard and Weil (2012) compare national income figures with satellite measures of light at night and use the relationship to improve estimates of growth where official figures are weak. -## 2.3 National statistics: what the whole online economy counts +National online sales can grow because existing sellers sell more or because more sellers enter. Trade economists make the same distinction for exports. Hummels and Klenow (2005) divide differences in exports between countries into an intensive margin, more of the same goods, and an extensive margin, a wider range of goods. The same split applied to online sales separates growth that comes from more sellers from growth that comes from higher sales per seller. -National statistics hold that the commerce a platform carries is not the platform's output. A country's accounts add up the goods and services it produces. They record the sale between a buyer and a seller separately from the platform's service of bringing the two together, and only the service counts as the platform's output (Ahmad and Schreyer 2016; International Monetary Fund 2018; Organisation for Economic Co-operation and Development [OECD] 2023; United Nations et al. 2025). The official view agrees with the pricing and accounting views: the commerce and the revenue are different amounts, and the commerce is the larger. +A count of sellers comes from a survey, and surveys change. Van den Brakel, Smith and Compton (2008) show that a change in survey design can produce a break in a series that looks like a real change, and van den Brakel, Zhang and Tam (2020) review methods for measuring such breaks. Respondents also misdate past events. In a study of household spending, Neter and Waksberg (1964) find that people tend to report events as more recent than they were. Firm exit matters as well: McKenzie and Paffhausen (2019) find that small firms in developing countries that stop operating are more often young firms with young owners. -Counting online commerce itself is harder, because the sources that report it count different things. Listed platforms report the revenue they record and often their transaction value. Statistics offices ask businesses about their online sales. Central banks report the value of payments. Industry reports estimate the size of whole markets from platform transaction values (Google, Temasek and Bain 2024; BPS 2025; Bank Indonesia 2025). The United Nations Conference on Trade and Development (UNCTAD) has shown that e-commerce values change with the definition used, and it has set business e-commerce sales beside the transaction values of online platforms (UNCTAD 2023, 2024). +Official statistics and survey research establish that platform transaction value and platform revenue measure different things, and that the figures used to describe online commerce come from different sources with different definitions. It does not test whether platform figures and national figures grow for the same reasons. It also offers no test of what a rise in a national count of online sellers contains: sellers who are new, or sellers whom the survey had not reached before. -A difference between two figures for the same activity does no harm while they move together. It matters when they part, because the choice of figure then changes the conclusion about size and growth. Economists have a standard answer to this problem: test each figure against the activity it stands for. Researchers do this when they check national income figures against satellite images of night-time lights (Henderson, Storeygard and Weil 2012). +## 2.3 Digital records and third-party information -The growth of national online sales has two possible sources. Existing sellers can sell more, or more sellers can enter. Trade economists split the growth of exports into the same two margins (Hummels and Klenow 2005). The split needs a count of sellers, and a count has weaknesses of its own. It depends on how the survey finds sellers, and a change in design can break a series in a way that looks like a change in the economy (van den Brakel, Smith and Compton 2008; van den Brakel, Zhang and Tam 2020). Respondents misdate events, and they tend to place them nearer to the interview than they were (Neter and Waksberg 1964). Small firms that stop selling are more often young firms with young owners (McKenzie and Paffhausen 2019), which matters when a count has to allow for sellers who stopped. +Transactions that do not enter a platform's revenue are still recorded in its systems. A platform stores who sold what, to whom, for how much and when. Many of the sellers and service providers involved are small, self-employed or weakly represented in conventional financial records. This differs from the setting of the shadow economy, where reliable records of activity are often missing (La Porta and Shleifer 2014; Ulyssea 2018). Digital records can carry information that conventional records lack. Berg, Burg, Gombovic and Puri (2020) find that simple digital traces, such as the type of device a customer uses, help predict loan default. Activity on platforms also leaves traces in financial and administrative data (Barrios, Hochberg and Yi 2022; Denes, Lagaras and Tsoutsoura 2025). -National statistics answer the thesis question in part. They say what the whole should be measured as, and they warn that the sources will differ and that a count can break. They do not say whether platform figures and national figures grow for the same reasons, and the studies reviewed here do not test it for one country over time. They also say little about what a rise in a count of online sellers contains: more sellers, or a better count of the sellers already there. +Information that a third party reports about a taxpayer improves tax compliance. In a field experiment in Denmark, Kleven et al. (2011) find that evasion is close to zero for income that third parties such as employers report, and substantial for income that taxpayers report themselves. Pomeranz (2015) shows that the paper trail created by the value-added tax in Chile raises compliance. Naritomi (2019) finds that rewarding consumers in Brazil for asking for receipts raised the sales that firms reported. Kleven, Kreiner and Saez (2016) and Slemrod (2019) review this evidence, and Brockmeyer and Hernandez (2016) study tax withheld by third parties in Costa Rica. -## 2.4 Platform records and tax: seeing the commerce directly +Rules for platforms now apply the principle of third-party reporting. The OECD, a group of mostly high-income countries, published model rules under which platforms report what their sellers earn (OECD 2020). The European Union's seventh Directive on Administrative Cooperation, known as DAC7, applies these rules in its member states (European Union 2021). Indonesia's rule for marketplaces goes further: it requires marketplaces to identify their sellers, report their transactions and collect tax from them (Ministry of Finance of the Republic of Indonesia 2025). -Tax research holds that the commerce missing from a platform's revenue is not lost, because the platform sees it. A platform records every sale: who sold, what, for how much and when. A report about a taxpayer made by someone else, such as an employer, a bank or a platform, is third-party information. Tax authorities value it because it does not depend on the taxpayer's own account. +Research on digital records and third-party information shows that platform records contain information that tax authorities can use, and that rules now oblige platforms to report it or to collect tax on it. It does not measure how much of a country's online selling those records cover. Where many sellers trade outside platforms, a rule built on platform records reaches only part of them, and the studies reviewed here do not estimate that part for Indonesia. -Platform records can help where other records are thin. Sellers and service providers may be small, self-employed or weakly represented in conventional financial records, yet the platform records what they sell. This differs from the shadow-economy setting, where reliable records may be sparse (La Porta and Shleifer 2014; Ulyssea 2018). Digital records can carry information that conventional records lack (Berg, Burg, Gombovic and Puri 2020), and platform-mediated work leaves traces in financial and administrative data (Barrios, Hochberg and Yi 2022; Denes, Lagaras and Tsoutsoura 2025). +## 2.4 Research gap -Information from a third party also changes how people comply with tax. Audit experiments in Denmark, and studies of the value-added tax and of receipt lotteries, show that reports from third parties help compliance (Kleven et al. 2011; Pomeranz 2015; Naritomi 2019; Kleven, Kreiner and Saez 2016; Slemrod 2019). Tax withheld through a third party rests on the same information (Brockmeyer and Hernandez 2016). +Existing research explains the parts of the problem separately. Platform economics explains why transaction value can exceed revenue, and accounting explains why the share recorded as revenue can move. Official statistics explain why the two quantities should not be treated as the same, and survey methods explain why a count of sellers can break. Research on third-party information explains why activity outside revenue can still be observed in platform records. Three questions remain open, and together they make up the research gap. -Several rules now build third-party reporting into the law for platforms. The OECD, a group of mostly high-income countries, published model rules under which platforms report what their sellers earn (OECD 2020). The European Union's DAC7 directive applies them (European Union 2021). Indonesia's marketplace tax rule goes further: it makes marketplaces collect tax from their sellers, not only report on them (Ministry of Finance of the Republic of Indonesia 2025). +1. **How large is the difference between transaction value and revenue in practice?** No study reviewed here measures it for the same platforms in a market such as Indonesia. +2. **Does the difference persist, or does it move over time?** The share a platform keeps can change, but no study reviewed here follows it across several years. +3. **Does the choice of figure change the conclusion about growth?** Platform, national and payment figures differ. No study reviewed here tests whether they grow for the same reasons, or what a rise in the count of sellers behind national growth contains. -Tax research answers the thesis question in part, by offering a way round revenue: read the records. The consensus is that platform records are useful and that rules can rest on them. The literature does not say how much of the online economy those records cover. The answer depends on how much selling happens on platforms, which is the point that national statistics left open, and the studies reviewed here do not estimate it for Indonesia. - -## 2.5 The research gap - -Pricing theory, accounting, national statistics and tax research each give a sound answer, and each breaks down at the same kind of point. Pricing theory and accounting explain why revenue and commerce differ, but they do not measure how far or how steadily. National statistics explain how the whole should be counted, but they do not test whether platform figures follow it. Tax research shows that records can reach the commerce, but not how much of it they reach. Three questions remain open. - -1. **How far does a platform's revenue follow the commerce it carries?** The reasons for a difference are known (Sections 2.1 and 2.2). The size of the difference, and how steady it stays over several years, have not been measured for the same platforms. -2. **Do platform figures follow national growth?** Official frameworks show that the figures differ (Section 2.3). Whether platform growth tracks national growth depends on where national growth comes from, and on whether the count of sellers behind it keeps pace with entry. The studies reviewed here test neither. -3. **How far do rules built on platform records reach?** Platform records can support tax rules (Section 2.4). How much of the online economy they cover is not known. - -The three questions set the agenda for the rest of the thesis. The framework in Chapter 3 turns them into measures and hypotheses, and the evidence in Chapters 4 and 5 answers them. +The thesis addresses these questions by measuring the invisible wedge, the difference between transaction value and revenue, over time. It traces the disclosed reasons for the largest movements and compares platform figures with national and payment figures. A further question follows from Section 2.3 and extends the thesis beyond measurement: how far rules built on platform records can reach. The thesis addresses it by estimating how many online sellers such rules can reach. diff --git a/papers/thesis_2026-10/ch3_framework.md b/papers/thesis_2026-10/ch3_framework.md index 01aa7a3..4469c6b 100644 --- a/papers/thesis_2026-10/ch3_framework.md +++ b/papers/thesis_2026-10/ch3_framework.md @@ -1,22 +1,16 @@ # Chapter 3. Framework and Hypotheses -This thesis builds its framework on one idea: a measure is a stand-in, and a stand-in is only as good as its link to what it stands for. Investors read a platform's revenue as a sign of the commerce the platform carries. Governments and analysts read platform figures as a sign of how fast online commerce is growing in a country. Each reading assumes a link, and the assumption is rarely tested. The framework turns each assumption into a claim that can be measured, so that the thesis can say how far the claim holds and what result would show that it does not. +Platform activity can be observed through several figures, and these figures do not measure the same economic object. Transaction value measures the commerce a platform carries. Revenue measures the part of that commerce the platform keeps. National statistics measure online sales across all sellers, whether or not they use a platform. The framework starts from the difference between the value a platform processes and the revenue it recognizes. It then asks whether that relationship stays stable over time, and whether figures from other sources give the same account of online activity. Figure 3.1 shows the structure. -Figure 3.1 shows the structure of the framework. Three circles sit inside one another. The smallest is a platform's revenue, the part of each sale that it keeps. Around it is platform commerce, the full value of the sales the platform handles. The largest is all online commerce in a country, which also includes sellers who never use a platform. The inner link joins revenue to platform commerce, and the outer link joins platform commerce to all online commerce. The first hypothesis, H1, tests the inner link, and the second, H2, tests the outer link (Section 3.2). +Figure 3.1 places the three quantities inside one another. Revenue sits inside platform commerce, because a platform keeps only part of each sale. Platform commerce sits inside all online commerce, because many sellers trade outside platforms. The first hypothesis, H1, tests the inner link between revenue and platform commerce. The second, H2, tests the outer link between platform commerce and national online commerce. The figure is illustrative, and the sizes are not to scale. The platform circle also differs from the marketplace channel in the survey of online sellers run by BPS, the national statistics office. [Figure 3.1 to be drawn: three nested circles, with revenue inside platform commerce inside all online commerce, H1 on the inner link and H2 on the outer link.] -The inner link and the outer link each hold only under a condition. The inner link holds while the share of each sale that the platform keeps stays steady, because then revenue rises and falls with the commerce. The outer link holds while national growth comes from the sellers that platforms record, because then platform figures show what the whole economy is doing. When a condition fails, the stand-in misleads. Revenue can rise while commerce falls, and platform growth can miss growth that happens elsewhere. +The research question asks how much activity remains invisible when platforms are measured through revenue, and when that choice changes the conclusion about growth. The two links answer it. The inner link measures how much commerce revenue leaves out and whether the share that revenue captures stays stable (Sections 3.1 and 3.2). The outer link tests whether the commerce on platforms tells the same story of growth as national figures (Section 3.2). A further test checks the count of sellers behind national growth (Section 3.3). The reach of rules built on platform records is examined separately, in Chapter 6. -The thesis asks how much of an online economy a platform's revenue leaves out, and when that changes the conclusion about growth. The two links answer it together. The inner link measures how much revenue leaves out and whether the share it keeps stays steady. The outer link asks whether the commerce that platforms carry is where the growth of the whole online economy happens. A third question sits beside them, because governments now write rules on platform records, and a rule can reach only what the records cover. So the framework asks three questions, and each becomes a measure, a test or both. +## 3.1 Measurement framework -1. **How far does revenue follow commerce?** The invisible wedge, the part of transaction value that revenue leaves out, measures how much is missing. Growth divergence, the difference between the growth of revenue and the growth of transaction value, measures whether revenue still follows commerce from year to year. Hypothesis H1 states the expected case (Sections 3.1 and 3.2). -2. **Do platform figures follow national growth?** Hypothesis H2 asks whether national growth comes mainly from more businesses selling online or from higher sales per business. An entry test checks what a rise in the count of businesses contains (Sections 3.2 and 3.3). -3. **How far do rules built on platform records reach?** A separate analysis counts the sellers that such a rule can reach (Section 3.4 and Chapter 6). - -## 3.1 Terms and measures - -Table 3.1 lists the terms used throughout the thesis, each defined once, with the symbol used in the formulas. The first rows describe a single platform, and the last rows describe the national survey. +Table 3.1 defines the terms used throughout the thesis, each once, with the symbol used in the formulas. **Table 3.1. Terms and symbols** @@ -33,70 +27,76 @@ Table 3.1 lists the terms used throughout the thesis, each defined once, with th | Online seller | | A business that the national statistics office, BPS, counts as selling online; the text also calls it a seller or a business. The count of online sellers is BPS's estimate of how many there are in a year. | | First-time entry | | A seller that began selling online in the year being measured. | -The framework builds three measures from a platform's revenue (R) and transaction value (V). Each is a formula: +Transaction value and revenue are paired only when they refer to the same period and the same business. Figures that companies report are kept apart from figures that the thesis constructs, and both are kept apart from national statistics and payment figures (Chapter 4). + +The invisible wedge measures, in currency, the part of transaction value that lies outside a platform's revenue: ``` -W = V − R -E = W / R = 1/m − 1 -D = g(R) − g(V) +W_it = V_it − R_it (1) ``` -The invisible wedge W is the part of transaction value that revenue leaves out, in currency. The ecosystem ratio E is the same part relative to revenue, so that large and small platforms can be compared. It restates the take rate. Growth divergence D shows whether revenue still follows the commerce from one year to the next. The wedge and growth divergence are plain arithmetic on reported figures. +where V_it is the transaction value of platform i in year t and R_it is the revenue it recognizes for the same year and business. The wedge is not an accounting error, because both amounts may appear in the same company report. It is a measure of transaction value, not of income or value added, so it does not measure missing output. -Growth divergence can also be written with the take rate alone. Let m0 be a platform's take rate in the first year and m1 its take rate in the second year. Revenue is the take rate times transaction value in each year, so revenue in the second year equals (1 + g(V)) × m1 / m0 times revenue in the first year. Subtracting the growth of transaction value gives: +To compare platforms of different size, the thesis also expresses the wedge relative to revenue, as the ecosystem ratio: ``` -D = (1 + g(V)) × (m1 − m0) / m0 +E_it = W_it / R_it = 1 / m_it − 1 (2) ``` -When the take rate is steady, m1 equals m0, D is zero, and revenue grows exactly as fast as the commerce. When the take rate rises, D is positive, and revenue grows faster than the commerce. +where m_it = R_it / V_it is the take rate, the share of each sale that the platform keeps. The ecosystem ratio is the author's own measure, and it restates the take rate. Its interpretation draws on two established results. Platform pricing means that revenue need not follow the commerce a platform carries, and revenue recognition means that the share recorded as revenue depends on whether the platform acts as principal or agent (Rochet and Tirole 2003; Hagiu and Wright 2015; International Accounting Standards Board 2014). An ecosystem ratio of 9 means that 9 units of transaction value lie outside revenue for every unit recorded as revenue. Equivalently, the platform keeps 10 percent of each sale. -Tokopedia between 2022 and 2023 shows the formula at work. Its take rate rose by about 68 percent, so that it kept about two thirds more of each sale. Its transaction value fell by about 9 percent, yet its revenue grew by about 53 percent. Growth divergence was 62 points, which is (1 − 0.09) × 0.68 = 0.62. +A single ratio does not show whether the relationship holds over time, so the thesis defines growth divergence: -Tokopedia's case also shows what each measure records. Its wedge was about 98 percent of its transaction value in both years, so the wedge moved with the commerce and fell by about 10 percent along with it. Its growth divergence was 62 points, because revenue followed the take rate and not the commerce. The two measures answer different halves of the thesis question. The wedge answers the question of size, and at the marketplaces studied in Indonesia it is almost all of transaction value. Growth divergence answers the question of growth, because it follows the take rate and shows whether revenue remains a fair guide. +``` +D_it = g(R_it) − g(V_it) (3) +``` -The size of the wedge next to revenue also decides how far a change in the take rate swings revenue growth. Take a platform that keeps 2 percent of each sale, with an ecosystem ratio of 49. If transaction value is flat, a rise in its take rate from 2 to 3 percent lifts revenue growth by about 50 points. A platform that keeps 17 percent has an ecosystem ratio near 5, and a rise from 17 to 18 percent lifts its revenue growth by about 6 points. A wedge that is large next to revenue means more than a big number. It also means that ordinary changes in fees or discounts can swing reported growth. +where g(X) is the growth of X from one year to the next, as a share of the first year. A positive D means that revenue grew faster than transaction value, so the take rate rose. A negative D means the reverse. Revenue equals the take rate times transaction value, so D can also be written with the take rate alone: -## 3.2 Hypotheses +``` +D_it = (1 + g(V_it)) × (m_it − m_i,t−1) / m_i,t−1 (4) +``` -The thesis answers its question through two checks, and each is stated as a hypothesis. The first is whether, within a platform, revenue grows in proportion to the commerce it comes from. The second is whether the growth of national online commerce comes mainly from more businesses selling online. H1 states the first check, and H2 states the second. +where m_i,t−1 is the take rate in the earlier year and m_it the take rate in the later year. If the take rate is steady, D is zero and revenue grows exactly as fast as transaction value. If revenue were a stable proxy for transaction value, D would stay close to zero. -The take rate depends on fees, on discounts and on how the platform's accounts treat each sale. Revenue stays a fair stand-in for the commerce only while the take rate is steady, which means that growth divergence stays close to zero. H1 states that case. +Tokopedia between 2022 and 2023 illustrates the three measures. Its take rate rose from about 1.5 to about 2.5 percent, so it kept about two thirds more of each sale. Its transaction value fell by about 9 percent and its revenue rose by about 53 percent, so D was 0.91 × 0.68 = 0.62, or 62 percentage points. Its wedge stayed at about 98 percent of transaction value and fell with it, by about 10 percent. -> **H1.** Transaction value and revenue recognized by the same platform grow in proportion over time. +The wedge, the ecosystem ratio and growth divergence do different jobs. The wedge gives the size of what revenue leaves out. At a marketplace that keeps a few percent of each sale, the wedge is almost all of transaction value, so it moves with the commerce. The ecosystem ratio expresses that size relative to revenue. Growth divergence carries the central test: whether revenue and transaction value keep the same relationship from year to year. -Growth rates never match exactly, so H1 is tested by asking whether growth divergence among Indonesian and regional platforms, measured in logs, is unusually large. The yardstick is growth divergence among listed platforms abroad, where revenue and transaction value broadly move together. Chapter 4 describes both groups. +The ecosystem ratio also governs how far a change in the take rate moves revenue growth. Equation (4) shows that D is the change in the take rate divided by its starting level. A platform that keeps 2 percent of each sale has an ecosystem ratio of 49. If its transaction value is flat, a rise in its take rate to 3 percent raises its revenue by 50 percent. A platform that keeps 17 percent has an ecosystem ratio near 5, and a rise to 18 percent raises its revenue by about 6 percent. So a large ecosystem ratio means that ordinary changes in fees or discounts can produce large swings in reported revenue growth. -National online sales can grow because existing sellers sell more or because more businesses sell online. BPS publishes both the number of businesses selling online and their total online sales, so the split can be made from published figures. The split matters for platform figures because new businesses often start in channels that platform records do not cover, such as messaging and social media, which most sellers use. If growth comes mainly from more businesses, platform figures can miss it even when every platform reports accurately. H2 states that case. +## 3.2 Hypothesis development -> **H2.** The growth of national e-commerce value comes mainly from more businesses selling online, and less from higher sales per business. +Two hypotheses follow from the research question. H1 asks whether revenue follows the commerce carried within a platform. H2 asks where national online growth comes from, which decides whether the commerce on platforms can stand for national online commerce. Together they test the two links in Figure 3.1. Table 3.2 pairs each hypothesis, and the entry test of Section 3.3, with its main test and with the result that would count against it. -The H2 split is arithmetic. It does not say which channels the new businesses use, or whether they are new. +**Table 3.2. Hypotheses, tests and what would count against them** -## 3.3 The entry test +| Test | What it asks | Main test | What would count against it | +|---|---|---|---| +| H1 | Do revenue and transaction value grow together within a platform? | Size of yearly changes in the take rate, Indonesian and regional platforms against listed platforms abroad | Changes in Indonesia far larger than abroad | +| H2 | Does national e-commerce value grow because of more businesses or because of higher sales per business? | BPS's national e-commerce value split into the number of businesses and sales per business | Higher sales per business accounting for most of the growth | +| Entry test | Does a rise in the count of online sellers come from first-time entry? | Share of 2023 sellers in BPS's survey files who began selling online in 2023 | Fewer new sellers than the rise requires | -H2 says that national growth comes mainly from more businesses selling online. That conclusion holds only if the count of businesses reflects real entry. A rise in the count can mean two different things: more businesses started selling, or the survey reached businesses that were already selling. The published figures cannot tell the two apart. The survey can, because it asks every seller in which year it began selling online. The thesis checks what the count contains, with a test that was written down before the survey files were opened. +Platform economics and accounting both suggest that the share of transaction value a platform records as revenue is not fixed. The share depends on fees, on customer incentives and on whether the platform acts as principal or agent, and each of these can change with the platform's pricing, business model and scope (Rochet and Tirole 2003; Hagiu and Wright 2015; International Accounting Standards Board 2014). Research on disclosed operating measures adds that a measure becomes less informative when the way it is computed changes (Givoly, Li, Lourie and Nekrasov 2019). Revenue follows transaction value only while the take rate stays constant, that is, while D stays close to zero. If the relationship were stable, revenue would be a workable proxy for the commerce a platform carries, D would stay near zero and the ecosystem ratio would be constant. The first hypothesis states this case. -> **Entry test.** In the survey file for 2023, at least 21.5 percent of online sellers must report that they began selling online in 2023. +> **H1.** Transaction value and revenue recognized by the same platform grow in proportion over time. -The 21.5 percent threshold comes from arithmetic. The published count of online sellers rose by 27.4 percent in 2023. Suppose every extra seller was new and no seller stopped. Then for every 100 sellers a year earlier there were 127.4, and 27.4 of those were new. New sellers would make up 21.5 percent of all sellers, which is 27.4 divided by 127.4. A smaller share means that part of the rise came from somewhere else. The test does not test H2 itself. It tests what "more businesses" means, and Chapter 4 describes how it was registered. +Growth rates never match exactly, so H1 is tested by comparison. The test compares the size of yearly changes in the take rate, measured in logs, between two groups. One is the Indonesian and regional platforms, and the other is listed platforms abroad, where revenue and transaction value broadly move together. Chapter 4 describes the two groups and the test. -If the share of new sellers falls short, part of the rise is something other than first-time entry. Two readings are open: the survey reached more of the sellers who were already there, or sellers came back after a pause. Chapter 5 weighs them. +National e-commerce value can grow because existing businesses sell more, the intensive margin, or because more businesses sell online, the extensive margin (Hummels and Klenow 2005). The distinction matters for measurement. Growth driven by new sellers can take place in channels that platform accounts do not cover, such as social media and instant messaging, which BPS reports are widely used (BPS 2025). Platform figures can then miss that growth even when every platform reports accurately. BPS publishes both the number of businesses selling online and their total online sales, so national growth can be split into the two margins from published figures. The second hypothesis states the case in which more businesses drive growth. -## 3.4 Further analyses +> **H2.** The growth of national e-commerce value comes mainly from more businesses selling online, and less from higher sales per business. -Four questions use the framework's measures but carry no hypothesis. Where did large movements in revenue come from: more commerce, higher fees or smaller discounts? Do platform, national and payment figures tell the same story where they cover the same sellers? Which channels do online sellers use, and do they keep financial statements? And do investors value a platform's growth in transaction value differently from growth in its take rate? Chapter 4 describes how each is answered. Chapter 6 uses the answers to ask how many sellers a rule built on platform records can reach. +The H2 split is arithmetic. It does not assign new businesses to channels, and it does not show whether the businesses in the count are new. -## 3.5 The tests together +## 3.3 The entry test -Table 3.2 pairs each test with the result that would count against it. +H2 rests on BPS's count of businesses selling online, and a rise in that count can mean two different things. Either more businesses started selling, or the survey reached businesses that were already selling. The published figures cannot tell the two apart. The survey can, because it asks every seller in which year it began selling online. The thesis checks what the count contains with a test written down before the survey files were opened. -**Table 3.2. Tests and what would count against them** +> **Entry test.** In the survey file for 2023, at least 21.5 percent of online sellers must report that they began selling online in 2023. -| Test | What it asks | What would count against it | -|---|---|---| -| H1: growth divergence | Do revenue and transaction value grow together within a platform? | Growth divergence in Indonesia far larger than among listed platforms abroad | -| H2: split of national growth | Does national e-commerce value grow because of more businesses or because of higher sales per business? | Higher sales per business accounting for most of the growth | -| Entry test | Does a rise in the count of online sellers come from first-time entry? | Fewer new sellers than the rise requires, so that the count reflects more than first-time entry | +The 21.5 percent threshold comes from arithmetic. The published count of online sellers rose by 27.4 percent in 2023. Suppose every extra seller was new and no seller stopped. Then for every 100 sellers a year earlier there were 127.4, and 27.4 of those were new. New sellers would make up 21.5 percent of all sellers, which is 27.4 divided by 127.4. A smaller share means that part of the rise came from somewhere else. The test does not test H2 itself. It tests what "more businesses" means. + +If the share of new sellers falls short, part of the rise is something other than first-time entry. Two readings are open: the survey reached more of the sellers who were already there, or sellers came back after a pause. Chapter 5 weighs them. -The first two rows test the hypotheses. The third row sits beside H2, because it tests what the count of online sellers behind H2 represents. Chapter 4 describes the data behind each test. +The split of national sales by channel and the comparison of record keeping between marketplace sellers and other sellers support the hypotheses without forming hypotheses of their own. The first shows whether national growth took place inside or outside the marketplace channel. The second shows whether marketplace sellers keep financial statements more often than other sellers, which bears on the information that platform records can supply to tax administration. Chapter 4 describes the data and methods behind each test and analysis. From e20b9331b74cb09716b6fe3e9b7a150133d44a07 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 13 Oct 2026 13:11:34 +0000 Subject: [PATCH 36/65] Thesis ch2: subject-verb agreement in two section closings Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/ch2_literature.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/papers/thesis_2026-10/ch2_literature.md b/papers/thesis_2026-10/ch2_literature.md index 34bead7..a56737e 100644 --- a/papers/thesis_2026-10/ch2_literature.md +++ b/papers/thesis_2026-10/ch2_literature.md @@ -18,7 +18,7 @@ Firms have used the room that revenue recognition rules leave. Bowen, Davis and Investors use operating measures alongside revenue. Trueman, Wong and Zhang (2000) find that measures of web traffic help explain the market values of internet firms beyond their financial statements, and Trueman, Wong and Zhang (2001) find that web traffic helps forecast those firms' revenue. Rajgopal, Venkatachalam and Kotha (2003) report similar results for measures of network advantage among e-commerce firms. -Platform economics and accounting research explain why a platform's revenue is smaller than the commerce it carries and why the share it keeps can change. It does not measure how large the difference is for actual platforms, or how much the share changes from one year to the next. The pricing models describe how charges are set, not how they move in a particular market. The accounting evidence comes from internet firms around 2000, and the investor evidence rests on web traffic, not on the transaction value of present-day platforms. None of the studies reviewed here follows the revenue and the transaction value of the same platforms over several years. +Platform economics and accounting research explain why a platform's revenue is smaller than the commerce it carries and why the share it keeps can change. They do not measure how large the difference is for actual platforms, or how much the share changes from one year to the next. The pricing models describe how charges are set, not how they move in a particular market. The accounting evidence comes from internet firms around 2000, and the investor evidence rests on web traffic, not on the transaction value of present-day platforms. None of the studies reviewed here follows the revenue and the transaction value of the same platforms over several years. ## 2.2 Measuring the digital economy @@ -32,7 +32,7 @@ National online sales can grow because existing sellers sell more or because mor A count of sellers comes from a survey, and surveys change. Van den Brakel, Smith and Compton (2008) show that a change in survey design can produce a break in a series that looks like a real change, and van den Brakel, Zhang and Tam (2020) review methods for measuring such breaks. Respondents also misdate past events. In a study of household spending, Neter and Waksberg (1964) find that people tend to report events as more recent than they were. Firm exit matters as well: McKenzie and Paffhausen (2019) find that small firms in developing countries that stop operating are more often young firms with young owners. -Official statistics and survey research establish that platform transaction value and platform revenue measure different things, and that the figures used to describe online commerce come from different sources with different definitions. It does not test whether platform figures and national figures grow for the same reasons. It also offers no test of what a rise in a national count of online sellers contains: sellers who are new, or sellers whom the survey had not reached before. +Official statistics and survey research establish that platform transaction value and platform revenue measure different things, and that the figures used to describe online commerce come from different sources with different definitions. They do not test whether platform figures and national figures grow for the same reasons. They also offer no test of what a rise in a national count of online sellers contains: sellers who are new, or sellers whom the survey had not reached before. ## 2.3 Digital records and third-party information From bff0bbfac404250190f687d342ada4f52ddcecc0 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 13 Oct 2026 13:42:54 +0000 Subject: [PATCH 37/65] Thesis ch2: three-part sections (situation, what it explains, where it stops) and a gap anchored to the stopping points Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 10 +++++ papers/thesis_2026-10/ch2_literature.md | 56 +++++++++++-------------- 2 files changed, 35 insertions(+), 31 deletions(-) diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index d5c0a8b..d8e4354 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -355,3 +355,13 @@ The researcher asked whether the proposal had been checked. It had not, properly - **Chapter 2** keeps the proposal's order and section titles and expands each step: what platform economics studies and its central finding, the price-structure result with an example, the platform–reseller distinction, revenue recognition with a worked ride example and the voucher rule with its exception, reporting choices, comparability and operating measures, investor evidence; official statistics, sources, night lights, margins, survey breaks, recall error and exit; digital records, third-party compliance evidence with what each study found, and the platform rules. Each section closes on what remains unmeasured. 2.4 keeps the proposal's three-part gap; the reach of rules is stated as a further question that extends the thesis. - **Chapter 3** keeps the proposal's declaration, figure note, pairing rule, equations (1) to (4) with "where" definitions, the E example, D as the central test, and hypothesis development from the literature. Added where the line reaches them: the terms table (Prof. Thomas), the take-rate form of D and the Tokopedia illustration, the thin-slice paragraph (Prof. Thomas's "just subtraction"), Table 3.2 placed before the hypotheses so they are anchored, and the entry test after H2's stated limit. The further analyses close the chapter in one paragraph. - Register: no rhetorical questions, vignettes, invented frames or announcements; studies are reported by what they find; openers name their subject; "indicator", "therefore", "rather than" and "Overall" avoided. + +### Chapter 2 applied in the three-part form (13 Oct 2026) + +After two previews the researcher confirmed the bones: an introduction that follows the platform's steps; each section in three parts (the situation, what the research explains, where it stops and why); and a gap anchored to those stopping points, with Table 2.1 and one question per section. The "where it stops" paragraphs explain what each body of work was built to answer and what would be needed, so they read as reasons, not as criticism. Cut for focus: Barrios et al., Denes et al., Kleven, Kreiner and Saez 2016, Slemrod 2019, Brockmeyer and Hernandez, La Porta and Shleifer, Ulyssea, De Franco et al. + +Queued as expansions, agreed not to be structural faults: +- A short finance section on how investors and analysts read platform figures (valuation multiples, disclosure of operating measures, value of non-financial metrics; reserve sources Liu, Nissim and Thomas; Damodaran; SEC 2020), which would also supply a source for "investors and analysts often read revenue as a sign of commerce". +- A targeted search for empirical studies of platform take rates over time, to confirm or narrow gap question 1. +- Checking each study finding against the paper (Kleven et al. 2011, Pomeranz 2015, Naritomi 2019, Berg et al. 2020, Bowen et al. 2002) and the reference list as a whole. +- Open choice: keep the reach of platform-based rules as gap question 3, or present it as an extension. diff --git a/papers/thesis_2026-10/ch2_literature.md b/papers/thesis_2026-10/ch2_literature.md index a56737e..a5f92a2 100644 --- a/papers/thesis_2026-10/ch2_literature.md +++ b/papers/thesis_2026-10/ch2_literature.md @@ -1,55 +1,49 @@ # Chapter 2. Literature Review -Digital platforms sit between economic activity and the figures used to measure it. A platform handles a transaction between a buyer and a seller, records part of the payment as its own revenue, and keeps a record of the whole sale. Each of these steps is studied by a different body of research. Platform economics and accounting explain why a platform records only part of each payment as revenue (Section 2.1). Official statistics and survey methods explain how the transactions themselves are measured across an economy (Section 2.2). Research on third-party information explains what a platform's records of each sale can show to others, such as tax authorities (Section 2.3). Each section sets out the main findings and the evidence behind them, and it closes with what the findings leave unmeasured. Section 2.4 brings these points together as the research gap. +Digital platforms sit between economic activity and the figures used to measure it. A platform carries a sale between a buyer and a seller. It records part of the payment as its own revenue, and it keeps a record of the whole sale. Different bodies of research study these steps. Platform economics and accounting explain why revenue is only part of each payment (Section 2.1). Official statistics explain how the sales themselves are measured (Section 2.2). Research on third-party information explains what a platform's records can show to tax authorities (Section 2.3). Each body of research explains why the figures differ, but none measures how far they differ in practice. Section 2.4 sets out this research gap. ## 2.1 Platform economics and revenue recognition -Platform economics studies firms that bring together two groups of users who benefit from each other's presence, such as buyers and sellers on a marketplace or riders and drivers on a ride-hailing service. Its central finding is that such a firm sets its prices for the two groups jointly, because the value of joining for one group depends on how many members of the other group join (Rochet and Tirole 2003; Caillaud and Jullien 2003; Armstrong 2006). The firm earns commissions, fees, advertising income or delivery charges for arranging transactions, and it does not take the full value of each sale (Evans and Schmalensee 2016). So the value of the commerce that passes through a platform can be much larger than the revenue the platform records. +A platform reports two figures that describe its business: the value of the sales it carries, and the revenue it keeps from them. Investors and analysts often read revenue as a sign of how much commerce a platform carries. That reading holds only if revenue keeps a steady share of the sales. -The way a platform divides its charges between its two groups of users also matters. Rochet and Tirole (2003) show that the number of transactions on a platform depends on how the total charge is split between the two sides, and not only on its level. A platform may charge one side little or nothing and recover its costs from the other, as when a marketplace lets shoppers browse for free and charges merchants a commission on each sale (Caillaud and Jullien 2003; Parker and Van Alstyne 2005; Armstrong 2006). Because both the level and the split are choices, a platform can change how much revenue it takes from the same volume of sales. +Platform economics explains why revenue is only a small share of the sales. A platform serves two groups that need each other, such as shoppers and merchants, or riders and drivers. It sets its charges for both groups together and earns fees for arranging trades, not the value of each sale (Rochet and Tirole 2003; Armstrong 2006; Evans and Schmalensee 2016). The level of its charges and the split between the two groups are choices, so the platform can take more or less revenue from the same sales (Rochet and Tirole 2003; Caillaud and Jullien 2003). -The business model sets how much of each sale can count as revenue in the first place. Hagiu and Wright (2015) distinguish a multi-sided platform, which lets sellers deal directly with buyers, from a reseller, which buys goods and sells them on its own account. A reseller's revenue includes the full price of what it sells. A platform's revenue is the charge it makes for the interaction, which is a fraction of the price. +Accounting rules add a second reason. Under the international standard, International Financial Reporting Standard 15 (IFRS 15), a company that only arranges a sale records its fee and not the full payment. If a rider pays 100 for a trip and the driver keeps 80, such a company records 20. Discounts and vouchers that it pays for are normally deducted from its revenue (International Accounting Standards Board 2014). So revenue can rise when a platform raises its fees or cuts its discounts, with no change in what customers buy. -Accounting rules decide how much of each payment a company records as revenue. These rules are known as revenue recognition, and International Financial Reporting Standard 15 (IFRS 15) is the international standard for them (International Accounting Standards Board 2014). The rules turn on whether the company is a principal, which controls the good or service before it reaches the customer, or an agent, which arranges for another party to provide it. A principal records the full payment as revenue, and an agent records only its fee or commission. If a rider pays 100 for a trip and the driver keeps 80, a ride-hailing company acting as an agent records 20 as revenue. - -IFRS 15 also treats payments that a company makes to its own customers, such as discounts and vouchers, as a reduction of revenue, unless the payment buys a distinct good or service from the customer (International Accounting Standards Board 2014). A platform that funds vouchers for shoppers reports less revenue on the same sales, and more when it cuts the vouchers. Changes in fees, customer incentives, the mix of services, acquisitions and the scope of the reported business can all move revenue without an equal change in the commerce behind it. - -Firms have used the room that revenue recognition rules leave. Bowen, Davis and Rajgopal (2002) examine how internet firms reported revenue around 2000 and find that the firms differed in whether they reported the full value of the sales they arranged, a practice known as reporting revenue gross. Comparability matters when such figures are read across firms: De Franco, Kothari and Verdi (2011) find that analysts forecast more accurately when a firm's accounting is comparable with that of its peers. Operating measures that firms disclose voluntarily, of which transaction value is one, raise a further problem. Givoly, Li, Lourie and Nekrasov (2019) find that such a measure loses information when the way it is computed changes. - -Investors use operating measures alongside revenue. Trueman, Wong and Zhang (2000) find that measures of web traffic help explain the market values of internet firms beyond their financial statements, and Trueman, Wong and Zhang (2001) find that web traffic helps forecast those firms' revenue. Rajgopal, Venkatachalam and Kotha (2003) report similar results for measures of network advantage among e-commerce firms. - -Platform economics and accounting research explain why a platform's revenue is smaller than the commerce it carries and why the share it keeps can change. They do not measure how large the difference is for actual platforms, or how much the share changes from one year to the next. The pricing models describe how charges are set, not how they move in a particular market. The accounting evidence comes from internet firms around 2000, and the investor evidence rests on web traffic, not on the transaction value of present-day platforms. None of the studies reviewed here follows the revenue and the transaction value of the same platforms over several years. +These findings explain why the share is small and why it can move, but they leave its actual size and movement to be measured. The pricing models were built to explain how a platform sets its charges, so they predict the direction of a change, not its size in a given market. The accounting studies asked how internet firms around 2000 chose to report revenue, not how far that revenue drifted from the sales behind it (Bowen, Davis and Rajgopal 2002). Studies of how investors valued those firms relied on web traffic as their measure of activity (Trueman, Wong and Zhang 2000, 2001; Rajgopal, Venkatachalam and Kotha 2003). Transaction value also needs care, because each company defines it for itself, and a figure of this kind loses information when its definition changes (Givoly, Li, Lourie and Nekrasov 2019). So measuring the share and its movement needs the revenue and the transaction value of the same platforms over several years, on a consistent definition. The studies reviewed here do not provide such a record. ## 2.2 Measuring the digital economy -Official statistics distinguish the commerce that passes through a platform from the service the platform provides. National accounts, which measure a country's production and income, record a sale between a buyer and a seller as the seller's output. They record the platform's charge for arranging the sale as the platform's output, and they do not treat the full transaction value as platform output (Ahmad and Schreyer 2016; International Monetary Fund 2018; Organisation for Economic Co-operation and Development [OECD] 2023; United Nations et al. 2025). - -In practice, different producers publish different quantities. Industry reports size the digital economy of Southeast Asia by gross merchandise value, the value of goods sold through platforms (Google, Temasek and Bain 2024). National statistics offices survey businesses about their online sales, as BPS, the Indonesian statistics office, does (BPS 2025). Listed platforms report the revenue they recognize under IFRS 15, and central banks report the value of electronic payments (Bank Indonesia 2025). The United Nations Conference on Trade and Development (UNCTAD) documents how far e-commerce values depend on the definition used, and it compares business e-commerce sales with the transaction values of large online platforms (UNCTAD 2023, 2024). +A country's online commerce is larger than what platforms carry, because many sellers trade outside platforms. Several producers measure it, and each counts something different. Industry reports add up the value of goods sold through platforms (Google, Temasek and Bain 2024). Statistics offices, such as BPS in Indonesia, survey businesses about their online sales (BPS 2025). Central banks count electronic payments (Bank Indonesia 2025). The resulting values change with the definition used (UNCTAD 2023, 2024). -A difference between two figures for the same activity is harmless while they move together. It matters when they part, because the choice of figure can then change the conclusion about size and growth. Research on night-time lights treats this as a question to be tested. Henderson, Storeygard and Weil (2012) compare national income figures with satellite measures of light at night and use the relationship to improve estimates of growth where official figures are weak. +Official statistics address the question in two ways. National accounts separate the sale between a buyer and a seller from the platform's fee, and they count only the fee as the platform's output (Ahmad and Schreyer 2016; International Monetary Fund 2018; Organisation for Economic Co-operation and Development [OECD] 2023; United Nations et al. 2025). Research on measurement also offers a method: test a figure against the activity it stands for, as studies of night-time lights do for national income (Henderson, Storeygard and Weil 2012). Growth in online sales can be split in the same spirit, into more sales by existing sellers and more sellers entering, as trade economists split the growth of exports (Hummels and Klenow 2005). -National online sales can grow because existing sellers sell more or because more sellers enter. Trade economists make the same distinction for exports. Hummels and Klenow (2005) divide differences in exports between countries into an intensive margin, more of the same goods, and an extensive margin, a wider range of goods. The same split applied to online sales separates growth that comes from more sellers from growth that comes from higher sales per seller. +These studies set out what should be counted and how one figure can be checked against another, but they were not designed to follow platform figures and national figures through time in one country. National accounting guidance defines the platform's output, and it does not track how platform sales and national sales grow. The test of one figure against another has been applied to national income and night-time lights, and, among the studies reviewed here, not to platform and national online sales. The growth split adds a further difficulty, because it rests on a count of sellers. Such counts can mislead for known reasons. A change in survey design can create a break that looks like real change (van den Brakel, Smith and Compton 2008; van den Brakel, Zhang and Tam 2020). People tend to report past events as more recent than they were (Neter and Waksberg 1964). Small firms that stop are more often young firms with young owners (McKenzie and Paffhausen 2019). Published totals cannot show whether a rise in the count reflects new sellers or a wider survey, because they do not record when each seller began. That question needs the answers of the sellers themselves. -A count of sellers comes from a survey, and surveys change. Van den Brakel, Smith and Compton (2008) show that a change in survey design can produce a break in a series that looks like a real change, and van den Brakel, Zhang and Tam (2020) review methods for measuring such breaks. Respondents also misdate past events. In a study of household spending, Neter and Waksberg (1964) find that people tend to report events as more recent than they were. Firm exit matters as well: McKenzie and Paffhausen (2019) find that small firms in developing countries that stop operating are more often young firms with young owners. +## 2.3 Digital records and third-party information -Official statistics and survey research establish that platform transaction value and platform revenue measure different things, and that the figures used to describe online commerce come from different sources with different definitions. They do not test whether platform figures and national figures grow for the same reasons. They also offer no test of what a rise in a national count of online sellers contains: sellers who are new, or sellers whom the survey had not reached before. +A platform records every sale it carries: who sold what, to whom, for how much and when. Governments have begun to use these records for tax. The OECD, a group of mostly high-income countries, set out model rules under which platforms report what their sellers earn (OECD 2020). The European Union applies these rules through its DAC7 directive (European Union 2021). Indonesia goes further and requires marketplaces to collect tax from their sellers (Ministry of Finance of the Republic of Indonesia 2025). -## 2.3 Digital records and third-party information +Tax research explains why such records matter. Reports from third parties improve compliance. In Denmark, evasion was close to zero on income that employers reported, and substantial on income that taxpayers reported themselves (Kleven et al. 2011). Paper trails and receipts have raised the sales that firms report in Chile and Brazil (Pomeranz 2015; Naritomi 2019). Digital records can also hold information that conventional records lack (Berg, Burg, Gombovic and Puri 2020). -Transactions that do not enter a platform's revenue are still recorded in its systems. A platform stores who sold what, to whom, for how much and when. Many of the sellers and service providers involved are small, self-employed or weakly represented in conventional financial records. This differs from the setting of the shadow economy, where reliable records of activity are often missing (La Porta and Shleifer 2014; Ulyssea 2018). Digital records can carry information that conventional records lack. Berg, Burg, Gombovic and Puri (2020) find that simple digital traces, such as the type of device a customer uses, help predict loan default. Activity on platforms also leaves traces in financial and administrative data (Barrios, Hochberg and Yi 2022; Denes, Lagaras and Tsoutsoura 2025). +Tax research shows that platform records are valuable where they exist. Its evidence comes from settings where a third party already sees the transaction, such as an employer paying wages or a firm buying from another firm. Online selling differs, because many sellers trade through messaging apps and social media, where no marketplace records the sale (BPS 2025). How far a rule built on platform records can reach depends on how much selling happens on platforms. The studies reviewed here do not estimate that share for Indonesia. -Information that a third party reports about a taxpayer improves tax compliance. In a field experiment in Denmark, Kleven et al. (2011) find that evasion is close to zero for income that third parties such as employers report, and substantial for income that taxpayers report themselves. Pomeranz (2015) shows that the paper trail created by the value-added tax in Chile raises compliance. Naritomi (2019) finds that rewarding consumers in Brazil for asking for receipts raised the sales that firms reported. Kleven, Kreiner and Saez (2016) and Slemrod (2019) review this evidence, and Brockmeyer and Hernandez (2016) study tax withheld by third parties in Costa Rica. +## 2.4 Research gap -Rules for platforms now apply the principle of third-party reporting. The OECD, a group of mostly high-income countries, published model rules under which platforms report what their sellers earn (OECD 2020). The European Union's seventh Directive on Administrative Cooperation, known as DAC7, applies these rules in its member states (European Union 2021). Indonesia's rule for marketplaces goes further: it requires marketplaces to identify their sellers, report their transactions and collect tax from them (Ministry of Finance of the Republic of Indonesia 2025). +The shortfalls identified in Sections 2.1 to 2.3 define the research gap. Table 2.1 sets them side by side. -Research on digital records and third-party information shows that platform records contain information that tax authorities can use, and that rules now oblige platforms to report it or to collect tax on it. It does not measure how much of a country's online selling those records cover. Where many sellers trade outside platforms, a rule built on platform records reaches only part of them, and the studies reviewed here do not estimate that part for Indonesia. +**Table 2.1. What the literature establishes and where it stops** -## 2.4 Research gap +| Section | What it establishes | Main sources | Where it stops | +|---|---|---|---| +| 2.1 Platform economics and accounting | Revenue is a fee that is a small share of the sales, and the share can move with pricing, discounts and accounting | Rochet and Tirole 2003; Armstrong 2006; IASB 2014 | The size of the share and its movement have not been measured for the same platforms over several years | +| 2.2 Official statistics and surveys | Platform sales and platform revenue differ; sources differ; a count of sellers can break | Ahmad and Schreyer 2016; Henderson et al. 2012; van den Brakel et al. 2008 | Platform and national figures have not been followed through time in one country, and published totals cannot show what a rise in the count of sellers contains | +| 2.3 Third-party information | Platform records show sales, and third-party reports improve compliance | Kleven et al. 2011; OECD 2020 | How much of online selling platform records cover has not been estimated for Indonesia | -Existing research explains the parts of the problem separately. Platform economics explains why transaction value can exceed revenue, and accounting explains why the share recorded as revenue can move. Official statistics explain why the two quantities should not be treated as the same, and survey methods explain why a count of sellers can break. Research on third-party information explains why activity outside revenue can still be observed in platform records. Three questions remain open, and together they make up the research gap. +Each shortfall becomes one question for the thesis. -1. **How large is the difference between transaction value and revenue in practice?** No study reviewed here measures it for the same platforms in a market such as Indonesia. -2. **Does the difference persist, or does it move over time?** The share a platform keeps can change, but no study reviewed here follows it across several years. -3. **Does the choice of figure change the conclusion about growth?** Platform, national and payment figures differ. No study reviewed here tests whether they grow for the same reasons, or what a rise in the count of sellers behind national growth contains. +1. **How large is the difference between transaction value and revenue, and does it last?** This follows from Section 2.1, which explains why the share is small and can move but leaves its size and movement unmeasured. +2. **Does the choice of figure change the conclusion about growth?** This follows from Section 2.2, which shows that figures differ but does not test whether they grow for the same reasons, or what a rise in the count of sellers contains. +3. **How far can rules built on platform records reach?** This follows from Section 2.3, which shows that platform records are valuable but not how much of online selling they cover. -The thesis addresses these questions by measuring the invisible wedge, the difference between transaction value and revenue, over time. It traces the disclosed reasons for the largest movements and compares platform figures with national and payment figures. A further question follows from Section 2.3 and extends the thesis beyond measurement: how far rules built on platform records can reach. The thesis addresses it by estimating how many online sellers such rules can reach. +The thesis answers the first question by measuring the invisible wedge, transaction value minus revenue, for the same platforms over several years, and by tracing the reasons for its largest moves. It answers the second by comparing platform figures with national figures and by using the sellers' own answers to test what the count of sellers contains. It answers the third by estimating how many online sellers a rule built on platform records can reach. From c0c90e659f24d8fdd7a5a187485c0a42d32165e0 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 13 Oct 2026 13:47:03 +0000 Subject: [PATCH 38/65] Research: literature search notes for Chapter 2 (take-rate studies, investor use of platform figures, checks on cited findings) Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- .../LIT_SEARCH_2026-10-13.md | 37 +++++++++++++++++++ 1 file changed, 37 insertions(+) create mode 100644 research/option_space_2026-10-01/LIT_SEARCH_2026-10-13.md diff --git a/research/option_space_2026-10-01/LIT_SEARCH_2026-10-13.md b/research/option_space_2026-10-01/LIT_SEARCH_2026-10-13.md new file mode 100644 index 0000000..e90dcb1 --- /dev/null +++ b/research/option_space_2026-10-01/LIT_SEARCH_2026-10-13.md @@ -0,0 +1,37 @@ +# Literature search for Chapter 2 (13 Oct 2026) + +Purpose: test gap question 1 (has anyone measured platform take rates over time?), find finance sources on how investors read platform figures, and check the findings Chapter 2 attributes to cited studies. Web searches only; abstracts and publisher or repository pages, not full texts. Treat every line below as "to confirm against the paper" before final submission. + +## A. Studies of platform commissions (bear on gap question 1) + +| Study | What it does | Bearing on the thesis | +|---|---|---| +| Li and Wang, "Regulating Powerful Platforms: Evidence from Commission Fee Caps", Information Systems Research 36(1), 126-140 (online 2024, DOI 10.1287/isre.2022.0191) | Commission caps on food delivery in 14 US cities and states; independent restaurants lost orders and revenue, chains gained, platforms raised consumer delivery fees | Commission changes studied for their effects on participants, not as a measurement question | +| Han et al. (TSE conference paper, 2024), creator platform commission rise from 5 to 20 percent in 2019 | Difference-in-differences on 7,186 creators over 51 months | Same: effect on participants; unpublished | +| Yu, "Commission Fee Structure and Innovation in Digital Platforms" (AEA 2025 programme) | Commission structure and innovation | Same; working paper | +| Binns and Stein (University of Oxford with Worker Info Exchange, 2025) | UK Uber driver data, 2016-2024, 1.5 million trips: median take rate rose from 25 to 29 percent after dynamic pricing (2023) | A take rate tracked over time, from drivers' data for one platform; advocacy-linked; company disputes it | +| COSTING journal (2026), Shopee and Tokopedia administrative fee increases | Qualitative, Islamic business ethics | Documents fee rises in Indonesia; no measurement | + +Reading: take rates are studied, but as prices whose changes affect sellers, drivers or restaurants, measured from one platform's fee schedule or users' data. None found asks whether a platform's reported revenue remains a fair guide to the commerce it carries, uses reported revenue against reported transaction value, or covers Indonesian platforms. Gap question 1 should be narrowed to that claim; "no study follows the share over several years" is too broad. + +## B. How investors and analysts read platform figures (finance section) + +| Study | Finding (from abstract) | +|---|---| +| Givoly, Li, Lourie and Nekrasov (2019), Review of Accounting Studies 24(4) | KPI news carries information beyond earnings for many KPIs; less when the computation is not disclosed or changes; analysts forecast KPIs more when earnings are less informative; KPI forecasts beat mechanical ones | +| Tang (2025), Journal of Business Finance and Accounting | Analysts' KPI questions on calls lead managers to disclose more KPIs; stronger when earnings are less relevant | +| Trueman, Wong and Zhang (2000), JAR supplement; (2001), RAST | Web traffic relates to internet stock prices beyond financial statements; helps forecast revenue | +| Rajgopal, Kotha and Venkatachalam (2000 working paper; 2003 JAR) | Web traffic value relevant; about US$700 per unique monthly visitor in 1999 (working paper), marked down in 2000 | +| McCarthy, Fader and Hardie (2017), Journal of Marketing 81(1), 17-35 | Customer-based corporate valuation from publicly disclosed customer data | +| U.S. SEC (2020), Release 33-10751 | Interpretive guidance: a key metric needs a clear definition and method, why it is useful, and discussion of changes in method and their effects | +| Liu, Nissim and Thomas (2002), JAR 40(1) | Valuation by multiples (reserve; finding to be confirmed from the paper before use) | + +## C. Checks on findings attributed in Chapter 2 + +| Study | Chapter 2 wording | Check | +|---|---|---| +| Kleven et al. (2011), Econometrica 79(3) | Evasion close to zero on third-party-reported income, substantial on self-reported | Matches the published abstract (working paper: about 0.3 and 37 percent) | +| Naritomi (2019), AER 109(9) | Receipt rewards raised firms' reported sales in Brazil | Matches: at least 21 percent over four years (São Paulo) | +| Pomeranz (2015), AER 105(8) | "Paper trails ... raised the sales that firms report in Chile" | Imprecise. The paper shows the VAT paper trail deters evasion and that enforcement spills over up the supply chain. Reword | +| Berg, Burg, Gombovic and Puri (2020), RFS 33(7) | Simple digital traces such as device type help predict default | Matches: footprint variables (device, email provider, channel, time) match or exceed credit-bureau scores | +| Bowen, Davis and Rajgopal (2002), CAR 19(4) | Firms differed in whether they reported revenue gross | Matches but thin. Grossed-up and barter revenue more likely at firms with higher cash burn and more message-board attention; not pervasive overall, common in some sectors | From e7dcecada01697511bd412f4cc11252990498f57 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 13 Oct 2026 13:51:04 +0000 Subject: [PATCH 39/65] Thesis ch2: narrow gap question 1 to what holds; correct the Pomeranz finding Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 4 ++++ papers/thesis_2026-10/ch2_literature.md | 8 ++++---- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index d8e4354..18a0c20 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -365,3 +365,7 @@ Queued as expansions, agreed not to be structural faults: - A targeted search for empirical studies of platform take rates over time, to confirm or narrow gap question 1. - Checking each study finding against the paper (Kleven et al. 2011, Pomeranz 2015, Naritomi 2019, Berg et al. 2020, Bowen et al. 2002) and the reference list as a whole. - Open choice: keep the reach of platform-based rules as gap question 3, or present it as an extension. + +### Two corrections from the literature search (13 Oct 2026) + +From `research/option_space_2026-10-01/LIT_SEARCH_2026-10-13.md`: (1) gap question 1 overclaimed; commission studies exist (Li and Wang on delivery fee caps; a creator-platform study; an Oxford study tracking an Uber take rate over 2016-2024), but they study commissions as prices and their effects on participants. The 2.1 closing, Table 2.1 and gap item 1 now claim only what holds: no study reviewed sets reported revenue against reported transaction value to test whether revenue stays a fair guide, and none covers Indonesian platforms. (2) Pomeranz (2015) was misdescribed; now: the VAT paper trail deters evasion and enforcement spreads up the supply chain. Expansions (commission studies in 2.1, a finance section, the Bowen detail) remain queued. diff --git a/papers/thesis_2026-10/ch2_literature.md b/papers/thesis_2026-10/ch2_literature.md index a5f92a2..a9bc842 100644 --- a/papers/thesis_2026-10/ch2_literature.md +++ b/papers/thesis_2026-10/ch2_literature.md @@ -10,7 +10,7 @@ Platform economics explains why revenue is only a small share of the sales. A pl Accounting rules add a second reason. Under the international standard, International Financial Reporting Standard 15 (IFRS 15), a company that only arranges a sale records its fee and not the full payment. If a rider pays 100 for a trip and the driver keeps 80, such a company records 20. Discounts and vouchers that it pays for are normally deducted from its revenue (International Accounting Standards Board 2014). So revenue can rise when a platform raises its fees or cuts its discounts, with no change in what customers buy. -These findings explain why the share is small and why it can move, but they leave its actual size and movement to be measured. The pricing models were built to explain how a platform sets its charges, so they predict the direction of a change, not its size in a given market. The accounting studies asked how internet firms around 2000 chose to report revenue, not how far that revenue drifted from the sales behind it (Bowen, Davis and Rajgopal 2002). Studies of how investors valued those firms relied on web traffic as their measure of activity (Trueman, Wong and Zhang 2000, 2001; Rajgopal, Venkatachalam and Kotha 2003). Transaction value also needs care, because each company defines it for itself, and a figure of this kind loses information when its definition changes (Givoly, Li, Lourie and Nekrasov 2019). So measuring the share and its movement needs the revenue and the transaction value of the same platforms over several years, on a consistent definition. The studies reviewed here do not provide such a record. +These findings explain why the share is small and why it can move, but they leave its actual size and movement to be measured. The pricing models were built to explain how a platform sets its charges, so they predict the direction of a change, not its size in a given market. The accounting studies asked how internet firms around 2000 chose to report revenue, not how far that revenue drifted from the sales behind it (Bowen, Davis and Rajgopal 2002). Studies of how investors valued those firms relied on web traffic as their measure of activity (Trueman, Wong and Zhang 2000, 2001; Rajgopal, Venkatachalam and Kotha 2003). Transaction value also needs care, because each company defines it for itself, and a figure of this kind loses information when its definition changes (Givoly, Li, Lourie and Nekrasov 2019). Testing whether revenue stays a fair guide to the commerce needs the reported revenue and the reported transaction value of the same platforms over several years, on a consistent definition. The studies reviewed here do not make that comparison, and none covers Indonesian platforms. ## 2.2 Measuring the digital economy @@ -24,7 +24,7 @@ These studies set out what should be counted and how one figure can be checked a A platform records every sale it carries: who sold what, to whom, for how much and when. Governments have begun to use these records for tax. The OECD, a group of mostly high-income countries, set out model rules under which platforms report what their sellers earn (OECD 2020). The European Union applies these rules through its DAC7 directive (European Union 2021). Indonesia goes further and requires marketplaces to collect tax from their sellers (Ministry of Finance of the Republic of Indonesia 2025). -Tax research explains why such records matter. Reports from third parties improve compliance. In Denmark, evasion was close to zero on income that employers reported, and substantial on income that taxpayers reported themselves (Kleven et al. 2011). Paper trails and receipts have raised the sales that firms report in Chile and Brazil (Pomeranz 2015; Naritomi 2019). Digital records can also hold information that conventional records lack (Berg, Burg, Gombovic and Puri 2020). +Tax research explains why such records matter. Reports from third parties improve compliance. In Denmark, evasion was close to zero on income that employers reported, and substantial on income that taxpayers reported themselves (Kleven et al. 2011). In Chile, the paper trail of the value-added tax deters evasion, and enforcement spreads up the supply chain (Pomeranz 2015). In Brazil, rewards for asking for receipts raised the sales that firms reported (Naritomi 2019). Digital records can also hold information that conventional records lack (Berg, Burg, Gombovic and Puri 2020). Tax research shows that platform records are valuable where they exist. Its evidence comes from settings where a third party already sees the transaction, such as an employer paying wages or a firm buying from another firm. Online selling differs, because many sellers trade through messaging apps and social media, where no marketplace records the sale (BPS 2025). How far a rule built on platform records can reach depends on how much selling happens on platforms. The studies reviewed here do not estimate that share for Indonesia. @@ -36,13 +36,13 @@ The shortfalls identified in Sections 2.1 to 2.3 define the research gap. Table | Section | What it establishes | Main sources | Where it stops | |---|---|---|---| -| 2.1 Platform economics and accounting | Revenue is a fee that is a small share of the sales, and the share can move with pricing, discounts and accounting | Rochet and Tirole 2003; Armstrong 2006; IASB 2014 | The size of the share and its movement have not been measured for the same platforms over several years | +| 2.1 Platform economics and accounting | Revenue is a fee that is a small share of the sales, and the share can move with pricing, discounts and accounting | Rochet and Tirole 2003; Armstrong 2006; IASB 2014 | Whether reported revenue stays a fair guide to reported transaction value has not been tested, and not for Indonesian platforms | | 2.2 Official statistics and surveys | Platform sales and platform revenue differ; sources differ; a count of sellers can break | Ahmad and Schreyer 2016; Henderson et al. 2012; van den Brakel et al. 2008 | Platform and national figures have not been followed through time in one country, and published totals cannot show what a rise in the count of sellers contains | | 2.3 Third-party information | Platform records show sales, and third-party reports improve compliance | Kleven et al. 2011; OECD 2020 | How much of online selling platform records cover has not been estimated for Indonesia | Each shortfall becomes one question for the thesis. -1. **How large is the difference between transaction value and revenue, and does it last?** This follows from Section 2.1, which explains why the share is small and can move but leaves its size and movement unmeasured. +1. **How large is the difference between transaction value and revenue, and does it last?** This follows from Section 2.1, which explains why the share is small and can move but does not test whether reported revenue stays a fair guide to the sales. 2. **Does the choice of figure change the conclusion about growth?** This follows from Section 2.2, which shows that figures differ but does not test whether they grow for the same reasons, or what a rise in the count of sellers contains. 3. **How far can rules built on platform records reach?** This follows from Section 2.3, which shows that platform records are valuable but not how much of online selling they cover. From 045dc14715a8b1c6be4eba40425490f1d62f817c Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 13 Oct 2026 15:36:54 +0000 Subject: [PATCH 40/65] Chapter 3: bridge from the research gap to the tests Map the three gap questions to the framework, use the Chapter 2 concerns where they apply (comparison rules, survey breaks, recall direction, returners), add the revenue split, the H2 identity and a further-analyses section with the market-value predictions. Paragraphs consolidated to one point each; sections renumbered 3.1 to 3.6. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 9 ++ papers/thesis_2026-10/README.md | 2 +- papers/thesis_2026-10/ch3_framework.md | 133 +++++++++++++++++++++---- 3 files changed, 123 insertions(+), 21 deletions(-) diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 18a0c20..4ce3c64 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -369,3 +369,12 @@ Queued as expansions, agreed not to be structural faults: ### Two corrections from the literature search (13 Oct 2026) From `research/option_space_2026-10-01/LIT_SEARCH_2026-10-13.md`: (1) gap question 1 overclaimed; commission studies exist (Li and Wang on delivery fee caps; a creator-platform study; an Oxford study tracking an Uber take rate over 2016-2024), but they study commissions as prices and their effects on participants. The 2.1 closing, Table 2.1 and gap item 1 now claim only what holds: no study reviewed sets reported revenue against reported transaction value to test whether revenue stays a fair guide, and none covers Indonesian platforms. (2) Pomeranz (2015) was misdescribed; now: the VAT paper trail deters evasion and enforcement spreads up the supply chain. Expansions (commission studies in 2.1, a finance section, the Bowen detail) remain queued. + +### Chapter 3 rebuilt as the bridge from the gap to the tests (13 Oct 2026) + +The researcher kept Chapter 2 as applied (three-part sections, gap anchored to the shortfalls; the queued Chapter 2 expansion was not applied) and asked for a complete Chapter 3: since this is the thesis and not the proposal, it may carry more than the proposal did. Chapter 3 now states how the literature and the gap become the approach. +- **Opening:** the declaration stays, with its basis named (test a figure against the activity it stands for; Henderson et al. 2012). The paragraph restating the research question became a numbered map of the three gap questions of 2.4 to the tools that answer each. Gap question 3 (reach of rules) stays a gap question, measured by the channel split. +- **Concerns raised in Chapter 2 now used where they apply:** Givoly et al. moved from the H1 reasoning to the comparison rules (a measure tells less when its computation changes); the own-goods rule (IFRS 15; Hagiu and Wright) stated in 3.1; van den Brakel et al. cited for the survey break in 3.5; Neter and Waksberg used for the direction of recall error (it favours passing the entry test, so a shortfall is cautious); McKenzie and Paffhausen give the reason the age of sellers bears on returners; exits only raise the share needed, so 21.5 percent is the smallest share that can produce the rise. +- **Added:** equations (5) and (6), splitting revenue growth into volume, fee rate and incentive rate (Objective 2); equation (7), the H2 margin identity with its decision rule; a new 3.6 with Table 3.3 (tracing, channels and reach, national figures, market values) and equation (8) with the two registered market-value predictions, anchored to the investor line in 2.1. Table 3.1 gains the fee rate and the incentive rate. +- **Form:** first draft too dense (paragraphs up to 181 words), a second too spread (64 paragraphs, many one-sentence pieces); the applied version joins the second without changing a sentence (checked by script): 51 paragraphs, longest 92 words, "where" definitions joined to what each measure means. Sections renumbered 3.1 to 3.6; no other chapter cites a Chapter 3 section number. +- **Open:** the researcher finds the chapter trustworthy but heavy to read, probably because of its eight equations and many symbols; accepted for now, cold read due. If still heavy, consider moving equation (8) to Chapter 4. Chapter 4 now repeats the revenue split (4.2) and the market regression (4.5) and should be cut to data and procedure; Chapter 1.3 may add the further analyses to its line on Chapter 3. One sentence is the author's reasoning, not a cited finding: "Growth from the take rate has a limit, because a platform cannot raise its share of each sale without end." diff --git a/papers/thesis_2026-10/README.md b/papers/thesis_2026-10/README.md index 1b7d29c..1424456 100644 --- a/papers/thesis_2026-10/README.md +++ b/papers/thesis_2026-10/README.md @@ -40,7 +40,7 @@ The thesis written in full. Same title, research question, hypotheses and measur |---|---|---| | `ch1_introduction.md` | 1 Introduction | drafted 12 Oct on the proposal's and deck's flow (opening, 1.1 question and objectives, 1.2 contributions, 1.3 structure); open concern on 1.1 in `EDIT_HISTORY.md` | | `ch5_results.md` | 5 Results | rewritten 13 Oct as 11 sections (5.1 wedge, 5.2 H1, 5.3 tracing, 5.4 market values, 5.5 the platforms in 2023, 5.6 what sellers reported, 5.7 count and entry test, 5.8 who the extra sellers were, 5.9 returners, 5.10 channels and records, 5.11 why the national figures disagree), awaiting the researcher's reading. Rules and reach of the 2026 rules go to Chapter 6. Figures not yet placed; exhibits in `research/option_space_2026-10-01/exhibits/` | -| `ch3_framework.md` | 3 Framework and Hypotheses | first full draft 12 Oct (evening), awaiting the researcher's reading: the chain of stand-ins, terms box (Table 3.1), the measures and the identity, H1, H2, the entry test, further analyses, tests table (Table 3.2); Figure 3.1 still to be drawn | +| `ch3_framework.md` | 3 Framework and Hypotheses | rewritten and accepted for now 13 Oct: opening declaration and map of the three gap questions, 3.1 terms and rules for comparison, 3.2 three measures (equations 1 to 4), 3.3 tracing a movement (equations 5 and 6), 3.4 hypotheses (equation 7), 3.5 the entry test, 3.6 further analyses (Table 3.3, market-value equation 8). Figure 3.1 still to be drawn; a cold read is still due (see `EDIT_HISTORY.md`) | | `ch2_literature.md` | 2 Literature Review | first full draft 12 Oct (evening), awaiting the researcher's reading: on the proposal's four sections (2.1 pricing and revenue recognition, 2.2 measuring the online economy, 2.3 digital records and third-party information, 2.4 the gap), with the survey-count strand added (breaks, recall, exit, margins). Citations are author-year, every one traced to `REFERENCES_WORKING.md`; the bibliography itself is not yet built | | `ch4_data_methods.md` | 4 Data and Methods | first full draft 12 Oct (night), awaiting the researcher's reading: platform data and the two groups (Table 4.1), the H1 test, tracing revenue movements, the BPS files and the entry test (Table 4.2), channels, records and reach, the comparison with national and payment figures (Table 4.3), market values, traceability. The appendix it points to (platform list, seven traced windows, registered tests) is not built | | `appendix_notes.md` | Appendices A to C | working draft 13 Oct: platform lists, the seven windows, wedge by platform-year, every check on the test of H1, registered tests with outcomes; the detail that Chapters 4 and 5 point to | diff --git a/papers/thesis_2026-10/ch3_framework.md b/papers/thesis_2026-10/ch3_framework.md index 4469c6b..7316ef2 100644 --- a/papers/thesis_2026-10/ch3_framework.md +++ b/papers/thesis_2026-10/ch3_framework.md @@ -1,14 +1,22 @@ # Chapter 3. Framework and Hypotheses -Platform activity can be observed through several figures, and these figures do not measure the same economic object. Transaction value measures the commerce a platform carries. Revenue measures the part of that commerce the platform keeps. National statistics measure online sales across all sellers, whether or not they use a platform. The framework starts from the difference between the value a platform processes and the revenue it recognizes. It then asks whether that relationship stays stable over time, and whether figures from other sources give the same account of online activity. Figure 3.1 shows the structure. +Platform activity can be observed through several figures, and these figures do not measure the same economic object. Transaction value measures the commerce a platform carries. Revenue measures the part of that commerce the platform keeps. National statistics measure online sales across all sellers, whether or not they use a platform. -Figure 3.1 places the three quantities inside one another. Revenue sits inside platform commerce, because a platform keeps only part of each sale. Platform commerce sits inside all online commerce, because many sellers trade outside platforms. The first hypothesis, H1, tests the inner link between revenue and platform commerce. The second, H2, tests the outer link between platform commerce and national online commerce. The figure is illustrative, and the sizes are not to scale. The platform circle also differs from the marketplace channel in the survey of online sellers run by BPS, the national statistics office. +The framework tests each figure against the activity it is used to represent, as studies of night-time lights do for national income (Henderson, Storeygard and Weil 2012). It starts from the difference between the value a platform processes and the revenue it recognizes. It then asks whether that relationship stays stable over time, and whether figures from other sources give the same account of online activity. Figure 3.1 shows the structure. + +Figure 3.1 places the three quantities inside one another. Revenue sits inside platform commerce, because a platform keeps only part of each sale. Platform commerce sits inside all online commerce, because many sellers trade outside platforms. The first hypothesis, H1, tests the inner link between revenue and platform commerce. The second, H2, tests the outer link between platform commerce and national online commerce. [Figure 3.1 to be drawn: three nested circles, with revenue inside platform commerce inside all online commerce, H1 on the inner link and H2 on the outer link.] -The research question asks how much activity remains invisible when platforms are measured through revenue, and when that choice changes the conclusion about growth. The two links answer it. The inner link measures how much commerce revenue leaves out and whether the share that revenue captures stays stable (Sections 3.1 and 3.2). The outer link tests whether the commerce on platforms tells the same story of growth as national figures (Section 3.2). A further test checks the count of sellers behind national growth (Section 3.3). The reach of rules built on platform records is examined separately, in Chapter 6. +*Note:* The figure is illustrative, and the sizes are not to scale. The platform circle differs from the marketplace channel in the survey of online sellers run by BPS, the national statistics office. + +The research question asks how much activity remains invisible when platforms are measured through revenue, and when that choice changes the conclusion about growth. The literature leaves three parts of it open (Section 2.4). The framework answers each with its own tools: + +1. **How large the difference between transaction value and revenue is, and whether it lasts:** three measures, a split of revenue growth, and H1 (Sections 3.2 to 3.4). +2. **Whether the choice of figure changes the conclusion about growth:** H2, the entry test and a comparison of national figures (Sections 3.4 to 3.6). +3. **How far rules built on platform records reach:** how online selling divides between marketplaces and other channels (Section 3.6). -## 3.1 Measurement framework +## 3.1 Terms and rules for comparison Table 3.1 defines the terms used throughout the thesis, each once, with the symbol used in the formulas. @@ -20,6 +28,8 @@ Table 3.1 defines the terms used throughout the thesis, each once, with the symb | Revenue | R | What the platform recognizes as its own income in its accounts. | | Take rate | m | Revenue divided by transaction value: the share of each sale that the platform keeps. | | Customer incentives | | Discounts, vouchers and other promotions that a platform pays for. Accounting rules normally deduct them from revenue, and Tokopedia reports the deduction as a separate line. | +| Fee rate | f | The platform's revenue before customer incentives, divided by transaction value. | +| Incentive rate | s | Customer incentives divided by transaction value. | | Invisible wedge | W | Transaction value minus revenue: what a revenue figure leaves out. It is defined for one platform. The thesis sums it over three platforms in Indonesia for 2023 and computes no national total. | | Ecosystem ratio | E | The wedge divided by revenue. It equals one divided by the take rate, minus one. This is the author's own measure, built for this thesis. | | Growth | g | The change from one year to the next as a share of the first year: 0.09 means 9 percent. | @@ -27,7 +37,15 @@ Table 3.1 defines the terms used throughout the thesis, each once, with the symb | Online seller | | A business that the national statistics office, BPS, counts as selling online; the text also calls it a seller or a business. The count of online sellers is BPS's estimate of how many there are in a year. | | First-time entry | | A seller that began selling online in the year being measured. | -Transaction value and revenue are paired only when they refer to the same period and the same business. Figures that companies report are kept apart from figures that the thesis constructs, and both are kept apart from national statistics and payment figures (Chapter 4). +Three rules decide which figures can be set against each other. First, transaction value and revenue are paired only when they refer to the same period and the same business. A take rate built from figures of different scope would measure the mismatch, not the platform. + +Second, each platform is compared only with itself from one year to the next. Companies define transaction value in their own ways, and a disclosed measure tells less when the way it is computed changes (Givoly, Li, Lourie and Nekrasov 2019). Within one company the definition cancels out of the yearly change, as long as the company keeps it. + +Third, companies that mainly sell goods themselves are kept out. Such a company reports the whole sale as revenue, while a platform that only arranges the sale reports its fee (International Accounting Standards Board 2014). The two business models differ in kind (Hagiu and Wright 2015), and their take rates cannot be compared. + +Every figure also keeps a label for where it comes from. Figures that companies report are kept apart from figures that the thesis constructs, and both are kept apart from national statistics and payment figures. Chapter 4 applies these rules platform by platform. + +## 3.2 Three measures The invisible wedge measures, in currency, the part of transaction value that lies outside a platform's revenue: @@ -43,7 +61,9 @@ To compare platforms of different size, the thesis also expresses the wedge rela E_it = W_it / R_it = 1 / m_it − 1 (2) ``` -where m_it = R_it / V_it is the take rate, the share of each sale that the platform keeps. The ecosystem ratio is the author's own measure, and it restates the take rate. Its interpretation draws on two established results. Platform pricing means that revenue need not follow the commerce a platform carries, and revenue recognition means that the share recorded as revenue depends on whether the platform acts as principal or agent (Rochet and Tirole 2003; Hagiu and Wright 2015; International Accounting Standards Board 2014). An ecosystem ratio of 9 means that 9 units of transaction value lie outside revenue for every unit recorded as revenue. Equivalently, the platform keeps 10 percent of each sale. +where m_it = R_it / V_it is the take rate, the share of each sale that the platform keeps. The ecosystem ratio is the author's own measure, and it restates the take rate. An ecosystem ratio of 9 means that 9 units of transaction value lie outside revenue for every unit recorded as revenue. Equivalently, the platform keeps 10 percent of each sale. + +Two established results explain why the ratio can be large and why it can move. Platform pricing and business models mean that revenue need not follow the commerce a platform carries (Rochet and Tirole 2003; Hagiu and Wright 2015). Revenue recognition means that the share recorded as revenue depends on whether the platform acts as principal or agent (International Accounting Standards Board 2014). A single ratio does not show whether the relationship holds over time, so the thesis defines growth divergence: @@ -51,23 +71,43 @@ A single ratio does not show whether the relationship holds over time, so the th D_it = g(R_it) − g(V_it) (3) ``` -where g(X) is the growth of X from one year to the next, as a share of the first year. A positive D means that revenue grew faster than transaction value, so the take rate rose. A negative D means the reverse. Revenue equals the take rate times transaction value, so D can also be written with the take rate alone: +where g(X) is the growth of X from one year to the next, as a share of the first year. A positive D means that revenue grew faster than transaction value, so the take rate rose. A negative D means the reverse. If revenue were a stable proxy for transaction value, D would stay close to zero. Revenue equals the take rate times transaction value, so D can also be written with the take rate alone: ``` D_it = (1 + g(V_it)) × (m_it − m_i,t−1) / m_i,t−1 (4) ``` -where m_i,t−1 is the take rate in the earlier year and m_it the take rate in the later year. If the take rate is steady, D is zero and revenue grows exactly as fast as transaction value. If revenue were a stable proxy for transaction value, D would stay close to zero. +where m_i,t−1 is the take rate in the earlier year and m_it the take rate in the later year. If the take rate is steady, D is zero and revenue grows exactly as fast as transaction value. Tokopedia between 2022 and 2023 illustrates the three measures. Its take rate rose from about 1.5 to about 2.5 percent, so it kept about two thirds more of each sale. Its transaction value fell by about 9 percent and its revenue rose by about 53 percent, so D was 0.91 × 0.68 = 0.62, or 62 percentage points. Its wedge stayed at about 98 percent of transaction value and fell with it, by about 10 percent. -The wedge, the ecosystem ratio and growth divergence do different jobs. The wedge gives the size of what revenue leaves out. At a marketplace that keeps a few percent of each sale, the wedge is almost all of transaction value, so it moves with the commerce. The ecosystem ratio expresses that size relative to revenue. Growth divergence carries the central test: whether revenue and transaction value keep the same relationship from year to year. +The three measures do different jobs. The wedge gives the size of what revenue leaves out, and at a marketplace that keeps a few percent of each sale it is almost all of transaction value. The ecosystem ratio expresses that size relative to revenue. Growth divergence carries the central test: whether revenue and transaction value keep the same relationship from year to year. + +The ecosystem ratio also governs how far a change in the take rate moves revenue growth. Equation (4) shows that D is the change in the take rate divided by its starting level. + +A platform that keeps 2 percent of each sale has an ecosystem ratio of 49. If its transaction value is flat, a rise in its take rate to 3 percent raises its revenue by 50 percent. A platform that keeps 17 percent has an ecosystem ratio near 5, and a rise to 18 percent raises its revenue by about 6 percent. So a large ecosystem ratio means that ordinary changes in fees or discounts can produce large swings in reported revenue growth. + +## 3.3 Tracing a movement + +A large divergence calls for an explanation, and the same definitions supply it. Measured in logarithms, the growth of revenue splits exactly into the growth of transaction value and the change in the take rate: -The ecosystem ratio also governs how far a change in the take rate moves revenue growth. Equation (4) shows that D is the change in the take rate divided by its starting level. A platform that keeps 2 percent of each sale has an ecosystem ratio of 49. If its transaction value is flat, a rise in its take rate to 3 percent raises its revenue by 50 percent. A platform that keeps 17 percent has an ecosystem ratio near 5, and a rise to 18 percent raises its revenue by about 6 percent. So a large ecosystem ratio means that ordinary changes in fees or discounts can produce large swings in reported revenue growth. +``` +Δln R_it = Δln V_it + Δln m_it (5) +``` + +where Δln X is the change in the natural logarithm of X from one year to the next. The first term is the volume effect, more or less commerce. The second is the take-rate effect, a larger or smaller share of each sale. A log change treats a rise and a fall by the same factor as equal in size, so the two effects can be compared directly. The take rate itself has two parts: + +``` +m_it = f_it − s_it (6) +``` -## 3.2 Hypothesis development +where f_it is the fee rate and s_it the incentive rate, both measured against transaction value. A take rate can rise because a platform charges more or because it spends less on discounts and vouchers. Accounting rules normally deduct such incentives from revenue (International Accounting Standards Board 2014). So a cut in incentives raises revenue even when no fee changes. -Two hypotheses follow from the research question. H1 asks whether revenue follows the commerce carried within a platform. H2 asks where national online growth comes from, which decides whether the commerce on platforms can stand for national online commerce. Together they test the two links in Figure 3.1. Table 3.2 pairs each hypothesis, and the entry test of Section 3.3, with its main test and with the result that would count against it. +Equations (5) and (6) together divide a movement in revenue into more commerce, higher fees and lower incentives. A change in the mix of a company's businesses is a further source, read from its reports where they describe one. The split shows how a movement happened. It does not show why the company chose it, and company explanations are read as reports. + +## 3.4 Hypotheses + +Two hypotheses follow from the research question. H1 asks whether revenue follows the commerce carried within a platform. H2 asks where national online growth comes from, which decides whether the commerce on platforms can stand for national online commerce. Together they test the two links in Figure 3.1. Table 3.2 pairs each hypothesis, and the entry test of Section 3.5, with its main test and with the result that would count against it. **Table 3.2. Hypotheses, tests and what would count against them** @@ -77,26 +117,79 @@ Two hypotheses follow from the research question. H1 asks whether revenue follow | H2 | Does national e-commerce value grow because of more businesses or because of higher sales per business? | BPS's national e-commerce value split into the number of businesses and sales per business | Higher sales per business accounting for most of the growth | | Entry test | Does a rise in the count of online sellers come from first-time entry? | Share of 2023 sellers in BPS's survey files who began selling online in 2023 | Fewer new sellers than the rise requires | -Platform economics and accounting both suggest that the share of transaction value a platform records as revenue is not fixed. The share depends on fees, on customer incentives and on whether the platform acts as principal or agent, and each of these can change with the platform's pricing, business model and scope (Rochet and Tirole 2003; Hagiu and Wright 2015; International Accounting Standards Board 2014). Research on disclosed operating measures adds that a measure becomes less informative when the way it is computed changes (Givoly, Li, Lourie and Nekrasov 2019). Revenue follows transaction value only while the take rate stays constant, that is, while D stays close to zero. If the relationship were stable, revenue would be a workable proxy for the commerce a platform carries, D would stay near zero and the ecosystem ratio would be constant. The first hypothesis states this case. +The share of transaction value that a platform keeps is not fixed. A platform sets its charges on both sides of the market together, and both the level and the split are choices (Rochet and Tirole 2003). Platforms often hold down the price to one side to draw it in. They can raise that price once both sides have joined (Evans and Schmalensee 2016). + +Accounting rules move the share in two ways. The discounts a platform pays for come out of its revenue. A platform that sells goods itself records more of each sale than one that only arranges it (International Accounting Standards Board 2014; Hagiu and Wright 2015). Both can change as a platform changes its business model or scope. + +Revenue follows transaction value only while the take rate holds steady, that is, while D stays near zero. In that case revenue would be a workable proxy for the commerce a platform carries, and the ecosystem ratio would not change. The first hypothesis states this case. > **H1.** Transaction value and revenue recognized by the same platform grow in proportion over time. -Growth rates never match exactly, so H1 is tested by comparison. The test compares the size of yearly changes in the take rate, measured in logs, between two groups. One is the Indonesian and regional platforms, and the other is listed platforms abroad, where revenue and transaction value broadly move together. Chapter 4 describes the two groups and the test. +Growth rates never match exactly, so H1 is tested by comparison. The test compares the size of yearly changes in the take rate, measured in logs as in equation (5), between two groups. One group is the Indonesian and regional platforms. The other is listed platforms abroad, where revenue and transaction value broadly move together. Changes far larger in Indonesia would count against H1. Chapter 4 describes the two groups and the test. + +National e-commerce value can grow in two ways. Existing businesses can sell more, which is the intensive margin. More businesses can sell online, which is the extensive margin. Trade economists split the growth of exports in the same way (Hummels and Klenow 2005). + +The distinction matters for measurement. Growth driven by new sellers can take place in channels that platform accounts do not cover, such as social media and instant messaging, which BPS reports are widely used (BPS 2025). Platform figures can then miss that growth even when every platform reports accurately. -National e-commerce value can grow because existing businesses sell more, the intensive margin, or because more businesses sell online, the extensive margin (Hummels and Klenow 2005). The distinction matters for measurement. Growth driven by new sellers can take place in channels that platform accounts do not cover, such as social media and instant messaging, which BPS reports are widely used (BPS 2025). Platform figures can then miss that growth even when every platform reports accurately. BPS publishes both the number of businesses selling online and their total online sales, so national growth can be split into the two margins from published figures. The second hypothesis states the case in which more businesses drive growth. +BPS publishes both the number of businesses selling online, N, and their total online sales value, Y. Since Y equals N times the sales per business, its growth in logarithms splits exactly in two: + +``` +Δln Y_t = Δln N_t + Δln (Y_t / N_t) (7) +``` + +where the first term is the extensive margin and the second the intensive margin. H2 holds if the first term is more than half of the total. The second hypothesis states the case in which more businesses drive growth. > **H2.** The growth of national e-commerce value comes mainly from more businesses selling online, and less from higher sales per business. The H2 split is arithmetic. It does not assign new businesses to channels, and it does not show whether the businesses in the count are new. -## 3.3 The entry test +## 3.5 The entry test + +H2 rests on BPS's count of businesses selling online, and a rise in that count can mean two different things. Either more businesses started selling, or the survey reached businesses that were already selling. A change in how a survey finds its respondents can create a break in a series that looks like real change (van den Brakel, Smith and Compton 2008). -H2 rests on BPS's count of businesses selling online, and a rise in that count can mean two different things. Either more businesses started selling, or the survey reached businesses that were already selling. The published figures cannot tell the two apart. The survey can, because it asks every seller in which year it began selling online. The thesis checks what the count contains with a test written down before the survey files were opened. +The published figures cannot tell the two apart. The survey can, because it asks every seller in which year it began selling online. The thesis checks what the count contains with a test written down before the survey files were opened. > **Entry test.** In the survey file for 2023, at least 21.5 percent of online sellers must report that they began selling online in 2023. -The 21.5 percent threshold comes from arithmetic. The published count of online sellers rose by 27.4 percent in 2023. Suppose every extra seller was new and no seller stopped. Then for every 100 sellers a year earlier there were 127.4, and 27.4 of those were new. New sellers would make up 21.5 percent of all sellers, which is 27.4 divided by 127.4. A smaller share means that part of the rise came from somewhere else. The test does not test H2 itself. It tests what "more businesses" means. +The 21.5 percent threshold comes from arithmetic. The published count of online sellers rose by 27.4 percent in 2023. Suppose every extra seller was new and no seller stopped. Then for every 100 sellers a year earlier there were 127.4, and 27.4 of those were new. New sellers would make up 21.5 percent of all sellers, which is 27.4 divided by 127.4. + +Sellers who stopped would have to be replaced as well, which only raises the share needed. The threshold is the smallest share of new sellers that can produce the rise. The test does not test H2 itself. It tests what "more businesses" means. -If the share of new sellers falls short, part of the rise is something other than first-time entry. Two readings are open: the survey reached more of the sellers who were already there, or sellers came back after a pause. Chapter 5 weighs them. +Sellers' memory works in favour of passing the test. People tend to report past events as more recent than they were (Neter and Waksberg 1964). A seller who began in 2022 but reports 2023 raises the share of new sellers. Recall error of this kind makes the threshold easier to reach, so a share below it is a cautious finding. + +If the share of new sellers falls short, part of the rise is something other than first-time entry. Two readings are open. The survey may have reached more of the sellers who were already there. Or sellers may have come back after a pause, since a returning seller keeps an old start year. Small firms that stop are more often young firms with young owners (McKenzie and Paffhausen 2019). So the age of the sellers behind the rise bears on the second reading. Chapter 5 weighs the two readings with that evidence. + +## 3.6 Further analyses + +Four further analyses complete the framework without forming hypotheses of their own. Each answers a part of the research question that H1, H2 and the entry test leave open. Table 3.3 lists them. + +**Table 3.3. Further analyses** + +| Analysis | What it asks | Main evidence | +|---|---|---| +| Tracing large movements | What moved revenue when it parted from transaction value? | Equations (5) and (6), applied to company reports | +| Channels and reach | How much online selling passes through marketplaces, and how far can rules built on platform records reach? | BPS's channel shares and survey files | +| Comparing national figures | Do national figures tell the same story of growth? | BPS, Bank Indonesia, industry estimates and household spending | +| Market values | Do markets value growth from more commerce differently from growth in the take rate? | Year-end market values of listed platforms | + +Tracing large movements applies the split of Section 3.3. Wherever H1 finds a large change in the take rate, the thesis divides the change in revenue into its parts. It also reads the company's reports for changes in the mix of businesses and in reporting scope. + +The channel analysis answers the third question of Section 2.4. A rule built on platform records reaches only the sellers whose sales pass through a platform. Research on tax shows that reports from a third party improve compliance where they exist (Kleven et al. 2011). + +The reach of a rule built on platform records depends on how online selling divides between marketplaces and channels that no marketplace records, such as messaging apps and social media. The thesis measures that division by sellers and by sales value. It also counts the marketplace sellers who may be large enough to fall under the marketplace tax. + +Record keeping is the second part of this analysis. The thesis compares how often marketplace sellers and other sellers keep financial statements. The comparison bears on the information that platform records can add. Chapter 6 develops the reach of the rules. + +The comparison of national figures extends the outer link in Figure 3.1. Platform reports, BPS's survey, Bank Indonesia's statistics and industry estimates count different things. Their values change with the definition used (UNCTAD 2024). If two national figures move in opposite directions in the same year, the conclusion about growth depends on the figure chosen. The thesis compares their growth rates, each in its own unit, and compares levels only after reconciling what each source covers. Household spending, from the national accounts, serves as a further point of comparison. + +The market-value analysis asks whether investors, who often read revenue as a sign of commerce (Section 2.1), treat its two sources alike. Equation (5) shows that revenue can grow in two ways, through more commerce or through a larger take rate. Early studies of internet companies found that markets priced measures of web activity beyond reported revenue (Trueman, Wong and Zhang 2000). + +The two sources of growth need not be worth the same. Growth from the take rate has a limit, because a platform cannot raise its share of each sale without end. A higher price to one side of a platform can also reduce how much that side uses it (Rochet and Tirole 2003). + +The thesis relates the yearly change in a platform's market value, MV, to the two sources of revenue growth: + +``` +Δln MV_it = β_V Δln V_it + β_m Δln m_it + year effect + error (8) +``` -The split of national sales by channel and the comparison of record keeping between marketplace sellers and other sellers support the hypotheses without forming hypotheses of their own. The first shows whether national growth took place inside or outside the marketplace channel. The second shows whether marketplace sellers keep financial statements more often than other sellers, which bears on the information that platform records can supply to tax administration. Chapter 4 describes the data and methods behind each test and analysis. +where β_V shows how market value moves with transaction value, and β_m how it moves with the take rate. Two predictions were written down before the market values were collected. The first is that market value moves with transaction value, so β_V is positive. The second is that markets value growth from more commerce more than growth from a higher take rate, so β_V is larger than β_m. Chapter 4 describes the data and methods behind each test and analysis. From 6653d16d376703738ded5b2451072b91fd968879 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 13 Oct 2026 15:53:52 +0000 Subject: [PATCH 41/65] Chapters 1 to 3: remove overlaps found in a cross-chapter read Chapter 2 hands over without repeating Chapter 3's map; Chapter 3 links Chapter 1's objectives to the gap questions, says where rules act in the take-rate split, and drops a repeated paragraph; roadmap and one ambiguous term fixed. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 11 +++++++++++ papers/thesis_2026-10/ch1_introduction.md | 2 +- papers/thesis_2026-10/ch2_literature.md | 6 ++---- papers/thesis_2026-10/ch3_framework.md | 6 ++++-- 4 files changed, 18 insertions(+), 7 deletions(-) diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 4ce3c64..68f530c 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -378,3 +378,14 @@ The researcher kept Chapter 2 as applied (three-part sections, gap anchored to t - **Added:** equations (5) and (6), splitting revenue growth into volume, fee rate and incentive rate (Objective 2); equation (7), the H2 margin identity with its decision rule; a new 3.6 with Table 3.3 (tracing, channels and reach, national figures, market values) and equation (8) with the two registered market-value predictions, anchored to the investor line in 2.1. Table 3.1 gains the fee rate and the incentive rate. - **Form:** first draft too dense (paragraphs up to 181 words), a second too spread (64 paragraphs, many one-sentence pieces); the applied version joins the second without changing a sentence (checked by script): 51 paragraphs, longest 92 words, "where" definitions joined to what each measure means. Sections renumbered 3.1 to 3.6; no other chapter cites a Chapter 3 section number. - **Open:** the researcher finds the chapter trustworthy but heavy to read, probably because of its eight equations and many symbols; accepted for now, cold read due. If still heavy, consider moving equation (8) to Chapter 4. Chapter 4 now repeats the revenue split (4.2) and the market regression (4.5) and should be cut to data and procedure; Chapter 1.3 may add the further analyses to its line on Chapter 3. One sentence is the author's reasoning, not a cited finding: "Growth from the take rate has a limit, because a platform cannot raise its share of each sale without end." + +### Cross-chapter read of Chapters 1 to 3 (13 Oct 2026) + +Read as one text after Chapter 3 was accepted. The line holds (Chapter 1 names what an answer needs, each Chapter 2 section stops there, Chapter 3 supplies the tools; the research question and the three gap questions match). Overlaps left by chapter-by-chapter work, fixed with the researcher's approval: +- Chapter 2's closing paragraph repeated Chapter 3's map; cut, and the handover moved into the sentence that introduces the three questions. Chapter 2 now ends on question 3. +- Chapter 1's three objectives and the three gap questions were never linked; Chapter 3 now says which objective serves which question, and that the third question is answered by a further analysis. +- Chapter 1.2 promises to set each recent rule against the figure it acts on; Chapter 3.3 now says where a rule acts in equation (6) (commission caps on the fee rate, discount limits on the incentive rate), pointing to Chapter 6. +- Chapter 3.2's "two established results" paragraph repeated 3.1 and 3.4; cut. +- Chapter 1.3 now names the four further analyses; Chapter 2's intro says "online sales across the country" where "the sales" was ambiguous. +- Left for the reference pass: "investors and analysts often read revenue as a sign of commerce" (2.1) has no source, and Chapter 3.6 relies on it. +- Practice from now on: a cross-chapter read after each chapter is accepted. diff --git a/papers/thesis_2026-10/ch1_introduction.md b/papers/thesis_2026-10/ch1_introduction.md index 74eeca2..1b35a61 100644 --- a/papers/thesis_2026-10/ch1_introduction.md +++ b/papers/thesis_2026-10/ch1_introduction.md @@ -40,4 +40,4 @@ Earlier work has already compared platform transaction values with national e-co ## 1.3 Structure of the thesis -The thesis moves from ideas to evidence and then to what follows from it. Chapter 2 reviews why platform revenue differs from the commerce behind it, how online selling is measured, and what platform records can show. It ends with the questions the literature leaves open. Chapter 3 turns those questions into measures, two hypotheses and the entry test. Chapter 4 describes the data, which come from platform reports, BPS survey files and national and market figures, and the tests that use them. Chapter 5 gives the results, first for the platforms and then for the national figures. Chapter 6 asks how far Indonesia's recent rules reach and what the findings mean for other countries. Chapter 7 concludes and states the limits of the evidence. +The thesis moves from ideas to evidence and then to what follows from it. Chapter 2 reviews why platform revenue differs from the commerce behind it, how online selling is measured, and what platform records can show. It ends with the questions the literature leaves open. Chapter 3 turns those questions into measures, two hypotheses, the entry test and four further analyses. Chapter 4 describes the data, which come from platform reports, BPS survey files and national and market figures, and the tests that use them. Chapter 5 gives the results, first for the platforms and then for the national figures. Chapter 6 asks how far Indonesia's recent rules reach and what the findings mean for other countries. Chapter 7 concludes and states the limits of the evidence. diff --git a/papers/thesis_2026-10/ch2_literature.md b/papers/thesis_2026-10/ch2_literature.md index a9bc842..7f26bd8 100644 --- a/papers/thesis_2026-10/ch2_literature.md +++ b/papers/thesis_2026-10/ch2_literature.md @@ -1,6 +1,6 @@ # Chapter 2. Literature Review -Digital platforms sit between economic activity and the figures used to measure it. A platform carries a sale between a buyer and a seller. It records part of the payment as its own revenue, and it keeps a record of the whole sale. Different bodies of research study these steps. Platform economics and accounting explain why revenue is only part of each payment (Section 2.1). Official statistics explain how the sales themselves are measured (Section 2.2). Research on third-party information explains what a platform's records can show to tax authorities (Section 2.3). Each body of research explains why the figures differ, but none measures how far they differ in practice. Section 2.4 sets out this research gap. +Digital platforms sit between economic activity and the figures used to measure it. A platform carries a sale between a buyer and a seller. It records part of the payment as its own revenue, and it keeps a record of the whole sale. Different bodies of research study these steps. Platform economics and accounting explain why revenue is only part of each payment (Section 2.1). Official statistics explain how online sales across the country are measured (Section 2.2). Research on third-party information explains what a platform's records can show to tax authorities (Section 2.3). Each body of research explains why the figures differ, but none measures how far they differ in practice. Section 2.4 sets out this research gap. ## 2.1 Platform economics and revenue recognition @@ -40,10 +40,8 @@ The shortfalls identified in Sections 2.1 to 2.3 define the research gap. Table | 2.2 Official statistics and surveys | Platform sales and platform revenue differ; sources differ; a count of sellers can break | Ahmad and Schreyer 2016; Henderson et al. 2012; van den Brakel et al. 2008 | Platform and national figures have not been followed through time in one country, and published totals cannot show what a rise in the count of sellers contains | | 2.3 Third-party information | Platform records show sales, and third-party reports improve compliance | Kleven et al. 2011; OECD 2020 | How much of online selling platform records cover has not been estimated for Indonesia | -Each shortfall becomes one question for the thesis. +Each shortfall becomes one question for the thesis, and Chapter 3 sets out the tools that answer each. 1. **How large is the difference between transaction value and revenue, and does it last?** This follows from Section 2.1, which explains why the share is small and can move but does not test whether reported revenue stays a fair guide to the sales. 2. **Does the choice of figure change the conclusion about growth?** This follows from Section 2.2, which shows that figures differ but does not test whether they grow for the same reasons, or what a rise in the count of sellers contains. 3. **How far can rules built on platform records reach?** This follows from Section 2.3, which shows that platform records are valuable but not how much of online selling they cover. - -The thesis answers the first question by measuring the invisible wedge, transaction value minus revenue, for the same platforms over several years, and by tracing the reasons for its largest moves. It answers the second by comparing platform figures with national figures and by using the sellers' own answers to test what the count of sellers contains. It answers the third by estimating how many online sellers a rule built on platform records can reach. diff --git a/papers/thesis_2026-10/ch3_framework.md b/papers/thesis_2026-10/ch3_framework.md index 7316ef2..47107ae 100644 --- a/papers/thesis_2026-10/ch3_framework.md +++ b/papers/thesis_2026-10/ch3_framework.md @@ -16,6 +16,8 @@ The research question asks how much activity remains invisible when platforms ar 2. **Whether the choice of figure changes the conclusion about growth:** H2, the entry test and a comparison of national figures (Sections 3.4 to 3.6). 3. **How far rules built on platform records reach:** how online selling divides between marketplaces and other channels (Section 3.6). +The three objectives set in Chapter 1 sit inside this map. Measuring the wedge and tracing its large moves serve the first question, and comparing platform figures with national and payment figures serves the second. The third question is answered by a further analysis. + ## 3.1 Terms and rules for comparison Table 3.1 defines the terms used throughout the thesis, each once, with the symbol used in the formulas. @@ -63,8 +65,6 @@ E_it = W_it / R_it = 1 / m_it − 1 (2) where m_it = R_it / V_it is the take rate, the share of each sale that the platform keeps. The ecosystem ratio is the author's own measure, and it restates the take rate. An ecosystem ratio of 9 means that 9 units of transaction value lie outside revenue for every unit recorded as revenue. Equivalently, the platform keeps 10 percent of each sale. -Two established results explain why the ratio can be large and why it can move. Platform pricing and business models mean that revenue need not follow the commerce a platform carries (Rochet and Tirole 2003; Hagiu and Wright 2015). Revenue recognition means that the share recorded as revenue depends on whether the platform acts as principal or agent (International Accounting Standards Board 2014). - A single ratio does not show whether the relationship holds over time, so the thesis defines growth divergence: ``` @@ -105,6 +105,8 @@ where f_it is the fee rate and s_it the incentive rate, both measured against tr Equations (5) and (6) together divide a movement in revenue into more commerce, higher fees and lower incentives. A change in the mix of a company's businesses is a further source, read from its reports where they describe one. The split shows how a movement happened. It does not show why the company chose it, and company explanations are read as reports. +Equation (6) also shows where a rule acts. A cap on commissions acts on the fee rate, and a limit on discounts acts on the incentive rate. Chapter 6 sets Indonesia's recent rules against these parts of the take rate. + ## 3.4 Hypotheses Two hypotheses follow from the research question. H1 asks whether revenue follows the commerce carried within a platform. H2 asks where national online growth comes from, which decides whether the commerce on platforms can stand for national online commerce. Together they test the two links in Figure 3.1. Table 3.2 pairs each hypothesis, and the entry test of Section 3.5, with its main test and with the result that would count against it. From 483e55dfb168c3fdcc3142605ae92afd1fd2d4ab Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 13 Oct 2026 16:54:15 +0000 Subject: [PATCH 42/65] Chapter 4: short neutral disclaimer, follow Chapter 5's order, point to Chapter 3 Opening states the sources' condition as a fact; repeated logic now points to Chapter 3's rules and equations; sections reordered to match the results; survey-file names made consistent (also in Chapter 3's entry-test box). Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 8 ++ papers/thesis_2026-10/README.md | 2 +- papers/thesis_2026-10/ch3_framework.md | 2 +- papers/thesis_2026-10/ch4_data_methods.md | 96 +++++++++++++++-------- 4 files changed, 74 insertions(+), 34 deletions(-) diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 68f530c..2cdf8bb 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -389,3 +389,11 @@ Read as one text after Chapter 3 was accepted. The line holds (Chapter 1 names w - Chapter 1.3 now names the four further analyses; Chapter 2's intro says "online sales across the country" where "the sales" was ambiguous. - Left for the reference pass: "investors and analysts often read revenue as a sign of commerce" (2.1) has no source, and Chapter 3.6 relies on it. - Practice from now on: a cross-chapter read after each chapter is accepted. + +### Chapter 4 restructured after Chapter 3 (13 Oct 2026) + +- **Opening:** the researcher wanted the disclaimer first, short and neutral, not an apology. After two rejected drafts ("No ready-made dataset…" opened on an absence; "The evidence this thesis needs is hard to obtain" stated a feeling and read as an excuse), the chapter now opens: "The figures this thesis compares come from sources that were never designed to be read together. Platforms publish in their own formats, and the answers of individual sellers are released only under a data-use agreement. The methods in this chapter are built around these conditions." The data-use agreement is said once. "The aim is modest…" dropped so the bar is not lowered twice. Rule learned: a disclaimer states the condition as a fact and lets the method answer it. +- **Repetition of Chapter 3 removed:** the definition rule, the own-goods theory, the log change, the revenue split, fees minus incentives, the H2 identity, the comparison rules and the market-value question now point to Sections 3.1, 3.3, 3.4 and 3.6 and equations (5) to (8). Kept in Chapter 4 because Chapter 3 lacks them: the business-mix condition on the definition rule, the overlap rule for comparing sources. +- **Order follows Chapter 5:** platform side (data, wedge, H1, tracing, market values), then the survey (files and H2, entry test and extra sellers, channels, records and reach), then national figures and labels. Sections 4.1 to 4.10; only internal reference changed (4.2 to 4.3 in 4.10). +- **Checked by script:** every data number kept; dropped only citations now carried by Chapter 3 (IFRS 15, Hummels and Klenow, Trueman et al., van den Brakel). Paragraphs 98 words or fewer. +- **Survey-file naming made consistent** (a file is named by its round; the 2024 file describes 2023): the cohort check now says "the 2024 file"; the Bank Indonesia test says "the 2023 file, which describes 2022"; Chapter 3's entry-test box now says "the survey file that describes 2023". Chapter 5 still says "the 2023 survey" in places; fix in its pass. diff --git a/papers/thesis_2026-10/README.md b/papers/thesis_2026-10/README.md index 1424456..b79b2e6 100644 --- a/papers/thesis_2026-10/README.md +++ b/papers/thesis_2026-10/README.md @@ -42,6 +42,6 @@ The thesis written in full. Same title, research question, hypotheses and measur | `ch5_results.md` | 5 Results | rewritten 13 Oct as 11 sections (5.1 wedge, 5.2 H1, 5.3 tracing, 5.4 market values, 5.5 the platforms in 2023, 5.6 what sellers reported, 5.7 count and entry test, 5.8 who the extra sellers were, 5.9 returners, 5.10 channels and records, 5.11 why the national figures disagree), awaiting the researcher's reading. Rules and reach of the 2026 rules go to Chapter 6. Figures not yet placed; exhibits in `research/option_space_2026-10-01/exhibits/` | | `ch3_framework.md` | 3 Framework and Hypotheses | rewritten and accepted for now 13 Oct: opening declaration and map of the three gap questions, 3.1 terms and rules for comparison, 3.2 three measures (equations 1 to 4), 3.3 tracing a movement (equations 5 and 6), 3.4 hypotheses (equation 7), 3.5 the entry test, 3.6 further analyses (Table 3.3, market-value equation 8). Figure 3.1 still to be drawn; a cold read is still due (see `EDIT_HISTORY.md`) | | `ch2_literature.md` | 2 Literature Review | first full draft 12 Oct (evening), awaiting the researcher's reading: on the proposal's four sections (2.1 pricing and revenue recognition, 2.2 measuring the online economy, 2.3 digital records and third-party information, 2.4 the gap), with the survey-count strand added (breaks, recall, exit, margins). Citations are author-year, every one traced to `REFERENCES_WORKING.md`; the bibliography itself is not yet built | -| `ch4_data_methods.md` | 4 Data and Methods | first full draft 12 Oct (night), awaiting the researcher's reading: platform data and the two groups (Table 4.1), the H1 test, tracing revenue movements, the BPS files and the entry test (Table 4.2), channels, records and reach, the comparison with national and payment figures (Table 4.3), market values, traceability. The appendix it points to (platform list, seven traced windows, registered tests) is not built | +| `ch4_data_methods.md` | 4 Data and Methods | restructured 13 Oct to follow Chapter 5's order and to point to Chapter 3 instead of repeating it: short opening disclaimer, 4.1 platform data, 4.2 the wedge in Indonesia, 4.3 testing H1, 4.4 tracing, 4.5 market values, 4.6 survey files and H2, 4.7 entry test and extra sellers, 4.8 channels, records and reach, 4.9 comparing national figures, 4.10 labels and licensed data | | `appendix_notes.md` | Appendices A to C | working draft 13 Oct: platform lists, the seven windows, wedge by platform-year, every check on the test of H1, registered tests with outcomes; the detail that Chapters 4 and 5 point to | | ch6, ch7 | | not started | diff --git a/papers/thesis_2026-10/ch3_framework.md b/papers/thesis_2026-10/ch3_framework.md index 47107ae..c5e5ced 100644 --- a/papers/thesis_2026-10/ch3_framework.md +++ b/papers/thesis_2026-10/ch3_framework.md @@ -151,7 +151,7 @@ H2 rests on BPS's count of businesses selling online, and a rise in that count c The published figures cannot tell the two apart. The survey can, because it asks every seller in which year it began selling online. The thesis checks what the count contains with a test written down before the survey files were opened. -> **Entry test.** In the survey file for 2023, at least 21.5 percent of online sellers must report that they began selling online in 2023. +> **Entry test.** In the survey file that describes 2023, at least 21.5 percent of online sellers must report that they began selling online in 2023. The 21.5 percent threshold comes from arithmetic. The published count of online sellers rose by 27.4 percent in 2023. Suppose every extra seller was new and no seller stopped. Then for every 100 sellers a year earlier there were 127.4, and 27.4 of those were new. New sellers would make up 21.5 percent of all sellers, which is 27.4 divided by 127.4. diff --git a/papers/thesis_2026-10/ch4_data_methods.md b/papers/thesis_2026-10/ch4_data_methods.md index 95daf21..cdd07d0 100644 --- a/papers/thesis_2026-10/ch4_data_methods.md +++ b/papers/thesis_2026-10/ch4_data_methods.md @@ -1,16 +1,20 @@ # Chapter 4. Data and Methods -No ready-made dataset records both what a platform keeps and what passes through it, so this thesis builds one. Each platform reports in its own format. The thesis reads revenue and transaction value from annual reports, audited statements and earnings releases, and the extraction tables keep the source file and the quoted passage beside every value. It keeps each company's own definition and compares a platform only with itself from one year to the next, so that differences in definition cancel. Where a platform does not report Indonesia alone, as with Shopee and Grab, the thesis states what it derives or borrows and labels it. +The figures this thesis compares come from sources that were never designed to be read together. Platforms publish in their own formats, and the answers of individual sellers are released only under a data-use agreement. The methods in this chapter are built around these conditions. -The national side rests on a survey that BPS, the national statistics office, runs among online sellers. BPS publishes a few totals, and the answers of individual sellers behind them are released only under a data-use agreement. The thesis obtained those answers for three survey rounds, which show when each seller began selling online and how much it sold. The central bank's own figure has no published method, so it serves only as a point of comparison. +On the platform side, the thesis reads revenue and transaction value from annual reports, audited statements and earnings releases, and the extraction tables keep the source file and the quoted passage beside every value. Where a platform does not report Indonesia alone, as with Shopee and Grab, the thesis states what it derives or borrows and labels it. -Setting the two sides against each other for one country is what makes the tests possible. Indonesia allows it. Five platform series report revenue and transaction value in the same year, three of them report what they pay out in incentives, and the survey asks every online seller when it began. The tests are built to ask only what these sources can answer, and each limit is stated where it applies. The aim is modest, and what the tests show adds to what the published figures give. +The national side rests on a survey that BPS, the national statistics office, runs among online sellers. BPS publishes a few totals, and the thesis obtained the answers behind them for three survey rounds, which show when each seller began selling online and how much it sold. The central bank's own figure has no published method, so it serves only as a point of comparison. -The chapter describes the evidence on each side and the tests that use it. It starts with the platforms, where H1 is tested and revenue movements are traced. It then turns to the survey, where H2 and the entry test are examined. It ends with the comparisons and market values that bring the two sides together. +Setting the two sides against each other for one country is what makes the tests possible. Indonesia allows it. Five platform series report revenue and transaction value in the same year, three of them report what they pay out in incentives, and the survey asks every online seller when it began. The tests are built to ask only what these sources can answer, and each limit is stated where it applies. -## 4.1 Choosing the platforms +The chapter describes the evidence on each side and the tests that use it. It starts with the platforms: the data, the wedge in Indonesia, the test of H1, the tracing of large moves and market values. It then turns to the survey: H2, the entry test, and the channels and records of sellers. It ends with the comparison of national figures and with how every number is labelled and traced. -The basic observation is one platform in one year, which this thesis calls a platform-year. Revenue comes from the company's financial statements and transaction value from its annual report or earnings release. Both refer to the same year and, as closely as the filings allow, the same business. Companies define transaction value in different ways, and some include shipping charges, digital goods or travel. The thesis keeps each company's own definition and records it. A take rate is built inside one company, so the definition cancels out of its yearly changes, as long as the company keeps the definition and the mix of its businesses. +## 4.1 The platform data + +The basic observation is one platform in one year, which this thesis calls a platform-year. Revenue comes from the company's financial statements and transaction value from its annual report or earnings release. Both refer to the same year and, as closely as the filings allow, the same business. + +Companies define transaction value in different ways, and some include shipping charges, digital goods or travel. The thesis keeps each company's own definition and records it. Under the second rule of Section 3.1, the definition cancels out of a take rate's yearly changes, as long as the company keeps both the definition and the mix of its businesses. Two groups of platforms are compared. The Indonesian and regional group has five platforms, listed in Table 4.1. Three are Indonesian marketplaces, and two are the on-demand businesses of Grab and GoTo, which cover rides and deliveries. The foreign group has 27 listed platforms in marketplaces, travel and ticketing, freelance work, resale and food delivery. Eight were chosen first, and 19 were added to widen the comparison. Ten more were examined and left out, six because they sell mostly their own goods and four because they do not disclose transaction value. @@ -28,33 +32,51 @@ Table 4.1 states the scope of each series because the series differ, and only To Platform-years are compared in pairs, and three rules keep the pairs comparable. Two consecutive platform-years form a pair, and nothing else does. A missing year stays missing and is never filled in. Annual and quarterly pairs are never pooled. A take rate is a ratio of two amounts in one currency, so the tests need no exchange rate. Amounts in running text are in US dollars at the World Bank's average rate for the year. -Platforms that sell goods themselves need a separate rule. In the foreign group, a company that sells goods itself reports the whole sale as revenue, while an agent reports only its commission (International Accounting Standards Board 2014). The take rate of a seller of goods then says little about a platform, and Hagiu and Wright (2015) draw the same line between a marketplace and a reseller. For the 19 platforms added, the thesis drops any year in which own goods exceed half of revenue. For the eight chosen first, it keeps a pair only when the company's reporting scope did not change between the two years. Zalando is one of the eight and sells part of what it carries, so Appendix B repeats the test without it. One added pair, Ozon from 2021 to 2022, ends in a kept year but starts in a dropped one. Appendix B repeats the test without Ozon as well. The result is 15 pairs for the Indonesian and regional group and 158 for the foreign group. +The third rule of Section 3.1, which keeps out companies that mainly sell their own goods, is applied in two ways. For the 19 platforms added, the thesis drops any year in which own goods exceed half of revenue. For the eight chosen first, it keeps a pair only when the company's reporting scope did not change between the two years. + +Two platforms sit close to that line. Zalando is one of the eight and sells part of what it carries, so Appendix B repeats the test without it. One added pair, Ozon from 2021 to 2022, ends in a kept year but starts in a dropped one. Appendix B repeats the test without Ozon as well. The result is 15 pairs for the Indonesian and regional group and 158 for the foreign group. + +## 4.2 Measuring the wedge in Indonesia + +The first objective, the size of the invisible wedge, needs amounts for Indonesia alone. The thesis sums the transaction value and revenue of three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. The wedge and the ecosystem ratio are then computed from the sums. -## 4.2 Testing H1 and tracing the moves +Each platform reaches the sum by its own route. Tokopedia reports both amounts for its segment. Shopee's transaction value is an outside estimate by Momentum Works. Its revenue assumes a take rate of 10 percent, which the thesis varies from 9 to 11 percent. Grab reports its Indonesian revenue, and the thesis derives its Indonesian transaction value from Grab's company-wide ratio of the two amounts. The thesis drops each platform in turn, and it repeats the exercise for every platform-year between 2020 and 2025 that the filings allow. -H1 says that revenue and transaction value grow in proportion, which means that the take rate stays close to steady. The test asks how far each platform's take rate moves from one year to the next. For each pair of years it takes the change in the logarithm of the take rate. A log change treats a rise by some factor and a fall by the same factor as equal in size. It equals the log growth of revenue minus the log growth of transaction value. This is growth divergence measured in logs, and it does not depend on how fast transaction value grew. Tokopedia's log change in 2023 is 0.52, which is a rise by a factor of 1.68, or 68 percent. +## 4.3 Testing H1 -A move in either direction counts against H1, so the test uses the size of each yearly change in the take rate. Each platform gets one number, the median size of its yearly changes, because the pairs of one platform are not independent of one another. The test compares five numbers for the Indonesian and regional group with 27 for the foreign group. It asks whether the first group ranks higher than chance would give, using a one-sided Mann–Whitney test (Mann and Whitney 1947). It repeats the question by shuffling the group labels 9,999 times. The p-value is the chance of a result at least this lopsided if the groups did not differ. +The test of H1 asks how far each platform's take rate moves from one year to the next. For each pair of years it takes the change in the logarithm of the take rate, the take-rate term of equation (5). This change is growth divergence measured in logs, and it does not depend on how fast transaction value grew. Tokopedia's log change in 2023 is 0.52, which is a rise by a factor of 1.68, or 68 percent. + +A move in either direction counts against H1, so the test uses the size of each yearly change in the take rate. Each platform gets one number, the median size of its yearly changes, because the pairs of one platform are not independent of one another. + +The test compares five numbers for the Indonesian and regional group with 27 for the foreign group. It asks whether the first group ranks higher than chance would give, using a one-sided Mann–Whitney test (Mann and Whitney 1947). It repeats the question by shuffling the group labels 9,999 times. The p-value is the chance of a result at least this lopsided if the groups did not differ. Appendix B lists the checks on this test. They drop each platform in turn and treat Tokopedia and GoTo as one company. They repeat the test with growth divergence in points as Chapter 3 defines it. They compare the change in the take rate in percentage points, to see whether a thin take rate explains the result. They narrow the test to the three marketplaces, correlate revenue growth with transaction value growth, and repeat the test with quarterly pairs. -The second objective, to reconcile large movements in revenue with disclosed changes in fees, discounts and scope, is answered in two steps. The first divides each movement into volume and take rate. In log terms, revenue growth equals transaction value growth plus the change in the take rate. The take-rate share is the size of the take-rate change divided by the sum of the sizes of both changes. The same split is made for the wedge, which equals transaction value minus revenue. +## 4.4 Tracing the moves + +The second objective, to reconcile large movements in revenue with disclosed changes in fees, discounts and scope, is answered in two steps. The first divides each movement into volume and take rate with equation (5). The take-rate share is the size of the take-rate change divided by the sum of the sizes of both changes. The same split is made for the wedge. + +The second step splits the take-rate change with equation (6). Wherever a company discloses both its gross fees and its incentives, the thesis splits the growth of revenue over a window into three parts: more commerce, a change in the fee rate and a change in the incentive rate. A change in the mix of businesses is recorded from the company's reports. + +The sources differ by company. Tokopedia's audited segment note gives gross revenue, incentives and net revenue, and the thesis checks them against the audited consolidated statements. For Tokopedia the thesis also reports the change line by line in rupiah, the usual way to read accounts, and says where the two views differ. -A take-rate change has three possible causes, and the thesis separates them. A platform's take rate equals the rate of its gross fees minus the rate of its incentives, each measured against transaction value. Accounting rules normally deduct such incentives from revenue (International Accounting Standards Board 2014). So a rise comes from higher fees, lower incentives or a change in the mix of businesses. Wherever a company discloses both, the thesis splits the growth of revenue over a window into three parts. They are more commerce, a change in the fee rate and a change in the incentive rate. +Grab is read from its annual filings and GoTo from its audited statements. Blibli does not report incentives, so its split rests on a proxy built from its releases. Seven windows are traced, and Appendix A lists them. The thesis also records where accounting treatment or reporting scope matters, such as the deduction of incentives from revenue and the travel business inside Blibli's retail segment. Company explanations are read as reports and are not tested. -The sources differ by company. Tokopedia's audited segment note gives gross revenue, incentives and net revenue, and the thesis checks them against the audited consolidated statements. For Tokopedia the thesis also reports the change line by line in rupiah, the usual way to read accounts, and says where the two views differ. Grab is read from its annual filings and GoTo from its audited statements. Blibli does not report incentives, so its split rests on a proxy built from its releases. Seven windows are traced, and Appendix A lists them. The thesis also records where accounting treatment or reporting scope matters, such as the deduction of incentives from revenue and the travel business inside Blibli's retail segment. Company explanations are read as reports and are not tested. +## 4.5 Market values -## 4.3 Measuring the wedge in Indonesia +The market-value analysis of Section 3.6 uses year-end market values from the financial data service LSEG, under the university licence. The main sample has 28 listed platforms and 138 platform-years, made up of Bukalapak and 27 platforms abroad, and it uses whole-company figures only. It is not the list of 27 used for H1. It adds Airbnb, Booking, DoorDash, Lyft and Uber, and it lacks Despegar, Mercado Libre, Ozon, Rakuten and Sea. -The first objective, the size of the invisible wedge, needs amounts for Indonesia alone. It sums the transaction value and revenue of three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. Tokopedia reports both amounts for its segment. Shopee's transaction value is an outside estimate by Momentum Works. Its revenue assumes a take rate of 10 percent, which the thesis varies from 9 to 11 percent. Grab reports its Indonesian revenue, and the thesis derives its Indonesian transaction value from Grab's company-wide ratio of the two amounts. The wedge and the ecosystem ratio are then computed from the sums. The thesis drops each platform in turn, and it repeats the exercise for every platform-year between 2020 and 2025 that the filings allow. +Equation (8) is estimated with a fixed effect for each year, which removes what all platforms share in that year. Standard errors are clustered by company, which allows for the repeated observations of one company. Each prediction names a direction, so both tests are one-sided. The test was written down before the market values were pulled. The result is an association, and market value also changes when a company issues shares, so the thesis does not call it a stock return. A second run adds three segment series and three more whole companies as a robustness check. -## 4.4 Evidence from the sellers +## 4.6 The survey files and H2 -The national evidence comes from the survey of online sellers that BPS runs. BPS publishes the number of businesses selling online, their total online sales and the shares by channel (BPS 2025). The survey rounds are named for the survey year, but their questions refer to the year before. The thesis names a file by its round and a year by the year of activity, so the file for the 2024 round describes 2023. Each round lists businesses in sampled areas and updates its sampling frame, so a change in who is found can change the count (van den Brakel, Smith and Compton 2008). +The national evidence comes from the survey of online sellers that BPS runs. BPS publishes the number of businesses selling online, their total online sales and the shares by channel (BPS 2025). The survey rounds are named for the survey year, but their questions refer to the year before. The thesis names a file by its round and a year by the year of activity, so the file for the 2024 round describes 2023. Each round lists businesses in sampled areas and updates its sampling frame, so a change in who is found can change the count. -H2 uses the published figures. National online sales value equals the number of businesses times the sales per business. In log terms, growth in value is growth in the number of businesses plus growth in sales per business. Trade economists split the growth of exports in the same way, into an extensive and an intensive margin (Hummels and Klenow 2005). H2 holds if the first part is more than half of the total. BPS prints two counts of businesses for 2023, so the thesis computes the split with both and reports both. +H2 uses the published figures and the split of equation (7). BPS prints two counts of businesses for 2023, so the thesis computes the split with both and reports both. -The published figures cannot tell a new seller from one counted for the first time, but the survey files can. Table 4.2 lists them. Each row is a business that sold online in the activity year. Its survey weight shows how many businesses it represents, so the weights add up to the national count. Two checks come before any test. The weighted total of each file must fall within 1 percent of the BPS count. The 2024 file must also reproduce two published shares of sellers who keep complete financial statements, 28.63 and 12.25 percent. All three files pass. The files carry no business identifier across rounds and no area variable below the province. So the rounds compare groups of sellers and not the same sellers over time. +The published figures cannot tell a new seller from one counted for the first time, but the survey files can. Table 4.2 lists them. Each row is a business that sold online in the activity year. Its survey weight shows how many businesses it represents, so the weights add up to the national count. + +Two checks come before any test. The weighted total of each file must fall within 1 percent of the BPS count. The 2024 file must also reproduce two published shares of sellers who keep complete financial statements, 28.63 and 12.25 percent. All three files pass. The files carry no business identifier across rounds and no area variable below the province. So the rounds compare groups of sellers and not the same sellers over time. **Table 4.2. The three BPS survey files** @@ -66,21 +88,35 @@ The published figures cannot tell a new seller from one counted for the first ti *Implied by the published count for 2023 and its published growth of 27.4 percent. +## 4.7 The entry test and the extra sellers + The entry test asks whether at least 21.5 percent of the 2023 sellers in the 2024 file began selling online in 2023, a threshold that Chapter 3 derives. If the weights had matched BPS's other count for 2023, the threshold would have been 23.9 percent, but they did not. The test was written down and dated in the project's code archive, called the repository, before the files were opened. -A failed entry test raises a second question: how much of the rise does first-time entry not explain? The sellers behind that part are called the extra sellers. A cohort is the group of sellers that began selling online by a given year. If two surveys count the same population, a cohort can only shrink between them, because sellers stop and no new seller can join it. The thesis compares the 2023 survey's count of sellers who began before 2023 with the whole 2022 count. It does so twice. The first time it assumes that no seller stopped. The second time it assumes that sellers stopped at about 5 percent a year, the rate at which one cohort shrank between the 2020 and 2022 surveys. +A failed entry test raises a second question: how much of the rise does first-time entry not explain? The sellers behind that part are called the extra sellers. A cohort is the group of sellers that began selling online by a given year. If two surveys count the same population, a cohort can only shrink between them, because sellers stop and no new seller can join it. + +The cohort check compares the 2024 file's count of sellers who began before 2023 with the whole 2022 count. It does so twice. The first time it assumes that no seller stopped. The second time it assumes that sellers stopped at about 5 percent a year, the rate at which one cohort shrank between the 2020 and 2022 surveys. + +Further checks ask where the extra sellers sit. They look across provinces and channels, using rank correlations, and at sellers' own reports of their sales. They use the sampling error that BPS publishes. They profile who the extra sellers are, with each group's count in 2023 compared with what its own yearly change in 2020 to 2022 would give. They look at part-year selling and test a scenario of returners. + +The cohort check and the further checks were designed after the entry test failed, so Chapter 5 labels them exploratory. The exception is the median change in sellers' sales, which was registered. The files cannot rule out returners, because a seller who stopped and returned keeps an old start year. -Further checks ask where the extra sellers sit. They look across provinces and channels, using rank correlations, and at sellers' own reports of their sales. They use the sampling error that BPS publishes. They profile who the extra sellers are, with each group's count in 2023 compared with what its own yearly change in 2020 to 2022 would give. They look at part-year selling and test a scenario of returners. The cohort check and these further checks were designed after the entry test failed, so Chapter 5 labels them exploratory. The exception is the median change in sellers' sales, which was registered. The files cannot rule out returners, because a seller who stopped and returned keeps an old start year. +## 4.8 Channels, records and reach -Three analyses beyond the entry test also use the survey files. One asks which channels sellers use and how sales divide between them. The published tables split the 2023 to 2024 rise in value between marketplaces and other channels, and BPS counts food and ride apps in its marketplace category. Value by channel rests on revenue brackets, which only the 2021 and 2023 files give. The thesis assigns each bracket a low, a middle and a high amount. The amounts were fixed before the file was opened. A validation rule applies: the middle scenario's 2022 total must lie between half and double BPS's published total of about US$53 billion. The 2023 questionnaire asks for monthly revenue but the bracket limits are annual size classes, so both readings are computed and the one that passes is kept. If the lower brackets alone already exceed the total, value shares are reported as ranges. +Three analyses beyond the entry test also use the survey files. One asks which channels sellers use and how sales divide between them. The published tables split the 2023 to 2024 rise in value between marketplaces and other channels, and BPS counts food and ride apps in its marketplace category. -Whether a seller keeps complete financial statements is the subject of a second analysis. It bears on what platform records can add, because a seller with no statements leaves fewer formal records. A third builds on the first two and is developed in Chapter 6. It counts the marketplace sellers whose revenue is above a bracket line and expresses the count as a share of all online sellers. At the 2022 rate, BPS's brackets divide at about US$20,000 a year. The marketplace tax exempts individual sellers up to about US$34,000. So the count above BPS's line is an upper limit on the sellers the tax can reach. +Value by channel rests on revenue brackets, which only the 2021 and 2023 files give. The thesis assigns each bracket a low, a middle and a high amount. The amounts were fixed before the file was opened. A validation rule applies: the middle scenario's 2022 total must lie between half and double BPS's published total of about US$53 billion. -World Bank Enterprise Surveys add a check on records. They cover unregistered firms in six Indonesian cities in 2023, and formal firms with five or more employees in seven countries. +The 2023 questionnaire asks for monthly revenue but the bracket limits are annual size classes, so both readings are computed and the one that passes is kept. If the lower brackets alone already exceed the total, value shares are reported as ranges. -## 4.5 Bringing the two sides together +Whether a seller keeps complete financial statements is the subject of a second analysis. It bears on what platform records can add, because a seller with no statements leaves fewer formal records. World Bank Enterprise Surveys add a check on records. They cover unregistered firms in six Indonesian cities in 2023, and formal firms with five or more employees in seven countries. -The third objective, to compare the platform evidence with national and payment figures, rests on the sources that Table 4.3 lists, with what each one counts. The sources differ in what they cover, so the comparison follows three rules. It compares growth rates across sources and keeps each source in its own unit. It compares levels only after reconciling the definitions. And it compares sources most closely where their coverage overlaps. The 2023 file lets the thesis separate the part of BPS's marketplace sales that went to final consumers. It compares that part with Bank Indonesia's figure for the same year, in a test written down before the file was opened. The thesis also sets quarterly platform growth beside quarterly household spending growth. It treats payment figures as context, because they show how people pay and not what they buy online. +A third analysis builds on the first two and is developed in Chapter 6. It counts the marketplace sellers whose revenue is above a bracket line and expresses the count as a share of all online sellers. At the 2022 rate, BPS's brackets divide at about US$20,000 a year. The marketplace tax exempts individual sellers up to about US$34,000. So the count above BPS's line is an upper limit on the sellers the tax can reach. + +## 4.9 Comparing national figures + +The third objective, to compare the platform evidence with national and payment figures, rests on the sources that Table 4.3 lists, with what each one counts. Beyond the rules of Section 3.6, the comparison is closest where the coverage of two sources overlaps. Payment figures serve as context, because they show how people pay and not what they buy online. + +The 2023 file, which describes 2022, lets the thesis separate the part of BPS's marketplace sales that went to final consumers. It compares that part with Bank Indonesia's figure for the same year, in a test written down before the file was opened. The thesis also sets quarterly platform growth beside quarterly household spending growth. **Table 4.3. Sources compared in Objective 3** @@ -94,14 +130,10 @@ The third objective, to compare the platform evidence with national and payment | Payment figures | Payments made through systems such as QRIS, the national QR-code system | Payment records, in person and online | | Adjacent figures | Parcels shipped in Southeast Asia, the value-added tax collected on foreign digital services (Ministry of Finance 2025), and the number of merchants a company reports | Used as context only, since none measures Indonesian e-commerce | -One last analysis asks whether markets value a platform's growth in transaction value differently from growth in its take rate. The data are year-end market values from the financial data service LSEG, under the university licence. The main sample has 28 listed platforms and 138 platform-years, made up of Bukalapak and 27 platforms abroad, and it uses whole-company figures only. It is not the list of 27 used for H1. It adds Airbnb, Booking, DoorDash, Lyft and Uber, and it lacks Despegar, Mercado Libre, Ozon, Rakuten and Sea. - -A regression relates the yearly change in log market value to the changes in log transaction value and log take rate. It adds a fixed effect for each year, which removes what all platforms share in that year. It clusters standard errors by company, which allows for the repeated observations of one company. Each prediction names a direction, so both tests are one-sided. The question follows early work on internet companies, which found that markets price web-traffic measures beyond reported revenue (Trueman, Wong and Zhang 2000). The result is an association, and market value also changes when a company issues shares, so the thesis does not call it a stock return. A second run adds three segment series and three more whole companies as a robustness check. The test was written down before the market values were pulled. - -## 4.6 Labels, traceability and licensed data +## 4.10 Labels, traceability and licensed data Every number in the thesis follows one path: source document, extract, analytic row, result. Each extract keeps the company's currency and units, and each value carries a label: direct company disclosure, direct segment disclosure, external estimate, derived value, conversion or scenario. The labels are never mixed in one sample. A restatement or a conflict between two reports is reported and never overwritten. A single script rebuilds every table that depends only on committed files, and 272 automatic checks confirm that each recorded result still matches its table. -The thesis also separates tests by when they were written. A registered test was written down, dated and stored in the repository before the data were opened. This applies to the entry test, the scenario rules for value, the comparison with Bank Indonesia and the market-value test, among others. Appendix C lists them with their dates. An exploratory test was designed after some results were seen. The test with the larger groups in Section 4.2 belongs here, because it was set up after a first test on the three marketplaces. A descriptive result reports numbers without a test. +The thesis also separates tests by when they were written. A registered test was written down, dated and stored in the repository before the data were opened. This applies to the entry test, the scenario rules for value, the comparison with Bank Indonesia and the market-value test, among others. Appendix C lists them with their dates. An exploratory test was designed after some results were seen. The test with the larger groups in Section 4.3 belongs here, because it was set up after a first test on the three marketplaces. A descriptive result reports numbers without a test. Three sources are licensed and are not in the repository: the BPS survey files, the LSEG market values and the World Bank survey files. The repository records the size and checksum of each BPS and LSEG file and of the World Bank cross-country query, so anyone who obtains the same files can check them. It holds the code that reads them and the national totals that the code produces, and no business-level record. BPS is cited as the source of every survey figure, in line with the data-use agreement. Chapter 7 states what these data cannot show. From 4da12c80b35620a2f23044d4cd3d19f9c555dddd Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 13 Oct 2026 19:16:06 +0000 Subject: [PATCH 43/65] Chapter 5: save plan, polished sample and direction count for review Sample of the opening, Table 5.1, 5.1 and 5.2 kept as a draft (chapter file unchanged); new script counts the direction of take-rate moves; edit history records the plan and a correction to the narrower H1 test. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 8 ++ .../drafts/ch5_sample_2026-10-13.md | 78 +++++++++++++++++++ .../h1_direction_count.py | 25 ++++++ 3 files changed, 111 insertions(+) create mode 100644 papers/thesis_2026-10/drafts/ch5_sample_2026-10-13.md create mode 100644 research/option_space_2026-10-01/h1_direction_count.py diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 2cdf8bb..adc90f2 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -397,3 +397,11 @@ Read as one text after Chapter 3 was accepted. The line holds (Chapter 1 names w - **Order follows Chapter 5:** platform side (data, wedge, H1, tracing, market values), then the survey (files and H2, entry test and extra sellers, channels, records and reach), then national figures and labels. Sections 4.1 to 4.10; only internal reference changed (4.2 to 4.3 in 4.10). - **Checked by script:** every data number kept; dropped only citations now carried by Chapter 3 (IFRS 15, Hummels and Klenow, Trueman et al., van den Brakel). Paragraphs 98 words or fewer. - **Survey-file naming made consistent** (a file is named by its round; the 2024 file describes 2023): the cohort check now says "the 2024 file"; the Bank Indonesia test says "the 2023 file, which describes 2022"; Chapter 3's entry-test box now says "the survey file that describes 2023". Chapter 5 still says "the 2023 survey" in places; fix in its pass. + +### Chapter 5: plan and a sample, not yet applied (13 Oct 2026) + +- **Plan agreed:** the proposal is a floor for Chapters 4 and 5, not a spine (the thesis evidence is much larger). Chapter 5 follows Figure 3.1's two links: inner link 5.1 to 5.4 (wedge, H1, what moved the take rate, markets), outer link 5.5 to 5.10 (2023 platforms and sellers, H2 and the entry test, what the rise contains, who the extra sellers were, channels and records, why the national figures disagree), ending back on Chapter 1's 2023 scene. Each section opens with its answer and ends on what stays open (no rhetorical questions). Table 5.1 lists every main result with its label and checks. Rounded numbers in prose, exact values in tables. Two halves named in the opening, no part headings; short two-sentence close; GDP-scale sentence (2.92 percent, ledger C39) left out until the researcher decides. +- **Sample written and polished in four passes:** opening, Table 5.1, 5.1 and 5.2, in `drafts/ch5_sample_2026-10-13.md`. Awaiting the researcher's reading for tone against Chapters 1 to 4. +- **Correction found:** the current 5.2 reports the narrower test planned in advance only as the platform shuffle (p = 0.06, "comes close without passing"). The Mann–Whitney test on one median per platform passes (p = 0.024; ledger C47, `build_numbers.py`). The sample reports both; Appendix B needs the 0.024 row. +- **New count:** `research/option_space_2026-10-01/h1_direction_count.py`: take rate rose in 13 of 15 Indonesian and regional pairs, opposite directions in 2; abroad it rose in 103 of 158 pairs. Size, not direction, separates the groups. +- **Still to fix in Chapter 5:** "about 750 thousand" new sellers in 2024 should read "at least 754 thousand" (FINDINGS); survey years named by activity year; section references 4.6 to 4.10 and 4.4 to 4.8; four rhetorical questions; repeats of Chapters 3 and 4; dense paragraphs in 5.4 to 5.9. diff --git a/papers/thesis_2026-10/drafts/ch5_sample_2026-10-13.md b/papers/thesis_2026-10/drafts/ch5_sample_2026-10-13.md new file mode 100644 index 0000000..58fe0dd --- /dev/null +++ b/papers/thesis_2026-10/drafts/ch5_sample_2026-10-13.md @@ -0,0 +1,78 @@ +# Chapter 5. Results + +The results follow the two links of Figure 3.1. On the inner link, revenue showed a small and unsteady part of the commerce that platforms carried. Platforms changed how much of each sale they kept, and revenue moved with those choices (Sections 5.1 to 5.4). On the outer link, the national count of online sellers rose by 27 percent in 2023. About half of that rise did not come from sellers entering for the first time (Sections 5.5 to 5.10). + +Every result in this chapter carries one of the three labels of Section 4.10: registered, exploratory or descriptive. Table 5.1 gives each main result with its label, the checks behind it and the section that reports it. + +**Table 5.1. Main results and the evidence behind them** + +| Result | Label | Main checks | Section | +|---|---|---|---| +| *Inner link: revenue and platform commerce* | | | | +| Revenue was about 7 percent of what three major platforms carried in 2023; the wedge was about US$40 billion | Descriptive | Shopee's take rate varied; each platform dropped in turn | 5.1 | +| Take rates moved about three times as much in Indonesia and the region as abroad, so H1 is rejected | Exploratory; the narrower test planned in advance points the same way | Each platform dropped in turn; Ozon and Zalando dropped; change measured in points; quarterly pairs do not reject | 5.2 | +| The thin share kept by Indonesian and regional platforms explains much of that difference | Exploratory | Changes in percentage points compared across groups | 5.2 | +| Pricing, not more commerce, moved revenue in the traced windows | Descriptive | Two views of Tokopedia; seven windows | 5.3 | +| Market value moves with transaction value | Registered; holds | Extreme values trimmed; a second run | 5.4 | +| Markets value growth from a higher take rate less | Registered; does not hold in the main sample | Holds only in a second run that mixes segment figures | 5.4 | +| *Outer link: platform commerce and all online commerce* | | | | +| Buying through platforms, and sellers' own sales, did not boom in 2023 | Descriptive; registered for sellers' sales, holds | Six platform measures against household spending | 5.5 | +| More businesses account for most national growth, so H2 holds | Descriptive | Both BPS counts for 2023; two periods | 5.6 | +| Too few 2023 sellers were new to produce the rise in the count | Registered; the entry test fails | Recall error would favour passing | 5.6 | +| About half of the rise is not explained by first-time entry | Exploratory | With and without exits; cohorts; provinces; sampling error | 5.7 | +| The extra count leaned toward long-running businesses; returning sellers cannot be ruled out | Exploratory | Part-year selling | 5.8 | +| Most online selling happens outside marketplaces, and records are thinner there | Registered and descriptive | Four survey years; World Bank surveys | 5.9 | +| National figures agree only where they cover the same sellers | Registered, with a mixed outcome, and descriptive | Three value scenarios | 5.10 | + +## 5.1 How much revenue leaves out + +Revenue showed only a small part of what Indonesia's major platforms carried. In 2023, Tokopedia, Shopee and Grab together carried about US$43 billion of transaction value and kept about US$3 billion as revenue. Revenue was about 7 percent of that commerce, and the invisible wedge was about US$40 billion. The ecosystem ratio was about 13, so almost 13 dollars of commerce passed through the platforms for every dollar of revenue. Table 5.2 shows each platform. + +**Table 5.2. The invisible wedge of three platforms, 2023 (US$ billion)** + +| Platform | Transaction value | Revenue | Wedge | Ecosystem ratio | How it is labelled | +|---|---|---|---|---|---| +| Tokopedia | 16.33 | 0.41 | 15.93 | 39.3 | Both direct segment disclosure | +| Shopee, Indonesia | 21.52 | 2.15 | 19.37 | 9.0 | Transaction value is an outside estimate (Momentum Works); revenue is derived at an assumed take rate of 10 percent | +| Grab, Indonesia | 5.38 | 0.60 | 4.78 | 7.9 | Revenue is a direct country disclosure; transaction value is derived from Grab's company-wide ratio | +| Total | 43.23 | 3.16 | 40.07 | 12.7 | Sum of three platforms, not a national total | + +*Note:* Rounded amounts may not add. With Shopee's take rate at 9 or 11 percent, the total wedge is US$39.9 or US$40.3 billion; at Sea's company-wide rate of 11.5 percent, it is US$39.8 billion. Source: company filings; Momentum Works (2024); author's calculations. + +The ratios differ because the amounts were obtained in different ways. Tokopedia reports both amounts, and its ratio is about 39. Shopee's ratio of 9 follows from the assumed take rate, and Grab's ratio of about 8 is its company-wide ratio. Neither is separate evidence for Indonesia. Blibli and Bukalapak also report both amounts, with ratios of about 43 and 36, close to Tokopedia's. They are left out of the sum because Blibli's figure includes online travel and Bukalapak reports for its whole group. + +The size of the wedge does not depend on the assumed rates. A take rate for Shopee between 9 and 11 percent keeps the total near US$40 billion. Dropping any one platform still leaves more than US$20 billion. For platforms that report both amounts, the wedge was 97 to 99 percent of transaction value in every year. It moved almost one for one with the commerce. Appendix A gives the wedge by platform and year. + +Platform commerce is itself only part of online commerce. In BPS's survey, marketplaces carry about a fifth of online sales value (Section 5.9), so platform revenue shows a smaller part still. A small share can still be a fair guide to the commerce, as long as it keeps its size from year to year. + +## 5.2 Testing H1 + +Revenue did not keep pace with transaction value at the Indonesian and regional platforms, and the thesis rejects H1. At the typical platform in this group, the take rate moved by about 20 percent a year, up or down. At the typical foreign platform it moved by about 6 percent, so the moves were about three times as large. Table 5.3 gives the test and its checks. + +**Table 5.3. The test of H1** + +| Test | Method | Indonesian and regional group | Foreign group | p-value | +|---|---|---|---|---| +| Yearly change in log take rate, 5 platforms against 27 | Mann–Whitney | 0.20 | 0.06 | 0.004 | +| The same | Shuffle | 0.20 | 0.06 | 0.022 | +| Growth divergence in points, 5 platforms against 26 | Mann–Whitney | 27.0 | 6.5 | 0.005 | +| Drop each platform in turn, or treat Tokopedia and GoTo as one company | Mann–Whitney | 0.17 to 0.23 | 0.06 | 0.003 to 0.016 | +| Quarterly pairs, 3 platforms against 8 | Mann–Whitney | 0.071 | 0.049 | 0.19 | + +*Note:* Group values are medians of each platform's median yearly change. Appendix B lists every check, including the tests without Ozon and Zalando. + +The main test compares the five platforms with 27 abroad, one median per platform. Both methods of Section 4.3 reject equal moves, with p = 0.004 and p = 0.022. No single platform carries the result. Dropping any one, or treating Tokopedia and GoTo as one company, keeps the Mann–Whitney p-value at 0.016 or below. The main test was set up after a first, narrower test, so it is exploratory. + +The test planned in advance was narrower: the three Indonesian marketplaces against the eight foreign platforms chosen first. It points the same way. With one median per platform, the Mann–Whitney test rejects equal moves (p = 0.024). The shuffle of platform labels comes close without passing (p = 0.06). Three platforms give a test little power. In that sample, revenue growth and transaction value growth had a correlation of 0.10 in Indonesia and 0.98 abroad, excluding Sea. + +The direction of the moves is less telling than their size. Revenue grew faster than transaction value in 13 of the 15 Indonesian and regional pairs. In two of them, revenue and transaction value moved in opposite directions. Abroad, take rates also rose in about two thirds of pairs, but by much less. What sets the Indonesian and regional platforms apart is the size of the moves, which is what the test measures. + +Quarterly pairs do not reject H1 (p = 0.19), contrary to a prediction registered before the test was run. The quarterly Indonesian pairs mostly cover 2023 to 2026, after most of the repricing, while the large moves came between 2021 and 2024. The rejection of H1 describes that period. + +The thin share that Indonesian and regional platforms keep explains much of the threefold difference, an exploratory finding. Measured in percentage points, both groups changed their take rates by about 1 point a year. The difference between them is nowhere near large enough to count (p = 0.65). + +The two groups differ in the share they start from. The typical Indonesian and regional platform keeps about 1.6 percent of each sale, against about 17 percent abroad. As Section 3.2 showed, the same 1-point rise then has very different effects. It lifts revenue by more than half at the thinner share and by about 6 percent at the thicker one. Figure 5.1 shows the three measures side by side. The test shows that take rates moved, but not what the platforms changed to move them. + +![Figure 5.1](figures/fig5_1_thin_slice.png) + +**Figure 5.1. The thin share.** Each dot is one platform's median over its years. Left: the share of each sale that the platform keeps, on a log scale. Middle: the yearly change in that share, in percentage points. Right: the same change relative to the share itself, as a log change in percent. Black bars mark group medians. The foreign group has 26 platforms here and 27 in the main test, because one platform has no pair of usable years for the change in points. Source: company filings; author's calculations. Exploratory. diff --git a/research/option_space_2026-10-01/h1_direction_count.py b/research/option_space_2026-10-01/h1_direction_count.py new file mode 100644 index 0000000..167ec49 --- /dev/null +++ b/research/option_space_2026-10-01/h1_direction_count.py @@ -0,0 +1,25 @@ +"""Direction of take-rate moves for the thesis groups (15 Indonesian and regional pairs, 158 foreign pairs). +Same inputs and pair rules as h1_extended.py. Run from this folder: python3 h1_direction_count.py "$PWD". +Result on 13 Oct 2026: take rate rose in 13 of 15 Indonesian and regional pairs, revenue and transaction value moved in +opposite directions in 2 (Tokopedia 2022-23, Blibli 2024-25); abroad the take rate rose in 103 of 158 pairs (65 percent).""" +import sys, numpy as np, pandas as pd +base=sys.argv[1] +T=pd.read_csv(base+"/tables/transitions_master.csv"); P=pd.read_csv(base+"/tables/l_panel.csv"); S=pd.read_csv(base+"/tables/spinoff_panel.csv") +def trans(df): + df=df.sort_values(["firm","year"]).copy(); df["m"]=df.R/df.V + ok=df.groupby("firm").year.diff()==1 + df["dlnm"]=np.where(ok,np.log(df.m)-np.log(df.groupby("firm").m.shift()),np.nan) + df["gV"]=np.where(ok,df.V/df.groupby("firm").V.shift()-1,np.nan); df["gR"]=np.where(ok,df.R/df.groupby("firm").R.shift()-1,np.nan) + return df.dropna(subset=["dlnm"]) +idn=T[T.set=="IDN_main"][["firm","y1","dlnm","gV","gR"]].rename(columns={"y1":"year"}); idn["gV"]/=100; idn["gR"]/=100 +grab=P[P.firm=="Grab"].groupby("year")[["V","R"]].sum().reset_index().assign(firm="Grab on-demand") +goto=P[(P.firm=="GoTo")&(P.seg=="On-demand")][["year","V","R"]].assign(firm="GoTo on-demand") +reg=trans(pd.concat([grab,goto])[lambda d:d.R>0])[["firm","year","dlnm","gV","gR"]] +ind=pd.concat([idn,reg]); +ext=T[T.set=="EXT_clean"][["firm","y1","dlnm"]]; new=S[(S.flag_1p!="exclude")&S.g_m.notna()].rename(columns={"entity":"firm","g_m":"dlnm"})[["firm","dlnm"]] +fo=pd.concat([ext,new]) +print("Indonesian/regional pairs:",len(ind),"firms",ind.firm.nunique()) +print(" take rate rose (revenue faster):",int((ind.dlnm>0).sum())) +print(" opposite directions:",int((np.sign(ind.gV)!=np.sign(ind.gR)).sum())) +print(ind.assign(up=ind.dlnm>0,opp=np.sign(ind.gV)!=np.sign(ind.gR))[["firm","year","dlnm","gV","gR","up","opp"]].round(3).to_string(index=False)) +print("Foreign pairs:",len(fo),"firms",fo.firm.nunique(),"take rate rose:",int((fo.dlnm>0).sum()), f"share {(fo.dlnm>0).mean():.2f}") From 9078acdbd7c0de20734355a18a15eb012a559888 Mon Sep 17 00:00:00 2001 From: Claude Date: Wed, 14 Oct 2026 10:18:34 +0000 Subject: [PATCH 44/65] Chapter 5: full draft in the agreed shape for review; take-rate share on the full groups Draft kept beside the chapter file (unchanged); new script extends the take-rate share of revenue and wedge moves to the 5-against-27 groups. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 8 + .../drafts/ch5_full_2026-10-14.md | 230 ++++++++++++++++++ .../tables/take_rate_share_full.json | 16 ++ .../take_rate_share_full.py | 43 ++++ 4 files changed, 297 insertions(+) create mode 100644 papers/thesis_2026-10/drafts/ch5_full_2026-10-14.md create mode 100644 research/option_space_2026-10-01/tables/take_rate_share_full.json create mode 100644 research/option_space_2026-10-01/take_rate_share_full.py diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index adc90f2..4e63357 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -405,3 +405,11 @@ Read as one text after Chapter 3 was accepted. The line holds (Chapter 1 names w - **Correction found:** the current 5.2 reports the narrower test planned in advance only as the platform shuffle (p = 0.06, "comes close without passing"). The Mann–Whitney test on one median per platform passes (p = 0.024; ledger C47, `build_numbers.py`). The sample reports both; Appendix B needs the 0.024 row. - **New count:** `research/option_space_2026-10-01/h1_direction_count.py`: take rate rose in 13 of 15 Indonesian and regional pairs, opposite directions in 2; abroad it rose in 103 of 158 pairs. Size, not direction, separates the groups. - **Still to fix in Chapter 5:** "about 750 thousand" new sellers in 2024 should read "at least 754 thousand" (FINDINGS); survey years named by activity year; section references 4.6 to 4.10 and 4.4 to 4.8; four rhetorical questions; repeats of Chapters 3 and 4; dense paragraphs in 5.4 to 5.9. + +### Chapter 5: full draft in the agreed shape, not yet applied (14 Oct 2026) + +`drafts/ch5_full_2026-10-14.md` continues the 13 Oct sample through 5.3 to 5.10 (ten sections, Tables 5.1 to 5.5, Figures 5.1 to 5.5), polished in four passes (sentence length, paragraph size and number count, openers, a reread for flow). Awaiting the researcher's reading; `ch5_results.md` is unchanged. +- **New evidence on the full thesis groups:** `research/option_space_2026-10-01/take_rate_share_full.py` (`tables/take_rate_share_full.json`). The take rate accounts for 0.66 of a revenue move at the median Indonesian and regional pair (15 pairs) and 0.25 abroad (139 foreign pairs with both changes, 26 platforms); about 95 percent of a wedge move comes from volume in both groups. This replaces the proposal-era 3-against-8 figures (66.5 and 26.7 percent) in 5.3; they agree. +- **Checked:** Chapter 1's 2024 levels (BPS about US$81 billion, Bank Indonesia about US$31 billion) against `tables/yardsticks_bi_bps.csv`, now given their results home in 5.10; payment growth from ledger C50 to C54; "at least 754 thousand" new sellers in 2024. +- **Removed from Chapter 5 because Chapters 3 and 4 carry them:** the literature sentence in 5.1, the thin-share arithmetic (now a pointer to 3.2), the incentive-deduction rule, the telescoping explanation (pointer to 3.5), the cohort definition, the list of market-value platforms (Chapter 4.5), "associations, not stock returns" (Chapter 4.5). +- **To do when applying:** Appendix B row for the narrower test's Mann–Whitney p = 0.024; an Appendix B note on the 139 foreign pairs in the take-rate share. diff --git a/papers/thesis_2026-10/drafts/ch5_full_2026-10-14.md b/papers/thesis_2026-10/drafts/ch5_full_2026-10-14.md new file mode 100644 index 0000000..79d80c3 --- /dev/null +++ b/papers/thesis_2026-10/drafts/ch5_full_2026-10-14.md @@ -0,0 +1,230 @@ +# Chapter 5. Results + +The results follow the two links of Figure 3.1. On the inner link, revenue showed a small and unsteady part of the commerce that platforms carried. Platforms changed how much of each sale they kept, and revenue moved with those choices (Sections 5.1 to 5.4). On the outer link, the national count of online sellers rose by 27 percent in 2023. About half of that rise did not come from sellers entering for the first time (Sections 5.5 to 5.10). + +Every result in this chapter carries one of the three labels of Section 4.10: registered, exploratory or descriptive. Table 5.1 gives each main result with its label, the checks behind it and the section that reports it. + +**Table 5.1. Main results and the evidence behind them** + +| Result | Label | Main checks | Section | +|---|---|---|---| +| *Inner link: revenue and platform commerce* | | | | +| Revenue was about 7 percent of what three major platforms carried in 2023; the wedge was about US$40 billion | Descriptive | Shopee's take rate varied; each platform dropped in turn | 5.1 | +| Take rates moved about three times as much in Indonesia and the region as abroad, so H1 is rejected | Exploratory; the narrower test planned in advance points the same way | Each platform dropped in turn; Ozon and Zalando dropped; change measured in points; quarterly pairs do not reject | 5.2 | +| The thin share kept by Indonesian and regional platforms explains much of that difference | Exploratory | Changes in percentage points compared across groups | 5.2 | +| Pricing, not more commerce, moved revenue in the traced windows | Descriptive | Two views of Tokopedia; seven windows | 5.3 | +| Market value moves with transaction value | Registered; holds | Extreme values trimmed; a second run | 5.4 | +| Markets value growth from a higher take rate less | Registered; does not hold in the main sample | Holds only in a second run that mixes segment figures | 5.4 | +| *Outer link: platform commerce and all online commerce* | | | | +| Buying through platforms, and sellers' own sales, did not boom in 2023 | Descriptive; registered for sellers' sales, holds | Six platform measures against household spending | 5.5 | +| More businesses account for most national growth, so H2 holds | Descriptive | Both BPS counts for 2023; two periods | 5.6 | +| Too few 2023 sellers were new to produce the rise in the count (13.45 against 21.5 percent) | Registered; the entry test fails | Recall error would favour passing | 5.6 | +| About half of the rise is not explained by first-time entry | Exploratory | With and without exits; cohorts; provinces; sampling error | 5.7 | +| The extra count leaned toward long-running businesses; returning sellers cannot be ruled out | Exploratory | Part-year selling | 5.8 | +| Most online selling happens outside marketplaces, and records are thinner there | Registered and descriptive | Four survey years; World Bank surveys | 5.9 | +| National figures agree only where they cover the same sellers | Registered, with a mixed outcome, and descriptive | Three value scenarios | 5.10 | + +## 5.1 How much revenue leaves out + +Revenue showed only a small part of what Indonesia's major platforms carried. In 2023, Tokopedia, Shopee and Grab together carried about US$43 billion of transaction value and kept about US$3 billion as revenue. Revenue was about 7 percent of that commerce, and the invisible wedge was about US$40 billion. The ecosystem ratio was about 13, so almost 13 dollars of commerce passed through the platforms for every dollar of revenue. Table 5.2 shows each platform. + +**Table 5.2. The invisible wedge of three platforms, 2023 (US$ billion)** + +| Platform | Transaction value | Revenue | Wedge | Ecosystem ratio | How it is labelled | +|---|---|---|---|---|---| +| Tokopedia | 16.33 | 0.41 | 15.93 | 39.3 | Both direct segment disclosure | +| Shopee, Indonesia | 21.52 | 2.15 | 19.37 | 9.0 | Transaction value is an outside estimate (Momentum Works); revenue is derived at an assumed take rate of 10 percent | +| Grab, Indonesia | 5.38 | 0.60 | 4.78 | 7.9 | Revenue is a direct country disclosure; transaction value is derived from Grab's company-wide ratio | +| Total | 43.23 | 3.16 | 40.07 | 12.7 | Sum of three platforms, not a national total | + +*Note:* Rounded amounts may not add. With Shopee's take rate at 9 or 11 percent, the total wedge is US$39.9 or US$40.3 billion; at Sea's company-wide rate of 11.5 percent, it is US$39.8 billion. Source: company filings; Momentum Works (2024); author's calculations. + +The ratios differ because the amounts were obtained in different ways. Tokopedia reports both amounts, and its ratio is about 39. Shopee's ratio of 9 follows from the assumed take rate, and Grab's ratio of about 8 is its company-wide ratio. Neither is separate evidence for Indonesia. Blibli and Bukalapak also report both amounts, with ratios of about 43 and 36, close to Tokopedia's. They are left out of the sum because Blibli's figure includes online travel and Bukalapak reports for its whole group. + +The size of the wedge does not depend on the assumed rates. A take rate for Shopee between 9 and 11 percent keeps the total near US$40 billion. Dropping any one platform still leaves more than US$20 billion. For platforms that report both amounts, the wedge was 97 to 99 percent of transaction value in every year. It moved almost one for one with the commerce. Appendix A gives the wedge by platform and year. + +Platform commerce is itself only part of online commerce. In BPS's survey, marketplaces carry about a fifth of online sales value (Section 5.9), so platform revenue shows a smaller part still. A small share can still be a fair guide to the commerce, as long as it keeps its size from year to year. + +## 5.2 Testing H1 + +Revenue did not keep pace with transaction value at the Indonesian and regional platforms, and the thesis rejects H1. At the typical platform in this group, the take rate moved by about 20 percent a year, up or down. At the typical foreign platform it moved by about 6 percent, so the moves were about three times as large. Table 5.3 gives the test and its checks. + +**Table 5.3. The test of H1** + +| Test | Method | Indonesian and regional group | Foreign group | p-value | +|---|---|---|---|---| +| Yearly change in log take rate, 5 platforms against 27 | Mann–Whitney | 0.20 | 0.06 | 0.004 | +| The same | Shuffle | 0.20 | 0.06 | 0.022 | +| Growth divergence in points, 5 platforms against 26 | Mann–Whitney | 27.0 | 6.5 | 0.005 | +| Drop each platform in turn, or treat Tokopedia and GoTo as one company | Mann–Whitney | 0.17 to 0.23 | 0.06 | 0.003 to 0.016 | +| Quarterly pairs, 3 platforms against 8 | Mann–Whitney | 0.071 | 0.049 | 0.19 | + +*Note:* Group values are medians of each platform's median yearly change. Appendix B lists every check, including the tests without Ozon and Zalando. + +The main test compares the five platforms with 27 abroad, one median per platform. Both methods of Section 4.3 reject equal moves, with p = 0.004 and p = 0.022. No single platform carries the result. Dropping any one, or treating Tokopedia and GoTo as one company, keeps the Mann–Whitney p-value at 0.016 or below. The main test was set up after a first, narrower test, so it is exploratory. + +The test planned in advance was narrower: the three Indonesian marketplaces against the eight foreign platforms chosen first. It points the same way. With one median per platform, the Mann–Whitney test rejects equal moves (p = 0.024). The shuffle of platform labels comes close without passing (p = 0.06). Three platforms give a test little power. In that sample, revenue growth and transaction value growth had a correlation of 0.10 in Indonesia and 0.98 abroad, excluding Sea. + +The direction of the moves is less telling than their size. Revenue grew faster than transaction value in 13 of the 15 Indonesian and regional pairs. In two of them, revenue and transaction value moved in opposite directions. Abroad, take rates also rose in about two thirds of pairs, but by much less. What sets the Indonesian and regional platforms apart is the size of the moves, which is what the test measures. + +Quarterly pairs do not reject H1 (p = 0.19), contrary to a prediction registered before the test was run. The quarterly Indonesian pairs mostly cover 2023 to 2026, after most of the repricing, while the large moves came between 2021 and 2024. The rejection of H1 describes that period. + +The thin share that Indonesian and regional platforms keep explains much of the threefold difference, an exploratory finding. Measured in percentage points, both groups changed their take rates by about 1 point a year. The difference between them is nowhere near large enough to count (p = 0.65). + +The two groups differ in the share they start from. The typical Indonesian and regional platform keeps about 1.6 percent of each sale, against about 17 percent abroad. As Section 3.2 showed, the same 1-point rise then has very different effects. It lifts revenue by more than half at the thinner share and by about 6 percent at the thicker one. Figure 5.1 shows the three measures side by side. The test shows that take rates moved, but not what the platforms changed to move them. + +![Figure 5.1](figures/fig5_1_thin_slice.png) + +**Figure 5.1. The thin share.** Each dot is one platform's median over its years. Left: the share of each sale that the platform keeps, on a log scale. Middle: the yearly change in that share, in percentage points. Right: the same change relative to the share itself, as a log change in percent. Black bars mark group medians. The foreign group has 26 platforms here and 27 in the main test, because one platform has no pair of usable years for the change in points. Source: company filings; author's calculations. Exploratory. + +## 5.3 What moved the take rate + +Pricing, not more commerce, moved revenue at the Indonesian and regional platforms. Splitting each yearly move with equation (5), the take rate accounts for about two thirds of the move in revenue at the typical pair. Abroad it accounts for about a quarter. The wedge, by contrast, follows the commerce: in both groups, about 95 percent of a move in the wedge comes from transaction value. + +Where companies disclose both their fees and their incentives, equation (6) splits the growth of revenue further. Table 5.4 gives the seven traced windows. Fees and incentives are measured per unit of transaction value, and a negative share means that a part pulled revenue down. The windows of one platform overlap, so they are not seven independent cases. + +**Table 5.4. What moved revenue in seven Indonesian and regional windows (share of the growth of revenue, percent)** + +| Platform and window | Growth of revenue | More commerce | Higher fees | Lower incentives | +|---|---|---|---|---| +| Tokopedia, 2022–23 | +53% | −22 | 76 | 45 | +| Blibli third-party retail, 2022–23 | +465% | 17 | 20 | 63 | +| Blibli third-party retail, 2022–25 | +679% | 18 | 63 | 20 | +| Grab on-demand, 2021–24 | +321% | 34 | 34 | 33 | +| Grab on-demand, 2022–24 | +86% | 42 | 11 | 47 | +| GoTo on-demand, 2022–25 | +111% | 12 | 36 | 52 | +| GoTo on-demand, 2023–25 | +114% | 27 | 6 | 67 | + +*Note:* Shares are rounded, so a row can add to 99 or 101. Source: company filings; author's calculations (Appendix A). + +Higher fees and lower incentives together outweighed more commerce in every window. Lower incentives were the larger of the two in four windows and higher fees in two. In the seventh, Grab from 2021 to 2024, the two were nearly equal. Three series need a note. GoTo's 2022 transaction value is an estimate. Blibli does not report incentives, so its split rests on a proxy, and part of its 2022–23 rise comes from travel and digital products. Bukalapak's reports do not allow the parts to be separated. + +Tokopedia can be read in two ways, and both point to pricing. Its incentives fell by about a third in 2023. Counted line by line in its accounts, lower incentives gave about 60 percent of the extra revenue. Higher gross fees gave about 40 percent. That count leaves the fall in volume inside both lines. Measured per unit of transaction value, as in Table 5.4, higher fees account for more of the rise in the take rate. The two views differ only on which lever led (Figure 5.2). + +![Figure 5.2](figures/fig5_2_tokopedia_two_views.png) + +**Figure 5.2. Tokopedia, 2022 to 2023: two ways to split the rise in revenue.** Left: the change in net revenue split by the lines of the accounts. Right: the growth of revenue split per unit of transaction value, where a negative share pulled revenue down. Source: GoTo annual report 2023, segment note; author's calculations. + +Tokopedia is not the only case of revenue rising while the commerce shrank. From 2024 to 2025, Blibli's transaction value fell by about 2 percent while its revenue rose by about 12 percent. In the windows traced here, revenue growth was not a fair guide to the commerce the platforms carried. Whether markets treated it as one is a separate question. + +## 5.4 What markets valued + +Market value moved with transaction value, a registered result. The sample has 28 listed platforms and 138 platform-years. In it, a 1 percent rise in transaction value goes with a rise of about 0.6 percent in market value (p = 0.002). Section 4.5 describes this sample, which differs from the 27 foreign platforms of H1. + +The second registered prediction does not hold in the main sample. It expected markets to value growth from a higher take rate less than growth from more commerce. A 1 percent rise in the take rate goes with a rise of about 0.3 percent, an estimate too uncertain to tell from zero. The gap between the two estimates is not large enough to count (p = 0.20). Trimming extreme values leaves it at p = 0.16. + +A second run adds three segment series and three more whole companies. There the gap counts (p = 0.004), with about 0.6 for transaction value against about 0.1 for the take rate. That run sets segment figures against whole-company market values, as the registration warned, so the thesis does not rely on it. The answer depends on which platforms are in the sample. + +Market value follows the commerce a platform carries. Early studies found a similar link, with markets pricing measures of web activity beyond revenue (Trueman, Wong and Zhang 2000). The data do not show that markets valued growth from pricing any less. Revenue, transaction value and market value all come from the platforms' own reports. The national figures give a separate view of the same commerce. + +## 5.5 The platforms and the sellers in 2023 + +Buying through platforms did not boom in 2023. Household spending rose by about 9 percent in current prices that year. Of six measures of buying through platforms, five grew more slowly, and three fell. Blibli is the one exception. Figure 5.3 sets the measures side by side, each in its own unit. + +The figures that described a boom were of other kinds. Tokopedia's revenue rose by 53 percent and BPS's online sales value by 41 percent, while the buying that platforms carried grew slowly or fell. Figure 5.3 shows the two kinds in different colours. + +![Figure 5.3](figures/fig5_3_growth_2023.png) + +**Figure 5.3. Growth in 2023 (percent).** Blue bars show measures of buying; orange bars show the figures that described a boom. Each measure is in its own unit. Sources: company reports, Bank Indonesia, Momentum Works and BPS; household spending from national accounts. GoTo's change rests on an estimated 2022 value, and Momentum Works' 3.5 is the author's calculation from its published levels (its 2024 release states 3.7). Appendix D gives the values. + +Sellers' own accounts of their sales show no boom either, a registered result. BPS asks each business how its online sales changed. For businesses already selling, the registered prediction set an upper limit on the median change. The limit was about 10 percent, the published growth in sales per business. The median change was zero. + +Fewer sellers reported a rise than a year earlier. About a quarter reported a rise and about a third a fall. A year before, more than a third had reported a rise and a quarter a fall. The mean change was about −4 percent. Lack of demand became the main obstacle, named by 41 percent of sellers against 35 percent in 2022. The published count of online sellers nonetheless rose by 27 percent in 2023. A rise of that size, in a year without a boom in sales, needs explaining. + +## 5.6 H2 and the entry test + +On BPS's published figures, H2 holds. From 2022 to 2023, online sales value grew by 41 percent and the number of businesses by 27 percent. More businesses account for 71 percent of that growth, and higher sales per business for the rest. + +H2 holds again from 2023 to 2024. Value grew by 17 percent, and more businesses account for 90 percent of the growth, or 71 percent on BPS's other count for 2023. The split of equation (7) is arithmetic, and it cannot tell a new seller from one counted for the first time. + +The registered entry test separates new sellers from sellers counted for the first time, and it fails. In the survey for 2023 (the 2024 file), about 13 percent of sellers began selling online that year. The test required 21.5 percent. Even with no sellers stopping, that many new sellers would raise the count by at most about 16 percent. The published rise was 27 percent. + +Recall error does not explain the shortfall. As Section 3.5 noted, sellers who report a start date later than the true one make new sellers look more numerous. Such errors push the share toward passing, so the shortfall is, if anything, understated. Part of the 2023 rise in the count is something other than first-time entry, and its size is the next result. + +## 5.7 What the rise in the count contains + +About half of the 2023 rise in the count is not explained by first-time entry, an exploratory result. Table 5.5 sets out the accounting. In the survey for 2023, sellers who began before 2023 outnumber the whole count for 2022 by about 306 thousand. They make up 37 percent of the rise if no seller stopped, and 56 percent if sellers stopped at about 5 percent a year. + +**Table 5.5. The 2023 rise in the count of online sellers (thousands)** + +| Part of the rise | If no seller stopped | If sellers stopped at 5 percent a year | +|---|---|---| +| Rise between the weighted totals of the two files | 819 (100%) | 819 (100%) | +| Net entry: sellers who began in 2023, less those who stopped | 513 (63%) | 361 (44%) | +| Earlier sellers newly counted above the whole 2022 count | 306 (37%) | 458 (56%) | + +*Note:* BPS's published counts give a rise of 821 thousand (BPS 2025). Source: BPS survey files, weighted; author's calculations. + +The cohort counts show the break directly. A cohort should only shrink between surveys, and the cohorts did so in every interval except 2023. Sellers who began by 2020 fell by about 10 percent between the surveys for 2020 and 2022. In the survey for 2023 they rose by 6 percent. + +The count behaved normally again in BPS's published table for 2024. The cohort that began before 2020 shrank by about 10 percent. At least 754 thousand new sellers appeared, more than the rise of 584 thousand. + +The break was uneven across places and channels. Across the 34 provinces, growth in the count had almost no link to the share of new sellers, a rank correlation of 0.10. It had only a weak link to growth in the sample, at 0.35. A rank correlation compares the order of provinces on two measures and runs from −1 to 1. The break sat in chat and social selling. Among sellers who began by 2022, sellers using only chat or social media rose by 13 percent, and marketplace users by 0.3 percent. + +Sampling noise does not explain the break. BPS's published error rates put the usual size of the sampling noise in the national count at about 1.4 percent. The 306 thousand extra sellers are about 4.6 times that noise in the change between the two years. The calculation applies the 2024 edition's error rates to both years and treats the two surveys as independent. + +Most of the 2023 rise in value has no source in the answers of existing sellers. BPS's online sales value rose from about US$53 billion to about US$72 billion. Entrants account for 25 to 30 percent of that rise, and sellers not explained by entry for 15 to 17 percent. Existing sellers reported a mean change of about −4 percent, so the remaining 53 to 59 percent cannot be traced to them. + +The checks point to a survey that reached more chat and social sellers in 2023, the kind of break that Section 3.5 described. The files cannot separate that reading from sellers returning after a pause. Who the extra sellers were bears on the choice between the two. + +## 5.8 Who the extra sellers were + +The extra count leaned toward long-running businesses, older owners and places outside Java, an exploratory result. Each group's extra count is its 2023 count above what its own yearly change from 2020 to 2022 would give (Section 4.7). The extra sellers total about 450 thousand, close to the 458 thousand in Table 5.5. Figure 5.4 compares each group's share of the extra count with its share of the 2022 count. + +![Figure 5.4](figures/fig5_4_extra_sellers.png) + +**Figure 5.4. Who the extra sellers were.** The extra sellers are the 2023 count above what each group's normal yearly change in 2020 to 2022 would give, about 450 thousand in total. Source: BPS survey files, weighted; author's calculations. Exploratory. + +The extra count did not lean toward young businesses. A lean toward men in the raw counts disappears once each group's normal change is taken out. This profile bears on whether the extra sellers were returning after a pause. + +Part-year selling weighs against returners. A returning seller would usually sell for only part of the year, unless it restarted in January, yet part-year selling fell in 2023. Among sellers who began by 2021, those selling for part of the year fell from about 470 thousand to about 320 thousand. The same fall appears for the groups that began by 2020 and by 2022. + +The age profile weighs against returners as well. Small firms that stop are more often young firms with young owners (McKenzie and Paffhausen 2019). Returners would then make the extra count lean young. It leaned old. + +Returning sellers still cannot be ruled out. Returners equal to 10 to 15 percent of the 2022 count would remove the unexplained part, which is a scenario and not an estimate. The files carry no linked histories, and BPS changed how it asks about months between the rounds. Exits, or a move to full-year selling, could also offset part-year returners. Where online sellers sell, and what records they keep, complete the survey evidence. + +## 5.9 Channels and records + +Most online selling happens outside marketplaces, and records are thinner there. The path of marketplace use, the value held by sellers without financial statements and the World Bank results were registered. The comparison within the group that began before 2020 is exploratory. + +Nearly all online sellers, about 96 percent, use chat or social media. Marketplace use fell every time it was measured, from about 22 percent of sellers in 2020 to about 17 percent in 2024. BPS worded the question slightly differently for 2023 and 2024. + +Almost all of the growth in value came from outside the marketplaces. From 2023 to 2024, BPS's marketplace value grew by about 1.5 percent and other channels by about 21 percent. BPS's marketplace category is itself broad: 43 percent of its sellers use only Gojek or Grab, and 47 percent use a shopping marketplace. + +![Figure 5.5](figures/fig5_5_marketplace_and_records.png) + +**Figure 5.5. Marketplace use and financial statements, 2020 to 2024.** Marketplace use for 2020, 2022 and 2023 comes from the survey files and for 2024 from BPS's published figure. Financial statements come from the survey files. BPS worded the marketplace question slightly differently for 2023 and 2024, and the survey has no point for 2021. Source: BPS; author's calculations. + +Records are thinner outside the marketplaces. In 2023, about 29 percent of marketplace sellers kept complete financial statements, against about 12 percent of other sellers. Figure 5.5 shows the share of all sellers who keep them falling. The fall from 2020 to 2022 came while the survey appears to have counted the same kinds of seller. The fall in 2023 may partly reflect the chat-only sellers added to the count. + +The fall reaches long-running sellers too, an exploratory finding. Among sellers who began before 2020, the share keeping complete statements fell from about 23 percent in 2022 to about 17 percent in 2023. + +Sellers without statements hold far less of the value than of the count. On the bracket scenarios of Section 4.8, they held 26 to 40 percent of online value in 2022. They made up 79 percent of the count. The annual reading of the brackets passes the validation rule, at 1.95 times BPS's total. The monthly reading fails, at 23 times. The lower brackets alone exceed BPS's total, so the shares are ranges. + +One registered prediction on value failed narrowly. It expected the value share of sellers without statements to fall by no more than 5 points between 2020 and 2022. The share fell by 5.8 points on the main version and by 3.3 points on a second version. + +World Bank surveys of Indonesian firms show the same thin records. In six Indonesian cities, 27 percent of unregistered firms use social media with their customers. Among those, 70 percent keep no written records, and their median sales are about US$4,000 a year. A registered prediction that their mean sales would stay below about US$6,600 failed, at about US$7,300. + +Among formal firms, about 40 percent in Indonesia have neither a website nor online tax filing. That is the highest share of seven countries, against about 9 percent in Malaysia and in Thailand. + +The split by channel bears on the third question of Section 2.4, because a rule built on platform records reaches only the marketplace part. Chapter 6 measures that reach. The same split also marks where the national figures part. + +## 5.10 Why the national figures disagree + +The national figures agree on growth only where they cover the same sellers, and they do not agree on scale. In 2023, Bank Indonesia's e-commerce figure fell by about 5 percent while BPS's rose by about 41 percent. That is the disagreement with which Chapter 1 began, and the third objective asks what lies behind it. + +Where Bank Indonesia's figure and BPS's cover the same ground, the growth is small. From 2022 to 2023, BPS's own marketplace slice grew by about 0.2 percent in the middle scenario. Across the scenarios it grew by 0.2 to 13 percent. From 2023 to 2024 it grew by about 1.5 percent. Most of the disagreement sits outside the marketplaces, among the sellers that only BPS reaches. + +A registered test on size gives a mixed answer. It predicted that the part of BPS's marketplace sales that went to final consumers in 2022 would fall below Bank Indonesia's figure. The prediction holds in the low and middle scenarios, at 0.16 and 0.68 times Bank Indonesia's figure. It fails in the high one, at 1.62 times. + +In 2024 the figures divide by coverage. Those that cover mainly platforms grew by 5 to 7 percent, and BPS's grew by about 17 percent. The adjacent figures of Table 4.3 point the same way as BPS but do not measure Indonesian e-commerce. Parcels shipped in Southeast Asia grew by about 25 percent, and the tax on foreign digital services by about 25 percent. + +Payment figures grew faster than any of these from 2023 to 2024. E-money shopping grew by about 30 percent, mobile-banking purchases by 83 percent and QRIS payments by 187 percent. They are context only, because they include in-person payments and the switch away from cash. + +Levels cannot be reconciled. In 2024, BPS put online sales at about US$81 billion and Bank Indonesia at about US$31 billion. In 2023, BPS's marketplace slice was about US$13 billion, against about US$38 billion for Tokopedia and the Shopee estimate together. In 2024 it was less than a quarter of Momentum Works' marketplace estimate of about US$57 billion. + +Platform figures and the seller survey count different things. Platform transaction value includes shipping, digital goods, travel and offline sales that a seller survey does not count in the same way. The counts of sellers differ too. Tokopedia alone said that it had about 15 million merchants in 2022, while BPS counts about 0.6 million marketplace sellers. + +Platform sales do not always keep pace with household spending. In each of the four quarters to mid-2026, GoTo's on-demand transaction value grew more slowly than household spending. In the last, it grew by 2 percent against 8 percent, while its revenue grew by 21 percent. Grab's and Shopee's figures grew faster, but they cover the region. + +The results answer the research question at two levels. On the inner link, revenue showed a small part of the commerce and stopped being a fair guide when the take rate moved. On the outer link, the 2023 rise in the count of sellers was only partly first-time entry. The figures part most where only the survey looks. Chapter 6 turns to the rules that Indonesia is writing. diff --git a/research/option_space_2026-10-01/tables/take_rate_share_full.json b/research/option_space_2026-10-01/tables/take_rate_share_full.json new file mode 100644 index 0000000..16de072 --- /dev/null +++ b/research/option_space_2026-10-01/tables/take_rate_share_full.json @@ -0,0 +1,16 @@ +{ + "Indonesian and regional": { + "pairs": 15, + "platforms": 5, + "median_take_rate_share_of_revenue_move_pairs": 0.658, + "median_take_rate_share_of_revenue_move_platforms": 0.658, + "median_take_rate_share_of_wedge_move_pairs": 0.055 + }, + "Foreign": { + "pairs": 139, + "platforms": 26, + "median_take_rate_share_of_revenue_move_pairs": 0.25, + "median_take_rate_share_of_revenue_move_platforms": 0.264, + "median_take_rate_share_of_wedge_move_pairs": 0.054 + } +} \ No newline at end of file diff --git a/research/option_space_2026-10-01/take_rate_share_full.py b/research/option_space_2026-10-01/take_rate_share_full.py new file mode 100644 index 0000000..8a8ae92 --- /dev/null +++ b/research/option_space_2026-10-01/take_rate_share_full.py @@ -0,0 +1,43 @@ +"""Take-rate share of revenue moves and of wedge moves, for the thesis groups (5 Indonesian and regional platforms, +27 foreign platforms), with the definitions of wedge_split.py: in logs, revenue growth = volume + take rate, and +wedge growth = volume + change in ln(1 - m). The take-rate share of a move is |take-rate term| / (|volume| + |take-rate term|). +Same inputs and pair rules as h1_extended.py and h1_direction_count.py. Run from this folder: +python3 take_rate_share_full.py "$PWD" -> tables/take_rate_share_full.json""" +import sys, json, numpy as np, pandas as pd +base = sys.argv[1] +T = pd.read_csv(base + "/tables/transitions_master.csv"); P = pd.read_csv(base + "/tables/l_panel.csv"); S = pd.read_csv(base + "/tables/spinoff_panel.csv") + +def pairs(df): + df = df.sort_values(["firm", "year"]).copy(); df["m"] = df.R / df.V + ok = df.groupby("firm").year.diff() == 1 + df["dlnV"] = np.where(ok, np.log(df.V) - np.log(df.groupby("firm").V.shift()), np.nan) + df["dlnm"] = np.where(ok, np.log(df.m) - np.log(df.groupby("firm").m.shift()), np.nan) + df["m0"] = df.groupby("firm").m.shift() + return df.dropna(subset=["dlnm"]) + +idn = T[T.set == "IDN_main"].assign(dlnV=lambda d: d.lnV, m0=lambda d: d.m0_pct / 100, m=lambda d: d.m1_pct / 100)[["firm", "dlnV", "dlnm", "m0", "m"]] +grab = P[P.firm == "Grab"].groupby("year")[["V", "R"]].sum().reset_index().assign(firm="Grab on-demand") +goto = P[(P.firm == "GoTo") & (P.seg == "On-demand")][["year", "V", "R"]].assign(firm="GoTo on-demand") +reg = pairs(pd.concat([grab, goto])[lambda d: d.R > 0])[["firm", "dlnV", "dlnm", "m0", "m"]] +ext = T[T.set == "EXT_clean"].assign(dlnV=lambda d: d.lnV, m0=lambda d: d.m0_pct / 100, m=lambda d: d.m1_pct / 100)[["firm", "dlnV", "dlnm", "m0", "m"]] +s = S[(S.flag_1p != "exclude") & S.g_m.notna() & S.g_V.notna()].rename(columns={"entity": "firm"}).sort_values(["firm", "year"]) +s["m0"] = s.groupby("firm").m.shift() +new = s.rename(columns={"g_V": "dlnV", "g_m": "dlnm"})[["firm", "dlnV", "dlnm", "m0", "m"]].dropna(subset=["m0"]) + +def shares(d): + d = d.copy() + d["share_R"] = d.dlnm.abs() / (d.dlnV.abs() + d.dlnm.abs()) + dw = np.log(1 - d.m) - np.log(1 - d.m0) + d["share_W"] = dw.abs() / (d.dlnV.abs() + dw.abs()) + return d + +groups = {"Indonesian and regional": shares(pd.concat([idn, reg])), "Foreign": shares(pd.concat([ext, new]))} +out = {} +for k, d in groups.items(): + firm = d.groupby("firm")[["share_R", "share_W"]].median() + out[k] = {"pairs": int(len(d)), "platforms": int(d.firm.nunique()), + "median_take_rate_share_of_revenue_move_pairs": round(float(d.share_R.median()), 3), + "median_take_rate_share_of_revenue_move_platforms": round(float(firm.share_R.median()), 3), + "median_take_rate_share_of_wedge_move_pairs": round(float(d.share_W.median()), 3)} +json.dump(out, open(base + "/tables/take_rate_share_full.json", "w"), indent=1) +print(json.dumps(out, indent=1)) From 53b71dfbfa10d98bbd96f64eef02155e0f31263e Mon Sep 17 00:00:00 2001 From: Claude Date: Wed, 14 Oct 2026 10:48:52 +0000 Subject: [PATCH 45/65] Chapter 5 draft: whole-chapter pass (seams, Table 5.1, a broken pointer, one overclaim) Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/drafts/ch5_full_2026-10-14.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/papers/thesis_2026-10/drafts/ch5_full_2026-10-14.md b/papers/thesis_2026-10/drafts/ch5_full_2026-10-14.md index 79d80c3..2a452b9 100644 --- a/papers/thesis_2026-10/drafts/ch5_full_2026-10-14.md +++ b/papers/thesis_2026-10/drafts/ch5_full_2026-10-14.md @@ -2,7 +2,7 @@ The results follow the two links of Figure 3.1. On the inner link, revenue showed a small and unsteady part of the commerce that platforms carried. Platforms changed how much of each sale they kept, and revenue moved with those choices (Sections 5.1 to 5.4). On the outer link, the national count of online sellers rose by 27 percent in 2023. About half of that rise did not come from sellers entering for the first time (Sections 5.5 to 5.10). -Every result in this chapter carries one of the three labels of Section 4.10: registered, exploratory or descriptive. Table 5.1 gives each main result with its label, the checks behind it and the section that reports it. +Every result in this chapter carries one of the three labels of Section 4.10: registered, exploratory or descriptive. A result given without a label is descriptive. Table 5.1 gives each main result with its label, the checks behind it and the section that reports it. **Table 5.1. Main results and the evidence behind them** @@ -12,7 +12,7 @@ Every result in this chapter carries one of the three labels of Section 4.10: re | Revenue was about 7 percent of what three major platforms carried in 2023; the wedge was about US$40 billion | Descriptive | Shopee's take rate varied; each platform dropped in turn | 5.1 | | Take rates moved about three times as much in Indonesia and the region as abroad, so H1 is rejected | Exploratory; the narrower test planned in advance points the same way | Each platform dropped in turn; Ozon and Zalando dropped; change measured in points; quarterly pairs do not reject | 5.2 | | The thin share kept by Indonesian and regional platforms explains much of that difference | Exploratory | Changes in percentage points compared across groups | 5.2 | -| Pricing, not more commerce, moved revenue in the traced windows | Descriptive | Two views of Tokopedia; seven windows | 5.3 | +| Pricing, not more commerce, moved revenue | Descriptive | All 15 pairs against 139 abroad; seven traced windows; two views of Tokopedia | 5.3 | | Market value moves with transaction value | Registered; holds | Extreme values trimmed; a second run | 5.4 | | Markets value growth from a higher take rate less | Registered; does not hold in the main sample | Holds only in a second run that mixes segment figures | 5.4 | | *Outer link: platform commerce and all online commerce* | | | | @@ -133,7 +133,7 @@ Fewer sellers reported a rise than a year earlier. About a quarter reported a ri ## 5.6 H2 and the entry test -On BPS's published figures, H2 holds. From 2022 to 2023, online sales value grew by 41 percent and the number of businesses by 27 percent. More businesses account for 71 percent of that growth, and higher sales per business for the rest. +H2 holds on BPS's published figures, but the registered entry test shows that first-time entry cannot explain the whole rise in the count. From 2022 to 2023, online sales value grew by 41 percent and the number of businesses by 27 percent. More businesses account for 71 percent of that growth, and higher sales per business for the rest. H2 holds again from 2023 to 2024. Value grew by 17 percent, and more businesses account for 90 percent of the growth, or 71 percent on BPS's other count for 2023. The split of equation (7) is arithmetic, and it cannot tell a new seller from one counted for the first time. @@ -189,7 +189,9 @@ Most online selling happens outside marketplaces, and records are thinner there. Nearly all online sellers, about 96 percent, use chat or social media. Marketplace use fell every time it was measured, from about 22 percent of sellers in 2020 to about 17 percent in 2024. BPS worded the question slightly differently for 2023 and 2024. -Almost all of the growth in value came from outside the marketplaces. From 2023 to 2024, BPS's marketplace value grew by about 1.5 percent and other channels by about 21 percent. BPS's marketplace category is itself broad: 43 percent of its sellers use only Gojek or Grab, and 47 percent use a shopping marketplace. +Marketplaces carry only about a fifth of online sales value. The share was 23 to 26 percent in 2022 on the survey files. In BPS's published figures it was 18 percent in 2023 and 16 percent in 2024. + +Almost all of the recent growth in value came from outside the marketplaces. From 2023 to 2024, BPS's marketplace value grew by about 1.5 percent and other channels by about 21 percent. BPS's marketplace category is itself broad: 43 percent of its sellers use only Gojek or Grab, and 47 percent use a shopping marketplace. ![Figure 5.5](figures/fig5_5_marketplace_and_records.png) From 3643652ea94da182ffcec593431d36e899627492 Mon Sep 17 00:00:00 2001 From: Claude Date: Wed, 14 Oct 2026 10:56:02 +0000 Subject: [PATCH 46/65] Fix take-rate share script: use all 158 foreign pairs The earlier year's take rate was taken after filtering, which dropped the first pair of 19 platforms. Result: 0.66 here against 0.24 abroad. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 4 ++-- papers/thesis_2026-10/drafts/ch5_full_2026-10-14.md | 2 +- .../tables/take_rate_share_full.json | 10 +++++----- .../option_space_2026-10-01/take_rate_share_full.py | 5 +++-- 4 files changed, 11 insertions(+), 10 deletions(-) diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 4e63357..523806e 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -409,7 +409,7 @@ Read as one text after Chapter 3 was accepted. The line holds (Chapter 1 names w ### Chapter 5: full draft in the agreed shape, not yet applied (14 Oct 2026) `drafts/ch5_full_2026-10-14.md` continues the 13 Oct sample through 5.3 to 5.10 (ten sections, Tables 5.1 to 5.5, Figures 5.1 to 5.5), polished in four passes (sentence length, paragraph size and number count, openers, a reread for flow). Awaiting the researcher's reading; `ch5_results.md` is unchanged. -- **New evidence on the full thesis groups:** `research/option_space_2026-10-01/take_rate_share_full.py` (`tables/take_rate_share_full.json`). The take rate accounts for 0.66 of a revenue move at the median Indonesian and regional pair (15 pairs) and 0.25 abroad (139 foreign pairs with both changes, 26 platforms); about 95 percent of a wedge move comes from volume in both groups. This replaces the proposal-era 3-against-8 figures (66.5 and 26.7 percent) in 5.3; they agree. +- **New evidence on the full thesis groups:** `research/option_space_2026-10-01/take_rate_share_full.py` (`tables/take_rate_share_full.json`). The take rate accounts for 0.66 of a revenue move at the median Indonesian and regional pair (15 pairs) and 0.24 abroad (158 pairs, 27 platforms; 0.22 with one median per platform); about 95 percent of a wedge move comes from volume in both groups. (A first run reported 139 foreign pairs because the script took the earlier year's take rate after filtering; fixed 14 Oct before use.) This replaces the proposal-era 3-against-8 figures (66.5 and 26.7 percent) in 5.3; they agree. - **Checked:** Chapter 1's 2024 levels (BPS about US$81 billion, Bank Indonesia about US$31 billion) against `tables/yardsticks_bi_bps.csv`, now given their results home in 5.10; payment growth from ledger C50 to C54; "at least 754 thousand" new sellers in 2024. - **Removed from Chapter 5 because Chapters 3 and 4 carry them:** the literature sentence in 5.1, the thin-share arithmetic (now a pointer to 3.2), the incentive-deduction rule, the telescoping explanation (pointer to 3.5), the cohort definition, the list of market-value platforms (Chapter 4.5), "associations, not stock returns" (Chapter 4.5). -- **To do when applying:** Appendix B row for the narrower test's Mann–Whitney p = 0.024; an Appendix B note on the 139 foreign pairs in the take-rate share. +- **To do when applying:** Appendix B row for the narrower test's Mann–Whitney p = 0.024. diff --git a/papers/thesis_2026-10/drafts/ch5_full_2026-10-14.md b/papers/thesis_2026-10/drafts/ch5_full_2026-10-14.md index 2a452b9..587a9f3 100644 --- a/papers/thesis_2026-10/drafts/ch5_full_2026-10-14.md +++ b/papers/thesis_2026-10/drafts/ch5_full_2026-10-14.md @@ -12,7 +12,7 @@ Every result in this chapter carries one of the three labels of Section 4.10: re | Revenue was about 7 percent of what three major platforms carried in 2023; the wedge was about US$40 billion | Descriptive | Shopee's take rate varied; each platform dropped in turn | 5.1 | | Take rates moved about three times as much in Indonesia and the region as abroad, so H1 is rejected | Exploratory; the narrower test planned in advance points the same way | Each platform dropped in turn; Ozon and Zalando dropped; change measured in points; quarterly pairs do not reject | 5.2 | | The thin share kept by Indonesian and regional platforms explains much of that difference | Exploratory | Changes in percentage points compared across groups | 5.2 | -| Pricing, not more commerce, moved revenue | Descriptive | All 15 pairs against 139 abroad; seven traced windows; two views of Tokopedia | 5.3 | +| Pricing, not more commerce, moved revenue | Descriptive | All 15 pairs against 158 abroad; seven traced windows; two views of Tokopedia | 5.3 | | Market value moves with transaction value | Registered; holds | Extreme values trimmed; a second run | 5.4 | | Markets value growth from a higher take rate less | Registered; does not hold in the main sample | Holds only in a second run that mixes segment figures | 5.4 | | *Outer link: platform commerce and all online commerce* | | | | diff --git a/research/option_space_2026-10-01/tables/take_rate_share_full.json b/research/option_space_2026-10-01/tables/take_rate_share_full.json index 16de072..f06a682 100644 --- a/research/option_space_2026-10-01/tables/take_rate_share_full.json +++ b/research/option_space_2026-10-01/tables/take_rate_share_full.json @@ -7,10 +7,10 @@ "median_take_rate_share_of_wedge_move_pairs": 0.055 }, "Foreign": { - "pairs": 139, - "platforms": 26, - "median_take_rate_share_of_revenue_move_pairs": 0.25, - "median_take_rate_share_of_revenue_move_platforms": 0.264, - "median_take_rate_share_of_wedge_move_pairs": 0.054 + "pairs": 158, + "platforms": 27, + "median_take_rate_share_of_revenue_move_pairs": 0.238, + "median_take_rate_share_of_revenue_move_platforms": 0.223, + "median_take_rate_share_of_wedge_move_pairs": 0.055 } } \ No newline at end of file diff --git a/research/option_space_2026-10-01/take_rate_share_full.py b/research/option_space_2026-10-01/take_rate_share_full.py index 8a8ae92..7ad0c25 100644 --- a/research/option_space_2026-10-01/take_rate_share_full.py +++ b/research/option_space_2026-10-01/take_rate_share_full.py @@ -20,8 +20,9 @@ def pairs(df): goto = P[(P.firm == "GoTo") & (P.seg == "On-demand")][["year", "V", "R"]].assign(firm="GoTo on-demand") reg = pairs(pd.concat([grab, goto])[lambda d: d.R > 0])[["firm", "dlnV", "dlnm", "m0", "m"]] ext = T[T.set == "EXT_clean"].assign(dlnV=lambda d: d.lnV, m0=lambda d: d.m0_pct / 100, m=lambda d: d.m1_pct / 100)[["firm", "dlnV", "dlnm", "m0", "m"]] -s = S[(S.flag_1p != "exclude") & S.g_m.notna() & S.g_V.notna()].rename(columns={"entity": "firm"}).sort_values(["firm", "year"]) -s["m0"] = s.groupby("firm").m.shift() +s = S.rename(columns={"entity": "firm"}).sort_values(["firm", "year"]) +s["m0"] = s.groupby("firm").m.shift() # previous year's take rate, taken before any row is filtered out +s = s[(s.flag_1p != "exclude") & s.g_m.notna() & s.g_V.notna()] new = s.rename(columns={"g_V": "dlnV", "g_m": "dlnm"})[["firm", "dlnV", "dlnm", "m0", "m"]].dropna(subset=["m0"]) def shares(d): From 79192dfc84890d6b701baaffb4acaacda0d2a4ef Mon Sep 17 00:00:00 2001 From: Claude Date: Wed, 14 Oct 2026 17:46:12 +0000 Subject: [PATCH 47/65] Chapters 1 to 5: align Chapters 3 to 5, apply Chapter 5, readability by reading in context Chapter 5 rewritten on the two links of Figure 3.1; Chapter 3's further analyses follow the order of Chapters 4 and 5; Chapter 4.9 gives 5.5 its method; one term for the thin take rate; appendix references renumbered and the narrower H1 test's Mann-Whitney row added. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 7 + papers/thesis_2026-10/README.md | 2 +- papers/thesis_2026-10/appendix_notes.md | 7 +- papers/thesis_2026-10/ch1_introduction.md | 8 +- papers/thesis_2026-10/ch2_literature.md | 8 +- papers/thesis_2026-10/ch3_framework.md | 24 ++- papers/thesis_2026-10/ch4_data_methods.md | 2 +- papers/thesis_2026-10/ch5_results.md | 188 +++++++++++++----- .../drafts/ch5_full_2026-10-14.md | 24 +-- 9 files changed, 180 insertions(+), 90 deletions(-) diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 523806e..e73211f 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -413,3 +413,10 @@ Read as one text after Chapter 3 was accepted. The line holds (Chapter 1 names w - **Checked:** Chapter 1's 2024 levels (BPS about US$81 billion, Bank Indonesia about US$31 billion) against `tables/yardsticks_bi_bps.csv`, now given their results home in 5.10; payment growth from ledger C50 to C54; "at least 754 thousand" new sellers in 2024. - **Removed from Chapter 5 because Chapters 3 and 4 carry them:** the literature sentence in 5.1, the thin-share arithmetic (now a pointer to 3.2), the incentive-deduction rule, the telescoping explanation (pointer to 3.5), the cohort definition, the list of market-value platforms (Chapter 4.5), "associations, not stock returns" (Chapter 4.5). - **To do when applying:** Appendix B row for the narrower test's Mann–Whitney p = 0.024. + +### Chapters 1 to 5 aligned, and Chapter 5 applied (14 Oct 2026) + +- **Chapter 5** replaced by the reviewed draft (`drafts/ch5_full_2026-10-14.md`): answer-first sections on Figure 3.1's two links, Table 5.1 of results and evidence, rounded numbers in prose. Appendix references renumbered (Tables 5.2 to 5.4); Appendix B gains the narrower test's Mann–Whitney row (p = 0.0242, ledger C47). +- **Alignment of Chapters 3 to 5:** Chapter 3's further analyses reordered (tracing, market values, channels, national figures) to match Chapters 4 and 5, wording unchanged; Chapter 4.9 now states the 2023 comparison of six platform measures with household spending that 5.5 reports; 5.4 names β_V and β_m of equation (8); "thin share" became "thin take rate" (one term); Chapter 3's entry-test box reads "BPS's survey of online sellers for 2023" (the file name arrives in Chapter 4). +- **Readability, judged by reading in context, not by counts** (the researcher's correction): kept the splits that help the line (Chapter 1's "two things… One is… The other is…", the 2024 BPS and Bank Indonesia figures, "In brief" as two points, the contribution sentence; Chapter 2's named openers and the 2.2 stopping point split into its two halves; Chapter 5 rejoins that tie a claim to its reason). Reverted the ones that broke it: the levy contrast (one sentence), the colon in "why this matters", "…are choices, so…", the 2.1 "where it stops" paragraph (kept whole as the researcher's device), "The median change was zero" (own sentence), a colon that promised a list. +- **Bug found and fixed:** the new take-rate share script first used 139 foreign pairs; corrected to all 158 (0.66 here against 0.24 abroad). diff --git a/papers/thesis_2026-10/README.md b/papers/thesis_2026-10/README.md index b79b2e6..e58ff66 100644 --- a/papers/thesis_2026-10/README.md +++ b/papers/thesis_2026-10/README.md @@ -39,7 +39,7 @@ The thesis written in full. Same title, research question, hypotheses and measur | File | Chapter | Status | |---|---|---| | `ch1_introduction.md` | 1 Introduction | drafted 12 Oct on the proposal's and deck's flow (opening, 1.1 question and objectives, 1.2 contributions, 1.3 structure); open concern on 1.1 in `EDIT_HISTORY.md` | -| `ch5_results.md` | 5 Results | rewritten 13 Oct as 11 sections (5.1 wedge, 5.2 H1, 5.3 tracing, 5.4 market values, 5.5 the platforms in 2023, 5.6 what sellers reported, 5.7 count and entry test, 5.8 who the extra sellers were, 5.9 returners, 5.10 channels and records, 5.11 why the national figures disagree), awaiting the researcher's reading. Rules and reach of the 2026 rules go to Chapter 6. Figures not yet placed; exhibits in `research/option_space_2026-10-01/exhibits/` | +| `ch5_results.md` | 5 Results | rewritten 14 Oct on Figure 3.1's two links: Table 5.1 (main results and evidence), inner link 5.1 to 5.4 (wedge, H1, what moved the take rate, markets), outer link 5.5 to 5.10 (2023 platforms and sellers, H2 and the entry test, what the rise contains, who the extra sellers were, channels and records, why the national figures disagree). Awaiting the researcher's reading with Chapters 3 and 4 | | `ch3_framework.md` | 3 Framework and Hypotheses | rewritten and accepted for now 13 Oct: opening declaration and map of the three gap questions, 3.1 terms and rules for comparison, 3.2 three measures (equations 1 to 4), 3.3 tracing a movement (equations 5 and 6), 3.4 hypotheses (equation 7), 3.5 the entry test, 3.6 further analyses (Table 3.3, market-value equation 8). Figure 3.1 still to be drawn; a cold read is still due (see `EDIT_HISTORY.md`) | | `ch2_literature.md` | 2 Literature Review | first full draft 12 Oct (evening), awaiting the researcher's reading: on the proposal's four sections (2.1 pricing and revenue recognition, 2.2 measuring the online economy, 2.3 digital records and third-party information, 2.4 the gap), with the survey-count strand added (breaks, recall, exit, margins). Citations are author-year, every one traced to `REFERENCES_WORKING.md`; the bibliography itself is not yet built | | `ch4_data_methods.md` | 4 Data and Methods | restructured 13 Oct to follow Chapter 5's order and to point to Chapter 3 instead of repeating it: short opening disclaimer, 4.1 platform data, 4.2 the wedge in Indonesia, 4.3 testing H1, 4.4 tracing, 4.5 market values, 4.6 survey files and H2, 4.7 entry test and extra sellers, 4.8 channels, records and reach, 4.9 comparing national figures, 4.10 labels and licensed data | diff --git a/papers/thesis_2026-10/appendix_notes.md b/papers/thesis_2026-10/appendix_notes.md index 5b54e3e..1f475c6 100644 --- a/papers/thesis_2026-10/appendix_notes.md +++ b/papers/thesis_2026-10/appendix_notes.md @@ -50,7 +50,7 @@ Left out after examination: DDL, JD, Jumei, LQDT, NEGG and VIPS (own goods are m | Grab on-demand | 4 | 0.20 | 0.50 | | Tokopedia e-commerce segment | 1 | 0.52 | 0.00 | -### A.3 The seven windows traced (Table 5.3 in full) +### A.3 The seven windows traced (Table 5.4 in full) Shares are of the log growth of revenue. Source: `tables/growth_decomposition.csv`, built from `tables/l_panel.csv` by `growth_decomposition.py`. Tokopedia, GoTo: audited statements. Grab: annual filings, summed over mobility and deliveries. Blibli: incentives are a proxy (gross profit before discounts less net revenue) and are marked `out` of the main sample in the panel. GoTo's 2022 transaction value is an estimate (2022 first and second quarters backed out of 2023 year-on-year comparisons). @@ -68,7 +68,7 @@ Tokopedia, 2022–23, line by line (Rp million, `tables/tokopedia_segment.csv`): ### A.4 Wedge and ecosystem ratio by platform-year, Indonesian series -Original currency and units, as in the filings: Rp million for Tokopedia and Bukalapak, Rp billion for Blibli, and US$ billion for Grab and Shopee, whose Indonesian amounts are derived or estimated. Shopee's revenue assumes a take rate of 8.4 percent in 2022, 10 percent in 2023 and 10.8 percent in 2024. Table 5.1 converts Tokopedia's 2023 rupiah amounts to US dollars. Take rate and ecosystem ratio carry no currency. Source: `tables/take_rate_levels.csv`. +Original currency and units, as in the filings: Rp million for Tokopedia and Bukalapak, Rp billion for Blibli, and US$ billion for Grab and Shopee, whose Indonesian amounts are derived or estimated. Shopee's revenue assumes a take rate of 8.4 percent in 2022, 10 percent in 2023 and 10.8 percent in 2024. Table 5.2 converts Tokopedia's 2023 rupiah amounts to US dollars. Take rate and ecosystem ratio carry no currency. Source: `tables/take_rate_levels.csv`. | Series | Year | Transaction value | Revenue | Wedge | Take rate (%) | Ecosystem ratio | Label | |---|---|---|---|---|---|---|---| @@ -94,7 +94,7 @@ Original currency and units, as in the filings: Rp million for Tokopedia and Buk ### B.1 The checks -The main test is in Table 5.2. This appendix lists each check with both p-values where both exist. Sources: `h1_extended.py` (`tables/h1_extended_results.json`), `tables/test_battery.csv`, `tables/h1_quarterly_results.json`, `tables/h1_points_vs_log.json`, `tables/h1_thesis_drops.json`. +The main test is in Table 5.3. This appendix lists each check with both p-values where both exist. Sources: `h1_extended.py` (`tables/h1_extended_results.json`), `tables/test_battery.csv`, `tables/h1_quarterly_results.json`, `tables/h1_points_vs_log.json`, `tables/h1_thesis_drops.json`. | Check | Indonesian and regional (median) | Foreign (median) | Mann–Whitney p | Shuffle p | |---|---|---|---|---| @@ -111,6 +111,7 @@ The main test is in Table 5.2. This appendix lists each check with both p-values | Without Grab on-demand | 0.194 | | 0.0094 | 0.0166 | | Without Tokopedia e-commerce segment | 0.169 | | 0.0164 | 0.0409 | | Three marketplaces against the 8 chosen first, comparing pairs of years, absolute growth divergence in points | 38.917 | 7.105 | 0.0022 | 0.0606 | +| Three marketplaces against the 8 chosen first, one median per platform, absolute growth divergence in points (claims ledger C47, `build_numbers.py`) | | | 0.0242 | | | Three marketplaces against the 8 chosen first, comparing pairs of years, absolute growth divergence in points (without Sea) | 38.917 | 4.192 | <0.0001 | 0.0083 | | Three marketplaces against the 8 chosen first, comparing pairs of years, absolute change in log take rate | 0.252 | 0.055 | 0.0037 | 0.0303 | | Three marketplaces against the 8 chosen first, comparing pairs of years, absolute change in log take rate (without Sea) | 0.252 | 0.033 | 0.0001 | 0.0167 | diff --git a/papers/thesis_2026-10/ch1_introduction.md b/papers/thesis_2026-10/ch1_introduction.md index 1b35a61..4a5a1d2 100644 --- a/papers/thesis_2026-10/ch1_introduction.md +++ b/papers/thesis_2026-10/ch1_introduction.md @@ -10,9 +10,9 @@ The share a platform keeps is not fixed. When it rises, revenue can grow even wh Platform and online sales figures carry weight well beyond Indonesia. Businesses in 43 economies sold almost US$27 trillion online in 2022, by the United Nations' estimate (UNCTAD 2024), and platforms carry a growing part of that trade. Investors value the companies that run them largely on reported growth. If part of that growth is a platform keeping more of each sale, then part of the price is a pricing decision. Governments use the same figures to size their digital economies and to tax online sellers. -Research explains why platform revenue and national sales figures can differ, but not when a figure stops moving with the activity it represents, or by how much. Answering that takes two things that are rarely found together: a platform's revenue and transaction value over several years, and a national count of online sellers whose underlying answers can be examined. +Research explains why platform revenue and national sales figures can differ, but not when a figure stops moving with the activity it represents, or by how much. Answering that takes two things that are rarely found together. One is a platform's revenue and transaction value over several years. The other is a national count of online sellers whose underlying answers can be examined. -In Indonesia, several platforms report revenue and transaction value each year, and BPS asks online sellers when they began and how their sales changed. The stakes are concrete. For 2024, BPS and the central bank give about US$81 billion and about US$31 billion for online sales (BPS 2025; Bank Indonesia 2025), a difference of more than two to one. The government is writing rules on platform commissions, discounts and tax. Its levy on marketplace sellers would raise about US$60 million a year on one measure of the market and about US$300 million on another, before exemptions (Ministry of Finance of the Republic of Indonesia 2025; author's calculation). Most online sellers work through messaging and social media, outside the marketplaces, so platform and survey figures are likely to part. The events of 2023 suggest that they did. +In Indonesia, several platforms report revenue and transaction value each year, and BPS asks online sellers when they began and how their sales changed. The stakes are concrete. For 2024, BPS gives about US$81 billion for online sales and the central bank about US$31 billion, more than two to one (BPS 2025; Bank Indonesia 2025). The government is writing rules on platform commissions, discounts and tax. Its levy on marketplace sellers would raise about US$60 million a year on one measure of the market and about US$300 million on another, before exemptions (Ministry of Finance of the Republic of Indonesia 2025; author's calculation). Most online sellers work through messaging and social media, outside the marketplaces, so platform and survey figures are likely to part. The events of 2023 suggest that they did. ## 1.1 Research question and objectives @@ -24,7 +24,7 @@ The research question is addressed through three objectives. The first is to mea The thesis tests two hypotheses. H1 says that a platform's revenue grows in proportion to its transaction value. H2 says that national e-commerce growth comes mainly from more businesses selling online, and less from higher sales per business. An entry test then asks whether a rise in the count of online sellers comes from sellers entering for the first time. A further analysis estimates how many sellers a rule built on platform records can reach. -In brief, revenue shows only a small part of the commerce that platforms carry, and in BPS's survey the marketplaces carry only about a fifth of online sales value. In Indonesia, platform revenue also grew faster than the commerce behind it, mainly because platforms kept a larger share of each sale and not because buyers bought more. National growth came mainly from more sellers, as H2 states, but about half of the 2023 rise in the count of sellers is not explained by first-time entry. +In brief, revenue shows only a small part of the commerce that platforms carry. In BPS's survey, the marketplaces carry only about a fifth of online sales value. In Indonesia, platform revenue also grew faster than the commerce behind it, mainly because platforms kept a larger share of each sale and not because buyers bought more. National growth came mainly from more sellers, as H2 states, but about half of the 2023 rise in the count of sellers is not explained by first-time entry. ## 1.2 Contributions @@ -36,7 +36,7 @@ For those who measure the economy, the thesis shows where the commerce outside t For those who design tax and regulation, the thesis measures how far rules based on platform records reach. A rule built on platform records reaches only the sellers who use platforms, so where most sellers work outside them, such rules cover a minority. The thesis also sets each recent rule, from the cap on ride-hailing commissions to the limit on discounts, against the figure it acts on. Several other countries have adopted similar rules, so the same question applies there. -Earlier work has already compared platform transaction values with national e-commerce statistics. The contribution of this thesis is the connected evidence for one country, drawn from the platforms' accounts, the sellers' own survey responses and the rules that act on those figures. +Earlier work has already compared platform transaction values with national e-commerce statistics. The contribution of this thesis is connected evidence for one country. It draws on the platforms' accounts, the sellers' own survey responses and the rules that act on those figures. ## 1.3 Structure of the thesis diff --git a/papers/thesis_2026-10/ch2_literature.md b/papers/thesis_2026-10/ch2_literature.md index 7f26bd8..a7ddf71 100644 --- a/papers/thesis_2026-10/ch2_literature.md +++ b/papers/thesis_2026-10/ch2_literature.md @@ -10,15 +10,17 @@ Platform economics explains why revenue is only a small share of the sales. A pl Accounting rules add a second reason. Under the international standard, International Financial Reporting Standard 15 (IFRS 15), a company that only arranges a sale records its fee and not the full payment. If a rider pays 100 for a trip and the driver keeps 80, such a company records 20. Discounts and vouchers that it pays for are normally deducted from its revenue (International Accounting Standards Board 2014). So revenue can rise when a platform raises its fees or cuts its discounts, with no change in what customers buy. -These findings explain why the share is small and why it can move, but they leave its actual size and movement to be measured. The pricing models were built to explain how a platform sets its charges, so they predict the direction of a change, not its size in a given market. The accounting studies asked how internet firms around 2000 chose to report revenue, not how far that revenue drifted from the sales behind it (Bowen, Davis and Rajgopal 2002). Studies of how investors valued those firms relied on web traffic as their measure of activity (Trueman, Wong and Zhang 2000, 2001; Rajgopal, Venkatachalam and Kotha 2003). Transaction value also needs care, because each company defines it for itself, and a figure of this kind loses information when its definition changes (Givoly, Li, Lourie and Nekrasov 2019). Testing whether revenue stays a fair guide to the commerce needs the reported revenue and the reported transaction value of the same platforms over several years, on a consistent definition. The studies reviewed here do not make that comparison, and none covers Indonesian platforms. +Platform economics and accounting explain why the share is small and why it can move, but they leave its actual size and movement to be measured. The pricing models were built to explain how a platform sets its charges, so they predict the direction of a change, not its size in a given market. The accounting studies asked how internet firms around 2000 chose to report revenue, not how far that revenue drifted from the sales behind it (Bowen, Davis and Rajgopal 2002). Studies of how investors valued those firms relied on web traffic as their measure of activity (Trueman, Wong and Zhang 2000, 2001; Rajgopal, Venkatachalam and Kotha 2003). Transaction value also needs care, because each company defines it for itself. A figure of this kind loses information when its definition changes (Givoly, Li, Lourie and Nekrasov 2019). Testing whether revenue stays a fair guide needs the reported revenue and transaction value of the same platforms, over several years and on one definition. The studies reviewed here do not make that comparison, and none covers Indonesian platforms. ## 2.2 Measuring the digital economy A country's online commerce is larger than what platforms carry, because many sellers trade outside platforms. Several producers measure it, and each counts something different. Industry reports add up the value of goods sold through platforms (Google, Temasek and Bain 2024). Statistics offices, such as BPS in Indonesia, survey businesses about their online sales (BPS 2025). Central banks count electronic payments (Bank Indonesia 2025). The resulting values change with the definition used (UNCTAD 2023, 2024). -Official statistics address the question in two ways. National accounts separate the sale between a buyer and a seller from the platform's fee, and they count only the fee as the platform's output (Ahmad and Schreyer 2016; International Monetary Fund 2018; Organisation for Economic Co-operation and Development [OECD] 2023; United Nations et al. 2025). Research on measurement also offers a method: test a figure against the activity it stands for, as studies of night-time lights do for national income (Henderson, Storeygard and Weil 2012). Growth in online sales can be split in the same spirit, into more sales by existing sellers and more sellers entering, as trade economists split the growth of exports (Hummels and Klenow 2005). +Official statistics address the question in two ways. National accounts separate the sale between a buyer and a seller from the platform's fee, and they count only the fee as the platform's output (Ahmad and Schreyer 2016; International Monetary Fund 2018; Organisation for Economic Co-operation and Development [OECD] 2023; United Nations et al. 2025). Research on measurement also offers a method: test a figure against the activity it stands for, as studies of night-time lights do for national income (Henderson, Storeygard and Weil 2012). Growth in online sales can be split in the same spirit, into more sales by existing sellers and more sellers entering. Trade economists split the growth of exports in this way (Hummels and Klenow 2005). -These studies set out what should be counted and how one figure can be checked against another, but they were not designed to follow platform figures and national figures through time in one country. National accounting guidance defines the platform's output, and it does not track how platform sales and national sales grow. The test of one figure against another has been applied to national income and night-time lights, and, among the studies reviewed here, not to platform and national online sales. The growth split adds a further difficulty, because it rests on a count of sellers. Such counts can mislead for known reasons. A change in survey design can create a break that looks like real change (van den Brakel, Smith and Compton 2008; van den Brakel, Zhang and Tam 2020). People tend to report past events as more recent than they were (Neter and Waksberg 1964). Small firms that stop are more often young firms with young owners (McKenzie and Paffhausen 2019). Published totals cannot show whether a rise in the count reflects new sellers or a wider survey, because they do not record when each seller began. That question needs the answers of the sellers themselves. +Official statistics and measurement research say what to count and how to check one figure against another. Neither follows platform figures and national figures through time in one country. National accounting guidance defines the platform's output, and it does not track how platform sales and national sales grow. The test of one figure against another has been applied to national income and night-time lights. Among the studies reviewed here, it has not been applied to platform and national online sales. + +The growth split has its own difficulty, because it rests on a count of sellers. Such counts can mislead for known reasons. A change in survey design can create a break that looks like real change (van den Brakel, Smith and Compton 2008; van den Brakel, Zhang and Tam 2020). People tend to report past events as more recent than they were (Neter and Waksberg 1964). Small firms that stop are more often young firms with young owners (McKenzie and Paffhausen 2019). Published totals cannot show whether a rise in the count reflects new sellers or a wider survey, because they do not record when each seller began. That question needs the answers of the sellers themselves. ## 2.3 Digital records and third-party information diff --git a/papers/thesis_2026-10/ch3_framework.md b/papers/thesis_2026-10/ch3_framework.md index c5e5ced..bccf079 100644 --- a/papers/thesis_2026-10/ch3_framework.md +++ b/papers/thesis_2026-10/ch3_framework.md @@ -151,7 +151,7 @@ H2 rests on BPS's count of businesses selling online, and a rise in that count c The published figures cannot tell the two apart. The survey can, because it asks every seller in which year it began selling online. The thesis checks what the count contains with a test written down before the survey files were opened. -> **Entry test.** In the survey file that describes 2023, at least 21.5 percent of online sellers must report that they began selling online in 2023. +> **Entry test.** In BPS's survey of online sellers for 2023, at least 21.5 percent of online sellers must report that they began selling online in 2023. The 21.5 percent threshold comes from arithmetic. The published count of online sellers rose by 27.4 percent in 2023. Suppose every extra seller was new and no seller stopped. Then for every 100 sellers a year earlier there were 127.4, and 27.4 of those were new. New sellers would make up 21.5 percent of all sellers, which is 27.4 divided by 127.4. @@ -170,20 +170,12 @@ Four further analyses complete the framework without forming hypotheses of their | Analysis | What it asks | Main evidence | |---|---|---| | Tracing large movements | What moved revenue when it parted from transaction value? | Equations (5) and (6), applied to company reports | +| Market values | Do markets value growth from more commerce differently from growth in the take rate? | Year-end market values of listed platforms | | Channels and reach | How much online selling passes through marketplaces, and how far can rules built on platform records reach? | BPS's channel shares and survey files | | Comparing national figures | Do national figures tell the same story of growth? | BPS, Bank Indonesia, industry estimates and household spending | -| Market values | Do markets value growth from more commerce differently from growth in the take rate? | Year-end market values of listed platforms | Tracing large movements applies the split of Section 3.3. Wherever H1 finds a large change in the take rate, the thesis divides the change in revenue into its parts. It also reads the company's reports for changes in the mix of businesses and in reporting scope. -The channel analysis answers the third question of Section 2.4. A rule built on platform records reaches only the sellers whose sales pass through a platform. Research on tax shows that reports from a third party improve compliance where they exist (Kleven et al. 2011). - -The reach of a rule built on platform records depends on how online selling divides between marketplaces and channels that no marketplace records, such as messaging apps and social media. The thesis measures that division by sellers and by sales value. It also counts the marketplace sellers who may be large enough to fall under the marketplace tax. - -Record keeping is the second part of this analysis. The thesis compares how often marketplace sellers and other sellers keep financial statements. The comparison bears on the information that platform records can add. Chapter 6 develops the reach of the rules. - -The comparison of national figures extends the outer link in Figure 3.1. Platform reports, BPS's survey, Bank Indonesia's statistics and industry estimates count different things. Their values change with the definition used (UNCTAD 2024). If two national figures move in opposite directions in the same year, the conclusion about growth depends on the figure chosen. The thesis compares their growth rates, each in its own unit, and compares levels only after reconciling what each source covers. Household spending, from the national accounts, serves as a further point of comparison. - The market-value analysis asks whether investors, who often read revenue as a sign of commerce (Section 2.1), treat its two sources alike. Equation (5) shows that revenue can grow in two ways, through more commerce or through a larger take rate. Early studies of internet companies found that markets priced measures of web activity beyond reported revenue (Trueman, Wong and Zhang 2000). The two sources of growth need not be worth the same. Growth from the take rate has a limit, because a platform cannot raise its share of each sale without end. A higher price to one side of a platform can also reduce how much that side uses it (Rochet and Tirole 2003). @@ -194,4 +186,14 @@ The thesis relates the yearly change in a platform's market value, MV, to the tw Δln MV_it = β_V Δln V_it + β_m Δln m_it + year effect + error (8) ``` -where β_V shows how market value moves with transaction value, and β_m how it moves with the take rate. Two predictions were written down before the market values were collected. The first is that market value moves with transaction value, so β_V is positive. The second is that markets value growth from more commerce more than growth from a higher take rate, so β_V is larger than β_m. Chapter 4 describes the data and methods behind each test and analysis. +where β_V shows how market value moves with transaction value, and β_m how it moves with the take rate. Two predictions were written down before the market values were collected. The first is that market value moves with transaction value, so β_V is positive. The second is that markets value growth from more commerce more than growth from a higher take rate, so β_V is larger than β_m. + +The channel analysis answers the third question of Section 2.4. A rule built on platform records reaches only the sellers whose sales pass through a platform. Research on tax shows that reports from a third party improve compliance where they exist (Kleven et al. 2011). + +The reach of a rule built on platform records depends on how online selling divides between marketplaces and channels that no marketplace records, such as messaging apps and social media. The thesis measures that division by sellers and by sales value. It also counts the marketplace sellers who may be large enough to fall under the marketplace tax. + +Record keeping is the second part of this analysis. The thesis compares how often marketplace sellers and other sellers keep financial statements. The comparison bears on the information that platform records can add. Chapter 6 develops the reach of the rules. + +The comparison of national figures extends the outer link in Figure 3.1. Platform reports, BPS's survey, Bank Indonesia's statistics and industry estimates count different things. Their values change with the definition used (UNCTAD 2024). + +If two national figures move in opposite directions in the same year, the conclusion about growth depends on the figure chosen. The thesis compares their growth rates, each in its own unit, and compares levels only after reconciling what each source covers. Household spending, from the national accounts, serves as a further point of comparison. Chapter 4 describes the data and methods behind each test and analysis. diff --git a/papers/thesis_2026-10/ch4_data_methods.md b/papers/thesis_2026-10/ch4_data_methods.md index cdd07d0..7c40ea6 100644 --- a/papers/thesis_2026-10/ch4_data_methods.md +++ b/papers/thesis_2026-10/ch4_data_methods.md @@ -116,7 +116,7 @@ A third analysis builds on the first two and is developed in Chapter 6. It count The third objective, to compare the platform evidence with national and payment figures, rests on the sources that Table 4.3 lists, with what each one counts. Beyond the rules of Section 3.6, the comparison is closest where the coverage of two sources overlaps. Payment figures serve as context, because they show how people pay and not what they buy online. -The 2023 file, which describes 2022, lets the thesis separate the part of BPS's marketplace sales that went to final consumers. It compares that part with Bank Indonesia's figure for the same year, in a test written down before the file was opened. The thesis also sets quarterly platform growth beside quarterly household spending growth. +The 2023 file, which describes 2022, lets the thesis separate the part of BPS's marketplace sales that went to final consumers. It compares that part with Bank Indonesia's figure for the same year, in a test written down before the file was opened. The thesis sets the 2023 growth of six measures of buying through platforms beside household spending, each in its own unit. It does the same with quarterly platform growth. **Table 4.3. Sources compared in Objective 3** diff --git a/papers/thesis_2026-10/ch5_results.md b/papers/thesis_2026-10/ch5_results.md index 320313e..0104b69 100644 --- a/papers/thesis_2026-10/ch5_results.md +++ b/papers/thesis_2026-10/ch5_results.md @@ -1,29 +1,55 @@ # Chapter 5. Results -This chapter follows the thesis from the platforms to the national figures. It first asks how much of the commerce a platform's revenue shows and whether revenue keeps pace with it. It then asks why the two parted in Indonesia and what that meant for 2023. The second half turns to the survey: where the growth in sellers came from, who the extra sellers were, and why the national figures disagree. Each result is marked registered, exploratory or descriptive, as section 4.6 defines. +The results follow the two links of Figure 3.1. On the inner link, revenue showed a small and unsteady part of the commerce that platforms carried. Platforms changed how much of each sale they kept, and revenue moved with those choices (Sections 5.1 to 5.4). On the outer link, the national count of online sellers rose by 27 percent in 2023. About half of that rise did not come from sellers entering for the first time (Sections 5.5 to 5.10). + +Every result in this chapter carries one of the three labels of Section 4.10: registered, exploratory or descriptive. A result given without a label is descriptive. Table 5.1 gives each main result with its label, the checks behind it and the section that reports it. + +**Table 5.1. Main results and the evidence behind them** + +| Result | Label | Main checks | Section | +|---|---|---|---| +| *Inner link: revenue and platform commerce* | | | | +| Revenue was about 7 percent of what three major platforms carried in 2023; the wedge was about US$40 billion | Descriptive | Shopee's take rate varied; each platform dropped in turn | 5.1 | +| Take rates moved about three times as much in Indonesia and the region as abroad, so H1 is rejected | Exploratory; the narrower test planned in advance points the same way | Each platform dropped in turn; Ozon and Zalando dropped; change measured in points; quarterly pairs do not reject | 5.2 | +| The thin take rate of Indonesian and regional platforms explains much of that difference | Exploratory | Changes in percentage points compared across groups | 5.2 | +| Pricing, not more commerce, moved revenue | Descriptive | All 15 pairs against 158 abroad; seven traced windows; two views of Tokopedia | 5.3 | +| Market value moves with transaction value | Registered; holds | Extreme values trimmed; a second run | 5.4 | +| Markets value growth from a higher take rate less | Registered; does not hold in the main sample | Holds only in a second run that mixes segment figures | 5.4 | +| *Outer link: platform commerce and all online commerce* | | | | +| Buying through platforms, and sellers' own sales, did not boom in 2023 | Descriptive; registered for sellers' sales, holds | Six platform measures against household spending | 5.5 | +| More businesses account for most national growth, so H2 holds | Descriptive | Both BPS counts for 2023; two periods | 5.6 | +| Too few 2023 sellers were new to produce the rise in the count (13.45 against 21.5 percent) | Registered; the entry test fails | Recall error would favour passing | 5.6 | +| About half of the rise is not explained by first-time entry | Exploratory | With and without exits; cohorts; provinces; sampling error | 5.7 | +| The extra count leaned toward long-running businesses; returning sellers cannot be ruled out | Exploratory | Part-year selling | 5.8 | +| Most online selling happens outside marketplaces, and records are thinner there | Registered and descriptive | Four survey years; World Bank surveys | 5.9 | +| National figures agree only where they cover the same sellers | Registered, with a mixed outcome, and descriptive | Three value scenarios | 5.10 | ## 5.1 How much revenue leaves out -Take three major platforms in Indonesia in 2023: Tokopedia, Shopee and Grab. Together they carried US$43 billion of transaction value and kept US$3 billion as revenue. The difference, the invisible wedge, was US$40 billion. Revenue was about 7 percent of what passed through them, and their ecosystem ratio, the wedge divided by revenue and the author's own measure, was 12.7. For every dollar of revenue, almost 13 dollars of commerce passed through the platforms without counting as revenue. A thin slice fits how platforms are paid and how agents book revenue (Rochet and Tirole 2003; International Accounting Standards Board 2014). Table 5.1 shows how each amount was obtained. +Revenue showed only a small part of what Indonesia's major platforms carried. In 2023, Tokopedia, Shopee and Grab together carried about US$43 billion of transaction value and kept about US$3 billion as revenue. Revenue was about 7 percent of that commerce, and the invisible wedge was about US$40 billion. The ecosystem ratio was about 13, so almost 13 dollars of commerce passed through the platforms for every dollar of revenue. Table 5.2 shows each platform. -**Table 5.1. The invisible wedge of three platforms, 2023 (US$ billion)** +**Table 5.2. The invisible wedge of three platforms, 2023 (US$ billion)** -| Platform | Transaction value | Revenue | Wedge | How it is labelled | -|---|---|---|---|---| -| Tokopedia | 16.33 | 0.41 | 15.93 | Both direct segment disclosure | -| Shopee, Indonesia | 21.52 | 2.15 | 19.37 | Transaction value is an outside estimate (Momentum Works); revenue is derived at an assumed take rate of 10 percent | -| Grab, Indonesia | 5.38 | 0.60 | 4.78 | Revenue is a direct country disclosure; transaction value is derived from Grab's company-wide ratio | -| Total | 43.23 | 3.16 | 40.07 | Rounded amounts may not add | +| Platform | Transaction value | Revenue | Wedge | Ecosystem ratio | How it is labelled | +|---|---|---|---|---|---| +| Tokopedia | 16.33 | 0.41 | 15.93 | 39.3 | Both direct segment disclosure | +| Shopee, Indonesia | 21.52 | 2.15 | 19.37 | 9.0 | Transaction value is an outside estimate (Momentum Works); revenue is derived at an assumed take rate of 10 percent | +| Grab, Indonesia | 5.38 | 0.60 | 4.78 | 7.9 | Revenue is a direct country disclosure; transaction value is derived from Grab's company-wide ratio | +| Total | 43.23 | 3.16 | 40.07 | 12.7 | Sum of three platforms, not a national total | + +*Note:* Rounded amounts may not add. With Shopee's take rate at 9 or 11 percent, the total wedge is US$39.9 or US$40.3 billion; at Sea's company-wide rate of 11.5 percent, it is US$39.8 billion. Source: company filings; Momentum Works (2024); author's calculations. -The wedge does not depend much on the assumptions. If Shopee kept 9 or 11 percent instead of 10, the total would stay between US$39.9 and US$40.3 billion. At Sea's company-wide rate of 11.5 percent it would be US$39.8 billion. Dropping any one platform leaves at least US$20.7 billion. Blibli's and Bukalapak's wedges, US$3.2 and US$10.5 billion, are left out because Blibli's measure includes online travel and Bukalapak reports for its whole group. For platforms that report both amounts directly, the wedge is 97 to 99 percent of transaction value in every year, so it moves almost one for one with it. Appendix A gives the wedge by platform and year. +The ratios differ because the amounts were obtained in different ways. Tokopedia reports both amounts, and its ratio is about 39. Shopee's ratio of 9 follows from the assumed take rate, and Grab's ratio of about 8 is its company-wide ratio. Neither is separate evidence for Indonesia. Blibli and Bukalapak also report both amounts, with ratios of about 43 and 36, close to Tokopedia's. They are left out of the sum because Blibli's figure includes online travel and Bukalapak reports for its whole group. -Platforms are only part of online selling. In the BPS survey, the marketplace channel carries between 16 and 26 percent of online sales value, and BPS counts food and ride apps in that channel. So BPS places most online sales value outside the marketplace channel, and platform revenue shows less still. Section 5.9 sets the platform totals beside BPS's. A small slice can still be a fair guide if it keeps its size. The next question is whether it does. +The size of the wedge does not depend on the assumed rates. A take rate for Shopee between 9 and 11 percent keeps the total near US$40 billion. Dropping any one platform still leaves more than US$20 billion. For platforms that report both amounts, the wedge was 97 to 99 percent of transaction value in every year. It moved almost one for one with the commerce, and Appendix A gives the wedge by platform and year. -## 5.2 Do revenue and transaction value grow together? +Platform commerce is itself only part of online commerce. In BPS's survey, marketplaces carry about a fifth of online sales value (Section 5.9), so platform revenue shows a smaller part still. A small share can still be a fair guide to the commerce, as long as it keeps its size from year to year. -Revenue and transaction value do not grow together among Indonesian and regional platforms, and the thesis rejects H1. The typical take rate moved by about 20 percent in a year, up or down. At the typical foreign platform it moved by about 6 percent, so the difference is about threefold. Table 5.2 gives the main test, which is exploratory, and its checks. +## 5.2 Testing H1 -**Table 5.2. The test of H1** +Revenue did not keep pace with transaction value at the Indonesian and regional platforms, and the thesis rejects H1. At the typical platform in this group, the take rate moved by about 20 percent a year, up or down. At the typical foreign platform it moved by about 6 percent, so the moves were about three times as large. Table 5.3 gives the test and its checks. + +**Table 5.3. The test of H1** | Test | Method | Indonesian and regional group | Foreign group | p-value | |---|---|---|---|---| @@ -33,23 +59,31 @@ Revenue and transaction value do not grow together among Indonesian and regional | Drop each platform in turn, or treat Tokopedia and GoTo as one company | Mann–Whitney | 0.17 to 0.23 | 0.06 | 0.003 to 0.016 | | Quarterly pairs, 3 platforms against 8 | Mann–Whitney | 0.071 | 0.049 | 0.19 | -Table 5.2's first two rows are the main test, using the two methods of Chapter 4. The third row repeats it with growth divergence in points, and the fourth shows that no single platform carries the result. +*Note:* Group values are medians of each platform's median yearly change. Appendix B lists every check, including the tests without Ozon and Zalando. + +The main test compares the five platforms with 27 abroad, one median per platform. Both methods of Section 4.3 reject equal moves, with p = 0.004 and p = 0.022. No single platform carries the result. Dropping any one, or treating Tokopedia and GoTo as one company, keeps the Mann–Whitney p-value at 0.016 or below. The main test was set up after a first, narrower test, so it is exploratory. -The test planned in advance was narrower than the main test: the three Indonesian marketplaces against the eight foreign platforms chosen first. It rejects H1 when it compares pairs of years (p = 0.002), but pairs from one platform are not independent. When it compares platforms, which is the fairer count, it comes close without passing (p = 0.06), because three platforms give little power. The larger groups were set up after that first result, which is why the main test is exploratory. In the narrower test, revenue growth and transaction value growth had a correlation of 0.10 in Indonesia and 0.98 abroad, excluding Sea (Appendix B). The quarterly test in the last row does not reject H1. Its Indonesian pairs mostly cover 2023 to 2026, after most of the repricing, and the large moves came between 2021 and 2024. So the rejection describes that period. +The test planned in advance was narrower: the three Indonesian marketplaces against the eight foreign platforms chosen first. It points the same way. With one median per platform, the Mann–Whitney test rejects equal moves (p = 0.024). The shuffle of platform labels comes close without passing (p = 0.06), since three platforms give a test little power. In that sample, revenue growth and transaction value growth had a correlation of 0.10 in Indonesia and 0.98 abroad, excluding Sea. -A thin slice, the small share of each sale that a platform keeps, explains much of the threefold difference, which is an exploratory finding. A log change in the take rate is roughly the change in points divided by the take rate itself. Indonesian and regional platforms changed their take rates by about 1 point a year, as foreign platforms did. But the typical Indonesian and regional platform keeps only 1.6 percent of each sale, against 16.9 percent abroad. Among the foreign platforms the share runs from 2.6 percent at Shopify to 71 percent at Zalando, which also sells goods itself. If transaction value is flat, a rise of 1 point lifts revenue by more than half at a platform that keeps 1.6 percent. It lifts revenue by about 6 percent at one that keeps 16.9 percent. Figure 5.1 shows the three measures side by side. What the platforms actually changed is the next question. +The direction of the moves is less telling than their size. Revenue grew faster than transaction value in 13 of the 15 Indonesian and regional pairs. In two of them, revenue and transaction value moved in opposite directions. Abroad, take rates also rose in about two thirds of pairs, but by much less. What sets the Indonesian and regional platforms apart is the size of the moves, which is what the test measures. + +Quarterly pairs do not reject H1 (p = 0.19), contrary to a prediction registered before the test was run. The quarterly Indonesian pairs mostly cover 2023 to 2026, after most of the repricing, while the large moves came between 2021 and 2024. The rejection of H1 describes that period. + +The thin take rate of Indonesian and regional platforms explains much of the threefold difference, an exploratory finding. Measured in percentage points, both groups changed their take rates by about 1 point a year. The difference between them is nowhere near large enough to count (p = 0.65). + +The two groups differ in the take rate they start from. The typical Indonesian and regional platform keeps about 1.6 percent of each sale, against about 17 percent abroad. As Section 3.2 showed, the same 1-point rise then has very different effects. It lifts revenue by more than half at the thinner take rate and by about 6 percent at the thicker one. Figure 5.1 shows the three measures side by side. The test shows that take rates moved, but not what the platforms changed to move them. ![Figure 5.1](figures/fig5_1_thin_slice.png) -**Figure 5.1. The thin slice.** Each dot is one platform's median over its years. Left: the share of each sale that the platform keeps, on a log scale. Middle: the yearly change in that share, in percentage points. Right: the same change relative to the share itself, as a log change in percent. Black bars mark group medians. The foreign group has 26 platforms here and 27 in the main test, because one platform has no pair of usable years for the change in points. Source: company filings; author's calculations. Exploratory. +**Figure 5.1. The thin take rate.** Each dot is one platform's median over its years. Left: the share of each sale that the platform keeps, on a log scale. Middle: the yearly change in that share, in percentage points. Right: the same change relative to the share itself, as a log change in percent. Black bars mark group medians. The foreign group has 26 platforms here and 27 in the main test, because one platform has no pair of usable years for the change in points. Source: company filings; author's calculations. Exploratory. ## 5.3 What moved the take rate -Tokopedia shows best how a take rate can move revenue. In 2023 its transaction value fell by 9 percent while its revenue rose by 53 percent. Across the three Indonesian marketplaces, the take rate accounts for two thirds of the movement in revenue at the median (66.5 percent). At the eight foreign platforms chosen first, it accounts for about a quarter (26.7 percent). The wedge follows the volume instead: at the median, about 96 percent of a change in the wedge comes from the change in transaction value, in both groups. +Pricing, not more commerce, moved revenue at the Indonesian and regional platforms. With equation (5), the take rate accounts for about two thirds of a revenue move at the typical pair, against about a quarter abroad. The wedge, by contrast, follows the commerce: in both groups, about 95 percent of a move in the wedge comes from transaction value. -Table 5.3 splits the growth of revenue into more commerce, higher fees and lower incentives, which are the discounts and vouchers a platform pays for. Fees and incentives are measured per unit of transaction value. A negative share means that the part pulled revenue down. The windows of one platform overlap, so they are not seven independent cases. +Where companies disclose both their fees and their incentives, equation (6) splits the growth of revenue further. Table 5.4 gives the seven traced windows. Fees and incentives are measured per unit of transaction value, and a negative share means that a part pulled revenue down. The windows of one platform overlap, so they are not seven independent cases. -**Table 5.3. What moved revenue in seven Indonesian and regional windows (share of the growth of revenue, percent)** +**Table 5.4. What moved revenue in seven Indonesian and regional windows (share of the growth of revenue, percent)** | Platform and window | Growth of revenue | More commerce | Higher fees | Lower incentives | |---|---|---|---|---| @@ -61,39 +95,57 @@ Table 5.3 splits the growth of revenue into more commerce, higher fees and lower | GoTo on-demand, 2022–25 | +111% | 12 | 36 | 52 | | GoTo on-demand, 2023–25 | +114% | 27 | 6 | 67 | -Lower incentives were the larger of the two take-rate parts in four windows and higher fees in two. In the seventh, Grab from 2021 to 2024, the two were nearly equal. Shares are rounded, so a row can add to 99 or 101. Two series need a note. GoTo's 2022 transaction value is an estimate. Blibli does not report incentives, so its split rests on a proxy, and part of its 2022–23 rise comes from travel and digital products. Bukalapak's reports do not allow the parts to be separated. +*Note:* Shares are rounded, so a row can add to 99 or 101. Source: company filings; author's calculations (Appendix A). + +Higher fees and lower incentives together outweighed more commerce in every window. Lower incentives were the larger of the two in four windows and higher fees in two. In the seventh, Grab from 2021 to 2024, the two were nearly equal. Three series need a note. GoTo's 2022 transaction value is an estimate. Blibli does not report incentives, so its split rests on a proxy, and part of its 2022–23 rise comes from travel and digital products. Bukalapak's reports do not allow the parts to be separated. -Tokopedia needs two views. Accounting rules normally deduct what a company pays its customers from its revenue (International Accounting Standards Board 2014), and Tokopedia's incentives fell by about one third in 2023. Counting the lines of its accounts, 61 percent of the extra net revenue came from lower incentives and 39 percent from higher gross fees. That count leaves the fall in volume inside both lines. Table 5.3 takes it out, and on that per-unit basis higher fees account for more of the rise in the take rate than lower incentives. Both views agree that pricing and not more commerce drove the rise, and they differ only on which lever led (Figure 5.2). Across these windows, revenue growth was not a fair guide to the commerce the platforms carried. Did investors see through it? +Tokopedia can be read in two ways, and both point to pricing. Its incentives fell by about a third in 2023. Counted line by line in its accounts, lower incentives gave about 60 percent of the extra revenue and higher gross fees about 40 percent. That count leaves the fall in volume inside both lines. Measured per unit of transaction value, as in Table 5.4, higher fees account for more of the rise in the take rate. The two views differ only on which lever led (Figure 5.2). ![Figure 5.2](figures/fig5_2_tokopedia_two_views.png) **Figure 5.2. Tokopedia, 2022 to 2023: two ways to split the rise in revenue.** Left: the change in net revenue split by the lines of the accounts. Right: the growth of revenue split per unit of transaction value, where a negative share pulled revenue down. Source: GoTo annual report 2023, segment note; author's calculations. +Tokopedia is not the only case of revenue rising while the commerce shrank. From 2024 to 2025, Blibli's transaction value fell by about 2 percent while its revenue rose by about 12 percent. In the windows traced here, revenue growth was not a fair guide to the commerce the platforms carried. Whether markets treated it as one is a separate question. + ## 5.4 What markets valued -Market value moved with transaction value, a registered result. Across 28 listed platforms and 138 platform-years, a 1 percent rise in transaction value goes with a 0.6 percent rise in market value (p = 0.002). The second prediction was that markets value growth from a higher take rate less. It did not hold in the main sample. A 1 percent rise in the take rate goes with a rise of 0.3 percent. That estimate is too uncertain to tell from zero. The difference between the two is not large enough to count (p = 0.20). Trimming extreme values leaves the difference at p = 0.16. A second run adds six series, three segments and three more whole companies, and gives 0.63 for transaction value and 0.07 for the take rate. There the difference counts (p = 0.004). That run mixes segment figures with whole-company market values, as the registration warned, so the thesis does not lean on it. The answer depends on which platforms are in the sample. +Across 28 listed platforms and 138 platform-years, market value moved with transaction value, a registered result. A 1 percent rise in transaction value goes with a rise of about 0.6 percent in market value (β_V in equation (8), p = 0.002). Section 4.5 describes this sample, which differs from the 27 foreign platforms of H1. + +The second registered prediction does not hold in the main sample. It expected markets to value growth from a higher take rate less than growth from more commerce. A 1 percent rise in the take rate goes with a rise of about 0.3 percent (β_m). That estimate is too uncertain to tell from zero. The gap between the two estimates is not large enough to count (p = 0.20), or p = 0.16 with extreme values trimmed. -The 28 platforms of the market-value sample differ from the 27 foreign platforms of H1. It adds Airbnb, Booking, DoorDash, Lyft and Uber, and it lacks Despegar, Mercado Libre, Ozon, Rakuten and Sea, which enter only the second run or not at all. So the data show that market value follows the commerce a platform carries. Early studies of internet companies found that markets price web-traffic measures too, which points the same way (Trueman, Wong and Zhang 2000; Rajgopal, Venkatachalam and Kotha 2003). The data do not show that markets saw through the pricing effect. Both results are associations and not stock returns, because market value also changes when a company issues shares. The next section asks what the commerce did in 2023. +A second run adds three segment series and three more whole companies. There the gap counts (p = 0.004), with about 0.6 for transaction value against about 0.1 for the take rate. That run sets segment figures against whole-company market values, as the registration warned, so the thesis does not rely on it. The answer depends on which platforms are in the sample. + +Market value follows the commerce a platform carries. Early studies found a similar link, with markets pricing measures of web activity beyond revenue (Trueman, Wong and Zhang 2000). The data do not show that markets valued growth from pricing any less. Revenue, transaction value and market value all come from the platforms' own reports. The national figures give a separate view of the same commerce. ## 5.5 The platforms and the sellers in 2023 -Buying through platforms did not boom in 2023. Figure 5.3 sets six measures of it against household spending, which rose by 9.4 percent in current prices. Five of the six grew more slowly, and three fell. Blibli is the exception. +Buying through platforms did not boom in 2023. Household spending rose by about 9 percent in current prices that year. Of six measures of buying through platforms, five grew more slowly, and three fell. Blibli is the one exception. Figure 5.3 sets the measures side by side, each in its own unit. + +The figures that described a boom were of other kinds. Tokopedia's revenue rose by 53 percent and BPS's online sales value by 41 percent, while the buying that platforms carried grew slowly or fell. Figure 5.3 shows the two kinds in different colours. ![Figure 5.3](figures/fig5_3_growth_2023.png) -**Figure 5.3. Growth in 2023 (percent).** Each measure is in its own unit. Sources: company reports, Bank Indonesia, Momentum Works and BPS; household spending from national accounts. GoTo's change rests on an estimated 2022 value, and Momentum Works' 3.5 is the author's calculation from its published levels (its 2024 release states 3.7). Appendix D gives the values. +**Figure 5.3. Growth in 2023 (percent).** Blue bars show measures of buying; orange bars show the figures that described a boom. Each measure is in its own unit. Sources: company reports, Bank Indonesia, Momentum Works and BPS; household spending from national accounts. GoTo's change rests on an estimated 2022 value, and Momentum Works' 3.5 is the author's calculation from its published levels (its 2024 release states 3.7). Appendix D gives the values. + +Sellers' own accounts of their sales show no boom either, a registered result. BPS asks each business how its online sales changed. For businesses already selling, the registered prediction set an upper limit on the median change. The limit was about 10 percent, the published growth in sales per business. The median change was zero. + +Fewer sellers reported a rise than a year earlier. About a quarter reported a rise and about a third a fall. A year before, more than a third had reported a rise and a quarter a fall. The mean change was about −4 percent. Lack of demand became the main obstacle, named by 41 percent of sellers against 35 percent in 2022. The published count of online sellers nonetheless rose by 27 percent in 2023. A rise of that size, in a year without a boom in sales, needs explaining. + +## 5.6 H2 and the entry test -Figure 5.3 compares only growth rates, because the measures are in different units. The blue bars show the buying, and the orange bars show the figures that described a boom. Revenue and BPS's value rose strongly while the buying through platforms did not. +H2 holds on BPS's published figures, but the registered entry test shows that first-time entry cannot explain the whole rise in the count. From 2022 to 2023, online sales value grew by 41 percent and the number of businesses by 27 percent. More businesses account for 71 percent of that growth, and higher sales per business for the rest. -Sellers' own accounts of their sales show no boom. BPS asks each business how its online sales changed. Among businesses already selling, the median change was zero. A registered prediction had set the limit at 10.4 percent, the published growth in sales per business. A share of 24 percent reported a rise, 32 percent a fall and 44 percent no change, against 37 percent up and 24 percent down a year earlier. The mean change was −4.3 percent, and lack of demand became the main obstacle, named by 41 percent of sellers against 35 percent in 2022. Yet the published count of online sellers rose by 27 percent that year. Where did those sellers come from? +H2 holds again from 2023 to 2024. Value grew by 17 percent, and more businesses account for 90 percent of the growth, or 71 percent on BPS's other count for 2023. The split of equation (7) is arithmetic, and it cannot tell a new seller from one counted for the first time. -## 5.6 Where the extra sellers came from +The registered entry test separates new sellers from sellers counted for the first time, and it fails. In the survey for 2023 (the 2024 file), about 13 percent of sellers began selling online that year. The test required 21.5 percent. Even with no sellers stopping, that many new sellers would raise the count by at most about 16 percent, against a published rise of 27 percent. -On BPS's published figures, H2 holds. From 2022 to 2023, online sales value grew by 40.6 percent, the number of businesses by 27.4 percent and sales per business by 10.4 percent. So more businesses account for 71 percent of the growth. From 2023 to 2024 the figures were 17.1, 15.3 and 1.5 percent, and more businesses account for 90 percent, or 71 percent on BPS's other count for 2023. The split is arithmetic, and it cannot tell a new seller from one counted for the first time. +Recall error does not explain the shortfall. As Section 3.5 noted, sellers who report a start date later than the true one make new sellers look more numerous. Such errors push the share toward passing, so the shortfall is, if anything, understated. Part of the 2023 rise in the count is something other than first-time entry, and its size is the next result. -The entry test separates new sellers from sellers counted for the first time, and its result is registered. In the 2024 file, 13.45 percent of sellers began selling online in 2023, and the rule required at least 21.5 percent. The test fails. Even if no seller stopped, that many new sellers lets the count grow by at most 15.5 percent over the sellers already there, against the published 27.4 percent. Respondents often place past events nearer to the interview than they were (Neter and Waksberg 1964). If sellers who began earlier reported 2023, that would make new sellers look more numerous, so the shortfall is if anything understated. +## 5.7 What the rise in the count contains -**Table 5.4. The 2023 rise in the count of online sellers (thousands)** +About half of the 2023 rise in the count is not explained by first-time entry, an exploratory result. Table 5.5 sets out the accounting. In the survey for 2023, sellers who began before 2023 outnumber the whole count for 2022 by about 306 thousand. They make up 37 percent of the rise if no seller stopped, and 56 percent if sellers stopped at about 5 percent a year. + +**Table 5.5. The 2023 rise in the count of online sellers (thousands)** | Part of the rise | If no seller stopped | If sellers stopped at 5 percent a year | |---|---|---| @@ -101,54 +153,80 @@ The entry test separates new sellers from sellers counted for the first time, an | Net entry: sellers who began in 2023, less those who stopped | 513 (63%) | 361 (44%) | | Earlier sellers newly counted above the whole 2022 count | 306 (37%) | 458 (56%) | -Source: BPS survey files, weighted; author's calculations. +*Note:* BPS's published counts give a rise of 821 thousand (BPS 2025). Source: BPS survey files, weighted; author's calculations. + +The cohort counts show the break directly. A cohort should only shrink between surveys, and the cohorts did so in every interval except 2023. Sellers who began by 2020 fell by about 10 percent between the surveys for 2020 and 2022. In the survey for 2023 they rose by 6 percent. -Table 5.4 shows the accounting. The rise of 819 thousand is the gap between the weighted totals of the two files, and BPS's own published counts give 821 thousand (BPS 2025). The 2023 survey counted 306 thousand more sellers who began before 2023 than the whole 2022 count held. That is 37 percent of the rise if no seller stopped and 56 percent if sellers stopped at 5 percent a year. So about half of the rise is not explained by first-time entry. This accounting and the checks below are exploratory, and they point the same way. +The count behaved normally again in BPS's published table for 2024. The cohort that began before 2020 shrank by about 10 percent. At least 754 thousand new sellers appeared, more than the rise of 584 thousand. -A cohort is the group of sellers who began by a given year, and a cohort should only shrink between surveys. Cohorts do, except in 2023. Sellers who began by 2020 numbered 9.9 percent fewer in the 2022 survey than in the 2020 one, over two years. In the 2023 survey they numbered 6.0 percent more than in the 2022 one. Between the 2023 and 2024 surveys, the cohort that began before 2020 shrank by 10.2 percent, and about 750 thousand new sellers appeared against a rise of 584 thousand. +The break was uneven across places and channels. Across the 34 provinces, growth in the count had almost no link to the share of new sellers, a rank correlation of 0.10. It had only a weak link to growth in the sample, at 0.35. A rank correlation compares the order of provinces on two measures and runs from −1 to 1. The break sat in chat and social selling. Among sellers who began by 2022, sellers using only chat or social media rose by 13 percent, and marketplace users by 0.3 percent. -The 2023 break in the cohort counts is also uneven. Across the 34 provinces, count growth had almost no link to the share of new sellers (0.10) and a weak link to growth in sample size (0.35). These are rank correlations, which compare the order of provinces on two measures and run from −1 to 1. Sellers who began by 2022 and use only chat or social media rose by 13 percent, while marketplace users rose by 0.3 percent. BPS's published error rates put the standard error of the national count, the usual size of its sampling noise, at about 1.4 percent. The 306 thousand sellers are about 4.6 times the standard error of the change. That calculation applies the 2024 edition's error rates to both years and treats the two surveys as independent. +Sampling noise does not explain the break. BPS's published error rates put the usual size of the sampling noise in the national count at about 1.4 percent. The 306 thousand extra sellers are about 4.6 times that noise in the change between the two years. The calculation applies the 2024 edition's error rates to both years and treats the two surveys as independent. -On the value side, BPS's measure rose from about US$53 billion to about US$72 billion. Sellers not explained by entry account for 15 to 17 percent of this, and entrants for 25 to 30 percent. The remaining 53 to 59 percent, about US$11 to 12 billion at the 2023 exchange rate, cannot be traced to existing sellers' answers, which show a mean change of −4.3 percent. These checks point to wider survey coverage of chat and social sellers. That is the pattern survey research warns about, because a change in how a survey finds respondents can break a series (van den Brakel, Smith and Compton 2008). The data cannot separate it from sellers returning after a pause. Who were the extra sellers? +Most of the 2023 rise in value has no source in the answers of existing sellers. BPS's online sales value rose from about US$53 billion to about US$72 billion. Entrants account for 25 to 30 percent of that rise, and sellers not explained by entry for 15 to 17 percent. Existing sellers reported a mean change of about −4 percent, so the remaining 53 to 59 percent cannot be traced to them. -## 5.7 Who the extra sellers were, and whether they returned +The checks point to a survey that reached more chat and social sellers in 2023, the kind of break that Section 3.5 described. The files cannot separate that reading from sellers returning after a pause. Who the extra sellers were bears on the choice between the two. -Comparing who the extra sellers were is exploratory. The surveys do not follow the same businesses, so the thesis compares groups. For each group it takes the 2023 count above what its normal yearly change in 2020 to 2022 would give. These extra sellers total about 450 thousand, close to the 458 thousand in Table 5.4. Figure 5.4 gives each group's share of this extra count next to its share of the 2022 count. +## 5.8 Who the extra sellers were + +The extra count leaned toward long-running businesses, older owners and places outside Java, an exploratory result. Each group's extra count is its 2023 count above what its own yearly change from 2020 to 2022 would give (Section 4.7). The extra sellers total about 450 thousand, close to the 458 thousand in Table 5.5. Figure 5.4 compares each group's share of the extra count with its share of the 2022 count. ![Figure 5.4](figures/fig5_4_extra_sellers.png) **Figure 5.4. Who the extra sellers were.** The extra sellers are the 2023 count above what each group's normal yearly change in 2020 to 2022 would give, about 450 thousand in total. Source: BPS survey files, weighted; author's calculations. Exploratory. -The extra count leaned toward long-running businesses, older owners and places outside Java, and the lean toward men in the raw data disappears once normal change is taken out. It does not look like the young start-ups of the usual growth story. That raises one question: could these be established sellers who paused and came back? +The extra count did not lean toward young businesses. A lean toward men in the raw counts disappears once each group's normal change is taken out. This profile bears on whether the extra sellers were returning after a pause. + +Part-year selling weighs against returners. A returning seller would usually sell for only part of the year, unless it restarted in January, yet part-year selling fell in 2023. Among sellers who began by 2021, those selling for part of the year fell from about 470 thousand to about 320 thousand. The same fall appears for the groups that began by 2020 and by 2022. + +The age profile weighs against returners as well. Small firms that stop are more often young firms with young owners (McKenzie and Paffhausen 2019). Returners would then make the extra count lean young. It leaned old. + +Returning sellers still cannot be ruled out. Returners equal to 10 to 15 percent of the 2022 count would remove the unexplained part, which is a scenario and not an estimate. The files carry no linked histories, and BPS changed how it asks about months between the rounds. Exits, or a move to full-year selling, could also offset part-year returners. Where online sellers sell, and what records they keep, complete the survey evidence. + +## 5.9 Channels and records -A returning seller would usually sell for only part of the year, unless the seller restarts in January, and part-year selling fell in 2023. Among sellers who began by 2021, the number selling for part of the year fell from 472 thousand to 318 thousand. It fell for the groups who began by 2020 and by 2022 as well. Small firms that stop are more often young, with young owners (McKenzie and Paffhausen 2019). If returners were mostly such firms, the extra count would lean young. It leaned old. Returners equal to 10 to 15 percent of the 2022 count would remove the unexplained part, which is a scenario and not an estimate. The check cannot rule out returners. The files carry no linked histories, BPS changed how it asks about months between the rounds, and exits or a move to full-year selling could offset them. +Most online selling happens outside marketplaces, and records are thinner there. The path of marketplace use, the value held by sellers without financial statements and the World Bank results were registered. The comparison within the group that began before 2020 is exploratory. -## 5.8 Channels and records +Nearly all online sellers, about 96 percent, use chat or social media. Marketplace use fell every time it was measured, from about 22 percent of sellers in 2020 to about 17 percent in 2024. BPS worded the question slightly differently for 2023 and 2024. -Two additional analyses show where online sellers sit. The path of marketplace use, the value held by sellers without statements and the World Bank results were registered. The comparison within the pre-2020 group is exploratory. +Marketplaces carry only about a fifth of online sales value. The share was 23 to 26 percent in 2022 on the survey files. In BPS's published figures it was 18 percent in 2023 and 16 percent in 2024. -Most online sellers do not use a marketplace, and about 96 percent use chat or social media. Use of marketplaces fell every time it was measured, from 21.6 percent of sellers in 2020 to 17.2 percent in 2024. BPS worded the question slightly differently for 2023 and 2024 than for the earlier years. From 2023 to 2024, BPS's marketplace value grew by 1.45 percent and other channels by 20.57 percent, so 98.5 percent of the increase came from outside the marketplaces. BPS's marketplace category includes many food and ride merchants: 43 percent of its sellers use only Gojek or Grab, and 47 percent use a shopping marketplace. +Almost all of the recent growth in value came from outside the marketplaces. From 2023 to 2024, BPS's marketplace value grew by about 1.5 percent and other channels by about 21 percent. BPS's marketplace category is itself broad: 43 percent of its sellers use only Gojek or Grab, and 47 percent use a shopping marketplace. ![Figure 5.5](figures/fig5_5_marketplace_and_records.png) **Figure 5.5. Marketplace use and financial statements, 2020 to 2024.** Marketplace use for 2020, 2022 and 2023 comes from the survey files and for 2024 from BPS's published figure. Financial statements come from the survey files. BPS worded the marketplace question slightly differently for 2023 and 2024, and the survey has no point for 2021. Source: BPS; author's calculations. -Records are thinner outside the marketplaces. In 2023, 28.6 percent of marketplace sellers kept complete financial statements, against 12.3 percent of other sellers, and Figure 5.5 shows the share of all sellers who keep them falling. The fall from 2020 to 2022 came while the survey appears to have counted the same kinds of seller. The fall in 2023 may partly reflect the chat-only sellers added to the count. Even among sellers who began before 2020, the share fell from 23.4 percent in 2022 to 16.8 percent in 2023. +Records are thinner outside the marketplaces. In 2023, about 29 percent of marketplace sellers kept complete financial statements, against about 12 percent of other sellers. Figure 5.5 shows the share of all sellers who keep them falling. The fall from 2020 to 2022 came while the survey appears to have counted the same kinds of seller. The fall in 2023 may partly reflect the chat-only sellers added to the count. + +The fall reaches long-running sellers too, an exploratory finding. Among sellers who began before 2020, the share keeping complete statements fell from about 23 percent in 2022 to about 17 percent in 2023. + +Sellers without statements hold far less of the value than of the count. On the bracket scenarios of Section 4.8, they held 26 to 40 percent of online value in 2022, against 79 percent of the count. The annual reading of the brackets passes the validation rule, at 1.95 times BPS's total, while the monthly reading fails at 23 times. The lower brackets alone exceed BPS's total, so the shares are ranges. + +One registered prediction on value failed narrowly. It expected the value share of sellers without statements to fall by no more than 5 points between 2020 and 2022. The share fell by 5.8 points on the main version and by 3.3 points on a second version. + +World Bank surveys of Indonesian firms show the same thin records. In six Indonesian cities, 27 percent of unregistered firms use social media with their customers. Among those, 70 percent keep no written records, and their median sales are about US$4,000 a year. A registered prediction that their mean sales would stay below about US$6,600 failed, at about US$7,300. + +Among formal firms, about 40 percent in Indonesia have neither a website nor online tax filing. That is the highest share of seven countries, against about 9 percent in Malaysia and in Thailand. + +The split by channel bears on the third question of Section 2.4, because a rule built on platform records reaches only the marketplace part. Chapter 6 measures that reach. The same split also marks where the national figures part. + +## 5.10 Why the national figures disagree -The value held by each kind of seller rests on the bracket scenarios that section 4.4 describes. The annual reading passes the validation rule, because the middle scenario gives 1.95 times BPS's total for 2022, and the monthly reading fails at 23 times. The lower brackets alone already exceed BPS's total, so the shares are ranges. Sellers without statements held between 26 and 40 percent of online value in 2022, against 79 percent of the count. The registered prediction was that their share of value would not fall by more than 5 points between 2020 and 2022. It fell by 5.8 points, so the prediction failed, narrowly. A second registered version gives a fall of 3.3 points, which is inside the limit. +The national figures agree on growth only where they cover the same sellers, and they do not agree on scale. In 2023, Bank Indonesia's e-commerce figure fell by about 5 percent while BPS's rose by about 41 percent. That is the disagreement with which Chapter 1 began, and the third objective asks what lies behind it. -World Bank surveys of Indonesian firms add evidence on records and online presence. In six Indonesian cities, 27 percent of unregistered firms use social media with their customers. Among those, 70 percent keep no written records, and their median sales are about US$4,000 a year. The registered prediction on their mean sales failed: it was below about US$6,600, and the mean is about US$7,300. Among formal firms, 39.9 percent in Indonesia have neither a website nor online tax filing, the highest of seven countries, against 9.1 percent in Malaysia and 8.7 percent in Thailand. +Where Bank Indonesia's figure and BPS's cover the same ground, the growth is small. From 2022 to 2023, BPS's own marketplace slice grew by about 0.2 percent in the middle scenario. Across the scenarios it grew by 0.2 to 13 percent. From 2023 to 2024 it grew by about 1.5 percent. Most of the disagreement sits outside the marketplaces, among the sellers that only BPS reaches. -## 5.9 Why the national figures disagree +A registered test on size gives a mixed answer. It predicted that the part of BPS's marketplace sales that went to final consumers in 2022 would fall below Bank Indonesia's figure. The prediction holds in the low and middle scenarios, at 0.16 and 0.68 times Bank Indonesia's figure. It fails in the high one, at 1.62 times. -Two official figures moved in opposite directions in 2023: Bank Indonesia's fell by 4.7 percent and BPS's rose by 40.6 percent. The third objective asks whether platform, national and payment figures give the same account. They do not give the same account of scale. They give the same account of growth only where they cover the same sellers. +In 2024 the figures divide by coverage. Those that cover mainly platforms grew by 5 to 7 percent, and BPS's grew by about 17 percent. The adjacent figures of Table 4.3 point the same way as BPS but do not measure Indonesian e-commerce. Parcels shipped in Southeast Asia grew by about 25 percent, and the tax on foreign digital services by about 25 percent. -Where Bank Indonesia's figure and BPS's cover the same ground, the growth is small. BPS's own marketplace slice grew by 0.2 percent from 2022 to 2023 in the middle scenario, and by 0.2 to 13.2 percent across the scenarios. It grew by 1.45 percent from 2023 to 2024. A registered test gives a mixed answer on size. The part of BPS's marketplace sales that went to final consumers in 2022 is 0.68 times Bank Indonesia's figure in the middle scenario and 0.16 times in the low one. In the high scenario it is 1.62 times. The prediction was that it would fall below Bank Indonesia's figure, so it holds in the first two scenarios and fails in the third. Most of the disagreement then sits outside the marketplaces, in the count of sellers that only BPS reaches. +Payment figures grew faster than any of these from 2023 to 2024. E-money shopping grew by about 30 percent, mobile-banking purchases by 83 percent and QRIS payments by 187 percent. They are context only, because they include in-person payments and the switch away from cash. -In 2024, the figures divide by coverage. Those that cover mainly platforms grew by 5 to 7 percent, and BPS's grew by 17.1 percent. Adjacent figures point the same way as BPS but do not measure Indonesian e-commerce. Southeast Asian parcels grew by 25.2 percent, and the tax on foreign digital services by 24.9 percent. +Levels cannot be reconciled. In 2024, BPS put online sales at about US$81 billion and Bank Indonesia at about US$31 billion. In 2023, BPS's marketplace slice was about US$13 billion, against about US$38 billion for Tokopedia and the Shopee estimate together. In 2024 it was less than a quarter of Momentum Works' marketplace estimate of about US$57 billion. -Sizes cannot be reconciled. In 2023, BPS's marketplace slice was about US$13 billion, against US$38 billion for Tokopedia and the Shopee estimate together. BPS's whole total for 2023, US$72 billion, lies above the three platforms' US$43 billion in Table 5.1, but the two count different things. In 2024, BPS's marketplace slice was less than a quarter of Momentum Works' marketplace estimate of about US$56.5 billion. Platform transaction value counts shipping, digital goods, travel and offline sales that a seller survey does not count in the same way. Counts differ too: Tokopedia alone said in a company statement that it had about 15 million merchants in 2022, while BPS counts about 0.6 million marketplace sellers. +Platform figures and the seller survey count different things. Platform transaction value includes shipping, digital goods, travel and offline sales that a seller survey does not count in the same way. The counts of sellers differ too. Tokopedia alone said that it had about 15 million merchants in 2022, while BPS counts about 0.6 million marketplace sellers. -Platform sales do not always keep pace with household spending. GoTo's on-demand transaction value grew more slowly than household spending in each of four quarters, from the third quarter of 2025 to the second quarter of 2026. In the last it grew by 2.0 percent against 8.3 percent, while its revenue grew by 20.5 percent. Grab and Shopee grew faster, but their figures cover the region. Payment figures such as QRIS are context only, because they include in-person payments and the switch from cash. +Platform sales do not always keep pace with household spending. In each of the four quarters to mid-2026, GoTo's on-demand transaction value grew more slowly than household spending. In the last, it grew by 2 percent against 8 percent, while its revenue grew by 21 percent. Grab's and Shopee's figures grew faster, but they cover the region. -Taken together, the results answer the research question at two levels. Revenue showed a small part of the commerce the platforms carried, and in the windows traced it was not a fair guide to that commerce, because the take rate moved. The official count of online sellers followed first-time entry only in part in 2023, and the platforms and the survey disagree most where only the survey looks. Chapter 6 asks what this means for the rules that Indonesia is writing. +The results answer the research question at two levels. On the inner link, revenue showed a small part of the commerce and stopped being a fair guide when the take rate moved. On the outer link, the 2023 rise in the count of sellers was only partly first-time entry. The figures part most where only the survey looks. Chapter 6 turns to the rules that Indonesia is writing. diff --git a/papers/thesis_2026-10/drafts/ch5_full_2026-10-14.md b/papers/thesis_2026-10/drafts/ch5_full_2026-10-14.md index 587a9f3..0104b69 100644 --- a/papers/thesis_2026-10/drafts/ch5_full_2026-10-14.md +++ b/papers/thesis_2026-10/drafts/ch5_full_2026-10-14.md @@ -11,7 +11,7 @@ Every result in this chapter carries one of the three labels of Section 4.10: re | *Inner link: revenue and platform commerce* | | | | | Revenue was about 7 percent of what three major platforms carried in 2023; the wedge was about US$40 billion | Descriptive | Shopee's take rate varied; each platform dropped in turn | 5.1 | | Take rates moved about three times as much in Indonesia and the region as abroad, so H1 is rejected | Exploratory; the narrower test planned in advance points the same way | Each platform dropped in turn; Ozon and Zalando dropped; change measured in points; quarterly pairs do not reject | 5.2 | -| The thin share kept by Indonesian and regional platforms explains much of that difference | Exploratory | Changes in percentage points compared across groups | 5.2 | +| The thin take rate of Indonesian and regional platforms explains much of that difference | Exploratory | Changes in percentage points compared across groups | 5.2 | | Pricing, not more commerce, moved revenue | Descriptive | All 15 pairs against 158 abroad; seven traced windows; two views of Tokopedia | 5.3 | | Market value moves with transaction value | Registered; holds | Extreme values trimmed; a second run | 5.4 | | Markets value growth from a higher take rate less | Registered; does not hold in the main sample | Holds only in a second run that mixes segment figures | 5.4 | @@ -41,7 +41,7 @@ Revenue showed only a small part of what Indonesia's major platforms carried. In The ratios differ because the amounts were obtained in different ways. Tokopedia reports both amounts, and its ratio is about 39. Shopee's ratio of 9 follows from the assumed take rate, and Grab's ratio of about 8 is its company-wide ratio. Neither is separate evidence for Indonesia. Blibli and Bukalapak also report both amounts, with ratios of about 43 and 36, close to Tokopedia's. They are left out of the sum because Blibli's figure includes online travel and Bukalapak reports for its whole group. -The size of the wedge does not depend on the assumed rates. A take rate for Shopee between 9 and 11 percent keeps the total near US$40 billion. Dropping any one platform still leaves more than US$20 billion. For platforms that report both amounts, the wedge was 97 to 99 percent of transaction value in every year. It moved almost one for one with the commerce. Appendix A gives the wedge by platform and year. +The size of the wedge does not depend on the assumed rates. A take rate for Shopee between 9 and 11 percent keeps the total near US$40 billion. Dropping any one platform still leaves more than US$20 billion. For platforms that report both amounts, the wedge was 97 to 99 percent of transaction value in every year. It moved almost one for one with the commerce, and Appendix A gives the wedge by platform and year. Platform commerce is itself only part of online commerce. In BPS's survey, marketplaces carry about a fifth of online sales value (Section 5.9), so platform revenue shows a smaller part still. A small share can still be a fair guide to the commerce, as long as it keeps its size from year to year. @@ -63,23 +63,23 @@ Revenue did not keep pace with transaction value at the Indonesian and regional The main test compares the five platforms with 27 abroad, one median per platform. Both methods of Section 4.3 reject equal moves, with p = 0.004 and p = 0.022. No single platform carries the result. Dropping any one, or treating Tokopedia and GoTo as one company, keeps the Mann–Whitney p-value at 0.016 or below. The main test was set up after a first, narrower test, so it is exploratory. -The test planned in advance was narrower: the three Indonesian marketplaces against the eight foreign platforms chosen first. It points the same way. With one median per platform, the Mann–Whitney test rejects equal moves (p = 0.024). The shuffle of platform labels comes close without passing (p = 0.06). Three platforms give a test little power. In that sample, revenue growth and transaction value growth had a correlation of 0.10 in Indonesia and 0.98 abroad, excluding Sea. +The test planned in advance was narrower: the three Indonesian marketplaces against the eight foreign platforms chosen first. It points the same way. With one median per platform, the Mann–Whitney test rejects equal moves (p = 0.024). The shuffle of platform labels comes close without passing (p = 0.06), since three platforms give a test little power. In that sample, revenue growth and transaction value growth had a correlation of 0.10 in Indonesia and 0.98 abroad, excluding Sea. The direction of the moves is less telling than their size. Revenue grew faster than transaction value in 13 of the 15 Indonesian and regional pairs. In two of them, revenue and transaction value moved in opposite directions. Abroad, take rates also rose in about two thirds of pairs, but by much less. What sets the Indonesian and regional platforms apart is the size of the moves, which is what the test measures. Quarterly pairs do not reject H1 (p = 0.19), contrary to a prediction registered before the test was run. The quarterly Indonesian pairs mostly cover 2023 to 2026, after most of the repricing, while the large moves came between 2021 and 2024. The rejection of H1 describes that period. -The thin share that Indonesian and regional platforms keep explains much of the threefold difference, an exploratory finding. Measured in percentage points, both groups changed their take rates by about 1 point a year. The difference between them is nowhere near large enough to count (p = 0.65). +The thin take rate of Indonesian and regional platforms explains much of the threefold difference, an exploratory finding. Measured in percentage points, both groups changed their take rates by about 1 point a year. The difference between them is nowhere near large enough to count (p = 0.65). -The two groups differ in the share they start from. The typical Indonesian and regional platform keeps about 1.6 percent of each sale, against about 17 percent abroad. As Section 3.2 showed, the same 1-point rise then has very different effects. It lifts revenue by more than half at the thinner share and by about 6 percent at the thicker one. Figure 5.1 shows the three measures side by side. The test shows that take rates moved, but not what the platforms changed to move them. +The two groups differ in the take rate they start from. The typical Indonesian and regional platform keeps about 1.6 percent of each sale, against about 17 percent abroad. As Section 3.2 showed, the same 1-point rise then has very different effects. It lifts revenue by more than half at the thinner take rate and by about 6 percent at the thicker one. Figure 5.1 shows the three measures side by side. The test shows that take rates moved, but not what the platforms changed to move them. ![Figure 5.1](figures/fig5_1_thin_slice.png) -**Figure 5.1. The thin share.** Each dot is one platform's median over its years. Left: the share of each sale that the platform keeps, on a log scale. Middle: the yearly change in that share, in percentage points. Right: the same change relative to the share itself, as a log change in percent. Black bars mark group medians. The foreign group has 26 platforms here and 27 in the main test, because one platform has no pair of usable years for the change in points. Source: company filings; author's calculations. Exploratory. +**Figure 5.1. The thin take rate.** Each dot is one platform's median over its years. Left: the share of each sale that the platform keeps, on a log scale. Middle: the yearly change in that share, in percentage points. Right: the same change relative to the share itself, as a log change in percent. Black bars mark group medians. The foreign group has 26 platforms here and 27 in the main test, because one platform has no pair of usable years for the change in points. Source: company filings; author's calculations. Exploratory. ## 5.3 What moved the take rate -Pricing, not more commerce, moved revenue at the Indonesian and regional platforms. Splitting each yearly move with equation (5), the take rate accounts for about two thirds of the move in revenue at the typical pair. Abroad it accounts for about a quarter. The wedge, by contrast, follows the commerce: in both groups, about 95 percent of a move in the wedge comes from transaction value. +Pricing, not more commerce, moved revenue at the Indonesian and regional platforms. With equation (5), the take rate accounts for about two thirds of a revenue move at the typical pair, against about a quarter abroad. The wedge, by contrast, follows the commerce: in both groups, about 95 percent of a move in the wedge comes from transaction value. Where companies disclose both their fees and their incentives, equation (6) splits the growth of revenue further. Table 5.4 gives the seven traced windows. Fees and incentives are measured per unit of transaction value, and a negative share means that a part pulled revenue down. The windows of one platform overlap, so they are not seven independent cases. @@ -99,7 +99,7 @@ Where companies disclose both their fees and their incentives, equation (6) spli Higher fees and lower incentives together outweighed more commerce in every window. Lower incentives were the larger of the two in four windows and higher fees in two. In the seventh, Grab from 2021 to 2024, the two were nearly equal. Three series need a note. GoTo's 2022 transaction value is an estimate. Blibli does not report incentives, so its split rests on a proxy, and part of its 2022–23 rise comes from travel and digital products. Bukalapak's reports do not allow the parts to be separated. -Tokopedia can be read in two ways, and both point to pricing. Its incentives fell by about a third in 2023. Counted line by line in its accounts, lower incentives gave about 60 percent of the extra revenue. Higher gross fees gave about 40 percent. That count leaves the fall in volume inside both lines. Measured per unit of transaction value, as in Table 5.4, higher fees account for more of the rise in the take rate. The two views differ only on which lever led (Figure 5.2). +Tokopedia can be read in two ways, and both point to pricing. Its incentives fell by about a third in 2023. Counted line by line in its accounts, lower incentives gave about 60 percent of the extra revenue and higher gross fees about 40 percent. That count leaves the fall in volume inside both lines. Measured per unit of transaction value, as in Table 5.4, higher fees account for more of the rise in the take rate. The two views differ only on which lever led (Figure 5.2). ![Figure 5.2](figures/fig5_2_tokopedia_two_views.png) @@ -109,9 +109,9 @@ Tokopedia is not the only case of revenue rising while the commerce shrank. From ## 5.4 What markets valued -Market value moved with transaction value, a registered result. The sample has 28 listed platforms and 138 platform-years. In it, a 1 percent rise in transaction value goes with a rise of about 0.6 percent in market value (p = 0.002). Section 4.5 describes this sample, which differs from the 27 foreign platforms of H1. +Across 28 listed platforms and 138 platform-years, market value moved with transaction value, a registered result. A 1 percent rise in transaction value goes with a rise of about 0.6 percent in market value (β_V in equation (8), p = 0.002). Section 4.5 describes this sample, which differs from the 27 foreign platforms of H1. -The second registered prediction does not hold in the main sample. It expected markets to value growth from a higher take rate less than growth from more commerce. A 1 percent rise in the take rate goes with a rise of about 0.3 percent, an estimate too uncertain to tell from zero. The gap between the two estimates is not large enough to count (p = 0.20). Trimming extreme values leaves it at p = 0.16. +The second registered prediction does not hold in the main sample. It expected markets to value growth from a higher take rate less than growth from more commerce. A 1 percent rise in the take rate goes with a rise of about 0.3 percent (β_m). That estimate is too uncertain to tell from zero. The gap between the two estimates is not large enough to count (p = 0.20), or p = 0.16 with extreme values trimmed. A second run adds three segment series and three more whole companies. There the gap counts (p = 0.004), with about 0.6 for transaction value against about 0.1 for the take rate. That run sets segment figures against whole-company market values, as the registration warned, so the thesis does not rely on it. The answer depends on which platforms are in the sample. @@ -137,7 +137,7 @@ H2 holds on BPS's published figures, but the registered entry test shows that fi H2 holds again from 2023 to 2024. Value grew by 17 percent, and more businesses account for 90 percent of the growth, or 71 percent on BPS's other count for 2023. The split of equation (7) is arithmetic, and it cannot tell a new seller from one counted for the first time. -The registered entry test separates new sellers from sellers counted for the first time, and it fails. In the survey for 2023 (the 2024 file), about 13 percent of sellers began selling online that year. The test required 21.5 percent. Even with no sellers stopping, that many new sellers would raise the count by at most about 16 percent. The published rise was 27 percent. +The registered entry test separates new sellers from sellers counted for the first time, and it fails. In the survey for 2023 (the 2024 file), about 13 percent of sellers began selling online that year. The test required 21.5 percent. Even with no sellers stopping, that many new sellers would raise the count by at most about 16 percent, against a published rise of 27 percent. Recall error does not explain the shortfall. As Section 3.5 noted, sellers who report a start date later than the true one make new sellers look more numerous. Such errors push the share toward passing, so the shortfall is, if anything, understated. Part of the 2023 rise in the count is something other than first-time entry, and its size is the next result. @@ -201,7 +201,7 @@ Records are thinner outside the marketplaces. In 2023, about 29 percent of marke The fall reaches long-running sellers too, an exploratory finding. Among sellers who began before 2020, the share keeping complete statements fell from about 23 percent in 2022 to about 17 percent in 2023. -Sellers without statements hold far less of the value than of the count. On the bracket scenarios of Section 4.8, they held 26 to 40 percent of online value in 2022. They made up 79 percent of the count. The annual reading of the brackets passes the validation rule, at 1.95 times BPS's total. The monthly reading fails, at 23 times. The lower brackets alone exceed BPS's total, so the shares are ranges. +Sellers without statements hold far less of the value than of the count. On the bracket scenarios of Section 4.8, they held 26 to 40 percent of online value in 2022, against 79 percent of the count. The annual reading of the brackets passes the validation rule, at 1.95 times BPS's total, while the monthly reading fails at 23 times. The lower brackets alone exceed BPS's total, so the shares are ranges. One registered prediction on value failed narrowly. It expected the value share of sellers without statements to fall by no more than 5 points between 2020 and 2022. The share fell by 5.8 points on the main version and by 3.3 points on a second version. From 0077fdcf10c6b3661871063841f972704f00cae1 Mon Sep 17 00:00:00 2001 From: Claude Date: Wed, 14 Oct 2026 18:37:49 +0000 Subject: [PATCH 48/65] Edit history: three Chapter 5 points to revisit after the researcher's reading Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index e73211f..1ae0589 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -420,3 +420,9 @@ Read as one text after Chapter 3 was accepted. The line holds (Chapter 1 names w - **Alignment of Chapters 3 to 5:** Chapter 3's further analyses reordered (tracing, market values, channels, national figures) to match Chapters 4 and 5, wording unchanged; Chapter 4.9 now states the 2023 comparison of six platform measures with household spending that 5.5 reports; 5.4 names β_V and β_m of equation (8); "thin share" became "thin take rate" (one term); Chapter 3's entry-test box reads "BPS's survey of online sellers for 2023" (the file name arrives in Chapter 4). - **Readability, judged by reading in context, not by counts** (the researcher's correction): kept the splits that help the line (Chapter 1's "two things… One is… The other is…", the 2024 BPS and Bank Indonesia figures, "In brief" as two points, the contribution sentence; Chapter 2's named openers and the 2.2 stopping point split into its two halves; Chapter 5 rejoins that tie a claim to its reason). Reverted the ones that broke it: the levy contrast (one sentence), the colon in "why this matters", "…are choices, so…", the 2.1 "where it stops" paragraph (kept whole as the researcher's device), "The median change was zero" (own sentence), a colon that promised a list. - **Bug found and fixed:** the new take-rate share script first used 139 foreign pairs; corrected to all 158 (0.66 here against 0.24 abroad). + +### Open for a later pass on Chapter 5 (14 Oct 2026, the researcher's decision to revisit after reading Chapters 3 to 5 together) + +1. The middle of Chapter 5 (5.6 to 5.9, the checks) may read close to Chapter 4's procedural voice; the verdict-first rhythm rests mainly on each section's first sentence. +2. The return to Chapter 1's opening scenes is one sentence in 5.10. A fuller bookend is possible, but must only set facts side by side (the 2023 break sat in chat and social selling; the same year the government banned sales on social media) without claiming one caused the other. +3. Economic scale: most results are percentages; US$ amounts appear mainly in 5.1, 5.7 and 5.10. The GDP-scale sentence (2.92 percent, ledger C39, magnitude only) is still the researcher's decision. From ecaf2bce55bee48dc7b01873cf104458fb4e1324 Mon Sep 17 00:00:00 2001 From: Claude Date: Wed, 14 Oct 2026 19:33:42 +0000 Subject: [PATCH 49/65] Chapters 6 and 7: first drafts for the researcher's reading Chapter 6 maps each 2023-26 rule to the part of the figures it acts on and measures its reach; 6.5 holds a slot for the predictions scored from late October. Chapter 7 gives the answer at both links, what failed, the limits (full audit list) and next steps. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 8 ++ papers/thesis_2026-10/README.md | 2 +- .../drafts/ch6_draft_2026-10-14.md | 108 ++++++++++++++++++ .../drafts/ch7_draft_2026-10-14.md | 75 ++++++++++++ 4 files changed, 192 insertions(+), 1 deletion(-) create mode 100644 papers/thesis_2026-10/drafts/ch6_draft_2026-10-14.md create mode 100644 papers/thesis_2026-10/drafts/ch7_draft_2026-10-14.md diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 1ae0589..8cecdf5 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -426,3 +426,11 @@ Read as one text after Chapter 3 was accepted. The line holds (Chapter 1 names w 1. The middle of Chapter 5 (5.6 to 5.9, the checks) may read close to Chapter 4's procedural voice; the verdict-first rhythm rests mainly on each section's first sentence. 2. The return to Chapter 1's opening scenes is one sentence in 5.10. A fuller bookend is possible, but must only set facts side by side (the 2023 break sat in chat and social selling; the same year the government banned sales on social media) without claiming one caused the other. 3. Economic scale: most results are percentages; US$ amounts appear mainly in 5.1, 5.7 and 5.10. The GDP-scale sentence (2.92 percent, ledger C39, magnitude only) is still the researcher's decision. + +### Chapters 6 and 7: first drafts, not yet applied (14 Oct 2026) + +`drafts/ch6_draft_2026-10-14.md` and `drafts/ch7_draft_2026-10-14.md`, written from Parts 6 and 7 (with their dated notes), `PREREGISTRATION.md` A and C, `tables/part6_checks.json`, `tables/tax_base_rulers.csv`, `tables/src/policy_timeline_web.csv` and `tables/src/platform_seller_rules_intl.csv`, in the voice of Chapters 1 to 5 and polished in three reading passes. Awaiting the researcher's reading; the chapter files are not created yet. +- **Chapter 6 shape:** opening with the tax as headline; 6.1 Table 6.1 (seven rules; regulation numbers only in the table) and equation (6) (a fee cap pushes the take rate down, an incentive limit pushes it up); 6.2 cap and discount rule; 6.3 marketplace tax reach (at most about 1 in 20 sellers and 17 to 24 percent of value; record of nearly one seller in five); 6.4 statistics rules (platform data to BPS, the 2026 census frame); 6.5 registered predictions (Table 6.2) with a marked results slot; 6.6 beyond Indonesia (OECD, DAC7, Philippines, Viet Nam; US delivery caps). Order changed from the plan: inner link to outer, as in Chapter 5, so the tax is 6.3; the statistics rules got their own section. +- **Chapter 7 shape:** opening answer (a figure is a fair guide while a condition holds); 7.1 the answer at both links; 7.2 what it adds, in Chapter 1.2's order plus research; 7.3 predictions that failed (E5, Q1, M1, E7, W3, RBI), matching Appendix C (S1, S2 and the discount-leverage rule are not in Appendix C and are left out); 7.4 limits, carrying the whole audit list (education codes, 19 top-bracket rows, Bank Indonesia's method, the exploratory 2023 accounting, the month question, Grab's filing vintages, one-sided tests) and the caveats kept out of the story; 7.5 next steps from the proposal's Section 8; a closing return to the 2023 figures. +- **To confirm before keeping:** the source of "two-wheel trips under 6 percent of Grab's rides business" (graded C in `cap_prediction.py`; marked in the draft). New citations for the reference check: GoTo Gojek Tokopedia (2026, 2Q26 call), Pasardana (2025), Katadata (2025), CNN Indonesia (2026), Republika (2024), Bureau of Internal Revenue (2023), Government of Viet Nam (2025), Li and Wang (2025, found 13 Oct, not yet read in full). +- **Later:** fill 6.5 after 27 Oct, early Nov and mid Nov; Appendix C rows for each outcome; 7.5's first step updated once scored. diff --git a/papers/thesis_2026-10/README.md b/papers/thesis_2026-10/README.md index e58ff66..be26fae 100644 --- a/papers/thesis_2026-10/README.md +++ b/papers/thesis_2026-10/README.md @@ -44,4 +44,4 @@ The thesis written in full. Same title, research question, hypotheses and measur | `ch2_literature.md` | 2 Literature Review | first full draft 12 Oct (evening), awaiting the researcher's reading: on the proposal's four sections (2.1 pricing and revenue recognition, 2.2 measuring the online economy, 2.3 digital records and third-party information, 2.4 the gap), with the survey-count strand added (breaks, recall, exit, margins). Citations are author-year, every one traced to `REFERENCES_WORKING.md`; the bibliography itself is not yet built | | `ch4_data_methods.md` | 4 Data and Methods | restructured 13 Oct to follow Chapter 5's order and to point to Chapter 3 instead of repeating it: short opening disclaimer, 4.1 platform data, 4.2 the wedge in Indonesia, 4.3 testing H1, 4.4 tracing, 4.5 market values, 4.6 survey files and H2, 4.7 entry test and extra sellers, 4.8 channels, records and reach, 4.9 comparing national figures, 4.10 labels and licensed data | | `appendix_notes.md` | Appendices A to C | working draft 13 Oct: platform lists, the seven windows, wedge by platform-year, every check on the test of H1, registered tests with outcomes; the detail that Chapters 4 and 5 point to | -| ch6, ch7 | | not started | +| ch6, ch7 | 6 The Rules and How Far They Reach; 7 Conclusion | first drafts 14 Oct in `drafts/ch6_draft_2026-10-14.md` and `drafts/ch7_draft_2026-10-14.md`, awaiting the researcher's reading; 6.5 has a results slot for the 27 Oct to mid-Nov scoring | diff --git a/papers/thesis_2026-10/drafts/ch6_draft_2026-10-14.md b/papers/thesis_2026-10/drafts/ch6_draft_2026-10-14.md new file mode 100644 index 0000000..07d8570 --- /dev/null +++ b/papers/thesis_2026-10/drafts/ch6_draft_2026-10-14.md @@ -0,0 +1,108 @@ +# Chapter 6. The Rules and How Far They Reach + +Indonesia's government is writing rules that act on the figures this thesis measures. In 2026 it capped a ride-hailing commission and limited discounts on marketplaces. It has also set a tax that marketplaces will collect from their sellers and required platforms to send data to BPS, and BPS has run a new economic census. Each rule acts on one part of what Chapter 5 measured, and each reaches only that part. + +The marketplace tax is the rule aimed most directly at online sellers. It can collect from at most about 1 in 20 online sellers and about a fifth of online sales value. It also gives the state a record of every marketplace seller, including those who pay nothing. This chapter measures what each rule can reach, using the data of Chapter 5. What each rule changes can be seen only after it takes effect, and Section 6.5 sets out the predictions that will be scored on those results. + +## 6.1 The rules and what each acts on + +The recent rules act on three parts of the figures: the platforms' take rate, the sales of sellers inside marketplaces, and BPS's coverage. Table 6.1 lists each rule with the part it acts on and the section of Chapter 5 that measures that part. + +**Table 6.1. Indonesia's recent rules and what each acts on** + +| Rule | What it does | What it acts on | Measured in | Status of the text | +|---|---|---|---|---| +| Commission cap (presidential directive, 2026; transport ministry decree, as named by GoTo) | Caps the commission on two-wheel ride-hailing trips at 8 percent from July 2026 | The fee rate, for one service | 5.3 | Announced; the legal text was not published when this thesis was written | +| Discount rule (Trade Ministry Regulation 19/2026, Art. 18) | Bars marketplaces from repeated price subsidies and discounts below fair cost | The incentive rate | 5.3 | Primary text read | +| Marketplace tax (Finance Ministry Regulation 37/2025) | Marketplaces collect 0.5 percent of domestic sellers' gross sales; individuals with sales up to Rp500 million a year are exempt; every seller gives a tax number or national ID | Sellers' sales inside marketplaces | 5.9 | Primary text read; collection due from November 2026 | +| Small-business final tax (Government Regulation 20/2026) | Keeps a 0.5 percent final tax on turnover up to Rp4.8 billion a year; excludes content creators | Small sellers in every channel | Not measured | Primary text read | +| Platform data to BPS (BPS Regulation 4/2023) | E-commerce operators must send BPS their revenue, transactions and numbers of sellers and buyers, with sanctions | BPS's coverage | 5.10 | Primary text read | +| Economic census 2026 | Door-to-door count of all businesses, including online sellers, affiliates and influencers | BPS's sampling frame | 5.7 | News reports; results not yet released | +| Ban on transactions on social media (Trade Ministry Regulation 31/2023) | Bars the sale of goods through social media platforms | Where online sales are paid | 5.9 | Primary text read | + +*Note:* At the 2024 rate, Rp500 million is about US$32,000 and Rp4.8 billion about US$300,000. Section 4.8 converts the exemption at the 2022 rate, about US$34,000, to match the survey year. Source: the legal texts; news reports where marked. + +Equation (6) shows where the first two rules act. The take rate is the fee rate less the incentive rate. A cap on a fee pushes the take rate down. A limit on incentives pushes it up. The two rules act on opposite sides of the same take rate. + +The marketplace tax acts further out, on the sales of sellers inside marketplaces. It leaves the take rate alone, but it depends on the marketplaces' records of each sale. The platform-data rule and the census act on the national figure: the first on what BPS receives, the second on where BPS looks. + +Two rules serve as context. The first is the 2023 ban on transactions on social media platforms, the step with which Chapter 1 began. Selling through chat and social media remained the norm, and in 2023 about 96 percent of online sellers used them. The second is the small-business tax, which applies in every channel. The survey files cannot identify the content creators it excludes, so the thesis does not measure its reach. + +The sections that follow take the other rules in the order of Figure 3.1, from the take rate outward. + +## 6.2 What the commission cap and the discount rule can move + +The commission cap reaches a small part of the take rates this thesis measures. It followed protests by ride-hailing drivers, who had demanded a smaller cut since 2025 (Pasardana 2025). From July 2026, a platform may keep at most 8 percent of the fare on a two-wheel ride. Car rides, food delivery and marketplaces are not covered. + +The trips under the cap are a small share of each company's business. At GoTo, the two-wheel ride service brings about 7 percent of group net revenue (GoTo Gojek Tokopedia 2026). Across Grab's whole region, rides are about 36 percent of on-demand transaction value, and two-wheel trips in Indonesia are less than 6 percent of its rides business. [Source of the 6 percent to be confirmed before this sentence is kept.] Both companies report rides and deliveries as one on-demand take rate, so the cap acts on a slice of the figure Chapter 5 measured. + +A cap on one fee does not fix the take rate, because the platform still sets its incentives. At GoTo, on-demand revenue rose by about 20 percent in the second quarter of 2026 while transaction value grew by 2 percent. The company attributed the rise to its mix of services and to "rationalization of incentives as we prepared for the implementation of the 8% commission rate" (GoTo Gojek Tokopedia 2026). Its take rate rose to about 22 percent in that quarter, the last before the cap. + +The discount rule acts on the lever that moved take rates most often. Lower incentives were the larger part of the rise in the take rate in four of the seven windows of Table 5.4. The rule covers marketplaces and other online trade. + +Where incentives can be followed over time, much of that lever had been pulled before the rule came. Tokopedia, the only marketplace that reported its incentives, cut them by about a third in 2023, its last full year of separate reporting. At GoTo's on-demand business, incentives fell from about 11 percent of transaction value in 2023 to about 4 percent in 2025. Grab still spent about 10 percent of on-demand transaction value on incentives in the four quarters to early 2026, across the region. The thesis did not register a test of the discount rule, because its effect on the take rate cannot be told apart from changes in fees. + +Marketplace fees moved the other way. Shopee raised the fees it charges sellers through 2025, and Shopee and TikTok Shop with Tokopedia raised them again in 2026 (Katadata 2025; CNN Indonesia 2026). The government declined to cap them. The rules on the take rate cap one fee on one ride-hailing service and leave the largest marketplace fees free. How far the cap moves the companies' take rates is the subject of the predictions in Section 6.5. + +## 6.3 How far the marketplace tax reaches + +The marketplace tax can collect from few online sellers, but it gives the state a record of every marketplace seller. Marketplaces will collect 0.5 percent of each domestic seller's gross sales and pass it to the tax office. Individual sellers with sales up to about US$34,000 a year are exempt (Section 4.8). Collection is due to start in November 2026, after the start date has already moved twice. + +At most about 1 in 20 online sellers falls under the tax. In 2022, marketplace sellers with sales above BPS's bracket line of about US$20,000 a year were about 5 percent of all online sellers. The exemption sits higher, at about US$34,000, so the true share is smaller. + +The value the tax reaches is larger, but it is still a minority of online sales. Marketplaces carried 23 to 26 percent of online sales value in 2022, and sellers above the bracket line held 77 to 94 percent of that. So the tax reaches at most 17 to 24 percent of online sales value, about a fifth. + +The record reaches further than the tax. Every domestic seller on a designated marketplace must give the marketplace a tax number or national ID and an address before being paid. Exempt sellers file a statement as well. Marketplace sellers were about 18 percent of online sellers in 2023, so the tax gives the state a record of nearly one seller in five, including the many who pay nothing. + +The tax leaves the other four in five sellers where they were. Sellers off the marketplaces sell through chat, social media or their own websites, and they keep complete financial statements less often (Section 5.9). Few of them plan to move. In the survey for 2023, about 13 percent of sellers off the marketplaces wanted to join one. + +The revenue at stake is small. At 0.5 percent, the tax would raise about US$60 million a year on BPS's marketplace sales for 2024, before exemptions. On e-Conomy's estimate of e-commerce transaction value, about five times larger, it would raise about US$300 million. Even the larger amount is about 0.2 percent of the government's tax target for 2026. + +The tax adds a record more than revenue. Whether collection starts in November, and whether sellers move away from marketplaces once it does, will be known only after the start. + +## 6.4 What the statistics rules can change + +The rules on statistics act on the outer link, where the national figures parted in 2023. Since 2024, e-commerce operators must send BPS their revenue, transactions and numbers of sellers and buyers. Operators that do not comply face sanctions. By late 2024, 61 operators had sent data and 75 registered operators had not (Republika 2024). BPS's publication for 2024 describes its figures as survey-based and does not mention the platform data. + +Full compliance would still leave most online selling to the survey. Platform data describe the marketplace slice, which holds about a fifth of online sales value and under a fifth of sellers (Section 5.9). The chat and social sellers, where the national figures part (Section 5.10), would still be counted only by BPS's survey. + +The economic census of 2026 acts on where the survey looks. BPS's e-commerce survey draws its areas from the economic census of 2016. The 2026 census visited businesses door to door from June to August 2026 and counted online sellers, affiliates and influencers for the first time. Its results had not been released when this thesis was written. + +A new frame can move the count of sellers without any change in selling. About half of the 2023 rise was not explained by first-time entry (Section 5.7). When BPS adopts a frame from the 2026 census, counts on either side of the change will need the same care. The entry test of Section 3.5 can be repeated on the first survey that uses the new frame. + +## 6.5 What the registered predictions test + +Ten predictions on the rules and on what they act on were registered before any result for the third quarter of 2026 existed. Eight concern the third-quarter results of GoTo, Grab and Sea. Six of them bear on the commission cap, one on marketplace fees and one on buying through platforms. Two concern the start of the marketplace tax and BPS's next figure for the marketplace share. Table 6.2 lists them with the date each is scored. + +**Table 6.2. Registered predictions on the 2026 rules** + +| Prediction | What it expects | Bears on | Scored | Outcome | +|---|---|---|---|---| +| P1 | GoTo's on-demand take rate falls by 0.3 to 2.5 points from the second quarter of 2026 | Commission cap | Late October 2026 | [to be filled] | +| P3 | GoTo's on-demand transaction value grows by 0 to 12 percent on a year earlier | Commission cap | Late October 2026 | [to be filled] | +| P5 | GoTo's on-demand transaction value grows by at least 5 percent | Commission cap, with spending on growth | Late October 2026 | [to be filled] | +| R1 | GoTo's on-demand revenue growth less transaction value growth lies between −0.5 and +12.5 points; it follows from P1 and P3 | Commission cap | Late October 2026 | [to be filled] | +| R2 | GoTo's on-demand transaction value grows more slowly than household spending | Buying through platforms | Early November 2026 | [to be filled] | +| P2 | Grab's on-demand take rate stays within −0.6 to +0.4 points of the second quarter | Commission cap | Mid-November 2026 | [to be filled] | +| P4 | Grab's change in the rides take rate, less its change in the deliveries take rate, is at most +0.3 points | Commission cap | Mid-November 2026 | [to be filled] | +| R3 | Shopee's revenue growth less its transaction value growth is at least +10 points | Marketplace fees | Mid-November 2026 | [to be filled] | +| Tax start | The start of the marketplace tax slips again | Marketplace tax | November 2026 | [to be filled] | +| Marketplace share | BPS's next marketplace share of online sales value is at most 15.79 percent | Reach of marketplace rules | Next BPS release | [to be filled] | + +*Note:* Ranges as registered; registration dates are in Appendix C. Sources for scoring: company results releases; BPS national accounts and e-commerce publication; the tax office's announcements. + +The predictions describe company numbers, not the effect of the cap. Both companies report rides and deliveries together, so any effect of the cap is diluted in their figures. P1 and P5 are read together. If both hold, GoTo met the cap partly by spending to grow. If P1 holds and P5 fails, the cap passed into the take rate. P4 has limited power, because its range cannot separate the cap from ordinary quarter-to-quarter noise. R2 and R3 concern single companies, and Shopee's figures cover Asia and Brazil. + +> **[Results slot.** To be written after the GoTo results (late October), the BPS national accounts (early November), the Grab and Sea results (mid-November) and the tax start (November). For each outcome: the result against its range in Table 6.2 and Appendix C, then one paragraph on what the outcomes show about the cap and one on marketplace fees and the tax.**]** + +The outcomes will be reported whatever their direction, as the other registered tests are. + +## 6.6 Beyond Indonesia + +Other countries are building rules on platform records too, and the same question of reach applies to them. The OECD's model rules ask platforms to report what their sellers earn, and a later module added the sale of goods (OECD 2020). The European Union applies such rules through its DAC7 directive, with reports on activity from 2023 (European Union 2021). The Philippines has platforms withhold tax from sellers' payouts, with small sellers exempt (Bureau of Internal Revenue 2023). Viet Nam has had platforms with a payment function withhold tax for household sellers since 2025 (Government of Viet Nam 2025). + +Indonesia's design goes further than reporting, because its marketplaces collect the tax as well as identify the seller. Every one of these rules reaches only the sales that pass through a platform. Where most selling runs through platforms, a rule built on their records reaches most sellers. Where it runs through chat and social media, as in Indonesia, it reaches a minority. + +Commission caps have been tried elsewhere as well. Several US cities and states capped the commissions that delivery platforms charged restaurants. The platforms raised the delivery fees they charged consumers, and independent restaurants lost orders while chains gained (Li and Wang 2025). In the terms of equation (6), a cap on one fee left the platform free to change another. Indonesia's cap also covers one fee on one service. + +The thesis does not measure the split between channels outside Indonesia. Any statistics office that asks sellers which channels they use can make the same count, and with it measure how far its own rules reach. diff --git a/papers/thesis_2026-10/drafts/ch7_draft_2026-10-14.md b/papers/thesis_2026-10/drafts/ch7_draft_2026-10-14.md new file mode 100644 index 0000000..7b9ed05 --- /dev/null +++ b/papers/thesis_2026-10/drafts/ch7_draft_2026-10-14.md @@ -0,0 +1,75 @@ +# Chapter 7. Conclusion + +Each figure of Indonesia's online economy is a fair guide to another only while a condition holds. Revenue follows transaction value while the take rate stays steady. A survey's count of sellers follows entry while the survey's reach stays steady. Between 2021 and 2024 the platforms' take rates moved, and revenue parted from the commerce it carried. In 2023 about half of the rise in BPS's count of online sellers was not explained by first-time entry. + +This chapter answers the research question at both links of Figure 3.1. It then says what the answer adds for those who use the figures, reports the predictions that failed, states the limits of the evidence and sets out the next steps. + +## 7.1 The answer to the research question + +The research question asked how much economic activity remains invisible when digital platforms are measured through their reported revenue, and when that choice changes the conclusion about growth. The answer to the first half is that most of the activity remains outside revenue. In 2023, three major platforms in Indonesia kept about US$3 billion of the US$43 billion that passed through them. The invisible wedge was about US$40 billion, and it moved almost one for one with the commerce. + +Platform commerce is itself only part of online commerce. In BPS's survey, marketplaces carry about a fifth of online sales value. Any measure built on platforms, whether revenue or transaction value, leaves out most online sales, which move through chat, social media and sellers' own websites. + +The answer to the second half differs by link. On the inner link, the choice of revenue changed the conclusion between 2021 and 2024. The Indonesian and regional platforms changed their take rates about three times as much as platforms abroad, measured against the take rate itself, so H1 is rejected. They keep a much thinner share of each sale, so price changes of a similar size in points moved their revenue far more. In the windows traced, pricing moved revenue more than commerce did. + +On the outer link, H2 holds on BPS's published figures: more businesses account for most of the growth in online sales value. The registered entry test failed, and about half of the 2023 rise in the count is not explained by first-time entry. The extra count leaned toward long-running businesses. In the same year, five of six measures of buying through platforms grew more slowly than household spending, and the median seller already online reported no change in sales. Reading BPS's 2023 figures as a boom in online selling overstates what sellers and platforms reported. + +The national figures agree on growth where they cover the same sellers. They part outside the marketplaces, among the chat and social sellers that only BPS reaches. The figure a reader picks decides whether 2023 looks like a stall or a boom. Market value, for its part, moved with transaction value, and the data do not show that markets valued growth from pricing any less. + +## 7.2 What the thesis adds + +For investors, the thesis adds a check that can be run on any platform that reports both amounts. Revenue growth splits into more commerce and a larger take rate with two public figures (equation (5)). In Indonesia, the take rate accounted for about two thirds of a revenue move at the typical pair, against about a quarter abroad. The thinner the take rate, the more a small change in fees or incentives moves revenue growth. + +For statistics offices, the thesis adds a test of what a count of sellers represents. The entry test needs no linked records, only a question on when each business began selling online. Applied to BPS's survey, it showed that the 2023 rise in the count was only partly first-time entry. The same test can mark a break in any survey that asks the question. + +For tax authorities and regulators, the thesis measures how far rules built on platform records reach. In Indonesia, the marketplace tax can collect from at most about 1 in 20 online sellers and about a fifth of online sales value. It still gives the state a record of every marketplace seller. Each of the other recent rules acts on one part of the take rate or of the national figure, and Chapter 6 maps each to the part it reaches. + +For research, the thesis adds connected evidence for one country: the platforms' accounts, the sellers' own answers and the rules that act on both. Its three measures, the invisible wedge, the ecosystem ratio and growth divergence, were built by the author. They use only figures that platforms already publish, so they can be computed for any platform that reports transaction value. + +## 7.3 Predictions that failed + +Several registered predictions failed, and the failures are part of the answer. The entry test is the clearest case. It required at least 21.5 percent of the sellers counted for 2023 to have begun that year, and the survey gave about 13 percent. That failure is the main result of the outer link. + +The test of H1 with quarterly pairs did not reject H1 (p = 0.19). The quarterly series cover mostly 2023 to 2026, after most of the repricing. So the rejection of H1 describes a period, 2021 to 2024, and not a lasting trait of Indonesian platforms. + +The second market-value prediction failed in the main sample (p = 0.20). It expected markets to value growth from pricing less than growth from more commerce. It held only in a second run that mixes segment figures with whole-company market values. Whether investors price the two sources of growth differently remains open. + +Three predictions on sellers' records and sales failed narrowly or in part. The value share of sellers without financial statements fell by 5.8 points between 2020 and 2022, beyond the registered limit of 5, though by only 3.3 points on a second version. The mean sales of unregistered firms that use social media were about US$7,300 a year, above the registered limit of about US$6,600, while their median was about US$4,000. The comparison with Bank Indonesia's figure held in two of three value scenarios and failed in the third. + +Appendix C lists every registered test with its outcome. The predictions on the 2026 rules are scored in Section 6.5. + +## 7.4 Limits of the evidence + +The invisible wedge is the part of each sale that a platform passes on to sellers, drivers and others. It is not missing output, unrecorded income or unpaid tax. The thesis does not find BPS's figures wrong in general, nor that total online buying in Indonesia fell. It finds that one year's count of sellers moved for reasons other than first-time entry, and that the figures part where they cover different sellers. + +The platform evidence rests on a small sample. Three Indonesian marketplaces report both revenue and transaction value. The main test of H1 adds the on-demand businesses of Grab and GoTo, which mix rides and deliveries, and Grab's figures cover the whole region. That test was set up after a narrower first test, so it is exploratory. The tests of H1 are one-sided, because each asks whether the Indonesian and regional group moves more. A two-sided test would about double each p-value, and the main Mann–Whitney result would still be below 1 percent. + +The platform figures carry their own limits. Shopee's and Grab's Indonesian amounts rest on an outside estimate, an assumed take rate or a company-wide ratio, and they stay out of the tests. Grab revised some figures between filings. The thesis takes each year from the latest filing that reports it, so one series can mix filings from before and after a revision. Blibli's incentives are a proxy, and Bukalapak's reports do not allow the parts of a revenue move to be separated. Company explanations of why revenue moved are reported as statements and are not tested. + +The survey files describe sellers in one year, not businesses followed over time. No business identifier links the files, so the thesis cannot follow a seller from one round to the next, and returning sellers cannot be ruled out. Revenue appears only in brackets, and only in the surveys for 2020 and 2022, so value shares are ranges. The survey for 2023 has no revenue amounts. At the top, sellers with sales above about US$3.4 million a year rest on 19 sample rows in the survey for 2022, yet they hold 18 to 39 percent of the marketplace value. + +Two changes in the questions also limit the survey checks. BPS changed how it asks about the months of selling between the rounds, which affects the check on part-year sellers. The education codes of the survey for 2023 were matched to the earlier groups by their shares, so education there is an inferred grouping. + +The accounting of the 2023 rise is exploratory, because it was designed after the files were opened. Its range depends on how many sellers stopped, and it assumes that no seller returned after a pause. Returners equal to 10 to 15 percent of the 2022 count would remove the unexplained part. The check on sampling noise applies one year's published error rates to both years and treats the two surveys as independent. The files carry no area or design variables, and the thesis did not contact BPS. So a change in BPS's frame cannot be told apart from other changes in coverage, and the break covers a single year. + +The national figures are read as published. Bank Indonesia does not publish its method. Momentum Works and e-Conomy publish estimates, not counts. Payment figures include payments made in person. The World Bank's survey of unregistered firms covers six cities, not the whole country. + +The market-value result is an association across 28 listed platforms. Market value also changes when a company issues shares, so the result is not a stock return. The thesis does not claim that transaction value causes market value to move. + +The rules are mapped, not evaluated. The legal text of the commission cap had not been published when the thesis was written, so its terms come from the government's announcement and GoTo's earnings call. The reach of the marketplace tax is an upper limit, because BPS's bracket line sits below the exemption. The thesis measures what each rule can reach and makes no claim about its effects. + +Three of the sources are licensed: the BPS survey files, the LSEG market values and the World Bank survey files. The repository holds the code and the national totals, and anyone who obtains the same files can rerun the analysis. The evidence concerns Indonesia, with regional series labelled as such. + +## 7.5 Next steps + +The first step is to score the registered predictions on the 2026 results, as Section 6.5 sets out. The outcomes will show how far the commission cap moved the companies' take rates, and whether the marketplace tax starts on time. + +Reconciling the levels of BPS's and the platforms' figures is the second step. It needs common definitions of what counts as a sale, including shipping, digital goods, travel and offline sales. The platform data that BPS now collects could supply the platform side, if BPS publishes them. + +The third step concerns the 2026 economic census. When BPS adopts a frame from it, the entry test can be repeated on the first survey that uses the new frame. A business identifier that links survey rounds would settle the question of returning sellers. + +The fourth step is a stock-return extension. A test of share prices around results announcements would ask whether investors react differently to growth from commerce and growth from pricing. It needs announcement timing and raw prices that can be reproduced. + +The last step takes the channel count abroad. Any country whose statistics office asks sellers which channels they use can measure how much online selling its platform rules reach. + +The thesis began with figures that moved apart in 2023. BPS's online sales rose by about 41 percent while Bank Indonesia's measure fell by about 5 percent, and Tokopedia's revenue rose while its transaction value fell. The figures disagreed because they measure different things. Revenue is what a platform keeps. Bank Indonesia's figure comes from large e-commerce firms, and BPS's survey counts the sellers it reaches. Each stops standing for another once a take rate or a survey's reach changes. A reader who checks which condition holds can use any of them. From 37064805df325d9ab768d864527505b1b9f1afa4 Mon Sep 17 00:00:00 2001 From: Claude Date: Thu, 15 Oct 2026 12:33:43 +0000 Subject: [PATCH 50/65] Edit history: pending Chapter 6 and 7 fixes, assessment and branch note for handover Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- papers/thesis_2026-10/EDIT_HISTORY.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/papers/thesis_2026-10/EDIT_HISTORY.md b/papers/thesis_2026-10/EDIT_HISTORY.md index 8cecdf5..844a985 100644 --- a/papers/thesis_2026-10/EDIT_HISTORY.md +++ b/papers/thesis_2026-10/EDIT_HISTORY.md @@ -434,3 +434,19 @@ Read as one text after Chapter 3 was accepted. The line holds (Chapter 1 names w - **Chapter 7 shape:** opening answer (a figure is a fair guide while a condition holds); 7.1 the answer at both links; 7.2 what it adds, in Chapter 1.2's order plus research; 7.3 predictions that failed (E5, Q1, M1, E7, W3, RBI), matching Appendix C (S1, S2 and the discount-leverage rule are not in Appendix C and are left out); 7.4 limits, carrying the whole audit list (education codes, 19 top-bracket rows, Bank Indonesia's method, the exploratory 2023 accounting, the month question, Grab's filing vintages, one-sided tests) and the caveats kept out of the story; 7.5 next steps from the proposal's Section 8; a closing return to the 2023 figures. - **To confirm before keeping:** the source of "two-wheel trips under 6 percent of Grab's rides business" (graded C in `cap_prediction.py`; marked in the draft). New citations for the reference check: GoTo Gojek Tokopedia (2026, 2Q26 call), Pasardana (2025), Katadata (2025), CNN Indonesia (2026), Republika (2024), Bureau of Internal Revenue (2023), Government of Viet Nam (2025), Li and Wang (2025, found 13 Oct, not yet read in full). - **Later:** fill 6.5 after 27 Oct, early Nov and mid Nov; Appendix C rows for each outcome; 7.5's first step updated once scored. + +### Pending: fixes to the Chapter 6 and 7 drafts, previewed 15 Oct 2026, not yet approved + +Found by checking Chapters 1 to 7 against each other and against the proposal. Apply only after the researcher approves; the exact before and after texts were shown in chat and are summarised here. +1. **6.3 and 7.4, BPS's "marketplace" includes food and ride apps** (in 2023, 47 percent of BPS's marketplace sellers used a shopping marketplace and 43 percent only Gojek or Grab; FINDINGS.md). Add after the value-reach sentence: "Both figures use BPS's marketplace category, which also counts food and ride apps, so both are upper limits." Replace the record sentence ("nearly one seller in five") with a range of about 8 to 18 percent of online sellers (0.47 × 17.8 to 17.8), depending on which apps the tax office designates. In 7.4, the tax-reach limit also names the food and ride apps. +2. **7.2, ownership of the measures:** follow Chapter 3 (only the ecosystem ratio is the author's own): "It tracks the invisible wedge and growth divergence over time, and its ecosystem ratio is the author's own measure." +3. **4.8, add at the end:** Chapter 6 sets each rule against the part of the figures it acts on; each legal text is read where published and labelled by status (primary text read, announced, news only). +4. **6.3, after "Exempt sellers file a statement as well.":** "Tax research finds that records held by a third party improve compliance where they exist (Section 2.3)." +5. **7.4, first paragraph, add the proposal's dropped boundaries:** "The splits of Table 5.4 are accounting, not causes. Marketplace sellers keep financial statements more often, but the thesis does not show that marketplaces lead them to." +6. **7.5, new paragraph before "The last step":** "The platform sample can grow as well. Lazada and TikTok Shop can join the test of H1 once they report Indonesian revenue and transaction value." +- Kept, after reconsideration: the "two thirds against a quarter" sentence in 7.2 (the one finding at economic scale for investors). +- Still open: source of "two-wheel trips under 6 percent of Grab's rides business" (6.2, marked in the draft). + +**Assessment shown to the researcher (15 Oct, the drafter's own judgement):** proposal about 6/10, thesis as drafted about 8/10, ceiling 8.5 to 9 after the reference check, Figure 3.1, a cold read and the 6.5 scoring. Largest integrity risk: the unchecked reference list. Largest content risk for a finance degree: the finance part is thin (M2 an association, M1 failed, stock returns deferred); framing the finance contribution deliberately in 1.2 and 7.2 is the researcher's decision. + +**Branch note:** this work is on `claude/happy-pascal-uc2eya`, 49 commits ahead of `proposal/integrated-2026-09-16`, which AGENTS.md and this README still name as the working branch. From e79f67a8ad370bfd20d7f58c0c69d52c89dd7ff5 Mon Sep 17 00:00:00 2001 From: Claude Date: Thu, 15 Oct 2026 13:04:37 +0000 Subject: [PATCH 51/65] Materials: draft Figure 3.1, GoTo-only scorer for 27 Oct, scoring sheet for Section 6.5 The GoTo-only scorer copies the registered formulas and thresholds; the registered scoring files are not edited. 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b/papers/thesis_2026-10/figures/make_fig3_1.py new file mode 100644 index 0000000..1eb2116 --- /dev/null +++ b/papers/thesis_2026-10/figures/make_fig3_1.py @@ -0,0 +1,37 @@ +"""Figure 3.1 of the thesis (draft, 15 Oct 2026): the three nested quantities and the two links tested by H1 and H2. +Illustrative only, not to scale. Same palette and ink as make_figures.py. Run from the repository root: +python3 papers/thesis_2026-10/figures/make_fig3_1.py""" +import pathlib +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from matplotlib.patches import Circle, FancyArrowPatch + +OUT = pathlib.Path(__file__).resolve().parent +BLUE, ORANGE, INK, INK2 = "#2a78d6", "#eb6834", "#0b0b0b", "#52514e" +plt.rcParams.update({"font.size": 9, "font.family": "DejaVu Sans", "savefig.dpi": 300}) + +fig, ax = plt.subplots(figsize=(7.2, 3.9)) +ax.set_xlim(0, 13.2); ax.set_ylim(0, 7); ax.set_aspect("equal"); ax.axis("off") +# circles share a bottom point so each sits inside the next +base = 0.4 +for r, face, edge, lw in [(3.2, "#eef4fc", BLUE, 1.6), (2.0, "#dbe8fa", BLUE, 1.6), (0.9, "#fbe3d9", ORANGE, 1.8)]: + ax.add_patch(Circle((3.6, base + r), r, facecolor=face, edgecolor=edge, linewidth=lw)) +ax.text(3.6, base + 2 * 3.2 - 0.45, "All online commerce", ha="center", va="top", fontsize=10, fontweight="bold", color=INK) +ax.text(3.6, base + 2 * 3.2 - 0.85, "every channel, including chat\nand social media", ha="center", va="top", fontsize=8, color=INK2) +ax.text(3.6, base + 2 * 2.0 - 0.35, "Platform commerce", ha="center", va="top", fontsize=10, fontweight="bold", color=INK) +ax.text(3.6, base + 2 * 2.0 - 0.72, "transaction value, V", ha="center", va="top", fontsize=8, color=INK2) +ax.text(3.6, base + 0.9 + 0.12, "Revenue", ha="center", va="center", fontsize=9.5, fontweight="bold", color=INK) +ax.text(3.6, base + 0.9 - 0.3, "R", ha="center", va="center", fontsize=8, color=INK2) +ax.text(3.6, base + 2.45, "invisible wedge W = V − R", ha="center", va="center", fontsize=8, color=INK2, style="italic") + +def link(y_from, y_to, label, sub, y_text): + ax.add_patch(FancyArrowPatch((7.3, y_from), (7.3, y_to), arrowstyle="<->", mutation_scale=12, color=INK, lw=1.2)) + ax.plot([3.8, 7.3], [y_from, y_from], color=INK2, lw=0.6, ls=":") + ax.plot([3.8, 7.3], [y_to, y_to], color=INK2, lw=0.6, ls=":") + ax.text(7.55, y_text + 0.2, label, ha="left", va="center", fontsize=10, fontweight="bold", color=INK) + ax.text(7.55, y_text - 0.3, sub, ha="left", va="center", fontsize=8, color=INK2) + +link(base + 1.8, base + 4.0, "H1, the inner link", "Does revenue grow in proportion\nto transaction value?", base + 2.75) +link(base + 4.0, base + 6.4, "H2, the outer link", "Does national online growth come\nmainly from more businesses?", base + 5.2) +fig.savefig(OUT / "fig3_1_two_links.png", bbox_inches="tight", facecolor="white"); plt.close(fig) diff --git a/research/option_space_2026-10-01/materials_2026-10-15/SCORING_PREP.md b/research/option_space_2026-10-01/materials_2026-10-15/SCORING_PREP.md new file mode 100644 index 0000000..fa38375 --- /dev/null +++ b/research/option_space_2026-10-01/materials_2026-10-15/SCORING_PREP.md @@ -0,0 +1,32 @@ +# Scoring the 2026 predictions: what to collect and where it goes (prepared 15 Oct 2026) + +Predictions: `PREREGISTRATION.md` sections A and C and the 3 Oct addendum. Thesis home: Section 6.5 (Table 6.2 and the results slot) and Appendix C. Nothing below changes a registered range. + +## 27 October 2026: GoTo 3Q26 release +Collect from GoTo's 3Q26 results presentation or financial statements (on-demand segment, IDR trillion): +- On-demand GTV, 3Q26 +- On-demand net revenue, 3Q26 +Run: `python3 score_q3_2026_goto_only.py GTV NET` (from `research/option_space_2026-10-01/`). Scores P1, P3, P5, R1. +Note: P5 has three outcomes as registered: confirmed at 5 percent or more, falsified below 2 percent, neither between 2 and 5. Table 6.2 should say so (pending fix). +Also note any management remarks on the 8% cap, incentives or "growth" in the call; quote with page or timestamp. + +## Early November 2026: BPS 3Q26 GDP release +Collect: household plus NPISH final consumption (the series in `three_rulers.py`: BPS national accounts, also via OECD quarterly national accounts, not seasonally adjusted), nominal (current prices), year-on-year percent, 3Q26. +Run: `python3 score_q3_2026_goto_only.py GTV NET HH_YOY`. Scores R2. + +## 1 November 2026: marketplace tax start +Registered expectation: the start slips again. Confirmed if DJP announces a postponement or collection does not begin on 1 November; falsified if collection starts on 1 November. Source: DJP announcement (pajak.go.id) and two news reports; record URL and date. + +## Mid-November 2026: Grab and Sea 3Q26 +Grab (US$ million, 3Q26): Mobility GMV and revenue; Deliveries GMV and revenue. Sea (US$ million): Shopee GMV and marketplace revenue (the series in `tables/shopee_quarterly_take_rate.csv`). +Fill the `IN` dictionary in `score_q3_2026.py` (registered file; only the inputs are filled) and run it. Scores P1 to P5 again (as a check on the GoTo-only run), P2, P4, R3. + +## Next BPS e-commerce publication (reference year 2025) +Collect the marketplace share of e-commerce value (exclusive split, as the 15.79 percent for 2024). Registered: at or below 15.79 percent. + +## Where each result goes +- Table 6.2, Outcome column: the value and CONFIRMED / FALSIFIED / NEITHER. +- Appendix C, row "P1 to P5, R1 to R3" and the tax and BPS-share row: outcome and date scored. +- Section 6.5 results slot: one paragraph on the cap (P1 to P5, R1), one on fees and the tax (R3, tax start), R2 in one sentence. +- Section 7.5 first step: change from "score" to what the scores showed. +- `EDIT_HISTORY.md`: one row per scoring date. diff --git a/research/option_space_2026-10-01/score_q3_2026_goto_only.py b/research/option_space_2026-10-01/score_q3_2026_goto_only.py new file mode 100644 index 0000000..7cec7a4 --- /dev/null +++ b/research/option_space_2026-10-01/score_q3_2026_goto_only.py @@ -0,0 +1,27 @@ +"""Score the GoTo-only predictions (P1, P3, P5, R1; R2 once BPS publishes) as soon as GoTo's 3Q26 release is out (27 Oct 2026), +without waiting for Grab and Sea (mid-November). Written 15 Oct 2026, before any 3Q26 number exists. +It copies the formulas and thresholds of cap_prediction.score_v2 and score_q3_2026.r_scores unchanged; those registered files are not edited. +Usage: python3 score_q3_2026_goto_only.py GTV_TN NET_REVENUE_TN [BPS_NOMINAL_HH_YOY_PCT] (GoTo on-demand, IDR trillion)""" +import sys +from cap_prediction import BASE2 +from score_q3_2026 import GOTO_3Q25 + +def score(gtv, net, hh=None): + take = 100 * net / gtv; d1 = take - BASE2["goto_net_take_2q26_pct"] + yoy = 100 * (gtv / GOTO_3Q25[0] - 1); rev = 100 * (net / GOTO_3Q25[1] - 1) + out = { + "P1 GoTo net take change vs 2Q26 (pt) in [-2.5, -0.3]": (round(d1, 2), -2.5 <= d1 <= -0.3), + "P3 GoTo GTV YoY (%) in [0, 12]": (round(yoy, 1), 0 <= yoy <= 12), + "P5 GoTo GTV YoY (%) >= 5; falsified only if < 2": (round(yoy, 1), True if yoy >= 5 else (False if yoy < 2 else None)), + "R1 GoTo revenue minus GTV growth (pt) in [-0.5, +12.5]": (round(rev - yoy, 1), -0.5 <= rev - yoy <= 12.5), + } + if hh is not None: + out["R2 GoTo GTV growth below nominal household spending growth"] = ((round(yoy, 1), hh), yoy < hh) + for k, (v, ok) in out.items(): + print(f"{k}: {v} -> {'CONFIRMED' if ok else ('NEITHER' if ok is None else 'FALSIFIED')}") + print(f"(3Q26 net take {take:.2f}% against 2Q26 {BASE2['goto_net_take_2q26_pct']}%; revenue YoY {rev:.1f}%)") + return out + +if __name__ == "__main__": + a = [float(v) for v in sys.argv[1:]] + score(*a) if len(a) >= 2 else print(__doc__) From eed8a398618267c29c0ddce0f18480df61a59687 Mon Sep 17 00:00:00 2001 From: Claude Date: Thu, 15 Oct 2026 13:16:37 +0000 Subject: [PATCH 52/65] Materials: cold read of Chapters 1 to 7 and finance framing sources (not yet acted on) Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- .../materials_2026-10-15/COLD_READ.md | 255 ++++++++++++++++++ .../materials_2026-10-15/FINANCE_FRAMING.md | 152 +++++++++++ 2 files changed, 407 insertions(+) create mode 100644 research/option_space_2026-10-01/materials_2026-10-15/COLD_READ.md create mode 100644 research/option_space_2026-10-01/materials_2026-10-15/FINANCE_FRAMING.md diff --git a/research/option_space_2026-10-01/materials_2026-10-15/COLD_READ.md b/research/option_space_2026-10-01/materials_2026-10-15/COLD_READ.md new file mode 100644 index 0000000..fc8e774 --- /dev/null +++ b/research/option_space_2026-10-01/materials_2026-10-15/COLD_READ.md @@ -0,0 +1,255 @@ +# Cold read of the thesis draft, 15 October 2026 + +Reader: an examiner for a finance master's who had not seen the project before. I read Chapters 1 to 5 (`papers/thesis_2026-10/ch1`–`ch5`), then `drafts/ch6_draft_2026-10-14.md` and `drafts/ch7_draft_2026-10-14.md`, in that order. I read no planning file first. I looked at the figures the chapters link to. I checked no number against a source. Where I say a number is wrong, I mean it is inconsistent with the thesis's own definitions or with another chapter. + +**Overall.** The draft is far easier to read than most theses I examine. Most sentences are short, active and point-first, and the voice holds steady from Chapter 1 to Chapter 7. The trouble spots are local. They are (a) a few sentences where the logic is packed into one clause, (b) terms and survey-year names that shift, (c) three or four places where two chapters contradict each other, and (d) Chapter 4 and Section 5.9, where the method detail gets dense. + +--- + +## 1. Chapter by chapter + +### Chapter 1. Introduction + +**Keep.** The opening is good. The Tanah Abang scene, then two official figures moving in opposite directions, then Tokopedia's revenue rising while its sales fell: three short scenes make the problem concrete before any term appears. "The two official figures moved in opposite directions" is a strong one-line point. + +**Where I got lost or had to reread** +- Opening, para 5: "If part of that growth is a platform keeping more of each sale, then part of the price is a pricing decision." I read "price" as the platform's price, not its share price, and the sentence went in a circle. I needed a second pass. +- Opening, para 4: "about 60 percent of the extra revenue came from smaller discounts and incentives, which a platform pays for out of what it earns." Sixty percent of what, and on which measure? The clause after the comma made me wonder whether incentives are a cost or a deduction from revenue. Chapter 3 later says they are a deduction. +- §1.1, research question: "how much economic activity remains invisible when digital platforms are measured through their reported revenue, and when does that choice change the conclusion about growth?" At about 30 words it is long. It is also inherited, so it may have to stay. The two-halves paragraph after it rescues it. + +**Paragraphs that did not follow** +- §1.1, para 1 is a change of voice ("This thesis sets out to change how the digital economy is measured … Decisions worth trillions of dollars rest on numbers … who is in it"). Everything before it is careful and evidence-led. This paragraph is advocacy, and it promises more than the thesis delivers. The thesis does not measure "who is in it", apart from the profile in §5.8, and it does not show "millions of the people who sell online are outside those figures". The word "missing" also invites the reading of the wedge as missing activity, which §3.2 and §7.4 reject. +- Opening, para 5 jumps from one company (Tokopedia) to US$27 trillion of world online sales and then to investors. The bridge sentence ("Platform and online sales figures carry weight well beyond Indonesia") works. The investor sentence that follows is the weak link (see above). + +**Terms used before I knew them.** In the objectives: "monetization", "revenue recognition", "reporting scope", "payment figures". "Entry test" appears in §1.1 with only a gloss. That is fine for an introduction, but "monetization" never comes back in plain words. + +**Numbers.** The levy figures, "about US$60 million a year on one measure of the market and about US$300 million on another", do not name the two measures. I found them only in §6.3. + +**Inconsistency with later chapters** +- §1.1 says "A further analysis estimates how many sellers a rule … can reach". §1.3 says Chapter 3 sets out "four further analyses". I could not tell which was right until Chapter 3. +- Chapter 1 gives Tokopedia's story as "about 60 percent of the extra revenue came from smaller discounts and incentives". Table 5.4 says the opposite on the thesis's main measure: higher fees 76, lower incentives 45. Section 5.3 explains that there are two views. But the introduction picks the view that Chapter 5 does not use as its main one, so it previews a result that Table 5.4 does not show. +- "In Indonesia, platform revenue also grew faster than the commerce behind it": the H1 group includes Grab's region-wide on-demand business. The scope is wider than "Indonesia". + +**What the chapter did, in one sentence:** In 2023 Indonesia's revenue figures and national figures told different growth stories. The thesis asks how much revenue leaves out and when that changes the growth conclusion, and it previews the answer: revenue is a thin and unsteady share, and about half of the 2023 rise in sellers was not new entry. *I could state this easily.* + +### Chapter 2. Literature Review + +**Keep.** This is the cleanest chapter. Each section opens with its point, gives the evidence and ends on what stays open. Table 2.1 is explained in the text, and the three numbered questions in §2.4 connect clearly to what follows. The IFRS 15 example ("a rider pays 100 … such a company records 20") is exactly the right size. + +**Where I had to reread** +- §2.2, para 2: "National accounts … count only the fee as the platform's output". "Output" is never defined, and it matters later because the wedge "does not measure missing output" (§3.2, §7.4). +- §2.2, para 2 moves from national accounts to night-time lights to export margins in four sentences. Each idea is fine, but the paragraph has three claims, and I lost the thread on what "in the same spirit" refers to. + +**Paragraphs that did not follow** +- §2.2, para 4: "Small firms that stop are more often young firms with young owners (McKenzie and Paffhausen 2019)." Here I could not see why it mattered. Its purpose appears only in §3.5 and §5.8. One clause on why it matters (returning sellers) would fix this. + +**As a finance examiner.** The finance literature here is thin. It consists of Bowen et al. 2002, Trueman et al. 2000/2001, Rajgopal et al. 2003 and Givoly et al. 2019. These cover only about one paragraph. Nothing covers the disclosure of non-GAAP key performance indicators by platforms, gross-versus-net revenue reporting after IFRS 15/ASC 606, or how analysts use GMV in valuing e-commerce firms. Yet the thesis claims a contribution "for investors" (§1.2, §7.2). Prof. Kong's "cite finance work" comment would apply here. + +**What the chapter did, in one sentence:** Platform economics, accounting, official statistics and tax research each explain why platform and national figures can differ, but none has measured how far they differ over time in one country, and none has done so for Indonesia. *Easy to state.* + +### Chapter 3. Framework and Hypotheses + +**Keep.** Table 3.1 as the single home for definitions is excellent. The 21.5 percent threshold in §3.5 is derived in plain arithmetic that a junior-high reader could follow ("for every 100 sellers a year earlier there were 127.4"). The recall-error paragraph is a model of turning a caveat into an argument. The worked Tokopedia example in §3.2 is the right idea. + +**Where I got lost or had to reread** +- Opening, para 3: "The three objectives set in Chapter 1 sit inside this map. Measuring the wedge and tracing its large moves serve the first question, and comparing platform figures with national and payment figures serves the second. The third question is answered by a further analysis." Objectives 1 to 3 and questions 1 to 3 do not line up. Objective 3 serves question 2, and question 3 has no objective. I reread this three times. +- Opening note under Figure 3.1: "The platform circle differs from the marketplace channel in the survey". I wanted to know how it differs. The answer (BPS's "marketplace" includes Gojek and Grab sellers) appears only in §4.8 and §5.9. +- §3.2: "D was 0.91 × 0.68 = 0.62". The 0.68 is not derived in the text. I had to work out (2.5 − 1.5)/1.5 myself, which gives 0.67, so the rounding left me unsure. +- §3.2: "Equation (4) shows that D is the change in the take rate divided by its starting level." Equation (4) also has the factor (1 + g(V)), so the sentence is not quite what the equation says. +- §3.2: "The ecosystem ratio also governs how far a change in the take rate moves revenue growth." The next paragraph's examples work, but the link from E to the result is never shown (E + 1 = 1/m). A reader is left to ask what E adds beyond the take rate itself. +- §3.3: "Measured in logarithms, the growth of revenue splits exactly …". The log explanation that follows is good. But this is the point where a non-finance reader would stop, and the plain-words version ("growth in revenue = growth in sales + growth in the share kept") could come first. + +**Paragraphs that did not follow** +- §3.4, after Table 3.2: "The share of transaction value that a platform keeps is not fixed … (Rochet and Tirole 2003) … (Evans and Schmalensee 2016)." This and the next paragraph repeat §2.1 almost line for line. Having just read the hypothesis table, I was taken back to theory. +- §3.6, the market-value paragraph moves from Trueman et al. to "the two sources of growth need not be worth the same" to the equation. It holds, but just barely. + +**Tables and numbers.** Table 3.2's H1 row says the test is "Changes in Indonesia far larger than abroad". "Far larger" is never given a threshold. I learn in Chapter 4 that it is a one-sided rank test, but the framework does not say what result would count. + +**One term per thing.** Table 3.1 itself admits that an online seller is "also called … a seller or a business". The national figure appears as "national e-commerce value", "online sales value", "online commerce" and "online selling". In the end I assumed these were one thing, but I was not sure until Chapter 5. + +**Section endings.** Most sections end on a bridge to the next chapter ("Chapter 4 describes …"), not on what stays open. + +**What the chapter did, in one sentence:** It defines transaction value, revenue, the take rate, the wedge, the ecosystem ratio and growth divergence, and it shows that revenue growth splits into volume, fees and incentives. It then sets H1 (take rates move more in Indonesia than abroad), H2 (more sellers drive most national growth), a 21.5 percent entry threshold and four further analyses. *I could state it, but only as a list. The chapter has more parts than a reader can hold.* + +### Chapter 4. Data and Methods + +**Keep.** The opening four paragraphs explain why Indonesia makes the tests possible, and that is persuasive. Table 4.1, with scope stated per series, is exactly what Prof. Kong asked for. Table 4.2's check that the weights reproduce the BPS count is reassuring. "The test was written down and dated in the project's code archive, called the repository, before the files were opened" is a good model of defining a term at first use. + +**Where I got lost or had to reread** +- §4.1, para 5: "applied in two ways. For the 19 platforms added, the thesis drops any year in which own goods exceed half of revenue. For the eight chosen first, it keeps a pair only when the company's reporting scope did not change". The second rule is not about own goods, so I did not see how it applies the third rule. I also never learned why there were "eight chosen first" and "19 added". +- §4.2, last sentence: "The thesis drops each platform in turn, and it repeats the exercise for every platform-year between 2020 and 2025 that the filings allow." Dropping a platform from a sum of three, and repeating a 2023 sum for every year, are two different things. I could not picture either. +- §4.3: "They repeat the test with growth divergence in points as Chapter 3 defines it." What does "in points" mean? Chapter 3 defines D as a share. +- §4.4: "Seven windows are traced". "Window" is never defined. "The same split is made for the wedge" leaves me to guess how. +- §4.6: "BPS prints two counts of businesses for 2023". Why two? This comes back in §4.7 (23.9 percent) and §5.6 (90 or 71 percent) and is never explained. +- §4.6: "reproduce two published shares of sellers who keep complete financial statements, 28.63 and 12.25 percent". Two shares of what? I found out only in §5.9 that they are marketplace and other sellers. +- §4.7: "If the weights had matched BPS's other count for 2023, the threshold would have been 23.9 percent, but they did not." This is a counterfactual about an unexplained second count, with "they" referring back to the weights. I reread it three times. +- §4.7, para 4 is a five-check list compressed into five sentences. "They profile who the extra sellers are, with each group's count in 2023 compared with what its own yearly change in 2020 to 2022 would give" was the hardest. +- §4.8: "The 2023 questionnaire asks for monthly revenue but the bracket limits are annual size classes, so both readings are computed and the one that passes is kept." As an examiner I read this as choosing a reading by its result (see question 3 below). As a reader I also tripped on the next sentence: "If the lower brackets alone already exceed the total, value shares are reported as ranges." +- §4.9: "Beyond the rules of Section 3.6". Section 3.6 has no rules, and the comparison rules are in §3.1. "Six measures of buying through platforms" are never listed in the text. + +**Terms used before defined** +- "Registered" first appears in §4.7 ("which was registered") and is defined only in §4.10. +- "Returners" appears in §4.7 without a gloss. + +**Survey-year names.** §4.6 sets a rule: name a file by its round and a year by its activity year. The text then uses "the 2023 file", "the 2024 file", "the 2023 questionnaire", "the survey for 2023" and "the 2021 and 2023 files". I had to keep Table 4.2 open from here to the end. In §7.4, "The survey for 2023 has no revenue amounts" looks as if it contradicts §4.8's "The 2023 questionnaire asks for monthly revenue". It does not, but only because of the naming rule. + +**What is buried.** §4.10 says the main H1 test is exploratory: "The test with the larger groups in Section 4.3 belongs here, because it was set up after a first test on the three marketplaces." For the headline hypothesis, that belongs in §4.3, not in a section about labels. + +**Market-value sample (§4.5).** It "adds Airbnb, Booking, DoorDash, Lyft and Uber, and it lacks Despegar, Mercado Libre, Ozon, Rakuten and Sea." I wanted one sentence on why. Why are Uber and Booking out of the H1 group, and why is Sea out of the market sample? The Indonesian side of the market sample is Bukalapak alone. + +**What the chapter did, in one sentence:** It sets five Indonesian and regional platform series (15 yearly pairs) against 27 foreign platforms (158 pairs), and three BPS survey files that reproduce the published counts. It then describes each test and labels each as registered, exploratory or descriptive. *I could state it, but I would not trust myself to explain the value-by-bracket method afterwards.* + +### Chapter 5. Results + +**Keep.** The chapter opening gives both links in four sentences, and that is the best summary in the thesis. Table 5.2 with its labels column is excellent. The cohort argument in §5.7 ("A cohort should only shrink between surveys … In the survey for 2023 they rose by 6 percent") is the most convincing evidence in the thesis and is told plainly. The part-year argument against returners in §5.8 is clear and clever. §5.3's "The windows of one platform overlap, so they are not seven independent cases" is the kind of honesty that disarms an examiner. + +**Where I got lost or had to reread** +- §5.1: "The ecosystem ratio was about 13, so almost 13 dollars of commerce passed through the platforms for every dollar of revenue." By the thesis's own definition, E is the *wedge* per dollar of revenue. Commerce per dollar of revenue is E + 1, about 14. A careful reader with Table 3.1 open will catch this. +- §5.1: "Shopee's ratio of 9 follows from the assumed take rate, and Grab's ratio of about 8 is its company-wide ratio. Neither is separate evidence for Indonesia." "Neither" came after three platforms had just been named, so I paused. +- §5.2: "Revenue grew faster than transaction value in 13 of the 15 … pairs. In two of them, revenue and transaction value moved in opposite directions." Is that two of the 13, or the other two? +- §5.2: "In that sample, revenue growth and transaction value growth had a correlation of 0.10 in Indonesia and 0.98 abroad, excluding Sea." Why exclude Sea? It is not explained. +- §5.2: "Figure 5.1 shows the three measures side by side." "The three measures" already means W, E and D (§3.2). Here it means three other things. +- §5.3: "That count leaves the fall in volume inside both lines." It is opaque, and it is the hinge of the two-views argument. +- §5.4, last para: "Revenue, transaction value and market value all come from the platforms' own reports." Market value comes from LSEG prices (§4.5), not from the platforms. The sentence is also a weak bridge to §5.5. +- §5.7: "It had only a weak link to growth in the sample, at 0.35." "Growth in the sample" means growth in the number of survey rows, I think. "Rank correlation" is used one sentence before it is defined. +- §5.7: "Entrants account for 25 to 30 percent of that rise, and sellers not explained by entry for 15 to 17 percent. Existing sellers reported a mean change of about −4 percent, so the remaining 53 to 59 percent cannot be traced to them." I could not reconstruct this. It is also, I think, one of the most striking findings in the thesis: most of BPS's 41 percent value rise has no source in the sellers' own answers. It is buried as the last paragraph of a section about counts. +- §5.9: "The path of marketplace use, the value held by sellers without financial statements and the World Bank results were registered." A label sentence with a stacked subject. It tells me nothing about the finding. +- §5.9: "The annual reading of the brackets passes the validation rule, at 1.95 times BPS's total, while the monthly reading fails at 23 times. The lower brackets alone exceed BPS's total, so the shares are ranges." I could not follow this. As an examiner I also note that 1.95 sits just under the limit of 2. +- §5.10: "BPS's own marketplace slice grew by about 0.2 percent in the middle scenario. Across the scenarios it grew by 0.2 to 13 percent." Is the middle scenario also the lowest? I reread it. + +**Tables and numbers not explained in the text** +- Table 5.1: "13.45 against 21.5 percent" is unrounded, while the text says "about 13". The label column mixes "Registered and descriptive" in one cell. +- Table 5.3: the quarterly row (0.071 against 0.049) and the "5 platforms against 26" row are not discussed. The 26 is explained only in the Figure 5.1 caption. +- Table 5.4: revenue growth of +465 and +679 percent at Blibli is never discussed. A reader wants one sentence on why revenue grew sevenfold. The note "Bukalapak's reports do not allow the parts to be separated" is attached to a table in which Bukalapak does not appear. +- §5.5: the "six measures of buying through platforms" are named only in Figure 5.3 and Appendix D. +- §5.8: the profile claims ("older owners and places outside Java") rest on Figure 5.4 alone, and the text gives no numbers. The figure also shows a sector bar (food and lodging, or making goods) that the text never mentions. +- §5.9: "43 percent of its sellers use only Gojek or Grab, and 47 percent use a shopping marketplace". What are the other 10 percent? It also changes how I read "marketplaces carry about a fifth", and the text does not say so. +- §5.9: "One registered prediction on value failed narrowly … 5.8 points on the main version and by 3.3 points on a second version." What is the second version? +- §5.9: the World Bank prediction ("mean sales would stay below about US$6,600") is never motivated. I did not know why it mattered. + +**Hedging inside the story.** "the shortfall is, if anything, understated" (§5.6); "The fall in 2023 may partly reflect …" and "appears to have counted" (§5.9). These are few, and most are defensible, but they sit in the story rather than in Chapter 7. + +**Voice.** It matches Chapters 1 to 4. §5.9 and §5.10 drift into lists of results with labels attached, and they read more like a log than an argument. + +**What the chapter did, in one sentence:** Revenue was about 7 percent of what three major platforms carried, and take rates in Indonesia and the region moved about three times as much as abroad, mostly because they start so thin, with pricing rather than volume driving revenue. On the national side, H2 holds on the published figures, but about half the 2023 rise in the seller count was not new entry, and most online selling and its growth sit outside marketplaces. *I could state it, but only in two sentences, one per link. The chapter has two findings, not one, and the opening paragraph says so. That is fine.* + +### Chapter 6. The Rules and How Far They Reach (draft) + +**Keep.** The opening states the headline at once: at most 1 in 20 sellers and a fifth of value. The equation (6) paragraph in §6.1 ("The two rules act on opposite sides of the same take rate") is the best use of the framework anywhere in the thesis. §6.3's revenue-at-stake paragraph ("about 0.2 percent of the government's tax target") gives the economic scale Prof. Kong asks for. §6.6's US delivery-cap example is apt. + +**Where I got lost or had to reread** +- §6.1, "Two rules serve as context": "Selling through chat and social media remained the norm, and in 2023 about 96 percent of online sellers used them." The ban was on *transactions* on social media, and the next sentence says 96 percent still use social media. Did the ban fail, or does "use" mean marketing only? I did not know. +- §6.2: "Across Grab's whole region, rides are about 36 percent of on-demand transaction value, and two-wheel trips in Indonesia are less than 6 percent of its rides business." This packs two shares into one sentence. A placeholder follows it ("[Source of the 6 percent to be confirmed …]"). +- §6.2: "Its take rate rose to about 22 percent in that quarter". Figure 5.1 shows no Indonesian or regional platform above about 11 percent, and the "thin take rate" story of Chapter 5 rests on a median of 1.6 percent. I could not reconcile the two. The likely reason is quarterly versus annual figures or a different scope, but the text does not say so. It also exposes that two of the five H1 platforms are not thin at all. +- §6.2: "Shopee and TikTok Shop with Tokopedia raised them again in 2026". Chapter 1 told me TikTok Shop *closed* in 2023. Nowhere does the thesis say that TikTok later took over Tokopedia. That would also explain why Tokopedia's series stops after 2023 ("its last full year of separate reporting"). This is a gap in the story, not just in a sentence. +- §6.3: "Marketplace sellers were about 18 percent of online sellers in 2023". Section 5.9 gives 18 percent as the marketplace share of *value* in 2023 and about 17 percent of sellers in 2024. With two different 18s, I checked back. +- §6.5: the labels P1, P3, P5, R1, R2, P2, P4 and R3 appear in an unexplained order with unexplained letters. "If P1 holds and P5 fails, the cap passed into the take rate." P1 is itself a fall in the take rate, so I could not follow the logic. +- §6.5, Table 6.2: "at most 15.79 percent" is unrounded, and its origin is not given. + +**Paragraphs that did not follow.** §6.4 introduces a fact that belongs in Chapter 4: "BPS's e-commerce survey draws its areas from the economic census of 2016". It matters a great deal for reading the 2023 break, and I met it only here. + +**Incomplete.** There is a results slot in §6.5 and a source placeholder in §6.2. The chapter cannot be judged finished until the October and November results arrive. + +**Voice.** It matches Chapters 1 to 5: short sentences, point first. It leans more on news and legal citations, which is natural for the topic. Table 6.1 is denser than any other table in the thesis. + +**What the chapter did, in one sentence:** Each of Indonesia's recent rules acts on one slice of what Chapter 5 measured: the commission cap on one fee, the discount rule on incentives, the marketplace tax on at most 1 in 20 sellers and a fifth of value, and the statistics rules on BPS's coverage. Ten registered predictions are waiting to be scored. *Easy to state.* + +### Chapter 7. Conclusion (draft) + +**Keep.** The opening paragraph ("Each figure … is a fair guide to another only while a condition holds") is the best single statement of the thesis. I would like to have read it in Chapter 1. §7.3, "Predictions that failed", is unusual and builds trust. The closing paragraph returns to the 2023 figures and ends on a usable rule ("A reader who checks which condition holds can use any of them"). + +**Where I got lost or had to reread** +- §7.2: "Its three measures, the invisible wedge, the ecosystem ratio and growth divergence, were built by the author." Section 3.2 and Table 3.1 say only the ecosystem ratio is the author's own. The wedge (V − R) and a growth difference are not new constructions. An examiner will pick on this. +- §7.2: "In Indonesia, the take rate accounted for about two thirds …" The sample is "Indonesian and regional" everywhere else. +- §7.4, para 4: "At the top, sellers with sales above about US$3.4 million a year rest on 19 sample rows in the survey for 2022, yet they hold 18 to 39 percent of the marketplace value." This is a new and important number that appears first in the limits. It changes how much I trust the "a fifth of value" result. +- §7.4, para 5: "The education codes of the survey for 2023 …" Education was never mentioned in Chapter 5. +- §7.4, para 3: "Grab revised some figures between filings" is a new fact about the H1 data, appearing first here. +- §7.4, para 2: the two-sided p-value remark covers only the main Mann–Whitney result. It does not cover the shuffle (0.022, which would become about 0.044) or the registered narrower test (0.024). + +**Paragraphs that did not follow.** §7.4 opens with what the wedge is *not*. That is a definition, not a limit, and it reads like a defence placed at the start of the limits. + +**Voice.** It is the same as Chapters 1 to 5. §7.4 has longer sentences carrying lists, which is acceptable in a limits section. + +**What the chapter did, in one sentence:** Revenue follows platform commerce only while take rates are steady, and BPS's seller count follows entry only while the survey's reach is steady. In Indonesia both conditions broke between 2021 and 2024. The chapter then sets out what this adds, which predictions failed, the limits and the next steps. *Easy to state.* + +--- + +## 2. Summaries: could I state what each chapter found? + +| Chapter | Could I state it? | Note | +|---|---|---| +| 1 | Yes | Weakened only by §1.1 para 1, which promises more than the thesis delivers | +| 2 | Yes | The clearest chapter | +| 3 | Yes, as a list | Too many parts: 6 terms, 7 equations, 2 hypotheses, 1 test, 4 analyses | +| 4 | Mostly | I could not re-explain the bracket-value method or the "two counts" | +| 5 | Yes, in two sentences | Two findings, one per link, as the chapter opening says | +| 6 | Yes | Not finished (results slot) | +| 7 | Yes | Its first paragraph is the thesis in five sentences | + +## 3. Does the story hold from Chapter 1 to Chapter 7? + +**Mostly yes.** The spine runs straight through. Two official figures disagree in 2023, revenue is a thin and moving share, the seller count jumped for reasons other than entry, the rules reach only the marketplace slice, and each figure is a fair guide only while its condition holds. Chapter 7's last paragraph closes the loop on Chapter 1's BPS–Bank Indonesia and Tokopedia scenes. + +**Where it breaks or sags** +1. **TikTok and Tokopedia.** Chapter 1 opens with TikTok Shop being banned and closing. The thesis never says what happened next (TikTok's tie-up with Tokopedia). That event explains why Tokopedia's own series ends after 2023, and it resurfaces without warning in §6.2 ("TikTok Shop with Tokopedia"). The Tanah Abang scene is never returned to. +2. **Tokopedia's lever.** Chapter 1 says incentives drove Tokopedia's revenue (60 percent). Table 5.4 says fees did (76 against 45). §5.3 reconciles the two views, but the introduction previews the minority view. +3. **"Thin take rate" and the on-demand platforms.** Chapter 5 explains H1's rejection by thin take rates, but two of the five platforms are on-demand businesses with take rates near 8 to 11 percent (Figure 5.1), or about 22 percent in one quarter (§6.2). The story never says whether the thin-slice explanation covers them. +4. **The ambition paragraph.** §1.1 para 1 promises to show "who is in it" and "millions" outside the figures. Chapter 7 does not come back to it, and the thesis does not deliver it at that scale. +5. **A finding that gets lost.** §5.7's result that most of BPS's 2023 *value* rise cannot be traced to sellers' own answers is stronger than anything in Chapter 7's summary of the outer link. Chapter 7 does not mention it. + +**Do Chapters 6 and 7 sound like 1 to 5?** Yes. The sentence length, point-first openings and plain words match. I could not have told that they were drafted separately. The two differences are that Chapter 6 relies more on news sources and legal citations, and that Chapter 7's limits section introduces new facts (19 sample rows, Grab revisions, education codes). New facts should not first appear in a conclusion. + +## 4. The three hardest questions I would ask at the defence + +**Q1. Is the rejection of H1 an Indonesian finding, or arithmetic about thin take rates?** Your own check shows that both groups changed take rates by about 1 point a year (p = 0.65), and the threefold gap in log changes comes from the starting level (1.6 against 17 percent). Any low-take-rate marketplace anywhere would then "reject H1". On top of that: +- The comparison group is mostly high-take-rate travel, freelance and delivery firms. +- The Indonesian group has five series, one of which (Tokopedia) contributes a single pair, and one (Grab) is region-wide. +- The main test was chosen after a narrower test. +- The quarterly test does not reject. + +What is left of H1 that a finance reader should act on? +- *Does the text answer it?* **Partly.** §5.2 (para 5–6) and §7.1 (para 3) concede the base effect openly. §7.2 turns it into the investor lesson ("The thinner the take rate, the more a small change … moves revenue growth"). §7.3 limits the rejection to 2021–2024, and §7.4 gives the sample size. **Not answered:** whether the result holds against foreign platforms with similar thin take rates, whether it holds for the two on-demand series (which are not thin), and why H1 should be read as "rejected" rather than "explained by base level". The defence needs a ready one-sentence answer: H1 is about proportional growth, and thin take rates are *why* proportionality fails. That answer is only implicit now. + +**Q2. What does the market-value analysis tell an investor about Indonesian platforms?** The sample is 28 firms and 138 firm-years, of which one firm (Bukalapak) is Indonesian. GoTo, Grab and Sea are absent. The dependent variable mixes price changes with share issuance. There are no controls for profitability, rates or dilution beyond year effects. The key prediction (β_V > β_m) fails in the main sample. The thesis's investor contribution (§1.2: "part of the price is a pricing decision") therefore has no market evidence behind it. +- *Does the text answer it?* **Partly.** §5.4 and §7.3 report the failure honestly. §7.4 calls the result an association, not a stock return. §7.5 names an event study as the next step. **Not answered:** why GoTo, Grab and Sea are excluded (§4.5 lists differences but gives no reasons), and what the result says about Indonesia specifically. Chapter 2 also does not place the analysis in the KPI-disclosure and valuation literature, so I cannot judge whether β_V ≈ 0.6 is new or expected. + +**Q3. If the 2023 BPS count is half unexplained, why trust BPS for H2, the channel shares and the reach of the tax?** H2 "holds" on the very count that the entry test discredits. The channel split rests on the following: +- a bracket method whose accepted reading lands at 1.95 times BPS's total, against a limit of 2; +- a choice between two readings made by which one passes; +- top sellers who hold 18 to 39 percent of marketplace value on 19 sample rows. + +Tokopedia alone reports about 15 million merchants, against BPS's 0.6 million marketplace sellers (§5.10). If BPS undercounts marketplace sellers, the "1 in 20" and "a fifth of value" figures in Chapter 6 move. +- *Does the text answer it?* **Partly.** + - §5.6 says the H2 split "is arithmetic" and cannot tell a new seller from one counted for the first time. + - §5.10 says platforms and BPS "count different things". + - §6.3 calls the reach an upper limit. + - §7.4 lists the bracket and top-row limits. + + **Not answered:** + - Whether H2's "holds" survives removing the unexplained part of the rise. The thesis has the number (§5.7), so this is a computation it could report. + - How 15 million merchants and 0.6 million sellers can both describe one market, beyond "count different things". For example: dormant accounts, multiple accounts per seller, or sellers outside BPS's business definition. + - Why picking the bracket reading that passes validation is not a forking path. + +## 5. The ten sentences most in need of rewriting (ranked) + +1. **§5.1:** "The ecosystem ratio was about 13, so almost 13 dollars of commerce passed through the platforms for every dollar of revenue." *Wrong by the thesis's own definition: E counts the wedge, not the commerce, per dollar of revenue (commerce per revenue dollar is about 14).* +2. **§5.4:** "Revenue, transaction value and market value all come from the platforms' own reports." *False: market value comes from LSEG prices (§4.5), and the sentence is a weak bridge to §5.5.* +3. **§7.2:** "Its three measures, the invisible wedge, the ecosystem ratio and growth divergence, were built by the author." *Contradicts §3.2 and Table 3.1, which claim only the ecosystem ratio as the author's own.* +4. **§1.1:** "Most online commerce is missing from the platform figures on which companies are valued and sellers are taxed, and millions of the people who sell online are outside those figures altogether." *Makes a claim before any evidence, in two clauses. "Missing" invites the missing-activity reading the thesis rejects, and "millions" is never shown.* +5. **§5.7:** "Existing sellers reported a mean change of about −4 percent, so the remaining 53 to 59 percent cannot be traced to them." *The arithmetic is hidden, and the reader cannot see how −4 percent leads to 53–59 percent, even though this may be the thesis's most striking national finding.* +6. **§4.7:** "If the weights had matched BPS's other count for 2023, the threshold would have been 23.9 percent, but they did not." *A counterfactual about a second count that is never explained, and "they" is ambiguous.* +7. **Ch1, opening para 5:** "If part of that growth is a platform keeping more of each sale, then part of the price is a pricing decision." *"Price" means share price but reads as the platform's price, so the sentence seems circular.* +8. **§7.4:** "The survey for 2023 has no revenue amounts." *Seems to contradict §4.8 ("The 2023 questionnaire asks for monthly revenue"); the survey-year naming has become a trap for readers.* +9. **§6.5:** "If P1 holds and P5 fails, the cap passed into the take rate." *P1 already is a fall in the take rate, so the reader cannot follow what the pair adds.* +10. **§5.3:** "That count leaves the fall in volume inside both lines." *Opaque, yet it is the hinge of the "two views of Tokopedia" argument.* + +Close behind: Chapter 3's opening note on objectives versus questions ("The third question is answered by a further analysis."). §4.2's "The thesis drops each platform in turn, and it repeats the exercise for every platform-year …". §5.9's "The annual reading of the brackets passes the validation rule, at 1.95 times BPS's total, while the monthly reading fails at 23 times." §5.2's "In two of them, revenue and transaction value moved in opposite directions." §3.2's "Equation (4) shows that D is the change in the take rate divided by its starting level." (It omits the (1 + g(V)) factor.) + +## Smaller items noticed in passing + +- Figure 3.1 is still marked "[to be drawn]" in Chapter 3, but `figures/fig3_1_two_links.png` exists. +- Unrounded numbers in prose or summary tables: 13.45 (Table 5.1), 28.63 and 12.25 (§4.6), 15.79 (Table 6.2), 1.95 (§5.9). +- GoTo's Q2 2026 revenue growth is "21 percent" in §5.10 and "about 20 percent" in §6.2. +- §4.9 refers to "the rules of Section 3.6". The rules are in §3.1. +- "Pricing" (§5.3, §7.1) is used as a synonym for a change in the take rate. Pick one term. +- Section endings: Chapter 2 and §6.3 end on what stays open. Most sections in Chapters 3 to 5 end on a pointer to the next chapter instead. diff --git a/research/option_space_2026-10-01/materials_2026-10-15/FINANCE_FRAMING.md b/research/option_space_2026-10-01/materials_2026-10-15/FINANCE_FRAMING.md new file mode 100644 index 0000000..d35b9ba --- /dev/null +++ b/research/option_space_2026-10-01/materials_2026-10-15/FINANCE_FRAMING.md @@ -0,0 +1,152 @@ +# Finance framing: sources and draft sentences (15 Oct 2026) + +Purpose: give the thesis a deliberate finance frame before Prof. Thomas asks "where is the finance?". This file gathers checked finance and accounting sources, drafts sentences for 1.2, 2.1, 3.6, 5.4 and 7.2, and lists the questions a finance examiner could still ask. Nothing here has been inserted into a chapter. The researcher decides what goes in. + +How the sources were checked (15 Oct 2026): Crossref metadata for every DOI (authors, year, title, journal, volume, issue, pages); the abstract read from Crossref, the SSRN version on Crossref, Semantic Scholar or IDEAS/RePEc. Abstracts only, no full texts. "Verified" below means the metadata and the abstract were both read. "Metadata only" means the work exists as cited but its finding was not read from an abstract, so it should not carry a claim until checked. Standards text was read from IASB and FASB staff papers that reproduce the paragraphs. + +## 1. Sources + +### 1a. Operating measures and the value of internet firms (around 2000) + +| Reference (APA 7) | DOI | Verified? | What it finds (from the abstract) | Where it fits | +|---|---|---|---|---| +| Trueman, B., Wong, M. H. F., & Zhang, X.-J. (2000). The eyeballs have it: Searching for the value in Internet stocks. *Journal of Accounting Research, 38*(Supplement), 137–162. | 10.2307/2672912 | Yes (metadata Crossref; abstract from the SSRN version, 10.2139/ssrn.206648) | Net income is not positively priced; gross profit is; unique visitors and pageviews add explanatory power for stock prices beyond the accounting figures | 3.6, 5.4, 7.2. **This is the paper that supports "markets priced web activity beyond the accounting figures".** See the wording note in Section 4 | +| Trueman, B., Wong, M. H. F., & Zhang, X.-J. (2001). Back to basics: Forecasting the revenues of Internet firms. *Review of Accounting Studies, 6*(2–3), 305–329. | 10.1023/A:1011623111051 | Yes (abstract from SSRN version, 10.2139/ssrn.224597) | Analysts' revenue forecasts almost always underestimate internet firms' revenue; web-usage growth helps predict e-tailers' revenue. Opens "in light of the importance of revenues in the valuation of internet stocks" | 2.1 (revenue forecasting). It is about forecasting revenue, **not** about pricing, so it does not support the 3.6/5.4 sentence | +| Rajgopal, S., Venkatachalam, M., & Kotha, S. (2003). The value relevance of network advantages: The case of e-commerce firms. *Journal of Accounting Research, 41*(1), 135–162. | 10.1111/1475-679X.00099 | Yes | Web traffic explains e-commerce firms' stock prices "over and above" earnings and book value; also linked to analysts' earnings forecasts | 2.1, 3.6, 5.4 | +| Demers, E., & Lev, B. (2001). A rude awakening: Internet shakeout in 2000. *Review of Accounting Studies, 6*(2–3), 331–359. | 10.1023/A:1011675227890 | Yes (abstract from SSRN version, 10.2139/ssrn.244231) | Web reach, "stickiness" and loyalty were value relevant in 1999 and 2000; firms the model rated overvalued saw larger falls in **price-to-sales** ratios after the crash | 2.1 (value measured against sales), reserve | +| Hand, J. R. M. (2001). The role of book income, Web traffic, and supply and demand in the pricing of U.S. Internet stocks. *Review of Finance, 5*(3), 295–317. | 10.1023/A:1013872530040 | Yes | "Beyond earnings, web traffic is significantly positively priced both at and after the Internet peak" | 3.6, 5.4 (second source for the web-activity sentence) | +| Hand, J. R. M. (2003). Profits, losses, and the nonlinear pricing of Internet stocks. In J. R. M. Hand & B. Lev (Eds.), *Intangible assets: Values, measures, and risks* (pp. 248–268). Oxford University Press. | 10.1093/oso/9780199256938.003.0011 | Partly (chapter metadata; abstract of the 2000 SSRN version, 10.2139/ssrn.204875) | Book equity and net income are value relevant in a nonlinear way for 167 internet firms, 1997–1999 | Reserve; not needed | +| Amir, E., & Lev, B. (1996). Value-relevance of nonfinancial information: The wireless communications industry. *Journal of Accounting and Economics, 22*(1–3), 3–30. | 10.1016/S0165-4101(96)00430-2 | Yes (abstract via Semantic Scholar) | For cellular firms, earnings, book value and cash flow alone are "largely irrelevant"; nonfinancial measures (population covered, POPS) dominate them | 2.1 (the founding study of operating measures and value) | +| Bartov, E., Mohanram, P., & Seethamraju, C. (2002). Valuation of Internet stocks—An IPO perspective. *Journal of Accounting Research, 40*(2), 321–346. | 10.1111/1475-679X.00050 | Yes | At internet IPOs earnings are not priced; sales growth is among the items that separate offer prices from first-day prices | Reserve | +| Core, J. E., Guay, W. R., & Van Buskirk, A. (2003). Market valuations in the New Economy: An investigation of what has changed. *Journal of Accounting and Economics, 34*(1–3), 43–67. | 10.1016/S0165-4101(02)00087-3 | Partly (abstract from SSRN version, 10.2139/ssrn.262619) | Traditional financial variables still explain value in the "New Economy" years, with more left unexplained | Reserve (answer if asked whether platforms need a special model) | + +### 1b. Revenue reporting, gross versus net, and how markets price revenue + +| Reference (APA 7) | DOI | Verified? | What it finds (from the abstract) | Where it fits | +|---|---|---|---|---| +| Bowen, R. M., Davis, A. K., & Rajgopal, S. (2002). Determinants of revenue-reporting practices for Internet firms. *Contemporary Accounting Research, 19*(4), 523–562. | 10.1506/9728-4YG8-GC3L-FPFA | Yes (already in the working list) | "Verifies the association between market capitalization and revenue"; regulators were concerned about pressure on internet firms to report high revenue; grossed-up and barter revenue are common in some sectors, not pervasive; more likely with high cash burn and message-board activity | 2.1 (the source for "investors read revenue"), 5.4 | +| Davis, A. K. (2002). The value relevance of revenue for Internet firms: Does reporting grossed-up or barter revenue make a difference? *Journal of Accounting Research, 40*(2), 445–477. | 10.1111/1475-679X.00056 | Yes | Revenue announcements are "highly associated with three-day market returns" beyond earnings announcements; the value relevance of revenue for firms reporting grossed-up or barter revenue **declined after the April 2000 crash**; higher pricing of grossed-up revenue where individual investors follow the firm | 2.1, 5.4 (the gross-vs-net link). **Note:** the brief's "Davis (2002), Do investors value gross vs net?, *The Accounting Review*" does not exist in Crossref. This JAR paper is the one meant | +| Bagnoli, M. E., Kallapur, S., & Watts, S. G. (2001). *Top line and bottom line forecasts: A comparison of Internet firms during and after the bubble* [Working paper]. SSRN. | 10.2139/ssrn.274178 | Abstract yes; **working paper only**, no journal version found in Crossref | For internet firms reporting losses, prices respond to revenue surprises but not earnings surprises, during and after the bubble; for profitable firms, revenue surprises mattered only during the bubble, and weakly | 2.1 (optional; cite as working paper or drop) | +| Chandra, U., & Ro, B. T. (2008). The role of revenue in firm valuation. *Accounting Horizons, 22*(2), 199–222. | 10.2308/acch.2008.22.2.199 | Yes | Revenue is useful for valuation beyond earnings, not only for technology or loss firms; more useful for technology firms; its new information has not declined over time | 2.1 | +| Ertimur, Y., Livnat, J., & Martikainen, M. (2003). Differential market reactions to revenue and expense surprises. *Review of Accounting Studies, 8*(2–3), 185–211. | 10.1023/A:1024409311267 | Metadata yes; abstract read only through the IDEAS/RePEc and search summary | Investors value a dollar of revenue surprise more than a dollar of expense surprise; the gap varies between growth and value firms | 3.6 (why the place where an incentive cut is recorded could matter). Read the abstract on IDEAS before citing | +| Ghosh, A., Gu, Z., & Jain, P. C. (2005). Sustained earnings and revenue growth, earnings quality, and earnings response coefficients. *Review of Accounting Studies, 10*(1), 33–57. | 10.1007/s11142-004-6339-3 | Partly (abstract from SSRN version, 10.2139/ssrn.404840) | Earnings growth backed by revenue growth is more persistent and earns larger earnings response coefficients than earnings growth alone | 3.6 (reserve: growth from the top line is valued more) | +| Jegadeesh, N., & Livnat, J. (2006). Revenue surprises and stock returns. *Journal of Accounting and Economics, 41*(1–2), 147–171. | 10.1016/j.jacceco.2005.10.003 | **Metadata only** (no abstract on Crossref or IDEAS; Semantic Scholar refused) | Not read. Do not cite for a finding until the abstract is read | Reserve | + +### 1c. Multiples and valuation practice + +| Reference (APA 7) | DOI | Verified? | What it finds (from the abstract) | Where it fits | +|---|---|---|---|---| +| Liu, J., Nissim, D., & Thomas, J. (2002). Equity valuation using multiples. *Journal of Accounting Research, 40*(1), 135–172. | 10.1111/1475-679X.00042 | Yes | Forward-earnings multiples price stocks best (errors within 15 percent for about half the sample); **sales performs the worst**; sample needs positive values and analyst forecasts, so many firm-years are excluded | 2.1 or defence. **Not cited in any chapter now** (only in `PART1_PROBLEM_AND_STAKES.md` and the working list). Use with care: it shows sales multiples are least accurate for profitable, covered firms | +| Liu, J., Nissim, D., & Thomas, J. (2007). Is cash flow king in valuations? *Financial Analysts Journal, 63*(2), 56–68. | 10.2469/faj.v63.n2.4522 | Partly (abstract from the 2006 SSRN version, 10.2139/ssrn.926428) | Earnings multiples dominate cash-flow multiples in all venues tested | Not needed | +| Kim, M., & Ritter, J. R. (1999). Valuing IPOs. *Journal of Financial Economics, 53*(3), 409–437. | 10.1016/S0304-405X(99)00027-6 | Metadata yes; abstract only through a secondary page | Price-to-earnings, market-to-book and price-to-sales multiples of comparable firms predict IPO values only modestly, because these ratios vary widely among young firms | Reserve | +| Bhojraj, S., & Lee, C. M. C. (2002). Who is my peer? A valuation-based approach to the selection of comparable firms. *Journal of Accounting Research, 40*(2), 407–439. | 10.1111/1475-679X.00054 | Yes | Builds "warranted" enterprise-value-to-sales and price-to-book multiples; tests include "new economy" stocks | Reserve (shows EV/sales is a standard target in research) | +| Damodaran, A. (2009). *Valuing young, start-up and growth companies: Estimation issues and valuation challenges* [Working paper]. SSRN. | 10.2139/ssrn.1418687 | Yes | Standard techniques for cash flows, growth and discount rates often fail for young firms with little revenue and operating losses | Reserve (replaces the vague "Damodaran" entry in the working list) | +| Mohrschladt, H., & Siedhoff, S. (2024). The valuation of loss firms: A stock market perspective. *Abacus, 60*(4), 752–776. | 10.1111/abac.12324 | Yes | Over 40 percent of listed firms report losses; revenue-to-price and book-to-market predict loss firms' returns | Reserve | + +### 1d. Operating measures (KPIs) and their disclosure today + +| Reference (APA 7) | DOI | Verified? | What it finds | Where it fits | +|---|---|---|---|---| +| Givoly, D., Li, Y., Lourie, B., & Nekrasov, A. (2019). Key performance indicators as supplements to earnings: Incremental informativeness, demand factors, measurement issues, and properties of their forecasts. *Review of Accounting Studies, 24*(4), 1147–1183. | 10.1007/s11142-019-09514-y | Yes (abstract via IDEAS) | KPI news adds information beyond earnings for many KPIs, "diminished when details about the computation of the KPI are absent or when the computation changes"; analysts forecast KPIs more when earnings are less informative | 2.1 (already cited), 7.4 | +| Tang, Q. R., & Huang, A. G. (2025). KPI information acquisition by analysts: Evidence from conference calls. *Journal of Business Finance & Accounting, 52*(1), 569–608. (Online 2024.) | 10.1111/jbfa.12822 | Yes | After analysts ask about KPIs on calls, managers disclose more KPIs, more so when earnings are less relevant; benefits for forecast dispersion, cost of capital and liquidity | 2.1 (reserve). **Correction:** `LIT_SEARCH_2026-10-13.md` lists it as "Tang (2025)"; it has two authors | +| U.S. Securities and Exchange Commission. (2020). *Commission guidance on management's discussion and analysis of financial condition and results of operations* (Release Nos. 33-10751; 34-88094; FR-87). *Federal Register, 85*(37), 10568–10571. | none (FR Doc. 2020-02296) | Yes (Federal Register text read) | Issued 30 Jan 2020, effective 25 Feb 2020. A key metric should come with "a clear definition of the metric and how it is calculated" and the reasons it is useful; changes in method should be disclosed with reasons and effects. Examples include "daily/monthly active users", "active customers", "average revenue per user" | 2.1, 4.x (why transaction value needs a fixed definition), 7.4 | +| McCarthy, D. M., Fader, P. S., & Hardie, B. G. S. (2017). Valuing subscription-based businesses using publicly disclosed customer data. *Journal of Marketing, 81*(1), 17–35. | 10.1509/jm.15.0519 | Yes | Firms now disclose customer counts and churn; builds a firm valuation from that public customer data (DISH, Sirius XM) | Reserve (valuation from operating data) | +| McCarthy, D. M., & Fader, P. S. (2018). Customer-based corporate valuation for publicly traded noncontractual firms. *Journal of Marketing Research, 55*(5), 617–635. | 10.1177/0022243718802843 | Yes | Extends customer-based valuation to firms without subscriptions, using public data | Reserve | + +Search note: no peer-reviewed accounting or finance study was found that tests the value relevance of gross merchandise value (GMV) or of DAU/MAU at platform firms (Crossref and web searches, 15 Oct). Only practitioner pages and one student thesis turned up. This supports, but does not prove, the claim that reported transaction value has not been studied as a valuation input. + +### 1e. Revenue recognition: principal or agent, and incentives + +| Reference (APA 7) | Identifier | Verified? | What it says | Where it fits | +|---|---|---|---|---| +| International Accounting Standards Board. (2014). *IFRS 15 Revenue from contracts with customers*. IFRS Foundation. | Paragraphs 70–72; B34–B38 | Paragraph text read as reproduced in IASB/FASB staff papers (below) | **B34:** the entity decides whether its promise is to provide the goods itself (principal) or "to arrange for the other party to provide" them (agent). **B35:** a principal records the gross amount. **B36:** an agent "recognises revenue in the amount of any fee or commission". **B37:** indicators of an agent. **70:** consideration payable to a customer (cash, or credits such as "a coupon or voucher") is a reduction of the transaction price "and, therefore, of revenue" unless it pays for a distinct good or service. **71:** the excess over fair value also reduces revenue. **72:** timing of the reduction | 2.1, 3.3 | +| International Accounting Standards Board. (2016). *Clarifications to IFRS 15 Revenue from contracts with customers*. IFRS Foundation. | Issued April 2016, effective 1 Jan 2018 | Secondary summaries only (BDO, Deloitte IAS Plus) | Amended B34–B38 (control as the basis; role of the indicators; credit-risk indicator removed). B36's fee-or-commission rule stands | Cite with IASB (2014) if the reference list gives paragraph numbers, since the 2014 B37 list was changed | +| FASB/IASB Joint Transition Resource Group for Revenue Recognition. (2015, January 26). *Consideration payable to a customer* (TRG Agenda Ref. 19) [Staff paper]. | ifrs.org staff paper | Yes (PDF read) | Reproduces paragraphs 70–71. Raises the platform case directly: an agent "may make incentive payments to parties that purchase the principal's good or service ... to increase the volume of transactions on which it earns its agency fee", and asks whether these fall under paragraph 70 | 2.1 note, 7.4 | +| Financial Accounting Standards Board, Emerging Issues Task Force. (2026, June). *Consideration payable to a customer* (Issue Summary No. 2). | storage.fasb.org | Yes (PDF read) | Says "significant diversity in practice" on whether payments to a customer's customer reduce revenue; names "online marketplaces" and platform intermediaries; cites an SEC staff view (2021) that a marketplace agent should consider implicit promises of incentives to end users | 7.4, 5.2 robustness (see Section 3) | +| Deloitte. (2026, August 21). *FASB holds August 19 meeting*. DART. | dart.deloitte.com | Secondary report read | The FASB added a project: such payments are consideration payable to a customer "when specified conditions are met"; a proposed update is being drafted | 7.4 (US GAAP only; IFRS 15 unchanged) | + +### 1f. Economics of take rates (note only) + +| Reference (APA 7) | DOI | Verified? | What it finds | Where it fits | +|---|---|---|---|---| +| Hagiu, A., & Wright, J. (2015). Marketplace or reseller? *Management Science, 61*(1), 184–203. | 10.1287/mnsc.2014.2042 | Yes | Models an intermediary's choice between marketplace mode and reseller mode | 2.1. This is the platform-or-reseller source. The working list cites Hagiu and Wright (2015) *IJIO* 43 for "platform vs reseller models"; the *IJIO* paper is "Multi-sided platforms". Check which one 2.1 needs | +| Hagiu, A., & Wright, J. (2024). Marketplace leakage. *Management Science, 70*(3), 1529–1553. | 10.1287/mnsc.2023.4757 | Yes | Sellers may take transactions off a marketplace to avoid its fees; models how a marketplace limits this | 2.3 or 5.9 (selling outside marketplaces), reserve | +| Rochet, J.-C., & Tirole, J. (2003). Platform competition in two-sided markets. *Journal of the European Economic Association, 1*(4), 990–1029. | already cited | not rechecked | Price structure across the two sides | already in 2.1 and 3.6 | + +No finance or accounting study of take rates as a reported ratio was found. The take-rate studies in `LIT_SEARCH_2026-10-13.md` (Li and Wang on fee caps; the Oxford Uber study) treat commissions as prices. + +## 2. Draft sentences + +Each sentence was counted at 13 to 18 words (citations excluded). Numbers in 7.2 repeat the headline from 5.2 to 5.4, as the conclusion may. + +### (i) Section 1.2: a finance paragraph (could replace the first contribution paragraph) + +> For investors, the thesis studies a measurement problem: which figure should carry a platform's growth? Market values of internet firms track revenue, and regulators worried about pressure to report high revenue (Bowen, Davis and Rajgopal 2002). Revenue growth, however, mixes two sources: more commerce and a larger share of each sale. Equation (5) separates them with two public figures, so an investor can check which one moved. When the take rate moves, a revenue multiple and a transaction-value multiple move by different amounts. In Indonesia, the share a platform keeps moved far more than at platforms abroad. The thesis also tests, in a prediction registered in advance, how market value moves with each source. + +Source check: sentence 2 rests on Bowen et al.'s abstract ("verifies the association between market capitalization and revenue"; "concern about pressures on Internet firms to report high levels of revenue"). Sentence 5 is an identity: Δln(MV/R) − Δln(MV/V) = −Δln m (the independent review's point; it describes the denominator, not investors). Sentence 6 has no number, because its home is 5.2. + +### (i) Section 7.2: the investor paragraph (could replace the current first paragraph) + +> For investors, the thesis shows that a platform's revenue growth can differ sharply from its commerce growth. Equation (5) splits revenue growth into more commerce and a larger take rate with two public figures. In Indonesia, the take rate accounted for about two thirds of a revenue move, against a quarter abroad. The same split shows when a revenue multiple and a transaction-value multiple will disagree. Across 28 listed platforms, market value moved with transaction value, as the registered prediction stated. A 1 percent rise in transaction value went with about 0.6 percent more market value. The data do not show that markets paid less for growth from a higher take rate. Earlier studies of internet firms found that markets priced measures of web activity beyond earnings (Trueman, Wong and Zhang 2000; Rajgopal, Venkatachalam and Kotha 2003). Here the activity measure is transaction value, which the platforms in the sample report themselves. + +Source check: two thirds and a quarter (5.3, `wedge_split.py`); 28 platforms, 0.6, p = 0.002 (5.4; `PREREGISTRATION.md` 8 Oct, b1 = 0.60); Trueman et al. (2000) and Rajgopal et al. (2003) abstracts say "incremental explanatory power" and "over and above ... earnings and book value". The last sentence is true of the M sample (whole-company transaction value from filings, `ch4` 4.5). + +### (ii) Section 2.1: grounding "investors read revenue as a sign of commerce" + +No source found says investors treat revenue as a measure of commerce in those words. The closest supportable version says that investors price revenue, and price it most where earnings say little. Pick one or two: + +- At internet firms, share prices respond to revenue news beyond what earnings news shows (Davis 2002). +- Market values of internet firms track revenue, and regulators worried about pressure to report high revenue (Bowen, Davis and Rajgopal 2002). +- Revenue helps value firms beyond earnings, and it is more useful for technology firms (Chandra and Ro 2008). +- Where internet firms report losses, prices respond to revenue surprises and not to earnings surprises (Bagnoli, Kallapur and Watts 2001). *(Working paper; cite as such or drop.)* + +Optional, to add the multiples point honestly: + +- Among the multiples studied, a multiple of sales tracks share prices least closely (Liu, Nissim and Thomas 2002). + +### (ii-b) Section 3.6: why the place of an incentive cut could matter (optional) + +- Markets value a dollar of revenue surprise more than a dollar of expense surprise (Ertimur, Livnat and Martikainen 2003). +- Under IFRS 15, a cut in vouchers raises revenue, though it works like a cut in costs. + +The second sentence is the author's reading of paragraph 70 and is marked as such. Read the Ertimur et al. abstract on IDEAS before using the first. + +### (iii) Section 5.4: link to gross-versus-net research + +- Earlier work found that revenue explained internet firms' prices less after 2000 where firms grossed it up (Davis 2002). + +A possible follow-on, the author's own reading: "So the way a platform records revenue can change what revenue tells investors." (13 words) + +## 3. What a finance examiner could still ask, and the answer in hand + +1. **"Where is the finance?"** The measurement problem for valuation: revenue and transaction value give different growth rates and different multiples whenever the take rate moves (equations (4) and (5)). The investor check splits revenue growth with two public numbers (5.3). The registered market-value test (M2) holds: β_V ≈ 0.6, p = 0.002. Put this in 1.2 and 7.2 in those words (Section 2 above). +2. **"Why not stock returns or an event study?"** Market-value changes include share issues, so they are not returns (4.5, 7.4). The return test is planned in the proposal's Section 8 and listed in 7.5. M2 is stated as an association. +3. **"Your second prediction failed. So what did you learn?"** β_m is positive (about 0.3) but too uncertain to tell from zero; the gap is not significant (p = 0.20; 0.16 trimmed). The registration itself warned that a higher take rate also means higher margins, so a positive β_m is a plausible result (`PREREGISTRATION.md`, 8 Oct). Report both numbers, as 5.4 does. +4. **"Is the market sample about Indonesia?"** No. It has Bukalapak and 27 platforms abroad (4.5). The test asks how markets price the two sources of growth. The Indonesian part is that its take rates move far more (5.2), so the split matters more there. Say so once, in 4.5 or 7.4. +5. **"Sales multiples are the worst multiple. Why does revenue matter?"** Liu, Nissim and Thomas (2002) rank sales last, but their sample needs positive earnings and forecasts. Where earnings are negative, prices respond to revenue (Bagnoli et al. 2001, working paper; Chandra and Ro 2008: revenue matters more in extreme earnings cases). Many platforms in the sample made losses in some years. +6. **"Is transaction value reliable? It is not audited."** It is a company-defined KPI. The SEC (2020) asks for a definition and disclosure of method changes, and Givoly et al. (2019) find a KPI tells investors less when its computation changes. The thesis pairs only figures on one definition (Chapter 3 pairing rule) and labels every value. Point to these in 4.x. +7. **"Do the foreign platforms book incentives the same way?"** This is the open risk. The FASB's task force found "significant diversity in practice" on whether a marketplace's incentives to end users reduce revenue (EITF 2026), and the FASB added a project in August 2026. If some US platforms in the 27 record consumer incentives as marketing expense, their take rates would move less when incentives change. That would widen the Indonesia–abroad gap for an accounting reason. **Check before the defence:** read each foreign platform's revenue-recognition note on incentives (at least Uber, DoorDash, Lyft, Airbnb, Booking, eBay, Etsy, Mercado Libre). If most deduct them, the answer is ready. If several do not, state it once in 7.4. +8. **"Does a bigger take rate mean a better firm?"** Not in itself. The split shows how revenue grew, not whether value was created. Margins and cash flow are outside the tests. Keep the claim at "how revenue grew". + +## 4. Corrections to existing text and notes (for the reference check) + +- **3.6 and 5.4 wording.** Both chapters say Trueman, Wong and Zhang (2000) found that markets priced web activity "beyond revenue". The abstract does not mention revenue. It says web usage adds explanatory power beyond net income and its components (gross profit is priced). Rajgopal et al. (2003) and Hand (2001) say "beyond earnings". Closest supportable version: "Early studies of internet companies found that markets priced measures of web activity beyond earnings (Trueman, Wong and Zhang 2000; Rajgopal, Venkatachalam and Kotha 2003)." (14 words.) Whether revenue entered their model can be confirmed only from the full text. +- **2.1, "Discounts and vouchers that it pays for are normally deducted from its revenue (IASB 2014)."** Paragraph 70 covers payments to the customer and to "other parties that purchase the entity's goods or services from the customer". For a marketplace acting as agent, the merchant is the customer and the shopper is the merchant's customer. Whether vouchers to shoppers fall under paragraph 70 is a judgement, raised by the TRG in 2015 and still debated by the FASB in 2026. The working list's audit note already says to lead with the issuer's disclosed deduction (GoTo annual report 2023, Note 29). Keep the sentence, and support it with GoTo's note as well as the standard. +- **Davis (2002).** It is in *Journal of Accounting Research* 40(2), not *The Accounting Review*. +- **Tang (2025).** It is Tang and Huang (2025), *JBFA* 52(1), 569–608. +- **Hagiu and Wright (2015).** Check that the cited paper is the one 2.1 needs: "Marketplace or reseller?" (*Management Science*) or "Multi-sided platforms" (*IJIO*). +- **Liu, Nissim and Thomas (2002).** Verified, but not cited in any chapter. Add it only if the multiples sentence goes into 2.1. +- **Damodaran.** Replace the vague entry with Damodaran (2009), SSRN 10.2139/ssrn.1418687, if it is used at all. +- **IFRS 15 citation.** If paragraphs are cited, add IASB (2016) *Clarifications*, which amended B34–B38. + +## Sources consulted (web) + +- Crossref REST API (api.crossref.org), all DOIs above +- Federal Register 85 FR 10568: https://www.govinfo.gov/content/pkg/FR-2020-02-25/html/2020-02296.htm +- TRG Agenda Ref. 19 (2015): https://www.ifrs.org/content/dam/ifrs/meetings/2015/january/trg-rev/rev-rec/ap19-consideration-payable.pdf +- IASB Agenda Paper 7E (March 2015), reproducing B34–B37: https://www.ifrs.org/content/dam/ifrs/meetings/2015/march/iasb/revenue-from-contracts-with-customers/ap7e-principal-v-agent-considerations-issues-emerging-trg-discussions.pdf +- FASB EITF Issue Summary No. 2 (June 2026): https://storage.fasb.org/EITF-Issue%20Summary-Consideration%20Payable%20to%20a%20Customer-20260623.pdf +- Deloitte DART, FASB 19 Aug 2026 meeting: https://dart.deloitte.com/USDART/home/news/all-news/2026/aug/fasb-aug-19-meeting +- IDEAS/RePEc pages for Givoly et al. (2019), Ertimur et al. (2003), Jegadeesh and Livnat (2006) +- Semantic Scholar API for Amir and Lev (1996) +- BDO and IAS Plus summaries of the 2016 Clarifications to IFRS 15 From 85ef648d56ddf45bedec863df802a10441863b1c Mon Sep 17 00:00:00 2001 From: Claude Date: Thu, 15 Oct 2026 13:16:52 +0000 Subject: [PATCH 53/65] Materials: YZU thesis format checklist and official guideline and template downloads (unmodified, with source URLs and checksums) Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01XxH9J9TaLYfAYFCaGAqS9y --- .../materials_2026-10-15/YZU_FORMAT.md | 167 ++++++++++++++++++ .../yzu_template/README.md | 90 ++++++++++ ...5\350\213\261\344\270\246\345\210\227.doc" | Bin 0 -> 40960 bytes ...5\350\213\261\344\270\246\345\210\227.doc" | Bin 0 -> 31744 bytes ...5\350\213\261\344\270\246\345\210\227.doc" | Bin 0 -> 27136 bytes .../yzu_template/aa_format/R-G-A00-new.doc | Bin 0 -> 62976 bytes .../yzu_template/aa_format/R-G-A00.doc | Bin 0 -> 54784 bytes 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a/research/option_space_2026-10-01/materials_2026-10-15/YZU_FORMAT.md b/research/option_space_2026-10-01/materials_2026-10-15/YZU_FORMAT.md new file mode 100644 index 0000000..ffca70b --- /dev/null +++ b/research/option_space_2026-10-01/materials_2026-10-15/YZU_FORMAT.md @@ -0,0 +1,167 @@ +# Yuan Ze University master's thesis format: rules checklist + +Compiled 15 October 2026 from official Yuan Ze University (YZU) documents and the YZU thesis system hosted by the National Central Library (NCL). +The files are in `yzu_template/`. Source URLs and download times are in `yzu_template/README.md`. +Scope: an English-language master's thesis in the College of Management, MS in Finance and Accounting (財務金融暨會計碩士班), Finance track (財務金融碩士學程). This is the program on the 27 September proposal cover. + +**Rule hierarchy.** The university guideline is a general rule. Departments and advisors may set field-specific conventions (G-zh p.5 註; G-en p.5 Note). No College of Management or program-level format guide was found (see "Not found" below). Confirm anything marked **ASK** with the advisor or the program office. + +## Source keys + +Page numbers for `.doc` files are pages as rendered (LibreOffice). They match the page counts stored by Word: 6 pages for G-zh, 5 for G-en, 1 for each appendix. PDF page numbers are physical PDF pages. + +| Key | File in `yzu_template/` | What it is | +|---|---|---| +| G-zh | `aa_format/R-G-A00-new.doc` | 元智大學研究所學位論文格式規範條例 (current Chinese guideline, saved 2023-11-23) | +| G-en | `aa_format/學位論文格式規範條例English.doc` | Yuan Ze University Thesis Format Guidelines (current English version, saved 2023-11-23) | +| G-old | `aa_format/R-G-A00.doc` | Superseded 2013 Chinese guideline (comparison only) | +| A1, A2, A5 ... | `aa_format/R-G-A1.doc`, `R-G-A2.doc`, ... | Office of Academic Affairs appendices (templates). A3 = `aa_degree_exam/審定書中英並列.doc`; A16 = `aa_degree_exam/推薦函中英並列.doc`; A17 spine = `R-G-A18.doc` | +| AA-98 | web page (README) | Academic Affairs page 學位論文格式規範 (notes above the appendix list) | +| AA-99 | web page (README) | Academic Affairs page 研究生學位考試申請程序 | +| R17 | `regulations/1-17_degree_exam_rules_en.pdf` (Chinese: `..._zh.pdf`) | Regulations for Master and Doctoral Degree Qualifying Examination (amended 2025-12-24) | +| R74 | `regulations/1-74_en.pdf` | Academic Research Ethics Education Course rules | +| ETD-en / ETD-zh | `ncl/ETD_submission_guidelines_en.pdf` / `_zh.pdf` | Graduate Electronic Thesis Submission Guidelines (May 2025), 1 page | +| WF | `ncl/upload_workflow.png` | Upload workflow chart (2025-12-09) | +| UM-zh / UM-en | `ncl/user_manual_zh_2024-01.pdf` / `ncl/user_manual_en_2024-01.pdf` | Thesis system user manual (library) | +| CONV-en | `ncl/ETD_conversion_guide_en.pdf` | ETD File Conversion Guide (watermark, PDF security) | +| REJ | `ncl/common_rejection_reasons.pdf` | Common reasons the department rejects an upload | +| EMB | `ncl/YZU_embargo_application_2025-09.pdf` | Application for Embargo of Thesis (2025.09 version) | +| NCL-up | https://cloud.ncl.edu.tw/yzu/upload.php (web page) | Thesis system notes on authorization and embargo | +| MS114 | `cm_ms_program/114CM-MS.docx` | MS in Finance and Accounting course list, 2025 entry (113 and 115 sheets carry the same thesis rules) | + +## 1. Language and the Chinese abstract + +- [ ] **Language.** "The thesis or dissertation (including the abstract) must be written in Chinese as a rule" (以中文撰寫為原則). R17 Art. 12, p.5 (Chinese p.4). G-zh II.7, p.2 repeats "文章主體以中文為原則". G-en II.7, p.2 drops this phrase. No written exception for English theses was found. The program runs an English-taught track (GMS, MS114 p.12–13). **ASK** the program office to confirm in writing that an English thesis is accepted. +- [ ] **A Chinese abstract is mandatory, also for an English thesis.** "須有中文摘要及英文摘要，分頁書寫" (G-zh II.4, p.1). G-en: "Chinese and English Abstracts (one for each) ... Abstracts in Chinese and English should be done on separate pages" (G-en I.4 and II.4, p.1). The thesis system requires both a Chinese and a foreign-language abstract (UM-zh p.8: "中文／外文摘要，兩者皆需填入"; UM-en p.4: "abstracts in both Chinese and English"). +- [ ] **A Chinese title is mandatory.** The title page carries the title in Chinese and English (G-en II.2, p.1; A2). The system requires both titles, and they must match the cover, abstract pages, approval sheet and authorization forms (UM-zh p.8). +- [ ] **Chinese keywords are mandatory in the system.** Chinese and foreign keywords are both required, one keyword per field (UM-zh p.8). The paper abstract templates have no keyword line (A5, A6). +- [ ] **Chinese reference list in the system.** The system says both Chinese and English reference lists must be filled in (UM-zh p.8: "中英文參考文獻，兩者皆需填入"). For a thesis with only English references, **ASK** the library (etds@saturn.yzu.edu.tw) what to enter. + +## 2. Front matter and binding order + +Current order (G-zh 一, p.1; G-en I, p.1): + +1. [ ] Front cover (封面), A1 +2. [ ] Title page / inside cover (書名頁), A2 +3. [ ] Oral defense committee approval sheet (論文口試委員審定書), A3. A copy is bound in the paper thesis; the originals go to the department (G-en II.3, p.1). Do **not** put it in the uploaded PDF (personal data) (AA-98). +4. [ ] Chinese abstract, then English abstract, each on its own page (A5, A6) +5. [ ] Acknowledgements or preface (誌謝辭或序言), A8 (English version: heading "Acknowledgements") +6. [ ] Table of contents, A9 / A9-1 (choose one layout with the advisor, G-en III.1, p.2) +7. [ ] List of tables, A10 +8. [ ] List of figures, A11 +9. [ ] List of symbols (符號說明), A12. All mathematical and special symbols on one page, unnumbered (G-en III.3, p.3). The thesis uses V, R, W, D, m and g(·), so this page applies. +10. [ ] Main text with tables and figures +11. [ ] References +12. [ ] Appendices, A15 +13. [ ] Back cover + +- [ ] **Authorization forms are not bound.** Put them loose inside each paper copy (G-zh PS, p.1; G-en p.s., p.1; AA-98). They are generated by the thesis system (Appendix 4, AA-98). +- [ ] The 2013 order bound the authorization form and ended with a vita (自傳). The current order does neither (G-old vs G-zh, p.1). The Chinese TOC template still shows 自傳 (A9). Follow the current order. + +## 3. Page, margins, paper + +- [ ] A4, 210 × 297 mm (G-en II.5, p.1). +- [ ] Margins: top 3.5 cm, left 4 cm, right 2 cm, bottom 2 cm (G-en II.6, p.1; diagram p.2). +- [ ] Page number centred, 1 cm from the bottom edge (G-en II.6, p.1). +- [ ] Covers: paper of at least 150 lb (cloth-grain or card, G-zh II.5, p.1). Cover colour set by the department (G-en II.5, p.1). **ASK** the colour. +- [ ] Note: the downloaded appendix templates use Word default margins (2.54/3.17 cm). They show layout and wording only. Set the margins yourself. + +## 4. Fonts and spacing + +- [ ] **No font face or font size is set in the written rules** (G-zh, G-en, all pages). **ASK** the department. +- [ ] What the official templates use (observation, not a rule): Chinese text in 標楷體 (DFKai-SB); Latin text falls back to Times New Roman 12 pt (template default style). Cover: university name 24 pt, department 28 pt, degree line 24 pt, title 14 pt, student and advisor 18 pt (A1). Title page and abstracts 14 pt (A2, A5, A6). Page headings ("Acknowledgements", "Table of Contents", "List of Tables", "References", chapter title) 20 pt (A8-en, A9-en, A10-en, A13, A14-en). TOC entries 13 pt (A9-en). +- [ ] **English line spacing: 1.5 or double, at least 28 lines per page.** Double space under the chapter title (G-en III.4(3), p.3). +- [ ] Chinese text (the Chinese abstract): double-spaced, at least 32 lines per page, at least 28 characters per line, dense character spacing (G-en III.4(3)–(4), p.3; G-zh p.3). +- [ ] Text runs left to right, horizontally. Foreign-language originals quoted in the text go in parentheses (G-en II.7, p.2). + +## 5. Page numbering + +- [ ] Lower-case Roman numerals (i, ii, iii ...) from the title page to the lists of tables and figures. The title page and approval sheet are counted but carry no printed number (G-en II.8(1), p.2; G-zh 8(1), p.2). +- [ ] The templates number them: title page i, approval sheet ii, Chinese abstract iii, English abstract iv, acknowledgements v, contents vi, list of tables vii, list of figures viii, (symbols ix) (A9-en; A9). The cover is not numbered. +- [ ] Arabic numerals (1, 2, 3 ...) from Chapter 1 through the appendices, continuous (G-en II.8(2), p.2). References continue the main-text numbering (G-en III.5(1), p.4). +- [ ] In the PDF: no page number and no watermark on the cover (UM-zh p.9; CONV-en p.3, p.12–14). PDF page numbers must match the paper copy (CONV-en p.6, p.21). + +## 6. Headings and text + +- [ ] Chapters numbered 1, 2, ...; sections 1.1, 1.1.1, then 1.1.1 (1) (G-en III.4(1), p.3). English TOC template: "Chapter 1 Introduction", "2.1", "2.1.1" (A9-en). +- [ ] Chapter number and title centred at the top of the page. Sections and paragraphs start at the left margin, indented one or two spaces by level (G-en III.4(2), p.3; A13). +- [ ] Equations: set apart from the text and indented. Number them in parentheses at the right of the last line, e.g. "(1)". Cite them by name and number in the text. Break a long equation only at +, −, ×, ÷, >, <; align lines at "=". In running text write fractions with "/" (G-en III.4(6), p.3). +- [ ] Footnotes: superscript numbers, numbered from 1 again in each chapter. Separate from the text by a rule at the page bottom; may continue on the next page (G-en III.4(7), p.3). +- [ ] Numbers in text: the rule is written for Chinese (Chinese numerals for simple descriptive numbers; concise form for large ones, e.g. "Three point three billion US dollar" rather than 3,300,000,000) (G-en III.4(5), p.3). + +## 7. Tables and figures + +- [ ] Table number and title **above** the table (G-en III.4(9), p.4). +- [ ] Figure number and title **below** the figure (G-en III.4(9), p.4; the English text says "below tables", which is a slip; the Chinese says 圖號及圖名置於圖下方, G-zh p.4). +- [ ] Source and notes **below** both tables and figures (G-en III.4(9), p.4). +- [ ] Number tables and figures consecutively through the whole thesis, not by chapter: Table 1, Table 2 ... (G-zh 三.2 "不分章節連續編號", p.2; G-en III.2, p.2; A10-en, A11-en). +- [ ] Text inside tables and figures must be typed (G-en III.4(9), p.4). + +## 8. Citations and references + +- [ ] **University default:** number citations in order of first citation, in square brackets, e.g. "Duncan [1]"; the list follows that order (G-en III.4(8), p.4; III.5(1), p.4). +- [ ] Default reference formats (G-en III.5(2), p.4–5; A14-en): + - Book (title italic): `Jelinek, F. (1968), Probabilistic Information Theory, McGraw-Hill, New York, pp. 79-82.` + - Journal (journal italic, article title in quotes): `Pathak, S. and Kolhe, M. V. (2016), "Title", Journal, vol. 2, no. 9, pp. 19-24.` + - Conference: `Huang, S. W. and Lin, S. W. (2008, Feb.), "Title", Conference Name, City, pp. 102-104.` + - Thesis (title italic): `Pollard, A. (1978), Flow in Tee Junction, Ph.D. Thesis, University of London, London, U. K.` A Taiwanese thesis cited from NDLTD needs its handle URL (https://hdl.handle.net/...) (G-zh p.5). +- [ ] **Field convention allowed.** "Each advisor may specify the format of references according to the specific field of study" (G-en p.5 Note; A14-en). The Chinese text adds that departments may too (G-zh p.5 註). No College or program rule names APA or another style. **ASK** Prof. Kong to approve author-year (for example APA) before the reference list is finalised. +- [ ] References start on a new page after the main text (G-en III.5(1), p.4). Works not cited in the text go in an appendix list (G-en III.6, p.5). + +## 9. Abstracts + +- [ ] Content: purpose, data sources, methods and results (G-en II.4, p.1). +- [ ] Length: "approximately 500~1000 words within one page" (G-en II.4, p.1). The Chinese says 約500～1000字（並以一頁為限）, i.e. characters (G-zh p.1). **One page each is the binding limit.** +- [ ] Header block (A5, A6): title; student; advisor(s) ("Advisors: Dr. ..."); "Submitted to Department of ... College of ... Yuan Ze University"; then "ABSTRACT" (English) or 摘要 (Chinese). +- [ ] Keywords: no paper rule; required in the system in Chinese and English (UM-zh p.8). No limit on number found. + +## 10. Cover, title page, spine + +- [ ] **Cover (A1):** Chinese-only template: 元智大學; department (學系/所); 碩士論文; title; 研究生; 指導教授; date as 中華民國 ___ 年 ___ 月. Departments with MOE-approved tracks may add the track name (G-en II.1, p.1). The proposal cover used 管理學院財務金融暨會計碩士班 / 財務金融碩士學程. No English cover template exists. +- [ ] **Title page (A2):** Chinese title, English title; 研究生/Student; 指導教授/Advisor; 元智大學, department, 碩士論文; then "A Thesis Submitted to Department of ... College of Management, Yuan Ze University in Partial Fulfillment of the Requirements for the Degree of Master of Science in ..." (omit "Department of" in the last line); month and year; "Chungli, Taiwan, Republic of China."; 中華民國 年 月 (A2; G-en II.2, p.1). **ASK** the exact English department and degree names. +- [ ] **Spine (A17, `R-G-A18.doc`):** bound on the left. The spine strip shows graduation year (畢業級別), 碩士論文, title, school/college/department, author (G-en II.9, p.2; A17). Type it on white paper and copy it onto coloured spine paper chosen by the department; 3–4 strips per sheet (A17). +- [ ] **Approval sheet (A3):** bilingual form "Verification Letter from the Oral Examination Committee"; committee signatures, advisor, head of department; date in ROC years (A3). Bring two originals to the defense (AA-99; one to the department, one kept and copied into the bound thesis). + +## 11. Defense and program rules (MS in Finance and Accounting) + +- [ ] Advisor chosen and registered by the end of the first year, from the Finance, Accounting or Digital Finance groups (MS114 p.3, p.13). +- [ ] Proposal defense: apply within 2 weeks of the start of the planned graduation semester; hold it within 4 weeks; a failed proposal must be retaken and passed by week 8. Committee: advisor(s) plus one internal or external member. Only then may the final defense be requested (MS114 p.3, p.13). +- [ ] Research ethics course certificate before applying for the defense (R74 Art. 4, p.1; R17 Art. 4, p.2; MS114 p.3). +- [ ] Defense application includes the draft thesis and abstract and the advisor's recommendation letter (A16) (R17 Art. 4, p.1–2). +- [ ] Originality (similarity) report given to the advisor and committee on the defense day; the threshold is set by each department (R17 Art. 4(4), p.2). The program threshold was not found. **ASK**. +- [ ] Committee of 3–5 members; at least 3 present; one third from outside YZU; pass mark 70, averaged (R17 Art. 5, p.2; Art. 9, p.4). Defense announced publicly in advance (R17 Art. 9(1), p.4). +- [ ] English-taught track (GMS): defense by end of December (fall) or end of June (spring) (MS114 p.13). +- [ ] Submission deadline for the final thesis: end of January (fall) or end of July (spring) (R17 Art. 12, p.5). + +## 12. Paper copies + +- [ ] At least two bound copies (G-en II.10, p.2): one for the NCL and one for the YZU library; the department may ask for more (ETD-zh 四). Submit them to the department office after the upload is approved (ETD-en 2.2; WF). +- [ ] Each copy carries the loose authorization forms and, if any, the embargo application (WF; ETD-zh 四; NCL-up). +- [ ] The approval sheet copy is bound in (AA-98). The 2013 rule of five copies is superseded (G-old vs G-zh II.10). + +## 13. Electronic submission (YZU thesis system on NCL) + +- [ ] System: https://cloud.ncl.edu.tw/yzu/ ; only students graduating that semester may open an account; log in with the YZU portal account (UM-en p.2; UM-zh p.3). +- [ ] Step 1, metadata: basic data, Chinese and English abstracts, table of contents, references (UM-en p.4). Foreign name as in passport, surname first in capitals, e.g. "SUN, YAT-SEN"; list every committee member; graduation year must match the diploma; publication year must match the cover; page count = last page number (UM-zh p.8). +- [ ] Step 2, one merged PDF of the whole thesis (UM-zh p.9; REJ). Add page numbers and the YZU watermark (`ncl/YZU_watermark.png`) centred on every page except the cover, without resizing it (CONV-en p.3, p.7; REJ). +- [ ] PDF security: printing allowed (high resolution), changes not allowed, copying not allowed, no password to open (CONV-en p.17, p.21; UM-zh p.9). Do not use Word's "Save as PDF"; use Acrobat (CONV-en p.15) or PDFill (`ncl/PDFill_security_guide_en.pdf`). File name without Chinese characters or special symbols (CONV-en p.21). Fonts in the PDF must match the paper copy (CONV-en p.21). +- [ ] Do not include the approval sheet in the PDF (AA-98). +- [ ] Step 3: print two authorization forms (YZU and NCL), single-sided; the student and advisor sign by hand; scan and upload both; hand the originals to the department (UM-zh p.13, p.16; UM-en p.6). Foreign students sign their Chinese name and their passport English name (UM-zh p.13). +- [ ] Choose on-campus and off-campus open-access dates for the electronic full text (UM-zh p.9). Since academic year 108, paper theses are treated as immediately public (NCL-up). +- [ ] Step 4: submit for department review; edits need "cancel validation" (UM-en p.9–10). +- [ ] **Embargo:** allowed only for a patent application, national secrets, or legal restrictions on disclosure. Journal submission has not been a valid reason since academic year 109 (NCL-up). File EMB with evidence, signed by student, advisor and committee, stamped by the department; maximum 5 years per application; a second application needs the original committee or a department meeting to confirm (R17 Art. 4(5)–(6), p.2; EMB notes 1–3). If no date is set, the NCL releases the thesis at once (UM-en p.5). For "withheld by law", attach e.g. the confidentiality contract (UM-zh p.11). Relevant if licensed data (`LICENSED_DATA.md`) would appear in the thesis. +- [ ] Contact: etds@saturn.yzu.edu.tw, ext. 2322 (ETD-en). + +## Not found + +1. **No College of Management, MS in Finance and Accounting, or GMS format guide or Word template** was found online. The program's thesis-examination notes (`dept.cm.yzu.edu.tw/ms/files/財會碩碩士論文考試注意事項.pdf`) are linked from https://ms.cm.yzu.edu.tw/ but the host was unreachable on 15 Oct 2026. The GMS Google Site needs a login. +2. **No full thesis template** (one Word file with styles). The university provides only single-page appendix samples, whose page setup does not match the margin rule. +3. **No written font rule** (face or size) for English or Chinese text, captions, footnotes or tables. Only template observations (section 4). +4. **No English cover template**; Appendix 1 is Chinese only. +5. **No written exception to "Chinese as a rule"** (R17 Art. 12) for English-taught programs. +6. **No program-chosen citation style** (APA or other); only the university's numbered default plus advisor discretion. +7. **No program similarity (Turnitin) threshold**, no program number of paper copies beyond two, no cover colour. +8. **No rule on keyword count, thesis length, landscape pages, or table font size.** +9. **How to fill the Chinese reference field** in the system when all references are English: not stated. +10. **YZU SharePoint material** (2026-05-28 upload briefing slides, similarity-check manual) needs a YZU login. +11. **NDLTD national submission rules beyond the YZU system:** the NCL deposit runs through the YZU system; the NCL embargo form is saved (`ncl/NCL_embargo_application_form.pdf`). The Degree Conferral Act itself (https://law.moj.gov.tw) was not downloaded because it is not a YZU or NCL document. diff --git a/research/option_space_2026-10-01/materials_2026-10-15/yzu_template/README.md b/research/option_space_2026-10-01/materials_2026-10-15/yzu_template/README.md new file mode 100644 index 0000000..4cbc78d --- /dev/null +++ b/research/option_space_2026-10-01/materials_2026-10-15/yzu_template/README.md @@ -0,0 +1,90 @@ +# Yuan Ze University thesis format: official downloads + +Downloaded on 15 October 2026 (UTC times below) for the thesis rewrite in `papers/thesis_2026-10/`. +Every file is an unmodified download from an official Yuan Ze University (YZU) site +(`www.yzu.edu.tw`) or from the YZU thesis system hosted by the National Central Library (NCL) +(`cloud.ncl.edu.tw/yzu/`, `nclfile.ncl.edu.tw`). Do not edit these files. Work on copies. + +The rules extracted from these files are in `../YZU_FORMAT.md`. + +## Where the files are linked from + +| Landing page | What it lists | +|---|---| +| https://www.yzu.edu.tw/admin/aa/index.php/tw/2016-01-14-06-58-46/2017-04-13-06-25-12/98-2016-03-23-06-06-30 | Office of Academic Affairs (教務處) page 學位論文格式規範: format guidelines (Chinese and English) and Appendices 1 to 17 | +| https://www.yzu.edu.tw/admin/aa/index.php/tw/2016-01-14-06-58-46/2017-04-13-06-25-12/99-2016-03-23-06-06-08 | 研究生學位考試申請程序: approval sheet, recommendation letter, oral defense score sheet, submission steps | +| https://cloud.ncl.edu.tw/yzu/download.php | YZU thesis system download area (library): upload workflow, PDF conversion and security guides, embargo form, watermark | +| https://cloud.ncl.edu.tw/yzu/upload.php | YZU thesis system user notes (authorization forms, embargo rules); links the NCL embargo form | +| https://www.yzu.edu.tw/admin/aa/index.php/tw/2016-01-14-06-58-46/42-2016-03-25-06-15-54/114-2016-03-25-13-32-54 | Required-course lists of the College of Management MS in Finance and Accounting (財務金融暨會計碩士班), by entry year | + +## Folders + +- `aa_format/`: Office of Academic Affairs format guidelines and Word templates (Appendices). `R-G-A00-new.doc` is the current Chinese guideline (saved 23 Nov 2023); `學位論文格式規範條例English.doc` is the current English guideline (saved 23 Nov 2023). `R-G-A00.doc` is the superseded 2013 Chinese version: it is still on the server but no longer linked; kept only for comparison. +- `aa_degree_exam/`: oral defense forms (approval sheet 審定書 = Appendix 3, recommendation letter = Appendix 16, score sheet). +- `regulations/`: YZU regulations 1-17 (degree examination rules, amended 24 Dec 2025, MOE noted 26 Jun 2026), 1-71 (academic ethics cases), 1-74 (research ethics course). +- `ncl/`: YZU electronic thesis (ETD) submission documents from the YZU thesis system on cloud.ncl.edu.tw, plus the NCL embargo form. +- `cm_ms_program/`: required-course lists of the MS in Finance and Accounting for entry years 113, 114, 115 (2024, 2025, 2026). They carry the program's thesis rules (advisor, proposal defense, deadlines). + +Appendix map (Office of Academic Affairs numbering): 1 cover `R-G-A1`; 2 title page `R-G-A2`; 3 approval sheet `aa_degree_exam/審定書中英並列.doc`; 4 authorization form (generated by the thesis system, not downloadable); 5 Chinese abstract `R-G-A5`; 6 English abstract `R-G-A6`; 7 missing-character form `R-G-A7`; 8 acknowledgements `R-G-A8` (Chinese) and `R-G-A8(誌謝格式-英)` (English); 9 table of contents `R-G-A9`, `R-G-A9目錄編排範例格式-英`, `R-G-A9-1`; 10 list of tables `R-G-A10`, `R-G-A10表目錄範例-英`; 11 list of figures `R-G-A11`, `R-G-A11圖目錄範例-英`; 12 list of symbols `R-G-A12`; 13 chapter layout `R-G-A13`; 14 references `R-G-A14`, `R-G-A14參考文獻-英`; 15 appendix `R-G-A15`; 16 recommendation letter `aa_degree_exam/推薦函中英並列.doc`; 17 spine `R-G-A18`; plus `R-G-A19` (committee appointment letter) and `R-G-A20` (cancellation of degree examination). + +## Provenance (source URL, download time, size in bytes, first 16 hex of SHA-256) + +| File | Source URL | Downloaded (UTC) | Bytes | SHA-256 (16) | +|---|---|---|---|---| +| `aa_format/R-G-A00-new.doc` | https://www.yzu.edu.tw/admin/aa/files/Registration/Graduation/R-G-A00-new.doc | 2026-10-15T13:05:03Z | 62976 | a27df46ba6dab725 | +| `aa_format/學位論文格式規範條例English.doc` | https://www.yzu.edu.tw/admin/aa/files/Registration/Graduation/%E5%AD%B8%E4%BD%8D%E8%AB%96%E6%96%87%E6%A0%BC%E5%BC%8F%E8%A6%8F%E7%AF%84%E6%A2%9D%E4%BE%8BEnglish.doc | 2026-10-15T13:05:04Z | 69632 | 6316e65ead62bd62 | +| `aa_format/R-G-A1.doc` | https://www.yzu.edu.tw/admin/aa/files/Registration/Graduation/R-G-A1.doc | 2026-10-15T13:05:05Z | 21504 | a3947bdc01603ed5 | +| `aa_format/R-G-A2.doc` | https://www.yzu.edu.tw/admin/aa/files/Registration/Graduation/R-G-A2.doc | 2026-10-15T13:05:05Z | 32768 | 08a09110f52df433 | +| `aa_format/R-G-A5.doc` | https://www.yzu.edu.tw/admin/aa/files/Registration/Graduation/R-G-A5.doc | 2026-10-15T13:05:07Z | 19456 | 4c0f42bd47a95523 | +| `aa_format/R-G-A6.doc` | https://www.yzu.edu.tw/admin/aa/files/Registration/Graduation/R-G-A6.doc | 2026-10-15T13:05:08Z | 23552 | 756030b90ef3c99f | +| `aa_format/R-G-A7.doc` | https://www.yzu.edu.tw/admin/aa/files/Registration/Graduation/R-G-A7.doc | 2026-10-15T13:05:08Z | 30720 | 6e9179228ec6c198 | +| `aa_format/R-G-A8.doc` | https://www.yzu.edu.tw/admin/aa/files/Registration/Graduation/R-G-A8.doc | 2026-10-15T13:05:09Z | 27136 | 3592c14aede808af | +| `aa_format/R-G-A8(誌謝格式-英).doc` | https://www.yzu.edu.tw/admin/aa/files/Registration/Graduation/R-G-A8%28%E8%AA%8C%E8%AC%9D%E6%A0%BC%E5%BC%8F-%E8%8B%B1%29.doc | 2026-10-15T13:05:10Z | 27648 | 36433d311260b4e7 | +| `aa_format/R-G-A9.doc` | https://www.yzu.edu.tw/admin/aa/files/Registration/Graduation/R-G-A9.doc | 2026-10-15T13:05:11Z | 33280 | 2aeaa0829c90865a | +| `aa_format/R-G-A9目錄編排範例格式-英.doc` | https://www.yzu.edu.tw/admin/aa/files/Registration/Graduation/R-G-A9%E7%9B%AE%E9%8C%84%E7%B7%A8%E6%8E%92%E7%AF%84%E4%BE%8B%E6%A0%BC%E5%BC%8F-%E8%8B%B1.doc | 2026-10-15T13:05:13Z | 30208 | 929f0516429f7392 | +| `aa_format/R-G-A9-1.doc` | https://www.yzu.edu.tw/admin/aa/files/Registration/Graduation/R-G-A9-1.doc | 2026-10-15T13:05:13Z | 33792 | 5cc12affc7372081 | +| `aa_format/R-G-A10.doc` | https://www.yzu.edu.tw/admin/aa/files/Registration/Graduation/R-G-A10.doc | 2026-10-15T13:05:14Z | 23040 | 1e6492fcaf9b9ae6 | +| `aa_format/R-G-A10表目錄範例-英.doc` | https://www.yzu.edu.tw/admin/aa/files/Registration/Graduation/R-G-A10%E8%A1%A8%E7%9B%AE%E9%8C%84%E7%AF%84%E4%BE%8B-%E8%8B%B1.doc | 2026-10-15T13:05:14Z | 22528 | f30f13d45a03c342 | +| `aa_format/R-G-A11.doc` | https://www.yzu.edu.tw/admin/aa/files/Registration/Graduation/R-G-A11.doc | 2026-10-15T13:05:15Z | 23040 | afc29f470c06e8e4 | +| `aa_format/R-G-A11圖目錄範例-英.doc` | https://www.yzu.edu.tw/admin/aa/files/Registration/Graduation/R-G-A11%E5%9C%96%E7%9B%AE%E9%8C%84%E7%AF%84%E4%BE%8B-%E8%8B%B1.doc | 2026-10-15T13:05:15Z | 22528 | 55477487c52e13f1 | +| `aa_format/R-G-A12.doc` | https://www.yzu.edu.tw/admin/aa/files/Registration/Graduation/R-G-A12.doc | 2026-10-15T13:05:16Z | 19456 | 73f504b7069420b8 | +| `aa_format/R-G-A13.doc` | https://www.yzu.edu.tw/admin/aa/files/Registration/Graduation/R-G-A13.doc | 2026-10-15T13:05:17Z | 28160 | 7ef2f5b95986c93d | +| `aa_format/R-G-A14.doc` | https://www.yzu.edu.tw/admin/aa/files/Registration/Graduation/R-G-A14.doc | 2026-10-15T13:05:18Z | 34816 | 493c7b9fbf4fcd0e | +| `aa_format/R-G-A14參考文獻-英.doc` | https://www.yzu.edu.tw/admin/aa/files/Registration/Graduation/R-G-A14%E5%8F%83%E8%80%83%E6%96%87%E7%8D%BB-%E8%8B%B1.doc | 2026-10-15T13:05:19Z | 32256 | 59e365f157c430e7 | +| `aa_format/R-G-A15.doc` | https://www.yzu.edu.tw/admin/aa/files/Registration/Graduation/R-G-A15.doc | 2026-10-15T13:05:20Z | 23552 | bf6f496f99717100 | +| `aa_format/R-G-A18.doc` | https://www.yzu.edu.tw/admin/aa/files/Registration/Graduation/R-G-A18.doc | 2026-10-15T13:05:21Z | 33280 | 03a8065e08e4c5a7 | +| `aa_format/R-G-A19.doc` | https://www.yzu.edu.tw/admin/aa/files/Registration/Graduation/R-G-A19.doc | 2026-10-15T13:05:22Z | 25088 | 8b4895819fa1fa4e | +| `aa_format/R-G-A20.doc` | https://www.yzu.edu.tw/admin/aa/files/Registration/Graduation/R-G-A20.doc | 2026-10-15T13:05:23Z | 26624 | c267debd8c5bd164 | +| `aa_format/R-G-A00.doc` | https://www.yzu.edu.tw/admin/aa/files/Registration/Graduation/R-G-A00.doc | 2026-10-15T13:05:23Z | 54784 | c1bb933d0f9a2f3f | +| `aa_degree_exam/審定書中英並列.doc` | https://www.yzu.edu.tw/admin/aa/files/DOCS/%E5%AF%A9%E5%AE%9A%E6%9B%B8%E4%B8%AD%E8%8B%B1%E4%B8%A6%E5%88%97.doc | 2026-10-15T13:05:24Z | 40960 | de2fb640767e38b9 | +| `aa_degree_exam/推薦函中英並列.doc` | https://www.yzu.edu.tw/admin/aa/files/DOCS/%E6%8E%A8%E8%96%A6%E5%87%BD%E4%B8%AD%E8%8B%B1%E4%B8%A6%E5%88%97.doc | 2026-10-15T13:05:26Z | 31744 | 4d3ee75728e04f32 | +| `aa_degree_exam/碩博士論文口試評分表中英並列.doc` | https://www.yzu.edu.tw/admin/aa/files/DOCS/%E7%A2%A9%E5%8D%9A%E5%A3%AB%E8%AB%96%E6%96%87%E5%8F%A3%E8%A9%A6%E8%A9%95%E5%88%86%E8%A1%A8%E4%B8%AD%E8%8B%B1%E4%B8%A6%E5%88%97.doc | 2026-10-15T13:05:26Z | 27136 | 2229c2e9a5aeb305 | +| `regulations/1-17_degree_exam_rules_zh.pdf` | https://www.yzu.edu.tw/admin/so/files/regulations/c/1-17.pdf | 2026-10-15T13:05:27Z | 203314 | e979ff8ff737c45e | +| `regulations/1-71_academic_ethics_zh.pdf` | https://www.yzu.edu.tw/admin/so/files/regulations/c/1-71.pdf | 2026-10-15T13:05:29Z | 206665 | 76247952676a0324 | +| `regulations/1-17_degree_exam_rules_en.pdf` | https://www.yzu.edu.tw/admin/so/files/regulations/c%20en/1-17.pdf | 2026-10-15T13:05:30Z | 148802 | 0b81aea45c3f5b30 | +| `ncl/upload_workflow.png` | https://cloud.ncl.edu.tw/school_file_download.php?Pact=FileDownLoad&Pval=aWNkb2ZvYmJkaGNnYmpiY2JnZ2hriXl/dw== | 2026-10-15T13:06:10Z | 200926 | 09d027d303a190d3 | +| `ncl/PDFill_security_guide_zh_20241106.pdf` | https://cloud.ncl.edu.tw/school_file_download.php?Pact=FileDownLoad&Pval=aWVoaGVvYmJkaGNnYmpiY2JnZ2hriXl/dw== | 2026-10-15T13:06:14Z | 728845 | d23cdef400dc5d5c | +| `ncl/PDFill_security_guide_en.pdf` | https://cloud.ncl.edu.tw/school_file_download.php?Pact=FileDownLoad&Pval=aWVoaGZvYmJkaGNnYmpiY2JnZ2hriXl/dw== | 2026-10-15T13:06:18Z | 653022 | 9e74388d1ff7f8c2 | +| `ncl/user_manual_zh_2024-01.pdf` | https://cloud.ncl.edu.tw/school_file_download.php?Pact=FileDownLoad&Pval=aGJjamZvYmJkaGNnYmpiY2JnZ2hriXl/dw== | 2026-10-15T13:06:25Z | 2282459 | 1a0fa92e81539595 | +| `ncl/ETD_conversion_guide_zh_2022-05.pdf` | https://cloud.ncl.edu.tw/school_file_download.php?Pact=FileDownLoad&Pval=ZWNib2VvYmJkaGNnYmpiY2JnZ2hriXl/dw== | 2026-10-15T13:06:29Z | 1078278 | 8495a01a7453fdd5 | +| `ncl/ETD_conversion_guide_en.pdf` | https://cloud.ncl.edu.tw/school_file_download.php?Pact=FileDownLoad&Pval=ZGZqbGlvYmJkaGNnYmpiY2JnZ2hriXl/dw== | 2026-10-15T13:06:36Z | 2346467 | a712fb591850fb4b | +| `ncl/user_manual_en_2024-01.pdf` | https://cloud.ncl.edu.tw/school_file_download.php?Pact=FileDownLoad&Pval=aGJjamdvYmJkaGNnYmpiY2JnZ2hriXl/dw== | 2026-10-15T13:06:42Z | 1569777 | b6f635c65adf89b4 | +| `ncl/YZU_embargo_application_2025-09.pdf` | https://cloud.ncl.edu.tw/school_file_download.php?Pact=FileDownLoad&Pval=aGVibWdvYmJkaGNnYmpiY2JnZ2hriXl/dw== | 2026-10-15T13:06:44Z | 178817 | aa3f94a35fbd6f9e | +| `ncl/ETD_submission_guidelines_zh.pdf` | https://cloud.ncl.edu.tw/school_file_download.php?Pact=FileDownLoad&Pval=aGJjaGlvYmJkaGNnYmpiY2JnZ2hriXl/dw== | 2026-10-15T13:06:46Z | 334456 | 0cac5627983bca8c | +| `ncl/ETD_submission_guidelines_en.pdf` | https://cloud.ncl.edu.tw/school_file_download.php?Pact=FileDownLoad&Pval=aGJjaGpvYmJkaGNnYmpiY2JnZ2hriXl/dw== | 2026-10-15T13:06:49Z | 217820 | 7d87d12ea10e9a6b | +| `ncl/YZU_watermark.png` | https://cloud.ncl.edu.tw/school_file_download.php?Pact=FileDownLoad&Pval=Y2lpa2dvYmJkaGNnYmpiY2JnZ2hriXl/dw== | 2026-10-15T13:06:50Z | 10516 | 01fdcaf68dbdfbee | +| `ncl/common_rejection_reasons.pdf` | https://cloud.ncl.edu.tw/school_file_download.php?Pact=FileDownLoad&Pval=Y2tmaGRvYmJkaGNnYmpiY2JnZ2hriXl/dw== | 2026-10-15T13:06:53Z | 262440 | 9cf15c2b038a5914 | +| `ncl/NCL_embargo_application_form.pdf` | https://nclfile.ncl.edu.tw/files/202007/89410681-3e9e-4564-8c36-ca0b41b4f81c.pdf | 2026-10-15T13:07:02Z | 115647 | 0b74e37768a24dba | +| `regulations/1-74_zh.pdf` | https://www.yzu.edu.tw/admin/so/files/regulations/c/1-74.pdf | 2026-10-15T13:07:04Z | 207598 | 7ecfc27eaa0746ab | +| `regulations/1-74_en.pdf` | https://www.yzu.edu.tw/admin/so/files/regulations/c%20en/1-74.pdf | 2026-10-15T13:07:06Z | 118808 | c972853985a27928 | +| `regulations/1-71_academic_ethics_en.pdf` | https://www.yzu.edu.tw/admin/so/files/regulations/c%20en/1-71.pdf | 2026-10-15T13:07:07Z | 177024 | 8f87ae8946f55f44 | +| `cm_ms_program/113CM-MS.docx` | https://www.yzu.edu.tw/admin/aa/files/COURSES/531CM-MS/113CM-MS.docx | 2026-10-15T13:12:32Z | 63053 | ea129f8748e4bb7a | +| `cm_ms_program/114CM-MS.docx` | https://www.yzu.edu.tw/admin/aa/files/COURSES/531CM-MS/114CM-MS.docx | 2026-10-15T13:12:33Z | 67726 | 05501de1a0580385 | +| `cm_ms_program/115CM-MS.docx` | https://www.yzu.edu.tw/admin/aa/files/COURSES/531CM-MS/115CM-MS.docx | 2026-10-15T13:12:34Z | 63932 | f310e31a814947bc | + +## Downloads that failed or were not attempted + +- `https://dept.cm.yzu.edu.tw/ms/files/財會碩碩士論文考試注意事項.pdf` (the MS program's "thesis examination notes", linked from the program menu at https://ms.cm.yzu.edu.tw/ under 學位考試): the host `dept.cm.yzu.edu.tw` reset or timed out on every attempt (HTTPS and HTTP, 15 Oct 2026). Not downloaded. Fetch it by hand or ask the program office. +- YZU SharePoint files linked from https://cloud.ncl.edu.tw/yzu/ (2026-05-28 upload briefing slides, similarity-check manual): return HTTP 403 without a YZU login. +- The GMS (English-taught track) Google Site linked from the program menu is an edit link that needs a login. +- Two links on the Academic Affairs page (`cloud.ncl.edu.tw/new_file_download.php?...Pval=31082/31083`) return an error page ("操作異常") outside a browser session; the same documents were taken from the thesis system's download area (`ncl/ETD_submission_guidelines_*.pdf`). diff --git "a/research/option_space_2026-10-01/materials_2026-10-15/yzu_template/aa_degree_exam/\345\257\251\345\256\232\346\233\270\344\270\255\350\213\261\344\270\246\345\210\227.doc" "b/research/option_space_2026-10-01/materials_2026-10-15/yzu_template/aa_degree_exam/\345\257\251\345\256\232\346\233\270\344\270\255\350\213\261\344\270\246\345\210\227.doc" new file mode 100644 index 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