Conversation
--html-dir given as a relative path crashed filepath.relative_to(BASE_DIR) (BASE_DIR is absolute), silently producing an empty prices CSV. Resolve the path first and fall back to the raw string. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- 13-recent-manifest.py: select recent-window snapshots (>=2 quarters, anchored 2024Q3, >=1 snapshot in trailing 12mo), 7 categories. - 14-recent-ipi.py: matched-model index (Jevons elementary per category, review-weighted geometric composite), quarterly + monthly, trailing-12mo headline. - run-recent-pipeline.sh: idempotent driver (download retry -> extract -> build). - gitignore: exclude html-recent/ raw HTML (22GB) and transient .out logs. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
15,150/15,309 snapshots (99%), 100% price extraction across 7 categories. Composite IPI flat over the past year (2025Q1->2026Q1: -0.3%); video -11.6%, coding -6.8%, writing -6.6%, design +2.1%. Includes per-category indices and volume-proxy weights for client-side recompute. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- code/15-build-site-data.py: reuse step 14's monthly build to emit per-category monthly index -> site/data.json (2.2 KB, trailing 12mo, rebased to window-start=100). - site/index.html + site/ipi.js: category checklist drives live in-browser composite recompute (matches pipeline's exp(Sum w.ln/Sum w)). - Verified: client recompute over all categories reproduces composite_all. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Page wasn't working and the user is building their own site. Removed site/index.html, site/ipi.js, scripts/deploy-site.sh and deleted the gh-pages branch. Kept code/15-build-site-data.py + site/data.json as the data layer for the user's own frontend. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Rebuilt CSRankings-style site now hand-rolls the trend chart and sparklines as inline SVG — no Plotly/CDN. Validated: JS syntax OK, data.json contract complete, client composite reproduces composite_all (headline -2.1% trailing 12mo). Adds deploy-site.sh redeploy helper. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…ting Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Serve the IPI frontend from /docs (branch mode) instead of the site/ subfolder, which Pages branch mode cannot serve. Drop the Actions workflow; update the data-build script output path. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Self-contained faq.html (no external libs): explains the purpose of the index and documents the exact formulas — price relatives, chained Jevons category index, weighted-geometric-mean composite, headline 12mo change, and review-based weights — with CSS-rendered math. Cross-linked from the main index header. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Polished, still fully self-contained (no external libs/CDN): Inter/system font stack, soft canvas with white rounded cards + subtle shadows, indigo accent, inline-SVG logo + favicon, pill basket toggles, refined table and tooltip, responsive tweaks. FAQ restyled to match (TOC card, paper card, boxed formulas). All JS hooks and the data contract are unchanged. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…ection - index.html: add full-sentence IPI definition directly beneath the page title - index.html: lay out trend chart (left) and category-selection table (right) side by side via CSS grid, collapsing to stacked below 900px; widen wrap - faq.html: matching aesthetic refresh Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- ipi.js: significantMoves() flags MoM moves past 0.8% plus the biggest rise/drop; drawChart overlays a green/red segment + labeled % on the composite line, recomputed live as categories are toggled - index.html: legend caption under the chart (green=rise, red=drop) Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…s it From a user question -- "look at specific AI launch dates and compare before and after, or did you do that already?" Honest answer was no: steps 52/55/57 searched for breaks and ranked milestones by fit, none compared before against after at a named date, and all three ran quarterly. Design 10 adds three things. Monthly resolution (the `month` field was on every row and unused). Twenty launches dated by PUBLIC AVAILABILITY rather than announcement, since an announcement cannot change a gig. And each launch matched to the category it targets -- Copilot/coding, ElevenLabs/audio, Midjourney/design -- with non-targets as controls, which needs no exposure score and so escapes the Eloundou measure entirely. PRICE MARGIN -- valid, and null. 7 confounded (every image-model date, Copilot, GPT-4o) 11 null 2 clear the pre-window gate (GPT-3 API, ChatGPT) Two survivors is FEWER than the ~3 that 60 tests give by chance, and GPT-3's API date is 2020-06, the pandemic inflection step 52 already flagged as wrong-signed. Design prices fall 4.5-5.1% at every image-model date and every one has a LARGER pre-window effect -- design was already diverging before any image model shipped. DEMAND MARGIN -- discarded on its own placebo. It returned 11 of 20 significant where chance predicts one, with incoherent signs: image tools raising design accrual while text tools cut writing's. Step 58b runs the identical design on 12 fake 2019 launches: price 1 of 12 = 8% false positives (nominal 5%) -> stands demand 9 of 12 = 75% false positives -> DISCARDED The eleven demand results are reported nowhere. The placebo ran before the write-up, which is why they never entered the draft. THE PREMISE FAILED, and that is the informative part. The first stage rejects the targeting assumption: ChatGPT produced no differential adoption in writing, and AI branding concentrated in coding regardless of which tool launched. That is what step 57 predicts -- diffusion came through entry, not incumbent conversion -- and it is the deepest diagnosis yet of designs 1-8: they searched for AI's effect inside the categories a crosswalk called exposed, and that is not where AI arrived. Caveats recorded, not buried: the first stage clusters on 7 categories (step 54's gate A defect) so only direction is read, and launches sit 1-3 months apart so each pre-window contains other launches. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
`code/59-review-order-audit.py` reads a block every archived gig page has carried since 2019 and that `code/09-extract-prices.py` has always discarded: one order record per displayed review, with an order id, the order date (not the capture date) and the amount the buyer paid in a band. Orders under $50 are 1.0% of all orders against a listed entry-package median of $25-30, so buyers essentially never buy the package the index prices. Two thirds land in $50-200. No new collection is needed — the 86 GB of stored HTML already holds it. Three consequences for `drafts/market-structure-answer.md`: - §1.3 stops being a caveat about something unmeasurable and becomes a measurement, with the paid distribution and its three limits (starts 2022, ~13% of orders recovered and display-ranked rather than sampled, amounts interval-censored with an open top band). - §2's implied "orders −18%" is flagged: the divisor is a price nobody pays, so it is an upper bound on the decline, now measured rather than assumed. - §5's entry saying realised order value is "not measurable at any effort" was wrong. Struck through rather than silently deleted — a section about what is unmeasurable had an entry in it that was measurable from data already on disk. Same pass makes the document readable without the econometrics vocabulary: a "How to read this document" glossary of thirteen terms, "In plain terms:" glosses at the five densest results, and an opening to §4 that explains what an identification design is and what it means for one to fail. `tests/structure-data.test.md`: R4 strengthened from asserted to measured, R8 added and recorded FAIL (the display-selection threat, open), R9 added PASS. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
…tion `drafts/plain-summary.md` is a standalone non-technical companion to `drafts/market-structure-answer.md` — same numbers, no econometrics vocabulary, and the data collection stated before the findings, which the answer doc never does because it was written for a reader who already knew where the panel came from. It leads with a correction the project must expect from every non-specialist reader: *prices rose during the AI launches and transactions fell.* Both directions are right. The equivocation is on **during** — the level movements span the AI period, but every turning point the project can date lands in 2020Q3-2021Q4. Stating that before Part 1 rather than inside §4 is deliberate: a reader arriving with the AI hypothesis otherwise reads the descriptive sections as confirmation of it. Three figures corrected while drafting: - pages downloaded is ~375,000 (291,997 balanced + 67,377 expanded + 15,150 recent), not the 384,983 gig-date *observations* — different quantities - realised-price bands recomputed from §1.3: $50-200 = 67.3%, >$200 = 31.7% - design count is nine run, nine failed (1-8 and 10). Design 9 is pre-registered and has never been estimated, and design 10's demand half was discarded on its own placebo rather than failing — an easy conflation Rendered to `drafts/plain-summary-2026-08-20.html` via `drafts/render.py --main`. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
`progress.md` gains the three 2026-08-20 entries in order — the order-record audit, the answer-doc rewrite, and the plain-language summary. `tests/structure-master.test.md`: U4 records the readability instruction, U5 the summary instruction, U6 the "during" correction. M9 added as a standing criterion and deliberately split — PASS in `market-structure-answer.md` and `plain-summary.md`, FAIL in `drafts/structure/`, which carries neither the plain-language pass nor the realised-price finding and still states the scope as six designs. `plans/todo.md` gains three Active items: - PRIORITY 1b, re-extract the order records — with the display-selection test as the first deliverable, not an index. Pages rank displayed reviews by relevancy, not at random; if display is selected on price, every realised price number is biased and unusable. - propagate the plain-language pass into the paper tree - keep `plain-summary.md` in sync — it is now a second place every headline number lives and `code/32-check-draft-numbers.py` points at neither it nor the answer doc Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
… missed From two reader questions in succession — "what does GMV mean" and "when you say buyers decreased 36 percent, do you mean transactions or revenue?" — both against a document published the same day whose purpose was to be readable without jargon. The readability pass had glossed every econometrics term and left every finance term untouched, because GMV and active buyers did not read as jargon to the person writing them. The document reported three quantities in one table (buyers, spend per buyer, GMV) and used a fourth word for them collectively (transactions), without ever saying they are different things. A reader who takes "buyers −36%" as a statement about sales volume was misled by the document. Added to `drafts/plain-summary.md`: - "Six words that get mixed up", placed before the "during" correction — buyers (people), orders (purchases, published by nobody and estimated here), GMV (dollars transacted), revenue (Fiverr's cut, ~1/3 of GMV), review accrual (our sales proxy), listed vs realised price. Plus the two consequences: GMV = buyers x spend per buyer is arithmetic rather than an estimate, and every transactions-fell claim in the document is derived rather than reported. - §2.2 spells GMV out on first use, states that it is not revenue, and glosses the column headers inline. - The implied order count now actually appears — −18% vs 2020, −38.6% from the 2021 peak, with its upper-bound caveat. The glossary was promising an estimate the document did not contain. - "Buyers −36%" restated as 1.5 million people who used to buy here and no longer do. Recorded as `tests/structure-master.test.md` U7. Re-rendered. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
From a user request for a before/after design on the project's headline question. Registered before any outcome is estimated. The move that makes a before/after work here is **staggered arrival**: each niche is dated by the quarter AI listings actually appear in it, not by a platform-wide calendar date. Design 10 died partly because one global date puts every niche's "after" in the same quarters as the pandemic unwind and the 2022 tech contraction; with staggered dates those are absorbed by quarter fixed effects and only the event-time profile is read. Fixed in advance: niches frozen and shared with design 9 so neither design can be accused of picking a definition to suit its result; arrival = AI share >=5% sustained two quarters; controls are **never-treated niches only**, because two-way FE under staggered adoption uses already-treated units as counterfactuals and can return a sign opposite to every underlying effect; SEs clustered on niche; outcomes log price and log review accrual, reported together. Three abandonment points, all pre-outcome: fewer than 100 usable niches (S2), arrivals not actually staggered (S3), and the pre-trend gate G1 — which is the one the question turns on, and the one that killed design 10's image-model result. Endogeneity declared now rather than after: AI sellers enter where AI works, so this can produce an association and never an identified effect. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
…e AI The project had no transaction series on the website and no category split anywhere. Two cards added, plus the paper figure behind them. PLATFORM (step 47). orders = real GMV / real IPI price, the project's only transaction-count series. Drawn as three lines rather than one because the finding is that the quotient falls while the numerator does not: real GMV +11.3% against a real price +35.8%, leaving orders -18.0% vs 2020 and -38.6% from the 2021 peak. Same 2020 = 100 base on all three, so one axis, never two. CATEGORIES (step 62, new). Fiverr publishes no category split, so within-gig review accrual is the only route. Reported as a SERIES for the first time - step 46 estimated the break and never wrote what it broke. Six of seven peak in 2020Q3 and the fall is a simultaneous step between 2021Q2 and 2021Q3, fifteen months before ChatGPT, which itself lands inside a flat stretch where five of seven categories are HIGHER in 2023Q3 than 2022Q3. Identification stated in the script rather than discovered later: within a gig age and calendar quarter move one-for-one, so the shape is identified and the trend is not. The peak quarter is the statistic that survives. A capture-span diagnostic is included for the same reason - mean span widens 1.2 -> 1.75 quarters, so part of the 2024 fall is the crawl. Two structural notes. write_site_block lives in steps 47 and 62 rather than 18, because both series are quotients of the index step 18 builds; step 18 writes data.json whole, so RERUNNING IT DROPS BOTH BLOCKS. And step 34 now takes figure names, so one figure regenerates without figure 4's subsampling curve. The category panel is small multiples, not seven overlaid lines, on a measurement rather than a preference: the site's seven category colours fail a colour-vision check on all 21 pairs, worst pair dE 6.1 deutan and one pair at dE 13.2 for NORMAL vision. Faceting removes the problem at its source. The underlying palette defect still affects the hero price chart and is filed as tech-debt TD2 rather than repainted, since that is a site-wide change. No causal claim anywhere. Both cards say so in their own text, because a chart with a ChatGPT line on it invites exactly that reading. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
From a user correction — "I'm trying to look at just the volume of transactions" — which exposed a real defect in how the page was arranged. Every quantity on the site went through the IPI. That is defensible for the implied order count (Fiverr reports dollars and never an order count, so orders = GMV / price is the only route to one), but it meant the question "what is the volume" was answered with a quotient whose denominator is a price index. Two of the three volume measures in this project never touch a price, and neither was presented as such. The new card is placed FIRST, above the implied-order card, because it is the direct answer and everything below it is derived from something. Pooled review accrual (quarterly, counted off the archived pages) and active buyers (annual, as Fiverr reports it), both indexed 2020 = 100 so they share ONE axis - the mixed cadence is only legitimate because of the shared base, and buyers carries open annual markers so its grain is visible rather than implied. pooled is equal-weighted across the seven categories, not review-weighted: review weight IS the outcome here, so weighting by it would let the largest category set its own denominator. Computed in step 62 rather than in the browser, so the definition lives with the data. The card states what it is not. Accrual is per SURVIVING listing and the archive cannot measure exit (n_404 = 0 across 509,339 captures), so it will not scale to a platform total; buyers counts people, and spend per buyer nearly doubled over the window. An actual count needs the dated order records still unextracted in the archived HTML - encrypted_order_id plus created_at, no price required - which stays PRIORITY 1b. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The data is 124 GB of flat files with no database, and which tool to reach for changes by tier — pandas for the KB indices and the 4-85 MB price panels, duckdb for the 1.3-5.8 GB cdx intermediates, gzip.open one page at a time for the 86 GB of archived HTML. That is not obvious from the tree, so this is one worked example per tier rather than any analysis. Two things it demonstrates that are easy to miss. The lineage hook: a price row's file_path resolves to the exact .html.gz it was scraped from, which is the check to run whenever a number looks wrong. And the download logs as the cheap index into the HTML store, so the 86 GB never gets globbed. duckdb reads the headerless 1.3 GB gig-month-index.tsv in ~3.5s: 190 months, 201006-202603. Note the parameterised read_csv(columns=$cols) form is silently ignored and then fails on dialect sniffing — the struct is rendered inline instead. Runs top-to-bottom clean under nbconvert --execute; outputs stripped. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The root was hardcoded to /home/exouser, so the notebook ran on exactly one machine. It is now discovered by walking up for CLAUDE.md + data/, and a preflight cell reports which of the four tiers are actually present. Sections degrade instead of raising. The price panel picks the largest file on hand, falling back from the gitignored balanced-prices.csv (292,447 rows) to the tracked recent-prices.csv (15,150), so a clone gets a real panel. The cdx and HTML sections print what they need and skip; duckdb is an optional import. Verified both ways: full collection machine, 0 errors, all four tiers OK; and a `git archive HEAD` tree — 44 MB, exactly what a clone gets — also 0 errors, with three clean SKIPs. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
… ChatGPT `balanced-prices.csv` is 88 MB and gitignored, so the notebook could not be run from a clone -- `first_present()` fell through to the thin `pilot-prices.csv` without saying so. The panels compress ~11x (88.6 -> 7.9 MB, 25.8 -> 2.9 MB), smaller than `pilot-prices.csv` which was already tracked, so the "exceeds GitHub's limits" reasoning held only for the raw CSV. Commit the gzips, keep the raw files ignored; `pd.read_csv` reads .gz transparently so no analysis code changed. `balanced-gig-category.csv.gz` (0.9 MB) joins them because the category source was a 51 MB manifest also ignored, which would have broken the index and event-study sections on a clone even with prices fixed. code/64 adds the panel builder and a two-way FE event study -- gig effects absorbed by within-gig demeaning, SEs clustered on gig, numpy only so a bare clone can run it. The GEKS index is imported from code/21 unchanged rather than reimplemented, so the notebook plots the papers' estimator. On the 16,128 gigs observed both sides of 2022Q4: the pre-trend is +0.047 log points per quarter (t = 42) and linear to within 0.02 across eleven quarters, nothing breaks at the launch, and the series ends 2024Q4 at -0.213 log points against the extrapolated trend, same sign in all seven categories. The last number is not identified -- no control group, and a linear counterfactual run eight quarters past its data. Held out of the drafts in plans/todo.md until a placebo cut or the step 61 niche design clears it. Verified by cloning the repo and executing the notebook there: 85 s, 0 errors. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01QfFt4d1Q4AkwPAnNCyYgTf
Section 7 was one chart of seven nominal lines. It is now two: the GEKS-Jevons matched-model index over 35 narrow buckets, nominal, and the same index deflated by CPI-U. Both grids share a y-axis, so the gap between them is general inflation. Nothing is reimplemented. code/66-narrow-real-geks.py wires together the estimator (step 21), the narrow taxonomy (step 16) and the deflator (step 23); the notebook imports it. Two results the broad chart could not show. All 35 buckets clear 20/20 quarters at pair_density 1.00, so step 16's thin-subcategory warning was about the recent monthly manifest, not about quarterly matched-model indexing. And deflation -- CPI-U rose 22.3% across the window -- changes no sign: real increases run +3% to +155%. The only two intervals covering zero are Subtitles & Transcribe (+3.1% real) and Translation (+5.5%), the most AI-exposed cell on any crosswalk the project uses. Its own keyword remainder, translation-other, is +51.2%, so the next move is auditing those keywords rather than reading the result. Still no break at the ChatGPT line, at either level, nominal or real. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_016G3pDU1a5BRbp2udySwGXt
…that name themselves
Working-tree files from the preceding sessions, checked in as they stand:
- code/60,61 -- niche construction and the niche-level event study (design 9)
- code/63,63b -- live-recovery calibration and the reach curve behind it
- code/65 -- the event-study figure, plus its PDF/PNG outputs
- data/pilot/niche-{arrival,assignment}.csv, the 2026-08-21 sitemap snapshot
- docs/ipi.js -- volume and transaction charts label which series each peak
belongs to, because accrual, buyers, orders and real GMV peak in different
years and an unqualified "peak" invited the wrong reading
One fix on the way in: the new real-GMV label referenced an undefined C_GMV,
which would have thrown on every render of the transactions chart. It is
TX_COLORS.gmv_real, the colour the series is already drawn in.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016G3pDU1a5BRbp2udySwGXt
A user looked at the fine-grained chart and said the coding line looked suspiciously high. It is high, and the reason is worth more than the chart. Two diagnostics in code/66-narrow-real-geks.py, both reusing step 21's walk: geks_variant() swaps the bilateral aggregator (median-of-logs in place of the mean Jevons is defined as), floor_shares() reports the cheap end. A cell in §7 runs them. The plotted index is unchanged. Coding's matched 2020Q1->2024Q4 log changes are 0.00 / 0.69 / 2.07 at the 20th/50th/90th: the typical matched gig exactly doubled, the top decile went eight-fold, and Jevons averages logs, so the plotted +145% sits far above the median-of-logs +74%. It is not outlier contamination -- 1% trimming moves it 0.5 index points. And the level is frame-dependent: the same estimator ranks coding first here and fourth on the panel frame the site publishes, where audio leads. The explanation that looks obvious fails, and is recorded as failing. Listings entering at or below $10 reprice 2-3x faster per observed quarter than dearer ones in every family, so the retiring $5 floor lifts every line -- but it does not order them: translation is the most floor-heavy family (64.5% at or below $5 in 2020) and the flattest series on the chart. Opened as findings.test.md R11 (FAIL): the draft reports the mean-of-logs level on one frame and says the category table is not a ranking. R9 promises that; this is the evidence, and it is stronger than the overlapping intervals given now. Untouched by all three checks: the within-family ordering and the nominal/real gap, including flat translation. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_016G3pDU1a5BRbp2udySwGXt
A second author appended a composite pre-trend analysis to the notebook, edited against the pre-section-7 version. GitHub's resolution dropped the deflated chart and left the new cells calling geks_df, a name section 7 no longer defines. Kept both sides. The real chart and the robustness cell are back; the new cells are section 9, verbatim, under a heading saying what the design is and that section 8 runs the same test with gig fixed effects and clustered SEs. geks_df is aliased to nom_b in the section 7 build cell so their code needs no edit, and their statsmodels cell is guarded, since statsmodels is not a project dependency and this notebook has to run on a bare clone. 39 cells, 0 errors, 7 figures, statsmodels absent. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_016G3pDU1a5BRbp2udySwGXt
…ises A user asked why marketing looked so high. It is high, +211% nominal and +172% real at the broad level, and unlike coding it survives every check. Marketing's listings are the shortest-lived on the panel -- median 9 quarters between first and last capture against design's 33 -- so entry composition was the obvious suspect. It is not: restricting to gigs spanning 8+ quarters moves marketing 210.79 -> 210.69, and no other domain by more than 0.2 index points, though 30 of 190 bilaterals change. That test is now a column in section 7's check cell, because it generalises to any category a reader gets suspicious of. The rest of the battery agrees. No single gig is worth more than 5% of the Ads & PPC level under a leave-one-out; the bilaterals are well populated (median 61 matched gigs per quarter pair, minimum 10); and marketing reads +282% on the published panel frame, higher than here, where coding's rank collapses from first to fourth. So the two suspicious categories have different diagnoses and only coding's is a measurement artefact. What marketing has is width. Email & Funnels reads 373 at 2024Q2 against 321 and 318 either side, 95% band [284, 491]. The spike is inside the noise. Also opened: section 6 asserts entrants price above incumbents, citing step 57. Step 57's finding was about AI-labelled entrants. On this panel the general claim inverts -- marketing entrants list at $15 against incumbents' $30 at 2023Q1 -- so the sentence needs restating or re-deriving. Verified: 39 cells, 0 errors. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_016G3pDU1a5BRbp2udySwGXt
Second merge with the same author, from the same stale base, dropping the same work: section 7's real chart, the check cell, the section 9 heading and the geks_df alias. Kept her newest cells verbatim except for two fixes -- the notebook as pushed carries both errors in its own committed outputs. NameError: geks_df, in cells 32 and 34. Section 7 renamed that table when it went to 35 categories. geks_df = nom_b is aliased in the section 7 build cell so her code reads the broad nominal table under its old name and needs no edit. KeyError: pre_fitted_index, in the section 8 plot. pre and post are copies taken before the fitted columns are added to g, so those columns do not exist on them. Now g.loc[pre.index, ...] and g.loc[post.index, ...], which is what the code intends. Structural fix, not exercised here: statsmodels is absent on this machine and the guarded cell skips. 39 cells, 0 errors, 7 figures. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_016G3pDU1a5BRbp2udySwGXt
Both section 7 charts have been in the file since 4ba13c6, but every output was stripped, so opening the notebook showed code and no images. That is the repo convention and it is the wrong one for a notebook whose point is two charts you are meant to compare. Committed with outputs: 7 figures, 0 errors. Section 7 cell 24 is the nominal chart, cell 25 is the same index deflated by CPI-U on the same y-axis. 1.4 MB. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_016G3pDU1a5BRbp2udySwGXt
…unning it §10 fits ln(real price) on the gig fixed effect (task value), quarter effects, reputation and AI exposure, over 169,337 gig-quarter observations. It imports code/76-price-model.py rather than restating it — build_panel, design, fe_ols, qdummies, mde and load_slug_exposure — so the notebook and runs/price-model/model.md are one fit. Every printed number reproduces the script's to the digit. What it establishes: reputation, +7.33% real price per doubling of cumulative reviews (t 26), an independent replication of steps 22/27 in 2020Q1 dollars; and task value, SD 1.249 log points, seven times the spread of everything time-varying put together, which is the argument for keeping it a fixed effect rather than a proxy. What it does not: Exposure x Post is -0.0333 (t -1.35) against an MDE of 0.0689, so the null rules nothing out — a silence, not a zero — and gate A fails anyway. The new event-study chart is the clearest statement the project has of why: the exposure gap opens across 2021, a year before ChatGPT exists, and is flat for the whole post-period. §10.5 carries the false positive that was caught, +13.76% per 10pp collapsing to +0.73% under one linear trend per category. Two of §10's dependencies were untracked, so a clone could not run it: step 76 itself, and runs/ai-slug-diffusion/diffusion.md, which §10.5 reads as its regressor. Both committed. Step 76 also gained a .csv.gz fallback for PRICES and path arguments to build_panel, since the 88 MB CSV is gitignored and only the 7.9 MB gzip is in the repo; its own output is byte-identical after the change. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_017CZzqPanjxFvPfB9TXK6se
§10 put task value in a gig fixed effect, which is the honest home for it and also a dead end: a fixed effect that ABSORBS task value can never REPORT it. `a_i` came back with an SD of 1.249 log points and no way to say what any part of it was. Worse, a gig fixed effect also absorbs the permanent part of reputation, so §10 could only ever watch one listing reprice itself and was structurally blind to the comparison the reputation question is about. Two new steps replace the gig anchor with a task anchor. `77-task-taxonomy.py` builds a two-level taxonomy. Domains are step 04's; subcategories are matched on the DELIVERABLE PHRASE stripped out of the title (`<seller>: I will <X> for $N on fiverr.com`, 99.99% coverage and far richer than the slug). Rules are ORDERED and first-match-wins, most specific first, so `shopify dropshipping store` reaches `ecommerce` before `web_dev` sees `store`; the order is part of the definition. 65 nodes, 77.5% of gigs named, nodes under 30 gigs folded into `<domain>/other`. Reference task value runs $6.66 (translation/subtitling) to $59.38 (marketing/content_strategy), a spread of 2.188 log points. Domain explains 5.7% of ln(real price), the node 10.9%, the gig 91.4% — the taxonomy roughly doubles what seven domains name and is still nowhere near the fixed effect. `78-reputation-price.py` fits reputation at three levels. Between nodes +6.94% per doubling; between gigs within a node -9.08% (t -28); within a gig +7.62% (t 31). B and C have OPPOSITE SIGNS, both precisely estimated on 270,965 observations. Step 25 saw this at category level as "near zero versus positive"; holding the task fixed at node level turns the cross-section significantly negative. A high review count marks two things at once — a seller who has been around, which raises price, and one running a cheap high-volume operation, which lowers it. Between sellers the second dominates; within one listing only the first can move. Neither number may be quoted without the other. Per-node slopes are positive in all 58 estimated nodes, 45 significant, +1.84% (translation/localization) to +27.11% (translation/interpreting). Commodity translation sits at the bottom, consultative work at the top. Notebook §11 (15 cells, four charts) imports both steps rather than restating them, the convention §10 uses for step 76; every printed number reproduces `runs/taxonomy/*.md`. The chart that carries the argument plots one node twice — one point per listing (slope -0.038), then each listing centred on its own mean (+0.091). The reversal is visible without a regression. Two corrections made while wiring it up. Step 77 was reporting reference values on the full panel while step 78 fit on rows carrying both reputation columns, so the project briefly held two different reference task values — marketing/content_strategy was $67.11 in one and $59.38 in the other; step 77 now computes on the estimation sample and says so. And §11.3's scatter was captioned as showing the reversal "before any regression", which is false: `adjusted` comes from the fit containing b1, so the gap is approximately b1 x (node mean log reviews - grand mean), measured correlation 0.978. The caption now states this and credits the sign to spec B. Open, both in todo: the taxonomy has no hand-labelled validation, and `translation/voiceover_leak` (721 gigs) shows step 04's classifier leak sits inside the nodes rather than repaired by them. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01TftqfBPLvNdb4GJYdSKXXe
The incoming commit replaced §7's chart colours with an explicit Okabe-Ito `DOMAIN_COLORS` map for the seven domains. §11 had landed with its own seven-colour palette, so a domain would have been one colour in §7 and another in §11's three charts. §11 now aliases `DOMAIN_COLORS` instead of defining a competing map, and its figures are re-executed against it. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01TftqfBPLvNdb4GJYdSKXXe
… approximates "Price increases 7.7% when review counts double within one gig" was a coefficient with no picture and two unasked questions behind it. §11.4a asks both. IS IT A STRAIGHT LINE? The left panel is a Frisch-Waugh partial-residual plot: gig, quarter and rating are swept out of the price AND out of the review count, so the plotted line's slope is spec C by construction — the cell asserts the FWL slope equals bC to 1e-9 — and the only thing free to move is the shape. Fourteen equal-count bins, gig-clustered 95% CIs. The answer is mostly, not exactly. A quadratic in ln(1+reviews) is -0.00277 (t -2.51), so strict log-linearity is REJECTED. The departure is small and monotone: +8.14% per doubling at 10 reviews, +7.23% at 100, +6.29% at 1,000, +5.63% at 5,000, against the pooled +7.62%. The median gig-quarter carries 111 reviews (p90 965), so the linear coefficient is a fair summary over the mass — but it is now stated as an approximation rather than a functional form, and the concavity is visible in the top bin without the parametric test. WHAT DOES THE RATE BUY? +7.6% per doubling is a rate, not an outcome, and it pays only if listings accumulate doublings. Most do not: the median listing travels 1.53 doublings over its whole observed life, p90 4.63, and 38% never manage even one. At spec C's slope the median listing's entire review history is worth +11.9% in real price. A secondary top axis converts doublings to implied gain directly. The cells reuse §11.4's y / X / igig / bC rather than refitting, so the section still holds one spec C. Outputs are executed and embedded, per the convention that committed charts are visible without running the notebook. Two reading hazards handled rather than left: the x-axis is doublings, not log points, so the coefficient is legible off the chart; and the histogram's last bar is a clipped 6+ tail (3.0% of listings), labelled so it is not misread as a mode. Logged as U6 (the instruction) and R12 (the functional-form critique, now answered) in tests/findings.test.md. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UzPM6JZMKR7bpHE5XciSU7
…r's sitemaps taken Two steps, tested against the live sites rather than the archive. FIVERR GIG PAGES ARE HARD-BLOCKED, MEASURED NOT ASSUMED. 15 gig pages, 3s apart, honest browser UA: 15/15 HTTP 403. Retried with complete standard browser headers: still 403. The homepage 403s on the first request, so it is not a rate limit. The body is a PerimeterX CAPTCHA wall. Passing it would need CAPTCHA-solving, proxy rotation or fingerprint spoofing — circumvention of an access control, refused. This closes todo PRIORITY 0b: its question (1), the paginated reviews endpoint, cannot be tested at all because no HTML is served, so the archive back-fill is dead by the plan's own abandonment criteria. 80-fiverr-sitemap-census.py RECOVERS WHAT WAS RECORDED AS IMPOSSIBLE. Step 39 found n_404 = 0 across 509,339 Wayback captures — the archive stops re-requesting a delisted URL rather than recording its death, so exit was unmeasurable. Fiverr publishes its live inventory as sitemaps, listed in its own robots.txt and served WITHOUT the PerimeterX wall. Eight requests give 291,068 live gig URLs and put 12,720 of the panel's 39,933 gigs (31.9%) still listed. Survival by year of last panel observation runs 2.2% (2018) to 55.0% (2024). That is an exit hazard, and it bears on §4.4's survivorship gap and on the objection that the index is computed on survivors. Two limits are in the docstring, not the commit only: absence means NOT LISTED, a lower bound — a gig may be paused, unindexed or rotated out rather than dead; and no sitemap carries a price, so this cannot extend the index. 79-mercor-board.py NEEDS NO CRAWLER. work.mercor.com/jobs server-renders every open listing into __NEXT_DATA__, so one GET is the whole cross-section — nothing to rate-limit. robots.txt allows /jobs/; /apply/, /api/ and /interview/ are untouched. 394 listings, 100% rate coverage, median hourly band $70-$100, plus capacity fields the archived copies lack (remainingSlots, 11,418 open slots; suppliedSlots; recentCandidatesCount; hoursPerWeek; postedAt). Stated in the docstring before anyone reaches for it: Mercor listings are employer-posted HOURLY RATE BANDS for contract roles, not seller-posted fixed prices for deliverables. Not comparable to Fiverr gig prices, and not to be spliced into the IPI. Today's file is a cross-section; the panel accrues only by re-running, where the version field separates a genuine reprice from a re-crawl. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UzPM6JZMKR7bpHE5XciSU7
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