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In the current working directory, `TASK.md` describes a bounded scientific task and the directory `out/` contains the deliverables submitted by another assistant (you have not seen how they were produced and you must not look for their working transcript). There is no network access. Use `~/wp29/venv/bin/python` if you need to re-run or test code. Do not modify any file under `out/` except that you must create `out/review/review.json`.\n\nReview the deliverables against `TASK.md` only. Specifically check:\n1. Does `out/results.json` contain every required key with legitimately computed values? Re-run `out/analysis.py` in a scratch copy if needed and compare.\n2. Are the information-availability rules of `TASK.md` respected by the code (for example no use of future information, no use of forbidden inputs)? Read the code to verify; do not trust the report.\n3. Are the central claims in `out/report.md` and `out/claims.json` supported by the numbers in `out/results.json`, with appropriate uncertainty and caveats? Flag any claim that is stronger than the evidence.\n4. Are counts, exclusions, units, signs and magnitudes internally consistent? Are baseline comparisons present when the task requires them?\n5. Are limitations stated honestly? Is anything declared complete that is not?\n\nYou have no reference answers; do not guess what the \"right\" number is. Report only issues you can justify from the files. Write `out/review/review.json` as {\"verdict\": \"accept\"|\"revise\", \"issues\": [{\"id\": \"R1\", \"severity\": \"critical\"|\"major\"|\"minor\", \"location\": \"<file or key>\", \"issue\": \"...\", \"suggested_fix\": \"...\"}], \"summary\": \"...\"}. Use \"revise\" if there is at least one critical or major issue. Be concise and specific. 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Never use words like \"complex\" or \"risk\" in the description - just describe what it does.\n\nSay what the command does in plain words: do not echo the command's text, its flags, or file paths - the user reads this description, often without seeing the command.\n\nFor simple commands (git, npm, standard CLI tools), keep it brief (5-10 words):\n- ls → \"List files in current directory\"\n- git status → \"Show working tree status\"\n- npm install → \"Install package dependencies\"\n\nFor commands that are harder to parse at a glance (piped commands, obscure flags, etc.), add enough context to clarify what it does:\n- find . -name \"*.tmp\" -exec rm {} \\; → \"Find and delete all .tmp files recursively\"\n- git reset --hard origin/main → \"Discard all local changes and match remote main\"\n- curl -s url | jq '.data[]' → \"Fetch JSON from URL and extract data array elements\"","type":"string"},"dangerouslyDisableSandbox":{"description":"Set this to true to dangerously override sandbox mode and run commands without sandboxing.","type":"boolean"}},"required":["command"],"additionalProperties":false}},{"name":"Edit","description":"modify file contents in place","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"file_path":{"description":"The absolute path to the file to modify","type":"string"},"old_string":{"description":"The text to replace","type":"string"},"new_string":{"description":"The text to replace it with (must be different from old_string)","type":"string"},"replace_all":{"description":"Replace all occurrences of old_string (default false)","default":false,"type":"boolean"}},"required":["file_path","old_string","new_string"],"additionalProperties":false}},{"name":"Read","description":"read files, images, PDFs, notebooks","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"file_path":{"description":"The absolute path to the file to read","type":"string"},"offset":{"description":"The line number to start reading from. 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the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0238","sessionId":"58628346-804e-4b01-8fcc-16f6b88a2208","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"58628346-804e-4b01-8fcc-16f6b88a2208"}12{"parentUuid":"bb51c137-0fa4-4394-8bbe-0f0388dc2d40","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrBGmUTQy7hDsLbSZH8","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01WcmcvJRj9FP6zX3yRVbyEJ","name":"Bash","input":{"command":"cat TASK.md; ls -la out; cat out/results.json; cat out/claims.json; cat out/report.md"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":716,"cache_read_input_tokens":1504,"output_tokens":84,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":716},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXrBGUMiWhwHWqkxDgjf","requestRef":"3fe4fa4b-d151-43fb-81b6-043b1a4f8cf9","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ee0aaedb-0fa7-4c7e-a04d-d628a9849b31","timestamp":"2026-10-30T05:12:37.660Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0238","sessionId":"58628346-804e-4b01-8fcc-16f6b88a2208","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"ee0aaedb-0fa7-4c7e-a04d-d628a9849b31","isSidechain":false,"promptId":"9045acf7-28b3-4ac7-b56a-73d028682e36","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01WcmcvJRj9FP6zX3yRVbyEJ","type":"tool_result","content":"# F4-conf-03 — Harmonising effect sizes reported under incompatible definitions\n\n## Scientific question\nA synthesis compares treatment effects from four open-access studies that report their estimates under different definitions: a semi-elasticity (coefficient on a log outcome), a percentage-point effect on a probability, an elasticity, and a linear-probability coefficient. For each of the five effects listed in `data/effects.json`, re-express the printed point estimate on the **target definition** stated for that item, using only quantities printed in the cited extract and an explicitly stated transformation; where the target definition cannot be reached from the printed quantities, say so and name the additional quantity that would be required. The estimands are the harmonised point estimates (no uncertainty propagation is required). Each item carries a locator for every printed quantity used.\n\n## Inputs\n- `data/effects.json` — the five items: {\"id\", \"source\", \"effect\" (what and where), \"reported_definition\", \"target_definition\"}.\n- `data/sources/arxiv-2609.27732v1.txt` — Armendariz (2026), *Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland*, arXiv:2609.27732v1, CC BY 4.0: abstract, Section 6 (Tables 6 and 7, Section 6.1), Appendix Table A19. Outcomes are log hospitalisation rates per 100,000 inhabitants; ATTs in log points; the tables also print a \"% change\" row for the overall effect.\n- `data/sources/arxiv-2606.14621v1.txt` — Li, Pearson-Merkowitz & Yokum (2026), *Evaluating the Impact of Rhode Island's Self-Sustaining RESEA Program on Employment Outcomes*, arXiv:2606.14621v1, CC BY 4.0: abstract, Section 3 (Table 2: treatment/control means, differences, ATEs with p-values and 95% CIs), Table 3. Reemployment is in percent (means) and percentage points (effects).\n- `data/sources/arxiv-2505.04555v3.txt` — Kanayama, Miyaji & Otani (2025/2026), *Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation*, arXiv:2505.04555v3, CC BY 4.0: abstract and Appendix A.1 (Table 2: elasticities and the size of the minimum-wage change, with note).\n- `data/sources/arxiv-2510.03658v1.txt` — Tossou (2025), *Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin*, arXiv:2510.03658v1, CC BY 4.0: abstract, Section 4.1 and Table 3 (linear probability model of infant death or stillbirth; \"Mean Dep. Var.\" row).\n- `data/README.md` — provenance, retrieval times, licences, SHA-256 of the extracts.\n\n## Information-availability rules\n- The extracts are the only admissible evidence; a quantity that is not printed in the cited extract is not available, even if you believe it is in the full paper or elsewhere.\n- Conventions: a log-point coefficient b corresponds to a relative change of 100·(exp(b) − 1) percent; a percentage-point effect Δ on a probability with counterfactual (control) level p corresponds to a relative change of 100·Δ/p percent; an elasticity η applied to a proportional change g of the treatment variable implies a relative change of 100·η·g percent. State the transformation you apply in `transformation`.\n- `harmonisable` is `false` only when the target definition requires a quantity that the cited extract does not print; then `value` is `null` and `missing_quantity` names the quantity. Do not substitute a guessed or externally remembered value.\n- `feasibility` refers to the task as a whole: the task is complete when every item has either a harmonised value or an explicit, justified statement of impossibility.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `harmonised` | object | — | {\"E1\": {\"value\": number or null, \"unit\": \"<unit of the target definition>\", \"transformation\": \"<formula with the printed inputs>\", \"harmonisable\": true/false, \"missing_quantity\": string or null, \"locator\": \"<source id | table | row | column for every printed input>\"}, … \"E5\": {…}} |\n| `n_harmonised` | integer | — | number of items with a non-null value |\n| `n_not_harmonisable` | integer | — | number of items with `harmonisable` = false |\n| `task_id` | string | — | \"F4-conf-03\" |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" — refers to the task as a whole |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the inputs (rendering defects, internal inconsistencies of a source, etc.) and how they were handled; empty list if none |\n| `n_input_rows` | object | lines / items | {file name: number of lines} for every file under `data/` that you read (text files: number of newline-terminated lines as reported by `wc -l`; JSON files: number of top-level items) |\n| `n_analysed` | integer or null | — | number of reported items (values, assertions, effects or references) you actually assessed |\n| `random_seed` | integer or null | — | null (no randomness is involved) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. \"Uncertainty\" must note that no standard errors were propagated and which conversions are exact versus approximate.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically (the script must print the source lines it relies on).\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available; the standard library is sufficient for this task). Do not modify `data/`.\n- The source extracts under `data/sources/` are the **only** admissible evidence. Do not rely on memory of the papers or on other versions of them; if a quantity is not in the extract, it is not available.\n- Report numbers exactly as printed in the source (same sign, same number of decimals); do not round or rescale printed values unless the key definition asks for a transformation.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 72\ndrwxr-xr-x@ 9 simon-pierreboucher staff 288 oct. 30 01:12 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 01:10 ..\n-rw-r--r--@ 1 simon-pierreboucher staff 11643 oct. 30 01:11 analysis.py\n-rw-r--r--@ 1 simon-pierreboucher staff 1943 oct. 30 01:12 claims.json\n-rw-r--r--@ 1 simon-pierreboucher staff 187 oct. 30 01:11 exclusions.csv\n-rw-r--r--@ 1 simon-pierreboucher staff 5016 oct. 30 01:12 report.md\n-rw-r--r--@ 1 simon-pierreboucher staff 5591 oct. 30 01:11 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 01:12 review\ndrwxr-xr-x@ 8 simon-pierreboucher staff 256 oct. 30 01:12 state\n{\n \"task_id\": \"F4-conf-03\",\n \"harmonised\": {\n \"E1\": {\n \"value\": -35.0141,\n \"unit\": \"percent (relative change vs. counterfactual hospitalisation rate)\",\n \"transformation\": \"100*(exp(b)-1) with b = -0.431 (printed ATT, log points) -> 100*(exp(-0.431)-1) = -35.0141%\",\n \"harmonisable\": true,\n \"missing_quantity\": null,\n \"locator\": \"arxiv-2609.27732v1 | Table 6 | row 'ATT' | column '5 km' (line 36); cross-check: row '% change' | column '5 km' = -35.0% (line 40)\"\n },\n \"E2\": {\n \"value\": 1.96,\n \"unit\": \"percent (relative change vs. control-group mean reemployment rate)\",\n \"transformation\": \"100*Delta/p with Delta = 1.47 pp (printed ATE) and p = 75.0% (printed control mean) -> 100*1.47/75.0 = 1.9600%\",\n \"harmonisable\": true,\n \"missing_quantity\": null,\n \"locator\": \"arxiv-2606.14621v1 | Table 2 | row 'Reemployment' | column 'Average treatment effect estimate' (1.47 pp) and column 'Control' (75.0%) (line 29)\"\n },\n \"E3\": {\n \"value\": -1.8236,\n \"unit\": \"percent (relative change in employment)\",\n \"transformation\": \"100*eta*g with eta = -0.388 (printed elasticity) and g = 0.047 (printed '% MW changes', proportional) -> 100*-0.388*0.047 = -1.8236%\",\n \"harmonisable\": true,\n \"missing_quantity\": null,\n \"locator\": \"arxiv-2505.04555v3 | Table 2 | row 'Employment elasticity w.r.t MW' | column 'Estimate (SE)' (line 50); row '% MW changes' | column 'Estimate (SE)' (line 60)\"\n },\n \"E4\": {\n \"value\": null,\n \"unit\": \"hospitalisations per 100,000 inhabitants per year\",\n \"transformation\": \"absolute change = R0*(exp(b)-1) with b = -0.354 (printed ATT, log points); exp(b)-1 = -0.2981 is computable, but R0 (counterfactual/baseline fatal rate) is not printed\",\n \"harmonisable\": false,\n \"missing_quantity\": \"baseline (counterfactual, e.g. pre-opening treated-region mean) level of the fatal drug-related hospitalisation rate per 100,000 inhabitants per year in the 5 km treated regions; not printed in the extract\",\n \"locator\": \"arxiv-2609.27732v1 | Table 7 | row 'Drug-related (fatal)' | column '5 km' (line 86)\"\n },\n \"E5\": {\n \"value\": -9.62,\n \"unit\": \"percentage points\",\n \"transformation\": \"100*beta with beta = -0.0962 (printed LPM coefficient, 0-1 scale) -> -9.6200 pp\",\n \"harmonisable\": true,\n \"missing_quantity\": null,\n \"locator\": \"arxiv-2510.03658v1 | Table 3 | row 'Treatment' | column '(4)' (line 49)\"\n }\n },\n \"n_harmonised\": 4,\n \"n_not_harmonisable\": 1,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"arxiv-2609.27732v1: the abstract says fatal drug-related hospitalisations 'fall by 35%', but Table 7 (5 km) prints -0.354 log points, i.e. 100*(exp(-0.354)-1) = -29.8%, matching Section 6.1 ('approximately 30% at 5 km'); 35% is the overall (Table 6) effect.\",\n \"handling\": \"Table 7 cell used as the printed estimate for E4; abstract figure not used. E4 is not harmonisable for a separate reason (missing baseline level).\"\n },\n {\n \"issue\": \"arxiv-2609.27732v1: the same CS-DR 5 km ATT (-0.431) is printed with SE (0.210) in Table 6 but (0.218) in Appendix Table A19; A19 note says 640 never-treated regions while its table and Table 6 say 608.\",\n \"handling\": \"Only the point estimate (identical in both tables) is used; SEs and region counts are not needed and were not used.\"\n },\n {\n \"issue\": \"arxiv-2505.04555v3: Table 2 prints '% MW changes' = 0.047 (4.7%), while the table note describes the 2023 increases as 'about 4%'.\",\n \"handling\": \"Per the target definition, the table value 0.047 is used for E3; with 'about 4%' the implied change would be 100*-0.388*0.04 = -1.5520% (sensitivity only, not the reported value).\"\n },\n {\n \"issue\": \"arxiv-2505.04555v3: Table 2 prints the same SE (0.091) for 'Affected employment' and 'Emp. elasticity w.r.t affected wage' (possible duplication); point estimates are internally consistent (-0.268/0.366 = -0.732).\",\n \"handling\": \"Not used; no uncertainty is propagated.\"\n },\n {\n \"issue\": \"arxiv-2510.03658v1: Table 3 prints '% Change' = -0.3685 for column (4), whereas -0.0962/0.261 = -0.36858 (rounds to -0.3686); the text also attributes the 0.0855 estimate to 'table 2' and calls an 18.79% relative change a 'percentage change' while interpreting it as lives saved per 100 deaths.\",\n \"handling\": \"Not material for E5, whose target (percentage points) needs only the printed coefficient -0.0962; noted for transparency.\"\n },\n {\n \"issue\": \"arxiv-2606.14621v1: the ATE (1.47 pp) is block(week)-adjusted while the 75.0% control mean is the raw aggregate control-group mean; the abstract rounds the ATE to 1.5 pp.\",\n \"handling\": \"Used the printed control mean 75.0% as the counterfactual level p, as the target definition specifies (percent of the control-group mean); 1.47 (table) used rather than 1.5 (abstract).\"\n }\n ],\n \"n_input_rows\": {\n \"effects.json\": 5,\n \"README.md\": 16,\n \"arxiv-2609.27732v1.txt\": 134,\n \"arxiv-2606.14621v1.txt\": 58,\n \"arxiv-2505.04555v3.txt\": 66,\n \"arxiv-2510.03658v1.txt\": 91\n },\n \"n_analysed\": 5,\n \"random_seed\": null,\n \"baseline_naive\": {\n \"E1_logpoints_times_100_pct\": -43.1,\n \"E2_printed_pp\": 1.47,\n \"E3_printed_elasticity\": -0.388,\n \"E4_logpoints_times_100_pct\": -35.4,\n \"E5_printed_coefficient\": -0.0962,\n \"note\": \"Naive values = printed numbers read at face value (log points x100 treated as percent; no rescaling). Not harmonised estimates.\"\n }\n}[\n {\"id\":\"C1\",\"text\":\"The 5 km DCR overall ATT of -0.431 log points corresponds to a -35.0141% relative change in the drug-related hospitalisation rate, consistent with the printed -35.0%.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Exact under the log-point convention; rate-level interpretation approximate (geometric mean).\"},\n {\"id\":\"C2\",\"text\":\"The RESEA reemployment ATE of 1.47 pp equals a 1.96% relative increase over the 75.0% control mean.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Block-adjusted ATE divided by raw control mean; inputs rounded as printed.\"},\n {\"id\":\"C3\",\"text\":\"The employment elasticity -0.388 applied to the printed 0.047 minimum-wage change implies a -1.8236% change in employment.\",\"results_keys\":[\"harmonised\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Linear approximation; table note says 'about 4%', which would give -1.5520%.\"},\n {\"id\":\"C4\",\"text\":\"The absolute change in the fatal drug-related hospitalisation rate (E4) cannot be computed because the extract does not print a baseline/counterfactual fatal rate per 100,000.\",\"results_keys\":[\"harmonised\",\"n_not_harmonisable\"],\"status\":\"supported\",\"caveat\":\"Only the relative change (about -29.8%) is computable.\"},\n {\"id\":\"C5\",\"text\":\"The Benin LPM coefficient -0.0962 corresponds to -9.62 percentage points.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Exact unit conversion.\"},\n {\"id\":\"C6\",\"text\":\"Four items are harmonised and one is not harmonisable; the task is complete.\",\"results_keys\":[\"n_harmonised\",\"n_not_harmonisable\",\"feasibility\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C7\",\"text\":\"The sources contain internal inconsistencies (DCR abstract fatal 35% vs table ~29.8%, DCR SE 0.210 vs 0.218, MW 0.047 vs 'about 4%', Benin % Change rounding) that do not affect the cells used.\",\"results_keys\":[\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Recorded, not resolved.\"}\n]\n# Harmonising effect sizes — F4-conf-03\n\n## Question\nRe-express five printed treatment effects (data/effects.json, E1–E5), reported as a semi-elasticity (log points), a percentage-point effect, an elasticity and a linear-probability coefficient, on the target definition stated for each item. We may use only quantities printed in the cited extract and an explicitly stated transformation. Where the target cannot be reached, we say so and name the missing quantity.\n\n## Data and exclusions\nThe inputs are the four source extracts under `data/sources/` plus `data/effects.json` and `data/README.md`. Line counts are in `n_input_rows`. All extract SHA-256 hashes match the README (`out/state/data_audit.md`). No item was excluded (`out/exclusions.csv`: 5 − 0 = 5), so `n_analysed` = 5. Problems found in the inputs are listed in `data_issues`. The ones that matter:\n- The DCR abstract says fatal hospitalisations \"fall by 35%\". Table 7 (5 km) prints −0.354 log points, which is ≈ −29.8%, in line with Section 6.1 (\"approximately 30%\").\n- The DCR paper prints the same ATT with SE 0.210 (Table 6) and 0.218 (Table A19), and gives 608 vs 640 control regions.\n- The minimum-wage Table 2 prints a 0.047 MW change, but its note says \"about 4%\".\n- In the Benin Table 3, the \"% Change\" cell (−0.3685) differs in the 4th decimal from −0.0962/0.261.\n- RESEA: the abstract's 1.5 pp is a rounding of the table's 1.47 pp.\n\n## Method\nEach transformation follows the TASK.md conventions (formula and printed inputs are in `harmonised.<id>.transformation`; source cells are in `harmonised.<id>.locator`):\n- E1: 100·(exp(b) − 1), with b = −0.431 (Table 6, ATT, 5 km).\n- E2: 100·Δ/p, with Δ = 1.47 pp (Table 2, Reemployment, ATE) and p = 75.0% (Table 2, Control mean).\n- E3: 100·η·g, with η = −0.388 (Table 2, employment elasticity w.r.t. MW) and g = 0.047 (Table 2, \"% MW changes\"). The target definition says to use the size of the increase as printed in the table.\n- E4: absolute change = R0·(exp(b) − 1), with b = −0.354 (Table 7, Drug-related (fatal), 5 km). This needs the counterfactual level R0 of the fatal rate per 100,000.\n- E5: 100·β, with β = −0.0962 (Table 3, Treatment, column (4)).\n\nAs a P2 baseline, the printed numbers read at face value are stored under `baseline_naive`. `out/analysis.py` locates each table row by pattern, prints the source lines it uses, and writes `out/results.json` deterministically. No randomness is involved (`random_seed` = null).\n\n## Results\n- E1: the DCR opening implies a −35.0141% change in the drug-related hospitalisation rate, 5 km buffer (`harmonised.E1.value`). This matches the printed \"% change\" of −35.0%. The naive log-point reading would give −43.1 (`baseline_naive.E1_logpoints_times_100_pct`).\n- E2: RESEA selection raises reemployment by 1.96% of the control mean (`harmonised.E2.value`).\n- E3: the 2023 minimum-wage rise (0.047) implies a −1.8236% change in employment (`harmonised.E3.value`).\n- E4: **not harmonisable** (`harmonised.E4.harmonisable` = false, `harmonised.E4.value` = null). The extract prints no baseline or counterfactual level of the fatal drug-related hospitalisation rate per 100,000 in the treated regions. That quantity is named in `harmonised.E4.missing_quantity`. Only the relative change (≈ −29.8%) can be computed, and it is not the requested target.\n- E5: free cesarean access lowers the probability of infant death or stillbirth by −9.62 percentage points (`harmonised.E5.value`).\n- Overall: 4 items harmonised (`n_harmonised`) and 1 not harmonisable (`n_not_harmonisable`). Every item has either a value or a justified statement of impossibility, so `feasibility` = \"complete\" (`feasibility_reason` = null).\n\n## Uncertainty\nNo standard errors or confidence intervals were propagated; the task asks only for point estimates.\n- **E5 is exact**: it is a pure change of units.\n- **E2 is exact** given the printed inputs. However, it divides a block-adjusted ATE by the raw control-group mean, and the inputs themselves are rounded (1.47, 75.0).\n- **E1 is exact under the stated convention.** As a reading of the true change in the rate level it is approximate: exp(ATT) − 1 is a change in the geometric mean, and Jensen's inequality applies. The input b is also rounded to 3 decimals.\n- **E3 is a linear approximation** (η·g). It also depends on which MW change is used: with the note's \"about 4%\" the result would be −1.5520% instead of −1.8236% (`data_issues`).\n\n## Limitations and open issues\n- E4 cannot be computed from the admissible evidence. No outside or remembered baseline rate was used.\n- E3 depends on which printed size of the minimum-wage change is used. We follow the target definition and use the table value, 0.047.\n- The source inconsistencies listed in `data_issues` were recorded but not resolved. None of them changes the cells used for E1–E3 and E5.\n- Values are kept to 4 decimals of the computed transformation. Printed inputs are used exactly as printed.","is_error":false}]},"uuid":"e1688193-8630-40a6-97af-a6230b08349c","timestamp":"2026-10-30T05:12:37.754Z","toolUseResult":{"stdout":"# F4-conf-03 — Harmonising effect sizes reported under incompatible definitions\n\n## Scientific question\nA synthesis compares treatment effects from four open-access studies that report their estimates under different definitions: a semi-elasticity (coefficient on a log outcome), a percentage-point effect on a probability, an elasticity, and a linear-probability coefficient. For each of the five effects listed in `data/effects.json`, re-express the printed point estimate on the **target definition** stated for that item, using only quantities printed in the cited extract and an explicitly stated transformation; where the target definition cannot be reached from the printed quantities, say so and name the additional quantity that would be required. The estimands are the harmonised point estimates (no uncertainty propagation is required). Each item carries a locator for every printed quantity used.\n\n## Inputs\n- `data/effects.json` — the five items: {\"id\", \"source\", \"effect\" (what and where), \"reported_definition\", \"target_definition\"}.\n- `data/sources/arxiv-2609.27732v1.txt` — Armendariz (2026), *Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland*, arXiv:2609.27732v1, CC BY 4.0: abstract, Section 6 (Tables 6 and 7, Section 6.1), Appendix Table A19. Outcomes are log hospitalisation rates per 100,000 inhabitants; ATTs in log points; the tables also print a \"% change\" row for the overall effect.\n- `data/sources/arxiv-2606.14621v1.txt` — Li, Pearson-Merkowitz & Yokum (2026), *Evaluating the Impact of Rhode Island's Self-Sustaining RESEA Program on Employment Outcomes*, arXiv:2606.14621v1, CC BY 4.0: abstract, Section 3 (Table 2: treatment/control means, differences, ATEs with p-values and 95% CIs), Table 3. Reemployment is in percent (means) and percentage points (effects).\n- `data/sources/arxiv-2505.04555v3.txt` — Kanayama, Miyaji & Otani (2025/2026), *Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation*, arXiv:2505.04555v3, CC BY 4.0: abstract and Appendix A.1 (Table 2: elasticities and the size of the minimum-wage change, with note).\n- `data/sources/arxiv-2510.03658v1.txt` — Tossou (2025), *Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin*, arXiv:2510.03658v1, CC BY 4.0: abstract, Section 4.1 and Table 3 (linear probability model of infant death or stillbirth; \"Mean Dep. Var.\" row).\n- `data/README.md` — provenance, retrieval times, licences, SHA-256 of the extracts.\n\n## Information-availability rules\n- The extracts are the only admissible evidence; a quantity that is not printed in the cited extract is not available, even if you believe it is in the full paper or elsewhere.\n- Conventions: a log-point coefficient b corresponds to a relative change of 100·(exp(b) − 1) percent; a percentage-point effect Δ on a probability with counterfactual (control) level p corresponds to a relative change of 100·Δ/p percent; an elasticity η applied to a proportional change g of the treatment variable implies a relative change of 100·η·g percent. State the transformation you apply in `transformation`.\n- `harmonisable` is `false` only when the target definition requires a quantity that the cited extract does not print; then `value` is `null` and `missing_quantity` names the quantity. Do not substitute a guessed or externally remembered value.\n- `feasibility` refers to the task as a whole: the task is complete when every item has either a harmonised value or an explicit, justified statement of impossibility.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `harmonised` | object | — | {\"E1\": {\"value\": number or null, \"unit\": \"<unit of the target definition>\", \"transformation\": \"<formula with the printed inputs>\", \"harmonisable\": true/false, \"missing_quantity\": string or null, \"locator\": \"<source id | table | row | column for every printed input>\"}, … \"E5\": {…}} |\n| `n_harmonised` | integer | — | number of items with a non-null value |\n| `n_not_harmonisable` | integer | — | number of items with `harmonisable` = false |\n| `task_id` | string | — | \"F4-conf-03\" |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" — refers to the task as a whole |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the inputs (rendering defects, internal inconsistencies of a source, etc.) and how they were handled; empty list if none |\n| `n_input_rows` | object | lines / items | {file name: number of lines} for every file under `data/` that you read (text files: number of newline-terminated lines as reported by `wc -l`; JSON files: number of top-level items) |\n| `n_analysed` | integer or null | — | number of reported items (values, assertions, effects or references) you actually assessed |\n| `random_seed` | integer or null | — | null (no randomness is involved) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. \"Uncertainty\" must note that no standard errors were propagated and which conversions are exact versus approximate.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically (the script must print the source lines it relies on).\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available; the standard library is sufficient for this task). Do not modify `data/`.\n- The source extracts under `data/sources/` are the **only** admissible evidence. Do not rely on memory of the papers or on other versions of them; if a quantity is not in the extract, it is not available.\n- Report numbers exactly as printed in the source (same sign, same number of decimals); do not round or rescale printed values unless the key definition asks for a transformation.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 72\ndrwxr-xr-x@ 9 simon-pierreboucher staff 288 oct. 30 01:12 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 01:10 ..\n-rw-r--r--@ 1 simon-pierreboucher staff 11643 oct. 30 01:11 analysis.py\n-rw-r--r--@ 1 simon-pierreboucher staff 1943 oct. 30 01:12 claims.json\n-rw-r--r--@ 1 simon-pierreboucher staff 187 oct. 30 01:11 exclusions.csv\n-rw-r--r--@ 1 simon-pierreboucher staff 5016 oct. 30 01:12 report.md\n-rw-r--r--@ 1 simon-pierreboucher staff 5591 oct. 30 01:11 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 01:12 review\ndrwxr-xr-x@ 8 simon-pierreboucher staff 256 oct. 30 01:12 state\n{\n \"task_id\": \"F4-conf-03\",\n \"harmonised\": {\n \"E1\": {\n \"value\": -35.0141,\n \"unit\": \"percent (relative change vs. counterfactual hospitalisation rate)\",\n \"transformation\": \"100*(exp(b)-1) with b = -0.431 (printed ATT, log points) -> 100*(exp(-0.431)-1) = -35.0141%\",\n \"harmonisable\": true,\n \"missing_quantity\": null,\n \"locator\": \"arxiv-2609.27732v1 | Table 6 | row 'ATT' | column '5 km' (line 36); cross-check: row '% change' | column '5 km' = -35.0% (line 40)\"\n },\n \"E2\": {\n \"value\": 1.96,\n \"unit\": \"percent (relative change vs. control-group mean reemployment rate)\",\n \"transformation\": \"100*Delta/p with Delta = 1.47 pp (printed ATE) and p = 75.0% (printed control mean) -> 100*1.47/75.0 = 1.9600%\",\n \"harmonisable\": true,\n \"missing_quantity\": null,\n \"locator\": \"arxiv-2606.14621v1 | Table 2 | row 'Reemployment' | column 'Average treatment effect estimate' (1.47 pp) and column 'Control' (75.0%) (line 29)\"\n },\n \"E3\": {\n \"value\": -1.8236,\n \"unit\": \"percent (relative change in employment)\",\n \"transformation\": \"100*eta*g with eta = -0.388 (printed elasticity) and g = 0.047 (printed '% MW changes', proportional) -> 100*-0.388*0.047 = -1.8236%\",\n \"harmonisable\": true,\n \"missing_quantity\": null,\n \"locator\": \"arxiv-2505.04555v3 | Table 2 | row 'Employment elasticity w.r.t MW' | column 'Estimate (SE)' (line 50); row '% MW changes' | column 'Estimate (SE)' (line 60)\"\n },\n \"E4\": {\n \"value\": null,\n \"unit\": \"hospitalisations per 100,000 inhabitants per year\",\n \"transformation\": \"absolute change = R0*(exp(b)-1) with b = -0.354 (printed ATT, log points); exp(b)-1 = -0.2981 is computable, but R0 (counterfactual/baseline fatal rate) is not printed\",\n \"harmonisable\": false,\n \"missing_quantity\": \"baseline (counterfactual, e.g. pre-opening treated-region mean) level of the fatal drug-related hospitalisation rate per 100,000 inhabitants per year in the 5 km treated regions; not printed in the extract\",\n \"locator\": \"arxiv-2609.27732v1 | Table 7 | row 'Drug-related (fatal)' | column '5 km' (line 86)\"\n },\n \"E5\": {\n \"value\": -9.62,\n \"unit\": \"percentage points\",\n \"transformation\": \"100*beta with beta = -0.0962 (printed LPM coefficient, 0-1 scale) -> -9.6200 pp\",\n \"harmonisable\": true,\n \"missing_quantity\": null,\n \"locator\": \"arxiv-2510.03658v1 | Table 3 | row 'Treatment' | column '(4)' (line 49)\"\n }\n },\n \"n_harmonised\": 4,\n \"n_not_harmonisable\": 1,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"arxiv-2609.27732v1: the abstract says fatal drug-related hospitalisations 'fall by 35%', but Table 7 (5 km) prints -0.354 log points, i.e. 100*(exp(-0.354)-1) = -29.8%, matching Section 6.1 ('approximately 30% at 5 km'); 35% is the overall (Table 6) effect.\",\n \"handling\": \"Table 7 cell used as the printed estimate for E4; abstract figure not used. E4 is not harmonisable for a separate reason (missing baseline level).\"\n },\n {\n \"issue\": \"arxiv-2609.27732v1: the same CS-DR 5 km ATT (-0.431) is printed with SE (0.210) in Table 6 but (0.218) in Appendix Table A19; A19 note says 640 never-treated regions while its table and Table 6 say 608.\",\n \"handling\": \"Only the point estimate (identical in both tables) is used; SEs and region counts are not needed and were not used.\"\n },\n {\n \"issue\": \"arxiv-2505.04555v3: Table 2 prints '% MW changes' = 0.047 (4.7%), while the table note describes the 2023 increases as 'about 4%'.\",\n \"handling\": \"Per the target definition, the table value 0.047 is used for E3; with 'about 4%' the implied change would be 100*-0.388*0.04 = -1.5520% (sensitivity only, not the reported value).\"\n },\n {\n \"issue\": \"arxiv-2505.04555v3: Table 2 prints the same SE (0.091) for 'Affected employment' and 'Emp. elasticity w.r.t affected wage' (possible duplication); point estimates are internally consistent (-0.268/0.366 = -0.732).\",\n \"handling\": \"Not used; no uncertainty is propagated.\"\n },\n {\n \"issue\": \"arxiv-2510.03658v1: Table 3 prints '% Change' = -0.3685 for column (4), whereas -0.0962/0.261 = -0.36858 (rounds to -0.3686); the text also attributes the 0.0855 estimate to 'table 2' and calls an 18.79% relative change a 'percentage change' while interpreting it as lives saved per 100 deaths.\",\n \"handling\": \"Not material for E5, whose target (percentage points) needs only the printed coefficient -0.0962; noted for transparency.\"\n },\n {\n \"issue\": \"arxiv-2606.14621v1: the ATE (1.47 pp) is block(week)-adjusted while the 75.0% control mean is the raw aggregate control-group mean; the abstract rounds the ATE to 1.5 pp.\",\n \"handling\": \"Used the printed control mean 75.0% as the counterfactual level p, as the target definition specifies (percent of the control-group mean); 1.47 (table) used rather than 1.5 (abstract).\"\n }\n ],\n \"n_input_rows\": {\n \"effects.json\": 5,\n \"README.md\": 16,\n \"arxiv-2609.27732v1.txt\": 134,\n \"arxiv-2606.14621v1.txt\": 58,\n \"arxiv-2505.04555v3.txt\": 66,\n \"arxiv-2510.03658v1.txt\": 91\n },\n \"n_analysed\": 5,\n \"random_seed\": null,\n \"baseline_naive\": {\n \"E1_logpoints_times_100_pct\": -43.1,\n \"E2_printed_pp\": 1.47,\n \"E3_printed_elasticity\": -0.388,\n \"E4_logpoints_times_100_pct\": -35.4,\n \"E5_printed_coefficient\": -0.0962,\n \"note\": \"Naive values = printed numbers read at face value (log points x100 treated as percent; no rescaling). Not harmonised estimates.\"\n }\n}[\n {\"id\":\"C1\",\"text\":\"The 5 km DCR overall ATT of -0.431 log points corresponds to a -35.0141% relative change in the drug-related hospitalisation rate, consistent with the printed -35.0%.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Exact under the log-point convention; rate-level interpretation approximate (geometric mean).\"},\n {\"id\":\"C2\",\"text\":\"The RESEA reemployment ATE of 1.47 pp equals a 1.96% relative increase over the 75.0% control mean.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Block-adjusted ATE divided by raw control mean; inputs rounded as printed.\"},\n {\"id\":\"C3\",\"text\":\"The employment elasticity -0.388 applied to the printed 0.047 minimum-wage change implies a -1.8236% change in employment.\",\"results_keys\":[\"harmonised\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Linear approximation; table note says 'about 4%', which would give -1.5520%.\"},\n {\"id\":\"C4\",\"text\":\"The absolute change in the fatal drug-related hospitalisation rate (E4) cannot be computed because the extract does not print a baseline/counterfactual fatal rate per 100,000.\",\"results_keys\":[\"harmonised\",\"n_not_harmonisable\"],\"status\":\"supported\",\"caveat\":\"Only the relative change (about -29.8%) is computable.\"},\n {\"id\":\"C5\",\"text\":\"The Benin LPM coefficient -0.0962 corresponds to -9.62 percentage points.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Exact unit conversion.\"},\n {\"id\":\"C6\",\"text\":\"Four items are harmonised and one is not harmonisable; the task is complete.\",\"results_keys\":[\"n_harmonised\",\"n_not_harmonisable\",\"feasibility\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C7\",\"text\":\"The sources contain internal inconsistencies (DCR abstract fatal 35% vs table ~29.8%, DCR SE 0.210 vs 0.218, MW 0.047 vs 'about 4%', Benin % Change rounding) that do not affect the cells used.\",\"results_keys\":[\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Recorded, not resolved.\"}\n]\n# Harmonising effect sizes — F4-conf-03\n\n## Question\nRe-express five printed treatment effects (data/effects.json, E1–E5), reported as a semi-elasticity (log points), a percentage-point effect, an elasticity and a linear-probability coefficient, on the target definition stated for each item. We may use only quantities printed in the cited extract and an explicitly stated transformation. Where the target cannot be reached, we say so and name the missing quantity.\n\n## Data and exclusions\nThe inputs are the four source extracts under `data/sources/` plus `data/effects.json` and `data/README.md`. Line counts are in `n_input_rows`. All extract SHA-256 hashes match the README (`out/state/data_audit.md`). No item was excluded (`out/exclusions.csv`: 5 − 0 = 5), so `n_analysed` = 5. Problems found in the inputs are listed in `data_issues`. The ones that matter:\n- The DCR abstract says fatal hospitalisations \"fall by 35%\". Table 7 (5 km) prints −0.354 log points, which is ≈ −29.8%, in line with Section 6.1 (\"approximately 30%\").\n- The DCR paper prints the same ATT with SE 0.210 (Table 6) and 0.218 (Table A19), and gives 608 vs 640 control regions.\n- The minimum-wage Table 2 prints a 0.047 MW change, but its note says \"about 4%\".\n- In the Benin Table 3, the \"% Change\" cell (−0.3685) differs in the 4th decimal from −0.0962/0.261.\n- RESEA: the abstract's 1.5 pp is a rounding of the table's 1.47 pp.\n\n## Method\nEach transformation follows the TASK.md conventions (formula and printed inputs are in `harmonised.<id>.transformation`; source cells are in `harmonised.<id>.locator`):\n- E1: 100·(exp(b) − 1), with b = −0.431 (Table 6, ATT, 5 km).\n- E2: 100·Δ/p, with Δ = 1.47 pp (Table 2, Reemployment, ATE) and p = 75.0% (Table 2, Control mean).\n- E3: 100·η·g, with η = −0.388 (Table 2, employment elasticity w.r.t. MW) and g = 0.047 (Table 2, \"% MW changes\"). The target definition says to use the size of the increase as printed in the table.\n- E4: absolute change = R0·(exp(b) − 1), with b = −0.354 (Table 7, Drug-related (fatal), 5 km). This needs the counterfactual level R0 of the fatal rate per 100,000.\n- E5: 100·β, with β = −0.0962 (Table 3, Treatment, column (4)).\n\nAs a P2 baseline, the printed numbers read at face value are stored under `baseline_naive`. `out/analysis.py` locates each table row by pattern, prints the source lines it uses, and writes `out/results.json` deterministically. No randomness is involved (`random_seed` = null).\n\n## Results\n- E1: the DCR opening implies a −35.0141% change in the drug-related hospitalisation rate, 5 km buffer (`harmonised.E1.value`). This matches the printed \"% change\" of −35.0%. The naive log-point reading would give −43.1 (`baseline_naive.E1_logpoints_times_100_pct`).\n- E2: RESEA selection raises reemployment by 1.96% of the control mean (`harmonised.E2.value`).\n- E3: the 2023 minimum-wage rise (0.047) implies a −1.8236% change in employment (`harmonised.E3.value`).\n- E4: **not harmonisable** (`harmonised.E4.harmonisable` = false, `harmonised.E4.value` = null). The extract prints no baseline or counterfactual level of the fatal drug-related hospitalisation rate per 100,000 in the treated regions. That quantity is named in `harmonised.E4.missing_quantity`. Only the relative change (≈ −29.8%) can be computed, and it is not the requested target.\n- E5: free cesarean access lowers the probability of infant death or stillbirth by −9.62 percentage points (`harmonised.E5.value`).\n- Overall: 4 items harmonised (`n_harmonised`) and 1 not harmonisable (`n_not_harmonisable`). Every item has either a value or a justified statement of impossibility, so `feasibility` = \"complete\" (`feasibility_reason` = null).\n\n## Uncertainty\nNo standard errors or confidence intervals were propagated; the task asks only for point estimates.\n- **E5 is exact**: it is a pure change of units.\n- **E2 is exact** given the printed inputs. However, it divides a block-adjusted ATE by the raw control-group mean, and the inputs themselves are rounded (1.47, 75.0).\n- **E1 is exact under the stated convention.** As a reading of the true change in the rate level it is approximate: exp(ATT) − 1 is a change in the geometric mean, and Jensen's inequality applies. The input b is also rounded to 3 decimals.\n- **E3 is a linear approximation** (η·g). It also depends on which MW change is used: with the note's \"about 4%\" the result would be −1.5520% instead of −1.8236% (`data_issues`).\n\n## Limitations and open issues\n- E4 cannot be computed from the admissible evidence. No outside or remembered baseline rate was used.\n- E3 depends on which printed size of the minimum-wage change is used. We follow the target definition and use the table value, 0.047.\n- The source inconsistencies listed in `data_issues` were recorded but not resolved. None of them changes the cells used for E1–E3 and E5.\n- Values are kept to 4 decimals of the computed transformation. 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{\n \"id\": \"E1\",\n \"source\": \"arxiv-2609.27732v1\",\n \"effect\": \"Overall ATT of a DCR opening on drug-related hospitalisations, 5 km buffer (Table 6, row ATT, column 5 km)\",\n \"reported_definition\": \"coefficient on a log outcome (log hospitalisation rate per 100,000), i.e. a log-point / semi-elasticity effect\",\n \"target_definition\": \"relative percent change in the hospitalisation rate attributable to the opening (percent of the counterfactual rate)\"\n },\n {\n \"id\": \"E2\",\n \"source\": \"arxiv-2606.14621v1\",\n \"effect\": \"Average treatment effect of RESEA selection on reemployment (Table 2, row Reemployment, column Average treatment effect estimate)\",\n \"reported_definition\": \"percentage-point change in the reemployment probability\",\n \"target_definition\": \"relative percent change in the reemployment probability (percent of the control-group mean)\"\n },\n {\n \"id\": \"E3\",\n \"source\": \"arxiv-2505.04555v3\",\n \"effect\": \"Employment elasticity with respect to the minimum wage (Table 2, row 'Employment elasticity w.r.t MW'), for the 2023 minimum-wage increase studied in the paper\",\n \"reported_definition\": \"elasticity: percent change in employment per percent change in the minimum wage\",\n \"target_definition\": \"relative percent change in employment implied for the minimum-wage increase actually observed in the study (use the size of that increase as printed in the same table)\"\n },\n {\n \"id\": \"E4\",\n \"source\": \"arxiv-2609.27732v1\",\n \"effect\": \"Overall ATT of a DCR opening on fatal drug-related hospitalisations, 5 km buffer (Table 7, row 'Drug-related (fatal)', column 5 km)\",\n \"reported_definition\": \"coefficient on a log outcome (log hospitalisation rate per 100,000), i.e. a log-point / semi-elasticity effect\",\n \"target_definition\": \"absolute change in the fatal drug-related hospitalisation rate, in hospitalisations per 100,000 inhabitants per year\"\n },\n {\n \"id\": \"E5\",\n \"source\": \"arxiv-2510.03658v1\",\n \"effect\": \"Effect of access to free cesarean sections on the probability of infant death or stillbirth, preferred specification (Table 3, row Treatment, column (4))\",\n \"reported_definition\": \"coefficient of a linear probability model (change in probability, on a 0-1 scale)\",\n \"target_definition\": \"change in the probability expressed in percentage points\"\n }\n]\n 66 arxiv-2505.04555v3.txt\n 91 arxiv-2510.03658v1.txt\n 58 arxiv-2606.14621v1.txt\n 134 arxiv-2609.27732v1.txt\n 349 total\n 1\tSOURCE EXTRACT — arxiv-2609.27732v1\n 2\tTitle: Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n 3\tAuthors: Ana Armendariz (University of St. Gallen)\n 4\tBibliographic record: arXiv:2609.27732v1 [econ.GN], submitted 23 Sep 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2609.27732v1\n 5\tRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\n 6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n 7\tExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\n 8\tOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\n 9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n 10\t================================================================\n 11\t\n 12\t# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n 13\t\n 14\tAna Armendariz*\n 15\t\n 16\tSeptember 23, 2026\n 17\t\n 18\tThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n 19\t\n 20\tKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n 21\t\n 22\t## 6 Results\n 23\t\n 24\tThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n 25\t\n 26\tFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n 27\t\n 28\tNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n 29\t\n 30\tFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n 31\t\n 32\tTable 6: Overall Average Treatment Effect on the Treated (ATT)\n 33\t\n 34\t| 1 km | 3 km | 5 km\n 35\t\n 36\tATT | -0.399 | -0.283 | -0.431**\n 37\t\n 38\t| (0.412) | (0.279) | (0.210)\n 39\t\n 40\t% change | -32.9% | -24.7% | -35.0%\n 41\t\n 42\tTreated regions | 7 | 35 | 50\n 43\t\n 44\tControl regions | 608 | 608 | 608\n 45\t\n 46\tObservations | 16,175 | 16,875 | 17,250\n 47\t\n 48\tNote: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\n 49\tthroughout. Regions near DCRs that opened before 1998\n 50\t(always-treated) are excluded. Standard errors clustered at the Medstat region level in\n 51\tparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n 52\t{}^{*}p<0.10 .\n 53\t\n 54\tTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\n 55\tcorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n 56\t\n 57\tTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\n 58\tsub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\n 59\t\n 60\tFigure 7: Event study: Sub-outcome estimates,\n 61\twithin 5 km, \\pm 5-year window\n 62\t\n 63\tNote: The figure plots event-study coefficients from the doubly robust Callaway & Sant’Anna (2021) estimator for four diagnostic sub-categories of drug-related hospitalizations. Each panel uses a separate y -axis to accommodate differences in scale across outcomes. The sample includes all staggered-treated MedStat regions within 5 km of a\n 64\tDCR that opened between 1998 and 2022. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded. 95% confidence intervals are based on standard errors clustered at the MedStat region level.\n 65\t\n 66\tTable 7: Overall ATT by Outcome Category and Buffer Zone\n 67\t\n 68\tOutcome | 1 km | 3 km | 5 km\n 69\t\n 70\tMental/Behavioral | -0.354 | -0.202 | -0.381*\n 71\t\n 72\t| (0.382) | (0.244) | (0.220)\n 73\t\n 74\tHepatitis | -0.270 | -0.413 | -0.406\n 75\t\n 76\t| (0.486) | (0.270) | (0.250)\n 77\t\n 78\tHIV | 0.063 | -0.239 | -0.340\n 79\t\n 80\t| (0.801) | (0.337) | (0.294)\n 81\t\n 82\tNarcotics | 1.186*** | 0.549*** | 0.390**\n 83\t\n 84\t| (0.288) | (0.187) | (0.165)\n 85\t\n 86\tDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n 87\t\n 88\t| (0.137) | (0.215) | (0.178)\n 89\t\n 90\tTreated regions | 7 | 35 | 50\n 91\t\n 92\tControl regions | 608 (never-treated) | |\n 93\t\n 94\tNote: Each cell reports the overall ATT from\n 95\tthe doubly robust Callaway & Sant’Anna (2021) estimator with\n 96\tnever-treated controls and a universal base period. Outcomes are\n 97\tlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\n 98\tmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\n 99\tHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\n 100\testimates are identified from a single treated region per cohort and\n 101\tshould be interpreted with caution. Regions near DCRs opened before\n 102\t1998 excluded. Standard errors clustered at the Medstat region level.\n 103\tSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n 104\t\n 105\tFigure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\n 106\t\n 107\t### 6.1 Mechanisms\n 108\t\n 109\tThe sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n 110\tsubstitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\n 111\t(Kerr et al., 2017; Hedrich, 2004).\n 112\t\n 113\tThe second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\n 114\t\n 115\tA third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\n 116\t\n 117\tTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n 118\t\n 119\t| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n 120\t\n 121\tATT | -0.431** | -0.438** | -0.362* | -0.149***\n 122\t\n 123\t| (0.218) | (0.208) | (0.205) | (0.038)\n 124\t\n 125\t% change | -35.0% | -35.5% | -30.3% | -13.9%\n 126\t\n 127\tTreated regions | 50 | | |\n 128\t\n 129\tControl regions | 608 | | |\n 130\t\n 131\tObservations | 17,250 | | |\n 132\t\n 133\tNote: All specifications use the 5 km buffer,\n 134\tnever-treated comparison regions (640 regions), and a \\pm 5-year window","is_error":false}]},"uuid":"66eea05c-6e2d-4f80-a8eb-d919f9261617","timestamp":"2026-10-30T05:12:40.094Z","toolUseResult":{"stdout":"[\n {\n \"id\": \"E1\",\n \"source\": \"arxiv-2609.27732v1\",\n \"effect\": \"Overall ATT of a DCR opening on drug-related hospitalisations, 5 km buffer (Table 6, row ATT, column 5 km)\",\n \"reported_definition\": \"coefficient on a log outcome (log hospitalisation rate per 100,000), i.e. a log-point / semi-elasticity effect\",\n \"target_definition\": \"relative percent change in the hospitalisation rate attributable to the opening (percent of the counterfactual rate)\"\n },\n {\n \"id\": \"E2\",\n \"source\": \"arxiv-2606.14621v1\",\n \"effect\": \"Average treatment effect of RESEA selection on reemployment (Table 2, row Reemployment, column Average treatment effect estimate)\",\n \"reported_definition\": \"percentage-point change in the reemployment probability\",\n \"target_definition\": \"relative percent change in the reemployment probability (percent of the control-group mean)\"\n },\n {\n \"id\": \"E3\",\n \"source\": \"arxiv-2505.04555v3\",\n \"effect\": \"Employment elasticity with respect to the minimum wage (Table 2, row 'Employment elasticity w.r.t MW'), for the 2023 minimum-wage increase studied in the paper\",\n \"reported_definition\": \"elasticity: percent change in employment per percent change in the minimum wage\",\n \"target_definition\": \"relative percent change in employment implied for the minimum-wage increase actually observed in the study (use the size of that increase as printed in the same table)\"\n },\n {\n \"id\": \"E4\",\n \"source\": \"arxiv-2609.27732v1\",\n \"effect\": \"Overall ATT of a DCR opening on fatal drug-related hospitalisations, 5 km buffer (Table 7, row 'Drug-related (fatal)', column 5 km)\",\n \"reported_definition\": \"coefficient on a log outcome (log hospitalisation rate per 100,000), i.e. a log-point / semi-elasticity effect\",\n \"target_definition\": \"absolute change in the fatal drug-related hospitalisation rate, in hospitalisations per 100,000 inhabitants per year\"\n },\n {\n \"id\": \"E5\",\n \"source\": \"arxiv-2510.03658v1\",\n \"effect\": \"Effect of access to free cesarean sections on the probability of infant death or stillbirth, preferred specification (Table 3, row Treatment, column (4))\",\n \"reported_definition\": \"coefficient of a linear probability model (change in probability, on a 0-1 scale)\",\n \"target_definition\": \"change in the probability expressed in percentage points\"\n }\n]\n 66 arxiv-2505.04555v3.txt\n 91 arxiv-2510.03658v1.txt\n 58 arxiv-2606.14621v1.txt\n 134 arxiv-2609.27732v1.txt\n 349 total\n 1\tSOURCE EXTRACT — arxiv-2609.27732v1\n 2\tTitle: Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n 3\tAuthors: Ana Armendariz (University of St. Gallen)\n 4\tBibliographic record: arXiv:2609.27732v1 [econ.GN], submitted 23 Sep 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2609.27732v1\n 5\tRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\n 6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n 7\tExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\n 8\tOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\n 9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n 10\t================================================================\n 11\t\n 12\t# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n 13\t\n 14\tAna Armendariz*\n 15\t\n 16\tSeptember 23, 2026\n 17\t\n 18\tThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n 19\t\n 20\tKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n 21\t\n 22\t## 6 Results\n 23\t\n 24\tThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n 25\t\n 26\tFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n 27\t\n 28\tNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n 29\t\n 30\tFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n 31\t\n 32\tTable 6: Overall Average Treatment Effect on the Treated (ATT)\n 33\t\n 34\t| 1 km | 3 km | 5 km\n 35\t\n 36\tATT | -0.399 | -0.283 | -0.431**\n 37\t\n 38\t| (0.412) | (0.279) | (0.210)\n 39\t\n 40\t% change | -32.9% | -24.7% | -35.0%\n 41\t\n 42\tTreated regions | 7 | 35 | 50\n 43\t\n 44\tControl regions | 608 | 608 | 608\n 45\t\n 46\tObservations | 16,175 | 16,875 | 17,250\n 47\t\n 48\tNote: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\n 49\tthroughout. Regions near DCRs that opened before 1998\n 50\t(always-treated) are excluded. Standard errors clustered at the Medstat region level in\n 51\tparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n 52\t{}^{*}p<0.10 .\n 53\t\n 54\tTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\n 55\tcorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n 56\t\n 57\tTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\n 58\tsub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\n 59\t\n 60\tFigure 7: Event study: Sub-outcome estimates,\n 61\twithin 5 km, \\pm 5-year window\n 62\t\n 63\tNote: The figure plots event-study coefficients from the doubly robust Callaway & Sant’Anna (2021) estimator for four diagnostic sub-categories of drug-related hospitalizations. Each panel uses a separate y -axis to accommodate differences in scale across outcomes. The sample includes all staggered-treated MedStat regions within 5 km of a\n 64\tDCR that opened between 1998 and 2022. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded. 95% confidence intervals are based on standard errors clustered at the MedStat region level.\n 65\t\n 66\tTable 7: Overall ATT by Outcome Category and Buffer Zone\n 67\t\n 68\tOutcome | 1 km | 3 km | 5 km\n 69\t\n 70\tMental/Behavioral | -0.354 | -0.202 | -0.381*\n 71\t\n 72\t| (0.382) | (0.244) | (0.220)\n 73\t\n 74\tHepatitis | -0.270 | -0.413 | -0.406\n 75\t\n 76\t| (0.486) | (0.270) | (0.250)\n 77\t\n 78\tHIV | 0.063 | -0.239 | -0.340\n 79\t\n 80\t| (0.801) | (0.337) | (0.294)\n 81\t\n 82\tNarcotics | 1.186*** | 0.549*** | 0.390**\n 83\t\n 84\t| (0.288) | (0.187) | (0.165)\n 85\t\n 86\tDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n 87\t\n 88\t| (0.137) | (0.215) | (0.178)\n 89\t\n 90\tTreated regions | 7 | 35 | 50\n 91\t\n 92\tControl regions | 608 (never-treated) | |\n 93\t\n 94\tNote: Each cell reports the overall ATT from\n 95\tthe doubly robust Callaway & Sant’Anna (2021) estimator with\n 96\tnever-treated controls and a universal base period. Outcomes are\n 97\tlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\n 98\tmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\n 99\tHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\n 100\testimates are identified from a single treated region per cohort and\n 101\tshould be interpreted with caution. Regions near DCRs opened before\n 102\t1998 excluded. Standard errors clustered at the Medstat region level.\n 103\tSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n 104\t\n 105\tFigure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\n 106\t\n 107\t### 6.1 Mechanisms\n 108\t\n 109\tThe sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n 110\tsubstitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\n 111\t(Kerr et al., 2017; Hedrich, 2004).\n 112\t\n 113\tThe second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\n 114\t\n 115\tA third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\n 116\t\n 117\tTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n 118\t\n 119\t| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n 120\t\n 121\tATT | -0.431** | -0.438** | -0.362* | -0.149***\n 122\t\n 123\t| (0.218) | (0.208) | (0.205) | (0.038)\n 124\t\n 125\t% change | -35.0% | -35.5% | -30.3% | -13.9%\n 126\t\n 127\tTreated regions | 50 | | |\n 128\t\n 129\tControl regions | 608 | | |\n 130\t\n 131\tObservations | 17,250 | | |\n 132\t\n 133\tNote: All specifications use the 5 km buffer,\n 134\tnever-treated comparison regions (640 regions), and a \\pm 5-year window","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"52af4377-26c2-4525-96bb-0c9e612128ea","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0238/data/sources","sessionId":"58628346-804e-4b01-8fcc-16f6b88a2208","version":"2.1.314","gitBranch":"HEAD"}21{"parentUuid":"66eea05c-6e2d-4f80-a8eb-d919f9261617","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrBQT6KoNNr3hbNFHPD","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LhJJcsk2V14Mbb7zyoHoWL","name":"Bash","input":{"command":"cd data/sources; cat -n arxiv-2606.14621v1.txt; cat -n arxiv-2505.04555v3.txt; cat -n arxiv-2510.03658v1.txt"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":8027,"cache_read_input_tokens":2220,"output_tokens":227,"output_tokens_details":{"thinking_tokens":20},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":8027},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXrBQAyVrpi4fzB77mYV","requestRef":"857de147-15bc-4690-94dd-c3fa1f79f19d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3481a5f2-316a-4958-aac8-76feed51bcfc","timestamp":"2026-10-30T05:12:40.613Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0238/data/sources","sessionId":"58628346-804e-4b01-8fcc-16f6b88a2208","version":"2.1.314","gitBranch":"HEAD"}22{"parentUuid":"3481a5f2-316a-4958-aac8-76feed51bcfc","isSidechain":false,"promptId":"9045acf7-28b3-4ac7-b56a-73d028682e36","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LhJJcsk2V14Mbb7zyoHoWL","type":"tool_result","content":"(eval):cd:1: no such file or directory: data/sources\n 1\tSOURCE EXTRACT — arxiv-2606.14621v1\n 2\tTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n 3\tAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\n 4\tBibliographic record: arXiv:2606.14621v1 [econ.GN], submitted 12 Jun 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2606.14621v1\n 5\tRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\n 6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n 7\tExtract contents: title, authors and abstract; Section 3 'Results' (Table 2 with caption, the results paragraphs, Section 3.1 'Cost-benefit analysis'); from Section 4.1, Table 3 with its caption and the paragraph that discusses it.\n 8\tOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\n 9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n 10\tEditorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n 11\t================================================================\n 12\t\n 13\t# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n 14\t\n 15\tHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n 16\t\n 17\t###### Abstract\n 18\t\n 19\tProlonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n 20\t\n 21\t## 3 Results\n 22\t\n 23\t[Table 1 (pre-treatment characteristics) omitted from this extract.]\n 24\t\n 25\t[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\n 26\t\n 27\tOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n 28\tWage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n 29\tReemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n 30\tWeeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n 31\tShort-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\n 32\tShort-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\n 33\tShort-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n 34\t\n 35\tTable 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n 36\t\n 37\tOverall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n 38\t\n 39\tThe results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n 40\t\n 41\tAs noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n 42\t\n 43\t### 3.1 Cost-benefit analysis\n 44\t\n 45\tWe find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n 46\t\n 47\t### 4.1 Methodological lessons (excerpt)\n 48\t\n 49\t[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n 50\t\n 51\tOutcome | Difference in means | Covariate adjusted | Fixed effects regression\n 52\tWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\n 53\tReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\n 54\tWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n 55\t\n 56\tTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n 57\t\n 58\tIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . These fixed effects regressions amount to estimating the coefficient in the regression model\n 1\tSOURCE EXTRACT — arxiv-2505.04555v3\n 2\tTitle: Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation\n 3\tAuthors: Hayato Kanayama; Sho Miyaji; Suguru Otani\n 4\tBibliographic record: arXiv:2505.04555v3 [econ.GN], version 1 submitted 7 May 2025, version 2 submitted 27 May 2025, version 3 (this extract) submitted 16 Mar 2026. Abstract page: https://arxiv.org/abs/2505.04555v3\n 5\tRetrieved: https://arxiv.org/html/2505.04555v3 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f2b68299cbc49f208a0340bd233007fe922f6d4245e0a642db526662d359789b)\n 6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n 7\tExtract contents: title, authors and abstract; Appendix A.1 'Elasticities Related to Minimum Wages' in full (Table 2 with its note and the discussion paragraph).\n 8\tOmitted: Sections 1-7, the other appendices, the references. The 'Current version' date printed in the HTML rendering is generated at rendering time and is not the submission date.\n 9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n 10\t================================================================\n 11\t\n 12\t# Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation\n 13\t\n 14\tHayato Kanayama (Waseda University), Sho Miyaji (Yale University), Suguru Otani (The University of Tokyo)\n 15\t\n 16\t###### Abstract\n 17\t\n 18\tHow labor markets adjust immediately after minimum wage hikes remains an open, policy-relevant question. This paper studies short-run minimum-wage effects in Japan’s spot labor market using Timee data and a wage-bin difference-in-differences design. We find a 2% employment decline in affected bins, driven by reduced vacancy creation rather than worker supply. Effects are more negative where the minimum-wage bite is higher and in low-wage occupations. Using job descriptions and amenity information, we document reallocation across job types: postings shift toward greater amenity provision and experienced-worker targeting, while female-targeted descriptions become less common, suggesting short-run labor-demand adjustments may foreshadow longer-run reallocation.\n 19\t\n 20\tKeywords: minimum wage, employment, job amenities, gig workers, spot work, difference-in-differences\n 21\t\n 22\tJEL codes: J21, J23, J38, J88\n 23\t\n 24\t## Appendix A Additional Analysis\n 25\t\n 26\t### A.1 Elasticities Related to Minimum Wages\n 27\t\n 28\tFollowing the method of Cengiz et al. (2019), we estimate (i) the elasticity of employment with respect to minimum wages and (ii) the employment elasticity with respect to the affected wages.\n 29\t\n 30\tTable 2: Impact of Minimum Wages on Employment and Wages\n 31\t\n 32\t| Estimate (SE)\n 33\t\n 34\tMissing jobs below new MW | -0.03\n 35\t\n 36\t| (0.006)\n 37\t\n 38\tExcess jobs above new MW | 0.012\n 39\t\n 40\t| (0.002)\n 41\t\n 42\tAffected wages | 0.366\n 43\t\n 44\t| (0.171)\n 45\t\n 46\tAffected employment | -0.268\n 47\t\n 48\t| (0.091)\n 49\t\n 50\tEmployment elasticity w.r.t MW | -0.388\n 51\t\n 52\t| (0.132)\n 53\t\n 54\tEmp. elasticity w.r.t affected wage | -0.732\n 55\t\n 56\t| (0.091)\n 57\t\n 58\tJob below new MW | 0.068\n 59\t\n 60\t% MW changes | 0.047\n 61\t\n 62\tNote: The table reports the effects of a minimum wage increase based on the event study analysis exploiting 47 prefecture-level minimum wage changes in 2023 (i.e., about 4% minimum wage increase). The table reports six-month averaged post-treatment estimates on missing jobs up to 100 JPY below the new minimum wage, excess jobs at and up to 300 JPY above it, employment, and wages. Robust standard errors in parentheses are clustered by wage bins separated by 10 JPY.\n 63\t\n 64\tTable 2 reports six-month–averaged post-treatment effects for the baseline specification depicted in Figure 7.\n 65\tThe magnitude of missing jobs below new minimum wages exceeds the magnitude of excess jobs above new minimum wages in the six-month window.\n 66\tThe estimated employment elasticity with respect to the minimum wage is - 0.388 , which is consistent with Figure 7. Unlike the findings of Cengiz et al. (2019) and Dube and Zipperer (2024), our own-wage employment elasticity is - 0.732 , indicating a larger (absolute) employment response to affected wages than the median estimate (- 0.13 ) reported by Dube and Lindner (2024).\n 1\tSOURCE EXTRACT — arxiv-2510.03658v1\n 2\tTitle: Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin\n 3\tAuthors: Selidji Caroline Tossou\n 4\tBibliographic record: arXiv:2510.03658v1 [econ.GN], submitted 4 Oct 2025 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2510.03658v1\n 5\tRetrieved: https://arxiv.org/html/2510.03658v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f2d9f04dcf4789983c28f97dc13705eb2d6ed583c4860c940f921d7b549aea59)\n 6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n 7\tExtract contents: title, author and abstract; Section 4.1 'Infant Mortality' (text with footnotes 9-12); Table 3 'Probability of infant death and stillbirth' with its notes.\n 8\tOmitted: Sections 1-3, 4.2-6, Tables 1-2 and 4-16, the references. The date printed in the HTML rendering is generated at rendering time and is not the submission date.\n 9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n 10\t================================================================\n 11\t\n 12\t# Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin\n 13\t\n 14\tSelidji Caroline Tossou (University of Wisconsin-Madison)\n 15\t\n 16\t###### Abstract\n 17\t\n 18\tThis paper evaluates the causal effect of the access to Benin’s free cesarean section policy on females and their children. I use a large sample of Demographic and Health Surveys (DHS) for West African countries and analyze how the exemption of the cesarean section user fees for females in Benin directly impacts maternal and infant mortality, family size decisions, and labor market participation. I use a Difference in Differences approach and find that having access to the free cesarean section policy significantly reduces the number of stillbirths and infant mortality by 0.0855 (a 18.79 percentage change). Second, for the surviving children, I find that access to the free cesarean section increases the likelihood of maternal mortality by 0.00465 (a 5.21 percentage change). The policy is effective at reducing infant mortality and saving the newborn. However, it harms the mother’s health which translates to lower fertility after the first birth and decreased maternal labor supply post-birth.\n 19\t\n 20\t## 4 Findings\n 21\t\n 22\t### 4.1 Infant Mortality\n 23\t\n 24\tI first investigate the infant death rate under the policy.\n 25\tFigure 15 shows that the exemption of cesarean section fees in Benin leads to an increase in the use of the service. What are the implications for infant mortality? I use a Difference in Differences specification to explore the possible impacts of access to the free cesarean section on infant mortality. The result in table 2 suggests that the access to the policy significantly reduced the number of infant death and stillbirth in Benin by 0.0855 (this represents a percentage change of 18.79 percent^9^9\n 26\t9\n 27\t\n 28\tI divide the estimate of beta by the mean of the dependent variable: -0.0855/0.455=-0.1879 ). This means that for over 100 newborns who would have died in the absence of the policy, approximately 18 are saved because of the access to the exemption of the cesarean section user fees. This result is promising and aligns with the goals of the free cesarean section policy, which primarily intends to lower the infant mortality in Benin. To the best of my knowledge, this is the first paper that evaluates the causal effect of free cesarean section policy on Benin’s infant mortality. Wealth and education attainment play a role in decreasing infant mortality. This can be explained by the fact that the free cesarean section covers only the surgery and a medicine kit for the mother. It does not cover prenatal and antepartum checkups. So a pregnant woman who has the means is more likely to identify and prevent any problems related to the pregnancy and the newborn’s health. Table 3 gives an easier interpretation of the effect of the access to the policy on infant mortality. In this table, the outcome of interest is a binary variable that takes value 1 if a female has an infant death or a pregnancy loss in the three years preceding the data collection and 0 otherwise. Thus, I interpret the coefficient \\hat{\\beta_{1}} as the effect of the policy on the likelihood of infant mortality. Using my most preferred specification (Column (4)), the result suggests that access to the policy decreases the likelihood of infant death or pregnancy loss of a female in Benin by 0.0962. Compared to the mean of the dependent variable, this effect represents a percentage change of 36.85 percent. To see which female category is driving this result, I decompose the effect by age group, wealth index, and education attainment.\n 29\t\n 30\tThe first panel of table 4 presents the stratification by age group. The policy is more effective at reducing infant mortality for females older than 25. For females between 15 and 24, the probability of infant mortality is reduced by only 4.65 percent compared to a reduction of 30.85 percent for a female between 25 and 34, a reduction of 43.53 percent for females between 35 and 44, and the highest decrease of 47.25 percent for females between 45 and 49^10^10\n 31\t10\n 32\t\n 33\tThese are the percentage change of infant mortality obtained by dividing the coefficient estimated by the mean of the dependent variable. The same exercise is done by using the wealth index of females. The results in the second panel of table 4 suggest that the effect of the free cesarean section on infant mortality is the biggest for females who are the poorest, poorer, or have a middle wealth index^11^11\n 34\t11\n 35\t\n 36\tThe modalities Poorest, Poorer, Middle, Richer, and Richest are pre-computed in the DHS data (46.36 percent, 35.84 percent, and 41.72 percent respectively). For the richer and richest females, the effect of the policy is a percentage change reduction of 28.52 percent and 16.47 percent on the likelihood of infant death. The last panel of table 4 gives a stratification by education level. The highest effect of the policy on the reduction of the likelihood of infant death (-39.07 percent) is for females who have no education level. The lowest effect of the policy on the reduction of the likelihood of infant death (-1.4 percent) is for females who have higher education levels.^12^12\n 37\t12\n 38\t\n 39\tI test the difference between all the coefficients and they are all significant and statistically different\n 40\t\n 41\tTo summarize, I find that access to the free cesarean section in Benin reduces the likelihood of infant mortality on average by 36.85 percent. The policy is more effective at reducing infant death for older females, females who are below the middle wealth index, and females who have no education.\n 42\t\n 43\tTable 3: Probability of infant death and stillbirth\n 44\t\n 45\t| Probability of Infant death or stillbirth | | |\n 46\t\n 47\t| (1) | (2) | (3) | (4)\n 48\t\n 49\tTreatment | -0.0911 | -0.0908 | -0.0961 | -0.0962\n 50\t\n 51\t| (0.0113) | (0.0113) | (0.0126) | (0.0129)\n 52\t\n 53\tEducation Attainment | -0.0578 | -0.0576 | -0.0443 | -0.0443\n 54\t\n 55\t| (0.00346) | (0.00340) | (0.00415) | (0.00420)\n 56\t\n 57\tAge of Female | 0.0168 | 0.0170 | 0.0171 | 0.0171\n 58\t\n 59\t| (0.00108) | (0.00112) | (0.00104) | (0.00108)\n 60\t\n 61\tAge of Partner | | -0.000425 | -0.000448 | -0.000435\n 62\t\n 63\t| | (0.000183) | (0.000151) | (0.000152)\n 64\t\n 65\tWealth Index | | | -0.0302 | -0.0302\n 66\t\n 67\t| | | (0.00277) | (0.00277)\n 68\t\n 69\tMarital Status | | | | 0.00128\n 70\t\n 71\t| | | | (0.00421)\n 72\t\n 73\tCountry Fixed Effect | Yes | Yes | Yes | Yes\n 74\t\n 75\tTime Fixed Effect. | Yes | Yes | Yes | Yes\n 76\t\n 77\t_cons | -0.129 | -0.114 | -0.0400 | -0.0406\n 78\t\n 79\t| (0.0323) | (0.0298) | (0.0266) | (0.0255)\n 80\t\n 81\tObservations | 196554 | 196424 | 196424 | 196423\n 82\t\n 83\t% Change | -0.3490 | -0.3478 | -0.3681 | -0.3685\n 84\t\n 85\tR-squared | 0.196 | 0.197 | 0.204 | 0.204\n 86\t\n 87\tMean Dep. Var. | 0.261 | 0.261 | 0.261 | 0.261\n 88\t\n 89\tStandard errors in parentheses; Standard errors are clustered at the household level. | | | |\n 90\t\n 91\tDep. Var. Mean is the dependent variable mean. Treatment is Benin Females after 2008 | | | |","is_error":false}]},"uuid":"31d6166a-1677-4ffe-b15e-f101879d7c40","timestamp":"2026-10-30T05:12:40.642Z","toolUseResult":{"stdout":"(eval):cd:1: no such file or directory: data/sources\n 1\tSOURCE EXTRACT — arxiv-2606.14621v1\n 2\tTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n 3\tAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\n 4\tBibliographic record: arXiv:2606.14621v1 [econ.GN], submitted 12 Jun 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2606.14621v1\n 5\tRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\n 6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n 7\tExtract contents: title, authors and abstract; Section 3 'Results' (Table 2 with caption, the results paragraphs, Section 3.1 'Cost-benefit analysis'); from Section 4.1, Table 3 with its caption and the paragraph that discusses it.\n 8\tOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\n 9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n 10\tEditorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n 11\t================================================================\n 12\t\n 13\t# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n 14\t\n 15\tHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n 16\t\n 17\t###### Abstract\n 18\t\n 19\tProlonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n 20\t\n 21\t## 3 Results\n 22\t\n 23\t[Table 1 (pre-treatment characteristics) omitted from this extract.]\n 24\t\n 25\t[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\n 26\t\n 27\tOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n 28\tWage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n 29\tReemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n 30\tWeeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n 31\tShort-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\n 32\tShort-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\n 33\tShort-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n 34\t\n 35\tTable 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n 36\t\n 37\tOverall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n 38\t\n 39\tThe results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n 40\t\n 41\tAs noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n 42\t\n 43\t### 3.1 Cost-benefit analysis\n 44\t\n 45\tWe find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n 46\t\n 47\t### 4.1 Methodological lessons (excerpt)\n 48\t\n 49\t[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n 50\t\n 51\tOutcome | Difference in means | Covariate adjusted | Fixed effects regression\n 52\tWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\n 53\tReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\n 54\tWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n 55\t\n 56\tTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n 57\t\n 58\tIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . These fixed effects regressions amount to estimating the coefficient in the regression model\n 1\tSOURCE EXTRACT — arxiv-2505.04555v3\n 2\tTitle: Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation\n 3\tAuthors: Hayato Kanayama; Sho Miyaji; Suguru Otani\n 4\tBibliographic record: arXiv:2505.04555v3 [econ.GN], version 1 submitted 7 May 2025, version 2 submitted 27 May 2025, version 3 (this extract) submitted 16 Mar 2026. Abstract page: https://arxiv.org/abs/2505.04555v3\n 5\tRetrieved: https://arxiv.org/html/2505.04555v3 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f2b68299cbc49f208a0340bd233007fe922f6d4245e0a642db526662d359789b)\n 6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n 7\tExtract contents: title, authors and abstract; Appendix A.1 'Elasticities Related to Minimum Wages' in full (Table 2 with its note and the discussion paragraph).\n 8\tOmitted: Sections 1-7, the other appendices, the references. The 'Current version' date printed in the HTML rendering is generated at rendering time and is not the submission date.\n 9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n 10\t================================================================\n 11\t\n 12\t# Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation\n 13\t\n 14\tHayato Kanayama (Waseda University), Sho Miyaji (Yale University), Suguru Otani (The University of Tokyo)\n 15\t\n 16\t###### Abstract\n 17\t\n 18\tHow labor markets adjust immediately after minimum wage hikes remains an open, policy-relevant question. This paper studies short-run minimum-wage effects in Japan’s spot labor market using Timee data and a wage-bin difference-in-differences design. We find a 2% employment decline in affected bins, driven by reduced vacancy creation rather than worker supply. Effects are more negative where the minimum-wage bite is higher and in low-wage occupations. Using job descriptions and amenity information, we document reallocation across job types: postings shift toward greater amenity provision and experienced-worker targeting, while female-targeted descriptions become less common, suggesting short-run labor-demand adjustments may foreshadow longer-run reallocation.\n 19\t\n 20\tKeywords: minimum wage, employment, job amenities, gig workers, spot work, difference-in-differences\n 21\t\n 22\tJEL codes: J21, J23, J38, J88\n 23\t\n 24\t## Appendix A Additional Analysis\n 25\t\n 26\t### A.1 Elasticities Related to Minimum Wages\n 27\t\n 28\tFollowing the method of Cengiz et al. (2019), we estimate (i) the elasticity of employment with respect to minimum wages and (ii) the employment elasticity with respect to the affected wages.\n 29\t\n 30\tTable 2: Impact of Minimum Wages on Employment and Wages\n 31\t\n 32\t| Estimate (SE)\n 33\t\n 34\tMissing jobs below new MW | -0.03\n 35\t\n 36\t| (0.006)\n 37\t\n 38\tExcess jobs above new MW | 0.012\n 39\t\n 40\t| (0.002)\n 41\t\n 42\tAffected wages | 0.366\n 43\t\n 44\t| (0.171)\n 45\t\n 46\tAffected employment | -0.268\n 47\t\n 48\t| (0.091)\n 49\t\n 50\tEmployment elasticity w.r.t MW | -0.388\n 51\t\n 52\t| (0.132)\n 53\t\n 54\tEmp. elasticity w.r.t affected wage | -0.732\n 55\t\n 56\t| (0.091)\n 57\t\n 58\tJob below new MW | 0.068\n 59\t\n 60\t% MW changes | 0.047\n 61\t\n 62\tNote: The table reports the effects of a minimum wage increase based on the event study analysis exploiting 47 prefecture-level minimum wage changes in 2023 (i.e., about 4% minimum wage increase). The table reports six-month averaged post-treatment estimates on missing jobs up to 100 JPY below the new minimum wage, excess jobs at and up to 300 JPY above it, employment, and wages. Robust standard errors in parentheses are clustered by wage bins separated by 10 JPY.\n 63\t\n 64\tTable 2 reports six-month–averaged post-treatment effects for the baseline specification depicted in Figure 7.\n 65\tThe magnitude of missing jobs below new minimum wages exceeds the magnitude of excess jobs above new minimum wages in the six-month window.\n 66\tThe estimated employment elasticity with respect to the minimum wage is - 0.388 , which is consistent with Figure 7. Unlike the findings of Cengiz et al. (2019) and Dube and Zipperer (2024), our own-wage employment elasticity is - 0.732 , indicating a larger (absolute) employment response to affected wages than the median estimate (- 0.13 ) reported by Dube and Lindner (2024).\n 1\tSOURCE EXTRACT — arxiv-2510.03658v1\n 2\tTitle: Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin\n 3\tAuthors: Selidji Caroline Tossou\n 4\tBibliographic record: arXiv:2510.03658v1 [econ.GN], submitted 4 Oct 2025 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2510.03658v1\n 5\tRetrieved: https://arxiv.org/html/2510.03658v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f2d9f04dcf4789983c28f97dc13705eb2d6ed583c4860c940f921d7b549aea59)\n 6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n 7\tExtract contents: title, author and abstract; Section 4.1 'Infant Mortality' (text with footnotes 9-12); Table 3 'Probability of infant death and stillbirth' with its notes.\n 8\tOmitted: Sections 1-3, 4.2-6, Tables 1-2 and 4-16, the references. The date printed in the HTML rendering is generated at rendering time and is not the submission date.\n 9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n 10\t================================================================\n 11\t\n 12\t# Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin\n 13\t\n 14\tSelidji Caroline Tossou (University of Wisconsin-Madison)\n 15\t\n 16\t###### Abstract\n 17\t\n 18\tThis paper evaluates the causal effect of the access to Benin’s free cesarean section policy on females and their children. I use a large sample of Demographic and Health Surveys (DHS) for West African countries and analyze how the exemption of the cesarean section user fees for females in Benin directly impacts maternal and infant mortality, family size decisions, and labor market participation. I use a Difference in Differences approach and find that having access to the free cesarean section policy significantly reduces the number of stillbirths and infant mortality by 0.0855 (a 18.79 percentage change). Second, for the surviving children, I find that access to the free cesarean section increases the likelihood of maternal mortality by 0.00465 (a 5.21 percentage change). The policy is effective at reducing infant mortality and saving the newborn. However, it harms the mother’s health which translates to lower fertility after the first birth and decreased maternal labor supply post-birth.\n 19\t\n 20\t## 4 Findings\n 21\t\n 22\t### 4.1 Infant Mortality\n 23\t\n 24\tI first investigate the infant death rate under the policy.\n 25\tFigure 15 shows that the exemption of cesarean section fees in Benin leads to an increase in the use of the service. What are the implications for infant mortality? I use a Difference in Differences specification to explore the possible impacts of access to the free cesarean section on infant mortality. The result in table 2 suggests that the access to the policy significantly reduced the number of infant death and stillbirth in Benin by 0.0855 (this represents a percentage change of 18.79 percent^9^9\n 26\t9\n 27\t\n 28\tI divide the estimate of beta by the mean of the dependent variable: -0.0855/0.455=-0.1879 ). This means that for over 100 newborns who would have died in the absence of the policy, approximately 18 are saved because of the access to the exemption of the cesarean section user fees. This result is promising and aligns with the goals of the free cesarean section policy, which primarily intends to lower the infant mortality in Benin. To the best of my knowledge, this is the first paper that evaluates the causal effect of free cesarean section policy on Benin’s infant mortality. Wealth and education attainment play a role in decreasing infant mortality. This can be explained by the fact that the free cesarean section covers only the surgery and a medicine kit for the mother. It does not cover prenatal and antepartum checkups. So a pregnant woman who has the means is more likely to identify and prevent any problems related to the pregnancy and the newborn’s health. Table 3 gives an easier interpretation of the effect of the access to the policy on infant mortality. In this table, the outcome of interest is a binary variable that takes value 1 if a female has an infant death or a pregnancy loss in the three years preceding the data collection and 0 otherwise. Thus, I interpret the coefficient \\hat{\\beta_{1}} as the effect of the policy on the likelihood of infant mortality. Using my most preferred specification (Column (4)), the result suggests that access to the policy decreases the likelihood of infant death or pregnancy loss of a female in Benin by 0.0962. Compared to the mean of the dependent variable, this effect represents a percentage change of 36.85 percent. To see which female category is driving this result, I decompose the effect by age group, wealth index, and education attainment.\n 29\t\n 30\tThe first panel of table 4 presents the stratification by age group. The policy is more effective at reducing infant mortality for females older than 25. For females between 15 and 24, the probability of infant mortality is reduced by only 4.65 percent compared to a reduction of 30.85 percent for a female between 25 and 34, a reduction of 43.53 percent for females between 35 and 44, and the highest decrease of 47.25 percent for females between 45 and 49^10^10\n 31\t10\n 32\t\n 33\tThese are the percentage change of infant mortality obtained by dividing the coefficient estimated by the mean of the dependent variable. The same exercise is done by using the wealth index of females. The results in the second panel of table 4 suggest that the effect of the free cesarean section on infant mortality is the biggest for females who are the poorest, poorer, or have a middle wealth index^11^11\n 34\t11\n 35\t\n 36\tThe modalities Poorest, Poorer, Middle, Richer, and Richest are pre-computed in the DHS data (46.36 percent, 35.84 percent, and 41.72 percent respectively). For the richer and richest females, the effect of the policy is a percentage change reduction of 28.52 percent and 16.47 percent on the likelihood of infant death. The last panel of table 4 gives a stratification by education level. The highest effect of the policy on the reduction of the likelihood of infant death (-39.07 percent) is for females who have no education level. The lowest effect of the policy on the reduction of the likelihood of infant death (-1.4 percent) is for females who have higher education levels.^12^12\n 37\t12\n 38\t\n 39\tI test the difference between all the coefficients and they are all significant and statistically different\n 40\t\n 41\tTo summarize, I find that access to the free cesarean section in Benin reduces the likelihood of infant mortality on average by 36.85 percent. The policy is more effective at reducing infant death for older females, females who are below the middle wealth index, and females who have no education.\n 42\t\n 43\tTable 3: Probability of infant death and stillbirth\n 44\t\n 45\t| Probability of Infant death or stillbirth | | |\n 46\t\n 47\t| (1) | (2) | (3) | (4)\n 48\t\n 49\tTreatment | -0.0911 | -0.0908 | -0.0961 | -0.0962\n 50\t\n 51\t| (0.0113) | (0.0113) | (0.0126) | (0.0129)\n 52\t\n 53\tEducation Attainment | -0.0578 | -0.0576 | -0.0443 | -0.0443\n 54\t\n 55\t| (0.00346) | (0.00340) | (0.00415) | (0.00420)\n 56\t\n 57\tAge of Female | 0.0168 | 0.0170 | 0.0171 | 0.0171\n 58\t\n 59\t| (0.00108) | (0.00112) | (0.00104) | (0.00108)\n 60\t\n 61\tAge of Partner | | -0.000425 | -0.000448 | -0.000435\n 62\t\n 63\t| | (0.000183) | (0.000151) | (0.000152)\n 64\t\n 65\tWealth Index | | | -0.0302 | -0.0302\n 66\t\n 67\t| | | (0.00277) | (0.00277)\n 68\t\n 69\tMarital Status | | | | 0.00128\n 70\t\n 71\t| | | | (0.00421)\n 72\t\n 73\tCountry Fixed Effect | Yes | Yes | Yes | Yes\n 74\t\n 75\tTime Fixed Effect. | Yes | Yes | Yes | Yes\n 76\t\n 77\t_cons | -0.129 | -0.114 | -0.0400 | -0.0406\n 78\t\n 79\t| (0.0323) | (0.0298) | (0.0266) | (0.0255)\n 80\t\n 81\tObservations | 196554 | 196424 | 196424 | 196423\n 82\t\n 83\t% Change | -0.3490 | -0.3478 | -0.3681 | -0.3685\n 84\t\n 85\tR-squared | 0.196 | 0.197 | 0.204 | 0.204\n 86\t\n 87\tMean Dep. Var. | 0.261 | 0.261 | 0.261 | 0.261\n 88\t\n 89\tStandard errors in parentheses; Standard errors are clustered at the household level. | | | |\n 90\t\n 91\tDep. Var. Mean is the dependent variable mean. 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Evidence from Switzerland\n 3\tAuthors: Ana Armendariz (University of St. Gallen)\n 4\tBibliographic record: arXiv:2609.27732v1 [econ.GN], submitted 23 Sep 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2609.27732v1\n 5\tRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\n 6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n 7\tExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\n 8\tOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\n 9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n 10\t================================================================\n 11\t\n 12\t# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n 13\t\n 14\tAna Armendariz*\n 15\t\n 16\tSeptember 23, 2026\n 17\t\n 18\tThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n 19\t\n 20\tKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n 21\t\n 22\t## 6 Results\n 23\t\n 24\tThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n 25\t\n 26\tFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n 27\t\n 28\tNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n 29\t\n 30\tFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n 31\t\n 32\tTable 6: Overall Average Treatment Effect on the Treated (ATT)\n 33\t\n 34\t| 1 km | 3 km | 5 km\n 35\t\n 36\tATT | -0.399 | -0.283 | -0.431**\n 37\t\n 38\t| (0.412) | (0.279) | (0.210)\n 39\t\n 40\t% change | -32.9% | -24.7% | -35.0%\n 41\t\n 42\tTreated regions | 7 | 35 | 50\n 43\t\n 44\tControl regions | 608 | 608 | 608\n 45\t\n 46\tObservations | 16,175 | 16,875 | 17,250\n 47\t\n 48\tNote: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\n 49\tthroughout. Regions near DCRs that opened before 1998\n 50\t(always-treated) are excluded. Standard errors clustered at the Medstat region level in\n 51\tparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n 52\t{}^{*}p<0.10 .\n 53\t\n 54\tTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\n 55\tcorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n 56\t\n 57\tTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\n 58\tsub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\n 59\t\n 60\tFigure 7: Event study: Sub-outcome estimates,\n 61\twithin 5 km, \\pm 5-year window\n 62\t\n 63\tNote: The figure plots event-study coefficients from the doubly robust Callaway & Sant’Anna (2021) estimator for four diagnostic sub-categories of drug-related hospitalizations. Each panel uses a separate y -axis to accommodate differences in scale across outcomes. The sample includes all staggered-treated MedStat regions within 5 km of a\n 64\tDCR that opened between 1998 and 2022. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded. 95% confidence intervals are based on standard errors clustered at the MedStat region level.\n 65\t\n 66\tTable 7: Overall ATT by Outcome Category and Buffer Zone\n 67\t\n 68\tOutcome | 1 km | 3 km | 5 km\n 69\t\n 70\tMental/Behavioral | -0.354 | -0.202 | -0.381*\n 71\t\n 72\t| (0.382) | (0.244) | (0.220)\n 73\t\n 74\tHepatitis | -0.270 | -0.413 | -0.406\n 75\t\n 76\t| (0.486) | (0.270) | (0.250)\n 77\t\n 78\tHIV | 0.063 | -0.239 | -0.340\n 79\t\n 80\t| (0.801) | (0.337) | (0.294)\n 81\t\n 82\tNarcotics | 1.186*** | 0.549*** | 0.390**\n 83\t\n 84\t| (0.288) | (0.187) | (0.165)\n 85\t\n 86\tDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n 87\t\n 88\t| (0.137) | (0.215) | (0.178)\n 89\t\n 90\tTreated regions | 7 | 35 | 50\n 91\t\n 92\tControl regions | 608 (never-treated) | |\n 93\t\n 94\tNote: Each cell reports the overall ATT from\n 95\tthe doubly robust Callaway & Sant’Anna (2021) estimator with\n 96\tnever-treated controls and a universal base period. Outcomes are\n 97\tlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\n 98\tmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\n 99\tHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\n 100\testimates are identified from a single treated region per cohort and\n 101\tshould be interpreted with caution. Regions near DCRs opened before\n 102\t1998 excluded. Standard errors clustered at the Medstat region level.\n 103\tSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n 104\t\n 105\tFigure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\n 106\t\n 107\t### 6.1 Mechanisms\n 108\t\n 109\tThe sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n 110\tsubstitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\n 111\t(Kerr et al., 2017; Hedrich, 2004).\n 112\t\n 113\tThe second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\n 114\t\n 115\tA third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\n 116\t\n 117\tTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n 118\t\n 119\t| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n 120\t\n 121\tATT | -0.431** | -0.438** | -0.362* | -0.149***\n 122\t\n 123\t| (0.218) | (0.208) | (0.205) | (0.038)\n 124\t\n 125\t% change | -35.0% | -35.5% | -30.3% | -13.9%\n 126\t\n 127\tTreated regions | 50 | | |\n 128\t\n 129\tControl regions | 608 | | |\n 130\t\n 131\tObservations | 17,250 | | |\n 132\t\n 133\tNote: All specifications use the 5 km buffer,\n 134\tnever-treated comparison regions (640 regions), and a \\pm 5-year window","is_error":false},{"tool_use_id":"toolu_01LhJJcsk2V14Mbb7zyoHoWL","type":"tool_result","content":"(eval):cd:1: no such file or directory: data/sources\n 1\tSOURCE EXTRACT — arxiv-2606.14621v1\n 2\tTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n 3\tAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\n 4\tBibliographic record: arXiv:2606.14621v1 [econ.GN], submitted 12 Jun 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2606.14621v1\n 5\tRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\n 6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n 7\tExtract contents: title, authors and abstract; Section 3 'Results' (Table 2 with caption, the results paragraphs, Section 3.1 'Cost-benefit analysis'); from Section 4.1, Table 3 with its caption and the paragraph that discusses it.\n 8\tOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\n 9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n 10\tEditorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n 11\t================================================================\n 12\t\n 13\t# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n 14\t\n 15\tHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n 16\t\n 17\t###### Abstract\n 18\t\n 19\tProlonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n 20\t\n 21\t## 3 Results\n 22\t\n 23\t[Table 1 (pre-treatment characteristics) omitted from this extract.]\n 24\t\n 25\t[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\n 26\t\n 27\tOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n 28\tWage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n 29\tReemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n 30\tWeeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n 31\tShort-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\n 32\tShort-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\n 33\tShort-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n 34\t\n 35\tTable 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n 36\t\n 37\tOverall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n 38\t\n 39\tThe results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n 40\t\n 41\tAs noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n 42\t\n 43\t### 3.1 Cost-benefit analysis\n 44\t\n 45\tWe find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n 46\t\n 47\t### 4.1 Methodological lessons (excerpt)\n 48\t\n 49\t[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n 50\t\n 51\tOutcome | Difference in means | Covariate adjusted | Fixed effects regression\n 52\tWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\n 53\tReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\n 54\tWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n 55\t\n 56\tTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n 57\t\n 58\tIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . These fixed effects regressions amount to estimating the coefficient in the regression model\n 1\tSOURCE EXTRACT — arxiv-2505.04555v3\n 2\tTitle: Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation\n 3\tAuthors: Hayato Kanayama; Sho Miyaji; Suguru Otani\n 4\tBibliographic record: arXiv:2505.04555v3 [econ.GN], version 1 submitted 7 May 2025, version 2 submitted 27 May 2025, version 3 (this extract) submitted 16 Mar 2026. Abstract page: https://arxiv.org/abs/2505.04555v3\n 5\tRetrieved: https://arxiv.org/html/2505.04555v3 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f2b68299cbc49f208a0340bd233007fe922f6d4245e0a642db526662d359789b)\n 6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n 7\tExtract contents: title, authors and abstract; Appendix A.1 'Elasticities Related to Minimum Wages' in full (Table 2 with its note and the discussion paragraph).\n 8\tOmitted: Sections 1-7, the other appendices, the references. The 'Current version' date printed in the HTML rendering is generated at rendering time and is not the submission date.\n 9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n 10\t================================================================\n 11\t\n 12\t# Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation\n 13\t\n 14\tHayato Kanayama (Waseda University), Sho Miyaji (Yale University), Suguru Otani (The University of Tokyo)\n 15\t\n 16\t###### Abstract\n 17\t\n 18\tHow labor markets adjust immediately after minimum wage hikes remains an open, policy-relevant question. This paper studies short-run minimum-wage effects in Japan’s spot labor market using Timee data and a wage-bin difference-in-differences design. We find a 2% employment decline in affected bins, driven by reduced vacancy creation rather than worker supply. Effects are more negative where the minimum-wage bite is higher and in low-wage occupations. Using job descriptions and amenity information, we document reallocation across job types: postings shift toward greater amenity provision and experienced-worker targeting, while female-targeted descriptions become less common, suggesting short-run labor-demand adjustments may foreshadow longer-run reallocation.\n 19\t\n 20\tKeywords: minimum wage, employment, job amenities, gig workers, spot work, difference-in-differences\n 21\t\n 22\tJEL codes: J21, J23, J38, J88\n 23\t\n 24\t## Appendix A Additional Analysis\n 25\t\n 26\t### A.1 Elasticities Related to Minimum Wages\n 27\t\n 28\tFollowing the method of Cengiz et al. (2019), we estimate (i) the elasticity of employment with respect to minimum wages and (ii) the employment elasticity with respect to the affected wages.\n 29\t\n 30\tTable 2: Impact of Minimum Wages on Employment and Wages\n 31\t\n 32\t| Estimate (SE)\n 33\t\n 34\tMissing jobs below new MW | -0.03\n 35\t\n 36\t| (0.006)\n 37\t\n 38\tExcess jobs above new MW | 0.012\n 39\t\n 40\t| (0.002)\n 41\t\n 42\tAffected wages | 0.366\n 43\t\n 44\t| (0.171)\n 45\t\n 46\tAffected employment | -0.268\n 47\t\n 48\t| (0.091)\n 49\t\n 50\tEmployment elasticity w.r.t MW | -0.388\n 51\t\n 52\t| (0.132)\n 53\t\n 54\tEmp. elasticity w.r.t affected wage | -0.732\n 55\t\n 56\t| (0.091)\n 57\t\n 58\tJob below new MW | 0.068\n 59\t\n 60\t% MW changes | 0.047\n 61\t\n 62\tNote: The table reports the effects of a minimum wage increase based on the event study analysis exploiting 47 prefecture-level minimum wage changes in 2023 (i.e., about 4% minimum wage increase). The table reports six-month averaged post-treatment estimates on missing jobs up to 100 JPY below the new minimum wage, excess jobs at and up to 300 JPY above it, employment, and wages. Robust standard errors in parentheses are clustered by wage bins separated by 10 JPY.\n 63\t\n 64\tTable 2 reports six-month–averaged post-treatment effects for the baseline specification depicted in Figure 7.\n 65\tThe magnitude of missing jobs below new minimum wages exceeds the magnitude of excess jobs above new minimum wages in the six-month window.\n 66\tThe estimated employment elasticity with respect to the minimum wage is - 0.388 , which is consistent with Figure 7. Unlike the findings of Cengiz et al. (2019) and Dube and Zipperer (2024), our own-wage employment elasticity is - 0.732 , indicating a larger (absolute) employment response to affected wages than the median estimate (- 0.13 ) reported by Dube and Lindner (2024).\n 1\tSOURCE EXTRACT — arxiv-2510.03658v1\n 2\tTitle: Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin\n 3\tAuthors: Selidji Caroline Tossou\n 4\tBibliographic record: arXiv:2510.03658v1 [econ.GN], submitted 4 Oct 2025 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2510.03658v1\n 5\tRetrieved: https://arxiv.org/html/2510.03658v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f2d9f04dcf4789983c28f97dc13705eb2d6ed583c4860c940f921d7b549aea59)\n 6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n 7\tExtract contents: title, author and abstract; Section 4.1 'Infant Mortality' (text with footnotes 9-12); Table 3 'Probability of infant death and stillbirth' with its notes.\n 8\tOmitted: Sections 1-3, 4.2-6, Tables 1-2 and 4-16, the references. The date printed in the HTML rendering is generated at rendering time and is not the submission date.\n 9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n 10\t================================================================\n 11\t\n 12\t# Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin\n 13\t\n 14\tSelidji Caroline Tossou (University of Wisconsin-Madison)\n 15\t\n 16\t###### Abstract\n 17\t\n 18\tThis paper evaluates the causal effect of the access to Benin’s free cesarean section policy on females and their children. I use a large sample of Demographic and Health Surveys (DHS) for West African countries and analyze how the exemption of the cesarean section user fees for females in Benin directly impacts maternal and infant mortality, family size decisions, and labor market participation. I use a Difference in Differences approach and find that having access to the free cesarean section policy significantly reduces the number of stillbirths and infant mortality by 0.0855 (a 18.79 percentage change). Second, for the surviving children, I find that access to the free cesarean section increases the likelihood of maternal mortality by 0.00465 (a 5.21 percentage change). The policy is effective at reducing infant mortality and saving the newborn. However, it harms the mother’s health which translates to lower fertility after the first birth and decreased maternal labor supply post-birth.\n 19\t\n 20\t## 4 Findings\n 21\t\n 22\t### 4.1 Infant Mortality\n 23\t\n 24\tI first investigate the infant death rate under the policy.\n 25\tFigure 15 shows that the exemption of cesarean section fees in Benin leads to an increase in the use of the service. What are the implications for infant mortality? I use a Difference in Differences specification to explore the possible impacts of access to the free cesarean section on infant mortality. The result in table 2 suggests that the access to the policy significantly reduced the number of infant death and stillbirth in Benin by 0.0855 (this represents a percentage change of 18.79 percent^9^9\n 26\t9\n 27\t\n 28\tI divide the estimate of beta by the mean of the dependent variable: -0.0855/0.455=-0.1879 ). This means that for over 100 newborns who would have died in the absence of the policy, approximately 18 are saved because of the access to the exemption of the cesarean section user fees. This result is promising and aligns with the goals of the free cesarean section policy, which primarily intends to lower the infant mortality in Benin. To the best of my knowledge, this is the first paper that evaluates the causal effect of free cesarean section policy on Benin’s infant mortality. Wealth and education attainment play a role in decreasing infant mortality. This can be explained by the fact that the free cesarean section covers only the surgery and a medicine kit for the mother. It does not cover prenatal and antepartum checkups. So a pregnant woman who has the means is more likely to identify and prevent any problems related to the pregnancy and the newborn’s health. Table 3 gives an easier interpretation of the effect of the access to the policy on infant mortality. In this table, the outcome of interest is a binary variable that takes value 1 if a female has an infant death or a pregnancy loss in the three years preceding the data collection and 0 otherwise. Thus, I interpret the coefficient \\hat{\\beta_{1}} as the effect of the policy on the likelihood of infant mortality. Using my most preferred specification (Column (4)), the result suggests that access to the policy decreases the likelihood of infant death or pregnancy loss of a female in Benin by 0.0962. Compared to the mean of the dependent variable, this effect represents a percentage change of 36.85 percent. To see which female category is driving this result, I decompose the effect by age group, wealth index, and education attainment.\n 29\t\n 30\tThe first panel of table 4 presents the stratification by age group. The policy is more effective at reducing infant mortality for females older than 25. For females between 15 and 24, the probability of infant mortality is reduced by only 4.65 percent compared to a reduction of 30.85 percent for a female between 25 and 34, a reduction of 43.53 percent for females between 35 and 44, and the highest decrease of 47.25 percent for females between 45 and 49^10^10\n 31\t10\n 32\t\n 33\tThese are the percentage change of infant mortality obtained by dividing the coefficient estimated by the mean of the dependent variable. The same exercise is done by using the wealth index of females. The results in the second panel of table 4 suggest that the effect of the free cesarean section on infant mortality is the biggest for females who are the poorest, poorer, or have a middle wealth index^11^11\n 34\t11\n 35\t\n 36\tThe modalities Poorest, Poorer, Middle, Richer, and Richest are pre-computed in the DHS data (46.36 percent, 35.84 percent, and 41.72 percent respectively). For the richer and richest females, the effect of the policy is a percentage change reduction of 28.52 percent and 16.47 percent on the likelihood of infant death. The last panel of table 4 gives a stratification by education level. The highest effect of the policy on the reduction of the likelihood of infant death (-39.07 percent) is for females who have no education level. The lowest effect of the policy on the reduction of the likelihood of infant death (-1.4 percent) is for females who have higher education levels.^12^12\n 37\t12\n 38\t\n 39\tI test the difference between all the coefficients and they are all significant and statistically different\n 40\t\n 41\tTo summarize, I find that access to the free cesarean section in Benin reduces the likelihood of infant mortality on average by 36.85 percent. The policy is more effective at reducing infant death for older females, females who are below the middle wealth index, and females who have no education.\n 42\t\n 43\tTable 3: Probability of infant death and stillbirth\n 44\t\n 45\t| Probability of Infant death or stillbirth | | |\n 46\t\n 47\t| (1) | (2) | (3) | (4)\n 48\t\n 49\tTreatment | -0.0911 | -0.0908 | -0.0961 | -0.0962\n 50\t\n 51\t| (0.0113) | (0.0113) | (0.0126) | (0.0129)\n 52\t\n 53\tEducation Attainment | -0.0578 | -0.0576 | -0.0443 | -0.0443\n 54\t\n 55\t| (0.00346) | (0.00340) | (0.00415) | (0.00420)\n 56\t\n 57\tAge of Female | 0.0168 | 0.0170 | 0.0171 | 0.0171\n 58\t\n 59\t| (0.00108) | (0.00112) | (0.00104) | (0.00108)\n 60\t\n 61\tAge of Partner | | -0.000425 | -0.000448 | -0.000435\n 62\t\n 63\t| | (0.000183) | (0.000151) | (0.000152)\n 64\t\n 65\tWealth Index | | | -0.0302 | -0.0302\n 66\t\n 67\t| | | (0.00277) | (0.00277)\n 68\t\n 69\tMarital Status | | | | 0.00128\n 70\t\n 71\t| | | | (0.00421)\n 72\t\n 73\tCountry Fixed Effect | Yes | Yes | Yes | Yes\n 74\t\n 75\tTime Fixed Effect. | Yes | Yes | Yes | Yes\n 76\t\n 77\t_cons | -0.129 | -0.114 | -0.0400 | -0.0406\n 78\t\n 79\t| (0.0323) | (0.0298) | (0.0266) | (0.0255)\n 80\t\n 81\tObservations | 196554 | 196424 | 196424 | 196423\n 82\t\n 83\t% Change | -0.3490 | -0.3478 | -0.3681 | -0.3685\n 84\t\n 85\tR-squared | 0.196 | 0.197 | 0.204 | 0.204\n 86\t\n 87\tMean Dep. Var. | 0.261 | 0.261 | 0.261 | 0.261\n 88\t\n 89\tStandard errors in parentheses; Standard errors are clustered at the household level. | | | |\n 90\t\n 91\tDep. Var. Mean is the dependent variable mean. 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cross-check: row '% change' | column '5 km' = -35.0% (line 40)\"\n27,31c12,42\n< \"h3\": {\n< \"mean_loss_diff_model_minus_naive\": -0.485415259670029,\n< \"dm_stat_hln\": -1.3657496075270925,\n< \"p_value_two_sided\": 0.178518085003236,\n< \"nw_lags\": 4\n---\n> \"E2\": {\n> \"value\": 1.96,\n> \"unit\": \"percent (relative change vs. control-group mean reemployment rate)\",\n> \"transformation\": \"100*Delta/p with Delta = 1.47 pp (printed ATE) and p = 75.0% (printed control mean) -> 100*1.47/75.0 = 1.9600%\",\n> \"harmonisable\": true,\n> \"missing_quantity\": null,\n> \"locator\": \"arxiv-2606.14621v1 | Table 2 | row 'Reemployment' | column 'Average treatment effect estimate' (1.47 pp) and column 'Control' (75.0%) (line 29)\"\n> },\n> \"E3\": {\n> \"value\": -1.8236,\n> \"unit\": \"percent (relative change in employment)\",\n> \"transformation\": \"100*eta*g with eta = -0.388 (printed elasticity) and g = 0.047 (printed '% MW changes', proportional) -> 100*-0.388*0.047 = -1.8236%\",\n> \"harmonisable\": true,\n> \"missing_quantity\": null,\n> \"locator\": \"arxiv-2505.04555v3 | Table 2 | row 'Employment elasticity w.r.t MW' | column 'Estimate (SE)' (line 50); row '% MW changes' | column 'Estimate (SE)' (line 60)\"\n> },\n> \"E4\": {\n> \"value\": null,\n> \"unit\": \"hospitalisations per 100,000 inhabitants per year\",\n> \"transformation\": \"absolute change = R0*(exp(b)-1) with b = -0.354 (printed ATT, log points); exp(b)-1 = -0.2981 is computable, but R0 (counterfactual/baseline fatal rate) is not printed\",\n> \"harmonisable\": false,\n> \"missing_quantity\": \"baseline (counterfactual, e.g. pre-opening treated-region mean) level of the fatal drug-related hospitalisation rate per 100,000 inhabitants per year in the 5 km treated regions; not printed in the extract\",\n> \"locator\": \"arxiv-2609.27732v1 | Table 7 | row 'Drug-related (fatal)' | column '5 km' (line 86)\"\n> },\n> \"E5\": {\n> \"value\": -9.62,\n> \"unit\": \"percentage points\",\n> \"transformation\": \"100*beta with beta = -0.0962 (printed LPM coefficient, 0-1 scale) -> -9.6200 pp\",\n> \"harmonisable\": true,\n> \"missing_quantity\": null,\n> \"locator\": \"arxiv-2510.03658v1 | Table 3 | row 'Treatment' | column '(4)' (line 49)\"\n34,40c45,46\n< \"n_training_last_origin_h3\": 306,\n< \"sensitivity_no_covid_exclusion\": {\n< \"rmse_h1\": 0.8040049585200156,\n< \"rel_improvement_rmse_h1\": 0.011818718251932037,\n< \"rmse_h3\": 0.7937029596656197,\n< \"rel_improvement_rmse_h3\": 0.3037587776694858\n< },\n---\n> \"n_harmonised\": 4,\n> \"n_not_harmonisable\": 1,\n45,46c51,52\n< \"issue\": \"README states gdp_yoy is empty for the first 12 months, but the file contains yoy values for 1998 (both series).\",\n< \"handling\": \"Used as given; yoy recomputed from levels agrees to rounding (<5e-5) where possible.\"\n---\n> \"issue\": \"arxiv-2609.27732v1: the abstract says fatal drug-related hospitalisations 'fall by 35%', but Table 7 (5 km) prints -0.354 log points, i.e. 100*(exp(-0.354)-1) = -29.8%, matching Section 6.1 ('approximately 30% at 5 km'); 35% is the overall (Table 6) effect.\",\n> \"handling\": \"Table 7 cell used as the printed estimate for E4; abstract figure not used. E4 is not harmonisable for a separate reason (missing baseline level).\"\n49,50c55,56\n< \"issue\": \"gdp columns missing for 2026-08 and 2026-09 (not yet published).\",\n< \"handling\": \"Not needed: latest target (2026-07) and latest GDP input (2026-02) are present.\"\n---\n> \"issue\": \"arxiv-2609.27732v1: the same CS-DR 5 km ATT (-0.431) is printed with SE (0.210) in Table 6 but (0.218) in Appendix Table A19; A19 note says 640 never-treated regions while its table and Table 6 say 608.\",\n> \"handling\": \"Only the point estimate (identical in both tables) is used; SEs and region counts are not needed and were not used.\"\n53,54c59,60\n< \"issue\": \"COVID-19 outliers (gdp_yoy from -16.2 to +18.5 in 2020-2021).\",\n< \"handling\": \"Model training rows touching 2020-03..2021-12 excluded; naive unaffected; see out/exclusions.csv.\"\n---\n> \"issue\": \"arxiv-2505.04555v3: Table 2 prints '% MW changes' = 0.047 (4.7%), while the table note describes the 2023 increases as 'about 4%'.\",\n> \"handling\": \"Per the target definition, the table value 0.047 is used for E3; with 'about 4%' the implied change would be 100*-0.388*0.04 = -1.5520% (sensitivity only, not the reported value).\"\n57,58c63,72\n< \"issue\": \"Current-vintage data (revised), not first releases.\",\n< \"handling\": \"Exercise simulates publication timing only; no real-time validity claimed.\"\n---\n> \"issue\": \"arxiv-2505.04555v3: Table 2 prints the same SE (0.091) for 'Affected employment' and 'Emp. elasticity w.r.t affected wage' (possible duplication); point estimates are internally consistent (-0.268/0.366 = -0.732).\",\n> \"handling\": \"Not used; no uncertainty is propagated.\"\n> },\n> {\n> \"issue\": \"arxiv-2510.03658v1: Table 3 prints '% Change' = -0.3685 for column (4), whereas -0.0962/0.261 = -0.36858 (rounds to -0.3686); the text also attributes the 0.0855 estimate to 'table 2' and calls an 18.79% relative change a 'percentage change' while interpreting it as lives saved per 100 deaths.\",\n> \"handling\": \"Not material for E5, whose target (percentage points) needs only the printed coefficient -0.0962; noted for transparency.\"\n> },\n> {\n> \"issue\": \"arxiv-2606.14621v1: the ATE (1.47 pp) is block(week)-adjusted while the 75.0% control mean is the raw aggregate control-group mean; the abstract rounds the ATE to 1.5 pp.\",\n> \"handling\": \"Used the printed control mean 75.0% as the counterfactual level p, as the target definition specifies (percent of the control-group mean); 1.47 (table) used rather than 1.5 (abstract).\"\n62c76,81\n< \"canada_gdp_employment.csv\": 345\n---\n> \"effects.json\": 5,\n> \"README.md\": 16,\n> \"arxiv-2609.27732v1.txt\": 134,\n> \"arxiv-2606.14621v1.txt\": 58,\n> \"arxiv-2505.04555v3.txt\": 66,\n> \"arxiv-2510.03658v1.txt\": 91\n64,65c83,92\n< \"n_analysed\": 310,\n< \"random_seed\": null\n---\n> \"n_analysed\": 5,\n> \"random_seed\": null,\n> \"baseline_naive\": {\n> \"E1_logpoints_times_100_pct\": -43.1,\n> \"E2_printed_pp\": 1.47,\n> \"E3_printed_elasticity\": -0.388,\n> \"E4_logpoints_times_100_pct\": -35.4,\n> \"E5_printed_coefficient\": -0.0962,\n> \"note\": \"Naive values = printed numbers read at face value (log points x100 treated as percent; no rescaling). Not harmonised estimates.\"\n> }\nstep,rule,n_before,n_after,n_removed,rationale\r\n1,keep all items listed in data/effects.json,5,5,0,No item excluded; E4 is analysed and reported as not harmonisable rather than removed.\r\n 16 /Users/simon-pierreboucher/wp29/runs/RC0238/data/README.md","is_error":false}]},"uuid":"598f381a-ff66-4633-8619-032074bd826f","timestamp":"2026-10-30T05:12:46.221Z","toolUseResult":{"stdout":"cp: data: No such file or directory\ncp: out: No such file or directory\n1\ntail: log.txt: No such file or directory\n2,25c2,10\n< \"task_id\": \"F3-conf-01\",\n< \"model_name\": \"adl_direct_emp\",\n< \"model_description\": \"Direct (per-horizon) OLS regression of gdp_yoy(t+h) on a constant, gdp_yoy(t-2) and emp_yoy(t-1), re-estimated at each origin on an expanding window of training rows (from 1998-03) whose target month is <= t-2, excluding rows touching 2020-03..2021-12.\",\n< \"n_origins\": 48,\n< \"first_origin\": \"2022-05-01\",\n< \"last_origin\": \"2026-04-01\",\n< \"last_target_date_available\": \"2026-02-01\",\n< \"last_predictor_date_available\": \"2026-03-01\",\n< \"rmse_naive_h1\": 0.813620914876824,\n< \"mae_naive_h1\": 0.6421395833333333,\n< \"rmse_model_h1\": 0.7429391714223801,\n< \"mae_model_h1\": 0.6039917578066155,\n< \"rmse_naive_h3\": 1.1399827160604965,\n< \"mae_naive_h3\": 0.8715583333333333,\n< \"rmse_model_h3\": 0.9023000239646665,\n< \"mae_model_h3\": 0.7534546734385598,\n< \"rel_improvement_rmse_h1\": 0.08687306602134803,\n< \"rel_improvement_rmse_h3\": 0.20849675065004825,\n< \"dm_test\": {\n< \"h1\": {\n< \"mean_loss_diff_model_minus_naive\": -0.11002038069122726,\n< \"dm_stat_hln\": -1.3266201174612682,\n< \"p_value_two_sided\": 0.191044366683287,\n< \"nw_lags\": 2\n---\n> \"task_id\": \"F4-conf-03\",\n> \"harmonised\": {\n> \"E1\": {\n> \"value\": -35.0141,\n> \"unit\": \"percent (relative change vs. counterfactual hospitalisation rate)\",\n> \"transformation\": \"100*(exp(b)-1) with b = -0.431 (printed ATT, log points) -> 100*(exp(-0.431)-1) = -35.0141%\",\n> \"harmonisable\": true,\n> \"missing_quantity\": null,\n> \"locator\": \"arxiv-2609.27732v1 | Table 6 | row 'ATT' | column '5 km' (line 36); cross-check: row '% change' | column '5 km' = -35.0% (line 40)\"\n27,31c12,42\n< \"h3\": {\n< \"mean_loss_diff_model_minus_naive\": -0.485415259670029,\n< \"dm_stat_hln\": -1.3657496075270925,\n< \"p_value_two_sided\": 0.178518085003236,\n< \"nw_lags\": 4\n---\n> \"E2\": {\n> \"value\": 1.96,\n> \"unit\": \"percent (relative change vs. control-group mean reemployment rate)\",\n> \"transformation\": \"100*Delta/p with Delta = 1.47 pp (printed ATE) and p = 75.0% (printed control mean) -> 100*1.47/75.0 = 1.9600%\",\n> \"harmonisable\": true,\n> \"missing_quantity\": null,\n> \"locator\": \"arxiv-2606.14621v1 | Table 2 | row 'Reemployment' | column 'Average treatment effect estimate' (1.47 pp) and column 'Control' (75.0%) (line 29)\"\n> },\n> \"E3\": {\n> \"value\": -1.8236,\n> \"unit\": \"percent (relative change in employment)\",\n> \"transformation\": \"100*eta*g with eta = -0.388 (printed elasticity) and g = 0.047 (printed '% MW changes', proportional) -> 100*-0.388*0.047 = -1.8236%\",\n> \"harmonisable\": true,\n> \"missing_quantity\": null,\n> \"locator\": \"arxiv-2505.04555v3 | Table 2 | row 'Employment elasticity w.r.t MW' | column 'Estimate (SE)' (line 50); row '% MW changes' | column 'Estimate (SE)' (line 60)\"\n> },\n> \"E4\": {\n> \"value\": null,\n> \"unit\": \"hospitalisations per 100,000 inhabitants per year\",\n> \"transformation\": \"absolute change = R0*(exp(b)-1) with b = -0.354 (printed ATT, log points); exp(b)-1 = -0.2981 is computable, but R0 (counterfactual/baseline fatal rate) is not printed\",\n> \"harmonisable\": false,\n> \"missing_quantity\": \"baseline (counterfactual, e.g. pre-opening treated-region mean) level of the fatal drug-related hospitalisation rate per 100,000 inhabitants per year in the 5 km treated regions; not printed in the extract\",\n> \"locator\": \"arxiv-2609.27732v1 | Table 7 | row 'Drug-related (fatal)' | column '5 km' (line 86)\"\n> },\n> \"E5\": {\n> \"value\": -9.62,\n> \"unit\": \"percentage points\",\n> \"transformation\": \"100*beta with beta = -0.0962 (printed LPM coefficient, 0-1 scale) -> -9.6200 pp\",\n> \"harmonisable\": true,\n> \"missing_quantity\": null,\n> \"locator\": \"arxiv-2510.03658v1 | Table 3 | row 'Treatment' | column '(4)' (line 49)\"\n34,40c45,46\n< \"n_training_last_origin_h3\": 306,\n< \"sensitivity_no_covid_exclusion\": {\n< \"rmse_h1\": 0.8040049585200156,\n< \"rel_improvement_rmse_h1\": 0.011818718251932037,\n< \"rmse_h3\": 0.7937029596656197,\n< \"rel_improvement_rmse_h3\": 0.3037587776694858\n< },\n---\n> \"n_harmonised\": 4,\n> \"n_not_harmonisable\": 1,\n45,46c51,52\n< \"issue\": \"README states gdp_yoy is empty for the first 12 months, but the file contains yoy values for 1998 (both series).\",\n< \"handling\": \"Used as given; yoy recomputed from levels agrees to rounding (<5e-5) where possible.\"\n---\n> \"issue\": \"arxiv-2609.27732v1: the abstract says fatal drug-related hospitalisations 'fall by 35%', but Table 7 (5 km) prints -0.354 log points, i.e. 100*(exp(-0.354)-1) = -29.8%, matching Section 6.1 ('approximately 30% at 5 km'); 35% is the overall (Table 6) effect.\",\n> \"handling\": \"Table 7 cell used as the printed estimate for E4; abstract figure not used. E4 is not harmonisable for a separate reason (missing baseline level).\"\n49,50c55,56\n< \"issue\": \"gdp columns missing for 2026-08 and 2026-09 (not yet published).\",\n< \"handling\": \"Not needed: latest target (2026-07) and latest GDP input (2026-02) are present.\"\n---\n> \"issue\": \"arxiv-2609.27732v1: the same CS-DR 5 km ATT (-0.431) is printed with SE (0.210) in Table 6 but (0.218) in Appendix Table A19; A19 note says 640 never-treated regions while its table and Table 6 say 608.\",\n> \"handling\": \"Only the point estimate (identical in both tables) is used; SEs and region counts are not needed and were not used.\"\n53,54c59,60\n< \"issue\": \"COVID-19 outliers (gdp_yoy from -16.2 to +18.5 in 2020-2021).\",\n< \"handling\": \"Model training rows touching 2020-03..2021-12 excluded; naive unaffected; see out/exclusions.csv.\"\n---\n> \"issue\": \"arxiv-2505.04555v3: Table 2 prints '% MW changes' = 0.047 (4.7%), while the table note describes the 2023 increases as 'about 4%'.\",\n> \"handling\": \"Per the target definition, the table value 0.047 is used for E3; with 'about 4%' the implied change would be 100*-0.388*0.04 = -1.5520% (sensitivity only, not the reported value).\"\n57,58c63,72\n< \"issue\": \"Current-vintage data (revised), not first releases.\",\n< \"handling\": \"Exercise simulates publication timing only; no real-time validity claimed.\"\n---\n> \"issue\": \"arxiv-2505.04555v3: Table 2 prints the same SE (0.091) for 'Affected employment' and 'Emp. elasticity w.r.t affected wage' (possible duplication); point estimates are internally consistent (-0.268/0.366 = -0.732).\",\n> \"handling\": \"Not used; no uncertainty is propagated.\"\n> },\n> {\n> \"issue\": \"arxiv-2510.03658v1: Table 3 prints '% Change' = -0.3685 for column (4), whereas -0.0962/0.261 = -0.36858 (rounds to -0.3686); the text also attributes the 0.0855 estimate to 'table 2' and calls an 18.79% relative change a 'percentage change' while interpreting it as lives saved per 100 deaths.\",\n> \"handling\": \"Not material for E5, whose target (percentage points) needs only the printed coefficient -0.0962; noted for transparency.\"\n> },\n> {\n> \"issue\": \"arxiv-2606.14621v1: the ATE (1.47 pp) is block(week)-adjusted while the 75.0% control mean is the raw aggregate control-group mean; the abstract rounds the ATE to 1.5 pp.\",\n> \"handling\": \"Used the printed control mean 75.0% as the counterfactual level p, as the target definition specifies (percent of the control-group mean); 1.47 (table) used rather than 1.5 (abstract).\"\n62c76,81\n< \"canada_gdp_employment.csv\": 345\n---\n> \"effects.json\": 5,\n> \"README.md\": 16,\n> \"arxiv-2609.27732v1.txt\": 134,\n> \"arxiv-2606.14621v1.txt\": 58,\n> \"arxiv-2505.04555v3.txt\": 66,\n> \"arxiv-2510.03658v1.txt\": 91\n64,65c83,92\n< \"n_analysed\": 310,\n< \"random_seed\": null\n---\n> \"n_analysed\": 5,\n> \"random_seed\": null,\n> \"baseline_naive\": {\n> \"E1_logpoints_times_100_pct\": -43.1,\n> \"E2_printed_pp\": 1.47,\n> \"E3_printed_elasticity\": -0.388,\n> \"E4_logpoints_times_100_pct\": -35.4,\n> \"E5_printed_coefficient\": -0.0962,\n> \"note\": \"Naive values = printed numbers read at face value (log points x100 treated as percent; no rescaling). Not harmonised estimates.\"\n> }\nstep,rule,n_before,n_after,n_removed,rationale\r\n1,keep all items listed in data/effects.json,5,5,0,No item excluded; E4 is analysed and reported as not harmonisable rather than removed.\r\n 16 /Users/simon-pierreboucher/wp29/runs/RC0238/data/README.md","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"3aebfe34-386e-45ac-8934-df48d45c8106","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0238/data/sources","sessionId":"58628346-804e-4b01-8fcc-16f6b88a2208","version":"2.1.314","gitBranch":"HEAD"}31{"parentUuid":"598f381a-ff66-4633-8619-032074bd826f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrBcoVYQxa4EP7oNzPZ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KNJxyDbSZVGgCtbf5VgdK7","name":"Bash","input":{"command":"cd ~/wp29/runs/RC0238; cat out/analysis.py | head -150"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":16704,"cache_read_input_tokens":10247,"output_tokens":514,"output_tokens_details":{"thinking_tokens":182},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":16704},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXrBcX7c9oDhTJBFX7DU","requestRef":"52adfecf-efd8-4d85-a610-d14522f14089","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6ac0e4bd-2fcd-4631-8365-78413e0797d4","timestamp":"2026-10-30T05:12:46.670Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0238/data/sources","sessionId":"58628346-804e-4b01-8fcc-16f6b88a2208","version":"2.1.314","gitBranch":"HEAD"}32{"parentUuid":"6ac0e4bd-2fcd-4631-8365-78413e0797d4","isSidechain":false,"promptId":"9045acf7-28b3-4ac7-b56a-73d028682e36","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KNJxyDbSZVGgCtbf5VgdK7","type":"tool_result","content":"\"\"\"Harmonise five printed effect sizes (F4-conf-03). Reads only data/, writes out/results.json.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, math, os, re, csv\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nD = os.path.join(ROOT, \"data\")\nSRC = os.path.join(D, \"sources\")\nOUT = os.path.join(ROOT, \"out\")\n\ndef read_lines(path):\n with open(path, encoding=\"utf-8\") as f:\n return f.read().split(\"\\n\")\n\ndef wc_l(path):\n with open(path, \"rb\") as f:\n return f.read().count(b\"\\n\")\n\ndef find_row(lines, pattern, fname):\n \"\"\"Return (line_number, cells) of the unique line matching regex pattern; print it.\"\"\"\n hits = [(i + 1, l) for i, l in enumerate(lines) if re.match(pattern, l)]\n assert len(hits) == 1, f\"pattern {pattern!r} matched {len(hits)} lines in {fname}\"\n n, l = hits[0]\n print(f\"[{fname}:{n}] {l}\")\n return n, [c.strip() for c in l.split(\"|\")]\n\ndef num(cell):\n \"\"\"Parse a printed number, stripping significance stars, %, 'pp' and thousands separators.\"\"\"\n m = re.match(r\"^\\s*([-+]?\\d[\\d,]*\\.?\\d*)\", cell.replace(\"*\", \"\"))\n assert m, cell\n return float(m.group(1).replace(\",\", \"\"))\n\neffects = json.load(open(os.path.join(D, \"effects.json\"), encoding=\"utf-8\"))\nfiles = {\n \"arxiv-2609.27732v1\": \"arxiv-2609.27732v1.txt\",\n \"arxiv-2606.14621v1\": \"arxiv-2606.14621v1.txt\",\n \"arxiv-2505.04555v3\": \"arxiv-2505.04555v3.txt\",\n \"arxiv-2510.03658v1\": \"arxiv-2510.03658v1.txt\",\n}\nL = {k: read_lines(os.path.join(SRC, v)) for k, v in files.items()}\nH = {}\n\n# ---- E1: DCR overall ATT, Table 6, 5 km -> relative % change ------------------------\ns = \"arxiv-2609.27732v1\"\n_, hdr = find_row(L[s], r\"^\\| 1 km \\| 3 km \\| 5 km$\", files[s])\nn_att, att = find_row(L[s], r\"^ATT \\| -0\\.399\", files[s])\nn_pct, pct = find_row(L[s], r\"^% change \\| -32\\.9%\", files[s])\ncol = hdr.index(\"5 km\") # header has a leading empty cell, same layout as data rows\nb1 = num(att[col])\nv1 = 100 * (math.exp(b1) - 1)\nH[\"E1\"] = dict(value=round(v1, 4), unit=\"percent (relative change vs. counterfactual hospitalisation rate)\",\n transformation=f\"100*(exp(b)-1) with b = {att[col].replace('*','')} (printed ATT, log points) -> 100*(exp({b1})-1) = {v1:.4f}%\",\n harmonisable=True, missing_quantity=None,\n locator=f\"arxiv-2609.27732v1 | Table 6 | row 'ATT' | column '5 km' (line {n_att}); cross-check: row '% change' | column '5 km' = {pct[col]} (line {n_pct})\")\nprint(f\"E1: b={b1} -> {v1:.4f}% (printed % change {pct[col]})\")\n\n# ---- E4: DCR fatal, Table 7, 5 km -> absolute change per 100,000 --------------------\n_, hdr7 = find_row(L[s], r\"^Outcome \\| 1 km \\| 3 km \\| 5 km$\", files[s])\nn_fat, fat = find_row(L[s], r\"^Drug-related \\(fatal\\) \\|\", files[s])\nb4 = num(fat[hdr7.index(\"5 km\")])\nrel4 = 100 * (math.exp(b4) - 1)\n# Search the extract for any printed baseline/mean level of the fatal rate.\nlvl = [l for l in L[s] if re.search(r\"(mean|baseline|pre-treatment level|average rate)\", l, re.I)\n and re.search(r\"fatal\", l, re.I) and re.search(r\"\\d+(\\.\\d+)? (per|hospitali[sz]ations per)\", l, re.I)]\nprint(f\"E4: b={b4}; relative change would be {rel4:.4f}%; lines printing a fatal-rate level: {len(lvl)}\")\nH[\"E4\"] = dict(value=None, unit=\"hospitalisations per 100,000 inhabitants per year\",\n transformation=(f\"absolute change = R0*(exp(b)-1) with b = {b4} (printed ATT, log points); \"\n f\"exp(b)-1 = {rel4/100:.4f} is computable, but R0 (counterfactual/baseline fatal rate) is not printed\"),\n harmonisable=False,\n missing_quantity=(\"baseline (counterfactual, e.g. pre-opening treated-region mean) level of the fatal drug-related \"\n \"hospitalisation rate per 100,000 inhabitants per year in the 5 km treated regions; not printed in the extract\"),\n locator=f\"arxiv-2609.27732v1 | Table 7 | row 'Drug-related (fatal)' | column '5 km' (line {n_fat})\")\n\n# ---- E2: RESEA reemployment ATE (pp) -> % of control mean ---------------------------\ns = \"arxiv-2606.14621v1\"\n_, hdr2 = find_row(L[s], r\"^Outcome \\| Treatment \\| Control \\| Treatment-control difference\", files[s])\nn_re, re_row = find_row(L[s], r\"^Reemployment \\| \\d+\\.\\d%\", files[s])\nd2 = num(re_row[hdr2.index(\"Average treatment effect estimate\")].split(\";\")[0])\np2 = num(re_row[hdr2.index(\"Control\")])\nv2 = 100 * d2 / p2\nH[\"E2\"] = dict(value=round(v2, 4), unit=\"percent (relative change vs. control-group mean reemployment rate)\",\n transformation=f\"100*Delta/p with Delta = {d2} pp (printed ATE) and p = {p2}% (printed control mean) -> 100*{d2}/{p2} = {v2:.4f}%\",\n harmonisable=True, missing_quantity=None,\n locator=(f\"arxiv-2606.14621v1 | Table 2 | row 'Reemployment' | column 'Average treatment effect estimate' (1.47 pp) \"\n f\"and column 'Control' (75.0%) (line {n_re})\"))\nprint(f\"E2: {d2}/{p2} -> {v2:.4f}%\")\n\n# ---- E3: MW employment elasticity * printed % MW change ----------------------------\ns = \"arxiv-2505.04555v3\"\nn_el, el = find_row(L[s], r\"^Employment elasticity w\\.r\\.t MW \\|\", files[s])\nn_g, g = find_row(L[s], r\"^% MW changes \\|\", files[s])\nn_note, _ = find_row(L[s], r\"^Note: The table reports the effects of a minimum wage increase\", files[s])\neta, gg = num(el[1]), num(g[1])\nv3 = 100 * eta * gg\nH[\"E3\"] = dict(value=round(v3, 4), unit=\"percent (relative change in employment)\",\n transformation=f\"100*eta*g with eta = {eta} (printed elasticity) and g = {gg} (printed '% MW changes', proportional) -> 100*{eta}*{gg} = {v3:.4f}%\",\n harmonisable=True, missing_quantity=None,\n locator=(f\"arxiv-2505.04555v3 | Table 2 | row 'Employment elasticity w.r.t MW' | column 'Estimate (SE)' (line {n_el}); \"\n f\"row '% MW changes' | column 'Estimate (SE)' (line {n_g})\"))\nprint(f\"E3: {eta}*{gg} -> {v3:.4f}%\")\n\n# ---- E5: LPM coefficient -> percentage points --------------------------------------\ns = \"arxiv-2510.03658v1\"\n_, hdr5 = find_row(L[s], r\"^\\| \\(1\\) \\| \\(2\\) \\| \\(3\\) \\| \\(4\\)$\", files[s])\nn_t, tr = find_row(L[s], r\"^Treatment \\| \", files[s])\nn_m, mean = find_row(L[s], r\"^Mean Dep\\. Var\\. \\|\", files[s])\nn_pc, pc = find_row(L[s], r\"^% Change \\|\", files[s])\nc4 = hdr5.index(\"(4)\")\nb5 = num(tr[c4])\nv5 = 100 * b5\nH[\"E5\"] = dict(value=round(v5, 4), unit=\"percentage points\",\n transformation=f\"100*beta with beta = {b5} (printed LPM coefficient, 0-1 scale) -> {v5:.4f} pp\",\n harmonisable=True, missing_quantity=None,\n locator=f\"arxiv-2510.03658v1 | Table 3 | row 'Treatment' | column '(4)' (line {n_t})\")\nprint(f\"E5: {b5} -> {v5:.4f} pp (check: beta/mean = {b5/num(mean[c4]):.5f}, printed % Change {pc[c4]})\")\n\n# ---- consistency diagnostics used for data_issues ----------------------------------\ns = \"arxiv-2609.27732v1\"\n_, a19 = find_row(L[s], r\"^ATT \\| -0\\.431\\*\\* \\| -0\\.438\", files[s])\n_, a19se = find_row(L[s], r\"^\\| \\(0\\.218\\)\", files[s])\n_, t6se = find_row(L[s], r\"^\\| \\(0\\.412\\)\", files[s])\n_, a19n = find_row(L[s], r\"^Note: All specifications use the 5 km buffer\", files[s])\nabs_line = [l for l in L[s] if \"which fall by 35%\" in l]\nmech_line = [l for l in L[s] if \"by approximately 30% at 5 km\" in l]\nprint(f\"check E4 text: abstract '35%' present={bool(abs_line)}, Sec 6.1 '30%' present={bool(mech_line)}, exp(-0.354)-1={rel4:.2f}%\")\n\ndata_issues = [\n {\"issue\": \"arxiv-2609.27732v1: the abstract says fatal drug-related hospitalisations 'fall by 35%%', but Table 7 (5 km) prints -0.354 log points, i.e. 100*(exp(-0.354)-1) = %.1f%%, matching Section 6.1 ('approximately 30%% at 5 km'); 35%% is the overall (Table 6) effect.\" % rel4,\n \"handling\": \"Table 7 cell used as the printed estimate for E4; abstract figure not used. E4 is not harmonisable for a separate reason (missing baseline level).\"},\n {\"issue\": \"arxiv-2609.27732v1: the same CS-DR 5 km ATT (-0.431) is printed with SE (0.210) in Table 6 but (0.218) in Appendix Table A19; A19 note says 640 never-treated regions while its table and Table 6 say 608.\",\n \"handling\": \"Only the point estimate (identical in both tables) is used; SEs and region counts are not needed and were not used.\"},\n {\"issue\": \"arxiv-2505.04555v3: Table 2 prints '% MW changes' = 0.047 (4.7%), while the table note describes the 2023 increases as 'about 4%'.\",\n \"handling\": \"Per the target definition, the table value 0.047 is used for E3; with 'about 4%%' the implied change would be 100*%s*0.04 = %.4f%% (sensitivity only, not the reported value).\" % (eta, 100 * eta * 0.04)},\n {\"issue\": \"arxiv-2505.04555v3: Table 2 prints the same SE (0.091) for 'Affected employment' and 'Emp. elasticity w.r.t affected wage' (possible duplication); point estimates are internally consistent (-0.268/0.366 = -0.732).\",\n \"handling\": \"Not used; no uncertainty is propagated.\"},\n {\"issue\": \"arxiv-2510.03658v1: Table 3 prints '%% Change' = -0.3685 for column (4), whereas -0.0962/0.261 = %.5f (rounds to -0.3686); the text also attributes the 0.0855 estimate to 'table 2' and calls an 18.79%% relative change a 'percentage change' while interpreting it as lives saved per 100 deaths.\" % (b5 / num(mean[c4])),\n \"handling\": \"Not material for E5, whose target (percentage points) needs only the printed coefficient -0.0962; noted for transparency.\"},\n {\"issue\": \"arxiv-2606.14621v1: the ATE (1.47 pp) is block(week)-adjusted while the 75.0% control mean is the raw aggregate control-group mean; the abstract rounds the ATE to 1.5 pp.\",\n \"handling\": \"Used the printed control mean 75.0% as the counterfactual level p, as the target definition specifies (percent of the control-group mean); 1.47 (table) used rather than 1.5 (abstract).\"},\n]\n\norder = [\"E1\", \"E2\", \"E3\", \"E4\", \"E5\"]\nassert [e[\"id\"] for e in effects] == order\nharm = {k: H[k] for k in order}\nn_h = sum(1 for k in order if harm[k][\"value\"] is not None)\nn_nh = sum(1 for k in order if harm[k][\"harmonisable\"] is False)\nn_input_rows = {\"effects.json\": len(effects), \"README.md\": wc_l(os.path.join(D, \"README.md\"))}\nfor v in files.values():\n n_input_rows[v] = wc_l(os.path.join(SRC, v))\n\nres = {","is_error":false}]},"uuid":"e4a36603-cca0-4489-8d79-f0cba9c5a529","timestamp":"2026-10-30T05:12:46.695Z","toolUseResult":{"stdout":"\"\"\"Harmonise five printed effect sizes (F4-conf-03). Reads only data/, writes out/results.json.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, math, os, re, csv\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nD = os.path.join(ROOT, \"data\")\nSRC = os.path.join(D, \"sources\")\nOUT = os.path.join(ROOT, \"out\")\n\ndef read_lines(path):\n with open(path, encoding=\"utf-8\") as f:\n return f.read().split(\"\\n\")\n\ndef wc_l(path):\n with open(path, \"rb\") as f:\n return f.read().count(b\"\\n\")\n\ndef find_row(lines, pattern, fname):\n \"\"\"Return (line_number, cells) of the unique line matching regex pattern; print it.\"\"\"\n hits = [(i + 1, l) for i, l in enumerate(lines) if re.match(pattern, l)]\n assert len(hits) == 1, f\"pattern {pattern!r} matched {len(hits)} lines in {fname}\"\n n, l = hits[0]\n print(f\"[{fname}:{n}] {l}\")\n return n, [c.strip() for c in l.split(\"|\")]\n\ndef num(cell):\n \"\"\"Parse a printed number, stripping significance stars, %, 'pp' and thousands separators.\"\"\"\n m = re.match(r\"^\\s*([-+]?\\d[\\d,]*\\.?\\d*)\", cell.replace(\"*\", \"\"))\n assert m, cell\n return float(m.group(1).replace(\",\", \"\"))\n\neffects = json.load(open(os.path.join(D, \"effects.json\"), encoding=\"utf-8\"))\nfiles = {\n \"arxiv-2609.27732v1\": \"arxiv-2609.27732v1.txt\",\n \"arxiv-2606.14621v1\": \"arxiv-2606.14621v1.txt\",\n \"arxiv-2505.04555v3\": \"arxiv-2505.04555v3.txt\",\n \"arxiv-2510.03658v1\": \"arxiv-2510.03658v1.txt\",\n}\nL = {k: read_lines(os.path.join(SRC, v)) for k, v in files.items()}\nH = {}\n\n# ---- E1: DCR overall ATT, Table 6, 5 km -> relative % change ------------------------\ns = \"arxiv-2609.27732v1\"\n_, hdr = find_row(L[s], r\"^\\| 1 km \\| 3 km \\| 5 km$\", files[s])\nn_att, att = find_row(L[s], r\"^ATT \\| -0\\.399\", files[s])\nn_pct, pct = find_row(L[s], r\"^% change \\| -32\\.9%\", files[s])\ncol = hdr.index(\"5 km\") # header has a leading empty cell, same layout as data rows\nb1 = num(att[col])\nv1 = 100 * (math.exp(b1) - 1)\nH[\"E1\"] = dict(value=round(v1, 4), unit=\"percent (relative change vs. counterfactual hospitalisation rate)\",\n transformation=f\"100*(exp(b)-1) with b = {att[col].replace('*','')} (printed ATT, log points) -> 100*(exp({b1})-1) = {v1:.4f}%\",\n harmonisable=True, missing_quantity=None,\n locator=f\"arxiv-2609.27732v1 | Table 6 | row 'ATT' | column '5 km' (line {n_att}); cross-check: row '% change' | column '5 km' = {pct[col]} (line {n_pct})\")\nprint(f\"E1: b={b1} -> {v1:.4f}% (printed % change {pct[col]})\")\n\n# ---- E4: DCR fatal, Table 7, 5 km -> absolute change per 100,000 --------------------\n_, hdr7 = find_row(L[s], r\"^Outcome \\| 1 km \\| 3 km \\| 5 km$\", files[s])\nn_fat, fat = find_row(L[s], r\"^Drug-related \\(fatal\\) \\|\", files[s])\nb4 = num(fat[hdr7.index(\"5 km\")])\nrel4 = 100 * (math.exp(b4) - 1)\n# Search the extract for any printed baseline/mean level of the fatal rate.\nlvl = [l for l in L[s] if re.search(r\"(mean|baseline|pre-treatment level|average rate)\", l, re.I)\n and re.search(r\"fatal\", l, re.I) and re.search(r\"\\d+(\\.\\d+)? (per|hospitali[sz]ations per)\", l, re.I)]\nprint(f\"E4: b={b4}; relative change would be {rel4:.4f}%; lines printing a fatal-rate level: {len(lvl)}\")\nH[\"E4\"] = dict(value=None, unit=\"hospitalisations per 100,000 inhabitants per year\",\n transformation=(f\"absolute change = R0*(exp(b)-1) with b = {b4} (printed ATT, log points); \"\n f\"exp(b)-1 = {rel4/100:.4f} is computable, but R0 (counterfactual/baseline fatal rate) is not printed\"),\n harmonisable=False,\n missing_quantity=(\"baseline (counterfactual, e.g. pre-opening treated-region mean) level of the fatal drug-related \"\n \"hospitalisation rate per 100,000 inhabitants per year in the 5 km treated regions; not printed in the extract\"),\n locator=f\"arxiv-2609.27732v1 | Table 7 | row 'Drug-related (fatal)' | column '5 km' (line {n_fat})\")\n\n# ---- E2: RESEA reemployment ATE (pp) -> % of control mean ---------------------------\ns = \"arxiv-2606.14621v1\"\n_, hdr2 = find_row(L[s], r\"^Outcome \\| Treatment \\| Control \\| Treatment-control difference\", files[s])\nn_re, re_row = find_row(L[s], r\"^Reemployment \\| \\d+\\.\\d%\", files[s])\nd2 = num(re_row[hdr2.index(\"Average treatment effect estimate\")].split(\";\")[0])\np2 = num(re_row[hdr2.index(\"Control\")])\nv2 = 100 * d2 / p2\nH[\"E2\"] = dict(value=round(v2, 4), unit=\"percent (relative change vs. control-group mean reemployment rate)\",\n transformation=f\"100*Delta/p with Delta = {d2} pp (printed ATE) and p = {p2}% (printed control mean) -> 100*{d2}/{p2} = {v2:.4f}%\",\n harmonisable=True, missing_quantity=None,\n locator=(f\"arxiv-2606.14621v1 | Table 2 | row 'Reemployment' | column 'Average treatment effect estimate' (1.47 pp) \"\n f\"and column 'Control' (75.0%) (line {n_re})\"))\nprint(f\"E2: {d2}/{p2} -> {v2:.4f}%\")\n\n# ---- E3: MW employment elasticity * printed % MW change ----------------------------\ns = \"arxiv-2505.04555v3\"\nn_el, el = find_row(L[s], r\"^Employment elasticity w\\.r\\.t MW \\|\", files[s])\nn_g, g = find_row(L[s], r\"^% MW changes \\|\", files[s])\nn_note, _ = find_row(L[s], r\"^Note: The table reports the effects of a minimum wage increase\", files[s])\neta, gg = num(el[1]), num(g[1])\nv3 = 100 * eta * gg\nH[\"E3\"] = dict(value=round(v3, 4), unit=\"percent (relative change in employment)\",\n transformation=f\"100*eta*g with eta = {eta} (printed elasticity) and g = {gg} (printed '% MW changes', proportional) -> 100*{eta}*{gg} = {v3:.4f}%\",\n harmonisable=True, missing_quantity=None,\n locator=(f\"arxiv-2505.04555v3 | Table 2 | row 'Employment elasticity w.r.t MW' | column 'Estimate (SE)' (line {n_el}); \"\n f\"row '% MW changes' | column 'Estimate (SE)' (line {n_g})\"))\nprint(f\"E3: {eta}*{gg} -> {v3:.4f}%\")\n\n# ---- E5: LPM coefficient -> percentage points --------------------------------------\ns = \"arxiv-2510.03658v1\"\n_, hdr5 = find_row(L[s], r\"^\\| \\(1\\) \\| \\(2\\) \\| \\(3\\) \\| \\(4\\)$\", files[s])\nn_t, tr = find_row(L[s], r\"^Treatment \\| \", files[s])\nn_m, mean = find_row(L[s], r\"^Mean Dep\\. Var\\. \\|\", files[s])\nn_pc, pc = find_row(L[s], r\"^% Change \\|\", files[s])\nc4 = hdr5.index(\"(4)\")\nb5 = num(tr[c4])\nv5 = 100 * b5\nH[\"E5\"] = dict(value=round(v5, 4), unit=\"percentage points\",\n transformation=f\"100*beta with beta = {b5} (printed LPM coefficient, 0-1 scale) -> {v5:.4f} pp\",\n harmonisable=True, missing_quantity=None,\n locator=f\"arxiv-2510.03658v1 | Table 3 | row 'Treatment' | column '(4)' (line {n_t})\")\nprint(f\"E5: {b5} -> {v5:.4f} pp (check: beta/mean = {b5/num(mean[c4]):.5f}, printed % Change {pc[c4]})\")\n\n# ---- consistency diagnostics used for data_issues ----------------------------------\ns = \"arxiv-2609.27732v1\"\n_, a19 = find_row(L[s], r\"^ATT \\| -0\\.431\\*\\* \\| -0\\.438\", files[s])\n_, a19se = find_row(L[s], r\"^\\| \\(0\\.218\\)\", files[s])\n_, t6se = find_row(L[s], r\"^\\| \\(0\\.412\\)\", files[s])\n_, a19n = find_row(L[s], r\"^Note: All specifications use the 5 km buffer\", files[s])\nabs_line = [l for l in L[s] if \"which fall by 35%\" in l]\nmech_line = [l for l in L[s] if \"by approximately 30% at 5 km\" in l]\nprint(f\"check E4 text: abstract '35%' present={bool(abs_line)}, Sec 6.1 '30%' present={bool(mech_line)}, exp(-0.354)-1={rel4:.2f}%\")\n\ndata_issues = [\n {\"issue\": \"arxiv-2609.27732v1: the abstract says fatal drug-related hospitalisations 'fall by 35%%', but Table 7 (5 km) prints -0.354 log points, i.e. 100*(exp(-0.354)-1) = %.1f%%, matching Section 6.1 ('approximately 30%% at 5 km'); 35%% is the overall (Table 6) effect.\" % rel4,\n \"handling\": \"Table 7 cell used as the printed estimate for E4; abstract figure not used. E4 is not harmonisable for a separate reason (missing baseline level).\"},\n {\"issue\": \"arxiv-2609.27732v1: the same CS-DR 5 km ATT (-0.431) is printed with SE (0.210) in Table 6 but (0.218) in Appendix Table A19; A19 note says 640 never-treated regions while its table and Table 6 say 608.\",\n \"handling\": \"Only the point estimate (identical in both tables) is used; SEs and region counts are not needed and were not used.\"},\n {\"issue\": \"arxiv-2505.04555v3: Table 2 prints '% MW changes' = 0.047 (4.7%), while the table note describes the 2023 increases as 'about 4%'.\",\n \"handling\": \"Per the target definition, the table value 0.047 is used for E3; with 'about 4%%' the implied change would be 100*%s*0.04 = %.4f%% (sensitivity only, not the reported value).\" % (eta, 100 * eta * 0.04)},\n {\"issue\": \"arxiv-2505.04555v3: Table 2 prints the same SE (0.091) for 'Affected employment' and 'Emp. elasticity w.r.t affected wage' (possible duplication); point estimates are internally consistent (-0.268/0.366 = -0.732).\",\n \"handling\": \"Not used; no uncertainty is propagated.\"},\n {\"issue\": \"arxiv-2510.03658v1: Table 3 prints '%% Change' = -0.3685 for column (4), whereas -0.0962/0.261 = %.5f (rounds to -0.3686); the text also attributes the 0.0855 estimate to 'table 2' and calls an 18.79%% relative change a 'percentage change' while interpreting it as lives saved per 100 deaths.\" % (b5 / num(mean[c4])),\n \"handling\": \"Not material for E5, whose target (percentage points) needs only the printed coefficient -0.0962; noted for transparency.\"},\n {\"issue\": \"arxiv-2606.14621v1: the ATE (1.47 pp) is block(week)-adjusted while the 75.0% control mean is the raw aggregate control-group mean; the abstract rounds the ATE to 1.5 pp.\",\n \"handling\": \"Used the printed control mean 75.0% as the counterfactual level p, as the target definition specifies (percent of the control-group mean); 1.47 (table) used rather than 1.5 (abstract).\"},\n]\n\norder = [\"E1\", \"E2\", \"E3\", \"E4\", \"E5\"]\nassert [e[\"id\"] for e in effects] == order\nharm = {k: H[k] for k in order}\nn_h = sum(1 for k in order if harm[k][\"value\"] is not None)\nn_nh = sum(1 for k in order if harm[k][\"harmonisable\"] is False)\nn_input_rows = {\"effects.json\": len(effects), \"README.md\": wc_l(os.path.join(D, \"README.md\"))}\nfor v in files.values():\n n_input_rows[v] = wc_l(os.path.join(SRC, v))\n\nres = {","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"6ac0e4bd-2fcd-4631-8365-78413e0797d4","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0238","sessionId":"58628346-804e-4b01-8fcc-16f6b88a2208","version":"2.1.314","gitBranch":"HEAD"}33{"type":"api-request-blob","sessionId":"58628346-804e-4b01-8fcc-16f6b88a2208","hash":"dc778473033b1e964e4a9676b84a57a85e4aa0805afc11e5fadc615ef09a40da","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01JwK4hboCwZtCDWM7QJmPXN","name":"Bash","input":{"command":"mkdir -p /tmp/rc && cp -r data out /tmp/rc/ && cd /tmp/rc && ~/wp29/venv/bin/python out/analysis.py >/tmp/rc/log.txt 2>&1; echo $?; tail -30 log.txt; diff <(python3 -m json.tool /tmp/rc/out/results.json) <(python3 -m json.tool ~/wp29/runs/RC0238/out/results.json) && echo SAME; cat ~/wp29/runs/RC0238/out/exclusions.csv; wc -l ~/wp29/runs/RC0238/data/README.md"}},{"type":"tool_use","id":"toolu_01KNJxyDbSZVGgCtbf5VgdK7","name":"Bash","input":{"command":"cd ~/wp29/runs/RC0238; cat out/analysis.py | head -150"}}]}}34{"type":"api-request-blob","sessionId":"58628346-804e-4b01-8fcc-16f6b88a2208","hash":"ae9bc1b911283580d12c74a3f848c67cbe76bd25c09c5e7ed9391f44b7df316d","message":{"role":"user","content":[{"tool_use_id":"toolu_01JwK4hboCwZtCDWM7QJmPXN","type":"tool_result","content":"cp: data: No such file or directory\ncp: out: No such file or directory\n1\ntail: log.txt: No such file or directory\n2,25c2,10\n< \"task_id\": \"F3-conf-01\",\n< \"model_name\": \"adl_direct_emp\",\n< \"model_description\": \"Direct (per-horizon) OLS regression of gdp_yoy(t+h) on a constant, gdp_yoy(t-2) and emp_yoy(t-1), re-estimated at each origin on an expanding window of training rows (from 1998-03) whose target month is <= t-2, excluding rows touching 2020-03..2021-12.\",\n< \"n_origins\": 48,\n< \"first_origin\": \"2022-05-01\",\n< \"last_origin\": \"2026-04-01\",\n< \"last_target_date_available\": \"2026-02-01\",\n< \"last_predictor_date_available\": \"2026-03-01\",\n< \"rmse_naive_h1\": 0.813620914876824,\n< \"mae_naive_h1\": 0.6421395833333333,\n< \"rmse_model_h1\": 0.7429391714223801,\n< \"mae_model_h1\": 0.6039917578066155,\n< \"rmse_naive_h3\": 1.1399827160604965,\n< \"mae_naive_h3\": 0.8715583333333333,\n< \"rmse_model_h3\": 0.9023000239646665,\n< \"mae_model_h3\": 0.7534546734385598,\n< \"rel_improvement_rmse_h1\": 0.08687306602134803,\n< \"rel_improvement_rmse_h3\": 0.20849675065004825,\n< \"dm_test\": {\n< \"h1\": {\n< \"mean_loss_diff_model_minus_naive\": -0.11002038069122726,\n< \"dm_stat_hln\": -1.3266201174612682,\n< \"p_value_two_sided\": 0.191044366683287,\n< \"nw_lags\": 2\n---\n> \"task_id\": \"F4-conf-03\",\n> \"harmonised\": {\n> \"E1\": {\n> \"value\": -35.0141,\n> \"unit\": \"percent (relative change vs. counterfactual hospitalisation rate)\",\n> \"transformation\": \"100*(exp(b)-1) with b = -0.431 (printed ATT, log points) -> 100*(exp(-0.431)-1) = -35.0141%\",\n> \"harmonisable\": true,\n> \"missing_quantity\": null,\n> \"locator\": \"arxiv-2609.27732v1 | Table 6 | row 'ATT' | column '5 km' (line 36); cross-check: row '% change' | column '5 km' = -35.0% (line 40)\"\n27,31c12,42\n< \"h3\": {\n< \"mean_loss_diff_model_minus_naive\": -0.485415259670029,\n< \"dm_stat_hln\": -1.3657496075270925,\n< \"p_value_two_sided\": 0.178518085003236,\n< \"nw_lags\": 4\n---\n> \"E2\": {\n> \"value\": 1.96,\n> \"unit\": \"percent (relative change vs. control-group mean reemployment rate)\",\n> \"transformation\": \"100*Delta/p with Delta = 1.47 pp (printed ATE) and p = 75.0% (printed control mean) -> 100*1.47/75.0 = 1.9600%\",\n> \"harmonisable\": true,\n> \"missing_quantity\": null,\n> \"locator\": \"arxiv-2606.14621v1 | Table 2 | row 'Reemployment' | column 'Average treatment effect estimate' (1.47 pp) and column 'Control' (75.0%) (line 29)\"\n> },\n> \"E3\": {\n> \"value\": -1.8236,\n> \"unit\": \"percent (relative change in employment)\",\n> \"transformation\": \"100*eta*g with eta = -0.388 (printed elasticity) and g = 0.047 (printed '% MW changes', proportional) -> 100*-0.388*0.047 = -1.8236%\",\n> \"harmonisable\": true,\n> \"missing_quantity\": null,\n> \"locator\": \"arxiv-2505.04555v3 | Table 2 | row 'Employment elasticity w.r.t MW' | column 'Estimate (SE)' (line 50); row '% MW changes' | column 'Estimate (SE)' (line 60)\"\n> },\n> \"E4\": {\n> \"value\": null,\n> \"unit\": \"hospitalisations per 100,000 inhabitants per year\",\n> \"transformation\": \"absolute change = R0*(exp(b)-1) with b = -0.354 (printed ATT, log points); exp(b)-1 = -0.2981 is computable, but R0 (counterfactual/baseline fatal rate) is not printed\",\n> \"harmonisable\": false,\n> \"missing_quantity\": \"baseline (counterfactual, e.g. pre-opening treated-region mean) level of the fatal drug-related hospitalisation rate per 100,000 inhabitants per year in the 5 km treated regions; not printed in the extract\",\n> \"locator\": \"arxiv-2609.27732v1 | Table 7 | row 'Drug-related (fatal)' | column '5 km' (line 86)\"\n> },\n> \"E5\": {\n> \"value\": -9.62,\n> \"unit\": \"percentage points\",\n> \"transformation\": \"100*beta with beta = -0.0962 (printed LPM coefficient, 0-1 scale) -> -9.6200 pp\",\n> \"harmonisable\": true,\n> \"missing_quantity\": null,\n> \"locator\": \"arxiv-2510.03658v1 | Table 3 | row 'Treatment' | column '(4)' (line 49)\"\n34,40c45,46\n< \"n_training_last_origin_h3\": 306,\n< \"sensitivity_no_covid_exclusion\": {\n< \"rmse_h1\": 0.8040049585200156,\n< \"rel_improvement_rmse_h1\": 0.011818718251932037,\n< \"rmse_h3\": 0.7937029596656197,\n< \"rel_improvement_rmse_h3\": 0.3037587776694858\n< },\n---\n> \"n_harmonised\": 4,\n> \"n_not_harmonisable\": 1,\n45,46c51,52\n< \"issue\": \"README states gdp_yoy is empty for the first 12 months, but the file contains yoy values for 1998 (both series).\",\n< \"handling\": \"Used as given; yoy recomputed from levels agrees to rounding (<5e-5) where possible.\"\n---\n> \"issue\": \"arxiv-2609.27732v1: the abstract says fatal drug-related hospitalisations 'fall by 35%', but Table 7 (5 km) prints -0.354 log points, i.e. 100*(exp(-0.354)-1) = -29.8%, matching Section 6.1 ('approximately 30% at 5 km'); 35% is the overall (Table 6) effect.\",\n> \"handling\": \"Table 7 cell used as the printed estimate for E4; abstract figure not used. E4 is not harmonisable for a separate reason (missing baseline level).\"\n49,50c55,56\n< \"issue\": \"gdp columns missing for 2026-08 and 2026-09 (not yet published).\",\n< \"handling\": \"Not needed: latest target (2026-07) and latest GDP input (2026-02) are present.\"\n---\n> \"issue\": \"arxiv-2609.27732v1: the same CS-DR 5 km ATT (-0.431) is printed with SE (0.210) in Table 6 but (0.218) in Appendix Table A19; A19 note says 640 never-treated regions while its table and Table 6 say 608.\",\n> \"handling\": \"Only the point estimate (identical in both tables) is used; SEs and region counts are not needed and were not used.\"\n53,54c59,60\n< \"issue\": \"COVID-19 outliers (gdp_yoy from -16.2 to +18.5 in 2020-2021).\",\n< \"handling\": \"Model training rows touching 2020-03..2021-12 excluded; naive unaffected; see out/exclusions.csv.\"\n---\n> \"issue\": \"arxiv-2505.04555v3: Table 2 prints '% MW changes' = 0.047 (4.7%), while the table note describes the 2023 increases as 'about 4%'.\",\n> \"handling\": \"Per the target definition, the table value 0.047 is used for E3; with 'about 4%' the implied change would be 100*-0.388*0.04 = -1.5520% (sensitivity only, not the reported value).\"\n57,58c63,72\n< \"issue\": \"Current-vintage data (revised), not first releases.\",\n< \"handling\": \"Exercise simulates publication timing only; no real-time validity claimed.\"\n---\n> \"issue\": \"arxiv-2505.04555v3: Table 2 prints the same SE (0.091) for 'Affected employment' and 'Emp. elasticity w.r.t affected wage' (possible duplication); point estimates are internally consistent (-0.268/0.366 = -0.732).\",\n> \"handling\": \"Not used; no uncertainty is propagated.\"\n> },\n> {\n> \"issue\": \"arxiv-2510.03658v1: Table 3 prints '% Change' = -0.3685 for column (4), whereas -0.0962/0.261 = -0.36858 (rounds to -0.3686); the text also attributes the 0.0855 estimate to 'table 2' and calls an 18.79% relative change a 'percentage change' while interpreting it as lives saved per 100 deaths.\",\n> \"handling\": \"Not material for E5, whose target (percentage points) needs only the printed coefficient -0.0962; noted for transparency.\"\n> },\n> {\n> \"issue\": \"arxiv-2606.14621v1: the ATE (1.47 pp) is block(week)-adjusted while the 75.0% control mean is the raw aggregate control-group mean; the abstract rounds the ATE to 1.5 pp.\",\n> \"handling\": \"Used the printed control mean 75.0% as the counterfactual level p, as the target definition specifies (percent of the control-group mean); 1.47 (table) used rather than 1.5 (abstract).\"\n62c76,81\n< \"canada_gdp_employment.csv\": 345\n---\n> \"effects.json\": 5,\n> \"README.md\": 16,\n> \"arxiv-2609.27732v1.txt\": 134,\n> \"arxiv-2606.14621v1.txt\": 58,\n> \"arxiv-2505.04555v3.txt\": 66,\n> \"arxiv-2510.03658v1.txt\": 91\n64,65c83,92\n< \"n_analysed\": 310,\n< \"random_seed\": null\n---\n> \"n_analysed\": 5,\n> \"random_seed\": null,\n> \"baseline_naive\": {\n> \"E1_logpoints_times_100_pct\": -43.1,\n> \"E2_printed_pp\": 1.47,\n> \"E3_printed_elasticity\": -0.388,\n> \"E4_logpoints_times_100_pct\": -35.4,\n> \"E5_printed_coefficient\": -0.0962,\n> \"note\": \"Naive values = printed numbers read at face value (log points x100 treated as percent; no rescaling). Not harmonised estimates.\"\n> }\nstep,rule,n_before,n_after,n_removed,rationale\r\n1,keep all items listed in data/effects.json,5,5,0,No item excluded; E4 is analysed and reported as not harmonisable rather than removed.\r\n 16 /Users/simon-pierreboucher/wp29/runs/RC0238/data/README.md","is_error":false},{"tool_use_id":"toolu_01KNJxyDbSZVGgCtbf5VgdK7","type":"tool_result","content":"\"\"\"Harmonise five printed effect sizes (F4-conf-03). Reads only data/, writes out/results.json.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, math, os, re, csv\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nD = os.path.join(ROOT, \"data\")\nSRC = os.path.join(D, \"sources\")\nOUT = os.path.join(ROOT, \"out\")\n\ndef read_lines(path):\n with open(path, encoding=\"utf-8\") as f:\n return f.read().split(\"\\n\")\n\ndef wc_l(path):\n with open(path, \"rb\") as f:\n return f.read().count(b\"\\n\")\n\ndef find_row(lines, pattern, fname):\n \"\"\"Return (line_number, cells) of the unique line matching regex pattern; print it.\"\"\"\n hits = [(i + 1, l) for i, l in enumerate(lines) if re.match(pattern, l)]\n assert len(hits) == 1, f\"pattern {pattern!r} matched {len(hits)} lines in {fname}\"\n n, l = hits[0]\n print(f\"[{fname}:{n}] {l}\")\n return n, [c.strip() for c in l.split(\"|\")]\n\ndef num(cell):\n \"\"\"Parse a printed number, stripping significance stars, %, 'pp' and thousands separators.\"\"\"\n m = re.match(r\"^\\s*([-+]?\\d[\\d,]*\\.?\\d*)\", cell.replace(\"*\", \"\"))\n assert m, cell\n return float(m.group(1).replace(\",\", \"\"))\n\neffects = json.load(open(os.path.join(D, \"effects.json\"), encoding=\"utf-8\"))\nfiles = {\n \"arxiv-2609.27732v1\": \"arxiv-2609.27732v1.txt\",\n \"arxiv-2606.14621v1\": \"arxiv-2606.14621v1.txt\",\n \"arxiv-2505.04555v3\": \"arxiv-2505.04555v3.txt\",\n \"arxiv-2510.03658v1\": \"arxiv-2510.03658v1.txt\",\n}\nL = {k: read_lines(os.path.join(SRC, v)) for k, v in files.items()}\nH = {}\n\n# ---- E1: DCR overall ATT, Table 6, 5 km -> relative % change ------------------------\ns = \"arxiv-2609.27732v1\"\n_, hdr = find_row(L[s], r\"^\\| 1 km \\| 3 km \\| 5 km$\", files[s])\nn_att, att = find_row(L[s], r\"^ATT \\| -0\\.399\", files[s])\nn_pct, pct = find_row(L[s], r\"^% change \\| -32\\.9%\", files[s])\ncol = hdr.index(\"5 km\") # header has a leading empty cell, same layout as data rows\nb1 = num(att[col])\nv1 = 100 * (math.exp(b1) - 1)\nH[\"E1\"] = dict(value=round(v1, 4), unit=\"percent (relative change vs. counterfactual hospitalisation rate)\",\n transformation=f\"100*(exp(b)-1) with b = {att[col].replace('*','')} (printed ATT, log points) -> 100*(exp({b1})-1) = {v1:.4f}%\",\n harmonisable=True, missing_quantity=None,\n locator=f\"arxiv-2609.27732v1 | Table 6 | row 'ATT' | column '5 km' (line {n_att}); cross-check: row '% change' | column '5 km' = {pct[col]} (line {n_pct})\")\nprint(f\"E1: b={b1} -> {v1:.4f}% (printed % change {pct[col]})\")\n\n# ---- E4: DCR fatal, Table 7, 5 km -> absolute change per 100,000 --------------------\n_, hdr7 = find_row(L[s], r\"^Outcome \\| 1 km \\| 3 km \\| 5 km$\", files[s])\nn_fat, fat = find_row(L[s], r\"^Drug-related \\(fatal\\) \\|\", files[s])\nb4 = num(fat[hdr7.index(\"5 km\")])\nrel4 = 100 * (math.exp(b4) - 1)\n# Search the extract for any printed baseline/mean level of the fatal rate.\nlvl = [l for l in L[s] if re.search(r\"(mean|baseline|pre-treatment level|average rate)\", l, re.I)\n and re.search(r\"fatal\", l, re.I) and re.search(r\"\\d+(\\.\\d+)? (per|hospitali[sz]ations per)\", l, re.I)]\nprint(f\"E4: b={b4}; relative change would be {rel4:.4f}%; lines printing a fatal-rate level: {len(lvl)}\")\nH[\"E4\"] = dict(value=None, unit=\"hospitalisations per 100,000 inhabitants per year\",\n transformation=(f\"absolute change = R0*(exp(b)-1) with b = {b4} (printed ATT, log points); \"\n f\"exp(b)-1 = {rel4/100:.4f} is computable, but R0 (counterfactual/baseline fatal rate) is not printed\"),\n harmonisable=False,\n missing_quantity=(\"baseline (counterfactual, e.g. pre-opening treated-region mean) level of the fatal drug-related \"\n \"hospitalisation rate per 100,000 inhabitants per year in the 5 km treated regions; not printed in the extract\"),\n locator=f\"arxiv-2609.27732v1 | Table 7 | row 'Drug-related (fatal)' | column '5 km' (line {n_fat})\")\n\n# ---- E2: RESEA reemployment ATE (pp) -> % of control mean ---------------------------\ns = \"arxiv-2606.14621v1\"\n_, hdr2 = find_row(L[s], r\"^Outcome \\| Treatment \\| Control \\| Treatment-control difference\", files[s])\nn_re, re_row = find_row(L[s], r\"^Reemployment \\| \\d+\\.\\d%\", files[s])\nd2 = num(re_row[hdr2.index(\"Average treatment effect estimate\")].split(\";\")[0])\np2 = num(re_row[hdr2.index(\"Control\")])\nv2 = 100 * d2 / p2\nH[\"E2\"] = dict(value=round(v2, 4), unit=\"percent (relative change vs. control-group mean reemployment rate)\",\n transformation=f\"100*Delta/p with Delta = {d2} pp (printed ATE) and p = {p2}% (printed control mean) -> 100*{d2}/{p2} = {v2:.4f}%\",\n harmonisable=True, missing_quantity=None,\n locator=(f\"arxiv-2606.14621v1 | Table 2 | row 'Reemployment' | column 'Average treatment effect estimate' (1.47 pp) \"\n f\"and column 'Control' (75.0%) (line {n_re})\"))\nprint(f\"E2: {d2}/{p2} -> {v2:.4f}%\")\n\n# ---- E3: MW employment elasticity * printed % MW change ----------------------------\ns = \"arxiv-2505.04555v3\"\nn_el, el = find_row(L[s], r\"^Employment elasticity w\\.r\\.t MW \\|\", files[s])\nn_g, g = find_row(L[s], r\"^% MW changes \\|\", files[s])\nn_note, _ = find_row(L[s], r\"^Note: The table reports the effects of a minimum wage increase\", files[s])\neta, gg = num(el[1]), num(g[1])\nv3 = 100 * eta * gg\nH[\"E3\"] = dict(value=round(v3, 4), unit=\"percent (relative change in employment)\",\n transformation=f\"100*eta*g with eta = {eta} (printed elasticity) and g = {gg} (printed '% MW changes', proportional) -> 100*{eta}*{gg} = {v3:.4f}%\",\n harmonisable=True, missing_quantity=None,\n locator=(f\"arxiv-2505.04555v3 | Table 2 | row 'Employment elasticity w.r.t MW' | column 'Estimate (SE)' (line {n_el}); \"\n f\"row '% MW changes' | column 'Estimate (SE)' (line {n_g})\"))\nprint(f\"E3: {eta}*{gg} -> {v3:.4f}%\")\n\n# ---- E5: LPM coefficient -> percentage points --------------------------------------\ns = \"arxiv-2510.03658v1\"\n_, hdr5 = find_row(L[s], r\"^\\| \\(1\\) \\| \\(2\\) \\| \\(3\\) \\| \\(4\\)$\", files[s])\nn_t, tr = find_row(L[s], r\"^Treatment \\| \", files[s])\nn_m, mean = find_row(L[s], r\"^Mean Dep\\. Var\\. \\|\", files[s])\nn_pc, pc = find_row(L[s], r\"^% Change \\|\", files[s])\nc4 = hdr5.index(\"(4)\")\nb5 = num(tr[c4])\nv5 = 100 * b5\nH[\"E5\"] = dict(value=round(v5, 4), unit=\"percentage points\",\n transformation=f\"100*beta with beta = {b5} (printed LPM coefficient, 0-1 scale) -> {v5:.4f} pp\",\n harmonisable=True, missing_quantity=None,\n locator=f\"arxiv-2510.03658v1 | Table 3 | row 'Treatment' | column '(4)' (line {n_t})\")\nprint(f\"E5: {b5} -> {v5:.4f} pp (check: beta/mean = {b5/num(mean[c4]):.5f}, printed % Change {pc[c4]})\")\n\n# ---- consistency diagnostics used for data_issues ----------------------------------\ns = \"arxiv-2609.27732v1\"\n_, a19 = find_row(L[s], r\"^ATT \\| -0\\.431\\*\\* \\| -0\\.438\", files[s])\n_, a19se = find_row(L[s], r\"^\\| \\(0\\.218\\)\", files[s])\n_, t6se = find_row(L[s], r\"^\\| \\(0\\.412\\)\", files[s])\n_, a19n = find_row(L[s], r\"^Note: All specifications use the 5 km buffer\", files[s])\nabs_line = [l for l in L[s] if \"which fall by 35%\" in l]\nmech_line = [l for l in L[s] if \"by approximately 30% at 5 km\" in l]\nprint(f\"check E4 text: abstract '35%' present={bool(abs_line)}, Sec 6.1 '30%' present={bool(mech_line)}, exp(-0.354)-1={rel4:.2f}%\")\n\ndata_issues = [\n {\"issue\": \"arxiv-2609.27732v1: the abstract says fatal drug-related hospitalisations 'fall by 35%%', but Table 7 (5 km) prints -0.354 log points, i.e. 100*(exp(-0.354)-1) = %.1f%%, matching Section 6.1 ('approximately 30%% at 5 km'); 35%% is the overall (Table 6) effect.\" % rel4,\n \"handling\": \"Table 7 cell used as the printed estimate for E4; abstract figure not used. E4 is not harmonisable for a separate reason (missing baseline level).\"},\n {\"issue\": \"arxiv-2609.27732v1: the same CS-DR 5 km ATT (-0.431) is printed with SE (0.210) in Table 6 but (0.218) in Appendix Table A19; A19 note says 640 never-treated regions while its table and Table 6 say 608.\",\n \"handling\": \"Only the point estimate (identical in both tables) is used; SEs and region counts are not needed and were not used.\"},\n {\"issue\": \"arxiv-2505.04555v3: Table 2 prints '% MW changes' = 0.047 (4.7%), while the table note describes the 2023 increases as 'about 4%'.\",\n \"handling\": \"Per the target definition, the table value 0.047 is used for E3; with 'about 4%%' the implied change would be 100*%s*0.04 = %.4f%% (sensitivity only, not the reported value).\" % (eta, 100 * eta * 0.04)},\n {\"issue\": \"arxiv-2505.04555v3: Table 2 prints the same SE (0.091) for 'Affected employment' and 'Emp. elasticity w.r.t affected wage' (possible duplication); point estimates are internally consistent (-0.268/0.366 = -0.732).\",\n \"handling\": \"Not used; no uncertainty is propagated.\"},\n {\"issue\": \"arxiv-2510.03658v1: Table 3 prints '%% Change' = -0.3685 for column (4), whereas -0.0962/0.261 = %.5f (rounds to -0.3686); the text also attributes the 0.0855 estimate to 'table 2' and calls an 18.79%% relative change a 'percentage change' while interpreting it as lives saved per 100 deaths.\" % (b5 / num(mean[c4])),\n \"handling\": \"Not material for E5, whose target (percentage points) needs only the printed coefficient -0.0962; noted for transparency.\"},\n {\"issue\": \"arxiv-2606.14621v1: the ATE (1.47 pp) is block(week)-adjusted while the 75.0% control mean is the raw aggregate control-group mean; the abstract rounds the ATE to 1.5 pp.\",\n \"handling\": \"Used the printed control mean 75.0% as the counterfactual level p, as the target definition specifies (percent of the control-group mean); 1.47 (table) used rather than 1.5 (abstract).\"},\n]\n\norder = [\"E1\", \"E2\", \"E3\", \"E4\", \"E5\"]\nassert [e[\"id\"] for e in effects] == order\nharm = {k: H[k] for k in order}\nn_h = sum(1 for k in order if harm[k][\"value\"] is not None)\nn_nh = sum(1 for k in order if harm[k][\"harmonisable\"] is False)\nn_input_rows = {\"effects.json\": len(effects), \"README.md\": wc_l(os.path.join(D, \"README.md\"))}\nfor v in files.values():\n n_input_rows[v] = wc_l(os.path.join(SRC, v))\n\nres = {","is_error":false}]}}35{"type":"api-request","sessionId":"58628346-804e-4b01-8fcc-16f6b88a2208","id":"d2a0ffef-8deb-4d25-acbf-7766001c48b7","timestamp":"2026-10-30T05:12:46.698Z","version":"2.1.314","querySource":"sdk","shapeHash":"8a4b6aae8fb8a6cfc0e3843f66dbf0b13e9fdbfa3624ae3c51fee271aac70a6e","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"52adfecf-efd8-4d85-a610-d14522f14089","keep":6,"tail":["dc778473033b1e964e4a9676b84a57a85e4aa0805afc11e5fadc615ef09a40da","ae9bc1b911283580d12c74a3f848c67cbe76bd25c09c5e7ed9391f44b7df316d"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",1],"value":{"type":"ephemeral"}}],"requestDigest":"a582ebaa152cb4e3c62679ea4612bcbb73b2107adb1d922e6f38845a0ba019c4"}36{"type":"last-prompt","lastPrompt":"Review","leafUuid":"e4a36603-cca0-4489-8d79-f0cba9c5a529","sessionId":"58628346-804e-4b01-8fcc-16f6b88a2208"}37{"type":"atis-latch","atis":"","sessionId":"58628346-804e-4b01-8fcc-16f6b88a2208"}38{"parentUuid":"e4a36603-cca0-4489-8d79-f0cba9c5a529","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrC4cGHtoqNLkAP49qP","type":"message","role":"assistant","content":[{"type":"text","text":"Cp failed due to cwd; the diff compared against a stale /tmp file. Redo."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":9270,"cache_read_input_tokens":26951,"output_tokens":216,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":9270},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXrC4QrYhU7cDBB9J4Vr","requestRef":"d2a0ffef-8deb-4d25-acbf-7766001c48b7","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3e5cdc27-fe5a-4831-9e92-3b9eeac62e00","timestamp":"2026-10-30T05:12:49.054Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0238","sessionId":"58628346-804e-4b01-8fcc-16f6b88a2208","version":"2.1.314","gitBranch":"HEAD"}39{"parentUuid":"3e5cdc27-fe5a-4831-9e92-3b9eeac62e00","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrC4cGHtoqNLkAP49qP","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SURESJ3MbZcpXicqwy65vM","name":"Bash","input":{"command":"cd ~/wp29/runs/RC0238; 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the diff compared against a stale /tmp file. Redo."},{"type":"tool_use","id":"toolu_01SURESJ3MbZcpXicqwy65vM","name":"Bash","input":{"command":"cd ~/wp29/runs/RC0238; rm -rf /tmp/rc2; mkdir /tmp/rc2 && cp -r data out /tmp/rc2/ && cd /tmp/rc2 && ~/wp29/venv/bin/python out/analysis.py | tail -8; cmp out/results.json ~/wp29/runs/RC0238/out/results.json && echo 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~/wp29/runs/RC0238; cat > out/review/review.json <<'EOF'\n{\n \"verdict\": \"accept\",\n \"issues\": [\n {\n \"id\": \"R1\",\n \"severity\": \"minor\",\n \"location\": \"out/report.md (Results, E5)\",\n \"issue\": \"Double negative: 'lowers the probability ... by −9.62 percentage points' reads as an increase. The stored value (-9.62 pp) is correct.\",\n \"suggested_fix\": \"Write either 'lowers ... by 9.62 percentage points' or 'changes ... by −9.62 pp'.\"\n },\n {\n \"id\": \"R2\",\n \"severity\": \"minor\",\n \"location\": \"harmonised.E3 / report.md Method\",\n \"issue\": \"The Table 2 cell is labelled '% MW changes' but its value 0.047 is read as a proportion (4.7%), not as 0.047%. This is the reasonable reading, since it agrees with the note's 'about 4%', but the report does not explain why it chose it.\",\n \"suggested_fix\": \"Add one sentence explaining the proportional reading of the '% MW changes' cell and pointing to the note's 'about 4%' as corroboration.\"\n },\n {\n \"id\": \"R3\",\n \"severity\": \"minor\",\n \"location\": \"out/analysis.py\",\n \"issue\": \"The row-matching regexes hard-code printed values (e.g. '^ATT \\\\| -0\\\\.399', '^% change \\\\| -32\\\\.9%'). The script is deterministic and reproduces results.json byte-for-byte, but these patterns only work with this exact extract.\",\n \"suggested_fix\": \"Match on row labels together with table context rather than on the numbers themselves (optional).\"\n },\n {\n \"id\": \"R4\",\n \"severity\": \"minor\",\n \"location\": \"claims.json C2 / harmonised.E2\",\n \"issue\": \"E2 divides a block-adjusted ATE by the raw aggregate control mean. The caveat says so. The Uncertainty section still calls E2 'exact', which slightly overstates its precision as a counterfactual-relative effect.\",\n \"suggested_fix\": \"Call E2 exact arithmetically but approximate as a relative-to-counterfactual effect, because the control mean is unadjusted.\"\n }\n ],\n \"summary\": \"All required keys are present. I re-ran analysis.py in a scratch copy and it reproduces results.json byte-for-byte. 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Only minor wording and robustness points remain.\"\n}\nEOF\n~/wp29/venv/bin/python -m json.tool out/review/review.json >/dev/null && echo 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~/wp29/runs/RC0238; cat > out/review/review.json <<'EOF'\n{\n \"verdict\": \"accept\",\n \"issues\": [\n {\n \"id\": \"R1\",\n \"severity\": \"minor\",\n \"location\": \"out/report.md (Results, E5)\",\n \"issue\": \"Double negative: 'lowers the probability ... by −9.62 percentage points' reads as an increase. The stored value (-9.62 pp) is correct.\",\n \"suggested_fix\": \"Write either 'lowers ... by 9.62 percentage points' or 'changes ... by −9.62 pp'.\"\n },\n {\n \"id\": \"R2\",\n \"severity\": \"minor\",\n \"location\": \"harmonised.E3 / report.md Method\",\n \"issue\": \"The Table 2 cell is labelled '% MW changes' but its value 0.047 is read as a proportion (4.7%), not as 0.047%. This is the reasonable reading, since it agrees with the note's 'about 4%', but the report does not explain why it chose it.\",\n \"suggested_fix\": \"Add one sentence explaining the proportional reading of the '% MW changes' cell and pointing to the note's 'about 4%' as corroboration.\"\n },\n {\n \"id\": \"R3\",\n \"severity\": \"minor\",\n \"location\": \"out/analysis.py\",\n \"issue\": \"The row-matching regexes hard-code printed values (e.g. '^ATT \\\\| -0\\\\.399', '^% change \\\\| -32\\\\.9%'). The script is deterministic and reproduces results.json byte-for-byte, but these patterns only work with this exact extract.\",\n \"suggested_fix\": \"Match on row labels together with table context rather than on the numbers themselves (optional).\"\n },\n {\n \"id\": \"R4\",\n \"severity\": \"minor\",\n \"location\": \"claims.json C2 / harmonised.E2\",\n \"issue\": \"E2 divides a block-adjusted ATE by the raw aggregate control mean. The caveat says so. The Uncertainty section still calls E2 'exact', which slightly overstates its precision as a counterfactual-relative effect.\",\n \"suggested_fix\": \"Call E2 exact arithmetically but approximate as a relative-to-counterfactual effect, because the control mean is unadjusted.\"\n }\n ],\n \"summary\": \"All required keys are present. I re-ran analysis.py in a scratch copy and it reproduces results.json byte-for-byte. Each input value matches the cited extract line: E1 −0.431 (Table 6 l.36), E2 1.47 pp and 75.0% (Table 2 l.29), E3 −0.388 and 0.047 (Table 2 l.50/60), E4 −0.354 (Table 7 l.86), E5 −0.0962 (Table 3 l.49). The transformations follow the TASK conventions and the arithmetic is correct. E4 is correctly declared not harmonisable: the extract prints no fatal-rate level, and the missing quantity is named. No outside values are used. The counts (4 harmonised, 1 not), feasibility 'complete', and the line counts are consistent. The data_issues list is accurate and thorough. Only minor wording and robustness points remain.\"\n}\nEOF\n~/wp29/venv/bin/python -m json.tool out/review/review.json >/dev/null && echo ok"}}]}}47{"type":"api-request","sessionId":"58628346-804e-4b01-8fcc-16f6b88a2208","id":"0be36307-071b-4f33-80e1-b3986c9b52ee","timestamp":"2026-10-30T05:13:06.702Z","version":"2.1.314","querySource":"sdk","shapeHash":"8a4b6aae8fb8a6cfc0e3843f66dbf0b13e9fdbfa3624ae3c51fee271aac70a6e","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"34061e49-bef1-406a-ba14-fa1dfececfe2","keep":10,"tail":["703997fba88e38c8707ed41dc523d1e541c1897b590880726ee82010cde61b20","f0177575da3f11fe2d78016a9ae3044595a02b2794e5b6ba259daea878b33ff4"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"2a030a24f46f108c0ed06ae0735a6f8049d77ca701598e08188b7a0a2397c38d"}48{"parentUuid":"4afd8b38-850a-47ae-be96-3a5554239a2c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrDYCiu6tfqrd62fNKk","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1679,"cache_read_input_tokens":36834,"output_tokens":804,"output_tokens_details":{"thinking_tokens":7},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1679},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":6630,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXrDXvbhb3ywPZycwcs2","requestRef":"0be36307-071b-4f33-80e1-b3986c9b52ee","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"749978bd-c4fc-4afb-9130-383f6abd6ddc","timestamp":"2026-10-30T05:13:14.846Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0238","sessionId":"58628346-804e-4b01-8fcc-16f6b88a2208","version":"2.1.314","gitBranch":"HEAD"}49{"parentUuid":"749978bd-c4fc-4afb-9130-383f6abd6ddc","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrDYCiu6tfqrd62fNKk","type":"message","role":"assistant","content":[{"type":"text","text":"My verdict is **accept**: I found four minor issues and nothing major or critical. The review is saved in `out/review/review.json`.\n\nI re-ran `out/analysis.py` on a scratch copy and it reproduced `out/results.json` byte-for-byte. Every required key is there. Each input value matches the line it cites in the source extracts:\n\n| Item | Input(s) and result | Status |\n|---|---|---|\n| E1 | −0.431 → −35.0141%, matching the printed −35.0% | correct |\n| E2 | 1.47 pp ÷ 75.0% → 1.96% | correct |\n| E3 | −0.388 × 0.047 → −1.8236% | correct |\n| E4 | −0.354, but no baseline rate printed | correctly marked not harmonisable |\n| E5 | −0.0962 → −9.62 pp | correct |\n\nThe transformations follow the formulas set out in `TASK.md`. E4 is rightly declared impossible: the extract never prints a baseline fatal hospitalisation rate, the missing quantity is named, and no remembered value is used. The counts (4 harmonised, 1 not), the \"complete\" status and the line counts all agree with each other. The list of source problems is accurate, including:\n- the drug consumption room paper's abstract saying fatal cases fall 35% when its table implies about 30%;\n- the two different standard errors (0.210 vs 0.218) and control-region counts (608 vs 640);\n- the minimum-wage table's 0.047 versus its note's \"about 4%\".\n\nThe claims are supported and appropriately caveated.\n\nThe four minor points:\n1. **Wording (report, E5):** \"lowers … by −9.62 percentage points\" is a double negative that reads as an increase. The stored value is correct.\n2. **E3 interpretation not explained:** the table cell is labelled \"% MW changes\" but its value 0.047 is read as a proportion (4.7%). That reading is reasonable and agrees with the note's \"about 4%\", but the report doesn't say why it was chosen.\n3. **Script fragility:** the code finds table rows by matching the printed numbers themselves, so it only works with this exact extract. It is still deterministic and reproduces the results.\n4. **E2 called \"exact\":** it divides a week-adjusted effect by the raw control-group mean. The claims file notes this, but the Uncertainty section still calls E2 exact, which slightly overstates it."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1679,"cache_read_input_tokens":36834,"output_tokens":804,"output_tokens_details":{"thinking_tokens":7},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1679},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrDXvbhb3ywPZycwcs2","requestRef":"0be36307-071b-4f33-80e1-b3986c9b52ee","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a5a17155-c4b2-469a-8422-2b5b9bae669b","timestamp":"2026-10-30T05:13:15.396Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0238","sessionId":"58628346-804e-4b01-8fcc-16f6b88a2208","version":"2.1.314","gitBranch":"HEAD"}50{"type":"last-prompt","lastPrompt":"Review","leafUuid":"a5a17155-c4b2-469a-8422-2b5b9bae669b","sessionId":"58628346-804e-4b01-8fcc-16f6b88a2208"}51{"type":"cost-state","sessionId":"58628346-804e-4b01-8fcc-16f6b88a2208","totalCostUSD":0.2782364,"totalAPIDuration":39264,"totalAPIDurationWithoutRetries":39261,"totalToolDuration":342,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":39693,"startTime":1793337155710,"modelUsage":{"claude-opus-5-5":{"inputTokens":14,"outputTokens":3517,"thinkingTokens":724,"cacheReadInputTokens":113977,"cacheCreationInputTokens":37009,"cacheCreationEphemeral5mInputTokens":37009,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.2782364}},"hasUnknownModelCost":false}52