1{"type":"atis-latch","atis":"","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T05:31:59.627Z","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T05:31:59.633Z","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83"}4{"parentUuid":null,"isSidechain":false,"promptId":"4c914a9d-d1ee-4047-9a6a-5177956ad95d","type":"user","message":{"role":"user","content":"Begin"},"uuid":"e7a1fe17-e3ba-4793-afec-b620f6d01e71","timestamp":"2026-10-30T05:31:59.638Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","shapeHash":"0ad9c323c6bed47240766f704f485176c15249bb7334cf52ef2f3a161dc0bcd8","timestamp":"2026-10-30T05:31:59.650Z","version":"2.1.314","shape":{"system":[{"type":"text","text":"You are Claude Code, Anthropic's official CLI for Claude, running within the Claude Agent SDK."},{"type":"text","text":"CWD: /Users/simon-pierreboucher/wp29/runs/RA0005\nDate: 2026-10-30\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md`. Below is some general advice about doing careful empirical research; it adds nothing specific to this task and you are free to ignore it.\n\nCareful empirical work usually begins by reading the task statement slowly and more than once, because the most common errors in applied research come not from difficult mathematics but from a misunderstanding of what exactly is being asked, which quantity is of interest, and which inputs are legitimately available. It is worth pausing to think about what the quantity of interest actually means, under what conditions it is well defined, and what would make an estimate of it misleading. Many published results have been overturned not because the estimator was wrong but because the sample was not what the authors believed it to be, or because information that should not have been available at the time of a decision had quietly entered the analysis.\n\nData deserve suspicion. Files assembled from several sources often carry inconsistencies: duplicated records, mixed units, shifted dates, columns whose definitions changed mid-sample, or values that were revised after the fact. Good practice is to look at the data before modelling them: distributions, ranges, missing values, identifiers, and whether counts add up. When something looks odd, it is better to document it and decide explicitly how to handle it than to let a default silently decide. The same applies to transformations: scaling, differencing, winsorising and imputing all change what is being estimated, and each deserves a sentence of justification.\n\nSimple baselines are underrated. A naïve or unconditional estimate computed with exactly the same evaluation rule as the main method tells the reader how much the method actually adds and protects against the illusion of progress created by a complicated pipeline. If the elaborate method does not beat the simple one, that is a finding, not a failure.\n\nUncertainty is part of the result. Standard errors must respect the dependence structure of the data; a confidence interval that ignores clustering, serial correlation or overlapping targets is not conservative, it is wrong. Where the sampling distribution is unknown, resampling or simulation can help, but the assumptions behind them should be stated. A result that is not statistically distinguishable from zero should be reported as such, in plain words.\n\nReproducibility is a courtesy to the reader and a safeguard for the author. Keeping the analysis in a script that reads the raw inputs and regenerates every reported number makes it possible to check the work, to find mistakes, and to rerun the analysis when the inputs change. Fixing random seeds, recording software versions and keeping intermediate files out of the final results all reduce the chance that a number in the report cannot be traced back to a computation.\n\nFinally, honesty about limitations is what distinguishes research from advocacy. Every analysis has weaknesses: assumptions that may not hold, data that may not be representative, methods that trade one bias for another. Stating them clearly, together with what would be needed to resolve them, is more valuable than a confident conclusion that the evidence does not support. When a task cannot be completed as specified, saying so, and saying why, is the correct outcome.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. Never use words like \"complex\" or \"risk\" in the description - just describe what it does.\n\nSay what the command does in plain words: do not echo the command's text, its flags, or file paths - the user reads this description, often without seeing the command.\n\nFor simple commands (git, npm, standard CLI tools), keep it brief (5-10 words):\n- ls → \"List files in current directory\"\n- git status → \"Show working tree status\"\n- npm install → \"Install package dependencies\"\n\nFor commands that are harder to parse at a glance (piped commands, obscure flags, etc.), add enough context to clarify what it does:\n- find . -name \"*.tmp\" -exec rm {} \\; → \"Find and delete all .tmp files recursively\"\n- git reset --hard origin/main → \"Discard all local changes and match remote main\"\n- curl -s url | jq '.data[]' → \"Fetch JSON from URL and extract data array elements\"","type":"string"},"dangerouslyDisableSandbox":{"description":"Set this to true to dangerously override sandbox mode and run commands without sandboxing.","type":"boolean"}},"required":["command"],"additionalProperties":false}},{"name":"Edit","description":"modify file contents in place","input_schema":{"$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. Only provide if the file is too large to read at once","type":"integer","minimum":0,"maximum":9007199254740991},"limit":{"description":"The number of lines to read. Only provide if the file is too large to read at once.","type":"integer","exclusiveMinimum":0,"maximum":9007199254740991},"pages":{"description":"Page range for PDF files (e.g., \"1-5\", \"3\", \"10-20\"). Only applicable to PDF files. Maximum 20 pages per request.","type":"string"},"allow_large":{"description":"Set to true to read a text file, or a line range of one, that is over the usual size limits, up to what still fits in your context. Only use this when you genuinely need all of it or the user asked for the whole file; otherwise read it in parts with offset and limit, or search it.","type":"boolean"}},"required":["file_path"],"additionalProperties":false}}]}}6{"type":"api-request-blob","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","hash":"d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9","message":{"role":"system","content":[],"output_config":{"effort":"medium"}}}7{"type":"api-request","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","id":"0ed685a3-fb8f-43f1-8771-eb3c55a8ef7a","timestamp":"2026-10-30T05:31:59.650Z","version":"2.1.314","querySource":"sdk","shapeHash":"0ad9c323c6bed47240766f704f485176c15249bb7334cf52ef2f3a161dc0bcd8","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"}},"keep":0,"tail":["252097215a6af3e04abe3f3c5a1cce73f5f388825c554cef2907d381aaf4102e","d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",0,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"093c98f85ddc278a354971108aa0120b2a3bf1aa1491b7ce12ebf8a94dd85fa2"}8{"parentUuid":"e7a1fe17-e3ba-4793-afec-b620f6d01e71","isSidechain":false,"attachment":{"type":"session_context","context":{}},"type":"attachment","uuid":"ac8df9d7-b4b8-4911-9db0-ff6c4d06b3ce","timestamp":"2026-10-30T05:31:59.642Z","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}9{"parentUuid":"ac8df9d7-b4b8-4911-9db0-ff6c4d06b3ce","isSidechain":false,"attachment":{"type":"date","date":"2026-10-30"},"type":"attachment","uuid":"f102a232-224d-4d17-88fa-48fd63bcd612","timestamp":"2026-10-30T05:31:59.643Z","rendered":[{"content":"<system-reminder>\nToday's date is 2026-10-30.\n</system-reminder>"}],"renderedRole":"system","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}10{"parentUuid":"f102a232-224d-4d17-88fa-48fd63bcd612","isSidechain":false,"attachment":{"type":"remote_session_change","url":null,"commit":"Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>","pr":"🤖 Generated with [Claude Code](https://claude.com/claude-code)","sendUserFileHint":false,"managedCommit":false,"managedPr":false},"type":"attachment","uuid":"7fa77379-08b7-4190-b358-6ae0e9de0df0","timestamp":"2026-10-30T05:31:59.643Z","rendered":[{"content":"<system-reminder>\nAttribution for git commits and pull requests you create from here on (this replaces Claude Code's own earlier attribution guidance, such as a previous copy of this reminder; 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/RA0005","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83"}12{"parentUuid":"7fa77379-08b7-4190-b358-6ae0e9de0df0","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsf4N14LXek6YDbrpTA","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01BhLj56nj1vVgtwTqz6eL6C","name":"Bash","input":{"command":"cat TASK.md; ls -R data | head -50"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":1682,"cache_read_input_tokens":1504,"output_tokens":63,"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":1682},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsf45ssdoSZ33Z2jJcW","requestRef":"0ed685a3-fb8f-43f1-8771-eb3c55a8ef7a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c4fef3e6-18fd-4bb0-b033-d8c5e3fa5667","timestamp":"2026-10-30T05:32:01.152Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"c4fef3e6-18fd-4bb0-b033-d8c5e3fa5667","isSidechain":false,"promptId":"4c914a9d-d1ee-4047-9a6a-5177956ad95d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BhLj56nj1vVgtwTqz6eL6C","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.\neffects.json\nREADME.md\nsources\n\ndata/sources:\narxiv-2505.04555v3.txt\narxiv-2510.03658v1.txt\narxiv-2606.14621v1.txt\narxiv-2609.27732v1.txt","is_error":false}]},"uuid":"4844061d-03a0-426b-b824-ef99a689d153","timestamp":"2026-10-30T05:32:01.261Z","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.\neffects.json\nREADME.md\nsources\n\ndata/sources:\narxiv-2505.04555v3.txt\narxiv-2510.03658v1.txt\narxiv-2606.14621v1.txt\narxiv-2609.27732v1.txt","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"c4fef3e6-18fd-4bb0-b033-d8c5e3fa5667","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","hash":"c9e2ffa2c24e1ba29f9053097dc5dfff8f41d1cb67e281f7d7f83bc9a2cc65d7","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01BhLj56nj1vVgtwTqz6eL6C","name":"Bash","input":{"command":"cat TASK.md; ls -R data | head -50"}}]}}15{"type":"api-request","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","id":"017caa40-9f13-435e-8198-c35c8e6bfec6","timestamp":"2026-10-30T05:32:01.266Z","version":"2.1.314","querySource":"sdk","shapeHash":"0ad9c323c6bed47240766f704f485176c15249bb7334cf52ef2f3a161dc0bcd8","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":"0ed685a3-fb8f-43f1-8771-eb3c55a8ef7a","keep":2,"tail":["c9e2ffa2c24e1ba29f9053097dc5dfff8f41d1cb67e281f7d7f83bc9a2cc65d7","d2604da4284854911759ece15fea310b6a720b1abb30b04e5a603eefbbb50137"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"edb61c47845776037a720f0bd96a0633d1b29f268f87dec2e09d6a5f3385801c"}16{"parentUuid":"4844061d-03a0-426b-b824-ef99a689d153","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsfAxdRYdbqA4d86doS","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0123RMwjPm9PQiNvfPr8j3uK","name":"Bash","input":{"command":"cd data; cat effects.json README.md; wc -l sources/* README.md effects.json"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2454,"cache_read_input_tokens":3186,"output_tokens":144,"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":2454},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsfAhVpCrL96omk9Aog","requestRef":"017caa40-9f13-435e-8198-c35c8e6bfec6","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5cfddc63-b02d-428d-b22b-236562a80722","timestamp":"2026-10-30T05:32:03.017Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005/data","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}17{"parentUuid":"5cfddc63-b02d-428d-b22b-236562a80722","isSidechain":false,"promptId":"4c914a9d-d1ee-4047-9a6a-5177956ad95d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0123RMwjPm9PQiNvfPr8j3uK","type":"tool_result","content":"[\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# Data provenance — F4-conf-03\n\nAll files under `data/sources/` are plain-text extracts of publicly accessible documents, prepared by the task designer on 2026-10-30 (UTC). Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables, the line following a coefficient row holds the standard errors in parentheses as printed in the original.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/arxiv-2609.27732v1.txt` | Armendariz, A. (2026). Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland. arXiv:2609.27732v1 [econ.GN]. https://arxiv.org/abs/2609.27732v1 (full text: https://arxiv.org/html/2609.27732v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b |\n| `sources/arxiv-2606.14621v1.txt` | Li, H. H., Pearson-Merkowitz, S., & Yokum, D. (2026). Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes. arXiv:2606.14621v1 [econ.GN]. https://arxiv.org/abs/2606.14621v1 (full text: https://arxiv.org/html/2606.14621v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 |\n| `sources/arxiv-2505.04555v3.txt` | Kanayama, H., Miyaji, S., & Otani, S. (2025/2026). Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation. arXiv:2505.04555v3 [econ.GN], v1 of 7 May 2025, v3 of 16 Mar 2026. https://arxiv.org/abs/2505.04555v3 (full text: https://arxiv.org/html/2505.04555v3) | 2026-10-30T03:23:18Z | CC BY 4.0 | b1731a9b1c40ae91c6be2cf2e92623bb6cf86c551587936a0270356f76ae95c1 |\n| `sources/arxiv-2510.03658v1.txt` | Tossou, S. C. (2025). Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin. arXiv:2510.03658v1 [econ.GN]. https://arxiv.org/abs/2510.03658v1 (full text: https://arxiv.org/html/2510.03658v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | f8782937eeaebd2dae0e653bbfc6aeb266c51587d09a4207bcaa3fa48a881127 |\n\nTask-specific input files (prepared by the task designer, not observed data):\n\n- `effects.json` — the five effects to harmonise (id, source id, description and location of the effect, reported definition, target definition), written by the task designer\n\nRights: arXiv e-prints are reproduced in part under the Creative Commons Attribution 4.0 licence shown on their arXiv abstract pages. No numbers were altered; where a table was re-set for legibility (RESEA Tables 2-3, London Table 3 header) the extract header says so.\n 66 sources/arxiv-2505.04555v3.txt\n 91 sources/arxiv-2510.03658v1.txt\n 58 sources/arxiv-2606.14621v1.txt\n 134 sources/arxiv-2609.27732v1.txt\n 16 README.md\n 37 effects.json\n 402 total","is_error":false}]},"uuid":"c7ac97b7-2a91-4c13-bcd9-87a244d1bafe","timestamp":"2026-10-30T05:32:03.061Z","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# Data provenance — F4-conf-03\n\nAll files under `data/sources/` are plain-text extracts of publicly accessible documents, prepared by the task designer on 2026-10-30 (UTC). Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables, the line following a coefficient row holds the standard errors in parentheses as printed in the original.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/arxiv-2609.27732v1.txt` | Armendariz, A. (2026). Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland. arXiv:2609.27732v1 [econ.GN]. https://arxiv.org/abs/2609.27732v1 (full text: https://arxiv.org/html/2609.27732v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b |\n| `sources/arxiv-2606.14621v1.txt` | Li, H. H., Pearson-Merkowitz, S., & Yokum, D. (2026). Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes. arXiv:2606.14621v1 [econ.GN]. https://arxiv.org/abs/2606.14621v1 (full text: https://arxiv.org/html/2606.14621v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 |\n| `sources/arxiv-2505.04555v3.txt` | Kanayama, H., Miyaji, S., & Otani, S. (2025/2026). Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation. arXiv:2505.04555v3 [econ.GN], v1 of 7 May 2025, v3 of 16 Mar 2026. https://arxiv.org/abs/2505.04555v3 (full text: https://arxiv.org/html/2505.04555v3) | 2026-10-30T03:23:18Z | CC BY 4.0 | b1731a9b1c40ae91c6be2cf2e92623bb6cf86c551587936a0270356f76ae95c1 |\n| `sources/arxiv-2510.03658v1.txt` | Tossou, S. C. (2025). Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin. arXiv:2510.03658v1 [econ.GN]. https://arxiv.org/abs/2510.03658v1 (full text: https://arxiv.org/html/2510.03658v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | f8782937eeaebd2dae0e653bbfc6aeb266c51587d09a4207bcaa3fa48a881127 |\n\nTask-specific input files (prepared by the task designer, not observed data):\n\n- `effects.json` — the five effects to harmonise (id, source id, description and location of the effect, reported definition, target definition), written by the task designer\n\nRights: arXiv e-prints are reproduced in part under the Creative Commons Attribution 4.0 licence shown on their arXiv abstract pages. No numbers were altered; where a table was re-set for legibility (RESEA Tables 2-3, London Table 3 header) the extract header says so.\n 66 sources/arxiv-2505.04555v3.txt\n 91 sources/arxiv-2510.03658v1.txt\n 58 sources/arxiv-2606.14621v1.txt\n 134 sources/arxiv-2609.27732v1.txt\n 16 README.md\n 37 effects.json\n 402 total","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"5cfddc63-b02d-428d-b22b-236562a80722","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005/data","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}18{"parentUuid":"c7ac97b7-2a91-4c13-bcd9-87a244d1bafe","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsfAxdRYdbqA4d86doS","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01CoAX7N2v2wsYDW4Hw6jmVA","name":"Bash","input":{"command":"cd data/sources; cat -n arxiv-2609.27732v1.txt"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2454,"cache_read_input_tokens":3186,"output_tokens":144,"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":2454},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsfAhVpCrL96omk9Aog","requestRef":"017caa40-9f13-435e-8198-c35c8e6bfec6","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4bb67133-df6c-4119-b2a1-930c30912406","timestamp":"2026-10-30T05:32:03.448Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005/data","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}19{"parentUuid":"4bb67133-df6c-4119-b2a1-930c30912406","isSidechain":false,"promptId":"4c914a9d-d1ee-4047-9a6a-5177956ad95d","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\n(eval):cd:1: no such file or directory: data/sources\ncat: arxiv-2609.27732v1.txt: No such file or directory","is_error":true,"tool_use_id":"toolu_01CoAX7N2v2wsYDW4Hw6jmVA"}]},"uuid":"4a8870c7-8f75-4e19-9d95-77df8c33363e","timestamp":"2026-10-30T05:32:03.490Z","toolUseResult":"Error: Exit code 1\n(eval):cd:1: no such file or directory: data/sources\ncat: arxiv-2609.27732v1.txt: No such file or directory","sandboxed":false,"sourceToolAssistantUUID":"4bb67133-df6c-4119-b2a1-930c30912406","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005/data","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}20{"type":"api-request-blob","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","hash":"65a20592e2a1f63521e376eb686b24dfbfa1e8bd88c2eead6b39eceab220c1e9","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_0123RMwjPm9PQiNvfPr8j3uK","name":"Bash","input":{"command":"cd data; cat effects.json README.md; wc -l sources/* README.md effects.json"}},{"type":"tool_use","id":"toolu_01CoAX7N2v2wsYDW4Hw6jmVA","name":"Bash","input":{"command":"cd data/sources; cat -n arxiv-2609.27732v1.txt"}}]}}21{"type":"api-request-blob","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","hash":"7963718abc8c34863c8b82f5ef6fefa0707bbd5e77006463d423913d1c961e84","message":{"role":"user","content":[{"tool_use_id":"toolu_0123RMwjPm9PQiNvfPr8j3uK","type":"tool_result","content":"[\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# Data provenance — F4-conf-03\n\nAll files under `data/sources/` are plain-text extracts of publicly accessible documents, prepared by the task designer on 2026-10-30 (UTC). Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables, the line following a coefficient row holds the standard errors in parentheses as printed in the original.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/arxiv-2609.27732v1.txt` | Armendariz, A. (2026). Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland. arXiv:2609.27732v1 [econ.GN]. https://arxiv.org/abs/2609.27732v1 (full text: https://arxiv.org/html/2609.27732v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b |\n| `sources/arxiv-2606.14621v1.txt` | Li, H. H., Pearson-Merkowitz, S., & Yokum, D. (2026). Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes. arXiv:2606.14621v1 [econ.GN]. https://arxiv.org/abs/2606.14621v1 (full text: https://arxiv.org/html/2606.14621v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 |\n| `sources/arxiv-2505.04555v3.txt` | Kanayama, H., Miyaji, S., & Otani, S. (2025/2026). Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation. arXiv:2505.04555v3 [econ.GN], v1 of 7 May 2025, v3 of 16 Mar 2026. https://arxiv.org/abs/2505.04555v3 (full text: https://arxiv.org/html/2505.04555v3) | 2026-10-30T03:23:18Z | CC BY 4.0 | b1731a9b1c40ae91c6be2cf2e92623bb6cf86c551587936a0270356f76ae95c1 |\n| `sources/arxiv-2510.03658v1.txt` | Tossou, S. C. (2025). Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin. arXiv:2510.03658v1 [econ.GN]. https://arxiv.org/abs/2510.03658v1 (full text: https://arxiv.org/html/2510.03658v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | f8782937eeaebd2dae0e653bbfc6aeb266c51587d09a4207bcaa3fa48a881127 |\n\nTask-specific input files (prepared by the task designer, not observed data):\n\n- `effects.json` — the five effects to harmonise (id, source id, description and location of the effect, reported definition, target definition), written by the task designer\n\nRights: arXiv e-prints are reproduced in part under the Creative Commons Attribution 4.0 licence shown on their arXiv abstract pages. No numbers were altered; where a table was re-set for legibility (RESEA Tables 2-3, London Table 3 header) the extract header says so.\n 66 sources/arxiv-2505.04555v3.txt\n 91 sources/arxiv-2510.03658v1.txt\n 58 sources/arxiv-2606.14621v1.txt\n 134 sources/arxiv-2609.27732v1.txt\n 16 README.md\n 37 effects.json\n 402 total","is_error":false},{"type":"tool_result","content":"Exit code 1\n(eval):cd:1: no such file or directory: data/sources\ncat: arxiv-2609.27732v1.txt: No such file or directory","is_error":true,"tool_use_id":"toolu_01CoAX7N2v2wsYDW4Hw6jmVA"}]}}22{"type":"api-request","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","id":"e5f2d15a-8580-4104-ac9e-5ba27b11c7b6","timestamp":"2026-10-30T05:32:03.493Z","version":"2.1.314","querySource":"sdk","shapeHash":"0ad9c323c6bed47240766f704f485176c15249bb7334cf52ef2f3a161dc0bcd8","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":"017caa40-9f13-435e-8198-c35c8e6bfec6","keep":4,"tail":["65a20592e2a1f63521e376eb686b24dfbfa1e8bd88c2eead6b39eceab220c1e9","7963718abc8c34863c8b82f5ef6fefa0707bbd5e77006463d423913d1c961e84"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",1],"value":{"type":"ephemeral"}}],"requestDigest":"7b1a7d1830921dc82c004361767bf6eff8951c02c4df80dcfa66e326d028d6d7"}23{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"4a8870c7-8f75-4e19-9d95-77df8c33363e","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83"}24{"type":"atis-latch","atis":"","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83"}25{"parentUuid":"4a8870c7-8f75-4e19-9d95-77df8c33363e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsfLVMKezLgtEtEqbjN","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SzrQCYDQ2TCsvm3VNkw1DU","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RA0005/data/sources; cat -n arxiv-2609.27732v1.txt"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2600,"cache_read_input_tokens":5640,"output_tokens":98,"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":2600},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsfLDk24FGk3m59d4Vo","requestRef":"e5f2d15a-8580-4104-ac9e-5ba27b11c7b6","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1c2cab74-ae38-40d6-9c32-6a7397680571","timestamp":"2026-10-30T05:32:05.210Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005/data","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}26{"parentUuid":"1c2cab74-ae38-40d6-9c32-6a7397680571","isSidechain":false,"promptId":"4c914a9d-d1ee-4047-9a6a-5177956ad95d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SzrQCYDQ2TCsvm3VNkw1DU","type":"tool_result","content":" 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":"b3c3c456-07af-40eb-b818-4aa382a7f2ff","timestamp":"2026-10-30T05:32:05.250Z","toolUseResult":{"stdout":" 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":"1c2cab74-ae38-40d6-9c32-6a7397680571","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005/data/sources","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}27{"type":"api-request-blob","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","hash":"1810e1651c113f1db8d5058dfdcfef1c3685c46605a35a408530274ff7334157","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01SzrQCYDQ2TCsvm3VNkw1DU","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RA0005/data/sources; cat -n arxiv-2609.27732v1.txt"}}]}}28{"type":"api-request","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","id":"1c4319de-174a-4928-a38b-902e3e7ed783","timestamp":"2026-10-30T05:32:05.253Z","version":"2.1.314","querySource":"sdk","shapeHash":"0ad9c323c6bed47240766f704f485176c15249bb7334cf52ef2f3a161dc0bcd8","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":"e5f2d15a-8580-4104-ac9e-5ba27b11c7b6","keep":6,"tail":["1810e1651c113f1db8d5058dfdcfef1c3685c46605a35a408530274ff7334157","44ff22af670950002649753720915b8f085d098c8c1dbb57c1d0ae33c6b9b9f6"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"0ff62c24093312e986d0475a30f6c3259ff317ff5f6878d57452df2ea2892246"}29{"parentUuid":"b3c3c456-07af-40eb-b818-4aa382a7f2ff","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsfU1Z3u8fj2Djrwev1","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5158,"cache_read_input_tokens":8240,"output_tokens":274,"output_tokens_details":{"thinking_tokens":160},"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":5158},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1201,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsfTjvVR12EoEGj5C5R","requestRef":"1c4319de-174a-4928-a38b-902e3e7ed783","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b6aac9a7-eb25-4afb-85e7-de45a4e85b05","timestamp":"2026-10-30T05:32:07.952Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005/data/sources","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}30{"parentUuid":"b6aac9a7-eb25-4afb-85e7-de45a4e85b05","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsfU1Z3u8fj2Djrwev1","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_016z36m53qF63m4wNnwtFV2Y","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RA0005/data/sources; cat -n arxiv-2606.14621v1.txt arxiv-2505.04555v3.txt"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5158,"cache_read_input_tokens":8240,"output_tokens":274,"output_tokens_details":{"thinking_tokens":160},"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":5158},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsfTjvVR12EoEGj5C5R","requestRef":"1c4319de-174a-4928-a38b-902e3e7ed783","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5be1208e-1a7a-4824-9e2c-e7704b19cddc","timestamp":"2026-10-30T05:32:08.619Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005/data/sources","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}31{"parentUuid":"5be1208e-1a7a-4824-9e2c-e7704b19cddc","isSidechain":false,"promptId":"4c914a9d-d1ee-4047-9a6a-5177956ad95d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016z36m53qF63m4wNnwtFV2Y","type":"tool_result","content":" 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).","is_error":false}]},"uuid":"12257fd4-a0e3-4b6f-9ea2-716e372cb799","timestamp":"2026-10-30T05:32:08.658Z","toolUseResult":{"stdout":" 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).","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"5be1208e-1a7a-4824-9e2c-e7704b19cddc","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005/data/sources","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}32{"type":"api-request-blob","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","hash":"f2fd83482be2fb7cb8304409c433d3d8876dbfa1133ff637df119586180d9c96","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_016z36m53qF63m4wNnwtFV2Y","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RA0005/data/sources; cat -n arxiv-2606.14621v1.txt arxiv-2505.04555v3.txt"}}]}}33{"type":"api-request","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","id":"afaa8d6f-82f2-4b2e-b453-dec662aaad18","timestamp":"2026-10-30T05:32:08.661Z","version":"2.1.314","querySource":"sdk","shapeHash":"0ad9c323c6bed47240766f704f485176c15249bb7334cf52ef2f3a161dc0bcd8","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":"1c4319de-174a-4928-a38b-902e3e7ed783","keep":8,"tail":["f2fd83482be2fb7cb8304409c433d3d8876dbfa1133ff637df119586180d9c96","f5efa959e6db57bcbf228a7cc5caccc5f6e3b5b735b391d9f9ac84485b699dbb"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"2880ae98c3a728ef5f021e37a172770e005567239761e01304297e241bcaf484"}34{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"12257fd4-a0e3-4b6f-9ea2-716e372cb799","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83"}35{"type":"atis-latch","atis":"","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83"}36{"parentUuid":"12257fd4-a0e3-4b6f-9ea2-716e372cb799","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsfiZty1J8CYGP9oRdw","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":7350,"cache_read_input_tokens":13398,"output_tokens":492,"output_tokens_details":{"thinking_tokens":393},"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":7350},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":3396,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsfiKGSCg5pDb6YzWVn","requestRef":"afaa8d6f-82f2-4b2e-b453-dec662aaad18","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"08fffa82-6b76-4a17-8879-93fbf0ecc34e","timestamp":"2026-10-30T05:32:13.526Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005/data/sources","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}37{"parentUuid":"08fffa82-6b76-4a17-8879-93fbf0ecc34e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsfiZty1J8CYGP9oRdw","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014qbJJPDXTDZ4yFgZ8Yw9Tb","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RA0005/data/sources; 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":7350,"cache_read_input_tokens":13398,"output_tokens":492,"output_tokens_details":{"thinking_tokens":393},"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":7350},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsfiKGSCg5pDb6YzWVn","requestRef":"afaa8d6f-82f2-4b2e-b453-dec662aaad18","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2b98f103-3dbc-46a2-aa23-f75513a5517a","timestamp":"2026-10-30T05:32:14.047Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005/data/sources","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}38{"parentUuid":"2b98f103-3dbc-46a2-aa23-f75513a5517a","isSidechain":false,"promptId":"4c914a9d-d1ee-4047-9a6a-5177956ad95d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014qbJJPDXTDZ4yFgZ8Yw9Tb","type":"tool_result","content":" 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":"6a54faff-9ff9-4846-a2dd-989d2feb0f86","timestamp":"2026-10-30T05:32:14.086Z","toolUseResult":{"stdout":" 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 | | | |","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"2b98f103-3dbc-46a2-aa23-f75513a5517a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005/data/sources","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}39{"type":"api-request-blob","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","hash":"58a284cd50487ca614c037aa6a3fcda950bfa29fa0e1d2d096b41b460dff1d85","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_014qbJJPDXTDZ4yFgZ8Yw9Tb","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RA0005/data/sources; cat -n arxiv-2510.03658v1.txt"}}]}}40{"type":"api-request","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","id":"916e2f1b-4462-4a52-9669-512c2b5031e5","timestamp":"2026-10-30T05:32:14.089Z","version":"2.1.314","querySource":"sdk","shapeHash":"0ad9c323c6bed47240766f704f485176c15249bb7334cf52ef2f3a161dc0bcd8","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":"afaa8d6f-82f2-4b2e-b453-dec662aaad18","keep":10,"tail":["58a284cd50487ca614c037aa6a3fcda950bfa29fa0e1d2d096b41b460dff1d85","a5ef6686b3d55997f613c5fe52ddc2f8cb6627699b49e83b79d827230d927fe3"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"3129752251c1c191d2d8d7170ccd57459ff683187f6ba814d2b7746c95b22360"}41{"parentUuid":"6a54faff-9ff9-4846-a2dd-989d2feb0f86","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsg7p91Nw5puxxSts8a","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3797,"cache_read_input_tokens":20748,"output_tokens":5912,"output_tokens_details":{"thinking_tokens":850},"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":3797},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":8033,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsg7X2W6WZoSfS2tfq3","requestRef":"916e2f1b-4462-4a52-9669-512c2b5031e5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"415b7f8b-d70a-4b31-9199-ec189103e773","timestamp":"2026-10-30T05:32:23.564Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005/data/sources","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}42{"parentUuid":"415b7f8b-d70a-4b31-9199-ec189103e773","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsg7p91Nw5puxxSts8a","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_016hKZ7oCE9cmA8GGCvhbkk4","name":"Bash","input":{"command":"mkdir -p /Users/simon-pierreboucher/wp29/runs/RA0005/out; cat > /Users/simon-pierreboucher/wp29/runs/RA0005/out/analysis.py <<'EOF'\n\"\"\"F4-conf-03: harmonise five effect sizes from printed quantities only.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads data/ only; prints every source line relied upon; writes out/results.json.\"\"\"\nimport json, math, os, re\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nD = os.path.join(ROOT, \"data\")\nSRC = os.path.join(D, \"sources\")\n\n\ndef lines(fn):\n with open(os.path.join(SRC, fn), encoding=\"utf-8\") as f:\n return f.read().split(\"\\n\")\n\n\ndef find(fn, pattern, after=None):\n \"\"\"Return (line_no, text) of the first line matching regex `pattern` (after line `after`).\"\"\"\n L = lines(fn)\n for i, t in enumerate(L, 1):\n if after is not None and i <= after:\n continue\n if re.search(pattern, t):\n print(f\"[{fn}:{i}] {t.strip()}\")\n return i, t\n raise ValueError(f\"pattern {pattern!r} not found in {fn}\")\n\n\ndef cells(t):\n return [c.strip() for c in t.split(\"|\")]\n\n\ndef num(s):\n return float(re.sub(r\"[^0-9.\\-]\", \"\", s.replace(\"−\", \"-\")))\n\n\nH = {}\n\n# ---- E1: Armendariz Table 6, ATT row, 5 km column (log points -> percent)\nf1 = \"arxiv-2609.27732v1.txt\"\ni6, _ = find(f1, r\"^Table 6:\")\nih, th = find(f1, r\"^\\| 1 km \\| 3 km \\| 5 km\", after=i6)\nia, ta = find(f1, r\"^ATT \\|\", after=i6)\nip, tp = find(f1, r\"^% change \\|\", after=i6)\ncol5 = cells(th).index(\"5 km\") # header has a leading empty cell, same as row label position\nb1_str = cells(ta)[col5]\nb1 = num(b1_str)\npct_printed = cells(tp)[col5]\nv1 = 100 * (math.exp(b1) - 1)\nH[\"E1\"] = {\n \"value\": round(v1, 2),\n \"unit\": \"percent change in the drug-related hospitalisation rate relative to the counterfactual rate\",\n \"transformation\": f\"100*(exp(b)-1) with b = {b1_str.rstrip('*')} log points -> 100*(exp(-0.431)-1) = {v1:.2f}% (printed '% change' row: {pct_printed})\",\n \"harmonisable\": True,\n \"missing_quantity\": None,\n \"locator\": f\"arxiv-2609.27732v1 | Table 6 | row 'ATT' | column '5 km' (extract line {ia}); cross-check: row '% change', column '5 km' (line {ip})\",\n}\n\n# ---- E2: RESEA Table 2, Reemployment row (pp -> percent of control mean)\nf2 = \"arxiv-2606.14621v1.txt\"\nih2, th2 = find(f2, r\"^Outcome \\| Treatment \\| Control\")\nir2, tr2 = find(f2, r\"^Reemployment \\|\", after=ih2)\nhdr = cells(th2); row = cells(tr2)\nctrl_str = row[hdr.index(\"Control\")]\nate_cell = row[hdr.index(\"Average treatment effect estimate\")]\nate_str = ate_cell.split(\";\")[0].strip()\np0 = num(ctrl_str); d2 = num(ate_str)\nv2 = 100 * d2 / p0\nH[\"E2\"] = {\n \"value\": round(v2, 2),\n \"unit\": \"percent change in the reemployment probability relative to the control-group mean\",\n \"transformation\": f\"100*Delta/p with Delta = {ate_str} and p = {ctrl_str} (control mean) -> 100*1.47/75.0 = {v2:.2f}%\",\n \"harmonisable\": True,\n \"missing_quantity\": None,\n \"locator\": f\"arxiv-2606.14621v1 | Table 2 | row 'Reemployment' | columns 'Average treatment effect estimate' (Delta) and 'Control' (p) (extract line {ir2})\",\n}\n\n# ---- E3: Kanayama Table 2 elasticity times printed % MW change\nf3 = \"arxiv-2505.04555v3.txt\"\ni2, _ = find(f3, r\"^Table 2:\")\nie, te = find(f3, r\"^Employment elasticity w\\.r\\.t MW \\|\", after=i2)\nig, tg = find(f3, r\"^% MW changes \\|\", after=i2)\nfind(f3, r\"^Note: The table reports the effects of a minimum wage increase\", after=i2)\neta_str = cells(te)[1]; g_str = cells(tg)[1]\neta = num(eta_str); g = num(g_str)\nv3 = 100 * eta * g\nH[\"E3\"] = {\n \"value\": round(v3, 2),\n \"unit\": \"percent change in employment implied for the observed 2023 minimum-wage increase\",\n \"transformation\": f\"100*eta*g with eta = {eta_str} and g = {g_str} (proportional MW change printed in Table 2) -> 100*(-0.388)*0.047 = {v3:.4f}%\",\n \"harmonisable\": True,\n \"missing_quantity\": None,\n \"locator\": f\"arxiv-2505.04555v3 | Table 2 | row 'Employment elasticity w.r.t MW' | column 'Estimate (SE)' (line {ie}); row '% MW changes' | column 'Estimate (SE)' (line {ig})\",\n}\n\n# ---- E4: Armendariz Table 7 fatal, 5 km: absolute change needs a level\ni7, _ = find(f1, r\"^Table 7:\")\nih7, th7 = find(f1, r\"^Outcome \\| 1 km \\| 3 km \\| 5 km\", after=i7)\nif7, tf7 = find(f1, r\"^Drug-related \\(fatal\\) \\|\", after=i7)\nb4_str = cells(tf7)[cells(th7).index(\"5 km\")]\nb4 = num(b4_str)\nrel4 = 100 * (math.exp(b4) - 1)\nprint(f\"E4 intermediate (relative change only, not the target): 100*(exp({b4})-1) = {rel4:.2f}%\")\n# Check whether any baseline/mean level of the fatal rate is printed in the extract\nlvl = [t for t in lines(f1) if re.search(r\"(?i)(mean|baseline|pre-treatment|average).*(rate|per 100,000)\", t) and re.search(r\"\\d+\\.\\d+\\s*(per|hospitali)\", t)]\nprint(f\"E4: lines printing a level of the fatal hospitalisation rate: {len(lvl)}\")\nH[\"E4\"] = {\n \"value\": None,\n \"unit\": \"hospitalisations per 100,000 inhabitants per year\",\n \"transformation\": f\"Would require R0*(exp(b)-1) with b = {b4_str.rstrip('*')} (relative change {rel4:.2f}%); R0 is not printed in the extract\",\n \"harmonisable\": False,\n \"missing_quantity\": \"the counterfactual (or pre-opening baseline) level of the fatal drug-related hospitalisation rate per 100,000 inhabitants per year for the treated regions within 5 km (e.g. a pre-treatment mean of the outcome in levels); the extract prints only log-point ATTs and no outcome level\",\n \"locator\": f\"arxiv-2609.27732v1 | Table 7 | row 'Drug-related (fatal)' | column '5 km' (extract line {if7})\",\n}\n\n# ---- E5: Tossou Table 3, Treatment row, column (4): 0-1 -> pp\nf5 = \"arxiv-2510.03658v1.txt\"\ni3, _ = find(f5, r\"^Table 3:\")\nih5, th5 = find(f5, r\"^\\| \\(1\\) \\| \\(2\\) \\| \\(3\\) \\| \\(4\\)\", after=i3)\nit5, tt5 = find(f5, r\"^Treatment \\|\", after=i3)\nim5, tm5 = find(f5, r\"^Mean Dep\\. Var\\. \\|\", after=i3)\nc4 = cells(th5).index(\"(4)\")\nb5_str = cells(tt5)[c4]\nb5 = num(b5_str)\nv5 = 100 * b5\nprint(f\"E5 cross-check: relative change = b/mean = {b5}/{num(cells(tm5)[c4])} = {b5/num(cells(tm5)[c4]):.4f}\")\nH[\"E5\"] = {\n \"value\": round(v5, 2),\n \"unit\": \"percentage points (change in the probability of infant death or stillbirth)\",\n \"transformation\": f\"100*beta with beta = {b5_str} (0-1 scale) -> {v5:.2f} pp (exact rescaling)\",\n \"harmonisable\": True,\n \"missing_quantity\": None,\n \"locator\": f\"arxiv-2510.03658v1 | Table 3 | row 'Treatment' | column '(4)' (extract line {it5})\",\n}\n\n# ---- data issues (detected by inspection; key checks recomputed here)\niA19, tA19 = find(f1, r\"^\\| \\(0\\.218\\)\")\nfind(f1, r\"never-treated comparison regions \\(640 regions\\)\")\nfind(f1, r\"fatal outcomes, which fall by 35%\")\nfind(f1, r\"by approximately 30% at 5 km\")\nfind(f3, r\"about 4% minimum wage increase\")\nfind(f2, r\"increased reemployment by 1\\.5 percentage points\")\nfind(f5, r\"by 0\\.0855 \\(a 18\\.79 percentage change\\)\")\nissues = [\n {\"issue\": \"Armendariz: the same CS-DR 5 km ATT (-0.431) has standard error (0.210) in Table 6 but (0.218) in Table A19.\",\n \"handling\": \"Point estimate identical, so E1 is unaffected; no uncertainty propagated. Recorded only.\"},\n {\"issue\": \"Armendariz: Tables 6/7 and the Figure 6 note state 608 never-treated control regions; the Table A19 note states 640.\",\n \"handling\": \"Does not enter any transformation; recorded only.\"},\n {\"issue\": f\"Armendariz: abstract says fatal hospitalisations 'fall by 35%', but the Table 7 5 km estimate -0.354 implies 100*(exp(-0.354)-1) = {rel4:.1f}%, matching Section 6.1 ('approximately 30% at 5 km'). The abstract figure appears to repeat the overall effect.\",\n \"handling\": \"Used the Table 7 coefficient as instructed; E4 target (absolute change) is in any case not reachable.\"},\n {\"issue\": \"Armendariz: Figure 7 note mentions 'four diagnostic sub-categories' and the Table 7 note lists ICD codes for four, while five outcomes (incl. fatal) are reported; the definition of the fatal outcome is not given in the extract.\",\n \"handling\": \"Recorded; does not affect the harmonised values.\"},\n {\"issue\": \"Kanayama: Table 2 note describes the 2023 increase as 'about 4%' while the table row '% MW changes' prints 0.047.\",\n \"handling\": \"Used the printed table value 0.047 as the task requires (target definition points to the table). With 0.04 the implied change would be about -1.55%; the 0.047-based -1.82% is consistent with missing+excess jobs (-0.03+0.012 = -0.018) and with the abstract's ~2% decline.\"},\n {\"issue\": \"RESEA: abstract rounds the reemployment ATE to 1.5 pp; Table 2 prints 1.47 pp. The 'Control' column is the raw aggregate control mean, not a block-adjusted counterfactual (the paper notes raw means are confounded by randomisation week).\",\n \"handling\": \"Used 1.47 pp and the printed control mean 75.0% as the target definition specifies (percent of control-group mean).\"},\n {\"issue\": \"Tossou: abstract/Section 4.1 headline (0.0855, 18.79 percentage change, mean 0.455) refers to a 'table 2' that is not in the extract and calls a relative change a 'percentage change'; Table 3 '% Change' is a relative change (b/mean, -0.0962/0.261 = -0.3686 vs printed -0.3685, rounding), not percentage points. Footnote markers are garbled by rendering ('^9^9 9').\",\n \"handling\": \"E5 uses Table 3 column (4) only; the target is percentage points (100*beta), distinct from the printed relative change.\"},\n {\"issue\": \"RESEA extract: rendering defect 'p ¡ 0.001' (for p < 0.001) in Section 3 text.\",\n \"handling\": \"Irrelevant to the harmonised values; recorded.\"},\n]\n\nn_rows = {}\nfor fn in sorted(os.listdir(SRC)):\n with open(os.path.join(SRC, fn), encoding=\"utf-8\") as f:\n n_rows[\"sources/\" + fn] = f.read().count(\"\\n\")\nwith open(os.path.join(D, \"README.md\"), encoding=\"utf-8\") as f:\n n_rows[\"README.md\"] = f.read().count(\"\\n\")\nwith open(os.path.join(D, \"effects.json\"), encoding=\"utf-8\") as f:\n eff = json.load(f)\nn_rows[\"effects.json\"] = len(eff)\nassert [e[\"id\"] for e in eff] == [\"E1\", \"E2\", \"E3\", \"E4\", \"E5\"]\n\nres = {\n \"task_id\": \"F4-conf-03\",\n \"harmonised\": H,\n \"n_harmonised\": sum(v[\"value\"] is not None for v in H.values()),\n \"n_not_harmonisable\": sum(not v[\"harmonisable\"] for v in H.values()),\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": n_rows,\n \"n_analysed\": len(H),\n \"random_seed\": None,\n}\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\", encoding=\"utf-8\") as f:\n json.dump(res, f, indent=2, ensure_ascii=False)\nprint(json.dumps({k: H[k][\"value\"] for k in H}))\nEOF\ncd /Users/simon-pierreboucher/wp29/runs/RA0005 && ~/wp29/venv/bin/python out/analysis.py"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3797,"cache_read_input_tokens":20748,"output_tokens":5912,"output_tokens_details":{"thinking_tokens":850},"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":3797},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsg7X2W6WZoSfS2tfq3","requestRef":"916e2f1b-4462-4a52-9669-512c2b5031e5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2b93a67c-c691-41b4-a512-c5c5d88bcc17","timestamp":"2026-10-30T05:33:01.579Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005/data/sources","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}43{"parentUuid":"2b93a67c-c691-41b4-a512-c5c5d88bcc17","isSidechain":false,"promptId":"4c914a9d-d1ee-4047-9a6a-5177956ad95d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016hKZ7oCE9cmA8GGCvhbkk4","type":"tool_result","content":"[arxiv-2609.27732v1.txt:32] Table 6: Overall Average Treatment Effect on the Treated (ATT)\n[arxiv-2609.27732v1.txt:34] | 1 km | 3 km | 5 km\n[arxiv-2609.27732v1.txt:36] ATT | -0.399 | -0.283 | -0.431**\n[arxiv-2609.27732v1.txt:40] % change | -32.9% | -24.7% | -35.0%\n[arxiv-2606.14621v1.txt:27] Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n[arxiv-2606.14621v1.txt:29] Reemployment | 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[arxiv-2505.04555v3.txt:30] Table 2: Impact of Minimum Wages on Employment and Wages\n[arxiv-2505.04555v3.txt:50] Employment elasticity w.r.t MW | -0.388\n[arxiv-2505.04555v3.txt:60] % MW changes | 0.047\n[arxiv-2505.04555v3.txt:62] Note: 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[arxiv-2609.27732v1.txt:66] Table 7: Overall ATT by Outcome Category and Buffer Zone\n[arxiv-2609.27732v1.txt:68] Outcome | 1 km | 3 km | 5 km\n[arxiv-2609.27732v1.txt:86] Drug-related (fatal) | -1.207*** | -0.290 | -0.354**\nE4 intermediate (relative change only, not the target): 100*(exp(-0.354)-1) = -29.81%\nE4: lines printing a level of the fatal hospitalisation rate: 0\n[arxiv-2510.03658v1.txt:43] Table 3: Probability of infant death and stillbirth\n[arxiv-2510.03658v1.txt:47] | (1) | (2) | (3) | (4)\n[arxiv-2510.03658v1.txt:49] Treatment | -0.0911 | -0.0908 | -0.0961 | -0.0962\n[arxiv-2510.03658v1.txt:87] Mean Dep. Var. | 0.261 | 0.261 | 0.261 | 0.261\nE5 cross-check: relative change = b/mean = -0.0962/0.261 = -0.3686\n[arxiv-2609.27732v1.txt:123] | (0.218) | (0.208) | (0.205) | (0.038)\n[arxiv-2609.27732v1.txt:134] never-treated comparison regions (640 regions), and a \\pm 5-year window\n[arxiv-2609.27732v1.txt:18] This 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[arxiv-2609.27732v1.txt:109] The 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[arxiv-2505.04555v3.txt:62] Note: 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[arxiv-2606.14621v1.txt:19] Prolonged 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[arxiv-2510.03658v1.txt:18] This 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{\"E1\": -35.01, \"E2\": 1.96, \"E3\": -1.82, \"E4\": null, \"E5\": -9.62}","is_error":false}]},"uuid":"c73dc16b-f22d-4438-9df9-988bcffb69d5","timestamp":"2026-10-30T05:33:01.653Z","toolUseResult":{"stdout":"[arxiv-2609.27732v1.txt:32] Table 6: Overall Average Treatment Effect on the Treated (ATT)\n[arxiv-2609.27732v1.txt:34] | 1 km | 3 km | 5 km\n[arxiv-2609.27732v1.txt:36] ATT | -0.399 | -0.283 | -0.431**\n[arxiv-2609.27732v1.txt:40] % change | -32.9% | -24.7% | -35.0%\n[arxiv-2606.14621v1.txt:27] Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n[arxiv-2606.14621v1.txt:29] Reemployment | 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[arxiv-2505.04555v3.txt:30] Table 2: Impact of Minimum Wages on Employment and Wages\n[arxiv-2505.04555v3.txt:50] Employment elasticity w.r.t MW | -0.388\n[arxiv-2505.04555v3.txt:60] % MW changes | 0.047\n[arxiv-2505.04555v3.txt:62] Note: 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[arxiv-2609.27732v1.txt:66] Table 7: Overall ATT by Outcome Category and Buffer Zone\n[arxiv-2609.27732v1.txt:68] Outcome | 1 km | 3 km | 5 km\n[arxiv-2609.27732v1.txt:86] Drug-related (fatal) | -1.207*** | -0.290 | -0.354**\nE4 intermediate (relative change only, not the target): 100*(exp(-0.354)-1) = -29.81%\nE4: lines printing a level of the fatal hospitalisation rate: 0\n[arxiv-2510.03658v1.txt:43] Table 3: Probability of infant death and stillbirth\n[arxiv-2510.03658v1.txt:47] | (1) | (2) | (3) | (4)\n[arxiv-2510.03658v1.txt:49] Treatment | -0.0911 | -0.0908 | -0.0961 | -0.0962\n[arxiv-2510.03658v1.txt:87] Mean Dep. Var. | 0.261 | 0.261 | 0.261 | 0.261\nE5 cross-check: relative change = b/mean = -0.0962/0.261 = -0.3686\n[arxiv-2609.27732v1.txt:123] | (0.218) | (0.208) | (0.205) | (0.038)\n[arxiv-2609.27732v1.txt:134] never-treated comparison regions (640 regions), and a \\pm 5-year window\n[arxiv-2609.27732v1.txt:18] This 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[arxiv-2609.27732v1.txt:109] The 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[arxiv-2505.04555v3.txt:62] Note: 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[arxiv-2606.14621v1.txt:19] Prolonged 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[arxiv-2510.03658v1.txt:18] This 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{\"E1\": -35.01, \"E2\": 1.96, \"E3\": -1.82, \"E4\": null, \"E5\": -9.62}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":true},"sandboxed":false,"sourceToolAssistantUUID":"2b93a67c-c691-41b4-a512-c5c5d88bcc17","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}44{"type":"api-request-blob","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","hash":"70eb3b62201590f88d9115e1f8f5d2be4f691ab142d2561ae75979d6f62ba83c","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_016hKZ7oCE9cmA8GGCvhbkk4","name":"Bash","input":{"command":"mkdir -p /Users/simon-pierreboucher/wp29/runs/RA0005/out; cat > /Users/simon-pierreboucher/wp29/runs/RA0005/out/analysis.py <<'EOF'\n\"\"\"F4-conf-03: harmonise five effect sizes from printed quantities only.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads data/ only; prints every source line relied upon; writes out/results.json.\"\"\"\nimport json, math, os, re\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nD = os.path.join(ROOT, \"data\")\nSRC = os.path.join(D, \"sources\")\n\n\ndef lines(fn):\n with open(os.path.join(SRC, fn), encoding=\"utf-8\") as f:\n return f.read().split(\"\\n\")\n\n\ndef find(fn, pattern, after=None):\n \"\"\"Return (line_no, text) of the first line matching regex `pattern` (after line `after`).\"\"\"\n L = lines(fn)\n for i, t in enumerate(L, 1):\n if after is not None and i <= after:\n continue\n if re.search(pattern, t):\n print(f\"[{fn}:{i}] {t.strip()}\")\n return i, t\n raise ValueError(f\"pattern {pattern!r} not found in {fn}\")\n\n\ndef cells(t):\n return [c.strip() for c in t.split(\"|\")]\n\n\ndef num(s):\n return float(re.sub(r\"[^0-9.\\-]\", \"\", s.replace(\"−\", \"-\")))\n\n\nH = {}\n\n# ---- E1: Armendariz Table 6, ATT row, 5 km column (log points -> percent)\nf1 = \"arxiv-2609.27732v1.txt\"\ni6, _ = find(f1, r\"^Table 6:\")\nih, th = find(f1, r\"^\\| 1 km \\| 3 km \\| 5 km\", after=i6)\nia, ta = find(f1, r\"^ATT \\|\", after=i6)\nip, tp = find(f1, r\"^% change \\|\", after=i6)\ncol5 = cells(th).index(\"5 km\") # header has a leading empty cell, same as row label position\nb1_str = cells(ta)[col5]\nb1 = num(b1_str)\npct_printed = cells(tp)[col5]\nv1 = 100 * (math.exp(b1) - 1)\nH[\"E1\"] = {\n \"value\": round(v1, 2),\n \"unit\": \"percent change in the drug-related hospitalisation rate relative to the counterfactual rate\",\n \"transformation\": f\"100*(exp(b)-1) with b = {b1_str.rstrip('*')} log points -> 100*(exp(-0.431)-1) = {v1:.2f}% (printed '% change' row: {pct_printed})\",\n \"harmonisable\": True,\n \"missing_quantity\": None,\n \"locator\": f\"arxiv-2609.27732v1 | Table 6 | row 'ATT' | column '5 km' (extract line {ia}); cross-check: row '% change', column '5 km' (line {ip})\",\n}\n\n# ---- E2: RESEA Table 2, Reemployment row (pp -> percent of control mean)\nf2 = \"arxiv-2606.14621v1.txt\"\nih2, th2 = find(f2, r\"^Outcome \\| Treatment \\| Control\")\nir2, tr2 = find(f2, r\"^Reemployment \\|\", after=ih2)\nhdr = cells(th2); row = cells(tr2)\nctrl_str = row[hdr.index(\"Control\")]\nate_cell = row[hdr.index(\"Average treatment effect estimate\")]\nate_str = ate_cell.split(\";\")[0].strip()\np0 = num(ctrl_str); d2 = num(ate_str)\nv2 = 100 * d2 / p0\nH[\"E2\"] = {\n \"value\": round(v2, 2),\n \"unit\": \"percent change in the reemployment probability relative to the control-group mean\",\n \"transformation\": f\"100*Delta/p with Delta = {ate_str} and p = {ctrl_str} (control mean) -> 100*1.47/75.0 = {v2:.2f}%\",\n \"harmonisable\": True,\n \"missing_quantity\": None,\n \"locator\": f\"arxiv-2606.14621v1 | Table 2 | row 'Reemployment' | columns 'Average treatment effect estimate' (Delta) and 'Control' (p) (extract line {ir2})\",\n}\n\n# ---- E3: Kanayama Table 2 elasticity times printed % MW change\nf3 = \"arxiv-2505.04555v3.txt\"\ni2, _ = find(f3, r\"^Table 2:\")\nie, te = find(f3, r\"^Employment elasticity w\\.r\\.t MW \\|\", after=i2)\nig, tg = find(f3, r\"^% MW changes \\|\", after=i2)\nfind(f3, r\"^Note: The table reports the effects of a minimum wage increase\", after=i2)\neta_str = cells(te)[1]; g_str = cells(tg)[1]\neta = num(eta_str); g = num(g_str)\nv3 = 100 * eta * g\nH[\"E3\"] = {\n \"value\": round(v3, 2),\n \"unit\": \"percent change in employment implied for the observed 2023 minimum-wage increase\",\n \"transformation\": f\"100*eta*g with eta = {eta_str} and g = {g_str} (proportional MW change printed in Table 2) -> 100*(-0.388)*0.047 = {v3:.4f}%\",\n \"harmonisable\": True,\n \"missing_quantity\": None,\n \"locator\": f\"arxiv-2505.04555v3 | Table 2 | row 'Employment elasticity w.r.t MW' | column 'Estimate (SE)' (line {ie}); row '% MW changes' | column 'Estimate (SE)' (line {ig})\",\n}\n\n# ---- E4: Armendariz Table 7 fatal, 5 km: absolute change needs a level\ni7, _ = find(f1, r\"^Table 7:\")\nih7, th7 = find(f1, r\"^Outcome \\| 1 km \\| 3 km \\| 5 km\", after=i7)\nif7, tf7 = find(f1, r\"^Drug-related \\(fatal\\) \\|\", after=i7)\nb4_str = cells(tf7)[cells(th7).index(\"5 km\")]\nb4 = num(b4_str)\nrel4 = 100 * (math.exp(b4) - 1)\nprint(f\"E4 intermediate (relative change only, not the target): 100*(exp({b4})-1) = {rel4:.2f}%\")\n# Check whether any baseline/mean level of the fatal rate is printed in the extract\nlvl = [t for t in lines(f1) if re.search(r\"(?i)(mean|baseline|pre-treatment|average).*(rate|per 100,000)\", t) and re.search(r\"\\d+\\.\\d+\\s*(per|hospitali)\", t)]\nprint(f\"E4: lines printing a level of the fatal hospitalisation rate: {len(lvl)}\")\nH[\"E4\"] = {\n \"value\": None,\n \"unit\": \"hospitalisations per 100,000 inhabitants per year\",\n \"transformation\": f\"Would require R0*(exp(b)-1) with b = {b4_str.rstrip('*')} (relative change {rel4:.2f}%); R0 is not printed in the extract\",\n \"harmonisable\": False,\n \"missing_quantity\": \"the counterfactual (or pre-opening baseline) level of the fatal drug-related hospitalisation rate per 100,000 inhabitants per year for the treated regions within 5 km (e.g. a pre-treatment mean of the outcome in levels); the extract prints only log-point ATTs and no outcome level\",\n \"locator\": f\"arxiv-2609.27732v1 | Table 7 | row 'Drug-related (fatal)' | column '5 km' (extract line {if7})\",\n}\n\n# ---- E5: Tossou Table 3, Treatment row, column (4): 0-1 -> pp\nf5 = \"arxiv-2510.03658v1.txt\"\ni3, _ = find(f5, r\"^Table 3:\")\nih5, th5 = find(f5, r\"^\\| \\(1\\) \\| \\(2\\) \\| \\(3\\) \\| \\(4\\)\", after=i3)\nit5, tt5 = find(f5, r\"^Treatment \\|\", after=i3)\nim5, tm5 = find(f5, r\"^Mean Dep\\. Var\\. \\|\", after=i3)\nc4 = cells(th5).index(\"(4)\")\nb5_str = cells(tt5)[c4]\nb5 = num(b5_str)\nv5 = 100 * b5\nprint(f\"E5 cross-check: relative change = b/mean = {b5}/{num(cells(tm5)[c4])} = {b5/num(cells(tm5)[c4]):.4f}\")\nH[\"E5\"] = {\n \"value\": round(v5, 2),\n \"unit\": \"percentage points (change in the probability of infant death or stillbirth)\",\n \"transformation\": f\"100*beta with beta = {b5_str} (0-1 scale) -> {v5:.2f} pp (exact rescaling)\",\n \"harmonisable\": True,\n \"missing_quantity\": None,\n \"locator\": f\"arxiv-2510.03658v1 | Table 3 | row 'Treatment' | column '(4)' (extract line {it5})\",\n}\n\n# ---- data issues (detected by inspection; key checks recomputed here)\niA19, tA19 = find(f1, r\"^\\| \\(0\\.218\\)\")\nfind(f1, r\"never-treated comparison regions \\(640 regions\\)\")\nfind(f1, r\"fatal outcomes, which fall by 35%\")\nfind(f1, r\"by approximately 30% at 5 km\")\nfind(f3, r\"about 4% minimum wage increase\")\nfind(f2, r\"increased reemployment by 1\\.5 percentage points\")\nfind(f5, r\"by 0\\.0855 \\(a 18\\.79 percentage change\\)\")\nissues = [\n {\"issue\": \"Armendariz: the same CS-DR 5 km ATT (-0.431) has standard error (0.210) in Table 6 but (0.218) in Table A19.\",\n \"handling\": \"Point estimate identical, so E1 is unaffected; no uncertainty propagated. Recorded only.\"},\n {\"issue\": \"Armendariz: Tables 6/7 and the Figure 6 note state 608 never-treated control regions; the Table A19 note states 640.\",\n \"handling\": \"Does not enter any transformation; recorded only.\"},\n {\"issue\": f\"Armendariz: abstract says fatal hospitalisations 'fall by 35%', but the Table 7 5 km estimate -0.354 implies 100*(exp(-0.354)-1) = {rel4:.1f}%, matching Section 6.1 ('approximately 30% at 5 km'). The abstract figure appears to repeat the overall effect.\",\n \"handling\": \"Used the Table 7 coefficient as instructed; E4 target (absolute change) is in any case not reachable.\"},\n {\"issue\": \"Armendariz: Figure 7 note mentions 'four diagnostic sub-categories' and the Table 7 note lists ICD codes for four, while five outcomes (incl. fatal) are reported; the definition of the fatal outcome is not given in the extract.\",\n \"handling\": \"Recorded; does not affect the harmonised values.\"},\n {\"issue\": \"Kanayama: Table 2 note describes the 2023 increase as 'about 4%' while the table row '% MW changes' prints 0.047.\",\n \"handling\": \"Used the printed table value 0.047 as the task requires (target definition points to the table). With 0.04 the implied change would be about -1.55%; the 0.047-based -1.82% is consistent with missing+excess jobs (-0.03+0.012 = -0.018) and with the abstract's ~2% decline.\"},\n {\"issue\": \"RESEA: abstract rounds the reemployment ATE to 1.5 pp; Table 2 prints 1.47 pp. The 'Control' column is the raw aggregate control mean, not a block-adjusted counterfactual (the paper notes raw means are confounded by randomisation week).\",\n \"handling\": \"Used 1.47 pp and the printed control mean 75.0% as the target definition specifies (percent of control-group mean).\"},\n {\"issue\": \"Tossou: abstract/Section 4.1 headline (0.0855, 18.79 percentage change, mean 0.455) refers to a 'table 2' that is not in the extract and calls a relative change a 'percentage change'; Table 3 '% Change' is a relative change (b/mean, -0.0962/0.261 = -0.3686 vs printed -0.3685, rounding), not percentage points. Footnote markers are garbled by rendering ('^9^9 9').\",\n \"handling\": \"E5 uses Table 3 column (4) only; the target is percentage points (100*beta), distinct from the printed relative change.\"},\n {\"issue\": \"RESEA extract: rendering defect 'p ¡ 0.001' (for p < 0.001) in Section 3 text.\",\n \"handling\": \"Irrelevant to the harmonised values; recorded.\"},\n]\n\nn_rows = {}\nfor fn in sorted(os.listdir(SRC)):\n with open(os.path.join(SRC, fn), encoding=\"utf-8\") as f:\n n_rows[\"sources/\" + fn] = f.read().count(\"\\n\")\nwith open(os.path.join(D, \"README.md\"), encoding=\"utf-8\") as f:\n n_rows[\"README.md\"] = f.read().count(\"\\n\")\nwith open(os.path.join(D, \"effects.json\"), encoding=\"utf-8\") as f:\n eff = json.load(f)\nn_rows[\"effects.json\"] = len(eff)\nassert [e[\"id\"] for e in eff] == [\"E1\", \"E2\", \"E3\", \"E4\", \"E5\"]\n\nres = {\n \"task_id\": \"F4-conf-03\",\n \"harmonised\": H,\n \"n_harmonised\": sum(v[\"value\"] is not None for v in H.values()),\n \"n_not_harmonisable\": sum(not v[\"harmonisable\"] for v in H.values()),\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": n_rows,\n \"n_analysed\": len(H),\n \"random_seed\": None,\n}\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\", encoding=\"utf-8\") as f:\n json.dump(res, f, indent=2, ensure_ascii=False)\nprint(json.dumps({k: H[k][\"value\"] for k in H}))\nEOF\ncd /Users/simon-pierreboucher/wp29/runs/RA0005 && ~/wp29/venv/bin/python out/analysis.py"}}]}}45{"type":"api-request","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","id":"0aca24a0-abc8-4e81-87ce-e621c034f6e9","timestamp":"2026-10-30T05:33:01.657Z","version":"2.1.314","querySource":"sdk","shapeHash":"0ad9c323c6bed47240766f704f485176c15249bb7334cf52ef2f3a161dc0bcd8","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":"916e2f1b-4462-4a52-9669-512c2b5031e5","keep":12,"tail":["70eb3b62201590f88d9115e1f8f5d2be4f691ab142d2561ae75979d6f62ba83c","ac6fb06d2cf0cca4af1741b56cfa68b2f174c26b94641461a0201088092da2ad"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"66e29f9ab39d276bb96e4212d3f318876461f6b78dad2fbad2a6551347656efd"}46{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"c73dc16b-f22d-4438-9df9-988bcffb69d5","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83"}47{"type":"atis-latch","atis":"","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83"}48{"parentUuid":"c73dc16b-f22d-4438-9df9-988bcffb69d5","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsjdDJYeZTe9Bes7aNf","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":8706,"cache_read_input_tokens":24545,"output_tokens":415,"output_tokens_details":{"thinking_tokens":268},"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":8706},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":2432,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsjcuCXU4VsJkXDNBLd","requestRef":"0aca24a0-abc8-4e81-87ce-e621c034f6e9","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3df4b722-6fd0-4803-bfb2-0cccaf59d1d3","timestamp":"2026-10-30T05:33:05.540Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}49{"parentUuid":"3df4b722-6fd0-4803-bfb2-0cccaf59d1d3","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsjdDJYeZTe9Bes7aNf","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015bwYd8SPRrhBp3TEbGnugb","name":"Bash","input":{"command":"~/wp29/venv/bin/python -c \"import json;r=json.load(open('out/results.json'));print(r['n_input_rows'],r['n_harmonised'],r['n_not_harmonisable'])\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":8706,"cache_read_input_tokens":24545,"output_tokens":415,"output_tokens_details":{"thinking_tokens":268},"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":8706},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsjcuCXU4VsJkXDNBLd","requestRef":"0aca24a0-abc8-4e81-87ce-e621c034f6e9","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"341da390-2a61-4f7d-a4fa-23bbc488eee6","timestamp":"2026-10-30T05:33:06.410Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}50{"parentUuid":"341da390-2a61-4f7d-a4fa-23bbc488eee6","isSidechain":false,"promptId":"4c914a9d-d1ee-4047-9a6a-5177956ad95d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015bwYd8SPRrhBp3TEbGnugb","type":"tool_result","content":"{'sources/arxiv-2505.04555v3.txt': 66, 'sources/arxiv-2510.03658v1.txt': 91, 'sources/arxiv-2606.14621v1.txt': 58, 'sources/arxiv-2609.27732v1.txt': 134, 'README.md': 16, 'effects.json': 5} 4 1","is_error":false}]},"uuid":"bc9d6154-6946-4437-adf7-59355ad5d203","timestamp":"2026-10-30T05:33:06.472Z","toolUseResult":{"stdout":"{'sources/arxiv-2505.04555v3.txt': 66, 'sources/arxiv-2510.03658v1.txt': 91, 'sources/arxiv-2606.14621v1.txt': 58, 'sources/arxiv-2609.27732v1.txt': 134, 'README.md': 16, 'effects.json': 5} 4 1","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"341da390-2a61-4f7d-a4fa-23bbc488eee6","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}51{"type":"api-request-blob","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","hash":"9fef4739e9203487cd4a3b486ffadb2f963490f6d5c20b4c5104008f499ae6fc","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_015bwYd8SPRrhBp3TEbGnugb","name":"Bash","input":{"command":"~/wp29/venv/bin/python -c \"import json;r=json.load(open('out/results.json'));print(r['n_input_rows'],r['n_harmonised'],r['n_not_harmonisable'])\""}}]}}52{"type":"api-request","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","id":"126c9f87-2baf-4d71-8985-c6400a82b786","timestamp":"2026-10-30T05:33:06.475Z","version":"2.1.314","querySource":"sdk","shapeHash":"0ad9c323c6bed47240766f704f485176c15249bb7334cf52ef2f3a161dc0bcd8","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":"0aca24a0-abc8-4e81-87ce-e621c034f6e9","keep":14,"tail":["9fef4739e9203487cd4a3b486ffadb2f963490f6d5c20b4c5104008f499ae6fc","69d71835da89cd25d98282b646ea884d34e4bdcf66bebfd5c05d106d0ea44f68"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"40187c84778bc3d191baa977036b4217dc6b5c0469360995cf43bc7ff58a6b84"}53{"parentUuid":"bc9d6154-6946-4437-adf7-59355ad5d203","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsjymZfTL3NBrdgnr7D","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LBZCeSbpYQd2MVJ19rfMVV","name":"Bash","input":{"command":"cd out; cat > report.md <<'EOF'\n# F4-conf-03 — Harmonising effect sizes reported under incompatible definitions\n\n## Question\nThe task is to re-express five printed treatment effects (`data/effects.json`) on a stated target definition. Only quantities printed in the cited extract may be used, and each transformation must be stated explicitly. Where the target definition cannot be reached from the printed quantities, I say so and name the quantity that is missing.\n\n## Data and exclusions\n- **Files read:** the four source extracts under `data/sources/` (66, 91, 58 and 134 lines), `data/README.md` (16 lines) and `data/effects.json` (5 items).\n- **Evidence used:** only numbers printed in the extracts. No outside values were used, including remembered values from the full papers.\n- **Exclusions:** none of the five items was excluded.\n- **Data issues:** inconsistencies and rendering problems are listed under `data_issues` in `results.json` and summarised under Limitations.\n\n## Method\nI used the conventions stated in TASK.md.\n- **E1 (log points → % of the counterfactual rate):** 100·(exp(b) − 1), with b = −0.431 (Armendariz, Table 6, row ATT, column 5 km).\n- **E2 (pp → % of the control mean):** 100·Δ/p, with Δ = 1.47 pp and p = 75.0%, the Control column (RESEA, Table 2, row Reemployment).\n- **E3 (elasticity → % change for the observed minimum-wage hike):** 100·η·g, with η = −0.388 and g = 0.047 (Kanayama et al., Table 2, rows \"Employment elasticity w.r.t MW\" and \"% MW changes\").\n- **E4 (log points → absolute change per 100,000 per year):** this would need R0·(exp(b) − 1), with b = −0.354 (Table 7, row \"Drug-related (fatal)\", column 5 km). The level R0 is not printed anywhere in the extract.\n- **E5 (LPM coefficient on a 0–1 scale → pp):** 100·β, with β = −0.0962 (Tossou, Table 3, row Treatment, column (4)).\n\n`out/analysis.py` finds each table row by pattern match, prints the source lines it uses, and recomputes all values.\n\n## Results\n| id | target | value | transformation | harmonisable |\n|---|---|---|---|---|\n| E1 | % change in the drug-related hospitalisation rate | **−35.01%** | 100·(exp(−0.431) − 1); the paper's own \"% change\" row prints −35.0% | yes |\n| E2 | % change in reemployment relative to the control mean | **+1.96%** | 100·1.47/75.0 | yes |\n| E3 | % change in employment for the 2023 hike | **−1.82%** | 100·(−0.388)·0.047 = −1.8236 | yes |\n| E4 | absolute change in fatal hospitalisations per 100,000 per year | **null** | missing: the counterfactual or pre-opening level of the fatal drug-related hospitalisation rate per 100,000 for treated regions within 5 km | **no** |\n| E5 | change in probability, in pp | **−9.62 pp** | 100·(−0.0962) | yes |\n\n**Totals:** 4 items harmonised, 1 not harmonisable. The task counts as complete, because every item has either a value or a justified statement of impossibility.\n\n**Supporting checks for E4 and E5.** These checks are not target values:\n- The relative change implied by E4's coefficient is 100·(exp(−0.354) − 1) = −29.81%. This agrees with the paper's Section 6.1 (\"approximately 30%\"), but not with its abstract (\"fall by 35%\").\n- For E5, the relative change is −0.0962/0.261 = −36.86%, against a printed \"% Change\" of −0.3685. This relative change is a different quantity from the pp target.\n\n## Uncertainty\nNo standard errors or confidence intervals were propagated; the task asks for point estimates only.\n- **Exact conversions:** E1 is an exact transformation of the log-point coefficient. E5 is an exact rescaling.\n- **E2 (approximate):** the arithmetic is exact, but the denominator is the raw aggregate control mean (75.0%). That is not a block-adjusted counterfactual level for the ATE.\n- **E3 (approximate):** the formula η·g is a linear, first-order approximation. The elasticity is a six-month average from an event study, and g is a summary (average) of 47 prefecture-level changes.\n- **Rounding of printed inputs:** all inputs are printed to 2–4 significant digits, which limits the precision of the outputs.\n\n## Limitations and open issues\n1. **E4 cannot be harmonised.** The extract prints only log-point ATTs and no outcome level, so an absolute change per 100,000 cannot be derived from it. I did not use any guessed or remembered baseline.\n2. **Kanayama (E3):** the Table 2 note says \"about 4%\" for the minimum-wage increase, but the table prints 0.047. I used the printed 0.047, as the target definition requires.\n - Using 0.04 instead would give about −1.55%.\n - The 0.047 figure is consistent with missing plus excess jobs (−0.03 + 0.012 = −0.018) and with the abstract's \"2% employment decline\".\n3. **Armendariz (E1, E4):**\n - The same 5 km ATT has a standard error of 0.210 in Table 6 but 0.218 in Table A19.\n - The number of control regions is 608 in the main tables but 640 in the Table A19 note.\n - The abstract's 35% for fatal hospitalisations does not match the Table 7 coefficient (which implies −29.8%).\n - Notes refer to \"four\" sub-categories, although five outcomes are reported, and the fatal outcome is not defined in the extract.\n\n None of these changes the point values above.\n4. **RESEA (E2):** the abstract rounds the effect to 1.5 pp. I used the Table 2 value of 1.47 pp.\n5. **Tossou (E5):**\n - The headline figures (0.0855, \"18.79 percentage change\", mean 0.455) cite a \"table 2\" that is not in the extract.\n - The author calls relative changes \"percentage change\", which is easy to confuse with percentage points. The pp value reported here (−9.62) is a different quantity from the paper's −36.85% relative change.\n - Footnote markers in the extract are garbled by the rendering.\n6. **Comparability across studies:** the harmonised values are on comparable scales, but the studies remain incomparable in population, design and treatment intensity. Harmonising the units does not make the effects comparable as estimates.\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The Armendariz 5 km overall ATT of -0.431 log points corresponds to a -35.01% change in the drug-related hospitalisation rate (100*(exp(-0.431)-1)), matching the printed -35.0%.\",\"results_keys\":[\"harmonised.E1\"],\"status\":\"supported\",\"caveat\":\"Exact transformation of the point estimate; no uncertainty propagated.\"},\n {\"id\":\"C2\",\"text\":\"The RESEA reemployment ATE of 1.47 pp equals a +1.96% relative change against the printed control mean of 75.0%.\",\"results_keys\":[\"harmonised.E2\"],\"status\":\"supported\",\"caveat\":\"Denominator is the raw aggregate control mean, not a block-adjusted counterfactual.\"},\n {\"id\":\"C3\",\"text\":\"The minimum-wage employment elasticity of -0.388 applied to the printed 0.047 proportional MW increase implies a -1.82% employment change.\",\"results_keys\":[\"harmonised.E3\"],\"status\":\"supported\",\"caveat\":\"Linear approximation; the table note says 'about 4%' whereas the table prints 0.047 (0.047 used).\"},\n {\"id\":\"C4\",\"text\":\"The fatal drug-related hospitalisation effect (E4) cannot be expressed as an absolute change per 100,000 per year because the extract prints no level of that rate.\",\"results_keys\":[\"harmonised.E4\",\"n_not_harmonisable\"],\"status\":\"supported\",\"caveat\":\"Its implied relative change (-29.81%) is computable but is not the target definition.\"},\n {\"id\":\"C5\",\"text\":\"The Tossou LPM coefficient -0.0962 (Table 3, col. 4) equals -9.62 percentage points.\",\"results_keys\":[\"harmonised.E5\"],\"status\":\"supported\",\"caveat\":\"Distinct from the paper's -36.85% relative change.\"},\n {\"id\":\"C6\",\"text\":\"The task is complete: 4 effects harmonised and 1 justified as not harmonisable.\",\"results_keys\":[\"n_harmonised\",\"n_not_harmonisable\",\"feasibility\"],\"status\":\"supported\",\"caveat\":\"Several internal inconsistencies in the sources are documented in data_issues.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('claims.json'))\"; ls"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":509,"cache_read_input_tokens":33251,"output_tokens":3100,"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":509},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsjyUheuGTp8yzAYK87","requestRef":"126c9f87-2baf-4d71-8985-c6400a82b786","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ccb54bf1-7acf-4675-80da-19f9dfdc6200","timestamp":"2026-10-30T05:33:32.997Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}54{"parentUuid":"ccb54bf1-7acf-4675-80da-19f9dfdc6200","isSidechain":false,"promptId":"4c914a9d-d1ee-4047-9a6a-5177956ad95d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LBZCeSbpYQd2MVJ19rfMVV","type":"tool_result","content":"analysis.py\nclaims.json\nreport.md\nresults.json","is_error":false}]},"uuid":"5bedba4b-dcfa-41ca-b88f-ad9602f8c9f6","timestamp":"2026-10-30T05:33:33.072Z","toolUseResult":{"stdout":"analysis.py\nclaims.json\nreport.md\nresults.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"ccb54bf1-7acf-4675-80da-19f9dfdc6200","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005/out","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}55{"type":"api-request-blob","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","hash":"be29c9e1c5886b13e8296bcd7ff5746d30ebd5a27dfe33baa22108e7a89ef481","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01LBZCeSbpYQd2MVJ19rfMVV","name":"Bash","input":{"command":"cd out; cat > report.md <<'EOF'\n# F4-conf-03 — Harmonising effect sizes reported under incompatible definitions\n\n## Question\nThe task is to re-express five printed treatment effects (`data/effects.json`) on a stated target definition. Only quantities printed in the cited extract may be used, and each transformation must be stated explicitly. Where the target definition cannot be reached from the printed quantities, I say so and name the quantity that is missing.\n\n## Data and exclusions\n- **Files read:** the four source extracts under `data/sources/` (66, 91, 58 and 134 lines), `data/README.md` (16 lines) and `data/effects.json` (5 items).\n- **Evidence used:** only numbers printed in the extracts. No outside values were used, including remembered values from the full papers.\n- **Exclusions:** none of the five items was excluded.\n- **Data issues:** inconsistencies and rendering problems are listed under `data_issues` in `results.json` and summarised under Limitations.\n\n## Method\nI used the conventions stated in TASK.md.\n- **E1 (log points → % of the counterfactual rate):** 100·(exp(b) − 1), with b = −0.431 (Armendariz, Table 6, row ATT, column 5 km).\n- **E2 (pp → % of the control mean):** 100·Δ/p, with Δ = 1.47 pp and p = 75.0%, the Control column (RESEA, Table 2, row Reemployment).\n- **E3 (elasticity → % change for the observed minimum-wage hike):** 100·η·g, with η = −0.388 and g = 0.047 (Kanayama et al., Table 2, rows \"Employment elasticity w.r.t MW\" and \"% MW changes\").\n- **E4 (log points → absolute change per 100,000 per year):** this would need R0·(exp(b) − 1), with b = −0.354 (Table 7, row \"Drug-related (fatal)\", column 5 km). The level R0 is not printed anywhere in the extract.\n- **E5 (LPM coefficient on a 0–1 scale → pp):** 100·β, with β = −0.0962 (Tossou, Table 3, row Treatment, column (4)).\n\n`out/analysis.py` finds each table row by pattern match, prints the source lines it uses, and recomputes all values.\n\n## Results\n| id | target | value | transformation | harmonisable |\n|---|---|---|---|---|\n| E1 | % change in the drug-related hospitalisation rate | **−35.01%** | 100·(exp(−0.431) − 1); the paper's own \"% change\" row prints −35.0% | yes |\n| E2 | % change in reemployment relative to the control mean | **+1.96%** | 100·1.47/75.0 | yes |\n| E3 | % change in employment for the 2023 hike | **−1.82%** | 100·(−0.388)·0.047 = −1.8236 | yes |\n| E4 | absolute change in fatal hospitalisations per 100,000 per year | **null** | missing: the counterfactual or pre-opening level of the fatal drug-related hospitalisation rate per 100,000 for treated regions within 5 km | **no** |\n| E5 | change in probability, in pp | **−9.62 pp** | 100·(−0.0962) | yes |\n\n**Totals:** 4 items harmonised, 1 not harmonisable. The task counts as complete, because every item has either a value or a justified statement of impossibility.\n\n**Supporting checks for E4 and E5.** These checks are not target values:\n- The relative change implied by E4's coefficient is 100·(exp(−0.354) − 1) = −29.81%. This agrees with the paper's Section 6.1 (\"approximately 30%\"), but not with its abstract (\"fall by 35%\").\n- For E5, the relative change is −0.0962/0.261 = −36.86%, against a printed \"% Change\" of −0.3685. This relative change is a different quantity from the pp target.\n\n## Uncertainty\nNo standard errors or confidence intervals were propagated; the task asks for point estimates only.\n- **Exact conversions:** E1 is an exact transformation of the log-point coefficient. E5 is an exact rescaling.\n- **E2 (approximate):** the arithmetic is exact, but the denominator is the raw aggregate control mean (75.0%). That is not a block-adjusted counterfactual level for the ATE.\n- **E3 (approximate):** the formula η·g is a linear, first-order approximation. The elasticity is a six-month average from an event study, and g is a summary (average) of 47 prefecture-level changes.\n- **Rounding of printed inputs:** all inputs are printed to 2–4 significant digits, which limits the precision of the outputs.\n\n## Limitations and open issues\n1. **E4 cannot be harmonised.** The extract prints only log-point ATTs and no outcome level, so an absolute change per 100,000 cannot be derived from it. I did not use any guessed or remembered baseline.\n2. **Kanayama (E3):** the Table 2 note says \"about 4%\" for the minimum-wage increase, but the table prints 0.047. I used the printed 0.047, as the target definition requires.\n - Using 0.04 instead would give about −1.55%.\n - The 0.047 figure is consistent with missing plus excess jobs (−0.03 + 0.012 = −0.018) and with the abstract's \"2% employment decline\".\n3. **Armendariz (E1, E4):**\n - The same 5 km ATT has a standard error of 0.210 in Table 6 but 0.218 in Table A19.\n - The number of control regions is 608 in the main tables but 640 in the Table A19 note.\n - The abstract's 35% for fatal hospitalisations does not match the Table 7 coefficient (which implies −29.8%).\n - Notes refer to \"four\" sub-categories, although five outcomes are reported, and the fatal outcome is not defined in the extract.\n\n None of these changes the point values above.\n4. **RESEA (E2):** the abstract rounds the effect to 1.5 pp. I used the Table 2 value of 1.47 pp.\n5. **Tossou (E5):**\n - The headline figures (0.0855, \"18.79 percentage change\", mean 0.455) cite a \"table 2\" that is not in the extract.\n - The author calls relative changes \"percentage change\", which is easy to confuse with percentage points. The pp value reported here (−9.62) is a different quantity from the paper's −36.85% relative change.\n - Footnote markers in the extract are garbled by the rendering.\n6. **Comparability across studies:** the harmonised values are on comparable scales, but the studies remain incomparable in population, design and treatment intensity. Harmonising the units does not make the effects comparable as estimates.\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The Armendariz 5 km overall ATT of -0.431 log points corresponds to a -35.01% change in the drug-related hospitalisation rate (100*(exp(-0.431)-1)), matching the printed -35.0%.\",\"results_keys\":[\"harmonised.E1\"],\"status\":\"supported\",\"caveat\":\"Exact transformation of the point estimate; no uncertainty propagated.\"},\n {\"id\":\"C2\",\"text\":\"The RESEA reemployment ATE of 1.47 pp equals a +1.96% relative change against the printed control mean of 75.0%.\",\"results_keys\":[\"harmonised.E2\"],\"status\":\"supported\",\"caveat\":\"Denominator is the raw aggregate control mean, not a block-adjusted counterfactual.\"},\n {\"id\":\"C3\",\"text\":\"The minimum-wage employment elasticity of -0.388 applied to the printed 0.047 proportional MW increase implies a -1.82% employment change.\",\"results_keys\":[\"harmonised.E3\"],\"status\":\"supported\",\"caveat\":\"Linear approximation; the table note says 'about 4%' whereas the table prints 0.047 (0.047 used).\"},\n {\"id\":\"C4\",\"text\":\"The fatal drug-related hospitalisation effect (E4) cannot be expressed as an absolute change per 100,000 per year because the extract prints no level of that rate.\",\"results_keys\":[\"harmonised.E4\",\"n_not_harmonisable\"],\"status\":\"supported\",\"caveat\":\"Its implied relative change (-29.81%) is computable but is not the target definition.\"},\n {\"id\":\"C5\",\"text\":\"The Tossou LPM coefficient -0.0962 (Table 3, col. 4) equals -9.62 percentage points.\",\"results_keys\":[\"harmonised.E5\"],\"status\":\"supported\",\"caveat\":\"Distinct from the paper's -36.85% relative change.\"},\n {\"id\":\"C6\",\"text\":\"The task is complete: 4 effects harmonised and 1 justified as not harmonisable.\",\"results_keys\":[\"n_harmonised\",\"n_not_harmonisable\",\"feasibility\"],\"status\":\"supported\",\"caveat\":\"Several internal inconsistencies in the sources are documented in data_issues.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('claims.json'))\"; ls"}}]}}56{"type":"api-request","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","id":"fbdb1f23-102b-4e71-b2e4-553cead1de65","timestamp":"2026-10-30T05:33:33.075Z","version":"2.1.314","querySource":"sdk","shapeHash":"0ad9c323c6bed47240766f704f485176c15249bb7334cf52ef2f3a161dc0bcd8","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":"126c9f87-2baf-4d71-8985-c6400a82b786","keep":16,"tail":["be29c9e1c5886b13e8296bcd7ff5746d30ebd5a27dfe33baa22108e7a89ef481","89d9b4a7b2886174d4a02a90e55bac3cbe3d79397ba98db68d17394fe831978d"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",17,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"ea897e3542c2af74403e5154c63caf1bd274962da8c9e90f606f17ef10086034"}57{"parentUuid":"5bedba4b-dcfa-41ca-b88f-ad9602f8c9f6","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsmwVwRjyt4G1SdQ6E7","type":"message","role":"assistant","content":[{"type":"text","text":"I harmonised four of the five effects. The fifth (E4) can't be converted to its target unit because the source extract never prints the baseline level it would need. That still counts as a complete task under its rules. Running `out/analysis.py` regenerates the results, `out/report.md` gives the full write-up, and `out/claims.json` lists the central claims.\n\n| Item | Printed estimate | Converted to | Result | How |\n|---|---|---|---|---|\n| E1 – drug consumption rooms, all drug-related hospitalisations, 5 km | −0.431 log points | % change in the rate | **−35.01%** | 100·(exp(−0.431)−1); the paper itself prints −35.0% |\n| E2 – re-employment programme | +1.47 percentage points | % of the control-group mean (75.0%) | **+1.96%** | 100·1.47/75.0 |\n| E3 – minimum-wage employment elasticity | −0.388 | % employment change for the observed rise | **−1.82%** | 100·(−0.388)·0.047 |\n| E4 – fatal drug-related hospitalisations, 5 km | −0.354 log points | change per 100,000 people per year | **not possible** | see below |\n| E5 – free caesarean policy, infant death or stillbirth | −0.0962 (probability, 0–1 scale) | percentage points | **−9.62 pp** | 100·(−0.0962) |\n\n- **Why E4 can't be done:** turning a log-point effect into an absolute change needs the level of the fatal hospitalisation rate before or without the intervention. The extract prints no level for this outcome. I didn't substitute a guess. The relative change it implies is −29.81%, but that isn't what the task asked for.\n- **Precision:** no standard errors were carried through, since the task asks for point estimates only. E1 and E5 are exact conversions.\n - E3 is a linear approximation.\n - E2's denominator is the raw control-group average, not a counterfactual adjusted for the week each person was randomised.\n- **Inconsistencies in the sources:** these are recorded in `data_issues` in `results.json`. Only the first one could affect a result (E3):\n - In the minimum-wage paper, the table note says the increase was \"about 4%\" but the table prints 0.047. I used 0.047; with 4% the answer would be about −1.55%.\n - In the drug-room paper, the abstract says fatal hospitalisations fall 35%, but the table coefficient implies about 30%, which matches the paper's own Section 6.1. The same estimate also has two different standard errors (0.210 and 0.218), and two different counts of control regions (608 and 640).\n - In the caesarean paper, the headline figures cite a table that isn't in the extract. The author also calls relative changes \"percentage change\", which is easy to misread as percentage points.\n\nMatching the units doesn't make these effects comparable as estimates: the studies differ in population, design and treatment intensity."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3123,"cache_read_input_tokens":33760,"output_tokens":976,"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":3123},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsmwDpy8eP2gUimxxrx","requestRef":"fbdb1f23-102b-4e71-b2e4-553cead1de65","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0145a190-7035-401e-987b-41ff5ceba9af","timestamp":"2026-10-30T05:33:43.857Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0005/out","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","version":"2.1.314","gitBranch":"HEAD"}58{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"0145a190-7035-401e-987b-41ff5ceba9af","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83"}59{"type":"atis-latch","atis":"","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83"}60{"type":"cost-state","sessionId":"38cbbe26-bc51-4c72-ab14-336a884add83","totalCostUSD":0.4353094,"totalAPIDuration":103761,"totalAPIDurationWithoutRetries":103757,"totalToolDuration":472,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":107392,"startTime":1793338316473,"modelUsage":{"claude-opus-5-5":{"inputTokens":20,"outputTokens":11474,"thinkingTokens":1671,"cacheReadInputTokens":144272,"cacheCreationInputTokens":35379,"cacheCreationEphemeral5mInputTokens":35379,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.4353094}},"hasUnknownModelCost":false}61