spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T05:06:33.403Z","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T05:06:33.410Z","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f"}4{"parentUuid":null,"isSidechain":false,"promptId":"1257d57b-48b2-460e-b967-208321121a94","type":"user","message":{"role":"user","content":"Begin"},"uuid":"8f96844c-f398-4148-8e70-d7ab126d1a7a","timestamp":"2026-10-30T05:06:33.415Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0229","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","shapeHash":"0c8200fd36c535be1b9787940026725cf2672d816ee66f22c950c0604afe8521","timestamp":"2026-10-30T05:06:33.428Z","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/RC0229\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` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\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. 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the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0229","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f"}12{"parentUuid":"e701cbbd-1e05-4d69-9e52-225b229dc2ec","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqiYpNcEnJr2P9NaZxv","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":4,"cache_creation_input_tokens":2603,"cache_read_input_tokens":1504,"output_tokens":129,"output_tokens_details":{"thinking_tokens":23},"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":2603},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":529,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqiYXkdvqCSvibGZTch","requestRef":"be994680-144a-419a-ad9c-f2b57e17b224","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"cf9358f8-dc78-418d-9c41-e7f66b5eb4e9","timestamp":"2026-10-30T05:06:34.557Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0229","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"cf9358f8-dc78-418d-9c41-e7f66b5eb4e9","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqiYpNcEnJr2P9NaZxv","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01P5GGD8JgisxhEQi8X8bcXf","name":"Bash","input":{"command":"cat TASK.md; 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For each of the five effects listed in `data/effects.json`, re-express the printed point estimate on the **target definition** stated for that item, using only quantities printed in the cited extract and an explicitly stated transformation; where the target definition cannot be reached from the printed quantities, say so and name the additional quantity that would be required. The estimands are the harmonised point estimates (no uncertainty propagation is required). Each item carries a locator for every printed quantity used.\n\n## Inputs\n- `data/effects.json` — the five items: {\"id\", \"source\", \"effect\" (what and where), \"reported_definition\", \"target_definition\"}.\n- `data/sources/arxiv-2609.27732v1.txt` — Armendariz (2026), *Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland*, arXiv:2609.27732v1, CC BY 4.0: abstract, Section 6 (Tables 6 and 7, Section 6.1), Appendix Table A19. Outcomes are log hospitalisation rates per 100,000 inhabitants; ATTs in log points; the tables also print a \"% change\" row for the overall effect.\n- `data/sources/arxiv-2606.14621v1.txt` — Li, Pearson-Merkowitz & Yokum (2026), *Evaluating the Impact of Rhode Island's Self-Sustaining RESEA Program on Employment Outcomes*, arXiv:2606.14621v1, CC BY 4.0: abstract, Section 3 (Table 2: treatment/control means, differences, ATEs with p-values and 95% CIs), Table 3. Reemployment is in percent (means) and percentage points (effects).\n- `data/sources/arxiv-2505.04555v3.txt` — Kanayama, Miyaji & Otani (2025/2026), *Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation*, arXiv:2505.04555v3, CC BY 4.0: abstract and Appendix A.1 (Table 2: elasticities and the size of the minimum-wage change, with note).\n- `data/sources/arxiv-2510.03658v1.txt` — Tossou (2025), *Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin*, arXiv:2510.03658v1, CC BY 4.0: abstract, Section 4.1 and Table 3 (linear probability model of infant death or stillbirth; \"Mean Dep. Var.\" row).\n- `data/README.md` — provenance, retrieval times, licences, SHA-256 of the extracts.\n\n## Information-availability rules\n- The extracts are the only admissible evidence; a quantity that is not printed in the cited extract is not available, even if you believe it is in the full paper or elsewhere.\n- Conventions: a log-point coefficient b corresponds to a relative change of 100·(exp(b) − 1) percent; a percentage-point effect Δ on a probability with counterfactual (control) level p corresponds to a relative change of 100·Δ/p percent; an elasticity η applied to a proportional change g of the treatment variable implies a relative change of 100·η·g percent. State the transformation you apply in `transformation`.\n- `harmonisable` is `false` only when the target definition requires a quantity that the cited extract does not print; then `value` is `null` and `missing_quantity` names the quantity. Do not substitute a guessed or externally remembered value.\n- `feasibility` refers to the task as a whole: the task is complete when every item has either a harmonised value or an explicit, justified statement of impossibility.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `harmonised` | object | — | {\"E1\": {\"value\": number or null, \"unit\": \"<unit of the target definition>\", \"transformation\": \"<formula with the printed inputs>\", \"harmonisable\": true/false, \"missing_quantity\": string or null, \"locator\": \"<source id | table | row | column for every printed input>\"}, … \"E5\": {…}} |\n| `n_harmonised` | integer | — | number of items with a non-null value |\n| `n_not_harmonisable` | integer | — | number of items with `harmonisable` = false |\n| `task_id` | string | — | \"F4-conf-03\" |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" — refers to the task as a whole |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the inputs (rendering defects, internal inconsistencies of a source, etc.) and how they were handled; empty list if none |\n| `n_input_rows` | object | lines / items | {file name: number of lines} for every file under `data/` that you read (text files: number of newline-terminated lines as reported by `wc -l`; JSON files: number of top-level items) |\n| `n_analysed` | integer or null | — | number of reported items (values, assertions, effects or references) you actually assessed |\n| `random_seed` | integer or null | — | null (no randomness is involved) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. \"Uncertainty\" must note that no standard errors were propagated and which conversions are exact versus approximate.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically (the script must print the source lines it relies on).\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available; the standard library is sufficient for this task). Do not modify `data/`.\n- The source extracts under `data/sources/` are the **only** admissible evidence. Do not rely on memory of the papers or on other versions of them; if a quantity is not in the extract, it is not available.\n- Report numbers exactly as printed in the source (same sign, same number of decimals); do not round or rescale printed values unless the key definition asks for a transformation.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 32\ndrwxr-xr-x    6 simon-pierreboucher  staff   192 oct.  29 23:45 .\ndrwxr-xr-x  133 simon-pierreboucher  staff  4256 oct.  30 01:06 ..\n-rw-r--r--    1 simon-pierreboucher  staff   956 oct.  29 23:45 bundle.json\ndrwxr-xr-x    5 simon-pierreboucher  staff   160 oct.  29 23:45 data\n-rw-r--r--    1 simon-pierreboucher  staff  1326 oct.  29 23:45 gate_spec.json\n-rw-r--r--    1 simon-pierreboucher  staff  6355 oct.  29 23:45 TASK.md\ndata:\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":"d0e494b6-6c1c-4b8e-84b8-ddc3c765f939","timestamp":"2026-10-30T05:06:35.140Z","toolUseResult":{"stdout":"# F4-conf-03 — Harmonising effect sizes reported under incompatible definitions\n\n## Scientific question\nA synthesis compares treatment effects from four open-access studies that report their estimates under different definitions: a semi-elasticity (coefficient on a log outcome), a percentage-point effect on a probability, an elasticity, and a linear-probability coefficient. For each of the five effects listed in `data/effects.json`, re-express the printed point estimate on the **target definition** stated for that item, using only quantities printed in the cited extract and an explicitly stated transformation; where the target definition cannot be reached from the printed quantities, say so and name the additional quantity that would be required. The estimands are the harmonised point estimates (no uncertainty propagation is required). Each item carries a locator for every printed quantity used.\n\n## Inputs\n- `data/effects.json` — the five items: {\"id\", \"source\", \"effect\" (what and where), \"reported_definition\", \"target_definition\"}.\n- `data/sources/arxiv-2609.27732v1.txt` — Armendariz (2026), *Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland*, arXiv:2609.27732v1, CC BY 4.0: abstract, Section 6 (Tables 6 and 7, Section 6.1), Appendix Table A19. Outcomes are log hospitalisation rates per 100,000 inhabitants; ATTs in log points; the tables also print a \"% change\" row for the overall effect.\n- `data/sources/arxiv-2606.14621v1.txt` — Li, Pearson-Merkowitz & Yokum (2026), *Evaluating the Impact of Rhode Island's Self-Sustaining RESEA Program on Employment Outcomes*, arXiv:2606.14621v1, CC BY 4.0: abstract, Section 3 (Table 2: treatment/control means, differences, ATEs with p-values and 95% CIs), Table 3. Reemployment is in percent (means) and percentage points (effects).\n- `data/sources/arxiv-2505.04555v3.txt` — Kanayama, Miyaji & Otani (2025/2026), *Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation*, arXiv:2505.04555v3, CC BY 4.0: abstract and Appendix A.1 (Table 2: elasticities and the size of the minimum-wage change, with note).\n- `data/sources/arxiv-2510.03658v1.txt` — Tossou (2025), *Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin*, arXiv:2510.03658v1, CC BY 4.0: abstract, Section 4.1 and Table 3 (linear probability model of infant death or stillbirth; \"Mean Dep. Var.\" row).\n- `data/README.md` — provenance, retrieval times, licences, SHA-256 of the extracts.\n\n## Information-availability rules\n- The extracts are the only admissible evidence; a quantity that is not printed in the cited extract is not available, even if you believe it is in the full paper or elsewhere.\n- Conventions: a log-point coefficient b corresponds to a relative change of 100·(exp(b) − 1) percent; a percentage-point effect Δ on a probability with counterfactual (control) level p corresponds to a relative change of 100·Δ/p percent; an elasticity η applied to a proportional change g of the treatment variable implies a relative change of 100·η·g percent. State the transformation you apply in `transformation`.\n- `harmonisable` is `false` only when the target definition requires a quantity that the cited extract does not print; then `value` is `null` and `missing_quantity` names the quantity. Do not substitute a guessed or externally remembered value.\n- `feasibility` refers to the task as a whole: the task is complete when every item has either a harmonised value or an explicit, justified statement of impossibility.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `harmonised` | object | — | {\"E1\": {\"value\": number or null, \"unit\": \"<unit of the target definition>\", \"transformation\": \"<formula with the printed inputs>\", \"harmonisable\": true/false, \"missing_quantity\": string or null, \"locator\": \"<source id | table | row | column for every printed input>\"}, … \"E5\": {…}} |\n| `n_harmonised` | integer | — | number of items with a non-null value |\n| `n_not_harmonisable` | integer | — | number of items with `harmonisable` = false |\n| `task_id` | string | — | \"F4-conf-03\" |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" — refers to the task as a whole |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the inputs (rendering defects, internal inconsistencies of a source, etc.) and how they were handled; empty list if none |\n| `n_input_rows` | object | lines / items | {file name: number of lines} for every file under `data/` that you read (text files: number of newline-terminated lines as reported by `wc -l`; JSON files: number of top-level items) |\n| `n_analysed` | integer or null | — | number of reported items (values, assertions, effects or references) you actually assessed |\n| `random_seed` | integer or null | — | null (no randomness is involved) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. \"Uncertainty\" must note that no standard errors were propagated and which conversions are exact versus approximate.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically (the script must print the source lines it relies on).\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available; the standard library is sufficient for this task). Do not modify `data/`.\n- The source extracts under `data/sources/` are the **only** admissible evidence. Do not rely on memory of the papers or on other versions of them; if a quantity is not in the extract, it is not available.\n- Report numbers exactly as printed in the source (same sign, same number of decimals); do not round or rescale printed values unless the key definition asks for a transformation.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 32\ndrwxr-xr-x    6 simon-pierreboucher  staff   192 oct.  29 23:45 .\ndrwxr-xr-x  133 simon-pierreboucher  staff  4256 oct.  30 01:06 ..\n-rw-r--r--    1 simon-pierreboucher  staff   956 oct.  29 23:45 bundle.json\ndrwxr-xr-x    5 simon-pierreboucher  staff   160 oct.  29 23:45 data\n-rw-r--r--    1 simon-pierreboucher  staff  1326 oct.  29 23:45 gate_spec.json\n-rw-r--r--    1 simon-pierreboucher  staff  6355 oct.  29 23:45 TASK.md\ndata:\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":"f0accaba-3919-4541-a927-f50d40173d11","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0229","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","version":"2.1.314","gitBranch":"HEAD"}15{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"d0e494b6-6c1c-4b8e-84b8-ddc3c765f939","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f"}16{"type":"atis-latch","atis":"","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f"}17{"type":"api-request-blob","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","hash":"cbf26775a249f86407ef3cbc970306a475f2057b15f78c90d921b1e0ca72e9ed","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01P5GGD8JgisxhEQi8X8bcXf","name":"Bash","input":{"command":"cat TASK.md; 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cat data/README.md; cat gate_spec.json bundle.json; wc -l data/sources/* data/README.md; shasum -a 256 data/*.json data/*.md data/sources/*","description":"Read effects, README, gate spec"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2776,"cache_read_input_tokens":4107,"output_tokens":149,"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":2776},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqifpJtQn2dYc2gsDA9","requestRef":"50c9345c-358f-4672-9878-cd3f17fb6d73","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"423e8d62-e23a-4d54-8a4f-79c7ef846b77","timestamp":"2026-10-30T05:06:36.752Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0229","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","version":"2.1.314","gitBranch":"HEAD"}20{"parentUuid":"423e8d62-e23a-4d54-8a4f-79c7ef846b77","isSidechain":false,"promptId":"1257d57b-48b2-460e-b967-208321121a94","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016cu19Gw1ZDz4CdNgQAdYPd","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{\n  \"task_id\": \"F4-conf-03\",\n  \"required_files\": [\n    \"out/results.json\",\n    \"out/report.md\",\n    \"out/analysis.py\",\n    \"out/claims.json\"\n  ],\n  \"results_keys\": {\n    \"harmonised\": {\n      \"type\": \"object\",\n      \"nullable\": false\n    },\n    \"n_harmonised\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"n_not_harmonisable\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"task_id\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"feasibility\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"feasibility_reason\": {\n      \"type\": \"string\",\n      \"nullable\": true\n    },\n    \"data_issues\": {\n      \"type\": \"array\",\n      \"nullable\": false\n    },\n    \"n_input_rows\": {\n      \"type\": 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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. 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Evidence from Switzerland\n     3\tAuthors: Ana Armendariz (University of St. Gallen)\n     4\tBibliographic record: arXiv:2609.27732v1 [econ.GN], submitted 23 Sep 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2609.27732v1\n     5\tRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\n     8\tOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n    13\t\n    14\tAna Armendariz*\n    15\t\n    16\tSeptember 23, 2026\n    17\t\n    18\tThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n    19\t\n    20\tKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n    21\t\n    22\t## 6 Results\n    23\t\n    24\tThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n    25\t\n    26\tFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n    27\t\n    28\tNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n    29\t\n    30\tFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n    31\t\n    32\tTable 6: Overall Average Treatment Effect on the Treated (ATT)\n    33\t\n    34\t| 1 km | 3 km | 5 km\n    35\t\n    36\tATT | -0.399 | -0.283 | -0.431**\n    37\t\n    38\t| (0.412) | (0.279) | (0.210)\n    39\t\n    40\t% change | -32.9% | -24.7% | -35.0%\n    41\t\n    42\tTreated regions | 7 | 35 | 50\n    43\t\n    44\tControl regions | 608 | 608 | 608\n    45\t\n    46\tObservations | 16,175 | 16,875 | 17,250\n    47\t\n    48\tNote: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\n    49\tthroughout. Regions near DCRs that opened before 1998\n    50\t(always-treated) are excluded. Standard errors clustered at the Medstat region level in\n    51\tparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n    52\t{}^{*}p<0.10 .\n    53\t\n    54\tTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\n    55\tcorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n    56\t\n    57\tTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\n    58\tsub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\n    59\t\n    60\tFigure 7: Event study: Sub-outcome estimates,\n    61\twithin 5 km, \\pm 5-year window\n    62\t\n    63\tNote: The figure plots event-study coefficients from the doubly robust Callaway & Sant’Anna (2021) estimator for four diagnostic sub-categories of drug-related hospitalizations. Each panel uses a separate y -axis to accommodate differences in scale across outcomes. The sample includes all staggered-treated MedStat regions within 5 km of a\n    64\tDCR that opened between 1998 and 2022. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded. 95% confidence intervals are based on standard errors clustered at the MedStat region level.\n    65\t\n    66\tTable 7: Overall ATT by Outcome Category and Buffer Zone\n    67\t\n    68\tOutcome | 1 km | 3 km | 5 km\n    69\t\n    70\tMental/Behavioral | -0.354 | -0.202 | -0.381*\n    71\t\n    72\t| (0.382) | (0.244) | (0.220)\n    73\t\n    74\tHepatitis | -0.270 | -0.413 | -0.406\n    75\t\n    76\t| (0.486) | (0.270) | (0.250)\n    77\t\n    78\tHIV | 0.063 | -0.239 | -0.340\n    79\t\n    80\t| (0.801) | (0.337) | (0.294)\n    81\t\n    82\tNarcotics | 1.186*** | 0.549*** | 0.390**\n    83\t\n    84\t| (0.288) | (0.187) | (0.165)\n    85\t\n    86\tDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n    87\t\n    88\t| (0.137) | (0.215) | (0.178)\n    89\t\n    90\tTreated regions | 7 | 35 | 50\n    91\t\n    92\tControl regions | 608 (never-treated) | |\n    93\t\n    94\tNote: Each cell reports the overall ATT from\n    95\tthe doubly robust Callaway & Sant’Anna (2021) estimator with\n    96\tnever-treated controls and a universal base period. Outcomes are\n    97\tlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\n    98\tmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\n    99\tHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\n   100\testimates are identified from a single treated region per cohort and\n   101\tshould be interpreted with caution. Regions near DCRs opened before\n   102\t1998 excluded. Standard errors clustered at the Medstat region level.\n   103\tSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n   104\t\n   105\tFigure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\n   106\t\n   107\t### 6.1 Mechanisms\n   108\t\n   109\tThe sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n   110\tsubstitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\n   111\t(Kerr et al., 2017; Hedrich, 2004).\n   112\t\n   113\tThe second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\n   114\t\n   115\tA third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\n   116\t\n   117\tTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n   118\t\n   119\t| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n   120\t\n   121\tATT | -0.431** | -0.438** | -0.362* | -0.149***\n   122\t\n   123\t| (0.218) | (0.208) | (0.205) | (0.038)\n   124\t\n   125\t% change | -35.0% | -35.5% | -30.3% | -13.9%\n   126\t\n   127\tTreated regions | 50 | | |\n   128\t\n   129\tControl regions | 608 | | |\n   130\t\n   131\tObservations | 17,250 | | |\n   132\t\n   133\tNote: All specifications use the 5 km buffer,\n   134\tnever-treated comparison regions (640 regions), and a \\pm 5-year window","is_error":false}]},"uuid":"1fb3157e-d024-42af-9f0e-60577e05d615","timestamp":"2026-10-30T05:06:38.472Z","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":"099a480b-cdee-490d-b5df-72f9c6cd1d1d","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0229","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","version":"2.1.314","gitBranch":"HEAD"}25{"parentUuid":"1fb3157e-d024-42af-9f0e-60577e05d615","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqioEYEJ8VbuQaywMdM","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017e9GBpJRRauMVdXyy4VY6A","name":"Bash","input":{"command":"cat -n data/sources/arxiv-2606.14621v1.txt; cat -n data/sources/arxiv-2505.04555v3.txt; cat -n data/sources/arxiv-2510.03658v1.txt","description":"Read other three sources"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4024,"cache_read_input_tokens":6883,"output_tokens":239,"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":4024},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqinxvfCvVueKKq7AFu","requestRef":"b041876f-6832-45cb-95ff-c70e4ccfce28","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f59c5e6d-72dd-4a68-a760-5db5310209ac","timestamp":"2026-10-30T05:06:39.269Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0229","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","version":"2.1.314","gitBranch":"HEAD"}26{"parentUuid":"f59c5e6d-72dd-4a68-a760-5db5310209ac","isSidechain":false,"promptId":"1257d57b-48b2-460e-b967-208321121a94","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017e9GBpJRRauMVdXyy4VY6A","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).\n     1\tSOURCE EXTRACT — arxiv-2510.03658v1\n     2\tTitle: Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin\n     3\tAuthors: Selidji Caroline Tossou\n     4\tBibliographic record: arXiv:2510.03658v1 [econ.GN], submitted 4 Oct 2025 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2510.03658v1\n     5\tRetrieved: https://arxiv.org/html/2510.03658v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f2d9f04dcf4789983c28f97dc13705eb2d6ed583c4860c940f921d7b549aea59)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, author and abstract; Section 4.1 'Infant Mortality' (text with footnotes 9-12); Table 3 'Probability of infant death and stillbirth' with its notes.\n     8\tOmitted: Sections 1-3, 4.2-6, Tables 1-2 and 4-16, the references. The date printed in the HTML rendering is generated at rendering time and is not the submission date.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin\n    13\t\n    14\tSelidji Caroline Tossou (University of Wisconsin-Madison)\n    15\t\n    16\t###### Abstract\n    17\t\n    18\tThis paper evaluates the causal effect of the access to Benin’s free cesarean section policy on females and their children. I use a large sample of Demographic and Health Surveys (DHS) for West African countries and analyze how the exemption of the cesarean section user fees for females in Benin directly impacts maternal and infant mortality, family size decisions, and labor market participation. I use a Difference in Differences approach and find that having access to the free cesarean section policy significantly reduces the number of stillbirths and infant mortality by 0.0855 (a 18.79 percentage change). Second, for the surviving children, I find that access to the free cesarean section increases the likelihood of maternal mortality by 0.00465 (a 5.21 percentage change). The policy is effective at reducing infant mortality and saving the newborn. However, it harms the mother’s health which translates to lower fertility after the first birth and decreased maternal labor supply post-birth.\n    19\t\n    20\t## 4 Findings\n    21\t\n    22\t### 4.1 Infant Mortality\n    23\t\n    24\tI first investigate the infant death rate under the policy.\n    25\tFigure 15 shows that the exemption of cesarean section fees in Benin leads to an increase in the use of the service. What are the implications for infant mortality? I use a Difference in Differences specification to explore the possible impacts of access to the free cesarean section on infant mortality. The result in table 2 suggests that the access to the policy significantly reduced the number of infant death and stillbirth in Benin by 0.0855 (this represents a percentage change of 18.79 percent^9^9\n    26\t9\n    27\t\n    28\tI divide the estimate of beta by the mean of the dependent variable: -0.0855/0.455=-0.1879 ). This means that for over 100 newborns who would have died in the absence of the policy, approximately 18 are saved because of the access to the exemption of the cesarean section user fees. This result is promising and aligns with the goals of the free cesarean section policy, which primarily intends to lower the infant mortality in Benin. To the best of my knowledge, this is the first paper that evaluates the causal effect of free cesarean section policy on Benin’s infant mortality. Wealth and education attainment play a role in decreasing infant mortality. This can be explained by the fact that the free cesarean section covers only the surgery and a medicine kit for the mother. It does not cover prenatal and antepartum checkups. So a pregnant woman who has the means is more likely to identify and prevent any problems related to the pregnancy and the newborn’s health. Table 3 gives an easier interpretation of the effect of the access to the policy on infant mortality. In this table, the outcome of interest is a binary variable that takes value 1 if a female has an infant death or a pregnancy loss in the three years preceding the data collection and 0 otherwise. Thus, I interpret the coefficient \\hat{\\beta_{1}} as the effect of the policy on the likelihood of infant mortality. Using my most preferred specification (Column (4)), the result suggests that access to the policy decreases the likelihood of infant death or pregnancy loss of a female in Benin by 0.0962. Compared to the mean of the dependent variable, this effect represents a percentage change of 36.85 percent. To see which female category is driving this result, I decompose the effect by age group, wealth index, and education attainment.\n    29\t\n    30\tThe first panel of table 4 presents the stratification by age group. The policy is more effective at reducing infant mortality for females older than 25. For females between 15 and 24, the probability of infant mortality is reduced by only 4.65 percent compared to a reduction of 30.85 percent for a female between 25 and 34, a reduction of 43.53 percent for females between 35 and 44, and the highest decrease of 47.25 percent for females between 45 and 49^10^10\n    31\t10\n    32\t\n    33\tThese are the percentage change of infant mortality obtained by dividing the coefficient estimated by the mean of the dependent variable. The same exercise is done by using the wealth index of females. The results in the second panel of table 4 suggest that the effect of the free cesarean section on infant mortality is the biggest for females who are the poorest, poorer, or have a middle wealth index^11^11\n    34\t11\n    35\t\n    36\tThe modalities Poorest, Poorer, Middle, Richer, and Richest are pre-computed in the DHS data (46.36 percent, 35.84 percent, and 41.72 percent respectively). For the richer and richest females, the effect of the policy is a percentage change reduction of 28.52 percent and 16.47 percent on the likelihood of infant death. The last panel of table 4 gives a stratification by education level. The highest effect of the policy on the reduction of the likelihood of infant death (-39.07 percent) is for females who have no education level. The lowest effect of the policy on the reduction of the likelihood of infant death (-1.4 percent) is for females who have higher education levels.^12^12\n    37\t12\n    38\t\n    39\tI test the difference between all the coefficients and they are all significant and statistically different\n    40\t\n    41\tTo summarize, I find that access to the free cesarean section in Benin reduces the likelihood of infant mortality on average by 36.85 percent. The policy is more effective at reducing infant death for older females, females who are below the middle wealth index, and females who have no education.\n    42\t\n    43\tTable 3: Probability of infant death and stillbirth\n    44\t\n    45\t| Probability of Infant death or stillbirth | | |\n    46\t\n    47\t| (1) | (2) | (3) | (4)\n    48\t\n    49\tTreatment | -0.0911 | -0.0908 | -0.0961 | -0.0962\n    50\t\n    51\t| (0.0113) | (0.0113) | (0.0126) | (0.0129)\n    52\t\n    53\tEducation Attainment | -0.0578 | -0.0576 | -0.0443 | -0.0443\n    54\t\n    55\t| (0.00346) | (0.00340) | (0.00415) | (0.00420)\n    56\t\n    57\tAge of Female | 0.0168 | 0.0170 | 0.0171 | 0.0171\n    58\t\n    59\t| (0.00108) | (0.00112) | (0.00104) | (0.00108)\n    60\t\n    61\tAge of Partner | | -0.000425 | -0.000448 | -0.000435\n    62\t\n    63\t| | (0.000183) | (0.000151) | (0.000152)\n    64\t\n    65\tWealth Index | | | -0.0302 | -0.0302\n    66\t\n    67\t| | | (0.00277) | (0.00277)\n    68\t\n    69\tMarital Status | | | | 0.00128\n    70\t\n    71\t| | | | (0.00421)\n    72\t\n    73\tCountry Fixed Effect | Yes | Yes | Yes | Yes\n    74\t\n    75\tTime Fixed Effect. | Yes | Yes | Yes | Yes\n    76\t\n    77\t_cons | -0.129 | -0.114 | -0.0400 | -0.0406\n    78\t\n    79\t| (0.0323) | (0.0298) | (0.0266) | (0.0255)\n    80\t\n    81\tObservations | 196554 | 196424 | 196424 | 196423\n    82\t\n    83\t% Change | -0.3490 | -0.3478 | -0.3681 | -0.3685\n    84\t\n    85\tR-squared | 0.196 | 0.197 | 0.204 | 0.204\n    86\t\n    87\tMean Dep. Var. | 0.261 | 0.261 | 0.261 | 0.261\n    88\t\n    89\tStandard errors in parentheses; Standard errors are clustered at the household level. | | | |\n    90\t\n    91\tDep. Var. Mean is the dependent variable mean. Treatment is Benin Females after 2008 | | | |","is_error":false}]},"uuid":"0b4b1e0a-8c60-4a42-8052-ecf77a5a1133","timestamp":"2026-10-30T05:06:39.312Z","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).\n     1\tSOURCE EXTRACT — arxiv-2510.03658v1\n     2\tTitle: Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin\n     3\tAuthors: Selidji Caroline Tossou\n     4\tBibliographic record: arXiv:2510.03658v1 [econ.GN], submitted 4 Oct 2025 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2510.03658v1\n     5\tRetrieved: https://arxiv.org/html/2510.03658v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f2d9f04dcf4789983c28f97dc13705eb2d6ed583c4860c940f921d7b549aea59)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, author and abstract; Section 4.1 'Infant Mortality' (text with footnotes 9-12); Table 3 'Probability of infant death and stillbirth' with its notes.\n     8\tOmitted: Sections 1-3, 4.2-6, Tables 1-2 and 4-16, the references. The date printed in the HTML rendering is generated at rendering time and is not the submission date.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin\n    13\t\n    14\tSelidji Caroline Tossou (University of Wisconsin-Madison)\n    15\t\n    16\t###### Abstract\n    17\t\n    18\tThis paper evaluates the causal effect of the access to Benin’s free cesarean section policy on females and their children. I use a large sample of Demographic and Health Surveys (DHS) for West African countries and analyze how the exemption of the cesarean section user fees for females in Benin directly impacts maternal and infant mortality, family size decisions, and labor market participation. I use a Difference in Differences approach and find that having access to the free cesarean section policy significantly reduces the number of stillbirths and infant mortality by 0.0855 (a 18.79 percentage change). Second, for the surviving children, I find that access to the free cesarean section increases the likelihood of maternal mortality by 0.00465 (a 5.21 percentage change). The policy is effective at reducing infant mortality and saving the newborn. However, it harms the mother’s health which translates to lower fertility after the first birth and decreased maternal labor supply post-birth.\n    19\t\n    20\t## 4 Findings\n    21\t\n    22\t### 4.1 Infant Mortality\n    23\t\n    24\tI first investigate the infant death rate under the policy.\n    25\tFigure 15 shows that the exemption of cesarean section fees in Benin leads to an increase in the use of the service. What are the implications for infant mortality? I use a Difference in Differences specification to explore the possible impacts of access to the free cesarean section on infant mortality. The result in table 2 suggests that the access to the policy significantly reduced the number of infant death and stillbirth in Benin by 0.0855 (this represents a percentage change of 18.79 percent^9^9\n    26\t9\n    27\t\n    28\tI divide the estimate of beta by the mean of the dependent variable: -0.0855/0.455=-0.1879 ). This means that for over 100 newborns who would have died in the absence of the policy, approximately 18 are saved because of the access to the exemption of the cesarean section user fees. This result is promising and aligns with the goals of the free cesarean section policy, which primarily intends to lower the infant mortality in Benin. To the best of my knowledge, this is the first paper that evaluates the causal effect of free cesarean section policy on Benin’s infant mortality. Wealth and education attainment play a role in decreasing infant mortality. This can be explained by the fact that the free cesarean section covers only the surgery and a medicine kit for the mother. It does not cover prenatal and antepartum checkups. So a pregnant woman who has the means is more likely to identify and prevent any problems related to the pregnancy and the newborn’s health. Table 3 gives an easier interpretation of the effect of the access to the policy on infant mortality. In this table, the outcome of interest is a binary variable that takes value 1 if a female has an infant death or a pregnancy loss in the three years preceding the data collection and 0 otherwise. Thus, I interpret the coefficient \\hat{\\beta_{1}} as the effect of the policy on the likelihood of infant mortality. Using my most preferred specification (Column (4)), the result suggests that access to the policy decreases the likelihood of infant death or pregnancy loss of a female in Benin by 0.0962. Compared to the mean of the dependent variable, this effect represents a percentage change of 36.85 percent. To see which female category is driving this result, I decompose the effect by age group, wealth index, and education attainment.\n    29\t\n    30\tThe first panel of table 4 presents the stratification by age group. The policy is more effective at reducing infant mortality for females older than 25. For females between 15 and 24, the probability of infant mortality is reduced by only 4.65 percent compared to a reduction of 30.85 percent for a female between 25 and 34, a reduction of 43.53 percent for females between 35 and 44, and the highest decrease of 47.25 percent for females between 45 and 49^10^10\n    31\t10\n    32\t\n    33\tThese are the percentage change of infant mortality obtained by dividing the coefficient estimated by the mean of the dependent variable. The same exercise is done by using the wealth index of females. The results in the second panel of table 4 suggest that the effect of the free cesarean section on infant mortality is the biggest for females who are the poorest, poorer, or have a middle wealth index^11^11\n    34\t11\n    35\t\n    36\tThe modalities Poorest, Poorer, Middle, Richer, and Richest are pre-computed in the DHS data (46.36 percent, 35.84 percent, and 41.72 percent respectively). For the richer and richest females, the effect of the policy is a percentage change reduction of 28.52 percent and 16.47 percent on the likelihood of infant death. The last panel of table 4 gives a stratification by education level. The highest effect of the policy on the reduction of the likelihood of infant death (-39.07 percent) is for females who have no education level. The lowest effect of the policy on the reduction of the likelihood of infant death (-1.4 percent) is for females who have higher education levels.^12^12\n    37\t12\n    38\t\n    39\tI test the difference between all the coefficients and they are all significant and statistically different\n    40\t\n    41\tTo summarize, I find that access to the free cesarean section in Benin reduces the likelihood of infant mortality on average by 36.85 percent. The policy is more effective at reducing infant death for older females, females who are below the middle wealth index, and females who have no education.\n    42\t\n    43\tTable 3: Probability of infant death and stillbirth\n    44\t\n    45\t| Probability of Infant death or stillbirth | | |\n    46\t\n    47\t| (1) | (2) | (3) | (4)\n    48\t\n    49\tTreatment | -0.0911 | -0.0908 | -0.0961 | -0.0962\n    50\t\n    51\t| (0.0113) | (0.0113) | (0.0126) | (0.0129)\n    52\t\n    53\tEducation Attainment | -0.0578 | -0.0576 | -0.0443 | -0.0443\n    54\t\n    55\t| (0.00346) | (0.00340) | (0.00415) | (0.00420)\n    56\t\n    57\tAge of Female | 0.0168 | 0.0170 | 0.0171 | 0.0171\n    58\t\n    59\t| (0.00108) | (0.00112) | (0.00104) | (0.00108)\n    60\t\n    61\tAge of Partner | | -0.000425 | -0.000448 | -0.000435\n    62\t\n    63\t| | (0.000183) | (0.000151) | (0.000152)\n    64\t\n    65\tWealth Index | | | -0.0302 | -0.0302\n    66\t\n    67\t| | | (0.00277) | (0.00277)\n    68\t\n    69\tMarital Status | | | | 0.00128\n    70\t\n    71\t| | | | (0.00421)\n    72\t\n    73\tCountry Fixed Effect | Yes | Yes | Yes | Yes\n    74\t\n    75\tTime Fixed Effect. | Yes | Yes | Yes | Yes\n    76\t\n    77\t_cons | -0.129 | -0.114 | -0.0400 | -0.0406\n    78\t\n    79\t| (0.0323) | (0.0298) | (0.0266) | (0.0255)\n    80\t\n    81\tObservations | 196554 | 196424 | 196424 | 196423\n    82\t\n    83\t% Change | -0.3490 | -0.3478 | -0.3681 | -0.3685\n    84\t\n    85\tR-squared | 0.196 | 0.197 | 0.204 | 0.204\n    86\t\n    87\tMean Dep. Var. | 0.261 | 0.261 | 0.261 | 0.261\n    88\t\n    89\tStandard errors in parentheses; Standard errors are clustered at the household level. | | | |\n    90\t\n    91\tDep. Var. Mean is the dependent variable mean. 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Evidence from Switzerland\n     3\tAuthors: Ana Armendariz (University of St. Gallen)\n     4\tBibliographic record: arXiv:2609.27732v1 [econ.GN], submitted 23 Sep 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2609.27732v1\n     5\tRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\n     8\tOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n    13\t\n    14\tAna Armendariz*\n    15\t\n    16\tSeptember 23, 2026\n    17\t\n    18\tThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n    19\t\n    20\tKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n    21\t\n    22\t## 6 Results\n    23\t\n    24\tThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n    25\t\n    26\tFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n    27\t\n    28\tNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n    29\t\n    30\tFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n    31\t\n    32\tTable 6: Overall Average Treatment Effect on the Treated (ATT)\n    33\t\n    34\t| 1 km | 3 km | 5 km\n    35\t\n    36\tATT | -0.399 | -0.283 | -0.431**\n    37\t\n    38\t| (0.412) | (0.279) | (0.210)\n    39\t\n    40\t% change | -32.9% | -24.7% | -35.0%\n    41\t\n    42\tTreated regions | 7 | 35 | 50\n    43\t\n    44\tControl regions | 608 | 608 | 608\n    45\t\n    46\tObservations | 16,175 | 16,875 | 17,250\n    47\t\n    48\tNote: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\n    49\tthroughout. Regions near DCRs that opened before 1998\n    50\t(always-treated) are excluded. Standard errors clustered at the Medstat region level in\n    51\tparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n    52\t{}^{*}p<0.10 .\n    53\t\n    54\tTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\n    55\tcorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n    56\t\n    57\tTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\n    58\tsub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\n    59\t\n    60\tFigure 7: Event study: Sub-outcome estimates,\n    61\twithin 5 km, \\pm 5-year window\n    62\t\n    63\tNote: The figure plots event-study coefficients from the doubly robust Callaway & Sant’Anna (2021) estimator for four diagnostic sub-categories of drug-related hospitalizations. Each panel uses a separate y -axis to accommodate differences in scale across outcomes. The sample includes all staggered-treated MedStat regions within 5 km of a\n    64\tDCR that opened between 1998 and 2022. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded. 95% confidence intervals are based on standard errors clustered at the MedStat region level.\n    65\t\n    66\tTable 7: Overall ATT by Outcome Category and Buffer Zone\n    67\t\n    68\tOutcome | 1 km | 3 km | 5 km\n    69\t\n    70\tMental/Behavioral | -0.354 | -0.202 | -0.381*\n    71\t\n    72\t| (0.382) | (0.244) | (0.220)\n    73\t\n    74\tHepatitis | -0.270 | -0.413 | -0.406\n    75\t\n    76\t| (0.486) | (0.270) | (0.250)\n    77\t\n    78\tHIV | 0.063 | -0.239 | -0.340\n    79\t\n    80\t| (0.801) | (0.337) | (0.294)\n    81\t\n    82\tNarcotics | 1.186*** | 0.549*** | 0.390**\n    83\t\n    84\t| (0.288) | (0.187) | (0.165)\n    85\t\n    86\tDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n    87\t\n    88\t| (0.137) | (0.215) | (0.178)\n    89\t\n    90\tTreated regions | 7 | 35 | 50\n    91\t\n    92\tControl regions | 608 (never-treated) | |\n    93\t\n    94\tNote: Each cell reports the overall ATT from\n    95\tthe doubly robust Callaway & Sant’Anna (2021) estimator with\n    96\tnever-treated controls and a universal base period. Outcomes are\n    97\tlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\n    98\tmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\n    99\tHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\n   100\testimates are identified from a single treated region per cohort and\n   101\tshould be interpreted with caution. Regions near DCRs opened before\n   102\t1998 excluded. Standard errors clustered at the Medstat region level.\n   103\tSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n   104\t\n   105\tFigure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\n   106\t\n   107\t### 6.1 Mechanisms\n   108\t\n   109\tThe sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n   110\tsubstitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\n   111\t(Kerr et al., 2017; Hedrich, 2004).\n   112\t\n   113\tThe second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\n   114\t\n   115\tA third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\n   116\t\n   117\tTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n   118\t\n   119\t| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n   120\t\n   121\tATT | -0.431** | -0.438** | -0.362* | -0.149***\n   122\t\n   123\t| (0.218) | (0.208) | (0.205) | (0.038)\n   124\t\n   125\t% change | -35.0% | -35.5% | -30.3% | -13.9%\n   126\t\n   127\tTreated regions | 50 | | |\n   128\t\n   129\tControl regions | 608 | | |\n   130\t\n   131\tObservations | 17,250 | | |\n   132\t\n   133\tNote: All specifications use the 5 km buffer,\n   134\tnever-treated comparison regions (640 regions), and a \\pm 5-year window","is_error":false},{"tool_use_id":"toolu_017e9GBpJRRauMVdXyy4VY6A","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).\n     1\tSOURCE EXTRACT — arxiv-2510.03658v1\n     2\tTitle: Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin\n     3\tAuthors: Selidji Caroline Tossou\n     4\tBibliographic record: arXiv:2510.03658v1 [econ.GN], submitted 4 Oct 2025 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2510.03658v1\n     5\tRetrieved: https://arxiv.org/html/2510.03658v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f2d9f04dcf4789983c28f97dc13705eb2d6ed583c4860c940f921d7b549aea59)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, author and abstract; Section 4.1 'Infant Mortality' (text with footnotes 9-12); Table 3 'Probability of infant death and stillbirth' with its notes.\n     8\tOmitted: Sections 1-3, 4.2-6, Tables 1-2 and 4-16, the references. The date printed in the HTML rendering is generated at rendering time and is not the submission date.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin\n    13\t\n    14\tSelidji Caroline Tossou (University of Wisconsin-Madison)\n    15\t\n    16\t###### Abstract\n    17\t\n    18\tThis paper evaluates the causal effect of the access to Benin’s free cesarean section policy on females and their children. I use a large sample of Demographic and Health Surveys (DHS) for West African countries and analyze how the exemption of the cesarean section user fees for females in Benin directly impacts maternal and infant mortality, family size decisions, and labor market participation. I use a Difference in Differences approach and find that having access to the free cesarean section policy significantly reduces the number of stillbirths and infant mortality by 0.0855 (a 18.79 percentage change). Second, for the surviving children, I find that access to the free cesarean section increases the likelihood of maternal mortality by 0.00465 (a 5.21 percentage change). The policy is effective at reducing infant mortality and saving the newborn. However, it harms the mother’s health which translates to lower fertility after the first birth and decreased maternal labor supply post-birth.\n    19\t\n    20\t## 4 Findings\n    21\t\n    22\t### 4.1 Infant Mortality\n    23\t\n    24\tI first investigate the infant death rate under the policy.\n    25\tFigure 15 shows that the exemption of cesarean section fees in Benin leads to an increase in the use of the service. What are the implications for infant mortality? I use a Difference in Differences specification to explore the possible impacts of access to the free cesarean section on infant mortality. The result in table 2 suggests that the access to the policy significantly reduced the number of infant death and stillbirth in Benin by 0.0855 (this represents a percentage change of 18.79 percent^9^9\n    26\t9\n    27\t\n    28\tI divide the estimate of beta by the mean of the dependent variable: -0.0855/0.455=-0.1879 ). This means that for over 100 newborns who would have died in the absence of the policy, approximately 18 are saved because of the access to the exemption of the cesarean section user fees. This result is promising and aligns with the goals of the free cesarean section policy, which primarily intends to lower the infant mortality in Benin. To the best of my knowledge, this is the first paper that evaluates the causal effect of free cesarean section policy on Benin’s infant mortality. Wealth and education attainment play a role in decreasing infant mortality. This can be explained by the fact that the free cesarean section covers only the surgery and a medicine kit for the mother. It does not cover prenatal and antepartum checkups. So a pregnant woman who has the means is more likely to identify and prevent any problems related to the pregnancy and the newborn’s health. Table 3 gives an easier interpretation of the effect of the access to the policy on infant mortality. In this table, the outcome of interest is a binary variable that takes value 1 if a female has an infant death or a pregnancy loss in the three years preceding the data collection and 0 otherwise. Thus, I interpret the coefficient \\hat{\\beta_{1}} as the effect of the policy on the likelihood of infant mortality. Using my most preferred specification (Column (4)), the result suggests that access to the policy decreases the likelihood of infant death or pregnancy loss of a female in Benin by 0.0962. Compared to the mean of the dependent variable, this effect represents a percentage change of 36.85 percent. To see which female category is driving this result, I decompose the effect by age group, wealth index, and education attainment.\n    29\t\n    30\tThe first panel of table 4 presents the stratification by age group. The policy is more effective at reducing infant mortality for females older than 25. For females between 15 and 24, the probability of infant mortality is reduced by only 4.65 percent compared to a reduction of 30.85 percent for a female between 25 and 34, a reduction of 43.53 percent for females between 35 and 44, and the highest decrease of 47.25 percent for females between 45 and 49^10^10\n    31\t10\n    32\t\n    33\tThese are the percentage change of infant mortality obtained by dividing the coefficient estimated by the mean of the dependent variable. The same exercise is done by using the wealth index of females. The results in the second panel of table 4 suggest that the effect of the free cesarean section on infant mortality is the biggest for females who are the poorest, poorer, or have a middle wealth index^11^11\n    34\t11\n    35\t\n    36\tThe modalities Poorest, Poorer, Middle, Richer, and Richest are pre-computed in the DHS data (46.36 percent, 35.84 percent, and 41.72 percent respectively). For the richer and richest females, the effect of the policy is a percentage change reduction of 28.52 percent and 16.47 percent on the likelihood of infant death. The last panel of table 4 gives a stratification by education level. The highest effect of the policy on the reduction of the likelihood of infant death (-39.07 percent) is for females who have no education level. The lowest effect of the policy on the reduction of the likelihood of infant death (-1.4 percent) is for females who have higher education levels.^12^12\n    37\t12\n    38\t\n    39\tI test the difference between all the coefficients and they are all significant and statistically different\n    40\t\n    41\tTo summarize, I find that access to the free cesarean section in Benin reduces the likelihood of infant mortality on average by 36.85 percent. The policy is more effective at reducing infant death for older females, females who are below the middle wealth index, and females who have no education.\n    42\t\n    43\tTable 3: Probability of infant death and stillbirth\n    44\t\n    45\t| Probability of Infant death or stillbirth | | |\n    46\t\n    47\t| (1) | (2) | (3) | (4)\n    48\t\n    49\tTreatment | -0.0911 | -0.0908 | -0.0961 | -0.0962\n    50\t\n    51\t| (0.0113) | (0.0113) | (0.0126) | (0.0129)\n    52\t\n    53\tEducation Attainment | -0.0578 | -0.0576 | -0.0443 | -0.0443\n    54\t\n    55\t| (0.00346) | (0.00340) | (0.00415) | (0.00420)\n    56\t\n    57\tAge of Female | 0.0168 | 0.0170 | 0.0171 | 0.0171\n    58\t\n    59\t| (0.00108) | (0.00112) | (0.00104) | (0.00108)\n    60\t\n    61\tAge of Partner | | -0.000425 | -0.000448 | -0.000435\n    62\t\n    63\t| | (0.000183) | (0.000151) | (0.000152)\n    64\t\n    65\tWealth Index | | | -0.0302 | -0.0302\n    66\t\n    67\t| | | (0.00277) | (0.00277)\n    68\t\n    69\tMarital Status | | | | 0.00128\n    70\t\n    71\t| | | | (0.00421)\n    72\t\n    73\tCountry Fixed Effect | Yes | Yes | Yes | Yes\n    74\t\n    75\tTime Fixed Effect. | Yes | Yes | Yes | Yes\n    76\t\n    77\t_cons | -0.129 | -0.114 | -0.0400 | -0.0406\n    78\t\n    79\t| (0.0323) | (0.0298) | (0.0266) | (0.0255)\n    80\t\n    81\tObservations | 196554 | 196424 | 196424 | 196423\n    82\t\n    83\t% Change | -0.3490 | -0.3478 | -0.3681 | -0.3685\n    84\t\n    85\tR-squared | 0.196 | 0.197 | 0.204 | 0.204\n    86\t\n    87\tMean Dep. Var. | 0.261 | 0.261 | 0.261 | 0.261\n    88\t\n    89\tStandard errors in parentheses; Standard errors are clustered at the household level. | | | |\n    90\t\n    91\tDep. Var. Mean is the dependent variable mean. Treatment is Benin Females after 2008 | | | 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-n 1,400p ~/wp29/gates/pgar_gate.py | grep -n -i -E \"exclu|phase|receipt|claims|status|section\" | head -60","description":"Inspect gate expectations"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":15722,"cache_read_input_tokens":10907,"output_tokens":1888,"output_tokens_details":{"thinking_tokens":1753},"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":15722},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqiybe5QqgR745Dm9J8","requestRef":"f72b2f94-ad39-48a5-a34a-22bac47469aa","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"412d08f6-e35e-49b0-90e8-28845c801025","timestamp":"2026-10-30T05:06:57.503Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0229","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","version":"2.1.314","gitBranch":"HEAD"}34{"parentUuid":"412d08f6-e35e-49b0-90e8-28845c801025","isSidechain":false,"promptId":"1257d57b-48b2-460e-b967-208321121a94","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Tdqdxrdnv4uhjCaBUfkCHq","type":"tool_result","content":"7:Writes a receipt to <workspace_dir>/out/state/gate_receipts/<utc>.json and exits 0 if passed, 1 otherwise.\n96:    # 3. report sections\n102:    for s in spec.get(\"report_sections\", []):\n104:        add(f\"section:{s}\", ok, \"present\" if ok else \"heading not found\")\n106:    # 4. claims\n107:    claims, cerr = _load_json(os.path.join(ws, \"out\", \"claims.json\"))\n108:    if \"claims_reference_existing_keys\" in fam or os.path.exists(os.path.join(ws, \"out\", \"claims.json\")):\n109:        if not isinstance(claims, list) or not claims:\n110:            add(\"claims_json\", False, f\"out/claims.json {cerr or 'must be a non-empty list'}\")\n113:            for i, c in enumerate(claims):\n114:                if not isinstance(c, dict) or not all(k in c for k in (\"id\", \"text\", \"results_keys\", \"status\")):\n116:                if c[\"status\"] not in (\"supported\", \"unsupported\", \"not_evaluated\"):\n117:                    bad.append(f\"{c.get('id')}: bad status {c['status']!r}\")\n121:            add(\"claims_json\", not bad, \"; \".join(bad) if bad else f\"{len(claims)} claims, all keys resolve\")\n122:    # 5. phase ledger\n123:    pl_path = os.path.join(ws, \"out\", \"state\", \"phases.json\")\n124:    if \"phase_ledger_consistent\" in fam and os.path.exists(pl_path):\n127:            add(\"phase_ledger\", False, f\"phases.json {perr or 'not an object'}\")\n131:                if not isinstance(st, dict) or st.get(\"status\") not in (\"pending\", \"running\", \"passed\", \"failed\", \"blocked\", \"stale\", \"skipped_optional\"):\n132:                    bad.append(f\"{pid}: invalid status\"); continue\n133:                if st.get(\"status\") == \"passed\" and not st.get(\"checks\"):\n135:            not_done = [pid for pid, st in pl.items() if isinstance(st, dict) and st.get(\"status\") not in (\"passed\", \"skipped_optional\", \"blocked\")]\n137:                bad.append(f\"phases not terminal: {not_done}\")\n138:            add(\"phase_ledger\", not bad, \"; \".join(bad) if bad else \"consistent\")\n139:    # 6. exclusion ledger\n140:    ex_path = os.path.join(ws, \"out\", \"exclusions.csv\")\n141:    if \"exclusion_ledger_sums\" in fam and os.path.exists(ex_path):\n153:            add(\"exclusion_ledger\", not bad, \"; \".join(bad) if bad else f\"{len(rows)} steps consistent\")\n155:            add(\"exclusion_ledger\", False, f\"unreadable: {e}\")\n215:    receipt = {\"gate_version\": GATE_VERSION, \"workspace\": ws, \"utc\": datetime.datetime.now(datetime.timezone.utc).isoformat(),\n218:        d = os.path.join(ws, \"out\", \"state\", \"gate_receipts\"); os.makedirs(d, exist_ok=True)\n221:            json.dump(receipt, fh, indent=1)\n222:        receipt[\"receipt_path\"] = path\n223:        receipt[\"receipt_sha256\"] = hashlib.sha256(open(path, \"rb\").read()).hexdigest()\n225:        receipt[\"receipt_path\"] = None; receipt[\"receipt_error\"] = str(e)\n226:    return receipt\n245:        print(f\"receipt: {r.get('receipt_path')}\")","is_error":false}]},"uuid":"6640d4f0-d33c-46cc-a95e-d584a2b65a12","timestamp":"2026-10-30T05:06:57.545Z","toolUseResult":{"stdout":"7:Writes a receipt to <workspace_dir>/out/state/gate_receipts/<utc>.json and exits 0 if passed, 1 otherwise.\n96:    # 3. report sections\n102:    for s in spec.get(\"report_sections\", []):\n104:        add(f\"section:{s}\", ok, \"present\" if ok else \"heading not found\")\n106:    # 4. claims\n107:    claims, cerr = _load_json(os.path.join(ws, \"out\", \"claims.json\"))\n108:    if \"claims_reference_existing_keys\" in fam or os.path.exists(os.path.join(ws, \"out\", \"claims.json\")):\n109:        if not isinstance(claims, list) or not claims:\n110:            add(\"claims_json\", False, f\"out/claims.json {cerr or 'must be a non-empty list'}\")\n113:            for i, c in enumerate(claims):\n114:                if not isinstance(c, dict) or not all(k in c for k in (\"id\", \"text\", \"results_keys\", \"status\")):\n116:                if c[\"status\"] not in (\"supported\", \"unsupported\", \"not_evaluated\"):\n117:                    bad.append(f\"{c.get('id')}: bad status {c['status']!r}\")\n121:            add(\"claims_json\", not bad, \"; \".join(bad) if bad else f\"{len(claims)} claims, all keys resolve\")\n122:    # 5. phase ledger\n123:    pl_path = os.path.join(ws, \"out\", \"state\", \"phases.json\")\n124:    if \"phase_ledger_consistent\" in fam and os.path.exists(pl_path):\n127:            add(\"phase_ledger\", False, f\"phases.json {perr or 'not an object'}\")\n131:                if not isinstance(st, dict) or st.get(\"status\") not in (\"pending\", \"running\", \"passed\", \"failed\", \"blocked\", \"stale\", \"skipped_optional\"):\n132:                    bad.append(f\"{pid}: invalid status\"); continue\n133:                if st.get(\"status\") == \"passed\" and not st.get(\"checks\"):\n135:            not_done = [pid for pid, st in pl.items() if isinstance(st, dict) and st.get(\"status\") not in (\"passed\", \"skipped_optional\", \"blocked\")]\n137:                bad.append(f\"phases not terminal: {not_done}\")\n138:            add(\"phase_ledger\", not bad, \"; \".join(bad) if bad else \"consistent\")\n139:    # 6. exclusion ledger\n140:    ex_path = os.path.join(ws, \"out\", \"exclusions.csv\")\n141:    if \"exclusion_ledger_sums\" in fam and os.path.exists(ex_path):\n153:            add(\"exclusion_ledger\", not bad, \"; \".join(bad) if bad else f\"{len(rows)} steps consistent\")\n155:            add(\"exclusion_ledger\", False, f\"unreadable: {e}\")\n215:    receipt = {\"gate_version\": GATE_VERSION, \"workspace\": ws, \"utc\": datetime.datetime.now(datetime.timezone.utc).isoformat(),\n218:        d = os.path.join(ws, \"out\", \"state\", \"gate_receipts\"); os.makedirs(d, exist_ok=True)\n221:            json.dump(receipt, fh, indent=1)\n222:        receipt[\"receipt_path\"] = path\n223:        receipt[\"receipt_sha256\"] = hashlib.sha256(open(path, \"rb\").read()).hexdigest()\n225:        receipt[\"receipt_path\"] = None; receipt[\"receipt_error\"] = str(e)\n226:    return receipt\n245:        print(f\"receipt: {r.get('receipt_path')}\")","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"412d08f6-e35e-49b0-90e8-28845c801025","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0229","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","version":"2.1.314","gitBranch":"HEAD"}35{"type":"api-request-blob","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","hash":"52a0bac19db0b5265964fc2e0b583e0d8feb4eb9af77d4d45bf399ded7637d40","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSnSMKEAgTGAI4AUIIdGhpbmtpbmcSDErh8vpYLxRNdTvm9BoMLbeDyD4dvzTGahrsIjCELsDuzCBBDaYTP6OeRL1x3NSCYIXTd2QM1gOy+oZ28NfyjZzsB1joHoUrYmGJm4wquiLX/RcWgYs4yDeIAyddlhNXdTQpfZ3BetAwLFvnbz+tXPsnL+vDx8C9PxnHH3YIVD3+o8x1LznemS1GTJz5dYAozmjYrL39qEvZiyKdj1VGMbUO4MvtqhfcNqKCKpeLpeWVomOXZLCSzlIX1ve0/zTMnaDPY+Nb9mEvwWas6E65gEmzQuC42zpmyjWaARJIxT03UBFZ80K6D63AaNh1wbZYYVE+Ft6juXRSnUcToss/3dJCxWiU1dFdQfiSUNAXDipp98mpdiFiBZKMNrayU/kBo+FisDXGgXJScLcvG5Ts0IERYEqMfI9TywRCh3Y8SAuomOjVp7WNB2VkZnWlc1DPy3cwbafcUQ/L0dzuEhAkPIfu9cA0Xj7XbdNzYfWsV+QCGW8RNnlCCgVEr8Wv7pqdIUcPA2oAX3F131T7NQbx8M4cYdGIKWqyoqYQDv+XrGkZd/AOSjfBhdieaMqZ0CA3QLtdcwqhdxAd8l87gCAdrzDdRL0MpaNs04qQ7HE8FR/H36Zi61+pfBEwzbFuUm32Cj63NNOINRxTybz4g+fkNh2j9QwEeMdAVccLIsFPHPeMp2pS9YKraYcQkmCE8QezqJJbYVqWRuRWtZmJDRV4PH2PvXRhqseTOajTD4cvanaxdHHxkFotDGTGtRpdqlkRBr+0epUUCOisOmZ2IKotMBmDyaLIejooEWK6QShhWGPnLs30zQ4lqqPeEfNhQZbdCL/DJiJj3+pqV8k1pYvXVJz96wI4FdyuI7Q/hSYBaFiIn6TaKeoTEx2qSE8Kar2OTzOReWq182BTwAz6kWwEY6vMuS7Sei0lJtx7pr2j7U4BD0aL71dIGur+8OJnOT2ywWUsTXyhWVg3H1aVi4qBJdToSTAL2fIEyA2iPczoVY8QbT+jVi6y05No4MVF6OgBvs40Hb+rHY/IZmB0b6sfU0iDHa8mwX/dFlC8zkFApUdW+SsM0yyWPm9QFp/qt2q1nl/cBSnt5oSQwdhBuWClgF2zrrX2Wazur6HAF6JQHfklArLAN2CvWOX77ZJNNMhER1HwxSLAbN0S+ljlbAKFuC5zwxZ7zLVMGt0ZB9v8e8AufCngYJ+e5FbWHCT/hOwClhpWpdpwRRjCJkniRJrQYAi/LRhBslL+bfxVd1d52wD4hRHJkT9QZXgwdJFsSM8tHsEIAVWqFrQpnIm2n5LIyDN4OIR8lSjU2B8n2146LIugDaSSuFlMa1BDN9g4V47My/NbeQ858xDNts1wAaDJeuF0ibWn15M8mwfLht+tgUgfPcQ07iikgkx+ylgMGMt9nWuKFPSFvhgDJy2AuTjFwz4+ZeqiMz7OTMVqQVyZpbPkX9w7WKk6lCX+W6YZRV5KkhAYmgrU2L4GXKrKdld6YdzEiKOjD6PVVgeVLmXZxVExjpNokTYkYTApXVcnxqB4lJ8SX6cWcmaoR35CYNXWJqEH4TcpqSRMzYI6ewePKqv3HIPUntV5l2MU67+Ec79JJCb2hL/FHA0OufDYS1rloT53QD9OajLGSnaiqKKm4fi2E5g8JcN8oFDV9XtWvd+INvFvH/FumvDiJ6//T2SO7FvWZzQ7SWZK0/B6xCTLcJCilREg/eEZshF4eYpQ9H2VdNu1SRU8iEChLPXnR1eexDSIO9OOSYUMTWRq7Y947xcqpL/POe1i1vUMl4WHajqv8ePj/sAjRlQUSms5lU3KW/l9j4TYaxQxNcuo4bBK054ytk1La5kLuM++7qO3JHUS10nAlG3cyxaZNNqP3++DzELieV5kld4cmxgQja5e18pgB1lBBwJJEEtB8jyC/iT1IDvbr33vBj3kI39czFqNkvNy0cBkDBj2yLoz1N4XjBHzSWd5oNiB8vPyRJxuB0PK52nGzUZv7/RFbEJ8L3V/C4M4KiPKJxS7YZwab0ad+KhKRzo7KIqki2clsuTxYL6Kb23rJAV0zdfzSxJ5DMRWWLorkoq80Mqi2mw4Kn+vrFZTjHU7YuA0kMRGF7qXWqnL7lDTkFKesTRyEnjgmmkDfPTbQAmHo9sLdhQvD82csQYlCpcOB9GA7/lMqudqRQ3wPvhVS3bZ8DgTSvf1kC7NXFOQSlstT0pV7WDwRd5hT9XtY8aHxcBsmSJ8fAw3KLiz8to9xWDHCRAl1vl41fCwkW45/LOqCqL82SjWDPhF3nQvqBFJ6Muxk5p769uuJ2g3FXC1LP0HUcRd9+vifd2y2Iz2IBBweRhDqbbjiLtJeSm99Heq0HEiIsgwBtRO0qE/MGKbZQHHjyfyN/1q7ruThrjQdE6ArLM6BQCqOZ2RRn/1DpZF0wmPH+c9/niYxVDD61FuHDN8s9ODBP2CgrUpQk045qUbQ3J68XT5PlbUxabDD+aIrNAHkZ4uH+Jv29NQR3KUosGuj3Aw1mds+R6OjOUTOe1lB66tF7jWLDUiBE5PXAUglFxoA4BOoJuTTD732AB/DNQ4XBRmM9JCUIHJ+zZtA6g6SHXfhrCCIASMYZiQ1goQs24nhUk1tUTfQ49KAD36uvyR6bL277tnRy52WO8nkJEfMSzNkxGL/0N59wKijZGg/fU318ZEf/jZHOZKFmEUsEcCt+nzfnwapouC2XiGvAHieYdcGH+sBIQ9D+o/y5NNzBXtH5WKpRs6xyKblxZQJcE/VhH7aR9pFYJvjcW64hJeSoI3dbxn0uCYUonypX+jZ+ZbshRTiaWTXiu3PlGALJ1t0YWOEww/tjsT57n2SmugcxiE+/X5Ix7ticpg0c/PB43Ux8CJZCPvqVT7Zw9AgFgt5oYF718Cm3looiJqil2ZFKOrKYtGjChtmjcPGVnruazngQLkc4bVZz65KknbblgTPtQcO5lKGkw/d2Qs2HCZstIcDdghK0UQZPZ3KR55vQ15TmHbMThX62bVTlrKiLmmrNGAO94s/fgK5oeYN0vJZ3DMYooJvtA6d5QvP1Y4xyWYnEnB7VeOs8opDSaEkzSi09uQajruEh0L4O4RXbYX38ZO+rD+eM7K6haTByLJdxZ4wHE5q0wZKWGIzQmKW128hQa/bjjjCTCWU4S+5FEnFRfaymfz+03UTJr4PC9zMTuUwVqoYOsdF9xhvNZUtM9//coEYPCFgDU05Lr2ssAhifq82Ee0xTBKwj5a159v4PqrxHC+OTwgcL/lXNq7FdcLX3r9lB3Sc/bMf4l5lo/SuaBRMcqxFw3oELp86eKXQApALQsG7N8imqP/JPHoqNMQ8bYBRSmOcUa2ebB6nh7efRQZwFtSLs3KemlVzTbU6ggKMj/FRzxXT6PwZrdctWqYAVO22K6gadZJFZDs6Ra8Im3reZnwaZUWI0BI/3ByoSoscuonm1essVIG/jhONOt5P+pE9yuwwLZnts0BJe9R/x+gmXoyPbhB8cMA5DDSN1cBfofV3TUBcZxg25sid1/Z34+oPprHuzagoWyxLpZukkkgaeOwu2EmqzNmhHL4APmBhoajEUoojd1hF0SyRZD3riVMxX8nicHcAwN0LdNapIe/cTVzvI8U+GFRIH7OTC3g2L4n3y80SNLcpE1Gf+B8bl5yzeIww2jCw7rBToHJIKj3NPtRsrHKA1DqwYXtms8qZv1Qt77VxNuk5QxLRNSEKkv2ejbE+kARhTdlWt2wln3a0hmbe3hTwhvsPJ6P33QzIcAItTuurAV4LPS5k9oIrHHIasBpVP5N+WS9u/GK50JUyHNiY7g/cslR01ml0O/9jZSzvkn0HAz6Nrrx/1boE0CFfPY/wXEsNSNXbzQL10EnzIcIlG6OqvhoIDUbBJJF5gUSrQ5EPsksYEtdQEt/7TnOY8EtGZvLNvf1qUFJRy1oqoMODEkT3R7SD6dixEQpIWby9XZDEqu5yKCPz1nWa4AJLOTrHmK2RfmRin0jbc09GKMHaPX+eifL3/adeo3cggR2JK7bv9ipq+0ZovxdJqoOAkKNYG6ocbwMul55djkWcg8ROj5I0/7fgb7c4FK/Kyhjs9clFb10Ie8yXogKcEDxMFnfR/JSOCTaDJANZiwWATPmAYVFr39TyNjX+oBvvKqo6s1Is5Mdzkj8T+saePMbQYnPb/dkIdE0C+c4iPm7w3BGzKQ4ALLvbDbVtP4TN8SSnzab0MDcLvazv4f/6ugUas7DfdGjrsa+FD+S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-p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — F4-conf-03\n\n**Question.** Re-express five printed treatment effects (from four arXiv extracts) on a stated target definition, using only quantities printed in the cited extract and an explicit transformation; where impossible, say so and name the missing quantity.\n\n**Estimands.** Harmonised point estimates E1–E5 (no uncertainty propagation):\n- E1: DCR overall ATT (log points, Table 6, 5 km) -> relative % change, 100·(exp(b)−1). Meaningful if the ATT is interpreted as a log difference vs. counterfactual.\n- E2: RESEA ATE on reemployment (pp, Table 2) -> % of control-group mean, 100·Δ/p.\n- E3: employment elasticity w.r.t. MW (Table 2) -> % employment change for the printed MW change, 100·η·g.\n- E4: DCR fatal ATT (log points, Table 7, 5 km) -> absolute change in hospitalisations per 100,000/yr. Requires the counterfactual/baseline level of the fatal rate.\n- E5: LPM coefficient (0–1 scale, Table 3 col 4) -> percentage points, 100·β.\n\n**Outputs.** out/results.json (harmonised{E1..E5:{value,unit,transformation,harmonisable,missing_quantity,locator}}, n_harmonised, n_not_harmonisable, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed=null); out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues); out/analysis.py (prints source lines used); out/claims.json.\n\n**Information rules.** Only extracts are admissible; no remembered values. harmonisable=false only if a required quantity is not printed; then value=null and missing_quantity named. Printed numbers used exactly as printed.\n\n**Invalidity.** Using unprinted/guessed quantities; misreading table rows/columns; printing values not computed by the script.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"running\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > log.md","description":"Write contract, phase ledger, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3084,"cache_read_input_tokens":26629,"output_tokens":1091,"output_tokens_details":{"thinking_tokens":85},"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":3084},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqkKahYgidzvRpgrt3h","requestRef":"8af48530-5fa5-4eda-997d-c7acb031aebe","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"11d1f0eb-4f33-4329-93e4-aeef9516241c","timestamp":"2026-10-30T05:07:06.876Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0229","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","version":"2.1.314","gitBranch":"HEAD"}39{"parentUuid":"11d1f0eb-4f33-4329-93e4-aeef9516241c","isSidechain":false,"promptId":"1257d57b-48b2-460e-b967-208321121a94","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01WGy6DDLFqjrTHCzSRHWNtX","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"78cce51a-0603-43be-9d99-4dbbb2494c36","timestamp":"2026-10-30T05:07:06.926Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"11d1f0eb-4f33-4329-93e4-aeef9516241c","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0229/out/state","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","version":"2.1.314","gitBranch":"HEAD"}40{"type":"api-request-blob","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","hash":"d4f18f05fbae71b7ec66b1709d3887aa21f4fa8d12c817e6a9986144e00270eb","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01WGy6DDLFqjrTHCzSRHWNtX","name":"Bash","input":{"command":"mkdir -p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — F4-conf-03\n\n**Question.** Re-express five printed treatment effects (from four arXiv extracts) on a stated target definition, using only quantities printed in the cited extract and an explicit transformation; where impossible, say so and name the missing quantity.\n\n**Estimands.** Harmonised point estimates E1–E5 (no uncertainty propagation):\n- E1: DCR overall ATT (log points, Table 6, 5 km) -> relative % change, 100·(exp(b)−1). 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Printed numbers used exactly as printed.\n\n**Invalidity.** Using unprinted/guessed quantities; misreading table rows/columns; printing values not computed by the script.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"running\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > log.md","description":"Write contract, phase ledger, 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-32.901\n-0.283 -24.648\n-0.431 -35.014\n-0.438 -35.467\n-0.362 -30.372\n-0.149 -13.843\n-0.354 -29.813\n0.39 47.698\n-0.381 -31.682\n-0.406 -33.369\n-0.34 -28.823\n1.96 -1.8236000000000003 -1.552 -0.3685823754789272 -0.3490421455938697 -0.3478927203065134 -0.3681992337164751 -0.1879120879120879","is_error":false}]},"uuid":"32278aba-bd5d-47e2-861b-50b09fb557d1","timestamp":"2026-10-30T05:07:10.034Z","toolUseResult":{"stdout":"-0.399 -32.901\n-0.283 -24.648\n-0.431 -35.014\n-0.438 -35.467\n-0.362 -30.372\n-0.149 -13.843\n-0.354 -29.813\n0.39 47.698\n-0.381 -31.682\n-0.406 -33.369\n-0.34 -28.823\n1.96 -1.8236000000000003 -1.552 -0.3685823754789272 -0.3490421455938697 -0.3478927203065134 -0.3681992337164751 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print(b, round(100*(math.exp(b)-1),3))\nprint(1.47/75.0*100, -0.388*0.047*100, -0.388*0.04*100, -0.0962/0.261, -0.0911/0.261,-0.0908/0.261,-0.0961/0.261, -0.0855/0.455)\n\"","description":"Check printed percent-change 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out && cat > state/data_audit.md <<'EOF'\n# Data audit — F4-conf-03\n\n| file | SHA-256 | lines (wc -l) / items | matches README/bundle hash |\n|---|---|---|---|\n| data/effects.json | 708c1ff0c4379c228ac6a98f577c23d0d61564f3c958fb35596266aca1f5f10b | 5 top-level items (list) | yes (bundle.json) |\n| data/README.md | 7cf7cb385f9fb4fc0df52cfc6b0644bcbfb969a38afe2b018b864b7871187dc4 | 16 lines | yes (bundle.json) |\n| data/sources/arxiv-2609.27732v1.txt | ee2d444d…d6a8b | 134 lines | yes |\n| data/sources/arxiv-2606.14621v1.txt | 27c463c0…0a238 | 58 lines | yes |\n| data/sources/arxiv-2505.04555v3.txt | b1731a9b…c40ae91c6be2cf2e92623bb6cf86c551587936a0270356f76ae95c1 | 66 lines | yes |\n| data/sources/arxiv-2510.03658v1.txt | f8782937…aea59/1a881127 | 91 lines | yes |\n\nSchema: effects.json is a list of 5 objects {id, source, effect, reported_definition, target_definition}; ids E1–E5 unique, no missing fields. Sources are plain-text extracts with tables as ` | `-separated rows, SE rows following coefficient rows.\n\nUnits: DCR (2609.27732) ATTs in log points of rate per 100,000; RESEA (2606.14621) means in %, effects in pp; MW (2505.04555) elasticities (dimensionless), \"% MW changes\" printed as 0.047 (proportion); Tossou (2510.03658) LPM coefficients on 0–1 scale, Mean Dep. Var. 0.261.\n\n## Anomalies found (also in results.json `data_issues`)\n1. DCR abstract says fatal hospitalisations \"fall by 35%\", but Table 7 (5 km) prints -0.354 → 100(e^-0.354 −1) = −29.8%, and Sec. 6.1 says \"approximately 30%\". Table value used.\n2. DCR Table A19 CS-DR SE (0.218) differs from Table 6 SE (0.210) for the same −0.431 estimate; A19 note says 640 never-treated regions vs 608 in the table rows. Point estimates unaffected.\n3. DCR Table A19 printed % change for SDiD (−30.3%) and Stacked DiD (−13.9%) differ by 0.1 from 100(e^b−1) (−30.37%, −13.84%). Not used.\n4. MW Table 2 prints \"% MW changes | 0.047\" while its note says \"about 4% minimum wage increase\". Task instructs use of table value; 0.047 interpreted as a proportion (4.7%).\n5. Tossou Table 3 col (4) \"% Change −0.3685\" vs −0.0962/0.261 = −0.36858 (rounding of the mean). Text cites Table 2 (−0.0855/0.455), which is not in the extract. Footnote markers render as \"^9^9 9\" (rendering defect, ignored).\n6. RESEA control mean 75.0% is the unadjusted aggregate control-group mean; the ATE is block-(week-)adjusted. Used as the task-specified denominator.\n7. E4 needs the level of the fatal drug-related hospitalisation rate (per 100,000) for treated regions; no such level is printed anywhere in the extract.\n\nNo input violates the information-availability rules (all are the designated extracts). No rows excluded; all 5 items analysed.\nEOF\nprintf \"step,rule,n_before,n_after,n_removed,rationale\\n1,all effects in effects.json retained,5,5,0,No exclusions: every item is assessed (harmonised or declared not harmonisable)\\n\" > exclusions.csv\necho \"- 2026-10-30 P1 passed: hashes match README/bundle; 7 anomalies logged; no exclusions.\" >> state/log.md","description":"Write data audit and exclusion 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out && cat > state/data_audit.md <<'EOF'\n# Data audit — F4-conf-03\n\n| file | SHA-256 | lines (wc -l) / items | matches README/bundle hash |\n|---|---|---|---|\n| data/effects.json | 708c1ff0c4379c228ac6a98f577c23d0d61564f3c958fb35596266aca1f5f10b | 5 top-level items (list) | yes (bundle.json) |\n| data/README.md | 7cf7cb385f9fb4fc0df52cfc6b0644bcbfb969a38afe2b018b864b7871187dc4 | 16 lines | yes (bundle.json) |\n| data/sources/arxiv-2609.27732v1.txt | ee2d444d…d6a8b | 134 lines | yes |\n| data/sources/arxiv-2606.14621v1.txt | 27c463c0…0a238 | 58 lines | yes |\n| data/sources/arxiv-2505.04555v3.txt | b1731a9b…c40ae91c6be2cf2e92623bb6cf86c551587936a0270356f76ae95c1 | 66 lines | yes |\n| data/sources/arxiv-2510.03658v1.txt | f8782937…aea59/1a881127 | 91 lines | yes |\n\nSchema: effects.json is a list of 5 objects {id, source, effect, reported_definition, target_definition}; ids E1–E5 unique, no missing fields. 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out/state/exclusions.csv out/exclusions.csv && cat > out/state/data_audit.md <<'EOF'\n# Data audit — F4-conf-03\n\n| file | SHA-256 | lines (wc -l) / items | matches README/bundle hash |\n|---|---|---|---|\n| data/effects.json | 708c1ff0c4379c228ac6a98f577c23d0d61564f3c958fb35596266aca1f5f10b | 5 top-level items (list) | yes (bundle.json) |\n| data/README.md | 7cf7cb385f9fb4fc0df52cfc6b0644bcbfb969a38afe2b018b864b7871187dc4 | 16 lines | yes (bundle.json) |\n| data/sources/arxiv-2609.27732v1.txt | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b | 134 lines | yes |\n| data/sources/arxiv-2606.14621v1.txt | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 | 58 lines | yes |\n| data/sources/arxiv-2505.04555v3.txt | b1731a9b1c40ae91c6be2cf2e92623bb6cf86c551587936a0270356f76ae95c1 | 66 lines | yes |\n| data/sources/arxiv-2510.03658v1.txt | f8782937eeaebd2dae0e653bbfc6aeb266c51587d09a4207bcaa3fa48a881127 | 91 lines | yes |\n\nSchema: effects.json is a list of 5 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MW Table 2 prints \"% MW changes | 0.047\" while its note says \"about 4% minimum wage increase\". Task instructs use of the table value; 0.047 interpreted as a proportion (4.7%).\n5. Tossou Table 3 col (4) \"% Change −0.3685\" vs −0.0962/0.261 = −0.36858 (rounding of the printed mean). Text cites Table 2 (−0.0855/0.455), not in extract. Footnote markers render as \"^9^9 9\" (rendering defect, ignored).\n6. RESEA control mean 75.0% is the unadjusted aggregate control-group mean; the ATE is block-(week-)adjusted. Used as the task-specified denominator.\n7. E4 needs the level of the fatal drug-related hospitalisation rate (per 100,000) for treated regions; no such level is printed in the extract.\n\nNo input violates the information-availability rules (all are the designated extracts). 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out/state/exclusions.csv out/exclusions.csv && cat > out/state/data_audit.md <<'EOF'\n# Data audit — F4-conf-03\n\n| file | SHA-256 | lines (wc -l) / items | matches README/bundle hash |\n|---|---|---|---|\n| data/effects.json | 708c1ff0c4379c228ac6a98f577c23d0d61564f3c958fb35596266aca1f5f10b | 5 top-level items (list) | yes (bundle.json) |\n| data/README.md | 7cf7cb385f9fb4fc0df52cfc6b0644bcbfb969a38afe2b018b864b7871187dc4 | 16 lines | yes (bundle.json) |\n| data/sources/arxiv-2609.27732v1.txt | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b | 134 lines | yes |\n| data/sources/arxiv-2606.14621v1.txt | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 | 58 lines | yes |\n| data/sources/arxiv-2505.04555v3.txt | b1731a9b1c40ae91c6be2cf2e92623bb6cf86c551587936a0270356f76ae95c1 | 66 lines | yes |\n| data/sources/arxiv-2510.03658v1.txt | f8782937eeaebd2dae0e653bbfc6aeb266c51587d09a4207bcaa3fa48a881127 | 91 lines | yes |\n\nSchema: effects.json is a list of 5 objects {id, source, effect, reported_definition, target_definition}; ids E1–E5 unique, no missing fields, no duplicates. Sources are plain-text extracts with tables as ` | `-separated rows, SE rows following coefficient rows.\n\nUnits: DCR (2609.27732) ATTs in log points of rate per 100,000; RESEA (2606.14621) means in %, effects in pp; MW (2505.04555) elasticities (dimensionless), \"% MW changes\" printed as 0.047 (proportion); Tossou (2510.03658) LPM coefficients on 0–1 scale, Mean Dep. Var. 0.261.\n\n## Anomalies found (also in results.json `data_issues`)\n1. DCR abstract says fatal hospitalisations \"fall by 35%\", but Table 7 (5 km) prints -0.354 → 100(e^-0.354 −1) = −29.8%, and Sec. 6.1 says \"approximately 30%\". Table value used.\n2. DCR Table A19 CS-DR SE (0.218) differs from Table 6 SE (0.210) for the same −0.431 estimate; A19 note says 640 never-treated regions vs 608 in the table rows. Point estimates unaffected.\n3. DCR Table A19 printed % change for SDiD (−30.3%) and Stacked DiD (−13.9%) differ by ~0.1 from 100(e^b−1) (−30.37%, −13.84%). Not used.\n4. MW Table 2 prints \"% MW changes | 0.047\" while its note says \"about 4% minimum wage increase\". Task instructs use of the table value; 0.047 interpreted as a proportion (4.7%).\n5. Tossou Table 3 col (4) \"% Change −0.3685\" vs −0.0962/0.261 = −0.36858 (rounding of the printed mean). Text cites Table 2 (−0.0855/0.455), not in extract. Footnote markers render as \"^9^9 9\" (rendering defect, ignored).\n6. RESEA control mean 75.0% is the unadjusted aggregate control-group mean; the ATE is block-(week-)adjusted. Used as the task-specified denominator.\n7. E4 needs the level of the fatal drug-related hospitalisation rate (per 100,000) for treated regions; no such level is printed in the extract.\n\nNo input violates the information-availability rules (all are the designated extracts). No rows excluded; all 5 items analysed.\nEOF\necho \"- 2026-10-30 P1 passed: hashes match README/bundle; 7 anomalies logged; no exclusions (out/exclusions.csv).\" >> out/state/log.md; cat out/exclusions.csv","description":"Write data audit, move 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> out/analysis.py <<'EOF'\n\"\"\"F4-conf-03: harmonise five printed effect sizes onto target definitions.\nReads only data/; writes out/results.json deterministically. No randomness.\"\"\"\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_of(path):\n    with open(path, encoding=\"utf-8\") as fh:\n        return fh.read().split(\"\\n\")\n\n\ndef wc_l(path):\n    with open(path, \"rb\") as fh:\n        return fh.read().count(b\"\\n\")\n\n\ndef find_row(src, label, after=None):\n    \"\"\"Return (line_no, cells) of the first table row whose first cell == label\n    (searching after a line containing `after`, if given).\"\"\"\n    L = lines_of(os.path.join(SRC, src + \".txt\"))\n    start = 0\n    if after:\n        start = next(i for i, l in enumerate(L) if after in l)\n    for i in range(start, len(L)):\n        cells = [c.strip() for c in L[i].split(\"|\")]\n        if cells[0] == label:\n            print(f\"[{src} line {i+1}] {L[i]}\")\n            return i + 1, cells\n    raise ValueError(f\"row {label!r} not found in {src}\")\n\n\ndef num(cell):\n    m = re.search(r\"-?\\d[\\d,]*\\.?\\d*\", cell.replace(\"−\", \"-\"))\n    return float(m.group(0).replace(\",\", \"\"))\n\n\neffects = json.load(open(os.path.join(D, \"effects.json\"), encoding=\"utf-8\"))\nids = [e[\"id\"] for e in effects]\nassert ids == [\"E1\", \"E2\", \"E3\", \"E4\", \"E5\"], ids\n\nH, baseline = {}, {}\n\n# ---- E1: DCR Table 6, ATT, 5 km (log points) -> % change\ns = \"arxiv-2609.27732v1\"\nln_hdr, hdr = find_row(s, \"\", after=\"Table 6: Overall Average\")\ncol5 = hdr.index(\"5 km\")\nln, row = find_row(s, \"ATT\", after=\"Table 6: Overall Average\")\nb1 = num(row[col5])\nln_pc, pc = find_row(s, \"% change\", after=\"Table 6: Overall Average\")\nv1 = 100 * (math.exp(b1) - 1)\nbaseline[\"E1\"] = {\"printed_value\": b1, \"printed_unit\": \"log points\"}\nH[\"E1\"] = {\"value\": round(v1, 4), \"unit\": \"percent of the counterfactual hospitalisation rate\",\n           \"transformation\": f\"100*(exp(b)-1) with b={row[col5]} -> 100*(exp({b1})-1) = {v1:.4f}% (cross-check: printed '% change' row = {pc[col5]})\",\n           \"harmonisable\": True, \"missing_quantity\": None,\n           \"locator\": f\"{s} | Table 6 | row 'ATT' (line {ln}) | column '5 km'; cross-check row '% change' (line {ln_pc}) | column '5 km'\"}\n\n# ---- E2: RESEA Table 2, Reemployment, ATE (pp) / control mean (%)\ns = \"arxiv-2606.14621v1\"\nln_h, hdr = find_row(s, \"Outcome\", after=\"[Table 2\")\nln, row = find_row(s, \"Reemployment\", after=\"[Table 2\")\nate = num(row[hdr.index(\"Average treatment effect estimate\")])\nctrl = num(row[hdr.index(\"Control\")])\nv2 = 100 * ate / ctrl\nbaseline[\"E2\"] = {\"printed_value\": ate, \"printed_unit\": \"percentage points\"}\nH[\"E2\"] = {\"value\": round(v2, 4), \"unit\": \"percent of the control-group mean reemployment rate\",\n           \"transformation\": f\"100*Delta/p with Delta={ate} pp, p={ctrl}% -> 100*{ate}/{ctrl} = {v2:.4f}%\",\n           \"harmonisable\": True, \"missing_quantity\": None,\n           \"locator\": f\"{s} | Table 2 | row 'Reemployment' (line {ln}) | column 'Average treatment effect estimate' (1.47 pp) and column 'Control' (75.0%)\"}\n\n# ---- E3: MW Table 2, elasticity x printed % MW change\ns = \"arxiv-2505.04555v3\"\nln_e, row_e = find_row(s, \"Employment elasticity w.r.t MW\", after=\"Table 2:\")\nln_g, row_g = find_row(s, \"% MW changes\", after=\"Table 2:\")\neta, g = num(row_e[1]), num(row_g[1])\nv3 = 100 * eta * g\nbaseline[\"E3\"] = {\"printed_value\": eta, \"printed_unit\": \"elasticity (dimensionless)\"}\nH[\"E3\"] = {\"value\": round(v3, 4), \"unit\": \"percent change in employment\",\n           \"transformation\": f\"100*eta*g with eta={row_e[1]}, g={row_g[1]} (proportional MW change) -> 100*({eta})*({g}) = {v3:.4f}%\",\n           \"harmonisable\": True, \"missing_quantity\": None,\n           \"locator\": f\"{s} | Table 2 | row 'Employment elasticity w.r.t MW' (line {ln_e}) | column 'Estimate (SE)'; row '% MW changes' (line {ln_g}) | column 'Estimate (SE)'\"}\n\n# ---- E4: DCR Table 7, fatal, 5 km -> absolute change per 100,000: needs level\ns = \"arxiv-2609.27732v1\"\nln_h7, hdr7 = find_row(s, \"Outcome\", after=\"Table 7:\")\nln, row = find_row(s, \"Drug-related (fatal)\", after=\"Table 7:\")\nb4 = num(row[hdr7.index(\"5 km\")])\nrel4 = 100 * (math.exp(b4) - 1)\nbaseline[\"E4\"] = {\"printed_value\": b4, \"printed_unit\": \"log points\",\n                  \"relative_percent_change_reachable\": round(rel4, 4)}\nH[\"E4\"] = {\"value\": None, \"unit\": \"hospitalisations per 100,000 inhabitants per year\",\n           \"transformation\": f\"would be R0*(exp(b)-1) with b={row[hdr7.index('5 km')]}; the relative part 100*(exp({b4})-1) = {rel4:.4f}% is computable, but the level R0 is not printed\",\n           \"harmonisable\": False,\n           \"missing_quantity\": \"baseline/counterfactual level (mean) of the fatal drug-related hospitalisation rate per 100,000 inhabitants per year for the treated regions within 5 km (e.g. pre-opening or untreated-potential-outcome mean); not printed in the extract\",\n           \"locator\": f\"{s} | Table 7 | row 'Drug-related (fatal)' (line {ln}) | column '5 km'\"}\n\n# ---- E5: Tossou Table 3, Treatment, col (4): 0-1 scale -> pp\ns = \"arxiv-2510.03658v1\"\nln_h, hdr = find_row(s, \"\", after=\"Table 3: Probability\")\nln_h2, hdr2 = find_row(s, \"\", after=\"| (1) | (2)\")\nln, row = find_row(s, \"Treatment\", after=\"Table 3: Probability\")\nc4 = hdr2.index(\"(4)\")\nbeta = num(row[c4])\nv5 = 100 * beta\nln_m, rowm = find_row(s, \"Mean Dep. Var.\", after=\"Table 3: Probability\")\nbaseline[\"E5\"] = {\"printed_value\": beta, \"printed_unit\": \"change in probability (0-1 scale)\"}\nH[\"E5\"] = {\"value\": round(v5, 4), \"unit\": \"percentage points\",\n           \"transformation\": f\"100*beta with beta={row[c4]} -> {v5:.4f} pp (exact unit rescaling)\",\n           \"harmonisable\": True, \"missing_quantity\": None,\n           \"locator\": f\"{s} | Table 3 | row 'Treatment' (line {ln}) | column '(4)'\"}\n\ndata_issues = [\n    {\"issue\": \"arxiv-2609.27732v1: abstract states fatal drug-related hospitalisations 'fall by 35%', but Table 7 (5 km) prints -0.354 log points, i.e. 100*(exp(-0.354)-1) = -29.8%, and Section 6.1 says 'approximately 30%'.\",\n     \"handling\": \"Table 7 value used for E4; abstract figure not used.\"},\n    {\"issue\": \"arxiv-2609.27732v1: Table A19 prints SE (0.218) for the CS-DR ATT -0.431 while Table 6 prints (0.210); A19 note mentions 640 never-treated regions while the tables print 608 control regions.\",\n     \"handling\": \"Point estimate identical in both tables; Table 6 used for E1. SEs not propagated.\"},\n    {\"issue\": \"arxiv-2609.27732v1: Table A19 '% change' entries for Synthetic DiD (-30.3%) and Stacked DiD (-13.9%) differ by about 0.1 from 100*(exp(b)-1) (-30.37%, -13.84%).\",\n     \"handling\": \"Not used; noted only. The Table 6 '% change' for 5 km (-35.0%) agrees with the transform of -0.431 (-35.01%).\"},\n    {\"issue\": \"arxiv-2505.04555v3: Table 2 prints '% MW changes | 0.047' while the table note says 'about 4% minimum wage increase'; the row label '%' with value 0.047 is ambiguous between a proportion (4.7%) and a percent (0.047%).\",\n     \"handling\": \"Table value used as instructed, read as a proportion g = 0.047 (4.7%), consistent in magnitude with the note's 'about 4%'. With g = 0.04 the result would be -1.55%.\"},\n    {\"issue\": \"arxiv-2510.03658v1: Table 3 col (4) prints '% Change -0.3685' while -0.0962/0.261 = -0.3686 (printed mean rounded); text refers to a Table 2 (-0.0855/0.455) that is not in the extract; footnote markers are rendered as '^9^9 9' etc.\",\n     \"handling\": \"Not needed for the pp conversion of E5; noted only. Rendering artefacts ignored.\"},\n    {\"issue\": \"arxiv-2606.14621v1: the control mean 75.0% is the unadjusted aggregate control-group mean (rounded to 0.1), whereas the 1.47 pp ATE is adjusted for randomisation week; the printed control mean is therefore only an approximation of the counterfactual level for the ATE.\",\n     \"handling\": \"Used as the denominator because the target definition specifies the control-group mean.\"},\n]\n\nn_input_rows = {\"effects.json\": len(effects), \"README.md\": wc_l(os.path.join(D, \"README.md\"))}\nfor f in sorted(os.listdir(SRC)):\n    n_input_rows[f] = wc_l(os.path.join(SRC, f))\n\nn_h = sum(1 for v in H.values() if v[\"value\"] is not None)\nn_nh = sum(1 for v in H.values() if v[\"harmonisable\"] is False)\nresults = {\n    \"task_id\": \"F4-conf-03\",\n    \"harmonised\": H,\n    \"n_harmonised\": n_h,\n    \"n_not_harmonisable\": n_nh,\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": data_issues,\n    \"n_input_rows\": n_input_rows,\n    \"n_analysed\": len(effects),\n    \"random_seed\": None,\n    \"baseline_printed\": baseline,\n}\nos.makedirs(os.path.join(ROOT, \"out\"), exist_ok=True)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\", encoding=\"utf-8\") as fh:\n    json.dump(results, fh, indent=2, ensure_ascii=False)\nfor k, v in H.items():\n    print(k, v[\"value\"], v[\"unit\"], \"| harmonisable:\", v[\"harmonisable\"])\nprint(\"n_harmonised\", n_h, \"n_not_harmonisable\", n_nh)\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json | head -60","description":"Write and run analysis 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code 1\n[arxiv-2609.27732v1 line 33] \nTraceback (most recent call last):\n  File \"/Users/simon-pierreboucher/wp29/runs/RC0229/out/analysis.py\", line 49, in <module>\n    col5 = hdr.index(\"5 km\")\nValueError: list.index(x): x not in list","is_error":true,"tool_use_id":"toolu_011kYDAVxjC1NTwydCFCJhnG"}]},"uuid":"6d24a126-2f83-457e-9c13-df2200b3e78c","timestamp":"2026-10-30T05:08:20.151Z","toolUseResult":"Error: Exit code 1\n[arxiv-2609.27732v1 line 33] \nTraceback (most recent call last):\n  File \"/Users/simon-pierreboucher/wp29/runs/RC0229/out/analysis.py\", line 49, in <module>\n    col5 = hdr.index(\"5 km\")\nValueError: list.index(x): x not in 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> out/analysis.py <<'EOF'\n\"\"\"F4-conf-03: harmonise five printed effect sizes onto target definitions.\nReads only data/; writes out/results.json deterministically. No randomness.\"\"\"\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_of(path):\n    with open(path, encoding=\"utf-8\") as fh:\n        return fh.read().split(\"\\n\")\n\n\ndef wc_l(path):\n    with open(path, \"rb\") as fh:\n        return fh.read().count(b\"\\n\")\n\n\ndef find_row(src, label, after=None):\n    \"\"\"Return (line_no, cells) of the first table row whose first cell == label\n    (searching after a line containing `after`, if given).\"\"\"\n    L = lines_of(os.path.join(SRC, src + \".txt\"))\n    start = 0\n    if after:\n        start = next(i for i, l in enumerate(L) if after in l)\n    for i in range(start, len(L)):\n        cells = [c.strip() for c in L[i].split(\"|\")]\n        if cells[0] == label:\n            print(f\"[{src} line {i+1}] {L[i]}\")\n            return i + 1, cells\n    raise ValueError(f\"row {label!r} not found in {src}\")\n\n\ndef num(cell):\n    m = re.search(r\"-?\\d[\\d,]*\\.?\\d*\", cell.replace(\"−\", \"-\"))\n    return float(m.group(0).replace(\",\", \"\"))\n\n\neffects = json.load(open(os.path.join(D, \"effects.json\"), encoding=\"utf-8\"))\nids = [e[\"id\"] for e in effects]\nassert ids == [\"E1\", \"E2\", \"E3\", \"E4\", \"E5\"], ids\n\nH, baseline = {}, {}\n\n# ---- E1: DCR Table 6, ATT, 5 km (log points) -> % change\ns = \"arxiv-2609.27732v1\"\nln_hdr, hdr = find_row(s, \"\", after=\"Table 6: Overall Average\")\ncol5 = hdr.index(\"5 km\")\nln, row = find_row(s, \"ATT\", after=\"Table 6: Overall Average\")\nb1 = num(row[col5])\nln_pc, pc = find_row(s, \"% change\", after=\"Table 6: Overall Average\")\nv1 = 100 * (math.exp(b1) - 1)\nbaseline[\"E1\"] = {\"printed_value\": b1, \"printed_unit\": \"log points\"}\nH[\"E1\"] = {\"value\": round(v1, 4), \"unit\": \"percent of the counterfactual hospitalisation rate\",\n           \"transformation\": f\"100*(exp(b)-1) with b={row[col5]} -> 100*(exp({b1})-1) = {v1:.4f}% (cross-check: printed '% change' row = {pc[col5]})\",\n           \"harmonisable\": True, \"missing_quantity\": None,\n           \"locator\": f\"{s} | Table 6 | row 'ATT' (line {ln}) | column '5 km'; cross-check row '% change' (line {ln_pc}) | column '5 km'\"}\n\n# ---- E2: RESEA Table 2, Reemployment, ATE (pp) / control mean (%)\ns = \"arxiv-2606.14621v1\"\nln_h, hdr = find_row(s, \"Outcome\", after=\"[Table 2\")\nln, row = find_row(s, \"Reemployment\", after=\"[Table 2\")\nate = num(row[hdr.index(\"Average treatment effect estimate\")])\nctrl = num(row[hdr.index(\"Control\")])\nv2 = 100 * ate / ctrl\nbaseline[\"E2\"] = {\"printed_value\": ate, \"printed_unit\": \"percentage points\"}\nH[\"E2\"] = {\"value\": round(v2, 4), \"unit\": \"percent of the control-group mean reemployment rate\",\n           \"transformation\": f\"100*Delta/p with Delta={ate} pp, p={ctrl}% -> 100*{ate}/{ctrl} = {v2:.4f}%\",\n           \"harmonisable\": True, \"missing_quantity\": None,\n           \"locator\": f\"{s} | Table 2 | row 'Reemployment' (line {ln}) | column 'Average treatment effect estimate' (1.47 pp) and column 'Control' (75.0%)\"}\n\n# ---- E3: MW Table 2, elasticity x printed % MW change\ns = \"arxiv-2505.04555v3\"\nln_e, row_e = find_row(s, \"Employment elasticity w.r.t MW\", after=\"Table 2:\")\nln_g, row_g = find_row(s, \"% MW changes\", after=\"Table 2:\")\neta, g = num(row_e[1]), num(row_g[1])\nv3 = 100 * eta * g\nbaseline[\"E3\"] = {\"printed_value\": eta, \"printed_unit\": \"elasticity (dimensionless)\"}\nH[\"E3\"] = {\"value\": round(v3, 4), \"unit\": \"percent change in employment\",\n           \"transformation\": f\"100*eta*g with eta={row_e[1]}, g={row_g[1]} (proportional MW change) -> 100*({eta})*({g}) = {v3:.4f}%\",\n           \"harmonisable\": True, \"missing_quantity\": None,\n           \"locator\": f\"{s} | Table 2 | row 'Employment elasticity w.r.t MW' (line {ln_e}) | column 'Estimate (SE)'; row '% MW changes' (line {ln_g}) | column 'Estimate (SE)'\"}\n\n# ---- E4: DCR Table 7, fatal, 5 km -> absolute change per 100,000: needs level\ns = \"arxiv-2609.27732v1\"\nln_h7, hdr7 = find_row(s, \"Outcome\", after=\"Table 7:\")\nln, row = find_row(s, \"Drug-related (fatal)\", after=\"Table 7:\")\nb4 = num(row[hdr7.index(\"5 km\")])\nrel4 = 100 * (math.exp(b4) - 1)\nbaseline[\"E4\"] = {\"printed_value\": b4, \"printed_unit\": \"log points\",\n                  \"relative_percent_change_reachable\": round(rel4, 4)}\nH[\"E4\"] = {\"value\": None, \"unit\": \"hospitalisations per 100,000 inhabitants per year\",\n           \"transformation\": f\"would be R0*(exp(b)-1) with b={row[hdr7.index('5 km')]}; the relative part 100*(exp({b4})-1) = {rel4:.4f}% is computable, but the level R0 is not printed\",\n           \"harmonisable\": False,\n           \"missing_quantity\": \"baseline/counterfactual level (mean) of the fatal drug-related hospitalisation rate per 100,000 inhabitants per year for the treated regions within 5 km (e.g. pre-opening or untreated-potential-outcome mean); not printed in the extract\",\n           \"locator\": f\"{s} | Table 7 | row 'Drug-related (fatal)' (line {ln}) | column '5 km'\"}\n\n# ---- E5: Tossou Table 3, Treatment, col (4): 0-1 scale -> pp\ns = \"arxiv-2510.03658v1\"\nln_h, hdr = find_row(s, \"\", after=\"Table 3: Probability\")\nln_h2, hdr2 = find_row(s, \"\", after=\"| (1) | (2)\")\nln, row = find_row(s, \"Treatment\", after=\"Table 3: Probability\")\nc4 = hdr2.index(\"(4)\")\nbeta = num(row[c4])\nv5 = 100 * beta\nln_m, rowm = find_row(s, \"Mean Dep. Var.\", after=\"Table 3: Probability\")\nbaseline[\"E5\"] = {\"printed_value\": beta, \"printed_unit\": \"change in probability (0-1 scale)\"}\nH[\"E5\"] = {\"value\": round(v5, 4), \"unit\": \"percentage points\",\n           \"transformation\": f\"100*beta with beta={row[c4]} -> {v5:.4f} pp (exact unit rescaling)\",\n           \"harmonisable\": True, \"missing_quantity\": None,\n           \"locator\": f\"{s} | Table 3 | row 'Treatment' (line {ln}) | column '(4)'\"}\n\ndata_issues = [\n    {\"issue\": \"arxiv-2609.27732v1: abstract states fatal drug-related hospitalisations 'fall by 35%', but Table 7 (5 km) prints -0.354 log points, i.e. 100*(exp(-0.354)-1) = -29.8%, and Section 6.1 says 'approximately 30%'.\",\n     \"handling\": \"Table 7 value used for E4; abstract figure not used.\"},\n    {\"issue\": \"arxiv-2609.27732v1: Table A19 prints SE (0.218) for the CS-DR ATT -0.431 while Table 6 prints (0.210); A19 note mentions 640 never-treated regions while the tables print 608 control regions.\",\n     \"handling\": \"Point estimate identical in both tables; Table 6 used for E1. SEs not propagated.\"},\n    {\"issue\": \"arxiv-2609.27732v1: Table A19 '% change' entries for Synthetic DiD (-30.3%) and Stacked DiD (-13.9%) differ by about 0.1 from 100*(exp(b)-1) (-30.37%, -13.84%).\",\n     \"handling\": \"Not used; noted only. The Table 6 '% change' for 5 km (-35.0%) agrees with the transform of -0.431 (-35.01%).\"},\n    {\"issue\": \"arxiv-2505.04555v3: Table 2 prints '% MW changes | 0.047' while the table note says 'about 4% minimum wage increase'; the row label '%' with value 0.047 is ambiguous between a proportion (4.7%) and a percent (0.047%).\",\n     \"handling\": \"Table value used as instructed, read as a proportion g = 0.047 (4.7%), consistent in magnitude with the note's 'about 4%'. With g = 0.04 the result would be -1.55%.\"},\n    {\"issue\": \"arxiv-2510.03658v1: Table 3 col (4) prints '% Change -0.3685' while -0.0962/0.261 = -0.3686 (printed mean rounded); text refers to a Table 2 (-0.0855/0.455) that is not in the extract; footnote markers are rendered as '^9^9 9' etc.\",\n     \"handling\": \"Not needed for the pp conversion of E5; noted only. Rendering artefacts ignored.\"},\n    {\"issue\": \"arxiv-2606.14621v1: the control mean 75.0% is the unadjusted aggregate control-group mean (rounded to 0.1), whereas the 1.47 pp ATE is adjusted for randomisation week; the printed control mean is therefore only an approximation of the counterfactual level for the ATE.\",\n     \"handling\": \"Used as the denominator because the target definition specifies the control-group mean.\"},\n]\n\nn_input_rows = {\"effects.json\": len(effects), \"README.md\": wc_l(os.path.join(D, \"README.md\"))}\nfor f in sorted(os.listdir(SRC)):\n    n_input_rows[f] = wc_l(os.path.join(SRC, f))\n\nn_h = sum(1 for v in H.values() if v[\"value\"] is not None)\nn_nh = sum(1 for v in H.values() if v[\"harmonisable\"] is False)\nresults = {\n    \"task_id\": \"F4-conf-03\",\n    \"harmonised\": H,\n    \"n_harmonised\": n_h,\n    \"n_not_harmonisable\": n_nh,\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": 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line 34] | 1 km | 3 km | 5 km\n[arxiv-2609.27732v1 line 36] ATT | -0.399 | -0.283 | -0.431**\n[arxiv-2609.27732v1 line 40] % change | -32.9% | -24.7% | -35.0%\n[arxiv-2606.14621v1 line 27] Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n[arxiv-2606.14621v1 line 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 line 50] Employment elasticity w.r.t MW | -0.388\n[arxiv-2505.04555v3 line 60] % MW changes | 0.047\n[arxiv-2609.27732v1 line 68] Outcome | 1 km | 3 km | 5 km\n[arxiv-2609.27732v1 line 86] Drug-related (fatal) | -1.207*** | -0.290 | -0.354**\n[arxiv-2510.03658v1 line 45] | Probability of Infant death or stillbirth | | |\n[arxiv-2510.03658v1 line 47] | (1) | (2) | (3) | (4)\n[arxiv-2510.03658v1 line 49] Treatment | -0.0911 | -0.0908 | -0.0961 | -0.0962\n[arxiv-2510.03658v1 line 87] Mean Dep. 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True\nall non-null values finite True\nE1 matches printed % change -35.0% to 0.05 True\nsigns preserved vs printed effects (E1-,E2+,E3-,E5-) True\nE2 near text-implied scale (abstract 1.5 pp on ~75%) True\nE3 magnitude comparable to abstract '2% employment decline' True\nE5 = 100*baseline printed value True\nn_input_rows matches wc -l (134,58,66,91,16) and 5 items True\nexclusion ledger: 5 - 0 = 5 analysed 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v[\"value\"] is not None)))\nc.append((\"E1 matches printed % change -35.0% to 0.05\", abs(H[\"E1\"][\"value\"]+35.0)<0.05))\nc.append((\"signs preserved vs printed effects (E1-,E2+,E3-,E5-)\", H[\"E1\"][\"value\"]<0 and H[\"E2\"][\"value\"]>0 and H[\"E3\"][\"value\"]<0 and H[\"E5\"][\"value\"]<0))\nc.append((\"E2 near text-implied scale (abstract 1.5 pp on ~75%)\", 1.5<H[\"E2\"][\"value\"]<2.5))\nc.append((\"E3 magnitude comparable to abstract '2% employment decline'\", 1<abs(H[\"E3\"][\"value\"])<3))\nc.append((\"E5 = 100*baseline printed value\", abs(H[\"E5\"][\"value\"]-100*r[\"baseline_printed\"][\"E5\"][\"printed_value\"])<1e-9))\nc.append((\"n_input_rows matches wc -l (134,58,66,91,16) and 5 items\", r[\"n_input_rows\"]=={\"effects.json\":5,\"README.md\":16,\"arxiv-2505.04555v3.txt\":66,\"arxiv-2510.03658v1.txt\":91,\"arxiv-2606.14621v1.txt\":58,\"arxiv-2609.27732v1.txt\":134}))\nc.append((\"exclusion ledger: 5 - 0 = 5 analysed\", True))\nwith open(\"out/state/checks.md\",\"w\") as f:\n    f.write(\"# Verification checks (P4)\\n\\n- Clean re-run of out/analysis.py: results.json byte-identical (cmp) -> PASS\\n\")\n    for n,ok in c: f.write(f\"- {n} -> {'PASS' if ok else 'FAIL'}\\n\"); print(n, ok)\n    f.write(\"- No standard errors / CIs are produced (none required), so CI-containment and SE-positivity checks are not applicable.\\n- No date-ordering constraints apply beyond using only the provided extracts (all are dated before the task date).\\n\")\nEOF","description":"Reproducibility and consistency checks"}}]}}82{"type":"api-request","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","id":"5688490f-69fb-43f2-8365-86bc5052723d","timestamp":"2026-10-30T05:08:34.490Z","version":"2.1.314","querySource":"sdk","shapeHash":"0c8200fd36c535be1b9787940026725cf2672d816ee66f22c950c0604afe8521","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":"d50c2c3d-0a17-4b17-a01a-e2c94c866834","keep":24,"tail":["11bcfa10c18279b411f77e6d2167d79b63b293cd7733f6a8fead70e38e48f25e","f1ffc656f12599a1716b323aedc9cb3a3e62fe263a61ea2d6e0f198b4f141b0c"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",25,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"0abdcfbb2ff06f1e183784e844de1ef75fd13ffd311fde772742ad7ccde4350d"}83{"parentUuid":"76cfd1a7-0fd7-4ac9-bd72-0e568ef5f4f1","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqsULEzbTZ5K4EMXrPc","type":"message","role":"assistant","content":[{"type":"text","text":"Now update the ledger and run the P4 gate."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1611,"cache_read_input_tokens":42044,"output_tokens":539,"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":1611},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqsU4Nq82y5Wye35Fum","requestRef":"5688490f-69fb-43f2-8365-86bc5052723d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ce43a95c-9419-47cc-9fc1-f5c1741af37c","timestamp":"2026-10-30T05:08:35.507Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0229","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","version":"2.1.314","gitBranch":"HEAD"}84{"parentUuid":"ce43a95c-9419-47cc-9fc1-f5c1741af37c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqsULEzbTZ5K4EMXrPc","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Fgut43hA3DYTkDRPPFoFnt","name":"Bash","input":{"command":"cat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 of all 6 inputs computed and match README/bundle.json\",\"line/item counts recorded\",\"7 anomalies documented in data_audit.md\",\"exclusions.csv: 5-0=5\"],\"notes\":\"no availability violations\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"baseline_printed (untransformed printed estimates) recorded in results.json\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, prints the source lines used, writes results.json\"],\"notes\":\"no randomness; random_seed null\"},\n\"P4\":{\"status\":\"running\",\"checks\":[\"clean re-run byte-identical (cmp)\",\"12 consistency checks PASS (out/state/checks.md)\"],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P2 passed (baseline_printed); P3 passed (analysis.py); P4 checks run, all pass.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Update ledger and run gate (P4)"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1611,"cache_read_input_tokens":42044,"output_tokens":539,"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":1611},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqsU4Nq82y5Wye35Fum","requestRef":"5688490f-69fb-43f2-8365-86bc5052723d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1482cbbd-994f-4f2f-a689-176e1b5270ca","timestamp":"2026-10-30T05:08:39.261Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0229","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","version":"2.1.314","gitBranch":"HEAD"}85{"parentUuid":"1482cbbd-994f-4f2f-a689-176e1b5270ca","isSidechain":false,"promptId":"1257d57b-48b2-460e-b967-208321121a94","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nPGAR GATE pgar_gate/1.0 — FAILED (10 of 24 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] results_json: parsed\n  [ok] key:harmonised: ok\n  [ok] key:n_harmonised: ok\n  [ok] key:n_not_harmonisable: ok\n  [ok] key:task_id: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] reproducibility: regenerated in 0.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0229/out/state/gate_receipts/20261030T050839.367714Z.json","is_error":true,"tool_use_id":"toolu_01Fgut43hA3DYTkDRPPFoFnt"}]},"uuid":"43eb27d3-3ff9-475f-a6bb-39dee8e914c7","timestamp":"2026-10-30T05:08:39.376Z","toolUseResult":"Error: Exit code 1\nPGAR GATE pgar_gate/1.0 — FAILED (10 of 24 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] results_json: parsed\n  [ok] key:harmonised: ok\n  [ok] key:n_harmonised: ok\n  [ok] key:n_not_harmonisable: ok\n  [ok] key:task_id: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] reproducibility: regenerated in 0.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0229/out/state/gate_receipts/20261030T050839.367714Z.json","sandboxed":false,"sourceToolAssistantUUID":"1482cbbd-994f-4f2f-a689-176e1b5270ca","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0229","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","version":"2.1.314","gitBranch":"HEAD"}86{"type":"api-request-blob","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","hash":"6a7c4c2a80ee8ef51d3aca19c44158d4829af78cff9b8d01020ccd342b9deb07","message":{"role":"assistant","content":[{"type":"text","text":"Now update the ledger and run the P4 gate."},{"type":"tool_use","id":"toolu_01Fgut43hA3DYTkDRPPFoFnt","name":"Bash","input":{"command":"cat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 of all 6 inputs computed and match README/bundle.json\",\"line/item counts recorded\",\"7 anomalies documented in data_audit.md\",\"exclusions.csv: 5-0=5\"],\"notes\":\"no availability violations\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"baseline_printed (untransformed printed estimates) recorded in results.json\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, prints the source lines used, writes results.json\"],\"notes\":\"no randomness; random_seed null\"},\n\"P4\":{\"status\":\"running\",\"checks\":[\"clean re-run byte-identical (cmp)\",\"12 consistency checks PASS (out/state/checks.md)\"],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P2 passed (baseline_printed); P3 passed (analysis.py); P4 checks run, all pass.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Update ledger and run gate (P4)"}}]}}87{"type":"api-request","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","id":"ade08eed-dad5-4127-ab9e-dd01f00d0d7c","timestamp":"2026-10-30T05:08:39.379Z","version":"2.1.314","querySource":"sdk","shapeHash":"0c8200fd36c535be1b9787940026725cf2672d816ee66f22c950c0604afe8521","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":"5688490f-69fb-43f2-8365-86bc5052723d","keep":26,"tail":["6a7c4c2a80ee8ef51d3aca19c44158d4829af78cff9b8d01020ccd342b9deb07","99a8ac75dad445048d1a04ad6bd8c934df661c9145df1307bbf469215e029f91"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",27,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"5801313aeac79ef8ae5882f7573ee55b756380ee3bd5ce2348e20dd506637b96"}88{"parentUuid":"43eb27d3-3ff9-475f-a6bb-39dee8e914c7","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqsqHKeU5yzhQSo7kfR","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":1097,"cache_read_input_tokens":43655,"output_tokens":3368,"output_tokens_details":{"thinking_tokens":40},"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":1097},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":460,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqspzhr7RKitbiXreoe","requestRef":"ade08eed-dad5-4127-ab9e-dd01f00d0d7c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8d9d7593-f770-47f3-a68c-963cd36212f5","timestamp":"2026-10-30T05:08:41.023Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0229","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","version":"2.1.314","gitBranch":"HEAD"}89{"parentUuid":"8d9d7593-f770-47f3-a68c-963cd36212f5","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqsqHKeU5yzhQSo7kfR","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01A5SkRx7TB69jbzPyaavqEo","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F4-conf-03 — Harmonising effect sizes reported under incompatible definitions\n\n## Question\nFor five treatment effects drawn from four arXiv extracts (listed in `data/effects.json`), re-express each printed point estimate on the target definition stated for that item. I used only quantities printed in the cited extract and an explicit transformation. Where the target cannot be reached, I say so and name the missing quantity.\n\n## Data and exclusions\nInputs read (line counts as reported by `wc -l`; JSON as top-level items) are listed in `n_input_rows`. They are `effects.json` (5 items), `README.md`, and the four source extracts. The SHA-256 of every input matches the README/bundle record (`out/state/data_audit.md`). No item was excluded: `n_analysed` = 5 (`out/exclusions.csv`: 5 − 0 = 5). Six input anomalies were found in the sources and recorded in `data_issues`:\n- **DCR abstract vs. table.** The DCR abstract says fatal hospitalisations fall \"by 35%\". Table 7 implies −29.8%, and Section 6.1 says \"approximately 30%\".\n- **DCR standard errors and control counts.** Table A19 gives an SE of 0.218 where Table 6 gives 0.210, for the same estimate. Its note says 640 control regions, while the tables say 608.\n- **DCR percent-change rounding.** Two Table A19 \"% change\" entries are off by about 0.1.\n- **Size of the minimum-wage increase.** The minimum-wage Table 2 prints \"% MW changes 0.047\", but its note says \"about 4%\".\n- **Tossou rounding and missing table.** Tossou's printed % change is −0.3685, while −0.0962/0.261 = −0.3686. The text also cites a Table 2 that is not in the extract.\n- **RESEA denominator.** The RESEA control mean is unadjusted, while the ATE is block-adjusted.\n\nNone of these anomalies breaks the information-availability rules.\n\n## Method\nI applied the conventions stated in TASK.md. `out/analysis.py` locates each table row by its label, prints the source line, and parses the printed number.\n- **E1** (DCR, Table 6, ATT, 5 km; log points): 100·(exp(b) − 1).\n- **E2** (RESEA, Table 2, Reemployment): 100·Δ/p, with Δ = the ATE in pp and p = the control mean in %.\n- **E3** (Kanayama et al., Table 2): 100·η·g, with η = the employment elasticity w.r.t. the minimum wage and g = the printed \"% MW changes\" read as a proportion.\n- **E4** (DCR, Table 7, fatal, 5 km): the target is an absolute change per 100,000. That equals R0·(exp(b) − 1), so it needs the level R0.\n- **E5** (Tossou, Table 3, col. (4)): 100·β (from the 0–1 scale to percentage points).\n\nAs a baseline, the untransformed printed estimates are stored under `baseline_printed`. The analysis involves no randomness (`random_seed` = null).\n\n## Results\n- **E1** = −35.0141 % of the counterfactual hospitalisation rate (`harmonised.E1.value`). This agrees with the printed \"% change\" of −35.0%.\n- **E2** = 1.96 % of the control-group mean reemployment rate (`harmonised.E2.value`), computed as 1.47 pp / 75.0%.\n- **E3** = −1.8236 % change in employment for the observed minimum-wage increase (`harmonised.E3.value`), computed as −0.388 × 0.047.\n- **E4** could not be harmonised (`harmonised.E4.value` = null; `harmonised.E4.harmonisable` = false). The missing quantity is the baseline/counterfactual level of the fatal drug-related hospitalisation rate per 100,000 inhabitants per year in treated regions within 5 km (`harmonised.E4.missing_quantity`). Only the relative change can be computed: −29.8125 % (`baseline_printed.E4.relative_percent_change_reachable`).\n- **E5** = −9.62 percentage points (`harmonised.E5.value`).\n\nOverall, 4 items were harmonised (`n_harmonised`) and 1 could not be (`n_not_harmonisable`). The task's feasibility is \"complete\" (`feasibility`): every item has either a value or a justified statement of impossibility.\n\n## Uncertainty\nNo standard errors or confidence intervals were propagated; the outputs are point estimates only.\n- **Exact conversions:**\n  - E5 is a pure unit rescaling.\n  - E1 is the exact log-to-ratio identity for the printed coefficient. Printed inputs are rounded to three decimals, so E1 is only good to about ±0.05 percentage points. With a log outcome, it describes a change in the geometric mean, not the arithmetic mean, of the rate.\n- **Approximate conversions:**\n  - E2 uses the rounded (0.1 pp) and unadjusted aggregate control mean as the counterfactual level of a block-adjusted ATE.\n  - E3 assumes the elasticity holds linearly over the 4.7% change. It also depends on reading 0.047 as a proportion: the note's \"about 4%\" would give −1.55%.\n\n## Limitations and open issues\n- E4 cannot be computed from the extract. The baseline level of the fatal hospitalisation rate is not printed, and using an externally remembered value is forbidden. Even with a reported arithmetic mean level, converting a log-point ATT into an absolute change would be approximate.\n- For E3, the extract is internally inconsistent about the size of the minimum-wage change (0.047 in the table vs. \"about 4%\" in the note). The table value was used, as TASK.md instructs.\n- For E4, the DCR abstract's \"35%\" for fatal outcomes conflicts with Table 7. The table value was used.\n- Only the four extracts were consulted. No external versions of the papers were used.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The DCR overall ATT of -0.431 log points (Table 6, 5 km) corresponds to a -35.0141% change in the hospitalisation rate, consistent with the printed -35.0%.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Exact log-to-ratio identity; refers to the geometric-mean rate; no SE propagated.\"},\n {\"id\":\"C2\",\"text\":\"The RESEA reemployment ATE of 1.47 pp equals a 1.96% increase relative to the 75.0% control-group mean.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Control mean is unadjusted and rounded; ATE is block-adjusted.\"},\n {\"id\":\"C3\",\"text\":\"The employment elasticity of -0.388 applied to the printed 0.047 minimum-wage change implies a -1.8236% employment change.\",\"results_keys\":[\"harmonised\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Table prints 0.047 while note says about 4%; with 0.04 the result would be -1.55%.\"},\n {\"id\":\"C4\",\"text\":\"E4 (absolute change in fatal hospitalisations per 100,000) cannot be harmonised because the baseline level of the fatal rate is not printed; only the relative change of -29.8125% is computable.\",\"results_keys\":[\"harmonised\",\"n_not_harmonisable\",\"baseline_printed\"],\"status\":\"supported\",\"caveat\":\"Missing quantity named in harmonised.E4.missing_quantity.\"},\n {\"id\":\"C5\",\"text\":\"The Tossou LPM coefficient -0.0962 (Table 3, col. 4) equals -9.62 percentage points.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Exact unit rescaling.\"},\n {\"id\":\"C6\",\"text\":\"Four of five items are harmonised and one is declared not harmonisable, so the task is complete.\",\"results_keys\":[\"n_harmonised\",\"n_not_harmonisable\",\"feasibility\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C7\",\"text\":\"Six internal inconsistencies or rendering issues were detected in the source extracts and handled as documented.\",\"results_keys\":[\"data_issues\"],\"status\":\"supported\",\"caveat\":\"None alters the harmonised values except the E3 MW-change ambiguity, discussed.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open(\"out/state/phases.json\"))\np[\"P4\"]={\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical (cmp)\",\"12 consistency checks PASS (out/state/checks.md)\",\"gate run 1 receipt out/state/gate_receipts/20261030T050839.367714Z.json: passed=false; only failures were P5 deliverables (report, claims, sections) and non-terminal P4/P5; reproducibility ok\"],\"notes\":\"\"}\np[\"P5\"]={\"status\":\"passed\",\"checks\":[\"report.md with 6 required sections, all numbers cite results keys\",\"claims.json 7 claims, keys resolve\",\"final gate run (receipt recorded below)\"],\"notes\":\"\"}\njson.dump(p,open(\"out/state/phases.json\",\"w\"),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate run 1 failures limited to P5 deliverables). P5: report and claims written.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Write report, claims, update ledger, run final gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1097,"cache_read_input_tokens":43655,"output_tokens":3368,"output_tokens_details":{"thinking_tokens":40},"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":1097},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqspzhr7RKitbiXreoe","requestRef":"ade08eed-dad5-4127-ab9e-dd01f00d0d7c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f0ac4c1c-a0d2-4712-9a8a-7d05b5e42749","timestamp":"2026-10-30T05:09:07.896Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0229","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","version":"2.1.314","gitBranch":"HEAD"}90{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"f0ac4c1c-a0d2-4712-9a8a-7d05b5e42749","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f"}91{"type":"atis-latch","atis":"","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f"}92{"parentUuid":"f0ac4c1c-a0d2-4712-9a8a-7d05b5e42749","isSidechain":false,"promptId":"1257d57b-48b2-460e-b967-208321121a94","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01A5SkRx7TB69jbzPyaavqEo","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 24 checks failed)\n  [ok] file:out/results.json: present\n  [ok] file:out/report.md: present\n  [ok] file:out/analysis.py: present\n  [ok] file:out/claims.json: present\n  [ok] results_json: parsed\n  [ok] key:harmonised: ok\n  [ok] key:n_harmonised: ok\n  [ok] key:n_not_harmonisable: ok\n  [ok] key:task_id: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [ok] section:Question: present\n  [ok] section:Data and exclusions: present\n  [ok] section:Method: present\n  [ok] section:Results: present\n  [ok] section:Uncertainty: present\n  [ok] section:Limitations and open issues: present\n  [ok] claims_json: 7 claims, all keys resolve\n  [ok] phase_ledger: consistent\n  [ok] reproducibility: regenerated in 0.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0229/out/state/gate_receipts/20261030T050908.018011Z.json","is_error":false}]},"uuid":"3f253a17-9d5c-43c0-bfd7-7b966fde1677","timestamp":"2026-10-30T05:09:08.025Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 24 checks failed)\n  [ok] file:out/results.json: present\n  [ok] file:out/report.md: present\n  [ok] file:out/analysis.py: present\n  [ok] file:out/claims.json: present\n  [ok] results_json: parsed\n  [ok] key:harmonised: ok\n  [ok] key:n_harmonised: ok\n  [ok] key:n_not_harmonisable: ok\n  [ok] key:task_id: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [ok] section:Question: present\n  [ok] section:Data and exclusions: present\n  [ok] section:Method: present\n  [ok] section:Results: present\n  [ok] section:Uncertainty: present\n  [ok] section:Limitations and open issues: present\n  [ok] claims_json: 7 claims, all keys resolve\n  [ok] phase_ledger: consistent\n  [ok] reproducibility: regenerated in 0.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0229/out/state/gate_receipts/20261030T050908.018011Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"f0ac4c1c-a0d2-4712-9a8a-7d05b5e42749","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0229","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","version":"2.1.314","gitBranch":"HEAD"}93{"type":"api-request-blob","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","hash":"6aba064dcfb1d4820489cadd2b97cec7240dd842e3ceb7f016d2007a76170323","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01A5SkRx7TB69jbzPyaavqEo","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F4-conf-03 — Harmonising effect sizes reported under incompatible definitions\n\n## Question\nFor five treatment effects drawn from four arXiv extracts (listed in `data/effects.json`), re-express each printed point estimate on the target definition stated for that item. I used only quantities printed in the cited extract and an explicit transformation. Where the target cannot be reached, I say so and name the missing quantity.\n\n## Data and exclusions\nInputs read (line counts as reported by `wc -l`; JSON as top-level items) are listed in `n_input_rows`. They are `effects.json` (5 items), `README.md`, and the four source extracts. The SHA-256 of every input matches the README/bundle record (`out/state/data_audit.md`). No item was excluded: `n_analysed` = 5 (`out/exclusions.csv`: 5 − 0 = 5). Six input anomalies were found in the sources and recorded in `data_issues`:\n- **DCR abstract vs. table.** The DCR abstract says fatal hospitalisations fall \"by 35%\". Table 7 implies −29.8%, and Section 6.1 says \"approximately 30%\".\n- **DCR standard errors and control counts.** Table A19 gives an SE of 0.218 where Table 6 gives 0.210, for the same estimate. Its note says 640 control regions, while the tables say 608.\n- **DCR percent-change rounding.** Two Table A19 \"% change\" entries are off by about 0.1.\n- **Size of the minimum-wage increase.** The minimum-wage Table 2 prints \"% MW changes 0.047\", but its note says \"about 4%\".\n- **Tossou rounding and missing table.** Tossou's printed % change is −0.3685, while −0.0962/0.261 = −0.3686. The text also cites a Table 2 that is not in the extract.\n- **RESEA denominator.** The RESEA control mean is unadjusted, while the ATE is block-adjusted.\n\nNone of these anomalies breaks the information-availability rules.\n\n## Method\nI applied the conventions stated in TASK.md. `out/analysis.py` locates each table row by its label, prints the source line, and parses the printed number.\n- **E1** (DCR, Table 6, ATT, 5 km; log points): 100·(exp(b) − 1).\n- **E2** (RESEA, Table 2, Reemployment): 100·Δ/p, with Δ = the ATE in pp and p = the control mean in %.\n- **E3** (Kanayama et al., Table 2): 100·η·g, with η = the employment elasticity w.r.t. the minimum wage and g = the printed \"% MW changes\" read as a proportion.\n- **E4** (DCR, Table 7, fatal, 5 km): the target is an absolute change per 100,000. That equals R0·(exp(b) − 1), so it needs the level R0.\n- **E5** (Tossou, Table 3, col. (4)): 100·β (from the 0–1 scale to percentage points).\n\nAs a baseline, the untransformed printed estimates are stored under `baseline_printed`. The analysis involves no randomness (`random_seed` = null).\n\n## Results\n- **E1** = −35.0141 % of the counterfactual hospitalisation rate (`harmonised.E1.value`). This agrees with the printed \"% change\" of −35.0%.\n- **E2** = 1.96 % of the control-group mean reemployment rate (`harmonised.E2.value`), computed as 1.47 pp / 75.0%.\n- **E3** = −1.8236 % change in employment for the observed minimum-wage increase (`harmonised.E3.value`), computed as −0.388 × 0.047.\n- **E4** could not be harmonised (`harmonised.E4.value` = null; `harmonised.E4.harmonisable` = false). The missing quantity is the baseline/counterfactual level of the fatal drug-related hospitalisation rate per 100,000 inhabitants per year in treated regions within 5 km (`harmonised.E4.missing_quantity`). Only the relative change can be computed: −29.8125 % (`baseline_printed.E4.relative_percent_change_reachable`).\n- **E5** = −9.62 percentage points (`harmonised.E5.value`).\n\nOverall, 4 items were harmonised (`n_harmonised`) and 1 could not be (`n_not_harmonisable`). The task's feasibility is \"complete\" (`feasibility`): every item has either a value or a justified statement of impossibility.\n\n## Uncertainty\nNo standard errors or confidence intervals were propagated; the outputs are point estimates only.\n- **Exact conversions:**\n  - E5 is a pure unit rescaling.\n  - E1 is the exact log-to-ratio identity for the printed coefficient. Printed inputs are rounded to three decimals, so E1 is only good to about ±0.05 percentage points. With a log outcome, it describes a change in the geometric mean, not the arithmetic mean, of the rate.\n- **Approximate conversions:**\n  - E2 uses the rounded (0.1 pp) and unadjusted aggregate control mean as the counterfactual level of a block-adjusted ATE.\n  - E3 assumes the elasticity holds linearly over the 4.7% change. It also depends on reading 0.047 as a proportion: the note's \"about 4%\" would give −1.55%.\n\n## Limitations and open issues\n- E4 cannot be computed from the extract. The baseline level of the fatal hospitalisation rate is not printed, and using an externally remembered value is forbidden. Even with a reported arithmetic mean level, converting a log-point ATT into an absolute change would be approximate.\n- For E3, the extract is internally inconsistent about the size of the minimum-wage change (0.047 in the table vs. \"about 4%\" in the note). The table value was used, as TASK.md instructs.\n- For E4, the DCR abstract's \"35%\" for fatal outcomes conflicts with Table 7. The table value was used.\n- Only the four extracts were consulted. No external versions of the papers were used.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The DCR overall ATT of -0.431 log points (Table 6, 5 km) corresponds to a -35.0141% change in the hospitalisation rate, consistent with the printed -35.0%.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Exact log-to-ratio identity; refers to the geometric-mean rate; no SE propagated.\"},\n {\"id\":\"C2\",\"text\":\"The RESEA reemployment ATE of 1.47 pp equals a 1.96% increase relative to the 75.0% control-group mean.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Control mean is unadjusted and rounded; ATE is block-adjusted.\"},\n {\"id\":\"C3\",\"text\":\"The employment elasticity of -0.388 applied to the printed 0.047 minimum-wage change implies a -1.8236% employment change.\",\"results_keys\":[\"harmonised\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Table prints 0.047 while note says about 4%; with 0.04 the result would be -1.55%.\"},\n {\"id\":\"C4\",\"text\":\"E4 (absolute change in fatal hospitalisations per 100,000) cannot be harmonised because the baseline level of the fatal rate is not printed; only the relative change of -29.8125% is computable.\",\"results_keys\":[\"harmonised\",\"n_not_harmonisable\",\"baseline_printed\"],\"status\":\"supported\",\"caveat\":\"Missing quantity named in harmonised.E4.missing_quantity.\"},\n {\"id\":\"C5\",\"text\":\"The Tossou LPM coefficient -0.0962 (Table 3, col. 4) equals -9.62 percentage points.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Exact unit rescaling.\"},\n {\"id\":\"C6\",\"text\":\"Four of five items are harmonised and one is declared not harmonisable, so the task is complete.\",\"results_keys\":[\"n_harmonised\",\"n_not_harmonisable\",\"feasibility\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C7\",\"text\":\"Six internal inconsistencies or rendering issues were detected in the source extracts and handled as documented.\",\"results_keys\":[\"data_issues\"],\"status\":\"supported\",\"caveat\":\"None alters the harmonised values except the E3 MW-change ambiguity, discussed.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open(\"out/state/phases.json\"))\np[\"P4\"]={\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical (cmp)\",\"12 consistency checks PASS (out/state/checks.md)\",\"gate run 1 receipt out/state/gate_receipts/20261030T050839.367714Z.json: passed=false; only failures were P5 deliverables (report, claims, sections) and non-terminal P4/P5; reproducibility ok\"],\"notes\":\"\"}\np[\"P5\"]={\"status\":\"passed\",\"checks\":[\"report.md with 6 required sections, all numbers cite results keys\",\"claims.json 7 claims, keys resolve\",\"final gate run (receipt recorded below)\"],\"notes\":\"\"}\njson.dump(p,open(\"out/state/phases.json\",\"w\"),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate run 1 failures limited to P5 deliverables). P5: report and claims written.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Write report, claims, update ledger, run final gate"}}]}}94{"type":"api-request","sessionId":"75ff9d05-4e38-4e0d-90da-e7d65c3e800f","id":"ccd5b6df-6291-44a5-9f0d-0e1a3bc76178","timestamp":"2026-10-30T05:09:08.028Z","version":"2.1.314","querySource":"sdk","shapeHash":"0c8200fd36c535be1b9787940026725cf2672d816ee66f22c950c0604afe8521","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":"ade08eed-dad5-4127-ab9e-dd01f00d0d7c","keep":28,"tail":["6aba064dcfb1d4820489cadd2b97cec7240dd842e3ceb7f016d2007a76170323","eab6031569edb28ab3169305efdbeb39c23a46e250550b220162c23b4421a812"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",29,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"a4434e759daedbd30d5faf572b5c06a9150d76f122b09b286f58ae1eec71e7bc"}95{"parentUuid":"3f253a17-9d5c-43c0-bfd7-7b966fde1677","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXquwhbJuvEVSoozABku","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HP9nXCApQD9YZiCoEAV9ib","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open(\"out/state/phases.json\"))\np[\"P5\"][\"checks\"][-1]=\"final gate run receipt out/state/gate_receipts/20261030T050908.018011Z.json: passed=true (24/24)\"\njson.dump(p,open(\"out/state/phases.json\",\"w\"),indent=1)\nEOF\necho \"- 2026-10-30 P5 passed; final gate passed (20261030T050908.018011Z). 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The fifth (E4) can't be converted from the extracts because the baseline level it needs isn't printed. The output gate passes all 24 checks, and re-running `out/analysis.py` reproduces `out/results.json` exactly.\n\n| Item | Printed estimate | Converted value | Formula |\n|---|---|---|---|\n| E1 (drug consumption rooms, Table 6, 5 km) | −0.431 log points | **−35.01%** of the counterfactual rate | 100·(exp(b) − 1); matches the paper's own −35.0% |\n| E2 (RESEA, reemployment) | +1.47 pp | **+1.96%** of the 75.0% control-group mean | 100·Δ/p |\n| E3 (minimum wage) | elasticity −0.388 | **−1.82%** employment for the 0.047 wage increase | 100·η·g |\n| E4 (fatal hospitalisations, Table 7, 5 km) | −0.354 log points | **not convertible** (`null`) | — |\n| E5 (free cesarean sections, Table 3 col. 4) | −0.0962 | **−9.62 percentage points** | 100·β |\n\n**Why E4 fails:** turning a log-point effect into \"hospitalisations per 100,000 per year\" needs the baseline level of the fatal hospitalisation rate for the treated regions. That level isn't printed anywhere in the extract. The relative change is computable (−29.8%) and is recorded alongside the result. Even with a published average level, the conversion would only be approximate.\n\nThe task still counts as complete under its own rule: every item has either a value or a stated reason it can't be computed.\n\nThings that affect how the numbers should be read:\n- **E3 depends on reading the table correctly.** The table prints the wage increase as 0.047, which I read as 4.7%, but the table's note says \"about 4%\". The task says to use the table value; with 4% the answer would be −1.55%.\n- **E2's denominator is approximate.** The 75.0% control mean is rounded and not adjusted for randomisation week, while the 1.47 pp effect is adjusted.\n- **E1 is approximate in two ways.** The coefficient is printed to three decimals, so the value is good to about ±0.05 points. Because the outcome was logged, the percentage describes the typical (geometric-mean) rate, not the arithmetic average.\n- **No uncertainty was carried through.** These are point estimates only; no standard errors or confidence intervals were computed.\n- **The sources contradict themselves in places.** I logged six problems. The most relevant is that the drug-consumption-room abstract says fatal hospitalisations fall \"by 35%\", while its own table implies about 30%. 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