spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T05:07:34.650Z","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T05:07:34.656Z","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474"}4{"parentUuid":null,"isSidechain":false,"promptId":"b69de4f4-97af-47d0-8ca5-67147b0766d7","type":"user","message":{"role":"user","content":"Begin"},"uuid":"8162503e-b888-412c-b173-2152dbe4732b","timestamp":"2026-10-30T05:07:34.660Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0234","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","shapeHash":"e01d2c50ee0140972e988dac6859d720b81c759454963c4578404a432423b1f2","timestamp":"2026-10-30T05:07:34.674Z","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/RC0234\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/RC0234","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474"}12{"parentUuid":"3ca9671a-fba9-4127-ae7f-34279fa93d1d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqo7kdZ5zkaCv6FQfcS","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":454,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqo7Sn5RXs1a7NqEZjf","requestRef":"3351587b-6eba-499c-a316-8a3b757ad49a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"636644b2-7da2-4172-80f4-447c1a2e7dc1","timestamp":"2026-10-30T05:07:36.715Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0234","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"636644b2-7da2-4172-80f4-447c1a2e7dc1","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqo7kdZ5zkaCv6FQfcS","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SAPxzrW5JC6mg365jG7zSX","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  135 simon-pierreboucher  staff  4320 oct.  30 01:07 ..\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":"b280f6f3-47cb-4c10-bc8d-026742306f50","timestamp":"2026-10-30T05:07:37.406Z","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  135 simon-pierreboucher  staff  4320 oct.  30 01:07 ..\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":"181f55ce-accb-4f74-bfcb-c1cd86a5f51e","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0234","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","version":"2.1.314","gitBranch":"HEAD"}15{"type":"api-request-blob","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","hash":"076c9158ec2ace2ed75133363651ff257e25c49849ea890c5f0fdbbf23cd6161","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01SAPxzrW5JC6mg365jG7zSX","name":"Bash","input":{"command":"cat TASK.md; 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wc -l data/sources/* data/README.md; shasum -a 256 data/*.json data/README.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":238,"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_011CgXqoG3xJrVDgd6La5PZQ","requestRef":"abdc3001-254f-4c76-bf89-970b52803f2a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3d0a9c94-e441-43be-9f8f-bbf5e74cf7c9","timestamp":"2026-10-30T05:07:39.695Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0234","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","version":"2.1.314","gitBranch":"HEAD"}20{"parentUuid":"3d0a9c94-e441-43be-9f8f-bbf5e74cf7c9","isSidechain":false,"promptId":"b69de4f4-97af-47d0-8ca5-67147b0766d7","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0191ZYXNhKocvba7GSbHmaV1","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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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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    1\tSOURCE EXTRACT — arxiv-2609.27732v1\n     2\tTitle: Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n     3\tAuthors: Ana Armendariz (University of St. Gallen)\n     4\tBibliographic record: arXiv:2609.27732v1 [econ.GN], submitted 23 Sep 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2609.27732v1\n     5\tRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\n     8\tOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n    13\t\n    14\tAna Armendariz*\n    15\t\n    16\tSeptember 23, 2026\n    17\t\n    18\tThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n    19\t\n    20\tKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n    21\t\n    22\t## 6 Results\n    23\t\n    24\tThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n    25\t\n    26\tFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n    27\t\n    28\tNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n    29\t\n    30\tFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n    31\t\n    32\tTable 6: Overall Average Treatment Effect on the Treated (ATT)\n    33\t\n    34\t| 1 km | 3 km | 5 km\n    35\t\n    36\tATT | -0.399 | -0.283 | -0.431**\n    37\t\n    38\t| (0.412) | (0.279) | (0.210)\n    39\t\n    40\t% change | -32.9% | -24.7% | -35.0%\n    41\t\n    42\tTreated regions | 7 | 35 | 50\n    43\t\n    44\tControl regions | 608 | 608 | 608\n    45\t\n    46\tObservations | 16,175 | 16,875 | 17,250\n    47\t\n    48\tNote: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\n    49\tthroughout. Regions near DCRs that opened before 1998\n    50\t(always-treated) are excluded. Standard errors clustered at the Medstat region level in\n    51\tparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n    52\t{}^{*}p<0.10 .\n    53\t\n    54\tTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\n    55\tcorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n    56\t\n    57\tTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\n    58\tsub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\n    59\t\n    60\tFigure 7: Event study: Sub-outcome estimates,\n    61\twithin 5 km, \\pm 5-year window\n    62\t\n    63\tNote: The figure plots event-study coefficients from the doubly robust Callaway & Sant’Anna (2021) estimator for four diagnostic sub-categories of drug-related hospitalizations. Each panel uses a separate y -axis to accommodate differences in scale across outcomes. The sample includes all staggered-treated MedStat regions within 5 km of a\n    64\tDCR that opened between 1998 and 2022. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded. 95% confidence intervals are based on standard errors clustered at the MedStat region level.\n    65\t\n    66\tTable 7: Overall ATT by Outcome Category and Buffer Zone\n    67\t\n    68\tOutcome | 1 km | 3 km | 5 km\n    69\t\n    70\tMental/Behavioral | -0.354 | -0.202 | -0.381*\n    71\t\n    72\t| (0.382) | (0.244) | (0.220)\n    73\t\n    74\tHepatitis | -0.270 | -0.413 | -0.406\n    75\t\n    76\t| (0.486) | (0.270) | (0.250)\n    77\t\n    78\tHIV | 0.063 | -0.239 | -0.340\n    79\t\n    80\t| (0.801) | (0.337) | (0.294)\n    81\t\n    82\tNarcotics | 1.186*** | 0.549*** | 0.390**\n    83\t\n    84\t| (0.288) | (0.187) | (0.165)\n    85\t\n    86\tDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n    87\t\n    88\t| (0.137) | (0.215) | (0.178)\n    89\t\n    90\tTreated regions | 7 | 35 | 50\n    91\t\n    92\tControl regions | 608 (never-treated) | |\n    93\t\n    94\tNote: Each cell reports the overall ATT from\n    95\tthe doubly robust Callaway & Sant’Anna (2021) estimator with\n    96\tnever-treated controls and a universal base period. Outcomes are\n    97\tlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\n    98\tmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\n    99\tHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\n   100\testimates are identified from a single treated region per cohort and\n   101\tshould be interpreted with caution. Regions near DCRs opened before\n   102\t1998 excluded. Standard errors clustered at the Medstat region level.\n   103\tSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n   104\t\n   105\tFigure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\n   106\t\n   107\t### 6.1 Mechanisms\n   108\t\n   109\tThe sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n   110\tsubstitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\n   111\t(Kerr et al., 2017; Hedrich, 2004).\n   112\t\n   113\tThe second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\n   114\t\n   115\tA third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\n   116\t\n   117\tTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n   118\t\n   119\t| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n   120\t\n   121\tATT | -0.431** | -0.438** | -0.362* | -0.149***\n   122\t\n   123\t| (0.218) | (0.208) | (0.205) | (0.038)\n   124\t\n   125\t% change | -35.0% | -35.5% | -30.3% | -13.9%\n   126\t\n   127\tTreated regions | 50 | | |\n   128\t\n   129\tControl regions | 608 | | |\n   130\t\n   131\tObservations | 17,250 | | |\n   132\t\n   133\tNote: All specifications use the 5 km buffer,\n   134\tnever-treated comparison regions (640 regions), and a \\pm 5-year window","is_error":false}]},"uuid":"aaed4c42-2baf-4778-94e1-55bc3666adc0","timestamp":"2026-10-30T05:07:40.258Z","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":"abca0064-7f14-4a2a-8688-cfd117a54943","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0234","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","version":"2.1.314","gitBranch":"HEAD"}23{"type":"api-request-blob","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","hash":"b1ecd101e548bd47764ee2c753df64799935fdb04a622e778665a044bc84cc48","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_0191ZYXNhKocvba7GSbHmaV1","name":"Bash","input":{"command":"cat data/effects.json data/README.md gate_spec.json bundle.json; wc -l data/sources/* data/README.md; shasum -a 256 data/*.json data/README.md data/sources/*","description":"Read effects, README, gate spec"}},{"type":"tool_use","id":"toolu_015gMg7JuRH9KDRx3Xweqt4N","name":"Bash","input":{"command":"cat -n data/sources/arxiv-2609.27732v1.txt","description":"Read Armendariz source"}}]}}24{"type":"api-request-blob","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","hash":"c95ac2845c08b2179738b97948b72b82153721b9810ca136919d5d2c2cf03e91","message":{"role":"user","content":[{"tool_use_id":"toolu_0191ZYXNhKocvba7GSbHmaV1","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. 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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 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    1\tSOURCE EXTRACT — arxiv-2606.14621v1\n     2\tTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n     3\tAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\n     4\tBibliographic record: arXiv:2606.14621v1 [econ.GN], submitted 12 Jun 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2606.14621v1\n     5\tRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Section 3 'Results' (Table 2 with caption, the results paragraphs, Section 3.1 'Cost-benefit analysis'); from Section 4.1, Table 3 with its caption and the paragraph that discusses it.\n     8\tOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\tEditorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n    11\t================================================================\n    12\t\n    13\t# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n    14\t\n    15\tHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n    16\t\n    17\t###### Abstract\n    18\t\n    19\tProlonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n    20\t\n    21\t## 3 Results\n    22\t\n    23\t[Table 1 (pre-treatment characteristics) omitted from this extract.]\n    24\t\n    25\t[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\n    26\t\n    27\tOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n    28\tWage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n    29\tReemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n    30\tWeeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n    31\tShort-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\n    32\tShort-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\n    33\tShort-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n    34\t\n    35\tTable 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n    36\t\n    37\tOverall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n    38\t\n    39\tThe results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n    40\t\n    41\tAs noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n    42\t\n    43\t### 3.1 Cost-benefit analysis\n    44\t\n    45\tWe find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n    46\t\n    47\t### 4.1 Methodological lessons (excerpt)\n    48\t\n    49\t[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n    50\t\n    51\tOutcome | Difference in means | Covariate adjusted | Fixed effects regression\n    52\tWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\n    53\tReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\n    54\tWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n    55\t\n    56\tTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n    57\t\n    58\tIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . These fixed effects regressions amount to estimating the coefficient in the regression model\n     1\tSOURCE EXTRACT — arxiv-2505.04555v3\n     2\tTitle: Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation\n     3\tAuthors: Hayato Kanayama; Sho Miyaji; Suguru Otani\n     4\tBibliographic record: arXiv:2505.04555v3 [econ.GN], version 1 submitted 7 May 2025, version 2 submitted 27 May 2025, version 3 (this extract) submitted 16 Mar 2026. Abstract page: https://arxiv.org/abs/2505.04555v3\n     5\tRetrieved: https://arxiv.org/html/2505.04555v3 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f2b68299cbc49f208a0340bd233007fe922f6d4245e0a642db526662d359789b)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Appendix A.1 'Elasticities Related to Minimum Wages' in full (Table 2 with its note and the discussion paragraph).\n     8\tOmitted: Sections 1-7, the other appendices, the references. The 'Current version' date printed in the HTML rendering is generated at rendering time and is not the submission date.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation\n    13\t\n    14\tHayato Kanayama (Waseda University), Sho Miyaji (Yale University), Suguru Otani (The University of Tokyo)\n    15\t\n    16\t###### Abstract\n    17\t\n    18\tHow labor markets adjust immediately after minimum wage hikes remains an open, policy-relevant question. This paper studies short-run minimum-wage effects in Japan’s spot labor market using Timee data and a wage-bin difference-in-differences design. We find a 2% employment decline in affected bins, driven by reduced vacancy creation rather than worker supply. Effects are more negative where the minimum-wage bite is higher and in low-wage occupations. Using job descriptions and amenity information, we document reallocation across job types: postings shift toward greater amenity provision and experienced-worker targeting, while female-targeted descriptions become less common, suggesting short-run labor-demand adjustments may foreshadow longer-run reallocation.\n    19\t\n    20\tKeywords: minimum wage, employment, job amenities, gig workers, spot work, difference-in-differences\n    21\t\n    22\tJEL codes: J21, J23, J38, J88\n    23\t\n    24\t## Appendix A Additional Analysis\n    25\t\n    26\t### A.1 Elasticities Related to Minimum Wages\n    27\t\n    28\tFollowing the method of Cengiz et al. (2019), we estimate (i) the elasticity of employment with respect to minimum wages and (ii) the employment elasticity with respect to the affected wages.\n    29\t\n    30\tTable 2: Impact of Minimum Wages on Employment and Wages\n    31\t\n    32\t| Estimate (SE)\n    33\t\n    34\tMissing jobs below new MW | -0.03\n    35\t\n    36\t| (0.006)\n    37\t\n    38\tExcess jobs above new MW | 0.012\n    39\t\n    40\t| (0.002)\n    41\t\n    42\tAffected wages | 0.366\n    43\t\n    44\t| (0.171)\n    45\t\n    46\tAffected employment | -0.268\n    47\t\n    48\t| (0.091)\n    49\t\n    50\tEmployment elasticity w.r.t MW | -0.388\n    51\t\n    52\t| (0.132)\n    53\t\n    54\tEmp. elasticity w.r.t affected wage | -0.732\n    55\t\n    56\t| (0.091)\n    57\t\n    58\tJob below new MW | 0.068\n    59\t\n    60\t% MW changes | 0.047\n    61\t\n    62\tNote: The table reports the effects of a minimum wage increase based on the event study analysis exploiting 47 prefecture-level minimum wage changes in 2023 (i.e., about 4% minimum wage increase). The table reports six-month averaged post-treatment estimates on missing jobs up to 100 JPY below the new minimum wage, excess jobs at and up to 300 JPY above it, employment, and wages. Robust standard errors in parentheses are clustered by wage bins separated by 10 JPY.\n    63\t\n    64\tTable 2 reports six-month–averaged post-treatment effects for the baseline specification depicted in Figure 7.\n    65\tThe magnitude of missing jobs below new minimum wages exceeds the magnitude of excess jobs above new minimum wages in the six-month window.\n    66\tThe estimated employment elasticity with respect to the minimum wage is - 0.388 , which is consistent with Figure 7. Unlike the findings of Cengiz et al. (2019) and Dube and Zipperer (2024), our own-wage employment elasticity is - 0.732 , indicating a larger (absolute) employment response to affected wages than the median estimate (- 0.13 ) reported by Dube and Lindner (2024).","is_error":false}]},"uuid":"efff09fa-f5cb-4e89-9096-0d1ac3d0fde9","timestamp":"2026-10-30T05:07:44.269Z","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 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    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":"2873c8c6-c83f-4caa-adb3-c486f8323808","timestamp":"2026-10-30T05:07:49.338Z","toolUseResult":{"stdout":"     1\tSOURCE EXTRACT — arxiv-2510.03658v1\n     2\tTitle: Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin\n     3\tAuthors: Selidji Caroline Tossou\n     4\tBibliographic record: arXiv:2510.03658v1 [econ.GN], submitted 4 Oct 2025 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2510.03658v1\n     5\tRetrieved: https://arxiv.org/html/2510.03658v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f2d9f04dcf4789983c28f97dc13705eb2d6ed583c4860c940f921d7b549aea59)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, author and abstract; Section 4.1 'Infant Mortality' (text with footnotes 9-12); Table 3 'Probability of infant death and stillbirth' with its notes.\n     8\tOmitted: Sections 1-3, 4.2-6, Tables 1-2 and 4-16, the references. The date printed in the HTML rendering is generated at rendering time and is not the submission date.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin\n    13\t\n    14\tSelidji Caroline Tossou (University of Wisconsin-Madison)\n    15\t\n    16\t###### Abstract\n    17\t\n    18\tThis paper evaluates the causal effect of the access to Benin’s free cesarean section policy on females and their children. I use a large sample of Demographic and Health Surveys (DHS) for West African countries and analyze how the exemption of the cesarean section user fees for females in Benin directly impacts maternal and infant mortality, family size decisions, and labor market participation. I use a Difference in Differences approach and find that having access to the free cesarean section policy significantly reduces the number of stillbirths and infant mortality by 0.0855 (a 18.79 percentage change). Second, for the surviving children, I find that access to the free cesarean section increases the likelihood of maternal mortality by 0.00465 (a 5.21 percentage change). The policy is effective at reducing infant mortality and saving the newborn. However, it harms the mother’s health which translates to lower fertility after the first birth and decreased maternal labor supply post-birth.\n    19\t\n    20\t## 4 Findings\n    21\t\n    22\t### 4.1 Infant Mortality\n    23\t\n    24\tI first investigate the infant death rate under the policy.\n    25\tFigure 15 shows that the exemption of cesarean section fees in Benin leads to an increase in the use of the service. What are the implications for infant mortality? I use a Difference in Differences specification to explore the possible impacts of access to the free cesarean section on infant mortality. The result in table 2 suggests that the access to the policy significantly reduced the number of infant death and stillbirth in Benin by 0.0855 (this represents a percentage change of 18.79 percent^9^9\n    26\t9\n    27\t\n    28\tI divide the estimate of beta by the mean of the dependent variable: -0.0855/0.455=-0.1879 ). This means that for over 100 newborns who would have died in the absence of the policy, approximately 18 are saved because of the access to the exemption of the cesarean section user fees. This result is promising and aligns with the goals of the free cesarean section policy, which primarily intends to lower the infant mortality in Benin. To the best of my knowledge, this is the first paper that evaluates the causal effect of free cesarean section policy on Benin’s infant mortality. Wealth and education attainment play a role in decreasing infant mortality. This can be explained by the fact that the free cesarean section covers only the surgery and a medicine kit for the mother. It does not cover prenatal and antepartum checkups. So a pregnant woman who has the means is more likely to identify and prevent any problems related to the pregnancy and the newborn’s health. Table 3 gives an easier interpretation of the effect of the access to the policy on infant mortality. In this table, the outcome of interest is a binary variable that takes value 1 if a female has an infant death or a pregnancy loss in the three years preceding the data collection and 0 otherwise. Thus, I interpret the coefficient \\hat{\\beta_{1}} as the effect of the policy on the likelihood of infant mortality. Using my most preferred specification (Column (4)), the result suggests that access to the policy decreases the likelihood of infant death or pregnancy loss of a female in Benin by 0.0962. Compared to the mean of the dependent variable, this effect represents a percentage change of 36.85 percent. To see which female category is driving this result, I decompose the effect by age group, wealth index, and education attainment.\n    29\t\n    30\tThe first panel of table 4 presents the stratification by age group. The policy is more effective at reducing infant mortality for females older than 25. For females between 15 and 24, the probability of infant mortality is reduced by only 4.65 percent compared to a reduction of 30.85 percent for a female between 25 and 34, a reduction of 43.53 percent for females between 35 and 44, and the highest decrease of 47.25 percent for females between 45 and 49^10^10\n    31\t10\n    32\t\n    33\tThese are the percentage change of infant mortality obtained by dividing the coefficient estimated by the mean of the dependent variable. The same exercise is done by using the wealth index of females. The results in the second panel of table 4 suggest that the effect of the free cesarean section on infant mortality is the biggest for females who are the poorest, poorer, or have a middle wealth index^11^11\n    34\t11\n    35\t\n    36\tThe modalities Poorest, Poorer, Middle, Richer, and Richest are pre-computed in the DHS data (46.36 percent, 35.84 percent, and 41.72 percent respectively). For the richer and richest females, the effect of the policy is a percentage change reduction of 28.52 percent and 16.47 percent on the likelihood of infant death. The last panel of table 4 gives a stratification by education level. The highest effect of the policy on the reduction of the likelihood of infant death (-39.07 percent) is for females who have no education level. The lowest effect of the policy on the reduction of the likelihood of infant death (-1.4 percent) is for females who have higher education levels.^12^12\n    37\t12\n    38\t\n    39\tI test the difference between all the coefficients and they are all significant and statistically different\n    40\t\n    41\tTo summarize, I find that access to the free cesarean section in Benin reduces the likelihood of infant mortality on average by 36.85 percent. The policy is more effective at reducing infant death for older females, females who are below the middle wealth index, and females who have no education.\n    42\t\n    43\tTable 3: Probability of infant death and stillbirth\n    44\t\n    45\t| Probability of Infant death or stillbirth | | |\n    46\t\n    47\t| (1) | (2) | (3) | (4)\n    48\t\n    49\tTreatment | -0.0911 | -0.0908 | -0.0961 | -0.0962\n    50\t\n    51\t| (0.0113) | (0.0113) | (0.0126) | (0.0129)\n    52\t\n    53\tEducation Attainment | -0.0578 | -0.0576 | -0.0443 | -0.0443\n    54\t\n    55\t| (0.00346) | (0.00340) | (0.00415) | (0.00420)\n    56\t\n    57\tAge of Female | 0.0168 | 0.0170 | 0.0171 | 0.0171\n    58\t\n    59\t| (0.00108) | (0.00112) | (0.00104) | (0.00108)\n    60\t\n    61\tAge of Partner | | -0.000425 | -0.000448 | -0.000435\n    62\t\n    63\t| | (0.000183) | (0.000151) | (0.000152)\n    64\t\n    65\tWealth Index | | | -0.0302 | -0.0302\n    66\t\n    67\t| | | (0.00277) | (0.00277)\n    68\t\n    69\tMarital Status | | | | 0.00128\n    70\t\n    71\t| | | | (0.00421)\n    72\t\n    73\tCountry Fixed Effect | Yes | Yes | Yes | Yes\n    74\t\n    75\tTime Fixed Effect. | Yes | Yes | Yes | Yes\n    76\t\n    77\t_cons | -0.129 | -0.114 | -0.0400 | -0.0406\n    78\t\n    79\t| (0.0323) | (0.0298) | (0.0266) | (0.0255)\n    80\t\n    81\tObservations | 196554 | 196424 | 196424 | 196423\n    82\t\n    83\t% Change | -0.3490 | -0.3478 | -0.3681 | -0.3685\n    84\t\n    85\tR-squared | 0.196 | 0.197 | 0.204 | 0.204\n    86\t\n    87\tMean Dep. Var. | 0.261 | 0.261 | 0.261 | 0.261\n    88\t\n    89\tStandard errors in parentheses; Standard errors are clustered at the household level. | | | |\n    90\t\n    91\tDep. Var. Mean is the dependent variable mean. Treatment is Benin Females after 2008 | | | 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> out/state/contract.md <<'EOF'\n# Contract — F4-conf-03\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**Estimands.** Harmonised point estimates E1–E5 (no uncertainty propagation). Meaningful under the task conventions: log-point b -> 100(exp(b)-1)%; pp effect Δ with control level p -> 100Δ/p %; elasticity η and proportional change g -> 100ηg %; LPM coefficient on 0-1 scale -> ×100 pp.\n**Outputs.** out/results.json keys: harmonised{E1..E5:{value,unit,transformation,harmonisable,missing_quantity,locator}}, n_harmonised, n_not_harmonisable, task_id=\"F4-conf-03\", feasibility, feasibility_reason, data_issues[{issue,handling}], n_input_rows{file: wc -l lines / JSON top-level items}, n_analysed, random_seed=null. Also out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues), out/analysis.py (prints relied-upon source lines), out/claims.json.\n**Information rules.** Extracts are the only admissible evidence; no remembered/external values; harmonisable=false only if a needed quantity is not printed (then value null + missing_quantity).\n**Invalidity.** Using unprinted numbers; guessing baselines; misreading table cells. Task \"complete\" when each item has a value or a justified impossibility statement.\nEOF\ncat > out/state/log.md <<'EOF'\n# Log\n- 2026-10-30 P0: read TASK.md, wrote contract. 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Meaningful under the task conventions: log-point b -> 100(exp(b)-1)%; pp effect Δ with control level p -> 100Δ/p %; elasticity η and proportional change g -> 100ηg %; LPM coefficient on 0-1 scale -> ×100 pp.\n**Outputs.** out/results.json keys: harmonised{E1..E5:{value,unit,transformation,harmonisable,missing_quantity,locator}}, n_harmonised, n_not_harmonisable, task_id=\"F4-conf-03\", feasibility, feasibility_reason, data_issues[{issue,handling}], n_input_rows{file: wc -l lines / JSON top-level items}, n_analysed, random_seed=null. Also out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues), out/analysis.py (prints relied-upon source lines), out/claims.json.\n**Information rules.** Extracts are the only admissible evidence; no remembered/external values; harmonisable=false only if a needed quantity is not printed (then value null + missing_quantity).\n**Invalidity.** Using unprinted numbers; guessing baselines; misreading table cells. Task \"complete\" when each item has a value or a justified impossibility statement.\nEOF\ncat > out/state/log.md <<'EOF'\n# Log\n- 2026-10-30 P0: read TASK.md, wrote contract. P0 passed.\nEOF\necho ok","description":"Write contract and log"}}]}}51{"type":"api-request","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","id":"85ec5a19-bf92-42fa-9abb-4b2caa3273ff","timestamp":"2026-10-30T05:08:07.497Z","version":"2.1.314","querySource":"sdk","shapeHash":"e01d2c50ee0140972e988dac6859d720b81c759454963c4578404a432423b1f2","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":"422fa2a5-7074-4e15-957d-27ffb49f7748","keep":12,"tail":["63018aca3b6ecc4a10cc0e27cb8626b14a254229323196f927a2b822305021fb","220d8e2030ae3bb73dd871b2bbd292ba72523db13a0c179d5655e8bd8a8435fa"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"5d436039b55d0fc79a8500a50b76c25fb4afc9f6fef41e5799bd4a6cb08ed04b"}52{"parentUuid":"c4dbf663-22b8-4918-bdb3-49c209989a39","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqqV2reJGbHCiSwJHLW","type":"message","role":"assistant","content":[{"type":"text","text":"Now the analysis script."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":752,"cache_read_input_tokens":28111,"output_tokens":4121,"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":752},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqqUhm6xp6V3qLuiMYk","requestRef":"85ec5a19-bf92-42fa-9abb-4b2caa3273ff","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"58eb1135-df59-4706-b20a-b8675effca8b","timestamp":"2026-10-30T05:08:08.857Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0234","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","version":"2.1.314","gitBranch":"HEAD"}53{"parentUuid":"58eb1135-df59-4706-b20a-b8675effca8b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqqV2reJGbHCiSwJHLW","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_016jPuFmNqymAyjByMezAJWc","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F4-conf-03: harmonise five printed effect sizes. Reads only data/, writes out/results.json.\nNo randomness. Every printed input is located by regex in the extract and its line is printed.\"\"\"\nimport json, math, os, re, sys\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nD = os.path.join(ROOT, \"data\")\nS = os.path.join(D, \"sources\")\n\ndef lines_of(fn):\n    with open(os.path.join(S, fn + \".txt\"), encoding=\"utf-8\") as f:\n        return f.read().split(\"\\n\")\n\ndef find(src, pattern):\n    \"\"\"Return (line_no, line, match) for the unique line matching pattern.\"\"\"\n    L = lines_of(src)\n    hits = [(i + 1, l, re.search(pattern, l)) for i, l in enumerate(L) if re.search(pattern, l)]\n    if len(hits) != 1:\n        sys.exit(f\"pattern {pattern!r} matched {len(hits)} lines in {src}\")\n    n, l, m = hits[0]\n    print(f\"[{src} L{n}] {l.strip()}\")\n    return n, l, m\n\ndef cell(line, idx):\n    return [c.strip() for c in line.split(\"|\")][idx]\n\ndef num(s):\n    return float(re.sub(r\"[^\\d.\\-]\", \"\", s))\n\neffects = json.load(open(os.path.join(D, \"effects.json\"), encoding=\"utf-8\"))\nassert [e[\"id\"] for e in effects] == [\"E1\", \"E2\", \"E3\", \"E4\", \"E5\"]\nH = {}\n\n# E1: Table 6, ATT row, 5 km column (4th cell)\nsrc = \"arxiv-2609.27732v1\"\nn, l, _ = find(src, r\"^ATT \\| -0\\.399\")\nb1_txt = cell(l, 3)                      # '-0.431**'\nb1 = num(b1_txt.replace(\"*\", \"\"))\nn_pc, l_pc, _ = find(src, r\"^% change \\| -32\\.9%\")\nprinted_pc = cell(l_pc, 3)\nv1 = 100 * (math.exp(b1) - 1)\nH[\"E1\"] = dict(value=v1, unit=\"percent (relative change vs counterfactual rate)\",\n    transformation=f\"100*(exp(b)-1) with b={b1} -> 100*(exp({b1})-1) = {v1:.4f}% (printed check: {printed_pc})\",\n    harmonisable=True, missing_quantity=None,\n    locator=f\"{src} | Table 6 | row 'ATT' (line {n}) | column '5 km' = {b1_txt}; cross-check row '% change' (line {n_pc}) | column '5 km' = {printed_pc}\")\n\n# E2: Table 2, Reemployment row\nsrc = \"arxiv-2606.14621v1\"\nn, l, _ = find(src, r\"^Reemployment \\| 75\\.2%\")\nctrl_txt = cell(l, 2); ate_txt = cell(l, 4).split(\";\")[0]\np = num(ctrl_txt); d = num(ate_txt)\nv2 = 100 * d / p\nH[\"E2\"] = dict(value=v2, unit=\"percent (relative change vs control-group mean)\",\n    transformation=f\"100*Delta/p with Delta={d} pp, p={p}% -> 100*{d}/{p} = {v2:.4f}%\",\n    harmonisable=True, missing_quantity=None,\n    locator=f\"{src} | Table 2 | row 'Reemployment' (line {n}) | column 'Average treatment effect estimate' = {ate_txt}; column 'Control' = {ctrl_txt}\")\n\n# E3: Table 2, elasticity row and '% MW changes' row\nsrc = \"arxiv-2505.04555v3\"\nn_e, l_e, _ = find(src, r\"^Employment elasticity w\\.r\\.t MW \\|\")\neta = num(cell(l_e, 1))\nn_g, l_g, _ = find(src, r\"^% MW changes \\|\")\ng = num(cell(l_g, 1))\nfind(src, r\"about 4% minimum wage increase\")\nv3 = 100 * eta * g\nH[\"E3\"] = dict(value=v3, unit=\"percent (relative change in employment)\",\n    transformation=f\"100*eta*g with eta={cell(l_e,1)}, g={cell(l_g,1)} (proportional MW change) -> 100*({eta})*({g}) = {v3:.4f}%\",\n    harmonisable=True, missing_quantity=None,\n    locator=f\"{src} | Table 2 | row 'Employment elasticity w.r.t MW' (line {n_e}) | column 'Estimate (SE)' = {cell(l_e,1)}; row '% MW changes' (line {n_g}) | column 'Estimate (SE)' = {cell(l_g,1)}\")\n\n# E4: Table 7, fatal row, 5 km column; absolute change needs the counterfactual level\nsrc = \"arxiv-2609.27732v1\"\nn, l, _ = find(src, r\"^Drug-related \\(fatal\\) \\|\")\nb4_txt = cell(l, 3); b4 = num(b4_txt.replace(\"*\", \"\"))\nrel4 = math.exp(b4) - 1\nH[\"E4\"] = dict(value=None, unit=\"hospitalisations per 100,000 inhabitants per year\",\n    transformation=(f\"Absolute change = R0*(exp(b)-1) with b={b4} -> R0*({rel4:.4f}); \"\n                    \"R0 (counterfactual/baseline fatal drug-related hospitalisation rate per 100,000 in treated 5 km regions) is not printed in the extract, so the value cannot be computed\"),\n    harmonisable=False,\n    missing_quantity=\"baseline (counterfactual, e.g. pre-opening or control-group mean) level of the fatal drug-related hospitalisation rate per 100,000 inhabitants per year for the treated 5 km regions\",\n    locator=f\"{src} | Table 7 | row 'Drug-related (fatal)' (line {n}) | column '5 km' = {b4_txt}\")\n\n# E5: Table 3, Treatment row, column (4)\nsrc = \"arxiv-2510.03658v1\"\nn, l, _ = find(src, r\"^Treatment \\| -0\\.0911\")\nb5_txt = cell(l, 4); b5 = num(b5_txt)\nv5 = 100 * b5\nH[\"E5\"] = dict(value=v5, unit=\"percentage points\",\n    transformation=f\"100*beta with beta={b5_txt} (0-1 scale) -> 100*({b5}) = {v5:.2f} pp\",\n    harmonisable=True, missing_quantity=None,\n    locator=f\"{src} | Table 3 | row 'Treatment' (line {n}) | column '(4)' = {b5_txt}\")\n\n# ---- internal-consistency evidence for data_issues (all from the extracts) ----\nsrc = \"arxiv-2609.27732v1\"\nfind(src, r\"fatal outcomes, which fall by 35%\")\nfind(src, r\"by approximately 30% at 5 km\")\nn_a19, l_a19, _ = find(src, r\"^\\| \\(0\\.218\\)\")\nn_se6, l_se6, _ = find(src, r\"^\\| \\(0\\.412\\)\")\nfind(src, r\"never-treated comparison regions \\(640 regions\\)\")\nsrc = \"arxiv-2510.03658v1\"\nfind(src, r\"by 0\\.0855 \\(a 18\\.79 percentage change\\)\")\nfind(src, r\"The result in table 2 suggests\")\nsrc = \"arxiv-2606.14621v1\"\nfind(src, r\"increased reemployment by 1\\.5 percentage points\")\n\nfatal_rel = 100 * rel4\ndata_issues = [\n    {\"issue\": f\"arxiv-2609.27732v1: abstract says fatal outcomes 'fall by 35%', whereas Section 6.1 says 'approximately 30% at 5 km'; Table 7 5 km fatal ATT {b4} implies 100*(exp({b4})-1) = {fatal_rel:.1f}%, consistent with ~30% not 35%.\",\n     \"handling\": \"E4 is based on the Table 7 coefficient only; the abstract figure is not used. Noted as an internal inconsistency of the source.\"},\n    {\"issue\": \"arxiv-2609.27732v1: the CS-DR 5 km ATT -0.431 has SE (0.210) in Table 6 but (0.218) in Table A19; Table 6 note says 608 control regions while the Table A19 note says 640 never-treated comparison regions (Table A19 body says 608).\",\n     \"handling\": \"Point estimate identical in both tables, so E1 is unaffected; no SE is propagated. Recorded only.\"},\n    {\"issue\": f\"arxiv-2505.04555v3: Table 2 prints '% MW changes' = {g} (4.7%) while the table note describes the 2023 change as 'about 4% minimum wage increase'.\",\n     \"handling\": f\"E3 uses the printed table value g={g}, as the target definition asks for the size printed in the same table; with 0.04 the implied change would be {100*eta*0.04:.4f}% (reported for transparency only).\"},\n    {\"issue\": \"arxiv-2510.03658v1: the abstract and Section 4.1 describe -0.0855 as 'a 18.79 percentage change' (a relative change computed against a mean of 0.455), and Section 4.1 attributes it to 'table 2', which is not in the extract; Table 3 relative change (-0.3685) is computed against the overall dependent-variable mean (0.261), not a counterfactual level.\",\n     \"handling\": \"E5 only requires rescaling the Table 3 column (4) LPM coefficient to percentage points (exact); the relative-change figures are not used.\"},\n    {\"issue\": \"arxiv-2606.14621v1: the abstract rounds the reemployment ATE to 1.5 pp; Table 2 prints 1.47 pp. The ATE is block-adjusted while the 75.0% control mean is the raw (unadjusted) aggregate control-group mean.\",\n     \"handling\": \"E2 uses the Table 2 values (1.47 pp, 75.0%) exactly as the target definition (percent of the control-group mean) specifies; noted as a caveat.\"},\n]\n\ndef nlines(path):\n    with open(path, \"rb\") as f:\n        return f.read().count(b\"\\n\")\n\nn_input_rows = {\"effects.json\": len(effects), \"README.md\": nlines(os.path.join(D, \"README.md\"))}\nfor fn in sorted(os.listdir(S)):\n    n_input_rows[fn] = nlines(os.path.join(S, fn))\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)\nres = {\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}\nassert n_h + n_nh == len(effects)\nos.makedirs(os.path.join(ROOT, \"out\"), exist_ok=True)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\", encoding=\"utf-8\") as f:\n    json.dump(res, f, indent=2, ensure_ascii=False)\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, \"feasibility complete\")\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":752,"cache_read_input_tokens":28111,"output_tokens":4121,"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":752},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqqUhm6xp6V3qLuiMYk","requestRef":"85ec5a19-bf92-42fa-9abb-4b2caa3273ff","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8d76188a-bfd4-4020-8c81-ccd73b94face","timestamp":"2026-10-30T05:08:40.251Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0234","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","version":"2.1.314","gitBranch":"HEAD"}54{"parentUuid":"8d76188a-bfd4-4020-8c81-ccd73b94face","isSidechain":false,"promptId":"b69de4f4-97af-47d0-8ca5-67147b0766d7","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016jPuFmNqymAyjByMezAJWc","type":"tool_result","content":"[arxiv-2609.27732v1 L36] ATT | -0.399 | -0.283 | -0.431**\n[arxiv-2609.27732v1 L40] % change | -32.9% | -24.7% | -35.0%\n[arxiv-2606.14621v1 L29] 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 L50] Employment elasticity w.r.t MW | -0.388\n[arxiv-2505.04555v3 L60] % MW changes | 0.047\n[arxiv-2505.04555v3 L62] Note: The table reports the effects of a minimum wage increase based on the event study analysis exploiting 47 prefecture-level minimum wage changes in 2023 (i.e., about 4% minimum wage increase). The table reports six-month averaged post-treatment estimates on missing jobs up to 100 JPY below the new minimum wage, excess jobs at and up to 300 JPY above it, employment, and wages. Robust standard errors in parentheses are clustered by wage bins separated by 10 JPY.\n[arxiv-2609.27732v1 L86] Drug-related (fatal) | -1.207*** | -0.290 | -0.354**\n[arxiv-2510.03658v1 L49] Treatment | -0.0911 | -0.0908 | -0.0961 | -0.0962\n[arxiv-2609.27732v1 L18] This paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n[arxiv-2609.27732v1 L109] The sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n[arxiv-2609.27732v1 L123] | (0.218) | (0.208) | (0.205) | (0.038)\n[arxiv-2609.27732v1 L38] | (0.412) | (0.279) | (0.210)\n[arxiv-2609.27732v1 L134] never-treated comparison regions (640 regions), and a \\pm 5-year window\n[arxiv-2510.03658v1 L18] This paper evaluates the causal effect of the access to Benin’s free cesarean section policy on females and their children. I use a large sample of Demographic and Health Surveys (DHS) for West African countries and analyze how the exemption of the cesarean section user fees for females in Benin directly impacts maternal and infant mortality, family size decisions, and labor market participation. I use a Difference in Differences approach and find that having access to the free cesarean section policy significantly reduces the number of stillbirths and infant mortality by 0.0855 (a 18.79 percentage change). Second, for the surviving children, I find that access to the free cesarean section increases the likelihood of maternal mortality by 0.00465 (a 5.21 percentage change). The policy is effective at reducing infant mortality and saving the newborn. However, it harms the mother’s health which translates to lower fertility after the first birth and decreased maternal labor supply post-birth.\n[arxiv-2510.03658v1 L25] Figure 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[arxiv-2606.14621v1 L19] Prolonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\nE1 -35.01410892252505 percent (relative change vs counterfactual rate) | harmonisable: True\nE2 1.96 percent (relative change vs control-group mean) | harmonisable: True\nE3 -1.8236 percent (relative change in employment) | harmonisable: True\nE4 None hospitalisations per 100,000 inhabitants per year | harmonisable: False\nE5 -9.62 percentage points | harmonisable: True\nn_harmonised 4 n_not_harmonisable 1 feasibility complete","is_error":false}]},"uuid":"74fffa2d-5476-476a-992f-b5ed4525cdda","timestamp":"2026-10-30T05:08:40.306Z","toolUseResult":{"stdout":"[arxiv-2609.27732v1 L36] ATT | -0.399 | -0.283 | -0.431**\n[arxiv-2609.27732v1 L40] % change | -32.9% | -24.7% | -35.0%\n[arxiv-2606.14621v1 L29] 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 L50] Employment elasticity w.r.t MW | -0.388\n[arxiv-2505.04555v3 L60] % MW changes | 0.047\n[arxiv-2505.04555v3 L62] Note: The table reports the effects of a minimum wage increase based on the event study analysis exploiting 47 prefecture-level minimum wage changes in 2023 (i.e., about 4% minimum wage increase). The table reports six-month averaged post-treatment estimates on missing jobs up to 100 JPY below the new minimum wage, excess jobs at and up to 300 JPY above it, employment, and wages. Robust standard errors in parentheses are clustered by wage bins separated by 10 JPY.\n[arxiv-2609.27732v1 L86] Drug-related (fatal) | -1.207*** | -0.290 | -0.354**\n[arxiv-2510.03658v1 L49] Treatment | -0.0911 | -0.0908 | -0.0961 | -0.0962\n[arxiv-2609.27732v1 L18] This paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n[arxiv-2609.27732v1 L109] The sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n[arxiv-2609.27732v1 L123] | (0.218) | (0.208) | (0.205) | (0.038)\n[arxiv-2609.27732v1 L38] | (0.412) | (0.279) | (0.210)\n[arxiv-2609.27732v1 L134] never-treated comparison regions (640 regions), and a \\pm 5-year window\n[arxiv-2510.03658v1 L18] This paper evaluates the causal effect of the access to Benin’s free cesarean section policy on females and their children. I use a large sample of Demographic and Health Surveys (DHS) for West African countries and analyze how the exemption of the cesarean section user fees for females in Benin directly impacts maternal and infant mortality, family size decisions, and labor market participation. I use a Difference in Differences approach and find that having access to the free cesarean section policy significantly reduces the number of stillbirths and infant mortality by 0.0855 (a 18.79 percentage change). Second, for the surviving children, I find that access to the free cesarean section increases the likelihood of maternal mortality by 0.00465 (a 5.21 percentage change). The policy is effective at reducing infant mortality and saving the newborn. However, it harms the mother’s health which translates to lower fertility after the first birth and decreased maternal labor supply post-birth.\n[arxiv-2510.03658v1 L25] Figure 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[arxiv-2606.14621v1 L19] Prolonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\nE1 -35.01410892252505 percent (relative change vs counterfactual rate) | harmonisable: True\nE2 1.96 percent (relative change vs control-group mean) | harmonisable: True\nE3 -1.8236 percent (relative change in employment) | harmonisable: True\nE4 None hospitalisations per 100,000 inhabitants per year | harmonisable: False\nE5 -9.62 percentage points | harmonisable: True\nn_harmonised 4 n_not_harmonisable 1 feasibility complete","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"8d76188a-bfd4-4020-8c81-ccd73b94face","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0234","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","version":"2.1.314","gitBranch":"HEAD"}55{"type":"api-request-blob","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","hash":"0be4767bdcad3d7d24450557fd53b079040e4226f8cb92a4e2f2228135de6a9c","message":{"role":"assistant","content":[{"type":"text","text":"Now the analysis script."},{"type":"tool_use","id":"toolu_016jPuFmNqymAyjByMezAJWc","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F4-conf-03: harmonise five printed effect sizes. Reads only data/, writes out/results.json.\nNo randomness. Every printed input is located by regex in the extract and its line is printed.\"\"\"\nimport json, math, os, re, sys\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nD = os.path.join(ROOT, \"data\")\nS = os.path.join(D, \"sources\")\n\ndef lines_of(fn):\n    with open(os.path.join(S, fn + \".txt\"), encoding=\"utf-8\") as f:\n        return f.read().split(\"\\n\")\n\ndef find(src, pattern):\n    \"\"\"Return (line_no, line, match) for the unique line matching pattern.\"\"\"\n    L = lines_of(src)\n    hits = [(i + 1, l, re.search(pattern, l)) for i, l in enumerate(L) if re.search(pattern, l)]\n    if len(hits) != 1:\n        sys.exit(f\"pattern {pattern!r} matched {len(hits)} lines in {src}\")\n    n, l, m = hits[0]\n    print(f\"[{src} L{n}] {l.strip()}\")\n    return n, l, m\n\ndef cell(line, idx):\n    return [c.strip() for c in line.split(\"|\")][idx]\n\ndef num(s):\n    return float(re.sub(r\"[^\\d.\\-]\", \"\", s))\n\neffects = json.load(open(os.path.join(D, \"effects.json\"), encoding=\"utf-8\"))\nassert [e[\"id\"] for e in effects] == [\"E1\", \"E2\", \"E3\", \"E4\", \"E5\"]\nH = {}\n\n# E1: Table 6, ATT row, 5 km column (4th cell)\nsrc = \"arxiv-2609.27732v1\"\nn, l, _ = find(src, r\"^ATT \\| -0\\.399\")\nb1_txt = cell(l, 3)                      # '-0.431**'\nb1 = num(b1_txt.replace(\"*\", \"\"))\nn_pc, l_pc, _ = find(src, r\"^% change \\| -32\\.9%\")\nprinted_pc = cell(l_pc, 3)\nv1 = 100 * (math.exp(b1) - 1)\nH[\"E1\"] = dict(value=v1, unit=\"percent (relative change vs counterfactual rate)\",\n    transformation=f\"100*(exp(b)-1) with b={b1} -> 100*(exp({b1})-1) = {v1:.4f}% (printed check: {printed_pc})\",\n    harmonisable=True, missing_quantity=None,\n    locator=f\"{src} | Table 6 | row 'ATT' (line {n}) | column '5 km' = {b1_txt}; cross-check row '% change' (line {n_pc}) | column '5 km' = {printed_pc}\")\n\n# E2: Table 2, Reemployment row\nsrc = \"arxiv-2606.14621v1\"\nn, l, _ = find(src, r\"^Reemployment \\| 75\\.2%\")\nctrl_txt = cell(l, 2); ate_txt = cell(l, 4).split(\";\")[0]\np = num(ctrl_txt); d = num(ate_txt)\nv2 = 100 * d / p\nH[\"E2\"] = dict(value=v2, unit=\"percent (relative change vs control-group mean)\",\n    transformation=f\"100*Delta/p with Delta={d} pp, p={p}% -> 100*{d}/{p} = {v2:.4f}%\",\n    harmonisable=True, missing_quantity=None,\n    locator=f\"{src} | Table 2 | row 'Reemployment' (line {n}) | column 'Average treatment effect estimate' = {ate_txt}; column 'Control' = {ctrl_txt}\")\n\n# E3: Table 2, elasticity row and '% MW changes' row\nsrc = \"arxiv-2505.04555v3\"\nn_e, l_e, _ = find(src, r\"^Employment elasticity w\\.r\\.t MW \\|\")\neta = num(cell(l_e, 1))\nn_g, l_g, _ = find(src, r\"^% MW changes \\|\")\ng = num(cell(l_g, 1))\nfind(src, r\"about 4% minimum wage increase\")\nv3 = 100 * eta * g\nH[\"E3\"] = dict(value=v3, unit=\"percent (relative change in employment)\",\n    transformation=f\"100*eta*g with eta={cell(l_e,1)}, g={cell(l_g,1)} (proportional MW change) -> 100*({eta})*({g}) = {v3:.4f}%\",\n    harmonisable=True, missing_quantity=None,\n    locator=f\"{src} | Table 2 | row 'Employment elasticity w.r.t MW' (line {n_e}) | column 'Estimate (SE)' = {cell(l_e,1)}; row '% MW changes' (line {n_g}) | column 'Estimate (SE)' = {cell(l_g,1)}\")\n\n# E4: Table 7, fatal row, 5 km column; absolute change needs the counterfactual level\nsrc = \"arxiv-2609.27732v1\"\nn, l, _ = find(src, r\"^Drug-related \\(fatal\\) \\|\")\nb4_txt = cell(l, 3); b4 = num(b4_txt.replace(\"*\", \"\"))\nrel4 = math.exp(b4) - 1\nH[\"E4\"] = dict(value=None, unit=\"hospitalisations per 100,000 inhabitants per year\",\n    transformation=(f\"Absolute change = R0*(exp(b)-1) with b={b4} -> R0*({rel4:.4f}); \"\n                    \"R0 (counterfactual/baseline fatal drug-related hospitalisation rate per 100,000 in treated 5 km regions) is not printed in the extract, so the value cannot be computed\"),\n    harmonisable=False,\n    missing_quantity=\"baseline (counterfactual, e.g. pre-opening or control-group mean) level of the fatal drug-related hospitalisation rate per 100,000 inhabitants per year for the treated 5 km regions\",\n    locator=f\"{src} | Table 7 | row 'Drug-related (fatal)' (line {n}) | column '5 km' = {b4_txt}\")\n\n# E5: Table 3, Treatment row, column (4)\nsrc = \"arxiv-2510.03658v1\"\nn, l, _ = find(src, r\"^Treatment \\| -0\\.0911\")\nb5_txt = cell(l, 4); b5 = num(b5_txt)\nv5 = 100 * b5\nH[\"E5\"] = dict(value=v5, unit=\"percentage points\",\n    transformation=f\"100*beta with beta={b5_txt} (0-1 scale) -> 100*({b5}) = {v5:.2f} pp\",\n    harmonisable=True, missing_quantity=None,\n    locator=f\"{src} | Table 3 | row 'Treatment' (line {n}) | column '(4)' = {b5_txt}\")\n\n# ---- internal-consistency evidence for data_issues (all from the extracts) ----\nsrc = \"arxiv-2609.27732v1\"\nfind(src, r\"fatal outcomes, which fall by 35%\")\nfind(src, r\"by approximately 30% at 5 km\")\nn_a19, l_a19, _ = find(src, r\"^\\| \\(0\\.218\\)\")\nn_se6, l_se6, _ = find(src, r\"^\\| \\(0\\.412\\)\")\nfind(src, r\"never-treated comparison regions \\(640 regions\\)\")\nsrc = \"arxiv-2510.03658v1\"\nfind(src, r\"by 0\\.0855 \\(a 18\\.79 percentage change\\)\")\nfind(src, r\"The result in table 2 suggests\")\nsrc = \"arxiv-2606.14621v1\"\nfind(src, r\"increased reemployment by 1\\.5 percentage points\")\n\nfatal_rel = 100 * rel4\ndata_issues = [\n    {\"issue\": f\"arxiv-2609.27732v1: abstract says fatal outcomes 'fall by 35%', whereas Section 6.1 says 'approximately 30% at 5 km'; Table 7 5 km fatal ATT {b4} implies 100*(exp({b4})-1) = {fatal_rel:.1f}%, consistent with ~30% not 35%.\",\n     \"handling\": \"E4 is based on the Table 7 coefficient only; the abstract figure is not used. Noted as an internal inconsistency of the source.\"},\n    {\"issue\": \"arxiv-2609.27732v1: the CS-DR 5 km ATT -0.431 has SE (0.210) in Table 6 but (0.218) in Table A19; Table 6 note says 608 control regions while the Table A19 note says 640 never-treated comparison regions (Table A19 body says 608).\",\n     \"handling\": \"Point estimate identical in both tables, so E1 is unaffected; no SE is propagated. Recorded only.\"},\n    {\"issue\": f\"arxiv-2505.04555v3: Table 2 prints '% MW changes' = {g} (4.7%) while the table note describes the 2023 change as 'about 4% minimum wage increase'.\",\n     \"handling\": f\"E3 uses the printed table value g={g}, as the target definition asks for the size printed in the same table; with 0.04 the implied change would be {100*eta*0.04:.4f}% (reported for transparency only).\"},\n    {\"issue\": \"arxiv-2510.03658v1: the abstract and Section 4.1 describe -0.0855 as 'a 18.79 percentage change' (a relative change computed against a mean of 0.455), and Section 4.1 attributes it to 'table 2', which is not in the extract; Table 3 relative change (-0.3685) is computed against the overall dependent-variable mean (0.261), not a counterfactual level.\",\n     \"handling\": \"E5 only requires rescaling the Table 3 column (4) LPM coefficient to percentage points (exact); the relative-change figures are not used.\"},\n    {\"issue\": \"arxiv-2606.14621v1: the abstract rounds the reemployment ATE to 1.5 pp; Table 2 prints 1.47 pp. The ATE is block-adjusted while the 75.0% control mean is the raw (unadjusted) aggregate control-group mean.\",\n     \"handling\": \"E2 uses the Table 2 values (1.47 pp, 75.0%) exactly as the target definition (percent of the control-group mean) specifies; noted as a caveat.\"},\n]\n\ndef nlines(path):\n    with open(path, \"rb\") as f:\n        return f.read().count(b\"\\n\")\n\nn_input_rows = {\"effects.json\": len(effects), \"README.md\": nlines(os.path.join(D, \"README.md\"))}\nfor fn in sorted(os.listdir(S)):\n    n_input_rows[fn] = nlines(os.path.join(S, fn))\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)\nres = {\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}\nassert n_h + n_nh == len(effects)\nos.makedirs(os.path.join(ROOT, \"out\"), exist_ok=True)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\", encoding=\"utf-8\") as f:\n    json.dump(res, f, indent=2, ensure_ascii=False)\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, \"feasibility complete\")\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis script"}}]}}56{"type":"api-request","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","id":"914904a9-ca5d-4f53-9731-cc13b71eef3c","timestamp":"2026-10-30T05:08:40.309Z","version":"2.1.314","querySource":"sdk","shapeHash":"e01d2c50ee0140972e988dac6859d720b81c759454963c4578404a432423b1f2","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":"85ec5a19-bf92-42fa-9abb-4b2caa3273ff","keep":14,"tail":["0be4767bdcad3d7d24450557fd53b079040e4226f8cb92a4e2f2228135de6a9c","f3784cf4c1359f6347debdbcd74b93090c245ca43caa8bc3ed0e6e1956aca15b"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"3e5c713763a6f3ba4b1f66c40a267c3f218702ed7f477a172da4223d89174252"}57{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"74fffa2d-5476-476a-992f-b5ed4525cdda","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474"}58{"type":"atis-latch","atis":"","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474"}59{"parentUuid":"74fffa2d-5476-476a-992f-b5ed4525cdda","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqsuHBXg8XbnKB92gzm","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":6556,"cache_read_input_tokens":28863,"output_tokens":472,"output_tokens_details":{"thinking_tokens":321},"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":6556},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":3288,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqstzom8L3r2BGFfyLu","requestRef":"914904a9-ca5d-4f53-9731-cc13b71eef3c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d36ed563-81c6-47db-b085-e1236b64caf8","timestamp":"2026-10-30T05:08:46.013Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/ru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section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. 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<<'EOF'\nimport json,math\nr=json.load(open('out/results.json'));H=r['harmonised']\nprint('counts', r['n_harmonised']+r['n_not_harmonisable']==5==r['n_analysed'])\nprint('null<->nonharm', all((v['value'] is None)==(not v['harmonisable'])==(v['missing_quantity'] is not None) for v in H.values()))\nprint('E1 vs printed -35.0%', round(H['E1']['value'],1)==-35.0, 'linear naive', -43.1)\nprint('E2 sign/size', 0<H['E2']['value']<5)\nprint('E3 abstract ~2%', H['E3']['value'], 'Cengiz check (missing+excess)/g', (-0.03+0.012)/0.047)\nprint('E5 rel check', -0.0962/0.261, 'printed -0.3685')\nEOF","description":"Rerun for reproducibility and consistency checks"}}]}}73{"type":"api-request","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","id":"80facf6f-728d-49ae-a271-2a1df91d5dcb","timestamp":"2026-10-30T05:09:06.342Z","version":"2.1.314","querySource":"sdk","shapeHash":"e01d2c50ee0140972e988dac6859d720b81c759454963c4578404a432423b1f2","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":"3e37571b-2564-4719-85ba-73d4c4c546cf","keep":20,"tail":["ae5d22b253ab0b56074f5b7995442eb619c5ac47afdbb7d14fa8e410216e492f","948551aac2c4f32cdeadf23c65269cf5665fe4b222777a05f21eacd1b4242a39"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",21,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"70052b2bd6792e18000259f401200ec5e153170d501f72fa6b45f27368485d7e"}74{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"97cab3a2-3bff-431b-93b7-121f4de0745a","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474"}75{"type":"atis-latch","atis":"","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474"}76{"parentUuid":"97cab3a2-3bff-431b-93b7-121f4de0745a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqupaDRknwmtdwgna3k","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019fTvc7YtjoThn6ftdSZQRA","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Checks (P2/P4)\n## Baseline (P2)\nNaive (untransformed / linear) readings: E1 100·b = -43.1% (linear log-point approximation); E2 1.47 pp; E3 elasticity -0.388; E4 -0.354 log points; E5 -0.0962 (0-1 scale). The harmonised values replace these with the exact task conventions.\n## Verification (P4)\n| check | outcome |\n|---|---|\n| Clean rerun of out/analysis.py (results.json deleted then regenerated) byte-identical | PASS |\n| n_harmonised + n_not_harmonisable = 5 = n_analysed; exclusions 5 − 0 = 5 | PASS |\n| value null ⇔ harmonisable false ⇔ missing_quantity non-null | PASS |\n| E1 −35.014% rounds to printed Table 6 \"% change\" −35.0% (5 km) | PASS |\n| E1 exact conversion differs from naive linear −43.1% as expected (exp convexity) | PASS (documented) |\n| E2 1.96% positive, small; consistent with 1.47 pp on a 75% base | PASS |\n| E3 −1.82% consistent in magnitude with abstract \"2% employment decline\"; elasticity −0.388 ≈ (missing+excess)/g = (−0.03+0.012)/0.047 = −0.383 | PASS (approximate internal consistency) |\n| E5 −0.0962/0.261 = −0.36858 vs printed −0.3685 (rounding/truncation) | PASS |\n| E4: grep of extract for mean/baseline rate found no printed level of the fatal hospitalisation rate | PASS → not harmonisable |\n| Every printed input matched by a unique regex line, printed by the script | PASS |\n| No SEs/CIs produced → no CI-containment checks applicable | N/A |\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . 2>&1 | tail -30","description":"Write checks and run gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":611,"cache_read_input_tokens":39256,"output_tokens":771,"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":611},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqupJqMxtBFEccc1vhr","requestRef":"80facf6f-728d-49ae-a271-2a1df91d5dcb","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"92945478-669f-43e2-9039-d3551917473d","timestamp":"2026-10-30T05:09:14.604Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0234","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","version":"2.1.314","gitBranch":"HEAD"}77{"parentUuid":"92945478-669f-43e2-9039-d3551917473d","isSidechain":false,"promptId":"b69de4f4-97af-47d0-8ca5-67147b0766d7","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019fTvc7YtjoThn6ftdSZQRA","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — FAILED (9 of 23 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  [ok] reproducibility: regenerated in 0.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0234/out/state/gate_receipts/20261030T050914.691235Z.json","is_error":false}]},"uuid":"b38948f8-7756-4f36-9e35-9f1f06ac808d","timestamp":"2026-10-30T05:09:14.699Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — FAILED (9 of 23 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  [ok] reproducibility: regenerated in 0.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0234/out/state/gate_receipts/20261030T050914.691235Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"92945478-669f-43e2-9039-d3551917473d","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0234","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","version":"2.1.314","gitBranch":"HEAD"}78{"type":"api-request-blob","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","hash":"fdc01482ad956fc55c3726e0e5334e76dbb42f54431677abacd9f831da746a55","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_019fTvc7YtjoThn6ftdSZQRA","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Checks (P2/P4)\n## Baseline (P2)\nNaive (untransformed / linear) readings: E1 100·b = -43.1% (linear log-point approximation); E2 1.47 pp; E3 elasticity -0.388; E4 -0.354 log points; E5 -0.0962 (0-1 scale). The harmonised values replace these with the exact task conventions.\n## Verification (P4)\n| check | outcome |\n|---|---|\n| Clean rerun of out/analysis.py (results.json deleted then regenerated) byte-identical | PASS |\n| n_harmonised + n_not_harmonisable = 5 = n_analysed; exclusions 5 − 0 = 5 | PASS |\n| value null ⇔ harmonisable false ⇔ missing_quantity non-null | PASS |\n| E1 −35.014% rounds to printed Table 6 \"% change\" −35.0% (5 km) | PASS |\n| E1 exact conversion differs from naive linear −43.1% as expected (exp convexity) | PASS (documented) |\n| E2 1.96% positive, small; consistent with 1.47 pp on a 75% base | PASS |\n| E3 −1.82% consistent in magnitude with abstract \"2% employment decline\"; elasticity −0.388 ≈ (missing+excess)/g = (−0.03+0.012)/0.047 = −0.383 | PASS (approximate internal consistency) |\n| E5 −0.0962/0.261 = −0.36858 vs printed −0.3685 (rounding/truncation) | PASS |\n| E4: grep of extract for mean/baseline rate found no printed level of the fatal hospitalisation rate | PASS → not harmonisable |\n| Every printed input matched by a unique regex line, printed by the script | PASS |\n| No SEs/CIs produced → no CI-containment checks applicable | N/A |\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . 2>&1 | tail -30","description":"Write checks and run gate"}}]}}79{"type":"api-request","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","id":"f90f072b-e635-4d82-900e-cc69c3d707bd","timestamp":"2026-10-30T05:09:14.702Z","version":"2.1.314","querySource":"sdk","shapeHash":"e01d2c50ee0140972e988dac6859d720b81c759454963c4578404a432423b1f2","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":"80facf6f-728d-49ae-a271-2a1df91d5dcb","keep":22,"tail":["fdc01482ad956fc55c3726e0e5334e76dbb42f54431677abacd9f831da746a55","483fdc496df34b717a334b050d624b5f21146d23478fd7250fd5e38529a8b0a7"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",23,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"9ab939a337520256f40129b22035322bb6b3bff2222b5dc554ba39bca797e2aa"}80{"parentUuid":"b38948f8-7756-4f36-9e35-9f1f06ac808d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqvSLX4uwU7gWPYsGRd","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":1284,"cache_read_input_tokens":39867,"output_tokens":2605,"output_tokens_details":{"thinking_tokens":46},"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":1284},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":807,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqvS3AHYP5aJaMvbNGJ","requestRef":"f90f072b-e635-4d82-900e-cc69c3d707bd","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d0070901-8984-4ece-85aa-02bbea9f93d7","timestamp":"2026-10-30T05:09:17.127Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0234","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","version":"2.1.314","gitBranch":"HEAD"}81{"parentUuid":"d0070901-8984-4ece-85aa-02bbea9f93d7","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqvSLX4uwU7gWPYsGRd","type":"message","role":"assistant","content":[{"type":"text","text":"Remaining gate failures are P5 deliverables (expected at this stage). Writing report and claims."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1284,"cache_read_input_tokens":39867,"output_tokens":2605,"output_tokens_details":{"thinking_tokens":46},"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":1284},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqvS3AHYP5aJaMvbNGJ","requestRef":"f90f072b-e635-4d82-900e-cc69c3d707bd","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d2f33f00-398f-4873-a7a4-7832ee8f64ad","timestamp":"2026-10-30T05:09:17.127Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0234","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","version":"2.1.314","gitBranch":"HEAD"}82{"parentUuid":"d2f33f00-398f-4873-a7a4-7832ee8f64ad","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqvSLX4uwU7gWPYsGRd","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_016ePb3otdQaMDVeDfqEdctb","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F4-conf-03 — Harmonising effect sizes reported under incompatible definitions\n\n## Question\nRe-express five printed treatment effects from four arXiv extracts on the target definition stated for each item in `data/effects.json`, using only quantities printed in the cited extract and an explicit transformation; where the target is unreachable, state the missing quantity.\n\n## Data and exclusions\nInputs: `data/effects.json` (5 items) and four source extracts (line counts in `n_input_rows`; SHA-256 hashes match `data/README.md`, see `out/state/data_audit.md`). All 5 items were assessed (`n_analysed` = 5); none excluded (`out/exclusions.csv`). Source anomalies are listed in `data_issues` (abstract vs Section 6.1 vs Table 7 inconsistency for fatal hospitalisations in the DCR paper; differing SE 0.210/0.218 and 608/640 control-region counts; \"about 4%\" note vs printed 0.047 MW change; Benin text citing an absent \"table 2\" and calling relative changes \"percentage changes\"; RESEA abstract rounding 1.47 pp to 1.5 pp). None changes a harmonised value.\n\n## Method\nTask conventions applied (`harmonised.*.transformation`): log-point b → 100·(exp(b)−1) %; pp effect Δ with control level p → 100·Δ/p %; elasticity η with proportional treatment change g → 100·η·g %; LPM coefficient on 0–1 scale → ×100 pp. `out/analysis.py` locates each printed input by a unique regex match in the extract, prints that source line, and computes the value. No randomness (`random_seed` = null).\n\n## Results\n- **E1** (DCR overall ATT, 5 km, Table 6, b = −0.431): 100·(exp(−0.431)−1) = **−35.01 %** of the counterfactual rate (`harmonised.E1.value`); matches the printed \"% change\" −35.0%.\n- **E2** (RESEA reemployment ATE 1.47 pp; control mean 75.0%, Table 2): 100·1.47/75.0 = **+1.96 %** of the control-group mean (`harmonised.E2.value`).\n- **E3** (MW employment elasticity −0.388; \"% MW changes\" 0.047, Table 2): 100·(−0.388)·0.047 = **−1.82 %** employment (`harmonised.E3.value`, −1.8236).\n- **E4** (DCR fatal hospitalisations ATT, 5 km, Table 7, b = −0.354): **not harmonisable** (`harmonised.E4.harmonisable` = false, `harmonised.E4.value` = null). An absolute change per 100,000 per year equals R0·(exp(−0.354)−1) = −0.298·R0, but the baseline/counterfactual fatal hospitalisation rate R0 is not printed in the extract (`harmonised.E4.missing_quantity`).\n- **E5** (Benin LPM, Table 3, column (4), β = −0.0962): **−9.62 percentage points** (`harmonised.E5.value`).\n\nSummary: `n_harmonised` = 4, `n_not_harmonisable` = 1, `feasibility` = \"complete\" (every item has a value or a justified impossibility statement).\n\n## Uncertainty\nNo standard errors or confidence intervals were propagated; only point estimates are harmonised. Exact conversions: E5 (pure rescaling) and E1 (exact mapping of a log-point coefficient to a percent change under the convention, though the ATT of a log outcome maps only approximately to the percent change in the average rate). Approximate: E2 (ratio uses the raw aggregate control mean while the ATE is block-adjusted; the ratio estimator is itself only approximately unbiased); E3 (linear elasticity extrapolation 100·η·g is a first-order approximation; the note's \"about 4%\" vs the printed 0.047 adds ambiguity — with 0.04 the value would be −1.55%, see `data_issues`). E4 has no value.\n\n## Limitations and open issues\n- E4 cannot be computed from the extract: it requires the baseline level of the fatal drug-related hospitalisation rate per 100,000 inhabitants per year for the treated 5 km regions (`harmonised.E4.missing_quantity`). No external or remembered value was substituted.\n- Source internal inconsistencies are recorded, not resolved (`data_issues`); in particular the DCR abstract's \"35%\" fall in fatal hospitalisations is not supported by Table 7 (−29.8% implied), which agrees with Section 6.1 (\"approximately 30%\").\n- E3 depends on reading 0.047 as the proportional MW change, as printed in the same table per the target definition.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The 5 km overall DCR ATT of -0.431 log points corresponds to a -35.01% relative change in drug-related hospitalisations, consistent with the printed -35.0%.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Exact under the 100*(exp(b)-1) convention; no SE propagated.\"},\n {\"id\":\"C2\",\"text\":\"The RESEA reemployment ATE of 1.47 pp is a +1.96% relative change versus the 75.0% control-group mean.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Control mean is the raw aggregate mean while the 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 -1.82% change in employment.\",\"results_keys\":[\"harmonised\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Linear approximation; the table note says 'about 4%', which would give -1.55%.\"},\n {\"id\":\"C4\",\"text\":\"The fatal drug-related hospitalisation effect (E4) cannot be expressed as an absolute change per 100,000 per year because the baseline rate is not printed in the extract.\",\"results_keys\":[\"harmonised\",\"n_not_harmonisable\"],\"status\":\"supported\",\"caveat\":\"Would require the baseline/counterfactual fatal hospitalisation rate for treated 5 km regions.\"},\n {\"id\":\"C5\",\"text\":\"The Benin LPM coefficient -0.0962 equals -9.62 percentage points.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Exact rescaling.\"},\n {\"id\":\"C6\",\"text\":\"Four of five items were harmonised, one is not harmonisable, and the task is complete.\",\"results_keys\":[\"n_harmonised\",\"n_not_harmonisable\",\"feasibility\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Complete means each item has a value or a justified impossibility statement.\"},\n {\"id\":\"C7\",\"text\":\"The DCR source is internally inconsistent about the size of the fatal-hospitalisation reduction (abstract 35% vs Section 6.1 ~30% vs Table 7 implied -29.8%).\",\"results_keys\":[\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Recorded only; does not affect any harmonised value.\"}\n]\nEOF\necho ok","description":"Write report and claims"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1284,"cache_read_input_tokens":39867,"output_tokens":2605,"output_tokens_details":{"thinking_tokens":46},"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":1284},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXqvS3AHYP5aJaMvbNGJ","requestRef":"f90f072b-e635-4d82-900e-cc69c3d707bd","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f4ba8d56-84d8-4cd1-832e-96ba02d16cd9","timestamp":"2026-10-30T05:09:37.482Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0234","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","version":"2.1.314","gitBranch":"HEAD"}83{"parentUuid":"f4ba8d56-84d8-4cd1-832e-96ba02d16cd9","isSidechain":false,"promptId":"b69de4f4-97af-47d0-8ca5-67147b0766d7","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016ePb3otdQaMDVeDfqEdctb","type":"tool_result","content":"ok","is_error":false}]},"uuid":"d940f30c-940e-4067-b420-adbca01fd19e","timestamp":"2026-10-30T05:09:37.507Z","toolUseResult":{"stdout":"ok","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"f4ba8d56-84d8-4cd1-832e-96ba02d16cd9","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0234","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","version":"2.1.314","gitBranch":"HEAD"}84{"type":"api-request-blob","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","hash":"f6a2c128b928103f5d67b1f4c771bf9ae73be18e2188c7641e8b0bc2d7adc26e","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"Remaining gate failures are P5 deliverables (expected at this stage). Writing report and claims."},{"type":"tool_use","id":"toolu_016ePb3otdQaMDVeDfqEdctb","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F4-conf-03 — Harmonising effect sizes reported under incompatible definitions\n\n## Question\nRe-express five printed treatment effects from four arXiv extracts on the target definition stated for each item in `data/effects.json`, using only quantities printed in the cited extract and an explicit transformation; where the target is unreachable, state the missing quantity.\n\n## Data and exclusions\nInputs: `data/effects.json` (5 items) and four source extracts (line counts in `n_input_rows`; SHA-256 hashes match `data/README.md`, see `out/state/data_audit.md`). All 5 items were assessed (`n_analysed` = 5); none excluded (`out/exclusions.csv`). Source anomalies are listed in `data_issues` (abstract vs Section 6.1 vs Table 7 inconsistency for fatal hospitalisations in the DCR paper; differing SE 0.210/0.218 and 608/640 control-region counts; \"about 4%\" note vs printed 0.047 MW change; Benin text citing an absent \"table 2\" and calling relative changes \"percentage changes\"; RESEA abstract rounding 1.47 pp to 1.5 pp). None changes a harmonised value.\n\n## Method\nTask conventions applied (`harmonised.*.transformation`): log-point b → 100·(exp(b)−1) %; pp effect Δ with control level p → 100·Δ/p %; elasticity η with proportional treatment change g → 100·η·g %; LPM coefficient on 0–1 scale → ×100 pp. `out/analysis.py` locates each printed input by a unique regex match in the extract, prints that source line, and computes the value. No randomness (`random_seed` = null).\n\n## Results\n- **E1** (DCR overall ATT, 5 km, Table 6, b = −0.431): 100·(exp(−0.431)−1) = **−35.01 %** of the counterfactual rate (`harmonised.E1.value`); matches the printed \"% change\" −35.0%.\n- **E2** (RESEA reemployment ATE 1.47 pp; control mean 75.0%, Table 2): 100·1.47/75.0 = **+1.96 %** of the control-group mean (`harmonised.E2.value`).\n- **E3** (MW employment elasticity −0.388; \"% MW changes\" 0.047, Table 2): 100·(−0.388)·0.047 = **−1.82 %** employment (`harmonised.E3.value`, −1.8236).\n- **E4** (DCR fatal hospitalisations ATT, 5 km, Table 7, b = −0.354): **not harmonisable** (`harmonised.E4.harmonisable` = false, `harmonised.E4.value` = null). An absolute change per 100,000 per year equals R0·(exp(−0.354)−1) = −0.298·R0, but the baseline/counterfactual fatal hospitalisation rate R0 is not printed in the extract (`harmonised.E4.missing_quantity`).\n- **E5** (Benin LPM, Table 3, column (4), β = −0.0962): **−9.62 percentage points** (`harmonised.E5.value`).\n\nSummary: `n_harmonised` = 4, `n_not_harmonisable` = 1, `feasibility` = \"complete\" (every item has a value or a justified impossibility statement).\n\n## Uncertainty\nNo standard errors or confidence intervals were propagated; only point estimates are harmonised. Exact conversions: E5 (pure rescaling) and E1 (exact mapping of a log-point coefficient to a percent change under the convention, though the ATT of a log outcome maps only approximately to the percent change in the average rate). Approximate: E2 (ratio uses the raw aggregate control mean while the ATE is block-adjusted; the ratio estimator is itself only approximately unbiased); E3 (linear elasticity extrapolation 100·η·g is a first-order approximation; the note's \"about 4%\" vs the printed 0.047 adds ambiguity — with 0.04 the value would be −1.55%, see `data_issues`). E4 has no value.\n\n## Limitations and open issues\n- E4 cannot be computed from the extract: it requires the baseline level of the fatal drug-related hospitalisation rate per 100,000 inhabitants per year for the treated 5 km regions (`harmonised.E4.missing_quantity`). No external or remembered value was substituted.\n- Source internal inconsistencies are recorded, not resolved (`data_issues`); in particular the DCR abstract's \"35%\" fall in fatal hospitalisations is not supported by Table 7 (−29.8% implied), which agrees with Section 6.1 (\"approximately 30%\").\n- E3 depends on reading 0.047 as the proportional MW change, as printed in the same table per the target definition.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The 5 km overall DCR ATT of -0.431 log points corresponds to a -35.01% relative change in drug-related hospitalisations, consistent with the printed -35.0%.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Exact under the 100*(exp(b)-1) convention; no SE propagated.\"},\n {\"id\":\"C2\",\"text\":\"The RESEA reemployment ATE of 1.47 pp is a +1.96% relative change versus the 75.0% control-group mean.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Control mean 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The final gate run passed (0 of 24 checks failed), and re-running `out/analysis.py` from scratch reproduces `out/results.json` exactly.\n\n| Item | Printed value | Target definition | Result |\n|---|---|---|---|\n| E1 – drug consumption rooms, all drug-related hospitalisations, 5 km (Table 6) | −0.431 log points | % change vs counterfactual rate | **−35.01 %**, using 100·(exp(b)−1); matches the printed −35.0% |\n| E2 – RESEA reemployment (Table 2) | 1.47 pp, control mean 75.0% | % of control mean | **+1.96 %** |\n| E3 – minimum-wage employment elasticity (Table 2) | −0.388, wage change 0.047 | % change in employment | **−1.82 %** (−1.8236) |\n| E4 – fatal drug-related hospitalisations, 5 km (Table 7) | −0.354 log points | change per 100,000 per year | **Not computable** (value `null`) |\n| E5 – Benin free cesarean section (Table 3, column 4) | −0.0962 (0–1 scale) | percentage points | **−9.62 pp** |\n\n**Why E4 can't be done:** the change in the rate would be −0.298 × the baseline rate. The extract never prints that baseline: the fatal drug-related hospitalisation rate per 100,000 per year in the treated 5 km regions. I didn't substitute any outside value. Because every item has either a value or a stated reason, the task counts as `complete`.\n\n**Uncertainty:** no standard errors were carried through. E5 is a plain rescaling, so it's exact. E1 is exact under the task's conversion rule. E2 and E3 are approximate:\n- **E2:** the 1.47 pp effect is adjusted for the randomisation week, but the 75.0% control mean is the unadjusted group average.\n- **E3:** this is a straight-line extrapolation of the elasticity.\n\n**Problems in the sources** (recorded under `data_issues`; none changes a result):\n- **Drug-room paper, fatal hospitalisations:** the abstract says they fall 35%, but Table 7 implies −29.8%, which agrees with Section 6.1's \"about 30%\".\n- **Drug-room paper, other mismatches:** the main estimate's standard error is 0.210 in Table 6 but 0.218 in Table A19. The control-region count is 608 in Table 6 but 640 in one table note.\n- **Minimum-wage paper:** the table prints a 0.047 (4.7%) wage increase but its note says \"about 4%\". I used 0.047, as the task asks for the value printed in the table; 0.04 would give −1.55%.\n- **Benin paper:** the text cites a \"table 2\" that isn't in the extract. It also calls relative changes \"percentage changes\".\n- **RESEA paper:** the abstract rounds 1.47 pp to 1.5 pp.\n\nEverything is in `out/`; start with `report.md`. The rest is `results.json`, `analysis.py` (prints every source line it uses), `claims.json`, `exclusions.csv`, and the working records in `state/`."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":402,"cache_read_input_tokens":44925,"output_tokens":1108,"output_tokens_details":{"thinking_tokens":14},"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":402},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqyDka6F3F1osznu7yf","requestRef":"89014303-255f-4568-9837-256839ec2824","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9a09d67a-616e-46b7-9e1f-c6e5b38a0098","timestamp":"2026-10-30T05:10:04.001Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0234","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","version":"2.1.314","gitBranch":"HEAD"}102{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"9a09d67a-616e-46b7-9e1f-c6e5b38a0098","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474"}103{"type":"cost-state","sessionId":"54720f4c-8f25-4674-9e8b-3043c4e43474","totalCostUSD":0.6098244000000002,"totalAPIDuration":148496,"totalAPIDurationWithoutRetries":148485,"totalToolDuration":847,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":149497,"startTime":1793336854510,"modelUsage":{"claude-opus-5-5":{"inputTokens":34,"outputTokens":14899,"thinkingTokens":1584,"cacheReadInputTokens":462967,"cacheCreationInputTokens":43823,"cacheCreationEphemeral5mInputTokens":43823,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.6098244000000002}},"hasUnknownModelCost":false}104