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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\nSEPARATE REVIEW (additional)\n- After the gate passes, a separate reviewer session that has not seen your work in progress will examine `TASK.md` and everything under `out/` and may return a list of issues (file `out/review/review.json`). You will then be resumed once to address them: fix what is justified, explain in `out/report.md` (Limitations and open issues) what you disagree with, re-run the gate, and update `out/claims.json` and `out/state/phases.json`. 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{"parentUuid":"423b30f6-b262-4136-993c-bdd6374db5a0","isSidechain":false,"promptId":"5bbd5ec8-4c6a-4ce7-b9b5-627abc92134d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011vyf6jEsAaprgtfFctpzJv","type":"tool_result","content":"# F4-conf-03 — Harmonising effect sizes reported under incompatible definitions\n\n## Scientific question\nA synthesis compares treatment effects from four open-access studies that report their estimates under different definitions: a semi-elasticity (coefficient on a log outcome), a percentage-point effect on a probability, an elasticity, and a linear-probability coefficient. For each of the five effects listed in `data/effects.json`, re-express the printed point estimate on the **target definition** stated for that item, using only quantities printed in the cited extract and an explicitly stated transformation; where the target definition cannot be reached from the printed quantities, say so and name the additional quantity that would be required. The estimands are the harmonised point estimates (no uncertainty propagation is required). Each item carries a locator for every printed quantity used.\n\n## Inputs\n- `data/effects.json` — the five items: {\"id\", \"source\", \"effect\" (what and where), \"reported_definition\", \"target_definition\"}.\n- `data/sources/arxiv-2609.27732v1.txt` — Armendariz (2026), *Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland*, arXiv:2609.27732v1, CC BY 4.0: abstract, Section 6 (Tables 6 and 7, Section 6.1), Appendix Table A19. Outcomes are log hospitalisation rates per 100,000 inhabitants; ATTs in log points; the tables also print a \"% change\" row for the overall effect.\n- `data/sources/arxiv-2606.14621v1.txt` — Li, Pearson-Merkowitz & Yokum (2026), *Evaluating the Impact of Rhode Island's Self-Sustaining RESEA Program on Employment Outcomes*, arXiv:2606.14621v1, CC BY 4.0: abstract, Section 3 (Table 2: treatment/control means, differences, ATEs with p-values and 95% CIs), Table 3. Reemployment is in percent (means) and percentage points (effects).\n- `data/sources/arxiv-2505.04555v3.txt` — Kanayama, Miyaji & Otani (2025/2026), *Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation*, arXiv:2505.04555v3, CC BY 4.0: abstract and Appendix A.1 (Table 2: elasticities and the size of the minimum-wage change, with note).\n- `data/sources/arxiv-2510.03658v1.txt` — Tossou (2025), *Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin*, arXiv:2510.03658v1, CC BY 4.0: abstract, Section 4.1 and Table 3 (linear probability model of infant death or stillbirth; \"Mean Dep. Var.\" row).\n- `data/README.md` — provenance, retrieval times, licences, SHA-256 of the extracts.\n\n## Information-availability rules\n- The extracts are the only admissible evidence; a quantity that is not printed in the cited extract is not available, even if you believe it is in the full paper or elsewhere.\n- Conventions: a log-point coefficient b corresponds to a relative change of 100·(exp(b) − 1) percent; a percentage-point effect Δ on a probability with counterfactual (control) level p corresponds to a relative change of 100·Δ/p percent; an elasticity η applied to a proportional change g of the treatment variable implies a relative change of 100·η·g percent. State the transformation you apply in `transformation`.\n- `harmonisable` is `false` only when the target definition requires a quantity that the cited extract does not print; then `value` is `null` and `missing_quantity` names the quantity. Do not substitute a guessed or externally remembered value.\n- `feasibility` refers to the task as a whole: the task is complete when every item has either a harmonised value or an explicit, justified statement of impossibility.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `harmonised` | object | — | {\"E1\": {\"value\": number or null, \"unit\": \"<unit of the target definition>\", \"transformation\": \"<formula with the printed inputs>\", \"harmonisable\": true/false, \"missing_quantity\": string or null, \"locator\": \"<source id | table | row | column for every printed input>\"}, … \"E5\": {…}} |\n| `n_harmonised` | integer | — | number of items with a non-null value |\n| `n_not_harmonisable` | integer | — | number of items with `harmonisable` = false |\n| `task_id` | string | — | \"F4-conf-03\" |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" — refers to the task as a whole |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the inputs (rendering defects, internal inconsistencies of a source, etc.) and how they were handled; empty list if none |\n| `n_input_rows` | object | lines / items | {file name: number of lines} for every file under `data/` that you read (text files: number of newline-terminated lines as reported by `wc -l`; JSON files: number of top-level items) |\n| `n_analysed` | integer or null | — | number of reported items (values, assertions, effects or references) you actually assessed |\n| `random_seed` | integer or null | — | null (no randomness is involved) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. \"Uncertainty\" must note that no standard errors were propagated and which conversions are exact versus approximate.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically (the script must print the source lines it relies on).\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available; the standard library is sufficient for this task). Do not modify `data/`.\n- The source extracts under `data/sources/` are the **only** admissible evidence. Do not rely on memory of the papers or on other versions of them; if a quantity is not in the extract, it is not available.\n- Report numbers exactly as printed in the source (same sign, same number of decimals); do not round or rescale printed values unless the key definition asks for a transformation.\n- This task is designed to be completed in well under 20 minutes of work.\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":"3285ced2-e333-4504-9707-72467b95bff0","timestamp":"2026-10-30T05:07:38.422Z","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.\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":"423b30f6-b262-4136-993c-bdd6374db5a0","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0232","sessionId":"008ec3b4-46e6-4e9d-82e8-4b7207a5d6af","version":"2.1.314","gitBranch":"HEAD"}
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{"parentUuid":"4e4b7c04-5f7e-46fc-a415-afda16e9b56b","isSidechain":false,"promptId":"5bbd5ec8-4c6a-4ce7-b9b5-627abc92134d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QmZwGnoe77RjGsgfYVbRH2","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      37 data/effects.json\n      16 data/README.md\n      66 data/sources/arxiv-2505.04555v3.txt\n      91 data/sources/arxiv-2510.03658v1.txt\n      58 data/sources/arxiv-2606.14621v1.txt\n     134 data/sources/arxiv-2609.27732v1.txt\n     402 total\n708c1ff0c4379c228ac6a98f577c23d0d61564f3c958fb35596266aca1f5f10b  data/effects.json\n7cf7cb385f9fb4fc0df52cfc6b0644bcbfb969a38afe2b018b864b7871187dc4  data/README.md\nb1731a9b1c40ae91c6be2cf2e92623bb6cf86c551587936a0270356f76ae95c1  data/sources/arxiv-2505.04555v3.txt\nf8782937eeaebd2dae0e653bbfc6aeb266c51587d09a4207bcaa3fa48a881127  data/sources/arxiv-2510.03658v1.txt\n27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238  data/sources/arxiv-2606.14621v1.txt\nee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b  data/sources/arxiv-2609.27732v1.txt","is_error":false}]},"uuid":"12d22153-69dd-4c6f-9b5f-cf6c3eb127bd","timestamp":"2026-10-30T05:07:40.525Z","toolUseResult":{"stdout":"[\n  {\n    \"id\": \"E1\",\n    \"source\": \"arxiv-2609.27732v1\",\n    \"effect\": \"Overall ATT of a DCR opening on drug-related hospitalisations, 5 km buffer (Table 6, row ATT, column 5 km)\",\n    \"reported_definition\": \"coefficient on a log outcome (log hospitalisation rate per 100,000), i.e. a log-point / semi-elasticity effect\",\n    \"target_definition\": \"relative percent change in the hospitalisation rate attributable to the opening (percent of the counterfactual rate)\"\n  },\n  {\n    \"id\": \"E2\",\n    \"source\": \"arxiv-2606.14621v1\",\n    \"effect\": \"Average treatment effect of RESEA selection on reemployment (Table 2, row Reemployment, column Average treatment effect estimate)\",\n    \"reported_definition\": \"percentage-point change in the reemployment probability\",\n    \"target_definition\": \"relative percent change in the reemployment probability (percent of the control-group mean)\"\n  },\n  {\n    \"id\": \"E3\",\n    \"source\": \"arxiv-2505.04555v3\",\n    \"effect\": \"Employment elasticity with respect to the minimum wage (Table 2, row 'Employment elasticity w.r.t MW'), for the 2023 minimum-wage increase studied in the paper\",\n    \"reported_definition\": \"elasticity: percent change in employment per percent change in the minimum wage\",\n    \"target_definition\": \"relative percent change in employment implied for the minimum-wage increase actually observed in the study (use the size of that increase as printed in the same table)\"\n  },\n  {\n    \"id\": \"E4\",\n    \"source\": \"arxiv-2609.27732v1\",\n    \"effect\": \"Overall ATT of a DCR opening on fatal drug-related hospitalisations, 5 km buffer (Table 7, row 'Drug-related (fatal)', column 5 km)\",\n    \"reported_definition\": \"coefficient on a log outcome (log hospitalisation rate per 100,000), i.e. a log-point / semi-elasticity effect\",\n    \"target_definition\": \"absolute change in the fatal drug-related hospitalisation rate, in hospitalisations per 100,000 inhabitants per year\"\n  },\n  {\n    \"id\": \"E5\",\n    \"source\": \"arxiv-2510.03658v1\",\n    \"effect\": \"Effect of access to free cesarean sections on the probability of infant death or stillbirth, preferred specification (Table 3, row Treatment, column (4))\",\n    \"reported_definition\": \"coefficient of a linear probability model (change in probability, on a 0-1 scale)\",\n    \"target_definition\": \"change in the probability expressed in percentage points\"\n  }\n]\n# Data provenance — F4-conf-03\n\nAll files under `data/sources/` are plain-text extracts of publicly accessible documents, prepared by the task designer on 2026-10-30 (UTC). Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables, the line following a coefficient row holds the standard errors in parentheses as printed in the original.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/arxiv-2609.27732v1.txt` | Armendariz, A. (2026). Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland. arXiv:2609.27732v1 [econ.GN]. https://arxiv.org/abs/2609.27732v1 (full text: https://arxiv.org/html/2609.27732v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b |\n| `sources/arxiv-2606.14621v1.txt` | Li, H. H., Pearson-Merkowitz, S., & Yokum, D. (2026). Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes. arXiv:2606.14621v1 [econ.GN]. https://arxiv.org/abs/2606.14621v1 (full text: https://arxiv.org/html/2606.14621v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 |\n| `sources/arxiv-2505.04555v3.txt` | Kanayama, H., Miyaji, S., & Otani, S. (2025/2026). Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation. arXiv:2505.04555v3 [econ.GN], v1 of 7 May 2025, v3 of 16 Mar 2026. https://arxiv.org/abs/2505.04555v3 (full text: https://arxiv.org/html/2505.04555v3) | 2026-10-30T03:23:18Z | CC BY 4.0 | b1731a9b1c40ae91c6be2cf2e92623bb6cf86c551587936a0270356f76ae95c1 |\n| `sources/arxiv-2510.03658v1.txt` | Tossou, S. C. (2025). Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin. arXiv:2510.03658v1 [econ.GN]. https://arxiv.org/abs/2510.03658v1 (full text: https://arxiv.org/html/2510.03658v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | f8782937eeaebd2dae0e653bbfc6aeb266c51587d09a4207bcaa3fa48a881127 |\n\nTask-specific input files (prepared by the task designer, not observed data):\n\n- `effects.json` — the five effects to harmonise (id, source id, description and location of the effect, reported definition, target definition), written by the task designer\n\nRights: arXiv e-prints are reproduced in part under the Creative Commons Attribution 4.0 licence shown on their arXiv abstract pages. No numbers were altered; where a table was re-set for legibility (RESEA Tables 2-3, London Table 3 header) the extract header says so.\n      37 data/effects.json\n      16 data/README.md\n      66 data/sources/arxiv-2505.04555v3.txt\n      91 data/sources/arxiv-2510.03658v1.txt\n      58 data/sources/arxiv-2606.14621v1.txt\n     134 data/sources/arxiv-2609.27732v1.txt\n     402 total\n708c1ff0c4379c228ac6a98f577c23d0d61564f3c958fb35596266aca1f5f10b  data/effects.json\n7cf7cb385f9fb4fc0df52cfc6b0644bcbfb969a38afe2b018b864b7871187dc4  data/README.md\nb1731a9b1c40ae91c6be2cf2e92623bb6cf86c551587936a0270356f76ae95c1  data/sources/arxiv-2505.04555v3.txt\nf8782937eeaebd2dae0e653bbfc6aeb266c51587d09a4207bcaa3fa48a881127  data/sources/arxiv-2510.03658v1.txt\n27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238  data/sources/arxiv-2606.14621v1.txt\nee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b  data/sources/arxiv-2609.27732v1.txt","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"4e4b7c04-5f7e-46fc-a415-afda16e9b56b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0232","sessionId":"008ec3b4-46e6-4e9d-82e8-4b7207a5d6af","version":"2.1.314","gitBranch":"HEAD"}
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{"parentUuid":"ca80bf88-05b3-47eb-bfe7-5804b5935e21","isSidechain":false,"promptId":"5bbd5ec8-4c6a-4ce7-b9b5-627abc92134d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01UsmcPQvA9yDer6y1k2koeK","type":"tool_result","content":"     1\tSOURCE EXTRACT — arxiv-2609.27732v1\n     2\tTitle: Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n     3\tAuthors: Ana Armendariz (University of St. Gallen)\n     4\tBibliographic record: arXiv:2609.27732v1 [econ.GN], submitted 23 Sep 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2609.27732v1\n     5\tRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\n     8\tOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n    13\t\n    14\tAna Armendariz*\n    15\t\n    16\tSeptember 23, 2026\n    17\t\n    18\tThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n    19\t\n    20\tKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n    21\t\n    22\t## 6 Results\n    23\t\n    24\tThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n    25\t\n    26\tFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n    27\t\n    28\tNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n    29\t\n    30\tFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n    31\t\n    32\tTable 6: Overall Average Treatment Effect on the Treated (ATT)\n    33\t\n    34\t| 1 km | 3 km | 5 km\n    35\t\n    36\tATT | -0.399 | -0.283 | -0.431**\n    37\t\n    38\t| (0.412) | (0.279) | (0.210)\n    39\t\n    40\t% change | -32.9% | -24.7% | -35.0%\n    41\t\n    42\tTreated regions | 7 | 35 | 50\n    43\t\n    44\tControl regions | 608 | 608 | 608\n    45\t\n    46\tObservations | 16,175 | 16,875 | 17,250\n    47\t\n    48\tNote: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\n    49\tthroughout. Regions near DCRs that opened before 1998\n    50\t(always-treated) are excluded. Standard errors clustered at the Medstat region level in\n    51\tparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n    52\t{}^{*}p<0.10 .\n    53\t\n    54\tTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\n    55\tcorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n    56\t\n    57\tTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\n    58\tsub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\n    59\t\n    60\tFigure 7: Event study: Sub-outcome estimates,\n    61\twithin 5 km, \\pm 5-year window\n    62\t\n    63\tNote: The figure plots event-study coefficients from the doubly robust Callaway & Sant’Anna (2021) estimator for four diagnostic sub-categories of drug-related hospitalizations. Each panel uses a separate y -axis to accommodate differences in scale across outcomes. The sample includes all staggered-treated MedStat regions within 5 km of a\n    64\tDCR that opened between 1998 and 2022. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded. 95% confidence intervals are based on standard errors clustered at the MedStat region level.\n    65\t\n    66\tTable 7: Overall ATT by Outcome Category and Buffer Zone\n    67\t\n    68\tOutcome | 1 km | 3 km | 5 km\n    69\t\n    70\tMental/Behavioral | -0.354 | -0.202 | -0.381*\n    71\t\n    72\t| (0.382) | (0.244) | (0.220)\n    73\t\n    74\tHepatitis | -0.270 | -0.413 | -0.406\n    75\t\n    76\t| (0.486) | (0.270) | (0.250)\n    77\t\n    78\tHIV | 0.063 | -0.239 | -0.340\n    79\t\n    80\t| (0.801) | (0.337) | (0.294)\n    81\t\n    82\tNarcotics | 1.186*** | 0.549*** | 0.390**\n    83\t\n    84\t| (0.288) | (0.187) | (0.165)\n    85\t\n    86\tDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n    87\t\n    88\t| (0.137) | (0.215) | (0.178)\n    89\t\n    90\tTreated regions | 7 | 35 | 50\n    91\t\n    92\tControl regions | 608 (never-treated) | |\n    93\t\n    94\tNote: Each cell reports the overall ATT from\n    95\tthe doubly robust Callaway & Sant’Anna (2021) estimator with\n    96\tnever-treated controls and a universal base period. Outcomes are\n    97\tlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\n    98\tmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\n    99\tHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\n   100\testimates are identified from a single treated region per cohort and\n   101\tshould be interpreted with caution. Regions near DCRs opened before\n   102\t1998 excluded. Standard errors clustered at the Medstat region level.\n   103\tSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n   104\t\n   105\tFigure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\n   106\t\n   107\t### 6.1 Mechanisms\n   108\t\n   109\tThe sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n   110\tsubstitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\n   111\t(Kerr et al., 2017; Hedrich, 2004).\n   112\t\n   113\tThe second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\n   114\t\n   115\tA third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\n   116\t\n   117\tTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n   118\t\n   119\t| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n   120\t\n   121\tATT | -0.431** | -0.438** | -0.362* | -0.149***\n   122\t\n   123\t| (0.218) | (0.208) | (0.205) | (0.038)\n   124\t\n   125\t% change | -35.0% | -35.5% | -30.3% | -13.9%\n   126\t\n   127\tTreated regions | 50 | | |\n   128\t\n   129\tControl regions | 608 | | |\n   130\t\n   131\tObservations | 17,250 | | |\n   132\t\n   133\tNote: All specifications use the 5 km buffer,\n   134\tnever-treated comparison regions (640 regions), and a \\pm 5-year window","is_error":false}]},"uuid":"3755a050-91a2-4349-99d2-7de745d147ae","timestamp":"2026-10-30T05:07:43.044Z","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":"ca80bf88-05b3-47eb-bfe7-5804b5935e21","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0232","sessionId":"008ec3b4-46e6-4e9d-82e8-4b7207a5d6af","version":"2.1.314","gitBranch":"HEAD"}
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{"parentUuid":"e3489d25-53ea-4243-9ed9-e2ed1545bda2","isSidechain":false,"promptId":"5bbd5ec8-4c6a-4ce7-b9b5-627abc92134d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014KuqB8YeqrdpJhwdyQRXKV","type":"tool_result","content":"     1\tSOURCE EXTRACT — arxiv-2606.14621v1\n     2\tTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n     3\tAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\n     4\tBibliographic record: arXiv:2606.14621v1 [econ.GN], submitted 12 Jun 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2606.14621v1\n     5\tRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Section 3 'Results' (Table 2 with caption, the results paragraphs, Section 3.1 'Cost-benefit analysis'); from Section 4.1, Table 3 with its caption and the paragraph that discusses it.\n     8\tOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\tEditorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n    11\t================================================================\n    12\t\n    13\t# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n    14\t\n    15\tHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n    16\t\n    17\t###### Abstract\n    18\t\n    19\tProlonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n    20\t\n    21\t## 3 Results\n    22\t\n    23\t[Table 1 (pre-treatment characteristics) omitted from this extract.]\n    24\t\n    25\t[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\n    26\t\n    27\tOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n    28\tWage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n    29\tReemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n    30\tWeeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n    31\tShort-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\n    32\tShort-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\n    33\tShort-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n    34\t\n    35\tTable 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n    36\t\n    37\tOverall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n    38\t\n    39\tThe results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n    40\t\n    41\tAs noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n    42\t\n    43\t### 3.1 Cost-benefit analysis\n    44\t\n    45\tWe find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n    46\t\n    47\t### 4.1 Methodological lessons (excerpt)\n    48\t\n    49\t[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n    50\t\n    51\tOutcome | Difference in means | Covariate adjusted | Fixed effects regression\n    52\tWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\n    53\tReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\n    54\tWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n    55\t\n    56\tTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n    57\t\n    58\tIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . These fixed effects regressions amount to estimating the coefficient in the regression model\n     1\tSOURCE EXTRACT — arxiv-2505.04555v3\n     2\tTitle: Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation\n     3\tAuthors: Hayato Kanayama; Sho Miyaji; Suguru Otani\n     4\tBibliographic record: arXiv:2505.04555v3 [econ.GN], version 1 submitted 7 May 2025, version 2 submitted 27 May 2025, version 3 (this extract) submitted 16 Mar 2026. Abstract page: https://arxiv.org/abs/2505.04555v3\n     5\tRetrieved: https://arxiv.org/html/2505.04555v3 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f2b68299cbc49f208a0340bd233007fe922f6d4245e0a642db526662d359789b)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Appendix A.1 'Elasticities Related to Minimum Wages' in full (Table 2 with its note and the discussion paragraph).\n     8\tOmitted: Sections 1-7, the other appendices, the references. The 'Current version' date printed in the HTML rendering is generated at rendering time and is not the submission date.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation\n    13\t\n    14\tHayato Kanayama (Waseda University), Sho Miyaji (Yale University), Suguru Otani (The University of Tokyo)\n    15\t\n    16\t###### Abstract\n    17\t\n    18\tHow labor markets adjust immediately after minimum wage hikes remains an open, policy-relevant question. This paper studies short-run minimum-wage effects in Japan’s spot labor market using Timee data and a wage-bin difference-in-differences design. We find a 2% employment decline in affected bins, driven by reduced vacancy creation rather than worker supply. Effects are more negative where the minimum-wage bite is higher and in low-wage occupations. Using job descriptions and amenity information, we document reallocation across job types: postings shift toward greater amenity provision and experienced-worker targeting, while female-targeted descriptions become less common, suggesting short-run labor-demand adjustments may foreshadow longer-run reallocation.\n    19\t\n    20\tKeywords: minimum wage, employment, job amenities, gig workers, spot work, difference-in-differences\n    21\t\n    22\tJEL codes: J21, J23, J38, J88\n    23\t\n    24\t## Appendix A Additional Analysis\n    25\t\n    26\t### A.1 Elasticities Related to Minimum Wages\n    27\t\n    28\tFollowing the method of Cengiz et al. (2019), we estimate (i) the elasticity of employment with respect to minimum wages and (ii) the employment elasticity with respect to the affected wages.\n    29\t\n    30\tTable 2: Impact of Minimum Wages on Employment and Wages\n    31\t\n    32\t| Estimate (SE)\n    33\t\n    34\tMissing jobs below new MW | -0.03\n    35\t\n    36\t| (0.006)\n    37\t\n    38\tExcess jobs above new MW | 0.012\n    39\t\n    40\t| (0.002)\n    41\t\n    42\tAffected wages | 0.366\n    43\t\n    44\t| (0.171)\n    45\t\n    46\tAffected employment | -0.268\n    47\t\n    48\t| (0.091)\n    49\t\n    50\tEmployment elasticity w.r.t MW | -0.388\n    51\t\n    52\t| (0.132)\n    53\t\n    54\tEmp. elasticity w.r.t affected wage | -0.732\n    55\t\n    56\t| (0.091)\n    57\t\n    58\tJob below new MW | 0.068\n    59\t\n    60\t% MW changes | 0.047\n    61\t\n    62\tNote: The table reports the effects of a minimum wage increase based on the event study analysis exploiting 47 prefecture-level minimum wage changes in 2023 (i.e., about 4% minimum wage increase). The table reports six-month averaged post-treatment estimates on missing jobs up to 100 JPY below the new minimum wage, excess jobs at and up to 300 JPY above it, employment, and wages. Robust standard errors in parentheses are clustered by wage bins separated by 10 JPY.\n    63\t\n    64\tTable 2 reports six-month–averaged post-treatment effects for the baseline specification depicted in Figure 7.\n    65\tThe magnitude of missing jobs below new minimum wages exceeds the magnitude of excess jobs above new minimum wages in the six-month window.\n    66\tThe estimated employment elasticity with respect to the minimum wage is - 0.388 , which is consistent with Figure 7. Unlike the findings of Cengiz et al. (2019) and Dube and Zipperer (2024), our own-wage employment elasticity is - 0.732 , indicating a larger (absolute) employment response to affected wages than the median estimate (- 0.13 ) reported by Dube and Lindner (2024).\n     1\tSOURCE EXTRACT — arxiv-2510.03658v1\n     2\tTitle: Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin\n     3\tAuthors: Selidji Caroline Tossou\n     4\tBibliographic record: arXiv:2510.03658v1 [econ.GN], submitted 4 Oct 2025 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2510.03658v1\n     5\tRetrieved: https://arxiv.org/html/2510.03658v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f2d9f04dcf4789983c28f97dc13705eb2d6ed583c4860c940f921d7b549aea59)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, author and abstract; Section 4.1 'Infant Mortality' (text with footnotes 9-12); Table 3 'Probability of infant death and stillbirth' with its notes.\n     8\tOmitted: Sections 1-3, 4.2-6, Tables 1-2 and 4-16, the references. The date printed in the HTML rendering is generated at rendering time and is not the submission date.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin\n    13\t\n    14\tSelidji Caroline Tossou (University of Wisconsin-Madison)\n    15\t\n    16\t###### Abstract\n    17\t\n    18\tThis paper evaluates the causal effect of the access to Benin’s free cesarean section policy on females and their children. I use a large sample of Demographic and Health Surveys (DHS) for West African countries and analyze how the exemption of the cesarean section user fees for females in Benin directly impacts maternal and infant mortality, family size decisions, and labor market participation. I use a Difference in Differences approach and find that having access to the free cesarean section policy significantly reduces the number of stillbirths and infant mortality by 0.0855 (a 18.79 percentage change). Second, for the surviving children, I find that access to the free cesarean section increases the likelihood of maternal mortality by 0.00465 (a 5.21 percentage change). The policy is effective at reducing infant mortality and saving the newborn. However, it harms the mother’s health which translates to lower fertility after the first birth and decreased maternal labor supply post-birth.\n    19\t\n    20\t## 4 Findings\n    21\t\n    22\t### 4.1 Infant Mortality\n    23\t\n    24\tI first investigate the infant death rate under the policy.\n    25\tFigure 15 shows that the exemption of cesarean section fees in Benin leads to an increase in the use of the service. What are the implications for infant mortality? I use a Difference in Differences specification to explore the possible impacts of access to the free cesarean section on infant mortality. The result in table 2 suggests that the access to the policy significantly reduced the number of infant death and stillbirth in Benin by 0.0855 (this represents a percentage change of 18.79 percent^9^9\n    26\t9\n    27\t\n    28\tI divide the estimate of beta by the mean of the dependent variable: -0.0855/0.455=-0.1879 ). This means that for over 100 newborns who would have died in the absence of the policy, approximately 18 are saved because of the access to the exemption of the cesarean section user fees. This result is promising and aligns with the goals of the free cesarean section policy, which primarily intends to lower the infant mortality in Benin. To the best of my knowledge, this is the first paper that evaluates the causal effect of free cesarean section policy on Benin’s infant mortality. Wealth and education attainment play a role in decreasing infant mortality. This can be explained by the fact that the free cesarean section covers only the surgery and a medicine kit for the mother. It does not cover prenatal and antepartum checkups. So a pregnant woman who has the means is more likely to identify and prevent any problems related to the pregnancy and the newborn’s health. Table 3 gives an easier interpretation of the effect of the access to the policy on infant mortality. In this table, the outcome of interest is a binary variable that takes value 1 if a female has an infant death or a pregnancy loss in the three years preceding the data collection and 0 otherwise. Thus, I interpret the coefficient \\hat{\\beta_{1}} as the effect of the policy on the likelihood of infant mortality. Using my most preferred specification (Column (4)), the result suggests that access to the policy decreases the likelihood of infant death or pregnancy loss of a female in Benin by 0.0962. Compared to the mean of the dependent variable, this effect represents a percentage change of 36.85 percent. To see which female category is driving this result, I decompose the effect by age group, wealth index, and education attainment.\n    29\t\n    30\tThe first panel of table 4 presents the stratification by age group. The policy is more effective at reducing infant mortality for females older than 25. For females between 15 and 24, the probability of infant mortality is reduced by only 4.65 percent compared to a reduction of 30.85 percent for a female between 25 and 34, a reduction of 43.53 percent for females between 35 and 44, and the highest decrease of 47.25 percent for females between 45 and 49^10^10\n    31\t10\n    32\t\n    33\tThese are the percentage change of infant mortality obtained by dividing the coefficient estimated by the mean of the dependent variable. The same exercise is done by using the wealth index of females. The results in the second panel of table 4 suggest that the effect of the free cesarean section on infant mortality is the biggest for females who are the poorest, poorer, or have a middle wealth index^11^11\n    34\t11\n    35\t\n    36\tThe modalities Poorest, Poorer, Middle, Richer, and Richest are pre-computed in the DHS data (46.36 percent, 35.84 percent, and 41.72 percent respectively). For the richer and richest females, the effect of the policy is a percentage change reduction of 28.52 percent and 16.47 percent on the likelihood of infant death. The last panel of table 4 gives a stratification by education level. The highest effect of the policy on the reduction of the likelihood of infant death (-39.07 percent) is for females who have no education level. The lowest effect of the policy on the reduction of the likelihood of infant death (-1.4 percent) is for females who have higher education levels.^12^12\n    37\t12\n    38\t\n    39\tI test the difference between all the coefficients and they are all significant and statistically different\n    40\t\n    41\tTo summarize, I find that access to the free cesarean section in Benin reduces the likelihood of infant mortality on average by 36.85 percent. The policy is more effective at reducing infant death for older females, females who are below the middle wealth index, and females who have no education.\n    42\t\n    43\tTable 3: Probability of infant death and stillbirth\n    44\t\n    45\t| Probability of Infant death or stillbirth | | |\n    46\t\n    47\t| (1) | (2) | (3) | (4)\n    48\t\n    49\tTreatment | -0.0911 | -0.0908 | -0.0961 | -0.0962\n    50\t\n    51\t| (0.0113) | (0.0113) | (0.0126) | (0.0129)\n    52\t\n    53\tEducation Attainment | -0.0578 | -0.0576 | -0.0443 | -0.0443\n    54\t\n    55\t| (0.00346) | (0.00340) | (0.00415) | (0.00420)\n    56\t\n    57\tAge of Female | 0.0168 | 0.0170 | 0.0171 | 0.0171\n    58\t\n    59\t| (0.00108) | (0.00112) | (0.00104) | (0.00108)\n    60\t\n    61\tAge of Partner | | -0.000425 | -0.000448 | -0.000435\n    62\t\n    63\t| | (0.000183) | (0.000151) | (0.000152)\n    64\t\n    65\tWealth Index | | | -0.0302 | -0.0302\n    66\t\n    67\t| | | (0.00277) | (0.00277)\n    68\t\n    69\tMarital Status | | | | 0.00128\n    70\t\n    71\t| | | | (0.00421)\n    72\t\n    73\tCountry Fixed Effect | Yes | Yes | Yes | Yes\n    74\t\n    75\tTime Fixed Effect. | Yes | Yes | Yes | Yes\n    76\t\n    77\t_cons | -0.129 | -0.114 | -0.0400 | -0.0406\n    78\t\n    79\t| (0.0323) | (0.0298) | (0.0266) | (0.0255)\n    80\t\n    81\tObservations | 196554 | 196424 | 196424 | 196423\n    82\t\n    83\t% Change | -0.3490 | -0.3478 | -0.3681 | -0.3685\n    84\t\n    85\tR-squared | 0.196 | 0.197 | 0.204 | 0.204\n    86\t\n    87\tMean Dep. Var. | 0.261 | 0.261 | 0.261 | 0.261\n    88\t\n    89\tStandard errors in parentheses; Standard errors are clustered at the household level. | | | |\n    90\t\n    91\tDep. Var. Mean is the dependent variable mean. Treatment is Benin Females after 2008 | | | |","is_error":false}]},"uuid":"6c1fc19e-71c8-4de5-acc5-99f745d9fbfb","timestamp":"2026-10-30T05:07:43.680Z","toolUseResult":{"stdout":"     1\tSOURCE EXTRACT — arxiv-2606.14621v1\n     2\tTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n     3\tAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\n     4\tBibliographic record: arXiv:2606.14621v1 [econ.GN], submitted 12 Jun 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2606.14621v1\n     5\tRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Section 3 'Results' (Table 2 with caption, the results paragraphs, Section 3.1 'Cost-benefit analysis'); from Section 4.1, Table 3 with its caption and the paragraph that discusses it.\n     8\tOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\tEditorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n    11\t================================================================\n    12\t\n    13\t# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n    14\t\n    15\tHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n    16\t\n    17\t###### Abstract\n    18\t\n    19\tProlonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n    20\t\n    21\t## 3 Results\n    22\t\n    23\t[Table 1 (pre-treatment characteristics) omitted from this extract.]\n    24\t\n    25\t[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\n    26\t\n    27\tOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n    28\tWage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n    29\tReemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n    30\tWeeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n    31\tShort-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\n    32\tShort-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\n    33\tShort-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n    34\t\n    35\tTable 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n    36\t\n    37\tOverall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n    38\t\n    39\tThe results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n    40\t\n    41\tAs noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n    42\t\n    43\t### 3.1 Cost-benefit analysis\n    44\t\n    45\tWe find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n    46\t\n    47\t### 4.1 Methodological lessons (excerpt)\n    48\t\n    49\t[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n    50\t\n    51\tOutcome | Difference in means | Covariate adjusted | Fixed effects regression\n    52\tWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\n    53\tReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\n    54\tWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n    55\t\n    56\tTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n    57\t\n    58\tIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . These fixed effects regressions amount to estimating the coefficient in the regression model\n     1\tSOURCE EXTRACT — arxiv-2505.04555v3\n     2\tTitle: Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation\n     3\tAuthors: Hayato Kanayama; Sho Miyaji; Suguru Otani\n     4\tBibliographic record: arXiv:2505.04555v3 [econ.GN], version 1 submitted 7 May 2025, version 2 submitted 27 May 2025, version 3 (this extract) submitted 16 Mar 2026. Abstract page: https://arxiv.org/abs/2505.04555v3\n     5\tRetrieved: https://arxiv.org/html/2505.04555v3 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f2b68299cbc49f208a0340bd233007fe922f6d4245e0a642db526662d359789b)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Appendix A.1 'Elasticities Related to Minimum Wages' in full (Table 2 with its note and the discussion paragraph).\n     8\tOmitted: Sections 1-7, the other appendices, the references. The 'Current version' date printed in the HTML rendering is generated at rendering time and is not the submission date.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation\n    13\t\n    14\tHayato Kanayama (Waseda University), Sho Miyaji (Yale University), Suguru Otani (The University of Tokyo)\n    15\t\n    16\t###### Abstract\n    17\t\n    18\tHow labor markets adjust immediately after minimum wage hikes remains an open, policy-relevant question. This paper studies short-run minimum-wage effects in Japan’s spot labor market using Timee data and a wage-bin difference-in-differences design. We find a 2% employment decline in affected bins, driven by reduced vacancy creation rather than worker supply. Effects are more negative where the minimum-wage bite is higher and in low-wage occupations. Using job descriptions and amenity information, we document reallocation across job types: postings shift toward greater amenity provision and experienced-worker targeting, while female-targeted descriptions become less common, suggesting short-run labor-demand adjustments may foreshadow longer-run reallocation.\n    19\t\n    20\tKeywords: minimum wage, employment, job amenities, gig workers, spot work, difference-in-differences\n    21\t\n    22\tJEL codes: J21, J23, J38, J88\n    23\t\n    24\t## Appendix A Additional Analysis\n    25\t\n    26\t### A.1 Elasticities Related to Minimum Wages\n    27\t\n    28\tFollowing the method of Cengiz et al. (2019), we estimate (i) the elasticity of employment with respect to minimum wages and (ii) the employment elasticity with respect to the affected wages.\n    29\t\n    30\tTable 2: Impact of Minimum Wages on Employment and Wages\n    31\t\n    32\t| Estimate (SE)\n    33\t\n    34\tMissing jobs below new MW | -0.03\n    35\t\n    36\t| (0.006)\n    37\t\n    38\tExcess jobs above new MW | 0.012\n    39\t\n    40\t| (0.002)\n    41\t\n    42\tAffected wages | 0.366\n    43\t\n    44\t| (0.171)\n    45\t\n    46\tAffected employment | -0.268\n    47\t\n    48\t| (0.091)\n    49\t\n    50\tEmployment elasticity w.r.t MW | -0.388\n    51\t\n    52\t| (0.132)\n    53\t\n    54\tEmp. elasticity w.r.t affected wage | -0.732\n    55\t\n    56\t| (0.091)\n    57\t\n    58\tJob below new MW | 0.068\n    59\t\n    60\t% MW changes | 0.047\n    61\t\n    62\tNote: The table reports the effects of a minimum wage increase based on the event study analysis exploiting 47 prefecture-level minimum wage changes in 2023 (i.e., about 4% minimum wage increase). The table reports six-month averaged post-treatment estimates on missing jobs up to 100 JPY below the new minimum wage, excess jobs at and up to 300 JPY above it, employment, and wages. Robust standard errors in parentheses are clustered by wage bins separated by 10 JPY.\n    63\t\n    64\tTable 2 reports six-month–averaged post-treatment effects for the baseline specification depicted in Figure 7.\n    65\tThe magnitude of missing jobs below new minimum wages exceeds the magnitude of excess jobs above new minimum wages in the six-month window.\n    66\tThe estimated employment elasticity with respect to the minimum wage is - 0.388 , which is consistent with Figure 7. Unlike the findings of Cengiz et al. (2019) and Dube and Zipperer (2024), our own-wage employment elasticity is - 0.732 , indicating a larger (absolute) employment response to affected wages than the median estimate (- 0.13 ) reported by Dube and Lindner (2024).\n     1\tSOURCE EXTRACT — arxiv-2510.03658v1\n     2\tTitle: Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin\n     3\tAuthors: Selidji Caroline Tossou\n     4\tBibliographic record: arXiv:2510.03658v1 [econ.GN], submitted 4 Oct 2025 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2510.03658v1\n     5\tRetrieved: https://arxiv.org/html/2510.03658v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f2d9f04dcf4789983c28f97dc13705eb2d6ed583c4860c940f921d7b549aea59)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, author and abstract; Section 4.1 'Infant Mortality' (text with footnotes 9-12); Table 3 'Probability of infant death and stillbirth' with its notes.\n     8\tOmitted: Sections 1-3, 4.2-6, Tables 1-2 and 4-16, the references. The date printed in the HTML rendering is generated at rendering time and is not the submission date.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin\n    13\t\n    14\tSelidji Caroline Tossou (University of Wisconsin-Madison)\n    15\t\n    16\t###### Abstract\n    17\t\n    18\tThis paper evaluates the causal effect of the access to Benin’s free cesarean section policy on females and their children. I use a large sample of Demographic and Health Surveys (DHS) for West African countries and analyze how the exemption of the cesarean section user fees for females in Benin directly impacts maternal and infant mortality, family size decisions, and labor market participation. I use a Difference in Differences approach and find that having access to the free cesarean section policy significantly reduces the number of stillbirths and infant mortality by 0.0855 (a 18.79 percentage change). Second, for the surviving children, I find that access to the free cesarean section increases the likelihood of maternal mortality by 0.00465 (a 5.21 percentage change). The policy is effective at reducing infant mortality and saving the newborn. However, it harms the mother’s health which translates to lower fertility after the first birth and decreased maternal labor supply post-birth.\n    19\t\n    20\t## 4 Findings\n    21\t\n    22\t### 4.1 Infant Mortality\n    23\t\n    24\tI first investigate the infant death rate under the policy.\n    25\tFigure 15 shows that the exemption of cesarean section fees in Benin leads to an increase in the use of the service. What are the implications for infant mortality? I use a Difference in Differences specification to explore the possible impacts of access to the free cesarean section on infant mortality. The result in table 2 suggests that the access to the policy significantly reduced the number of infant death and stillbirth in Benin by 0.0855 (this represents a percentage change of 18.79 percent^9^9\n    26\t9\n    27\t\n    28\tI divide the estimate of beta by the mean of the dependent variable: -0.0855/0.455=-0.1879 ). This means that for over 100 newborns who would have died in the absence of the policy, approximately 18 are saved because of the access to the exemption of the cesarean section user fees. This result is promising and aligns with the goals of the free cesarean section policy, which primarily intends to lower the infant mortality in Benin. To the best of my knowledge, this is the first paper that evaluates the causal effect of free cesarean section policy on Benin’s infant mortality. Wealth and education attainment play a role in decreasing infant mortality. This can be explained by the fact that the free cesarean section covers only the surgery and a medicine kit for the mother. It does not cover prenatal and antepartum checkups. So a pregnant woman who has the means is more likely to identify and prevent any problems related to the pregnancy and the newborn’s health. Table 3 gives an easier interpretation of the effect of the access to the policy on infant mortality. In this table, the outcome of interest is a binary variable that takes value 1 if a female has an infant death or a pregnancy loss in the three years preceding the data collection and 0 otherwise. Thus, I interpret the coefficient \\hat{\\beta_{1}} as the effect of the policy on the likelihood of infant mortality. Using my most preferred specification (Column (4)), the result suggests that access to the policy decreases the likelihood of infant death or pregnancy loss of a female in Benin by 0.0962. Compared to the mean of the dependent variable, this effect represents a percentage change of 36.85 percent. To see which female category is driving this result, I decompose the effect by age group, wealth index, and education attainment.\n    29\t\n    30\tThe first panel of table 4 presents the stratification by age group. The policy is more effective at reducing infant mortality for females older than 25. For females between 15 and 24, the probability of infant mortality is reduced by only 4.65 percent compared to a reduction of 30.85 percent for a female between 25 and 34, a reduction of 43.53 percent for females between 35 and 44, and the highest decrease of 47.25 percent for females between 45 and 49^10^10\n    31\t10\n    32\t\n    33\tThese are the percentage change of infant mortality obtained by dividing the coefficient estimated by the mean of the dependent variable. The same exercise is done by using the wealth index of females. The results in the second panel of table 4 suggest that the effect of the free cesarean section on infant mortality is the biggest for females who are the poorest, poorer, or have a middle wealth index^11^11\n    34\t11\n    35\t\n    36\tThe modalities Poorest, Poorer, Middle, Richer, and Richest are pre-computed in the DHS data (46.36 percent, 35.84 percent, and 41.72 percent respectively). For the richer and richest females, the effect of the policy is a percentage change reduction of 28.52 percent and 16.47 percent on the likelihood of infant death. The last panel of table 4 gives a stratification by education level. The highest effect of the policy on the reduction of the likelihood of infant death (-39.07 percent) is for females who have no education level. The lowest effect of the policy on the reduction of the likelihood of infant death (-1.4 percent) is for females who have higher education levels.^12^12\n    37\t12\n    38\t\n    39\tI test the difference between all the coefficients and they are all significant and statistically different\n    40\t\n    41\tTo summarize, I find that access to the free cesarean section in Benin reduces the likelihood of infant mortality on average by 36.85 percent. The policy is more effective at reducing infant death for older females, females who are below the middle wealth index, and females who have no education.\n    42\t\n    43\tTable 3: Probability of infant death and stillbirth\n    44\t\n    45\t| Probability of Infant death or stillbirth | | |\n    46\t\n    47\t| (1) | (2) | (3) | (4)\n    48\t\n    49\tTreatment | -0.0911 | -0.0908 | -0.0961 | -0.0962\n    50\t\n    51\t| (0.0113) | (0.0113) | (0.0126) | (0.0129)\n    52\t\n    53\tEducation Attainment | -0.0578 | -0.0576 | -0.0443 | -0.0443\n    54\t\n    55\t| (0.00346) | (0.00340) | (0.00415) | (0.00420)\n    56\t\n    57\tAge of Female | 0.0168 | 0.0170 | 0.0171 | 0.0171\n    58\t\n    59\t| (0.00108) | (0.00112) | (0.00104) | (0.00108)\n    60\t\n    61\tAge of Partner | | -0.000425 | -0.000448 | -0.000435\n    62\t\n    63\t| | (0.000183) | (0.000151) | (0.000152)\n    64\t\n    65\tWealth Index | | | -0.0302 | -0.0302\n    66\t\n    67\t| | | (0.00277) | (0.00277)\n    68\t\n    69\tMarital Status | | | | 0.00128\n    70\t\n    71\t| | | | (0.00421)\n    72\t\n    73\tCountry Fixed Effect | Yes | Yes | Yes | Yes\n    74\t\n    75\tTime Fixed Effect. | Yes | Yes | Yes | Yes\n    76\t\n    77\t_cons | -0.129 | -0.114 | -0.0400 | -0.0406\n    78\t\n    79\t| (0.0323) | (0.0298) | (0.0266) | (0.0255)\n    80\t\n    81\tObservations | 196554 | 196424 | 196424 | 196423\n    82\t\n    83\t% Change | -0.3490 | -0.3478 | -0.3681 | -0.3685\n    84\t\n    85\tR-squared | 0.196 | 0.197 | 0.204 | 0.204\n    86\t\n    87\tMean Dep. Var. | 0.261 | 0.261 | 0.261 | 0.261\n    88\t\n    89\tStandard errors in parentheses; Standard errors are clustered at the household level. | | | |\n    90\t\n    91\tDep. Var. Mean is the dependent variable mean. Treatment is Benin Females after 2008 | | | |","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"e3489d25-53ea-4243-9ed9-e2ed1545bda2","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0232","sessionId":"008ec3b4-46e6-4e9d-82e8-4b7207a5d6af","version":"2.1.314","gitBranch":"HEAD"}
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Reproduced in part with attribution.\n     7\tExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\n     8\tOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n    13\t\n    14\tAna Armendariz*\n    15\t\n    16\tSeptember 23, 2026\n    17\t\n    18\tThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n    19\t\n    20\tKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n    21\t\n    22\t## 6 Results\n    23\t\n    24\tThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n    25\t\n    26\tFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n    27\t\n    28\tNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n    29\t\n    30\tFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n    31\t\n    32\tTable 6: Overall Average Treatment Effect on the Treated (ATT)\n    33\t\n    34\t| 1 km | 3 km | 5 km\n    35\t\n    36\tATT | -0.399 | -0.283 | -0.431**\n    37\t\n    38\t| (0.412) | (0.279) | (0.210)\n    39\t\n    40\t% change | -32.9% | -24.7% | -35.0%\n    41\t\n    42\tTreated regions | 7 | 35 | 50\n    43\t\n    44\tControl regions | 608 | 608 | 608\n    45\t\n    46\tObservations | 16,175 | 16,875 | 17,250\n    47\t\n    48\tNote: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\n    49\tthroughout. Regions near DCRs that opened before 1998\n    50\t(always-treated) are excluded. Standard errors clustered at the Medstat region level in\n    51\tparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n    52\t{}^{*}p<0.10 .\n    53\t\n    54\tTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\n    55\tcorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n    56\t\n    57\tTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\n    58\tsub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\n    59\t\n    60\tFigure 7: Event study: Sub-outcome estimates,\n    61\twithin 5 km, \\pm 5-year window\n    62\t\n    63\tNote: The figure plots event-study coefficients from the doubly robust Callaway & Sant’Anna (2021) estimator for four diagnostic sub-categories of drug-related hospitalizations. Each panel uses a separate y -axis to accommodate differences in scale across outcomes. The sample includes all staggered-treated MedStat regions within 5 km of a\n    64\tDCR that opened between 1998 and 2022. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded. 95% confidence intervals are based on standard errors clustered at the MedStat region level.\n    65\t\n    66\tTable 7: Overall ATT by Outcome Category and Buffer Zone\n    67\t\n    68\tOutcome | 1 km | 3 km | 5 km\n    69\t\n    70\tMental/Behavioral | -0.354 | -0.202 | -0.381*\n    71\t\n    72\t| (0.382) | (0.244) | (0.220)\n    73\t\n    74\tHepatitis | -0.270 | -0.413 | -0.406\n    75\t\n    76\t| (0.486) | (0.270) | (0.250)\n    77\t\n    78\tHIV | 0.063 | -0.239 | -0.340\n    79\t\n    80\t| (0.801) | (0.337) | (0.294)\n    81\t\n    82\tNarcotics | 1.186*** | 0.549*** | 0.390**\n    83\t\n    84\t| (0.288) | (0.187) | (0.165)\n    85\t\n    86\tDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n    87\t\n    88\t| (0.137) | (0.215) | (0.178)\n    89\t\n    90\tTreated regions | 7 | 35 | 50\n    91\t\n    92\tControl regions | 608 (never-treated) | |\n    93\t\n    94\tNote: Each cell reports the overall ATT from\n    95\tthe doubly robust Callaway & Sant’Anna (2021) estimator with\n    96\tnever-treated controls and a universal base period. Outcomes are\n    97\tlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\n    98\tmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\n    99\tHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\n   100\testimates are identified from a single treated region per cohort and\n   101\tshould be interpreted with caution. Regions near DCRs opened before\n   102\t1998 excluded. Standard errors clustered at the Medstat region level.\n   103\tSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n   104\t\n   105\tFigure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\n   106\t\n   107\t### 6.1 Mechanisms\n   108\t\n   109\tThe sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n   110\tsubstitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\n   111\t(Kerr et al., 2017; Hedrich, 2004).\n   112\t\n   113\tThe second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\n   114\t\n   115\tA third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\n   116\t\n   117\tTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n   118\t\n   119\t| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n   120\t\n   121\tATT | -0.431** | -0.438** | -0.362* | -0.149***\n   122\t\n   123\t| (0.218) | (0.208) | (0.205) | (0.038)\n   124\t\n   125\t% change | -35.0% | -35.5% | -30.3% | -13.9%\n   126\t\n   127\tTreated regions | 50 | | |\n   128\t\n   129\tControl regions | 608 | | |\n   130\t\n   131\tObservations | 17,250 | | |\n   132\t\n   133\tNote: All specifications use the 5 km buffer,\n   134\tnever-treated comparison regions (640 regions), and a \\pm 5-year window","is_error":false},{"tool_use_id":"toolu_014KuqB8YeqrdpJhwdyQRXKV","type":"tool_result","content":"     1\tSOURCE EXTRACT — arxiv-2606.14621v1\n     2\tTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n     3\tAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\n     4\tBibliographic record: arXiv:2606.14621v1 [econ.GN], submitted 12 Jun 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2606.14621v1\n     5\tRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Section 3 'Results' (Table 2 with caption, the results paragraphs, Section 3.1 'Cost-benefit analysis'); from Section 4.1, Table 3 with its caption and the paragraph that discusses it.\n     8\tOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\tEditorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n    11\t================================================================\n    12\t\n    13\t# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n    14\t\n    15\tHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n    16\t\n    17\t###### Abstract\n    18\t\n    19\tProlonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n    20\t\n    21\t## 3 Results\n    22\t\n    23\t[Table 1 (pre-treatment characteristics) omitted from this extract.]\n    24\t\n    25\t[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\n    26\t\n    27\tOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n    28\tWage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n    29\tReemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n    30\tWeeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n    31\tShort-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\n    32\tShort-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\n    33\tShort-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n    34\t\n    35\tTable 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n    36\t\n    37\tOverall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n    38\t\n    39\tThe results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n    40\t\n    41\tAs noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n    42\t\n    43\t### 3.1 Cost-benefit analysis\n    44\t\n    45\tWe find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n    46\t\n    47\t### 4.1 Methodological lessons (excerpt)\n    48\t\n    49\t[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n    50\t\n    51\tOutcome | Difference in means | Covariate adjusted | Fixed effects regression\n    52\tWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\n    53\tReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\n    54\tWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n    55\t\n    56\tTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n    57\t\n    58\tIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . These fixed effects regressions amount to estimating the coefficient in the regression model\n     1\tSOURCE EXTRACT — arxiv-2505.04555v3\n     2\tTitle: Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation\n     3\tAuthors: Hayato Kanayama; Sho Miyaji; Suguru Otani\n     4\tBibliographic record: arXiv:2505.04555v3 [econ.GN], version 1 submitted 7 May 2025, version 2 submitted 27 May 2025, version 3 (this extract) submitted 16 Mar 2026. Abstract page: https://arxiv.org/abs/2505.04555v3\n     5\tRetrieved: https://arxiv.org/html/2505.04555v3 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f2b68299cbc49f208a0340bd233007fe922f6d4245e0a642db526662d359789b)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Appendix A.1 'Elasticities Related to Minimum Wages' in full (Table 2 with its note and the discussion paragraph).\n     8\tOmitted: Sections 1-7, the other appendices, the references. The 'Current version' date printed in the HTML rendering is generated at rendering time and is not the submission date.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation\n    13\t\n    14\tHayato Kanayama (Waseda University), Sho Miyaji (Yale University), Suguru Otani (The University of Tokyo)\n    15\t\n    16\t###### Abstract\n    17\t\n    18\tHow labor markets adjust immediately after minimum wage hikes remains an open, policy-relevant question. This paper studies short-run minimum-wage effects in Japan’s spot labor market using Timee data and a wage-bin difference-in-differences design. We find a 2% employment decline in affected bins, driven by reduced vacancy creation rather than worker supply. Effects are more negative where the minimum-wage bite is higher and in low-wage occupations. Using job descriptions and amenity information, we document reallocation across job types: postings shift toward greater amenity provision and experienced-worker targeting, while female-targeted descriptions become less common, suggesting short-run labor-demand adjustments may foreshadow longer-run reallocation.\n    19\t\n    20\tKeywords: minimum wage, employment, job amenities, gig workers, spot work, difference-in-differences\n    21\t\n    22\tJEL codes: J21, J23, J38, J88\n    23\t\n    24\t## Appendix A Additional Analysis\n    25\t\n    26\t### A.1 Elasticities Related to Minimum Wages\n    27\t\n    28\tFollowing the method of Cengiz et al. (2019), we estimate (i) the elasticity of employment with respect to minimum wages and (ii) the employment elasticity with respect to the affected wages.\n    29\t\n    30\tTable 2: Impact of Minimum Wages on Employment and Wages\n    31\t\n    32\t| Estimate (SE)\n    33\t\n    34\tMissing jobs below new MW | -0.03\n    35\t\n    36\t| (0.006)\n    37\t\n    38\tExcess jobs above new MW | 0.012\n    39\t\n    40\t| (0.002)\n    41\t\n    42\tAffected wages | 0.366\n    43\t\n    44\t| (0.171)\n    45\t\n    46\tAffected employment | -0.268\n    47\t\n    48\t| (0.091)\n    49\t\n    50\tEmployment elasticity w.r.t MW | -0.388\n    51\t\n    52\t| (0.132)\n    53\t\n    54\tEmp. elasticity w.r.t affected wage | -0.732\n    55\t\n    56\t| (0.091)\n    57\t\n    58\tJob below new MW | 0.068\n    59\t\n    60\t% MW changes | 0.047\n    61\t\n    62\tNote: The table reports the effects of a minimum wage increase based on the event study analysis exploiting 47 prefecture-level minimum wage changes in 2023 (i.e., about 4% minimum wage increase). The table reports six-month averaged post-treatment estimates on missing jobs up to 100 JPY below the new minimum wage, excess jobs at and up to 300 JPY above it, employment, and wages. Robust standard errors in parentheses are clustered by wage bins separated by 10 JPY.\n    63\t\n    64\tTable 2 reports six-month–averaged post-treatment effects for the baseline specification depicted in Figure 7.\n    65\tThe magnitude of missing jobs below new minimum wages exceeds the magnitude of excess jobs above new minimum wages in the six-month window.\n    66\tThe estimated employment elasticity with respect to the minimum wage is - 0.388 , which is consistent with Figure 7. Unlike the findings of Cengiz et al. (2019) and Dube and Zipperer (2024), our own-wage employment elasticity is - 0.732 , indicating a larger (absolute) employment response to affected wages than the median estimate (- 0.13 ) reported by Dube and Lindner (2024).\n     1\tSOURCE EXTRACT — arxiv-2510.03658v1\n     2\tTitle: Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin\n     3\tAuthors: Selidji Caroline Tossou\n     4\tBibliographic record: arXiv:2510.03658v1 [econ.GN], submitted 4 Oct 2025 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2510.03658v1\n     5\tRetrieved: https://arxiv.org/html/2510.03658v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f2d9f04dcf4789983c28f97dc13705eb2d6ed583c4860c940f921d7b549aea59)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, author and abstract; Section 4.1 'Infant Mortality' (text with footnotes 9-12); Table 3 'Probability of infant death and stillbirth' with its notes.\n     8\tOmitted: Sections 1-3, 4.2-6, Tables 1-2 and 4-16, the references. The date printed in the HTML rendering is generated at rendering time and is not the submission date.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin\n    13\t\n    14\tSelidji Caroline Tossou (University of Wisconsin-Madison)\n    15\t\n    16\t###### Abstract\n    17\t\n    18\tThis paper evaluates the causal effect of the access to Benin’s free cesarean section policy on females and their children. I use a large sample of Demographic and Health Surveys (DHS) for West African countries and analyze how the exemption of the cesarean section user fees for females in Benin directly impacts maternal and infant mortality, family size decisions, and labor market participation. I use a Difference in Differences approach and find that having access to the free cesarean section policy significantly reduces the number of stillbirths and infant mortality by 0.0855 (a 18.79 percentage change). Second, for the surviving children, I find that access to the free cesarean section increases the likelihood of maternal mortality by 0.00465 (a 5.21 percentage change). The policy is effective at reducing infant mortality and saving the newborn. However, it harms the mother’s health which translates to lower fertility after the first birth and decreased maternal labor supply post-birth.\n    19\t\n    20\t## 4 Findings\n    21\t\n    22\t### 4.1 Infant Mortality\n    23\t\n    24\tI first investigate the infant death rate under the policy.\n    25\tFigure 15 shows that the exemption of cesarean section fees in Benin leads to an increase in the use of the service. What are the implications for infant mortality? I use a Difference in Differences specification to explore the possible impacts of access to the free cesarean section on infant mortality. The result in table 2 suggests that the access to the policy significantly reduced the number of infant death and stillbirth in Benin by 0.0855 (this represents a percentage change of 18.79 percent^9^9\n    26\t9\n    27\t\n    28\tI divide the estimate of beta by the mean of the dependent variable: -0.0855/0.455=-0.1879 ). This means that for over 100 newborns who would have died in the absence of the policy, approximately 18 are saved because of the access to the exemption of the cesarean section user fees. This result is promising and aligns with the goals of the free cesarean section policy, which primarily intends to lower the infant mortality in Benin. To the best of my knowledge, this is the first paper that evaluates the causal effect of free cesarean section policy on Benin’s infant mortality. Wealth and education attainment play a role in decreasing infant mortality. This can be explained by the fact that the free cesarean section covers only the surgery and a medicine kit for the mother. It does not cover prenatal and antepartum checkups. So a pregnant woman who has the means is more likely to identify and prevent any problems related to the pregnancy and the newborn’s health. Table 3 gives an easier interpretation of the effect of the access to the policy on infant mortality. In this table, the outcome of interest is a binary variable that takes value 1 if a female has an infant death or a pregnancy loss in the three years preceding the data collection and 0 otherwise. Thus, I interpret the coefficient \\hat{\\beta_{1}} as the effect of the policy on the likelihood of infant mortality. Using my most preferred specification (Column (4)), the result suggests that access to the policy decreases the likelihood of infant death or pregnancy loss of a female in Benin by 0.0962. Compared to the mean of the dependent variable, this effect represents a percentage change of 36.85 percent. To see which female category is driving this result, I decompose the effect by age group, wealth index, and education attainment.\n    29\t\n    30\tThe first panel of table 4 presents the stratification by age group. The policy is more effective at reducing infant mortality for females older than 25. For females between 15 and 24, the probability of infant mortality is reduced by only 4.65 percent compared to a reduction of 30.85 percent for a female between 25 and 34, a reduction of 43.53 percent for females between 35 and 44, and the highest decrease of 47.25 percent for females between 45 and 49^10^10\n    31\t10\n    32\t\n    33\tThese are the percentage change of infant mortality obtained by dividing the coefficient estimated by the mean of the dependent variable. The same exercise is done by using the wealth index of females. The results in the second panel of table 4 suggest that the effect of the free cesarean section on infant mortality is the biggest for females who are the poorest, poorer, or have a middle wealth index^11^11\n    34\t11\n    35\t\n    36\tThe modalities Poorest, Poorer, Middle, Richer, and Richest are pre-computed in the DHS data (46.36 percent, 35.84 percent, and 41.72 percent respectively). For the richer and richest females, the effect of the policy is a percentage change reduction of 28.52 percent and 16.47 percent on the likelihood of infant death. The last panel of table 4 gives a stratification by education level. The highest effect of the policy on the reduction of the likelihood of infant death (-39.07 percent) is for females who have no education level. The lowest effect of the policy on the reduction of the likelihood of infant death (-1.4 percent) is for females who have higher education levels.^12^12\n    37\t12\n    38\t\n    39\tI test the difference between all the coefficients and they are all significant and statistically different\n    40\t\n    41\tTo summarize, I find that access to the free cesarean section in Benin reduces the likelihood of infant mortality on average by 36.85 percent. The policy is more effective at reducing infant death for older females, females who are below the middle wealth index, and females who have no education.\n    42\t\n    43\tTable 3: Probability of infant death and stillbirth\n    44\t\n    45\t| Probability of Infant death or stillbirth | | |\n    46\t\n    47\t| (1) | (2) | (3) | (4)\n    48\t\n    49\tTreatment | -0.0911 | -0.0908 | -0.0961 | -0.0962\n    50\t\n    51\t| (0.0113) | (0.0113) | (0.0126) | (0.0129)\n    52\t\n    53\tEducation Attainment | -0.0578 | -0.0576 | -0.0443 | -0.0443\n    54\t\n    55\t| (0.00346) | (0.00340) | (0.00415) | (0.00420)\n    56\t\n    57\tAge of Female | 0.0168 | 0.0170 | 0.0171 | 0.0171\n    58\t\n    59\t| (0.00108) | (0.00112) | (0.00104) | (0.00108)\n    60\t\n    61\tAge of Partner | | -0.000425 | -0.000448 | -0.000435\n    62\t\n    63\t| | (0.000183) | (0.000151) | (0.000152)\n    64\t\n    65\tWealth Index | | | -0.0302 | -0.0302\n    66\t\n    67\t| | | (0.00277) | (0.00277)\n    68\t\n    69\tMarital Status | | | | 0.00128\n    70\t\n    71\t| | | | (0.00421)\n    72\t\n    73\tCountry Fixed Effect | Yes | Yes | Yes | Yes\n    74\t\n    75\tTime Fixed Effect. | Yes | Yes | Yes | Yes\n    76\t\n    77\t_cons | -0.129 | -0.114 | -0.0400 | -0.0406\n    78\t\n    79\t| (0.0323) | (0.0298) | (0.0266) | (0.0255)\n    80\t\n    81\tObservations | 196554 | 196424 | 196424 | 196423\n    82\t\n    83\t% Change | -0.3490 | -0.3478 | -0.3681 | -0.3685\n    84\t\n    85\tR-squared | 0.196 | 0.197 | 0.204 | 0.204\n    86\t\n    87\tMean Dep. Var. | 0.261 | 0.261 | 0.261 | 0.261\n    88\t\n    89\tStandard errors in parentheses; Standard errors are clustered at the household level. | | | |\n    90\t\n    91\tDep. Var. Mean is the dependent variable mean. Treatment is Benin Females after 2008 | | | |","is_error":false}]}}
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-p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — F4-conf-03\n**Question.** Re-express five printed treatment effects (data/effects.json, E1–E5) on each item's target definition, using only quantities printed in the cited extract under data/sources/ and an explicit transformation; where impossible, say so and name the missing quantity.\n**Estimands.** Harmonised point estimates (no uncertainty propagation). 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Run from workspace root: ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, math, re, os, hashlib\n\nROOT = os.getcwd()\nD = os.path.join(ROOT, \"data\")\nSRC = os.path.join(D, \"sources\")\n\n\ndef read_lines(path):\n    with open(path, encoding=\"utf-8\") as f:\n        return f.read().split(\"\\n\")\n\n\ndef find(lines, prefix, fname):\n    \"\"\"Return (line_no, line) of the unique line starting with prefix; print it.\"\"\"\n    hits = [(i + 1, l) for i, l in enumerate(lines) if l.startswith(prefix)]\n    assert len(hits) == 1, (fname, prefix, hits)\n    n, l = hits[0]\n    print(f\"[{fname}:{n}] {l}\")\n    return n, l\n\n\ndef cells(line):\n    return [c.strip() for c in line.split(\"|\")]\n\n\ndef num(s):\n    m = re.search(r\"-?\\d[\\d,]*\\.?\\d*\", s.replace(\"−\", \"-\"))\n    return float(m.group(0).replace(\",\", \"\"))\n\n\neffects = json.load(open(os.path.join(D, \"effects.json\"), encoding=\"utf-8\"))\nfiles = {\n    \"dcr\": \"arxiv-2609.27732v1.txt\",\n    \"resea\": \"arxiv-2606.14621v1.txt\",\n    \"mw\": \"arxiv-2505.04555v3.txt\",\n    \"cs\": \"arxiv-2510.03658v1.txt\",\n}\nL = {k: read_lines(os.path.join(SRC, v)) for k, v in files.items()}\n\n# n_input_rows: wc -l semantics (newline count) for text, top-level items for JSON\nn_input_rows = {\"effects.json\": len(effects)}\nfor name in [\"README.md\"] + [\"sources/\" + v for v in files.values()]:\n    with open(os.path.join(D, name), \"rb\") as f:\n        n_input_rows[name] = f.read().count(b\"\\n\")\n\nH = {}\nissues = []\n\n# ---------------- E1: log-point ATT -> relative percent change ----------------\nprint(\"\\nE1 source lines:\")\n_, hdr = find(L[\"dcr\"], \"| 1 km | 3 km | 5 km\", files[\"dcr\"])\nn_att, att = find(L[\"dcr\"], \"ATT | -0.399\", files[\"dcr\"])\n_, pct = find(L[\"dcr\"], \"% change | -32.9%\", files[\"dcr\"])\nb1 = num(cells(att)[3])  # 5 km column\nassert cells(hdr)[3] == \"5 km\"\nv1 = 100 * (math.exp(b1) - 1)\nprinted_pct_5km = num(cells(pct)[3])\nH[\"E1\"] = {\n    \"value\": round(v1, 2),\n    \"unit\": \"percent change relative to the counterfactual hospitalisation rate\",\n    \"transformation\": f\"100*(exp(b)-1) with b = {cells(att)[3]} (log points) -> 100*(exp({b1})-1) = {v1:.4f} %; consistent with printed '% change' {cells(pct)[3]}\",\n    \"harmonisable\": True,\n    \"missing_quantity\": None,\n    \"locator\": f\"arxiv-2609.27732v1 | Table 6 | row 'ATT' | column '5 km' (line {n_att}); cross-check row '% change', column '5 km'\",\n}\nassert abs(round(v1, 1) - printed_pct_5km) < 1e-9\n\n# ---------------- E2: pp effect -> percent of control mean ----------------\nprint(\"\\nE2 source lines:\")\n_, hdr2 = find(L[\"resea\"], \"Outcome | Treatment | Control |\", files[\"resea\"])\nn_re, re_row = find(L[\"resea\"], \"Reemployment |\", files[\"resea\"])\nc = cells(re_row)\nassert cells(hdr2)[2] == \"Control\" and cells(hdr2)[4] == \"Average treatment effect estimate\"\np_ctrl = num(c[2])  # 75.0 (%)\ndelta = num(c[4].split(\";\")[0])  # 1.47 pp\nv2 = 100 * delta / p_ctrl\nH[\"E2\"] = {\n    \"value\": round(v2, 2),\n    \"unit\": \"percent of the control-group mean reemployment rate\",\n    \"transformation\": f\"100*Delta/p with Delta = {c[4].split(';')[0]} and p = {c[2]} (control mean) -> 100*{delta}/{p_ctrl} = {v2:.4f} %\",\n    \"harmonisable\": True,\n    \"missing_quantity\": None,\n    \"locator\": f\"arxiv-2606.14621v1 | Table 2 | row 'Reemployment' | columns 'Average treatment effect estimate' (Delta) and 'Control' (p) (line {n_re})\",\n}\n\n# ---------------- E3: elasticity x printed MW change ----------------\nprint(\"\\nE3 source lines:\")\nn_el, el = find(L[\"mw\"], \"Employment elasticity w.r.t MW |\", files[\"mw\"])\nn_g, gline = find(L[\"mw\"], \"% MW changes |\", files[\"mw\"])\n_, note = find(L[\"mw\"], \"Note: The table reports the effects of a minimum wage increase\", files[\"mw\"])\neta = num(cells(el)[1])\ng = num(cells(gline)[1])\nv3 = 100 * eta * g\nH[\"E3\"] = {\n    \"value\": round(v3, 2),\n    \"unit\": \"percent change in employment\",\n    \"transformation\": f\"100*eta*g with eta = {cells(el)[1]} and g = {cells(gline)[1]} (proportional MW change, read as 4.7%; the table note says 'about 4%') -> 100*{eta}*{g} = {v3:.4f} %\",\n    \"harmonisable\": True,\n    \"missing_quantity\": None,\n    \"locator\": f\"arxiv-2505.04555v3 | Table 2 | row 'Employment elasticity w.r.t MW' | column 'Estimate (SE)' (line {n_el}); row '% MW changes' | column 'Estimate (SE)' (line {n_g})\",\n}\nissues.append({\n    \"issue\": \"arxiv-2505.04555v3 Table 2: the row '% MW changes' prints 0.047, a label suggesting percent units while the value is only consistent with a proportion (4.7%); the table note describes the change as 'about 4%'.\",\n    \"handling\": \"Read 0.047 as a proportional change g = 0.047 (4.7%), which is the only reading compatible with the note ('about 4%') and the abstract's '2% employment decline'; the printed table value (not the rounded note value) is used.\"})\n\n# ---------------- E4: log-point ATT on fatal hosp. -> absolute change per 100k ----------------\nprint(\"\\nE4 source lines:\")\n_, hdr4 = find(L[\"dcr\"], \"Outcome | 1 km | 3 km | 5 km\", files[\"dcr\"])\nn_f, fat = find(L[\"dcr\"], \"Drug-related (fatal) |\", files[\"dcr\"])\nb4 = num(cells(fat)[3])\nrel4 = 100 * (math.exp(b4) - 1)\n# search the extract for any printed level of the outcome (mean/baseline rate); none expected\nlevel_hits = [l for l in L[\"dcr\"] if re.search(r\"(mean|baseline rate|pre-treatment (mean|level)|average rate)\", l, re.I)\n              and re.search(r\"fatal\", l, re.I)]\nprint(f\"Lines printing a level of the fatal-hospitalisation rate: {len(level_hits)}\")\nH[\"E4\"] = {\n    \"value\": None,\n    \"unit\": \"hospitalisations per 100,000 inhabitants per year\",\n    \"transformation\": f\"Absolute change = R0*(exp(b)-1) with b = {cells(fat)[3]} (log points), i.e. relative change 100*(exp({b4})-1) = {rel4:.4f} %; R0 (counterfactual/baseline fatal rate per 100,000 in treated regions) is not printed in the extract, so the absolute change 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 in the 5 km treated regions\",\n    \"locator\": f\"arxiv-2609.27732v1 | Table 7 | row 'Drug-related (fatal)' | column '5 km' (line {n_f})\",\n}\nassert not level_hits\n\n# ---------------- E5: LPM coefficient -> percentage points ----------------\nprint(\"\\nE5 source lines:\")\n_, hdr5 = find(L[\"cs\"], \"| (1) | (2) | (3) | (4)\", files[\"cs\"])\nn_t, tr = find(L[\"cs\"], \"Treatment |\", files[\"cs\"])\n_, mean5 = find(L[\"cs\"], \"Mean Dep. Var. |\", files[\"cs\"])\n_, pc5 = find(L[\"cs\"], \"% Change |\", files[\"cs\"])\nb5 = num(cells(tr)[4])\nv5 = 100 * b5\nH[\"E5\"] = {\n    \"value\": round(v5, 2),\n    \"unit\": \"percentage points\",\n    \"transformation\": f\"100*beta with beta = {cells(tr)[4]} (change in probability, 0-1 scale) -> 100*({b5}) = {v5:.4f} pp\",\n    \"harmonisable\": True,\n    \"missing_quantity\": None,\n    \"locator\": f\"arxiv-2510.03658v1 | Table 3 | row 'Treatment' | column '(4)' (line {n_t})\",\n}\n\n# ---------------- consistency checks on the sources (recorded as data issues) ----------------\nprint(\"\\nSource-consistency checks:\")\n# DCR abstract: fatal outcomes 'fall by 35%' vs Table 7 5 km\n_, abs_dcr = find(L[\"dcr\"], \"This paper estimates the causal effect of drug consumption room\", files[\"dcr\"])\nissues.append({\n    \"issue\": f\"arxiv-2609.27732v1: the abstract states that fatal outcomes 'fall by 35%', but Table 7 (5 km) prints {cells(fat)[3]} log points, i.e. 100*(exp({b4})-1) = {rel4:.1f}%; Section 6.1 says 'approximately 30%', consistent with Table 7. The 35% matches the overall ATT (Table 6), not the fatal outcome.\",\n    \"handling\": \"Table 7 is used as the authoritative printed estimate for E4; the abstract figure is not used.\"})\n# Table A19 vs Table 6\nn_a19, a19 = find(L[\"dcr\"], \"ATT | -0.431** | -0.438**\", files[\"dcr\"])\na19_se = L[\"dcr\"][n_a19 + 1]\nt6_se = L[\"dcr\"][n_att + 1]\nprint(f\"[{files['dcr']}:{n_att+2}] {t6_se}\")\nprint(f\"[{files['dcr']}:{n_a19+2}] {a19_se}\")\nissues.append({\n    \"issue\": f\"arxiv-2609.27732v1: the 5 km CS-DR ATT -0.431 has SE {cells(t6_se)[3]} in Table 6 but {cells(a19_se)[1]} in Table A19; the Table A19 note mentions 640 never-treated comparison regions while the table body and Tables 6-7 print 608.\",\n    \"handling\": \"Does not affect the point estimate used in E1 (identical -0.431 in both tables); Table 6 is used as the cited location. No uncertainty is propagated.\"})\n_, a19pct = find(L[\"dcr\"], \"% change | -35.0% | -35.5%\", files[\"dcr\"])\nmism = []\nfor b, p in zip(cells(a19)[1:], cells(a19pct)[1:]):\n    bb, pp = num(b), num(p)\n    implied = 100 * (math.exp(bb) - 1)\n    if abs(round(implied, 1) - pp) > 1e-9:\n        mism.append(f\"{b} -> {implied:.2f}% vs printed {p}\")\nprint(\"A19 % change mismatches:\", mism)\nif mism:\n    issues.append({\"issue\": \"arxiv-2609.27732v1 Table A19: printed '% change' differs in the last decimal from 100*(exp(ATT)-1) for: \" + \"; \".join(mism) + \".\",\n                   \"handling\": \"Minor rounding inconsistency in a robustness table not used for any item; recorded only.\"})\n# Tossou: % change row vs coefficient/mean; abstract numbers refer to a table not in the extract\nratio = b5 / num(cells(mean5)[4])\nprint(f\"Tossou col (4): beta/mean = {ratio:.4f} vs printed % Change {cells(pc5)[4]}\")\nissues.append({\n    \"issue\": f\"arxiv-2510.03658v1: Table 3 column (4) prints '% Change' {cells(pc5)[4]} while {b5}/{num(cells(mean5)[4])} = {ratio:.4f} (last-digit difference, presumably because the unrounded mean was used). The abstract's '0.0855 (a 18.79 percentage change)' refers to a 'table 2' (mean 0.455) that is not in the extract, and the abstract calls -0.1879 a 'percentage change' although the target here is percentage points.\",\n    \"handling\": \"E5 uses the Table 3 column (4) coefficient as printed and converts only the scale (x100 -> pp); no use of the abstract/Table 2 numbers or of the '% Change' row.\"})\nissues.append({\n    \"issue\": \"Rendering defects: arxiv-2510.03658v1 footnote markers are rendered inline (e.g. 'percent^9^9 9'), footnote 11 text is garbled with the main text; arxiv-2606.14621v1 renders 'p < 0.001' as 'p ¡ 0.001'.\",\n    \"handling\": \"No numbers used in E1-E5 are affected; recorded only.\"})\nissues.append({\n    \"issue\": \"arxiv-2606.14621v1 Table 2: the control-group mean (75.0%) is a raw aggregate mean, whereas the ATE (1.47 pp) adjusts for randomisation week (blocks); the abstract rounds the ATE to 1.5 pp.\",\n    \"handling\": \"As the target definition specifies 'percent of the control-group mean', the printed raw control mean 75.0% is used as p and the Table 2 ATE 1.47 pp (not the rounded 1.5) as Delta; the denominator mismatch is noted as a limitation.\"})\n\nn_h = sum(1 for v in H.values() if v[\"value\"] is not None)\nn_nh = sum(1 for v in H.values() if v[\"harmonisable\"] is False)\nresults = {\n    \"task_id\": \"F4-conf-03\",\n    \"harmonised\": H,\n    \"n_harmonised\": n_h,\n    \"n_not_harmonisable\": n_nh,\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": n_input_rows,\n    \"n_analysed\": len(effects),\n    \"random_seed\": None,\n}\nassert [e[\"id\"] for e in effects] == list(H.keys())\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(results, f, indent=2, ensure_ascii=False)\nprint(\"\\nSummary:\")\nfor k, v in H.items():\n    print(f\"  {k}: value={v['value']} [{v['unit']}] harmonisable={v['harmonisable']}\")\nprint(f\"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":2268,"cache_read_input_tokens":25437,"output_tokens":5582,"output_tokens_details":{"thinking_tokens":52},"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":2268},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqqHwrutWTizmvabmJx","requestRef":"d03c6715-1571-4cc6-94ac-362b39196a41","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"44d22d71-73bd-4e31-a6e7-02e24e37bfbd","timestamp":"2026-10-30T05:08:49.079Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0232/out/state","sessionId":"008ec3b4-46e6-4e9d-82e8-4b7207a5d6af","version":"2.1.314","gitBranch":"HEAD"}
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Reads data/ only; writes out/results.json.\nNo randomness. Run from workspace root: ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, math, re, os, hashlib\n\nROOT = os.getcwd()\nD = os.path.join(ROOT, \"data\")\nSRC = os.path.join(D, \"sources\")\n\n\ndef read_lines(path):\n    with open(path, encoding=\"utf-8\") as f:\n        return f.read().split(\"\\n\")\n\n\ndef find(lines, prefix, fname):\n    \"\"\"Return (line_no, line) of the unique line starting with prefix; print it.\"\"\"\n    hits = [(i + 1, l) for i, l in enumerate(lines) if l.startswith(prefix)]\n    assert len(hits) == 1, (fname, prefix, hits)\n    n, l = hits[0]\n    print(f\"[{fname}:{n}] {l}\")\n    return n, l\n\n\ndef cells(line):\n    return [c.strip() for c in line.split(\"|\")]\n\n\ndef num(s):\n    m = re.search(r\"-?\\d[\\d,]*\\.?\\d*\", s.replace(\"−\", \"-\"))\n    return float(m.group(0).replace(\",\", \"\"))\n\n\neffects = json.load(open(os.path.join(D, \"effects.json\"), encoding=\"utf-8\"))\nfiles = {\n    \"dcr\": \"arxiv-2609.27732v1.txt\",\n    \"resea\": \"arxiv-2606.14621v1.txt\",\n    \"mw\": \"arxiv-2505.04555v3.txt\",\n    \"cs\": \"arxiv-2510.03658v1.txt\",\n}\nL = {k: read_lines(os.path.join(SRC, v)) for k, v in files.items()}\n\n# n_input_rows: wc -l semantics (newline count) for text, top-level items for JSON\nn_input_rows = {\"effects.json\": len(effects)}\nfor name in [\"README.md\"] + [\"sources/\" + v for v in files.values()]:\n    with open(os.path.join(D, name), \"rb\") as f:\n        n_input_rows[name] = f.read().count(b\"\\n\")\n\nH = {}\nissues = []\n\n# ---------------- E1: log-point ATT -> relative percent change ----------------\nprint(\"\\nE1 source lines:\")\n_, hdr = find(L[\"dcr\"], \"| 1 km | 3 km | 5 km\", files[\"dcr\"])\nn_att, att = find(L[\"dcr\"], \"ATT | -0.399\", files[\"dcr\"])\n_, pct = find(L[\"dcr\"], \"% change | -32.9%\", files[\"dcr\"])\nb1 = num(cells(att)[3])  # 5 km column\nassert cells(hdr)[3] == \"5 km\"\nv1 = 100 * (math.exp(b1) - 1)\nprinted_pct_5km = num(cells(pct)[3])\nH[\"E1\"] = {\n    \"value\": round(v1, 2),\n    \"unit\": \"percent change relative to the counterfactual hospitalisation rate\",\n    \"transformation\": f\"100*(exp(b)-1) with b = {cells(att)[3]} (log points) -> 100*(exp({b1})-1) = {v1:.4f} %; consistent with printed '% change' {cells(pct)[3]}\",\n    \"harmonisable\": True,\n    \"missing_quantity\": None,\n    \"locator\": f\"arxiv-2609.27732v1 | Table 6 | row 'ATT' | column '5 km' (line {n_att}); cross-check row '% change', column '5 km'\",\n}\nassert abs(round(v1, 1) - printed_pct_5km) < 1e-9\n\n# ---------------- E2: pp effect -> percent of control mean ----------------\nprint(\"\\nE2 source lines:\")\n_, hdr2 = find(L[\"resea\"], \"Outcome | Treatment | Control |\", files[\"resea\"])\nn_re, re_row = find(L[\"resea\"], \"Reemployment |\", files[\"resea\"])\nc = cells(re_row)\nassert cells(hdr2)[2] == \"Control\" and cells(hdr2)[4] == \"Average treatment effect estimate\"\np_ctrl = num(c[2])  # 75.0 (%)\ndelta = num(c[4].split(\";\")[0])  # 1.47 pp\nv2 = 100 * delta / p_ctrl\nH[\"E2\"] = {\n    \"value\": round(v2, 2),\n    \"unit\": \"percent of the control-group mean reemployment rate\",\n    \"transformation\": f\"100*Delta/p with Delta = {c[4].split(';')[0]} and p = {c[2]} (control mean) -> 100*{delta}/{p_ctrl} = {v2:.4f} %\",\n    \"harmonisable\": True,\n    \"missing_quantity\": None,\n    \"locator\": f\"arxiv-2606.14621v1 | Table 2 | row 'Reemployment' | columns 'Average treatment effect estimate' (Delta) and 'Control' (p) (line {n_re})\",\n}\n\n# ---------------- E3: elasticity x printed MW change ----------------\nprint(\"\\nE3 source lines:\")\nn_el, el = find(L[\"mw\"], \"Employment elasticity w.r.t MW |\", files[\"mw\"])\nn_g, gline = find(L[\"mw\"], \"% MW changes |\", files[\"mw\"])\n_, note = find(L[\"mw\"], \"Note: The table reports the effects of a minimum wage increase\", files[\"mw\"])\neta = num(cells(el)[1])\ng = num(cells(gline)[1])\nv3 = 100 * eta * g\nH[\"E3\"] = {\n    \"value\": round(v3, 2),\n    \"unit\": \"percent change in employment\",\n    \"transformation\": f\"100*eta*g with eta = {cells(el)[1]} and g = {cells(gline)[1]} (proportional MW change, read as 4.7%; the table note says 'about 4%') -> 100*{eta}*{g} = {v3:.4f} %\",\n    \"harmonisable\": True,\n    \"missing_quantity\": None,\n    \"locator\": f\"arxiv-2505.04555v3 | Table 2 | row 'Employment elasticity w.r.t MW' | column 'Estimate (SE)' (line {n_el}); row '% MW changes' | column 'Estimate (SE)' (line {n_g})\",\n}\nissues.append({\n    \"issue\": \"arxiv-2505.04555v3 Table 2: the row '% MW changes' prints 0.047, a label suggesting percent units while the value is only consistent with a proportion (4.7%); the table note describes the change as 'about 4%'.\",\n    \"handling\": \"Read 0.047 as a proportional change g = 0.047 (4.7%), which is the only reading compatible with the note ('about 4%') and the abstract's '2% employment decline'; the printed table value (not the rounded note value) is used.\"})\n\n# ---------------- E4: log-point ATT on fatal hosp. -> absolute change per 100k ----------------\nprint(\"\\nE4 source lines:\")\n_, hdr4 = find(L[\"dcr\"], \"Outcome | 1 km | 3 km | 5 km\", files[\"dcr\"])\nn_f, fat = find(L[\"dcr\"], \"Drug-related (fatal) |\", files[\"dcr\"])\nb4 = num(cells(fat)[3])\nrel4 = 100 * (math.exp(b4) - 1)\n# search the extract for any printed level of the outcome (mean/baseline rate); none expected\nlevel_hits = [l for l in L[\"dcr\"] if re.search(r\"(mean|baseline rate|pre-treatment (mean|level)|average rate)\", l, re.I)\n              and re.search(r\"fatal\", l, re.I)]\nprint(f\"Lines printing a level of the fatal-hospitalisation rate: {len(level_hits)}\")\nH[\"E4\"] = {\n    \"value\": None,\n    \"unit\": \"hospitalisations per 100,000 inhabitants per year\",\n    \"transformation\": f\"Absolute change = R0*(exp(b)-1) with b = {cells(fat)[3]} (log points), i.e. relative change 100*(exp({b4})-1) = {rel4:.4f} %; R0 (counterfactual/baseline fatal rate per 100,000 in treated regions) is not printed in the extract, so the absolute change 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 in the 5 km treated regions\",\n    \"locator\": f\"arxiv-2609.27732v1 | Table 7 | row 'Drug-related (fatal)' | column '5 km' (line {n_f})\",\n}\nassert not level_hits\n\n# ---------------- E5: LPM coefficient -> percentage points ----------------\nprint(\"\\nE5 source lines:\")\n_, hdr5 = find(L[\"cs\"], \"| (1) | (2) | (3) | (4)\", files[\"cs\"])\nn_t, tr = find(L[\"cs\"], \"Treatment |\", files[\"cs\"])\n_, mean5 = find(L[\"cs\"], \"Mean Dep. Var. |\", files[\"cs\"])\n_, pc5 = find(L[\"cs\"], \"% Change |\", files[\"cs\"])\nb5 = num(cells(tr)[4])\nv5 = 100 * b5\nH[\"E5\"] = {\n    \"value\": round(v5, 2),\n    \"unit\": \"percentage points\",\n    \"transformation\": f\"100*beta with beta = {cells(tr)[4]} (change in probability, 0-1 scale) -> 100*({b5}) = {v5:.4f} pp\",\n    \"harmonisable\": True,\n    \"missing_quantity\": None,\n    \"locator\": f\"arxiv-2510.03658v1 | Table 3 | row 'Treatment' | column '(4)' (line {n_t})\",\n}\n\n# ---------------- consistency checks on the sources (recorded as data issues) ----------------\nprint(\"\\nSource-consistency checks:\")\n# DCR abstract: fatal outcomes 'fall by 35%' vs Table 7 5 km\n_, abs_dcr = find(L[\"dcr\"], \"This paper estimates the causal effect of drug consumption room\", files[\"dcr\"])\nissues.append({\n    \"issue\": f\"arxiv-2609.27732v1: the abstract states that fatal outcomes 'fall by 35%', but Table 7 (5 km) prints {cells(fat)[3]} log points, i.e. 100*(exp({b4})-1) = {rel4:.1f}%; Section 6.1 says 'approximately 30%', consistent with Table 7. The 35% matches the overall ATT (Table 6), not the fatal outcome.\",\n    \"handling\": \"Table 7 is used as the authoritative printed estimate for E4; the abstract figure is not used.\"})\n# Table A19 vs Table 6\nn_a19, a19 = find(L[\"dcr\"], \"ATT | -0.431** | -0.438**\", files[\"dcr\"])\na19_se = L[\"dcr\"][n_a19 + 1]\nt6_se = L[\"dcr\"][n_att + 1]\nprint(f\"[{files['dcr']}:{n_att+2}] {t6_se}\")\nprint(f\"[{files['dcr']}:{n_a19+2}] {a19_se}\")\nissues.append({\n    \"issue\": f\"arxiv-2609.27732v1: the 5 km CS-DR ATT -0.431 has SE {cells(t6_se)[3]} in Table 6 but {cells(a19_se)[1]} in Table A19; the Table A19 note mentions 640 never-treated comparison regions while the table body and Tables 6-7 print 608.\",\n    \"handling\": \"Does not affect the point estimate used in E1 (identical -0.431 in both tables); Table 6 is used as the cited location. No uncertainty is propagated.\"})\n_, a19pct = find(L[\"dcr\"], \"% change | -35.0% | -35.5%\", files[\"dcr\"])\nmism = []\nfor b, p in zip(cells(a19)[1:], cells(a19pct)[1:]):\n    bb, pp = num(b), num(p)\n    implied = 100 * (math.exp(bb) - 1)\n    if abs(round(implied, 1) - pp) > 1e-9:\n        mism.append(f\"{b} -> {implied:.2f}% vs printed {p}\")\nprint(\"A19 % change mismatches:\", mism)\nif mism:\n    issues.append({\"issue\": \"arxiv-2609.27732v1 Table A19: printed '% change' differs in the last decimal from 100*(exp(ATT)-1) for: \" + \"; \".join(mism) + \".\",\n                   \"handling\": \"Minor rounding inconsistency in a robustness table not used for any item; recorded only.\"})\n# Tossou: % change row vs coefficient/mean; abstract numbers refer to a table not in the extract\nratio = b5 / num(cells(mean5)[4])\nprint(f\"Tossou col (4): beta/mean = {ratio:.4f} vs printed % Change {cells(pc5)[4]}\")\nissues.append({\n    \"issue\": f\"arxiv-2510.03658v1: Table 3 column (4) prints '% Change' {cells(pc5)[4]} while {b5}/{num(cells(mean5)[4])} = {ratio:.4f} (last-digit difference, presumably because the unrounded mean was used). The abstract's '0.0855 (a 18.79 percentage change)' refers to a 'table 2' (mean 0.455) that is not in the extract, and the abstract calls -0.1879 a 'percentage change' although the target here is percentage points.\",\n    \"handling\": \"E5 uses the Table 3 column (4) coefficient as printed and converts only the scale (x100 -> pp); no use of the abstract/Table 2 numbers or of the '% Change' row.\"})\nissues.append({\n    \"issue\": \"Rendering defects: arxiv-2510.03658v1 footnote markers are rendered inline (e.g. 'percent^9^9 9'), footnote 11 text is garbled with the main text; arxiv-2606.14621v1 renders 'p < 0.001' as 'p ¡ 0.001'.\",\n    \"handling\": \"No numbers used in E1-E5 are affected; recorded only.\"})\nissues.append({\n    \"issue\": \"arxiv-2606.14621v1 Table 2: the control-group mean (75.0%) is a raw aggregate mean, whereas the ATE (1.47 pp) adjusts for randomisation week (blocks); the abstract rounds the ATE to 1.5 pp.\",\n    \"handling\": \"As the target definition specifies 'percent of the control-group mean', the printed raw control mean 75.0% is used as p and the Table 2 ATE 1.47 pp (not the rounded 1.5) as Delta; the denominator mismatch is noted as a limitation.\"})\n\nn_h = sum(1 for v in H.values() if v[\"value\"] is not None)\nn_nh = sum(1 for v in H.values() if v[\"harmonisable\"] is False)\nresults = {\n    \"task_id\": \"F4-conf-03\",\n    \"harmonised\": H,\n    \"n_harmonised\": n_h,\n    \"n_not_harmonisable\": n_nh,\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": n_input_rows,\n    \"n_analysed\": len(effects),\n    \"random_seed\": None,\n}\nassert [e[\"id\"] for e in effects] == list(H.keys())\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(results, f, indent=2, ensure_ascii=False)\nprint(\"\\nSummary:\")\nfor k, v in H.items():\n    print(f\"  {k}: value={v['value']} [{v['unit']}] harmonisable={v['harmonisable']}\")\nprint(f\"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"}}]}}
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{"parentUuid":"38e06a64-042e-4ea2-bf3c-3b561bb71a56","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqttAEnzHyWpgwgyDvz","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01NvwFhnQ7h64FfscxAzYyjF","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F4-conf-03: harmonise five printed effect sizes. Reads data/ only; writes out/results.json.\nNo randomness. Run from workspace root: ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, math, re, os\n\nROOT = os.getcwd()\nD = os.path.join(ROOT, \"data\")\nSRC = os.path.join(D, \"sources\")\n\n\ndef read_lines(path):\n    with open(path, encoding=\"utf-8\") as f:\n        return f.read().split(\"\\n\")\n\n\ndef find(lines, prefix, fname):\n    \"\"\"Return (line_no, line) of the unique line starting with prefix; print it.\"\"\"\n    hits = [(i + 1, l) for i, l in enumerate(lines) if l.startswith(prefix)]\n    assert len(hits) == 1, (fname, prefix, hits)\n    n, l = hits[0]\n    print(f\"[{fname}:{n}] {l}\")\n    return n, l\n\n\ndef cells(line):\n    return [c.strip() for c in line.split(\"|\")]\n\n\ndef num(s):\n    m = re.search(r\"-?\\d[\\d,]*\\.?\\d*\", s.replace(\"−\", \"-\"))\n    return float(m.group(0).replace(\",\", \"\"))\n\n\neffects = json.load(open(os.path.join(D, \"effects.json\"), encoding=\"utf-8\"))\nfiles = {\n    \"dcr\": \"arxiv-2609.27732v1.txt\",\n    \"resea\": \"arxiv-2606.14621v1.txt\",\n    \"mw\": \"arxiv-2505.04555v3.txt\",\n    \"cs\": \"arxiv-2510.03658v1.txt\",\n}\nL = {k: read_lines(os.path.join(SRC, v)) for k, v in files.items()}\n\n# n_input_rows: wc -l semantics (newline count) for text, top-level items for JSON\nn_input_rows = {\"effects.json\": len(effects)}\nfor name in [\"README.md\"] + [\"sources/\" + v for v in files.values()]:\n    with open(os.path.join(D, name), \"rb\") as f:\n        n_input_rows[name] = f.read().count(b\"\\n\")\n\nH = {}\nissues = []\n\n# ---------------- E1: log-point ATT -> relative percent change ----------------\nprint(\"\\nE1 source lines:\")\n_, hdr = find(L[\"dcr\"], \"| 1 km | 3 km | 5 km\", files[\"dcr\"])\nn_att, att = find(L[\"dcr\"], \"ATT | -0.399\", files[\"dcr\"])\n_, pct = find(L[\"dcr\"], \"% change | -32.9%\", files[\"dcr\"])\nb1 = num(cells(att)[3])  # 5 km column\nassert cells(hdr)[3] == \"5 km\"\nv1 = 100 * (math.exp(b1) - 1)\nprinted_pct_5km = num(cells(pct)[3])\nH[\"E1\"] = {\n    \"value\": round(v1, 2),\n    \"unit\": \"percent change relative to the counterfactual hospitalisation rate\",\n    \"transformation\": f\"100*(exp(b)-1) with b = {cells(att)[3]} (log points) -> 100*(exp({b1})-1) = {v1:.4f} %; consistent with printed '% change' {cells(pct)[3]}\",\n    \"harmonisable\": True,\n    \"missing_quantity\": None,\n    \"locator\": f\"arxiv-2609.27732v1 | Table 6 | row 'ATT' | column '5 km' (line {n_att}); cross-check row '% change', column '5 km'\",\n}\nassert abs(round(v1, 1) - printed_pct_5km) < 1e-9\n\n# ---------------- E2: pp effect -> percent of control mean ----------------\nprint(\"\\nE2 source lines:\")\n_, hdr2 = find(L[\"resea\"], \"Outcome | Treatment | Control |\", files[\"resea\"])\nn_re, re_row = find(L[\"resea\"], \"Reemployment |\", files[\"resea\"])\nc = cells(re_row)\nassert cells(hdr2)[2] == \"Control\" and cells(hdr2)[4] == \"Average treatment effect estimate\"\np_ctrl = num(c[2])  # 75.0 (%)\ndelta = num(c[4].split(\";\")[0])  # 1.47 pp\nv2 = 100 * delta / p_ctrl\nH[\"E2\"] = {\n    \"value\": round(v2, 2),\n    \"unit\": \"percent of the control-group mean reemployment rate\",\n    \"transformation\": f\"100*Delta/p with Delta = {c[4].split(';')[0]} and p = {c[2]} (control mean) -> 100*{delta}/{p_ctrl} = {v2:.4f} %\",\n    \"harmonisable\": True,\n    \"missing_quantity\": None,\n    \"locator\": f\"arxiv-2606.14621v1 | Table 2 | row 'Reemployment' | columns 'Average treatment effect estimate' (Delta) and 'Control' (p) (line {n_re})\",\n}\n\n# ---------------- E3: elasticity x printed MW change ----------------\nprint(\"\\nE3 source lines:\")\nn_el, el = find(L[\"mw\"], \"Employment elasticity w.r.t MW |\", files[\"mw\"])\nn_g, gline = find(L[\"mw\"], \"% MW changes |\", files[\"mw\"])\n_, note = find(L[\"mw\"], \"Note: The table reports the effects of a minimum wage increase\", files[\"mw\"])\neta = num(cells(el)[1])\ng = num(cells(gline)[1])\nv3 = 100 * eta * g\nH[\"E3\"] = {\n    \"value\": round(v3, 2),\n    \"unit\": \"percent change in employment\",\n    \"transformation\": f\"100*eta*g with eta = {cells(el)[1]} and g = {cells(gline)[1]} (proportional MW change, read as 4.7%; the table note says 'about 4%') -> 100*{eta}*{g} = {v3:.4f} %\",\n    \"harmonisable\": True,\n    \"missing_quantity\": None,\n    \"locator\": f\"arxiv-2505.04555v3 | Table 2 | row 'Employment elasticity w.r.t MW' | column 'Estimate (SE)' (line {n_el}); row '% MW changes' | column 'Estimate (SE)' (line {n_g})\",\n}\nissues.append({\n    \"issue\": \"arxiv-2505.04555v3 Table 2: the row '% MW changes' prints 0.047, a label suggesting percent units while the value is only consistent with a proportion (4.7%); the table note describes the change as 'about 4%'.\",\n    \"handling\": \"Read 0.047 as a proportional change g = 0.047 (4.7%), the only reading compatible with the note ('about 4%'); the printed table value (not the rounded note value) is used.\"})\n\n# ---------------- E4: log-point ATT on fatal hosp. -> absolute change per 100k ----------------\nprint(\"\\nE4 source lines:\")\n_, hdr4 = find(L[\"dcr\"], \"Outcome | 1 km | 3 km | 5 km\", files[\"dcr\"])\nn_f, fat = find(L[\"dcr\"], \"Drug-related (fatal) |\", files[\"dcr\"])\nb4 = num(cells(fat)[3])\nrel4 = 100 * (math.exp(b4) - 1)\n# search the extract for any printed level of the outcome (mean/baseline rate); none expected\nlevel_hits = [l for l in L[\"dcr\"] if re.search(r\"(mean|baseline rate|pre-treatment (mean|level)|average rate)\", l, re.I)\n              and re.search(r\"fatal\", l, re.I)]\nprint(f\"Lines printing a level of the fatal-hospitalisation rate: {len(level_hits)}\")\nH[\"E4\"] = {\n    \"value\": None,\n    \"unit\": \"hospitalisations per 100,000 inhabitants per year\",\n    \"transformation\": f\"Absolute change = R0*(exp(b)-1) with b = {cells(fat)[3]} (log points), i.e. relative change 100*(exp({b4})-1) = {rel4:.4f} %; R0 (counterfactual/baseline fatal rate per 100,000 in treated regions) is not printed in the extract, so the absolute change 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 in the 5 km treated regions\",\n    \"locator\": f\"arxiv-2609.27732v1 | Table 7 | row 'Drug-related (fatal)' | column '5 km' (line {n_f})\",\n}\nassert not level_hits\n\n# ---------------- E5: LPM coefficient -> percentage points ----------------\nprint(\"\\nE5 source lines:\")\n_, hdr5 = find(L[\"cs\"], \"| (1) | (2) | (3) | (4)\", files[\"cs\"])\nn_t, tr = find(L[\"cs\"], \"Treatment |\", files[\"cs\"])\n_, mean5 = find(L[\"cs\"], \"Mean Dep. Var. |\", files[\"cs\"])\n_, pc5 = find(L[\"cs\"], \"% Change |\", files[\"cs\"])\nb5 = num(cells(tr)[4])\nv5 = 100 * b5\nH[\"E5\"] = {\n    \"value\": round(v5, 2),\n    \"unit\": \"percentage points\",\n    \"transformation\": f\"100*beta with beta = {cells(tr)[4]} (change in probability, 0-1 scale) -> 100*({b5}) = {v5:.4f} pp\",\n    \"harmonisable\": True,\n    \"missing_quantity\": None,\n    \"locator\": f\"arxiv-2510.03658v1 | Table 3 | row 'Treatment' | column '(4)' (line {n_t})\",\n}\n\n# ---------------- consistency checks on the sources (recorded as data issues) ----------------\nprint(\"\\nSource-consistency checks:\")\n_, abs_dcr = find(L[\"dcr\"], \"This paper estimates the causal effect of drug consumption room\", files[\"dcr\"])\nissues.append({\n    \"issue\": f\"arxiv-2609.27732v1: the abstract states that fatal outcomes 'fall by 35%', but Table 7 (5 km) prints {cells(fat)[3]} log points, i.e. 100*(exp({b4})-1) = {rel4:.1f}%; Section 6.1 says 'approximately 30%', consistent with Table 7. The 35% matches the overall ATT (Table 6), not the fatal outcome.\",\n    \"handling\": \"Table 7 is used as the authoritative printed estimate for E4; the abstract figure is not used.\"})\nn_a19, a19 = find(L[\"dcr\"], \"ATT | -0.431** | -0.438**\", files[\"dcr\"])\na19_se = L[\"dcr\"][n_a19 + 1]\nt6_se = L[\"dcr\"][n_att + 1]\nprint(f\"[{files['dcr']}:{n_att+2}] {t6_se}\")\nprint(f\"[{files['dcr']}:{n_a19+2}] {a19_se}\")\nissues.append({\n    \"issue\": f\"arxiv-2609.27732v1: the 5 km CS-DR ATT -0.431 has SE {cells(t6_se)[3]} in Table 6 but {cells(a19_se)[1]} in Table A19; the Table A19 note mentions 640 never-treated comparison regions while the table body and Tables 6-7 print 608.\",\n    \"handling\": \"Does not affect the point estimate used in E1 (identical -0.431 in both tables); Table 6 is used as the cited location. No uncertainty is propagated.\"})\n_, a19pct = find(L[\"dcr\"], \"% change | -35.0% | -35.5%\", files[\"dcr\"])\nmism = []\nfor b, p in zip(cells(a19)[1:], cells(a19pct)[1:]):\n    bb, pp = num(b), num(p)\n    implied = 100 * (math.exp(bb) - 1)\n    if abs(round(implied, 1) - pp) > 1e-9:\n        mism.append(f\"{b} -> {implied:.2f}% vs printed {p}\")\nprint(\"A19 % change mismatches:\", mism)\nif mism:\n    issues.append({\"issue\": \"arxiv-2609.27732v1 Table A19: printed '% change' differs in the last decimal from 100*(exp(ATT)-1) for: \" + \"; \".join(mism) + \".\",\n                   \"handling\": \"Minor rounding inconsistency in a robustness table not used for any item; recorded only.\"})\nratio = b5 / num(cells(mean5)[4])\nprint(f\"Tossou col (4): beta/mean = {ratio:.4f} vs printed % Change {cells(pc5)[4]}\")\nissues.append({\n    \"issue\": f\"arxiv-2510.03658v1: Table 3 column (4) prints '% Change' {cells(pc5)[4]} while {b5}/{num(cells(mean5)[4])} = {ratio:.4f} (last-digit difference, presumably from an unrounded mean). The abstract's '0.0855 (a 18.79 percentage change)' refers to a 'table 2' (mean 0.455) that is not in the extract.\",\n    \"handling\": \"E5 uses the Table 3 column (4) coefficient as printed and converts only the scale (x100 -> pp); the abstract/Table 2 numbers and the '% Change' row are not used.\"})\nissues.append({\n    \"issue\": \"Rendering defects: arxiv-2510.03658v1 footnote markers are rendered inline (e.g. 'percent^9^9 9') and footnote 11 is interleaved with main text; arxiv-2606.14621v1 renders 'p < 0.001' as 'p ¡ 0.001'.\",\n    \"handling\": \"No numbers used in E1-E5 are affected; recorded only.\"})\nissues.append({\n    \"issue\": \"arxiv-2606.14621v1 Table 2: the control-group mean (75.0%) is a raw aggregate mean, whereas the ATE (1.47 pp) adjusts for randomisation week (blocks); the abstract rounds the ATE to 1.5 pp.\",\n    \"handling\": \"Following the target definition ('percent of the control-group mean'), the printed control mean 75.0% is used as p and the Table 2 ATE 1.47 pp (not the rounded 1.5) as Delta; the denominator mismatch is noted as a limitation.\"})\n\nn_h = sum(1 for v in H.values() if v[\"value\"] is not None)\nn_nh = sum(1 for v in H.values() if v[\"harmonisable\"] is False)\nresults = {\n    \"task_id\": \"F4-conf-03\",\n    \"harmonised\": H,\n    \"n_harmonised\": n_h,\n    \"n_not_harmonisable\": n_nh,\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": n_input_rows,\n    \"n_analysed\": len(effects),\n    \"random_seed\": None,\n}\nassert [e[\"id\"] for e in effects] == list(H.keys())\nassert n_h + n_nh == len(effects)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\", encoding=\"utf-8\") as f:\n    json.dump(results, f, indent=2, ensure_ascii=False)\nprint(\"\\nSummary:\")\nfor k, v in H.items():\n    print(f\"  {k}: value={v['value']} [{v['unit']}] harmonisable={v['harmonisable']}\")\nprint(f\"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 from root"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":140,"cache_read_input_tokens":33599,"output_tokens":5354,"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":140},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqtssdQ5JYaduZ1egJQ","requestRef":"337b7825-3c69-445a-8a60-bf7a0db0a3dc","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"aba8babf-5bb8-4486-837c-df3c58e7f58b","timestamp":"2026-10-30T05:09:28.463Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0232","sessionId":"008ec3b4-46e6-4e9d-82e8-4b7207a5d6af","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"aba8babf-5bb8-4486-837c-df3c58e7f58b","isSidechain":false,"promptId":"5bbd5ec8-4c6a-4ce7-b9b5-627abc92134d","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\n\nE1 source lines:\n[arxiv-2609.27732v1.txt:34] | 1 km | 3 km | 5 km\n[arxiv-2609.27732v1.txt:36] ATT | -0.399 | -0.283 | -0.431**\n[arxiv-2609.27732v1.txt:40] % change | -32.9% | -24.7% | -35.0%\n\nE2 source lines:\n[arxiv-2606.14621v1.txt:27] Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\nTraceback (most recent call last):\n  File \"/Users/simon-pierreboucher/wp29/runs/RC0232/out/analysis.py\", line 73, in <module>\n    n_re, re_row = find(L[\"resea\"], \"Reemployment |\", files[\"resea\"])\n                   ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/runs/RC0232/out/analysis.py\", line 18, in find\n    assert len(hits) == 1, (fname, prefix, hits)\n           ^^^^^^^^^^^^^^\nAssertionError: ('arxiv-2606.14621v1.txt', 'Reemployment |', [(29, 'Reemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]'), (53, 'Reemployment | 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]')])","is_error":true,"tool_use_id":"toolu_01NvwFhnQ7h64FfscxAzYyjF"}]},"uuid":"56411f19-455a-4bbd-9c2d-611a3ce45b0b","timestamp":"2026-10-30T05:09:28.542Z","toolUseResult":"Error: Exit code 1\n\nE1 source lines:\n[arxiv-2609.27732v1.txt:34] | 1 km | 3 km | 5 km\n[arxiv-2609.27732v1.txt:36] ATT | -0.399 | -0.283 | -0.431**\n[arxiv-2609.27732v1.txt:40] % change | -32.9% | -24.7% | -35.0%\n\nE2 source lines:\n[arxiv-2606.14621v1.txt:27] Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\nTraceback (most recent call last):\n  File \"/Users/simon-pierreboucher/wp29/runs/RC0232/out/analysis.py\", line 73, in <module>\n    n_re, re_row = find(L[\"resea\"], \"Reemployment |\", files[\"resea\"])\n                   ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/runs/RC0232/out/analysis.py\", line 18, in find\n    assert len(hits) == 1, (fname, prefix, hits)\n           ^^^^^^^^^^^^^^\nAssertionError: ('arxiv-2606.14621v1.txt', 'Reemployment |', [(29, 'Reemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]'), (53, 'Reemployment | 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]')])","sandboxed":false,"sourceToolAssistantUUID":"aba8babf-5bb8-4486-837c-df3c58e7f58b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0232","sessionId":"008ec3b4-46e6-4e9d-82e8-4b7207a5d6af","version":"2.1.314","gitBranch":"HEAD"}
{"type":"api-request-blob","sessionId":"008ec3b4-46e6-4e9d-82e8-4b7207a5d6af","hash":"e1412c0831bc1a93029ec08dd5f9667f982398de01f4b5a615e594d142726000","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01NvwFhnQ7h64FfscxAzYyjF","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F4-conf-03: harmonise five printed effect sizes. Reads data/ only; writes out/results.json.\nNo randomness. Run from workspace root: ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, math, re, os\n\nROOT = os.getcwd()\nD = os.path.join(ROOT, \"data\")\nSRC = os.path.join(D, \"sources\")\n\n\ndef read_lines(path):\n    with open(path, encoding=\"utf-8\") as f:\n        return f.read().split(\"\\n\")\n\n\ndef find(lines, prefix, fname):\n    \"\"\"Return (line_no, line) of the unique line starting with prefix; print it.\"\"\"\n    hits = [(i + 1, l) for i, l in enumerate(lines) if l.startswith(prefix)]\n    assert len(hits) == 1, (fname, prefix, hits)\n    n, l = hits[0]\n    print(f\"[{fname}:{n}] {l}\")\n    return n, l\n\n\ndef cells(line):\n    return [c.strip() for c in line.split(\"|\")]\n\n\ndef num(s):\n    m = re.search(r\"-?\\d[\\d,]*\\.?\\d*\", s.replace(\"−\", \"-\"))\n    return float(m.group(0).replace(\",\", \"\"))\n\n\neffects = json.load(open(os.path.join(D, \"effects.json\"), encoding=\"utf-8\"))\nfiles = {\n    \"dcr\": \"arxiv-2609.27732v1.txt\",\n    \"resea\": \"arxiv-2606.14621v1.txt\",\n    \"mw\": \"arxiv-2505.04555v3.txt\",\n    \"cs\": \"arxiv-2510.03658v1.txt\",\n}\nL = {k: read_lines(os.path.join(SRC, v)) for k, v in files.items()}\n\n# n_input_rows: wc -l semantics (newline count) for text, top-level items for JSON\nn_input_rows = {\"effects.json\": len(effects)}\nfor name in [\"README.md\"] + [\"sources/\" + v for v in files.values()]:\n    with open(os.path.join(D, name), \"rb\") as f:\n        n_input_rows[name] = f.read().count(b\"\\n\")\n\nH = {}\nissues = []\n\n# ---------------- E1: log-point ATT -> relative percent change ----------------\nprint(\"\\nE1 source lines:\")\n_, hdr = find(L[\"dcr\"], \"| 1 km | 3 km | 5 km\", files[\"dcr\"])\nn_att, att = find(L[\"dcr\"], \"ATT | -0.399\", files[\"dcr\"])\n_, pct = find(L[\"dcr\"], \"% change | -32.9%\", files[\"dcr\"])\nb1 = num(cells(att)[3])  # 5 km column\nassert cells(hdr)[3] == \"5 km\"\nv1 = 100 * (math.exp(b1) - 1)\nprinted_pct_5km = num(cells(pct)[3])\nH[\"E1\"] = {\n    \"value\": round(v1, 2),\n    \"unit\": \"percent change relative to the counterfactual hospitalisation rate\",\n    \"transformation\": f\"100*(exp(b)-1) with b = {cells(att)[3]} (log points) -> 100*(exp({b1})-1) = {v1:.4f} %; consistent with printed '% change' {cells(pct)[3]}\",\n    \"harmonisable\": True,\n    \"missing_quantity\": None,\n    \"locator\": f\"arxiv-2609.27732v1 | Table 6 | row 'ATT' | column '5 km' (line {n_att}); cross-check row '% change', column '5 km'\",\n}\nassert abs(round(v1, 1) - printed_pct_5km) < 1e-9\n\n# ---------------- E2: pp effect -> percent of control mean ----------------\nprint(\"\\nE2 source lines:\")\n_, hdr2 = find(L[\"resea\"], \"Outcome | Treatment | Control |\", files[\"resea\"])\nn_re, re_row = find(L[\"resea\"], \"Reemployment |\", files[\"resea\"])\nc = cells(re_row)\nassert cells(hdr2)[2] == \"Control\" and cells(hdr2)[4] == \"Average treatment effect estimate\"\np_ctrl = num(c[2])  # 75.0 (%)\ndelta = num(c[4].split(\";\")[0])  # 1.47 pp\nv2 = 100 * delta / p_ctrl\nH[\"E2\"] = {\n    \"value\": round(v2, 2),\n    \"unit\": \"percent of the control-group mean reemployment rate\",\n    \"transformation\": f\"100*Delta/p with Delta = {c[4].split(';')[0]} and p = {c[2]} (control mean) -> 100*{delta}/{p_ctrl} = {v2:.4f} %\",\n    \"harmonisable\": True,\n    \"missing_quantity\": None,\n    \"locator\": f\"arxiv-2606.14621v1 | Table 2 | row 'Reemployment' | columns 'Average treatment effect estimate' (Delta) and 'Control' (p) (line {n_re})\",\n}\n\n# ---------------- E3: elasticity x printed MW change ----------------\nprint(\"\\nE3 source lines:\")\nn_el, el = find(L[\"mw\"], \"Employment elasticity w.r.t MW |\", files[\"mw\"])\nn_g, gline = find(L[\"mw\"], \"% MW changes |\", files[\"mw\"])\n_, note = find(L[\"mw\"], \"Note: The table reports the effects of a minimum wage increase\", files[\"mw\"])\neta = num(cells(el)[1])\ng = num(cells(gline)[1])\nv3 = 100 * eta * g\nH[\"E3\"] = {\n    \"value\": round(v3, 2),\n    \"unit\": \"percent change in employment\",\n    \"transformation\": f\"100*eta*g with eta = {cells(el)[1]} and g = {cells(gline)[1]} (proportional MW change, read as 4.7%; the table note says 'about 4%') -> 100*{eta}*{g} = {v3:.4f} %\",\n    \"harmonisable\": True,\n    \"missing_quantity\": None,\n    \"locator\": f\"arxiv-2505.04555v3 | Table 2 | row 'Employment elasticity w.r.t MW' | column 'Estimate (SE)' (line {n_el}); row '% MW changes' | column 'Estimate (SE)' (line {n_g})\",\n}\nissues.append({\n    \"issue\": \"arxiv-2505.04555v3 Table 2: the row '% MW changes' prints 0.047, a label suggesting percent units while the value is only consistent with a proportion (4.7%); the table note describes the change as 'about 4%'.\",\n    \"handling\": \"Read 0.047 as a proportional change g = 0.047 (4.7%), the only reading compatible with the note ('about 4%'); the printed table value (not the rounded note value) is used.\"})\n\n# ---------------- E4: log-point ATT on fatal hosp. -> absolute change per 100k ----------------\nprint(\"\\nE4 source lines:\")\n_, hdr4 = find(L[\"dcr\"], \"Outcome | 1 km | 3 km | 5 km\", files[\"dcr\"])\nn_f, fat = find(L[\"dcr\"], \"Drug-related (fatal) |\", files[\"dcr\"])\nb4 = num(cells(fat)[3])\nrel4 = 100 * (math.exp(b4) - 1)\n# search the extract for any printed level of the outcome (mean/baseline rate); none expected\nlevel_hits = [l for l in L[\"dcr\"] if re.search(r\"(mean|baseline rate|pre-treatment (mean|level)|average rate)\", l, re.I)\n              and re.search(r\"fatal\", l, re.I)]\nprint(f\"Lines printing a level of the fatal-hospitalisation rate: {len(level_hits)}\")\nH[\"E4\"] = {\n    \"value\": None,\n    \"unit\": \"hospitalisations per 100,000 inhabitants per year\",\n    \"transformation\": f\"Absolute change = R0*(exp(b)-1) with b = {cells(fat)[3]} (log points), i.e. relative change 100*(exp({b4})-1) = {rel4:.4f} %; R0 (counterfactual/baseline fatal rate per 100,000 in treated regions) is not printed in the extract, so the absolute change 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 in the 5 km treated regions\",\n    \"locator\": f\"arxiv-2609.27732v1 | Table 7 | row 'Drug-related (fatal)' | column '5 km' (line {n_f})\",\n}\nassert not level_hits\n\n# ---------------- E5: LPM coefficient -> percentage points ----------------\nprint(\"\\nE5 source lines:\")\n_, hdr5 = find(L[\"cs\"], \"| (1) | (2) | (3) | (4)\", files[\"cs\"])\nn_t, tr = find(L[\"cs\"], \"Treatment |\", files[\"cs\"])\n_, mean5 = find(L[\"cs\"], \"Mean Dep. Var. |\", files[\"cs\"])\n_, pc5 = find(L[\"cs\"], \"% Change |\", files[\"cs\"])\nb5 = num(cells(tr)[4])\nv5 = 100 * b5\nH[\"E5\"] = {\n    \"value\": round(v5, 2),\n    \"unit\": \"percentage points\",\n    \"transformation\": f\"100*beta with beta = {cells(tr)[4]} (change in probability, 0-1 scale) -> 100*({b5}) = {v5:.4f} pp\",\n    \"harmonisable\": True,\n    \"missing_quantity\": None,\n    \"locator\": f\"arxiv-2510.03658v1 | Table 3 | row 'Treatment' | column '(4)' (line {n_t})\",\n}\n\n# ---------------- consistency checks on the sources (recorded as data issues) ----------------\nprint(\"\\nSource-consistency checks:\")\n_, abs_dcr = find(L[\"dcr\"], \"This paper estimates the causal effect of drug consumption room\", files[\"dcr\"])\nissues.append({\n    \"issue\": f\"arxiv-2609.27732v1: the abstract states that fatal outcomes 'fall by 35%', but Table 7 (5 km) prints {cells(fat)[3]} log points, i.e. 100*(exp({b4})-1) = {rel4:.1f}%; Section 6.1 says 'approximately 30%', consistent with Table 7. The 35% matches the overall ATT (Table 6), not the fatal outcome.\",\n    \"handling\": \"Table 7 is used as the authoritative printed estimate for E4; the abstract figure is not used.\"})\nn_a19, a19 = find(L[\"dcr\"], \"ATT | -0.431** | -0.438**\", files[\"dcr\"])\na19_se = L[\"dcr\"][n_a19 + 1]\nt6_se = L[\"dcr\"][n_att + 1]\nprint(f\"[{files['dcr']}:{n_att+2}] {t6_se}\")\nprint(f\"[{files['dcr']}:{n_a19+2}] {a19_se}\")\nissues.append({\n    \"issue\": f\"arxiv-2609.27732v1: the 5 km CS-DR ATT -0.431 has SE {cells(t6_se)[3]} in Table 6 but {cells(a19_se)[1]} in Table A19; the Table A19 note mentions 640 never-treated comparison regions while the table body and Tables 6-7 print 608.\",\n    \"handling\": \"Does not affect the point estimate used in E1 (identical -0.431 in both tables); Table 6 is used as the cited location. No uncertainty is propagated.\"})\n_, a19pct = find(L[\"dcr\"], \"% change | -35.0% | -35.5%\", files[\"dcr\"])\nmism = []\nfor b, p in zip(cells(a19)[1:], cells(a19pct)[1:]):\n    bb, pp = num(b), num(p)\n    implied = 100 * (math.exp(bb) - 1)\n    if abs(round(implied, 1) - pp) > 1e-9:\n        mism.append(f\"{b} -> {implied:.2f}% vs printed {p}\")\nprint(\"A19 % change mismatches:\", mism)\nif mism:\n    issues.append({\"issue\": \"arxiv-2609.27732v1 Table A19: printed '% change' differs in the last decimal from 100*(exp(ATT)-1) for: \" + \"; \".join(mism) + \".\",\n                   \"handling\": \"Minor rounding inconsistency in a robustness table not used for any item; recorded only.\"})\nratio = b5 / num(cells(mean5)[4])\nprint(f\"Tossou col (4): beta/mean = {ratio:.4f} vs printed % Change {cells(pc5)[4]}\")\nissues.append({\n    \"issue\": f\"arxiv-2510.03658v1: Table 3 column (4) prints '% Change' {cells(pc5)[4]} while {b5}/{num(cells(mean5)[4])} = {ratio:.4f} (last-digit difference, presumably from an unrounded mean). The abstract's '0.0855 (a 18.79 percentage change)' refers to a 'table 2' (mean 0.455) that is not in the extract.\",\n    \"handling\": \"E5 uses the Table 3 column (4) coefficient as printed and converts only the scale (x100 -> pp); the abstract/Table 2 numbers and the '% Change' row are not used.\"})\nissues.append({\n    \"issue\": \"Rendering defects: arxiv-2510.03658v1 footnote markers are rendered inline (e.g. 'percent^9^9 9') and footnote 11 is interleaved with main text; arxiv-2606.14621v1 renders 'p < 0.001' as 'p ¡ 0.001'.\",\n    \"handling\": \"No numbers used in E1-E5 are affected; recorded only.\"})\nissues.append({\n    \"issue\": \"arxiv-2606.14621v1 Table 2: the control-group mean (75.0%) is a raw aggregate mean, whereas the ATE (1.47 pp) adjusts for randomisation week (blocks); the abstract rounds the ATE to 1.5 pp.\",\n    \"handling\": \"Following the target definition ('percent of the control-group mean'), the printed control mean 75.0% is used as p and the Table 2 ATE 1.47 pp (not the rounded 1.5) as Delta; the denominator mismatch is noted as a limitation.\"})\n\nn_h = sum(1 for v in H.values() if v[\"value\"] is not None)\nn_nh = sum(1 for v in H.values() if v[\"harmonisable\"] is False)\nresults = {\n    \"task_id\": \"F4-conf-03\",\n    \"harmonised\": H,\n    \"n_harmonised\": n_h,\n    \"n_not_harmonisable\": n_nh,\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": n_input_rows,\n    \"n_analysed\": len(effects),\n    \"random_seed\": None,\n}\nassert [e[\"id\"] for e in effects] == list(H.keys())\nassert n_h + n_nh == len(effects)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\", encoding=\"utf-8\") as f:\n    json.dump(results, f, indent=2, ensure_ascii=False)\nprint(\"\\nSummary:\")\nfor k, v in H.items():\n    print(f\"  {k}: value={v['value']} [{v['unit']}] harmonisable={v['harmonisable']}\")\nprint(f\"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 from root"}}]}}
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{"parentUuid":"9a6dd908-9a32-4f0b-9318-e3d9a87550a2","isSidechain":false,"promptId":"5bbd5ec8-4c6a-4ce7-b9b5-627abc92134d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01119fP57gySZ2n7k9sdRDvN","type":"tool_result","content":"E1 source lines:\n[arxiv-2609.27732v1.txt:34] | 1 km | 3 km | 5 km\n[arxiv-2609.27732v1.txt:36] ATT | -0.399 | -0.283 | -0.431**\n[arxiv-2609.27732v1.txt:40] % change | -32.9% | -24.7% | -35.0%\n\nE2 source lines:\n[arxiv-2606.14621v1.txt:27] Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n[arxiv-2606.14621v1.txt:29] Reemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n\nE3 source lines:\n[arxiv-2505.04555v3.txt:50] Employment elasticity w.r.t MW | -0.388\n[arxiv-2505.04555v3.txt:60] % MW changes | 0.047\n[arxiv-2505.04555v3.txt:62] Note: The table reports the effects of a minimum wage increase based on the event study analysis exploiting 47 prefecture-level minimum wage changes in 2023 (i.e., about 4% minimum wage increase). The table reports six-month averaged post-treatment estimates on missing jobs up to 100 JPY below the new minimum wage, excess jobs at and up to 300 JPY above it, employment, and wages. Robust standard errors in parentheses are clustered by wage bins separated by 10 JPY.\n\nE4 source lines:\n[arxiv-2609.27732v1.txt:68] Outcome | 1 km | 3 km | 5 km\n[arxiv-2609.27732v1.txt:86] Drug-related (fatal) | -1.207*** | -0.290 | -0.354**\nLines printing a level of the fatal-hospitalisation rate: 0\n\nE5 source lines:\n[arxiv-2510.03658v1.txt:47] | (1) | (2) | (3) | (4)\n[arxiv-2510.03658v1.txt:49] Treatment | -0.0911 | -0.0908 | -0.0961 | -0.0962\n[arxiv-2510.03658v1.txt:87] Mean Dep. Var. | 0.261 | 0.261 | 0.261 | 0.261\n[arxiv-2510.03658v1.txt:83] % Change | -0.3490 | -0.3478 | -0.3681 | -0.3685\n\nSource-consistency checks:\n[arxiv-2609.27732v1.txt:18] This paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n[arxiv-2609.27732v1.txt:121] ATT | -0.431** | -0.438** | -0.362* | -0.149***\n[arxiv-2609.27732v1.txt:38] | (0.412) | (0.279) | (0.210)\n[arxiv-2609.27732v1.txt:123] | (0.218) | (0.208) | (0.205) | (0.038)\n[arxiv-2609.27732v1.txt:125] % change | -35.0% | -35.5% | -30.3% | -13.9%\nA19 % change mismatches: ['-0.362* -> -30.37% vs printed -30.3%', '-0.149*** -> -13.84% vs printed -13.9%']\nTossou col (4): beta/mean = -0.3686 vs printed % Change -0.3685\n\nSummary:\n  E1: value=-35.01 [percent change relative to the counterfactual hospitalisation rate] harmonisable=True\n  E2: value=1.96 [percent of the control-group mean reemployment rate] harmonisable=True\n  E3: value=-1.82 [percent change in employment] harmonisable=True\n  E4: value=None [hospitalisations per 100,000 inhabitants per year] harmonisable=False\n  E5: value=-9.62 [percentage points] harmonisable=True\nn_harmonised=4 n_not_harmonisable=1 feasibility=complete\n{\n  \"task_id\": \"F4-conf-03\",\n  \"harmonised\": {\n    \"E1\": {\n      \"value\": -35.01,\n      \"unit\": \"percent change relative to the counterfactual hospitalisation rate\",\n      \"transformation\": \"100*(exp(b)-1) with b = -0.431** (log points) -> 100*(exp(-0.431)-1) = -35.0141 %; consistent with printed '% change' -35.0%\",\n      \"harmonisable\": true,\n      \"missing_quantity\": null,\n      \"locator\": \"arxiv-2609.27732v1 | Table 6 | row 'ATT' | column '5 km' (line 36); cross-check row '% change', column '5 km'\"\n    },\n    \"E2\": {\n      \"value\": 1.96,\n      \"unit\": \"percent of the control-group mean reemployment rate\",\n      \"transformation\": \"100*Delta/p with Delta = 1.47 pp and p = 75.0% (control mean) -> 100*1.47/75.0 = 1.9600 %\",\n      \"harmonisable\": true,\n      \"missing_quantity\": null,\n      \"locator\": \"arxiv-2606.14621v1 | Table 2 | row 'Reemployment' | columns 'Average treatment effect estimate' (Delta) and 'Control' (p) (line 29)\"\n    },\n    \"E3\": {\n      \"value\": -1.82,\n      \"unit\": \"percent change in employment\",\n      \"transformation\": \"100*eta*g with eta = -0.388 and g = 0.047 (proportional MW change, read as 4.7%; the table note says 'about 4%') -> 100*-0.388*0.047 = -1.8236 %\",\n      \"harmonisable\": true,\n      \"missing_quantity\": null,\n      \"locator\": \"arxiv-2505.04555v3 | Table 2 | row 'Employment elasticity w.r.t MW' | column 'Estimate (SE)' (line 50); row '% MW changes' | column 'Estimate (SE)' (line 60)\"\n    },\n    \"E4\": {\n      \"value\": null,\n      \"unit\": \"hospitalisations per 100,000 inhabitants per year\",\n      \"transformation\": \"Absolute change = R0*(exp(b)-1) with b = -0.354** (log points), i.e. relative change 100*(exp(-0.354)-1) = -29.8125 %; R0 (counterfactual/baseline fatal rate per 100,000 in treated regions) is not printed in the extract, so the absolute change 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 in the 5 km treated regions\",\n      \"locator\": \"arxiv-2609.27732v1 | Table 7 | row 'Drug-related (fatal)' | column '5 km' (line 86)\"\n    },\n    \"E5\": {\n      \"value\": -9.62,\n      \"unit\": \"percentage points\",\n      \"transformation\": \"100*beta with beta = -0.0962 (change in probability, 0-1 scale) -> 100*(-0.0962) = -9.6200 pp\",\n      \"harmonisable\": true,\n      \"missing_quantity\": null,\n      \"locator\": \"arxiv-2510.03658v1 | Table 3 | row 'Treatment' | column '(4)' (line 49)\"\n    }\n  },\n  \"n_harmonised\": 4,\n  \"n_not_harmonisable\": 1,\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [\n    {\n      \"issue\": \"arxiv-2505.04555v3 Table 2: the row '% MW changes' prints 0.047, a label suggesting percent units while the value is only consistent with a proportion (4.7%); the table note describes the change as 'about 4%'.\",\n      \"handling\": \"Read 0.047 as a proportional change g = 0.047 (4.7%), the only reading compatible with the note ('about 4%'); the printed table value (not the rounded note value) is used.\"\n    },\n    {\n      \"issue\": \"arxiv-2609.27732v1: the abstract states that fatal outcomes 'fall by 35%', but Table 7 (5 km) prints -0.354** log points, i.e. 100*(exp(-0.354)-1) = -29.8%; Section 6.1 says 'approximately 30%', consistent with Table 7. The 35% matches the overall ATT (Table 6), not the fatal outcome.\",\n      \"handling\": \"Table 7 is used as the authoritative printed estimate for E4; the abstract figure is not used.\"\n    },\n    {\n      \"issue\": \"arxiv-2609.27732v1: the 5 km CS-DR ATT -0.431 has SE (0.210) in Table 6 but (0.218) in Table A19; the Table A19 note mentions 640 never-treated comparison regions while the table body and Tables 6-7 print 608.\",\n      \"handling\": \"Does not affect the point estimate used in E1 (identical -0.431 in both tables); Table 6 is used as the cited location. No uncertainty is propagated.\"","is_error":false}]},"uuid":"6ab85f1c-c94c-4dac-8260-5a7a8bec8eb9","timestamp":"2026-10-30T05:09:31.370Z","toolUseResult":{"stdout":"E1 source lines:\n[arxiv-2609.27732v1.txt:34] | 1 km | 3 km | 5 km\n[arxiv-2609.27732v1.txt:36] ATT | -0.399 | -0.283 | -0.431**\n[arxiv-2609.27732v1.txt:40] % change | -32.9% | -24.7% | -35.0%\n\nE2 source lines:\n[arxiv-2606.14621v1.txt:27] Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n[arxiv-2606.14621v1.txt:29] Reemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n\nE3 source lines:\n[arxiv-2505.04555v3.txt:50] Employment elasticity w.r.t MW | -0.388\n[arxiv-2505.04555v3.txt:60] % MW changes | 0.047\n[arxiv-2505.04555v3.txt:62] Note: The table reports the effects of a minimum wage increase based on the event study analysis exploiting 47 prefecture-level minimum wage changes in 2023 (i.e., about 4% minimum wage increase). The table reports six-month averaged post-treatment estimates on missing jobs up to 100 JPY below the new minimum wage, excess jobs at and up to 300 JPY above it, employment, and wages. Robust standard errors in parentheses are clustered by wage bins separated by 10 JPY.\n\nE4 source lines:\n[arxiv-2609.27732v1.txt:68] Outcome | 1 km | 3 km | 5 km\n[arxiv-2609.27732v1.txt:86] Drug-related (fatal) | -1.207*** | -0.290 | -0.354**\nLines printing a level of the fatal-hospitalisation rate: 0\n\nE5 source lines:\n[arxiv-2510.03658v1.txt:47] | (1) | (2) | (3) | (4)\n[arxiv-2510.03658v1.txt:49] Treatment | -0.0911 | -0.0908 | -0.0961 | -0.0962\n[arxiv-2510.03658v1.txt:87] Mean Dep. Var. | 0.261 | 0.261 | 0.261 | 0.261\n[arxiv-2510.03658v1.txt:83] % Change | -0.3490 | -0.3478 | -0.3681 | -0.3685\n\nSource-consistency checks:\n[arxiv-2609.27732v1.txt:18] This paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n[arxiv-2609.27732v1.txt:121] ATT | -0.431** | -0.438** | -0.362* | -0.149***\n[arxiv-2609.27732v1.txt:38] | (0.412) | (0.279) | (0.210)\n[arxiv-2609.27732v1.txt:123] | (0.218) | (0.208) | (0.205) | (0.038)\n[arxiv-2609.27732v1.txt:125] % change | -35.0% | -35.5% | -30.3% | -13.9%\nA19 % change mismatches: ['-0.362* -> -30.37% vs printed -30.3%', '-0.149*** -> -13.84% vs printed -13.9%']\nTossou col (4): beta/mean = -0.3686 vs printed % Change -0.3685\n\nSummary:\n  E1: value=-35.01 [percent change relative to the counterfactual hospitalisation rate] harmonisable=True\n  E2: value=1.96 [percent of the control-group mean reemployment rate] harmonisable=True\n  E3: value=-1.82 [percent change in employment] harmonisable=True\n  E4: value=None [hospitalisations per 100,000 inhabitants per year] harmonisable=False\n  E5: value=-9.62 [percentage points] harmonisable=True\nn_harmonised=4 n_not_harmonisable=1 feasibility=complete\n{\n  \"task_id\": \"F4-conf-03\",\n  \"harmonised\": {\n    \"E1\": {\n      \"value\": -35.01,\n      \"unit\": \"percent change relative to the counterfactual hospitalisation rate\",\n      \"transformation\": \"100*(exp(b)-1) with b = -0.431** (log points) -> 100*(exp(-0.431)-1) = -35.0141 %; consistent with printed '% change' -35.0%\",\n      \"harmonisable\": true,\n      \"missing_quantity\": null,\n      \"locator\": \"arxiv-2609.27732v1 | Table 6 | row 'ATT' | column '5 km' (line 36); cross-check row '% change', column '5 km'\"\n    },\n    \"E2\": {\n      \"value\": 1.96,\n      \"unit\": \"percent of the control-group mean reemployment rate\",\n      \"transformation\": \"100*Delta/p with Delta = 1.47 pp and p = 75.0% (control mean) -> 100*1.47/75.0 = 1.9600 %\",\n      \"harmonisable\": true,\n      \"missing_quantity\": null,\n      \"locator\": \"arxiv-2606.14621v1 | Table 2 | row 'Reemployment' | columns 'Average treatment effect estimate' (Delta) and 'Control' (p) (line 29)\"\n    },\n    \"E3\": {\n      \"value\": -1.82,\n      \"unit\": \"percent change in employment\",\n      \"transformation\": \"100*eta*g with eta = -0.388 and g = 0.047 (proportional MW change, read as 4.7%; the table note says 'about 4%') -> 100*-0.388*0.047 = -1.8236 %\",\n      \"harmonisable\": true,\n      \"missing_quantity\": null,\n      \"locator\": \"arxiv-2505.04555v3 | Table 2 | row 'Employment elasticity w.r.t MW' | column 'Estimate (SE)' (line 50); row '% MW changes' | column 'Estimate (SE)' (line 60)\"\n    },\n    \"E4\": {\n      \"value\": null,\n      \"unit\": \"hospitalisations per 100,000 inhabitants per year\",\n      \"transformation\": \"Absolute change = R0*(exp(b)-1) with b = -0.354** (log points), i.e. relative change 100*(exp(-0.354)-1) = -29.8125 %; R0 (counterfactual/baseline fatal rate per 100,000 in treated regions) is not printed in the extract, so the absolute change 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 in the 5 km treated regions\",\n      \"locator\": \"arxiv-2609.27732v1 | Table 7 | row 'Drug-related (fatal)' | column '5 km' (line 86)\"\n    },\n    \"E5\": {\n      \"value\": -9.62,\n      \"unit\": \"percentage points\",\n      \"transformation\": \"100*beta with beta = -0.0962 (change in probability, 0-1 scale) -> 100*(-0.0962) = -9.6200 pp\",\n      \"harmonisable\": true,\n      \"missing_quantity\": null,\n      \"locator\": \"arxiv-2510.03658v1 | Table 3 | row 'Treatment' | column '(4)' (line 49)\"\n    }\n  },\n  \"n_harmonised\": 4,\n  \"n_not_harmonisable\": 1,\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [\n    {\n      \"issue\": \"arxiv-2505.04555v3 Table 2: the row '% MW changes' prints 0.047, a label suggesting percent units while the value is only consistent with a proportion (4.7%); the table note describes the change as 'about 4%'.\",\n      \"handling\": \"Read 0.047 as a proportional change g = 0.047 (4.7%), the only reading compatible with the note ('about 4%'); the printed table value (not the rounded note value) is used.\"\n    },\n    {\n      \"issue\": \"arxiv-2609.27732v1: the abstract states that fatal outcomes 'fall by 35%', but Table 7 (5 km) prints -0.354** log points, i.e. 100*(exp(-0.354)-1) = -29.8%; Section 6.1 says 'approximately 30%', consistent with Table 7. The 35% matches the overall ATT (Table 6), not the fatal outcome.\",\n      \"handling\": \"Table 7 is used as the authoritative printed estimate for E4; the abstract figure is not used.\"\n    },\n    {\n      \"issue\": \"arxiv-2609.27732v1: the 5 km CS-DR ATT -0.431 has SE (0.210) in Table 6 but (0.218) in Table A19; the Table A19 note mentions 640 never-treated comparison regions while the table body and Tables 6-7 print 608.\",\n      \"handling\": \"Does not affect the point estimate used in E1 (identical -0.431 in both tables); Table 6 is used as the cited location. 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{"parentUuid":"fb3cd68d-5948-4f0b-938e-008e04a4c4bd","isSidechain":false,"promptId":"5bbd5ec8-4c6a-4ce7-b9b5-627abc92134d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DLsFp66uqYdnG2HbFnKXYz","type":"tool_result","content":"                        diffs.append(k)\n                except Exception:  # noqa: BLE001\n                    diffs.append(k)\n        elif va != vb:\n            diffs.append(k)\n    return sorted(diffs)\n\ndef run_gate(ws, python=None, do_repro=True):\n    ws = os.path.abspath(ws)\n    checks = []\n    def add(cid, ok, detail=\"\"):\n        checks.append({\"id\": cid, \"passed\": bool(ok), \"detail\": detail})\n    spec, err = _load_json(os.path.join(ws, \"gate_spec.json\"))\n    if spec is None:\n        add(\"gate_spec\", False, f\"gate_spec.json {err}\")\n        return _finish(ws, checks)\n    # 1. required files\n    for rel in spec.get(\"required_files\", []):\n        p = os.path.join(ws, rel)\n        ok = os.path.isfile(p) and os.path.getsize(p) > 0\n        add(f\"file:{rel}\", ok, \"present\" if ok else \"missing or empty\")\n    # 2. results.json keys/types\n    res, err = _load_json(os.path.join(ws, \"out\", \"results.json\"))\n    if not isinstance(res, dict):\n        add(\"results_json\", False, f\"out/results.json {err or 'is not an object'}\")\n        res = {}\n    else:\n        add(\"results_json\", True, \"parsed\")\n        for k, rule in spec.get(\"results_keys\", {}).items():\n            if k not in res:\n                add(f\"key:{k}\", False, \"missing\")\n            else:\n                ok = _type_ok(res[k], rule.get(\"type\"), rule.get(\"nullable\", False))\n                add(f\"key:{k}\", ok, \"ok\" if ok else f\"type/nullability mismatch (expected {rule.get('type')}, nullable={rule.get('nullable', False)}, got {type(res[k]).__name__}={res[k]!r:.60})\")\n        if spec.get(\"task_id\") and res.get(\"task_id\") not in (None, spec[\"task_id\"]):\n            add(\"task_id_matches\", False, f\"results.task_id={res.get('task_id')!r} != {spec['task_id']!r}\")\n    # 3. report sections\n    rep = \"\"\n    try:\n        rep = open(os.path.join(ws, \"out\", \"report.md\"), encoding=\"utf-8\", errors=\"replace\").read()\n    except Exception:  # noqa: BLE001\n        pass\n    for s in spec.get(\"report_sections\", []):\n        ok = re.search(NUMERIC_RE + re.escape(s) + r\"\\s*$\", rep, re.M | re.I) is not None\n        add(f\"section:{s}\", ok, \"present\" if ok else \"heading not found\")\n    fam = set(spec.get(\"family_checks\", []))\n    # 4. claims\n    claims, cerr = _load_json(os.path.join(ws, \"out\", \"claims.json\"))\n    if \"claims_reference_existing_keys\" in fam or os.path.exists(os.path.join(ws, \"out\", \"claims.json\")):\n        if not isinstance(claims, list) or not claims:\n            add(\"claims_json\", False, f\"out/claims.json {cerr or 'must be a non-empty list'}\")\n        else:\n            bad = []\n            for i, c in enumerate(claims):\n                if not isinstance(c, dict) or not all(k in c for k in (\"id\", \"text\", \"results_keys\", \"status\")):\n                    bad.append(f\"#{i}: missing fields\"); continue\n                if c[\"status\"] not in (\"supported\", \"unsupported\", \"not_evaluated\"):\n                    bad.append(f\"{c.get('id')}: bad status {c['status']!r}\")\n                missing = [k for k in (c.get(\"results_keys\") or []) if k not in res]\n                if missing:\n                    bad.append(f\"{c.get('id')}: results_keys not in results.json: {missing}\")\n            add(\"claims_json\", not bad, \"; \".join(bad) if bad else f\"{len(claims)} claims, all keys resolve\")\n    # 5. phase ledger\n    pl_path = os.path.join(ws, \"out\", \"state\", \"phases.json\")\n    if \"phase_ledger_consistent\" in fam and os.path.exists(pl_path):\n        pl, perr = _load_json(pl_path)\n        if not isinstance(pl, dict):\n            add(\"phase_ledger\", False, f\"phases.json {perr or 'not an object'}\")\n        else:\n            bad = []\n            for pid, st in pl.items():\n                if not isinstance(st, dict) or st.get(\"status\") not in (\"pending\", \"running\", \"passed\", \"failed\", \"blocked\", \"stale\", \"skipped_optional\"):\n                    bad.append(f\"{pid}: invalid status\"); continue\n                if st.get(\"status\") == \"passed\" and not st.get(\"checks\"):\n                    bad.append(f\"{pid}: passed without recorded checks\")\n            not_done = [pid for pid, st in pl.items() if isinstance(st, dict) and st.get(\"status\") not in (\"passed\", \"skipped_optional\", \"blocked\")]\n            if not_done:\n                bad.append(f\"phases not terminal: {not_done}\")\n            add(\"phase_ledger\", not bad, \"; \".join(bad) if bad else \"consistent\")\n    # 6. exclusion ledger\n    ex_path = os.path.join(ws, \"out\", \"exclusions.csv\")\n    if \"exclusion_ledger_sums\" in fam and os.path.exists(ex_path):\n        try:\n            rows = list(csv.DictReader(open(ex_path, newline=\"\")))\n            bad = []\n            prev_after = None\n            for i, r in enumerate(rows):\n                nb, na, nr = int(float(r[\"n_before\"])), int(float(r[\"n_after\"])), int(float(r[\"n_removed\"]))\n                if nb - nr != na:\n                    bad.append(f\"row {i}: n_before - n_removed != n_after\")\n                if prev_after is not None and nb != prev_after:\n                    bad.append(f\"row {i}: n_before != previous n_after\")\n                prev_after = na\n            add(\"exclusion_ledger\", not bad, \"; \".join(bad) if bad else f\"{len(rows)} steps consistent\")\n        except Exception as e:  # noqa: BLE001\n            add(\"exclusion_ledger\", False, f\"unreadable: {e}\")\n    # 7. numeric sanity checks\n    if \"ci_contains_estimate\" in fam:\n        for est, lo, hi in spec.get(\"ci_pairs\", []):\n            e, l, h = res.get(est), res.get(lo), res.get(hi)\n            if all(_is_num(v) for v in (e, l, h)):\n                add(f\"ci:{est}\", l <= e <= h, f\"{l} <= {e} <= {h}\")\n            elif e is None and l is None and h is None:\n                add(f\"ci:{est}\", True, \"all null (allowed if reported infeasible)\")\n            else:\n                add(f\"ci:{est}\", False, \"non-numeric CI triple\")\n    if \"positive_se\" in fam:\n        for k in spec.get(\"positive_keys\", []):\n            v = res.get(k)\n            add(f\"positive:{k}\", (v is None) or (_is_num(v) and v > 0), f\"{v}\")\n    if \"chronology\" in fam:\n        for a, b in spec.get(\"chronology_pairs\", []):\n            da, db = _parse_date(res.get(a)), _parse_date(res.get(b))\n            add(f\"chronology:{a}<={b}\", da is not None and db is not None and da <= db, f\"{res.get(a)} vs {res.get(b)}\")\n    if \"forecasts_csv_chronology\" in fam:\n        fp = os.path.join(ws, \"out\", \"forecasts.csv\")\n        try:\n            rows = list(csv.DictReader(open(fp, newline=\"\")))\n            bad = sum(1 for r in rows if not (_parse_date(r.get(\"origin_date\")) and _parse_date(r.get(\"target_date\")) and _parse_date(r[\"origin_date\"]) < _parse_date(r[\"target_date\"])))\n            add(\"forecasts_chronology\", rows and bad == 0, f\"{bad} rows with target_date <= origin_date out of {len(rows)}\")\n        except Exception as e:  # noqa: BLE001","is_error":false}]},"uuid":"ef76c971-ad9b-4473-b80b-7f1178762845","timestamp":"2026-10-30T05:09:39.485Z","toolUseResult":{"stdout":"                        diffs.append(k)\n                except Exception:  # noqa: BLE001\n                    diffs.append(k)\n        elif va != vb:\n            diffs.append(k)\n    return sorted(diffs)\n\ndef run_gate(ws, python=None, do_repro=True):\n    ws = os.path.abspath(ws)\n    checks = []\n    def add(cid, ok, detail=\"\"):\n        checks.append({\"id\": cid, \"passed\": bool(ok), \"detail\": detail})\n    spec, err = _load_json(os.path.join(ws, \"gate_spec.json\"))\n    if spec is None:\n        add(\"gate_spec\", False, f\"gate_spec.json {err}\")\n        return _finish(ws, checks)\n    # 1. required files\n    for rel in spec.get(\"required_files\", []):\n        p = os.path.join(ws, rel)\n        ok = os.path.isfile(p) and os.path.getsize(p) > 0\n        add(f\"file:{rel}\", ok, \"present\" if ok else \"missing or empty\")\n    # 2. results.json keys/types\n    res, err = _load_json(os.path.join(ws, \"out\", \"results.json\"))\n    if not isinstance(res, dict):\n        add(\"results_json\", False, f\"out/results.json {err or 'is not an object'}\")\n        res = {}\n    else:\n        add(\"results_json\", True, \"parsed\")\n        for k, rule in spec.get(\"results_keys\", {}).items():\n            if k not in res:\n                add(f\"key:{k}\", False, \"missing\")\n            else:\n                ok = _type_ok(res[k], rule.get(\"type\"), rule.get(\"nullable\", False))\n                add(f\"key:{k}\", ok, \"ok\" if ok else f\"type/nullability mismatch (expected {rule.get('type')}, nullable={rule.get('nullable', False)}, got {type(res[k]).__name__}={res[k]!r:.60})\")\n        if spec.get(\"task_id\") and res.get(\"task_id\") not in (None, spec[\"task_id\"]):\n            add(\"task_id_matches\", False, f\"results.task_id={res.get('task_id')!r} != {spec['task_id']!r}\")\n    # 3. report sections\n    rep = \"\"\n    try:\n        rep = open(os.path.join(ws, \"out\", \"report.md\"), encoding=\"utf-8\", errors=\"replace\").read()\n    except Exception:  # noqa: BLE001\n        pass\n    for s in spec.get(\"report_sections\", []):\n        ok = re.search(NUMERIC_RE + re.escape(s) + r\"\\s*$\", rep, re.M | re.I) is not None\n        add(f\"section:{s}\", ok, \"present\" if ok else \"heading not found\")\n    fam = set(spec.get(\"family_checks\", []))\n    # 4. claims\n    claims, cerr = _load_json(os.path.join(ws, \"out\", \"claims.json\"))\n    if \"claims_reference_existing_keys\" in fam or os.path.exists(os.path.join(ws, \"out\", \"claims.json\")):\n        if not isinstance(claims, list) or not claims:\n            add(\"claims_json\", False, f\"out/claims.json {cerr or 'must be a non-empty list'}\")\n        else:\n            bad = []\n            for i, c in enumerate(claims):\n                if not isinstance(c, dict) or not all(k in c for k in (\"id\", \"text\", \"results_keys\", \"status\")):\n                    bad.append(f\"#{i}: missing fields\"); continue\n                if c[\"status\"] not in (\"supported\", \"unsupported\", \"not_evaluated\"):\n                    bad.append(f\"{c.get('id')}: bad status {c['status']!r}\")\n                missing = [k for k in (c.get(\"results_keys\") or []) if k not in res]\n                if missing:\n                    bad.append(f\"{c.get('id')}: results_keys not in results.json: {missing}\")\n            add(\"claims_json\", not bad, \"; \".join(bad) if bad else f\"{len(claims)} claims, all keys resolve\")\n    # 5. phase ledger\n    pl_path = os.path.join(ws, \"out\", \"state\", \"phases.json\")\n    if \"phase_ledger_consistent\" in fam and os.path.exists(pl_path):\n        pl, perr = _load_json(pl_path)\n        if not isinstance(pl, dict):\n            add(\"phase_ledger\", False, f\"phases.json {perr or 'not an object'}\")\n        else:\n            bad = []\n            for pid, st in pl.items():\n                if not isinstance(st, dict) or st.get(\"status\") not in (\"pending\", \"running\", \"passed\", \"failed\", \"blocked\", \"stale\", \"skipped_optional\"):\n                    bad.append(f\"{pid}: invalid status\"); continue\n                if st.get(\"status\") == \"passed\" and not st.get(\"checks\"):\n                    bad.append(f\"{pid}: passed without recorded checks\")\n            not_done = [pid for pid, st in pl.items() if isinstance(st, dict) and st.get(\"status\") not in (\"passed\", \"skipped_optional\", \"blocked\")]\n            if not_done:\n                bad.append(f\"phases not terminal: {not_done}\")\n            add(\"phase_ledger\", not bad, \"; \".join(bad) if bad else \"consistent\")\n    # 6. exclusion ledger\n    ex_path = os.path.join(ws, \"out\", \"exclusions.csv\")\n    if \"exclusion_ledger_sums\" in fam and os.path.exists(ex_path):\n        try:\n            rows = list(csv.DictReader(open(ex_path, newline=\"\")))\n            bad = []\n            prev_after = None\n            for i, r in enumerate(rows):\n                nb, na, nr = int(float(r[\"n_before\"])), int(float(r[\"n_after\"])), int(float(r[\"n_removed\"]))\n                if nb - nr != na:\n                    bad.append(f\"row {i}: n_before - n_removed != n_after\")\n                if prev_after is not None and nb != prev_after:\n                    bad.append(f\"row {i}: n_before != previous n_after\")\n                prev_after = na\n            add(\"exclusion_ledger\", not bad, \"; \".join(bad) if bad else f\"{len(rows)} steps consistent\")\n        except Exception as e:  # noqa: BLE001\n            add(\"exclusion_ledger\", False, f\"unreadable: {e}\")\n    # 7. numeric sanity checks\n    if \"ci_contains_estimate\" in fam:\n        for est, lo, hi in spec.get(\"ci_pairs\", []):\n            e, l, h = res.get(est), res.get(lo), res.get(hi)\n            if all(_is_num(v) for v in (e, l, h)):\n                add(f\"ci:{est}\", l <= e <= h, f\"{l} <= {e} <= {h}\")\n            elif e is None and l is None and h is None:\n                add(f\"ci:{est}\", True, \"all null (allowed if reported infeasible)\")\n            else:\n                add(f\"ci:{est}\", False, \"non-numeric CI triple\")\n    if \"positive_se\" in fam:\n        for k in spec.get(\"positive_keys\", []):\n            v = res.get(k)\n            add(f\"positive:{k}\", (v is None) or (_is_num(v) and v > 0), f\"{v}\")\n    if \"chronology\" in fam:\n        for a, b in spec.get(\"chronology_pairs\", []):\n            da, db = _parse_date(res.get(a)), _parse_date(res.get(b))\n            add(f\"chronology:{a}<={b}\", da is not None and db is not None and da <= db, f\"{res.get(a)} vs {res.get(b)}\")\n    if \"forecasts_csv_chronology\" in fam:\n        fp = os.path.join(ws, \"out\", \"forecasts.csv\")\n        try:\n            rows = list(csv.DictReader(open(fp, newline=\"\")))\n            bad = sum(1 for r in rows if not (_parse_date(r.get(\"origin_date\")) and _parse_date(r.get(\"target_date\")) and _parse_date(r[\"origin_date\"]) < _parse_date(r[\"target_date\"])))\n            add(\"forecasts_chronology\", rows and bad == 0, f\"{bad} rows with target_date <= origin_date out of {len(rows)}\")\n        except Exception as e:  # noqa: BLE001","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"fb3cd68d-5948-4f0b-938e-008e04a4c4bd","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0232","sessionId":"008ec3b4-46e6-4e9d-82e8-4b7207a5d6af","version":"2.1.314","gitBranch":"HEAD"}
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Sources are plain-text extracts with provenance header; tables one row per line, \" | \" separators, SE on following line.\n\nUnits: DCR outcomes = log hospitalisation rate per 100,000 (ATT in log points). RESEA reemployment means in %, effects in pp. MW Table 2: elasticities unitless; '% MW changes' = 0.047 (interpreted as a proportion, see anomalies). Tossou Table 3: LPM coefficients on 0–1 probability; Mean Dep. Var. 0.261.\n\nAnomalies (all also recorded in results.json `data_issues`):\n- DCR abstract \"fatal outcomes fall by 35%\" vs Table 7 5 km -0.354 (=-29.8%); Section 6.1 says ~30%.\n- DCR Table A19 SE (0.218) vs Table 6 SE (0.210) for the same -0.431; A19 note \"640 regions\" vs 608 in tables.\n- DCR Table A19 % change last-digit mismatches for -0.362 and -0.149.\n- MW Table 2 '% MW changes' 0.047 vs note \"about 4%\".\n- Tossou % Change -0.3685 vs -0.0962/0.261=-0.3686; abstract cites Table 2 (not in extract).\n- RESEA control mean is raw (not block-adjusted); abstract rounds 1.47 to 1.5 pp.\n- Rendering defects (footnotes inline, 'p ¡ 0.001').\n- No fatal-hospitalisation rate level (mean/baseline) printed anywhere in the DCR extract -> E4 not harmonisable.\n\nInformation-availability: no input violates the rules; only the extracts are used. No remembered values used. Exclusions: none (out/exclusions.csv).\nEOF\ncat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\nEOF\nrm -f out/results.json; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cp out/results.json /tmp/r1.json; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp /tmp/r1.json out/results.json && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport json,math\nr=json.load(open(\"out/results.json\")); H=r[\"harmonised\"]\nck=[]\nck.append((\"E1 matches printed % change -35.0 at 1dp\", round(H[\"E1\"][\"value\"],1)==-35.0))\nck.append((\"E1 recompute\", abs(H[\"E1\"][\"value\"]-round(100*(math.exp(-0.431)-1),2))<1e-12))\nck.append((\"E2 recompute 100*1.47/75.0\", H[\"E2\"][\"value\"]==round(100*1.47/75.0,2)))\nck.append((\"E3 recompute 100*-0.388*0.047\", H[\"E3\"][\"value\"]==round(-0.388*0.047*100,2)))\nck.append((\"E3 magnitude vs abstract '2% employment decline' (same order, negative)\", -3<H[\"E3\"][\"value\"]<-1))\nck.append((\"E4 null with missing_quantity and harmonisable false\", H[\"E4\"][\"value\"] is None and H[\"E4\"][\"harmonisable\"] is False and H[\"E4\"][\"missing_quantity\"]))\nck.append((\"E5 = 100*-0.0962\", H[\"E5\"][\"value\"]==-9.62))\nck.append((\"signs preserved vs baseline printed values\", all((H[k][\"value\"]>0)==(r[\"baseline_printed_values\"][k]>0) for k in H if H[k][\"value\"] is not None)))\nck.append((\"n_harmonised + n_not_harmonisable == n_analysed == 5\", r[\"n_harmonised\"]+r[\"n_not_harmonisable\"]==r[\"n_analysed\"]==5))\nck.append((\"harmonisable items have null missing_quantity\", all(H[k][\"missing_quantity\"] is None for k in H if H[k][\"harmonisable\"])))\nck.append((\"n_input_rows matches wc -l\", r[\"n_input_rows\"]=={\"effects.json\":5,\"README.md\":16,\"sources/arxiv-2609.27732v1.txt\":134,\"sources/arxiv-2606.14621v1.txt\":58,\"sources/arxiv-2505.04555v3.txt\":66,\"sources/arxiv-2510.03658v1.txt\":91}))\nck.append((\"feasibility complete consistent (every item has value or justified impossibility)\", r[\"feasibility\"]==\"complete\" and all(H[k][\"value\"] is not None or H[k][\"missing_quantity\"] for k in H)))\nck.append((\"exclusions: 5 raw - 0 removed = 5 analysed\", True))\nwith open(\"out/state/checks.md\",\"a\") as f:\n    f.write(\"- Clean re-run (results.json deleted, regenerated twice, byte-identical via cmp): PASS\\n\")\n    for n,ok in ck: f.write(f\"- {n}: {'PASS' if ok else 'FAIL'}\\n\")\nfor n,ok in ck: print(ok,n)\nEOF","description":"Write audit, rerun for reproducibility, run consistency checks"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":653,"cache_read_input_tokens":47016,"output_tokens":2417,"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":653},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqxfvtekfpLwrPF7GpT","requestRef":"10862746-1dd4-462f-a922-b57e7bb738a3","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"dbdf1f10-d1a8-4428-a06a-b74bbcc63e14","timestamp":"2026-10-30T05:10:09.212Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0232","sessionId":"008ec3b4-46e6-4e9d-82e8-4b7207a5d6af","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"dbdf1f10-d1a8-4428-a06a-b74bbcc63e14","isSidechain":false,"promptId":"5bbd5ec8-4c6a-4ce7-b9b5-627abc92134d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01AXuyMWLHFCNByX1fRr4mQ3","type":"tool_result","content":"IDENTICAL\nTrue E1 matches printed % change -35.0 at 1dp\nTrue E1 recompute\nTrue E2 recompute 100*1.47/75.0\nTrue E3 recompute 100*-0.388*0.047\nTrue E3 magnitude vs abstract '2% employment decline' (same order, negative)\nbaseline (counterfactual, e.g. pre-opening or control-group mean) level of the fatal drug-related hospitalisation rate per 100,000 inhabitants per year in the 5 km treated regions E4 null with missing_quantity and harmonisable false\nTrue E5 = 100*-0.0962\nTrue signs preserved vs baseline printed values\nTrue n_harmonised + n_not_harmonisable == n_analysed == 5\nTrue harmonisable items have null missing_quantity\nTrue n_input_rows matches wc -l\nTrue feasibility complete consistent (every item has value or justified impossibility)\nTrue exclusions: 5 raw - 0 removed = 5 analysed","is_error":false}]},"uuid":"52812f63-feab-4c47-84c2-0607b9591d39","timestamp":"2026-10-30T05:10:09.334Z","toolUseResult":{"stdout":"IDENTICAL\nTrue E1 matches printed % change -35.0 at 1dp\nTrue E1 recompute\nTrue E2 recompute 100*1.47/75.0\nTrue E3 recompute 100*-0.388*0.047\nTrue E3 magnitude vs abstract '2% employment decline' (same order, negative)\nbaseline (counterfactual, e.g. pre-opening or control-group mean) level of the fatal drug-related hospitalisation rate per 100,000 inhabitants per year in the 5 km treated regions E4 null with missing_quantity and harmonisable false\nTrue E5 = 100*-0.0962\nTrue signs preserved vs baseline printed values\nTrue n_harmonised + n_not_harmonisable == n_analysed == 5\nTrue harmonisable items have null missing_quantity\nTrue n_input_rows matches wc -l\nTrue feasibility complete consistent (every item has value or justified impossibility)\nTrue exclusions: 5 raw - 0 removed = 5 analysed","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"dbdf1f10-d1a8-4428-a06a-b74bbcc63e14","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0232","sessionId":"008ec3b4-46e6-4e9d-82e8-4b7207a5d6af","version":"2.1.314","gitBranch":"HEAD"}
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Sources are plain-text extracts with provenance header; tables one row per line, \" | \" separators, SE on following line.\n\nUnits: DCR outcomes = log hospitalisation rate per 100,000 (ATT in log points). RESEA reemployment means in %, effects in pp. MW Table 2: elasticities unitless; '% MW changes' = 0.047 (interpreted as a proportion, see anomalies). Tossou Table 3: LPM coefficients on 0–1 probability; Mean Dep. Var. 0.261.\n\nAnomalies (all also recorded in results.json `data_issues`):\n- DCR abstract \"fatal outcomes fall by 35%\" vs Table 7 5 km -0.354 (=-29.8%); Section 6.1 says ~30%.\n- DCR Table A19 SE (0.218) vs Table 6 SE (0.210) for the same -0.431; A19 note \"640 regions\" vs 608 in tables.\n- DCR Table A19 % change last-digit mismatches for -0.362 and -0.149.\n- MW Table 2 '% MW changes' 0.047 vs note \"about 4%\".\n- Tossou % Change -0.3685 vs -0.0962/0.261=-0.3686; abstract cites Table 2 (not in extract).\n- RESEA control mean is raw (not block-adjusted); abstract rounds 1.47 to 1.5 pp.\n- Rendering defects (footnotes inline, 'p ¡ 0.001').\n- No fatal-hospitalisation rate level (mean/baseline) printed anywhere in the DCR extract -> E4 not harmonisable.\n\nInformation-availability: no input violates the rules; only the extracts are used. No remembered values used. Exclusions: none (out/exclusions.csv).\nEOF\ncat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\nEOF\nrm -f out/results.json; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cp out/results.json /tmp/r1.json; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp /tmp/r1.json out/results.json && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport json,math\nr=json.load(open(\"out/results.json\")); H=r[\"harmonised\"]\nck=[]\nck.append((\"E1 matches printed % change -35.0 at 1dp\", round(H[\"E1\"][\"value\"],1)==-35.0))\nck.append((\"E1 recompute\", abs(H[\"E1\"][\"value\"]-round(100*(math.exp(-0.431)-1),2))<1e-12))\nck.append((\"E2 recompute 100*1.47/75.0\", H[\"E2\"][\"value\"]==round(100*1.47/75.0,2)))\nck.append((\"E3 recompute 100*-0.388*0.047\", H[\"E3\"][\"value\"]==round(-0.388*0.047*100,2)))\nck.append((\"E3 magnitude vs abstract '2% employment decline' (same order, negative)\", -3<H[\"E3\"][\"value\"]<-1))\nck.append((\"E4 null with missing_quantity and harmonisable false\", H[\"E4\"][\"value\"] is None and H[\"E4\"][\"harmonisable\"] is False and H[\"E4\"][\"missing_quantity\"]))\nck.append((\"E5 = 100*-0.0962\", H[\"E5\"][\"value\"]==-9.62))\nck.append((\"signs preserved vs baseline printed values\", all((H[k][\"value\"]>0)==(r[\"baseline_printed_values\"][k]>0) for k in H if H[k][\"value\"] is not None)))\nck.append((\"n_harmonised + n_not_harmonisable == n_analysed == 5\", r[\"n_harmonised\"]+r[\"n_not_harmonisable\"]==r[\"n_analysed\"]==5))\nck.append((\"harmonisable items have null missing_quantity\", all(H[k][\"missing_quantity\"] is None for k in H if H[k][\"harmonisable\"])))\nck.append((\"n_input_rows matches wc -l\", r[\"n_input_rows\"]=={\"effects.json\":5,\"README.md\":16,\"sources/arxiv-2609.27732v1.txt\":134,\"sources/arxiv-2606.14621v1.txt\":58,\"sources/arxiv-2505.04555v3.txt\":66,\"sources/arxiv-2510.03658v1.txt\":91}))\nck.append((\"feasibility complete consistent (every item has value or justified impossibility)\", r[\"feasibility\"]==\"complete\" and all(H[k][\"value\"] is not None or H[k][\"missing_quantity\"] for k in H)))\nck.append((\"exclusions: 5 raw - 0 removed = 5 analysed\", True))\nwith open(\"out/state/checks.md\",\"a\") as f:\n    f.write(\"- Clean re-run (results.json deleted, regenerated twice, byte-identical via cmp): PASS\\n\")\n    for n,ok in ck: f.write(f\"- {n}: {'PASS' if ok else 'FAIL'}\\n\")\nfor n,ok in ck: print(ok,n)\nEOF","description":"Write audit, rerun for reproducibility, run consistency checks"}}]}}
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{"parentUuid":"2df3a55a-73b5-4bfa-9522-919ebd054683","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqzTjxps8xQ46W86WAk","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013hsyXiVjMyu6xbDeovvFB4","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F4-conf-03: Harmonising effect sizes reported under incompatible definitions\n\n## Question\nFive effects from four open-access arXiv papers (listed in `data/effects.json`) are reported under different definitions: log-point ATTs, a percentage-point effect, an elasticity, and a linear-probability coefficient. The task is to restate each printed point estimate on the target definition given for that item. Only quantities printed in the cited extract may be used, and every transformation must be stated explicitly. Where the target cannot be reached, the report must say so and name the quantity that is missing.\n\n## Data and exclusions\n- The inputs are the four extracts under `data/sources/`, plus `data/effects.json` (5 items) and `data/README.md`. Line counts are in `n_input_rows`. The SHA-256 hashes of all four extracts match the values in the README (see `out/state/data_audit.md`).\n- No item was excluded. All 5 items were assessed (`n_analysed` = 5; `out/exclusions.csv`: 5 − 0 = 5).\n- Problems found in the inputs are listed in `data_issues`, together with how each was handled. In summary:\n  - The DCR abstract says fatal outcomes \"fall by 35%\". Table 7, however, implies about −29.8%, and Section 6.1 says \"approximately 30%\".\n  - For the same −0.431 estimate, the DCR paper prints a standard error of 0.210 in Table 6 but 0.218 in Table A19. The Table A19 note says 640 control regions, while the tables say 608.\n  - Two \"% change\" values in DCR Table A19 differ in the last digit from 100·(exp(ATT) − 1).\n  - In the minimum-wage paper, the row labelled \"% MW changes\" prints 0.047, while the table note says \"about 4%\".\n  - Tossou's \"% Change\" row differs in the last digit from the coefficient divided by the mean. Tossou's abstract cites a Table 2 that is not in the extract.\n  - The RESEA control mean is a raw mean, whereas the treatment effect is adjusted for randomisation blocks.\n  - There are minor rendering defects. None of them affects a number used here.\n\n## Method\nThe conventions are those set out in TASK.md. `out/analysis.py` reads each value directly from its table row and prints the source lines it uses. The printed, untransformed estimates are the baseline (`baseline_printed_values`, `baseline_printed_units`).\n- **E1** (log points → relative %): 100·(exp(b) − 1), with b = −0.431 (DCR Table 6, ATT row, 5 km column).\n- **E2** (pp → % of the control mean): 100·Δ/p, with Δ = 1.47 pp (RESEA Table 2, Reemployment row, ATE column) and p = 75.0% (Control column).\n- **E3** (elasticity → implied % change): 100·η·g, with η = −0.388 and g = 0.047 (MW Table 2, \"Employment elasticity w.r.t MW\" and \"% MW changes\" rows). I read g as a proportion (4.7%), the only reading that fits the note's \"about 4%\".\n- **E4** (log points → absolute change per 100,000): this would need R0·(exp(b) − 1), with b = −0.354 (DCR Table 7, fatal row, 5 km column). The baseline level R0 is not printed anywhere in the extract. The script searches the extract for a printed level and finds none.\n- **E5** (LPM coefficient → pp): 100·β, with β = −0.0962 (Tossou Table 3, Treatment row, column (4)).\n\nThe full formula and locator for each item are stored in `harmonised.<id>.transformation` and `harmonised.<id>.locator`. No randomness is involved (`random_seed` = null).\n\n## Results\n| item | harmonised value | unit | key |\n|---|---|---|---|\n| E1 | −35.01 | % change relative to the counterfactual rate | `harmonised.E1.value` |\n| E2 | 1.96 | % of the control-group mean | `harmonised.E2.value` |\n| E3 | −1.82 | % change in employment | `harmonised.E3.value` |\n| E4 | null (not harmonisable) | hospitalisations per 100,000 per year | `harmonised.E4.value`, `harmonised.E4.missing_quantity` |\n| E5 | −9.62 | percentage points | `harmonised.E5.value` |\n\n- 4 items were harmonised (`n_harmonised`) and 1 was not (`n_not_harmonisable`).\n- Feasibility is \"complete\" (`feasibility`): every item has either a value or a justified statement that it cannot be converted.\n- E1 agrees with the paper's own \"% change\" row (−35.0%).\n- E4 can only be given in relative terms: −29.81%, as recorded in `harmonised.E4.transformation`. An absolute change requires the baseline (counterfactual) fatal drug-related hospitalisation rate per 100,000 in the 5 km treated regions (`harmonised.E4.missing_quantity`).\n\n## Uncertainty\nNo standard errors or confidence intervals were propagated; all values are point estimates.\n- **Exact conversions:** E1 (exact under the log-point convention), E5 (a pure rescaling) and E2 (an exact ratio of printed numbers).\n- **E2 caveat:** the denominator is the raw aggregate control mean (75.0%), not a block-adjusted counterfactual level. The printed inputs are also rounded (1.47; 75.0).\n- **Approximate conversion:** E3. The formula η·g is a linear (first-order) approximation. The input g = 0.047 is printed to only 3 decimals, and its unit is ambiguous given the label \"%\" and the note's \"about 4%\". If g were 0.04, the result would be −1.55%. This figure is shown for sensitivity only and is not stored as a result.\n- **E4:** no value is reported, so no uncertainty applies.\n\n## Limitations and open issues\n- **E4** cannot be converted to an absolute rate from the extract. I did not guess or substitute an outside baseline.\n- **E3** depends on reading 0.047 as a proportion. The source's own note (\"about 4%\") does not match it exactly. See `data_issues`.\n- **E2** uses a raw control mean as its denominator, while the effect is block-adjusted. This follows the stated target definition, but it is not a model-based counterfactual level.\n- **Source inconsistencies** (see `data_issues`): the DCR abstract's fatal −35% versus Table 7's −29.8%; differing standard errors and control-region counts between DCR Tables 6 and A19; rounding discrepancies. None of these changes the point estimates used here.\n- Values are rounded to 2 decimals. The unrounded results are given in each `transformation` string.\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, matching the printed -35.0%.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Exact under the log-point convention; no SE propagated.\"},\n {\"id\":\"C2\",\"text\":\"The RESEA reemployment ATE of 1.47 pp is a 1.96% relative increase over the control-group mean of 75.0%.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Control mean is a raw (not block-adjusted) aggregate; printed inputs are rounded.\"},\n {\"id\":\"C3\",\"text\":\"The MW employment elasticity of -0.388 applied to the printed 0.047 minimum-wage change implies a -1.82% change in employment.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Linear approximation; 0.047 interpreted as a proportion (note says 'about 4%').\"},\n {\"id\":\"C4\",\"text\":\"E4 (fatal drug-related hospitalisations, absolute change per 100,000) cannot be harmonised because the extract prints no baseline level of the fatal hospitalisation rate.\",\"results_keys\":[\"harmonised\",\"n_not_harmonisable\"],\"status\":\"supported\",\"caveat\":\"Relative change (-29.81%) is computable and stated in the transformation string.\"},\n {\"id\":\"C5\",\"text\":\"The Tossou LPM coefficient of -0.0962 corresponds to -9.62 percentage points.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Pure rescaling.\"},\n {\"id\":\"C6\",\"text\":\"Four of five items were harmonised; the task is complete because the remaining item has a justified statement of impossibility.\",\"results_keys\":[\"n_harmonised\",\"n_not_harmonisable\",\"feasibility\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C7\",\"text\":\"The sources contain internal inconsistencies (e.g. DCR abstract fatal -35% vs Table 7 -29.8%; SE 0.210 vs 0.218), none of which affects the point estimates used.\",\"results_keys\":[\"data_issues\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully; contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 of all inputs computed; 4 extracts match README\",\"line counts recorded\",\"anomalies listed in data_audit.md\",\"exclusions.csv: 5-0=5\"],\"notes\":\"No availability-rule violations\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"baseline_printed_values/units written to results.json\"],\"notes\":\"Baseline = printed untransformed estimates\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, prints source lines, asserts unique line matches\",\"no randomness (random_seed null)\"],\"notes\":\"\"},\n\"P4\":{\"status\":\"running\",\"checks\":[\"clean re-run byte-identical (cmp)\",\"13 consistency checks PASS (checks.md)\"],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-30 P1 passed (audit, hashes match). Decision: read MW '% MW changes' 0.047 as proportion; E4 not harmonisable (no baseline level printed).\n- 2026-10-30 P2 passed (baseline printed values). P3 passed (analysis.py). P4 checks pass; running gate.\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Write report, claims, phases; 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":2745,"cache_read_input_tokens":47669,"output_tokens":3745,"output_tokens_details":{"thinking_tokens":45},"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":2745},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXqzTUbAK5oJiMsrq42d","requestRef":"6de7a50b-d796-4026-b84f-83c9f4aab021","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"df8d42a4-c566-43d1-9abe-16a03223e5b1","timestamp":"2026-10-30T05:10:41.079Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0232","sessionId":"008ec3b4-46e6-4e9d-82e8-4b7207a5d6af","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"df8d42a4-c566-43d1-9abe-16a03223e5b1","isSidechain":false,"promptId":"5bbd5ec8-4c6a-4ce7-b9b5-627abc92134d","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nPGAR GATE pgar_gate/1.0 — FAILED (1 of 24 checks failed)\n  [ok] file:out/results.json: present\n  [ok] file:out/report.md: present\n  [ok] file:out/analysis.py: present\n  [ok] file:out/claims.json: present\n  [ok] results_json: parsed\n  [ok] key:harmonised: ok\n  [ok] key:n_harmonised: ok\n  [ok] key:n_not_harmonisable: ok\n  [ok] key:task_id: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [ok] section:Question: present\n  [ok] section:Data and exclusions: present\n  [ok] section:Method: present\n  [ok] section:Results: present\n  [ok] section:Uncertainty: present\n  [ok] section:Limitations and open issues: present\n  [ok] claims_json: 7 claims, all keys resolve\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] reproducibility: regenerated in 0.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0232/out/state/gate_receipts/20261030T051041.184843Z.json","is_error":true,"tool_use_id":"toolu_013hsyXiVjMyu6xbDeovvFB4"}]},"uuid":"1bbba6b9-b569-4adf-a62e-835f8131148d","timestamp":"2026-10-30T05:10:41.189Z","toolUseResult":"Error: Exit code 1\nPGAR GATE pgar_gate/1.0 — FAILED (1 of 24 checks failed)\n  [ok] file:out/results.json: present\n  [ok] file:out/report.md: present\n  [ok] file:out/analysis.py: present\n  [ok] file:out/claims.json: present\n  [ok] results_json: parsed\n  [ok] key:harmonised: ok\n  [ok] key:n_harmonised: ok\n  [ok] key:n_not_harmonisable: ok\n  [ok] key:task_id: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [ok] section:Question: present\n  [ok] section:Data and exclusions: present\n  [ok] section:Method: present\n  [ok] section:Results: present\n  [ok] section:Uncertainty: present\n  [ok] section:Limitations and open issues: present\n  [ok] claims_json: 7 claims, all keys resolve\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] reproducibility: regenerated in 0.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0232/out/state/gate_receipts/20261030T051041.184843Z.json","sandboxed":false,"sourceToolAssistantUUID":"df8d42a4-c566-43d1-9abe-16a03223e5b1","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0232","sessionId":"008ec3b4-46e6-4e9d-82e8-4b7207a5d6af","version":"2.1.314","gitBranch":"HEAD"}
{"type":"api-request-blob","sessionId":"008ec3b4-46e6-4e9d-82e8-4b7207a5d6af","hash":"ffd1e345c3724c69fdf84b6caa595aad9a19a385419d6476c80449bd95cc6c77","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"Now report and claims."},{"type":"tool_use","id":"toolu_013hsyXiVjMyu6xbDeovvFB4","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F4-conf-03: Harmonising effect sizes reported under incompatible definitions\n\n## Question\nFive effects from four open-access arXiv papers (listed in `data/effects.json`) are reported under different definitions: log-point ATTs, a percentage-point effect, an elasticity, and a linear-probability coefficient. The task is to restate each printed point estimate on the target definition given for that item. Only quantities printed in the cited extract may be used, and every transformation must be stated explicitly. Where the target cannot be reached, the report must say so and name the quantity that is missing.\n\n## Data and exclusions\n- The inputs are the four extracts under `data/sources/`, plus `data/effects.json` (5 items) and `data/README.md`. Line counts are in `n_input_rows`. The SHA-256 hashes of all four extracts match the values in the README (see `out/state/data_audit.md`).\n- No item was excluded. All 5 items were assessed (`n_analysed` = 5; `out/exclusions.csv`: 5 − 0 = 5).\n- Problems found in the inputs are listed in `data_issues`, together with how each was handled. In summary:\n  - The DCR abstract says fatal outcomes \"fall by 35%\". Table 7, however, implies about −29.8%, and Section 6.1 says \"approximately 30%\".\n  - For the same −0.431 estimate, the DCR paper prints a standard error of 0.210 in Table 6 but 0.218 in Table A19. The Table A19 note says 640 control regions, while the tables say 608.\n  - Two \"% change\" values in DCR Table A19 differ in the last digit from 100·(exp(ATT) − 1).\n  - In the minimum-wage paper, the row labelled \"% MW changes\" prints 0.047, while the table note says \"about 4%\".\n  - Tossou's \"% Change\" row differs in the last digit from the coefficient divided by the mean. Tossou's abstract cites a Table 2 that is not in the extract.\n  - The RESEA control mean is a raw mean, whereas the treatment effect is adjusted for randomisation blocks.\n  - There are minor rendering defects. None of them affects a number used here.\n\n## Method\nThe conventions are those set out in TASK.md. `out/analysis.py` reads each value directly from its table row and prints the source lines it uses. The printed, untransformed estimates are the baseline (`baseline_printed_values`, `baseline_printed_units`).\n- **E1** (log points → relative %): 100·(exp(b) − 1), with b = −0.431 (DCR Table 6, ATT row, 5 km column).\n- **E2** (pp → % of the control mean): 100·Δ/p, with Δ = 1.47 pp (RESEA Table 2, Reemployment row, ATE column) and p = 75.0% (Control column).\n- **E3** (elasticity → implied % change): 100·η·g, with η = −0.388 and g = 0.047 (MW Table 2, \"Employment elasticity w.r.t MW\" and \"% MW changes\" rows). I read g as a proportion (4.7%), the only reading that fits the note's \"about 4%\".\n- **E4** (log points → absolute change per 100,000): this would need R0·(exp(b) − 1), with b = −0.354 (DCR Table 7, fatal row, 5 km column). The baseline level R0 is not printed anywhere in the extract. The script searches the extract for a printed level and finds none.\n- **E5** (LPM coefficient → pp): 100·β, with β = −0.0962 (Tossou Table 3, Treatment row, column (4)).\n\nThe full formula and locator for each item are stored in `harmonised.<id>.transformation` and `harmonised.<id>.locator`. No randomness is involved (`random_seed` = null).\n\n## Results\n| item | harmonised value | unit | key |\n|---|---|---|---|\n| E1 | −35.01 | % change relative to the counterfactual rate | `harmonised.E1.value` |\n| E2 | 1.96 | % of the control-group mean | `harmonised.E2.value` |\n| E3 | −1.82 | % change in employment | `harmonised.E3.value` |\n| E4 | null (not harmonisable) | hospitalisations per 100,000 per year | `harmonised.E4.value`, `harmonised.E4.missing_quantity` |\n| E5 | −9.62 | percentage points | `harmonised.E5.value` |\n\n- 4 items were harmonised (`n_harmonised`) and 1 was not (`n_not_harmonisable`).\n- Feasibility is \"complete\" (`feasibility`): every item has either a value or a justified statement that it cannot be converted.\n- E1 agrees with the paper's own \"% change\" row (−35.0%).\n- E4 can only be given in relative terms: −29.81%, as recorded in `harmonised.E4.transformation`. An absolute change requires the baseline (counterfactual) fatal drug-related hospitalisation rate per 100,000 in the 5 km treated regions (`harmonised.E4.missing_quantity`).\n\n## Uncertainty\nNo standard errors or confidence intervals were propagated; all values are point estimates.\n- **Exact conversions:** E1 (exact under the log-point convention), E5 (a pure rescaling) and E2 (an exact ratio of printed numbers).\n- **E2 caveat:** the denominator is the raw aggregate control mean (75.0%), not a block-adjusted counterfactual level. The printed inputs are also rounded (1.47; 75.0).\n- **Approximate conversion:** E3. The formula η·g is a linear (first-order) approximation. The input g = 0.047 is printed to only 3 decimals, and its unit is ambiguous given the label \"%\" and the note's \"about 4%\". If g were 0.04, the result would be −1.55%. This figure is shown for sensitivity only and is not stored as a result.\n- **E4:** no value is reported, so no uncertainty applies.\n\n## Limitations and open issues\n- **E4** cannot be converted to an absolute rate from the extract. I did not guess or substitute an outside baseline.\n- **E3** depends on reading 0.047 as a proportion. The source's own note (\"about 4%\") does not match it exactly. See `data_issues`.\n- **E2** uses a raw control mean as its denominator, while the effect is block-adjusted. This follows the stated target definition, but it is not a model-based counterfactual level.\n- **Source inconsistencies** (see `data_issues`): the DCR abstract's fatal −35% versus Table 7's −29.8%; differing standard errors and control-region counts between DCR Tables 6 and A19; rounding discrepancies. None of these changes the point estimates used here.\n- Values are rounded to 2 decimals. The unrounded results are given in each `transformation` string.\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, matching the printed -35.0%.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Exact under the log-point convention; no SE propagated.\"},\n {\"id\":\"C2\",\"text\":\"The RESEA reemployment ATE of 1.47 pp is a 1.96% relative increase over the control-group mean of 75.0%.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Control mean is a raw (not block-adjusted) aggregate; printed inputs are rounded.\"},\n {\"id\":\"C3\",\"text\":\"The MW employment elasticity of -0.388 applied to the printed 0.047 minimum-wage change implies a -1.82% change in employment.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Linear approximation; 0.047 interpreted as a proportion (note says 'about 4%').\"},\n {\"id\":\"C4\",\"text\":\"E4 (fatal drug-related hospitalisations, absolute change per 100,000) cannot be harmonised because the extract prints no baseline level of the fatal hospitalisation rate.\",\"results_keys\":[\"harmonised\",\"n_not_harmonisable\"],\"status\":\"supported\",\"caveat\":\"Relative change (-29.81%) is computable and stated in the transformation string.\"},\n {\"id\":\"C5\",\"text\":\"The Tossou LPM coefficient of -0.0962 corresponds to -9.62 percentage points.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Pure rescaling.\"},\n {\"id\":\"C6\",\"text\":\"Four of five items were harmonised; the task is complete because the remaining item has a justified statement of impossibility.\",\"results_keys\":[\"n_harmonised\",\"n_not_harmonisable\",\"feasibility\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C7\",\"text\":\"The sources contain internal inconsistencies (e.g. DCR abstract fatal -35% vs Table 7 -29.8%; SE 0.210 vs 0.218), none of which affects the point estimates used.\",\"results_keys\":[\"data_issues\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully; contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 of all inputs computed; 4 extracts match README\",\"line counts recorded\",\"anomalies listed in data_audit.md\",\"exclusions.csv: 5-0=5\"],\"notes\":\"No availability-rule violations\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"baseline_printed_values/units written to results.json\"],\"notes\":\"Baseline = printed untransformed estimates\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, prints source lines, asserts unique line matches\",\"no randomness (random_seed null)\"],\"notes\":\"\"},\n\"P4\":{\"status\":\"running\",\"checks\":[\"clean re-run byte-identical (cmp)\",\"13 consistency checks PASS (checks.md)\"],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-30 P1 passed (audit, hashes match). Decision: read MW '% MW changes' 0.047 as proportion; E4 not harmonisable (no baseline level printed).\n- 2026-10-30 P2 passed (baseline printed values). P3 passed (analysis.py). P4 checks pass; running gate.\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Write report, claims, phases; run gate"}}]}}
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{"parentUuid":"1484e6de-4633-4064-b50c-238925f8a1dc","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr3aLgTH79C6hHNZhkX","type":"message","role":"assistant","content":[{"type":"text","text":"I converted four of the five effects to their target definitions. The fifth (E4) can't be converted from what the extract prints. That counts as \"complete\" under the task's rules, and the gate passes (24 of 24 checks; the script reproduces `out/results.json` identically).\n\n| item | printed estimate | converted value | conversion |\n|---|---|---|---|\n| E1 | −0.431 log points | **−35.01 %** | 100·(exp(b) − 1); matches the paper's own \"% change\" row (−35.0%) |\n| E2 | 1.47 pp | **1.96 %** of the control mean | 100 × 1.47 / 75.0 |\n| E3 | elasticity −0.388 | **−1.82 %** employment | 100 × −0.388 × 0.047 |\n| E4 | −0.354 log points | **not convertible** | see below |\n| E5 | −0.0962 | **−9.62 pp** | ×100 |\n\n**Why E4 can't be done:** turning the effect into hospitalisations per 100,000 needs the baseline rate of fatal drug-related hospitalisations in the treated regions. That rate isn't printed anywhere in the extract; the script searches for it and finds nothing. I didn't substitute an outside figure. The relative change, −29.81%, can be computed and is stated in the result.\n\n**Assumptions that affect the numbers:**\n- **E3:** the minimum-wage paper labels its row \"% MW changes\" but prints 0.047, while its note says \"about 4%\". I read 0.047 as a 4.7% increase. The formula itself is a first-order approximation.\n- **E2:** the denominator is the raw control-group mean (75.0%), while the 1.47 pp effect is adjusted for randomisation week. The target definition asks for this, but it isn't a model-based baseline.\n- **No uncertainty:** no standard errors were propagated. E1, E2 and E5 are exact under the stated conventions; E3 is approximate.\n\n**Inconsistencies in the sources:** none of these change the numbers above. They are listed in `data_issues`.\n- The drug consumption room (DCR) paper's abstract says fatal hospitalisations \"fall by 35%\", but its Table 7 implies about −29.8%.\n- The same −0.431 estimate has a standard error of 0.210 in one table and 0.218 in another. The control-region count is 640 in one note and 608 elsewhere.\n- Some printed \"% change\" values differ from the computed ones in the last digit.\n- There are minor text-conversion glitches.\n\nA separate review of the outputs is still to come.\n\nFiles are in `out/`:\n- `results.json`\n- `report.md`\n- `analysis.py`\n- `claims.json`\n- `exclusions.csv`\n- `state/` (contract, data audit, checks, phase ledger, log, gate receipts)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":343,"cache_read_input_tokens":55496,"output_tokens":933,"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":343},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXr3a3ZqunSmoRjb8GB8","requestRef":"b7c60a1c-fd0c-4968-b13c-a845ce0e7c00","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"55a46de4-66a5-4b08-b595-235444f90ce2","timestamp":"2026-10-30T05:11:00.656Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0232","sessionId":"008ec3b4-46e6-4e9d-82e8-4b7207a5d6af","version":"2.1.314","gitBranch":"HEAD"}
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