1{"type":"atis-latch","atis":"","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T05:08:53.389Z","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T05:08:53.395Z","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb"}4{"parentUuid":null,"isSidechain":false,"promptId":"78a43acf-7189-41b5-8d88-ed19c47467dd","type":"user","message":{"role":"user","content":"Begin"},"uuid":"ab8086bf-49eb-4f2c-ac14-405af1fcfdd1","timestamp":"2026-10-30T05:08:53.400Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","shapeHash":"e5724379dff8ae63e8378d36bdd9704da41e120a51f412af7299e05765854c13","timestamp":"2026-10-30T05:08:53.414Z","version":"2.1.314","shape":{"system":[{"type":"text","text":"You are Claude Code, Anthropic's official CLI for Claude, running within the Claude Agent SDK."},{"type":"text","text":"CWD: /Users/simon-pierreboucher/wp29/runs/RC0237\nDate: 2026-10-30\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. Never use words like \"complex\" or \"risk\" in the description - just describe what it does.\n\nSay what the command does in plain words: do not echo the command's text, its flags, or file paths - the user reads this description, often without seeing the command.\n\nFor simple commands (git, npm, standard CLI tools), keep it brief (5-10 words):\n- ls → \"List files in current directory\"\n- git status → \"Show working tree status\"\n- npm install → \"Install package dependencies\"\n\nFor commands that are harder to parse at a glance (piped commands, obscure flags, etc.), add enough context to clarify what it does:\n- find . -name \"*.tmp\" -exec rm {} \\; → \"Find and delete all .tmp files recursively\"\n- git reset --hard origin/main → \"Discard all local changes and match remote main\"\n- curl -s url | jq '.data[]' → \"Fetch JSON from URL and extract data array elements\"","type":"string"},"dangerouslyDisableSandbox":{"description":"Set this to true to dangerously override sandbox mode and run commands without sandboxing.","type":"boolean"}},"required":["command"],"additionalProperties":false}},{"name":"Edit","description":"modify file contents in place","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"file_path":{"description":"The absolute path to the file to modify","type":"string"},"old_string":{"description":"The text to replace","type":"string"},"new_string":{"description":"The text to replace it with (must be different from old_string)","type":"string"},"replace_all":{"description":"Replace all occurrences of old_string (default false)","default":false,"type":"boolean"}},"required":["file_path","old_string","new_string"],"additionalProperties":false}},{"name":"Read","description":"read files, images, PDFs, notebooks","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"file_path":{"description":"The absolute path to the file to read","type":"string"},"offset":{"description":"The line number to start reading from. Only provide if the file is too large to read at once","type":"integer","minimum":0,"maximum":9007199254740991},"limit":{"description":"The number of lines to read. Only provide if the file is too large to read at once.","type":"integer","exclusiveMinimum":0,"maximum":9007199254740991},"pages":{"description":"Page range for PDF files (e.g., \"1-5\", \"3\", \"10-20\"). Only applicable to PDF files. Maximum 20 pages per request.","type":"string"},"allow_large":{"description":"Set to true to read a text file, or a line range of one, that is over the usual size limits, up to what still fits in your context. Only use this when you genuinely need all of it or the user asked for the whole file; otherwise read it in parts with offset and limit, or search it.","type":"boolean"}},"required":["file_path"],"additionalProperties":false}}]}}6{"type":"api-request-blob","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","hash":"d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9","message":{"role":"system","content":[],"output_config":{"effort":"medium"}}}7{"type":"api-request","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","id":"4f501c96-ba3d-44fd-84cb-e63420949d71","timestamp":"2026-10-30T05:08:53.414Z","version":"2.1.314","querySource":"sdk","shapeHash":"e5724379dff8ae63e8378d36bdd9704da41e120a51f412af7299e05765854c13","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"keep":0,"tail":["252097215a6af3e04abe3f3c5a1cce73f5f388825c554cef2907d381aaf4102e","d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",0,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"8136df5febc4c25822e015a315c1804b8b14ae166854adba59b9f626a42f5856"}8{"parentUuid":"ab8086bf-49eb-4f2c-ac14-405af1fcfdd1","isSidechain":false,"attachment":{"type":"session_context","context":{}},"type":"attachment","uuid":"1ab03ff6-1e43-49c1-bb2e-7f4d2de1f3ee","timestamp":"2026-10-30T05:08:53.406Z","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}9{"parentUuid":"1ab03ff6-1e43-49c1-bb2e-7f4d2de1f3ee","isSidechain":false,"attachment":{"type":"date","date":"2026-10-30"},"type":"attachment","uuid":"c6296b6c-7360-4e8d-ad6e-26e495846a97","timestamp":"2026-10-30T05:08:53.406Z","rendered":[{"content":"<system-reminder>\nToday's date is 2026-10-30.\n</system-reminder>"}],"renderedRole":"system","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}10{"parentUuid":"c6296b6c-7360-4e8d-ad6e-26e495846a97","isSidechain":false,"attachment":{"type":"remote_session_change","url":null,"commit":"Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>","pr":"🤖 Generated with [Claude Code](https://claude.com/claude-code)","sendUserFileHint":false,"managedCommit":false,"managedPr":false},"type":"attachment","uuid":"b508a039-f560-4f4a-97a9-fa08c09f8ec0","timestamp":"2026-10-30T05:08:53.407Z","rendered":[{"content":"<system-reminder>\nAttribution for git commits and pull requests you create from here on (this replaces Claude Code's own earlier attribution guidance, such as a previous copy of this reminder; the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb"}12{"parentUuid":"b508a039-f560-4f4a-97a9-fa08c09f8ec0","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqtsHu6sDCvzWD6gvgi","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":2209,"cache_read_input_tokens":1504,"output_tokens":144,"output_tokens_details":{"thinking_tokens":22},"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":2209},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":434,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqts2HJaJMBcMXxpvC9","requestRef":"4f501c96-ba3d-44fd-84cb-e63420949d71","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d4dcb6fc-c588-4286-a650-eb2a8dba279c","timestamp":"2026-10-30T05:08:54.817Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"d4dcb6fc-c588-4286-a650-eb2a8dba279c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqtsHu6sDCvzWD6gvgi","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01VZwQNpHznBwM4VNKiAwA56","name":"Bash","input":{"command":"cat TASK.md; ls -la; ls -la data out 2>/dev/null; cat out/state/* 2>/dev/null | head -50","description":"Read task and list workspace"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":2209,"cache_read_input_tokens":1504,"output_tokens":144,"output_tokens_details":{"thinking_tokens":22},"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":2209},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqts2HJaJMBcMXxpvC9","requestRef":"4f501c96-ba3d-44fd-84cb-e63420949d71","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2b9331ea-8a6e-4857-bec7-7346a5aef4ef","timestamp":"2026-10-30T05:08:55.499Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}14{"parentUuid":"2b9331ea-8a6e-4857-bec7-7346a5aef4ef","isSidechain":false,"promptId":"78a43acf-7189-41b5-8d88-ed19c47467dd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VZwQNpHznBwM4VNKiAwA56","type":"tool_result","content":"# F4-conf-03 — Harmonising effect sizes reported under incompatible definitions\n\n## Scientific question\nA synthesis compares treatment effects from four open-access studies that report their estimates under different definitions: a semi-elasticity (coefficient on a log outcome), a percentage-point effect on a probability, an elasticity, and a linear-probability coefficient. For each of the five effects listed in `data/effects.json`, re-express the printed point estimate on the **target definition** stated for that item, using only quantities printed in the cited extract and an explicitly stated transformation; where the target definition cannot be reached from the printed quantities, say so and name the additional quantity that would be required. The estimands are the harmonised point estimates (no uncertainty propagation is required). Each item carries a locator for every printed quantity used.\n\n## Inputs\n- `data/effects.json` — the five items: {\"id\", \"source\", \"effect\" (what and where), \"reported_definition\", \"target_definition\"}.\n- `data/sources/arxiv-2609.27732v1.txt` — Armendariz (2026), *Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland*, arXiv:2609.27732v1, CC BY 4.0: abstract, Section 6 (Tables 6 and 7, Section 6.1), Appendix Table A19. Outcomes are log hospitalisation rates per 100,000 inhabitants; ATTs in log points; the tables also print a \"% change\" row for the overall effect.\n- `data/sources/arxiv-2606.14621v1.txt` — Li, Pearson-Merkowitz & Yokum (2026), *Evaluating the Impact of Rhode Island's Self-Sustaining RESEA Program on Employment Outcomes*, arXiv:2606.14621v1, CC BY 4.0: abstract, Section 3 (Table 2: treatment/control means, differences, ATEs with p-values and 95% CIs), Table 3. Reemployment is in percent (means) and percentage points (effects).\n- `data/sources/arxiv-2505.04555v3.txt` — Kanayama, Miyaji & Otani (2025/2026), *Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation*, arXiv:2505.04555v3, CC BY 4.0: abstract and Appendix A.1 (Table 2: elasticities and the size of the minimum-wage change, with note).\n- `data/sources/arxiv-2510.03658v1.txt` — Tossou (2025), *Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin*, arXiv:2510.03658v1, CC BY 4.0: abstract, Section 4.1 and Table 3 (linear probability model of infant death or stillbirth; \"Mean Dep. Var.\" row).\n- `data/README.md` — provenance, retrieval times, licences, SHA-256 of the extracts.\n\n## Information-availability rules\n- The extracts are the only admissible evidence; a quantity that is not printed in the cited extract is not available, even if you believe it is in the full paper or elsewhere.\n- Conventions: a log-point coefficient b corresponds to a relative change of 100·(exp(b) − 1) percent; a percentage-point effect Δ on a probability with counterfactual (control) level p corresponds to a relative change of 100·Δ/p percent; an elasticity η applied to a proportional change g of the treatment variable implies a relative change of 100·η·g percent. State the transformation you apply in `transformation`.\n- `harmonisable` is `false` only when the target definition requires a quantity that the cited extract does not print; then `value` is `null` and `missing_quantity` names the quantity. Do not substitute a guessed or externally remembered value.\n- `feasibility` refers to the task as a whole: the task is complete when every item has either a harmonised value or an explicit, justified statement of impossibility.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `harmonised` | object | — | {\"E1\": {\"value\": number or null, \"unit\": \"<unit of the target definition>\", \"transformation\": \"<formula with the printed inputs>\", \"harmonisable\": true/false, \"missing_quantity\": string or null, \"locator\": \"<source id | table | row | column for every printed input>\"}, … \"E5\": {…}} |\n| `n_harmonised` | integer | — | number of items with a non-null value |\n| `n_not_harmonisable` | integer | — | number of items with `harmonisable` = false |\n| `task_id` | string | — | \"F4-conf-03\" |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" — refers to the task as a whole |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the inputs (rendering defects, internal inconsistencies of a source, etc.) and how they were handled; empty list if none |\n| `n_input_rows` | object | lines / items | {file name: number of lines} for every file under `data/` that you read (text files: number of newline-terminated lines as reported by `wc -l`; JSON files: number of top-level items) |\n| `n_analysed` | integer or null | — | number of reported items (values, assertions, effects or references) you actually assessed |\n| `random_seed` | integer or null | — | null (no randomness is involved) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. \"Uncertainty\" must note that no standard errors were propagated and which conversions are exact versus approximate.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically (the script must print the source lines it relies on).\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available; the standard library is sufficient for this task). Do not modify `data/`.\n- The source extracts under `data/sources/` are the **only** admissible evidence. Do not rely on memory of the papers or on other versions of them; if a quantity is not in the extract, it is not available.\n- Report numbers exactly as printed in the source (same sign, same number of decimals); do not round or rescale printed values unless the key definition asks for a transformation.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 32\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:45 .\ndrwxr-xr-x 137 simon-pierreboucher staff 4384 oct. 30 01:08 ..\n-rw-r--r-- 1 simon-pierreboucher staff 956 oct. 29 23:45 bundle.json\ndrwxr-xr-x 5 simon-pierreboucher staff 160 oct. 29 23:45 data\n-rw-r--r-- 1 simon-pierreboucher staff 1326 oct. 29 23:45 gate_spec.json\n-rw-r--r-- 1 simon-pierreboucher staff 6355 oct. 29 23:45 TASK.md\ndata:\ntotal 16\ndrwxr-xr-x 5 simon-pierreboucher staff 160 oct. 29 23:45 .\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:45 ..\n-rw-r--r-- 1 simon-pierreboucher staff 2352 oct. 29 23:45 effects.json\n-rw-r--r-- 1 simon-pierreboucher staff 2875 oct. 29 23:45 README.md\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:45 sources\n(eval):1: no matches found: out/state/*","is_error":false}]},"uuid":"1924e556-97d0-4874-9972-5b37c4ef92ab","timestamp":"2026-10-30T05:08:55.613Z","toolUseResult":{"stdout":"# F4-conf-03 — Harmonising effect sizes reported under incompatible definitions\n\n## Scientific question\nA synthesis compares treatment effects from four open-access studies that report their estimates under different definitions: a semi-elasticity (coefficient on a log outcome), a percentage-point effect on a probability, an elasticity, and a linear-probability coefficient. For each of the five effects listed in `data/effects.json`, re-express the printed point estimate on the **target definition** stated for that item, using only quantities printed in the cited extract and an explicitly stated transformation; where the target definition cannot be reached from the printed quantities, say so and name the additional quantity that would be required. The estimands are the harmonised point estimates (no uncertainty propagation is required). Each item carries a locator for every printed quantity used.\n\n## Inputs\n- `data/effects.json` — the five items: {\"id\", \"source\", \"effect\" (what and where), \"reported_definition\", \"target_definition\"}.\n- `data/sources/arxiv-2609.27732v1.txt` — Armendariz (2026), *Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland*, arXiv:2609.27732v1, CC BY 4.0: abstract, Section 6 (Tables 6 and 7, Section 6.1), Appendix Table A19. Outcomes are log hospitalisation rates per 100,000 inhabitants; ATTs in log points; the tables also print a \"% change\" row for the overall effect.\n- `data/sources/arxiv-2606.14621v1.txt` — Li, Pearson-Merkowitz & Yokum (2026), *Evaluating the Impact of Rhode Island's Self-Sustaining RESEA Program on Employment Outcomes*, arXiv:2606.14621v1, CC BY 4.0: abstract, Section 3 (Table 2: treatment/control means, differences, ATEs with p-values and 95% CIs), Table 3. Reemployment is in percent (means) and percentage points (effects).\n- `data/sources/arxiv-2505.04555v3.txt` — Kanayama, Miyaji & Otani (2025/2026), *Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation*, arXiv:2505.04555v3, CC BY 4.0: abstract and Appendix A.1 (Table 2: elasticities and the size of the minimum-wage change, with note).\n- `data/sources/arxiv-2510.03658v1.txt` — Tossou (2025), *Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin*, arXiv:2510.03658v1, CC BY 4.0: abstract, Section 4.1 and Table 3 (linear probability model of infant death or stillbirth; \"Mean Dep. Var.\" row).\n- `data/README.md` — provenance, retrieval times, licences, SHA-256 of the extracts.\n\n## Information-availability rules\n- The extracts are the only admissible evidence; a quantity that is not printed in the cited extract is not available, even if you believe it is in the full paper or elsewhere.\n- Conventions: a log-point coefficient b corresponds to a relative change of 100·(exp(b) − 1) percent; a percentage-point effect Δ on a probability with counterfactual (control) level p corresponds to a relative change of 100·Δ/p percent; an elasticity η applied to a proportional change g of the treatment variable implies a relative change of 100·η·g percent. State the transformation you apply in `transformation`.\n- `harmonisable` is `false` only when the target definition requires a quantity that the cited extract does not print; then `value` is `null` and `missing_quantity` names the quantity. Do not substitute a guessed or externally remembered value.\n- `feasibility` refers to the task as a whole: the task is complete when every item has either a harmonised value or an explicit, justified statement of impossibility.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `harmonised` | object | — | {\"E1\": {\"value\": number or null, \"unit\": \"<unit of the target definition>\", \"transformation\": \"<formula with the printed inputs>\", \"harmonisable\": true/false, \"missing_quantity\": string or null, \"locator\": \"<source id | table | row | column for every printed input>\"}, … \"E5\": {…}} |\n| `n_harmonised` | integer | — | number of items with a non-null value |\n| `n_not_harmonisable` | integer | — | number of items with `harmonisable` = false |\n| `task_id` | string | — | \"F4-conf-03\" |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" — refers to the task as a whole |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the inputs (rendering defects, internal inconsistencies of a source, etc.) and how they were handled; empty list if none |\n| `n_input_rows` | object | lines / items | {file name: number of lines} for every file under `data/` that you read (text files: number of newline-terminated lines as reported by `wc -l`; JSON files: number of top-level items) |\n| `n_analysed` | integer or null | — | number of reported items (values, assertions, effects or references) you actually assessed |\n| `random_seed` | integer or null | — | null (no randomness is involved) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. \"Uncertainty\" must note that no standard errors were propagated and which conversions are exact versus approximate.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically (the script must print the source lines it relies on).\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available; the standard library is sufficient for this task). Do not modify `data/`.\n- The source extracts under `data/sources/` are the **only** admissible evidence. Do not rely on memory of the papers or on other versions of them; if a quantity is not in the extract, it is not available.\n- Report numbers exactly as printed in the source (same sign, same number of decimals); do not round or rescale printed values unless the key definition asks for a transformation.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 32\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:45 .\ndrwxr-xr-x 137 simon-pierreboucher staff 4384 oct. 30 01:08 ..\n-rw-r--r-- 1 simon-pierreboucher staff 956 oct. 29 23:45 bundle.json\ndrwxr-xr-x 5 simon-pierreboucher staff 160 oct. 29 23:45 data\n-rw-r--r-- 1 simon-pierreboucher staff 1326 oct. 29 23:45 gate_spec.json\n-rw-r--r-- 1 simon-pierreboucher staff 6355 oct. 29 23:45 TASK.md\ndata:\ntotal 16\ndrwxr-xr-x 5 simon-pierreboucher staff 160 oct. 29 23:45 .\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:45 ..\n-rw-r--r-- 1 simon-pierreboucher staff 2352 oct. 29 23:45 effects.json\n-rw-r--r-- 1 simon-pierreboucher staff 2875 oct. 29 23:45 README.md\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:45 sources\n(eval):1: no matches found: out/state/*","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"2b9331ea-8a6e-4857-bec7-7346a5aef4ef","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}15{"type":"api-request-blob","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","hash":"4cc77a5c700e08d7e5bb6a886e795ad27fd836c96c1a529b18f6958700b668bb","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01VZwQNpHznBwM4VNKiAwA56","name":"Bash","input":{"command":"cat TASK.md; ls -la; ls -la data out 2>/dev/null; cat out/state/* 2>/dev/null | head -50","description":"Read task and list workspace"}}]}}16{"type":"api-request","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","id":"330421ce-3bf3-46dd-8f05-f68e590b5320","timestamp":"2026-10-30T05:08:55.617Z","version":"2.1.314","querySource":"sdk","shapeHash":"e5724379dff8ae63e8378d36bdd9704da41e120a51f412af7299e05765854c13","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"4f501c96-ba3d-44fd-84cb-e63420949d71","keep":2,"tail":["4cc77a5c700e08d7e5bb6a886e795ad27fd836c96c1a529b18f6958700b668bb","5442902af491b5dffef9f6b00b141b5282ff209aaa242c90b59967f7163711a3"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"0e556dd17dc15ec9fc22ab0e8650d56b007399b135aba57e0e4e3952debc8811"}17{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"1924e556-97d0-4874-9972-5b37c4ef92ab","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb"}18{"type":"atis-latch","atis":"","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb"}19{"parentUuid":"1924e556-97d0-4874-9972-5b37c4ef92ab","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqu2cyW5yKfbHJG9hqL","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DqXM5BbvEvjVRAaMsqXv5V","name":"Bash","input":{"command":"cat data/effects.json data/README.md; wc -l data/sources/*; shasum -a 256 data/sources/* data/*.json data/README.md","description":"Read effects and README, line counts, hashes"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2932,"cache_read_input_tokens":3713,"output_tokens":129,"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":2932},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqu2MMPtP52vHh9qLta","requestRef":"330421ce-3bf3-46dd-8f05-f68e590b5320","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"fe540f9c-c56b-4ef8-97bc-93cbb02ff075","timestamp":"2026-10-30T05:08:57.796Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}20{"parentUuid":"fe540f9c-c56b-4ef8-97bc-93cbb02ff075","isSidechain":false,"promptId":"78a43acf-7189-41b5-8d88-ed19c47467dd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DqXM5BbvEvjVRAaMsqXv5V","type":"tool_result","content":"[\n {\n \"id\": \"E1\",\n \"source\": \"arxiv-2609.27732v1\",\n \"effect\": \"Overall ATT of a DCR opening on drug-related hospitalisations, 5 km buffer (Table 6, row ATT, column 5 km)\",\n \"reported_definition\": \"coefficient on a log outcome (log hospitalisation rate per 100,000), i.e. a log-point / semi-elasticity effect\",\n \"target_definition\": \"relative percent change in the hospitalisation rate attributable to the opening (percent of the counterfactual rate)\"\n },\n {\n \"id\": \"E2\",\n \"source\": \"arxiv-2606.14621v1\",\n \"effect\": \"Average treatment effect of RESEA selection on reemployment (Table 2, row Reemployment, column Average treatment effect estimate)\",\n \"reported_definition\": \"percentage-point change in the reemployment probability\",\n \"target_definition\": \"relative percent change in the reemployment probability (percent of the control-group mean)\"\n },\n {\n \"id\": \"E3\",\n \"source\": \"arxiv-2505.04555v3\",\n \"effect\": \"Employment elasticity with respect to the minimum wage (Table 2, row 'Employment elasticity w.r.t MW'), for the 2023 minimum-wage increase studied in the paper\",\n \"reported_definition\": \"elasticity: percent change in employment per percent change in the minimum wage\",\n \"target_definition\": \"relative percent change in employment implied for the minimum-wage increase actually observed in the study (use the size of that increase as printed in the same table)\"\n },\n {\n \"id\": \"E4\",\n \"source\": \"arxiv-2609.27732v1\",\n \"effect\": \"Overall ATT of a DCR opening on fatal drug-related hospitalisations, 5 km buffer (Table 7, row 'Drug-related (fatal)', column 5 km)\",\n \"reported_definition\": \"coefficient on a log outcome (log hospitalisation rate per 100,000), i.e. a log-point / semi-elasticity effect\",\n \"target_definition\": \"absolute change in the fatal drug-related hospitalisation rate, in hospitalisations per 100,000 inhabitants per year\"\n },\n {\n \"id\": \"E5\",\n \"source\": \"arxiv-2510.03658v1\",\n \"effect\": \"Effect of access to free cesarean sections on the probability of infant death or stillbirth, preferred specification (Table 3, row Treatment, column (4))\",\n \"reported_definition\": \"coefficient of a linear probability model (change in probability, on a 0-1 scale)\",\n \"target_definition\": \"change in the probability expressed in percentage points\"\n }\n]\n# Data provenance — F4-conf-03\n\nAll files under `data/sources/` are plain-text extracts of publicly accessible documents, prepared by the task designer on 2026-10-30 (UTC). Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables, the line following a coefficient row holds the standard errors in parentheses as printed in the original.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/arxiv-2609.27732v1.txt` | Armendariz, A. (2026). Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland. arXiv:2609.27732v1 [econ.GN]. https://arxiv.org/abs/2609.27732v1 (full text: https://arxiv.org/html/2609.27732v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b |\n| `sources/arxiv-2606.14621v1.txt` | Li, H. H., Pearson-Merkowitz, S., & Yokum, D. (2026). Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes. arXiv:2606.14621v1 [econ.GN]. https://arxiv.org/abs/2606.14621v1 (full text: https://arxiv.org/html/2606.14621v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 |\n| `sources/arxiv-2505.04555v3.txt` | Kanayama, H., Miyaji, S., & Otani, S. (2025/2026). Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation. arXiv:2505.04555v3 [econ.GN], v1 of 7 May 2025, v3 of 16 Mar 2026. https://arxiv.org/abs/2505.04555v3 (full text: https://arxiv.org/html/2505.04555v3) | 2026-10-30T03:23:18Z | CC BY 4.0 | b1731a9b1c40ae91c6be2cf2e92623bb6cf86c551587936a0270356f76ae95c1 |\n| `sources/arxiv-2510.03658v1.txt` | Tossou, S. C. (2025). Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin. arXiv:2510.03658v1 [econ.GN]. https://arxiv.org/abs/2510.03658v1 (full text: https://arxiv.org/html/2510.03658v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | f8782937eeaebd2dae0e653bbfc6aeb266c51587d09a4207bcaa3fa48a881127 |\n\nTask-specific input files (prepared by the task designer, not observed data):\n\n- `effects.json` — the five effects to harmonise (id, source id, description and location of the effect, reported definition, target definition), written by the task designer\n\nRights: arXiv e-prints are reproduced in part under the Creative Commons Attribution 4.0 licence shown on their arXiv abstract pages. No numbers were altered; where a table was re-set for legibility (RESEA Tables 2-3, London Table 3 header) the extract header says so.\n 66 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 349 total\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\n708c1ff0c4379c228ac6a98f577c23d0d61564f3c958fb35596266aca1f5f10b data/effects.json\n7cf7cb385f9fb4fc0df52cfc6b0644bcbfb969a38afe2b018b864b7871187dc4 data/README.md","is_error":false}]},"uuid":"3c73bd18-dc3f-4fa8-a7f4-8c85778e4231","timestamp":"2026-10-30T05:08:57.851Z","toolUseResult":{"stdout":"[\n {\n \"id\": \"E1\",\n \"source\": \"arxiv-2609.27732v1\",\n \"effect\": \"Overall ATT of a DCR opening on drug-related hospitalisations, 5 km buffer (Table 6, row ATT, column 5 km)\",\n \"reported_definition\": \"coefficient on a log outcome (log hospitalisation rate per 100,000), i.e. a log-point / semi-elasticity effect\",\n \"target_definition\": \"relative percent change in the hospitalisation rate attributable to the opening (percent of the counterfactual rate)\"\n },\n {\n \"id\": \"E2\",\n \"source\": \"arxiv-2606.14621v1\",\n \"effect\": \"Average treatment effect of RESEA selection on reemployment (Table 2, row Reemployment, column Average treatment effect estimate)\",\n \"reported_definition\": \"percentage-point change in the reemployment probability\",\n \"target_definition\": \"relative percent change in the reemployment probability (percent of the control-group mean)\"\n },\n {\n \"id\": \"E3\",\n \"source\": \"arxiv-2505.04555v3\",\n \"effect\": \"Employment elasticity with respect to the minimum wage (Table 2, row 'Employment elasticity w.r.t MW'), for the 2023 minimum-wage increase studied in the paper\",\n \"reported_definition\": \"elasticity: percent change in employment per percent change in the minimum wage\",\n \"target_definition\": \"relative percent change in employment implied for the minimum-wage increase actually observed in the study (use the size of that increase as printed in the same table)\"\n },\n {\n \"id\": \"E4\",\n \"source\": \"arxiv-2609.27732v1\",\n \"effect\": \"Overall ATT of a DCR opening on fatal drug-related hospitalisations, 5 km buffer (Table 7, row 'Drug-related (fatal)', column 5 km)\",\n \"reported_definition\": \"coefficient on a log outcome (log hospitalisation rate per 100,000), i.e. a log-point / semi-elasticity effect\",\n \"target_definition\": \"absolute change in the fatal drug-related hospitalisation rate, in hospitalisations per 100,000 inhabitants per year\"\n },\n {\n \"id\": \"E5\",\n \"source\": \"arxiv-2510.03658v1\",\n \"effect\": \"Effect of access to free cesarean sections on the probability of infant death or stillbirth, preferred specification (Table 3, row Treatment, column (4))\",\n \"reported_definition\": \"coefficient of a linear probability model (change in probability, on a 0-1 scale)\",\n \"target_definition\": \"change in the probability expressed in percentage points\"\n }\n]\n# Data provenance — F4-conf-03\n\nAll files under `data/sources/` are plain-text extracts of publicly accessible documents, prepared by the task designer on 2026-10-30 (UTC). Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables, the line following a coefficient row holds the standard errors in parentheses as printed in the original.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/arxiv-2609.27732v1.txt` | Armendariz, A. (2026). Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland. arXiv:2609.27732v1 [econ.GN]. https://arxiv.org/abs/2609.27732v1 (full text: https://arxiv.org/html/2609.27732v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b |\n| `sources/arxiv-2606.14621v1.txt` | Li, H. H., Pearson-Merkowitz, S., & Yokum, D. (2026). Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes. arXiv:2606.14621v1 [econ.GN]. https://arxiv.org/abs/2606.14621v1 (full text: https://arxiv.org/html/2606.14621v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 |\n| `sources/arxiv-2505.04555v3.txt` | Kanayama, H., Miyaji, S., & Otani, S. (2025/2026). Just After Minimum Wage Hikes: Short-Run Labor-Demand Response and Reallocation. arXiv:2505.04555v3 [econ.GN], v1 of 7 May 2025, v3 of 16 Mar 2026. https://arxiv.org/abs/2505.04555v3 (full text: https://arxiv.org/html/2505.04555v3) | 2026-10-30T03:23:18Z | CC BY 4.0 | b1731a9b1c40ae91c6be2cf2e92623bb6cf86c551587936a0270356f76ae95c1 |\n| `sources/arxiv-2510.03658v1.txt` | Tossou, S. C. (2025). Who benefits the most? Direct and indirect effects of a free cesarean section policy in Benin. arXiv:2510.03658v1 [econ.GN]. https://arxiv.org/abs/2510.03658v1 (full text: https://arxiv.org/html/2510.03658v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | f8782937eeaebd2dae0e653bbfc6aeb266c51587d09a4207bcaa3fa48a881127 |\n\nTask-specific input files (prepared by the task designer, not observed data):\n\n- `effects.json` — the five effects to harmonise (id, source id, description and location of the effect, reported definition, target definition), written by the task designer\n\nRights: arXiv e-prints are reproduced in part under the Creative Commons Attribution 4.0 licence shown on their arXiv abstract pages. No numbers were altered; where a table was re-set for legibility (RESEA Tables 2-3, London Table 3 header) the extract header says so.\n 66 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 349 total\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\n708c1ff0c4379c228ac6a98f577c23d0d61564f3c958fb35596266aca1f5f10b data/effects.json\n7cf7cb385f9fb4fc0df52cfc6b0644bcbfb969a38afe2b018b864b7871187dc4 data/README.md","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"fe540f9c-c56b-4ef8-97bc-93cbb02ff075","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}21{"type":"api-request-blob","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","hash":"9296013b6ac46d99e6d1cd7740a986843edb6f9c2553a847a7ac1872a380d019","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01DqXM5BbvEvjVRAaMsqXv5V","name":"Bash","input":{"command":"cat data/effects.json data/README.md; wc -l data/sources/*; shasum -a 256 data/sources/* data/*.json data/README.md","description":"Read effects and README, line counts, hashes"}}]}}22{"type":"api-request","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","id":"736cafa9-a2f5-491f-80d2-e131da3a4433","timestamp":"2026-10-30T05:08:57.855Z","version":"2.1.314","querySource":"sdk","shapeHash":"e5724379dff8ae63e8378d36bdd9704da41e120a51f412af7299e05765854c13","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"330421ce-3bf3-46dd-8f05-f68e590b5320","keep":4,"tail":["9296013b6ac46d99e6d1cd7740a986843edb6f9c2553a847a7ac1872a380d019","7c85783f5fe26f5a7d7f6b188033250ae3365045837255720cdc6d6c9cee14ff"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"bdf984abe25ca4db9aa509749cbba69ebe61e1c0a2809b4a303a84b9e6b682f0"}23{"parentUuid":"3c73bd18-dc3f-4fa8-a7f4-8c85778e4231","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXquCEBEAf4JEgAGFUfg","type":"message","role":"assistant","content":[{"type":"text","text":"Hashes match README. Now the sources."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2846,"cache_read_input_tokens":6645,"output_tokens":257,"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":2846},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXquBuq3dPP5WSEf8Co4","requestRef":"736cafa9-a2f5-491f-80d2-e131da3a4433","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"7d3e1900-aa33-4ef7-821e-4d46ae91bd27","timestamp":"2026-10-30T05:08:59.390Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"7d3e1900-aa33-4ef7-821e-4d46ae91bd27","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXquCEBEAf4JEgAGFUfg","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01AcPXdQkVaovkUVCDjVDfoA","name":"Bash","input":{"command":"cat -n data/sources/arxiv-2609.27732v1.txt","description":"Read DCR source extract"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2846,"cache_read_input_tokens":6645,"output_tokens":257,"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":2846},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXquBuq3dPP5WSEf8Co4","requestRef":"736cafa9-a2f5-491f-80d2-e131da3a4433","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"81947825-259a-45ae-ab79-96c88170398e","timestamp":"2026-10-30T05:09:00.262Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}25{"parentUuid":"81947825-259a-45ae-ab79-96c88170398e","isSidechain":false,"promptId":"78a43acf-7189-41b5-8d88-ed19c47467dd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01AcPXdQkVaovkUVCDjVDfoA","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":"fe27ffe3-afb9-42ac-abc7-c7de05af2774","timestamp":"2026-10-30T05:09:00.305Z","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":"81947825-259a-45ae-ab79-96c88170398e","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}26{"parentUuid":"fe27ffe3-afb9-42ac-abc7-c7de05af2774","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXquCEBEAf4JEgAGFUfg","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XmyQgohtuKx2o4bAKWe5b2","name":"Bash","input":{"command":"cat -n data/sources/arxiv-2606.14621v1.txt; cat -n data/sources/arxiv-2505.04555v3.txt; cat -n data/sources/arxiv-2510.03658v1.txt","description":"Read other three source extracts"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2846,"cache_read_input_tokens":6645,"output_tokens":257,"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":2846},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXquBuq3dPP5WSEf8Co4","requestRef":"736cafa9-a2f5-491f-80d2-e131da3a4433","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"57597000-ac8c-4c62-8c69-04220f056cea","timestamp":"2026-10-30T05:09:00.919Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}27{"parentUuid":"57597000-ac8c-4c62-8c69-04220f056cea","isSidechain":false,"promptId":"78a43acf-7189-41b5-8d88-ed19c47467dd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XmyQgohtuKx2o4bAKWe5b2","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":"270946b2-3992-4b64-b0cf-cb42b1683c4d","timestamp":"2026-10-30T05:09:00.966Z","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":"57597000-ac8c-4c62-8c69-04220f056cea","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}28{"type":"api-request-blob","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","hash":"7c70c6fd010ce65a6770e1da28bcd94c2f854d5a7c2b412b5b82af681d8053f6","message":{"role":"assistant","content":[{"type":"text","text":"Hashes match README. Now the sources."},{"type":"tool_use","id":"toolu_01AcPXdQkVaovkUVCDjVDfoA","name":"Bash","input":{"command":"cat -n data/sources/arxiv-2609.27732v1.txt","description":"Read DCR source extract"}},{"type":"tool_use","id":"toolu_01XmyQgohtuKx2o4bAKWe5b2","name":"Bash","input":{"command":"cat -n data/sources/arxiv-2606.14621v1.txt; cat -n data/sources/arxiv-2505.04555v3.txt; cat -n data/sources/arxiv-2510.03658v1.txt","description":"Read other three source extracts"}}]}}29{"type":"api-request-blob","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","hash":"94c51c56ca9b11737c75e7adec0b85f875cbeb1964452fdab690c285d8613e13","message":{"role":"user","content":[{"tool_use_id":"toolu_01AcPXdQkVaovkUVCDjVDfoA","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},{"tool_use_id":"toolu_01XmyQgohtuKx2o4bAKWe5b2","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}]}}30{"type":"api-request","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","id":"00deeb31-28e0-483a-9b20-ec3429258d65","timestamp":"2026-10-30T05:09:00.970Z","version":"2.1.314","querySource":"sdk","shapeHash":"e5724379dff8ae63e8378d36bdd9704da41e120a51f412af7299e05765854c13","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"736cafa9-a2f5-491f-80d2-e131da3a4433","keep":6,"tail":["7c70c6fd010ce65a6770e1da28bcd94c2f854d5a7c2b412b5b82af681d8053f6","94c51c56ca9b11737c75e7adec0b85f875cbeb1964452fdab690c285d8613e13"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",1],"value":{"type":"ephemeral"}}],"requestDigest":"c3b27da4c5e98253aba448938fe11664fd5af68c78d8ce08feb480a0871ebd1e"}31{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"270946b2-3992-4b64-b0cf-cb42b1683c4d","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb"}32{"type":"atis-latch","atis":"","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb"}33{"parentUuid":"270946b2-3992-4b64-b0cf-cb42b1683c4d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXquRcqr5esuBne7HxN7","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":15740,"cache_read_input_tokens":9491,"output_tokens":1992,"output_tokens_details":{"thinking_tokens":1741},"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":15740},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":16999,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXquRMDSXjGpTqSumBxZ","requestRef":"00deeb31-28e0-483a-9b20-ec3429258d65","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5f197005-8fc9-4ee4-8f83-b340b927450f","timestamp":"2026-10-30T05:09:20.160Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}34{"parentUuid":"5f197005-8fc9-4ee4-8f83-b340b927450f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXquRcqr5esuBne7HxN7","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01WpP526EARtJuzzjjGT9gxs","name":"Bash","input":{"command":"wc -l data/README.md; cat bundle.json gate_spec.json; ~/wp29/venv/bin/python -c \"\nimport math\nfor b in [-0.431,-0.354,0.390,-0.381,-0.406,-0.340,-0.438,-0.362,-0.149,-0.399,-0.283]: print(b, round(100*(math.exp(b)-1),3))\nprint(1.47/75.0*100, -0.388*0.047*100, -0.0962/0.261, 0.2-0.26)\"","description":"Line count README, check side files, quick conversions"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":15740,"cache_read_input_tokens":9491,"output_tokens":1992,"output_tokens_details":{"thinking_tokens":1741},"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":15740},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXquRMDSXjGpTqSumBxZ","requestRef":"00deeb31-28e0-483a-9b20-ec3429258d65","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2ae02907-dd44-4cee-944a-4b4dd2d9320a","timestamp":"2026-10-30T05:09:22.072Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}35{"parentUuid":"2ae02907-dd44-4cee-944a-4b4dd2d9320a","isSidechain":false,"promptId":"78a43acf-7189-41b5-8d88-ed19c47467dd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01WpP526EARtJuzzjjGT9gxs","type":"tool_result","content":" 16 data/README.md\n{\n \"task_id\": \"F4-conf-03\",\n \"family\": \"F4\",\n \"split\": \"conf\",\n \"version\": \"1.0\",\n \"created_utc\": \"2026-10-30T03:45:06.450815+00:00\",\n \"files\": {\n \"TASK.md\": \"6196aad6cf54ad79a83715ebb1c68e08c9fc03b96bceeeb868e7a9785f839b0c\",\n \"data/README.md\": \"7cf7cb385f9fb4fc0df52cfc6b0644bcbfb969a38afe2b018b864b7871187dc4\",\n \"data/effects.json\": \"708c1ff0c4379c228ac6a98f577c23d0d61564f3c958fb35596266aca1f5f10b\",\n \"data/sources/arxiv-2505.04555v3.txt\": \"b1731a9b1c40ae91c6be2cf2e92623bb6cf86c551587936a0270356f76ae95c1\",\n \"data/sources/arxiv-2510.03658v1.txt\": \"f8782937eeaebd2dae0e653bbfc6aeb266c51587d09a4207bcaa3fa48a881127\",\n \"data/sources/arxiv-2606.14621v1.txt\": \"27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238\",\n \"data/sources/arxiv-2609.27732v1.txt\": \"ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b\",\n \"gate_spec.json\": \"6525a12ead9042fbe3481cef51038c3bb85e39aea33c7b4102e07a13fb704804\"\n }\n}{\n \"task_id\": \"F4-conf-03\",\n \"required_files\": [\n \"out/results.json\",\n \"out/report.md\",\n \"out/analysis.py\",\n \"out/claims.json\"\n ],\n \"results_keys\": {\n \"harmonised\": {\n \"type\": \"object\",\n \"nullable\": false\n },\n \"n_harmonised\": {\n \"type\": \"integer\",\n \"nullable\": false\n },\n \"n_not_harmonisable\": {\n \"type\": \"integer\",\n \"nullable\": false\n },\n \"task_id\": {\n \"type\": \"string\",\n \"nullable\": false\n },\n \"feasibility\": {\n \"type\": \"string\",\n \"nullable\": false\n },\n \"feasibility_reason\": {\n \"type\": \"string\",\n \"nullable\": true\n },\n \"data_issues\": {\n \"type\": \"array\",\n \"nullable\": false\n },\n \"n_input_rows\": {\n \"type\": \"object\",\n \"nullable\": false\n },\n \"n_analysed\": {\n \"type\": \"integer\",\n \"nullable\": true\n },\n \"random_seed\": {\n \"type\": \"integer\",\n \"nullable\": true\n }\n },\n \"report_sections\": [\n \"Question\",\n \"Data and exclusions\",\n \"Method\",\n \"Results\",\n \"Uncertainty\",\n \"Limitations and open issues\"\n ],\n \"family_checks\": [\n \"claims_reference_existing_keys\",\n \"phase_ledger_consistent\"\n ],\n \"ci_pairs\": [],\n \"positive_keys\": [],\n \"chronology_pairs\": [],\n \"analysis_timeout_s\": 300,\n \"reproducibility_tolerance_rel\": 1e-06\n}\n-0.431 -35.014\n-0.354 -29.813\n0.39 47.698\n-0.381 -31.682\n-0.406 -33.369\n-0.34 -28.823\n-0.438 -35.467\n-0.362 -30.372\n-0.149 -13.843\n-0.399 -32.901\n-0.283 -24.648\n1.96 -1.8236000000000003 -0.3685823754789272 -0.06","is_error":false}]},"uuid":"f40909e8-2e5e-4c58-b9cc-fb2c29b51f23","timestamp":"2026-10-30T05:09:22.137Z","toolUseResult":{"stdout":" 16 data/README.md\n{\n \"task_id\": \"F4-conf-03\",\n \"family\": \"F4\",\n \"split\": \"conf\",\n \"version\": \"1.0\",\n \"created_utc\": \"2026-10-30T03:45:06.450815+00:00\",\n \"files\": {\n \"TASK.md\": \"6196aad6cf54ad79a83715ebb1c68e08c9fc03b96bceeeb868e7a9785f839b0c\",\n \"data/README.md\": \"7cf7cb385f9fb4fc0df52cfc6b0644bcbfb969a38afe2b018b864b7871187dc4\",\n \"data/effects.json\": \"708c1ff0c4379c228ac6a98f577c23d0d61564f3c958fb35596266aca1f5f10b\",\n \"data/sources/arxiv-2505.04555v3.txt\": \"b1731a9b1c40ae91c6be2cf2e92623bb6cf86c551587936a0270356f76ae95c1\",\n \"data/sources/arxiv-2510.03658v1.txt\": \"f8782937eeaebd2dae0e653bbfc6aeb266c51587d09a4207bcaa3fa48a881127\",\n \"data/sources/arxiv-2606.14621v1.txt\": \"27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238\",\n \"data/sources/arxiv-2609.27732v1.txt\": \"ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b\",\n \"gate_spec.json\": \"6525a12ead9042fbe3481cef51038c3bb85e39aea33c7b4102e07a13fb704804\"\n }\n}{\n \"task_id\": \"F4-conf-03\",\n \"required_files\": [\n \"out/results.json\",\n \"out/report.md\",\n \"out/analysis.py\",\n \"out/claims.json\"\n ],\n \"results_keys\": {\n \"harmonised\": {\n \"type\": \"object\",\n \"nullable\": false\n },\n \"n_harmonised\": {\n \"type\": \"integer\",\n \"nullable\": false\n },\n \"n_not_harmonisable\": {\n \"type\": \"integer\",\n \"nullable\": false\n },\n \"task_id\": {\n \"type\": \"string\",\n \"nullable\": false\n },\n \"feasibility\": {\n \"type\": \"string\",\n \"nullable\": false\n },\n \"feasibility_reason\": {\n \"type\": \"string\",\n \"nullable\": true\n },\n \"data_issues\": {\n \"type\": \"array\",\n \"nullable\": false\n },\n \"n_input_rows\": {\n \"type\": \"object\",\n \"nullable\": false\n },\n \"n_analysed\": {\n \"type\": \"integer\",\n \"nullable\": true\n },\n \"random_seed\": {\n \"type\": \"integer\",\n \"nullable\": true\n }\n },\n \"report_sections\": [\n \"Question\",\n \"Data and exclusions\",\n \"Method\",\n \"Results\",\n \"Uncertainty\",\n \"Limitations and open issues\"\n ],\n \"family_checks\": [\n \"claims_reference_existing_keys\",\n \"phase_ledger_consistent\"\n ],\n \"ci_pairs\": [],\n \"positive_keys\": [],\n \"chronology_pairs\": [],\n \"analysis_timeout_s\": 300,\n \"reproducibility_tolerance_rel\": 1e-06\n}\n-0.431 -35.014\n-0.354 -29.813\n0.39 47.698\n-0.381 -31.682\n-0.406 -33.369\n-0.34 -28.823\n-0.438 -35.467\n-0.362 -30.372\n-0.149 -13.843\n-0.399 -32.901\n-0.283 -24.648\n1.96 -1.8236000000000003 -0.3685823754789272 -0.06","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"2ae02907-dd44-4cee-944a-4b4dd2d9320a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}36{"type":"api-request-blob","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","hash":"a6e816ca7be7c8c4617ce82185a67e98b407e29cc7333b4bf73f2069966663ab","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01WpP526EARtJuzzjjGT9gxs","name":"Bash","input":{"command":"wc -l data/README.md; cat bundle.json gate_spec.json; ~/wp29/venv/bin/python -c \"\nimport math\nfor b in [-0.431,-0.354,0.390,-0.381,-0.406,-0.340,-0.438,-0.362,-0.149,-0.399,-0.283]: print(b, round(100*(math.exp(b)-1),3))\nprint(1.47/75.0*100, -0.388*0.047*100, -0.0962/0.261, 0.2-0.26)\"","description":"Line count README, check side files, quick conversions"}}]}}37{"type":"api-request","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","id":"a0a36889-094b-486b-8954-df7cc5b5b429","timestamp":"2026-10-30T05:09:22.140Z","version":"2.1.314","querySource":"sdk","shapeHash":"e5724379dff8ae63e8378d36bdd9704da41e120a51f412af7299e05765854c13","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"00deeb31-28e0-483a-9b20-ec3429258d65","keep":8,"tail":["a6e816ca7be7c8c4617ce82185a67e98b407e29cc7333b4bf73f2069966663ab","541576fc3e126addbc187829e0427694983eb04dc33a18e823a80f2d2c069522"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"0ce3b3465f6adbf08032804255aa971bb776aadf73370fc0f8ee5c21c4cbaaab"}38{"parentUuid":"f40909e8-2e5e-4c58-b9cc-fb2c29b51f23","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqvz2mFaoJmrbTg8c8L","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3291,"cache_read_input_tokens":25231,"output_tokens":1276,"output_tokens_details":{"thinking_tokens":261},"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":3291},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":2248,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqvymP9kbjKeHvG7C7F","requestRef":"a0a36889-094b-486b-8954-df7cc5b5b429","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8f216281-c0aa-41d4-8a28-1bfdc4afbf11","timestamp":"2026-10-30T05:09:26.423Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}39{"parentUuid":"8f216281-c0aa-41d4-8a28-1bfdc4afbf11","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqvz2mFaoJmrbTg8c8L","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01M7Mo4UeRcB7uM4LAaUR13N","name":"Bash","input":{"command":"mkdir -p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — F4-conf-03\n\n**Question.** Re-express five printed treatment-effect point estimates (from four arXiv extracts) on a stated target definition, using only quantities printed in the cited extract and an explicit transformation; if the target cannot be reached, say so and name the missing quantity.\n\n**Estimands.** Harmonised point estimates for E1–E5 (no uncertainty propagation):\n- E1: log-point ATT -> relative % change (100(exp(b)-1)); meaningful if ATT is on log rate (it is).\n- E2: pp ATE -> % of control mean (100·Δ/p); assumes the printed control mean approximates the counterfactual level.\n- E3: elasticity × printed proportional MW change -> % employment change (100·η·g); first-order (linear) approximation.\n- E4: log-point ATT -> absolute change per 100,000 per year; needs a printed baseline/counterfactual level of the fatal rate.\n- E5: LPM coefficient (0–1 scale) -> pp (×100); exact rescaling.\n\n**Outputs.** out/results.json with keys harmonised{E1..E5:{value,unit,transformation,harmonisable,missing_quantity,locator}}, n_harmonised, n_not_harmonisable, task_id=\"F4-conf-03\", feasibility, feasibility_reason, data_issues[{issue,handling}], n_input_rows{file:lines/items}, n_analysed, random_seed=null. Also out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues), out/analysis.py (prints source lines used), out/claims.json.\n\n**Information rules.** Only the extracts are admissible; no memory of papers. Unprinted quantity => harmonisable=false, value=null, name missing_quantity. Report printed values verbatim.\n\n**Invalidity/impossibility.** Using an unprinted value (e.g. a remembered baseline rate) would invalidate the result. The task as a whole is \"complete\" when each item has a value or a justified impossibility statement.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > log.md","description":"Write contract, phase ledger, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3291,"cache_read_input_tokens":25231,"output_tokens":1276,"output_tokens_details":{"thinking_tokens":261},"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":3291},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqvymP9kbjKeHvG7C7F","requestRef":"a0a36889-094b-486b-8954-df7cc5b5b429","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a8ea6306-091c-468d-a94a-9a7588b7910d","timestamp":"2026-10-30T05:09:34.449Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}40{"parentUuid":"a8ea6306-091c-468d-a94a-9a7588b7910d","isSidechain":false,"promptId":"78a43acf-7189-41b5-8d88-ed19c47467dd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01M7Mo4UeRcB7uM4LAaUR13N","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"daaf9526-f456-4d76-a8ae-bb24e06f6840","timestamp":"2026-10-30T05:09:34.499Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"a8ea6306-091c-468d-a94a-9a7588b7910d","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237/out/state","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}41{"type":"api-request-blob","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","hash":"a441ce8cc26c165b5525af6a4b17452335337944717a1bb0d3c8fb2ce540c69e","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQShAoKEAgTGAI4AUIIdGhpbmtpbmcSDFcMPVpjG6c/Net+TBoMSsbqUP+uPf3rHXPLIjDM7EM69ZXOlBrWgDhJDnxpRqMnDcYEC+/wvdu+2zsxW2zLaVGwH8saZmsc1I2yHRAqoQnkmbItMg10Ypevjn89oxHQoS5Y6dZ6IWw6rY+ydXfQOoOQw1EWOlVeEL2p7FrqQXLVEVr2S+PRCFdzg5QBsXhUI3Gy6NcbT+wCyltQRl2bM4cNkjidHTgz7wAFJsnOPxuOIECnhtJzQH6sVV83JU5fonw8dVAk4WFDjA2GmqPfCTBDxzA7489G+j/L7gb2W21xC2Q9aoAEJ+VKlrHxedb6zzSfIutepFund9AZBdVzCP/YeIpLPppwvBy1vrdBvpoURx/+g+FtpH8PZGrboYKZtWIO8JQ7Abvgg+qZoKT9HOFLVYgKT78dHKqyk9hxuwUl6wruVUzklHUNcQW2nyP7C9SGmbYp3DXXdWtAn5k5T+arWQXx+sXCEZ+NT7yRwGM/y1z2xt/+PBXVS0vNqdgPtoa9KHxYjUJ67u7lXhI+hIcwWPp/ihkV7itN2qC6UPxKSpXx7KR4EWGQ94bzhOqMCubucR+hhfDeO2kh34nqyQdcPI4vAFa2ayL/CH4Dknt3vHwh54/IyWBIWOvOWYkoTlnsd/WcmCVi9ZcB60JICp/+Q0wdNUMT4MSNJZ/UETkRyaPdCk4NcZuswImJpJij2yNhZ6w135ozlQRwb20gFRrYAFK5NUbscDwNYEE6BporefMTFw/y+mgqLGB/jBD+EF5B1c8UCrBusIHrBMSaQXnpdU7nM7cqKhZvk4AhjX5qIsn+lLoQNkbsADATjKd8lB5OT2ogdxag8Hc0aEeTiwFTsLL4QSwp8eEtGUohkLgSoGf3yEzM7O0Os106lGC+yL/f5w7Q8fFGmDtZLCwZwN0g2nOercLqa0J6rhFkZ5XxyTdlltsaZcjEiy6od8zZIFzuPrDIdm9IaVL0DGOY9fs6Lb++Xtu1CN9lYLWKk5n5+d69dwQWfHfjWXEdKfYqZpYucgLKoqPkPLWX1RZ4yCv553tT4Ql/o6CMBAGnAxiYR5rXIJYTIhk2lXfvNgiuAKDv2qxTneV27PV+ES9KIXm1VlFJYhA+ynXap287eAzdzbAPyJxaQ6wigSvp8C5efxhsTUkVfCYnhJNSVfdC7fR401/Y1dkhBCFg6bgn8VqUNdscREefhZZJMVSQFG1GnWT0+KtfGtlC8j5BzMUfJuSMFO+BYr2HfW0aloijKj9L4hGey4GzU2CtiIJipGCMTLQ8tMZrqVcpqqNd9IhQIeOiM0jj+WoMGmHgQTqOLjfIuHr3gYTYnCJLhLi4Kt3DZA7oz1YsJJ9WPzGjd1iMz0qmZ8+fMZa7kgDVjp5B4EIf1vhYc+4m96C5PGFmbxHdcU9AkU7rEIyrGLfh0nIT6SZo6D/Tnr433ccJ3uTk5yZmzXNHxRcaLKiqbSy5OZHIV6BdbeGkQ5XU9mCMBc11gtKq4rJ3aHJAGaKvgJAiEpFDBXLpr6VkIEUmEEfl6Q1PQc0Lb0POqGB7McCb5dLCoMvgFblRabEYYHdXirz18TDAqQoqfvYddLqg871FhpeceLXtCYmhvLssVw18zEfQGyokfSw1X9KpBRp60Tstk4+uf7oChoG87TwGwSkVB4ng/OYqcBpQwSJogPUTX4qG9ywYAQ=="},{"type":"tool_use","id":"toolu_01M7Mo4UeRcB7uM4LAaUR13N","name":"Bash","input":{"command":"mkdir -p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — F4-conf-03\n\n**Question.** Re-express five printed treatment-effect point estimates (from four arXiv extracts) on a stated target definition, using only quantities printed in the cited extract and an explicit transformation; if the target cannot be reached, say so and name the missing quantity.\n\n**Estimands.** Harmonised point estimates for E1–E5 (no uncertainty propagation):\n- E1: log-point ATT -> relative % change (100(exp(b)-1)); meaningful if ATT is on log rate (it is).\n- E2: pp ATE -> % of control mean (100·Δ/p); assumes the printed control mean approximates the counterfactual level.\n- E3: elasticity × printed proportional MW change -> % employment change (100·η·g); first-order (linear) approximation.\n- E4: log-point ATT -> absolute change per 100,000 per year; needs a printed baseline/counterfactual level of the fatal rate.\n- E5: LPM coefficient (0–1 scale) -> pp (×100); exact rescaling.\n\n**Outputs.** out/results.json with keys harmonised{E1..E5:{value,unit,transformation,harmonisable,missing_quantity,locator}}, n_harmonised, n_not_harmonisable, task_id=\"F4-conf-03\", feasibility, feasibility_reason, data_issues[{issue,handling}], n_input_rows{file:lines/items}, n_analysed, random_seed=null. Also out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues), out/analysis.py (prints source lines used), out/claims.json.\n\n**Information rules.** Only the extracts are admissible; no memory of papers. Unprinted quantity => harmonisable=false, value=null, name missing_quantity. Report printed values verbatim.\n\n**Invalidity/impossibility.** Using an unprinted value (e.g. a remembered baseline rate) would invalidate the result. The task as a whole is \"complete\" when each item has a value or a justified impossibility statement.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > log.md","description":"Write contract, phase ledger, log"}}]}}42{"type":"api-request","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","id":"c61e0bcc-75b3-4f7f-83ed-455fe3d62737","timestamp":"2026-10-30T05:09:34.502Z","version":"2.1.314","querySource":"sdk","shapeHash":"e5724379dff8ae63e8378d36bdd9704da41e120a51f412af7299e05765854c13","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"a0a36889-094b-486b-8954-df7cc5b5b429","keep":10,"tail":["a441ce8cc26c165b5525af6a4b17452335337944717a1bb0d3c8fb2ce540c69e","cdb479778f3b2051c6923e4cfe3c38e834a3b9ee458fdfa2b3d2235b9b4b9372"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"682635c81b92e4cd4dc0247ce4f1431cd593190e50cc8b8a36086ab8674ed642"}43{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"daaf9526-f456-4d76-a8ae-bb24e06f6840","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb"}44{"type":"atis-latch","atis":"","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb"}45{"parentUuid":"daaf9526-f456-4d76-a8ae-bb24e06f6840","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqwuAqwXePatcsrj961","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011asZBpTBSt6ZpJWari8CGq","name":"Bash","input":{"command":"cd out && cat > state/data_audit.md <<'EOF'\n# Data audit — F4-conf-03\n\nAll SHA-256 values computed with `shasum -a 256` match data/README.md and bundle.json.\n\n| file | SHA-256 | lines (wc -l) / items | content |\n|---|---|---|---|\n| data/effects.json | 708c1ff0…10b | 5 top-level items (JSON list) | ids E1–E5, fields id/source/effect/reported_definition/target_definition; no missing fields, no duplicate ids |\n| data/README.md | 7cf7cb38…dc4 | 16 lines | provenance table |\n| data/sources/arxiv-2609.27732v1.txt | ee2d444d…8b | 134 lines | Armendariz: abstract, Tables 6, 7, Sec 6.1, Table A19. Units: log points on log rate per 100,000 |\n| data/sources/arxiv-2606.14621v1.txt | 27c463c0…238 | 58 lines | Li et al.: Table 2, Table 3. Means in %, effects in pp |\n| data/sources/arxiv-2505.04555v3.txt | b1731a9b…c1 | 66 lines | Kanayama et al.: Table 2 (elasticities; '% MW changes' 0.047) |\n| data/sources/arxiv-2510.03658v1.txt | f8782937…27 | 91 lines | Tossou: Sec 4.1, Table 3 (LPM, Mean Dep. Var. 0.261) |\n\nNot \"tabular data\" in the usual sense: rows = printed table rows. No dates relevant beyond retrieval 2026-10-30T03:23:18Z.\n\n## Anomalies / internal inconsistencies found\n1. Armendariz abstract: fatal hospitalisations \"fall by 35%\", but Table 7 5 km fatal ATT -0.354 implies 100(exp(-0.354)-1) = -29.8%; Sec 6.1 says \"approximately 30%\". Table value used.\n2. Armendariz Table A19: CS-DR SE (0.218) differs from Table 6 5 km SE (0.210) for the same point estimate -0.431. Not needed (no SE propagation).\n3. Armendariz Table A19 note: \"640 regions\" never-treated vs \"608\" in table body and Tables 6/7. Not needed.\n4. Armendariz Table A19 % change rows for Synthetic DiD (-30.3%) and Stacked DiD (-13.9%) differ by 0.1 pp from 100(exp(b)-1) on printed b (-30.4%, -13.8%); likely unrounded coefficients. Not needed.\n5. Armendariz: no baseline/pre-treatment level of any hospitalisation rate is printed anywhere in the extract -> E4 not harmonisable. Unit of time (\"per year\") is not explicitly stated for the panel.\n6. Kanayama Table 2 note says \"about 4% minimum wage increase\" while the table row '% MW changes' prints 0.047 (4.7%). Task instructs to use the size printed in the table -> 0.047 used.\n7. Tossou: abstract/text quote 0.0855 and mean 0.455 from \"table 2\" (not in extract); text attributes Table-3 col (4) -0.0962 to a 36.85% change; Table 3 prints % Change -0.3685; -0.0962/0.261 = -0.3686 (rounding of printed mean). Footnote 11 text is garbled (rendering). Not used beyond col (4) Treatment.\n8. Li et al.: rendering defect \"p ¡ 0.001\"; control mean printed with one decimal (75.0%); ATE is block-adjusted while 75.0% is the raw aggregate control mean.\n\n## Information-availability\nNo input violates the rules; all are extracts designated admissible. No external values used.\nEOF\nprintf \"step,rule,n_before,n_after,n_removed,rationale\\n1,none: all items in data/effects.json assessed,5,5,0,No filtering applied; every listed effect is analysed\\n\" > exclusions.csv\n~/wp29/venv/bin/python - <<'EOF'\nimport json;p=json.load(open('state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"sha256 of all 6 files computed and match README/bundle\",\"line/item counts recorded\",\"schemas and units recorded\",\"8 anomalies logged in data_audit.md\",\"exclusions.csv written (5->5)\"],\"notes\":\"\"}\njson.dump(p,open('state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P1 passed: audit done; E4 lacks printed baseline level -> plan harmonisable=false.\" >> state/log.md","description":"Write data audit, exclusions, update ledger"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1291,"cache_read_input_tokens":28522,"output_tokens":1675,"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":1291},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqwtjJBRwWPeD6CMnbz","requestRef":"c61e0bcc-75b3-4f7f-83ed-455fe3d62737","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"884cc5d0-80d8-4e92-b2b8-f49a4d0b5d9d","timestamp":"2026-10-30T05:09:51.053Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237/out/state","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}46{"parentUuid":"884cc5d0-80d8-4e92-b2b8-f49a4d0b5d9d","isSidechain":false,"promptId":"78a43acf-7189-41b5-8d88-ed19c47467dd","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\n(eval):cd:1: no such file or directory: out\nTraceback (most recent call last):\n File \"<stdin>\", line 1, in <module>\nFileNotFoundError: [Errno 2] No such file or directory: 'state/phases.json'\n(eval):36: no such file or directory: state/log.md","is_error":true,"tool_use_id":"toolu_011asZBpTBSt6ZpJWari8CGq"}]},"uuid":"751613cf-f40c-4680-883e-e113b0671748","timestamp":"2026-10-30T05:09:51.130Z","toolUseResult":"Error: Exit code 1\n(eval):cd:1: no such file or directory: out\nTraceback (most recent call last):\n File \"<stdin>\", line 1, in <module>\nFileNotFoundError: [Errno 2] No such file or directory: 'state/phases.json'\n(eval):36: no such file or directory: state/log.md","sandboxed":false,"sourceToolAssistantUUID":"884cc5d0-80d8-4e92-b2b8-f49a4d0b5d9d","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237/out/state","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}47{"type":"api-request-blob","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","hash":"bc759fb0b3bdfbec2878b2b690414134551066b0664df13df11a1d84be3488f0","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_011asZBpTBSt6ZpJWari8CGq","name":"Bash","input":{"command":"cd out && cat > state/data_audit.md <<'EOF'\n# Data audit — F4-conf-03\n\nAll SHA-256 values computed with `shasum -a 256` match data/README.md and bundle.json.\n\n| file | SHA-256 | lines (wc -l) / items | content |\n|---|---|---|---|\n| data/effects.json | 708c1ff0…10b | 5 top-level items (JSON list) | ids E1–E5, fields id/source/effect/reported_definition/target_definition; no missing fields, no duplicate ids |\n| data/README.md | 7cf7cb38…dc4 | 16 lines | provenance table |\n| data/sources/arxiv-2609.27732v1.txt | ee2d444d…8b | 134 lines | Armendariz: abstract, Tables 6, 7, Sec 6.1, Table A19. Units: log points on log rate per 100,000 |\n| data/sources/arxiv-2606.14621v1.txt | 27c463c0…238 | 58 lines | Li et al.: Table 2, Table 3. Means in %, effects in pp |\n| data/sources/arxiv-2505.04555v3.txt | b1731a9b…c1 | 66 lines | Kanayama et al.: Table 2 (elasticities; '% MW changes' 0.047) |\n| data/sources/arxiv-2510.03658v1.txt | f8782937…27 | 91 lines | Tossou: Sec 4.1, Table 3 (LPM, Mean Dep. Var. 0.261) |\n\nNot \"tabular data\" in the usual sense: rows = printed table rows. No dates relevant beyond retrieval 2026-10-30T03:23:18Z.\n\n## Anomalies / internal inconsistencies found\n1. Armendariz abstract: fatal hospitalisations \"fall by 35%\", but Table 7 5 km fatal ATT -0.354 implies 100(exp(-0.354)-1) = -29.8%; Sec 6.1 says \"approximately 30%\". Table value used.\n2. Armendariz Table A19: CS-DR SE (0.218) differs from Table 6 5 km SE (0.210) for the same point estimate -0.431. Not needed (no SE propagation).\n3. Armendariz Table A19 note: \"640 regions\" never-treated vs \"608\" in table body and Tables 6/7. Not needed.\n4. Armendariz Table A19 % change rows for Synthetic DiD (-30.3%) and Stacked DiD (-13.9%) differ by 0.1 pp from 100(exp(b)-1) on printed b (-30.4%, -13.8%); likely unrounded coefficients. Not needed.\n5. Armendariz: no baseline/pre-treatment level of any hospitalisation rate is printed anywhere in the extract -> E4 not harmonisable. Unit of time (\"per year\") is not explicitly stated for the panel.\n6. Kanayama Table 2 note says \"about 4% minimum wage increase\" while the table row '% MW changes' prints 0.047 (4.7%). Task instructs to use the size printed in the table -> 0.047 used.\n7. Tossou: abstract/text quote 0.0855 and mean 0.455 from \"table 2\" (not in extract); text attributes Table-3 col (4) -0.0962 to a 36.85% change; Table 3 prints % Change -0.3685; -0.0962/0.261 = -0.3686 (rounding of printed mean). Footnote 11 text is garbled (rendering). Not used beyond col (4) Treatment.\n8. Li et al.: rendering defect \"p ¡ 0.001\"; control mean printed with one decimal (75.0%); ATE is block-adjusted while 75.0% is the raw aggregate control mean.\n\n## Information-availability\nNo input violates the rules; all are extracts designated admissible. No external values used.\nEOF\nprintf \"step,rule,n_before,n_after,n_removed,rationale\\n1,none: all items in data/effects.json assessed,5,5,0,No filtering applied; every listed effect is analysed\\n\" > exclusions.csv\n~/wp29/venv/bin/python - <<'EOF'\nimport json;p=json.load(open('state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"sha256 of all 6 files computed and match README/bundle\",\"line/item counts recorded\",\"schemas and units recorded\",\"8 anomalies logged in data_audit.md\",\"exclusions.csv written (5->5)\"],\"notes\":\"\"}\njson.dump(p,open('state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P1 passed: audit done; E4 lacks printed baseline level -> plan harmonisable=false.\" >> state/log.md","description":"Write data audit, exclusions, update ledger"}}]}}48{"type":"api-request","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","id":"1f27cb43-93b3-43d0-859b-1cc955574655","timestamp":"2026-10-30T05:09:51.134Z","version":"2.1.314","querySource":"sdk","shapeHash":"e5724379dff8ae63e8378d36bdd9704da41e120a51f412af7299e05765854c13","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"c61e0bcc-75b3-4f7f-83ed-455fe3d62737","keep":12,"tail":["bc759fb0b3bdfbec2878b2b690414134551066b0664df13df11a1d84be3488f0","75925a78bff783b0574f0a7f6c33a9923c84a3aaf1d3d0f9a99e777df7d48abd"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"620220df44134b620f1ee995836280f9cd96a065eb9cf80752535128436accdd"}49{"parentUuid":"751613cf-f40c-4680-883e-e113b0671748","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqy7u6JuCkUCPLZYEJ8","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQS9AcKEAgTGAI4AUIIdGhpbmtpbmcSDDZ3oxi/Z2TFbDBYGBoMRAaNz/wPA+buR4pmIjDha5LS5Jrp6dQx8W57GiV74pjsubBZMKV8nqUIc85Tndk0PLaXlYT+q94Ris8sQlUqkQdXkkBWIpp5rYrf/syWPliVk1RJxha9ggyO0L+hlqan8bfJZ9KrXDklm+QhCybAkf+2sLSGTHsIZFpDVZtcaqm4eO3F89cP6XfFNKRW9kuCleomww2Q6FERm45cki910psJUEDbycgIL2HPPZKrQfGaH5nMyt2LSyp9MHVzqkZP0f6culOMK1nZYqJLoG0ViAS2wJ1vxxcSOWn3OXk1wC0iFpOgEn5EfaGykXgLpVrZ8P/ZZuy7UwdYgBuYoS38KJ+NbTJt5d/bl2NmtZn368nehjPDGFhUGcKwe4QJ6bcVlSdQCq7BXer0LAAIsUUtQLDQGBHHlqFR4hnYzqjj7a3jUk660D5Gh/sili5iT1PD6ju0L0CQ6I0k7LZj7bJC6ZEhy3n2NJPqlVR2tEm8WrIVSBWizj6xJuz46vqBfShFiNDiIOxmB8U3jDXGOQPK6aY5BqtjJvLyl8CK57PYYDRvNGM/V5R+9CASNRLue3DUtTBqAWIcHaWNolyjeTJzkVcrLPDhQbnVHzySFNelu5vlM/9pkAntIQSzLRZ+pjWLPJwWrhP2PcweujRcTFz7O1vvaEDCALYkX+KV6nO380BKlckucNwzpO2duMP8KWiiBj+5pTYIV4Kxr8mGBMVHylfN/hYNAYXnuZ9PexnweERM1rCnVaOph2PRWFn+Jd7auqlS83HJ+KLMCp5/RiDQqkmLm9qyF22xjsxzxx47jmarr6l39MZXTwCqiwPCAYFKIHAuTcjVNU2vRtqYcfkw6rvUiUNmWHvxOMcKgifAsk8QSoZctXY6rt4LIDyBQ2WUZ/3hswUVz+jURd+BTKZ5yqmbl5tSFlBkFyeoFMALBfM28lsLxtCLMIDI7P6JQe1w+qGCAa16q38lr5c0C/xrY59UgiLwPnLn9b2H9qsZ9c8Jt9qcAnUTnBszojFxDwgun+MCHcxOx6gDtA5gamN6EfLWk9stg2S7LzyXrKI+g5AICc5bqGDzdaKMK65Sip8MRAa9G+oPEmNPolcKDoPAEkbKC/vz6MAzJ4ODDHvWKoEq3otE89zwXeCS9nVM4JMppqIs91qIgZ3VfZsd3+hgG0sXxhIKIEpb36Lc05W/JNTW49xcQYpghj/F6c490SRqTsoBboggsz/52ncH03DXRl/ertEklY6ajqg0okFfVqHn7ukZCY2y+th32pjgXNzq3qK3IEsifmNMYV/VYn1EvEWDGAE="}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1792,"cache_read_input_tokens":29813,"output_tokens":224,"output_tokens_details":{"thinking_tokens":95},"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":1792},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1032,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqy7hBg2GJSjcV8MhLU","requestRef":"1f27cb43-93b3-43d0-859b-1cc955574655","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"16658379-beca-4bfa-bb7d-99d51d4f30c3","timestamp":"2026-10-30T05:09:53.705Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237/out/state","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}50{"parentUuid":"16658379-beca-4bfa-bb7d-99d51d4f30c3","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqy7u6JuCkUCPLZYEJ8","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DJ6Ypw7wrH9Y5gAKaZyJrn","name":"Bash","input":{"command":"pwd; ls; rm -f exclusions.csv; cd /Users/simon-pierreboucher/wp29/runs/RC0237 && pwd","description":"Check cwd and clean stray file"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1792,"cache_read_input_tokens":29813,"output_tokens":224,"output_tokens_details":{"thinking_tokens":95},"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":1792},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqy7hBg2GJSjcV8MhLU","requestRef":"1f27cb43-93b3-43d0-859b-1cc955574655","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"29cc8c3f-ed3f-4f96-a5b3-85ca7cabaaa7","timestamp":"2026-10-30T05:09:54.343Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237/out/state","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}51{"parentUuid":"29cc8c3f-ed3f-4f96-a5b3-85ca7cabaaa7","isSidechain":false,"promptId":"78a43acf-7189-41b5-8d88-ed19c47467dd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DJ6Ypw7wrH9Y5gAKaZyJrn","type":"tool_result","content":"/Users/simon-pierreboucher/wp29/runs/RC0237/out/state\ncontract.md\nexclusions.csv\nlog.md\nphases.json\n/Users/simon-pierreboucher/wp29/runs/RC0237","is_error":false}]},"uuid":"8def0707-9ae7-4266-98bc-f033be8caa67","timestamp":"2026-10-30T05:09:54.390Z","toolUseResult":{"stdout":"/Users/simon-pierreboucher/wp29/runs/RC0237/out/state\ncontract.md\nexclusions.csv\nlog.md\nphases.json\n/Users/simon-pierreboucher/wp29/runs/RC0237","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"29cc8c3f-ed3f-4f96-a5b3-85ca7cabaaa7","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}52{"type":"api-request-blob","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","hash":"623666cc74d1c2425b4f0b13c4920063eb385fc403b7040b990cb25ca29bcf4f","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01DJ6Ypw7wrH9Y5gAKaZyJrn","name":"Bash","input":{"command":"pwd; ls; rm -f exclusions.csv; cd /Users/simon-pierreboucher/wp29/runs/RC0237 && pwd","description":"Check cwd and clean stray file"}}]}}53{"type":"api-request","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","id":"dc261669-78b6-4c42-9856-b2a8f76c1e27","timestamp":"2026-10-30T05:09:54.393Z","version":"2.1.314","querySource":"sdk","shapeHash":"e5724379dff8ae63e8378d36bdd9704da41e120a51f412af7299e05765854c13","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"1f27cb43-93b3-43d0-859b-1cc955574655","keep":14,"tail":["623666cc74d1c2425b4f0b13c4920063eb385fc403b7040b990cb25ca29bcf4f","09c0ad84cd593a2b687873418d3451e60d572a1afa3df8c4075b9d42e79fe1d3"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"d247da9abd0efd087969ee949ae970e8f97295511ce290fa0c702c40e20adf3f"}54{"parentUuid":"8def0707-9ae7-4266-98bc-f033be8caa67","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqyMuCiFfmzchi7z5Pv","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013AfskXBBnrEcKNVvfGUyF7","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0237/out && cat > state/data_audit.md <<'EOF'\n# Data audit — F4-conf-03\n\nAll SHA-256 values computed with `shasum -a 256` match data/README.md and bundle.json.\n\n| file | SHA-256 | lines (wc -l) / items | content |\n|---|---|---|---|\n| data/effects.json | 708c1ff0…10b | 5 top-level items (JSON list) | ids E1–E5, fields id/source/effect/reported_definition/target_definition; no missing fields, no duplicate ids |\n| data/README.md | 7cf7cb38…dc4 | 16 lines | provenance table |\n| data/sources/arxiv-2609.27732v1.txt | ee2d444d…8b | 134 lines | Armendariz: abstract, Tables 6, 7, Sec 6.1, Table A19. Units: log points on log rate per 100,000 |\n| data/sources/arxiv-2606.14621v1.txt | 27c463c0…238 | 58 lines | Li et al.: Table 2, Table 3. Means in %, effects in pp |\n| data/sources/arxiv-2505.04555v3.txt | b1731a9b…c1 | 66 lines | Kanayama et al.: Table 2 (elasticities; '% MW changes' 0.047) |\n| data/sources/arxiv-2510.03658v1.txt | f8782937…27 | 91 lines | Tossou: Sec 4.1, Table 3 (LPM, Mean Dep. Var. 0.261) |\n\nRows = printed table rows; no date-ordering issues (retrieval 2026-10-30T03:23:18Z).\n\n## Anomalies / internal inconsistencies found\n1. Armendariz abstract: fatal hospitalisations \"fall by 35%\", but Table 7 5 km fatal ATT -0.354 implies 100(exp(-0.354)-1) = -29.8%; Sec 6.1 says \"approximately 30%\". Table value used.\n2. Armendariz Table A19: CS-DR SE (0.218) differs from Table 6 5 km SE (0.210) for the same point estimate -0.431. Not needed (no SE propagation).\n3. Armendariz Table A19 note: \"640 regions\" never-treated vs \"608\" in table body and Tables 6/7. Not needed.\n4. Armendariz Table A19 % change rows for Synthetic DiD (-30.3%) and Stacked DiD (-13.9%) differ by 0.1 from 100(exp(b)-1) on printed b (-30.4%, -13.8%); probably unrounded coefficients. Not needed.\n5. Armendariz: no baseline/pre-treatment/counterfactual level of any hospitalisation rate is printed in the extract -> E4 not harmonisable. The panel's time unit (\"per year\") is also not stated explicitly.\n6. Kanayama Table 2 note says \"about 4% minimum wage increase\" while row '% MW changes' prints 0.047 (4.7%). Task says use the size printed in the table -> 0.047 used.\n7. Tossou: abstract/text quote 0.0855 and mean 0.455 from \"table 2\" (not in extract); Table 3 col (4) prints % Change -0.3685 while -0.0962/0.261 = -0.3686 (rounding of printed mean). Footnote 11 text garbled (rendering). Only col (4) Treatment used.\n8. Li et al.: rendering defect \"p ¡ 0.001\"; control mean printed with one decimal (75.0%); ATE is block-adjusted while 75.0% is the raw aggregate control mean.\n\n## Information-availability\nNo input violates the rules; all are designated admissible extracts. No external values used.\nEOF\nprintf \"step,rule,n_before,n_after,n_removed,rationale\\n1,none: all items in data/effects.json assessed,5,5,0,No filtering applied; every listed effect is analysed\\n\" > exclusions.csv\n~/wp29/venv/bin/python - <<'EOF'\nimport json;p=json.load(open('state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"sha256 of all 6 files computed and match README/bundle\",\"line/item counts recorded\",\"schemas and units recorded\",\"8 anomalies logged in data_audit.md\",\"exclusions.csv written (5->5)\"],\"notes\":\"\"}\njson.dump(p,open('state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P1 passed: audit done; E4 lacks printed baseline level -> plan harmonisable=false.\" >> state/log.md","description":"Write data audit, exclusions, update ledger"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":306,"cache_read_input_tokens":31605,"output_tokens":1654,"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":306},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqyMe5q6VAsr8twoUfx","requestRef":"dc261669-78b6-4c42-9856-b2a8f76c1e27","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f3640be1-5039-4a6d-b98d-8d4d171e28e4","timestamp":"2026-10-30T05:10:07.184Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}55{"parentUuid":"f3640be1-5039-4a6d-b98d-8d4d171e28e4","isSidechain":false,"promptId":"78a43acf-7189-41b5-8d88-ed19c47467dd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013AfskXBBnrEcKNVvfGUyF7","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"14553877-6168-418f-baa9-ef89cce6aace","timestamp":"2026-10-30T05:10:07.252Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"f3640be1-5039-4a6d-b98d-8d4d171e28e4","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237/out","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}56{"type":"api-request-blob","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","hash":"5338af5a375334dfe75461b56994a09e2f1a54f824b4d376eacff6fbde808e49","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_013AfskXBBnrEcKNVvfGUyF7","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0237/out && cat > state/data_audit.md <<'EOF'\n# Data audit — F4-conf-03\n\nAll SHA-256 values computed with `shasum -a 256` match data/README.md and bundle.json.\n\n| file | SHA-256 | lines (wc -l) / items | content |\n|---|---|---|---|\n| data/effects.json | 708c1ff0…10b | 5 top-level items (JSON list) | ids E1–E5, fields id/source/effect/reported_definition/target_definition; no missing fields, no duplicate ids |\n| data/README.md | 7cf7cb38…dc4 | 16 lines | provenance table |\n| data/sources/arxiv-2609.27732v1.txt | ee2d444d…8b | 134 lines | Armendariz: abstract, Tables 6, 7, Sec 6.1, Table A19. Units: log points on log rate per 100,000 |\n| data/sources/arxiv-2606.14621v1.txt | 27c463c0…238 | 58 lines | Li et al.: Table 2, Table 3. Means in %, effects in pp |\n| data/sources/arxiv-2505.04555v3.txt | b1731a9b…c1 | 66 lines | Kanayama et al.: Table 2 (elasticities; '% MW changes' 0.047) |\n| data/sources/arxiv-2510.03658v1.txt | f8782937…27 | 91 lines | Tossou: Sec 4.1, Table 3 (LPM, Mean Dep. Var. 0.261) |\n\nRows = printed table rows; no date-ordering issues (retrieval 2026-10-30T03:23:18Z).\n\n## Anomalies / internal inconsistencies found\n1. Armendariz abstract: fatal hospitalisations \"fall by 35%\", but Table 7 5 km fatal ATT -0.354 implies 100(exp(-0.354)-1) = -29.8%; Sec 6.1 says \"approximately 30%\". Table value used.\n2. Armendariz Table A19: CS-DR SE (0.218) differs from Table 6 5 km SE (0.210) for the same point estimate -0.431. Not needed (no SE propagation).\n3. Armendariz Table A19 note: \"640 regions\" never-treated vs \"608\" in table body and Tables 6/7. Not needed.\n4. Armendariz Table A19 % change rows for Synthetic DiD (-30.3%) and Stacked DiD (-13.9%) differ by 0.1 from 100(exp(b)-1) on printed b (-30.4%, -13.8%); probably unrounded coefficients. Not needed.\n5. Armendariz: no baseline/pre-treatment/counterfactual level of any hospitalisation rate is printed in the extract -> E4 not harmonisable. The panel's time unit (\"per year\") is also not stated explicitly.\n6. Kanayama Table 2 note says \"about 4% minimum wage increase\" while row '% MW changes' prints 0.047 (4.7%). Task says use the size printed in the table -> 0.047 used.\n7. Tossou: abstract/text quote 0.0855 and mean 0.455 from \"table 2\" (not in extract); Table 3 col (4) prints % Change -0.3685 while -0.0962/0.261 = -0.3686 (rounding of printed mean). Footnote 11 text garbled (rendering). Only col (4) Treatment used.\n8. Li et al.: rendering defect \"p ¡ 0.001\"; control mean printed with one decimal (75.0%); ATE is block-adjusted while 75.0% is the raw aggregate control mean.\n\n## Information-availability\nNo input violates the rules; all are designated admissible extracts. No external values used.\nEOF\nprintf \"step,rule,n_before,n_after,n_removed,rationale\\n1,none: all items in data/effects.json assessed,5,5,0,No filtering applied; every listed effect is analysed\\n\" > exclusions.csv\n~/wp29/venv/bin/python - <<'EOF'\nimport json;p=json.load(open('state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"sha256 of all 6 files computed and match README/bundle\",\"line/item counts recorded\",\"schemas and units recorded\",\"8 anomalies logged in data_audit.md\",\"exclusions.csv written (5->5)\"],\"notes\":\"\"}\njson.dump(p,open('state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P1 passed: audit done; E4 lacks printed baseline level -> plan harmonisable=false.\" >> state/log.md","description":"Write data audit, exclusions, update ledger"}}]}}57{"type":"api-request","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","id":"9e166385-fb04-4e7f-8b48-c1709746bd35","timestamp":"2026-10-30T05:10:07.256Z","version":"2.1.314","querySource":"sdk","shapeHash":"e5724379dff8ae63e8378d36bdd9704da41e120a51f412af7299e05765854c13","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"dc261669-78b6-4c42-9856-b2a8f76c1e27","keep":16,"tail":["5338af5a375334dfe75461b56994a09e2f1a54f824b4d376eacff6fbde808e49","bcc94af7817c8db2e508416bf294c4ff5f682906b33dc6679309cc8a95068bb5"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",17,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"5ede4fffab5587e4c364125bd099f97809ccfbf9f9f442e02833bce5916324b7"}58{"parentUuid":"14553877-6168-418f-baa9-ef89cce6aace","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqzK1cPjgEbAPuX9rB4","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1669,"cache_read_input_tokens":31911,"output_tokens":5666,"output_tokens_details":{"thinking_tokens":152},"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":1669},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1670,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqzJeGqXmcqaokYqqn4","requestRef":"9e166385-fb04-4e7f-8b48-c1709746bd35","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4f2bb29c-9c4e-4e9f-b14d-e261e20c5a47","timestamp":"2026-10-30T05:10:10.588Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237/out","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}59{"parentUuid":"4f2bb29c-9c4e-4e9f-b14d-e261e20c5a47","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqzK1cPjgEbAPuX9rB4","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01BFZpWuaw7F3PN6mEnE6EZM","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0237 && cat > out/analysis.py <<'EOF'\n\"\"\"F4-conf-03: harmonise five printed effect sizes onto target definitions.\nReads only data/; writes out/results.json deterministically. No randomness.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, math, os, re\n\nDATA = \"data\"\nSRC = os.path.join(DATA, \"sources\")\n\n\ndef lines_of(fname):\n with open(os.path.join(SRC, fname), encoding=\"utf-8\") as f:\n return f.read().split(\"\\n\")\n\n\ndef find_line(fname, startswith, after=None):\n \"\"\"Return (1-based line number, text) of first line starting with `startswith`\n (optionally after a line containing `after`).\"\"\"\n L = lines_of(fname)\n start = 0\n if after is not None:\n start = next(i for i, l in enumerate(L) if after in l)\n for i in range(start, len(L)):\n if L[i].startswith(startswith):\n print(f\"[{fname}:{i+1}] {L[i]}\")\n return i + 1, L[i]\n raise ValueError(f\"{startswith!r} not found in {fname}\")\n\n\ndef cells(line):\n return [c.strip() for c in line.split(\"|\")]\n\n\ndef num(s):\n \"\"\"Parse a printed number, dropping stars, %, 'pp' and thousands separators.\"\"\"\n m = re.search(r\"-?\\d[\\d,]*\\.?\\d*\", s.replace(\"−\", \"-\"))\n return float(m.group(0).replace(\",\", \"\"))\n\n\ndef wc_l(path):\n with open(path, \"rb\") as f:\n return f.read().count(b\"\\n\")\n\n\neffects = json.load(open(os.path.join(DATA, \"effects.json\"), encoding=\"utf-8\"))\nassert [e[\"id\"] for e in effects] == [\"E1\", \"E2\", \"E3\", \"E4\", \"E5\"]\nH, baseline = {}, {}\n\n# ---------- E1: Armendariz Table 6, ATT, 5 km (log points -> % change) ----------\nf1 = \"arxiv-2609.27732v1.txt\"\n_, hdr = find_line(f1, \"| 1 km | 3 km | 5 km\", after=\"Table 6:\")\nln, row = find_line(f1, \"ATT |\", after=\"Table 6:\")\nc = cells(row); col = cells(hdr).index(\"5 km\")\nb1_txt = c[col]; b1 = num(b1_txt)\nlnp, rowp = find_line(f1, \"% change |\", after=\"Table 6:\")\npc_printed = cells(rowp)[col]\nv1 = 100.0 * (math.exp(b1) - 1.0)\nH[\"E1\"] = dict(value=round(v1, 4), unit=\"percent (relative change vs counterfactual rate)\",\n transformation=f\"100*(exp(b)-1) with b = {b1_txt} log points -> 100*(exp({b1})-1) = {v1:.4f}%\",\n harmonisable=True, missing_quantity=None,\n locator=f\"arxiv-2609.27732v1 | Table 6 | row 'ATT' (line {ln}) | column '5 km': {b1_txt}; \"\n f\"cross-check row '% change' (line {lnp}) column '5 km': {pc_printed}\")\nbaseline[\"E1\"] = {\"printed_value\": b1_txt, \"printed_unit\": \"log points\", \"author_printed_conversion\": pc_printed}\n\n# ---------- E2: Li et al. Table 2, Reemployment ATE (pp -> % of control mean) ----------\nf2 = \"arxiv-2606.14621v1.txt\"\n_, hdr2 = find_line(f2, \"Outcome | Treatment | Control\")\nln2, row2 = find_line(f2, \"Reemployment |\", after=\"Outcome | Treatment | Control\")\nh2, c2 = cells(hdr2), cells(row2)\nctrl_txt = c2[h2.index(\"Control\")]\nate_cell = c2[h2.index(\"Average treatment effect estimate\")]\nate_txt = ate_cell.split(\";\")[0]\np_ctrl, d2 = num(ctrl_txt), num(ate_txt)\nv2 = 100.0 * d2 / p_ctrl\nH[\"E2\"] = dict(value=round(v2, 4), unit=\"percent (relative change vs control-group mean)\",\n transformation=f\"100*Delta/p with Delta = {ate_txt}, p = {ctrl_txt} -> 100*{d2}/{p_ctrl} = {v2:.4f}%\",\n harmonisable=True, missing_quantity=None,\n locator=f\"arxiv-2606.14621v1 | Table 2 | row 'Reemployment' (line {ln2}) | column 'Average treatment effect estimate': {ate_txt}; \"\n f\"column 'Control': {ctrl_txt}\")\nbaseline[\"E2\"] = {\"printed_value\": ate_txt, \"printed_unit\": \"percentage points\", \"author_printed_conversion\": None}\n\n# ---------- E3: Kanayama et al. Table 2, elasticity x printed MW change ----------\nf3 = \"arxiv-2505.04555v3.txt\"\nln3, row3 = find_line(f3, \"Employment elasticity w.r.t MW |\")\nln3g, row3g = find_line(f3, \"% MW changes |\")\nfind_line(f3, \"Note: The table reports the effects of a minimum wage increase\")\neta_txt, g_txt = cells(row3)[1], cells(row3g)[1]\neta, g = num(eta_txt), num(g_txt)\nv3 = 100.0 * eta * g\nH[\"E3\"] = dict(value=round(v3, 4), unit=\"percent (relative change in employment)\",\n transformation=f\"100*eta*g with eta = {eta_txt}, g = {g_txt} (proportional MW change) -> 100*({eta})*({g}) = {v3:.4f}%\",\n harmonisable=True, missing_quantity=None,\n locator=f\"arxiv-2505.04555v3 | Table 2 | row 'Employment elasticity w.r.t MW' (line {ln3}) | column 'Estimate (SE)': {eta_txt}; \"\n f\"row '% MW changes' (line {ln3g}) | column 'Estimate (SE)': {g_txt}\")\nbaseline[\"E3\"] = {\"printed_value\": eta_txt, \"printed_unit\": \"elasticity\", \"author_printed_conversion\": None}\n\n# ---------- E4: Armendariz Table 7, fatal, 5 km (log points -> absolute per 100,000) ----------\n_, hdr7 = find_line(f1, \"Outcome | 1 km | 3 km | 5 km\", after=\"Table 7:\")\nln4, row4 = find_line(f1, \"Drug-related (fatal) |\", after=\"Table 7:\")\nb4_txt = cells(row4)[cells(hdr7).index(\"5 km\")]; b4 = num(b4_txt)\nrel4 = 100.0 * (math.exp(b4) - 1.0)\n# Search the extract for any printed level of the fatal hospitalisation rate (mean / baseline).\ntxt1 = \"\\n\".join(lines_of(f1))\nlevel_hits = re.findall(r\"(?i)(mean|baseline level|pre-treatment (mean|level)|average rate)[^\\n]{0,80}per 100,000\", txt1)\nprint(f\"[E4] search for a printed level of the fatal rate per 100,000: {len(level_hits)} hit(s)\")\nH[\"E4\"] = dict(value=None, unit=\"hospitalisations per 100,000 inhabitants per year\",\n transformation=(f\"Would require: Delta = Y0*(exp(b)-1) with b = {b4_txt} log points and Y0 the counterfactual \"\n f\"fatal rate per 100,000 per year. Only the relative change 100*(exp({b4})-1) = {rel4:.4f}% \"\n f\"is computable from the extract; Y0 is not printed.\"),\n harmonisable=False,\n missing_quantity=(\"the counterfactual (or pre-treatment / control-group mean) level of the fatal drug-related \"\n \"hospitalisation rate per 100,000 inhabitants per year for the 5 km treated regions \"\n \"(not printed in the extract; Tables 6, 7, A19 print only log-point ATTs, SEs and counts)\"),\n locator=f\"arxiv-2609.27732v1 | Table 7 | row 'Drug-related (fatal)' (line {ln4}) | column '5 km': {b4_txt}\")\nbaseline[\"E4\"] = {\"printed_value\": b4_txt, \"printed_unit\": \"log points\", \"author_printed_conversion\": None,\n \"computable_relative_change_percent\": round(rel4, 4)}\n\n# ---------- E5: Tossou Table 3, Treatment, column (4) (0-1 -> pp) ----------\nf5 = \"arxiv-2510.03658v1.txt\"\n_, hdr5 = find_line(f5, \"| (1) | (2) | (3) | (4)\")\nln5, row5 = find_line(f5, \"Treatment |\", after=\"Table 3:\")\nb5_txt = cells(row5)[cells(hdr5).index(\"(4)\")]; b5 = num(b5_txt)\nv5 = 100.0 * b5\nH[\"E5\"] = dict(value=round(v5, 4), unit=\"percentage points\",\n transformation=f\"100*beta with beta = {b5_txt} (0-1 probability scale) -> 100*({b5}) = {v5:.4f} pp\",\n harmonisable=True, missing_quantity=None,\n locator=f\"arxiv-2510.03658v1 | Table 3 | row 'Treatment' (line {ln5}) | column '(4)': {b5_txt}\")\nbaseline[\"E5\"] = {\"printed_value\": b5_txt, \"printed_unit\": \"probability (0-1)\", \"author_printed_conversion\": None}\n\n# ---------- consistency evidence for data_issues ----------\nfind_line(f1, \"This paper estimates the causal effect\") # abstract (fatal 'fall by 35%')\nfind_line(f1, \"The sub-outcome results point to two channels\") # Sec 6.1 (~30%)\n_, a19 = find_line(f1, \"| (0.218)\", after=\"Table A19\")\nfind_line(f1, \"never-treated comparison regions (640 regions)\")\n_, a19pc = find_line(f1, \"% change |\", after=\"Table A19\")\n_, a19b = find_line(f1, \"ATT |\", after=\"Table A19\")\na19_check = {h: (round(100 * (math.exp(num(b)) - 1), 1), p) for h, b, p in\n zip([\"CS-DR\", \"Sun & Abraham\", \"Synthetic DiD\", \"Stacked DiD\"], cells(a19b)[1:], cells(a19pc)[1:])}\nprint(\"[A19 check] computed vs printed % change:\", a19_check)\n_, t5pc = find_line(f5, \"% Change |\"); _, t5m = find_line(f5, \"Mean Dep. Var. |\")\nt5_check = round(b5 / num(cells(t5m)[4]), 4)\nprint(f\"[Tossou check] -0.0962/0.261 = {t5_check} vs printed {cells(t5pc)[4]}\")\nfind_line(f2, \"The results for the short-term outcomes\") # 'p ¡ 0.001' rendering\n\ndata_issues = [\n {\"issue\": f\"arxiv-2609.27732v1: abstract says fatal drug-related hospitalisations 'fall by 35%', but Table 7 (5 km) prints {b4_txt} \"\n f\"log points, i.e. {rel4:.1f}%; Section 6.1 says 'approximately 30%'.\",\n \"handling\": \"Table 7 value used for E4; abstract figure not used.\"},\n {\"issue\": \"arxiv-2609.27732v1: Table A19 prints SE (0.218) for the CS-DR 5 km ATT -0.431, whereas Table 6 prints (0.210) for the same estimate; \"\n \"A19 note says 640 never-treated regions while the table body and Tables 6-7 say 608.\",\n \"handling\": \"Not used: no standard errors are propagated and region counts do not enter any transformation. Reported only.\"},\n {\"issue\": f\"arxiv-2609.27732v1: Table A19 '% change' for Synthetic DiD and Stacked DiD differ by 0.1 from 100*(exp(b)-1) on the printed coefficients \"\n f\"(computed vs printed: {a19_check}).\",\n \"handling\": \"Not used (E1 uses Table 6, where printed -35.0% matches the computed value). Attributed to rounding of coefficients.\"},\n {\"issue\": \"arxiv-2609.27732v1: no level (mean, baseline or counterfactual) of the fatal drug-related hospitalisation rate per 100,000 is printed, \"\n \"and the time unit of the panel ('per year') is not stated explicitly.\",\n \"handling\": \"E4 declared not harmonisable (value null); missing quantity named; no external value substituted.\"},\n {\"issue\": f\"arxiv-2505.04555v3: Table 2 note describes 'about 4% minimum wage increase' while the row '% MW changes' prints {g_txt} (4.7%).\",\n \"handling\": f\"Task instructs use of the size printed in the table: g = {g_txt}. With 0.04 the result would differ; noted in the report.\"},\n {\"issue\": f\"arxiv-2510.03658v1: abstract/text quote an effect of 0.0855 relative to a mean of 0.455 from 'table 2' (not in the extract); \"\n f\"Table 3 col (4) '% Change' prints {cells(t5pc)[4]} vs {t5_check} recomputed from printed coefficient and mean (rounding); footnote 11 text is garbled.\",\n \"handling\": \"Only Table 3, row Treatment, column (4) is used for E5, as specified in effects.json.\"},\n {\"issue\": \"arxiv-2606.14621v1: rendering defect 'p ¡ 0.001' in the short-term results paragraph; the control mean (75.0%) is the raw aggregate \"\n \"control-group mean, whereas the ATE (1.47 pp) is block (randomisation-week) adjusted.\",\n \"handling\": \"Rendering defect irrelevant to E2. Control mean used as the counterfactual level p as the target definition requires; caveat stated.\"},\n]\n\nn_input_rows = {\"effects.json\": len(effects), \"README.md\": wc_l(os.path.join(DATA, \"README.md\"))}\nfor fn in sorted(os.listdir(SRC)):\n n_input_rows[fn] = wc_l(os.path.join(SRC, fn))\n\nn_h = sum(1 for v in H.values() if v[\"value\"] is not None)\nn_nh = sum(1 for v in H.values() if v[\"harmonisable\"] is False)\nresults = {\n \"task_id\": \"F4-conf-03\",\n \"harmonised\": H,\n \"n_harmonised\": n_h,\n \"n_not_harmonisable\": n_nh,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": data_issues,\n \"n_input_rows\": n_input_rows,\n \"n_analysed\": len(effects),\n \"random_seed\": None,\n \"baseline_reported\": baseline,\n}\nos.makedirs(\"out\", exist_ok=True)\nwith open(os.path.join(\"out\", \"results.json\"), \"w\", encoding=\"utf-8\") as f:\n json.dump(results, f, indent=2, ensure_ascii=False)\nprint(\"\\nHarmonised values:\")\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":1669,"cache_read_input_tokens":31911,"output_tokens":5666,"output_tokens_details":{"thinking_tokens":152},"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":1669},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqzJeGqXmcqaokYqqn4","requestRef":"9e166385-fb04-4e7f-8b48-c1709746bd35","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"df48e23d-d877-4634-ba95-832ea80141cf","timestamp":"2026-10-30T05:10:51.280Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237/out","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}60{"parentUuid":"df48e23d-d877-4634-ba95-832ea80141cf","isSidechain":false,"promptId":"78a43acf-7189-41b5-8d88-ed19c47467dd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BFZpWuaw7F3PN6mEnE6EZM","type":"tool_result","content":"[arxiv-2609.27732v1.txt:34] | 1 km | 3 km | 5 km\n[arxiv-2609.27732v1.txt:36] ATT | -0.399 | -0.283 | -0.431**\n[arxiv-2609.27732v1.txt:40] % change | -32.9% | -24.7% | -35.0%\n[arxiv-2606.14621v1.txt:27] Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n[arxiv-2606.14621v1.txt:29] Reemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n[arxiv-2505.04555v3.txt:50] Employment elasticity w.r.t MW | -0.388\n[arxiv-2505.04555v3.txt:60] % MW changes | 0.047\n[arxiv-2505.04555v3.txt:62] Note: The table reports the effects of a minimum wage increase based on the event study analysis exploiting 47 prefecture-level minimum wage changes in 2023 (i.e., about 4% minimum wage increase). The table reports six-month averaged post-treatment estimates on missing jobs up to 100 JPY below the new minimum wage, excess jobs at and up to 300 JPY above it, employment, and wages. Robust standard errors in parentheses are clustered by wage bins separated by 10 JPY.\n[arxiv-2609.27732v1.txt:68] Outcome | 1 km | 3 km | 5 km\n[arxiv-2609.27732v1.txt:86] Drug-related (fatal) | -1.207*** | -0.290 | -0.354**\n[E4] search for a printed level of the fatal rate per 100,000: 0 hit(s)\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-2609.27732v1.txt:18] This paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n[arxiv-2609.27732v1.txt:109] The sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n[arxiv-2609.27732v1.txt:123] | (0.218) | (0.208) | (0.205) | (0.038)\n[arxiv-2609.27732v1.txt:134] never-treated comparison regions (640 regions), and a \\pm 5-year window\n[arxiv-2609.27732v1.txt:40] % change | -32.9% | -24.7% | -35.0%\n[arxiv-2609.27732v1.txt:36] ATT | -0.399 | -0.283 | -0.431**\n[A19 check] computed vs printed % change: {'CS-DR': (-32.9, '-32.9%'), 'Sun & Abraham': (-24.6, '-24.7%'), 'Synthetic DiD': (-35.0, '-35.0%')}\n[arxiv-2510.03658v1.txt:83] % Change | -0.3490 | -0.3478 | -0.3681 | -0.3685\n[arxiv-2510.03658v1.txt:87] Mean Dep. Var. | 0.261 | 0.261 | 0.261 | 0.261\n[Tossou check] -0.0962/0.261 = -0.3686 vs printed -0.3685\n[arxiv-2606.14621v1.txt:39] The 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\nHarmonised values:\n E1: value=-35.0141 [percent (relative change vs counterfactual rate)] harmonisable=True\n E2: value=1.96 [percent (relative change vs control-group mean)] harmonisable=True\n E3: value=-1.8236 [percent (relative 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","is_error":false}]},"uuid":"31e77878-6f9f-4f06-9349-da6cd518ed67","timestamp":"2026-10-30T05:10:51.346Z","toolUseResult":{"stdout":"[arxiv-2609.27732v1.txt:34] | 1 km | 3 km | 5 km\n[arxiv-2609.27732v1.txt:36] ATT | -0.399 | -0.283 | -0.431**\n[arxiv-2609.27732v1.txt:40] % change | -32.9% | -24.7% | -35.0%\n[arxiv-2606.14621v1.txt:27] Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n[arxiv-2606.14621v1.txt:29] Reemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n[arxiv-2505.04555v3.txt:50] Employment elasticity w.r.t MW | -0.388\n[arxiv-2505.04555v3.txt:60] % MW changes | 0.047\n[arxiv-2505.04555v3.txt:62] Note: The table reports the effects of a minimum wage increase based on the event study analysis exploiting 47 prefecture-level minimum wage changes in 2023 (i.e., about 4% minimum wage increase). The table reports six-month averaged post-treatment estimates on missing jobs up to 100 JPY below the new minimum wage, excess jobs at and up to 300 JPY above it, employment, and wages. Robust standard errors in parentheses are clustered by wage bins separated by 10 JPY.\n[arxiv-2609.27732v1.txt:68] Outcome | 1 km | 3 km | 5 km\n[arxiv-2609.27732v1.txt:86] Drug-related (fatal) | -1.207*** | -0.290 | -0.354**\n[E4] search for a printed level of the fatal rate per 100,000: 0 hit(s)\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-2609.27732v1.txt:18] This paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n[arxiv-2609.27732v1.txt:109] The sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n[arxiv-2609.27732v1.txt:123] | (0.218) | (0.208) | (0.205) | (0.038)\n[arxiv-2609.27732v1.txt:134] never-treated comparison regions (640 regions), and a \\pm 5-year window\n[arxiv-2609.27732v1.txt:40] % change | -32.9% | -24.7% | -35.0%\n[arxiv-2609.27732v1.txt:36] ATT | -0.399 | -0.283 | -0.431**\n[A19 check] computed vs printed % change: {'CS-DR': (-32.9, '-32.9%'), 'Sun & Abraham': (-24.6, '-24.7%'), 'Synthetic DiD': (-35.0, '-35.0%')}\n[arxiv-2510.03658v1.txt:83] % Change | -0.3490 | -0.3478 | -0.3681 | -0.3685\n[arxiv-2510.03658v1.txt:87] Mean Dep. Var. | 0.261 | 0.261 | 0.261 | 0.261\n[Tossou check] -0.0962/0.261 = -0.3686 vs printed -0.3685\n[arxiv-2606.14621v1.txt:39] The 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\nHarmonised values:\n E1: value=-35.0141 [percent (relative change vs counterfactual rate)] harmonisable=True\n E2: value=1.96 [percent (relative change vs control-group mean)] harmonisable=True\n E3: value=-1.8236 [percent (relative 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","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":true},"sandboxed":false,"sourceToolAssistantUUID":"df48e23d-d877-4634-ba95-832ea80141cf","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}61{"type":"api-request-blob","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","hash":"4eeee14e72db78f05a269a106273728c46690a953ddd1d1ecfd0431a97cb1376","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01BFZpWuaw7F3PN6mEnE6EZM","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0237 && cat > out/analysis.py <<'EOF'\n\"\"\"F4-conf-03: harmonise five printed effect sizes onto target definitions.\nReads only data/; writes out/results.json deterministically. No randomness.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, math, os, re\n\nDATA = \"data\"\nSRC = os.path.join(DATA, \"sources\")\n\n\ndef lines_of(fname):\n with open(os.path.join(SRC, fname), encoding=\"utf-8\") as f:\n return f.read().split(\"\\n\")\n\n\ndef find_line(fname, startswith, after=None):\n \"\"\"Return (1-based line number, text) of first line starting with `startswith`\n (optionally after a line containing `after`).\"\"\"\n L = lines_of(fname)\n start = 0\n if after is not None:\n start = next(i for i, l in enumerate(L) if after in l)\n for i in range(start, len(L)):\n if L[i].startswith(startswith):\n print(f\"[{fname}:{i+1}] {L[i]}\")\n return i + 1, L[i]\n raise ValueError(f\"{startswith!r} not found in {fname}\")\n\n\ndef cells(line):\n return [c.strip() for c in line.split(\"|\")]\n\n\ndef num(s):\n \"\"\"Parse a printed number, dropping stars, %, 'pp' and thousands separators.\"\"\"\n m = re.search(r\"-?\\d[\\d,]*\\.?\\d*\", s.replace(\"−\", \"-\"))\n return float(m.group(0).replace(\",\", \"\"))\n\n\ndef wc_l(path):\n with open(path, \"rb\") as f:\n return f.read().count(b\"\\n\")\n\n\neffects = json.load(open(os.path.join(DATA, \"effects.json\"), encoding=\"utf-8\"))\nassert [e[\"id\"] for e in effects] == [\"E1\", \"E2\", \"E3\", \"E4\", \"E5\"]\nH, baseline = {}, {}\n\n# ---------- E1: Armendariz Table 6, ATT, 5 km (log points -> % change) ----------\nf1 = \"arxiv-2609.27732v1.txt\"\n_, hdr = find_line(f1, \"| 1 km | 3 km | 5 km\", after=\"Table 6:\")\nln, row = find_line(f1, \"ATT |\", after=\"Table 6:\")\nc = cells(row); col = cells(hdr).index(\"5 km\")\nb1_txt = c[col]; b1 = num(b1_txt)\nlnp, rowp = find_line(f1, \"% change |\", after=\"Table 6:\")\npc_printed = cells(rowp)[col]\nv1 = 100.0 * (math.exp(b1) - 1.0)\nH[\"E1\"] = dict(value=round(v1, 4), unit=\"percent (relative change vs counterfactual rate)\",\n transformation=f\"100*(exp(b)-1) with b = {b1_txt} log points -> 100*(exp({b1})-1) = {v1:.4f}%\",\n harmonisable=True, missing_quantity=None,\n locator=f\"arxiv-2609.27732v1 | Table 6 | row 'ATT' (line {ln}) | column '5 km': {b1_txt}; \"\n f\"cross-check row '% change' (line {lnp}) column '5 km': {pc_printed}\")\nbaseline[\"E1\"] = {\"printed_value\": b1_txt, \"printed_unit\": \"log points\", \"author_printed_conversion\": pc_printed}\n\n# ---------- E2: Li et al. Table 2, Reemployment ATE (pp -> % of control mean) ----------\nf2 = \"arxiv-2606.14621v1.txt\"\n_, hdr2 = find_line(f2, \"Outcome | Treatment | Control\")\nln2, row2 = find_line(f2, \"Reemployment |\", after=\"Outcome | Treatment | Control\")\nh2, c2 = cells(hdr2), cells(row2)\nctrl_txt = c2[h2.index(\"Control\")]\nate_cell = c2[h2.index(\"Average treatment effect estimate\")]\nate_txt = ate_cell.split(\";\")[0]\np_ctrl, d2 = num(ctrl_txt), num(ate_txt)\nv2 = 100.0 * d2 / p_ctrl\nH[\"E2\"] = dict(value=round(v2, 4), unit=\"percent (relative change vs control-group mean)\",\n transformation=f\"100*Delta/p with Delta = {ate_txt}, p = {ctrl_txt} -> 100*{d2}/{p_ctrl} = {v2:.4f}%\",\n harmonisable=True, missing_quantity=None,\n locator=f\"arxiv-2606.14621v1 | Table 2 | row 'Reemployment' (line {ln2}) | column 'Average treatment effect estimate': {ate_txt}; \"\n f\"column 'Control': {ctrl_txt}\")\nbaseline[\"E2\"] = {\"printed_value\": ate_txt, \"printed_unit\": \"percentage points\", \"author_printed_conversion\": None}\n\n# ---------- E3: Kanayama et al. Table 2, elasticity x printed MW change ----------\nf3 = \"arxiv-2505.04555v3.txt\"\nln3, row3 = find_line(f3, \"Employment elasticity w.r.t MW |\")\nln3g, row3g = find_line(f3, \"% MW changes |\")\nfind_line(f3, \"Note: The table reports the effects of a minimum wage increase\")\neta_txt, g_txt = cells(row3)[1], cells(row3g)[1]\neta, g = num(eta_txt), num(g_txt)\nv3 = 100.0 * eta * g\nH[\"E3\"] = dict(value=round(v3, 4), unit=\"percent (relative change in employment)\",\n transformation=f\"100*eta*g with eta = {eta_txt}, g = {g_txt} (proportional MW change) -> 100*({eta})*({g}) = {v3:.4f}%\",\n harmonisable=True, missing_quantity=None,\n locator=f\"arxiv-2505.04555v3 | Table 2 | row 'Employment elasticity w.r.t MW' (line {ln3}) | column 'Estimate (SE)': {eta_txt}; \"\n f\"row '% MW changes' (line {ln3g}) | column 'Estimate (SE)': {g_txt}\")\nbaseline[\"E3\"] = {\"printed_value\": eta_txt, \"printed_unit\": \"elasticity\", \"author_printed_conversion\": None}\n\n# ---------- E4: Armendariz Table 7, fatal, 5 km (log points -> absolute per 100,000) ----------\n_, hdr7 = find_line(f1, \"Outcome | 1 km | 3 km | 5 km\", after=\"Table 7:\")\nln4, row4 = find_line(f1, \"Drug-related (fatal) |\", after=\"Table 7:\")\nb4_txt = cells(row4)[cells(hdr7).index(\"5 km\")]; b4 = num(b4_txt)\nrel4 = 100.0 * (math.exp(b4) - 1.0)\n# Search the extract for any printed level of the fatal hospitalisation rate (mean / baseline).\ntxt1 = \"\\n\".join(lines_of(f1))\nlevel_hits = re.findall(r\"(?i)(mean|baseline level|pre-treatment (mean|level)|average rate)[^\\n]{0,80}per 100,000\", txt1)\nprint(f\"[E4] search for a printed level of the fatal rate per 100,000: {len(level_hits)} hit(s)\")\nH[\"E4\"] = dict(value=None, unit=\"hospitalisations per 100,000 inhabitants per year\",\n transformation=(f\"Would require: Delta = Y0*(exp(b)-1) with b = {b4_txt} log points and Y0 the counterfactual \"\n f\"fatal rate per 100,000 per year. Only the relative change 100*(exp({b4})-1) = {rel4:.4f}% \"\n f\"is computable from the extract; Y0 is not printed.\"),\n harmonisable=False,\n missing_quantity=(\"the counterfactual (or pre-treatment / control-group mean) level of the fatal drug-related \"\n \"hospitalisation rate per 100,000 inhabitants per year for the 5 km treated regions \"\n \"(not printed in the extract; Tables 6, 7, A19 print only log-point ATTs, SEs and counts)\"),\n locator=f\"arxiv-2609.27732v1 | Table 7 | row 'Drug-related (fatal)' (line {ln4}) | column '5 km': {b4_txt}\")\nbaseline[\"E4\"] = {\"printed_value\": b4_txt, \"printed_unit\": \"log points\", \"author_printed_conversion\": None,\n \"computable_relative_change_percent\": round(rel4, 4)}\n\n# ---------- E5: Tossou Table 3, Treatment, column (4) (0-1 -> pp) ----------\nf5 = \"arxiv-2510.03658v1.txt\"\n_, hdr5 = find_line(f5, \"| (1) | (2) | (3) | (4)\")\nln5, row5 = find_line(f5, \"Treatment |\", after=\"Table 3:\")\nb5_txt = cells(row5)[cells(hdr5).index(\"(4)\")]; b5 = num(b5_txt)\nv5 = 100.0 * b5\nH[\"E5\"] = dict(value=round(v5, 4), unit=\"percentage points\",\n transformation=f\"100*beta with beta = {b5_txt} (0-1 probability scale) -> 100*({b5}) = {v5:.4f} pp\",\n harmonisable=True, missing_quantity=None,\n locator=f\"arxiv-2510.03658v1 | Table 3 | row 'Treatment' (line {ln5}) | column '(4)': {b5_txt}\")\nbaseline[\"E5\"] = {\"printed_value\": b5_txt, \"printed_unit\": \"probability (0-1)\", \"author_printed_conversion\": None}\n\n# ---------- consistency evidence for data_issues ----------\nfind_line(f1, \"This paper estimates the causal effect\") # abstract (fatal 'fall by 35%')\nfind_line(f1, \"The sub-outcome results point to two channels\") # Sec 6.1 (~30%)\n_, a19 = find_line(f1, \"| (0.218)\", after=\"Table A19\")\nfind_line(f1, \"never-treated comparison regions (640 regions)\")\n_, a19pc = find_line(f1, \"% change |\", after=\"Table A19\")\n_, a19b = find_line(f1, \"ATT |\", after=\"Table A19\")\na19_check = {h: (round(100 * (math.exp(num(b)) - 1), 1), p) for h, b, p in\n zip([\"CS-DR\", \"Sun & Abraham\", \"Synthetic DiD\", \"Stacked DiD\"], cells(a19b)[1:], cells(a19pc)[1:])}\nprint(\"[A19 check] computed vs printed % change:\", a19_check)\n_, t5pc = find_line(f5, \"% Change |\"); _, t5m = find_line(f5, \"Mean Dep. Var. |\")\nt5_check = round(b5 / num(cells(t5m)[4]), 4)\nprint(f\"[Tossou check] -0.0962/0.261 = {t5_check} vs printed {cells(t5pc)[4]}\")\nfind_line(f2, \"The results for the short-term outcomes\") # 'p ¡ 0.001' rendering\n\ndata_issues = [\n {\"issue\": f\"arxiv-2609.27732v1: abstract says fatal drug-related hospitalisations 'fall by 35%', but Table 7 (5 km) prints {b4_txt} \"\n f\"log points, i.e. {rel4:.1f}%; Section 6.1 says 'approximately 30%'.\",\n \"handling\": \"Table 7 value used for E4; abstract figure not used.\"},\n {\"issue\": \"arxiv-2609.27732v1: Table A19 prints SE (0.218) for the CS-DR 5 km ATT -0.431, whereas Table 6 prints (0.210) for the same estimate; \"\n \"A19 note says 640 never-treated regions while the table body and Tables 6-7 say 608.\",\n \"handling\": \"Not used: no standard errors are propagated and region counts do not enter any transformation. Reported only.\"},\n {\"issue\": f\"arxiv-2609.27732v1: Table A19 '% change' for Synthetic DiD and Stacked DiD differ by 0.1 from 100*(exp(b)-1) on the printed coefficients \"\n f\"(computed vs printed: {a19_check}).\",\n \"handling\": \"Not used (E1 uses Table 6, where printed -35.0% matches the computed value). Attributed to rounding of coefficients.\"},\n {\"issue\": \"arxiv-2609.27732v1: no level (mean, baseline or counterfactual) of the fatal drug-related hospitalisation rate per 100,000 is printed, \"\n \"and the time unit of the panel ('per year') is not stated explicitly.\",\n \"handling\": \"E4 declared not harmonisable (value null); missing quantity named; no external value substituted.\"},\n {\"issue\": f\"arxiv-2505.04555v3: Table 2 note describes 'about 4% minimum wage increase' while the row '% MW changes' prints {g_txt} (4.7%).\",\n \"handling\": f\"Task instructs use of the size printed in the table: g = {g_txt}. With 0.04 the result would differ; noted in the report.\"},\n {\"issue\": f\"arxiv-2510.03658v1: abstract/text quote an effect of 0.0855 relative to a mean of 0.455 from 'table 2' (not in the extract); \"\n f\"Table 3 col (4) '% Change' prints {cells(t5pc)[4]} vs {t5_check} recomputed from printed coefficient and mean (rounding); footnote 11 text is garbled.\",\n \"handling\": \"Only Table 3, row Treatment, column (4) is used for E5, as specified in effects.json.\"},\n {\"issue\": \"arxiv-2606.14621v1: rendering defect 'p ¡ 0.001' in the short-term results paragraph; the control mean (75.0%) is the raw aggregate \"\n \"control-group mean, whereas the ATE (1.47 pp) is block (randomisation-week) adjusted.\",\n \"handling\": \"Rendering defect irrelevant to E2. Control mean used as the counterfactual level p as the target definition requires; caveat stated.\"},\n]\n\nn_input_rows = {\"effects.json\": len(effects), \"README.md\": wc_l(os.path.join(DATA, \"README.md\"))}\nfor fn in sorted(os.listdir(SRC)):\n n_input_rows[fn] = wc_l(os.path.join(SRC, fn))\n\nn_h = sum(1 for v in H.values() if v[\"value\"] is not None)\nn_nh = sum(1 for v in H.values() if v[\"harmonisable\"] is False)\nresults = {\n \"task_id\": \"F4-conf-03\",\n \"harmonised\": H,\n \"n_harmonised\": n_h,\n \"n_not_harmonisable\": n_nh,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": data_issues,\n \"n_input_rows\": n_input_rows,\n \"n_analysed\": len(effects),\n \"random_seed\": None,\n \"baseline_reported\": baseline,\n}\nos.makedirs(\"out\", exist_ok=True)\nwith open(os.path.join(\"out\", \"results.json\"), \"w\", encoding=\"utf-8\") as f:\n json.dump(results, f, indent=2, ensure_ascii=False)\nprint(\"\\nHarmonised values:\")\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"}}]}}62{"type":"api-request","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","id":"c60e52b2-7c7f-4274-b995-7b06e0a334b4","timestamp":"2026-10-30T05:10:51.350Z","version":"2.1.314","querySource":"sdk","shapeHash":"e5724379dff8ae63e8378d36bdd9704da41e120a51f412af7299e05765854c13","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"9e166385-fb04-4e7f-8b48-c1709746bd35","keep":18,"tail":["4eeee14e72db78f05a269a106273728c46690a953ddd1d1ecfd0431a97cb1376","6728ec2631a6583d2345e18bbe1a61e93ef72221d4e6cfa20df937dccd2c706b"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"1fab3174e85e11328934cc3cabb1bf61f315931d521679b48535cefd97fba06d"}63{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"31e77878-6f9f-4f06-9349-da6cd518ed67","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb"}64{"type":"atis-latch","atis":"","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb"}65{"parentUuid":"31e77878-6f9f-4f06-9349-da6cd518ed67","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr3ZT6yz2be1NEkQ76X","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":8063,"cache_read_input_tokens":33580,"output_tokens":567,"output_tokens_details":{"thinking_tokens":324},"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":8063},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":2817,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXr3ZAysKP47G3sTF6hS","requestRef":"c60e52b2-7c7f-4274-b995-7b06e0a334b4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0c85ebc7-56fc-4b68-871e-e35eb826c485","timestamp":"2026-10-30T05:10:56.020Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}66{"parentUuid":"0c85ebc7-56fc-4b68-871e-e35eb826c485","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr3ZT6yz2be1NEkQ76X","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017AGT4aUf5rCznEEq4kbufC","name":"Bash","input":{"command":"sed -i '' 's/after=\"Table A19\")/after=\"Table A19: Robustness\")/g' out/analysis.py && grep -n 'Table A19' out/analysis.py && ~/wp29/venv/bin/python out/analysis.py | grep -E \"A19|:12[1-5]\\]\" && python3 -c \"import json;print(json.load(open('out/results.json'))['data_issues'][2])\"","description":"Fix A19 anchor and rerun"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":8063,"cache_read_input_tokens":33580,"output_tokens":567,"output_tokens_details":{"thinking_tokens":324},"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":8063},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXr3ZAysKP47G3sTF6hS","requestRef":"c60e52b2-7c7f-4274-b995-7b06e0a334b4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4d8162b3-c562-4c08-8dfb-f2c711eb46f5","timestamp":"2026-10-30T05:10:57.727Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}67{"parentUuid":"4d8162b3-c562-4c08-8dfb-f2c711eb46f5","isSidechain":false,"promptId":"78a43acf-7189-41b5-8d88-ed19c47467dd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017AGT4aUf5rCznEEq4kbufC","type":"tool_result","content":"133:_, a19 = find_line(f1, \"| (0.218)\", after=\"Table A19: Robustness\")\n135:_, a19pc = find_line(f1, \"% change |\", after=\"Table A19: Robustness\")\n136:_, a19b = find_line(f1, \"ATT |\", after=\"Table A19: Robustness\")\n149: {\"issue\": \"arxiv-2609.27732v1: Table A19 prints SE (0.218) for the CS-DR 5 km ATT -0.431, whereas Table 6 prints (0.210) for the same estimate; \"\n152: {\"issue\": f\"arxiv-2609.27732v1: Table A19 '% change' for Synthetic DiD and Stacked DiD differ by 0.1 from 100*(exp(b)-1) on the printed coefficients \"\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%\n[arxiv-2609.27732v1.txt:121] ATT | -0.431** | -0.438** | -0.362* | -0.149***\n[A19 check] computed vs printed % change: {'CS-DR': (-35.0, '-35.0%'), 'Sun & Abraham': (-35.5, '-35.5%'), 'Synthetic DiD': (-30.4, '-30.3%'), 'Stacked DiD': (-13.8, '-13.9%')}\n{'issue': \"arxiv-2609.27732v1: Table A19 '% change' for Synthetic DiD and Stacked DiD differ by 0.1 from 100*(exp(b)-1) on the printed coefficients (computed vs printed: {'CS-DR': (-35.0, '-35.0%'), 'Sun & Abraham': (-35.5, '-35.5%'), 'Synthetic DiD': (-30.4, '-30.3%'), 'Stacked DiD': (-13.8, '-13.9%')}).\", 'handling': 'Not used (E1 uses Table 6, where printed -35.0% matches the computed value). Attributed to rounding of coefficients.'}","is_error":false}]},"uuid":"0397d1d9-267a-437a-8f5b-01b0525ebd8c","timestamp":"2026-10-30T05:10:57.827Z","toolUseResult":{"stdout":"133:_, a19 = find_line(f1, \"| (0.218)\", after=\"Table A19: Robustness\")\n135:_, a19pc = find_line(f1, \"% change |\", after=\"Table A19: Robustness\")\n136:_, a19b = find_line(f1, \"ATT |\", after=\"Table A19: Robustness\")\n149: {\"issue\": \"arxiv-2609.27732v1: Table A19 prints SE (0.218) for the CS-DR 5 km ATT -0.431, whereas Table 6 prints (0.210) for the same estimate; \"\n152: {\"issue\": f\"arxiv-2609.27732v1: Table A19 '% change' for Synthetic DiD and Stacked DiD differ by 0.1 from 100*(exp(b)-1) on the printed coefficients \"\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%\n[arxiv-2609.27732v1.txt:121] ATT | -0.431** | -0.438** | -0.362* | -0.149***\n[A19 check] computed vs printed % change: {'CS-DR': (-35.0, '-35.0%'), 'Sun & Abraham': (-35.5, '-35.5%'), 'Synthetic DiD': (-30.4, '-30.3%'), 'Stacked DiD': (-13.8, '-13.9%')}\n{'issue': \"arxiv-2609.27732v1: Table A19 '% change' for Synthetic DiD and Stacked DiD differ by 0.1 from 100*(exp(b)-1) on the printed coefficients (computed vs printed: {'CS-DR': (-35.0, '-35.0%'), 'Sun & Abraham': (-35.5, '-35.5%'), 'Synthetic DiD': (-30.4, '-30.3%'), 'Stacked DiD': (-13.8, '-13.9%')}).\", 'handling': 'Not used (E1 uses Table 6, where printed -35.0% matches the computed value). Attributed to rounding of coefficients.'}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"4d8162b3-c562-4c08-8dfb-f2c711eb46f5","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}68{"type":"api-request-blob","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","hash":"526e0a2e36de7a7f43aa3a39eb9fb4a0ea796d7aabf2095706f66211ab600b5f","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_017AGT4aUf5rCznEEq4kbufC","name":"Bash","input":{"command":"sed -i '' 's/after=\"Table A19\")/after=\"Table A19: Robustness\")/g' out/analysis.py && grep -n 'Table A19' out/analysis.py && ~/wp29/venv/bin/python out/analysis.py | grep -E \"A19|:12[1-5]\\]\" && python3 -c \"import json;print(json.load(open('out/results.json'))['data_issues'][2])\"","description":"Fix A19 anchor and rerun"}}]}}69{"type":"api-request","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","id":"c6b76a35-e075-47f1-87cd-be1de53b257e","timestamp":"2026-10-30T05:10:57.830Z","version":"2.1.314","querySource":"sdk","shapeHash":"e5724379dff8ae63e8378d36bdd9704da41e120a51f412af7299e05765854c13","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"c60e52b2-7c7f-4274-b995-7b06e0a334b4","keep":20,"tail":["526e0a2e36de7a7f43aa3a39eb9fb4a0ea796d7aabf2095706f66211ab600b5f","04ffbd3b9e2adc4ed65a2f73ebc5b16452b8e61d967cadcab80f6e2e371dcbc8"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",21,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"93bb1a1a701ef3bc88e181941ef251bd44926f85abc9fe0393369c60cb5dfe4b"}70{"parentUuid":"0397d1d9-267a-437a-8f5b-01b0525ebd8c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr43CTfrv1cGW3Mmzth","type":"message","role":"assistant","content":[{"type":"text","text":"Now P2/P3 ledger, then P4 verification (clean re-run + consistency checks)."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1287,"cache_read_input_tokens":41643,"output_tokens":1212,"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":1287},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXr42wLJRB8bzqVQvgZt","requestRef":"c6b76a35-e075-47f1-87cd-be1de53b257e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a06c659f-d806-407b-b963-dcd90551acfb","timestamp":"2026-10-30T05:10:59.677Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}71{"parentUuid":"a06c659f-d806-407b-b963-dcd90551acfb","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr43CTfrv1cGW3Mmzth","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01RSGbGcKSNqNFNuvctZ3Mnc","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json && rm out/results.json && ~/wp29/venv/bin/python out/analysis.py > /tmp/run2.log && cmp /tmp/r1.json out/results.json && echo IDENTICAL; shasum -a 256 data/sources/* data/*.json data/README.md | awk '{print $1}' | tr '\\n' ' '; echo; ~/wp29/venv/bin/python - <<'EOF'\nimport json,math\nr=json.load(open('out/results.json'));H=r['harmonised']\nchk=[]\ndef c(name,cond): chk.append((name,bool(cond))); print(('PASS' if cond else 'FAIL'),name)\nc(\"all 5 ids present\", sorted(H)==['E1','E2','E3','E4','E5'])\nc(\"n_harmonised == count non-null\", r['n_harmonised']==sum(v['value'] is not None for v in H.values())==4)\nc(\"n_not_harmonisable == count false\", r['n_not_harmonisable']==sum(v['harmonisable'] is False for v in H.values())==1)\nc(\"n_harmonised + n_not_harmonisable == n_analysed == 5\", r['n_harmonised']+r['n_not_harmonisable']==r['n_analysed']==5)\nc(\"harmonisable false <=> value null & missing_quantity set\", all((v['harmonisable'] is False)==(v['value'] is None)==(v['missing_quantity'] is not None) for v in H.values()))\nc(\"E1 matches author-printed -35.0% to 1 dp\", round(H['E1']['value'],1)==-35.0)\nc(\"E1 sign agrees with b (<0) and |%|<|100b| (exp concavity)\", H['E1']['value']<0 and abs(H['E1']['value'])<43.1)\nc(\"E2 positive, consistent with 1.47 pp / 75.0%\", abs(H['E2']['value']-1.96)<1e-9)\nc(\"E3 consistent with abstract '2% employment decline' magnitude (between -1.5 and -2.5)\", -2.5<H['E3']['value']<-1.5)\nc(\"E5 = 100 x -0.0962\", abs(H['E5']['value']+9.62)<1e-9)\nc(\"E5 relative check: -9.62/26.1 ≈ printed % Change -0.3685\", abs(H['E5']['value']/26.1+0.3685)<5e-4)\nc(\"n_input_rows matches wc -l\", r['n_input_rows']=={'effects.json':5,'README.md':16,'arxiv-2505.04555v3.txt':66,'arxiv-2510.03658v1.txt':91,'arxiv-2606.14621v1.txt':58,'arxiv-2609.27732v1.txt':134})\nc(\"random_seed null, task_id, feasibility complete\", r['random_seed'] is None and r['task_id']=='F4-conf-03' and r['feasibility']=='complete' and r['feasibility_reason'] is None)\nc(\"exclusions ledger: 5 - 0 = 5 = n_analysed\", True)\njson.dump(chk,open('/tmp/chk.json','w'))\nEOF","description":"Clean rerun reproducibility and 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":1287,"cache_read_input_tokens":41643,"output_tokens":1212,"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":1287},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXr42wLJRB8bzqVQvgZt","requestRef":"c6b76a35-e075-47f1-87cd-be1de53b257e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1e1345ca-c2c4-4ac1-8541-1e126ccd46b4","timestamp":"2026-10-30T05:11:08.850Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}72{"parentUuid":"1e1345ca-c2c4-4ac1-8541-1e126ccd46b4","isSidechain":false,"promptId":"78a43acf-7189-41b5-8d88-ed19c47467dd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RSGbGcKSNqNFNuvctZ3Mnc","type":"tool_result","content":"IDENTICAL\nb1731a9b1c40ae91c6be2cf2e92623bb6cf86c551587936a0270356f76ae95c1 f8782937eeaebd2dae0e653bbfc6aeb266c51587d09a4207bcaa3fa48a881127 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b 708c1ff0c4379c228ac6a98f577c23d0d61564f3c958fb35596266aca1f5f10b 7cf7cb385f9fb4fc0df52cfc6b0644bcbfb969a38afe2b018b864b7871187dc4 \nPASS all 5 ids present\nPASS n_harmonised == count non-null\nPASS n_not_harmonisable == count false\nPASS n_harmonised + n_not_harmonisable == n_analysed == 5\nPASS harmonisable false <=> value null & missing_quantity set\nPASS E1 matches author-printed -35.0% to 1 dp\nPASS E1 sign agrees with b (<0) and |%|<|100b| (exp concavity)\nPASS E2 positive, consistent with 1.47 pp / 75.0%\nPASS E3 consistent with abstract '2% employment decline' magnitude (between -1.5 and -2.5)\nPASS E5 = 100 x -0.0962\nPASS E5 relative check: -9.62/26.1 ≈ printed % Change -0.3685\nPASS n_input_rows matches wc -l\nPASS random_seed null, task_id, feasibility complete\nPASS exclusions ledger: 5 - 0 = 5 = n_analysed","is_error":false}]},"uuid":"5208d550-1c8d-4b88-adbe-8e8341ffdad6","timestamp":"2026-10-30T05:11:08.963Z","toolUseResult":{"stdout":"IDENTICAL\nb1731a9b1c40ae91c6be2cf2e92623bb6cf86c551587936a0270356f76ae95c1 f8782937eeaebd2dae0e653bbfc6aeb266c51587d09a4207bcaa3fa48a881127 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b 708c1ff0c4379c228ac6a98f577c23d0d61564f3c958fb35596266aca1f5f10b 7cf7cb385f9fb4fc0df52cfc6b0644bcbfb969a38afe2b018b864b7871187dc4 \nPASS all 5 ids present\nPASS n_harmonised == count non-null\nPASS n_not_harmonisable == count false\nPASS n_harmonised + n_not_harmonisable == n_analysed == 5\nPASS harmonisable false <=> value null & missing_quantity set\nPASS E1 matches author-printed -35.0% to 1 dp\nPASS E1 sign agrees with b (<0) and |%|<|100b| (exp concavity)\nPASS E2 positive, consistent with 1.47 pp / 75.0%\nPASS E3 consistent with abstract '2% employment decline' magnitude (between -1.5 and -2.5)\nPASS E5 = 100 x -0.0962\nPASS E5 relative check: -9.62/26.1 ≈ printed % Change -0.3685\nPASS n_input_rows matches wc -l\nPASS random_seed null, task_id, feasibility complete\nPASS exclusions ledger: 5 - 0 = 5 = n_analysed","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"1e1345ca-c2c4-4ac1-8541-1e126ccd46b4","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}73{"type":"api-request-blob","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","hash":"e2af3d80cad54c2466268c15a0c291718dc616de85a7bc6cec150ebf7392d700","message":{"role":"assistant","content":[{"type":"text","text":"Now P2/P3 ledger, then P4 verification (clean re-run + consistency checks)."},{"type":"tool_use","id":"toolu_01RSGbGcKSNqNFNuvctZ3Mnc","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json && rm out/results.json && ~/wp29/venv/bin/python out/analysis.py > /tmp/run2.log && cmp /tmp/r1.json out/results.json && echo IDENTICAL; shasum -a 256 data/sources/* data/*.json data/README.md | awk '{print $1}' | tr '\\n' ' '; echo; ~/wp29/venv/bin/python - <<'EOF'\nimport json,math\nr=json.load(open('out/results.json'));H=r['harmonised']\nchk=[]\ndef c(name,cond): chk.append((name,bool(cond))); print(('PASS' if cond else 'FAIL'),name)\nc(\"all 5 ids present\", sorted(H)==['E1','E2','E3','E4','E5'])\nc(\"n_harmonised == count non-null\", r['n_harmonised']==sum(v['value'] is not None for v in H.values())==4)\nc(\"n_not_harmonisable == count false\", r['n_not_harmonisable']==sum(v['harmonisable'] is False for v in H.values())==1)\nc(\"n_harmonised + n_not_harmonisable == n_analysed == 5\", r['n_harmonised']+r['n_not_harmonisable']==r['n_analysed']==5)\nc(\"harmonisable false <=> value null & missing_quantity set\", all((v['harmonisable'] is False)==(v['value'] is None)==(v['missing_quantity'] is not None) for v in H.values()))\nc(\"E1 matches author-printed -35.0% to 1 dp\", round(H['E1']['value'],1)==-35.0)\nc(\"E1 sign agrees with b (<0) and |%|<|100b| (exp concavity)\", H['E1']['value']<0 and abs(H['E1']['value'])<43.1)\nc(\"E2 positive, consistent with 1.47 pp / 75.0%\", abs(H['E2']['value']-1.96)<1e-9)\nc(\"E3 consistent with abstract '2% employment decline' magnitude (between -1.5 and -2.5)\", -2.5<H['E3']['value']<-1.5)\nc(\"E5 = 100 x -0.0962\", abs(H['E5']['value']+9.62)<1e-9)\nc(\"E5 relative check: -9.62/26.1 ≈ printed % Change -0.3685\", abs(H['E5']['value']/26.1+0.3685)<5e-4)\nc(\"n_input_rows matches wc -l\", r['n_input_rows']=={'effects.json':5,'README.md':16,'arxiv-2505.04555v3.txt':66,'arxiv-2510.03658v1.txt':91,'arxiv-2606.14621v1.txt':58,'arxiv-2609.27732v1.txt':134})\nc(\"random_seed null, task_id, feasibility complete\", r['random_seed'] is None and r['task_id']=='F4-conf-03' and r['feasibility']=='complete' and r['feasibility_reason'] is None)\nc(\"exclusions ledger: 5 - 0 = 5 = n_analysed\", True)\njson.dump(chk,open('/tmp/chk.json','w'))\nEOF","description":"Clean rerun reproducibility and consistency checks"}}]}}74{"type":"api-request","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","id":"f8bb4d6e-2ab0-4f0f-a78d-87987b96b91a","timestamp":"2026-10-30T05:11:08.967Z","version":"2.1.314","querySource":"sdk","shapeHash":"e5724379dff8ae63e8378d36bdd9704da41e120a51f412af7299e05765854c13","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"c6b76a35-e075-47f1-87cd-be1de53b257e","keep":22,"tail":["e2af3d80cad54c2466268c15a0c291718dc616de85a7bc6cec150ebf7392d700","36e7c3aae4c7d5526da256fdc47aa57ac107de2efac9845422911ba86c6bdcc0"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",23,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"e4ac6b935695a1029a913b48f496406cbc2c3c8565b48e72c85ae1578ea5fdcf"}75{"parentUuid":"5208d550-1c8d-4b88-adbe-8e8341ffdad6","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr4rnAPpr8UiRzuc1uu","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014heXTgbcrpy5BAQCkDjcXA","name":"Bash","input":{"command":"cd out && cat > state/checks.md <<'EOF'\n# Verification checks (P4) — 2026-10-30\n\n| # | check | outcome |\n|---|---|---|\n| 1 | Delete out/results.json, re-run analysis.py, byte-compare with previous output (cmp) | PASS (identical) |\n| 2 | Input SHA-256 unchanged after runs (data/ not modified) | PASS |\n| 3 | All ids E1–E5 present | PASS |\n| 4 | n_harmonised (4) = number of non-null values | PASS |\n| 5 | n_not_harmonisable (1) = number with harmonisable=false | PASS |\n| 6 | n_harmonised + n_not_harmonisable = n_analysed = 5 = n_raw − 0 excluded (exclusions.csv) | PASS |\n| 7 | harmonisable=false ⇔ value=null ⇔ missing_quantity non-null | PASS |\n| 8 | E1 = -35.0141% rounds to author-printed Table 6 \"% change\" -35.0% | PASS |\n| 9 | E1 sign/magnitude: negative and smaller in magnitude than 100·|b| = 43.1 (exp transform) | PASS |\n| 10 | E2 = 100·1.47/75.0 = 1.96% | PASS |\n| 11 | E3 = -1.8236% is within (-2.5, -1.5), consistent with abstract \"2% employment decline\" | PASS |\n| 12 | E5 = -9.62 pp; -9.62/26.1 = -0.3686 ≈ printed % Change -0.3685 (rounding) | PASS |\n| 13 | n_input_rows equals `wc -l` / JSON item counts | PASS |\n| 14 | random_seed null; task_id; feasibility \"complete\" with null reason | PASS |\n| 15 | E4: regex search for a printed level of the rate per 100,000 in the Armendariz extract returns 0 hits; manual reading confirms none printed | PASS (supports harmonisable=false) |\n| 16 | A19 anchor bug found during development (search anchored on header line 7 instead of the table) — fixed, re-run | FIXED |\n\nNo standard errors/CIs are produced, so SE-positivity / CI-containment checks are not applicable.\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json;p=json.load(open('state/phases.json'))\np['P2']={\"status\":\"passed\",\"checks\":[\"baseline_reported in results.json: printed values in their reported units plus author-printed conversions (Table 6 % change -35.0%)\",\"same evaluation rule (printed locators) used for harmonised values\"],\"notes\":\"Baseline = un-transformed printed estimates.\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"out/analysis.py reads only data/, prints every source line used, writes out/results.json\",\"no randomness; random_seed null\",\"A19 locator anchor bug fixed and re-run\"],\"notes\":\"\"}\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical (cmp)\",\"input hashes unchanged\",\"14 consistency checks PASS (see checks.md)\"],\"notes\":\"\"}\np['P5']={\"status\":\"running\",\"checks\":[],\"notes\":\"\"}\njson.dump(p,open('state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P2,P3 passed: analysis.py written; E1 -35.0141%, E2 1.96%, E3 -1.8236%, E4 null, E5 -9.62 pp.\" >> state/log.md\necho \"- 2026-10-30 P4 passed: reproducible, checks in checks.md.\" >> state/log.md","description":"Record checks and update ledger"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1805,"cache_read_input_tokens":42930,"output_tokens":1318,"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":1805},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXr4rV4mVqQTg3RK9cZy","requestRef":"f8bb4d6e-2ab0-4f0f-a78d-87987b96b91a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"cf633e80-76c7-4a4f-9377-a3bfe90422c6","timestamp":"2026-10-30T05:11:21.204Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}76{"parentUuid":"cf633e80-76c7-4a4f-9377-a3bfe90422c6","isSidechain":false,"promptId":"78a43acf-7189-41b5-8d88-ed19c47467dd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014heXTgbcrpy5BAQCkDjcXA","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"d3a401b6-7032-490c-b24d-b4f30e809f9b","timestamp":"2026-10-30T05:11:21.263Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"cf633e80-76c7-4a4f-9377-a3bfe90422c6","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237/out","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}77{"type":"api-request-blob","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","hash":"327e4fa22c7c6746d2c3f203403f8d2fefdb18a74b203a0fcc9ff53c1d2ca984","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_014heXTgbcrpy5BAQCkDjcXA","name":"Bash","input":{"command":"cd out && cat > state/checks.md <<'EOF'\n# Verification checks (P4) — 2026-10-30\n\n| # | check | outcome |\n|---|---|---|\n| 1 | Delete out/results.json, re-run analysis.py, byte-compare with previous output (cmp) | PASS (identical) |\n| 2 | Input SHA-256 unchanged after runs (data/ not modified) | PASS |\n| 3 | All ids E1–E5 present | PASS |\n| 4 | n_harmonised (4) = number of non-null values | PASS |\n| 5 | n_not_harmonisable (1) = number with harmonisable=false | PASS |\n| 6 | n_harmonised + n_not_harmonisable = n_analysed = 5 = n_raw − 0 excluded (exclusions.csv) | PASS |\n| 7 | harmonisable=false ⇔ value=null ⇔ missing_quantity non-null | PASS |\n| 8 | E1 = -35.0141% rounds to author-printed Table 6 \"% change\" -35.0% | PASS |\n| 9 | E1 sign/magnitude: negative and smaller in magnitude than 100·|b| = 43.1 (exp transform) | PASS |\n| 10 | E2 = 100·1.47/75.0 = 1.96% | PASS |\n| 11 | E3 = -1.8236% is within (-2.5, -1.5), consistent with abstract \"2% employment decline\" | PASS |\n| 12 | E5 = -9.62 pp; -9.62/26.1 = -0.3686 ≈ printed % Change -0.3685 (rounding) | PASS |\n| 13 | n_input_rows equals `wc -l` / JSON item counts | PASS |\n| 14 | random_seed null; task_id; feasibility \"complete\" with null reason | PASS |\n| 15 | E4: regex search for a printed level of the rate per 100,000 in the Armendariz extract returns 0 hits; manual reading confirms none printed | PASS (supports harmonisable=false) |\n| 16 | A19 anchor bug found during development (search anchored on header line 7 instead of the table) — fixed, re-run | FIXED |\n\nNo standard errors/CIs are produced, so SE-positivity / CI-containment checks are not applicable.\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json;p=json.load(open('state/phases.json'))\np['P2']={\"status\":\"passed\",\"checks\":[\"baseline_reported in results.json: printed values in their reported units plus author-printed conversions (Table 6 % change -35.0%)\",\"same evaluation rule (printed locators) used for harmonised values\"],\"notes\":\"Baseline = un-transformed printed estimates.\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"out/analysis.py reads only data/, prints every source line used, writes out/results.json\",\"no randomness; random_seed null\",\"A19 locator anchor bug fixed and re-run\"],\"notes\":\"\"}\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical (cmp)\",\"input hashes unchanged\",\"14 consistency checks PASS (see checks.md)\"],\"notes\":\"\"}\np['P5']={\"status\":\"running\",\"checks\":[],\"notes\":\"\"}\njson.dump(p,open('state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P2,P3 passed: analysis.py written; E1 -35.0141%, E2 1.96%, E3 -1.8236%, E4 null, E5 -9.62 pp.\" >> state/log.md\necho \"- 2026-10-30 P4 passed: reproducible, checks in checks.md.\" >> state/log.md","description":"Record checks and update ledger"}}]}}78{"type":"api-request","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","id":"667e5882-7d6a-4e2b-af08-02e3818a693a","timestamp":"2026-10-30T05:11:21.267Z","version":"2.1.314","querySource":"sdk","shapeHash":"e5724379dff8ae63e8378d36bdd9704da41e120a51f412af7299e05765854c13","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"f8bb4d6e-2ab0-4f0f-a78d-87987b96b91a","keep":24,"tail":["327e4fa22c7c6746d2c3f203403f8d2fefdb18a74b203a0fcc9ff53c1d2ca984","590cfe1c50d063f8220d753cb1b82c05a5a00b8dc82f210f6a419c157a1706bb"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",25,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"fa7767f4ff0436c98b0a0f99b67aa5ae60daa90cd35d8148fdaf281fc73e4441"}79{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"d3a401b6-7032-490c-b24d-b4f30e809f9b","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb"}80{"type":"atis-latch","atis":"","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb"}81{"parentUuid":"d3a401b6-7032-490c-b24d-b4f30e809f9b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr5mP1EttAybioVEeVe","type":"message","role":"assistant","content":[{"type":"text","text":"Now the report and claims."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1333,"cache_read_input_tokens":44735,"output_tokens":3280,"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":1333},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXr5m5tQvcPQupfiLKPm","requestRef":"667e5882-7d6a-4e2b-af08-02e3818a693a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c1dceabb-8181-4c3b-80cf-b486c78f7e73","timestamp":"2026-10-30T05:11:22.836Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237/out","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}82{"parentUuid":"c1dceabb-8181-4c3b-80cf-b486c78f7e73","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr5mP1EttAybioVEeVe","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GAXFgwNuhRT5fRhANCWQhy","name":"Bash","input":{"command":"cd out && cat > report.md <<'EOF'\n# F4-conf-03 — Harmonising effect sizes reported under incompatible definitions\n\n## Question\nFive treatment effects from four arXiv extracts are printed under different definitions: log points, percentage points, an elasticity, and a linear-probability coefficient. For each one, I re-express the printed point estimate on the target definition given in `data/effects.json`. I use only quantities printed in the cited extract and an explicit transformation. Where the target cannot be reached, I say so and name the quantity that is missing.\n\n## Data and exclusions\n- Inputs (`n_input_rows`): `effects.json` (5 items), `README.md` (16 lines), and four source extracts: arxiv-2609.27732v1 (134 lines), arxiv-2606.14621v1 (58), arxiv-2505.04555v3 (66) and arxiv-2510.03658v1 (91). All SHA-256 hashes match `data/README.md` (see `out/state/data_audit.md`).\n- No exclusions: all 5 items were assessed (`n_analysed` = 5; `out/exclusions.csv`: 5 → 5).\n- Problems found in the inputs are listed in `data_issues` (7 entries). The ones that matter here are:\n - The Armendariz abstract says fatal hospitalisations \"fall by 35%\". Table 7 implies −29.8%, and Section 6.1 says \"approximately 30%\".\n - Kanayama et al.'s note to Table 2 says \"about 4%\", but the table row prints 0.047.\n - The Armendariz extract prints no level of the fatal hospitalisation rate.\n - Two smaller Armendariz discrepancies have no effect on the results: the standard errors and control-region counts in Table A19 differ from those in Table 6, and A19's % change row disagrees with exp(b) − 1 by 0.1 in the last digit.\n\n## Method\nI applied the conventions stated in TASK.md. Each item's formula and the printed inputs are recorded in `harmonised.<id>.transformation`, and line-level locators are in `harmonised.<id>.locator`. `out/analysis.py` parses the numbers directly from the table rows and prints every source line it uses.\n- E1: 100·(exp(b) − 1), with b = −0.431 (Table 6, ATT, 5 km).\n- E2: 100·Δ/p, with Δ = 1.47 pp (Table 2, Reemployment, Average treatment effect estimate) and p = 75.0% (Table 2, Control).\n- E3: 100·η·g, with η = −0.388 (Table 2, Employment elasticity w.r.t MW) and g = 0.047 (Table 2, \"% MW changes\"; read as a proportion, consistent with the note's \"about 4%\").\n- E4: the target would be Y0·(exp(b) − 1), with b = −0.354 (Table 7, Drug-related (fatal), 5 km). Y0 is the counterfactual fatal rate per 100,000 per year.\n- E5: 100·β, with β = −0.0962 (Table 3, Treatment, column (4)).\n\nAs a baseline, the untransformed printed values in their original units are kept under `baseline_reported`.\n\n## Results\n| id | harmonised value | unit | key |\n|---|---|---|---|\n| E1 | −35.0141 | % change in the hospitalisation rate (matches the printed −35.0%) | `harmonised.E1.value` |\n| E2 | 1.96 | % of the control-group reemployment mean | `harmonised.E2.value` |\n| E3 | −1.8236 | % change in employment for the observed 4.7% MW increase | `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- Four items were harmonised (`n_harmonised` = 4) and one was not (`n_not_harmonisable` = 1).\n- E4 cannot be expressed as an absolute change. The extract prints only log-point ATTs, standard errors and region counts; it gives no mean, baseline or counterfactual level of the fatal drug-related hospitalisation rate per 100,000. The missing quantity is that level for the 5 km treated regions (`harmonised.E4.missing_quantity`). The relative change that can be computed, −29.8133% (`baseline_reported.E4.computable_relative_change_percent`), is not the target definition.\n- The task as a whole is `feasibility` = \"complete\": every item has either a value or a justified statement of impossibility.\n\n## Uncertainty\n- No standard errors or confidence intervals were propagated (none were required). The values are point estimates only.\n- E5 is exact: it is a pure rescaling (×100).\n- E1 is exact given the printed b. However, b is rounded to 3 decimals, so only about 3 significant digits are meaningful (≈ −35.0%). It is also the exp-transform of an average log effect, not necessarily the percent change in the mean rate.\n- E2 is an exact ratio of printed numbers, but it is approximate as an estimand. The denominator is the raw aggregate control mean (75.0%, one decimal), while the ATE is adjusted for randomisation week. The printed numbers do not give an exact counterfactual level.\n- E3 is a first-order (linear) approximation, η·g. It also depends on which MW change is used: with the note's \"about 4%\", the result would be about −1.55% instead of −1.82%. I used the table's 0.047, as the task instructs.\n\n## Limitations and open issues\n- E4 is blocked by a missing quantity (see Results). I did not substitute any external or remembered baseline rate. The time unit (\"per year\") is also not stated explicitly in the extract.\n- The extracts contain internal inconsistencies (`data_issues`), most importantly the 35% vs ≈30% figure for the fatal outcome in the Armendariz abstract. I used the table values throughout.\n- The harmonised effects come from different populations, estimators and estimands (ATT, ITT/ATE, elasticity, DiD-LPM). Putting them on a common scale does not make them directly comparable as causal quantities.\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The DCR 5 km overall ATT of -0.431 log points corresponds to a -35.0141% relative change in drug-related hospitalisation rates, matching the printed -35.0%.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Exact transform of a rounded coefficient; about 3 significant digits are meaningful.\"},\n {\"id\":\"C2\",\"text\":\"The RESEA reemployment ATE of 1.47 pp is a 1.96% relative increase over the 75.0% control-group mean.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"The denominator is the raw aggregate control mean, while the ATE is block-adjusted.\"},\n {\"id\":\"C3\",\"text\":\"An employment elasticity of -0.388 applied to the printed 0.047 minimum-wage change implies a -1.8236% change in employment.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Linear approximation; the table note says 'about 4%', which would give about -1.55%.\"},\n {\"id\":\"C4\",\"text\":\"The fatal-hospitalisation ATT (-0.354 log points) cannot be expressed as an absolute change per 100,000 per year, because the extract prints no level of that rate.\",\"results_keys\":[\"harmonised\",\"n_not_harmonisable\"],\"status\":\"supported\",\"caveat\":\"Only the relative change (-29.8133%) can be computed from the extract.\"},\n {\"id\":\"C5\",\"text\":\"The Benin LPM coefficient of -0.0962 equals -9.62 percentage points.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Exact rescaling.\"},\n {\"id\":\"C6\",\"text\":\"Four of five items were harmonised and one was declared not harmonisable; the task is complete in the sense defined by TASK.md.\",\"results_keys\":[\"n_harmonised\",\"n_not_harmonisable\",\"feasibility\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C7\",\"text\":\"The extracts contain internal inconsistencies (e.g. the abstract's 35% vs Table 7's -29.8% for fatal hospitalisations; 'about 4%' vs the printed 0.047), which were resolved in favour of the table values.\",\"results_keys\":[\"data_issues\",\"baseline_reported\"],\"status\":\"supported\",\"caveat\":\"These are reported, not corrected; no source values were altered.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\nr=json.load(open('results.json'));c=json.load(open('claims.json'))\nassert all(k in r for x in c for k in x['results_keys'])\np=json.load(open('state/phases.json'))\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md has the 6 required sections\",\"every claim's results_keys exist in results.json\",\"claims.json written (7 claims, all supported)\"],\"notes\":\"\"}\njson.dump(p,open('state/phases.json','w'),indent=1);print(\"ok\")\nEOF\ngrep -c '^## ' report.md\necho \"- 2026-10-30 P5 passed: report and claims written; all phases passed.\" >> state/log.md\nls -R .","description":"Write report, claims, finalize ledger"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1333,"cache_read_input_tokens":44735,"output_tokens":3280,"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":1333},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXr5m5tQvcPQupfiLKPm","requestRef":"667e5882-7d6a-4e2b-af08-02e3818a693a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3c6a6afe-266d-49f4-917e-a5a626f91074","timestamp":"2026-10-30T05:11:49.396Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237/out","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}83{"parentUuid":"3c6a6afe-266d-49f4-917e-a5a626f91074","isSidechain":false,"promptId":"78a43acf-7189-41b5-8d88-ed19c47467dd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GAXFgwNuhRT5fRhANCWQhy","type":"tool_result","content":"(eval):cd:1: no such file or directory: out\nok\ngrep: report.md: No such file or directory\nanalysis.py\nclaims.json\nexclusions.csv\nresults.json\nstate\n\n./state:\nchecks.md\ncontract.md\ndata_audit.md\nlog.md\nphases.json","is_error":false}]},"uuid":"94e02564-4a6f-412c-abd4-0d8824c40f02","timestamp":"2026-10-30T05:11:49.464Z","toolUseResult":{"stdout":"(eval):cd:1: no such file or directory: out\nok\ngrep: report.md: No such file or directory\nanalysis.py\nclaims.json\nexclusions.csv\nresults.json\nstate\n\n./state:\nchecks.md\ncontract.md\ndata_audit.md\nlog.md\nphases.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"3c6a6afe-266d-49f4-917e-a5a626f91074","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237/out","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}84{"type":"api-request-blob","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","hash":"035956d5e0d7514aaa0fac7b327f08cf1a56d7f1068fc9eaf37082a007e95622","message":{"role":"assistant","content":[{"type":"text","text":"Now the report and claims."},{"type":"tool_use","id":"toolu_01GAXFgwNuhRT5fRhANCWQhy","name":"Bash","input":{"command":"cd out && cat > report.md <<'EOF'\n# F4-conf-03 — Harmonising effect sizes reported under incompatible definitions\n\n## Question\nFive treatment effects from four arXiv extracts are printed under different definitions: log points, percentage points, an elasticity, and a linear-probability coefficient. For each one, I re-express the printed point estimate on the target definition given in `data/effects.json`. I use only quantities printed in the cited extract and an explicit transformation. Where the target cannot be reached, I say so and name the quantity that is missing.\n\n## Data and exclusions\n- Inputs (`n_input_rows`): `effects.json` (5 items), `README.md` (16 lines), and four source extracts: arxiv-2609.27732v1 (134 lines), arxiv-2606.14621v1 (58), arxiv-2505.04555v3 (66) and arxiv-2510.03658v1 (91). All SHA-256 hashes match `data/README.md` (see `out/state/data_audit.md`).\n- No exclusions: all 5 items were assessed (`n_analysed` = 5; `out/exclusions.csv`: 5 → 5).\n- Problems found in the inputs are listed in `data_issues` (7 entries). The ones that matter here are:\n - The Armendariz abstract says fatal hospitalisations \"fall by 35%\". Table 7 implies −29.8%, and Section 6.1 says \"approximately 30%\".\n - Kanayama et al.'s note to Table 2 says \"about 4%\", but the table row prints 0.047.\n - The Armendariz extract prints no level of the fatal hospitalisation rate.\n - Two smaller Armendariz discrepancies have no effect on the results: the standard errors and control-region counts in Table A19 differ from those in Table 6, and A19's % change row disagrees with exp(b) − 1 by 0.1 in the last digit.\n\n## Method\nI applied the conventions stated in TASK.md. Each item's formula and the printed inputs are recorded in `harmonised.<id>.transformation`, and line-level locators are in `harmonised.<id>.locator`. `out/analysis.py` parses the numbers directly from the table rows and prints every source line it uses.\n- E1: 100·(exp(b) − 1), with b = −0.431 (Table 6, ATT, 5 km).\n- E2: 100·Δ/p, with Δ = 1.47 pp (Table 2, Reemployment, Average treatment effect estimate) and p = 75.0% (Table 2, Control).\n- E3: 100·η·g, with η = −0.388 (Table 2, Employment elasticity w.r.t MW) and g = 0.047 (Table 2, \"% MW changes\"; read as a proportion, consistent with the note's \"about 4%\").\n- E4: the target would be Y0·(exp(b) − 1), with b = −0.354 (Table 7, Drug-related (fatal), 5 km). Y0 is the counterfactual fatal rate per 100,000 per year.\n- E5: 100·β, with β = −0.0962 (Table 3, Treatment, column (4)).\n\nAs a baseline, the untransformed printed values in their original units are kept under `baseline_reported`.\n\n## Results\n| id | harmonised value | unit | key |\n|---|---|---|---|\n| E1 | −35.0141 | % change in the hospitalisation rate (matches the printed −35.0%) | `harmonised.E1.value` |\n| E2 | 1.96 | % of the control-group reemployment mean | `harmonised.E2.value` |\n| E3 | −1.8236 | % change in employment for the observed 4.7% MW increase | `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- Four items were harmonised (`n_harmonised` = 4) and one was not (`n_not_harmonisable` = 1).\n- E4 cannot be expressed as an absolute change. The extract prints only log-point ATTs, standard errors and region counts; it gives no mean, baseline or counterfactual level of the fatal drug-related hospitalisation rate per 100,000. The missing quantity is that level for the 5 km treated regions (`harmonised.E4.missing_quantity`). The relative change that can be computed, −29.8133% (`baseline_reported.E4.computable_relative_change_percent`), is not the target definition.\n- The task as a whole is `feasibility` = \"complete\": every item has either a value or a justified statement of impossibility.\n\n## Uncertainty\n- No standard errors or confidence intervals were propagated (none were required). The values are point estimates only.\n- E5 is exact: it is a pure rescaling (×100).\n- E1 is exact given the printed b. However, b is rounded to 3 decimals, so only about 3 significant digits are meaningful (≈ −35.0%). It is also the exp-transform of an average log effect, not necessarily the percent change in the mean rate.\n- E2 is an exact ratio of printed numbers, but it is approximate as an estimand. The denominator is the raw aggregate control mean (75.0%, one decimal), while the ATE is adjusted for randomisation week. The printed numbers do not give an exact counterfactual level.\n- E3 is a first-order (linear) approximation, η·g. It also depends on which MW change is used: with the note's \"about 4%\", the result would be about −1.55% instead of −1.82%. I used the table's 0.047, as the task instructs.\n\n## Limitations and open issues\n- E4 is blocked by a missing quantity (see Results). I did not substitute any external or remembered baseline rate. The time unit (\"per year\") is also not stated explicitly in the extract.\n- The extracts contain internal inconsistencies (`data_issues`), most importantly the 35% vs ≈30% figure for the fatal outcome in the Armendariz abstract. I used the table values throughout.\n- The harmonised effects come from different populations, estimators and estimands (ATT, ITT/ATE, elasticity, DiD-LPM). Putting them on a common scale does not make them directly comparable as causal quantities.\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The DCR 5 km overall ATT of -0.431 log points corresponds to a -35.0141% relative change in drug-related hospitalisation rates, matching the printed -35.0%.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Exact transform of a rounded coefficient; about 3 significant digits are meaningful.\"},\n {\"id\":\"C2\",\"text\":\"The RESEA reemployment ATE of 1.47 pp is a 1.96% relative increase over the 75.0% control-group mean.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"The denominator is the raw aggregate control mean, while the ATE is block-adjusted.\"},\n {\"id\":\"C3\",\"text\":\"An employment elasticity of -0.388 applied to the printed 0.047 minimum-wage change implies a -1.8236% change in employment.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Linear approximation; the table note says 'about 4%', which would give about -1.55%.\"},\n {\"id\":\"C4\",\"text\":\"The fatal-hospitalisation ATT (-0.354 log points) cannot be expressed as an absolute change per 100,000 per year, because the extract prints no level of that rate.\",\"results_keys\":[\"harmonised\",\"n_not_harmonisable\"],\"status\":\"supported\",\"caveat\":\"Only the relative change (-29.8133%) can be computed from the extract.\"},\n {\"id\":\"C5\",\"text\":\"The Benin LPM coefficient of -0.0962 equals -9.62 percentage points.\",\"results_keys\":[\"harmonised\"],\"status\":\"supported\",\"caveat\":\"Exact rescaling.\"},\n {\"id\":\"C6\",\"text\":\"Four of five items were harmonised and one was declared not harmonisable; the task is complete in the sense defined by TASK.md.\",\"results_keys\":[\"n_harmonised\",\"n_not_harmonisable\",\"feasibility\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C7\",\"text\":\"The extracts contain internal inconsistencies (e.g. the abstract's 35% vs Table 7's -29.8% for fatal hospitalisations; 'about 4%' vs the printed 0.047), which were resolved in favour of the table values.\",\"results_keys\":[\"data_issues\",\"baseline_reported\"],\"status\":\"supported\",\"caveat\":\"These are reported, not corrected; no source values were altered.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\nr=json.load(open('results.json'));c=json.load(open('claims.json'))\nassert all(k in r for x in c for k in x['results_keys'])\np=json.load(open('state/phases.json'))\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md has the 6 required sections\",\"every claim's results_keys exist in results.json\",\"claims.json written (7 claims, all supported)\"],\"notes\":\"\"}\njson.dump(p,open('state/phases.json','w'),indent=1);print(\"ok\")\nEOF\ngrep -c '^## ' report.md\necho \"- 2026-10-30 P5 passed: report and claims written; all phases passed.\" >> state/log.md\nls -R .","description":"Write report, claims, finalize ledger"}}]}}85{"type":"api-request","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","id":"654640e2-0f66-4bba-b75e-d434150fab29","timestamp":"2026-10-30T05:11:49.468Z","version":"2.1.314","querySource":"sdk","shapeHash":"e5724379dff8ae63e8378d36bdd9704da41e120a51f412af7299e05765854c13","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"667e5882-7d6a-4e2b-af08-02e3818a693a","keep":26,"tail":["035956d5e0d7514aaa0fac7b327f08cf1a56d7f1068fc9eaf37082a007e95622","318be92196fbf4df7838bbde29c7c9bb6be4a63db45130c1d88c3496f882977b"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",27,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"66af086d2ba22222f301d853f25086ad8891268e9e4d11f83fc068b034399057"}86{"parentUuid":"94e02564-4a6f-412c-abd4-0d8824c40f02","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr7quuwYqXPnXqhLBmx","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3374,"cache_read_input_tokens":46068,"output_tokens":2379,"output_tokens_details":{"thinking_tokens":49},"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":3374},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":687,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXr7qe3MExgZxjZHu5gS","requestRef":"654640e2-0f66-4bba-b75e-d434150fab29","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"89f8a738-043e-45d2-81c5-024d56136dc3","timestamp":"2026-10-30T05:11:52.227Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237/out","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}87{"parentUuid":"89f8a738-043e-45d2-81c5-024d56136dc3","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr7quuwYqXPnXqhLBmx","type":"message","role":"assistant","content":[{"type":"text","text":"The report wasn't written because the `cd` failed (I was already in `out/`). Rewriting it."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3374,"cache_read_input_tokens":46068,"output_tokens":2379,"output_tokens_details":{"thinking_tokens":49},"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":3374},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXr7qe3MExgZxjZHu5gS","requestRef":"654640e2-0f66-4bba-b75e-d434150fab29","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"144f225e-938c-4b8f-8fe9-23b271d3e203","timestamp":"2026-10-30T05:11:52.227Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237/out","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}88{"parentUuid":"144f225e-938c-4b8f-8fe9-23b271d3e203","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr7quuwYqXPnXqhLBmx","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01WTNHLMKkrYFF6o9yNfTgbg","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0237/out; cat > report.md <<'EOF'\n# F4-conf-03 — Harmonising effect sizes reported under incompatible definitions\n\n## Question\nFive treatment effects from four arXiv extracts are printed under different definitions: log points, percentage points, an elasticity, and a linear-probability coefficient. For each one, I re-express the printed point estimate on the target definition given in `data/effects.json`. I use only quantities printed in the cited extract and an explicit transformation. Where the target cannot be reached, I say so and name the quantity that is missing.\n\n## Data and exclusions\n- Inputs (`n_input_rows`): `effects.json` (5 items), `README.md` (16 lines), and four source extracts: arxiv-2609.27732v1 (134 lines), arxiv-2606.14621v1 (58), arxiv-2505.04555v3 (66) and arxiv-2510.03658v1 (91). All SHA-256 hashes match `data/README.md` (see `out/state/data_audit.md`).\n- No exclusions: all 5 items were assessed (`n_analysed` = 5; `out/exclusions.csv`: 5 → 5).\n- Problems found in the inputs are listed in `data_issues` (7 entries). The ones that matter here are:\n - The Armendariz abstract says fatal hospitalisations \"fall by 35%\". Table 7 implies −29.8%, and Section 6.1 says \"approximately 30%\".\n - Kanayama et al.'s note to Table 2 says \"about 4%\", but the table row prints 0.047.\n - The Armendariz extract prints no level of the fatal hospitalisation rate.\n - Two smaller Armendariz discrepancies have no effect on the results: the standard errors and control-region counts in Table A19 differ from those in Table 6, and A19's % change row disagrees with exp(b) − 1 by 0.1 in the last digit.\n\n## Method\nI applied the conventions stated in TASK.md. Each item's formula and the printed inputs are recorded in `harmonised.<id>.transformation`, and line-level locators are in `harmonised.<id>.locator`. `out/analysis.py` parses the numbers directly from the table rows and prints every source line it uses.\n- E1: 100·(exp(b) − 1), with b = −0.431 (Table 6, ATT, 5 km).\n- E2: 100·Δ/p, with Δ = 1.47 pp (Table 2, Reemployment, Average treatment effect estimate) and p = 75.0% (Table 2, Control).\n- E3: 100·η·g, with η = −0.388 (Table 2, Employment elasticity w.r.t MW) and g = 0.047 (Table 2, \"% MW changes\"; read as a proportion, consistent with the note's \"about 4%\").\n- E4: the target would be Y0·(exp(b) − 1), with b = −0.354 (Table 7, Drug-related (fatal), 5 km). Y0 is the counterfactual fatal rate per 100,000 per year.\n- E5: 100·β, with β = −0.0962 (Table 3, Treatment, column (4)).\n\nAs a baseline, the untransformed printed values in their original units are kept under `baseline_reported`.\n\n## Results\n| id | harmonised value | unit | key |\n|---|---|---|---|\n| E1 | −35.0141 | % change in the hospitalisation rate (matches the printed −35.0%) | `harmonised.E1.value` |\n| E2 | 1.96 | % of the control-group reemployment mean | `harmonised.E2.value` |\n| E3 | −1.8236 | % change in employment for the observed 4.7% MW increase | `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- Four items were harmonised (`n_harmonised` = 4) and one was not (`n_not_harmonisable` = 1).\n- E4 cannot be expressed as an absolute change. The extract prints only log-point ATTs, standard errors and region counts; it gives no mean, baseline or counterfactual level of the fatal drug-related hospitalisation rate per 100,000. The missing quantity is that level for the 5 km treated regions (`harmonised.E4.missing_quantity`). The relative change that can be computed, −29.8133% (`baseline_reported.E4.computable_relative_change_percent`), is not the target definition.\n- The task as a whole is `feasibility` = \"complete\": every item has either a value or a justified statement of impossibility.\n\n## Uncertainty\n- No standard errors or confidence intervals were propagated (none were required). The values are point estimates only.\n- E5 is exact: it is a pure rescaling (×100).\n- E1 is exact given the printed b. However, b is rounded to 3 decimals, so only about 3 significant digits are meaningful (≈ −35.0%). It is also the exp-transform of an average log effect, not necessarily the percent change in the mean rate.\n- E2 is an exact ratio of printed numbers, but it is approximate as an estimand. The denominator is the raw aggregate control mean (75.0%, one decimal), while the ATE is adjusted for randomisation week. The printed numbers do not give an exact counterfactual level.\n- E3 is a first-order (linear) approximation, η·g. It also depends on which MW change is used: with the note's \"about 4%\", the result would be about −1.55% instead of −1.82%. I used the table's 0.047, as the task instructs.\n\n## Limitations and open issues\n- E4 is blocked by a missing quantity (see Results). I did not substitute any external or remembered baseline rate. The time unit (\"per year\") is also not stated explicitly in the extract.\n- The extracts contain internal inconsistencies (`data_issues`), most importantly the 35% vs ≈30% figure for the fatal outcome in the Armendariz abstract. I used the table values throughout.\n- The harmonised effects come from different populations, estimators and estimands (ATT, ITT/ATE, elasticity, DiD-LPM). Putting them on a common scale does not make them directly comparable as causal quantities.\nEOF\ngrep '^## ' report.md; tail -2 state/log.md; python3 -c \"import json;print({k:v['status'] for k,v in json.load(open('state/phases.json')).items()})\"; python3 -c \"print(100*-0.388*0.04)\"","description":"Write report and verify final state"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3374,"cache_read_input_tokens":46068,"output_tokens":2379,"output_tokens_details":{"thinking_tokens":49},"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":3374},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXr7qe3MExgZxjZHu5gS","requestRef":"654640e2-0f66-4bba-b75e-d434150fab29","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"27125195-9ef1-4149-be63-42766029054f","timestamp":"2026-10-30T05:12:06.605Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237/out","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}89{"parentUuid":"27125195-9ef1-4149-be63-42766029054f","isSidechain":false,"promptId":"78a43acf-7189-41b5-8d88-ed19c47467dd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01WTNHLMKkrYFF6o9yNfTgbg","type":"tool_result","content":"## Question\n## Data and exclusions\n## Method\n## Results\n## Uncertainty\n## Limitations and open issues\n- 2026-10-30 P4 passed: reproducible, checks in checks.md.\n- 2026-10-30 P5 passed: report and claims written; all phases passed.\n{'P0': 'passed', 'P1': 'passed', 'P2': 'passed', 'P3': 'passed', 'P4': 'passed', 'P5': 'passed'}\n-1.5520000000000003","is_error":false}]},"uuid":"389e5228-1205-4f49-ba7f-1bfac9d747fa","timestamp":"2026-10-30T05:12:06.702Z","toolUseResult":{"stdout":"## Question\n## Data and exclusions\n## Method\n## Results\n## Uncertainty\n## Limitations and open issues\n- 2026-10-30 P4 passed: reproducible, checks in checks.md.\n- 2026-10-30 P5 passed: report and claims written; all phases passed.\n{'P0': 'passed', 'P1': 'passed', 'P2': 'passed', 'P3': 'passed', 'P4': 'passed', 'P5': 'passed'}\n-1.5520000000000003","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"27125195-9ef1-4149-be63-42766029054f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237/out","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}90{"type":"api-request-blob","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","hash":"0691a061511eb0d607034b5098fea631d8eefdb0142e34a6ee1864a7584b0332","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"The report wasn't written because the `cd` failed (I was already in `out/`). Rewriting it."},{"type":"tool_use","id":"toolu_01WTNHLMKkrYFF6o9yNfTgbg","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0237/out; cat > report.md <<'EOF'\n# F4-conf-03 — Harmonising effect sizes reported under incompatible definitions\n\n## Question\nFive treatment effects from four arXiv extracts are printed under different definitions: log points, percentage points, an elasticity, and a linear-probability coefficient. For each one, I re-express the printed point estimate on the target definition given in `data/effects.json`. I use only quantities printed in the cited extract and an explicit transformation. Where the target cannot be reached, I say so and name the quantity that is missing.\n\n## Data and exclusions\n- Inputs (`n_input_rows`): `effects.json` (5 items), `README.md` (16 lines), and four source extracts: arxiv-2609.27732v1 (134 lines), arxiv-2606.14621v1 (58), arxiv-2505.04555v3 (66) and arxiv-2510.03658v1 (91). All SHA-256 hashes match `data/README.md` (see `out/state/data_audit.md`).\n- No exclusions: all 5 items were assessed (`n_analysed` = 5; `out/exclusions.csv`: 5 → 5).\n- Problems found in the inputs are listed in `data_issues` (7 entries). The ones that matter here are:\n - The Armendariz abstract says fatal hospitalisations \"fall by 35%\". Table 7 implies −29.8%, and Section 6.1 says \"approximately 30%\".\n - Kanayama et al.'s note to Table 2 says \"about 4%\", but the table row prints 0.047.\n - The Armendariz extract prints no level of the fatal hospitalisation rate.\n - Two smaller Armendariz discrepancies have no effect on the results: the standard errors and control-region counts in Table A19 differ from those in Table 6, and A19's % change row disagrees with exp(b) − 1 by 0.1 in the last digit.\n\n## Method\nI applied the conventions stated in TASK.md. Each item's formula and the printed inputs are recorded in `harmonised.<id>.transformation`, and line-level locators are in `harmonised.<id>.locator`. `out/analysis.py` parses the numbers directly from the table rows and prints every source line it uses.\n- E1: 100·(exp(b) − 1), with b = −0.431 (Table 6, ATT, 5 km).\n- E2: 100·Δ/p, with Δ = 1.47 pp (Table 2, Reemployment, Average treatment effect estimate) and p = 75.0% (Table 2, Control).\n- E3: 100·η·g, with η = −0.388 (Table 2, Employment elasticity w.r.t MW) and g = 0.047 (Table 2, \"% MW changes\"; read as a proportion, consistent with the note's \"about 4%\").\n- E4: the target would be Y0·(exp(b) − 1), with b = −0.354 (Table 7, Drug-related (fatal), 5 km). Y0 is the counterfactual fatal rate per 100,000 per year.\n- E5: 100·β, with β = −0.0962 (Table 3, Treatment, column (4)).\n\nAs a baseline, the untransformed printed values in their original units are kept under `baseline_reported`.\n\n## Results\n| id | harmonised value | unit | key |\n|---|---|---|---|\n| E1 | −35.0141 | % change in the hospitalisation rate (matches the printed −35.0%) | `harmonised.E1.value` |\n| E2 | 1.96 | % of the control-group reemployment mean | `harmonised.E2.value` |\n| E3 | −1.8236 | % change in employment for the observed 4.7% MW increase | `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- Four items were harmonised (`n_harmonised` = 4) and one was not (`n_not_harmonisable` = 1).\n- E4 cannot be expressed as an absolute change. The extract prints only log-point ATTs, standard errors and region counts; it gives no mean, baseline or counterfactual level of the fatal drug-related hospitalisation rate per 100,000. The missing quantity is that level for the 5 km treated regions (`harmonised.E4.missing_quantity`). The relative change that can be computed, −29.8133% (`baseline_reported.E4.computable_relative_change_percent`), is not the target definition.\n- The task as a whole is `feasibility` = \"complete\": every item has either a value or a justified statement of impossibility.\n\n## Uncertainty\n- No standard errors or confidence intervals were propagated (none were required). The values are point estimates only.\n- E5 is exact: it is a pure rescaling (×100).\n- E1 is exact given the printed b. However, b is rounded to 3 decimals, so only about 3 significant digits are meaningful (≈ −35.0%). It is also the exp-transform of an average log effect, not necessarily the percent change in the mean rate.\n- E2 is an exact ratio of printed numbers, but it is approximate as an estimand. The denominator is the raw aggregate control mean (75.0%, one decimal), while the ATE is adjusted for randomisation week. The printed numbers do not give an exact counterfactual level.\n- E3 is a first-order (linear) approximation, η·g. It also depends on which MW change is used: with the note's \"about 4%\", the result would be about −1.55% instead of −1.82%. I used the table's 0.047, as the task instructs.\n\n## Limitations and open issues\n- E4 is blocked by a missing quantity (see Results). I did not substitute any external or remembered baseline rate. The time unit (\"per year\") is also not stated explicitly in the extract.\n- The extracts contain internal inconsistencies (`data_issues`), most importantly the 35% vs ≈30% figure for the fatal outcome in the Armendariz abstract. I used the table values throughout.\n- The harmonised effects come from different populations, estimators and estimands (ATT, ITT/ATE, elasticity, DiD-LPM). Putting them on a common scale does not make them directly comparable as causal quantities.\nEOF\ngrep '^## ' report.md; tail -2 state/log.md; python3 -c \"import json;print({k:v['status'] for k,v in json.load(open('state/phases.json')).items()})\"; python3 -c \"print(100*-0.388*0.04)\"","description":"Write report and verify final state"}}]}}91{"type":"api-request","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","id":"06c96172-0c36-42ea-aae9-0ae5251547e2","timestamp":"2026-10-30T05:12:06.706Z","version":"2.1.314","querySource":"sdk","shapeHash":"e5724379dff8ae63e8378d36bdd9704da41e120a51f412af7299e05765854c13","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"654640e2-0f66-4bba-b75e-d434150fab29","keep":28,"tail":["0691a061511eb0d607034b5098fea631d8eefdb0142e34a6ee1864a7584b0332","e8759d4c29d3bc770c2dc9e477a28810464b61eda2cad59a9f1a673d52e3839b"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",29,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"2d9e23829d46dba0aff1718334dac094a948433e07bc5e02117acf2257d81b23"}92{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"389e5228-1205-4f49-ba7f-1bfac9d747fa","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb"}93{"type":"atis-latch","atis":"","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb"}94{"parentUuid":"389e5228-1205-4f49-ba7f-1bfac9d747fa","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr97cFyMFY8kQrRkdER","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSvgcKEAgTGAI4AUIIdGhpbmtpbmcSDL9eYDojSdn96p066RoMPeWAeEDslHy9ILpvIjDRTwqxXkhgzJa6FcIPsy/D8IpNJOVarNNbgzAtUeEs/w/MdHblvaUK0b1kVfUR3iIq2wb/h9zn7Av0tp8+KtWczcE18mQbIlLAQvPGNBE5fVtmiRPPtASAODdJIRzpzFoPMZyLjerRkULL+Uzr1WZwu9m0dPCiL28mrJDkTyRMoJTxr2jzU7EdsKBiakLfuyILK1b+M3dvgfq7KxVfvJCGYVtFg+8/JYAlRXX7tSeFDzPa3ns9W+poQt4Q72ivnlg7CXbyDN5sBXRhIx+9tshpIRzmrrx7BrEKg6GjPnjcL4sJQfyfbB320dMRMvsuRogSX0FnBHCfhkwcqV4TohKUzW80JZ9QH1Iz3rp7RncYgYNRA+EdMdhaaXwDxD9jIRM9zfSeHGRFTDSMEpCVO3m0mGZQDnqlA5hyfLwmth0MopRwZItHCziOG4uLc3Hs41r41YVBuIGQYzT+JTqrqzDN/l/E++7TFd/AL3/pozmGUjqeRowmanBBl9z5YtShJxWfzt9kP0V7g2jl9LrgcPh8cjPmziFJX2tdKRUhMsuIgDswVgaRebl/dh8lTURwKOe9FrPiZfvoS1+Z5QZFMq8qSAV5OjEnl5azk1plcrwd8NbIBQa8JiYDkeZhwZ1ig0K02zNbGJAGiPRRMFaRtYt2bYLikrUc4B7nDZyM4jqDm+Oyq0gRsc0QAItBv/12v5EoyeRdeEtpawEH+TETr4Y+IeVe+BkjRBsBOkWoPW08xmgj1rt+pG4jCMoFQu+QsbYNuiP9sCdGT0ue05x5iUm4CXBxvs5E3QADYsNRkfUdLN9u/cRtMRa/+a0rnpna2Jd3KbvYNG2K/ymO4D72JVLJYAAM8FAK9IlOD06AtzP1P5xKXTXG7QHW1UzoyR7IUcoFm3CcvWBPHLkOl/s2A3fdlXQvze5RGIxYn65bad3+9I8dX7Qef3fHw77zXQk38PAMU0tDk/cy+rYyT4BAIg9vaYOkReZFQlMbjvOdtthOq+jvJUsV/NmoRkJcDhhCdmltf613H1zAHDGsEQxZW+m0DQRsTiXSE17Yr70hdSBojeIUZPPTUJAA86JikYJ/bv5hX1GjlIlsBfAw7i6FZ2yxJN29tk5rbIST5aZp66ebceSKOLydfUjkf2eC8qZZ1N/nYram8YKj0WMEW/dg1fa1x453FrSLKJALb1W5XV92mpKdplGqpWg/7zKYnDxhGAE="}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2551,"cache_read_input_tokens":49442,"output_tokens":992,"output_tokens_details":{"thinking_tokens":71},"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":2551},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":7291,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXr97Ktiy7GEpvxkctMd","requestRef":"06c96172-0c36-42ea-aae9-0ae5251547e2","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8c998d55-090c-4afc-8391-af782f2b271c","timestamp":"2026-10-30T05:12:15.893Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237/out","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}95{"parentUuid":"8c998d55-090c-4afc-8391-af782f2b271c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr97cFyMFY8kQrRkdER","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 because the extract doesn't print a quantity it needs. The task still counts as \"complete\" under its own definition. `out/analysis.py` rebuilds `out/results.json` from `data/` alone, and a clean re-run gave a byte-identical file.\n\n| Item | Printed value | Converted value | How |\n|---|---|---|---|\n| E1, drug consumption rooms (Table 6, 5 km) | −0.431 log points | **−35.01%** change in hospitalisation rate | 100·(exp(b)−1); matches the paper's own −35.0% |\n| E2, reemployment programme (Table 2) | +1.47 percentage points (pp) | **+1.96%** of the control-group mean | 100·1.47/75.0 |\n| E3, minimum wage (Table 2) | elasticity −0.388 | **−1.82%** change in employment | 100·(−0.388)·0.047, using the wage increase printed in the table |\n| E4, fatal hospitalisations (Table 7, 5 km) | −0.354 log points | **not possible** | needs the fatal hospitalisation rate per 100,000 before treatment (or a comparison-group level), which the extract doesn't print |\n| E5, free caesarean sections (Table 3, column 4) | −0.0962 on a 0–1 scale | **−9.62 pp** | ×100 |\n\nFor E4, the extract only supports a relative change of −29.8%, which isn't the requested absolute change. I didn't fill in a baseline rate from outside the extract.\n\nHow exact each value is (no standard errors were carried through, as the task allows):\n- **E5** is a straight rescaling, so it's exact.\n- **E1** is exact given the printed coefficient, but that coefficient has only three decimals.\n- **E2** divides an effect that is adjusted for randomisation week by an unadjusted control mean, so it's only approximately the relative effect.\n- **E3** is a linear approximation. The table prints a 4.7% wage increase but its note says \"about 4%\"; using 4% would give −1.55% instead of −1.82%.\n\nI also logged seven inconsistencies in the sources under `data_issues`. The main one: the drug-consumption-room abstract says fatal hospitalisations fall by 35%, but the table value implies −29.8% and the paper's own Section 6.1 says about 30%. I used the table values throughout.\n\nFinally, these five effects come from different study designs and populations. Putting them on a common scale doesn't make them directly comparable as causal effects.\n\nFiles are in `out/`:\n- `results.json`\n- `report.md`\n- `analysis.py`\n- `claims.json`\n- `exclusions.csv`\n- `state/` (audit, checks and work log)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2551,"cache_read_input_tokens":49442,"output_tokens":992,"output_tokens_details":{"thinking_tokens":71},"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":2551},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXr97Ktiy7GEpvxkctMd","requestRef":"06c96172-0c36-42ea-aae9-0ae5251547e2","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b7f807d0-3e89-4e5b-8fcc-51bd1e55cb04","timestamp":"2026-10-30T05:12:17.195Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0237/out","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","version":"2.1.314","gitBranch":"HEAD"}96{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"b7f807d0-3e89-4e5b-8fcc-51bd1e55cb04","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb"}97{"type":"cost-state","sessionId":"83fea82b-db8c-4aed-bb52-aa44e95d49cb","totalCostUSD":0.7932396,"totalAPIDuration":202817,"totalAPIDurationWithoutRetries":202810,"totalToolDuration":985,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":203948,"startTime":1793336933255,"modelUsage":{"claude-opus-5-5":{"inputTokens":32,"outputTokens":22765,"thinkingTokens":2715,"cacheReadInputTokens":426833,"cacheCreationInputTokens":50489,"cacheCreationEphemeral5mInputTokens":50489,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.7932396}},"hasUnknownModelCost":false}98