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standard errors in parentheses) and a randomised evaluation of Rhode Island's RESEA programme (average treatment effects with 95% confidence intervals). The task is to extract specified point estimates, standard errors, confidence-interval bounds and sample counts **exactly as printed**, to attach to each value a locator (source id, table number, row label, column label), to derive two quantities that the sources do not print (a 95% confidence interval from a standard error, and a standard error from a confidence interval) while stating the convention used, and to recognise which requested quantity is not reported at all. The estimands are the printed numbers themselves; nothing is re-estimated.\n\n## Inputs\n- `data/sources/arxiv-2609.27732v1.txt` — extract of Armendariz (2026), *Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland*, arXiv:2609.27732v1 [econ.GN], CC BY 4.0; contains the abstract, Section 6 (Results, with Table 6 \"Overall Average Treatment Effect on the Treated (ATT)\" by buffer distance and Table 7 \"Overall ATT by Outcome Category and Buffer Zone\") and Appendix Table A19. Outcomes are log hospitalisation rates per 100,000 inhabitants; ATTs are in log points; the line below each coefficient row holds standard errors in parentheses; stars denote p < 0.01 (***), p < 0.05 (**), p < 0.10 (*).\n- `data/sources/arxiv-2606.14621v1.txt` — extract of Li, Pearson-Merkowitz & Yokum (2026), *Evaluating the Impact of Rhode Island's Self-Sustaining RESEA Program on Employment Outcomes*, arXiv:2606.14621v1 [econ.GN], CC BY 4.0; contains the abstract, Section 3 (Results, with Table 2: group means, treatment-control differences and average treatment effect (ATE) estimates with two-sided p-values and 95% confidence intervals) and Table 3 (ATE estimates under three estimators). Wages are annualised US dollars; reemployment is in percent / percentage points (pp); weeks on UI are weeks. Tables 2 and 3 were re-set in a one-row-per-outcome layout (see the extract header); values are verbatim.\n- `data/README.md` — provenance, retrieval times, licences and SHA-256 of the extracts.\n\n## Information-availability rules\n- The two extracts are the only admissible evidence; do not use remembered values from the papers or other versions.\n- \"As printed\" means the sign and the number of decimals shown in the table that the key names (e.g. `-0.431`, `0.210`, `17250` for \"17,250\"). Where a value appears in more than one table, use the table named in the key's definition.\n- Derived keys must be computed from the printed values of the named table with a stated convention (normal 1.96 or 2.0, or a t critical value); the convention must be written in `derivation_notes`.\n- A locator has the form `<source id> | Table <n> | row \"<row label>\" | column \"<column label>\"`; for derived keys, name the table the inputs came from and say \"derived\".\n- If a requested quantity is not printed in the source (and cannot be derived from printed quantities without additional assumptions), report `null` and say so in the locator (e.g. `not reported in arxiv-2609.27732v1`).\n\n## Required outputs\n- `out/results.json` with the keys (all numbers \"as printed\" unless marked derived):\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `dcr_att_5km` | number | log points | Table 6, row \"ATT\", column \"5 km\" |\n| `dcr_att_5km_se` | number | log points | Table 6, standard error of the 5 km ATT |\n| `dcr_att_3km` | number | log points | Table 6, row \"ATT\", column \"3 km\" |\n| `dcr_att_3km_se` | number | log points | Table 6, standard error of the 3 km ATT |\n| `dcr_obs_5km` | integer | region-years | Table 6, row \"Observations\", column \"5 km\" |\n| `dcr_narcotics_att_3km` | number | log points | Table 7, row \"Narcotics\", column \"3 km\" |\n| `dcr_narcotics_att_3km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_fatal_att_1km` | number | log points | Table 7, row \"Drug-related (fatal)\", column \"1 km\" |\n| `dcr_fatal_att_1km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_hiv_att_5km` | number | log points | Table 7, row \"HIV\", column \"5 km\" |\n| `dcr_hiv_att_5km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_hiv_att_5km_p_value` | number or null | — | the exact two-sided p-value of the HIV 5 km ATT if the source prints it; otherwise null |\n| `dcr_hiv_att_5km_significant_10pct` | boolean | — | whether the source marks the HIV 5 km ATT as significant at the 10% level |\n| `dcr_att_5km_ci_low`, `dcr_att_5km_ci_high` | number | log points | **derived**: 95% confidence interval of the Table 6 5 km ATT from its printed SE |\n| `ri_wage_ate` | number | USD per year | Table 2, row \"Wage\", column \"Average treatment effect estimate\", point estimate |\n| `ri_wage_ate_ci_low`, `ri_wage_ate_ci_high` | number | USD per year | Table 2, the 95% CI bounds printed for that ATE |\n| `ri_wage_ate_se_reported` | boolean | — | whether the source prints a standard error for the wage ATE |\n| `ri_wage_ate_se_derived` | number | USD per year | **derived**: standard error implied by the printed 95% CI (state the critical value used) |\n| `ri_control_mean_wage` | number | USD per year | Table 2, row \"Wage\", column \"Control\" (mean) |\n| `ri_reemployment_ate_pp` | number | percentage points | Table 2, row \"Reemployment\", ATE point estimate |\n| `ri_weeks_ui_ate` | number | weeks | Table 2, row \"Weeks on UI\", ATE point estimate (sign as printed: negative = fewer weeks) |\n| `ri_weeks_ui_ate_ci_low`, `ri_weeks_ui_ate_ci_high` | number | weeks | Table 2, the 95% CI bounds printed for that ATE (as printed, lower bound first) |\n| `locators` | object | — | one locator string per key above (except the two booleans) |\n| `derivation_notes` | object | — | {\"dcr_att_5km_ci\": \"<convention>\", \"ri_wage_ate_se\": \"<convention>\"} |\n| `task_id` | string | — | \"F4-conf-01\" |\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. The Uncertainty section must say which reported uncertainties are standard errors and which are confidence intervals, and what the derived quantities assume.\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 (text parsing is acceptable; hard-coding the numbers is acceptable only if the script also prints the matched source lines).\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.\nREADME.md\nsources\n\ndata/sources:\narxiv-2606.14621v1.txt\narxiv-2609.27732v1.txt","is_error":false}]},"uuid":"52842b62-3fdb-4f39-847c-953b86134c31","timestamp":"2026-10-30T05:21:40.038Z","toolUseResult":{"stdout":"# F4-conf-01 — Extracting treatment effects and their uncertainty from two published results tables\n\n## Scientific question\nTwo open-access studies report treatment effects with uncertainty: a staggered difference-in-differences study of drug consumption room (DCR) openings in Switzerland (log hospitalisation rates; standard errors in parentheses) and a randomised evaluation of Rhode Island's RESEA programme (average treatment effects with 95% confidence intervals). The task is to extract specified point estimates, standard errors, confidence-interval bounds and sample counts **exactly as printed**, to attach to each value a locator (source id, table number, row label, column label), to derive two quantities that the sources do not print (a 95% confidence interval from a standard error, and a standard error from a confidence interval) while stating the convention used, and to recognise which requested quantity is not reported at all. The estimands are the printed numbers themselves; nothing is re-estimated.\n\n## Inputs\n- `data/sources/arxiv-2609.27732v1.txt` — extract of Armendariz (2026), *Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland*, arXiv:2609.27732v1 [econ.GN], CC BY 4.0; contains the abstract, Section 6 (Results, with Table 6 \"Overall Average Treatment Effect on the Treated (ATT)\" by buffer distance and Table 7 \"Overall ATT by Outcome Category and Buffer Zone\") and Appendix Table A19. Outcomes are log hospitalisation rates per 100,000 inhabitants; ATTs are in log points; the line below each coefficient row holds standard errors in parentheses; stars denote p < 0.01 (***), p < 0.05 (**), p < 0.10 (*).\n- `data/sources/arxiv-2606.14621v1.txt` — extract of Li, Pearson-Merkowitz & Yokum (2026), *Evaluating the Impact of Rhode Island's Self-Sustaining RESEA Program on Employment Outcomes*, arXiv:2606.14621v1 [econ.GN], CC BY 4.0; contains the abstract, Section 3 (Results, with Table 2: group means, treatment-control differences and average treatment effect (ATE) estimates with two-sided p-values and 95% confidence intervals) and Table 3 (ATE estimates under three estimators). Wages are annualised US dollars; reemployment is in percent / percentage points (pp); weeks on UI are weeks. Tables 2 and 3 were re-set in a one-row-per-outcome layout (see the extract header); values are verbatim.\n- `data/README.md` — provenance, retrieval times, licences and SHA-256 of the extracts.\n\n## Information-availability rules\n- The two extracts are the only admissible evidence; do not use remembered values from the papers or other versions.\n- \"As printed\" means the sign and the number of decimals shown in the table that the key names (e.g. `-0.431`, `0.210`, `17250` for \"17,250\"). Where a value appears in more than one table, use the table named in the key's definition.\n- Derived keys must be computed from the printed values of the named table with a stated convention (normal 1.96 or 2.0, or a t critical value); the convention must be written in `derivation_notes`.\n- A locator has the form `<source id> | Table <n> | row \"<row label>\" | column \"<column label>\"`; for derived keys, name the table the inputs came from and say \"derived\".\n- If a requested quantity is not printed in the source (and cannot be derived from printed quantities without additional assumptions), report `null` and say so in the locator (e.g. `not reported in arxiv-2609.27732v1`).\n\n## Required outputs\n- `out/results.json` with the keys (all numbers \"as printed\" unless marked derived):\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `dcr_att_5km` | number | log points | Table 6, row \"ATT\", column \"5 km\" |\n| `dcr_att_5km_se` | number | log points | Table 6, standard error of the 5 km ATT |\n| `dcr_att_3km` | number | log points | Table 6, row \"ATT\", column \"3 km\" |\n| `dcr_att_3km_se` | number | log points | Table 6, standard error of the 3 km ATT |\n| `dcr_obs_5km` | integer | region-years | Table 6, row \"Observations\", column \"5 km\" |\n| `dcr_narcotics_att_3km` | number | log points | Table 7, row \"Narcotics\", column \"3 km\" |\n| `dcr_narcotics_att_3km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_fatal_att_1km` | number | log points | Table 7, row \"Drug-related (fatal)\", column \"1 km\" |\n| `dcr_fatal_att_1km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_hiv_att_5km` | number | log points | Table 7, row \"HIV\", column \"5 km\" |\n| `dcr_hiv_att_5km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_hiv_att_5km_p_value` | number or null | — | the exact two-sided p-value of the HIV 5 km ATT if the source prints it; otherwise null |\n| `dcr_hiv_att_5km_significant_10pct` | boolean | — | whether the source marks the HIV 5 km ATT as significant at the 10% level |\n| `dcr_att_5km_ci_low`, `dcr_att_5km_ci_high` | number | log points | **derived**: 95% confidence interval of the Table 6 5 km ATT from its printed SE |\n| `ri_wage_ate` | number | USD per year | Table 2, row \"Wage\", column \"Average treatment effect estimate\", point estimate |\n| `ri_wage_ate_ci_low`, `ri_wage_ate_ci_high` | number | USD per year | Table 2, the 95% CI bounds printed for that ATE |\n| `ri_wage_ate_se_reported` | boolean | — | whether the source prints a standard error for the wage ATE |\n| `ri_wage_ate_se_derived` | number | USD per year | **derived**: standard error implied by the printed 95% CI (state the critical value used) |\n| `ri_control_mean_wage` | number | USD per year | Table 2, row \"Wage\", column \"Control\" (mean) |\n| `ri_reemployment_ate_pp` | number | percentage points | Table 2, row \"Reemployment\", ATE point estimate |\n| `ri_weeks_ui_ate` | number | weeks | Table 2, row \"Weeks on UI\", ATE point estimate (sign as printed: negative = fewer weeks) |\n| `ri_weeks_ui_ate_ci_low`, `ri_weeks_ui_ate_ci_high` | number | weeks | Table 2, the 95% CI bounds printed for that ATE (as printed, lower bound first) |\n| `locators` | object | — | one locator string per key above (except the two booleans) |\n| `derivation_notes` | object | — | {\"dcr_att_5km_ci\": \"<convention>\", \"ri_wage_ate_se\": \"<convention>\"} |\n| `task_id` | string | — | \"F4-conf-01\" |\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. The Uncertainty section must say which reported uncertainties are standard errors and which are confidence intervals, and what the derived quantities assume.\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 (text parsing is acceptable; hard-coding the numbers is acceptable only if the script also prints the matched source lines).\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.\nREADME.md\nsources\n\ndata/sources:\narxiv-2606.14621v1.txt\narxiv-2609.27732v1.txt","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"5aa968e3-c22a-4abd-af35-b8deeeea73b5","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0278","sessionId":"f5918bad-04e6-40d5-a2c9-d6b80bb40a78","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"f5918bad-04e6-40d5-a2c9-d6b80bb40a78","hash":"ca947cfd0f77f457e6e9a9795411b71934f99d77ccce756809e78f7f4391cfae","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01S7NYsNTt8cKotJ8Rr8hjuz","name":"Bash","input":{"command":"cat TASK.md; ls -R data | head -50"}}]}}15{"type":"api-request","sessionId":"f5918bad-04e6-40d5-a2c9-d6b80bb40a78","id":"ca948a8a-189a-435d-81c6-19cc98c11f18","timestamp":"2026-10-30T05:21:40.044Z","version":"2.1.314","querySource":"sdk","shapeHash":"3125ad8154de8f98cc9ea6c19a88dec04c7eaec797399fa15a04bf0a8906a4d5","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":"41c12df1-a908-42e5-89a3-75e35ceeb633","keep":2,"tail":["ca947cfd0f77f457e6e9a9795411b71934f99d77ccce756809e78f7f4391cfae","1d2f2bbea1fce156eb50a34f824ff3419f382b67c8dcb333d3046c89c0e5e962"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"bb507d728df40e3e567565127a7bceefb870d6b33eb3c8ed5f281940fec4fe21"}16{"parentUuid":"52842b62-3fdb-4f39-847c-953b86134c31","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrsNnErk7cksARYk5pq","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01YbbCBRWyKucL6Ka8f15DtF","name":"Bash","input":{"command":"cd data; cat README.md; wc -l README.md sources/*; cat -A sources/arxiv-2609.27732v1.txt | head -300"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3351,"cache_read_input_tokens":2234,"output_tokens":161,"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":3351},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXrsNb5xDt44Jc77xyep","requestRef":"ca948a8a-189a-435d-81c6-19cc98c11f18","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4b97f3ad-07a5-40e4-a383-1a365439a580","timestamp":"2026-10-30T05:21:42.082Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0278/data","sessionId":"f5918bad-04e6-40d5-a2c9-d6b80bb40a78","version":"2.1.314","gitBranch":"HEAD"}17{"parentUuid":"4b97f3ad-07a5-40e4-a383-1a365439a580","isSidechain":false,"promptId":"ecaf2d52-147b-420e-9fd9-88edb739ee47","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01YbbCBRWyKucL6Ka8f15DtF","type":"tool_result","content":"# Data provenance — F4-conf-01\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\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 10 README.md\n 58 sources/arxiv-2606.14621v1.txt\n 134 sources/arxiv-2609.27732v1.txt\n 202 total\ncat: illegal option -- A\nusage: cat [-belnstuv] [file ...]","is_error":false}]},"uuid":"23afc7f0-1b5c-4ec2-bcb9-d35a1277c056","timestamp":"2026-10-30T05:21:42.114Z","toolUseResult":{"stdout":"# Data provenance — F4-conf-01\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\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 10 README.md\n 58 sources/arxiv-2606.14621v1.txt\n 134 sources/arxiv-2609.27732v1.txt\n 202 total\ncat: illegal option -- A\nusage: cat [-belnstuv] [file ...]","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"4b97f3ad-07a5-40e4-a383-1a365439a580","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0278/data","sessionId":"f5918bad-04e6-40d5-a2c9-d6b80bb40a78","version":"2.1.314","gitBranch":"HEAD"}18{"parentUuid":"23afc7f0-1b5c-4ec2-bcb9-d35a1277c056","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrsNnErk7cksARYk5pq","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01YVHxoZnewTkS9XiwoCA4gf","name":"Bash","input":{"command":"cd data; cat sources/arxiv-2606.14621v1.txt"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3351,"cache_read_input_tokens":2234,"output_tokens":161,"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":3351},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrsNb5xDt44Jc77xyep","requestRef":"ca948a8a-189a-435d-81c6-19cc98c11f18","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4f4e19bb-edce-4838-b00c-0fc8c971526f","timestamp":"2026-10-30T05:21:42.459Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0278/data","sessionId":"f5918bad-04e6-40d5-a2c9-d6b80bb40a78","version":"2.1.314","gitBranch":"HEAD"}19{"parentUuid":"4f4e19bb-edce-4838-b00c-0fc8c971526f","isSidechain":false,"promptId":"ecaf2d52-147b-420e-9fd9-88edb739ee47","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01YVHxoZnewTkS9XiwoCA4gf","type":"tool_result","content":"(eval):cd:1: no such file or directory: data\nSOURCE EXTRACT — arxiv-2606.14621v1\nTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\nAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\nBibliographic 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\nRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\nLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\nExtract 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.\nOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\nRendering 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.\nEditorial 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================================================================\n\n# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n\nHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n\n###### Abstract\n\nProlonged 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\n## 3 Results\n\n[Table 1 (pre-treatment characteristics) omitted from this extract.]\n\n[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\nOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\nWage | $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]\nReemployment | 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]\nWeeks 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]\nShort-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]\nShort-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]\nShort-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\nTable 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\nOverall, 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\nThe 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\nAs 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\n### 3.1 Cost-benefit analysis\n\nWe 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\n### 4.1 Methodological lessons (excerpt)\n\n[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\nOutcome | Difference in means | Covariate adjusted | Fixed effects regression\nWage | $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]\nReemployment | 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]\nWeeks 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\nTable 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\nIf 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","is_error":false}]},"uuid":"a0e2af6a-64d9-426a-b925-47f09c8cde7e","timestamp":"2026-10-30T05:21:42.483Z","toolUseResult":{"stdout":"(eval):cd:1: no such file or directory: data\nSOURCE EXTRACT — arxiv-2606.14621v1\nTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\nAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\nBibliographic 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\nRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\nLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\nExtract 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.\nOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\nRendering 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.\nEditorial 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================================================================\n\n# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n\nHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n\n###### Abstract\n\nProlonged 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\n## 3 Results\n\n[Table 1 (pre-treatment characteristics) omitted from this extract.]\n\n[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\nOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\nWage | $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]\nReemployment | 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]\nWeeks 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]\nShort-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]\nShort-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]\nShort-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\nTable 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\nOverall, 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\nThe 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\nAs 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\n### 3.1 Cost-benefit analysis\n\nWe 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\n### 4.1 Methodological lessons (excerpt)\n\n[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\nOutcome | Difference in means | Covariate adjusted | Fixed effects regression\nWage | $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]\nReemployment | 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]\nWeeks 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\nTable 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\nIf 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","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"4f4e19bb-edce-4838-b00c-0fc8c971526f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0278/data","sessionId":"f5918bad-04e6-40d5-a2c9-d6b80bb40a78","version":"2.1.314","gitBranch":"HEAD"}20{"type":"api-request-blob","sessionId":"f5918bad-04e6-40d5-a2c9-d6b80bb40a78","hash":"36613b8912674c1916898a8c447704552206b32c377838c2a52cecb066b8b3b0","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01YbbCBRWyKucL6Ka8f15DtF","name":"Bash","input":{"command":"cd data; cat README.md; wc -l README.md sources/*; cat -A sources/arxiv-2609.27732v1.txt | head -300"}},{"type":"tool_use","id":"toolu_01YVHxoZnewTkS9XiwoCA4gf","name":"Bash","input":{"command":"cd data; cat sources/arxiv-2606.14621v1.txt"}}]}}21{"type":"api-request-blob","sessionId":"f5918bad-04e6-40d5-a2c9-d6b80bb40a78","hash":"052db7e4e05ebdb6e9d8aa35802a5f8870e45a39487080b5c42886185e094e69","message":{"role":"user","content":[{"tool_use_id":"toolu_01YbbCBRWyKucL6Ka8f15DtF","type":"tool_result","content":"# Data provenance — F4-conf-01\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\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 10 README.md\n 58 sources/arxiv-2606.14621v1.txt\n 134 sources/arxiv-2609.27732v1.txt\n 202 total\ncat: illegal option -- A\nusage: cat [-belnstuv] [file ...]","is_error":false},{"tool_use_id":"toolu_01YVHxoZnewTkS9XiwoCA4gf","type":"tool_result","content":"(eval):cd:1: no such file or directory: data\nSOURCE EXTRACT — arxiv-2606.14621v1\nTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\nAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\nBibliographic 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\nRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\nLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\nExtract 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.\nOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\nRendering 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.\nEditorial 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================================================================\n\n# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n\nHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n\n###### Abstract\n\nProlonged 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\n## 3 Results\n\n[Table 1 (pre-treatment characteristics) omitted from this extract.]\n\n[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\nOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\nWage | $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]\nReemployment | 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]\nWeeks 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]\nShort-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]\nShort-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]\nShort-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\nTable 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\nOverall, 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\nThe 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\nAs 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\n### 3.1 Cost-benefit analysis\n\nWe 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\n### 4.1 Methodological lessons (excerpt)\n\n[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\nOutcome | Difference in means | Covariate adjusted | Fixed effects regression\nWage | $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]\nReemployment | 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]\nWeeks 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\nTable 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\nIf 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) . 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EXTRACT — arxiv-2609.27732v1\nTitle: Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\nAuthors: Ana Armendariz (University of St. Gallen)\nBibliographic 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\nRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\nLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\nExtract 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.\nOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\nRendering 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================================================================\n\n# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n\nAna Armendariz*\n\nSeptember 23, 2026\n\nThis 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\nKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n\n## 6 Results\n\nThis 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\nFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n\nNote: 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\nFigure 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\nTable 6: Overall Average Treatment Effect on the Treated (ATT)\n\n| 1 km | 3 km | 5 km\n\nATT | -0.399 | -0.283 | -0.431**\n\n| (0.412) | (0.279) | (0.210)\n\n% change | -32.9% | -24.7% | -35.0%\n\nTreated regions | 7 | 35 | 50\n\nControl regions | 608 | 608 | 608\n\nObservations | 16,175 | 16,875 | 17,250\n\nNote: 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\nthroughout. Regions near DCRs that opened before 1998\n(always-treated) are excluded. Standard errors clustered at the Medstat region level in\nparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n{}^{*}p<0.10 .\n\nTable 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\ncorresponding 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\nTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\nsub-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\nFigure 7: Event study: Sub-outcome estimates,\nwithin 5 km, \\pm 5-year window\n\nNote: 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\nDCR 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\nTable 7: Overall ATT by Outcome Category and Buffer Zone\n\nOutcome | 1 km | 3 km | 5 km\n\nMental/Behavioral | -0.354 | -0.202 | -0.381*\n\n| (0.382) | (0.244) | (0.220)\n\nHepatitis | -0.270 | -0.413 | -0.406\n\n| (0.486) | (0.270) | (0.250)\n\nHIV | 0.063 | -0.239 | -0.340\n\n| (0.801) | (0.337) | (0.294)\n\nNarcotics | 1.186*** | 0.549*** | 0.390**\n\n| (0.288) | (0.187) | (0.165)\n\nDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n\n| (0.137) | (0.215) | (0.178)\n\nTreated regions | 7 | 35 | 50\n\nControl regions | 608 (never-treated) | |\n\nNote: Each cell reports the overall ATT from\nthe doubly robust Callaway & Sant’Anna (2021) estimator with\nnever-treated controls and a universal base period. Outcomes are\nlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\nmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\nHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\nestimates are identified from a single treated region per cohort and\nshould be interpreted with caution. Regions near DCRs opened before\n1998 excluded. Standard errors clustered at the Medstat region level.\nSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n\nFigure 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\n### 6.1 Mechanisms\n\nThe 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\nsubstitution 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(Kerr et al., 2017; Hedrich, 2004).\n\nThe 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\nA 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\nTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n\n| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n\nATT | -0.431** | -0.438** | -0.362* | -0.149***\n\n| (0.218) | (0.208) | (0.205) | (0.038)\n\n% change | -35.0% | -35.5% | -30.3% | -13.9%\n\nTreated regions | 50 | | |\n\nControl regions | 608 | | |\n\nObservations | 17,250 | | |\n\nNote: All specifications use the 5 km buffer,\nnever-treated comparison regions (640 regions), and a \\pm 5-year window","is_error":false}]},"uuid":"3a17786b-c301-4eab-86ed-85eda03735be","timestamp":"2026-10-30T05:21:47.317Z","toolUseResult":{"stdout":"/Users/simon-pierreboucher/wp29/runs/RC0278/data\nSOURCE EXTRACT — arxiv-2609.27732v1\nTitle: Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\nAuthors: Ana Armendariz (University of St. Gallen)\nBibliographic 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\nRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\nLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\nExtract 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.\nOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\nRendering 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================================================================\n\n# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n\nAna Armendariz*\n\nSeptember 23, 2026\n\nThis 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\nKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n\n## 6 Results\n\nThis 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\nFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n\nNote: 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\nFigure 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\nTable 6: Overall Average Treatment Effect on the Treated (ATT)\n\n| 1 km | 3 km | 5 km\n\nATT | -0.399 | -0.283 | -0.431**\n\n| (0.412) | (0.279) | (0.210)\n\n% change | -32.9% | -24.7% | -35.0%\n\nTreated regions | 7 | 35 | 50\n\nControl regions | 608 | 608 | 608\n\nObservations | 16,175 | 16,875 | 17,250\n\nNote: 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\nthroughout. Regions near DCRs that opened before 1998\n(always-treated) are excluded. Standard errors clustered at the Medstat region level in\nparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n{}^{*}p<0.10 .\n\nTable 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\ncorresponding 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\nTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\nsub-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\nFigure 7: Event study: Sub-outcome estimates,\nwithin 5 km, \\pm 5-year window\n\nNote: 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\nDCR 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\nTable 7: Overall ATT by Outcome Category and Buffer Zone\n\nOutcome | 1 km | 3 km | 5 km\n\nMental/Behavioral | -0.354 | -0.202 | -0.381*\n\n| (0.382) | (0.244) | (0.220)\n\nHepatitis | -0.270 | -0.413 | -0.406\n\n| (0.486) | (0.270) | (0.250)\n\nHIV | 0.063 | -0.239 | -0.340\n\n| (0.801) | (0.337) | (0.294)\n\nNarcotics | 1.186*** | 0.549*** | 0.390**\n\n| (0.288) | (0.187) | (0.165)\n\nDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n\n| (0.137) | (0.215) | (0.178)\n\nTreated regions | 7 | 35 | 50\n\nControl regions | 608 (never-treated) | |\n\nNote: Each cell reports the overall ATT from\nthe doubly robust Callaway & Sant’Anna (2021) estimator with\nnever-treated controls and a universal base period. Outcomes are\nlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\nmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\nHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\nestimates are identified from a single treated region per cohort and\nshould be interpreted with caution. Regions near DCRs opened before\n1998 excluded. Standard errors clustered at the Medstat region level.\nSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n\nFigure 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\n### 6.1 Mechanisms\n\nThe 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\nsubstitution 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(Kerr et al., 2017; Hedrich, 2004).\n\nThe 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\nA 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\nTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n\n| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n\nATT | -0.431** | -0.438** | -0.362* | -0.149***\n\n| (0.218) | (0.208) | (0.205) | (0.038)\n\n% change | -35.0% | -35.5% | -30.3% | -13.9%\n\nTreated regions | 50 | | |\n\nControl regions | 608 | | |\n\nObservations | 17,250 | | |\n\nNote: All specifications use the 5 km buffer,\nnever-treated comparison regions (640 regions), and a \\pm 5-year 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-p out; cat > out/analysis.py <<'EOF'\n\"\"\"Replication script for F4-conf-01. Parses the two source extracts in data/sources/,\nprints the matched source lines, and writes out/results.json. Deterministic; no randomness.\"\"\"\nimport json, os, re, math\nfrom scipy.stats import norm\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nDCR_ID, RI_ID = \"arxiv-2609.27732v1\", \"arxiv-2606.14621v1\"\nP_DCR = os.path.join(ROOT, \"data\", \"sources\", DCR_ID + \".txt\")\nP_RI = os.path.join(ROOT, \"data\", \"sources\", RI_ID + \".txt\")\nP_README = os.path.join(ROOT, \"data\", \"README.md\")\n\ndef read(p):\n with open(p, encoding=\"utf-8\") as f:\n return f.read()\n\ndef wc_l(p):\n return read(p).count(\"\\n\")\n\ndcr = read(P_DCR).splitlines()\nri = read(P_RI).splitlines()\n\ndef block(lines, start_pat, end_pat):\n i0 = next(i for i, l in enumerate(lines) if l.startswith(start_pat))\n i1 = next(i for i in range(i0 + 1, len(lines)) if lines[i].startswith(end_pat))\n return lines[i0:i1]\n\ndef cells(line):\n return [c.strip() for c in line.split(\"|\")]\n\ndef parse_reg_table(blk, header_line_idx_pred):\n \"\"\"Return header cols and dict row -> (coef cells, se cells or None).\"\"\"\n nonempty = [l for l in blk if l.strip()]\n hdr = next(l for l in nonempty if header_line_idx_pred(l))\n cols = cells(hdr)[1:]\n rows = {}\n for k, l in enumerate(nonempty):\n c = cells(l)\n if l is hdr or \"|\" not in l or c[0] == \"\":\n continue\n se = None\n if k + 1 < len(nonempty) and cells(nonempty[k + 1])[0] == \"\" and nonempty[k + 1].strip().startswith(\"| (\"):\n se = cells(nonempty[k + 1])[1:]\n print(\" matched:\", l, \"\\n \", nonempty[k + 1])\n else:\n print(\" matched:\", l)\n rows[c[0]] = (c[1:], se)\n return cols, rows\n\ndef num(s):\n m = re.match(r\"^\\(?(-?[\\d,]*\\.?\\d+)\\)?(\\**)\", s.replace(\"$\", \"\").strip())\n return m.group(1).replace(\",\", \"\"), m.group(2)\n\ndef as_printed(s):\n v, _ = num(s)\n return int(v) if re.fullmatch(r\"-?\\d+\", v) else float(v)\n\nprint(\"== DCR Table 6 ==\")\nt6 = block(dcr, \"Table 6:\", \"Note:\")\nc6, r6 = parse_reg_table(t6, lambda l: l.strip().startswith(\"| 1 km\"))\nprint(\"== DCR Table 7 ==\")\nt7 = block(dcr, \"Table 7:\", \"Note:\")\nc7, r7 = parse_reg_table(t7, lambda l: l.startswith(\"Outcome |\"))\nprint(\"== DCR Table A19 ==\")\nta = block(dcr, \"Table A19:\", \"Note:\")\nca, ra = parse_reg_table(ta, lambda l: l.strip().startswith(\"| CS-DR\"))\n\ndef g(cols, rows, row, col, se=False):\n j = cols.index(col)\n c, s = rows[row]\n return (s if se else c)[j]\n\ndef loc(src, tab, row, col, extra=\"\"):\n return f'{src} | Table {tab} | row \"{row}\" | column \"{col}\"' + extra\n\nR, L, issues = {}, {}, []\n\ndef put(key, cols, rows, tab, row, col, se=False):\n raw = g(cols, rows, row, col, se)\n R[key] = as_printed(raw)\n L[key] = loc(DCR_ID, tab, row, col, \" (standard error, line below coefficient)\" if se else \"\")\n\nput(\"dcr_att_5km\", c6, r6, 6, \"ATT\", \"5 km\")\nput(\"dcr_att_5km_se\", c6, r6, 6, \"ATT\", \"5 km\", se=True)\nput(\"dcr_att_3km\", c6, r6, 6, \"ATT\", \"3 km\")\nput(\"dcr_att_3km_se\", c6, r6, 6, \"ATT\", \"3 km\", se=True)\nput(\"dcr_obs_5km\", c6, r6, 6, \"Observations\", \"5 km\")\nput(\"dcr_narcotics_att_3km\", c7, r7, 7, \"Narcotics\", \"3 km\")\nput(\"dcr_narcotics_att_3km_se\", c7, r7, 7, \"Narcotics\", \"3 km\", se=True)\nput(\"dcr_fatal_att_1km\", c7, r7, 7, \"Drug-related (fatal)\", \"1 km\")\nput(\"dcr_fatal_att_1km_se\", c7, r7, 7, \"Drug-related (fatal)\", \"1 km\", se=True)\nput(\"dcr_hiv_att_5km\", c7, r7, 7, \"HIV\", \"5 km\")\nput(\"dcr_hiv_att_5km_se\", c7, r7, 7, \"HIV\", \"5 km\", se=True)\n\n# HIV p-value: search the whole extract for any printed p-value near HIV\nhiv_p = [l for l in dcr if \"HIV\" in l and re.search(r\"p\\s*[=<]\\s*0?\\.\\d\", l)]\nprint(\"Lines mentioning HIV with an exact p-value:\", hiv_p)\nR[\"dcr_hiv_att_5km_p_value\"] = None\nL[\"dcr_hiv_att_5km_p_value\"] = (f\"not reported in {DCR_ID} (Table 7 prints only the coefficient, SE in parentheses \"\n \"and significance stars; no exact p-value is printed anywhere in the extract)\")\nhiv_stars = num(g(c7, r7, \"HIV\", \"5 km\"))[1]\nR[\"dcr_hiv_att_5km_significant_10pct\"] = len(hiv_stars) >= 1\n\n# derived 95% CI for Table 6 5km ATT\nZ = 1.96\nb, se = R[\"dcr_att_5km\"], R[\"dcr_att_5km_se\"]\nR[\"dcr_att_5km_ci_low\"] = round(b - Z * se, 4)\nR[\"dcr_att_5km_ci_high\"] = round(b + Z * se, 4)\nfor k in (\"dcr_att_5km_ci_low\", \"dcr_att_5km_ci_high\"):\n L[k] = f'{DCR_ID} | Table 6 | row \"ATT\" | column \"5 km\" | derived from printed ATT (-0.431) and SE (0.210)'\n\n# ---------- RI ----------\nprint(\"== RI Table 2 ==\")\nt2 = block(ri, \"Outcome | Treatment | Control\", \"Table 2:\")\nhdr2 = cells(t2[0])\nrows2 = {}\nfor l in t2[1:]:\n if l.strip():\n print(\" matched:\", l)\n c = cells(l); rows2[c[0]] = c[1:]\ncols2 = hdr2[1:]\nATE = \"Average treatment effect estimate\"\n\ndef parse_effect(s):\n m = re.match(r\"^(-?)\\$?(-?[\\d,]*\\.?\\d+)\\s*(pp|weeks|wks)?;\\s*p\\s*([=<])\\s*([\\d.]+);\\s*95% CI:\\s*\\[([^,\\]]+(?:,\\d{3})*),\\s*([^\\]]+)\\]$\", s)\n # split CI robustly: bounds may contain thousands separators (\"2,141\")\n est = m.group(1) + m.group(2).replace(\",\", \"\")\n ci = re.search(r\"\\[(.*)\\]\", s).group(1)\n parts = re.split(r\",\\s+\", ci)\n lo, hi = [p.replace(\",\", \"\") for p in parts]\n return est, m.group(4) + m.group(5), lo, hi\n\ndef tonum(v):\n return int(v) if re.fullmatch(r\"-?\\d+\", v) else float(v)\n\nw_est, w_p, w_lo, w_hi = parse_effect(rows2[\"Wage\"][cols2.index(ATE)])\nR[\"ri_wage_ate\"] = tonum(w_est)\nR[\"ri_wage_ate_ci_low\"], R[\"ri_wage_ate_ci_high\"] = tonum(w_lo), tonum(w_hi)\nfor k, what in ((\"ri_wage_ate\", \"point estimate\"), (\"ri_wage_ate_ci_low\", \"95% CI lower bound\"),\n (\"ri_wage_ate_ci_high\", \"95% CI upper bound\")):\n L[k] = f'{RI_ID} | Table 2 | row \"Wage\" | column \"{ATE}\" ({what})'\nse_printed = any(re.search(r\"\\bSE\\b|standard error\", l, re.I) for l in t2)\nR[\"ri_wage_ate_se_reported\"] = bool(se_printed)\nR[\"ri_wage_ate_se_derived\"] = round((R[\"ri_wage_ate_ci_high\"] - R[\"ri_wage_ate_ci_low\"]) / (2 * Z), 2)\nL[\"ri_wage_ate_se_derived\"] = (f'{RI_ID} | Table 2 | row \"Wage\" | column \"{ATE}\" | derived from printed 95% CI '\n f'[166, 2,141]; SE not reported in {RI_ID}')\ncm = rows2[\"Wage\"][cols2.index(\"Control\")]\nR[\"ri_control_mean_wage\"] = tonum(re.match(r\"\\$([\\d,]+)\", cm).group(1).replace(\",\", \"\"))\nL[\"ri_control_mean_wage\"] = f'{RI_ID} | Table 2 | row \"Wage\" | column \"Control\" (mean; SD printed separately)'\nr_est, r_p, r_lo, r_hi = parse_effect(rows2[\"Reemployment\"][cols2.index(ATE)])\nR[\"ri_reemployment_ate_pp\"] = tonum(r_est)\nL[\"ri_reemployment_ate_pp\"] = f'{RI_ID} | Table 2 | row \"Reemployment\" | column \"{ATE}\" (point estimate)'\nu_est, u_p, u_lo, u_hi = parse_effect(rows2[\"Weeks on UI\"][cols2.index(ATE)])\nR[\"ri_weeks_ui_ate\"] = tonum(u_est)\nR[\"ri_weeks_ui_ate_ci_low\"], R[\"ri_weeks_ui_ate_ci_high\"] = tonum(u_lo), tonum(u_hi)\nfor k, what in ((\"ri_weeks_ui_ate\", \"point estimate\"), (\"ri_weeks_ui_ate_ci_low\", \"95% CI lower bound\"),\n (\"ri_weeks_ui_ate_ci_high\", \"95% CI upper bound\")):\n L[k] = f'{RI_ID} | Table 2 | row \"Weeks on UI\" | column \"{ATE}\" ({what})'\n\n# ---------- consistency checks ----------\ndef stars_expected(b, s):\n p = 2 * (1 - norm.cdf(abs(b / s)))\n return (\"***\" if p < .01 else \"**\" if p < .05 else \"*\" if p < .10 else \"\"), p\n\nstar_mismatch = []\nfor tab, cols, rows in ((\"6\", c6, r6), (\"7\", c7, r7), (\"A19\", ca, ra)):\n for row, (c, s) in rows.items():\n if s is None:\n continue\n for j, col in enumerate(cols):\n if j >= len(c) or not c[j]:\n continue\n bv, st = num(c[j]); sv, _ = num(s[j])\n exp, p = stars_expected(float(bv), float(sv))\n if exp != st:\n star_mismatch.append(f\"Table {tab} {row}/{col}: printed '{st}' vs normal-approx '{exp}' (p={p:.3f})\")\nprint(\"Star/SE mismatches:\", star_mismatch)\n\na19_se = as_printed(g(ca, ra, \"ATT\", \"CS-DR\", se=True))\nif a19_se != R[\"dcr_att_5km_se\"]:\n issues.append({\"issue\": f\"Internal inconsistency in {DCR_ID}: the 5 km CS-DR overall ATT (-0.431**) has SE \"\n f\"{R['dcr_att_5km_se']} in Table 6 but SE {a19_se} in Appendix Table A19 (same estimator, same 5 km buffer).\",\n \"handling\": \"Used Table 6 as the key definition requires (SE 0.210); derived CI uses 0.210. \"\n \"With 0.218 the CI would be [\" + f\"{b - Z * a19_se:.4f}, {b + Z * a19_se:.4f}\" + \"].\"})\nn_ctrl = [l for l in dcr if \"640 regions\" in l]\nobs = {col: as_printed(g(c6, r6, \"Observations\", col)) for col in c6}\ntr = {col: as_printed(g(c6, r6, \"Treated regions\", col)) for col in c6}\nimplied = {col: obs[col] / 25 - tr[col] for col in c6}\nprint(\"Observations/25 years (1998-2022) minus treated regions:\", implied)\nif n_ctrl:\n issues.append({\"issue\": f\"Internal inconsistency in {DCR_ID}: Tables 6/7/A19 state 608 never-treated control regions, but the \"\n \"Table A19 note says 640 never-treated regions. The printed observation counts (16,175 / 16,875 / 17,250) \"\n \"equal 25 years x (640 + 7 / 35 / 50 treated regions), i.e. are consistent with 640 rather than 608 controls \"\n f\"(implied controls: {implied}). The 25-year panel length (1998-2022) is our assumption.\",\n \"handling\": \"Reported dcr_obs_5km = 17250 exactly as printed in Table 6; flagged, not corrected.\"})\nissues.append({\"issue\": f\"Text-vs-table wording in {DCR_ID}: the abstract says fatal outcomes 'fall by 35%', while Table 7 \"\n \"5 km fatal ATT is -0.354 log points (exp(-0.354)-1 = -29.8%, which Section 6.1 reports as ~30%); the text also \"\n \"calls HIV estimates 'close to zero' although the 5 km HIV ATT is -0.340 (insignificant).\",\n \"handling\": \"No effect on extracted keys; values taken from tables only.\"})\nissues.append({\"issue\": f\"In {RI_ID} the results paragraph prints the Weeks-on-UI CI as magnitudes '[1.75 weeks, 2.24 weeks]' \"\n \"for a 'reduction', whereas Table 2 prints the signed interval [-2.24, -1.75].\",\n \"handling\": \"Used Table 2 values with sign as printed (lower bound -2.24, upper bound -1.75).\"})\nissues.append({\"issue\": f\"In {RI_ID} the printed wage-ATE CI [166, 2,141] is very slightly asymmetric around $1,153 \"\n \"(987 below, 988 above), consistent with rounding of printed values; the CI method ('described in the main text') is not \"\n \"in the extract.\",\n \"handling\": \"Derived SE from the full CI width/(2*1.96); implied z = 1153/SE gives p close to the printed p=0.022 (checked).\"})\nif star_mismatch:\n issues.append({\"issue\": \"Significance stars not matching a normal approximation of coef/SE: \" + \"; \".join(star_mismatch),\n \"handling\": \"Stars reported as printed; flagged only.\"})\n\nz_w = R[\"ri_wage_ate\"] / R[\"ri_wage_ate_se_derived\"]\np_w = 2 * (1 - norm.cdf(z_w))\nprint(f\"Check: wage ATE z = {z_w:.3f}, implied two-sided p = {p_w:.4f} (printed {w_p})\")\nprint(f\"Check: HIV 5km z = {R['dcr_hiv_att_5km']/R['dcr_hiv_att_5km_se']:.3f}; stars printed: '{hiv_stars}'\")\n\norder = [\"dcr_att_5km\", \"dcr_att_5km_se\", \"dcr_att_3km\", \"dcr_att_3km_se\", \"dcr_obs_5km\",\n \"dcr_narcotics_att_3km\", \"dcr_narcotics_att_3km_se\", \"dcr_fatal_att_1km\", \"dcr_fatal_att_1km_se\",\n \"dcr_hiv_att_5km\", \"dcr_hiv_att_5km_se\", \"dcr_hiv_att_5km_p_value\", \"dcr_hiv_att_5km_significant_10pct\",\n \"dcr_att_5km_ci_low\", \"dcr_att_5km_ci_high\", \"ri_wage_ate\", \"ri_wage_ate_ci_low\", \"ri_wage_ate_ci_high\",\n \"ri_wage_ate_se_reported\", \"ri_wage_ate_se_derived\", \"ri_control_mean_wage\", \"ri_reemployment_ate_pp\",\n \"ri_weeks_ui_ate\", \"ri_weeks_ui_ate_ci_low\", \"ri_weeks_ui_ate_ci_high\"]\nout = {k: R[k] for k in order}\nout[\"locators\"] = {k: L[k] for k in order if k in L}\nout[\"derivation_notes\"] = {\n \"dcr_att_5km_ci\": \"Normal approximation, critical value 1.96: CI = ATT +/- 1.96*SE = -0.431 +/- 1.96*0.210 \"\n \"(Table 6 printed values; rounded to 4 decimals). Assumes asymptotic normality of the CS-DR ATT \"\n \"and that the printed clustered SE is the relevant SE.\",\n \"ri_wage_ate_se\": \"Normal approximation, critical value 1.96: SE = (upper - lower)/(2*1.96) = (2141 - 166)/3.92 \"\n \"(Table 2 printed CI; rounded to 2 decimals). Assumes the printed CI is symmetric and normal-based; with \"\n \"N = 23,549 a t critical value would be indistinguishable from 1.96.\",\n}\nout[\"task_id\"] = \"F4-conf-01\"\nout[\"feasibility\"] = \"complete\"\nout[\"feasibility_reason\"] = None\nout[\"data_issues\"] = issues\nout[\"n_input_rows\"] = {\"README.md\": wc_l(P_README), f\"{DCR_ID}.txt\": wc_l(P_DCR), f\"{RI_ID}.txt\": wc_l(P_RI)}\nout[\"n_analysed\"] = len(order)\nout[\"random_seed\"] = None\nos.makedirs(os.path.join(ROOT, \"out\"), exist_ok=True)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\") as f:\n json.dump(out, f, indent=2)\nprint(json.dumps({k: out[k] for k in order}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4734,"cache_read_input_tokens":11896,"output_tokens":7534,"output_tokens_details":{"thinking_tokens":905},"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":4734},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrsuiq6nwnRK8caqVtn","requestRef":"7fd43797-421d-4540-abcd-f6e4be4d2744","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5e7cf21d-232c-4d46-9382-8deaa007905b","timestamp":"2026-10-30T05:22:46.985Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0278","sessionId":"f5918bad-04e6-40d5-a2c9-d6b80bb40a78","version":"2.1.314","gitBranch":"HEAD"}38{"parentUuid":"5e7cf21d-232c-4d46-9382-8deaa007905b","isSidechain":false,"promptId":"ecaf2d52-147b-420e-9fd9-88edb739ee47","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016pvCTwnDKnuG3z649uZnCS","type":"tool_result","content":"== DCR Table 6 ==\n matched: ATT | -0.399 | -0.283 | -0.431** \n | (0.412) | (0.279) | (0.210)\n matched: % change | -32.9% | -24.7% | -35.0%\n matched: Treated regions | 7 | 35 | 50\n matched: Control regions | 608 | 608 | 608\n matched: Observations | 16,175 | 16,875 | 17,250\n== DCR Table 7 ==\n matched: Mental/Behavioral | -0.354 | -0.202 | -0.381* \n | (0.382) | (0.244) | (0.220)\n matched: Hepatitis | -0.270 | -0.413 | -0.406 \n | (0.486) | (0.270) | (0.250)\n matched: HIV | 0.063 | -0.239 | -0.340 \n | (0.801) | (0.337) | (0.294)\n matched: Narcotics | 1.186*** | 0.549*** | 0.390** \n | (0.288) | (0.187) | (0.165)\n matched: Drug-related (fatal) | -1.207*** | -0.290 | -0.354** \n | (0.137) | (0.215) | (0.178)\n matched: Treated regions | 7 | 35 | 50\n matched: Control regions | 608 (never-treated) | |\n== DCR Table A19 ==\n matched: ATT | -0.431** | -0.438** | -0.362* | -0.149*** \n | (0.218) | (0.208) | (0.205) | (0.038)\n matched: % change | -35.0% | -35.5% | -30.3% | -13.9%\n matched: Treated regions | 50 | | |\n matched: Control regions | 608 | | |\n matched: Observations | 17,250 | | |\nLines mentioning HIV with an exact p-value: []\n== RI Table 2 ==\n matched: Wage | $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 matched: 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 matched: Weeks 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 matched: Short-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 matched: Short-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 matched: Short-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]\nStar/SE mismatches: []\nObservations/25 years (1998-2022) minus treated regions: {'1 km': 640.0, '3 km': 640.0, '5 km': 640.0}\nCheck: wage ATE z = 2.288, implied two-sided p = 0.0221 (printed =0.022)\nCheck: HIV 5km z = -1.156; stars printed: ''\n{\n \"dcr_att_5km\": -0.431,\n \"dcr_att_5km_se\": 0.21,\n \"dcr_att_3km\": -0.283,\n \"dcr_att_3km_se\": 0.279,\n \"dcr_obs_5km\": 17250,\n \"dcr_narcotics_att_3km\": 0.549,\n \"dcr_narcotics_att_3km_se\": 0.187,\n \"dcr_fatal_att_1km\": -1.207,\n \"dcr_fatal_att_1km_se\": 0.137,\n \"dcr_hiv_att_5km\": -0.34,\n \"dcr_hiv_att_5km_se\": 0.294,\n \"dcr_hiv_att_5km_p_value\": null,\n \"dcr_hiv_att_5km_significant_10pct\": false,\n \"dcr_att_5km_ci_low\": -0.8426,\n \"dcr_att_5km_ci_high\": -0.0194,\n \"ri_wage_ate\": 1153,\n \"ri_wage_ate_ci_low\": 166,\n \"ri_wage_ate_ci_high\": 2141,\n \"ri_wage_ate_se_reported\": false,\n \"ri_wage_ate_se_derived\": 503.83,\n \"ri_control_mean_wage\": 33043,\n \"ri_reemployment_ate_pp\": 1.47,\n \"ri_weeks_ui_ate\": -1.99,\n \"ri_weeks_ui_ate_ci_low\": -2.24,\n \"ri_weeks_ui_ate_ci_high\": -1.75\n}","is_error":false}]},"uuid":"8b4f9e80-7635-43dd-b22c-646f25690973","timestamp":"2026-10-30T05:22:47.488Z","toolUseResult":{"stdout":"== DCR Table 6 ==\n matched: ATT | -0.399 | -0.283 | -0.431** \n | (0.412) | (0.279) | (0.210)\n matched: % change | -32.9% | -24.7% | -35.0%\n matched: Treated regions | 7 | 35 | 50\n matched: Control regions | 608 | 608 | 608\n matched: Observations | 16,175 | 16,875 | 17,250\n== DCR Table 7 ==\n matched: Mental/Behavioral | -0.354 | -0.202 | -0.381* \n | (0.382) | (0.244) | (0.220)\n matched: Hepatitis | -0.270 | -0.413 | -0.406 \n | (0.486) | (0.270) | (0.250)\n matched: HIV | 0.063 | -0.239 | -0.340 \n | (0.801) | (0.337) | (0.294)\n matched: Narcotics | 1.186*** | 0.549*** | 0.390** \n | (0.288) | (0.187) | (0.165)\n matched: Drug-related (fatal) | -1.207*** | -0.290 | -0.354** \n | (0.137) | (0.215) | (0.178)\n matched: Treated regions | 7 | 35 | 50\n matched: Control regions | 608 (never-treated) | |\n== DCR Table A19 ==\n matched: ATT | -0.431** | -0.438** | -0.362* | -0.149*** \n | (0.218) | (0.208) | (0.205) | (0.038)\n matched: % change | -35.0% | -35.5% | -30.3% | -13.9%\n matched: Treated regions | 50 | | |\n matched: Control regions | 608 | | |\n matched: Observations | 17,250 | | |\nLines mentioning HIV with an exact p-value: []\n== RI Table 2 ==\n matched: Wage | $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 matched: 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 matched: Weeks 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 matched: Short-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 matched: Short-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 matched: Short-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]\nStar/SE mismatches: []\nObservations/25 years (1998-2022) minus treated regions: {'1 km': 640.0, '3 km': 640.0, '5 km': 640.0}\nCheck: wage ATE z = 2.288, implied two-sided p = 0.0221 (printed =0.022)\nCheck: HIV 5km z = -1.156; stars printed: ''\n{\n \"dcr_att_5km\": -0.431,\n \"dcr_att_5km_se\": 0.21,\n \"dcr_att_3km\": -0.283,\n \"dcr_att_3km_se\": 0.279,\n \"dcr_obs_5km\": 17250,\n \"dcr_narcotics_att_3km\": 0.549,\n \"dcr_narcotics_att_3km_se\": 0.187,\n \"dcr_fatal_att_1km\": -1.207,\n \"dcr_fatal_att_1km_se\": 0.137,\n \"dcr_hiv_att_5km\": -0.34,\n \"dcr_hiv_att_5km_se\": 0.294,\n \"dcr_hiv_att_5km_p_value\": null,\n \"dcr_hiv_att_5km_significant_10pct\": false,\n \"dcr_att_5km_ci_low\": -0.8426,\n \"dcr_att_5km_ci_high\": -0.0194,\n \"ri_wage_ate\": 1153,\n \"ri_wage_ate_ci_low\": 166,\n \"ri_wage_ate_ci_high\": 2141,\n \"ri_wage_ate_se_reported\": false,\n \"ri_wage_ate_se_derived\": 503.83,\n \"ri_control_mean_wage\": 33043,\n \"ri_reemployment_ate_pp\": 1.47,\n \"ri_weeks_ui_ate\": -1.99,\n \"ri_weeks_ui_ate_ci_low\": -2.24,\n \"ri_weeks_ui_ate_ci_high\": 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-p out; cat > out/analysis.py <<'EOF'\n\"\"\"Replication script for F4-conf-01. Parses the two source extracts in data/sources/,\nprints the matched source lines, and writes out/results.json. Deterministic; no randomness.\"\"\"\nimport json, os, re, math\nfrom scipy.stats import norm\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nDCR_ID, RI_ID = \"arxiv-2609.27732v1\", \"arxiv-2606.14621v1\"\nP_DCR = os.path.join(ROOT, \"data\", \"sources\", DCR_ID + \".txt\")\nP_RI = os.path.join(ROOT, \"data\", \"sources\", RI_ID + \".txt\")\nP_README = os.path.join(ROOT, \"data\", \"README.md\")\n\ndef read(p):\n with open(p, encoding=\"utf-8\") as f:\n return f.read()\n\ndef wc_l(p):\n return read(p).count(\"\\n\")\n\ndcr = read(P_DCR).splitlines()\nri = read(P_RI).splitlines()\n\ndef block(lines, start_pat, end_pat):\n i0 = next(i for i, l in enumerate(lines) if l.startswith(start_pat))\n i1 = next(i for i in range(i0 + 1, len(lines)) if lines[i].startswith(end_pat))\n return lines[i0:i1]\n\ndef cells(line):\n return [c.strip() for c in line.split(\"|\")]\n\ndef parse_reg_table(blk, header_line_idx_pred):\n \"\"\"Return header cols and dict row -> (coef cells, se cells or None).\"\"\"\n nonempty = [l for l in blk if l.strip()]\n hdr = next(l for l in nonempty if header_line_idx_pred(l))\n cols = cells(hdr)[1:]\n rows = {}\n for k, l in enumerate(nonempty):\n c = cells(l)\n if l is hdr or \"|\" not in l or c[0] == \"\":\n continue\n se = None\n if k + 1 < len(nonempty) and cells(nonempty[k + 1])[0] == \"\" and nonempty[k + 1].strip().startswith(\"| (\"):\n se = cells(nonempty[k + 1])[1:]\n print(\" matched:\", l, \"\\n \", nonempty[k + 1])\n else:\n print(\" matched:\", l)\n rows[c[0]] = (c[1:], se)\n return cols, rows\n\ndef num(s):\n m = re.match(r\"^\\(?(-?[\\d,]*\\.?\\d+)\\)?(\\**)\", s.replace(\"$\", \"\").strip())\n return m.group(1).replace(\",\", \"\"), m.group(2)\n\ndef as_printed(s):\n v, _ = num(s)\n return int(v) if re.fullmatch(r\"-?\\d+\", v) else float(v)\n\nprint(\"== DCR Table 6 ==\")\nt6 = block(dcr, \"Table 6:\", \"Note:\")\nc6, r6 = parse_reg_table(t6, lambda l: l.strip().startswith(\"| 1 km\"))\nprint(\"== DCR Table 7 ==\")\nt7 = block(dcr, \"Table 7:\", \"Note:\")\nc7, r7 = parse_reg_table(t7, lambda l: l.startswith(\"Outcome |\"))\nprint(\"== DCR Table A19 ==\")\nta = block(dcr, \"Table A19:\", \"Note:\")\nca, ra = parse_reg_table(ta, lambda l: l.strip().startswith(\"| CS-DR\"))\n\ndef g(cols, rows, row, col, se=False):\n j = cols.index(col)\n c, s = rows[row]\n return (s if se else c)[j]\n\ndef loc(src, tab, row, col, extra=\"\"):\n return f'{src} | Table {tab} | row \"{row}\" | column \"{col}\"' + extra\n\nR, L, issues = {}, {}, []\n\ndef put(key, cols, rows, tab, row, col, se=False):\n raw = g(cols, rows, row, col, se)\n R[key] = as_printed(raw)\n L[key] = loc(DCR_ID, tab, row, col, \" (standard error, line below coefficient)\" if se else \"\")\n\nput(\"dcr_att_5km\", c6, r6, 6, \"ATT\", \"5 km\")\nput(\"dcr_att_5km_se\", c6, r6, 6, \"ATT\", \"5 km\", se=True)\nput(\"dcr_att_3km\", c6, r6, 6, \"ATT\", \"3 km\")\nput(\"dcr_att_3km_se\", c6, r6, 6, \"ATT\", \"3 km\", se=True)\nput(\"dcr_obs_5km\", c6, r6, 6, \"Observations\", \"5 km\")\nput(\"dcr_narcotics_att_3km\", c7, r7, 7, \"Narcotics\", \"3 km\")\nput(\"dcr_narcotics_att_3km_se\", c7, r7, 7, \"Narcotics\", \"3 km\", se=True)\nput(\"dcr_fatal_att_1km\", c7, r7, 7, \"Drug-related (fatal)\", \"1 km\")\nput(\"dcr_fatal_att_1km_se\", c7, r7, 7, \"Drug-related (fatal)\", \"1 km\", se=True)\nput(\"dcr_hiv_att_5km\", c7, r7, 7, \"HIV\", \"5 km\")\nput(\"dcr_hiv_att_5km_se\", c7, r7, 7, \"HIV\", \"5 km\", se=True)\n\n# HIV p-value: search the whole extract for any printed p-value near HIV\nhiv_p = [l for l in dcr if \"HIV\" in l and re.search(r\"p\\s*[=<]\\s*0?\\.\\d\", l)]\nprint(\"Lines mentioning HIV with an exact p-value:\", hiv_p)\nR[\"dcr_hiv_att_5km_p_value\"] = None\nL[\"dcr_hiv_att_5km_p_value\"] = (f\"not reported in {DCR_ID} (Table 7 prints only the coefficient, SE in parentheses \"\n \"and significance stars; no exact p-value is printed anywhere in the extract)\")\nhiv_stars = num(g(c7, r7, \"HIV\", \"5 km\"))[1]\nR[\"dcr_hiv_att_5km_significant_10pct\"] = len(hiv_stars) >= 1\n\n# derived 95% CI for Table 6 5km ATT\nZ = 1.96\nb, se = R[\"dcr_att_5km\"], R[\"dcr_att_5km_se\"]\nR[\"dcr_att_5km_ci_low\"] = round(b - Z * se, 4)\nR[\"dcr_att_5km_ci_high\"] = round(b + Z * se, 4)\nfor k in (\"dcr_att_5km_ci_low\", \"dcr_att_5km_ci_high\"):\n L[k] = f'{DCR_ID} | Table 6 | row \"ATT\" | column \"5 km\" | derived from printed ATT (-0.431) and SE (0.210)'\n\n# ---------- RI ----------\nprint(\"== RI Table 2 ==\")\nt2 = block(ri, \"Outcome | Treatment | Control\", \"Table 2:\")\nhdr2 = cells(t2[0])\nrows2 = {}\nfor l in t2[1:]:\n if l.strip():\n print(\" matched:\", l)\n c = cells(l); rows2[c[0]] = c[1:]\ncols2 = hdr2[1:]\nATE = \"Average treatment effect estimate\"\n\ndef parse_effect(s):\n m = re.match(r\"^(-?)\\$?(-?[\\d,]*\\.?\\d+)\\s*(pp|weeks|wks)?;\\s*p\\s*([=<])\\s*([\\d.]+);\\s*95% CI:\\s*\\[([^,\\]]+(?:,\\d{3})*),\\s*([^\\]]+)\\]$\", s)\n # split CI robustly: bounds may contain thousands separators (\"2,141\")\n est = m.group(1) + m.group(2).replace(\",\", \"\")\n ci = re.search(r\"\\[(.*)\\]\", s).group(1)\n parts = re.split(r\",\\s+\", ci)\n lo, hi = [p.replace(\",\", \"\") for p in parts]\n return est, m.group(4) + m.group(5), lo, hi\n\ndef tonum(v):\n return int(v) if re.fullmatch(r\"-?\\d+\", v) else float(v)\n\nw_est, w_p, w_lo, w_hi = parse_effect(rows2[\"Wage\"][cols2.index(ATE)])\nR[\"ri_wage_ate\"] = tonum(w_est)\nR[\"ri_wage_ate_ci_low\"], R[\"ri_wage_ate_ci_high\"] = tonum(w_lo), tonum(w_hi)\nfor k, what in ((\"ri_wage_ate\", \"point estimate\"), (\"ri_wage_ate_ci_low\", \"95% CI lower bound\"),\n (\"ri_wage_ate_ci_high\", \"95% CI upper bound\")):\n L[k] = f'{RI_ID} | Table 2 | row \"Wage\" | column \"{ATE}\" ({what})'\nse_printed = any(re.search(r\"\\bSE\\b|standard error\", l, re.I) for l in t2)\nR[\"ri_wage_ate_se_reported\"] = bool(se_printed)\nR[\"ri_wage_ate_se_derived\"] = round((R[\"ri_wage_ate_ci_high\"] - R[\"ri_wage_ate_ci_low\"]) / (2 * Z), 2)\nL[\"ri_wage_ate_se_derived\"] = (f'{RI_ID} | Table 2 | row \"Wage\" | column \"{ATE}\" | derived from printed 95% CI '\n f'[166, 2,141]; SE not reported in {RI_ID}')\ncm = rows2[\"Wage\"][cols2.index(\"Control\")]\nR[\"ri_control_mean_wage\"] = tonum(re.match(r\"\\$([\\d,]+)\", cm).group(1).replace(\",\", \"\"))\nL[\"ri_control_mean_wage\"] = f'{RI_ID} | Table 2 | row \"Wage\" | column \"Control\" (mean; SD printed separately)'\nr_est, r_p, r_lo, r_hi = parse_effect(rows2[\"Reemployment\"][cols2.index(ATE)])\nR[\"ri_reemployment_ate_pp\"] = tonum(r_est)\nL[\"ri_reemployment_ate_pp\"] = f'{RI_ID} | Table 2 | row \"Reemployment\" | column \"{ATE}\" (point estimate)'\nu_est, u_p, u_lo, u_hi = parse_effect(rows2[\"Weeks on UI\"][cols2.index(ATE)])\nR[\"ri_weeks_ui_ate\"] = tonum(u_est)\nR[\"ri_weeks_ui_ate_ci_low\"], R[\"ri_weeks_ui_ate_ci_high\"] = tonum(u_lo), tonum(u_hi)\nfor k, what in ((\"ri_weeks_ui_ate\", \"point estimate\"), (\"ri_weeks_ui_ate_ci_low\", \"95% CI lower bound\"),\n (\"ri_weeks_ui_ate_ci_high\", \"95% CI upper bound\")):\n L[k] = f'{RI_ID} | Table 2 | row \"Weeks on UI\" | column \"{ATE}\" ({what})'\n\n# ---------- consistency checks ----------\ndef stars_expected(b, s):\n p = 2 * (1 - norm.cdf(abs(b / s)))\n return (\"***\" if p < .01 else \"**\" if p < .05 else \"*\" if p < .10 else \"\"), p\n\nstar_mismatch = []\nfor tab, cols, rows in ((\"6\", c6, r6), (\"7\", c7, r7), (\"A19\", ca, ra)):\n for row, (c, s) in rows.items():\n if s is None:\n continue\n for j, col in enumerate(cols):\n if j >= len(c) or not c[j]:\n continue\n bv, st = num(c[j]); sv, _ = num(s[j])\n exp, p = stars_expected(float(bv), float(sv))\n if exp != st:\n star_mismatch.append(f\"Table {tab} {row}/{col}: printed '{st}' vs normal-approx '{exp}' (p={p:.3f})\")\nprint(\"Star/SE mismatches:\", star_mismatch)\n\na19_se = as_printed(g(ca, ra, \"ATT\", \"CS-DR\", se=True))\nif a19_se != R[\"dcr_att_5km_se\"]:\n issues.append({\"issue\": f\"Internal inconsistency in {DCR_ID}: the 5 km CS-DR overall ATT (-0.431**) has SE \"\n f\"{R['dcr_att_5km_se']} in Table 6 but SE {a19_se} in Appendix Table A19 (same estimator, same 5 km buffer).\",\n \"handling\": \"Used Table 6 as the key definition requires (SE 0.210); derived CI uses 0.210. \"\n \"With 0.218 the CI would be [\" + f\"{b - Z * a19_se:.4f}, {b + Z * a19_se:.4f}\" + \"].\"})\nn_ctrl = [l for l in dcr if \"640 regions\" in l]\nobs = {col: as_printed(g(c6, r6, \"Observations\", col)) for col in c6}\ntr = {col: as_printed(g(c6, r6, \"Treated regions\", col)) for col in c6}\nimplied = {col: obs[col] / 25 - tr[col] for col in c6}\nprint(\"Observations/25 years (1998-2022) minus treated regions:\", implied)\nif n_ctrl:\n issues.append({\"issue\": f\"Internal inconsistency in {DCR_ID}: Tables 6/7/A19 state 608 never-treated control regions, but the \"\n \"Table A19 note says 640 never-treated regions. The printed observation counts (16,175 / 16,875 / 17,250) \"\n \"equal 25 years x (640 + 7 / 35 / 50 treated regions), i.e. are consistent with 640 rather than 608 controls \"\n f\"(implied controls: {implied}). The 25-year panel length (1998-2022) is our assumption.\",\n \"handling\": \"Reported dcr_obs_5km = 17250 exactly as printed in Table 6; flagged, not corrected.\"})\nissues.append({\"issue\": f\"Text-vs-table wording in {DCR_ID}: the abstract says fatal outcomes 'fall by 35%', while Table 7 \"\n \"5 km fatal ATT is -0.354 log points (exp(-0.354)-1 = -29.8%, which Section 6.1 reports as ~30%); the text also \"\n \"calls HIV estimates 'close to zero' although the 5 km HIV ATT is -0.340 (insignificant).\",\n \"handling\": \"No effect on extracted keys; values taken from tables only.\"})\nissues.append({\"issue\": f\"In {RI_ID} the results paragraph prints the Weeks-on-UI CI as magnitudes '[1.75 weeks, 2.24 weeks]' \"\n \"for a 'reduction', whereas Table 2 prints the signed interval [-2.24, -1.75].\",\n \"handling\": \"Used Table 2 values with sign as printed (lower bound -2.24, upper bound -1.75).\"})\nissues.append({\"issue\": f\"In {RI_ID} the printed wage-ATE CI [166, 2,141] is very slightly asymmetric around $1,153 \"\n \"(987 below, 988 above), consistent with rounding of printed values; the CI method ('described in the main text') is not \"\n \"in the extract.\",\n \"handling\": \"Derived SE from the full CI width/(2*1.96); implied z = 1153/SE gives p close to the printed p=0.022 (checked).\"})\nif star_mismatch:\n issues.append({\"issue\": \"Significance stars not matching a normal approximation of coef/SE: \" + \"; \".join(star_mismatch),\n \"handling\": \"Stars reported as printed; flagged only.\"})\n\nz_w = R[\"ri_wage_ate\"] / R[\"ri_wage_ate_se_derived\"]\np_w = 2 * (1 - norm.cdf(z_w))\nprint(f\"Check: wage ATE z = {z_w:.3f}, implied two-sided p = {p_w:.4f} (printed {w_p})\")\nprint(f\"Check: HIV 5km z = {R['dcr_hiv_att_5km']/R['dcr_hiv_att_5km_se']:.3f}; stars printed: '{hiv_stars}'\")\n\norder = [\"dcr_att_5km\", \"dcr_att_5km_se\", \"dcr_att_3km\", \"dcr_att_3km_se\", \"dcr_obs_5km\",\n \"dcr_narcotics_att_3km\", \"dcr_narcotics_att_3km_se\", \"dcr_fatal_att_1km\", \"dcr_fatal_att_1km_se\",\n \"dcr_hiv_att_5km\", \"dcr_hiv_att_5km_se\", \"dcr_hiv_att_5km_p_value\", \"dcr_hiv_att_5km_significant_10pct\",\n \"dcr_att_5km_ci_low\", \"dcr_att_5km_ci_high\", \"ri_wage_ate\", \"ri_wage_ate_ci_low\", \"ri_wage_ate_ci_high\",\n \"ri_wage_ate_se_reported\", \"ri_wage_ate_se_derived\", \"ri_control_mean_wage\", \"ri_reemployment_ate_pp\",\n \"ri_weeks_ui_ate\", \"ri_weeks_ui_ate_ci_low\", \"ri_weeks_ui_ate_ci_high\"]\nout = {k: R[k] for k in order}\nout[\"locators\"] = {k: L[k] for k in order if k in L}\nout[\"derivation_notes\"] = {\n \"dcr_att_5km_ci\": \"Normal approximation, critical value 1.96: CI = ATT +/- 1.96*SE = -0.431 +/- 1.96*0.210 \"\n \"(Table 6 printed values; rounded to 4 decimals). Assumes asymptotic normality of the CS-DR ATT \"\n \"and that the printed clustered SE is the relevant SE.\",\n \"ri_wage_ate_se\": \"Normal approximation, critical value 1.96: SE = (upper - lower)/(2*1.96) = (2141 - 166)/3.92 \"\n \"(Table 2 printed CI; rounded to 2 decimals). Assumes the printed CI is symmetric and normal-based; with \"\n \"N = 23,549 a t critical value would be indistinguishable from 1.96.\",\n}\nout[\"task_id\"] = \"F4-conf-01\"\nout[\"feasibility\"] = \"complete\"\nout[\"feasibility_reason\"] = None\nout[\"data_issues\"] = issues\nout[\"n_input_rows\"] = {\"README.md\": wc_l(P_README), f\"{DCR_ID}.txt\": wc_l(P_DCR), f\"{RI_ID}.txt\": wc_l(P_RI)}\nout[\"n_analysed\"] = len(order)\nout[\"random_seed\"] = None\nos.makedirs(os.path.join(ROOT, \"out\"), exist_ok=True)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\") as f:\n json.dump(out, f, indent=2)\nprint(json.dumps({k: out[k] for k in order}, indent=1))\nEOF\n~/wp29/venv/bin/python 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> out/report.md <<'EOF'\n# F4-conf-01 — Extraction of treatment effects and uncertainty from two published tables\n\n## Question\nExtract, exactly as printed, specified point estimates, standard errors (SEs), 95% confidence-interval (CI) bounds and sample counts from (a) Armendariz (2026), arXiv:2609.27732v1 (DCR openings in Switzerland, staggered DiD, Tables 6–7) and (b) Li, Pearson-Merkowitz & Yokum (2026), arXiv:2606.14621v1 (Rhode Island RESEA RCT, Table 2). Attach a locator to each value, derive one CI from an SE and one SE from a CI, and identify the requested quantity that is not reported.\n\n## Data and exclusions\nOnly the two extracts in `data/sources/` were used, plus `data/README.md` for provenance (line counts: README 10, arxiv-2609.27732v1.txt 134, arxiv-2606.14621v1.txt 58). Nothing was excluded. Where a value appears in more than one table (DCR 5 km ATT in Table 6 and Appendix Table A19; RESEA ATEs in Tables 2 and 3), the table named in the key definition (Table 6, Table 2) was used.\n\n## Method\n`out/analysis.py` parses the pipe-delimited tables. For regression tables it reads the line below each coefficient row as the SE line, then removes stars, `$` and thousands separators. It prints every matched source line so that each value can be checked by eye. It also runs consistency checks: stars against a normal approximation of coef/SE, the p-value implied by the derived SE, and observation counts against region counts. Derived quantities use the normal critical value 1.96.\n\n## Results\n| key | value | locator (abridged) |\n|---|---|---|\n| dcr_att_5km / se | -0.431** / 0.210 | 2609.27732v1, Table 6, \"ATT\", \"5 km\" |\n| dcr_att_3km / se | -0.283 / 0.279 | Table 6, \"ATT\", \"3 km\" |\n| dcr_obs_5km | 17250 (printed \"17,250\") | Table 6, \"Observations\", \"5 km\" |\n| dcr_narcotics_att_3km / se | 0.549*** / 0.187 | Table 7, \"Narcotics\", \"3 km\" |\n| dcr_fatal_att_1km / se | -1.207*** / 0.137 | Table 7, \"Drug-related (fatal)\", \"1 km\" |\n| dcr_hiv_att_5km / se | -0.340 / 0.294 (no star) | Table 7, \"HIV\", \"5 km\" |\n| dcr_hiv_att_5km_p_value | **null — not reported** | only stars are printed, no exact p-values |\n| dcr_hiv_att_5km_significant_10pct | false | no star on the cell |\n| dcr_att_5km CI (derived) | [-0.8426, -0.0194] | Table 6 inputs, derived |\n| ri_wage_ate [95% CI] | 1153 [166, 2141] USD/yr | 2606.14621v1, Table 2, \"Wage\", \"Average treatment effect estimate\" |\n| ri_wage_ate_se_reported | false | Table 2 prints p and CI, not an SE |\n| ri_wage_ate_se_derived | 503.83 USD/yr | Table 2 CI, derived |\n| ri_control_mean_wage | 33043 USD/yr | Table 2, \"Wage\", \"Control\" |\n| ri_reemployment_ate_pp | 1.47 pp | Table 2, \"Reemployment\", ATE |\n| ri_weeks_ui_ate [95% CI] | -1.99 [-2.24, -1.75] weeks | Table 2, \"Weeks on UI\", ATE |\n\nNote: JSON cannot store trailing zeros. In `results.json`, 0.210 is stored as `0.21` and -0.340 as `-0.34`. The printed strings are 0.210 and -0.340, and the numeric values are the same.\n\nThe full locator strings are in `results.json[\"locators\"]`.\n\n## Uncertainty\n- **DCR study (arXiv:2609.27732v1):** every uncertainty measure is a **standard error** clustered at the MedStat region level, printed in parentheses below each coefficient, with significance stars. The source prints no CIs or exact p-values for the table cells, so the HIV 5 km p-value is reported as null. The **derived 95% CI** for the Table 6 5 km ATT is −0.431 ± 1.96 × 0.210 = [−0.8426, −0.0194] log points. It assumes the estimator is asymptotically normal and that the printed (rounded) SE is the right SE. With critical value 2.0 it would be [−0.851, −0.011].\n- **RESEA study (arXiv:2606.14621v1):** the uncertainties in Table 2 are **95% confidence intervals** plus two-sided p-values; no SEs are printed. The **derived SE** for the wage ATE is (2141 − 166)/(2 × 1.96) = 503.83 USD/yr. This assumes the CI is a symmetric normal-based interval. With N = 23,549, a t critical value would make no practical difference. As a check, 1153/503.83 gives z = 2.29 and a two-sided p of 0.022, which matches the printed p = 0.022. The construction of the CI is described only in the paper's main text, which is not in the extract.\n- All printed stars in DCR Tables 6, 7 and A19 match a normal approximation of coef/SE.\n\n## Limitations and open issues\n- **There is an internal inconsistency in the DCR source.** The 5 km CS-DR ATT (−0.431**) has SE 0.210 in Table 6 but SE 0.218 in Appendix Table A19. The Table 6 value is used, as the key specifies. With 0.218, the derived CI would be [−0.8583, −0.0037].\n- **The control-region counts in the DCR source disagree.** Tables 6, 7 and A19 state 608 never-treated control regions, but the A19 note says 640. The printed observation counts are 16,175, 16,875 and 17,250. Each equals 25 × (640 + treated regions), which fits 640 controls rather than 608. This check assumes a 25-year panel (1998–2022), which is my inference. The value 17250 is reported as printed, without correction.\n- **Some DCR text does not match the tables.** The abstract says fatal hospitalisations fall \"by 35%\". The Table 7 5 km fatal ATT of −0.354 log points corresponds to about −29.8%, and Section 6.1 itself says about 30%. The text also calls the HIV estimates \"close to zero\", while the 5 km cell is −0.340 (not significant). None of this affects the extracted keys.\n- **Two minor points in the RESEA source:**\n - The results paragraph prints the Weeks-on-UI CI as magnitudes, [1.75, 2.24]. Table 2 prints the signed interval [−2.24, −1.75], which is the one used.\n - The wage CI is very slightly asymmetric about the estimate (987 below, 988 above), which is consistent with rounding.\n- Overall the task is complete. The one quantity that is not available (the HIV 5 km exact p-value) is reported as null because the source does not print it, as the task anticipates.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Table 6 of arXiv:2609.27732v1 prints a 5 km overall ATT of -0.431** (SE 0.210) and a 3 km ATT of -0.283 (SE 0.279), with 17,250 observations at 5 km.\",\"results_keys\":[\"dcr_att_5km\",\"dcr_att_5km_se\",\"dcr_att_3km\",\"dcr_att_3km_se\",\"dcr_obs_5km\"],\"status\":\"supported\",\"caveat\":\"Appendix Table A19 prints SE 0.218 for the same 5 km CS-DR estimate; Table 6 used per key definition.\"},\n {\"id\":\"C2\",\"text\":\"Table 7 prints Narcotics 3 km ATT 0.549*** (0.187), Drug-related (fatal) 1 km ATT -1.207*** (0.137), and HIV 5 km ATT -0.340 (0.294) without stars.\",\"results_keys\":[\"dcr_narcotics_att_3km\",\"dcr_narcotics_att_3km_se\",\"dcr_fatal_att_1km\",\"dcr_fatal_att_1km_se\",\"dcr_hiv_att_5km\",\"dcr_hiv_att_5km_se\",\"dcr_hiv_att_5km_significant_10pct\"],\"status\":\"supported\",\"caveat\":\"The source notes 1 km estimates rest on a single treated region per cohort.\"},\n {\"id\":\"C3\",\"text\":\"The exact p-value of the HIV 5 km ATT is not reported in the DCR source (only stars), so it is null.\",\"results_keys\":[\"dcr_hiv_att_5km_p_value\"],\"status\":\"supported\",\"caveat\":\"A normal approximation would give p of roughly 0.25, but that is not a printed value and is not reported as one.\"},\n {\"id\":\"C4\",\"text\":\"Derived 95% CI of the Table 6 5 km ATT (normal, 1.96) is [-0.8426, -0.0194] log points, excluding zero.\",\"results_keys\":[\"dcr_att_5km_ci_low\",\"dcr_att_5km_ci_high\"],\"status\":\"supported\",\"caveat\":\"Depends on normality and on the rounded printed SE; using A19's SE 0.218 gives [-0.8583, -0.0037].\"},\n {\"id\":\"C5\",\"text\":\"Table 2 of arXiv:2606.14621v1 prints a wage ATE of $1,153 (95% CI [166, 2,141]) against a control mean of $33,043; no SE is printed.\",\"results_keys\":[\"ri_wage_ate\",\"ri_wage_ate_ci_low\",\"ri_wage_ate_ci_high\",\"ri_wage_ate_se_reported\",\"ri_control_mean_wage\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C6\",\"text\":\"The SE implied by the printed wage-ATE CI (normal, 1.96) is 503.83 USD/yr, consistent with the printed p=0.022.\",\"results_keys\":[\"ri_wage_ate_se_derived\"],\"status\":\"supported\",\"caveat\":\"Assumes a symmetric normal-based CI; the CI method is not in the extract.\"},\n {\"id\":\"C7\",\"text\":\"Table 2 prints a reemployment ATE of 1.47 pp and a Weeks-on-UI ATE of -1.99 weeks (95% CI [-2.24, -1.75]).\",\"results_keys\":[\"ri_reemployment_ate_pp\",\"ri_weeks_ui_ate\",\"ri_weeks_ui_ate_ci_low\",\"ri_weeks_ui_ate_ci_high\"],\"status\":\"supported\",\"caveat\":\"Body text prints the UI CI as unsigned magnitudes [1.75, 2.24].\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import 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> out/report.md <<'EOF'\n# F4-conf-01 — Extraction of treatment effects and uncertainty from two published tables\n\n## Question\nExtract, exactly as printed, specified point estimates, standard errors (SEs), 95% confidence-interval (CI) bounds and sample counts from (a) Armendariz (2026), arXiv:2609.27732v1 (DCR openings in Switzerland, staggered DiD, Tables 6–7) and (b) Li, Pearson-Merkowitz & Yokum (2026), arXiv:2606.14621v1 (Rhode Island RESEA RCT, Table 2). Attach a locator to each value, derive one CI from an SE and one SE from a CI, and identify the requested quantity that is not reported.\n\n## Data and exclusions\nOnly the two extracts in `data/sources/` were used, plus `data/README.md` for provenance (line counts: README 10, arxiv-2609.27732v1.txt 134, arxiv-2606.14621v1.txt 58). Nothing was excluded. Where a value appears in more than one table (DCR 5 km ATT in Table 6 and Appendix Table A19; RESEA ATEs in Tables 2 and 3), the table named in the key definition (Table 6, Table 2) was used.\n\n## Method\n`out/analysis.py` parses the pipe-delimited tables. For regression tables it reads the line below each coefficient row as the SE line, then removes stars, `$` and thousands separators. It prints every matched source line so that each value can be checked by eye. It also runs consistency checks: stars against a normal approximation of coef/SE, the p-value implied by the derived SE, and observation counts against region counts. Derived quantities use the normal critical value 1.96.\n\n## Results\n| key | value | locator (abridged) |\n|---|---|---|\n| dcr_att_5km / se | -0.431** / 0.210 | 2609.27732v1, Table 6, \"ATT\", \"5 km\" |\n| dcr_att_3km / se | -0.283 / 0.279 | Table 6, \"ATT\", \"3 km\" |\n| dcr_obs_5km | 17250 (printed \"17,250\") | Table 6, \"Observations\", \"5 km\" |\n| dcr_narcotics_att_3km / se | 0.549*** / 0.187 | Table 7, \"Narcotics\", \"3 km\" |\n| dcr_fatal_att_1km / se | -1.207*** / 0.137 | Table 7, \"Drug-related (fatal)\", \"1 km\" |\n| dcr_hiv_att_5km / se | -0.340 / 0.294 (no star) | Table 7, \"HIV\", \"5 km\" |\n| dcr_hiv_att_5km_p_value | **null — not reported** | only stars are printed, no exact p-values |\n| dcr_hiv_att_5km_significant_10pct | false | no star on the cell |\n| dcr_att_5km CI (derived) | [-0.8426, -0.0194] | Table 6 inputs, derived |\n| ri_wage_ate [95% CI] | 1153 [166, 2141] USD/yr | 2606.14621v1, Table 2, \"Wage\", \"Average treatment effect estimate\" |\n| ri_wage_ate_se_reported | false | Table 2 prints p and CI, not an SE |\n| ri_wage_ate_se_derived | 503.83 USD/yr | Table 2 CI, derived |\n| ri_control_mean_wage | 33043 USD/yr | Table 2, \"Wage\", \"Control\" |\n| ri_reemployment_ate_pp | 1.47 pp | Table 2, \"Reemployment\", ATE |\n| ri_weeks_ui_ate [95% CI] | -1.99 [-2.24, -1.75] weeks | Table 2, \"Weeks on UI\", ATE |\n\nNote: JSON cannot store trailing zeros. In `results.json`, 0.210 is stored as `0.21` and -0.340 as `-0.34`. The printed strings are 0.210 and -0.340, and the numeric values are the same.\n\nThe full locator strings are in `results.json[\"locators\"]`.\n\n## Uncertainty\n- **DCR study (arXiv:2609.27732v1):** every uncertainty measure is a **standard error** clustered at the MedStat region level, printed in parentheses below each coefficient, with significance stars. The source prints no CIs or exact p-values for the table cells, so the HIV 5 km p-value is reported as null. The **derived 95% CI** for the Table 6 5 km ATT is −0.431 ± 1.96 × 0.210 = [−0.8426, −0.0194] log points. It assumes the estimator is asymptotically normal and that the printed (rounded) SE is the right SE. With critical value 2.0 it would be [−0.851, −0.011].\n- **RESEA study (arXiv:2606.14621v1):** the uncertainties in Table 2 are **95% confidence intervals** plus two-sided p-values; no SEs are printed. The **derived SE** for the wage ATE is (2141 − 166)/(2 × 1.96) = 503.83 USD/yr. This assumes the CI is a symmetric normal-based interval. With N = 23,549, a t critical value would make no practical difference. As a check, 1153/503.83 gives z = 2.29 and a two-sided p of 0.022, which matches the printed p = 0.022. The construction of the CI is described only in the paper's main text, which is not in the extract.\n- All printed stars in DCR Tables 6, 7 and A19 match a normal approximation of coef/SE.\n\n## Limitations and open issues\n- **There is an internal inconsistency in the DCR source.** The 5 km CS-DR ATT (−0.431**) has SE 0.210 in Table 6 but SE 0.218 in Appendix Table A19. The Table 6 value is used, as the key specifies. With 0.218, the derived CI would be [−0.8583, −0.0037].\n- **The control-region counts in the DCR source disagree.** Tables 6, 7 and A19 state 608 never-treated control regions, but the A19 note says 640. The printed observation counts are 16,175, 16,875 and 17,250. Each equals 25 × (640 + treated regions), which fits 640 controls rather than 608. This check assumes a 25-year panel (1998–2022), which is my inference. The value 17250 is reported as printed, without correction.\n- **Some DCR text does not match the tables.** The abstract says fatal hospitalisations fall \"by 35%\". The Table 7 5 km fatal ATT of −0.354 log points corresponds to about −29.8%, and Section 6.1 itself says about 30%. The text also calls the HIV estimates \"close to zero\", while the 5 km cell is −0.340 (not significant). None of this affects the extracted keys.\n- **Two minor points in the RESEA source:**\n - The results paragraph prints the Weeks-on-UI CI as magnitudes, [1.75, 2.24]. Table 2 prints the signed interval [−2.24, −1.75], which is the one used.\n - The wage CI is very slightly asymmetric about the estimate (987 below, 988 above), which is consistent with rounding.\n- Overall the task is complete. The one quantity that is not available (the HIV 5 km exact p-value) is reported as null because the source does not print it, as the task anticipates.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Table 6 of arXiv:2609.27732v1 prints a 5 km overall ATT of -0.431** (SE 0.210) and a 3 km ATT of -0.283 (SE 0.279), with 17,250 observations at 5 km.\",\"results_keys\":[\"dcr_att_5km\",\"dcr_att_5km_se\",\"dcr_att_3km\",\"dcr_att_3km_se\",\"dcr_obs_5km\"],\"status\":\"supported\",\"caveat\":\"Appendix Table A19 prints SE 0.218 for the same 5 km CS-DR estimate; Table 6 used per key definition.\"},\n {\"id\":\"C2\",\"text\":\"Table 7 prints Narcotics 3 km ATT 0.549*** (0.187), Drug-related (fatal) 1 km ATT -1.207*** (0.137), and HIV 5 km ATT -0.340 (0.294) without stars.\",\"results_keys\":[\"dcr_narcotics_att_3km\",\"dcr_narcotics_att_3km_se\",\"dcr_fatal_att_1km\",\"dcr_fatal_att_1km_se\",\"dcr_hiv_att_5km\",\"dcr_hiv_att_5km_se\",\"dcr_hiv_att_5km_significant_10pct\"],\"status\":\"supported\",\"caveat\":\"The source notes 1 km estimates rest on a single treated region per cohort.\"},\n {\"id\":\"C3\",\"text\":\"The exact p-value of the HIV 5 km ATT is not reported in the DCR source (only stars), so it is null.\",\"results_keys\":[\"dcr_hiv_att_5km_p_value\"],\"status\":\"supported\",\"caveat\":\"A normal approximation would give p of roughly 0.25, but that is not a printed value and is not reported as one.\"},\n {\"id\":\"C4\",\"text\":\"Derived 95% CI of the Table 6 5 km ATT (normal, 1.96) is [-0.8426, -0.0194] log points, excluding zero.\",\"results_keys\":[\"dcr_att_5km_ci_low\",\"dcr_att_5km_ci_high\"],\"status\":\"supported\",\"caveat\":\"Depends on normality and on the rounded printed SE; using A19's SE 0.218 gives [-0.8583, -0.0037].\"},\n {\"id\":\"C5\",\"text\":\"Table 2 of arXiv:2606.14621v1 prints a wage ATE of $1,153 (95% CI [166, 2,141]) against a control mean of $33,043; no SE is printed.\",\"results_keys\":[\"ri_wage_ate\",\"ri_wage_ate_ci_low\",\"ri_wage_ate_ci_high\",\"ri_wage_ate_se_reported\",\"ri_control_mean_wage\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C6\",\"text\":\"The SE implied by the printed wage-ATE CI (normal, 1.96) is 503.83 USD/yr, consistent with the printed p=0.022.\",\"results_keys\":[\"ri_wage_ate_se_derived\"],\"status\":\"supported\",\"caveat\":\"Assumes a symmetric normal-based CI; the CI method is not in the extract.\"},\n {\"id\":\"C7\",\"text\":\"Table 2 prints a reemployment ATE of 1.47 pp and a Weeks-on-UI ATE of -1.99 weeks (95% CI [-2.24, -1.75]).\",\"results_keys\":[\"ri_reemployment_ate_pp\",\"ri_weeks_ui_ate\",\"ri_weeks_ui_ate_ci_low\",\"ri_weeks_ui_ate_ci_high\"],\"status\":\"supported\",\"caveat\":\"Body text prints the UI CI as unsigned magnitudes [1.75, 2.24].\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'));d=json.load(open('out/results.json'));print(len(d['locators']),d['n_input_rows'])\""}}]}}47{"type":"api-request","sessionId":"f5918bad-04e6-40d5-a2c9-d6b80bb40a78","id":"80157928-62ca-489a-ab34-e2b51e731f5d","timestamp":"2026-10-30T05:23:20.141Z","version":"2.1.314","querySource":"sdk","shapeHash":"3125ad8154de8f98cc9ea6c19a88dec04c7eaec797399fa15a04bf0a8906a4d5","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":"92ba88eb-6250-4de6-8c5d-438dc0dd8908","keep":12,"tail":["5565a9507dfb67f301569d65e57a4d813cefabd08802b607564ba973016554a4","051d4c64ba74eb3211e58a2c89003c5f6bdedc905121620fc97e400f09923d35"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"a5ac97b8a781d65e0bd6c6e35c22dc09f6acc6ecb4142b791e3c4402d05e1f5a"}48{"parentUuid":"03d9eb9b-b0ed-4d92-aacd-a306b2eaeaa4","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrzkrEBVKiSAfFuC6tA","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4049,"cache_read_input_tokens":26049,"output_tokens":1089,"output_tokens_details":{"thinking_tokens":85},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":4049},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":7815,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXrzkZcUG7NEqVEDhTCJ","requestRef":"80157928-62ca-489a-ab34-e2b51e731f5d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"52ec7a8f-09e4-4e62-bd2c-c69aad0dc1b7","timestamp":"2026-10-30T05:23:31.041Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0278","sessionId":"f5918bad-04e6-40d5-a2c9-d6b80bb40a78","version":"2.1.314","gitBranch":"HEAD"}49{"parentUuid":"52ec7a8f-09e4-4e62-bd2c-c69aad0dc1b7","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrzkrEBVKiSAfFuC6tA","type":"message","role":"assistant","content":[{"type":"text","text":"I've completed all the required outputs and marked the task as complete. Every requested value came out of the two extracts, except one that the source doesn't print: the exact p-value for the HIV 5 km effect, which is `null`.\n\n**Values taken as printed**\n- **Drug consumption room study, Table 6:**\n - Overall effect at 5 km: −0.431** (SE 0.210).\n - Overall effect at 3 km: −0.283 (SE 0.279).\n - Observations at 5 km: 17250.\n- **Drug consumption room study, Table 7:**\n - Narcotics at 3 km: 0.549*** (0.187).\n - Fatal at 1 km: −1.207*** (0.137).\n - HIV at 5 km: −0.340 (0.294), with no star, so it is not significant at 10%.\n- **RESEA study, Table 2:**\n - Wage effect: $1,153, 95% CI [166, 2141]. No SE is printed.\n - Control-group mean wage: $33,043.\n - Reemployment effect: 1.47 pp.\n - Weeks on UI: −1.99, 95% CI [−2.24, −1.75].\n\nJSON can't keep trailing zeros, so `results.json` stores 0.210 as `0.21` and −0.340 as `−0.34`; the report notes this.\n\n**Derived values (both use the 1.96 normal critical value)**\n- **95% CI for the 5 km overall effect:** [−0.8426, −0.0194], which excludes zero.\n- **SE implied by the wage CI:** 503.83 USD per year. As a check, this gives a p-value of 0.022, matching the printed one.\n\n**Problems in the sources (flagged, not corrected)**\n- **Two SEs for one estimate:** Table 6 gives 0.210 for the 5 km effect, but Appendix Table A19 gives 0.218 for the same estimate. I used 0.210 as the task specifies; with 0.218 the CI would be [−0.8583, −0.0037].\n- **Control-region count:** the tables say 608 control regions, but the A19 note says 640. The printed observation counts fit 640, assuming a 25-year panel (1998–2022). That panel length is my inference, not stated in the extract.\n- **Text vs tables:**\n - The abstract says fatal hospitalisations fall \"by 35%\", but the 5 km estimate works out to about −30%.\n - The text calls the HIV estimates \"close to zero\", though the 5 km figure is −0.340.\n - The RESEA text gives the Weeks-on-UI CI as unsigned magnitudes, while Table 2 gives it with signs; I used Table 2.\n\nEvery value comes with a locator, `derivation_notes` states the conventions, and the stars printed in all three DCR tables match their SEs. `analysis.py` reads the tables from `data/`, prints each source line it matched, and runs the consistency checks.\n\nFiles are in `out/`:\n- results.json\n- report.md\n- analysis.py\n- claims.json"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4049,"cache_read_input_tokens":26049,"output_tokens":1089,"output_tokens_details":{"thinking_tokens":85},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":4049},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrzkZcUG7NEqVEDhTCJ","requestRef":"80157928-62ca-489a-ab34-e2b51e731f5d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"552dbb81-0ebf-4ad2-b043-12c78bc3e070","timestamp":"2026-10-30T05:23:32.586Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0278","sessionId":"f5918bad-04e6-40d5-a2c9-d6b80bb40a78","version":"2.1.314","gitBranch":"HEAD"}50{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"552dbb81-0ebf-4ad2-b043-12c78bc3e070","sessionId":"f5918bad-04e6-40d5-a2c9-d6b80bb40a78"}51{"type":"cost-state","sessionId":"f5918bad-04e6-40d5-a2c9-d6b80bb40a78","totalCostUSD":0.419933,"totalAPIDuration":113808,"totalAPIDurationWithoutRetries":113802,"totalToolDuration":705,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":114674,"startTime":1793337697919,"modelUsage":{"claude-opus-5-5":{"inputTokens":16,"outputTokens":13089,"thinkingTokens":1220,"cacheReadInputTokens":75595,"cacheCreationInputTokens":28594,"cacheCreationEphemeral5mInputTokens":28594,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.419933}},"hasUnknownModelCost":false}52