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It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md`. Below is some general advice about doing careful empirical research; it adds nothing specific to this task and you are free to ignore it.\n\nCareful empirical work usually begins by reading the task statement slowly and more than once, because the most common errors in applied research come not from difficult mathematics but from a misunderstanding of what exactly is being asked, which quantity is of interest, and which inputs are legitimately available. It is worth pausing to think about what the quantity of interest actually means, under what conditions it is well defined, and what would make an estimate of it misleading. Many published results have been overturned not because the estimator was wrong but because the sample was not what the authors believed it to be, or because information that should not have been available at the time of a decision had quietly entered the analysis.\n\nData deserve suspicion. Files assembled from several sources often carry inconsistencies: duplicated records, mixed units, shifted dates, columns whose definitions changed mid-sample, or values that were revised after the fact. Good practice is to look at the data before modelling them: distributions, ranges, missing values, identifiers, and whether counts add up. When something looks odd, it is better to document it and decide explicitly how to handle it than to let a default silently decide. The same applies to transformations: scaling, differencing, winsorising and imputing all change what is being estimated, and each deserves a sentence of justification.\n\nSimple baselines are underrated. A naïve or unconditional estimate computed with exactly the same evaluation rule as the main method tells the reader how much the method actually adds and protects against the illusion of progress created by a complicated pipeline. If the elaborate method does not beat the simple one, that is a finding, not a failure.\n\nUncertainty is part of the result. Standard errors must respect the dependence structure of the data; a confidence interval that ignores clustering, serial correlation or overlapping targets is not conservative, it is wrong. Where the sampling distribution is unknown, resampling or simulation can help, but the assumptions behind them should be stated. A result that is not statistically distinguishable from zero should be reported as such, in plain words.\n\nReproducibility is a courtesy to the reader and a safeguard for the author. Keeping the analysis in a script that reads the raw inputs and regenerates every reported number makes it possible to check the work, to find mistakes, and to rerun the analysis when the inputs change. Fixing random seeds, recording software versions and keeping intermediate files out of the final results all reduce the chance that a number in the report cannot be traced back to a computation.\n\nFinally, honesty about limitations is what distinguishes research from advocacy. Every analysis has weaknesses: assumptions that may not hold, data that may not be representative, methods that trade one bias for another. Stating them clearly, together with what would be needed to resolve them, is more valuable than a confident conclusion that the evidence does not support. 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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. 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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.\ntotal 8\ndrwxr-xr-x 4 simon-pierreboucher staff 128 oct. 29 23:45 .\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:45 ..\n-rw-r--r-- 1 simon-pierreboucher staff 1822 oct. 29 23:45 README.md\ndrwxr-xr-x 4 simon-pierreboucher staff 128 oct. 29 23:45 sources","sandboxed":false,"sourceToolAssistantUUID":"d36e4ed2-1050-4c71-8300-61a23a6e056c","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0012","sessionId":"94f70ee6-af4b-4af7-86e9-dc589a45519a","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"94f70ee6-af4b-4af7-86e9-dc589a45519a","hash":"3d953faed5dc3887abc7d8711f01647e19e8e73a1589193dd22f66106fcc7589","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_0189Vz3VEYzA4jqg2yJJ3BLh","name":"Bash","input":{"command":"cat TASK.md; ls -la data; ls out 2>/dev/null"}}]}}15{"type":"api-request","sessionId":"94f70ee6-af4b-4af7-86e9-dc589a45519a","id":"a6ec7fe7-cb74-40f1-bf1e-cabbaf9c2cd4","timestamp":"2026-10-30T05:34:23.826Z","version":"2.1.314","querySource":"sdk","shapeHash":"139d5b2dd4dcca6229785092195dc55e592fd5b9ac99cafb1558d315273a3bdb","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":"47f509c9-f537-4eed-80cf-831d6467dedd","keep":2,"tail":["3d953faed5dc3887abc7d8711f01647e19e8e73a1589193dd22f66106fcc7589","13ddf747e4f3888b79d30511f510d38937609d4900a0aa2da88e6465fdd8f0f1"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"263e81508f3c86c1ef3684078c0aae6d9e4291299b43cb5deede65dd59abc12a"}16{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"4510db31-2212-4aba-8fc9-5ee955f95cd8","sessionId":"94f70ee6-af4b-4af7-86e9-dc589a45519a"}17{"type":"atis-latch","atis":"","sessionId":"94f70ee6-af4b-4af7-86e9-dc589a45519a"}18{"parentUuid":"4510db31-2212-4aba-8fc9-5ee955f95cd8","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsqgTBwqpPKBqgWN6ja","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01VN76oXpXUATqb2QPsTZqma","name":"Bash","input":{"command":"cat data/README.md; wc -l data/README.md data/sources/*; cat -n data/sources/arxiv-2609.27732v1.txt"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3491,"cache_read_input_tokens":3186,"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":3491},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsqgFnFiMxiKuAz6wFK","requestRef":"a6ec7fe7-cb74-40f1-bf1e-cabbaf9c2cd4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6beabd8a-f6b7-4b65-818c-8d6d0bafbe46","timestamp":"2026-10-30T05:34:25.764Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0012","sessionId":"94f70ee6-af4b-4af7-86e9-dc589a45519a","version":"2.1.314","gitBranch":"HEAD"}19{"parentUuid":"6beabd8a-f6b7-4b65-818c-8d6d0bafbe46","isSidechain":false,"promptId":"da06a6bd-f279-4847-9e66-b6c01275d027","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VN76oXpXUATqb2QPsTZqma","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 data/README.md\n 58 data/sources/arxiv-2606.14621v1.txt\n 134 data/sources/arxiv-2609.27732v1.txt\n 202 total\n 1\tSOURCE EXTRACT — arxiv-2609.27732v1\n 2\tTitle: Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n 3\tAuthors: Ana Armendariz (University of St. Gallen)\n 4\tBibliographic record: arXiv:2609.27732v1 [econ.GN], submitted 23 Sep 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2609.27732v1\n 5\tRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\n 6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n 7\tExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\n 8\tOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\n 9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n 10\t================================================================\n 11\t\n 12\t# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n 13\t\n 14\tAna Armendariz*\n 15\t\n 16\tSeptember 23, 2026\n 17\t\n 18\tThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n 19\t\n 20\tKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n 21\t\n 22\t## 6 Results\n 23\t\n 24\tThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n 25\t\n 26\tFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n 27\t\n 28\tNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n 29\t\n 30\tFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n 31\t\n 32\tTable 6: Overall Average Treatment Effect on the Treated (ATT)\n 33\t\n 34\t| 1 km | 3 km | 5 km\n 35\t\n 36\tATT | -0.399 | -0.283 | -0.431**\n 37\t\n 38\t| (0.412) | (0.279) | (0.210)\n 39\t\n 40\t% change | -32.9% | -24.7% | -35.0%\n 41\t\n 42\tTreated regions | 7 | 35 | 50\n 43\t\n 44\tControl regions | 608 | 608 | 608\n 45\t\n 46\tObservations | 16,175 | 16,875 | 17,250\n 47\t\n 48\tNote: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\n 49\tthroughout. Regions near DCRs that opened before 1998\n 50\t(always-treated) are excluded. Standard errors clustered at the Medstat region level in\n 51\tparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n 52\t{}^{*}p<0.10 .\n 53\t\n 54\tTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\n 55\tcorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n 56\t\n 57\tTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\n 58\tsub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\n 59\t\n 60\tFigure 7: Event study: Sub-outcome estimates,\n 61\twithin 5 km, \\pm 5-year window\n 62\t\n 63\tNote: The figure plots event-study coefficients from the doubly robust Callaway & Sant’Anna (2021) estimator for four diagnostic sub-categories of drug-related hospitalizations. Each panel uses a separate y -axis to accommodate differences in scale across outcomes. The sample includes all staggered-treated MedStat regions within 5 km of a\n 64\tDCR that opened between 1998 and 2022. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded. 95% confidence intervals are based on standard errors clustered at the MedStat region level.\n 65\t\n 66\tTable 7: Overall ATT by Outcome Category and Buffer Zone\n 67\t\n 68\tOutcome | 1 km | 3 km | 5 km\n 69\t\n 70\tMental/Behavioral | -0.354 | -0.202 | -0.381*\n 71\t\n 72\t| (0.382) | (0.244) | (0.220)\n 73\t\n 74\tHepatitis | -0.270 | -0.413 | -0.406\n 75\t\n 76\t| (0.486) | (0.270) | (0.250)\n 77\t\n 78\tHIV | 0.063 | -0.239 | -0.340\n 79\t\n 80\t| (0.801) | (0.337) | (0.294)\n 81\t\n 82\tNarcotics | 1.186*** | 0.549*** | 0.390**\n 83\t\n 84\t| (0.288) | (0.187) | (0.165)\n 85\t\n 86\tDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n 87\t\n 88\t| (0.137) | (0.215) | (0.178)\n 89\t\n 90\tTreated regions | 7 | 35 | 50\n 91\t\n 92\tControl regions | 608 (never-treated) | |\n 93\t\n 94\tNote: Each cell reports the overall ATT from\n 95\tthe doubly robust Callaway & Sant’Anna (2021) estimator with\n 96\tnever-treated controls and a universal base period. Outcomes are\n 97\tlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\n 98\tmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\n 99\tHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\n 100\testimates are identified from a single treated region per cohort and\n 101\tshould be interpreted with caution. Regions near DCRs opened before\n 102\t1998 excluded. Standard errors clustered at the Medstat region level.\n 103\tSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n 104\t\n 105\tFigure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\n 106\t\n 107\t### 6.1 Mechanisms\n 108\t\n 109\tThe sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n 110\tsubstitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\n 111\t(Kerr et al., 2017; Hedrich, 2004).\n 112\t\n 113\tThe second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\n 114\t\n 115\tA third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\n 116\t\n 117\tTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n 118\t\n 119\t| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n 120\t\n 121\tATT | -0.431** | -0.438** | -0.362* | -0.149***\n 122\t\n 123\t| (0.218) | (0.208) | (0.205) | (0.038)\n 124\t\n 125\t% change | -35.0% | -35.5% | -30.3% | -13.9%\n 126\t\n 127\tTreated regions | 50 | | |\n 128\t\n 129\tControl regions | 608 | | |\n 130\t\n 131\tObservations | 17,250 | | |\n 132\t\n 133\tNote: All specifications use the 5 km buffer,\n 134\tnever-treated comparison regions (640 regions), and a \\pm 5-year window","is_error":false}]},"uuid":"934c2d70-1640-48b1-ac00-6d15bf587028","timestamp":"2026-10-30T05:34:25.815Z","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 data/README.md\n 58 data/sources/arxiv-2606.14621v1.txt\n 134 data/sources/arxiv-2609.27732v1.txt\n 202 total\n 1\tSOURCE EXTRACT — arxiv-2609.27732v1\n 2\tTitle: Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n 3\tAuthors: Ana Armendariz (University of St. Gallen)\n 4\tBibliographic record: arXiv:2609.27732v1 [econ.GN], submitted 23 Sep 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2609.27732v1\n 5\tRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\n 6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n 7\tExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\n 8\tOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\n 9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n 10\t================================================================\n 11\t\n 12\t# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n 13\t\n 14\tAna Armendariz*\n 15\t\n 16\tSeptember 23, 2026\n 17\t\n 18\tThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n 19\t\n 20\tKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n 21\t\n 22\t## 6 Results\n 23\t\n 24\tThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n 25\t\n 26\tFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n 27\t\n 28\tNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n 29\t\n 30\tFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n 31\t\n 32\tTable 6: Overall Average Treatment Effect on the Treated (ATT)\n 33\t\n 34\t| 1 km | 3 km | 5 km\n 35\t\n 36\tATT | -0.399 | -0.283 | -0.431**\n 37\t\n 38\t| (0.412) | (0.279) | (0.210)\n 39\t\n 40\t% change | -32.9% | -24.7% | -35.0%\n 41\t\n 42\tTreated regions | 7 | 35 | 50\n 43\t\n 44\tControl regions | 608 | 608 | 608\n 45\t\n 46\tObservations | 16,175 | 16,875 | 17,250\n 47\t\n 48\tNote: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\n 49\tthroughout. Regions near DCRs that opened before 1998\n 50\t(always-treated) are excluded. Standard errors clustered at the Medstat region level in\n 51\tparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n 52\t{}^{*}p<0.10 .\n 53\t\n 54\tTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\n 55\tcorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n 56\t\n 57\tTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\n 58\tsub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\n 59\t\n 60\tFigure 7: Event study: Sub-outcome estimates,\n 61\twithin 5 km, \\pm 5-year window\n 62\t\n 63\tNote: The figure plots event-study coefficients from the doubly robust Callaway & Sant’Anna (2021) estimator for four diagnostic sub-categories of drug-related hospitalizations. Each panel uses a separate y -axis to accommodate differences in scale across outcomes. The sample includes all staggered-treated MedStat regions within 5 km of a\n 64\tDCR that opened between 1998 and 2022. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded. 95% confidence intervals are based on standard errors clustered at the MedStat region level.\n 65\t\n 66\tTable 7: Overall ATT by Outcome Category and Buffer Zone\n 67\t\n 68\tOutcome | 1 km | 3 km | 5 km\n 69\t\n 70\tMental/Behavioral | -0.354 | -0.202 | -0.381*\n 71\t\n 72\t| (0.382) | (0.244) | (0.220)\n 73\t\n 74\tHepatitis | -0.270 | -0.413 | -0.406\n 75\t\n 76\t| (0.486) | (0.270) | (0.250)\n 77\t\n 78\tHIV | 0.063 | -0.239 | -0.340\n 79\t\n 80\t| (0.801) | (0.337) | (0.294)\n 81\t\n 82\tNarcotics | 1.186*** | 0.549*** | 0.390**\n 83\t\n 84\t| (0.288) | (0.187) | (0.165)\n 85\t\n 86\tDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n 87\t\n 88\t| (0.137) | (0.215) | (0.178)\n 89\t\n 90\tTreated regions | 7 | 35 | 50\n 91\t\n 92\tControl regions | 608 (never-treated) | |\n 93\t\n 94\tNote: Each cell reports the overall ATT from\n 95\tthe doubly robust Callaway & Sant’Anna (2021) estimator with\n 96\tnever-treated controls and a universal base period. Outcomes are\n 97\tlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\n 98\tmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\n 99\tHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\n 100\testimates are identified from a single treated region per cohort and\n 101\tshould be interpreted with caution. Regions near DCRs opened before\n 102\t1998 excluded. Standard errors clustered at the Medstat region level.\n 103\tSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n 104\t\n 105\tFigure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\n 106\t\n 107\t### 6.1 Mechanisms\n 108\t\n 109\tThe sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n 110\tsubstitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\n 111\t(Kerr et al., 2017; Hedrich, 2004).\n 112\t\n 113\tThe second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\n 114\t\n 115\tA third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\n 116\t\n 117\tTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n 118\t\n 119\t| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n 120\t\n 121\tATT | -0.431** | -0.438** | -0.362* | -0.149***\n 122\t\n 123\t| (0.218) | (0.208) | (0.205) | (0.038)\n 124\t\n 125\t% change | -35.0% | -35.5% | -30.3% | -13.9%\n 126\t\n 127\tTreated regions | 50 | | |\n 128\t\n 129\tControl regions | 608 | | |\n 130\t\n 131\tObservations | 17,250 | | |\n 132\t\n 133\tNote: All specifications use the 5 km buffer,\n 134\tnever-treated comparison regions (640 regions), and a \\pm 5-year window","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"6beabd8a-f6b7-4b65-818c-8d6d0bafbe46","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0012","sessionId":"94f70ee6-af4b-4af7-86e9-dc589a45519a","version":"2.1.314","gitBranch":"HEAD"}20{"parentUuid":"934c2d70-1640-48b1-ac00-6d15bf587028","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsqgTBwqpPKBqgWN6ja","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SGJWUrkYpuDU3hzGvWm81o","name":"Bash","input":{"command":"cat -n data/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":3491,"cache_read_input_tokens":3186,"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":3491},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsqgFnFiMxiKuAz6wFK","requestRef":"a6ec7fe7-cb74-40f1-bf1e-cabbaf9c2cd4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"93711694-eddd-4b4f-bbea-19ef8994d8f7","timestamp":"2026-10-30T05:34:26.141Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0012","sessionId":"94f70ee6-af4b-4af7-86e9-dc589a45519a","version":"2.1.314","gitBranch":"HEAD"}21{"parentUuid":"93711694-eddd-4b4f-bbea-19ef8994d8f7","isSidechain":false,"promptId":"da06a6bd-f279-4847-9e66-b6c01275d027","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SGJWUrkYpuDU3hzGvWm81o","type":"tool_result","content":" 1\tSOURCE EXTRACT — arxiv-2606.14621v1\n 2\tTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n 3\tAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\n 4\tBibliographic record: arXiv:2606.14621v1 [econ.GN], submitted 12 Jun 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2606.14621v1\n 5\tRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\n 6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n 7\tExtract contents: title, authors and abstract; Section 3 'Results' (Table 2 with caption, the results paragraphs, Section 3.1 'Cost-benefit analysis'); from Section 4.1, Table 3 with its caption and the paragraph that discusses it.\n 8\tOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\n 9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n 10\tEditorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n 11\t================================================================\n 12\t\n 13\t# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n 14\t\n 15\tHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n 16\t\n 17\t###### Abstract\n 18\t\n 19\tProlonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n 20\t\n 21\t## 3 Results\n 22\t\n 23\t[Table 1 (pre-treatment characteristics) omitted from this extract.]\n 24\t\n 25\t[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\n 26\t\n 27\tOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n 28\tWage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n 29\tReemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n 30\tWeeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n 31\tShort-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\n 32\tShort-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\n 33\tShort-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n 34\t\n 35\tTable 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n 36\t\n 37\tOverall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n 38\t\n 39\tThe results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n 40\t\n 41\tAs noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n 42\t\n 43\t### 3.1 Cost-benefit analysis\n 44\t\n 45\tWe find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n 46\t\n 47\t### 4.1 Methodological lessons (excerpt)\n 48\t\n 49\t[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n 50\t\n 51\tOutcome | Difference in means | Covariate adjusted | Fixed effects regression\n 52\tWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\n 53\tReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\n 54\tWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n 55\t\n 56\tTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n 57\t\n 58\tIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . These fixed effects regressions amount to estimating the coefficient in the regression model","is_error":false}]},"uuid":"e54ce180-69b7-4c74-9d5e-b64722361047","timestamp":"2026-10-30T05:34:26.183Z","toolUseResult":{"stdout":" 1\tSOURCE EXTRACT — arxiv-2606.14621v1\n 2\tTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n 3\tAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\n 4\tBibliographic record: arXiv:2606.14621v1 [econ.GN], submitted 12 Jun 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2606.14621v1\n 5\tRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\n 6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n 7\tExtract contents: title, authors and abstract; Section 3 'Results' (Table 2 with caption, the results paragraphs, Section 3.1 'Cost-benefit analysis'); from Section 4.1, Table 3 with its caption and the paragraph that discusses it.\n 8\tOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\n 9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n 10\tEditorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n 11\t================================================================\n 12\t\n 13\t# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n 14\t\n 15\tHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n 16\t\n 17\t###### Abstract\n 18\t\n 19\tProlonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n 20\t\n 21\t## 3 Results\n 22\t\n 23\t[Table 1 (pre-treatment characteristics) omitted from this extract.]\n 24\t\n 25\t[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\n 26\t\n 27\tOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n 28\tWage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n 29\tReemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n 30\tWeeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n 31\tShort-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\n 32\tShort-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\n 33\tShort-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n 34\t\n 35\tTable 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n 36\t\n 37\tOverall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n 38\t\n 39\tThe results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n 40\t\n 41\tAs noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n 42\t\n 43\t### 3.1 Cost-benefit analysis\n 44\t\n 45\tWe find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n 46\t\n 47\t### 4.1 Methodological lessons (excerpt)\n 48\t\n 49\t[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n 50\t\n 51\tOutcome | Difference in means | Covariate adjusted | Fixed effects regression\n 52\tWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\n 53\tReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\n 54\tWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n 55\t\n 56\tTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n 57\t\n 58\tIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . These fixed effects regressions amount to estimating the coefficient in the regression model","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"93711694-eddd-4b4f-bbea-19ef8994d8f7","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0012","sessionId":"94f70ee6-af4b-4af7-86e9-dc589a45519a","version":"2.1.314","gitBranch":"HEAD"}22{"type":"api-request-blob","sessionId":"94f70ee6-af4b-4af7-86e9-dc589a45519a","hash":"1c9d8eaca25784d02e50b00493be337e284704c88cca9d6181038a4fe10a0942","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01VN76oXpXUATqb2QPsTZqma","name":"Bash","input":{"command":"cat data/README.md; wc -l data/README.md data/sources/*; cat -n data/sources/arxiv-2609.27732v1.txt"}},{"type":"tool_use","id":"toolu_01SGJWUrkYpuDU3hzGvWm81o","name":"Bash","input":{"command":"cat -n data/sources/arxiv-2606.14621v1.txt"}}]}}23{"type":"api-request-blob","sessionId":"94f70ee6-af4b-4af7-86e9-dc589a45519a","hash":"5bff5205601d09af6da4ea9def02fe0c0e1fcff2235a146541777b570c895633","message":{"role":"user","content":[{"tool_use_id":"toolu_01VN76oXpXUATqb2QPsTZqma","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 data/README.md\n 58 data/sources/arxiv-2606.14621v1.txt\n 134 data/sources/arxiv-2609.27732v1.txt\n 202 total\n 1\tSOURCE EXTRACT — arxiv-2609.27732v1\n 2\tTitle: Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n 3\tAuthors: Ana Armendariz (University of St. Gallen)\n 4\tBibliographic record: arXiv:2609.27732v1 [econ.GN], submitted 23 Sep 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2609.27732v1\n 5\tRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\n 6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n 7\tExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\n 8\tOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\n 9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n 10\t================================================================\n 11\t\n 12\t# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n 13\t\n 14\tAna Armendariz*\n 15\t\n 16\tSeptember 23, 2026\n 17\t\n 18\tThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n 19\t\n 20\tKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n 21\t\n 22\t## 6 Results\n 23\t\n 24\tThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n 25\t\n 26\tFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n 27\t\n 28\tNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n 29\t\n 30\tFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n 31\t\n 32\tTable 6: Overall Average Treatment Effect on the Treated (ATT)\n 33\t\n 34\t| 1 km | 3 km | 5 km\n 35\t\n 36\tATT | -0.399 | -0.283 | -0.431**\n 37\t\n 38\t| (0.412) | (0.279) | (0.210)\n 39\t\n 40\t% change | -32.9% | -24.7% | -35.0%\n 41\t\n 42\tTreated regions | 7 | 35 | 50\n 43\t\n 44\tControl regions | 608 | 608 | 608\n 45\t\n 46\tObservations | 16,175 | 16,875 | 17,250\n 47\t\n 48\tNote: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\n 49\tthroughout. Regions near DCRs that opened before 1998\n 50\t(always-treated) are excluded. Standard errors clustered at the Medstat region level in\n 51\tparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n 52\t{}^{*}p<0.10 .\n 53\t\n 54\tTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\n 55\tcorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n 56\t\n 57\tTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\n 58\tsub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\n 59\t\n 60\tFigure 7: Event study: Sub-outcome estimates,\n 61\twithin 5 km, \\pm 5-year window\n 62\t\n 63\tNote: The figure plots event-study coefficients from the doubly robust Callaway & Sant’Anna (2021) estimator for four diagnostic sub-categories of drug-related hospitalizations. Each panel uses a separate y -axis to accommodate differences in scale across outcomes. The sample includes all staggered-treated MedStat regions within 5 km of a\n 64\tDCR that opened between 1998 and 2022. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded. 95% confidence intervals are based on standard errors clustered at the MedStat region level.\n 65\t\n 66\tTable 7: Overall ATT by Outcome Category and Buffer Zone\n 67\t\n 68\tOutcome | 1 km | 3 km | 5 km\n 69\t\n 70\tMental/Behavioral | -0.354 | -0.202 | -0.381*\n 71\t\n 72\t| (0.382) | (0.244) | (0.220)\n 73\t\n 74\tHepatitis | -0.270 | -0.413 | -0.406\n 75\t\n 76\t| (0.486) | (0.270) | (0.250)\n 77\t\n 78\tHIV | 0.063 | -0.239 | -0.340\n 79\t\n 80\t| (0.801) | (0.337) | (0.294)\n 81\t\n 82\tNarcotics | 1.186*** | 0.549*** | 0.390**\n 83\t\n 84\t| (0.288) | (0.187) | (0.165)\n 85\t\n 86\tDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n 87\t\n 88\t| (0.137) | (0.215) | (0.178)\n 89\t\n 90\tTreated regions | 7 | 35 | 50\n 91\t\n 92\tControl regions | 608 (never-treated) | |\n 93\t\n 94\tNote: Each cell reports the overall ATT from\n 95\tthe doubly robust Callaway & Sant’Anna (2021) estimator with\n 96\tnever-treated controls and a universal base period. Outcomes are\n 97\tlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\n 98\tmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\n 99\tHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\n 100\testimates are identified from a single treated region per cohort and\n 101\tshould be interpreted with caution. Regions near DCRs opened before\n 102\t1998 excluded. Standard errors clustered at the Medstat region level.\n 103\tSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n 104\t\n 105\tFigure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\n 106\t\n 107\t### 6.1 Mechanisms\n 108\t\n 109\tThe sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n 110\tsubstitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\n 111\t(Kerr et al., 2017; Hedrich, 2004).\n 112\t\n 113\tThe second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\n 114\t\n 115\tA third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\n 116\t\n 117\tTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n 118\t\n 119\t| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n 120\t\n 121\tATT | -0.431** | -0.438** | -0.362* | -0.149***\n 122\t\n 123\t| (0.218) | (0.208) | (0.205) | (0.038)\n 124\t\n 125\t% change | -35.0% | -35.5% | -30.3% | -13.9%\n 126\t\n 127\tTreated regions | 50 | | |\n 128\t\n 129\tControl regions | 608 | | |\n 130\t\n 131\tObservations | 17,250 | | |\n 132\t\n 133\tNote: All specifications use the 5 km buffer,\n 134\tnever-treated comparison regions (640 regions), and a \\pm 5-year window","is_error":false},{"tool_use_id":"toolu_01SGJWUrkYpuDU3hzGvWm81o","type":"tool_result","content":" 1\tSOURCE EXTRACT — arxiv-2606.14621v1\n 2\tTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n 3\tAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\n 4\tBibliographic record: arXiv:2606.14621v1 [econ.GN], submitted 12 Jun 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2606.14621v1\n 5\tRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\n 6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n 7\tExtract contents: title, authors and abstract; Section 3 'Results' (Table 2 with caption, the results paragraphs, Section 3.1 'Cost-benefit analysis'); from Section 4.1, Table 3 with its caption and the paragraph that discusses it.\n 8\tOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\n 9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n 10\tEditorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n 11\t================================================================\n 12\t\n 13\t# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n 14\t\n 15\tHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n 16\t\n 17\t###### Abstract\n 18\t\n 19\tProlonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n 20\t\n 21\t## 3 Results\n 22\t\n 23\t[Table 1 (pre-treatment characteristics) omitted from this extract.]\n 24\t\n 25\t[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\n 26\t\n 27\tOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n 28\tWage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n 29\tReemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n 30\tWeeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n 31\tShort-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\n 32\tShort-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\n 33\tShort-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n 34\t\n 35\tTable 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n 36\t\n 37\tOverall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n 38\t\n 39\tThe results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n 40\t\n 41\tAs noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n 42\t\n 43\t### 3.1 Cost-benefit analysis\n 44\t\n 45\tWe find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n 46\t\n 47\t### 4.1 Methodological lessons (excerpt)\n 48\t\n 49\t[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n 50\t\n 51\tOutcome | Difference in means | Covariate adjusted | Fixed effects regression\n 52\tWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\n 53\tReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\n 54\tWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n 55\t\n 56\tTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n 57\t\n 58\tIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . 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-p out; cat > out/analysis.py <<'EOF'\n\"\"\"F4-conf-01: extract printed values from two source extracts and derive two quantities.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nValues are parsed from the text; every matched source line is printed.\"\"\"\nimport json, re, os\nfrom scipy.stats import norm\n\nDCR = \"data/sources/arxiv-2609.27732v1.txt\"\nRI = \"data/sources/arxiv-2606.14621v1.txt\"\nSID_D, SID_R = \"arxiv-2609.27732v1\", \"arxiv-2606.14621v1\"\n\ndef lines(p):\n with open(p, encoding=\"utf-8\") as f:\n return f.read().split(\"\\n\")\n\ndef wc_l(p):\n with open(p, \"rb\") as f:\n return f.read().count(b\"\\n\")\n\ndef cells(s):\n return [c.strip() for c in s.split(\"|\")]\n\ndef block(L, title, end_marker):\n i = next(k for k, s in enumerate(L) if s.startswith(title))\n j = next(k for k in range(i + 1, len(L)) if L[k].startswith(end_marker))\n return [s for s in L[i:j] if s.strip()], i\n\ndef num(c):\n \"\"\"Return the printed number string (sign + decimals kept), stars stripped.\"\"\"\n m = re.search(r\"-?[\\d,]*\\.?\\d+\", c.replace(\"$\", \"\"))\n return m.group(0)\n\ndef to_num(s):\n s = s.replace(\",\", \"\")\n return int(s) if re.fullmatch(r\"-?\\d+\", s) else float(s)\n\ndef stars(c):\n return len(re.search(r\"\\**$\", c.strip()).group(0))\n\nD = lines(DCR)\nR = lines(RI)\n\n# ---- Table 6\nt6, _ = block(D, \"Table 6:\", \"Note:\")\nhdr6 = cells(t6[1]) # ['', '1 km', '3 km', '5 km']\ndef t6row(label):\n for k, s in enumerate(t6):\n if cells(s)[0] == label:\n print(\"[T6 match]\", s)\n return cells(s), (cells(t6[k + 1]) if label == \"ATT\" else None)\natt6, se6 = t6row(\"ATT\")\nobs6, _ = t6row(\"Observations\")\nprint(\"[T6 SE line]\", \" | \".join(se6))\ncol6 = {h: i for i, h in enumerate(hdr6) if h}\n\n# ---- Table 7\nt7, _ = block(D, \"Table 7:\", \"Note:\")\nhdr7 = cells(t7[1])\ncol7 = {h: i for i, h in enumerate(hdr7)}\ndef t7cell(row, col):\n for k, s in enumerate(t7):\n if cells(s)[0] == row:\n print(\"[T7 match]\", s, \" || SE line:\", t7[k + 1])\n return cells(s)[col7[col]], cells(t7[k + 1])[col7[col]]\n raise KeyError(row)\n\nres = {\"task_id\": \"F4-conf-01\"}\nloc = {}\ndef L6(row, col, extra=\"\"):\n return f'{SID_D} | Table 6 | row \"{row}\" | column \"{col}\"' + extra\ndef L7(row, col, extra=\"\"):\n return f'{SID_D} | Table 7 | row \"{row}\" | column \"{col}\"' + extra\n\nfor km in [\"5\", \"3\"]:\n c = col6[f\"{km} km\"]\n res[f\"dcr_att_{km}km\"] = to_num(num(att6[c]))\n res[f\"dcr_att_{km}km_se\"] = to_num(num(se6[c]))\n loc[f\"dcr_att_{km}km\"] = L6(\"ATT\", f\"{km} km\")\n loc[f\"dcr_att_{km}km_se\"] = L6(\"ATT\", f\"{km} km\", \" (standard error in parentheses on the line below the ATT row)\")\nres[\"dcr_obs_5km\"] = to_num(num(obs6[col6[\"5 km\"]]))\nloc[\"dcr_obs_5km\"] = L6(\"Observations\", \"5 km\")\n\nfor key, row, col in [(\"dcr_narcotics_att_3km\", \"Narcotics\", \"3 km\"),\n (\"dcr_fatal_att_1km\", \"Drug-related (fatal)\", \"1 km\"),\n (\"dcr_hiv_att_5km\", \"HIV\", \"5 km\")]:\n v, se = t7cell(row, col)\n res[key] = to_num(num(v)); res[key + \"_se\"] = to_num(num(se))\n loc[key] = L7(row, col)\n loc[key + \"_se\"] = L7(row, col, \" (standard error in parentheses on the line below)\")\n if key == \"dcr_hiv_att_5km\":\n hiv_stars = stars(v)\n\n# HIV p-value: the extract prints only stars, no exact p-values\nres[\"dcr_hiv_att_5km_p_value\"] = None\nloc[\"dcr_hiv_att_5km_p_value\"] = (f\"not reported in {SID_D} (Table 7 prints only significance stars; \"\n f'cell row \"HIV\" column \"5 km\" carries no star)')\nres[\"dcr_hiv_att_5km_significant_10pct\"] = hiv_stars >= 1\n\n# derived CI\nz = 1.96\nb, s = res[\"dcr_att_5km\"], res[\"dcr_att_5km_se\"]\nres[\"dcr_att_5km_ci_low\"] = round(b - z * s, 4)\nres[\"dcr_att_5km_ci_high\"] = round(b + z * s, 4)\nfor k in [\"dcr_att_5km_ci_low\", \"dcr_att_5km_ci_high\"]:\n loc[k] = f'{SID_D} | Table 6 | row \"ATT\" (estimate and SE) | column \"5 km\" | derived'\n\n# ---- RI Table 2\ni2 = next(k for k, s in enumerate(R) if s.startswith(\"Outcome | Treatment | Control\"))\nhdr2 = cells(R[i2]); col2 = {h: i for i, h in enumerate(hdr2)}\ndef t2(row):\n for s in R[i2 + 1:]:\n if cells(s)[0] == row:\n print(\"[RI T2 match]\", s)\n return cells(s)\nATE = \"Average treatment effect estimate\"\ndef parse_effect(c):\n parts = [p.strip() for p in c.split(\";\")]\n est = num(parts[0])\n ci = re.search(r\"\\[(.*?)\\]\", c).group(1)\n lo, hi = [x.strip() for x in re.split(r\",\\s(?=-?\\d)\", ci)]\n return to_num(est), to_num(num(lo)), to_num(num(hi)), parts\n\ndef L2(row, col, extra=\"\"):\n return f'{SID_R} | Table 2 | row \"{row}\" | column \"{col}\"' + extra\n\nw = t2(\"Wage\")\nest, lo, hi, parts = parse_effect(w[col2[ATE]])\nres[\"ri_wage_ate\"], res[\"ri_wage_ate_ci_low\"], res[\"ri_wage_ate_ci_high\"] = est, lo, hi\nloc[\"ri_wage_ate\"] = L2(\"Wage\", ATE, \" (point estimate)\")\nloc[\"ri_wage_ate_ci_low\"] = L2(\"Wage\", ATE, \" (95% CI, lower bound)\")\nloc[\"ri_wage_ate_ci_high\"] = L2(\"Wage\", ATE, \" (95% CI, upper bound)\")\nres[\"ri_wage_ate_se_reported\"] = any(re.search(r\"\\bSE\\b|standard error\", p, re.I) for p in parts)\nres[\"ri_wage_ate_se_derived\"] = round((hi - lo) / (2 * z), 2)\nloc[\"ri_wage_ate_se_derived\"] = L2(\"Wage\", ATE, \" (95% CI bounds) | derived\")\nres[\"ri_control_mean_wage\"] = to_num(num(w[col2[\"Control\"]].split(\"(\")[0]))\nloc[\"ri_control_mean_wage\"] = L2(\"Wage\", \"Control\", \" (mean; SD in parentheses not used)\")\n\nr = t2(\"Reemployment\")\nres[\"ri_reemployment_ate_pp\"] = parse_effect(r[col2[ATE]])[0]\nloc[\"ri_reemployment_ate_pp\"] = L2(\"Reemployment\", ATE, \" (point estimate)\")\n\nu = t2(\"Weeks on UI\")\nest, lo, hi, _ = parse_effect(u[col2[ATE]])\nres[\"ri_weeks_ui_ate\"], res[\"ri_weeks_ui_ate_ci_low\"], res[\"ri_weeks_ui_ate_ci_high\"] = est, lo, hi\nloc[\"ri_weeks_ui_ate\"] = L2(\"Weeks on UI\", ATE, \" (point estimate)\")\nloc[\"ri_weeks_ui_ate_ci_low\"] = L2(\"Weeks on UI\", ATE, \" (95% CI, lower bound)\")\nloc[\"ri_weeks_ui_ate_ci_high\"] = L2(\"Weeks on UI\", ATE, \" (95% CI, upper bound)\")\n\n# ---- consistency checks (printed for the report, not stored as keys)\nwl, wh, we = res[\"ri_wage_ate_ci_low\"], res[\"ri_wage_ate_ci_high\"], res[\"ri_wage_ate\"]\nprint(f\"[check] wage CI half-widths: {we-wl} below, {wh-we} above (symmetric -> normal-type CI)\")\nprint(f\"[check] implied z for wage ATE = {we/res['ri_wage_ate_se_derived']:.3f}, \"\n f\"normal two-sided p = {2*norm.sf(abs(we/res['ri_wage_ate_se_derived'])):.4f} (printed p=0.022)\")\nprint(f\"[check] Table 6 5km SE {res['dcr_att_5km_se']} vs Table A19 CS-DR SE line:\",\n [s for s in D if s.strip().startswith(\"| (0.218)\")])\nfor n_ctl in (608, 640):\n print(f\"[check] (treated + {n_ctl}) * 25 years:\", [(t + n_ctl) * 25 for t in (7, 35, 50)])\nimport math\nprint(f\"[check] exp(-0.354)-1 = {math.exp(-0.354)-1:.3f}; exp(0.390)-1 = {math.exp(0.390)-1:.3f}; \"\n f\"exp(-0.431)-1 = {math.exp(-0.431)-1:.3f}\")\n\nres[\"locators\"] = loc\nres[\"derivation_notes\"] = {\n \"dcr_att_5km_ci\": (\"Normal approximation with critical value 1.96: CI = ATT +/- 1.96*SE = -0.431 +/- 1.96*0.210, \"\n \"using the Table 6 printed values (estimate -0.431, clustered SE 0.210); rounded to 4 decimals. \"\n \"The paper's own inference may use a different (e.g. bootstrap/t) critical value; not stated in the extract.\"),\n \"ri_wage_ate_se\": (\"SE = (upper - lower)/(2*1.96) = (2141 - 166)/3.92, using the printed Table 2 95% CI; assumes a \"\n \"symmetric normal-approximation (Wald) interval, which is consistent with the printed bounds \"\n \"(half-widths 987 and 988) and with the printed p=0.022 (implied normal p ~ 0.022). Rounded to cents.\"),\n}\nres[\"feasibility\"] = \"complete\"\nres[\"feasibility_reason\"] = None\nres[\"data_issues\"] = [\n {\"issue\": \"Table A19 (CS-DR column) prints SE (0.218) for the same 5 km ATT (-0.431) for which Table 6 prints SE (0.210).\",\n \"handling\": \"Key definitions name Table 6, so 0.210 is used for dcr_att_5km_se and the derived CI; discrepancy documented.\"},\n {\"issue\": \"Number of control regions is internally inconsistent: Tables 6/7 and Figure 6 note say 608 never-treated regions, the Table A19 note says 640; the printed Observations (16,175 / 16,875 / 17,250) equal (treated + 640) x 25 exactly, not (treated + 608) x 25.\",\n \"handling\": \"Observations reported as printed (17250); inconsistency flagged; nothing re-estimated.\"},\n {\"issue\": \"Abstract says fatal hospitalisations fall by 35%, but Table 7 5 km fatal ATT -0.354 implies exp(-0.354)-1 = -29.8% (Section 6.1 says ~30%). Text also calls HIV estimates 'close to zero' though the 3/5 km point estimates are -0.239/-0.340 (insignificant).\",\n \"handling\": \"Narrative inconsistencies noted; table values used as required.\"},\n {\"issue\": \"RESEA text reports the weeks-on-UI CI as magnitudes '[1.75 weeks, 2.24 weeks]' while Table 2 prints [-2.24, -1.75].\",\n \"handling\": \"Table 2 signed values used (lower bound -2.24 first).\"},\n {\"issue\": \"Rendering defect in the RESEA extract: 'p ¡ 0.001' (should read p < 0.001); Tables 2-3 were re-set by the extract editor.\",\n \"handling\": \"No keyed value affected; values taken from the re-set Table 2 as verbatim.\"},\n {\"issue\": \"DCR Table 7 reports only significance stars, no exact p-values; RESEA Table 2 reports CIs and p-values but no standard errors.\",\n \"handling\": \"dcr_hiv_att_5km_p_value = null (not reported); RESEA wage SE derived from CI with stated convention; ri_wage_ate_se_reported = false.\"},\n]\nfiles = [\"data/README.md\", DCR, RI]\nres[\"n_input_rows\"] = {os.path.basename(p): wc_l(p) for p in files}\nres[\"n_analysed\"] = 26 # 25 extracted/derived value keys + HIV p-value availability check, see report\nres[\"random_seed\"] = None\n\nwith open(\"out/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps({k: v for k, v in res.items() if k not in (\"locators\", \"derivation_notes\", \"data_issues\")}, indent=1))\nEOF\n~/wp29/venv/bin/python 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match] ATT | -0.399 | -0.283 | -0.431**\n[T6 match] Observations | 16,175 | 16,875 | 17,250\n[T6 SE line] | (0.412) | (0.279) | (0.210)\n[T7 match] Narcotics | 1.186*** | 0.549*** | 0.390** || SE line: | (0.288) | (0.187) | (0.165)\n[T7 match] Drug-related (fatal) | -1.207*** | -0.290 | -0.354** || SE line: | (0.137) | (0.215) | (0.178)\n[T7 match] HIV | 0.063 | -0.239 | -0.340 || SE line: | (0.801) | (0.337) | (0.294)\n[RI T2 match] 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[RI T2 match] 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[RI T2 match] 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[check] wage CI half-widths: 987 below, 988 above (symmetric -> normal-type CI)\n[check] implied z for wage ATE = 2.288, normal two-sided p = 0.0221 (printed p=0.022)\n[check] Table 6 5km SE 0.21 vs Table A19 CS-DR SE line: ['| (0.218) | (0.208) | (0.205) | (0.038)']\n[check] (treated + 608) * 25 years: [15375, 16075, 16450]\n[check] (treated + 640) * 25 years: [16175, 16875, 17250]\n[check] exp(-0.354)-1 = -0.298; exp(0.390)-1 = 0.477; exp(-0.431)-1 = -0.350\n{\n \"task_id\": \"F4-conf-01\",\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 \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"n_input_rows\": {\n \"README.md\": 10,\n \"arxiv-2609.27732v1.txt\": 134,\n \"arxiv-2606.14621v1.txt\": 58\n },\n \"n_analysed\": 26,\n \"random_seed\": null\n}","is_error":false}]},"uuid":"710339fd-8e4f-4823-b8a6-b659e33de822","timestamp":"2026-10-30T05:35:12.234Z","toolUseResult":{"stdout":"[T6 match] ATT | -0.399 | -0.283 | -0.431**\n[T6 match] Observations | 16,175 | 16,875 | 17,250\n[T6 SE line] | (0.412) | (0.279) | (0.210)\n[T7 match] Narcotics | 1.186*** | 0.549*** | 0.390** || SE line: | (0.288) | (0.187) | (0.165)\n[T7 match] Drug-related (fatal) | -1.207*** | -0.290 | -0.354** || SE line: | (0.137) | (0.215) | (0.178)\n[T7 match] HIV | 0.063 | -0.239 | -0.340 || SE line: | (0.801) | (0.337) | (0.294)\n[RI T2 match] 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[RI T2 match] 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[RI T2 match] 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[check] wage CI half-widths: 987 below, 988 above (symmetric -> normal-type CI)\n[check] implied z for wage ATE = 2.288, normal two-sided p = 0.0221 (printed p=0.022)\n[check] Table 6 5km SE 0.21 vs Table A19 CS-DR SE line: ['| (0.218) | (0.208) | (0.205) | (0.038)']\n[check] (treated + 608) * 25 years: [15375, 16075, 16450]\n[check] (treated + 640) * 25 years: [16175, 16875, 17250]\n[check] exp(-0.354)-1 = -0.298; exp(0.390)-1 = 0.477; exp(-0.431)-1 = -0.350\n{\n \"task_id\": \"F4-conf-01\",\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 \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"n_input_rows\": {\n \"README.md\": 10,\n \"arxiv-2609.27732v1.txt\": 134,\n \"arxiv-2606.14621v1.txt\": 58\n },\n \"n_analysed\": 26,\n 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-p out; cat > out/analysis.py <<'EOF'\n\"\"\"F4-conf-01: extract printed values from two source extracts and derive two quantities.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nValues are parsed from the text; every matched source line is printed.\"\"\"\nimport json, re, os\nfrom scipy.stats import norm\n\nDCR = \"data/sources/arxiv-2609.27732v1.txt\"\nRI = \"data/sources/arxiv-2606.14621v1.txt\"\nSID_D, SID_R = \"arxiv-2609.27732v1\", \"arxiv-2606.14621v1\"\n\ndef lines(p):\n with open(p, encoding=\"utf-8\") as f:\n return f.read().split(\"\\n\")\n\ndef wc_l(p):\n with open(p, \"rb\") as f:\n return f.read().count(b\"\\n\")\n\ndef cells(s):\n return [c.strip() for c in s.split(\"|\")]\n\ndef block(L, title, end_marker):\n i = next(k for k, s in enumerate(L) if s.startswith(title))\n j = next(k for k in range(i + 1, len(L)) if L[k].startswith(end_marker))\n return [s for s in L[i:j] if s.strip()], i\n\ndef num(c):\n \"\"\"Return the printed number string (sign + decimals kept), stars stripped.\"\"\"\n m = re.search(r\"-?[\\d,]*\\.?\\d+\", c.replace(\"$\", \"\"))\n return m.group(0)\n\ndef to_num(s):\n s = s.replace(\",\", \"\")\n return int(s) if re.fullmatch(r\"-?\\d+\", s) else float(s)\n\ndef stars(c):\n return len(re.search(r\"\\**$\", c.strip()).group(0))\n\nD = lines(DCR)\nR = lines(RI)\n\n# ---- Table 6\nt6, _ = block(D, \"Table 6:\", \"Note:\")\nhdr6 = cells(t6[1]) # ['', '1 km', '3 km', '5 km']\ndef t6row(label):\n for k, s in enumerate(t6):\n if cells(s)[0] == label:\n print(\"[T6 match]\", s)\n return cells(s), (cells(t6[k + 1]) if label == \"ATT\" else None)\natt6, se6 = t6row(\"ATT\")\nobs6, _ = t6row(\"Observations\")\nprint(\"[T6 SE line]\", \" | \".join(se6))\ncol6 = {h: i for i, h in enumerate(hdr6) if h}\n\n# ---- Table 7\nt7, _ = block(D, \"Table 7:\", \"Note:\")\nhdr7 = cells(t7[1])\ncol7 = {h: i for i, h in enumerate(hdr7)}\ndef t7cell(row, col):\n for k, s in enumerate(t7):\n if cells(s)[0] == row:\n print(\"[T7 match]\", s, \" || SE line:\", t7[k + 1])\n return cells(s)[col7[col]], cells(t7[k + 1])[col7[col]]\n raise KeyError(row)\n\nres = {\"task_id\": \"F4-conf-01\"}\nloc = {}\ndef L6(row, col, extra=\"\"):\n return f'{SID_D} | Table 6 | row \"{row}\" | column \"{col}\"' + extra\ndef L7(row, col, extra=\"\"):\n return f'{SID_D} | Table 7 | row \"{row}\" | column \"{col}\"' + extra\n\nfor km in [\"5\", \"3\"]:\n c = col6[f\"{km} km\"]\n res[f\"dcr_att_{km}km\"] = to_num(num(att6[c]))\n res[f\"dcr_att_{km}km_se\"] = to_num(num(se6[c]))\n loc[f\"dcr_att_{km}km\"] = L6(\"ATT\", f\"{km} km\")\n loc[f\"dcr_att_{km}km_se\"] = L6(\"ATT\", f\"{km} km\", \" (standard error in parentheses on the line below the ATT row)\")\nres[\"dcr_obs_5km\"] = to_num(num(obs6[col6[\"5 km\"]]))\nloc[\"dcr_obs_5km\"] = L6(\"Observations\", \"5 km\")\n\nfor key, row, col in [(\"dcr_narcotics_att_3km\", \"Narcotics\", \"3 km\"),\n (\"dcr_fatal_att_1km\", \"Drug-related (fatal)\", \"1 km\"),\n (\"dcr_hiv_att_5km\", \"HIV\", \"5 km\")]:\n v, se = t7cell(row, col)\n res[key] = to_num(num(v)); res[key + \"_se\"] = to_num(num(se))\n loc[key] = L7(row, col)\n loc[key + \"_se\"] = L7(row, col, \" (standard error in parentheses on the line below)\")\n if key == \"dcr_hiv_att_5km\":\n hiv_stars = stars(v)\n\n# HIV p-value: the extract prints only stars, no exact p-values\nres[\"dcr_hiv_att_5km_p_value\"] = None\nloc[\"dcr_hiv_att_5km_p_value\"] = (f\"not reported in {SID_D} (Table 7 prints only significance stars; \"\n f'cell row \"HIV\" column \"5 km\" carries no star)')\nres[\"dcr_hiv_att_5km_significant_10pct\"] = hiv_stars >= 1\n\n# derived CI\nz = 1.96\nb, s = res[\"dcr_att_5km\"], res[\"dcr_att_5km_se\"]\nres[\"dcr_att_5km_ci_low\"] = round(b - z * s, 4)\nres[\"dcr_att_5km_ci_high\"] = round(b + z * s, 4)\nfor k in [\"dcr_att_5km_ci_low\", \"dcr_att_5km_ci_high\"]:\n loc[k] = f'{SID_D} | Table 6 | row \"ATT\" (estimate and SE) | column \"5 km\" | derived'\n\n# ---- RI Table 2\ni2 = next(k for k, s in enumerate(R) if s.startswith(\"Outcome | Treatment | Control\"))\nhdr2 = cells(R[i2]); col2 = {h: i for i, h in enumerate(hdr2)}\ndef t2(row):\n for s in R[i2 + 1:]:\n if cells(s)[0] == row:\n print(\"[RI T2 match]\", s)\n return cells(s)\nATE = \"Average treatment effect estimate\"\ndef parse_effect(c):\n parts = [p.strip() for p in c.split(\";\")]\n est = num(parts[0])\n ci = re.search(r\"\\[(.*?)\\]\", c).group(1)\n lo, hi = [x.strip() for x in re.split(r\",\\s(?=-?\\d)\", ci)]\n return to_num(est), to_num(num(lo)), to_num(num(hi)), parts\n\ndef L2(row, col, extra=\"\"):\n return f'{SID_R} | Table 2 | row \"{row}\" | column \"{col}\"' + extra\n\nw = t2(\"Wage\")\nest, lo, hi, parts = parse_effect(w[col2[ATE]])\nres[\"ri_wage_ate\"], res[\"ri_wage_ate_ci_low\"], res[\"ri_wage_ate_ci_high\"] = est, lo, hi\nloc[\"ri_wage_ate\"] = L2(\"Wage\", ATE, \" (point estimate)\")\nloc[\"ri_wage_ate_ci_low\"] = L2(\"Wage\", ATE, \" (95% CI, lower bound)\")\nloc[\"ri_wage_ate_ci_high\"] = L2(\"Wage\", ATE, \" (95% CI, upper bound)\")\nres[\"ri_wage_ate_se_reported\"] = any(re.search(r\"\\bSE\\b|standard error\", p, re.I) for p in parts)\nres[\"ri_wage_ate_se_derived\"] = round((hi - lo) / (2 * z), 2)\nloc[\"ri_wage_ate_se_derived\"] = L2(\"Wage\", ATE, \" (95% CI bounds) | derived\")\nres[\"ri_control_mean_wage\"] = to_num(num(w[col2[\"Control\"]].split(\"(\")[0]))\nloc[\"ri_control_mean_wage\"] = L2(\"Wage\", \"Control\", \" (mean; SD in parentheses not used)\")\n\nr = t2(\"Reemployment\")\nres[\"ri_reemployment_ate_pp\"] = parse_effect(r[col2[ATE]])[0]\nloc[\"ri_reemployment_ate_pp\"] = L2(\"Reemployment\", ATE, \" (point estimate)\")\n\nu = t2(\"Weeks on UI\")\nest, lo, hi, _ = parse_effect(u[col2[ATE]])\nres[\"ri_weeks_ui_ate\"], res[\"ri_weeks_ui_ate_ci_low\"], res[\"ri_weeks_ui_ate_ci_high\"] = est, lo, hi\nloc[\"ri_weeks_ui_ate\"] = L2(\"Weeks on UI\", ATE, \" (point estimate)\")\nloc[\"ri_weeks_ui_ate_ci_low\"] = L2(\"Weeks on UI\", ATE, \" (95% CI, lower bound)\")\nloc[\"ri_weeks_ui_ate_ci_high\"] = L2(\"Weeks on UI\", ATE, \" (95% CI, upper bound)\")\n\n# ---- consistency checks (printed for the report, not stored as keys)\nwl, wh, we = res[\"ri_wage_ate_ci_low\"], res[\"ri_wage_ate_ci_high\"], res[\"ri_wage_ate\"]\nprint(f\"[check] wage CI half-widths: {we-wl} below, {wh-we} above (symmetric -> normal-type CI)\")\nprint(f\"[check] implied z for wage ATE = {we/res['ri_wage_ate_se_derived']:.3f}, \"\n f\"normal two-sided p = {2*norm.sf(abs(we/res['ri_wage_ate_se_derived'])):.4f} (printed p=0.022)\")\nprint(f\"[check] Table 6 5km SE {res['dcr_att_5km_se']} vs Table A19 CS-DR SE line:\",\n [s for s in D if s.strip().startswith(\"| (0.218)\")])\nfor n_ctl in (608, 640):\n print(f\"[check] (treated + {n_ctl}) * 25 years:\", [(t + n_ctl) * 25 for t in (7, 35, 50)])\nimport math\nprint(f\"[check] exp(-0.354)-1 = {math.exp(-0.354)-1:.3f}; exp(0.390)-1 = {math.exp(0.390)-1:.3f}; \"\n f\"exp(-0.431)-1 = {math.exp(-0.431)-1:.3f}\")\n\nres[\"locators\"] = loc\nres[\"derivation_notes\"] = {\n \"dcr_att_5km_ci\": (\"Normal approximation with critical value 1.96: CI = ATT +/- 1.96*SE = -0.431 +/- 1.96*0.210, \"\n \"using the Table 6 printed values (estimate -0.431, clustered SE 0.210); rounded to 4 decimals. \"\n \"The paper's own inference may use a different (e.g. bootstrap/t) critical value; not stated in the extract.\"),\n \"ri_wage_ate_se\": (\"SE = (upper - lower)/(2*1.96) = (2141 - 166)/3.92, using the printed Table 2 95% CI; assumes a \"\n \"symmetric normal-approximation (Wald) interval, which is consistent with the printed bounds \"\n \"(half-widths 987 and 988) and with the printed p=0.022 (implied normal p ~ 0.022). Rounded to cents.\"),\n}\nres[\"feasibility\"] = \"complete\"\nres[\"feasibility_reason\"] = None\nres[\"data_issues\"] = [\n {\"issue\": \"Table A19 (CS-DR column) prints SE (0.218) for the same 5 km ATT (-0.431) for which Table 6 prints SE (0.210).\",\n \"handling\": \"Key definitions name Table 6, so 0.210 is used for dcr_att_5km_se and the derived CI; discrepancy documented.\"},\n {\"issue\": \"Number of control regions is internally inconsistent: Tables 6/7 and Figure 6 note say 608 never-treated regions, the Table A19 note says 640; the printed Observations (16,175 / 16,875 / 17,250) equal (treated + 640) x 25 exactly, not (treated + 608) x 25.\",\n \"handling\": \"Observations reported as printed (17250); inconsistency flagged; nothing re-estimated.\"},\n {\"issue\": \"Abstract says fatal hospitalisations fall by 35%, but Table 7 5 km fatal ATT -0.354 implies exp(-0.354)-1 = -29.8% (Section 6.1 says ~30%). Text also calls HIV estimates 'close to zero' though the 3/5 km point estimates are -0.239/-0.340 (insignificant).\",\n \"handling\": \"Narrative inconsistencies noted; table values used as required.\"},\n {\"issue\": \"RESEA text reports the weeks-on-UI CI as magnitudes '[1.75 weeks, 2.24 weeks]' while Table 2 prints [-2.24, -1.75].\",\n \"handling\": \"Table 2 signed values used (lower bound -2.24 first).\"},\n {\"issue\": \"Rendering defect in the RESEA extract: 'p ¡ 0.001' (should read p < 0.001); Tables 2-3 were re-set by the extract editor.\",\n \"handling\": \"No keyed value affected; values taken from the re-set Table 2 as verbatim.\"},\n {\"issue\": \"DCR Table 7 reports only significance stars, no exact p-values; RESEA Table 2 reports CIs and p-values but no standard errors.\",\n \"handling\": \"dcr_hiv_att_5km_p_value = null (not reported); RESEA wage SE derived from CI with stated convention; ri_wage_ate_se_reported = false.\"},\n]\nfiles = [\"data/README.md\", DCR, RI]\nres[\"n_input_rows\"] = {os.path.basename(p): wc_l(p) for p in files}\nres[\"n_analysed\"] = 26 # 25 extracted/derived value keys + HIV p-value availability check, see report\nres[\"random_seed\"] = None\n\nwith open(\"out/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps({k: v for k, v in res.items() if k not in (\"locators\", \"derivation_notes\", \"data_issues\")}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py"}}]}}31{"type":"api-request","sessionId":"94f70ee6-af4b-4af7-86e9-dc589a45519a","id":"15e8d4df-7add-4cb3-8e3f-2b7ead67bbc4","timestamp":"2026-10-30T05:35:12.237Z","version":"2.1.314","querySource":"sdk","shapeHash":"139d5b2dd4dcca6229785092195dc55e592fd5b9ac99cafb1558d315273a3bdb","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":"ec6df375-6a70-4fb7-80e2-40f0128e0b76","keep":6,"tail":["99dec36f3bd1420b1f615e4e21dbc6833082c3dc7777f4a238e901ac00e038a0","8e404900e84c1875238f2081d143de3bc40c832d42e8c910e1a60117ec12e43b"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"557702aaf3d4bb802924704b720aca636a8c9a2657e3808e047b23fcd94f9c1e"}32{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"710339fd-8e4f-4823-b8a6-b659e33de822","sessionId":"94f70ee6-af4b-4af7-86e9-dc589a45519a"}33{"type":"atis-latch","atis":"","sessionId":"94f70ee6-af4b-4af7-86e9-dc589a45519a"}34{"parentUuid":"710339fd-8e4f-4823-b8a6-b659e33de822","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsuFX7HFMuHQ43PShU1","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":7120,"cache_read_input_tokens":17996,"output_tokens":355,"output_tokens_details":{"thinking_tokens":163},"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":7120},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1398,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsuFEVRf8AJXeRGyTJY","requestRef":"15e8d4df-7add-4cb3-8e3f-2b7ead67bbc4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"7ac27885-1023-4c29-a6c0-7c0183ef5cfb","timestamp":"2026-10-30T05:35:15.578Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0012","sessionId":"94f70ee6-af4b-4af7-86e9-dc589a45519a","version":"2.1.314","gitBranch":"HEAD"}35{"parentUuid":"7ac27885-1023-4c29-a6c0-7c0183ef5cfb","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsuFX7HFMuHQ43PShU1","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01AgMbLMpC9YtQ8mbEAMJzrq","name":"Bash","input":{"command":"sed 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Nothing is re-estimated.\n\n## Data and exclusions\nWe read `data/README.md` (10 lines), `arxiv-2609.27732v1.txt` (134 lines) and `arxiv-2606.14621v1.txt` (58 lines). Only these extracts were used. Table A19 (DCR) and Table 3 (RESEA) were read for consistency checks only, because every key names Table 6, 7 or 2.\n\n## Method\n`out/analysis.py` finds the table rows by their labels and the columns by their headers, then parses the printed numbers and prints each line it matched.\n- **Stars** are stripped from estimates and recorded separately.\n- **DCR standard errors** come from the parenthesised line under each coefficient row.\n- **RESEA ATE cells** are split into the estimate, the p-value and the CI bounds.\n- **Formatting of printed numbers.** Thousands separators are dropped (17,250 → 17250; $2,141 → 2141). JSON cannot store trailing zeros, so printed `0.210` appears as `0.21` and `-0.340` as `-0.34`. The numeric values are identical to the printed ones.\n\n## Results\n| key | value | source |\n|---|---|---|\n| dcr_att_5km / se | -0.431** / (0.210) | Table 6, ATT, 5 km |\n| dcr_att_3km / se | -0.283 / (0.279) | Table 6, ATT, 3 km |\n| dcr_obs_5km | 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) | Table 7, HIV, 5 km |\n| dcr_hiv_att_5km_p_value | **null**: not reported (stars only) | Table 7 |\n| dcr_hiv_att_5km_significant_10pct | false (no star) | Table 7 |\n| dcr_att_5km CI (derived) | [-0.8426, -0.0194] | from Table 6 |\n| ri_wage_ate | $1,153; 95% CI [166, 2,141] | Table 2, Wage, ATE |\n| ri_wage_ate_se_reported | false | Table 2 |\n| ri_wage_ate_se_derived | 503.83 | from Table 2 CI |\n| ri_control_mean_wage | $33,043 | Table 2, Wage, Control |\n| ri_reemployment_ate_pp | 1.47 pp | Table 2, Reemployment, ATE |\n| ri_weeks_ui_ate | -1.99 weeks; 95% CI [-2.24, -1.75] | Table 2, Weeks on UI, ATE |\n\nThe full locator strings are in `results.json` under `locators`.\n\n## Uncertainty\n- **DCR study (Tables 6 and 7).** The uncertainty is printed as **standard errors** (clustered at the MedStat region level, in parentheses) plus significance stars. No exact p-values or CIs are printed.\n- **RESEA study (Table 2).** The uncertainty is printed as **95% confidence intervals** and two-sided p-values. No standard errors are printed.\n\nHow the derived quantities were computed:\n- **DCR 95% CI.** -0.431 ± 1.96 × 0.210 = [-0.8426, -0.0194].\n - This uses a normal approximation with critical value 1.96.\n - The extract does not say which critical value the paper uses (it could be a bootstrap or t critical value). Using 2.0 instead would give [-0.851, -0.011].\n - The interval excludes zero, which matches the ** (p<0.05) star.\n- **RESEA wage SE.** (2141 − 166) / (2 × 1.96) = 503.83.\n - This assumes the printed interval is a symmetric Wald interval with a normal critical value of 1.96.\n - The assumption is supported in two ways. The half-widths are 987 and 988. The implied z of 2.288 gives a normal two-sided p of 0.0221, against a printed p of 0.022.\n - Using 2.0 instead would give an SE of about 493.75.\n\n## Limitations and open issues\n- **Two conflicting SEs for the 5 km ATT.** Table A19 prints an SE of (0.218) for the same CS-DR 5 km ATT of -0.431, whereas Table 6 prints (0.210). We used Table 6, as the key names it.\n- **Control-region count does not match the observation count.** Tables 6 and 7 say there are 608 never-treated control regions; the A19 note says 640. The printed Observations (16,175 / 16,875 / 17,250) equal (treated + 640) × 25 exactly. With 608 controls the counts would be 15,375 / 16,075 / 16,450. The observation counts are reported as printed.\n- **Narrative text does not match the tables in places:**\n - The abstract says fatal hospitalisations fall by 35%. Table 7's -0.354 at 5 km implies a fall of 29.8%, and Section 6.1 says about 30%.\n - The text calls the HIV estimates \"close to zero\", but the 5 km point estimate is -0.340. It is insignificant, not near zero.\n- **Weeks-on-UI CI sign.** The RESEA prose gives this CI as magnitudes ([1.75, 2.24] weeks). We used the signed Table 2 bounds [-2.24, -1.75].\n- **Rendering and layout.** The RESEA extract has a rendering defect (\"p ¡ 0.001\"). Its Tables 2 and 3 were re-set by the extract editor, so we rely on the editor's statement that the values are verbatim. We could not check them against the original PDF.\n- **Missing p-value.** The exact p-value for the HIV 5 km ATT is not printed, so it is reported as null. A normal-approximation p could be computed from the estimate and SE, but that would be a derived value, not a printed one.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Table 6 reports a 5 km DCR ATT of -0.431 (SE 0.210), significant at 5%; a derived normal 95% CI is [-0.8426, -0.0194].\",\"results_keys\":[\"dcr_att_5km\",\"dcr_att_5km_se\",\"dcr_att_5km_ci_low\",\"dcr_att_5km_ci_high\"],\"status\":\"supported\",\"caveat\":\"CI uses 1.96; Table A19 prints a different SE (0.218) for the same estimate.\"},\n {\"id\":\"C2\",\"text\":\"The HIV 5 km ATT (-0.340, SE 0.294) is not marked significant at 10%, and no exact p-value is printed.\",\"results_keys\":[\"dcr_hiv_att_5km\",\"dcr_hiv_att_5km_se\",\"dcr_hiv_att_5km_p_value\",\"dcr_hiv_att_5km_significant_10pct\"],\"status\":\"supported\",\"caveat\":\"p-value is null because it is not reported.\"},\n {\"id\":\"C3\",\"text\":\"Table 7 prints a narcotics ATT of 0.549*** (0.187) at 3 km and a fatal ATT of -1.207*** (0.137) at 1 km.\",\"results_keys\":[\"dcr_narcotics_att_3km\",\"dcr_narcotics_att_3km_se\",\"dcr_fatal_att_1km\",\"dcr_fatal_att_1km_se\"],\"status\":\"supported\",\"caveat\":\"Paper warns 1 km estimates rest on a single treated region per cohort.\"},\n {\"id\":\"C4\",\"text\":\"The RESEA wage ATE is $1,153 with printed 95% CI [166, 2,141]; no SE is printed and the implied SE is about 503.83 under a symmetric normal (1.96) interval.\",\"results_keys\":[\"ri_wage_ate\",\"ri_wage_ate_ci_low\",\"ri_wage_ate_ci_high\",\"ri_wage_ate_se_reported\",\"ri_wage_ate_se_derived\"],\"status\":\"supported\",\"caveat\":\"Derived SE assumes a Wald-type interval; consistent with printed p=0.022.\"},\n {\"id\":\"C5\",\"text\":\"RESEA Table 2 prints a control-group mean wage of $33,043, 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_control_mean_wage\",\"ri_reemployment_ate_pp\",\"ri_weeks_ui_ate\",\"ri_weeks_ui_ate_ci_low\",\"ri_weeks_ui_ate_ci_high\"],\"status\":\"supported\",\"caveat\":\"Prose states the weeks CI as unsigned magnitudes.\"},\n {\"id\":\"C6\",\"text\":\"The DCR source is internally inconsistent on the number of control regions (608 vs 640); printed observation counts match 640.\",\"results_keys\":[\"dcr_obs_5km\"],\"status\":\"supported\",\"caveat\":\"Inferred from arithmetic on printed counts; 25-year panel length is an inference (1998-2022).\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'));print('ok')\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":371,"cache_read_input_tokens":25116,"output_tokens":3510,"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":371},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsucH2UAtn9qfByekHe","requestRef":"a6cea120-4764-4469-b956-f03312cfab1a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d5e733b6-c64f-4f8e-bff7-365070107b1c","timestamp":"2026-10-30T05:35:45.982Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0012","sessionId":"94f70ee6-af4b-4af7-86e9-dc589a45519a","version":"2.1.314","gitBranch":"HEAD"}40{"parentUuid":"d5e733b6-c64f-4f8e-bff7-365070107b1c","isSidechain":false,"promptId":"da06a6bd-f279-4847-9e66-b6c01275d027","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GBnfuUnGoUshkAFDPyLxdP","type":"tool_result","content":"ok","is_error":false}]},"uuid":"5a81205e-5304-4f91-a75f-9d5ff6a3cb21","timestamp":"2026-10-30T05:35:46.016Z","toolUseResult":{"stdout":"ok","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"d5e733b6-c64f-4f8e-bff7-365070107b1c","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0012","sessionId":"94f70ee6-af4b-4af7-86e9-dc589a45519a","version":"2.1.314","gitBranch":"HEAD"}41{"type":"api-request-blob","sessionId":"94f70ee6-af4b-4af7-86e9-dc589a45519a","hash":"968cbc08882fc1277942f39b308cf358191fc8c9c549a6f86e049c78a9d77509","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01GBnfuUnGoUshkAFDPyLxdP","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F4-conf-01: Treatment effects and their uncertainty taken from two published results tables\n\n## Question\nThe task is to copy specified point estimates, uncertainty measures and counts exactly as printed from two arXiv extracts, and to give each one a locator:\n- **DCR study** (arxiv-2609.27732v1): staggered DiD on Swiss drug consumption room openings, Tables 6 and 7.\n- **RESEA study** (arxiv-2606.14621v1): randomised trial in Rhode Island, Table 2.\n\nTwo quantities that the sources do not print are derived, with the convention stated:\n- a 95% CI for the Table 6 5 km ATT, computed from its SE;\n- an SE for the RESEA wage ATE, computed from its CI.\n\nThe task also asks us to identify which requested quantity is not reported at all. Nothing is re-estimated.\n\n## Data and exclusions\nWe read `data/README.md` (10 lines), `arxiv-2609.27732v1.txt` (134 lines) and `arxiv-2606.14621v1.txt` (58 lines). Only these extracts were used. Table A19 (DCR) and Table 3 (RESEA) were read for consistency checks only, because every key names Table 6, 7 or 2.\n\n## Method\n`out/analysis.py` finds the table rows by their labels and the columns by their headers, then parses the printed numbers and prints each line it matched.\n- **Stars** are stripped from estimates and recorded separately.\n- **DCR standard errors** come from the parenthesised line under each coefficient row.\n- **RESEA ATE cells** are split into the estimate, the p-value and the CI bounds.\n- **Formatting of printed numbers.** Thousands separators are dropped (17,250 → 17250; $2,141 → 2141). JSON cannot store trailing zeros, so printed `0.210` appears as `0.21` and `-0.340` as `-0.34`. The numeric values are identical to the printed ones.\n\n## Results\n| key | value | source |\n|---|---|---|\n| dcr_att_5km / se | -0.431** / (0.210) | Table 6, ATT, 5 km |\n| dcr_att_3km / se | -0.283 / (0.279) | Table 6, ATT, 3 km |\n| dcr_obs_5km | 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) | Table 7, HIV, 5 km |\n| dcr_hiv_att_5km_p_value | **null**: not reported (stars only) | Table 7 |\n| dcr_hiv_att_5km_significant_10pct | false (no star) | Table 7 |\n| dcr_att_5km CI (derived) | [-0.8426, -0.0194] | from Table 6 |\n| ri_wage_ate | $1,153; 95% CI [166, 2,141] | Table 2, Wage, ATE |\n| ri_wage_ate_se_reported | false | Table 2 |\n| ri_wage_ate_se_derived | 503.83 | from Table 2 CI |\n| ri_control_mean_wage | $33,043 | Table 2, Wage, Control |\n| ri_reemployment_ate_pp | 1.47 pp | Table 2, Reemployment, ATE |\n| ri_weeks_ui_ate | -1.99 weeks; 95% CI [-2.24, -1.75] | Table 2, Weeks on UI, ATE |\n\nThe full locator strings are in `results.json` under `locators`.\n\n## Uncertainty\n- **DCR study (Tables 6 and 7).** The uncertainty is printed as **standard errors** (clustered at the MedStat region level, in parentheses) plus significance stars. No exact p-values or CIs are printed.\n- **RESEA study (Table 2).** The uncertainty is printed as **95% confidence intervals** and two-sided p-values. No standard errors are printed.\n\nHow the derived quantities were computed:\n- **DCR 95% CI.** -0.431 ± 1.96 × 0.210 = [-0.8426, -0.0194].\n - This uses a normal approximation with critical value 1.96.\n - The extract does not say which critical value the paper uses (it could be a bootstrap or t critical value). Using 2.0 instead would give [-0.851, -0.011].\n - The interval excludes zero, which matches the ** (p<0.05) star.\n- **RESEA wage SE.** (2141 − 166) / (2 × 1.96) = 503.83.\n - This assumes the printed interval is a symmetric Wald interval with a normal critical value of 1.96.\n - The assumption is supported in two ways. The half-widths are 987 and 988. The implied z of 2.288 gives a normal two-sided p of 0.0221, against a printed p of 0.022.\n - Using 2.0 instead would give an SE of about 493.75.\n\n## Limitations and open issues\n- **Two conflicting SEs for the 5 km ATT.** Table A19 prints an SE of (0.218) for the same CS-DR 5 km ATT of -0.431, whereas Table 6 prints (0.210). We used Table 6, as the key names it.\n- **Control-region count does not match the observation count.** Tables 6 and 7 say there are 608 never-treated control regions; the A19 note says 640. The printed Observations (16,175 / 16,875 / 17,250) equal (treated + 640) × 25 exactly. With 608 controls the counts would be 15,375 / 16,075 / 16,450. The observation counts are reported as printed.\n- **Narrative text does not match the tables in places:**\n - The abstract says fatal hospitalisations fall by 35%. Table 7's -0.354 at 5 km implies a fall of 29.8%, and Section 6.1 says about 30%.\n - The text calls the HIV estimates \"close to zero\", but the 5 km point estimate is -0.340. It is insignificant, not near zero.\n- **Weeks-on-UI CI sign.** The RESEA prose gives this CI as magnitudes ([1.75, 2.24] weeks). We used the signed Table 2 bounds [-2.24, -1.75].\n- **Rendering and layout.** The RESEA extract has a rendering defect (\"p ¡ 0.001\"). Its Tables 2 and 3 were re-set by the extract editor, so we rely on the editor's statement that the values are verbatim. We could not check them against the original PDF.\n- **Missing p-value.** The exact p-value for the HIV 5 km ATT is not printed, so it is reported as null. A normal-approximation p could be computed from the estimate and SE, but that would be a derived value, not a printed one.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Table 6 reports a 5 km DCR ATT of -0.431 (SE 0.210), significant at 5%; a derived normal 95% CI is [-0.8426, -0.0194].\",\"results_keys\":[\"dcr_att_5km\",\"dcr_att_5km_se\",\"dcr_att_5km_ci_low\",\"dcr_att_5km_ci_high\"],\"status\":\"supported\",\"caveat\":\"CI uses 1.96; Table A19 prints a different SE (0.218) for the same estimate.\"},\n {\"id\":\"C2\",\"text\":\"The HIV 5 km ATT (-0.340, SE 0.294) is not marked significant at 10%, and no exact p-value is printed.\",\"results_keys\":[\"dcr_hiv_att_5km\",\"dcr_hiv_att_5km_se\",\"dcr_hiv_att_5km_p_value\",\"dcr_hiv_att_5km_significant_10pct\"],\"status\":\"supported\",\"caveat\":\"p-value is null because it is not reported.\"},\n {\"id\":\"C3\",\"text\":\"Table 7 prints a narcotics ATT of 0.549*** (0.187) at 3 km and a fatal ATT of -1.207*** (0.137) at 1 km.\",\"results_keys\":[\"dcr_narcotics_att_3km\",\"dcr_narcotics_att_3km_se\",\"dcr_fatal_att_1km\",\"dcr_fatal_att_1km_se\"],\"status\":\"supported\",\"caveat\":\"Paper warns 1 km estimates rest on a single treated region per cohort.\"},\n {\"id\":\"C4\",\"text\":\"The RESEA wage ATE is $1,153 with printed 95% CI [166, 2,141]; no SE is printed and the implied SE is about 503.83 under a symmetric normal (1.96) interval.\",\"results_keys\":[\"ri_wage_ate\",\"ri_wage_ate_ci_low\",\"ri_wage_ate_ci_high\",\"ri_wage_ate_se_reported\",\"ri_wage_ate_se_derived\"],\"status\":\"supported\",\"caveat\":\"Derived SE assumes a Wald-type interval; consistent with printed p=0.022.\"},\n {\"id\":\"C5\",\"text\":\"RESEA Table 2 prints a control-group mean wage of $33,043, 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_control_mean_wage\",\"ri_reemployment_ate_pp\",\"ri_weeks_ui_ate\",\"ri_weeks_ui_ate_ci_low\",\"ri_weeks_ui_ate_ci_high\"],\"status\":\"supported\",\"caveat\":\"Prose states the weeks CI as unsigned magnitudes.\"},\n {\"id\":\"C6\",\"text\":\"The DCR source is internally inconsistent on the number of control regions (608 vs 640); printed observation counts match 640.\",\"results_keys\":[\"dcr_obs_5km\"],\"status\":\"supported\",\"caveat\":\"Inferred from arithmetic on printed counts; 25-year panel length is an inference (1998-2022).\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'));print('ok')\""}}]}}42{"type":"api-request","sessionId":"94f70ee6-af4b-4af7-86e9-dc589a45519a","id":"76115851-6fc4-4f1d-9d96-38545db185b5","timestamp":"2026-10-30T05:35:46.019Z","version":"2.1.314","querySource":"sdk","shapeHash":"139d5b2dd4dcca6229785092195dc55e592fd5b9ac99cafb1558d315273a3bdb","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":"a6cea120-4764-4469-b956-f03312cfab1a","keep":10,"tail":["968cbc08882fc1277942f39b308cf358191fc8c9c549a6f86e049c78a9d77509","ce8ba83a17fcd713cc806fcf1ea3ff9eee4d5a2627edeb5647a83f8275ace31a"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"cb39b35a641494e7a62450cab79b10edca0a507bc4dee81b1b60c538d2ecf3a7"}43{"parentUuid":"5a81205e-5304-4f91-a75f-9d5ff6a3cb21","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXswjygjXZKKXzQ2Pz1R","type":"message","role":"assistant","content":[{"type":"text","text":"I've extracted all the requested values from both papers, derived the two missing quantities, and written all four files; `out/analysis.py` regenerates `out/results.json` from the source extracts. Only one requested value, the exact p-value for the HIV 5 km estimate, is not printed in its paper, so it is `null`. I've still marked the task \"complete\", because naming that missing value is part of the task.\n\n**Swiss drug consumption room study (log points, standard errors in parentheses)**\n\n| Value | Table, row, column | As printed |\n|---|---|---|\n| Effect at 5 km | Table 6, ATT, 5 km | -0.431** (0.210) |\n| Effect at 3 km | Table 6, ATT, 3 km | -0.283 (0.279) |\n| Observations at 5 km | Table 6, Observations, 5 km | 17,250 |\n| Narcotics at 3 km | Table 7, Narcotics, 3 km | 0.549*** (0.187) |\n| Fatal at 1 km | Table 7, Drug-related (fatal), 1 km | -1.207*** (0.137) |\n| HIV at 5 km | Table 7, HIV, 5 km | -0.340 (0.294), no star |\n\n- **HIV p-value:** the paper prints only significance stars, so this is `null`. Since there is no star, the estimate is not marked significant at 10%.\n- **Derived 95% interval for the 5 km effect:** [-0.8426, -0.0194], using estimate ± 1.96 × SE. It excludes zero, which matches the paper's 5% significance star.\n\n**Rhode Island RESEA trial (Table 2, which prints confidence intervals but no standard errors)**\n\n| Value | As printed |\n|---|---|\n| Wage effect | $1,153, 95% CI [166, 2,141] |\n| Control-group mean wage | $33,043 |\n| Reemployment effect | 1.47 percentage points |\n| Weeks on unemployment insurance | -1.99, 95% CI [-2.24, -1.75] |\n\n- **Derived standard error for the wage effect:** 503.83, i.e. (2141 − 166) / (2 × 1.96). This assumes a symmetric normal interval. That assumption fits the printed numbers: the interval is symmetric (987 below, 988 above) and gives back the printed p-value of 0.022.\n\n**Problems in the sources (logged in `data_issues`)**\n- **Two standard errors for one estimate:** an appendix table prints 0.218 for the same 5 km effect that Table 6 gives as 0.210. I used Table 6, as the task specifies.\n- **Control-region count:** the main tables say 608 control regions, but an appendix note says 640. The printed observation counts equal (treated + 640) × 25 exactly, not 608.\n- **Text contradicts tables:** the abstract says fatal hospitalisations fall 35%, but the table's -0.354 implies about 30%, which is the figure Section 6.1 gives. The text also calls the HIV estimates \"close to zero\", although the 5 km estimate is -0.340.\n- **Sign dropped in prose:** the RESEA text gives the weeks interval without signs; I used the signed table values.\n- **Garbled character:** the RESEA extract shows \"p ¡ 0.001\" where \"p < 0.001\" is meant.\n\n**Limitations**\n- JSON can't keep trailing zeros, so 0.210 is stored as 0.21 and -0.340 as -0.34; the values are the same.\n- The 1.96 critical value is my choice. 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