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The estimands are the **balanced-panel means**: for each year, the simple (unweighted) mean of life expectancy over the countries that have a non-missing value in every year from 2000 to 2022 inclusive, each country counted once; the main quantities are the balanced means for 2000 and 2022 and their difference, together with the composition of the balanced set. A naive mean over whatever countries happen to be available in a year is not the estimand, but it is requested as a descriptive baseline for 2022.\n\n## Inputs\n- `data/life_expectancy_panel.csv` — observed data: life expectancy at birth, total (years), World Bank World Development Indicators indicator `SP.DYN.LE00.IN`, 20 countries, years 2000–2022. Columns `country_name` (string), `iso3` (ISO alpha-3 code), `year` (integer), `life_expectancy` (years). Source URL, retrieval date, license and SHA-256 are in `data/README.md`. 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Do not impute, interpolate or look up missing values: a country-year without a recorded value is missing.\n- A country is identified by its `iso3` code; each country contributes at most one observation per year.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F1-conf-02\" |\n| `n_countries_total` | integer | countries | distinct countries (iso3) present in the file |\n| `n_countries_balanced` | integer | countries | countries with a recorded value in every year 2000–2022 |\n| `n_countries_incomplete` | integer | countries | distinct countries not in the balanced set |\n| `balanced_countries` | array of strings | — | iso3 codes of the balanced set, sorted |\n| `n_analysed` | integer | country-years | rows of the balanced analytic dataset (countries × years) |\n| `mean_2000_balanced` | number | years | balanced-panel mean life expectancy in 2000 |\n| `mean_2022_balanced` | number | years | balanced-panel mean life expectancy in 2022 |\n| `change_2000_2022_balanced` | number | years | `mean_2022_balanced − mean_2000_balanced` |\n| `mean_2022_all_available` | number | years | baseline: mean over all distinct countries with a recorded 2022 value, each counted once |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | every data problem detected and how it was handled (empty list if none) |\n| `n_input_rows` | object | rows | {\"life_expectancy_panel.csv\": number of data rows read (excluding the header)} |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/analytic.csv` — the balanced analytic dataset: one row per country-year of the balanced set, columns `iso3`, `country_name`, `year`, `life_expectancy`; exactly `n_analysed` rows, one row per (iso3, year) pair.\n- `out/exclusions.csv` — the exclusion ledger with columns `step`, `rule`, `n_before`, `n_after`, `n_removed`, `rationale`; one row per step from the number of rows read to `n_analysed` (`n_before − n_removed = n_after` on every row; each step's `n_before` equals the previous step's `n_after`).\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/analytic.csv` and `out/exclusions.csv` from `data/` only, deterministically.\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). Do not modify `data/`.\n- This task is designed to be completed in well under 15 minutes of work.\nlife_expectancy_panel.csv\nREADME.md","is_error":false}]},"uuid":"c28c0106-feff-432c-bf4f-586b51234f5e","timestamp":"2026-10-30T04:44:27.506Z","toolUseResult":{"stdout":"# F1-conf-02 — Balanced-panel mean life expectancy, 2000–2022\n\n## Scientific question\nMeasure the change in life expectancy at birth between 2000 and 2022 across a fixed set of countries. The estimands are the **balanced-panel means**: for each year, the simple (unweighted) mean of life expectancy over the countries that have a non-missing value in every year from 2000 to 2022 inclusive, each country counted once; the main quantities are the balanced means for 2000 and 2022 and their difference, together with the composition of the balanced set. 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Do not impute, interpolate or look up missing values: a country-year without a recorded value is missing.\n- A country is identified by its `iso3` code; each country contributes at most one observation per year.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F1-conf-02\" |\n| `n_countries_total` | integer | countries | distinct countries (iso3) present in the file |\n| `n_countries_balanced` | integer | countries | countries with a recorded value in every year 2000–2022 |\n| `n_countries_incomplete` | integer | countries | distinct countries not in the balanced set |\n| `balanced_countries` | array of strings | — | iso3 codes of the balanced set, sorted |\n| `n_analysed` | integer | country-years | rows of the balanced analytic dataset (countries × years) |\n| `mean_2000_balanced` | number | years | balanced-panel mean life expectancy in 2000 |\n| `mean_2022_balanced` | number | years | balanced-panel mean life expectancy in 2022 |\n| `change_2000_2022_balanced` | number | years | `mean_2022_balanced − mean_2000_balanced` |\n| `mean_2022_all_available` | number | years | baseline: mean over all distinct countries with a recorded 2022 value, each counted once |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | every data problem detected and how it was handled (empty list if none) |\n| `n_input_rows` | object | rows | {\"life_expectancy_panel.csv\": number of data rows read (excluding the header)} |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/analytic.csv` — the balanced analytic dataset: one row per country-year of the balanced set, columns `iso3`, `country_name`, `year`, `life_expectancy`; exactly `n_analysed` rows, one row per (iso3, year) pair.\n- `out/exclusions.csv` — the exclusion ledger with columns `step`, `rule`, `n_before`, `n_after`, `n_removed`, `rationale`; one row per step from the number of rows read to `n_analysed` (`n_before − n_removed = n_after` on every row; each step's `n_before` equals the previous step's `n_after`).\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/analytic.csv` and `out/exclusions.csv` from `data/` only, deterministically.\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). Do not modify `data/`.\n- This task is designed to be completed in well under 15 minutes of work.\nlife_expectancy_panel.csv\nREADME.md","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"dacd737f-48c0-4c91-8375-ed5dde7df9cf","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0154","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd","hash":"8b5ff2ba961b98987786280418894cae35715f358a969330fe3c50e12f0219d4","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_0169ECfoV6YKpKY26GUuyULv","name":"Bash","input":{"command":"cat TASK.md; ls -R data"}}]}}15{"type":"api-request","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd","id":"1124b64b-5199-4e4d-8cf2-f4a9769697f6","timestamp":"2026-10-30T04:44:27.511Z","version":"2.1.314","querySource":"sdk","shapeHash":"bfd8cb89d4bc7816fce8f6e316bd34b9efce3d2fb178d4d9fe610b2de8b519a5","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":"58fb7d33-2f38-4639-9107-6b366321123e","keep":2,"tail":["8b5ff2ba961b98987786280418894cae35715f358a969330fe3c50e12f0219d4","1a9757f1c2378fdc5c04744e250797159cfc770e991d76b84a3d12dfebd54a0b"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"141a9036943e3309aacb641e8ea3950b6688e699113fe1019849994d8a9aaedc"}16{"parentUuid":"c28c0106-feff-432c-bf4f-586b51234f5e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp2pMnUQTepzkFjnDs7","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01NZvDhiEXNMsTFbRDbnnmLh","name":"Bash","input":{"command":"cat data/README.md; cd data; wc -l *.csv; head -5 *.csv; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('life_expectancy_panel.csv')\nprint(d.dtypes); print(d.describe(include='all'))\nprint(d.groupby('iso3').agg(n=('year','size'),nu=('year','nunique'),nn=('life_expectancy','count'),names=('country_name','nunique')))\ndup=d[d.duplicated(['iso3','year'],keep=False)].sort_values(['iso3','year']); print(dup)\nprint(d[d.life_expectancy.isna()])\nprint(d.year.value_counts().sort_index())\nprint(d.groupby('iso3').country_name.unique())\nprint(d.sort_values('life_expectancy').head(), d.sort_values('life_expectancy').tail())\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1685,"cache_read_input_tokens":2234,"output_tokens":347,"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":1685},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXp2p98QtTo65n58Eua2","requestRef":"1124b64b-5199-4e4d-8cf2-f4a9769697f6","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"603ea8d6-6d1e-42de-9097-6b9add1f2d09","timestamp":"2026-10-30T04:44:30.695Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0154","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd","version":"2.1.314","gitBranch":"HEAD"}17{"parentUuid":"603ea8d6-6d1e-42de-9097-6b9add1f2d09","isSidechain":false,"promptId":"f037acb4-2173-4d94-b354-2fc9a8b4d1c9","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NZvDhiEXNMsTFbRDbnnmLh","type":"tool_result","content":"# Data provenance — F1-conf-02\n\n`life_expectancy_panel.csv` — observed data (not simulated): **Life expectancy at birth, total (years)**, indicator `SP.DYN.LE00.IN`, World Development Indicators (World Bank, source id 2, release last updated 2026-10-08), for 20 countries (Australia, Austria, Bangladesh, Belgium, Canada, Ethiopia, France, Germany, India, Italy, Japan, Korea Rep., Netherlands, Nigeria, Spain, Sweden, Switzerland, Türkiye, United Kingdom, United States), years 2000–2022.\n\n- Source URL (JSON API): `https://api.worldbank.org/v2/country/CAN;USA;GBR;FRA;DEU;ITA;ESP;JPN;KOR;AUS;SWE;NLD;CHE;BEL;AUT;TUR;IND;NGA;ETH;BGD/indicator/SP.DYN.LE00.IN?format=json&per_page=2000&date=2000:2022`\n- Retrieved: 2026-10-30T03:25Z. SHA-256 of the raw API response: `ead88ee94d5b6cc6f8a055c9f2ec79f232c37faf8baca0ea122dbfc0550ff27e`\n- License: World Bank open data, Creative Commons Attribution 4.0 (CC BY-4.0).\n- Columns: `country_name` (string), `iso3` (ISO-3166 alpha-3 code as returned by the API), `year` (integer), `life_expectancy` (years, 3 decimals). Values were rounded to 3 decimals; nothing else was changed in the values.\n- The panel was assembled by the task author from two extractions of the API response (one earlier, narrower extraction and one later, wider one) and may contain assembly errors. An empty `life_expectancy` cell means no value was recorded for that country-year in the file.\n 440 life_expectancy_panel.csv\ncountry_name,iso3,year,life_expectancy\nAustralia,AUS,2000,79.234\nAustralia,AUS,2001,79.634\nAustralia,AUS,2002,79.937\nAustralia,AUS,2003,80.239\ncountry_name object\niso3 object\nyear int64\nlife_expectancy float64\ndtype: object\n country_name iso3 year life_expectancy\ncount 439 439 439.000000 438.000000\nunique 21 20 NaN NaN\ntop Australia TUR NaN NaN\nfreq 23 46 NaN NaN\nmean NaN NaN 2011.601367 78.466521\nstd NaN NaN 6.598263 6.073816\nmin NaN NaN 2000.000000 51.490000\n25% NaN NaN 2006.000000 77.738000\n50% NaN NaN 2012.000000 80.517500\n75% NaN NaN 2017.000000 81.803500\nmax NaN NaN 2022.000000 84.560000\n n nu nn names\niso3 \nAUS 23 23 23 1\nAUT 23 23 23 1\nBEL 23 23 23 1\nBGD 12 12 12 1\nCAN 23 23 23 1\nCHE 23 23 22 1\nDEU 23 23 23 1\nESP 23 23 23 1\nETH 12 12 12 1\nFRA 23 23 23 1\nGBR 23 23 23 1\nIND 12 12 12 1\nITA 23 23 23 1\nJPN 23 23 23 1\nKOR 23 23 23 1\nNGA 12 12 12 1\nNLD 23 23 23 1\nSWE 23 23 23 1\nTUR 46 23 46 2\nUSA 23 23 23 1\n country_name iso3 year life_expectancy\n347 Turkey TUR 2000 71.931\n370 Turkiye TUR 2000 71.931\n348 Turkey TUR 2001 72.271\n371 Turkiye TUR 2001 72.271\n349 Turkey TUR 2002 72.605\n372 Turkiye TUR 2002 72.605\n350 Turkey TUR 2003 72.906\n373 Turkiye TUR 2003 72.906\n351 Turkey TUR 2004 73.223\n374 Turkiye TUR 2004 73.223\n352 Turkey TUR 2005 73.510\n375 Turkiye TUR 2005 73.510\n353 Turkey TUR 2006 73.804\n376 Turkiye TUR 2006 73.804\n354 Turkey TUR 2007 74.129\n377 Turkiye TUR 2007 74.129\n355 Turkey TUR 2008 74.380\n378 Turkiye TUR 2008 74.380\n356 Turkey TUR 2009 74.679\n379 Turkiye TUR 2009 74.679\n357 Turkey TUR 2010 74.964\n380 Turkiye TUR 2010 74.964\n358 Turkey TUR 2011 74.823\n381 Turkiye TUR 2011 74.823\n359 Turkey TUR 2012 75.552\n382 Turkiye TUR 2012 75.552\n360 Turkey TUR 2013 76.171\n383 Turkiye TUR 2013 76.171\n361 Turkey TUR 2014 76.452\n384 Turkiye TUR 2014 76.452\n362 Turkey TUR 2015 76.539\n385 Turkiye TUR 2015 76.539\n363 Turkey TUR 2016 76.595\n386 Turkiye TUR 2016 76.595\n364 Turkey TUR 2017 77.037\n387 Turkiye TUR 2017 77.037\n365 Turkey TUR 2018 77.454\n388 Turkiye TUR 2018 77.454\n366 Turkey TUR 2019 77.737\n389 Turkiye TUR 2019 77.737\n367 Turkey TUR 2020 76.525\n390 Turkiye TUR 2020 76.525\n368 Turkey TUR 2021 75.722\n391 Turkiye TUR 2021 75.722\n369 Turkey TUR 2022 77.591\n392 Turkiye TUR 2022 77.591\n country_name iso3 year life_expectancy\n331 Switzerland CHE 2007 NaN\nyear\n2000 17\n2001 17\n2002 17\n2003 17\n2004 17\n2005 17\n2006 17\n2007 17\n2008 17\n2009 17\n2010 17\n2011 21\n2012 21\n2013 21\n2014 21\n2015 21\n2016 21\n2017 21\n2018 21\n2019 21\n2020 21\n2021 21\n2022 21\nName: count, dtype: int64\niso3\nAUS [Australia]\nAUT [Austria]\nBEL [Belgium]\nBGD [Bangladesh]\nCAN [Canada]\nCHE [Switzerland]\nDEU [Germany]\nESP [Spain]\nETH [Ethiopia]\nFRA [France]\nGBR [United Kingdom]\nIND [India]\nITA [Italy]\nJPN [Japan]\nKOR [Korea, Rep.]\nNGA [Nigeria]\nNLD [Netherlands]\nSWE [Sweden]\nTUR [Turkey, Turkiye]\nUSA [United States]\nName: country_name, dtype: object\n country_name iso3 year life_expectancy\n266 Nigeria NGA 2011 51.490\n267 Nigeria NGA 2012 51.688\n268 Nigeria NGA 2013 51.827\n270 Nigeria NGA 2015 51.939\n269 Nigeria NGA 2014 51.940 country_name iso3 year life_expectancy\n215 Japan JPN 2018 84.211\n216 Japan JPN 2019 84.356\n218 Japan JPN 2021 84.446\n217 Japan JPN 2020 84.560\n331 Switzerland CHE 2007 NaN","is_error":false}]},"uuid":"3d12266f-b1ae-4c3c-907c-78667b94c7ed","timestamp":"2026-10-30T04:44:30.990Z","toolUseResult":{"stdout":"# Data provenance — F1-conf-02\n\n`life_expectancy_panel.csv` — observed data (not simulated): **Life expectancy at birth, total (years)**, indicator `SP.DYN.LE00.IN`, World Development Indicators (World Bank, source id 2, release last updated 2026-10-08), for 20 countries (Australia, Austria, Bangladesh, Belgium, Canada, Ethiopia, France, Germany, India, Italy, Japan, Korea Rep., Netherlands, Nigeria, Spain, Sweden, Switzerland, Türkiye, United Kingdom, United States), years 2000–2022.\n\n- Source URL (JSON API): `https://api.worldbank.org/v2/country/CAN;USA;GBR;FRA;DEU;ITA;ESP;JPN;KOR;AUS;SWE;NLD;CHE;BEL;AUT;TUR;IND;NGA;ETH;BGD/indicator/SP.DYN.LE00.IN?format=json&per_page=2000&date=2000:2022`\n- Retrieved: 2026-10-30T03:25Z. SHA-256 of the raw API response: `ead88ee94d5b6cc6f8a055c9f2ec79f232c37faf8baca0ea122dbfc0550ff27e`\n- License: World Bank open data, Creative Commons Attribution 4.0 (CC BY-4.0).\n- Columns: `country_name` (string), `iso3` (ISO-3166 alpha-3 code as returned by the API), `year` (integer), `life_expectancy` (years, 3 decimals). Values were rounded to 3 decimals; nothing else was changed in the values.\n- The panel was assembled by the task author from two extractions of the API response (one earlier, narrower extraction and one later, wider one) and may contain assembly errors. An empty `life_expectancy` cell means no value was recorded for that country-year in the file.\n 440 life_expectancy_panel.csv\ncountry_name,iso3,year,life_expectancy\nAustralia,AUS,2000,79.234\nAustralia,AUS,2001,79.634\nAustralia,AUS,2002,79.937\nAustralia,AUS,2003,80.239\ncountry_name object\niso3 object\nyear int64\nlife_expectancy float64\ndtype: object\n country_name iso3 year life_expectancy\ncount 439 439 439.000000 438.000000\nunique 21 20 NaN NaN\ntop Australia TUR NaN NaN\nfreq 23 46 NaN NaN\nmean NaN NaN 2011.601367 78.466521\nstd NaN NaN 6.598263 6.073816\nmin NaN NaN 2000.000000 51.490000\n25% NaN NaN 2006.000000 77.738000\n50% NaN NaN 2012.000000 80.517500\n75% NaN NaN 2017.000000 81.803500\nmax NaN NaN 2022.000000 84.560000\n n nu nn names\niso3 \nAUS 23 23 23 1\nAUT 23 23 23 1\nBEL 23 23 23 1\nBGD 12 12 12 1\nCAN 23 23 23 1\nCHE 23 23 22 1\nDEU 23 23 23 1\nESP 23 23 23 1\nETH 12 12 12 1\nFRA 23 23 23 1\nGBR 23 23 23 1\nIND 12 12 12 1\nITA 23 23 23 1\nJPN 23 23 23 1\nKOR 23 23 23 1\nNGA 12 12 12 1\nNLD 23 23 23 1\nSWE 23 23 23 1\nTUR 46 23 46 2\nUSA 23 23 23 1\n country_name iso3 year life_expectancy\n347 Turkey TUR 2000 71.931\n370 Turkiye TUR 2000 71.931\n348 Turkey TUR 2001 72.271\n371 Turkiye TUR 2001 72.271\n349 Turkey TUR 2002 72.605\n372 Turkiye TUR 2002 72.605\n350 Turkey TUR 2003 72.906\n373 Turkiye TUR 2003 72.906\n351 Turkey TUR 2004 73.223\n374 Turkiye TUR 2004 73.223\n352 Turkey TUR 2005 73.510\n375 Turkiye TUR 2005 73.510\n353 Turkey TUR 2006 73.804\n376 Turkiye TUR 2006 73.804\n354 Turkey TUR 2007 74.129\n377 Turkiye TUR 2007 74.129\n355 Turkey TUR 2008 74.380\n378 Turkiye TUR 2008 74.380\n356 Turkey TUR 2009 74.679\n379 Turkiye TUR 2009 74.679\n357 Turkey TUR 2010 74.964\n380 Turkiye TUR 2010 74.964\n358 Turkey TUR 2011 74.823\n381 Turkiye TUR 2011 74.823\n359 Turkey TUR 2012 75.552\n382 Turkiye TUR 2012 75.552\n360 Turkey TUR 2013 76.171\n383 Turkiye TUR 2013 76.171\n361 Turkey TUR 2014 76.452\n384 Turkiye TUR 2014 76.452\n362 Turkey TUR 2015 76.539\n385 Turkiye TUR 2015 76.539\n363 Turkey TUR 2016 76.595\n386 Turkiye TUR 2016 76.595\n364 Turkey TUR 2017 77.037\n387 Turkiye TUR 2017 77.037\n365 Turkey TUR 2018 77.454\n388 Turkiye TUR 2018 77.454\n366 Turkey TUR 2019 77.737\n389 Turkiye TUR 2019 77.737\n367 Turkey TUR 2020 76.525\n390 Turkiye TUR 2020 76.525\n368 Turkey TUR 2021 75.722\n391 Turkiye TUR 2021 75.722\n369 Turkey TUR 2022 77.591\n392 Turkiye TUR 2022 77.591\n country_name iso3 year life_expectancy\n331 Switzerland CHE 2007 NaN\nyear\n2000 17\n2001 17\n2002 17\n2003 17\n2004 17\n2005 17\n2006 17\n2007 17\n2008 17\n2009 17\n2010 17\n2011 21\n2012 21\n2013 21\n2014 21\n2015 21\n2016 21\n2017 21\n2018 21\n2019 21\n2020 21\n2021 21\n2022 21\nName: count, dtype: int64\niso3\nAUS [Australia]\nAUT [Austria]\nBEL [Belgium]\nBGD [Bangladesh]\nCAN [Canada]\nCHE [Switzerland]\nDEU [Germany]\nESP [Spain]\nETH [Ethiopia]\nFRA [France]\nGBR [United Kingdom]\nIND [India]\nITA [Italy]\nJPN [Japan]\nKOR [Korea, Rep.]\nNGA [Nigeria]\nNLD [Netherlands]\nSWE [Sweden]\nTUR [Turkey, Turkiye]\nUSA [United States]\nName: country_name, dtype: object\n country_name iso3 year life_expectancy\n266 Nigeria NGA 2011 51.490\n267 Nigeria NGA 2012 51.688\n268 Nigeria NGA 2013 51.827\n270 Nigeria NGA 2015 51.939\n269 Nigeria NGA 2014 51.940 country_name iso3 year life_expectancy\n215 Japan JPN 2018 84.211\n216 Japan JPN 2019 84.356\n218 Japan JPN 2021 84.446\n217 Japan JPN 2020 84.560\n331 Switzerland CHE 2007 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print(d.describe(include='all'))\nprint(d.groupby('iso3').agg(n=('year','size'),nu=('year','nunique'),nn=('life_expectancy','count'),names=('country_name','nunique')))\ndup=d[d.duplicated(['iso3','year'],keep=False)].sort_values(['iso3','year']); print(dup)\nprint(d[d.life_expectancy.isna()])\nprint(d.year.value_counts().sort_index())\nprint(d.groupby('iso3').country_name.unique())\nprint(d.sort_values('life_expectancy').head(), d.sort_values('life_expectancy').tail())\n\""}}]}}19{"type":"api-request","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd","id":"b6ed9149-e88e-4c96-a453-5622a30eb471","timestamp":"2026-10-30T04:44:30.994Z","version":"2.1.314","querySource":"sdk","shapeHash":"bfd8cb89d4bc7816fce8f6e316bd34b9efce3d2fb178d4d9fe610b2de8b519a5","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":"1124b64b-5199-4e4d-8cf2-f4a9769697f6","keep":4,"tail":["5380a33212e6d9e1ac505017a2ebb72dba0716c92c2f243a01fbf7c3ca919e81","3f726ce59b703236c7742f071b2c6e7275fede487cb7ae357403d10e33c1f20f"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"0a18f118095d15e7b04e76451c4e7475a7fd223adaaf8ef5285d051884744a87"}20{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"3d12266f-b1ae-4c3c-907c-78667b94c7ed","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd"}21{"type":"atis-latch","atis":"","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd"}22{"parentUuid":"3d12266f-b1ae-4c3c-907c-78667b94c7ed","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp35LguZ89h2mNJoPCg","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":3105,"cache_read_input_tokens":3919,"output_tokens":591,"output_tokens_details":{"thinking_tokens":391},"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":3105},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":3782,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXp352KubF5ZV43tzCM7","requestRef":"b6ed9149-e88e-4c96-a453-5622a30eb471","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"71cbe102-5e57-475c-a4a0-7a437060da68","timestamp":"2026-10-30T04:44:35.851Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0154/data","sessionId":"5fc116bc-7a4a-4757-ae66-de08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data; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('life_expectancy_panel.csv')\nd=d.sort_values(['iso3','year']); d['dif']=d.groupby('iso3').life_expectancy.diff()\nprint(d[d.dif.abs()>1.2])\nfor c in ['country_name','iso3']: print((d[c]!=d[c].str.strip()).sum())\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3105,"cache_read_input_tokens":3919,"output_tokens":591,"output_tokens_details":{"thinking_tokens":391},"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":3105},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXp352KubF5ZV43tzCM7","requestRef":"b6ed9149-e88e-4c96-a453-5622a30eb471","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3dbc0999-de0b-4d42-9189-f6080dbe75e0","timestamp":"2026-10-30T04:44:37.137Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0154/data","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"3dbc0999-de0b-4d42-9189-f6080dbe75e0","isSidechain":false,"promptId":"f037acb4-2173-4d94-b354-2fc9a8b4d1c9","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QDgtjjDQEbHKLfnkCmVxhe","type":"tool_result","content":"(eval):cd:1: no such file or directory: data\n country_name iso3 year life_expectancy dif\n78 Belgium BEL 2020 80.695 -1.300\n55 Bangladesh BGD 2020 71.421 -1.204\n57 Bangladesh BGD 2022 74.265 3.162\n298 Spain ESP 2020 82.232 -1.600\n115 Ethiopia ETH 2022 66.897 1.567\n172 India IND 2021 67.282 -2.874\n173 India IND 2022 71.698 4.416\n194 Italy ITA 2020 82.195 -1.303\n367 Turkey TUR 2020 76.525 -1.212\n369 Turkey TUR 2022 77.591 1.869\n436 United States USA 2020 76.980 -1.808\n0\n0","is_error":false}]},"uuid":"37c369ec-42c5-48bd-b5e0-acef868c021d","timestamp":"2026-10-30T04:44:37.412Z","toolUseResult":{"stdout":"(eval):cd:1: no such file or directory: data\n country_name iso3 year life_expectancy dif\n78 Belgium BEL 2020 80.695 -1.300\n55 Bangladesh BGD 2020 71.421 -1.204\n57 Bangladesh BGD 2022 74.265 3.162\n298 Spain ESP 2020 82.232 -1.600\n115 Ethiopia ETH 2022 66.897 1.567\n172 India IND 2021 67.282 -2.874\n173 India IND 2022 71.698 4.416\n194 Italy ITA 2020 82.195 -1.303\n367 Turkey TUR 2020 76.525 -1.212\n369 Turkey TUR 2022 77.591 1.869\n436 United States USA 2020 76.980 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data; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('life_expectancy_panel.csv')\nd=d.sort_values(['iso3','year']); d['dif']=d.groupby('iso3').life_expectancy.diff()\nprint(d[d.dif.abs()>1.2])\nfor c in ['country_name','iso3']: print((d[c]!=d[c].str.strip()).sum())\n\""}}]}}26{"type":"api-request","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd","id":"f942f921-e2c8-401f-85ca-f196830818de","timestamp":"2026-10-30T04:44:37.415Z","version":"2.1.314","querySource":"sdk","shapeHash":"bfd8cb89d4bc7816fce8f6e316bd34b9efce3d2fb178d4d9fe610b2de8b519a5","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":"b6ed9149-e88e-4c96-a453-5622a30eb471","keep":6,"tail":["273f8da0e2471bf7a54564b32ce3ed38d4ce8ff80a18f2c421d4c3f3cc68efb5","4d49989c533abc3463997e16217b0591bb3c07ce3b940ca44aa113e713d5f8f7"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"4c780041f87708a01218fe9a6ebb8aa75007a5c6b570cb56b1b228cd943b8b29"}27{"parentUuid":"37c369ec-42c5-48bd-b5e0-acef868c021d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp3YnBVQGEqA1mrANwa","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":879,"cache_read_input_tokens":7024,"output_tokens":2351,"output_tokens_details":{"thinking_tokens":15},"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":879},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":358,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXp3YVaKt5aUkTh9pZ5D","requestRef":"f942f921-e2c8-401f-85ca-f196830818de","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"28231e8d-1434-47dc-ba85-f9db0214c7c4","timestamp":"2026-10-30T04:44:38.570Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0154/data","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd","version":"2.1.314","gitBranch":"HEAD"}28{"parentUuid":"28231e8d-1434-47dc-ba85-f9db0214c7c4","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp3YnBVQGEqA1mrANwa","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01C9GwqVBsoBBy1QH4nuPFVW","name":"Bash","input":{"command":"mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-02: balanced-panel mean life expectancy 2000-2022. Run from workspace root.\"\"\"\nimport json, os\nimport pandas as pd\n\nIN = \"data/life_expectancy_panel.csv\"\nOUT = \"out\"\nYEARS = list(range(2000, 2023))\nos.makedirs(OUT, exist_ok=True)\n\nraw = pd.read_csv(IN, dtype={\"country_name\": str, \"iso3\": str})\nn_read = len(raw)\nfor c in [\"country_name\", \"iso3\"]:\n raw[c] = raw[c].str.strip()\nraw[\"year\"] = raw[\"year\"].astype(int)\nd = raw[raw[\"year\"].between(2000, 2022)].copy()\nassert len(d) == n_read, \"rows outside 2000-2022 found\"\n\nissues, ledger = [], []\ndef step(rule, before, after, rationale):\n ledger.append(dict(step=len(ledger) + 1, rule=rule, n_before=before, n_after=after,\n n_removed=before - after, rationale=rationale))\n\n# 1. Duplicate (iso3, year) rows\nnames = d.groupby(\"iso3\")[\"country_name\"].unique()\nmulti = {k: sorted(v) for k, v in names.items() if len(v) > 1}\ndupkey = d.duplicated([\"iso3\", \"year\"], keep=False)\ndups = d[dupkey]\nconflict = dups.groupby([\"iso3\", \"year\"])[\"life_expectancy\"].nunique(dropna=False).gt(1).any()\nassert not conflict, \"conflicting duplicate values - needs manual decision\"\n# canonical name: the current World Bank spelling (\"Turkiye\") where alternatives exist\ncanon = {}\nfor k, v in multi.items():\n canon[k] = \"Turkiye\" if \"Turkiye\" in v else v[-1]\nd[\"country_name\"] = d.apply(lambda r: canon.get(r[\"iso3\"], r[\"country_name\"]), axis=1)\nb = len(d)\nd = d.drop_duplicates([\"iso3\", \"year\"], keep=\"first\")\nstep(\"Drop duplicate (iso3, year) rows (identical values)\", b, len(d),\n \"Panel assembled from two extractions; some countries appear twice under different names. iso3 identifies the country; one observation per country-year.\")\nfor k, v in multi.items():\n n = int(dups[dups.iso3 == k].shape[0] // 2)\n issues.append({\"issue\": f\"{k} appears under two country names {v} with {n} duplicated country-years (identical values) — an assembly artifact of the two extractions.\",\n \"handling\": f\"Deduplicated by (iso3, year), keeping one row per year; values identical so no choice of value needed; name harmonised to '{canon[k]}'. Counted as one country.\"})\n\n# 2. Missing values\nmiss = d[d[\"life_expectancy\"].isna()]\nb = len(d)\nd = d.dropna(subset=[\"life_expectancy\"])\nstep(\"Drop country-years with empty life_expectancy\", b, len(d),\n \"Empty cell = no recorded value; no imputation allowed.\")\nfor _, r in miss.iterrows():\n issues.append({\"issue\": f\"{r.iso3} has an empty life_expectancy cell for {r.year} (row present, value missing).\",\n \"handling\": \"Treated as missing (not imputed/interpolated); country therefore excluded from the balanced set.\"})\n\n# 3. Restrict to balanced countries\ncov = d.groupby(\"iso3\")[\"year\"].apply(lambda s: set(s))\nbalanced = sorted([k for k, s in cov.items() if set(YEARS) <= s])\nall_iso = sorted(raw[\"iso3\"].unique())\nincomplete = [k for k in all_iso if k not in balanced]\nfor k in incomplete:\n have = sorted(cov.get(k, set()))\n missing_years = sorted(set(YEARS) - set(have))\n if k not in set(miss.iso3):\n issues.append({\"issue\": f\"{k} has no rows for years {missing_years[0]}–{missing_years[-1]} ({len(missing_years)} years missing; only the later, wider extraction covers it).\" if missing_years == list(range(missing_years[0], missing_years[-1] + 1)) else f\"{k} missing years {missing_years}.\",\n \"handling\": \"Country excluded from the balanced panel; its available rows are used only in the 2022 all-available baseline.\"})\nb = len(d)\nan = d[d[\"iso3\"].isin(balanced)].copy()\nstep(\"Keep only countries with a recorded value in every year 2000-2022\", b, len(an),\n f\"Balanced-panel estimand; excluded incomplete countries: {', '.join(incomplete)}.\")\n\nan = an.sort_values([\"iso3\", \"year\"])[[\"iso3\", \"country_name\", \"year\", \"life_expectancy\"]]\nassert len(an) == len(balanced) * len(YEARS)\nan.to_csv(f\"{OUT}/analytic.csv\", index=False)\npd.DataFrame(ledger).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\nm00 = an.loc[an.year == 2000, \"life_expectancy\"].mean()\nm22 = an.loc[an.year == 2022, \"life_expectancy\"].mean()\nall22 = d[d.year == 2022] # deduplicated, non-missing; one row per iso3\nassert all22.iso3.is_unique\nres = {\n \"task_id\": \"F1-conf-02\",\n \"n_countries_total\": len(all_iso),\n \"n_countries_balanced\": len(balanced),\n \"n_countries_incomplete\": len(incomplete),\n \"balanced_countries\": balanced,\n \"n_analysed\": len(an),\n \"mean_2000_balanced\": round(float(m00), 4),\n \"mean_2022_balanced\": round(float(m22), 4),\n \"change_2000_2022_balanced\": round(float(m22 - m00), 4),\n \"mean_2022_all_available\": round(float(all22.life_expectancy.mean()), 4),\n \"n_countries_2022_all_available\": int(all22.iso3.nunique()),\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"life_expectancy_panel.csv\": n_read},\n \"random_seed\": None,\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n json.dump(res, f, indent=2, ensure_ascii=False)\nprint(json.dumps(res, indent=2, ensure_ascii=False))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/exclusions.csv; wc -l out/analytic.csv"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":879,"cache_read_input_tokens":7024,"output_tokens":2351,"output_tokens_details":{"thinking_tokens":15},"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":879},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXp3YVaKt5aUkTh9pZ5D","requestRef":"f942f921-e2c8-401f-85ca-f196830818de","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9e6c7a1b-10fa-46e1-9f48-59ff3ff7102a","timestamp":"2026-10-30T04:44:55.533Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0154/data","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"9e6c7a1b-10fa-46e1-9f48-59ff3ff7102a","isSidechain":false,"promptId":"f037acb4-2173-4d94-b354-2fc9a8b4d1c9","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nTraceback (most recent call last):\n File \"/Users/simon-pierreboucher/wp29/runs/RC0154/data/out/analysis.py\", line 10, in <module>\n raw = pd.read_csv(IN, dtype={\"country_name\": str, \"iso3\": str})\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n return _read(filepath_or_buffer, kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n parser = TextFileReader(filepath_or_buffer, **kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n self._engine = self._make_engine(f, self.engine)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n self.handles = get_handle(\n ~~~~~~~~~~^\n f,\n ^^\n ...<6 lines>...\n storage_options=self.options.get(\"storage_options\", None),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n handle = open(\n handle,\n ...<3 lines>...\n newline=\"\",\n )\nFileNotFoundError: [Errno 2] No such file or directory: 'data/life_expectancy_panel.csv'\ncat: out/exclusions.csv: No such file or directory\nwc: out/analytic.csv: open: No such file or directory","is_error":true,"tool_use_id":"toolu_01C9GwqVBsoBBy1QH4nuPFVW"}]},"uuid":"d0f2aaef-64b1-481d-9ec6-41aff42ab5fc","timestamp":"2026-10-30T04:44:55.829Z","toolUseResult":"Error: Exit code 1\nTraceback (most recent call last):\n File \"/Users/simon-pierreboucher/wp29/runs/RC0154/data/out/analysis.py\", line 10, in <module>\n raw = pd.read_csv(IN, dtype={\"country_name\": str, \"iso3\": str})\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n return _read(filepath_or_buffer, kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n parser = TextFileReader(filepath_or_buffer, **kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n self._engine = self._make_engine(f, self.engine)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n self.handles = get_handle(\n ~~~~~~~~~~^\n f,\n ^^\n ...<6 lines>...\n storage_options=self.options.get(\"storage_options\", None),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n handle = open(\n handle,\n ...<3 lines>...\n newline=\"\",\n )\nFileNotFoundError: [Errno 2] No such file or directory: 'data/life_expectancy_panel.csv'\ncat: out/exclusions.csv: No such file or directory\nwc: out/analytic.csv: open: No such file or 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-p out; cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-02: balanced-panel mean life expectancy 2000-2022. Run from workspace root.\"\"\"\nimport json, os\nimport pandas as pd\n\nIN = \"data/life_expectancy_panel.csv\"\nOUT = \"out\"\nYEARS = list(range(2000, 2023))\nos.makedirs(OUT, exist_ok=True)\n\nraw = pd.read_csv(IN, dtype={\"country_name\": str, \"iso3\": str})\nn_read = len(raw)\nfor c in [\"country_name\", \"iso3\"]:\n raw[c] = raw[c].str.strip()\nraw[\"year\"] = raw[\"year\"].astype(int)\nd = raw[raw[\"year\"].between(2000, 2022)].copy()\nassert len(d) == n_read, \"rows outside 2000-2022 found\"\n\nissues, ledger = [], []\ndef step(rule, before, after, rationale):\n ledger.append(dict(step=len(ledger) + 1, rule=rule, n_before=before, n_after=after,\n n_removed=before - after, rationale=rationale))\n\n# 1. Duplicate (iso3, year) rows\nnames = d.groupby(\"iso3\")[\"country_name\"].unique()\nmulti = {k: sorted(v) for k, v in names.items() if len(v) > 1}\ndupkey = d.duplicated([\"iso3\", \"year\"], keep=False)\ndups = d[dupkey]\nconflict = dups.groupby([\"iso3\", \"year\"])[\"life_expectancy\"].nunique(dropna=False).gt(1).any()\nassert not conflict, \"conflicting duplicate values - needs manual decision\"\n# canonical name: the current World Bank spelling (\"Turkiye\") where alternatives exist\ncanon = {}\nfor k, v in multi.items():\n canon[k] = \"Turkiye\" if \"Turkiye\" in v else v[-1]\nd[\"country_name\"] = d.apply(lambda r: canon.get(r[\"iso3\"], r[\"country_name\"]), axis=1)\nb = len(d)\nd = d.drop_duplicates([\"iso3\", \"year\"], keep=\"first\")\nstep(\"Drop duplicate (iso3, year) rows (identical values)\", b, len(d),\n \"Panel assembled from two extractions; some countries appear twice under different names. iso3 identifies the country; one observation per country-year.\")\nfor k, v in multi.items():\n n = int(dups[dups.iso3 == k].shape[0] // 2)\n issues.append({\"issue\": f\"{k} appears under two country names {v} with {n} duplicated country-years (identical values) — an assembly artifact of the two extractions.\",\n \"handling\": f\"Deduplicated by (iso3, year), keeping one row per year; values identical so no choice of value needed; name harmonised to '{canon[k]}'. Counted as one country.\"})\n\n# 2. Missing values\nmiss = d[d[\"life_expectancy\"].isna()]\nb = len(d)\nd = d.dropna(subset=[\"life_expectancy\"])\nstep(\"Drop country-years with empty life_expectancy\", b, len(d),\n \"Empty cell = no recorded value; no imputation allowed.\")\nfor _, r in miss.iterrows():\n issues.append({\"issue\": f\"{r.iso3} has an empty life_expectancy cell for {r.year} (row present, value missing).\",\n \"handling\": \"Treated as missing (not imputed/interpolated); country therefore excluded from the balanced set.\"})\n\n# 3. Restrict to balanced countries\ncov = d.groupby(\"iso3\")[\"year\"].apply(lambda s: set(s))\nbalanced = sorted([k for k, s in cov.items() if set(YEARS) <= s])\nall_iso = sorted(raw[\"iso3\"].unique())\nincomplete = [k for k in all_iso if k not in balanced]\nfor k in incomplete:\n have = sorted(cov.get(k, set()))\n missing_years = sorted(set(YEARS) - set(have))\n if k not in set(miss.iso3):\n issues.append({\"issue\": f\"{k} has no rows for years {missing_years[0]}–{missing_years[-1]} ({len(missing_years)} years missing; only the later, wider extraction covers it).\" if missing_years == list(range(missing_years[0], missing_years[-1] + 1)) else f\"{k} missing years {missing_years}.\",\n \"handling\": \"Country excluded from the balanced panel; its available rows are used only in the 2022 all-available baseline.\"})\nb = len(d)\nan = d[d[\"iso3\"].isin(balanced)].copy()\nstep(\"Keep only countries with a recorded value in every year 2000-2022\", b, len(an),\n f\"Balanced-panel estimand; excluded incomplete countries: {', '.join(incomplete)}.\")\n\nan = an.sort_values([\"iso3\", \"year\"])[[\"iso3\", \"country_name\", \"year\", \"life_expectancy\"]]\nassert len(an) == len(balanced) * len(YEARS)\nan.to_csv(f\"{OUT}/analytic.csv\", index=False)\npd.DataFrame(ledger).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\nm00 = an.loc[an.year == 2000, \"life_expectancy\"].mean()\nm22 = an.loc[an.year == 2022, \"life_expectancy\"].mean()\nall22 = d[d.year == 2022] # deduplicated, non-missing; one row per iso3\nassert all22.iso3.is_unique\nres = {\n \"task_id\": \"F1-conf-02\",\n \"n_countries_total\": len(all_iso),\n \"n_countries_balanced\": len(balanced),\n \"n_countries_incomplete\": len(incomplete),\n \"balanced_countries\": balanced,\n \"n_analysed\": len(an),\n \"mean_2000_balanced\": round(float(m00), 4),\n \"mean_2022_balanced\": round(float(m22), 4),\n \"change_2000_2022_balanced\": round(float(m22 - m00), 4),\n \"mean_2022_all_available\": round(float(all22.life_expectancy.mean()), 4),\n \"n_countries_2022_all_available\": int(all22.iso3.nunique()),\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"life_expectancy_panel.csv\": n_read},\n \"random_seed\": None,\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n json.dump(res, f, indent=2, ensure_ascii=False)\nprint(json.dumps(res, indent=2, ensure_ascii=False))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/exclusions.csv; wc -l out/analytic.csv"}}]}}31{"type":"api-request","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd","id":"ca6436cb-21a9-4920-bc54-4a16ec3db96c","timestamp":"2026-10-30T04:44:55.833Z","version":"2.1.314","querySource":"sdk","shapeHash":"bfd8cb89d4bc7816fce8f6e316bd34b9efce3d2fb178d4d9fe610b2de8b519a5","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":"f942f921-e2c8-401f-85ca-f196830818de","keep":8,"tail":["2a89ed6d181754b785464f504e5d8e9145f6fc44e5385f8fc5ebabff330d66c8","88419dc2f70eaf255c9444586a55a426d58aa19c7388ef294576dc8f75ad46ae"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"0ff083205a531af459b88f031a7206841da79caeb471abd6e95cd9578c3b56e8"}32{"parentUuid":"d0f2aaef-64b1-481d-9ec6-41aff42ab5fc","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp4uW963rrn6Ya76chk","type":"message","role":"assistant","content":[{"type":"text","text":"Oops, the shell was in data/; I created data/out by mistake. Moving it."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3122,"cache_read_input_tokens":7903,"output_tokens":184,"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":3122},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXp4uE2QV8CP4o6tfehS","requestRef":"ca6436cb-21a9-4920-bc54-4a16ec3db96c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"82d4bab5-d69d-4896-8cde-11af34c7d81b","timestamp":"2026-10-30T04:44:57.145Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0154/data","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd","version":"2.1.314","gitBranch":"HEAD"}33{"parentUuid":"82d4bab5-d69d-4896-8cde-11af34c7d81b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp4uW963rrn6Ya76chk","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KaekQPeS6fd3KCw9fwhsBV","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0154 && mkdir -p out && mv data/out/analysis.py out/ && rmdir data/out && ls data && ~/wp29/venv/bin/python out/analysis.py; cat out/exclusions.csv; wc -l out/analytic.csv"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3122,"cache_read_input_tokens":7903,"output_tokens":184,"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":3122},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXp4uE2QV8CP4o6tfehS","requestRef":"ca6436cb-21a9-4920-bc54-4a16ec3db96c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8f89d8f1-942f-46dd-83fc-89b6f342522b","timestamp":"2026-10-30T04:44:58.035Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0154/data","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd","version":"2.1.314","gitBranch":"HEAD"}34{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"8f89d8f1-942f-46dd-83fc-89b6f342522b","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd"}35{"type":"atis-latch","atis":"","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd"}36{"parentUuid":"8f89d8f1-942f-46dd-83fc-89b6f342522b","isSidechain":false,"promptId":"f037acb4-2173-4d94-b354-2fc9a8b4d1c9","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KaekQPeS6fd3KCw9fwhsBV","type":"tool_result","content":"life_expectancy_panel.csv\nREADME.md\n{\n \"task_id\": \"F1-conf-02\",\n \"n_countries_total\": 20,\n \"n_countries_balanced\": 15,\n \"n_countries_incomplete\": 5,\n \"balanced_countries\": [\n \"AUS\",\n \"AUT\",\n \"BEL\",\n \"CAN\",\n \"DEU\",\n \"ESP\",\n \"FRA\",\n \"GBR\",\n \"ITA\",\n \"JPN\",\n \"KOR\",\n \"NLD\",\n \"SWE\",\n \"TUR\",\n \"USA\"\n ],\n \"n_analysed\": 345,\n \"mean_2000_balanced\": 78.0603,\n \"mean_2022_balanced\": 81.5531,\n \"change_2000_2022_balanced\": 3.4928,\n \"mean_2022_all_available\": 78.692,\n \"n_countries_2022_all_available\": 20,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"TUR appears under two country names ['Turkey', 'Turkiye'] with 23 duplicated country-years (identical values) — an assembly artifact of the two extractions.\",\n \"handling\": \"Deduplicated by (iso3, year), keeping one row per year; values identical so no choice of value needed; name harmonised to 'Turkiye'. Counted as one country.\"\n },\n {\n \"issue\": \"CHE has an empty life_expectancy cell for 2007 (row present, value missing).\",\n \"handling\": \"Treated as missing (not imputed/interpolated); country therefore excluded from the balanced set.\"\n },\n {\n \"issue\": \"BGD has no rows for years 2000–2010 (11 years missing; only the later, wider extraction covers it).\",\n \"handling\": \"Country excluded from the balanced panel; its available rows are used only in the 2022 all-available baseline.\"\n },\n {\n \"issue\": \"ETH has no rows for years 2000–2010 (11 years missing; only the later, wider extraction covers it).\",\n \"handling\": \"Country excluded from the balanced panel; its available rows are used only in the 2022 all-available baseline.\"\n },\n {\n \"issue\": \"IND has no rows for years 2000–2010 (11 years missing; only the later, wider extraction covers it).\",\n \"handling\": \"Country excluded from the balanced panel; its available rows are used only in the 2022 all-available baseline.\"\n },\n {\n \"issue\": \"NGA has no rows for years 2000–2010 (11 years missing; only the later, wider extraction covers it).\",\n \"handling\": \"Country excluded from the balanced panel; its available rows are used only in the 2022 all-available baseline.\"\n }\n ],\n \"n_input_rows\": {\n \"life_expectancy_panel.csv\": 439\n },\n \"random_seed\": null\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1,\"Drop duplicate (iso3, year) rows (identical values)\",439,416,23,Panel assembled from two extractions; some countries appear twice under different names. iso3 identifies the country; one observation per country-year.\n2,Drop country-years with empty life_expectancy,416,415,1,Empty cell = no recorded value; no imputation allowed.\n3,Keep only countries with a recorded value in every year 2000-2022,415,345,70,\"Balanced-panel estimand; excluded incomplete countries: BGD, CHE, ETH, IND, NGA.\"\n 346 out/analytic.csv","is_error":false}]},"uuid":"687d12d7-4674-420f-8ffe-062e05793551","timestamp":"2026-10-30T04:44:58.343Z","toolUseResult":{"stdout":"life_expectancy_panel.csv\nREADME.md\n{\n \"task_id\": \"F1-conf-02\",\n \"n_countries_total\": 20,\n \"n_countries_balanced\": 15,\n \"n_countries_incomplete\": 5,\n \"balanced_countries\": [\n \"AUS\",\n \"AUT\",\n \"BEL\",\n \"CAN\",\n \"DEU\",\n \"ESP\",\n \"FRA\",\n \"GBR\",\n \"ITA\",\n \"JPN\",\n \"KOR\",\n \"NLD\",\n \"SWE\",\n \"TUR\",\n \"USA\"\n ],\n \"n_analysed\": 345,\n \"mean_2000_balanced\": 78.0603,\n \"mean_2022_balanced\": 81.5531,\n \"change_2000_2022_balanced\": 3.4928,\n \"mean_2022_all_available\": 78.692,\n \"n_countries_2022_all_available\": 20,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"TUR appears under two country names ['Turkey', 'Turkiye'] with 23 duplicated country-years (identical values) — an assembly artifact of the two extractions.\",\n \"handling\": \"Deduplicated by (iso3, year), keeping one row per year; values identical so no choice of value needed; name harmonised to 'Turkiye'. Counted as one country.\"\n },\n {\n \"issue\": \"CHE has an empty life_expectancy cell for 2007 (row present, value missing).\",\n \"handling\": \"Treated as missing (not imputed/interpolated); country therefore excluded from the balanced set.\"\n },\n {\n \"issue\": \"BGD has no rows for years 2000–2010 (11 years missing; only the later, wider extraction covers it).\",\n \"handling\": \"Country excluded from the balanced panel; its available rows are used only in the 2022 all-available baseline.\"\n },\n {\n \"issue\": \"ETH has no rows for years 2000–2010 (11 years missing; only the later, wider extraction covers it).\",\n \"handling\": \"Country excluded from the balanced panel; its available rows are used only in the 2022 all-available baseline.\"\n },\n {\n \"issue\": \"IND has no rows for years 2000–2010 (11 years missing; only the later, wider extraction covers it).\",\n \"handling\": \"Country excluded from the balanced panel; its available rows are used only in the 2022 all-available baseline.\"\n },\n {\n \"issue\": \"NGA has no rows for years 2000–2010 (11 years missing; only the later, wider extraction covers it).\",\n \"handling\": \"Country excluded from the balanced panel; its available rows are used only in the 2022 all-available baseline.\"\n }\n ],\n \"n_input_rows\": {\n \"life_expectancy_panel.csv\": 439\n },\n \"random_seed\": null\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1,\"Drop duplicate (iso3, year) rows (identical values)\",439,416,23,Panel assembled from two extractions; some countries appear twice under different names. iso3 identifies the country; one observation per country-year.\n2,Drop country-years with empty life_expectancy,416,415,1,Empty cell = no recorded value; no imputation allowed.\n3,Keep only countries with a recorded value in every year 2000-2022,415,345,70,\"Balanced-panel estimand; excluded incomplete countries: BGD, CHE, ETH, IND, NGA.\"\n 346 out/analytic.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"8f89d8f1-942f-46dd-83fc-89b6f342522b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0154","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd","version":"2.1.314","gitBranch":"HEAD"}37{"type":"api-request-blob","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd","hash":"608bf834661d824b98d59ced222165b2a4131442e9405753021e19d66766dc17","message":{"role":"assistant","content":[{"type":"text","text":"Oops, the shell was in data/; I created data/out by mistake. Moving it."},{"type":"tool_use","id":"toolu_01KaekQPeS6fd3KCw9fwhsBV","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0154 && mkdir -p out && mv data/out/analysis.py out/ && rmdir data/out && ls data && ~/wp29/venv/bin/python out/analysis.py; cat out/exclusions.csv; wc -l out/analytic.csv"}}]}}38{"type":"api-request","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd","id":"84195cdc-907a-48c5-9a13-9e28513727fb","timestamp":"2026-10-30T04:44:58.346Z","version":"2.1.314","querySource":"sdk","shapeHash":"bfd8cb89d4bc7816fce8f6e316bd34b9efce3d2fb178d4d9fe610b2de8b519a5","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":"ca6436cb-21a9-4920-bc54-4a16ec3db96c","keep":10,"tail":["608bf834661d824b98d59ced222165b2a4131442e9405753021e19d66766dc17","b29f7475a31d66a6e3b3256f3ab2c2cbc9a2b662354528f2e9de3613d271a69e"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"01faa5a0f91fb3d028388813818dbd5e615a95525b751e636efb121801317b1c"}39{"parentUuid":"687d12d7-4674-420f-8ffe-062e05793551","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp56E4x2SDv1Gzpjr9x","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":1370,"cache_read_input_tokens":11025,"output_tokens":613,"output_tokens_details":{"thinking_tokens":155},"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":1370},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1373,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXp55yBpNLocUMGEZV44","requestRef":"84195cdc-907a-48c5-9a13-9e28513727fb","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0c0a3c1a-9a32-45c3-b5a1-9f8c943c0f46","timestamp":"2026-10-30T04:45:00.833Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0154","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd","version":"2.1.314","gitBranch":"HEAD"}40{"parentUuid":"0c0a3c1a-9a32-45c3-b5a1-9f8c943c0f46","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp56E4x2SDv1Gzpjr9x","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01PWo5YzTmPqepriSTUtuu5s","name":"Bash","input":{"command":"sed -i '' 's/ missing; only the later, wider extraction covers it)/ missing from the file; the country is present only from 2011)/' out/analysis.py && ~/wp29/venv/bin/python out/analysis.py >/dev/null && grep BGD out/results.json; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\na=pd.read_csv('out/analytic.csv'); w=a.pivot(index='iso3',columns='year',values='life_expectancy')\nch=w[2022]-w[2000]; print(ch.sort_values().round(3).to_dict()); print('sd',ch.std(),'se',ch.std()/len(ch)**.5, 'min/max')\nfrom scipy import stats; print(stats.t.ppf(.975,14)*ch.std()/15**.5)\nprint(w.mean().round(3).to_dict())\nd=pd.read_csv('data/life_expectancy_panel.csv').drop_duplicates(['iso3','year'])\nprint('naive2000',d[d.year==2000].life_expectancy.mean(), d[d.year==2000].life_expectancy.count())\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1370,"cache_read_input_tokens":11025,"output_tokens":613,"output_tokens_details":{"thinking_tokens":155},"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":1370},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXp55yBpNLocUMGEZV44","requestRef":"84195cdc-907a-48c5-9a13-9e28513727fb","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"33e82889-2b42-426a-9654-127b923e8b23","timestamp":"2026-10-30T04:45:04.380Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0154","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd","version":"2.1.314","gitBranch":"HEAD"}41{"parentUuid":"33e82889-2b42-426a-9654-127b923e8b23","isSidechain":false,"promptId":"f037acb4-2173-4d94-b354-2fc9a8b4d1c9","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PWo5YzTmPqepriSTUtuu5s","type":"tool_result","content":" \"issue\": \"BGD has no rows for years 2000–2010 (11 years missing from the file; the country is present only from 2011).\",\n{'USA': 0.797, 'CAN': 1.928, 'DEU': 2.681, 'JPN': 2.92, 'ITA': 2.922, 'FRA': 3.073, 'AUT': 3.168, 'GBR': 3.27, 'SWE': 3.415, 'NLD': 3.627, 'AUS': 3.966, 'BEL': 4.027, 'ESP': 4.168, 'TUR': 5.66, 'KOR': 6.77}\nsd 1.4088058874704616 se 0.3637521160141372 min/max\n0.7801706961287351\n{2000: 78.06, 2001: 78.365, 2002: 78.519, 2003: 78.651, 2004: 79.119, 2005: 79.292, 2006: 79.671, 2007: 79.93, 2008: 80.088, 2009: 80.351, 2010: 80.541, 2011: 80.85, 2012: 80.942, 2013: 81.192, 2014: 81.524, 2015: 81.364, 2016: 81.602, 2017: 81.665, 2018: 81.783, 2019: 82.073, 2020: 81.32, 2021: 81.447, 2022: 81.553}\nnaive2000 78.1615 16","is_error":false}]},"uuid":"08cae184-18a8-4b9e-a55e-8d7319d51ad0","timestamp":"2026-10-30T04:45:05.320Z","toolUseResult":{"stdout":" \"issue\": \"BGD has no rows for years 2000–2010 (11 years missing from the file; the country is present only from 2011).\",\n{'USA': 0.797, 'CAN': 1.928, 'DEU': 2.681, 'JPN': 2.92, 'ITA': 2.922, 'FRA': 3.073, 'AUT': 3.168, 'GBR': 3.27, 'SWE': 3.415, 'NLD': 3.627, 'AUS': 3.966, 'BEL': 4.027, 'ESP': 4.168, 'TUR': 5.66, 'KOR': 6.77}\nsd 1.4088058874704616 se 0.3637521160141372 min/max\n0.7801706961287351\n{2000: 78.06, 2001: 78.365, 2002: 78.519, 2003: 78.651, 2004: 79.119, 2005: 79.292, 2006: 79.671, 2007: 79.93, 2008: 80.088, 2009: 80.351, 2010: 80.541, 2011: 80.85, 2012: 80.942, 2013: 81.192, 2014: 81.524, 2015: 81.364, 2016: 81.602, 2017: 81.665, 2018: 81.783, 2019: 82.073, 2020: 81.32, 2021: 81.447, 2022: 81.553}\nnaive2000 78.1615 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-i '' 's/ missing; only the later, wider extraction covers it)/ missing from the file; the country is present only from 2011)/' out/analysis.py && ~/wp29/venv/bin/python out/analysis.py >/dev/null && grep BGD out/results.json; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\na=pd.read_csv('out/analytic.csv'); w=a.pivot(index='iso3',columns='year',values='life_expectancy')\nch=w[2022]-w[2000]; print(ch.sort_values().round(3).to_dict()); print('sd',ch.std(),'se',ch.std()/len(ch)**.5, 'min/max')\nfrom scipy import stats; print(stats.t.ppf(.975,14)*ch.std()/15**.5)\nprint(w.mean().round(3).to_dict())\nd=pd.read_csv('data/life_expectancy_panel.csv').drop_duplicates(['iso3','year'])\nprint('naive2000',d[d.year==2000].life_expectancy.mean(), 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- <<'EOF'\np='out/analysis.py'; s=open(p).read()\ns=s.replace(''' \"feasibility\": \"complete\",''',''' \"descriptive_country_change_sd\": round(float(chg.std(ddof=1)), 4),\n \"descriptive_country_change_min\": {chg.idxmin(): round(float(chg.min()), 3)},\n \"descriptive_country_change_max\": {chg.idxmax(): round(float(chg.max()), 3)},\n \"descriptive_change_t95_halfwidth_across_countries\": round(float(t95 * chg.std(ddof=1) / len(chg) ** 0.5), 4),\n \"descriptive_mean_2000_all_available\": round(float(d.loc[d.year == 2000, \"life_expectancy\"].mean()), 4),\n \"descriptive_n_countries_2000_all_available\": int((d.year == 2000).sum()),\n \"feasibility\": \"complete\",''')\ns=s.replace('res = {','''w = an.pivot(index=\"iso3\", columns=\"year\", values=\"life_expectancy\")\nchg = w[2022] - w[2000]\nfrom scipy import stats\nt95 = stats.t.ppf(0.975, len(chg) - 1)\nres = {''',1)\nopen(p,'w').write(s)\nEOF\n~/wp29/venv/bin/python out/analysis.py | grep descr; md5 out/results.json; 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> out/report.md <<'EOF'\n# F1-conf-02 — Balanced-panel mean life expectancy, 2000–2022\n\n## Question\nHow did life expectancy at birth (WDI `SP.DYN.LE00.IN`) change from 2000 to 2022? The estimand is the unweighted mean taken over a **fixed balanced set** of countries, i.e. those with a recorded value in every year from 2000 to 2022, each counted once. The naive 2022 mean over all available countries is also reported, but only as a descriptive baseline.\n\n## Data and exclusions\nInput: `data/life_expectancy_panel.csv`, which has 439 data rows, 20 distinct iso3 codes and covers 2000–2022. I checked it for duplicates, empty cells, coverage gaps, name inconsistencies, stray whitespace and implausible year-to-year jumps. I found these problems:\n\n1. **TUR appears twice.** It is listed once as \"Turkey\" and once as \"Turkiye\", with all 23 years duplicated and identical values. This is an artifact of how the file was assembled from two extractions. I kept one row per (iso3, year) and used the name \"Turkiye\", so TUR counts as one country. Since the values are identical, no value had to be chosen.\n2. **CHE 2007 is empty.** The row is present but has no value. It is treated as missing and not imputed, so CHE drops out of the balanced set.\n3. **BGD, ETH, IND and NGA start in 2011.** The file has no rows for 2000–2010 for these countries, so they are excluded from the balanced set.\n\nThe jumps I flagged (falls in 2020–2021 and rebounds in 2022 for several countries) match the COVID-19 period, so I did not treat them as errors.\n\nExclusion ledger (`out/exclusions.csv`):\n\n| step | rule | before | after | removed |\n|---|---|---|---|---|\n| 1 | drop duplicate (iso3, year) rows (identical values) | 439 | 416 | 23 |\n| 2 | drop country-years with empty value | 416 | 415 | 1 |\n| 3 | keep only countries complete for 2000–2022 | 415 | 345 | 70 |\n\nThe balanced set has 15 countries: AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA. That gives 15 × 23 = 345 country-years (`out/analytic.csv`). Five countries are incomplete: BGD, CHE, ETH, IND, NGA.\n\n## Method\nFor each year I took the simple mean of life expectancy over the 15 balanced countries. The change is the 2022 balanced mean minus the 2000 balanced mean. The 2022 baseline is the mean over every distinct iso3 that has a recorded 2022 value (20 countries, after deduplication). No weighting, imputation or interpolation was used, and nothing is random, so no seed is needed. `out/analysis.py` regenerates all outputs deterministically.\n\n## Results\n- Balanced mean in 2000: **78.0603 years**\n- Balanced mean in 2022: **81.5531 years**\n- Change from 2000 to 2022 (balanced): **+3.4928 years**\n- Baseline 2022 mean over all available countries (20): **78.692 years**. For comparison, the 2000 mean over all available countries (16) is 78.1615.\n\nThe naive 2022 baseline is about 2.9 years lower than the balanced 2022 mean. This is because it adds four lower-life-expectancy countries (BGD, ETH, IND, NGA) plus CHE. Composition changes therefore have a large effect: comparing naive means across years would mostly reflect which countries are in the sample, not a change in life expectancy.\n\nAcross the balanced countries, changes range from +0.797 years (USA) to +6.77 years (KOR), with a standard deviation of 1.4088. The balanced mean peaked in 2019 at about 82.07 (by year: 2020 = 81.32, 2021 = 81.45, 2022 = 81.55), so 2022 had not yet returned to its pre-pandemic level.\n\n## Uncertainty\nThe estimand is a descriptive mean over a fixed, non-random set of countries. It is not an estimate for a wider population, so I report no sampling standard error for it. As a purely descriptive measure of spread, I also computed a t-based 95% half-width across the 15 country-level changes: ±0.78 years. Read it as heterogeneity across countries, not as inference about countries outside the set. WDI values are themselves modelled estimates (from UN WPP and national vital statistics) and are revised between releases, so they carry measurement and revision uncertainty that is not quantified here.\n\n## Limitations and open issues\n- The balanced set is mostly high-income countries; all four low- and lower-middle-income countries drop out because of coverage. The result therefore describes that group, not the 20 countries listed in the README.\n- CHE is excluded because of a single empty cell (2007). Per the task rules it is not imputed. Its absence probably comes from the file assembly rather than from the World Bank source, but the task does not allow looking it up.\n- The coverage gaps for BGD, ETH, IND and NGA before 2011 also look like extraction artifacts (the \"narrower extraction\"), not true missingness at the source. They are treated as missing as the task requires.\n- The means are unweighted (no population weights), as the task specifies.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"15 of the 20 countries have a recorded life expectancy value in every year 2000-2022 (345 country-years); BGD, CHE, ETH, IND and NGA are incomplete.\",\"results_keys\":[\"n_countries_total\",\"n_countries_balanced\",\"n_countries_incomplete\",\"balanced_countries\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"CHE is excluded solely due to one empty cell (2007); the 4 others lack 2000-2010 rows in the file, likely an assembly artifact.\"},\n {\"id\":\"C2\",\"text\":\"Balanced-panel mean life expectancy rose from 78.06 years in 2000 to 81.55 years in 2022, an increase of 3.49 years.\",\"results_keys\":[\"mean_2000_balanced\",\"mean_2022_balanced\",\"change_2000_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Unweighted mean over a mostly high-income set; descriptive, no sampling inference.\"},\n {\"id\":\"C3\",\"text\":\"The naive 2022 mean over all 20 available countries (78.69) is about 2.9 years below the balanced 2022 mean, because of composition differences.\",\"results_keys\":[\"mean_2022_all_available\",\"mean_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Baseline only; not the estimand.\"},\n {\"id\":\"C4\",\"text\":\"TUR was duplicated under two names with identical values and was deduplicated to one country.\",\"results_keys\":[\"data_issues\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"Values identical, so deduplication does not affect results.\"}\n]\nEOF\nls out"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":837,"cache_read_input_tokens":13448,"output_tokens":2479,"output_tokens_details":{"thinking_tokens":39},"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":837},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXp6CLhouFnTBhhqz8Hb","requestRef":"7bfd4a8a-679e-44e6-b0e4-ce8856e3f980","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"725305e0-277e-4b91-93b2-e562065a45f9","timestamp":"2026-10-30T04:45:35.032Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0154","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd","version":"2.1.314","gitBranch":"HEAD"}51{"parentUuid":"725305e0-277e-4b91-93b2-e562065a45f9","isSidechain":false,"promptId":"f037acb4-2173-4d94-b354-2fc9a8b4d1c9","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0129BUY8oBv52Z4TXW8kcaoj","type":"tool_result","content":"analysis.py\nanalytic.csv\nclaims.json\nexclusions.csv\nreport.md\nresults.json","is_error":false}]},"uuid":"a0d580e7-4b7c-4600-8241-a58b87a1948c","timestamp":"2026-10-30T04:45:35.062Z","toolUseResult":{"stdout":"analysis.py\nanalytic.csv\nclaims.json\nexclusions.csv\nreport.md\nresults.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"725305e0-277e-4b91-93b2-e562065a45f9","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0154","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd","version":"2.1.314","gitBranch":"HEAD"}52{"type":"api-request-blob","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd","hash":"8f451fde29c18b504dd0ae71f483c2d9a2771bdda7bf98f113028cebcb2bd993","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQS2QYKEAgTGAI4AUIIdGhpbmtpbmcSDMvd6PcXzk5MpxTqShoMmhv2ejfDhdcNuirdIjDFWDjZ3V38aqeW2scxsP/qMhFpeFbQekb5tMy/hnzkWhQ/LGuZBzFSjK71AEU+TZIq9gXy+sRwKiHK78xdu25rmxokfnVZ0LJhx8ZFBp7NSF91Fs30bGe52s0uyjyn83IzmULLtFS3Y/HzRLZUJ+kXxE/WjVaEZWSiSPgyR2z42uMyGxgYaaSDTNP7RqNS1MWiMZrL7GLKBHIglYlcCYUj4qZSjJbyClvDuDF5b0frUdHROcf7CR7BgI4aPdhMLKAFjb3+syjOz0zUpfGJScQMTkcUO918EZjaKtDHhMiTKNEC+F6GvGn+cZnR7LfhJ++RcjA6QDZjK4IlrB6qjjQMMp9qSDJabSx7rguIJfPqdcS6POLDLE8o1zEAgCBOfvJRrDqkcetjJYeRtX3iOgIgaPgKRrqgnPaH4JjW5DzTXPFD7ic2oWq6VjfFaxPKaFUleuPdAk+vBIiq5rTvxD2SCdwgl9UbxVPQCf46M3VSavVqRisgirpKYYZie6iJq64fbCky2juyk+m3EmZ2sKwX5ME4VCmh25XWRnVEkbsi1dvCTTSvHXro5N9RLfQaohFnONQO7s6AiDqFa9TV9auPYQkkz7Zy76KSEqdr3k6FaejDFrRkzWgWCnN+eNhqhT71Xme1Yw9BAKear19DERux8+PH7nXJhgBXlA4hNLHUUywZIbL4wugwr1dF08oqGqFdbyzUvuzfIW2P/YgqOYvL5cPFztMHkoSpNDcCejH3JvcOi/0L9RLvxciVRbnCS6P4+9zgKykKshD8xKVWTwipl9itqzWRuXa2zZLhkOmD6p+jw2dbQcw4+p55e9/sZQTAQFRHEYYSG1ke1ys5VmhDDXfqvQRv1Hw0KJv30GT4N+et1++ZaolGcJqYPtXe/bLPMJW/cJ545VvkyJ3N77fH+qHBcyhZLHCwCvDnip0pfTINbpkzwVGfe4U+fwg47/JGuE6s/TPM6aBKbQh1wSdroSKyiO+XalhMYSpIP/IKUr4tH4Eonq1oxeqfiEgL98Qb45hF3GKdpSqtEXu9FXiq4BEo6qg9tyT04kGeleYbDWnbWMiF2ZgH0RgB"},{"type":"tool_use","id":"toolu_0129BUY8oBv52Z4TXW8kcaoj","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F1-conf-02 — Balanced-panel mean life expectancy, 2000–2022\n\n## Question\nHow did life expectancy at birth (WDI `SP.DYN.LE00.IN`) change from 2000 to 2022? The estimand is the unweighted mean taken over a **fixed balanced set** of countries, i.e. those with a recorded value in every year from 2000 to 2022, each counted once. The naive 2022 mean over all available countries is also reported, but only as a descriptive baseline.\n\n## Data and exclusions\nInput: `data/life_expectancy_panel.csv`, which has 439 data rows, 20 distinct iso3 codes and covers 2000–2022. I checked it for duplicates, empty cells, coverage gaps, name inconsistencies, stray whitespace and implausible year-to-year jumps. I found these problems:\n\n1. **TUR appears twice.** It is listed once as \"Turkey\" and once as \"Turkiye\", with all 23 years duplicated and identical values. This is an artifact of how the file was assembled from two extractions. I kept one row per (iso3, year) and used the name \"Turkiye\", so TUR counts as one country. Since the values are identical, no value had to be chosen.\n2. **CHE 2007 is empty.** The row is present but has no value. It is treated as missing and not imputed, so CHE drops out of the balanced set.\n3. **BGD, ETH, IND and NGA start in 2011.** The file has no rows for 2000–2010 for these countries, so they are excluded from the balanced set.\n\nThe jumps I flagged (falls in 2020–2021 and rebounds in 2022 for several countries) match the COVID-19 period, so I did not treat them as errors.\n\nExclusion ledger (`out/exclusions.csv`):\n\n| step | rule | before | after | removed |\n|---|---|---|---|---|\n| 1 | drop duplicate (iso3, year) rows (identical values) | 439 | 416 | 23 |\n| 2 | drop country-years with empty value | 416 | 415 | 1 |\n| 3 | keep only countries complete for 2000–2022 | 415 | 345 | 70 |\n\nThe balanced set has 15 countries: AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA. That gives 15 × 23 = 345 country-years (`out/analytic.csv`). Five countries are incomplete: BGD, CHE, ETH, IND, NGA.\n\n## Method\nFor each year I took the simple mean of life expectancy over the 15 balanced countries. The change is the 2022 balanced mean minus the 2000 balanced mean. The 2022 baseline is the mean over every distinct iso3 that has a recorded 2022 value (20 countries, after deduplication). No weighting, imputation or interpolation was used, and nothing is random, so no seed is needed. `out/analysis.py` regenerates all outputs deterministically.\n\n## Results\n- Balanced mean in 2000: **78.0603 years**\n- Balanced mean in 2022: **81.5531 years**\n- Change from 2000 to 2022 (balanced): **+3.4928 years**\n- Baseline 2022 mean over all available countries (20): **78.692 years**. For comparison, the 2000 mean over all available countries (16) is 78.1615.\n\nThe naive 2022 baseline is about 2.9 years lower than the balanced 2022 mean. This is because it adds four lower-life-expectancy countries (BGD, ETH, IND, NGA) plus CHE. Composition changes therefore have a large effect: comparing naive means across years would mostly reflect which countries are in the sample, not a change in life expectancy.\n\nAcross the balanced countries, changes range from +0.797 years (USA) to +6.77 years (KOR), with a standard deviation of 1.4088. The balanced mean peaked in 2019 at about 82.07 (by year: 2020 = 81.32, 2021 = 81.45, 2022 = 81.55), so 2022 had not yet returned to its pre-pandemic level.\n\n## Uncertainty\nThe estimand is a descriptive mean over a fixed, non-random set of countries. It is not an estimate for a wider population, so I report no sampling standard error for it. As a purely descriptive measure of spread, I also computed a t-based 95% half-width across the 15 country-level changes: ±0.78 years. Read it as heterogeneity across countries, not as inference about countries outside the set. WDI values are themselves modelled estimates (from UN WPP and national vital statistics) and are revised between releases, so they carry measurement and revision uncertainty that is not quantified here.\n\n## Limitations and open issues\n- The balanced set is mostly high-income countries; all four low- and lower-middle-income countries drop out because of coverage. The result therefore describes that group, not the 20 countries listed in the README.\n- CHE is excluded because of a single empty cell (2007). Per the task rules it is not imputed. Its absence probably comes from the file assembly rather than from the World Bank source, but the task does not allow looking it up.\n- The coverage gaps for BGD, ETH, IND and NGA before 2011 also look like extraction artifacts (the \"narrower extraction\"), not true missingness at the source. They are treated as missing as the task requires.\n- The means are unweighted (no population weights), as the task specifies.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"15 of the 20 countries have a recorded life expectancy value in every year 2000-2022 (345 country-years); BGD, CHE, ETH, IND and NGA are incomplete.\",\"results_keys\":[\"n_countries_total\",\"n_countries_balanced\",\"n_countries_incomplete\",\"balanced_countries\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"CHE is excluded solely due to one empty cell (2007); the 4 others lack 2000-2010 rows in the file, likely an assembly artifact.\"},\n {\"id\":\"C2\",\"text\":\"Balanced-panel mean life expectancy rose from 78.06 years in 2000 to 81.55 years in 2022, an increase of 3.49 years.\",\"results_keys\":[\"mean_2000_balanced\",\"mean_2022_balanced\",\"change_2000_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Unweighted mean over a mostly high-income set; descriptive, no sampling inference.\"},\n {\"id\":\"C3\",\"text\":\"The naive 2022 mean over all 20 available countries (78.69) is about 2.9 years below the balanced 2022 mean, because of composition differences.\",\"results_keys\":[\"mean_2022_all_available\",\"mean_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Baseline only; not the estimand.\"},\n {\"id\":\"C4\",\"text\":\"TUR was duplicated under two names with identical values and was deduplicated to one country.\",\"results_keys\":[\"data_issues\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"Values identical, so deduplication does not affect results.\"}\n]\nEOF\nls out"}}]}}53{"type":"api-request","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd","id":"b2de871f-ad6e-4559-b89a-fba1fc9fd53b","timestamp":"2026-10-30T04:45:35.065Z","version":"2.1.314","querySource":"sdk","shapeHash":"bfd8cb89d4bc7816fce8f6e316bd34b9efce3d2fb178d4d9fe610b2de8b519a5","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":"7bfd4a8a-679e-44e6-b0e4-ce8856e3f980","keep":16,"tail":["8f451fde29c18b504dd0ae71f483c2d9a2771bdda7bf98f113028cebcb2bd993","342560decdd8848e9c380e496ecbbd262ebfc5e7059543e298b7b08f7b24fed2"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",17,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"c19d812c5c06e756ac53113412e6512cc76c4ee977cc963d878cb86a517acb98"}54{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"a0d580e7-4b7c-4600-8241-a58b87a1948c","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd"}55{"type":"atis-latch","atis":"","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd"}56{"parentUuid":"a0d580e7-4b7c-4600-8241-a58b87a1948c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp7oEhBY7JNrsdK67Ji","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2517,"cache_read_input_tokens":14285,"output_tokens":949,"output_tokens_details":{"thinking_tokens":61},"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":2517},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":7124,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXp7nyKLyAQDigZYUm91","requestRef":"b2de871f-ad6e-4559-b89a-fba1fc9fd53b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"393b094a-bae8-4304-a01a-473003fac120","timestamp":"2026-10-30T04:45:43.375Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0154","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd","version":"2.1.314","gitBranch":"HEAD"}57{"parentUuid":"393b094a-bae8-4304-a01a-473003fac120","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp7oEhBY7JNrsdK67Ji","type":"message","role":"assistant","content":[{"type":"text","text":"I computed the balanced-panel means. Fifteen of the 20 countries have a recorded value in every year from 2000 to 2022 (345 country-years). Their unweighted mean life expectancy rose from **78.06 years in 2000 to 81.55 in 2022, an increase of 3.49 years**. I ran `out/analysis.py` twice and got identical outputs.\n\n- **Balanced set:** AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA.\n- **Naive 2022 baseline:** the mean over all 20 countries with a 2022 value is 78.69 years, about 2.9 years below the balanced figure. Most of that gap comes from including four countries with lower life expectancy, so comparing naive means across years would mostly reflect which countries are in the sample.\n- **Spread across countries:** changes range from +0.80 years (USA) to +6.77 (KOR). The balanced mean peaked in 2019 at about 82.07, so 2022 was still below its pre-pandemic level.\n\n**Data problems found and how I handled them:**\n- **Turkey duplicated:** all 23 years of TUR appear twice, under \"Turkey\" and \"Turkiye\", with identical values. I kept one row per country-year, so this doesn't affect any result.\n- **Switzerland:** one empty cell (2007). Under the no-imputation rule, that alone drops it from the balanced set.\n- **BGD, ETH, IND, NGA:** the file has no rows for 2000–2010, so all four are excluded from the balanced set.\n- **2020–2021 drops:** several countries fall sharply and rebound in 2022. This matches the COVID-19 period, so I kept these values as real.\n\n**Limitations:**\n- With all four lower-income countries excluded, the result describes a mostly high-income group, not the 20 countries named in the data README.\n- The Swiss gap and the missing 2000–2010 rows look like errors from how the file was put together rather than real gaps at the World Bank. The rules don't allow looking them up, so they stay missing.\n- There is no sampling standard error, because the set of countries is fixed rather than sampled. The ±0.78-year spread across country changes describes how much countries differ, not uncertainty about a wider population.\n\nI also added a few extra descriptive keys to `results.json` (the country-change spread and the naive 2000 mean over 16 countries) beyond those the task requires.\n\nFiles are in `out/`:\n- `analysis.py`\n- `results.json`\n- `analytic.csv`\n- `exclusions.csv`\n- `report.md`\n- `claims.json`"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2517,"cache_read_input_tokens":14285,"output_tokens":949,"output_tokens_details":{"thinking_tokens":61},"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":2517},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXp7nyKLyAQDigZYUm91","requestRef":"b2de871f-ad6e-4559-b89a-fba1fc9fd53b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2c26b5e4-2059-44e0-bd65-9f3c641ff737","timestamp":"2026-10-30T04:45:44.551Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0154","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd","version":"2.1.314","gitBranch":"HEAD"}58{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"2c26b5e4-2059-44e0-bd65-9f3c641ff737","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd"}59{"type":"cost-state","sessionId":"5fc116bc-7a4a-4757-ae66-de083633a7fd","totalCostUSD":0.2562374,"totalAPIDuration":77143,"totalAPIDurationWithoutRetries":77139,"totalToolDuration":3559,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":80888,"startTime":1793335463672,"modelUsage":{"claude-opus-5-5":{"inputTokens":20,"outputTokens":8246,"thinkingTokens":739,"cacheReadInputTokens":73737,"cacheCreationInputTokens":15298,"cacheCreationEphemeral5mInputTokens":15298,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.2562374}},"hasUnknownModelCost":false}60