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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`. The file was assembled from two extractions of the API response and may contain assembly errors; an empty cell means no value is recorded for that country-year.\n\n## Information-availability rules\n- Use only `data/life_expectancy_panel.csv`. 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":"19146365-94b2-4c6d-ae55-74fae1af0f6e","timestamp":"2026-10-30T04:44:29.427Z","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. 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`. The file was assembled from two extractions of the API response and may contain assembly errors; an empty cell means no value is recorded for that country-year.\n\n## Information-availability rules\n- Use only `data/life_expectancy_panel.csv`. 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. 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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":"fe85b8f8-d7e3-469c-98ec-936c385e6e4a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0153","sessionId":"1c433fe8-2bfa-4f11-9528-a1f0dd584bf3","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"1c433fe8-2bfa-4f11-9528-a1f0dd584bf3","hash":"8e32877d43f46d45ca86b335a12b428bd736177cc4b369b3f4c3f407b975db8e","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_0176TGeqmjwcWydsz8r1zYY2","name":"Bash","input":{"command":"cat TASK.md; ls -R data | head -50"}}]}}15{"type":"api-request","sessionId":"1c433fe8-2bfa-4f11-9528-a1f0dd584bf3","id":"20e86773-9870-427e-bca8-4578034ab924","timestamp":"2026-10-30T04:44:29.431Z","version":"2.1.314","querySource":"sdk","shapeHash":"5ae44f7f11cc66e598e3b8e94d55a2e090d33af12721f21fb60e43bc34a365a1","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":"4569000d-2bcd-47fb-bd13-6be2a3f633b1","keep":2,"tail":["8e32877d43f46d45ca86b335a12b428bd736177cc4b369b3f4c3f407b975db8e","2d3cf40f46763a83c7367c26c31cf6f5a05876af4324ccc3de4e6a172cceddc6"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"5a4ea7bc637cc12aa5a624fa6ae0e973151e928320d33b59afbfd766e76be686"}16{"parentUuid":"19146365-94b2-4c6d-ae55-74fae1af0f6e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp2x7r82g1Nq9vizsK4","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015GTNDGSrgYqPsD6afaE5Ub","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',dtype=str,keep_default_na=False)\nprint(d.describe())\nprint(d.groupby('iso3').size())\nprint(d[d.duplicated(['iso3','year'],keep=False)].sort_values(['iso3','year']))\nprint(d[d.life_expectancy==''])\nprint(d.year.value_counts().sort_index())\nprint(d.groupby('iso3').country_name.unique())\nx=pd.to_numeric(d.life_expectancy,errors='coerce');print(x.describe()); print(d[x.isna()&(d.life_expectancy!='')])\nprint(d[(x<35)|(x>90)])\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1689,"cache_read_input_tokens":2234,"output_tokens":333,"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":1689},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXp2wpkDbY6TtTwYcZVq","requestRef":"20e86773-9870-427e-bca8-4578034ab924","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"835dbbd3-06dc-4805-bc92-f4d2c5272279","timestamp":"2026-10-30T04:44:32.679Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0153","sessionId":"1c433fe8-2bfa-4f11-9528-a1f0dd584bf3","version":"2.1.314","gitBranch":"HEAD"}17{"parentUuid":"835dbbd3-06dc-4805-bc92-f4d2c5272279","isSidechain":false,"promptId":"289c70d6-9993-447c-b1d2-3cbdbfc94a12","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015GTNDGSrgYqPsD6afaE5Ub","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\n country_name iso3 year life_expectancy\ncount 439 439 439 439\nunique 21 20 23 391\ntop Australia TUR 2011 77.037\nfreq 23 46 21 3\niso3\nAUS 23\nAUT 23\nBEL 23\nBGD 12\nCAN 23\nCHE 23\nDEU 23\nESP 23\nETH 12\nFRA 23\nGBR 23\nIND 12\nITA 23\nJPN 23\nKOR 23\nNGA 12\nNLD 23\nSWE 23\nTUR 46\nUSA 23\ndtype: int64\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 \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\ncount 438.000000\nmean 78.466521\nstd 6.073816\nmin 51.490000\n25% 77.738000\n50% 80.517500\n75% 81.803500\nmax 84.560000\nName: life_expectancy, dtype: float64\nEmpty DataFrame\nColumns: [country_name, iso3, year, life_expectancy]\nIndex: []\nEmpty DataFrame\nColumns: [country_name, iso3, year, life_expectancy]\nIndex: []","is_error":false}]},"uuid":"f60ce3c9-d429-4a17-9791-64bf7e272dc6","timestamp":"2026-10-30T04:44:33.002Z","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\n country_name iso3 year life_expectancy\ncount 439 439 439 439\nunique 21 20 23 391\ntop Australia TUR 2011 77.037\nfreq 23 46 21 3\niso3\nAUS 23\nAUT 23\nBEL 23\nBGD 12\nCAN 23\nCHE 23\nDEU 23\nESP 23\nETH 12\nFRA 23\nGBR 23\nIND 12\nITA 23\nJPN 23\nKOR 23\nNGA 12\nNLD 23\nSWE 23\nTUR 46\nUSA 23\ndtype: int64\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 \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\ncount 438.000000\nmean 78.466521\nstd 6.073816\nmin 51.490000\n25% 77.738000\n50% 80.517500\n75% 81.803500\nmax 84.560000\nName: life_expectancy, dtype: float64\nEmpty DataFrame\nColumns: [country_name, iso3, year, life_expectancy]\nIndex: []\nEmpty DataFrame\nColumns: [country_name, iso3, year, life_expectancy]\nIndex: 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pd\nd=pd.read_csv('life_expectancy_panel.csv',dtype=str,keep_default_na=False)\nprint(d.describe())\nprint(d.groupby('iso3').size())\nprint(d[d.duplicated(['iso3','year'],keep=False)].sort_values(['iso3','year']))\nprint(d[d.life_expectancy==''])\nprint(d.year.value_counts().sort_index())\nprint(d.groupby('iso3').country_name.unique())\nx=pd.to_numeric(d.life_expectancy,errors='coerce');print(x.describe()); print(d[x.isna()&(d.life_expectancy!='')])\nprint(d[(x<35)|(x>90)])\n\""}}]}}19{"type":"api-request","sessionId":"1c433fe8-2bfa-4f11-9528-a1f0dd584bf3","id":"a1c5a596-605a-45ce-93cb-23250c27f69f","timestamp":"2026-10-30T04:44:33.005Z","version":"2.1.314","querySource":"sdk","shapeHash":"5ae44f7f11cc66e598e3b8e94d55a2e090d33af12721f21fb60e43bc34a365a1","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":"20e86773-9870-427e-bca8-4578034ab924","keep":4,"tail":["9b4d087b4e3a2516a2e411b5c1b781f96ab2756bfcda63ccd7cb359ef76c47a3","c09ce2362d61317ed39893585c7512b1238060db26aea0ef4d36e6ac5dddc04b"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"ff6138a1cbc357e7e9de9d57bb4a4ee6c971a8ab5cfcbe8eb459ec6457c84a72"}20{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"f60ce3c9-d429-4a17-9791-64bf7e272dc6","sessionId":"1c433fe8-2bfa-4f11-9528-a1f0dd584bf3"}21{"type":"atis-latch","atis":"","sessionId":"1c433fe8-2bfa-4f11-9528-a1f0dd584bf3"}22{"parentUuid":"f60ce3c9-d429-4a17-9791-64bf7e272dc6","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp3DMdnVqvHDsPoEU5e","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":2696,"cache_read_input_tokens":3923,"output_tokens":507,"output_tokens_details":{"thinking_tokens":254},"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":2696},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":2183,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXp3D61QRXiKAiDKT2e5","requestRef":"a1c5a596-605a-45ce-93cb-23250c27f69f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8f426458-38ba-48f8-a195-9a3c696ac52a","timestamp":"2026-10-30T04:44:36.956Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0153/data","sessionId":"1c433fe8-2bfa-4f11-9528-a1f0dd584bf3","version":"2.1.314","gitBranch":"HEAD"}23{"parentUuid":"8f426458-38ba-48f8-a195-9a3c696ac52a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp3DMdnVqvHDsPoEU5e","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01EDS3k3Dc17EWtwPSJKpbgN","name":"Bash","input":{"command":"cd data; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('life_expectancy_panel.csv').drop_duplicates(['iso3','year'])\nd=d.sort_values(['iso3','year']);d['dif']=d.groupby('iso3').life_expectancy.diff()\nprint(d[d.dif.abs()>1.2])\nprint(d.groupby('iso3').year.agg(['min','max']))\nprint(repr(d.iso3.unique()))\nprint(d[d.year==2022].groupby('iso3').life_expectancy.first().to_string())\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2696,"cache_read_input_tokens":3923,"output_tokens":507,"output_tokens_details":{"thinking_tokens":254},"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":2696},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXp3D61QRXiKAiDKT2e5","requestRef":"a1c5a596-605a-45ce-93cb-23250c27f69f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"22165ba3-5314-4314-b7fc-f2c3ed0d7c4c","timestamp":"2026-10-30T04:44:38.656Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0153/data","sessionId":"1c433fe8-2bfa-4f11-9528-a1f0dd584bf3","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"22165ba3-5314-4314-b7fc-f2c3ed0d7c4c","isSidechain":false,"promptId":"289c70d6-9993-447c-b1d2-3cbdbfc94a12","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01EDS3k3Dc17EWtwPSJKpbgN","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\n min max\niso3 \nAUS 2000 2022\nAUT 2000 2022\nBEL 2000 2022\nBGD 2011 2022\nCAN 2000 2022\nCHE 2000 2022\nDEU 2000 2022\nESP 2000 2022\nETH 2011 2022\nFRA 2000 2022\nGBR 2000 2022\nIND 2011 2022\nITA 2000 2022\nJPN 2000 2022\nKOR 2000 2022\nNGA 2011 2022\nNLD 2000 2022\nSWE 2000 2022\nTUR 2000 2022\nUSA 2000 2022\narray(['AUS', 'AUT', 'BEL', 'BGD', 'CAN', 'CHE', 'DEU', 'ESP', 'ETH',\n 'FRA', 'GBR', 'IND', 'ITA', 'JPN', 'KOR', 'NGA', 'NLD', 'SWE',\n 'TUR', 'USA'], dtype=object)\niso3\nAUS 83.200\nAUT 81.295\nBEL 81.749\nBGD 74.265\nCAN 81.095\nCHE 83.605\nDEU 80.608\nESP 83.134\nETH 66.897\nFRA 82.129\nGBR 81.011\nIND 71.698\nITA 82.700\nJPN 83.996\nKOR 82.680\nNGA 54.079\nNLD 81.615\nSWE 83.059\nTUR 77.591\nUSA 77.434","is_error":false}]},"uuid":"7bb4473f-b6e5-4562-a726-f3ed9cfa59fa","timestamp":"2026-10-30T04:44:38.950Z","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 -1.808\n min max\niso3 \nAUS 2000 2022\nAUT 2000 2022\nBEL 2000 2022\nBGD 2011 2022\nCAN 2000 2022\nCHE 2000 2022\nDEU 2000 2022\nESP 2000 2022\nETH 2011 2022\nFRA 2000 2022\nGBR 2000 2022\nIND 2011 2022\nITA 2000 2022\nJPN 2000 2022\nKOR 2000 2022\nNGA 2011 2022\nNLD 2000 2022\nSWE 2000 2022\nTUR 2000 2022\nUSA 2000 2022\narray(['AUS', 'AUT', 'BEL', 'BGD', 'CAN', 'CHE', 'DEU', 'ESP', 'ETH',\n 'FRA', 'GBR', 'IND', 'ITA', 'JPN', 'KOR', 'NGA', 'NLD', 'SWE',\n 'TUR', 'USA'], dtype=object)\niso3\nAUS 83.200\nAUT 81.295\nBEL 81.749\nBGD 74.265\nCAN 81.095\nCHE 83.605\nDEU 80.608\nESP 83.134\nETH 66.897\nFRA 82.129\nGBR 81.011\nIND 71.698\nITA 82.700\nJPN 83.996\nKOR 82.680\nNGA 54.079\nNLD 81.615\nSWE 83.059\nTUR 77.591\nUSA 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data; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('life_expectancy_panel.csv').drop_duplicates(['iso3','year'])\nd=d.sort_values(['iso3','year']);d['dif']=d.groupby('iso3').life_expectancy.diff()\nprint(d[d.dif.abs()>1.2])\nprint(d.groupby('iso3').year.agg(['min','max']))\nprint(repr(d.iso3.unique()))\nprint(d[d.year==2022].groupby('iso3').life_expectancy.first().to_string())\n\""}}]}}26{"type":"api-request","sessionId":"1c433fe8-2bfa-4f11-9528-a1f0dd584bf3","id":"d426ecb3-6236-4a4f-9e0e-fc6e18b509df","timestamp":"2026-10-30T04:44:38.958Z","version":"2.1.314","querySource":"sdk","shapeHash":"5ae44f7f11cc66e598e3b8e94d55a2e090d33af12721f21fb60e43bc34a365a1","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":"a1c5a596-605a-45ce-93cb-23250c27f69f","keep":6,"tail":["a7ed758aeb549086e2a7b292afae1535359b68e4566897d97e7ec3434dafe71a","c2a040aff842b714c386ca60d49b06a43a707ed4ae23958a5b9aaa0ef19d72b6"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"fba37360c8de9b6d8baa4f00bedc6194c51f498ad31a569b3b4489c222af7560"}27{"parentUuid":"7bb4473f-b6e5-4562-a726-f3ed9cfa59fa","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp3eroJaufsGTVAv832","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":1290,"cache_read_input_tokens":6619,"output_tokens":2541,"output_tokens_details":{"thinking_tokens":258},"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":1290},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":2193,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXp3eZTEPTzdCiqXNLPj","requestRef":"d426ecb3-6236-4a4f-9e0e-fc6e18b509df","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0d2d6d1d-5385-4e78-9793-9dfee59c0c76","timestamp":"2026-10-30T04:44:42.326Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0153/data","sessionId":"1c433fe8-2bfa-4f11-9528-a1f0dd584bf3","version":"2.1.314","gitBranch":"HEAD"}28{"parentUuid":"0d2d6d1d-5385-4e78-9793-9dfee59c0c76","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp3eroJaufsGTVAv832","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LPvzGuK2tZf3vziAvfn8Le","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, \"year\": int})\nraw[\"iso3\"] = raw[\"iso3\"].str.strip()\nn_read = len(raw)\nissues, ledger = [], []\n\ndef step(name, rule, before, after, why):\n ledger.append(dict(step=len(ledger) + 1, rule=rule, n_before=before, n_after=after,\n n_removed=before - after, rationale=why))\n\nn_total = raw[\"iso3\"].nunique()\n\n# 1. duplicate (iso3, year) rows\ndup_mask = raw.duplicated([\"iso3\", \"year\"], keep=False)\ndups = raw[dup_mask]\nconflict = dups.groupby([\"iso3\", \"year\"])[\"life_expectancy\"].nunique(dropna=False).gt(1)\nif conflict.any():\n raise SystemExit(f\"Conflicting duplicate values: {conflict[conflict].index.tolist()}\")\nfor iso, g in dups.groupby(\"iso3\"):\n names = sorted(g[\"country_name\"].unique())\n issues.append({\"issue\": f\"{iso}: every (iso3, year) for {g['year'].nunique()} years appears twice \"\n f\"under different country_name labels {names} (assembly of two extractions); \"\n \"values are identical across the copies.\",\n \"handling\": \"Identified countries by iso3 and kept one row per (iso3, year) \"\n \"(the later-listed copy, i.e. current API label); values unaffected.\"})\nd = raw.drop_duplicates([\"iso3\", \"year\"], keep=\"last\").copy()\nstep(\"deduplicate\", \"keep one row per (iso3, year)\", n_read, len(d),\n \"Exact-duplicate country-years from merging two extractions (same values, different name label).\")\n# harmonise name to one label per iso3 (last-listed)\nd[\"country_name\"] = d[\"iso3\"].map(d.groupby(\"iso3\")[\"country_name\"].last())\n\n# 2. missing values\nmiss = d[d[\"life_expectancy\"].isna()]\nfor _, r in miss.iterrows():\n issues.append({\"issue\": f\"{r.iso3} {r.year}: empty life_expectancy cell (no recorded value).\",\n \"handling\": \"Treated as missing; not imputed. Country therefore not in balanced set.\"})\nbefore = len(d)\nd = d[d[\"life_expectancy\"].notna()]\nstep(\"drop_missing\", \"drop rows with empty life_expectancy\", before, len(d),\n \"No recorded value; imputation/interpolation is prohibited.\")\n\n# 3. restrict to study years (sanity)\nbefore = len(d)\nd = d[d[\"year\"].between(2000, 2022)]\nstep(\"year_window\", \"keep years 2000-2022\", before, len(d), \"Study window.\")\n\n# 4. balanced set\ncov = d.groupby(\"iso3\")[\"year\"].apply(lambda s: set(s))\nbalanced = sorted(i for i, s in cov.items() if set(YEARS) <= s)\nincomplete = sorted(set(raw[\"iso3\"]) - set(balanced))\nfor iso in incomplete:\n have = cov.get(iso, set())\n missing_years = sorted(set(YEARS) - have)\n if missing_years and len(missing_years) > 1 and missing_years == list(range(missing_years[0], missing_years[-1] + 1)):\n desc = f\"{missing_years[0]}-{missing_years[-1]}\"\n else:\n desc = \",\".join(map(str, missing_years))\n if iso not in set(miss[\"iso3\"]):\n issues.append({\"issue\": f\"{iso}: no rows at all for years {desc} (series only present \"\n f\"{min(have)}-{max(have)}; likely from the narrower extraction).\",\n \"handling\": \"Country excluded from balanced panel; still counted in the 2022 all-available baseline.\"})\nbefore = len(d)\nd = d[d[\"iso3\"].isin(balanced)]\nstep(\"balanced_panel\", \"keep countries with a recorded value in every year 2000-2022\", before, len(d),\n f\"Excluded incomplete countries: {', '.join(incomplete)}.\")\n\nanalytic = d[[\"iso3\", \"country_name\", \"year\", \"life_expectancy\"]].sort_values([\"iso3\", \"year\"])\nassert len(analytic) == len(balanced) * len(YEARS)\nassert not analytic.duplicated([\"iso3\", \"year\"]).any()\nanalytic.to_csv(f\"{OUT}/analytic.csv\", index=False)\npd.DataFrame(ledger).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\nm = analytic.groupby(\"year\")[\"life_expectancy\"].mean()\navail22 = raw.drop_duplicates([\"iso3\", \"year\"])\navail22 = avail22[(avail22[\"year\"] == 2022) & avail22[\"life_expectancy\"].notna()]\nres = {\n \"task_id\": \"F1-conf-02\",\n \"n_countries_total\": int(n_total),\n \"n_countries_balanced\": len(balanced),\n \"n_countries_incomplete\": len(incomplete),\n \"balanced_countries\": balanced,\n \"n_analysed\": int(len(analytic)),\n \"mean_2000_balanced\": round(float(m[2000]), 4),\n \"mean_2022_balanced\": round(float(m[2022]), 4),\n \"change_2000_2022_balanced\": round(float(m[2022] - m[2000]), 4),\n \"mean_2022_all_available\": round(float(avail22[\"life_expectancy\"].mean()), 4),\n \"n_countries_2022_available\": int(avail22[\"iso3\"].nunique()),\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"life_expectancy_panel.csv\": int(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))\nprint(pd.DataFrame(ledger).to_string())\nEOF\n~/wp29/venv/bin/python out/analysis.py"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1290,"cache_read_input_tokens":6619,"output_tokens":2541,"output_tokens_details":{"thinking_tokens":258},"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":1290},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXp3eZTEPTzdCiqXNLPj","requestRef":"d426ecb3-6236-4a4f-9e0e-fc6e18b509df","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"bfd38eaf-140b-4a09-976d-3cad0f9be110","timestamp":"2026-10-30T04:44:59.387Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0153/data","sessionId":"1c433fe8-2bfa-4f11-9528-a1f0dd584bf3","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"bfd38eaf-140b-4a09-976d-3cad0f9be110","isSidechain":false,"promptId":"289c70d6-9993-447c-b1d2-3cbdbfc94a12","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/RC0153/data/out/analysis.py\", line 10, in <module>\n raw = pd.read_csv(IN, dtype={\"country_name\": str, \"iso3\": str, \"year\": int})\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'","is_error":true,"tool_use_id":"toolu_01LPvzGuK2tZf3vziAvfn8Le"}]},"uuid":"cea732c5-e5ec-425e-9c84-3757fe2e47fe","timestamp":"2026-10-30T04:44:59.669Z","toolUseResult":"Error: Exit code 1\nTraceback (most recent call last):\n File \"/Users/simon-pierreboucher/wp29/runs/RC0153/data/out/analysis.py\", line 10, in <module>\n raw = pd.read_csv(IN, dtype={\"country_name\": str, \"iso3\": str, \"year\": int})\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: 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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, \"year\": int})\nraw[\"iso3\"] = raw[\"iso3\"].str.strip()\nn_read = len(raw)\nissues, ledger = [], []\n\ndef step(name, rule, before, after, why):\n ledger.append(dict(step=len(ledger) + 1, rule=rule, n_before=before, n_after=after,\n n_removed=before - after, rationale=why))\n\nn_total = raw[\"iso3\"].nunique()\n\n# 1. duplicate (iso3, year) rows\ndup_mask = raw.duplicated([\"iso3\", \"year\"], keep=False)\ndups = raw[dup_mask]\nconflict = dups.groupby([\"iso3\", \"year\"])[\"life_expectancy\"].nunique(dropna=False).gt(1)\nif conflict.any():\n raise SystemExit(f\"Conflicting duplicate values: {conflict[conflict].index.tolist()}\")\nfor iso, g in dups.groupby(\"iso3\"):\n names = sorted(g[\"country_name\"].unique())\n issues.append({\"issue\": f\"{iso}: every (iso3, year) for {g['year'].nunique()} years appears twice \"\n f\"under different country_name labels {names} (assembly of two extractions); \"\n \"values are identical across the copies.\",\n \"handling\": \"Identified countries by iso3 and kept one row per (iso3, year) \"\n \"(the later-listed copy, i.e. current API label); values unaffected.\"})\nd = raw.drop_duplicates([\"iso3\", \"year\"], keep=\"last\").copy()\nstep(\"deduplicate\", \"keep one row per (iso3, year)\", n_read, len(d),\n \"Exact-duplicate country-years from merging two extractions (same values, different name label).\")\n# harmonise name to one label per iso3 (last-listed)\nd[\"country_name\"] = d[\"iso3\"].map(d.groupby(\"iso3\")[\"country_name\"].last())\n\n# 2. missing values\nmiss = d[d[\"life_expectancy\"].isna()]\nfor _, r in miss.iterrows():\n issues.append({\"issue\": f\"{r.iso3} {r.year}: empty life_expectancy cell (no recorded value).\",\n \"handling\": \"Treated as missing; not imputed. Country therefore not in balanced set.\"})\nbefore = len(d)\nd = d[d[\"life_expectancy\"].notna()]\nstep(\"drop_missing\", \"drop rows with empty life_expectancy\", before, len(d),\n \"No recorded value; imputation/interpolation is prohibited.\")\n\n# 3. restrict to study years (sanity)\nbefore = len(d)\nd = d[d[\"year\"].between(2000, 2022)]\nstep(\"year_window\", \"keep years 2000-2022\", before, len(d), \"Study window.\")\n\n# 4. balanced set\ncov = d.groupby(\"iso3\")[\"year\"].apply(lambda s: set(s))\nbalanced = sorted(i for i, s in cov.items() if set(YEARS) <= s)\nincomplete = sorted(set(raw[\"iso3\"]) - set(balanced))\nfor iso in incomplete:\n have = cov.get(iso, set())\n missing_years = sorted(set(YEARS) - have)\n if missing_years and len(missing_years) > 1 and missing_years == list(range(missing_years[0], missing_years[-1] + 1)):\n desc = f\"{missing_years[0]}-{missing_years[-1]}\"\n else:\n desc = \",\".join(map(str, missing_years))\n if iso not in set(miss[\"iso3\"]):\n issues.append({\"issue\": f\"{iso}: no rows at all for years {desc} (series only present \"\n f\"{min(have)}-{max(have)}; likely from the narrower extraction).\",\n \"handling\": \"Country excluded from balanced panel; still counted in the 2022 all-available baseline.\"})\nbefore = len(d)\nd = d[d[\"iso3\"].isin(balanced)]\nstep(\"balanced_panel\", \"keep countries with a recorded value in every year 2000-2022\", before, len(d),\n f\"Excluded incomplete countries: {', '.join(incomplete)}.\")\n\nanalytic = d[[\"iso3\", \"country_name\", \"year\", \"life_expectancy\"]].sort_values([\"iso3\", \"year\"])\nassert len(analytic) == len(balanced) * len(YEARS)\nassert not analytic.duplicated([\"iso3\", \"year\"]).any()\nanalytic.to_csv(f\"{OUT}/analytic.csv\", index=False)\npd.DataFrame(ledger).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\nm = analytic.groupby(\"year\")[\"life_expectancy\"].mean()\navail22 = raw.drop_duplicates([\"iso3\", \"year\"])\navail22 = avail22[(avail22[\"year\"] == 2022) & avail22[\"life_expectancy\"].notna()]\nres = {\n \"task_id\": \"F1-conf-02\",\n \"n_countries_total\": int(n_total),\n \"n_countries_balanced\": len(balanced),\n \"n_countries_incomplete\": len(incomplete),\n \"balanced_countries\": balanced,\n \"n_analysed\": int(len(analytic)),\n \"mean_2000_balanced\": round(float(m[2000]), 4),\n \"mean_2022_balanced\": round(float(m[2022]), 4),\n \"change_2000_2022_balanced\": round(float(m[2022] - m[2000]), 4),\n \"mean_2022_all_available\": round(float(avail22[\"life_expectancy\"].mean()), 4),\n \"n_countries_2022_available\": int(avail22[\"iso3\"].nunique()),\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"life_expectancy_panel.csv\": int(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))\nprint(pd.DataFrame(ledger).to_string())\nEOF\n~/wp29/venv/bin/python out/analysis.py"}}]}}31{"type":"api-request","sessionId":"1c433fe8-2bfa-4f11-9528-a1f0dd584bf3","id":"12bbd7a7-4ec2-428d-83a2-d1a5746bd820","timestamp":"2026-10-30T04:44:59.673Z","version":"2.1.314","querySource":"sdk","shapeHash":"5ae44f7f11cc66e598e3b8e94d55a2e090d33af12721f21fb60e43bc34a365a1","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":"d426ecb3-6236-4a4f-9e0e-fc6e18b509df","keep":8,"tail":["1eb3924d99dbff3c532e54bf15035eff2e23d87ff1cee1cf1226e6146c238acf","431b376acdebba9998913ee9110acef96d8b1dbb1b29198245a8c41c65a1a7a1"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"8b2c7fcf0c1a81984460be6752ab4e7602e789247c97eb011f0b5bbe2d283ecb"}32{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"cea732c5-e5ec-425e-9c84-3757fe2e47fe","sessionId":"1c433fe8-2bfa-4f11-9528-a1f0dd584bf3"}33{"type":"atis-latch","atis":"","sessionId":"1c433fe8-2bfa-4f11-9528-a1f0dd584bf3"}34{"parentUuid":"cea732c5-e5ec-425e-9c84-3757fe2e47fe","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp5BPNTcbSh6rHEi9NE","type":"message","role":"assistant","content":[{"type":"text","text":"Shell was still in data/; I'll move the misplaced out folder."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3276,"cache_read_input_tokens":7909,"output_tokens":144,"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":3276},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXp5B6kt8PmgcV8mDZha","requestRef":"12bbd7a7-4ec2-428d-83a2-d1a5746bd820","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4eefaf75-e1be-438c-b8ca-f7e8d0ed9e20","timestamp":"2026-10-30T04:45:01.430Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0153/data","sessionId":"1c433fe8-2bfa-4f11-9528-a1f0dd584bf3","version":"2.1.314","gitBranch":"HEAD"}35{"parentUuid":"4eefaf75-e1be-438c-b8ca-f7e8d0ed9e20","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp5BPNTcbSh6rHEi9NE","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015MRRFB6N8LmQ2Rv2gnSP8a","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0153 && mv data/out ./out && ls data && ~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3276,"cache_read_input_tokens":7909,"output_tokens":144,"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":3276},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXp5B6kt8PmgcV8mDZha","requestRef":"12bbd7a7-4ec2-428d-83a2-d1a5746bd820","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"21edbdbf-f247-439e-8db7-077d00b26cc5","timestamp":"2026-10-30T04:45:02.136Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0153/data","sessionId":"1c433fe8-2bfa-4f11-9528-a1f0dd584bf3","version":"2.1.314","gitBranch":"HEAD"}36{"parentUuid":"21edbdbf-f247-439e-8db7-077d00b26cc5","isSidechain":false,"promptId":"289c70d6-9993-447c-b1d2-3cbdbfc94a12","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015MRRFB6N8LmQ2Rv2gnSP8a","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_available\": 20,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"TUR: every (iso3, year) for 23 years appears twice under different country_name labels ['Turkey', 'Turkiye'] (assembly of two extractions); values are identical across the copies.\",\n \"handling\": \"Identified countries by iso3 and kept one row per (iso3, year) (the later-listed copy, i.e. current API label); values unaffected.\"\n },\n {\n \"issue\": \"CHE 2007: empty life_expectancy cell (no recorded value).\",\n \"handling\": \"Treated as missing; not imputed. Country therefore not in balanced set.\"\n },\n {\n \"issue\": \"BGD: no rows at all for years 2000-2010 (series only present 2011-2022; likely from the narrower extraction).\",\n \"handling\": \"Country excluded from balanced panel; still counted in the 2022 all-available baseline.\"\n },\n {\n \"issue\": \"ETH: no rows at all for years 2000-2010 (series only present 2011-2022; likely from the narrower extraction).\",\n \"handling\": \"Country excluded from balanced panel; still counted in the 2022 all-available baseline.\"\n },\n {\n \"issue\": \"IND: no rows at all for years 2000-2010 (series only present 2011-2022; likely from the narrower extraction).\",\n \"handling\": \"Country excluded from balanced panel; still counted in the 2022 all-available baseline.\"\n },\n {\n \"issue\": \"NGA: no rows at all for years 2000-2010 (series only present 2011-2022; likely from the narrower extraction).\",\n \"handling\": \"Country excluded from balanced panel; still counted in the 2022 all-available baseline.\"\n }\n ],\n \"n_input_rows\": {\n \"life_expectancy_panel.csv\": 439\n },\n \"random_seed\": null\n}\n step rule n_before n_after n_removed rationale\n0 1 keep one row per (iso3, year) 439 416 23 Exact-duplicate country-years from merging two extractions (same values, different name label).\n1 2 drop rows with empty life_expectancy 416 415 1 No recorded value; imputation/interpolation is prohibited.\n2 3 keep years 2000-2022 415 415 0 Study window.\n3 4 keep countries with a recorded value in every year 2000-2022 415 345 70 Excluded incomplete countries: BGD, CHE, ETH, IND, NGA.\nstep,rule,n_before,n_after,n_removed,rationale\n1,\"keep one row per (iso3, year)\",439,416,23,\"Exact-duplicate country-years from merging two extractions (same values, different name label).\"\n2,drop rows with empty life_expectancy,416,415,1,No recorded value; imputation/interpolation is prohibited.\n3,keep years 2000-2022,415,415,0,Study window.\n4,keep countries with a recorded value in every year 2000-2022,415,345,70,\"Excluded incomplete countries: BGD, CHE, ETH, IND, NGA.\"","is_error":false}]},"uuid":"e3ecbd51-d282-4953-a708-cf50c9a73a64","timestamp":"2026-10-30T04:45:02.449Z","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_available\": 20,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"TUR: every (iso3, year) for 23 years appears twice under different country_name labels ['Turkey', 'Turkiye'] (assembly of two extractions); values are identical across the copies.\",\n \"handling\": \"Identified countries by iso3 and kept one row per (iso3, year) (the later-listed copy, i.e. current API label); values unaffected.\"\n },\n {\n \"issue\": \"CHE 2007: empty life_expectancy cell (no recorded value).\",\n \"handling\": \"Treated as missing; not imputed. Country therefore not in balanced set.\"\n },\n {\n \"issue\": \"BGD: no rows at all for years 2000-2010 (series only present 2011-2022; likely from the narrower extraction).\",\n \"handling\": \"Country excluded from balanced panel; still counted in the 2022 all-available baseline.\"\n },\n {\n \"issue\": \"ETH: no rows at all for years 2000-2010 (series only present 2011-2022; likely from the narrower extraction).\",\n \"handling\": \"Country excluded from balanced panel; still counted in the 2022 all-available baseline.\"\n },\n {\n \"issue\": \"IND: no rows at all for years 2000-2010 (series only present 2011-2022; likely from the narrower extraction).\",\n \"handling\": \"Country excluded from balanced panel; still counted in the 2022 all-available baseline.\"\n },\n {\n \"issue\": \"NGA: no rows at all for years 2000-2010 (series only present 2011-2022; likely from the narrower extraction).\",\n \"handling\": \"Country excluded from balanced panel; still counted in the 2022 all-available baseline.\"\n }\n ],\n \"n_input_rows\": {\n \"life_expectancy_panel.csv\": 439\n },\n \"random_seed\": null\n}\n step rule n_before n_after n_removed rationale\n0 1 keep one row per (iso3, year) 439 416 23 Exact-duplicate country-years from merging two extractions (same values, different name label).\n1 2 drop rows with empty life_expectancy 416 415 1 No recorded value; imputation/interpolation is prohibited.\n2 3 keep years 2000-2022 415 415 0 Study window.\n3 4 keep countries with a recorded value in every year 2000-2022 415 345 70 Excluded incomplete countries: BGD, CHE, ETH, IND, NGA.\nstep,rule,n_before,n_after,n_removed,rationale\n1,\"keep one row per (iso3, year)\",439,416,23,\"Exact-duplicate country-years from merging two extractions (same values, different name label).\"\n2,drop rows with empty life_expectancy,416,415,1,No recorded value; imputation/interpolation is prohibited.\n3,keep years 2000-2022,415,415,0,Study window.\n4,keep countries with a recorded 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> out/report.md <<'EOF'\n# F1-conf-02 — Balanced-panel mean life expectancy, 2000–2022\n\n## Question\nHow much did life expectancy at birth (WDI `SP.DYN.LE00.IN`) change between 2000 and 2022? The estimand is the unweighted mean over a fixed, balanced set of countries, meaning countries with a recorded value in every year from 2000 to 2022. As a descriptive baseline, the report also gives the naive 2022 mean over all countries with a recorded 2022 value.\n\n## Data and exclusions\nInput: `data/life_expectancy_panel.csv`, with 439 data rows covering 20 distinct iso3 codes. Data problems found:\n\n1. **Duplicate Türkiye series.** All 23 TUR country-years appear twice: once labelled \"Turkey\" and once \"Turkiye\". This is an artefact of merging two extractions. The values in each pair are identical. Countries are identified by iso3, so one row was kept per (iso3, year), and the label was set to \"Turkiye\". 23 rows were removed.\n2. **Missing value for CHE 2007.** The `life_expectancy` cell is empty. It was treated as missing and not imputed, as the task rules require. Switzerland is therefore not in the balanced set. 1 row was removed at the missing-value step, and CHE's other 22 rows were removed at the balanced-panel step.\n3. **Truncated series for BGD, ETH, IND and NGA.** These countries have no rows at all for 2000–2010; they are present only for 2011–2022, probably because they came from the narrower extraction. They were excluded from the balanced panel (48 rows) but kept in the 2022 baseline.\n\nExclusion ledger (`out/exclusions.csv`): 439 rows → dedupe 416 → drop missing 415 → year window 2000–2022 415 → balanced panel **345** (15 countries × 23 years).\n\nThere are no other problems: no out-of-window years, no non-numeric values, and no implausible values. The largest year-on-year changes fall in 2020–2022 and are consistent with COVID-19.\n\n## Method\n- Balanced set: the iso3 codes with a non-missing value in every year 2000–2022, after deduplication.\n- For each year, the simple mean of `life_expectancy` over those 15 countries.\n- Change = balanced mean for 2022 minus balanced mean for 2000.\n- Baseline: the simple mean over all distinct iso3 codes with a recorded 2022 value (20 countries, each counted once after deduplication).\n- No weighting, imputation or randomness was used. Running `out/analysis.py` reproduces every output.\n\n## Results\n- Balanced set (15 countries): AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA. Five countries are incomplete: BGD, CHE, ETH, IND, NGA.\n- Balanced mean, 2000: **78.0603 years**.\n- Balanced mean, 2022: **81.5531 years**.\n- Change 2000–2022: **+3.4928 years**.\n- Naive 2022 mean over all 20 countries: **78.692 years**. This is 2.86 years below the balanced 2022 mean, because it includes the low-life-expectancy countries (NGA, ETH, IND, BGD) that drop out of the balanced set. It should not be compared with the balanced 2000 mean.\n- Change by country, 2000–2022: from +0.797 years (USA) to +6.770 years (KOR).\n\n## Uncertainty\nThese values are exact descriptive statistics for a fixed, purposively chosen set of countries, so there is no sampling uncertainty in the usual sense. The changes vary across countries with SD = 1.41 years. For illustration only: if the 15 countries were treated as a random sample, the standard error of the mean change would be 0.36 years. That figure has no formal inferential meaning here. The WDI estimates themselves are modelled values, and they get revised between releases; no uncertainty for them is available in the input.\n\n## Limitations and open issues\n- The balanced set is almost entirely high-income countries; Türkiye is the only exception. The result therefore says nothing about the excluded lower-income countries (BGD, ETH, IND, NGA), nor about Switzerland, which was dropped for a single missing year.\n- The CHE 2007 gap and the missing 2000–2010 rows for four countries are probably assembly errors and not true gaps in the WDI data. The task rules forbid looking up or imputing values, so these countries are excluded. If the original extraction were recovered, the balanced set (and the estimates) could change.\n- The unweighted mean is not a population-weighted mean.\n- 2022 values come from a single WDI release (updated 2026-10-08) and may be revised later.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"15 of 20 countries have a recorded value in every year 2000-2022 (AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA); BGD, CHE, ETH, IND and NGA are incomplete.\",\"results_keys\":[\"n_countries_total\",\"n_countries_balanced\",\"n_countries_incomplete\",\"balanced_countries\"],\"status\":\"supported\",\"caveat\":\"Incompleteness probably reflects file assembly errors; no imputation or lookup was allowed.\"},\n {\"id\":\"C2\",\"text\":\"Balanced-panel mean life expectancy rose from 78.06 years in 2000 to 81.55 years in 2022, a gain of 3.49 years.\",\"results_keys\":[\"mean_2000_balanced\",\"mean_2022_balanced\",\"change_2000_2022_balanced\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Unweighted mean over a mostly high-income set of countries; descriptive, not an estimate for a wider population.\"},\n {\"id\":\"C3\",\"text\":\"The naive 2022 mean over all 20 available countries (78.69 years) is about 2.86 years lower than the balanced 2022 mean, because it includes low-life-expectancy countries absent from the balanced set.\",\"results_keys\":[\"mean_2022_all_available\",\"mean_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Baseline only; it is not comparable with the balanced 2000 mean.\"},\n {\"id\":\"C4\",\"text\":\"The Turkey/Turkiye duplicate series had identical values and was collapsed to one row per iso3-year, so the deduplication does not change any estimate.\",\"results_keys\":[\"data_issues\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'))\"; ls out"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":760,"cache_read_input_tokens":13216,"output_tokens":2347,"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":760},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXp67GbzKSSuwraoJDvo","requestRef":"49432695-937b-4a54-b0b8-be80c5b96624","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"39fd258e-ee5a-43ce-9a2d-be20c53452b3","timestamp":"2026-10-30T04:45:32.567Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0153","sessionId":"1c433fe8-2bfa-4f11-9528-a1f0dd584bf3","version":"2.1.314","gitBranch":"HEAD"}52{"parentUuid":"39fd258e-ee5a-43ce-9a2d-be20c53452b3","isSidechain":false,"promptId":"289c70d6-9993-447c-b1d2-3cbdbfc94a12","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011sLMdX1eTDrrmdtQhm1KsV","type":"tool_result","content":"analysis.py\nanalytic.csv\nclaims.json\nexclusions.csv\nreport.md\nresults.json","is_error":false}]},"uuid":"33b62be0-318f-457a-8162-2104e9b26378","timestamp":"2026-10-30T04:45:32.649Z","toolUseResult":{"stdout":"analysis.py\nanalytic.csv\nclaims.json\nexclusions.csv\nreport.md\nresults.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"39fd258e-ee5a-43ce-9a2d-be20c53452b3","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0153","sessionId":"1c433fe8-2bfa-4f11-9528-a1f0dd584bf3","version":"2.1.314","gitBranch":"HEAD"}53{"type":"api-request-blob","sessionId":"1c433fe8-2bfa-4f11-9528-a1f0dd584bf3","hash":"afae274e3d6734751413f79942319ba7a7088d54c789679019f028b248595d69","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_011sLMdX1eTDrrmdtQhm1KsV","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F1-conf-02 — Balanced-panel mean life expectancy, 2000–2022\n\n## Question\nHow much did life expectancy at birth (WDI `SP.DYN.LE00.IN`) change between 2000 and 2022? The estimand is the unweighted mean over a fixed, balanced set of countries, meaning countries with a recorded value in every year from 2000 to 2022. As a descriptive baseline, the report also gives the naive 2022 mean over all countries with a recorded 2022 value.\n\n## Data and exclusions\nInput: `data/life_expectancy_panel.csv`, with 439 data rows covering 20 distinct iso3 codes. Data problems found:\n\n1. **Duplicate Türkiye series.** All 23 TUR country-years appear twice: once labelled \"Turkey\" and once \"Turkiye\". This is an artefact of merging two extractions. The values in each pair are identical. Countries are identified by iso3, so one row was kept per (iso3, year), and the label was set to \"Turkiye\". 23 rows were removed.\n2. **Missing value for CHE 2007.** The `life_expectancy` cell is empty. It was treated as missing and not imputed, as the task rules require. Switzerland is therefore not in the balanced set. 1 row was removed at the missing-value step, and CHE's other 22 rows were removed at the balanced-panel step.\n3. **Truncated series for BGD, ETH, IND and NGA.** These countries have no rows at all for 2000–2010; they are present only for 2011–2022, probably because they came from the narrower extraction. They were excluded from the balanced panel (48 rows) but kept in the 2022 baseline.\n\nExclusion ledger (`out/exclusions.csv`): 439 rows → dedupe 416 → drop missing 415 → year window 2000–2022 415 → balanced panel **345** (15 countries × 23 years).\n\nThere are no other problems: no out-of-window years, no non-numeric values, and no implausible values. The largest year-on-year changes fall in 2020–2022 and are consistent with COVID-19.\n\n## Method\n- Balanced set: the iso3 codes with a non-missing value in every year 2000–2022, after deduplication.\n- For each year, the simple mean of `life_expectancy` over those 15 countries.\n- Change = balanced mean for 2022 minus balanced mean for 2000.\n- Baseline: the simple mean over all distinct iso3 codes with a recorded 2022 value (20 countries, each counted once after deduplication).\n- No weighting, imputation or randomness was used. Running `out/analysis.py` reproduces every output.\n\n## Results\n- Balanced set (15 countries): AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA. Five countries are incomplete: BGD, CHE, ETH, IND, NGA.\n- Balanced mean, 2000: **78.0603 years**.\n- Balanced mean, 2022: **81.5531 years**.\n- Change 2000–2022: **+3.4928 years**.\n- Naive 2022 mean over all 20 countries: **78.692 years**. This is 2.86 years below the balanced 2022 mean, because it includes the low-life-expectancy countries (NGA, ETH, IND, BGD) that drop out of the balanced set. It should not be compared with the balanced 2000 mean.\n- Change by country, 2000–2022: from +0.797 years (USA) to +6.770 years (KOR).\n\n## Uncertainty\nThese values are exact descriptive statistics for a fixed, purposively chosen set of countries, so there is no sampling uncertainty in the usual sense. The changes vary across countries with SD = 1.41 years. For illustration only: if the 15 countries were treated as a random sample, the standard error of the mean change would be 0.36 years. That figure has no formal inferential meaning here. The WDI estimates themselves are modelled values, and they get revised between releases; no uncertainty for them is available in the input.\n\n## Limitations and open issues\n- The balanced set is almost entirely high-income countries; Türkiye is the only exception. The result therefore says nothing about the excluded lower-income countries (BGD, ETH, IND, NGA), nor about Switzerland, which was dropped for a single missing year.\n- The CHE 2007 gap and the missing 2000–2010 rows for four countries are probably assembly errors and not true gaps in the WDI data. The task rules forbid looking up or imputing values, so these countries are excluded. If the original extraction were recovered, the balanced set (and the estimates) could change.\n- The unweighted mean is not a population-weighted mean.\n- 2022 values come from a single WDI release (updated 2026-10-08) and may be revised later.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"15 of 20 countries have a recorded value in every year 2000-2022 (AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA); BGD, CHE, ETH, IND and NGA are incomplete.\",\"results_keys\":[\"n_countries_total\",\"n_countries_balanced\",\"n_countries_incomplete\",\"balanced_countries\"],\"status\":\"supported\",\"caveat\":\"Incompleteness probably reflects file assembly errors; no imputation or lookup was allowed.\"},\n {\"id\":\"C2\",\"text\":\"Balanced-panel mean life expectancy rose from 78.06 years in 2000 to 81.55 years in 2022, a gain of 3.49 years.\",\"results_keys\":[\"mean_2000_balanced\",\"mean_2022_balanced\",\"change_2000_2022_balanced\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Unweighted mean over a mostly high-income set of countries; descriptive, not an estimate for a wider population.\"},\n {\"id\":\"C3\",\"text\":\"The naive 2022 mean over all 20 available countries (78.69 years) is about 2.86 years lower than the balanced 2022 mean, because it includes low-life-expectancy countries absent from the balanced set.\",\"results_keys\":[\"mean_2022_all_available\",\"mean_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Baseline only; it is not comparable with the balanced 2000 mean.\"},\n {\"id\":\"C4\",\"text\":\"The Turkey/Turkiye duplicate series had identical values and was collapsed to one row per iso3-year, so the deduplication does not change any estimate.\",\"results_keys\":[\"data_issues\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\n~/wp29/venv/bin/python -c 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written all the required files in `out/`, and running `out/analysis.py` regenerates the results, the analytic dataset and the exclusion ledger from `data/`. Life expectancy across the 15 balanced-panel countries rose by about 3.5 years between 2000 and 2022.\n\n| Measure | Value |\n|---|---|\n| Countries in the file | 20 |\n| Countries with a value in every year 2000–2022 | 15 (AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA) |\n| Rows in the balanced dataset | 345 (15 countries × 23 years) |\n| Balanced mean, 2000 | 78.0603 years |\n| Balanced mean, 2022 | 81.5531 years |\n| Change, 2000–2022 | +3.4928 years |\n| Simple 2022 mean over all 20 countries (baseline) | 78.692 years |\n\nThe simple 2022 mean is 2.86 years below the balanced one because it includes Nigeria, Ethiopia, India and Bangladesh, which are not in the balanced set. Across countries, the 2000–2022 gain runs from +0.8 years (USA) to +6.8 years (KOR).\n\n**Data problems found and how I handled them:**\n- **Türkiye listed twice:** every Türkiye year appears twice, once as \"Turkey\" and once as \"Turkiye\", with identical values. I kept one row per country-year (23 rows removed), which doesn't change any number.\n- **Switzerland:** its 2007 value is blank. The task rules forbid filling gaps, so Switzerland is left out of the balanced set.\n- **Bangladesh, Ethiopia, India and Nigeria:** these have no rows at all before 2011. They are left out of the balanced set but counted in the 2022 baseline.\n\n**Limitations:**\n- **Mostly rich countries:** the balanced set is almost all high-income countries, so the result doesn't speak for the five excluded ones. The gaps look like errors from assembling the file rather than real missing data; with the original data, the balanced set and the means could change.\n- **Unweighted:** the means are simple averages, not weighted by population.\n- **No real margin of error:** this is a fixed, hand-picked set of countries, so there is no sampling uncertainty in the usual sense. The report gives a standard error of 0.36 years for the change, for illustration only.\n\nI added two keys that the task didn't ask for to `results.json`: `n_countries_2022_available` (20) and `descriptive_extra` (the per-country change figures above).\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":2385,"cache_read_input_tokens":13976,"output_tokens":993,"output_tokens_details":{"thinking_tokens":57},"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":2385},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXp7c7DyaZfVPqP7jCB3","requestRef":"ef731608-30da-43ed-91f2-51165103d8c3","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"bd5c5f5c-d9f7-4d5d-82c3-95d020fb52fb","timestamp":"2026-10-30T04:45:42.751Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0153","sessionId":"1c433fe8-2bfa-4f11-9528-a1f0dd584bf3","version":"2.1.314","gitBranch":"HEAD"}57{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"bd5c5f5c-d9f7-4d5d-82c3-95d020fb52fb","sessionId":"1c433fe8-2bfa-4f11-9528-a1f0dd584bf3"}58{"type":"cost-state","sessionId":"1c433fe8-2bfa-4f11-9528-a1f0dd584bf3","totalCostUSD":0.2475174,"totalAPIDuration":76702,"totalAPIDurationWithoutRetries":76690,"totalToolDuration":1939,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":78831,"startTime":1793335463928,"modelUsage":{"claude-opus-5-5":{"inputTokens":20,"outputTokens":7925,"thinkingTokens":817,"cacheReadInputTokens":73262,"cacheCreationInputTokens":14857,"cacheCreationEphemeral5mInputTokens":14857,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.2475174}},"hasUnknownModelCost":false}59