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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. 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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. 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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.\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(439, 4)\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\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\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 \n['2000' '2001' '2002' '2003' '2004' '2005' '2006' '2007' '2008' '2009'\n '2010' '2011' '2012' '2013' '2014' '2015' '2016' '2017' '2018' '2019'\n '2020' '2021' '2022']\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: []\ncountry_name 0\niso3 0\nyear 0\nlife_expectancy 0\nb3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac life_expectancy_panel.csv","is_error":false}]},"uuid":"c23d613d-e054-45fe-bb90-7a901c2facb2","timestamp":"2026-10-30T04:42:20.063Z","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.\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(439, 4)\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\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\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 \n['2000' '2001' '2002' '2003' '2004' '2005' '2006' '2007' '2008' '2009'\n '2010' '2011' '2012' '2013' '2014' '2015' '2016' '2017' '2018' '2019'\n '2020' '2021' '2022']\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: []\ncountry_name 0\niso3 0\nyear 0\nlife_expectancy 0\nb3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac 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data; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('life_expectancy_panel.csv')\nfor c in ['BGD','ETH','IND','NGA']: print(c, sorted(d[d.iso3==c].year))\nd=d.drop_duplicates(['iso3','year']).sort_values(['iso3','year'])\nd['dif']=d.groupby('iso3').life_expectancy.diff()\nprint(d[d.dif.abs()>1.2])\nprint(d[d.year.isin([2000,2022])].pivot(index='iso3',columns='year',values='life_expectancy'))\n\"","description":"Check gaps and jumps"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2747,"cache_read_input_tokens":4132,"output_tokens":412,"output_tokens_details":{"thinking_tokens":116},"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":2747},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXosRHDTPuhZVoeq4gEN","requestRef":"44ec9562-60c7-4ca9-9e5c-6e8a3377f22b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3e43a72d-67b4-46de-9fbc-0a64f17188b4","timestamp":"2026-10-30T04:42:23.979Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0146/data","sessionId":"3241f7fb-acf3-4db9-b133-bd6218d5ec1c","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"3e43a72d-67b4-46de-9fbc-0a64f17188b4","isSidechain":false,"promptId":"87fb3d01-2536-4f45-9405-99492fd13532","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011AMrNL3GS2H89U492i855p","type":"tool_result","content":"(eval):cd:1: no such file or directory: data\nBGD [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]\nETH [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]\nIND [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]\nNGA [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]\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\nyear 2000 2022\niso3 \nAUS 79.234 83.200\nAUT 78.127 81.295\nBEL 77.722 81.749\nBGD NaN 74.265\nCAN 79.167 81.095\nCHE 79.680 83.605\nDEU 77.927 80.608\nESP 78.966 83.134\nETH NaN 66.897\nFRA 79.056 82.129\nGBR 77.741 81.011\nIND NaN 71.698\nITA 79.778 82.700\nJPN 81.076 83.996\nKOR 75.910 82.680\nNGA NaN 54.079\nNLD 77.988 81.615\nSWE 79.644 83.059\nTUR 71.931 77.591\nUSA 76.637 77.434","is_error":false}]},"uuid":"bc4d94bf-ec54-48a7-8be8-b7f6489edde6","timestamp":"2026-10-30T04:42:24.262Z","toolUseResult":{"stdout":"(eval):cd:1: no such file or directory: data\nBGD [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]\nETH [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]\nIND [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]\nNGA [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]\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\nyear 2000 2022\niso3 \nAUS 79.234 83.200\nAUT 78.127 81.295\nBEL 77.722 81.749\nBGD NaN 74.265\nCAN 79.167 81.095\nCHE 79.680 83.605\nDEU 77.927 80.608\nESP 78.966 83.134\nETH NaN 66.897\nFRA 79.056 82.129\nGBR 77.741 81.011\nIND NaN 71.698\nITA 79.778 82.700\nJPN 81.076 83.996\nKOR 75.910 82.680\nNGA NaN 54.079\nNLD 77.988 81.615\nSWE 79.644 83.059\nTUR 71.931 77.591\nUSA 76.637 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data; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('life_expectancy_panel.csv')\nfor c in ['BGD','ETH','IND','NGA']: print(c, sorted(d[d.iso3==c].year))\nd=d.drop_duplicates(['iso3','year']).sort_values(['iso3','year'])\nd['dif']=d.groupby('iso3').life_expectancy.diff()\nprint(d[d.dif.abs()>1.2])\nprint(d[d.year.isin([2000,2022])].pivot(index='iso3',columns='year',values='life_expectancy'))\n\"","description":"Check gaps and jumps"}}]}}26{"type":"api-request","sessionId":"3241f7fb-acf3-4db9-b133-bd6218d5ec1c","id":"884b4ce5-cc84-40cd-a848-462ba4afaaa4","timestamp":"2026-10-30T04:42:24.265Z","version":"2.1.314","querySource":"sdk","shapeHash":"dda230fb3163780039b904a7cab335cbb247d7724e70550da8ed8cf793d411bb","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":"44ec9562-60c7-4ca9-9e5c-6e8a3377f22b","keep":6,"tail":["f8870078268292503965d9436e5be24348ddf3a6543527d2dbde894dd9a6d898","62fa228417e5ee9b94e150064c5f35b4b7405e3f383f6e5d448be8fdd33c2f71"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"da34eb32abfd0cca0685afaf4cca93d68a7a74813b70808dd415895a21447a69"}27{"parentUuid":"bc4d94bf-ec54-48a7-8be8-b7f6489edde6","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXosjUqXy4aifkd5acRm","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":1154,"cache_read_input_tokens":6879,"output_tokens":2165,"output_tokens_details":{"thinking_tokens":54},"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":1154},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":342,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXosjCijGp94xAkX2kiD","requestRef":"884b4ce5-cc84-40cd-a848-462ba4afaaa4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6cd265d8-ee7f-4d9d-8dfc-403020829db2","timestamp":"2026-10-30T04:42:25.811Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0146/data","sessionId":"3241f7fb-acf3-4db9-b133-bd6218d5ec1c","version":"2.1.314","gitBranch":"HEAD"}28{"parentUuid":"6cd265d8-ee7f-4d9d-8dfc-403020829db2","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXosjUqXy4aifkd5acRm","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01UHLy9p9mcVS3kEfPEn4San","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={\"iso3\": str, \"country_name\": str})\nn_read = len(raw)\nissues, ledger = [], []\n\ndef step(name, rule, before, after, why):\n ledger.append(dict(step=len(ledger) + 1, rule=f\"{name}: {rule}\", n_before=before,\n n_after=after, n_removed=before - after, rationale=why))\n\nd = raw.copy()\n# 1. Rows outside 2000-2022 (none expected)\nb = len(d); d = d[d.year.isin(YEARS)]\nstep(\"year_window\", \"keep rows with year in 2000-2022\", b, len(d), \"Study period.\")\n\n# 2. Duplicate (iso3, year) rows from assembly of two extractions\ndup = d[d.duplicated([\"iso3\", \"year\"], keep=False)]\nif len(dup):\n conflicts = dup.groupby([\"iso3\", \"year\"]).life_expectancy.nunique(dropna=False).gt(1).sum()\n names = {k: sorted(v.unique()) for k, v in dup.groupby(\"iso3\").country_name}\n if conflicts:\n raise SystemExit(f\"Conflicting duplicate values: {conflicts}\")\n issues.append({\"issue\": f\"{len(dup) - dup[['iso3','year']].drop_duplicates().shape[0]} duplicate (iso3, year) rows \"\n f\"from the two-extraction assembly: {names} (same iso3, different country_name spelling); \"\n \"life_expectancy values identical in every duplicated pair.\",\n \"handling\": \"Identified countries by iso3; kept one row per (iso3, year) (first occurrence), \"\n \"using the latest-spelling country name as the label for the country.\"})\n# canonical name: last-listed spelling per iso3 (later, wider extraction)\ncanon = d.groupby(\"iso3\").country_name.last()\nb = len(d); d = d.drop_duplicates([\"iso3\", \"year\"], keep=\"first\").copy()\nd[\"country_name\"] = d.iso3.map(canon)\nstep(\"dedupe\", \"one row per (iso3, year)\", b, len(d), \"Each country counted once per year; duplicates were identical.\")\n\n# 3. Missing values\nmiss = d[d.life_expectancy.isna()]\nif len(miss):\n issues.append({\"issue\": \"Empty life_expectancy cell(s): \" + \", \".join(f\"{r.iso3} {r.year}\" for r in miss.itertuples()),\n \"handling\": \"Treated as missing (no imputation); country excluded from the balanced set.\"})\nb = len(d); d = d[d.life_expectancy.notna()]\nstep(\"drop_missing\", \"drop country-years with empty life_expectancy\", b, len(d), \"No imputation allowed.\")\n\n# 4. Incomplete coverage\ncov = d.groupby(\"iso3\").year.nunique()\nall_iso = sorted(raw.iso3.unique())\nbalanced = sorted(cov[cov == len(YEARS)].index)\nincomplete = [c for c in all_iso if c not in balanced]\nfor c in incomplete:\n yrs = sorted(d[d.iso3 == c].year)\n absent = [y for y in YEARS if y not in yrs]\n if c not in set(miss.iso3):\n issues.append({\"issue\": f\"{c} has no row at all for {len(absent)} years ({min(absent)}-{max(absent)}); \"\n f\"only {len(yrs)} of 23 years present (likely only in the narrower extraction).\",\n \"handling\": \"Not imputed; country excluded from the balanced set (included in the 2022 all-available baseline).\"})\nb = len(d); an = d[d.iso3.isin(balanced)].sort_values([\"iso3\", \"year\"])\nstep(\"balanced_panel\", \"keep countries with a recorded value in every year 2000-2022\", b, len(an),\n f\"Balanced panel; removed countries: {', '.join(incomplete)}.\")\n\nan[[\"iso3\", \"country_name\", \"year\", \"life_expectancy\"]].to_csv(f\"{OUT}/analytic.csv\", index=False)\npd.DataFrame(ledger).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\nm = an.groupby(\"year\").life_expectancy.mean()\nall22 = d[d.year == 2022].drop_duplicates(\"iso3\").life_expectancy.mean()\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\": int(len(an)),\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(all22), 4),\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(m.round(3).to_string())\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/exclusions.csv","description":"Write and run analysis"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1154,"cache_read_input_tokens":6879,"output_tokens":2165,"output_tokens_details":{"thinking_tokens":54},"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":1154},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXosjCijGp94xAkX2kiD","requestRef":"884b4ce5-cc84-40cd-a848-462ba4afaaa4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c2d22127-5251-4cee-90e0-72bde3571cf9","timestamp":"2026-10-30T04:42:41.772Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0146/data","sessionId":"3241f7fb-acf3-4db9-b133-bd6218d5ec1c","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"c2d22127-5251-4cee-90e0-72bde3571cf9","isSidechain":false,"promptId":"87fb3d01-2536-4f45-9405-99492fd13532","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/RC0146/data/out/analysis.py\", line 10, in <module>\n raw = pd.read_csv(IN, dtype={\"iso3\": str, \"country_name\": 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","is_error":true,"tool_use_id":"toolu_01UHLy9p9mcVS3kEfPEn4San"}]},"uuid":"66f832cb-b46d-492c-8e09-21019437ed21","timestamp":"2026-10-30T04:42:42.041Z","toolUseResult":"Error: Exit code 1\nTraceback (most recent call last):\n File \"/Users/simon-pierreboucher/wp29/runs/RC0146/data/out/analysis.py\", line 10, in <module>\n raw = pd.read_csv(IN, dtype={\"iso3\": str, \"country_name\": 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 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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={\"iso3\": str, \"country_name\": str})\nn_read = len(raw)\nissues, ledger = [], []\n\ndef step(name, rule, before, after, why):\n ledger.append(dict(step=len(ledger) + 1, rule=f\"{name}: {rule}\", n_before=before,\n n_after=after, n_removed=before - after, rationale=why))\n\nd = raw.copy()\n# 1. Rows outside 2000-2022 (none expected)\nb = len(d); d = d[d.year.isin(YEARS)]\nstep(\"year_window\", \"keep rows with year in 2000-2022\", b, len(d), \"Study period.\")\n\n# 2. Duplicate (iso3, year) rows from assembly of two extractions\ndup = d[d.duplicated([\"iso3\", \"year\"], keep=False)]\nif len(dup):\n conflicts = dup.groupby([\"iso3\", \"year\"]).life_expectancy.nunique(dropna=False).gt(1).sum()\n names = {k: sorted(v.unique()) for k, v in dup.groupby(\"iso3\").country_name}\n if conflicts:\n raise SystemExit(f\"Conflicting duplicate values: {conflicts}\")\n issues.append({\"issue\": f\"{len(dup) - dup[['iso3','year']].drop_duplicates().shape[0]} duplicate (iso3, year) rows \"\n f\"from the two-extraction assembly: {names} (same iso3, different country_name spelling); \"\n \"life_expectancy values identical in every duplicated pair.\",\n \"handling\": \"Identified countries by iso3; kept one row per (iso3, year) (first occurrence), \"\n \"using the latest-spelling country name as the label for the country.\"})\n# canonical name: last-listed spelling per iso3 (later, wider extraction)\ncanon = d.groupby(\"iso3\").country_name.last()\nb = len(d); d = d.drop_duplicates([\"iso3\", \"year\"], keep=\"first\").copy()\nd[\"country_name\"] = d.iso3.map(canon)\nstep(\"dedupe\", \"one row per (iso3, year)\", b, len(d), \"Each country counted once per year; duplicates were identical.\")\n\n# 3. Missing values\nmiss = d[d.life_expectancy.isna()]\nif len(miss):\n issues.append({\"issue\": \"Empty life_expectancy cell(s): \" + \", \".join(f\"{r.iso3} {r.year}\" for r in miss.itertuples()),\n \"handling\": \"Treated as missing (no imputation); country excluded from the balanced set.\"})\nb = len(d); d = d[d.life_expectancy.notna()]\nstep(\"drop_missing\", \"drop country-years with empty life_expectancy\", b, len(d), \"No imputation allowed.\")\n\n# 4. Incomplete coverage\ncov = d.groupby(\"iso3\").year.nunique()\nall_iso = sorted(raw.iso3.unique())\nbalanced = sorted(cov[cov == len(YEARS)].index)\nincomplete = [c for c in all_iso if c not in balanced]\nfor c in incomplete:\n yrs = sorted(d[d.iso3 == c].year)\n absent = [y for y in YEARS if y not in yrs]\n if c not in set(miss.iso3):\n issues.append({\"issue\": f\"{c} has no row at all for {len(absent)} years ({min(absent)}-{max(absent)}); \"\n f\"only {len(yrs)} of 23 years present (likely only in the narrower extraction).\",\n \"handling\": \"Not imputed; country excluded from the balanced set (included in the 2022 all-available baseline).\"})\nb = len(d); an = d[d.iso3.isin(balanced)].sort_values([\"iso3\", \"year\"])\nstep(\"balanced_panel\", \"keep countries with a recorded value in every year 2000-2022\", b, len(an),\n f\"Balanced panel; removed countries: {', '.join(incomplete)}.\")\n\nan[[\"iso3\", \"country_name\", \"year\", \"life_expectancy\"]].to_csv(f\"{OUT}/analytic.csv\", index=False)\npd.DataFrame(ledger).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\nm = an.groupby(\"year\").life_expectancy.mean()\nall22 = d[d.year == 2022].drop_duplicates(\"iso3\").life_expectancy.mean()\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\": int(len(an)),\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(all22), 4),\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(m.round(3).to_string())\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/exclusions.csv","description":"Write and run 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\"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 \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"23 duplicate (iso3, year) rows from the two-extraction assembly: {'TUR': ['Turkey', 'Turkiye']} (same iso3, different country_name spelling); life_expectancy values identical in every duplicated pair.\",\n \"handling\": \"Identified countries by iso3; kept one row per (iso3, year) (first occurrence), using the latest-spelling country name as the label for the country.\"\n },\n {\n \"issue\": \"Empty life_expectancy cell(s): CHE 2007\",\n \"handling\": \"Treated as missing (no imputation); country excluded from the balanced set.\"\n },\n {\n \"issue\": \"BGD has no row at all for 11 years (2000-2010); only 12 of 23 years present (likely only in the narrower extraction).\",\n \"handling\": \"Not imputed; country excluded from the balanced set (included in the 2022 all-available baseline).\"\n },\n {\n \"issue\": \"ETH has no row at all for 11 years (2000-2010); only 12 of 23 years present (likely only in the narrower extraction).\",\n \"handling\": \"Not imputed; country excluded from the balanced set (included in the 2022 all-available baseline).\"\n },\n {\n \"issue\": \"IND has no row at all for 11 years (2000-2010); only 12 of 23 years present (likely only in the narrower extraction).\",\n \"handling\": \"Not imputed; country excluded from the balanced set (included in the 2022 all-available baseline).\"\n },\n {\n \"issue\": \"NGA has no row at all for 11 years (2000-2010); only 12 of 23 years present (likely only in the narrower extraction).\",\n \"handling\": \"Not imputed; country excluded from the balanced set (included in the 2022 all-available baseline).\"\n }\n ],\n \"n_input_rows\": {\n \"life_expectancy_panel.csv\": 439\n },\n \"random_seed\": null\n}\nyear\n2000 78.060\n2001 78.365\n2002 78.519\n2003 78.651\n2004 79.119\n2005 79.292\n2006 79.671\n2007 79.930\n2008 80.088\n2009 80.351\n2010 80.541\n2011 80.850\n2012 80.942\n2013 81.192\n2014 81.524\n2015 81.364\n2016 81.602\n2017 81.665\n2018 81.783\n2019 82.073\n2020 81.320\n2021 81.447\n2022 81.553\nstep,rule,n_before,n_after,n_removed,rationale\n1,year_window: keep rows with year in 2000-2022,439,439,0,Study period.\n2,\"dedupe: one row per (iso3, year)\",439,416,23,Each country counted once per year; duplicates were identical.\n3,drop_missing: drop country-years with empty life_expectancy,416,415,1,No imputation allowed.\n4,balanced_panel: keep countries with a recorded value in every year 2000-2022,415,345,70,\"Balanced panel; removed countries: BGD, CHE, ETH, IND, NGA.\"\n 346 out/analytic.csv","is_error":false}]},"uuid":"1a20e520-944a-4c5b-a5e6-966c9e10f673","timestamp":"2026-10-30T04:42:44.721Z","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 \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"23 duplicate (iso3, year) rows from the two-extraction assembly: {'TUR': ['Turkey', 'Turkiye']} (same iso3, different country_name spelling); life_expectancy values identical in every duplicated pair.\",\n \"handling\": \"Identified countries by iso3; kept one row per (iso3, year) (first occurrence), using the latest-spelling country name as the label for the country.\"\n },\n {\n \"issue\": \"Empty life_expectancy cell(s): CHE 2007\",\n \"handling\": \"Treated as missing (no imputation); country excluded from the balanced set.\"\n },\n {\n \"issue\": \"BGD has no row at all for 11 years (2000-2010); only 12 of 23 years present (likely only in the narrower extraction).\",\n \"handling\": \"Not imputed; country excluded from the balanced set (included in the 2022 all-available baseline).\"\n },\n {\n \"issue\": \"ETH has no row at all for 11 years (2000-2010); only 12 of 23 years present (likely only in the narrower extraction).\",\n \"handling\": \"Not imputed; country excluded from the balanced set (included in the 2022 all-available baseline).\"\n },\n {\n \"issue\": \"IND has no row at all for 11 years (2000-2010); only 12 of 23 years present (likely only in the narrower extraction).\",\n \"handling\": \"Not imputed; country excluded from the balanced set (included in the 2022 all-available baseline).\"\n },\n {\n \"issue\": \"NGA has no row at all for 11 years (2000-2010); only 12 of 23 years present (likely only in the narrower extraction).\",\n \"handling\": \"Not imputed; country excluded from the balanced set (included in the 2022 all-available baseline).\"\n }\n ],\n \"n_input_rows\": {\n \"life_expectancy_panel.csv\": 439\n },\n \"random_seed\": null\n}\nyear\n2000 78.060\n2001 78.365\n2002 78.519\n2003 78.651\n2004 79.119\n2005 79.292\n2006 79.671\n2007 79.930\n2008 80.088\n2009 80.351\n2010 80.541\n2011 80.850\n2012 80.942\n2013 81.192\n2014 81.524\n2015 81.364\n2016 81.602\n2017 81.665\n2018 81.783\n2019 82.073\n2020 81.320\n2021 81.447\n2022 81.553\nstep,rule,n_before,n_after,n_removed,rationale\n1,year_window: keep rows with year in 2000-2022,439,439,0,Study period.\n2,\"dedupe: one row per (iso3, year)\",439,416,23,Each country counted once per year; duplicates were identical.\n3,drop_missing: drop country-years with empty life_expectancy,416,415,1,No imputation allowed.\n4,balanced_panel: keep countries with a recorded value in every year 2000-2022,415,345,70,\"Balanced panel; removed countries: BGD, CHE, ETH, IND, NGA.\"\n 346 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>> out/analysis.py <<'EOF'\n\n# Descriptive sensitivity checks (printed only; not part of results.json)\nnaive_dup = raw[(raw.year == 2022) & raw.life_expectancy.notna()].life_expectancy.mean()\nwith_che = d[d.iso3.isin(balanced + [\"CHE\"])]\nprint(\"SENS naive 2022 mean if TUR duplicate rows were not removed:\", round(float(naive_dup), 4))\nprint(\"SENS 15+CHE mean 2000/2022/change:\",\n [round(float(with_che[with_che.year == y].life_expectancy.mean()), 4) for y in (2000, 2022)],\n round(float(with_che[with_che.year == 2022].life_expectancy.mean() - with_che[with_che.year == 2000].life_expectancy.mean()), 4))\nprint(\"SENS n countries with 2022 value:\", d[d.year == 2022].iso3.nunique())\nprint(\"SENS per-country change range (balanced):\",\n (an[an.year == 2022].set_index(\"iso3\").life_expectancy - an[an.year == 2000].set_index(\"iso3\").life_expectancy).agg([\"min\", \"idxmin\", \"max\", \"idxmax\"]).to_dict())\nEOF\n~/wp29/venv/bin/python out/analysis.py | grep 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naive 2022 mean if TUR duplicate rows were not removed: 78.6396\nSENS 15+CHE mean 2000/2022/change: [78.1615, 81.6813] 3.5198\nSENS n countries with 2022 value: 20\nSENS per-country change range (balanced): {'min': 0.796999999999997, 'idxmin': 'USA', 'max': 6.77000000000001, 'idxmax': 'KOR'}","is_error":false}]},"uuid":"2121762f-599e-4fb1-8f47-6ef4239f0cf6","timestamp":"2026-10-30T04:42:52.343Z","toolUseResult":{"stdout":"SENS naive 2022 mean if TUR duplicate rows were not removed: 78.6396\nSENS 15+CHE mean 2000/2022/change: [78.1615, 81.6813] 3.5198\nSENS n countries with 2022 value: 20\nSENS per-country change range (balanced): {'min': 0.796999999999997, 'idxmin': 'USA', 'max': 6.77000000000001, 'idxmax': 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>> out/analysis.py <<'EOF'\n\n# Descriptive sensitivity checks (printed only; not part of results.json)\nnaive_dup = raw[(raw.year == 2022) & raw.life_expectancy.notna()].life_expectancy.mean()\nwith_che = d[d.iso3.isin(balanced + [\"CHE\"])]\nprint(\"SENS naive 2022 mean if TUR duplicate rows were not removed:\", round(float(naive_dup), 4))\nprint(\"SENS 15+CHE mean 2000/2022/change:\",\n [round(float(with_che[with_che.year == y].life_expectancy.mean()), 4) for y in (2000, 2022)],\n round(float(with_che[with_che.year == 2022].life_expectancy.mean() - with_che[with_che.year == 2000].life_expectancy.mean()), 4))\nprint(\"SENS n countries with 2022 value:\", d[d.year == 2022].iso3.nunique())\nprint(\"SENS per-country change range (balanced):\",\n (an[an.year == 2022].set_index(\"iso3\").life_expectancy - an[an.year == 2000].set_index(\"iso3\").life_expectancy).agg([\"min\", \"idxmin\", \"max\", \"idxmax\"]).to_dict())\nEOF\n~/wp29/venv/bin/python out/analysis.py | grep 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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 simple unweighted mean across a fixed set of countries: those with a recorded value in every year from 2000 to 2022, each country counted once. As a descriptive baseline, the report also gives the naive 2022 mean over every country with a 2022 value.\n\n## Data and exclusions\nInput: `data/life_expectancy_panel.csv`, 439 data rows, 20 distinct iso3 codes, years 2000–2022. The file was assembled from two extractions. I found these problems:\n\n1. **Duplicate country (TUR).** Türkiye appears twice, once as \"Turkey\" and once as \"Turkiye\", so there are 46 rows for 23 years. The values are identical in all 23 duplicated pairs. Countries are identified by iso3, so I kept one row per (iso3, year) and labelled the country with the later spelling, \"Turkiye\". This removed 23 rows.\n2. **Missing cell.** Switzerland (CHE) has an empty value for 2007. It is treated as missing, with no imputation, so CHE is not in the balanced set. Its 2022 value is still used in the baseline.\n3. **Truncated series.** Bangladesh (BGD), Ethiopia (ETH), India (IND) and Nigeria (NGA) only have rows for 2011–2022; there are no rows for 2000–2010. They are excluded from the balanced set but included in the 2022 baseline.\n\nI screened the values for implausible entries (all fall between 51.5 and 84.6 years) and for large year-to-year jumps. The largest jumps are the 2020–2022 COVID-era drops and rebounds (for example IND 2021 −2.9 and 2022 +4.4, USA 2020 −1.8). These match known patterns, so I left them unchanged.\n\nExclusion ledger (`out/exclusions.csv`): 439 rows read → 439 after the year-window filter → 416 after removing duplicates (−23) → 415 after dropping the missing cell (−1) → **345** in the balanced panel (−70). The 345 rows are 15 countries × 23 years.\n\n## Method\n- Balanced set: the 15 countries with a non-missing value in all 23 years: AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA.\n- For each year, the unweighted mean over these 15 countries. Change = 2022 mean − 2000 mean.\n- Baseline: the unweighted mean of 2022 values over all distinct iso3 codes with a 2022 value. That is 20 countries, with TUR counted once.\n- No randomness was used. `out/analysis.py` regenerates every output deterministically.\n\n## Results\n| quantity | value (years) |\n|---|---|\n| Balanced mean, 2000 (n=15) | 78.0603 |\n| Balanced mean, 2022 (n=15) | 81.5531 |\n| Change 2000→2022 (balanced) | **+3.4928** |\n| Naive 2022 mean, all available (n=20) | 78.6920 |\n\nIn the balanced panel, mean life expectancy rose fairly steadily to a peak of 82.07 in 2019. It fell to 81.32 in 2020 and had only partly recovered by 2022 (81.55). By country, the 2000–2022 change ranges from +0.80 years (USA) to +6.77 years (KOR).\n\nThe naive 2022 mean (78.69) is about 2.9 years below the balanced 2022 mean. This is because it adds four lower-life-expectancy countries (BGD, ETH, IND, NGA) and CHE. It is a different population, so it should not be compared with the 2000 balanced mean.\n\nSensitivity checks (printed by `analysis.py`, not part of the estimand):\n- If the TUR duplicates were not removed, the naive 2022 mean would be 78.6396.\n- If CHE were kept despite its missing 2007 value, the means would be 78.1615 for 2000 and 81.6813 for 2022, a change of +3.5198.\n\n## Uncertainty\nThese figures are exact descriptive statistics for a fixed set of countries, not estimates from a sample, so I report no sampling standard errors or confidence intervals. The only remaining uncertainty is measurement uncertainty in the WDI figures themselves, much of which are modelled estimates (UN WPP). That uncertainty is not quantified here, and the WDI may revise these values in later releases.\n\n## Limitations and open issues\n- The balanced set has 15 countries, mostly high-income OECD members plus Türkiye. The result does not describe the full 20-country set, and certainly not the world.\n- The means are unweighted (each country counts equally), not population-weighted.\n- I cannot tell from the file alone whether the missing CHE 2007 value and the missing 2000–2010 rows for BGD/ETH/IND/NGA are real gaps in the source or artefacts of the two-extraction assembly. The WDI normally does have values for these. Following the task rules, I treated them as missing and did not fill them from any other source.\n- Choosing which Türkiye spelling to keep as the label does not affect any number, because the duplicated values are identical.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Among the 15 countries with complete 2000-2022 data, unweighted mean life expectancy rose from 78.06 years in 2000 to 81.55 years in 2022, an increase of about 3.49 years.\",\"results_keys\":[\"mean_2000_balanced\",\"mean_2022_balanced\",\"change_2000_2022_balanced\",\"n_countries_balanced\"],\"status\":\"supported\",\"caveat\":\"Unweighted mean over a mostly high-income set; not representative of the 20 countries or the world.\"},\n {\"id\":\"C2\",\"text\":\"5 of 20 countries were excluded from the balanced set: CHE (missing 2007) and BGD, ETH, IND, NGA (no rows for 2000-2010).\",\"results_keys\":[\"n_countries_total\",\"n_countries_incomplete\",\"balanced_countries\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Missingness may be an assembly artefact rather than a true gap in the source; no imputation was done, per the task rules.\"},\n {\"id\":\"C3\",\"text\":\"Türkiye appeared twice (as 'Turkey' and 'Turkiye') with identical values; it was deduplicated by iso3, giving a 345-row balanced analytic dataset.\",\"results_keys\":[\"n_analysed\",\"n_input_rows\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Deduplication does not change any value because the duplicates were identical.\"},\n {\"id\":\"C4\",\"text\":\"The naive 2022 mean over all 20 countries with a 2022 value is 78.69 years, about 2.9 years below the balanced 2022 mean, because of the composition difference.\",\"results_keys\":[\"mean_2022_all_available\",\"mean_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Descriptive baseline only; it is not comparable with the 2000 balanced mean.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'))\" && ls out","description":"Write report and claims"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":903,"cache_read_input_tokens":12500,"output_tokens":2668,"output_tokens_details":{"thinking_tokens":102},"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":903},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXouoFN6zGs37CgMd4ki","requestRef":"21175fcd-3067-4ef3-b887-d2ada00e667e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"bce1953a-d926-47fb-83a0-a013372c578c","timestamp":"2026-10-30T04:43:16.549Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0146","sessionId":"3241f7fb-acf3-4db9-b133-bd6218d5ec1c","version":"2.1.314","gitBranch":"HEAD"}46{"parentUuid":"bce1953a-d926-47fb-83a0-a013372c578c","isSidechain":false,"promptId":"87fb3d01-2536-4f45-9405-99492fd13532","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VbVXESy6HyqoM12QoUXvv4","type":"tool_result","content":"analysis.py\nanalytic.csv\nclaims.json\nexclusions.csv\nreport.md\nresults.json","is_error":false}]},"uuid":"868d9e1d-b47d-4581-8a5e-5186dedf26ca","timestamp":"2026-10-30T04:43:16.604Z","toolUseResult":{"stdout":"analysis.py\nanalytic.csv\nclaims.json\nexclusions.csv\nreport.md\nresults.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"bce1953a-d926-47fb-83a0-a013372c578c","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0146","sessionId":"3241f7fb-acf3-4db9-b133-bd6218d5ec1c","version":"2.1.314","gitBranch":"HEAD"}47{"type":"api-request-blob","sessionId":"3241f7fb-acf3-4db9-b133-bd6218d5ec1c","hash":"8ef730a5b764a7826af0cb3391d97fa4704f3664587b1c817c386954cea9a093","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01VbVXESy6HyqoM12QoUXvv4","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 simple unweighted mean across a fixed set of countries: those with a recorded value in every year from 2000 to 2022, each country counted once. As a descriptive baseline, the report also gives the naive 2022 mean over every country with a 2022 value.\n\n## Data and exclusions\nInput: `data/life_expectancy_panel.csv`, 439 data rows, 20 distinct iso3 codes, years 2000–2022. The file was assembled from two extractions. I found these problems:\n\n1. **Duplicate country (TUR).** Türkiye appears twice, once as \"Turkey\" and once as \"Turkiye\", so there are 46 rows for 23 years. The values are identical in all 23 duplicated pairs. Countries are identified by iso3, so I kept one row per (iso3, year) and labelled the country with the later spelling, \"Turkiye\". This removed 23 rows.\n2. **Missing cell.** Switzerland (CHE) has an empty value for 2007. It is treated as missing, with no imputation, so CHE is not in the balanced set. Its 2022 value is still used in the baseline.\n3. **Truncated series.** Bangladesh (BGD), Ethiopia (ETH), India (IND) and Nigeria (NGA) only have rows for 2011–2022; there are no rows for 2000–2010. They are excluded from the balanced set but included in the 2022 baseline.\n\nI screened the values for implausible entries (all fall between 51.5 and 84.6 years) and for large year-to-year jumps. The largest jumps are the 2020–2022 COVID-era drops and rebounds (for example IND 2021 −2.9 and 2022 +4.4, USA 2020 −1.8). These match known patterns, so I left them unchanged.\n\nExclusion ledger (`out/exclusions.csv`): 439 rows read → 439 after the year-window filter → 416 after removing duplicates (−23) → 415 after dropping the missing cell (−1) → **345** in the balanced panel (−70). The 345 rows are 15 countries × 23 years.\n\n## Method\n- Balanced set: the 15 countries with a non-missing value in all 23 years: AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA.\n- For each year, the unweighted mean over these 15 countries. Change = 2022 mean − 2000 mean.\n- Baseline: the unweighted mean of 2022 values over all distinct iso3 codes with a 2022 value. That is 20 countries, with TUR counted once.\n- No randomness was used. `out/analysis.py` regenerates every output deterministically.\n\n## Results\n| quantity | value (years) |\n|---|---|\n| Balanced mean, 2000 (n=15) | 78.0603 |\n| Balanced mean, 2022 (n=15) | 81.5531 |\n| Change 2000→2022 (balanced) | **+3.4928** |\n| Naive 2022 mean, all available (n=20) | 78.6920 |\n\nIn the balanced panel, mean life expectancy rose fairly steadily to a peak of 82.07 in 2019. It fell to 81.32 in 2020 and had only partly recovered by 2022 (81.55). By country, the 2000–2022 change ranges from +0.80 years (USA) to +6.77 years (KOR).\n\nThe naive 2022 mean (78.69) is about 2.9 years below the balanced 2022 mean. This is because it adds four lower-life-expectancy countries (BGD, ETH, IND, NGA) and CHE. It is a different population, so it should not be compared with the 2000 balanced mean.\n\nSensitivity checks (printed by `analysis.py`, not part of the estimand):\n- If the TUR duplicates were not removed, the naive 2022 mean would be 78.6396.\n- If CHE were kept despite its missing 2007 value, the means would be 78.1615 for 2000 and 81.6813 for 2022, a change of +3.5198.\n\n## Uncertainty\nThese figures are exact descriptive statistics for a fixed set of countries, not estimates from a sample, so I report no sampling standard errors or confidence intervals. The only remaining uncertainty is measurement uncertainty in the WDI figures themselves, much of which are modelled estimates (UN WPP). That uncertainty is not quantified here, and the WDI may revise these values in later releases.\n\n## Limitations and open issues\n- The balanced set has 15 countries, mostly high-income OECD members plus Türkiye. The result does not describe the full 20-country set, and certainly not the world.\n- The means are unweighted (each country counts equally), not population-weighted.\n- I cannot tell from the file alone whether the missing CHE 2007 value and the missing 2000–2010 rows for BGD/ETH/IND/NGA are real gaps in the source or artefacts of the two-extraction assembly. The WDI normally does have values for these. Following the task rules, I treated them as missing and did not fill them from any other source.\n- Choosing which Türkiye spelling to keep as the label does not affect any number, because the duplicated values are identical.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Among the 15 countries with complete 2000-2022 data, unweighted mean life expectancy rose from 78.06 years in 2000 to 81.55 years in 2022, an increase of about 3.49 years.\",\"results_keys\":[\"mean_2000_balanced\",\"mean_2022_balanced\",\"change_2000_2022_balanced\",\"n_countries_balanced\"],\"status\":\"supported\",\"caveat\":\"Unweighted mean over a mostly high-income set; not representative of the 20 countries or the world.\"},\n {\"id\":\"C2\",\"text\":\"5 of 20 countries were excluded from the balanced set: CHE (missing 2007) and BGD, ETH, IND, NGA (no rows for 2000-2010).\",\"results_keys\":[\"n_countries_total\",\"n_countries_incomplete\",\"balanced_countries\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Missingness may be an assembly artefact rather than a true gap in the source; no imputation was done, per the task rules.\"},\n {\"id\":\"C3\",\"text\":\"Türkiye appeared twice (as 'Turkey' and 'Turkiye') with identical values; it was deduplicated by iso3, giving a 345-row balanced analytic dataset.\",\"results_keys\":[\"n_analysed\",\"n_input_rows\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Deduplication does not change any value because the duplicates were identical.\"},\n {\"id\":\"C4\",\"text\":\"The naive 2022 mean over all 20 countries with a 2022 value is 78.69 years, about 2.9 years below the balanced 2022 mean, because of the composition difference.\",\"results_keys\":[\"mean_2022_all_available\",\"mean_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Descriptive baseline only; it is not comparable with the 2000 balanced mean.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'))\" && ls out","description":"Write report and claims"}}]}}48{"type":"api-request","sessionId":"3241f7fb-acf3-4db9-b133-bd6218d5ec1c","id":"b7206f25-1424-4088-b404-18808dbfcb91","timestamp":"2026-10-30T04:43:16.608Z","version":"2.1.314","querySource":"sdk","shapeHash":"dda230fb3163780039b904a7cab335cbb247d7724e70550da8ed8cf793d411bb","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":"21175fcd-3067-4ef3-b887-d2ada00e667e","keep":14,"tail":["8ef730a5b764a7826af0cb3391d97fa4704f3664587b1c817c386954cea9a093","2f626a8b083bdb3460115bbd079f38eb76bd329ff4c94e6cc713c0aac64cb4c3"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"afdddd014413e3b2847c99705faabee3b70fc6034e05776f4ff2b4f2d8bc3f20"}49{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"868d9e1d-b47d-4581-8a5e-5186dedf26ca","sessionId":"3241f7fb-acf3-4db9-b133-bd6218d5ec1c"}50{"type":"atis-latch","atis":"","sessionId":"3241f7fb-acf3-4db9-b133-bd6218d5ec1c"}51{"parentUuid":"868d9e1d-b47d-4581-8a5e-5186dedf26ca","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXowbK4aWhqUZTPm7UCM","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":2706,"cache_read_input_tokens":13403,"output_tokens":964,"output_tokens_details":{"thinking_tokens":23},"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":2706},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":6793,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXowb2TgwT7hAya4zLQR","requestRef":"b7206f25-1424-4088-b404-18808dbfcb91","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9993c1b1-b949-433a-9242-802e5421b174","timestamp":"2026-10-30T04:43:24.761Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0146","sessionId":"3241f7fb-acf3-4db9-b133-bd6218d5ec1c","version":"2.1.314","gitBranch":"HEAD"}52{"parentUuid":"9993c1b1-b949-433a-9242-802e5421b174","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXowbK4aWhqUZTPm7UCM","type":"message","role":"assistant","content":[{"type":"text","text":"I've finished the analysis. Across the 15 countries with a recorded value in every year from 2000 to 2022, the simple average life expectancy rose by **+3.49 years**, from 78.06 to 81.55. The task counts as complete, and running `out/analysis.py` regenerates all the results from `data/`.\n\n| Quantity | Value (years) |\n|---|---|\n| Balanced-set average, 2000 (15 countries) | 78.0603 |\n| Balanced-set average, 2022 (15 countries) | 81.5531 |\n| Change, 2000 to 2022 | +3.4928 |\n| Simple 2022 average, every country with a 2022 value (20) | 78.6920 |\n\n- **Which countries are included:** The balanced set is AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR and USA, giving 345 country-year rows.\n- **The 2022 baseline is a different group:** It is about 2.9 years lower because it adds lower-life-expectancy countries. It shouldn't be compared with the 2000 figure.\n- **Spread and trend:** The country-level change runs from +0.80 years (USA) to +6.77 years (KOR). The balanced average peaked at 82.07 in 2019, fell in 2020 and had only partly recovered by 2022.\n\n**Problems in the data file and how I handled them:**\n- **Türkiye appears twice,** as \"Turkey\" and \"Turkiye\", with identical values in all 23 years. I kept one row per country code and year, which removed 23 rows and doesn't change any number.\n- **Switzerland has no value for 2007,** so it is left out of the balanced set. Its 2022 value is still in the baseline.\n- **Bangladesh, Ethiopia, India and Nigeria have no rows for 2000–2010,** so they are also left out of the balanced set but included in the baseline.\n\n**Limitations:**\n- **Mostly rich countries:** The balanced set is largely high-income OECD countries plus Türkiye, so it doesn't represent all 20 countries, let alone the world.\n- **Unweighted averages:** Each country counts equally, with no weighting by population.\n- **Possibly artificial gaps:** The World Bank normally publishes values for these countries and years. So the missing Swiss 2007 value and the missing early years may be errors from how the file was assembled rather than real gaps. As the task requires, I didn't fill them in.\n- **Swiss sensitivity check:** Including Switzerland despite its gap would make the change +3.52 instead of +3.49.\n\nNo randomness was used.\n\nFiles are in `out/`:\n- `results.json`\n- `analytic.csv`\n- `exclusions.csv`\n- `report.md`\n- `claims.json`\n- `analysis.py`"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2706,"cache_read_input_tokens":13403,"output_tokens":964,"output_tokens_details":{"thinking_tokens":23},"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":2706},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXowb2TgwT7hAya4zLQR","requestRef":"b7206f25-1424-4088-b404-18808dbfcb91","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5a5589e9-4607-41a2-920f-d1f18db2b39b","timestamp":"2026-10-30T04:43:26.166Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0146","sessionId":"3241f7fb-acf3-4db9-b133-bd6218d5ec1c","version":"2.1.314","gitBranch":"HEAD"}53{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"5a5589e9-4607-41a2-920f-d1f18db2b39b","sessionId":"3241f7fb-acf3-4db9-b133-bd6218d5ec1c"}54{"type":"cost-state","sessionId":"3241f7fb-acf3-4db9-b133-bd6218d5ec1c","totalCostUSD":0.23890359999999997,"totalAPIDuration":71527,"totalAPIDurationWithoutRetries":71522,"totalToolDuration":1519,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":73237,"startTime":1793335332936,"modelUsage":{"claude-opus-5-5":{"inputTokens":18,"outputTokens":7694,"thinkingTokens":514,"cacheReadInputTokens":59633,"cacheCreationInputTokens":14605,"cacheCreationEphemeral5mInputTokens":14605,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.23890359999999997}},"hasUnknownModelCost":false}55