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It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md`. Below is some general advice about doing careful empirical research; it adds nothing specific to this task and you are free to ignore it.\n\nCareful empirical work usually begins by reading the task statement slowly and more than once, because the most common errors in applied research come not from difficult mathematics but from a misunderstanding of what exactly is being asked, which quantity is of interest, and which inputs are legitimately available. It is worth pausing to think about what the quantity of interest actually means, under what conditions it is well defined, and what would make an estimate of it misleading. Many published results have been overturned not because the estimator was wrong but because the sample was not what the authors believed it to be, or because information that should not have been available at the time of a decision had quietly entered the analysis.\n\nData deserve suspicion. Files assembled from several sources often carry inconsistencies: duplicated records, mixed units, shifted dates, columns whose definitions changed mid-sample, or values that were revised after the fact. Good practice is to look at the data before modelling them: distributions, ranges, missing values, identifiers, and whether counts add up. When something looks odd, it is better to document it and decide explicitly how to handle it than to let a default silently decide. The same applies to transformations: scaling, differencing, winsorising and imputing all change what is being estimated, and each deserves a sentence of justification.\n\nSimple baselines are underrated. A naïve or unconditional estimate computed with exactly the same evaluation rule as the main method tells the reader how much the method actually adds and protects against the illusion of progress created by a complicated pipeline. If the elaborate method does not beat the simple one, that is a finding, not a failure.\n\nUncertainty is part of the result. Standard errors must respect the dependence structure of the data; a confidence interval that ignores clustering, serial correlation or overlapping targets is not conservative, it is wrong. Where the sampling distribution is unknown, resampling or simulation can help, but the assumptions behind them should be stated. A result that is not statistically distinguishable from zero should be reported as such, in plain words.\n\nReproducibility is a courtesy to the reader and a safeguard for the author. Keeping the analysis in a script that reads the raw inputs and regenerates every reported number makes it possible to check the work, to find mistakes, and to rerun the analysis when the inputs change. Fixing random seeds, recording software versions and keeping intermediate files out of the final results all reduce the chance that a number in the report cannot be traced back to a computation.\n\nFinally, honesty about limitations is what distinguishes research from advocacy. Every analysis has weaknesses: assumptions that may not hold, data that may not be representative, methods that trade one bias for another. Stating them clearly, together with what would be needed to resolve them, is more valuable than a confident conclusion that the evidence does not support. 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the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0004","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099"}12{"parentUuid":"8d978f7c-c40f-4dc1-bb67-c61d40f742ad","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsbL1jBeazu8tVYxguW","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Ep4NegVKKsEVzdaiQngjDE","name":"Bash","input":{"command":"cat TASK.md; 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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.\ntotal 32\ndrwxr-xr-x 4 simon-pierreboucher staff 128 oct. 29 23:35 .\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:41 ..\n-rw-r--r-- 1 simon-pierreboucher staff 11030 oct. 29 23:33 life_expectancy_panel.csv\n-rw-r--r-- 1 simon-pierreboucher staff 1416 oct. 29 23:35 README.md\n==> data/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\n==> data/README.md <==\n# 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`","is_error":false}]},"uuid":"fe69cf86-aa15-4909-8b53-4cf9d2892cff","timestamp":"2026-10-30T05:31:11.426Z","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. 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.\ntotal 32\ndrwxr-xr-x 4 simon-pierreboucher staff 128 oct. 29 23:35 .\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:41 ..\n-rw-r--r-- 1 simon-pierreboucher staff 11030 oct. 29 23:33 life_expectancy_panel.csv\n-rw-r--r-- 1 simon-pierreboucher staff 1416 oct. 29 23:35 README.md\n==> data/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\n==> data/README.md <==\n# 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`","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"b0c49ddc-1473-4b82-93ea-53eeca37c631","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0004","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","hash":"4bbaf0d6f6a6412c6beebd454078f5d307c90f7cdfacb6889818b8a1111bf7f4","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01Ep4NegVKKsEVzdaiQngjDE","name":"Bash","input":{"command":"cat TASK.md; ls -la data; head -5 data/*"}}]}}15{"type":"api-request","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","id":"4cebecde-03cc-4f06-ae30-5e1effc15aea","timestamp":"2026-10-30T05:31:11.429Z","version":"2.1.314","querySource":"sdk","shapeHash":"216a004784203def40e37f4e7512971c54f2a007b72c5bd931bcb5dbc4a0cfda","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":"21b0e1bc-4984-454d-be74-4c9bc4cfc1ca","keep":2,"tail":["4bbaf0d6f6a6412c6beebd454078f5d307c90f7cdfacb6889818b8a1111bf7f4","15ac16d5246bdca14c0cba4bb643b903844fecb17594b7e9f1440b775bf3cf9b"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"bf170bf8d0b86e8973932c3fbcbc1c0859de172f47ecfd597912b820e478ac1c"}16{"parentUuid":"fe69cf86-aa15-4909-8b53-4cf9d2892cff","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsbVx1sS3HnGo1egxME","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XBh5MiNoo67cYLKwd32tGf","name":"Bash","input":{"command":"cat data/README.md | tail -5; cd data; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('life_expectancy_panel.csv')\nprint(d.shape, d.dtypes)\nprint(d.isna().sum())\nprint(d.groupby('iso3').agg(n=('year','size'),nu=('year','nunique'),na=('life_expectancy',lambda s:s.isna().sum()),names=('country_name','nunique'),mn=('life_expectancy','min'),mx=('life_expectancy','max')))\nprint(d.year.value_counts().sort_index())\ndup=d[d.duplicated(['iso3','year'],keep=False)];print(dup)\nprint(d[d.isna().any(axis=1)])\nprint(d.country_name.unique()); print(d.iso3.unique())\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2318,"cache_read_input_tokens":3186,"output_tokens":330,"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":2318},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsbVg9Q5PLvnSkmpm1L","requestRef":"4cebecde-03cc-4f06-ae30-5e1effc15aea","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0f82ec9a-57cb-4dc1-8e0c-03cc29a94298","timestamp":"2026-10-30T05:31:14.693Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0004","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","version":"2.1.314","gitBranch":"HEAD"}17{"parentUuid":"0f82ec9a-57cb-4dc1-8e0c-03cc29a94298","isSidechain":false,"promptId":"1b36e6a4-33d7-42a9-8e14-a5ad7ff3ec18","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XBh5MiNoo67cYLKwd32tGf","type":"tool_result","content":"- 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(439, 4) country_name object\niso3 object\nyear int64\nlife_expectancy float64\ndtype: object\ncountry_name 0\niso3 0\nyear 0\nlife_expectancy 1\ndtype: int64\n n nu na names mn mx\niso3 \nAUS 23 23 0 1 79.234 83.300\nAUT 23 23 0 1 78.127 81.895\nBEL 23 23 0 1 77.722 81.995\nBGD 12 12 0 1 68.499 74.265\nCAN 23 23 0 1 79.167 82.159\nCHE 23 23 1 1 79.680 83.905\nDEU 23 23 0 1 77.927 81.293\nESP 23 23 0 1 78.966 83.832\nETH 12 12 0 1 60.595 66.897\nFRA 23 23 0 1 79.056 82.827\nGBR 23 23 0 1 77.741 81.369\nIND 12 12 0 1 67.282 71.698\nITA 23 23 0 1 79.778 83.498\nJPN 23 23 0 1 81.076 84.560\nKOR 23 23 0 1 75.910 83.527\nNGA 12 12 0 1 51.490 54.079\nNLD 23 23 0 1 77.988 82.112\nSWE 23 23 0 1 79.644 83.110\nTUR 46 23 0 2 71.931 77.737\nUSA 23 23 0 1 76.329 78.841\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\n country_name iso3 year life_expectancy\n347 Turkey TUR 2000 71.931\n348 Turkey TUR 2001 72.271\n349 Turkey TUR 2002 72.605\n350 Turkey TUR 2003 72.906\n351 Turkey TUR 2004 73.223\n352 Turkey TUR 2005 73.510\n353 Turkey TUR 2006 73.804\n354 Turkey TUR 2007 74.129\n355 Turkey TUR 2008 74.380\n356 Turkey TUR 2009 74.679\n357 Turkey TUR 2010 74.964\n358 Turkey TUR 2011 74.823\n359 Turkey TUR 2012 75.552\n360 Turkey TUR 2013 76.171\n361 Turkey TUR 2014 76.452\n362 Turkey TUR 2015 76.539\n363 Turkey TUR 2016 76.595\n364 Turkey TUR 2017 77.037\n365 Turkey TUR 2018 77.454\n366 Turkey TUR 2019 77.737\n367 Turkey TUR 2020 76.525\n368 Turkey TUR 2021 75.722\n369 Turkey TUR 2022 77.591\n370 Turkiye TUR 2000 71.931\n371 Turkiye TUR 2001 72.271\n372 Turkiye TUR 2002 72.605\n373 Turkiye TUR 2003 72.906\n374 Turkiye TUR 2004 73.223\n375 Turkiye TUR 2005 73.510\n376 Turkiye TUR 2006 73.804\n377 Turkiye TUR 2007 74.129\n378 Turkiye TUR 2008 74.380\n379 Turkiye TUR 2009 74.679\n380 Turkiye TUR 2010 74.964\n381 Turkiye TUR 2011 74.823\n382 Turkiye TUR 2012 75.552\n383 Turkiye TUR 2013 76.171\n384 Turkiye TUR 2014 76.452\n385 Turkiye TUR 2015 76.539\n386 Turkiye TUR 2016 76.595\n387 Turkiye TUR 2017 77.037\n388 Turkiye TUR 2018 77.454\n389 Turkiye TUR 2019 77.737\n390 Turkiye TUR 2020 76.525\n391 Turkiye TUR 2021 75.722\n392 Turkiye TUR 2022 77.591\n country_name iso3 year life_expectancy\n331 Switzerland CHE 2007 NaN\n['Australia' 'Austria' 'Bangladesh' 'Belgium' 'Canada' 'Ethiopia' 'France'\n 'Germany' 'India' 'Italy' 'Japan' 'Korea, Rep.' 'Netherlands' 'Nigeria'\n 'Spain' 'Sweden' 'Switzerland' 'Turkey' 'Turkiye' 'United Kingdom'\n 'United States']\n['AUS' 'AUT' 'BGD' 'BEL' 'CAN' 'ETH' 'FRA' 'DEU' 'IND' 'ITA' 'JPN' 'KOR'\n 'NLD' 'NGA' 'ESP' 'SWE' 'CHE' 'TUR' 'GBR' 'USA']","is_error":false}]},"uuid":"819ed568-1df3-4643-a5d5-20d58eb92060","timestamp":"2026-10-30T05:31:14.894Z","toolUseResult":{"stdout":"- 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(439, 4) country_name object\niso3 object\nyear int64\nlife_expectancy float64\ndtype: object\ncountry_name 0\niso3 0\nyear 0\nlife_expectancy 1\ndtype: int64\n n nu na names mn mx\niso3 \nAUS 23 23 0 1 79.234 83.300\nAUT 23 23 0 1 78.127 81.895\nBEL 23 23 0 1 77.722 81.995\nBGD 12 12 0 1 68.499 74.265\nCAN 23 23 0 1 79.167 82.159\nCHE 23 23 1 1 79.680 83.905\nDEU 23 23 0 1 77.927 81.293\nESP 23 23 0 1 78.966 83.832\nETH 12 12 0 1 60.595 66.897\nFRA 23 23 0 1 79.056 82.827\nGBR 23 23 0 1 77.741 81.369\nIND 12 12 0 1 67.282 71.698\nITA 23 23 0 1 79.778 83.498\nJPN 23 23 0 1 81.076 84.560\nKOR 23 23 0 1 75.910 83.527\nNGA 12 12 0 1 51.490 54.079\nNLD 23 23 0 1 77.988 82.112\nSWE 23 23 0 1 79.644 83.110\nTUR 46 23 0 2 71.931 77.737\nUSA 23 23 0 1 76.329 78.841\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\n country_name iso3 year life_expectancy\n347 Turkey TUR 2000 71.931\n348 Turkey TUR 2001 72.271\n349 Turkey TUR 2002 72.605\n350 Turkey TUR 2003 72.906\n351 Turkey TUR 2004 73.223\n352 Turkey TUR 2005 73.510\n353 Turkey TUR 2006 73.804\n354 Turkey TUR 2007 74.129\n355 Turkey TUR 2008 74.380\n356 Turkey TUR 2009 74.679\n357 Turkey TUR 2010 74.964\n358 Turkey TUR 2011 74.823\n359 Turkey TUR 2012 75.552\n360 Turkey TUR 2013 76.171\n361 Turkey TUR 2014 76.452\n362 Turkey TUR 2015 76.539\n363 Turkey TUR 2016 76.595\n364 Turkey TUR 2017 77.037\n365 Turkey TUR 2018 77.454\n366 Turkey TUR 2019 77.737\n367 Turkey TUR 2020 76.525\n368 Turkey TUR 2021 75.722\n369 Turkey TUR 2022 77.591\n370 Turkiye TUR 2000 71.931\n371 Turkiye TUR 2001 72.271\n372 Turkiye TUR 2002 72.605\n373 Turkiye TUR 2003 72.906\n374 Turkiye TUR 2004 73.223\n375 Turkiye TUR 2005 73.510\n376 Turkiye TUR 2006 73.804\n377 Turkiye TUR 2007 74.129\n378 Turkiye TUR 2008 74.380\n379 Turkiye TUR 2009 74.679\n380 Turkiye TUR 2010 74.964\n381 Turkiye TUR 2011 74.823\n382 Turkiye TUR 2012 75.552\n383 Turkiye TUR 2013 76.171\n384 Turkiye TUR 2014 76.452\n385 Turkiye TUR 2015 76.539\n386 Turkiye TUR 2016 76.595\n387 Turkiye TUR 2017 77.037\n388 Turkiye TUR 2018 77.454\n389 Turkiye TUR 2019 77.737\n390 Turkiye TUR 2020 76.525\n391 Turkiye TUR 2021 75.722\n392 Turkiye TUR 2022 77.591\n country_name iso3 year life_expectancy\n331 Switzerland CHE 2007 NaN\n['Australia' 'Austria' 'Bangladesh' 'Belgium' 'Canada' 'Ethiopia' 'France'\n 'Germany' 'India' 'Italy' 'Japan' 'Korea, Rep.' 'Netherlands' 'Nigeria'\n 'Spain' 'Sweden' 'Switzerland' 'Turkey' 'Turkiye' 'United Kingdom'\n 'United States']\n['AUS' 'AUT' 'BGD' 'BEL' 'CAN' 'ETH' 'FRA' 'DEU' 'IND' 'ITA' 'JPN' 'KOR'\n 'NLD' 'NGA' 'ESP' 'SWE' 'CHE' 'TUR' 'GBR' 'USA']","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"0f82ec9a-57cb-4dc1-8e0c-03cc29a94298","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0004/data","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","version":"2.1.314","gitBranch":"HEAD"}18{"type":"api-request-blob","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","hash":"569a700e3e0bba4cd5584a53eaf6d76f93abdfc87db1a15da758bc217993c471","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01XBh5MiNoo67cYLKwd32tGf","name":"Bash","input":{"command":"cat data/README.md | tail -5; cd data; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('life_expectancy_panel.csv')\nprint(d.shape, d.dtypes)\nprint(d.isna().sum())\nprint(d.groupby('iso3').agg(n=('year','size'),nu=('year','nunique'),na=('life_expectancy',lambda s:s.isna().sum()),names=('country_name','nunique'),mn=('life_expectancy','min'),mx=('life_expectancy','max')))\nprint(d.year.value_counts().sort_index())\ndup=d[d.duplicated(['iso3','year'],keep=False)];print(dup)\nprint(d[d.isna().any(axis=1)])\nprint(d.country_name.unique()); print(d.iso3.unique())\n\""}}]}}19{"type":"api-request","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","id":"7e973548-b14d-494b-a2f0-275fa05678af","timestamp":"2026-10-30T05:31:14.896Z","version":"2.1.314","querySource":"sdk","shapeHash":"216a004784203def40e37f4e7512971c54f2a007b72c5bd931bcb5dbc4a0cfda","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":"4cebecde-03cc-4f06-ae30-5e1effc15aea","keep":4,"tail":["569a700e3e0bba4cd5584a53eaf6d76f93abdfc87db1a15da758bc217993c471","6dc915107cbcd7bdb00a8d10153c72ac281885bdccae8f7fa5167b141cd11e50"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"9a2a042e2ffdfa921b7ecb32e1f56f0ede1594bb9986f8e248817d124d106cb3"}20{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"819ed568-1df3-4643-a5d5-20d58eb92060","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099"}21{"type":"atis-latch","atis":"","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099"}22{"parentUuid":"819ed568-1df3-4643-a5d5-20d58eb92060","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsbknF7qonGFcX8Fptk","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":2564,"cache_read_input_tokens":5504,"output_tokens":2644,"output_tokens_details":{"thinking_tokens":348},"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":2564},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":3329,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsbkV8rFoX7wBbRPegC","requestRef":"7e973548-b14d-494b-a2f0-275fa05678af","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"02e25e8d-95d0-4c6e-b0d0-7dac2266f8a2","timestamp":"2026-10-30T05:31:19.564Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0004/data","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","version":"2.1.314","gitBranch":"HEAD"}23{"parentUuid":"02e25e8d-95d0-4c6e-b0d0-7dac2266f8a2","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsbknF7qonGFcX8Fptk","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012G2i5GXfGdaz7CtMNUSRnz","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\"\nYEARS = list(range(2000, 2023))\nos.makedirs(\"out\", exist_ok=True)\n\nraw = pd.read_csv(IN, dtype={\"country_name\": str, \"iso3\": str})\nfor c in [\"country_name\", \"iso3\"]:\n raw[c] = raw[c].str.strip()\nn_read = len(raw)\nissues, ledger = [], []\n\ndef step(rule, before, after, rationale):\n ledger.append(dict(step=len(ledger) + 1, rule=rule, n_before=before, n_after=after,\n n_removed=before - after, rationale=rationale))\n\n# --- integrity checks\nassert raw[\"year\"].between(2000, 2022).all()\nnames = raw.groupby(\"iso3\")[\"country_name\"].unique()\nmulti = names[names.apply(len) > 1]\nfor iso, nm in multi.items():\n issues.append({\"issue\": f\"iso3 {iso} appears under several country names {sorted(nm)} (assembly of two extractions)\",\n \"handling\": \"Countries identified by iso3 only; a single name kept (the last listed in the file, i.e. the later spelling).\"})\n\n# --- step 1: duplicate (iso3, year) rows\nd = raw.copy()\ndupmask = d.duplicated([\"iso3\", \"year\"], keep=False)\nif dupmask.any():\n g = d[dupmask].groupby([\"iso3\", \"year\"])[\"life_expectancy\"].nunique(dropna=False)\n conflicting = g[g > 1]\n if len(conflicting):\n raise SystemExit(f\"Conflicting duplicate values: {conflicting}\")\n n_dup_rows = int(d.duplicated([\"iso3\", \"year\"]).sum())\n issues.append({\"issue\": f\"{n_dup_rows} duplicate (iso3, year) rows for {sorted(d[dupmask].iso3.unique())}; values identical across duplicates\",\n \"handling\": \"Kept one row per (iso3, year) (last occurrence); since values are identical the choice does not affect results.\"})\nbefore = len(d)\nd = d.drop_duplicates([\"iso3\", \"year\"], keep=\"last\").copy()\nstep(\"Remove duplicate (iso3, year) rows\", before, len(d),\n \"Each country contributes at most one observation per year; duplicates had identical values.\")\n# harmonise name per iso3 (last listed)\nd[\"country_name\"] = d[\"iso3\"].map(raw.groupby(\"iso3\")[\"country_name\"].last())\n\n# --- step 2: rows with missing value\nmiss = d[d[\"life_expectancy\"].isna()]\nfor _, r in miss.iterrows():\n issues.append({\"issue\": f\"Missing life_expectancy for {r.iso3} {r.year} (empty cell)\",\n \"handling\": \"Treated as missing (no imputation); country therefore excluded from the balanced set.\"})\nbefore = len(d)\nd = d.dropna(subset=[\"life_expectancy\"])\nstep(\"Remove country-years with no recorded life_expectancy\", before, len(d),\n \"Empty cell = no recorded value; no imputation allowed.\")\n\n# --- coverage gaps (absent rows)\ncov = d.groupby(\"iso3\")[\"year\"].apply(lambda s: sorted(set(s)))\nbalanced = sorted([i for i, ys in cov.items() if ys == YEARS])\nall_iso = sorted(raw[\"iso3\"].unique())\nfor iso in all_iso:\n if iso in balanced:\n continue\n ys = cov.get(iso, [])\n missing_years = [y for y in YEARS if y not in ys]\n absent = [y for y in missing_years if not ((raw.iso3 == iso) & (raw.year == y)).any()]\n if absent:\n issues.append({\"issue\": f\"{iso} has no rows for years {absent[0]}-{absent[-1]} ({len(absent)} years absent from file)\",\n \"handling\": \"Not imputed; country excluded from the balanced set (still counted in the 2022 all-available baseline if 2022 recorded).\"})\n\n# --- step 3: restrict to balanced countries\nbefore = len(d)\nexcluded = [i for i in all_iso if i not in balanced]\nan = d[d[\"iso3\"].isin(balanced)].sort_values([\"iso3\", \"year\"])[[\"iso3\", \"country_name\", \"year\", \"life_expectancy\"]]\nstep(f\"Keep only countries with a recorded value in every year 2000-2022 (drops {', '.join(excluded)})\",\n before, len(an), \"Balanced-panel estimand: fixed country set across all years.\")\nassert len(an) == len(balanced) * len(YEARS)\nassert not an.duplicated([\"iso3\", \"year\"]).any()\n\nan.to_csv(\"out/analytic.csv\", index=False)\npd.DataFrame(ledger).to_csv(\"out/exclusions.csv\", index=False)\n\nm = an.groupby(\"year\")[\"life_expectancy\"].mean()\ndedup = d # one row per iso3-year, non-missing\nm22_all = dedup[dedup.year == 2022][\"life_expectancy\"].mean()\nn22_all = int((dedup.year == 2022).sum())\n\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(all_iso) - len(balanced),\n \"balanced_countries\": balanced,\n \"n_analysed\": int(len(an)),\n \"mean_2000_balanced\": round(float(m[2000]), 6),\n \"mean_2022_balanced\": round(float(m[2022]), 6),\n \"change_2000_2022_balanced\": round(float(m[2022] - m[2000]), 6),\n \"mean_2022_all_available\": round(float(m22_all), 6),\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(\"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(\"n countries with 2022 value:\", n22_all)\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":2564,"cache_read_input_tokens":5504,"output_tokens":2644,"output_tokens_details":{"thinking_tokens":348},"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":2564},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsbkV8rFoX7wBbRPegC","requestRef":"7e973548-b14d-494b-a2f0-275fa05678af","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0c3061a2-d4fc-4430-8963-90549a9daadb","timestamp":"2026-10-30T05:31:37.029Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0004/data","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"0c3061a2-d4fc-4430-8963-90549a9daadb","isSidechain":false,"promptId":"1b36e6a4-33d7-42a9-8e14-a5ad7ff3ec18","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/RA0004/data/out/analysis.py\", line 9, in <module>\n raw = pd.read_csv(IN, dtype={\"country_name\": str, \"iso3\": str})\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n return _read(filepath_or_buffer, kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n parser = TextFileReader(filepath_or_buffer, **kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n self._engine = self._make_engine(f, self.engine)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n self.handles = get_handle(\n ~~~~~~~~~~^\n f,\n ^^\n ...<6 lines>...\n storage_options=self.options.get(\"storage_options\", None),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n handle = open(\n handle,\n ...<3 lines>...\n newline=\"\",\n )\nFileNotFoundError: [Errno 2] No such file or directory: 'data/life_expectancy_panel.csv'","is_error":true,"tool_use_id":"toolu_012G2i5GXfGdaz7CtMNUSRnz"}]},"uuid":"524bbb6a-76fa-48f6-9bbc-a53cfd2e07d0","timestamp":"2026-10-30T05:31:37.221Z","toolUseResult":"Error: Exit code 1\nTraceback (most recent call last):\n File \"/Users/simon-pierreboucher/wp29/runs/RA0004/data/out/analysis.py\", line 9, in <module>\n raw = pd.read_csv(IN, dtype={\"country_name\": str, \"iso3\": str})\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n return _read(filepath_or_buffer, kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n parser = TextFileReader(filepath_or_buffer, **kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n self._engine = self._make_engine(f, self.engine)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n self.handles = get_handle(\n ~~~~~~~~~~^\n f,\n ^^\n ...<6 lines>...\n storage_options=self.options.get(\"storage_options\", None),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n handle = open(\n handle,\n ...<3 lines>...\n newline=\"\",\n )\nFileNotFoundError: [Errno 2] No such file or directory: 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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\"\nYEARS = list(range(2000, 2023))\nos.makedirs(\"out\", exist_ok=True)\n\nraw = pd.read_csv(IN, dtype={\"country_name\": str, \"iso3\": str})\nfor c in [\"country_name\", \"iso3\"]:\n raw[c] = raw[c].str.strip()\nn_read = len(raw)\nissues, ledger = [], []\n\ndef step(rule, before, after, rationale):\n ledger.append(dict(step=len(ledger) + 1, rule=rule, n_before=before, n_after=after,\n n_removed=before - after, rationale=rationale))\n\n# --- integrity checks\nassert raw[\"year\"].between(2000, 2022).all()\nnames = raw.groupby(\"iso3\")[\"country_name\"].unique()\nmulti = names[names.apply(len) > 1]\nfor iso, nm in multi.items():\n issues.append({\"issue\": f\"iso3 {iso} appears under several country names {sorted(nm)} (assembly of two extractions)\",\n \"handling\": \"Countries identified by iso3 only; a single name kept (the last listed in the file, i.e. the later spelling).\"})\n\n# --- step 1: duplicate (iso3, year) rows\nd = raw.copy()\ndupmask = d.duplicated([\"iso3\", \"year\"], keep=False)\nif dupmask.any():\n g = d[dupmask].groupby([\"iso3\", \"year\"])[\"life_expectancy\"].nunique(dropna=False)\n conflicting = g[g > 1]\n if len(conflicting):\n raise SystemExit(f\"Conflicting duplicate values: {conflicting}\")\n n_dup_rows = int(d.duplicated([\"iso3\", \"year\"]).sum())\n issues.append({\"issue\": f\"{n_dup_rows} duplicate (iso3, year) rows for {sorted(d[dupmask].iso3.unique())}; values identical across duplicates\",\n \"handling\": \"Kept one row per (iso3, year) (last occurrence); since values are identical the choice does not affect results.\"})\nbefore = len(d)\nd = d.drop_duplicates([\"iso3\", \"year\"], keep=\"last\").copy()\nstep(\"Remove duplicate (iso3, year) rows\", before, len(d),\n \"Each country contributes at most one observation per year; duplicates had identical values.\")\n# harmonise name per iso3 (last listed)\nd[\"country_name\"] = d[\"iso3\"].map(raw.groupby(\"iso3\")[\"country_name\"].last())\n\n# --- step 2: rows with missing value\nmiss = d[d[\"life_expectancy\"].isna()]\nfor _, r in miss.iterrows():\n issues.append({\"issue\": f\"Missing life_expectancy for {r.iso3} {r.year} (empty cell)\",\n \"handling\": \"Treated as missing (no imputation); country therefore excluded from the balanced set.\"})\nbefore = len(d)\nd = d.dropna(subset=[\"life_expectancy\"])\nstep(\"Remove country-years with no recorded life_expectancy\", before, len(d),\n \"Empty cell = no recorded value; no imputation allowed.\")\n\n# --- coverage gaps (absent rows)\ncov = d.groupby(\"iso3\")[\"year\"].apply(lambda s: sorted(set(s)))\nbalanced = sorted([i for i, ys in cov.items() if ys == YEARS])\nall_iso = sorted(raw[\"iso3\"].unique())\nfor iso in all_iso:\n if iso in balanced:\n continue\n ys = cov.get(iso, [])\n missing_years = [y for y in YEARS if y not in ys]\n absent = [y for y in missing_years if not ((raw.iso3 == iso) & (raw.year == y)).any()]\n if absent:\n issues.append({\"issue\": f\"{iso} has no rows for years {absent[0]}-{absent[-1]} ({len(absent)} years absent from file)\",\n \"handling\": \"Not imputed; country excluded from the balanced set (still counted in the 2022 all-available baseline if 2022 recorded).\"})\n\n# --- step 3: restrict to balanced countries\nbefore = len(d)\nexcluded = [i for i in all_iso if i not in balanced]\nan = d[d[\"iso3\"].isin(balanced)].sort_values([\"iso3\", \"year\"])[[\"iso3\", \"country_name\", \"year\", \"life_expectancy\"]]\nstep(f\"Keep only countries with a recorded value in every year 2000-2022 (drops {', '.join(excluded)})\",\n before, len(an), \"Balanced-panel estimand: fixed country set across all years.\")\nassert len(an) == len(balanced) * len(YEARS)\nassert not an.duplicated([\"iso3\", \"year\"]).any()\n\nan.to_csv(\"out/analytic.csv\", index=False)\npd.DataFrame(ledger).to_csv(\"out/exclusions.csv\", index=False)\n\nm = an.groupby(\"year\")[\"life_expectancy\"].mean()\ndedup = d # one row per iso3-year, non-missing\nm22_all = dedup[dedup.year == 2022][\"life_expectancy\"].mean()\nn22_all = int((dedup.year == 2022).sum())\n\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(all_iso) - len(balanced),\n \"balanced_countries\": balanced,\n \"n_analysed\": int(len(an)),\n \"mean_2000_balanced\": round(float(m[2000]), 6),\n \"mean_2022_balanced\": round(float(m[2022]), 6),\n \"change_2000_2022_balanced\": round(float(m[2022] - m[2000]), 6),\n \"mean_2022_all_available\": round(float(m22_all), 6),\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(\"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(\"n countries with 2022 value:\", n22_all)\nprint(pd.DataFrame(ledger).to_string())\nEOF\n~/wp29/venv/bin/python out/analysis.py"}}]}}26{"type":"api-request","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","id":"388cc81d-a380-4e40-bdcb-63e9eeae2f64","timestamp":"2026-10-30T05:31:37.224Z","version":"2.1.314","querySource":"sdk","shapeHash":"216a004784203def40e37f4e7512971c54f2a007b72c5bd931bcb5dbc4a0cfda","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":"7e973548-b14d-494b-a2f0-275fa05678af","keep":6,"tail":["a7148492d377dbf547b449076d97377896ebb3be9bc2e9a8c4a6a7440bac0e0e","631216b78f84b55edc88cc3b703c363b89adda99ec91738298b85db87370b5ff"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"09465055522c7b9de31f1ba5213c7570eaef4f9e6bb7f14f33234dd03232b711"}27{"parentUuid":"524bbb6a-76fa-48f6-9bbc-a53cfd2e07d0","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsdQD3ByhBfei5G5Khq","type":"message","role":"assistant","content":[{"type":"text","text":"Oops, the shell was still in `data/`; I created out/ there. Fix that."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3374,"cache_read_input_tokens":8068,"output_tokens":184,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":3374},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsdPwv4Y3St4yH6rjQg","requestRef":"388cc81d-a380-4e40-bdcb-63e9eeae2f64","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"bd1156ea-4d5c-4352-8d1a-a73f6fd6c93f","timestamp":"2026-10-30T05:31:38.610Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0004/data","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","version":"2.1.314","gitBranch":"HEAD"}28{"parentUuid":"bd1156ea-4d5c-4352-8d1a-a73f6fd6c93f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsdQD3ByhBfei5G5Khq","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01UxBWFEDNaa4hbLczHBmvbA","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RA0004 && mkdir -p out && mv data/out/analysis.py out/ && rmdir data/out && ls data && ~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && head -3 out/analytic.csv"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3374,"cache_read_input_tokens":8068,"output_tokens":184,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":3374},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsdPwv4Y3St4yH6rjQg","requestRef":"388cc81d-a380-4e40-bdcb-63e9eeae2f64","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2036ecf1-af1e-4b59-b278-823bb6243006","timestamp":"2026-10-30T05:31:39.533Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0004/data","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"2036ecf1-af1e-4b59-b278-823bb6243006","isSidechain":false,"promptId":"1b36e6a4-33d7-42a9-8e14-a5ad7ff3ec18","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01UxBWFEDNaa4hbLczHBmvbA","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\": 78.060267,\n \"mean_2022_balanced\": 81.553067,\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\": \"iso3 TUR appears under several country names ['Turkey', 'Turkiye'] (assembly of two extractions)\",\n \"handling\": \"Countries identified by iso3 only; a single name kept (the last listed in the file, i.e. the later spelling).\"\n },\n {\n \"issue\": \"23 duplicate (iso3, year) rows for ['TUR']; values identical across duplicates\",\n \"handling\": \"Kept one row per (iso3, year) (last occurrence); since values are identical the choice does not affect results.\"\n },\n {\n \"issue\": \"Missing life_expectancy for CHE 2007 (empty cell)\",\n \"handling\": \"Treated as missing (no imputation); country therefore excluded from the balanced set.\"\n },\n {\n \"issue\": \"BGD has no rows for years 2000-2010 (11 years absent from file)\",\n \"handling\": \"Not imputed; country excluded from the balanced set (still counted in the 2022 all-available baseline if 2022 recorded).\"\n },\n {\n \"issue\": \"ETH has no rows for years 2000-2010 (11 years absent from file)\",\n \"handling\": \"Not imputed; country excluded from the balanced set (still counted in the 2022 all-available baseline if 2022 recorded).\"\n },\n {\n \"issue\": \"IND has no rows for years 2000-2010 (11 years absent from file)\",\n \"handling\": \"Not imputed; country excluded from the balanced set (still counted in the 2022 all-available baseline if 2022 recorded).\"\n },\n {\n \"issue\": \"NGA has no rows for years 2000-2010 (11 years absent from file)\",\n \"handling\": \"Not imputed; country excluded from the balanced set (still counted in the 2022 all-available baseline if 2022 recorded).\"\n }\n ],\n \"n_input_rows\": {\n \"life_expectancy_panel.csv\": 439\n },\n \"random_seed\": null\n}\nn countries with 2022 value: 20\n step rule n_before n_after n_removed rationale\n0 1 Remove duplicate (iso3, year) rows 439 416 23 Each country contributes at most one observation per year; duplicates had identical values.\n1 2 Remove country-years with no recorded life_expectancy 416 415 1 Empty cell = no recorded value; no imputation allowed.\n2 3 Keep only countries with a recorded value in every year 2000-2022 (drops BGD, CHE, ETH, IND, NGA) 415 345 70 Balanced-panel estimand: fixed country set across all years.\nstep,rule,n_before,n_after,n_removed,rationale\n1,\"Remove duplicate (iso3, year) rows\",439,416,23,Each country contributes at most one observation per year; duplicates had identical values.\n2,Remove country-years with no recorded life_expectancy,416,415,1,Empty cell = no recorded value; no imputation allowed.\n3,\"Keep only countries with a recorded value in every year 2000-2022 (drops BGD, CHE, ETH, IND, NGA)\",415,345,70,Balanced-panel estimand: fixed country set across all years.\niso3,country_name,year,life_expectancy\nAUS,Australia,2000,79.234\nAUS,Australia,2001,79.634","is_error":false}]},"uuid":"c741e3c5-2871-448e-85a7-815c49d5c967","timestamp":"2026-10-30T05:31:39.783Z","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\": 78.060267,\n \"mean_2022_balanced\": 81.553067,\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\": \"iso3 TUR appears under several country names ['Turkey', 'Turkiye'] (assembly of two extractions)\",\n \"handling\": \"Countries identified by iso3 only; a single name kept (the last listed in the file, i.e. the later spelling).\"\n },\n {\n \"issue\": \"23 duplicate (iso3, year) rows for ['TUR']; values identical across duplicates\",\n \"handling\": \"Kept one row per (iso3, year) (last occurrence); since values are identical the choice does not affect results.\"\n },\n {\n \"issue\": \"Missing life_expectancy for CHE 2007 (empty cell)\",\n \"handling\": \"Treated as missing (no imputation); country therefore excluded from the balanced set.\"\n },\n {\n \"issue\": \"BGD has no rows for years 2000-2010 (11 years absent from file)\",\n \"handling\": \"Not imputed; country excluded from the balanced set (still counted in the 2022 all-available baseline if 2022 recorded).\"\n },\n {\n \"issue\": \"ETH has no rows for years 2000-2010 (11 years absent from file)\",\n \"handling\": \"Not imputed; country excluded from the balanced set (still counted in the 2022 all-available baseline if 2022 recorded).\"\n },\n {\n \"issue\": \"IND has no rows for years 2000-2010 (11 years absent from file)\",\n \"handling\": \"Not imputed; country excluded from the balanced set (still counted in the 2022 all-available baseline if 2022 recorded).\"\n },\n {\n \"issue\": \"NGA has no rows for years 2000-2010 (11 years absent from file)\",\n \"handling\": \"Not imputed; country excluded from the balanced set (still counted in the 2022 all-available baseline if 2022 recorded).\"\n }\n ],\n \"n_input_rows\": {\n \"life_expectancy_panel.csv\": 439\n },\n \"random_seed\": null\n}\nn countries with 2022 value: 20\n step rule n_before n_after n_removed rationale\n0 1 Remove duplicate (iso3, year) rows 439 416 23 Each country contributes at most one observation per year; duplicates had identical values.\n1 2 Remove country-years with no recorded life_expectancy 416 415 1 Empty cell = no recorded value; no imputation allowed.\n2 3 Keep only countries with a recorded value in every year 2000-2022 (drops BGD, CHE, ETH, IND, NGA) 415 345 70 Balanced-panel estimand: fixed country set across all years.\nstep,rule,n_before,n_after,n_removed,rationale\n1,\"Remove duplicate (iso3, year) rows\",439,416,23,Each country contributes at most one observation per year; duplicates had identical values.\n2,Remove country-years with no recorded life_expectancy,416,415,1,Empty cell = no recorded value; no imputation allowed.\n3,\"Keep only countries with a recorded value in every year 2000-2022 (drops BGD, CHE, ETH, IND, NGA)\",415,345,70,Balanced-panel estimand: fixed country set across all years.\niso3,country_name,year,life_expectancy\nAUS,Australia,2000,79.234\nAUS,Australia,2001,79.634","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"2036ecf1-af1e-4b59-b278-823bb6243006","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0004","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","version":"2.1.314","gitBranch":"HEAD"}30{"type":"api-request-blob","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","hash":"311fe9f2dc9dd42514b3d25347ea99288458c8fc75cf94952e13cb457ae10942","message":{"role":"assistant","content":[{"type":"text","text":"Oops, the shell was still in `data/`; I created out/ there. Fix that."},{"type":"tool_use","id":"toolu_01UxBWFEDNaa4hbLczHBmvbA","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RA0004 && mkdir -p out && mv data/out/analysis.py out/ && rmdir data/out && ls data && ~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && head -3 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-c \"\nimport pandas as pd,numpy as np\na=pd.read_csv('out/analytic.csv');w=a.pivot(index='iso3',columns='year',values='life_expectancy')\nch=w[2022]-w[2000];print(ch.round(3).sort_values().to_dict());print(ch.mean(),ch.std(ddof=1),ch.std(ddof=1)/np.sqrt(15))\nfrom scipy import stats;print(stats.t.ppf(.975,14)*ch.std(ddof=1)/np.sqrt(15))\nr=pd.read_csv('data/life_expectancy_panel.csv');print('naive raw 2022 rows',r[r.year==2022].life_expectancy.mean())\nprint('naive 2000 avail',r.drop_duplicates(['iso3','year'])[lambda x:x.year==2000].life_expectancy.mean())\nprint(r[r.iso3=='CHE'].set_index('year').life_expectancy.loc[[2000,2022]])\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1633,"cache_read_input_tokens":11442,"output_tokens":576,"output_tokens_details":{"thinking_tokens":175},"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":1633},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsdayCbjh8EPhLZMVvo","requestRef":"1463ff3a-7c5a-493b-a9ee-e3ca57e1d924","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"067a67e7-04f6-46ac-8831-b2226a4dd5b2","timestamp":"2026-10-30T05:31:45.802Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0004","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","version":"2.1.314","gitBranch":"HEAD"}36{"parentUuid":"067a67e7-04f6-46ac-8831-b2226a4dd5b2","isSidechain":false,"promptId":"1b36e6a4-33d7-42a9-8e14-a5ad7ff3ec18","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014tQDj5MCKw8yd9Yfnfu2PL","type":"tool_result","content":"{'USA': 0.797, 'CAN': 1.928, 'DEU': 2.681, 'JPN': 2.92, 'ITA': 2.922, 'FRA': 3.073, 'AUT': 3.168, 'GBR': 3.27, 'SWE': 3.415, 'NLD': 3.627, 'AUS': 3.966, 'BEL': 4.027, 'ESP': 4.168, 'TUR': 5.66, 'KOR': 6.77}\n3.492800000000001 1.4088058874704616 0.3637521160141372\n0.7801706961287351\nnaive raw 2022 rows 78.63957142857141\nnaive 2000 avail 78.1615\nyear\n2000 79.680\n2022 83.605\nName: life_expectancy, dtype: float64","is_error":false}]},"uuid":"c40261bb-1607-4bf3-bf49-8e66df755602","timestamp":"2026-10-30T05:31:46.305Z","toolUseResult":{"stdout":"{'USA': 0.797, 'CAN': 1.928, 'DEU': 2.681, 'JPN': 2.92, 'ITA': 2.922, 'FRA': 3.073, 'AUT': 3.168, 'GBR': 3.27, 'SWE': 3.415, 'NLD': 3.627, 'AUS': 3.966, 'BEL': 4.027, 'ESP': 4.168, 'TUR': 5.66, 'KOR': 6.77}\n3.492800000000001 1.4088058874704616 0.3637521160141372\n0.7801706961287351\nnaive raw 2022 rows 78.63957142857141\nnaive 2000 avail 78.1615\nyear\n2000 79.680\n2022 83.605\nName: life_expectancy, dtype: 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-c \"\nimport pandas as pd,numpy as np\na=pd.read_csv('out/analytic.csv');w=a.pivot(index='iso3',columns='year',values='life_expectancy')\nch=w[2022]-w[2000];print(ch.round(3).sort_values().to_dict());print(ch.mean(),ch.std(ddof=1),ch.std(ddof=1)/np.sqrt(15))\nfrom scipy import stats;print(stats.t.ppf(.975,14)*ch.std(ddof=1)/np.sqrt(15))\nr=pd.read_csv('data/life_expectancy_panel.csv');print('naive raw 2022 rows',r[r.year==2022].life_expectancy.mean())\nprint('naive 2000 avail',r.drop_duplicates(['iso3','year'])[lambda x:x.year==2000].life_expectancy.mean())\nprint(r[r.iso3=='CHE'].set_index('year').life_expectancy.loc[[2000,2022]])\n\""}}]}}38{"type":"api-request","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","id":"d95b8de5-e795-4748-84a1-27b92e27bf59","timestamp":"2026-10-30T05:31:46.308Z","version":"2.1.314","querySource":"sdk","shapeHash":"216a004784203def40e37f4e7512971c54f2a007b72c5bd931bcb5dbc4a0cfda","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":"1463ff3a-7c5a-493b-a9ee-e3ca57e1d924","keep":10,"tail":["1a7c4eac46a64e977ffe84416170cc31c2da55d53d5d9d4662469bcf6f61d209","76db4d47daebdf0ed39762a30732f3573b0edc88596c4ca49d3d3cae9f64d0b4"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"486863192bbcef9bee262b3d79a6bc796a329596180cc83d96daca8038ae9d25"}39{"parentUuid":"c40261bb-1607-4bf3-bf49-8e66df755602","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXse5LkSHHkF4YYHRFHc","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":846,"cache_read_input_tokens":13075,"output_tokens":851,"output_tokens_details":{"thinking_tokens":182},"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":846},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1739,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXse53eLSnbtnwukLEgP","requestRef":"d95b8de5-e795-4748-84a1-27b92e27bf59","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0204977f-239c-4774-9b87-84ab35c0fb56","timestamp":"2026-10-30T05:31:49.378Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0004","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","version":"2.1.314","gitBranch":"HEAD"}40{"parentUuid":"0204977f-239c-4774-9b87-84ab35c0fb56","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXse5LkSHHkF4YYHRFHc","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01G3YKm6QnvcYrrXuCEDgF1m","name":"Bash","input":{"command":"cat >> out/analysis.py <<'EOF'\n\n# --- supplementary descriptives (not part of results.json schema)\nimport numpy as np\nfrom scipy import stats\nw = an.pivot(index=\"iso3\", columns=\"year\", values=\"life_expectancy\")\nch = w[2022] - w[2000]\nse = ch.std(ddof=1) / np.sqrt(len(ch))\ntcrit = stats.t.ppf(0.975, len(ch) - 1)\nsupp = {\n \"country_change_2000_2022\": {k: round(float(v), 3) for k, v in ch.sort_values().items()},\n \"sd_country_change\": round(float(ch.std(ddof=1)), 4),\n \"se_mean_change_superpopulation\": round(float(se), 4),\n \"ci95_mean_change_superpopulation\": [round(float(ch.mean() - tcrit * se), 4), round(float(ch.mean() + tcrit * se), 4)],\n \"n_countries_2022_all_available\": n22_all,\n \"mean_2000_all_available\": round(float(d[d.year == 2000][\"life_expectancy\"].mean()), 6),\n \"n_countries_2000_all_available\": int((d.year == 2000).sum()),\n \"mean_2022_raw_rows_with_duplicates\": round(float(raw[raw.year == 2022][\"life_expectancy\"].mean()), 6),\n}\nwith open(\"out/supplementary.json\", \"w\") as f:\n json.dump(supp, f, indent=2)\nprint(json.dumps(supp, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py | tail -30; md5 out/results.json; ~/wp29/venv/bin/python out/analysis.py >/dev/null; md5 out/results.json"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":846,"cache_read_input_tokens":13075,"output_tokens":851,"output_tokens_details":{"thinking_tokens":182},"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":846},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXse53eLSnbtnwukLEgP","requestRef":"d95b8de5-e795-4748-84a1-27b92e27bf59","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4eb5f958-082f-471d-b5b9-623f0dee6f17","timestamp":"2026-10-30T05:31:53.952Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0004","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","version":"2.1.314","gitBranch":"HEAD"}41{"parentUuid":"4eb5f958-082f-471d-b5b9-623f0dee6f17","isSidechain":false,"promptId":"1b36e6a4-33d7-42a9-8e14-a5ad7ff3ec18","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01G3YKm6QnvcYrrXuCEDgF1m","type":"tool_result","content":"2 3 Keep only countries with a recorded value in every year 2000-2022 (drops BGD, CHE, ETH, IND, NGA) 415 345 70 Balanced-panel estimand: fixed country set across all years.\n{\n \"country_change_2000_2022\": {\n \"USA\": 0.797,\n \"CAN\": 1.928,\n \"DEU\": 2.681,\n \"JPN\": 2.92,\n \"ITA\": 2.922,\n \"FRA\": 3.073,\n \"AUT\": 3.168,\n \"GBR\": 3.27,\n \"SWE\": 3.415,\n \"NLD\": 3.627,\n \"AUS\": 3.966,\n \"BEL\": 4.027,\n \"ESP\": 4.168,\n \"TUR\": 5.66,\n \"KOR\": 6.77\n },\n \"sd_country_change\": 1.4088,\n \"se_mean_change_superpopulation\": 0.3638,\n \"ci95_mean_change_superpopulation\": [\n 2.7126,\n 4.273\n ],\n \"n_countries_2022_all_available\": 20,\n \"mean_2000_all_available\": 78.1615,\n \"n_countries_2000_all_available\": 16,\n \"mean_2022_raw_rows_with_duplicates\": 78.639571\n}\nMD5 (out/results.json) = 7ae885ab525fdaf97a90b9d81a2fd132\nMD5 (out/results.json) = 7ae885ab525fdaf97a90b9d81a2fd132","is_error":false}]},"uuid":"fd7e057d-08de-4ae6-9768-0b5b259bf042","timestamp":"2026-10-30T05:31:54.913Z","toolUseResult":{"stdout":"2 3 Keep only countries with a recorded value in every year 2000-2022 (drops BGD, CHE, ETH, IND, NGA) 415 345 70 Balanced-panel estimand: fixed country set across all years.\n{\n \"country_change_2000_2022\": {\n \"USA\": 0.797,\n \"CAN\": 1.928,\n \"DEU\": 2.681,\n \"JPN\": 2.92,\n \"ITA\": 2.922,\n \"FRA\": 3.073,\n \"AUT\": 3.168,\n \"GBR\": 3.27,\n \"SWE\": 3.415,\n \"NLD\": 3.627,\n \"AUS\": 3.966,\n \"BEL\": 4.027,\n \"ESP\": 4.168,\n \"TUR\": 5.66,\n \"KOR\": 6.77\n },\n \"sd_country_change\": 1.4088,\n \"se_mean_change_superpopulation\": 0.3638,\n \"ci95_mean_change_superpopulation\": [\n 2.7126,\n 4.273\n ],\n \"n_countries_2022_all_available\": 20,\n \"mean_2000_all_available\": 78.1615,\n \"n_countries_2000_all_available\": 16,\n \"mean_2022_raw_rows_with_duplicates\": 78.639571\n}\nMD5 (out/results.json) = 7ae885ab525fdaf97a90b9d81a2fd132\nMD5 (out/results.json) = 7ae885ab525fdaf97a90b9d81a2fd132","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"4eb5f958-082f-471d-b5b9-623f0dee6f17","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0004","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","version":"2.1.314","gitBranch":"HEAD"}42{"type":"api-request-blob","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","hash":"57d5bcd7c912a2a0bed4d058c2b416ec0b384de0ddd69e0f287e257d1afc0ff1","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01G3YKm6QnvcYrrXuCEDgF1m","name":"Bash","input":{"command":"cat >> out/analysis.py <<'EOF'\n\n# --- supplementary descriptives (not part of results.json schema)\nimport numpy as np\nfrom scipy import stats\nw = an.pivot(index=\"iso3\", columns=\"year\", values=\"life_expectancy\")\nch = w[2022] - w[2000]\nse = ch.std(ddof=1) / np.sqrt(len(ch))\ntcrit = stats.t.ppf(0.975, len(ch) - 1)\nsupp = {\n \"country_change_2000_2022\": {k: round(float(v), 3) for k, v in ch.sort_values().items()},\n \"sd_country_change\": round(float(ch.std(ddof=1)), 4),\n \"se_mean_change_superpopulation\": round(float(se), 4),\n \"ci95_mean_change_superpopulation\": [round(float(ch.mean() - tcrit * se), 4), round(float(ch.mean() + tcrit * se), 4)],\n \"n_countries_2022_all_available\": n22_all,\n \"mean_2000_all_available\": round(float(d[d.year == 2000][\"life_expectancy\"].mean()), 6),\n \"n_countries_2000_all_available\": int((d.year == 2000).sum()),\n \"mean_2022_raw_rows_with_duplicates\": round(float(raw[raw.year == 2022][\"life_expectancy\"].mean()), 6),\n}\nwith open(\"out/supplementary.json\", \"w\") as f:\n json.dump(supp, f, indent=2)\nprint(json.dumps(supp, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py | tail -30; md5 out/results.json; ~/wp29/venv/bin/python out/analysis.py >/dev/null; md5 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> out/report.md <<'EOF'\n# F1-conf-02 — Balanced-panel mean life expectancy, 2000–2022\n\n## Question\nHow did unweighted mean life expectancy at birth (WDI `SP.DYN.LE00.IN`) change between 2000 and 2022 across a **fixed** set of countries, defined as the countries in `data/life_expectancy_panel.csv` that have a recorded value in every year 2000–2022? As a descriptive baseline, I also report the naive 2022 mean over every country with a recorded 2022 value.\n\n## Data and exclusions\n- I read 439 data rows covering 20 distinct iso3 codes and years 2000–2022.\n- **Data issues found** (all are also listed in `results.json` → `data_issues`):\n 1. **Duplicated country (TUR).** TUR appears twice, under the names \"Turkey\" and \"Turkiye\", with 23 rows each. This looks like an artefact of combining two extractions. The values are identical row for row. I kept one row per (iso3, year) and one name per iso3, which removed 23 rows. If the duplicates had been left in, the naive 2022 mean over raw rows would have been 78.640 instead of 78.692.\n 2. **Missing value.** CHE 2007 is an empty cell. I treated it as missing and did not impute it, which removed 1 row. Because of this, CHE is not in the balanced set.\n 3. **Absent years.** BGD, ETH, IND and NGA have no rows at all for 2000–2010 (they have 12 rows each, 2011–2022). I did not impute them, so all four are excluded from the balanced set.\n- Exclusion ledger (`out/exclusions.csv`): 439 → 416 (duplicates removed) → 415 (missing value removed) → 345 (only balanced countries kept, which drops BGD, CHE, ETH, IND and NGA).\n- Balanced set: 15 countries (AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA) × 23 years = 345 country-years. These are written to `out/analytic.csv`.\n\n## Method\nCountries are identified by iso3. For each year, the balanced mean is the simple unweighted mean of life expectancy over the 15 balanced countries. The change is the 2022 balanced mean minus the 2000 balanced mean. The baseline is the unweighted mean over all distinct countries with a recorded 2022 value: there are 20 of them after de-duplication, each counted once. The script uses no random elements. `out/analysis.py` regenerates all outputs deterministically, and I checked this by running it twice and getting identical output.\n\n## Results\n| quantity | value (years) |\n|---|---|\n| Balanced mean 2000 (15 countries) | 78.060 |\n| Balanced mean 2022 (15 countries) | 81.553 |\n| Change 2000→2022, balanced | **+3.493** |\n| Naive 2022 mean, all available (20 countries) | 78.692 |\n\nLife expectancy rose in all 15 balanced countries. The smallest gain was USA (+0.80) and the largest was KOR (+6.77); the per-country changes are in `out/supplementary.json`.\n\nThe naive 2022 mean is about 2.9 years lower than the balanced 2022 mean. This gap comes from composition, not from any change over time: the naive mean includes four countries with much lower life expectancy (BGD, ETH, IND, NGA) plus CHE. Comparing naive means across years would mix these composition shifts with real change. For example, the 2000 naive mean (16 countries available) is 78.162, so a naive \"change\" would come out at only about +0.53 years.\n\n## Uncertainty\nThe balanced means are exact descriptive statistics for a fixed, fully observed set of countries. If that set is the population of interest, there is no sampling error. The data themselves (WDI modelled estimates) carry measurement uncertainty, but the file gives no way to quantify it. As a supplementary result only, I treated the 15 countries as a sample from a wider population. The mean change is then 3.49 years (SE 0.36), with a t-based 95% CI of [2.71, 4.27] and a cross-country SD of changes of 1.41. This interpretation is questionable because the countries were not randomly sampled.\n\n## Limitations and open issues\n- **The balanced set is selective.** It contains 14 high-income OECD countries plus Türkiye. All the lower-income countries in the file (BGD, ETH, IND, NGA) are excluded because their 2000–2010 rows are missing, so the result does not describe a broader global trend.\n- **CHE is excluded because of a single missing year (2007).** This follows the stated estimand. Its recorded change from 2000 to 2022 (79.680 → 83.605) is in line with the other countries, but I did not use it.\n- The missing 2000–2010 rows for four countries are probably an assembly error (the earlier, narrower extraction). The real WDI series likely has those values, but I could not and did not retrieve them.\n- I resolved the TUR duplicate safely because the duplicate values are identical. I did not verify the values against the original API response, because the raw response is not provided.\n- The means are unweighted (not population-weighted), as the task specifies.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Balanced-panel mean life expectancy over 15 countries rose from 78.060 years in 2000 to 81.553 years in 2022, a change of +3.493 years.\",\"results_keys\":[\"mean_2000_balanced\",\"mean_2022_balanced\",\"change_2000_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Unweighted; balanced set is mostly high-income OECD countries plus Turkiye.\"},\n {\"id\":\"C2\",\"text\":\"Of 20 countries in the file, 15 have a recorded value in every year 2000-2022; BGD, ETH, IND, NGA (no rows 2000-2010) and CHE (missing 2007) are excluded, giving 345 country-years.\",\"results_keys\":[\"n_countries_total\",\"n_countries_balanced\",\"n_countries_incomplete\",\"balanced_countries\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Missing years likely reflect an assembly error, not true absence in WDI; not imputed per task rules.\"},\n {\"id\":\"C3\",\"text\":\"The naive 2022 mean over all 20 available countries (78.692) is about 2.9 years below the balanced 2022 mean because of composition, so naive cross-year comparisons are misleading.\",\"results_keys\":[\"mean_2022_all_available\",\"mean_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Descriptive baseline only.\"},\n {\"id\":\"C4\",\"text\":\"TUR was duplicated under two names (Turkey/Turkiye) with identical values; de-duplication to one row per iso3-year does not change any value.\",\"results_keys\":[\"data_issues\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"Not checked against the raw API response.\"}\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":1332,"cache_read_input_tokens":13921,"output_tokens":2457,"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":1332},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsehaqfbmMjD41SueYj","requestRef":"73123a49-840c-463a-8cca-21a4f98d7066","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6142a605-d01b-40b0-9fa2-f17a746a6ad1","timestamp":"2026-10-30T05:32:16.879Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0004","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","version":"2.1.314","gitBranch":"HEAD"}45{"parentUuid":"6142a605-d01b-40b0-9fa2-f17a746a6ad1","isSidechain":false,"promptId":"1b36e6a4-33d7-42a9-8e14-a5ad7ff3ec18","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DEab2MaU73vsygeqiw4fPp","type":"tool_result","content":"analysis.py\nanalytic.csv\nclaims.json\nexclusions.csv\nreport.md\nresults.json\nsupplementary.json","is_error":false}]},"uuid":"ef833fa2-d0d7-491a-9ce0-6f24b2f3a0b8","timestamp":"2026-10-30T05:32:16.929Z","toolUseResult":{"stdout":"analysis.py\nanalytic.csv\nclaims.json\nexclusions.csv\nreport.md\nresults.json\nsupplementary.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"6142a605-d01b-40b0-9fa2-f17a746a6ad1","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0004","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","version":"2.1.314","gitBranch":"HEAD"}46{"type":"api-request-blob","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","hash":"f0932952d61dfbb1fca0847fbad94c43cab61483850c00c082d2efb01e61817e","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01DEab2MaU73vsygeqiw4fPp","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F1-conf-02 — Balanced-panel mean life expectancy, 2000–2022\n\n## Question\nHow did unweighted mean life expectancy at birth (WDI `SP.DYN.LE00.IN`) change between 2000 and 2022 across a **fixed** set of countries, defined as the countries in `data/life_expectancy_panel.csv` that have a recorded value in every year 2000–2022? As a descriptive baseline, I also report the naive 2022 mean over every country with a recorded 2022 value.\n\n## Data and exclusions\n- I read 439 data rows covering 20 distinct iso3 codes and years 2000–2022.\n- **Data issues found** (all are also listed in `results.json` → `data_issues`):\n 1. **Duplicated country (TUR).** TUR appears twice, under the names \"Turkey\" and \"Turkiye\", with 23 rows each. This looks like an artefact of combining two extractions. The values are identical row for row. I kept one row per (iso3, year) and one name per iso3, which removed 23 rows. If the duplicates had been left in, the naive 2022 mean over raw rows would have been 78.640 instead of 78.692.\n 2. **Missing value.** CHE 2007 is an empty cell. I treated it as missing and did not impute it, which removed 1 row. Because of this, CHE is not in the balanced set.\n 3. **Absent years.** BGD, ETH, IND and NGA have no rows at all for 2000–2010 (they have 12 rows each, 2011–2022). I did not impute them, so all four are excluded from the balanced set.\n- Exclusion ledger (`out/exclusions.csv`): 439 → 416 (duplicates removed) → 415 (missing value removed) → 345 (only balanced countries kept, which drops BGD, CHE, ETH, IND and NGA).\n- Balanced set: 15 countries (AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA) × 23 years = 345 country-years. These are written to `out/analytic.csv`.\n\n## Method\nCountries are identified by iso3. For each year, the balanced mean is the simple unweighted mean of life expectancy over the 15 balanced countries. The change is the 2022 balanced mean minus the 2000 balanced mean. The baseline is the unweighted mean over all distinct countries with a recorded 2022 value: there are 20 of them after de-duplication, each counted once. The script uses no random elements. `out/analysis.py` regenerates all outputs deterministically, and I checked this by running it twice and getting identical output.\n\n## Results\n| quantity | value (years) |\n|---|---|\n| Balanced mean 2000 (15 countries) | 78.060 |\n| Balanced mean 2022 (15 countries) | 81.553 |\n| Change 2000→2022, balanced | **+3.493** |\n| Naive 2022 mean, all available (20 countries) | 78.692 |\n\nLife expectancy rose in all 15 balanced countries. The smallest gain was USA (+0.80) and the largest was KOR (+6.77); the per-country changes are in `out/supplementary.json`.\n\nThe naive 2022 mean is about 2.9 years lower than the balanced 2022 mean. This gap comes from composition, not from any change over time: the naive mean includes four countries with much lower life expectancy (BGD, ETH, IND, NGA) plus CHE. Comparing naive means across years would mix these composition shifts with real change. For example, the 2000 naive mean (16 countries available) is 78.162, so a naive \"change\" would come out at only about +0.53 years.\n\n## Uncertainty\nThe balanced means are exact descriptive statistics for a fixed, fully observed set of countries. If that set is the population of interest, there is no sampling error. The data themselves (WDI modelled estimates) carry measurement uncertainty, but the file gives no way to quantify it. As a supplementary result only, I treated the 15 countries as a sample from a wider population. The mean change is then 3.49 years (SE 0.36), with a t-based 95% CI of [2.71, 4.27] and a cross-country SD of changes of 1.41. This interpretation is questionable because the countries were not randomly sampled.\n\n## Limitations and open issues\n- **The balanced set is selective.** It contains 14 high-income OECD countries plus Türkiye. All the lower-income countries in the file (BGD, ETH, IND, NGA) are excluded because their 2000–2010 rows are missing, so the result does not describe a broader global trend.\n- **CHE is excluded because of a single missing year (2007).** This follows the stated estimand. Its recorded change from 2000 to 2022 (79.680 → 83.605) is in line with the other countries, but I did not use it.\n- The missing 2000–2010 rows for four countries are probably an assembly error (the earlier, narrower extraction). The real WDI series likely has those values, but I could not and did not retrieve them.\n- I resolved the TUR duplicate safely because the duplicate values are identical. I did not verify the values against the original API response, because the raw response is not provided.\n- The means are unweighted (not population-weighted), as the task specifies.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Balanced-panel mean life expectancy over 15 countries rose from 78.060 years in 2000 to 81.553 years in 2022, a change of +3.493 years.\",\"results_keys\":[\"mean_2000_balanced\",\"mean_2022_balanced\",\"change_2000_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Unweighted; balanced set is mostly high-income OECD countries plus Turkiye.\"},\n {\"id\":\"C2\",\"text\":\"Of 20 countries in the file, 15 have a recorded value in every year 2000-2022; BGD, ETH, IND, NGA (no rows 2000-2010) and CHE (missing 2007) are excluded, giving 345 country-years.\",\"results_keys\":[\"n_countries_total\",\"n_countries_balanced\",\"n_countries_incomplete\",\"balanced_countries\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Missing years likely reflect an assembly error, not true absence in WDI; not imputed per task rules.\"},\n {\"id\":\"C3\",\"text\":\"The naive 2022 mean over all 20 available countries (78.692) is about 2.9 years below the balanced 2022 mean because of composition, so naive cross-year comparisons are misleading.\",\"results_keys\":[\"mean_2022_all_available\",\"mean_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Descriptive baseline only.\"},\n {\"id\":\"C4\",\"text\":\"TUR was duplicated under two names (Turkey/Turkiye) with identical values; de-duplication to one row per iso3-year does not change any value.\",\"results_keys\":[\"data_issues\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"Not checked against the raw API response.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'))\"; ls 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finished the analysis and written all the required outputs. Across the 15 countries with a recorded value in every year, mean life expectancy rose from **78.060 years in 2000 to 81.553 years in 2022, an increase of 3.493 years**. The script reproduces identical results when run twice.\n\n| | value (years) |\n|---|---|\n| Mean, 2000 (15 countries) | 78.060 |\n| Mean, 2022 (15 countries) | 81.553 |\n| Change, 2000 to 2022 | +3.493 |\n| Simple 2022 mean over all 20 countries with a 2022 value | 78.692 |\n\n**Which countries count:** the file has 439 rows covering 20 countries. The 15 complete ones are AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR and USA, which gives 345 country-years. Life expectancy rose in all 15, from +0.80 years (USA) to +6.77 (KOR).\n\nThe simple 2022 mean is about 2.9 years lower than the 15-country mean because it includes different countries, not because of any change over time. A naive 2000-to-2022 comparison using whatever countries are available each year would show a gain of only about +0.53 years, which is misleading.\n\n**Problems in the data and how I handled them:**\n- **Turkey appears twice**, as \"Turkey\" and \"Turkiye\", with identical values. I kept one copy, removing 23 rows; no values change.\n- **Switzerland has no value for 2007**, so it is left out of the complete set even though every other year is present.\n- **Bangladesh, Ethiopia, India and Nigeria have no rows for 2000–2010**, so they are left out of the complete set too. This is probably an error in how the file was put together, but the rules forbid filling gaps or looking values up.\n\n**Limitations:**\n- **Mostly rich countries:** the complete set is 14 high-income OECD countries plus Türkiye. Every lower-income country in the file drops out, so the result says nothing about a global trend.\n- **Uncertainty:** the means are exact for this fixed set of countries, so there is no sampling error in the main result. As an extra I also gave a 95% interval of [2.71, 4.27] years for the change, which assumes the countries are a sample from a wider population. That assumption is weak because they weren't chosen at random.\n- **Not checked against the source:** I couldn't compare any values with the original World Bank data, because that response isn't in the workspace.\n\nFiles are in `out/`:\n- results.json\n- report.md\n- analysis.py\n- analytic.csv\n- exclusions.csv\n- claims.json\n- supplementary.json (extra per-country figures; not required by the task)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2502,"cache_read_input_tokens":15253,"output_tokens":962,"output_tokens_details":{"thinking_tokens":50},"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":2502},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsgKjFWkpkjeqoAnAfz","requestRef":"d2703506-2905-47cc-8748-bc35785a9897","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"28ff4fb4-7392-45c5-b877-1fd1e4b855a7","timestamp":"2026-10-30T05:32:27.003Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0004","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","version":"2.1.314","gitBranch":"HEAD"}52{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"28ff4fb4-7392-45c5-b877-1fd1e4b855a7","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099"}53{"type":"cost-state","sessionId":"247a606c-7197-40a6-8218-7e1ae9fbe099","totalCostUSD":0.2571376,"totalAPIDuration":76170,"totalAPIDurationWithoutRetries":76168,"totalToolDuration":2092,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":81448,"startTime":1793338265582,"modelUsage":{"claude-opus-5-5":{"inputTokens":18,"outputTokens":8071,"thinkingTokens":755,"cacheReadInputTokens":71953,"cacheCreationInputTokens":16251,"cacheCreationEphemeral5mInputTokens":16251,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.2571376}},"hasUnknownModelCost":false}54