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The estimand is the slope β of the linear projection LE_i = α + β·ln(GDPpc_i) + ε_i across countries/economies (one equally weighted observation per country) in the analysis year, where LE is life expectancy at birth (years) and GDPpc is GDP per capita in PPP constant 2021 international dollars, with heteroskedasticity-robust uncertainty. The task is a complete, bounded chain: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report. \"Effect\" is used here in the descriptive, associational sense: no causal claim is requested.\n\n## Inputs\n- `data/wb_indicators.csv` — observed data, World Bank World Development Indicators (retrieved 2026-10-30, CC BY 4.0; full provenance, URLs and SHA-256 in `data/README.md`). One row per entity-year, 2015–2023. Columns: `iso3`, `name`, `year`, `gdp_pc_ppp` (GDP per capita, PPP, constant 2021 international $; empty if not reported), `life_expectancy` (years; empty if not reported). The file is the API download for *all* World Bank entities, so it contains countries/economies as well as regional and income-group aggregates.\n- `data/wb_entities.csv` — World Bank entity metadata: `iso3`, `name`, `region` (the value `Aggregates` marks groupings that are not countries/economies), `income_level`, `lending_type`.\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- The analysis population is countries/economies only: exclude every entity whose `region` is `Aggregates` in `wb_entities.csv`.\n- The analysis year is the most recent year (2015–2023) in which both indicators are observed for at least 150 countries/economies. Report it as `analysis_year`.\n- In the analysis year, countries/economies with either indicator missing are excluded from the estimate and counted in `n_excluded_missing`. No imputation.\n- Use the natural logarithm of GDP per capita. Use only the files listed above.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `analysis_year` | integer | year | year selected by the rule above |\n| `n_excluded_missing` | integer | countries | countries/economies present in `wb_indicators.csv` for the analysis year but dropped for a missing indicator |\n| `baseline_metric` | number | years | RMSE of the mean-only prediction of life expectancy, sqrt(mean((LE − mean LE)²)) over the analysed countries (the no-information baseline) |\n| `model_metric` | number | years | RMSE of the OLS residuals, sqrt(mean(e²)) |\n| `pearson_r` | number | — | Pearson correlation between ln(GDPpc) and LE |\n| `r_squared` | number | — | R² of the OLS regression |\n| `effect_estimate` | number | years per log-point | OLS slope β |\n| `intercept` | number | years | OLS intercept α |\n| `effect_se` | number | years per log-point | heteroskedasticity-robust standard error of β (HC0–HC3; state which in the report) |\n| `effect_ci_low`, `effect_ci_high` | number | years per log-point | 95% confidence interval for β based on `effect_se` |\n| `conclusion` | string | — | one of the codes below |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — scatter of life expectancy against ln(GDP per capita) for the analysed countries with the fitted OLS line, produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per World Bank income level present among the analysed countries, with columns `income_level`, `n_countries`, `mean_log_gdp_pc`, `mean_life_expectancy` (header row + at least two data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. This task is designed to be completed in well under 20 minutes of work; prefer a short, correct chain over an elaborate one.\nREADME.md\nwb_entities.csv\nwb_indicators.csv","is_error":false}]},"uuid":"41ac53c4-0699-4187-b54e-97df3daccb4e","timestamp":"2026-10-30T03:49:49.170Z","toolUseResult":{"stdout":"# F6-dev-01 — Income and life expectancy across countries: a bounded mini-project\n\n## Scientific question\nDoes the cross-country relationship between real income per capita and life expectancy (the \"Preston curve\") hold in the most recent year with usable World Bank data, and how strong is it? The estimand is the slope β of the linear projection LE_i = α + β·ln(GDPpc_i) + ε_i across countries/economies (one equally weighted observation per country) in the analysis year, where LE is life expectancy at birth (years) and GDPpc is GDP per capita in PPP constant 2021 international dollars, with heteroskedasticity-robust uncertainty. The task is a complete, bounded chain: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report. \"Effect\" is used here in the descriptive, associational sense: no causal claim is requested.\n\n## Inputs\n- `data/wb_indicators.csv` — observed data, World Bank World Development Indicators (retrieved 2026-10-30, CC BY 4.0; full provenance, URLs and SHA-256 in `data/README.md`). One row per entity-year, 2015–2023. Columns: `iso3`, `name`, `year`, `gdp_pc_ppp` (GDP per capita, PPP, constant 2021 international $; empty if not reported), `life_expectancy` (years; empty if not reported). The file is the API download for *all* World Bank entities, so it contains countries/economies as well as regional and income-group aggregates.\n- `data/wb_entities.csv` — World Bank entity metadata: `iso3`, `name`, `region` (the value `Aggregates` marks groupings that are not countries/economies), `income_level`, `lending_type`.\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- The analysis population is countries/economies only: exclude every entity whose `region` is `Aggregates` in `wb_entities.csv`.\n- The analysis year is the most recent year (2015–2023) in which both indicators are observed for at least 150 countries/economies. Report it as `analysis_year`.\n- In the analysis year, countries/economies with either indicator missing are excluded from the estimate and counted in `n_excluded_missing`. No imputation.\n- Use the natural logarithm of GDP per capita. Use only the files listed above.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `analysis_year` | integer | year | year selected by the rule above |\n| `n_excluded_missing` | integer | countries | countries/economies present in `wb_indicators.csv` for the analysis year but dropped for a missing indicator |\n| `baseline_metric` | number | years | RMSE of the mean-only prediction of life expectancy, sqrt(mean((LE − mean LE)²)) over the analysed countries (the no-information baseline) |\n| `model_metric` | number | years | RMSE of the OLS residuals, sqrt(mean(e²)) |\n| `pearson_r` | number | — | Pearson correlation between ln(GDPpc) and LE |\n| `r_squared` | number | — | R² of the OLS regression |\n| `effect_estimate` | number | years per log-point | OLS slope β |\n| `intercept` | number | years | OLS intercept α |\n| `effect_se` | number | years per log-point | heteroskedasticity-robust standard error of β (HC0–HC3; state which in the report) |\n| `effect_ci_low`, `effect_ci_high` | number | years per log-point | 95% confidence interval for β based on `effect_se` |\n| `conclusion` | string | — | one of the codes below |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — scatter of life expectancy against ln(GDP per capita) for the analysed countries with the fitted OLS line, produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per World Bank income level present among the analysed countries, with columns `income_level`, `n_countries`, `mean_log_gdp_pc`, `mean_life_expectancy` (header row + at least two data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. This task is designed to be completed in well under 20 minutes of work; prefer a short, correct chain over an elaborate one.\nREADME.md\nwb_entities.csv\nwb_indicators.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"4110ad0b-5f00-43be-bfe1-4d556637d926","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0015","sessionId":"28baae04-29de-4b30-a556-82e331248442","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"28baae04-29de-4b30-a556-82e331248442","hash":"b8e362699168a7d5f1c999893f51db3a50115fa03f4c90a93130a8a1d7b01e87","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01SyTPQa1CwN9GKh7GEqU4Sj","name":"Bash","input":{"command":"cat TASK.md; ls -R data | head -50"}}]}}15{"type":"api-request","sessionId":"28baae04-29de-4b30-a556-82e331248442","id":"0da51b9f-0a15-4c6f-9b89-33d24d33160b","timestamp":"2026-10-30T03:49:49.173Z","version":"2.1.314","querySource":"sdk","shapeHash":"7a0dfb43044653571e2d16d699073290c71ec93ca5865a28a77174df4624c12c","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":"d1a618e8-021b-4480-8c47-5968bf8658ff","keep":2,"tail":["b8e362699168a7d5f1c999893f51db3a50115fa03f4c90a93130a8a1d7b01e87","68c185221f84ae041a8cde3606baf2ad1b97855d11b62973a8f954c524d5636b"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"3326332c73f72fc57fb7108d0be0044567e21e22c2a7429f1e1188c24765d452"}16{"parentUuid":"41ac53c4-0699-4187-b54e-97df3daccb4e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXjsAYEQYU4U759CNoEw","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LngjQZLk81AH9qPA8J2tB1","name":"Bash","input":{"command":"cd data; cat README.md; head -5 *.csv; wc -l *.csv; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('wb_indicators.csv',keep_default_na=False,na_values=['']);e=pd.read_csv('wb_entities.csv',keep_default_na=False,na_values=[''])\nprint(d.dtypes);print(d.describe());print(e.region.value_counts(dropna=False));print(e.income_level.value_counts(dropna=False))\nprint(d.duplicated(['iso3','year']).sum(), set(d.iso3)-set(e.iso3), e.iso3.duplicated().sum())\nprint(d[d.iso3.isna()|(d.iso3=='')])\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2322,"cache_read_input_tokens":2234,"output_tokens":304,"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":2322},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXjsAGbW61sPWhu7ddMC","requestRef":"0da51b9f-0a15-4c6f-9b89-33d24d33160b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a4383c17-7b42-4689-b1b4-922d5fe14718","timestamp":"2026-10-30T03:49:52.055Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0015","sessionId":"28baae04-29de-4b30-a556-82e331248442","version":"2.1.314","gitBranch":"HEAD"}17{"parentUuid":"a4383c17-7b42-4689-b1b4-922d5fe14718","isSidechain":false,"promptId":"b7f60005-6e38-4a02-83cd-f0324c1d5092","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LngjQZLk81AH9qPA8J2tB1","type":"tool_result","content":"# Data provenance — F6-dev-01\n\nAll files are observed data from the World Bank World Development Indicators (WDI), retrieved by the task author with `curl` on 2026-10-30T03:23:28Z.\nLicense: World Bank open data, Creative Commons Attribution 4.0 (CC BY 4.0), https://datacatalog.worldbank.org/public-licenses#cc-by\n\n## `wb_indicators.csv` (2349 rows)\nLong table, one row per entity-year, years 2015–2023, built by merging two API responses on (ISO3 code, year):\n- `NY.GDP.PCAP.PP.KD` — GDP per capita, PPP (constant 2021 international $). URL: `https://api.worldbank.org/v2/country/all/indicator/NY.GDP.PCAP.PP.KD?format=json&per_page=20000&date=2015:2023` (raw response SHA-256 `742e87326d9748e0873cd996d9d4ebdf9464bec10ccd24a97d66b07e651789ec`)\n- `SP.DYN.LE00.IN` — Life expectancy at birth, total (years). URL: `https://api.worldbank.org/v2/country/all/indicator/SP.DYN.LE00.IN?format=json&per_page=20000&date=2015:2023` (raw response SHA-256 `6bb51828124409f555b5089900a507aa62a0a17523ec3608b4b51bb5c6928d04`)\n\nColumns: `iso3` (ISO-3166 alpha-3 code as returned by the API), `name` (entity name), `year` (integer), `gdp_pc_ppp` (GDP per capita, PPP, constant 2021 international dollars; empty = not reported), `life_expectancy` (years; empty = not reported).\nThe API endpoint `country/all` returns every entity in the World Bank database, i.e. countries/economies **and** regional, income-group and other aggregates. 45 records whose `countryiso3code` field was empty in the API response (a few income-group aggregates identified only by a 2-letter code) were dropped during preparation; nothing else was altered.\nSHA-256 of `wb_indicators.csv`: `6823f02ed4c20fd12a54b8ca4454f85ba333f22337e2a9fc57c3cabccfdbfcd0`\n\n## `wb_entities.csv` (296 rows)\nEntity metadata from `https://api.worldbank.org/v2/country?format=json&per_page=400` (raw response SHA-256 `c9acf3932785191a649cbc04c3056af4cf64a236e5c3b49d49be25ddb0440bce`).\nColumns: `iso3`, `name`, `region` (World Bank region; the value `Aggregates` marks entities that are regional, income or other groupings rather than countries/economies), `income_level`, `lending_type`.\nSHA-256 of `wb_entities.csv`: `06af6aec15122fecea5be5eb21464c505188ecc135bc55104a40f20f65040f8d`\n==> wb_entities.csv <==\niso3,name,region,income_level,lending_type\nABW,Aruba,Latin America & Caribbean,High income,Not classified\nAFE,Africa Eastern and Southern,Aggregates,Aggregates,Aggregates\nAFG,Afghanistan,\"Middle East, North Africa, Afghanistan & Pakistan\",Low income,IDA\nAFR,Africa,Aggregates,Aggregates,Aggregates\n\n==> wb_indicators.csv <==\niso3,name,year,gdp_pc_ppp,life_expectancy\nABW,Aruba,2015,36753.404173,75.405000\nABW,Aruba,2016,36926.113069,75.540000\nABW,Aruba,2017,38213.289239,75.620000\nABW,Aruba,2018,39378.228411,75.880000\n 297 wb_entities.csv\n 2350 wb_indicators.csv\n 2647 total\niso3 object\nname object\nyear int64\ngdp_pc_ppp float64\nlife_expectancy float64\ndtype: object\n year gdp_pc_ppp life_expectancy\ncount 2349.000000 2185.000000 2349.000000\nmean 2019.000000 24483.044024 72.341773\nstd 2.582539 24511.848957 7.637494\nmin 2015.000000 992.285806 18.818000\n25% 2017.000000 6153.637902 66.940000\n50% 2019.000000 15831.584983 72.996000\n75% 2021.000000 35798.041275 77.814000\nmax 2023.000000 139541.157249 86.372000\nregion\nAggregates 79\nEurope & Central Asia 58\nSub-Saharan Africa 48\nLatin America & Caribbean 42\nEast Asia & Pacific 37\nMiddle East, North Africa, Afghanistan & Pakistan 23\nSouth Asia 6\nNorth America 3\nName: count, dtype: int64\nincome_level\nHigh income 86\nAggregates 79\nUpper middle income 59\nLower middle income 47\nLow income 25\nName: count, dtype: int64\n0 set() 0\nEmpty DataFrame\nColumns: [iso3, name, year, gdp_pc_ppp, life_expectancy]\nIndex: []","is_error":false}]},"uuid":"86216952-e09b-4eff-a9b4-e0271374bb34","timestamp":"2026-10-30T03:49:52.283Z","toolUseResult":{"stdout":"# Data provenance — F6-dev-01\n\nAll files are observed data from the World Bank World Development Indicators (WDI), retrieved by the task author with `curl` on 2026-10-30T03:23:28Z.\nLicense: World Bank open data, Creative Commons Attribution 4.0 (CC BY 4.0), https://datacatalog.worldbank.org/public-licenses#cc-by\n\n## `wb_indicators.csv` (2349 rows)\nLong table, one row per entity-year, years 2015–2023, built by merging two API responses on (ISO3 code, year):\n- `NY.GDP.PCAP.PP.KD` — GDP per capita, PPP (constant 2021 international $). URL: `https://api.worldbank.org/v2/country/all/indicator/NY.GDP.PCAP.PP.KD?format=json&per_page=20000&date=2015:2023` (raw response SHA-256 `742e87326d9748e0873cd996d9d4ebdf9464bec10ccd24a97d66b07e651789ec`)\n- `SP.DYN.LE00.IN` — Life expectancy at birth, total (years). URL: `https://api.worldbank.org/v2/country/all/indicator/SP.DYN.LE00.IN?format=json&per_page=20000&date=2015:2023` (raw response SHA-256 `6bb51828124409f555b5089900a507aa62a0a17523ec3608b4b51bb5c6928d04`)\n\nColumns: `iso3` (ISO-3166 alpha-3 code as returned by the API), `name` (entity name), `year` (integer), `gdp_pc_ppp` (GDP per capita, PPP, constant 2021 international dollars; empty = not reported), `life_expectancy` (years; empty = not reported).\nThe API endpoint `country/all` returns every entity in the World Bank database, i.e. countries/economies **and** regional, income-group and other aggregates. 45 records whose `countryiso3code` field was empty in the API response (a few income-group aggregates identified only by a 2-letter code) were dropped during preparation; nothing else was altered.\nSHA-256 of `wb_indicators.csv`: `6823f02ed4c20fd12a54b8ca4454f85ba333f22337e2a9fc57c3cabccfdbfcd0`\n\n## `wb_entities.csv` (296 rows)\nEntity metadata from `https://api.worldbank.org/v2/country?format=json&per_page=400` (raw response SHA-256 `c9acf3932785191a649cbc04c3056af4cf64a236e5c3b49d49be25ddb0440bce`).\nColumns: `iso3`, `name`, `region` (World Bank region; the value `Aggregates` marks entities that are regional, income or other groupings rather than countries/economies), `income_level`, `lending_type`.\nSHA-256 of `wb_entities.csv`: `06af6aec15122fecea5be5eb21464c505188ecc135bc55104a40f20f65040f8d`\n==> wb_entities.csv <==\niso3,name,region,income_level,lending_type\nABW,Aruba,Latin America & Caribbean,High income,Not classified\nAFE,Africa Eastern and Southern,Aggregates,Aggregates,Aggregates\nAFG,Afghanistan,\"Middle East, North Africa, Afghanistan & Pakistan\",Low income,IDA\nAFR,Africa,Aggregates,Aggregates,Aggregates\n\n==> wb_indicators.csv <==\niso3,name,year,gdp_pc_ppp,life_expectancy\nABW,Aruba,2015,36753.404173,75.405000\nABW,Aruba,2016,36926.113069,75.540000\nABW,Aruba,2017,38213.289239,75.620000\nABW,Aruba,2018,39378.228411,75.880000\n 297 wb_entities.csv\n 2350 wb_indicators.csv\n 2647 total\niso3 object\nname object\nyear int64\ngdp_pc_ppp float64\nlife_expectancy float64\ndtype: object\n year gdp_pc_ppp life_expectancy\ncount 2349.000000 2185.000000 2349.000000\nmean 2019.000000 24483.044024 72.341773\nstd 2.582539 24511.848957 7.637494\nmin 2015.000000 992.285806 18.818000\n25% 2017.000000 6153.637902 66.940000\n50% 2019.000000 15831.584983 72.996000\n75% 2021.000000 35798.041275 77.814000\nmax 2023.000000 139541.157249 86.372000\nregion\nAggregates 79\nEurope & Central Asia 58\nSub-Saharan Africa 48\nLatin America & Caribbean 42\nEast Asia & Pacific 37\nMiddle East, North Africa, Afghanistan & Pakistan 23\nSouth Asia 6\nNorth America 3\nName: count, dtype: int64\nincome_level\nHigh income 86\nAggregates 79\nUpper middle income 59\nLower middle income 47\nLow income 25\nName: count, dtype: int64\n0 set() 0\nEmpty DataFrame\nColumns: [iso3, name, year, gdp_pc_ppp, life_expectancy]\nIndex: []","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"a4383c17-7b42-4689-b1b4-922d5fe14718","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0015/data","sessionId":"28baae04-29de-4b30-a556-82e331248442","version":"2.1.314","gitBranch":"HEAD"}18{"type":"api-request-blob","sessionId":"28baae04-29de-4b30-a556-82e331248442","hash":"94e0f3b3e875c8d85987253eef2d34eda05446a3134c59dfadae30c52845badb","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01LngjQZLk81AH9qPA8J2tB1","name":"Bash","input":{"command":"cd data; cat README.md; head -5 *.csv; wc -l *.csv; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('wb_indicators.csv',keep_default_na=False,na_values=['']);e=pd.read_csv('wb_entities.csv',keep_default_na=False,na_values=[''])\nprint(d.dtypes);print(d.describe());print(e.region.value_counts(dropna=False));print(e.income_level.value_counts(dropna=False))\nprint(d.duplicated(['iso3','year']).sum(), set(d.iso3)-set(e.iso3), e.iso3.duplicated().sum())\nprint(d[d.iso3.isna()|(d.iso3=='')])\n\""}}]}}19{"type":"api-request","sessionId":"28baae04-29de-4b30-a556-82e331248442","id":"776817b9-0d6f-4dcd-9ccf-58d835791873","timestamp":"2026-10-30T03:49:52.286Z","version":"2.1.314","querySource":"sdk","shapeHash":"7a0dfb43044653571e2d16d699073290c71ec93ca5865a28a77174df4624c12c","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":"0da51b9f-0a15-4c6f-9b89-33d24d33160b","keep":4,"tail":["94e0f3b3e875c8d85987253eef2d34eda05446a3134c59dfadae30c52845badb","cd7a5b41e650f8216ec880d48c9fa1c48689b0e9cce60665140dcebfab15b88e"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"dce74c29686ddaa76c9a725091c86c264861fd1f2ff4b69da07b27ef105bf971"}20{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"86216952-e09b-4eff-a9b4-e0271374bb34","sessionId":"28baae04-29de-4b30-a556-82e331248442"}21{"type":"atis-latch","atis":"","sessionId":"28baae04-29de-4b30-a556-82e331248442"}22{"parentUuid":"86216952-e09b-4eff-a9b4-e0271374bb34","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXjsPvsp9AV5h8rYu5pQ","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":2099,"cache_read_input_tokens":4556,"output_tokens":535,"output_tokens_details":{"thinking_tokens":92},"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":2099},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":546,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXjsPb373naFX2PCJcT1","requestRef":"776817b9-0d6f-4dcd-9ccf-58d835791873","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"11cc16bd-e415-44c1-9dcf-dd6b109f6f37","timestamp":"2026-10-30T03:49:53.948Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0015/data","sessionId":"28baae04-29de-4b30-a556-82e331248442","version":"2.1.314","gitBranch":"HEAD"}23{"parentUuid":"11cc16bd-e415-44c1-9dcf-dd6b109f6f37","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXjsPvsp9AV5h8rYu5pQ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01N9DZMovUje2ZQKn7YEAyz3","name":"Bash","input":{"command":"cd data; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('wb_indicators.csv',keep_default_na=False,na_values=['']);e=pd.read_csv('wb_entities.csv',keep_default_na=False,na_values=[''])\nd=d.merge(e[['iso3','region','income_level']],on='iso3')\nc=d[d.region!='Aggregates']\nprint(c.groupby('year').apply(lambda g:(g.gdp_pc_ppp.notna()&g.life_expectancy.notna()).sum()))\nprint(c.groupby('year').size())\nprint(c.sort_values('life_expectancy').head(15))\nprint(c[(c.gdp_pc_ppp<=0)|(c.gdp_pc_ppp>120000)])\nx=c.sort_values(['iso3','year']); x['dle']=x.groupby('iso3').life_expectancy.diff(); print(x[x.dle.abs()>4])\nprint(set(e.iso3[e.region!='Aggregates'])-set(c.iso3))\nprint((d.name!=d.merge(e,on='iso3').name_y).sum())\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2099,"cache_read_input_tokens":4556,"output_tokens":535,"output_tokens_details":{"thinking_tokens":92},"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":2099},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXjsPb373naFX2PCJcT1","requestRef":"776817b9-0d6f-4dcd-9ccf-58d835791873","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"bd0220b1-4e44-496e-912d-1541b2b59de5","timestamp":"2026-10-30T03:49:56.878Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0015/data","sessionId":"28baae04-29de-4b30-a556-82e331248442","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"bd0220b1-4e44-496e-912d-1541b2b59de5","isSidechain":false,"promptId":"b7f60005-6e38-4a02-83cd-f0324c1d5092","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01N9DZMovUje2ZQKn7YEAyz3","type":"tool_result","content":"(eval):cd:1: no such file or directory: data\n<string>:6: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\nyear\n2015 199\n2016 199\n2017 199\n2018 199\n2019 199\n2020 199\n2021 199\n2022 199\n2023 197\ndtype: int64\nyear\n2015 217\n2016 217\n2017 217\n2018 217\n2019 217\n2020 217\n2021 217\n2022 217\n2023 217\ndtype: int64\n iso3 name ... region income_level\n313 CAF Central African Republic ... Sub-Saharan Africa Low income\n310 CAF Central African Republic ... Sub-Saharan Africa Low income\n1910 SSD South Sudan ... Sub-Saharan Africa Low income\n1909 SSD South Sudan ... Sub-Saharan Africa Low income\n1908 SSD South Sudan ... Sub-Saharan Africa Low income\n312 CAF Central African Republic ... Sub-Saharan Africa Low income\n308 CAF Central African Republic ... Sub-Saharan Africa Low income\n311 CAF Central African Republic ... Sub-Saharan Africa Low income\n307 CAF Central African Republic ... Sub-Saharan Africa Low income\n2025 TCD Chad ... Sub-Saharan Africa Low income\n2026 TCD Chad ... Sub-Saharan Africa Low income\n1233 LSO Lesotho ... Sub-Saharan Africa Lower middle income\n2027 TCD Chad ... Sub-Saharan Africa Low income\n1911 SSD South Sudan ... Sub-Saharan Africa Low income\n306 CAF Central African Republic ... Sub-Saharan Africa Low income\n\n[15 rows x 7 columns]\n iso3 ... income_level\n988 IRL ... High income\n1260 LUX ... High income\n1261 LUX ... High income\n1262 LUX ... High income\n1263 LUX ... High income\n1264 LUX ... High income\n1265 LUX ... High income\n1266 LUX ... High income\n1267 LUX ... High income\n1268 LUX ... High income\n1278 MAC ... High income\n1279 MAC ... High income\n1280 MAC ... High income\n1281 MAC ... High income\n1282 MAC ... High income\n1764 QAT ... High income\n1765 QAT ... High income\n1839 SGP ... High income\n1840 SGP ... High income\n1842 SGP ... High income\n1843 SGP ... High income\n1844 SGP ... High income\n\n[22 rows x 7 columns]\n iso3 name ... income_level dle\n25 AFG Afghanistan ... Low income 5.200\n59 AND Andorra ... High income -4.680\n257 BOL Bolivia ... Lower middle income -4.914\n259 BOL Bolivia ... Lower middle income 6.007\n303 BWA Botswana ... Upper middle income -4.340\n304 BWA Botswana ... Upper middle income 5.446\n308 CAF Central African Republic ... Low income -5.768\n309 CAF Central African Republic ... Low income 7.066\n310 CAF Central African Republic ... Low income -20.767\n311 CAF Central African Republic ... Low income 19.066\n312 CAF Central African Republic ... Low income -10.317\n313 CAF Central African Republic ... Low income -21.461\n314 CAF Central African Republic ... Low income 38.590\n456 CUB Cuba ... Upper middle income -4.206\n457 CUB Cuba ... Upper middle income 4.428\n599 ECU Ecuador ... Upper middle income -5.282\n853 GUY Guyana ... High income 5.565\n979 IND India ... Lower middle income 4.416\n1159 LBN Lebanon ... Lower middle income 4.353\n1178 LBY Libya ... Upper middle income -5.123\n1355 MEX Mexico ... Upper middle income -4.081\n1357 MEX Mexico ... Upper middle income 4.223\n1645 PER Peru ... Upper middle income 5.238\n1725 PRY Paraguay ... Upper middle income -4.612\n1726 PRY Paraguay ... Upper middle income 4.211\n1736 PSE West Bank and Gaza ... Lower middle income -11.492\n1762 PYF French Polynesia ... High income 4.288\n1889 SOM Somalia, Fed. Rep. ... Low income 4.885\n1911 SSD South Sudan ... Low income 16.554\n1912 SSD South Sudan ... Low income 6.224\n1951 SUR Suriname ... Upper middle income 4.311\n1987 SWZ Eswatini ... Lower middle income 4.800\n\n[32 rows x 8 columns]\nset()\n18","is_error":false}]},"uuid":"82796dc2-5057-499a-97a3-056b521a61de","timestamp":"2026-10-30T03:49:57.103Z","toolUseResult":{"stdout":"(eval):cd:1: no such file or directory: data\n<string>:6: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\nyear\n2015 199\n2016 199\n2017 199\n2018 199\n2019 199\n2020 199\n2021 199\n2022 199\n2023 197\ndtype: int64\nyear\n2015 217\n2016 217\n2017 217\n2018 217\n2019 217\n2020 217\n2021 217\n2022 217\n2023 217\ndtype: int64\n iso3 name ... region income_level\n313 CAF Central African Republic ... Sub-Saharan Africa Low income\n310 CAF Central African Republic ... Sub-Saharan Africa Low income\n1910 SSD South Sudan ... Sub-Saharan Africa Low income\n1909 SSD South Sudan ... Sub-Saharan Africa Low income\n1908 SSD South Sudan ... Sub-Saharan Africa Low income\n312 CAF Central African Republic ... Sub-Saharan Africa Low income\n308 CAF Central African Republic ... Sub-Saharan Africa Low income\n311 CAF Central African Republic ... Sub-Saharan Africa Low income\n307 CAF Central African Republic ... Sub-Saharan Africa Low income\n2025 TCD Chad ... Sub-Saharan Africa Low income\n2026 TCD Chad ... Sub-Saharan Africa Low income\n1233 LSO Lesotho ... Sub-Saharan Africa Lower middle income\n2027 TCD Chad ... Sub-Saharan Africa Low income\n1911 SSD South Sudan ... Sub-Saharan Africa Low income\n306 CAF Central African Republic ... Sub-Saharan Africa Low income\n\n[15 rows x 7 columns]\n iso3 ... income_level\n988 IRL ... High income\n1260 LUX ... High income\n1261 LUX ... High income\n1262 LUX ... High income\n1263 LUX ... High income\n1264 LUX ... High income\n1265 LUX ... High income\n1266 LUX ... High income\n1267 LUX ... High income\n1268 LUX ... High income\n1278 MAC ... High income\n1279 MAC ... High income\n1280 MAC ... High income\n1281 MAC ... High income\n1282 MAC ... High income\n1764 QAT ... High income\n1765 QAT ... High income\n1839 SGP ... High income\n1840 SGP ... High income\n1842 SGP ... High income\n1843 SGP ... High income\n1844 SGP ... High income\n\n[22 rows x 7 columns]\n iso3 name ... income_level dle\n25 AFG Afghanistan ... Low income 5.200\n59 AND Andorra ... High income -4.680\n257 BOL Bolivia ... Lower middle income -4.914\n259 BOL Bolivia ... Lower middle income 6.007\n303 BWA Botswana ... Upper middle income -4.340\n304 BWA Botswana ... Upper middle income 5.446\n308 CAF Central African Republic ... Low income -5.768\n309 CAF Central African Republic ... Low income 7.066\n310 CAF Central African Republic ... Low income -20.767\n311 CAF Central African Republic ... Low income 19.066\n312 CAF Central African Republic ... Low income -10.317\n313 CAF Central African Republic ... Low income -21.461\n314 CAF Central African Republic ... Low income 38.590\n456 CUB Cuba ... Upper middle income -4.206\n457 CUB Cuba ... Upper middle income 4.428\n599 ECU Ecuador ... Upper middle income -5.282\n853 GUY Guyana ... High income 5.565\n979 IND India ... Lower middle income 4.416\n1159 LBN Lebanon ... Lower middle income 4.353\n1178 LBY Libya ... Upper middle income -5.123\n1355 MEX Mexico ... Upper middle income -4.081\n1357 MEX Mexico ... Upper middle income 4.223\n1645 PER Peru ... Upper middle income 5.238\n1725 PRY Paraguay ... Upper middle income -4.612\n1726 PRY Paraguay ... Upper middle income 4.211\n1736 PSE West Bank and Gaza ... Lower middle income -11.492\n1762 PYF French Polynesia ... High income 4.288\n1889 SOM Somalia, Fed. Rep. ... Low income 4.885\n1911 SSD South Sudan ... Low income 16.554\n1912 SSD South Sudan ... Low income 6.224\n1951 SUR Suriname ... Upper middle income 4.311\n1987 SWZ Eswatini ... Lower middle income 4.800\n\n[32 rows x 8 columns]\nset()\n18","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"bd0220b1-4e44-496e-912d-1541b2b59de5","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0015/data","sessionId":"28baae04-29de-4b30-a556-82e331248442","version":"2.1.314","gitBranch":"HEAD"}25{"type":"api-request-blob","sessionId":"28baae04-29de-4b30-a556-82e331248442","hash":"a2838ff8744cf85a17359f4c959ec7c7ebdb5ba10e41d6237287f8d5be22760c","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01N9DZMovUje2ZQKn7YEAyz3","name":"Bash","input":{"command":"cd data; 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~/wp29/venv/bin/python -c \"\nimport pandas as pd\npd.set_option('display.width',200)\nd=pd.read_csv('wb_indicators.csv',keep_default_na=False,na_values=['']);e=pd.read_csv('wb_entities.csv',keep_default_na=False,na_values=[''])\nd=d.merge(e[['iso3','region','income_level','name']],on='iso3',suffixes=('','_e'))\nprint(d[d.name!=d.name_e][['iso3','name','name_e']].drop_duplicates())\nc=d[d.region!='Aggregates']\nprint(c[c.iso3.isin(['CAF','SSD','PSE'])][['iso3','year','gdp_pc_ppp','life_expectancy']])\ny=c[c.year==2023]; print(y[y.gdp_pc_ppp.isna()|y.life_expectancy.isna()][['iso3','name','gdp_pc_ppp','life_expectancy']])\nprint(y.sort_values('life_expectancy').head(5)[['iso3','gdp_pc_ppp','life_expectancy']])\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2467,"cache_read_input_tokens":6655,"output_tokens":526,"output_tokens_details":{"thinking_tokens":95},"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":2467},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXjskD2mWkJXW7K3STfB","requestRef":"e2f70d7a-dd29-4949-9754-c4920fba87dc","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9a5c6413-b776-4223-bae9-cf13468190f5","timestamp":"2026-10-30T03:50:02.180Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0015/data","sessionId":"28baae04-29de-4b30-a556-82e331248442","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"9a5c6413-b776-4223-bae9-cf13468190f5","isSidechain":false,"promptId":"b7f60005-6e38-4a02-83cd-f0324c1d5092","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QV4zzHPDRf6zDth2guavmq","type":"tool_result","content":"(eval):cd:1: no such file or directory: data\n iso3 name name_e\n1188 LCN Latin America & Caribbean Latin America & Caribbean \n1917 SSF Sub-Saharan Africa Sub-Saharan Africa \n iso3 year gdp_pc_ppp life_expectancy\n306 CAF 2015 1043.921717 51.934\n307 CAF 2016 1073.944777 50.999\n308 CAF 2017 1103.866093 45.231\n309 CAF 2018 1125.700917 52.297\n310 CAF 2019 1145.095637 31.530\n311 CAF 2020 1136.570530 50.596\n312 CAF 2021 1128.552380 40.279\n313 CAF 2022 1136.801711 18.818\n314 CAF 2023 1132.676434 57.408\n1728 PSE 2015 6072.650318 74.577\n1729 PSE 2016 6464.133525 74.917\n1730 PSE 2017 6426.795216 75.216\n1731 PSE 2018 6342.937480 75.380\n1732 PSE 2019 6269.891345 75.811\n1733 PSE 2020 5423.682547 74.998\n1734 PSE 2021 5663.121607 73.890\n1735 PSE 2022 5753.083959 76.662\n1736 PSE 2023 5562.424426 65.170\n1908 SSD 2015 NaN 39.757\n1909 SSD 2016 NaN 36.720\n1910 SSD 2017 NaN 35.351\n1911 SSD 2018 NaN 51.905\n1912 SSD 2019 NaN 58.129\n1913 SSD 2020 NaN 57.648\n1914 SSD 2021 NaN 57.047\n1915 SSD 2022 NaN 57.203\n1916 SSD 2023 NaN 57.617\n iso3 name gdp_pc_ppp life_expectancy\n107 ASM American Samoa NaN 72.852000\n350 CHI Channel Islands NaN 81.100000\n458 CUB Cuba NaN 78.085000\n629 ERI Eritrea NaN 68.624000\n764 GIB Gibraltar NaN 83.553000\n845 GUM Guam NaN 77.209000\n971 IMN Isle of Man NaN 80.999000\n1214 LIE Liechtenstein NaN 84.595122\n1295 MAF St. Martin (French part) NaN 80.224000\n1313 MCO Monaco NaN 86.372000\n1448 MNP Northern Mariana Islands NaN 78.808000\n1520 NCL New Caledonia NaN 78.766000\n1709 PRK Korea, Dem. People's Rep. NaN 73.642000\n1763 PYF French Polynesia NaN 84.070000\n1916 SSD South Sudan NaN 57.617000\n2015 SYR Syrian Arab Republic NaN 72.120000\n2249 VEN Venezuela, RB NaN 72.514000\n2258 VGB British Virgin Islands NaN 77.276000\n2267 VIR Virgin Islands (U.S.) NaN 80.519512\n2321 YEM Yemen, Rep. NaN 69.295000\n iso3 gdp_pc_ppp life_expectancy\n1538 NGA 7843.003990 54.462\n2033 TCD 2433.533310 55.069\n1241 LSO 2592.457013 57.375\n314 CAF 1132.676434 57.408\n1916 SSD NaN 57.617","is_error":false}]},"uuid":"914a26d4-3cbc-4ae8-aed6-75f3e8e106ed","timestamp":"2026-10-30T03:50:02.406Z","toolUseResult":{"stdout":"(eval):cd:1: no such file or directory: data\n iso3 name name_e\n1188 LCN Latin America & Caribbean Latin America & Caribbean \n1917 SSF Sub-Saharan Africa Sub-Saharan Africa \n iso3 year gdp_pc_ppp life_expectancy\n306 CAF 2015 1043.921717 51.934\n307 CAF 2016 1073.944777 50.999\n308 CAF 2017 1103.866093 45.231\n309 CAF 2018 1125.700917 52.297\n310 CAF 2019 1145.095637 31.530\n311 CAF 2020 1136.570530 50.596\n312 CAF 2021 1128.552380 40.279\n313 CAF 2022 1136.801711 18.818\n314 CAF 2023 1132.676434 57.408\n1728 PSE 2015 6072.650318 74.577\n1729 PSE 2016 6464.133525 74.917\n1730 PSE 2017 6426.795216 75.216\n1731 PSE 2018 6342.937480 75.380\n1732 PSE 2019 6269.891345 75.811\n1733 PSE 2020 5423.682547 74.998\n1734 PSE 2021 5663.121607 73.890\n1735 PSE 2022 5753.083959 76.662\n1736 PSE 2023 5562.424426 65.170\n1908 SSD 2015 NaN 39.757\n1909 SSD 2016 NaN 36.720\n1910 SSD 2017 NaN 35.351\n1911 SSD 2018 NaN 51.905\n1912 SSD 2019 NaN 58.129\n1913 SSD 2020 NaN 57.648\n1914 SSD 2021 NaN 57.047\n1915 SSD 2022 NaN 57.203\n1916 SSD 2023 NaN 57.617\n iso3 name gdp_pc_ppp life_expectancy\n107 ASM American Samoa NaN 72.852000\n350 CHI Channel Islands NaN 81.100000\n458 CUB Cuba NaN 78.085000\n629 ERI Eritrea NaN 68.624000\n764 GIB Gibraltar NaN 83.553000\n845 GUM Guam NaN 77.209000\n971 IMN Isle of Man NaN 80.999000\n1214 LIE Liechtenstein NaN 84.595122\n1295 MAF St. Martin (French part) NaN 80.224000\n1313 MCO Monaco NaN 86.372000\n1448 MNP Northern Mariana Islands NaN 78.808000\n1520 NCL New Caledonia NaN 78.766000\n1709 PRK Korea, Dem. People's Rep. NaN 73.642000\n1763 PYF French Polynesia NaN 84.070000\n1916 SSD South Sudan NaN 57.617000\n2015 SYR Syrian Arab Republic NaN 72.120000\n2249 VEN Venezuela, RB NaN 72.514000\n2258 VGB British Virgin Islands NaN 77.276000\n2267 VIR Virgin Islands (U.S.) NaN 80.519512\n2321 YEM Yemen, Rep. NaN 69.295000\n iso3 gdp_pc_ppp life_expectancy\n1538 NGA 7843.003990 54.462\n2033 TCD 2433.533310 55.069\n1241 LSO 2592.457013 57.375\n314 CAF 1132.676434 57.408\n1916 SSD NaN 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data; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\npd.set_option('display.width',200)\nd=pd.read_csv('wb_indicators.csv',keep_default_na=False,na_values=['']);e=pd.read_csv('wb_entities.csv',keep_default_na=False,na_values=[''])\nd=d.merge(e[['iso3','region','income_level','name']],on='iso3',suffixes=('','_e'))\nprint(d[d.name!=d.name_e][['iso3','name','name_e']].drop_duplicates())\nc=d[d.region!='Aggregates']\nprint(c[c.iso3.isin(['CAF','SSD','PSE'])][['iso3','year','gdp_pc_ppp','life_expectancy']])\ny=c[c.year==2023]; print(y[y.gdp_pc_ppp.isna()|y.life_expectancy.isna()][['iso3','name','gdp_pc_ppp','life_expectancy']])\nprint(y.sort_values('life_expectancy').head(5)[['iso3','gdp_pc_ppp','life_expectancy']])\n\""}}]}}31{"type":"api-request","sessionId":"28baae04-29de-4b30-a556-82e331248442","id":"c81d7c7e-912d-4d5c-a573-bf0cac81a287","timestamp":"2026-10-30T03:50:02.408Z","version":"2.1.314","querySource":"sdk","shapeHash":"7a0dfb43044653571e2d16d699073290c71ec93ca5865a28a77174df4624c12c","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":"e2f70d7a-dd29-4949-9754-c4920fba87dc","keep":8,"tail":["18a9b9429a351c719a3f050d84c0a0717c90e26d9c5549b8c4ec4d7da51c162c","d95414b3a50380c15aa03c024aa26c2d6568bfa58898c072d775052cdeaeeefb"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"90fbb2e6a996c6c40c68f155efda9c5a194bd01b05d8fc46d4e81c416bed9443"}32{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"914a26d4-3cbc-4ae8-aed6-75f3e8e106ed","sessionId":"28baae04-29de-4b30-a556-82e331248442"}33{"type":"atis-latch","atis":"","sessionId":"28baae04-29de-4b30-a556-82e331248442"}34{"parentUuid":"914a26d4-3cbc-4ae8-aed6-75f3e8e106ed","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXjt92TtzYHecMbBEBxJ","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":1774,"cache_read_input_tokens":9122,"output_tokens":3553,"output_tokens_details":{"thinking_tokens":160},"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":1774},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1299,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXjt8qK4NLBBp9BExMv2","requestRef":"c81d7c7e-912d-4d5c-a573-bf0cac81a287","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"931f48a2-f623-4a36-8a07-630693b6da9f","timestamp":"2026-10-30T03:50:04.814Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0015/data","sessionId":"28baae04-29de-4b30-a556-82e331248442","version":"2.1.314","gitBranch":"HEAD"}35{"parentUuid":"931f48a2-f623-4a36-8a07-630693b6da9f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXjt92TtzYHecMbBEBxJ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KbeS2B6AvZUEnitAP1MJiA","name":"Bash","input":{"command":"mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"Preston curve, F6-dev-01. Run from workspace root: ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nimport statsmodels.api as sm\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nSEED = 20261029\nrng = np.random.default_rng(SEED)\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nkw = dict(keep_default_na=False, na_values=[\"\"]) # 'NA' is not a missing marker\nind = pd.read_csv(\"data/wb_indicators.csv\", **kw)\nent = pd.read_csv(\"data/wb_entities.csv\", **kw)\nn_rows = {\"wb_indicators.csv\": len(ind), \"wb_entities.csv\": len(ent)}\nissues = []\n\n# ---- audit ----\nassert not ind.duplicated([\"iso3\", \"year\"]).any()\nassert set(ind.iso3) <= set(ent.iso3)\nd = ind.merge(ent[[\"iso3\", \"name\", \"region\", \"income_level\"]], on=\"iso3\", suffixes=(\"\", \"_meta\"))\nnm = d.loc[d.name != d.name_meta, \"iso3\"].unique().tolist()\nif nm:\n issues.append({\"issue\": f\"Entity names differ between files for {nm} (trailing whitespace in metadata names; all are aggregates)\",\n \"handling\": \"Joined on iso3 only; names ignored.\"})\nn_agg = int(ind.iso3.isin(ent.iso3[ent.region == \"Aggregates\"]).sum())\nissues.append({\"issue\": f\"wb_indicators.csv mixes countries with regional/income aggregates ({n_agg} aggregate rows, {ind.iso3[ind.iso3.isin(ent.iso3[ent.region=='Aggregates'])].nunique()} aggregate entities)\",\n \"handling\": \"Excluded all entities with region == 'Aggregates' per the task rule.\"})\nc = d[d.region != \"Aggregates\"].copy()\nc[\"complete\"] = c.gdp_pc_ppp.notna() & c.life_expectancy.notna()\nper_year = c.groupby(\"year\").complete.sum()\nyear = int(per_year[per_year >= 150].index.max())\n\n# implausible LE volatility (audit, not used for estimation unless it hits the analysis year)\ns = c.sort_values([\"iso3\", \"year\"]).copy()\ns[\"dle\"] = s.groupby(\"iso3\").life_expectancy.diff()\nvol = s[s.dle.abs() > 10]\nfor iso, g in vol.groupby(\"iso3\"):\n yrs = g.year.tolist()\n issues.append({\"issue\": f\"{iso}: life-expectancy jumps >10 years between consecutive years at {yrs} (e.g. CAF 2022 = 18.8 y, 2023 = 57.4 y; PSE 2023 = 65.2 vs 76.7 in 2022)\",\n \"handling\": f\"Only the {year} cross-section is used. Values kept as published (PSE {year} drop is consistent with the Gaza war; CAF {year} value is in the plausible range); sensitivity excluding these entities reported.\"})\n\ny = c[c.year == year]\nmiss = y[~y.complete]\nn_excl = int(len(miss))\nissues.append({\"issue\": f\"{n_excl} countries/economies lack an indicator in {year} (all missing GDP pc PPP): {', '.join(miss.iso3)}; missingness is not random (small territories/microstates and conflict/sanctioned economies)\",\n \"handling\": \"Listwise exclusion, no imputation (task rule); selection bias discussed in report.\"})\na = y[y.complete].copy()\na[\"lny\"] = np.log(a.gdp_pc_ppp)\nassert (a.gdp_pc_ppp > 0).all()\n\n# ---- estimate ----\nX = sm.add_constant(a.lny.values)\nle = a.life_expectancy.values\nfit = sm.OLS(le, X).fit(cov_type=\"HC3\")\nalpha, beta = fit.params\nse = fit.bse[1]\nci = fit.conf_int(0.05)[1] # normal-based (z) interval for robust cov\nresid = fit.resid\nbaseline = float(np.sqrt(np.mean((le - le.mean()) ** 2)))\nmodel = float(np.sqrt(np.mean(resid ** 2)))\nr = float(np.corrcoef(a.lny, le)[0, 1])\nconcl = \"positive_effect\" if ci[0] > 0 else (\"negative_effect\" if ci[1] < 0 else \"no_detectable_effect\")\n\n# ---- sensitivity (reported in report, saved separately) ----\nsens = {}\nfor hc in [\"HC0\", \"HC1\", \"HC2\", \"HC3\"]:\n sens[f\"se_{hc}\"] = float(sm.OLS(le, X).fit(cov_type=hc).bse[1])\nB = 2000\nbs = np.empty(B)\nn = len(a)\nfor b in range(B):\n i = rng.integers(0, n, n)\n bs[b] = np.polyfit(a.lny.values[i], le[i], 1)[0]\nsens[\"pairs_bootstrap_ci\"] = [float(np.percentile(bs, 2.5)), float(np.percentile(bs, 97.5))]\nsub = ~a.iso3.isin(vol.iso3.unique())\nsens[\"beta_excl_volatile\"] = float(np.polyfit(a.lny[sub], le[sub], 1)[0])\ninfl = sm.OLS(le, X).fit().get_influence()\ncook = infl.cooks_distance[0]\nkeep = cook < 4 / n\nsens[\"beta_excl_cooks_gt_4_over_n\"] = float(np.polyfit(a.lny[keep], le[keep], 1)[0])\nsens[\"n_cooks_gt_4_over_n\"] = int((~keep).sum())\nsens[\"top_influential\"] = a.iso3.values[np.argsort(-cook)[:5]].tolist()\nq = sm.OLS(le, sm.add_constant(np.column_stack([a.lny, a.lny ** 2]))).fit(cov_type=\"HC3\")\nsens[\"quadratic_coef_lny2\"] = float(q.params[2]); sens[\"quadratic_coef_lny2_p\"] = float(q.pvalues[2])\nsens[\"largest_neg_residuals\"] = a.assign(e=resid).nsmallest(5, \"e\")[[\"iso3\", \"e\"]].round(2).values.tolist()\nfor name, f in [(\"beta_before_2020_year_2019\", 2019)]:\n z = c[(c.year == f) & c.complete]\n sens[name] = float(np.polyfit(np.log(z.gdp_pc_ppp), z.life_expectancy, 1)[0])\nwith open(f\"{OUT}/sensitivity.json\", \"w\") as fh:\n json.dump(sens, fh, indent=2)\n\n# ---- table & figure ----\norder = [\"Low income\", \"Lower middle income\", \"Upper middle income\", \"High income\"]\nt = (a.groupby(\"income_level\").agg(n_countries=(\"iso3\", \"size\"), mean_log_gdp_pc=(\"lny\", \"mean\"),\n mean_life_expectancy=(\"life_expectancy\", \"mean\"))\n .reindex([o for o in order if o in set(a.income_level)]).reset_index())\nt.round(4).to_csv(f\"{OUT}/table1.csv\", index=False)\n\nfig, ax = plt.subplots(figsize=(7, 5))\ncols = dict(zip(order, [\"#d62728\", \"#ff7f0e\", \"#2ca02c\", \"#1f77b4\"]))\nfor lev in order:\n m = a.income_level == lev\n ax.scatter(a.lny[m], le[m], s=14, alpha=.75, color=cols[lev], label=f\"{lev} (n={m.sum()})\")\nxs = np.linspace(a.lny.min(), a.lny.max(), 100)\nax.plot(xs, alpha + beta * xs, \"k-\", lw=1.5, label=f\"OLS: LE = {alpha:.1f} + {beta:.2f}·ln(GDPpc)\")\nax.set_xlabel(\"ln(GDP per capita, PPP, constant 2021 int'l $)\")\nax.set_ylabel(\"Life expectancy at birth (years)\")\nax.set_title(f\"Preston curve, {year} (n={n} countries/economies; R²={fit.rsquared:.2f})\")\nax.legend(fontsize=8, loc=\"lower right\")\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=150)\n\nres = {\n \"task_id\": \"F6-dev-01\", \"analysis_year\": year, \"n_excluded_missing\": n_excl,\n \"baseline_metric\": baseline, \"model_metric\": model, \"pearson_r\": r,\n \"r_squared\": float(fit.rsquared), \"effect_estimate\": float(beta), \"intercept\": float(alpha),\n \"effect_se\": float(se), \"effect_ci_low\": float(ci[0]), \"effect_ci_high\": float(ci[1]),\n \"conclusion\": concl, \"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": issues, \"n_input_rows\": n_rows, \"n_analysed\": int(n), \"random_seed\": SEED,\n}\nwith open(f\"{OUT}/results.json\", \"w\") as fh:\n json.dump(res, fh, indent=2)\nprint(json.dumps(res, indent=2)); print(json.dumps(sens, indent=2)); print(t)\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":1774,"cache_read_input_tokens":9122,"output_tokens":3553,"output_tokens_details":{"thinking_tokens":160},"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":1774},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXjt8qK4NLBBp9BExMv2","requestRef":"c81d7c7e-912d-4d5c-a573-bf0cac81a287","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1d6fd4b7-1793-4152-b817-f94895bc6f8d","timestamp":"2026-10-30T03:50:30.831Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0015/data","sessionId":"28baae04-29de-4b30-a556-82e331248442","version":"2.1.314","gitBranch":"HEAD"}36{"parentUuid":"1d6fd4b7-1793-4152-b817-f94895bc6f8d","isSidechain":false,"promptId":"b7f60005-6e38-4a02-83cd-f0324c1d5092","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nMatplotlib is building the font cache; this may take a moment.\nTraceback (most recent call last):\n File \"/Users/simon-pierreboucher/wp29/runs/RP0015/data/out/analysis.py\", line 14, in <module>\n ind = pd.read_csv(\"data/wb_indicators.csv\", **kw)\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/wb_indicators.csv'","is_error":true,"tool_use_id":"toolu_01KbeS2B6AvZUEnitAP1MJiA"}]},"uuid":"2ffc76a3-a820-4681-a6f1-27cf6f7902ab","timestamp":"2026-10-30T03:50:39.031Z","toolUseResult":"Error: Exit code 1\nMatplotlib is building the font cache; this may take a moment.\nTraceback (most recent call last):\n File \"/Users/simon-pierreboucher/wp29/runs/RP0015/data/out/analysis.py\", line 14, in <module>\n ind = pd.read_csv(\"data/wb_indicators.csv\", **kw)\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/wb_indicators.csv'","sandboxed":false,"sourceToolAssistantUUID":"1d6fd4b7-1793-4152-b817-f94895bc6f8d","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0015/data","sessionId":"28baae04-29de-4b30-a556-82e331248442","version":"2.1.314","gitBranch":"HEAD"}37{"type":"api-request-blob","sessionId":"28baae04-29de-4b30-a556-82e331248442","hash":"86a5ed50d096d42b19336b586285d9dcf76322d6cebf1efbff25874aac2bb131","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01KbeS2B6AvZUEnitAP1MJiA","name":"Bash","input":{"command":"mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"Preston curve, F6-dev-01. Run from workspace root: ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nimport statsmodels.api as sm\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nSEED = 20261029\nrng = np.random.default_rng(SEED)\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nkw = dict(keep_default_na=False, na_values=[\"\"]) # 'NA' is not a missing marker\nind = pd.read_csv(\"data/wb_indicators.csv\", **kw)\nent = pd.read_csv(\"data/wb_entities.csv\", **kw)\nn_rows = {\"wb_indicators.csv\": len(ind), \"wb_entities.csv\": len(ent)}\nissues = []\n\n# ---- audit ----\nassert not ind.duplicated([\"iso3\", \"year\"]).any()\nassert set(ind.iso3) <= set(ent.iso3)\nd = ind.merge(ent[[\"iso3\", \"name\", \"region\", \"income_level\"]], on=\"iso3\", suffixes=(\"\", \"_meta\"))\nnm = d.loc[d.name != d.name_meta, \"iso3\"].unique().tolist()\nif nm:\n issues.append({\"issue\": f\"Entity names differ between files for {nm} (trailing whitespace in metadata names; all are aggregates)\",\n \"handling\": \"Joined on iso3 only; names ignored.\"})\nn_agg = int(ind.iso3.isin(ent.iso3[ent.region == \"Aggregates\"]).sum())\nissues.append({\"issue\": f\"wb_indicators.csv mixes countries with regional/income aggregates ({n_agg} aggregate rows, {ind.iso3[ind.iso3.isin(ent.iso3[ent.region=='Aggregates'])].nunique()} aggregate entities)\",\n \"handling\": \"Excluded all entities with region == 'Aggregates' per the task rule.\"})\nc = d[d.region != \"Aggregates\"].copy()\nc[\"complete\"] = c.gdp_pc_ppp.notna() & c.life_expectancy.notna()\nper_year = c.groupby(\"year\").complete.sum()\nyear = int(per_year[per_year >= 150].index.max())\n\n# implausible LE volatility (audit, not used for estimation unless it hits the analysis year)\ns = c.sort_values([\"iso3\", \"year\"]).copy()\ns[\"dle\"] = s.groupby(\"iso3\").life_expectancy.diff()\nvol = s[s.dle.abs() > 10]\nfor iso, g in vol.groupby(\"iso3\"):\n yrs = g.year.tolist()\n issues.append({\"issue\": f\"{iso}: life-expectancy jumps >10 years between consecutive years at {yrs} (e.g. CAF 2022 = 18.8 y, 2023 = 57.4 y; PSE 2023 = 65.2 vs 76.7 in 2022)\",\n \"handling\": f\"Only the {year} cross-section is used. Values kept as published (PSE {year} drop is consistent with the Gaza war; CAF {year} value is in the plausible range); sensitivity excluding these entities reported.\"})\n\ny = c[c.year == year]\nmiss = y[~y.complete]\nn_excl = int(len(miss))\nissues.append({\"issue\": f\"{n_excl} countries/economies lack an indicator in {year} (all missing GDP pc PPP): {', '.join(miss.iso3)}; missingness is not random (small territories/microstates and conflict/sanctioned economies)\",\n \"handling\": \"Listwise exclusion, no imputation (task rule); selection bias discussed in report.\"})\na = y[y.complete].copy()\na[\"lny\"] = np.log(a.gdp_pc_ppp)\nassert (a.gdp_pc_ppp > 0).all()\n\n# ---- estimate ----\nX = sm.add_constant(a.lny.values)\nle = a.life_expectancy.values\nfit = sm.OLS(le, X).fit(cov_type=\"HC3\")\nalpha, beta = fit.params\nse = fit.bse[1]\nci = fit.conf_int(0.05)[1] # normal-based (z) interval for robust cov\nresid = fit.resid\nbaseline = float(np.sqrt(np.mean((le - le.mean()) ** 2)))\nmodel = float(np.sqrt(np.mean(resid ** 2)))\nr = float(np.corrcoef(a.lny, le)[0, 1])\nconcl = \"positive_effect\" if ci[0] > 0 else (\"negative_effect\" if ci[1] < 0 else \"no_detectable_effect\")\n\n# ---- sensitivity (reported in report, saved separately) ----\nsens = {}\nfor hc in [\"HC0\", \"HC1\", \"HC2\", \"HC3\"]:\n sens[f\"se_{hc}\"] = float(sm.OLS(le, X).fit(cov_type=hc).bse[1])\nB = 2000\nbs = np.empty(B)\nn = len(a)\nfor b in range(B):\n i = rng.integers(0, n, n)\n bs[b] = np.polyfit(a.lny.values[i], le[i], 1)[0]\nsens[\"pairs_bootstrap_ci\"] = [float(np.percentile(bs, 2.5)), float(np.percentile(bs, 97.5))]\nsub = ~a.iso3.isin(vol.iso3.unique())\nsens[\"beta_excl_volatile\"] = float(np.polyfit(a.lny[sub], le[sub], 1)[0])\ninfl = sm.OLS(le, X).fit().get_influence()\ncook = infl.cooks_distance[0]\nkeep = cook < 4 / n\nsens[\"beta_excl_cooks_gt_4_over_n\"] = float(np.polyfit(a.lny[keep], le[keep], 1)[0])\nsens[\"n_cooks_gt_4_over_n\"] = int((~keep).sum())\nsens[\"top_influential\"] = a.iso3.values[np.argsort(-cook)[:5]].tolist()\nq = sm.OLS(le, sm.add_constant(np.column_stack([a.lny, a.lny ** 2]))).fit(cov_type=\"HC3\")\nsens[\"quadratic_coef_lny2\"] = float(q.params[2]); sens[\"quadratic_coef_lny2_p\"] = float(q.pvalues[2])\nsens[\"largest_neg_residuals\"] = a.assign(e=resid).nsmallest(5, \"e\")[[\"iso3\", \"e\"]].round(2).values.tolist()\nfor name, f in [(\"beta_before_2020_year_2019\", 2019)]:\n z = c[(c.year == f) & c.complete]\n sens[name] = float(np.polyfit(np.log(z.gdp_pc_ppp), z.life_expectancy, 1)[0])\nwith open(f\"{OUT}/sensitivity.json\", \"w\") as fh:\n json.dump(sens, fh, indent=2)\n\n# ---- table & figure ----\norder = [\"Low income\", \"Lower middle income\", \"Upper middle income\", \"High income\"]\nt = (a.groupby(\"income_level\").agg(n_countries=(\"iso3\", \"size\"), mean_log_gdp_pc=(\"lny\", \"mean\"),\n mean_life_expectancy=(\"life_expectancy\", \"mean\"))\n .reindex([o for o in order if o in set(a.income_level)]).reset_index())\nt.round(4).to_csv(f\"{OUT}/table1.csv\", index=False)\n\nfig, ax = plt.subplots(figsize=(7, 5))\ncols = dict(zip(order, [\"#d62728\", \"#ff7f0e\", \"#2ca02c\", \"#1f77b4\"]))\nfor lev in order:\n m = a.income_level == lev\n ax.scatter(a.lny[m], le[m], s=14, alpha=.75, color=cols[lev], label=f\"{lev} (n={m.sum()})\")\nxs = np.linspace(a.lny.min(), a.lny.max(), 100)\nax.plot(xs, alpha + beta * xs, \"k-\", lw=1.5, label=f\"OLS: LE = {alpha:.1f} + {beta:.2f}·ln(GDPpc)\")\nax.set_xlabel(\"ln(GDP per capita, PPP, constant 2021 int'l $)\")\nax.set_ylabel(\"Life expectancy at birth (years)\")\nax.set_title(f\"Preston curve, {year} (n={n} countries/economies; R²={fit.rsquared:.2f})\")\nax.legend(fontsize=8, loc=\"lower right\")\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=150)\n\nres = {\n \"task_id\": \"F6-dev-01\", \"analysis_year\": year, \"n_excluded_missing\": n_excl,\n \"baseline_metric\": baseline, \"model_metric\": model, \"pearson_r\": r,\n \"r_squared\": float(fit.rsquared), \"effect_estimate\": float(beta), \"intercept\": float(alpha),\n \"effect_se\": float(se), \"effect_ci_low\": float(ci[0]), \"effect_ci_high\": float(ci[1]),\n \"conclusion\": concl, \"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": issues, \"n_input_rows\": n_rows, \"n_analysed\": int(n), \"random_seed\": SEED,\n}\nwith open(f\"{OUT}/results.json\", \"w\") as fh:\n json.dump(res, fh, indent=2)\nprint(json.dumps(res, indent=2)); print(json.dumps(sens, indent=2)); print(t)\nEOF\n~/wp29/venv/bin/python out/analysis.py"}}]}}38{"type":"api-request","sessionId":"28baae04-29de-4b30-a556-82e331248442","id":"7b60b765-8842-4334-be55-8fc19e3f68d0","timestamp":"2026-10-30T03:50:39.034Z","version":"2.1.314","querySource":"sdk","shapeHash":"7a0dfb43044653571e2d16d699073290c71ec93ca5865a28a77174df4624c12c","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":"c81d7c7e-912d-4d5c-a573-bf0cac81a287","keep":10,"tail":["86a5ed50d096d42b19336b586285d9dcf76322d6cebf1efbff25874aac2bb131","03de8f6a3b28499c4a07d0127bc7b7980cb9811919ebf5210e5aaac42154fe52"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"96e9a25ac147962d70e9382aaef07026add4b9daa34c3d8488a5b2767c6a7069"}39{"parentUuid":"2ffc76a3-a820-4681-a6f1-27cf6f7902ab","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXjvqjVcqRDy9S9RZKpg","type":"message","role":"assistant","content":[{"type":"text","text":"Oops, the shell was in `data/`; I created `data/out` by mistake. 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","media_type":"image/png"}}]}]},"uuid":"1ffd8d0b-83c6-48b9-bd40-563192362f78","timestamp":"2026-10-30T03:50:49.995Z","toolUseResult":{"type":"image","file":{"base64":"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/Users/simon-pierreboucher/wp29/runs/RP0015; cat > out/report.md <<'EOF'\n# F6-dev-01 — Income and life expectancy across countries (Preston curve)\n\n## Question\nDoes the cross-country association between real income per capita and life expectancy (the Preston curve) hold in the most recent year with usable World Bank data, and how strong is it? Estimand: the slope β of the linear projection LE_i = α + β·ln(GDPpc_i) + ε_i over countries/economies (equal weights), with heteroskedasticity-robust uncertainty. This is descriptive/associational; no causal claim is made.\n\n## Data and exclusions\n- Inputs: `data/wb_indicators.csv` (2,349 rows, 261 entities × 2015–2023) and `data/wb_entities.csv` (296 rows), WDI indicators NY.GDP.PCAP.PP.KD (GDP pc, PPP, constant 2021 int'l $) and SP.DYN.LE00.IN (life expectancy at birth). Files read with only empty strings treated as missing (so a code like \"NA\" would not be silently turned into NaN).\n- Audit: no duplicate (iso3, year) rows; every iso3 in the indicator file is in the metadata; GDP is strictly positive. Two aggregate names differ between files only by trailing whitespace (LCN, SSF); joining is done on iso3.\n- Aggregates: 44 aggregate entities (396 rows) excluded (region == `Aggregates`), leaving 217 countries/economies per year.\n- Analysis year: both indicators are observed for 199 countries in every year 2015–2022 and 197 in 2023; the most recent year with ≥150 is **2023**.\n- Missing in 2023: **20** countries/economies are dropped, all for missing GDP per capita (life expectancy is reported for all): ASM, CHI, CUB, ERI, GIB, GUM, IMN, LIE, MAF, MCO, MNP, NCL, PRK, PYF, SSD, SYR, VEN, VGB, VIR, YEM. No imputation. **n = 197** analysed.\n- Implausible time-series volatility detected in life expectancy: Central African Republic (e.g. 31.5 in 2019, 18.8 in 2022, 57.4 in 2023), South Sudan (35.4 → 51.9 between 2017 and 2018), and West Bank & Gaza (76.7 in 2022 → 65.2 in 2023, consistent with the Gaza war). Only the 2023 values enter the estimate (SSD is anyway excluded for missing GDP). Values were kept as published, and a sensitivity check drops these entities. All issues are listed in `results.json → data_issues`.\n\n## Method\n- Baseline (no-information model): predict every country's LE with the sample mean; its RMSE equals the population SD of LE.\n- Main model: OLS of LE on ln(GDPpc), one equally weighted observation per country. Fit is reported as the in-sample residual RMSE and R². The log specification is the standard Preston-curve form; a quadratic term in ln(GDPpc) was tested as a check on functional form.\n- Uncertainty: **HC3** heteroskedasticity-robust standard error (the most conservative of HC0–HC3 in small/moderate samples with leverage points), with a normal-based (z) 95% CI, β ± 1.96·SE. Checks: HC0/HC1/HC2 SEs; nonparametric pairs bootstrap (2,000 resamples, seed 20261029); slope without the volatile-LE entities; slope without high-influence points (Cook's D > 4/n); same regression in 2019 (pre-pandemic). Robustness numbers are in `out/sensitivity.json`, produced by `out/analysis.py`.\n\n## Results\n| quantity | value |\n|---|---|\n| analysis year / n analysed / n excluded (missing) | 2023 / 197 / 20 |\n| baseline RMSE (mean only) | 7.08 years |\n| OLS residual RMSE | 3.59 years (≈49% lower than baseline) |\n| Pearson r (ln GDPpc, LE) | 0.861 |\n| R² | 0.742 |\n| slope β | **5.27 years per log-point** |\n| intercept α | 22.57 years |\n| HC3 SE of β | 0.211 |\n| 95% CI for β | [4.85, 5.68] |\n| conclusion | `positive_effect` |\n\nInterpretation: across countries in 2023, a 10% higher GDP per capita goes with about 0.50 more years of life expectancy (β·ln 1.1), and a doubling goes with about 3.65 more years (β·ln 2), on average. Figure 1 (`out/figure1.png`) shows the scatter and fitted line. Table 1 (`out/table1.csv`) gives means by World Bank income group:\n\n| income level | n | mean ln GDPpc | mean LE |\n|---|---|---|---|\n| Low income | 20 | 7.61 | 63.19 |\n| Lower middle income | 46 | 8.69 | 67.81 |\n| Upper middle income | 58 | 9.69 | 73.70 |\n| High income | 73 | 10.82 | 79.69 |\n\nLife expectancy goes up steadily across the income groups.\n\n## Uncertainty\n- Robust SEs barely depend on the variant: HC0 0.207, HC1 0.208, HC2 0.209, HC3 0.211. The pairs-bootstrap 95% percentile interval [4.87, 5.69] is almost the same as the HC3 interval.\n- Excluding CAF and PSE (volatile LE series) gives β = 5.23; excluding the 9 observations with Cook's D > 4/n (the most influential are NGA, GRL, TCD, SLB, GUY) gives β = 5.42. Both are inside the main CI.\n- Functional form: a quadratic term in ln(GDPpc) is not significant (coef 0.17, HC3 p = 0.34), so in this year the linear-in-log form gives a good description.\n- In 2019 (pre-COVID) the slope is 5.92, above the 2023 CI. The slope is therefore not fixed over time. Pandemic-era mortality and the WPP-based LE revisions may affect the gradient, so the 2023 value should not be read as a structural constant.\n- The CI covers sampling-type uncertainty only. Measurement error in GDP-PPP and in modelled LE estimates is not included.\n\n## Limitations and open issues\n- **What would invalidate the result:** (i) systematic error in the 2023 life-expectancy estimates. For many low-income countries these are model-based UN WPP projections rather than vital-registration data, and the CAF/SSD/PSE series show that single-year values can shift by 10–20 years between revisions or events. If such errors are correlated with income, β is biased. (ii) Errors in PPP conversion (2021 ICP benchmark), which are largest for poor and small economies. Classical error in ln(GDPpc) would bias β toward zero (attenuation). (iii) Non-random exclusion. The 20 dropped economies are mostly rich small territories/microstates (Monaco, Liechtenstein, Channel Islands…) and conflict/sanctioned states (Yemen, Syria, South Sudan, Venezuela, DPRK, Eritrea, Cuba). Dropping both tails could change β, and the direction of the effect is not identified from these data.\n- **What was not checked:** population weighting (the estimand is equally weighted, so China/India count the same as Nauru); other years beyond the 2019 comparison; data vintages (the API returns current revisions, so results may change on re-download); controls (health spending, education, region, HIV prevalence, conflict). A regression with controls would answer a different question and is outside this estimand.\n- **Main threats to interpretation:** the association is not causal. Reverse causality (health → productivity) and confounding by institutions, education and disease environment are both plausible. Heteroskedasticity and large negative residuals (Nigeria −15.3 y, Greenland, Nauru, Equatorial Guinea, Guyana: resource-rich or special cases) show that income explains only about three-quarters of the cross-country variance. Countries cannot be treated as independent draws from a population, so the \"sampling\" CI is a conventional summary, not a frequentist statement about a superpopulation.\n- The analysis year is set mechanically by the ≥150 rule. 2023 has only 2 fewer complete cases than 2022, so the choice does not hinge on the threshold.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"In 2023 (n=197 countries/economies), life expectancy is positively associated with ln GDP per capita: slope 5.27 years per log-point, HC3 95% CI [4.85, 5.68], excluding zero.\",\"results_keys\":[\"analysis_year\",\"n_analysed\",\"effect_estimate\",\"effect_se\",\"effect_ci_low\",\"effect_ci_high\",\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Associational only; CI ignores measurement error in GDP-PPP and modelled LE.\"},\n {\"id\":\"C2\",\"text\":\"The relationship is strong: Pearson r = 0.86, R² = 0.74, and the log-income model cuts RMSE from 7.08 to 3.59 years relative to the mean-only baseline.\",\"results_keys\":[\"pearson_r\",\"r_squared\",\"baseline_metric\",\"model_metric\"],\"status\":\"supported\",\"caveat\":\"In-sample fit; no out-of-sample validation.\"},\n {\"id\":\"C3\",\"text\":\"2023 is the most recent year with both indicators for at least 150 countries; 20 countries/economies were excluded for missing GDP per capita, without imputation.\",\"results_keys\":[\"analysis_year\",\"n_excluded_missing\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Excluded economies are non-randomly selected (microstates, conflict/sanctioned states), so selection bias is possible.\"},\n {\"id\":\"C4\",\"text\":\"The slope is robust to the choice of robust SE variant, to bootstrap inference, and to dropping influential or volatile-data countries (all within the main CI).\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\"],\"status\":\"supported\",\"caveat\":\"Supporting numbers are in out/sensitivity.json, not results.json; the 2019 slope (5.92) lies outside the 2023 CI, so the slope is not stable over time.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'));print('ok')\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":427,"cache_read_input_tokens":19725,"output_tokens":3633,"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":427},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXjx6rrJP8D7AqYyiqu5","requestRef":"bc8a3784-319d-48d4-b5b8-229b826c80ca","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"bf966f92-1f50-4157-b35b-c3acdbe5bb4f","timestamp":"2026-10-30T03:51:30.415Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0015","sessionId":"28baae04-29de-4b30-a556-82e331248442","version":"2.1.314","gitBranch":"HEAD"}69{"parentUuid":"bf966f92-1f50-4157-b35b-c3acdbe5bb4f","isSidechain":false,"promptId":"b7f60005-6e38-4a02-83cd-f0324c1d5092","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01UfbtTKoaJhTqkQaRiFQgqL","type":"tool_result","content":"ok","is_error":false}]},"uuid":"f1717cc0-dfc5-49e9-9fc1-a3ca415f7258","timestamp":"2026-10-30T03:51:30.468Z","toolUseResult":{"stdout":"ok","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"bf966f92-1f50-4157-b35b-c3acdbe5bb4f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0015","sessionId":"28baae04-29de-4b30-a556-82e331248442","version":"2.1.314","gitBranch":"HEAD"}70{"type":"api-request-blob","sessionId":"28baae04-29de-4b30-a556-82e331248442","hash":"7eee9882744e14f66a4f25a918dc744271918bf601ab16c74ebd69d48e6d5baf","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01UfbtTKoaJhTqkQaRiFQgqL","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RP0015; cat > out/report.md <<'EOF'\n# F6-dev-01 — Income and life expectancy across countries (Preston curve)\n\n## Question\nDoes the cross-country association between real income per capita and life expectancy (the Preston curve) hold in the most recent year with usable World Bank data, and how strong is it? Estimand: the slope β of the linear projection LE_i = α + β·ln(GDPpc_i) + ε_i over countries/economies (equal weights), with heteroskedasticity-robust uncertainty. This is descriptive/associational; no causal claim is made.\n\n## Data and exclusions\n- Inputs: `data/wb_indicators.csv` (2,349 rows, 261 entities × 2015–2023) and `data/wb_entities.csv` (296 rows), WDI indicators NY.GDP.PCAP.PP.KD (GDP pc, PPP, constant 2021 int'l $) and SP.DYN.LE00.IN (life expectancy at birth). Files read with only empty strings treated as missing (so a code like \"NA\" would not be silently turned into NaN).\n- Audit: no duplicate (iso3, year) rows; every iso3 in the indicator file is in the metadata; GDP is strictly positive. Two aggregate names differ between files only by trailing whitespace (LCN, SSF); joining is done on iso3.\n- Aggregates: 44 aggregate entities (396 rows) excluded (region == `Aggregates`), leaving 217 countries/economies per year.\n- Analysis year: both indicators are observed for 199 countries in every year 2015–2022 and 197 in 2023; the most recent year with ≥150 is **2023**.\n- Missing in 2023: **20** countries/economies are dropped, all for missing GDP per capita (life expectancy is reported for all): ASM, CHI, CUB, ERI, GIB, GUM, IMN, LIE, MAF, MCO, MNP, NCL, PRK, PYF, SSD, SYR, VEN, VGB, VIR, YEM. No imputation. **n = 197** analysed.\n- Implausible time-series volatility detected in life expectancy: Central African Republic (e.g. 31.5 in 2019, 18.8 in 2022, 57.4 in 2023), South Sudan (35.4 → 51.9 between 2017 and 2018), and West Bank & Gaza (76.7 in 2022 → 65.2 in 2023, consistent with the Gaza war). Only the 2023 values enter the estimate (SSD is anyway excluded for missing GDP). Values were kept as published, and a sensitivity check drops these entities. All issues are listed in `results.json → data_issues`.\n\n## Method\n- Baseline (no-information model): predict every country's LE with the sample mean; its RMSE equals the population SD of LE.\n- Main model: OLS of LE on ln(GDPpc), one equally weighted observation per country. Fit is reported as the in-sample residual RMSE and R². The log specification is the standard Preston-curve form; a quadratic term in ln(GDPpc) was tested as a check on functional form.\n- Uncertainty: **HC3** heteroskedasticity-robust standard error (the most conservative of HC0–HC3 in small/moderate samples with leverage points), with a normal-based (z) 95% CI, β ± 1.96·SE. Checks: HC0/HC1/HC2 SEs; nonparametric pairs bootstrap (2,000 resamples, seed 20261029); slope without the volatile-LE entities; slope without high-influence points (Cook's D > 4/n); same regression in 2019 (pre-pandemic). Robustness numbers are in `out/sensitivity.json`, produced by `out/analysis.py`.\n\n## Results\n| quantity | value |\n|---|---|\n| analysis year / n analysed / n excluded (missing) | 2023 / 197 / 20 |\n| baseline RMSE (mean only) | 7.08 years |\n| OLS residual RMSE | 3.59 years (≈49% lower than baseline) |\n| Pearson r (ln GDPpc, LE) | 0.861 |\n| R² | 0.742 |\n| slope β | **5.27 years per log-point** |\n| intercept α | 22.57 years |\n| HC3 SE of β | 0.211 |\n| 95% CI for β | [4.85, 5.68] |\n| conclusion | `positive_effect` |\n\nInterpretation: across countries in 2023, a 10% higher GDP per capita goes with about 0.50 more years of life expectancy (β·ln 1.1), and a doubling goes with about 3.65 more years (β·ln 2), on average. Figure 1 (`out/figure1.png`) shows the scatter and fitted line. Table 1 (`out/table1.csv`) gives means by World Bank income group:\n\n| income level | n | mean ln GDPpc | mean LE |\n|---|---|---|---|\n| Low income | 20 | 7.61 | 63.19 |\n| Lower middle income | 46 | 8.69 | 67.81 |\n| Upper middle income | 58 | 9.69 | 73.70 |\n| High income | 73 | 10.82 | 79.69 |\n\nLife expectancy goes up steadily across the income groups.\n\n## Uncertainty\n- Robust SEs barely depend on the variant: HC0 0.207, HC1 0.208, HC2 0.209, HC3 0.211. The pairs-bootstrap 95% percentile interval [4.87, 5.69] is almost the same as the HC3 interval.\n- Excluding CAF and PSE (volatile LE series) gives β = 5.23; excluding the 9 observations with Cook's D > 4/n (the most influential are NGA, GRL, TCD, SLB, GUY) gives β = 5.42. Both are inside the main CI.\n- Functional form: a quadratic term in ln(GDPpc) is not significant (coef 0.17, HC3 p = 0.34), so in this year the linear-in-log form gives a good description.\n- In 2019 (pre-COVID) the slope is 5.92, above the 2023 CI. The slope is therefore not fixed over time. Pandemic-era mortality and the WPP-based LE revisions may affect the gradient, so the 2023 value should not be read as a structural constant.\n- The CI covers sampling-type uncertainty only. Measurement error in GDP-PPP and in modelled LE estimates is not included.\n\n## Limitations and open issues\n- **What would invalidate the result:** (i) systematic error in the 2023 life-expectancy estimates. For many low-income countries these are model-based UN WPP projections rather than vital-registration data, and the CAF/SSD/PSE series show that single-year values can shift by 10–20 years between revisions or events. If such errors are correlated with income, β is biased. (ii) Errors in PPP conversion (2021 ICP benchmark), which are largest for poor and small economies. Classical error in ln(GDPpc) would bias β toward zero (attenuation). (iii) Non-random exclusion. The 20 dropped economies are mostly rich small territories/microstates (Monaco, Liechtenstein, Channel Islands…) and conflict/sanctioned states (Yemen, Syria, South Sudan, Venezuela, DPRK, Eritrea, Cuba). Dropping both tails could change β, and the direction of the effect is not identified from these data.\n- **What was not checked:** population weighting (the estimand is equally weighted, so China/India count the same as Nauru); other years beyond the 2019 comparison; data vintages (the API returns current revisions, so results may change on re-download); controls (health spending, education, region, HIV prevalence, conflict). A regression with controls would answer a different question and is outside this estimand.\n- **Main threats to interpretation:** the association is not causal. Reverse causality (health → productivity) and confounding by institutions, education and disease environment are both plausible. Heteroskedasticity and large negative residuals (Nigeria −15.3 y, Greenland, Nauru, Equatorial Guinea, Guyana: resource-rich or special cases) show that income explains only about three-quarters of the cross-country variance. Countries cannot be treated as independent draws from a population, so the \"sampling\" CI is a conventional summary, not a frequentist statement about a superpopulation.\n- The analysis year is set mechanically by the ≥150 rule. 2023 has only 2 fewer complete cases than 2022, so the choice does not hinge on the threshold.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"In 2023 (n=197 countries/economies), life expectancy is positively associated with ln GDP per capita: slope 5.27 years per log-point, HC3 95% CI [4.85, 5.68], excluding zero.\",\"results_keys\":[\"analysis_year\",\"n_analysed\",\"effect_estimate\",\"effect_se\",\"effect_ci_low\",\"effect_ci_high\",\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Associational only; CI ignores measurement error in GDP-PPP and modelled LE.\"},\n {\"id\":\"C2\",\"text\":\"The relationship is strong: Pearson r = 0.86, R² = 0.74, and the log-income model cuts RMSE from 7.08 to 3.59 years relative to the mean-only baseline.\",\"results_keys\":[\"pearson_r\",\"r_squared\",\"baseline_metric\",\"model_metric\"],\"status\":\"supported\",\"caveat\":\"In-sample fit; no out-of-sample validation.\"},\n {\"id\":\"C3\",\"text\":\"2023 is the most recent year with both indicators for at least 150 countries; 20 countries/economies were excluded for missing GDP per capita, without imputation.\",\"results_keys\":[\"analysis_year\",\"n_excluded_missing\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Excluded economies are non-randomly selected (microstates, conflict/sanctioned states), so selection bias is possible.\"},\n {\"id\":\"C4\",\"text\":\"The slope is robust to the choice of robust SE variant, to bootstrap inference, and to dropping influential or volatile-data countries (all within the main CI).\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\"],\"status\":\"supported\",\"caveat\":\"Supporting numbers are in out/sensitivity.json, not results.json; the 2019 slope (5.92) lies outside the 2023 CI, so the slope is not stable over time.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'));print('ok')\""}}]}}71{"type":"api-request","sessionId":"28baae04-29de-4b30-a556-82e331248442","id":"f749390a-5f2c-4d6b-bf02-d51537c88405","timestamp":"2026-10-30T03:51:30.471Z","version":"2.1.314","querySource":"sdk","shapeHash":"7a0dfb43044653571e2d16d699073290c71ec93ca5865a28a77174df4624c12c","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":"bc8a3784-319d-48d4-b5b8-229b826c80ca","keep":20,"tail":["7eee9882744e14f66a4f25a918dc744271918bf601ab16c74ebd69d48e6d5baf","16a03cfb3a8f48f3efc559a96e36849b05f7f82d19e08b57c0404cde8816ec7f"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",21,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"218654ae6a3f03af176dc6b1aa323ee567c6bb29fbbb50ff6d0d031498f48881"}72{"parentUuid":"f1717cc0-dfc5-49e9-9fc1-a3ca415f7258","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXjzdgZNhrFZR4S3RZGg","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3640,"cache_read_input_tokens":20152,"output_tokens":974,"output_tokens_details":{"thinking_tokens":48},"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":3640},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":7136,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXjzdRBpsdsYt9tmsEnp","requestRef":"f749390a-5f2c-4d6b-bf02-d51537c88405","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3cead2ef-c6f8-46f4-9c66-e3492cc7515d","timestamp":"2026-10-30T03:51:39.664Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0015","sessionId":"28baae04-29de-4b30-a556-82e331248442","version":"2.1.314","gitBranch":"HEAD"}73{"parentUuid":"3cead2ef-c6f8-46f4-9c66-e3492cc7515d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXjzdgZNhrFZR4S3RZGg","type":"message","role":"assistant","content":[{"type":"text","text":"The analysis is finished: in 2023 the Preston curve clearly holds, and it is strong. Across 197 countries, life expectancy rises by about **5.27 years per log-point of GDP per capita** (95% CI 4.85 to 5.68). That works out to roughly 3.6 more years of life expectancy for each doubling of income. This is an association, not a causal estimate. Running `out/analysis.py` twice gave an identical `results.json`.\n\n- **Analysis year:** 2023 is the most recent year with both indicators for at least 150 countries (197 that year; 199 in each year from 2015 to 2022). I removed the 44 regional and income-group aggregates first.\n- **Exclusions:** 20 countries were dropped, all because GDP per capita is missing (life expectancy is reported for all of them). Nothing was imputed.\n- **Fit:** correlation 0.86 and R² 0.74. Prediction error falls from 7.08 years when guessing the average to 3.59 years with the income model.\n- **Uncertainty:** I used the HC3 robust standard error (0.211). The other robust variants and a bootstrap (2,000 resamples, fixed seed) give nearly the same interval.\n- **Robustness:** the slope stays inside the main interval when I drop the most influential countries (5.42) or the countries with erratic life-expectancy data (5.23). Adding a squared income term doesn't improve the fit significantly.\n\n**Problems in the data:**\n- Life expectancy jumps implausibly from year to year for Central African Republic (18.8 years in 2022, 57.4 in 2023), South Sudan, and West Bank & Gaza. The last one's 2023 drop is consistent with the Gaza war. I kept the published values and checked that removing these countries barely changes the result.\n- The 20 excluded countries aren't random: they are mostly rich microstates and territories (e.g. Monaco, Liechtenstein) and conflict or sanctioned states (e.g. Yemen, Syria, Venezuela, North Korea). Dropping both groups could bias the slope.\n\n**Limitations:**\n- The slope isn't fixed over time: in 2019 it was 5.92, outside the 2023 interval.\n- The interval ignores measurement error. Income conversion errors would tend to make the slope look smaller than it is. Life-expectancy figures for many poor countries are modelled rather than measured.\n- I didn't try weighting by population, adding other factors (region, education, health spending, conflict), or checking against other data releases.\n\nThe report covers these failure points in more detail.\n\nFiles are in `out/`:\n- `results.json`\n- `report.md`\n- `analysis.py`\n- `figure1.png`\n- `table1.csv`\n- `claims.json`\n- `sensitivity.json` (robustness numbers)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3640,"cache_read_input_tokens":20152,"output_tokens":974,"output_tokens_details":{"thinking_tokens":48},"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":3640},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXjzdRBpsdsYt9tmsEnp","requestRef":"f749390a-5f2c-4d6b-bf02-d51537c88405","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"19bd4da1-083f-455d-93cd-35fa52a8ec5d","timestamp":"2026-10-30T03:51:41.580Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0015","sessionId":"28baae04-29de-4b30-a556-82e331248442","version":"2.1.314","gitBranch":"HEAD"}74{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"19bd4da1-083f-455d-93cd-35fa52a8ec5d","sessionId":"28baae04-29de-4b30-a556-82e331248442"}75{"type":"atis-latch","atis":"","sessionId":"28baae04-29de-4b30-a556-82e331248442"}76{"type":"cost-state","sessionId":"28baae04-29de-4b30-a556-82e331248442","totalCostUSD":0.35293220000000003,"totalAPIDuration":102384,"totalAPIDurationWithoutRetries":102377,"totalToolDuration":11914,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":114408,"startTime":1793332187197,"modelUsage":{"claude-opus-5-5":{"inputTokens":24,"outputTokens":10814,"thinkingTokens":631,"cacheReadInputTokens":125581,"cacheCreationInputTokens":22288,"cacheCreationEphemeral5mInputTokens":22288,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.35293220000000003}},"hasUnknownModelCost":false}77