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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":"b9198017-6079-4517-ad5a-64cf88b68b68","timestamp":"2026-10-30T05:30:56.824Z","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":"c2260cc8-a29f-43ff-bfde-3fafed04c16b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0007","sessionId":"0b454686-9378-4035-afb9-eec048b51078","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"0b454686-9378-4035-afb9-eec048b51078","hash":"c17f26e44e126cad9133b211d00e7ff416383fdbc9a3f818686a6db262eb4d06","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01PzcdFRQMfz5W6hWCgA5eY5","name":"Bash","input":{"command":"cat TASK.md; ls -R data | head -50","description":"Read task and list data"}}]}}15{"type":"api-request","sessionId":"0b454686-9378-4035-afb9-eec048b51078","id":"f40d0a03-5dc3-4fdd-93e4-fc0b7e12a0b9","timestamp":"2026-10-30T05:30:56.827Z","version":"2.1.314","querySource":"sdk","shapeHash":"6d1426322e6f01939b80ae4fb18cd6ff3e8aa95eea4dd5cf5b965767a8a5da0e","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":"000c7527-aef9-408f-8947-8f24a35bcced","keep":2,"tail":["c17f26e44e126cad9133b211d00e7ff416383fdbc9a3f818686a6db262eb4d06","7a20ddddef20860e1e40462c286e2abaf65e819f138be377b252a53231b2025e"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"9c7afa43caddd7af7fe9aebc049a417617786722024b37a4c1bcf080b82a88f6"}16{"parentUuid":"b9198017-6079-4517-ad5a-64cf88b68b68","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsaRWMHfbyPwvEssCWw","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HahSoN2Az8wCjf2jPQUfFr","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('dups',d.duplicated(['iso3','year']).sum(), e.duplicated('iso3').sum())\nprint('not in ent', set(d.iso3)-set(e.iso3)); print(d[d.iso3.isna()|(d.iso3=='')])\nm=d.merge(e[['iso3','region']],on='iso3',how='left'); c=m[m.region!='Aggregates']\nprint(c.groupby('year').apply(lambda g:(g.gdp_pc_ppp.notna()&g.life_expectancy.notna()).sum()))\nprint(c.groupby('year').size())\n\"","description":"Audit data"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2348,"cache_read_input_tokens":2234,"output_tokens":452,"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":2348},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsaRFDh9dVdey6yihDr","requestRef":"f40d0a03-5dc3-4fdd-93e4-fc0b7e12a0b9","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5e81dba2-af5b-4556-85c1-7c2a45db12a1","timestamp":"2026-10-30T05:31:00.901Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0007","sessionId":"0b454686-9378-4035-afb9-eec048b51078","version":"2.1.314","gitBranch":"HEAD"}17{"parentUuid":"5e81dba2-af5b-4556-85c1-7c2a45db12a1","isSidechain":false,"promptId":"a8ac1cff-661a-42ef-a5d1-c5f87749bc76","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HahSoN2Az8wCjf2jPQUfFr","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\n<string>:8: 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.\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\ndups 0 0\nnot in ent set()\nEmpty DataFrame\nColumns: [iso3, name, year, gdp_pc_ppp, life_expectancy]\nIndex: []\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","is_error":false}]},"uuid":"4a834d93-c0b7-436c-8457-36f8d7a10979","timestamp":"2026-10-30T05:31:01.140Z","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\n<string>:8: 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.\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\ndups 0 0\nnot in ent set()\nEmpty DataFrame\nColumns: [iso3, name, year, gdp_pc_ppp, life_expectancy]\nIndex: []\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: 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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('dups',d.duplicated(['iso3','year']).sum(), e.duplicated('iso3').sum())\nprint('not in ent', set(d.iso3)-set(e.iso3)); print(d[d.iso3.isna()|(d.iso3=='')])\nm=d.merge(e[['iso3','region']],on='iso3',how='left'); c=m[m.region!='Aggregates']\nprint(c.groupby('year').apply(lambda g:(g.gdp_pc_ppp.notna()&g.life_expectancy.notna()).sum()))\nprint(c.groupby('year').size())\n\"","description":"Audit 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missingness"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2477,"cache_read_input_tokens":4582,"output_tokens":617,"output_tokens_details":{"thinking_tokens":96},"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":2477},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsajgjQTgHw5uTyjMde","requestRef":"247848d7-a621-4cd9-9080-b80cd812cf21","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"966baeb1-cbd6-40c9-b050-cceb23a71e60","timestamp":"2026-10-30T05:31:06.893Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0007/data","sessionId":"0b454686-9378-4035-afb9-eec048b51078","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"966baeb1-cbd6-40c9-b050-cceb23a71e60","isSidechain":false,"promptId":"a8ac1cff-661a-42ef-a5d1-c5f87749bc76","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019ShErFRRdiPrWJLPrQNtRH","type":"tool_result","content":"(eval):cd:1: no such file or directory: data\n iso3 name year gdp_pc_ppp life_expectancy\n310 CAF Central African Republic 2019 1145.095637 31.530\n312 CAF Central African Republic 2021 1128.552380 40.279\n313 CAF Central African Republic 2022 1136.801711 18.818\n1908 SSD South Sudan 2015 NaN 39.757\n1909 SSD South Sudan 2016 NaN 36.720\n1910 SSD South Sudan 2017 NaN 35.351\niso3\nSOM 6.626\nBOL 7.154\nNAM 7.377\nSWZ 9.182\nSYR 9.525\nPSE 11.492\nSSD 22.778\nCAF 38.590\nName: life_expectancy, dtype: float64\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 name gdp_pc_ppp life_expectancy\n1538 NGA Nigeria 7843.003990 54.462\n2033 TCD Chad 2433.533310 55.069\n1241 LSO Lesotho 2592.457013 57.375\n314 CAF Central African Republic 1132.676434 57.408\n1916 SSD South Sudan NaN 57.617\n iso3 name gdp_pc_ppp life_expectancy\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\n18\nincome_level\nHigh income 86\nUpper middle income 59\nLower middle income 47\nLow income 25\nName: count, dtype: int64\n1505:NAM,Namibia,2015,12166.565839,60.008000\n1506:NAM,Namibia,2016,11846.788083,60.810000","is_error":false}]},"uuid":"21ad0572-902a-4abf-bd28-135589a8bf09","timestamp":"2026-10-30T05:31:07.131Z","toolUseResult":{"stdout":"(eval):cd:1: no such file or directory: data\n iso3 name year gdp_pc_ppp life_expectancy\n310 CAF Central African Republic 2019 1145.095637 31.530\n312 CAF Central African Republic 2021 1128.552380 40.279\n313 CAF Central African Republic 2022 1136.801711 18.818\n1908 SSD South Sudan 2015 NaN 39.757\n1909 SSD South Sudan 2016 NaN 36.720\n1910 SSD South Sudan 2017 NaN 35.351\niso3\nSOM 6.626\nBOL 7.154\nNAM 7.377\nSWZ 9.182\nSYR 9.525\nPSE 11.492\nSSD 22.778\nCAF 38.590\nName: life_expectancy, dtype: float64\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 name gdp_pc_ppp life_expectancy\n1538 NGA Nigeria 7843.003990 54.462\n2033 TCD Chad 2433.533310 55.069\n1241 LSO Lesotho 2592.457013 57.375\n314 CAF Central African Republic 1132.676434 57.408\n1916 SSD South Sudan NaN 57.617\n iso3 name gdp_pc_ppp life_expectancy\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\n18\nincome_level\nHigh income 86\nUpper middle income 59\nLower middle income 47\nLow income 25\nName: count, dtype: 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mismatches"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1809,"cache_read_input_tokens":7059,"output_tokens":389,"output_tokens_details":{"thinking_tokens":116},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1809},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsbBJMYgVugZjFi4xtF","requestRef":"68e0a219-fc51-4786-95d8-97aa7ad5bc80","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1502d6c2-4fbb-4c4c-8f06-38c4fb77e7ba","timestamp":"2026-10-30T05:31:11.072Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0007/data","sessionId":"0b454686-9378-4035-afb9-eec048b51078","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"1502d6c2-4fbb-4c4c-8f06-38c4fb77e7ba","isSidechain":false,"promptId":"a8ac1cff-661a-42ef-a5d1-c5f87749bc76","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit 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...<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_01JZ5zXfQWE1Vm215FhgKBgr"}]},"uuid":"4582f37f-51c9-458c-bb02-4ee0ec90ff8c","timestamp":"2026-10-30T05:31:11.299Z","toolUseResult":"Error: Exit code 1\nTraceback (most recent call last):\n File \"<string>\", line 3, in <module>\n d=pd.read_csv('data/wb_indicators.csv',keep_default_na=False,na_values=['']); e=pd.read_csv('data/wb_entities.csv',keep_default_na=False,na_values=[''])\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 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iso3 name year gdp_pc_ppp life_expectancy\n306 CAF Central African Republic 2015 1043.921717 51.934\n307 CAF Central African Republic 2016 1073.944777 50.999\n308 CAF Central African Republic 2017 1103.866093 45.231\n309 CAF Central African Republic 2018 1125.700917 52.297\n310 CAF Central African Republic 2019 1145.095637 31.530\n311 CAF Central African Republic 2020 1136.570530 50.596\n312 CAF Central African Republic 2021 1128.552380 40.279\n313 CAF Central African Republic 2022 1136.801711 18.818\n314 CAF Central African Republic 2023 1132.676434 57.408\n1908 SSD South Sudan 2015 NaN 39.757\n1909 SSD South Sudan 2016 NaN 36.720\n1910 SSD South Sudan 2017 NaN 35.351\n1911 SSD South Sudan 2018 NaN 51.905\n1912 SSD South Sudan 2019 NaN 58.129\n1913 SSD South Sudan 2020 NaN 57.648\n1914 SSD South Sudan 2021 NaN 57.047\n1915 SSD South Sudan 2022 NaN 57.203\n1916 SSD South Sudan 2023 NaN 57.617\n iso3 name name_e\n1188 LCN Latin America & Caribbean Latin America & Caribbean \n1917 SSF 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57.047\n1915 SSD South Sudan 2022 NaN 57.203\n1916 SSD South Sudan 2023 NaN 57.617\n iso3 name name_e\n1188 LCN Latin America & Caribbean Latin America & Caribbean \n1917 SSF Sub-Saharan Africa Sub-Saharan Africa","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"4222a653-5572-4e22-9af8-19efefb525ec","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0007","sessionId":"0b454686-9378-4035-afb9-eec048b51078","version":"2.1.314","gitBranch":"HEAD"}35{"type":"api-request-blob","sessionId":"0b454686-9378-4035-afb9-eec048b51078","hash":"f2cbfe7a2b3dc933f116e1fa42dedfb154787ee61e162d2a93f36928570483d8","message":{"role":"assistant","content":[{"type":"text","text":"The shell is still in data/ from 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-p out; cat > out/analysis.py <<'EOF'\n\"\"\"Replication entry point for F6-dev-01 (Preston curve). Run from workspace root:\n ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nimport statsmodels.api as sm\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nSEED = 0 # no randomness used; fixed for completeness\nnp.random.seed(SEED)\nDATA, OUT = \"data\", \"out\"\nos.makedirs(OUT, exist_ok=True)\n\n# keep_default_na=False so that ISO code \"NAM\" (Namibia) etc. is never parsed as NaN\nrd = dict(keep_default_na=False, na_values=[\"\"])\nind = pd.read_csv(os.path.join(DATA, \"wb_indicators.csv\"), **rd)\nent = pd.read_csv(os.path.join(DATA, \"wb_entities.csv\"), **rd)\nn_input_rows = {\"wb_indicators.csv\": int(len(ind)), \"wb_entities.csv\": int(len(ent))}\nissues = []\n\n# ---- audit ----\nassert not ind.duplicated([\"iso3\", \"year\"]).any()\nassert not ent.duplicated(\"iso3\").any()\nunmatched = sorted(set(ind.iso3) - set(ent.iso3))\nassert not unmatched, unmatched\nent[\"region\"] = ent[\"region\"].str.strip()\nent[\"income_level\"] = ent[\"income_level\"].str.strip()\nent[\"name\"] = ent[\"name\"].str.strip()\n\nm = ind.merge(ent[[\"iso3\", \"region\", \"income_level\"]], on=\"iso3\", how=\"left\")\nn_agg_ent = int(m.loc[m.region == \"Aggregates\", \"iso3\"].nunique())\nissues.append({\"issue\": f\"wb_indicators.csv mixes countries/economies with {n_agg_ent} regional/income/other aggregates (region == 'Aggregates').\",\n \"handling\": \"All aggregate entities excluded before any computation, per TASK rules.\"})\nissues.append({\"issue\": \"Some entity names carry trailing whitespace in wb_entities.csv (e.g. 'Latin America & Caribbean ', 'Sub-Saharan Africa ').\",\n \"handling\": \"Strings stripped; join is on iso3 so no effect on the analysis.\"})\nissues.append({\"issue\": \"ISO3 code 'NAM' (Namibia) would be read as missing by pandas' default NA parsing.\",\n \"handling\": \"CSV read with keep_default_na=False and only empty strings treated as missing.\"})\nc = m[m.region != \"Aggregates\"].copy()\n\n# ---- analysis year ----\nc[\"complete\"] = c.gdp_pc_ppp.notna() & c.life_expectancy.notna()\nper_year = c.groupby(\"year\")[\"complete\"].sum()\neligible = per_year[per_year >= 150]\nanalysis_year = int(eligible.index.max())\n\ny = c[c.year == analysis_year]\nmissing = y[~y.complete]\nn_excluded_missing = int(len(missing))\nissues.append({\"issue\": f\"In {analysis_year}, {n_excluded_missing} countries/economies lack an indicator (GDP pc PPP missing for: \"\n + \", \".join(missing.iso3) + \"; LE missing for \"\n + str(int(missing.life_expectancy.isna().sum())) + \"). Missingness is non-random (small territories, conflict/sanctioned economies such as SSD, SYR, YEM, VEN, PRK, CUB, ERI).\",\n \"handling\": \"Listwise exclusion, no imputation, counted in n_excluded_missing; discussed as a selection threat.\"})\n\n# implausible LE swings in the panel (outside analysis year)\nswing = c.groupby(\"iso3\").life_expectancy.agg(lambda s: s.max() - s.min())\nbig = swing[swing > 15].index.tolist()\nissues.append({\"issue\": \"Very large year-to-year swings in life expectancy for \" + \", \".join(big)\n + \" (e.g. CAF 18.8 years in 2022 vs 57.4 in 2023; SSD 35-40 in 2015-2017), reflecting crisis-mortality estimates/revisions.\",\n \"handling\": f\"Only {analysis_year} values enter the estimate; CAF {analysis_year} value kept (plausible); a sensitivity excluding CAF is reported in the report.\"})\n\nd = y[y.complete].copy()\nd[\"log_gdp\"] = np.log(d.gdp_pc_ppp)\nassert (d.gdp_pc_ppp > 0).all()\nn = len(d)\n\n# ---- baseline and model ----\nle = d.life_expectancy.values\nbaseline_rmse = float(np.sqrt(np.mean((le - le.mean()) ** 2)))\nX = sm.add_constant(d.log_gdp.values)\nfit = sm.OLS(le, X).fit(cov_type=\"HC3\")\nalpha, beta = map(float, fit.params)\nresid = fit.resid\nmodel_rmse = float(np.sqrt(np.mean(resid ** 2)))\nse = float(fit.bse[1])\nci = fit.conf_int(alpha=0.05)[1] # normal-based 95% CI with HC3 SE\nr = float(np.corrcoef(d.log_gdp, le)[0, 1])\nr2 = float(fit.rsquared)\n\nlo, hi = float(ci[0]), float(ci[1])\nconclusion = \"positive_effect\" if lo > 0 else (\"negative_effect\" if hi < 0 else \"no_detectable_effect\")\n\n# ---- sensitivity (printed, saved to out/sensitivity.json) ----\nsens = {}\nfor k in [\"nonrobust\", \"HC0\", \"HC1\", \"HC2\", \"HC3\"]:\n f = sm.OLS(le, X).fit(cov_type=k)\n sens[f\"se_{k}\"] = float(f.bse[1])\nnoc = d[d.iso3 != \"CAF\"]\nf = sm.OLS(noc.life_expectancy.values, sm.add_constant(noc.log_gdp.values)).fit(cov_type=\"HC3\")\nsens[\"beta_excl_CAF\"] = float(f.params[1]); sens[\"se_excl_CAF_HC3\"] = float(f.bse[1])\ninfl = sm.OLS(le, X).fit().get_influence()\ncooks = infl.cooks_distance[0]\ntop = np.argsort(cooks)[::-1][:5]\nsens[\"top5_cooks\"] = {d.iso3.iloc[i]: float(cooks[i]) for i in top}\nkeep = cooks < 4 / n\nf = sm.OLS(le[keep], X[keep]).fit(cov_type=\"HC3\")\nsens[\"beta_excl_cooks_gt_4_over_n\"] = float(f.params[1]); sens[\"n_excl_cooks\"] = int((~keep).sum())\nXq = np.column_stack([X, (d.log_gdp.values - d.log_gdp.mean()) ** 2])\nfq = sm.OLS(le, Xq).fit(cov_type=\"HC3\")\nsens[\"quadratic_term\"] = float(fq.params[2]); sens[\"quadratic_term_p_HC3\"] = float(fq.pvalues[2])\nsens[\"rho_spearman\"] = float(d[[\"log_gdp\", \"life_expectancy\"]].corr(method=\"spearman\").iloc[0, 1])\n# slope by year (all eligible years) for stability\nsens[\"beta_by_year\"] = {}\nfor yr in eligible.index:\n dy = c[(c.year == yr) & c.complete]\n fy = sm.OLS(dy.life_expectancy.values, sm.add_constant(np.log(dy.gdp_pc_ppp.values))).fit()\n sens[\"beta_by_year\"][int(yr)] = float(fy.params[1])\nsens[\"complete_per_year\"] = {int(k): int(v) for k, v in per_year.items()}\nsens[\"largest_neg_resid\"] = {d.iso3.iloc[i]: float(resid[i]) for i in np.argsort(resid)[:5]}\nsens[\"largest_pos_resid\"] = {d.iso3.iloc[i]: float(resid[i]) for i in np.argsort(resid)[::-1][:5]}\nwith open(os.path.join(OUT, \"sensitivity.json\"), \"w\") as fh:\n json.dump(sens, fh, indent=2)\n\n# ---- table ----\norder = [\"Low income\", \"Lower middle income\", \"Upper middle income\", \"High income\"]\ntab = (d.groupby(\"income_level\")\n .agg(n_countries=(\"iso3\", \"size\"), mean_log_gdp_pc=(\"log_gdp\", \"mean\"),\n mean_life_expectancy=(\"life_expectancy\", \"mean\"))\n .reindex([o for o in order if o in set(d.income_level)] +\n sorted(set(d.income_level) - set(order)))\n .reset_index())\ntab.round(4).to_csv(os.path.join(OUT, \"table1.csv\"), index=False)\n\n# ---- figure ----\nfig, ax = plt.subplots(figsize=(7, 5))\ncols = {\"Low income\": \"tab:red\", \"Lower middle income\": \"tab:orange\",\n \"Upper middle income\": \"tab:green\", \"High income\": \"tab:blue\"}\nfor lvl, g in d.groupby(\"income_level\"):\n ax.scatter(g.log_gdp, g.life_expectancy, s=16, alpha=0.75, label=lvl, color=cols.get(lvl, \"grey\"))\nxs = np.linspace(d.log_gdp.min(), d.log_gdp.max(), 100)\nax.plot(xs, alpha + beta * xs, \"k-\", lw=1.5,\n label=f\"OLS: LE = {alpha:.1f} + {beta:.2f}·ln(GDPpc), R²={r2:.2f}\")\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, {analysis_year} (n={n} countries/economies)\")\nax.legend(fontsize=8, loc=\"lower right\")\nfig.tight_layout()\nfig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=150)\n\nresults = {\n \"task_id\": \"F6-dev-01\",\n \"analysis_year\": analysis_year,\n \"n_excluded_missing\": n_excluded_missing,\n \"baseline_metric\": baseline_rmse,\n \"model_metric\": model_rmse,\n \"pearson_r\": r,\n \"r_squared\": r2,\n \"effect_estimate\": beta,\n \"intercept\": alpha,\n \"effect_se\": se,\n \"effect_ci_low\": lo,\n \"effect_ci_high\": hi,\n \"conclusion\": conclusion,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": n_input_rows,\n \"n_analysed\": int(n),\n \"random_seed\": SEED,\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as fh:\n json.dump(results, fh, indent=2)\nprint(json.dumps(results, indent=2)); 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Run from workspace root:\n ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nimport statsmodels.api as sm\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nSEED = 0 # no randomness used; fixed for completeness\nnp.random.seed(SEED)\nDATA, OUT = \"data\", \"out\"\nos.makedirs(OUT, exist_ok=True)\n\n# keep_default_na=False so that ISO code \"NAM\" (Namibia) etc. is never parsed as NaN\nrd = dict(keep_default_na=False, na_values=[\"\"])\nind = pd.read_csv(os.path.join(DATA, \"wb_indicators.csv\"), **rd)\nent = pd.read_csv(os.path.join(DATA, \"wb_entities.csv\"), **rd)\nn_input_rows = {\"wb_indicators.csv\": int(len(ind)), \"wb_entities.csv\": int(len(ent))}\nissues = []\n\n# ---- audit ----\nassert not ind.duplicated([\"iso3\", \"year\"]).any()\nassert not ent.duplicated(\"iso3\").any()\nunmatched = sorted(set(ind.iso3) - set(ent.iso3))\nassert not unmatched, unmatched\nent[\"region\"] = ent[\"region\"].str.strip()\nent[\"income_level\"] = ent[\"income_level\"].str.strip()\nent[\"name\"] = ent[\"name\"].str.strip()\n\nm = ind.merge(ent[[\"iso3\", \"region\", \"income_level\"]], on=\"iso3\", how=\"left\")\nn_agg_ent = int(m.loc[m.region == \"Aggregates\", \"iso3\"].nunique())\nissues.append({\"issue\": f\"wb_indicators.csv mixes countries/economies with {n_agg_ent} regional/income/other aggregates (region == 'Aggregates').\",\n \"handling\": \"All aggregate entities excluded before any computation, per TASK rules.\"})\nissues.append({\"issue\": \"Some entity names carry trailing whitespace in wb_entities.csv (e.g. 'Latin America & Caribbean ', 'Sub-Saharan Africa ').\",\n \"handling\": \"Strings stripped; join is on iso3 so no effect on the analysis.\"})\nissues.append({\"issue\": \"ISO3 code 'NAM' (Namibia) would be read as missing by pandas' default NA parsing.\",\n \"handling\": \"CSV read with keep_default_na=False and only empty strings treated as missing.\"})\nc = m[m.region != \"Aggregates\"].copy()\n\n# ---- analysis year ----\nc[\"complete\"] = c.gdp_pc_ppp.notna() & c.life_expectancy.notna()\nper_year = c.groupby(\"year\")[\"complete\"].sum()\neligible = per_year[per_year >= 150]\nanalysis_year = int(eligible.index.max())\n\ny = c[c.year == analysis_year]\nmissing = y[~y.complete]\nn_excluded_missing = int(len(missing))\nissues.append({\"issue\": f\"In {analysis_year}, {n_excluded_missing} countries/economies lack an indicator (GDP pc PPP missing for: \"\n + \", \".join(missing.iso3) + \"; LE missing for \"\n + str(int(missing.life_expectancy.isna().sum())) + \"). Missingness is non-random (small territories, conflict/sanctioned economies such as SSD, SYR, YEM, VEN, PRK, CUB, ERI).\",\n \"handling\": \"Listwise exclusion, no imputation, counted in n_excluded_missing; discussed as a selection threat.\"})\n\n# implausible LE swings in the panel (outside analysis year)\nswing = c.groupby(\"iso3\").life_expectancy.agg(lambda s: s.max() - s.min())\nbig = swing[swing > 15].index.tolist()\nissues.append({\"issue\": \"Very large year-to-year swings in life expectancy for \" + \", \".join(big)\n + \" (e.g. CAF 18.8 years in 2022 vs 57.4 in 2023; SSD 35-40 in 2015-2017), reflecting crisis-mortality estimates/revisions.\",\n \"handling\": f\"Only {analysis_year} values enter the estimate; CAF {analysis_year} value kept (plausible); a sensitivity excluding CAF is reported in the report.\"})\n\nd = y[y.complete].copy()\nd[\"log_gdp\"] = np.log(d.gdp_pc_ppp)\nassert (d.gdp_pc_ppp > 0).all()\nn = len(d)\n\n# ---- baseline and model ----\nle = d.life_expectancy.values\nbaseline_rmse = float(np.sqrt(np.mean((le - le.mean()) ** 2)))\nX = sm.add_constant(d.log_gdp.values)\nfit = sm.OLS(le, X).fit(cov_type=\"HC3\")\nalpha, beta = map(float, fit.params)\nresid = fit.resid\nmodel_rmse = float(np.sqrt(np.mean(resid ** 2)))\nse = float(fit.bse[1])\nci = fit.conf_int(alpha=0.05)[1] # normal-based 95% CI with HC3 SE\nr = float(np.corrcoef(d.log_gdp, le)[0, 1])\nr2 = float(fit.rsquared)\n\nlo, hi = float(ci[0]), float(ci[1])\nconclusion = \"positive_effect\" if lo > 0 else (\"negative_effect\" if hi < 0 else \"no_detectable_effect\")\n\n# ---- sensitivity (printed, saved to out/sensitivity.json) ----\nsens = {}\nfor k in [\"nonrobust\", \"HC0\", \"HC1\", \"HC2\", \"HC3\"]:\n f = sm.OLS(le, X).fit(cov_type=k)\n sens[f\"se_{k}\"] = float(f.bse[1])\nnoc = d[d.iso3 != \"CAF\"]\nf = sm.OLS(noc.life_expectancy.values, sm.add_constant(noc.log_gdp.values)).fit(cov_type=\"HC3\")\nsens[\"beta_excl_CAF\"] = float(f.params[1]); sens[\"se_excl_CAF_HC3\"] = float(f.bse[1])\ninfl = sm.OLS(le, X).fit().get_influence()\ncooks = infl.cooks_distance[0]\ntop = np.argsort(cooks)[::-1][:5]\nsens[\"top5_cooks\"] = {d.iso3.iloc[i]: float(cooks[i]) for i in top}\nkeep = cooks < 4 / n\nf = sm.OLS(le[keep], X[keep]).fit(cov_type=\"HC3\")\nsens[\"beta_excl_cooks_gt_4_over_n\"] = float(f.params[1]); sens[\"n_excl_cooks\"] = int((~keep).sum())\nXq = np.column_stack([X, (d.log_gdp.values - d.log_gdp.mean()) ** 2])\nfq = sm.OLS(le, Xq).fit(cov_type=\"HC3\")\nsens[\"quadratic_term\"] = float(fq.params[2]); sens[\"quadratic_term_p_HC3\"] = float(fq.pvalues[2])\nsens[\"rho_spearman\"] = float(d[[\"log_gdp\", \"life_expectancy\"]].corr(method=\"spearman\").iloc[0, 1])\n# slope by year (all eligible years) for stability\nsens[\"beta_by_year\"] = {}\nfor yr in eligible.index:\n dy = c[(c.year == yr) & c.complete]\n fy = sm.OLS(dy.life_expectancy.values, sm.add_constant(np.log(dy.gdp_pc_ppp.values))).fit()\n sens[\"beta_by_year\"][int(yr)] = float(fy.params[1])\nsens[\"complete_per_year\"] = {int(k): int(v) for k, v in per_year.items()}\nsens[\"largest_neg_resid\"] = {d.iso3.iloc[i]: float(resid[i]) for i in np.argsort(resid)[:5]}\nsens[\"largest_pos_resid\"] = {d.iso3.iloc[i]: float(resid[i]) for i in np.argsort(resid)[::-1][:5]}\nwith open(os.path.join(OUT, \"sensitivity.json\"), \"w\") as fh:\n json.dump(sens, fh, indent=2)\n\n# ---- table ----\norder = [\"Low income\", \"Lower middle income\", \"Upper middle income\", \"High income\"]\ntab = (d.groupby(\"income_level\")\n .agg(n_countries=(\"iso3\", \"size\"), mean_log_gdp_pc=(\"log_gdp\", \"mean\"),\n mean_life_expectancy=(\"life_expectancy\", \"mean\"))\n .reindex([o for o in order if o in set(d.income_level)] +\n sorted(set(d.income_level) - set(order)))\n .reset_index())\ntab.round(4).to_csv(os.path.join(OUT, \"table1.csv\"), index=False)\n\n# ---- figure ----\nfig, ax = plt.subplots(figsize=(7, 5))\ncols = {\"Low income\": 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Next I'll write the report and the claims file."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2047,"cache_read_input_tokens":15360,"output_tokens":3470,"output_tokens_details":{"thinking_tokens":77},"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":2047},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsezKQxUtdNo78WKMhX","requestRef":"8eefac80-2577-4729-8272-dc1b8645b3a1","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c4b1a36e-7b88-4779-8504-b58eea6ff6c2","timestamp":"2026-10-30T05:32:03.072Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0007","sessionId":"0b454686-9378-4035-afb9-eec048b51078","version":"2.1.314","gitBranch":"HEAD"}68{"parentUuid":"c4b1a36e-7b88-4779-8504-b58eea6ff6c2","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsezaJAafBgQq8Ac7Yc","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Cz4rmzt9nU3BfXareZ9V3P","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RR0007; cat > out/report.md <<'EOF'\n# F6-dev-01 — Income and life expectancy across countries (Preston curve), 2023\n\n## Question\nAcross countries/economies (each weighted equally), how does life expectancy at birth (LE, years) relate to the natural log of GDP per capita (PPP, constant 2021 international $) in the most recent year with usable World Bank WDI data? The estimand is the slope β of the linear projection LE = α + β·ln(GDPpc) + ε. This is a descriptive association, not a causal effect.\n\n## Data and exclusions\n- Inputs: `wb_indicators.csv` (2,349 entity-year rows, 2015–2023) and `wb_entities.csv` (296 entities). Files were read with only empty strings treated as missing, so the ISO code `NAM` (Namibia) is not parsed as NA. There are no duplicate (iso3, year) keys, and every iso3 code in the indicators file matches an entity.\n- Aggregates: 44 entities in the indicators file have `region == \"Aggregates\"` (regions, income groups and so on). They were all dropped, leaving 217 countries/economies.\n- Analysis year: there are 199 complete countries in each year 2015–2022 and 197 in 2023. Every year clears the 150 threshold, so the rule picks **2023**.\n- Missing data in 2023: **20** countries/economies are dropped. Each has life expectancy but no GDP per capita: ASM, CHI, CUB, ERI, GIB, GUM, IMN, LIE, MAF, MCO, MNP, NCL, PRK, PYF, SSD, SYR, VEN, VGB, VIR, YEM. Nothing was imputed. **n = 197** countries are analysed.\n- Other data issues found: (i) some entity names have trailing whitespace (harmless, since the join is on iso3); (ii) the LE series for CAF and SSD jump around implausibly within the panel (for example, CAF is 18.8 years in 2022 and 57.4 in 2023). Only 2023 values are used, and CAF's 2023 value looks plausible. I report a sensitivity check that drops CAF. All issues are also listed in `results.json → data_issues`.\n\n## Method\n- Baseline (no information): predict every country's LE with the cross-country mean. Its RMSE is the population SD of LE.\n- Model: OLS of LE on a constant and ln(GDPpc), one unweighted observation per country. In-sample RMSE and R² are reported.\n- Uncertainty: **HC3** heteroskedasticity-robust standard error for β. The 95% CI is β ± 1.96·SE (normal approximation; with 195 df a t-based interval would be practically the same).\n- Sensitivity checks (`out/sensitivity.json`, produced by `analysis.py`): other SE types (classical, HC0–HC2); dropping CAF; dropping the 9 high-influence points with Cook's D > 4/n; adding a quadratic term in ln(GDPpc); Spearman correlation; and the slope in each year from 2015 to 2023.\n- Deliverables: Figure 1 (`figure1.png`) is a scatter by income group with the OLS line. Table 1 (`table1.csv`) gives means by World Bank income level. No randomness is involved (the seed is fixed at 0 anyway).\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| Model RMSE (OLS residuals) | 3.59 years |\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\nThe Preston curve holds clearly in 2023. A 1-unit (about 2.7-fold) difference in GDP per capita goes with about 5.3 more years of life expectancy, or about 3.65 years (5.27·ln 2) per doubling. Knowing income roughly halves the prediction error compared with the mean-only baseline (7.08 → 3.59 years). Table 1 shows the same pattern by income level: mean LE is 63.2 years in low-income countries (n=20), 67.8 in lower-middle (n=46), 73.7 in upper-middle (n=58) and 79.7 in high-income countries (n=73).\n\nThe largest negative residuals (countries living much shorter than their income predicts) are Nigeria (−15.3 years), Greenland, Nauru, Equatorial Guinea and Guyana. Several of these are economies driven by oil or other resources. The largest positive residuals are Solomon Islands, Jordan, Lebanon, Malawi and the Maldives.\n\n## Uncertainty\n- HC3 SE = 0.211, so the **95% CI for β is [4.85, 5.68]**. It lies entirely above zero, giving conclusion code `positive_effect`.\n- The SE barely changes with the estimator: classical 0.222, HC0 0.207, HC1 0.208, HC2 0.209.\n- The point estimate is stable across sensitivity checks: 5.24 without CAF, and 5.42 after dropping the 9 points with Cook's D > 4/n. The quadratic term in ln(GDPpc) is small and not significant (0.17, HC3 p = 0.34), so over this range the log-linear form is adequate. Spearman ρ = 0.87.\n- Over time, the slope drifts down from about 5.9 (2015–2019) to 5.6–5.7 (2020–2022) and 5.27 (2023). That is a descriptive observation only. I did not formally test it, and it partly reflects WDI vintage and estimation choices.\n\n## Limitations and open issues\n- **What would invalidate the result:** (i) a systematic revision of the UN WPP life-expectancy estimates or the ICP 2021 PPP benchmarks (2023 values for many low-income countries are modelled or extrapolated, not observed); (ii) mismeasured GDP per capita that correlates with LE, for example in conflict economies or where resource rents inflate GDP. Either would bias β. The sign is extremely robust (the CI is about 23 SEs from zero), but the size of β is less secure.\n- **Selection from missing data:** the 20 excluded economies are not random. They include conflict or sanctioned states (SSD, SYR, YEM, VEN, PRK, CUB, ERI) and small rich territories (MCO, LIE, GIB, IMN, CHI, etc.). The poor conflict states would probably sit below the line and the rich territories near or above it, so their net effect on β is unknown. I did not try any bounding or imputation.\n- **Not causal:** β mixes up the effect of income with health technology, institutions, education, disease environment (HIV, malaria), inequality and reverse causation (health → income). Nothing is claimed about what would happen if a country's income changed.\n- **Equal weighting:** each economy, including micro-states, counts the same as India or China. A population-weighted slope would answer a different question and was not estimated.\n- **Not checked:** out-of-sample prediction (the RMSEs are in-sample, and with two parameters and n = 197 optimism is small); spatial or regional correlation of residuals, which robust SEs do not handle and which would probably widen the CI; alternative functional forms beyond a quadratic in logs; uncertainty in the underlying WDI estimates themselves (treated here as fixed and error-free); and the reasons for the 2023 drop in slope.\n- **Data quality flags:** the volatile CAF and SSD LE series suggest crisis-mortality modelling or revision artefacts. I did not audit these values beyond spot checks, and similar problems may exist undetected in other countries.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"In 2023 (n=197 countries/economies), life expectancy rises with ln(GDP per capita): OLS slope 5.27 years per log-point, HC3 95% CI [4.85, 5.68], entirely above zero.\",\"results_keys\":[\"analysis_year\",\"n_analysed\",\"effect_estimate\",\"effect_se\",\"effect_ci_low\",\"effect_ci_high\",\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Descriptive association, not causal; 20 economies excluded for missing GDP, non-randomly.\"},\n {\"id\":\"C2\",\"text\":\"The relationship is strong: Pearson r = 0.86, R² = 0.74.\",\"results_keys\":[\"pearson_r\",\"r_squared\"],\"status\":\"supported\",\"caveat\":\"In-sample fit; equal country weights.\"},\n {\"id\":\"C3\",\"text\":\"Log income roughly halves prediction error relative to the mean-only baseline (RMSE 7.08 -> 3.59 years).\",\"results_keys\":[\"baseline_metric\",\"model_metric\"],\"status\":\"supported\",\"caveat\":\"In-sample RMSE; no cross-validation performed.\"},\n {\"id\":\"C4\",\"text\":\"The analysis year is 2023, the latest year with >=150 complete countries; 20 countries/economies were excluded for missing GDP per capita.\",\"results_keys\":[\"analysis_year\",\"n_excluded_missing\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"All excluded cases lack GDP pc PPP; LE was complete.\"}\n]\nEOF\nmd5 out/results.json out/table1.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; md5 out/results.json out/table1.csv; ~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'))\"; ls out","description":"Write report and claims, verify deterministic rerun"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2047,"cache_read_input_tokens":15360,"output_tokens":3470,"output_tokens_details":{"thinking_tokens":77},"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":2047},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXsezKQxUtdNo78WKMhX","requestRef":"8eefac80-2577-4729-8272-dc1b8645b3a1","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"56447f7d-c8cd-4ae2-98c2-f9ec90b6c4f1","timestamp":"2026-10-30T05:32:32.772Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0007","sessionId":"0b454686-9378-4035-afb9-eec048b51078","version":"2.1.314","gitBranch":"HEAD"}69{"parentUuid":"56447f7d-c8cd-4ae2-98c2-f9ec90b6c4f1","isSidechain":false,"promptId":"ef6f0154-1eb0-4dee-895a-1aaccd87907a","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Cz4rmzt9nU3BfXareZ9V3P","type":"tool_result","content":"MD5 (out/results.json) = 1ac96c99b712a342e422b8a15ba7e17a\nMD5 (out/table1.csv) = 07318f07084fcfbb1188da7cf0529979\nMD5 (out/results.json) = 1ac96c99b712a342e422b8a15ba7e17a\nMD5 (out/table1.csv) = 07318f07084fcfbb1188da7cf0529979\nanalysis.py\nclaims.json\nfigure1.png\nreport.md\nresults.json\nsensitivity.json\ntable1.csv","is_error":false}]},"uuid":"ae785dd4-34a7-4ce8-8ee9-3ad4646e10c4","timestamp":"2026-10-30T05:32:33.688Z","toolUseResult":{"stdout":"MD5 (out/results.json) = 1ac96c99b712a342e422b8a15ba7e17a\nMD5 (out/table1.csv) = 07318f07084fcfbb1188da7cf0529979\nMD5 (out/results.json) = 1ac96c99b712a342e422b8a15ba7e17a\nMD5 (out/table1.csv) = 07318f07084fcfbb1188da7cf0529979\nanalysis.py\nclaims.json\nfigure1.png\nreport.md\nresults.json\nsensitivity.json\ntable1.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"56447f7d-c8cd-4ae2-98c2-f9ec90b6c4f1","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0007","sessionId":"0b454686-9378-4035-afb9-eec048b51078","version":"2.1.314","gitBranch":"HEAD"}70{"type":"api-request-blob","sessionId":"0b454686-9378-4035-afb9-eec048b51078","hash":"0b44174b49489fe7388e4741e99f42531c10d53a6e982d949604126b0bb954a4","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"Everything ran. Next I'll write the report and the claims file."},{"type":"tool_use","id":"toolu_01Cz4rmzt9nU3BfXareZ9V3P","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RR0007; cat > out/report.md <<'EOF'\n# F6-dev-01 — Income and life expectancy across countries (Preston curve), 2023\n\n## Question\nAcross countries/economies (each weighted equally), how does life expectancy at birth (LE, years) relate to the natural log of GDP per capita (PPP, constant 2021 international $) in the most recent year with usable World Bank WDI data? The estimand is the slope β of the linear projection LE = α + β·ln(GDPpc) + ε. This is a descriptive association, not a causal effect.\n\n## Data and exclusions\n- Inputs: `wb_indicators.csv` (2,349 entity-year rows, 2015–2023) and `wb_entities.csv` (296 entities). Files were read with only empty strings treated as missing, so the ISO code `NAM` (Namibia) is not parsed as NA. There are no duplicate (iso3, year) keys, and every iso3 code in the indicators file matches an entity.\n- Aggregates: 44 entities in the indicators file have `region == \"Aggregates\"` (regions, income groups and so on). They were all dropped, leaving 217 countries/economies.\n- Analysis year: there are 199 complete countries in each year 2015–2022 and 197 in 2023. Every year clears the 150 threshold, so the rule picks **2023**.\n- Missing data in 2023: **20** countries/economies are dropped. Each has life expectancy but no GDP per capita: ASM, CHI, CUB, ERI, GIB, GUM, IMN, LIE, MAF, MCO, MNP, NCL, PRK, PYF, SSD, SYR, VEN, VGB, VIR, YEM. Nothing was imputed. **n = 197** countries are analysed.\n- Other data issues found: (i) some entity names have trailing whitespace (harmless, since the join is on iso3); (ii) the LE series for CAF and SSD jump around implausibly within the panel (for example, CAF is 18.8 years in 2022 and 57.4 in 2023). Only 2023 values are used, and CAF's 2023 value looks plausible. I report a sensitivity check that drops CAF. All issues are also listed in `results.json → data_issues`.\n\n## Method\n- Baseline (no information): predict every country's LE with the cross-country mean. Its RMSE is the population SD of LE.\n- Model: OLS of LE on a constant and ln(GDPpc), one unweighted observation per country. In-sample RMSE and R² are reported.\n- Uncertainty: **HC3** heteroskedasticity-robust standard error for β. The 95% CI is β ± 1.96·SE (normal approximation; with 195 df a t-based interval would be practically the same).\n- Sensitivity checks (`out/sensitivity.json`, produced by `analysis.py`): other SE types (classical, HC0–HC2); dropping CAF; dropping the 9 high-influence points with Cook's D > 4/n; adding a quadratic term in ln(GDPpc); Spearman correlation; and the slope in each year from 2015 to 2023.\n- Deliverables: Figure 1 (`figure1.png`) is a scatter by income group with the OLS line. Table 1 (`table1.csv`) gives means by World Bank income level. No randomness is involved (the seed is fixed at 0 anyway).\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| Model RMSE (OLS residuals) | 3.59 years |\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\nThe Preston curve holds clearly in 2023. A 1-unit (about 2.7-fold) difference in GDP per capita goes with about 5.3 more years of life expectancy, or about 3.65 years (5.27·ln 2) per doubling. Knowing income roughly halves the prediction error compared with the mean-only baseline (7.08 → 3.59 years). Table 1 shows the same pattern by income level: mean LE is 63.2 years in low-income countries (n=20), 67.8 in lower-middle (n=46), 73.7 in upper-middle (n=58) and 79.7 in high-income countries (n=73).\n\nThe largest negative residuals (countries living much shorter than their income predicts) are Nigeria (−15.3 years), Greenland, Nauru, Equatorial Guinea and Guyana. Several of these are economies driven by oil or other resources. The largest positive residuals are Solomon Islands, Jordan, Lebanon, Malawi and the Maldives.\n\n## Uncertainty\n- HC3 SE = 0.211, so the **95% CI for β is [4.85, 5.68]**. It lies entirely above zero, giving conclusion code `positive_effect`.\n- The SE barely changes with the estimator: classical 0.222, HC0 0.207, HC1 0.208, HC2 0.209.\n- The point estimate is stable across sensitivity checks: 5.24 without CAF, and 5.42 after dropping the 9 points with Cook's D > 4/n. The quadratic term in ln(GDPpc) is small and not significant (0.17, HC3 p = 0.34), so over this range the log-linear form is adequate. Spearman ρ = 0.87.\n- Over time, the slope drifts down from about 5.9 (2015–2019) to 5.6–5.7 (2020–2022) and 5.27 (2023). That is a descriptive observation only. I did not formally test it, and it partly reflects WDI vintage and estimation choices.\n\n## Limitations and open issues\n- **What would invalidate the result:** (i) a systematic revision of the UN WPP life-expectancy estimates or the ICP 2021 PPP benchmarks (2023 values for many low-income countries are modelled or extrapolated, not observed); (ii) mismeasured GDP per capita that correlates with LE, for example in conflict economies or where resource rents inflate GDP. Either would bias β. The sign is extremely robust (the CI is about 23 SEs from zero), but the size of β is less secure.\n- **Selection from missing data:** the 20 excluded economies are not random. They include conflict or sanctioned states (SSD, SYR, YEM, VEN, PRK, CUB, ERI) and small rich territories (MCO, LIE, GIB, IMN, CHI, etc.). The poor conflict states would probably sit below the line and the rich territories near or above it, so their net effect on β is unknown. I did not try any bounding or imputation.\n- **Not causal:** β mixes up the effect of income with health technology, institutions, education, disease environment (HIV, malaria), inequality and reverse causation (health → income). Nothing is claimed about what would happen if a country's income changed.\n- **Equal weighting:** each economy, including micro-states, counts the same as India or China. A population-weighted slope would answer a different question and was not estimated.\n- **Not checked:** out-of-sample prediction (the RMSEs are in-sample, and with two parameters and n = 197 optimism is small); spatial or regional correlation of residuals, which robust SEs do not handle and which would probably widen the CI; alternative functional forms beyond a quadratic in logs; uncertainty in the underlying WDI estimates themselves (treated here as fixed and error-free); and the reasons for the 2023 drop in slope.\n- **Data quality flags:** the volatile CAF and SSD LE series suggest crisis-mortality modelling or revision artefacts. I did not audit these values beyond spot checks, and similar problems may exist undetected in other countries.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"In 2023 (n=197 countries/economies), life expectancy rises with ln(GDP per capita): OLS slope 5.27 years per log-point, HC3 95% CI [4.85, 5.68], entirely above zero.\",\"results_keys\":[\"analysis_year\",\"n_analysed\",\"effect_estimate\",\"effect_se\",\"effect_ci_low\",\"effect_ci_high\",\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Descriptive association, not causal; 20 economies excluded for missing GDP, non-randomly.\"},\n {\"id\":\"C2\",\"text\":\"The relationship is strong: Pearson r = 0.86, R² = 0.74.\",\"results_keys\":[\"pearson_r\",\"r_squared\"],\"status\":\"supported\",\"caveat\":\"In-sample fit; equal country weights.\"},\n {\"id\":\"C3\",\"text\":\"Log income roughly halves prediction error relative to the mean-only baseline (RMSE 7.08 -> 3.59 years).\",\"results_keys\":[\"baseline_metric\",\"model_metric\"],\"status\":\"supported\",\"caveat\":\"In-sample RMSE; no cross-validation performed.\"},\n {\"id\":\"C4\",\"text\":\"The analysis year is 2023, the latest year with >=150 complete countries; 20 countries/economies were excluded for missing GDP per capita.\",\"results_keys\":[\"analysis_year\",\"n_excluded_missing\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"All excluded cases lack GDP pc PPP; LE was complete.\"}\n]\nEOF\nmd5 out/results.json out/table1.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; md5 out/results.json out/table1.csv; ~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'))\"; ls out","description":"Write report and claims, verify deterministic rerun"}}]}}71{"type":"api-request","sessionId":"0b454686-9378-4035-afb9-eec048b51078","id":"26f7384c-051b-41c7-830a-54e10f7f2002","timestamp":"2026-10-30T05:32:33.690Z","version":"2.1.314","querySource":"sdk","shapeHash":"6d1426322e6f01939b80ae4fb18cd6ff3e8aa95eea4dd5cf5b965767a8a5da0e","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":"8eefac80-2577-4729-8272-dc1b8645b3a1","keep":20,"tail":["0b44174b49489fe7388e4741e99f42531c10d53a6e982d949604126b0bb954a4","f578996f8fb5c5097260d266a23b9e76b61a606b70f462de039d8afda21575f3"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",21,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"5ad4fdc70169125b529c7fb088bb2c99de68680ee2b9536fdc07691bdfa072c7"}72{"parentUuid":"ae785dd4-34a7-4ce8-8ee9-3ad4646e10c4","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXshZfvRY3uC4G1dhLGh","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":3655,"cache_read_input_tokens":17407,"output_tokens":1125,"output_tokens_details":{"thinking_tokens":16},"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":3655},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":6792,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXshZPo9YxzmzENqW2v5","requestRef":"26f7384c-051b-41c7-830a-54e10f7f2002","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"36a6c36b-be18-498d-9a77-6c76b1980b8c","timestamp":"2026-10-30T05:32:41.938Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0007","sessionId":"0b454686-9378-4035-afb9-eec048b51078","version":"2.1.314","gitBranch":"HEAD"}73{"parentUuid":"36a6c36b-be18-498d-9a77-6c76b1980b8c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXshZfvRY3uC4G1dhLGh","type":"message","role":"assistant","content":[{"type":"text","text":"The income–life expectancy relationship (the \"Preston curve\") holds clearly in 2023: life expectancy rises with log GDP per capita, and the link is strong. All required files are in `out/`, and re-running `out/analysis.py` gives identical outputs.\n\n**Data and year**\n- After removing the 44 regional and income-group aggregates, 217 countries/economies remain.\n- Every year from 2015 to 2023 has at least 150 countries with both indicators, so the rule picks 2023.\n- In 2023, 20 countries/economies are dropped because GDP per capita is missing (life expectancy is never missing), leaving 197 in the estimate.\n\n**Results**\n\n| Quantity | Value |\n|---|---|\n| Slope (years of life expectancy per 1-unit rise in log GDP per capita) | 5.27 |\n| Robust standard error (HC3) | 0.211 |\n| 95% interval | 4.85 to 5.68 |\n| Intercept | 22.57 years |\n| Correlation (Pearson r) | 0.861 |\n| R² | 0.742 |\n| Prediction error using the average only | 7.08 years |\n| Prediction error using the regression | 3.59 years |\n\nIn plain terms, a doubling of income goes with about 3.65 more years of life expectancy. The whole interval is above zero, so the conclusion code is `positive_effect`.\n\n**How robust it is**\n- The standard error barely changes with the choice of estimator.\n- The slope stays at 5.24 without the Central African Republic and 5.42 after dropping the 9 most influential points.\n- Adding a squared term doesn't improve the fit (p = 0.34), so the straight-line-in-logs form is adequate.\n- The slope has drifted down from about 5.9 in 2015–2019 to 5.27 in 2023. I describe this but did not test it.\n\n**Data problems found**\n- **Swings in life expectancy:** the Central African Republic and South Sudan series jump around implausibly within the panel (the Central African Republic is 18.8 years in 2022 and 57.4 in 2023). The 2023 value used looks plausible.\n- **Missing countries aren't random:** they are mostly conflict or sanctioned states (South Sudan, Syria, Yemen, Venezuela, North Korea, Cuba, Eritrea) and small rich territories (Monaco, Liechtenstein, Gibraltar and others). Their effect on the slope is unknown, and I didn't try to bound it.\n- **Two minor issues:** some entity names have trailing spaces, which doesn't matter because the join uses country codes. Namibia's code \"NAM\" would be read as missing by default, so the files are read with only empty cells treated as missing.\n\n**Limitations**\n- The result is an association, not a causal effect.\n- Each country counts equally, including micro-states.\n- The fit and prediction errors are measured on the same data, with no out-of-sample check.\n- Regional correlation of the residuals isn't accounted for and would probably widen the interval.\n- The World Bank figures are treated as exact, although many 2023 values for low-income countries are modelled.\n\n**Files** (all in `out/`):\n- results.json\n- report.md\n- analysis.py\n- figure1.png\n- table1.csv\n- claims.json (4 claims, all supported)\n- sensitivity.json (an extra file with the robustness checks)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3655,"cache_read_input_tokens":17407,"output_tokens":1125,"output_tokens_details":{"thinking_tokens":16},"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":3655},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXshZPo9YxzmzENqW2v5","requestRef":"26f7384c-051b-41c7-830a-54e10f7f2002","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a6040471-c785-4a75-a10c-3591ed5d4560","timestamp":"2026-10-30T05:32:44.856Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0007","sessionId":"0b454686-9378-4035-afb9-eec048b51078","version":"2.1.314","gitBranch":"HEAD"}74{"type":"last-prompt","lastPrompt":"Your","leafUuid":"a6040471-c785-4a75-a10c-3591ed5d4560","sessionId":"0b454686-9378-4035-afb9-eec048b51078"}75{"type":"mode","mode":"normal","sessionId":"0b454686-9378-4035-afb9-eec048b51078"}76{"type":"atis-latch","atis":"","sessionId":"0b454686-9378-4035-afb9-eec048b51078"}77{"type":"cost-state","sessionId":"0b454686-9378-4035-afb9-eec048b51078","totalCostUSD":0.33409259999999996,"totalAPIDuration":107468,"totalAPIDurationWithoutRetries":107463,"totalToolDuration":2658,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":110302,"startTime":1793338253301,"modelUsage":{"claude-opus-5-5":{"inputTokens":24,"outputTokens":10881,"thinkingTokens":443,"cacheReadInputTokens":92933,"cacheCreationInputTokens":19558,"cacheCreationEphemeral5mInputTokens":19558,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.33409259999999996}},"hasUnknownModelCost":false}78