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In the current working directory, `TASK.md` describes a bounded scientific task and the directory `out/` contains the deliverables submitted by another assistant (you have not seen how they were produced and you must not look for their working transcript). There is no network access. Use `~/wp29/venv/bin/python` if you need to re-run or test code. Do not modify any file under `out/` except that you must create `out/review/review.json`.\n\nReview the deliverables against `TASK.md` only. Specifically check:\n1. Does `out/results.json` contain every required key with legitimately computed values? Re-run `out/analysis.py` in a scratch copy if needed and compare.\n2. Are the information-availability rules of `TASK.md` respected by the code (for example no use of future information, no use of forbidden inputs)? Read the code to verify; do not trust the report.\n3. 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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.\ntotal 192\ndrwxr-xr-x@ 11 simon-pierreboucher staff 352 oct. 29 23:53 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 29 23:50 ..\n-rw-r--r--@ 1 simon-pierreboucher staff 7592 oct. 29 23:52 analysis.py\n-rw-r--r--@ 1 simon-pierreboucher staff 1845 oct. 29 23:53 claims.json\n-rw-r--r--@ 1 simon-pierreboucher staff 432 oct. 29 23:52 exclusions.csv\n-rw-r--r--@ 1 simon-pierreboucher staff 62212 oct. 29 23:52 figure1.png\n-rw-r--r--@ 1 simon-pierreboucher staff 6817 oct. 29 23:53 report.md\n-rw-r--r--@ 1 simon-pierreboucher staff 2644 oct. 29 23:52 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 29 23:53 review\ndrwxr-xr-x@ 9 simon-pierreboucher staff 288 oct. 29 23:52 state\n-rw-r--r--@ 1 simon-pierreboucher staff 198 oct. 29 23:52 table1.csv\n{\n \"task_id\": \"F6-dev-01\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"analysis_year\": 2023,\n \"n_excluded_missing\": 20,\n \"n_analysed\": 197,\n \"baseline_metric\": 7.07645404460588,\n \"model_metric\": 3.5949979094078177,\n \"pearson_r\": 0.8613437957840087,\n \"r_squared\": 0.7419131345356038,\n \"effect_estimate\": 5.267173451958655,\n \"intercept\": 22.56709274982858,\n \"effect_se\": 0.21067528691162227,\n \"effect_ci_low\": 4.854257477179233,\n \"effect_ci_high\": 5.6800894267380775,\n \"conclusion\": \"positive_effect\",\n \"data_issues\": [\n {\n \"issue\": \"wb_indicators.csv contains 44 regional/income/other aggregate entities (396 entity-year rows)\",\n \"handling\": \"excluded via region == 'Aggregates' (rule)\"\n },\n {\n \"issue\": \"20 countries/economies lack an indicator in 2023 (gdp missing: 20, LE missing: 0): ASM, CHI, CUB, ERI, GIB, GUM, IMN, LIE, MAF, MCO, MNP, NCL, PRK, PYF, SSD, SYR, VEN, VGB, VIR, YEM\",\n \"handling\": \"excluded, counted in n_excluded_missing; possible selection bias discussed in report\"\n },\n {\n \"issue\": \"Central African Republic life expectancy series is implausibly volatile in 2015-2022 (e.g. 18.8 years in 2022, 31.5 in 2019); 2023 value 57.4 is plausible\",\n \"handling\": \"affects non-analysis years only; 2023 value kept; noted as data-quality caveat\"\n },\n {\n \"issue\": \"West Bank and Gaza life expectancy drops from 76.7 (2022) to 65.2 (2023), consistent with conflict mortality but a large outlier relative to its income\",\n \"handling\": \"kept (genuine reported value, no rule permits dropping it); a leave-5-most-influential-out (Cook distance) sensitivity is reported\"\n }\n ],\n \"n_input_rows\": {\n \"wb_indicators.csv\": 2349,\n \"wb_entities.csv\": 296\n },\n \"random_seed\": null,\n \"se_type\": \"HC3\",\n \"complete_countries_by_year\": {\n \"2015\": 199,\n \"2016\": 199,\n \"2017\": 199,\n \"2018\": 199,\n \"2019\": 199,\n \"2020\": 199,\n \"2021\": 199,\n \"2022\": 199,\n \"2023\": 197\n },\n \"n_countries_in_analysis_year\": 217,\n \"supp_effect_se_by_type\": {\n \"HC0\": 0.20736485591704354,\n \"HC1\": 0.2084255526375312,\n \"HC2\": 0.20901105806015902,\n \"HC3\": 0.21067528691162227\n },\n \"supp_effect_se_classical\": 0.22246758287864518,\n \"supp_mean_life_expectancy\": 73.47408158883249,\n \"supp_sens_drop5_cooks_iso3\": [\n \"NGA\",\n \"GRL\",\n \"TCD\",\n \"SLB\",\n \"GUY\"\n ],\n \"supp_sens_drop5_effect_estimate\": 5.317920624114932,\n \"supp_sens_drop5_effect_se\": 0.18313966978067114,\n \"supp_sens_drop5_r_squared\": 0.7826492360851807,\n \"supp_largest_neg_residual_iso3\": \"NGA\",\n \"supp_largest_neg_residual\": -15.337823881620139\n}[\n {\"id\":\"C1\",\"text\":\"The analysis year is 2023, the latest year with at least 150 countries/economies observing both indicators; 197 countries are analysed and 20 are excluded for missing GDP.\",\"results_keys\":[\"analysis_year\",\"n_analysed\",\"n_excluded_missing\",\"complete_countries_by_year\"],\"status\":\"supported\",\"caveat\":\"Missing countries are not random (small territories and conflict/sanctioned states).\"},\n {\"id\":\"C2\",\"text\":\"The OLS slope of life expectancy on ln(GDP per capita) is about 5.27 years per log point, with HC3 95% CI [4.85, 5.68]; conclusion positive_effect.\",\"results_keys\":[\"effect_estimate\",\"effect_se\",\"effect_ci_low\",\"effect_ci_high\",\"conclusion\",\"se_type\"],\"status\":\"supported\",\"caveat\":\"Associational only; CI ignores measurement error and misspecification.\"},\n {\"id\":\"C3\",\"text\":\"The relationship is strong: Pearson r = 0.861, R^2 = 0.742, and log income reduces prediction RMSE from 7.08 (mean-only baseline) to 3.59 years.\",\"results_keys\":[\"pearson_r\",\"r_squared\",\"baseline_metric\",\"model_metric\"],\"status\":\"supported\",\"caveat\":\"In-sample fit; linear-in-log form misses curvature at high incomes.\"},\n {\"id\":\"C4\",\"text\":\"The slope is robust to the choice of robust SE type and to dropping the 5 most influential countries (beta = 5.32).\",\"results_keys\":[\"supp_effect_se_by_type\",\"supp_effect_se_classical\",\"supp_sens_drop5_effect_estimate\",\"supp_sens_drop5_effect_se\",\"supp_sens_drop5_cooks_iso3\"],\"status\":\"supported\",\"caveat\":\"Limited sensitivity set; no clustering, bootstrap or nonlinear specifications.\"},\n {\"id\":\"C5\",\"text\":\"Mean life expectancy rises monotonically across World Bank income groups, from 63.2 (Low) to 79.7 years (High).\",\"results_keys\":[\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Numbers are in out/table1.csv, generated by analysis.py; group sizes are unequal (20 low-income countries).\"}\n]","is_error":false}]},"uuid":"f80697b8-dec2-4e9a-b4c6-53d91eb1f9e8","timestamp":"2026-10-30T03:53:39.776Z","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.\ntotal 192\ndrwxr-xr-x@ 11 simon-pierreboucher staff 352 oct. 29 23:53 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 29 23:50 ..\n-rw-r--r--@ 1 simon-pierreboucher staff 7592 oct. 29 23:52 analysis.py\n-rw-r--r--@ 1 simon-pierreboucher staff 1845 oct. 29 23:53 claims.json\n-rw-r--r--@ 1 simon-pierreboucher staff 432 oct. 29 23:52 exclusions.csv\n-rw-r--r--@ 1 simon-pierreboucher staff 62212 oct. 29 23:52 figure1.png\n-rw-r--r--@ 1 simon-pierreboucher staff 6817 oct. 29 23:53 report.md\n-rw-r--r--@ 1 simon-pierreboucher staff 2644 oct. 29 23:52 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 29 23:53 review\ndrwxr-xr-x@ 9 simon-pierreboucher staff 288 oct. 29 23:52 state\n-rw-r--r--@ 1 simon-pierreboucher staff 198 oct. 29 23:52 table1.csv\n{\n \"task_id\": \"F6-dev-01\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"analysis_year\": 2023,\n \"n_excluded_missing\": 20,\n \"n_analysed\": 197,\n \"baseline_metric\": 7.07645404460588,\n \"model_metric\": 3.5949979094078177,\n \"pearson_r\": 0.8613437957840087,\n \"r_squared\": 0.7419131345356038,\n \"effect_estimate\": 5.267173451958655,\n \"intercept\": 22.56709274982858,\n \"effect_se\": 0.21067528691162227,\n \"effect_ci_low\": 4.854257477179233,\n \"effect_ci_high\": 5.6800894267380775,\n \"conclusion\": \"positive_effect\",\n \"data_issues\": [\n {\n \"issue\": \"wb_indicators.csv contains 44 regional/income/other aggregate entities (396 entity-year rows)\",\n \"handling\": \"excluded via region == 'Aggregates' (rule)\"\n },\n {\n \"issue\": \"20 countries/economies lack an indicator in 2023 (gdp missing: 20, LE missing: 0): ASM, CHI, CUB, ERI, GIB, GUM, IMN, LIE, MAF, MCO, MNP, NCL, PRK, PYF, SSD, SYR, VEN, VGB, VIR, YEM\",\n \"handling\": \"excluded, counted in n_excluded_missing; possible selection bias discussed in report\"\n },\n {\n \"issue\": \"Central African Republic life expectancy series is implausibly volatile in 2015-2022 (e.g. 18.8 years in 2022, 31.5 in 2019); 2023 value 57.4 is plausible\",\n \"handling\": \"affects non-analysis years only; 2023 value kept; noted as data-quality caveat\"\n },\n {\n \"issue\": \"West Bank and Gaza life expectancy drops from 76.7 (2022) to 65.2 (2023), consistent with conflict mortality but a large outlier relative to its income\",\n \"handling\": \"kept (genuine reported value, no rule permits dropping it); a leave-5-most-influential-out (Cook distance) sensitivity is reported\"\n }\n ],\n \"n_input_rows\": {\n \"wb_indicators.csv\": 2349,\n \"wb_entities.csv\": 296\n },\n \"random_seed\": null,\n \"se_type\": \"HC3\",\n \"complete_countries_by_year\": {\n \"2015\": 199,\n \"2016\": 199,\n \"2017\": 199,\n \"2018\": 199,\n \"2019\": 199,\n \"2020\": 199,\n \"2021\": 199,\n \"2022\": 199,\n \"2023\": 197\n },\n \"n_countries_in_analysis_year\": 217,\n \"supp_effect_se_by_type\": {\n \"HC0\": 0.20736485591704354,\n \"HC1\": 0.2084255526375312,\n \"HC2\": 0.20901105806015902,\n \"HC3\": 0.21067528691162227\n },\n \"supp_effect_se_classical\": 0.22246758287864518,\n \"supp_mean_life_expectancy\": 73.47408158883249,\n \"supp_sens_drop5_cooks_iso3\": [\n \"NGA\",\n \"GRL\",\n \"TCD\",\n \"SLB\",\n \"GUY\"\n ],\n \"supp_sens_drop5_effect_estimate\": 5.317920624114932,\n \"supp_sens_drop5_effect_se\": 0.18313966978067114,\n \"supp_sens_drop5_r_squared\": 0.7826492360851807,\n \"supp_largest_neg_residual_iso3\": \"NGA\",\n \"supp_largest_neg_residual\": -15.337823881620139\n}[\n {\"id\":\"C1\",\"text\":\"The analysis year is 2023, the latest year with at least 150 countries/economies observing both indicators; 197 countries are analysed and 20 are excluded for missing GDP.\",\"results_keys\":[\"analysis_year\",\"n_analysed\",\"n_excluded_missing\",\"complete_countries_by_year\"],\"status\":\"supported\",\"caveat\":\"Missing countries are not random (small territories and conflict/sanctioned states).\"},\n {\"id\":\"C2\",\"text\":\"The OLS slope of life expectancy on ln(GDP per capita) is about 5.27 years per log point, with HC3 95% CI [4.85, 5.68]; conclusion positive_effect.\",\"results_keys\":[\"effect_estimate\",\"effect_se\",\"effect_ci_low\",\"effect_ci_high\",\"conclusion\",\"se_type\"],\"status\":\"supported\",\"caveat\":\"Associational only; CI ignores measurement error and misspecification.\"},\n {\"id\":\"C3\",\"text\":\"The relationship is strong: Pearson r = 0.861, R^2 = 0.742, and log income reduces prediction RMSE from 7.08 (mean-only baseline) to 3.59 years.\",\"results_keys\":[\"pearson_r\",\"r_squared\",\"baseline_metric\",\"model_metric\"],\"status\":\"supported\",\"caveat\":\"In-sample fit; linear-in-log form misses curvature at high incomes.\"},\n {\"id\":\"C4\",\"text\":\"The slope is robust to the choice of robust SE type and to dropping the 5 most influential countries (beta = 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entry point for F6-dev-01 (Preston curve).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/figure1.png, out/table1.csv, out/exclusions.csv.\nNo randomness is used (random_seed recorded as null).\"\"\"\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\nDATA, OUT = \"data\", \"out\"\nos.makedirs(OUT, exist_ok=True)\nread = lambda f: pd.read_csv(os.path.join(DATA, f), keep_default_na=False, na_values=[\"\"])\nind, ent = read(\"wb_indicators.csv\"), read(\"wb_entities.csv\")\nn_input_rows = {\"wb_indicators.csv\": int(len(ind)), \"wb_entities.csv\": int(len(ent))}\nissues, excl = [], []\n\ndef log_excl(step, rule, before, after, why):\n excl.append(dict(step=step, rule=rule, n_before=int(before), n_after=int(after),\n n_removed=int(before - after), rationale=why))\n\n# integrity checks\nassert not ind.duplicated([\"iso3\", \"year\"]).any() and not ent.iso3.duplicated().any()\nassert set(ind.iso3) <= set(ent.iso3)\nassert (ind.gdp_pc_ppp.dropna() > 0).all()\n\n# Step 1: drop aggregates\nm = ind.merge(ent[[\"iso3\", \"region\", \"income_level\"]], on=\"iso3\", how=\"left\")\nn0 = len(m)\nn_agg_ent = int(m.loc[m.region == \"Aggregates\", \"iso3\"].nunique())\nc = m[m.region != \"Aggregates\"].copy()\nlog_excl(1, \"drop entity-years whose region == 'Aggregates' in wb_entities.csv\", n0, len(c),\n \"information rule: countries/economies only\")\nissues.append({\"issue\": f\"wb_indicators.csv contains {n_agg_ent} regional/income/other aggregate entities \"\n f\"({n0 - len(c)} entity-year rows)\", \"handling\": \"excluded via region == 'Aggregates' (rule)\"})\n\n# Step 2: analysis year selection\ncov = c.assign(both=c.gdp_pc_ppp.notna() & c.life_expectancy.notna()).groupby(\"year\").both.sum()\nelig = cov[cov >= 150]\nanalysis_year = int(elig.index.max())\nn1 = len(c)\ny = c[c.year == analysis_year].copy()\nlog_excl(2, f\"keep analysis year {analysis_year} (latest year with >=150 complete countries)\", n1, len(y),\n \"information rule: single cross-section in analysis year\")\n\n# Step 3: drop missing\nmiss = y[y.gdp_pc_ppp.isna() | y.life_expectancy.isna()]\nn_excluded_missing = int(len(miss))\nn2 = len(y)\nd = y.dropna(subset=[\"gdp_pc_ppp\", \"life_expectancy\"]).copy()\nlog_excl(3, \"drop countries with gdp_pc_ppp or life_expectancy missing in analysis year\", n2, len(d),\n \"information rule: no imputation\")\nissues.append({\"issue\": f\"{n_excluded_missing} countries/economies lack an indicator in {analysis_year} \"\n f\"(gdp missing: {int(miss.gdp_pc_ppp.isna().sum())}, LE missing: {int(miss.life_expectancy.isna().sum())}): \"\n + \", \".join(sorted(miss.iso3)),\n \"handling\": \"excluded, counted in n_excluded_missing; possible selection bias discussed in report\"})\nissues.append({\"issue\": \"Central African Republic life expectancy series is implausibly volatile in 2015-2022 \"\n \"(e.g. 18.8 years in 2022, 31.5 in 2019); 2023 value 57.4 is plausible\",\n \"handling\": \"affects non-analysis years only; 2023 value kept; noted as data-quality caveat\"})\nissues.append({\"issue\": \"West Bank and Gaza life expectancy drops from 76.7 (2022) to 65.2 (2023), consistent with \"\n \"conflict mortality but a large outlier relative to its income\",\n \"handling\": \"kept (genuine reported value, no rule permits dropping it); a leave-5-most-influential-out (Cook distance) sensitivity is reported\"})\n\n# Estimation\nd[\"ln_gdp\"] = np.log(d.gdp_pc_ppp)\nX = sm.add_constant(d.ln_gdp.values)\nyv = d.life_expectancy.values\nfit = sm.OLS(yv, X).fit(cov_type=\"HC3\")\nalpha, beta = float(fit.params[0]), float(fit.params[1])\nse = float(fit.bse[1])\nz = 1.959963984540054\nci_lo, ci_hi = beta - z * se, beta + z * se\nresid = yv - (alpha + beta * d.ln_gdp.values)\nbaseline_rmse = float(np.sqrt(np.mean((yv - yv.mean()) ** 2)))\nmodel_rmse = float(np.sqrt(np.mean(resid ** 2)))\nr = float(np.corrcoef(d.ln_gdp, yv)[0, 1])\nconclusion = \"positive_effect\" if ci_lo > 0 else (\"negative_effect\" if ci_hi < 0 else \"no_detectable_effect\")\n\n# Supplementary (not estimand): other robust SEs, sensitivity dropping the 5 most influential obs (Cook's D)\nfit_ols = sm.OLS(yv, X).fit()\nse_alt = {k: float(sm.OLS(yv, X).fit(cov_type=k).bse[1]) for k in [\"HC0\", \"HC1\", \"HC2\", \"HC3\"]}\ncooks = fit_ols.get_influence().cooks_distance[0]\ntop = np.argsort(cooks)[::-1][:5]\nkeep = np.setdiff1d(np.arange(len(d)), top)\nfit_s = sm.OLS(yv[keep], X[keep]).fit(cov_type=\"HC3\")\nd[\"resid\"] = resid\n\nresults = {\n \"task_id\": \"F6-dev-01\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"analysis_year\": analysis_year,\n \"n_excluded_missing\": n_excluded_missing,\n \"n_analysed\": int(len(d)),\n \"baseline_metric\": baseline_rmse,\n \"model_metric\": model_rmse,\n \"pearson_r\": r,\n \"r_squared\": float(fit.rsquared),\n \"effect_estimate\": beta,\n \"intercept\": alpha,\n \"effect_se\": se,\n \"effect_ci_low\": float(ci_lo),\n \"effect_ci_high\": float(ci_hi),\n \"conclusion\": conclusion,\n \"data_issues\": issues,\n \"n_input_rows\": n_input_rows,\n \"random_seed\": None,\n \"se_type\": \"HC3\",\n \"complete_countries_by_year\": {str(k): int(v) for k, v in cov.items()},\n \"n_countries_in_analysis_year\": int(n2),\n \"supp_effect_se_by_type\": se_alt,\n \"supp_effect_se_classical\": float(fit_ols.bse[1]),\n \"supp_mean_life_expectancy\": float(yv.mean()),\n \"supp_sens_drop5_cooks_iso3\": [str(s) for s in d.iso3.values[top]],\n \"supp_sens_drop5_effect_estimate\": float(fit_s.params[1]),\n \"supp_sens_drop5_effect_se\": float(fit_s.bse[1]),\n \"supp_sens_drop5_r_squared\": float(fit_s.rsquared),\n \"supp_largest_neg_residual_iso3\": str(d.iso3.values[np.argmin(resid)]),\n \"supp_largest_neg_residual\": float(resid.min()),\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(results, f, indent=2)\n\npd.DataFrame(excl, columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]).to_csv(\n os.path.join(OUT, \"exclusions.csv\"), index=False)\n\norder = [\"Low income\", \"Lower middle income\", \"Upper middle income\", \"High income\"]\nt = (d.groupby(\"income_level\").agg(n_countries=(\"iso3\", \"size\"), mean_log_gdp_pc=(\"ln_gdp\", \"mean\"),\n mean_life_expectancy=(\"life_expectancy\", \"mean\")).reset_index())\nt[\"o\"] = t.income_level.map({k: i for i, k in enumerate(order)}).fillna(99)\nt = t.sort_values([\"o\", \"income_level\"]).drop(columns=\"o\")\nt.to_csv(os.path.join(OUT, \"table1.csv\"), index=False, float_format=\"%.4f\")\n\nfig, ax = plt.subplots(figsize=(7, 5))\ncols = dict(zip(order, [\"#d7191c\", \"#fdae61\", \"#abd9e9\", \"#2c7bb6\"]))\nfor lvl in order:\n s = d[d.income_level == lvl]\n ax.scatter(s.ln_gdp, s.life_expectancy, s=18, c=cols[lvl], label=f\"{lvl} (n={len(s)})\", edgecolor=\"k\", lw=0.3)\nxs = np.linspace(d.ln_gdp.min(), d.ln_gdp.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 intl $)\")\nax.set_ylabel(\"Life expectancy at birth (years)\")\nax.set_title(f\"Preston curve, {analysis_year} (n={len(d)} countries/economies)\")\nax.legend(fontsize=8, loc=\"lower right\")\nfig.tight_layout()\nfig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=120, metadata={\"Software\": None})\nplt.close(fig)\n\nprint(f\"year={analysis_year} n={len(d)} excluded_missing={n_excluded_missing} beta={beta:.4f} \"\n f\"HC3 se={se:.4f} CI=[{ci_lo:.4f},{ci_hi:.4f}] R2={fit.rsquared:.4f} conclusion={conclusion}\")\n# F6-dev-01 — Preston curve in the latest usable WDI year\n\n## Question\nAcross countries/economies, how strongly is life expectancy at birth (LE, years) associated with the natural log of GDP per capita (PPP, constant 2021 intl $) in the most recent year with usable World Bank data? Estimand: slope β of the linear projection LE = α + β·ln(GDPpc) + ε, one equally weighted observation per country. The interpretation is descriptive/associational, not causal.\n\n## Data and exclusions\nInputs: `data/wb_indicators.csv` (2349 rows) and `data/wb_entities.csv` (296 rows) (`n_input_rows`); SHA-256 hashes match `data/README.md` (see `out/state/data_audit.md`). Exclusion ledger (`out/exclusions.csv`):\n1. Dropped 396 entity-year rows belonging to 44 aggregate entities (region = `Aggregates`) → 1953 rows, 217 countries/economies.\n2. Analysis year: countries with both indicators number 199 in every year 2015–2022 and 197 in 2023 (`complete_countries_by_year`); the latest year with ≥150 is **2023** (`analysis_year`). Kept 217 country rows (`n_countries_in_analysis_year`).\n3. Dropped 20 countries with a missing indicator in 2023 (`n_excluded_missing`); all 20 lack GDP (life expectancy is never missing). No imputation was done. The analysis uses **197** countries (`n_analysed`).\n\n2349 − 396 − 1736 − 20 = 197. Data problems and how each was handled are listed in `data_issues`: the aggregates; the 20 missing countries (ASM, CHI, CUB, ERI, GIB, GUM, IMN, LIE, MAF, MCO, MNP, NCL, PRK, PYF, SSD, SYR, VEN, VGB, VIR, YEM); an implausibly volatile Central African Republic LE series in earlier years (its 2023 value of 57.4 looks plausible and was kept); and the drop in West Bank and Gaza LE in 2023 (kept as reported).\n\n## Method\n- Baseline: predict every country's LE with the cross-country mean. Its RMSE is `baseline_metric`.\n- Main model: OLS of LE on ln(GDPpc) over the 197 countries, unweighted. Standard errors are heteroskedasticity-robust **HC3** (`se_type`). HC3 was picked because it is the most conservative of HC0–HC3 in moderate samples. The 95% CI is β ± 1.96·SE (normal approximation). The fit metric is the RMSE of the in-sample residuals (`model_metric`).\n- Conclusion rule as given in TASK.md: `positive_effect` if the 95% CI lies entirely above 0.\n- Supplementary check, not part of the estimand: re-estimate after dropping the 5 countries with the largest Cook's distance (`supp_sens_drop5_*`). HC0–HC2 and classical SEs are also reported (`supp_effect_se_by_type`, `supp_effect_se_classical`).\n- `out/analysis.py` regenerates everything deterministically. It uses no randomness (`random_seed` = null).\n- Outputs: `out/figure1.png` (scatter plot with the OLS line) and `out/table1.csv` (means by income group).\n\n## Results\n- Slope: **β = 5.27 years per log point** of GDP per capita (`effect_estimate` = 5.267). Intercept 22.57 years (`intercept`). A 10% income difference therefore goes with about 0.50 more years of LE (5.267·ln 1.1, derived from `effect_estimate`). A doubling goes with about 3.65 years (5.267·ln 2).\n- Strength: Pearson r = 0.861 (`pearson_r`), R² = 0.742 (`r_squared`).\n- Fit: residual RMSE is 3.59 years (`model_metric`), against 7.08 years for the mean-only baseline (`baseline_metric`). ln(GDPpc) roughly halves the prediction error.\n- Conclusion: `positive_effect`. In 2023 the Preston-curve association holds and is strong.\n- Table 1 (`out/table1.csv`): mean LE rises with income group, from 63.2 years (Low income, n=20) to 67.8 (Lower middle income, n=46), 73.7 (Upper middle income, n=58) and 79.7 (High income, n=73).\n- Largest negative residual: Nigeria, −15.3 years (`supp_largest_neg_residual_iso3`, `supp_largest_neg_residual`).\n\n## Uncertainty\n- HC3 SE = 0.211 (`effect_se`). 95% CI for β: [4.85, 5.68] (`effect_ci_low`, `effect_ci_high`), well away from zero.\n- The choice among robust SEs makes little difference: HC0 0.207, HC1 0.208, HC2 0.209, HC3 0.211, classical 0.222 (`supp_effect_se_by_type`, `supp_effect_se_classical`).\n- Dropping the 5 most influential countries (NGA, GRL, TCD, SLB, GUY; `supp_sens_drop5_cooks_iso3`) gives β = 5.32 (SE 0.183), inside the main CI (`supp_sens_drop5_effect_estimate`, `supp_sens_drop5_effect_se`).\n- The CI covers sampling-style uncertainty only, treating countries as independent draws from a superpopulation. The 197 countries are close to the full population of economies, so this is a modelling convention. The CI does not cover measurement error, model misspecification or non-random missingness.\n\n## Limitations and open issues\n- **No causal interpretation.** β is an association. Confounders such as institutions, education, health technology, disease environment and conflict affect both income and LE, and causation can also run from health to income.\n- **Functional form.** The linear-in-log line is a projection. The figure shows curvature at the top: LE flattens among high-income economies, and the low-income residuals are widely spread. A nonlinear or spline fit was not estimated, so β is an average slope over the income range, not a constant marginal effect.\n- **Non-random missingness.** The 20 excluded economies are either small high-income territories (e.g. MCO, LIE, GIB) or conflict-affected/sanctioned states (SSD, SYR, YEM, VEN, PRK, CUB, ERI). Their direction of bias is unclear, and no bounding analysis was done.\n- **Measurement and data quality.** GDP PPP figures for fragile states and small territories are imprecise, and errors in ln(GDPpc) would bias β toward zero (attenuation). LE estimates for countries without complete vital registration are modelled, not observed. The CAF series shows implausible year-to-year swings (18.8 years in 2022), which suggests vintage or modelling artefacts in the LE data. 2023 values were not validated against external sources.\n- **Single year.** 2023 can still carry post-COVID mortality effects and conflict shocks (e.g. West Bank and Gaza). Stability across 2015–2022 was not checked because the task restricts the analysis to the selected year.\n- **Equal weighting.** Each economy counts once whatever its population, so tiny territories weigh as much as India or China. A population-weighted slope would answer a different question and was not computed.\n- **What would invalidate the result:** a misclassification of aggregates as countries (checked: all 44 aggregate entities in the file were removed via the region flag); a systematic revision of the 2023 WDI vintage; or leverage concentrated in a few points (partly checked with the leave-5-out sensitivity, which did not change the conclusion). Not checked: heavier-tailed inference (e.g. bootstrap), spatial or regional correlation of errors (regional clustering would probably widen the CI), and outliers beyond the 5 most influential points.\nincome_level,n_countries,mean_log_gdp_pc,mean_life_expectancy\nLow income,20,7.6100,63.1880\nLower middle income,46,8.6899,67.8095\nUpper middle income,58,9.6899,73.6954\nHigh income,73,10.8226,79.6858\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop entity-years whose region == 'Aggregates' in wb_entities.csv,2349,1953,396,information rule: countries/economies only\n2,keep analysis year 2023 (latest year with >=150 complete countries),1953,217,1736,information rule: single cross-section in analysis year\n3,drop countries with gdp_pc_ppp or life_expectancy missing in analysis year,217,197,20,information rule: no imputation\nchecks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json\nrepro","is_error":false}]},"uuid":"115095b0-a29f-4a88-b147-44186981dbfe","timestamp":"2026-10-30T03:53:41.536Z","toolUseResult":{"stdout":"\"\"\"Replication entry point for F6-dev-01 (Preston curve).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/figure1.png, out/table1.csv, out/exclusions.csv.\nNo randomness is used (random_seed recorded as null).\"\"\"\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\nDATA, OUT = \"data\", \"out\"\nos.makedirs(OUT, exist_ok=True)\nread = lambda f: pd.read_csv(os.path.join(DATA, f), keep_default_na=False, na_values=[\"\"])\nind, ent = read(\"wb_indicators.csv\"), read(\"wb_entities.csv\")\nn_input_rows = {\"wb_indicators.csv\": int(len(ind)), \"wb_entities.csv\": int(len(ent))}\nissues, excl = [], []\n\ndef log_excl(step, rule, before, after, why):\n excl.append(dict(step=step, rule=rule, n_before=int(before), n_after=int(after),\n n_removed=int(before - after), rationale=why))\n\n# integrity checks\nassert not ind.duplicated([\"iso3\", \"year\"]).any() and not ent.iso3.duplicated().any()\nassert set(ind.iso3) <= set(ent.iso3)\nassert (ind.gdp_pc_ppp.dropna() > 0).all()\n\n# Step 1: drop aggregates\nm = ind.merge(ent[[\"iso3\", \"region\", \"income_level\"]], on=\"iso3\", how=\"left\")\nn0 = len(m)\nn_agg_ent = int(m.loc[m.region == \"Aggregates\", \"iso3\"].nunique())\nc = m[m.region != \"Aggregates\"].copy()\nlog_excl(1, \"drop entity-years whose region == 'Aggregates' in wb_entities.csv\", n0, len(c),\n \"information rule: countries/economies only\")\nissues.append({\"issue\": f\"wb_indicators.csv contains {n_agg_ent} regional/income/other aggregate entities \"\n f\"({n0 - len(c)} entity-year rows)\", \"handling\": \"excluded via region == 'Aggregates' (rule)\"})\n\n# Step 2: analysis year selection\ncov = c.assign(both=c.gdp_pc_ppp.notna() & c.life_expectancy.notna()).groupby(\"year\").both.sum()\nelig = cov[cov >= 150]\nanalysis_year = int(elig.index.max())\nn1 = len(c)\ny = c[c.year == analysis_year].copy()\nlog_excl(2, f\"keep analysis year {analysis_year} (latest year with >=150 complete countries)\", n1, len(y),\n \"information rule: single cross-section in analysis year\")\n\n# Step 3: drop missing\nmiss = y[y.gdp_pc_ppp.isna() | y.life_expectancy.isna()]\nn_excluded_missing = int(len(miss))\nn2 = len(y)\nd = y.dropna(subset=[\"gdp_pc_ppp\", \"life_expectancy\"]).copy()\nlog_excl(3, \"drop countries with gdp_pc_ppp or life_expectancy missing in analysis year\", n2, len(d),\n \"information rule: no imputation\")\nissues.append({\"issue\": f\"{n_excluded_missing} countries/economies lack an indicator in {analysis_year} \"\n f\"(gdp missing: {int(miss.gdp_pc_ppp.isna().sum())}, LE missing: {int(miss.life_expectancy.isna().sum())}): \"\n + \", \".join(sorted(miss.iso3)),\n \"handling\": \"excluded, counted in n_excluded_missing; possible selection bias discussed in report\"})\nissues.append({\"issue\": \"Central African Republic life expectancy series is implausibly volatile in 2015-2022 \"\n \"(e.g. 18.8 years in 2022, 31.5 in 2019); 2023 value 57.4 is plausible\",\n \"handling\": \"affects non-analysis years only; 2023 value kept; noted as data-quality caveat\"})\nissues.append({\"issue\": \"West Bank and Gaza life expectancy drops from 76.7 (2022) to 65.2 (2023), consistent with \"\n \"conflict mortality but a large outlier relative to its income\",\n \"handling\": \"kept (genuine reported value, no rule permits dropping it); a leave-5-most-influential-out (Cook distance) sensitivity is reported\"})\n\n# Estimation\nd[\"ln_gdp\"] = np.log(d.gdp_pc_ppp)\nX = sm.add_constant(d.ln_gdp.values)\nyv = d.life_expectancy.values\nfit = sm.OLS(yv, X).fit(cov_type=\"HC3\")\nalpha, beta = float(fit.params[0]), float(fit.params[1])\nse = float(fit.bse[1])\nz = 1.959963984540054\nci_lo, ci_hi = beta - z * se, beta + z * se\nresid = yv - (alpha + beta * d.ln_gdp.values)\nbaseline_rmse = float(np.sqrt(np.mean((yv - yv.mean()) ** 2)))\nmodel_rmse = float(np.sqrt(np.mean(resid ** 2)))\nr = float(np.corrcoef(d.ln_gdp, yv)[0, 1])\nconclusion = \"positive_effect\" if ci_lo > 0 else (\"negative_effect\" if ci_hi < 0 else \"no_detectable_effect\")\n\n# Supplementary (not estimand): other robust SEs, sensitivity dropping the 5 most influential obs (Cook's D)\nfit_ols = sm.OLS(yv, X).fit()\nse_alt = {k: float(sm.OLS(yv, X).fit(cov_type=k).bse[1]) for k in [\"HC0\", \"HC1\", \"HC2\", \"HC3\"]}\ncooks = fit_ols.get_influence().cooks_distance[0]\ntop = np.argsort(cooks)[::-1][:5]\nkeep = np.setdiff1d(np.arange(len(d)), top)\nfit_s = sm.OLS(yv[keep], X[keep]).fit(cov_type=\"HC3\")\nd[\"resid\"] = resid\n\nresults = {\n \"task_id\": \"F6-dev-01\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"analysis_year\": analysis_year,\n \"n_excluded_missing\": n_excluded_missing,\n \"n_analysed\": int(len(d)),\n \"baseline_metric\": baseline_rmse,\n \"model_metric\": model_rmse,\n \"pearson_r\": r,\n \"r_squared\": float(fit.rsquared),\n \"effect_estimate\": beta,\n \"intercept\": alpha,\n \"effect_se\": se,\n \"effect_ci_low\": float(ci_lo),\n \"effect_ci_high\": float(ci_hi),\n \"conclusion\": conclusion,\n \"data_issues\": issues,\n \"n_input_rows\": n_input_rows,\n \"random_seed\": None,\n \"se_type\": \"HC3\",\n \"complete_countries_by_year\": {str(k): int(v) for k, v in cov.items()},\n \"n_countries_in_analysis_year\": int(n2),\n \"supp_effect_se_by_type\": se_alt,\n \"supp_effect_se_classical\": float(fit_ols.bse[1]),\n \"supp_mean_life_expectancy\": float(yv.mean()),\n \"supp_sens_drop5_cooks_iso3\": [str(s) for s in d.iso3.values[top]],\n \"supp_sens_drop5_effect_estimate\": float(fit_s.params[1]),\n \"supp_sens_drop5_effect_se\": float(fit_s.bse[1]),\n \"supp_sens_drop5_r_squared\": float(fit_s.rsquared),\n \"supp_largest_neg_residual_iso3\": str(d.iso3.values[np.argmin(resid)]),\n \"supp_largest_neg_residual\": float(resid.min()),\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(results, f, indent=2)\n\npd.DataFrame(excl, columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]).to_csv(\n os.path.join(OUT, \"exclusions.csv\"), index=False)\n\norder = [\"Low income\", \"Lower middle income\", \"Upper middle income\", \"High income\"]\nt = (d.groupby(\"income_level\").agg(n_countries=(\"iso3\", \"size\"), mean_log_gdp_pc=(\"ln_gdp\", \"mean\"),\n mean_life_expectancy=(\"life_expectancy\", \"mean\")).reset_index())\nt[\"o\"] = t.income_level.map({k: i for i, k in enumerate(order)}).fillna(99)\nt = t.sort_values([\"o\", \"income_level\"]).drop(columns=\"o\")\nt.to_csv(os.path.join(OUT, \"table1.csv\"), index=False, float_format=\"%.4f\")\n\nfig, ax = plt.subplots(figsize=(7, 5))\ncols = dict(zip(order, [\"#d7191c\", \"#fdae61\", \"#abd9e9\", \"#2c7bb6\"]))\nfor lvl in order:\n s = d[d.income_level == lvl]\n ax.scatter(s.ln_gdp, s.life_expectancy, s=18, c=cols[lvl], label=f\"{lvl} (n={len(s)})\", edgecolor=\"k\", lw=0.3)\nxs = np.linspace(d.ln_gdp.min(), d.ln_gdp.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 intl $)\")\nax.set_ylabel(\"Life expectancy at birth (years)\")\nax.set_title(f\"Preston curve, {analysis_year} (n={len(d)} countries/economies)\")\nax.legend(fontsize=8, loc=\"lower right\")\nfig.tight_layout()\nfig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=120, metadata={\"Software\": None})\nplt.close(fig)\n\nprint(f\"year={analysis_year} n={len(d)} excluded_missing={n_excluded_missing} beta={beta:.4f} \"\n f\"HC3 se={se:.4f} CI=[{ci_lo:.4f},{ci_hi:.4f}] R2={fit.rsquared:.4f} conclusion={conclusion}\")\n# F6-dev-01 — Preston curve in the latest usable WDI year\n\n## Question\nAcross countries/economies, how strongly is life expectancy at birth (LE, years) associated with the natural log of GDP per capita (PPP, constant 2021 intl $) in the most recent year with usable World Bank data? Estimand: slope β of the linear projection LE = α + β·ln(GDPpc) + ε, one equally weighted observation per country. The interpretation is descriptive/associational, not causal.\n\n## Data and exclusions\nInputs: `data/wb_indicators.csv` (2349 rows) and `data/wb_entities.csv` (296 rows) (`n_input_rows`); SHA-256 hashes match `data/README.md` (see `out/state/data_audit.md`). Exclusion ledger (`out/exclusions.csv`):\n1. Dropped 396 entity-year rows belonging to 44 aggregate entities (region = `Aggregates`) → 1953 rows, 217 countries/economies.\n2. Analysis year: countries with both indicators number 199 in every year 2015–2022 and 197 in 2023 (`complete_countries_by_year`); the latest year with ≥150 is **2023** (`analysis_year`). Kept 217 country rows (`n_countries_in_analysis_year`).\n3. Dropped 20 countries with a missing indicator in 2023 (`n_excluded_missing`); all 20 lack GDP (life expectancy is never missing). No imputation was done. The analysis uses **197** countries (`n_analysed`).\n\n2349 − 396 − 1736 − 20 = 197. Data problems and how each was handled are listed in `data_issues`: the aggregates; the 20 missing countries (ASM, CHI, CUB, ERI, GIB, GUM, IMN, LIE, MAF, MCO, MNP, NCL, PRK, PYF, SSD, SYR, VEN, VGB, VIR, YEM); an implausibly volatile Central African Republic LE series in earlier years (its 2023 value of 57.4 looks plausible and was kept); and the drop in West Bank and Gaza LE in 2023 (kept as reported).\n\n## Method\n- Baseline: predict every country's LE with the cross-country mean. Its RMSE is `baseline_metric`.\n- Main model: OLS of LE on ln(GDPpc) over the 197 countries, unweighted. Standard errors are heteroskedasticity-robust **HC3** (`se_type`). HC3 was picked because it is the most conservative of HC0–HC3 in moderate samples. The 95% CI is β ± 1.96·SE (normal approximation). The fit metric is the RMSE of the in-sample residuals (`model_metric`).\n- Conclusion rule as given in TASK.md: `positive_effect` if the 95% CI lies entirely above 0.\n- Supplementary check, not part of the estimand: re-estimate after dropping the 5 countries with the largest Cook's distance (`supp_sens_drop5_*`). HC0–HC2 and classical SEs are also reported (`supp_effect_se_by_type`, `supp_effect_se_classical`).\n- `out/analysis.py` regenerates everything deterministically. It uses no randomness (`random_seed` = null).\n- Outputs: `out/figure1.png` (scatter plot with the OLS line) and `out/table1.csv` (means by income group).\n\n## Results\n- Slope: **β = 5.27 years per log point** of GDP per capita (`effect_estimate` = 5.267). Intercept 22.57 years (`intercept`). A 10% income difference therefore goes with about 0.50 more years of LE (5.267·ln 1.1, derived from `effect_estimate`). A doubling goes with about 3.65 years (5.267·ln 2).\n- Strength: Pearson r = 0.861 (`pearson_r`), R² = 0.742 (`r_squared`).\n- Fit: residual RMSE is 3.59 years (`model_metric`), against 7.08 years for the mean-only baseline (`baseline_metric`). ln(GDPpc) roughly halves the prediction error.\n- Conclusion: `positive_effect`. In 2023 the Preston-curve association holds and is strong.\n- Table 1 (`out/table1.csv`): mean LE rises with income group, from 63.2 years (Low income, n=20) to 67.8 (Lower middle income, n=46), 73.7 (Upper middle income, n=58) and 79.7 (High income, n=73).\n- Largest negative residual: Nigeria, −15.3 years (`supp_largest_neg_residual_iso3`, `supp_largest_neg_residual`).\n\n## Uncertainty\n- HC3 SE = 0.211 (`effect_se`). 95% CI for β: [4.85, 5.68] (`effect_ci_low`, `effect_ci_high`), well away from zero.\n- The choice among robust SEs makes little difference: HC0 0.207, HC1 0.208, HC2 0.209, HC3 0.211, classical 0.222 (`supp_effect_se_by_type`, `supp_effect_se_classical`).\n- Dropping the 5 most influential countries (NGA, GRL, TCD, SLB, GUY; `supp_sens_drop5_cooks_iso3`) gives β = 5.32 (SE 0.183), inside the main CI (`supp_sens_drop5_effect_estimate`, `supp_sens_drop5_effect_se`).\n- The CI covers sampling-style uncertainty only, treating countries as independent draws from a superpopulation. The 197 countries are close to the full population of economies, so this is a modelling convention. The CI does not cover measurement error, model misspecification or non-random missingness.\n\n## Limitations and open issues\n- **No causal interpretation.** β is an association. Confounders such as institutions, education, health technology, disease environment and conflict affect both income and LE, and causation can also run from health to income.\n- **Functional form.** The linear-in-log line is a projection. The figure shows curvature at the top: LE flattens among high-income economies, and the low-income residuals are widely spread. A nonlinear or spline fit was not estimated, so β is an average slope over the income range, not a constant marginal effect.\n- **Non-random missingness.** The 20 excluded economies are either small high-income territories (e.g. MCO, LIE, GIB) or conflict-affected/sanctioned states (SSD, SYR, YEM, VEN, PRK, CUB, ERI). Their direction of bias is unclear, and no bounding analysis was done.\n- **Measurement and data quality.** GDP PPP figures for fragile states and small territories are imprecise, and errors in ln(GDPpc) would bias β toward zero (attenuation). LE estimates for countries without complete vital registration are modelled, not observed. The CAF series shows implausible year-to-year swings (18.8 years in 2022), which suggests vintage or modelling artefacts in the LE data. 2023 values were not validated against external sources.\n- **Single year.** 2023 can still carry post-COVID mortality effects and conflict shocks (e.g. West Bank and Gaza). Stability across 2015–2022 was not checked because the task restricts the analysis to the selected year.\n- **Equal weighting.** Each economy counts once whatever its population, so tiny territories weigh as much as India or China. A population-weighted slope would answer a different question and was not computed.\n- **What would invalidate the result:** a misclassification of aggregates as countries (checked: all 44 aggregate entities in the file were removed via the region flag); a systematic revision of the 2023 WDI vintage; or leverage concentrated in a few points (partly checked with the leave-5-out sensitivity, which did not change the conclusion). Not checked: heavier-tailed inference (e.g. bootstrap), spatial or regional correlation of errors (regional clustering would probably widen the CI), and outliers beyond the 5 most influential points.\nincome_level,n_countries,mean_log_gdp_pc,mean_life_expectancy\nLow income,20,7.6100,63.1880\nLower middle income,46,8.6899,67.8095\nUpper middle income,58,9.6899,73.6954\nHigh income,73,10.8226,79.6858\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop entity-years whose region == 'Aggregates' in wb_entities.csv,2349,1953,396,information rule: countries/economies only\n2,keep analysis year 2023 (latest year with >=150 complete countries),1953,217,1736,information rule: single cross-section in analysis year\n3,drop countries with gdp_pc_ppp or life_expectancy missing in analysis year,217,197,20,information rule: no 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classified')])\nprint(d.life_expectancy.describe(), d.gdp_pc_ppp.describe())\nprint(set(e[e.region!='Aggregates'].iso3)-set(c.iso3))\n\"","description":"Rerun analysis in scratch and independently check 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n=197 excluded_missing=20 beta=5.2672 HC3 se=0.2107 CI=[4.8543,5.6801] R2=0.7419 conclusion=positive_effect\nSAME\n2349 296\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\nEmpty DataFrame\nColumns: [iso3, name, region, income_level, lending_type]\nIndex: []\nyear\n2015 217\n2016 217\n2017 217\n2018 217\n2019 217\n2020 217\n2021 217\n2022 217\n2023 217\nName: iso3, dtype: int64\nincome_level\nHigh income 86\nUpper middle income 59\nLower middle income 47\nLow income 25\nName: count, dtype: int64\nEmpty DataFrame\nColumns: [iso3, name_x, year, gdp_pc_ppp, life_expectancy, name_y, region, income_level, lending_type]\nIndex: []\ncount 197.000000\nmean 73.474082\nstd 7.094483\nmin 54.462000\n25% 67.689000\n50% 74.402000\n75% 78.473171\nmax 85.706000\nName: life_expectancy, dtype: float64 count 197.000000\nmean 27404.732511\nstd 26991.485580\nmin 1033.009373\n25% 6520.898865\n50% 17685.858593\n75% 41320.257996\nmax 130296.843789\nName: gdp_pc_ppp, dtype: float64\nset()\nShell cwd was reset to /Users/simon-pierreboucher/wp29/runs/RP0014","is_error":false}]},"uuid":"20bef6e2-f046-4762-a3bc-d30d118e42bf","timestamp":"2026-10-30T03:53:51.297Z","toolUseResult":{"stdout":"year=2023 n=197 excluded_missing=20 beta=5.2672 HC3 se=0.2107 CI=[4.8543,5.6801] R2=0.7419 conclusion=positive_effect\nSAME\n2349 296\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\nEmpty DataFrame\nColumns: [iso3, name, region, income_level, lending_type]\nIndex: []\nyear\n2015 217\n2016 217\n2017 217\n2018 217\n2019 217\n2020 217\n2021 217\n2022 217\n2023 217\nName: iso3, dtype: int64\nincome_level\nHigh income 86\nUpper middle income 59\nLower middle income 47\nLow income 25\nName: count, dtype: int64\nEmpty DataFrame\nColumns: [iso3, name_x, year, gdp_pc_ppp, life_expectancy, name_y, region, income_level, lending_type]\nIndex: []\ncount 197.000000\nmean 73.474082\nstd 7.094483\nmin 54.462000\n25% 67.689000\n50% 74.402000\n75% 78.473171\nmax 85.706000\nName: life_expectancy, dtype: float64 count 197.000000\nmean 27404.732511\nstd 26991.485580\nmin 1033.009373\n25% 6520.898865\n50% 17685.858593\n75% 41320.257996\nmax 130296.843789\nName: gdp_pc_ppp, dtype: float64\nset()","stderr":"\nShell cwd was reset to 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/tmp && rm -rf rp14 && mkdir rp14 && cp -r ~/wp29/runs/RP0014/data rp14/ && mkdir rp14/out && cp ~/wp29/runs/RP0014/out/analysis.py rp14/out/ && cd rp14 && ~/wp29/venv/bin/python out/analysis.py && diff <(python3 -m json.tool out/results.json) <(python3 -m json.tool ~/wp29/runs/RP0014/out/results.json) && diff out/table1.csv ~/wp29/runs/RP0014/out/table1.csv && echo SAME\n~/wp29/venv/bin/python -c \"\nimport pandas as pd,numpy as np\ni=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=[''])\nprint(len(i),len(e)); print(e.region.value_counts().head(10)); print(e[e.region.isna()|(e.region=='')])\nm=i.merge(e,on='iso3',how='left'); c=m[m.region!='Aggregates']\nprint(c.groupby('year').iso3.nunique())\ny=c[c.year==2023]; print(y.income_level.value_counts(dropna=False))\nd=y.dropna(subset=['gdp_pc_ppp','life_expectancy'])\nprint(d[d.income_level.isna()|(d.income_level=='')|(d.income_level=='Not classified')])\nprint(d.life_expectancy.describe(), d.gdp_pc_ppp.describe())\nprint(set(e[e.region!='Aggregates'].iso3)-set(c.iso3))\n\"","description":"Rerun analysis in scratch and independently check data"}}]}}29{"type":"api-request","sessionId":"6fcb0c24-be55-47de-b0e0-caf598a26592","id":"dd9a4939-e6cd-4f24-9a33-3b9056ec118f","timestamp":"2026-10-30T03:53:51.301Z","version":"2.1.314","querySource":"sdk","shapeHash":"92ce147de194a12327c5e1615ea501eabd943da6d84ec52126e1dd947551cc1a","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":"547c40f3-a602-48cb-8f10-eb9dab7e847d","keep":6,"tail":["446fde3a2d0ba0d403c25d513601a4bbf46a9d8123fb0aced8d5af675b5fc8f8","bf9a0d1732b9af3f70d98c72a93e4b8a44c17e9e8ece7071ee5268c54c48906d"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"4d53c78c9ba316c0ce5ca05c5466daa4fd7427f579757ff30e28945293262079"}30{"parentUuid":"20bef6e2-f046-4762-a3bc-d30d118e42bf","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXkB1G8B8nFmNwnkVCd9","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":1496,"cache_read_input_tokens":13883,"output_tokens":179,"output_tokens_details":{"thinking_tokens":61},"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":1496},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":412,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXkAzeeqsBXstwiFv4KB","requestRef":"dd9a4939-e6cd-4f24-9a33-3b9056ec118f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e0a0cf22-83ad-46a2-a762-6a66b50629ec","timestamp":"2026-10-30T03:53:53.023Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0014","sessionId":"6fcb0c24-be55-47de-b0e0-caf598a26592","version":"2.1.314","gitBranch":"HEAD"}31{"parentUuid":"e0a0cf22-83ad-46a2-a762-6a66b50629ec","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXkB1G8B8nFmNwnkVCd9","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0151PbtgeS9BQu2jzFjsYLTY","name":"Bash","input":{"command":"grep 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African Republic,2015,1043.921717,51.934000\nCAF,Central African Republic,2016,1073.944777,50.999000\nCAF,Central African Republic,2017,1103.866093,45.231000\nCAF,Central African Republic,2018,1125.700917,52.297000\nCAF,Central African Republic,2019,1145.095637,31.530000\nCAF,Central African Republic,2020,1136.570530,50.596000\nCAF,Central African Republic,2021,1128.552380,40.279000\nCAF,Central African Republic,2022,1136.801711,18.818000\nCAF,Central African Republic,2023,1132.676434,57.408000\nNGA,Nigeria,2015,8892.057987,51.939000\nNGA,Nigeria,2016,8534.692815,52.189000\nNGA,Nigeria,2017,8396.784522,52.397000\nNGA,Nigeria,2018,8362.623309,52.669000\nNGA,Nigeria,2019,8361.786648,53.009000\nNGA,Nigeria,2020,7664.210996,53.072000\nNGA,Nigeria,2021,7588.478856,53.455000\nNGA,Nigeria,2022,7752.262065,54.079000\nNGA,Nigeria,2023,7843.003990,54.462000\nPSE,West Bank and Gaza,2015,6072.650318,74.577000\nPSE,West Bank and Gaza,2016,6464.133525,74.917000\nPSE,West Bank and Gaza,2017,6426.795216,75.216000\nPSE,West Bank and Gaza,2018,6342.937480,75.380000\nPSE,West Bank and Gaza,2019,6269.891345,75.811000\nPSE,West Bank and Gaza,2020,5423.682547,74.998000\nPSE,West Bank and Gaza,2021,5663.121607,73.890000\nPSE,West Bank and Gaza,2022,5753.083959,76.662000\nPSE,West Bank and Gaza,2023,5562.424426,65.170000\n# 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`","is_error":false}]},"uuid":"a57077cb-6020-4d37-aefe-5da680a5e4f1","timestamp":"2026-10-30T03:53:53.728Z","toolUseResult":{"stdout":"CAF,Central African Republic,2015,1043.921717,51.934000\nCAF,Central African Republic,2016,1073.944777,50.999000\nCAF,Central African Republic,2017,1103.866093,45.231000\nCAF,Central African Republic,2018,1125.700917,52.297000\nCAF,Central African Republic,2019,1145.095637,31.530000\nCAF,Central African Republic,2020,1136.570530,50.596000\nCAF,Central African Republic,2021,1128.552380,40.279000\nCAF,Central African Republic,2022,1136.801711,18.818000\nCAF,Central African Republic,2023,1132.676434,57.408000\nNGA,Nigeria,2015,8892.057987,51.939000\nNGA,Nigeria,2016,8534.692815,52.189000\nNGA,Nigeria,2017,8396.784522,52.397000\nNGA,Nigeria,2018,8362.623309,52.669000\nNGA,Nigeria,2019,8361.786648,53.009000\nNGA,Nigeria,2020,7664.210996,53.072000\nNGA,Nigeria,2021,7588.478856,53.455000\nNGA,Nigeria,2022,7752.262065,54.079000\nNGA,Nigeria,2023,7843.003990,54.462000\nPSE,West Bank and Gaza,2015,6072.650318,74.577000\nPSE,West Bank and Gaza,2016,6464.133525,74.917000\nPSE,West Bank and Gaza,2017,6426.795216,75.216000\nPSE,West Bank and Gaza,2018,6342.937480,75.380000\nPSE,West Bank and Gaza,2019,6269.891345,75.811000\nPSE,West Bank and Gaza,2020,5423.682547,74.998000\nPSE,West Bank and Gaza,2021,5663.121607,73.890000\nPSE,West Bank and Gaza,2022,5753.083959,76.662000\nPSE,West Bank and Gaza,2023,5562.424426,65.170000\n# 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`: 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","media_type":"image/png"}}]}]},"uuid":"fc0399d6-5b26-4915-901e-28e8226883ef","timestamp":"2026-10-30T03:53:58.329Z","toolUseResult":{"type":"image","file":{"base64":"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> out/review/review.json <<'EOF'\n{\n \"verdict\": \"accept\",\n \"issues\": [\n {\n \"id\": \"R1\",\n \"severity\": \"minor\",\n \"location\": \"out/report.md (Limitations: Functional form)\",\n \"issue\": \"The report says the figure 'shows curvature at the top: LE flattens among high-income economies'. No nonlinear specification was estimated, and the scatter does not clearly show a plateau: high-income points mostly sit around or above the line. This is a visual impression stated as an observation.\",\n \"suggested_fix\": \"Either soften it to 'possible curvature, not tested' or add a simple quadratic or spline check and report the result.\"\n },\n {\n \"id\": \"R2\",\n \"severity\": \"minor\",\n \"location\": \"results.json data_issues (West Bank and Gaza)\",\n \"issue\": \"The handling text points to the leave-5-out Cook's-distance sensitivity, but PSE is not among the five countries dropped (NGA, GRL, TCD, SLB, GUY). So that sensitivity does not address this flagged observation.\",\n \"suggested_fix\": \"Say that PSE was not among the five most influential points, or report a leave-PSE-out estimate.\"\n },\n {\n \"id\": \"R3\",\n \"severity\": \"minor\",\n \"location\": \"out/claims.json C5\",\n \"issue\": \"C5 (income-group means of 63.2 to 79.7) references only `n_analysed`. The numbers it cites live in table1.csv, not in results.json, so the listed key cannot verify the claim.\",\n \"suggested_fix\": \"Add the group means to results.json as supplementary keys and reference them, or mark the claim as backed by table1.csv in the caveat (this is partly done already).\"\n },\n {\n \"id\": \"R4\",\n \"severity\": \"minor\",\n \"location\": \"out/claims.json C4\",\n \"issue\": \"'Robust' rests on one leave-5-out refit and a comparison of SE types. The SE comparison concerns inference only, not the slope. The caveat admits the sensitivity set is limited, but the claim text is a bit stronger than the evidence.\",\n \"suggested_fix\": \"Rephrase as 'the slope changes little (5.27 to 5.32) when the 5 highest-Cook's-D countries are dropped; SE type has negligible impact'.\"\n }\n ],\n \"summary\": \"A rerun of analysis.py in a scratch copy reproduces results.json and table1.csv exactly. All required keys are present and correctly computed. The code follows the information rules: it filters aggregates via region, selects the year by rule (2023 with 197 complete countries out of 217), excludes 20 countries with missing GDP without imputation, uses the natural log, reads only data/, and reads 'NA' safely (Namibia). Counts add up (2349 to 1953 to 217 to 197; table groups sum to 197). HC3 is stated and the CI is consistent with it, and the conclusion follows the rule. The mean-only baseline is reported. Limitations are substantive. Only minor wording and traceability issues remain.\"\n}\nEOF\n~/wp29/venv/bin/python -m json.tool out/review/review.json >/dev/null && echo ok","description":"Write review JSON and validate it"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1085,"cache_read_input_tokens":17271,"output_tokens":1193,"output_tokens_details":{"thinking_tokens":50},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1085},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXkBWkAEuVGEP2cuqdTb","requestRef":"91864427-75e2-4902-a1f0-ce37ef6eb89e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"447a4ce2-9bc2-438a-951e-f789ce1d41ca","timestamp":"2026-10-30T03:54:09.655Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0014","sessionId":"6fcb0c24-be55-47de-b0e0-caf598a26592","version":"2.1.314","gitBranch":"HEAD"}45{"parentUuid":"447a4ce2-9bc2-438a-951e-f789ce1d41ca","isSidechain":false,"promptId":"b924b1ef-7477-4863-aeb7-7adc9de0ab4f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017ze7zd1xzwAeuJTRhPhrSr","type":"tool_result","content":"ok","is_error":false}]},"uuid":"71751ec4-6151-4610-8fe0-e721f5d6e0e2","timestamp":"2026-10-30T03:54:09.714Z","toolUseResult":{"stdout":"ok","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"447a4ce2-9bc2-438a-951e-f789ce1d41ca","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0014","sessionId":"6fcb0c24-be55-47de-b0e0-caf598a26592","version":"2.1.314","gitBranch":"HEAD"}46{"type":"api-request-blob","sessionId":"6fcb0c24-be55-47de-b0e0-caf598a26592","hash":"3c4f73cc8e058bb3aae0a602f716cabfa39747a1309db63d1e5289f6e751537a","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_017ze7zd1xzwAeuJTRhPhrSr","name":"Bash","input":{"command":"cat > out/review/review.json <<'EOF'\n{\n \"verdict\": \"accept\",\n \"issues\": [\n {\n \"id\": \"R1\",\n \"severity\": \"minor\",\n \"location\": \"out/report.md (Limitations: Functional form)\",\n \"issue\": \"The report says the figure 'shows curvature at the top: LE flattens among high-income economies'. No nonlinear specification was estimated, and the scatter does not clearly show a plateau: high-income points mostly sit around or above the line. This is a visual impression stated as an observation.\",\n \"suggested_fix\": \"Either soften it to 'possible curvature, not tested' or add a simple quadratic or spline check and report the result.\"\n },\n {\n \"id\": \"R2\",\n \"severity\": \"minor\",\n \"location\": \"results.json data_issues (West Bank and Gaza)\",\n \"issue\": \"The handling text points to the leave-5-out Cook's-distance sensitivity, but PSE is not among the five countries dropped (NGA, GRL, TCD, SLB, GUY). So that sensitivity does not address this flagged observation.\",\n \"suggested_fix\": \"Say that PSE was not among the five most influential points, or report a leave-PSE-out estimate.\"\n },\n {\n \"id\": \"R3\",\n \"severity\": \"minor\",\n \"location\": \"out/claims.json C5\",\n \"issue\": \"C5 (income-group means of 63.2 to 79.7) references only `n_analysed`. The numbers it cites live in table1.csv, not in results.json, so the listed key cannot verify the claim.\",\n \"suggested_fix\": \"Add the group means to results.json as supplementary keys and reference them, or mark the claim as backed by table1.csv in the caveat (this is partly done already).\"\n },\n {\n \"id\": \"R4\",\n \"severity\": \"minor\",\n \"location\": \"out/claims.json C4\",\n \"issue\": \"'Robust' rests on one leave-5-out refit and a comparison of SE types. The SE comparison concerns inference only, not the slope. The caveat admits the sensitivity set is limited, but the claim text is a bit stronger than the evidence.\",\n \"suggested_fix\": \"Rephrase as 'the slope changes little (5.27 to 5.32) when the 5 highest-Cook's-D countries are dropped; SE type has negligible impact'.\"\n }\n ],\n \"summary\": \"A rerun of analysis.py in a scratch copy reproduces results.json and table1.csv exactly. All required keys are present and correctly computed. The code follows the information rules: it filters aggregates via region, selects the year by rule (2023 with 197 complete countries out of 217), excludes 20 countries with missing GDP without imputation, uses the natural log, reads only data/, and reads 'NA' safely (Namibia). Counts add up (2349 to 1953 to 217 to 197; table groups sum to 197). HC3 is stated and the CI is consistent with it, and the conclusion follows the rule. The mean-only baseline is reported. Limitations are substantive. Only minor wording and traceability issues remain.\"\n}\nEOF\n~/wp29/venv/bin/python -m json.tool out/review/review.json >/dev/null && echo ok","description":"Write review JSON and validate it"}}]}}47{"type":"api-request","sessionId":"6fcb0c24-be55-47de-b0e0-caf598a26592","id":"859e1dd5-1b54-49ba-8d34-317572adf570","timestamp":"2026-10-30T03:54:09.717Z","version":"2.1.314","querySource":"sdk","shapeHash":"92ce147de194a12327c5e1615ea501eabd943da6d84ec52126e1dd947551cc1a","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":"91864427-75e2-4902-a1f0-ce37ef6eb89e","keep":12,"tail":["3c4f73cc8e058bb3aae0a602f716cabfa39747a1309db63d1e5289f6e751537a","86ef7f24847a0165dfd186585d7763ccdfe5f1d091ab5eccfb69f3f0b184004f"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"b87776c968f27034b558b6d9e3020fa31e061552ce491ff8173e47db76323e83"}48{"parentUuid":"71751ec4-6151-4610-8fe0-e721f5d6e0e2","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXkCMjSteAsxki5Qu1ky","type":"message","role":"assistant","content":[{"type":"text","text":"My verdict is **accept**: the deliverables hold up against `TASK.md`, with four minor issues and nothing critical or major. I re-ran `out/analysis.py` on a scratch copy, and it reproduced `results.json` and `table1.csv` exactly.\n\n**What checks out:**\n- **Required keys:** every key is present and its value is computed correctly.\n- **Analysis year:** 2023 is right by the task's rule. In 2023, 197 of 217 countries have both indicators.\n- **Exclusions:** aggregates are removed using the `region` flag. 20 countries are dropped for missing GDP, with no imputation.\n- **Inputs and missing values:** the code reads only `data/`, uses the natural log, and handles missing values correctly (Namibia's code \"NA\" is not mistaken for a blank).\n- **Counts:** rows go 2349 → 1953 → 217 → 197, and the income-group table also sums to 197.\n- **Uncertainty and conclusion:** the report says which robust standard error it uses (HC3), and the confidence interval matches it. The `positive_effect` conclusion follows the task's rule. The mean-only baseline is reported (prediction error 7.08 years against 3.59 for the model).\n- **Data issues and limitations:** the flagged data problems (the Central African Republic's swings in earlier years, West Bank and Gaza's 2023 drop) match the raw file. The limitations section is substantive.\n\n**Minor issues:**\n1. **Curvature claim:** the report says the figure shows life expectancy flattening among high-income countries. No nonlinear model was fitted, and the scatter doesn't clearly show that.\n2. **West Bank and Gaza handling:** the stated handling points to the drop-the-5-most-influential-countries check, but West Bank and Gaza is not one of those five, so that check doesn't address it.\n3. **Claim C5 traceability:** the income-group means it cites are in `table1.csv`, but the claim references only `n_analysed`, which can't verify them.\n4. **Claim C4 wording:** \"robust\" is a bit strong for one drop-5 refit plus a comparison of standard-error types, though the claim's caveat admits the limited checks.\n\nThe review is in `out/review/review.json`."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1200,"cache_read_input_tokens":18356,"output_tokens":748,"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":1200},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXkCMSLfHwjnFsvP1UXE","requestRef":"859e1dd5-1b54-49ba-8d34-317572adf570","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b765d274-8a86-4ad2-a418-ec662101b436","timestamp":"2026-10-30T03:54:17.618Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0014","sessionId":"6fcb0c24-be55-47de-b0e0-caf598a26592","version":"2.1.314","gitBranch":"HEAD"}49{"type":"last-prompt","lastPrompt":"Review","leafUuid":"b765d274-8a86-4ad2-a418-ec662101b436","sessionId":"6fcb0c24-be55-47de-b0e0-caf598a26592"}50{"type":"cost-state","sessionId":"6fcb0c24-be55-47de-b0e0-caf598a26592","totalCostUSD":0.1784616,"totalAPIDuration":38853,"totalAPIDurationWithoutRetries":38851,"totalToolDuration":1641,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":40679,"startTime":1793332416946,"modelUsage":{"claude-opus-5-5":{"inputTokens":16,"outputTokens":3650,"thinkingTokens":656,"cacheReadInputTokens":75688,"cacheCreationInputTokens":18052,"cacheCreationEphemeral5mInputTokens":18052,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.1784616}},"hasUnknownModelCost":false}51