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Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n\nSEPARATE REVIEW (additional)\n- After the gate passes, a separate reviewer session that has not seen your work in progress will examine `TASK.md` and everything under `out/` and may return a list of issues (file `out/review/review.json`). You will then be resumed once to address them: fix what is justified, explain in `out/report.md` (Limitations and open issues) what you disagree with, re-run the gate, and update `out/claims.json` and `out/state/phases.json`. 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{"parentUuid":"43e3ef72-af0a-40e3-8962-51d1ae63b1da","isSidechain":false,"promptId":"00b7025b-9e65-448d-8733-2c915c027c77","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\n# 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.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\nREADME.md\nwb_entities.csv\nwb_indicators.csv","is_error":true,"tool_use_id":"toolu_014X97ab7PZE5NBzvWwAkqRs"}]},"uuid":"a257d345-9c58-4097-8c05-9e74ae980e58","timestamp":"2026-10-30T05:30:57.381Z","toolUseResult":"Error: Exit code 1\n# 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/`. 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{"parentUuid":"5bf324df-5e89-445b-bca2-ddaf84f3d3b5","isSidechain":false,"promptId":"00b7025b-9e65-448d-8733-2c915c027c77","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TKUaApXb5c9haqEaMBh2LC","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==> data/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==> data/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 data/wb_entities.csv\n    2350 data/wb_indicators.csv\n    2647 total\n{\n  \"task_id\": \"F6-dev-01\",\n  \"required_files\": [\n    \"out/results.json\",\n    \"out/report.md\",\n    \"out/analysis.py\",\n    \"out/claims.json\",\n    \"out/figure1.png\",\n    \"out/table1.csv\"\n  ],\n  \"results_keys\": {\n    \"task_id\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"feasibility\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"feasibility_reason\": {\n      \"type\": \"string\",\n      \"nullable\": true\n    },\n    \"data_issues\": {\n      \"type\": \"array\",\n      \"nullable\": false\n    },\n    \"n_input_rows\": {\n      \"type\": \"object\",\n      \"nullable\": false\n    },\n    \"n_analysed\": {\n      \"type\": \"integer\",\n      \"nullable\": true\n    },\n    \"random_seed\": {\n      \"type\": \"integer\",\n      \"nullable\": true\n    },\n    \"analysis_year\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"n_excluded_missing\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"baseline_metric\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"model_metric\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"pearson_r\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"r_squared\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"effect_estimate\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"intercept\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"effect_se\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"effect_ci_low\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"effect_ci_high\": {","is_error":false}]},"uuid":"8e9a7537-ae61-4e5e-a47e-454b48f791b5","timestamp":"2026-10-30T05:30:59.051Z","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==> data/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==> data/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 data/wb_entities.csv\n    2350 data/wb_indicators.csv\n    2647 total\n{\n  \"task_id\": \"F6-dev-01\",\n  \"required_files\": [\n    \"out/results.json\",\n    \"out/report.md\",\n    \"out/analysis.py\",\n    \"out/claims.json\",\n    \"out/figure1.png\",\n    \"out/table1.csv\"\n  ],\n  \"results_keys\": {\n    \"task_id\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"feasibility\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"feasibility_reason\": {\n      \"type\": \"string\",\n      \"nullable\": true\n    },\n    \"data_issues\": {\n      \"type\": \"array\",\n      \"nullable\": false\n    },\n    \"n_input_rows\": {\n      \"type\": \"object\",\n      \"nullable\": false\n    },\n    \"n_analysed\": {\n      \"type\": \"integer\",\n      \"nullable\": true\n    },\n    \"random_seed\": {\n      \"type\": \"integer\",\n      \"nullable\": true\n    },\n    \"analysis_year\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"n_excluded_missing\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"baseline_metric\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"model_metric\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"pearson_r\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"r_squared\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"effect_estimate\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"intercept\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"effect_se\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"effect_ci_low\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"effect_ci_high\": {","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"5bf324df-5e89-445b-bca2-ddaf84f3d3b5","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0006","sessionId":"5ba6b8fb-8eed-4eae-9ff2-e173ef6821b3","version":"2.1.314","gitBranch":"HEAD"}
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Meaningful as a best-linear-predictor slope (no causal interpretation); requires finite variance and enough countries; robust (HC) SE for heteroskedasticity, assuming independence across countries.\n\n**Required outputs.** out/results.json (analysis_year, n_excluded_missing, baseline_metric, model_metric, pearson_r, r_squared, effect_estimate, intercept, effect_se, effect_ci_low, effect_ci_high, conclusion, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/figure1.png (scatter + OLS line); out/table1.csv (income_level, n_countries, mean_log_gdp_pc, mean_life_expectancy); out/report.md (6 sections in order); out/analysis.py; out/claims.json; plus state files, exclusions.csv.\n\n**Information rules.** Exclude entities with region == \"Aggregates\" in wb_entities.csv. Analysis year = most recent year 2015–2023 with both indicators observed for >=150 countries/economies. In that year drop countries with any missing indicator (count in n_excluded_missing), no imputation. Natural log. Only the listed files.\n\n**Invalidity / impossibility.** No year with >=150 complete countries → infeasible (premise_invalid). Non-positive GDP (log undefined). Entities in indicators not matched to metadata (ambiguous country status) must be handled explicitly. Using aggregates would invalidate the estimate.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"running\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > out/state/log.md\n~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, numpy as np\nind=pd.read_csv('data/wb_indicators.csv',keep_default_na=False,na_values=[''])\nent=pd.read_csv('data/wb_entities.csv',keep_default_na=False,na_values=[''])\nprint(ind.dtypes, ind.shape, ent.shape)\nprint(ind.isna().sum()); print(ent.isna().sum())\nprint('dup iso3-year',ind.duplicated(['iso3','year']).sum(),'dup ent',ent.duplicated('iso3').sum())\nprint(ind.year.value_counts().sort_index())\nprint(ind.describe())\ns=set(ind.iso3)-set(ent.iso3); print('unmatched',s, ind[ind.iso3.isin(s)].name.unique())\nprint('ent not in ind', set(ent.iso3)-set(ind.iso3))\nprint(ent.region.value_counts()); print(ent.income_level.value_counts())\nm=ind.merge(ent[['iso3','region','income_level']],on='iso3',how='left')\nc=m[m.region!='Aggregates']\nprint(c.assign(ok=c.gdp_pc_ppp.notna()&c.life_expectancy.notna()).groupby('year').agg(n=('iso3','size'),ok=('ok','sum'),g=('gdp_pc_ppp',lambda x:x.notna().sum()),l=('life_expectancy',lambda x:x.notna().sum())))\nprint((ind.gdp_pc_ppp<=0).sum())\nprint(c[c.year==2023].sort_values('life_expectancy').head(3)[['name','gdp_pc_ppp','life_expectancy']])\nprint(c[c.year==2023][c[c.year==2023].isna().any(axis=1)][['iso3','name','gdp_pc_ppp','life_expectancy']])\nEOF","description":"Write state files and 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":186,"cache_read_input_tokens":10966,"output_tokens":1672,"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":186},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsbZGCL313wiLp388RN","requestRef":"0026ca48-d595-47a0-bc26-df6f6648fec8","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"caffbfc4-ae80-4076-8f6f-d2229acae3a7","timestamp":"2026-10-30T05:31:24.319Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0006","sessionId":"5ba6b8fb-8eed-4eae-9ff2-e173ef6821b3","version":"2.1.314","gitBranch":"HEAD"}
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{"parentUuid":"7ec78857-beb4-4830-a72b-3b000550e07e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXscikjXGcYBzoXHFSDa","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01D3N4Cbev9ZS4W7JC9Uy6My","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, numpy as np\nind=pd.read_csv('data/wb_indicators.csv',keep_default_na=False,na_values=[''])\nent=pd.read_csv('data/wb_entities.csv',keep_default_na=False,na_values=[''])\nprint(ind.dtypes, ind.shape, ent.shape)\nprint(ind.isna().sum()); print(ent.isna().sum())\nprint('dup iso3-year',ind.duplicated(['iso3','year']).sum(),'dup ent',ent.duplicated('iso3').sum())\nprint(ind.year.value_counts().sort_index().to_dict())\nprint(ind.describe())\ns=set(ind.iso3)-set(ent.iso3); print('unmatched',s, ind[ind.iso3.isin(s)].name.unique())\nprint('ent not in ind', set(ent.iso3)-set(ind.iso3))\nprint(ent.region.value_counts()); print(ent.income_level.value_counts())\nm=ind.merge(ent[['iso3','region','income_level']],on='iso3',how='left')\nc=m[m.region!='Aggregates']\nprint(c.assign(ok=c.gdp_pc_ppp.notna()&c.life_expectancy.notna()).groupby('year').agg(n=('iso3','size'),ok=('ok','sum'),g=('gdp_pc_ppp',lambda x:x.notna().sum()),l=('life_expectancy',lambda x:x.notna().sum())))\nprint((ind.gdp_pc_ppp<=0).sum())\nEOF","description":"Audit input data"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":660,"cache_read_input_tokens":12899,"output_tokens":639,"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":660},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsciUMdcDbPe3EZfkVt","requestRef":"96aa0779-921d-4b31-ae38-c7d07d1979a3","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"093e09cb-59bc-42cc-b3c5-aefd307e458b","timestamp":"2026-10-30T05:31:33.020Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0006","sessionId":"5ba6b8fb-8eed-4eae-9ff2-e173ef6821b3","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"093e09cb-59bc-42cc-b3c5-aefd307e458b","isSidechain":false,"promptId":"4a656fe6-0a24-4f6d-a633-385670d0e6ec","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01D3N4Cbev9ZS4W7JC9Uy6My","type":"tool_result","content":"iso3                object\nname                object\nyear                 int64\ngdp_pc_ppp         float64\nlife_expectancy    float64\ndtype: object (2349, 5) (296, 5)\niso3                 0\nname                 0\nyear                 0\ngdp_pc_ppp         164\nlife_expectancy      0\ndtype: int64\niso3            0\nname            0\nregion          0\nincome_level    0\nlending_type    0\ndtype: int64\ndup iso3-year 0 dup ent 0\n{2015: 261, 2016: 261, 2017: 261, 2018: 261, 2019: 261, 2020: 261, 2021: 261, 2022: 261, 2023: 261}\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\nunmatched set() []\nent not in ind {'BHI', 'BMN', 'BEC', 'FXS', 'BSS', 'DSA', 'CME', 'INX', 'NAF', 'HIC', 'DNS', 'CLA', 'LIC', 'BEA', 'MDE', 'CEU', 'LMC', 'UMC', 'NRS', 'DEA', 'DMN', 'CAA', 'NXS', 'RRS', 'DEC', 'DSF', 'SXZ', 'AFR', 'DSS', 'CEA', 'CSA', 'IBB', 'DLA', 'XZN', 'BLA'}\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\n        n   ok    g    l\nyear                    \n2015  217  199  199  217\n2016  217  199  199  217\n2017  217  199  199  217\n2018  217  199  199  217\n2019  217  199  199  217\n2020  217  199  199  217\n2021  217  199  199  217\n2022  217  199  199  217\n2023  217  197  197  217\n0","is_error":false}]},"uuid":"15366ac5-c8a2-42d0-86e0-2e7cae6b9c5c","timestamp":"2026-10-30T05:31:33.326Z","toolUseResult":{"stdout":"iso3                object\nname                object\nyear                 int64\ngdp_pc_ppp         float64\nlife_expectancy    float64\ndtype: object (2349, 5) (296, 5)\niso3                 0\nname                 0\nyear                 0\ngdp_pc_ppp         164\nlife_expectancy      0\ndtype: int64\niso3            0\nname            0\nregion          0\nincome_level    0\nlending_type    0\ndtype: int64\ndup iso3-year 0 dup ent 0\n{2015: 261, 2016: 261, 2017: 261, 2018: 261, 2019: 261, 2020: 261, 2021: 261, 2022: 261, 2023: 261}\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\nunmatched set() []\nent not in ind {'BHI', 'BMN', 'BEC', 'FXS', 'BSS', 'DSA', 'CME', 'INX', 'NAF', 'HIC', 'DNS', 'CLA', 'LIC', 'BEA', 'MDE', 'CEU', 'LMC', 'UMC', 'NRS', 'DEA', 'DMN', 'CAA', 'NXS', 'RRS', 'DEC', 'DSF', 'SXZ', 'AFR', 'DSS', 'CEA', 'CSA', 'IBB', 'DLA', 'XZN', 'BLA'}\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\n        n   ok    g    l\nyear                    \n2015  217  199  199  217\n2016  217  199  199  217\n2017  217  199  199  217\n2018  217  199  199  217\n2019  217  199  199  217\n2020  217  199  199  217\n2021  217  199  199  217\n2022  217  199  199  217\n2023  217  197  197  217\n0","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"093e09cb-59bc-42cc-b3c5-aefd307e458b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0006","sessionId":"5ba6b8fb-8eed-4eae-9ff2-e173ef6821b3","version":"2.1.314","gitBranch":"HEAD"}
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{"parentUuid":"18dfa819-b193-40b2-8874-2156c2b53a9d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsd7N1j9QJgwsDvHivo","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013EaHtgG5yVCDnTziV5Qnxd","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, numpy as np\nind=pd.read_csv('data/wb_indicators.csv',keep_default_na=False,na_values=[''])\nent=pd.read_csv('data/wb_entities.csv',keep_default_na=False,na_values=[''])\nm=ind.merge(ent[['iso3','region','income_level']],on='iso3',how='left')\nc=m[m.region!='Aggregates']\nprint(c.sort_values('life_expectancy').head(12)[['iso3','name','year','gdp_pc_ppp','life_expectancy']])\nprint(c.sort_values('life_expectancy').tail(5)[['iso3','name','year','gdp_pc_ppp','life_expectancy']])\nd=c[c.year==2023]\nprint(d[d.isna().any(axis=1)][['iso3','name','gdp_pc_ppp','life_expectancy','income_level']])\nprint(d.income_level.value_counts())\n# year-over-year jumps in LE\np=c.pivot(index='iso3',columns='year',values='life_expectancy'); dd=p.diff(axis=1).abs()\nprint(dd.max(axis=1).sort_values().tail(10))\npg=c.pivot(index='iso3',columns='year',values='gdp_pc_ppp'); r=np.log(pg).diff(axis=1).abs()\nprint(r.max(axis=1).sort_values().tail(8))\n# exact repeated LE values across years (possible fills)\nprint((p.nunique(axis=1)<=2).sum(), p[p.nunique(axis=1)<=3].index.tolist())\nprint(m[m.region=='Aggregates'].iso3.nunique(), c.iso3.nunique())\n# name mismatches\nx=ind.merge(ent[['iso3','name']],on='iso3'); print((x.name_x!=x.name_y).sum(), x[x.name_x!=x.name_y][['name_x','name_y']].drop_duplicates().head())\nEOF","description":"Investigate anomalies"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1554,"cache_read_input_tokens":13559,"output_tokens":879,"output_tokens_details":{"thinking_tokens":115},"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":1554},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsd77dv53Roh6G6M96n","requestRef":"f0333116-1e38-4515-9f29-77c1493c6303","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f44f995f-18f8-4400-bf21-98b1547d3f5e","timestamp":"2026-10-30T05:31:41.157Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0006","sessionId":"5ba6b8fb-8eed-4eae-9ff2-e173ef6821b3","version":"2.1.314","gitBranch":"HEAD"}
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{"parentUuid":"f44f995f-18f8-4400-bf21-98b1547d3f5e","isSidechain":false,"promptId":"4a656fe6-0a24-4f6d-a633-385670d0e6ec","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013EaHtgG5yVCDnTziV5Qnxd","type":"tool_result","content":"     iso3                      name  year   gdp_pc_ppp  life_expectancy\n313   CAF  Central African Republic  2022  1136.801711           18.818\n310   CAF  Central African Republic  2019  1145.095637           31.530\n1910  SSD               South Sudan  2017          NaN           35.351\n1909  SSD               South Sudan  2016          NaN           36.720\n1908  SSD               South Sudan  2015          NaN           39.757\n312   CAF  Central African Republic  2021  1128.552380           40.279\n308   CAF  Central African Republic  2017  1103.866093           45.231\n311   CAF  Central African Republic  2020  1136.570530           50.596\n307   CAF  Central African Republic  2016  1073.944777           50.999\n2025  TCD                      Chad  2015  2822.579299           51.320\n2026  TCD                      Chad  2016  2622.597808           51.603\n1233  LSO                   Lesotho  2015  2986.958667           51.830\n     iso3    name  year  gdp_pc_ppp  life_expectancy\n1307  MCO  Monaco  2017         NaN           85.878\n1308  MCO  Monaco  2018         NaN           86.084\n1310  MCO  Monaco  2020         NaN           86.089\n1309  MCO  Monaco  2019         NaN           86.151\n1313  MCO  Monaco  2023         NaN           86.372\n     iso3                       name  ...  life_expectancy         income_level\n107   ASM             American Samoa  ...        72.852000          High income\n350   CHI            Channel Islands  ...        81.100000          High income\n458   CUB                       Cuba  ...        78.085000  Upper middle income\n629   ERI                    Eritrea  ...        68.624000           Low income\n764   GIB                  Gibraltar  ...        83.553000          High income\n845   GUM                       Guam  ...        77.209000          High income\n971   IMN                Isle of Man  ...        80.999000          High income\n1214  LIE              Liechtenstein  ...        84.595122          High income\n1295  MAF   St. Martin (French part)  ...        80.224000          High income\n1313  MCO                     Monaco  ...        86.372000          High income\n1448  MNP   Northern Mariana Islands  ...        78.808000          High income\n1520  NCL              New Caledonia  ...        78.766000          High income\n1709  PRK  Korea, Dem. People's Rep.  ...        73.642000           Low income\n1763  PYF           French Polynesia  ...        84.070000          High income\n1916  SSD                South Sudan  ...        57.617000           Low income\n2015  SYR       Syrian Arab Republic  ...        72.120000           Low income\n2249  VEN              Venezuela, RB  ...        72.514000  Lower middle income\n2258  VGB     British Virgin Islands  ...        77.276000          High income\n2267  VIR      Virgin Islands (U.S.)  ...        80.519512          High income\n2321  YEM                Yemen, Rep.  ...        69.295000           Low income\n\n[20 rows x 5 columns]\nincome_level\nHigh income            86\nUpper middle income    59\nLower middle income    47\nLow income             25\nName: count, dtype: int64\niso3\nLBY     5.123\nAFG     5.200\nPER     5.238\nECU     5.282\nBWA     5.446\nGUY     5.565\nBOL     6.007\nPSE    11.492\nSSD    16.554\nCAF    38.590\ndtype: float64\niso3\nMNP   NaN\nNCL   NaN\nPRK   NaN\nPYF   NaN\nSSD   NaN\nVEN   NaN\nVGB   NaN\nYEM   NaN\ndtype: float64\n0 []\n44 217\n18                          name_x                      name_y\n1188  Latin America & Caribbean  Latin America & Caribbean \n1917         Sub-Saharan Africa         Sub-Saharan Africa","is_error":false}]},"uuid":"ff12f43d-a242-484d-b719-f8b88d5d556e","timestamp":"2026-10-30T05:31:41.492Z","toolUseResult":{"stdout":"     iso3                      name  year   gdp_pc_ppp  life_expectancy\n313   CAF  Central African Republic  2022  1136.801711           18.818\n310   CAF  Central African Republic  2019  1145.095637           31.530\n1910  SSD               South Sudan  2017          NaN           35.351\n1909  SSD               South Sudan  2016          NaN           36.720\n1908  SSD               South Sudan  2015          NaN           39.757\n312   CAF  Central African Republic  2021  1128.552380           40.279\n308   CAF  Central African Republic  2017  1103.866093           45.231\n311   CAF  Central African Republic  2020  1136.570530           50.596\n307   CAF  Central African Republic  2016  1073.944777           50.999\n2025  TCD                      Chad  2015  2822.579299           51.320\n2026  TCD                      Chad  2016  2622.597808           51.603\n1233  LSO                   Lesotho  2015  2986.958667           51.830\n     iso3    name  year  gdp_pc_ppp  life_expectancy\n1307  MCO  Monaco  2017         NaN           85.878\n1308  MCO  Monaco  2018         NaN           86.084\n1310  MCO  Monaco  2020         NaN           86.089\n1309  MCO  Monaco  2019         NaN           86.151\n1313  MCO  Monaco  2023         NaN           86.372\n     iso3                       name  ...  life_expectancy         income_level\n107   ASM             American Samoa  ...        72.852000          High income\n350   CHI            Channel Islands  ...        81.100000          High income\n458   CUB                       Cuba  ...        78.085000  Upper middle income\n629   ERI                    Eritrea  ...        68.624000           Low income\n764   GIB                  Gibraltar  ...        83.553000          High income\n845   GUM                       Guam  ...        77.209000          High income\n971   IMN                Isle of Man  ...        80.999000          High income\n1214  LIE              Liechtenstein  ...        84.595122          High income\n1295  MAF   St. Martin (French part)  ...        80.224000          High income\n1313  MCO                     Monaco  ...        86.372000          High income\n1448  MNP   Northern Mariana Islands  ...        78.808000          High income\n1520  NCL              New Caledonia  ...        78.766000          High income\n1709  PRK  Korea, Dem. People's Rep.  ...        73.642000           Low income\n1763  PYF           French Polynesia  ...        84.070000          High income\n1916  SSD                South Sudan  ...        57.617000           Low income\n2015  SYR       Syrian Arab Republic  ...        72.120000           Low income\n2249  VEN              Venezuela, RB  ...        72.514000  Lower middle income\n2258  VGB     British Virgin Islands  ...        77.276000          High income\n2267  VIR      Virgin Islands (U.S.)  ...        80.519512          High income\n2321  YEM                Yemen, Rep.  ...        69.295000           Low income\n\n[20 rows x 5 columns]\nincome_level\nHigh income            86\nUpper middle income    59\nLower middle income    47\nLow income             25\nName: count, dtype: int64\niso3\nLBY     5.123\nAFG     5.200\nPER     5.238\nECU     5.282\nBWA     5.446\nGUY     5.565\nBOL     6.007\nPSE    11.492\nSSD    16.554\nCAF    38.590\ndtype: float64\niso3\nMNP   NaN\nNCL   NaN\nPRK   NaN\nPYF   NaN\nSSD   NaN\nVEN   NaN\nVGB   NaN\nYEM   NaN\ndtype: float64\n0 []\n44 217\n18                          name_x                      name_y\n1188  Latin America & Caribbean  Latin America & Caribbean \n1917         Sub-Saharan Africa         Sub-Saharan Africa","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"f44f995f-18f8-4400-bf21-98b1547d3f5e","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0006","sessionId":"5ba6b8fb-8eed-4eae-9ff2-e173ef6821b3","version":"2.1.314","gitBranch":"HEAD"}
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{"parentUuid":"9357f27b-b665-4cb2-8229-75ba9efc60a4","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsdxBeZk6wHdrAeWmA8","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KeFrtZ8sf7NWEG5c45uVJ4","name":"Bash","input":{"command":"mkdir -p out && cat > out/analysis.py <<'EOF'\n\"\"\"Replication entry point for F6-dev-01 (Preston curve, WDI).\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\"\nMIN_COUNTRIES = 150\nHC = \"HC3\"\nZ = 1.959963984540054  # 97.5% standard-normal quantile\n\nind = pd.read_csv(os.path.join(DATA, \"wb_indicators.csv\"), keep_default_na=False, na_values=[\"\"])\nent = pd.read_csv(os.path.join(DATA, \"wb_entities.csv\"), keep_default_na=False, na_values=[\"\"])\nn_input_rows = {\"wb_indicators.csv\": int(len(ind)), \"wb_entities.csv\": int(len(ent))}\ndata_issues = []\nexcl = []\n\ndef log_ex(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(), \"duplicate iso3-year rows\"\nassert not ent.duplicated(\"iso3\").any(), \"duplicate entity rows\"\nunmatched = sorted(set(ind.iso3) - set(ent.iso3))\nif unmatched:\n    data_issues.append({\"issue\": f\"{len(unmatched)} indicator iso3 codes absent from entity metadata: {unmatched}\",\n                        \"handling\": \"excluded (country status cannot be verified)\"})\nassert (ind.gdp_pc_ppp.dropna() > 0).all(), \"non-positive GDP per capita\"\n\nm = ind.merge(ent[[\"iso3\", \"region\", \"income_level\"]], on=\"iso3\", how=\"left\")\nn0 = len(m)\nm = m[m.region.notna()]\nlog_ex(1, \"drop indicator rows whose iso3 is not in wb_entities.csv\", n0, len(m),\n       \"country/aggregate status cannot be established\")\nn1 = len(m)\nn_agg = m.loc[m.region == \"Aggregates\", \"iso3\"].nunique()\nc = m[m.region != \"Aggregates\"].copy()\nlog_ex(2, \"drop entities with region == 'Aggregates' (all years)\", n1, len(c),\n       f\"information rule: countries/economies only ({n_agg} aggregate entities)\")\ndata_issues.append({\"issue\": f\"wb_indicators.csv contains {n_agg} regional/income/other aggregate entities \"\n                             \"mixed with countries/economies\",\n                    \"handling\": \"excluded via region == 'Aggregates' in wb_entities.csv\"})\n\n# --- analysis year selection ---\nc[\"complete\"] = c.gdp_pc_ppp.notna() & c.life_expectancy.notna()\ncounts = c.groupby(\"year\")[\"complete\"].sum().astype(int)\neligible = counts[counts >= MIN_COUNTRIES]\nif eligible.empty:\n    raise SystemExit(\"No year with >= 150 complete countries: premise invalid\")\nyear = int(eligible.index.max())\n\nn2 = len(c)\ny = c[c.year == year].copy()\nlog_ex(3, f\"keep analysis year {year} only\", n2, len(y),\n       f\"most recent year with >= {MIN_COUNTRIES} countries having both indicators\")\nn3 = len(y)\nmiss = y[~y.complete]\nd = y[y.complete].copy()\nn_excluded_missing = int(len(miss))\nlog_ex(4, \"drop countries with GDPpc or LE missing in analysis year (no imputation)\", n3, len(d),\n       \"information rule; missing: \" + \"; \".join(miss.iso3))\nmiss_g = int(miss.gdp_pc_ppp.isna().sum()); miss_l = int(miss.life_expectancy.isna().sum())\ndata_issues.append({\"issue\": f\"{n_excluded_missing} countries/economies in {year} lack an indicator \"\n                             f\"(GDPpc missing: {miss_g}, LE missing: {miss_l}): {', '.join(miss.iso3)}; \"\n                             \"mostly small territories and conflict/sanctioned economies, so missingness is not random\",\n                    \"handling\": \"excluded from the estimate, counted in n_excluded_missing; no imputation\"})\n\n# anomalies outside the analysis year (documented, not used)\nle_p = c.pivot(index=\"iso3\", columns=\"year\", values=\"life_expectancy\")\njumps = le_p.diff(axis=1).abs().max(axis=1)\nbig = jumps[jumps > 10].sort_values(ascending=False)\nif len(big):\n    data_issues.append({\"issue\": \"implausibly large year-to-year life-expectancy swings (>10 years) in: \"\n                        + \", \".join(f\"{k} (max |dLE|={v:.1f})\" for k, v in big.items())\n                        + \"; e.g. CAF LE 18.8 in 2022 vs 57.4 in 2023; PSE 2023 LE 65.2 vs 76.7 in 2022 (Gaza war)\",\n                        \"handling\": f\"only {year} values enter the estimate; values kept as published (no editing); \"\n                                    \"influence checked with leave-one-out slopes\"})\naggname = ent[ent.name != ent.name.str.strip()]\nif len(aggname):\n    data_issues.append({\"issue\": f\"{len(aggname)} entity names in wb_entities.csv carry trailing whitespace\",\n                        \"handling\": \"cosmetic; joins use iso3 only\"})\n\n# --- estimation ---\nd[\"log_gdp_pc\"] = np.log(d.gdp_pc_ppp)\nx = d.log_gdp_pc.to_numpy(); le = d.life_expectancy.to_numpy()\nn = len(d)\nbaseline_rmse = float(np.sqrt(np.mean((le - le.mean()) ** 2)))\nX = sm.add_constant(x)\nfit = sm.OLS(le, X).fit(cov_type=HC)\nalpha, beta = (float(v) for v in fit.params)\nse = float(fit.bse[1])\nresid = le - (alpha + beta * x)\nmodel_rmse = float(np.sqrt(np.mean(resid ** 2)))\nr = float(np.corrcoef(x, le)[0, 1])\nr2 = float(fit.rsquared)\nlo, hi = beta - Z * se, beta + Z * se\nse_hc = {h: float(sm.OLS(le, X).fit(cov_type=h).bse[1]) for h in [\"HC0\", \"HC1\", \"HC2\", \"HC3\"]}\nse_classical = float(sm.OLS(le, X).fit().bse[1])\n\n# leave-one-out influence\nloo = []\nfor i in range(n):\n    k = np.arange(n) != i\n    b = np.polyfit(x[k], le[k], 1)[0]\n    loo.append(b)\nloo = np.array(loo)\niso = d.iso3.to_numpy()\n\nif lo > 0:\n    concl = \"positive_effect\"\nelif hi < 0:\n    concl = \"negative_effect\"\nelse:\n    concl = \"no_detectable_effect\"\n\nresults = {\n    \"task_id\": \"F6-dev-01\",\n    \"analysis_year\": 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\": float(lo),\n    \"effect_ci_high\": float(hi),\n    \"conclusion\": concl,\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": data_issues,\n    \"n_input_rows\": n_input_rows,\n    \"n_analysed\": int(n),\n    \"random_seed\": None,\n    # supplementary diagnostics\n    \"se_type\": HC,\n    \"ci_method\": \"beta +/- 1.959964 * HC3 SE (normal approximation)\",\n    \"complete_countries_by_year\": {str(k): int(v) for k, v in counts.items()},\n    \"n_countries_in_year\": int(n3),\n    \"n_aggregate_entities_excluded\": int(n_agg),\n    \"effect_se_hc0\": se_hc[\"HC0\"], \"effect_se_hc1\": se_hc[\"HC1\"],\n    \"effect_se_hc2\": se_hc[\"HC2\"], \"effect_se_hc3\": se_hc[\"HC3\"],\n    \"effect_se_classical\": se_classical,\n    \"loo_slope_min\": float(loo.min()), \"loo_slope_min_dropped\": str(iso[loo.argmin()]),\n    \"loo_slope_max\": float(loo.max()), \"loo_slope_max_dropped\": str(iso[loo.argmax()]),\n    \"rmse_reduction_share\": float(1 - model_rmse / baseline_rmse),\n    \"le_gain_per_doubling_gdp\": float(beta * np.log(2)),\n    \"mean_life_expectancy\": float(le.mean()),\n    \"mean_log_gdp_pc\": float(x.mean()),\n}\n\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(results, f, indent=2)\n\n# --- table ---\norder = [\"Low income\", \"Lower middle income\", \"Upper middle income\", \"High income\"]\nt = (d.groupby(\"income_level\")\n       .agg(n_countries=(\"iso3\", \"size\"), mean_log_gdp_pc=(\"log_gdp_pc\", \"mean\"),\n            mean_life_expectancy=(\"life_expectancy\", \"mean\"))\n       .reset_index())\nt[\"k\"] = t.income_level.map({v: i for i, v in enumerate(order)}).fillna(99)\nt = t.sort_values([\"k\", \"income_level\"]).drop(columns=\"k\")\nassert t.n_countries.sum() == n\nt.to_csv(os.path.join(OUT, \"table1.csv\"), index=False, float_format=\"%.6f\")\n\n# --- exclusions ledger ---\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\n# --- figure ---\nfig, ax = plt.subplots(figsize=(7.5, 5.2))\ncols = dict(zip(order, [\"#d7191c\", \"#fdae61\", \"#abd9e9\", \"#2c7bb6\"]))\nfor lvl in order:\n    s = d[d.income_level == lvl]\n    ax.scatter(s.log_gdp_pc, s.life_expectancy, s=18, alpha=0.8, color=cols[lvl], label=f\"{lvl} (n={len(s)})\")\nxx = np.linspace(x.min(), x.max(), 100)\nax.plot(xx, alpha + beta * xx, color=\"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, {year}: {n} countries/economies (WDI)\")\nax.legend(fontsize=8, loc=\"lower right\")\nfig.tight_layout()\nfig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=120)\n\nprint(f\"year={year} n={n} excluded_missing={n_excluded_missing} beta={beta:.4f} se({HC})={se:.4f} \"\n      f\"CI=[{lo:.4f},{hi:.4f}] r={r:.4f} R2={r2:.4f} RMSE base={baseline_rmse:.4f} model={model_rmse:.4f} -> {concl}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/table1.csv out/exclusions.csv","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1187,"cache_read_input_tokens":17324,"output_tokens":4949,"output_tokens_details":{"thinking_tokens":559},"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":1187},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXsdwuGwoDckqMGDq8LS","requestRef":"084617ac-d9db-46ca-a1e8-38ca05a7e25e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3bd9c1dc-9f5e-483d-8ef5-5a9248b48bda","timestamp":"2026-10-30T05:32:24.784Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0006","sessionId":"5ba6b8fb-8eed-4eae-9ff2-e173ef6821b3","version":"2.1.314","gitBranch":"HEAD"}
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{"parentUuid":"3bd9c1dc-9f5e-483d-8ef5-5a9248b48bda","isSidechain":false,"promptId":"4a656fe6-0a24-4f6d-a633-385670d0e6ec","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KeFrtZ8sf7NWEG5c45uVJ4","type":"tool_result","content":"year=2023 n=197 excluded_missing=20 beta=5.2672 se(HC3)=0.2107 CI=[4.8543,5.6801] r=0.8613 R2=0.7419 RMSE base=7.0765 model=3.5950 -> positive_effect\n{\n  \"task_id\": \"F6-dev-01\",\n  \"analysis_year\": 2023,\n  \"n_excluded_missing\": 20,\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  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [\n    {\n      \"issue\": \"wb_indicators.csv contains 44 regional/income/other aggregate entities mixed with countries/economies\",\n      \"handling\": \"excluded via region == 'Aggregates' in wb_entities.csv\"\n    },\n    {\n      \"issue\": \"20 countries/economies in 2023 lack an indicator (GDPpc 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; mostly small territories and conflict/sanctioned economies, so missingness is not random\",\n      \"handling\": \"excluded from the estimate, counted in n_excluded_missing; no imputation\"\n    },\n    {\n      \"issue\": \"implausibly large year-to-year life-expectancy swings (>10 years) in: CAF (max |dLE|=38.6), SSD (max |dLE|=16.6), PSE (max |dLE|=11.5); e.g. CAF LE 18.8 in 2022 vs 57.4 in 2023; PSE 2023 LE 65.2 vs 76.7 in 2022 (Gaza war)\",\n      \"handling\": \"only 2023 values enter the estimate; values kept as published (no editing); influence checked with leave-one-out slopes\"\n    },\n    {\n      \"issue\": \"4 entity names in wb_entities.csv carry trailing whitespace\",\n      \"handling\": \"cosmetic; joins use iso3 only\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"wb_indicators.csv\": 2349,\n    \"wb_entities.csv\": 296\n  },\n  \"n_analysed\": 197,\n  \"random_seed\": null,\n  \"se_type\": \"HC3\",\n  \"ci_method\": \"beta +/- 1.959964 * HC3 SE (normal approximation)\",\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_year\": 217,\n  \"n_aggregate_entities_excluded\": 44,\n  \"effect_se_hc0\": 0.20736485591704354,\n  \"effect_se_hc1\": 0.2084255526375312,\n  \"effect_se_hc2\": 0.20901105806015902,\n  \"effect_se_hc3\": 0.21067528691162227,\n  \"effect_se_classical\": 0.22246758287864518,\n  \"loo_slope_min\": 5.205387994234399,\n  \"loo_slope_min_dropped\": \"TCD\",\n  \"loo_slope_max\": 5.327563424394044,\n  \"loo_slope_max_dropped\": \"GRL\",\n  \"rmse_reduction_share\": 0.491977495120151,\n  \"le_gain_per_doubling_gdp\": 3.650926427745336,\n  \"mean_life_expectancy\": 73.47408158883249,\n  \"mean_log_gdp_pc\": 9.664953946043608\n}income_level,n_countries,mean_log_gdp_pc,mean_life_expectancy\nLow income,20,7.610046,63.188050\nLower middle income,46,8.689852,67.809452\nUpper middle income,58,9.689900,73.695373\nHigh income,73,10.822570,79.685845\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop indicator rows whose iso3 is not in wb_entities.csv,2349,2349,0,country/aggregate status cannot be established\n2,drop entities with region == 'Aggregates' (all years),2349,1953,396,information rule: countries/economies only (44 aggregate entities)\n3,keep analysis year 2023 only,1953,217,1736,most recent year with >= 150 countries having both indicators\n4,drop countries with GDPpc or LE missing in analysis year (no imputation),217,197,20,information rule; missing: ASM; CHI; CUB; ERI; GIB; GUM; IMN; LIE; MAF; MCO; MNP; NCL; PRK; PYF; SSD; SYR; VEN; VGB; VIR; YEM","is_error":false}]},"uuid":"eabcdc3f-9b2e-4636-aa09-4b62cec24b45","timestamp":"2026-10-30T05:32:25.986Z","toolUseResult":{"stdout":"year=2023 n=197 excluded_missing=20 beta=5.2672 se(HC3)=0.2107 CI=[4.8543,5.6801] r=0.8613 R2=0.7419 RMSE base=7.0765 model=3.5950 -> positive_effect\n{\n  \"task_id\": \"F6-dev-01\",\n  \"analysis_year\": 2023,\n  \"n_excluded_missing\": 20,\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  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [\n    {\n      \"issue\": \"wb_indicators.csv contains 44 regional/income/other aggregate entities mixed with countries/economies\",\n      \"handling\": \"excluded via region == 'Aggregates' in wb_entities.csv\"\n    },\n    {\n      \"issue\": \"20 countries/economies in 2023 lack an indicator (GDPpc 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; mostly small territories and conflict/sanctioned economies, so missingness is not random\",\n      \"handling\": \"excluded from the estimate, counted in n_excluded_missing; no imputation\"\n    },\n    {\n      \"issue\": \"implausibly large year-to-year life-expectancy swings (>10 years) in: CAF (max |dLE|=38.6), SSD (max |dLE|=16.6), PSE (max |dLE|=11.5); e.g. CAF LE 18.8 in 2022 vs 57.4 in 2023; PSE 2023 LE 65.2 vs 76.7 in 2022 (Gaza war)\",\n      \"handling\": \"only 2023 values enter the estimate; values kept as published (no editing); influence checked with leave-one-out slopes\"\n    },\n    {\n      \"issue\": \"4 entity names in wb_entities.csv carry trailing whitespace\",\n      \"handling\": \"cosmetic; joins use iso3 only\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"wb_indicators.csv\": 2349,\n    \"wb_entities.csv\": 296\n  },\n  \"n_analysed\": 197,\n  \"random_seed\": null,\n  \"se_type\": \"HC3\",\n  \"ci_method\": \"beta +/- 1.959964 * HC3 SE (normal approximation)\",\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_year\": 217,\n  \"n_aggregate_entities_excluded\": 44,\n  \"effect_se_hc0\": 0.20736485591704354,\n  \"effect_se_hc1\": 0.2084255526375312,\n  \"effect_se_hc2\": 0.20901105806015902,\n  \"effect_se_hc3\": 0.21067528691162227,\n  \"effect_se_classical\": 0.22246758287864518,\n  \"loo_slope_min\": 5.205387994234399,\n  \"loo_slope_min_dropped\": \"TCD\",\n  \"loo_slope_max\": 5.327563424394044,\n  \"loo_slope_max_dropped\": \"GRL\",\n  \"rmse_reduction_share\": 0.491977495120151,\n  \"le_gain_per_doubling_gdp\": 3.650926427745336,\n  \"mean_life_expectancy\": 73.47408158883249,\n  \"mean_log_gdp_pc\": 9.664953946043608\n}income_level,n_countries,mean_log_gdp_pc,mean_life_expectancy\nLow income,20,7.610046,63.188050\nLower middle income,46,8.689852,67.809452\nUpper middle income,58,9.689900,73.695373\nHigh income,73,10.822570,79.685845\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop indicator rows whose iso3 is not in wb_entities.csv,2349,2349,0,country/aggregate status cannot be established\n2,drop entities with region == 'Aggregates' (all years),2349,1953,396,information rule: countries/economies only (44 aggregate entities)\n3,keep analysis year 2023 only,1953,217,1736,most recent year with >= 150 countries having both indicators\n4,drop countries with GDPpc or LE missing in analysis year (no imputation),217,197,20,information rule; missing: ASM; CHI; CUB; ERI; GIB; GUM; IMN; LIE; MAF; MCO; MNP; NCL; PRK; PYF; SSD; SYR; VEN; VGB; VIR; YEM","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"3bd9c1dc-9f5e-483d-8ef5-5a9248b48bda","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0006","sessionId":"5ba6b8fb-8eed-4eae-9ff2-e173ef6821b3","version":"2.1.314","gitBranch":"HEAD"}
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Now writing the audit and analysis script."},{"type":"tool_use","id":"toolu_01KeFrtZ8sf7NWEG5c45uVJ4","name":"Bash","input":{"command":"mkdir -p out && cat > out/analysis.py <<'EOF'\n\"\"\"Replication entry point for F6-dev-01 (Preston curve, WDI).\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\"\nMIN_COUNTRIES = 150\nHC = \"HC3\"\nZ = 1.959963984540054  # 97.5% standard-normal quantile\n\nind = pd.read_csv(os.path.join(DATA, \"wb_indicators.csv\"), keep_default_na=False, na_values=[\"\"])\nent = pd.read_csv(os.path.join(DATA, \"wb_entities.csv\"), keep_default_na=False, na_values=[\"\"])\nn_input_rows = {\"wb_indicators.csv\": int(len(ind)), \"wb_entities.csv\": int(len(ent))}\ndata_issues = []\nexcl = []\n\ndef log_ex(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(), \"duplicate iso3-year rows\"\nassert not ent.duplicated(\"iso3\").any(), \"duplicate entity rows\"\nunmatched = sorted(set(ind.iso3) - set(ent.iso3))\nif unmatched:\n    data_issues.append({\"issue\": f\"{len(unmatched)} indicator iso3 codes absent from entity metadata: {unmatched}\",\n                        \"handling\": \"excluded (country status cannot be verified)\"})\nassert (ind.gdp_pc_ppp.dropna() > 0).all(), \"non-positive GDP per capita\"\n\nm = ind.merge(ent[[\"iso3\", \"region\", \"income_level\"]], on=\"iso3\", how=\"left\")\nn0 = len(m)\nm = m[m.region.notna()]\nlog_ex(1, \"drop indicator rows whose iso3 is not in wb_entities.csv\", n0, len(m),\n       \"country/aggregate status cannot be established\")\nn1 = len(m)\nn_agg = m.loc[m.region == \"Aggregates\", \"iso3\"].nunique()\nc = m[m.region != \"Aggregates\"].copy()\nlog_ex(2, \"drop entities with region == 'Aggregates' (all years)\", n1, len(c),\n       f\"information rule: countries/economies only ({n_agg} aggregate entities)\")\ndata_issues.append({\"issue\": f\"wb_indicators.csv contains {n_agg} regional/income/other aggregate entities \"\n                             \"mixed with countries/economies\",\n                    \"handling\": \"excluded via region == 'Aggregates' in wb_entities.csv\"})\n\n# --- analysis year selection ---\nc[\"complete\"] = c.gdp_pc_ppp.notna() & c.life_expectancy.notna()\ncounts = c.groupby(\"year\")[\"complete\"].sum().astype(int)\neligible = counts[counts >= MIN_COUNTRIES]\nif eligible.empty:\n    raise SystemExit(\"No year with >= 150 complete countries: premise invalid\")\nyear = int(eligible.index.max())\n\nn2 = len(c)\ny = c[c.year == year].copy()\nlog_ex(3, f\"keep analysis year {year} only\", n2, len(y),\n       f\"most recent year with >= {MIN_COUNTRIES} countries having both indicators\")\nn3 = len(y)\nmiss = y[~y.complete]\nd = y[y.complete].copy()\nn_excluded_missing = int(len(miss))\nlog_ex(4, \"drop countries with GDPpc or LE missing in analysis year (no imputation)\", n3, len(d),\n       \"information rule; missing: \" + \"; \".join(miss.iso3))\nmiss_g = int(miss.gdp_pc_ppp.isna().sum()); miss_l = int(miss.life_expectancy.isna().sum())\ndata_issues.append({\"issue\": f\"{n_excluded_missing} countries/economies in {year} lack an indicator \"\n                             f\"(GDPpc missing: {miss_g}, LE missing: {miss_l}): {', '.join(miss.iso3)}; \"\n                             \"mostly small territories and conflict/sanctioned economies, so missingness is not random\",\n                    \"handling\": \"excluded from the estimate, counted in n_excluded_missing; no imputation\"})\n\n# anomalies outside the analysis year (documented, not used)\nle_p = c.pivot(index=\"iso3\", columns=\"year\", values=\"life_expectancy\")\njumps = le_p.diff(axis=1).abs().max(axis=1)\nbig = jumps[jumps > 10].sort_values(ascending=False)\nif len(big):\n    data_issues.append({\"issue\": \"implausibly large year-to-year life-expectancy swings (>10 years) in: \"\n                        + \", \".join(f\"{k} (max |dLE|={v:.1f})\" for k, v in big.items())\n                        + \"; e.g. CAF LE 18.8 in 2022 vs 57.4 in 2023; PSE 2023 LE 65.2 vs 76.7 in 2022 (Gaza war)\",\n                        \"handling\": f\"only {year} values enter the estimate; values kept as published (no editing); \"\n                                    \"influence checked with leave-one-out slopes\"})\naggname = ent[ent.name != ent.name.str.strip()]\nif len(aggname):\n    data_issues.append({\"issue\": f\"{len(aggname)} entity names in wb_entities.csv carry trailing whitespace\",\n                        \"handling\": \"cosmetic; joins use iso3 only\"})\n\n# --- estimation ---\nd[\"log_gdp_pc\"] = np.log(d.gdp_pc_ppp)\nx = d.log_gdp_pc.to_numpy(); le = d.life_expectancy.to_numpy()\nn = len(d)\nbaseline_rmse = float(np.sqrt(np.mean((le - le.mean()) ** 2)))\nX = sm.add_constant(x)\nfit = sm.OLS(le, X).fit(cov_type=HC)\nalpha, beta = (float(v) for v in fit.params)\nse = float(fit.bse[1])\nresid = le - (alpha + beta * x)\nmodel_rmse = float(np.sqrt(np.mean(resid ** 2)))\nr = float(np.corrcoef(x, le)[0, 1])\nr2 = float(fit.rsquared)\nlo, hi = beta - Z * se, beta + Z * se\nse_hc = {h: float(sm.OLS(le, X).fit(cov_type=h).bse[1]) for h in [\"HC0\", \"HC1\", \"HC2\", \"HC3\"]}\nse_classical = float(sm.OLS(le, X).fit().bse[1])\n\n# leave-one-out influence\nloo = []\nfor i in range(n):\n    k = np.arange(n) != i\n    b = np.polyfit(x[k], le[k], 1)[0]\n    loo.append(b)\nloo = np.array(loo)\niso = d.iso3.to_numpy()\n\nif lo > 0:\n    concl = \"positive_effect\"\nelif hi < 0:\n    concl = \"negative_effect\"\nelse:\n    concl = \"no_detectable_effect\"\n\nresults = {\n    \"task_id\": \"F6-dev-01\",\n    \"analysis_year\": 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\": float(lo),\n    \"effect_ci_high\": float(hi),\n    \"conclusion\": concl,\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": data_issues,\n    \"n_input_rows\": n_input_rows,\n    \"n_analysed\": int(n),\n    \"random_seed\": None,\n    # supplementary diagnostics\n    \"se_type\": HC,\n    \"ci_method\": \"beta +/- 1.959964 * HC3 SE (normal approximation)\",\n    \"complete_countries_by_year\": {str(k): int(v) for k, v in counts.items()},\n    \"n_countries_in_year\": int(n3),\n    \"n_aggregate_entities_excluded\": int(n_agg),\n    \"effect_se_hc0\": se_hc[\"HC0\"], \"effect_se_hc1\": se_hc[\"HC1\"],\n    \"effect_se_hc2\": se_hc[\"HC2\"], \"effect_se_hc3\": se_hc[\"HC3\"],\n    \"effect_se_classical\": se_classical,\n    \"loo_slope_min\": float(loo.min()), \"loo_slope_min_dropped\": str(iso[loo.argmin()]),\n    \"loo_slope_max\": float(loo.max()), \"loo_slope_max_dropped\": str(iso[loo.argmax()]),\n    \"rmse_reduction_share\": float(1 - model_rmse / baseline_rmse),\n    \"le_gain_per_doubling_gdp\": float(beta * np.log(2)),\n    \"mean_life_expectancy\": float(le.mean()),\n    \"mean_log_gdp_pc\": float(x.mean()),\n}\n\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(results, f, indent=2)\n\n# --- table ---\norder = [\"Low income\", \"Lower middle income\", \"Upper middle income\", \"High income\"]\nt = (d.groupby(\"income_level\")\n       .agg(n_countries=(\"iso3\", \"size\"), mean_log_gdp_pc=(\"log_gdp_pc\", \"mean\"),\n            mean_life_expectancy=(\"life_expectancy\", \"mean\"))\n       .reset_index())\nt[\"k\"] = t.income_level.map({v: i for i, v in enumerate(order)}).fillna(99)\nt = t.sort_values([\"k\", \"income_level\"]).drop(columns=\"k\")\nassert t.n_countries.sum() == n\nt.to_csv(os.path.join(OUT, \"table1.csv\"), index=False, float_format=\"%.6f\")\n\n# --- exclusions ledger ---\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\n# --- figure ---\nfig, ax = plt.subplots(figsize=(7.5, 5.2))\ncols = dict(zip(order, [\"#d7191c\", \"#fdae61\", \"#abd9e9\", \"#2c7bb6\"]))\nfor lvl in order:\n    s = d[d.income_level == lvl]\n    ax.scatter(s.log_gdp_pc, s.life_expectancy, s=18, alpha=0.8, color=cols[lvl], label=f\"{lvl} (n={len(s)})\")\nxx = np.linspace(x.min(), x.max(), 100)\nax.plot(xx, alpha + beta * xx, color=\"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, {year}: {n} countries/economies (WDI)\")\nax.legend(fontsize=8, loc=\"lower right\")\nfig.tight_layout()\nfig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=120)\n\nprint(f\"year={year} n={n} excluded_missing={n_excluded_missing} beta={beta:.4f} se({HC})={se:.4f} \"\n      f\"CI=[{lo:.4f},{hi:.4f}] r={r:.4f} R2={r2:.4f} RMSE base={baseline_rmse:.4f} model={model_rmse:.4f} -> {concl}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/table1.csv out/exclusions.csv","description":"Write and run analysis script"}}]}}
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{"parentUuid":"636f0188-aa1f-422a-9f19-81c281006403","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsgzX86pVQJHwor512R","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01TjhNXMByhFrPMTf5mjt4jg","name":"Bash","input":{"command":"sed -i '' 's/vs 76.7 in 2022 (Gaza war)\"/vs 76.7 in 2022 (plausibly war-related; not verified)\"/' out/analysis.py && grep -n \"plausibly war\" out/analysis.py\ncat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n\n| file | SHA-256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/wb_indicators.csv | 6823f02ed4c20fd12a54b8ca4454f85ba333f22337e2a9fc57c3cabccfdbfcd0 | 2349 | 5 |\n| data/wb_entities.csv | 06af6aec15122fecea5be5eb21464c505188ecc135bc55104a40f20f65040f8d | 296 | 5 |\n| data/README.md | 76cb5af488d97339fb6319a5c9a7ba6ec98112ab6a0a8116e7bec6acd1f9f67a | – | – |\n\nAll hashes match `data/README.md` and `bundle.json`.\n\n**wb_indicators.csv** — iso3 (str), name (str), year (int, 2015–2023, 261 entities × 9 years), gdp_pc_ppp (float, PPP const. 2021 intl $, range 992–139,541, 164 missing, none ≤ 0), life_expectancy (float, years, range 18.8–86.4, 0 missing). No duplicate (iso3, year). Every iso3 matched in wb_entities.csv.\n\n**wb_entities.csv** — 296 entities, 79 with region `Aggregates` (35 of these do not appear in the indicator file), 217 countries/economies. Income levels among countries: High 86, Upper middle 59, Lower middle 47, Low 25 (no \"Not classified\"). 4 names have trailing whitespace (cosmetic). No duplicates, no missing.\n\n**Countries/economies with both indicators, by year:** 199 each year 2015–2022, 197 in 2023 → analysis year 2023 (≥150 rule). In 2023, 20 of 217 countries lack GDPpc (none lack LE): ASM, CHI, CUB, ERI, GIB, GUM, IMN, LIE, MAF, MCO, MNP, NCL, PRK, PYF, SSD, SYR, VEN, VGB, VIR, YEM.\n\n**Anomalies:** large LE swings outside the analysis year — CAF (18.8 in 2022, 31.5 in 2019, then 57.4 in 2023), SSD (35→52 in 2017–18), PSE (76.7 in 2022 → 65.2 in 2023). Only PSE 2023 enters the estimate; kept as published, influence assessed with leave-one-out.\n\n**Information-availability:** no input violates the rules; aggregates are present in the indicator file and are removed by the region rule. Only the listed files are used.\nEOF\necho \"- 2026-10-30 P1 passed: audit done, analysis year 2023, 197 analysed, 20 excluded missing GDP. Decision: keep anomalous LE values as published (only 2023 used); HC3 SE with normal 95% CI.\" >> out/state/log.md\necho \"- 2026-10-30 P2 passed: baseline mean-only RMSE computed (baseline_metric) in analysis.py with the same RMSE rule as model_metric.\" >> out/state/log.md\necho \"- 2026-10-30 P3 passed: out/analysis.py written; OLS + HC3, figure, table, exclusions ledger.\" >> out/state/log.md","description":"Fix wording, write audit and log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":6748,"cache_read_input_tokens":18511,"output_tokens":1444,"output_tokens_details":{"thinking_tokens":155},"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":6748},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsgzGVoEw7dcRj9N27G","requestRef":"ef47a89a-8501-4c38-89e3-5f7100177ec1","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"33d9170a-50dc-4aaa-b9c9-b85fe78ed2c9","timestamp":"2026-10-30T05:32:40.926Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0006","sessionId":"5ba6b8fb-8eed-4eae-9ff2-e173ef6821b3","version":"2.1.314","gitBranch":"HEAD"}
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{"parentUuid":"b60b3457-d626-4f79-8e24-e95139c61419","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsi6eF8gRML9BNGFAFb","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01JjmsLa8KsXEhojCRr1phST","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; rm -f out/results.json out/figure1.png out/table1.csv out/exclusions.csv\n~/wp29/venv/bin/python out/analysis.py >/dev/null; cp out/results.json /tmp/r2.json\n~/wp29/venv/bin/python out/analysis.py >/dev/null\n~/wp29/venv/bin/python - <<'EOF'\nimport json,pandas as pd,numpy as np, scipy.stats as st\na=json.load(open('/tmp/r2.json')); b=json.load(open('out/results.json'))\nprint('identical rerun', a==b, 'vs first (only wording change expected):', {k for k in a if a[k]!=json.load(open('/tmp/r1.json')).get(k)})\nr=b; e=pd.read_csv('out/exclusions.csv')\nprint('excl sums', 2349-e.n_removed.sum()==r['n_analysed'], (e.n_before-e.n_removed==e.n_after).all())\nprint('n_countries_in_year - missing = analysed', r['n_countries_in_year']-r['n_excluded_missing']==r['n_analysed'])\nprint('ci contains', r['effect_ci_low']<r['effect_estimate']<r['effect_ci_high'], 'se>0', r['effect_se']>0)\nprint('r^2==R2', abs(r['pearson_r']**2-r['r_squared'])<1e-12)\nprint('R2 == 1-(model/base)^2', abs(1-(r['model_metric']/r['baseline_metric'])**2-r['r_squared'])<1e-12)\nprint('model<=baseline', r['model_metric']<=r['baseline_metric'])\nt=pd.read_csv('out/table1.csv'); print('table n sum', t.n_countries.sum()==r['n_analysed'], len(t))\n# independent recomputation with numpy\nind=pd.read_csv('data/wb_indicators.csv'); ent=pd.read_csv('data/wb_entities.csv')\nd=ind[(ind.year==2023)&ind.iso3.isin(ent[ent.region!='Aggregates'].iso3)].dropna()\nx=np.log(d.gdp_pc_ppp.values); y=d.life_expectancy.values\nres=st.linregress(x,y); print('linregress slope/int', res.slope, res.intercept, len(d))\nX=np.c_[np.ones_like(x),x]; XtXi=np.linalg.inv(X.T@X); e_=y-X@np.array([res.intercept,res.slope]); h=np.sum(X@XtXi*X,1)\nV=XtXi@(X.T*(e_**2/(1-h)**2))@X@XtXi; print('manual HC3 se', np.sqrt(V[1,1]))\nprint('mean-LE vs table weighted', np.average(t.mean_life_expectancy,weights=t.n_countries), r['mean_life_expectancy'])\nEOF","description":"Rerun and run consistency checks"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1527,"cache_read_input_tokens":25259,"output_tokens":1103,"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":1527},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsi6PNdG8x3ULbULXox","requestRef":"dd507f64-30fa-42a9-a9e8-9ff6ab574025","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1fa81cdb-ae3a-4961-bbe6-6cc0062bf53d","timestamp":"2026-10-30T05:32:50.283Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0006","sessionId":"5ba6b8fb-8eed-4eae-9ff2-e173ef6821b3","version":"2.1.314","gitBranch":"HEAD"}
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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`):\n\n1. Indicator rows with an iso3 absent from the entity file: 0 removed (all matched).\n2. Entities with region `Aggregates`: 44 aggregate entities × 9 years = 396 rows removed (`n_aggregate_entities_excluded`).\n3. Analysis-year restriction: the number of countries/economies with both indicators is 199 in every year 2015–2022 and 197 in 2023 (`complete_countries_by_year`), so the most recent year with ≥150 is **2023** (`analysis_year`); 1736 rows of other years removed, leaving 217 countries/economies (`n_countries_in_year`).\n4. Countries missing an indicator in 2023: 20 removed (`n_excluded_missing`), all because GDP per capita is missing (life expectancy is never missing): ASM, CHI, CUB, ERI, GIB, GUM, IMN, LIE, MAF, MCO, MNP, NCL, PRK, PYF, SSD, SYR, VEN, VGB, VIR, YEM. No imputation.\n\nAnalysed: 197 countries/economies (`n_analysed`); 2349 − 2152 removed = 197. Data issues found and how they were handled are listed in `data_issues`. They include implausibly large year-to-year life-expectancy swings outside 2023 (e.g. Central African Republic at 18.8 years in 2022), which do not enter the estimate, and a sharp 2023 drop for West Bank and Gaza (65.2 vs 76.7 in 2022), which does enter it and was kept as published.\n\n## Method\n- Baseline: predict every country's LE by the sample mean; the metric is the RMSE (`baseline_metric`).\n- Main model: OLS of LE on the natural log of GDP per capita (PPP, constant 2021 $), one equally weighted observation per country. Model fit is the residual RMSE (`model_metric`) computed with the same rule, plus R² (`r_squared`) and the Pearson correlation (`pearson_r`).\n- Uncertainty: **HC3** heteroskedasticity-robust SE for β (`effect_se`, `se_type`). The 95% CI is β ± 1.96·SE (normal approximation, `ci_method`). HC0/HC1/HC2 and classical SEs are reported for comparison. Leave-one-out slopes measure the influence of single countries.\n- Conclusion rule: `positive_effect` if the 95% CI lies above zero.\n- No randomness is used (`random_seed` = null). `out/analysis.py` regenerates `results.json`, `figure1.png`, `table1.csv` and `exclusions.csv` from `data/` alone.\n\n## Results\n- In 2023, across 197 countries, the OLS slope is **β = 5.27 years of life expectancy per log-point of GDP per capita** (`effect_estimate` = 5.267). The intercept is 22.57 years (`intercept`). One log-point is about a 2.72× income ratio. Doubling income goes with about 3.65 more years (`le_gain_per_doubling_gdp`).\n- Strength: Pearson r = 0.861 (`pearson_r`), R² = 0.742 (`r_squared`). The residual RMSE is 3.59 years (`model_metric`), against 7.08 years for the mean-only baseline (`baseline_metric`). That is a 49% reduction in RMSE (`rmse_reduction_share`).\n- Conclusion: `positive_effect` (`conclusion`). The Preston-curve association holds clearly in 2023.\n- Table 1 (`out/table1.csv`): mean LE rises steadily across income groups: Low income 63.2 years (n=20), Lower middle 67.8 (n=46), Upper middle 73.7 (n=58), High 79.7 (n=73). These figures are produced by `analysis.py` but are not keys in results.json; the group counts sum to `n_analysed`.\n- Figure 1 (`out/figure1.png`): scatter with the fitted line, coloured by income group.\n\n## Uncertainty\nHC3 SE = 0.211 (`effect_se`), giving a 95% CI of [4.85, 5.68] years per log-point (`effect_ci_low`, `effect_ci_high`). The choice of SE type matters little: HC0 0.207, HC1 0.208, HC2 0.209, classical 0.222 (`effect_se_hc0`, `effect_se_hc1`, `effect_se_hc2`, `effect_se_classical`). Dropping any single country moves β only within [5.21, 5.33] (`loo_slope_min`, dropping TCD; `loo_slope_max`, dropping GRL). This CI covers sampling-type variation only, under the assumption that countries are independent observations. It does not cover measurement error in the WDI series, model misspecification, or the selection effects described below.\n\n## Limitations and open issues\n- **Not causal.** β is a descriptive linear projection. Income is correlated with health systems, education, institutions and geography, and causality runs both ways (health also raises income). Nothing here identifies the effect of raising income.\n- **Non-random missingness.** The 20 excluded economies (`n_excluded_missing`) lack GDP data, not life expectancy. They are mostly small high-income territories (Monaco, Liechtenstein, Channel Islands, …) and conflict-affected or sanctioned economies (South Sudan, Syria, Yemen, Venezuela, DPRK, Eritrea, Cuba). The second group probably sits at low income or low LE. Leaving out both tails could bias β in either direction. I did not quantify this, because the rules forbid imputation.\n- **Data quality.** WDI life expectancy for 2023 comes largely from modelled UN WPP estimates rather than direct vital registration in many countries. The audit found implausible swings in some series (CAF, SSD, PSE; `data_issues`). Only PSE's 2023 value enters the estimate, and leave-one-out shows no single country drives β. PPP GDP for small or conflict-affected economies is also uncertain. Errors in ln(GDPpc) would attenuate β.\n- **Functional form.** One linear-in-log slope summarises a relationship that may flatten at high income. The figure shows several high-income economies well below the line. I did not test nonlinearity, piecewise slopes, or residual patterns by region. Robust SEs keep the inference valid for the linear projection, but β should not be read as a constant marginal gradient.\n- **Equal weighting.** Tiny economies count as much as China or India. A population-weighted estimand would answer a different question; it was not computed.\n- **Single cross-section.** Only 2023 is analysed. Stability across 2015–2022 (including the COVID-19 years) was not checked.\n- **Independence.** Countries within a region share shocks, so cross-sectional dependence could make the SEs too small. Clustered or spatial SEs were not computed.\n- What would invalidate the result: a systematic error in the 2023 WDI vintage, a mis-classification of aggregates as countries (checked: all 44 aggregate codes are excluded), or a different reading of the year-selection rule. Under the stated rule, 2023 qualifies (197 ≥ 150).\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The analysis year is 2023, the most recent year with at least 150 countries/economies having both indicators; 197 countries are analysed and 20 are excluded for missing GDP per capita.\",\"results_keys\":[\"analysis_year\",\"n_analysed\",\"n_excluded_missing\",\"complete_countries_by_year\"],\"status\":\"supported\",\"caveat\":\"Excluded economies are not missing at random.\"},\n {\"id\":\"C2\",\"text\":\"In 2023 the OLS slope of life expectancy on ln(GDP per capita) is about 5.27 years per log-point (about 3.65 years per doubling of income).\",\"results_keys\":[\"effect_estimate\",\"intercept\",\"le_gain_per_doubling_gdp\"],\"status\":\"supported\",\"caveat\":\"Descriptive linear projection, not a causal effect; equal country weights.\"},\n {\"id\":\"C3\",\"text\":\"The association is positive and statistically clear: the HC3-robust 95% CI [4.85, 5.68] excludes zero (conclusion positive_effect).\",\"results_keys\":[\"effect_se\",\"effect_ci_low\",\"effect_ci_high\",\"conclusion\",\"se_type\"],\"status\":\"supported\",\"caveat\":\"Assumes independent countries; ignores measurement error and selection from missing data.\"},\n {\"id\":\"C4\",\"text\":\"The relationship is strong: r = 0.86, R^2 = 0.74, and log income cuts the RMSE of life expectancy from 7.08 years (mean-only baseline) to 3.59 years.\",\"results_keys\":[\"pearson_r\",\"r_squared\",\"baseline_metric\",\"model_metric\",\"rmse_reduction_share\"],\"status\":\"supported\",\"caveat\":\"In-sample fit; nonlinearity at high income not tested.\"},\n {\"id\":\"C5\",\"text\":\"The slope is robust to the choice of HC estimator and to dropping any single country (leave-one-out range 5.21 to 5.33).\",\"results_keys\":[\"effect_se_hc0\",\"effect_se_hc1\",\"effect_se_hc2\",\"effect_se_hc3\",\"effect_se_classical\",\"loo_slope_min\",\"loo_slope_max\"],\"status\":\"supported\",\"caveat\":\"Does not address joint influence of groups of countries or other years.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 of all inputs computed and matched README\",\"schema/missing/duplicates/ranges audited (data_audit.md)\",\"exclusion ledger written and sums verified\"],\"notes\":\"analysis year 2023\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"baseline_metric (mean-only RMSE) computed with same RMSE rule as model_metric = 7.0765\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, writes results/figure/table/exclusions; no randomness (seed null)\"],\"notes\":\"OLS + HC3\"}\np['P4']={\"status\":\"passed\",\"checks\":[\"clean rerun identical\",\"13 consistency checks PASS (checks.md)\",\"gate run 20261030T053301.912267Z: only P5 deliverables (report, claims, sections, ledger) missing; all results/reproducibility checks ok\"],\"notes\":\"gate_receipts: out/state/gate_receipts/20261030T053301.912267Z.json\"}\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md with 6 sections citing results keys\",\"claims.json 5 claims referencing existing keys\",\"final gate run (see receipts)\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nr=json.load(open('out/results.json'))\nfor c in json.load(open('out/claims.json')): assert all(k in r for k in c['results_keys']),c['id']\nprint('claims keys ok')\nEOF\necho \"- 2026-10-30 P4 passed: clean rerun identical, checks.md all PASS, gate run 1 (failures only for P5 files).\" >> out/state/log.md\necho \"- 2026-10-30 P5: report.md and claims.json written; running final gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v \"\\[ok\\]\"","description":"Write report, claims, update ledger, run gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":934,"cache_read_input_tokens":29785,"output_tokens":4314,"output_tokens_details":{"thinking_tokens":46},"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":934},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsjoCpgpcoo3nUzZmRB","requestRef":"e7da2be4-b345-4c6b-b0d0-2c43a2a6c22b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e0c06262-979c-46da-b6f1-22b5697c4769","timestamp":"2026-10-30T05:33:41.701Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0006","sessionId":"5ba6b8fb-8eed-4eae-9ff2-e173ef6821b3","version":"2.1.314","gitBranch":"HEAD"}
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Estimand: the slope β of the linear projection LE_i = α + β·ln(GDPpc_i) + ε_i over countries/economies (equal weights) in the analysis year, with heteroskedasticity-robust uncertainty. 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`):\n\n1. Indicator rows with an iso3 absent from the entity file: 0 removed (all matched).\n2. Entities with region `Aggregates`: 44 aggregate entities × 9 years = 396 rows removed (`n_aggregate_entities_excluded`).\n3. Analysis-year restriction: the number of countries/economies with both indicators is 199 in every year 2015–2022 and 197 in 2023 (`complete_countries_by_year`), so the most recent year with ≥150 is **2023** (`analysis_year`); 1736 rows of other years removed, leaving 217 countries/economies (`n_countries_in_year`).\n4. Countries missing an indicator in 2023: 20 removed (`n_excluded_missing`), all because GDP per capita is missing (life expectancy is never missing): ASM, CHI, CUB, ERI, GIB, GUM, IMN, LIE, MAF, MCO, MNP, NCL, PRK, PYF, SSD, SYR, VEN, VGB, VIR, YEM. No imputation.\n\nAnalysed: 197 countries/economies (`n_analysed`); 2349 − 2152 removed = 197. Data issues found and how they were handled are listed in `data_issues`. They include implausibly large year-to-year life-expectancy swings outside 2023 (e.g. Central African Republic at 18.8 years in 2022), which do not enter the estimate, and a sharp 2023 drop for West Bank and Gaza (65.2 vs 76.7 in 2022), which does enter it and was kept as published.\n\n## Method\n- Baseline: predict every country's LE by the sample mean; the metric is the RMSE (`baseline_metric`).\n- Main model: OLS of LE on the natural log of GDP per capita (PPP, constant 2021 $), one equally weighted observation per country. Model fit is the residual RMSE (`model_metric`) computed with the same rule, plus R² (`r_squared`) and the Pearson correlation (`pearson_r`).\n- Uncertainty: **HC3** heteroskedasticity-robust SE for β (`effect_se`, `se_type`). The 95% CI is β ± 1.96·SE (normal approximation, `ci_method`). HC0/HC1/HC2 and classical SEs are reported for comparison. Leave-one-out slopes measure the influence of single countries.\n- Conclusion rule: `positive_effect` if the 95% CI lies above zero.\n- No randomness is used (`random_seed` = null). `out/analysis.py` regenerates `results.json`, `figure1.png`, `table1.csv` and `exclusions.csv` from `data/` alone.\n\n## Results\n- In 2023, across 197 countries, the OLS slope is **β = 5.27 years of life expectancy per log-point of GDP per capita** (`effect_estimate` = 5.267). The intercept is 22.57 years (`intercept`). One log-point is about a 2.72× income ratio. Doubling income goes with about 3.65 more years (`le_gain_per_doubling_gdp`).\n- Strength: Pearson r = 0.861 (`pearson_r`), R² = 0.742 (`r_squared`). The residual RMSE is 3.59 years (`model_metric`), against 7.08 years for the mean-only baseline (`baseline_metric`). That is a 49% reduction in RMSE (`rmse_reduction_share`).\n- Conclusion: `positive_effect` (`conclusion`). The Preston-curve association holds clearly in 2023.\n- Table 1 (`out/table1.csv`): mean LE rises steadily across income groups: Low income 63.2 years (n=20), Lower middle 67.8 (n=46), Upper middle 73.7 (n=58), High 79.7 (n=73). These figures are produced by `analysis.py` but are not keys in results.json; the group counts sum to `n_analysed`.\n- Figure 1 (`out/figure1.png`): scatter with the fitted line, coloured by income group.\n\n## Uncertainty\nHC3 SE = 0.211 (`effect_se`), giving a 95% CI of [4.85, 5.68] years per log-point (`effect_ci_low`, `effect_ci_high`). The choice of SE type matters little: HC0 0.207, HC1 0.208, HC2 0.209, classical 0.222 (`effect_se_hc0`, `effect_se_hc1`, `effect_se_hc2`, `effect_se_classical`). Dropping any single country moves β only within [5.21, 5.33] (`loo_slope_min`, dropping TCD; `loo_slope_max`, dropping GRL). This CI covers sampling-type variation only, under the assumption that countries are independent observations. It does not cover measurement error in the WDI series, model misspecification, or the selection effects described below.\n\n## Limitations and open issues\n- **Not causal.** β is a descriptive linear projection. Income is correlated with health systems, education, institutions and geography, and causality runs both ways (health also raises income). Nothing here identifies the effect of raising income.\n- **Non-random missingness.** The 20 excluded economies (`n_excluded_missing`) lack GDP data, not life expectancy. They are mostly small high-income territories (Monaco, Liechtenstein, Channel Islands, …) and conflict-affected or sanctioned economies (South Sudan, Syria, Yemen, Venezuela, DPRK, Eritrea, Cuba). The second group probably sits at low income or low LE. Leaving out both tails could bias β in either direction. I did not quantify this, because the rules forbid imputation.\n- **Data quality.** WDI life expectancy for 2023 comes largely from modelled UN WPP estimates rather than direct vital registration in many countries. The audit found implausible swings in some series (CAF, SSD, PSE; `data_issues`). Only PSE's 2023 value enters the estimate, and leave-one-out shows no single country drives β. PPP GDP for small or conflict-affected economies is also uncertain. Errors in ln(GDPpc) would attenuate β.\n- **Functional form.** One linear-in-log slope summarises a relationship that may flatten at high income. The figure shows several high-income economies well below the line. I did not test nonlinearity, piecewise slopes, or residual patterns by region. Robust SEs keep the inference valid for the linear projection, but β should not be read as a constant marginal gradient.\n- **Equal weighting.** Tiny economies count as much as China or India. A population-weighted estimand would answer a different question; it was not computed.\n- **Single cross-section.** Only 2023 is analysed. Stability across 2015–2022 (including the COVID-19 years) was not checked.\n- **Independence.** Countries within a region share shocks, so cross-sectional dependence could make the SEs too small. Clustered or spatial SEs were not computed.\n- What would invalidate the result: a systematic error in the 2023 WDI vintage, a mis-classification of aggregates as countries (checked: all 44 aggregate codes are excluded), or a different reading of the year-selection rule. Under the stated rule, 2023 qualifies (197 ≥ 150).\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The analysis year is 2023, the most recent year with at least 150 countries/economies having both indicators; 197 countries are analysed and 20 are excluded for missing GDP per capita.\",\"results_keys\":[\"analysis_year\",\"n_analysed\",\"n_excluded_missing\",\"complete_countries_by_year\"],\"status\":\"supported\",\"caveat\":\"Excluded economies are not missing at random.\"},\n {\"id\":\"C2\",\"text\":\"In 2023 the OLS slope of life expectancy on ln(GDP per capita) is about 5.27 years per log-point (about 3.65 years per doubling of income).\",\"results_keys\":[\"effect_estimate\",\"intercept\",\"le_gain_per_doubling_gdp\"],\"status\":\"supported\",\"caveat\":\"Descriptive linear projection, not a causal effect; equal country weights.\"},\n {\"id\":\"C3\",\"text\":\"The association is positive and statistically clear: the HC3-robust 95% CI [4.85, 5.68] excludes zero (conclusion positive_effect).\",\"results_keys\":[\"effect_se\",\"effect_ci_low\",\"effect_ci_high\",\"conclusion\",\"se_type\"],\"status\":\"supported\",\"caveat\":\"Assumes independent countries; ignores measurement error and selection from missing data.\"},\n {\"id\":\"C4\",\"text\":\"The relationship is strong: r = 0.86, R^2 = 0.74, and log income cuts the RMSE of life expectancy from 7.08 years (mean-only baseline) to 3.59 years.\",\"results_keys\":[\"pearson_r\",\"r_squared\",\"baseline_metric\",\"model_metric\",\"rmse_reduction_share\"],\"status\":\"supported\",\"caveat\":\"In-sample fit; nonlinearity at high income not tested.\"},\n {\"id\":\"C5\",\"text\":\"The slope is robust to the choice of HC estimator and to dropping any single country (leave-one-out range 5.21 to 5.33).\",\"results_keys\":[\"effect_se_hc0\",\"effect_se_hc1\",\"effect_se_hc2\",\"effect_se_hc3\",\"effect_se_classical\",\"loo_slope_min\",\"loo_slope_max\"],\"status\":\"supported\",\"caveat\":\"Does not address joint influence of groups of countries or other years.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 of all inputs computed and matched README\",\"schema/missing/duplicates/ranges audited (data_audit.md)\",\"exclusion ledger written and sums verified\"],\"notes\":\"analysis year 2023\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"baseline_metric (mean-only RMSE) computed with same RMSE rule as model_metric = 7.0765\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, writes results/figure/table/exclusions; no randomness (seed null)\"],\"notes\":\"OLS + HC3\"}\np['P4']={\"status\":\"passed\",\"checks\":[\"clean rerun identical\",\"13 consistency checks PASS (checks.md)\",\"gate run 20261030T053301.912267Z: only P5 deliverables (report, claims, sections, ledger) missing; all results/reproducibility checks ok\"],\"notes\":\"gate_receipts: out/state/gate_receipts/20261030T053301.912267Z.json\"}\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md with 6 sections citing results keys\",\"claims.json 5 claims referencing existing keys\",\"final gate run (see receipts)\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nr=json.load(open('out/results.json'))\nfor c in json.load(open('out/claims.json')): assert all(k in r for k in c['results_keys']),c['id']\nprint('claims keys ok')\nEOF\necho \"- 2026-10-30 P4 passed: clean rerun identical, checks.md all PASS, gate run 1 (failures only for P5 files).\" >> out/state/log.md\necho \"- 2026-10-30 P5: report.md and claims.json written; running final gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v \"\\[ok\\]\"","description":"Write report, claims, update ledger, run gate"}}]}}
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{"parentUuid":"584c465c-ebca-40b4-9738-b4bef5853929","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXso2CFS6Nx6QGGazFAN","type":"message","role":"assistant","content":[{"type":"text","text":"The analysis is finished and the gate passes (0 of 40 checks failed). The Preston curve clearly holds in 2023: across 197 countries, life expectancy is about 5.27 years higher per log-point of GDP per capita.\n\n**How the year and sample were chosen**\n- **Year:** 2023 is the most recent year with at least 150 countries reporting both indicators (197 do; 2015–2022 each had 199).\n- **Aggregates:** the 44 regional and income-group aggregates in the indicator file were removed.\n- **Missing data:** 20 countries were dropped in 2023, all because GDP per capita is missing (life expectancy is never missing). No values were imputed.\n\n**Results**\n| | value |\n|---|---|\n| Slope (years per log-point) | 5.267 |\n| Robust standard error (HC3) | 0.211 |\n| 95% confidence interval | 4.85 to 5.68 |\n| Intercept | 22.57 years |\n| Correlation r | 0.861 |\n| R² | 0.742 |\n| Prediction error, regression | 3.59 years |\n| Prediction error, mean-only baseline | 7.08 years |\n\n- **Per doubling:** doubling income goes with about 3.65 more years of life expectancy.\n- **Conclusion code:** `positive_effect`.\n- **Robustness:** the choice of robust standard error barely matters (0.207–0.222 across types). Dropping any single country keeps the slope between 5.21 and 5.33.\n- **Checks:** the script was re-run from scratch and gave identical results, and the slope and standard error were recomputed independently and matched.\n\n**Limitations (detailed in the report)**\n- **Not causal:** this is an association only; income and health affect each other.\n- **Missing countries aren't random:** the 20 dropped countries are mostly small rich territories and conflict-affected or sanctioned economies, so the slope could be biased either way. The impact wasn't quantified because imputation is forbidden.\n- **Odd values in the data:** some life-expectancy series jump implausibly, e.g. Central African Republic at 18.8 years in 2022. Only one such value, West Bank and Gaza's sharp 2023 drop, enters the estimate, and it was kept as published.\n- **Not tested:**\n  - whether the curve flattens at high income;\n  - whether the slope is stable in other years;\n  - weighting by population;\n  - correlation between neighbouring countries, which could make the standard errors too small.\n\nFiles are in `out/`:\n- results.json\n- report.md\n- claims.json (5 supported claims)\n- analysis.py\n- figure1.png\n- table1.csv\n- exclusions.csv\n- `state/` (contract, data audit, checks, phase ledger, log, gate receipts)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":370,"cache_read_input_tokens":35135,"output_tokens":936,"output_tokens_details":{"thinking_tokens":23},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":370},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXso1vcyvJA48CtTG7RJ","requestRef":"25c83ad6-69bb-4431-a2d8-986ed03d8b99","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"17cfba51-c01b-4896-a10e-6cbf444cb9be","timestamp":"2026-10-30T05:33:57.344Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0006","sessionId":"5ba6b8fb-8eed-4eae-9ff2-e173ef6821b3","version":"2.1.314","gitBranch":"HEAD"}
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