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1{"type":"atis-latch","atis":"","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T03:49:49.729Z","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T03:49:49.733Z","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f"}4{"parentUuid":null,"isSidechain":false,"promptId":"3ff5636a-5eeb-458b-90a1-abc20dff2ccd","type":"user","message":{"role":"user","content":"Begin"},"uuid":"144a53a0-fca1-46b5-94a8-df2c479b4600","timestamp":"2026-10-30T03:49:49.736Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0016","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","shapeHash":"44d4b330eab5c7a7324586f4f5db3a4e7c34456a294bfb55428af5658878540b","timestamp":"2026-10-30T03:49:49.745Z","version":"2.1.314","shape":{"system":[{"type":"text","text":"You are Claude Code, Anthropic's official CLI for Claude, running within the Claude Agent SDK."},{"type":"text","text":"CWD: /Users/simon-pierreboucher/wp29/runs/RP0016\nDate: 2026-10-29\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` 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"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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The estimand is the slope β of the linear projection LE_i = α + β·ln(GDPpc_i) + ε_i across countries/economies (one equally weighted observation per country) in the analysis year, where LE is life expectancy at birth (years) and GDPpc is GDP per capita in PPP constant 2021 international dollars, with heteroskedasticity-robust uncertainty. The task is a complete, bounded chain: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report. \"Effect\" is used here in the descriptive, associational sense: no causal claim is requested.\n\n## Inputs\n- `data/wb_indicators.csv` — observed data, World Bank World Development Indicators (retrieved 2026-10-30, CC BY 4.0; full provenance, URLs and SHA-256 in `data/README.md`). One row per entity-year, 2015–2023. Columns: `iso3`, `name`, `year`, `gdp_pc_ppp` (GDP per capita, PPP, constant 2021 international $; empty if not reported), `life_expectancy` (years; empty if not reported). The file is the API download for *all* World Bank entities, so it contains countries/economies as well as regional and income-group aggregates.\n- `data/wb_entities.csv` — World Bank entity metadata: `iso3`, `name`, `region` (the value `Aggregates` marks groupings that are not countries/economies), `income_level`, `lending_type`.\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- The analysis population is countries/economies only: exclude every entity whose `region` is `Aggregates` in `wb_entities.csv`.\n- The analysis year is the most recent year (2015–2023) in which both indicators are observed for at least 150 countries/economies. Report it as `analysis_year`.\n- In the analysis year, countries/economies with either indicator missing are excluded from the estimate and counted in `n_excluded_missing`. No imputation.\n- Use the natural logarithm of GDP per capita. Use only the files listed above.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `analysis_year` | integer | year | year selected by the rule above |\n| `n_excluded_missing` | integer | countries | countries/economies present in `wb_indicators.csv` for the analysis year but dropped for a missing indicator |\n| `baseline_metric` | number | years | RMSE of the mean-only prediction of life expectancy, sqrt(mean((LE − mean LE)²)) over the analysed countries (the no-information baseline) |\n| `model_metric` | number | years | RMSE of the OLS residuals, sqrt(mean(e²)) |\n| `pearson_r` | number | — | Pearson correlation between ln(GDPpc) and LE |\n| `r_squared` | number | — | R² of the OLS regression |\n| `effect_estimate` | number | years per log-point | OLS slope β |\n| `intercept` | number | years | OLS intercept α |\n| `effect_se` | number | years per log-point | heteroskedasticity-robust standard error of β (HC0–HC3; state which in the report) |\n| `effect_ci_low`, `effect_ci_high` | number | years per log-point | 95% confidence interval for β based on `effect_se` |\n| `conclusion` | string | — | one of the codes below |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — scatter of life expectancy against ln(GDP per capita) for the analysed countries with the fitted OLS line, produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per World Bank income level present among the analysed countries, with columns `income_level`, `n_countries`, `mean_log_gdp_pc`, `mean_life_expectancy` (header row + at least two data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. 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The estimand is the slope β of the linear projection LE_i = α + β·ln(GDPpc_i) + ε_i across countries/economies (one equally weighted observation per country) in the analysis year, where LE is life expectancy at birth (years) and GDPpc is GDP per capita in PPP constant 2021 international dollars, with heteroskedasticity-robust uncertainty. The task is a complete, bounded chain: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report. \"Effect\" is used here in the descriptive, associational sense: no causal claim is requested.\n\n## Inputs\n- `data/wb_indicators.csv` — observed data, World Bank World Development Indicators (retrieved 2026-10-30, CC BY 4.0; full provenance, URLs and SHA-256 in `data/README.md`). One row per entity-year, 2015–2023. Columns: `iso3`, `name`, `year`, `gdp_pc_ppp` (GDP per capita, PPP, constant 2021 international $; empty if not reported), `life_expectancy` (years; empty if not reported). The file is the API download for *all* World Bank entities, so it contains countries/economies as well as regional and income-group aggregates.\n- `data/wb_entities.csv` — World Bank entity metadata: `iso3`, `name`, `region` (the value `Aggregates` marks groupings that are not countries/economies), `income_level`, `lending_type`.\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- The analysis population is countries/economies only: exclude every entity whose `region` is `Aggregates` in `wb_entities.csv`.\n- The analysis year is the most recent year (2015–2023) in which both indicators are observed for at least 150 countries/economies. Report it as `analysis_year`.\n- In the analysis year, countries/economies with either indicator missing are excluded from the estimate and counted in `n_excluded_missing`. No imputation.\n- Use the natural logarithm of GDP per capita. Use only the files listed above.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `analysis_year` | integer | year | year selected by the rule above |\n| `n_excluded_missing` | integer | countries | countries/economies present in `wb_indicators.csv` for the analysis year but dropped for a missing indicator |\n| `baseline_metric` | number | years | RMSE of the mean-only prediction of life expectancy, sqrt(mean((LE − mean LE)²)) over the analysed countries (the no-information baseline) |\n| `model_metric` | number | years | RMSE of the OLS residuals, sqrt(mean(e²)) |\n| `pearson_r` | number | — | Pearson correlation between ln(GDPpc) and LE |\n| `r_squared` | number | — | R² of the OLS regression |\n| `effect_estimate` | number | years per log-point | OLS slope β |\n| `intercept` | number | years | OLS intercept α |\n| `effect_se` | number | years per log-point | heteroskedasticity-robust standard error of β (HC0–HC3; state which in the report) |\n| `effect_ci_low`, `effect_ci_high` | number | years per log-point | 95% confidence interval for β based on `effect_se` |\n| `conclusion` | string | — | one of the codes below |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — scatter of life expectancy against ln(GDP per capita) for the analysed countries with the fitted OLS line, produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per World Bank income level present among the analysed countries, with columns `income_level`, `n_countries`, `mean_log_gdp_pc`, `mean_life_expectancy` (header row + at least two data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. 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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\n76cb5af488d97339fb6319a5c9a7ba6ec98112ab6a0a8116e7bec6acd1f9f67a  data/README.md\n06af6aec15122fecea5be5eb21464c505188ecc135bc55104a40f20f65040f8d  data/wb_entities.csv\n6823f02ed4c20fd12a54b8ca4454f85ba333f22337e2a9fc57c3cabccfdbfcd0  data/wb_indicators.csv\n     297 data/wb_entities.csv\n    2350 data/wb_indicators.csv\n    2647 total\n{\n  \"task_id\": \"F6-dev-01\",\n  \"family\": \"F6\",\n  \"split\": \"dev\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:42:41.723569+00:00\",\n  \"files\": {\n    \"TASK.md\": \"6aa11fe5b23688a6084aeca7e83157ee4240169267dc7bc77d3da5e9fd8567e5\",\n    \"data/README.md\": \"76cb5af488d97339fb6319a5c9a7ba6ec98112ab6a0a8116e7bec6acd1f9f67a\",\n    \"data/wb_entities.csv\": \"06af6aec15122fecea5be5eb21464c505188ecc135bc55104a40f20f65040f8d\",\n    \"data/wb_indicators.csv\": \"6823f02ed4c20fd12a54b8ca4454f85ba333f22337e2a9fc57c3cabccfdbfcd0\",\n    \"gate_spec.json\": \"2f6efaa4406577bd9a1133182ff2b15374cd250cad0229cf79c8e3f48ed6b3c3\"\n  }\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\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"conclusion\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    }\n  },\n  \"report_sections\": [\n    \"Question\",\n    \"Data and exclusions\",\n    \"Method\",\n    \"Results\",\n    \"Uncertainty\",\n    \"Limitations and open issues\"\n  ],\n  \"family_checks\": [\n    \"ci_contains_estimate\",\n    \"positive_se\",\n    \"claims_reference_existing_keys\",\n    \"phase_ledger_consistent\",\n    \"exclusion_ledger_sums\"\n  ],\n  \"ci_pairs\": [\n    [\n      \"effect_estimate\",\n      \"effect_ci_low\",\n      \"effect_ci_high\"\n    ]\n  ],\n  \"positive_keys\": [\n    \"effect_se\",\n    \"baseline_metric\",\n    \"model_metric\"\n  ],\n  \"chronology_pairs\": [],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-06\n}","is_error":false}]},"uuid":"434f56da-067d-4d16-9c9d-058e6ec67d18","timestamp":"2026-10-30T03:49:53.816Z","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\n76cb5af488d97339fb6319a5c9a7ba6ec98112ab6a0a8116e7bec6acd1f9f67a  data/README.md\n06af6aec15122fecea5be5eb21464c505188ecc135bc55104a40f20f65040f8d  data/wb_entities.csv\n6823f02ed4c20fd12a54b8ca4454f85ba333f22337e2a9fc57c3cabccfdbfcd0  data/wb_indicators.csv\n     297 data/wb_entities.csv\n    2350 data/wb_indicators.csv\n    2647 total\n{\n  \"task_id\": \"F6-dev-01\",\n  \"family\": \"F6\",\n  \"split\": \"dev\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:42:41.723569+00:00\",\n  \"files\": {\n    \"TASK.md\": \"6aa11fe5b23688a6084aeca7e83157ee4240169267dc7bc77d3da5e9fd8567e5\",\n    \"data/README.md\": \"76cb5af488d97339fb6319a5c9a7ba6ec98112ab6a0a8116e7bec6acd1f9f67a\",\n    \"data/wb_entities.csv\": \"06af6aec15122fecea5be5eb21464c505188ecc135bc55104a40f20f65040f8d\",\n    \"data/wb_indicators.csv\": \"6823f02ed4c20fd12a54b8ca4454f85ba333f22337e2a9fc57c3cabccfdbfcd0\",\n    \"gate_spec.json\": \"2f6efaa4406577bd9a1133182ff2b15374cd250cad0229cf79c8e3f48ed6b3c3\"\n  }\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\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"conclusion\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    }\n  },\n  \"report_sections\": [\n    \"Question\",\n    \"Data and exclusions\",\n    \"Method\",\n    \"Results\",\n    \"Uncertainty\",\n    \"Limitations and open issues\"\n  ],\n  \"family_checks\": [\n    \"ci_contains_estimate\",\n    \"positive_se\",\n    \"claims_reference_existing_keys\",\n    \"phase_ledger_consistent\",\n    \"exclusion_ledger_sums\"\n  ],\n  \"ci_pairs\": [\n    [\n      \"effect_estimate\",\n      \"effect_ci_low\",\n      \"effect_ci_high\"\n    ]\n  ],\n  \"positive_keys\": [\n    \"effect_se\",\n    \"baseline_metric\",\n    \"model_metric\"\n  ],\n  \"chronology_pairs\": [],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-06\n}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"5ac16b97-935d-4a08-96a3-b396951b8523","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0016","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","version":"2.1.314","gitBranch":"HEAD"}21{"type":"api-request-blob","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","hash":"fdfa20a8dc2e50016b36a1813f3ef9a597bc58420932bd0e9a3ee76e39243092","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_011Ck4Hisrc7BFWzGYjxPwMh","name":"Bash","input":{"command":"cat data/README.md; head -5 data/*.csv; shasum -a 256 data/*; wc -l data/*.csv; cat bundle.json gate_spec.json","description":"Inspect data files and provenance"}}]}}22{"type":"api-request","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","id":"f852ab8a-4d03-4736-98e8-1a16b17caa3f","timestamp":"2026-10-30T03:49:53.819Z","version":"2.1.314","querySource":"sdk","shapeHash":"44d4b330eab5c7a7324586f4f5db3a4e7c34456a294bfb55428af5658878540b","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"4593681e-6c2b-43cd-ad6c-29a9912fa15f","keep":4,"tail":["fdfa20a8dc2e50016b36a1813f3ef9a597bc58420932bd0e9a3ee76e39243092","9d5921b0d03c60fb3423ff43aae5ddf6da9f4a757418396d4f8e6d52a9ab1af0"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"6f8b85f4d338b6c4f67a3a0d7f702cb1f0befef1f3887d41d22bfa57e38a1597"}23{"parentUuid":"434f56da-067d-4d16-9c9d-058e6ec67d18","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXjsVSmbF5nohFLTd6RW","type":"message","role":"assistant","content":[{"type":"text","text":"P0: contract and ledger."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2971,"cache_read_input_tokens":6576,"output_tokens":960,"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":2971},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXjsVFsAecMGPyeiGHmR","requestRef":"f852ab8a-4d03-4736-98e8-1a16b17caa3f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a7133541-ea11-4d0d-9603-26f408dc9ed6","timestamp":"2026-10-30T03:49:54.895Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0016","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"a7133541-ea11-4d0d-9603-26f408dc9ed6","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXjsVSmbF5nohFLTd6RW","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015AgqFpyCaDcxbNA1iXra1W","name":"Bash","input":{"command":"mkdir -p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — F6-dev-01\n\n**Question.** Does the cross-country Preston curve (life expectancy vs log real income per capita) hold in the most recent usable year of WDI data, and how strong is it?\n\n**Estimand.** β in the linear projection LE_i = α + β ln(GDPpc_i) + ε_i over countries/economies (equal weights) in the analysis year. LE = life expectancy at birth (years); GDPpc = GDP pc PPP const. 2021 intl $. Descriptive/associational only. Meaningful as a best linear predictor; robust (HC) SE for inference treating countries as draws from a superpopulation (questionable: countries are close to a census).\n\n**Rules.** Exclude entities with region == \"Aggregates\" in wb_entities.csv. Analysis year = most recent year in 2015–2023 with both indicators observed for >=150 countries. Missing-indicator countries in that year excluded and counted (n_excluded_missing); no imputation. Natural log. Only the provided files.\n\n**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; out/table1.csv (income_level, n_countries, mean_log_gdp_pc, mean_life_expectancy); out/report.md (6 sections); out/analysis.py; out/claims.json; plus protocol files (state/, exclusions.csv).\n\n**Invalidity / impossibility.** No year with >=150 complete countries; entities in indicators not matchable to metadata (could leak aggregates); non-positive GDP (log undefined); duplicates of entity-year.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"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 \"- 2026-10-29: P0 passed; contract written.\" > log.md","description":"Write contract, phase ledger, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2971,"cache_read_input_tokens":6576,"output_tokens":960,"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":2971},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXjsVFsAecMGPyeiGHmR","requestRef":"f852ab8a-4d03-4736-98e8-1a16b17caa3f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a27b340e-7102-4c34-b83c-0c97cb20b9f7","timestamp":"2026-10-30T03:50:02.218Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0016","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","version":"2.1.314","gitBranch":"HEAD"}25{"parentUuid":"a27b340e-7102-4c34-b83c-0c97cb20b9f7","isSidechain":false,"promptId":"3ff5636a-5eeb-458b-90a1-abc20dff2ccd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015AgqFpyCaDcxbNA1iXra1W","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"098c0903-cc31-41d5-9c38-3bacdf77076b","timestamp":"2026-10-30T03:50:02.253Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"a27b340e-7102-4c34-b83c-0c97cb20b9f7","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0016/out/state","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","version":"2.1.314","gitBranch":"HEAD"}26{"type":"api-request-blob","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","hash":"0edf5678e7d353f6a8eac59f8c7cabb4d530fbd9a5353a05915e7e01931065aa","message":{"role":"assistant","content":[{"type":"text","text":"P0: contract and ledger."},{"type":"tool_use","id":"toolu_015AgqFpyCaDcxbNA1iXra1W","name":"Bash","input":{"command":"mkdir -p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — F6-dev-01\n\n**Question.** Does the cross-country Preston curve (life expectancy vs log real income per capita) hold in the most recent usable year of WDI data, and how strong is it?\n\n**Estimand.** β in the linear projection LE_i = α + β ln(GDPpc_i) + ε_i over countries/economies (equal weights) in the analysis year. LE = life expectancy at birth (years); GDPpc = GDP pc PPP const. 2021 intl $. Descriptive/associational only. Meaningful as a best linear predictor; robust (HC) SE for inference treating countries as draws from a superpopulation (questionable: countries are close to a census).\n\n**Rules.** Exclude entities with region == \"Aggregates\" in wb_entities.csv. Analysis year = most recent year in 2015–2023 with both indicators observed for >=150 countries. Missing-indicator countries in that year excluded and counted (n_excluded_missing); no imputation. Natural log. Only the provided files.\n\n**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; out/table1.csv (income_level, n_countries, mean_log_gdp_pc, mean_life_expectancy); out/report.md (6 sections); out/analysis.py; out/claims.json; plus protocol files (state/, exclusions.csv).\n\n**Invalidity / impossibility.** No year with >=150 complete countries; entities in indicators not matchable to metadata (could leak aggregates); non-positive GDP (log undefined); duplicates of entity-year.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"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 \"- 2026-10-29: P0 passed; contract written.\" > log.md","description":"Write contract, phase ledger, 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classified'])|c.income_level.isna()][['iso3','name','year','gdp_pc_ppp']].drop_duplicates('iso3'))\nna=e[e.iso3.isin(['NAM'])]; print(na)\nEOF","description":"Audit data schema, missingness, 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code 1\nTraceback (most recent call last):\n  File \"<stdin>\", line 2, in <module>\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n    return _read(filepath_or_buffer, kwds)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n    parser = TextFileReader(filepath_or_buffer, **kwds)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n    self._engine = self._make_engine(f, self.engine)\n                   ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n    self.handles = get_handle(\n                   ~~~~~~~~~~^\n        f,\n        ^^\n    ...<6 lines>...\n        storage_options=self.options.get(\"storage_options\", None),\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n    )\n    ^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n    handle = open(\n        handle,\n    ...<3 lines>...\n        newline=\"\",\n    )\nFileNotFoundError: [Errno 2] No such file or directory: 'data/wb_indicators.csv'","is_error":true,"tool_use_id":"toolu_01A9hsfd2pc3YTp3P56gjfgP"}]},"uuid":"933950d9-d9b7-4e07-8694-8ea35e9f5e6c","timestamp":"2026-10-30T03:50:08.046Z","toolUseResult":"Error: Exit code 1\nTraceback (most recent call last):\n  File \"<stdin>\", line 2, in <module>\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n    return _read(filepath_or_buffer, kwds)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n    parser = TextFileReader(filepath_or_buffer, **kwds)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n    self._engine = self._make_engine(f, self.engine)\n                   ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n    self.handles = get_handle(\n                   ~~~~~~~~~~^\n        f,\n        ^^\n    ...<6 lines>...\n        storage_options=self.options.get(\"storage_options\", None),\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n    )\n    ^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n    handle = open(\n        handle,\n    ...<3 lines>...\n        newline=\"\",\n    )\nFileNotFoundError: [Errno 2] No such file or directory: 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coverage"}}]}}31{"type":"api-request","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","id":"75c74801-24fa-496e-9c45-4a64863b8145","timestamp":"2026-10-30T03:50:08.049Z","version":"2.1.314","querySource":"sdk","shapeHash":"44d4b330eab5c7a7324586f4f5db3a4e7c34456a294bfb55428af5658878540b","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"bfedf4a1-b4f4-49cb-8914-c1a70df8f23f","keep":8,"tail":["cde9f458c1a03551234e963bd6161ddb2f04637f630e3034542c29c145dc4c41","69818a8a5541830af187c82bf1c3b1bb240ab18ef92d304dd00d571f5faa9522"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"024fba8b86ce6cdd29688beee17ef7944d305a8ab17f62d0669f55e9555f2656"}32{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"933950d9-d9b7-4e07-8694-8ea35e9f5e6c","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f"}33{"type":"atis-latch","atis":"","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f"}34{"parentUuid":"933950d9-d9b7-4e07-8694-8ea35e9f5e6c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXjtYLvXdoE8o4Je2aaH","type":"message","role":"assistant","content":[{"type":"text","text":"The shell had moved into out/state; rerunning from root."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1264,"cache_read_input_tokens":10522,"output_tokens":645,"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":1264},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXjtY5ntEMsxs9kyWPea","requestRef":"75c74801-24fa-496e-9c45-4a64863b8145","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"7934489d-8213-41ad-9763-8f2e82bcd0b3","timestamp":"2026-10-30T03:50:09.171Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0016/out/state","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","version":"2.1.314","gitBranch":"HEAD"}35{"parentUuid":"7934489d-8213-41ad-9763-8f2e82bcd0b3","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXjtYLvXdoE8o4Je2aaH","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01VgQ6ppH8ACpQctECqWwteo","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RP0016 && ~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd\ni=pd.read_csv('data/wb_indicators.csv',keep_default_na=False,na_values=['']); e=pd.read_csv('data/wb_entities.csv',keep_default_na=False,na_values=[''])\nprint(i.shape,e.shape); print(i.dtypes); print(i.isna().sum()); print(e.isna().sum())\nprint('dup',i.duplicated(['iso3','year']).sum(), e.duplicated('iso3').sum())\nprint(i.year.value_counts().sort_index())\nprint('not in meta', sorted(set(i.iso3)-set(e.iso3)))\nprint('meta not in ind', sorted(set(e.iso3)-set(i.iso3)))\nprint(e.region.value_counts()); print(e.income_level.value_counts())\nc=i.merge(e[['iso3','region','income_level']],on='iso3',how='left')\nc=c[c.region!='Aggregates']\nprint('n countries',c.iso3.nunique())\nprint(c.assign(ok=c.gdp_pc_ppp.notna()&c.life_expectancy.notna()).groupby('year').ok.agg(['sum','size']))\nprint(i.describe())\nprint(c[c.income_level.isin(['Not classified'])|c.income_level.isna()][['iso3','name','year','gdp_pc_ppp']].drop_duplicates('iso3'))\nprint(e[e.iso3.isin(['NAM'])])\nEOF","description":"Audit data schema, missingness, coverage"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1264,"cache_read_input_tokens":10522,"output_tokens":645,"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":1264},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXjtY5ntEMsxs9kyWPea","requestRef":"75c74801-24fa-496e-9c45-4a64863b8145","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"37806a81-377e-4789-b45a-992cca50c289","timestamp":"2026-10-30T03:50:12.965Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0016/out/state","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","version":"2.1.314","gitBranch":"HEAD"}36{"parentUuid":"37806a81-377e-4789-b45a-992cca50c289","isSidechain":false,"promptId":"3ff5636a-5eeb-458b-90a1-abc20dff2ccd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VgQ6ppH8ACpQctECqWwteo","type":"tool_result","content":"(2349, 5) (296, 5)\niso3                object\nname                object\nyear                 int64\ngdp_pc_ppp         float64\nlife_expectancy    float64\ndtype: object\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 0 0\nyear\n2015    261\n2016    261\n2017    261\n2018    261\n2019    261\n2020    261\n2021    261\n2022    261\n2023    261\nName: count, dtype: int64\nnot in meta []\nmeta not in ind ['AFR', 'BEA', 'BEC', 'BHI', 'BLA', 'BMN', 'BSS', 'CAA', 'CEA', 'CEU', 'CLA', 'CME', 'CSA', 'DEA', 'DEC', 'DLA', 'DMN', 'DNS', 'DSA', 'DSF', 'DSS', 'FXS', 'HIC', 'IBB', 'INX', 'LIC', 'LMC', 'MDE', 'NAF', 'NRS', 'NXS', 'RRS', 'SXZ', 'UMC', 'XZN']\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\nn countries 217\n      sum  size\nyear           \n2015  199   217\n2016  199   217\n2017  199   217\n2018  199   217\n2019  199   217\n2020  199   217\n2021  199   217\n2022  199   217\n2023  197   217\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\nEmpty DataFrame\nColumns: [iso3, name, year, gdp_pc_ppp]\nIndex: []\n    iso3     name              region         income_level lending_type\n196  NAM  Namibia  Sub-Saharan Africa  Lower middle income         IBRD","is_error":false}]},"uuid":"b01da592-df18-4a12-a007-a33cdb95b6df","timestamp":"2026-10-30T03:50:13.179Z","toolUseResult":{"stdout":"(2349, 5) (296, 5)\niso3                object\nname                object\nyear                 int64\ngdp_pc_ppp         float64\nlife_expectancy    float64\ndtype: object\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 0 0\nyear\n2015    261\n2016    261\n2017    261\n2018    261\n2019    261\n2020    261\n2021    261\n2022    261\n2023    261\nName: count, dtype: int64\nnot in meta []\nmeta not in ind ['AFR', 'BEA', 'BEC', 'BHI', 'BLA', 'BMN', 'BSS', 'CAA', 'CEA', 'CEU', 'CLA', 'CME', 'CSA', 'DEA', 'DEC', 'DLA', 'DMN', 'DNS', 'DSA', 'DSF', 'DSS', 'FXS', 'HIC', 'IBB', 'INX', 'LIC', 'LMC', 'MDE', 'NAF', 'NRS', 'NXS', 'RRS', 'SXZ', 'UMC', 'XZN']\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\nn countries 217\n      sum  size\nyear           \n2015  199   217\n2016  199   217\n2017  199   217\n2018  199   217\n2019  199   217\n2020  199   217\n2021  199   217\n2022  199   217\n2023  197   217\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\nEmpty DataFrame\nColumns: [iso3, name, year, gdp_pc_ppp]\nIndex: []\n    iso3     name              region         income_level lending_type\n196  NAM  Namibia  Sub-Saharan Africa  Lower middle income         IBRD","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"37806a81-377e-4789-b45a-992cca50c289","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0016","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","version":"2.1.314","gitBranch":"HEAD"}37{"type":"api-request-blob","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","hash":"835ed4afcdf6d4bc323e35915b0ce784c63574507ecaa50e289c323799b02baf","message":{"role":"assistant","content":[{"type":"text","text":"The shell had moved into out/state; rerunning from root."},{"type":"tool_use","id":"toolu_01VgQ6ppH8ACpQctECqWwteo","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RP0016 && ~/wp29/venv/bin/python - <<'EOF'\nimport pandas as 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classified'])|c.income_level.isna()][['iso3','name','year','gdp_pc_ppp']].drop_duplicates('iso3'))\nprint(e[e.iso3.isin(['NAM'])])\nEOF","description":"Audit data schema, missingness, coverage"}}]}}38{"type":"api-request","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","id":"cc415940-746b-4b9d-b0e8-c19230e05796","timestamp":"2026-10-30T03:50:13.182Z","version":"2.1.314","querySource":"sdk","shapeHash":"44d4b330eab5c7a7324586f4f5db3a4e7c34456a294bfb55428af5658878540b","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"75c74801-24fa-496e-9c45-4a64863b8145","keep":10,"tail":["835ed4afcdf6d4bc323e35915b0ce784c63574507ecaa50e289c323799b02baf","248c8f715d69c1c764661748ddaaaf5d194058b7e2649e185b984b098cff713a"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"5c3cb72cd7b98f1d59f40661c94438a31b3c620143034baba741895c5069db0d"}39{"parentUuid":"b01da592-df18-4a12-a007-a33cdb95b6df","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXjtvJHFKRxT7xXgfNCY","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1597,"cache_read_input_tokens":11786,"output_tokens":578,"output_tokens_details":{"thinking_tokens":118},"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":1597},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":936,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXjtv2QHQoj8asiPMykm","requestRef":"cc415940-746b-4b9d-b0e8-c19230e05796","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6e9f9695-270e-4a69-9f9a-d6c6b6b7d4fb","timestamp":"2026-10-30T03:50:15.335Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0016","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","version":"2.1.314","gitBranch":"HEAD"}40{"parentUuid":"6e9f9695-270e-4a69-9f9a-d6c6b6b7d4fb","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXjtvJHFKRxT7xXgfNCY","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01ATDRMB8pAEciydgZSHZ4wJ","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd\ni=pd.read_csv('data/wb_indicators.csv',keep_default_na=False,na_values=['']); e=pd.read_csv('data/wb_entities.csv',keep_default_na=False,na_values=[''])\nc=i.merge(e,on='iso3',how='left',suffixes=('','_m')); c=c[c.region!='Aggregates']\nprint(c[c.life_expectancy<55][['iso3','name','year','life_expectancy']])\nd=c[c.year==2023]; print(d[d.gdp_pc_ppp.isna()][['iso3','name']].to_string())\nprint((c.name!=c.name_m).sum())\n# year-on-year jumps\nc=c.sort_values(['iso3','year']); c['dle']=c.groupby('iso3').life_expectancy.diff()\nprint(c[c.dle.abs()>5][['iso3','name','year','life_expectancy','dle']])\nprint(d.describe())\nEOF","description":"Check anomalies and 2023 missing list"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1597,"cache_read_input_tokens":11786,"output_tokens":578,"output_tokens_details":{"thinking_tokens":118},"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":1597},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXjtv2QHQoj8asiPMykm","requestRef":"cc415940-746b-4b9d-b0e8-c19230e05796","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"96815f65-803e-41b9-a942-38f2af69bfdd","timestamp":"2026-10-30T03:50:18.479Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0016","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","version":"2.1.314","gitBranch":"HEAD"}41{"parentUuid":"96815f65-803e-41b9-a942-38f2af69bfdd","isSidechain":false,"promptId":"3ff5636a-5eeb-458b-90a1-abc20dff2ccd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01ATDRMB8pAEciydgZSHZ4wJ","type":"tool_result","content":"     iso3                      name  year  life_expectancy\n306   CAF  Central African Republic  2015           51.934\n307   CAF  Central African Republic  2016           50.999\n308   CAF  Central African Republic  2017           45.231\n309   CAF  Central African Republic  2018           52.297\n310   CAF  Central African Republic  2019           31.530\n311   CAF  Central African Republic  2020           50.596\n312   CAF  Central African Republic  2021           40.279\n313   CAF  Central African Republic  2022           18.818\n1233  LSO                   Lesotho  2015           51.830\n1234  LSO                   Lesotho  2016           52.908\n1235  LSO                   Lesotho  2017           53.874\n1236  LSO                   Lesotho  2018           54.638\n1239  LSO                   Lesotho  2021           54.209\n1530  NGA                   Nigeria  2015           51.939\n1531  NGA                   Nigeria  2016           52.189\n1532  NGA                   Nigeria  2017           52.397\n1533  NGA                   Nigeria  2018           52.669\n1534  NGA                   Nigeria  2019           53.009\n1535  NGA                   Nigeria  2020           53.072\n1536  NGA                   Nigeria  2021           53.455\n1537  NGA                   Nigeria  2022           54.079\n1538  NGA                   Nigeria  2023           54.462\n1883  SOM        Somalia, Fed. Rep.  2017           52.190\n1884  SOM        Somalia, Fed. Rep.  2018           54.767\n1888  SOM        Somalia, Fed. Rep.  2022           53.931\n1908  SSD               South Sudan  2015           39.757\n1909  SSD               South Sudan  2016           36.720\n1910  SSD               South Sudan  2017           35.351\n1911  SSD               South Sudan  2018           51.905\n1980  SWZ                  Eswatini  2015           54.941\n2025  TCD                      Chad  2015           51.320\n2026  TCD                      Chad  2016           51.603\n2027  TCD                      Chad  2017           51.875\n2028  TCD                      Chad  2018           52.447\n2029  TCD                      Chad  2019           52.993\n2030  TCD                      Chad  2020           53.082\n2031  TCD                      Chad  2021           53.136\n2032  TCD                      Chad  2022           54.528\n     iso3                       name\n107   ASM             American Samoa\n350   CHI            Channel Islands\n458   CUB                       Cuba\n629   ERI                    Eritrea\n764   GIB                  Gibraltar\n845   GUM                       Guam\n971   IMN                Isle of Man\n1214  LIE              Liechtenstein\n1295  MAF   St. Martin (French part)\n1313  MCO                     Monaco\n1448  MNP   Northern Mariana Islands\n1520  NCL              New Caledonia\n1709  PRK  Korea, Dem. People's Rep.\n1763  PYF           French Polynesia\n1916  SSD                South Sudan\n2015  SYR       Syrian Arab Republic\n2249  VEN              Venezuela, RB\n2258  VGB     British Virgin Islands\n2267  VIR      Virgin Islands (U.S.)\n2321  YEM                Yemen, Rep.\n0\n     iso3                      name  year  life_expectancy     dle\n25    AFG               Afghanistan  2022           65.617   5.200\n259   BOL                   Bolivia  2022           67.434   6.007\n304   BWA                  Botswana  2022           68.749   5.446\n308   CAF  Central African Republic  2017           45.231  -5.768\n309   CAF  Central African Republic  2018           52.297   7.066\n310   CAF  Central African Republic  2019           31.530 -20.767\n311   CAF  Central African Republic  2020           50.596  19.066\n312   CAF  Central African Republic  2021           40.279 -10.317\n313   CAF  Central African Republic  2022           18.818 -21.461\n314   CAF  Central African Republic  2023           57.408  38.590\n599   ECU                   Ecuador  2020           72.004  -5.282\n853   GUY                    Guyana  2022           69.888   5.565\n1178  LBY                     Libya  2023           69.339  -5.123\n1645  PER                      Peru  2022           76.834   5.238\n1736  PSE        West Bank and Gaza  2023           65.170 -11.492\n1911  SSD               South Sudan  2018           51.905  16.554\n1912  SSD               South Sudan  2019           58.129   6.224\n         year     gdp_pc_ppp  life_expectancy\ncount   217.0     197.000000       217.000000\nmean   2023.0   27404.732511        73.790943\nstd       0.0   26991.485580         7.122870\nmin    2023.0    1033.009373        54.462000\n25%    2023.0    6520.898865        68.484000\n50%    2023.0   17685.858593        74.552000\n75%    2023.0   41320.257996        79.594000\nmax    2023.0  130296.843789        86.372000","is_error":false}]},"uuid":"48c59ef1-d148-4598-89f0-1629ce0f8c02","timestamp":"2026-10-30T03:50:18.711Z","toolUseResult":{"stdout":"     iso3                      name  year  life_expectancy\n306   CAF  Central African Republic  2015           51.934\n307   CAF  Central African Republic  2016           50.999\n308   CAF  Central African Republic  2017           45.231\n309   CAF  Central African Republic  2018           52.297\n310   CAF  Central African Republic  2019           31.530\n311   CAF  Central African Republic  2020           50.596\n312   CAF  Central African Republic  2021           40.279\n313   CAF  Central African Republic  2022           18.818\n1233  LSO                   Lesotho  2015           51.830\n1234  LSO                   Lesotho  2016           52.908\n1235  LSO                   Lesotho  2017           53.874\n1236  LSO                   Lesotho  2018           54.638\n1239  LSO                   Lesotho  2021           54.209\n1530  NGA                   Nigeria  2015           51.939\n1531  NGA                   Nigeria  2016           52.189\n1532  NGA                   Nigeria  2017           52.397\n1533  NGA                   Nigeria  2018           52.669\n1534  NGA                   Nigeria  2019           53.009\n1535  NGA                   Nigeria  2020           53.072\n1536  NGA                   Nigeria  2021           53.455\n1537  NGA                   Nigeria  2022           54.079\n1538  NGA                   Nigeria  2023           54.462\n1883  SOM        Somalia, Fed. Rep.  2017           52.190\n1884  SOM        Somalia, Fed. Rep.  2018           54.767\n1888  SOM        Somalia, Fed. Rep.  2022           53.931\n1908  SSD               South Sudan  2015           39.757\n1909  SSD               South Sudan  2016           36.720\n1910  SSD               South Sudan  2017           35.351\n1911  SSD               South Sudan  2018           51.905\n1980  SWZ                  Eswatini  2015           54.941\n2025  TCD                      Chad  2015           51.320\n2026  TCD                      Chad  2016           51.603\n2027  TCD                      Chad  2017           51.875\n2028  TCD                      Chad  2018           52.447\n2029  TCD                      Chad  2019           52.993\n2030  TCD                      Chad  2020           53.082\n2031  TCD                      Chad  2021           53.136\n2032  TCD                      Chad  2022           54.528\n     iso3                       name\n107   ASM             American Samoa\n350   CHI            Channel Islands\n458   CUB                       Cuba\n629   ERI                    Eritrea\n764   GIB                  Gibraltar\n845   GUM                       Guam\n971   IMN                Isle of Man\n1214  LIE              Liechtenstein\n1295  MAF   St. Martin (French part)\n1313  MCO                     Monaco\n1448  MNP   Northern Mariana Islands\n1520  NCL              New Caledonia\n1709  PRK  Korea, Dem. People's Rep.\n1763  PYF           French Polynesia\n1916  SSD                South Sudan\n2015  SYR       Syrian Arab Republic\n2249  VEN              Venezuela, RB\n2258  VGB     British Virgin Islands\n2267  VIR      Virgin Islands (U.S.)\n2321  YEM                Yemen, Rep.\n0\n     iso3                      name  year  life_expectancy     dle\n25    AFG               Afghanistan  2022           65.617   5.200\n259   BOL                   Bolivia  2022           67.434   6.007\n304   BWA                  Botswana  2022           68.749   5.446\n308   CAF  Central African Republic  2017           45.231  -5.768\n309   CAF  Central African Republic  2018           52.297   7.066\n310   CAF  Central African Republic  2019           31.530 -20.767\n311   CAF  Central African Republic  2020           50.596  19.066\n312   CAF  Central African Republic  2021           40.279 -10.317\n313   CAF  Central African Republic  2022           18.818 -21.461\n314   CAF  Central African Republic  2023           57.408  38.590\n599   ECU                   Ecuador  2020           72.004  -5.282\n853   GUY                    Guyana  2022           69.888   5.565\n1178  LBY                     Libya  2023           69.339  -5.123\n1645  PER                      Peru  2022           76.834   5.238\n1736  PSE        West Bank and Gaza  2023           65.170 -11.492\n1911  SSD               South Sudan  2018           51.905  16.554\n1912  SSD               South Sudan  2019           58.129   6.224\n         year     gdp_pc_ppp  life_expectancy\ncount   217.0     197.000000       217.000000\nmean   2023.0   27404.732511        73.790943\nstd       0.0   26991.485580         7.122870\nmin    2023.0    1033.009373        54.462000\n25%    2023.0    6520.898865        68.484000\n50%    2023.0   17685.858593        74.552000\n75%    2023.0   41320.257996        79.594000\nmax    2023.0  130296.843789        86.372000","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"96815f65-803e-41b9-a942-38f2af69bfdd","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0016","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","version":"2.1.314","gitBranch":"HEAD"}42{"type":"api-request-blob","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","hash":"7d24a2539c14cf9684b46f49a4e8f8a95a2dd0ac45ba8c18e217485b383bd66b","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01ATDRMB8pAEciydgZSHZ4wJ","name":"Bash","input":{"command":"~/wp29/venv/bin/python 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> 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 (matches README) | 2349 | 5 |\n| data/wb_entities.csv | 06af6aec15122fecea5be5eb21464c505188ecc135bc55104a40f20f65040f8d (matches README) | 296 | 5 |\n| data/README.md | 76cb5af488d97339fb6319a5c9a7ba6ec98112ab6a0a8116e7bec6acd1f9f67a (matches bundle.json) | – | – |\n\n**Schema.** Indicators: iso3 (str), name (str), year (int, 2015–2023, 261 entities × 9 years, balanced), gdp_pc_ppp (float, const. 2021 PPP $, range 992–139,541), life_expectancy (float, years, range 18.8–86.4). Entities: iso3, name, region, income_level, lending_type; no missing.\n**Duplicates.** None on (iso3, year) or entity iso3. Names agree between files for all rows.\n**Matching.** Every indicator iso3 is in wb_entities. 35 metadata entities (all Aggregates-type codes) have no indicator rows.\n**Aggregates.** 44 of 261 indicator entities have region == \"Aggregates\" → excluded (396 rows). 217 countries/economies remain.\n**Missingness.** gdp_pc_ppp missing in 164 rows overall; life_expectancy never missing. Among countries, complete pairs: 199 per year 2015–2022, 197 in 2023. All years ≥150, so analysis year = 2023.\n**2023 missing GDP (20):** ASM, CHI, CUB, ERI, GIB, GUM, IMN, LIE, MAF, MCO, MNP, NCL, PRK, PYF, SSD, SYR, VEN, VGB, VIR, YEM — mostly small territories plus several conflict/sanctioned economies (non-random missingness).\n**Anomalies.** Central African Republic LE series is implausibly volatile (31.5 in 2019, 18.8 in 2022, 57.4 in 2023); 2023 value itself is plausible and kept (no rule permits removing it); flagged. West Bank and Gaza LE falls 11.5 years in 2023 (war; kept). LE complete for all territories → modelled (UN WPP) estimates, latest year likely provisional.\n**Availability rules.** No input violates them; only provided files used; aggregates removed via metadata region field.\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-29: P1 audit done. Analysis year 2023 (197 complete of 217). Decision: keep CAF/PSE values (no exclusion rule), flag as data issues. 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Entities: iso3, name, region, income_level, lending_type; no missing.\n**Duplicates.** None on (iso3, year) or entity iso3. Names agree between files for all rows.\n**Matching.** Every indicator iso3 is in wb_entities. 35 metadata entities (all Aggregates-type codes) have no indicator rows.\n**Aggregates.** 44 of 261 indicator entities have region == \"Aggregates\" → excluded (396 rows). 217 countries/economies remain.\n**Missingness.** gdp_pc_ppp missing in 164 rows overall; life_expectancy never missing. Among countries, complete pairs: 199 per year 2015–2022, 197 in 2023. All years ≥150, so analysis year = 2023.\n**2023 missing GDP (20):** ASM, CHI, CUB, ERI, GIB, GUM, IMN, LIE, MAF, MCO, MNP, NCL, PRK, PYF, SSD, SYR, VEN, VGB, VIR, YEM — mostly small territories plus several conflict/sanctioned economies (non-random missingness).\n**Anomalies.** Central African Republic LE series is implausibly volatile (31.5 in 2019, 18.8 in 2022, 57.4 in 2023); 2023 value itself is plausible and kept (no rule permits removing it); flagged. West Bank and Gaza LE falls 11.5 years in 2023 (war; kept). LE complete for all territories → modelled (UN WPP) estimates, latest year likely provisional.\n**Availability rules.** No input violates them; only provided files used; aggregates removed via metadata region field.\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-29: P1 audit done. Analysis year 2023 (197 complete of 217). Decision: keep CAF/PSE values (no exclusion rule), flag as data issues. HC3 robust SE, normal-quantile 95% CI.\nEOF","description":"Write data audit and 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the analysis script (baseline included; it writes exclusions.csv 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> out/analysis.py <<'EOF'\n\"\"\"F6-dev-01: Preston curve, replication entry point.\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, pandas as pd\nimport statsmodels.api as sm\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nrd = dict(keep_default_na=False, na_values=[\"\"])\nind = pd.read_csv(\"data/wb_indicators.csv\", **rd)\nent = pd.read_csv(\"data/wb_entities.csv\", **rd)\nn_input_rows = {\"wb_indicators.csv\": int(len(ind)), \"wb_entities.csv\": int(len(ent))}\n\nexcl = []\ndef log(step, rule, before, after, why):\n    excl.append(dict(step=step, rule=rule, n_before=before, n_after=after,\n                     n_removed=before - after, rationale=why))\n\nassert not ind.duplicated([\"iso3\", \"year\"]).any()\nunmatched = sorted(set(ind.iso3) - set(ent.iso3))\nassert not unmatched, unmatched\ndf = ind.merge(ent[[\"iso3\", \"region\", \"income_level\"]], on=\"iso3\", how=\"left\")\n\nn0 = len(df)\ndf = df[df.region != \"Aggregates\"].copy()\nlog(1, \"drop entities with region == 'Aggregates'\", n0, len(df),\n    \"analysis population is countries/economies only (TASK rule)\")\n\ndf[\"complete\"] = df.gdp_pc_ppp.notna() & df.life_expectancy.notna()\ncounts = df.groupby(\"year\").complete.sum()\neligible = counts[counts >= 150]\nif eligible.empty:\n    raise SystemExit(\"no year with >=150 complete countries\")\nyear = int(eligible.index.max())\n\nn1 = len(df)\nd = df[df.year == year].copy()\nlog(2, f\"keep analysis year {year} (most recent year with >=150 complete countries)\", n1, len(d),\n    \"estimand is a single cross-section in the analysis year\")\nn2 = len(d)\nd = d[d.complete].copy()\nn_excl_missing = n2 - len(d)\nlog(3, \"drop countries with missing gdp_pc_ppp or life_expectancy\", n2, len(d),\n    \"no imputation (TASK rule)\")\nassert (d.gdp_pc_ppp > 0).all()\npd.DataFrame(excl).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\nd[\"log_gdp_pc\"] = np.log(d.gdp_pc_ppp)\ny, x = d.life_expectancy.to_numpy(), d.log_gdp_pc.to_numpy()\nn = len(d)\n\n# Baseline: mean-only prediction\nbaseline_rmse = float(np.sqrt(np.mean((y - y.mean()) ** 2)))\n\n# OLS with HC3 robust SE\nX = sm.add_constant(x)\nfit = sm.OLS(y, X).fit(cov_type=\"HC3\")\nalpha, beta = map(float, fit.params)\nse = float(fit.bse[1])\nci_lo, ci_hi = map(float, fit.conf_int(alpha=0.05)[1])  # normal quantile with HC3\nmodel_rmse = float(np.sqrt(np.mean(fit.resid ** 2)))\nr = float(np.corrcoef(x, y)[0, 1])\nr2 = float(fit.rsquared)\n\nif ci_lo > 0: concl = \"positive_effect\"\nelif ci_hi < 0: concl = \"negative_effect\"\nelse: concl = \"no_detectable_effect\"\n\nmissing_list = sorted(df[(df.year == year) & ~df.complete].iso3)\ncaf = df[(df.iso3 == \"CAF\")].set_index(\"year\").life_expectancy\ndata_issues = [\n    {\"issue\": f\"wb_indicators.csv mixes countries with {int((ind.merge(ent, on='iso3').region == 'Aggregates').sum() // 9)} aggregate entities (regions/income groups)\",\n     \"handling\": \"Excluded every entity whose region is 'Aggregates' in wb_entities.csv\"},\n    {\"issue\": f\"{n_excl_missing} countries/economies lack gdp_pc_ppp in {year}: {', '.join(missing_list)} (small territories plus conflict/sanctioned economies; missingness likely not random)\",\n     \"handling\": \"Excluded and counted in n_excluded_missing; no imputation\"},\n    {\"issue\": \"Central African Republic life-expectancy series is implausibly volatile in earlier years (e.g. \"\n              f\"{caf[2019]:.1f} in 2019, {caf[2022]:.1f} in 2022, {caf[2023]:.1f} in 2023)\",\n     \"handling\": f\"Its {year} value is plausible and kept (no task rule allows dropping it); flagged as a data-quality concern\"},\n    {\"issue\": \"Life expectancy is reported for every entity-year including tiny territories, indicating modelled (UN WPP) estimates; latest-year values may be provisional\",\n     \"handling\": \"Used as published; noted as a limitation\"},\n]\n\nres = {\n    \"task_id\": \"F6-dev-01\",\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    \"analysis_year\": year,\n    \"n_excluded_missing\": int(n_excl_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_se_type\": \"HC3\",\n    \"effect_ci_low\": ci_lo,\n    \"effect_ci_high\": ci_hi,\n    \"conclusion\": concl,\n    \"n_countries_total\": int(df.iso3.nunique()),\n    \"complete_counts_by_year\": {str(k): int(v) for k, v in counts.items()},\n    \"effect_se_classical\": float(sm.OLS(y, X).fit().bse[1]),\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\n\n# Table 1\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[\"_o\"] = t.income_level.map({k: i for i, k in enumerate(order)}).fillna(99)\nt = t.sort_values([\"_o\", \"income_level\"]).drop(columns=\"_o\")\nt.round(4).to_csv(f\"{OUT}/table1.csv\", index=False)\n\n# Figure 1\nfig, ax = plt.subplots(figsize=(7, 5))\ncolors = dict(zip(order, [\"tab:red\", \"tab:orange\", \"tab:green\", \"tab:blue\"]))\nfor lvl, g in d.groupby(\"income_level\"):\n    ax.scatter(g.log_gdp_pc, g.life_expectancy, s=14, alpha=0.75, label=lvl, color=colors.get(lvl, \"grey\"))\nxs = np.linspace(x.min(), x.max(), 100)\nax.plot(xs, alpha + beta * xs, \"k-\", lw=1.5, label=f\"OLS: LE = {alpha:.1f} + {beta:.2f} ln(GDPpc)\")\nax.set_xlabel(\"ln(GDP per capita, PPP, constant 2021 int. $)\")\nax.set_ylabel(\"Life expectancy at birth (years)\")\nax.set_title(f\"Preston curve, {year} (n = {n} countries/economies)\")\nax.legend(fontsize=8, loc=\"lower right\")\nfig.tight_layout()\nfig.savefig(f\"{OUT}/figure1.png\", dpi=150)\n\nprint(f\"year={year} n={n} excluded_missing={n_excl_missing} beta={beta:.3f} se(HC3)={se:.3f} \"\n      f\"CI=[{ci_lo:.3f},{ci_hi:.3f}] R2={r2:.3f} RMSE base={baseline_rmse:.3f} model={model_rmse:.3f} -> {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 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is building the font cache; this may take a moment.\nyear=2023 n=197 excluded_missing=20 beta=5.267 se(HC3)=0.211 CI=[4.854,5.680] R2=0.742 RMSE base=7.076 model=3.595 -> positive_effect\n{\n  \"task_id\": \"F6-dev-01\",\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [\n    {\n      \"issue\": \"wb_indicators.csv mixes countries with 44 aggregate entities (regions/income groups)\",\n      \"handling\": \"Excluded every entity whose region is 'Aggregates' in wb_entities.csv\"\n    },\n    {\n      \"issue\": \"20 countries/economies lack gdp_pc_ppp in 2023: ASM, CHI, CUB, ERI, GIB, GUM, IMN, LIE, MAF, MCO, MNP, NCL, PRK, PYF, SSD, SYR, VEN, VGB, VIR, YEM (small territories plus conflict/sanctioned economies; missingness likely not random)\",\n      \"handling\": \"Excluded and counted in n_excluded_missing; no imputation\"\n    },\n    {\n      \"issue\": \"Central African Republic life-expectancy series is implausibly volatile in earlier years (e.g. 31.5 in 2019, 18.8 in 2022, 57.4 in 2023)\",\n      \"handling\": \"Its 2023 value is plausible and kept (no task rule allows dropping it); flagged as a data-quality concern\"\n    },\n    {\n      \"issue\": \"Life expectancy is reported for every entity-year including tiny territories, indicating modelled (UN WPP) estimates; latest-year values may be provisional\",\n      \"handling\": \"Used as published; noted as a limitation\"\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  \"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_se_type\": \"HC3\",\n  \"effect_ci_low\": 4.854257477179233,\n  \"effect_ci_high\": 5.6800894267380775,\n  \"conclusion\": \"positive_effect\",\n  \"n_countries_total\": 217,\n  \"complete_counts_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  \"effect_se_classical\": 0.22246758287864518\n}income_level,n_countries,mean_log_gdp_pc,mean_life_expectancy\nLow income,20,7.61,63.188\nLower middle income,46,8.6899,67.8095\nUpper middle income,58,9.6899,73.6954\nHigh income,73,10.8226,79.6858\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop entities with region == 'Aggregates',2349,1953,396,analysis population is countries/economies only (TASK rule)\n2,keep analysis year 2023 (most recent year with >=150 complete countries),1953,217,1736,estimand is a single cross-section in the analysis year\n3,drop countries with missing gdp_pc_ppp or life_expectancy,217,197,20,no imputation (TASK rule)","is_error":false}]},"uuid":"5861a9b0-440b-458e-b5a2-bdcbdd683beb","timestamp":"2026-10-30T03:51:10.015Z","toolUseResult":{"stdout":"Matplotlib is building the font cache; this may take a moment.\nyear=2023 n=197 excluded_missing=20 beta=5.267 se(HC3)=0.211 CI=[4.854,5.680] R2=0.742 RMSE base=7.076 model=3.595 -> positive_effect\n{\n  \"task_id\": \"F6-dev-01\",\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [\n    {\n      \"issue\": \"wb_indicators.csv mixes countries with 44 aggregate entities (regions/income groups)\",\n      \"handling\": \"Excluded every entity whose region is 'Aggregates' in wb_entities.csv\"\n    },\n    {\n      \"issue\": \"20 countries/economies lack gdp_pc_ppp in 2023: ASM, CHI, CUB, ERI, GIB, GUM, IMN, LIE, MAF, MCO, MNP, NCL, PRK, PYF, SSD, SYR, VEN, VGB, VIR, YEM (small territories plus conflict/sanctioned economies; missingness likely not random)\",\n      \"handling\": \"Excluded and counted in n_excluded_missing; no imputation\"\n    },\n    {\n      \"issue\": \"Central African Republic life-expectancy series is implausibly volatile in earlier years (e.g. 31.5 in 2019, 18.8 in 2022, 57.4 in 2023)\",\n      \"handling\": \"Its 2023 value is plausible and kept (no task rule allows dropping it); flagged as a data-quality concern\"\n    },\n    {\n      \"issue\": \"Life expectancy is reported for every entity-year including tiny territories, indicating modelled (UN WPP) estimates; latest-year values may be provisional\",\n      \"handling\": \"Used as published; noted as a limitation\"\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  \"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_se_type\": \"HC3\",\n  \"effect_ci_low\": 4.854257477179233,\n  \"effect_ci_high\": 5.6800894267380775,\n  \"conclusion\": \"positive_effect\",\n  \"n_countries_total\": 217,\n  \"complete_counts_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  \"effect_se_classical\": 0.22246758287864518\n}income_level,n_countries,mean_log_gdp_pc,mean_life_expectancy\nLow income,20,7.61,63.188\nLower middle income,46,8.6899,67.8095\nUpper middle income,58,9.6899,73.6954\nHigh income,73,10.8226,79.6858\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop entities with region == 'Aggregates',2349,1953,396,analysis population is countries/economies only (TASK rule)\n2,keep analysis year 2023 (most recent year with >=150 complete countries),1953,217,1736,estimand is a single cross-section in the analysis year\n3,drop countries with missing gdp_pc_ppp or life_expectancy,217,197,20,no imputation (TASK rule)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"16639168-b26c-4df5-933d-207a72af3c4c","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0016","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","version":"2.1.314","gitBranch":"HEAD"}54{"type":"api-request-blob","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","hash":"85b3817b775f768a02456d5ca130f3497c94f8c94453a497e311d1e400d879f5","message":{"role":"assistant","content":[{"type":"text","text":"Now the analysis script (baseline included; it writes exclusions.csv too)."},{"type":"tool_use","id":"toolu_018aFhZ33Xv6NZgStVS2P14x","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F6-dev-01: Preston curve, replication entry point.\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, pandas as pd\nimport statsmodels.api as sm\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nrd = dict(keep_default_na=False, na_values=[\"\"])\nind = pd.read_csv(\"data/wb_indicators.csv\", **rd)\nent = pd.read_csv(\"data/wb_entities.csv\", **rd)\nn_input_rows = {\"wb_indicators.csv\": int(len(ind)), \"wb_entities.csv\": int(len(ent))}\n\nexcl = []\ndef log(step, rule, before, after, why):\n    excl.append(dict(step=step, rule=rule, n_before=before, n_after=after,\n                     n_removed=before - after, rationale=why))\n\nassert not ind.duplicated([\"iso3\", \"year\"]).any()\nunmatched = sorted(set(ind.iso3) - set(ent.iso3))\nassert not unmatched, unmatched\ndf = ind.merge(ent[[\"iso3\", \"region\", \"income_level\"]], on=\"iso3\", how=\"left\")\n\nn0 = len(df)\ndf = df[df.region != \"Aggregates\"].copy()\nlog(1, \"drop entities with region == 'Aggregates'\", n0, len(df),\n    \"analysis population is countries/economies only (TASK rule)\")\n\ndf[\"complete\"] = df.gdp_pc_ppp.notna() & df.life_expectancy.notna()\ncounts = df.groupby(\"year\").complete.sum()\neligible = counts[counts >= 150]\nif eligible.empty:\n    raise SystemExit(\"no year with >=150 complete countries\")\nyear = int(eligible.index.max())\n\nn1 = len(df)\nd = df[df.year == year].copy()\nlog(2, f\"keep analysis year {year} (most recent year with >=150 complete countries)\", n1, len(d),\n    \"estimand is a single cross-section in the analysis year\")\nn2 = len(d)\nd = d[d.complete].copy()\nn_excl_missing = n2 - len(d)\nlog(3, \"drop countries with missing gdp_pc_ppp or life_expectancy\", n2, len(d),\n    \"no imputation (TASK rule)\")\nassert (d.gdp_pc_ppp > 0).all()\npd.DataFrame(excl).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\nd[\"log_gdp_pc\"] = np.log(d.gdp_pc_ppp)\ny, x = d.life_expectancy.to_numpy(), d.log_gdp_pc.to_numpy()\nn = len(d)\n\n# Baseline: mean-only prediction\nbaseline_rmse = float(np.sqrt(np.mean((y - y.mean()) ** 2)))\n\n# OLS with HC3 robust SE\nX = sm.add_constant(x)\nfit = sm.OLS(y, X).fit(cov_type=\"HC3\")\nalpha, beta = map(float, fit.params)\nse = float(fit.bse[1])\nci_lo, ci_hi = map(float, fit.conf_int(alpha=0.05)[1])  # normal quantile with HC3\nmodel_rmse = float(np.sqrt(np.mean(fit.resid ** 2)))\nr = float(np.corrcoef(x, y)[0, 1])\nr2 = float(fit.rsquared)\n\nif ci_lo > 0: concl = \"positive_effect\"\nelif ci_hi < 0: concl = \"negative_effect\"\nelse: concl = \"no_detectable_effect\"\n\nmissing_list = sorted(df[(df.year == year) & ~df.complete].iso3)\ncaf = df[(df.iso3 == \"CAF\")].set_index(\"year\").life_expectancy\ndata_issues = [\n    {\"issue\": f\"wb_indicators.csv mixes countries with {int((ind.merge(ent, on='iso3').region == 'Aggregates').sum() // 9)} aggregate entities (regions/income groups)\",\n     \"handling\": \"Excluded every entity whose region is 'Aggregates' in wb_entities.csv\"},\n    {\"issue\": f\"{n_excl_missing} countries/economies lack gdp_pc_ppp in {year}: {', '.join(missing_list)} (small territories plus conflict/sanctioned economies; missingness likely not random)\",\n     \"handling\": \"Excluded and counted in n_excluded_missing; no imputation\"},\n    {\"issue\": \"Central African Republic life-expectancy series is implausibly volatile in earlier years (e.g. \"\n              f\"{caf[2019]:.1f} in 2019, {caf[2022]:.1f} in 2022, {caf[2023]:.1f} in 2023)\",\n     \"handling\": f\"Its {year} value is plausible and kept (no task rule allows dropping it); flagged as a data-quality concern\"},\n    {\"issue\": \"Life expectancy is reported for every entity-year including tiny territories, indicating modelled (UN WPP) estimates; latest-year values may be provisional\",\n     \"handling\": \"Used as published; noted as a limitation\"},\n]\n\nres = {\n    \"task_id\": \"F6-dev-01\",\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    \"analysis_year\": year,\n    \"n_excluded_missing\": int(n_excl_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_se_type\": \"HC3\",\n    \"effect_ci_low\": ci_lo,\n    \"effect_ci_high\": ci_hi,\n    \"conclusion\": concl,\n    \"n_countries_total\": int(df.iso3.nunique()),\n    \"complete_counts_by_year\": {str(k): int(v) for k, v in counts.items()},\n    \"effect_se_classical\": float(sm.OLS(y, X).fit().bse[1]),\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\n\n# Table 1\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[\"_o\"] = t.income_level.map({k: i for i, k in enumerate(order)}).fillna(99)\nt = t.sort_values([\"_o\", \"income_level\"]).drop(columns=\"_o\")\nt.round(4).to_csv(f\"{OUT}/table1.csv\", index=False)\n\n# Figure 1\nfig, ax = plt.subplots(figsize=(7, 5))\ncolors = dict(zip(order, [\"tab:red\", \"tab:orange\", \"tab:green\", \"tab:blue\"]))\nfor lvl, g in d.groupby(\"income_level\"):\n    ax.scatter(g.log_gdp_pc, g.life_expectancy, s=14, alpha=0.75, label=lvl, color=colors.get(lvl, \"grey\"))\nxs = np.linspace(x.min(), x.max(), 100)\nax.plot(xs, alpha + beta * xs, \"k-\", lw=1.5, label=f\"OLS: LE = {alpha:.1f} + {beta:.2f} ln(GDPpc)\")\nax.set_xlabel(\"ln(GDP per capita, PPP, constant 2021 int. $)\")\nax.set_ylabel(\"Life expectancy at birth (years)\")\nax.set_title(f\"Preston curve, {year} (n = {n} countries/economies)\")\nax.legend(fontsize=8, loc=\"lower right\")\nfig.tight_layout()\nfig.savefig(f\"{OUT}/figure1.png\", dpi=150)\n\nprint(f\"year={year} n={n} excluded_missing={n_excl_missing} beta={beta:.3f} se(HC3)={se:.3f} \"\n      f\"CI=[{ci_lo:.3f},{ci_hi:.3f}] R2={r2:.3f} RMSE base={baseline_rmse:.3f} model={model_rmse:.3f} -> {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 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> out/report.md <<'EOF'\n# F6-dev-01 — Income and life expectancy across countries (Preston curve)\n\n## Question\nDoes the cross-country relationship between real income per capita and life expectancy 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 = α + β·ln(GDPpc) + ε across countries/economies (equal weights), with heteroskedasticity-robust uncertainty. The interpretation is descriptive (associational), not causal.\n\n## Data and exclusions\nInputs: `data/wb_indicators.csv` (2,349 entity-year rows) and `data/wb_entities.csv` (296 rows) (`n_input_rows`). SHA-256 hashes match `data/README.md`. Full audit: `out/state/data_audit.md`.\n\nExclusions (`out/exclusions.csv`):\n1. I dropped the 44 aggregate entities, i.e. those with region = \"Aggregates\" (396 rows). That left 217 countries/economies.\n2. I selected the analysis year. In 2015–2022, 199 countries have both indicators each year; in 2023, 197 do. Every year therefore passes the ≥150 threshold, so the most recent year is used: `analysis_year` = 2023.\n3. I dropped countries missing an indicator in 2023: `n_excluded_missing` = 20. Every one of them is missing GDP per capita; no life-expectancy value is missing. They are ASM, CHI, CUB, ERI, GIB, GUM, IMN, LIE, MAF, MCO, MNP, NCL, PRK, PYF, SSD, SYR, VEN, VGB, VIR, YEM. Nothing was imputed.\n\n`n_analysed` = 197 (2349 − 396 − 1736 − 20). The data problems I found, and how each was handled, are listed in `data_issues`. Besides the aggregates and the missing values, they are: an implausibly volatile life-expectancy series for the Central African Republic in earlier years (its 2023 value of 57.4 is plausible and was kept), and life expectancy being reported for every territory, which suggests modelled estimates.\n\n## Method\n- **Baseline:** predict every country's life expectancy with the sample mean. Its RMSE is `baseline_metric`.\n- **Main model:** OLS of LE on a constant and ln(GDPpc) (natural log), one observation per country, unweighted (statsmodels).\n- **Uncertainty:** HC3 heteroskedasticity-robust standard error (`effect_se_type` = \"HC3\"). The 95% CI is β ± 1.96·SE (normal quantile).\n- **Fit statistics:** model fit is the RMSE of the residuals (`model_metric`). Pearson r and R² are also reported.\n- **Conclusion rule:** `positive_effect` if the 95% CI lies above zero.\n- **Reproducibility:** no randomness is used (`random_seed` = null). `out/analysis.py` regenerates `results.json`, `table1.csv`, `figure1.png` and `exclusions.csv`.\n\n## Results\n- **Slope:** `effect_estimate` = 5.27 years of life expectancy per log-point of GDP per capita. So a doubling of income is associated with about 0.693 × 5.27 ≈ 3.65 more years.\n- **Intercept:** `intercept` = 22.57 years.\n- **Correlation and fit:** `pearson_r` = 0.861 and `r_squared` = 0.742.\n- **Against the baseline:** the residual RMSE (`model_metric`) is 3.59 years, compared with 7.08 years for the mean-only baseline (`baseline_metric`). That is roughly half the prediction error.\n- **Conclusion:** `conclusion` = `positive_effect`. The Preston-curve association holds strongly in 2023.\n\nTable 1 (`out/table1.csv`) shows mean life expectancy rising 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). Figure 1 (`out/figure1.png`) shows the scatter with the fitted line.\n\n## Uncertainty\n- **HC3 robust SE:** `effect_se` = 0.211. The 95% CI is [`effect_ci_low` = 4.85, `effect_ci_high` = 5.68], which is far from zero.\n- **Classical SE:** the conventional OLS SE is similar (`effect_se_classical` = 0.222), so the inference does not depend on the heteroskedasticity correction.\n- **Caveat:** these standard errors treat countries as draws from a superpopulation. The 197 countries are nearly the whole population, so the CI reflects model or sampling uncertainty in that notional sense. It does not include measurement error in GDP or life expectancy.\n\n## Limitations and open issues\n- **Missing data are not random.** The 20 excluded economies include conflict-affected or sanctioned states (Syria, Yemen, South Sudan, Venezuela, North Korea, Eritrea, Cuba) and small rich territories (Monaco, Liechtenstein, Isle of Man). Their exclusion could shift the slope in either direction. I did not run a bounds or sensitivity analysis for it.\n- **The linear-in-log form is a simplification.** The scatter suggests curvature: a flatter slope at high incomes and large negative residuals among some middle-income African and oil economies (e.g. one lower-middle-income country at ~54.5 years). Nonlinearity, influential observations and leverage were not formally tested. The slope is a best-linear-projection summary, not a structural parameter.\n- **Measurement quality.** Life expectancy for many countries comes from modelled UN WPP estimates, and 2023 values may be provisional and revised later. The Central African Republic's series is erratic. PPP GDP for low-income and conflict economies is also uncertain. Errors in ln(GDPpc) would bias β toward zero.\n- **It is one year only.** Stability across 2015–2022 (including the COVID-19 years) was not examined, because the task asks for a single cross-section.\n- **No causal interpretation.** Income is confounded with health systems, education, governance and disease environment, and reverse causality is plausible. Nothing here identifies the effect of raising income.\n- **Equal weighting.** Each country counts once regardless of population. A population-weighted slope could differ and was not estimated.\n- **What would invalidate the result:** a misclassified aggregate left in the sample (checked: every iso3 matched the metadata, and the aggregates were removed); a different indicator vintage; or a data error in the 2023 extract. None of these was detected, but the data were not cross-checked against an independent source.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The analysis year is 2023; 197 countries/economies are analysed and 20 are excluded for missing GDP per capita.\",\"results_keys\":[\"analysis_year\",\"n_analysed\",\"n_excluded_missing\"],\"status\":\"supported\",\"caveat\":\"Missingness is concentrated in territories and conflict/sanctioned economies.\"},\n {\"id\":\"C2\",\"text\":\"Life expectancy rises by about 5.27 years per log-point of GDP per capita (OLS slope), with a 95% HC3 CI of [4.85, 5.68] that excludes zero; conclusion positive_effect.\",\"results_keys\":[\"effect_estimate\",\"effect_se\",\"effect_ci_low\",\"effect_ci_high\",\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Associational only; SE treats countries as a sample from a superpopulation.\"},\n {\"id\":\"C3\",\"text\":\"The relationship is strong: Pearson r = 0.861, R^2 = 0.742.\",\"results_keys\":[\"pearson_r\",\"r_squared\"],\"status\":\"supported\",\"caveat\":\"Linear-in-log specification; curvature not tested.\"},\n {\"id\":\"C4\",\"text\":\"Log income roughly halves prediction error relative to the mean-only baseline (RMSE 3.59 vs 7.08 years).\",\"results_keys\":[\"model_metric\",\"baseline_metric\"],\"status\":\"supported\",\"caveat\":\"In-sample fit, not out-of-sample prediction.\"},\n {\"id\":\"C5\",\"text\":\"Inference is insensitive to the heteroskedasticity correction (classical SE 0.222 vs HC3 0.211).\",\"results_keys\":[\"effect_se\",\"effect_se_classical\"],\"status\":\"supported\",\"caveat\":\"Other robustness (outliers, weighting, other years) not evaluated.\"}\n]\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written with estimand, outputs, rules, invalidity conditions\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 of all inputs computed and match README/bundle\",\"schema, ranges, missingness, duplicates (0), iso3 matching (all matched) audited\",\"exclusion ledger written and sums to n_analysed (2349-2152=197)\",\"no availability-rule violations found\"],\"notes\":\"see data_audit.md\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"mean-only baseline RMSE computed over the same 197 countries: baseline_metric=7.076\"],\"notes\":\"computed inside analysis.py\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, writes results.json/table1.csv/figure1.png/exclusions.csv\",\"no randomness; random_seed null\",\"Agg backend\"],\"notes\":\"OLS + HC3\"},\n\"P4\":{\"status\":\"passed\",\"checks\":[\"clean re-run produced byte-identical results.json\",\"12 consistency checks all PASS (see checks.md)\",\"data/ hashes unchanged\"],\"notes\":\"\"},\n\"P5\":{\"status\":\"passed\",\"checks\":[\"report.md has the six required sections in order, quantities cite results keys\",\"claims.json: 5 claims, all keys exist in results.json\"],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-29: P2-P4 passed (baseline, OLS HC3, clean rerun identical, checks pass). P5 report+claims written; all phases passed.\" >> out/state/log.md\n~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('out/results.json'));c=json.load(open('out/claims.json'))\nprint(all(k in r for x in c for k in x['results_keys']))\nimport re;print(re.findall(r'^## .*',open('out/report.md').read(),re.M))\"","description":"Write report, claims, final phase ledger; validate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":855,"cache_read_input_tokens":23716,"output_tokens":3672,"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":855},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXjzQbG3WugPeqV5wju7","requestRef":"76a95ef1-1d72-4bb2-a283-406bf78da5c6","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4b25393f-ff8e-40c5-a17c-3855a7a27a56","timestamp":"2026-10-30T03:51:58.897Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0016","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","version":"2.1.314","gitBranch":"HEAD"}79{"parentUuid":"4b25393f-ff8e-40c5-a17c-3855a7a27a56","isSidechain":false,"promptId":"3ff5636a-5eeb-458b-90a1-abc20dff2ccd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01K1caxzSmfYBZh1TCN91RxZ","type":"tool_result","content":"True\n['## Question', '## Data and exclusions', '## Method', '## Results', '## Uncertainty', '## Limitations and open issues']","is_error":false}]},"uuid":"e2e7a98a-1435-49e0-b6c7-e25f67e8d43e","timestamp":"2026-10-30T03:51:58.970Z","toolUseResult":{"stdout":"True\n['## Question', '## Data and exclusions', '## Method', '## Results', '## Uncertainty', '## Limitations and open issues']","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"4b25393f-ff8e-40c5-a17c-3855a7a27a56","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0016","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","version":"2.1.314","gitBranch":"HEAD"}80{"type":"api-request-blob","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","hash":"c8fc99e0ce23aea13cfa29e766aa9dea4390d9005a82fabb13134a228b275779","message":{"role":"assistant","content":[{"type":"text","text":"Now the report, claims, and final ledger."},{"type":"tool_use","id":"toolu_01K1caxzSmfYBZh1TCN91RxZ","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F6-dev-01 — Income and life expectancy across countries (Preston curve)\n\n## Question\nDoes the cross-country relationship between real income per capita and life expectancy 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 = α + β·ln(GDPpc) + ε across countries/economies (equal weights), with heteroskedasticity-robust uncertainty. The interpretation is descriptive (associational), not causal.\n\n## Data and exclusions\nInputs: `data/wb_indicators.csv` (2,349 entity-year rows) and `data/wb_entities.csv` (296 rows) (`n_input_rows`). SHA-256 hashes match `data/README.md`. Full audit: `out/state/data_audit.md`.\n\nExclusions (`out/exclusions.csv`):\n1. I dropped the 44 aggregate entities, i.e. those with region = \"Aggregates\" (396 rows). That left 217 countries/economies.\n2. I selected the analysis year. In 2015–2022, 199 countries have both indicators each year; in 2023, 197 do. Every year therefore passes the ≥150 threshold, so the most recent year is used: `analysis_year` = 2023.\n3. I dropped countries missing an indicator in 2023: `n_excluded_missing` = 20. Every one of them is missing GDP per capita; no life-expectancy value is missing. They are ASM, CHI, CUB, ERI, GIB, GUM, IMN, LIE, MAF, MCO, MNP, NCL, PRK, PYF, SSD, SYR, VEN, VGB, VIR, YEM. Nothing was imputed.\n\n`n_analysed` = 197 (2349 − 396 − 1736 − 20). The data problems I found, and how each was handled, are listed in `data_issues`. Besides the aggregates and the missing values, they are: an implausibly volatile life-expectancy series for the Central African Republic in earlier years (its 2023 value of 57.4 is plausible and was kept), and life expectancy being reported for every territory, which suggests modelled estimates.\n\n## Method\n- **Baseline:** predict every country's life expectancy with the sample mean. Its RMSE is `baseline_metric`.\n- **Main model:** OLS of LE on a constant and ln(GDPpc) (natural log), one observation per country, unweighted (statsmodels).\n- **Uncertainty:** HC3 heteroskedasticity-robust standard error (`effect_se_type` = \"HC3\"). The 95% CI is β ± 1.96·SE (normal quantile).\n- **Fit statistics:** model fit is the RMSE of the residuals (`model_metric`). Pearson r and R² are also reported.\n- **Conclusion rule:** `positive_effect` if the 95% CI lies above zero.\n- **Reproducibility:** no randomness is used (`random_seed` = null). `out/analysis.py` regenerates `results.json`, `table1.csv`, `figure1.png` and `exclusions.csv`.\n\n## Results\n- **Slope:** `effect_estimate` = 5.27 years of life expectancy per log-point of GDP per capita. So a doubling of income is associated with about 0.693 × 5.27 ≈ 3.65 more years.\n- **Intercept:** `intercept` = 22.57 years.\n- **Correlation and fit:** `pearson_r` = 0.861 and `r_squared` = 0.742.\n- **Against the baseline:** the residual RMSE (`model_metric`) is 3.59 years, compared with 7.08 years for the mean-only baseline (`baseline_metric`). That is roughly half the prediction error.\n- **Conclusion:** `conclusion` = `positive_effect`. The Preston-curve association holds strongly in 2023.\n\nTable 1 (`out/table1.csv`) shows mean life expectancy rising 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). Figure 1 (`out/figure1.png`) shows the scatter with the fitted line.\n\n## Uncertainty\n- **HC3 robust SE:** `effect_se` = 0.211. The 95% CI is [`effect_ci_low` = 4.85, `effect_ci_high` = 5.68], which is far from zero.\n- **Classical SE:** the conventional OLS SE is similar (`effect_se_classical` = 0.222), so the inference does not depend on the heteroskedasticity correction.\n- **Caveat:** these standard errors treat countries as draws from a superpopulation. The 197 countries are nearly the whole population, so the CI reflects model or sampling uncertainty in that notional sense. It does not include measurement error in GDP or life expectancy.\n\n## Limitations and open issues\n- **Missing data are not random.** The 20 excluded economies include conflict-affected or sanctioned states (Syria, Yemen, South Sudan, Venezuela, North Korea, Eritrea, Cuba) and small rich territories (Monaco, Liechtenstein, Isle of Man). Their exclusion could shift the slope in either direction. I did not run a bounds or sensitivity analysis for it.\n- **The linear-in-log form is a simplification.** The scatter suggests curvature: a flatter slope at high incomes and large negative residuals among some middle-income African and oil economies (e.g. one lower-middle-income country at ~54.5 years). Nonlinearity, influential observations and leverage were not formally tested. The slope is a best-linear-projection summary, not a structural parameter.\n- **Measurement quality.** Life expectancy for many countries comes from modelled UN WPP estimates, and 2023 values may be provisional and revised later. The Central African Republic's series is erratic. PPP GDP for low-income and conflict economies is also uncertain. Errors in ln(GDPpc) would bias β toward zero.\n- **It is one year only.** Stability across 2015–2022 (including the COVID-19 years) was not examined, because the task asks for a single cross-section.\n- **No causal interpretation.** Income is confounded with health systems, education, governance and disease environment, and reverse causality is plausible. Nothing here identifies the effect of raising income.\n- **Equal weighting.** Each country counts once regardless of population. A population-weighted slope could differ and was not estimated.\n- **What would invalidate the result:** a misclassified aggregate left in the sample (checked: every iso3 matched the metadata, and the aggregates were removed); a different indicator vintage; or a data error in the 2023 extract. None of these was detected, but the data were not cross-checked against an independent source.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The analysis year is 2023; 197 countries/economies are analysed and 20 are excluded for missing GDP per capita.\",\"results_keys\":[\"analysis_year\",\"n_analysed\",\"n_excluded_missing\"],\"status\":\"supported\",\"caveat\":\"Missingness is concentrated in territories and conflict/sanctioned economies.\"},\n {\"id\":\"C2\",\"text\":\"Life expectancy rises by about 5.27 years per log-point of GDP per capita (OLS slope), with a 95% HC3 CI of [4.85, 5.68] that excludes zero; conclusion positive_effect.\",\"results_keys\":[\"effect_estimate\",\"effect_se\",\"effect_ci_low\",\"effect_ci_high\",\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Associational only; SE treats countries as a sample from a superpopulation.\"},\n {\"id\":\"C3\",\"text\":\"The relationship is strong: Pearson r = 0.861, R^2 = 0.742.\",\"results_keys\":[\"pearson_r\",\"r_squared\"],\"status\":\"supported\",\"caveat\":\"Linear-in-log specification; curvature not tested.\"},\n {\"id\":\"C4\",\"text\":\"Log income roughly halves prediction error relative to the mean-only baseline (RMSE 3.59 vs 7.08 years).\",\"results_keys\":[\"model_metric\",\"baseline_metric\"],\"status\":\"supported\",\"caveat\":\"In-sample fit, not out-of-sample prediction.\"},\n {\"id\":\"C5\",\"text\":\"Inference is insensitive to the heteroskedasticity correction (classical SE 0.222 vs HC3 0.211).\",\"results_keys\":[\"effect_se\",\"effect_se_classical\"],\"status\":\"supported\",\"caveat\":\"Other robustness (outliers, weighting, other years) not evaluated.\"}\n]\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written with estimand, outputs, rules, invalidity conditions\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 of all inputs computed and match README/bundle\",\"schema, ranges, missingness, duplicates (0), iso3 matching (all matched) audited\",\"exclusion ledger written and sums to n_analysed (2349-2152=197)\",\"no availability-rule violations found\"],\"notes\":\"see data_audit.md\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"mean-only baseline RMSE computed over the same 197 countries: baseline_metric=7.076\"],\"notes\":\"computed inside analysis.py\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, writes results.json/table1.csv/figure1.png/exclusions.csv\",\"no randomness; random_seed null\",\"Agg backend\"],\"notes\":\"OLS + HC3\"},\n\"P4\":{\"status\":\"passed\",\"checks\":[\"clean re-run produced byte-identical results.json\",\"12 consistency checks all PASS (see checks.md)\",\"data/ hashes unchanged\"],\"notes\":\"\"},\n\"P5\":{\"status\":\"passed\",\"checks\":[\"report.md has the six required sections in order, quantities cite results keys\",\"claims.json: 5 claims, all keys exist in results.json\"],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-29: P2-P4 passed (baseline, OLS HC3, clean rerun identical, checks pass). P5 report+claims written; all phases passed.\" >> out/state/log.md\n~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('out/results.json'));c=json.load(open('out/claims.json'))\nprint(all(k in r for x in c for k in x['results_keys']))\nimport re;print(re.findall(r'^## .*',open('out/report.md').read(),re.M))\"","description":"Write report, claims, final phase ledger; validate"}}]}}81{"type":"api-request","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","id":"7bca04e7-f693-4463-8942-48722e1ce325","timestamp":"2026-10-30T03:51:58.973Z","version":"2.1.314","querySource":"sdk","shapeHash":"44d4b330eab5c7a7324586f4f5db3a4e7c34456a294bfb55428af5658878540b","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"76a95ef1-1d72-4bb2-a283-406bf78da5c6","keep":24,"tail":["c8fc99e0ce23aea13cfa29e766aa9dea4390d9005a82fabb13134a228b275779","579a5f4555f7310ee7b00b462a00e3e74cf3634be14217f0adb037c6a02d9b39"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",25,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"66bb5c05f65705120c3e2d80965647aea6f3f0b823bff92be6d530c5f37667a2"}82{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"e2e7a98a-1435-49e0-b6c7-e25f67e8d43e","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f"}83{"type":"atis-latch","atis":"","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f"}84{"parentUuid":"e2e7a98a-1435-49e0-b6c7-e25f67e8d43e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXk2iemRduM1K6xm2DPN","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3724,"cache_read_input_tokens":24571,"output_tokens":1048,"output_tokens_details":{"thinking_tokens":92},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":3724},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":7027,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXk2iPdwdQK5cDqMhS42","requestRef":"7bca04e7-f693-4463-8942-48722e1ce325","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ca18b376-2c55-4f7e-9b38-4bcf573cc92e","timestamp":"2026-10-30T03:52:07.593Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0016","sessionId":"19c13962-3714-4f87-bf1f-7bce5b43d66f","version":"2.1.314","gitBranch":"HEAD"}85{"parentUuid":"ca18b376-2c55-4f7e-9b38-4bcf573cc92e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXk2iemRduM1K6xm2DPN","type":"message","role":"assistant","content":[{"type":"text","text":"I've finished the task: the income–life expectancy relationship holds strongly in 2023. A clean re-run of `out/analysis.py` reproduced `out/results.json` byte for byte, and all 12 consistency checks passed.\n\n**Data used:**\n- **Countries:** I removed the 44 regional and income-group aggregates, which left 217 countries/economies.\n- **Year:** every year from 2015 to 2023 has at least 150 countries with both indicators, so the rule picks the most recent one, 2023.\n- **Exclusions:** 20 countries were dropped for missing GDP per capita in 2023, leaving 197 in the estimate. No life-expectancy values were missing, and nothing was imputed.\n\n**Results:**\n\n| Measure | Value |\n|---|---|\n| Slope β | 5.27 years per log-point of income (a doubling of income ≈ +3.65 years) |\n| Robust standard error (HC3) | 0.211 |\n| 95% confidence interval | [4.85, 5.68] |\n| Intercept | 22.57 years |\n| Correlation r | 0.861 |\n| R² | 0.742 |\n| Prediction error (RMSE), model | 3.59 years |\n| Prediction error (RMSE), mean-only baseline | 7.08 years |\n\nThe interval sits well above zero, so the conclusion code is `positive_effect`. The ordinary standard error is similar (0.222), so the result doesn't depend on the robustness correction. Mean life expectancy by income group runs from 63.2 years (low income, 20 countries) to 79.7 years (high income, 73 countries).\n\n**Limitations:**\n- **Excluded countries are not a random set.** They include conflict-hit or sanctioned states (Syria, Yemen, South Sudan, Venezuela, North Korea) and small rich territories (Monaco, Liechtenstein). Leaving them out could move the slope either way, and I didn't test how much.\n- **The straight-line-in-log fit is a simplification.** The scatter suggests the curve flattens at high incomes, and I didn't formally test for curvature or influential points.\n- **Data quality is uneven.** Life expectancy appears to be modelled estimates, and 2023 values may be revised. The Central African Republic's earlier figures swing wildly (18.8 years in 2022); its 2023 value of 57.4 looks plausible and was kept.\n- **Scope:** this is one year, with each country weighted equally regardless of population. The confidence interval treats countries as a sample, even though 197 is close to all of them. The relationship is an association, not evidence that raising income causes longer lives.\n\nEverything is in `out/`. The report is `report.md`; `figure1.png` and `table1.csv` are the required figure and table; `analysis.py` regenerates the results. 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