spb/wp29_uqo

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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\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n\nSEPARATE REVIEW (additional)\n- After the gate passes, a separate reviewer session that has not seen your work in progress will examine `TASK.md` and everything under `out/` and may return a list of issues (file `out/review/review.json`). You will then be resumed once to address them: fix what is justified, explain in `out/report.md` (Limitations and open issues) what you disagree with, re-run the gate, and update `out/claims.json` and `out/state/phases.json`. The reviewer has no reference answers; its role is to challenge unsupported claims, violated information rules, unverified computations, and inconsistencies between your report and your results.\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 user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0014","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91"}12{"parentUuid":"39256d4f-dfa4-482d-8774-9fa57b38878b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXjv153mHE3dbMQiUMAu","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":4,"cache_creation_input_tokens":2811,"cache_read_input_tokens":1504,"output_tokens":129,"output_tokens_details":{"thinking_tokens":23},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2811},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":417,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXjuznBaXrURtjSwNbSD","requestRef":"1d323b73-4bb7-443f-86bb-a7be21a249b0","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e09d1bc5-10df-42e4-a8c2-ed97be45b3b5","timestamp":"2026-10-30T03:50:29.317Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0014","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"e09d1bc5-10df-42e4-a8c2-ed97be45b3b5","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXjv153mHE3dbMQiUMAu","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DrhFhqYbFSmQxgYzQUmL97","name":"Bash","input":{"command":"cat TASK.md; 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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*. 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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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{\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}{\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}==> data/wb_entities.csv <==\niso3,name,region,income_level,lending_type\nABW,Aruba,Latin America & Caribbean,High income,Not classified\nAFE,Africa Eastern and Southern,Aggregates,Aggregates,Aggregates\nAFG,Afghanistan,\"Middle East, North Africa, Afghanistan & Pakistan\",Low income,IDA\nAFR,Africa,Aggregates,Aggregates,Aggregates\n\n==> data/wb_indicators.csv <==\niso3,name,year,gdp_pc_ppp,life_expectancy\nABW,Aruba,2015,36753.404173,75.405000\nABW,Aruba,2016,36926.113069,75.540000\nABW,Aruba,2017,38213.289239,75.620000\nABW,Aruba,2018,39378.228411,75.880000\n     297 data/wb_entities.csv\n    2350 data/wb_indicators.csv\n    2647 total\n76cb5af488d97339fb6319a5c9a7ba6ec98112ab6a0a8116e7bec6acd1f9f67a  data/README.md\n06af6aec15122fecea5be5eb21464c505188ecc135bc55104a40f20f65040f8d  data/wb_entities.csv\n6823f02ed4c20fd12a54b8ca4454f85ba333f22337e2a9fc57c3cabccfdbfcd0  data/wb_indicators.csv","is_error":false}]},"uuid":"d87f5ce9-8abb-404d-82cc-b8b674194195","timestamp":"2026-10-30T03:50:32.202Z","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{\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}{\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}==> data/wb_entities.csv <==\niso3,name,region,income_level,lending_type\nABW,Aruba,Latin America & Caribbean,High income,Not classified\nAFE,Africa Eastern and Southern,Aggregates,Aggregates,Aggregates\nAFG,Afghanistan,\"Middle East, North Africa, Afghanistan & Pakistan\",Low income,IDA\nAFR,Africa,Aggregates,Aggregates,Aggregates\n\n==> data/wb_indicators.csv <==\niso3,name,year,gdp_pc_ppp,life_expectancy\nABW,Aruba,2015,36753.404173,75.405000\nABW,Aruba,2016,36926.113069,75.540000\nABW,Aruba,2017,38213.289239,75.620000\nABW,Aruba,2018,39378.228411,75.880000\n     297 data/wb_entities.csv\n    2350 data/wb_indicators.csv\n    2647 total\n76cb5af488d97339fb6319a5c9a7ba6ec98112ab6a0a8116e7bec6acd1f9f67a  data/README.md\n06af6aec15122fecea5be5eb21464c505188ecc135bc55104a40f20f65040f8d  data/wb_entities.csv\n6823f02ed4c20fd12a54b8ca4454f85ba333f22337e2a9fc57c3cabccfdbfcd0  data/wb_indicators.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"d6b37a41-cd8a-45f2-b8fd-a83f9e4a9774","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0014","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","version":"2.1.314","gitBranch":"HEAD"}21{"type":"api-request-blob","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","hash":"b81469c9628be278bf3b36753605ae6888ac6899b59506397d8099ab5ab4e085","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01PLrGaHA1TtPPJF4RQQDvvC","name":"Bash","input":{"command":"cat data/README.md; cat gate_spec.json bundle.json; head -5 data/*.csv; wc -l data/*.csv; shasum -a 256 data/*","description":"Inspect data files and specs"}}]}}22{"type":"api-request","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","id":"4e3ba354-d48b-4527-a11b-81da4315224e","timestamp":"2026-10-30T03:50:32.206Z","version":"2.1.314","querySource":"sdk","shapeHash":"a1a7fae70b72e9ac39b3275721793ee12a68cf4c21397754b79cb5e12e871183","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":"b3abb3fc-cbef-4d34-94c5-4bdfb5e904d2","keep":4,"tail":["b81469c9628be278bf3b36753605ae6888ac6899b59506397d8099ab5ab4e085","19044923ab489d23817e5069391be58d0a01380f3d1199f057a0a7861c851c4c"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"d9f3e70021c115b949458c059c357657a28286a7899f540f403b2f3a89c5fd2b"}23{"parentUuid":"d87f5ce9-8abb-404d-82cc-b8b674194195","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXjvKixMJrL3itnwbk13","type":"message","role":"assistant","content":[{"type":"text","text":"Hashes match. Now a quick audit."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2969,"cache_read_input_tokens":6958,"output_tokens":598,"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":2969},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXjvKU5J9VmSVcwukE2m","requestRef":"4e3ba354-d48b-4527-a11b-81da4315224e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"47548b4d-da91-4e93-8a1a-51c734f39581","timestamp":"2026-10-30T03:50:33.569Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0014","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"47548b4d-da91-4e93-8a1a-51c734f39581","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXjvKixMJrL3itnwbk13","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01EduKcSDw7xchTqu1J7iCPR","name":"Bash","input":{"command":"mkdir -p out/state; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\ne=pd.read_csv('data/wb_entities.csv',keep_default_na=False,na_values=['']); d=pd.read_csv('data/wb_indicators.csv',keep_default_na=False,na_values=[''])\nprint(e.isna().sum()); print(e.region.value_counts()); print(e.income_level.value_counts())\nprint(d.dtypes); print(d.isna().sum()); print(d.year.value_counts().sort_index())\nprint('dup',d.duplicated(['iso3','year']).sum(), e.iso3.duplicated().sum())\nprint('not in entities', sorted(set(d.iso3)-set(e.iso3)))\nprint('entities not in ind', sorted(set(e.iso3)-set(d.iso3)))\nm=d.merge(e[['iso3','region','income_level']],on='iso3',how='left')\nc=m[m.region!='Aggregates']\nprint('countries',c.iso3.nunique())\nprint(c.groupby('year').apply(lambda g: pd.Series({'n':len(g),'both':(g.gdp_pc_ppp.notna()&g.life_expectancy.notna()).sum()})))\nprint(d.describe())\nprint(c[c.income_level.isin(['','Not classified'])|c.income_level.isna()][['iso3','name','income_level']].drop_duplicates())\nprint(e[e.iso3.str.len()!=3])\n\"","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":2969,"cache_read_input_tokens":6958,"output_tokens":598,"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":2969},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXjvKU5J9VmSVcwukE2m","requestRef":"4e3ba354-d48b-4527-a11b-81da4315224e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b158df82-792e-4800-8fa7-b81918eb6988","timestamp":"2026-10-30T03:50:37.801Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0014","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","version":"2.1.314","gitBranch":"HEAD"}25{"parentUuid":"b158df82-792e-4800-8fa7-b81918eb6988","isSidechain":false,"promptId":"cced29be-8a0c-46b8-9fc9-c2501f16d592","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01EduKcSDw7xchTqu1J7iCPR","type":"tool_result","content":"<string>:12: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\niso3            0\nname            0\nregion          0\nincome_level    0\nlending_type    0\ndtype: int64\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\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\nyear\n2015    261\n2016    261\n2017    261\n2018    261\n2019    261\n2020    261\n2021    261\n2022    261\n2023    261\nName: count, dtype: int64\ndup 0 0\nnot in entities []\nentities 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']\ncountries 217\n        n  both\nyear           \n2015  217   199\n2016  217   199\n2017  217   199\n2018  217   199\n2019  217   199\n2020  217   199\n2021  217   199\n2022  217   199\n2023  217   197\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, income_level]\nIndex: []\nEmpty DataFrame\nColumns: [iso3, name, region, income_level, lending_type]\nIndex: []","is_error":false}]},"uuid":"b687bec9-a2cd-4e62-9ee6-015a3a1319b0","timestamp":"2026-10-30T03:50:38.110Z","toolUseResult":{"stdout":"<string>:12: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\niso3            0\nname            0\nregion          0\nincome_level    0\nlending_type    0\ndtype: int64\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\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\nyear\n2015    261\n2016    261\n2017    261\n2018    261\n2019    261\n2020    261\n2021    261\n2022    261\n2023    261\nName: count, dtype: int64\ndup 0 0\nnot in entities []\nentities 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']\ncountries 217\n        n  both\nyear           \n2015  217   199\n2016  217   199\n2017  217   199\n2018  217   199\n2019  217   199\n2020  217   199\n2021  217   199\n2022  217   199\n2023  217   197\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, income_level]\nIndex: []\nEmpty DataFrame\nColumns: [iso3, name, region, income_level, lending_type]\nIndex: []","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"b158df82-792e-4800-8fa7-b81918eb6988","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0014","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","version":"2.1.314","gitBranch":"HEAD"}26{"type":"api-request-blob","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","hash":"6f545af86746a8d4e5fefb4186e93ada357f037720b84f585be83193eb2183aa","message":{"role":"assistant","content":[{"type":"text","text":"Hashes match. Now a quick audit."},{"type":"tool_use","id":"toolu_01EduKcSDw7xchTqu1J7iCPR","name":"Bash","input":{"command":"mkdir -p out/state; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\ne=pd.read_csv('data/wb_entities.csv',keep_default_na=False,na_values=['']); d=pd.read_csv('data/wb_indicators.csv',keep_default_na=False,na_values=[''])\nprint(e.isna().sum()); print(e.region.value_counts()); print(e.income_level.value_counts())\nprint(d.dtypes); print(d.isna().sum()); print(d.year.value_counts().sort_index())\nprint('dup',d.duplicated(['iso3','year']).sum(), e.iso3.duplicated().sum())\nprint('not in entities', sorted(set(d.iso3)-set(e.iso3)))\nprint('entities not in ind', sorted(set(e.iso3)-set(d.iso3)))\nm=d.merge(e[['iso3','region','income_level']],on='iso3',how='left')\nc=m[m.region!='Aggregates']\nprint('countries',c.iso3.nunique())\nprint(c.groupby('year').apply(lambda g: pd.Series({'n':len(g),'both':(g.gdp_pc_ppp.notna()&g.life_expectancy.notna()).sum()})))\nprint(d.describe())\nprint(c[c.income_level.isin(['','Not classified'])|c.income_level.isna()][['iso3','name','income_level']].drop_duplicates())\nprint(e[e.iso3.str.len()!=3])\n\"","description":"Audit data schema, missingness, coverage"}}]}}27{"type":"api-request","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","id":"3a62e4fb-1fd6-450c-8308-c336e37e7670","timestamp":"2026-10-30T03:50:38.115Z","version":"2.1.314","querySource":"sdk","shapeHash":"a1a7fae70b72e9ac39b3275721793ee12a68cf4c21397754b79cb5e12e871183","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":"4e3ba354-d48b-4527-a11b-81da4315224e","keep":6,"tail":["6f545af86746a8d4e5fefb4186e93ada357f037720b84f585be83193eb2183aa","516baf49527cb177eb4a21e124e354611a53d2b6b38e3224fd437bbf9e1b8cef"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"1cd92d0d771485b03dc3ca34a8bcd96659e8fb2bd616a6426b3fbe2cc075a65c"}28{"parentUuid":"b687bec9-a2cd-4e62-9ee6-015a3a1319b0","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXjvm1ynTJZJpML6Gd6M","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSngcKEAgTGAI4AUIIdGhpbmtpbmcSDKC8D1RDpkfXPiOFmBoMWeZDIrMfNMkHigUjIjCaLiN1VUKGMZmeTOGYBpYk2ox5JE6OvF33kHqSqvZqUNolGatf0NuVMt4YBGrhtkYquwazpKSj5YMKqpW14h5xcJwM5ytLFbenNtsJ3sKT46YQYV6a3sdma5lcPM36bRuMSgRzljy01cuvr64F0ETpsuqadefwY9pY4KfUYMO4S5jv3T1LOBU1QuWpMKoCfk5G4oG552eIGMRawbNviAoGIPQg+scFEjSVS4pKnXd91jS/cFExoErYZj1LKaCqEbMxyTpNaKK5ClDI8Fh7JY0+FIaD0mHzeWj0s/LFh6FRJIMasxLKHEF9TDVXNYt31eiIhrs9Ao5IP4ah2ygft3yDd7W3FI4tDi8adoSmjRovvT+s3J+Pv+JrFP1shf5sjBYiNuo3J4CPmnjRGZKJnB+KrqOt9TqPSluaFmZBEAg5UJ33c76AvxAppKIzzwmLPT1u9S1E0ioT+Y1DIwxeG3VdRz0YnZ9wR16wo+HKFXfJsoukh5OaRUJT+yoi6Ix0mtusJdKuvNzk3T5Uyhhvj+uxqTXh6Xd4T30QXh9UMOgHc2ScAv0ey1ehWx+EX4oyk6nSr1rEWYStYuxY52nnDdFtuTAwafzHINo8l96cEFkIByDkkKgqTKaYt0Z5Qx+iPfytzTJb2gZZ8kGpAjgYm7Gw54G3icMGHj33Abj3l+KKd3XQaX6UK/rfGeB/FR/H+f8QsD+f71jbwpg3mohlw6668iQ6ZGvxy/V1KJ8i/cYcfKX/k3pbuq+bvgtjS+0Y8ophNORuzfSD64Fnok+qNFsYgkjAqV26LvCS0K/pZ9Ea4I20lWHUiYX+9ms0zGD4Qqho9VdE2Cvl6DT8lsdFL2cqcNWp30569EupFs4Z71UCgSZyarMmIoVM4e49qeGXTfqhMXJ7G3KgeVj/+hi1zzmH8FNQhHLu8twcEpqRLdYs4iKr0fXbVbLEpm5Z5zUvKYzeM0gsu0Gk6YM0S/43Y9G00YoOoTxwkCRM22BjPbG524W+7Dx5mggHlGKVLxSd2GZ/IUO6+qP1k7k9m+cd/NNU+KxlM+Dh6tDWozA7e8209CwhfFxQ6eluXOKCFrU+YQi6miOiCRiJFIEwCkQpXeWotEbW1ONOsux/GzZgcU9McgSYzhC8CXgo7f0XFWhOk97oE6sjcfAUGHBIbTj7sxgB"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1625,"cache_read_input_tokens":9927,"output_tokens":601,"output_tokens_details":{"thinking_tokens":82},"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":1625},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":434,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXjvki882CSriKQBmCXe","requestRef":"3a62e4fb-1fd6-450c-8308-c336e37e7670","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5c5aeefa-b7d5-4322-8158-a389e90a155b","timestamp":"2026-10-30T03:50:40.177Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0014","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"5c5aeefa-b7d5-4322-8158-a389e90a155b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXjvm1ynTJZJpML6Gd6M","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012QWtsYxsBbCcYiNroN7xmo","name":"Bash","input":{"command":"~/wp29/venv/bin/python -c \"\nimport pandas as pd\ne=pd.read_csv('data/wb_entities.csv',keep_default_na=False,na_values=['']); d=pd.read_csv('data/wb_indicators.csv',keep_default_na=False,na_values=[''])\nm=d.merge(e[['iso3','region','income_level']],on='iso3')\nc=m[m.region!='Aggregates']\nprint(c.sort_values('life_expectancy').head(12))\nprint(c.sort_values('life_expectancy').tail(5))\nc=c.sort_values(['iso3','year']); c['dle']=c.groupby('iso3').life_expectancy.diff(); c['dg']=c.groupby('iso3').gdp_pc_ppp.pct_change(fill_method=None)\nprint(c.loc[c.dle.abs()>4]); print(c.loc[c.dg.abs()>0.3])\ny=c[c.year==2023]; print(y[y.gdp_pc_ppp.isna()|y.life_expectancy.isna()][['iso3','name','income_level','gdp_pc_ppp','life_expectancy']])\nprint(y.gdp_pc_ppp.describe())\nprint((c.life_expectancy.round(6)==c.life_expectancy).all())\n\"","description":"Check anomalies and 2023 missing countries"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1625,"cache_read_input_tokens":9927,"output_tokens":601,"output_tokens_details":{"thinking_tokens":82},"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":1625},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXjvki882CSriKQBmCXe","requestRef":"3a62e4fb-1fd6-450c-8308-c336e37e7670","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"52b96107-84bd-49b6-b39d-e5f70ffe93ab","timestamp":"2026-10-30T03:50:43.875Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0014","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","version":"2.1.314","gitBranch":"HEAD"}30{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"52b96107-84bd-49b6-b39d-e5f70ffe93ab","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91"}31{"type":"atis-latch","atis":"","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91"}32{"parentUuid":"52b96107-84bd-49b6-b39d-e5f70ffe93ab","isSidechain":false,"promptId":"cced29be-8a0c-46b8-9fc9-c2501f16d592","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012QWtsYxsBbCcYiNroN7xmo","type":"tool_result","content":"     iso3                      name  ...              region         income_level\n313   CAF  Central African Republic  ...  Sub-Saharan Africa           Low income\n310   CAF  Central African Republic  ...  Sub-Saharan Africa           Low income\n1910  SSD               South Sudan  ...  Sub-Saharan Africa           Low income\n1909  SSD               South Sudan  ...  Sub-Saharan Africa           Low income\n1908  SSD               South Sudan  ...  Sub-Saharan Africa           Low income\n312   CAF  Central African Republic  ...  Sub-Saharan Africa           Low income\n308   CAF  Central African Republic  ...  Sub-Saharan Africa           Low income\n311   CAF  Central African Republic  ...  Sub-Saharan Africa           Low income\n307   CAF  Central African Republic  ...  Sub-Saharan Africa           Low income\n2025  TCD                      Chad  ...  Sub-Saharan Africa           Low income\n2026  TCD                      Chad  ...  Sub-Saharan Africa           Low income\n1233  LSO                   Lesotho  ...  Sub-Saharan Africa  Lower middle income\n\n[12 rows x 7 columns]\n     iso3    name  year  ...  life_expectancy                 region income_level\n1307  MCO  Monaco  2017  ...           85.878  Europe & Central Asia  High income\n1308  MCO  Monaco  2018  ...           86.084  Europe & Central Asia  High income\n1310  MCO  Monaco  2020  ...           86.089  Europe & Central Asia  High income\n1309  MCO  Monaco  2019  ...           86.151  Europe & Central Asia  High income\n1313  MCO  Monaco  2023  ...           86.372  Europe & Central Asia  High income\n\n[5 rows x 7 columns]\n     iso3                      name  ...     dle        dg\n25    AFG               Afghanistan  ...   5.200 -0.075767\n59    AND                   Andorra  ...  -4.680 -0.122238\n257   BOL                   Bolivia  ...  -4.914 -0.137899\n259   BOL                   Bolivia  ...   6.007  0.025465\n303   BWA                  Botswana  ...  -4.340  0.102596\n304   BWA                  Botswana  ...   5.446  0.038236\n308   CAF  Central African Republic  ...  -5.768  0.027861\n309   CAF  Central African Republic  ...   7.066  0.019780\n310   CAF  Central African Republic  ... -20.767  0.017229\n311   CAF  Central African Republic  ...  19.066 -0.007445\n312   CAF  Central African Republic  ... -10.317 -0.007055\n313   CAF  Central African Republic  ... -21.461  0.007310\n314   CAF  Central African Republic  ...  38.590 -0.003629\n456   CUB                      Cuba  ...  -4.206       NaN\n457   CUB                      Cuba  ...   4.428       NaN\n599   ECU                   Ecuador  ...  -5.282 -0.103108\n853   GUY                    Guyana  ...   5.565  0.621111\n979   IND                     India  ...   4.416  0.067624\n1159  LBN                   Lebanon  ...   4.353 -0.010778\n1178  LBY                     Libya  ...  -5.123  0.089653\n1355  MEX                    Mexico  ...  -4.081 -0.091029\n1357  MEX                    Mexico  ...   4.223  0.029316\n1645  PER                      Peru  ...   5.238  0.018367\n1725  PRY                  Paraguay  ...  -4.612  0.027431\n1726  PRY                  Paraguay  ...   4.211 -0.011561\n1736  PSE        West Bank and Gaza  ... -11.492 -0.033140\n1762  PYF          French Polynesia  ...   4.288       NaN\n1889  SOM        Somalia, Fed. Rep.  ...   4.885  0.010162\n1911  SSD               South Sudan  ...  16.554       NaN\n1912  SSD               South Sudan  ...   6.224       NaN\n1951  SUR                  Suriname  ...   4.311  0.015405\n1987  SWZ                  Eswatini  ...   4.800 -0.006289\n\n[32 rows x 9 columns]\n     iso3                      name  year  ...         income_level    dle        dg\n851   GUY                    Guyana  2020  ...          High income -1.316  0.435123\n853   GUY                    Guyana  2022  ...          High income  5.565  0.621111\n854   GUY                    Guyana  2023  ...          High income  0.292  0.330314\n1172  LBY                     Libya  2017  ...  Upper middle income  0.922  0.303951\n1175  LBY                     Libya  2020  ...  Upper middle income -0.553 -0.304002\n1283  MAC          Macao SAR, China  2020  ...          High income  0.200 -0.552936\n1286  MAC          Macao SAR, China  2023  ...          High income  0.100  0.749210\n1337  MDV                  Maldives  2020  ...  Upper middle income -0.994 -0.348312\n1338  MDV                  Maldives  2021  ...  Upper middle income -0.661  0.337686\n1826  SDN                     Sudan  2023  ...           Low income  0.636 -0.303632\n2018  TCA  Turks and Caicos Islands  2017  ...          High income  0.068  0.675980\n2021  TCA  Turks and Caicos Islands  2020  ...          High income -0.192 -0.356362\n\n[12 rows x 9 columns]\n     iso3                       name  ... gdp_pc_ppp  life_expectancy\n107   ASM             American Samoa  ...        NaN        72.852000\n350   CHI            Channel Islands  ...        NaN        81.100000\n458   CUB                       Cuba  ...        NaN        78.085000\n629   ERI                    Eritrea  ...        NaN        68.624000\n764   GIB                  Gibraltar  ...        NaN        83.553000\n845   GUM                       Guam  ...        NaN        77.209000\n971   IMN                Isle of Man  ...        NaN        80.999000\n1214  LIE              Liechtenstein  ...        NaN        84.595122\n1295  MAF   St. Martin (French part)  ...        NaN        80.224000\n1313  MCO                     Monaco  ...        NaN        86.372000\n1448  MNP   Northern Mariana Islands  ...        NaN        78.808000\n1520  NCL              New Caledonia  ...        NaN        78.766000\n1709  PRK  Korea, Dem. People's Rep.  ...        NaN        73.642000\n1763  PYF           French Polynesia  ...        NaN        84.070000\n1916  SSD                South Sudan  ...        NaN        57.617000\n2015  SYR       Syrian Arab Republic  ...        NaN        72.120000\n2249  VEN              Venezuela, RB  ...        NaN        72.514000\n2258  VGB     British Virgin Islands  ...        NaN        77.276000\n2267  VIR      Virgin Islands (U.S.)  ...        NaN        80.519512\n2321  YEM                Yemen, Rep.  ...        NaN        69.295000\n\n[20 rows x 5 columns]\ncount       197.000000\nmean      27404.732511\nstd       26991.485580\nmin        1033.009373\n25%        6520.898865\n50%       17685.858593\n75%       41320.257996\nmax      130296.843789\nName: gdp_pc_ppp, dtype: float64\nTrue","is_error":false}]},"uuid":"4b654600-ff7e-42d1-b6a8-a03e6a1d2a45","timestamp":"2026-10-30T03:50:44.166Z","toolUseResult":{"stdout":"     iso3                      name  ...              region         income_level\n313   CAF  Central African Republic  ...  Sub-Saharan Africa           Low income\n310   CAF  Central African Republic  ...  Sub-Saharan Africa           Low income\n1910  SSD               South Sudan  ...  Sub-Saharan Africa           Low income\n1909  SSD               South Sudan  ...  Sub-Saharan Africa           Low income\n1908  SSD               South Sudan  ...  Sub-Saharan Africa           Low income\n312   CAF  Central African Republic  ...  Sub-Saharan Africa           Low income\n308   CAF  Central African Republic  ...  Sub-Saharan Africa           Low income\n311   CAF  Central African Republic  ...  Sub-Saharan Africa           Low income\n307   CAF  Central African Republic  ...  Sub-Saharan Africa           Low income\n2025  TCD                      Chad  ...  Sub-Saharan Africa           Low income\n2026  TCD                      Chad  ...  Sub-Saharan Africa           Low income\n1233  LSO                   Lesotho  ...  Sub-Saharan Africa  Lower middle income\n\n[12 rows x 7 columns]\n     iso3    name  year  ...  life_expectancy                 region income_level\n1307  MCO  Monaco  2017  ...           85.878  Europe & Central Asia  High income\n1308  MCO  Monaco  2018  ...           86.084  Europe & Central Asia  High income\n1310  MCO  Monaco  2020  ...           86.089  Europe & Central Asia  High income\n1309  MCO  Monaco  2019  ...           86.151  Europe & Central Asia  High income\n1313  MCO  Monaco  2023  ...           86.372  Europe & Central Asia  High income\n\n[5 rows x 7 columns]\n     iso3                      name  ...     dle        dg\n25    AFG               Afghanistan  ...   5.200 -0.075767\n59    AND                   Andorra  ...  -4.680 -0.122238\n257   BOL                   Bolivia  ...  -4.914 -0.137899\n259   BOL                   Bolivia  ...   6.007  0.025465\n303   BWA                  Botswana  ...  -4.340  0.102596\n304   BWA                  Botswana  ...   5.446  0.038236\n308   CAF  Central African Republic  ...  -5.768  0.027861\n309   CAF  Central African Republic  ...   7.066  0.019780\n310   CAF  Central African Republic  ... -20.767  0.017229\n311   CAF  Central African Republic  ...  19.066 -0.007445\n312   CAF  Central African Republic  ... -10.317 -0.007055\n313   CAF  Central African Republic  ... -21.461  0.007310\n314   CAF  Central African Republic  ...  38.590 -0.003629\n456   CUB                      Cuba  ...  -4.206       NaN\n457   CUB                      Cuba  ...   4.428       NaN\n599   ECU                   Ecuador  ...  -5.282 -0.103108\n853   GUY                    Guyana  ...   5.565  0.621111\n979   IND                     India  ...   4.416  0.067624\n1159  LBN                   Lebanon  ...   4.353 -0.010778\n1178  LBY                     Libya  ...  -5.123  0.089653\n1355  MEX                    Mexico  ...  -4.081 -0.091029\n1357  MEX                    Mexico  ...   4.223  0.029316\n1645  PER                      Peru  ...   5.238  0.018367\n1725  PRY                  Paraguay  ...  -4.612  0.027431\n1726  PRY                  Paraguay  ...   4.211 -0.011561\n1736  PSE        West Bank and Gaza  ... -11.492 -0.033140\n1762  PYF          French Polynesia  ...   4.288       NaN\n1889  SOM        Somalia, Fed. Rep.  ...   4.885  0.010162\n1911  SSD               South Sudan  ...  16.554       NaN\n1912  SSD               South Sudan  ...   6.224       NaN\n1951  SUR                  Suriname  ...   4.311  0.015405\n1987  SWZ                  Eswatini  ...   4.800 -0.006289\n\n[32 rows x 9 columns]\n     iso3                      name  year  ...         income_level    dle        dg\n851   GUY                    Guyana  2020  ...          High income -1.316  0.435123\n853   GUY                    Guyana  2022  ...          High income  5.565  0.621111\n854   GUY                    Guyana  2023  ...          High income  0.292  0.330314\n1172  LBY                     Libya  2017  ...  Upper middle income  0.922  0.303951\n1175  LBY                     Libya  2020  ...  Upper middle income -0.553 -0.304002\n1283  MAC          Macao SAR, China  2020  ...          High income  0.200 -0.552936\n1286  MAC          Macao SAR, China  2023  ...          High income  0.100  0.749210\n1337  MDV                  Maldives  2020  ...  Upper middle income -0.994 -0.348312\n1338  MDV                  Maldives  2021  ...  Upper middle income -0.661  0.337686\n1826  SDN                     Sudan  2023  ...           Low income  0.636 -0.303632\n2018  TCA  Turks and Caicos Islands  2017  ...          High income  0.068  0.675980\n2021  TCA  Turks and Caicos Islands  2020  ...          High income -0.192 -0.356362\n\n[12 rows x 9 columns]\n     iso3                       name  ... gdp_pc_ppp  life_expectancy\n107   ASM             American Samoa  ...        NaN        72.852000\n350   CHI            Channel Islands  ...        NaN        81.100000\n458   CUB                       Cuba  ...        NaN        78.085000\n629   ERI                    Eritrea  ...        NaN        68.624000\n764   GIB                  Gibraltar  ...        NaN        83.553000\n845   GUM                       Guam  ...        NaN        77.209000\n971   IMN                Isle of Man  ...        NaN        80.999000\n1214  LIE              Liechtenstein  ...        NaN        84.595122\n1295  MAF   St. Martin (French part)  ...        NaN        80.224000\n1313  MCO                     Monaco  ...        NaN        86.372000\n1448  MNP   Northern Mariana Islands  ...        NaN        78.808000\n1520  NCL              New Caledonia  ...        NaN        78.766000\n1709  PRK  Korea, Dem. People's Rep.  ...        NaN        73.642000\n1763  PYF           French Polynesia  ...        NaN        84.070000\n1916  SSD                South Sudan  ...        NaN        57.617000\n2015  SYR       Syrian Arab Republic  ...        NaN        72.120000\n2249  VEN              Venezuela, RB  ...        NaN        72.514000\n2258  VGB     British Virgin Islands  ...        NaN        77.276000\n2267  VIR      Virgin Islands (U.S.)  ...        NaN        80.519512\n2321  YEM                Yemen, Rep.  ...        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countries"}}]}}34{"type":"api-request","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","id":"4723b749-1da9-4f0e-aafa-ddb38e90d9d0","timestamp":"2026-10-30T03:50:44.170Z","version":"2.1.314","querySource":"sdk","shapeHash":"a1a7fae70b72e9ac39b3275721793ee12a68cf4c21397754b79cb5e12e871183","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":"3a62e4fb-1fd6-450c-8308-c336e37e7670","keep":8,"tail":["abbf8496747c0428836d14be20c8b679e9a5abc40637c81f5744389ec7df0976","d91b993b42ce0aa33ad5f6abe4b3115461bbbef750db5c3af606c4b6ecedfc6e"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"58058b9d981b5d0963b15726b6a047291dea49ae3675fa2b70568c6fa3dd955f"}35{"parentUuid":"4b654600-ff7e-42d1-b6a8-a03e6a1d2a45","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXjwCsyTVis8npF7zwiu","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013WWjf2dQjR44sHrY5ZZFBD","name":"Bash","input":{"command":"grep 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African Republic,2015,1043.921717,51.934000\nCAF,Central African Republic,2016,1073.944777,50.999000\nCAF,Central African Republic,2017,1103.866093,45.231000\nCAF,Central African Republic,2018,1125.700917,52.297000\nCAF,Central African Republic,2019,1145.095637,31.530000\nCAF,Central African Republic,2020,1136.570530,50.596000\nCAF,Central African Republic,2021,1128.552380,40.279000\nCAF,Central African Republic,2022,1136.801711,18.818000\nCAF,Central African Republic,2023,1132.676434,57.408000\nPSE,West Bank and Gaza,2015,6072.650318,74.577000\nPSE,West Bank and Gaza,2016,6464.133525,74.917000\nPSE,West Bank and Gaza,2017,6426.795216,75.216000\nPSE,West Bank and Gaza,2018,6342.937480,75.380000\nPSE,West Bank and Gaza,2019,6269.891345,75.811000\nPSE,West Bank and Gaza,2020,5423.682547,74.998000\nPSE,West Bank and Gaza,2021,5663.121607,73.890000\nPSE,West Bank and Gaza,2022,5753.083959,76.662000\nPSE,West Bank and Gaza,2023,5562.424426,65.170000\nSSD,South Sudan,2015,,39.757000\nSSD,South Sudan,2016,,36.720000\nSSD,South Sudan,2017,,35.351000\nSSD,South Sudan,2018,,51.905000\nSSD,South Sudan,2019,,58.129000\nSSD,South Sudan,2020,,57.648000\nSSD,South Sudan,2021,,57.047000\nSSD,South Sudan,2022,,57.203000\nSSD,South Sudan,2023,,57.617000","is_error":false}]},"uuid":"5afd1af2-3a74-45be-995f-4c3b5d9b95f4","timestamp":"2026-10-30T03:50:45.762Z","toolUseResult":{"stdout":"CAF,Central African Republic,2015,1043.921717,51.934000\nCAF,Central African Republic,2016,1073.944777,50.999000\nCAF,Central African Republic,2017,1103.866093,45.231000\nCAF,Central African Republic,2018,1125.700917,52.297000\nCAF,Central African Republic,2019,1145.095637,31.530000\nCAF,Central African Republic,2020,1136.570530,50.596000\nCAF,Central African Republic,2021,1128.552380,40.279000\nCAF,Central African Republic,2022,1136.801711,18.818000\nCAF,Central African Republic,2023,1132.676434,57.408000\nPSE,West Bank and Gaza,2015,6072.650318,74.577000\nPSE,West Bank and 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-p out/state/gate_receipts && cd out/state && cat > contract.md <<'EOF'\n# Contract — F6-dev-01\n**Question.** Does the cross-country Preston curve (life expectancy rising with ln income) hold in the most recent usable WDI year, and how strong is it? Descriptive/associational only.\n\n**Estimand.** Slope β of the linear projection LE_i = α + β ln(GDPpc_i) + ε_i over countries/economies (equal weights, one obs per country) in the analysis year. LE = life expectancy at birth (years); GDPpc = GDP pc PPP constant 2021 intl $. β is a best-linear-predictor coefficient (meaningful without linearity/exogeneity assumptions as a projection); inference with heteroskedasticity-robust SE (HC3 chosen), countries treated as independent draws.\n\n**Outputs.** out/results.json with keys 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. Plus out/figure1.png (scatter + OLS line), out/table1.csv (income_level, n_countries, mean_log_gdp_pc, mean_life_expectancy), out/report.md (6 sections), out/analysis.py, out/claims.json, out/exclusions.csv, out/state/*.\n\n**Information rules.** Exclude entities with region == Aggregates. Analysis year = latest year 2015–2023 with both indicators for ≥150 countries. Drop countries missing either indicator in that year (count n_excluded_missing), no imputation. Natural log. Only the listed files.\n\n**Invalidity / impossibility.** No year with ≥150 complete countries; inability to identify aggregates; non-positive GDP (log undefined); duplicated country-years. Any of these → premise_invalid / partial.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\",\"data hashes match README/bundle.json\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"running\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-29 P0 passed; contract written. 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Descriptive/associational only.\n\n**Estimand.** Slope β of the linear projection LE_i = α + β ln(GDPpc_i) + ε_i over countries/economies (equal weights, one obs per country) in the analysis year. LE = life expectancy at birth (years); GDPpc = GDP pc PPP constant 2021 intl $. β is a best-linear-predictor coefficient (meaningful without linearity/exogeneity assumptions as a projection); inference with heteroskedasticity-robust SE (HC3 chosen), countries treated as independent draws.\n\n**Outputs.** out/results.json with keys 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. Plus out/figure1.png (scatter + OLS line), out/table1.csv (income_level, n_countries, mean_log_gdp_pc, mean_life_expectancy), out/report.md (6 sections), out/analysis.py, out/claims.json, out/exclusions.csv, out/state/*.\n\n**Information rules.** Exclude entities with region == Aggregates. Analysis year = latest year 2015–2023 with both indicators for ≥150 countries. Drop countries missing either indicator in that year (count n_excluded_missing), no imputation. Natural log. Only the listed files.\n\n**Invalidity / impossibility.** No year with ≥150 complete countries; inability to identify aggregates; non-positive GDP (log undefined); duplicated country-years. Any of these → premise_invalid / partial.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\",\"data hashes match README/bundle.json\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"running\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-29 P0 passed; contract written. 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the analysis script (also writes 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> out/analysis.py <<'EOF'\n\"\"\"Replication entry point for F6-dev-01 (Preston curve).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/figure1.png, out/table1.csv, out/exclusions.csv.\nNo randomness is used (random_seed recorded as null).\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nimport statsmodels.api as sm\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nDATA, OUT = \"data\", \"out\"\nos.makedirs(OUT, exist_ok=True)\nread = lambda f: pd.read_csv(os.path.join(DATA, f), keep_default_na=False, na_values=[\"\"])\nind, ent = read(\"wb_indicators.csv\"), read(\"wb_entities.csv\")\nn_input_rows = {\"wb_indicators.csv\": int(len(ind)), \"wb_entities.csv\": int(len(ent))}\nissues, excl = [], []\n\ndef log_excl(step, rule, before, after, why):\n    excl.append(dict(step=step, rule=rule, n_before=int(before), n_after=int(after),\n                     n_removed=int(before - after), rationale=why))\n\n# integrity checks\nassert not ind.duplicated([\"iso3\", \"year\"]).any() and not ent.iso3.duplicated().any()\nassert set(ind.iso3) <= set(ent.iso3)\nassert (ind.gdp_pc_ppp.dropna() > 0).all()\n\n# Step 1: drop aggregates\nm = ind.merge(ent[[\"iso3\", \"region\", \"income_level\"]], on=\"iso3\", how=\"left\")\nn0 = len(m)\nn_agg_ent = int(m.loc[m.region == \"Aggregates\", \"iso3\"].nunique())\nc = m[m.region != \"Aggregates\"].copy()\nlog_excl(1, \"drop entity-years whose region == 'Aggregates' in wb_entities.csv\", n0, len(c),\n         \"information rule: countries/economies only\")\nissues.append({\"issue\": f\"wb_indicators.csv contains {n_agg_ent} regional/income/other aggregate entities \"\n               f\"({n0 - len(c)} entity-year rows)\", \"handling\": \"excluded via region == 'Aggregates' (rule)\"})\n\n# Step 2: analysis year selection\ncov = c.assign(both=c.gdp_pc_ppp.notna() & c.life_expectancy.notna()).groupby(\"year\").both.sum()\nelig = cov[cov >= 150]\nanalysis_year = int(elig.index.max())\nn1 = len(c)\ny = c[c.year == analysis_year].copy()\nlog_excl(2, f\"keep analysis year {analysis_year} (latest year with >=150 complete countries)\", n1, len(y),\n         \"information rule: single cross-section in analysis year\")\n\n# Step 3: drop missing\nmiss = y[y.gdp_pc_ppp.isna() | y.life_expectancy.isna()]\nn_excluded_missing = int(len(miss))\nn2 = len(y)\nd = y.dropna(subset=[\"gdp_pc_ppp\", \"life_expectancy\"]).copy()\nlog_excl(3, \"drop countries with gdp_pc_ppp or life_expectancy missing in analysis year\", n2, len(d),\n         \"information rule: no imputation\")\nissues.append({\"issue\": f\"{n_excluded_missing} countries/economies lack an indicator in {analysis_year} \"\n               f\"(gdp missing: {int(miss.gdp_pc_ppp.isna().sum())}, LE missing: {int(miss.life_expectancy.isna().sum())}): \"\n               + \", \".join(sorted(miss.iso3)),\n               \"handling\": \"excluded, counted in n_excluded_missing; possible selection bias discussed in report\"})\nissues.append({\"issue\": \"Central African Republic life expectancy series is implausibly volatile in 2015-2022 \"\n               \"(e.g. 18.8 years in 2022, 31.5 in 2019); 2023 value 57.4 is plausible\",\n               \"handling\": \"affects non-analysis years only; 2023 value kept; noted as data-quality caveat\"})\nissues.append({\"issue\": \"West Bank and Gaza life expectancy drops from 76.7 (2022) to 65.2 (2023), consistent with \"\n               \"conflict mortality but a large outlier relative to its income\",\n               \"handling\": \"kept (genuine reported value); influence assessed in leave-out sensitivity\"})\n\n# Estimation\nd[\"ln_gdp\"] = np.log(d.gdp_pc_ppp)\nX = sm.add_constant(d.ln_gdp.values)\nyv = d.life_expectancy.values\nfit = sm.OLS(yv, X).fit(cov_type=\"HC3\")\nalpha, beta = float(fit.params[0]), float(fit.params[1])\nse = float(fit.bse[1])\nz = 1.959963984540054\nci_lo, ci_hi = beta - z * se, beta + z * se\nresid = yv - (alpha + beta * d.ln_gdp.values)\nbaseline_rmse = float(np.sqrt(np.mean((yv - yv.mean()) ** 2)))\nmodel_rmse = float(np.sqrt(np.mean(resid ** 2)))\nr = float(np.corrcoef(d.ln_gdp, yv)[0, 1])\nconclusion = \"positive_effect\" if ci_lo > 0 else (\"negative_effect\" if ci_hi < 0 else \"no_detectable_effect\")\n\n# Supplementary (not estimand): other robust SEs, sensitivity dropping the 5 most influential obs (Cook's D)\nfit_ols = sm.OLS(yv, X).fit()\nse_alt = {k: float(sm.OLS(yv, X).fit(cov_type=k).bse[1]) for k in [\"HC0\", \"HC1\", \"HC2\", \"HC3\"]}\ncooks = fit_ols.get_influence().cooks_distance[0]\ntop = np.argsort(cooks)[::-1][:5]\nkeep = np.setdiff1d(np.arange(len(d)), top)\nfit_s = sm.OLS(yv[keep], X[keep]).fit(cov_type=\"HC3\")\nd[\"resid\"] = resid\n\nresults = {\n    \"task_id\": \"F6-dev-01\",\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"analysis_year\": analysis_year,\n    \"n_excluded_missing\": n_excluded_missing,\n    \"n_analysed\": int(len(d)),\n    \"baseline_metric\": baseline_rmse,\n    \"model_metric\": model_rmse,\n    \"pearson_r\": r,\n    \"r_squared\": float(fit.rsquared),\n    \"effect_estimate\": beta,\n    \"intercept\": alpha,\n    \"effect_se\": se,\n    \"effect_ci_low\": float(ci_lo),\n    \"effect_ci_high\": float(ci_hi),\n    \"conclusion\": conclusion,\n    \"data_issues\": issues,\n    \"n_input_rows\": n_input_rows,\n    \"random_seed\": None,\n    \"se_type\": \"HC3\",\n    \"complete_countries_by_year\": {str(k): int(v) for k, v in cov.items()},\n    \"n_countries_in_analysis_year\": int(n2),\n    \"supp_effect_se_by_type\": se_alt,\n    \"supp_effect_se_classical\": float(fit_ols.bse[1]),\n    \"supp_mean_life_expectancy\": float(yv.mean()),\n    \"supp_sens_drop5_cooks_iso3\": [str(s) for s in d.iso3.values[top]],\n    \"supp_sens_drop5_effect_estimate\": float(fit_s.params[1]),\n    \"supp_sens_drop5_effect_se\": float(fit_s.bse[1]),\n    \"supp_sens_drop5_r_squared\": float(fit_s.rsquared),\n    \"supp_largest_neg_residual_iso3\": str(d.iso3.values[np.argmin(resid)]),\n    \"supp_largest_neg_residual\": float(resid.min()),\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(results, f, indent=2)\n\npd.DataFrame(excl, columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]).to_csv(\n    os.path.join(OUT, \"exclusions.csv\"), index=False)\n\norder = [\"Low income\", \"Lower middle income\", \"Upper middle income\", \"High income\"]\nt = (d.groupby(\"income_level\").agg(n_countries=(\"iso3\", \"size\"), mean_log_gdp_pc=(\"ln_gdp\", \"mean\"),\n                                   mean_life_expectancy=(\"life_expectancy\", \"mean\")).reset_index())\nt[\"o\"] = t.income_level.map({k: i for i, k in enumerate(order)}).fillna(99)\nt = t.sort_values([\"o\", \"income_level\"]).drop(columns=\"o\")\nt.to_csv(os.path.join(OUT, \"table1.csv\"), index=False, float_format=\"%.4f\")\n\nfig, ax = plt.subplots(figsize=(7, 5))\ncols = dict(zip(order, [\"#d7191c\", \"#fdae61\", \"#abd9e9\", \"#2c7bb6\"]))\nfor lvl in order:\n    s = d[d.income_level == lvl]\n    ax.scatter(s.ln_gdp, s.life_expectancy, s=18, c=cols[lvl], label=f\"{lvl} (n={len(s)})\", edgecolor=\"k\", lw=0.3)\nxs = np.linspace(d.ln_gdp.min(), d.ln_gdp.max(), 100)\nax.plot(xs, alpha + beta * xs, \"k-\", lw=1.5, label=f\"OLS: LE = {alpha:.1f} + {beta:.2f} ln(GDPpc)\")\nax.set_xlabel(\"ln(GDP per capita, PPP, constant 2021 intl $)\")\nax.set_ylabel(\"Life expectancy at birth (years)\")\nax.set_title(f\"Preston curve, {analysis_year} (n={len(d)} countries/economies)\")\nax.legend(fontsize=8, loc=\"lower right\")\nfig.tight_layout()\nfig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=120, metadata={\"Software\": None})\nplt.close(fig)\n\nprint(f\"year={analysis_year} n={len(d)} excluded_missing={n_excluded_missing} beta={beta:.4f} \"\n      f\"HC3 se={se:.4f} CI=[{ci_lo:.4f},{ci_hi:.4f}] R2={fit.rsquared:.4f} conclusion={conclusion}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/table1.csv out/exclusions.csv","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1260,"cache_read_input_tokens":15508,"output_tokens":3824,"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":1260},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXjx9turXPhHpurJf3Bn","requestRef":"29fdd5c1-9631-4df4-ab62-a9fd5dab85de","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"798cf327-1444-4828-8631-099c7c8f2180","timestamp":"2026-10-30T03:51:26.638Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0014/out/state","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","version":"2.1.314","gitBranch":"HEAD"}48{"parentUuid":"798cf327-1444-4828-8631-099c7c8f2180","isSidechain":false,"promptId":"cced29be-8a0c-46b8-9fc9-c2501f16d592","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 2\n(eval):1: no such file or directory: out/analysis.py\n/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/simon-pierreboucher/wp29/runs/RP0014/out/state/out/analysis.py': [Errno 2] No such file or directory","is_error":true,"tool_use_id":"toolu_01HQn373L2URPHQBjpnFAUrm"}]},"uuid":"dcb0e51f-3d97-4c60-9775-e6c844cd2adb","timestamp":"2026-10-30T03:51:26.684Z","toolUseResult":"Error: Exit code 2\n(eval):1: no such file or directory: out/analysis.py\n/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/simon-pierreboucher/wp29/runs/RP0014/out/state/out/analysis.py': [Errno 2] No such file or directory","sandboxed":false,"sourceToolAssistantUUID":"798cf327-1444-4828-8631-099c7c8f2180","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0014/out/state","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","version":"2.1.314","gitBranch":"HEAD"}49{"type":"api-request-blob","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","hash":"304b7281cd399f54c0f8cf66f02caff8aa48748e479b7b2ab9cb3cc0982791e7","message":{"role":"assistant","content":[{"type":"text","text":"Now the analysis script (also writes exclusions.csv)."},{"type":"tool_use","id":"toolu_01HQn373L2URPHQBjpnFAUrm","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"Replication entry point for F6-dev-01 (Preston curve).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/figure1.png, out/table1.csv, out/exclusions.csv.\nNo randomness is used (random_seed recorded as null).\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nimport statsmodels.api as sm\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nDATA, OUT = \"data\", \"out\"\nos.makedirs(OUT, exist_ok=True)\nread = lambda f: pd.read_csv(os.path.join(DATA, f), keep_default_na=False, na_values=[\"\"])\nind, ent = read(\"wb_indicators.csv\"), read(\"wb_entities.csv\")\nn_input_rows = {\"wb_indicators.csv\": int(len(ind)), \"wb_entities.csv\": int(len(ent))}\nissues, excl = [], []\n\ndef log_excl(step, rule, before, after, why):\n    excl.append(dict(step=step, rule=rule, n_before=int(before), n_after=int(after),\n                     n_removed=int(before - after), rationale=why))\n\n# integrity checks\nassert not ind.duplicated([\"iso3\", \"year\"]).any() and not ent.iso3.duplicated().any()\nassert set(ind.iso3) <= set(ent.iso3)\nassert (ind.gdp_pc_ppp.dropna() > 0).all()\n\n# Step 1: drop aggregates\nm = ind.merge(ent[[\"iso3\", \"region\", \"income_level\"]], on=\"iso3\", how=\"left\")\nn0 = len(m)\nn_agg_ent = int(m.loc[m.region == \"Aggregates\", \"iso3\"].nunique())\nc = m[m.region != \"Aggregates\"].copy()\nlog_excl(1, \"drop entity-years whose region == 'Aggregates' in wb_entities.csv\", n0, len(c),\n         \"information rule: countries/economies only\")\nissues.append({\"issue\": f\"wb_indicators.csv contains {n_agg_ent} regional/income/other aggregate entities \"\n               f\"({n0 - len(c)} entity-year rows)\", \"handling\": \"excluded via region == 'Aggregates' (rule)\"})\n\n# Step 2: analysis year selection\ncov = c.assign(both=c.gdp_pc_ppp.notna() & c.life_expectancy.notna()).groupby(\"year\").both.sum()\nelig = cov[cov >= 150]\nanalysis_year = int(elig.index.max())\nn1 = len(c)\ny = c[c.year == analysis_year].copy()\nlog_excl(2, f\"keep analysis year {analysis_year} (latest year with >=150 complete countries)\", n1, len(y),\n         \"information rule: single cross-section in analysis year\")\n\n# Step 3: drop missing\nmiss = y[y.gdp_pc_ppp.isna() | y.life_expectancy.isna()]\nn_excluded_missing = int(len(miss))\nn2 = len(y)\nd = y.dropna(subset=[\"gdp_pc_ppp\", \"life_expectancy\"]).copy()\nlog_excl(3, \"drop countries with gdp_pc_ppp or life_expectancy missing in analysis year\", n2, len(d),\n         \"information rule: no imputation\")\nissues.append({\"issue\": f\"{n_excluded_missing} countries/economies lack an indicator in {analysis_year} \"\n               f\"(gdp missing: {int(miss.gdp_pc_ppp.isna().sum())}, LE missing: {int(miss.life_expectancy.isna().sum())}): \"\n               + \", \".join(sorted(miss.iso3)),\n               \"handling\": \"excluded, counted in n_excluded_missing; possible selection bias discussed in report\"})\nissues.append({\"issue\": \"Central African Republic life expectancy series is implausibly volatile in 2015-2022 \"\n               \"(e.g. 18.8 years in 2022, 31.5 in 2019); 2023 value 57.4 is plausible\",\n               \"handling\": \"affects non-analysis years only; 2023 value kept; noted as data-quality caveat\"})\nissues.append({\"issue\": \"West Bank and Gaza life expectancy drops from 76.7 (2022) to 65.2 (2023), consistent with \"\n               \"conflict mortality but a large outlier relative to its income\",\n               \"handling\": \"kept (genuine reported value); influence assessed in leave-out sensitivity\"})\n\n# Estimation\nd[\"ln_gdp\"] = np.log(d.gdp_pc_ppp)\nX = sm.add_constant(d.ln_gdp.values)\nyv = d.life_expectancy.values\nfit = sm.OLS(yv, X).fit(cov_type=\"HC3\")\nalpha, beta = float(fit.params[0]), float(fit.params[1])\nse = float(fit.bse[1])\nz = 1.959963984540054\nci_lo, ci_hi = beta - z * se, beta + z * se\nresid = yv - (alpha + beta * d.ln_gdp.values)\nbaseline_rmse = float(np.sqrt(np.mean((yv - yv.mean()) ** 2)))\nmodel_rmse = float(np.sqrt(np.mean(resid ** 2)))\nr = float(np.corrcoef(d.ln_gdp, yv)[0, 1])\nconclusion = \"positive_effect\" if ci_lo > 0 else (\"negative_effect\" if ci_hi < 0 else \"no_detectable_effect\")\n\n# Supplementary (not estimand): other robust SEs, sensitivity dropping the 5 most influential obs (Cook's D)\nfit_ols = sm.OLS(yv, X).fit()\nse_alt = {k: float(sm.OLS(yv, X).fit(cov_type=k).bse[1]) for k in [\"HC0\", \"HC1\", \"HC2\", \"HC3\"]}\ncooks = fit_ols.get_influence().cooks_distance[0]\ntop = np.argsort(cooks)[::-1][:5]\nkeep = np.setdiff1d(np.arange(len(d)), top)\nfit_s = sm.OLS(yv[keep], X[keep]).fit(cov_type=\"HC3\")\nd[\"resid\"] = resid\n\nresults = {\n    \"task_id\": \"F6-dev-01\",\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"analysis_year\": analysis_year,\n    \"n_excluded_missing\": n_excluded_missing,\n    \"n_analysed\": int(len(d)),\n    \"baseline_metric\": baseline_rmse,\n    \"model_metric\": model_rmse,\n    \"pearson_r\": r,\n    \"r_squared\": float(fit.rsquared),\n    \"effect_estimate\": beta,\n    \"intercept\": alpha,\n    \"effect_se\": se,\n    \"effect_ci_low\": float(ci_lo),\n    \"effect_ci_high\": float(ci_hi),\n    \"conclusion\": conclusion,\n    \"data_issues\": issues,\n    \"n_input_rows\": n_input_rows,\n    \"random_seed\": None,\n    \"se_type\": \"HC3\",\n    \"complete_countries_by_year\": {str(k): int(v) for k, v in cov.items()},\n    \"n_countries_in_analysis_year\": int(n2),\n    \"supp_effect_se_by_type\": se_alt,\n    \"supp_effect_se_classical\": float(fit_ols.bse[1]),\n    \"supp_mean_life_expectancy\": float(yv.mean()),\n    \"supp_sens_drop5_cooks_iso3\": [str(s) for s in d.iso3.values[top]],\n    \"supp_sens_drop5_effect_estimate\": float(fit_s.params[1]),\n    \"supp_sens_drop5_effect_se\": float(fit_s.bse[1]),\n    \"supp_sens_drop5_r_squared\": float(fit_s.rsquared),\n    \"supp_largest_neg_residual_iso3\": str(d.iso3.values[np.argmin(resid)]),\n    \"supp_largest_neg_residual\": float(resid.min()),\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(results, f, indent=2)\n\npd.DataFrame(excl, columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]).to_csv(\n    os.path.join(OUT, \"exclusions.csv\"), index=False)\n\norder = [\"Low income\", \"Lower middle income\", \"Upper middle income\", \"High income\"]\nt = (d.groupby(\"income_level\").agg(n_countries=(\"iso3\", \"size\"), mean_log_gdp_pc=(\"ln_gdp\", \"mean\"),\n                                   mean_life_expectancy=(\"life_expectancy\", \"mean\")).reset_index())\nt[\"o\"] = t.income_level.map({k: i for i, k in enumerate(order)}).fillna(99)\nt = t.sort_values([\"o\", \"income_level\"]).drop(columns=\"o\")\nt.to_csv(os.path.join(OUT, \"table1.csv\"), index=False, float_format=\"%.4f\")\n\nfig, ax = plt.subplots(figsize=(7, 5))\ncols = dict(zip(order, [\"#d7191c\", \"#fdae61\", \"#abd9e9\", \"#2c7bb6\"]))\nfor lvl in order:\n    s = d[d.income_level == lvl]\n    ax.scatter(s.ln_gdp, s.life_expectancy, s=18, c=cols[lvl], label=f\"{lvl} (n={len(s)})\", edgecolor=\"k\", lw=0.3)\nxs = np.linspace(d.ln_gdp.min(), d.ln_gdp.max(), 100)\nax.plot(xs, alpha + beta * xs, \"k-\", lw=1.5, label=f\"OLS: LE = {alpha:.1f} + {beta:.2f} ln(GDPpc)\")\nax.set_xlabel(\"ln(GDP per capita, PPP, constant 2021 intl $)\")\nax.set_ylabel(\"Life expectancy at birth (years)\")\nax.set_title(f\"Preston curve, {analysis_year} (n={len(d)} countries/economies)\")\nax.legend(fontsize=8, loc=\"lower right\")\nfig.tight_layout()\nfig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=120, metadata={\"Software\": None})\nplt.close(fig)\n\nprint(f\"year={analysis_year} n={len(d)} excluded_missing={n_excluded_missing} beta={beta:.4f} \"\n      f\"HC3 se={se:.4f} CI=[{ci_lo:.4f},{ci_hi:.4f}] R2={fit.rsquared:.4f} conclusion={conclusion}\")\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/analysis.py <<'EOF'\n\"\"\"Replication entry point for F6-dev-01 (Preston curve).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/figure1.png, out/table1.csv, out/exclusions.csv.\nNo randomness is used (random_seed recorded as null).\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nimport statsmodels.api as sm\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nDATA, OUT = \"data\", \"out\"\nos.makedirs(OUT, exist_ok=True)\nread = lambda f: pd.read_csv(os.path.join(DATA, f), keep_default_na=False, na_values=[\"\"])\nind, ent = read(\"wb_indicators.csv\"), read(\"wb_entities.csv\")\nn_input_rows = {\"wb_indicators.csv\": int(len(ind)), \"wb_entities.csv\": int(len(ent))}\nissues, excl = [], []\n\ndef log_excl(step, rule, before, after, why):\n    excl.append(dict(step=step, rule=rule, n_before=int(before), n_after=int(after),\n                     n_removed=int(before - after), rationale=why))\n\n# integrity checks\nassert not ind.duplicated([\"iso3\", \"year\"]).any() and not ent.iso3.duplicated().any()\nassert set(ind.iso3) <= set(ent.iso3)\nassert (ind.gdp_pc_ppp.dropna() > 0).all()\n\n# Step 1: drop aggregates\nm = ind.merge(ent[[\"iso3\", \"region\", \"income_level\"]], on=\"iso3\", how=\"left\")\nn0 = len(m)\nn_agg_ent = int(m.loc[m.region == \"Aggregates\", \"iso3\"].nunique())\nc = m[m.region != \"Aggregates\"].copy()\nlog_excl(1, \"drop entity-years whose region == 'Aggregates' in wb_entities.csv\", n0, len(c),\n         \"information rule: countries/economies only\")\nissues.append({\"issue\": f\"wb_indicators.csv contains {n_agg_ent} regional/income/other aggregate entities \"\n               f\"({n0 - len(c)} entity-year rows)\", \"handling\": \"excluded via region == 'Aggregates' (rule)\"})\n\n# Step 2: analysis year selection\ncov = c.assign(both=c.gdp_pc_ppp.notna() & c.life_expectancy.notna()).groupby(\"year\").both.sum()\nelig = cov[cov >= 150]\nanalysis_year = int(elig.index.max())\nn1 = len(c)\ny = c[c.year == analysis_year].copy()\nlog_excl(2, f\"keep analysis year {analysis_year} (latest year with >=150 complete countries)\", n1, len(y),\n         \"information rule: single cross-section in analysis year\")\n\n# Step 3: drop missing\nmiss = y[y.gdp_pc_ppp.isna() | y.life_expectancy.isna()]\nn_excluded_missing = int(len(miss))\nn2 = len(y)\nd = y.dropna(subset=[\"gdp_pc_ppp\", \"life_expectancy\"]).copy()\nlog_excl(3, \"drop countries with gdp_pc_ppp or life_expectancy missing in analysis year\", n2, len(d),\n         \"information rule: no imputation\")\nissues.append({\"issue\": f\"{n_excluded_missing} countries/economies lack an indicator in {analysis_year} \"\n               f\"(gdp missing: {int(miss.gdp_pc_ppp.isna().sum())}, LE missing: {int(miss.life_expectancy.isna().sum())}): \"\n               + \", \".join(sorted(miss.iso3)),\n               \"handling\": \"excluded, counted in n_excluded_missing; possible selection bias discussed in report\"})\nissues.append({\"issue\": \"Central African Republic life expectancy series is implausibly volatile in 2015-2022 \"\n               \"(e.g. 18.8 years in 2022, 31.5 in 2019); 2023 value 57.4 is plausible\",\n               \"handling\": \"affects non-analysis years only; 2023 value kept; noted as data-quality caveat\"})\nissues.append({\"issue\": \"West Bank and Gaza life expectancy drops from 76.7 (2022) to 65.2 (2023), consistent with \"\n               \"conflict mortality but a large outlier relative to its income\",\n               \"handling\": \"kept (genuine reported value); influence assessed in leave-out sensitivity\"})\n\n# Estimation\nd[\"ln_gdp\"] = np.log(d.gdp_pc_ppp)\nX = sm.add_constant(d.ln_gdp.values)\nyv = d.life_expectancy.values\nfit = sm.OLS(yv, X).fit(cov_type=\"HC3\")\nalpha, beta = float(fit.params[0]), float(fit.params[1])\nse = float(fit.bse[1])\nz = 1.959963984540054\nci_lo, ci_hi = beta - z * se, beta + z * se\nresid = yv - (alpha + beta * d.ln_gdp.values)\nbaseline_rmse = float(np.sqrt(np.mean((yv - yv.mean()) ** 2)))\nmodel_rmse = float(np.sqrt(np.mean(resid ** 2)))\nr = float(np.corrcoef(d.ln_gdp, yv)[0, 1])\nconclusion = \"positive_effect\" if ci_lo > 0 else (\"negative_effect\" if ci_hi < 0 else \"no_detectable_effect\")\n\n# Supplementary (not estimand): other robust SEs, sensitivity dropping the 5 most influential obs (Cook's D)\nfit_ols = sm.OLS(yv, X).fit()\nse_alt = {k: float(sm.OLS(yv, X).fit(cov_type=k).bse[1]) for k in [\"HC0\", \"HC1\", \"HC2\", \"HC3\"]}\ncooks = fit_ols.get_influence().cooks_distance[0]\ntop = np.argsort(cooks)[::-1][:5]\nkeep = np.setdiff1d(np.arange(len(d)), top)\nfit_s = sm.OLS(yv[keep], X[keep]).fit(cov_type=\"HC3\")\nd[\"resid\"] = resid\n\nresults = {\n    \"task_id\": \"F6-dev-01\",\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"analysis_year\": analysis_year,\n    \"n_excluded_missing\": n_excluded_missing,\n    \"n_analysed\": int(len(d)),\n    \"baseline_metric\": baseline_rmse,\n    \"model_metric\": model_rmse,\n    \"pearson_r\": r,\n    \"r_squared\": float(fit.rsquared),\n    \"effect_estimate\": beta,\n    \"intercept\": alpha,\n    \"effect_se\": se,\n    \"effect_ci_low\": float(ci_lo),\n    \"effect_ci_high\": float(ci_hi),\n    \"conclusion\": conclusion,\n    \"data_issues\": issues,\n    \"n_input_rows\": n_input_rows,\n    \"random_seed\": None,\n    \"se_type\": \"HC3\",\n    \"complete_countries_by_year\": {str(k): int(v) for k, v in cov.items()},\n    \"n_countries_in_analysis_year\": int(n2),\n    \"supp_effect_se_by_type\": se_alt,\n    \"supp_effect_se_classical\": float(fit_ols.bse[1]),\n    \"supp_mean_life_expectancy\": float(yv.mean()),\n    \"supp_sens_drop5_cooks_iso3\": [str(s) for s in d.iso3.values[top]],\n    \"supp_sens_drop5_effect_estimate\": float(fit_s.params[1]),\n    \"supp_sens_drop5_effect_se\": float(fit_s.bse[1]),\n    \"supp_sens_drop5_r_squared\": float(fit_s.rsquared),\n    \"supp_largest_neg_residual_iso3\": str(d.iso3.values[np.argmin(resid)]),\n    \"supp_largest_neg_residual\": float(resid.min()),\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(results, f, indent=2)\n\npd.DataFrame(excl, columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]).to_csv(\n    os.path.join(OUT, \"exclusions.csv\"), index=False)\n\norder = [\"Low income\", \"Lower middle income\", \"Upper middle income\", \"High income\"]\nt = (d.groupby(\"income_level\").agg(n_countries=(\"iso3\", \"size\"), mean_log_gdp_pc=(\"ln_gdp\", \"mean\"),\n                                   mean_life_expectancy=(\"life_expectancy\", \"mean\")).reset_index())\nt[\"o\"] = t.income_level.map({k: i for i, k in enumerate(order)}).fillna(99)\nt = t.sort_values([\"o\", \"income_level\"]).drop(columns=\"o\")\nt.to_csv(os.path.join(OUT, \"table1.csv\"), index=False, float_format=\"%.4f\")\n\nfig, ax = plt.subplots(figsize=(7, 5))\ncols = dict(zip(order, [\"#d7191c\", \"#fdae61\", \"#abd9e9\", \"#2c7bb6\"]))\nfor lvl in order:\n    s = d[d.income_level == lvl]\n    ax.scatter(s.ln_gdp, s.life_expectancy, s=18, c=cols[lvl], label=f\"{lvl} (n={len(s)})\", edgecolor=\"k\", lw=0.3)\nxs = np.linspace(d.ln_gdp.min(), d.ln_gdp.max(), 100)\nax.plot(xs, alpha + beta * xs, \"k-\", lw=1.5, label=f\"OLS: LE = {alpha:.1f} + {beta:.2f} ln(GDPpc)\")\nax.set_xlabel(\"ln(GDP per capita, PPP, constant 2021 intl $)\")\nax.set_ylabel(\"Life expectancy at birth (years)\")\nax.set_title(f\"Preston curve, {analysis_year} (n={len(d)} countries/economies)\")\nax.legend(fontsize=8, loc=\"lower right\")\nfig.tight_layout()\nfig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=120, metadata={\"Software\": None})\nplt.close(fig)\n\nprint(f\"year={analysis_year} n={len(d)} excluded_missing={n_excluded_missing} beta={beta:.4f} \"\n      f\"HC3 se={se:.4f} CI=[{ci_lo:.4f},{ci_hi:.4f}] R2={fit.rsquared:.4f} conclusion={conclusion}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/table1.csv out/exclusions.csv","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":158,"cache_read_input_tokens":20754,"output_tokens":3804,"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":158},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXjzUzBh9H7tXrWSc5Tc","requestRef":"35217c51-0397-406f-b74b-4bd0aa4540a1","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"455bce80-12e4-4d66-9dcf-3fcbddb7b58c","timestamp":"2026-10-30T03:51:53.388Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0014","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","version":"2.1.314","gitBranch":"HEAD"}57{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"455bce80-12e4-4d66-9dcf-3fcbddb7b58c","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91"}58{"type":"atis-latch","atis":"","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91"}59{"parentUuid":"455bce80-12e4-4d66-9dcf-3fcbddb7b58c","isSidechain":false,"promptId":"cced29be-8a0c-46b8-9fc9-c2501f16d592","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PrLa2RLCVB81QS3BGYkZoc","type":"tool_result","content":"year=2023 n=197 excluded_missing=20 beta=5.2672 HC3 se=0.2107 CI=[4.8543,5.6801] R2=0.7419 conclusion=positive_effect\n{\n  \"task_id\": \"F6-dev-01\",\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"analysis_year\": 2023,\n  \"n_excluded_missing\": 20,\n  \"n_analysed\": 197,\n  \"baseline_metric\": 7.07645404460588,\n  \"model_metric\": 3.5949979094078177,\n  \"pearson_r\": 0.8613437957840087,\n  \"r_squared\": 0.7419131345356038,\n  \"effect_estimate\": 5.267173451958655,\n  \"intercept\": 22.56709274982858,\n  \"effect_se\": 0.21067528691162227,\n  \"effect_ci_low\": 4.854257477179233,\n  \"effect_ci_high\": 5.6800894267380775,\n  \"conclusion\": \"positive_effect\",\n  \"data_issues\": [\n    {\n      \"issue\": \"wb_indicators.csv contains 44 regional/income/other aggregate entities (396 entity-year rows)\",\n      \"handling\": \"excluded via region == 'Aggregates' (rule)\"\n    },\n    {\n      \"issue\": \"20 countries/economies lack an indicator in 2023 (gdp missing: 20, LE missing: 0): ASM, CHI, CUB, ERI, GIB, GUM, IMN, LIE, MAF, MCO, MNP, NCL, PRK, PYF, SSD, SYR, VEN, VGB, VIR, YEM\",\n      \"handling\": \"excluded, counted in n_excluded_missing; possible selection bias discussed in report\"\n    },\n    {\n      \"issue\": \"Central African Republic life expectancy series is implausibly volatile in 2015-2022 (e.g. 18.8 years in 2022, 31.5 in 2019); 2023 value 57.4 is plausible\",\n      \"handling\": \"affects non-analysis years only; 2023 value kept; noted as data-quality caveat\"\n    },\n    {\n      \"issue\": \"West Bank and Gaza life expectancy drops from 76.7 (2022) to 65.2 (2023), consistent with conflict mortality but a large outlier relative to its income\",\n      \"handling\": \"kept (genuine reported value); influence assessed in leave-out sensitivity\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"wb_indicators.csv\": 2349,\n    \"wb_entities.csv\": 296\n  },\n  \"random_seed\": null,\n  \"se_type\": \"HC3\",\n  \"complete_countries_by_year\": {\n    \"2015\": 199,\n    \"2016\": 199,\n    \"2017\": 199,\n    \"2018\": 199,\n    \"2019\": 199,\n    \"2020\": 199,\n    \"2021\": 199,\n    \"2022\": 199,\n    \"2023\": 197\n  },\n  \"n_countries_in_analysis_year\": 217,\n  \"supp_effect_se_by_type\": {\n    \"HC0\": 0.20736485591704354,\n    \"HC1\": 0.2084255526375312,\n    \"HC2\": 0.20901105806015902,\n    \"HC3\": 0.21067528691162227\n  },\n  \"supp_effect_se_classical\": 0.22246758287864518,\n  \"supp_mean_life_expectancy\": 73.47408158883249,\n  \"supp_sens_drop5_cooks_iso3\": [\n    \"NGA\",\n    \"GRL\",\n    \"TCD\",\n    \"SLB\",\n    \"GUY\"\n  ],\n  \"supp_sens_drop5_effect_estimate\": 5.317920624114932,\n  \"supp_sens_drop5_effect_se\": 0.18313966978067114,\n  \"supp_sens_drop5_r_squared\": 0.7826492360851807,\n  \"supp_largest_neg_residual_iso3\": \"NGA\",\n  \"supp_largest_neg_residual\": -15.337823881620139\n}income_level,n_countries,mean_log_gdp_pc,mean_life_expectancy\nLow income,20,7.6100,63.1880\nLower middle income,46,8.6899,67.8095\nUpper middle income,58,9.6899,73.6954\nHigh income,73,10.8226,79.6858\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop entity-years whose region == 'Aggregates' in wb_entities.csv,2349,1953,396,information rule: countries/economies only\n2,keep analysis year 2023 (latest year with >=150 complete countries),1953,217,1736,information rule: single cross-section in analysis year\n3,drop countries with gdp_pc_ppp or life_expectancy missing in analysis year,217,197,20,information rule: no imputation","is_error":false}]},"uuid":"7b8de243-728b-45b2-8969-fb46dbfdbd54","timestamp":"2026-10-30T03:51:58.499Z","toolUseResult":{"stdout":"year=2023 n=197 excluded_missing=20 beta=5.2672 HC3 se=0.2107 CI=[4.8543,5.6801] R2=0.7419 conclusion=positive_effect\n{\n  \"task_id\": \"F6-dev-01\",\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"analysis_year\": 2023,\n  \"n_excluded_missing\": 20,\n  \"n_analysed\": 197,\n  \"baseline_metric\": 7.07645404460588,\n  \"model_metric\": 3.5949979094078177,\n  \"pearson_r\": 0.8613437957840087,\n  \"r_squared\": 0.7419131345356038,\n  \"effect_estimate\": 5.267173451958655,\n  \"intercept\": 22.56709274982858,\n  \"effect_se\": 0.21067528691162227,\n  \"effect_ci_low\": 4.854257477179233,\n  \"effect_ci_high\": 5.6800894267380775,\n  \"conclusion\": \"positive_effect\",\n  \"data_issues\": [\n    {\n      \"issue\": \"wb_indicators.csv contains 44 regional/income/other aggregate entities (396 entity-year rows)\",\n      \"handling\": \"excluded via region == 'Aggregates' (rule)\"\n    },\n    {\n      \"issue\": \"20 countries/economies lack an indicator in 2023 (gdp missing: 20, LE missing: 0): ASM, CHI, CUB, ERI, GIB, GUM, IMN, LIE, MAF, MCO, MNP, NCL, PRK, PYF, SSD, SYR, VEN, VGB, VIR, YEM\",\n      \"handling\": \"excluded, counted in n_excluded_missing; possible selection bias discussed in report\"\n    },\n    {\n      \"issue\": \"Central African Republic life expectancy series is implausibly volatile in 2015-2022 (e.g. 18.8 years in 2022, 31.5 in 2019); 2023 value 57.4 is plausible\",\n      \"handling\": \"affects non-analysis years only; 2023 value kept; noted as data-quality caveat\"\n    },\n    {\n      \"issue\": \"West Bank and Gaza life expectancy drops from 76.7 (2022) to 65.2 (2023), consistent with conflict mortality but a large outlier relative to its income\",\n      \"handling\": \"kept (genuine reported value); influence assessed in leave-out sensitivity\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"wb_indicators.csv\": 2349,\n    \"wb_entities.csv\": 296\n  },\n  \"random_seed\": null,\n  \"se_type\": \"HC3\",\n  \"complete_countries_by_year\": {\n    \"2015\": 199,\n    \"2016\": 199,\n    \"2017\": 199,\n    \"2018\": 199,\n    \"2019\": 199,\n    \"2020\": 199,\n    \"2021\": 199,\n    \"2022\": 199,\n    \"2023\": 197\n  },\n  \"n_countries_in_analysis_year\": 217,\n  \"supp_effect_se_by_type\": {\n    \"HC0\": 0.20736485591704354,\n    \"HC1\": 0.2084255526375312,\n    \"HC2\": 0.20901105806015902,\n    \"HC3\": 0.21067528691162227\n  },\n  \"supp_effect_se_classical\": 0.22246758287864518,\n  \"supp_mean_life_expectancy\": 73.47408158883249,\n  \"supp_sens_drop5_cooks_iso3\": [\n    \"NGA\",\n    \"GRL\",\n    \"TCD\",\n    \"SLB\",\n    \"GUY\"\n  ],\n  \"supp_sens_drop5_effect_estimate\": 5.317920624114932,\n  \"supp_sens_drop5_effect_se\": 0.18313966978067114,\n  \"supp_sens_drop5_r_squared\": 0.7826492360851807,\n  \"supp_largest_neg_residual_iso3\": \"NGA\",\n  \"supp_largest_neg_residual\": -15.337823881620139\n}income_level,n_countries,mean_log_gdp_pc,mean_life_expectancy\nLow income,20,7.6100,63.1880\nLower middle income,46,8.6899,67.8095\nUpper middle income,58,9.6899,73.6954\nHigh income,73,10.8226,79.6858\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop entity-years whose region == 'Aggregates' in wb_entities.csv,2349,1953,396,information rule: countries/economies only\n2,keep analysis year 2023 (latest year with >=150 complete countries),1953,217,1736,information rule: single cross-section in analysis year\n3,drop countries with gdp_pc_ppp or life_expectancy missing in analysis year,217,197,20,information rule: no imputation","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"455bce80-12e4-4d66-9dcf-3fcbddb7b58c","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0014","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","version":"2.1.314","gitBranch":"HEAD"}60{"type":"api-request-blob","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","hash":"1131ca30837c1250b439dc687ce35953974da3c097ed4bd7b3b6e8999aaa6e65","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01PrLa2RLCVB81QS3BGYkZoc","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"Replication entry point for F6-dev-01 (Preston curve).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/figure1.png, out/table1.csv, out/exclusions.csv.\nNo randomness is used (random_seed recorded as null).\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nimport statsmodels.api as sm\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nDATA, OUT = \"data\", \"out\"\nos.makedirs(OUT, exist_ok=True)\nread = lambda f: pd.read_csv(os.path.join(DATA, f), keep_default_na=False, na_values=[\"\"])\nind, ent = read(\"wb_indicators.csv\"), read(\"wb_entities.csv\")\nn_input_rows = {\"wb_indicators.csv\": int(len(ind)), \"wb_entities.csv\": int(len(ent))}\nissues, excl = [], []\n\ndef log_excl(step, rule, before, after, why):\n    excl.append(dict(step=step, rule=rule, n_before=int(before), n_after=int(after),\n                     n_removed=int(before - after), rationale=why))\n\n# integrity checks\nassert not ind.duplicated([\"iso3\", \"year\"]).any() and not ent.iso3.duplicated().any()\nassert set(ind.iso3) <= set(ent.iso3)\nassert (ind.gdp_pc_ppp.dropna() > 0).all()\n\n# Step 1: drop aggregates\nm = ind.merge(ent[[\"iso3\", \"region\", \"income_level\"]], on=\"iso3\", how=\"left\")\nn0 = len(m)\nn_agg_ent = int(m.loc[m.region == \"Aggregates\", \"iso3\"].nunique())\nc = m[m.region != \"Aggregates\"].copy()\nlog_excl(1, \"drop entity-years whose region == 'Aggregates' in wb_entities.csv\", n0, len(c),\n         \"information rule: countries/economies only\")\nissues.append({\"issue\": f\"wb_indicators.csv contains {n_agg_ent} regional/income/other aggregate entities \"\n               f\"({n0 - len(c)} entity-year rows)\", \"handling\": \"excluded via region == 'Aggregates' (rule)\"})\n\n# Step 2: analysis year selection\ncov = c.assign(both=c.gdp_pc_ppp.notna() & c.life_expectancy.notna()).groupby(\"year\").both.sum()\nelig = cov[cov >= 150]\nanalysis_year = int(elig.index.max())\nn1 = len(c)\ny = c[c.year == analysis_year].copy()\nlog_excl(2, f\"keep analysis year {analysis_year} (latest year with >=150 complete countries)\", n1, len(y),\n         \"information rule: single cross-section in analysis year\")\n\n# Step 3: drop missing\nmiss = y[y.gdp_pc_ppp.isna() | y.life_expectancy.isna()]\nn_excluded_missing = int(len(miss))\nn2 = len(y)\nd = y.dropna(subset=[\"gdp_pc_ppp\", \"life_expectancy\"]).copy()\nlog_excl(3, \"drop countries with gdp_pc_ppp or life_expectancy missing in analysis year\", n2, len(d),\n         \"information rule: no imputation\")\nissues.append({\"issue\": f\"{n_excluded_missing} countries/economies lack an indicator in {analysis_year} \"\n               f\"(gdp missing: {int(miss.gdp_pc_ppp.isna().sum())}, LE missing: {int(miss.life_expectancy.isna().sum())}): \"\n               + \", \".join(sorted(miss.iso3)),\n               \"handling\": \"excluded, counted in n_excluded_missing; possible selection bias discussed in report\"})\nissues.append({\"issue\": \"Central African Republic life expectancy series is implausibly volatile in 2015-2022 \"\n               \"(e.g. 18.8 years in 2022, 31.5 in 2019); 2023 value 57.4 is plausible\",\n               \"handling\": \"affects non-analysis years only; 2023 value kept; noted as data-quality caveat\"})\nissues.append({\"issue\": \"West Bank and Gaza life expectancy drops from 76.7 (2022) to 65.2 (2023), consistent with \"\n               \"conflict mortality but a large outlier relative to its income\",\n               \"handling\": \"kept (genuine reported value); influence assessed in leave-out sensitivity\"})\n\n# Estimation\nd[\"ln_gdp\"] = np.log(d.gdp_pc_ppp)\nX = sm.add_constant(d.ln_gdp.values)\nyv = d.life_expectancy.values\nfit = sm.OLS(yv, X).fit(cov_type=\"HC3\")\nalpha, beta = float(fit.params[0]), float(fit.params[1])\nse = float(fit.bse[1])\nz = 1.959963984540054\nci_lo, ci_hi = beta - z * se, beta + z * se\nresid = yv - (alpha + beta * d.ln_gdp.values)\nbaseline_rmse = float(np.sqrt(np.mean((yv - yv.mean()) ** 2)))\nmodel_rmse = float(np.sqrt(np.mean(resid ** 2)))\nr = float(np.corrcoef(d.ln_gdp, yv)[0, 1])\nconclusion = \"positive_effect\" if ci_lo > 0 else (\"negative_effect\" if ci_hi < 0 else \"no_detectable_effect\")\n\n# Supplementary (not estimand): other robust SEs, sensitivity dropping the 5 most influential obs (Cook's D)\nfit_ols = sm.OLS(yv, X).fit()\nse_alt = {k: float(sm.OLS(yv, X).fit(cov_type=k).bse[1]) for k in [\"HC0\", \"HC1\", \"HC2\", \"HC3\"]}\ncooks = fit_ols.get_influence().cooks_distance[0]\ntop = np.argsort(cooks)[::-1][:5]\nkeep = np.setdiff1d(np.arange(len(d)), top)\nfit_s = sm.OLS(yv[keep], X[keep]).fit(cov_type=\"HC3\")\nd[\"resid\"] = resid\n\nresults = {\n    \"task_id\": \"F6-dev-01\",\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"analysis_year\": analysis_year,\n    \"n_excluded_missing\": n_excluded_missing,\n    \"n_analysed\": int(len(d)),\n    \"baseline_metric\": baseline_rmse,\n    \"model_metric\": model_rmse,\n    \"pearson_r\": r,\n    \"r_squared\": float(fit.rsquared),\n    \"effect_estimate\": beta,\n    \"intercept\": alpha,\n    \"effect_se\": se,\n    \"effect_ci_low\": float(ci_lo),\n    \"effect_ci_high\": float(ci_hi),\n    \"conclusion\": conclusion,\n    \"data_issues\": issues,\n    \"n_input_rows\": n_input_rows,\n    \"random_seed\": None,\n    \"se_type\": \"HC3\",\n    \"complete_countries_by_year\": {str(k): int(v) for k, v in cov.items()},\n    \"n_countries_in_analysis_year\": int(n2),\n    \"supp_effect_se_by_type\": se_alt,\n    \"supp_effect_se_classical\": float(fit_ols.bse[1]),\n    \"supp_mean_life_expectancy\": float(yv.mean()),\n    \"supp_sens_drop5_cooks_iso3\": [str(s) for s in d.iso3.values[top]],\n    \"supp_sens_drop5_effect_estimate\": float(fit_s.params[1]),\n    \"supp_sens_drop5_effect_se\": float(fit_s.bse[1]),\n    \"supp_sens_drop5_r_squared\": float(fit_s.rsquared),\n    \"supp_largest_neg_residual_iso3\": str(d.iso3.values[np.argmin(resid)]),\n    \"supp_largest_neg_residual\": float(resid.min()),\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(results, f, indent=2)\n\npd.DataFrame(excl, columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]).to_csv(\n    os.path.join(OUT, \"exclusions.csv\"), index=False)\n\norder = [\"Low income\", \"Lower middle income\", \"Upper middle income\", \"High income\"]\nt = (d.groupby(\"income_level\").agg(n_countries=(\"iso3\", \"size\"), mean_log_gdp_pc=(\"ln_gdp\", \"mean\"),\n                                   mean_life_expectancy=(\"life_expectancy\", \"mean\")).reset_index())\nt[\"o\"] = t.income_level.map({k: i for i, k in enumerate(order)}).fillna(99)\nt = t.sort_values([\"o\", \"income_level\"]).drop(columns=\"o\")\nt.to_csv(os.path.join(OUT, \"table1.csv\"), index=False, float_format=\"%.4f\")\n\nfig, ax = plt.subplots(figsize=(7, 5))\ncols = dict(zip(order, [\"#d7191c\", \"#fdae61\", \"#abd9e9\", \"#2c7bb6\"]))\nfor lvl in order:\n    s = d[d.income_level == lvl]\n    ax.scatter(s.ln_gdp, s.life_expectancy, s=18, c=cols[lvl], label=f\"{lvl} (n={len(s)})\", edgecolor=\"k\", lw=0.3)\nxs = np.linspace(d.ln_gdp.min(), d.ln_gdp.max(), 100)\nax.plot(xs, alpha + beta * xs, \"k-\", lw=1.5, label=f\"OLS: LE = {alpha:.1f} + {beta:.2f} ln(GDPpc)\")\nax.set_xlabel(\"ln(GDP per capita, PPP, constant 2021 intl $)\")\nax.set_ylabel(\"Life expectancy at birth (years)\")\nax.set_title(f\"Preston curve, {analysis_year} (n={len(d)} countries/economies)\")\nax.legend(fontsize=8, loc=\"lower right\")\nfig.tight_layout()\nfig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=120, metadata={\"Software\": None})\nplt.close(fig)\n\nprint(f\"year={analysis_year} n={len(d)} excluded_missing={n_excluded_missing} beta={beta:.4f} \"\n      f\"HC3 se={se:.4f} CI=[{ci_lo:.4f},{ci_hi:.4f}] R2={fit.rsquared:.4f} conclusion={conclusion}\")\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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","media_type":"image/png"}}]}]},"uuid":"ce964a41-e841-4e6b-b673-ed639526318c","timestamp":"2026-10-30T03:52:04.130Z","toolUseResult":{"type":"image","file":{"base64":"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cross-check of the numbers, then audit/check files."},{"type":"tool_use","id":"toolu_012MGAobj6JCdm1qffqxavg4","name":"Bash","input":{"command":"rm -f out/results.json out/table1.csv out/figure1.png out/exclusions.csv && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp out/results.json /tmp/r1.json && echo IDENTICAL; ~/wp29/venv/bin/python -c \"\nimport json,numpy as np,pandas as pd\nR=json.load(open('out/results.json'))\ni=pd.read_csv('data/wb_indicators.csv');e=pd.read_csv('data/wb_entities.csv',keep_default_na=False)\nagg=set(e.iso3[e.region=='Aggregates']); d=i[(i.year==2023)&~i.iso3.isin(agg)].dropna()\nx=np.log(d.gdp_pc_ppp.values);y=d.life_expectancy.values;n=len(x)\nX=np.c_[np.ones(n),x];b=np.linalg.solve(X.T@X,X.T@y);e_=y-X@b\nXtXi=np.linalg.inv(X.T@X);h=np.einsum('ij,jk,ik->i',X,XtXi,X)\nV=XtXi@(X.T*(e_**2/(1-h)**2))@X@XtXi\nprint(n,b,np.sqrt(V[1,1]), np.sqrt(np.mean(e_**2)), np.std(y))\nprint('slope match',abs(b[1]-R['effect_estimate'])<1e-9,'se match',abs(np.sqrt(V[1,1])-R['effect_se'])<1e-9)\nprint('r2=r^2',abs(R['pearson_r']**2-R['r_squared'])<1e-12,'rmse identity',abs(R['model_metric']**2-(1-R['r_squared'])*R['baseline_metric']**2)<1e-9)\nprint('ci contains',R['effect_ci_low']<R['effect_estimate']<R['effect_ci_high'])\nt=pd.read_csv('out/table1.csv');print('table n sum',t.n_countries.sum())\n\"","description":"Clean rerun and independent 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  \"task_id\": \"F6-dev-01\",\n---\n>   \"task_id\": \"F5-dev-01\",\n>   \"x1_demand\": 16.8,\n>   \"x2_demand\": 19.5,\n>   \"indirect_utility\": 2.9182520128635305,\n>   \"budget_identity_residual\": 0.0,\n>   \"mrs_at_optimum\": 0.625,\n>   \"price_ratio\": 0.625,\n>   \"expenditure_share_good1\": 0.35,\n>   \"indirect_utility_numerical\": 2.9182520128635305,\n5,35c13\n<   \"analysis_year\": 2023,\n<   \"n_excluded_missing\": 20,\n<   \"n_analysed\": 197,\n<   \"baseline_metric\": 7.07645404460588,\n<   \"model_metric\": 3.5949979094078177,\n<   \"pearson_r\": 0.8613437957840087,\n<   \"r_squared\": 0.7419131345356038,\n<   \"effect_estimate\": 5.267173451958655,\n<   \"intercept\": 22.56709274982858,\n<   \"effect_se\": 0.21067528691162227,\n<   \"effect_ci_low\": 4.854257477179233,\n<   \"effect_ci_high\": 5.6800894267380775,\n<   \"conclusion\": \"positive_effect\",\n<   \"data_issues\": [\n<     {\n<       \"issue\": \"wb_indicators.csv contains 44 regional/income/other aggregate entities (396 entity-year rows)\",\n<       \"handling\": \"excluded via region == 'Aggregates' (rule)\"\n<     },\n<     {\n<       \"issue\": \"20 countries/economies lack an indicator in 2023 (gdp missing: 20, LE missing: 0): ASM, CHI, CUB, ERI, GIB, GUM, IMN, LIE, MAF, MCO, MNP, NCL, PRK, PYF, SSD, SYR, VEN, VGB, VIR, YEM\",\n<       \"handling\": \"excluded, counted in n_excluded_missing; possible selection bias discussed in report\"\n<     },\n<     {\n<       \"issue\": \"Central African Republic life expectancy series is implausibly volatile in 2015-2022 (e.g. 18.8 years in 2022, 31.5 in 2019); 2023 value 57.4 is plausible\",\n<       \"handling\": \"affects non-analysis years only; 2023 value kept; noted as data-quality caveat\"\n<     },\n<     {\n<       \"issue\": \"West Bank and Gaza life expectancy drops from 76.7 (2022) to 65.2 (2023), consistent with conflict mortality but a large outlier relative to its income\",\n<       \"handling\": \"kept (genuine reported value, no rule permits dropping it); a leave-5-most-influential-out (Cook distance) sensitivity is reported\"\n<     }\n<   ],\n---\n>   \"data_issues\": [],\n37,38c15\n<     \"wb_indicators.csv\": 2349,\n<     \"wb_entities.csv\": 296\n---\n>     \"parameters.csv\": 4\n39a17\n>   \"n_analysed\": 4,\n41,73c19,40\n<   \"se_type\": \"HC3\",\n<   \"complete_countries_by_year\": {\n<     \"2015\": 199,\n<     \"2016\": 199,\n<     \"2017\": 199,\n<     \"2018\": 199,\n<     \"2019\": 199,\n<     \"2020\": 199,\n<     \"2021\": 199,\n<     \"2022\": 199,\n<     \"2023\": 197\n<   },\n<   \"n_countries_in_analysis_year\": 217,\n<   \"supp_effect_se_by_type\": {\n<     \"HC0\": 0.20736485591704354,\n<     \"HC1\": 0.2084255526375312,\n<     \"HC2\": 0.20901105806015902,\n<     \"HC3\": 0.21067528691162227\n<   },\n<   \"supp_effect_se_classical\": 0.22246758287864518,\n<   \"supp_mean_life_expectancy\": 73.47408158883249,\n<   \"supp_sens_drop5_cooks_iso3\": [\n<     \"NGA\",\n<     \"GRL\",\n<     \"TCD\",\n<     \"SLB\",\n<     \"GUY\"\n<   ],\n<   \"supp_sens_drop5_effect_estimate\": 5.317920624114932,\n<   \"supp_sens_drop5_effect_se\": 0.18313966978067114,\n<   \"supp_sens_drop5_r_squared\": 0.7826492360851807,\n<   \"supp_largest_neg_residual_iso3\": \"NGA\",\n<   \"supp_largest_neg_residual\": -15.337823881620139\n---\n>   \"numerical_validation\": {\n>     \"bounded_scalar\": {\n>       \"x1\": 16.800000228799448,\n>       \"x2\": 19.499999857000347,\n>       \"u\": 2.9182520128635305,\n>       \"success\": true\n>     },\n>     \"grid\": {\n>       \"n_points\": 2000001,\n>       \"x1\": 16.799990400048,\n>       \"x2\": 19.50000599997,\n>       \"u\": 2.9182520128634426\n>     },\n>     \"slsqp_2d\": {\n>       \"x1\": 16.79999279458207,\n>       \"x2\": 19.500004503386222,\n>       \"u\": 2.9182520128634817,\n>       \"success\": true,\n>       \"budget_slack\": -5.684341886080802e-14\n>     },\n>     \"abs_diff_v_closed_vs_numerical\": 0.0\n>   }\n{\n  \"task_id\": \"F5-dev-01\",\n  \"x1_demand\": 16.8,","is_error":false}]},"uuid":"de03f198-df8b-485f-9c2a-309d892a884a","timestamp":"2026-10-30T03:52:16.027Z","toolUseResult":{"stdout":"2c2,10\n<   \"task_id\": \"F6-dev-01\",\n---\n>   \"task_id\": \"F5-dev-01\",\n>   \"x1_demand\": 16.8,\n>   \"x2_demand\": 19.5,\n>   \"indirect_utility\": 2.9182520128635305,\n>   \"budget_identity_residual\": 0.0,\n>   \"mrs_at_optimum\": 0.625,\n>   \"price_ratio\": 0.625,\n>   \"expenditure_share_good1\": 0.35,\n>   \"indirect_utility_numerical\": 2.9182520128635305,\n5,35c13\n<   \"analysis_year\": 2023,\n<   \"n_excluded_missing\": 20,\n<   \"n_analysed\": 197,\n<   \"baseline_metric\": 7.07645404460588,\n<   \"model_metric\": 3.5949979094078177,\n<   \"pearson_r\": 0.8613437957840087,\n<   \"r_squared\": 0.7419131345356038,\n<   \"effect_estimate\": 5.267173451958655,\n<   \"intercept\": 22.56709274982858,\n<   \"effect_se\": 0.21067528691162227,\n<   \"effect_ci_low\": 4.854257477179233,\n<   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0.21067528691162227\n<   },\n<   \"supp_effect_se_classical\": 0.22246758287864518,\n<   \"supp_mean_life_expectancy\": 73.47408158883249,\n<   \"supp_sens_drop5_cooks_iso3\": [\n<     \"NGA\",\n<     \"GRL\",\n<     \"TCD\",\n<     \"SLB\",\n<     \"GUY\"\n<   ],\n<   \"supp_sens_drop5_effect_estimate\": 5.317920624114932,\n<   \"supp_sens_drop5_effect_se\": 0.18313966978067114,\n<   \"supp_sens_drop5_r_squared\": 0.7826492360851807,\n<   \"supp_largest_neg_residual_iso3\": \"NGA\",\n<   \"supp_largest_neg_residual\": -15.337823881620139\n---\n>   \"numerical_validation\": {\n>     \"bounded_scalar\": {\n>       \"x1\": 16.800000228799448,\n>       \"x2\": 19.499999857000347,\n>       \"u\": 2.9182520128635305,\n>       \"success\": true\n>     },\n>     \"grid\": {\n>       \"n_points\": 2000001,\n>       \"x1\": 16.799990400048,\n>       \"x2\": 19.50000599997,\n>       \"u\": 2.9182520128634426\n>     },\n>     \"slsqp_2d\": {\n>       \"x1\": 16.79999279458207,\n>       \"x2\": 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PSE 2023 LE 65.2 vs 76.7 in 2022 (Gaza war) — kept. Large GDP jumps (GUY oil boom, MAC Covid) are real.\n\n**Information-availability**: no input violates the rules; aggregates are removed by the region flag; only the listed files are used.\nEOF\ncat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n| check | outcome |\n|---|---|\n| Clean re-run (deleted outputs, reran analysis.py): results.json byte-identical, table1.csv & figure1.png SHA-256 identical | PASS |\n| Independent numpy re-implementation of OLS slope and HC3 SE matches results to <1e-9 | PASS |\n| r_squared == pearson_r^2 | PASS |\n| model_metric^2 == (1−R²)·baseline_metric^2 | PASS |\n| effect_se > 0, baseline_metric > 0, model_metric > 0 | PASS |\n| effect_ci_low < effect_estimate < effect_ci_high; CI = β ± 1.96·SE | PASS |\n| conclusion consistent with CI (lower bound 4.85 > 0 → positive_effect) | PASS |\n| Exclusion ledger: 2349 − (396+1736+20) = 197 = n_analysed | PASS |\n| n_countries_in_analysis_year 217 = n_analysed 197 + n_excluded_missing 20 | PASS |\n| table1 n_countries sums to 197; mean LE increases monotonically with income group | PASS |\n| model_metric (3.59) < baseline_metric (7.08) | PASS |\n| Magnitude plausibility: slope ~5.3 years per log point, in the range commonly reported for Preston-curve cross-sections | PASS (informal) |\n| Information rule: analysis year 2023 is latest year with ≥150 complete countries (197) | PASS |\n| Note: a first reproducibility attempt compared against a /tmp copy that was overwritten by an unrelated process; redone with a workspace-local copy (out/state/repro/) | resolved |\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 recorded and match README\",\"schema/missing/duplicates/ranges audited (data_audit.md)\",\"exclusions.csv sums: 2349-2152=197\"],\"notes\":\"\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"baseline_metric (mean-only RMSE) = 7.076 computed on same 197 countries\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, no randomness (random_seed null), Agg backend\",\"outputs results.json, figure1.png, table1.csv, exclusions.csv\"],\"notes\":\"\"}\np['P4']={\"status\":\"running\",\"checks\":[\"clean rerun byte-identical (out/state/repro)\",\"independent HC3 recomputation matches\",\"all identities in checks.md pass\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-29 P1 passed (audit; analysis year 2023, 197 countries, 20 GDP-missing excluded).\n- 2026-10-29 P2/P3 passed: baseline RMSE 7.08; OLS HC3 β=5.27. Decision: HC3 SE, normal 1.96 CI; supplementary leave-5-out sensitivity.\n- 2026-10-29 P4 checks done; running gate.\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Write audit, checks, update ledger, run gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":384,"cache_read_input_tokens":30513,"output_tokens":2207,"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":384},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXk4LNNwbGStAhgZuU8X","requestRef":"f3233800-555b-405b-b943-f84a9c5ccfcb","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ce81678f-301d-4d4e-a76d-dd16aa35da96","timestamp":"2026-10-30T03:52:41.531Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0014","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","version":"2.1.314","gitBranch":"HEAD"}91{"parentUuid":"ce81678f-301d-4d4e-a76d-dd16aa35da96","isSidechain":false,"promptId":"cced29be-8a0c-46b8-9fc9-c2501f16d592","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nPGAR GATE pgar_gate/1.0 — FAILED (10 of 40 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] file:out/figure1.png: present\n  [ok] file:out/table1.csv: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [ok] key:analysis_year: ok\n  [ok] key:n_excluded_missing: ok\n  [ok] key:baseline_metric: ok\n  [ok] key:model_metric: ok\n  [ok] key:pearson_r: ok\n  [ok] key:r_squared: ok\n  [ok] key:effect_estimate: ok\n  [ok] key:intercept: ok\n  [ok] key:effect_se: ok\n  [ok] key:effect_ci_low: ok\n  [ok] key:effect_ci_high: ok\n  [ok] key:conclusion: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] exclusion_ledger: 3 steps consistent\n  [ok] ci:effect_estimate: 4.854257477179233 <= 5.267173451958655 <= 5.6800894267380775\n  [ok] positive:effect_se: 0.21067528691162227\n  [ok] positive:baseline_metric: 7.07645404460588\n  [ok] positive:model_metric: 3.5949979094078177\n  [ok] reproducibility: regenerated in 1.1s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RP0014/out/state/gate_receipts/20261030T035242.771389Z.json","is_error":true,"tool_use_id":"toolu_01WRnGCi45vLifZRoSa8S7VM"}]},"uuid":"5c65ff46-676e-4c56-9567-4e7dd776d879","timestamp":"2026-10-30T03:52:42.780Z","toolUseResult":"Error: Exit code 1\nPGAR GATE pgar_gate/1.0 — FAILED (10 of 40 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] file:out/figure1.png: present\n  [ok] file:out/table1.csv: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [ok] key:analysis_year: ok\n  [ok] key:n_excluded_missing: ok\n  [ok] key:baseline_metric: ok\n  [ok] key:model_metric: ok\n  [ok] key:pearson_r: ok\n  [ok] key:r_squared: ok\n  [ok] key:effect_estimate: ok\n  [ok] key:intercept: ok\n  [ok] key:effect_se: ok\n  [ok] key:effect_ci_low: ok\n  [ok] key:effect_ci_high: ok\n  [ok] key:conclusion: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] exclusion_ledger: 3 steps consistent\n  [ok] ci:effect_estimate: 4.854257477179233 <= 5.267173451958655 <= 5.6800894267380775\n  [ok] positive:effect_se: 0.21067528691162227\n  [ok] positive:baseline_metric: 7.07645404460588\n  [ok] positive:model_metric: 3.5949979094078177\n  [ok] reproducibility: regenerated in 1.1s; 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No duplicate (iso3, year). All iso3 codes present in wb_entities.csv.\nNote: \"NA\" (Namibia's ISO2) is not an issue since codes are ISO3; reading uses keep_default_na=False with only '' as missing, to be safe.\n\n**wb_entities.csv**: 296 entities, no missing; region == Aggregates for 79 (44 of which appear in indicators; 35 have no indicator rows). 217 countries/economies; income levels: High 86, UMC 59, LMC 47, Low 25 (in metadata; no \"Not classified\" among countries).\n\n**Coverage (countries with both indicators)**: 199 each year 2015–2022, 197 in 2023 → analysis year 2023 (≥150 rule).\n**2023 missing (20, all GDP missing)**: ASM, CHI, CUB, ERI, GIB, GUM, IMN, LIE, MAF, MCO, MNP, NCL, PRK, PYF, SSD, SYR, VEN, VGB, VIR, YEM — mix of small high-income territories and conflict/sanctioned low/middle-income states (non-random missingness).\n\n**Anomalies**: CAF life expectancy volatile 2015–2022 (18.8 in 2022, 31.5 in 2019) — not in analysis year; 2023 = 57.4 plausible. PSE 2023 LE 65.2 vs 76.7 in 2022 (Gaza war) — kept. Large GDP jumps (GUY oil boom, MAC Covid) are real.\n\n**Information-availability**: no input violates the rules; aggregates are removed by the region flag; only the listed files are used.\nEOF\ncat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n| check | outcome |\n|---|---|\n| Clean re-run (deleted outputs, reran analysis.py): results.json byte-identical, table1.csv & figure1.png SHA-256 identical | PASS |\n| Independent numpy re-implementation of OLS slope and HC3 SE matches results to <1e-9 | PASS |\n| r_squared == pearson_r^2 | PASS |\n| model_metric^2 == (1−R²)·baseline_metric^2 | PASS |\n| effect_se > 0, baseline_metric > 0, model_metric > 0 | PASS |\n| effect_ci_low < effect_estimate < effect_ci_high; CI = β ± 1.96·SE | PASS |\n| conclusion consistent with CI (lower bound 4.85 > 0 → positive_effect) | PASS |\n| Exclusion ledger: 2349 − (396+1736+20) = 197 = n_analysed | PASS |\n| n_countries_in_analysis_year 217 = n_analysed 197 + n_excluded_missing 20 | PASS |\n| table1 n_countries sums to 197; mean LE increases monotonically with income group | PASS |\n| model_metric (3.59) < baseline_metric (7.08) | PASS |\n| Magnitude plausibility: slope ~5.3 years per log point, in the range commonly reported for Preston-curve cross-sections | PASS (informal) |\n| Information rule: analysis year 2023 is latest year with ≥150 complete countries (197) | PASS |\n| Note: a first reproducibility attempt compared against a /tmp copy that was overwritten by an unrelated process; redone with a workspace-local copy (out/state/repro/) | resolved |\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 recorded and match README\",\"schema/missing/duplicates/ranges audited (data_audit.md)\",\"exclusions.csv sums: 2349-2152=197\"],\"notes\":\"\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"baseline_metric (mean-only RMSE) = 7.076 computed on same 197 countries\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, no randomness (random_seed null), Agg backend\",\"outputs results.json, figure1.png, table1.csv, exclusions.csv\"],\"notes\":\"\"}\np['P4']={\"status\":\"running\",\"checks\":[\"clean rerun byte-identical (out/state/repro)\",\"independent HC3 recomputation matches\",\"all identities in checks.md pass\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-29 P1 passed (audit; analysis year 2023, 197 countries, 20 GDP-missing excluded).\n- 2026-10-29 P2/P3 passed: baseline RMSE 7.08; OLS HC3 β=5.27. Decision: HC3 SE, normal 1.96 CI; supplementary leave-5-out sensitivity.\n- 2026-10-29 P4 checks done; running gate.\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Write audit, checks, update ledger, run gate"}}]}}93{"type":"api-request","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","id":"993dc9cb-4c1e-417d-ab5c-112cbe46e339","timestamp":"2026-10-30T03:52:42.784Z","version":"2.1.314","querySource":"sdk","shapeHash":"a1a7fae70b72e9ac39b3275721793ee12a68cf4c21397754b79cb5e12e871183","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":"f3233800-555b-405b-b943-f84a9c5ccfcb","keep":28,"tail":["8e9a706125431a9f5d950ca6dc99800f62d82c895e8ac53b706186991a20337e","59e173eec62c645c4c6dccd8ce8609426249c01fbf1af1c166c22049cd9a13bf"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",29,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"a73c634365127c97c75cd92dd606a84f51b1cd061884e7a9c2e22126409af48a"}94{"parentUuid":"5c65ff46-676e-4c56-9567-4e7dd776d879","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXk5x1mMKkAavKfXhDdM","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":3082,"cache_read_input_tokens":30897,"output_tokens":3687,"output_tokens_details":{"thinking_tokens":63},"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":3082},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":850,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXk5wktHZco2CwMevwvA","requestRef":"993dc9cb-4c1e-417d-ab5c-112cbe46e339","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a6fcffee-6cef-43e3-b1c3-4486b065ebea","timestamp":"2026-10-30T03:52:45.082Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0014","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","version":"2.1.314","gitBranch":"HEAD"}95{"parentUuid":"a6fcffee-6cef-43e3-b1c3-4486b065ebea","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXk5x1mMKkAavKfXhDdM","type":"message","role":"assistant","content":[{"type":"text","text":"P4-relevant gate checks pass (keys, CI, positivity, exclusions, reproducibility); the remaining failures are P5 deliverables. Writing report and claims."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3082,"cache_read_input_tokens":30897,"output_tokens":3687,"output_tokens_details":{"thinking_tokens":63},"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":3082},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXk5wktHZco2CwMevwvA","requestRef":"993dc9cb-4c1e-417d-ab5c-112cbe46e339","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ab16a12d-b706-43c1-b17f-b557f2eb0391","timestamp":"2026-10-30T03:52:45.082Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0014","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","version":"2.1.314","gitBranch":"HEAD"}96{"parentUuid":"ab16a12d-b706-43c1-b17f-b557f2eb0391","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXk5x1mMKkAavKfXhDdM","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KAUowYPKcuMtsy2N2eWBPJ","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F6-dev-01 — Preston curve in the latest usable WDI year\n\n## Question\nAcross countries/economies, how strongly is life expectancy at birth (LE, years) associated with the natural log of GDP per capita (PPP, constant 2021 intl $) in the most recent year with usable World Bank data? Estimand: slope β of the linear projection LE = α + β·ln(GDPpc) + ε, one equally weighted observation per country. The interpretation is descriptive/associational, not causal.\n\n## Data and exclusions\nInputs: `data/wb_indicators.csv` (2349 rows) and `data/wb_entities.csv` (296 rows) (`n_input_rows`); SHA-256 hashes match `data/README.md` (see `out/state/data_audit.md`). Exclusion ledger (`out/exclusions.csv`):\n1. Dropped 396 entity-year rows belonging to 44 aggregate entities (region = `Aggregates`) → 1953 rows, 217 countries/economies.\n2. Analysis year: countries with both indicators number 199 in every year 2015–2022 and 197 in 2023 (`complete_countries_by_year`); the latest year with ≥150 is **2023** (`analysis_year`). Kept 217 country rows (`n_countries_in_analysis_year`).\n3. Dropped 20 countries with a missing indicator in 2023 (`n_excluded_missing`); all 20 lack GDP (life expectancy is never missing). No imputation was done. The analysis uses **197** countries (`n_analysed`).\n\n2349 − 396 − 1736 − 20 = 197. Data problems and how each was handled are listed in `data_issues`: the aggregates; the 20 missing countries (ASM, CHI, CUB, ERI, GIB, GUM, IMN, LIE, MAF, MCO, MNP, NCL, PRK, PYF, SSD, SYR, VEN, VGB, VIR, YEM); an implausibly volatile Central African Republic LE series in earlier years (its 2023 value of 57.4 looks plausible and was kept); and the drop in West Bank and Gaza LE in 2023 (kept as reported).\n\n## Method\n- Baseline: predict every country's LE with the cross-country mean. Its RMSE is `baseline_metric`.\n- Main model: OLS of LE on ln(GDPpc) over the 197 countries, unweighted. Standard errors are heteroskedasticity-robust **HC3** (`se_type`). HC3 was picked because it is the most conservative of HC0–HC3 in moderate samples. The 95% CI is β ± 1.96·SE (normal approximation). The fit metric is the RMSE of the in-sample residuals (`model_metric`).\n- Conclusion rule as given in TASK.md: `positive_effect` if the 95% CI lies entirely above 0.\n- Supplementary check, not part of the estimand: re-estimate after dropping the 5 countries with the largest Cook's distance (`supp_sens_drop5_*`). HC0–HC2 and classical SEs are also reported (`supp_effect_se_by_type`, `supp_effect_se_classical`).\n- `out/analysis.py` regenerates everything deterministically. It uses no randomness (`random_seed` = null).\n- Outputs: `out/figure1.png` (scatter plot with the OLS line) and `out/table1.csv` (means by income group).\n\n## Results\n- Slope: **β = 5.27 years per log point** of GDP per capita (`effect_estimate` = 5.267). Intercept 22.57 years (`intercept`). A 10% income difference therefore goes with about 0.50 more years of LE (5.267·ln 1.1, derived from `effect_estimate`). A doubling goes with about 3.65 years (5.267·ln 2).\n- Strength: Pearson r = 0.861 (`pearson_r`), R² = 0.742 (`r_squared`).\n- Fit: residual RMSE is 3.59 years (`model_metric`), against 7.08 years for the mean-only baseline (`baseline_metric`). ln(GDPpc) roughly halves the prediction error.\n- Conclusion: `positive_effect`. In 2023 the Preston-curve association holds and is strong.\n- Table 1 (`out/table1.csv`): mean LE rises with income group, from 63.2 years (Low income, n=20) to 67.8 (Lower middle income, n=46), 73.7 (Upper middle income, n=58) and 79.7 (High income, n=73).\n- Largest negative residual: Nigeria, −15.3 years (`supp_largest_neg_residual_iso3`, `supp_largest_neg_residual`).\n\n## Uncertainty\n- HC3 SE = 0.211 (`effect_se`). 95% CI for β: [4.85, 5.68] (`effect_ci_low`, `effect_ci_high`), well away from zero.\n- The choice among robust SEs makes little difference: HC0 0.207, HC1 0.208, HC2 0.209, HC3 0.211, classical 0.222 (`supp_effect_se_by_type`, `supp_effect_se_classical`).\n- Dropping the 5 most influential countries (NGA, GRL, TCD, SLB, GUY; `supp_sens_drop5_cooks_iso3`) gives β = 5.32 (SE 0.183), inside the main CI (`supp_sens_drop5_effect_estimate`, `supp_sens_drop5_effect_se`).\n- The CI covers sampling-style uncertainty only, treating countries as independent draws from a superpopulation. The 197 countries are close to the full population of economies, so this is a modelling convention. The CI does not cover measurement error, model misspecification or non-random missingness.\n\n## Limitations and open issues\n- **No causal interpretation.** β is an association. Confounders such as institutions, education, health technology, disease environment and conflict affect both income and LE, and causation can also run from health to income.\n- **Functional form.** The linear-in-log line is a projection. The figure shows curvature at the top: LE flattens among high-income economies, and the low-income residuals are widely spread. A nonlinear or spline fit was not estimated, so β is an average slope over the income range, not a constant marginal effect.\n- **Non-random missingness.** The 20 excluded economies are either small high-income territories (e.g. MCO, LIE, GIB) or conflict-affected/sanctioned states (SSD, SYR, YEM, VEN, PRK, CUB, ERI). Their direction of bias is unclear, and no bounding analysis was done.\n- **Measurement and data quality.** GDP PPP figures for fragile states and small territories are imprecise, and errors in ln(GDPpc) would bias β toward zero (attenuation). LE estimates for countries without complete vital registration are modelled, not observed. The CAF series shows implausible year-to-year swings (18.8 years in 2022), which suggests vintage or modelling artefacts in the LE data. 2023 values were not validated against external sources.\n- **Single year.** 2023 can still carry post-COVID mortality effects and conflict shocks (e.g. West Bank and Gaza). Stability across 2015–2022 was not checked because the task restricts the analysis to the selected year.\n- **Equal weighting.** Each economy counts once whatever its population, so tiny territories weigh as much as India or China. A population-weighted slope would answer a different question and was not computed.\n- **What would invalidate the result:** a misclassification of aggregates as countries (checked: all 44 aggregate entities in the file were removed via the region flag); a systematic revision of the 2023 WDI vintage; or leverage concentrated in a few points (partly checked with the leave-5-out sensitivity, which did not change the conclusion). Not checked: heavier-tailed inference (e.g. bootstrap), spatial or regional correlation of errors (regional clustering would probably widen the CI), and outliers beyond the 5 most influential points.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The analysis year is 2023, the latest year with at least 150 countries/economies observing both indicators; 197 countries are analysed and 20 are excluded for missing GDP.\",\"results_keys\":[\"analysis_year\",\"n_analysed\",\"n_excluded_missing\",\"complete_countries_by_year\"],\"status\":\"supported\",\"caveat\":\"Missing countries are not random (small territories and conflict/sanctioned states).\"},\n {\"id\":\"C2\",\"text\":\"The OLS slope of life expectancy on ln(GDP per capita) is about 5.27 years per log point, with HC3 95% CI [4.85, 5.68]; conclusion positive_effect.\",\"results_keys\":[\"effect_estimate\",\"effect_se\",\"effect_ci_low\",\"effect_ci_high\",\"conclusion\",\"se_type\"],\"status\":\"supported\",\"caveat\":\"Associational only; CI ignores measurement error and misspecification.\"},\n {\"id\":\"C3\",\"text\":\"The relationship is strong: Pearson r = 0.861, R^2 = 0.742, and log income reduces prediction RMSE from 7.08 (mean-only baseline) to 3.59 years.\",\"results_keys\":[\"pearson_r\",\"r_squared\",\"baseline_metric\",\"model_metric\"],\"status\":\"supported\",\"caveat\":\"In-sample fit; linear-in-log form misses curvature at high incomes.\"},\n {\"id\":\"C4\",\"text\":\"The slope is robust to the choice of robust SE type and to dropping the 5 most influential countries (beta = 5.32).\",\"results_keys\":[\"supp_effect_se_by_type\",\"supp_effect_se_classical\",\"supp_sens_drop5_effect_estimate\",\"supp_sens_drop5_effect_se\",\"supp_sens_drop5_cooks_iso3\"],\"status\":\"supported\",\"caveat\":\"Limited sensitivity set; no clustering, bootstrap or nonlinear specifications.\"},\n {\"id\":\"C5\",\"text\":\"Mean life expectancy rises monotonically across World Bank income groups, from 63.2 (Low) to 79.7 years (High).\",\"results_keys\":[\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Numbers are in out/table1.csv, generated by analysis.py; group sizes are unequal (20 low-income countries).\"}\n]\nEOF\necho ok","description":"Write report and claims"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3082,"cache_read_input_tokens":30897,"output_tokens":3687,"output_tokens_details":{"thinking_tokens":63},"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":3082},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXk5wktHZco2CwMevwvA","requestRef":"993dc9cb-4c1e-417d-ab5c-112cbe46e339","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"abda5b7f-f50c-4fbd-b968-b8b107119ec7","timestamp":"2026-10-30T03:53:15.088Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0014","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","version":"2.1.314","gitBranch":"HEAD"}97{"parentUuid":"abda5b7f-f50c-4fbd-b968-b8b107119ec7","isSidechain":false,"promptId":"cced29be-8a0c-46b8-9fc9-c2501f16d592","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KAUowYPKcuMtsy2N2eWBPJ","type":"tool_result","content":"ok","is_error":false}]},"uuid":"0afd27a8-9463-4587-9ca2-97d18bf15c33","timestamp":"2026-10-30T03:53:15.117Z","toolUseResult":{"stdout":"ok","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"abda5b7f-f50c-4fbd-b968-b8b107119ec7","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0014","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","version":"2.1.314","gitBranch":"HEAD"}98{"type":"api-request-blob","sessionId":"99ab1392-00fc-4b7a-bd81-cec3805b6c91","hash":"2d2e364bc5f3afbdae977add4bdd9e47f040b99570c4aa6dfac7cbe5277c2746","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"P4-relevant gate checks pass (keys, CI, positivity, exclusions, reproducibility); the remaining failures are P5 deliverables. Writing report and claims."},{"type":"tool_use","id":"toolu_01KAUowYPKcuMtsy2N2eWBPJ","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F6-dev-01 — Preston curve in the latest usable WDI year\n\n## Question\nAcross countries/economies, how strongly is life expectancy at birth (LE, years) associated with the natural log of GDP per capita (PPP, constant 2021 intl $) in the most recent year with usable World Bank data? Estimand: slope β of the linear projection LE = α + β·ln(GDPpc) + ε, one equally weighted observation per country. The interpretation is descriptive/associational, not causal.\n\n## Data and exclusions\nInputs: `data/wb_indicators.csv` (2349 rows) and `data/wb_entities.csv` (296 rows) (`n_input_rows`); SHA-256 hashes match `data/README.md` (see `out/state/data_audit.md`). Exclusion ledger (`out/exclusions.csv`):\n1. Dropped 396 entity-year rows belonging to 44 aggregate entities (region = `Aggregates`) → 1953 rows, 217 countries/economies.\n2. Analysis year: countries with both indicators number 199 in every year 2015–2022 and 197 in 2023 (`complete_countries_by_year`); the latest year with ≥150 is **2023** (`analysis_year`). Kept 217 country rows (`n_countries_in_analysis_year`).\n3. Dropped 20 countries with a missing indicator in 2023 (`n_excluded_missing`); all 20 lack GDP (life expectancy is never missing). No imputation was done. The analysis uses **197** countries (`n_analysed`).\n\n2349 − 396 − 1736 − 20 = 197. Data problems and how each was handled are listed in `data_issues`: the aggregates; the 20 missing countries (ASM, CHI, CUB, ERI, GIB, GUM, IMN, LIE, MAF, MCO, MNP, NCL, PRK, PYF, SSD, SYR, VEN, VGB, VIR, YEM); an implausibly volatile Central African Republic LE series in earlier years (its 2023 value of 57.4 looks plausible and was kept); and the drop in West Bank and Gaza LE in 2023 (kept as reported).\n\n## Method\n- Baseline: predict every country's LE with the cross-country mean. Its RMSE is `baseline_metric`.\n- Main model: OLS of LE on ln(GDPpc) over the 197 countries, unweighted. Standard errors are heteroskedasticity-robust **HC3** (`se_type`). HC3 was picked because it is the most conservative of HC0–HC3 in moderate samples. The 95% CI is β ± 1.96·SE (normal approximation). The fit metric is the RMSE of the in-sample residuals (`model_metric`).\n- Conclusion rule as given in TASK.md: `positive_effect` if the 95% CI lies entirely above 0.\n- Supplementary check, not part of the estimand: re-estimate after dropping the 5 countries with the largest Cook's distance (`supp_sens_drop5_*`). HC0–HC2 and classical SEs are also reported (`supp_effect_se_by_type`, `supp_effect_se_classical`).\n- `out/analysis.py` regenerates everything deterministically. It uses no randomness (`random_seed` = null).\n- Outputs: `out/figure1.png` (scatter plot with the OLS line) and `out/table1.csv` (means by income group).\n\n## Results\n- Slope: **β = 5.27 years per log point** of GDP per capita (`effect_estimate` = 5.267). Intercept 22.57 years (`intercept`). A 10% income difference therefore goes with about 0.50 more years of LE (5.267·ln 1.1, derived from `effect_estimate`). A doubling goes with about 3.65 years (5.267·ln 2).\n- Strength: Pearson r = 0.861 (`pearson_r`), R² = 0.742 (`r_squared`).\n- Fit: residual RMSE is 3.59 years (`model_metric`), against 7.08 years for the mean-only baseline (`baseline_metric`). ln(GDPpc) roughly halves the prediction error.\n- Conclusion: `positive_effect`. In 2023 the Preston-curve association holds and is strong.\n- Table 1 (`out/table1.csv`): mean LE rises with income group, from 63.2 years (Low income, n=20) to 67.8 (Lower middle income, n=46), 73.7 (Upper middle income, n=58) and 79.7 (High income, n=73).\n- Largest negative residual: Nigeria, −15.3 years (`supp_largest_neg_residual_iso3`, `supp_largest_neg_residual`).\n\n## Uncertainty\n- HC3 SE = 0.211 (`effect_se`). 95% CI for β: [4.85, 5.68] (`effect_ci_low`, `effect_ci_high`), well away from zero.\n- The choice among robust SEs makes little difference: HC0 0.207, HC1 0.208, HC2 0.209, HC3 0.211, classical 0.222 (`supp_effect_se_by_type`, `supp_effect_se_classical`).\n- Dropping the 5 most influential countries (NGA, GRL, TCD, SLB, GUY; `supp_sens_drop5_cooks_iso3`) gives β = 5.32 (SE 0.183), inside the main CI (`supp_sens_drop5_effect_estimate`, `supp_sens_drop5_effect_se`).\n- The CI covers sampling-style uncertainty only, treating countries as independent draws from a superpopulation. The 197 countries are close to the full population of economies, so this is a modelling convention. The CI does not cover measurement error, model misspecification or non-random missingness.\n\n## Limitations and open issues\n- **No causal interpretation.** β is an association. Confounders such as institutions, education, health technology, disease environment and conflict affect both income and LE, and causation can also run from health to income.\n- **Functional form.** The linear-in-log line is a projection. The figure shows curvature at the top: LE flattens among high-income economies, and the low-income residuals are widely spread. A nonlinear or spline fit was not estimated, so β is an average slope over the income range, not a constant marginal effect.\n- **Non-random missingness.** The 20 excluded economies are either small high-income territories (e.g. MCO, LIE, GIB) or conflict-affected/sanctioned states (SSD, SYR, YEM, VEN, PRK, CUB, ERI). Their direction of bias is unclear, and no bounding analysis was done.\n- **Measurement and data quality.** GDP PPP figures for fragile states and small territories are imprecise, and errors in ln(GDPpc) would bias β toward zero (attenuation). LE estimates for countries without complete vital registration are modelled, not observed. The CAF series shows implausible year-to-year swings (18.8 years in 2022), which suggests vintage or modelling artefacts in the LE data. 2023 values were not validated against external sources.\n- **Single year.** 2023 can still carry post-COVID mortality effects and conflict shocks (e.g. West Bank and Gaza). Stability across 2015–2022 was not checked because the task restricts the analysis to the selected year.\n- **Equal weighting.** Each economy counts once whatever its population, so tiny territories weigh as much as India or China. A population-weighted slope would answer a different question and was not computed.\n- **What would invalidate the result:** a misclassification of aggregates as countries (checked: all 44 aggregate entities in the file were removed via the region flag); a systematic revision of the 2023 WDI vintage; or leverage concentrated in a few points (partly checked with the leave-5-out sensitivity, which did not change the conclusion). Not checked: heavier-tailed inference (e.g. bootstrap), spatial or regional correlation of errors (regional clustering would probably widen the CI), and outliers beyond the 5 most influential points.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The analysis year is 2023, the latest year with at least 150 countries/economies observing both indicators; 197 countries are analysed and 20 are excluded for missing GDP.\",\"results_keys\":[\"analysis_year\",\"n_analysed\",\"n_excluded_missing\",\"complete_countries_by_year\"],\"status\":\"supported\",\"caveat\":\"Missing countries are not random (small territories and conflict/sanctioned states).\"},\n {\"id\":\"C2\",\"text\":\"The OLS slope of life expectancy on ln(GDP per capita) is about 5.27 years per log point, with HC3 95% CI [4.85, 5.68]; conclusion positive_effect.\",\"results_keys\":[\"effect_estimate\",\"effect_se\",\"effect_ci_low\",\"effect_ci_high\",\"conclusion\",\"se_type\"],\"status\":\"supported\",\"caveat\":\"Associational only; CI ignores measurement error and misspecification.\"},\n {\"id\":\"C3\",\"text\":\"The relationship is strong: Pearson r = 0.861, R^2 = 0.742, and log income reduces prediction RMSE from 7.08 (mean-only baseline) to 3.59 years.\",\"results_keys\":[\"pearson_r\",\"r_squared\",\"baseline_metric\",\"model_metric\"],\"status\":\"supported\",\"caveat\":\"In-sample fit; linear-in-log form misses curvature at high incomes.\"},\n {\"id\":\"C4\",\"text\":\"The slope is robust to the choice of robust SE type and to dropping the 5 most influential countries (beta = 5.32).\",\"results_keys\":[\"supp_effect_se_by_type\",\"supp_effect_se_classical\",\"supp_sens_drop5_effect_estimate\",\"supp_sens_drop5_effect_se\",\"supp_sens_drop5_cooks_iso3\"],\"status\":\"supported\",\"caveat\":\"Limited sensitivity set; no clustering, bootstrap or nonlinear specifications.\"},\n {\"id\":\"C5\",\"text\":\"Mean life expectancy rises monotonically across World Bank income groups, from 63.2 (Low) to 79.7 years (High).\",\"results_keys\":[\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Numbers are in out/table1.csv, generated by analysis.py; 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Each extra log point of GDP per capita goes with about 5.27 more years of life expectancy (95% CI [4.85, 5.68]), so the conclusion code is `positive_effect`. The final gate run passed all 40 checks, including the re-run of `out/analysis.py`, which reproduced `results.json` exactly.\n\n| Quantity | Value |\n|---|---|\n| Analysis year | 2023 (197 countries have both indicators; 199 in every year 2015–2022) |\n| Countries analysed | 197 |\n| Countries excluded for missing data | 20, all missing GDP; no imputation |\n| Slope β (years per log point) | 5.267 |\n| Robust standard error (HC3) | 0.211 |\n| Pearson r / R² | 0.861 / 0.742 |\n| Prediction error (RMSE) | 3.59 years with log income, against 7.08 for the mean-only baseline |\n\n- **Size of the slope:** a doubling of income goes with about 3.65 more years of life expectancy, and a 10% difference with about 0.50 years.\n- **Robustness:** the four robust standard-error types (HC0–HC3) range from 0.207 to 0.211. Dropping the 5 most influential countries (Nigeria, Greenland, Chad, Solomon Islands, Guyana) gives β = 5.32, inside the main interval.\n- **By income group (`table1.csv`):** mean life expectancy rises from 63.2 years in low-income countries to 79.7 in high-income ones.\n\n**Data issues** (all recorded in `results.json` and the report):\n- 44 regional and income-group aggregates were removed, as the task requires.\n- The 20 excluded economies are not a random sample. They are mostly small high-income territories (Monaco, Liechtenstein, Gibraltar) and conflict-hit or sanctioned states (South Sudan, Syria, Yemen, Venezuela, North Korea, Cuba, Eritrea).\n- The Central African Republic's life expectancy series jumps around implausibly before 2023, down to 18.8 years in 2022. Its 2023 value (57.4) looks plausible and was kept.\n- Life expectancy in the West Bank and Gaza fell sharply in 2023 (76.7 to 65.2). It was kept as reported.\n\n**Limitations:**\n- The result is an association, not a causal effect.\n- A straight line in log income misses the flattening among high-income countries.\n- Every economy counts once, regardless of population.\n- I did not check other years, bootstrap or regionally clustered standard errors, or curved fits.\n\nEverything is in `out/`. Start with `report.md`; `results.json`, `figure1.png` and `table1.csv` are regenerated by `analysis.py`. 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