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{"call_id":"call_ca072f1c","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q008/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"62cdbe0860ad","inputs":{"tools":["calc__annuity","calc__breakeven","calc__combine","calc__discount","calc__fiscal_impact","core__atkinson","core__equivalent_variation","core__fgt","core__gini","core__indirect_incidence","core__kakwani","core__mvpf","core__palma","core__quantile_shares","core__reynolds_smolensky","core__scale_effect","core__theil","core__welfare_change","data__cite","data__describe","data__siconfi","data__sidra","evidence__pool_dl","infer__ate","infer__density_test","infer__did_2x2","infer__did_staggered","infer__event_study","infer__fe","infer__fisher_exact","infer__iv_2sls","infer__linear_hypothesis","infer__mde","infer__neyman","infer__ols","infer__power","infer__randomization_inference","infer__rdd","infer__sample_size","infer__twfe","infer__wald_late","submit_answer"],"exemplars":[]},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:00:51.500674Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"reference","prompt":"# Role\nYou are MAGI's economist. A municipal analyst in Brazil asks a question; you answer it by choosing registered tools and their arguments. The tools compute every number. Your job is to recognise how the data were generated, pick the tool whose design matches, read what it reports, and say what the result means for the analyst.\n\n# Rules\n1. You never compute a number. Every number in your answer must have been stated by a tool call in this run (a figure, its standard error or interval, a diagnostic, a table cell) or written in the question. No mental arithmetic: for a ratio, difference, sum, product, percent change or share of figures already computed, call calc.combine; to restate a log-point coefficient as a percent or an effect in standard deviations, call core.scale_effect.\n2. Before estimating, work out the design: how units came to be treated (a lottery, an offer with partial take-up, a threshold of a running variable, a policy that started on one date or on different dates, self-selection), what a row is, and which columns hold the outcome, treatment, unit, time, running variable, instrument, cluster and survey weight. Call data.describe when the columns are unclear. Then choose the tool whose \"Use when\" matches and heed its \"Avoid when\".\n3. Read the diagnostics, warnings and \"Check after\" items a result returns before you trust a figure. If a diagnostic says the design fails — a weak instrument, sorting at the cutoff, few clusters, non-parallel pre-trends — say so plainly and do not present the estimate as causal.\n4. If no tool fits the design, or the data cannot identify what is asked, decline and say what is missing. Never invent a design, a column or a number.\n5. A tool that errors or declines returns its reason; fix the arguments and call again, or choose another tool.\n6. Before each tool call, write one sentence on why this tool and these arguments fit the design. It is recorded as your rationale.\n7. Finish with submit_answer. Write the answer in the language of the question (Portuguese: decimal comma), short and direct: the estimate with its uncertainty, the design that identifies it, and what could undermine it. List in figure_refs every figure you quote, as '<call_id>:<key>' (each tool result carries its call_id), and put diagnostics that limit the conclusion in caveats. Write numbers as the tools stated them; do not quote rule-of-thumb thresholds as numbers.\n\n# Datasets\nPass a dataset's id as the `dataset` argument.\n{\n \"id\": \"ds_0005\",\n \"rows\": 2481,\n \"description\": \"| Variable Name | Description |\\n| --- | --- |\\n| year | Calendar year of observation (1995–2012). Each row is a country–year. |\\n| percenthealth | Share of total general government expenditure allocated to public health (%). Drawn from the World Bank’s World Development Indicators (sectoral spending as a fraction of total government spending). |\\n| country | Country name corresponding to the national polity whose lower or single parliamentary chamber is referenced for legislative variables. |\\n| ever_high_adopt_treat | Indicator equal to 1 if the country ever experienced a large first-year jump in women’s parliamentary seat share associated with the first election using a quota (above the sample mean “shock”) during 1995–2012; 0 otherwise. Time-invariant within a country. |\\n| time_trend | Numeric time index equal to the calendar year, included to capture linear secular change. |\\n| time_trend_2 | Square of the numeric time index (year^2), allowing flexible nonlinear trends over time. |\\n| demlag | Democracy indicator lagged one year: 1 if the prior year’s Polity IV combined score was ≥ 6; 0 otherwise. |\\n| dem_cumlag | Cumulative years of democracy since 1945 up through the previous year (count based on Polity IV threshold ≥ 6). |\\n| autoclag | Autocracy indicator lagged one year: 1 if the prior year’s Polity IV combined score was ≤ −6; 0 otherwise. |\\n| leftlag | Indicator lagged one year for whether the governing/prime minister’s party was coded as left-of-center in the Database of Political Institutions. |\\n| left_cumlag | Cumulative years of left-leaning executive rule since 1945 up through the previous year (based on DPI coding). |\\n| lnODAlag | Natural log of official development assistance received as a percentage of GDP, lagged one year (World Bank). |\\n| lnOillag | Natural log of oil rents as a percentage of GDP, lagged one year (World Bank). Oil rents reflect the value of crude oil production minus costs as a share of the economy. |\\n| lnTradelag | Natural log of total trade as a percentage of GDP, lagged one year (World Bank). Total trade is exports plus imports of goods and services. |\\n| lnFDIlag | Natural log of foreign direct investment, net inflows, as a percentage of GDP, lagged one year (World Bank). |\\n| increaselag | Percentage-point increase in women’s share of seats in the lower/single parliamentary chamber at the first election conducted under a gender quota (“quota shock”), lagged one year. Calculated as the difference between the last pre-quota election and the first quota-based election; coded 0 where no implementation occurred in the period. |\\n| adopt_quotalag | Indicator for the presence of a parliamentary gender quota from the year of adoption onward, lagged one year. Adoption covers reserved-seat provisions, candidate-list gender requirements, and voluntary party quotas that collectively cover at least ~30% of lower-house seats; compiled from the International IDEA Quota Project and harmonized scholarly sources. |\\n\\nDataset Context:\\nThis dataset is a country–year panel spanning 1995–2012, a period marked by widespread introduction and use of electoral gender quota policies across regions. Fiscal variables come from the World Bank’s World Development Indicators, which report sectoral expenditures as shares of total general government spending. The health-spending series used here captures the proportion of national budgets allocated to public health, including both recurrent and capital outlays, enabling consistent comparison across countries and years.\\n\\nInformation on quota policies—adoption dates, first election under a quota, and the magnitude of changes in women’s legislative representation—was compiled from public administrative and research sources, notably the International IDEA Quota Project, and cross-checked with scholarly datasets and case documentation. Adoption covers three institutional designs: reserved seats for women, legal candidate-list requirements, and voluntary party quotas that meet a threshold of parliamentary seat coverage. In many systems, there is a lag between legal adoption and first use in an election; some adopters did not functionally implement quotas within the 1995–2012 window.\\n\\nLegislative composition is measured for the lower or single parliamentary chamber. The dataset includes a derived “quota shock” measure that records the first percentage-point jump in women’s seat share at the initial quota-based election; this value reflects the immediate numerical change produced by the reform in each country. To align political and economic covariates with subsequent budgeting, several variables are lagged one year. Regime characteristics (democracy/autocracy status and cumulative democratic experience) are drawn from Polity IV. Governing party ideology is coded using the Database of Political Institutions. Macroeconomic indicators—official development assistance, oil rents, trade openness, and foreign direct investment—come from the World Bank and are expressed as percentages of GDP with natural-log transformations. The panel also includes linear and quadratic time indices to summarize long-run temporal patterns.\\n\\nCoverage emphasizes countries with available fiscal and political data in the post-1995 period. Some early quota adopters prior to 1995 are outside scope to maintain consistent timing and measurement. As with most cross-national administrative series, occasional missingness may reflect gaps in national reporting or differences in when reforms occurred relative to fiscal years. Overall, the file provides a harmonized basis for examining how legislative reforms and macro-political contexts coincide with evolving public budget compositions worldwide.\",\n \"columns\": {\n  \"year\": \"float64\",\n  \"percenthealth\": \"float64\",\n  \"country\": \"str\",\n  \"ever_high_adopt_treat\": \"int64\",\n  \"time_trend\": \"float64\",\n  \"time_trend_2\": \"float64\",\n  \"demlag\": \"float64\",\n  \"dem_cumlag\": \"float64\",\n  \"autoclag\": \"float64\",\n  \"leftlag\": \"float64\",\n  \"left_cumlag\": \"float64\",\n  \"lnODAlag\": \"float64\",\n  \"lnOillag\": \"float64\",\n  \"lnTradelag\": \"float64\",\n  \"lnFDIlag\": \"float64\",\n  \"increaselag\": \"float64\",\n  \"adopt_quotalag\": \"float64\"\n }\n}\n{\n \"id\": \"ref_a_pre\",\n \"rows\": 127,\n \"description\": \"year == 1997 and adopt_quotalag == 0\",\n \"columns\": {\n  \"country\": \"str\",\n  \"a_pre\": \"int64\"\n }\n}\n{\n \"id\": \"ref_c_post\",\n \"rows\": 120,\n \"description\": \"year == 1998 and adopt_quotalag == 0\",\n \"columns\": {\n  \"country\": \"str\",\n  \"c_post\": \"int64\"\n }\n}\n{\n \"id\": \"ref_never\",\n \"rows\": 139,\n \"description\": \"Each country's max adopt_quotalag; never = 1 when it is 0 or missing\",\n \"columns\": {\n  \"country\": \"str\",\n  \"amax\": \"float64\",\n  \"never\": \"float64\"\n }\n}\n{\n \"id\": \"ref_panel\",\n \"rows\": 168,\n \"description\": \"Rows of 1997 and 1998: countries adopting the quota (1997 at 0, 1998 at 1) and never-adopters at 0 in both years\",\n \"columns\": {\n  \"year\": \"float64\",\n  \"percenthealth\": \"float64\",\n  \"country\": \"str\",\n  \"ever_high_adopt_treat\": \"int64\",\n  \"time_trend\": \"float64\",\n  \"time_trend_2\": \"float64\",\n  \"demlag\": \"float64\",\n  \"dem_cumlag\": \"float64\",\n  \"autoclag\": \"float64\",\n  \"leftlag\": \"float64\",\n  \"left_cumlag\": \"float64\",\n  \"lnODAlag\": \"float64\",\n  \"lnOillag\": \"float64\",\n  \"lnTradelag\": \"float64\",\n  \"lnFDIlag\": \"float64\",\n  \"increaselag\": \"float64\",\n  \"adopt_quotalag\": \"float64\",\n  \"a_pre\": \"float64\",\n  \"t_post\": \"float64\",\n  \"c_post\": \"float64\",\n  \"amax\": \"float64\",\n  \"never\": \"float64\",\n  \"treat\": \"float64\",\n  \"post\": \"int64\",\n  \"treat_post\": \"float64\"\n }\n}\n{\n \"id\": \"ref_t_post\",\n \"rows\": 15,\n \"description\": \"year == 1998 and adopt_quotalag == 1\",\n \"columns\": {\n  \"country\": \"str\",\n  \"t_post\": \"int64\"\n }\n}\n\n# Question\nAmong countries that adopted a parliamentary gender quota in 1997, how did their public-health spending share change from 1997 to 1998 relative to countries that never adopted a quota?","response":"OLS of patsy 'percenthealth ~ treat + post + treat:post + controls', cluster by country\ninfer__ols({\"dataset\": \"ref_panel\", \"y\": \"percenthealth\", \"x\": [\"treat_post\"], \"controls\": [\"treat\", \"post\", \"demlag\", \"dem_cumlag\", \"autoclag\", \"leftlag\", \"left_cumlag\", \"lnODAlag\", \"lnOillag\", \"lnTradelag\"], \"cluster\": \"country\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_ca072f1c","text":"OLS of patsy 'percenthealth ~ treat + post + treat:post + controls', cluster by country","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"ref_panel","y":"percenthealth","x":["treat_post"],"controls":["treat","post","demlag","dem_cumlag","autoclag","leftlag","left_cumlag","lnODAlag","lnOillag","lnTradelag"],"cluster":"country"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"6f376f958d094a0dc5159425249152ec75236b27f90f70fcf0166bca5490351e","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["bf9061d993a67251ba9e74560635aa45df96d587aa68c4e2959257cfbf290fe2"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"reference","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.7270839996635914}}
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