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{"call_id":"call_1d6d251f","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q046/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":"2026-10-15T17:00:53.986819Z","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_0024\",\n \"rows\": 27068,\n \"description\": \"| Variable Name | Description |\\n| --- | --- |\\n| turnout | County presidential-election turnout rate in the general election, measured as total ballots cast divided by the county voting-age population (unit interval 0–1). Votes and voting-age population were assembled from standard U.S. election and Census tabulations for each election year. |\\n| tr_ar | Indicator (0/1) that at least one Black church arson incident was recorded in the county within the election-cycle window associated with that year. Incidents are drawn from the U.S. Department of Justice’s National Church Arson Task Force records (primarily 1995–2000) and mapped to the relevant presidential-election cycle for comparability across counties and years. |\\n| perc_bl | Share of county residents identifying as Black or African American in that year (0–1), based on U.S. Census/ACS population statistics for the county. |\\n| pop_tot | Total county population (persons) in that year, from U.S. Census/ACS population counts. |\\n| income | County median household income in U.S. dollars for that year (nominal dollars as reported by the Census/ACS). |\\n| fips | Five-digit Federal Information Processing Standards (FIPS) code uniquely identifying the county (state–county concatenated code). |\\n| year | Presidential election year for the observation (e.g., 1980, 1984, …, 2016). |\\n\\nDataset Context:\\nThis file is a county-by–presidential-election-year panel covering U.S. general elections from 1980 through 2016. Each record is keyed by a county’s five-digit FIPS code and the presidential election year. Turnout is constructed from county-level counts of ballots cast in the presidential general election combined with county voting-age population from the U.S. Census Bureau. Using voting-age population standardizes the denominator across states, which vary in registration rules and reporting conventions, but note that VAP includes individuals ineligible to vote (e.g., noncitizens), a common feature of administrative turnout measures.\\n\\nDemographic indicators—total population, the share identifying as Black or African American, and median household income—are taken from decennial Census and American Community Survey tabulations aligned to the election year. Income is recorded in nominal dollars for the reported year.\\n\\nThe Black church arson indicator comes from administrative records compiled by the U.S. Department of Justice’s National Church Arson Task Force established in the mid-1990s. Those incident locations are assigned to counties and linked to the presidential-election cycle that encompasses the incident window to ensure a consistent temporal frame across units. Because the Task Force’s comprehensive catalog primarily spans 1995–2000, county-years outside that period are generally coded as having no recorded incident.\\n\\nUsers should be aware that counties occasionally experience boundary changes or renamings over long panels; the dataset relies on FIPS coding to maintain consistent identification. As with most historical administrative compilations, reporting conventions can vary across states and years; the assembly here harmonizes these inputs to produce a cohesive, national county-level panel suitable for studying election-period patterns alongside population and community characteristics.\",\n \"columns\": {\n  \"turnout\": \"float64\",\n  \"tr_ar\": \"float64\",\n  \"perc_bl\": \"float64\",\n  \"pop_tot\": \"float64\",\n  \"income\": \"float64\",\n  \"fips\": \"float64\",\n  \"year\": \"float64\"\n }\n}\n{\n \"id\": \"ref_evp\",\n \"rows\": 2889,\n \"description\": \"Each county's max of tr_ar over years up to 1996 (evp)\",\n \"columns\": {\n  \"fips\": \"float64\",\n  \"evp\": \"float64\"\n }\n}\n{\n \"id\": \"ref_panel\",\n \"rows\": 5764,\n \"description\": \"County rows of 1992 and 1996: tr_ar 0 then 1 (treated), or 0 and 0 never treated by 1996, with the post indicator interacted with the pre-period covariates\",\n \"columns\": {\n  \"turnout\": \"float64\",\n  \"tr_ar\": \"float64\",\n  \"perc_bl\": \"float64\",\n  \"pop_tot\": \"float64\",\n  \"income\": \"float64\",\n  \"fips\": \"float64\",\n  \"year\": \"float64\",\n  \"nyears\": \"int64\",\n  \"tr_pre\": \"float64\",\n  \"perc_bl_pre\": \"float64\",\n  \"pop_tot_pre\": \"float64\",\n  \"income_pre\": \"float64\",\n  \"tr_post\": \"float64\",\n  \"evp\": \"float64\",\n  \"treated_group\": \"int64\",\n  \"post\": \"int64\",\n  \"post_x_perc_bl_pre\": \"float64\",\n  \"post_x_pop_tot_pre\": \"float64\",\n  \"post_x_income_pre\": \"float64\",\n  \"did\": \"int64\"\n }\n}\n{\n \"id\": \"ref_post\",\n \"rows\": 2884,\n \"description\": \"Each county's 1996 tr_ar (tr_post)\",\n \"columns\": {\n  \"fips\": \"float64\",\n  \"tr_post\": \"float64\"\n }\n}\n{\n \"id\": \"ref_pre\",\n \"rows\": 2882,\n \"description\": \"Each county's 1992 tr_ar (tr_pre) and covariates (perc_bl_pre, pop_tot_pre, income_pre)\",\n \"columns\": {\n  \"fips\": \"float64\",\n  \"tr_pre\": \"float64\",\n  \"perc_bl_pre\": \"float64\",\n  \"pop_tot_pre\": \"float64\",\n  \"income_pre\": \"float64\"\n }\n}\n{\n \"id\": \"ref_years\",\n \"rows\": 2884,\n \"description\": \"How many of 1992 and 1996 each county is observed in\",\n \"columns\": {\n  \"fips\": \"float64\",\n  \"nyears\": \"int64\"\n }\n}\n\n# Question\nHow did county presidential-election turnout change between 1992 and 1996 in counties that experienced a recorded Black church arson incident, compared with counties that did not experience such incidents during that period?","response":"sm.OLS(turnout, add_constant([treated_group, post, did, post_x_*_pre]), missing='drop').fit(cov_type='cluster', groups=fips) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)\ninfer__ols({\"dataset\": \"ref_panel\", \"y\": \"turnout\", \"x\": [\"did\"], \"controls\": [\"treated_group\", \"post\", \"post_x_perc_bl_pre\", \"post_x_pop_tot_pre\", \"post_x_income_pre\"], \"cluster\": \"fips\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_1d6d251f","text":"sm.OLS(turnout, add_constant([treated_group, post, did, post_x_*_pre]), missing='drop').fit(cov_type='cluster', groups=fips) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"ref_panel","y":"turnout","x":["did"],"controls":["treated_group","post","post_x_perc_bl_pre","post_x_pop_tot_pre","post_x_income_pre"],"cluster":"fips"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"28a1157a8f59e3b20561eaaec0792e135aa7af4e12a8d8686c47f8fd44b582e0","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["0fe896272b9ca332152e570d75651a16a7bba3126e5dd2ca6393ec2843fabd3a"],"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":0.7708340417593718}}
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