{"call_id":"call_10be58ff","run_id":"20261015T170037Z-suite-qrdata_socsci-naive-761cb5/qrdata_socsci.394/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":"2026-10-15T17:00:38.455720Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"naive-ols","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\": \"pres08\",\n \"rows\": 51,\n \"description\": \"Regression towards the mean represents an empirical phenomenon where an observation with a value of the predictor further away from the distribution’s mean tends to have a value of an outcome variable closer to that mean. This tendency can be explained by chance alone.\\n\\nWe will examine whether or not the US presidential election data exhibit the regression towards the mean phenomenon. To do this, we use Obama’s vote share in the 2008 election to predict his vote share in his 2012 reelection.\\n\\nThe CSV data file pres08.csv contains the election results by state of the 2008 US presidential election.\\nVariable Description\\nstate: abbreviated name of the state\\nstate.name: unabbreviated name of the state\\nObama: Obama’s vote share (percentage)\\nMcCain: McCain’s vote share (percentage)\\nEV: number of Electoral College votes for the state\\n\\nIn addition, we have the CSV file pres12.csv, which contains the election results by state of the 2012 US presidential election.\\nVariable Description\\nstate: abbreviated name of the state\\nObama: Obama’s vote share (percentage)\\nRomney: Romney’s vote share (percentage)\\nEV: number of Electoral College votes for the state\",\n \"columns\": {\n  \"state.name\": \"str\",\n  \"state\": \"str\",\n  \"Obama\": \"int64\",\n  \"McCain\": \"int64\",\n  \"EV\": \"int64\"\n }\n}\n{\n \"id\": \"pres12\",\n \"rows\": 51,\n \"description\": \"Data file pres12.csv of this question; the question's data description is the description of dataset 'pres08'.\",\n \"columns\": {\n  \"state\": \"str\",\n  \"Obama\": \"int64\",\n  \"Romney\": \"int64\",\n  \"EV\": \"int64\"\n }\n}\n\n# Question\nWe standardize vote shares across elections by computing their z-scores so that we can measure Obama’s electoral performance in each state relative to his average performance of that year. That is, we subtract the mean from Obama’s vote share in each election and then divide it by the standard deviation.\nWhat is the proportion of states where Obama received a greater share of standardized votes in 2012 than he did in 2008, in the bottom quartile of Obama’s 2008 (standardized) vote share? Please round to the nearest thousandth.","response":"submit_answer({\"answer\": \"Linha de base ingênua: o item não declara o resultado e o tratamento, então não há regressão ingênua a fazer e nenhuma estimativa é citada.\", \"figure_refs\": [], \"caveats\": []})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_10be58ff","text":"","tool_calls":[{"vendor_id":"script_1","llm_name":"submit_answer","tool_id":null,"args":{"answer":"Linha de base ingênua: o item não declara o resultado e o tratamento, então não há regressão ingênua a fazer e nenhuma estimativa é citada.","figure_refs":[],"caveats":[]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"3f79b2fa2bf5f8f6468425986a7e8f23f42a6bc87680fa073287313d49aac8db","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["cc3ea1e76b453a0a9538f554c9232725eda446be8c8268e4ac97fa163504e1df"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"naive-ols","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.26237498968839645}}
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