{"call_id":"call_d951bbfd","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q102/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"2925aeab2262","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":"2026-10-15T17:10:30.319321Z","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\": \"ds_0068\",\n \"rows\": 566,\n \"description\": \"| Variable Name   | Description                                                                                                                             |\\n|-----------------|-----------------------------------------------------------------------------------------------------------------------------------------|\\n| (index)         | Row identifier from the CSV export; sequential numeric index with no substantive meaning.                                               |\\n| pres_change_reg_1 | Change in a state’s Democratic share of the two‑party presidential vote from the previous presidential election, demeaned by election year; percentage points. |\\n| rv              | Democratic margin in the most recent prior gubernatorial election (Democratic two‑party share minus 50); positive values = Democratic win, negative = Republican win; percentage points. |\\n\\nData Description: \\nThis file is a state–year dataset assembled from historical U.S. election returns to examine how party control at the state level relates to subsequent presidential voting patterns. Each row corresponds to a state in a presidential election year and includes the shift in that state’s Democratic two‑party presidential vote relative to the previous presidential contest, along with the Democratic margin from the preceding gubernatorial election. The subset shown here focuses on cases where the prior gubernatorial race was close (roughly within ±5 percentage points of parity).\\n\\nElection data were compiled from the Inter‑university Consortium for Political and Social Research (ICPSR) archives and official publications of state election authorities, following the paper’s coding conventions: vote shares are recorded as Democratic two‑party percentages, changes are computed across presidential cycles, and margins are centered at 50 percent. The broader study spans elections from the late 19th century through 2012; values are reported in percentage points and are suitable for reproducing the specific specification referenced in the paper.\\n\",\n \"columns\": {\n  \"pres_change_reg_1\": \"float64\",\n  \"rv\": \"float64\"\n }\n}\n\n# Question\nAmong states with razor-close gubernatorial elections, what is the average effect of having a Democratic (vs. Republican) governor on the subsequent change in the Democratic share of the two-party presidential vote in that state?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"ds_0068\", \"y\": \"pres_change_reg_1\", \"x\": [\"rv\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_d951bbfd","text":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"ds_0068","y":"pres_change_reg_1","x":["rv"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"1addf2b7d1a94715cd2fb81f94fd8c0cf5f604921e976cf2ee1a8670421fb552","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["a2c74258108ca79c100969e44a4c0381c1a9bf09d8b4fd42290290464fa598ad"],"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":0.7398338057100773}}
{"call_id":"call_4b11ea1d","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q102/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"ds_0068","y":"pres_change_reg_1","x":["rv"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"c647f8c4383d850181aca21ead43aab81a4999a5f9fc63c65621d4c583c570f2","dataset_digests":{"ds_0068":"080a987711e187795f5856c35fb499a3360071e7510be26f1b1b2ffbb27a00a3"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_4b11ea1d","status":"ok","reason":"","figures":[{"key":"coef.rv","value":-0.037594758663053264,"unit":"","label":"Coefficient on rv in the regression of pres_change_reg_1","se":0.028062309531866263,"ci_low":-0.09271415848262472,"ci_high":0.017524641156518195,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de rv na regressão por mínimos quadrados ordinários de pres_change_reg_1.","formula":"","terms":[{"symbol":"n","value":566.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":-0.037594758663053264,"unit":""},{"text_pt":"Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.","formula":"ep = √V_jj","terms":[{"symbol":"V_jj","value":0.0007874932162622721,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.028062309531866263,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 564 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-0.037594758663053264,"unit":"","note":""},{"symbol":"c","value":1.9641790265687296,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.028062309531866263,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":566,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.0050474702666511995,"flag":false,"note":""},{"name":"df_resid","value":564,"flag":false,"note":""},{"name":"se_type","value":"HC1","flag":false,"note":"heteroskedasticity-robust standard errors (HC1)"},{"name":"n_clusters","value":null,"flag":false,"note":"errors not clustered"}],"tables":{"coefficients":[{"term":"Intercept","coef":0.06519021075069506,"se":0.22242051439356042,"t":0.2930944159015148,"p":0.7695577909736103,"ci_low":-0.3716834986997646,"ci_high":0.5020639202011546},{"term":"rv","coef":-0.037594758663053264,"se":0.028062309531866263,"t":-1.339688688857287,"p":0.18088611750205802,"ci_low":-0.09271415848262472,"ci_high":0.017524641156518195}]},"datasets":{},"warnings":[],"assumptions":["the conditional mean of the outcome is linear in the regressors","errors are independent across observations; their variance may differ"],"notes":""},"outputs":null,"output_digest":"4eb424817991f7bb0f19241f9d77e73be989e6a96499c38a8f98f07c70d07e07","params_versions":[],"sources":[],"started_at":"2026-10-15T17:10:30.321894Z","duration_ms":2.114916918799281,"error":null,"decided_by":"model","rationale":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_d951bbfd","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"63445635a0c49425b5e7a9638433208617e3822544802936db250fe482ec40fc"}}
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