{"call_id":"call_8df9f20f","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q104/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":"2026-10-15T17:10:30.363585Z","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_0070\",\n \"rows\": 873,\n \"description\": \"| Variable Name  | Description                                                                                                                              |\\n|----------------|------------------------------------------------------------------------------------------------------------------------------------------|\\n| Unnamed: 0     | -                                                                                                                                        |\\n| dem_lhpct_chng | Change in the Democratic share of seats in the state lower house between the prior gubernatorial election year and the subsequent midterm election (percentage points, two‑party share). |\\n| D_1            | Democratic gubernatorial vote margin from 50 percent in the previous gubernatorial election (percentage points; positive if the Democrat won, negative if the Republican won).       |\\n\\nData Description: \\nThis file is a pared‑down extract constructed to study midterm seat swings in U.S. state lower‑house elections following gubernatorial contests. Each row corresponds to a state midterm legislative election year, and variables record (a) how the Democratic two‑party seat share changed from the last cycle and (b) the Democratic win/lose margin in the immediately preceding gubernatorial election. The coverage used in the original study spans 1882–2010, focusing on lower chambers because most upper chambers are staggered and often small. Cases with nonpartisan legislatures or strong third‑party dominance were excluded, and the analysis centers on years when the governor was not on the ballot but the lower house was.\\n\\nThe data were assembled from historical administrative sources: state legislative seat counts from Dubin (2007) with recent updates from the National Conference of State Legislatures, and gubernatorial election returns from ICPSR compilations and state election officials. The extract you have contains just the variables required to reproduce the core state‑level midterm seat‑change analysis.\",\n \"columns\": {\n  \"dem_lhpct_chng\": \"float64\",\n  \"D_1\": \"float64\"\n }\n}\n\n# Question\nAmong gubernatorial elections decided by very small margins, what is the causal effect of a Democratic (vs. Republican) gubernatorial victory on the subsequent midterm change in the Democratic share of lower-house seats?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"ds_0070\", \"y\": \"dem_lhpct_chng\", \"x\": [\"D_1\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_8df9f20f","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_0070","y":"dem_lhpct_chng","x":["D_1"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"bba927f7e19e63259302bea8564ab53b2e82626f01efc68bc349a8102cd1d662","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["48c30171a36c6e330aa789fc330cc411fcc1468a56bc6cae5d054251e051769d"],"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":0.8374580647796392}}
{"call_id":"call_5189e9bf","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q104/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"ds_0070","y":"dem_lhpct_chng","x":["D_1"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"521e37f835537731bf6cc46303099139f0fb101fc96a1aa2b75c5a6212dd4ad5","dataset_digests":{"ds_0070":"c926fb8fe6ff7408427a65205dffe4fd87f76883a3eaa589adb1eea87284732a"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_5189e9bf","status":"ok","reason":"","figures":[{"key":"coef.D_1","value":-0.23324501786413865,"unit":"","label":"Coefficient on D_1 in the regression of dem_lhpct_chng","se":0.03588139995104481,"ci_low":-0.3036691303423126,"ci_high":-0.16282090538596472,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de D_1 na regressão por mínimos quadrados ordinários de dem_lhpct_chng.","formula":"","terms":[{"symbol":"n","value":873.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":-0.23324501786413865,"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":0.0012874748624468382,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.03588139995104481,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 871 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-0.23324501786413865,"unit":"","note":""},{"symbol":"c","value":1.962691326822751,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.03588139995104481,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":873,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.027478520083585645,"flag":false,"note":""},{"name":"df_resid","value":871,"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":0.06121176339982759,"se":0.4384076847038512,"t":0.13962292527143258,"p":0.8889902043371423,"ci_low":-0.7992471969808644,"ci_high":0.9216707237805196},{"term":"D_1","coef":-0.23324501786413865,"se":0.03588139995104481,"t":-6.500443633257596,"p":1.3469601726954206e-10,"ci_low":-0.3036691303423126,"ci_high":-0.16282090538596472}]},"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":"4b04eb85d2582b288ad0cbcea708e986df17b13e7ea50c278ad559ae66b95429","params_versions":[],"sources":[],"started_at":"2026-10-15T17:10:30.367217Z","duration_ms":2.5457090232521296,"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_8df9f20f","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"433561caafd46b9d43d9a7d5aa9e8e19190b93fbc0f37f44bd57e543c0992888"}}
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