{"call_id":"call_1ac1a1bb","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q118/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":"2026-10-15T17:10:30.703534Z","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_0083\",\n \"rows\": 628,\n \"description\": \"| Variable Name | Description                                                                                   |\\n|---------------|-----------------------------------------------------------------------------------------------|\\n| petpooled     | Indicator equal to 1 if the member signed at least one pro–civil rights discharge petition in the specified Congress; 0 otherwise. |\\n| Dvotespercent | Democratic candidate’s two‑party vote share in the district in the most recent U.S. House election before the Congress (0–1).      |\\n| icpsr         | ICPSR legislator identification code for the U.S. House member.                                |\\n\\nData Description: \\nThis dataset links individual U.S. House members to their participation in civil rights discharge petitions during a specific Congress. The underlying historical collection spans 1933–1972. The core variable captures whether a member signed any of the pro–civil rights discharge petitions filed in that Congress, merged with district electoral information from the preceding House election and a stable legislator identifier.\\n\\nDischarge petition signatures were transcribed from archival petition sheets housed at the National Archives, while district vote shares were drawn from compiled U.S. House election returns (ICPSR archives). Legislator identifiers come from ICPSR coding. The file corresponds to observations of House members (ICPSR-coded) and, consistent with the study’s focus, covers non‑Southern districts as defined in the paper. The “petpooled” measure pools signatures across all relevant pro–civil rights discharge petitions filed within the Congress represented in the file.\\n\",\n \"columns\": {\n  \"petpooled\": \"int64\",\n  \"Dvotespercent\": \"float64\",\n  \"icpsr\": \"int64\"\n }\n}\n\n# Question\nAmong U.S. House races decided near the 50% threshold prior to the 83rd Congress, what is the effect of electing a Democrat (versus a Republican) on the representative's likelihood of supporting civil-rights discharge petitions during the 83rd Congress?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"ds_0083\", \"y\": \"petpooled\", \"x\": [\"Dvotespercent\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_1ac1a1bb","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_0083","y":"petpooled","x":["Dvotespercent"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"116378cd82e73484bfda7908b2df37d1abaf7b27120e0ca4c339899d6f3be757","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["753920a4a96bf3d4fe486395f90dda6adc6b2a7a2b8fdf384106d22c6b34bc8e"],"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":0.7479998748749495}}
{"call_id":"call_bea54b2a","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q118/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"ds_0083","y":"petpooled","x":["Dvotespercent"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"9fd06c2a44e2bd1b6c542910ae75473f8da46325b35fdc3031b183dc1eb5d6d4","dataset_digests":{"ds_0083":"0282318219984e89c4c5f921ae12513bc9871deb30b9ef38449c39f8bd18f9f6"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_bea54b2a","status":"ok","reason":"","figures":[{"key":"coef.Dvotespercent","value":1.6058507807751772,"unit":"","label":"Coefficient on Dvotespercent in the regression of petpooled","se":0.13538625606868893,"ci_low":1.339937898539091,"ci_high":1.8717636630112633,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de Dvotespercent na regressão por mínimos quadrados ordinários de petpooled.","formula":"","terms":[{"symbol":"n","value":576.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":1.6058507807751772,"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":0.01832943833229661,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.13538625606868893,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 574 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":1.6058507807751772,"unit":"","note":""},{"symbol":"c","value":1.9641054414059111,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.13538625606868893,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":576,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.26377775050523045,"flag":false,"note":""},{"name":"df_resid","value":574,"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":-0.5060082625366609,"se":0.057123720875409636,"t":-8.85811104007556,"p":1.0149778934830634e-17,"ci_low":-0.6182052735414054,"ci_high":-0.3938112515319164},{"term":"Dvotespercent","coef":1.6058507807751772,"se":0.13538625606868893,"t":11.861254069692572,"p":3.533821589022089e-29,"ci_low":1.339937898539091,"ci_high":1.8717636630112633}]},"datasets":{},"warnings":["52 rows were dropped because one of 'petpooled', 'Dvotespercent' was missing"],"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":"503dcce9b6744f12678a1bf891f7f46ca8af6d0d1493f4dd9fff9e818d74b6d3","params_versions":[],"sources":[],"started_at":"2026-10-15T17:10:30.706155Z","duration_ms":11.479665990918875,"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_1ac1a1bb","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"4f03dca746d6db82b0704590b3aa24f7b84f7a788ddc25d57de91fc799521acb"}}
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