{"call_id":"call_94c5825c","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q114/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":"2026-10-15T17:10:30.637274Z","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_0079\",\n \"rows\": 429,\n \"description\": \"| Variable Name | Description |\\n| --- | --- |\\n| Unnamed: 0 | Row identifier for the candidate observation (index carried over from the source CSV; not a substantive variable). |\\n| d_board0 | Binary indicator equal to 1 if the individual ever served as a director on the board of at least one publicly traded corporation observed in BoardEx during 2000–2013, excluding any board positions held before the candidate’s first Senate election; 0 otherwise. |\\n| margin | Vote margin in the candidate’s first U.S. Senate general election used for the dataset, measured as (candidate vote share − 0.50). Units are vote-share proportion (e.g., 0.03 = 3 percentage points above 50%; −0.03 = 3 points below 50%). |\\n| everwon | Binary indicator equal to 1 if the candidate ever went on to hold U.S. Senate office at any point (i.e., ultimately won a Senate election and served), and 0 otherwise. This can differ from the first-election result captured by `margin` (e.g., a candidate with a negative first-election `margin` may later win and thus have `everwon = 1`). |\\n\\nDataset Context:\\nThis dataset is a candidate-level file focused on U.S. Senate contests. Each row corresponds to an individual candidate, keyed to the candidate’s first Senate election used in the dataset, and includes (i) an election vote margin for that first contest and (ii) information on whether the individual later appeared as a director on the board of a publicly traded firm during the BoardEx coverage period.\\n\\nCorporate board service outcomes are based on BoardEx’s historical database of publicly traded firm directorships, which compiles board membership information from public disclosures for firms traded on U.S. exchanges. The BoardEx coverage window used here spans 2000–2013. Election results used to construct the vote margin were drawn from official election returns and standard political data sources. The board-service outcome variables are constructed to exclude board positions held prior to the candidate’s first Senate election. The dataset also applies eligibility restrictions consistent with the underlying data availability window (e.g., board outcomes are only observed in 2000–2013) and excludes cases where board service cannot be meaningfully assessed (such as candidates who did not survive into the relevant observation window or other similar sample restrictions implemented in the source study).\\n\",\n \"columns\": {\n  \"Unnamed: 0\": \"int64\",\n  \"d_board0\": \"int64\",\n  \"margin\": \"float64\",\n  \"everwon\": \"int64\"\n }\n}\n\n# Question\nWhat is the effect of serving a Senate term on the probability of ever serving on a corporate board, for candidates whose service is determined by election outcomes?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"ds_0079\", \"y\": \"d_board0\", \"x\": [\"everwon\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_94c5825c","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_0079","y":"d_board0","x":["everwon"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"cccaccf2d41fc7d71b9a16d4005396f446c59f83442781d4c9f2b67880b5bd50","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["f8238defcb18457875bc18790c1477582b159cddd7445253b0f483e77914dd33"],"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":0.793833052739501}}
{"call_id":"call_67b00546","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q114/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"ds_0079","y":"d_board0","x":["everwon"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"b978d60b737a875a400086e750b5e7f051c7bde7a1165853c1876136b5ac2291","dataset_digests":{"ds_0079":"c73a3376b9d9bc36db221cdbfa92366c5cad696f4d515eab51da090693091c8d"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_67b00546","status":"ok","reason":"","figures":[{"key":"coef.everwon","value":0.3426823049464559,"unit":"","label":"Coefficient on everwon in the regression of d_board0","se":0.0510021648362006,"ci_low":0.24243575631699207,"ci_high":0.4429288535759197,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de everwon na regressão por mínimos quadrados ordinários de d_board0.","formula":"","terms":[{"symbol":"n","value":429.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":0.3426823049464559,"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":0.0026012208179789767,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.0510021648362006,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 427 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.3426823049464559,"unit":"","note":""},{"symbol":"c","value":1.965535167995659,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.0510021648362006,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":429,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.13369517354639238,"flag":false,"note":""},{"name":"df_resid","value":427,"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":0.12578616352201258,"se":0.018639163684953303,"t":6.748487520583073,"p":4.883914424386667e-11,"ci_low":0.0891502317972093,"ci_high":0.16242209524681586},{"term":"everwon","coef":0.3426823049464559,"se":0.0510021648362006,"t":6.718975675778079,"p":5.867230542055249e-11,"ci_low":0.24243575631699207,"ci_high":0.4429288535759197}]},"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":"4e6a8dce846afadce5bde542b2afe96a76f2a60cfba7e5942fb223c862b1961f","params_versions":[],"sources":[],"started_at":"2026-10-15T17:10:30.639920Z","duration_ms":2.647415967658162,"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_94c5825c","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"452157f4c816694a5a11a4064d724af625e21c233509d6fb5c530323cdab81fc"}}
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