{"call_id":"call_d32a9904","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q115/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":"2026-10-15T17:10:30.654892Z","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_0080\",\n \"rows\": 375,\n \"description\": \"| Variable Name | Description |\\n| --- | --- |\\n| Unnamed: 0 | Row index from the source CSV; included for reference only. |\\n| d_board0 | Binary indicator equal to 1 if the candidate ever served as a director on at least one board of a publicly traded firm **in the post-election/post-office period** covered by the dataset, based on BoardEx directorship records (BoardEx coverage window used here: 2000–2013). Board positions held **before the candidate’s first gubernatorial election** are excluded from this measure (i.e., pre-election directorships are not counted). |\\n| margin | Vote-share margin relative to the 50% threshold in the candidate’s **first** gubernatorial election (proportion). Constructed so that 0 corresponds to exactly 50% of the vote; negative values indicate the candidate received less than 50% (lost), positive values indicate more than 50% (won). Example: 0.02 = 2 percentage points above 50%. |\\n| everwon | Binary indicator equal to 1 if the candidate **ever served as governor** at any point in their career (i.e., won a gubernatorial election at some point and entered/held the office). This is not necessarily the same as winning the first gubernatorial election reflected in `margin`, because some candidates who lose their first race later win a subsequent gubernatorial election and serve. |\\n\\nDataset Context:\\nThis dataset contains candidate-level records for U.S. state gubernatorial candidates drawn from elections spanning the period used in the source study (candidates who ran for or served in office during 1992–2012, with each candidate linked to their first gubernatorial election margin). The unit of observation is an individual candidate (not an election-year); candidates are characterized by their first-election vote margin (measured around the 50% threshold) and indicators for later outcomes such as whether they ever served as governor and whether they later held corporate board directorships.\\n\\nCorporate board membership information comes from BoardEx, which compiles board rosters for publicly traded firms using SEC filings (e.g., proxy statements) and related public disclosures. BoardEx coverage used for these data is 2000–2013; board service outside this window is not captured. Candidates were matched to BoardEx records (including manual name matching in the source study) to determine whether they ever served on a board within the observation window. Consistent with the source study’s construction of board outcomes, board positions held prior to a candidate’s first gubernatorial election are excluded from the binary board-service outcome so that it reflects post-election/post-office board participation rather than pre-existing directorships.\\n\",\n \"columns\": {\n  \"Unnamed: 0\": \"int64\",\n  \"d_board0\": \"int64\",\n  \"margin\": \"float64\",\n  \"everwon\": \"int64\"\n }\n}\n\n# Question\nAmong candidates whose electoral outcomes are marginal enough that winning changes their probability of ever holding office, what is the effect of having served as governor on the probability of ever serving on a corporate board?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"ds_0080\", \"y\": \"d_board0\", \"x\": [\"everwon\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_d32a9904","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_0080","y":"d_board0","x":["everwon"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"70f4faf0cde2647a58dcd9165b40ade7c02f0d2b71f6c45a07081534b1d8c4f0","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["8a3fb3e11b8d6e7bc41fcc7779fbb203c8ce479898c814250756fcbac4b130b1"],"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":0.7307920604944229}}
{"call_id":"call_18a19014","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q115/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"ds_0080","y":"d_board0","x":["everwon"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"afcd20d3cac73e80b03a9f10cfdcf3a49590c4fdc2ea032ac58c8e65860a8d4a","dataset_digests":{"ds_0080":"43d04ead23e183e1d34831aa20a2a752fc9a99352f9d846dd9e86bd284642ead"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_18a19014","status":"ok","reason":"","figures":[{"key":"coef.everwon","value":0.3265367316341829,"unit":"","label":"Coefficient on everwon in the regression of d_board0","se":0.04671390916364295,"ci_low":0.23468110274662984,"ci_high":0.41839236052173595,"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":375.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":0.3265367316341829,"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":0.0021821893093490847,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.04671390916364295,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 373 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.3265367316341829,"unit":"","note":""},{"symbol":"c","value":1.9663442972793106,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.04671390916364295,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":375,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.13559006358179893,"flag":false,"note":""},{"name":"df_resid","value":373,"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":0.12173913043478261,"se":0.021618430668462044,"t":5.631265853741235,"p":3.5252350295948014e-8,"ci_low":0.07922985257372411,"ci_high":0.16424840829584111},{"term":"everwon","coef":0.3265367316341829,"se":0.04671390916364295,"t":6.990139285716721,"p":1.2731171699629955e-11,"ci_low":0.23468110274662984,"ci_high":0.41839236052173595}]},"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":"7cc820881a9959d1c41c7cc96ee4ec743fcbc267d007f1b8ed86923acd4f3dae","params_versions":[],"sources":[],"started_at":"2026-10-15T17:10:30.657415Z","duration_ms":2.287666080519557,"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_d32a9904","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"c6760f0084aa9027a062a2aca0204a8fb045313445ea8a6a23e47696854eb6d3"}}
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