{"call_id":"call_0b34ace9","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q119/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":"2026-10-15T17:10:30.730522Z","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_0084\",\n \"rows\": 315,\n \"description\": \"| Variable Name | Description |\\n| --- | --- |\\n| Unnamed: 0 | Row index from the source CSV; not a substantive data field. |\\n| petpooled | Outcome indicator for the **88th U.S. Congress (1963–1964)**: equals 1 if the House member signed **at least one** pro–civil-rights discharge petition during the 88th Congress (as coded from archival discharge-petition signature records); 0 otherwise. |\\n| Dvotespercent | Democratic candidate’s share of the **two-party** vote in the member’s U.S. House district in the **immediately preceding House general election** (Democratic votes ÷ (Democratic + Republican votes)); recorded on a 0–1 scale (e.g., 0.50 = 50%). For the 88th Congress, this corresponds to the election held just before the 1963–1964 term. |\\n| icpsr | ICPSR numeric identifier for the legislator (member-level ID used to link to other historical congressional datasets). |\\n\\nDataset Context:\\nThis file is a member-level snapshot drawn from a larger project on congressional civil rights politics in the mid‑twentieth century. **This particular extract contains one record per U.S. House member serving in the 88th Congress (1963–1964) only**; accordingly, there is no separate “Congress”/session identifier column in this file. The included fields capture (i) whether the member signed any pro–civil-rights discharge petition during the 88th Congress, (ii) the district’s Democratic two‑party vote share from the immediately prior House election, and (iii) the member’s ICPSR identifier.\\n\\nThe underlying study assembled archival evidence on discharge petition signatures from the National Archives (covering 1933–1976) and linked those signatures to legislators. Election returns were matched using standard historical House election datasets, and member identifiers come from ICPSR. The broader research situates these data within an effort to trace how members advanced civil rights outside of roll‑call votes—particularly through discharge petitions that could bring bills to the floor despite committee gatekeeping—providing insight into party coalitions and legislative activity during 1933–1972.\\n\",\n \"columns\": {\n  \"petpooled\": \"int64\",\n  \"Dvotespercent\": \"float64\",\n  \"icpsr\": \"int64\"\n }\n}\n\n# Question\nWhat is the average effect, at the 50% two-party vote threshold, of a Democrat narrowly winning a House seat on that representative's probability of signing a civil-rights discharge petition in the 88th Congress?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"ds_0084\", \"y\": \"petpooled\", \"x\": [\"Dvotespercent\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_0b34ace9","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_0084","y":"petpooled","x":["Dvotespercent"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"92e8bdcc044aec1f7595e44ed2450f8dea35b3324ad6a1f81ccbe3fd2873136a","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["c587ce91f4ae9c28e363fc161ab0f86b13037d64786bba9b05879937d6e8532c"],"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":1.0129169095307589}}
{"call_id":"call_9ec85880","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q119/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"ds_0084","y":"petpooled","x":["Dvotespercent"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"f9edbc666c54c257f966b97deaf284731bc3a5df5d4e4436da25adf42b7f67d7","dataset_digests":{"ds_0084":"5aeb031ddaf2fede78265be6f0fa1adffd5ff0557ac97464854bab4bac361bf4"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_9ec85880","status":"ok","reason":"","figures":[{"key":"coef.Dvotespercent","value":2.0960928858168635,"unit":"","label":"Coefficient on Dvotespercent in the regression of petpooled","se":0.15994121031715117,"ci_low":1.7813483316483123,"ci_high":2.4108374399854147,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de Dvotespercent na regressão por mínimos quadrados ordinários de petpooled.","formula":"","terms":[{"symbol":"n","value":303.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":2.0960928858168635,"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":0.025581190757715182,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.15994121031715117,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 301 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":2.0960928858168635,"unit":"","note":""},{"symbol":"c","value":1.9678765312856952,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.15994121031715117,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":303,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.35377698421228865,"flag":false,"note":""},{"name":"df_resid","value":301,"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":-0.5720412388813813,"se":0.08700664786383226,"t":-6.574684267535983,"p":2.1506608262862913e-10,"ci_low":-0.7432595792784554,"ci_high":-0.4008228984843071},{"term":"Dvotespercent","coef":2.0960928858168635,"se":0.15994121031715117,"t":13.105395924292882,"p":2.3534382579330522e-31,"ci_low":1.7813483316483123,"ci_high":2.4108374399854147}]},"datasets":{},"warnings":["12 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":"de7bafbb59925e6b14fa879b9313f5e537e7064c14450c7fe3de52a8c936c65c","params_versions":[],"sources":[],"started_at":"2026-10-15T17:10:30.733401Z","duration_ms":2.5631659664213657,"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_0b34ace9","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"d9b71dd3827ba5f2470f816559681318f200ea63843ede4e6dc9b09d9749673e"}}
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