{"call_id":"call_8d45a34f","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q116/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":"2026-10-15T17:10:30.670302Z","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_0081\",\n \"rows\": 307,\n \"description\": \"| Variable Name | Description |\\n| --- | --- |\\n| Unnamed: 0 | Row index from the source CSV export. |\\n| petpooled | Indicator (0/1) for whether the House member signed any of the pro–civil-rights discharge petition(s) included in this file for the 73rd Congress (Congress years 1933–1934). Coded 1 if the member signed at least one such petition; 0 otherwise. |\\n| Dvotespercent | Democratic candidate’s two-party vote share in the immediately preceding U.S. House election for the seat (for the 73rd Congress, the 1932 House election). Stored as a proportion on \\\\[0, 1\\\\]. |\\n| icpsr | ICPSR numeric identifier for the U.S. House member. |\\n\\nDataset Context:\\nThis file is a member-level dataset constructed for the study of U.S. House civil-rights politics in the mid-20th century. It focuses on signatures on pro–civil-rights discharge petitions, an agenda-setting mechanism used to bring stalled bills to the floor. Each row represents a U.S. House member serving in the **73rd Congress (1933–1934)**, indicating whether the member signed the relevant pro–civil-rights discharge petition(s) covered by this file, along with linked electoral information from the prior House election and a standardized legislator identifier.\\n\\nPetition signatory data were transcribed from original House discharge petitions housed at the National Archives and compiled in prior research, then merged to district-level election returns (two-party vote shares; for this Congress, from the 1932 election) and legislator identifiers (ICPSR). The broader project from which this file is drawn assembles multiple indicators of members’ civil-rights-related legislative activity (including discharge petition signatures, floor speeches, and bill sponsorship) across many Congresses spanning 1933 to 1972; this particular file provides the 73rd-Congress member-level signature indicator and associated electoral context.\\n\",\n \"columns\": {\n  \"petpooled\": \"int64\",\n  \"Dvotespercent\": \"float64\",\n  \"icpsr\": \"int64\"\n }\n}\n\n# Question\nAmong districts with elections decided around the 50% Democratic vote-share threshold, what is the effect of electing a Democrat (versus a Republican) on the representative's likelihood of signing civil-rights discharge petitions in the 73rd Congress?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"ds_0081\", \"y\": \"petpooled\", \"x\": [\"Dvotespercent\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_8d45a34f","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_0081","y":"petpooled","x":["Dvotespercent"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"4de652566c0538f958813044165b743e9740ef69abbae175a63f48f7b6818b9a","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["16fea7e3da94fe5f363fa5b123fbf96a2bb866f1bafaf2affabb34bea2b9ef55"],"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":0.728582963347435}}
{"call_id":"call_fbd756e6","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q116/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"ds_0081","y":"petpooled","x":["Dvotespercent"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"7947087ccdb4347b43ec03645f02f20551b9a4358487e3dd363c1e94e0d69376","dataset_digests":{"ds_0081":"47ba1f4aee9818b9fc74de07acd75130a813004d3a1333f72fa83e5b9e52156e"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_fbd756e6","status":"ok","reason":"","figures":[{"key":"coef.Dvotespercent","value":-1.00025217094351,"unit":"","label":"Coefficient on Dvotespercent in the regression of petpooled","se":0.2850601127045052,"ci_low":-1.561532823912395,"ci_high":-0.43897151797462497,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de Dvotespercent na regressão por mínimos quadrados ordinários de petpooled.","formula":"","terms":[{"symbol":"n","value":266.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":-1.00025217094351,"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":0.0812592678551052,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.2850601127045052,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 264 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-1.00025217094351,"unit":"","note":""},{"symbol":"c","value":1.9689904969297174,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.2850601127045052,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":266,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.05405879151645754,"flag":false,"note":""},{"name":"df_resid","value":264,"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":0.9740330913528529,"se":0.1555316383127618,"t":6.262604200144472,"p":1.5279907595858188e-9,"ci_low":0.667792773543115,"ci_high":1.2802734091625907},{"term":"Dvotespercent","coef":-1.00025217094351,"se":0.2850601127045052,"t":-3.50891663324492,"p":0.0005287833887452628,"ci_low":-1.561532823912395,"ci_high":-0.43897151797462497}]},"datasets":{},"warnings":["41 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":"47c9c0660be2a87b6f3501fbba46c7bb920b543044fb6848f8c0262bc6929987","params_versions":[],"sources":[],"started_at":"2026-10-15T17:10:30.672808Z","duration_ms":2.2015830036252737,"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_8d45a34f","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"d00a483c6a72fa51e9abd6babc06b70f30409d0a28b4d7db48fed8bf86c835db"}}
{"call_id":"call_6884a56e","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q116/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"2925aeab2262","inputs":{},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:10:30.675180Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"naive-ols","prompt":null,"response":"submit_answer({\"answer\": \"Estimativa ingênua (infer.ols): coef.Dvotespercent = -1,000.\", \"figure_refs\": [\"call_fbd756e6:coef.Dvotespercent\"], \"caveats\": []})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_6884a56e","text":"","tool_calls":[{"vendor_id":"script_2","llm_name":"submit_answer","tool_id":null,"args":{"answer":"Estimativa ingênua (infer.ols): coef.Dvotespercent = -1,000.","figure_refs":["call_fbd756e6:coef.Dvotespercent"],"caveats":[]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"4de652566c0538f958813044165b743e9740ef69abbae175a63f48f7b6818b9a","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["16fea7e3da94fe5f363fa5b123fbf96a2bb866f1bafaf2affabb34bea2b9ef55","808a2fb8de2808a1a417acfc23a244e634969f06e2b7824905127693f6c0fb51","1c8dc2613959564b3ec40d3ccb8187a4504ec2b78496aae34b9dac624448d7e2"],"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":0.22624991834163666}}
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