{"call_id":"call_03a914c2","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q117/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":"2026-10-15T17:10:30.686236Z","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_0082\",\n \"rows\": 915,\n \"description\": \"| Variable Name | Description |\\n| --- | --- |\\n| (unnamed) | Row index carried over from the CSV export; not a substantive variable. |\\n| petpooled | Indicator equal to 1 if the House member signed at least one civil-rights–related discharge petition during the **78th Congress (1943–1944)**; 0 otherwise. |\\n| Dvotespercent | Democratic candidate’s share of the **two-party vote** in the general election immediately preceding service in the 78th Congress (i.e., the **1942** U.S. House election for the district/seat), coded as a proportion from 0 to 1 (e.g., 0.50 = 50%). |\\n| icpsr | ICPSR numeric identifier for the member of the U.S. House of Representatives. |\\n\\nDataset Context:\\nThis file contains member-level records from the U.S. House of Representatives for the **78th Congress (1943–1944)**. Each observation corresponds to a single House member/district in that Congress and includes (i) whether the member signed any civil-rights–related discharge petition(s) during the Congress, (ii) the district’s Democratic two-party vote share in the immediately preceding House election (1942), and (iii) a stable legislator identifier. Because this extract includes only one Congress, there is **no separate Congress/session/year column** in the file.\\n\\nThe data were assembled by combining archival records of discharge petition signatures (from National Archives holdings on House discharge petitions), historical U.S. House election returns (two-party vote shares by district), and ICPSR legislator identification codes. The broader underlying project described in the accompanying documentation spans multiple Congresses across the mid-20th century (1933–1976) and compiles repeated measures of legislative activity and election results; this benchmark CSV is a single-Congress subset for 1943–44.\\n\",\n \"columns\": {\n  \"petpooled\": \"int64\",\n  \"Dvotespercent\": \"float64\",\n  \"icpsr\": \"int64\"\n }\n}\n\n# Question\nAmong U.S. House districts with very close elections around the 50% Democratic vote threshold in the 78th Congress, what is the causal effect of electing a Democrat (versus a Republican) on the probability that the representative signs a civil-rights discharge petition?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"ds_0082\", \"y\": \"petpooled\", \"x\": [\"Dvotespercent\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_03a914c2","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_0082","y":"petpooled","x":["Dvotespercent"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"6b02e9829e2c4216905dc66a4e8e7da03e588c80c161e7b8826c66606868d96e","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["d0ce6324832abaf3a0a323e5fbc1dc39d25adef82a3b5cd3abdf4674fa71d3d7"],"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":0.7947920821607113}}
{"call_id":"call_be7020ee","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q117/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"ds_0082","y":"petpooled","x":["Dvotespercent"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"361b3e42e075485c5d328bdfb49441a7b6713c94929a720f7e81822c4b14063e","dataset_digests":{"ds_0082":"39e2e6852e5940b1797b82277695a8528505fea6358f41a9d0302e5be403f512"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_be7020ee","status":"ok","reason":"","figures":[{"key":"coef.Dvotespercent","value":0.8749535645527936,"unit":"","label":"Coefficient on Dvotespercent in the regression of petpooled","se":0.12280165872525636,"ci_low":0.6339148213696816,"ci_high":1.1159923077359055,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de Dvotespercent na regressão por mínimos quadrados ordinários de petpooled.","formula":"","terms":[{"symbol":"n","value":831.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":0.8749535645527936,"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":0.015080247385674332,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.12280165872525636,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 829 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.8749535645527936,"unit":"","note":""},{"symbol":"c","value":1.9628297018559577,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.12280165872525636,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":831,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.06628114350597902,"flag":false,"note":""},{"name":"df_resid","value":829,"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":-0.04845169170590989,"se":0.05743301496724223,"t":-0.843620898773032,"p":0.3991246378963572,"ci_low":-0.16118291935075071,"ci_high":0.06427953593893093},{"term":"Dvotespercent","coef":0.8749535645527936,"se":0.12280165872525636,"t":7.124932787026302,"p":2.263155641877603e-12,"ci_low":0.6339148213696816,"ci_high":1.1159923077359055}]},"datasets":{},"warnings":["84 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":"ca93878c190439ad10657f5ffca52d55cdb4c4274c70c21229de8367444c1f9b","params_versions":[],"sources":[],"started_at":"2026-10-15T17:10:30.688919Z","duration_ms":2.5904581416398287,"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_03a914c2","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"2d26ae01137e657c6b760db982e57a9b6a456b856b3678627524770d6c6976e6"}}
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