{"call_id":"call_65ab4c6d","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q088/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":"2026-10-15T17:10:29.819260Z","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_0057\",\n \"rows\": 1400,\n \"description\": \"| Variable Name | Description                                                                                 |\\n|---------------|---------------------------------------------------------------------------------------------|\\n| (index)       | Row index from the CSV export                                                               |\\n| Unnamed: 0    | Secondary row index carried over from the original export                                   |\\n| state         | State name                                                                                  |\\n| stcd          | -                                                                                           |\\n| year          | Election year                                                                               |\\n| cdID          | -                                                                                           |\\n| dv            | Democratic share of the two-party vote in the district’s U.S. House election (percent)      |\\n| dvp           | Democratic share of the two-party vote in the district’s most recent prior House election (%)|\\n| dpres         | Democratic share of the two-party presidential vote in the district-year (percent)          |\\n| party         | Party of the most recent House seat winner, coded +1 for Democrat and -1 for opposition     |\\n| pres_elec     | Indicator for presidential election year (1 = presidential year, 0 = otherwise)             |\\n| elecdate      | Timing of the House election relative to November presidential election (pre, nov, post)    |\\n| margin        | Absolute margin of the most recent prior House election in percentage points                |\\n| dem_seat      | Indicator that the most recent prior House seat was won by a Democrat (1 = yes, 0 = no)     |\\n| rv            | Signed margin of the most recent prior House election in percentage points (Dem win = +)    |\\n\\nData Description: \\nThis dataset assembles district-level election information for nineteenth-century U.S. congressional and presidential contests during the era of party ballots, covering multiple election years when House races often occurred on nonuniform dates across states. It includes measures of Democratic two-party vote shares for both House and presidential elections by district-year, indicators of election timing relative to November, and derived variables summarizing prior House results (e.g., margins, party control). Values expressed as vote shares are on a 0–100 percentage scale.\\n\\nThe data were constructed from historical election returns compiled from authoritative sources on U.S. congressional elections and political parties, with presidential returns aggregated from county to congressional districts where necessary. The coding captures party identification of House winners, whether an election year coincided with a presidential contest, and whether districts’ House elections were held before, during, or after November. The dataset provides structured context for analyzing patterns in electoral outcomes and the institutional environment of nineteenth-century U.S. elections under party ballots.\",\n \"columns\": {\n  \"Unnamed: 0\": \"int64\",\n  \"state\": \"str\",\n  \"stcd\": \"int64\",\n  \"year\": \"int64\",\n  \"cdID\": \"int64\",\n  \"dv\": \"float64\",\n  \"dvp\": \"float64\",\n  \"dpres\": \"float64\",\n  \"party\": \"int64\",\n  \"pres_elec\": \"int64\",\n  \"elecdate\": \"str\",\n  \"margin\": \"float64\",\n  \"dem_seat\": \"int64\",\n  \"rv\": \"float64\"\n }\n}\n\n# Question\nAmong districts where the previous U.S. House race was decided by an infinitesimal margin, what is the average effect of the Democratic party holding that House seat on the subsequent Democratic presidential vote share in that district during November elections?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"ds_0057\", \"y\": \"dpres\", \"x\": [\"dem_seat\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_65ab4c6d","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_0057","y":"dpres","x":["dem_seat"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"26cc9274d4e27e13631c6edf9d4faa439788cf2b3eed0a10f9df3d413beeb1a7","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["dcce0f5d114203586d12c13830600fd50c8bf3f1999feec5bb41cf6337b2e34a"],"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":0.8187501225620508}}
{"call_id":"call_5d358551","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q088/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"ds_0057","y":"dpres","x":["dem_seat"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"ba8e4c7c8be9d14dc434678362fa1009621bdd66de706dc724978eba32d07afe","dataset_digests":{"ds_0057":"10ebc79bf97720b099053bc24726292e57a3f0014d938eb12351c3602bea9090"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_5d358551","status":"ok","reason":"","figures":[{"key":"coef.dem_seat","value":12.145777836452924,"unit":"","label":"Coefficient on dem_seat in the regression of dpres","se":0.4928711773248835,"ci_low":11.178931012751255,"ci_high":13.112624660154593,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de dem_seat na regressão por mínimos quadrados ordinários de dpres.","formula":"","terms":[{"symbol":"n","value":1400.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":12.145777836452924,"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":0.24292199743761675,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.4928711773248835,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 1398 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":12.145777836452924,"unit":"","note":""},{"symbol":"c","value":1.9616623332476932,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.4928711773248835,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":1400,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.30760759622663003,"flag":false,"note":""},{"name":"df_resid","value":1398,"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":42.721403210376344,"se":0.3040112403320902,"t":140.52573570539406,"p":0.0,"ci_low":42.12503581133297,"ci_high":43.31777060941972},{"term":"dem_seat","coef":12.145777836452924,"se":0.4928711773248835,"t":24.64290547963378,"p":1.1968949633955244e-111,"ci_low":11.178931012751255,"ci_high":13.112624660154593}]},"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":"a83d8d2a993703b0cbc93eefea0b2df23dad103125483b5021d59b04fc5b9482","params_versions":[],"sources":[],"started_at":"2026-10-15T17:10:29.822260Z","duration_ms":5.865958984941244,"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_65ab4c6d","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"2cb95190d23f0b909b7e065155b18dcde34cbc3858a7e3d995c8e7402b59c3fe"}}
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