{"call_id":"call_db31b178","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q106/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":"2026-10-15T17:10:30.425566Z","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_0072\",\n \"rows\": 32670,\n \"description\": \"| Variable Name | Description                                                                                                                                                |\\n|---------------|------------------------------------------------------------------------------------------------------------------------------------------------------------|\\n| (index)       | Row identifier created during CSV export; no substantive meaning.                                                                                           |\\n| dv_money      | Democratic Party’s share of total campaign contributions in the same district in the subsequent election cycle (percentage points, 0–100); excludes candidate self-financing and party-committee funds, derived from FollowTheMoney records for state legislative general elections. |\\n| rv            | Democratic two-party vote-share margin in the referenced election (percentage points; Democratic share minus 50), where positive values indicate a Democratic advantage. |\\n\\nData Description: This dataset records campaign finance surrounding U.S. state legislative elections. It is built by merging district-level general election returns with donation data maintained by the National Institute on Money in State Politics (FollowTheMoney) for 1990–2010. The merge uses state, chamber (upper/lower), year, and district number. Races in which a third-party candidate placed first or second were removed, and observations where the next cycle occurred under new district lines were excluded to maintain consistent district boundaries.\\n\\nContributions are compiled at the district–election level and classified from individual and organizational donors as reported to state disclosure agencies. Candidate self-financing and party-committee funds are excluded, and monetary aggregates in the source study were adjusted to constant 1990 dollars using the CPI (the variable here is a percentage share). The column dv_money records the Democratic share of total contributions received in the next election cycle within the same district, while rv records the Democratic two-party vote-share margin from the referenced election, providing a measure of the party’s prior electoral standing in that district.\\n\",\n \"columns\": {\n  \"dv_money\": \"float64\",\n  \"rv\": \"float64\"\n }\n}\n\n# Question\nWhat is the average effect of party incumbency on the Democratic Party's share of total campaign contributions in the subsequent election cycle among state legislative districts with very close prior elections?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"ds_0072\", \"y\": \"dv_money\", \"x\": [\"rv\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_db31b178","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_0072","y":"dv_money","x":["rv"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"f95d6bec8d679f3e2f38c73fd25150287775f3138ee03e05d16deb3dfb3c6be9","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["4dc69100efc573c9821512c4e680d498396c2d2520a89aa0d8b5ee84998b5d97"],"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":0.719917006790638}}
{"call_id":"call_ff3779fe","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q106/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"ds_0072","y":"dv_money","x":["rv"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"f0f136533689603a0a5c4b14aee9986927f93e19efb918392abbc56b21de9a06","dataset_digests":{"ds_0072":"ba45942c1035889e069a08dd54f2d1ccad01beac12c0d1934e124053fa44abaf"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_ff3779fe","status":"ok","reason":"","figures":[{"key":"coef.rv","value":0.9071328513061394,"unit":"","label":"Coefficient on rv in the regression of dv_money","se":0.00474534635436816,"ci_low":0.8978317294850867,"ci_high":0.9164339731271921,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de rv na regressão por mínimos quadrados ordinários de dv_money.","formula":"","terms":[{"symbol":"n","value":27203.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":0.9071328513061394,"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":0.000022518312022915186,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.00474534635436816,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 27201 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.9071328513061394,"unit":"","note":""},{"symbol":"c","value":1.96005120100262,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.00474534635436816,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":27203,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.5196618869625949,"flag":false,"note":""},{"name":"df_resid","value":27201,"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":49.0770207716093,"se":0.16638423335648103,"t":294.96196713820206,"p":0.0,"ci_low":48.75089915519103,"ci_high":49.40314238802757},{"term":"rv","coef":0.9071328513061394,"se":0.00474534635436816,"t":191.1626219803978,"p":0.0,"ci_low":0.8978317294850867,"ci_high":0.9164339731271921}]},"datasets":{},"warnings":["5467 rows were dropped because one of 'dv_money', 'rv' 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":"ae3ee629926de2183e6a200ed27b7afcd7f48a315a19410be736185d5db83704","params_versions":[],"sources":[],"started_at":"2026-10-15T17:10:30.428024Z","duration_ms":5.145584000274539,"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_db31b178","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"fd1fa75ce4f2f368363441bccbdc55de3a1e5828d07ffa2527b76fd54711525d"}}
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