{"call_id":"call_4a18869c","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q007/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":"2026-10-15T17:10:27.987330Z","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_0004\",\n \"rows\": 3772,\n \"description\": \"| Variable Name | Description |\\n| --- | --- |\\n| congress | Two-year session of the U.S. Congress (numeric, 97–113 corresponding to 1981–2014). |\\n| icpsr | Stable numeric identifier for the legislator/candidate from the ICPSR member/candidate coding scheme; constant across Congresses. |\\n| party_code | Party identifier using standard ICPSR party codes (e.g., 100 = Democrat, 200 = Republican). |\\n| nominate_dim1 | DW-NOMINATE first-dimension roll-call ideology score, scaled roughly liberal (negative) to conservative (positive) across all Congresses. |\\n| nokken_poole_dim1 | Nokken–Poole first-dimension roll-call ideology score estimated separately within each Congress, allowing movement over time; liberal (negative) to conservative (positive). |\\n| prog | Indicator for affiliation with the Congressional Progressive Caucus in that Congress (1 = member, 0 = not a member; may be blank before the caucus existed or if unknown). |\\n| pop | Indicator for affiliation with the Populist Caucus in that Congress (1/0; may be blank when not applicable). |\\n| new | Indicator for affiliation with the New Democrat Coalition in that Congress (1/0; may be blank when not applicable). |\\n| bdc | Indicator for affiliation with the Blue Dog Coalition in that Congress (1/0; may be blank when not applicable). |\\n| rmsp | Indicator for affiliation with the Republican Main Street Partnership in that Congress (1/0; may be blank when not applicable). |\\n| rsc | Indicator for affiliation with the Republican Study Committee in that Congress (1/0; may be blank when not applicable). |\\n| tea | Indicator for affiliation with the Tea Party Caucus in that Congress (1/0; may be blank when not applicable). |\\n| lib | Indicator for affiliation with the House Liberty Caucus in that Congress (1/0; may be blank when not applicable). |\\n| post109 | Convenience flag for Congresses at or after the 109th (1 for 2005–2006 and later; 0 otherwise). |\\n| general_percent | Candidate’s vote share in the most recent U.S. House general election in percent (0–100). |\\n| median_donor_current | Median contributor ideology (CF-score) among all donors to the candidate in the current two-year period; negative values indicate more liberal donor pools and positive values more conservative donor pools. |\\n| mean_donor_current | Mean contributor ideology (CF-score) among all donors to the candidate in the current two-year period; same left–right orientation as the median. |\\n| districtpres | District-level vote share for the Democratic presidential nominee in the most recent presidential election, in percent (0–100). |\\n| primary_percent | Candidate’s vote share in the most recent U.S. House primary election in percent (0–100). |\\n| bdc_incumbonly | Incumbent-only Blue Dog Coalition indicator in that Congress (1 if the sitting House member is affiliated; 0 otherwise). |\\n| new_incumbonly | Incumbent-only New Democrat Coalition indicator in that Congress (1 if the sitting House member is affiliated; 0 otherwise). |\\n| tea_incumbonly | Incumbent-only Tea Party Caucus indicator in that Congress (1 if the sitting House member is affiliated; 0 otherwise). |\\n\\nDataset Context:\\nThis panel links U.S. House candidates to their party identification, recorded ideology from roll-call voting, affiliations with major ideological congressional caucuses, and election and donor environments over the 97th–113th Congresses (1981–2014). Roll-call ideology is drawn from widely used spatial scaling procedures: DW-NOMINATE provides a common scale across time, while Nokken–Poole estimates are produced separately within each Congress to capture short-run movement.\\n\\nCaucus affiliation indicators reflect publicly documented rosters of congressional membership organizations that articulate ideological or brand-like identities within each party. Rosters were compiled from official congressional registries of Member Organizations, reference volumes that track congressional activity, archived faction webpages, interviews, and contemporaneous reporting. Many ideological caucuses began or expanded in the mid-1990s and 2000s as the House formalized procedures for registering member organizations.\\n\\nElection results come from administrative and research consolidations of Federal Election Commission returns and related archives, providing general- and primary-election vote shares. District presidential vote share aligns each House district with the most recent presidential election result to summarize local partisan leanings.\\n\\nDonor-ideology measures are constructed from the Database on Ideology, Money in Politics, and Elections (DIME). Individual donations, PACs, party committees, and organizational contributions are linked to recipients and mapped onto a common ideological scale (CF-scores). For each candidate-by-Congress, the dataset summarizes the ideological composition of contributors using both the mean and median CF-score, allowing analysts to characterize the donor pool associated with a candidate in a given two-year period.\\n\\nTogether, these sources create a candidate-by-Congress panel suitable for studying ideological positioning in the House, participation in intra-party caucuses, and how electoral and donor environments vary over time within the institutional setting of congressional sessions.\",\n \"columns\": {\n  \"congress\": \"float64\",\n  \"icpsr\": \"int64\",\n  \"party_code\": \"float64\",\n  \"nominate_dim1\": \"float64\",\n  \"nokken_poole_dim1\": \"float64\",\n  \"prog\": \"float64\",\n  \"pop\": \"float64\",\n  \"new\": \"int64\",\n  \"bdc\": \"int64\",\n  \"rmsp\": \"int64\",\n  \"rsc\": \"float64\",\n  \"tea\": \"int64\",\n  \"lib\": \"int64\",\n  \"post109\": \"float64\",\n  \"general_percent\": \"float64\",\n  \"median_donor_current\": \"float64\",\n  \"mean_donor_current\": \"float64\",\n  \"districtpres\": \"float64\",\n  \"primary_percent\": \"float64\",\n  \"bdc_incumbonly\": \"float64\",\n  \"new_incumbonly\": \"float64\",\n  \"tea_incumbonly\": \"float64\"\n }\n}\n\n# Question\nDid Democrats who newly joined the Blue Dog Coalition experience a change in the mean ideology of their contributors after joining compared with comparable Democrats who did not join?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"ds_0004\", \"y\": \"mean_donor_current\", \"x\": [\"bdc\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_4a18869c","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_0004","y":"mean_donor_current","x":["bdc"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"f61d282209e38f9474e6b3291a3a014e7c575b5498244802fcce335e581a0fd5","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["899f9d10d182e0c5715727f9fa1c279fcea64913047fc42588bc6a0e344069a1"],"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":0.7693332154303789}}
{"call_id":"call_4605deea","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q007/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"ds_0004","y":"mean_donor_current","x":["bdc"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"1b749882e52f11fd4661c5050a2d5739b1fc4961cc56298f7d18deaa4e56eb91","dataset_digests":{"ds_0004":"fb79d8328cb5575dafc431c56db335c7c98451e2721b72a562ef084eb84589db"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_4605deea","status":"ok","reason":"","figures":[{"key":"coef.bdc","value":0.1564587852478964,"unit":"","label":"Coefficient on bdc in the regression of mean_donor_current","se":0.01931960239491873,"ci_low":0.11858089966737603,"ci_high":0.19433667082841674,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de bdc na regressão por mínimos quadrados ordinários de mean_donor_current.","formula":"","terms":[{"symbol":"n","value":3772.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":0.1564587852478964,"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":0.0003732470366977496,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.01931960239491873,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 3770 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.1564587852478964,"unit":"","note":""},{"symbol":"c","value":1.9605934328380716,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.01931960239491873,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":3772,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.015788923570371427,"flag":false,"note":""},{"name":"df_resid","value":3770,"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":-0.32008661747571093,"se":0.0056658312030494346,"t":-56.49420288120048,"p":0.0,"ci_low":-0.3311950089239787,"ci_high":-0.3089782260274432},{"term":"bdc","coef":0.1564587852478964,"se":0.01931960239491873,"t":8.098447475764138,"p":7.443208777769911e-16,"ci_low":0.11858089966737603,"ci_high":0.19433667082841674}]},"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":"dff74167fbfb83351ec0c7c197a0a4bf77790f8291ea8aebceb758a2769cba81","params_versions":[],"sources":[],"started_at":"2026-10-15T17:10:27.989866Z","duration_ms":3.960083005949855,"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_4a18869c","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"a01a95cf010d83b35643d066a71636847cc085cfde01c702e4a60481d730c60b"}}
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