{"call_id":"call_cf18959c","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.lmb_2004.tab1_c1.agent/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"62cdbe0860ad","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":"2026-10-15T16:57:56.453865Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"reference","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\": \"lmb\",\n \"rows\": 13588,\n \"description\": \"U.S. House elections and roll-call voting, the extract of the data of Lee, Moretti and Butler (2004), 'Do Voters Affect or Elect Policies? Evidence from the U.S. House', distributed with the causaldata R package: one row per district and congressional session (13,588 rows). Each row pairs a district's election with the one before it for the same seat. Columns: rownames, the row number; state, ICPSR state code; district, district code; id, an election identifier; score, the adjusted ADA voting score of the district's representative in the session that follows the row's election (0 to 100, higher means a more liberal roll-call record); year, year of the election; demvoteshare, the Democratic candidate's share of the two-party vote in the row's election; democrat, 1 if the Democratic candidate won the row's election; lagdemvoteshare, the Democratic share of the two-party vote in the previous election for the same seat; lagdemocrat, 1 if the Democratic candidate won that previous election. In the paper's notation the previous election is time t and the row's election, and the session after it, are time t+1.\",\n \"columns\": {\n  \"rownames\": \"int64\",\n  \"state\": \"int64\",\n  \"district\": \"int64\",\n  \"id\": \"int64\",\n  \"score\": \"float64\",\n  \"year\": \"int64\",\n  \"demvoteshare\": \"float64\",\n  \"democrat\": \"int64\",\n  \"lagdemocrat\": \"int64\",\n  \"lagdemvoteshare\": \"float64\"\n }\n}\n\n# Question\nLee, Moretti and Butler (2004), 'Do Voters Affect or Elect Policies? Evidence from the U.S. House', Table I, column (1), the total effect gamma: in the paper's close-elections sample, the House elections at time t in which the Democratic candidate's share of the two-party vote was strictly between 48 and 52 percent, estimate the difference in the average adjusted ADA voting index of the district's representative at t+1 between districts where the Democratic candidate's share at t was strictly between 50 and 52 percent and districts where it was strictly between 48 and 50 percent. Estimate it as the coefficient of a regression of the t+1 ADA index on an indicator that the Democratic candidate won at t, with no other regressors. Report that difference.","response":"Table I, column (1): the difference in mean ADA score at t+1 between districts the Democrat won and lost at t, among elections with the Democratic share at t strictly between 48 and 52 percent (915 observations), as the coefficient of a regression on the win indicator; heteroskedasticity-robust errors, which equal the Mixtape replication's errors clustered by election id (id is unique in this window).\ninfer__ols({\"dataset\": \"lmb\", \"y\": \"score\", \"x\": [\"lagdemocrat\"], \"controls\": [], \"fe\": [], \"subset\": \"lagdemvoteshare > 0.48 and lagdemvoteshare < 0.52\", \"se\": \"HC1\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_cf18959c","text":"Table I, column (1): the difference in mean ADA score at t+1 between districts the Democrat won and lost at t, among elections with the Democratic share at t strictly between 48 and 52 percent (915 observations), as the coefficient of a regression on the win indicator; heteroskedasticity-robust errors, which equal the Mixtape replication's errors clustered by election id (id is unique in this window).","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"lmb","y":"score","x":["lagdemocrat"],"controls":[],"fe":[],"subset":"lagdemvoteshare > 0.48 and lagdemvoteshare < 0.52","se":"HC1"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"b464788742358119415ce56dc044de30838474cd0ce71792d48a4fd545c86f9f","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["e3b0f18612990ebf782ce04c0eb6fb34db52cb08ea9fb40606b8f2e410949f8c"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"reference","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.9430840145796537}}
{"call_id":"call_ef74efc9","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.lmb_2004.tab1_c1.agent/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"62cdbe0860ad","inputs":{"dataset":"lmb","y":"score","x":["lagdemocrat"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":"lagdemvoteshare > 0.48 and lagdemvoteshare < 0.52","intercept":true,"level":0.95},"input_digest":"8543766fcac8ad3aa6ad6830ce84f820a4207a158ca40548913badbc3949ee87","dataset_digests":{"lmb":"6f2fbc93d91c4156cab244ba7581cc8eae45210626f97f85ac8b2d366e4cbc20"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_ef74efc9","status":"ok","reason":"","figures":[{"key":"coef.lagdemocrat","value":21.283872256260693,"unit":"","label":"Coefficient on lagdemocrat in the regression of score","se":1.9512339082202579,"ci_low":17.454447516296263,"ci_high":25.113296996225124,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de lagdemocrat na regressão por mínimos quadrados ordinários de score.","formula":"","terms":[{"symbol":"n","value":915.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":21.283872256260693,"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":3.8073137645885016,"unit":"","note":"diagonal da matriz de covariância"}],"result":1.9512339082202579,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 913 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":21.283872256260693,"unit":"","note":""},{"symbol":"c","value":1.962565699494885,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":1.9512339082202579,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":915,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.11521622412798271,"flag":false,"note":""},{"name":"df_resid","value":913,"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":31.195758241906272,"se":1.3339826939068975,"t":23.38542949949508,"p":3.787615957246003e-95,"ci_low":28.577729563124812,"ci_high":33.81378692068773},{"term":"lagdemocrat","coef":21.283872256260693,"se":1.9512339082202579,"t":10.907904053222378,"p":3.9868192567845213e-26,"ci_low":17.454447516296263,"ci_high":25.113296996225124}]},"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":"188abe14b5d0434bbf5d49f330ba1ab5662346a8a0839025fe6a0846e8e6cbf8","params_versions":[],"sources":[],"started_at":"2026-10-15T16:57:56.456654Z","duration_ms":2.9876669868826866,"error":null,"decided_by":"model","rationale":"Table I, column (1): the difference in mean ADA score at t+1 between districts the Democrat won and lost at t, among elections with the Democratic share at t strictly between 48 and 52 percent (915 observations), as the coefficient of a regression on the win indicator; heteroskedasticity-robust errors, which equal the Mixtape replication's errors clustered by election id (id is unique in this window).","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_cf18959c","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"f1452eb8cc4ec23c0268f7bf40b646da206c6b44e4740bf52c2a97982a62a778"}}
{"call_id":"call_96e11c15","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.lmb_2004.tab1_c1.agent/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"62cdbe0860ad","inputs":{},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T16:57:56.459811Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"reference","prompt":null,"response":"submit_answer({\"answer\": \"Estimativa (infer.ols): coef.lagdemocrat = 21,28.\", \"figure_refs\": [\"call_ef74efc9:coef.lagdemocrat\"], \"caveats\": []})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_96e11c15","text":"","tool_calls":[{"vendor_id":"script_2","llm_name":"submit_answer","tool_id":null,"args":{"answer":"Estimativa (infer.ols): coef.lagdemocrat = 21,28.","figure_refs":["call_ef74efc9:coef.lagdemocrat"],"caveats":[]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"b464788742358119415ce56dc044de30838474cd0ce71792d48a4fd545c86f9f","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["e3b0f18612990ebf782ce04c0eb6fb34db52cb08ea9fb40606b8f2e410949f8c","3cf1589872c2062426b508d433f6f04425881a44d4f5bbb3b7095e1daf0beb9d","26dce876d4ad65954753ef74d1c50cd80107a6bac56578583262f014cfc2f670"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"reference","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.2420828677713871}}
{"call_id":"call_4e8e899f","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.lmb_2004.tab1_c1.agent/e1a1","step":"analysis","kind":"note","name":"analyst.guard","version":"","code_version":"62cdbe0860ad","inputs":{},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T16:57:56.461325Z","duration_ms":0.0,"error":null,"decided_by":"code","rationale":"","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_96e11c15","numbers_found":["21,28"],"unbacked":[],"uncited":[],"invalid_refs":[],"empty":false,"verdict":"accepted","retries_left":1,"vendor_id":"script_2"}}