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{"call_id":"call_733c4e3a","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q075/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":"2026-10-15T17:00:55.751774Z","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\": \"ds_0045\",\n \"rows\": 114803,\n \"description\": \"| Variable Name      | Description                                                                 |\\n|--------------------|-----------------------------------------------------------------------------|\\n| (unnamed)          | Row identifier / dataset index (row number)                                 |\\n| D                  | Log of the number of broadband providers in the county (natural log of provider count from FCC records) |\\n| outcome            | Rescaled measure of partisan affect: difference in feeling‑thermometer scores toward the in‑ and out‑party presidential candidates (scaled 0–1) |\\n| Total_log          | Log of the state right‑of‑way (ROW) regulation index (Beyer & Kende 2003)    |\\n| region             | Census region categorical label (e.g., \\\"1: Northeast\\\", \\\"2: Midwest\\\", \\\"3: South\\\", \\\"4: West\\\") |\\n| percent_black      | Proportion of the county population identifying as Black (fraction between 0 and 1) |\\n| percent_white      | Proportion of the county population identifying as White (fraction between 0 and 1) |\\n| percent_male       | Proportion of the county population that is male (fraction between 0 and 1)  |\\n| lowed              | Indicator for low‑education county status (e.g., \\\"0: Not low‑education type\\\") |\\n| unemploymentrate   | County unemployment rate (percent)                                          |\\n| density            | County population density (as provided in the ICPSR County Characteristics file) |\\n| HHINC_log          | Log of median household income in the county (natural log of HH income)     |\\n| year               | Survey year (2004 or 2008)                                                  |\\n| state              | U.S. state name (state of residence / county mapping)                       |\\n\\nData Description: This merged file brings together individual survey records from national rolling‑cross‑section studies (NAES 2004 and 2008) with county‑level socioeconomic indicators (ICPSR County Characteristics), broadband provider counts (FCC Form 477), and a state‑level right‑of‑way (ROW) regulation index (Beyer & Kende 2003). Each row represents a respondent observation linked to county and state characteristics for the survey year; variables include a constructed index of partisan affect, log‑transformed measures of broadband/provider and income, regional codes, and standard county demographic and economic controls. The data were assembled to enable descriptive and comparative analyses of regional policy indicators, local broadband availability, and survey‑measured attitudes.\",\n \"columns\": {\n  \"D\": \"float64\",\n  \"outcome\": \"float64\",\n  \"Total_log\": \"float64\",\n  \"region\": \"str\",\n  \"percent_black\": \"float64\",\n  \"percent_white\": \"float64\",\n  \"percent_male\": \"float64\",\n  \"lowed\": \"str\",\n  \"unemploymentrate\": \"float64\",\n  \"density\": \"float64\",\n  \"HHINC_log\": \"float64\",\n  \"year\": \"float64\",\n  \"state\": \"str\"\n }\n}\n{\n \"id\": \"ref_iv\",\n \"rows\": 114803,\n \"description\": \"Every row, with region, low-education-type and year dummies\",\n \"columns\": {\n  \"outcome\": \"float64\",\n  \"D\": \"float64\",\n  \"Total_log\": \"float64\",\n  \"percent_black\": \"float64\",\n  \"percent_white\": \"float64\",\n  \"percent_male\": \"float64\",\n  \"unemploymentrate\": \"float64\",\n  \"density\": \"float64\",\n  \"HHINC_log\": \"float64\",\n  \"region\": \"str\",\n  \"lowed\": \"str\",\n  \"year\": \"float64\",\n  \"state\": \"str\",\n  \"region_2: Midwest\": \"int64\",\n  \"region_3: South\": \"int64\",\n  \"region_4: West\": \"int64\",\n  \"lowed_1: Low-education type\": \"int64\",\n  \"year_2008.0\": \"int64\"\n }\n}\n\n# Question\nWhat is the causal effect of a one-unit increase in log(number of broadband providers) on affective partisan polarization (outcome scaled 0-1), estimated for compliers using county-level data pooled across 2004 and 2008?","response":"IV2SLS.from_formula('outcome ~ 1 + controls + C(region) + C(lowed) + C(year) + [D ~ Total_log]').fit(cov_type='clustered', clusters=state)\ninfer__iv_2sls({\"dataset\": \"ref_iv\", \"y\": \"outcome\", \"endog\": [\"D\"], \"instruments\": [\"Total_log\"], \"exog\": [\"percent_black\", \"percent_white\", \"percent_male\", \"unemploymentrate\", \"density\", \"HHINC_log\", \"region_2: Midwest\", \"region_3: South\", \"region_4: West\", \"lowed_1: Low-education type\", \"year_2008.0\"], \"cluster\": \"state\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_733c4e3a","text":"IV2SLS.from_formula('outcome ~ 1 + controls + C(region) + C(lowed) + C(year) + [D ~ Total_log]').fit(cov_type='clustered', clusters=state)","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__iv_2sls","tool_id":"infer.iv_2sls","args":{"dataset":"ref_iv","y":"outcome","endog":["D"],"instruments":["Total_log"],"exog":["percent_black","percent_white","percent_male","unemploymentrate","density","HHINC_log","region_2: Midwest","region_3: South","region_4: West","lowed_1: Low-education type","year_2008.0"],"cluster":"state"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"a347625809a5a43f3650ed9f9cf4dccd065850e03f51877133682fb0d7d367c0","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["66c0f14d2e5c6da97669339b4ac008c7d690a61499da7fcdfadf37d4c4bffeab"],"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":0.7166247814893723}}
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liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.031577464920755464,"unit":"","note":""},{"symbol":"c","value":2.0117405137297655,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.014119552673326995,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"first_stage_F.D","value":11.163168071167823,"flag":false,"note":"Wald F of the excluded instruments in the first stage for D (cluster-robust standard errors (CR1) by 'state')"},{"name":"partial_r2.D","value":0.07662616610864015,"flag":false,"note":"share of the variation the instruments explain beyond the controls"},{"name":"weak_instrument","value":false,"flag":false,"note":"every first-stage F is at least 10"},{"name":"n_obs","value":114803,"flag":false,"note":""},{"name":"se_type","value":"cluster","flag":false,"note":"cluster-robust standard errors (CR1) by 'state'"},{"name":"n_clusters","value":48,"flag":false,"note":"48 clusters of 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