{"call_id":"call_5f546a86","run_id":"20261015T170925Z-suite-crb_papers-scripted-2dbcf5/crb_papers.q076/e1a1","step":"data","kind":"note","name":"data.transform","version":"","code_version":"2925aeab2262","inputs":{"dataset":"ref_iv","index":0,"op":"select","args":{"columns":["suppafford","privpubins3r","byr4647","rep","ind","con","mod","ideostrength","hcsocial","fininsur","healthcaresupport","child18","male","married","labor","mobility","homeowner","religimp","employed","votereg","vote08","black","hispanic2","military","fincome","newsint","publicemp","bornagain"]}},"input_digest":"","dataset_digests":{},"result":null,"outputs":{"rows_before":5221,"rows_after":5221,"frame_sha256_before":"689c88964b32920f16710072893eca0946d0bfffd668f2f88d265ce3cc17f55f","frame_sha256_after":"dfea1bcb946f0fd6dcbfd6e2d0d44f7271e75880241d570da8a39ad310ae39b2"},"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:09:30.486755Z","duration_ms":0.6647501140832901,"error":null,"decided_by":"code","rationale":"","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{}}
{"call_id":"call_f7e7bedc","run_id":"20261015T170925Z-suite-crb_papers-scripted-2dbcf5/crb_papers.q076/e1a1","step":"data","kind":"note","name":"data.transform","version":"","code_version":"2925aeab2262","inputs":{"dataset":"ref_iv","index":1,"op":"dropna","args":{"columns":["suppafford","privpubins3r","byr4647","rep","ind","con","mod","ideostrength","hcsocial","fininsur","healthcaresupport","child18","male","married","labor","mobility","homeowner","religimp","employed","votereg","vote08","black","hispanic2","military","fincome","newsint","publicemp","bornagain"]}},"input_digest":"","dataset_digests":{},"result":null,"outputs":{"rows_before":5221,"rows_after":4389,"frame_sha256_before":"dfea1bcb946f0fd6dcbfd6e2d0d44f7271e75880241d570da8a39ad310ae39b2","frame_sha256_after":"c6be60d3c844bbd766dd5bb0d1810d6f2df234c1defba10fd9e06e0b2ed42610"},"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:09:30.507003Z","duration_ms":2.2908751852810383,"error":null,"decided_by":"code","rationale":"","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{}}
{"call_id":"call_75d477ce","run_id":"20261015T170925Z-suite-crb_papers-scripted-2dbcf5/crb_papers.q076/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":"2026-10-15T17:09:30.530569Z","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_0046\",\n \"rows\": 5221,\n \"description\": \"| Variable Name | Description |\\n| --- | --- |\\n| row_id | Row identifier / case index (sequential record number). |\\n| educ | Education level (ordered categorical from the CCES; coded 1–6 with higher values indicating more education). |\\n| votereg | Voter registration status (binary indicator; 1 = registered to vote, 0 = not registered). |\\n| child18 | Child under age 18 present in household (binary indicator; 1 = yes, 0 = no). |\\n| newsint | How closely respondent follows news about government and public affairs (ordinal scale; CCES item typically coded 1–4 with higher values = follows more closely). |\\n| suppafford | Support for the Affordable Care Act (ACA) (dichotomous; 1 = support, 0 = oppose). |\\n| dontcutmedicare | Budget-priority item: respondent selected “Don’t cut domestic spending such as Medicare and Social Security” as the option they would least prefer among deficit-reduction choices (dichotomous; 1 = selected this option, 0 = otherwise). |\\n| byr4647 | Indicator for birth cohort in this extract: 1 = born in 1946–1947, 0 = born in 1948–1949 (no other birth years included in this file). |\\n| privpubins3r | Health insurance type (recoded dichotomy used in the study): 1 = has any public insurance (including public-only or public + private), 0 = private insurance only. (Respondents with other/none/unknown coverage may appear as missing, depending on CCES source coding.) |\\n| con | Ideology: conservative (binary indicator; 1 = self-identifies as conservative, 0 = otherwise). |\\n| mod | Ideology: moderate (binary indicator; 1 = self-identifies as moderate, 0 = otherwise). |\\n| ideostrength | Strength of ideological identification (ordinal measure; higher values indicate stronger ideology). |\\n| rep | Party identification: Republican (binary indicator; 1 = Republican, 0 = otherwise). |\\n| ind | Party identification: Independent (binary indicator; 1 = Independent, 0 = otherwise). |\\n| employed | Employment status (binary indicator; 1 = employed full-time or part-time, 0 = otherwise). |\\n| healthcaresupport | Occupation indicator: works in “healthcare support” occupation category (binary indicator; 1 = yes, 0 = no). |\\n| hcsocial | Industry indicator: works in “healthcare or social assistance” industry category (binary indicator; 1 = yes, 0 = no). |\\n| fininsur | Industry indicator: works in “finance or insurance” industry category (binary indicator; 1 = yes, 0 = no). |\\n| hispanic2 | Hispanic ethnicity (binary indicator; 1 = Hispanic, 0 = not Hispanic). |\\n| black | Race: Black/African American (binary indicator; 1 = Black, 0 = not Black). |\\n| military | Military household affiliation (binary indicator; 1 = respondent or household member is affiliated with the military, 0 = otherwise). |\\n| mobility | Residential mobility (binary indicator; 1 = recently moved, 0 = otherwise; exact reference period follows CCES coding). |\\n| married | Marital status (binary indicator; 1 = married, 0 = not married). |\\n| homeowner | Homeownership (binary indicator; 1 = owns home, 0 = does not own). |\\n| labor | Labor union household indicator (binary indicator; 1 = respondent or someone in household belongs to a labor union, 0 = otherwise). |\\n| publicemp | Public-sector employment (binary indicator; 1 = public employee, 0 = otherwise). |\\n| vote08 | Self-reported voting in the 2008 election (binary indicator; 1 = voted, 0 = did not vote). |\\n| bornagain | Religious identification as “born again” (binary indicator; 1 = yes, 0 = no). |\\n| religimp | Importance of religion (ordinal scale; higher values indicate religion is more important to the respondent). |\\n| male | Gender (binary indicator; 1 = male, 0 = not male). |\\n| fincome | Family income (ordered categorical bins from the CCES; coded 1–16 with higher values indicating higher income; some respondents may have missing/refused). |\\n\\nDataset Context:\\nThis file is an analysis-ready extract derived from the 2012 Cooperative Congressional Election Study (CCES), an online national survey administered by YouGov/Polymetrix. It contains individual-level responses covering demographic characteristics (education, income, sex, race/ethnicity, marital and homeownership status), political identifiers and behavior (party identification, ideology and its strength, voter registration and 2008 turnout, news consumption), work/household indicators (employment, public-employee and military household flags, union household, mobility), religiosity measures, and items about health insurance source and attitudes toward health-related policy questions. Variable codings mix binary indicators and ordered categorical scales; the table above summarizes each column as present in the provided CSV.\\n\\nThis extract is restricted to respondents born in 1946–1949 (corresponding to roughly ages 63–66 at the time of the October–November 2012 CCES field period).\\n\",\n \"columns\": {\n  \"educ\": \"float64\",\n  \"votereg\": \"float64\",\n  \"child18\": \"float64\",\n  \"newsint\": \"float64\",\n  \"suppafford\": \"float64\",\n  \"dontcutmedicare\": \"float64\",\n  \"byr4647\": \"float64\",\n  \"privpubins3r\": \"float64\",\n  \"con\": \"float64\",\n  \"mod\": \"float64\",\n  \"ideostrength\": \"float64\",\n  \"rep\": \"float64\",\n  \"ind\": \"float64\",\n  \"employed\": \"float64\",\n  \"healthcaresupport\": \"float64\",\n  \"hcsocial\": \"float64\",\n  \"fininsur\": \"float64\",\n  \"hispanic2\": \"float64\",\n  \"black\": \"float64\",\n  \"military\": \"float64\",\n  \"mobility\": \"float64\",\n  \"married\": \"float64\",\n  \"homeowner\": \"float64\",\n  \"labor\": \"float64\",\n  \"publicemp\": \"float64\",\n  \"vote08\": \"float64\",\n  \"bornagain\": \"float64\",\n  \"religimp\": \"float64\",\n  \"male\": \"float64\",\n  \"fincome\": \"float64\"\n }\n}\n{\n \"id\": \"ref_iv\",\n \"rows\": 4389,\n \"description\": \"Complete rows of the script's columns (educ, the CSV's first column, is the index and not a control)\",\n \"columns\": {\n  \"suppafford\": \"float64\",\n  \"privpubins3r\": \"float64\",\n  \"byr4647\": \"float64\",\n  \"rep\": \"float64\",\n  \"ind\": \"float64\",\n  \"con\": \"float64\",\n  \"mod\": \"float64\",\n  \"ideostrength\": \"float64\",\n  \"hcsocial\": \"float64\",\n  \"fininsur\": \"float64\",\n  \"healthcaresupport\": \"float64\",\n  \"child18\": \"float64\",\n  \"male\": \"float64\",\n  \"married\": \"float64\",\n  \"labor\": \"float64\",\n  \"mobility\": \"float64\",\n  \"homeowner\": \"float64\",\n  \"religimp\": \"float64\",\n  \"employed\": \"float64\",\n  \"votereg\": \"float64\",\n  \"vote08\": \"float64\",\n  \"black\": \"float64\",\n  \"hispanic2\": \"float64\",\n  \"military\": \"float64\",\n  \"fincome\": \"float64\",\n  \"newsint\": \"float64\",\n  \"publicemp\": \"float64\",\n  \"bornagain\": \"float64\"\n }\n}\n\n# Question\nWhat is the causal effect of being enrolled in public health insurance (versus only private insurance) on an individual's support for the Affordable Care Act, measured for the subgroup whose insurance enrollment was shifted by the exogenous eligibility variation exploited in this analysis?","response":"2SLS by matrices: X = [1, controls, D], Z = [1, controls, byr4647]\ninfer__iv_2sls({\"dataset\": \"ref_iv\", \"y\": \"suppafford\", \"endog\": [\"privpubins3r\"], \"instruments\": [\"byr4647\"], \"exog\": [\"rep\", \"ind\", \"con\", \"mod\", \"ideostrength\", \"hcsocial\", \"fininsur\", \"healthcaresupport\", \"child18\", \"male\", \"married\", \"labor\", \"mobility\", \"homeowner\", \"religimp\", \"employed\", \"votereg\", \"vote08\", \"black\", \"hispanic2\", \"military\", \"fincome\", \"newsint\", \"publicemp\", \"bornagain\"], \"se\": \"HC1\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_75d477ce","text":"2SLS by matrices: X = [1, controls, D], Z = [1, controls, byr4647]","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__iv_2sls","tool_id":"infer.iv_2sls","args":{"dataset":"ref_iv","y":"suppafford","endog":["privpubins3r"],"instruments":["byr4647"],"exog":["rep","ind","con","mod","ideostrength","hcsocial","fininsur","healthcaresupport","child18","male","married","labor","mobility","homeowner","religimp","employed","votereg","vote08","black","hispanic2","military","fincome","newsint","publicemp","bornagain"],"se":"HC1"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"90d4f3c0aac6e0c4e6a4a40eead88660f5b78313d781862687cbd0b59238713c","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["9fcc390af7328ffe4d9021756a291c7374cc54830728b6a4349f140939a2e25c"],"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":0.7982500828802586}}
{"call_id":"call_85585a75","run_id":"20261015T170925Z-suite-crb_papers-scripted-2dbcf5/crb_papers.q076/e1a1","step":"analysis","kind":"tool","name":"infer.iv_2sls","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"ref_iv","y":"suppafford","endog":["privpubins3r"],"instruments":["byr4647"],"exog":["rep","ind","con","mod","ideostrength","hcsocial","fininsur","healthcaresupport","child18","male","married","labor","mobility","homeowner","religimp","employed","votereg","vote08","black","hispanic2","military","fincome","newsint","publicemp","bornagain"],"cluster":null,"se":"HC1","subset":null,"level":0.95},"input_digest":"07898f5e194331251aa88c85a49de5c1bb29ba4eb55ce9499a2bce0522e8de2a","dataset_digests":{"ref_iv":"c6be60d3c844bbd766dd5bb0d1810d6f2df234c1defba10fd9e06e0b2ed42610"},"result":{"tool":"infer.iv_2sls","version":"0.1.0","call_id":"call_85585a75","status":"ok","reason":"","figures":[{"key":"coef.privpubins3r","value":0.04598858983362574,"unit":"","label":"2SLS effect of privpubins3r on suppafford","se":0.022942665851068515,"ci_low":0.0010093102963070547,"ci_high":0.09096786937094442,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Com um instrumento e um regressor endógeno, o MQ2E é a razão entre o coeficiente de byr4647 na forma reduzida (suppafford sobre byr4647 e os controles) e no primeiro estágio (privpubins3r sobre byr4647 e os controles).","formula":"β = ρ / π","terms":[{"symbol":"ρ","value":0.020179140901986383,"unit":"","note":"forma reduzida"},{"symbol":"π","value":0.43878581567708563,"unit":"","note":"primeiro estágio"}],"result":0.04598858983362574,"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":0.0005263659163537854,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.022942665851068515,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 4362 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.04598858983362574,"unit":"","note":""},{"symbol":"c","value":1.9605079823460818,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.022942665851068515,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"first_stage_F.privpubins3r","value":1186.4814767053833,"flag":false,"note":"Wald F of the excluded instruments in the first stage for privpubins3r (heteroskedasticity-robust standard errors (HC1))"},{"name":"partial_r2.privpubins3r","value":0.22526566832883976,"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":4389,"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 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