{"call_id":"call_5af1018b","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.card_1995.ex15_4_iv.design/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":"2026-10-15T16:57:55.076992Z","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\": \"card\",\n \"rows\": 3010,\n \"description\": \"Wooldridge's CARD data set (the R package wooldridge, served by Rdatasets): David Card's (1995) sample of men from the National Longitudinal Survey of Young Men, observed in 1976; one row per man (3,010 rows). Columns used here: lwage, log hourly wage in 1976; educ, years of schooling in 1976; nearc4, 1 if the man grew up near a four-year college in 1966; nearc2, 1 if near a two-year college in 1966; exper, years of experience (age - educ - 6); expersq, exper squared; black, 1 if black; smsa, 1 if living in an SMSA in 1976; south, 1 if living in the South in 1976; smsa66, 1 if living in an SMSA in 1966; reg661 to reg669, indicators of the region of residence in 1966; also age, fatheduc, motheduc, IQ, KWW, married, wage (cents per hour) and the family-background indicators momdad14, sinmom14, step14, south66, libcrd14, enroll, weight, id and rownames.\",\n \"columns\": {\n  \"rownames\": \"int64\",\n  \"id\": \"int64\",\n  \"nearc2\": \"int64\",\n  \"nearc4\": \"int64\",\n  \"educ\": \"int64\",\n  \"age\": \"int64\",\n  \"fatheduc\": \"float64\",\n  \"motheduc\": \"float64\",\n  \"weight\": \"int64\",\n  \"momdad14\": \"int64\",\n  \"sinmom14\": \"int64\",\n  \"step14\": \"int64\",\n  \"reg661\": \"int64\",\n  \"reg662\": \"int64\",\n  \"reg663\": \"int64\",\n  \"reg664\": \"int64\",\n  \"reg665\": \"int64\",\n  \"reg666\": \"int64\",\n  \"reg667\": \"int64\",\n  \"reg668\": \"int64\",\n  \"reg669\": \"int64\",\n  \"south66\": \"int64\",\n  \"black\": \"int64\",\n  \"smsa\": \"int64\",\n  \"south\": \"int64\",\n  \"smsa66\": \"int64\",\n  \"wage\": \"int64\",\n  \"enroll\": \"int64\",\n  \"KWW\": \"float64\",\n  \"IQ\": \"float64\",\n  \"married\": \"float64\",\n  \"libcrd14\": \"float64\",\n  \"exper\": \"int64\",\n  \"lwage\": \"float64\",\n  \"expersq\": \"int64\"\n }\n}\n\n# Question\nWhat is the causal return to a year of schooling for young men in the United States? Using Card's sample of men from the National Longitudinal Survey of Young Men observed in 1976, estimate the effect of education on the log hourly wage, using a source of variation in schooling that is not driven by ability or family background.","response":"Example 15.4, Table 15.1, IV column: log wage on education instrumented by college proximity, with experience, its square, race, SMSA, South, SMSA in 1966 and the 1966 region dummies (classical errors with the degrees-of-freedom adjustment, as the example states).\ninfer__iv_2sls({\"dataset\": \"card\", \"y\": \"lwage\", \"endog\": [\"educ\"], \"instruments\": [\"nearc4\"], \"exog\": [\"exper\", \"expersq\", \"black\", \"smsa\", \"south\", \"smsa66\", \"reg662\", \"reg663\", \"reg664\", \"reg665\", \"reg666\", \"reg667\", \"reg668\", \"reg669\"], \"se\": \"classical\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_5af1018b","text":"Example 15.4, Table 15.1, IV column: log wage on education instrumented by college proximity, with experience, its square, race, SMSA, South, SMSA in 1966 and the 1966 region dummies (classical errors with the degrees-of-freedom adjustment, as the example states).","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__iv_2sls","tool_id":"infer.iv_2sls","args":{"dataset":"card","y":"lwage","endog":["educ"],"instruments":["nearc4"],"exog":["exper","expersq","black","smsa","south","smsa66","reg662","reg663","reg664","reg665","reg666","reg667","reg668","reg669"],"se":"classical"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"bf5824a1b2b681c3f4f1fcc6e88fb6802db52aa3a73d31da4c3db8f4e1ff0cc9","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["27ad8764a374d9cff882afdb70213102f60b5287c9b860244dc96694b503584b"],"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":0.8442080579698086}}
{"call_id":"call_32eba4df","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.card_1995.ex15_4_iv.design/e1a1","step":"analysis","kind":"tool","name":"infer.iv_2sls","version":"0.1.0","code_version":"62cdbe0860ad","inputs":{"dataset":"card","y":"lwage","endog":["educ"],"instruments":["nearc4"],"exog":["exper","expersq","black","smsa","south","smsa66","reg662","reg663","reg664","reg665","reg666","reg667","reg668","reg669"],"cluster":null,"se":"classical","subset":null,"level":0.95},"input_digest":"97083fef85e2718ee2ccdbec438ba3057ff67d591ac6af7bb615083325cd3111","dataset_digests":{"card":"4ec4da8d7939f11a46d67bd248416cc7330bd0dddaddcd6c49c9872c5a427413"},"result":{"tool":"infer.iv_2sls","version":"0.1.0","call_id":"call_32eba4df","status":"ok","reason":"","figures":[{"key":"coef.educ","value":0.13150383624470474,"unit":"","label":"2SLS effect of educ on lwage","se":0.054963672601175226,"ci_low":0.023733450163648154,"ci_high":0.23927422232576134,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Com um instrumento e um regressor endógeno, o MQ2E é a razão entre o coeficiente de nearc4 na forma reduzida (lwage sobre nearc4 e os controles) e no primeiro estágio (educ sobre nearc4 e os controles).","formula":"β = ρ / π","terms":[{"symbol":"ρ","value":0.04206793783264049,"unit":"","note":"forma reduzida"},{"symbol":"π","value":0.3198989400914623,"unit":"","note":"primeiro estágio"}],"result":0.13150383624470474,"unit":""},{"text_pt":"Erro-padrão clássico (homocedástico): a raiz da variância estimada do coeficiente.","formula":"ep = √V_jj","terms":[{"symbol":"V_jj","value":0.0030210053058091803,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.054963672601175226,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 2994 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.13150383624470474,"unit":"","note":""},{"symbol":"c","value":1.9607566412647661,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.054963672601175226,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"first_stage_F.educ","value":13.255785330576108,"flag":false,"note":"Wald F of the excluded instruments in the first stage for educ (classical (homoskedastic) standard errors)"},{"name":"partial_r2.educ","value":0.004407934102325983,"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":3010,"flag":false,"note":""},{"name":"se_type","value":"classical","flag":false,"note":"classical (homoskedastic) standard errors"},{"name":"n_clusters","value":null,"flag":false,"note":"errors not clustered"}],"tables":{"coefficients":[{"term":"Intercept","coef":3.666150908427464,"se":0.9248295310138733,"t":3.9641369414407985,"p":0.00007538415974785308,"ci_low":1.8527852634542328,"ci_high":5.479516553400695},{"term":"exper","coef":0.10827110610049366,"se":0.023658571085405363,"t":4.576400903911081,"p":4.922903208547169e-6,"ci_low":0.06188240572195053,"ci_high":0.1546598064790368},{"term":"expersq","coef":-0.002334937677505689,"se":0.00033349713755244625,"t":-7.001372469466829,"p":3.1161246944085553e-12,"ci_low":-0.0029888444048044374,"ci_high":-0.001681030950206941},{"term":"black","coef":-0.1467757471841765,"se":0.05389985881009377,"t":-2.7231193258096242,"p":0.006504376545512747,"ci_low":-0.25246025330930105,"ci_high":-0.04109124105905192},{"term":"smsa","coef":0.11180830860064371,"se":0.03166198833336174,"t":3.5313103972953286,"p":0.0004197459454284399,"ci_low":0.04972685470035713,"ci_high":0.1738897625009303},{"term":"south","coef":-0.144671500689114,"se":0.027284622963645526,"t":-5.302308955556272,"p":1.2266258857565786e-7,"ci_low":-0.19817000636948712,"ci_high":-0.09117299500874089},{"term":"smsa66","coef":0.01853110449719602,"se":0.021608588970484666,"t":0.8575804983151744,"p":0.39119278809555685,"ci_low":-0.023838079835042367,"ci_high":0.06090028882943441},{"term":"reg662","coef":0.10076778091986964,"se":0.0376857165904629,"t":2.6738984962109194,"p":0.007538241795889867,"ci_low":0.026875261834297726,"ci_high":0.17466030000544155},{"term":"reg663","coef":0.1482587783887083,"se":0.03681413427830229,"t":4.027224360837132,"p":0.000057840797772050855,"ci_low":0.07607522011011421,"ci_high":0.2204423366673024},{"term":"reg664","coef":0.049897078854400416,"se":0.04373982315600082,"t":1.1407700181237441,"p":0.25405688043284264,"ci_low":-0.035866069886474596,"ci_high":0.13566022759527543},{"term":"reg665","coef":0.14627191305252435,"se":0.04706394912130858,"t":3.107939639223742,"p":0.001901645058086012,"ci_low":0.05399096224877149,"ci_high":0.23855286385627722},{"term":"reg666","coef":0.16290294187588075,"se":0.05190957477135003,"t":3.1382060553073585,"p":0.001716385181435308,"ci_low":0.06112089839772622,"ci_high":0.2646849853540353},{"term":"reg667","coef":0.13457220953081087,"se":0.04940230562501896,"t":2.72400665977539,"p":0.006486970827583493,"ci_low":0.03770631068276323,"ci_high":0.2314381083788585},{"term":"reg668","coef":-0.08307699307386351,"se":0.05933135339638065,"t":-1.4002207655511092,"p":0.161550835975611,"ci_low":-0.1994113382810437,"ci_high":0.03325735213331668},{"term":"reg669","coef":0.10781423263664079,"se":0.041813678752432255,"t":2.578444084649437,"p":0.009971984091442317,"ci_low":0.0258277843270978,"ci_high":0.1898006809461838},{"term":"educ","coef":0.13150383624470474,"se":0.054963672601175226,"t":2.392559121711473,"p":0.016792621890736437,"ci_low":0.023733450163648154,"ci_high":0.23927422232576134}],"first_stage":[{"endog":"educ","instrument":"nearc4","coef":0.3198989400914623,"se":0.08786381779522222,"t":3.640849534185134,"p":0.00027634008572957033,"ci_low":0.147619375822603,"ci_high":0.4921785043603216}],"reduced_form":[{"instrument":"nearc4","coef":0.04206793783264049,"se":0.01807760095364329,"t":2.3270752540957202,"p":0.020027629759561513,"ci_low":0.006622161704650145,"ci_high":0.07751371396063084}]},"datasets":{},"warnings":[],"assumptions":["the instruments affect the outcome only through the endogenous regressors (exclusion restriction)","the instruments are as good as randomly assigned given the controls","errors are independent and have the same variance for every observation"],"notes":""},"outputs":null,"output_digest":"28687165695c6e205d43536e1ead504f60fb8de746cdd5b2099cea2cdf178c89","params_versions":[],"sources":[],"started_at":"2026-10-15T16:57:55.082649Z","duration_ms":20.1579169370234,"error":null,"decided_by":"model","rationale":"Example 15.4, Table 15.1, IV column: log wage on education instrumented by college proximity, with experience, its square, race, SMSA, South, SMSA in 1966 and the 1966 region dummies (classical errors with the degrees-of-freedom adjustment, as the example 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