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{"call_id":"call_102473e2","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.angrist_krueger_1991.tab5_c2.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":"2026-10-15T16:57:58.774032Z","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\": \"asciiqob\",\n \"rows\": 329509,\n \"description\": \"Joshua Angrist's minimal extract of the 1980 Census used by Angrist and Krueger (1991), 'Does Compulsory School Attendance Affect Schooling and Earnings?' (asciiqob.txt in asciiqob.zip): one row per man born in the United States in 1930-1939, from the 5 percent public-use sample of the 1980 Census (329,509 rows). Columns: lwklywge, the log of weekly earnings in 1979; educ, years of education; yob, year of birth (30 for 1930 to 39 for 1939); qob, quarter of birth (1 to 4); pob, state of birth (census code). Added by the declared transforms: yob_30 to yob_39, one indicator per year of birth; qtr_1 to qtr_4, one indicator per quarter of birth; and qtr1_yob30 to qtr3_yob39, thirty columns, each the product of the indicator for quarter of birth 1, 2 or 3 and the indicator for one year of birth (qtr2_yob35 is 1 for men born in the second quarter of 1935).\",\n \"columns\": {\n  \"lwklywge\": \"float64\",\n  \"educ\": \"int64\",\n  \"yob\": \"int64\",\n  \"qob\": \"int64\",\n  \"pob\": \"int64\",\n  \"yob_30\": \"int64\",\n  \"yob_31\": \"int64\",\n  \"yob_32\": \"int64\",\n  \"yob_33\": \"int64\",\n  \"yob_34\": \"int64\",\n  \"yob_35\": \"int64\",\n  \"yob_36\": \"int64\",\n  \"yob_37\": \"int64\",\n  \"yob_38\": \"int64\",\n  \"yob_39\": \"int64\",\n  \"qtr_1\": \"int64\",\n  \"qtr_2\": \"int64\",\n  \"qtr_3\": \"int64\",\n  \"qtr_4\": \"int64\",\n  \"qtr1_yob30\": \"int64\",\n  \"qtr1_yob31\": \"int64\",\n  \"qtr1_yob32\": \"int64\",\n  \"qtr1_yob33\": \"int64\",\n  \"qtr1_yob34\": \"int64\",\n  \"qtr1_yob35\": \"int64\",\n  \"qtr1_yob36\": \"int64\",\n  \"qtr1_yob37\": \"int64\",\n  \"qtr1_yob38\": \"int64\",\n  \"qtr1_yob39\": \"int64\",\n  \"qtr2_yob30\": \"int64\",\n  \"qtr2_yob31\": \"int64\",\n  \"qtr2_yob32\": \"int64\",\n  \"qtr2_yob33\": \"int64\",\n  \"qtr2_yob34\": \"int64\",\n  \"qtr2_yob35\": \"int64\",\n  \"qtr2_yob36\": \"int64\",\n  \"qtr2_yob37\": \"int64\",\n  \"qtr2_yob38\": \"int64\",\n  \"qtr2_yob39\": \"int64\",\n  \"qtr3_yob30\": \"int64\",\n  \"qtr3_yob31\": \"int64\",\n  \"qtr3_yob32\": \"int64\",\n  \"qtr3_yob33\": \"int64\",\n  \"qtr3_yob34\": \"int64\",\n  \"qtr3_yob35\": \"int64\",\n  \"qtr3_yob36\": \"int64\",\n  \"qtr3_yob37\": \"int64\",\n  \"qtr3_yob38\": \"int64\",\n  \"qtr3_yob39\": \"int64\"\n }\n}\n\n# Question\nIs the earnings gain from schooling causal? For men born in the United States in 1930-1939, observed in the 1980 Census, estimate the causal effect of a year of education on log weekly earnings, using a source of variation in schooling that is unrelated to ability or family background.","response":"Table V, column (2): 2SLS of the log weekly wage on years of education, instrumented by the 30 quarter-of-birth (1-3) by year-of-birth interactions, with nine year-of-birth dummies (the authors' 'QOB Table V.do': ivregress 2sls, classical errors).\ninfer__iv_2sls({\"dataset\": \"asciiqob\", \"y\": \"lwklywge\", \"endog\": [\"educ\"], \"instruments\": [\"qtr1_yob30\", \"qtr1_yob31\", \"qtr1_yob32\", \"qtr1_yob33\", \"qtr1_yob34\", \"qtr1_yob35\", \"qtr1_yob36\", \"qtr1_yob37\", \"qtr1_yob38\", \"qtr1_yob39\", \"qtr2_yob30\", \"qtr2_yob31\", \"qtr2_yob32\", \"qtr2_yob33\", \"qtr2_yob34\", \"qtr2_yob35\", \"qtr2_yob36\", \"qtr2_yob37\", \"qtr2_yob38\", \"qtr2_yob39\", \"qtr3_yob30\", \"qtr3_yob31\", \"qtr3_yob32\", \"qtr3_yob33\", \"qtr3_yob34\", \"qtr3_yob35\", \"qtr3_yob36\", \"qtr3_yob37\", \"qtr3_yob38\", \"qtr3_yob39\"], \"exog\": [\"yob_30\", \"yob_31\", \"yob_32\", \"yob_33\", \"yob_34\", \"yob_35\", \"yob_36\", \"yob_37\", \"yob_38\"], \"se\": \"classical\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_102473e2","text":"Table V, column (2): 2SLS of the log weekly wage on years of education, instrumented by the 30 quarter-of-birth (1-3) by year-of-birth interactions, with nine year-of-birth dummies (the authors' 'QOB Table V.do': ivregress 2sls, classical errors).","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__iv_2sls","tool_id":"infer.iv_2sls","args":{"dataset":"asciiqob","y":"lwklywge","endog":["educ"],"instruments":["qtr1_yob30","qtr1_yob31","qtr1_yob32","qtr1_yob33","qtr1_yob34","qtr1_yob35","qtr1_yob36","qtr1_yob37","qtr1_yob38","qtr1_yob39","qtr2_yob30","qtr2_yob31","qtr2_yob32","qtr2_yob33","qtr2_yob34","qtr2_yob35","qtr2_yob36","qtr2_yob37","qtr2_yob38","qtr2_yob39","qtr3_yob30","qtr3_yob31","qtr3_yob32","qtr3_yob33","qtr3_yob34","qtr3_yob35","qtr3_yob36","qtr3_yob37","qtr3_yob38","qtr3_yob39"],"exog":["yob_30","yob_31","yob_32","yob_33","yob_34","yob_35","yob_36","yob_37","yob_38"],"se":"classical"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"5dcbb49d15a67a349804a691c05e962a49db6126344761c58f491d7f4d3e6c37","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["9e1e2503568b031628b4f5add2a0a6b786eb766b96455e334f3519adb11eb2e9"],"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":0.8407500572502613}}
{"call_id":"call_c1f07f1f","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.angrist_krueger_1991.tab5_c2.design/e1a1","step":"analysis","kind":"tool","name":"infer.iv_2sls","version":"0.1.0","code_version":"62cdbe0860ad","inputs":{"dataset":"asciiqob","y":"lwklywge","endog":["educ"],"instruments":["qtr1_yob30","qtr1_yob31","qtr1_yob32","qtr1_yob33","qtr1_yob34","qtr1_yob35","qtr1_yob36","qtr1_yob37","qtr1_yob38","qtr1_yob39","qtr2_yob30","qtr2_yob31","qtr2_yob32","qtr2_yob33","qtr2_yob34","qtr2_yob35","qtr2_yob36","qtr2_yob37","qtr2_yob38","qtr2_yob39","qtr3_yob30","qtr3_yob31","qtr3_yob32","qtr3_yob33","qtr3_yob34","qtr3_yob35","qtr3_yob36","qtr3_yob37","qtr3_yob38","qtr3_yob39"],"exog":["yob_30","yob_31","yob_32","yob_33","yob_34","yob_35","yob_36","yob_37","yob_38"],"cluster":null,"se":"classical","subset":null,"level":0.95},"input_digest":"f696ec88b4f06094d263f300e85e678edb33ee2a0029a83961f26ce207f83e2c","dataset_digests":{"asciiqob":"654074d5953a60c49422fd28f157a9ede3a844493e1280ba284d386a05958cf7"},"result":{"tool":"infer.iv_2sls","version":"0.1.0","call_id":"call_c1f07f1f","status":"ok","reason":"","figures":[{"key":"coef.educ","value":0.08911545635097498,"unit":"","label":"2SLS effect of educ on lwklywge","se":0.01611008909296629,"ci_low":0.05754014595356108,"ci_high":0.12069076674838888,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de educ por mínimos quadrados em dois estágios: lwklywge sobre as projeções dos regressores endógenos nos instrumentos ['qtr1_yob30', 'qtr1_yob31', 'qtr1_yob32', 'qtr1_yob33', 'qtr1_yob34', 'qtr1_yob35', 'qtr1_yob36', 'qtr1_yob37', 'qtr1_yob38', 'qtr1_yob39', 'qtr2_yob30', 'qtr2_yob31', 'qtr2_yob32', 'qtr2_yob33', 'qtr2_yob34', 'qtr2_yob35', 'qtr2_yob36', 'qtr2_yob37', 'qtr2_yob38', 'qtr2_yob39', 'qtr3_yob30', 'qtr3_yob31', 'qtr3_yob32', 'qtr3_yob33', 'qtr3_yob34', 'qtr3_yob35', 'qtr3_yob36', 'qtr3_yob37', 'qtr3_yob38', 'qtr3_yob39'] e os controles; resíduos com os valores observados de educ.","formula":"","terms":[{"symbol":"n","value":329509.0,"unit":"observações","note":""},{"symbol":"instrumentos","value":30.0,"unit":"","note":""}],"result":0.08911545635097498,"unit":""},{"text_pt":"Erro-padrão clássico (homocedástico): a raiz da variância estimada do coeficiente.","formula":"ep = √V_jj","terms":[{"symbol":"V_jj","value":0.0002595349705833114,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.01611008909296629,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 329498 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.08911545635097498,"unit":"","note":""},{"symbol":"c","value":1.9599711842189484,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.01611008909296629,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"first_stage_F.educ","value":4.907068900445309,"flag":true,"note":"Wald F of the excluded instruments in the first stage for educ (classical (homoskedastic) standard errors); below 10: weak instrument, the estimate and its interval are 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