{"call_id":"call_45194e0c","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.mroz_1987.ex15_5.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":"2026-10-15T16:57:56.659576Z","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\": \"mroz\",\n \"rows\": 753,\n \"description\": \"Wooldridge's MROZ data set (the R package wooldridge, served by Rdatasets): Mroz's (1987) sample of married women from the Panel Study of Income Dynamics, for 1975; one row per woman (753 rows). Columns used here: inlf, 1 if the woman was in the labour force in 1975; lwage, log hourly wage (missing for women who did not work); wage, hourly wage; educ, years of schooling; exper, years of labour-market experience; expersq, exper squared; fatheduc, father's years of schooling; motheduc, mother's years of schooling; also hours, kidslt6, kidsge6, age, repwage, hushrs, husage, huseduc, huswage, faminc, mtr, unem, city, nwifeinc and rownames.\",\n \"columns\": {\n  \"rownames\": \"int64\",\n  \"inlf\": \"int64\",\n  \"hours\": \"int64\",\n  \"kidslt6\": \"int64\",\n  \"kidsge6\": \"int64\",\n  \"age\": \"int64\",\n  \"educ\": \"int64\",\n  \"wage\": \"float64\",\n  \"repwage\": \"float64\",\n  \"hushrs\": \"int64\",\n  \"husage\": \"int64\",\n  \"huseduc\": \"int64\",\n  \"huswage\": \"float64\",\n  \"faminc\": \"int64\",\n  \"mtr\": \"float64\",\n  \"motheduc\": \"int64\",\n  \"fatheduc\": \"int64\",\n  \"unem\": \"float64\",\n  \"city\": \"int64\",\n  \"exper\": \"int64\",\n  \"nwifeinc\": \"float64\",\n  \"lwage\": \"float64\",\n  \"expersq\": \"int64\"\n }\n}\n\n# Question\nWooldridge, Introductory Econometrics (7th edition), Example 15.5, using Mroz's (1987) data on married women: among the women who worked in 1975 (those with an observed wage), estimate by two-stage least squares the log hourly wage on years of schooling, years of experience and experience squared, using both the mother's and the father's years of schooling as instruments for schooling. Report the coefficient on years of schooling.","response":"Example 15.5: 2SLS of the log wage on education, experience and its square, with mother's and father's education as instruments, for the working women.\ninfer__iv_2sls({\"dataset\": \"mroz\", \"y\": \"lwage\", \"endog\": [\"educ\"], \"instruments\": [\"motheduc\", \"fatheduc\"], \"exog\": [\"exper\", \"expersq\"], \"subset\": \"inlf == 1\", \"se\": \"classical\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_45194e0c","text":"Example 15.5: 2SLS of the log wage on education, experience and its square, with mother's and father's education as instruments, for the working women.","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__iv_2sls","tool_id":"infer.iv_2sls","args":{"dataset":"mroz","y":"lwage","endog":["educ"],"instruments":["motheduc","fatheduc"],"exog":["exper","expersq"],"subset":"inlf == 1","se":"classical"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"8608f24abbb4c8b5a1d7c95c77aa8c2077bd078f304c68340b4c6ccf0ba43b46","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["06011a2e399541cdf9f925616b148913d2c049b5f093f753d45f8a2a57431afa"],"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":0.8421249222010374}}
{"call_id":"call_685d19b4","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.mroz_1987.ex15_5.agent/e1a1","step":"analysis","kind":"tool","name":"infer.iv_2sls","version":"0.1.0","code_version":"62cdbe0860ad","inputs":{"dataset":"mroz","y":"lwage","endog":["educ"],"instruments":["motheduc","fatheduc"],"exog":["exper","expersq"],"cluster":null,"se":"classical","subset":"inlf == 1","level":0.95},"input_digest":"3565adb5ab78445c1390f62b4724acad1e3a50676d99ef7c3db4299d46b1531e","dataset_digests":{"mroz":"909e3d99ac8070dc661ce7915c99f27631797719369832e227d4005096cbd479"},"result":{"tool":"infer.iv_2sls","version":"0.1.0","call_id":"call_685d19b4","status":"ok","reason":"","figures":[{"key":"coef.educ","value":0.06139662866015408,"unit":"","label":"2SLS effect of educ on lwage","se":0.031436695644695214,"ci_low":-0.00039454487276216477,"ci_high":0.12318780219307032,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de educ por mínimos quadrados em dois estágios: lwage sobre as projeções dos regressores endógenos nos instrumentos ['motheduc', 'fatheduc'] e os controles; resíduos com os valores observados de educ.","formula":"","terms":[{"symbol":"n","value":428.0,"unit":"observações","note":""},{"symbol":"instrumentos","value":2.0,"unit":"","note":""}],"result":0.06139662866015408,"unit":""},{"text_pt":"Erro-padrão clássico (homocedástico): a raiz da variância estimada do coeficiente.","formula":"ep = √V_jj","terms":[{"symbol":"V_jj","value":0.000988265833057199,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.031436695644695214,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 424 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.06139662866015408,"unit":"","note":""},{"symbol":"c","value":1.965574697522104,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.031436695644695214,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"first_stage_F.educ","value":55.40030042777673,"flag":false,"note":"Wald F of the excluded instruments in the first stage for educ (classical (homoskedastic) standard errors)"},{"name":"partial_r2.educ","value":0.20756926964482014,"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":"overid_p","value":0.5386372330714904,"flag":false,"note":"Sargan test of the overidentifying restrictions (assumes homoskedastic errors)"},{"name":"sargan","value":0.3780713419638187,"flag":false,"note":"n·R² of the structural residuals on the instruments"},{"name":"sargan_df","value":1,"flag":false,"note":""},{"name":"n_obs","value":428,"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":0.048100306932180106,"se":0.4003280776041123,"t":0.12015221919994053,"p":0.9044194793612478,"ci_low":-0.7387744331141283,"ci_high":0.8349750469784886},{"term":"exper","coef":0.04417039294876266,"se":0.013432475529443424,"t":3.2883285625157486,"p":0.0010918384252699615,"ci_low":0.017767858923003838,"ci_high":0.07057292697452149},{"term":"expersq","coef":-0.0008989695881555219,"se":0.0004016856118761863,"t":-2.2379930014337086,"p":0.025740027334257214,"ci_low":-0.001688512663218038,"ci_high":-0.00010942651309300572},{"term":"educ","coef":0.06139662866015408,"se":0.031436695644695214,"t":1.9530242412902723,"p":0.05147417391505394,"ci_low":-0.00039454487276216477,"ci_high":0.12318780219307032}],"first_stage":[{"endog":"educ","instrument":"motheduc","coef":0.1575970327485951,"se":0.03589411554668934,"t":4.390609166664114,"p":0.000014298403672048712,"ci_low":0.08704398999594125,"ci_high":0.22815007550124894},{"endog":"educ","instrument":"fatheduc","coef":0.1895484101549542,"se":0.03375646678192402,"t":5.615173275671551,"p":3.561512366875694e-8,"ci_low":0.12319710416036223,"ci_high":0.25589971614954615}],"reduced_form":[{"instrument":"motheduc","coef":0.0030693943221808697,"se":0.012463808356456057,"t":0.24626456331791813,"p":0.8055968958254257,"ci_low":-0.021429317804883524,"ci_high":0.02756810644924526},{"instrument":"fatheduc","coef":0.017419890532518457,"se":0.01172153503026715,"t":1.48614413449579,"p":0.1379859225819088,"ci_low":-0.0056198180526244,"ci_high":0.04045959911766131}]},"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":"2a93c35aae145805f534469ff904723beea7d5d6b4b213088ba41f00243f02ce","params_versions":[],"sources":[],"started_at":"2026-10-15T16:57:56.662156Z","duration_ms":5.358792142942548,"error":null,"decided_by":"model","rationale":"Example 15.5: 2SLS of the log wage on education, experience and its square, with mother's and father's education as instruments, for the working women.","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_45194e0c","llm_name":"infer__iv_2sls","vendor_id":"script_1","result_for_model_ref":"739b5c77529032a324fa968e87aa80699087bb1a1f5ef0dcbbcfd5749b6470af"}}
{"call_id":"call_ca4e37dc","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.mroz_1987.ex15_5.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":"2026-10-15T16:57:56.667807Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"reference","prompt":null,"response":"submit_answer({\"answer\": \"Estimativa (infer.iv_2sls): coef.educ = 0,06140.\", \"figure_refs\": [\"call_685d19b4:coef.educ\"], \"caveats\": []})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_ca4e37dc","text":"","tool_calls":[{"vendor_id":"script_2","llm_name":"submit_answer","tool_id":null,"args":{"answer":"Estimativa (infer.iv_2sls): coef.educ = 0,06140.","figure_refs":["call_685d19b4:coef.educ"],"caveats":[]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"8608f24abbb4c8b5a1d7c95c77aa8c2077bd078f304c68340b4c6ccf0ba43b46","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["06011a2e399541cdf9f925616b148913d2c049b5f093f753d45f8a2a57431afa","a0e525a3700cd10f36d74134992e2b724a731a7e80d7b52b54796ef16b40825c","3077d7a273667b96cd0b055ad2888301a6d00c1fde4f55c93e5e5a5d8b8fba68"],"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":0.32466696575284004}}
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