{"call_id":"call_ef7e5b68","run_id":"20261015T165911Z-suite-paper_repro-baseline-8b4f85/paper_repro.card_1995.ex15_4_iv.agent/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.baseline","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":"2026-10-15T16:59:21.322523Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"rules-1","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\nWooldridge, Introductory Econometrics (7th edition), Example 15.4, Table 15.1, IV column, using Card's (1995) data on men in 1976: estimate by instrumental variables the return to education, the effect of years of schooling on the log hourly wage, using as the instrument whether the man grew up near a four-year college in 1966. Include as exogenous controls years of experience and its square, a race indicator, indicators for living in a standard metropolitan statistical area and in a southern state in 1976, an indicator for living in a metropolitan area in 1966, and the 1966 region-of-residence dummies with the first region omitted. Report the coefficient on years of schooling.","response":"submit_answer({\"answer\": \"Pelas regras deste roteador não identifiquei um desenho que responda à pergunta; nenhuma estimativa foi feita.\", \"figure_refs\": [], \"caveats\": []})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_ef7e5b68","text":"","tool_calls":[{"vendor_id":"baseline_answer","llm_name":"submit_answer","tool_id":null,"args":{"answer":"Pelas regras deste roteador não identifiquei um desenho que responda à pergunta; nenhuma estimativa foi feita.","figure_refs":[],"caveats":[]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"bf5824a1b2b681c3f4f1fcc6e88fb6802db52aa3a73d31da4c3db8f4e1ff0cc9","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["3736f20bf48ddb73bab9c39a50e4ba56a1b245357c08e8f8450a77e55f29323e"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"rules-1","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.585624948143959}}
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