{"call_id":"call_7cd714dd","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q052/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":"2026-10-15T17:11:43.739658Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"naive-ols","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\": \"sabia_are_minimum\",\n \"rows\": 1173,\n \"description\": \"This dataset is an annual, state-by-year panel for U.S. states combining fatal crash counts, alcohol-related crash measures for young drivers, minimum wage measures, alcohol policy/tax variables, price indices, and population and income controls, assembled to study alcohol-involved traffic outcomes in relation to policy and economic conditions. The crash measures align with the Fatality Analysis Reporting System (FARS) conventions on alcohol involvement and timing characteristics, while policy and macro controls capture state minimum wages, beer excise taxes, inflation indexes, and demographic scale and income levels at the state-year level.\\n\\nVariables:\\ntot_acc_all: Total fatal traffic accidents in the state-year, as counted in FARS aggregates for all crash circumstances.\\nDUI_1620: Alcohol-involved fatal accidents or fatalities involving drivers aged 16–20, consistent with FARS alcohol-impaired measures and age-specific tallies; FARS imputes BAC where missing using established methods.\\nnonDUI_1620: Fatal accidents or fatalities for ages 16–20 that are not classified as alcohol-involved under FARS definitions for the same state-year.\\ntot_acc_nt / tot_acc_dt: Fatal accidents occurring at night versus daytime, matching FARS time-of-day categorizations commonly used in alcohol-impaired crash reporting.\\ntot_acc_we / tot_acc_wd: Fatal accidents occurring on weekends versus weekdays, consistent with FARS day-of-week splits used to describe alcohol-involved crash patterns.\\nur1620_r: Unemployment rate for ages 16–20 in the state-year, expressed as a rate; captures youth labor market conditions relevant to the age group used in the alcohol-involved outcomes.\\npop: Total state population, annual.\\npop1620: State population ages 16–20, annual.\\nlpop: Log of total state population, facilitating scale normalization in regression specifications and descriptive summaries.\\npc_perinc: Per capita personal income (nominal) at the state-year level.\\nlpc_perinc: Log of per capita personal income, providing a scaled income control.\\ncpi: Consumer Price Index level used to convert nominal variables; precise index definition follows the replication package documentation.\\ncpi2006: CPI re-based to a 2006 index (e.g., 2006=100) to align deflation across series within the dataset.\\nmw2: State minimum wage level (nominal), defined as the binding minimum in the state-year (state or federal), consistent with standard minimum wage series used in policy analyses.\\nmwdef: Deflated or standardized minimum wage measure (e.g., real minimum wage using the included CPI), enabling comparisons over time within states.\\nbeertax: Nominal beer excise tax per unit (e.g., per gallon) at the state-year level, capturing alcohol price policy.realbeertax: Beer tax adjusted for inflation using the included CPI series for real comparisons across years.\\nbac: Indicator for the state per se blood alcohol concentration (BAC) legal limit regime (e.g., .08 per se), reflecting contemporaneous legal environment for impaired driving enforcement.\\\"\",\n \"columns\": {\n  \"state\": \"float64\",\n  \"year\": \"float64\",\n  \"tot_acc_all\": \"int64\",\n  \"DUI_1620\": \"float64\",\n  \"tot_acc_nt\": \"int64\",\n  \"tot_acc_dt\": \"int64\",\n  \"tot_acc_we\": \"int64\",\n  \"tot_acc_wd\": \"int64\",\n  \"nonDUI_1620\": \"float64\",\n  \"ur1620_r\": \"float64\",\n  \"mw2\": \"float64\",\n  \"mwdef\": \"float64\",\n  \"bac\": \"int64\",\n  \"pc_perinc\": \"int64\",\n  \"lpc_perinc\": \"float64\",\n  \"beertax\": \"float64\",\n  \"realbeertax\": \"float64\",\n  \"cpi\": \"float64\",\n  \"cpi2006\": \"float64\",\n  \"pop\": \"float64\",\n  \"pop1620\": \"float64\",\n  \"lpop\": \"float64\"\n }\n}\n\n# Question\nWhat is the effect of the minimum wage on alcohol‑related fatal accidents involving drivers ages 16–20?","response":"submit_answer({\"answer\": \"Linha de base ingênua: o item não declara o resultado e o tratamento, então não há regressão ingênua a fazer e nenhuma estimativa é citada.\", \"figure_refs\": [], \"caveats\": []})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_7cd714dd","text":"","tool_calls":[{"vendor_id":"script_1","llm_name":"submit_answer","tool_id":null,"args":{"answer":"Linha de base ingênua: o item não declara o resultado e o tratamento, então não há regressão ingênua a fazer e nenhuma estimativa é citada.","figure_refs":[],"caveats":[]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"7fd4f29aa8c2a78387bcf24ac5a5b8632b201cc9de72ba6e315a44291a126e75","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["6f1b3b9f0f28025643c11b915fab87445d6c9b4a3af01ea079f797df6e6153ba"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"naive-ols","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.2626669593155384}}
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