{"call_id":"call_7eb96a7a","run_id":"20261015T170957Z-suite-crb_papers-baseline-dae0c1/crb_papers.q111/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.baseline","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":"2026-10-15T17:10:00.246206Z","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\": \"ds_0076\",\n \"rows\": 186575,\n \"description\": \"| Variable Name | Description |\\n| --- | --- |\\n| Unnamed: 0 | Row index from the CSV export; not a substantive data field. |\\n| voted_2012 | Turnout indicator for the 2012 Florida general election from official vote history (1 = voted, 0 = did not vote). |\\n| preregistered_2008 | Preregistration indicator based on Florida voter-file registration history for the cohort around the 2008 eligibility cutoff (1 = the registrant was added to the voter rolls via preregistration—i.e., registered before turning 18; 0 = registered through standard/regular registration after reaching voting age). |\\n| days_ratio_flipped | Running variable measuring the registrant’s date-of-birth position relative to the November 4, 1990 cutoff. **days_ratio_flipped = 0 corresponds exactly to DOB = 1990-11-04**. Values < 0 indicate DOBs before 1990-11-04 (older; eligible by the cutoff), and values > 0 indicate DOBs after 1990-11-04 (younger; ineligible by the cutoff). (This variable is a scaled/normalized distance derived from full date of birth.) |\\n\\nDataset Context:\\nThis file is a derived extract from Florida’s statewide voter registration database used in a study of youth political participation and preregistration. It contains individual-level records for registrants from a Florida voter-file snapshot obtained from the Florida Division of Elections (downloaded around May 2013). Each row represents a registrant and includes official vote history (e.g., whether the person voted in the 2012 general election), an indicator derived from registration history for whether the registrant entered the rolls via preregistration (registered before age 18), and a constructed date-of-birth proximity measure centered on a key birthdate cutoff.\\n\\nThe analytic extract is restricted to young registrants born within a narrow window around **November 4, 1990**, which aligns with eligibility timing for the 2008 general election. Full dates of birth in the voter file are used to compute the distance-to-cutoff running variable (days_ratio_flipped), where 0 denotes an exact match to the cutoff date and the sign indicates whether the registrant was born before or after the cutoff. The resulting dataset provides a focused view of registration-mode differences (preregistration vs regular registration) and subsequent turnout behavior among adjacent birth cohorts near the 2008 eligibility threshold.\\n\",\n \"columns\": {\n  \"voted_2012\": \"int64\",\n  \"days_ratio_flipped\": \"float64\"\n }\n}\n\n# Question\nAmong young Florida registrants near voting age during the 2008 election, what is the effect of being offered preregistration during the 2008 campaign (versus registering normally) on the probability of voting in 2012?","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_7eb96a7a","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":"113ce94982fa5ff563b68d5d0be47e1555b2472596875ff736e4902c3ea8e7f2","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["7ec6451f67e9b8aae0a5b7506c4cca45bdd0ae4509e27b6be41e0f4a1c8dc4ec"],"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":0.2711659763008356}}
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