{"call_id":"call_b0b94255","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q093/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":"2026-10-15T17:10:30.041893Z","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\": \"ds_0061\",\n \"rows\": 64685,\n \"description\": \"| Variable Name | Description                                                                                         |\\n|---------------|-----------------------------------------------------------------------------------------------------|\\n| voted         | Indicator (1/0) that the parent cast a ballot in the election, based on validated official records. |\\n| days          | Child’s age relative to the 18-year voting-eligibility cutoff, measured in days (0 = cutoff day; negative = before; positive = after). |\\n\\nData Description: \\nThis dataset is derived from Danish administrative registers linked to validated election-day records and focuses on families in which a child was near the age of enfranchisement. Each observation corresponds to a parent, with a binary indicator of whether that parent voted in a specific Danish election and a continuous measure capturing the child’s age in days relative to the legal voting-age threshold (18 years) on election day. The “days” variable is centered so that zero denotes that the child’s eligibility date coincided with election day, negative values indicate the child was not yet of age, and positive values indicate the child had passed the threshold.\\n\\nDenmark’s civil registration system enables accurate linkage of de-identified parent–child pairs using exact birthdates and addresses, while municipalities’ official polling lists provide validated turnout. For the election covered here, turnout data were obtained from participating municipalities and merged to the register-based parent–child file. The resulting analytic file allows analysts to study how parental voting coincides with a child’s timing of entry into the electorate in a high-quality administrative context.\",\n \"columns\": {\n  \"voted\": \"int64\",\n  \"days\": \"float64\"\n }\n}\n\n# Question\nAmong parents whose child's age is arbitrarily close to the voting-age threshold, what is the average effect of the child being just eligible to vote on the parent's probability of voting in 2009?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"ds_0061\", \"y\": \"voted\", \"x\": [\"days\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_b0b94255","text":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"ds_0061","y":"voted","x":["days"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"a8f7f18aef46339acd95f950b71dff7d1ef55ebdd85876ef730a1f8619a76b35","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["07719eae1375c2d1ce52b0b7d20e947447e6e40a826330d25308df263765a0e2"],"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":0.745957950130105}}
{"call_id":"call_25927f94","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q093/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"ds_0061","y":"voted","x":["days"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"cc1c74834f923caff1cb2a9aa8d4993eb3e2778c5e6a776f43dde45130939f8a","dataset_digests":{"ds_0061":"202040073773ad7f2a95dc5393c0294791be792dd489a42165d5955e8ba801b5"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_25927f94","status":"ok","reason":"","figures":[{"key":"coef.days","value":0.000109518598072353,"unit":"","label":"Coefficient on days in the regression of voted","se":0.0000166091734715892,"ci_low":0.00007696460709671627,"ci_high":0.00014207258904798972,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de days na regressão por mínimos quadrados ordinários de voted.","formula":"","terms":[{"symbol":"n","value":64685.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":0.000109518598072353,"unit":""},{"text_pt":"Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.","formula":"ep = √V_jj","terms":[{"symbol":"V_jj","value":2.7586464340934247e-10,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.0000166091734715892,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 64683 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.000109518598072353,"unit":"","note":""},{"symbol":"c","value":1.9600006605579685,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.0000166091734715892,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":64685,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.0006667585585791169,"flag":false,"note":""},{"name":"df_resid","value":64683,"flag":false,"note":""},{"name":"se_type","value":"HC1","flag":false,"note":"heteroskedasticity-robust standard errors (HC1)"},{"name":"n_clusters","value":null,"flag":false,"note":"errors not clustered"}],"tables":{"coefficients":[{"term":"Intercept","coef":0.7054077039683305,"se":0.0017917285093264493,"t":393.70233843826543,"p":0.0,"ci_low":0.7018959149065102,"ci_high":0.7089194930301509},{"term":"days","coef":0.000109518598072353,"se":0.0000166091734715892,"t":6.59386201605335,"p":4.3181477448166093e-11,"ci_low":0.00007696460709671627,"ci_high":0.00014207258904798972}]},"datasets":{},"warnings":[],"assumptions":["the conditional mean of the outcome is linear in the regressors","errors are independent across observations; their variance may differ"],"notes":""},"outputs":null,"output_digest":"dde24a0f414b700fd4526ebe06aeee3e73dd02af9289e3de9d4a9d4258fd7faf","params_versions":[],"sources":[],"started_at":"2026-10-15T17:10:30.044630Z","duration_ms":8.8903340511024,"error":null,"decided_by":"model","rationale":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_b0b94255","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"d953d6692a4f6cbfc2e1d92d3d9cbc3650da08b0c8cff04a05ddfa1d9b44c0c7"}}
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