{"call_id":"call_3412d274","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q074/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":"2026-10-15T17:10:29.457972Z","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_0044\",\n \"rows\": 40452,\n \"description\": \"| Variable Name  | Description                                                                                 |\\n|----------------|---------------------------------------------------------------------------------------------|\\n| Unnamed/Index  | Row identifier / index column carried over from the source CSV (sequence number).          |\\n| city           | Municipality name and state (lowercase string, e.g., \\\"akron, oh\\\").                         |\\n| YEAR           | Census year (decennial year) for the observation (numeric).                                |\\n| occ            | Occupation class for municipal employment (e.g., \\\"blue_collar\\\", \\\"white_collar\\\", \\\"police\\\").  |\\n| name           | Demographic group label combining race and nativity (e.g., \\\"white_x_native_born\\\").         |\\n| govt           | Proportion (share) of municipal government jobs in this city–year–occupation held by group. |\\n| pop            | Proportion (share) of the municipality's total population comprised by the named group.     |\\n| treat_assign   | Binary indicator whether a statewide legislative mandate applied to the municipality that decade (0/1). |\\n| treat_actual   | Binary indicator whether the municipality had the municipal civil service commission in effect that decade (0/1). |\\n| bandwidth      | Numeric distance in residents between the municipality's population and the state cutoff used in assignment rules. |\\n\\nData Description: This dataset is a municipality-by-decade panel that links full-count U.S. decennial census records (1850–1940) on municipal employment and population composition with administrative indicators derived from state statutes and municipal surveys. Each observation identifies a city and census year, an occupation class, and a demographic group, and reports group shares in the municipal population and in the local government workforce; the file also includes binary flags recording whether a state-level mandate applied and whether a municipal civil service commission was in place, plus a numeric distance-from-cutoff measure used for threshold-based comparisons. The underlying sources include full-count census enumerations (industry/occupation and birthplace fields), historical surveys of municipal civil service adoption, and compiled state session-law information assembled to create a harmonized longitudinal dataset on municipal staff composition.\",\n \"columns\": {\n  \"city\": \"str\",\n  \"YEAR\": \"float64\",\n  \"occ\": \"str\",\n  \"name\": \"str\",\n  \"govt\": \"float64\",\n  \"pop\": \"float64\",\n  \"treat_assign\": \"float64\",\n  \"treat_actual\": \"float64\",\n  \"bandwidth\": \"float64\"\n }\n}\n\n# Question\nWhat is the causal effect of municipal civil service reform adoption on the share of blue-collar municipal government jobs held by native-born white workers, estimated for municipalities whose adoption was externally induced (local compliers)?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"ds_0044\", \"y\": \"govt\", \"x\": [\"treat_actual\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_3412d274","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_0044","y":"govt","x":["treat_actual"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"b8cc5f573355ab714d2aaeac916ed255964cdc6a5a1d6995ddacbea63b178b6d","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["3cd6d0361e36ea0ca3ad9caef752058bb923038e1baab70b1b2b0e4a21e23709"],"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":0.627333065494895}}
{"call_id":"call_9916e16b","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q074/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"ds_0044","y":"govt","x":["treat_actual"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"6c4f7624c367141a1206e7a0a3323748eb7b1da9e00d0f68a230996bcac03945","dataset_digests":{"ds_0044":"70ec9feb2c796cfd98963f1948c8a52286056dadf4df3fc19d3c7179994c358f"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_9916e16b","status":"ok","reason":"","figures":[{"key":"coef.treat_actual","value":-4.437399801659122e-18,"unit":"","label":"Coefficient on treat_actual in the regression of govt","se":0.0032884140813934522,"ci_low":-0.0064453660270854364,"ci_high":0.006445366027085428,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de treat_actual na regressão por mínimos quadrados ordinários de govt.","formula":"","terms":[{"symbol":"n","value":40452.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":-4.437399801659122e-18,"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":0.000010813667170706742,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.0032884140813934522,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 40450 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-4.437399801659122e-18,"unit":"","note":""},{"symbol":"c","value":1.960022633267108,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.0032884140813934522,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":40452,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":-4.440892098500626e-16,"flag":false,"note":""},{"name":"df_resid","value":40450,"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":0.16666666666666666,"se":0.0024581591305653325,"t":67.8014147230315,"p":0.0,"ci_low":0.1618486191345864,"ci_high":0.17148471419874692},{"term":"treat_actual","coef":-4.437399801659122e-18,"se":0.0032884140813934522,"t":-1.3494042087846772e-15,"p":0.9999999999999989,"ci_low":-0.0064453660270854364,"ci_high":0.006445366027085428}]},"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":"a614c69e1b519c36904d003aaf1b67d882703ad432ba4f4cb69beb882b1b0101","params_versions":[],"sources":[],"started_at":"2026-10-15T17:10:29.460341Z","duration_ms":39.8592499550432,"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_3412d274","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"f100c7292f841ea254b53e70ff7ecd5704abe89cc6438d9768da7c505a34a688"}}
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