{"call_id":"call_78d20a62","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.goodman_bacon_2021.fig5_dd.design/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","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":"2026-10-15T16:57:56.308945Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"reference","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\": \"bacon_example\",\n \"rows\": 1617,\n \"description\": \"The state-year panel Goodman-Bacon (2021), 'Difference-in-Differences with Variation in Treatment Timing', uses to replicate Stevenson and Wolfers (2006) on unilateral (no-fault) divorce and female suicide: one row per U.S. state and year, 1964-1996 (1,617 rows: 49 states, 33 years). Columns: stfips, the state's FIPS code; year; _nfd, the year the state adopted unilateral (no-fault) divorce, for the 36 states that adopted it between 1969 and 1985 (missing for the states that adopted it before 1964 and for those that never adopted it in the window); post, the unilateral-divorce indicator: 1 in the years in which the state had unilateral divorce (from _nfd on, and in every year for the states that adopted it before 1964) and 0 otherwise; asmrs, the age-adjusted suicide mortality of women (suicides per million women); pcinc, personal income per capita (BEA); asmrh, the age-adjusted homicide mortality of women; cases, AFDC (welfare) cases; weight, a population weight; copop, the state's population.\",\n \"columns\": {\n  \"stfips\": \"int8\",\n  \"year\": \"float32\",\n  \"_nfd\": \"float64\",\n  \"post\": \"float32\",\n  \"asmrs\": \"float32\",\n  \"pcinc\": \"float64\",\n  \"asmrh\": \"float32\",\n  \"cases\": \"float64\",\n  \"weight\": \"float32\",\n  \"copop\": \"float64\"\n }\n}\n\n# Question\nDid the unilateral (no-fault) divorce laws that U.S. states adopted at different times reduce female suicide? Using the state panel for 1964-1996, estimate the effect of unilateral divorce on the age-adjusted female suicide rate.","response":"Figure 5: two-way fixed effects DD of the female suicide rate on the unilateral-divorce indicator with state and year fixed effects, unweighted, no controls, heteroskedasticity-robust errors.\ninfer__ols({\"dataset\": \"bacon_example\", \"y\": \"asmrs\", \"x\": [\"post\"], \"controls\": [], \"fe\": [\"stfips\", \"year\"], \"weights\": null, \"se\": \"HC1\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_78d20a62","text":"Figure 5: two-way fixed effects DD of the female suicide rate on the unilateral-divorce indicator with state and year fixed effects, unweighted, no controls, heteroskedasticity-robust errors.","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"bacon_example","y":"asmrs","x":["post"],"controls":[],"fe":["stfips","year"],"weights":null,"se":"HC1"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"9004a611ae134402b1a817abede5693d9b9cebafa996240f403fefe84cfb7ccd","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["eb4a284f466f496cb803fdbe7ca93ae3e201c2e31c37c2ef4a5b9c119753f597"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"reference","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":1.0846660006791353}}
{"call_id":"call_bd71b042","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.goodman_bacon_2021.fig5_dd.design/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"62cdbe0860ad","inputs":{"dataset":"bacon_example","y":"asmrs","x":["post"],"controls":[],"fe":["stfips","year"],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"695f369ca3bbb6da4edc290ddb3e4699e5f15d4a378f245958627c6891f995ff","dataset_digests":{"bacon_example":"2e60642b02d0d3cb7932bb330f7cd97434f0eb86b21224a60ef6f440e4bf2374"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_bd71b042","status":"ok","reason":"","figures":[{"key":"coef.post","value":-3.079925765539901,"unit":"","label":"Coefficient on post in the regression of asmrs","se":1.2677049453118925,"ci_low":-5.566542499975666,"ci_high":-0.5933090311041358,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de post na regressão por mínimos quadrados ordinários de asmrs, efeitos fixos de stfips, year.","formula":"","terms":[{"symbol":"n","value":1617.0,"unit":"observações","note":""},{"symbol":"k","value":82.0,"unit":"parâmetros","note":""}],"result":-3.079925765539901,"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":1.6070758283682283,"unit":"","note":"diagonal da matriz de covariância"}],"result":1.2677049453118925,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 1535 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-3.079925765539901,"unit":"","note":""},{"symbol":"c","value":1.9615106367073332,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":1.2677049453118925,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":1617,"flag":false,"note":"observations used"},{"name":"k","value":82,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.7007060114875596,"flag":false,"note":""},{"name":"df_resid","value":1535,"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"},{"name":"fixed_effects","value":"stfips (49 levels), year (33 levels)","flag":false,"note":"absorbed as dummies; their coefficients are not tabulated"}],"tables":{"coefficients":[{"term":"Intercept","coef":42.50232151737817,"se":2.4120769637515935,"t":17.62063240771253,"p":1.9611748414667094e-63,"ci_low":37.77100689642269,"ci_high":47.23363613833365},{"term":"post","coef":-3.079925765539901,"se":1.2677049453118925,"t":-2.4295288717850267,"p":0.015232525598551252,"ci_low":-5.566542499975666,"ci_high":-0.5933090311041358}]},"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":"573abcfcdaeb0e83dcb37a323778ceb0c18d0957cc53c04c098c9cdf14686b0a","params_versions":[],"sources":[],"started_at":"2026-10-15T16:57:56.311844Z","duration_ms":18.50679120980203,"error":null,"decided_by":"model","rationale":"Figure 5: two-way fixed effects DD of the female suicide rate on the unilateral-divorce indicator with state and year fixed effects, unweighted, no controls, heteroskedasticity-robust errors.","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_78d20a62","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"a47eb25de99b255e956532475d0e7a08bbe1642b319710f187018fd8680ea8af"}}
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