{"call_id":"call_fc7d0273","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.callaway_santanna_2021.mpdta_att_gt.design/e1a1","step":"data","kind":"note","name":"data.transform","version":"","code_version":"62cdbe0860ad","inputs":{"dataset":"mpdta","index":0,"op":"rename","args":{"map":{"first.treat":"first_treat"}}},"input_digest":"","dataset_digests":{},"result":null,"outputs":{"rows_before":2500,"rows_after":2500,"frame_sha256_before":"76542064d0d831b0c765315df8d21d3899447f08caef3e54fd4ec6d199cfaeb6","frame_sha256_after":"ede9279f5a99110cfc103b63bc6f1b9052eb1f6f5df8290bc4fd8388ae23fac3"},"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T16:57:54.915116Z","duration_ms":0.45316596515476704,"error":null,"decided_by":"code","rationale":"","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{}}
{"call_id":"call_30b20a66","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.callaway_santanna_2021.mpdta_att_gt.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":"2026-10-15T16:57:54.924289Z","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\": \"mpdta\",\n \"rows\": 2500,\n \"description\": \"The did package's example panel mpdta (Callaway and Sant'Anna 2021): county-level teen employment in 500 U.S. counties, 2003-2007, one row per county and year. Some states first raised their minimum wage in 2004, some in 2006 and some in 2007; the others did not in the window. Columns: countyreal, the county id; year; lemp, the log of county teen employment; lpop, the log of county population; first_treat, the year the county's state first raised its minimum wage (2004, 2006 or 2007; 0 for counties never treated in the window); treat, 1 for counties in states that raised it at some point in the window.\",\n \"columns\": {\n  \"year\": \"Int32\",\n  \"countyreal\": \"float64\",\n  \"lpop\": \"float64\",\n  \"lemp\": \"float64\",\n  \"first_treat\": \"float64\",\n  \"treat\": \"float64\"\n }\n}\n\n# Question\nDid state minimum-wage increases, adopted by different states in different years between 2004 and 2007, reduce teen employment? Using the county panel for 2003-2007, estimate how the effect evolved with time since each state's increase, allowing it to differ across adoption cohorts.","response":"att_gt(yname = 'lemp', gname = 'first.treat', idname = 'countyreal', tname = 'year', xformla = ~1, data = mpdta, est_method = 'reg'): never-treated control group, no covariates, varying base period (the package defaults)\ninfer__did_staggered({\"dataset\": \"mpdta\", \"y\": \"lemp\", \"unit\": \"countyreal\", \"time\": \"year\", \"first_treat\": \"first_treat\", \"control_group\": \"never_treated\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_30b20a66","text":"att_gt(yname = 'lemp', gname = 'first.treat', idname = 'countyreal', tname = 'year', xformla = ~1, data = mpdta, est_method = 'reg'): never-treated control group, no covariates, varying base period (the package defaults)","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__did_staggered","tool_id":"infer.did_staggered","args":{"dataset":"mpdta","y":"lemp","unit":"countyreal","time":"year","first_treat":"first_treat","control_group":"never_treated"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"251e40d7841502c15e27205bc0f261086ffd9705d0ec26c9ace5a540c9c1488f","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["67f977ad0b0854161b13cbe06f19fa8fa4a9dc1778687d264db97ecd22a34b6b"],"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":0.7268749177455902}}
{"call_id":"call_a839c6a8","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.callaway_santanna_2021.mpdta_att_gt.design/e1a1","step":"analysis","kind":"tool","name":"infer.did_staggered","version":"0.1.0","code_version":"62cdbe0860ad","inputs":{"dataset":"mpdta","y":"lemp","unit":"countyreal","time":"year","first_treat":"first_treat","control_group":"never_treated","n_boot":199,"seed":0,"level":0.95},"input_digest":"997ff34c3084b175e1111650d4b2a0473818a9ab1633941d1ac51035890759b9","dataset_digests":{"mpdta":"ede9279f5a99110cfc103b63bc6f1b9052eb1f6f5df8290bc4fd8388ae23fac3"},"result":{"tool":"infer.did_staggered","version":"0.1.0","call_id":"call_a839c6a8","status":"ok","reason":"","figures":[{"key":"att","value":-0.03995127515517701,"unit":"","label":"Average effect on lemp across post-adoption cohort-periods (Callaway–Sant'Anna)","se":0.011941084528484509,"ci_low":-0.0633553707673551,"ci_high":-0.016547179542998922,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Para cada coorte g e período t ≥ g: variação da coorte entre g − 1 e t menos a variação das unidades nunca tratadas (ATT(g,t), na tabela att_gt).","formula":"","terms":[{"symbol":"células pós-adoção","value":7.0,"unit":"","note":""},{"symbol":"coortes","value":3.0,"unit":"","note":""}],"result":null,"unit":""},{"text_pt":"Agregação simples: média das células pós-adoção, cada uma ponderada pelo tamanho da sua coorte.","formula":"ATT = S / W","terms":[{"symbol":"S","value":-11.625821070156508,"unit":"","note":"Σ n_g · ATT(g,t)"},{"symbol":"W","value":291.0,"unit":"unidades","note":"Σ n_g"}],"result":-0.03995127515517701,"unit":""},{"text_pt":"Erro-padrão: desvio-padrão do ATT em 199 reamostragens de unidades (cada unidade leva todos os seus períodos).","formula":"","terms":[],"result":0.011941084528484509,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição normal vezes o erro-padrão.","formula":"IC = ATT ± c · ep","terms":[{"symbol":"ATT","value":-0.03995127515517701,"unit":"","note":""},{"symbol":"c","value":1.959963984540054,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.011941084528484509,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"pretrend_mean","value":-0.0005969576739334104,"flag":false,"note":"cohort-weighted mean of the placebo ATT(g,t) before adoption (consecutive periods); it should be near zero"},{"name":"pretrend_se","value":0.006825271151463481,"flag":false,"note":"bootstrap standard error of pretrend_mean"},{"name":"n_cohorts","value":3,"flag":false,"note":""},{"name":"n_never_treated","value":309,"flag":false,"note":""},{"name":"n_units","value":500,"flag":false,"note":""},{"name":"control_group","value":"never_treated","flag":false,"note":""},{"name":"n_boot","value":199,"flag":false,"note":""},{"name":"n_boot_failed","value":0,"flag":false,"note":"replicates discarded because a drawn cohort had no comparison unit in some cell"}],"tables":{"att_gt":[{"g":2004,"t":2004,"e":0,"att":-0.010503246220963533,"se":0.022882360180335775,"n_g":20,"n_control":309,"post":true},{"g":2004,"t":2005,"e":1,"att":-0.07042315810314909,"se":0.035349338097653976,"n_g":20,"n_control":309,"post":true},{"g":2004,"t":2006,"e":2,"att":-0.13725873888940443,"se":0.037707820794609354,"n_g":20,"n_control":309,"post":true},{"g":2004,"t":2007,"e":3,"att":-0.10081136308540525,"se":0.03498817563354677,"n_g":20,"n_control":309,"post":true},{"g":2006,"t":2004,"e":-2,"att":0.006520112424232907,"se":0.02331943920931109,"n_g":40,"n_control":309,"post":false},{"g":2006,"t":2005,"e":-1,"att":-0.002750818750518684,"se":0.01848490029541098,"n_g":40,"n_control":309,"post":false},{"g":2006,"t":2006,"e":0,"att":-0.00459460695286272,"se":0.018221346748964883,"n_g":40,"n_control":309,"post":true},{"g":2006,"t":2007,"e":1,"att":-0.04122447154621793,"se":0.019524036066045164,"n_g":40,"n_control":309,"post":true},{"g":2007,"t":2004,"e":-3,"att":0.030506655583292096,"se":0.014885220499805493,"n_g":131,"n_control":309,"post":false},{"g":2007,"t":2005,"e":-2,"att":-0.0027258928861159572,"se":0.01728908780159486,"n_g":131,"n_control":309,"post":false},{"g":2007,"t":2006,"e":-1,"att":-0.031087119389688136,"se":0.01864182705314265,"n_g":131,"n_control":309,"post":false},{"g":2007,"t":2007,"e":0,"att":-0.026054410719197237,"se":0.017105063505205884,"n_g":131,"n_control":309,"post":true}],"event_time":[{"e":-3,"att":0.030506655583292096,"se":0.014885220499805493,"n_cohorts":1},{"e":-2,"att":-0.0005630846263852289,"se":0.013665058460471256,"n_cohorts":2},{"e":-1,"att":-0.024458744971168966,"se":0.014563335173162988,"n_cohorts":2},{"e":0,"att":-0.019931816789259777,"se":0.01203765078223572,"n_cohorts":3},{"e":1,"att":-0.050957367065194985,"se":0.016800409715261998,"n_cohorts":2},{"e":2,"att":-0.13725873888940443,"se":0.037707820794609354,"n_cohorts":1},{"e":3,"att":-0.10081136308540525,"se":0.03498817563354677,"n_cohorts":1}],"by_cohort":[{"g":2004,"att":-0.07974912657473057,"n_g":20,"post_periods":4},{"g":2006,"att":-0.022909539249540326,"n_g":40,"post_periods":2},{"g":2007,"att":-0.026054410719197237,"n_g":131,"post_periods":1}]},"datasets":{},"warnings":[],"assumptions":["parallel trends: without the policy each cohort would have moved like the comparison units","no anticipation: outcomes do not respond before adoption","adoption is irreversible: once treated, units stay treated"],"notes":""},"outputs":null,"output_digest":"36da0dc6562d5ce39016a4774c2c3d176bd989d4e5b2b1509b116f4bd502bb60","params_versions":[],"sources":[],"started_at":"2026-10-15T16:57:54.927976Z","duration_ms":8.423167048022151,"error":null,"decided_by":"model","rationale":"att_gt(yname = 'lemp', gname = 'first.treat', idname = 'countyreal', tname = 'year', xformla = ~1, data = mpdta, est_method = 'reg'): never-treated control group, no covariates, varying base period (the package defaults)","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_30b20a66","llm_name":"infer__did_staggered","vendor_id":"script_1","result_for_model_ref":"1d01ea8b29f073b908af8a63dbf0f1edaa20e0b6cd3e89d34f907bdef5aaca82"}}
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