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{"call_id":"call_7395ca22","run_id":"20261015T165911Z-suite-paper_repro-baseline-8b4f85/paper_repro.card_krueger_1994.tab3_r3_ciii.agent/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.baseline","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":"2026-10-15T16:59:21.415202Z","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. 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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. 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Columns: store, a unique restaurant id (the sheet number, plus 1000 for New Jersey restaurants, because one sheet number is used by one restaurant in each state); sheet, the survey's sheet number; wave, 1 or 2; wave2, 1 for the second wave (after the rise) and 0 for the first; nj, 1 for a restaurant in New Jersey and 0 for one in Pennsylvania; fte, full-time-equivalent employment in that wave (full-time employees plus managers and assistant managers plus half the part-time employees, the authors' definition; restaurants closed for good in wave 2 report zero employees); chain, 1 Burger King, 2 KFC, 3 Roy Rogers, 4 Wendy's; co_owned, 1 if company-owned; southj, centralj, northj, 1 for southern, central and northern New Jersey; pa1, pa2, 1 for the northeast suburbs of Philadelphia and for Easton and nearby areas; shore, 1 on the New Jersey shore; wage_st, starting wage in wave 1 ($ per hour); status2, status of the second interview (0 refused, 1 answered, 2 closed for renovations, 3 closed permanently, 4 closed for highway construction, 5 closed by a mall fire).\",\n \"columns\": {\n  \"store\": \"float64\",\n  \"wave\": \"int64\",\n  \"centralj\": \"int64\",\n  \"chain\": \"int64\",\n  \"co_owned\": \"int64\",\n  \"nj\": \"int64\",\n  \"northj\": \"int64\",\n  \"pa1\": \"int64\",\n  \"pa2\": \"int64\",\n  \"sheet\": \"int64\",\n  \"shore\": \"int64\",\n  \"southj\": \"int64\",\n  \"status2\": \"int64\",\n  \"wage_st\": \"float64\",\n  \"fte\": \"float64\",\n  \"wave2\": \"int64\"\n }\n}\n\n# Question\nCard and Krueger (1994), 'Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania', Table 3, row 3, column (iii): New Jersey raised its minimum wage from $4.25 to $5.05 per hour on April 1, 1992, while Pennsylvania's did not change. Using all available observations in each survey wave (February-March 1992, before the rise, and November-December 1992, after it), estimate the difference in differences of mean full-time-equivalent employment per restaurant: the change in the New Jersey mean minus the change in the Pennsylvania mean, where full-time-equivalent employment counts full-time employees and managers in full and each part-time worker as half a full-time worker. 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