{"call_id":"call_e55cedc3","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q044/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":"2026-10-15T17:11:00.793181Z","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\": \"autor_china_syndrome\",\n \"rows\": 1444,\n \"description\": \"This study assembles a commuting-zone panel for 1990-2007 to quantify how changes in Chinese import exposure relate to U.S. local labor-market outcomes, merging County Business Patterns, Census/ACS, and trade data to track employment, wages, nonemployment, and transfer receipts across places and time. The unit of observation is the commuting zone-year, with baseline characteristics recorded at the start of each period and subsequent changes measured over decade-length intervals.\\nVariables include:\\nczone: Commuting zone identifier\\nstatefip: State FIPS code for the commuting zone\\ncity: City/metropolitan label linked to the commuting zone\\nyr: Calendar year of observation\\nt2: Time-period indicator (e.g., 1990-2000 vs. 2000-2007 window)\\ntimepwt48: Period weight used for aggregating across time intervals\\nreg_midatl: Mid-Atlantic region dummy\\nreg_encen: East North Central region dummy\\nreg_wncen: West North Central region dummy\\nreg_satl: South Atlantic region dummy\\nreg_escen: East South Central region dummy\\nreg_wscen: West South Central region dummy\\nreg_mount: Mountain region dummy\\nreg_pacif: Pacific region dummy\\nl_popcount: Log working-age population count at baseline\\nl_no_workers_totcbp: Log total employment (CBP) at baseline\\nl_shind_manuf_cbp: Baseline share employed in manufacturing industries (CBP)\\nl_sh_popedu_c: Baseline share of population with college education\\nl_sh_popfborn: Baseline share of foreign-born population\\nl_sh_empl_f: Baseline female employment share\\nl_sh_routine33: Baseline share in routine-task occupations\\nl_task_outsource: Baseline task offshorability index (higher = more offshorable)\\nl_sh_empl: Baseline employment-to-population share\\nl_sh_empl_mfg: Baseline manufacturing employment share\\nl_sh_empl_mfg_m: Baseline male manufacturing employment share\\nl_sh_empl_mfg_f: Baseline female manufacturing employment share\\nl_sh_empl_mfg_edu_nc: Baseline manufacturing share, non-college\\nl_sh_empl_mfg_edu_c: Baseline manufacturing share, college\\nl_sh_empl_mfg_age1634: Baseline manufacturing share, ages 16-34\\nl_sh_empl_mfg_age3549: Baseline manufacturing share, ages 35-49\\nl_sh_empl_mfg_age5064: Baseline manufacturing share, ages 50-64\\nl_sh_empl_nmfg: Baseline nonmanufacturing employment share\\nl_sh_empl_nmfg_m: Baseline male nonmanufacturing employment share\\nl_sh_empl_nmfg_f: Baseline female nonmanufacturing employment share\\nl_sh_empl_nmfg_edu_nc: Baseline nonmanufacturing share, non-college\\nl_sh_empl_nmfg_edu_c: Baseline nonmanufacturing share, college\\nl_sh_empl_nmfg_age1634: Baseline nonmanufacturing share, ages 16-34\\nl_sh_empl_nmfg_age3549: Baseline nonmanufacturing share, ages 35-49\\nl_sh_empl_nmfg_age5064: Baseline nonmanufacturing share, ages 50-64\\nl_sh_unempl: Baseline unemployment share\\nl_sh_unempl_m: Baseline male unemployment share\\nl_sh_unempl_f: Baseline female unemployment share\\nl_sh_unempl_edu_nc: Baseline unemployment share, non-college\\nl_sh_unempl_edu_c: Baseline unemployment share, college\\nl_sh_unempl_age1634: Baseline unemployment share, ages 16-34\\nl_sh_unempl_age3549: Baseline unemployment share, ages 35-49\\nl_sh_unempl_age5064: Baseline unemployment share, ages 50-64\\nl_sh_nilf: Baseline not-in-labor-force share\\nl_sh_nilf_m: Baseline NILF share, male\\nl_sh_nilf_f: Baseline NILF share, female\\nl_sh_nilf_edu_nc: Baseline NILF share, non-college\\nl_sh_nilf_edu_c: Baseline NILF share, college\\nl_sh_nilf_age1634: Baseline NILF share, ages 16-34\\nl_sh_nilf_age3549: Baseline NILF share, ages 35-49\\nl_sh_nilf_age5064: Baseline NILF share, ages 50-64\\nl_sh_ssadiswkrs: Baseline share on SSDI (disabled workers)\\nl_avg_lnwkwage_mfg: Baseline log average weekly wage in manufacturing\\nl_avg_lnwkwage_nmfg: Baseline log average weekly wage in nonmanufacturing\\nl_trans_totindiv_pc: Baseline per-capita total individual transfers\\nl_trans_totmed_pc: Baseline per-capita total Medicare transfers\\nl_trans_fedinc_pc: Baseline per-capita federal income assistance\\nl_trans_othinc_pc: Baseline per-capita other income assistance\\nl_trans_unemp_pc: Baseline per-capita unemployment benefits\\nl_trans_taaimp_pc: Baseline per-capita Trade Adjustment Assistance payments\\nl_trans_totedu_pc: Baseline per-capita education-related transfers\\nl_trans_ssaret_pc: Baseline per-capita Social Security retirement\\nl_trans_ssadis_pc: Baseline per-capita Social Security disability\\nl_avg_hhincsum_pc_pw: Baseline per-worker average household total income per capita\\nl_avg_hhincwage_pc_pw: Baseline per-worker average household wage income per capita\\nl_tradeusch_pw: Baseline U.S. imports-from-China exposure per worker\\nl_tradeotch_pw: Baseline China exports to other high-income markets exposure per worker\\nd_tradeusch_pw: Change in U.S. imports-from-China exposure per worker\\nd_tradeotch_pw_lag: Lagged change in China-to-others exposure per worker\\nlnchg_popworkage: Log change in working-age population\\nlnchg_popworkage_edu_c: Log change in working-age population, college\\nlnchg_popworkage_edu_nc: Log change in working-age population, non-college\\nlnchg_popworkage_age1634: Log change, ages 16-34\\nlnchg_popworkage_age3549: Log change, ages 35-49\\nlnchg_popworkage_age5064: Log change, ages 50-64\\nd_popcount: Change in population count\\nd_sh_empl: Change in employment-to-population share\\nd_sh_empl_mfg: Change in manufacturing employment share\\nd_sh_empl_nmfg: Change in nonmanufacturing employment share\\nd_sh_empl_mfg_m: Change in male manufacturing employment share\\nd_sh_empl_nmfg_m: Change in male nonmanufacturing employment share\\nd_sh_empl_mfg_f: Change in female manufacturing employment share\\nd_sh_empl_nmfg_f: Change in female nonmanufacturing employment share\\nd_sh_empl_mfg_edu_nc: Change in manufacturing share, non-college\\nd_sh_empl_nmfg_edu_nc: Change in nonmanufacturing share, non-college\\nd_sh_empl_mfg_edu_c: Change in manufacturing share, college\\nd_sh_empl_nmfg_edu_c: Change in nonmanufacturing share, college\\nd_sh_empl_mfg_age1634: Change in manufacturing share, ages 16-34\\nd_sh_empl_nmfg_age1634: Change in nonmanufacturing share, ages 16-34\\nd_sh_empl_mfg_age3549: Change in manufacturing share, ages 35-49\\nd_sh_empl_nmfg_age3549: Change in nonmanufacturing share, ages 35-49\\nd_sh_empl_mfg_age5064: Change in manufacturing share, ages 50-64\\nd_sh_empl_nmfg_age5064: Change in nonmanufacturing share, ages 50-64\\nd_sh_empl_edu_c: Change in overall employment share, college\\nd_sh_empl_edu_nc: Change in overall employment share, non-college\\nd_sh_unempl: Change in unemployment share\\nd_sh_unempl_m: Change in male unemployment share\\nd_sh_unempl_f: Change in female unemployment share\\nd_sh_unempl_edu_nc: Change in unemployment share, non-college\\nd_sh_unempl_edu_c: Change in unemployment share, college\\nd_sh_unempl_age1634: Change in unemployment share, ages 16-34\\nd_sh_unempl_age3549: Change in unemployment share, ages 35-49\\nd_sh_unempl_age5064: Change in unemployment share, ages 50-64\\nd_sh_nilf: Change in not-in-labor-force share\\nd_sh_nilf_m: Change in NILF share, male\\nd_sh_nilf_f: Change in NILF share, female\\nd_sh_nilf_edu_nc: Change in NILF share, non-college\\nd_sh_nilf_edu_c: Change in NILF share, college\\nd_sh_nilf_age1634: Change in NILF share, ages 16-34\\nd_sh_nilf_age3549: Change in NILF share, ages 35-49\\nd_sh_nilf_age5064: Change in NILF share, ages 50-64\\nd_sh_ssadiswkrs: Change in SSDI recipient share\\nd_avg_lnwkwage: Change in log average weekly wage (overall)\\nd_avg_lnwkwage_c: Change in log average weekly wage, college\\nd_avg_lnwkwage_nc: Change in log average weekly wage, non-college\\nd_avg_lnwkwage_m: Change in log average weekly wage, male\\nd_avg_lnwkwage_f: Change in log average weekly wage, female\\nd_avg_lnwkwage_c_m: Change in log average weekly wage, college males\\nd_avg_lnwkwage_c_f: Change in log average weekly wage, college females\\nd_avg_lnwkwage_nc_m: Change in log average weekly wage, non-college males\\nd_avg_lnwkwage_nc_f: Change in log average weekly wage, non-college females\\nd_avg_lnwkwage_mfg: Change in log average weekly wage in manufacturing\\nd_avg_lnwkwage_mfg_c: Change in log manufacturing wage, college\\nd_avg_lnwkwage_mfg_nc: Change in log manufacturing wage, non-college\\nd_avg_lnwkwage_nmfg: Change in log average weekly wage in nonmanufacturing\\nd_avg_lnwkwage_nmfg_c: Change in log nonmanufacturing wage, college\\nd_avg_lnwkwage_nmfg_nc: Change in log nonmanufacturing wage, non-college\\nlnchg_no_empl: Log change in number employed\\nlnchg_no_empl_mfg: Log change in manufacturing employment count\\nlnchg_no_empl_nmfg: Log change in nonmanufacturing employment count\\nlnchg_no_empl_mfg_edu_nc: Log change in manufacturing employment, non-college\\nlnchg_no_empl_nmfg_edu_nc: Log change in nonmanufacturing employment, non-college\\nlnchg_no_empl_mfg_edu_c: Log change in manufacturing employment, college\\nlnchg_no_empl_nmfg_edu_c: Log change in nonmanufacturing employment, college\\nlnchg_no_unempl: Log change in number unemployed\\nlnchg_no_nilf: Log change in number not in labor force\\nlnchg_no_ssadiswkrs: Log change in number receiving SSDI\\nlnchg_trans_totindiv_pc: Log change in per-capita total transfers\\nlnchg_trans_totmed_pc: Log change in per-capita Medicare transfers\\nlnchg_trans_fedinc_pc: Log change in per-capita federal income assistance\\nlnchg_trans_othinc_pc: Log change in per-capita other income assistance\\nlnchg_trans_unemp_pc: Log change in per-capita unemployment benefits\\nlnchg_trans_taaimp_pc: Log change in per-capita TAA payments\\nlnchg_trans_totedu_pc: Log change in per-capita education transfers\\nlnchg_trans_ssaret_pc: Log change in per-capita Social Security retirement\\nlnchg_trans_ssadis_pc: Log change in per-capita Social Security disability\\nd_trans_totindiv_pc: Change in per-capita total transfers\\nd_trans_totmed_pc: Change in per-capita Medicare transfers\\nd_trans_fedinc_pc: Change in per-capita federal income assistance\\nd_trans_othinc_pc: Change in per-capita other income assistance\\nd_trans_unemp_pc: Change in per-capita unemployment benefits\\nd_trans_taaimp_pc: Change in per-capita TAA payments\\nd_trans_totedu_pc: Change in per-capita education transfers\\nd_trans_ssaret_pc: Change in per-capita Social Security retirement\\nd_trans_ssadis_pc: Change in per-capita Social Security disability\\nrelchg_avg_hhincsum_pc_pw: Relative change in per-worker average household total income per capita\\nrelchg_avg_hhincwage_pc_pw: Relative change in per-worker average household wage income per capita\\nrelchg_avg_hhincbusinv_pc_pw: Relative change in per-worker average household business/investment income per capita\\nrelchg_avg_hhinctrans_pc_pw: Relative change in per-worker average household transfer income per capita\\nrelchg_avg_hhincrest_pc_pw: Relative change in per-worker average household residual income per capita\\nrelchg_med_hhincsum_pc_pw: Relative change in per-worker median household total income per capita\\nrelchg_med_hhincwage_pc_pw: Relative change in per-worker median household wage income per capita\\nd_avg_hhincsum_pc_pw: Change in per-worker average household total income per capita\\nd_avg_hhincwage_pc_pw: Change in per-worker average household wage income per capita\\nd_avg_hhincbusinv_pc_pw: Change in per-worker average household business/investment income per capita\\nd_avg_hhinctrans_pc_pw: Change in per-worker average household transfer income per capita\\nd_avg_hhincrest_pc_pw: Change in per-worker average household residual income per capita\\nd_med_hhincsum_pc_pw: Change in per-worker median household total income per capita\\nd_med_hhincwage_pc_pw: Change in per-worker median household wage income per capita\\nd_tradex_usch_pw: Change in exposure to U.S. exports to China per worker\\nd_tradex_otch_pw_lag: Lagged change in exposure to China's exports to other high-income markets per worker\\nd_tradeusch_netinput_pw: Change in net trade exposure per worker using input linkages\\nd_inputotch_pw_lag: Lagged change in input-based China-to-others exposure per worker\\nd_netimpusch_pw: Change in net imports-from-China exposure per worker\\nd_expotch_pw_lag: Lagged change in exposure to Chinese exports per worker\\nd_traderes_pw_lag: Lagged change in residual trade exposure per worker\\nd_nettradefactor_usch_io: Change in factor-based net trade exposure (U.S.-China, input-output weighted)\\nd_tradefactor_otch_lag_io: Lagged change in factor-based China-to-others exposure (IO weighted)\\nd_expfactor_otch_lag_io: Lagged change in factor-based China export exposure (IO weighted)\\nd_tradeuschlw_pw: Change in low-wage-weighted U.S. import exposure per worker\\nd_tradeotchlw_pw_lag: Lagged change in low-wage-weighted China-to-others exposure per worker\\nd_tradeuschce_pw: Change in consumption-expenditure-weighted U.S. import exposure per worker\\nd_tradeotchce_pw_lag: Lagged change in consumption-expenditure-weighted China-to-others exposure per worker\\nd_tradeusce_pw: Change in U.S. consumption-weighted import exposure per worker\\nd_tradeotce_pw_lag: Lagged change in others' consumption-weighted China exposure per worker\\nd_tradeushi_pw: Change in high-income-weighted U.S. import exposure per worker\\nd_tradeothi_pw_lag: Lagged change in high-income-weighted China-to-others exposure per worker\",\n \"columns\": {\n  \"czone\": \"float64\",\n  \"statefip\": \"int64\",\n  \"city\": \"str\",\n  \"yr\": \"int64\",\n  \"t2\": \"int64\",\n  \"timepwt48\": \"float64\",\n  \"reg_midatl\": \"int64\",\n  \"reg_encen\": \"int64\",\n  \"reg_wncen\": \"int64\",\n  \"reg_satl\": \"int64\",\n  \"reg_escen\": \"int64\",\n  \"reg_wscen\": \"int64\",\n  \"reg_mount\": \"int64\",\n  \"reg_pacif\": \"int64\",\n  \"l_popcount\": \"float64\",\n  \"l_no_workers_totcbp\": \"float64\",\n  \"l_shind_manuf_cbp\": \"float64\",\n  \"l_sh_popedu_c\": \"float64\",\n  \"l_sh_popfborn\": \"float64\",\n  \"l_sh_empl_f\": \"float64\",\n  \"l_sh_routine33\": \"float64\",\n  \"l_task_outsource\": \"float64\",\n  \"l_sh_empl\": \"float64\",\n  \"l_sh_empl_mfg\": \"float64\",\n  \"l_sh_empl_mfg_m\": \"float64\",\n  \"l_sh_empl_mfg_f\": \"float64\",\n  \"l_sh_empl_mfg_edu_nc\": \"float64\",\n  \"l_sh_empl_mfg_edu_c\": \"float64\",\n  \"l_sh_empl_mfg_age1634\": \"float64\",\n  \"l_sh_empl_mfg_age3549\": \"float64\",\n  \"l_sh_empl_mfg_age5064\": \"float64\",\n  \"l_sh_empl_nmfg\": \"float64\",\n  \"l_sh_empl_nmfg_m\": \"float64\",\n  \"l_sh_empl_nmfg_f\": \"float64\",\n  \"l_sh_empl_nmfg_edu_nc\": \"float64\",\n  \"l_sh_empl_nmfg_edu_c\": \"float64\",\n  \"l_sh_empl_nmfg_age1634\": \"float64\",\n  \"l_sh_empl_nmfg_age3549\": \"float64\",\n  \"l_sh_empl_nmfg_age5064\": \"float64\",\n  \"l_sh_unempl\": \"float64\",\n  \"l_sh_unempl_m\": \"float64\",\n  \"l_sh_unempl_f\": \"float64\",\n  \"l_sh_unempl_edu_nc\": \"float64\",\n  \"l_sh_unempl_edu_c\": \"float64\",\n  \"l_sh_unempl_age1634\": \"float64\",\n  \"l_sh_unempl_age3549\": \"float64\",\n  \"l_sh_unempl_age5064\": \"float64\",\n  \"l_sh_nilf\": \"float64\",\n  \"l_sh_nilf_m\": \"float64\",\n  \"l_sh_nilf_f\": \"float64\",\n  \"l_sh_nilf_edu_nc\": \"float64\",\n  \"l_sh_nilf_edu_c\": \"float64\",\n  \"l_sh_nilf_age1634\": \"float64\",\n  \"l_sh_nilf_age3549\": \"float64\",\n  \"l_sh_nilf_age5064\": \"float64\",\n  \"l_sh_ssadiswkrs\": \"float64\",\n  \"l_avg_lnwkwage_mfg\": \"float64\",\n  \"l_avg_lnwkwage_nmfg\": \"float64\",\n  \"l_trans_totindiv_pc\": \"float64\",\n  \"l_trans_totmed_pc\": \"float64\",\n  \"l_trans_fedinc_pc\": \"float64\",\n  \"l_trans_othinc_pc\": \"float64\",\n  \"l_trans_unemp_pc\": \"float64\",\n  \"l_trans_taaimp_pc\": \"float64\",\n  \"l_trans_totedu_pc\": \"float64\",\n  \"l_trans_ssaret_pc\": \"float64\",\n  \"l_trans_ssadis_pc\": \"float64\",\n  \"l_avg_hhincsum_pc_pw\": \"float64\",\n  \"l_avg_hhincwage_pc_pw\": \"float64\",\n  \"l_tradeusch_pw\": \"float64\",\n  \"l_tradeotch_pw\": \"float64\",\n  \"d_tradeusch_pw\": \"float64\",\n  \"d_tradeotch_pw_lag\": \"float64\",\n  \"lnchg_popworkage\": \"float64\",\n  \"lnchg_popworkage_edu_c\": \"float64\",\n  \"lnchg_popworkage_edu_nc\": \"float64\",\n  \"lnchg_popworkage_age1634\": \"float64\",\n  \"lnchg_popworkage_age3549\": \"float64\",\n  \"lnchg_popworkage_age5064\": \"float64\",\n  \"d_popcount\": \"float64\",\n  \"d_sh_empl\": \"float64\",\n  \"d_sh_empl_mfg\": \"float64\",\n  \"d_sh_empl_nmfg\": \"float64\",\n  \"d_sh_empl_mfg_m\": \"float64\",\n  \"d_sh_empl_nmfg_m\": \"float64\",\n  \"d_sh_empl_mfg_f\": \"float64\",\n  \"d_sh_empl_nmfg_f\": \"float64\",\n  \"d_sh_empl_mfg_edu_nc\": \"float64\",\n  \"d_sh_empl_nmfg_edu_nc\": \"float64\",\n  \"d_sh_empl_mfg_edu_c\": \"float64\",\n  \"d_sh_empl_nmfg_edu_c\": \"float64\",\n  \"d_sh_empl_mfg_age1634\": \"float64\",\n  \"d_sh_empl_nmfg_age1634\": \"float64\",\n  \"d_sh_empl_mfg_age3549\": \"float64\",\n  \"d_sh_empl_nmfg_age3549\": \"float64\",\n  \"d_sh_empl_mfg_age5064\": \"float64\",\n  \"d_sh_empl_nmfg_age5064\": \"float64\",\n  \"d_sh_empl_edu_c\": \"float64\",\n  \"d_sh_empl_edu_nc\": \"float64\",\n  \"d_sh_unempl\": \"float64\",\n  \"d_sh_unempl_m\": \"float64\",\n  \"d_sh_unempl_f\": \"float64\",\n  \"d_sh_unempl_edu_nc\": \"float64\",\n  \"d_sh_unempl_edu_c\": \"float64\",\n  \"d_sh_unempl_age1634\": \"float64\",\n  \"d_sh_unempl_age3549\": \"float64\",\n  \"d_sh_unempl_age5064\": \"float64\",\n  \"d_sh_nilf\": \"float64\",\n  \"d_sh_nilf_m\": \"float64\",\n  \"d_sh_nilf_f\": \"float64\",\n  \"d_sh_nilf_edu_nc\": \"float64\",\n  \"d_sh_nilf_edu_c\": \"float64\",\n  \"d_sh_nilf_age1634\": \"float64\",\n  \"d_sh_nilf_age3549\": \"float64\",\n  \"d_sh_nilf_age5064\": \"float64\",\n  \"d_sh_ssadiswkrs\": \"float64\",\n  \"d_avg_lnwkwage\": \"float64\",\n  \"d_avg_lnwkwage_c\": \"float64\",\n  \"d_avg_lnwkwage_nc\": \"float64\",\n  \"d_avg_lnwkwage_m\": \"float64\",\n  \"d_avg_lnwkwage_f\": \"float64\",\n  \"d_avg_lnwkwage_c_m\": \"float64\",\n  \"d_avg_lnwkwage_c_f\": \"float64\",\n  \"d_avg_lnwkwage_nc_m\": \"float64\",\n  \"d_avg_lnwkwage_nc_f\": \"float64\",\n  \"d_avg_lnwkwage_mfg\": \"float64\",\n  \"d_avg_lnwkwage_mfg_c\": \"float64\",\n  \"d_avg_lnwkwage_mfg_nc\": \"float64\",\n  \"d_avg_lnwkwage_nmfg\": \"float64\",\n  \"d_avg_lnwkwage_nmfg_c\": \"float64\",\n  \"d_avg_lnwkwage_nmfg_nc\": \"float64\",\n  \"lnchg_no_empl\": \"float64\",\n  \"lnchg_no_empl_mfg\": \"float64\",\n  \"lnchg_no_empl_nmfg\": \"float64\",\n  \"lnchg_no_empl_mfg_edu_nc\": \"float64\",\n  \"lnchg_no_empl_nmfg_edu_nc\": \"float64\",\n  \"lnchg_no_empl_mfg_edu_c\": \"float64\",\n  \"lnchg_no_empl_nmfg_edu_c\": \"float64\",\n  \"lnchg_no_unempl\": \"float64\",\n  \"lnchg_no_nilf\": \"float64\",\n  \"lnchg_no_ssadiswkrs\": \"float64\",\n  \"lnchg_trans_totindiv_pc\": \"float64\",\n  \"lnchg_trans_totmed_pc\": \"float64\",\n  \"lnchg_trans_fedinc_pc\": \"float64\",\n  \"lnchg_trans_othinc_pc\": \"float64\",\n  \"lnchg_trans_unemp_pc\": \"float64\",\n  \"lnchg_trans_taaimp_pc\": \"float64\",\n  \"lnchg_trans_totedu_pc\": \"float64\",\n  \"lnchg_trans_ssaret_pc\": \"float64\",\n  \"lnchg_trans_ssadis_pc\": \"float64\",\n  \"d_trans_totindiv_pc\": \"float64\",\n  \"d_trans_totmed_pc\": \"float64\",\n  \"d_trans_fedinc_pc\": \"float64\",\n  \"d_trans_othinc_pc\": \"float64\",\n  \"d_trans_unemp_pc\": \"float64\",\n  \"d_trans_taaimp_pc\": \"float64\",\n  \"d_trans_totedu_pc\": \"float64\",\n  \"d_trans_ssaret_pc\": \"float64\",\n  \"d_trans_ssadis_pc\": \"float64\",\n  \"relchg_avg_hhincsum_pc_pw\": \"float64\",\n  \"relchg_avg_hhincwage_pc_pw\": \"float64\",\n  \"relchg_avg_hhincbusinv_pc_pw\": \"float64\",\n  \"relchg_avg_hhinctrans_pc_pw\": \"float64\",\n  \"relchg_avg_hhincrest_pc_pw\": \"float64\",\n  \"relchg_med_hhincsum_pc_pw\": \"float64\",\n  \"relchg_med_hhincwage_pc_pw\": \"float64\",\n  \"d_avg_hhincsum_pc_pw\": \"float64\",\n  \"d_avg_hhincwage_pc_pw\": \"float64\",\n  \"d_avg_hhincbusinv_pc_pw\": \"float64\",\n  \"d_avg_hhinctrans_pc_pw\": \"float64\",\n  \"d_avg_hhincrest_pc_pw\": \"float64\",\n  \"d_med_hhincsum_pc_pw\": \"float64\",\n  \"d_med_hhincwage_pc_pw\": \"float64\",\n  \"d_tradex_usch_pw\": \"float64\",\n  \"d_tradex_otch_pw_lag\": \"float64\",\n  \"d_tradeusch_netinput_pw\": \"float64\",\n  \"d_inputotch_pw_lag\": \"float64\",\n  \"d_netimpusch_pw\": \"float64\",\n  \"d_expotch_pw_lag\": \"float64\",\n  \"d_traderes_pw_lag\": \"float64\",\n  \"d_nettradefactor_usch_io\": \"float64\",\n  \"d_tradefactor_otch_lag_io\": \"float64\",\n  \"d_expfactor_otch_lag_io\": \"float64\",\n  \"d_tradeuschlw_pw\": \"float64\",\n  \"d_tradeotchlw_pw_lag\": \"float64\",\n  \"d_tradeuschce_pw\": \"float64\",\n  \"d_tradeotchce_pw_lag\": \"float64\",\n  \"d_tradeusce_pw\": \"float64\",\n  \"d_tradeotce_pw_lag\": \"float64\",\n  \"d_tradeushi_pw\": \"float64\",\n  \"d_tradeothi_pw_lag\": \"float64\"\n }\n}\n\n# Question\nDoes an increase in Chinese import competition reduce the manufacturing employment share in local labor markets?","response":"causcibench_metadata_rule/1: iv\ninfer__iv_2sls({\"dataset\": \"autor_china_syndrome\", \"y\": \"d_sh_empl_mfg\", \"endog\": [\"d_tradeusch_pw\"], \"instruments\": [\"d_tradeotch_pw_lag\"], \"exog\": [\"t2\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_e55cedc3","text":"causcibench_metadata_rule/1: 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{"call_id":"call_35778d9b","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q044/e1a1","step":"analysis","kind":"tool","name":"infer.iv_2sls","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"autor_china_syndrome","y":"d_sh_empl_mfg","endog":["d_tradeusch_pw"],"instruments":["d_tradeotch_pw_lag"],"exog":["t2"],"cluster":null,"se":"HC1","subset":null,"level":0.95},"input_digest":"932cfd224a41209497d1e8d09c132cda999d7a6bc61f848d5a9d6dc8d2459775","dataset_digests":{"autor_china_syndrome":"dff483b164debe4f7940c1b62d049aa15131912009e244d7aecab186f60327ff"},"result":{"tool":"infer.iv_2sls","version":"0.1.0","call_id":"call_35778d9b","status":"ok","reason":"","figures":[{"key":"coef.d_tradeusch_pw","value":-0.6215758121089948,"unit":"","label":"2SLS effect of d_tradeusch_pw on d_sh_empl_mfg","se":0.12917824069875605,"ci_low":-0.8749733490814211,"ci_high":-0.3681782751365685,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Com um instrumento e um regressor endógeno, o MQ2E é a razão entre o coeficiente de d_tradeotch_pw_lag na forma reduzida (d_sh_empl_mfg sobre d_tradeotch_pw_lag e os controles) e no primeiro estágio (d_tradeusch_pw sobre d_tradeotch_pw_lag e os controles).","formula":"β = ρ / π","terms":[{"symbol":"ρ","value":-0.5389958133083622,"unit":"","note":"forma reduzida"},{"symbol":"π","value":0.867144124349948,"unit":"","note":"primeiro estágio"}],"result":-0.6215758121089948,"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":0.016687017870025755,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.12917824069875605,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 1441 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-0.6215758121089948,"unit":"","note":""},{"symbol":"c","value":1.96161161200012,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.12917824069875605,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"first_stage_F.d_tradeusch_pw","value":51.56145914069501,"flag":false,"note":"Wald F of the excluded instruments in the first stage for d_tradeusch_pw (heteroskedasticity-robust standard errors (HC1))"},{"name":"partial_r2.d_tradeusch_pw","value":0.46298812988549665,"flag":false,"note":"share of the variation the instruments explain beyond the controls"},{"name":"weak_instrument","value":false,"flag":false,"note":"every first-stage F is at least 10"},{"name":"n_obs","value":1444,"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":-0.21368378978842553,"se":0.16828170300201287,"t":-1.2697981181345044,"p":0.20436156483465448,"ci_low":-0.5437871324843294,"ci_high":0.11641955290747832},{"term":"t2","coef":-0.44987013713085827,"se":0.2031852768675569,"t":-2.214088264988309,"p":0.026978757222309684,"ci_low":-0.8484407356217173,"ci_high":-0.051299538639999254},{"term":"d_tradeusch_pw","coef":-0.6215758121089948,"se":0.12917824069875605,"t":-4.811768675178903,"p":1.6531087415123596e-6,"ci_low":-0.8749733490814211,"ci_high":-0.3681782751365685}],"first_stage":[{"endog":"d_tradeusch_pw","instrument":"d_tradeotch_pw_lag","coef":0.867144124349948,"se":0.12076155210844738,"t":7.180630831667577,"p":1.1079723089122809e-12,"ci_low":0.6302568614508601,"ci_high":1.104031387249036}],"reduced_form":[{"instrument":"d_tradeotch_pw_lag","coef":-0.5389958133083622,"se":0.08224027269778712,"t":-6.553915686649532,"p":7.783686691322959e-11,"ci_low":-0.7003192872063979,"ci_high":-0.3776723394103266}]},"datasets":{},"warnings":[],"assumptions":["the instruments affect the outcome only through the endogenous regressors (exclusion restriction)","the instruments are as good as randomly assigned given the controls","errors are independent across observations; their variance may differ"],"notes":""},"outputs":null,"output_digest":"b3835d37c24cd43d96da6014e0a2a5b1d1c918fe9aa139529c16d4db6823cf2a","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:00.796483Z","duration_ms":17.70579209551215,"error":null,"decided_by":"model","rationale":"causcibench_metadata_rule/1: iv","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_e55cedc3","llm_name":"infer__iv_2sls","vendor_id":"script_1","result_for_model_ref":"07534828e88d40849a30b164f2bef53371865def26cfbabe45c1aba5a260af18"}}
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