Tarefa causcibench_real.q044

Veredito

Pergunta feita ao analista

Does an increase in Chinese import competition reduce the manufacturing employment share in local labor markets?

Valores esperados e de onde vêm

Verificações

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Trajetória

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1. 0Início

   Does an increase in Chinese import competition reduce the manufacturing employment share in local labor markets?

   autor_china_syndrome (agent)

   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. Variables include: czone: Commuting zone identifier statefip: State FIPS code for the commuting zone city: City/metropolitan label linked to the commuting zone yr: Calendar year of observation t2: Time-period indicator (e.g., 1990-2000 vs. 2000-2007 window) timepwt48: Period weight used for aggregating across time intervals reg_midatl: Mid-Atlantic region dummy reg_encen: East North Central region dummy reg_wncen: West North Central region dummy reg_satl: South Atlantic region dummy reg_escen: East South Central region dummy reg_wscen: West South Central region dummy reg_mount: Mountain region dummy reg_pacif: Pacific region dummy l_popcount: Log working-age population count at baseline l_no_workers_totcbp: Log total employment (CBP) at baseline l_shind_manuf_cbp: Baseline share employed in manufacturing industries (CBP) l_sh_popedu_c: Baseline share of population with college education l_sh_popfborn: Baseline share of foreign-born population l_sh_empl_f: Baseline female employment share l_sh_routine33: Baseline share in routine-task occupations l_task_outsource: Baseline task offshorability index (higher = more offshorable) l_sh_empl: Baseline employment-to-population share l_sh_empl_mfg: Baseline manufacturing employment share l_sh_empl_mfg_m: Baseline male manufacturing employment share l_sh_empl_mfg_f: Baseline female manufacturing employment share l_sh_empl_mfg_edu_nc: Baseline manufacturing share, non-college l_sh_empl_mfg_edu_c: Baseline manufacturing share, college l_sh_empl_mfg_age1634: Baseline manufacturing share, ages 16-34 l_sh_empl_mfg_age3549: Baseline manufacturing share, ages 35-49 l_sh_empl_mfg_age5064: Baseline manufacturing share, ages 50-64 l_sh_empl_nmfg: Baseline nonmanufacturing employment share l_sh_empl_nmfg_m: Baseline male nonmanufacturing employment share l_sh_empl_nmfg_f: Baseline female nonmanufacturing employment share l_sh_empl_nmfg_edu_nc: Baseline nonmanufacturing share, non-college l_sh_empl_nmfg_edu_c: Baseline nonmanufacturing share, college l_sh_empl_nmfg_age1634: Baseline nonmanufacturing share, ages 16-34 l_sh_empl_nmfg_age3549: Baseline nonmanufacturing share, ages 35-49 l_sh_empl_nmfg_age5064: Baseline nonmanufacturing share, ages 50-64 l_sh_unempl: Baseline unemployment share l_sh_unempl_m: Baseline male unemployment share l_sh_unempl_f: Baseline female unemployment share l_sh_unempl_edu_nc: Baseline unemployment share, non-college l_sh_unempl_edu_c: Baseline unemployment share, college l_sh_unempl_age1634: Baseline unemployment share, ages 16-34 l_sh_unempl_age3549: Baseline unemployment share, ages 35-49 l_sh_unempl_age5064: Baseline unemployment share, ages 50-64 l_sh_nilf: Baseline not-in-labor-force share l_sh_nilf_m: Baseline NILF share, male l_sh_nilf_f: Baseline NILF share, female l_sh_nilf_edu_nc: Baseline NILF share, non-college l_sh_nilf_edu_c: Baseline NILF share, college l_sh_nilf_age1634: Baseline NILF share, ages 16-34 l_sh_nilf_age3549: Baseline NILF share, ages 35-49 l_sh_nilf_age5064: Baseline NILF share, ages 50-64 l_sh_ssadiswkrs: Baseline share on SSDI (disabled workers) l_avg_lnwkwage_mfg: Baseline log average weekly wage in manufacturing l_avg_lnwkwage_nmfg: Baseline log average weekly wage in nonmanufacturing l_trans_totindiv_pc: Baseline per-capita total individual transfers l_trans_totmed_pc: Baseline per-capita total Medicare transfers l_trans_fedinc_pc: Baseline per-capita federal income assistance l_trans_othinc_pc: Baseline per-capita other income assistance l_trans_unemp_pc: Baseline per-capita unemployment benefits l_trans_taaimp_pc: Baseline per-capita Trade Adjustment Assistance payments l_trans_totedu_pc: Baseline per-capita education-related transfers l_trans_ssaret_pc: Baseline per-capita Social Security retirement l_trans_ssadis_pc: Baseline per-capita Social Security disability l_avg_hhincsum_pc_pw: Baseline per-worker average household total income per capita l_avg_hhincwage_pc_pw: Baseline per-worker average household wage income per capita l_tradeusch_pw: Baseline U.S. imports-from-China exposure per worker l_tradeotch_pw: Baseline China exports to other high-income markets exposure per worker d_tradeusch_pw: Change in U.S. imports-from-China exposure per worker d_tradeotch_pw_lag: Lagged change in China-to-others exposure per worker lnchg_popworkage: Log change in working-age population lnchg_popworkage_edu_c: Log change in working-age population, college lnchg_popworkage_edu_nc: Log change in working-age population, non-college lnchg_popworkage_age1634: Log change, ages 16-34 lnchg_popworkage_age3549: Log change, ages 35-49 lnchg_popworkage_age5064: Log change, ages 50-64 d_popcount: Change in population count d_sh_empl: Change in employment-to-population share d_sh_empl_mfg: Change in manufacturing employment share d_sh_empl_nmfg: Change in nonmanufacturing employment share d_sh_empl_mfg_m: Change in male manufacturing employment share d_sh_empl_nmfg_m: Change in male nonmanufacturing employment share d_sh_empl_mfg_f: Change in female manufacturing employment share d_sh_empl_nmfg_f: Change in female nonmanufacturing employment share d_sh_empl_mfg_edu_nc: Change in manufacturing share, non-college d_sh_empl_nmfg_edu_nc: Change in nonmanufacturing share, non-college d_sh_empl_mfg_edu_c: Change in manufacturing share, college d_sh_empl_nmfg_edu_c: Change in nonmanufacturing share, college d_sh_empl_mfg_age1634: Change in manufacturing share, ages 16-34 d_sh_empl_nmfg_age1634: Change in nonmanufacturing share, ages 16-34 d_sh_empl_mfg_age3549: Change in manufacturing share, ages 35-49 d_sh_empl_nmfg_age3549: Change in nonmanufacturing share, ages 35-49 d_sh_empl_mfg_age5064: Change in manufacturing share, ages 50-64 d_sh_empl_nmfg_age5064: Change in nonmanufacturing share, ages 50-64 d_sh_empl_edu_c: Change in overall employment share, college d_sh_empl_edu_nc: Change in overall employment share, non-college d_sh_unempl: Change in unemployment share d_sh_unempl_m: Change in male unemployment share d_sh_unempl_f: Change in female unemployment share d_sh_unempl_edu_nc: Change in unemployment share, non-college d_sh_unempl_edu_c: Change in unemployment share, college d_sh_unempl_age1634: Change in unemployment share, ages 16-34 d_sh_unempl_age3549: Change in unemployment share, ages 35-49 d_sh_unempl_age5064: Change in unemployment share, ages 50-64 d_sh_nilf: Change in not-in-labor-force share d_sh_nilf_m: Change in NILF share, male d_sh_nilf_f: Change in NILF share, female d_sh_nilf_edu_nc: Change in NILF share, non-college d_sh_nilf_edu_c: Change in NILF share, college d_sh_nilf_age1634: Change in NILF share, ages 16-34 d_sh_nilf_age3549: Change in NILF share, ages 35-49 d_sh_nilf_age5064: Change in NILF share, ages 50-64 d_sh_ssadiswkrs: Change in SSDI recipient share d_avg_lnwkwage: Change in log average weekly wage (overall) d_avg_lnwkwage_c: Change in log average weekly wage, college d_avg_lnwkwage_nc: Change in log average weekly wage, non-college d_avg_lnwkwage_m: Change in log average weekly wage, male d_avg_lnwkwage_f: Change in log average weekly wage, female d_avg_lnwkwage_c_m: Change in log average weekly wage, college males d_avg_lnwkwage_c_f: Change in log average weekly wage, college females d_avg_lnwkwage_nc_m: Change in log average weekly wage, non-college males d_avg_lnwkwage_nc_f: Change in log average weekly wage, non-college females d_avg_lnwkwage_mfg: Change in log average weekly wage in manufacturing d_avg_lnwkwage_mfg_c: Change in log manufacturing wage, college d_avg_lnwkwage_mfg_nc: Change in log manufacturing wage, non-college d_avg_lnwkwage_nmfg: Change in log average weekly wage in nonmanufacturing d_avg_lnwkwage_nmfg_c: Change in log nonmanufacturing wage, college d_avg_lnwkwage_nmfg_nc: Change in log nonmanufacturing wage, non-college lnchg_no_empl: Log change in number employed lnchg_no_empl_mfg: Log change in manufacturing employment count lnchg_no_empl_nmfg: Log change in nonmanufacturing employment count lnchg_no_empl_mfg_edu_nc: Log change in manufacturing employment, non-college lnchg_no_empl_nmfg_edu_nc: Log change in nonmanufacturing employment, non-college lnchg_no_empl_mfg_edu_c: Log change in manufacturing employment, college lnchg_no_empl_nmfg_edu_c: Log change in nonmanufacturing employment, college lnchg_no_unempl: Log change in number unemployed lnchg_no_nilf: Log change in number not in labor force lnchg_no_ssadiswkrs: Log change in number receiving SSDI lnchg_trans_totindiv_pc: Log change in per-capita total transfers lnchg_trans_totmed_pc: Log change in per-capita Medicare transfers lnchg_trans_fedinc_pc: Log change in per-capita federal income assistance lnchg_trans_othinc_pc: Log change in per-capita other income assistance lnchg_trans_unemp_pc: Log change in per-capita unemployment benefits lnchg_trans_taaimp_pc: Log change in per-capita TAA payments lnchg_trans_totedu_pc: Log change in per-capita education transfers lnchg_trans_ssaret_pc: Log change in per-capita Social Security retirement lnchg_trans_ssadis_pc: Log change in per-capita Social Security disability d_trans_totindiv_pc: Change in per-capita total transfers d_trans_totmed_pc: Change in per-capita Medicare transfers d_trans_fedinc_pc: Change in per-capita federal income assistance d_trans_othinc_pc: Change in per-capita other income assistance d_trans_unemp_pc: Change in per-capita unemployment benefits d_trans_taaimp_pc: Change in per-capita TAA payments d_trans_totedu_pc: Change in per-capita education transfers d_trans_ssaret_pc: Change in per-capita Social Security retirement d_trans_ssadis_pc: Change in per-capita Social Security disability relchg_avg_hhincsum_pc_pw: Relative change in per-worker average household total income per capita relchg_avg_hhincwage_pc_pw: Relative change in per-worker average household wage income per capita relchg_avg_hhincbusinv_pc_pw: Relative change in per-worker average household business/investment income per capita relchg_avg_hhinctrans_pc_pw: Relative change in per-worker average household transfer income per capita relchg_avg_hhincrest_pc_pw: Relative change in per-worker average household residual income per capita relchg_med_hhincsum_pc_pw: Relative change in per-worker median household total income per capita relchg_med_hhincwage_pc_pw: Relative change in per-worker median household wage income per capita d_avg_hhincsum_pc_pw: Change in per-worker average household total income per capita d_avg_hhincwage_pc_pw: Change in per-worker average household wage income per capita d_avg_hhincbusinv_pc_pw: Change in per-worker average household business/investment income per capita d_avg_hhinctrans_pc_pw: Change in per-worker average household transfer income per capita d_avg_hhincrest_pc_pw: Change in per-worker average household residual income per capita d_med_hhincsum_pc_pw: Change in per-worker median household total income per capita d_med_hhincwage_pc_pw: Change in per-worker median household wage income per capita d_tradex_usch_pw: Change in exposure to U.S. exports to China per worker d_tradex_otch_pw_lag: Lagged change in exposure to China's exports to other high-income markets per worker d_tradeusch_netinput_pw: Change in net trade exposure per worker using input linkages d_inputotch_pw_lag: Lagged change in input-based China-to-others exposure per worker d_netimpusch_pw: Change in net imports-from-China exposure per worker d_expotch_pw_lag: Lagged change in exposure to Chinese exports per worker d_traderes_pw_lag: Lagged change in residual trade exposure per worker d_nettradefactor_usch_io: Change in factor-based net trade exposure (U.S.-China, input-output weighted) d_tradefactor_otch_lag_io: Lagged change in factor-based China-to-others exposure (IO weighted) d_expfactor_otch_lag_io: Lagged change in factor-based China export exposure (IO weighted) d_tradeuschlw_pw: Change in low-wage-weighted U.S. import exposure per worker d_tradeotchlw_pw_lag: Lagged change in low-wage-weighted China-to-others exposure per worker d_tradeuschce_pw: Change in consumption-expenditure-weighted U.S. import exposure per worker d_tradeotchce_pw_lag: Lagged change in consumption-expenditure-weighted China-to-others exposure per worker d_tradeusce_pw: Change in U.S. consumption-weighted import exposure per worker d_tradeotce_pw_lag: Lagged change in others' consumption-weighted China exposure per worker d_tradeushi_pw: Change in high-income-weighted U.S. import exposure per worker d_tradeothi_pw_lag: Lagged change in high-income-weighted China-to-others exposure per worker

   file 603a026947701601a2216badd278060457f392a999eebf58ecbd09d41cb6b155 · frame —
2. 1Turno do modelo
   call_e55cedc3referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   causcibench_metadata_rule/1: iv

   Chamadas pedidas

   • infer.iv_2sls

     {
       "dataset": "autor_china_syndrome",
       "endog": [
         "d_tradeusch_pw"
       ],
       "exog": [
         "t2"
       ],
       "instruments": [
         "d_tradeotch_pw_lag"
       ],
       "y": "d_sh_empl_mfg"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.iv_2slsv0.1.0call_35778d9bdecidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: iv

   Argumentos

   {
     "cluster": null,
     "dataset": "autor_china_syndrome",
     "endog": [
       "d_tradeusch_pw"
     ],
     "exog": [
       "t2"
     ],
     "instruments": [
       "d_tradeotch_pw_lag"
     ],
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "y": "d_sh_empl_mfg"
   }

   Figuras call_35778d9b

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.d_tradeusch_pw 2SLS effect of d_tradeusch_pw on d_sh_empl_mfg	-0,621576	-0.6215758121089948	0,1291780.12917824069875605	-0,874973 – -0,368178

   Diagnósticos

   • first_stage_F.d_tradeusch_pw = 51,561551.56145914069501
   • partial_r2.d_tradeusch_pw = 0,4629880.46298812988549665
   • weak_instrument = false
   • n_obs = 1.4441444
   • se_type = "HC1"
   • n_clusters = —

   Tabelas (3)

   {
     "coefficients": [
       {
         "ci_high": 0.11641955290747832,
         "ci_low": -0.5437871324843294,
         "coef": -0.21368378978842553,
         "p": 0.20436156483465448,
         "se": 0.16828170300201287,
         "t": -1.2697981181345044,
         "term": "Intercept"
       },
       {
         "ci_high": -0.051299538639999254,
         "ci_low": -0.8484407356217173,
         "coef": -0.44987013713085827,
         "p": 0.026978757222309684,
         "se": 0.2031852768675569,
         "t": -2.214088264988309,
         "term": "t2"
       },
       {
         "ci_high": -0.3681782751365685,
         "ci_low": -0.8749733490814211,
         "coef": -0.6215758121089948,
         "p": 0.0000016531087415123596,
         "se": 0.12917824069875605,
         "t": -4.811768675178903,
         "term": "d_tradeusch_pw"
       }
     ],
     "first_stage": [
       {
         "ci_high": 1.104031387249036,
         "ci_low": 0.6302568614508601,
         "coef": 0.867144124349948,
         "endog": "d_tradeusch_pw",
         "instrument": "d_tradeotch_pw_lag",
         "p": 1.1079723089122809e-12,
         "se": 0.12076155210844738,
         "t": 7.180630831667577
       }
     ],
     "reduced_form": [
       {
         "ci_high": -0.3776723394103266,
         "ci_low": -0.7003192872063979,
         "coef": -0.5389958133083622,
         "instrument": "d_tradeotch_pw_lag",
         "p": 7.783686691322959e-11,
         "se": 0.08224027269778712,
         "t": -6.553915686649532
       }
     ]
   }

   Digests dos dados

   autor_china_syndrome dff483b164debe4f7940c1b62d049aa15131912009e244d7aecab186f60327ff

   sha256 do resultado

   c7c02a6c4304034d0c683c2c0c3d482f0c659a63b4e30bb73e63802a6501c420

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_eebff546referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa (infer.iv_2sls): coef.d_tradeusch_pw = -0,6216.",
       "caveats": [],
       "figure_refs": [
         "call_35778d9b:coef.d_tradeusch_pw"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
5. 4Guarda
   resposta aceita

   Números encontrados

   -0,6216

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
6. 5Resposta

   Estimativa (infer.iv_2sls): coef.d_tradeusch_pw = -0,6216.

   Figuras citadas: call_35778d9b:coef.d_tradeusch_pw · Parada: answered

   Figura da resposta: call_35778d9b:coef.d_tradeusch_pw = -0,621576-0.6215758121089948 (se 0,1291780.12917824069875605)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximafalhouobrigatória-0.6215758121 vs -0.746

Épocas × verificações

Tentativas

Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.

• época 1, tentativa 1falhoupontuada(exibindo)

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