Tarefa paper_repro.mroz_1987.ex15_5.design

Veredito

Pergunta feita ao analista

What is the return to a year of education for married women who work, holding their labour-market experience (and its square) fixed? Using Mroz's sample of married women in 1975, estimate the causal effect of a year of schooling on the log hourly wage of the women who worked, using variation in schooling that does not come from the woman's own ability.

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1. 0Início

   What is the return to a year of education for married women who work, holding their labour-market experience (and its square) fixed? Using Mroz's sample of married women in 1975, estimate the causal effect of a year of schooling on the log hourly wage of the women who worked, using variation in schooling that does not come from the woman's own ability.

   mroz (agent)

   Wooldridge's MROZ data set (the R package wooldridge, served by Rdatasets): Mroz's (1987) sample of married women from the Panel Study of Income Dynamics, for 1975; one row per woman (753 rows). Columns used here: inlf, 1 if the woman was in the labour force in 1975; lwage, log hourly wage (missing for women who did not work); wage, hourly wage; educ, years of schooling; exper, years of labour-market experience; expersq, exper squared; fatheduc, father's years of schooling; motheduc, mother's years of schooling; also hours, kidslt6, kidsge6, age, repwage, hushrs, husage, huseduc, huswage, faminc, mtr, unem, city, nwifeinc and rownames.

   file bcfc04b6a87746b185ed972f1149c459cc1af5a6ff44443ce5dd004eb8204a78 · frame —
2. 1Turno do modelo
   call_ec33b9b6referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   Example 15.5: 2SLS of the log wage on education, experience and its square, with mother's and father's education as instruments, for the working women.

   Chamadas pedidas

   • infer.iv_2sls

     {
       "dataset": "mroz",
       "endog": [
         "educ"
       ],
       "exog": [
         "exper",
         "expersq"
       ],
       "instruments": [
         "motheduc",
         "fatheduc"
       ],
       "se": "classical",
       "subset": "inlf == 1",
       "y": "lwage"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.iv_2slsv0.1.0call_07dbd066decidido pelo modeloreexecutável

   Motivo: Example 15.5: 2SLS of the log wage on education, experience and its square, with mother's and father's education as instruments, for the working women.

   Argumentos

   {
     "cluster": null,
     "dataset": "mroz",
     "endog": [
       "educ"
     ],
     "exog": [
       "exper",
       "expersq"
     ],
     "instruments": [
       "motheduc",
       "fatheduc"
     ],
     "level": 0.95,
     "se": "classical",
     "subset": "inlf == 1",
     "y": "lwage"
   }

   Figuras call_07dbd066

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.educ 2SLS effect of educ on lwage	0,0613966	0.06139662866015408	0,03143670.031436695644695214	-0,000394545 – 0,123188

   Diagnósticos

   • first_stage_F.educ = 55,400355.40030042777673
   • partial_r2.educ = 0,2075690.20756926964482014
   • weak_instrument = false
   • overid_p = 0,5386370.5386372330714904
   • sargan = 0,3780710.3780713419638187
   • sargan_df = 11
   • n_obs = 428428
   • se_type = "classical"
   • n_clusters = —

   Tabelas (3)

   {
     "coefficients": [
       {
         "ci_high": 0.8349750469784886,
         "ci_low": -0.7387744331141283,
         "coef": 0.048100306932180106,
         "p": 0.9044194793612478,
         "se": 0.4003280776041123,
         "t": 0.12015221919994053,
         "term": "Intercept"
       },
       {
         "ci_high": 0.07057292697452149,
         "ci_low": 0.017767858923003838,
         "coef": 0.04417039294876266,
         "p": 0.0010918384252699615,
         "se": 0.013432475529443424,
         "t": 3.2883285625157486,
         "term": "exper"
       },
       {
         "ci_high": -0.00010942651309300572,
         "ci_low": -0.001688512663218038,
         "coef": -0.0008989695881555219,
         "p": 0.025740027334257214,
         "se": 0.0004016856118761863,
         "t": -2.2379930014337086,
         "term": "expersq"
       },
       {
         "ci_high": 0.12318780219307032,
         "ci_low": -0.00039454487276216477,
         "coef": 0.06139662866015408,
         "p": 0.05147417391505394,
         "se": 0.031436695644695214,
         "t": 1.9530242412902723,
         "term": "educ"
       }
     ],
     "first_stage": [
       {
         "ci_high": 0.22815007550124894,
         "ci_low": 0.08704398999594125,
         "coef": 0.1575970327485951,
         "endog": "educ",
         "instrument": "motheduc",
         "p": 0.000014298403672048712,
         "se": 0.03589411554668934,
         "t": 4.390609166664114
       },
       {
         "ci_high": 0.25589971614954615,
         "ci_low": 0.12319710416036223,
         "coef": 0.1895484101549542,
         "endog": "educ",
         "instrument": "fatheduc",
         "p": 3.561512366875694e-8,
         "se": 0.03375646678192402,
         "t": 5.615173275671551
       }
     ],
     "reduced_form": [
       {
         "ci_high": 0.02756810644924526,
         "ci_low": -0.021429317804883524,
         "coef": 0.0030693943221808697,
         "instrument": "motheduc",
         "p": 0.8055968958254257,
         "se": 0.012463808356456057,
         "t": 0.24626456331791813
       },
       {
         "ci_high": 0.04045959911766131,
         "ci_low": -0.0056198180526244,
         "coef": 0.017419890532518457,
         "instrument": "fatheduc",
         "p": 0.1379859225819088,
         "se": 0.01172153503026715,
         "t": 1.48614413449579
       }
     ]
   }

   Digests dos dados

   mroz 909e3d99ac8070dc661ce7915c99f27631797719369832e227d4005096cbd479

   sha256 do resultado

   4b19cca1eefc91b44936db415e6de161a880cb50c8d30838be2dc18124ec737a

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_a706a508referenceFim: 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.educ = 0,06140.",
       "caveats": [],
       "figure_refs": [
         "call_07dbd066:coef.educ"
       ]
     }

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

   Números encontrados

   0,06140

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.iv_2sls): coef.educ = 0,06140.

   Figuras citadas: call_07dbd066:coef.educ · Parada: answered

   Figura da resposta: call_07dbd066:coef.educ = 0,06139660.06139662866015408 (se 0,03143670.031436695644695214)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta escolhidapassouobrigatóriaanswer figure call_07dbd066:coef.educ (infer.iv_2sls)
9. 8Pontuação
   #2ferramenta chamadapassouobrigatóriaanswer call is infer.iv_2sls
10. 9Pontuação
    #3argumento igualpassouobrigatóriainfer.iv_2sls.y = 'lwage'
11. 10Pontuação
    #4argumento igualpassouobrigatóriainfer.iv_2sls.endog[0] = 'educ'
12. 11Pontuação
    #5argumento igualpassouregistradainfer.iv_2sls.y = 'lwage'
13. 12Pontuação
    #6argumento igualpassouregistradainfer.iv_2sls.endog = ['educ']
14. 13Pontuação
    #7argumento igualpassouregistradainfer.iv_2sls.instruments = ['motheduc', 'fatheduc']
15. 14Pontuação
    #8argumento igualpassouregistradainfer.iv_2sls.exog = ['exper', 'expersq']
16. 15Pontuação
    #9diagnósticopassouregistrada428 vs 428
17. 16Pontuação
    #10figura da resposta próximapassouregistrada0.06139662866 vs 0.061396628660153496
18. 17Pontuação
    #11figura da resposta próximapassouregistrada0.03143669564 vs 0.0314366956446951
19. 18Pontuação
    #12figura da resposta próximapassouregistrada0.06139662866 vs 0.061
20. 19Pontuação
    #13figura da resposta próximapassouregistrada0.03143669564 vs 0.031

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• época 1, tentativa 1passoupontuada(exibindo)

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