Tarefa paper_repro.card_1995.ex15_4_iv.design

Veredito

Pergunta feita ao analista

What is the causal return to a year of schooling for young men in the United States? Using Card's sample of men from the National Longitudinal Survey of Young Men observed in 1976, estimate the effect of education on the log hourly wage, using a source of variation in schooling that is not driven by ability or family background.

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Verificações

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Trajetória

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1. 0Início

   What is the causal return to a year of schooling for young men in the United States? Using Card's sample of men from the National Longitudinal Survey of Young Men observed in 1976, estimate the effect of education on the log hourly wage, using a source of variation in schooling that is not driven by ability or family background.

   card (agent)

   Wooldridge's CARD data set (the R package wooldridge, served by Rdatasets): David Card's (1995) sample of men from the National Longitudinal Survey of Young Men, observed in 1976; one row per man (3,010 rows). Columns used here: lwage, log hourly wage in 1976; educ, years of schooling in 1976; nearc4, 1 if the man grew up near a four-year college in 1966; nearc2, 1 if near a two-year college in 1966; exper, years of experience (age - educ - 6); expersq, exper squared; black, 1 if black; smsa, 1 if living in an SMSA in 1976; south, 1 if living in the South in 1976; smsa66, 1 if living in an SMSA in 1966; reg661 to reg669, indicators of the region of residence in 1966; also age, fatheduc, motheduc, IQ, KWW, married, wage (cents per hour) and the family-background indicators momdad14, sinmom14, step14, south66, libcrd14, enroll, weight, id and rownames.

   file 326dad817467b20d51bdfd015033e3e3e313931f51f812c38c0776249e7fd711 · frame —
2. 1Turno do modelo
   call_5af1018breferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   Example 15.4, Table 15.1, IV column: log wage on education instrumented by college proximity, with experience, its square, race, SMSA, South, SMSA in 1966 and the 1966 region dummies (classical errors with the degrees-of-freedom adjustment, as the example states).

   Chamadas pedidas

   • infer.iv_2sls

     {
       "dataset": "card",
       "endog": [
         "educ"
       ],
       "exog": [
         "exper",
         "expersq",
         "black",
         "smsa",
         "south",
         "smsa66",
         "reg662",
         "reg663",
         "reg664",
         "reg665",
         "reg666",
         "reg667",
         "reg668",
         "reg669"
       ],
       "instruments": [
         "nearc4"
       ],
       "se": "classical",
       "y": "lwage"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.iv_2slsv0.1.0call_32eba4dfdecidido pelo modeloreexecutável

   Motivo: Example 15.4, Table 15.1, IV column: log wage on education instrumented by college proximity, with experience, its square, race, SMSA, South, SMSA in 1966 and the 1966 region dummies (classical errors with the degrees-of-freedom adjustment, as the example states).

   Argumentos

   {
     "cluster": null,
     "dataset": "card",
     "endog": [
       "educ"
     ],
     "exog": [
       "exper",
       "expersq",
       "black",
       "smsa",
       "south",
       "smsa66",
       "reg662",
       "reg663",
       "reg664",
       "reg665",
       "reg666",
       "reg667",
       "reg668",
       "reg669"
     ],
     "instruments": [
       "nearc4"
     ],
     "level": 0.95,
     "se": "classical",
     "subset": null,
     "y": "lwage"
   }

   Figuras call_32eba4df

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.educ 2SLS effect of educ on lwage	0,131504	0.13150383624470474	0,05496370.054963672601175226	0,0237335 – 0,239274

   Diagnósticos

   • first_stage_F.educ = 13,255813.255785330576108
   • partial_r2.educ = 0,004407930.004407934102325983
   • weak_instrument = false
   • n_obs = 3.0103010
   • se_type = "classical"
   • n_clusters = —

   Tabelas (3)

   {
     "coefficients": [
       {
         "ci_high": 5.479516553400695,
         "ci_low": 1.8527852634542328,
         "coef": 3.666150908427464,
         "p": 0.00007538415974785308,
         "se": 0.9248295310138733,
         "t": 3.9641369414407985,
         "term": "Intercept"
       },
       {
         "ci_high": 0.1546598064790368,
         "ci_low": 0.06188240572195053,
         "coef": 0.10827110610049366,
         "p": 0.000004922903208547169,
         "se": 0.023658571085405363,
         "t": 4.576400903911081,
         "term": "exper"
       },
       {
         "ci_high": -0.001681030950206941,
         "ci_low": -0.0029888444048044374,
         "coef": -0.002334937677505689,
         "p": 3.1161246944085553e-12,
         "se": 0.00033349713755244625,
         "t": -7.001372469466829,
         "term": "expersq"
       },
       {
         "ci_high": -0.04109124105905192,
         "ci_low": -0.25246025330930105,
         "coef": -0.1467757471841765,
         "p": 0.006504376545512747,
         "se": 0.05389985881009377,
         "t": -2.7231193258096242,
         "term": "black"
       },
       {
         "ci_high": 0.1738897625009303,
         "ci_low": 0.04972685470035713,
         "coef": 0.11180830860064371,
         "p": 0.0004197459454284399,
         "se": 0.03166198833336174,
         "t": 3.5313103972953286,
         "term": "smsa"
       },
       {
         "ci_high": -0.09117299500874089,
         "ci_low": -0.19817000636948712,
         "coef": -0.144671500689114,
         "p": 1.2266258857565786e-7,
         "se": 0.027284622963645526,
         "t": -5.302308955556272,
         "term": "south"
       },
       {
         "ci_high": 0.06090028882943441,
         "ci_low": -0.023838079835042367,
         "coef": 0.01853110449719602,
         "p": 0.39119278809555685,
         "se": 0.021608588970484666,
         "t": 0.8575804983151744,
         "term": "smsa66"
       },
       {
         "ci_high": 0.17466030000544155,
         "ci_low": 0.026875261834297726,
         "coef": 0.10076778091986964,
         "p": 0.007538241795889867,
         "se": 0.0376857165904629,
         "t": 2.6738984962109194,
         "term": "reg662"
       },
       {
         "ci_high": 0.2204423366673024,
         "ci_low": 0.07607522011011421,
         "coef": 0.1482587783887083,
         "p": 0.000057840797772050855,
         "se": 0.03681413427830229,
         "t": 4.027224360837132,
         "term": "reg663"
       },
       {
         "ci_high": 0.13566022759527543,
         "ci_low": -0.035866069886474596,
         "coef": 0.049897078854400416,
         "p": 0.25405688043284264,
         "se": 0.04373982315600082,
         "t": 1.1407700181237441,
         "term": "reg664"
       },
       {
         "ci_high": 0.23855286385627722,
         "ci_low": 0.05399096224877149,
         "coef": 0.14627191305252435,
         "p": 0.001901645058086012,
         "se": 0.04706394912130858,
         "t": 3.107939639223742,
         "term": "reg665"
       },
       {
         "ci_high": 0.2646849853540353,
         "ci_low": 0.06112089839772622,
         "coef": 0.16290294187588075,
         "p": 0.001716385181435308,
         "se": 0.05190957477135003,
         "t": 3.1382060553073585,
         "term": "reg666"
       },
       {
         "ci_high": 0.2314381083788585,
         "ci_low": 0.03770631068276323,
         "coef": 0.13457220953081087,
         "p": 0.006486970827583493,
         "se": 0.04940230562501896,
         "t": 2.72400665977539,
         "term": "reg667"
       },
       {
         "ci_high": 0.03325735213331668,
         "ci_low": -0.1994113382810437,
         "coef": -0.08307699307386351,
         "p": 0.161550835975611,
         "se": 0.05933135339638065,
         "t": -1.4002207655511092,
         "term": "reg668"
       },
       {
         "ci_high": 0.1898006809461838,
         "ci_low": 0.0258277843270978,
         "coef": 0.10781423263664079,
         "p": 0.009971984091442317,
         "se": 0.041813678752432255,
         "t": 2.578444084649437,
         "term": "reg669"
       },
       {
         "ci_high": 0.23927422232576134,
         "ci_low": 0.023733450163648154,
         "coef": 0.13150383624470474,
         "p": 0.016792621890736437,
         "se": 0.054963672601175226,
         "t": 2.392559121711473,
         "term": "educ"
       }
     ],
     "first_stage": [
       {
         "ci_high": 0.4921785043603216,
         "ci_low": 0.147619375822603,
         "coef": 0.3198989400914623,
         "endog": "educ",
         "instrument": "nearc4",
         "p": 0.00027634008572957033,
         "se": 0.08786381779522222,
         "t": 3.640849534185134
       }
     ],
     "reduced_form": [
       {
         "ci_high": 0.07751371396063084,
         "ci_low": 0.006622161704650145,
         "coef": 0.04206793783264049,
         "instrument": "nearc4",
         "p": 0.020027629759561513,
         "se": 0.01807760095364329,
         "t": 2.3270752540957202
       }
     ]
   }

   Digests dos dados

   card 4ec4da8d7939f11a46d67bd248416cc7330bd0dddaddcd6c49c9872c5a427413

   sha256 do resultado

   6265cba433bc72e2fb80a63a587174ec8231b3a31dc3e70a228c8cf886fa6f29

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_df86acafreferenceFim: 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 = 0,1315.",
       "caveats": [],
       "figure_refs": [
         "call_32eba4df:coef.educ"
       ]
     }

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

   Números encontrados

   0,1315

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.iv_2sls): coef.educ = 0,1315.

   Figuras citadas: call_32eba4df:coef.educ · Parada: answered

   Figura da resposta: call_32eba4df:coef.educ = 0,1315040.13150383624470474 (se 0,05496370.054963672601175226)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta escolhidapassouobrigatóriaanswer figure call_32eba4df: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 = ['nearc4']
15. 14Pontuação
    #8argumento igualpassouregistradainfer.iv_2sls.exog = ['exper', 'expersq', 'black', 'smsa', 'south', 'smsa66', 'reg662', 'reg663', 'reg664', 'reg665', 'reg666', 'reg667', 'reg668', 'reg669']
16. 15Pontuação
    #9diagnósticopassouregistrada3010 vs 3010
17. 16Pontuação
    #10figura da resposta próximapassouregistrada0.1315038362 vs 0.13150383624451933
18. 17Pontuação
    #11figura da resposta próximapassouregistrada0.0549636726 vs 0.054963672601153514
19. 18Pontuação
    #12figura da resposta próximapassouregistrada0.1315038362 vs 0.132
20. 19Pontuação
    #13figura da resposta próximapassouregistrada0.0549636726 vs 0.055

Épocas × verificações

Tentativas

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• época 1, tentativa 1passoupontuada(exibindo)

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