Tarefa qrdata_socsci.362

Veredito

Pergunta feita ao analista

What is the average additional percentage wage increase associated with each additional year of education based on the instrumental variable of whether the individual is born in the last quarter q4? Please adjust for all the other variables, and round your answer to the nearest hundredth of a percent.

Como o agente resolveu e como foi validado

Resumo passo a passo do que o agente executou, por que o caminho está correto e como cada verificação conferiu o resultado.

1. 1Preparação da base de dados
   ak91— The dataset provided, ak91.csv, contains information on individuals' log wages, years of schooling, year of birth, quarter of birth, and state of birth. The purpose of the analysis is to estimate the effect of education on wage, using linear regression and quarter of birth as an instrumental variable (IV). This idea takes advantage of US compulsory attendance law. Usually, they state that a kid must have turned 6 years by January 1 of the year they enter school. For this reason, kids that are born at the beginning of the year will enter school at an older age. Compulsory attendance law also requires students to be in school until they turn 16, at which point they are legally allowed to drop out. The result is that people born later in the year have, on average, more years of education than those born in the beginning of the year.
   ak91_iv
   Transformação "derive" aplicada à base "ak91_iv" (329509 → 329509 linhas). {"args":{"cmp":"eq","column":"quarter_of_birth","value":4},"name":"q4","op":"indicator"}
   Transformação "cast" aplicada à base "ak91_iv" (329509 → 329509 linhas). {"columns":["year_of_birth","state_of_birth"],"to":"int"}
   Transformação "dummies" aplicada à base "ak91_iv" (329509 → 329509 linhas). {"column":"year_of_birth","drop_first":true,"prefix":"yob"}
   Transformação "dummies" aplicada à base "ak91_iv" (329509 → 329509 linhas). {"column":"state_of_birth","drop_first":true,"prefix":"sob"}
2. 2Chamada que gerou a resposta finalinfer.iv_2sls(Variáveis instrumentais por mínimos quadrados em dois estágios (MQ2E))citada na respostacoincide com a referência

   Ver chamada call_f3d49934 nos detalhes

   2SLS of log wage on years of schooling instrumented by the fourth-quarter indicator, with year-of-birth and state-of-birth dummies as exogenous controls (Brave and True, ch. 8).

   Por que este é o caminho de referência: 2SLS of log wage on years of schooling instrumented by the fourth-quarter indicator, with year-of-birth and state-of-birth dummies as exogenous controls (Brave and True, ch. 8).
   Argumentos principais

   dataset:

   "ak91_iv"

   endog:

   ["years_of_schooling"]

   exog:

   ["yob_31","yob_32","yob_33","yob_34","yob_35","yob_36","yob_37","yob_38","yob_39","sob_2","sob_4","sob_5","sob_6","sob_8","sob_9","sob_10","sob_11","sob_12","sob_13","sob_15","sob_16","sob_17","sob_18","sob_19","sob_20","sob_21","sob_22","sob_23","sob_24","sob_25","sob_26","sob_27","sob_28","sob_29","sob_30","sob_31","sob_32","sob_33","sob_34","sob_35","sob_36","sob_37","sob_38","sob_39","sob_40","sob_41","sob_42","sob_44","sob_45","sob_46","sob_47","sob_48","sob_49","sob_50","sob_51","sob_53","sob_54","sob_55","sob_56"]

   instruments:

   ["q4"]

   level:

   0,950.95

   se:

   "HC1"

   y:

   "log_wage"

   Resultado calculado

   • coef.years_of_schooling = 0,0853029 0.08530286492088814
     2SLS effect of years_of_schooling on log_wage
   Diagnósticos: first_stage_F.years_of_schooling = 61,782761.782739389417884 (Wald F of the excluded instruments in the first stage for years_of_schooling (heteroskedasticity-robust standard errors (HC1))) · partial_r2.years_of_schooling = 0,0001854010.0001854009158526182 (share of the variation the instruments explain beyond the controls) · weak_instrument = false (every first-stage F is at least 10) · n_obs = 329.509329509 · se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
3. 3Conferência de lastro e resposta final

   Todos os 1 números escritos na resposta foram conferidos contra as ferramentas e o enunciado (nenhum número sem lastro).

   Números encontrados: 0,08530
   Figuras citadas: call_f3d49934:coef.years_of_schooling
   • ✓
     #0resposta com lastro

     every number of the answer is stated by a tool call
   • ✓
     #1figura da resposta próxima

     QRData eval.py: strictly inside gold ± 3% (a '%' gold divided by 100), on the answer figure after meta.answer_format

Valores esperados e de onde vêm

Verificações

Caminho de referência e caminho do agente

A chamada de referência ao lado da chamada que deu a resposta, argumento por argumento.

Trajetória

Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.

1. 0Início

   What is the average additional percentage wage increase associated with each additional year of education based on the instrumental variable of whether the individual is born in the last quarter q4? Please adjust for all the other variables, and round your answer to the nearest hundredth of a percent.

   ak91 (agent)

   The dataset provided, ak91.csv, contains information on individuals' log wages, years of schooling, year of birth, quarter of birth, and state of birth. The purpose of the analysis is to estimate the effect of education on wage, using linear regression and quarter of birth as an instrumental variable (IV). This idea takes advantage of US compulsory attendance law. Usually, they state that a kid must have turned 6 years by January 1 of the year they enter school. For this reason, kids that are born at the beginning of the year will enter school at an older age. Compulsory attendance law also requires students to be in school until they turn 16, at which point they are legally allowed to drop out. The result is that people born later in the year have, on average, more years of education than those born in the beginning of the year.

   file 3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970 · frame —

   ak91_iv (reference)

   file 3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970 · frame —

   1. derive {"args":{"cmp":"eq","column":"quarter_of_birth","value":4},"name":"q4","op":"indicator"}
   2. cast {"columns":["year_of_birth","state_of_birth"],"to":"int"}
   3. dummies {"column":"year_of_birth","drop_first":true,"prefix":"yob"}
   4. dummies {"column":"state_of_birth","drop_first":true,"prefix":"sob"}
2. 1Transformação
   ak91_iv · derive {"args":{"cmp":"eq","column":"quarter_of_birth","value":4},"name":"q4","op":"indicator"}

   329509 → 329509 linhas

   Frame antes → depois: af6529c7b49b → ce8d66b6716e
3. 2Transformação
   ak91_iv · cast {"columns":["year_of_birth","state_of_birth"],"to":"int"}

   329509 → 329509 linhas

   Frame antes → depois: ce8d66b6716e → ec1c6797fcab
4. 3Transformação
   ak91_iv · dummies {"column":"year_of_birth","drop_first":true,"prefix":"yob"}

   329509 → 329509 linhas

   Frame antes → depois: ec1c6797fcab → c138d8254047
5. 4Transformação
   ak91_iv · dummies {"column":"state_of_birth","drop_first":true,"prefix":"sob"}

   329509 → 329509 linhas

   Frame antes → depois: c138d8254047 → bde6e4a59971
6. 5Turno do modelo
   call_9fe06b70referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   2SLS of log wage on years of schooling instrumented by the fourth-quarter indicator, with year-of-birth and state-of-birth dummies as exogenous controls (Brave and True, ch. 8).

   Chamadas pedidas

   • infer.iv_2sls

     {
       "dataset": "ak91_iv",
       "endog": [
         "years_of_schooling"
       ],
       "exog": [
         "yob_31",
         "yob_32",
         "yob_33",
         "yob_34",
         "yob_35",
         "yob_36",
         "yob_37",
         "yob_38",
         "yob_39",
         "sob_2",
         "sob_4",
         "sob_5",
         "sob_6",
         "sob_8",
         "sob_9",
         "sob_10",
         "sob_11",
         "sob_12",
         "sob_13",
         "sob_15",
         "sob_16",
         "sob_17",
         "sob_18",
         "sob_19",
         "sob_20",
         "sob_21",
         "sob_22",
         "sob_23",
         "sob_24",
         "sob_25",
         "sob_26",
         "sob_27",
         "sob_28",
         "sob_29",
         "sob_30",
         "sob_31",
         "sob_32",
         "sob_33",
         "sob_34",
         "sob_35",
         "sob_36",
         "sob_37",
         "sob_38",
         "sob_39",
         "sob_40",
         "sob_41",
         "sob_42",
         "sob_44",
         "sob_45",
         "sob_46",
         "sob_47",
         "sob_48",
         "sob_49",
         "sob_50",
         "sob_51",
         "sob_53",
         "sob_54",
         "sob_55",
         "sob_56"
       ],
       "instruments": [
         "q4"
       ],
       "y": "log_wage"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
7. 6Ferramenta
   infer.iv_2sls— Variáveis instrumentais por mínimos quadrados em dois estágios (MQ2E)v0.1.0call_f3d49934decidido pelo modeloreexecutável

   Motivo: 2SLS of log wage on years of schooling instrumented by the fourth-quarter indicator, with year-of-birth and state-of-birth dummies as exogenous controls (Brave and True, ch. 8).

   Argumentos

   {
     "cluster": null,
     "dataset": "ak91_iv",
     "endog": [
       "years_of_schooling"
     ],
     "exog": [
       "yob_31",
       "yob_32",
       "yob_33",
       "yob_34",
       "yob_35",
       "yob_36",
       "yob_37",
       "yob_38",
       "yob_39",
       "sob_2",
       "sob_4",
       "sob_5",
       "sob_6",
       "sob_8",
       "sob_9",
       "sob_10",
       "sob_11",
       "sob_12",
       "sob_13",
       "sob_15",
       "sob_16",
       "sob_17",
       "sob_18",
       "sob_19",
       "sob_20",
       "sob_21",
       "sob_22",
       "sob_23",
       "sob_24",
       "sob_25",
       "sob_26",
       "sob_27",
       "sob_28",
       "sob_29",
       "sob_30",
       "sob_31",
       "sob_32",
       "sob_33",
       "sob_34",
       "sob_35",
       "sob_36",
       "sob_37",
       "sob_38",
       "sob_39",
       "sob_40",
       "sob_41",
       "sob_42",
       "sob_44",
       "sob_45",
       "sob_46",
       "sob_47",
       "sob_48",
       "sob_49",
       "sob_50",
       "sob_51",
       "sob_53",
       "sob_54",
       "sob_55",
       "sob_56"
     ],
     "instruments": [
       "q4"
     ],
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "y": "log_wage"
   }

   Figuras call_f3d49934

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.years_of_schooling 2SLS effect of years_of_schooling on log_wage	0,0853029	0.08530286492088814	0,02554320.025543177250445984	0,035239 – 0,135367

   Diagnósticos

   • first_stage_F.years_of_schooling = 61,782761.782739389417884 (Wald F of the excluded instruments in the first stage for years_of_schooling (heteroskedasticity-robust standard errors (HC1)))
   • partial_r2.years_of_schooling = 0,0001854010.0001854009158526182 (share of the variation the instruments explain beyond the controls)
   • weak_instrument = false (every first-stage F is at least 10)
   • n_obs = 329.509329509
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   coef.years_of_schooling

   1. Com um instrumento e um regressor endógeno, o MQ2E é a razão entre o coeficiente de q4 na forma reduzida (log_wage sobre q4 e os controles) e no primeiro estágio (years_of_schooling sobre q4 e os controles).β = ρ / π→ 0,0853029 (0.08530286492088814)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,0255432 (0.025543177250445984)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 329448 graus de liberdade vezes o erro-padrão.IC = β ± c · ep

   Pressupostos do estimador

   • 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

   Tabelas (3)

   {
     "coefficients": [
       {
         "ci_high": 5.315898594278442,
         "ci_low": 4.177629399485408,
         "coef": 4.746763996881925,
         "p": 4.8426057751719305e-60,
         "se": 0.2903790635605813,
         "t": 16.346784574197155,
         "term": "Intercept"
       },
       {
         "ci_high": 0.004546659725242786,
         "ci_low": -0.02014899153819063,
         "coef": -0.007801165906473921,
         "p": 0.21561329348538055,
         "se": 0.006300003655285049,
         "t": -1.23827958416017,
         "term": "yob_31"
       },
       {
         "ci_high": -0.00007111629874588521,
         "ci_low": -0.028843419723120947,
         "coef": -0.014457268010933416,
         "p": 0.048878177544792435,
         "se": 0.00733998123033635,
         "t": -1.9696600791267858,
         "term": "yob_32"
       },
       {
         "ci_high": -0.0006554671077250267,
         "ci_low": -0.03420526536578866,
         "coef": -0.017430366236756846,
         "p": 0.04169538906337667,
         "se": 0.008558747829940452,
         "t": -2.03655564845379,
         "term": "yob_33"
       },
       {
         "ci_high": -0.0032123076698963512,
         "ci_low": -0.040138154008912304,
         "coef": -0.021675230839404327,
         "p": 0.021393349404883858,
         "se": 0.009419997246833157,
         "t": -2.3009805917609127,
         "term": "yob_34"
       },
       {
         "ci_high": -0.013208526332850221,
         "ci_low": -0.05558344589758296,
         "coef": -0.03439598611521659,
         "p": 0.0014635538543801196,
         "se": 0.010810087383502785,
         "t": -3.18184163503693,
         "term": "yob_35"
       },
       {
         "ci_high": -0.011492833704452372,
         "ci_low": -0.05790677251915096,
         "coef": -0.034699803111801666,
         "p": 0.003383186524140447,
         "se": 0.011840464585023643,
         "t": -2.930611621075372,
         "term": "yob_36"
       },
       {
         "ci_high": -0.015057087621178026,
         "ci_low": -0.06695350349298683,
         "coef": -0.041005295557082426,
         "p": 0.0019531327157528405,
         "se": 0.013239076232530716,
         "t": -3.097292804789905,
         "term": "yob_37"
       },
       {
         "ci_high": -0.013526637959811495,
         "ci_low": -0.07298402002575227,
         "coef": -0.04325532899278189,
         "p": 0.004348012660808795,
         "se": 0.015167922495882738,
         "t": -2.851763582291731,
         "term": "yob_38"
       },
       {
         "ci_high": -0.02306414396027861,
         "ci_low": -0.0863568546250717,
         "coef": -0.05471049929267516,
         "p": 0.0007030590215531625,
         "se": 0.01614633703268688,
         "t": -3.3884155385780947,
         "term": "yob_39"
       },
       {
         "ci_high": 0.4350471566111351,
         "ci_low": 0.019412980307373706,
         "coef": 0.2272300684592544,
         "p": 0.0321090505800597,
         "se": 0.10603068540461077,
         "t": 2.1430595076524255,
         "term": "sob_2"
       },
       {
         "ci_high": 0.20073654074533173,
         "ci_low": 0.04234602780287669,
         "coef": 0.12154128427410421,
         "p": 0.0026301392708818145,
         "se": 0.040406337126142625,
         "t": 3.0079758007925896,
         "term": "sob_4"
       },
       {
         "ci_high": 0.06709778182181278,
         "ci_low": 0.01901351499182987,
         "coef": 0.04305564840682132,
         "p": 0.0004481705560768453,
         "se": 0.012266574934961957,
         "t": 3.509997585724189,
         "term": "sob_5"
       },
       {
         "ci_high": 0.24425702285876905,
         "ci_low": 0.025980069275713558,
         "coef": 0.1351185460672413,
         "p": 0.01524426533679295,
         "se": 0.055683714949194225,
         "t": 2.426536128031748,
         "term": "sob_6"
       },
       {
         "ci_high": 0.17737469267428033,
         "ci_low": 0.006711367140046998,
         "coef": 0.09204302990716366,
         "p": 0.03450545690277291,
         "se": 0.04353720269287974,
         "t": 2.1141236509027435,
         "term": "sob_8"
       },
       {
         "ci_high": 0.18914047920798355,
         "ci_low": 0.02104915164429433,
         "coef": 0.10509481542613894,
         "p": 0.01425268695221718,
         "se": 0.04288107111558434,
         "t": 2.4508439899474466,
         "term": "sob_9"
       },
       {
         "ci_high": 0.13457270712245234,
         "ci_low": 0.024685823609266064,
         "coef": 0.07962926536585921,
         "p": 0.004503478932639642,
         "se": 0.028032780363481204,
         "t": 2.8405767937879487,
         "term": "sob_10"
       },
       {
         "ci_high": 0.22823145204405088,
         "ci_low": 0.004092985779976757,
         "coef": 0.11616221891201382,
         "p": 0.04219971614168689,
         "se": 0.057179020779439545,
         "t": 2.031553134848078,
         "term": "sob_11"
       },
       {
         "ci_high": 0.038753516381874756,
         "ci_low": -0.07014788778211954,
         "coef": -0.015697185700122396,
         "p": 0.5720568483581701,
         "se": 0.027781378874373267,
         "t": -0.5650254356021959,
         "term": "sob_12"
       },
       {
         "ci_high": 0.007870306978340114,
         "ci_low": -0.03889045902887449,
         "coef": -0.015510076025267185,
         "p": 0.19353146274559033,
         "se": 0.011928942210387523,
         "t": -1.3002054793895532,
         "term": "sob_13"
       },
       {
         "ci_high": 0.23821339576217443,
         "ci_low": 0.01296004758154938,
         "coef": 0.1255867216718619,
         "p": 0.028852413687147416,
         "se": 0.05746343361287966,
         "t": 2.185506743608745,
         "term": "sob_15"
       },
       {
         "ci_high": 0.16929456938807064,
         "ci_low": -0.025610838497139396,
         "coef": 0.07184186544546561,
         "p": 0.1484910375040201,
         "se": 0.0497214982918791,
         "t": 1.4448853697797635,
         "term": "sob_16"
       },
       {
         "ci_high": 0.26027785939632087,
         "ci_low": 0.09212554756144514,
         "coef": 0.176201703478883,
         "p": 0.00003999077977006447,
         "se": 0.042896628556337434,
         "t": 4.107588624301139,
         "term": "sob_17"
       },
       {
         "ci_high": 0.19057917478541006,
         "ci_low": 0.07754151451722599,
         "coef": 0.13406034465131803,
         "p": 0.0000033372383855349286,
         "se": 0.02883656176052679,
         "t": 4.64897118334086,
         "term": "sob_18"
       },
       {
         "ci_high": 0.1482854106743441,
         "ci_low": -0.0013394416383843283,
         "coef": 0.07347298451797987,
         "p": 0.05424524980075849,
         "se": 0.038170166335618254,
         "t": 1.9248798622451644,
         "term": "sob_19"
       },
       {
         "ci_high": 0.13263023202135077,
         "ci_low": -0.048645007008910786,
         "coef": 0.04199261250621999,
         "p": 0.36384768944074514,
         "se": 0.04624436328165669,
         "t": 0.9080590482013794,
         "term": "sob_20"
       },
       {
         "ci_high": 0.14948536087636177,
         "ci_low": 0.08934346193028057,
         "coef": 0.11941441140332117,
         "p": 7.090532123190034e-15,
         "se": 0.015342546716195336,
         "t": 7.783219670908306,
         "term": "sob_21"
       },
       {
         "ci_high": 0.11763954260275168,
         "ci_low": 0.05893388120744315,
         "coef": 0.08828671190509742,
         "p": 3.7469839052596315e-9,
         "se": 0.014976154199423643,
         "t": 5.895152435629645,
         "term": "sob_22"
       },
       {
         "ci_high": 0.019334584550068777,
         "ci_low": -0.06657883177953572,
         "coef": -0.023622123614733467,
         "p": 0.28112441596593496,
         "se": 0.021917010049294174,
         "t": -1.0777986395774002,
         "term": "sob_23"
       },
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       {
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       {
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         "term": "years_of_schooling"
       }
     ],
     "first_stage": [
       {
         "ci_high": 0.12600744808677325,
         "ci_low": 0.07570873736896201,
         "coef": 0.10085809272786762,
         "endog": "years_of_schooling",
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     ],
     "reduced_form": [
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     ]
   }

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   ak91_iv bde6e4a5997113d378703b104e7cbce2209be4aa4c18126fa1755f5365803fef

   sha256 do resultado

   85a1b8917183d194477e9a8e5e09283c6a0ad7ef5bff25d86d24439278596e92

   Resultado mostrado ao modelo
8. 7Turno do modelo
   call_22971485referenceFim: 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.years_of_schooling = 0,08530. Caminho de referência inferido à mão.",
       "caveats": [],
       "figure_refs": [
         "call_f3d49934:coef.years_of_schooling"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
9. 8Guarda
   resposta aceita

   Números encontrados

   0,08530

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
10. 9Resposta

    Estimativa (infer.iv_2sls): coef.years_of_schooling = 0,08530. Caminho de referência inferido à mão.

    Figuras citadas: call_f3d49934:coef.years_of_schooling · Parada: answered

    Figura da resposta: call_f3d49934:coef.years_of_schooling = 0,08530290.08530286492088814 (se 0,02554320.025543177250445984)
11. 10Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
12. 11Pontuação
    #1figura da resposta próximapassouobrigatória0.0853 in (0.082741, 0.087859)?

Épocas × verificações

Tentativas

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

• época 1, tentativa 1passoupontuada(exibindo)

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