Tarefa qrdata_socsci.376

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Pergunta feita ao analista

What is the racial gap in callback rate among white and black female applicants? Please round to the nearest thousandth.

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Trajetória

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1. 0Início

   What is the racial gap in callback rate among white and black female applicants? Please round to the nearest thousandth.

   resume (agent)

   Does racial discrimination exist in the labor market? Or, should racial disparities in the unemployment rate be attributed to other factors such as racial gaps in educational attainment? To answer this question, two social scientists conducted the following experiment.1 In response to newspaper ads, the researchers sent out résumés of fictitious job candidates to potential employers. They varied only the names of job applicants, while leaving the other information in the résumés unchanged. For some candidates, stereotypically African-American-sounding names such as Lakisha Washington or Jamal Jones were used, whereas other résumés contained stereotypically white-sounding names, such as Emily Walsh or Greg Baker. The researchers then compared the callback rates between these two groups and examined whether applicants with stereotypically black names received fewer callbacks than those with stereotypically white names. The positions to which the applications were sent were either in sales, administrative support, clerical, or customer services. Data is in the CSV data file resume.csv. Variable Description firstname: first name of the fictitious job applicant sex: sex of applicant (female or male) race: race of applicant (black or white) call: whether a callback was made (1= yes, 0= no)

   file 3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970 · frame —

   resume_white (reference)

   file 3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970 · frame —

   1. derive {"args":{"cmp":"eq","column":"race","value":"white"},"name":"white","op":"indicator"}
2. 1Transformação
   resume_white · derive {"args":{"cmp":"eq","column":"race","value":"white"},"name":"white","op":"indicator"}

   4870 → 4870 linhas

   Frame antes → depois: 7b2ee50bc3aa → 13005fa64ddf
3. 2Turno do modelo
   call_fab00405referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   Among female applicants, OLS of callback on the white-name indicator: the difference of the two callback rates.

   Chamadas pedidas

   • infer.ols

     {
       "dataset": "resume_white",
       "subset": "sex == 'female'",
       "x": [
         "white"
       ],
       "y": "call"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
4. 3Ferramenta
   infer.olsv0.1.0call_ee6a244cdecidido pelo modeloreexecutável

   Motivo: Among female applicants, OLS of callback on the white-name indicator: the difference of the two callback rates.

   Argumentos

   {
     "cluster": null,
     "controls": [],
     "dataset": "resume_white",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": "sex == 'female'",
     "weights": null,
     "x": [
       "white"
     ],
     "y": "call"
   }

   Figuras call_ee6a244c

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.white Coefficient on white in the regression of call	0,0326469	0.0326468944913853	0,008987770.008987769417675224	0,0150255 – 0,0502683

   Diagnósticos

   • n_obs = 3.7463746
   • k = 22
   • r2 = 0,003520470.003520467767697877
   • df_resid = 3.7443744
   • se_type = "HC1"
   • n_clusters = —

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": 0.07751163521340439,
         "ci_low": 0.05504403816941641,
         "coef": 0.0662778366914104,
         "p": 1.9753969122581188e-30,
         "se": 0.00572978225879644,
         "t": 11.56725224412494,
         "term": "Intercept"
       },
       {
         "ci_high": 0.05026829548708581,
         "ci_low": 0.015025493495684796,
         "coef": 0.0326468944913853,
         "p": 0.00028459551980354093,
         "se": 0.008987769417675224,
         "t": 3.6323689420850482,
         "term": "white"
       }
     ]
   }

   Digests dos dados

   resume_white 13005fa64ddf114e6818f9359859e4255102fad2fc6cfd0e0e835afac2a823bb

   sha256 do resultado

   a0c25ad52a23707694cf61b1b343bc011e39914b07e3124ff62499cd96a69298

   Resultado mostrado ao modelo
5. 4Turno do modelo
   call_1ee94114referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa (infer.ols): coef.white = 0,03265. Caminho de referência inferido à mão.",
       "caveats": [],
       "figure_refs": [
         "call_ee6a244c:coef.white"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
6. 5Guarda
   resposta aceita

   Números encontrados

   0,03265

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
7. 6Resposta

   Estimativa (infer.ols): coef.white = 0,03265. Caminho de referência inferido à mão.

   Figuras citadas: call_ee6a244c:coef.white · Parada: answered

   Figura da resposta: call_ee6a244c:coef.white = 0,03264690.0326468944913853 (se 0,008987770.008987769417675224)
8. 7Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
9. 8Pontuação
   #1figura da resposta próximapassouobrigatória0.033 in (0.03201, 0.03399)?

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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