Tarefa causcibench_real.q108

Veredito

Pergunta feita ao analista

Does a greater inequality in income lead to a higher average mortality rate among males and females?

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Verificações

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Trajetória

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1. 0Início

   Does a greater inequality in income lead to a higher average mortality rate among males and females?

   rebeira_does_rising (agent)

   This dataset aims to study the effect of income inequality on mortality rates for men and women in a subset of OECD countries from 1950 to 2000 using adult mortality rates at age sixty-five as the measure of mortality. Likewise, for measuring income inequality, the inverted Pareto-Lorenz coefficient was used. The coefficient is directly obtained from the World Top Income Database. More specifically, the measure was derived from individual taxation and income data as well as national account statistics. The Pareto-Lorenz coefficient ranges from 1.5 to 3. Values in the range of 1.5 to 1.8 are considered low inequality, while values above 2.5 are considered high inequality. Data variables: year: Year observation was taken cc: Country code qxall: Averaged rate between qxfemale and qxmale qxfemale: Five-year mortality rate for females at age sixty-five (the probability of dying within 5 years for 65-year-old women) invertedplc: Inverted Pareto-Lorenz coefficient (measure of income inequality at the top end of distribution, ranging from ~1.5-3) exfemale: Life expectancy for females at age 65 qxmale: Five-year mortality rate for males at age sixty-five (the probability of dying within 5 years for 65-year-old men) exmale: Life expectancy for males (at age 65) country: Country name rgdpe: Real GDP obtained from Penn World Table; values adjusted for purchasing power parity pop: Population (in millions) hc: Health capital index - a measure capturing educational attainment that serves as a proxy for human capital stock rpcgdp: Real per capita GDP - GDP per person adjusted for purchasing power parity

   file a45e6b911277627d6b75a1c955aef5765405b77452b959c26188af44c238bd91 · frame —
2. 1Turno do modelo
   call_8536916bnaive-olsFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   Linha de base ingênua: regressão do resultado no tratamento, sem controles.

   Chamadas pedidas

   • infer.ols

     {
       "dataset": "rebeira_does_rising",
       "x": [
         "invertedplc"
       ],
       "y": "qxall"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.olsv0.1.0call_46a012bcdecidido pelo modeloreexecutável

   Motivo: Linha de base ingênua: regressão do resultado no tratamento, sem controles.

   Argumentos

   {
     "cluster": null,
     "controls": [],
     "dataset": "rebeira_does_rising",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "invertedplc"
     ],
     "y": "qxall"
   }

   Figuras call_46a012bc

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.invertedplc Coefficient on invertedplc in the regression of qxall	-0,0398781	-0.03987812733583945	0,002719250.002719246365218814	-0,0452186 – -0,0345377

   Diagnósticos

   • n_obs = 600600
   • k = 22
   • r2 = 0,1724890.17248915940553133
   • df_resid = 598598
   • se_type = "HC1"
   • n_clusters = —

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": 0.19232173693592364,
         "ci_low": 0.17239235542376097,
         "coef": 0.1823570461798423,
         "p": 1.532771789698792e-151,
         "se": 0.005073829316149994,
         "t": 35.940713574932445,
         "term": "Intercept"
       },
       {
         "ci_high": -0.034537693625790185,
         "ci_low": -0.04521856104588872,
         "coef": -0.03987812733583945,
         "p": 8.057193375262738e-42,
         "se": 0.002719246365218814,
         "t": -14.665139520240018,
         "term": "invertedplc"
       }
     ]
   }

   Digests dos dados

   rebeira_does_rising b8002d51d33a7f9a1278ba8db1cc069b65e05527316e73f35294457839af47ce

   sha256 do resultado

   c6870979785729bcec898bc0e80e4e19aecae232a8ad655153689803c30e217f

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_614f8170naive-olsFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa ingênua (infer.ols): coef.invertedplc = -0,03988.",
       "caveats": [],
       "figure_refs": [
         "call_46a012bc:coef.invertedplc"
       ]
     }

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

   Números encontrados

   -0,03988

   Sem lastro

   —

   Não citados

   —

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

   Estimativa ingênua (infer.ols): coef.invertedplc = -0,03988.

   Figuras citadas: call_46a012bc:coef.invertedplc · Parada: answered

   Figura da resposta: call_46a012bc:coef.invertedplc = -0,0398781-0.03987812733583945 (se 0,002719250.002719246365218814)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximafalhouobrigatória-0.03987812734 vs -0.025

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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