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?

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
   rebeira_does_rising— 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
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na respostacoincide com a referência

   Ver chamada call_bf96e082 nos detalhes

   causcibench_metadata_rule/1: ols

   Por que este é o caminho de referência: causcibench_metadata_rule/1: ols
   Argumentos principais

   controls:

   ["pop","hc","rgdpe"]

   dataset:

   "rebeira_does_rising"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["invertedplc"]

   y:

   "qxall"

   Resultado calculado

   • coef.invertedplc = -0,0248628 -0.02486275668419051
     Coefficient on invertedplc in the regression of qxall
   Diagnósticos: n_obs = 600600 (observations used) · k = 55 (parameters estimated, fixed-effect dummies included) · r2 = 0,4811450.4811452120632217 · df_resid = 595595 · 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: -0,02486
   Figuras citadas: call_bf96e082:coef.invertedplc
   • ✓
     #0resposta com lastro

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

     CauSciBench effect accuracy: |answer figure − gold| ≤ 5% of |gold|

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

   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_9f539f9dreferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   causcibench_metadata_rule/1: ols

   Chamadas pedidas

   • infer.ols

     {
       "controls": [
         "pop",
         "hc",
         "rgdpe"
       ],
       "dataset": "rebeira_does_rising",
       "x": [
         "invertedplc"
       ],
       "y": "qxall"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_bf96e082decidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: ols

   Argumentos

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

   Figuras call_bf96e082

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.invertedplc Coefficient on invertedplc in the regression of qxall	-0,0248628	-0.02486275668419051	0,003603450.0036034497719140807	-0,0319398 – -0,0177857

   Diagnósticos

   • n_obs = 600600 (observations used)
   • k = 55 (parameters estimated, fixed-effect dummies included)
   • r2 = 0,4811450.4811452120632217
   • df_resid = 595595
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   coef.invertedplc

   1. Coeficiente de invertedplc na regressão por mínimos quadrados ordinários de qxall.→ -0,0248628 (-0.02486275668419051)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,00360345 (0.0036034497719140807)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 595 graus de liberdade vezes o erro-padrão.IC = β ± c · ep

   Pressupostos do estimador

   • the conditional mean of the outcome is linear in the regressors
   • errors are independent across observations; their variance may differ

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": 0.2939387901330393,
         "ci_low": 0.2646452864355017,
         "coef": 0.2792920382842705,
         "p": 1.3052970655245668e-158,
         "se": 0.007457768721117592,
         "t": 37.449812233171116,
         "term": "Intercept"
       },
       {
         "ci_high": -0.017785729146546685,
         "ci_low": -0.031939784221834334,
         "coef": -0.02486275668419051,
         "p": 1.3372361435289914e-11,
         "se": 0.0036034497719140807,
         "t": -6.899709516690144,
         "term": "invertedplc"
       },
       {
         "ci_high": 0.00027918861749123684,
         "ci_low": 0.0001579945443996569,
         "coef": 0.00021859158094544688,
         "p": 3.955960032361521e-12,
         "se": 0.00003085453269160159,
         "t": 7.0845856954089,
         "term": "pop"
       },
       {
         "ci_high": -0.03997390482184768,
         "ci_low": -0.05010820459705159,
         "coef": -0.045041054709449635,
         "p": 2.0636766592398935e-55,
         "se": 0.0025800691052295715,
         "t": -17.45730554974493,
         "term": "hc"
       },
       {
         "ci_high": -2.0337664978423645e-9,
         "ci_low": -6.29589944951053e-9,
         "coef": -4.164832973676447e-9,
         "p": 0.0001371772012989165,
         "se": 1.0850870602708892e-9,
         "t": -3.8382477555641548,
         "term": "rgdpe"
       }
     ]
   }

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   rebeira_does_rising b8002d51d33a7f9a1278ba8db1cc069b65e05527316e73f35294457839af47ce

   sha256 do resultado

   49d1e16ece50fde2877ca47279ccfc6902965311fb25471299f54a922f5e795d

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_4fe99e23referenceFim: 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.invertedplc = -0,02486.",
       "caveats": [],
       "figure_refs": [
         "call_bf96e082:coef.invertedplc"
       ]
     }

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

   Números encontrados

   -0,02486

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.ols): coef.invertedplc = -0,02486.

   Figuras citadas: call_bf96e082:coef.invertedplc · Parada: answered

   Figura da resposta: call_bf96e082:coef.invertedplc = -0,0248628-0.02486275668419051 (se 0,003603450.0036034497719140807)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximapassouobrigatória-0.02486275668 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 1passoupontuada(exibindo)

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