Tarefa causcibench_real.q112

Veredito

Pergunta feita ao analista

Does an increase in corruption lead to higher income inequality?

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
   dwiputri_the_corruption— The existence of ambiguity in the study of the impact of corruption on economic growth, namely the grease the wheel hypothesis and sand the wheel hypothesis, triggered this research to look at the impact of corruption through another perspective, i.e. income inequality. This study seeks to understand the mutual influence between corruption and income inequality in Asian countries. The data is collected from a variety of sources, including the World Bank, Transparency International, and The Institute Worldwide Governance Index. Dataset variables: - trade: Merchandise trade (% of GDP) - id: Unique country identifier - gini: GINI index (World Bank estimate) - corr: corruption indicator - country (str): Country name - year: Year collected - popgrow: Population growth (annual %) - unemptot: Unemployment, total (% of total labor force) (national estimate) - healthtot: Health expenditure, total (% of GDP) - educsec: Gross enrolment ratio, secondary, both sexes (%) - educprim: Gross enrollment ratio, primary, both sexes (%) - gdpcapgrow: GDP per capita growth (annual %) - gdpcapcons: GDP per capita (constant 2010 US$) - cap: Gross fixed capital formation (% of GDP) - capgrow: Gross fixed capital formation (annual % growth) - expense: Expense (% of GDP) - taxrev: Tax revenue (% of GDP) - fdi: Foreign direct investment, net inflows (% of GDP) - al_ethnic: Ethnic Fractionalization - al_language: Language Fractionalization - al_religion: Religion Fractionalization - p_democ: Institutionalized Democracy - ln_gdpcapcons: natural log of gdp per capita growth - gdpcapcor: interaction between corruption and gdp per capita growth - gov: Governance indicator"
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_39a112a7 nos detalhes

   causcibench_metadata_rule/1: iv

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

   dataset:

   "dwiputri_the_corruption"

   endog:

   ["corr"]

   exog:

   ["unemptot","ln_gdpcapcons","educprim","educsec","taxrev","p_democ","healthtot","fdi","gov","cap","popgrow"]

   instruments:

   ["al_ethnic","al_language","al_religion"]

   level:

   0,950.95

   se:

   "HC1"

   y:

   "gini"

   Resultado calculado

   • coef.corr = -14,9612 -14.961169281273891
     2SLS effect of corr on gini
   Diagnósticos: first_stage_F.corr = 4,015894.0158866968054046 (Wald F of the excluded instruments in the first stage for corr (heteroskedasticity-robust standard errors (HC1)); below 10: weak instrument, the estimate and its interval are unreliable) · partial_r2.corr = 0,1082860.10828581009841975 (share of the variation the instruments explain beyond the controls) · weak_instrument = true (at least one first-stage F is below 10) · overid_p = 0,113360.11336029857830167 (Sargan test of the overidentifying restrictions (assumes homoskedastic errors)) · sargan = 4,354374.354368099416988 (n·R² of the structural residuals on the instruments) · sargan_df = 22 · n_obs = 8383 · 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: -14,96
   Figuras citadas: call_39a112a7:coef.corr
   • ✓
     #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 an increase in corruption lead to higher income inequality?

   dwiputri_the_corruption (agent)

   The existence of ambiguity in the study of the impact of corruption on economic growth, namely the grease the wheel hypothesis and sand the wheel hypothesis, triggered this research to look at the impact of corruption through another perspective, i.e. income inequality. This study seeks to understand the mutual influence between corruption and income inequality in Asian countries. The data is collected from a variety of sources, including the World Bank, Transparency International, and The Institute Worldwide Governance Index. Dataset variables: - trade: Merchandise trade (% of GDP) - id: Unique country identifier - gini: GINI index (World Bank estimate) - corr: corruption indicator - country (str): Country name - year: Year collected - popgrow: Population growth (annual %) - unemptot: Unemployment, total (% of total labor force) (national estimate) - healthtot: Health expenditure, total (% of GDP) - educsec: Gross enrolment ratio, secondary, both sexes (%) - educprim: Gross enrollment ratio, primary, both sexes (%) - gdpcapgrow: GDP per capita growth (annual %) - gdpcapcons: GDP per capita (constant 2010 US$) - cap: Gross fixed capital formation (% of GDP) - capgrow: Gross fixed capital formation (annual % growth) - expense: Expense (% of GDP) - taxrev: Tax revenue (% of GDP) - fdi: Foreign direct investment, net inflows (% of GDP) - al_ethnic: Ethnic Fractionalization - al_language: Language Fractionalization - al_religion: Religion Fractionalization - p_democ: Institutionalized Democracy - ln_gdpcapcons: natural log of gdp per capita growth - gdpcapcor: interaction between corruption and gdp per capita growth - gov: Governance indicator"

   file 10cc9110630a581a2fe10095f748e14a6787c416f3b7367d9870c06ff4a18973 · frame —
2. 1Turno do modelo
   call_c356e6bcreferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   causcibench_metadata_rule/1: iv

   Chamadas pedidas

   • infer.iv_2sls

     {
       "dataset": "dwiputri_the_corruption",
       "endog": [
         "corr"
       ],
       "exog": [
         "unemptot",
         "ln_gdpcapcons",
         "educprim",
         "educsec",
         "taxrev",
         "p_democ",
         "healthtot",
         "fdi",
         "gov",
         "cap",
         "popgrow"
       ],
       "instruments": [
         "al_ethnic",
         "al_language",
         "al_religion"
       ],
       "y": "gini"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.iv_2sls— Variáveis instrumentais por mínimos quadrados em dois estágios (MQ2E)v0.1.0call_39a112a7decidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: iv

   Argumentos

   {
     "cluster": null,
     "dataset": "dwiputri_the_corruption",
     "endog": [
       "corr"
     ],
     "exog": [
       "unemptot",
       "ln_gdpcapcons",
       "educprim",
       "educsec",
       "taxrev",
       "p_democ",
       "healthtot",
       "fdi",
       "gov",
       "cap",
       "popgrow"
     ],
     "instruments": [
       "al_ethnic",
       "al_language",
       "al_religion"
     ],
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "y": "gini"
   }

   Figuras call_39a112a7

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.corr 2SLS effect of corr on gini	-14,9612	-14.961169281273891	5,723735.72373144022489	-26,3768 – -3,54555

   Diagnósticos

   • first_stage_F.corr = 4,015894.0158866968054046 (Wald F of the excluded instruments in the first stage for corr (heteroskedasticity-robust standard errors (HC1)); below 10: weak instrument, the estimate and its interval are unreliable)
   • partial_r2.corr = 0,1082860.10828581009841975 (share of the variation the instruments explain beyond the controls)
   • weak_instrument = true (at least one first-stage F is below 10)
   • overid_p = 0,113360.11336029857830167 (Sargan test of the overidentifying restrictions (assumes homoskedastic errors))
   • sargan = 4,354374.354368099416988 (n·R² of the structural residuals on the instruments)
   • sargan_df = 22
   • n_obs = 8383
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   coef.corr

   1. Coeficiente de corr por mínimos quadrados em dois estágios: gini sobre as projeções dos regressores endógenos nos instrumentos ['al_ethnic', 'al_language', 'al_religion'] e os controles; resíduos com os valores observados de corr.→ -14,9612 (-14.961169281273891)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 5,72373 (5.72373144022489)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 70 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": 109.29662998533796,
         "ci_low": 2.887493505939389,
         "coef": 56.092061745638674,
         "p": 0.03909146748927349,
         "se": 26.676483267226345,
         "t": 2.1026782722350483,
         "term": "Intercept"
       },
       {
         "ci_high": 0.43790098063718114,
         "ci_low": -0.12049629378007304,
         "coef": 0.15870234342855405,
         "p": 0.2607966578986255,
         "se": 0.13998868932030706,
         "t": 1.1336797579797924,
         "term": "unemptot"
       },
       {
         "ci_high": 10.69656797398935,
         "ci_low": -1.7398145400721976,
         "coef": 4.478376716958576,
         "p": 0.15534149311436668,
         "se": 3.117767524646905,
         "t": 1.4364049537227004,
         "term": "ln_gdpcapcons"
       },
       {
         "ci_high": 0.15643198737828312,
         "ci_low": -0.46803934528881963,
         "coef": -0.15580367895526825,
         "p": 0.3230610904621762,
         "se": 0.1565532773586761,
         "t": -0.9952118638711698,
         "term": "educprim"
       },
       {
         "ci_high": 0.1380135725156077,
         "ci_low": -0.40792074152556274,
         "coef": -0.13495358450497752,
         "p": 0.32750971125593964,
         "se": 0.1368642587973973,
         "t": -0.9860396402303381,
         "term": "educsec"
       },
       {
         "ci_high": 0.7847711302566615,
         "ci_low": -0.8679585619099105,
         "coef": -0.04159371582662451,
         "p": 0.9203242834362781,
         "se": 0.4143348723336891,
         "t": -0.10038671278705828,
         "term": "taxrev"
       },
       {
         "ci_high": 0.8587660077675867,
         "ci_low": -0.768709245205955,
         "coef": 0.04502838128081586,
         "p": 0.9124375464419223,
         "se": 0.4080036526015706,
         "t": 0.11036269159283141,
         "term": "p_democ"
       },
       {
         "ci_high": 4.441553826416248,
         "ci_low": -0.3514936383330105,
         "coef": 2.045030094041619,
         "p": 0.09320868684003365,
         "se": 1.2016040607298795,
         "t": 1.7019167634964756,
         "term": "healthtot"
       },
       {
         "ci_high": -0.28841680573794404,
         "ci_low": -1.9688494094394886,
         "coef": -1.1286331075887164,
         "p": 0.009195172555090812,
         "se": 0.4212799174723575,
         "t": -2.679057464596024,
         "term": "fdi"
       },
       {
         "ci_high": 44.347464047614906,
         "ci_low": 9.834656875130808,
         "coef": 27.09106046137286,
         "p": 0.0025412146085860184,
         "se": 8.652267591890764,
         "t": 3.1310936900245245,
         "term": "gov"
       },
       {
         "ci_high": 1.1508954047343822,
         "ci_low": -0.01330986039099813,
         "coef": 0.5687927721716921,
         "p": 0.0553238881225621,
         "se": 0.2918631172309797,
         "t": 1.9488340204409969,
         "term": "cap"
       },
       {
         "ci_high": 10.073265538847462,
         "ci_low": 3.2341590476385766,
         "coef": 6.653712293243019,
         "p": 0.00023291624417196276,
         "se": 1.7145455353905157,
         "t": 3.8807439965293935,
         "term": "popgrow"
       },
       {
         "ci_high": -3.545546879077829,
         "ci_low": -26.376791683469953,
         "coef": -14.961169281273891,
         "p": 0.010951711215348858,
         "se": 5.72373144022489,
         "t": -2.613883868857071,
         "term": "corr"
       }
     ],
     "first_stage": [
       {
         "ci_high": 2.1207932355304324,
         "ci_low": 0.23617729081795713,
         "coef": 1.1784852631741949,
         "endog": "corr",
         "instrument": "al_ethnic",
         "p": 0.015005600534817812,
         "se": 0.47222382544489516,
         "t": 2.495607378691474
       },
       {
         "ci_high": -0.5168626036674658,
         "ci_low": -2.311135516701333,
         "coef": -1.4139990601843995,
         "endog": "corr",
         "instrument": "al_language",
         "p": 0.0024624134597656245,
         "se": 0.449586782528403,
         "t": -3.145108164061894
       },
       {
         "ci_high": 0.744967375974066,
         "ci_low": -0.4053540806666047,
         "coef": 0.16980664765373066,
         "endog": "corr",
         "instrument": "al_religion",
         "p": 0.5577268806395919,
         "se": 0.28823336673460853,
         "t": 0.5891290435159108
       }
     ],
     "reduced_form": [
       {
         "ci_high": -3.729881090917143,
         "ci_low": -19.83765604337549,
         "coef": -11.783768567146316,
         "instrument": "al_ethnic",
         "p": 0.004749609961194706,
         "se": 4.036087633024785,
         "t": -2.9196017625403115
       },
       {
         "ci_high": 21.210120986890676,
         "ci_low": 9.099980012128832,
         "coef": 15.155050499509754,
         "instrument": "al_language",
         "p": 0.000004364540123890831,
         "se": 3.03440980313444,
         "t": 4.994398081582492
       },
       {
         "ci_high": -6.4785409185591005,
         "ci_low": -19.248627022918306,
         "coef": -12.863583970738704,
         "instrument": "al_religion",
         "p": 0.00014816058103212553,
         "se": 3.199770716352087,
         "t": -4.020158039762265
       }
     ]
   }

   • 862 rows were dropped because one of 'gini', 'corr', 'al_ethnic', 'al_language', 'al_religion', 'unemptot', 'ln_gdpcapcons', 'educprim', 'educsec', 'taxrev', 'p_democ', 'healthtot', 'fdi', 'gov', 'cap', 'popgrow' was missing

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   dwiputri_the_corruption e1fda7779831d5b03ea894cd13c84f710e02b4b6c8178046f03c03a311e3911b

   sha256 do resultado

   fd43ba22cf65cf1389191a776d42214b0ea0dcd74e504a6494d8f7e5bf0e2d93

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_c04ece11referenceFim: 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.corr = -14,96.",
       "caveats": [],
       "figure_refs": [
         "call_39a112a7:coef.corr"
       ]
     }

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

   Números encontrados

   -14,96

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.iv_2sls): coef.corr = -14,96.

   Figuras citadas: call_39a112a7:coef.corr · Parada: answered

   Figura da resposta: call_39a112a7:coef.corr = -14,9612-14.961169281273891 (se 5,723735.72373144022489)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximafalhouobrigatória-14.96116928 vs -18.126

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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