Tarefa causcibench_real.q039

Veredito

Pergunta feita ao analista

Does an increase in trust in local government increase support for public-citizen partnerships?

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
   liu_public_trust2— The dataset comes from a pre-registered vignette experiment examining the effect of trust in government on perceived collaborative governance in a hypothetical environmental program. Respondents were randomly assigned to three experimental conditions: Corrupt, Control, and Honest, about government integrity in a fictional U.S. city, Midtown. The information provided to the corrupt group explicitly states that the (hypothetical) municipality has corrupt officials. Similarly, for the honest group, the information states that officials are not corrupt. The control group is provided with no information. After reading the information about government integrity, subjects reported trust in government, perceptions of collaborative governance, and willingness to coproduce public services. Variables: trust: Trust in government (0-100; 0=never, 100=always) PPP: Support for public-private partnerships (0-100; 0=never, 100=always) PCP: Support for public-citizen partnerships (0-100; 0=never, 100=always) coproduce: Willingness to coproduce public services (0-100; 0=never, 100=always) placebo1: Placebo outcome variable for robustness checks (0-100; 0=never, 100=always) placebo2: Placebo outcome variable for robustness checks (0-100; 0=never, 100=always) duration: Time spent on the survey sex: 0=male; 1=female white: Race indicator (1=yes, 0=no) black: Race indicator (1=yes, 0=no) hispanic: Race indicator (1=yes, 0=no) asian: Race indicator (1=yes, 0=no) other: Race indicator (1=yes, 0=no) age: Age in years (18-100) income: 1=<25k; 2=25k-34,999; 3=35k-49,999; 4=50k-74,999; 5=75k-99,999; 6=100k-149,999; 7=150k+ education: 1=<high school; 2=high school/GED; 3=some college; 4=2-year degree; 5=4-year degree; 6=master's; 7=doctoral; 8=professional (JD, MD) ideology: 1=very liberal; 2=liberal; 3=moderate; 4=conservative; 5=very conservative gw: Belief that global warming is happening (-1=no, 0=don't know, 1=yes) gwh: Belief about human causation of global warming (-1=neither, 0=mostly natural, 1=mostly human) covid: Concern about COVID-19 (1=not at all, ..., 5=extremely concerned) covid_job: Concern about job loss due to the pandemic (1=strongly disagree, ..., 5=strongly agree) rand: The randomly assigned government integrity group: Corrupt, Control, and Honest trustprime: Encoding of government integrity group to which participants are assigned (0=Corrupt, 1=Control, 2=Honest) at: Response to the question, ""What is 17 + 63 equal to?"" The question was meant to test the attentiveness of the participants atpass: 1 = participant provided correct answer to attention test question, 0 otherwise mc: Response to the manipulation check question: Have you seen the information below from any previous part of this survey? ""Local and state government have more corruption than we assumed"" mcpass: 1 = participant passed the manipulation check, 0 = otherwise mcatpass: 1 = passed both attention and manipulation test, 0 = otherwise climatebelief: Average of gw and gwh"
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_3f5b55e3 nos detalhes

   causcibench_metadata_rule/1: iv

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

   dataset:

   "liu_public_trust2"

   endog:

   ["trust"]

   instruments:

   ["trustprime"]

   level:

   0,950.95

   se:

   "HC1"

   y:

   "PCP"

   Resultado calculado

   • coef.trust = 0,674003 0.6740034765158338
     2SLS effect of trust on PCP
   Diagnósticos: first_stage_F.trust = 135,729135.72900899797403 (Wald F of the excluded instruments in the first stage for trust (heteroskedasticity-robust standard errors (HC1))) · partial_r2.trust = 0,09584360.0958435875458935 (share of the variation the instruments explain beyond the controls) · weak_instrument = false (every first-stage F is at least 10) · n_obs = 1.3371337 · 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: 0,6740
   Figuras citadas: call_3f5b55e3:coef.trust
   • ✓
     #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 trust in local government increase support for public-citizen partnerships?

   liu_public_trust2 (agent)

   The dataset comes from a pre-registered vignette experiment examining the effect of trust in government on perceived collaborative governance in a hypothetical environmental program. Respondents were randomly assigned to three experimental conditions: Corrupt, Control, and Honest, about government integrity in a fictional U.S. city, Midtown. The information provided to the corrupt group explicitly states that the (hypothetical) municipality has corrupt officials. Similarly, for the honest group, the information states that officials are not corrupt. The control group is provided with no information. After reading the information about government integrity, subjects reported trust in government, perceptions of collaborative governance, and willingness to coproduce public services. Variables: trust: Trust in government (0-100; 0=never, 100=always) PPP: Support for public-private partnerships (0-100; 0=never, 100=always) PCP: Support for public-citizen partnerships (0-100; 0=never, 100=always) coproduce: Willingness to coproduce public services (0-100; 0=never, 100=always) placebo1: Placebo outcome variable for robustness checks (0-100; 0=never, 100=always) placebo2: Placebo outcome variable for robustness checks (0-100; 0=never, 100=always) duration: Time spent on the survey sex: 0=male; 1=female white: Race indicator (1=yes, 0=no) black: Race indicator (1=yes, 0=no) hispanic: Race indicator (1=yes, 0=no) asian: Race indicator (1=yes, 0=no) other: Race indicator (1=yes, 0=no) age: Age in years (18-100) income: 1=<25k; 2=25k-34,999; 3=35k-49,999; 4=50k-74,999; 5=75k-99,999; 6=100k-149,999; 7=150k+ education: 1=<high school; 2=high school/GED; 3=some college; 4=2-year degree; 5=4-year degree; 6=master's; 7=doctoral; 8=professional (JD, MD) ideology: 1=very liberal; 2=liberal; 3=moderate; 4=conservative; 5=very conservative gw: Belief that global warming is happening (-1=no, 0=don't know, 1=yes) gwh: Belief about human causation of global warming (-1=neither, 0=mostly natural, 1=mostly human) covid: Concern about COVID-19 (1=not at all, ..., 5=extremely concerned) covid_job: Concern about job loss due to the pandemic (1=strongly disagree, ..., 5=strongly agree) rand: The randomly assigned government integrity group: Corrupt, Control, and Honest trustprime: Encoding of government integrity group to which participants are assigned (0=Corrupt, 1=Control, 2=Honest) at: Response to the question, ""What is 17 + 63 equal to?"" The question was meant to test the attentiveness of the participants atpass: 1 = participant provided correct answer to attention test question, 0 otherwise mc: Response to the manipulation check question: Have you seen the information below from any previous part of this survey? ""Local and state government have more corruption than we assumed"" mcpass: 1 = participant passed the manipulation check, 0 = otherwise mcatpass: 1 = passed both attention and manipulation test, 0 = otherwise climatebelief: Average of gw and gwh"

   file fc860c838198dcffd59dcd954f0a1f1743f18b6fdb1a88ee81b4490493d3c3cf · frame —
2. 1Turno do modelo
   call_66f6a02freferenceFim: 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": "liu_public_trust2",
       "endog": [
         "trust"
       ],
       "exog": [],
       "instruments": [
         "trustprime"
       ],
       "y": "PCP"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.iv_2sls— Variáveis instrumentais por mínimos quadrados em dois estágios (MQ2E)v0.1.0call_3f5b55e3decidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: iv

   Argumentos

   {
     "cluster": null,
     "dataset": "liu_public_trust2",
     "endog": [
       "trust"
     ],
     "exog": [],
     "instruments": [
       "trustprime"
     ],
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "y": "PCP"
   }

   Figuras call_3f5b55e3

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.trust 2SLS effect of trust on PCP	0,674003	0.6740034765158338	1,070611.0706075622477977	-1,42625 – 2,77426

   Diagnósticos

   • first_stage_F.trust = 135,729135.72900899797403 (Wald F of the excluded instruments in the first stage for trust (heteroskedasticity-robust standard errors (HC1)))
   • partial_r2.trust = 0,09584360.0958435875458935 (share of the variation the instruments explain beyond the controls)
   • weak_instrument = false (every first-stage F is at least 10)
   • n_obs = 1.3371337
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   coef.trust

   1. Com um instrumento e um regressor endógeno, o MQ2E é a razão entre o coeficiente de trustprime na forma reduzida (PCP sobre trustprime) e no primeiro estágio (trust sobre trustprime).β = ρ / π→ 0,674003 (0.6740034765158338)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 1,07061 (1.0706075622477977)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 1335 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": 88.55132694599837,
         "ci_low": 69.75059285615217,
         "coef": 79.15095990107527,
         "p": 6.5239548330663855e-56,
         "se": 4.7918454119876746,
         "t": 16.51784502543941,
         "term": "Intercept"
       },
       {
         "ci_high": 2.7742598873387845,
         "ci_low": -1.4262529343071169,
         "coef": 0.6740034765158338,
         "p": 0.5290952470772582,
         "se": 1.0706075622477977,
         "t": 0.6295523217683308,
         "term": "trust"
       }
     ],
     "first_stage": [
       {
         "ci_high": 0.6315374020998727,
         "ci_low": 0.44950510693073226,
         "coef": 0.5405212545153025,
         "endog": "trust",
         "instrument": "trustprime",
         "p": 6.082820714072249e-30,
         "se": 0.04639556169835534,
         "t": 11.650279352786956
       }
     ],
     "reduced_form": [
       {
         "ci_high": 1.5030310420630881,
         "ci_low": -0.7744046327150649,
         "coef": 0.3643132046740117,
         "instrument": "trustprime",
         "p": 0.5303564255655533,
         "se": 0.5804624243463261,
         "t": 0.6276258193358067
       }
     ]
   }

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   liu_public_trust2 075a3e2ce885dad588f2e9e6bee224ee62b6c7eb389d59cb0f989acf9a8d97b6

   sha256 do resultado

   904b660521aae1648da690a9ede7d3ae974c483f3e3c1a4eb51e8287782f95ed

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_c5361dcbreferenceFim: 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.trust = 0,6740.",
       "caveats": [],
       "figure_refs": [
         "call_3f5b55e3:coef.trust"
       ]
     }

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

   Números encontrados

   0,6740

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.iv_2sls): coef.trust = 0,6740.

   Figuras citadas: call_3f5b55e3:coef.trust · Parada: answered

   Figura da resposta: call_3f5b55e3:coef.trust = 0,6740030.6740034765158338 (se 1,070611.0706075622477977)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximapassouobrigatória0.6740034765 vs 0.674

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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