Tarefa causcibench_real.q069

Veredito

Pergunta feita ao analista

Does PEPFAR funding increase approval of U.S. leadership?

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
   goldsmith_doing_well— The dataset combines multinational survey data on public opinion with country-level information on aid flows, health indicators, governance, and economic conditions. Public opinion measures come from the Gallup World Poll (GWP), an annual global survey launched in 2006 that samples individuals aged 15 and older. In countries with high telephone penetration (over 80%), data are collected through Random-Digit-Dial (RDD) telephone interviews; elsewhere, surveys are conducted face-to-face. Each survey includes about 1,000 respondents per country per year. Aid-related data include figures from the U.S. President's Emergency Plan for AIDS Relief (PEPFAR), one of the largest U.S. bilateral health initiatives, launched in 2003 and first disbursed in 2004, aimed at combating HIV/AIDS in dozens of countries, especially in Sub-Saharan Africa. The dataset also incorporates U.S. Official Development Assistance (ODA) compiled from international aid databases. Additional country-level indicators are drawn from international sources: population and economic statistics from the World Bank's World Development Indicators (WDI), health data from UNAIDS and global monitoring agencies, governance measures from the World Bank Governance Indicators, civil liberties scores from Freedom House, UN General Assembly voting data, and trade flows from the U.S. Department of Commerce. Data variables: country: Country name ccode: Numeric country code y_us: Net approval of the U.S. (log(%approve) - log(%disapprove)) y_ch: Net approval of China y_fr: Net approval of France y_ge: Net approval of Germany y_in: Net approval of India y_ja: Net approval of Japan y_ru: Net approval of Russia y_uk: Net approval of the U.K. y_ownc: Net approval of respondent's own country x: PEPFAR funds per capita (log) x_imputed: PEPFAR funds per capita (log, imputed) z_ratio: HIV prevalence rate (log) z_death: HIV-related deaths per capita (log) usaid: U.S. ODA per capita (log) usaid_imputed: U.S. ODA per capita (log, imputed) gdppc: GDP per capita (log) wbgi: World Bank governance indicator fhscore: Freedom House civil liberties score africa: 1 if African country, 0 otherwise trade_i: Imports to the U.S. (log) trade_e: Exports to the U.S. (log) unvote_key: UN voting alignment with the U.S.
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_95801693 nos detalhes

   causcibench_metadata_rule/1: iv

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

   dataset:

   "goldsmith_doing_well"

   endog:

   ["x"]

   exog:

   ["usaid","gdppc","wbgi","fhscore","africa","trade_i","trade_e","unvote_key"]

   instruments:

   ["z_ratio","z_death"]

   level:

   0,950.95

   se:

   "HC1"

   y:

   "y_us"

   Resultado calculado

   • coef.x = 0,201617 0.2016168028931283
     2SLS effect of x on y_us
   Diagnósticos: first_stage_F.x = 25,508925.508852768251494 (Wald F of the excluded instruments in the first stage for x (heteroskedasticity-robust standard errors (HC1))) · partial_r2.x = 0,4514590.45145917151222803 (share of the variation the instruments explain beyond the controls) · weak_instrument = false (every first-stage F is at least 10) · overid_p = 0,3457980.3457975250962223 (Sargan test of the overidentifying restrictions (assumes homoskedastic errors)) · sargan = 0,8888190.8888190860442153 (n·R² of the structural residuals on the instruments) · sargan_df = 11 · n_obs = 5757 · 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: 0,2016
   Figuras citadas: call_95801693:coef.x
   • ✓
     #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 PEPFAR funding increase approval of U.S. leadership?

   goldsmith_doing_well (agent)

   The dataset combines multinational survey data on public opinion with country-level information on aid flows, health indicators, governance, and economic conditions. Public opinion measures come from the Gallup World Poll (GWP), an annual global survey launched in 2006 that samples individuals aged 15 and older. In countries with high telephone penetration (over 80%), data are collected through Random-Digit-Dial (RDD) telephone interviews; elsewhere, surveys are conducted face-to-face. Each survey includes about 1,000 respondents per country per year. Aid-related data include figures from the U.S. President's Emergency Plan for AIDS Relief (PEPFAR), one of the largest U.S. bilateral health initiatives, launched in 2003 and first disbursed in 2004, aimed at combating HIV/AIDS in dozens of countries, especially in Sub-Saharan Africa. The dataset also incorporates U.S. Official Development Assistance (ODA) compiled from international aid databases. Additional country-level indicators are drawn from international sources: population and economic statistics from the World Bank's World Development Indicators (WDI), health data from UNAIDS and global monitoring agencies, governance measures from the World Bank Governance Indicators, civil liberties scores from Freedom House, UN General Assembly voting data, and trade flows from the U.S. Department of Commerce. Data variables: country: Country name ccode: Numeric country code y_us: Net approval of the U.S. (log(%approve) - log(%disapprove)) y_ch: Net approval of China y_fr: Net approval of France y_ge: Net approval of Germany y_in: Net approval of India y_ja: Net approval of Japan y_ru: Net approval of Russia y_uk: Net approval of the U.K. y_ownc: Net approval of respondent's own country x: PEPFAR funds per capita (log) x_imputed: PEPFAR funds per capita (log, imputed) z_ratio: HIV prevalence rate (log) z_death: HIV-related deaths per capita (log) usaid: U.S. ODA per capita (log) usaid_imputed: U.S. ODA per capita (log, imputed) gdppc: GDP per capita (log) wbgi: World Bank governance indicator fhscore: Freedom House civil liberties score africa: 1 if African country, 0 otherwise trade_i: Imports to the U.S. (log) trade_e: Exports to the U.S. (log) unvote_key: UN voting alignment with the U.S.

   file e45a2c15808d35983e45405dd1eb70737617764feccf2852c6c51ba329b094dc · frame —
2. 1Turno do modelo
   call_9b58f99dreferenceFim: 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": "goldsmith_doing_well",
       "endog": [
         "x"
       ],
       "exog": [
         "usaid",
         "gdppc",
         "wbgi",
         "fhscore",
         "africa",
         "trade_i",
         "trade_e",
         "unvote_key"
       ],
       "instruments": [
         "z_ratio",
         "z_death"
       ],
       "y": "y_us"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.iv_2sls— Variáveis instrumentais por mínimos quadrados em dois estágios (MQ2E)v0.1.0call_95801693decidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: iv

   Argumentos

   {
     "cluster": null,
     "dataset": "goldsmith_doing_well",
     "endog": [
       "x"
     ],
     "exog": [
       "usaid",
       "gdppc",
       "wbgi",
       "fhscore",
       "africa",
       "trade_i",
       "trade_e",
       "unvote_key"
     ],
     "instruments": [
       "z_ratio",
       "z_death"
     ],
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "y": "y_us"
   }

   Figuras call_95801693

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.x 2SLS effect of x on y_us	0,201617	0.2016168028931283	0,07539450.07539448157092168	0,0499427 – 0,353291

   Diagnósticos

   • first_stage_F.x = 25,508925.508852768251494 (Wald F of the excluded instruments in the first stage for x (heteroskedasticity-robust standard errors (HC1)))
   • partial_r2.x = 0,4514590.45145917151222803 (share of the variation the instruments explain beyond the controls)
   • weak_instrument = false (every first-stage F is at least 10)
   • overid_p = 0,3457980.3457975250962223 (Sargan test of the overidentifying restrictions (assumes homoskedastic errors))
   • sargan = 0,8888190.8888190860442153 (n·R² of the structural residuals on the instruments)
   • sargan_df = 11
   • n_obs = 5757
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   coef.x

   1. Coeficiente de x por mínimos quadrados em dois estágios: y_us sobre as projeções dos regressores endógenos nos instrumentos ['z_ratio', 'z_death'] e os controles; resíduos com os valores observados de x.→ 0,201617 (0.2016168028931283)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,0753945 (0.07539448157092168)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 47 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": 7.8977616887121265,
         "ci_low": 0.2810032976934078,
         "coef": 4.089382493202767,
         "p": 0.035890660011518125,
         "se": 1.8930767509615973,
         "t": 2.1601778644872933,
         "term": "Intercept"
       },
       {
         "ci_high": 0.0419619322175393,
         "ci_low": -0.255633484831684,
         "coef": -0.10683577630707236,
         "p": 0.1552557603276624,
         "se": 0.07396466269337133,
         "t": -1.4444164607357415,
         "term": "usaid"
       },
       {
         "ci_high": 0.10472757863965182,
         "ci_low": -0.7151251846946072,
         "coef": -0.3051988030274777,
         "p": 0.1408761453518549,
         "se": 0.20376702605005767,
         "t": -1.4977830758176849,
         "term": "gdppc"
       },
       {
         "ci_high": 0.3902976879919897,
         "ci_low": -1.4453650453833893,
         "coef": -0.5275336786956998,
         "p": 0.2534144322784223,
         "se": 0.4562374523074205,
         "t": -1.1562699993779526,
         "term": "wbgi"
       },
       {
         "ci_high": 0.0837604042568798,
         "ci_low": -0.5844022236347064,
         "coef": -0.2503209096889133,
         "p": 0.13841118107678746,
         "se": 0.16606580802332532,
         "t": -1.5073597188275725,
         "term": "fhscore"
       },
       {
         "ci_high": 1.5072591608424075,
         "ci_low": 0.10020905622278065,
         "coef": 0.8037341085325941,
         "p": 0.0260402996208304,
         "se": 0.3497096407356625,
         "t": 2.2982898236429183,
         "term": "africa"
       },
       {
         "ci_high": 0.277077323734156,
         "ci_low": -0.11624003415204469,
         "coef": 0.08041864479105566,
         "p": 0.4148608857009515,
         "se": 0.0977554896374261,
         "t": 0.8226509333575784,
         "term": "trade_i"
       },
       {
         "ci_high": 0.23641770243626825,
         "ci_low": -0.33977667608061435,
         "coef": -0.05167948682217305,
         "p": 0.7198134085002114,
         "se": 0.1432079273108187,
         "t": -0.3608702939328758,
         "term": "trade_e"
       },
       {
         "ci_high": 2.5064564978937827,
         "ci_low": -0.5660250799866152,
         "coef": 0.9702157089535838,
         "p": 0.21015332447655968,
         "se": 0.7636376453402579,
         "t": 1.2705184387829385,
         "term": "unvote_key"
       },
       {
         "ci_high": 0.35329093598100364,
         "ci_low": 0.04994266980525297,
         "coef": 0.2016168028931283,
         "p": 0.010272245995103067,
         "se": 0.07539448157092168,
         "t": 2.674158621323929,
         "term": "x"
       }
     ],
     "first_stage": [
       {
         "ci_high": 1.7243983105177778,
         "ci_low": 0.3368998966929577,
         "coef": 1.0306491036053678,
         "endog": "x",
         "instrument": "z_ratio",
         "p": 0.00446407948881963,
         "se": 0.34465235419304974,
         "t": 2.9904020415542307
       },
       {
         "ci_high": 0.4983168856705443,
         "ci_low": -0.4747560604950842,
         "coef": 0.01178041258773006,
         "endog": "x",
         "instrument": "z_death",
         "p": 0.9613392159101395,
         "se": 0.24170974060651668,
         "t": 0.04873784795833938
       }
     ],
     "reduced_form": [
       {
         "ci_high": 0.45277687069189726,
         "ci_low": -0.3732109219697897,
         "coef": 0.039782974361053755,
         "instrument": "z_ratio",
         "p": 0.8471089345405091,
         "se": 0.2051740271837986,
         "t": 0.1938986864327396
       },
       {
         "ci_high": 0.4513912862000947,
         "ci_low": -0.19064539286118695,
         "coef": 0.1303729466694539,
         "instrument": "z_death",
         "p": 0.41786647218140255,
         "se": 0.15948086910367892,
         "t": 0.8174832969131747
       }
     ]
   }

   • 96 rows were dropped because one of 'y_us', 'x', 'z_ratio', 'z_death', 'usaid', 'gdppc', 'wbgi', 'fhscore', 'africa', 'trade_i', 'trade_e', 'unvote_key' was missing

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   goldsmith_doing_well 86960799c51564c41b8eff9ae72ea65bbe56b8bf41e1fd2d667fac0748f19b98

   sha256 do resultado

   3f74877bb896b89b028d407bb0db2efbecd1bbe1d511dac89b809913244648ec

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_a4aea0eareferenceFim: 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.x = 0,2016.",
       "caveats": [],
       "figure_refs": [
         "call_95801693:coef.x"
       ]
     }

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

   Números encontrados

   0,2016

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.iv_2sls): coef.x = 0,2016.

   Figuras citadas: call_95801693:coef.x · Parada: answered

   Figura da resposta: call_95801693:coef.x = 0,2016170.2016168028931283 (se 0,07539450.07539448157092168)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximapassouobrigatória0.2016168029 vs 0.202

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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