Tarefa causcibench_real.q087

Veredito

Pergunta feita ao analista

Does having a strategic partnership with China increase the total Chinese foreign aid a country receives during COVID-19?

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
   urdinez_china— This dataset contains information on 537 donations across 33 Latin American and Caribbean countries between 11 February and 20 June 2020. It details China's and Taiwan's mask diplomacy, including donors, recipients, donated items, and the political determinants of aid. The data highlight the influence of strategic partnerships, the One China Policy, and regime type in allocation decisions. Data variables: cow_code: country code donor code: Donor code (1 = central government, 2 = provincial government, 3 = municipal government, 4 = university, 5 = enterprise, 6 = individual, 7 = foundation, 8 = other, 100 = Taiwan) amount_usd: Total value of donations (in millions) country: Name of the recipient country iso_code: iso_code of the country str_partnership: Partnership status with China (0 = No partnership, 1 = Partnership, 2 = Strategic Partnership, 3 = Comprehensive Strategic Partnership) taiwan: dummy variable that is 1 if the country has diplomatic relations with Taiwan gdp_per_capita_log: the logarithm of gdp per capita total_deaths_log: total deaths due to COVID-19 chinese_exports: total Chinese exports chinese_exports_log: logarithm of the total Chinese exports to the country democracy: Discrete variable for democracy status (0 = Autocracy; 1 = Anocracy; 2 = Weak democracy; 3 = Full democracy) amount_usd_log: logarithm of total donations customs: total customs customs_log: logarithm of total customs agree_us: Measure of affinity with the United States agree_cn: Measure of affinity with China total_china: Total donations from China (in millions) total_china_ln: Logarithm of total donations from China
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na respostacoincide com a referência

   Ver chamada call_555e513e nos detalhes

   causcibench_metadata_rule/1: ols

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

   controls:

   ["democracy","taiwan","agree_us","chinese_exports_log","total_deaths_log","gdp_per_capita_log"]

   dataset:

   "urdinez_china"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["str_partnership"]

   y:

   "total_china_ln"

   Resultado calculado

   • coef.str_partnership = 0,47986 0.4798602909914369
     Coefficient on str_partnership in the regression of total_china_ln
   Diagnósticos: n_obs = 264264 (observations used) · k = 88 (parameters estimated, fixed-effect dummies included) · r2 = 0,8032330.8032331306426179 · df_resid = 256256 · 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: 0,4799
   Figuras citadas: call_555e513e:coef.str_partnership
   • ✓
     #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 having a strategic partnership with China increase the total Chinese foreign aid a country receives during COVID-19?

   urdinez_china (agent)

   This dataset contains information on 537 donations across 33 Latin American and Caribbean countries between 11 February and 20 June 2020. It details China's and Taiwan's mask diplomacy, including donors, recipients, donated items, and the political determinants of aid. The data highlight the influence of strategic partnerships, the One China Policy, and regime type in allocation decisions. Data variables: cow_code: country code donor code: Donor code (1 = central government, 2 = provincial government, 3 = municipal government, 4 = university, 5 = enterprise, 6 = individual, 7 = foundation, 8 = other, 100 = Taiwan) amount_usd: Total value of donations (in millions) country: Name of the recipient country iso_code: iso_code of the country str_partnership: Partnership status with China (0 = No partnership, 1 = Partnership, 2 = Strategic Partnership, 3 = Comprehensive Strategic Partnership) taiwan: dummy variable that is 1 if the country has diplomatic relations with Taiwan gdp_per_capita_log: the logarithm of gdp per capita total_deaths_log: total deaths due to COVID-19 chinese_exports: total Chinese exports chinese_exports_log: logarithm of the total Chinese exports to the country democracy: Discrete variable for democracy status (0 = Autocracy; 1 = Anocracy; 2 = Weak democracy; 3 = Full democracy) amount_usd_log: logarithm of total donations customs: total customs customs_log: logarithm of total customs agree_us: Measure of affinity with the United States agree_cn: Measure of affinity with China total_china: Total donations from China (in millions) total_china_ln: Logarithm of total donations from China

   file f74c78cc3d6c50207291c4826731647739aeb407a77c31665ce9255c74deb420 · frame —
2. 1Turno do modelo
   call_c116708creferenceFim: 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": [
         "democracy",
         "taiwan",
         "agree_us",
         "chinese_exports_log",
         "total_deaths_log",
         "gdp_per_capita_log"
       ],
       "dataset": "urdinez_china",
       "x": [
         "str_partnership"
       ],
       "y": "total_china_ln"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_555e513edecidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: ols

   Argumentos

   {
     "cluster": null,
     "controls": [
       "democracy",
       "taiwan",
       "agree_us",
       "chinese_exports_log",
       "total_deaths_log",
       "gdp_per_capita_log"
     ],
     "dataset": "urdinez_china",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "str_partnership"
     ],
     "y": "total_china_ln"
   }

   Figuras call_555e513e

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.str_partnership Coefficient on str_partnership in the regression of total_china_ln	0,47986	0.4798602909914369	0,04112960.04112962772367135	0,398865 – 0,560856

   Diagnósticos

   • n_obs = 264264 (observations used)
   • k = 88 (parameters estimated, fixed-effect dummies included)
   • r2 = 0,8032330.8032331306426179
   • df_resid = 256256
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   coef.str_partnership

   1. Coeficiente de str_partnership na regressão por mínimos quadrados ordinários de total_china_ln.→ 0,47986 (0.4798602909914369)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,0411296 (0.04112962772367135)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 256 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": 1.4832780649830488,
         "ci_low": -0.28768607734138363,
         "coef": 0.5977959938208326,
         "p": 0.1848756210394685,
         "se": 0.4496490182566915,
         "t": 1.3294724764184147,
         "term": "Intercept"
       },
       {
         "ci_high": 0.5608557929589215,
         "ci_low": 0.3988647890239522,
         "coef": 0.4798602909914369,
         "p": 1.6641866086638344e-25,
         "se": 0.04112962772367135,
         "t": 11.667022473808164,
         "term": "str_partnership"
       },
       {
         "ci_high": -0.4793503984418121,
         "ci_low": -0.8038953947446383,
         "coef": -0.6416228965932252,
         "p": 1.7126788999431735e-13,
         "se": 0.08240219859909138,
         "t": -7.786477879247996,
         "term": "democracy"
       },
       {
         "ci_high": -0.6343627051724173,
         "ci_low": -0.8420121437897367,
         "coef": -0.738187424481077,
         "p": 1.8567620111987278e-33,
         "se": 0.05272233580815547,
         "t": -14.001417296213361,
         "term": "taiwan"
       },
       {
         "ci_high": 3.0431052453234155,
         "ci_low": 1.0442732260676377,
         "coef": 2.0436892356955267,
         "p": 0.00007450758329668867,
         "se": 0.5075048295098399,
         "t": 4.026935542011236,
         "term": "agree_us"
       },
       {
         "ci_high": -0.022586674675154322,
         "ci_low": -0.047335442058918833,
         "coef": -0.03496105836703658,
         "p": 6.64478429095237e-8,
         "se": 0.00628372912314692,
         "t": -5.563743707260239,
         "term": "chinese_exports_log"
       },
       {
         "ci_high": 0.13679347638300088,
         "ci_low": 0.06108791719099864,
         "coef": 0.09894069678699977,
         "p": 5.26802646688378e-7,
         "se": 0.019221693739421588,
         "t": 5.147345396731779,
         "term": "total_deaths_log"
       },
       {
         "ci_high": 0.1898211385389041,
         "ci_low": 0.018930418924878484,
         "coef": 0.10437577873189129,
         "p": 0.01685728632392524,
         "se": 0.043389271678177924,
         "t": 2.4055665074550157,
         "term": "gdp_per_capita_log"
       }
     ]
   }

   • 33 rows were dropped because one of 'total_china_ln', 'str_partnership', 'democracy', 'taiwan', 'agree_us', 'chinese_exports_log', 'total_deaths_log', 'gdp_per_capita_log' was missing

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   urdinez_china 1ad6a4f8dd4dac236433e58763ee29509e326480601d43cae0c5921dcf42e743

   sha256 do resultado

   cc3d7e2f61f121186279e75f37ca9c5faaf32ec5c231eddba7d3088d0bd29edd

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_c81680c7referenceFim: 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.str_partnership = 0,4799.",
       "caveats": [],
       "figure_refs": [
         "call_555e513e:coef.str_partnership"
       ]
     }

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

   Números encontrados

   0,4799

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.ols): coef.str_partnership = 0,4799.

   Figuras citadas: call_555e513e:coef.str_partnership · Parada: answered

   Figura da resposta: call_555e513e:coef.str_partnership = 0,479860.4798602909914369 (se 0,04112960.04112962772367135)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximapassouobrigatória0.479860291 vs 0.477

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)

Downloads desta tarefa