Tarefa crb_papers.q062

Veredito

Pergunta feita ao analista

Did Zhejiang counties historically classified as marginal-faction counties experience larger increases in the non-state (collective and private) share of industrial output from 1965 to 1998 than dominant-faction counties?

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
   ds_0032— | Variable Name | Description | | --- | --- | | countyid | Numeric identifier for a county in Zhejiang province, China. The same identifier links its observations across years. | | year | Calendar year. The file contains rows for 1952, 1957, 1965, 1978, and 1998. The nonsoe outcome is observed only in 1965, 1978, and 1998. | | nonsoe | Share of county industrial output produced by collective and private firms, on a 0–1 scale. Calculated as total industrial output minus state-owned industrial output, divided by total industrial output. | | mfc1978 | Indicator equal to 1 for a historically classified marginal-faction county in 1978, and 0 otherwise. This column combines the county's historical classification with the year being 1978; its zeros in other years do not mean that the historical classification changed. | | mfc1998 | Indicator equal to 1 for a historically classified marginal-faction county in 1998, and 0 otherwise. This column combines the same historical county classification with the year being 1998. | Dataset Context: This extract contains 61 counties in Zhejiang province, with 305 county-year rows. Industrial statistics were compiled from provincial statistical records. The non-state sector includes collective and private firms. Non-state industrial-output shares are unavailable in the file for 1952 and 1957; the numbers of counties with observed shares are 52 in 1965, 53 in 1978, and 61 in 1998. Reporting was disrupted during the Cultural Revolution, so the observations are benchmark years rather than a continuous annual series. Historical county classifications were constructed from organizational histories and county gazetteers. In Zhejiang, counties maintaining independent guerrilla forces during 1945–1948 were classified as marginal-faction counties, reflecting the subsequent marginalization of those local political elites within the provincial power structure. Other counties form the dominant-faction category. This historical classification is constant for a county; 1978 and 1998 in the two column names identify observation years, not dates when the classification was introduced. The county-level classification can be recovered from either column's value in its named year.
   ref_panel
   ref_post
   ref_treat
   Transformação "select" aplicada à base "ref_treat" (305 → 305 linhas). {"columns":["countyid","year","nonsoe","mfc1998"]}
   Transformação "filter" aplicada à base "ref_treat" (305 → 122 linhas). {"query":"year in [1965, 1998]"}
   Transformação "dropna" aplicada à base "ref_treat" (122 → 113 linhas). {"columns":["nonsoe"]}
   Transformação "collapse" aplicada à base "ref_treat" (113 → 61 linhas). {"aggs":[{"column":"mfc1998","fn":"max","name":"treated"}],"by":["countyid"]}
   Transformação "select" aplicada à base "ref_post" (305 → 305 linhas). {"columns":["countyid","year","nonsoe","mfc1998"]}
   Transformação "filter" aplicada à base "ref_post" (305 → 122 linhas). {"query":"year in [1965, 1998]"}
   Transformação "dropna" aplicada à base "ref_post" (122 → 113 linhas). {"columns":["nonsoe"]}
   Transformação "filter" aplicada à base "ref_post" (113 → 61 linhas). {"query":"year == 1998"}
   Transformação "rename" aplicada à base "ref_post" (61 → 61 linhas). {"map":{"nonsoe":"y_post"}}
   Transformação "select" aplicada à base "ref_post" (61 → 61 linhas). {"columns":["countyid","y_post"]}
   Transformação "select" aplicada à base "ref_panel" (305 → 305 linhas). {"columns":["countyid","year","nonsoe","mfc1998"]}
   Transformação "filter" aplicada à base "ref_panel" (305 → 122 linhas). {"query":"year in [1965, 1998]"}
   Transformação "dropna" aplicada à base "ref_panel" (122 → 113 linhas). {"columns":["nonsoe"]}
   Transformação "filter" aplicada à base "ref_panel" (113 → 52 linhas). {"query":"year == 1965"}
   Transformação "rename" aplicada à base "ref_panel" (52 → 52 linhas). {"map":{"nonsoe":"y_pre"}}
   Transformação "select" aplicada à base "ref_panel" (52 → 52 linhas). {"columns":["countyid","y_pre"]}
   Transformação "merge" aplicada à base "ref_panel" (52 → 52 linhas). {"how":"inner","on":["countyid"],"with":"ref_post"}
   Transformação "merge" aplicada à base "ref_panel" (52 → 52 linhas). {"how":"inner","on":["countyid"],"with":"ref_treat"}
   Transformação "derive" aplicada à base "ref_panel" (52 → 52 linhas). {"args":{"left":"y_post","right":"y_pre"},"name":"delta","op":"diff"}
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na respostacoincide com a referência

   Ver chamada call_f72720d6 nos detalhes

   sm.OLS(delta, add_constant(treated)).fit(cov_type='HC1')

   Por que este é o caminho de referência: sm.OLS(delta, add_constant(treated)).fit(cov_type='HC1')
   Argumentos principais

   dataset:

   "ref_panel"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["treated"]

   y:

   "delta"

   Resultado calculado

   • coef.treated = -0,0401814 -0.04018143506236438
     Coefficient on treated in the regression of delta
   Diagnósticos: n_obs = 5252 (observations used) · k = 22 (parameters estimated, fixed-effect dummies included) · r2 = 0,01384780.013847813919122376 · df_resid = 5050 · 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: -0,04018
   Figuras citadas: call_f72720d6:coef.treated
   • ✓
     #0resposta com lastro

     every number traced to a tool call or the question
   • ✓
     #1figura da resposta próxima

     CRB estimate_within_solution_ci: the answer figure inside the gold 95% interval effect ± 1.96·|SE| of source crb_papers.q062.gold (closed, evaluate_result.py; no --auto-rescale)
   • ✓
     #2figura da resposta próxima

     replication cell: the gold effect to rel 1e-6 (abs floor 1e-6·SE); never required
   • ✓
     #3figura da resposta próxima

     replication cell: the gold SE to rel 1e-6; never required

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

   Did Zhejiang counties historically classified as marginal-faction counties experience larger increases in the non-state (collective and private) share of industrial output from 1965 to 1998 than dominant-faction counties?

   ds_0032 (agent)

   | Variable Name | Description | | --- | --- | | countyid | Numeric identifier for a county in Zhejiang province, China. The same identifier links its observations across years. | | year | Calendar year. The file contains rows for 1952, 1957, 1965, 1978, and 1998. The nonsoe outcome is observed only in 1965, 1978, and 1998. | | nonsoe | Share of county industrial output produced by collective and private firms, on a 0–1 scale. Calculated as total industrial output minus state-owned industrial output, divided by total industrial output. | | mfc1978 | Indicator equal to 1 for a historically classified marginal-faction county in 1978, and 0 otherwise. This column combines the county's historical classification with the year being 1978; its zeros in other years do not mean that the historical classification changed. | | mfc1998 | Indicator equal to 1 for a historically classified marginal-faction county in 1998, and 0 otherwise. This column combines the same historical county classification with the year being 1998. | Dataset Context: This extract contains 61 counties in Zhejiang province, with 305 county-year rows. Industrial statistics were compiled from provincial statistical records. The non-state sector includes collective and private firms. Non-state industrial-output shares are unavailable in the file for 1952 and 1957; the numbers of counties with observed shares are 52 in 1965, 53 in 1978, and 61 in 1998. Reporting was disrupted during the Cultural Revolution, so the observations are benchmark years rather than a continuous annual series. Historical county classifications were constructed from organizational histories and county gazetteers. In Zhejiang, counties maintaining independent guerrilla forces during 1945–1948 were classified as marginal-faction counties, reflecting the subsequent marginalization of those local political elites within the provincial power structure. Other counties form the dominant-faction category. This historical classification is constant for a county; 1978 and 1998 in the two column names identify observation years, not dates when the classification was introduced. The county-level classification can be recovered from either column's value in its named year.

   file 6eede5a4f1540d2b6513ec2483909477a548f32e838e4e0bb0a385ab6edde649 · frame —

   ref_panel (reference)

   file 6eede5a4f1540d2b6513ec2483909477a548f32e838e4e0bb0a385ab6edde649 · frame —

   1. select {"columns":["countyid","year","nonsoe","mfc1998"]}
   2. filter {"query":"year in [1965, 1998]"}
   3. dropna {"columns":["nonsoe"]}
   4. filter {"query":"year == 1965"}
   5. rename {"map":{"nonsoe":"y_pre"}}
   6. select {"columns":["countyid","y_pre"]}
   7. merge {"how":"inner","on":["countyid"],"with":"ref_post"}
   8. merge {"how":"inner","on":["countyid"],"with":"ref_treat"}
   9. derive {"args":{"left":"y_post","right":"y_pre"},"name":"delta","op":"diff"}

   ref_post (reference)

   file 6eede5a4f1540d2b6513ec2483909477a548f32e838e4e0bb0a385ab6edde649 · frame —

   1. select {"columns":["countyid","year","nonsoe","mfc1998"]}
   2. filter {"query":"year in [1965, 1998]"}
   3. dropna {"columns":["nonsoe"]}
   4. filter {"query":"year == 1998"}
   5. rename {"map":{"nonsoe":"y_post"}}
   6. select {"columns":["countyid","y_post"]}

   ref_treat (reference)

   file 6eede5a4f1540d2b6513ec2483909477a548f32e838e4e0bb0a385ab6edde649 · frame —

   1. select {"columns":["countyid","year","nonsoe","mfc1998"]}
   2. filter {"query":"year in [1965, 1998]"}
   3. dropna {"columns":["nonsoe"]}
   4. collapse {"aggs":[{"column":"mfc1998","fn":"max","name":"treated"}],"by":["countyid"]}
2. 1Transformação
   ref_treat · select {"columns":["countyid","year","nonsoe","mfc1998"]}

   305 → 305 linhas

   Frame antes → depois: d968a5cceb73 → 1f4ce73d8d48
3. 2Transformação
   ref_treat · filter {"query":"year in [1965, 1998]"}

   305 → 122 linhas

   Frame antes → depois: 1f4ce73d8d48 → 5ec4c1983546
4. 3Transformação
   ref_treat · dropna {"columns":["nonsoe"]}

   122 → 113 linhas

   Frame antes → depois: 5ec4c1983546 → 826695938e0c
5. 4Transformação
   ref_treat · collapse {"aggs":[{"column":"mfc1998","fn":"max","name":"treated"}],"by":["countyid"]}

   113 → 61 linhas

   Frame antes → depois: 826695938e0c → a7b160ede862
6. 5Transformação
   ref_post · select {"columns":["countyid","year","nonsoe","mfc1998"]}

   305 → 305 linhas

   Frame antes → depois: d968a5cceb73 → 1f4ce73d8d48
7. 6Transformação
   ref_post · filter {"query":"year in [1965, 1998]"}

   305 → 122 linhas

   Frame antes → depois: 1f4ce73d8d48 → 5ec4c1983546
8. 7Transformação
   ref_post · dropna {"columns":["nonsoe"]}

   122 → 113 linhas

   Frame antes → depois: 5ec4c1983546 → 826695938e0c
9. 8Transformação
   ref_post · filter {"query":"year == 1998"}

   113 → 61 linhas

   Frame antes → depois: 826695938e0c → f954d9b27048
10. 9Transformação
    ref_post · rename {"map":{"nonsoe":"y_post"}}

    61 → 61 linhas

    Frame antes → depois: f954d9b27048 → dc3857ff2af1
11. 10Transformação
    ref_post · select {"columns":["countyid","y_post"]}

    61 → 61 linhas

    Frame antes → depois: dc3857ff2af1 → 54a7e60bad0d
12. 11Transformação
    ref_panel · select {"columns":["countyid","year","nonsoe","mfc1998"]}

    305 → 305 linhas

    Frame antes → depois: d968a5cceb73 → 1f4ce73d8d48
13. 12Transformação
    ref_panel · filter {"query":"year in [1965, 1998]"}

    305 → 122 linhas

    Frame antes → depois: 1f4ce73d8d48 → 5ec4c1983546
14. 13Transformação
    ref_panel · dropna {"columns":["nonsoe"]}

    122 → 113 linhas

    Frame antes → depois: 5ec4c1983546 → 826695938e0c
15. 14Transformação
    ref_panel · filter {"query":"year == 1965"}

    113 → 52 linhas

    Frame antes → depois: 826695938e0c → ea67b198fa5c
16. 15Transformação
    ref_panel · rename {"map":{"nonsoe":"y_pre"}}

    52 → 52 linhas

    Frame antes → depois: ea67b198fa5c → ae4cd0921e69
17. 16Transformação
    ref_panel · select {"columns":["countyid","y_pre"]}

    52 → 52 linhas

    Frame antes → depois: ae4cd0921e69 → 67bf5384a0c5
18. 17Transformação
    ref_panel · merge {"how":"inner","on":["countyid"],"with":"ref_post"}

    52 → 52 linhas

    Frame antes → depois: 67bf5384a0c5 → a5b203fedac5
19. 18Transformação
    ref_panel · merge {"how":"inner","on":["countyid"],"with":"ref_treat"}

    52 → 52 linhas

    Frame antes → depois: a5b203fedac5 → fc79873206f6
20. 19Transformação
    ref_panel · derive {"args":{"left":"y_post","right":"y_pre"},"name":"delta","op":"diff"}

    52 → 52 linhas

    Frame antes → depois: fc79873206f6 → 49a9a71b09f5
21. 20Turno do modelo
    call_2280c480referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    sm.OLS(delta, add_constant(treated)).fit(cov_type='HC1')

    Chamadas pedidas

    • infer.ols

      {
        "dataset": "ref_panel",
        "se": "HC1",
        "x": [
          "treated"
        ],
        "y": "delta"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
22. 21Ferramenta
    infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_f72720d6decidido pelo modeloreexecutável

    Motivo: sm.OLS(delta, add_constant(treated)).fit(cov_type='HC1')

    Argumentos

    {
      "cluster": null,
      "controls": [],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "treated"
      ],
      "y": "delta"
    }

    Figuras call_f72720d6

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.treated Coefficient on treated in the regression of delta	-0,0401814	-0.04018143506236438	0,04936980.049369793828992044	-0,139344 – 0,0589807

    Diagnósticos

    • n_obs = 5252 (observations used)
    • k = 22 (parameters estimated, fixed-effect dummies included)
    • r2 = 0,01384780.013847813919122376
    • df_resid = 5050
    • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
    • n_clusters = — (errors not clustered)

    Derivação matemática e pressupostos

    coef.treated

    1. Coeficiente de treated na regressão por mínimos quadrados ordinários de delta.→ -0,0401814 (-0.04018143506236438)
    2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,0493698 (0.049369793828992044)
    3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 50 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": 0.7404125360499775,
          "ci_low": 0.5764718671160406,
          "coef": 0.658442201583009,
          "p": 1.845847113772468e-21,
          "se": 0.04081051634135644,
          "t": 16.134130626417946,
          "term": "Intercept"
        },
        {
          "ci_high": 0.0589807141953935,
          "ci_low": -0.13934358432012225,
          "coef": -0.04018143506236438,
          "p": 0.41956890802937447,
          "se": 0.049369793828992044,
          "t": -0.813887033872281,
          "term": "treated"
        }
      ]
    }

    Hashes SHA-256 e metadados de reprodução

    Digests dos dados

    ref_panel 49a9a71b09f5e6d0c38422c8ea1cf5800835bce8819e47e9be6c7906db3df3b5

    sha256 do resultado

    34ce8a4fb962557ebcb6de71019aff79a32c04d15040a61b5a503bdb6c78baa9

    Resultado mostrado ao modelo
23. 22Turno do modelo
    call_6b068c3areferenceFim: 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.treated = -0,04018. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_f72720d6:coef.treated"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
24. 23Guarda
    resposta aceita

    Números encontrados

    -0,04018

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
25. 24Resposta

    Estimativa (infer.ols): coef.treated = -0,04018. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_f72720d6:coef.treated · Parada: answered

    Figura da resposta: call_f72720d6:coef.treated = -0,0401814-0.04018143506236438 (se 0,04936980.049369793828992044)
26. 25Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
27. 26Pontuação
    #1figura da resposta próximapassouobrigatória-0.04018143506 in [-0.136946231, 0.05658336084]?
28. 27Pontuação
    #2figura da resposta próximapassouregistrada-0.04018143506 vs -0.04018143506236438
29. 28Pontuação
    #3figura da resposta próximapassouregistrada0.04936979383 vs 0.04936979382899201

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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