Tarefa crb_papers.q046

Veredito

Pergunta feita ao analista

How did county presidential-election turnout change between 1992 and 1996 in counties that experienced a recorded Black church arson incident, compared with counties that did not experience such incidents during that period?

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

   How did county presidential-election turnout change between 1992 and 1996 in counties that experienced a recorded Black church arson incident, compared with counties that did not experience such incidents during that period?

   ds_0024 (agent)

   | Variable Name | Description | | --- | --- | | turnout | County presidential-election turnout rate in the general election, measured as total ballots cast divided by the county voting-age population (unit interval 0–1). Votes and voting-age population were assembled from standard U.S. election and Census tabulations for each election year. | | tr_ar | Indicator (0/1) that at least one Black church arson incident was recorded in the county within the election-cycle window associated with that year. Incidents are drawn from the U.S. Department of Justice’s National Church Arson Task Force records (primarily 1995–2000) and mapped to the relevant presidential-election cycle for comparability across counties and years. | | perc_bl | Share of county residents identifying as Black or African American in that year (0–1), based on U.S. Census/ACS population statistics for the county. | | pop_tot | Total county population (persons) in that year, from U.S. Census/ACS population counts. | | income | County median household income in U.S. dollars for that year (nominal dollars as reported by the Census/ACS). | | fips | Five-digit Federal Information Processing Standards (FIPS) code uniquely identifying the county (state–county concatenated code). | | year | Presidential election year for the observation (e.g., 1980, 1984, …, 2016). | Dataset Context: This file is a county-by–presidential-election-year panel covering U.S. general elections from 1980 through 2016. Each record is keyed by a county’s five-digit FIPS code and the presidential election year. Turnout is constructed from county-level counts of ballots cast in the presidential general election combined with county voting-age population from the U.S. Census Bureau. Using voting-age population standardizes the denominator across states, which vary in registration rules and reporting conventions, but note that VAP includes individuals ineligible to vote (e.g., noncitizens), a common feature of administrative turnout measures. Demographic indicators—total population, the share identifying as Black or African American, and median household income—are taken from decennial Census and American Community Survey tabulations aligned to the election year. Income is recorded in nominal dollars for the reported year. The Black church arson indicator comes from administrative records compiled by the U.S. Department of Justice’s National Church Arson Task Force established in the mid-1990s. Those incident locations are assigned to counties and linked to the presidential-election cycle that encompasses the incident window to ensure a consistent temporal frame across units. Because the Task Force’s comprehensive catalog primarily spans 1995–2000, county-years outside that period are generally coded as having no recorded incident. Users should be aware that counties occasionally experience boundary changes or renamings over long panels; the dataset relies on FIPS coding to maintain consistent identification. As with most historical administrative compilations, reporting conventions can vary across states and years; the assembly here harmonizes these inputs to produce a cohesive, national county-level panel suitable for studying election-period patterns alongside population and community characteristics.

   file d73be27ea1d43f15d8e48b09dfc8299de15152438ea0d0b908fcad88c89b3c43 · frame —

   ref_evp (reference)

   file d73be27ea1d43f15d8e48b09dfc8299de15152438ea0d0b908fcad88c89b3c43 · frame —

   1. select {"columns":["turnout","tr_ar","perc_bl","pop_tot","income","fips","year"]}
   2. dropna {"columns":["turnout","tr_ar","fips","year"]}
   3. filter {"query":"year <= 1996"}
   4. collapse {"aggs":[{"column":"tr_ar","fn":"max","name":"evp"}],"by":["fips"]}

   ref_panel (reference)

   file d73be27ea1d43f15d8e48b09dfc8299de15152438ea0d0b908fcad88c89b3c43 · frame —

   1. select {"columns":["turnout","tr_ar","perc_bl","pop_tot","income","fips","year"]}
   2. dropna {"columns":["turnout","tr_ar","fips","year"]}
   3. filter {"query":"year in [1992, 1996]"}
   4. merge {"how":"left","on":["fips"],"with":"ref_years"}
   5. filter {"query":"nyears == 2"}
   6. merge {"how":"inner","on":["fips"],"with":"ref_pre"}
   7. merge {"how":"inner","on":["fips"],"with":"ref_post"}
   8. merge {"how":"left","on":["fips"],"with":"ref_evp"}
   9. filter {"query":"tr_pre == 0 and (tr_post == 1 or (tr_post == 0 and evp == 0))"}
   10. derive {"args":{"cmp":"eq","column":"tr_post","value":1},"name":"treated_group","op":"indicator"}
   11. derive {"args":{"cmp":"eq","column":"year","value":1996},"name":"post","op":"indicator"}
   12. derive {"args":{"columns":["post","perc_bl_pre"]},"name":"post_x_perc_bl_pre","op":"product"}
   13. derive {"args":{"columns":["post","pop_tot_pre"]},"name":"post_x_pop_tot_pre","op":"product"}
   14. derive {"args":{"columns":["post","income_pre"]},"name":"post_x_income_pre","op":"product"}
   15. derive {"args":{"columns":["treated_group","post"]},"name":"did","op":"interaction"}

   ref_post (reference)

   file d73be27ea1d43f15d8e48b09dfc8299de15152438ea0d0b908fcad88c89b3c43 · frame —

   1. select {"columns":["turnout","tr_ar","perc_bl","pop_tot","income","fips","year"]}
   2. dropna {"columns":["turnout","tr_ar","fips","year"]}
   3. filter {"query":"year == 1996"}
   4. select {"columns":["fips","tr_ar"]}
   5. rename {"map":{"tr_ar":"tr_post"}}

   ref_pre (reference)

   file d73be27ea1d43f15d8e48b09dfc8299de15152438ea0d0b908fcad88c89b3c43 · frame —

   1. select {"columns":["turnout","tr_ar","perc_bl","pop_tot","income","fips","year"]}
   2. dropna {"columns":["turnout","tr_ar","fips","year"]}
   3. filter {"query":"year == 1992"}
   4. select {"columns":["fips","tr_ar","perc_bl","pop_tot","income"]}
   5. rename {"map":{"income":"income_pre","perc_bl":"perc_bl_pre","pop_tot":"pop_tot_pre","tr_ar":"tr_pre"}}

   ref_years (reference)

   file d73be27ea1d43f15d8e48b09dfc8299de15152438ea0d0b908fcad88c89b3c43 · frame —

   1. select {"columns":["turnout","tr_ar","perc_bl","pop_tot","income","fips","year"]}
   2. dropna {"columns":["turnout","tr_ar","fips","year"]}
   3. filter {"query":"year in [1992, 1996]"}
   4. collapse {"aggs":[{"column":"turnout","fn":"count","name":"n"}],"by":["fips","year"]}
   5. collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["fips"]}
2. 1Transformação
   ref_years · select {"columns":["turnout","tr_ar","perc_bl","pop_tot","income","fips","year"]}

   27068 → 27068 linhas

   Frame antes → depois: 7cb9b7f722be → 7cb9b7f722be
3. 2Transformação
   ref_years · dropna {"columns":["turnout","tr_ar","fips","year"]}

   27068 → 27068 linhas

   Frame antes → depois: 7cb9b7f722be → 7cb9b7f722be
4. 3Transformação
   ref_years · filter {"query":"year in [1992, 1996]"}

   27068 → 5766 linhas

   Frame antes → depois: 7cb9b7f722be → 8f00fce3da38
5. 4Transformação
   ref_years · collapse {"aggs":[{"column":"turnout","fn":"count","name":"n"}],"by":["fips","year"]}

   5766 → 5766 linhas

   Frame antes → depois: 8f00fce3da38 → 67f643a4c73a
6. 5Transformação
   ref_years · collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["fips"]}

   5766 → 2884 linhas

   Frame antes → depois: 67f643a4c73a → 4ac28a548a05
7. 6Transformação
   ref_evp · select {"columns":["turnout","tr_ar","perc_bl","pop_tot","income","fips","year"]}

   27068 → 27068 linhas

   Frame antes → depois: 7cb9b7f722be → 7cb9b7f722be
8. 7Transformação
   ref_evp · dropna {"columns":["turnout","tr_ar","fips","year"]}

   27068 → 27068 linhas

   Frame antes → depois: 7cb9b7f722be → 7cb9b7f722be
9. 8Transformação
   ref_evp · filter {"query":"year <= 1996"}

   27068 → 14400 linhas

   Frame antes → depois: 7cb9b7f722be → a018b48e55a7
10. 9Transformação
    ref_evp · collapse {"aggs":[{"column":"tr_ar","fn":"max","name":"evp"}],"by":["fips"]}

    14400 → 2889 linhas

    Frame antes → depois: a018b48e55a7 → 79a35451bda6
11. 10Transformação
    ref_pre · select {"columns":["turnout","tr_ar","perc_bl","pop_tot","income","fips","year"]}

    27068 → 27068 linhas

    Frame antes → depois: 7cb9b7f722be → 7cb9b7f722be
12. 11Transformação
    ref_pre · dropna {"columns":["turnout","tr_ar","fips","year"]}

    27068 → 27068 linhas

    Frame antes → depois: 7cb9b7f722be → 7cb9b7f722be
13. 12Transformação
    ref_pre · filter {"query":"year == 1992"}

    27068 → 2882 linhas

    Frame antes → depois: 7cb9b7f722be → 905b6e5130d9
14. 13Transformação
    ref_pre · select {"columns":["fips","tr_ar","perc_bl","pop_tot","income"]}

    2882 → 2882 linhas

    Frame antes → depois: 905b6e5130d9 → d425b9251fd3
15. 14Transformação
    ref_pre · rename {"map":{"income":"income_pre","perc_bl":"perc_bl_pre","pop_tot":"pop_tot_pre","tr_ar":"tr_pre"}}

    2882 → 2882 linhas

    Frame antes → depois: d425b9251fd3 → fd371983f9de
16. 15Transformação
    ref_post · select {"columns":["turnout","tr_ar","perc_bl","pop_tot","income","fips","year"]}

    27068 → 27068 linhas

    Frame antes → depois: 7cb9b7f722be → 7cb9b7f722be
17. 16Transformação
    ref_post · dropna {"columns":["turnout","tr_ar","fips","year"]}

    27068 → 27068 linhas

    Frame antes → depois: 7cb9b7f722be → 7cb9b7f722be
18. 17Transformação
    ref_post · filter {"query":"year == 1996"}

    27068 → 2884 linhas

    Frame antes → depois: 7cb9b7f722be → 87c1db8f0e6e
19. 18Transformação
    ref_post · select {"columns":["fips","tr_ar"]}

    2884 → 2884 linhas

    Frame antes → depois: 87c1db8f0e6e → 9d43066f5d64
20. 19Transformação
    ref_post · rename {"map":{"tr_ar":"tr_post"}}

    2884 → 2884 linhas

    Frame antes → depois: 9d43066f5d64 → fbefe8eb0909
21. 20Transformação
    ref_panel · select {"columns":["turnout","tr_ar","perc_bl","pop_tot","income","fips","year"]}

    27068 → 27068 linhas

    Frame antes → depois: 7cb9b7f722be → 7cb9b7f722be
22. 21Transformação
    ref_panel · dropna {"columns":["turnout","tr_ar","fips","year"]}

    27068 → 27068 linhas

    Frame antes → depois: 7cb9b7f722be → 7cb9b7f722be
23. 22Transformação
    ref_panel · filter {"query":"year in [1992, 1996]"}

    27068 → 5766 linhas

    Frame antes → depois: 7cb9b7f722be → 8f00fce3da38
24. 23Transformação
    ref_panel · merge {"how":"left","on":["fips"],"with":"ref_years"}

    5766 → 5766 linhas

    Frame antes → depois: 8f00fce3da38 → ed34a9b99b09
25. 24Transformação
    ref_panel · filter {"query":"nyears == 2"}

    5766 → 5764 linhas

    Frame antes → depois: ed34a9b99b09 → 3d4bcc6a7f7a
26. 25Transformação
    ref_panel · merge {"how":"inner","on":["fips"],"with":"ref_pre"}

    5764 → 5764 linhas

    Frame antes → depois: 3d4bcc6a7f7a → 1189d98953e0
27. 26Transformação
    ref_panel · merge {"how":"inner","on":["fips"],"with":"ref_post"}

    5764 → 5764 linhas

    Frame antes → depois: 1189d98953e0 → 95092d1f4998
28. 27Transformação
    ref_panel · merge {"how":"left","on":["fips"],"with":"ref_evp"}

    5764 → 5764 linhas

    Frame antes → depois: 95092d1f4998 → 80678c6f4c17
29. 28Transformação
    ref_panel · filter {"query":"tr_pre == 0 and (tr_post == 1 or (tr_post == 0 and evp == 0))"}

    5764 → 5764 linhas

    Frame antes → depois: 80678c6f4c17 → 80678c6f4c17
30. 29Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"tr_post","value":1},"name":"treated_group","op":"indicator"}

    5764 → 5764 linhas

    Frame antes → depois: 80678c6f4c17 → 4039fa67e292
31. 30Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"year","value":1996},"name":"post","op":"indicator"}

    5764 → 5764 linhas

    Frame antes → depois: 4039fa67e292 → 9f9b1692eb94
32. 31Transformação
    ref_panel · derive {"args":{"columns":["post","perc_bl_pre"]},"name":"post_x_perc_bl_pre","op":"product"}

    5764 → 5764 linhas

    Frame antes → depois: 9f9b1692eb94 → c62ad73619b7
33. 32Transformação
    ref_panel · derive {"args":{"columns":["post","pop_tot_pre"]},"name":"post_x_pop_tot_pre","op":"product"}

    5764 → 5764 linhas

    Frame antes → depois: c62ad73619b7 → 41d49c5de852
34. 33Transformação
    ref_panel · derive {"args":{"columns":["post","income_pre"]},"name":"post_x_income_pre","op":"product"}

    5764 → 5764 linhas

    Frame antes → depois: 41d49c5de852 → 80769bb1d393
35. 34Transformação
    ref_panel · derive {"args":{"columns":["treated_group","post"]},"name":"did","op":"interaction"}

    5764 → 5764 linhas

    Frame antes → depois: 80769bb1d393 → 92dd38bde845
36. 35Turno do modelo
    call_1d6d251freferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    sm.OLS(turnout, add_constant([treated_group, post, did, post_x_*_pre]), missing='drop').fit(cov_type='cluster', groups=fips) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "fips",
        "controls": [
          "treated_group",
          "post",
          "post_x_perc_bl_pre",
          "post_x_pop_tot_pre",
          "post_x_income_pre"
        ],
        "dataset": "ref_panel",
        "x": [
          "did"
        ],
        "y": "turnout"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
37. 36Ferramenta
    infer.olsv0.1.0call_14fb399fdecidido pelo modeloreexecutável

    Motivo: sm.OLS(turnout, add_constant([treated_group, post, did, post_x_*_pre]), missing='drop').fit(cov_type='cluster', groups=fips) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Argumentos

    {
      "cluster": "fips",
      "controls": [
        "treated_group",
        "post",
        "post_x_perc_bl_pre",
        "post_x_pop_tot_pre",
        "post_x_income_pre"
      ],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "did"
      ],
      "y": "turnout"
    }

    Figuras call_14fb399f

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.did Coefficient on did in the regression of turnout	0,05766	0.05765999456111044	0,00773840.007738399871744608	0,0424866 – 0,0728334

    Diagnósticos

    • n_obs = 5.7645764
    • k = 77
    • r2 = 0,1696620.1696623041900176
    • df_resid = 5.7575757
    • se_type = "cluster"
    • n_clusters = 2.8822882

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 0.6063211735601218,
          "ci_low": 0.5989054359923606,
          "coef": 0.6026133047762412,
          "p": 0,
          "se": 0.0018910097714945715,
          "t": 318.6727609027435,
          "term": "Intercept"
        },
        {
          "ci_high": 0.07283335418841155,
          "ci_low": 0.04248663493380934,
          "coef": 0.05765999456111044,
          "p": 1.216414531941813e-13,
          "se": 0.007738399871744608,
          "t": 7.451152113713543,
          "term": "did"
        },
        {
          "ci_high": -0.03388722607021363,
          "ci_low": -0.06555998821438755,
          "coef": -0.04972360714230059,
          "p": 8.46731558566046e-10,
          "se": 0.008076540217015486,
          "t": -6.156547953236701,
          "term": "treated_group"
        },
        {
          "ci_high": -0.08245918420606417,
          "ci_low": -0.11250607808434014,
          "coef": -0.09748263114520216,
          "p": 4.05598926650631e-36,
          "se": 0.007661944534538532,
          "t": -12.722962259224106,
          "term": "post"
        },
        {
          "ci_high": -0.18047064993595477,
          "ci_low": -0.23684241302805706,
          "coef": -0.2086565314820059,
          "p": 3.948288315512396e-46,
          "se": 0.014374774440099845,
          "t": -14.515464736610964,
          "term": "post_x_perc_bl_pre"
        },
        {
          "ci_high": -3.863512790398138e-8,
          "ci_low": -9.18500837473365e-8,
          "coef": -6.524260582565894e-8,
          "p": 0.0000016032842997875152,
          "se": 1.3569789999973824e-8,
          "t": -4.807930397285793,
          "term": "post_x_pop_tot_pre"
        },
        {
          "ci_high": 0.0000024237011021242226,
          "ci_low": 0.0000013600900051754614,
          "coef": 0.0000018918955536498419,
          "p": 3.76576106548367e-12,
          "se": 2.7122035522723663e-7,
          "t": 6.975492499686296,
          "term": "post_x_income_pre"
        }
      ]
    }

    Digests dos dados

    ref_panel 92dd38bde845d95aa9c611a059b0cfc780c1246546a5f5e387c04a84148123d0

    sha256 do resultado

    bb5babf3ba44c5ea5e20e6d65bb176053e5252cc04cfc06b8b52863399e557d2

    Resultado mostrado ao modelo
38. 37Turno do modelo
    call_15f55f46referenceFim: 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.did = 0,05766. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_14fb399f:coef.did"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
39. 38Guarda
    resposta aceita

    Números encontrados

    0,05766

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
40. 39Resposta

    Estimativa (infer.ols): coef.did = 0,05766. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_14fb399f:coef.did · Parada: answered

    Figura da resposta: call_14fb399f:coef.did = 0,057660.05765999456111044 (se 0,00773840.007738399871744608)
41. 40Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
42. 41Pontuação
    #1figura da resposta próximapassouobrigatória0.05765999456 in [0.04249273081, 0.07282725831]?
43. 42Pontuação
    #2figura da resposta próximapassouregistrada0.05765999456 vs 0.05765999455808379
44. 43Pontuação
    #3figura da resposta próximapassouregistrada0.007738399872 vs 0.007738399871299681

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