Tarefa crb_papers.q018

Veredito

Pergunta feita ao analista

By how many percentage points did Section 5 preclearance coverage change Black voter registration (share of the Black voting-age population) in covered North Carolina counties from 1960 to 1966 compared with uncovered counties?

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Verificações

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Trajetória

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1. 0Início

   By how many percentage points did Section 5 preclearance coverage change Black voter registration (share of the Black voting-age population) in covered North Carolina counties from 1960 to 1966 compared with uncovered counties?

   ds_0010 (agent)

   | Variable Name | Description | | --- | --- | | county_id | Numeric identifier for a North Carolina county (1–100). Stable across years. | | year | Calendar year of the observation (1958–1993). Not all measures are available every year. | | pop_t_i_ln | Natural logarithm of total county resident population. Population counts come from the U.S. Census via NHGIS and are linearly interpolated for non-decennial years. | | pop_b_i_perc | Share of the county’s total population that is Black (0–1). Based on U.S. Census counts via NHGIS, linearly interpolated between decennial enumerations. | | inc_hh_med_i | Median household income for the county, as reported in Census data via NHGIS. Values for non-decennial years are linearly interpolated; units reflect source-year dollars. | | educ_25p_i | Median years of schooling among county residents age 25 and older. Derived from U.S. Census data via NHGIS and linearly interpolated for non-decennial years. | | p_vreg_w | White voter registration as a share of the white voting-age population (0–1+, where values can exceed 1.0 due to unpurged names on voter rolls). Compiled from state and federal administrative sources. | | p_vreg_bnw | Black voter registration as a share of the Black voting-age population (0–1+, where values can exceed 1.0 due to unpurged names on voter rolls). Compiled from state and federal administrative sources. | | post_x_covered | Indicator equal to 1 for county-years from 1966 onward in counties subject to Section 5 preclearance of the Voting Rights Act; 0 otherwise. North Carolina had partial county coverage (40 of 100 counties), determined using a federal turnout benchmark from the 1964 election. | | tag_vreg_bnw | Auxiliary tag associated with the Black registration series. Coding details are not specified in this file; treat as a metadata flag accompanying p_vreg_bnw. | Dataset Context: This file is a county–year panel for North Carolina covering 1958–1993. It brings together historical measures of voter registration by race with core county demographics to document changes in political participation over a period spanning the passage and implementation of the Voting Rights Act of 1965 (VRA). Registration by race prior to the mid-1960s is available for 1958 and 1960 from the U.S. Commission on Civil Rights. After the VRA, registration figures were compiled from the North Carolina State Board of Elections, Inter-university Consortium for Political and Social Research (ICPSR) series, and the Voter Education Project. Post-1965 registration is observed in 1966–1968, then generally every two years from 1970 through 1988, with additional coverage in 1971 and 1993. Registration measures use the voting-age population (VAP) of the corresponding racial group as the denominator. Because county rolls were not always purged of deceased or migrated individuals (“deadwood”), the registration share can occasionally exceed 100%. Demographic measures—total population (logged), racial composition, median household income, and median years of schooling among adults 25+—are drawn from the U.S. Census via NHGIS. To provide annual coverage, values are linearly interpolated between decennial census years. Units for income reflect source-year dollars, and education is recorded in years. The period also spans a major policy environment: Section 5 of the VRA required certain jurisdictions to obtain federal approval (“preclearance”) for changes to voting practices. North Carolina was unique among the initially covered states in having partial county coverage—40 of its 100 counties—based on a federally applied turnout benchmark from the 1964 election. The dataset includes an indicator flagging county-years after 1965 in covered counties to reflect when preclearance was in effect. Overall, the panel structure allows users to observe evolving registration patterns by race alongside demographic shifts under this policy regime, with variable availability driven by the timing of administrative releases and census collections.

   file 5a85d499ade6470cd3f43aa2a2a4ccda06ae13281bb710aa464f6b1747fc6cd1 · frame —

   ref_cov (reference)

   file 5a85d499ade6470cd3f43aa2a2a4ccda06ae13281bb710aa464f6b1747fc6cd1 · frame —

   1. select {"columns":["county_id","year","post_x_covered","p_vreg_bnw"]}
   2. collapse {"aggs":[{"column":"post_x_covered","fn":"max","name":"covered"}],"by":["county_id"]}

   ref_panel (reference)

   file 5a85d499ade6470cd3f43aa2a2a4ccda06ae13281bb710aa464f6b1747fc6cd1 · frame —

   1. select {"columns":["county_id","year","post_x_covered","p_vreg_bnw"]}
   2. filter {"query":"year in [1960, 1966]"}
   3. dropna {"columns":["p_vreg_bnw"]}
   4. merge {"how":"inner","on":["county_id"],"with":"ref_cov"}
   5. merge {"how":"left","on":["county_id"],"with":"ref_years"}
   6. filter {"query":"nyears == 2"}
   7. derive {"args":{"cmp":"eq","column":"year","value":1966},"name":"post","op":"indicator"}
   8. derive {"args":{"columns":["covered","post"]},"name":"did","op":"interaction"}

   ref_years (reference)

   file 5a85d499ade6470cd3f43aa2a2a4ccda06ae13281bb710aa464f6b1747fc6cd1 · frame —

   1. select {"columns":["county_id","year","post_x_covered","p_vreg_bnw"]}
   2. filter {"query":"year in [1960, 1966]"}
   3. dropna {"columns":["p_vreg_bnw"]}
   4. collapse {"aggs":[{"column":"p_vreg_bnw","fn":"count","name":"n"}],"by":["county_id","year"]}
   5. collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["county_id"]}
2. 1Transformação
   ref_cov · select {"columns":["county_id","year","post_x_covered","p_vreg_bnw"]}

   1695 → 1695 linhas

   Frame antes → depois: 640cd4195615 → b654f624a0ee
3. 2Transformação
   ref_cov · collapse {"aggs":[{"column":"post_x_covered","fn":"max","name":"covered"}],"by":["county_id"]}

   1695 → 100 linhas

   Frame antes → depois: b654f624a0ee → 9382adf0f69e
4. 3Transformação
   ref_years · select {"columns":["county_id","year","post_x_covered","p_vreg_bnw"]}

   1695 → 1695 linhas

   Frame antes → depois: 640cd4195615 → b654f624a0ee
5. 4Transformação
   ref_years · filter {"query":"year in [1960, 1966]"}

   1695 → 198 linhas

   Frame antes → depois: b654f624a0ee → 35e6a32688cd
6. 5Transformação
   ref_years · dropna {"columns":["p_vreg_bnw"]}

   198 → 198 linhas

   Frame antes → depois: 35e6a32688cd → 35e6a32688cd
7. 6Transformação
   ref_years · collapse {"aggs":[{"column":"p_vreg_bnw","fn":"count","name":"n"}],"by":["county_id","year"]}

   198 → 198 linhas

   Frame antes → depois: 35e6a32688cd → fbe291ec584a
8. 7Transformação
   ref_years · collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["county_id"]}

   198 → 100 linhas

   Frame antes → depois: fbe291ec584a → a6e1f7e9e4d5
9. 8Transformação
   ref_panel · select {"columns":["county_id","year","post_x_covered","p_vreg_bnw"]}

   1695 → 1695 linhas

   Frame antes → depois: 640cd4195615 → b654f624a0ee
10. 9Transformação
    ref_panel · filter {"query":"year in [1960, 1966]"}

    1695 → 198 linhas

    Frame antes → depois: b654f624a0ee → 35e6a32688cd
11. 10Transformação
    ref_panel · dropna {"columns":["p_vreg_bnw"]}

    198 → 198 linhas

    Frame antes → depois: 35e6a32688cd → 35e6a32688cd
12. 11Transformação
    ref_panel · merge {"how":"inner","on":["county_id"],"with":"ref_cov"}

    198 → 198 linhas

    Frame antes → depois: 35e6a32688cd → b9cc235cb260
13. 12Transformação
    ref_panel · merge {"how":"left","on":["county_id"],"with":"ref_years"}

    198 → 198 linhas

    Frame antes → depois: b9cc235cb260 → fe5ba638bbb2
14. 13Transformação
    ref_panel · filter {"query":"nyears == 2"}

    198 → 196 linhas

    Frame antes → depois: fe5ba638bbb2 → 285f5af9e107
15. 14Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"year","value":1966},"name":"post","op":"indicator"}

    196 → 196 linhas

    Frame antes → depois: 285f5af9e107 → d11aa4aaf153
16. 15Transformação
    ref_panel · derive {"args":{"columns":["covered","post"]},"name":"did","op":"interaction"}

    196 → 196 linhas

    Frame antes → depois: d11aa4aaf153 → 60c7ae3d4cda
17. 16Turno do modelo
    call_89b7755areferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    sm.OLS(p_vreg_bnw, add_constant([covered, post, did])).fit(cov_type='cluster', groups=county_id) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "county_id",
        "controls": [
          "covered",
          "post"
        ],
        "dataset": "ref_panel",
        "x": [
          "did"
        ],
        "y": "p_vreg_bnw"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
18. 17Ferramenta
    infer.olsv0.1.0call_9c3adb53decidido pelo modeloreexecutável

    Motivo: sm.OLS(p_vreg_bnw, add_constant([covered, post, did])).fit(cov_type='cluster', groups=county_id) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Argumentos

    {
      "cluster": "county_id",
      "controls": [
        "covered",
        "post"
      ],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "did"
      ],
      "y": "p_vreg_bnw"
    }

    Figuras call_9c3adb53

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.did Coefficient on did in the regression of p_vreg_bnw	0,0348603	0.03486034259007409	0,07797850.0779784966228882	-0,119905 – 0,189626

    Diagnósticos

    • n_obs = 196196
    • k = 44
    • r2 = 0,1663980.16639788404982125
    • df_resid = 192192
    • se_type = "cluster"
    • n_clusters = 9898

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 0.6399658450366662,
          "ci_low": 0.42368933357189925,
          "coef": 0.5318275893042828,
          "p": 4.401740188156352e-16,
          "se": 0.05448530883017636,
          "t": 9.760935575531384,
          "term": "Intercept"
        },
        {
          "ci_high": 0.18962607284803346,
          "ci_low": -0.11990538766788528,
          "coef": 0.03486034259007409,
          "p": 0.6558350591475929,
          "se": 0.0779784966228882,
          "t": 0.44705071397647217,
          "term": "did"
        },
        {
          "ci_high": -0.1198118005525676,
          "ci_low": -0.39514337193609106,
          "coef": -0.2574775862443293,
          "p": 0.0003431374490498885,
          "se": 0.06936271348159266,
          "t": -3.7120460449209243,
          "term": "covered"
        },
        {
          "ci_high": 0.21900445563237192,
          "ci_low": -0.011625144566122556,
          "coef": 0.10368965553312467,
          "p": 0.07744617588042844,
          "se": 0.05810120066710136,
          "t": 1.7846387741146434,
          "term": "post"
        }
      ]
    }

    Digests dos dados

    ref_panel 60c7ae3d4cda6f6235920cb3f12694414d7caeb9c1805f3ed15620f50935699d

    sha256 do resultado

    df3e41b65e861e0ed6ec38d4629cba154d08b4c6f1e636599fa8db6737673e9d

    Resultado mostrado ao modelo
19. 18Turno do modelo
    call_1acb1cfcreferenceFim: 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 = 0,03486. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_9c3adb53:coef.did"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
20. 19Guarda
    resposta aceita

    Números encontrados

    0,03486

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
21. 20Resposta

    Estimativa (infer.ols): coef.did = 0,03486. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_9c3adb53:coef.did · Parada: answered

    Figura da resposta: call_9c3adb53:coef.did = 0,03486030.03486034259007409 (se 0,07797850.0779784966228882)
22. 21Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
23. 22Pontuação
    #1figura da resposta próximapassouobrigatória0.03486034259 in [-0.1179775108, 0.187698196]?
24. 23Pontuação
    #2figura da resposta próximapassouregistrada0.03486034259 vs 0.03486034259007323
25. 24Pontuação
    #3figura da resposta próximapassouregistrada0.07797849662 vs 0.07797849662288812

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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