Tarefa crb_papers.q019

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 1970 compared with uncovered 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_0010— | 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.
   ref_cov
   ref_panel
   ref_years
   Transformação "select" aplicada à base "ref_cov" (1695 → 1695 linhas). {"columns":["county_id","year","post_x_covered","p_vreg_bnw"]}
   Transformação "collapse" aplicada à base "ref_cov" (1695 → 100 linhas). {"aggs":[{"column":"post_x_covered","fn":"max","name":"covered"}],"by":["county_id"]}
   Transformação "select" aplicada à base "ref_years" (1695 → 1695 linhas). {"columns":["county_id","year","post_x_covered","p_vreg_bnw"]}
   Transformação "filter" aplicada à base "ref_years" (1695 → 200 linhas). {"query":"year in [1960, 1970]"}
   Transformação "dropna" aplicada à base "ref_years" (200 → 200 linhas). {"columns":["p_vreg_bnw"]}
   Transformação "collapse" aplicada à base "ref_years" (200 → 200 linhas). {"aggs":[{"column":"p_vreg_bnw","fn":"count","name":"n"}],"by":["county_id","year"]}
   Transformação "collapse" aplicada à base "ref_years" (200 → 100 linhas). {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["county_id"]}
   Transformação "select" aplicada à base "ref_panel" (1695 → 1695 linhas). {"columns":["county_id","year","post_x_covered","p_vreg_bnw"]}
   Transformação "filter" aplicada à base "ref_panel" (1695 → 200 linhas). {"query":"year in [1960, 1970]"}
   Transformação "dropna" aplicada à base "ref_panel" (200 → 200 linhas). {"columns":["p_vreg_bnw"]}
   Transformação "merge" aplicada à base "ref_panel" (200 → 200 linhas). {"how":"inner","on":["county_id"],"with":"ref_cov"}
   Transformação "merge" aplicada à base "ref_panel" (200 → 200 linhas). {"how":"left","on":["county_id"],"with":"ref_years"}
   Transformação "filter" aplicada à base "ref_panel" (200 → 200 linhas). {"query":"nyears == 2"}
   Transformação "derive" aplicada à base "ref_panel" (200 → 200 linhas). {"args":{"cmp":"eq","column":"year","value":1970},"name":"post","op":"indicator"}
   Transformação "derive" aplicada à base "ref_panel" (200 → 200 linhas). {"args":{"columns":["covered","post"]},"name":"did","op":"interaction"}
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na respostacoincide com a referência

   Ver chamada call_720d452d nos detalhes

   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)

   Por que este é o caminho de referência: 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 principais

   cluster:

   "county_id"

   controls:

   ["covered","post"]

   dataset:

   "ref_panel"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["did"]

   y:

   "p_vreg_bnw"

   Resultado calculado

   • coef.did = 0,236547 0.2365474758980175
     Coefficient on did in the regression of p_vreg_bnw
   Diagnósticos: n_obs = 200200 (observations used) · k = 44 (parameters estimated, fixed-effect dummies included) · r2 = 0,1233760.12337625859258017 · df_resid = 196196 · se_type = "cluster" (cluster-robust standard errors (CR1) by 'county_id') · n_clusters = 100100 (100 clusters of 'county_id')
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: 0,2365
   Figuras citadas: call_720d452d:coef.did
   • ✓
     #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.q019.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

   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 1970 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, 1970]"}
   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":1970},"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, 1970]"}
   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, 1970]"}

   1695 → 200 linhas

   Frame antes → depois: b654f624a0ee → 1afcc03043ff
6. 5Transformação
   ref_years · dropna {"columns":["p_vreg_bnw"]}

   200 → 200 linhas

   Frame antes → depois: 1afcc03043ff → 1afcc03043ff
7. 6Transformação
   ref_years · collapse {"aggs":[{"column":"p_vreg_bnw","fn":"count","name":"n"}],"by":["county_id","year"]}

   200 → 200 linhas

   Frame antes → depois: 1afcc03043ff → cbab375ed598
8. 7Transformação
   ref_years · collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["county_id"]}

   200 → 100 linhas

   Frame antes → depois: cbab375ed598 → 4074da81ad52
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, 1970]"}

    1695 → 200 linhas

    Frame antes → depois: b654f624a0ee → 1afcc03043ff
11. 10Transformação
    ref_panel · dropna {"columns":["p_vreg_bnw"]}

    200 → 200 linhas

    Frame antes → depois: 1afcc03043ff → 1afcc03043ff
12. 11Transformação
    ref_panel · merge {"how":"inner","on":["county_id"],"with":"ref_cov"}

    200 → 200 linhas

    Frame antes → depois: 1afcc03043ff → 5b33bdb59631
13. 12Transformação
    ref_panel · merge {"how":"left","on":["county_id"],"with":"ref_years"}

    200 → 200 linhas

    Frame antes → depois: 5b33bdb59631 → 0789c1760992
14. 13Transformação
    ref_panel · filter {"query":"nyears == 2"}

    200 → 200 linhas

    Frame antes → depois: 0789c1760992 → 0789c1760992
15. 14Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"year","value":1970},"name":"post","op":"indicator"}

    200 → 200 linhas

    Frame antes → depois: 0789c1760992 → 852b6f371c18
16. 15Transformação
    ref_panel · derive {"args":{"columns":["covered","post"]},"name":"did","op":"interaction"}

    200 → 200 linhas

    Frame antes → depois: 852b6f371c18 → cb2a74f56ffa
17. 16Turno do modelo
    call_83e59ad4referenceFim: 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.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_720d452ddecidido 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_720d452d

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.did Coefficient on did in the regression of p_vreg_bnw	0,236547	0.2365474758980175	0,06975180.06975179388853711	0,0981448 – 0,37495

    Diagnósticos

    • n_obs = 200200 (observations used)
    • k = 44 (parameters estimated, fixed-effect dummies included)
    • r2 = 0,1233760.12337625859258017
    • df_resid = 196196
    • se_type = "cluster" (cluster-robust standard errors (CR1) by 'county_id')
    • n_clusters = 100100 (100 clusters of 'county_id')

    Derivação matemática e pressupostos

    coef.did

    1. Coeficiente de did na regressão por mínimos quadrados ordinários de p_vreg_bnw.→ 0,236547 (0.2365474758980175)
    2. Erro-padrão robusto por conglomerado (CR1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,0697518 (0.06975179388853711)
    3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 99 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 clusters of 'county_id' and may be correlated within them

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 0.6742697381000241,
          "ci_low": 0.4370636008162775,
          "coef": 0.5556666694581508,
          "p": 3.751210592025455e-15,
          "se": 0.059773236261815035,
          "t": 9.296245346734347,
          "term": "Intercept"
        },
        {
          "ci_high": 0.3749501677352147,
          "ci_low": 0.0981447840608203,
          "coef": 0.2365474758980175,
          "p": 0.0010008311437818616,
          "se": 0.06975179388853711,
          "t": 3.3912744420024343,
          "term": "did"
        },
        {
          "ci_high": -0.13531255876485893,
          "ci_low": -0.4273207740315359,
          "coef": -0.2813166663981974,
          "p": 0.00023042216238505778,
          "se": 0.07358273374118973,
          "t": -3.8231342068325946,
          "term": "covered"
        },
        {
          "ci_high": 0.12785044570081874,
          "ci_low": -0.09369828014883838,
          "coef": 0.017076082775990178,
          "p": 0.760345213707892,
          "se": 0.055827747483087706,
          "t": 0.3058708894024993,
          "term": "post"
        }
      ]
    }

    Hashes SHA-256 e metadados de reprodução

    Digests dos dados

    ref_panel cb2a74f56ffa87f37dd63eeb4ee5cd736d2c4730ab19cd6e7e27213f64eaa6ec

    sha256 do resultado

    d0e0b5fd38effd799eb6265dce7e0f00dee420fbcf89eedbce2bc5d767cb1cec

    Resultado mostrado ao modelo
19. 18Turno do modelo
    call_ce810cc4referenceFim: 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 = 0,2365. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_720d452d:coef.did"
        ]
      }

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

    Números encontrados

    0,2365

    Sem lastro

    —

    Não citados

    —

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

    Estimativa (infer.ols): coef.did = 0,2365. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_720d452d:coef.did · Parada: answered

    Figura da resposta: call_720d452d:coef.did = 0,2365470.2365474758980175 (se 0,06975180.06975179388853711)
22. 21Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
23. 22Pontuação
    #1figura da resposta próximapassouobrigatória0.2365474759 in [0.09983395988, 0.3732609919]?
24. 23Pontuação
    #2figura da resposta próximapassouregistrada0.2365474759 vs 0.2365474758980179
25. 24Pontuação
    #3figura da resposta próximapassouregistrada0.06975179389 vs 0.06975179388853704

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