Tarefa crb_papers.q025

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Pergunta feita ao analista

By how many percentage points did adopting same-day voter registration in 1996 alter a state's CPS-based voter turnout between the prior and subsequent election?

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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 adopting same-day voter registration in 1996 alter a state's CPS-based voter turnout between the prior and subsequent election?

   ds_0013 (agent)

   | Variable Name | Description | | --- | --- | | voted | State-year turnout rate (0–1), aggregated from the Census Bureau’s Current Population Survey Voting and Registration Supplement. Computed as the share of adult residents surveyed in that state and year who report having voted in the November general election. Includes both presidential and midterm cycles and reflects CPS’s biennial November fielding and weighting. | | temp_var | Indicator (0/1) that residents could register and cast a ballot on the same day in that state and year. Status reflects the first general election in which the option was implemented, and remains 1 thereafter. Codings draw on legislative and administrative sources and treat Election Day registration as part of the same “same-day” category. | | state | U.S. state name corresponding to each panel unit. | | year | Calendar election year (even-numbered), matching the CPS Voter Supplement cycle and covering both presidential and midterm general elections. | Dataset Context: This file is a state-by-year panel spanning 1980–2018 that aligns survey-based measures of participation with the timing of state registration rules. Turnout is derived from the Census Bureau’s Current Population Survey (CPS) Voting and Registration Supplement, a large, nationally representative survey of the civilian, noninstitutionalized population conducted every two years in November. The CPS asks respondents whether they voted in the most recent general election and provides sampling weights to recover population-representative estimates. Aggregating these responses to the state-year level yields a consistent series of turnout rates across both presidential and midterm elections. As with any self-reported voting measure, the CPS can modestly overstate participation, but it is widely used for its coverage, consistent questionnaire, and long time span. Policy status for same-day registration is compiled from legislative and administrative references, including nonpartisan compilations and state records, and is coded to reflect the first election in which residents could register and vote on the same day. Jurisdictions that permit registration on Election Day itself are included under the same umbrella as those allowing same-day registration during an early voting window. Once a state implements the option, its indicator remains active in subsequent even-year elections unless the policy is rescinded. By harmonizing CPS-based turnout with the timing of registration options, the dataset supports comparisons of participation patterns across places and over time. The panel includes both high-salience presidential contests and midterm cycles, allowing researchers to examine differences that can arise across election types while working with a uniform measurement framework.

   file 5b884bcf1e4ee621cad50fd984e81c33cf38702b12ff606d79bfa868f56d9acb · frame —

   ref_panel (reference)

   file 5b884bcf1e4ee621cad50fd984e81c33cf38702b12ff606d79bfa868f56d9acb · frame —

   1. select {"columns":["voted","temp_var","state","year"]}
   2. dropna {"columns":["voted","temp_var","state","year"]}
   3. filter {"query":"year in [1994, 1996]"}
   4. merge {"how":"left","on":["state"],"with":"ref_years"}
   5. filter {"query":"nyears == 2"}
   6. merge {"how":"inner","on":["state"],"with":"ref_pre"}
   7. merge {"how":"inner","on":["state"],"with":"ref_post"}
   8. filter {"query":"t_pre == 0 and (t_post == 1 or t_post == 0)"}
   9. derive {"args":{"cmp":"eq","column":"t_post","value":1},"name":"treated","op":"indicator"}
   10. derive {"args":{"cmp":"eq","column":"year","value":1996},"name":"post","op":"indicator"}
   11. derive {"args":{"columns":["treated","post"]},"name":"treated_post","op":"interaction"}

   ref_post (reference)

   file 5b884bcf1e4ee621cad50fd984e81c33cf38702b12ff606d79bfa868f56d9acb · frame —

   1. select {"columns":["voted","temp_var","state","year"]}
   2. dropna {"columns":["voted","temp_var","state","year"]}
   3. filter {"query":"year == 1996"}
   4. collapse {"aggs":[{"column":"temp_var","fn":"max","name":"t_post"}],"by":["state"]}

   ref_pre (reference)

   file 5b884bcf1e4ee621cad50fd984e81c33cf38702b12ff606d79bfa868f56d9acb · frame —

   1. select {"columns":["voted","temp_var","state","year"]}
   2. dropna {"columns":["voted","temp_var","state","year"]}
   3. filter {"query":"year == 1994"}
   4. collapse {"aggs":[{"column":"temp_var","fn":"max","name":"t_pre"}],"by":["state"]}

   ref_years (reference)

   file 5b884bcf1e4ee621cad50fd984e81c33cf38702b12ff606d79bfa868f56d9acb · frame —

   1. select {"columns":["voted","temp_var","state","year"]}
   2. dropna {"columns":["voted","temp_var","state","year"]}
   3. filter {"query":"year in [1994, 1996]"}
   4. collapse {"aggs":[{"column":"voted","fn":"count","name":"n"}],"by":["state","year"]}
   5. collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["state"]}
2. 1Transformação
   ref_years · select {"columns":["voted","temp_var","state","year"]}

   980 → 980 linhas

   Frame antes → depois: 9b5a550f37a1 → 9b5a550f37a1
3. 2Transformação
   ref_years · dropna {"columns":["voted","temp_var","state","year"]}

   980 → 980 linhas

   Frame antes → depois: 9b5a550f37a1 → 9b5a550f37a1
4. 3Transformação
   ref_years · filter {"query":"year in [1994, 1996]"}

   980 → 98 linhas

   Frame antes → depois: 9b5a550f37a1 → 682aff89cf3d
5. 4Transformação
   ref_years · collapse {"aggs":[{"column":"voted","fn":"count","name":"n"}],"by":["state","year"]}

   98 → 98 linhas

   Frame antes → depois: 682aff89cf3d → 6eecefe2750c
6. 5Transformação
   ref_years · collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["state"]}

   98 → 49 linhas

   Frame antes → depois: 6eecefe2750c → a499c60b2466
7. 6Transformação
   ref_pre · select {"columns":["voted","temp_var","state","year"]}

   980 → 980 linhas

   Frame antes → depois: 9b5a550f37a1 → 9b5a550f37a1
8. 7Transformação
   ref_pre · dropna {"columns":["voted","temp_var","state","year"]}

   980 → 980 linhas

   Frame antes → depois: 9b5a550f37a1 → 9b5a550f37a1
9. 8Transformação
   ref_pre · filter {"query":"year == 1994"}

   980 → 49 linhas

   Frame antes → depois: 9b5a550f37a1 → 0de274830856
10. 9Transformação
    ref_pre · collapse {"aggs":[{"column":"temp_var","fn":"max","name":"t_pre"}],"by":["state"]}

    49 → 49 linhas

    Frame antes → depois: 0de274830856 → 5fe40380287f
11. 10Transformação
    ref_post · select {"columns":["voted","temp_var","state","year"]}

    980 → 980 linhas

    Frame antes → depois: 9b5a550f37a1 → 9b5a550f37a1
12. 11Transformação
    ref_post · dropna {"columns":["voted","temp_var","state","year"]}

    980 → 980 linhas

    Frame antes → depois: 9b5a550f37a1 → 9b5a550f37a1
13. 12Transformação
    ref_post · filter {"query":"year == 1996"}

    980 → 49 linhas

    Frame antes → depois: 9b5a550f37a1 → 7a067db3bd1a
14. 13Transformação
    ref_post · collapse {"aggs":[{"column":"temp_var","fn":"max","name":"t_post"}],"by":["state"]}

    49 → 49 linhas

    Frame antes → depois: 7a067db3bd1a → 3a0438c3620e
15. 14Transformação
    ref_panel · select {"columns":["voted","temp_var","state","year"]}

    980 → 980 linhas

    Frame antes → depois: 9b5a550f37a1 → 9b5a550f37a1
16. 15Transformação
    ref_panel · dropna {"columns":["voted","temp_var","state","year"]}

    980 → 980 linhas

    Frame antes → depois: 9b5a550f37a1 → 9b5a550f37a1
17. 16Transformação
    ref_panel · filter {"query":"year in [1994, 1996]"}

    980 → 98 linhas

    Frame antes → depois: 9b5a550f37a1 → 682aff89cf3d
18. 17Transformação
    ref_panel · merge {"how":"left","on":["state"],"with":"ref_years"}

    98 → 98 linhas

    Frame antes → depois: 682aff89cf3d → e0e09a90e8e4
19. 18Transformação
    ref_panel · filter {"query":"nyears == 2"}

    98 → 98 linhas

    Frame antes → depois: e0e09a90e8e4 → e0e09a90e8e4
20. 19Transformação
    ref_panel · merge {"how":"inner","on":["state"],"with":"ref_pre"}

    98 → 98 linhas

    Frame antes → depois: e0e09a90e8e4 → f3ae1b6dc72f
21. 20Transformação
    ref_panel · merge {"how":"inner","on":["state"],"with":"ref_post"}

    98 → 98 linhas

    Frame antes → depois: f3ae1b6dc72f → a1c65947eadc
22. 21Transformação
    ref_panel · filter {"query":"t_pre == 0 and (t_post == 1 or t_post == 0)"}

    98 → 88 linhas

    Frame antes → depois: a1c65947eadc → 5ddd70de4628
23. 22Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"t_post","value":1},"name":"treated","op":"indicator"}

    88 → 88 linhas

    Frame antes → depois: 5ddd70de4628 → f6f6462373c6
24. 23Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"year","value":1996},"name":"post","op":"indicator"}

    88 → 88 linhas

    Frame antes → depois: f6f6462373c6 → f02b9a1718c3
25. 24Transformação
    ref_panel · derive {"args":{"columns":["treated","post"]},"name":"treated_post","op":"interaction"}

    88 → 88 linhas

    Frame antes → depois: f02b9a1718c3 → 031fec230094
26. 25Turno do modelo
    call_199d9a06referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    smf.ols('voted ~ treated + post + treated:post').fit(cov_type='cluster', groups=state) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "state",
        "controls": [
          "treated",
          "post"
        ],
        "dataset": "ref_panel",
        "x": [
          "treated_post"
        ],
        "y": "voted"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
27. 26Ferramenta
    infer.olsv0.1.0call_585d9c63decidido pelo modeloreexecutável

    Motivo: smf.ols('voted ~ treated + post + treated:post').fit(cov_type='cluster', groups=state) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Argumentos

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

    Figuras call_585d9c63

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.treated_post Coefficient on treated_post in the regression of voted	0,0394458	0.039445779320614834	0,01136660.011366633602856013	0,0165228 – 0,0623688

    Diagnósticos

    • n_obs = 8888
    • k = 44
    • r2 = 0,5422450.5422451000136433
    • df_resid = 8484
    • se_type = "cluster"
    • n_clusters = 4444

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 0.23724266853011353,
          "ci_low": 0.19828204417637876,
          "coef": 0.21776235635324614,
          "p": 1.9079388042161804e-25,
          "se": 0.009659536633466545,
          "t": 22.54376836242684,
          "term": "Intercept"
        },
        {
          "ci_high": 0.06236878063897541,
          "ci_low": 0.016522778002254258,
          "coef": 0.039445779320614834,
          "p": 0.0011955619138895586,
          "se": 0.011366633602856013,
          "t": 3.4703132606212956,
          "term": "treated_post"
        },
        {
          "ci_high": -0.05874716045544859,
          "ci_low": -0.09770778480918332,
          "coef": -0.07822747263231596,
          "p": 3.439873765182963e-10,
          "se": 0.00965953663346654,
          "t": -8.098470516824603,
          "term": "treated"
        },
        {
          "ci_high": 0.14279291298945923,
          "ci_low": 0.09694691035273809,
          "coef": 0.11986991167109866,
          "p": 1.6786458792281135e-13,
          "se": 0.011366633602856007,
          "t": 10.545770705671366,
          "term": "post"
        }
      ]
    }

    Digests dos dados

    ref_panel 031fec230094506391c96b11ffdf3068bd9f29ddafd4165f7012691497e5ef8d

    sha256 do resultado

    d059f4a24d2b64fc5123209e11a41a075cf19c17d18da638dd73797beb394eaa

    Resultado mostrado ao modelo
28. 27Turno do modelo
    call_8969a9aareferenceFim: 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_post = 0,03945. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_585d9c63:coef.treated_post"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
29. 28Guarda
    resposta aceita

    Números encontrados

    0,03945

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
30. 29Resposta

    Estimativa (infer.ols): coef.treated_post = 0,03945. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_585d9c63:coef.treated_post · Parada: answered

    Figura da resposta: call_585d9c63:coef.treated_post = 0,03944580.039445779320614834 (se 0,01136660.011366633602856013)
31. 30Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
32. 31Pontuação
    #1figura da resposta próximapassouobrigatória0.03944577932 in [0.01716717746, 0.06172438118]?
33. 32Pontuação
    #2figura da resposta próximapassouregistrada0.03944577932 vs 0.03944577932061476
34. 33Pontuação
    #3figura da resposta próximapassouregistrada0.0113666336 vs 0.01136663360285569

Épocas × verificações

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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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