Tarefa crb_papers.q058

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Pergunta feita ao analista

For districts that moved from no Section 5 preclearance coverage in analysis period 1 to coverage in period 2, how did gaining coverage affect the representative's civil-rights support score between periods 1 and 2, relative to districts covered in both periods?

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1. 0Início

   For districts that moved from no Section 5 preclearance coverage in analysis period 1 to coverage in period 2, how did gaining coverage affect the representative's civil-rights support score between periods 1 and 2, relative to districts covered in both periods?

   ds_0030 (agent)

   | Variable Name | Description | | --- | --- | | party | Member’s party identification using standard congressional numeric codes (e.g., 100 = Democrat, 200 = Republican; other parties/independents use their conventional codes). Party is measured for the member serving in that Congress (two-year House term). | | correct | Civil-rights roll-call support score for the member in that Congress (two-year House term). Constructed as the proportion (0–1) of included civil-rights–related House roll calls on which the member voted in the direction coded as expanding (rather than restricting) civil rights; abstentions are treated as votes to restrict civil rights. The set of roll calls varies by Congress. | | dpres | District presidential partisanship: Democratic presidential vote share for the most recent presidential election prior to (or associated with) the Congress observation. Higher values indicate greater support for the Democratic presidential candidate (scale follows the replication file’s coding/transformation of election returns). | | percentblack | Share of the congressional district population that is Black (0–1), aligned to district boundaries within redistricting cycles using decennial census data and standard crosswalk/harmonization to congressional districts. | | competitive | Electoral competitiveness measure based on the margin of victory in the most recent presidential election in the district, multiplied by −1 so that larger values correspond to closer (more competitive) contests. Scale reflects the transformation used in the replication files. | | covered_new2 | Indicator (1/0) that the congressional district contained any geographic overlap with a jurisdiction subject to Section 5 preclearance during that Congress. Coverage is based on the list of covered states/counties/townships and subsequent changes due to statutory amendments and court actions (including “bail-in” and “bail-out”). Time-varying at the district–Congress level. | | black_legis | Indicator (1/0) that the district’s U.S. House member was Black in that Congress, based on congressional rosters/biographical sources. | | redistrict_decade | District identifier that is stable within a redistricting cycle (roughly a decade). This ID is intended to allow districts to be tracked consistently within-cycle despite renumbering or boundary changes across cycles. | | year3 | Ordered analysis-period index taking values 1 through 5. Use it with redistrict_decade to order observations within a district-cycle. The extract does not retain calendar years, Congress numbers, or a separate redistricting-cycle label; an index value must not be interpreted as a unique calendar Congress across the whole study. Periods named in the questions refer to these recorded index values. | | treatment | Inverse-coded coverage indicator: 1 means not covered by Section 5 preclearance and 0 means covered. In every row, treatment = 1 - covered_new2. Either existing column determines coverage, provided its coding is respected; gaining coverage means covered_new2 changes from 0 to 1, or treatment changes from 1 to 0. Neither column is an ever-covered flag. | Dataset Context: This file contains 902 district-period observations for 261 district-cycle identifiers, with at most five ordered analysis periods per identifier. It is an unbalanced extract: some units appear in only one period. The source analysis for this extract concerns Texas and the Deep South states of Alabama, Georgia, Louisiana, Mississippi, and South Carolina. It is not the full national panel. The parent collection covers House terms from 1959–60 through 1997–98, but this extract does not preserve the calendar Congress labels needed to date each row. Questions therefore specify windows using year3 rather than asserting calendar dates. The observational unit is a congressional district in a House term, with districts tracked inside redistricting cycles using redistrict_decade. The parent collection includes representatives who served the full Congress in single-member districts. Do not join different redistrict_decade identifiers as if their district boundaries were unchanged. Missing periods are absent observations, not zeros or evidence that a district was uncovered. Legislative behavior is summarized with a civil-rights support score. To build it, House roll-call votes were identified from policy agenda categories focused on civil rights, minority issues, and voting rights, and votes not directly pertinent to these topics were excluded. For each Congress-member pair, the score is the proportion of included roll calls on which the member voted in the direction coded as expanding civil rights, yielding a value between 0 and 1. Jurisdictional coverage under the Voting Rights Act’s Section 5 preclearance provision is mapped to congressional districts by geographic overlap. Coverage operated at the state, county, or township level and changed over time due to statutory amendments and court actions, including “bail-in” (placing additional jurisdictions under review) and “bail-out” (removing jurisdictions meeting statutory criteria). A district is flagged as covered in a given Congress if any part of it overlapped a covered jurisdiction during that period. District context draws on administrative sources. Demographic composition, including the share of the population that is Black, comes from the U.S. Census, harmonized to congressional boundaries within each redistricting cycle. Presidential election returns are used to compute district-level Democratic vote strength and a competitiveness proxy based on recent presidential margins. Member characteristics, including party codes and whether the representative is Black, come from standard congressional rosters and roll-call archives. The observed coverage histories include districts covered throughout a window, districts moving into coverage, and districts moving out of coverage. Six district-cycle units move from uncovered in period 1 to covered in period 2 and remain covered in period 3. There are no units observed in both periods 1 and 2 that remain uncovered in both. The questions identify their respective observation windows and use districts covered throughout those windows as the comparison population. These recorded histories describe the available data; they do not by themselves establish that the groups would have followed the same outcome trends.

   file 04dd7c4cc12ed66ead5ebdb56002ef0ce3382585e7404e50c3fea64977886219 · frame —

   ref_c1 (reference)

   file 04dd7c4cc12ed66ead5ebdb56002ef0ce3382585e7404e50c3fea64977886219 · frame —

   1. select {"columns":["redistrict_decade","year3","correct","covered_new2"]}
   2. dropna {"columns":["redistrict_decade","year3","covered_new2"]}
   3. filter {"query":"year3 == 1"}
   4. rename {"map":{"covered_new2":"c1"}}
   5. select {"columns":["redistrict_decade","c1"]}

   ref_c2 (reference)

   file 04dd7c4cc12ed66ead5ebdb56002ef0ce3382585e7404e50c3fea64977886219 · frame —

   1. select {"columns":["redistrict_decade","year3","correct","covered_new2"]}
   2. dropna {"columns":["redistrict_decade","year3","covered_new2"]}
   3. filter {"query":"year3 == 2"}
   4. rename {"map":{"covered_new2":"c2"}}
   5. select {"columns":["redistrict_decade","c2"]}

   ref_panel (reference)

   file 04dd7c4cc12ed66ead5ebdb56002ef0ce3382585e7404e50c3fea64977886219 · frame —

   1. select {"columns":["redistrict_decade","year3","correct","covered_new2"]}
   2. dropna {"columns":["redistrict_decade","year3","covered_new2"]}
   3. filter {"query":"year3 in [1, 2]"}
   4. dropna {"columns":["correct"]}
   5. merge {"how":"inner","on":["redistrict_decade"],"with":"ref_c1"}
   6. merge {"how":"inner","on":["redistrict_decade"],"with":"ref_c2"}
   7. filter {"query":"(c1 == 0 and c2 == 1) or (c1 == 1 and c2 == 1)"}
   8. merge {"how":"inner","on":["redistrict_decade"],"with":"ref_years"}
   9. filter {"query":"nyears == 2"}
   10. derive {"args":{"cmp":"eq","column":"c1","value":0},"name":"gained_coverage","op":"indicator"}
   11. derive {"args":{"cmp":"eq","column":"year3","value":2},"name":"post","op":"indicator"}
   12. derive {"args":{"columns":["gained_coverage","post"]},"name":"gained_post","op":"interaction"}

   ref_years (reference)

   file 04dd7c4cc12ed66ead5ebdb56002ef0ce3382585e7404e50c3fea64977886219 · frame —

   1. select {"columns":["redistrict_decade","year3","correct","covered_new2"]}
   2. dropna {"columns":["redistrict_decade","year3","covered_new2"]}
   3. filter {"query":"year3 in [1, 2]"}
   4. dropna {"columns":["correct"]}
   5. collapse {"aggs":[{"column":"correct","fn":"count","name":"n"}],"by":["redistrict_decade","year3"]}
   6. collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["redistrict_decade"]}
2. 1Transformação
   ref_c1 · select {"columns":["redistrict_decade","year3","correct","covered_new2"]}

   902 → 902 linhas

   Frame antes → depois: 5f2712a54402 → a5168e14d499
3. 2Transformação
   ref_c1 · dropna {"columns":["redistrict_decade","year3","covered_new2"]}

   902 → 902 linhas

   Frame antes → depois: a5168e14d499 → a5168e14d499
4. 3Transformação
   ref_c1 · filter {"query":"year3 == 1"}

   902 → 217 linhas

   Frame antes → depois: a5168e14d499 → f45b8b52e465
5. 4Transformação
   ref_c1 · rename {"map":{"covered_new2":"c1"}}

   217 → 217 linhas

   Frame antes → depois: f45b8b52e465 → df27d8e5ca07
6. 5Transformação
   ref_c1 · select {"columns":["redistrict_decade","c1"]}

   217 → 217 linhas

   Frame antes → depois: df27d8e5ca07 → 17bee07be7c0
7. 6Transformação
   ref_c2 · select {"columns":["redistrict_decade","year3","correct","covered_new2"]}

   902 → 902 linhas

   Frame antes → depois: 5f2712a54402 → a5168e14d499
8. 7Transformação
   ref_c2 · dropna {"columns":["redistrict_decade","year3","covered_new2"]}

   902 → 902 linhas

   Frame antes → depois: a5168e14d499 → a5168e14d499
9. 8Transformação
   ref_c2 · filter {"query":"year3 == 2"}

   902 → 211 linhas

   Frame antes → depois: a5168e14d499 → 04cc1162517c
10. 9Transformação
    ref_c2 · rename {"map":{"covered_new2":"c2"}}

    211 → 211 linhas

    Frame antes → depois: 04cc1162517c → 81ee550b4aaa
11. 10Transformação
    ref_c2 · select {"columns":["redistrict_decade","c2"]}

    211 → 211 linhas

    Frame antes → depois: 81ee550b4aaa → 582407f69ee4
12. 11Transformação
    ref_years · select {"columns":["redistrict_decade","year3","correct","covered_new2"]}

    902 → 902 linhas

    Frame antes → depois: 5f2712a54402 → a5168e14d499
13. 12Transformação
    ref_years · dropna {"columns":["redistrict_decade","year3","covered_new2"]}

    902 → 902 linhas

    Frame antes → depois: a5168e14d499 → a5168e14d499
14. 13Transformação
    ref_years · filter {"query":"year3 in [1, 2]"}

    902 → 428 linhas

    Frame antes → depois: a5168e14d499 → ab6be55ec54f
15. 14Transformação
    ref_years · dropna {"columns":["correct"]}

    428 → 428 linhas

    Frame antes → depois: ab6be55ec54f → ab6be55ec54f
16. 15Transformação
    ref_years · collapse {"aggs":[{"column":"correct","fn":"count","name":"n"}],"by":["redistrict_decade","year3"]}

    428 → 428 linhas

    Frame antes → depois: ab6be55ec54f → 5dfea533f993
17. 16Transformação
    ref_years · collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["redistrict_decade"]}

    428 → 228 linhas

    Frame antes → depois: 5dfea533f993 → b43db9aa2821
18. 17Transformação
    ref_panel · select {"columns":["redistrict_decade","year3","correct","covered_new2"]}

    902 → 902 linhas

    Frame antes → depois: 5f2712a54402 → a5168e14d499
19. 18Transformação
    ref_panel · dropna {"columns":["redistrict_decade","year3","covered_new2"]}

    902 → 902 linhas

    Frame antes → depois: a5168e14d499 → a5168e14d499
20. 19Transformação
    ref_panel · filter {"query":"year3 in [1, 2]"}

    902 → 428 linhas

    Frame antes → depois: a5168e14d499 → ab6be55ec54f
21. 20Transformação
    ref_panel · dropna {"columns":["correct"]}

    428 → 428 linhas

    Frame antes → depois: ab6be55ec54f → ab6be55ec54f
22. 21Transformação
    ref_panel · merge {"how":"inner","on":["redistrict_decade"],"with":"ref_c1"}

    428 → 417 linhas

    Frame antes → depois: ab6be55ec54f → 2cfc94b00b7d
23. 22Transformação
    ref_panel · merge {"how":"inner","on":["redistrict_decade"],"with":"ref_c2"}

    417 → 400 linhas

    Frame antes → depois: 2cfc94b00b7d → d66432be7e05
24. 23Transformação
    ref_panel · filter {"query":"(c1 == 0 and c2 == 1) or (c1 == 1 and c2 == 1)"}

    400 → 392 linhas

    Frame antes → depois: d66432be7e05 → fa7cd5b939b1
25. 24Transformação
    ref_panel · merge {"how":"inner","on":["redistrict_decade"],"with":"ref_years"}

    392 → 392 linhas

    Frame antes → depois: fa7cd5b939b1 → 8ed75f4d28cb
26. 25Transformação
    ref_panel · filter {"query":"nyears == 2"}

    392 → 392 linhas

    Frame antes → depois: 8ed75f4d28cb → 8ed75f4d28cb
27. 26Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"c1","value":0},"name":"gained_coverage","op":"indicator"}

    392 → 392 linhas

    Frame antes → depois: 8ed75f4d28cb → 4ade0701ef5e
28. 27Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"year3","value":2},"name":"post","op":"indicator"}

    392 → 392 linhas

    Frame antes → depois: 4ade0701ef5e → f8452e779149
29. 28Transformação
    ref_panel · derive {"args":{"columns":["gained_coverage","post"]},"name":"gained_post","op":"interaction"}

    392 → 392 linhas

    Frame antes → depois: f8452e779149 → fa5cc67ee764
30. 29Turno do modelo
    call_823b3771referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    smf.ols('correct ~ gained_coverage * post').fit(cov_type='cluster', groups=redistrict_decade)

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "redistrict_decade",
        "controls": [
          "gained_coverage",
          "post"
        ],
        "dataset": "ref_panel",
        "x": [
          "gained_post"
        ],
        "y": "correct"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
31. 30Ferramenta
    infer.olsv0.1.0call_a4f16cdddecidido pelo modeloreexecutável

    Motivo: smf.ols('correct ~ gained_coverage * post').fit(cov_type='cluster', groups=redistrict_decade)

    Argumentos

    {
      "cluster": "redistrict_decade",
      "controls": [
        "gained_coverage",
        "post"
      ],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "gained_post"
      ],
      "y": "correct"
    }

    Figuras call_a4f16cdd

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.gained_post Coefficient on gained_post in the regression of correct	0,357169	0.3571685232744928	0,1163080.11630770632720876	0,127786 – 0,586551

    Diagnósticos

    • n_obs = 392392
    • k = 44
    • r2 = 0,01664520.016645242571212027
    • df_resid = 388388
    • se_type = "cluster"
    • n_clusters = 196196

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 0.5424257294017982,
          "ci_low": 0.4399140492191475,
          "coef": 0.4911698893104729,
          "p": 5.966215643224097e-46,
          "se": 0.02598911611522127,
          "t": 18.899060942776934,
          "term": "Intercept"
        },
        {
          "ci_high": 0.5865510528867542,
          "ci_low": 0.12778599366223134,
          "coef": 0.3571685232744928,
          "p": 0.0024387895438131263,
          "se": 0.11630770632720876,
          "t": 3.0708930177822413,
          "term": "gained_post"
        },
        {
          "ci_high": -0.18180807154337153,
          "ci_low": -0.46719836753542376,
          "coef": -0.32450321953939765,
          "p": 0.000012435134752240488,
          "se": 0.07235313602781127,
          "t": -4.4849917689077685,
          "term": "gained_coverage"
        },
        {
          "ci_high": 0.0009775369302551379,
          "ci_low": -0.10503943661736966,
          "coef": -0.05203094984355726,
          "p": 0.05433392464362622,
          "se": 0.026877790226482623,
          "t": -1.9358343600840848,
          "term": "post"
        }
      ]
    }

    Digests dos dados

    ref_panel fa5cc67ee7642ce3ffc5a43656757484e5b976ebdd7809fa4b2677c4ad9b8608

    sha256 do resultado

    c35dfea31f05229627420a716d2de94dc9cdad72b0201e7b3d2a0cc280c4fe98

    Resultado mostrado ao modelo
32. 31Turno do modelo
    call_937ef30breferenceFim: 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.gained_post = 0,3572. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_a4f16cdd:coef.gained_post"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
33. 32Guarda
    resposta aceita

    Números encontrados

    0,3572

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
34. 33Resposta

    Estimativa (infer.ols): coef.gained_post = 0,3572. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_a4f16cdd:coef.gained_post · Parada: answered

    Figura da resposta: call_a4f16cdd:coef.gained_post = 0,3571690.3571685232744928 (se 0,1163080.11630770632720876)
35. 34Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
36. 35Pontuação
    #1figura da resposta próximapassouobrigatória0.3571685233 in [0.1292054189, 0.5851316277]?
37. 36Pontuação
    #2figura da resposta próximapassouregistrada0.3571685233 vs 0.3571685232744938
38. 37Pontuação
    #3figura da resposta próximapassouregistrada0.1163077063 vs 0.11630770632720892

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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