Tarefa crb_papers.q057

Veredito

Pergunta feita ao analista

For districts that moved from no Section 5 preclearance coverage in analysis period 1 to coverage in period 2 and remained covered in period 3, how did gaining coverage affect the representative's civil-rights support score between periods 1 and 3, relative to districts covered in all three periods?

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

   For districts that moved from no Section 5 preclearance coverage in analysis period 1 to coverage in period 2 and remained covered in period 3, how did gaining coverage affect the representative's civil-rights support score between periods 1 and 3, relative to districts covered in all three 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_c3 (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 == 3"}
   4. rename {"map":{"covered_new2":"c3"}}
   5. select {"columns":["redistrict_decade","c3"]}

   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, 3]"}
   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. merge {"how":"inner","on":["redistrict_decade"],"with":"ref_c3"}
   8. filter {"query":"(c1 == 0 and c2 == 1 and c3 == 1) or (c1 == 1 and c2 == 1 and c3 == 1)"}
   9. merge {"how":"inner","on":["redistrict_decade"],"with":"ref_years"}
   10. filter {"query":"nyears == 2"}
   11. derive {"args":{"cmp":"eq","column":"c1","value":0},"name":"gained_coverage","op":"indicator"}
   12. derive {"args":{"cmp":"eq","column":"year3","value":3},"name":"post","op":"indicator"}
   13. 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, 3]"}
   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_c3 · select {"columns":["redistrict_decade","year3","correct","covered_new2"]}

    902 → 902 linhas

    Frame antes → depois: 5f2712a54402 → a5168e14d499
13. 12Transformação
    ref_c3 · dropna {"columns":["redistrict_decade","year3","covered_new2"]}

    902 → 902 linhas

    Frame antes → depois: a5168e14d499 → a5168e14d499
14. 13Transformação
    ref_c3 · filter {"query":"year3 == 3"}

    902 → 207 linhas

    Frame antes → depois: a5168e14d499 → 573dd1b0f5bc
15. 14Transformação
    ref_c3 · rename {"map":{"covered_new2":"c3"}}

    207 → 207 linhas

    Frame antes → depois: 573dd1b0f5bc → c1153ca78751
16. 15Transformação
    ref_c3 · select {"columns":["redistrict_decade","c3"]}

    207 → 207 linhas

    Frame antes → depois: c1153ca78751 → cc11a5b89cb4
17. 16Transformação
    ref_years · select {"columns":["redistrict_decade","year3","correct","covered_new2"]}

    902 → 902 linhas

    Frame antes → depois: 5f2712a54402 → a5168e14d499
18. 17Transformação
    ref_years · dropna {"columns":["redistrict_decade","year3","covered_new2"]}

    902 → 902 linhas

    Frame antes → depois: a5168e14d499 → a5168e14d499
19. 18Transformação
    ref_years · filter {"query":"year3 in [1, 3]"}

    902 → 424 linhas

    Frame antes → depois: a5168e14d499 → fbf35091a194
20. 19Transformação
    ref_years · dropna {"columns":["correct"]}

    424 → 424 linhas

    Frame antes → depois: fbf35091a194 → fbf35091a194
21. 20Transformação
    ref_years · collapse {"aggs":[{"column":"correct","fn":"count","name":"n"}],"by":["redistrict_decade","year3"]}

    424 → 424 linhas

    Frame antes → depois: fbf35091a194 → 5eab569fe836
22. 21Transformação
    ref_years · collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["redistrict_decade"]}

    424 → 231 linhas

    Frame antes → depois: 5eab569fe836 → 47c7bae8f6ac
23. 22Transformação
    ref_panel · select {"columns":["redistrict_decade","year3","correct","covered_new2"]}

    902 → 902 linhas

    Frame antes → depois: 5f2712a54402 → a5168e14d499
24. 23Transformação
    ref_panel · dropna {"columns":["redistrict_decade","year3","covered_new2"]}

    902 → 902 linhas

    Frame antes → depois: a5168e14d499 → a5168e14d499
25. 24Transformação
    ref_panel · filter {"query":"year3 in [1, 3]"}

    902 → 424 linhas

    Frame antes → depois: a5168e14d499 → fbf35091a194
26. 25Transformação
    ref_panel · dropna {"columns":["correct"]}

    424 → 424 linhas

    Frame antes → depois: fbf35091a194 → fbf35091a194
27. 26Transformação
    ref_panel · merge {"how":"inner","on":["redistrict_decade"],"with":"ref_c1"}

    424 → 410 linhas

    Frame antes → depois: fbf35091a194 → 3b3986c24f71
28. 27Transformação
    ref_panel · merge {"how":"inner","on":["redistrict_decade"],"with":"ref_c2"}

    410 → 393 linhas

    Frame antes → depois: 3b3986c24f71 → b2a5eb17d2e3
29. 28Transformação
    ref_panel · merge {"how":"inner","on":["redistrict_decade"],"with":"ref_c3"}

    393 → 386 linhas

    Frame antes → depois: b2a5eb17d2e3 → 89034e4ddf85
30. 29Transformação
    ref_panel · filter {"query":"(c1 == 0 and c2 == 1 and c3 == 1) or (c1 == 1 and c2 == 1 and c3 == 1)"}

    386 → 366 linhas

    Frame antes → depois: 89034e4ddf85 → 5f6d7229cf34
31. 30Transformação
    ref_panel · merge {"how":"inner","on":["redistrict_decade"],"with":"ref_years"}

    366 → 366 linhas

    Frame antes → depois: 5f6d7229cf34 → e0f1d414e5bb
32. 31Transformação
    ref_panel · filter {"query":"nyears == 2"}

    366 → 366 linhas

    Frame antes → depois: e0f1d414e5bb → e0f1d414e5bb
33. 32Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"c1","value":0},"name":"gained_coverage","op":"indicator"}

    366 → 366 linhas

    Frame antes → depois: e0f1d414e5bb → 660e4276f9cb
34. 33Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"year3","value":3},"name":"post","op":"indicator"}

    366 → 366 linhas

    Frame antes → depois: 660e4276f9cb → ca1a76639076
35. 34Transformação
    ref_panel · derive {"args":{"columns":["gained_coverage","post"]},"name":"gained_post","op":"interaction"}

    366 → 366 linhas

    Frame antes → depois: ca1a76639076 → 71b663e24581
36. 35Turno do modelo
    call_c6a352ccreferenceFim: 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
37. 36Ferramenta
    infer.olsv0.1.0call_1293b374decidido 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_1293b374

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.gained_post Coefficient on gained_post in the regression of correct	0,383051	0.3830510450811204	0,09299230.09299225335317259	0,19957 – 0,566533

    Diagnósticos

    • n_obs = 366366
    • k = 44
    • r2 = 0,01391030.013910311988308943
    • df_resid = 362362
    • se_type = "cluster"
    • n_clusters = 183183

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 0.5463830652925685,
          "ci_low": 0.4390373663334765,
          "coef": 0.49271021581302255,
          "p": 1.374403602300822e-42,
          "se": 0.02720251513661606,
          "t": 18.112671322432533,
          "term": "Intercept"
        },
        {
          "ci_high": 0.5665325794878515,
          "ci_low": 0.19956951067438936,
          "coef": 0.3830510450811204,
          "p": 0.0000576468724261452,
          "se": 0.09299225335317259,
          "t": 4.119171557509656,
          "term": "gained_post"
        },
        {
          "ci_high": -0.18235080537761145,
          "ci_low": -0.46973628670628315,
          "coef": -0.3260435460419473,
          "p": 0.000013328173598598794,
          "se": 0.07282646609684906,
          "t": -4.47699254840067,
          "term": "gained_coverage"
        },
        {
          "ci_high": 0.03585822155041029,
          "ci_low": -0.06795355541689066,
          "coef": -0.01604766693324019,
          "p": 0.5426124385743728,
          "se": 0.02630698259637006,
          "t": -0.610015492063864,
          "term": "post"
        }
      ]
    }

    Digests dos dados

    ref_panel 71b663e24581b9d9bdc913daab4d0cbc2d12c800c22cafd50502b06e6cd72c02

    sha256 do resultado

    7c24e292cdb3df60b75798d14925081d6e7fa9e541bc6c803321d00644c3ac86

    Resultado mostrado ao modelo
38. 37Turno do modelo
    call_59ee7fb4referenceFim: 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 = 0,3831. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_1293b374:coef.gained_post"
        ]
      }

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

    Números encontrados

    0,3831

    Sem lastro

    —

    Não citados

    —

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

    Estimativa (infer.ols): coef.gained_post = 0,3831. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_1293b374:coef.gained_post · Parada: answered

    Figura da resposta: call_1293b374:coef.gained_post = 0,3830510.3830510450811204 (se 0,09299230.09299225335317259)
41. 40Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
42. 41Pontuação
    #1figura da resposta próximapassouobrigatória0.3830510451 in [0.2007862285, 0.5653158617]?
43. 42Pontuação
    #2figura da resposta próximapassouregistrada0.3830510451 vs 0.3830510450811192
44. 43Pontuação
    #3figura da resposta próximapassouregistrada0.09299225335 vs 0.09299225335317234

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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