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Party is measured for the member serving in that Congress (two-year House term). |\n| correct | Civil-rights roll-call support score for the member in that Congress (two-year House term). Constructed as the proportion (0\u20131) of included civil-rights\u2013related 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. |\n| 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\u2019s coding/transformation of election returns). |\n| percentblack | Share of the congressional district population that is Black (0\u20131), aligned to district boundaries within redistricting cycles using decennial census data and standard crosswalk/harmonization to congressional districts. |\n| competitive | Electoral competitiveness measure based on the margin of victory in the most recent presidential election in the district, multiplied by \u22121 so that larger values correspond to closer (more competitive) contests. Scale reflects the transformation used in the replication files. |\n| 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 \u201cbail-in\u201d and \u201cbail-out\u201d). Time-varying at the district\u2013Congress level. |\n| black_legis | Indicator (1/0) that the district\u2019s U.S. House member was Black in that Congress, based on congressional rosters/biographical sources. |\n| 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. |\n| 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. |\n| 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. |\n\nDataset Context:\nThis 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\u201360 through 1997\u201398, 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.\n\nThe 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.\n\nLegislative 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.\n\nJurisdictional coverage under the Voting Rights Act\u2019s 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 \u201cbail-in\u201d (placing additional jurisdictions under review) and \u201cbail-out\u201d (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.\n\nDistrict 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.\n\nThe 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. 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In this setting: (1) party is plausibly affected by VRA coverage through partisan realignment and electoral selection: the paper notes the VRA \u201chas\u2026 contributed to the radical realignment of Southern politics\u201d (Schuit & Rogowski 2017, p. 513). Conditioning on legislators\u2019 party would partial out a pathway by which preclearance operates (electing different kinds of representatives). (2) competitive is a downstream electoral outcome of preclearance. The authors report that \u201cdistricts subject to the preclearance requirement were approximately 5 percentage points less likely to feature an uncontested congressional race, and approximately 4 percentage points more likely to be decided in a close contest\u201d (p. 524). Adjusting for competitiveness would condition on a mediator (see Chernozhukov et al., Fig. 11.9 on mediators), inducing over-control bias. (3) black_legis (indicator that the member is Black) is also a mediator. The VRA is credited with \u201cincreased numbers of Black elected officials \u2026 and Congress\u201d (p. 513\u2013514), and the authors explicitly add this variable \u201cto distinguish effects from the preclearance provision from effects that resulted from increased levels of descriptive representation\u201d (p. 519\u2013520). Controlling for it blocks part of the treatment effect on civil-rights voting (Chernozhukov et al., Fig. 11.9a). (4) dpres (district Democratic presidential vote) can also be mechanically affected by the VRA through turnout and mobilization: \u201cBlack voter registration and turnout surged after the VRA was passed\u201d (p. 516). As such, dpres is plausibly post-treatment in the post period and conditioning on it risks absorbing a treatment pathway (Chernozhukov et al., Sec. 11.4).","strategy":"Difference-in-Differences","time_variable":"year3","treatments":["treatment/covered_new2"]},"identification_strategy":"Difference-in-Differences"},"headline_keys":{"infer.ols":["coef.gained_post"]},"naive":{"dataset":"ds_0030","treatment":"treatment","y":"correct"},"native_id":"58","reference":{"cite":1,"note":"Reference path transcribed from the item's own estimation script.","steps":[{"args":{"cluster":"redistrict_decade","controls":["gained_coverage","post"],"dataset":"ref_panel","x":["gained_post"],"y":"correct"},"tool":"infer.ols","why":"smf.ols('correct ~ gained_coverage * post').fit(cov_type='cluster', groups=redistrict_decade)"}]},"reference_path":{"calls":[{"args":{"cluster":"redistrict_decade","controls":["gained_coverage","post"],"dataset":"ref_panel","x":["gained_post"],"y":"correct"},"tool":"infer.ols"}],"needs":[],"provenance":"benchmark_script","quotes":{},"reference_datasets":["ref_c1","ref_c2","ref_panel","ref_years"],"source_id":"crb_papers.q058.script"}},"question":"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?","requires":[],"runner":null,"sources":["crb_papers.q058.gold","crb_papers.q058.script"],"split":"public","suite":"crb_papers","tags":["econ-bench","external","crb","did","ref_benchmark_script"],"target":"agent","tool":null,"truth":"CausalReasoningBenchmark gold at the pinned revision: causal_queries.json effect and standard_error (the value the item's estimation.py prints), scored as evaluate_result.py scores estimate_within_solution_ci"}