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Stable across years. |\n| year | Calendar year of the observation (1958\u20131993). Not all measures are available every year. |\n| 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. |\n| pop_b_i_perc | Share of the county\u2019s total population that is Black (0\u20131). Based on U.S. Census counts via NHGIS, linearly interpolated between decennial enumerations. |\n| 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. |\n| 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. |\n| p_vreg_w | White voter registration as a share of the white voting-age population (0\u20131+, where values can exceed 1.0 due to unpurged names on voter rolls). Compiled from state and federal administrative sources. |\n| p_vreg_bnw | Black voter registration as a share of the Black voting-age population (0\u20131+, where values can exceed 1.0 due to unpurged names on voter rolls). Compiled from state and federal administrative sources. |\n| 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. |\n| 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. |\n\nDataset Context:\nThis file is a county\u2013year panel for North Carolina covering 1958\u20131993. 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).\n\nRegistration 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\u20131968, 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 (\u201cdeadwood\u201d), the registration share can occasionally exceed 100%.\n\nDemographic measures\u2014total population (logged), racial composition, median household income, and median years of schooling among adults 25+\u2014are 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.\n\nThe period also spans a major policy environment: Section 5 of the VRA required certain jurisdictions to obtain federal approval (\u201cpreclearance\u201d) for changes to voting practices. North Carolina was unique among the initially covered states in having partial county coverage\u201440 of its 100 counties\u2014based 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_sha256":"5a85d499ade6470cd3f43aa2a2a4ccda06ae13281bb710aa464f6b1747fc6cd1","format":"csv","frame_sha256":null,"generator":null,"kind":"file","licence":"redistributed by CausalReasoningBenchmark (MIT) under the original replication data's terms","member":null,"object":null,"params":{},"path":null,"read_kw":{},"records":null,"role":"agent","sha256":"5a85d499ade6470cd3f43aa2a2a4ccda06ae13281bb710aa464f6b1747fc6cd1","transforms":[],"url":"https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/data/ds_0010/data/data.csv"},"ref_cov":{"columns":null,"description":"Each county's coverage: the max of post_x_covered over all its 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\u00b1 1.96\u00b7|standard_error| (evaluate_result.ci_bounds)","effect":0.03486034259007323,"standard_error":0.07797849662288812},"identification":{"bad_controls":["pop_t_i_ln","pop_b_i_perc","inc_hh_med_i","educ_25p_i"],"causal_quantity":"ATT","controls":[],"group_variable":"county_id","minimal_controlling_set":[],"outcomes":["p_vreg_bnw"],"reason_for_bad_controls":"These are time-varying covariates that can be affected by Section 5 after 1965 (e.g., migration changing population size and racial composition; enfranchisement shifting income/education), so conditioning on them risks post-treatment/over-control bias. The paper notes potential migration spillovers: \u201cMobile voters are the most likely SUTVA violation. If black voters chose to move to counties covered by the VRA, or whites chose to move out of covered counties\u2026 this might bias (overestimate) the effect of coverage.\u201d (Empirical approach). Methodologically, \u201ccontrolling for post-treatment variables is almost always a bad idea\u201d (Applied Causal Inference, Deeper Dive into DAGs, Post-Treatment Variables).","reason_for_minimal_controlling_set":"Under DiD with county and year fixed effects, the identifying variation is post-1965 \u00d7 covered. The paper shows \u201cgraphical analysis (app. F) and formal econometric test (app. C) demonstrate that pre-VRA trends in political outcomes are parallel across covered and uncovered counties with the exception of Democratic vote share,\u201d supporting unconditional parallel trends for registration. Thus no additional time-varying covariates are required for identification.","strategy":"Difference-in-Differences","time_variable":"year","treatments":["post_x_covered"]},"identification_strategy":"Difference-in-Differences"},"headline_keys":{"infer.ols":["coef.did"]},"item_caveats":[{"kind":"unit_percentage_points","note":"the question asks for percentage points while the outcome and the gold are 0-1 fractions; the answer figure is compared on the fraction scale (the gold's), and the answer may state 100 x the fraction"}],"naive":{"dataset":"ds_0010","treatment":"post_x_covered","y":"p_vreg_bnw"},"native_id":"18","reference":{"cite":1,"note":"Reference path transcribed from the item's own estimation script.","steps":[{"args":{"cluster":"county_id","controls":["covered","post"],"dataset":"ref_panel","x":["did"],"y":"p_vreg_bnw"},"tool":"infer.ols","why":"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)\u00b7(N-1)/(N-K) correction)"}]},"reference_path":{"calls":[{"args":{"cluster":"county_id","controls":["covered","post"],"dataset":"ref_panel","x":["did"],"y":"p_vreg_bnw"},"tool":"infer.ols"}],"needs":[],"provenance":"benchmark_script","quotes":{},"reference_datasets":["ref_cov","ref_panel","ref_years"],"source_id":"crb_papers.q018.script"}},"question":"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 1966 compared with uncovered counties?","requires":[],"runner":null,"sources":["crb_papers.q018.gold","crb_papers.q018.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"}