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Incidents are drawn from the U.S. Department of Justice\u2019s National Church Arson Task Force records (primarily 1995\u20132000) and mapped to the relevant presidential-election cycle for comparability across counties and years. |\n| perc_bl | Share of county residents identifying as Black or African American in that year (0\u20131), based on U.S. Census/ACS population statistics for the county. |\n| pop_tot | Total county population (persons) in that year, from U.S. Census/ACS population counts. |\n| income | County median household income in U.S. dollars for that year (nominal dollars as reported by the Census/ACS). |\n| fips | Five-digit Federal Information Processing Standards (FIPS) code uniquely identifying the county (state\u2013county concatenated code). |\n| year | Presidential election year for the observation (e.g., 1980, 1984, \u2026, 2016). |\n\nDataset Context:\nThis file is a county-by\u2013presidential-election-year panel covering U.S. general elections from 1980 through 2016. Each record is keyed by a county\u2019s five-digit FIPS code and the presidential election year. Turnout is constructed from county-level counts of ballots cast in the presidential general election combined with county voting-age population from the U.S. Census Bureau. Using voting-age population standardizes the denominator across states, which vary in registration rules and reporting conventions, but note that VAP includes individuals ineligible to vote (e.g., noncitizens), a common feature of administrative turnout measures.\n\nDemographic indicators\u2014total population, the share identifying as Black or African American, and median household income\u2014are taken from decennial Census and American Community Survey tabulations aligned to the election year. Income is recorded in nominal dollars for the reported year.\n\nThe Black church arson indicator comes from administrative records compiled by the U.S. Department of Justice\u2019s National Church Arson Task Force established in the mid-1990s. Those incident locations are assigned to counties and linked to the presidential-election cycle that encompasses the incident window to ensure a consistent temporal frame across units. Because the Task Force\u2019s comprehensive catalog primarily spans 1995\u20132000, county-years outside that period are generally coded as having no recorded incident.\n\nUsers should be aware that counties occasionally experience boundary changes or renamings over long panels; the dataset relies on FIPS coding to maintain consistent identification. As with most historical administrative compilations, reporting conventions can vary across states and years; the assembly here harmonizes these inputs to produce a cohesive, national county-level panel suitable for studying election-period patterns alongside population and community characteristics.","file_sha256":"d73be27ea1d43f15d8e48b09dfc8299de15152438ea0d0b908fcad88c89b3c43","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":"d73be27ea1d43f15d8e48b09dfc8299de15152438ea0d0b908fcad88c89b3c43","transforms":[],"url":"https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/data/ds_0024/data/data.csv"},"ref_evp":{"columns":null,"description":"Each county's max of tr_ar over years up to 1996 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Conditioning on post\u2011treatment variables risks over\u2011control and collider bias. Guidance: \u201ccontrolling for post-treatment variables is almost always a bad idea\u201d (Applied Causal Inference, Sec. 11.3 Post\u2011Treatment Variables, p. 309). Post\u2011treatment controls that lie on causal paths induce \u201cover\u2011control bias\u201d (p. 310) and can introduce \u201ccollider stratification bias\u201d (p. 311). In this study, these controls vary by year: \u201cDemographic measures (percent Black, total population, median household income) were taken from Census/ACS data\u201d (APSR 2023, Data, p. 1042), and traumatic events can cause \u201cdisplacement\u201d (APSR 2023, Cases: Natural disasters \u201c\u2026cause loss of life\u2026 and displacement,\u201d p. 1040). Because tr_ar can occur between elections and these covariates are recorded in the election year, they are plausibly descendants of treatment in the post period and thus bad controls.","reason_for_minimal_controlling_set":"Identification comes from comparing within\u2011county changes across time with county and year fixed effects, relying on parallel trends without needing time\u2011varying covariates. The paper states: \u201cThe generalized TWFE model\u2026 compares the change in a county experiencing a traumatic event (treated) with the same change in a county that did not experience a traumatic event (untreated), controlling for the possibility of secular temporal trends\u201d (APSR 2023, Methods, p. 1043), and that this \u201callows me to account for unobserved or difficult\u2011to\u2011observe time\u2011invariant confounders that are constant within a county within election cycles\u201d (p. 1043). Since the author\u2019s added demographics are time\u2011varying and plausibly post\u2011treatment, the minimal sufficient set among the provided controls is none to avoid post\u2011treatment bias while maintaining the DiD identification via fixed effects and parallel trends.","strategy":"Difference-in-Differences","time_variable":"year","treatments":["tr_ar"]},"identification_strategy":"Difference-in-Differences"},"headline_keys":{"infer.ols":["coef.did"]},"naive":{"dataset":"ds_0024","treatment":"tr_ar","y":"turnout"},"native_id":"46","reference":{"cite":1,"note":"Reference path transcribed from the item's own estimation script.","steps":[{"args":{"cluster":"fips","controls":["treated_group","post","post_x_perc_bl_pre","post_x_pop_tot_pre","post_x_income_pre"],"dataset":"ref_panel","x":["did"],"y":"turnout"},"tool":"infer.ols","why":"sm.OLS(turnout, add_constant([treated_group, post, did, post_x_*_pre]), missing='drop').fit(cov_type='cluster', groups=fips) (CR1 errors with statsmodels' default G/(G-1)\u00b7(N-1)/(N-K) correction)"}]},"reference_path":{"calls":[{"args":{"cluster":"fips","controls":["treated_group","post","post_x_perc_bl_pre","post_x_pop_tot_pre","post_x_income_pre"],"dataset":"ref_panel","x":["did"],"y":"turnout"},"tool":"infer.ols"}],"needs":[],"provenance":"benchmark_script","quotes":{},"reference_datasets":["ref_evp","ref_panel","ref_post","ref_pre","ref_years"],"source_id":"crb_papers.q046.script"}},"question":"How did county presidential-election turnout change between 1992 and 1996 in counties that experienced a recorded Black church arson incident, compared with counties that did not experience such incidents during that period?","requires":[],"runner":null,"sources":["crb_papers.q046.gold","crb_papers.q046.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"}