Jian CaoCalifornia Institute of Technology
Seo-young Silvia KimCalifornia Institute of Technology
R. Michael AlvarezCalifornia Institute of Technology
How do we ensure the accuracy and integrity of a statewide voter registration database, which often depends on aggregating decentralized, sub-state data with different list maintenance practices? We present Bayesian multivariate multilevel model to account for common patterns in local data while detecting anomalous patterns, using Florida as our example. We use monthly snapshots of state's voter database to estimate countywide change rates for multiple response variables (e.g., changes in voter's partisan affiliation), and then jointly model their changes. We show that there is much heterogeneity in how counties manage voter lists, resulting in very different patterns in additions, deletions, or changes of records. Our method identifies several Florida counties with anomalous rates of changes in the 2016 election.
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