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Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Records

  • Kosuke Imai (a1) and Kabir Khanna (a2)


In both political behavior research and voting rights litigation, turnout and vote choice for different racial groups are often inferred using aggregate election results and racial composition. Over the past several decades, many statistical methods have been proposed to address this ecological inference problem. We propose an alternative method to reduce aggregation bias by predicting individual-level ethnicity from voter registration records. Building on the existing methodological literature, we use Bayes's rule to combine the Census Bureau's Surname List with various information from geocoded voter registration records. We evaluate the performance of the proposed methodology using approximately nine million voter registration records from Florida, where self-reported ethnicity is available. We find that it is possible to reduce the false positive rate among Black and Latino voters to 6% and 3%, respectively, while maintaining the true positive rate above 80%. Moreover, we use our predictions to estimate turnout by race and find that our estimates yields substantially less amounts of bias and root mean squared error than standard ecological inference estimates. We provide open-source software to implement the proposed methodology.


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Authors' note: We thank Bruce Willsie, the CEO of L2, for the data and answering numerous questions, and the participants of “Building the Evidence to Win Voting Rights Cases” conference at the American Constitutional Society for Law and Policy for their helpful comments. Two anonymous reviewers provided helpful suggestions. The R package, wru: Who Are You? Bayesian Prediction of Racial Category Using Surname and Geolocation, is freely available for download at Replication files for this study are available on the Political Analysis Dataverse at Supplementary materials for this article are available on the Political Analysis Web site.



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