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This repository contains the code and the intermediary outputs generated for Kim, Seo-young Silvia, Spencer Schneider, and R. Michael Alvarez. 2019. "Evaluating the Quality of Changes in Voter Registration Databases." American Politics Research. Article first published online: Sep 9, 2019. DOI: 10.1177/1532673X19.

The administration of elections depends crucially upon the quality and integrity of voter registration databases. In addition, political scientists are increasingly using these databases in their research. However, these databases are dynamic and may be subject to external manipulation and unintentional errors. In this article, using data from Orange County, California, we develop two methods for evaluating the quality of voter registration data as it changes over time: (a) generating audit data by repeated record linkage across periodic snapshots of a given database and monitoring it for sudden anomalous changes and (b) identifying duplicates via an efficient, automated duplicate detection, and tracking new duplicates and deduplication efforts over time. We show that the generated data can serve not only to evaluate voter file quality and election integrity but also as a novel source of data on election administration practices.

The main package used is sysilviakim/voterdiffR. See its GitHub repository for detailed examples (vignettes coming soon.)