Seo-young Silvia Kim,  California Institute of Technology

Spencer Schneider, California Institute of Technology

R. Michael Alvarez, California Institute of Technology

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 paper, using data from Orange County, California, we develop

two methods for evaluating the quality of voter registration data as it changes

over time: (1) generating audit data by repeated record linkage across periodic

snapshots of a given database, and monitoring it for sudden anomalous changes;

and (2) 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.