Dartmouth Events

Title: When Do Voter Files Accurately Measure Turnout? How Transitory Voter File Snapshots Impact Research and Representation

Speaker: Seo-young Silvia Kim, Assistant Professor of Government, School of Public Affairs, American University

Abstract: Voter files are an essential tool for both election research and campaigns, but relatively little work has established best practices for using these data. We focus on how the timing of voter file snapshots affects the most commonly cited advantage of voter file data: accurate measures of who votes. Outlining the panel structure inherent in voter file data, we demonstrate that opposing patterns of accretion and attrition in the voter registration list result in temporally-dependent bias in estimates of voter turnout for a given election. This bias impacts samples for surveys, experiments, or campaign activities by skewing estimates of the potential and actual voter populations; low-propensity voters are particularly impacted. We provide an approach that allows researchers to measure the impact of this bias on their inferences. We then outline methods that measurably reduce this bias, including combining multiple snapshots to preserve the turnout histories of dropped voters.