Resources

This research focuses on how the timing of voter file snapshots affects the most commonly cited advantage of voter file data: accurate measures of who votes.

This research uses difference-in-differences estimates that suggest that same day voter registration disproportionately increases turnout among individuals aged 18–24 (an effect between 3.1 and 7.3 percentage points).

In researching how to ensure a statewide voter registration database’s accuracy and integrity, this analysis develops a Bayesian multivariate multilevel model to account for correlated patterns of change over time in multiple response variables, and label statewide anomalies using deviations from model predictions.

Using monthly data from the Colorado Department of Motor Vehicles from 2017 to 2021, the research studies a series of reforms to the voter registration process conducted by the DMV between 2018 and 2020. Prior to the reforms, a large majority of unregistered DMV patrons declined the opportunity to register when conducting a transaction. When voter registration became the clear default option for certain unregistered Colorado DMV patrons in 2020, very few of them subsequently opted out, which resulted in a sudden, large increase in the rate at which DMV patrons registered to vote.

The results in this article suggest that while convenience measures are designed to improve registration and voting rates, they may not result in across-the-board increases that some policymakers and advocates hope for. Nevertheless, there may be some differential impacts of these innovations on registration and turnout, particularly for youth and minority voters.

In this paper, authors examine the effects of automatic voter registration (AVR) on both registration and turnout. They find that ind it does raise registration rates substantially, that the effect of AVR gradually builds the longer it is in place, and that the different types of AVR have significantly different effects on both registration and turnout.

This book examines the dynamics behind shifts in voter registration rates across the states.

In this paper, authors use snapshots of voter registration files (VRF) over time and machine learning models to test the effectiveness of unsupervised anomaly detection methods in detecting VRF modifications. They find that statistical models comparing administrative districts within a short time span and non-negative matrix factorization are most effective for surfacing anomalous events for review.

In this paper, authors match a high-quality, random sample of the U.S. population to multiple lists revealing that at least 11% of the adult citizenry is not on a voter list. An additional 12% is mislisted (i.e., not living at their recorded address).

This research analyzes registrants in Wisconsin who were identified as potential movers and did not respond to a subsequent postcard. At least 4% of these registrants cast a ballot at their address of registration, with minority registrants twice as likely as white registrants to do so.

This research develops and applies a method to estimate how many people voted twice in the 2012 presidential election. It estimates that about one in 4,000 voters cast two ballots, although an audit suggests that the true rate may be lower due to small errors in electronic vote records.

This research argues that local challenges remain when maintaining voters’ registration and voting history information, which undermines the quality of voter lists and the integrity of the electoral process. It analyzes Mississippi’s Statewide Election Management System (SEMS) records and finds that voter registration and voting history errors are linked to the county’s active and inactive registered voter rates and demographic characteristics.