Resources

This report highlights key factors influencing poll worker recruitment and retention, including poll worker pay, implementation of partisan balance requirements, and harassment. It also offers potential strategies legislators can use to improvement address these factors and improvement poll worker recruitment and retention.

This research studies whether characteristics such as election results, turnout, and policies in similar sized counties differ based on the political affiliation of directly elected local election officials. Authors find that regardless of political affiliation, local election officials are more likely to agree on election policies across parties than the general public and that these officials generally do not use their positions to advantage their party.

This study aims to understand poll worker incentives and motivations. Authors analyze original data from a 2015 survey of poll workers during the 2015 British general election. They found that a range of relationships exist between individuals' decision to serve as a poll worker and various incentives, as well as poll workers' socio-economic, social capital, and satisfaction profiles.

This June 2023 report outlines best practices for improving poll worker recruitment. It identifies barriers such as overly restrictive residency and age requirements, heavy reliance on political parties for hiring, and insufficient compensation and training standards. The report recommends concrete reforms to help election administrators build a more diverse and sufficient poll worker workforce.

This report summarizes existing academic literature related to election official and poll worker recruitment, training, and retention. Authors discuss the demographic characteristics of the elections workforce, methods of selection, training programs and barriers to retaining elections workers.

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.

The 2022 Local Election Official Survey provides insight into the challenges and successes facing election administrators by analyzing their workloads, job satisfaction, and efforts and views related to voter education and election confidence. The survey also highlights election officials' growing workloads, feelings of preparedness for the election, and growing voter-centric attitudes.

This report surveys temporary election-worker policies across all 50 states, highlighting both the protections in place and the gaps that remain. It includes a dataset of election worker requirements, including data points for what trainings are required, who is required to attend them, if election worker oaths are required, and what policies are in place to achieve political parity of poll workers.

The 2022 Local Election Official Survey explored the views and opinions of 596 local election officials, finding that most election officials need more support in their roles overall. Election officials noted the mounting stress of political attacks on the election system and threats against election officials. Concerns regarding the impacts of misinformation related to elections were also widespread.

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.