Resources

This MIT Election Data + Science Lab analysis explains the 2020 “blue shift,” where later-counted ballots disproportionately favored Democrats, and why that pattern mattered for public interpretation of results.

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.

Employing national surveys from 2012, 2016, 2018, and 2020, this paper that beliefs in election fraud are common and stable across time, and only occasionally relate to partisanship.

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 evaluation report examines philanthropy & trust-building in relation to the entry’s stated focus on election security; confidence; field-building. It is relevant to the dataset because it connects election rules, information environments, or administrative performance to public confidence and perceived legitimacy.

This research finds that a majority of Trump voters in the survey sample falsely believed that election fraud was widespread, and that Trump won the election. It also finds that Trump conceding or losing his legal challenges would likely lead a majority of Trump voters to accept Biden’s victory as legitimate, although 40% said they would continue to view Biden as illegitimate regardless.

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

This analysis focuses on whether counties that had previously been “covered” purged voters at a higher rate than noncovered counties after the coverage formula was struck down. It finds increases in purge rate of between 1.5 and 4.5 points in formerly covered jurisdictions post-Shelby, compared with counties that had not been subject to preclearance. Most of the increase came immediately, as the effect in 2014 is substantively and significantly higher than that in 2016.

There is increasing evidence that voters’ confidence in the outcome of elections, and more specifically, that their vote was counted accurately, is dominated by the whether the voter supported the winning or losing candidate in an election. Authors ask whether this winner (loser) effect is consistent over time and parties. Additionally, they test whether the strength of this effect on voter confidence varies across electoral level (i.e., confidence in a county, state, and nations vote counting).

Utilizing the 2008–2016 Survey on the Performance of American Elections (SPAE), the analysis finds that wait times have a negative effect on confidence as do challenges with the voting equipment and voter registration.

CEIR has surveyed states about voter registration database security every two years since 2018. These surveys have demonstrated widespread best practices in respondent states.