Lessons Learned from the 2020 U.S. Presidential Election: Hindsight is 2020 3031445481, 9783031445484

Table of contents :
Contents
Notes on Contributors
List of Figures
List of Tables
1 Lessons Learned from the 2020 US Presidential Election: Hindsight Is 2020
Introduction
Lessons Learned from the 2020 US Presidential Election: Hindsight Is 2020
Election Administration
Vote Methods, Vote Choice, and Voter Turnout
Election Perceptions
Conclusion
References
Part I The Administration of the 2020 Election
2 Ain’t No Mountain High Enough: What Motivates Poll Workers to Serve During a Pandemic Emergency and Political Unrest
Introduction
Election Administration in the U.S.
Poll Workers as Election Administrators
State of the Poll Worker Field
Motivation and Poll Workers
Challenges to Delivering the 2020 Election
COVID-19
Election Denialism and Violence
Strategies and Innovations Mid-pandemic
Case Selection: Miami-Dade County
Election Administration in Miami-Dade County
Linking Case Selection to the Broader 2020 Electoral Environment
Methods and Design
Measures
Results
Respondent Profile
Descriptive Statistics
Inferential Statistics
Implications and the Future
Appendix A: Miami-Dade County Survey Instrument
References
3 The Impacts of COVID-19 on Election Administration: Perspectives from Local Election Officials in the United States
Introduction
Existing Research
Factors Influencing LEO Confidence in Election Preparedness
Jurisdiction Size
Party Affiliation
Selection Method
Political Culture
Data and Methods
Results
Conclusion
Future Study
Appendix
References
Part II Vote Methods, Vote Choice, and Voter Turnout
4 The Pandemic and Vote Mode Choice in the 2020 Election
The Pandemic and Vote Mode Choice in the 2020 Election
Federal Voting Recommendations
Calculating Risk During the Pandemic
Data, Methods, and Hypotheses
Data
Hypotheses
Cases
Pandemic Effects on Vote-by-Mail
Modeling Effects of Risk on Vote Mode Choice
Results
Primary Election Vote Mode
Discussion and Conclusion
References
5 Access to Voting and Participation: Does the Policy of Limiting Mail-In Ballot Dropbox Locations in Ohio Suppress Voter Turnout?
Introduction
Background and Literature Review
Data and Methods
The Effect of Accessibility of Drop Box on Voting
The Effect of Accessibility on Early Voting (Drop Box Plus In-Person Early Voting)
Discussion and Conclusion
Appendices
A1: Materials and Methods
Data
Empirical Strategies
Case Validation
Graphical Presentation of Our RD Design
A2: Internal Validation
For Distance
For Travel Time
For Auto-Driving Distance
References
6 Vote Choice During a Pandemic: How Health Concerns Shaped the 2020 Presidential Election
Introduction
Trump’s Handling of the COVID-19 Crisis
Candidate Vote Choice and Health Concerns
Data and Empirical Framework
Findings
Discussion
Appendix A: ANES 2020 Survey: Variable Coding
References
Part III The Voter Experience
7 How COVID-19 Election Access Policies Shaped Voter Fraud Beliefs and Voter Confidence in the 2020 US Election
Introduction
The Effects of Election Changes in 2020 on the Public and Partisans
Election Changes, Voter Fraud Beliefs, and Voter Confidence in the General Public
Election Changes, Voter Fraud Beliefs, and Voter Confidence in Partisan Publics
Data and Methods
Analyses
The General Public
Partisan Publics
Conclusion
Appendix A: Summary Statistics
Appendix B: Additional Tables
Appendix C: Voter Fraud Scale Reliability Analyses
References
8 The Tradeoff Between Protecting Voters and Ensuring Access for In-Person Voters During the COVID-19 Pandemic
Introduction
The Importance of Voter Wait Times and Voter Evaluations
COVID-19 and In-Person Voting During the 2020 Election
Data and Methods
Results
Conclusion
Appendix: Summary Statistics
References
Index

Citation preview

ELECTIONS, VOTING, TECHNOLOGY

Lessons Learned from the 2020 U.S. Presidential Election Hindsight is 2020 Edited by Joseph A. Coll · Joseph Anthony

Elections, Voting, Technology

Series Editor Kathleen Hale, Department of Political Science, Auburn University, Auburn, AL, USA

This series examines the relationships between people, electoral processes and technologies, and democracy. Elections are a fundamental aspect of a free and democratic society and, at their core, they involve a citizenry making selections for who will represent them. This series examines the ways in which citizens select their candidates—the voting technologies used, the rules of the game that govern the process—and considers how changes in processes and technologies affect the voter and the democratic process.

Joseph A. Coll · Joseph Anthony Editors

Lessons Learned from the 2020 U.S. Presidential Election Hindsight is 2020

Editors Joseph A. Coll Department of Political Science College of Wooster Wooster, OH, USA

Joseph Anthony Political Science State University of New York Cortland, NY, USA

ISSN 2945-7610 ISSN 2945-7629 (electronic) Elections, Voting, Technology ISBN 978-3-031-44548-4 ISBN 978-3-031-44549-1 (eBook) https://doi.org/10.1007/978-3-031-44549-1 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover illustration: smartboy10/DigitalVision Vectors/Getty Image This Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

Contents

1

Lessons Learned from the 2020 US Presidential Election: Hindsight Is 2020 Joseph Anthony and Joseph A. Coll

1

Part I The Administration of the 2020 Election 2

3

Ain’t No Mountain High Enough: What Motivates Poll Workers to Serve During a Pandemic Emergency and Political Unrest Christina S. Barsky, Amanda D. Clark, Monica A. Bustinza, and M. Blake Emidy The Impacts of COVID-19 on Election Administration: Perspectives from Local Election Officials in the United States Joseph Anthony and Paul Manson

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Part II Vote Methods, Vote Choice, and Voter Turnout 4

The Pandemic and Vote Mode Choice in the 2020 Election Lonna Rae Atkeson, Wendy L. Hansen, Cherie D. Maestas, Eric Weimer, and Maggie Toulouse Oliver

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CONTENTS

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Access to Voting and Participation: Does the Policy of Limiting Mail-In Ballot Dropbox Locations in Ohio Suppress Voter Turnout? Jiehong Lou, Dana Rowangould, Alex Karner, and Deb A. Niemeier

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Vote Choice During a Pandemic: How Health Concerns Shaped the 2020 Presidential Election Enrijeta Shino and Daniel A. Smith

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Part III The Voter Experience 7

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How COVID-19 Election Access Policies Shaped Voter Fraud Beliefs and Voter Confidence in the 2020 US Election Joseph A. Coll The Tradeoff Between Protecting Voters and Ensuring Access for In-Person Voters During the COVID-19 Pandemic Joseph A. Coll

Index

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Notes on Contributors

Dr. Joseph Anthony is an Assistant Professor of Political Science at the State University of New York, College at Cortland (SUNY-Cortland). He has two primary research areas: the first is on elections administration and public policies, and the second is on political parties and organizations in the United States. In the elections arena, he focuses on the work of elections officials, their practices, and their impacts, as well as on electoral reforms such as ranked choice voting. He also has a parallel research area examining political parties, in particular the levels and impacts of decline of local political party organizations in rural areas. His research priorities broadly investigate the electoral and institutional structures that impact political participation, as well as the organizational structures that mobilize this participation. He lives in Syracuse, New York, with his dog and three cats. Lonna Rae Atkeson is the LeRoy Collins Eminent Scholar in Civic Education and Political Science at Florida State University where she also directs of the LeRoy Collins Institute. She is a member of the MIT Data and Election Science Board, the American National Election Studies Board, and an Associate Editor for Political Analysis. Her research focuses on election science, survey methodology, public policy, voting rights, and political behavior. Her research has been supported by the NSF, Pew, Golisano Foundation, Thornburg Foundation, New Mexico Department of Transportation, MEDSL, New Mexico Secretary of State, and Bernalillo County. She received her B.A. from the vii

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University of California, Riverside and her Ph.D. from the University of Colorado at Boulder. Christina S. Barsky, Ph.D. is an assistant professor in the Baucus Institute Department of Public Administration and Policy at the University of Montana. Barsky’s research centers on the nexus of citizen-state encounters in civil society; including, election administration, administrators, and processes; the provision of public health and social services; public perceptions of policy implementation; and, the scholarship of teaching and learning. A sought-after contributor to popular media, Barsky’s recent academic work has appeared in Administration and Society, the International Journal of Public Administration, the Journal of Public Affairs Education, the Journal of Political Science Education, and Public Administration Quarterly. Monica A. Bustinza is a Ph.D. candidate in the Department of Public Policy and Administration at Florida International University (FIU). Her research focuses on local policy implementation and citizen participation in the U.S. with an emphasis on election administration. Currently, Bustinza is the Senior Program Manager for Voter Engagement at Engage Miami, is a board member and Voter Services Chair of the League of Women Voters of Miami-Dade County, and a committee member of FIU’s Voting and Civic Engagement Committee. Dr. Joseph A. Coll is an Assistant Professor of Political Science at the College of Wooster. His research examines the factors that influence the access and administration of elections, how that access and administration affects political behavior and public opinion, and how this then goes on to affect public policy. His work focuses on the general American public, as well as racial, ethnic, and youth groups. His work can be found in American Politics Research; Election Law Journal: Rules, Politics, and Policy; Journal of Race, Ethnicity, and Politics; Politics, Groups, and Identities; Public Opinion Quarterly and more. Amanda D. Clark, Ph.D. is an assistant teaching professor with the Department of Public Policy and Administration in the Steven J. Green School of International and Public Affairs at Florida International University. Dr. Clark’s research focuses on social movements, election administration, and the U.S. policy process. She is the author of Framing the Fight Against Human Trafficking: Movement Coalitions and Tactical Diffusion (Lexington Books, 2019). Her recent journal articles have

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been published in Administration and Society, Research in Social Movements, Conflicts and Change, Administrative Theory & Praxis, and Urban Affairs Review. M. Blake Emidy, Ph.D. is an assistant professor of Public Administration at the University of Montana. His research looks at the organizational factors that contribute to employee motivation and well-being in the public sector, including the effects of downsizing and organizational turbulence. He also investigates differences in employee perceptions of organizational justice at the intersection of gender, race, sexual orientation, and disability status. Emidy received his Ph.D. from the Andrew Young School of Policy Studies at Georgia State University. Wendy L. Hansen is a Professor of Political Science at the University of New Mexico. Her research focuses on public policy broadly, including areas such as campaign finance and voting behavior, with an emphasis on econometric modeling of government, corporate and individual-level decision-making. Her research has been supported by the NSF, Joyce Foundation, and Thornburg Foundation. She received her BA from Lawrence University and her Ph.D. from the California Institute of Technology. Alex Karner is an associate professor in the Graduate Program in Community and Regional Planning at The University of Texas at Austin. His work critically engages with transportation planning practice to achieve progress toward equity and justice. Jiehong Lou is an assistant research professor in the University of Maryland School of Public Policy. Dr. Lou’s research includes climate finance, energy economics, environmental and energy policy, sustainable development, and equity and transportation justice. Her research focuses on mobilization of private finance in financing climate action, evaluation of co-benefits from climate finance projects, behavioral change interventions on energy consumption, and how transportation accessibility imposes disparate impacts on different economic and minority groups. She was a postdoctoral fellow with the Maryland Transportation Institute. She holds a Ph.D. in policy sciences with a focus on environmental and energy policy, an MA in public policy and an MA in applied economics from the University of Maryland; and a BA in public policy from Tongji University in China.

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Cherie D. Maestas is Professor and Head in the Department of Political Science at Purdue University. Her research focuses on representation, accountability, risk attitudes, public opinions, and the political significance of emotions in motivating attitudes and behavior. Her research has been supported by the National Science Foundation, the Army Office of Research, the Smith-Richardson Foundation, and the Carnegie Corporation. She received her Ph.D. from the University of Colorado at Boulder. Paul Manson is a research assistant professor with the Center for Public Service at Portland State University. He is also the Research Director for the Elections and Voting Information Center (EVIC) located at Reed College. Paul’s research in election administration includes four annual surveys of election administrators and in-depth interviews with these administrators. Results of these surveys have been shared in professional and academic publications aimed at elevating the voices of those that administer elections. Paul also conducts public opinion research on environmental policy issues exploring how the public constructs ideas of power and deservingness across communities involved in environmental impacts and climate change. In addition to scholarly research, Paul works with local governments to explore opportunities and challenges in workforce development and diversity, equity and inclusion initiatives through cooperative research programs. He holds a Ph.D. and M.P.A. from Portland State University. Deb A. Niemeier is the Clark Distinguished Chair in Energy and Sustainability at the University of Maryland, College Park; she serves as a professor in the Dept. of Civil and Environmental Engineering and an affiliate professor in the College of Information Studies. She studies how to characterize and respond to risk associated with outcomes in the intersection of the built environment and environmental hazards. Maggie Toulouse Oliver is New Mexico’s 26th Secretary of State. She has served as New Mexico’s Chief Election Official for 6 years and served prior to that as the County Clerk in Bernalillo County, the state’s largest jurisdiction, for 10 years. Toulouse Oliver earned a B.A. in Political Science and Spanish from the University of New Mexico in 2001 and an M.A. in Political Science in 2005. She is currently a Ph.D. Candidate in Political Science at the University of New Mexico. Ms. Toulouse Oliver is the author of several articles and chapters in

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refereed publications. She is a past-president of the National Association of Secretaries of State, among other distinctions, and serves on a number of national advisory committees related to election administration. Dana Rowangould is an assistant professor in the Department of Civil and Environmental Engineering at the University of Vermont. Drawing from the fields of engineering, economics, and the social sciences, Dr. Rowangould’s research includes transportation and land-use policy and planning, transportation justice, rural travel, and accessibility. Dana is also a founding principal of Sustainable Systems Research, an independent consulting firm that conducts research on the health, environmental, and equity impacts of transportation systems in partnership with communitybased organizations. Enrijeta Shino is Assistant Professor of Political Science at the University of Alabama. Dr. Shino holds a Ph.D. in Political Science from the University of Florida. Her research interests focus on elections, voting behavior, public opinion, political methodology, and survey statistics. Her primary research examines how election reform laws affect turnout, voter behavior, and representation, and how survey methodology affects our understanding of mass public opinion. These themes figure prominently in her peer-reviewed articles. Dr. Shino’s work has been quoted in local, national, and international media such as The Washington Post, The New York Times, The Wall Street Journal, Vox, VG, Newsweek, and NPR Daniel A. Smith is Professor and Chair of Political Science at the University of Florida. Dr. Smith holds a Ph.D. in Political Science from the University of Wisconsin-Madison and earned his undergraduate degrees in History and Foreign Affairs from Penn State. His research broadly examines how political institutions affect political behavior across and within the American states. In addition to publishing over 100 peerreviewed articles and book chapters, his books include Tax Crusaders and the Politics of Direct Democracy (Routledge, 1998), Educated by Initiative (University of Michigan Press, 2004), and State and Local Politics: Institutions and Reform (4th edition, Cengage, 2015). He served as a Senior Fulbright Scholar in Ghana, West Africa, and is president of ElectionSmith, Inc. Dr. Smith has served as an expert witness in dozens of voting rights lawsuits in Florida and across the country, several of which have dealt with the curing of VBM ballots. He has worked closely with

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the ACLU, the NAACP LDF, Demos, Common Cause, All Voting is Local, the League of Women Voters, Latino Justice, the Campaign Legal Center, Mi Familia, SEIU, and numerous other voting rights groups. He is widely quoted in the Florida and national media. Eric Weimer is a Junior Data Analyst at Citizen Data where he utilizes a range of research methodologies in data science and survey research to understand broader narratives and insights into key challenges facing society. Previously, he was a postdoctoral researcher in Political Science at Purdue. He received his Ph.D. in Communication in 2021 from the Brian Lamb School of Communication.

List of Figures

Fig. 3.1 Fig. 4.1 Fig. 4.2

Fig. 4.3

Fig. 5.1

COVID-19 battery percent confidence by jurisdiction size Florida and New Mexico vote mode 2004–2020. a Florida. b New Mexico Logistic regression marginal effects of Florida and New Mexico General Election vote mode by age and party. a Florida VBM. b New Mexico VBM. c Florida early vote. d New Mexico early vote. e Florida Election Day. f New Mexico Election Day. g Florida 2016. h Florida 2020 Logistic regression marginal effects of Florida and New Mexico Primary Election vote mode by age and party. a Florida VBM. b New Mexico VBM. c Florida early vote. d New Mexico early vote. e Florida Election Day. f New Mexico Election Day. g Florida 2016. h Florida 2020 Comparison of Ohio absentee presidential general election voting rates by county from 2008 to 2020 (panel A), and overall voting turnout between 2008 and 2020 (panel B). The orange line indicates the statewide average, and the blue and green lines represent Belmont County and Jefferson County, respectively. Absentee voter turnout is the share of votes cast that were absentee over the total ballot votes cast (Data source The Ohio Secretary of State)

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LIST OF FIGURES

Fig. 5.2

Fig. 5.3

Fig. 5.4

Fig. 5.5 Fig. 5.6 Fig. 5.7

Fig. 6.1

Case selection. The county boundary between Belmont County and Jefferson County is short enough to ensure that the residents on/near both sides of the border will have a distinct difference in terms of accessibility to their corresponding drop box locations Travel Distance to the Drop Boxes. The Y -axis is the distance between the voters and their corresponding drop boxes while the x axis represents the S score, represented as the distance to the border, with negative values assigned for the control group (Jefferson). Here, all voters within the bandwidth are plotted. The threshold discontinuity in Case 1 confirms that we have identified a threshold in which the treatment (distance to drop box) is present First-difference (2020 and 2016) in Voter Turnout Global (fourth order) and Local Estimates (first order). The Y -axis is the voter outcome. The X -axis is the score. The scores of Panel A and Panel B are the perpendicular distance from the residential address to the border. The scores of Panel C and Panel D are the travel time from the residential address to the border. The scores of Panel E and Panel F are the auto driving distance from the residential address to the border. The global estimate (left) used a polynomial to fit the observed outcome on the score. The local estimate (right) employed only observations with scores near the county border Continuity of covariates at the cutoff: a graphical analysis (global) Continuity of covariates at the cutoff: a graphical analysis (local) Additional Tests for Interval Validity. Panel A. Placebo cutoffs. Panel B. Sensitivity to observations near the cutoff (donut hole approach). The red circles represent the point estimates of interest, which are obtained from running the GRD specification separately. The blue vertical bars represent the 95% confidence intervals of the estimations Predicted probabilities for vote choice by party registration, ANES 2020 survey (Note Predicted probabilities for support for Trump by party identification, conditioning on statistically significant issues as shown in Model 6 shown in Table 6.2)

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LIST OF FIGURES

Fig. 7.1 Fig. 8.1

Fig. 8.2

Scree plot of Eigen values (see Table 7.9) Voter Wait times in the 2008–2020 Presidential Elections (Source Survey of the Performance of American Elections [2008–2020]) Voter evaluations in the 2008–2020 presidential elections (Source Survey of the Performance of American Elections [2008–2020])

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List of Tables

Table Table Table Table Table

2.1 2.2 2.3 2.4 3.1

Table 3.2 Table 3.3 Table 3.4 Table 3.5

Table 3.6 Table 3.7 Table 5.1 Table 5.2

Table 5.3 Table 5.4

Miami-Dade respondent profile Miami-Dade summary statistics Miami-Dade correlation matrix Miami-Dade regression results Overall LEO responses to COVID-19 and VBM questions Confidence in recruiting poll workers in 2020 Confidence in being able utilize traditional polling places Confidence in being able to safely physically accommodate staff and volunteers Confidence having sufficient staff and resources to process increased numbers of absentee or by-mail ballot applications Confidence in being able to have sufficient time for voters to resolve issues with their absentee ballots Regression models exploring responses to COVID-19 pandemic Comparison among three cases: FD-GRD estimates of voters’ drop box accessibility on voter turnout Geographic regression discontinuity estimates of voters’ distance to drop box on 2020 presidential election turnout Formal continuity-based analysis for covariates Formal continuity-based analysis for covariates (for case 1): travel time

31 31 32 33 57 58 58 59

59 59 66 111

114 125 128

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LIST OF TABLES

Table 5.5 Table 5.6 Table 5.7 Table 5.8 Table 5.9 Table 6.1 Table 6.2 Table 7.1 Table Table Table Table Table

7.2 7.3 7.4 7.5 7.6

Table 7.7 Table Table Table Table

7.8 7.9 7.10 8.1

Table 8.2 Table 8.3

Continuity-based analysis for alternative cutoffs: travel time Continuity-based analysis for the donut-hole approach: travel time Formal continuity-based analysis for covariates (for case 1): driving distance Continuity-based analysis for alternative cutoffs: driving distance Continuity-based analysis for the donut-hole approach: driving distance Descriptive statistics for COVID-19 and vote choice Logistic regression model for COVID-19 and economy effect on vote choice in the 2020 presidential election Effects of election access on voter fraud and voter confidence Summary statistics for the full sample Summary statistics for republicans Summary statistics for democrats Fraud beliefs by fraud type, year, and partisanship The effects of election changes on voter fraud beliefs and voter confidence among the general public The effects of election changes on voter fraud beliefs and voter confidence among partisans Cronbach’s alpha diagnostics of fraud scale Principal factor analysis Factor loadings Effects of COVID-19 safety measures on voter wait times and evaluations Effects of COVID-19 safety policies on Voter Wait Times and Voter Experiences Summary statistics

129 130 131 132 133 150 152 174 178 179 180 181 182 184 187 187 187 204 206 210

CHAPTER 1

Lessons Learned from the 2020 US Presidential Election: Hindsight Is 2020 Joseph Anthony and Joseph A. Coll

Introduction The COVID-19 pandemic upended almost every aspect of society in 2020, causing major shifts in how people interacted with one another as well as to the policies and practices of major American institutions. Congress, state legislatures, and even the Supreme Court, for example, moved to virtual deliberations which changed how policies were discussed and developed in these arenas. 2020 was also a major presidential election cycle, although election officials did not have the same capacity to move all their operations to a virtual format as did other governmental bodies.

J. Anthony (B) Department of Political Science, State University of New York, College at Cortland, Cortland, NY, USA e-mail: [email protected] J. A. Coll Department of Political Science, The College of Wooster, Wooster, OH, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. A. Coll and J. Anthony (eds.), Lessons Learned from the 2020 U.S. Presidential Election, Elections, Voting, Technology, https://doi.org/10.1007/978-3-031-44549-1_1

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J. ANTHONY AND J. A. COLL

Because of the nature of their work, local election officials (LEOs)1 were still required to engage with the public—namely election workers and voters—during the novel pandemic which required them to make many changes to how they administered elections in 2020. Additionally, since the country did not fully lock down until March of 2020, most LEOs did not know these adjustments would be needed until months, or even just weeks, before elections were scheduled. LEOs across the nation had to carefully balance maintaining access to the ballot for all eligible voters with preserving the safety of voters and election workers amid a global public health crisis. Despite these monumental challenges, the 2020 elections were run smoothly and with no major problems or instances of systematic fraud (Departments of Justice and Homeland Security 2021; Eggers et al. 2021). This outcome illuminates the innovative and resilient nature of elections administration in the United States. Much like the proverbial duck that appears to be smoothly gliding across the water in effortless fashion, LEOs were furiously paddling and working below the surface to ensure the country’s elections were run with integrity and without complications that could impact electoral outcomes or compromise public health. This book gives us a chance to step into the world of how LEOs viewed the pandemic from an administrative point of view, and it also

1 The term “local election official,” often shortened to “LEO,” is used throughout this book. This term is used broadly and somewhat differently across the chapters, depending on the objective of the respective authors. While there is a myriad of professional positions in modern election administration (e.g., registrar, assistant/deputy clerk, etc.), the title of local election official/LEO is used consistently throughout the volume to describe the individuals who are in formal, professional positions, and who are the lead public officials for administering elections within their jurisdictions. These officials are chosen either by election or appointment, depending on the state. In some cases, the jurisdiction of the LEO is at the county level, in others it is the city level, village, township, or other level of municipal government. The term “LEO” in this book does not include statewide election officials/authorities, such as Secretaries of State or state Boards of Elections. The titles of local election officials vary; for instance, some are called County Clerks, others are referred to as Directors of Elections, and in Florida local election officials are called the “Supervisors” of elections, among others. In some instances, LEOs work only and entirely on elections, and in other cases running elections is only one part of their jobs. The field of election administration has grown increasingly professionalized and more administratively complex over time, particularly after the 2000 election and subsequent passage of the 2002 Help America Vote Act. This book aims to reflect some of the complexity and illuminate modern challenges facing election administration in the United States.

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allows us to see some of this below-the-surface paddling work done by these “stewards of democracy” in the 2020 elections (Adona et al. 2019). While the immediate danger of the COVID-19 pandemic has largely now been abated, LEOs continue to face substantial challenges to their work, such as increasing public narratives questioning the integrity of US elections broadly, as well as personal attacks directed at LEOs from those who believe fraud is rampant in US elections (Draeger 2023). It is as important as ever to understand the work of LEOs, and how major events like a health pandemic impact the work of these public officials, because the work they do is critical to our democracy and its legitimacy. The 2020 election also witnessed historic changes to the rules and regulations surrounding elections. At the federal level, we saw the first large influx of election administration funds since the 2002 Help America Vote Act. Dubbed the CARES Act, the federal government provided $400 million to the US states to assist in preventing, preparing for, and responding to the pandemic (CARES State Reports 2020). Many states used these funds to expand their vote-by-mail capabilities, others bought protective equipment like face masks and hand sanitizer, while some simply refused the funds. There were also major changes to the voting process across and within states. Nearly half of US states added or altered their ballot drop box policies, one-third removed excuse requirements or allowed COVID-19 as an excuse to request an absentee ballot, several removed notary/witness requirements for absentee voting, a handful automatically mailed registered voters ballots ahead of the election, among other state level changes (Garrett et al. 2020; National Governors Association 2020; Raifman et al. 2020). Locally, election administrators overhauled elections at the jurisdiction-level. Many jurisdictions switched to larger polling places or vote centers to allow for socially distanced voting, equipped these locations with a myriad of COVID-19 prevention policies like protective barriers, engaged in greater poll worker recruitment to help stave off a poll worker shortage, and much more (Election Assistance Commission 2020). How did these changes and the reactions to these changes affect the 2020 election? In this edited volume, emerging and established election scholars document the changes that occurred in 2020, examine their effects, and highlight their broader implications with a focus on the work of local election officials, the motivation of poll workers, and the experiences and behavior of the American electorate. The content of these chapters reflects in-depth work aimed at understanding what happened

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regarding COVID-19 and elections in 2020, what went right (and wrong), and why it matters. The remainder of this introduction recaps the findings of the subsequent chapters, highlights the lessons learned from each study, and provides tangible take-aways and recommendations based upon the problems faced in 2020.

Lessons Learned from the 2020 US Presidential Election: Hindsight Is 2020 Election Administration Part “The Administration of the 2020 Election” of this edited volume is concerned with election administration from the perspective of poll workers and election officials, examining who volunteers to be a poll worker during a pandemic and how election officials perceived the changes that occurred during the 2020 election. Utilizing a unique survey of Florida poll workers, Christine Barsky, Amanda D. Clark, Monica Bustinza, and M. Blake Emidy examine what motivates individuals to volunteer to be on the frontline of elections during a health pandemic. They find normative reasons (e.g., civic duty, make a difference), affective reasons (e.g., enthusiasm to be a poll worker), and financial motivations (e.g., wanting to make money) all play significant roles. They also find, however, that normative motivations may be a stronger driving force than either affective or financial. Two lessons are immediately clear from this chapter: first, to motivate individuals to work the polls during times of crisis, election administrators may need to appeal to their normative sense of civic duty. Second, we should also be mindful of the differential role of financial motivations by demographics. For instance, while the average poll worker may not sign up for the money, monetary compensation may be a motivating factor for some (e.g., students). Last, the authors highlight several issues of concern for poll workers, including election denialism, election violence, the exodus of experienced poll workers, and the difficulty in recruiting new poll workers, particularly absent ample funding. Chapter 3 of this volume examines how local election officials perceived changes to elections in 2020, particularly how they logistically prepared for elections during a global pandemic and how they viewed the shift to substantially more people choosing to vote by mail (VBM). From an invaluable survey of local election officials conducted by the Elections

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and Voting Information Center at Reed College, Joseph Anthony and Paul Manson document some of the first reactions to how COVID-19 impacted elections administration. Specifically, the authors explore how confident LEOs felt in preparing to keep voters and election workers safe through the voting process, as well as how confident they felt in their abilities to ramp up their vote-by-mail systems. Anthony and Manson look for patterns in LEOs’ confidence levels across different dimensions related to LEOs, such as jurisdiction size, partisanship of LEO, selection method, and the political culture of the states in which they work, among other factors. In this chapter, Anthony and Manson find that jurisdiction size is an important factor in LEOs levels of confidence in being prepared for COVID-19 and a substantial increase in vote-by-mail ballots. Their study finds mixed and interesting results for understanding LEOs confidence levels across partisanship, selection method, and states’ political culture as well. Vote Methods, Vote Choice, and Voter Turnout In Part “Vote Methods, Vote Choice, and Voter Turnout” of this volume, three teams of researchers examine how the COVID-19 pandemic influenced whether Americans voted, how they voted, and who they voted for. Starting with Jiehong Lou, Dana Rowangould, Alex Karner, and Deb A. Niemeier’s chapter on ballot drop box access, the authors examine how availability of ballot drop boxes influences voter turnout. Utilizing a firstdifference-geographic-discontinuity design with information on ballot drop boxes in the state of Ohio, the authors find greater access to ballot drop boxes resulted in significantly and substantially larger turnout rates. Simply put, when you allow citizens to vote at drop boxes and those drop boxes are conveniently located, it is easier for individuals to cast a ballot. What can we learn from the role of drop boxes during a pandemic? First, greater ballot drop box access can increase voter turnout by making voting more accessible to the electorate (see also Collingwood et al. 2018; McGuire et al. 2020). This becomes particularly important during a health pandemic that was exacerbated by greater in-person gatherings (Cotti et al. 2021). Second, however, we also see how claims of voter fraud at drop boxes spurred a call to arms for citizen militias to occupy drop box locations. For example, Maricopa County, Arizona, made headline news after voters filed complaints of voter intimidation

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at a ballot drop box outside of the Maricopa County election headquarters. The complainants alleged, and photos showed, armed vigilantes clad in camouflage and military gear stationing themselves near ballot drop boxes. There were also claims of verbal voter intimidation sometimes accompanying these instances. This necessitates that we not only think about the number of drop boxes and where to put them, but also how to ensure election security and prevent voter intimidation at drop box locations. Moving forward, election administrators need to confront the “drop box location problem (Schmidt and Albert 2023),” that is, how to maximize the utility of each drop box, as well as how to prevent voter intimidation while simultaneously ensuring election security. Assuming one chose to vote in 2020, what determined how they decided to cast their ballot? Lonna Rae Atkeson, Wendy L. Hansen, Cherie D. Maestas, Eric Weimer, and Maggie Toulouse Oliver examine how perceptions of risk interact with partisanship to influence whether someone voted by mail, early in-person, or in-person on Election Day. Analyses of voter file data from Florida and New Mexico show how age and partisanship interacted to influence vote mode choice. Prior to the 2020 election, few voters voted by mail in either state, while many voted early or on Election Day. However, the authors demonstrate large switches to voting by mail for the 2020 election, and though Democrats were more likely to vote by mail than Republicans, older individuals of either party were more likely to vote by mail than younger co-partisans, suggesting the perceived health risks of voting in-person attenuated the partisan signals regarding the (un)reliability of voting by mail. The authors note several lessons that can be gleaned from their study. Most notably, the divergent partisan elite rhetoric surrounding the threat of COVID-19 and the reliability of mail voting likely caused partisan and polarized decision making. As the authors note, “Democrats emphasized that VBM was a safe and reliable alternative to in-person voting, while Republicans, especially President Trump, emphasized the potential for fraud.” They contend, “[t]hese narratives about the integrity (or lack thereof) of VBM provided incentives for Democrats and Republicans to polarize around vote mode creating a party gap in behaviors that did not exist prior to the pandemic.” Given these results, election administrators should not only focus on providing access to voting, but also consider the ways in which they can disseminate information about the measures taken to ensure the safety of voters to counteract (misinformed) partisan rhetoric (see also Suttmann-Lea and Merivaki 2023).

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These previously mentioned chapters have examined issues related to whether one voted and through which method they voted, but how did the COVID-19 pandemic influence for whom they voted? Enrijeta Shino and Daniel A. Smith examine this question via an analysis of the American National Election Study. They argue the divergent partisan rhetoric surrounding the seriousness of the pandemic—Republicans downplaying the health threat while Democrats did the opposite—led those who perceived COVID-19 as a greater health threat to be more likely to vote against the incumbent Republican president. Their findings largely support this assertion: those who had higher (lower) approval of how Trump handled the COVID-19 crisis were more (less) likely to vote for his reelection. Further, though these beliefs did not fully override partisan effects, the authors also find that these perceptions were important for both parties. For both Republicans and Democrats, concerns with the COVID-19 pandemic moderated the extent to which they were willing to support Trump’s second bid for the White House. Shino and Smith demonstrate the persistent role of health considerations on political behavior during health crises, including the potential for these considerations to overcome pervasive partisan attachments. As such, one major lesson we can take away from this study is the ability of the American people to hold elected officials accountable. Like with the economy (Lewis-Beck and Martini 2020), when voters feel as if the sitting president is doing a poor job handling a health crisis, they voice their dissatisfaction through the ballot box. At the same time, partisanship still played a large role, suggesting limits to the extent to which voters punish co-partisan elected officials. Election Perceptions The changes to the 2020 US election not only affected how elections were administered and how voters behaved, but also their perceptions of the safety, efficiency, and legitimacy of the election. In the last section of the edited volume, Joseph Coll examines how the changes to voter access influenced voter confidence, and how the use of in-person COVID-19 safety protocols altered voter perceptions of election administration. Starting with the effects of changing election access, Coll investigates whether the policy changes made to accommodate the COVID-19 pandemic influenced voter fraud beliefs and whether voters were confident in their ballot being counted as intended. As mentioned above, many

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states made substantial changes to their registration and voting process, including automatically mailing ballots to registered voters, reducing signature requirements, removing excuse requirements, and much more. At the same time, however, these changes were viewed very differently by Republicans and Democrats, with Republicans against and Democrats in favor. How did these changes affect fraud beliefs and confidence and were there partisan asymmetries to these effects? Interestingly, Coll finds that election changes, on the whole, do influence fraud and confidence perceptions, but the specific changes made to accommodate COVID-19 did not. What can we learn from this chapter? First, election changes can affect voter perceptions of election legitimacy in the general public and among partisans. Second, however, we also learn that large changes are needed to bring about substantive changes in election perceptions on the part of voters, suggesting any single change will not drastically alter voter fraud beliefs or voter confidence. Lastly, and in line with existing work (Bowler and Donovan 2016), it is important to consider partisan differences in these effects. Given the invalidity of many of the claims of the antivoter access proponents (Auerbach and Pierson 2021; Eggers et al. 2021) and the ability of election officials to correct the record (Suttmann-Lea and Merivaki 2023), these results suggest the dissemination of counterpartisan signals may be needed to avoid divergent effects of election access changes, but, again, offer little evidence that the COVID-19-specific changes influenced fraud beliefs or voter confidence. The last chapter of this section examines how the addition of COVID19 protective policies (e.g., face masks, routine booth cleaning, and socially distanced voting) may have created a tradeoff between perceived safety and positive election evaluations on the one hand, and voter access on the other. Examining six COVID-19 protection policies, this last chapter demonstrates how two policies, routine booth cleaning and socially distanced voting, incurred such a tradeoff by increasing wait times while also increasing feelings of safety and positive evaluations. At the same time, several policies were found to either decrease wait times or increase election perceptions. These results suggest some COVID19 policies may have negative effects, but that many did not and were beneficial to the voting process. Given these results, this chapter provides clear suggestions for how to protect in-person voters from a health crisis while also having little to no negative effects on voter access or evaluations. At the same time, for

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those two policies that do demonstrate a tradeoff, it is worth emphasizing that the takeaway is not that these policies should be removed, but rather they should be reexamined to determine how to implement them without causing negative effects. Where such negative effects cannot be reduced, administrators should look toward ways to allow access to the ballot box without needing such policies (e.g., increased mail and absentee voting). Further, the results of this chapter should be combined with evidence on the protection efficacy of these policies when deciding which, whether, and how to implement them.

Conclusion This edited volume sets out to improve our understanding of the changes that occurred to elections in 2020 and their impacts, specifically with an eye toward how LEOs adjusted to holding elections during a global pandemic. By focusing on the work of election officials and pollworkers, as well as illuminating the impacts on voter behavior and voter perceptions, this volume provides insights into how election administration shifted during the 2020 US elections and provides several lessons gleaned from these changes. The common thread of these chapters is that elections matter, even—and maybe especially—during a global health crisis. Americans stepped up to do their duty as poll workers, election administrators faced their jobs effectively and head-on, voters demonstrated sophisticated voting behavior, and all of this culminated in a unique election environment that affected how voters and LEOs interacted during the 2020 election. May we never have to conduct another election during such uncertain times, but if we do, the lessons laid out in this edited volume should serve as a guide for those administering and researching US elections. After all, hindsight is 2020!

References Adona, Natialie, Paul Gronke, Paul Manson, and Sara Cole. 2019. “Stewards of Democracy: The Views of American Local Election Officials.” Report issued by the Democracy Fund. https://democracyfund.org/idea/stewards-of-dem ocracy-the-views-of-american-local-election-officials/. Auerbach, Jonathan, and Steve Pierson. 2021. “Does Voting by Mail Increase Fraud? Estimating the Change in Reported Voter Fraud When States Switch to Elections by Mail.” Statistics and Public Policy 8 (1): 18–41.

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Bowler, Shaun, and Todd Donovan. 2016. “A Partisan Model of Electoral Reform: Voter Identification Laws and Confidence in State Elections.” State Politics & Policy Quarterly 16 (3): 340–61. CARES State Reports. 2020. U.S. Election Assistance Commission. https:// www.eac.gov/payments-and-grants/cares-state-reports (May 24, 2023). Collingwood, Loren et al. 2018. “Do Drop Boxes Improve Voter Turnout? Evidence from King County, Washington.” Election Law Journal 17 (1): 58–72. Cotti, Chad et al. 2021. “The Relationship Between In-person Voting and COVID-19: Evidence from the Wisconsin Primary.” Contemporary Economic Policy 39 (4): 760–77. Departments of Justice and Homeland Security. 2021. Joint Statement from the Departments of Justice and Homeland Security Assessing the Impact of Foreign Interference During the 2020 U.S. Elections. Press Release. https://www.jus tice.gov/opa/pr/joint-statement-departments-justice-and-homeland-securityassessing-impact-foreign (June 14, 2023). Draeger, Saige. 2023. “As 2024 Campaigns Begin, States Confront Threats to Election Workers.” National Council of State Legislatures (April 26). https://www.ncsl.org/state-legislatures-news/details/as-2024campaigns-begin-states-confront-threats-to-election-workers. Eggers, Andrew C., Haritz Garro, and Justin Grimmer. 2021. “No Evidence for Systematic Voter Fraud: A Guide to Statistical Claims About the 2020 Election.” Proceedings of the National Academy of Sciences 118 (45). Election Assistance Commission. 2020. Election Administration and Voting Survey 2020 Comprehensive Report: A Report from the U.S. Election Assistance Commission to the 117th Congress. Election Assistance Commission. Garrett, R. Sam, Sarah J. Echman, and Karen L. Shanton. 2020. Congressional Research Service Report R46455: COVID-19 and Other Election Emergencies: Frequently Asked Questions and Recent Policy Developments. Congressional Research Service. https://crsreports.congress.gov/product/ pdf/R/R46455. Lewis-Beck, Colin, and Nicholas F. Martini. 2020. “Economic Perceptions and Voting Behavior in US Presidential Elections.” Research & Politics 7 (4): 2053168020972811. McGuire, William et al. 2020. “Does Distance Matter? Evaluating the Impact of Drop Boxes on Voter Turnout.” Social Science Quarterly 101 (5): 1789–1809. National Governors Association. 2020. “COVID-19 Health and Safety Measures for Elections.” National Governors Association. https://www.nga.org/public ations/election-health-safety-covid-19/ (December 1, 2022). Raifman, Julia et al. 2020. “COVID-19 US State Policies.” www.tinyurl.com/ statepolicies.

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Schmidt, Adam, and Laura A. Albert. 2023. “The Drop Box Location Problem.” IISE Transactions: 1–24. Suttmann-Lea, Mara, and Thessalia Merivaki. 2023. “The Impact of Voter Education on Voter Confidence: Evidence from the 2020 US Presidential Election.” Election Law Journal: Rules, Politics, and Policy.

PART I

The Administration of the 2020 Election

CHAPTER 2

Ain’t No Mountain High Enough: What Motivates Poll Workers to Serve During a Pandemic Emergency and Political Unrest Christina S. Barsky , Amanda D. Clark , Monica A. Bustinza, and M. Blake Emidy

Introduction The U.S. 2020 presidential election was unlike any before. The hotly contested presidential contest, wide-ranging conspiracy theories, and the global COVID-19 pandemic contributed to a contentious electoral environment. COVID-19 forced election administrators throughout the U.S. to improvise: requiring shifts in the electoral process designed to keep a scarce election workforce safe and public education related to mail balloting and processing. These shifts were expensive, with experts estimating that administering the 2020 elections cost about $10 billion, with many individual districts stating their budgets increased by at least 50%, if not more (Stewart 2022). These extraordinary costs were funded through

C. S. Barsky (B) · M. B. Emidy University of Montana, Missoula, MT, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. A. Coll and J. Anthony (eds.), Lessons Learned from the 2020 U.S. Presidential Election, Elections, Voting, Technology, https://doi.org/10.1007/978-3-031-44549-1_2

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a combination of state and local funds, federal funds, and outside grants. For example, the federal CARES Act, administered through the Election Assistance Commission (EAC), provided $400 million dollars across the country, while the Center for Tech and Civic Life provided another $350 million dollars (Center for Tech & Civic Life, n.d.) to provide election officials with resources to prepare for and respond to COVID-19 during the 2020 election cycle. In addition to managing and addressing administrative challenges posed by the pandemic, local election officials (LEOs) also experienced difficulties in recruiting individuals to serve as poll workers in their jurisdictions. Election after election, LEOs across the country depend on the same group of mostly senior-aged workers to run elections. The susceptibility of senior citizens to COVID-19 caused LEOs to scramble for poll workers as many decided that it was not worth the health risk to serve during 2020. Many voters were also concerned about potential COVID19 exposure from voting in person and looked for safer alternatives to cast their ballot. The need for personal protective equipment (PPE), reliance on new and younger poll workers, and an increase in mail voting created an atmosphere ripe for confusion and mistakes. In addition, tactics to spread misinformation and disinformation about election integrity were weaponized by President Trump in order to cast doubt on any potential loss. Despite all this, in November 2020, nearly six in ten Americans indicated they believed the November election was administered very or somewhat well (Pew Research 2020). This chapter reviews the impact of COVID-19 and election denialism on poll workers’ recruitment, retention, and training in 2020. We begin with a brief primer on how elections

M. B. Emidy e-mail: [email protected] A. D. Clark · M. A. Bustinza Florida International University, Miami, FL, USA e-mail: [email protected] M. A. Bustinza Engage Miami, Miami, FL, USA e-mail: [email protected]

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are administered in the U.S., followed by a review of the academic literature on the role of poll workers in American elections. We also analyze the innovations LEOs used to keep their poll workers, employees, and voters safe and whether or not those extraordinary measures can be used in the future without special funding. Finally, we ask what motivated individuals to serve as poll workers in the unprecedented environment surrounding the 2020 general election. We present the case of Miami-Dade County, Florida, and review its innovations and strategic response to the challenges of the 2020 election and analyze survey data from the county’s poll workers.

Election Administration in the U.S. Elections in the U.S. are an outlier in the democratic world as they are not administered centrally, but locally, with state and local governments in control of the materials, procedures, personnel, and, in general, costs of running elections. While there are layers of federal government oversight, mostly through interpreting election rules in the courts and keeping minimum standards of uniformity through laws like the National Voter Registration Act (NVRA) of 1993 and the Help America Vote Act (HAVA) of 2002, elections in the U.S. remain a local affair. Today, the rules for how elections are delivered is an amalgamation of state and local laws organized beneath “an umbrella of federal policies” (Montjoy 2008, 788). In essence, federal elections, like those for the presidency, are concurrent statewide (and territorial) elections administered by more than 8,000 local jurisdictions. Local control over elections provides both benefits and costs. Despite claims to the contrary, it is actually very difficult to mount large-scale fraud over such a fragmented system (Feldman 2020). Study after study and audit after audit show isolated cases of misconduct, none so egregious as to ever swing an election at the state or national level. While this system offers some protection against large-scale fraud, imbalances in resources across jurisdictions can lead to issues of quality and fairness. The perceived quality of local elections depends on the confidence in local officials and poll workers (Claassen et al. 2008; Garnett 2019). However, the quality of poll worker training, polling locations, voting equipment, and materials is dependent on local capacity. Further, less affluent, or more sparsely populated, voting districts may lack access to the same levels of resources as larger, richer districts (Creek and Karnes 2010; James and Jervier 2017;

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Montjoy 2010). The importance of well-trained, supported, and representative poll workers is essential to the perceived and actual quality of U.S. elections. We review this concept in more depth in the next section.

Poll Workers as Election Administrators Before and on Election Day, voters who cast their ballots at in-person polling places interface with a workforce of citizen poll workers. Scholars who investigate the electoral process have classified these individuals as stipend volunteers (Clark and James 2021), street-level bureaucrats (Kimball and Kropf 2006), or temporary workers (Suttmann-Lea 2020). Whatever their moniker, it is an “army of poll workers” that is responsible for interacting face-to-face with voters at polling places throughout the U.S. (Burden and Milyo 2015, 38). In short, U.S. elections rely on the participation of over one million election workers for the delivery of the democratic process. The individuals who staff voting locations have many names; including, poll workers, election workers, election judges, polling officials, polling managers, and more. For the purpose of this chapter, these individuals are broadly conceptualized as poll workers. Poll workers undertake vital tasks including greeting and directing voters, verifying voter eligibility, configuring equipment and polling places, providing troubleshooting and technical support, facilitating the provisional ballot process, transporting materials, and in some locations, registering voters and updating voter information on Election Day. In addition to tasks like these performed at polling locations, poll workers may also serve behind the scenes and undertake tasks like opening and preparing mail and absentee ballots, tabulating votes, verifying signatures, canvassing votes, among other responsibilities.

State of the Poll Worker Field Experienced poll workers are the foundation of successful election administration in the U.S. During the 2016 election cycle, LEOs operated approximately 120,000 polling locations staffed by close to 918,000 poll workers throughout the country (EAC 2017). Recruiting nearly one million individuals to work the long hours for little pay synonymous with elections is no easy task. In fact, 65% of LEOs reported poll worker

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recruitment was very or somewhat difficult in 2016. Furthermore, available data from the EAC indicates that poll workers are disproportionately older than the citizens they serve. Thirty-two percent of poll workers in 2016 were between the ages of 61 and 70 while nearly one quarter were aged 71 or older (EAC 2017). Data collected following the 2018 federal election cycle indicated that more than two-thirds of poll workers were over the age of 61 in the U.S. (EAC 2018). Individuals who identify as women, white, and have completed some level of post-secondary education are well represented in the poll worker ranks (Manson et al. 2020). Yet, the challenges associated with the 2020 election cycle seemingly contributed to a diversification of the sociological characteristics of the poll worker workforce. In particular, findings from the EAC’s Election Administration and Voting Surveys (EAVS) uncovered significant changes in the age distributions of American poll workers between 2016 and 2020. In 2020, lower rates of participation in poll work were reported for individuals over the age of 61. At the same time, younger workers aged 26–40 served as poll workers at significantly higher rates when compared to 2016. Prior scholarship suggests that the increased participation of first-time poll workers in 2020 was due to an uptick during a historic period in American history (Velten and Hughes 2020). The anticipated shortage of election workers in 2020 was well documented by popular media. Beyond the work of state and local officials, nongovernmental groups contributed to efforts designed to shore-up the ranks of community poll workers. For instance, entities ranging from Comedy Central, Uber, Levi Strauss & Co., the Fair Elections Center, and Patagonia worked to recruit poll workers in advance of the 2020 general election (Sprunt 2020). Student groups like the Alliance for Students at the Polls and other dedicated youth poll worker programs contributed to bolstering the ranks of poll workers under the age of 25. Furthermore, initiatives like Power to the Polls emerged mid-pandemic and have continued following the 2020 election cycle, working to increase civic participation in election work. Questions remain about whether LEOs can maintain the interest of younger people to continue to serve, particularly in nonpresidential election years. While some poll workers were hesitant to continue working in the 2020 environment, others felt a renewed sense of purpose to help defend democracy and learn more about the process (Brangham et al. 2022; Wang 2022). In an atmosphere of increased partisan scrutiny

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and an ongoing pandemic, LEOs need to understand the motivations of those who want to serve and what can be done to protect them from both physical threats and health concerns. In the next section, we discuss the concept of motivation broadly and the motivations of poll workers specifically.

Motivation and Poll Workers The provision of essential public services, like democratic elections, is reliant upon human resources. In general, workers are the primary asset of public organizations, meaning these organizations depend upon their human capital to achieve mandated outcomes. As a result, motivation is considered a critical factor in “both the provision of public service and the quality of public sector work” (Ritz et al. 2016, 2). As voter perceptions of quality elections have been shown to contribute to public belief in democratic legitimacy (Bowler et al. 2015) and positive, high-quality interactions with poll workers have been documented to have a significant impact in whether citizens believe elections produce fair outcomes (Atkeson and Saunders 2007; Hall et al. 2009), ensuring qualified and competent poll workers are motivated to serve is essential for successful election administration. For the purpose of this chapter, motivation refers to “forces that energize, sustain, and direct behavior” (Perry and Porter 1982; Perry et al. 2010, 681). The concept of motivation is pivotal to public administration and therefore election administration for several reasons. First, demographic trends including a declining birth rate will produce a future glut in the labor supply. Given the preponderance of retirement-age individuals in the election workforce, these shifts will be felt significantly by LEOs, who will need to strengthen their understanding of motivation in order to recruit, and retain, sufficient numbers of poll workers. Second, because organizational performance has been tied to worker motivation, increased pressure for enhanced public sector service delivery means managers interested in organizational performance must consider what motivates their workers. In addition, because societal goals, like free and fair elections and guaranteeing citizens’ rights to access the ballot box, are measures of public sector and electoral performance, it is important to uncover whether these broad goals make up the foundation of motivation and public sector performance. In sum, the “motivation of individuals is essential in keeping a balance between outcome and process values, such as equity and quality,

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regardless of whether or not these aspects are monitored” (Ritz et al. 2016, 2; Vandenabeele and Van Loon 2015). Although their work is essential for the delivery of elections, relatively little is known about why individuals are motivated to participate in poll work (Clark and James 2021). Largely due to the unusual circumstance of their work, poll workers have previously been described as a “singular hybrid of volunteer and public servant” and are motivated to work American elections due to a sense of civic responsibility and interests in supporting the democratic process (McAuliffe 2009, 16). Others suggest that poll worker motivation is driven by personally-held beliefs about their individual contributions to society, the expectations of citizenship, and an interest in learning more about the American electoral process (Atkeson et al. 2011). Recent research investigating why poll workers serve finds motivations clustered around four facets: (1) civic duty; (2) social engagement; (3) material compensation, and (4) social desirability (Barsky 2020). Scholars in Great Britain conceptualize poll worker motivations to belong in three categories: (1) solidary, or being civically-engaged with other like-minded individuals; (2) purposive, or achieving identified aims and goals; and (3) material, or tangible pay or career benefits (Clark and James 2021). In Mexico, researchers argue that the reasons workers choose to participate in delivering elections depend upon “the context in which voters socialize;” highlighting new processes that influence motivation (1) electoral competition and (2) criminal violence (Cantú and Ley 2017, 1). Yet, the environment surrounding the 2020 elections presented novel challenges to delivering American elections, and in turn, created an unprecedented window into understanding why individuals choose to participate in poll work.

Challenges to Delivering the 2020 Election COVID-19 The environment surrounding the 2020 election cycle introduced new, acute pressures on the administrative process. On January 20 of that year, the Centers for Disease Control and Prevention (CDC) reported the first laboratory-confirmed case of what became known as COVID-19 in the U.S., just two weeks before Iowa’s first-in-the-nation primary (caucus) contest on February 3. By March 11, after 118,000 confirmed cases

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and 4,291 deaths worldwide, the World Health Organization (WHO) declared COVID-19 a pandemic (CDC 2022). By this date, primaries and caucuses had already taken place in 25 states.1 On March 15, states throughout the U.S. began implementing shutdowns and other preventative measures to stave the spread of COVID-19, yet by April 13, most states reported widespread cases of COVID-19. With the pandemic expanding, shortages of PPE like gowns, masks, and eye protection were reported across America. In October 2020, staff at the White House and the President of the U.S. tested positive for COVID-19. Following record participation in the November 3 election, the U.S. reports 100,000 new cases of COVID-19 24 hours later, on November 4, 2020. On December 14, about six weeks after the election, the first American outside of clinical trials received a COVID-19 vaccine (CDC 2022). The risk of severe illness, and death, from contracting COVID-19 increases with age (CDC 2021). Detailed in the prior section, poll workers are typically older Americans. Many experienced poll workers choose to stay home during the spring 2020 primaries, leading to issues including the closure of voting locations, understaffed polling locations, and long lines at polling places (Sprunt 2020), leaving LEOs scrambling to ameliorate anticipated staff and poll worker shortages for the November general election (EAC n.d., 7). Reporting on lessons learned from the 2020 primaries, the Cybersecurity and Infrastructure Security Agency (CISA) Elections Infrastructure Government Coordinating Council and Sector Coordinating Council’s Joint COVID-19 Working Group articulated that: the loss of experienced poll workers can have ripple effects throughout the electoral process…whether it is the loss of experience in election operations or a loss of workers at a level that necessitates closing or consolidating voting locations, recruitment, training, and retaining poll workers has been a major thread of 2020 primary elections. (EAC n.d., 7)

Despite concerns stemming from the pandemic, LEOs and poll workers endeavored to provide voters with a sense of safety. While a 1 Iowa (February 3); New Hampshire (February 11); Nevada (February 22); South Carolina (February 29); Wyoming (February–March); Alabama, Arkansas, California, Colorado, Maine, Massachusetts, Minnesota, North Carolina, Oklahoma, Tennessee, Texas, Utah, Vermont, Virginia (March 3); Idaho, Michigan, Mississippi, Missouri, North Dakota, Washington (March 10) (NCSL 2022).

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significant number of voters chose to take advantage of vote-by-mail to avoid exposure to COVID-19, in-person voter turnout was still very high in 2020 (Desilver 2021). The MIT Election Science and Data Lab found that most voters reported seeing their poll workers employ a plethora of health precautions; 87% of voters reported poll workers wearing masks, 74% noted the availability of hand sanitizer, and 68% reported adequate six-feet social distancing indicators. In that same report, 98% of voters stated they had no problem with their voter registration, 97% reported that there were no problems with voting equipment, and 80% agreed with the statement that their polling place was run well (Stewart 2021). Importantly, prior work uncovered that voters viewed their overall experience with voting and interactions with poll workers more positively in locations with more COVID-19 safety precautions during 2020 (Coll 2022). Unfortunately, poll workers had to deal with other challenges during the 2020 election cycle, including election denialism, threats, and violence some of which were endorsed and amplified by elected U.S. officials. Election Denialism and Violence Poll workers in 2020 experienced increasing threats to their safety and expertise by election deniers. While most Americans felt the 2020 elections were run fairly and accurately, this number was made up of mostly Democrats and Independents. In a 2021 poll, two-thirds of Republicans did not trust 2020 election results (Montanaro 2021). Distrust of official results and processes are felt keenly by those on the front lines as they are the ones most likely to face questions, accusations, threats, and violence at the hands of election deniers. In addition to their normal training, starting in 2020, poll workers also had to learn how to better control non-solicitation zones, protect voters from intimidation, and remain calm in the face of increased scrutiny by poll watchers, many of whom are not trained by LEOs (Sherman and Stepnick 2022). For example, in West Boca Raton, Florida, a poll worker alleged that supporters of then-president Donald Trump used their vehicles, adorned with “MAGA, KAG and QAnon flags,” to block voter access to the polls; poll workers were “threatened, harassed, taunted, harangued and even physically assaulted” (Kasky 2020). In the week prior to the general election, the Armed Conflict Location and Event Data project (ACLED) and MilitiaWatch, two organizations that track extremist activity in the U.S., issued a warning that extremist groups and other non-state actors posed

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a “serious threat to the safety and security of American voters” (2020, 1; Westphal 2021). These documented and undocumented instances of denialism and violence have consequences to the continuation of free and fair American elections, and the individuals responsible for their delivery. Violent contexts, according to Cantú and Ley (2017), add significantly to an already burdensome load shouldered by poll workers and that “in an insecure environment, the incentives to take part in electoral administration are likely to decrease” (2). Also worrisome is that election deniers are signing up to serve as poll workers themselves, pledging to disrupt processes they see as corrupt or opaque; monitoring these threats causes more work for their fellow poll workers as they now need to serve the public and keep a close eye on deliberate sabotage (Miller 2022b). Research has also shown that believers in conspiracy theories often believe in many different ones, compounding the pressures felt by poll workers in the 2020 election cycle. Those that believed COVID-19 was a hoax were more likely to believe the 2020 elections were fraudulent. As LEOs developed safety precautions to protect their volunteer work force, election/COVID-19 deniers sought to circumvent those precautions (Caldwell 2022). Many refused to wear masks, and due to legal battles leading up to the 2020 elections, voters could not be denied entry to vote in most places due to not wearing a mask (Izaguirre 2020). The inability to mandate masks for voters in polling places led to more poll worker shortages. The COVID-19 pandemic and growth in election denialism contributed to accelerated concerns, and realities, of an aging and understaffed election workforce. While these issues underscore the unique complexities faced by voters and election administrators in 2020, they also illuminate the precarious nature of U.S. elections. Understanding how and why individuals choose to serve as poll workers is essential for the continued successful administration of free and fair elections in America. In the next section, we highlight strategies and innovations created during the pandemic.

Strategies and Innovations Mid-pandemic Innovation in election administration is essential to the successful delivery of the democratic process (Barsky 2020). Following the workforce shortages that affected the 2020 primary elections, states adjusted their requirements for poll worker eligibility and sought creative solutions to

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inadequate polling place staffing. Between the spring primaries and the November general election, the requirements for poll workers to reside in the jurisdiction they serve were waived in some states. In other states, emergency backup teams of poll workers, or in some localities, off-duty National Guard members, were established to be deployed in the event of workforce shortages on Election Day (EAC, n.d.). For example, in Michigan, Secretary of State Jocelyn Benson developed and launched the Democracy MVP program in May 2020 to address the state’s need for individuals to serve as poll workers during the COVID-19 pandemic. Michigan jurisdictions are consistently recruiting younger poll workers and retaining the individuals who served for the first-time during the 2020 election cycle (Reinhardt 2023). Some states were able to increase pay or create bonuses for service. For example, in Alaska, the Division of Elections enacted an emergency pay increase for poll workers from $12 to $15 an hour for working during the COVID-19 pandemic (Kitchenman 2020). Across the nation, LEO’s secured and strengthened partnerships with community groups to support poll worker recruitment. Together the National Association of Secretaries of State (NASS), the National Association of State Election Directors (NASED), and the American Bar Association (ABA) mobilized lawyers to serve as poll workers during the 2020 election cycle (ABA 2020). In West Virginia, real estate agents, brokers, and associate brokers received seven hours of continuing education credit for completing poll worker training and working on Election Day (West Virginia Secretary of State 2020). In Florida, Hillsborough County’s LEO recruited “twice as many” poll workers through their partnerships with various groups including #TeamTampaBay, which consists of Tampa Bay sporting groups including the Buccaneers and Rays (Tampa Bay Sports Commission 2020). In the next section, we explore the motivations of election poll workers that served in Miami-Dade County, Florida during the 2020 general election.

Case Selection: Miami-Dade County Miami-Dade County is the most populous county in Florida and is considered the seventh most populous county in the U.S. with a population of slightly more than 2.7 million residents and almost 1.6 million active voters in November 2020 (Miami-Dade County, n.d., U.S. Census, n.d.). Miami-Dade County is home to 34 incorporated cities, and many

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unincorporated areas, with the City of Miami serving as the county seat. Along with heavily urbanized areas, the southern part of the county contains agricultural production in the Redland and Homestead areas, and portions of two national parks to the west (Everglades National Park) and east (Biscayne National Park). Residents of Miami-Dade County are diverse, multi-lingual, and identify with various cultures and identities. A majority of people living in Miami-Dade County identify as Hispanic or Latinx (69%) and residents are less likely to identify as “white alone” than in any other Florida county (30%) (U.S. Census, n.d.). On the whole, those who reside within MiamiDade County are more likely to be foreign-born (54% compared to 14%), speak a language other than English in their home (75% compared to 22%), and identify as Hispanic or Latinx (69% compared to 19%) than the modal American (U.S. Census, n.d.). Election Administration in Miami-Dade County Unlike the other 66 counties in Florida, elections in Miami-Dade County are administered by an appointed Supervisor of Elections (SOE). While the other SOEs in Florida are elected to four-year terms, the MiamiDade SOE is appointed by the county mayor. However, following a 2018 amendment to the Florida Constitution whereby the constitutional grant of authority for certain charter county officials to be appointed was revoked, the SOE in Miami-Dade County will be elected beginning in 2024 (Miami-Dade County Attorney 2022). The SOE in Miami-Dade County formulates and directs all operations for the Elections Department and is supported by 107 full-time equivalent positions that are organized within seven divisions. The seven divisions of the Department cover all aspects of elections including operations, technology, governmental affairs, and voter services. During presidential and midterm election cycles, the Department relies heavily on seasonal workers and employees. In 2020, Miami-Dade County’s SOE was responsible for conducting elections in over 900 precincts for more than 1.6 million registered voters. To assist voters on Election Day, the Miami-Dade County Elections Department’s Poll Worker Recruitment and Training Division recruited, trained, and deployed over 6,000 poll workers. This is an increase over the number of poll workers previously required to administer elections in the County.

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Like other states, Florida has faced challenges in recruiting and retaining poll workers, and notably, recruiting bilingual poll workers (Miller 2022a). In Miami-Dade County, to become a poll worker, individuals must be registered or pre-registered to vote in the county and must complete an online or paper application. Additional requirements include: the ability to read and write in the English language, attending a mandatory training prior to every election, remaining nonpartisan while at the polls, and having excellent customer service skills and transportation to and from the polls. Depending on their competency, poll workers may serve as a clerk, assistant clerk, inspector, poll deputy, elections specialist, or EViD inspector in Miami-Dade County and pay varies from $180.33 to $346.31 depending on the position served. Regardless of their position, each poll worker is compensated $40 for attending the four-hour mandatory pre-Election Day training. Linking Case Selection to the Broader 2020 Electoral Environment The challenges faced by LEOs broadly during the 2020 election cycle were acutely present in Miami-Dade County. Florida’s presidential primary election took place on March 17, 2020, one day after the first shelter-in-place order in the U.S. went into force. In some Florida counties, significant numbers of poll workers canceled at the last minute, leaving LEOs scrambling to staff polling places for the primary election (Ross 2020). In July 2020, Miami-Dade County’s COVID-19 positivity rate rose and was declared the worst impacted area in the state (Associated Press 2020). The county continued to grapple with the impacts of COVID-19, and a second spike in cases, in the months immediately preceding the November election. In addition to COVID-19 concerns, the days surrounding the 2020 general election saw rumors circulating across various social media platforms about many threats including riots, protesters planning to destroy retail stores (Torres et al. 2020), and claims about non delivery of mail ballots (Greenberg 2020). After conducting an investigation, federal agents determined the threats to wreak havoc regardless of electoral outcomes were not credible. Nevertheless, a strong law enforcement presence permeated South Florida on Election Night and in the Design District, along Miami Beach, and in Midtown some Miami businesses boarded their doors and windows to limit damage should a riot erupt (Torres et al. 2020).

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Methods and Design Following the 2020 U.S. presidential election, the Miami-Dade County Elections Department created and disseminated a post-election survey via email to all workers that served the polls on Election Day. The survey included questions related to poll workers’ motivations and experiences, assessment of required training, and basic demographics including the position worked on Election Day, longevity of poll work experience, age, and employment status. For the purpose of this chapter, we explore what respondents in Miami-Dade County identify as motivational factors in their decision to serve as an election poll worker (see Appendix for complete questionnaire). The electronic survey was sent on January 29, 2021, and remained open for 25 days. In total, 1,729 MiamiDade County poll workers participated in the post-election survey.2 ,3 The modal age range of survey respondents was 56–60 years (age was reported ordinally). Roughly half of the participants were employed, while a quarter identified as being retired. Noting that in Miami-Dade County, each polling precinct is normally staffed by one county employee serving as an Election Specialist poll worker, 29% of respondents reported working as county employees. Measures In this chapter, we use three conceptual measures to analyze the motivations of Miami-Dade County poll workers. The measures are comprised of five-point4 Likert-scale questions exploring the degree of importance respondents place on motivational factors to their service. Factors explored in Miami-Dade County align with prior investigations of election worker motivation (for instance, Atkeson et al. 2011; Barsky forthcoming; Clark and James 2021; McAuliffe 2009). Detailed below, measures of normative, affective, and financial motivation are discussed. 2 Data collected from approximately 375 respondents are excluded from analysis due to missing information on key indicator variables. 3 The Miami-Dade County-designed survey collected limited demographic data. 4 The five-point Likert-style scale provided respondents the option to report the impor-

tance of each motivational prompt as not at all (5), slightly (4), moderately (3), very (2), and extremely (1) important reasons for their decision to be a poll worker. For this analysis, items were reverse coded so that higher numbers indicate a greater degree of importance.

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Normative motivation: Normative motivation assesses a respondent’s motives to serve as a poll worker due to a sense of civic duty or societal obligation. Three items make up the measure of normative motivation employed here: (1) to be part of the democratic process; (2) to perform my civic duty/serve my community; and, (3) to make a difference. Epistemologically correlated, the three items return a Cronbach’s alpha of 0.85, indicating good reliability of the measure. Affective motivation: Affective motivation relates to a respondent’s interest in being involved in the electoral process and their excitement to learn more. The measure is designed to capture an individual’s enthusiasm to serve as a poll worker. In this chapter, two items are used to construct the measure of affective motivation: (1) to learn new skills and (2) to keep myself busy. Together, the items produced a Cronbach’s alpha of 0.72. Financial motivation: Financial motivation is a relatively understudied factor in public service work generally, and poll work specifically (Clark and James 2021). A single item from the survey, “I wanted to make some extra money” is used to assess the role of fiscal motivation in an individual’s participation in poll work. Employing the three constructed measures of motivation, we analyze poll worker motivation in Miami-Dade County immediately following the 2020 general election. Using summary statistics (e.g., mean, standard deviation) and correlation matrices, we determine the relative strength of each measure of motivation, and the measures’ relationships to one another. Then, a series of Ordinary Least Squares (OLS) regression models are employed, one per dependent variable, uncovering motivational differences among demographic groups. The analysis in the following section allows us to understand whether motivations vary across identity groups.

Results In this section, we discuss the findings on motivational factors influencing Miami-Dade County poll workers service during the 2020 general election. The first subsection presents descriptive statistics to establish overall trends and the second subsection provides regression analyses to tease out differences among self-reported identity groups. The findings presented in the following pages provide a snapshot of poll worker motivation during a critical general election, while facing unprecedented challenges

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presented by the global COVID-19 pandemic, election denialism, and increasing instances of violence directed at the very individuals responsible for free and fair elections. Respondent Profile The Miami-Dade County survey collected limited demographic information from poll worker respondents. However, what we do know is that the majority (56%) of respondents are 55 years of age or older, a plurality are employed full-time (46%) with an entity other than county government (71%), and have served in elections prior to 2020 (75%) (see Table 2.1). Survey responses indicate that even in the face of the COVID19 pandemic and increasing election denialism and violence, those that served during the 2020 general election are likely (93%) return to work the polls in future elections. Finally, survey participants were asked on a scale of zero to ten, where higher numbers indicate a greater likelihood, whether they would recommend serving as a poll worker to their friends, family, and/or colleagues. The overwhelming majority (77%) indicated strong likelihood (nine and ten) and a small minority (six percent) indicated they would be unlikely (zero to five) to recommend others serve as a Miami-Dade County poll worker. Descriptive Statistics Normative motivation is uncovered as the strongest driver of respondents’ decisions to serve as election poll workers (see Table 2.2). Unlike Clark and James’ (2021) recent finding that British election workers report substantial financial motivation, Miami-Dade County respondents report the weakest importance of financial motivations in their decision to serve. In fact, on a one-to-five scale where higher numbers equate to greater importance, Miami-Dade poll workers score financial incentives below normative motivation by a whole point. For the sake of comparison, poll workers in Miami-Dade County earned between $180.33 and $346.31 on Election Day whereas workers in the U.K. earned between £195 and £285 (approximately $298.06–$435.625 ) (Clark and James 2021). Overall, findings from the 2020 survey of Miami-Dade poll workers

5 Using the 2015 average exchange rate of 1.5285 U.S. dollars per British pound.

2

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Table 2.1 Miami-Dade respondent profile Category Age

Employment status

County employee First-time poll worker Return as poll worker in future elections

Recommend poll work to a friend/colleague/family (0–10)

25 and under 26–35 36–45 46–55 56–65 66 and over Employed full-time Employed part-time Self-employed Unemployed Retired Student Other Yes No Yes No Very likely Likely Neither likely nor unlikely Unlikely Very unlikely 0–2 3–5 6–8 9–10

Frequency

Percent

42 122 163 313 393 349 634 110 66 160 362 27 30 414 1,007 365 1,104 1,186 178 65 16 24 14 66 239 1,080

3 9 12 23 28 28 46 8 5 12 26 2 2 29 71 25 75 81 12 4 1 2 1 5 17 77

Table 2.2 Miami-Dade summary statistics Motivation variable

Mean

Std. dev

Min

Max

Normative Affective Financial

4.47 3.54 3.43

0.79 1.25 1.40

1 1 1

5 5 5

align with previous works which highlight the important role of normative motivational factors in an individual’s decision to serve (for instance Atkeson et al. 2014).

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Table 2.3 Miami-Dade correlation matrix

Normative Affective Financial

Normative

Affective

1 0.34 0.08

1 0.58

Correlation matrices (see Table 2.3) detail the relationships among the three types of motivation explored in this chapter. Normative motivation displays a fairly strong, positive correlation with affective motivation (0.34). Conversely, normative motivation is largely unrelated to financial motivation (0.08). Broadly, findings indicate that in 2020, respondents in Miami-Dade County were driven by normative factors like civic duty and community service to participate in poll work and that financial incentives were by and large less important to poll worker’s motivations to serve. Inferential Statistics Although normative motivation presents as a dominant driver of poll worker participation, it is important to understand whether poll workers are uniform in their motivations, or whether different groups ascribe stronger or weaker importance across normative, affective, and financial motivations. To examine these differences, we employ a series of OLS regression models (see Table 2.4) with each measure of motivation as a separate dependent variable. Despite the limited collection of independent demographic variables, we glean useful findings that may be used to inform future poll worker recruitment and retention. Overall, first-time poll workers in Miami-Dade County were significantly less likely than returning workers to report financial motivations as important in their decision to serve. These findings suggest monetary incentives are of minor importance to new poll workers, even in the midst of a pandemic that resulted in South Florida job losses in excess of 677,000 during the first months of COVID-19 (FIU 2021) and calls from Miami-Dade County human service agencies for increased employment and financial assistance to whether the pandemic storm (FIU 2020). Yet, financial motivations may be less important for recruitment of new poll workers but may become increasingly important for retaining poll workers from election to election.

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Table 2.4 Miami-Dade regression results

First-time worker Age group 25 and under 26–35 36–45 46–55 56–65 66 and over Employment status Employed full-time Employed part-time Self-employed Unemployed Retired Student Other County employee Constant Observations R2

Normative motivation

Affective motivation

Financial motivation

0.16*** (2.99)

−0.70*** (−8.37)

−1.05*** (−11.19)

– 0.01 (0.08) 0.07 (0.51) 0.19 (1.36) 0.27* (1.94) 0.24 (1.61)

– 0.19 (0.84) 0.30 (1.29) 0.21 (0.93) 0.08 (0.37) −0.05 (−0.23)

– −0.12 (−0.45) −0.06 (−0.23) −0.15 (−0.60) −0.26 (−1.05) −0.37 (−1.41)

– −0.03 (−0.40) 0.04 (0.38) −0.02 (−0.30) −0.11 (−1.41) 0.09 (0.53) 0.03 (0.21) −0.37*** (−5.88) 4.39*** 1,353 0.07

– 0.71*** (5.33) 0.14 (0.86) 0.81*** (6.75) 0.67*** (5.42) 0.86*** (3.25) 0.31 (1.35) −0.14 (−1.40) 3.28*** 1,353 0.12

– 0.65*** (4.38) 0.14 (0.75) 0.66*** (4.93) 0.38*** (2.70) 0.98*** (3.27) 0.14 (0.52) 0.17 (1.58) 3.60*** 1,353 0.13

t-statistics in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1

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Considering all Miami-Dade County respondents, financial motivators were of greater importance to specific population groups: students, unemployed individuals, and part-time workers all emphasized their motivational importance. We believe that it is important for this study and others that seek to explore poll worker motivations, to be mindful of social desirability bias (Arnold and Feldman 1981; Wright and Grant 2010). Prior studies of public sector performance show that while individuals often self-report financial rewards as unimportant to their motivation, in reality, their behavior suggests otherwise (Rynes et al. 2004; Wright and Grant 2010). We are reluctant to draw conclusions about financial incentives broadly, but believe it accurate to say that financial motivations serve a lesser role in selecting to serve as a poll worker, even in the early days of COVID-19 financial precarity, than an individual’s normative desires. First-time poll workers reported significant normative motivation when compared to those who served during previous elections. These findings run counter to findings of a 2019 study conducted in Maricopa County, AZ where first-time poll workers reported lower normative motivation than returning poll workers (Bustinza et al. 2022). These findings suggest that temporal differences between surveys conducted prior to 2020 and those conducted during and following 2020 may influence first-time poll workers’ stronger expression of normative motivations for participating in poll work. Popular reporting during the 2020 general election (Velten and Hughes 2020; Fuchs 2020) provides anecdotal confirmation that the environmental context surrounding the 2020 election motivated new ranks of citizens to participate in poll work, though further research is necessary to solidify this conclusion. Since we know that the vast majority of poll workers across the U.S. are of retirement age, understanding whether motivational factors are expressed differently by various age cohorts is essential for future recruitment and retention efforts. Somewhat surprisingly, in Miami-Dade County, there were no significant differences (at the 0.05 level) in expression of motivational factors by age group.

Implications and the Future Poll workers are the lifeblood of U.S. elections. Concerns about recruiting new generations of poll workers have permeated the work of local officials and election scholars since at least 2000. The pandemic introduced

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novel, critical challenges to maintaining a sufficient force of workers to administer U.S. elections. COVID-19 uncovered the Achilles heel of the electoral process in the U.S., the system’s reliance on senior citizens to serve on the front lines of elections. LEOs in various parts of the country can leverage these findings to recruit a new force of front line workers. These results demonstrate the potential role messaging can have on recruiting poll workers. Yet, the ability to attract, train, and retain poll workers is dependent on funding. The 2020 elections were unique in terms of the perfect storm of a global pandemic, unprecedented turnout, and partisan rancor. Yet, other challenges have always been present for LEOs. Prior elections (2016) encountered new threats due to cybersecurity concerns and many states have dealt with natural and human-made disasters during election cycles (e.g., Hurricanes Sandy [2012], Michael [2018], and Ian [2022]) (Morris and Miller 2023). All of these situations require increased resources whether that be people or materials. Lack of sufficient funding for elections and election infrastructure is a dire threat not just to democracy, but the dedicated individuals who make it all possible (Adona et al. 2019, 6). Despite evidence that philanthropic donations, either direct cash or in-kind donations, made a huge difference in covering budget shortfalls for election officials in 2020 (Stewart 2022), some states have begun to limit the amount of outside funding that can be used for elections (NCSL 2023). Public officials should heed the call for increased, not decreased, financial support for election administration. U.S. democracy depends on it.

Appendix A: Miami-Dade County Survey Instrument 1. What position did you serve in on Election Day? a. Clerk b. Assistant Clerk c. Inspector d. Deputy e. Election Specialist f. Standby 2. Was this your first-time serving as a poll worker? a. Yes b. No

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3. Please indicate the year you began serving as a poll worker. a. 2020 (1) b. 2019 (2) c. 2018 (3) d. 2017 (4) e. 2016 (5) f. 2015 (6) g. 2014 (7) h. 2013 (8) i. 2012 (9) j. 2011 (10) k. 2010 (11) l. 2009 (12) m. 2008 (13) n. 2007+ (14) 4. Please indicate how important each of these reasons were in helping you to decide to become a poll worker: Extremely important (1) To be a part of the democratic process (1) To perform my civic duty/ serve my community (2) To make a difference (3) To make extra money (4) To learn new skills (5) To keep myself busy (6)

Very important (2)

Moderately important (3)

Slightly important (4)

Not at all important (5)

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5. How did you find out about the Election Day poll worker opportunity? a. Word of mouth/current poll worker (1) b. College/University or school (2) c. County website, social media, or email (3) d. Civic or community organization (4) e. Department of Motor Vehicles (DMV) (5) f. Elections Department website, social media, or email (6) g. E-mail (7) h. Employer (8) i. Flyer (9) j. Political Party (10) k. Other (please specify below) (11) 6. Overall, how satisfied were you with your poll worker experience? a Extremely satisfied (1) b. Somewhat satisfied (2) c. Neither satisfied nor dissatisfied (3) d. Somewhat dissatisfied (4) e. Extremely dissatisfied (5) 7. What challenges, if any, did you face during the November 3 General Election (please select all that apply or N/A): a. Polling place/location does not open on time (1) b. Insufficient supplies (2) c. Insufficient poll workers (3) d. Equipment issues (4) e. Uncertainty about procedures or protocols (5) f. Volume of voters (6) g. Fellow poll worker(s) (7) h. Campaigners/solicitors (8) i. Poll watchers (9) j. N/A (10) k. Other (please specify below) (11) 8. Based on your most recent experience serving as a poll worker, how likely are you to return for future elections? a. Very likely (1) b. Likely (2)

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c. Neither likely nor unlikely (3) d. Unlikely (4) e. Very unlikely (5) 9. Thinking back to your poll worker training, how satisfied were you with each of the following: Extremely satisfied (1)

Moderately satisfied (2)

Slightly satisfied (3)

Neither satisfied nor dissatisfied (4)

Slightly dissatisfied (5)

N/A (6)

Training content (1) Length of class (2) Trainer(s) (3) Poll worker manual (4) Scheduling (5) Location/ facility (6)

10. On Election Day, how confident did you feel about your ability to apply what you learned during training? a. Confident (1) b. Neutral (2) c. Not confident (3) 11. How can we improve training to increase your level of confidence? _______________________________________________ _______________________________________________ _______________________________________________ _______________________________________________ _______________________________________________ 12. Please indicate your age bracket below: a. 16–17 b. 18–25 c. 26–30 d. 31–35

(pre-registered to vote) (1) (2) (3) (4)

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e. 36–40 (5) f. 41–45 (6) g. 46–50 (7) h. 51–55 (8) i. 56–60 (9) j. 61–65 (10) k. 66–70 (11) l. 71 or older (12) m. Prefer not to answer (13) 13. I am currently: a. Employed full-time (1) b. Employed part-time (2) c. Self-employed (3) d. Unemployed (4) e. Retired (5) f. Student (6) g. Other (please specify below) (7) h. Prefer not to answer (8) 14. Are you a Miami-Dade County employee? a. Yes (1) b. No (2) 15. Which department? ▼ Animal Services (1) … Zoo (71) 16. On a scale from 0 to 10, how likely are you to recommend serving as a Miami-Dade County Election Day poll worker to a friend, colleague, or family member? 0 (0) 1 (1) 2 (2) 3 (3) 4 (4) 5 (5) 6 (6) 7 (7) 8 (8) 9 (9) 10 (10)

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References Adona, Natalie, Paul Gronke, Paul Manson, and Sarah Cole. 2019. Stewards of Democracy: The Views of American Election Officials. Democracy Fund. https://democracyfund.org/wp-content/uploads/2020/06/2019_D emocracyFund_StewardsOfDemocracy.pdf American Bar Association. 2020. “ABA Joins NASS and NASED to Mobilize Lawyers as Poll Workers for Election Day.” https://www.americanbar.org/ news/abanews/aba-news-archives/2020/08/aba-joins-nass-and-nased-tomobilize-lawyers-as-poll-workers-for/ Armed Conflict Location and Event Data Project (ACLED). 2020. Standing by: Right-Wing Militia Groups & the U.S. Election. https://acleddata.com/acl eddatanew/wp-content/uploads/2020/10/ACLEDMilitiaWatch_StandingB yMilitiaGroups_2020Web.pdf. Arnold, Hugh J., and Daniel C. Feldman. 1981. “Social Desirability Response Bias in Self-Report Choice Situations.” The Academy of Management Journal 24 (2): 377–85. Associated Press. 2020. “Miami Now ‘Epicenter’ of Coronavirus Pandemic, Top Doctor Says.” ABC7 Eyewitness News, July 14. https://abc7.com/floridaepicenter-of-covid-19-outbreak-coronavirus-covid-cases/6316617/ Atkeson, Lonna Rae, Yann P. Kerevel, R. Michael Alvarez, and Hall, Thad E. 2014. “Who Asks for Voter Identification? Explaining Poll-Worker Discretion.” The Journal of Politics 76( 4): 944–57. Atkeson, Lonna Rae, R. Michael Alvarez, Alex N. Adams, and Lisa Ann Bryant. 2011. Assessing Electoral Performance in the New Mexico 2010 General Election. The University of New Mexico. https://polisci.unm.edu/common/csved/papers/nm-2010-general-election.pdf. Atkeson, Lonna Rae, and Kyle L. Saunders. 2007. “The Effect of Election Administration on Voter Confidence: A Local Matter?” PS: Political Science & Politics 40 (4): 655–60. Barsky, Christina S. 2020. “Administrators of Democracy: Implementing and Innovating in Election Administration.” Ph.D. diss. Northern Arizona University. Bowler, Shaun, Thomas Brunell, Todd Donovan, and Paul Gronke. 2015. “Election Administration and Perceptions of Fair Elections.” Electoral Studies 38: 1–9. Brangham, William, Matt Loffman, and Ian Couzens. 2022. “Election Officials Struggle to Recruit Poll Workers for Midterms Amid Growing Threats.” PBS Newshour, September 19. https://www.pbs.org/newshour/show/electionofficials-struggle-to-recruit-poll-workers-for-midterms-amid-growing-threats Burden, Barry C., and Jeffrey Milyo. 2015. “The Quantities and Qualities of Poll Workers.” Election Law Journal 14(1): 38–46.

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Bustinza, Monica A., M. Blake Emidy, Christina S. Barsky, and Amanda, D. Clark. 2022. “‘Helping My Fellow Americans to Vote’ Is ‘My Civic Duty’: Exploring Poll Worker Motivations in Recent U.S. Elections.” Paper presentation, Election Science, Reform, and Administration. Caldwell, Emily. 2022. “Considering COVID a Hoax Is a ‘Gateway’ to Belief in Conspiracy Theories.” Ohio State News. https://news.osu.edu/consideringcovid-a-hoax-is-gateway-to-belief-in-conspiracy-theories/. Cantú, Francisco, and Sandra Ley. 2017. “Poll Worker Recruitment: Evidence from the Mexican Case.” Election Law Journal: Rules, Politics, and Policy 16(4): 495–510. Center for Tech and Civic Life. “COVID-19 Response Grants.” https://www. techandciviclife.org/our-work/election-officials/grants/. Centers for Disease Control and Prevention (CDC). 2021. “COVID-19 Recommendations for Older Adults.” https://www.cdc.gov/aging/covid19guidance.html. ———. 2022. “CDC Museum COVID-19 Timeline.” https://www.cdc.gov/ museum/timeline/covid19.html. Claassen, Ryan L., David B. Magleby, J. Quin Monson, and Kelly D. Patterson. 2008. “‘At Your Service’: Voter Evaluations of Poll Worker Performance.” American Politics Research 36 (4): 612–34. Clark, Alistair, and Toby S James. 2021. “Electoral Administration and the Problem of Poll Worker Recruitment: Who Volunteers, and Why?” Public Policy and Administration: 09520767211021203. Coll, Joseph. 2022. “Proper Protective (Voting) Equipment: How Covid-19 Safety Measures Shaped In-person Voting Experiences During the 2020 Election.” American Politics Research 50 (6): 798–810. https://doi.org/10. 1177/1532673X221112396. Creek, Heather M., and Kimberly A. Karnes. 2010. “Federalism and Election Law: Implementation Issues in Rural America.” Publius 40 (2): 275–95. Desilver, Drew. 2021. “Turnout Soared in 2020 as Nearly Two-Thirds of Eligible U.S. Voters Cast Ballots for President.” Pew Research Center. https://www.pewresearch.org/fact-tank/2021/01/28/turnout-soared-in2020-as-nearly-two-thirds-of-eligible-u-s-voters-cast-ballots-for-president/. Feldman, Max. 2020. “10 Voter Fraud Lies Debunked.” Brennan Center for Justice. https://www.brennancenter.org/our-work/research-reports/10voter-fraud-lies-debunked Florida International University (FIU). 2021. “COVID-19 Recovery Quarterly Report: Behind the Numbers.” Jorge M. Pérez Metropolitan Center. https:// metropolitan.fiu.edu/recovery-index/quarterly-reports/q2-2021-1.pdf Florida International University (FIU). 2020. “Economic Impact of the Nonprofit Sector in Miami Dade County.” Jorge M. Pérez Metropolitan

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Center. https://philanthropymiami.org/wp-content/uploads/2020/06/ MDC_Nonprofit_Report-2.pdf Fuchs, Hailey. 2020. “With Polling Sites Lacking Workers, a New Generation Steps Up.” The New York Times. https://www.nytimes.com/2020/10/12/ us/politics/poll-workers-teenagers-young-people.html Garnett, Holly Ann. 2019. “On the Front Lines of Democracy: Perceptions of Electoral Officials and Democratic Elections.” Democratization 26 (8): 1399– 1418. Greenberg, Joel. 2020. “Claim That Postal Service Failed to Deliver 27% of Mail-in Ballots in South Florida Is 100% Wrong.” Tampa Bay Times. https://www.tampabay.com/news/florida-politics/elections/2020/11/05/ claim-that-postal-service-failed-to-deliver-27-of-mail-in-ballots-in-south-flo rida-is-100-wrong-politifact/ Hall, Thad E., J. Quin Monson, and Kelly D. Patterson. 2009. “The Human Dimension of Elections: How Poll Workers Shape Public Confidence in Elections.” Political Research Quarterly 62(3): 507–22. Izaguirre, Anthony. 2020. “Polling Places Are Latest Front in Battle over Face Masks.” Associated Press. https://www.wptv.com/news/election-2020/pol ling-places-are-latest-front-in-battle-over-face-masks. James, Toby S, and Tyrone Jervier. 2017. “The Cost of Elections: The Effects of Public Sector Austerity on Electoral Integrity and Voter Engagement.” Public money & management 37(7): 461–68. Kasky, Jeffrey. 2020. “Poll Workers Signed up to Help Voters. Instead We Were Abused by Trump Supporters.” South Florida Sun Sentinel. https://www. sun-sentinel.com/opinion/commentary/fl-op-com-polling-sites-campaign-int imidation-20201104-rfr6d6npune7daoihytqsogbhq-story.html. Kimball, David C., and Martha Kropf. 2006. “The Street-Level Bureaucrats of Elections: Selection Methods for Local Election Officials.” The Review of policy research 23(6): 1257–68. Kitchenman, Andrew. 2020. “In Order to Recruit More Election Workers, Alaska Increases Pay by $3 Per Hour.” Alaska Public Media & KTOO. https://www.ktoo.org/2020/07/30/in-order-to-recruit-more-election-wor kers-alaska-increases-pay-by-3-per-hour/ Manson, Paul, Natalie Adona, and Paul Gronke. 2020. “Staffing the Stewards of Democracy: The Demographic and Professional Profile of America’s Election Officials.” In San Juan, Puerto Rico. McAuliffe, Elizabeth W. 2009. ProQuest Dissertations and Theses “The Unexamined Element of Election Administration: Why Citizens Choose to Serve as Poll Workers on Election Day.” Ph.D. The Florida State University. https:// www.proquest.com/dissertations-theses/unexamined-element-election-adm inistration-why/docview/304886730/se-2?accountid=10901.

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Miami-Dade County Attorney. 2022. “Memorandum: Special Item No. 8.” https://www.miamidade.gov/govaction/legistarfiles/Matters/Y2022/220 800/pdf Miami-Dade County. “Voter Registration Statistics.” Miami-Dade County Elections. https://www.miamidade.gov/global/elections/voter-registration-statis tics.page Miller, Lauren. 2022a. “Florida Poll Workers: Rules and Constraints.” Brennan Center for Justice. https://www.brennancenter.org/our-work/research-rep orts/florida-poll-workers-rules-and-constraints. ———. 2022b. “Protections in Place Against Rogue Poll Workers.” Brennan Center for Justice. https://www.brennancenter.org/our-work/analysis-opi nion/protections-place-against-rogue-poll-workers. Montanaro, Domenico. 2021. “Most Americans Trust Elections Are Fair, But Sharp Divides Exist, a New Poll Finds.” National Public Radio. https:// www.npr.org/2021/11/01/1050291610/most-americans-trust-electionsare-fair-but-sharp-divides-exist-a-new-poll-finds. Montjoy, Robert S. 2010. “The Changing Nature . . . and Costs . . . of Election Administration.” Public Administration Review 70 (6): 867–75. Morris, Kevin, and Peter Miller. 2023. “Authority After the Tempest: Hurricane Michael and the 2018 Elections.” The Journal of Politics 85 (2): 405–20. https://doi.org/10.1086/722772. Montjoy, Robert S. 2008. “The Public Administration of Elections.” Public Administration Review 68 (5): 788–33. National Conference of State Legislatures (NCSL). 2022. 2020 State Primary Election Dates. https://www.ncsl.org/elections-and-campaigns/-2020-stateprimary-election-dates#chronological. National Conference of State Legislatures (NCSL). 2023. “Prohibiting Private Funding of Elections.” https://www.ncsl.org/elections-and-campaigns/pro hibiting-private-funding-of-elections. Perry, James L., Annie Hondeghem, and Lois Recascino Wise. 2010. “Revisiting the Motivational Bases of Public Service: Twenty Years of Research and an Agenda for the Future.” Public Administration Review 70 (5): 681–90. Perry, James L., and L.W. Porter. 1982. “Factors Affecting the Context for Motivation in Public Organizations.” Academy of Management Review (7): 89–98. Pew Research Center. 2020. Voters’ Evaluations of the 2020 Election Process. https://www.pewresearch.org/politics/2020/11/20/voters-evalua tions-of-the-2020-election-process/. Reinhardt, Sarah. 2023. 2023 NASS Ideas Award Submission. https://www. nass.org/sites/default/files/awards/2023/MI-NASS-IDEAS-Nomination2023.pdf.

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Ritz, Adrian, Oliver Neumann, and Wouter Vandenabeele. 2016. “Motivation in the Public Sector.” In The Routledge Handbook of Global Public Policy and Administration, edited by Thomas R. Klassen, Denita Cepiku, and T.J. Lah, 368–81. Ross, Allison. 2020, July 30. “Help Wanted: Florida Poll Workers to Brave the Coronavirus.” The Tampa Bay Times. https://www.tampabay.com/news/ health/2020/07/30/help-wanted-florida-poll-workers-to-brave-the-corona virus/ Rynes, Sara L., Barry Gerhart, and Kathleen A. Minette. 2004. “The Importance of Pay in Employee Motivation: Discrepancies Between What People Say and What They Do.” Human Resource Management 43 (4): 381–94. Sherman, Amy, and Hana Stepnick. 2022. “Poll Workers Are Short-Staffed, Under Attack—And Quietly Defending Democracy.” Poynter. https://www. poynter.org/fact-checking/2022/poll-workers-are-short-staffed-under-att ack-and-quietly-defending-democracy/. Sprunt, Barbara. 2020. “Wanted: Young People to Work the Polls This November.” National Public Radio. https://www.npr.org/2020/08/05/894 331965/wanted-young-people-to-work-the-polls-this-november. Stewart, Charles. 2021. How We Voted in 2020. MIT Election Science and Data Lab. https://electionlab.mit.edu/sites/default/files/2021-03/HowWeVote dIn2020-March2021.pdf. ———. 2022. The Cost of Conducting Elections. MIT Election Data + Science Lab. https://electionlab.mit.edu/sites/default/files/2022-05/The CostofConductingElections-2022.pdf. Suttmann-Lea, Mara. 2020. “Poll Worker Decision Making at the American Ballot Box: Part of Special Symposium on Election Sciences.” American Politics Research 48 (6): 714–18. Tampa Bay Sports Commission. 2020. “#TeamTampaBay Recognizes Poll Worker Recruitment Day on September 1.” https://www.tampabaysports. org/News/NPWRD_2020 Torres, Andrea, Amanda Batchelor, and Liane Morejon. 2020. “‘No Credible Threat of Planned Attacks’ on Election Day in Miami-Dade, Policy Say.” Local10.com. https://www.local10.com/vote-2020/2020/11/03/nocredible-threat-of-planned-attacks-in-miami-dade-police-say/ U.S. Census. “QuickFacts: Miami-Dade County Florida.” https://www.census. gov/quickfacts/fact/table/miamidadecountyflorida/POP060210 U.S. Election Assistance Commission (EAC). 2017. EAVS Deep Dive: Poll Workers and Polling Places. https://www.eac.gov/sites/default/files/doc ument_library/files/EAVSDeepDive_pollworkers_pollingplaces_nov17.pdf. ———. 2018. Election Administration and Voting Survey: 2018 Comprehensive Report—A Report to the 116th Congress2020 Grant Expenditure Report.

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https://www.eac.gov/sites/default/files/eac_assets/1/6/2018_EAVS_Rep ort.pdf. ———. 2020a. 2020 Grant Expenditure Report. https://www.eac.gov/sites/ default/files/paymentgrants/expenditures/2020_State_Grant_Expenditure_ Report_FINAL.pdf. ———. 2020b. Lessons Learned from the 2020 Primary During COVID-19. https://www.eac.gov/sites/default/files/electionofficials/workinggroup/ Lessons_Learned_From_the_2020_Primary.pdf. Vandenabeele, Wouter, and Nina M. van Loon. 2015. “Motivating Employees Using Public Service.” In Handbook of Public Administration, edited by James L. Perry and Robert K. Christensen, 353–65. Velten, Elspeth, and Jazmine Hughes. 2020. “N.Y.C. Poll Workers: Young, Engaged and Tired of Their Apartments.” The New York Times. https://www.nytimes.com/2020/10/28/nyregion/coronavirus-nycpoll-workers-election.html. Wang, Hansi Lo. 2022. “The Midterm Elections Need Workers. Teens, Veterans and Lawyers Are Stepping Up.” NPR. https://www.npr.org/2022/09/07/ 1120065844/midterms-elections-poll-worker-shortage-recruitment. West Virginia Secretary of State. 2020. “WV Realtors Answer Poll Worker Call to Serve in June 9 Primary Election.” EIN News. https://www.einnews. com/pr_news/518370451/wv-realtors-answer-poll-worker-call-to-serve-injune-9-primary-election Westphal, Tom. 2021. “Violence and the 2020 General Election.” StanfordMIT Healthy Elections Project. https://web.mit.edu/healthyelections/ www/sites/default/files/2021-06/Violence_2020_Election.pdf. Wright, Bradley E., and Adam M. Grant. 2010. “Unanswered Questions About Public Service Motivation: Designing Research to Address Key Issues of Emergence and Effects. Public Administration Review 70 (5): 691-700.

CHAPTER 3

The Impacts of COVID-19 on Election Administration: Perspectives from Local Election Officials in the United States Joseph Anthony and Paul Manson

Introduction The COVID-19 pandemic in 2020 created many new obstacles that local election officials (LEO) had to overcome to run smooth—and safe—elections in their communities. Most LEOs held at least two major elections in 2020; presidential primary and general elections. They did so amid a global pandemic that was not foreseen until just weeks or months before these elections were scheduled. Even given these monumental and unique challenges, voter turnout in the 2020 election reached an historic high of over 66%, and the elections were largely reported to

J. Anthony (B) State University of New York, Cortland, NY, USA e-mail: [email protected] P. Manson Portland State University, Portland, OR, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. A. Coll and J. Anthony (eds.), Lessons Learned from the 2020 U.S. Presidential Election, Elections, Voting, Technology, https://doi.org/10.1007/978-3-031-44549-1_3

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be problem-free (Departments of Justice and Homeland Security 2021; Eggers et al. 2021). This outcome is remarkable, given that LEOs had to make substantial changes in how they conducted elections, including physical changes to polling places to allow for social distancing, prioritizing poll worker safety with PPE equipment, covering massive worker shortages, as well as making large-scale shifts to vote-by-mail systems. This chapter examines how LEOs around the country reported their levels of confidence in making the necessary changes to prepare for holding safe and accessible in-person elections during COVID-19, as well as their confidence in their abilities to scale up their vote-by-mail (VBM) operations. We use data from the 2020 Local Election Official Survey conducted by the Elections and Voting, and Information Center (EVIC) at Reed College. We examine whether levels of confidence in preparation are impacted by the size of the jurisdiction LEOs’ serve, their partisan affiliation, their selection method to office, as well as across dimensions of the political culture in their states. We acknowledge that LEOs in 2020 were faced with difficult tensions around their decisions in 2020, as they had to balance both voter accessibility to the ballot and protecting voters’ safety during a pandemic. Additionally, new policies and changes to practices have the potential to increase the administrative burden upon election officials, which may have been especially onerous in 2020 given the rapidness and scale of changes needed to react and adjust to COVID-19.

Existing Research Elections in the United States are highly decentralized, and election officials make many important decisions about their administration at the local level (Ewald 2009). Election officials represent a wide variety of jurisdiction types and sizes, differing political compositions in the electorate, as well as having differing (or no) partisan affiliations themselves as public officials (Kimball and Kropf 2006). Some studies show that factors such as jurisdiction size and partisanship can impact the opinions of local election officials (Moynihan and Silva 2008; Montjoy 2008). While local administrators are required to comply with state and federal election laws, those laws leave some room for interpretation, and local officials may vary in how they implement them. Existing research shows that decisions made at the local level in election administration can impact outcomes such as turnout and how

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provisional ballots are counted. (e.g., Ansolabehere and Stewart 2005; Kropf et al. 2006; Burden et al. 2013; Fullmer 2015). A range of evidence indicates that the attitudes of local government officials can influence how they enforce the law (e.g., Farris and Holman 2015). Additionally, local officials’ views toward election policies help determine whether they support or oppose those policies (Moynihan and Silva 2008; Burden et al. 2012).

Factors Influencing LEO Confidence in Election Preparedness Jurisdiction Size Given the discretionary control afforded to local administrators who represent a spectrum of jurisdictions, this chapter examines connections between the levels of confidence reported by LEOs in their 2020 election preparedness and the size of the communities (number of voters) they represent. Larger jurisdictions—urban and suburban—have higher volumes of voters to serve and more ballots to process, which requires more staff, election workers, space, and technology. These large jurisdictions, however, also typically have at least one full-time staff person who is solely focused on administering elections. Rural and less-populated jurisdictions, on the other hand, serve smaller numbers of voters and process fewer ballots and registration applications. Therefore, policy changes and their implementation could potentially be more nimble to adaptation in less-populated areas. It is important to remember, however, that often rural jurisdictions do not have full-time staff devoted entirely to elections; it is not uncommon that these officials will have other municipal duties in addition to running elections. For instance, many county clerks are also responsible for processing local licensing applications, doing property assessments, and engaging in other time-consuming public duties. As one former, rural election official from a midwestern state pointed out, “[s]ometimes we are even the ones cleaning the office bathroom at the end of the day!” (Personal communication, 12/6/2022). We expect that larger jurisdictions will have higher levels of confidence in their COVID-19 election preparedness than smaller jurisdictions, given their higher staffing levels, higher budgets, and higher likelihood that they have employees who are solely committed to running elections (Hypothesis

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1). We also expect to find that larger jurisdictions will be more confident in their preparation to administer large-scale shifts to VBM, given that they are more likely to have the staffing capacity to carry out the application and ballot-counting processes to scale (Hypothesis 2). Party Affiliation The party affiliations of election officials have been previously associated with their decisions to purge voter rolls (Stuart 2004), select early voting locations (McBrayer et al. 2020), choose titles for ballot measures (Lund 1998), and implement straight-party voting mechanisms (Hamilton and Ladd 1996). A study done by Martha Kropf, David C. Kimball, and Lindsay Battles (2006) shows partisan differences in how LEOs count provisional ballots, with Democratic LEOs being slightly more likely to do so than GOP officials. Additionally, the likelihood of provisional ballots being counted also increases when the political makeup of the electorate matches the partisan affiliation of the LEO in charge (Kropf et al. 2006). In Maine, some research shows that partisan affiliation is related to how LEOs feel about ranked choice voting (RCV) reforms in that state and may influence how LEOs educate voters about RCV (Anthony et al. 2020). Importantly, a body of research shows that if local election officials have or perceive an increased administrative burden due to election reforms, their opposition toward these policies increases (Burden et al. 2012). Additionally, LEOs tend to support existing policies related to elections; however, they are more resistant to new policies (Burden et al. 2011). At least one study has shown that LEOs are less favorable toward election reforms that may increase their administrative burden, even when the general public is favorable toward these reforms (Manion et al. 2021). During the 2020 election cycle, however, it is possible that concerns for safety were tantamount to LEOs, and that they were more consistently supportive of changes to policies and practices that would maximize public safety regardless of the increased administrative burden. In 2020, governors in conservative states tended to see COVID-19 as less of a problem than those liberal states, at least some of the time during the first year of the pandemic (Weissert et al. 2021). Studies have also shown that conservatives took the pandemic less seriously and vaccinated at lower rates in the early stages of the pandemic than liberals (Brownstein 2020; Gadarian et al. 2021). It is possible then that in early 2020,

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LEOs’ perceptions of the seriousness of and need for preparations were more muted in conservative states. Therefore, we expect that LEOs who identify as Republican will be more confident about their preparedness for COVID-19 and the ability to scale up VBM than Democratic LEOs (Hypothesis 3). Selection Method Local election officials are selected for their positions through a variety of methods in the United States, including being elected in partisan or nonpartisan elections, appointed by a statewide official, or appointed by an elections board (Kimball and Kropf 2006). Additional studies have shown that the way an election administrator is selected can impact several important outcomes including voter turnout and the policy preferences of an LEO (see Burden et al. 2013). In a study of Wisconsin LEOs, for instance, Barry Burden, David T. Canon, Stéphane Lavertu, Kenneth R. Mayer, and Donald P. Moynihan found that LEOs who were elected to their positions had a preference for policy measures that increased voter access, and they were not as focused on issues like administrative costs (2013). We examine this survey sample to see if the selection methods of LEOs impact their confidence levels around preparing for elections during a pandemic and their ability to make substantial shifts to VBM. The LEO survey asks respondents if they are elected, appointed, or hired. The hired category presumes civil service protections, where the appointed positions are at-will and able to be dismissed by boards or commissions. Because elected LEOs are mostly in smaller, rural jurisdictions (NCSL 2022), we expect they will feel less confident in their preparations for COVID-19 and VBM, due to their smaller staff sizes and limited resources (Hypothesis 4). Political Culture This chapter also examines if there are any patterns to LEOs’ levels of confidence in election preparedness across their states’ political cultures (Elazar 1966). Daniel Elazar writes that culture is “the particular pattern of orientation to political action in which each political system is embedded” (Elazar 1966, 78). Daniel Elazar argues that states and regions within the United States have developed their own internal political

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cultures that influence which policies are prioritized, how decisions are made, and who participates within their borders (1966). Importantly, Elazar’s theory is tied to westward expansion; as pioneers moved West, they brought their religious and political traditions with them to their new homes. States often reflect the culture of neighboring states, creating a regional effect as well. Political culture is rooted in the perception of the role of government, how state government works (for instance, in a centralized or decentralized fashion), the role of the public in decisionmaking processes, as well as how much innovation should occur in state and local policymaking. Using Elazar’s frame of political culture, we examine if patterns exist in levels of confidence in election preparedness for LEOs across the typology’s three categories: moralistic, individualistic, and traditionalistic states. States that are aligned with the “moralistic” culture are those in which the highest value is placed upon government actions that benefit the common good and greater public.1 Public policies reflect community participation and revolve around the premise of creating a better overall society. In these states, citizens value the role of government in helping to develop and maintain healthy communities, and there is little tolerance for government corruption. Because these states highly value citizen participation, they also tend to make registration and voting as accessible as possible. With a few important exceptions, states in this category tend to be more liberal-leaning than traditionalist states, or at least desire a more central role for government in everyday society (Elazar 1966). Under the individualistic frame, states see themselves as a “marketplace” of individual concerns that compete to fulfill their own interests in a democracy, similar to an economic free market (Elazar 1966). In this sense, the role of government is not as much about furthering the public good as it is about being responsive to the demands of [organized] individual interests and the presiding political parties. Given this premise, states that fall into this category could be more conservative or more liberal, depending upon their political landscape. Individualistic states2 have generally leaned more conservative in nature as of the past 1 California, Colorado, Idaho, Iowa, Kansas, Maine, Michigan, Minnesota, Montana, New Hampshire, North Dakota, Oregon, South Dakota, Utah, Vermont, Washington, Wisconsin. 2 Connecticut, Delaware, Illinois, Indiana, Maryland, Massachusetts, Missouri, Nebraska, Nevada, New Jersey, New York, Ohio, Pennsylvania, Rhode Island, Wyoming.

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several election cycles, though this category holds some true battleground states as well. Individualistic states arguably fall somewhere in the middle politically as well as geographically on the spectrum; in this category the ideal role of government is that it is responsive to individual interests and to parties; therefore, the most organized and well-resourced groups will most likely have control of government. In the last category, states with traditionalistic cultures tend to be the most conservative of the three categories about policy change and prefer to stick with the status quo.3 Policy change is often difficult to accomplish, and innovation takes longer in traditionalistic states than in other categories of states. Political competition is typically lower in traditionalistic states, which tend to be dominated by one party. Voter turnout in traditionalistic states tends to be lower as well. This category of states is also regionally contiguous; all traditionalistic states are located in the US south, and all are considered to be moderately to consistently conservative. With these criteria in mind, we expect that in the traditionalistic states, LEOs were more confident about COVID-19 preparedness measures and the shift to VBM systems in 2020, based upon studies showing that the pandemic was downplayed and not taken as seriously in conservative states as in liberal states (Hypothesis 5). Due to the mixed political landscape across individualistic states, we expect that LEOs were less confident about their COVID-19 preparedness measures and the shift to VBM systems than those in traditionalistic states, but more confident about preparedness than the moralistic states which are often more liberalleaning (Hypothesis 6). In the moralistic states, we expect that LEOs will be less confident than traditionalistic and individualistic states in their ability to prepare for COVID-19 and in shifting to VBM systems (Hypothesis 7 ), based on the premise that COVID-19 was presented as more of a threat in these states than in conservative states. One important addendum has been made to Elazar’s original typology since 1966 which also applies to the analyses in this chapter. The “new political culture” argues that as the population has become more mobile, people have been able to become more selective in where they live; and

3 Alabama, Arizona, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, New Mexico, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia.

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typically, people prefer to live near like-minded others and in communities with which they relate (Clark and Hoffmann-Martinot 1998). This practice also explains the growing urban–rural political divide in American politics (Bishop and Cushing 2009). Therefore, scholars argue, it has become increasingly important to not just examine differences in political culture across states, but within states and across local jurisdictions as well. This frame applies well to analyzing the opinions of LEOs, because they exercise a substantial amount of discretion in decision-making at the local level, and the size of jurisdictions within states varies greatly. This foundation buttresses our expectations of observing differences in how LEOs respond to election preparedness and VBM policies across jurisdiction sizes.

Data and Methods To test these hypotheses, we use data from the Local Election Official Survey fielded in 2020, conducted by the Elections & Voting Information Center (EVIC) at Reed College in Portland, Oregon. To survey these offices, a sample was drawn from a national listing of local election offices provided by the US Election Assistance Commission. Approximately 8000 offices are charged with administering elections, and in this survey those offices are defined as the ones that are responsible for Election Day duties (Adona et al. 2019). Each state has a series of historical features that impact how election administration responsibilities are assigned, and most importantly, how many offices there may be in a state and at what level of government. A common model in the United States is for county clerks, recorders, or election directors to be the key local official responsible for Election Day duties in their jurisdiction (just to capture some of the titles that encompass this role). In this example, the clerk or recorder may be elected, or in some counties the office may be an administrative position that reports to an elected commission. In other parts of the United States, the election may be overseen by a board or pair of partisan officials. Further complicating this is that counties themselves are unequal units, when comparing across states. Some states may have very few (or no counties) while other states may have many counties of varying size, such as the 254 counties in the state of Texas. Portions of the Midwest and Northeast further complicate this population by administering elections at the town or township level, a level that is subsidiary to counties.

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Due to these varying sizes of jurisdictions, voters are not equally distributed across our unit of analysis. Approximately 8% of jurisdictions cover 75% of voters in the United States, because so many voters are located in a smaller set of very populous geographies (Lee & Gronke, 2020). To address this jurisdictional diversity, the sample is not a simple random draw. Rather the sample uses weights based on the size of the jurisdiction to adjust the probability of being sampled. In general, all jurisdictions with approximately 15,000 registered voters are included in the sample, and then probability of being sampled decreases as a jurisdiction is increasingly smaller in total registered voters. This means that the many small jurisdictions are sampled in proportion to the larger ones, and final results are adjusted with design and post-stratification weights. In 2020, due to the pandemic, the LEO survey was conducted entirely online using the Qualtrics survey platform. LEO offices were recruited via email and received a link by email. An additional follow up mailer was also sent to encourage participation. The LEO survey had a response rate of 28.5% in 2020. In 2020, the LEO survey contained a total of 20 questions related to elections administration during COVID-19. In this study, we closely examine a subset of these items focused on the impact of the global pandemic on election administration. We focus on two batteries in this chapter: Election preparedness in light of COVID-19 impact and preparedness for the expansion of absentee or vote-by-mail options. Importantly, the survey was fielded to LEOs in 2020 before the November election, though some LEOs had conducted primary elections in June of the survey year which also may have informed their responses. We use two main dependent variables in this study, both are indexes created using questions from the LEO survey responses from 2020. Indexes allow us to combine several different yet similar variables into a single unit of analysis. The first index is on LEOs COVID-19 preparedness measures in 2020, including the following items below: • My jurisdiction will have sufficient personal protective equipment for my staff, poll workers, and volunteers for Election Day (e.g., masks, gloves, sanitizing equipment or cleaners) • I will be able to utilize my traditional locations for polling places. • My offices and workspaces can safely accommodate staff and volunteers for administering the election.

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• My permanent workforce will be available for the November 2020 election. • I can recruit sufficient poll workers for the November 2020 election. Response options offered for these questions were: very confident, confident, somewhat confident and not at all confident. A “not applicable” option was also offered. The second index is a series of questions related to absentee and vote-by-mail (VBM) opinions and practices on the part of LEOs. Understanding how LEOs perceived shifts to VBM is important, because nearly half of all voters (46%) cast their ballot by mail in 2020 compared with 23.5% of voters in 2016. These items included: • I have sufficient staff and resources to process increased numbers of absentee or by-mail ballot applications. • My office can print or obtain sufficient ballots and envelopes to meet expanded demand for absentee or by-mail voting. • My office can acquire the technology to sort and manage expanded absentee or by-mail ballots. Response options offered for these questions where: very confident, confident, somewhat confident, and not at all confident. A not applicable option was also offered. For both the COVID-19 and VBM batteries, we initially tested the validity of combining these items by calculating the Cronbach alpha for each battery. We found these items do correlate well with each other to construct an index. The index was created by calculating the mean across each item after converting the response option to a numerical value for the order in the response scale. We examine similarities and differences in responses to these questions across the jurisdiction size (number of voters), self-reported partisanship, selection method, as well across the political culture in the states represented by the LEO (Elazar 1966).

Results Table 3.1 reports the overall responses to the COVID-19 and VBM batteries. A key takeaway here is the overall confidence of local officials to rapidly meet and overcome the challenges posed by COVID-19, and their

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ability to effectively run safe and accessible elections in its midst. Where weakness in confidence levels emerges, it is centered on LEOs’ concerns of safety for their staff, volunteers, and poll workers. These areas see LEOs’ levels of confidence drop, and where we see the greatest reporting of low confidence. Table 3.1 Overall LEO responses to COVID-19 and VBM questions

COVID-19 Preparedness My jurisdiction will have sufficient personal protective equipment for my staff, poll workers, and volunteers for Election Day I will be able to utilize my traditional locations for polling places My offices and workspaces can safely accommodate staff and volunteers for administering the election My permanent workforce will be available for the November 2020 election I can recruit sufficient poll workers for the November 2020 election VBM Preparedness I have sufficient staff and resources to process increased numbers of absentee or by-mail ballot applications My office can print or obtain sufficient ballots and envelopes to meet expanded demand for absentee or by-mail voting My office can acquire the technology to sort and manage expanded absentee or by-mail ballots

Very confident (%)

Confident (%)

Somewhat confident (%)

Not at all confident (%)

60

27

12

2

60

23

9

7

50

29

14

7

52

29

14

4

35

29

26

10

42

22

24

12

55

30

12

2.3

39

32

16

13

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We find some variation in responses to these items across jurisdictions based on the total number of registered voters in a jurisdiction. These dynamics across size play out in several key areas represented in Tables 3.1, 3.2, 3.3, 3.4, 3.5, 3.6 that select some key variables around staff, space, and voter engagement. The differences reported here are all statistically significant at a p-value of 0.05 or less using chi-squared tests and with Rao and Scott’s second-order correction. Figure 3.1 presents the two upper confidence responses combined across COVID-19 battery and by jurisdiction size, error bars represent 95% confidence interval. Two key patterns emerge here. First, smaller jurisdictions tend to report more confidence around all of the items. While the largest jurisdictions report the least confidence across several items measured here, suggesting that stresses of larger numbers of voters might have made the response to the pandemic more challenging despite presumably having greater resources and staff specialization. At the same time, the category “Very confident” as a response level varies greatly, though combining “Confident” with “Very confident” responses into one group makes the swings between jurisdictions much less drastic. Table 3.2 Confidence in recruiting poll workers in 2020

Very confident Confident Somewhat confident Not at all confident

Small (100,000 voters) (%)

38 29 24 9

16 31 32 20

16 35 35 13

Table 3.3 Confidence in being able utilize traditional polling places

Very confident Confident Somewhat confident Not at all confident

Small (100,000 voters) (%)

65 22 6 6

42 28 21 10

16 36 33 17

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Table 3.4 Confidence in being able to safely physically accommodate staff and volunteers

Very confident Confident Somewhat confident Not at all confident

Small (100,000 voters) (%)

54 28 11 7

37 27 27 9

13 40 33 15

Table 3.5 Confidence having sufficient staff and resources to process increased numbers of absentee or by-mail ballot applications

Very confident Confident Somewhat confident Not at all confident

Small (100,000 voters) (%)

45 20 23 12

24 28 29 18

20 34 36 11

Table 3.6 Confidence in being able to have sufficient time for voters to resolve issues with their absentee ballots

Very confident Confident Somewhat confident Not at all confident

Small (100,000 voters) (%)

30 30 28 11

18 23 45 14

9 35 42 15

Second, there do seem to be cases where the medium-sized jurisdictions report more challenges in making changes to adjust to the pandemic compared to their colleagues in smaller and larger jurisdictions. This effect is shown in the two tables on processing mail ballot applications and in the ability to administratively resolve any issues with ballots. Here, we see that

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Fig. 3.1 COVID-19 battery percent confidence by jurisdiction size

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the “Confident” response levels for small and large-sized jurisdictions are above those of the medium-sized jurisdictions. This result suggests there might be a size dynamic where small and larger jurisdictions felt more right-sized in terms of their tools and resources to prepare for the largescale changes needed in 2020. Perhaps medium-sized jurisdictions occupy an organizational inflection point between small and large election jurisdictions, where their offices are processing more ballots and in need of more temporary election workers than the smaller jurisdictions, though they do not have the specialized and larger staffing capacities often found in the larger jurisdictions. To explore these dynamics further, we used a generalized linear model (GLM) with robust standard errors and constructed two models. We conduct data analysis in the R statistical language using the Survey package for complex survey design (Lumley 2010; R Core Team 2020). We construct two models, one for the COVID-19 concerns and one for VBM preparedness. Table 3.7 in the appendix shares the results of these models. The dependent variable here is the index of each battery as described above. A one unit increase in these indices is an increase from one level of confidence to the next. The Elazar political culture models are set up as factors in this model, with the individualistic model as the reference category. The reported coefficients can be compared to that reference category. Similarly, size is an ordered factor, and the small category is set as the reference category for comparisons. The COVID-19 model presents several insights. First, states from the moralistic model are slightly less likely to be confident in their preparedness for administering elections during the pandemic. While significant, the effect here is muted; if the local election official is an elected official (versus appointed or staff), higher confidence is reported. The strongest effect in this model can be found in the size of the jurisdiction—the largest jurisdictions, when compared to small, are half a point less confident about being ready for COVID-19. The VBM model has very similar dynamics with the exception of the political cultural variable shifting to the traditionalistic model. Some of the states categorized in this model were also ones that kept higher barriers to absentee or vote-by-mail options, thus we would expect their confidence in administering these options to be higher. Our model also included the LEO selection method, party identification, and relative election workload for each office. Of these three, only selection types was significant. Here we find for both the COVID-19 and

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VBM models, elected LEOs were more confident that they would be able to face the challenges of the pandemic. We interpret this as in line with previous literature exploring the role of selection method among LEOs where those that are elected are more focused on the voter experience, while non-elected LEOs are concerned with administrative challenges (Kimball and Kropf 2006; Burden et al. 2013). Further, elected officials are more common in smaller jurisdictions where we also generally see higher confidence among LEOs. Party identification does not have a statistically significant effect. We will note again that this could be a limitation of the data as many LEOs (around one-third) in the survey did not share a party affiliation. In June of 2020, the partisan aspect of the pandemic was not as divided as we witnessed later in the pandemic response. So, it may also be equally plausible that these data capture a snapshot of party perspectives that were still not attached to one interpretation of the pandemic, at least not yet. Finally, on workload we included a measure of whether an office was a full-time election office, versus split duties with other functions such as recording, budgets, or other tasks. Here, being a full-time election office was not statistically significant, and we suspect this may have to do with the mixed perspectives across jurisdiction sizes. The 2020 survey found that small jurisdictions in particular shared their percentage of election-related tasks. Thus, more small jurisdictions were reporting that all or almost all their work was election related. This shift likely impacted the significance of our workload measure. Overall, both the COVID-19 and VBM model, when assessed on goodness-of-fit, only explain a part of the COVID-19 story. While the R-squared value is low, we believe it is still meaningful for two reasons. First, even at this level, the power of this model is to focus our attention on the key issues that might predict future levels of concerns to other massive shocks to election administration. The size of the jurisdiction is critical here and suggests that medium and large-sized jurisdictions may need more support to adapt quickly to large-scale changes (though smaller jurisdictions have their challenges as well). The second reason this model is promising is that this area of research is novel and directly reflects the perspectives of the officials running elections during this crucial time in American history. We hope these efforts assist in setting the stage for future research.

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Conclusion This chapter builds upon emergent research related to the importance of understanding the opinions of local election officials toward policies and changes that impact election administration. It is important to understand LEOs’ perspectives on how prepared they felt to administer elections during a global pandemic, because we can learn more from their experiences about how to better prepare for future public health crises, as well as understand the additional burdens and resource-needs that are identified by LEOs in such challenging times. Using data from the Local Election Official Survey administered by the Early Voting Information Center, we have some interesting take-aways from LEOs about their responses to COVID-19 during the 2020 election cycle. We examined how confident LEOs felt in their preparation for running the 2020 elections safely during COVID-19, as well as how confident they were in preparing for large-scale shifts to using vote-bymail in jurisdictions where VBM had not been the primary vote method previously. We expected that larger jurisdictions would have higher confidence in their election preparedness and shifts to VBM, given that larger jurisdictions tend to have full-time staff devoted primarily or exclusively to elections, they have more employees, and because they tend to have more resources than smaller jurisdictions (Hypotheses 1 and 2). Our hypothesis was supported by the survey results presented here; in fact the biggest effect in the models can be found in size. The largest jurisdictions, when compared to small ones, are half a point less confident about being ready for COVID-19. While these effects are small, they are significant and demonstrate that all LEOs faced major challenges addressing the challenges of running an election amid the pandemic, even in the largest jurisdictions. We found two other interesting patterns related to jurisdiction size in our analysis. Smaller jurisdictions tend to report more confidence around all the items in our battery, while the largest jurisdictions consistently report the least confidence, which runs counter to our expectations. This result indicates that managing a large number of voters in an environment heightened by COVID-19 was challenging for the large areas, even with more staffing and resources. Additionally, a substantial number of LEOs in medium-sized jurisdictions reported having more challenges than LEOs in either the small, medium, or large jurisdictions.

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Given the documented differences in how the pandemic was perceived by Republicans and Democrats, we expected that Republican LEOs would be more confident about their preparedness for COVID-19 and the ability to scale up VBM than Democratic LEOs (Hypothesis 3). Instead, we do not find a statistically significant effect for party identification in our analysis. In part this may be attributed to the dynamic environment of the period when the survey was fielded. By June of 2020 election officials in some states had already made a shift to no-excuse absentee or all vote-by-mail elections. For some officials, this experience may have been able to assuage some of their fears. Further, the rhetoric by June of 2020 was still largely centered on public health. By June the nation started to see the expansion of mask mandates and the policy feedback into partisan discourse. But the timing of those dynamics may not have been captured by these data, given that the survey was administered before and just around this time. Because elected LEOs are mostly in smaller, rural jurisdictions (NCSL 2022), we expected they would feel less confident in their preparations for COVID-19 and VBM, due to their smaller staff sizes and limited resources (Hypothesis 4). Here again the results run counter to our expectations; our analyses show that if the local election official is elected (versus appointed or staff), they report higher levels of confidence in their election preparedness. This finding also aligns with the results of confidence levels reported by LEOs representing smaller jurisdictions, who are usually elected; there could be an overlapping effect here. In regard to states’ political cultures, we expected that LEOs in traditionalistic states LEOs would be the most confident about COVID-19 preparedness measures and the shift to VBM systems in 2020, given that the seriousness of the pandemic was downplayed more in conservative states (Hypothesis 5). Because individualistic states have more varied political landscapes, we hypothesized that LEOs in these states would be less confident about their COVID-19 preparedness measures and the shift to VBM systems than those in traditionalistic states, but more confident about preparedness than the moralistic states which are often more liberalleaning (Hypothesis 6). Finally, we thought that LEOs in moralistic would be less confident than both traditionalistic and individualistic states in their ability to prepare for COVID-19 and in shifting to VBM systems (Hypothesis 7 ), based on information that COVID-19 was taken more seriously in left-leaning states and those who generally want government to make important decisions to better society.

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The results in this analysis showed mixed support for our hypotheses around political culture and some surprising findings. One takeaway is that states from the moralistic states are slightly less likely to be confident in election preparedness than the other two categories of states. The effect is statistically significant though it is also quite small. Traditionalistic and individualistic states tend to band together in their results, and they tend to be more confident in their election preparedness measures, though again these effects are small. Our expectations on how LEOs might differ in their responses across factors of partisanship and, for the most part, political culture was not met with the findings in this study. Additionally, most LEOs reported being confident or somewhat confident in their abilities to prepare for COVID19 and for scaling up their vote-by-mail processes. These results show that LEOs were able to meet monumental challenges effectively and with alacrity at a time democracy needed it most. The fact that partisanship and a state’s overall culture did not substantially impact the responses of LEOs in this survey indicates that whether they are red or blue, LEOs know they have a job to perform, and that the types of logistical challenges they faced were similar regardless of the partisan makeup of their offices and jurisdictions..

Future Study The realm of election administration is vast and LEOs vary a great deal in how they are selected, their partisan affiliations, the size of the jurisdictions they serve, as well as the scope and duties of their work. This chapter can only address a small piece of the innumerable challenges faced by LEOs in the 2020 election cycle and how prepared they felt to meet these challenges. Future research could examine additional measures of preparedness captured in the survey instrument, as well as how LEOs feel voters responded to these changes. Additionally, more granular analyses could be conducted among LEOs within a state to capture a more comprehensive view of patterns across different jurisdictions within the same state. Finally, a state-by-state analysis could add more depth and build upon the regional analyses covered in this chapter. We hope that the findings in this chapter illuminate some of the obstacles LEOs faced in running elections amid a global pandemic, with only a short time to prepare themselves and their elections workforces for these

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Table 3.7 Regression models exploring responses to COVID-19 pandemic

Variable Intercept Years of Service Moralistic Traditionalistic Elected LEO Democrat Election Workload—All or Almost All Medium Size Large Size R-squared:

COVID-19 model

VBM model

3.33*** −0.01 −0.16* 0.08 0.27* −0.19 0.17 −0.35*** −0.55*** 0.097

2.88*** −0.01 −0.01 0.27* 0.33* −0.06 0.19 −0.28** −0.30* 0.047

p-value: 0.5 = *, 0.01 = **, 0.001 = ***

changes. We think there is much to be learned about levels of preparedness and how LEOs were able to conduct smooth-running elections at such an unprecedented time in American history.

Appendix See Table 3.7.

References Adona, N., P. Gronke, P. Manson, and S. Cole. 2019. Stewards of Democracy: The Views of American Local Election officials. Democracy Fund. Ansolabehere, Stephen, and Charles Stewart III. 2005. “Residual Votes Attributable to Technology.” Journal of Politics 67: 365–389. Anthony, Joseph, Amy Fried, Robert Glover, and David Kimball. 2020. “Ranked Choice Voting in Maine from the Perspective of Local Election Officials.” Election Law Journal 20 (3): 254–271. https://doi.org/10.1089/elj.2020. 0650 Bishop, Bill, and Robert G. Cushing. 2009. The Big Sort: Why the Clustering of Like-Minded America is Tearing us Apart. Houghton Mifflin Harcourt. Brownstein, Ronald. 2020. “Red and Blue Aren’t Experiencing the Same Pandemic.” Atlantic Monthly, March 20. https://www.theatlantic.com/pol itics/archive/2020/03/how-republicans-and-democrats-think-about-corona virus/608395/

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Burden, Barry C., David T. Canon, Kenneth R. Mayer, and Donald P. Moynihan. 2011. “Early Voting and Election Day Registration in the Trenches: Local Officials’ Perceptions of Election Reform.” Election Law Journal 10: 89–102. Burden, Barry C., David T. Canon, Kenneth R. Mayer, and Donald P. Moynihan. 2012. “The Effect of Administrative Burden on Bureaucratic Perception of Policies: Evidence from Election Administration.” Public Administration Review 72: 741–751. Burden, Barry C., David T. Canon, Stephane Lavertu, Kenneth R. Mayer, and Donald P. Moynihan. 2013. “Selection Method, Partisanship, and the Administration of Elections.” American Politics Research 41: 903–936. Cark, Terry Nichols, and Vincent Hoffmann-Martinot, eds. 1998. The New Political Culture. Boulder: Westview Press. Departments of Justice and Homeland Security. 2021. Joint Statement from the Departments of Justice and Homeland Security Assessing the Impact of Foreign Interference During the 2020 U.S. Elections. Press Release. Accessed June 14, 2023. https://www.justice.gov/opa/pr/joint-statement-departments-justiceand-homeland-security-assessing-impact-foreign. Eggers, Andrew C., Haritz Garro, and Justin Grimmer. 2021. “No Evidence for Systematic Voter Fraud: A Guide to Statistical Claims about the 2020 Election.” Proceedings of the National Academy of Sciences 118 (45). Elazar, Daniel. 1966. American Federalism: A View from the States. New York: Thomas Crowell Publishing. Ewald, Alec. 2009. The Way We Vote: The Local Dimension of American Suffrage. Nashville: Vanderbilt University Press. Farris, Emily M., and Mirya R. Holman. 2015. “Public Officials and a ‘Private’ Matter: Attitudes and Policies in the County Sheriff Office Regarding Violence Against Women.” Social Science Quarterly 96: 1117–1135. Fullmer, Elliott B. 2015. “Early Voting: Do More Sites Lead to Higher Turnout?” Election Law Journal 14: 81–96. Gadarian, Shana, Sara Wallace Goodman, and Thomas B. Pepinsky. 2021. “Partisanship, Health Behavior, and Policy Attitudes in the Early Stages of the COVID-19 Pandemic.” PloS ONE 16 (4). https://doi.org/10.1371/journal. pone.0249596. Hamilton, James T., and Helen F. Ladd. 1996. “Biased Ballots? The Impact of Ballot Structure on North Carolina Elections in 1992.” Public Choice 87: 259–280. Kimball, David C., and Martha Kropf. 2006. “The Street-Level Bureaucrats of Elections: Selection Methods for Local Election Officials.” Review of Policy Research 6 (23): 1257–1268. Kimball, David C., Martha Kropf, D. Moynihan, and C. L. Silva. 2013. “Policy Views of Partisan Election Officials.” UC Irvine Law Review 3: 551.

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Kropf, Martha, David C. Kimball, and Lindsay Battles. 2006. “Helping America Vote? Election Administration, Partisanship, and Provisional Voting in the 2004 Election.” Election Law Journal 5 (4): 447–461. Lee, J., and P. Gronke. 2020. “Considerations for an Establishment Survey of Local Election Officials.” Southern Political Science Association Annual Meeting. San Juan, PR. Lumley, Thomas. 2010. Complex Surveys: A Guide to Analysis Using R. Hoboken: Wiley. Lund, William A. 1998. “What’s in a Name? The Battle Over Ballot Titles in Oregon.” Willamette Law Review 34: 143–167. Manion, Anita, Joseph Anthony, David C. Kimball, Adriano Udani, and Paul Gronke. 2021. “Comparing Elite and Public Opinion on Election Administration and Reform.” Paper presented at the annual meeting of the Election Science, Reform, and Administration conference. McBrayer, Markie, R. Lucas Williams, and Andrea Eckelman. 2020. “Local Officials as Partisan Operatives: The Effect of County Officials on Early Voting Administration.” Social Science Quarterly 101: 1475–1488. Montjoy, Robert S. 2008. “The Public Administration of Elections.” Public Administration Review 68: 788–799. Moynihan, Donald P., and Carol L. Silva. 2008. “The Administrators of Democracy: A Research Note on Local Election Officials.” Public Administration Review 68: 816–827. National Conference of State Legislatures (NCSL). 2022. “Election Administration at the State and Local Levels.” https://www.ncsl.org/elections-and-cam paigns/election-administration-at-state-and-local-levels R Core Team. 2020. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-pro ject.org/ Stuart, Guy. 2004. “Databases, Felons, and Voting: Bias and Partisanship of the Florida Felons List in the 2000 Elections.” Political Science Quarterly 119: 453–475. Weissert, C. S., M. J. Uttermark, K. R. Mackie, and A. Artiles. 2021. “Governors in Control: Executive Orders, State-Local Preemption, and the COVID-19 Pandemic.” Publius: The Journal of Federalism 51 (3): 396–428.

PART II

Vote Methods, Vote Choice, and Voter Turnout

CHAPTER 4

The Pandemic and Vote Mode Choice in the 2020 Election Lonna Rae Atkeson , Wendy L. Hansen , Cherie D. Maestas , Eric Weimer, and Maggie Toulouse Oliver

The Pandemic and Vote Mode Choice in the 2020 Election The possibility of the spread of COVID-19 in Vote Centers and election precincts across the country created challenges for voters and election officials in both the 2020 state primaries and general elections. Consequently, for the voter the choice of when, where, and how to vote in the 2020 presidential election was anything but routine. In typical years, voters choose how to vote based upon the costs and benefits of different modes (Stein and Vonnahme 2008), with mobilization factors by parties

L. R. Atkeson (B) Florida State University, Tallahassee, FL, USA e-mail: [email protected] W. L. Hansen · M. T. Oliver University of New Mexico, Albuquerque, NM, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. A. Coll and J. Anthony (eds.), Lessons Learned from the 2020 U.S. Presidential Election, Elections, Voting, Technology, https://doi.org/10.1007/978-3-031-44549-1_4

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and candidates likely playing the most prominent role in voter behavior (Rosenston and Hansen 1993). In 2020, COVID-19 introduced a new potential cost of in-person voting—exposing oneself to the risk of illness and death, leading the CDC, media, and some political elites to emphasize vote modes that minimized risk of exposure. However, other political elites, especially President Trump, introduced an alternative concern that votes cast remotely by mail or at ballot drop off locations could result in miscounted ballots or outright vote fraud (Chalfant 2020; Chen et al. 2021). These conflicting risks offer an opportunity to explore how different factors shaped who chose to vote-by-mail (VBM) or vote in-person. While Chapter 6 examines how COVID-19 risks impacted vote choice, this chapter examines how COVID-19 impacted how people voted. Specifically, we ask, what role did party polarization and COVID-19related risk factors, such as age, play in the vote mode decision? We first addressed these questions in the context of New Mexico in our article “Should I vote-by-mail or in-person? The impact of COVID-19 risk factors and partisanship on vote mode decisions in the 2020 presidential election” (Atkeson et al. 2022). In this chapter, we update and extend our analysis to Florida and compare New Mexico results to Florida to get a better sense of the generalizability of the New Mexico findings and where and why differences might exist between different election ecosystems.1

1 This chapter follows the same logic and analysis as our 2020 study in New Mexico. Hence, we have similar descriptions throughout this chapter. There may be phrases of words at times that appear nearly identical in our other text. We also have copied our figures from the New Mexico article over into this chapter to allow for comparability. The New Mexico PLoS One article is open source and all of the same authors are on each publication. Therefore, other than acknowledging the previous work as we have done here, there are no additional requirements for usage.

C. D. Maestas Purdue University, West Lafayette, IN, USA e-mail: [email protected] E. Weimer Princeton, Princeton, NJ, USA e-mail: [email protected] M. T. Oliver New Mexico Secretary of State, Santa Fe, NM, USA e-mail: [email protected]

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Federal Voting Recommendations Given the dangers of COVID-19, the Centers for Disease Control issued safety guidelines for voting in the 2020 election with the following guiding principles featured on their website (CDC 2019): The more an individual interacts with others, and the longer that interaction, the higher the risk of COVID-19 spread. Elections with only in-person voting on a single day are higher risk for COVID-19 spread because there will be larger crowds and longer wait times. Lower risk election polling settings include those with: • a wide variety of voting options • longer voting periods (more days and/or more hours) • any other feasible options for reducing the number of voters who congregate indoors in polling locations at the same time.

For election officials, they suggested the following for general operations: Maintaining Healthy Operations Where available in your jurisdiction, offer alternative voting methods that minimize direct contact and reduce crowd size at polling locations. • Consider offering alternatives to in-person voting if allowed in the jurisdiction. • Offer early voting or extended hours, where voter crowds may be smaller throughout the day. • Consider drive-up voting for eligible voters if allowed in the jurisdiction. • Encourage voters planning to vote in-person on Election Day to arrive at off-peak times. For example, if voter crowds are lighter mid-morning, advertise that in advance to the community.

The CDC recommendations for voters included: • Consider voting alternatives available in your jurisdiction that minimize contact. Voting alternatives that limit the number of people you come in contact with or the amount of time you are in contact with others can help reduce the spread of COVID-19. Check your local election office website for more information on voting alternatives available in your jurisdiction.

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• Avoid crowds – Use early voting, if available in your jurisdiction. – Vote at off-peak times, such as mid-morning. – If driving to the polls and your schedule allows, monitor the voter line from your car and join it when it’s shorter.

States took very seriously the need to reduce risk of infection, and as a result, many states instituted policy changes in line with the CDC recommendations, such as reducing polling place density and making mail balloting easier.2 Some states, such as California and Montana, moved from mostly vote-by-mail to all vote-by-mail in which all registered voters in the state were sent a ballot in the mail to encourage voters to vote safely from the privacy of their homes. Other states, such as Alabama and Massachusetts, suspended their more restrictive absentee ballot rules allowing all voters to cast a VBM ballot. Still other states, such as Texas, expanded their in-person early voting hours to accommodate a larger spread of voters over the course of the election, and other states, such as New Mexico, actively encouraged voters to vote-by-mail by sending out mail ballot applications to all voters. States were generally successful at shifting voters away from densely packed Election Day voting into other modes of voting. Nationally, voteby-mail went from 17% of the electorate in 2016 to 41%, a 141% increase, according to Dr. Michael McDonald.3 ,4 The shift from an estimated 24,218,607 vote-by-mail voters in 2016 to 65,642,049 in 2020 was striking. In addition, according to Dr. McDonald, another 22% of voters chose to vote early in-person instead of on Election Day. Early voting was up 5% from 2016 when approximately 17% of voters voted in-person early. Election Day voting, however, dropped appreciably. Only 37% of voters chose this mode, a significant decrease from a large majority (67%) voting on Election Day in 2016.

2 For an overview of state-by-state changes in election laws in response to COVID-19 and the 2020 election, see https://ballotpedia.org/Changes_to_election_dates,_procedure s,_and_administration_in_response_to_the_coronavirus_(COVID-19)_pandemic,_2020. 3 The data in this paragraph come from the Professor Michael McDonald’s Election Project webpage and can be found at https://electproject.github.io/Early-Vote-2020G/ index.html and http://www.electproject.org/early_2016. 4 See https://fivethirtyeight.com/features/what-absentee-voting-looked-like-in-all-50states/ for an alternative calculation.

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Taken together, the aggregate data suggest that actions taken by state and local election officials, along with information about risk, substantially altered how people voted in the 2020 election. However, the aggregate numbers do not help us understand what factors led to individual choices of mode, or how the competing risk messages might have affected vote choice differently across individuals.

Calculating Risk During the Pandemic We focus on understanding two types of risk messages voters were exposed to—risks of infection from participating in different election modes, and the risk of having one’s ballot lost or not correctly counted. The recommendations by the CDC suggest two major strategies for a healthy election environment. The first is to vote-by-mail, but the second is clearly to vote in-person early to spread out voting (see Chapter 3 for an examination of how COVID-19 safety policies influenced feelings of safety while voting in-person). Although vote-by-mail is generally safe and reliable, it does increase opportunities for errors. Vote-by-mail ballots, for example, may not arrive on time to be counted or may be rejected for some procedural errors—for example, the ballot may not be authenticated through signature matching or some other identification process. These potential problems may be why some research shows that vote-by-mail voters are less confident that their ballot is counted (Bryant 2020; Atkeson and Saunders 2007; Menger and Stein 2020; but see Atkeson 2014). On the other hand, voting in-person offers several advantages to voters over VBM because it reduces uncertainty about ballot arrival, acceptance, and, for many voters, counting.5 It is also the case, that where we see fraud in voting, it is largely found in VBM ballots. For example, in 2020, a local election in Patterson, New Jersey was so enveloped in voter mail fraud that the election was voided and rerun (Sturla 2020), and in 2018 the 9th congressional race in North Carolina was not certified because of fraud found in mail ballots in a close race that left the outcome uncertain (Graff and Ochsner 2021).

5 Most urban and suburban in-person voters place their paper ballot directly into the tabulator.

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Considering vote mode choice in 2020 through a theoretical lens of competing risk messages is important because we can draw from key theories about how risk perceptions shape human decision-making. Such perceptions should be an important factor in deciding how to vote during a pandemic. Extraordinary events, such as a pandemic, naturally stimulate people to think more deeply and hence with less bias from predispositions, such as partisanship, as they are motivated to prioritize accuracy in their understanding of risk (Atkeson and Maestas 2009; 2012; Maestas et al. 2008; Kunda 1990). This is especially true when the event produces anxiety. The 2020 pandemic according to the American Psychiatric Association dramatically increased citizen anxiety. Fully three-fifths (62%) of Americans felt more anxious in 2020 compared to last year, while in the previous 3 years responses to the same question ranged between 32% and 39% (APA 2020). Respondents indicated that their anxiety was caused by their worries about keeping their family safe (80%), COVID-19 (75%), and their health (73%).6 Given high anxiety levels we might expect that voters in the 2020 election would be more immune to partisan influences because health and health-related behavior are generally nonpartisan. Voters engaging in accuracy-motivated reasoning would be expected to seek out information about how to vote based upon their perceived health and safety risks in which case politics, political cues, and affective polarization7 should have little influence on choice. However, there is a great deal of heterogeneity or diversity in a riskmessaging environment and in how individuals perceive and respond to risk (Allcott et al. 2020; Barrios and Hochberg 2021; Gennaioli et al. 2016). It was clear early in the election process that candidates took very different positions on their support for vote-by-mail. For example, President Trump presented concerns about possible VBM fraud, a concern amplified by traditional and social media outlets. President Trump first tweeted about potential problems in mail balloting as early as April 8th (Shino et al. 2022). This had the effect of magnifying partisan polarization around the expansion of vote-by-mail, something that was largely

6 See https://www.psychiatry.org/news-room/news-releases/anxiety-poll-2020. 7 Affective polarization is the dislike or distrust partisans tend to feel toward those from

the other party.

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nonpartisan before the 2020 election (Clinton et al. 2022). Also in April, at a news conference, the President said, “Mail ballots – they cheat…..People cheat. Mail ballots are a very dangerous thing for this country because there’re cheaters. They go and collect them. They’re fraudulent in many cases.” And, in May, he tweeted, “There is NO WAY (ZERO!) that Mail-In Ballots will be anything less than substantially fraudulent. Mailboxes will be robbed, ballots will be forged & even illegally printed out & fraudulently signed” (Lemongello 2020). In contrast, VBM was promoted as the preferred option by Democratic elites. In the US Senate, Democrats introduced legislation to expand vote-by-mail and early voting options (US Vote Foundation 2020) and across the country Democrats filed lawsuits to do the same (Abramson 2020). Meanwhile Republicans blocked legislation promoting universal mail balloting, a policy whereby states automatically send ballots out to each eligible voter, and for which President Trump took a strong position against. This partisan difference in messaging around health and ballot risks is important because political polarization encourages partisan-motivated reasoning, influences how voters assess information, and shapes citizen attitudes and behavior in our social (Iyengar et al. 2019), economic (Gerber and Huber 2010) and political (Clinton et al. 2021; Atkeson et al. 2022) lives. Therefore, while health perceptions are important across both parties, the assessments of risk may not be immune from partisan perturbations. For example, Druckman et al. (2021) show a strong correlation between out-party animosity and citizen attitudes toward the pandemic policies. To summarize, we argue that all voters were, at least in part, making decisions based upon the perception of their risk of COVID-19 contraction and its consequences for their own or their families’ health, and thus should be more likely to VBM than in-person. Vote-by-mail addressed this risk most clearly because voters would have virtually no interaction with other voters, reducing the risk of both contraction and transmission to zero. But we contend that some voters in this election also felt keenly that they were balancing health risks against the risk that their ballot would be lost, stolen, or not accurately processed and counted by local election officials. For voters who found this a concern, voting early in-person, over an extended period, in environments where there would presumably be fewer voters, offered, as the CDC suggested, a viable alternative. Early voting is less risky than Election Day voting, while mitigating the risk of potential ballot problems. We surmise that risk perceptions are

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tied to health factors and partisan elite divides about the efficacy of VBM. By drawing on these intuitions, we can specify a set of empirical expectations for which individuals should be most likely to select vote-by-mail versus in-person voting.

Data, Methods, and Hypotheses Data We utilize data from official voter administrative records drawn from the Secretary of State offices in Florida and New Mexico to explore how pandemic-related risk factors, especially voter age and partisanship, influenced whether voters decided to VBM or to vote in-person early (also see Niebler 2020; Scheller 2021). The dataset is the panel of voters participating in the 2016, 2018, and 2020 elections. While survey research has advanced our understanding of COVID-19 related attitudes and behavior (e.g., Druckman et al. 2021; Gollwitzer et al. 2020), administrative data offer the clearest evidence of behavioral choice in elections because they are not subject to survey misreports or errors. This is particularly important since Shino et al. (2022) show that Trump voters in Florida were more likely to misremember their previous VBM experiences than Biden voters. The reliable and valid nature of the administrative voter data provides strong external validity for our tests because we use the known universe of voters, while the comparative nature of our tests and use of a panel difference-in-differences analysis across election years provides strong internal validity allowing us greater purchase on causal inference. Hypotheses Our first hypothesis is that more voters should VBM in 2020 than in previous elections due to the risk of COVID-19 exposure. However, risk varies across individuals based on their personal health circumstances, and we operationalize this through age since age was emphasized by the CDC and in media as a key risk criterion. Two of the primary risk factors for COVID-19 complications, including death, are age, since older adults are more at risk than younger adults, and other medical conditions, which

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are associated with age (Franceschi et al. 2018).8 Even today, the CDC (2023) indicates that while 30–39 year-olds are 1.5 times more likely to be hospitalized than 18–29 year-olds (the base category), and 3.5 times more likely to die, 75–84 year-olds are 8.6 times more likely to be hospitalized and 140 times more likely to die. This suggests that older voters, regardless of party, should be more likely to choose to VBM than younger voters and represents our second hypothesis. Risk of ballot error is operationalized as a voter’s party registration. While we should see evidence of age and party effects related to decisionmaking, we should also see evidence that partisan-based reasoning offsets and mediates the effects of age. Elite policy-making and political polarization happened very quickly. States with stronger support for Clinton in 2016 issued more mask mandates and stay-at-home orders (Makridis and Rothwell 2020) than states where her support was lower. Evidence also suggests that COVID-19-related policy preferences, such as approving or restricting nonessential travel, concerns about the virus, and self-reported COVID-19 behaviors such as wearing a mask or practicing social distancing, were polarized with Democrats taking more conservative pandemic positions and actions than Republicans (Gollwitzer et al. 2020; Druckman et al. 2021). Therefore, while all voters should likely increase their use of mail balloting, Democrats and Independents should, on average, be more likely to VBM than Republicans, holding age constant (hypothesis 3). And Democrats, on average, should be more likely to vote-by-mail than Independents (hypothesis 4) given the partisan messaging environment. Given their greater concern about mail balloting, we also expect Republicans to be more likely to vote early than either Democrats or Independents (hypothesis 5). Cases Because of their similarities in election ecosystems, New Mexico and Florida are both excellent cases in which to examine and compare the relationship between age, party, and vote mode. In 2020, both states had no excuse absentee voting laws, which makes VBM easily requestable by any qualified elector. Voters in New Mexico had to request every election year to obtain a mail ballot, while Florida voters in 2020 could make 8 The third risk factor is pregnancy, which would apply to a small percentage of women aged 18–50.

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a single request every federal election cycle, or every 2 years. In both states, there were large VBM campaigns being waged by both parties, and in both states some local county election officials chose to send out VBM applications to all registered voters in advance of the election. In New Mexico, VBM ballots were authenticated through a process that had voters include the last 4 digits of their social security number on the outside ballot envelope. Florida’s method of ballot authentication was a more traditional signature match. In both states voters who request a VBM ballot can change their mind and vote in-person using a regular ballot. Both states also had drop box locations in which a voter could insert their VBM ballot into a secure receptacle for direct pick-up by local election officials. However, the two states had somewhat different early voting rules. New Mexico law requires 28 days of early voting, and Florida only requires a minimum of 8 days. New Mexico starts early in-person voting 28 days before the election in every county’s County Clerk Office. At 21 days before the election, the process expands to a larger number of locations across each county. In Florida, counties were required to have early voting available from Saturday, October 24 to Saturday, October 31 and were given the option of adding early days from Monday, October 19 to Friday, October 23 and Sunday, November 1. In addition, two counties, Bay and Gulf County, were under Executive Order 19-262 due to damage from Hurricane Michael in 2018 and thus were allowed to extend early voting hours even further. Bay County extended early voting through Monday, November 2. Gulf County also added extra days at the end of the election cycle for their voters. Election Day voting is also different across the two states. New Mexico uses a Vote Center model. Voter Centers are large facilities that provide many voting stations which any voter in the county can use. Because any voter can vote at any location in the county, there are usually many fewer voting locations than in precinct voting. Florida uses a precinct model. Precinct voting results in many more voting locations on Election Day because voters are required to vote in their precinct. Other differences exist between the two states. Florida was considered a toss-up state in the 2016 and 2020 presidential elections, while New Mexico was not (Ballotpedia 2020). But, in terms of state politics, both states have been trending different directions, New Mexico toward becoming a deep blue state and Florida increasingly becoming a very red state. New Mexico and Florida both have very rural and

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very urban counties, but in 2020 Florida was 10 times the population of New Mexico (21,538,187 vs. 2,117,522) with 12 times the voters (11,144,855 vs 928,230). In terms of state legislative politics, both states were trifectas, with New Mexico’s three branches of government controlled by Democrats, while Florida’s were controlled by Republicans. State institutional differences are important because they provide opportunities for voting that can reduce voting costs, making it easier to VBM or in-person. While both states had a very open and easily accessible VBM system, Florida’s early voting operation was shorter and its Election Day operations, using traditional precincts, offered many more and less crowded options compared to New Mexico’s Vote Center model. These different voting contexts may help us understand the differences we observe in the two states. Pandemic Effects on Vote-by-Mail Figure 4.1 shows how the similarities and differences in election ecosystems affected voter choices over time and supports our first hypothesis that vote-by-mail increased in both states in 2020 relative to 2016 and 2018. In Florida VBM went from 29% in 2016 to 44%, a 15% increase. In New Mexico vote-by-mail went from 10% to 35%, a 25% increase. These are large and significant changes and are suggestive that the pandemic played a major role in voter behavior. It is also important to note that the effects differ across states in part because of differences in the baseline use of each vote mode. New Mexico voters were overwhelmingly in-person voters throughout the last decade. However, New Mexico voters shifted to early in-person voting from Election Day voting in 2014 and 2016 when the initial move to Vote Centers resulted in long lines (Atkeson et al. 2015, 2017). By 2016 and 2018 early voting was the preferred voting method for a majority of New Mexicans, with 57% of voters choosing to vote this way in 2016, 54% in 2018, and in 2020 it was the mode at 49%. Florida’s VBM mode, however, had been growing over time and trending upward. VBM voters were static in New Mexico at about 10% while Florida’s VBM rose from about 22% of voters in 2008 to 32% in 2018, with each federal election year monotonically increasing. As a result, since the baseline of VBM is 20 percentage points higher in Florida, we should expect to see smaller effects of the pandemic on VBM

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in Florida than in New Mexico. Florida simply has fewer voters to shift from in-person to VBM.

Modeling Effects of Risk on Vote Mode Choice We estimate a series of logit models where our dependent variables are a set of binary vote mode choices (VBM, early, or Election Day), and our treatment variable is an interaction with election year (2016, 2018, 2020) multiplied by party (Democrat, Independent, or Republican) and age categories. In other words, we seek to predict voters’ choices of how to vote, looking at variation across party ID and age, by comparing each voter’s behavior across three elections. We expect risk perceptions and partisanship to affect behavior in 2020 compared to 2018 or 2016. While we might expect some age effects in 2016 or 2018, since older voters are

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more likely to have health concerns that lead to a greater likelihood of voting at home, we expect them to be much larger and more dramatic in 2020. In terms of party, we have no reason to expect strong partisan differences in VBM prior to 2020. Age is the primary variable we use to measure perceived risk of COVID-19. We measure age in categories as defined by the CDC in graphics it had on its website during COVID-19 (see Atkeson et al. 2022). These are: 18–29, 30–39, 40–49, 50–64, 65–74, 75–84, and 85+. To examine the role of party polarization, we rely on voters’ stated party registration, defined as Democrat, Independent/no major party/ other party, and Republican in the voter file. We use a panel differencein-differences (DID) analysis using logistic regression models of voters’ individual vote mode decisions in New Mexico and Florida in 2020 compared to those made in 2016 and 2018. The DID model is a quasiexperimental approach to estimating the effects of the pandemic on vote mode choice at different levels of our main risk proxies—age and partisanship—in the pre- and post-pandemic contexts. Matching voters across elections reduces the chance that unmeasured confounders that correlate with party, age, and location affect outcomes across elections, and ensures that the differences we see in 2020 are the result of the pandemic context and not the result of differences in the groups of voters participating in the election. The underlying assumption of our model is that vote mode choices would not change much from election to election in the absence of the pandemic. We expect individual characteristics like age and party to have little systematic influence on vote mode choice in 2016 compared to 2018 and expect the marginal effects for 2016 compared to 2018 to be very similar and more or less overlapping one another. But these variables take on special meaning during 2020 because of the pandemic context when they become proxies for individual exposure to risk. As a result, we expect greater differences in average marginal age and party effects on vote mode choice in 2020 compared to 2016. We control for sex and for self-identified race and ethnicity or, in the case of NM, imputed race and ethnicity using Imai and Khanna’s (2016) method of considering both last name and location of the voter. We include fixed effects for county, since election administration implementation happens at this level, and we cluster by the individual voter. We use graphs to show the change in the predicted probabilities of selecting each vote mode by age and party. We also present a multinomial logit

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model of voting mode and show age and party effects by election year to observe the predicted probabilities and understand the substantive size of the effects in each election context.9

Results We estimate subgroup treatment effects to see how risk context for different age and party groups affected the probability of selecting one vote mode over another. The empirical design assumes parallel trends such that voter behavior in the treated category, if untreated, would have followed a similar trend as prior years. Although the parallel trends assumption is not directly testable, the similarities between 2016 and 2018 suggest consistency in voting behavior prior to 2020. VBM voters mostly VBM, early voters mostly vote early, and Election Day voters mostly vote on Election Day—thus parallel trends are a plausible assumption. Changes in 2020, then, suggest a deviation associated with factors that made voters rethink their typical routines, resulting in different choices. This is exactly what we find in Fig. 4.2, with similarities between 2016 and 2018 and large differences between 2016 and 2020. Importantly, we clearly see evidence of both age risk and partisan-based motivated reasoning in the results, consistent with our hypotheses. The first six panels, Fig. 4.2a–f, present visualizations of the differences in the probability of vote mode choice in the pandemic context (election year) for different party and age combinations, including 95% confidence intervals, generated from the models for each vote mode: VBM (top panel), early (second panel), and Election Day (third panel). The lefthand side of these panels are the Florida graphs, and the righthand side are the New Mexico graphs (taken from our published article, Atkeson et al. 2022). In Fig. 4.2g and h, we present the predicted probabilities from the multinomial logit for Florida only. Each groups’ predicted probabilities across vote modes sums to 1. These can be found for New Mexico in our publication. Essentially the multinomial logit graphs show the predicted probability for each mode by party and age, and if we subtract, for example, VBM Democrat 2020 (Fig. 4.2g) from 2016 (Fig. 4.2h) for 9 Logit and multinomial logit model coefficients are available from the first author upon request.

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each point these are the “differences” we see in Fig. 4.2a. We include these bottom panel figures to help provide context and understanding for what we see above. For example, we can see that as voters get older, regardless of COVID-19, they are more likely to VBM. Figure 4.2g shows us that VBM propensity was about equal for 18–49-year-olds, but voters over 50 are more likely to VBM, and this increases monotonically for each decade older. Thus, when we see that the oldest voters’ “difference” in Fig. 4.2a is less than those a decade younger than them, we can see that this is because these voters were already voting by mail at a rather high probability of 0.58. The results demonstrate how drastically voting changed during the pandemic and how both health risks and partisanship affected decisionmaking. Figure 4.2a–f show incredibly similar patterns for both New Mexico and Florida. The top 3 lines show the difference between 2020 and 2016 and the bottom 3 lines show the difference between 2018 and 2016. Importantly, in the 2018 and 2016 differences, we see two important trends that are consistent with our hypotheses in both states. First, we see the bottom 3 lines are very close to zero, which means that there was not much change in VBM behavior between 2016 and 2018. There are small, substantively uninteresting increases in Florida, consistent with the upward trend we saw in Fig. 4.1, and, in New Mexico, we also see very small and substantively uninteresting changes for partisans. Figure 4.2g also supports this conclusion by showing that there were not large party differences in vote mode behavior. A 2018 multinomial graph (not shown) shows the same result. For example, the youngest Democrats had the same probability of VBM as the youngest Republicans and Independents, and the oldest Democrats had the same probability of voting by mail as the oldest Republicans and Independents. It is also worth noting that in both states, the predicted probabilities between party members and across the age categories is similar. Importantly, all the data point to consistent and nonpolarized VBM behavior prior to 2020. When we compare 2020 to 2016, however, we see a large shift in vote-by-mail, particularly for Democrats and Independents across all age groups, and the gap increases dramatically as voters age (see top 3 lines in Fig. 4.2a and b). The youngest Republicans, especially in Florida, were only slightly more likely to vote-by-mail than in 2016, but with each decade of age there are monotonic increases. New Mexico shows much larger differences than Florida, in part because so many fewer voters were

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taking advantage of VBM in NM before the 2020 elections. But the results are amazingly consistent in scope, if not magnitude. The results for both New Mexico and Florida are consistent with hypothesis 1, more voters are voting by mail in 2020 than in 2016; hypothesis 2, older voters are more likely to VBM in 2020 than in 2016 due to the pandemic; hypothesis 3, Democrats and Independents are more likely to VBM than Republicans; and hypothesis 4, Independents are more likely to VBM than Republicans. The youngest Democrats aged 18–49 in Florida were about 2.2 and in New Mexico about 2.9 times more likely to VBM in 2020 than in 2016 (see Fig. 4.2a), while Democrats in Florida aged 65+ were, on average, about 3.5 and in New Mexico 4.5 times more likely to VBM. Republicans were significantly more likely to VBM in 2020, but the effects were smaller, especially for younger voters aged 18–49, and particularly in Florida. In 2020, younger Republicans in Florida were always above the 2016 compared to 2018 differences, consistent with hypothesis 1, but not much different than their choices in 2020 versus 2016, while New Mexico young Republicans averaged a somewhat larger 0.075 increase. For the oldest voters 85+ the difference between 2020 and 2016 was about 0.18 in Florida and 0.31 in New Mexico. Republicans in both states show a monotonic rise across age categories consistent with hypothesis 2 about the role of age and risk properties. The oldest partisans are also the least far apart on selecting VBM suggesting that they were motivated to vote most similarly, which is also consistent with hypothesis 2 about the relationship between health risk, defined as age, and VBM. For example, in Florida the gap between the youngest Democrats and youngest Republicans is about 0.2, but the gap is only about 0.1 between the oldest Democrats and Republicans. Similarly in New Mexico the gap between the youngest partisan voters is about 0.25, but for the oldest partisan voters it is only 0.15. Thus, there are larger differences in partisan choices between those least at risk from illness and death, but much less so for those partisans who were at the greatest risk for illness and death, consistent with hypothesis 2. We also see similar patterns between the two states when we look at early and Election Day voting in Fig. 4.2c–f, but again we see larger effects in New Mexico. For both New Mexico and Florida, the lines that are more closely clustered together represent the difference between 2018 and 2016. These more tightly clustered lines demonstrate the similarities in vote mode choices among Democrats, Republicans, and Independents

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in 2018 compared to 2016. Clearly, 2018 and 2016 are more closely aligned across party and age than the 3 lines that represent the differences between 2020 and 2016. In Fig. 4.2c and d, we see that for both Florida and New Mexico these lines are below 0 telling us that fewer voters voted early in-person in 2018 than in 2016. This is not surprising since mobilization patterns change somewhat between presidential and midterm elections. This interpretation is also supported by Fig. 4.2e and f, where the top 3 lines in both graphs show that Election Day voting increases particularly for younger voters in both states. The similarity between the differences in the 2016 and 2018 early and Election Day figures shows us that party mattered only a little and is not substantively interesting. For younger Democrats in both states, the predicted probabilities in voting early are just slightly higher than for Independents and Republicans. However, once again, when we examine the differences between 2020 and 2016 in Fig. 4.2c and d we see both strong party and age effects with the same ordering of partisans. Younger Republicans in both states increase their likelihood of voting early, while older Republicans were less likely to vote early because they were more likely to VBM (see Fig. 4.2a and b, and Fig. 4.2g and h). Importantly Republicans of all ages are more likely to vote in-person early than Democrats or Independents consistent with hypothesis 5. Differences between the two states are smaller for younger votes, with the age group 30–39 showing the largest difference of 0.07 with New Mexico on top. At the other extreme, we see that the oldest New Mexican Democrats were about 0.35 less likely to vote early and the oldest Floridian Democrats were about 0.2 less likely to vote early because they are more likely to VBM. Turning to Election Day, in Fig. 4.2e and f, the top three lines show the change in the predicted probability from 2018 to 2016. The lines show that younger partisans in both states were more likely to vote on Election Day in 2018 than in 2016. This difference is likely due to changes in campaign mobilization patterns between presidential and midterm federal elections as we discussed before. But for the most part, the party lines are overlapping and for older partisans, who are likely more habitual voters than younger voters, very close to zero. This shows that Election Day voters in 2018 had about the same likelihood of Election Day voting as they did in 2016. Comparing 2020 to 2016, for both states we see that partisans across age groups are equally likely to vote on Election Day, but less likely to vote on Election Day than in 2016.

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All voters abandoned Election Day voting equally. And, based upon the other graphs in Fig. 4.2, most Democrats and Republicans instead selected vote modes consistent with the government’s messaging regarding health risk, and party messaging regarding vote-count risks. More specifically, Democrats selected mainly VBM and Republicans, especially younger Republicans selected mainly early voting, with older Republicans more likely to VBM.

Primary Election Vote Mode To validate our models, we turn to the primary election held the first week of June in New Mexico, 5 months before the general election, and in August in Florida, 11 weeks before the general election. The NM state primary occurs at the same time as the presidential primary, but Florida separates the presidential preference primary from their state primary. Prior to the June primary in New Mexico, 27 county clerks petitioned the New Mexico Supreme Court to allow them to conduct an all-mail election of all eligible electors (Gerstein 2020). In response, the New Mexico GOP sued claiming such changes were against New Mexico law and the New Mexico Supreme Court agreed, charging the county clerks to instead mail out requests for VBM applications to all eligible electors. The Florida election had no changes to their VBM. It is worth noting the differences in the pandemic context across the two primary election contexts. Although the governor of Florida issued a stay-at-home order for 30 days on April 1,10 by April 17, Governor DeSantis allowed Florida beaches to reopen and on July 6, 2020, the Florida Department of Education ordered all public schools to reopen in person in the fall.11 Thus, Florida was moving toward normalcy by the August 2020 state primary. In New Mexico, in contrast, voters were completely locked down during the June primary. On March 23, Governor Lujan Grisham issued a stay-at-home order for all nonessential workers.12 After some attempts at reopening, on June 25 the governor reimposed restrictions that were largely maintained through the entire 10 See Florida Executive Order 20–91. 11 For a chronology of the actions Florida took related to COVID-19 see https://en.

wikipedia.org/wiki/COVID-19_pandemic_in_Florida#cite_note-executiveorder-52. 12 For a chronology of the actions New Mexico took related to COVID-19 see https:// en.wikipedia.org/wiki/COVID-19_pandemic_in_New_Mexico.

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election cycle, including no indoor seating in restaurants, face coverings required when exercising in public, and K-12 education online from home. Figure 4.3 shows the primary vote mode graphs. Just as we did for the general election, for the primary we present multiple figures highlighting how both age and party influenced vote mode decisions. Both states have closed primaries so one difference is that there are no Independents included in these graphs.13 In Fig. 4.3a and b, the Florida and New Mexico primary VBM graphs, respectively, we present the difference in the marginal effects of vote mode differences for 2018 compared to 2016 and 2020 compared to 2016 between the two parties and across age categories. Once again at the bottom, we present the multinomial logits for vote modes in Florida for 2016 (Fig. 4.3g) and for 2020 (Fig. 4.3h) to provide the context for the difference-in-differences graphs, and ask you to refer to our PLoS One article (Atkeson et al. 2022) for New Mexico. We start with Fig. 4.3g and h to consider the different context that VBM is taking place in the primary election between the two states so we can better understand the difference-in-differences in Fig. 4.3a–f. The three lines at the bottom of Fig. 4.3g that start with the youngest voters are the predicted probabilities for VBM. Notice that Floridian voters, under 50, have between a 0.2 and 0.25 predicted probability of voting by mail, while the oldest Floridian voters are most likely to VBM with a predicted probability over 0.6. In New Mexico by contrast less than 0.1 of younger voters are voting by mail and for the oldest voters it averages only about 0.2. Because the starting probability of VBM is much lower in New Mexico than Florida, we expect the difference-in-differences to be much higher there, which is what we see in Fig. 4.3a. Overall, the results are very similar to the general election story where we see both age and party effects as expected, but the differences are much more striking in NM. We follow a similar discussion strategy. We first look at 2018 compared to 2016 to see if our assumptions hold that elections were fairly typical, and parties behaved rather similarly before 2020, and then we compare 2020 to 2016 to see how the pandemic changed behavior.

13 Some counties in Florida include local nonpartisan elections with their state elections. However, for consistency across the state, we limited this to just Democratic and Republican voters who voted in all 3 elections.

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Fig. 4.3 Logistic regression marginal effects of Florida and New Mexico Primary Election vote mode by age and party. a Florida VBM. b New Mexico VBM. c Florida early vote. d New Mexico early vote. e Florida Election Day. f New Mexico Election Day. g Florida 2016. h Florida 2020

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First, notice that the placebo model, comparing 2018 to 2016, shows no difference for VBM in New Mexico and very tiny differences in Florida as the line hovers around zero for both parties and the party lines are very close to one another. We see similar patterns for early and Election Day voting with vote mode choices in 2018 being largely like vote mode choices in 2016, the point being that the parties largely overlapped with not a great deal of polarization over vote mode between the two election years. In contrast, however, we see a big difference comparing 2020 to 2016, especially in VBM, and as we hypothesized, these differences are related to age and party. Focusing on party differences first, once again we see more muted effects in Florida compared to New Mexico, demonstrating how state vote mode history mattered to these differences. Nevertheless, the results show that both Democrats and Republicans regardless of age were more likely to VBM in the primary in 2020, consistent with hypothesis 1, and our general election models in Fig. 4.2. There were smaller differences in early voting. In both New Mexico and Florida, we see that younger Republicans were more likely to vote early consistent with hypothesis 5, while Democrats in both states were much less likely to vote early because they were so much more likely to VBM, consistent with hypothesis 3. For both states and both parties, we see large differences in the reduction in Election Day voting in 2020 relative to 2016. We also find that there is a strong VBM effect by party that makes it more likely that Democrats VBM than Republicans, consistent with hypothesis 3. The party polarization effect is found in the gap between the Democrats and Republicans in both states. Across all age categories, Democrats are more likely to VBM than Republicans. Regardless of party, there are clear signs of behavior associated with risk because as voters age, they increase their likelihood of voting by mail. While both younger and older voters are more likely to VBM, older voters are much more likely to be responsive to the pandemic health message than the VBM ballot message. The party gap across age is much larger in NM, starting at 0.35 between the youngest Democrat and Republican voters, and ending at their closest point with a difference gap of only 0.2. We also see a party gap in Florida with almost a 0.2 gap between the youngest Democrats and Republicans, which declines to a gap of only 0.1 for the oldest voters.

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While in both states the gap for the oldest voters is still large, the fact that the party gap declines as voters age increases our confidence that age is a good proxy for risk for COVID-19. As the tradeoff for death and illness increases, so do changes in vote mode choice. These findings are consistent with hypothesis 2 and consistent with our findings in the general election. In terms of early voting, we see similar patterns between the two states although the New Mexico results are spread out more for the differences between 2020 and 2016. This is because they have more of a change from in-person voting to VBM. Democrats are much less likely to vote early because they are so much more likely to VBM, but we do see differences with younger Democratic voters in both states more likely to vote early than older voters. Florida Republican voters under age 65 were just a little bit more likely to vote early while voters 75 and above were a little less likely to vote early. In New Mexico, we see larger effects for younger voters, under age 50, who are a bit more likely to vote early, while voters over 65 are much less likely to vote early. For Election Day, the two top lines in both graphs show the change from 2018 to 2016. Florida Republicans basically show small and uninteresting changes in their voting behavior between 2018 and 2016, the line hovers right at 0. Democrats consistently hover just below zero suggesting that they are, on average, very slightly (0.04) less likely to vote on Election Day, but in general these data support our assumption of parallel outcomes between 2018 and 2016 as vote mode behavior is mostly similar across the years. In New Mexico, we see Democrats under 40, are, on average, about 0.05 more likely to vote on Election Day and similarly aged Republicans are about 0.08 less likely to vote on Election Day. But when we consider the whole series, we see that, on average, the lines hover around 0. This suggests that vote mode decisions were similar between 2016 and 2018. Both states show that prior to the 2020 election voting is largely constant with the parties showing little difference in vote mode choices. However, in both states in 2020 compared to 2016, we see that all voters decrease their likelihood of voting on Election Day although the gap is larger for Democrats than for Republicans. Of course, we see the turnout declines on Election Day because voters are moving to VBM and for younger Republicans to early voting.

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Discussion and Conclusion By replicating our analysis of differences in voting in New Mexico using Florida administrative data, we show that our general expectations about risk-driven vote mode choices hold beyond the case of New Mexico. We are also able to see important differences that arise due to the voting context in each state. We rely on administrative data because it provides a large and accurate set of data that allows us to construct a causal test of our hypotheses by focusing on the same voters over time and comparing the differences between 2018 and 2016 to the differences between 2020 and 2016. The electoral rules pertaining to vote mode are similar enough across Florida and New Mexico to support similar expectations for behavior across the states. The comparison also allows us to see whether or not our results are robust to differences in state baseline vote conditions and differences in party control in the state. All things considered, the data and contextual features result in a strong research design with both internal and external validity. The results are rather amazing because they hold up so well across the states. In both states we find consistent evidence to support all of our hypotheses. The pandemic led more voters to choose to vote-by-mail, older voters were more likely to choose to VBM than younger voters, Democrats chose this option more than Independents and Republicans, Independents chose this option more than Republicans, and Republicans were, on average, more likely to vote early than Independents and Democrats. We also see interesting differences between the two states. But these differences help us to identify how context increased or decreased the size of the effects. There were two major institutional differences between New Mexico and Florida in 2020. First, in Florida vote-by-mail was already trending up and nearly one-third of voters chose this option in 2018. In New Mexico, however, VBM use was static at a mere 10% of the voting population and was not trending upward. Also, New Mexico has a much longer early voting period than Florida. These factors help to explain the differences we see, especially the more muted appearance in the difference-in-differences in Florida due to the fact that so many voters were already voting by mail. Substantively, the findings are suggestive of how risks play out differently given historical patterns and institutional rules in elections.

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Of course, voters who were already VBM voters had their own experiences to rely on to reflect on the quality and confidence in their VBM ballot. We know from previous research that voters’ personal experiences with the election process influence their voter confidence (Atkeson and Saunders 2007; Clayton et al. 2021; Claassen et al. 2013; Hall et al. 2009; Persily and Stewart 2021), and thus we would expect voters who were already mail voters to be much less likely to be deterred by partisan rhetoric in their vote mode decision. The fact that we find similar results across two states suggests that our theory is more generalizable. In our PLoS One article (Atkeson et al. 2022), we argued that our theory and results would generalize to other “no excuse” VBM states that maintained or expanded VBM or early inperson. We confirm our intuition with the Florida test. Age was always a clear risk-factor for both illness and death throughout the panic. In both Florida and New Mexico, we find strong age effects. Older voters were more likely to VBM than younger voters. Younger Republicans in New Mexico and Florida and Independent voters in Florida were also more likely to vote early in-person. A number of implications arise from our findings. First, they suggest that polarized and diverse information environments can shape voting behavior. In other words, in the climate of the 2020 election, where the political parties provided competing risk messages regarding COVID-19 and ballot security, voter behavior diverged. Our results consistently show evidence of partisan and polarized decision-making in both the primary and general elections. This is true even among voters who faced similar levels of age risk from COVID-19; Democrats were always the most likely to VBM regardless of age. The two parties produced different causal narratives about risk in the 2020 election. Democrats emphasized that VBM was a safe and reliable alternative to in-person voting, while Republicans, especially President Trump, emphasized the potential for fraud. These narratives about the integrity (or lack thereof) of VBM provided incentives for Democrats and Republicans to polarize around vote mode creating a party gap in behaviors that did not exist prior to the pandemic. These results also highlight how extraordinary events like a pandemic can result in a complex decision-making process for voters. Moreover, they suggest that calculations in decision-making are affected by risk, as well as partisan and polarized information flows. In our study, this leads to somewhat different outcomes for younger voters compared to older voters and different outcomes between identifiers of different parties.

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Importantly, there is survey evidence for this in other areas of life. Data show that Republicans are less likely to take the vaccine, less likely to wear masks, and less likely to engage in social distancing (Hamel et al. 2021). Therefore, we also contribute to a growing literature that finds that behavioral choices of partisans during the pandemic were very polarized.

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Druckman, James N., Samara Klar, Yanna Krupnikov, Matthew Levendusky, and John Barry Ryan. 2021. “Affective Polarization, Local Contexts and Public Opinion in America.” Nature Human Behaviour 5: 28–38. Franceschi, Claudio, Paolo Garagnani, Cristina Morsiani, Maria Conte, Aurelia Santoro, Andrea Grignolio, Daniela Monti, Miriam Capri, and Stefano Salvioli. 2018. “The Continuum of Aging and Age-Related Diseases: Common Mechanisms But Different Rates.” Frontiers in Medicine 5: 61. https://www.frontiersin.org/article/10.3389/fmed.2018.00061. Gennaioli, Nicola, Yueran Ma, and Andrei Shleifer. 2016. “Expectations and Investment.” NBER Macroeconomics Annual 30: 379–431. Gerber, Alan S., and Gregory A. Huber. 2010. “Partisanship, Political Control, and Economic Assessment.” American Journal of Political Science 54 (1): 153–73. Gerstein, Michael. 2020. “New Mexico High Court Halts Automatic Mail-in Election in Victory for GOP. Santa Fe New Mexican, April 14. Available at: https://www.santafenewmexican.com/news/coronavirus/new-mexico-highcourt-halts-automatic-mail-in-election-in-victory-for-gop/article_a68f335c7e66-11ea-b08c-5b8e087c4a21.html. Gollwitzer, Anton, Cameron Martel, William J Brady, Philip Pärnamets, Isaac G Freedman, Eric D. Knowles, and Jay J. Van Bavel. 2020. “Partisan Differences in Physical Distancing are Linked to Health Outcomes During the COVID19 Pandemic.” Nature human behaviour 4 (11): 1186–1197. https://doi. org/10.1038/s41562-020-00977-7. Graff, Michael, and Nick Ochsner. 2021. The Vote Collectors. Chapel Hill: University of North Carolina Press. Hall, Thad E., J. Quin Monson, and Kelly D. Patterson. 2009. “The Human Dimension of Elections: How Poll Workers Shape Public Confidence in Elections.” Political Research Quarterly 62 (3): 507–22. https://doi.org/10. 1177/1065912908324870. Hamel, Liz, Lunna Lopes, Audrey Kearney, Grace Sparks, Millisha Stokes, and Mollyann Brodie. 2021. KFF COVID-19 Vaccine Monitor, June 2021 Kaiser Family Foundation. Available at: https://www.kff.org/coronavirus-covid-19/ poll-finding/kff-covid-19-vaccine-monitor-june-2021/. Imai, Kosuke, and Kabir Khanna. 2016. “Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Records.” Political Analysis 24: 263–72. Iyengar, Shanto, Yphtach Lelkes, Matthew Levendusky, Neil Malhotra, and Sean J. Westwood. 2019. “The Origins and Consequences of Affective Polarization in the United States.” Annual Review of Political Science 22 (1): 129–46. https://doi.org/10.1146/annurev-polisci-051117-073034. Kunda, Ziva. 1990. “The Case for Motivated Reasoning.” Psychological Bulletin 108: 480–98.

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Lemongello, Steven. 2020. “Vote by Mail has a Long History in Florida, But in t2020 It’s a Coronavirus Salvation and Battleground.” Orlando Sentinel, July 7. Available at: https://www.orlandosentinel.com/politics/os-ne-mail-in-bal lots-pace-20200703-sj4pklrlvveujj3eadoo5g5i5u-story.html. Maestas Cherie D, Lonna Rae Atkeson, Thomas Croom, and Lisa A. Bryant. 2008. “Shifting the Blame: Federalism, Causal Attribution and Political Accountability Following Hurricane Katrina.” Publius Journal of Federalism 38: 609–632. Makridis, Christos A., and Jonathan T. Rothwell. 2020. “The Real Cost of Political Polarization: Evidence from the COVID-19 Pandemic.” Centre for Economic Policy Research Press 34 (3): 50–87. Menger, Andrew, and Robert M. Stein. 2020. “Choosing the Less Convenient Way to Vote: An Anomaly in Vote by Mail Elections.” Political Research Quarterly 73: 196–207. https://doi.org/10.1177/1065912919890009. Niebler, Sarah. 2020. “Vote-by-Mail: COVID-19 and the 2020 Presidential Primaries.” Society 57: 547–53. https://doi.org/10.1007/s12115-020-005 31-1. Persily, Nate, and Charles Stewart III. 2021. “The Miracle and Tragedy of the 2020 Election.” Journal of Democracy 32: 159–78. Rosenston, Steven J., and Mark Hansen. 1993. Mobilization, Participation and Democracy in America. New York: MacMillan. Scheller, Daniel S. 2021. “Pandemic Primary: The Interactive Effects of COVID19 Prevalence of Age on Voter Turnout.” Journal of Elections, Public Opinion and Parties 31 (1): 180–190. Shino, Enrijeta, Daniel Smith, and Laura Uribe. 2022. “Lying for Trump? Elite Cue-Taking and Expressive Responding on Vote Method.” Public Opinion Quarterly 86 (4): 837–861. Stein Robert M., and Greg Vonnahme. 2008. “Engaging the Unengaged Voter: Vote Centers and Voter Turnout.” Journal of Politics 70: 487–497. Sturla, Anna. 2020. “Judge Invalidates Paterson, NJ, City Council Election after Allegations of Mail-in Voter Fraud.” CNN, August 20. Available at: https://www.cnn.com/2020/08/20/politics/paterson-new-jerseycity-council-voter-fraud/index.html. US Vote Foundation. 2020. “Senate Introduces No-excuse Vote-by-Mail Bill to Address Pandemics and Disasters.” Available at: https://www.usvotefounda tion.org/Senate-Vote-by-Mail-Bill-S3529.

CHAPTER 5

Access to Voting and Participation: Does the Policy of Limiting Mail-In Ballot Dropbox Locations in Ohio Suppress Voter Turnout? Jiehong Lou , Dana Rowangould , Alex Karner , and Deb A. Niemeier

Introduction The universal ability to vote is an essential feature of a democracy. This ability is determined, in part, by the convenience and availability of voting locations in time and space. The 2020 presidential election occurred in the midst of the COVID-19 pandemic, with an attendant desire to

J. Lou (B) School of Public Policy, University of Maryland, College Park, MD, USA e-mail: [email protected] D. Rowangould Department of Civil and Environmental Engineering, University of Vermont, Burlington, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. A. Coll and J. Anthony (eds.), Lessons Learned from the 2020 U.S. Presidential Election, Elections, Voting, Technology, https://doi.org/10.1007/978-3-031-44549-1_5

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reduce exposure to others in crowded public spaces. In response to these concerns, many regions expanded access to absentee and in-person voting. Not surprisingly, this election exhibited a marked increase in the number of voters using absentee and mail-in ballots, with nearly 46% of voters using these methods (Pew Research Center 2020), compared to about 25% in the 2016 general election (Desilver 2020). Many of those voting by absentee or mail-in ballots also had the option of returning their ballots to a designated drop box. A ballot drop box is a location, supervised or unsupervised, with security features, such as cameras, where voters can drop off mail ballots in sealed and signed envelopes (NCSL 2020). Drop boxes provide voters with certainty that their ballot has arrived on time, and they often allow voters to submit a ballot later than a regular USPS mail-in ballot. These benefits may have been more pronounced during the 2020 election, which coincided with concerns about US postal service delays. In the 2016 presidential election, around 16% of voters used drop boxes, mostly concentrated in three states with vote-by-mail systems for all elections: Washington, Oregon, and Colorado (Fessler 2020). In 2020, voters using drop boxes grew by more than 20% nationally, with many states adding drop boxes due to the pandemic (Pew Research Center 2020; Stanford-MIT Healthy Elections Project 2020b). One of the issues associated with using drop boxes is the lack of clear, specific rules, or laws guiding their implementation. Among the 41 states (including the District of Columbia) that provide drop boxes, only eight have laws regulating their use (Stanford-MIT Healthy Elections Project 2020a). In the days leading up to the 2020 presidential election, lawsuits on the number, and sometimes placement of drop boxes were filed in Texas, Ohio, and Pennsylvania (PEW 2020). Decisions about drop box placements can have disproportionate effects on access for populations

A. Karner Graduate Program in Community and Regional Planning, The University of Texas, Austin, TX, USA e-mail: [email protected] D. A. Niemeier Department of Civil and Environmental Engineering, University of Maryland, College Park, MD, USA e-mail: [email protected]

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that have been the target of disenfranchisement efforts, as happened in Texas during the 2020 election (Karner and Rowangould 2021). In Ohio, drop boxes were first added statewide for the 2020 primary election based on House Bill 197, passed on March 27, 2020 (the Ohio Legislature 2020). The public debate quickly turned into a battle between the Republican Secretary of State Frank LaRose and county election officials on how many drop boxes to allow in each county. The battle ended in a victory for LaRose, who issued a directive on October 12, indicating that “boards of elections are prohibited from installing a drop box at any other location other than the board of elections” (Ohio Secretary of State 2020), which effectively limited drop boxes to one location in each county. The sparse voter dropbox locations in Ohio’s 2020 presidential election provide a unique opportunity to observe variation in accessibility that can be used to evaluate our question. Our research provides a new analytical perspective for investigating the effect of drop box accessibility on voter turnout. Using a first-difference geographic regression discontinuity (FD-GRD) design to compare cross-county voters with different levels of accessibility, we show that increasing the accessibility of drop boxes results in a significant increase in voter turnout. We find that greater drop box accessibility improves overall voter turnout in the range of 3.9–5.2% in the 2020 presidential election in Ohio relative to 2016. In addition, we argue that measuring accessibility in terms of not just distance, but also travel time and auto driving distance, provides a more accurate estimate of the travel costs that voters must bear to cast their vote. By incorporating contextual information such as traffic conditions, our measures of accessibility reflect the convenience factor that plays a role in voting decisions. Overall, our study offers a new analytical perspective on the impact of drop box accessibility on voter turnout and highlights the importance of accessibility as a key factor in facilitating democratic participation.

Background and Literature Review In-person early voting was not an option in the United States before the 1970s, and California was the first state to adopt this option in 1978 (Biggers and Hanmer 2015). Since this time, statewide adoption of inperson early voting provisions has been uneven. Ohio, in particular, did not begin allowing pre-election-day ballots until 2002 (Gronke 2004; Gronke et al. 2007), and formal legislation for in-person early voting

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was established in 2005 (Kaplan and Yuan 2020). Since then, Ohio’s absentee and mail-in balloting has grown in popularity, with 2020 representing a high-water mark. On the first day of general election early voting, October 5, 2020, over 2 million Ohio voters submitted absentee ballot applications to the county boards of elections in their jurisdictions. That number surpassed the total number of voters who voted absentee in the 2016 general election (Turman 2020). Figure 5.1 illustrates that vote by absentee and mail-in turnout rates were higher in 2020 than any of the previous years across all Ohio counties (Panel A), while the overall voting turnout was relatively stable (Panel B) during the same period. The average turnout rate of absentee and mail-in voting in 2020 was over half of all votes, doubling the rate of 2008. Ohio was a key 2020 presidential election battleground, and the debate about drop box locations during the pandemic took on heightened intensity. The controversy over the one-drop-box-per-county decision for the presidential election continued through the March primary, raising criticism from the Democratic Party, voting rights groups, and several large counties and cities.1 Populous counties, such as Cuyahoga, home to 860,000 registered voters, initially had plans to expand the number of drop boxes, but these were ultimately shelved. The Ohio decision to reduce the number of drop box locations also potentially reduced voter turnout. Previous studies on consolidating, changing, or expanding the number of polling locations suggest that voter participation is affected. Polling consolidation substantially reduces overall voter turnout (Brady and McNulty 2011; McNulty et al. 2009). During the pandemic, polling-place consolidation not only decreased overall turnout in Milwaukee, but it also had a disproportionate effect on black voters (Morris and Miller 2021). Changing polling locations also affects the decision to vote (Haspel and Knotts 2005). For example, Oregon’s move to all-mail ballots2 resulted in increased voter participation by reducing the burden of traveling to the polls (Cantoni 2020; Dyck and Gimpel 2005). Finally, a large expansion in the number of drop boxes 1 Six major cities, Columbus, Cleveland, Cincinnati, Akron, Dayton, and Toledo, were fighting alongside the voting rights groups to expand access to off-site drop box locations (Dejak 2020). 2 Oregon made history on November 7, 2000, by becoming the first state in the United States to conduct a presidential election entirely by mail (Oregon Secretary of State 2007).

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Fig. 5.1 Comparison of Ohio absentee presidential general election voting rates by county from 2008 to 2020 (panel A), and overall voting turnout between 2008 and 2020 (panel B). The orange line indicates the statewide average, and the blue and green lines represent Belmont County and Jefferson County, respectively. Absentee voter turnout is the share of votes cast that were absentee over the total ballot votes cast (Data source The Ohio Secretary of State)

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in King County, Washington, increased the likelihood of voting (Collingwood et al. 2018). McGuire found a similar (positive) effect in Pierce County, WA with the expansion in the number of drop boxes (McGuire et al. 2020). Both studies occur in all-mail ballot states, which limits generalizability. Prior to the 2020 election, only five states had all-mail balloting (CO, WA, OR, UT, and HI). Consistent with observed effects of polling locations on voter participation, there is evidence that even slight differences in the distance to polling locations can significantly affect voter turnout (Haspel and Knotts 2005). This is thought to be the case because distance may have a nonlinear relationship with political participation (Gimpel and Schuknecht 2003). Increased distance also induces high travel costs, and higher voting costs can reduce the likelihood of registration (Cantoni 2020) and reduce voter participation (Dyck and Gimpel 2005). Voters with mobility limitations (Schur et al. 2017) and low-propensity voting populations (Barreto et al. 2009) can be disproportionately affected by increased travel costs for the purposes of voting. Our study employs a FD-GRD design to examine the impact of drop box accessibility on voter turnout. While GRD methodology is common in certain fields, FD-GRD and difference-in-discontinuities are less frequently used in social science. However, these methods have been effective in assessing the outcomes of social programs, as demonstrated in prior research (Grembi 2016; Lemieux and Milligan 2008). Previous studies have used GRD to investigate the impact of early voting on voter turnout, with some focusing on the policy’s effectiveness by comparing a control group without the policy to a treatment group with the policy implemented (Cantoni 2020; Kaplan and Yuan 2020), and others considering only distance as the main variable of interest (Cantoni 2020). In contrast, our research contributes by differentiating the control and treatment groups based on the level of accessibility to drop boxes, providing a more nuanced perspective on the relationship between accessibility and voter turnout. Data and Methods The number and location of drop boxes determines their utility and convenience, which may affect voter turnout. In this chapter, we use accessibility metrics to evaluate the effect that access to drop boxes

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has on voter turnout on Ohio’s 2020 presidential election. Accessibility is the ease with which people can reach destinations, factors such as available transportation options, surrounding land-use patterns, and socio-economic characteristics, among others, can play a role in determining the levels of accessibility (Geurs and van Wee 2004; Handy and Niemeier 1997; Levine et al. 2019). By utilizing the 2020 voter registration records and voting records from the Ohio Secretary of State (Ohio SOS 2021) and drop box location information for each county from the SafeGraph (SafeGraph 2020), we are able to estimate three measures of drop box accessibility: haversine distance,3 travel time, and auto driving distance. Distance4 has long served as a proxy for travel time and costs when evaluating voter turnout (Brady and McNulty 2011; Cantoni 2020; Collingwood et al. 2018; McGuire et al. 2020; McNulty et al. 2009). While distance can represent accessibility, measures such as travel time or driving distance along a network to one or more destinations that capture transportation options and traffic conditions more closely represent the ease with which potential voters can reach voting locations. Automobile congestion conditions have a dramatic effect on apparent accessibility that is not captured when using distance alone. Accessibility measures including public transit and auto travel times to the nearest location have been used to evaluate the equity of ballot drop boxes (Karner and Rowangould 2021), but these types of measures have not been used to evaluate voter turnout. In this study, we extend previous work on voter turnout by accounting for voting accessibility, measured as haversine distance (hereinafter referred to as “distance”), travel time and auto driving distance. We evaluate all three measures of accessibility based on the location of a voter’s residence and their county drop box. In Ohio’s 2020 presidential election, both in-person early voting sites and drop boxes were located at county board of election offices.5 3 Haversine distance is the distance between two points on a sphere and takes into account the curvature of the Earth’s surface. 4 In the literature of voter turnout, distance was measured as the point-to-point distance on the map or the surface. The most common measures are haversine distance, Manhattan box, and straight-line distance. 5 The drop box(es) were placed outside the building and the in-person early voting site was inside the building.

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Although early voting sites have existed for almost 20 years, drop boxes were added in 2020. Because of this, our FD-GRD design can compare the 2016 and 2020 election participation for voters living along the border of two Ohio counties, using one county as a treatment group (Belmont County) and one as a control group (Jefferson County), where the treatment group experiences greater access to the drop box than the control because its county drop box is located closer to the border. The areas proximate to the county borders are similar in terms of voter sociodemographics. The two counties differ in terms of drop box accessibility. We identify two additional pairs of treatment and control groups (pair one: Belmont County and Noble County; pair two: Belmont County and Guernsey County) which are similar across county borders in terms of voter characteristics and also similar in terms of access to drop boxes. These additions add robustness to our research design and serve as the external validity to our proposed FD-GRD model. This research design helps us to identify the causal effect of drop box accessibility on voter turnout. A detailed empirical strategy and data is available in “A1: Materials and Methods”. We illustrate the identification strategy based on the FD-GRD research framework in Fig. 5.2, using the boundaries for four counties to form three different cases in Ohio. The county-case selection is based on two prerequisites. First, we select two counties that share a border that is short enough to ensure that residents on/near both sides of the border will have a distinct difference in terms of accessibility to their corresponding drop box locations. Second, each comparison must pass a set of internal validity tests of RD design; this is critical because RD designs depend on a high level of internal validity (Keele and Titiunik 2015; Lee 2008). Our FD-GRD design with three measures of accessibility pass all internal validation tests (see “A2: Internal validation”). Case 1 (Belmont and Jefferson County) is our research’s geography of interest. In this case, we treat voters with a shorter distance to the drop box as the treatment group (Belmont County) and voters on the other side of the border, with a longer distance to the drop box, as the control group (Jefferson County). As we will discuss later, we use Case 2 (Belmont and Noble County) and Case 3 (Belmont and Guernsey County) to validate our main case selection strategy externally. In every case, Belmont County is the treatment group.

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Fig. 5.2 Case selection. The county boundary between Belmont County and Jefferson County is short enough to ensure that the residents on/near both sides of the border will have a distinct difference in terms of accessibility to their corresponding drop box locations

The Effect of Accessibility of Drop Box on Voting Our primary sample consists of 61,563 individuals who are registered to vote within two counties in Case 1, where 22,362 individuals assigned to the treatment group. In the treatment group, the average distance to the drop box is 9.3 miles, while in the control group, it is 5.0 miles. Additionally, we have a sample size of 25,347 individuals in Case 2 and 39,979 in Case 3. To isolate the effect of the drop box alone, we employ a two-step methodology. Firstly, we utilize the first-differences strategy, where we compare the outcomes between the 2020 and 2016 general

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elections. This approach allows us to isolate the changes directly related to the presence of drop boxes. Secondly, we integrate the results of this analysis with the GRD. By adopting this combined approach, we are able to compare voters residing in different counties with varying levels of accessibility, as voters located on either side of the county border have different levels of accessibility to their respective drop boxes. We specify three regressions using the FD-GDR design for our three different measures of accessibility.6 Table 5.1 presents our point estimates and standard errors for the coefficients of interest. In Column (1), we employ the direct distance measure. The coefficient on the treatment indicator is 0.041, indicating that with a bandwidth of 0.098 degrees (or 6.8 miles) on each side of the county border, the average voter turnout rate increases by 4.1% for those voters located closer to drop off ballot locations (9.3 miles less). The result is statistically significant at the 5% level. Moving to Column (4), we consider the travel time measure. We observe a 3.9% increase in the turnout rate for voters with a shorter travel time (3.5 mins less) to their drop box. This finding is statistically significant at the 10% level. Lastly, in Column (7), we introduce the auto distance accessibility measure. The coefficient on the treatment indicator is 0.052 and is statistically significant at the 5% level, indicating greater turnout of 5.2% for those with shorter auto distances (7.6 miles less) to their drop box. Overall, the results from the three measures of accessibility align consistently. The findings indicate that greater accessibility to drop boxes, as measured by direct distance, travel time, and auto distance, positively affects voter turnout, and demonstrate the impact of proximity to drop-off locations on voter behavior. The inclusion of Case 2 and Case 3 in our analysis further strengthens the robustness of our main findings from Case 1. In Case 1, voters on each side of the county line had different levels of accessibility to the drop box, which allowed us to observe a clear discontinuity in voter turnout at the boundary. However, in Case 2 and 3, our case selection strategy ensured that voters on each side of the county line had similar levels of accessibility. As a result, we did not observe a distinct jump at 6 The typical GRD will require a plot to show the jump around the cut off. We plot the binned means of first difference in voter outcome against the voters’ distance to the county line with a global fit (Panel A) and the means voter turnout with a local fit (Panel B) for the treatment and control observations in Fig. 5.4.

(0.07) 26,347 3671

(0.02) 61,563 13,114

(0.04) 39,979 5483

0.022

− 0.072 (0.03) 61,563 17,402

0.039*

(4) Main model case 1

0.041**

Travel time (3) Robust case 3

(2) Robust case 2

Distance

(1) Main model case 1

0.05 26,347 13,801

0.009

(5) Robust case 2

(0.04) 39,979 8507

0.031

(6) Robust case 3

(0.03) 61,563 14,543

0.052**

(7) Main model case 1

(0.05) 26,347 4119

− 0.003

(8) Robust case 2

Auto driving distance

(0.03) 39,979 5029

0.034

(9) Robust case 3

Comparison among three cases: FD-GRD estimates of voters’ drop box accessibility on voter turnout

Standard errors are in parentheses ***Significant at the 1% level. **Significant at the 5% level. *Significant at the 10% level a We use rdrobust software, which is a data-drive bandwidth selection methods from STATA to generate the robust confidence intervals (Cattaneo et al. 2019; Keele et al. 2017; Keele and Titiunik 2015)

Observation Effective observationa

FD-GRD estimates

Table 5.1

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the boundary in either Case 2 or 3 based on the FD-GRD results across all measures of accessibility. In other words, voters residing across the county border in Case 2 and 3 have comparable levels of accessibility to their respective drop boxes. The average voter turnout is similar between the treatment and control groups. Taking the travel time as an example, the model specifications in columns (5) and (6) show coefficients on the treatment indicator of 0.009 (Case 2) and 0.031 (Case 3), and these results are not statistically significant at the p < 0.1. In other words, when voters have to travel a similar time (distance or driving distance) to the drop boxes, there is no statistically significant difference in voter turnout between the treatment and control groups. The parallel trend assumption is a crucial prerequisite for traditional Difference-in-Differences (DID) models. It suggests that in the absence of the treatment, the treatment and control groups would exhibit similar trends in the outcome variable over time. This assumption is essential for unbiased estimation of the treatment effect. To assess this assumption, we conduct an additional set of FD-GRD models using voter turnout outcomes from the 2016 and 2012 elections. Upon analyzing the results, none of the point estimates is statistically significant at the 10% level. This indicates that there was no significant difference in voter turnout rates between the two counties during the pre-treatment period. Therefore, we can infer that the parallel trend assumption holds, and the treatment effect estimated by the FD-GRD models is more likely to be unbiased. Finally, FD-GRD design depends on a high level of internal validity (Keele and Titiunik 2015; Lee 2008). Our second (2012–2016) FD-GRD design with three measures of accessibility pass all internal validation tests7 , reinforcing the robustness of our research design strategy for internal validity. In conclusion, the estimates obtained from the FD-GRD models presented in this section provide strong evidence that improved accessibility to drop boxes leads to increased voter turnout rates. By meeting the assumptions required for a valid causal inference, we can have greater confidence in the findings of our research.

7 We conducted several standard tests for Case 1, including testing the continuity of covariates, various placebo cutoffs, and sensitivity to observations near the boundary. The full set of analysis and tests is provided in the “A2: Internal validation”.

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The Effect of Accessibility on Early Voting (Drop Box Plus In-Person Early Voting) The FD-GRD estimates provide strong evidence that better drop box accessibility increases voter turnout. This analysis makes a valuable contribution by offering a practical and analytical method to examine the accessibility of the voting process. Building upon this, in this section, we extend our methodology to analyze the impact of early voting locations, which encompass both drop boxes and in-person early voting sites, on voter turnout in the Ohio 2020 presidential election. In Ohio, the regulations mandated that the drop box and in-person early voting locations (vote over the counter) be situated at the County Board of Election. The fact that voters have the same distance to drop off their ballots or go (in person) to their county board of elections office and ask for an absentee ballot allows us to use the GRD alone to identify the effects of the early voting on voter turnout. By employing our methodology to analyze the accessibility of both early voting locations and drop box, we aim to provide valuable insights into the effects of these factors on voter participation in the Ohio 2020 presidential election. This analysis expands our understanding of the broader voting process and its impact on democratic participation. We estimate three regression specifications for our GDR design. Table 5.2 presents the point estimates and standard errors for coefficients of interest for each regression model. In Column (1), the coefficient on the treatment indicator is 0.113, indicating that with a bandwidth of 0.07 degree (5.2 miles) on each side of the county line, the average voter turnout rate increases by 11.3% when voters are located closer to early voting locations. The result is statistically significant at the 1% level. Similarly, when considering travel time and auto driving distances as alternative measures of accessibility, we observe consistent results. We find a 14.9% increase in the voter turnout rate when voters travel less time and a 9.8% increase in voter turnout rate when voters drive less distance.

Discussion and Conclusion During the COVID-19 pandemic, a number of states initiated the use of ballot drop boxes, intentionally combining security, convenience, and flexibility. This triggered an intense debate of their effect on overall voter turnout. This analysis provides a robust empirical research design that

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Table 5.2 Geographic regression discontinuity estimates of voters’ distance to drop box on 2020 presidential election turnout

GRD estimates Observation Effective observationa

(1) Travel distance

(2) Travel time by driving

(3) Auto driving distance

0.113*** (0.03) 61,563 8495

0.149*** (0.04) 61,563 5165

0.098*** (0.03) 61,563 5929

Standard errors are in parentheses ***Significant at the 1% level. **Significant at the 5% level. *Significant at the 10% level a We use rdrobust software, which is a data-drive bandwidth selection methods from STATA to generate the robust confidence intervals (Cattaneo et al. 2019; Keele et al. 2017; Keele and Titiunik 2015)

extends previous work to identify the effect of voter accessibility to drop boxes on voter turnout. The evidence indicates that greater drop box accessibility improves overall voter turnout. Our approach presents a practical tool to study the effects of drop boxes. The results illustrate the crucial importance of ensuring accessibility within the voting process, and our research implications call for deeper consideration of populations with low accessibility, and policies to address the inequality of accessibility. Our research contributes to the broader literature on voting accessibility and extends the limited research on the effect of drop boxes on voting behavior. Our study suggests that the one-drop-box-per-county policy in Ohio may have a disproportionate effect on voters with limited transport options (such as, low income, mobility limited, and lack of a vehicle) and generally low accessibility. This finding is consistent with past research that limiting the geographic accessibility of polling places results in disproportionate effects on certain groups, such as people with disabilities (Schur et al. 2017), females (Kaplan and Yuan 2020), low-education workers (Collingwood et al. 2018), low-income and minority populations (Cantoni 2020; Collingwood et al. 2018; Karner and Rowangould 2021), and those who rely on transit (Karner and Rowangould 2021). Our study also confirms that accessibility is important for ensuring adequate voting locations (Burden et al. 2014; Dyck and Gimpel 2005; Haspel and Knotts 2005) and number of polling places (Brady and McNulty 2011;

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Collingwood et al. 2018; McNulty et al. 2009). Failing to address the growing disparity in accessibility will lead to social inequalities and a disenfranchised population who cannot fully practice their voting rights. Findings from this study provide timely suggestions for policymakers, election officials at local and state levels, and other stakeholders. Our model is suitable for policymakers in a broader setting. Curtailing drop-off boxes for mail-in ballots in Ohio is not unique in the political sphere. Texas is experiencing a similar trend in restriction on the number of drop boxes per county. There has also been an expansion of drop boxes for mail-in ballots in certain states, such as Pennsylvania and Georgia. Most studies of political participation through drop boxes rely on natural experiments (McGuire et al. 2020) or variation in drop box policy between different years and elections (Collingwood et al. 2018). Our research capitalized and relied on limited variations in the policy implementation to establish a causal connection between drop box accessibility and voter turnout using a FD-GRD design. Thus, our approach provides a way to better link policy and voter turnout robustly. Although we incorporated auto accessibility, there is a need to better understand how mode choice can affect turnout. In our work, there is limited public transit coverage in the two counties evaluated. In Belmont County and Jefferson County, the Eastern Ohio Regional Transit Authority (EORTA) and the Steel Valley Regional Transit Authority (SVRTA) serve as the primary public transportation authority, respectively. However, both public transit systems provide limited coverage (i.e., within or near proximity to cities) (Ohio DOT 2018a, 2018b). Applying our research strategy to metropolitan areas where we can include public transit accessibility will provide another perspective on the value of accessibility to different population groups. Our work provides strong evidence that accessibility played a key role in election participation in Ohio in the 2020 and 2016 general elections. Nevertheless, two limitations are worth noting. First, although we are confident that increased voter turnout was due to increased drop box accessibility, our research design limits our conclusions to the relationship between accessibility and voter turnout. Second, our sample focuses on the two counties in Ohio, and there is a need to replicate this work in other regions and settings.

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Appendices A1: Materials and Methods Data Voter Registration Data. We obtained 2020 voter registration records and voting records from the Ohio Secretary of State (Ohio SOS 2021). Our data includes the voting-related information of all active registered voters at the individual level, including full name, date of birth, registration date, voter status, party affiliation, residential address, precinct, and voting histories for elections (Ohio SOS 2018). Due to the partially open primary system in Ohio (NCSL 2021), voters cannot be affiliated with a political party when they complete the registration form (Open Primaries 2020). Thus, we do not use the party affiliation variable since it is often not designated. The registration list includes 8,073,163 voters. The absentee and in-person early voting lists of each county are assembled from the county boards of elections. The list differentiates methods of absentee and in-person early voting, such as voting by mail or in the board of election office. Using drop boxes to cast ballots is included in the mail category. We merged the dataset and the voter registration records based on a unique voter ID number assigned by the Ohio Secretary of State. We obtained drop box location information for each county from the SafeGraph, 2020 Polling Location Dataset (SafeGraph 2020). The dataset contains drop-off location, places, operation hours, latitude and longitude, and start and end date. To ensure the quality of the dataset, we cross-referenced the list of drop boxes with a few other sources, such as state and county voting information posted online (SOS website, county board of elections websites), and local news media (Buchanan, 2020). We also obtained the list of election day polling locations from SafeGraph. We use these locations to ensure that polling locations are reasonably balanced across the control and treatment locations. We use Nominatim, a tool provided by OpenStreetMap (OSM), to geocode the latitude and longitude for the residential addresses of individual voters. We calculate the haversine distance (in miles and kilometers) between each voter and their corresponding drop boxes using Python. Research shows that the haversine measure is a good fit for calculating relatively close distance, especially within a county (Collingwood et al. 2018). We used the Mapbox driving profile, which incorporates a duration-optimized route between the voter’s residential address and their

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corresponding drop box coordinates to compute the travel time and auto traveling distance (Mapbox 2021). Socio-economic Data. We obtained demographic data, including income, population density, race/ethnicity, education level, number of vehicles, and poverty level, at the US Census Block group level from the 2020 American Community Survey (ACS) five-year estimates. We then used the geocoded location of each voter to map voters into the block group boundary. Thus, each voter is attached to a set of demographic covariates obtained from their home location’s block group. Empirical Strategies We use the FD-GRD methodology to evaluate the effects of differences in drop box accessibility on voting outcomes. To identify the causal effect of drop box accessibility on voter turnout, we select two counties, Belmont County and Jefferson County, which meet our identification strategy. We validate our methodology and identification strategy by applying the same research design to the other two pairs of counties. Determining whether accessibility to the drop box affects voter turnout is challenging for two reasons: first, the introduction of drop boxes and limiting the number of drop boxes per county happened in the same year, 2020, across the entire state. Therefore, we are not able to identify the treatment effect of drop boxes with the traditional difference-indifference technique due to the lack of a control group. Second, in Ohio, the in-person early voting site and the drop box location in a county were both at the county board of elections office, which leads to a biased estimate of the treatment effect of accessibility on voter turnout due to the combined effects. To address these complexities, we adopt a two-step approach to isolate the effects of accessibility to the drop box on voter turnout. For the first step, we use a GRD approach. It is usual to see RD/ GRD applied to policy evaluations where one side of a boundary has a policy and the other side does not. However, RD can be applied to study a discontinuity present in a policy that is the same across a boundary as long as the variation produced by the policy is large enough (Lemieux and Milligan 2008). In our case, the single allowable drop box per county policy produces different levels of accessibility along the border of the counties due to the location of the drop box within each county. In the second step, we isolate the effect of accessibility to the drop box from the effects of in-person early voting since both are co-located.

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We cannot solely rely on a GRD approach because when drop boxes were added in Ohio in 2020, they were placed at the same location where early voting occurred, so GRD measures the effects of both the accessibility to the drop box and the accessibility to in-person early voting. To address this issue, we take advantage of the fact that Ohio legislation for inperson early voting was established in 2005, while the drop box policy was introduced in 2020. By comparing voting behavior before and after the introduction of the drop box policy, we can evaluate the policy and eliminate the effect of the accessibility to the in-person early voting site using a methodology called a “first-difference-in-geographical-discontinuity” design. In this approach, we take the first differences between the 2020 and 2016 presidential elections for voters near the shared border, then apply the geographical discontinuities approach to the first difference. Differences between counties observed using GRD in 2016 reflect the effect of the location of in-person early voting while differences between counties observed using GRD in 2020 reflects the combined effects of inperson early voting and drop box locations; the difference between 2016 and 2020 indicates the effect of drop box locations alone. The geographic regression discontinuity experimental design shares characteristics with the traditional regression discontinuity design (RD). It is a quasi-experimental design suitable for assessing whether a “forcing” variable exceeds a certain threshold (Lee and Lemieux 2010). These methods are ideally suited for evaluating the efficacy of program or policy changes. The causal inferences are considered more credible than those drawn from natural experiments. Our study area includes residents in geographically defined jurisdictions on either side of a county administrative boundary. Voters on either side of the boundary are within close proximity to each other and share similar socio-economic characteristics. They differ in terms of their access to their voting drop box and in-person early voting locations. If access to a drop box/in-person early voting location affects voter turnout, we expect to observe a discrete jump in voter turnout at the county border while our control variables change smoothly. This approach allows us to isolate the relationship between drop box/ in-person early voting locations accessibility and voter turnout. The strengths of using GRD over the traditional RD include, first, that by design socio-demographics tend to be similar on both sides of and in proximity to a boundary (in our case, the county border). Second, the high cost of moving to a new location helps prevent fuzzy regression

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discontinuity.8 We have two additional specific strengths that increase the robustness of using GRD. First, voters are not able to cross borders to experience a different treatment; registered voters in the treatment county cannot vote in a control county. In addition, the registration deadline for the 2020 general election was October 5, 2020, while the decision of onedrop-box-per-county was finalized on October 12, making it very hard to manipulate voter accessibility (e.g., moving to the adjacent county). The treatment effect of election accessibility (including both accessibility of the drop box and in-person early voting locations) on voter turnout is shown in Eq. 5.1, where treatment is determined based on geography. Yit = βo + β ∗ (Treatia ) + δa (X i ) + εia

(5.1)

where i = 1, 2, . . ., nvoters. Yit is the voter turnout variables for voter i of year t. The dependent variable, Yit , is a dummy variable in our data set, where 1 signals an individual voter returned ballot in year t; 0 otherwise. The binary treatment of interest is denoted by Treatia , which captures the better accessibility to the early voting/dropbox location in the treatment county. Voters located in our treatment county (higher drop box and early voting accessibility) have Treati = 1; voters across the county line have lower accessibility is denoted as Treati = 0. δ(.) is the continuous function that our GRD strategy rests on. It means that accessibility is the only source of discontinuity in our voter turnout across county borders. Therefore, X i is the assignment variable centered at the cutoff point, which is the boundary between the two counties. The coefficient of interest, β, is a biased estimator.9 In order to reduce the bias, we use information from the 2016 presidential election to isolate the effect of the in-person early voting accessibility on voter turnout from that of drop box accessibility by estimating the following FD-GRD model. Generally, the RD estimator with first differences were explored in Lemieux and Milligan’s (2008) first-difference RD estimator, and 8 In a fuzzy regression discontinuity, the treatment assignment is no longer determined by the assignment variable (Hahn et al. 2001; Jacob and Zhu 2012). Therefore, enforcement is not perfect. 9 As we mentioned, in 2020 presidential election, the accessibility of the in-person early voting locations may contaminate of the estimation of the one-drop-box-policy, since the in-person early voting site and the drop box location in a county were both at the county boards of elections office.

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Grembi’s, (2016) difference-in-discontinuities. However, our FD-GRD is different from these. In the setting of Grembi (2016), the identification relies on the differences between two cross-sectional estimators; our estimator rests on the within-unit variation due to treatment assignment over time. Building on the Lemieux and Milligan’s (2008) model, our FD-GRD model incorporates the geographical dimension. Therefore, the model of the voter turnout in year 2020 is ( ) Yit_2020 = βo + β1 treatdropbox + treatearlyvoting + δa (X i ) + εia (5.2) While the model of the voter turnout in year 2016 is ) ( Yit_2016 = β0 ' + β1 ' treatearlyvoting + δa '(X i ) + εia '

(5.3)

Yit_2020 − Yit_2016 = (βo − β0 ') + (β1 − β1 ')treatearlyvoting ] [ + β1 ∗ treatdropbox + δa (X i ) − δa '(X i ) + (εia − εia ') (5.4)

Under the assumption β1 = β1 ', we finally obtain our FD-GRD estimator from the following regression model Yit_2020 − Yit_2016 = (βo − β0 ') + β1 ∗ treatdropbox + θa + εia

(5.5)

Again, θa is the continuous function of accessibility of voters across border. And treatearlyvoting and treatdropbox are a dummy for voter. The assumption of β1 = β1 ' is fulfilled by the parallel test we conduct in Supplementary Table 4. The coefficient β1 is the FD-GRD estimator and identifies the treatment effect of drop box accessibility on voter turnout. For the εia error term, we cluster the standard errors at the individual voter’s level. Our causal identification requires that, with the exception of accessibility to the drop box, voters who live close to but on opposite sides of the county line share similar determinants of voter turnout (e.g., identical socio-economic background). Selecting pairs of counties that meet this requirement becomes crucial. Identification and Case Selection. GDR is a version of the traditional RD where a geographic boundary serves as the treatment threshold

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(Imbens and Lemieux 2008; Moore 2015). In traditional RD the treatment is a deterministic function of the assignment variable (Hahn et al. 2001); the treatment in the GRD setting is a deterministic function of the unit’s geographic boundary (Keele et al. 2017). Under this framework, the treatment assignment must meet two conditions: first, there must be a clear distinctive boundary. Second, experimental units on either side of the boundary (i.e., voters on each side of the county border) must receive a different treatment. Thus, the variation comes from differences in accessibility to the drop boxes between the treatment and control groups. Our study design easily meets the first condition and we have previously discussed it as one of the strengths of our design. Use of the county administrative boundary ensures that the treatment effect changes; voters on the one side of the county border will vote in one location and voters on the other side will vote in a different location. In terms of the second condition, we use two strategies to ensure that we meet the condition. First, we rely on the haversine distance, travel time and auto driving distance between the voter’s residential location and the drop box to measure accessibility. Second, we limit our case selection samples to pairs of counties in which the shared administrative border is very short. This strategy has two advantages in that first, it limits the effect of the boundary itself on the voter’s accessibility to a drop box (Keele and Titiunik 2015), and second, we can use a perpendicular distance from the residential address to the border, the shortest distance to the boundary, to identify the coordinates of the corresponding point on the border. Then, we are able to use the two pairs of coordinates to calculate the distance, travel time, and auto driving distance separately, which we denote as the score, S. Since our cutoff is the boundary, which is 0, we set the S in the control group as negative. Therefore, the treatment assignment is Treati = Treat(S i ), with Treati =1 for S > 0, and Treati =0 for S < 0. Case Validation To validate our case selection, we expect to observe a discrete jump in voters’ accessibility (for distance, travel time, and auto travel distance) to their drop boxes near the county boundary in Case 1. If our design is robust, we should observe a smooth change of voters’ accessibility to their drop boxes for our Case 2 and 3 based on our selection strategy.

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Figure 5.3 illustrates the geographic regression discontinuity for voters at the administrative border for each of our three cases using the distance accessibility metric. We also plotted geographic regression discontinuity using the travel time and auto driving distance. The results are similar. The points represent the average binned distance to the drop boxes regressed against the voters’ distance to the border. We have plotted a global polynomial fit based on a fourth-order polynomial10 regression fit of the accessibility to the drop boxes on the score, using the voter registration data. Panel A clearly shows that distance to the drop box is significantly lower in the treatment group compared to the control group at the county administrative boundary (Case 1). Within the bandwidth11 of 0.01 degree,12 the average distance to the drop box in the treatment group is 5.5 miles less compared to the control group. The threshold discontinuity confirms that our case selection strategy is robust. Panels B and C (Case 2 and 3) further validate our case selection strategy. There is no significant difference in distance to the drop box among voters on either side of the borders. Recall that we chose these counties expressly to test our selection strategy and we expect voters to have similar distances to their corresponding drop boxes. As a final validation test, we also examined the distribution of election day polling locations for the 2020 election, given that voter turnout might be affected by access to election day polling locations. These locations are evenly spread across borders between each pair of counties. Thus, we do not observe obvious differences in distance between residential addresses to election day polling locations across borders. Graphical Presentation of Our RD Design We plot the binned means of first difference in voter outcome against the voters’ distance to the county line with a global fit (Panel A) and the means voter turnout with a local fit (Panel B) for the treatment and 10 A fourth-order polynomial is the default order of polynomial in the Stata RD package (Cattaneo et al. 2019). 11 The bandwidth can be interpreted as the bandwidth we used to estimate the RD effect (Cattaneo et al. 2019). 12 1°(degree) = 69 miles (or 111 km).

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Fig. 5.3 Travel Distance to the Drop Boxes. The Y -axis is the distance between the voters and their corresponding drop boxes while the x axis represents the S score, represented as the distance to the border, with negative values assigned for the control group (Jefferson). Here, all voters within the bandwidth are plotted. The threshold discontinuity in Case 1 confirms that we have identified a threshold in which the treatment (distance to drop box) is present

control observations in Fig. 5.4. Panel A shows a positive jump at the county border which indicates that the average voter turnout near the cutoff tends to be higher in the treatment county where voters travel a shorter distance to their drop box than in the control county where voters travel further to drop off their ballots. Panel B illustrates a similar positive trend by focusing on observations close to the cutoff. The pair of panel C and panel D (travel time), and pair of panel E and panel F (auto driving distance) displays the same trend where we observe a positive jump at the cutoff in both global and local settings. These plots indicate that the average voter turnout tends to be higher in the treatment county where voters accessibility is higher (shorter travel times or distances) to their drop box than in the control county where voters have longer travel times (or drive more distance) to ballot drop off. A2: Internal Validation For Distance In general, GRD designs depend on a high level of internal validity (Keele and Titiunik 2015; Lee 2008). Thus, we conducted several standard tests for Case 1, including testing the continuity of covariates, various placebo cutoffs, and sensitivity to observations near the boundary. The GRD

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Fig. 5.4 First-difference (2020 and 2016) in Voter Turnout Global (fourth order) and Local Estimates (first order). The Y -axis is the voter outcome. The X -axis is the score. The scores of Panel A and Panel B are the perpendicular distance from the residential address to the border. The scores of Panel C and Panel D are the travel time from the residential address to the border. The scores of Panel E and Panel F are the auto driving distance from the residential address to the border. The global estimate (left) used a polynomial to fit the observed outcome on the score. The local estimate (right) employed only observations with scores near the county border

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assumes that the sole change occurring at the boundary is a shift in treatment status (de la Cuesta and Imai 2016). In other words, the rest of the pre-treatment covariates will continue to change in a smooth manner at the cutoff. We conducted a graphical analysis with global and local techniques for every covariate in Figs. 5.5 and 5.6. From our statistical analysis (Table 5.3), we do not observe apparent discontinuities at the boundary for either age or registration period, the only individual level data to which we have access. The robust p-value is 0.835 and 0.289, respectively. We also plotted the point estimates of interest with a 95th percentile confidence interval (Fig. 5.7). Panel A illustrates the results from the placebo cutoffs test, which replaces the true boundary (value = 0) with a set of artificial boundaries. A treatment effect should not be observed in theory while using these placebo cutoffs (Cattaneo et al. 2019). We do not observe any significant treatment effects at any of the placebo cutoffs. Panel B evaluates how sensitive the results are to those voters residing Table 5.3 Formal continuity-based analysis for covariates Variable

MES-optional

RD

Robust inference

Eff. number observation

Bandwidth

Estimator

p-value

Conf. Int

Ntr

Nco

Age

0.079

−0.17

0.835

3800

5174

Registration period

0.091

0.788

0.289

3800

5174

Household median income Population density

0.018

−3744

0

1325

344

0.016

−629.37

0

1292

342

Population with minority (%) Population in poverty (%) HH with no vehicle (%) Population with college degree (%)

0.011

−2.682

0

818

230

0.009

1.078

0

[−1.512, 1.881] [−0.962, 3.225] [−6068.46, − 2174.25] [−662.682, − 572.781] [−3.064, − 2.062] [0.868, 1.423]

559

217

0.009

2.488

0

[1.957, 3.355]

559

217

0.017

−7.673

0

[−8.298, − 7.258]

1313

344

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Fig. 5.5 Continuity of covariates at the cutoff: a graphical analysis (global)

Fig. 5.6 Continuity of covariates at the cutoff: a graphical analysis (local)

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Fig. 5.7 Additional Tests for Interval Validity. Panel A. Placebo cutoffs. Panel B. Sensitivity to observations near the cutoff (donut hole approach). The red circles represent the point estimates of interest, which are obtained from running the GRD specification separately. The blue vertical bars represent the 95% confidence intervals of the estimations

very close to the cutoff. Because of the concerns that voters closest to the cutoff have a higher likelihood of engaging in manipulation (e.g., voting across the administrative boundary), we exclude these voters using a “donut hole” approach. From Panel B, we observe similar estimated effects for the treatment using the new data compared to the original estimates at the 1% significant level. This indicates that our research design strategy is robust for internal validity. For Travel Time See Tables 5.4, 5.5, and 5.6. For Auto-Driving Distance See Tables 5.7, 5.8, and 5.9.

1.233 −0.259 3987.110 −599.700 0.080 −5.551 −10.937 −6.144

Estimator

Bandwidth (L, R) 10.494, 13.797 8.435, 29.509 2.287, 2.362 1.496, 2.095 1.483, 1.418 1.829, 1.774 1.543, 2.481 1.537, 2.786

RD

MES-optional

0.276 0.950 0.001 0 0.341 0 0 0

p-value

[−1.479, 5.185] [−1.914, 2.040] [1435.785, 5554.539] [−662.601, −493.281] [−0.126, 0.365] [−6.426, −4.294] [−14.972, −5.822] [−7.477, −4.901]

Conf. Int

Robust inference

Formal continuity-based analysis for covariates (for case 1): travel time

Age Registration period Household median income Population density Population with minority (%) Population in poverty (%) HH with no vehicle (%) Population with college degree (%)

Variable

Table 5.4

2704 2346 713 373 364 455 392 379

Ntr

3284 12,670 85 79 59 66 87 114

Nco

Eff. number observation

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−4 −3 −2 −1 0 1 2 3 4

−0.016 −0.026 −0.071 0.053 0.039 0.511 −0.301 −0.101 −0.083

Estimator

Bandwidth (L, R)

(21.189, 0.989) (16.937, 0.776) (20.057, 1.428) (20.363, 0.963) (23.207, 18.716) (0.966, 21.361) (1.966, 18.493) (1.731, 22.526) (1.187, 20.152)

RD

MES-optional

0.507 0.524 0.086 0.217 0.099 0.344 0.415 0.288 0.464

p-value [−0.155, [−0.204, [−0.205, [−0.051, [−0.009, [−0.331, [−0.750, [−0.291, [−0.349,

Conf. Int

Robust inference

Continuity-based analysis for alternative cutoffs: travel time

Alternative cutoff

Table 5.5

0.077] 0.104] 0.014] 0.223] 0.111] 0.949] 0.309] 0.087] 0.159]

11,925 5894 8338 7731 10,467 40 70 81 87

Ntr

Eff. number observation

384 238 438 184 6935 8650 7767 9916 9203

Nco

5 ACCESS TO VOTING AND PARTICIPATION …

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0.00 1 1.2 1.4 1.6 1.8

18.716) 14.703) 14.781) 15.417) 15.550) 15.924)

0.042 0.079 0.083 0.068 0.064 0.054

Estimator

Bandwidth (23.207, (21.692, (22.085, (20.304, (20.534, (20.460,

RD

MES-optional

0.099 0.020 0.016 0.042 0.053 0.095

p-value

[−0.009, 0.111] [0.015, 0.171] [0.018, 0.175] [0.003, 0.160] [−0.001, 0.155] [−0.011, 0.142]

Conf. Int

Robust inference

Continuity-based analysis for the donut-hole approach: travel time

Donut-hole radius (mins)

Table 5.6

10,467 8179 8666 6699 6803 6705

Ntr

Eff. number observation

6935 3697 3738 4019 4081 4361

Nco

130 J. LOU ET AL.

2.555 −0.818 −220.268 −820.624 0.956 −3.701 −7.533 −6.798

Estimator

Bandwidth (L, R) 10.520, 11.461 10.450, 22.453 1.433, 1.917 3.626, 4.639 1.764, 3.449 1.829, 1.774 1.063, 1.581 1.326, 3.249

RD

MES-optional

0.01 0.330 0.652 0 0 0 0 0

p-value

[0.825, 6.023] [−2.369, 0.795] [−4267.21, 2672.491] [−846.544, −774.631] [0.637, 1.151] [−4.123, −2.921] [−9.558, −3.334] [−7.474, −6.150]

Conf. Int

Robust inference

Formal continuity-based analysis for covariates (for case 1): driving distance

Age Registration period Household median income Population density Population with minority (%) Population in poverty (%) HH with no vehicle (%) Population with college degree (%)

Variable

Table 5.7

3334 3323 753 2091 1006 502 705 705

Ntr

3334 11,183 165 467 281 130 113 276

Nco

Eff. number observation

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−4 −3 −2 −1 0 1 2 3 4

−0.103 −0.057 −0.011 −0.048 0.052 −0.127 −0.027 −0.067 −0.214

Estimator

Bandwidth (L, R)

(24.259, 0.638) (34.330, 1.002) (24.633, 1.947) (25.436, 0.981) (23.273, 14.291) (0.988, 13.966) (1.798, 11.787) (2.305, 10.205) (1.481, 15.547)

RD

MES-optional

0.633 0.053 0.923 0.298 0.015 0.990 0.309 0.628 0.161

p-value

[−0.578, 0.351] [−0.182, 0.001] [−0.120, 0.109] [−0.218, 0.067] [0.013, 0.123] [−1.346, 1.363] [−0.611, 0.193] [−0.218, 0.131] [−0.450, 0.075]

Conf. Int

Robust inference

Continuity-based analysis for alternative cutoffs: driving distance

Alternative cutoff

Table 5.8

9502 23,328 7965 8506 7454 70 140 197 83

Ntr

Eff. number observation

155 608 1101 482 7089 7208 6710 5883 8863

Nco

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0.00 1 1.2 1.4 1.6 1.8

14.291) 12.264) 12.788) 11.990) 9.881) 10.110)

0.052 0.047 0.043 0.039 0.099 0.081

Estimator

Bandwidth (23.273, (20.649, (19.823, (19.239, (19.281, (18.987,

RD

MES-optional

0.015 0.064 0.078 0.122 0.027 0.057

p-value

[0.013, 0.123] [−0.004, 0.130] [−0.007, 0.125] [−0.015, 0.125] [0.013, 0.214] [−0.003, 0.195]

Conf. Int

Robust inference

Continuity-based analysis for the donut-hole approach: driving distance

Donut-hole radius (km)

Table 5.9

7454 5351 4961 4717 4544 4361

Ntr

Eff. number observation

7089 4285 5337 3888 1599 1736

Nco

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References Barreto, Matt A., Stephen A. Nuño, and Gabriel R. Sanchez. 2009. “The Disproportionate Impact of Voter-ID Requirements on the Electorate—New Evidence from Indiana.” PS: Political Science & Politics 42(01): 111–16. Biggers, Daniel R., and Michael J. Hanmer. 2015. “Who Makes Voting Convenient? Explaining the Adoption of Early and No-Excuse Absentee Voting in the American States.” State Politics & Policy Quarterly 15(2): 192–210. Brady, Henry E., and John E. McNulty. 2011. “Turning Out to Vote: The Costs of Finding and Getting to the Polling Place.” The American Political Science Review 105(1): 115–34. Buchanan, Tyler. 2020. “Here Is Where to Drop off Your Ohio Absentee Ballot.” Ohio Capital Journal. https://ohiocapitaljournal.com/2020/10/20/here-iswhere-to-drop-off-your-ohio-absentee-ballot/ (July 26, 2021). Burden, Barry C., David T. Canon, Kenneth R. Mayer, and Donald P. Moynihan. 2014. “Election Laws, Mobilization, and Turnout: The Unanticipated Consequences of Election Reform.” American Journal of Political Science 58(1): 95–109. Cantoni, Enrico. 2020. “A Precinct Too Far: Turnout and Voting Costs.” American Economic Journal: Applied Economics 12(1): 61–85. Cattaneo, Matias D., Nicolas Idrobo, and Rocio Titiunik. 2019. A Practical Introduction to Regression Discontinuity Designs: Foundations. Cambridge University Press. Collingwood, Loren et al. 2018. “Do Drop Boxes Improve Voter Turnout? Evidence from King County, Washington.” Election Law Journal: Rules, Politics, and Policy 17(1): 58–72. de la Cuesta, Brandon, and Kosuke Imai. 2016. “Misunderstandings About the Regression Discontinuity Design in the Study of Close Elections.” Annual Review of Political Science 19(1): 375–96. Dejak, Tony. 2020. “Dispute Over Ohio Mail Ballot Drop Box Limit Ends as Advocates Drop Suit.” NBC News. https://www.nbcnews.com/politics/ 2020-election/dispute-over-ohio-mail-ballot-drop-box-limit-ends-advocatesn1244584 (May 17, 2021). Desilver, Drew. 2020. “Mail-in Voting Became Much More Common in 2020 Primaries as COVID-19 Spread.” Pew Research Center. https://www.pewres earch.org/fact-tank/2020/10/13/mail-in-voting-became-much-more-com mon-in-2020-primaries-as-covid-19-spread/ (May 17, 2021). Dyck, Joshua J., and James G. Gimpel. 2005. “Distance, Turnout, and the Convenience of Voting.” Social Science Quarterly 86(3): 531–48. Fessler, Pam. 2020. “Ballot Drop Boxes Become Latest Front In Voting Legal Fights.” NPR.org. https://www.npr.org/2020/08/11/901066396/ballotdrop-boxes-become-latest-front-in-voting-legal-fights (May 17, 2021). Geurs, Karst T, and Bert van Wee. 2004. “Accessibility Evaluation of LandUse and Transport Strategies: Review and Research Directions.” Journal of Transport Geography: 14.

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Gimpel, J.G., and J.E. Schuknecht. 2003. “Political Participation and the Accessibility of the Ballot Box.” Political Geography 22(5): 471–88. Grembi, Veronica. 2016. “Do Fiscal Rules Matter?” Applied Economics 8(3): 35. Gronke, Paul. 2004. Early Voting Reforms and American Elections. Reed College. Presented at the Annual Meeting of the American Political Science Association. Gronke, Paul, Eva Galanes-Rosenbaum, and Peter A. Miller. 2007. “Early Voting and Turnout.” PS: Political Science & Politics 40(04): 639–45. Hahn, Jinyong, Petra Todd, and Wilbert Van der Klaauw. 2001. “Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design.” Econometrica 69(1): 201–9. Handy, Susan, and Deb Niemeier. 1997. “Measuring Accessibility: An Exploration of Issues and Alternatives.” Environment and Planning A: Economy and Space 29(7): 1175–94. Haspel, Moshe, and H. Gibbs Knotts. 2005. “Location, Location, Location: Precinct Placement and the Costs of Voting.” The Journal of Politics 67(2): 560–73. Imbens, Guido W., and Thomas Lemieux. 2008. “Regression Discontinuity Designs: A Guide to Practice.” Journal of Econometrics 142(2): 615–35. Jacob, Robin, and Pei Zhu. 2012. “A Practical Guide to Regression Discontinuity.” Building Knowledge to Improve Social Policy. https://www.mdrc.org/ sites/default/files/regression_discontinuity_full.pdf (July 26, 2021). Kaplan, Ethan, and Haishan Yuan. 2020. “Early Voting Laws, Voter Turnout, and Partisan Vote Composition: Evidence from Ohio—Online Appendix.” Applied Economics: 8. Karner, Alex, and Dana Rowangould. 2021. “Access to Secure Ballot Drop-off Locations in Texas.” Findings. https://findingspress.org/article/24080-acc ess-to-secure-ballot-drop-off-locations-in-texas (June 3, 2021). Keele, Luke et al. 2017. “An Overview of Geographically Discontinuous Treatment Assignments with an Application to Children’s Health Insurance.” In Advances in Econometrics, edited by Matias D. Cattaneo and Juan Carlos Escanciano, 147–94. Emerald Publishing Limited. https://www.emerald.com/insight/content/doi/10.1108/S0731905320170000038007/full/html (May 5, 2021). Keele, Luke J., and Rocío Titiunik. 2015. “Geographic Boundaries as Regression Discontinuities.” Political Analysis 23(1): 127–55. Lee, David S. 2008. “Randomized Experiments from Non-Random Selection in U.S. House Elections.” Journal of Econometrics 142(2): 675–97. Lee, David S, and Thomas Lemieux. 2010. “Regression Discontinuity Designs in Economics.” Journal of Economic Literature 48(2): 281–355. Lemieux, Thomas, and Kevin Milligan. 2008. “Incentive Effects of Social Assistance: A Regression Discontinuity Approach.” Journal of Econometrics 142(2): 807–28.

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Ohio SOS. 2018. “Voter File Layout.” Ohio SOS. 2021. “Ohio Secretary of State’s Voter Files Download Page.” Ohio Secretary of State. https://www6.ohiosos.gov/ords/f?p=VOTERFTP: STWD:::#stwdVtrFiles (April 30, 2021). Open Primaries. 2020. “Ohio.” Open Primaries. https://www.openprimaries. org/states_ohio (May 1, 2021). PEW. 2020. “Rise in Use of Ballot Drop Boxes Sparks Partisan Battles.” The Pew Charitable Trusts. https://pew.org/2FxLTRt (May 5, 2021). Pew Research Center. 2020. “The 2020 Voting Experience: Coronavirus, Mail Concerns Factored into Deciding How to Vote.” https://www.pewresearch. org/politics/2020/11/20/the-voting-experience-in-2020/ (May 5, 2021). SafeGraph. 2020. “2020 Polling Location Data.” https://www.safegraph.com/ 2020-polling-location-data?utm_source=marketo&utm_medium=email& utm_campaign=newsletter&mkt_tok=eyJpIjoiWmpCa1lqSXlNRFk1TWpNNS IsInQiOiJreHRsaXA3UmZxWUFPN05BckVjRG4rbjlpZEhHRzNsVmRa SDNcL20zSEdcL0RxQUNpOFlwbVN3MDkwV29hYnJPQ0hwc0FkcndhbW 83MmFoamtkb0tqR2RVMHhVZWhYa2E1V29tRjJQOWlzWThkY0d3M1F6 YVwvZVVpdFhnTEYxMzlHbCJ9 (May 1, 2021). Schur, Lisa, Mason Ameri, and Meera Adya. 2017. “Disability, Voter Turnout, and Polling Place Accessibility: Disability, Voter Turnout, and Polling Place Accessibility.” Social Science Quarterly 98(5): 1374–90. Stanford-MIT Healthy Elections Project. 2020a. Mail Voting Litigation During the Coronavirus Pandemic. Stanford-MIT Healthy Elections Project. https:// healthyelections.org/sites/default/files/2020-11/Mail_Voting_Litigation. pdf (May 17, 2021). Stanford-MIT Healthy Elections Project. 2020b. Where Can You Drop Off Your Ballot? A 50 State Analysis. Stanford-MIT Healthy Elections Project. https://healthyelections.org/sites/default/files/2020-10/Bal lot_Drop_Off_0.pdf (May 17, 2021). the Ohio Legislature. 2020. “House Bill Number 197.” https://search-prod. lis.state.oh.us/solarapi/v1/general_assembly_133/bills/hb197/EN/06?for mat=pdf (May 17, 2021). Turman, Jack. 2020. “More Drop Boxes to Be Allowed in Ohio—But Only Where They Already Were.” CBS News. https://www.cbsnews.com/news/ ohio-drop-boxes-election/ (May 17, 2021).

CHAPTER 6

Vote Choice During a Pandemic: How Health Concerns Shaped the 2020 Presidential Election Enrijeta Shino

and Daniel A. Smith

Introduction In the run-up to the 2020 presidential election, millions of Americans were infected by the COVID-19 virus, and thousands more were dying weekly from the disease. Early on during the pandemic, President Donald J. Trump “repeatedly played down the seriousness of the virus and focused on other issues,” while “an array of figures inside his government—from top White House advisers to experts deep in the cabinet departments and intelligence agencies—identified the threat, sounded

E. Shino (B) Department of Political Science, University of Alabama, Tuscaloosa, AL, USA e-mail: [email protected] D. A. Smith Department of Political Science, University of Florida, Gainesville, FL, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. A. Coll and J. Anthony (eds.), Lessons Learned from the 2020 U.S. Presidential Election, Elections, Voting, Technology, https://doi.org/10.1007/978-3-031-44549-1_6

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alarms and made clear the need for aggressive action.”1 His handling of the crisis only got worse over the Summer, as cases surged across the states, health care workers on the front lines were in short supply of personal protective gear, demands for testing outpaced supply, and hospital beds filled up, costing the lives of countless Americans. As the Washington Post reported, “It may never be known how many thousands of deaths, or millions of infections, might have been prevented with a response that was more coherent, urgent and effective. But even now, there are many indications that the administration’s handling of the crisis had potentially devastating consequences.”2 Given this background concerning the President’s handling of the health crisis, we are interested in how much of a role COVID-19 played in determining the 2020 General Election outcome. We know from the discussion in Chapter 4, which examines how COVID-19 risks impacted vote method in the election, that both Republicans and Democrats who were at greater risk for COVID-19 were more likely to vote by mail. Here, we are interested in the impact of COVID-19 on vote choice in the presidential election. We argue that just as personal and national evaluations of the economy can influence candidate vote choice, either retrospectively or prospectively (Kinder and Kiewiet 1981; MacKuen et al. 1992; Healy et al. 2017), there is reason to suspect that in the 2020 General Election voters’ concerns over their personal health, and public health more generally, affected candidate vote choice in the presidential contest. More generally, we suggest that considerations of personal health in the midst of a pandemic conditioned the support of Donald Trump’s reelection. Building on our previous research (Shino and Smith 2021), we argue in this chapter that voters who were concerned about becoming ill in the 2020 presidential election were more likely to alter their vote choice to better protect themselves from the disease, and that those who valued public health over the economy were less likely to support the reelection of an incumbent whose duties included overseeing the response to the pandemic. 1 “He Could Have Seen What Was Coming: Behind Trump’s Failure on the Virus,” New York Times, April 11, 2020, available https://www.nytimes.com/2020/04/11/us/ politics/coronavirus-trump-response.html (last accessed January 24, 2023). 2 “The U.S. Was Beset by Denial and Dysfunction as the Coronavirus Raged,” Washington Post, April 4, 2020, available https://www.washingtonpost.com/national-security/ 2020/04/04/coronavirus-government-dysfunction/ (last accessed January 24, 2023).

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In order to test these propositions, we draw on public opinion data in the United States fielded prior to the 2020 General Election. When it comes to one’s health and candidate vote choice in the era of COVID19, particularly in races where public health is included in the portfolio of candidates standing for reelection, we argue that voters are able to retrospectively “ascertain whether the incumbents have performed poorly or well” by “calculat[ing] the changes in their own welfare” (Fiorina 1981, p. 5), as well as prospectively evaluate their well-being during the pandemic. Here, with regard to vote choice in the 2020 presidential election, we argue that Republicans who expressed health concerns due to COVID-19 were particularly less likely to support Trump’s reelection.

Trump’s Handling of the COVID-19 Crisis There is considerable evidence that while in office, Trump’s handling of the COVID-19 crisis was wanting, if not wholly inadequate. Trump’s initial response to the pandemic might be most generously characterized as trying to minimize the virus’s severity, downplaying its impact, and dismissing concerns raised by health experts. “You know, a lot of people think that goes away in April with the heat — as the heat comes in,” Trump declared early on in the pandemic, continuing, “Typically, that will go away in April.” Later in February, 2020, he claimed that China is “getting it more and more under control. So I think that’s a problem that’s going to go away,” and that “within a couple of days [the death count] is going to be down to close to zero, that’s a pretty good job we’ve done.” Never one to shy away from self-congratulation, Trump claimed in early March, “We have done an incredible job. We’re going to continue. It’s going to disappear. One day—it’s like a miracle—it will disappear,” but then later hedged, saying, “you know, it could get worse before it gets better. It could maybe go away. We’ll see what happens. Nobody really knows.” By mid-March, with documented COVID-19 cases rising into the thousands but death tolls still under 100, Trump said, “We’re prepared, and we’re doing a great job with it. And it will go away. Just stay calm. It will go away.” Even in April, with more than a quartermillion cases and over 7000 documented deaths, Trump continued to

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deny the severity of the virus, saying, “It’s going to go away, hopefully at the end of the month. And, if not, hopefully it will be soon after that.”3 As the number of confirmed cases and death toll continued to rise during the Summer of 2020, Trump repeatedly claimed that the virus would disappear on its own. Touting the need to keep the economy open, he downplayed the importance of wearing masks and social distancing, despite increasing evidence of how the highly contagious respiratory illness was transmitted. In late June, with testing of the coronavirus more widely available and showing a resurgence of rising positive cases, Trump reasoned that, “Our Coronavirus testing is so much greater and so much more advanced, that it makes us look like we have more cases, especially proportionally, than other countries.” “So when you do 30 million,” Trump said at the end of June about the number of COVID-19 tests, “you’re going to have a kid with the sniffles, and they’ll say it’s coronavirus—whatever you want to call it.” “In some cases, downplaying the severity of the virus,” Trump averred that “it’s people that didn’t even know they were sick. Maybe they weren’t. But it shows up in a test.” Rather than evidence of the pandemic spreading, Trump argued that the increase in positive cases was just a symptom of increased testing.4 Not until the virus reached pandemic levels did Trump finally begin to shift his tone and implement a series of measures to address the crisis. In late July, 2020, with over 140,000 recorded deaths caused by the virus, Trump urged Americans to wear masks and to continue to maintain social distance, warning that there would be more cases to come. “It will probably, unfortunately, get worse before it gets better,” Trump admitted at his first White House press briefing in months on July 21, 2020, continuing that it was “something I don’t like saying about things, but that’s the way it is.” Just a few days earlier, the president publicly wore a face mask for the first time during a visit to a military hospital. “We’re asking everybody that when you are not able to socially distance, wear a mask, get a mask,” he said at the press conference, as “whether you like the

3 “Yet again, Trump Pledges That the Coronavirus Will Simply Go Away,” The Washington Post, April 4, 2020, available https://www.washingtonpost.com/politics/2020/ 04/28/yet-again-trump-pledges-that-coronavirus-will-simply-go-away/ (last accessed May 15, 2023). 4 “President Trump, Coronavirus Truther,” The Washington Post, July 6, 2020, available https://www.washingtonpost.com/politics/2020/07/06/trump-throwscaution-wind-coronavirus/ (last accessed May 15, 2023).

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mask or not, they have an impact. They’ll have an effect. And we need everything we can get.”5 To be sure, during the Summer of 2020 and into the Fall as the presidential campaign was heating up, the debate over mask-wearing had become a partisan issue, just as the President’s handling of the COVID19 crisis had become a salient issue for the American public. Democrats were quick to criticize Trump’s response to the pandemic. Democratic Speaker of House, Nancy Pelosi, responded immediately, lambasting the President, saying, “This is not a hoax—it is a pandemic that has gotten worse before it will get better because of his inaction, and in fact clearly it is the Trump virus.”6 For his part, Democratic presidential nominee, Joe Biden, criticized the President for minimizing the risks of the disease. In September, 2020, on the campaign trail, Biden accused Trump of lying about the severity of COVID-19. “He’s failed our economy and our country,” said Biden. Trump’s misleading the public was “beyond despicable,” “a disgrace,” and “a dereliction of duty.” “He knew how dangerous it was,” Biden continued, “[a]nd while this deadly disease ripped through our nation, he failed to do his job on purpose. It was a life-and-death betrayal of the American people.”7 With this background to the handling of the pandemic by Trump and the partisan politics that underpinned the COVID-19 crisis in the Summer of 2020, we turn to how health concerns might shape candidate vote choice. Did voters’ perceptions of Trump’s response to the spread of coronavirus affect vote choice in the November 2020 election? We are primarily interested in understanding the drivers of presidential vote choice for individuals who were particularly concerned about becoming ill from the virus, as well as those who valued public health over the economy.

5 “Trump Shifts Rhetoric as He Urges Mask-Wearing, Warns of Worsening Pandemic,” Reuters, July 21, 2020, available https://www.reuters.com/article/us-health-coronavirustrump-idUSKCN24M2X3 (last accessed May 15, 2023). 6 “Pelosi Calls Coronavirus the ‘Trump Virus,’” The Hill, July 21, 2020, available https://thehill.com/homenews/house/508449-pelosi-refers-to-coronavirus-asthe-trump-virus/ (last accessed May 15, 2023). 7 “Trump Called the Coronavirus ‘Deadly’ in Private While Minimizing Its Risks in Public, Book Reveals,” New York Times, September 9, 2020, available https://www. nytimes.com/live/2020/09/09/us/trump-vs-biden#biden-speaking-in-michigan-calls-tru mps-minimizing-of-the-virus-beyond-despicable (last accessed May 15, 2023).

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Candidate Vote Choice and Health Concerns There is surprising little scholarship on how health concerns, even without the severity of a pandemic shaping perceptions, might affect vote choice. Some scholars have written about how health care policies and the salience of social programs might affect vote choice (Bélanger and Meguid 2008). Others have examined how personal health considerations might shape which candidates voters will support. For example, Denny and Doyle (2007) draw on the British National Child Development Study to examine the impact of childhood health indicators on party vote choice across three national elections (1979, 1987, and 1997), finding that individuals who were in poor health as children were more likely to vote for the Labor Party. Because they were prone to sickness as children, the authors reason that as adults they were more likely to rely on the National Health System, and thus support Labor. In an epidemiological study leveraging ecological inference, Smith and Dorling (1996) find that mortality rates in England and Wales were related to negative vote shares for the Conservative Party in the 1983, 1987, and 1992 elections. In the American context, there is evidence of a partisan divide regarding the federal government’s management of disease outbreaks—from the avian influenza in 2006, to the H1N1 “swine” flu in 2009, to the Ebola outbreak in 2014.8 Yet, given the novelty of the latest coronavirus, there is understandably scant scholarship on how personal health concerns during a pandemic might shape candidate vote choice. True, there is considerable literature on how health care policy is polarized in the United States (Henderson and Hillygus 2011; Thompson 2013). This partisan polarization was furthered with the introduction and passage of the Affordable Care Act in 2010, popularly known as “Obamacare.” After four years of Donald Trump, it is almost quaint reading a 2015 account concerning Republican efforts to defund the Affordable Care Act, and how “it is difficult to imagine that Obama’s successor will be as polarizing a figure as he has become” (Jacobson 2015, p. 90). At first blush, there might be good reason to be skeptical as to whether individual health concerns, much less public health considerations more generally, might impact determinants of candidate choice. Given the

8 See “The Partisan Divide on Ebola Preparedness,” New York Times, October 16, 2014, available https://www.nytimes.com/2014/10/17/upshot/the-partisan-divide-onebola-preparedness.html (last accessed September 12, 2020).

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strength of partisan heuristics driving perceptions of the current health crisis in the United States, might individual-level worries about the coronavirus really alter support for Trump? As we go on to detail in this chapter, drawing on national data collected during the COVID-19 pandemic in the months leading up to the 2020 General Election, and accounting for several model specifications, we consistently find that health concerns were important predictors of candidate vote choice. Partisanship, of course, is sticky and cannot be dismissed from vote choice calculus, even during a pandemic. But we find that during a period of extreme partisan polarization in the United States (Hetherington and Rudolph 2015), individual health concerns in 2020 lessened partisan attachments, particularly among Republicans (also see Shino and Binder 2020). Although our study is limited to explaining American voter behavior during a pandemic, it provides insight for voter behavior in other contexts, as the COVID-19 pandemic continues to alter the political landscape worldwide. Theoretically, we draw on economic voting literature when theorizing about how voters might think about vote choice in the midst of a health pandemic (Duch and Stevenson 2008; Lewis-Beck and Paldam 2000; Markus 1988; Ansolabehere et al. 2014; Healy et al. 2017). We suggest that when it comes to personal health, similar to economic considerations, “[r]etrospective voting,” Achen and Bartels (2016, p. 102) write, “can be a powerful mechanism for electoral accountability, but only insofar as voters can discern and set aside irrelevant factors contributing to their subjective well-being.” Electoral accountability might remain as elusive due to the instability of the mass public on policy issues, including health issues (Converse 1964; Zaller 1992; Zaller and Feldman 1992). Even voters who learn an incumbent’s position on an issue, as Lenz (2012) finds, do not always modify their support for or against the candidate. That voters do not inevitably cast a ballot for candidates who reflect their own policy preferences is borne out in a recent survey conducted in the United States during the height of the coronavirus. Issues of public health might pose even more difficulties for voters when trying to ascertain candidates’ policy positions. For example, despite Trump’s opposition to mask wearing, a majority of his supporters reported that they supported such a policy. One survey even found that four-in-five of Trump’s backers said that they thought the president

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supported a policy that individuals should wear masks.9 Despite being in the midst of a severe pandemic with thousands of fellow Americans dying every week, there was considerable uncertainty as to which major presidential candidate supported wearing a mask in public. Nevertheless, we suggest that deciding for whom to vote in the throes of a public health pandemic might be just as easy for voters to consider as when they think about the economy. Americans have an uncanny ability to discern their subjective well-being. For nearly all voters, the COVID19 crisis was a salient issue in 2020. As such, in a health crisis with life and death implications, we argue that the mass public did take health issues into consideration and did hold the incumbent president accountable based on their own health status as well as their perspective on the broader state of public health. Compared to making retrospective or prospective evaluations of the economy when it comes to vote choice, we suggest that weighing one’s own health and that of the general public during a pandemic is a relatively light lift. So, why might an individual’s health considerations operate similarly to economic evaluations when it comes to assigning responsibility to an incumbent? With Democrats and Republicans becoming increasingly ideologically homogeneous in their membership (Levendusky 2009; Mason 2018), and with individuals increasingly viewing events through a partisan “perceptual screen” (Campbell et al. 1960, p. 130), we argue that COVID-19 has elevated personal health concerns so that they may even temper partisanship. In the run-up to the 2020 General Election, the potential life-altering effects of COVID-19, we think, may dampen support for Trump’s reelection among those with health concerns. Despite the pull of partisanship, and irrespective of whether an individual knows Trump’s policy positions on the coronavirus, we suspect that individual perceptions and behavior in response to the coronavirus—specifically, expressing concern about contracting COVID-19, choosing to wear a mask in public, and privileging public health over the economy—will diminish support for Trump’s reelection. We argue that health concerns stemming from the pandemic should hold across party lines. Republicans who are concerned about contracting COVID-19, who mask up, and who value public health above the 9 See, “Even with 190,000 Dead, There’s a Lot That Voters Don’t Know,” New York Times, October 10, 2020, available https://www.nytimes.com/2020/09/10/upshot/vot ers-trump-virus-projection.html (last accessed September 12, 2020).

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economy should be particularly less likely to support Trump in the November 2020 election. However, as we discuss below, not all concerns related to the coronavirus will have a negative impact on the support for Trump. For example, individuals who had taken a COVID-19 test might not have been as concerned about their health. And because deaths from COVID-19 remain relatively rare events, individuals who report having had a close friend or family member die from COVID-19 may not have much of an impact on support for the president. Of course, these factors may change if positive rates of infection and mortality rates increase markedly as a proportion of the overall population.

Data and Empirical Framework To examine the impact of COVID-19 on vote choice in the 2020 presidential election, we draw on survey data from the 2020 American National Election Studies (ANES). The 2020 ANES study, which was conducted in August-November, is a probability sample of the voting age population. Data were collected using three different survey modes: internet, phone, and video. Our empirical strategy focuses primarily on understanding how different issues related to COVID-19 affected presidential vote choice among American adults. The outcome variable in our analysis is vote choice, a binary variable taking the value of one if the respondent reported vote preference for Trump in the 2020 election, and zero if the respondent reported voting for Biden. To gauge voters’ perceptions of COVID-19, we focus on four different items: 1. if they approved President Trump’s handling of COVID-19, 2. if someone in their family contracted COVID-19 or showed symptoms, 3. how important science should be for decisions on COVID-19, and 4. if they believed that COVID-19 was intentionally developed in a lab. The specific questions we use from the ANES 2020 questionnaire and how we code the items for this study are as follows. First, on President Trump’s handling of COVID-19, the ANES 2020 includes a branching question. It starts by asking about the approval of Trump’s handling

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of the situation: “Do you approve or disapprove of the way Donald Trump has handled the coronavirus, or COVID-19, pandemic?” Then it asks about the strength of this opinion: “Do you [approve/disapprove] strongly or not strongly?” In our analysis we code handling COVID-19 variable to vary from 1 (strongly disapprove) to 4 (strongly approve). Second, when it comes to voters’ personal experience with COVID-19, we use two ANES questions: (1) “Has anyone in your household tested positive for the coronavirus disease, COVID19, or has no one tested positive?” and (2) “Has anyone in your household been suspected of having COVID-19 based on their symptoms, or not?” Our variable, contracted/ symptoms COVID-19, is coded 1 if someone in the household has tested positive or has shown COVID-19 symptoms and 0 otherwise. Third, we are interested in how respondents considered the role that science should play in government decisions about COVID-19. The ANES 2020 questionnaire includes the following question: “In general, how important should science be for making government decisions about. COVID-19?” We code our science importance COVID-19 variable 1 (not important at all) to 5 (extremely important). Finally, the COVID-19 pandemic sparked many conspiracy theories related to its origin. One of these widespread beliefs was that the virus was intentionally developed in a lab. The question we use to measure voters’ perceptions about this issue is: “Which of these two statements do you think is most likely to be true? (1) The novel coronavirus (COVID-19) was developed intentionally in a lab. (2) The novel coronavirus (COVID19) was not developed intentionally in a lab.” Our lab manufactured COVID-19 variable is coded 1 if the respondent believed that COVID19 was intentionally manufactured in a lab and 0 if they did not believe that statement. While COVID-19 may have been the most salient issue Americans were facing prior to the 2020 General Election, it was not the only issue voters had to weigh when making their voting decisions. In order to analyze the importance of the four COVID-19-related issues relative to other issues, we control for both national and pocketbook economic concerns, as well as general trust in the election and support for vote by mail. First, we code National economy as a 1 if respondents thought that national economy was doing worse than a year ago to 5 if they reported that it was doing better. Second, we code Pocketbook economy as a 1 if over

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the past 12 months someone in respondent’s family or a close personal friend lost their job, and 0 if no one did. In addition, due to the speculation revolving around the accuracy of the election results and how vote by mail (VBM) would be the main tool rigging the election, we want to make sure to control for respondents’ perceptions on election integrity and mail voting. Trust in the election is coded 1 if the respondent said that votes will be counted accurately to 5 not accurately at all. Support for VBM is coded 1 if respondents opposed conducting all elections by mail, 2 neither nor, and 3 if they favored this practice. In the section below, we show findings from a series of logistic regression models estimating candidate vote choice, controlling for a set of variables including a respondent’s attitudes on COVID-19 issues, economic and political concerns. All model estimations control for demographics (age, gender, race/ethnicity, education, social status), party of registration, ideology, political awareness, survey mode, and state fixed-effects.10

Findings As COVID-19 was upending the life of millions of Americans, Trump was running for reelection. In the analysis that follows we explain what aspects of COVID-19 affected vote choice for president and whether these considerations vary across party lines. We begin with some descriptive statistics from the national survey. In Table 6.1, we show weighted descriptive statistics for the four main COVID-19-related issues and their relationship with presidential vote preference. We find that 5.6% of those who strongly disagreed with how Trump handled the COVID-19 crisis reported they would vote for Trump. Conversely, we find that 96.7% of those who strongly approved said that they would vote for him in the 2020 election. In addition, we find the association between the two variables, Trump handling COVID-19 and vote choice, is statistically significant. We also find, as shown in Table 6.1, that less than half of all respondents—42.2%—who dealt with a COVID-19 infection in their household reported they would support Trump in the 2020 election. Interestingly,

10 See Appendix A for variable coding information.

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Table 6.1 Descriptive statistics for COVID-19 and vote choice COVID-19 issues

Vote Trump (%)

Trump handling COVID-19 Disapprove strongly 5.6 Disapprove not strongly 69.2 Approve not strongly 92 Approve strongly 96.7 Family contracting COVID-19 or show symptoms Yes 42.2 No 45.3 Science importance on COVID-19 decisions Not at all important 82.4 A little important 92.2 Moderately important 86.1 Very important 61.1 Extremely important 17.8 COVID-19 manufactured in a lab Yes 68.6 No 22.8

χ 2 ( p)

3595.7(0.00)

2.37(0.12)

1621.6(0.00)

964.8(0.00)

Notes Weighted crosstabultions for COVID-19 and vote choice in the 2020 presidential election

though, we find that having someone in the household who tested positive or showed COVID-19 symptoms did not have a substantial differing effect on presidential vote choice. We also find no statistically significant association between the two variables. We do, however, find that respondents’ perceptions of whether science should guide decisions related to the COVID-19 pandemic response did affect vote choice. Specifically, as we report in Table 6.1, 82.4% of those who thought that science should not be important at all in affecting COVID-19 decisions reported they would vote for Trump. This compares to just 17.8% support for Trump among those who thought that science was extremely important in guiding COVID-19 public health matters. Given this distribution of responses, we find that there is a statistically significant association between perceptions on the importance of science in COVID-19 decision-making and voting for Trump. Finally, Table 6.1 reports the relationship between belief in one of the major conspiracy theories related to the origins of COVID-19 and support for Trump. Over two-thirds of respondents, some 68.6% of those surveyed, who believed that COVID-19 was intentionally developed in a

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lab reported supporting Trump’s reelection. The association between the two variables is statistically significant. Overall, as the descriptive statistics reported in Table 6.1 reveal, with the exception of family members exposure to COVID-19, the perceptions of the pandemic were strongly related to support of Trump. While, COVID-19 was one of the main issues voters used to inform their vote choice in the 2020 presidential election, partisanship is probably the most stable and important factor to explain vote choice. In our sample, 91.3% of Republicans reported support for Trump in the 2020 presidential election, followed by 40.0% of no party affiliates (NPA), and only 4.0% of Democrats. We now turn to our models predicting vote choice in the 2020 presidential election, which consider not only COVID-19 perceptions, but a host of other factors. The findings in Table 6.1 are descriptive in nature and do not allow us to predict vote choice when controlling for other important factors that might affect responses of the national sample of registered voters. We now turn to our findings reported in Table 6.2, which report logistic regression estimates from a series of models for Trump support in the November 2020 election. All model estimations control for an array of demographic information for each respondent (e.g., race and ethnicity, gender, age, education, marital status), as well as party identification and political awareness. The full model also include controls for support for other issues, such as trust in the election and mail voting, attitudes concerning the economy (pocketbook and national), as well as controls for survey mode effect and state fixed-effects. In Model 1, we present a baseline model which controls only for the four COVID-19 issues, as reported above. Consistent with our descriptive findings discussed above, we find that those who approved of Trump’s handling of COVID-19 were more likely to vote for him in the 2020 presidential election than for Biden. On the other hand, those who believed that science should play an important role in COVID-19 decision-making were less likely to vote for Trump than Biden. In contrast, those who believed in the theory that COVID-19 was intentionally manufactured in a lab were more likely to support Trump than Biden in the 2020 election. As with the descriptive findings, reported above, our multivariate model reveals that exposure to COVID-19 infection in the respondent’s household was not a significant determinant of vote choice in the 2020 presidential election. This is not a surprising finding as the majority of

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Table 6.2 Logistic regression model for COVID-19 and economy effect on vote choice in the 2020 presidential election

(Intercept) Handling COVID-19 Contracted COVID-19 Science importance COVID-19 Lab manufactured COVID-19 National economy Pocketbook economy Democrat Republican Ideology Black Hispanic Other race Female Education

Model 1

Model 2

−3.357*** (0.808) 2.297*** (0.072) −0.085 (0.179) −0.747*** (0.075) 1.162*** (0.126)

Model 3

Model 5

Model 6

−5.486*** −2.561*** −6.492*** (1.225) (0.354) (0.803) 1.676*** (0.089) −0.084 (0.239) −0.754*** (0.110)

−4.054*** (0.835) 2.179*** (0.073) −0.029 (0.184) −0.703*** (0.077)

−5.373*** (1.359) 1.442*** (0.091) 0.011 (0.248) −0.673*** (0.113)

1.556*** (0.178)

1.076*** (0.129)

1.240*** (0.186)

0.421*** (0.054) −0.179 (0.129)

0.365*** (0.074) −0.096 (0.177) −1.659*** (0.254) 1.034*** (0.200) 0.630*** (0.082) −2.118*** (0.443) −0.415 (0.337) 0.080 (0.288) −0.100 (0.177) −0.036

0.986*** (0.030) −0.334*** (0.068) −1.668*** (0.247) 0.996*** (0.192) 0.726*** (0.079) −1.917*** (0.448) −0.477 (0.336) 0.276 (0.270) −0.033 (0.170) −0.038

Model 4

0.699*** (0.052) −0.195 (0.121) −2.070*** (0.185) 1.556*** (0.134) 1.078*** (0.056) −1.820*** (0.348) −0.691** (0.245) 0.160 (0.203) 0.295* (0.120) −0.245***

(continued)

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Table 6.2 (continued) Model 1

Model 3

(0.087) 0.004 (0.005) 0.308 (0.172) −0.057 (0.088)

Age Married Political awareness Election trust VBM support State FE Survey mode Log Likelihood Num. obs.

Model 2

Model 4

Model 5

Model 6

Yes Yes

(0.090) 0.010 (0.006) 0.360* (0.180) −0.041 (0.091) 0.280** (0.088) −0.766*** (0.130) Yes Yes

(0.061) −0.001 (0.004) 0.237 (0.122) −0.014 (0.063)

Yes Yes

Yes Yes

Yes Yes

Yes Yes

−991.945

−551.591

−2817.566 −1011.676 −952.893

−511.651

5451

4549

5555

4523

4622

5428

Note Dependent variable is coded 1 for vote intent for Trump and 0 for Biden. All model estimations control for state fixed-effects ***p < 0.001, **p < 0.01, *p < 0.05

people who contracted COVID-19 had a high chance of survival. To be more specific, 98.2% of known COVID-19 patients in the United States survived the infection (Lajka and Joffe-Block 2021). Furthermore, to test the consistency of these findings, in Table 6.2 Model 2, we replicate the analysis controlling for demographic and political interest variables, as mentioned above. As expected, the effects of handling COVID-19, the importance of science in COVID-19 decisionmaking, and believing that COVID-19 was intentionally manufactured in a lab remain similar to those discussed in Model 1. Given that in the months before an election voters evaluate different issues to inform their candidate vote choice and perceptions of the national and pocketbook economy are salient to voters, in Model 3 we estimate a baseline model for economic perceptions. As expected, those who perceived the national economy to be doing better than the previous year were more likely to support Trump in the 2020 presidential election. On the other hand, those who lost their jobs were less likely to support the incumbent candidate. In addition, in Model 4 we estimate the full economic perception

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model including other controlling variables. As shown in Model 4, those holding positive perceptions about the state of the national economy were more likely to support the incumbent candidate. Since in a given election year voters evaluate the salience of several issues relative to one another, in Model 5, we estimate a vote choice model controlling for both COVID-19 and economic perceptions. Similar to findings discussed in Model 1 through Model 4, we find that those who believe that Trump handled COVID-19 well, believed the conspiracy theory that COVID-19 was manufactured in a lab, and perceived the national economy to be doing well were more likely to support Trump in the 2020 election. While, whose who believed in the importance of science in handling COVID-19 were less likely to express support for Trump’s reelection bid in 2020. In other words, analyzing the relative importance of COVID-19 to economy we find that both these issues are strong predictors in explaining vote choice in the 2020 presidential election. In addition, to test the consistency of our findings, in Model 6, we replicate Model 5, but include voters’ perceptions on the accuracy of vote count and support for mail voting, as well as the other control variables. These were all salient issues prior to the November 2020 election. As shown in Table 6.2 Model 6, the effect of the three issues related to COVID-19 remains consistent and robust to different model specification. To sum up, as shown in Table 6.2, we find three consistent COVID19 predictors for Trump support across all model specifications. First, respondents who approved Trump’s handling of COVID-19 were more likely to express support for the incumbent President in the 2020 election. Second, respondents who reported that science should be important informing COVID-19 decision-making were less likely to express greater support for Trump in the November 2020 election compared to their counterparts. Last but not least, those who believed that COVID-19 was intentionally manufactured in a lab were more likely to support Trump’s reelection in 2020. That these effects remain strong even after including other common predictors for candidate vote choice, gives us confidence that the pandemic played a sizeable role in influencing the vote for president in 2020. But how much are these findings driven by partisanship? We now turn to how voters’ considerations about the pandemic might differ across partisan lines when it comes to vote choice. In Fig. 6.1, we plot predicted

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probabilities for the statistically significant issues, shown in Model 6 in Table 6.2, broken down by party identification. In Fig. 6.1a, we find that support for Trump was greater than 90 among Republicans who approved of the president’s handling of COVID-19, holding all other variables constant.

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 0 No party affiliate

1 Democrat

Republican

(c) Lab manufactured COVID-19

Fig. 6.1 Predicted probabilities for vote choice by party registration, ANES 2020 survey (Note Predicted probabilities for support for Trump by party identification, conditioning on statistically significant issues as shown in Model 6 shown in Table 6.2)

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Among those who supported the president’s handling of COVID-19, Trump received about 80% from those with no party affiliation (Independents); Trump received just under 50% support from Democrats. What should be highlighted in Fig. 6.1a is that regardless of partisanship, respondents who did not approve of Trump’s handling of COVID-19 were less than 12% likely to vote for Trump’s reelection. Our estimates of support for Trump range as high as 41% for Republicans, but they still fall below 50%, indicating they would vote for Biden. Figure 6.1b looks like a mirror image of Fig. 6.1a. In Fig. 6.1b, we find that support for Trump was about 90% among Republicans who believed that science should not be important in COVID-19 decisionmaking. The support among Republicans drops below 50% among those who thought science should be important to making COVID-19 decisions. Finally, as expected, in Fig. 6.1c shows that, all else equal, support for Trump was about 60% among Republicans who believed that COVID19 was developed in a lab, followed by NPAs at 38%, and Democrats at about 10%.

Discussion The United States 2020 presidential election took place in the midst of an unprecedented global health crisis, affecting all strata of the American public. To date, there is little research to understand voter behavior, much less candidate vote choice, under such conditions. Importantly, our study shows that voters—irrespective of partisan ties—are willing to adjust their preferences for candidates when taking into consideration their health. Drawing on national data collected just as COVID-19 infection rates across the United States were continuing to rise, and holding for a battery of model specifications, we consistently find that health concerns are important predictors of candidate vote choice. Individuals who are concerned about contracting COVID-19, and who have taken the most basic preventative action (wearing a mask), and who value public health above the economy were more likely to moderate their support for Trump in 2020. Partisanship is tough to trump, so to speak, and cannot be dismissed from vote choice calculus that includes health considerations, even during a pandemic. But even during a period of extreme partisan polarization in the United States (Hetherington and Rudolph 2015), we find

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that individual health concerns appear to have mitigated partisan attachments, particularly among Republicans, dampening support for President Trump’s reelection bid. Although our study is limited to explaining American voter behavior during a pandemic in a single election, it provides insight for voter behavior in other contexts as the COVID-19 pandemic continues to alter the political landscape worldwide.

Appendix A: ANES 2020 Survey: Variable Coding • Vote Trump 2020: is a dummy variable coded 1 if the respondent intends to vote for Trump in the November 2020 election and 0 vote for Biden. • Vote counts: is a five point scale variable varying from 1 if they reported that votes in November 2020 will be counted accurately to 5 not at all accurately. • Contract COVID-19: is a five point scale variable varying from 1 not at all worried about contracting the virus to 5 extremely worried. • Party ID: are dummy variables for Republican, Democrat, and independent. Republican is the base category. • Race: are dummy variables for white, black, Hispanic, and other race. White is the base category. • Age: is a continuous variable. • Female: is a dummy variable coded 1 if the respondent is a female and 0 if male. • Political awareness: is a four point scale varying from 1 not paying attention at all to 4 paying a great deal of attention on news about government and politics. • Ideology: is a seven point scale item varying from 1 very conservative to 7 very liberal.

References Achen, Christopher H., and Larry M. Bartels. 2016. Democracy for Realists: Why Elections do not Produce Responsive Government. Princeton: Princeton University Press. Ansolabehere, Stephen, Marc Meredith, and Erik Snowberg. 2014. “Mecroeconomic Voting: Local Information and Micro-perceptions of the MacroEconomy.” Economics & Politics 26 (3): 380–410.

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Bélanger, Éric, and Bonnie M. Meguid. 2008. “Issue Salience, Issue Ownership, and Issue-Based Vote Choice.” Electoral Studies 27 (3): 477–91. Campbell, Angus, Philip E. Converse, Warren E. Miller, and Donald E. Stokes. 1960. The American Voter. New York: Wiley Sons. Converse, Philip E. 1964. “The Nature of Belief Systems in Mass Publics.” In Ideology and Discontent, edited by David Apter. New York: Free Press. Denny, Kevin J., and Orla M. Doyle. 2007. ““... Take up thy Bed, and Vote” Measuring the Relationship Between Voting Behaviour and Indicators of Health.” The European Journal of Public Health 17 (4): 400–401. Duch, Raymond M., and Randolph T. Stevenson. 2008. The Economic Vote: How Political and Economic Institutions Condition Election Results. Cambridge: Cambridge University Press. Fiorina, Morris P. 1981. Retrospective Voting in American National Elections. London: Yale University Press. Healy, Andrew J., Mikael Persson, and Erik Snowberg. 2017. “Digging Into the Pocketbook: Evidence on Economic Voting From Income Registry Data Matched to a Voter Survey.” American Political Science Review 111 (4): 771– 85. Henderson, Michael, and D. Sunshine Hillygus. 2011. “The Dynamics of Health Care Opinion, 2008–2010: Partisanship, Self-Interest, and Racial Resentment.” Journal of Health Politics, Policy and Law 36 (6): 945–60. Hetherington, Marc J., and Thomas J. Rudolph. 2015. Why Washington Won’t Work: Polarization, Political Trust, and the Governing Crisis. Chicago: University of Chicago Press. Jacobson, Gary C. 2015. “Eroding the Electoral Foundations of Partisan Polarization.” In Solutions to Political Polarization in America, edited by N. Persily, 83–95. New York: Cambridge University Press. Kinder, Donald R., and D. Roderick Kiewiet. 1981. “Sociotropic Politics: the American Case.” British Journal of Political Science 11 (2): 129–61. Lajka, Arijeta, and Jude Joffe-Block. 2021. “Survival Rates for COVID-19 Misrepresented in Posts.” AP News. https://apnews.com/article/fact-che cking-970830023526. Lenz, Gabriel S. 2012. Follow the Leader?: How Voters Respond to Politicians’ Policies and Performance. Chicago: University of Chicago Press. Levendusky, Matthew. 2009. The Partisan Sort: How Liberals Became Democrats and Conservatives Became Republicans. Chicago: University of Chicago Press. Lewis-Beck, Michael S., and Martin Paldam. 2000. “Economic Voting: An Introduction.” Electoral Studies 19 (2–3): 113–21. MacKuen, Michael B., Robert S. Erikson, and James A. Stimson. 1992. “Peasants or Bankers? The American Electorate and the Us Economy.” American Political Science Review 86 (3): 597–611.

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Markus, Gregory B. 1988. “The Impact of Personal and National Economic Conditions on the Presidential Vote: A Pooled Cross-Sectional Analysis.” American Journal of Political Science 32 (1): 137–54. Mason, Liliana. 2018. Uncivil Agreement: How Politics Became Our Identity. Chicago: University of Chicago Press. Shino, Enrijeta, and Michael Binder. 2020. “Defying the Rally During COVID19 Pandemic: A Regression Discontinuity Approach.” Social Science Quarterly 101 (5): 1979–94. Shino, Enrijeta, and Daniel A. Smith. 2021. “Pandemic Politics: COVID-19, Health Concerns, and Vote Choice in the 2020 General Election.” Journal of Elections, Public Opinion and Parties 31 (1): 191–205. Smith, George Davey, and Daniel Dorling. 1996. ““I’m all Right, John”: Voting Patterns and Mortality in England and Wales, 1981–92.” BMJ 313 (7072): 1573–77. Thompson, Frank J. 2013. “Health Reform, Polarization, and Public Administration.” Public Administration Review 73 (1): S3–S12. Zaller, John R. 1992. The Nature and Origins of Mass Opinion. Cambridge: Cambridge University Press. Zaller, John R., and Stanley Feldman. 1992. “A Simple Theory of the Survey Response: Answering Questions Versus Revealing Preferences.” American Journal of Political Science 36 (3): 579–616.

PART III

The Voter Experience

CHAPTER 7

How COVID-19 Election Access Policies Shaped Voter Fraud Beliefs and Voter Confidence in the 2020 US Election Joseph A. Coll

Introduction In the lead-up to the 2020 November election, several hundred Americans were dying each day from COVID-19 (New York Times 2020). This disease, which may spread quickly through large gatherings like those that occur at polling places (Cotti et al. 2021; National Governors Association 2020), posed a threat for in-person voters. In order to stave off the spread of COVID-19, states and localities made drastic changes to the way elections were conducted. Some states removed excuse requirements for casting absentee ballots or allowed voters to cite COVID-19 worries/ complications as reasons for voting absentee, others provided absentee/ mail ballots to all voters, a few changed witness requirements for absentee

J. A. Coll (B) The College of Wooster, Wooster, OH, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. A. Coll and J. Anthony (eds.), Lessons Learned from the 2020 U.S. Presidential Election, Elections, Voting, Technology, https://doi.org/10.1007/978-3-031-44549-1_7

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voting, many states provided public drop boxes for voters to deposit their ballots in, there were numerous changes to polling locations, and more. These adjustments were made in the hopes of protecting voters from the COVID-19 pandemic while still allowing them to access the election. At the same time, these changes also had the capacity to affect voter fraud beliefs and voter confidence in the election. Many of the reforms listed above reflect permissive changes to the election process— changes that make it easier for voters to access the ballot box. Yet, these changes not only make it easier for law-abiding citizens to cast their ballot, but, theoretically, also make it easier for malicious actors to subvert the democratic process by stealing ballots, impersonating voters, and casting fraudulent votes.1 This perception that fraudulent voters may be more likely to interfere with the democratic process under more permissive voting rules may then lead voters to perceive higher rates of fraud and have lower confidence in elections (Crawford v. Marion County Election Board 2008). At the same time, compounding evidence suggests it is not the changes to election access, themselves, that affect voter fraud beliefs and voter confidence, but rather the motivated reasoning and partisan spin put on these changes (Bowler and Donovan 2016; McCarthy 2019). Instead of voters viewing election changes in a purely democratic light, their perceptions of these reforms are heavily colored by partisan lenses. That is, partisan publics evaluate changes to the election process in light of how those changes will affect their own or the out party’s electoral fortunes (Conover and Miller 2018; Kane 2017; McCarthy 2019) and how partisan elites signal the need and consequences of those reforms (Bowler and Donovan 2016; Gronke et al. 2019). And, in the lead-up to the 2020 election, there were a plethora of headlines, news statements, social media messages, and more highlighting the potentially partisan impacts of these reforms on voter turnout, as well as divergent partisan signals regarding their ability to invite rampant voter fraud and affect the legitimacy of the election (Parks 2020). Given the potential for these changes to affect voter fraud beliefs and voter confidence, and for partisanship to affect the degree to which 1 The argument that easing restrictions theoretically make it easier for fraudulent actors to cast ballots stems from an assumption that fraudulent voters will be more likely to see lower costs to committing fraud under more permissive rules. However, see Minnite (2011, ch. 5) for a discussion of the (ir)rationality of committing voter fraud.

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these changes affect these outcomes, this chapter asks two questions: first, whether the COVID-related changes made to election access are linked to changes in voter fraud beliefs and voter confidence in the 2020 election, and second, whether there are divergent partisan effects in this relationship. Examining voter fraud beliefs and voter confidence via the 2020 Survey of the Performance of American Elections (Stewart III 2021) and voter restrictions via the Cost of Voting Indices ranks (Li et al. 2018), results suggest more permanent policies guiding election access did affect voter fraud beliefs and voter confidence in the 2020 election, but that the temporary changes that were instituted to accommodate voting during the pandemic did not. Specifically, this study finds general election access matters for voter fraud beliefs among the general public, Republicans, and Democrats, as well as for voter confidence for Republicans and Democrats. However, when examining only the temporary COVIDrelated election policies, these same relationships are not uncovered.

The Effects of Election Changes in 2020 on the Public and Partisans Election Changes, Voter Fraud Beliefs, and Voter Confidence in the General Public Proponents of restrictive voting requirements argue making the registration and voting process harder decreases the ability of individuals to commit voter fraud, resulting in greater election legitimacy as fewer votes are fraudulently cast. When the registration and voting process is made easier, the opposite occurs. Easier access leads to greater fraud beliefs and lower voter confidence as malicious actors are seen as more easily able to taint the democratic process. And, even if these reforms do not affect actual fraud occurrences or election outcome legitimacy, they have the potential to go on to affect perceptions of fraud and election legitimacy. This logic was well-articulated by the US Supreme Court in the case of Crawford v. Marion County (2008). In 2006, Indiana passed the most restrictive voter identification law seen in the USA at that time, arguing the reform was necessary to prevent fraud. Opponents, however, sued the state, arguing there was little evidence of voter fraud and that the identification requirement unfairly burdened older and non-white voters. While recognizing the lack of evidence of in-person voter fraud, the Supreme

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Court reasoned that Indiana’s voter ID law was constitutional on the grounds that, even if it did not prevent fraud, it had the capacity to increase voter confidence. This argument that restrictive reforms are necessary to increase voter confidence and that permissive reforms decrease confidence has since become a common claim among voting restriction advocates (e.g., Lucas 2023). However, little evidence supports the assertion that restrictive voting policies decrease voter fraud beliefs or increase voter confidence in the general public. In support of this argument, Endres and Panagopoulos (2021) find that providing information to Virginian residents about Virginia’s voter ID law decreased fraud perceptions compared to those who were not provided the information. Contrary to this argument, other studies have found that individuals in states with voter identification laws do not perceive less fraud or are not more confident than those in states without (Ansolabehere 2009; Stewart III et al. 2016). At the same time, some works have found that voting via more permissive means may affect voter fraud beliefs and voter confidence. Numerous studies tend to show that absentee and mail voters are more likely to think fraud occurs or have lower confidence in elections (Alvarez et al. 2021; Alvarez et al. 2008; Atkeson and Saunders 2007; Bryant 2020; Burden and Gaines 2015; Clark 2021). Bryant (2020) offers two potential explanations for why those who vote by absentee/mail ballots are less confident in vote counts. First, differences in the voting experience (e.g., lack of interaction with poll workers/other voters, not seeing ballots opened or votes tabulated) may account for lower voter confidence. Second, lower confidence may be an artifact of low confidence in the postal system or not knowing whether their ballot was accepted or rejected during the counting process. Concerning fraud beliefs and confidence that votes were counted accurately, both of these avenues can increase the potential for fraudulent actors to interfere with votes or vote counts, leading to decreased confidence. If the logic underpinning the Crawford decision is correct, then it is expected that voters will be more likely to think fraud occurred and less likely to be confident in elections when in a state that liberalized their voting processes during the 2020 election. H1: Voters in states that made it easier to vote in 2020 will have higher fraud beliefs.

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H2: Voters in states that made it easier to vote in 2020 will have lower confidence in elections.

Election Changes, Voter Fraud Beliefs, and Voter Confidence in Partisan Publics Other studies have critiqued the standard logic described above, particularly for its assumption that most individuals view voting policies similarly. Most notable, a large body of evidence suggests that partisans see the needs and consequences of election reforms differently (Park-Ozee and Jarvis 2021; Sheagley and Udani 2021), and that these different perceptions may be an artifact of partisan elite signaling (Bowler and Donovan 2016; Gronke et al. 2019) and/or partisan strategic/motivated reasoning (Conover and Miller 2018; Kane 2017; McCarthy 2019). These partisan perceptions then go on to color the extent to which individuals of different parties see these policies as affecting fraud and election legitimacy. Starting, first, with the concept of partisan elite signaling, Bowler and Donovan (2016) argue for a partisan model of electoral reforms to explain partisan patterns to voter identification support and perceptions of their need and consequences. Under this partisan model, partisan elites send different signals regarding the needs and consequences of electoral reforms, with partisans in the public receiving these signals and adjusting their views accordingly. These needs refer to the belief in the existence of voter fraud, while consequences are typically things like reduced voter fraud, increased voter suppression, and increased voter confidence, depending on the partisan group under study. Republican elites signal high need and positive consequences of these reforms via claims of rampant voter fraud and that such restrictions can curtail fraud. Democrat elites signal low need and negative consequences by arguing there is little voter fraud and that these reforms suppress otherwise legal voters. This leads to Republicans (Democrats) in the electorate viewing these reforms as necessary/beneficial (unnecessary/harmful), resulting in increased (decreased) confidence in elections when conducted under restrictive voting policies. These divergent partisan signals have led to Republicans and Democrats in the electorate viewing the need, consequences, and support for reforms differently (Bowler and Donovan 2016; Gronke et al. 2019).

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Further concerning the need for reforms (i.e., voter fraud), conceptualizations and prevalence of voter fraud beliefs differ among partisans. First, Republicans and Democrats conceptualize voter fraud differently (ParkOzee and Jarvis 2021; Sheagley and Udani 2021). Republicans view voter fraud in line with the more common definition of what actually constitutes voter fraud: individuals voting more than once, non-citizens voting, dead people voting, stealing ballots, impersonating voters, and the like. However, Democrats conceptualize voter fraud as more akin to election fraud: acts of voter suppression, elite manipulation, institutional fraud, etc. Second, and as a potential artifact of the first point and partisan elite signaling, they also view the frequency of voter fraud differently (Atkeson et al. 2014). Examining rates of voter fraud in the Survey of Performance of American Elections (2012–2020), Republicans are more likely to believe every type of voter fraud occurs more frequently than Democrats, even regardless of which party won the election (see Table 7.5 in Appendix B). Looking at the consequences of these reforms, Republicans are more likely to believe these reforms shore up the security of the ballot box while Democrats see them as causing voter suppression. Through a study in New Mexico, Atkeson et al. (2014) find 41% of Democrats think voter identification laws restrict access and 66% think they prevent fraud. This is compared to 20% and 77% of Republicans. Similarly, Gronke et al. (2019), using a 2,000 person nationally representative sample, find Republicans are more likely to think voter identification requirements protect the legitimacy of elections than are Democrats. This then leads to differences in voter confidence under different reforms between the parties, as Republicans see restrictive (permissive) reforms as securing (threatening) the legitimacy of the election and Democrats see restrictive (permissive) reforms as (not) suppressing otherwise legal votes. Other work has also shown support for reforms varies based on how the reform is expected to affect one’s own or their opponent’s electoral fortunes (Biggers 2019; Biggers and Bowler 2022; Conover and Miller 2018; Kane 2017; McCarthy 2019), which may also be an artifact of partisan elites signaling to partisan voters the consequences of these reforms. Through a series of different experiments, McCarthy (2019) shows telling partisans that same day registration will increase opposing party turnout decreases support for the reform, though signaling it will increase in-party turnout has no significant impact. Biggers (2019) finds Republicans and Democrats are more supportive of reforms that are

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framed as advancing in-party electoral prospects than those that help out-party prospects. Biggers and Bowler (2022) find that partisans are more supportive for reforms that advance their own partisan interests even if the voters, themselves, see these reforms are reducing electoral fairness. These differences in the perceived need and consequences of these reforms go on to affect partisan support for these electoral changes. Previous work has repeatedly shown that Republicans are more likely to support reforms that tighten voting restrictions while Democrats are more supportive of reforms that increase access to the ballot box (Alvarez et al. 2011; Bowler and Donovan 2018; Coll et al. 2022). Coll et al. (2022) find that a (super)majority of Democrats support reforms like felon reenfranchisement, automatic voter registration, Election Day registration, and all mail voting, while less than half of Republicans support these same reforms. Further, the authors also show that nearly 89% of Republicans support voter identification requirements while only 55% of Democrats do. This is all to say that there is strong support for a partisan model of electoral reforms (Bowler and Donovan 2016; Coll et al. 2022; Gronke et al. 2019; Park-Ozee and Jarvis 2021; Sheagley and Udani 2021). Partisan elites send signals to partisan publics about the need and consequences of reforms and partisans update their perspectives accordingly. And these patterns of partisan signaling regarding how the above discussed reforms will affect electoral fortunes, voter fraud, and election outcomes, as well as partisan splits in support for reforms, were all on display during the 2020 election. On the Republican side, elites argue that increased access to mail/ absentee voting in 2020 would electorally benefit Democrats, (possibly because of) inviting rampant voter fraud, leading to an illegitimate election. Most notorious are the claims originating from then-President Donald Trump (Wise 2020). Trump frequently criticized these changes, particularly those pertaining to mail/absentee voting, as opening the floodgates of voter fraud, and even blamed them for inviting fraud that led to his downfall. Despite voting by mail, Trump stated mail voting was a disaster and out of control, claiming the election was fraudulent due to voters being mailed unsolicited ballots and that mail-in ballots will be printed by foreign countries (e.g., Parks 2020). These claims were frequently picked up by other Republican elites, including state and federal elected officials, political pundits, and conservative think tanks.

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These elite signals likely went on to color how Republican partisans in the electorate viewed the potential of these reforms to affect voter fraud and voter confidence, resulting in increased fraud beliefs and decreased voter confidence when voting in a state that allowed these permissive changes. H3a: Republicans will have higher fraud beliefs in states that made it easier to vote in 2020. H4a: Republicans will have lower confidence in elections in states that made it easier to vote in 2020.

Among Democrats, elites argued these laws will not have a partisan electoral effect, will not lead to widespread fraud, and will not affect the legitimacy of the election, while also supporting these reforms as necessary for protecting voters during a health crisis, particularly for those individuals who may have a harder time accessing the ballot box like elderly or non-white voters (Barrow 2022). In support of mail/absentee voting during the pandemic, then-Presidential candidate Joe Biden remarked, “We have to make it easier for everybody to be able to vote, particularly if we are still basically in the kind of lockdown circumstances we are in now.” And, in response to Trump’s claims about fraudulent voting, Biden claimed, “[Trump]’s already trying to undermine the election with false claims of voter fraud,” and, “Voting by mail is safe and secure. And don’t take my word for it: Take it from [Trump] who just requested his mail-in ballot.” These elite signals likely went on to color how Democrat partisans in the electorate viewed the potential of these reforms to affect voter fraud and voter confidence, likely having no effect on fraud beliefs and potentially increasing confidence if Democrats saw these reforms as giving greater access to those who would otherwise not be able to participate when voting in a state that allowed these permissive changes. H3b: Democrats will not have higher fraud beliefs in states that made it easier to vote in 2020. H4b: Democrats will have higher confidence in elections in states that made it easier to vote in 2020.

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Data and Methods Data on voter fraud beliefs and voter confidence come from the 2020 Survey of the Performance of American Elections (SPAE, see Stewart III [2021]), while data on registration and voting difficulty are measured using the Cost of Voting Indices (see Li et al. 2018; Schraufnagel et al. 2022). The SPAE is an election survey beginning the day after the November election. The survey is designed by the MIT Election and Data Science Lab and conducted by phone and internet via YouGov with support from Pew Charitable Trusts and the Democracy Fund. The 2020 SPAE interviewed over 16,000 self-reported voters, including 200 voters from every state as well as oversamples of voters from a few states (AZ, FL, GA, IA, MI, NC, NV, OH, PA, WI). Crucial for this study, the SPAE is a largescale, state representative study that includes questions over voter fraud beliefs, voter confidence, and other relevant information. The SPAE has routinely been used to understand voter fraud beliefs, voter confidence, and other election-related phenomena (Bowler and Donovan 2016; King 2017, 2020; King and Barnes 2019; Stewart III et al. 2016). To measure voter fraud, this study uses an additive index that captures how often respondents think voter fraud occurs. The index includes voter fraud acts of repeat voting, voter impersonation, non-citizen voting, and forging absentee ballots. The index ranges from 0 to 100, where higher values reflect greater belief in widespread voter fraud. Additional analyses (see Appendix C) support the use of an index, as does prior work (Gronke et al. 2019). Voter confidence is measured as whether the voter was very confident that votes in their state were counted as intended (1) or not (0).2 Overall, 55.26% of respondents were very confident votes in their state were counted correctly. However, this majority confidence masks statistically significant and substantively large partisan differences: 64.40% of Democrats are very confident, while only 40.20% of Republicans report the same (p ≤ 0.0001).

2 Though the question originally ranged from 1 (not at all confident) to 4 (very confident), a majority of respondents reported being very confident in their state’s elections. As such, this study opts collapses the variable to a 0/1 dichotomy (see also Alvarez et al. 2021; Sances and Stewart III 2015).

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To isolate the effect of changes to election access due to COVID19, voter access is measured multiple ways using rankings of how easy it is to vote in a state.3 First, Li et al. (2018) create a Cost of Voting Index for each state and year, then rank the states based on how permissive (1) or restrictive (50) the state is (this was reversed such that higher values represent more permissive states given the changes in 2020 were largely permissive). This ranking takes into consideration the ‘permanent’ changes to how citizens access elections, such as changes in early voting availability, same/Election Day registration options, voter identification requirements, and more. However, the COVI does not include temporary changes made to accommodate COVID-19. Using this measure provides a baseline comparison of how changes outside of COVID-19 affected voter fraud beliefs and voter confidence. Second, for the 2020 election, specifically, Schraufnagel et al. (2022) created the COVID Cost of Voting Index (CCOVI) which includes all the aspects of the standard Cost of Voting Index, but also those ‘temporary’ changes made to accommodate voters during the COVID-19 pandemic (e.g., allowing COVID-19 as an excuse to vote absentee). Using this second measure allows this study to examine how the addition of COVID-19 policies to a state’s already existing policies influences these beliefs. Third, to isolate only the changes due to COVID, I create a measure that is the difference in ranking between the COVI and CCOVI (Diff. C/COVI), with the resulting value denoting the extent to which changes due to COVID affected the states respective difficulty ranking. This last measure isolates the influence of COVID-19 related changes from other changes states made in 2020. Together, these three measures allow this study to examine the extent to which election access policies affect voter fraud beliefs and voter confidence in the 2020 election and whether the specific temporary changes made to accommodate the COVID pandemic exacerbated these relationships. For example, if this study finds the COVI affects these beliefs but neither the CCOVI nor the Difference C/COVI measures affect these beliefs, this would suggest the COVID-specific policies did not alter these outcomes. To account for alternative influences on voter fraud beliefs and voter confidence, a series of other variables are included in the analyses below. 3 Rankings, as opposed to raw values, are used for comparability across different indices. Results are similar using raw values.

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These include basic demographic information (age, gender, income, education, marital status, race, and ethnicity), political factors (partisanship, ideology, political interest, and state-level presidential winning margin), and voting experience (if it is the first time they have voted, how they voted, how long they waited to vote, if they had problems with registration or voting, if they were asked to show identification to vote, and how difficult it was to find their polling place). Linear regression is used to predict voter fraud due to a continuous dependent variable, while logistic regression is used to examine voter confidence due to a binary dependent variable. All models are estimated including robust standard errors clustered by state. To examine the impacts of voter access changes on fraud beliefs and confidence, models include the Cost of Voting Index rank (COVI), then the COVID Cost of Voting Index rank (CCOVI), and last, the Difference Between the Cost of Voting Index rank and COVID Cost of Voting Index rank (Diff. C/COVI). Additionally, a second set of models interact the index rankings with partisanship to test for heterogeneous effects by party affiliation between Republicans and Democrats.

Analyses Do changes to election access affect voter evaluations of the legitimacy of the election? If so, to what extent were the changes due to COVID in 2020 responsible for these shifts? Last, are there are partisan asymmetries to these relationships? To answer these questions, Table 7.1 displays the effects of the costs of voting on fraud beliefs (columns I–III) and voter confidence (columns IV–VI). Within each group of dependent variables, the first column displays the effects for the Cost of Voting Index rank (COVI), which captures the permanent election policies but not the temporary COVID policies; the second displays the same effects for the COVID Cost of Voting Rank (CCOVI), which captures both temporary COVID policies and permanent policies; and the third shows the difference in rank between the two previous variables (Diff. C/COVI), which captures just the temporary COVID policies. Results are shown for the general public (row 1), Republicans (row 2), and Democrats (row 3). Tables 7.6 and 7.7 in Appendix B display the full regression table.

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Table 7.1 Effects of election access on voter fraud and voter confidence Fraud beliefs

Full sample Republicans Democrats

Confidence

I COVI rank

II CCOVI rank

III IV Diff. C/ COVI COVI rank rank

V CCOVI rank

VI Diff. C/ COVI rank

7.99 13.79 4.12

8.85 15.25 4.63

– – –

– −27.54 12.53

– – –

– −25.17 10.88

Note Cells show the percentage point change in fraud beliefs or voter confidence varying independent variables ±2 standard deviations, reflecting changes from more restrictive to more permissive states. Results derived from Tables 7.6 and 7.7 in Appendix B. Results significant at the p ≤ 0.10 level or greater; ‘–’ denotes insignificance. COVI = Cost of Voting Index, measures the permissiveness of the permanent election access policies. CCOVI = COVID Cost of Voting Index, measures the permissiveness of the permanent policies and the temporary policies due to COVID. Diff. C/COVI = Difference between the COVI and CCOVI, measures only the permissiveness of the specific changes due to COVID

The General Public This study begins by examining the effects of voter access on voter fraud and voter confidence among the general public. According to hypotheses 1 and 2, greater voter access should lead to increased beliefs of voter fraud and decreased voter confidence. Table 7.1 provides evidence in support of hypothesis 1, no evidence in support of hypothesis 2, and little evidence of any unique effect of the election changes due to COVID. Looking first at the effects of the permanent election policies via the Cost of Voting Index rank in row 1 column I, results suggest respondents in the most permissive states have higher fraud beliefs by roughly 7.99 percentage points compared to those in the most restrictive states. This provides some evidence that changes to election access can affect voter fraud beliefs, in line with hypothesis 1. Next, row 1, column II displays the effects for the COVID Cost of Voting Index rank that captures both permanent and temporary changes. The effects are slightly larger at 8.85 percentage points, but this difference is not substantively or statistically different from the effect of the COVI in column 1, suggesting the temporary changes did not budge fraud beliefs to any large degree. This is further supported by row 1, column III using the change in rank related only to COVID election policies, which suggests an insignificant effect.

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These results suggest changes to election access can affect fraud beliefs, but there was not a unique or significant effect of COVID-19 election policies. Despite the significant effects to voter fraud beliefs, Table 7.1 shows no significant effects on voter confidence. On the one hand, this may be surprising given the theoretical link between voter fraud beliefs and voter confidence (Coll 2022a). On the other hand, this may mask partisan asymmetries in voter confidence under restrictive reforms. For example, Bowler and Donovan (2016) find no significant partisan differences in voter fraud beliefs between states with or without voter ID requirements, but that Republicans (Democrats) were more (less) confident under restrictive voting laws. To examine whether this is the case, this study now turns to examining partisan differences via interacting partisanship with the separate index rankings. Partisan Publics Hypotheses 3ab and 4ab expect easing voting difficulty to increase fraud beliefs and decrease confidence among Republicans but have no effect on fraud beliefs and increase confidence for Democrats. Table 7.1 provides strong support regarding voter confidence, but mixed evidence in support of voter fraud changes. Starting with Republican fraud beliefs (row 2, columns I–III), results suggest making the registration and voting process more stringent has significant and substantive effects on the extent to which Republicans think fraud occurred. Going from a more restrictive ranked state to a more permissive ranked state increases voter fraud beliefs by 13.79 percentage points using the Cost of Voting Index rank (row 2, column I). This increases slightly to 15.25 percentage points using the COVID Cost of Voting Index rank (row 2, column II), but again this effect is not substantively or statistically different from that uncovered in row 2, column I. Last, there is no effect of the policies specifically due to COVID-19 (row 2, column III). In line with the results for the general public, these findings suggest registration and voting difficulty did affect voter fraud beliefs among Republicans in the 2020 election, but that these effects are very weakly if at all linked to the specific COVID-related changes. Contrary to expectations, results also suggest fraud beliefs are reduced among Democrats when it is more difficult to access elections (row 3,

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columns I-III), though the effects are much smaller than those uncovered for Republicans. Moving from a state ranked more restrictive to one ranked more permissive via the Cost of Voting Index rank increases fraud beliefs by 4.12 percentage points (row 3, column I). This increases to 4.63 when examining the COVID Cost of Voting Index (row 3, column II), but this change is not significant or substantively different from row 3, column I. And, again, no effects are found for the COVID changes specifically (row 3, column III). Like above, results suggest changing voter access can affect fraud beliefs, but this is largest for Republicans and not due to the specific COVID-19 changes. Turning to voter confidence, evidence is more in line with expectations. Republicans in more permissive states are 25.17 percentage points less confident than their counterparts (row 2, column IV). This increases slightly but insignificantly to 27.54 using the COVID Cost of Voting Index (row 2, column V), but there is no unique effect of the COVIDspecific changes (row 2, column VI). Among Democrats, the opposite occurs. Democrats in more permissive states are 10.89 percentage points more confidence than those in restrictive states (row 3, column IV). This increases insignificantly to 12.53 percentage points under the COVID Cost of Voting Index (row 3, column V), but, like above, no significant effects of the changes due specifically to COVID (row 3, column VI). In line with previous studies (Bowler and Donovan 2016), these results suggest voter restrictions have divergent effects on partisans’ confidence in elections. Adding to this literature, this study finds the changes made specifically to accommodate the COVID pandemic did not exacerbate these relationships.

Conclusion In light of the ongoing pandemic during the 2020 election, states loosened their restrictions for how voters can access elections. Some states allowed voters to vote absentee without an excuse/use COVID-19 as an excuse, relaxed signature requirements for absentee voting, sent out mail ballots to all voters, and provided drop boxes for ballot drop off. These policies were put in place to provide greater access to voters during the pandemic, but in doing so, may have also opened the election process up to greater accusations of voter fraud and election illegitimacy as voters perceive these policies as allowing fraudulent voters access to the voting process. Further, given the partisan divergence in perceptions of these

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policies (Bowler and Donovan 2016, 2018; Coll et al. 2022), it is likely the effects of these policies on voter fraud beliefs and voter confidence differ drastically between partisans. Using the 2020 Survey of the Performance of American Elections and the Cost of Voting Index rankings, this study examines whether the changes made to election access to accommodate the COVID pandemic affected voter fraud beliefs and voter confidence among the general American public, as well as partisans. Results strongly suggest changes to election access have the capacity to influence these beliefs, but there are partisan asymmetries to these effects. Further, though changes to election access more broadly affected these outcomes, temporary changes put in place due to COVID-19 did not. Together, these results suggest the temporary election policies put in place during the 2020 pandemic did not drastically alter voter fraud beliefs or voter confidence, but that election changes more broadly do matter. Why the insignificant findings for COVID policies? It could be that these policies were too small or subtle to affect these beliefs, though ample evidence suggests these changes were salient (Barrow 2022; Dale 2021; Parks 2020) and previous work has shown even small and singular changes to elections can matter (Coll 2022b, 2022c). Another explanation may be that voters saw these policies are necessary and did not negatively evaluate elections for having them (Coll 2022b). Last, it could also be that voters saw some COVID policies as beneficial while others were harmful, leading to a washing out effect. This last explanation suggests future work should replicate the analyses here with specific COVID policies.

Appendix A: Summary Statistics See Tables 7.2, 7.3, and 7.4.

Appendix B: Additional Tables See Tables 7.5, 7.6, and 7.7.

Appendix C: Voter Fraud Scale Reliability Analyses See Tables 7.8, 7.9, 7.10, and Fig. 7.1.

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Table 7.2 Summary statistics for the full sample Variable

Obs

Mean

Std. dev.

Min

Max

Voter fraud beliefs Voter confidence (state) COVI CCOVI Diff. C/COVI First time voter Voted early Voted mail Wait time Registration problems Machine problems Difficulty finding polling place Asked for Photo ID Democrat Republican Liberalism Political interest Vote margin Age Income Education Married Female Hispanic Black Other race

16,806 16,806 16,806 16,806 16,806 16,806 16,806 16,806 16,806 16,806 16,806 16,806 16,806 16,806 16,806 16,806 16,806 16,806 16,806 16,806 16,806 16,806 16,806 16,806 16,806 16,806

34.88 0.54 24.86 25.04 −0.17 0.07 0.24 0.47 1.67 0.48 0.01 1.10 0.22 0.47 0.42 2.93 0.61 11.06 51.36 6.35 2.67 0.51 0.53 0.06 0.09 0.05

34.00 0.50 12.31 12.08 2.38 0.25 0.42 0.50 1.01 0.50 0.11 0.36 0.42 0.50 0.49 1.23 0.49 11.38 17.46 3.05 1.45 0.50 0.50 0.24 0.29 0.21

0.00 0.00 1.00 1.00 −8.00 0.00 0.00 0.00 1.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 0.00 0.24 18.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00

100.00 1.00 50.00 50.00 3.00 1.00 1.00 1.00 4.00 1.00 1.00 4.00 1.00 1.00 1.00 5.00 1.00 44.96 94.00 12.00 5.00 1.00 1.00 1.00 1.00 1.00

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Table 7.3 Summary statistics for republicans Variable

Obs

Mean

Std. dev.

Min

Max

Voter fraud beliefs Voter confidence (state) COVI CCOVI Diff. C/COVI First time voter Voted early Voted mail Wait time Registration problems Machine problems Difficulty finding polling place Asked for Photo ID Democrat Republican Liberalism Political interest Vote margin Age Income Education Married Female Hispanic Black Other race

6,542 6,542 6,542 6,542 6,542 6,542 6,542 6,542 6,542 6,542 6,542 6,542 6,542 6,542 6,542 6,542 6,542 6,542 6,542 6,542 6,542 6,542 6,542 6,542 6,542 6,542

55.65 0.32 23.63 23.78 −0.15 0.06 0.27 0.32 1.83 0.33 0.02 1.09 0.30 0.00 1.00 1.95 0.61 11.23 54.79 6.46 2.48 0.60 0.49 0.05 0.02 0.04

31.71 0.47 12.18 11.97 2.34 0.23 0.44 0.47 1.05 0.47 0.14 0.36 0.46 0.01 0.00 0.81 0.49 11.56 16.84 2.96 1.40 0.49 0.50 0.21 0.13 0.19

0.00 0.00 1.00 1.00 −8.00 0.00 0.00 0.00 1.00 0.00 0.00 1.00 0.00 0.00 1.00 1.00 0.00 0.24 18.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00

100.00 1.00 50.00 50.00 3.00 1.00 1.00 1.00 4.00 1.00 1.00 4.00 1.00 1.00 1.00 5.00 1.00 44.96 91.00 12.00 5.00 1.00 1.00 1.00 1.00 1.00

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Table 7.4 Summary statistics for democrats Variable

Obs

Mean

Std. dev.

Min

Max

Voter fraud beliefs Voter confidence (State) COVI CCOVI Diff. C/COVI First time voter Voted early Voted mail Wait time Registration problems Machine problems Difficulty finding polling place Asked for Photo ID Democrat Republican Liberalism Political interest Vote margin Age Income Education Married Female Hispanic Black Other race

8,262 8,262 8,262 8,262 8,262 8,262 8,262 8,262 8,262 8,262 8,262 8,262 8,262 8,262 8,262 8,262 8,262 8,262 8,262 8,262 8,262 8,262 8,262 8,262 8,262 8,262

15.69 0.74 25.78 25.95 −0.17 0.07 0.22 0.60 1.54 0.60 0.01 1.09 0.16 1.00 0.00 3.80 0.62 10.69 48.78 6.27 2.85 0.43 0.59 0.07 0.16 0.05

23.19 0.44 12.23 12.00 2.40 0.26 0.41 0.49 0.95 0.49 0.08 0.35 0.37 0.00 0.01 0.92 0.48 11.08 17.75 3.14 1.47 0.50 0.49 0.26 0.37 0.21

0.00 0.00 1.00 1.00 −8.00 0.00 0.00 0.00 1.00 0.00 0.00 1.00 0.00 1.00 0.00 1.00 0.00 0.24 18.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00

100.00 1.00 50.00 50.00 3.00 1.00 1.00 1.00 4.00 1.00 1.00 4.00 1.00 1.00 1.00 5.00 1.00 44.96 94.00 12.00 5.00 1.00 1.00 1.00 1.00 1.00

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Table 7.5 Fraud beliefs by fraud type, year, and partisanship Fraud-year

Republican

Democrat

Difference (Republican-Democrat)

Repeat voter-2012 Repeat voter-2016 Repeat voter-2020 Voter impersonation-2012 Voter impersonation-2016 Voter impersonation-2020 Non-citizen voting-2012 Non-citizen voting-2016 Non-citizen voting-2020 Stealing absentee-2012 Stealing absentee-2016 Stealing absentee-2020

26.20 34.95 21.97 23.24 32.28 21.07 18.93 25.39 16.81 18.03 24.56 13.82

57.62 62.49 73.25 53.64 60.33 71.79 52.07 59.44 69.45 39.90 44.82 63.48

−31.42*** −27.54*** −51.28*** −30.4*** −28.05*** −50.72*** −33.14*** −34.05*** −52.64*** −21.87*** −20.26*** −49.66***

Note Cells show percent agreeing that the type of fraud almost never occurs. *0.10; **0.05; ***0.01

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Table 7.6 The effects of election changes on voter fraud beliefs and voter confidence among the general public Fraud beliefs

Voter confidence

(1) COVI

(2) CCOVI

b/se

b/se

COVI rank 0.163*** (0.049) CCOVI rank Diff. C/ COVI rank First time 3.242*** voter (1.184) Voted early −1.907** (0.894) Voted by −11.912*** mail (2.945) Wait time 0.668** (0.286) Registration 7.955*** problems (2.647) Voting 18.370*** problems (2.336) Difficulty 2.756*** finding (0.864) polling place Asked for −0.787 Photo ID (0.796) Democrat −16.071*** (0.969) Republican 12.820*** (0.990)

(3) Diff. C/ COVI b/se

(4) COVI

(5) CCOVI

b/se

b/se

(6) Diff. C/ COVI b/se

−0.004 (0.003) −0.004 (0.003)

0.181*** (0.046) −0.278 (0.264) 3.198*** 3.278** (1.183) (1.245) −1.962** −1.518 (0.913) (1.007) −12.090*** −12.332*** (2.982) (2.912) 0.675** 0.591** (0.286) (0.285) 7.909*** 8.511*** (2.651) (2.716) 18.227*** 18.709*** (2.326) (2.368) 2.731*** 2.903*** (0.865) (0.867)

0.001 (0.014) 0.182* 0.182* 0.180* (0.098) (0.097) (0.097) −0.078 −0.077 −0.087 (0.089) (0.089) (0.087) 0.386 0.390 0.391 (0.278) (0.277) (0.274) −0.032 −0.032 −0.030 (0.031) (0.031) (0.030) −0.268 −0.267 −0.281 (0.268) (0.268) (0.263) −1.049*** −1.048*** −1.058*** (0.309) (0.309) (0.312) −0.354*** −0.353*** −0.357*** (0.087) (0.087) (0.087)

−0.860 (0.764) −16.036*** (0.968) 12.841*** (0.987)

0.143* (0.079) 0.503*** (0.104) −0.518*** (0.095)

−1.796** (0.827) −16.053*** (0.998) 12.706*** (1.029)

0.146* (0.079) 0.502*** (0.103) −0.519*** (0.095)

0.161** (0.077) 0.502*** (0.104) −0.513*** (0.095)

(continued)

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Table 7.6 (continued) Fraud beliefs

Voter confidence (4) COVI

(5) CCOVI

b/se

(3) Diff. C/ COVI b/se

b/se

b/se

(6) Diff. C/ COVI b/se

−5.982*** (0.350) −2.544*** (0.691) −0.241*** (0.049) −0.031** (0.015) −0.221** (0.087) −1.518*** (0.190) 0.892** (0.423) 0.778 (0.475) 4.092*** (1.362) 7.459*** (1.111) 5.722*** (1.005)

−5.980*** (0.347) −2.543*** (0.689) −0.241*** (0.048) −0.031** (0.015) −0.223** (0.086) −1.525*** (0.189) 0.897** (0.417) 0.789 (0.476) 4.019*** (1.316) 7.427*** (1.124) 5.686*** (0.990)

−5.966*** (0.335) −2.563*** (0.693) −0.216*** (0.055) −0.032** (0.015) −0.179* (0.090) −1.527*** (0.190) 0.788* (0.415) 0.721 (0.473) 4.460*** (1.224) 7.554*** (1.149) 6.337*** (0.995)

Voter fraud belief scale Constant 57.511*** (2.914) Observations 16,806

57.201*** (2.872) 16,806

61.110*** (2.712) 16,806

0.049 (0.031) 0.056 (0.038) 0.020*** (0.003) −0.001 (0.001) −0.002 (0.009) −0.001 (0.019) 0.093* (0.049) 0.027 (0.051) 0.085 (0.115) 0.046 (0.090) 0.003 (0.087) −0.021*** (0.001) 0.953*** (0.246) 16,806

0.049 (0.031) 0.056 (0.038) 0.020*** (0.003) −0.001 (0.001) −0.002 (0.009) −0.001 (0.019) 0.093* (0.049) 0.027 (0.051) 0.086 (0.117) 0.047 (0.090) 0.003 (0.087) −0.021*** (0.001) 0.956*** (0.248) 16,806

0.047 (0.031) 0.057 (0.038) 0.020*** (0.003) −0.001 (0.001) −0.003 (0.009) −0.002 (0.019) 0.095* (0.049) 0.029 (0.051) 0.076 (0.118) 0.044 (0.091) −0.010 (0.090) −0.021*** (0.001) 0.875*** (0.238) 16,806

Liberalism Political interest Vote margin Age Income Education Married Female Hispanic Black Other

(1) COVI

(2) CCOVI

b/se

Linear regression predicting fraud belief scale (models 1–3), logistic regression predicting voter confidence (models 4–6). All models estimated with robust standard errors clustered by state and year fixed effects. *0.10; **0.05; ***0.01. COVI = Cost of Voting Index, measures the permanent election access policies. CCOVI = COVID Cost of Voting Index, measures the permanent policies and the temporary policies due to COVID. Diff. C/COVI = Difference between the COVI and CCOVI, measures only the specific changes due to COVID. All COVI ranks reversed such that higher values represent more permissive states

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Table 7.7 The effects of election changes on voter fraud beliefs and voter confidence among partisans Fraud beliefs

Republican COVI rank

Voter confidence

(1) COVI

(2) CCOVI

(4) COVI

(5) CCOVI

b/se

(3) Diff. C/ COVI b/se

b/se

b/se

b/se

18.639*** (2.513) 0.084** (0.037) 0.197* (0.100)

18.114*** (2.387)

23.349*** (1.084)

−0.125 −0.031 (0.302) (0.295) 0.010* (0.006) −0.031*** (0.010)

Republican × COVI rank CCOVI 0.095** rank (0.036) Republican 0.217** × CCOVI (0.095) rank Diff. C/ −0.166 COVI Rank (0.177) Republican −0.333 × Diff. C/ (0.530) COVI rank First time 3.320** 3.291** 3.346** 0.181* voter (1.263) (1.260) (1.288) (0.096) Voted early −2.508*** −2.596*** −2.087** −0.056 (0.915) (0.933) (1.028) (0.089) Voted by −13.255*** −13.461*** −13.891*** 0.403 mail (3.145) (3.199) (3.056) (0.281) Wait time 0.625** 0.635** 0.574* −0.033 (0.290) (0.290) (0.292) (0.031) Registration 8.543*** 8.487*** 9.340*** −0.258 problems (2.840) (2.850) (2.891) (0.270)

(6) Diff. C/ COVI b/se −0.880*** (0.110)

0.011** (0.005) −0.035*** (0.010) −0.025 (0.027) 0.063 (0.059) 0.179* (0.096) −0.050 (0.090) 0.409 (0.281) −0.034 (0.031) −0.256 (0.271)

0.182* (0.098) −0.061 (0.087) 0.431 (0.276) −0.034 (0.031) −0.306 (0.265)

(continued)

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Table 7.7 (continued) Fraud beliefs

Voter confidence (4) COVI

(5) CCOVI

b/se

(3) Diff. C/ COVI b/se

b/se

b/se

18.502*** (2.372) 2.922*** (0.884)

18.270*** (2.357) 2.894*** (0.885)

18.774*** (2.374) 3.140*** (0.882)

−1.017*** −1.007*** −1.018*** (0.309) (0.310) (0.304) −0.344*** −0.343*** −0.360*** (0.085) (0.084) (0.086)

−0.677 (0.852) 3.891*** (0.528) Liberalism −7.390*** (0.328) Political −3.113*** interest (0.688) Vote margin −0.227*** (0.051) Age −0.028* (0.015) Income −0.215** (0.088) Education −1.560*** (0.181) Married 0.863* (0.439) Female 0.495 (0.459) Hispanic 4.035*** (1.319)

−0.769 (0.812) 3.866*** (0.528) −7.388*** (0.326) −3.102*** (0.685) −0.226*** (0.050) −0.028* (0.015) −0.217** (0.087) −1.563*** (0.182) 0.862* (0.434) 0.510 (0.461) 3.978*** (1.276)

−1.862** (0.850) 3.877*** (0.539) −7.349*** (0.313) −3.163*** (0.696) −0.210*** (0.055) −0.030** (0.015) −0.180* (0.091) −1.558*** (0.180) 0.789* (0.436) 0.375 (0.462) 4.380*** (1.190)

0.125 0.129 0.161** (0.080) (0.081) (0.077) −0.336*** −0.336*** −0.340*** (0.063) (0.063) (0.063) 0.086** 0.087*** 0.078** (0.034) (0.034) (0.034) 0.059 0.058 0.062* (0.038) (0.038) (0.037) 0.019*** 0.019*** 0.019*** (0.003) (0.003) (0.003) −0.002 −0.002 −0.001 (0.001) (0.001) (0.001) −0.003 −0.003 −0.003 (0.009) (0.009) (0.009) 0.003 0.003 0.001 (0.019) (0.019) (0.019) 0.101** 0.102** 0.099** (0.049) (0.049) (0.049) 0.007 0.006 0.019 (0.051) (0.051) (0.051) 0.073 0.071 0.062 (0.117) (0.117) (0.113)

Voting problems Difficulty finding polling place Asked for Photo ID Independent

(1) COVI

(2) CCOVI

b/se

(6) Diff. C/ COVI b/se

(continued)

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Table 7.7 (continued) Fraud beliefs

Black Other Voter fraud belief scale Constant

Voter confidence

(1) COVI

(2) CCOVI

b/se

b/se

(3) Diff. C/ COVI b/se

6.010*** (1.130) 6.284*** (1.020)

5.980*** (1.134) 6.248*** (1.004)

6.152*** (1.166) 6.838*** (0.961)

51.110*** (2.462) 16,806

52.764*** (2.426) 16,806

51.176*** (2.471) Observations 16,806

(4) COVI

(5) CCOVI

b/se

b/se

(6) Diff. C/ COVI b/se

0.063 0.062 0.052 (0.088) (0.088) (0.088) 0.001 −0.001 −0.001 (0.091) (0.092) (0.092) −0.021*** −0.021*** −0.021*** (0.001) (0.001) (0.001) 1.027*** 0.983*** 1.307*** (0.256) (0.243) (0.213) 16,806 16,806 16,806

Linear regression predicting fraud belief scale (models 1–3), logistic regression predicting voter confidence (models 4–6). All models estimated with robust standard errors clustered by state and year fixed effects. *0.10 **0.05 ***0.01. COVI = Cost of Voting Index, measures the permanent election access policies. CCOVI = COVID Cost of Voting Index, measures the permanent policies and the temporary policies due to COVID. Diff. C/COVI = Difference between the COVI and CCOVI, measures only the specific changes due to COVID. All COVI ranks reversed such that higher values represent more permissive states

Fig. 7.1 Scree plot of Eigen values (see Table 7.9)

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Table 7.8 Cronbach’s alpha diagnostics of fraud scale

Average interitem covariance: Number of items in the scale: Scale reliability coefficient:

187

1.522389 4 0.9265

Test scale = mean (unstandardized items)

Table 7.9 Principal factor analysis Factor Factor Factor Factor Factor

1 2 3 4

Eigenvalue

Difference

Proportion

Cumulative

2.97359 −0.05994 −0.06675 −0.08319

3.03353 0.0068 0.01644

1.0759 −0.0217 −0.0242 −0.0301

1.0759 1.0543 1.0301 1

LR test: independent vs. saturated: chi2(6) = 1.1e+05 Prob > chi2 = 0.0000

Table 7.10 Factor loadings

Variable

Factor 1

Uniqueness

Repeat Voting Voter Impersonation Non-Citizen Voting Fraudulent Absentee Ballots

0.877 0.8853 0.8449 0.8407

0.2309 0.2162 0.2861 0.2932

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Li, Quan, Michael J. Pomante, and Scot Schraufnagel. 2018. “Cost of Voting in the American States.” Election Law Journal: Rules, Politics, and Policy 17(3): 234–47. Lucas, Fred. 2023. “Voter ID Laws Are Popular for Good Reasons.” The Heritage Foundation. https://www.heritage.org/election-integrity/commen tary/voter-id-laws-are-popular-good-reasons (February 12, 2023). McCarthy, Devin. 2019. “Partisanship vs. Principle: Understanding Public Opinion on Same-Day Registration.” Public Opinion Quarterly 83 (3): 568–83. Minnite, Lorraine C. 2011. The Myth of Voter Fraud. Cornell University Press. National Governors Association. 2020. “COVID-19 Health and Safety Measures for Elections.” National Governors Association. https://www.nga.org/public ations/election-health-safety-covid-19/ (December 1, 2022). New York Times. 2020. “Coronavirus in the U.S.: Latest Map and Case Count.” The New York Times. https://www.nytimes.com/interactive/2021/us/covidcases.html (February 12, 2023). Park-Ozee, Dakota, and Sharon E Jarvis. 2021. “What Does Rigged Mean? Partisan and Widely Shared Perceptions of Threats to Elections.” American Behavioral Scientist 65 (4): 587–99. Parks, Miles. 2020. “Ignoring FBI And Fellow Republicans, Trump Continues Assault On Mail-in Voting.” NPR. https://www.npr.org/2020/08/28/ 906676695/ignoring-fbi-and-fellow-republicans-trump-continues-assault-onmail-in-voting (August 8, 2022). Sances, Michael W, and Charles Stewart III. 2015. “Partisanship and Confidence in the Vote Count: Evidence from US National Elections Since 2000.” Electoral Studies 40: 176–88. Schraufnagel, Scot. 2022. “State Voting Restrictions and State Voter Fraud Cases.” In 2022 State Politics and Policy Conference. Tallahassee, Florida. Sheagley, Geoffrey, and Adriano Udani. 2021. “Multiple Meanings? The Link between Partisanship and Definitions of Voter Fraud.” Electoral Studies 69: 102244. Stewart III, Charles. 2021. “2020 Survey of the Performance of American Elections.” Stewart III, Charles, Stephen Ansolabehere, and Nathaniel Persily. 2016. “Revisiting Public Opinion on Voter Identification and Voter Fraud in an Era of Increasing Partisan Polarization.” Stanford Law Review 68: 1455. Wise, Alana. 2020. “Trump Declines to Promise Peaceful Transfer of Power After Election.” NPR. https://www.npr.org/2020/09/23/916221894/ trump-says-he-expect-election-results-to-end-up-at-supreme-court (February 25, 2023).

CHAPTER 8

The Tradeoff Between Protecting Voters and Ensuring Access for In-Person Voters During the COVID-19 Pandemic Joseph A. Coll

Introduction As discussed in Chapter 1, the COVID-19 pandemic forced drastic changes to the way US elections were conducted. These changes were acutely obvious to those voters who voted in-person as polling places were outfitted to protect voters from the spread of COVID-19. Many poll workers wore face coverings, protective barriers were erected, voting lines and booths were spread further apart to allow for socially distanced voting, voting booths were routinely cleaned, and single use ballot marking devices and hand sanitizer were made available to voters (Garrett et al. 2020). These policies were put in place to provide a safer voting experience during a pandemic that has been exacerbated by large public gatherings.

J. A. Coll (B) The College of Wooster, Wooster, OH, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. A. Coll and J. Anthony (eds.), Lessons Learned from the 2020 U.S. Presidential Election, Elections, Voting, Technology, https://doi.org/10.1007/978-3-031-44549-1_8

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Past affecting how safe in-person voting was, these policies also changed the way voters interacted with the voting process. Socially distanced voting caused longer lines and less room for voting booths, while cleaning voting booths between voters created a perception of inperson voter safety and that poll workers were working hard to keep voters safe. With these changes likely also came changes to how quickly voters traversed the voting process and how highly they rated their experiences doing so. By extending voting lines, decreasing the number of voting booths available, and taking the time to clean those booths between voters, these policies likely went on to affect how long it took a voter to complete the in-person voting process (Coll 2022b; Schmidt and Albert 2022). At the same time, these same policies may make voters feel safer casting their ballot in-person, while also rating their voting experience more positively as perceptions of safety spillover into perceptions of the better performing election administration (Coll 2022a). The potential for these policies to simultaneously increase wait times while increasing voter evaluations suggest election officials may face a tradeoff in enacting these protocols (Peña-López 2020). Local election officials often take a voter-centric perspective to administering elections (Adona et al. 2019), and as such, likely desire that voters are able to quickly traverse the voting process and do not feel negatively about their experiences doing so. However, if a COVID-19 policy makes the voting process take longer while also providing a more positive (and safer) experience, this suggests there may be a tradeoff between accessibility on the one hand and positive (and safe) voting experiences on the other. This tradeoff is inherently important to administering an election during a health pandemic. These policies were seen as necessary for slowing the spread of the COVID-19 pandemic (National Governors Association 2020), but local election officials are underfunded (Adona et al. 2019) and may have to pick and choose which policies they can reasonably implement. When weighing whether and which of these protective policies to implement, election officials should weigh the efficacy of these policies alongside the potential affects they may have on the ability of voters to access elections and their evaluations of the voting process. While the safety of voting in-person is undoubtedly important (Cotti et al. 2021), so is how long voters have to wait in line

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(Cottrell et al. 2020; King 2020) and their evaluations of their voting process (Claassen et al. 2008; King 2017). Election officials should assess the tradeoff of the advantages of these policies—increased (perceptions) of safety and voting experience positivity—against their disadvantages— decreased election access—when deciding whether and which policies to institute. This chapter investigates the extent to which COVID-19 safety policies represent such a tradeoff by examining whether COVID-19 safety policies increase how long it takes a voter to cast a ballot and whether they affect the positivity of their voting experience. Policies that increase voter wait times while also increasing voter evaluations exhibit such a tradeoff. Using information on the types of COVID-19 safety policies voters encountered—poll workers wearing face coverings, protective barriers between workers/voters, socially distanced voting protocols, hand sanitizer availability, single use pen availability, and routine booth cleaning—results suggest only two policies present such a tradeoff: socially distanced voting and routine booth cleaning. These two policies increased both wait times and voter evaluations of the voting process. Protective barriers and hand sanitizer were found to positively affect voter experiences without significantly affecting wait times, face coverings had no significant effects on any outcome, and, notably, single use voting pens were found to provide the best of both worlds by decreasing wait times while also increasing voter evaluations. Last, no policy was found to negatively affect voter evaluations. These results suggest socially distanced voting booths and routine booth cleaning may represent a tradeoff between access and evaluations/safety for election officials. However, these results should not be construed to suggest policies exhibiting such a tradeoff should be eliminated. In fact, though these policies do increase wait times, they also had sizeable effects on voter evaluations, including perceived voter safety. Instead of dismissing these policies, they should be re-examined to determine how they can be implemented without negatively affecting voter access. If solutions to the tradeoff of these policies cannot be found, election officials should consider ways of alleviating the need for COVID-19 in-person protection policies in the first place, such as by allowing more voters to cast their ballot through means other than in-person voting methods.

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The Importance of Voter Wait Times and Voter Evaluations The ability of voters to traverse the voting process and their approval of it is integral to voter turnout and confidence in election outcomes. Starting, first, with voter wait times, casting a ballot is a costly act. Voting requires voters divest their free time, disposable income, and civic skills into the voting process (Verba et al. 1995). Of these resources, free time has been found to be one of the most important for determining whether a voter casts their ballot (Brady et al. 1995). One way the voting process eats up this resource of free time is by extending how long it takes a voter to cast their ballot, such as by encountering longer lines at polling places. When voters face longer lines, they are more likely to renege on voting and leave the voting line (Lamb 2021). Additionally, the memory of the time tax may linger, causing individuals to opt out of voting the next year, too (Pettigrew 2021). Further, this poor experience with elections sews doubt of electoral integrity in the mind of the voter—if administrators cannot quickly move a voter through the voting line, how can they be confident administrators are competently counting ballots? This then leads to lower voter confidence when encountering greater wait times (King 2020).1 Second, and similarly, poor election administration can go on to affect voter evaluations of the voting process, also including voter confidence. When faced with worse administration, voters are more likely to report negative perceptions of poll workers (Claassen et al. 2008) as they blame poll workers for the long wait times, and negative perceptions of polling places (Stein and Vonnahme 2012) as they think their polling places were more poorly operated. These negative evaluations may then lead to lower perceptions of the ability of election administrators to accurately count ballots as voters cast them, resulting in lower confidence in the accuracy and fairness of election outcomes (Bowler et al. 2015; King 2020).

1 It is worth highlighting the racially disparate presence and impact of long wait times. Black and Latino Americans tend to possess less free time than their white counterparts (Coll and Juelich 2022; Verba et al. 1995), meaning they are less able to pay the costs of long wait times. At the same time, non-white voters tend to face longer wait times than white voters (Cottrell et al. 2020; Herron and Smith 2015; Mukherjee 2009; Pettigrew 2017). As such, non-white voters may be more likely to renege on voting (Lamb 2021) and less likely to return the following year (Cottrell et al. 2020), leading to lower nonwhite turnout. Future work should examine whether the patterns uncovered here differ among racial and ethnic groups.

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How long were voting wait times in the 2020 election? Figure 8.1 displays the percent of voters that reported a wait time of 30 minutes or more during the 2008–2020 presidential elections (Survey of the Performance of American Elections, 2008–2020). Around 13% of voters waited more 30 minutes or more in 2008. This decreased to by 2.7 percentage points in 2012 to just under 11% and again decreased by 2.2 percentage points in 2016 for an average of 8% of voters waiting a half an hour or more. Yet, despite the trend of decreased wait times over the past handful of elections and much fewer voters casting their ballots in-person, wait times doubled in 2020 from 2016, with over 17% of voters waiting 30 minutes or more, the longest average wait time since the SPAE began asking voters about how long it took them to cast a ballot. The same story is not seen when looking at how highly voters rated poll workers and polling places (Fig. 8.2). Given the negative relationship between voter wait times and voter evaluations, it is notable that voters faced longer wait times in 2020 (Fig. 8.1) but also ratted their voting

Fig. 8.1 Voter Wait times in the 2008–2020 Presidential Elections (Source Survey of the Performance of American Elections [2008–2020])

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experiences just as positively as previous years. Given the widespread use of COVID-19 safety policies, this finding is in line with the argument furthered in this study—voters had higher evaluations of the voting process when voting in places with COVID-19 safety policies in place, potentially offsetting the negative effects of voter wait times.

COVID-19 and In-Person Voting During the 2020 Election As discussed in Chapter 1, many states relaxed their voting restrictions. Some automatically mailed registered voters ballots, others transitioned to no-excuse absentee voting, many allowed COVID-19 as an excise to vote absentee, and several also provided ballot drop boxes. These policies likely accounted for part of the increase to voter turnout in the 2020 election (e.g., Lou et al., Chapter 5, this volume). However, many made no changes, including a few that still required a non-COVID-related excuse to vote in-person (Raifman et al. 2020). Additionally, many citizens did not want to or chose not to vote via alternative means (Gramlich 2020, see also Atkeson et al., Chapter 4, this volume). As such, the 2020 presidential still witnessed nearly over 100 million Americans cast their ballot in-person, accounting for 61.1% of the total votes cast (Election Assistance Commission 2020). Complicating in-person voting, COVID-19 was argued to spread quicker in areas where people gathered (Cotti et al. 2021), a concern for in-person voting (National Governors Association 2020). To offset the negative impacts of COVID-19 while still allowing for in-person voting, polling places across the country were outfitted with a host of protective policies, including poll workers wearing face coverings, protective barriers were erected to separate voters/poll workers, voting lines and booths spread further apart for socially distanced voting, voting booths routinely cleaned, and single use ballot marking devices and hand sanitizer available (Garrett et al. 2020; National Governors Association 2020). These policies were put in place in an attempt to slow the spread of COVID-19 while also making voters feel safer about voting in-person. In the shadow of a global pandemic and some states’ lack of desire to allow for non-in-person voting options, the goal of these policies was to make voting safer or at least feel safer. Both of these goals are desirable, as in-person voting and (lack of) COVID-19 prevention policies have been associated with COVID-19 spread (Cotti et al. 2021) as well as how/

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Fig. 8.2 Voter evaluations in the 2008–2020 presidential elections (Source Survey of the Performance of American Elections [2008–2020])

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if voters cast a ballot (see Atkeson et al., Chapter 4, this volume; Lou et al., Chapter 5, this volume). Understanding whether and the extent to which COVID-19 policies affect voter evaluations of safety is important for the ability of these policies to instill confidence in the safety of inperson voting. However, these policies not only altered the safety of voting in-person, but also how voters interact with the voting process. For example, face coverings may mask the transmission of information from poll workers to voters (Saunders et al. 2021), potentially causing the worker and/or voter to ask more questions, repeat themselves, or make mistakes. Socially distanced voting booths meant that fewer voting booths could be implemented in a single voting place, while socially distanced lines pushed voters out the door. Cleaning voting booths between voters also essentially shutdowns a voting booth for the time needed to wipe down the booth. These and other COVID-19 policies may not just affect spread of the illness, but also how safe voters feel voting in-person, their evaluations of the voting process, and how long it took them to vote. Because these policies altered how voters interact with the voting process, they may also affect how long it takes them to navigate it. Previous work has shown that changes to election administration can alter voter wait times or voter turnout. For example, Stein et al. (2020) find that voter identification requirements and the administration of those policies (e.g., poll books) affect voter wait times (see also Hostetter 2022). Directly related to administering elections during the COVID-19 pandemic, Highton (2006) finds fewer voting booths may lead to lower voter turnout. Like other changes to election administration, the changes that came about due to the pandemic may also affect voter wait times. Many poll workers wore facemasks, which may help stop the spread of COVID-19 but may also hinder the ability of poll workers to provide instructions to voters or answer their questions (Saunders et al. 2021). There were also a myriad of policies that separated workers from voters or voters from other voters, such as protective barriers and socially distanced voting. Protective barriers create literal and figurative barriers to the voting process, such as stifling speech and causing longer lines as voters navigate the erected barriers. Socially distanced voting limited the number of voters and voting booths that can be in a polling place, causing longer lines while also reducing the number of voters that can cast a ballot once. Hand sanitizer and single use pens were also made available to voters, with the former

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potentially increasing line length if voters lathered up at the voting booth before casting their ballot, and the latter potentially increasing line length if polling stations ran out of pens. Last, cleaning voting booths may shut down a voting booth as poll workers wipe them down, increasing how long it takes to cast a ballot. By altering the in-person voting process, these COVID-19 policies may affect how long it takes a voter to cast a ballot (Spencer and Markovits 2010). H1: Voters will report longer wait times when casting a ballot at a polling place with more COVID-19 prevention policies in place.

Despite increasing wait times, which may go on to negatively affect voter evaluations (Claassen et al. 2008), these same policies likely increased how positively voters felt about their voting experience. Many Americans were worried about the COVID-19 pandemic at the time of the election, with roughly 70% citing the pandemic has them worried for themselves or a member of their family (Kamisar and Holzberg 2020). Many also believed COVID-19 prevention policies could affect COVID19 spread and supported these policies in and outside of the voting booth (Funk and Tyson 2021; Kortum et al. 2020). For example, 65–80% of voters approve of routine booth cleaning, social distancing, or face masks (Douglas and Zilis 2020), and both workers and voters said they would feel more comfortable working/voting in-person if there were COVID19 safety policies in place (Kousser et al. 2021). Given the apprehension toward COVID-19 and support for these policies at the voting booth, this chapter argues voters likely felt safer voting in-person when more COVID-19 prevention policies were in place. H2: Voters will feel more confident in the safety of voting in-person when casting a ballot at a polling place with more COVID-19 prevention policies in place.

This feeling of safety while voting likely spills over into evaluations of other factors in the voting process—namely poll worker, polling place, and overall voting experience evaluations. Poll workers, as the face of elections, are often blamed or praised for how the voting process affects voters (Claassen et al. 2008). This blame or praise may reflect how safe a voter felt casting their ballot during health pandemics. Given the above evidence suggesting voters desired these policies, voters may be more

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likely to praise poll workers when protective policies are in place as they evaluate workers higher for putting in the effort to protect voters. In a similar manner, voters may also evaluate their polling places more positively when it is outfitted with protective policies. That is, polling places with more protective policies may be seen as being more safely and effectively operated, leading to greater satisfaction among voters with their polling place. And, because feelings of safety, poll worker performance, and polling place operation all likely feed into overall voting experience evaluation, voters may also report a more positive voting evaluation when casting a ballot at a polling place with more protective policies. H3: Voters will have more positive evaluations of the voting process when casting a ballot at a polling place with more COVID-19 prevention policies in place.

In summary, COVID-19 policies are expected to increase feelings of safety and voter experience evaluations while also increasing wait times. As such, COVID-19 policies may be thought of as presenting a tradeoff to election officials: the same policies that increase feelings of safety and voter evaluations also decrease access by increasing how long it takes to cast a ballot. As such, these policies likely represent a tradeoff for election officials wherein trying to protect voters from the COVID-19 pandemic may also limit their ability to participate in the election. H4: There is a safety and positive evaluations/access tradeoff with COVID19 policies.

Identifying if these policies represent such a tradeoff is intrinsically important for the question of how to administer elections during health pandemics. At a minimum, election officials are assumed to want to provide access to elections. From an optimistic view, they are also expected to want to provide a positive experience for voters. However, if the effects of these policies run counter to these goals, election officials face a difficult tradeoff between evaluations and access. Importantly, results affirming such a tradeoff do not suggest these policies will always have a tradeoff, but rather if they have a tradeoff as currently implemented. Any policies exhibiting such a tradeoff should be examined further to determine how to alleviate that tradeoff and evaluated against the efficacy of that policy at deterring the spread of COVID-19 (and similar diseases).

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Data and Methods Data for this chapter come from the Survey of the Performance of Americans (SPAE) (Stewart III 2021).2 The SPAE is a post-election internet survey conducted by YouGov with funding from MIT and Pew. The survey is designed to capture American’s experiences with the voting process. Using the SPAE provides several key advantages for understanding how COVID-19 safety policies affect voter wait times and evaluations of the voting process. First, it is, to the best of my knowledge, the only large-scale voter survey that polls respondents on the availability of COVID-19 protective policies at the voting booth. The SPAE surveyed over 8,000 in-person voters following the 2020 election and asked respondents whether they saw poll workers wearing face coverings, protective barriers between workers/voters, socially distanced voting protocols, hand sanitizer availability, single use pen availability, and routine booth cleaning happening at their polling place.3 These indicators represent the key independent variables of this study, with each measured as 1 if the voter reported seeing that policy in place when voting at their polling place, 0 otherwise.4

2 See Appendix A for summary statistics. 3 There are three key benefits to using these policies. First, they represent protocols

that were frequently suggested and used during the 2020 presidential election (Garrett et al. 2020; National Governors Association 2020). Looking at rates of COVID-19 policy prevalence, 89% of voters reported poll workers wearing face coverings, 60% saw protective barriers, 79% voted at polling places with socially distanced voting policies, 75% said hand sanitizer was available, 44% had access to single use pens, and 43% witnessed booths being cleaned between voters. Second, these policies represent two strains of COVID-19 prevention strategies. Some of these policies are focused on preventing COVID-19 via air transmission (face coverings, protective barriers, and socially distanced voting) while others focus on surface transmission (single use voting pens, hand sanitizer, and routine booth cleaning)—important information that should be taken into consideration when weighing the (dis)advantages of these policies as uncovered in this study. Last, some of these policies represent subtle changes to the voting process, providing an examination of how even small changes can have significant effects on the ability of voters to traverse the voting process and their evaluations of it. Election officials should evaluate the findings of this study in light of these factors. 4 The SPAE differentiated face masks from face shields, protective barriers between workers and voters from barriers separating voting booths, and socially distanced voting lines from socially distanced voting booths. Given the similarity of these policies, the frequency at which one type of COVID-19 prevention strategy was seen with the other

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Second, the SPAE routinely asks questions over poll worker, polling place, and overall voting experience evaluations. These questions have been used in past studies (King and Barnes 2019), providing comparability and evidence of validity. And, for the 2020 election, specifically, they asked about how safe voters felt voting in-person. These represent the key dependent variables. Each is measured binarily such that 1 represents reporting any wait time, high confidence in in-person voting safety, excellent poll worker evaluations, very well polling place operations, or mostly positive voting experiences, and 0 represents all other responses.5 Third, the SPAE also asks about a host of issues that may also affect the dependent variables, such as whether the respondent was asked for identification, whether they had issues voting, and important political and demographic characteristics. This allows this study to control for confounding factors in the relationship between COVID-19 protocols and voter wait times or evaluations. Specifically, this study controls for how worried the respondent was about COVID-19, whether it was their first time voting, if they voted early (as opposed to on Election Day), if they encountered registration or voting problems, if they were asked to show identification, how difficult it was to find their polling place, their race, ethnicity, age, income, education, marital status, gender, partisanship, ideology, and political interest, as many of these variables have been observed to affect voter wait times or evaluations (Atkeson and Saunders 2007; Claassen et al. 2008; Hall and Stewart III 2013; King 2017; King and Barnes 2019). Methodologically, this study employs logistic regression with robust standard errors clustered by state (Arceneaux and Nickerson 2009; Long 1997). Logistic regression is a standard modeling strategy for when (within the same group), and out of concern for model parsimony and avoiding multicollinearity, these are combined into categories of face coverings, socially distanced voting, or protective barriers. Results are robust to using separate indicators. 5 These variables were originally measured categorically or ordinally. However, the distribution of the variables is highly skewed with many respondents reporting the shortest wait times or highest evaluations. For both simplicity and to account for this distribution, variables were collapsed to binary responses. Results reliably robust to alternative specifications, including using the original scales or abbreviated version of the original scales via ordered or multinomial logistic regression (Coll 2022a, 2022b).

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the dependent variable takes on one of two values (e.g., did not wait to vote (0), waited to vote (1)), while robust and clustered standard errors help account for issues of non-constant error variance and nonindependence. Each model predicts one of the dependent variables (voter wait times, voter safety, poll worker evaluations, polling place evaluations, and overall voting positivity) contingent on the main independent variables of interest (the COVID-19 safety policies), controlling for other factors (e.g., respondent political and demographic characteristics). Because logistic regression coefficients are not directly interpretable, an additional table is shown below that computes the predicted probability of having no wait or highly positive evaluations varying whether or not the respondent voted in a polling place with COVID-19 safety policies, holding all other variables at their mean or modal values.

Results Table 8.1 displays the models predicting voter wait times (model 1), perceptions of voter safety (model 2), poll worker evaluations (model 3), polling place evaluations (model 4), and overall voting evaluations (model 5). To aid in interpretation of the logistic regression coefficients, Table 8.2 displays the change in the probability of reporting having to wait to vote (column 1), being highly confident in the safety of voting (column 2), rating poll worker performance as excellent (column 3), rating polling place operations as very well (column 4), and rating the overall voting experience as mostly positive (column 5). To first assess hypothesis 1, that COVID-19 policies increased voter wait times, model 1 suggests socially distanced voting protocols and routine booth cleaning increased the likelihood a voter incurred a wait (+3.5 and +6.3 percentage points, respectively), while single use voting pens actually decreased likelihood of having to wait to cast a ballot (−3.4 percentage points). Face coverings, protective barriers, and hand sanitizer had no significant effect on wait times. These results provide some support for hypothesis 1, but also highlight the limited impact of some policies on voter wait times. Model 2 examines whether these same policies go on to affect perceptions of in-person voter safety. Other than face coverings and hand sanitizer, each policy positively impacts voter safety and no policy

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Table 8.1 Effects of COVID-19 safety measures on voter wait times and evaluations (1) Voter wait times b/se Face coverings 0.08 (0.08) Protective 0.07 barriers (0.06) Socially 0.15** distanced (0.07) Sanitizer −0.07 available (0.08) Single use −0.15** pens (0.07) Booths 0.28*** cleaned (0.07) Waited to vote Confidence in social distancing measures Poll worker eval

(2) Voter safety

(4) Polling place eval. b/se

(5) Voting eval.

b/se

(3) Poll worker eval. b/se

−0.13 (0.11) 0.28***

−0.04 (0.11) −0.03

0.01 (0.15) −0.07

−0.01 (0.09) 0.12**

(0.05) 0.26***

(0.07) 0.07

(0.08) −0.00

(0.06) 0.16**

(0.08) 0.11

(0.10) 0.22***

(0.12) −0.05

(0.07) 0.04

(0.09) 0.24***

(0.08) 0.11*

(0.10) 0.18**

(0.06) 0.13**

(0.07) 0.82***

(0.07) 0.29***

(0.08) 0.11

(0.05) −0.01

(0.06) −0.27***

(0.08) −0.22***

(0.12) −0.99***

(0.06) −0.17***

(0.06)

(0.06) 1.10***

(0.13) 0.82***

(0.06) 0.62***

(0.08)

(0.09) 1.94***

(0.07) 1.53***

(0.11)

(0.08) 0.70*** (0.10)

−0.28***

(0.06) −0.19***

(0.06) Poll place eval −0.98*** (0.13) Voting −0.17*** experience (0.06) First time −0.03 voter (0.13) Voted early 0.31**

1.08***

b/se

(0.08) 0.78*** (0.09) 0.62***

1.95*** (0.10) 1.54***

0.73***

(0.07) −0.15

(0.08) 0.05

(0.10) 0.20

−0.02

(0.13) −0.02

(0.17) 0.01

(0.19) 0.33***

(0.17) −0.13**

(continued)

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Table 8.1 (continued) (1) Voter wait times b/se (0.15) Reg. problems −0.03 (0.26) Voting 0.10 problems (0.28) Difficulty 0.60*** Finding Polling place (0.10) Age −0.00 (0.00) Income 0.02** (0.01) Education 0.01 (0.02) Married 0.05 (0.06) Female −0.09 (0.08) Hispanic 0.34*** (0.10) Black 0.48*** (0.08) Other 0.28** (0.13) COVID-19 0.01 Worry (0.04) Showed 0.37*** identification (0.13) Democrat 0.31*** (0.09) Republican 0.17** (0.07) Liberalism −0.03 (0.03) Political 0.00 interest

(2) Voter safety b/se

(3) Poll worker eval. b/se

(4) Polling place eval. b/se

(5) Voting eval. b/se

(0.07) 0.29* (0.16) 0.10

(0.09) −0.13 (0.22) −0.14

(0.09) −1.61*** (0.28) −1.83***

(0.06) 0.13 (0.25) −1.15***

(0.20) −0.23***

(0.17) −0.38***

(0.27) −0.62***

(0.25) −0.31***

(0.07) 0.01*** (0.00) −0.03** (0.01) −0.04* (0.02) 0.07 (0.05) 0.00 (0.06) 0.27** (0.13) 0.45*** (0.11) 0.33 (0.20) −0.62***

(0.07) 0.01*** (0.00) 0.01 (0.01) 0.07** (0.03) 0.03 (0.07) −0.11 (0.08) −0.59*** (0.20) −0.60*** (0.11) −0.19 (0.15) 0.26***

(0.07) 0.00 (0.00) −0.02** (0.01) −0.05* (0.02) −0.03 (0.08) −0.11 (0.11) 0.42** (0.18) 0.26** (0.11) −0.14 (0.17) 0.17***

(0.07) 0.01** (0.00) 0.02 (0.01) 0.04** (0.02) −0.05 (0.06) 0.30*** (0.06) −0.08 (0.13) 0.30*** (0.07) 0.16 (0.15) 0.12***

(0.03) −0.11

(0.04) 0.20*

(0.04) 0.29***

(0.04) 0.06

(0.08) −0.05 (0.07) 0.17** (0.07) −0.21*** (0.03) 0.18***

(0.10) 0.06 (0.09) −0.15** (0.07) 0.13*** (0.05) 0.01

(0.10) 0.33** (0.16) 0.05 (0.12) −0.09* (0.05) −0.12

(0.08) 0.49*** (0.09) −0.07 (0.07) 0.11*** (0.03) 0.35***

(continued)

206

J. A. COLL

Table 8.1 (continued)

Constant Observations

(1) Voter wait times b/se

(2) Voter safety b/se

(3) Poll worker eval. b/se

(4) Polling place eval. b/se

(0.07) 0.57** (0.24) 8246

(5) Voting eval. b/se

(0.07) 0.48*** (0.18) 8246

(0.08) −3.15*** (0.28) 8246

(0.08) 1.01*** (0.36) 8246

(0.06) −3.87*** (0.25) 8246

Dependent Variables: M1, whether voter waited to vote, where higher values reflecting having to wait; M2, whether voter was confident COVID-19 safety measures protected voters from COVID19 at the voting place, where higher values reflect greater confidence; M3, Whether voter rated poll worker performance very highly, where higher values reflect higher performance ratings; M4, whether voter rated polling place operation very highly, where higher values reflect better operation; M5, whether voter had a mostly positive voting experience, where higher values reflect more positive experiences. Models estimated using logistic regression with robust standard errors clustered by state. *0.1, **0.05, ***0.01

Table 8.2 Effects of COVID-19 safety policies on Voter Wait Times and Voter Experiences COVID-19 Incurred Highly Rated poll worker Rated polling Rated Trade safety wait to confident performance High place Operations overall off? policy vote in safety High voting experience mostly positive Face coverings Protective Barriers Socially Distanced Sanitizer Available Single use pens Booths cleaned

–

–

–

–

–

No

–

6.4

–

–

2.4

No

3.5

6.1

–

–

3.1

Yes

–

–

3.1

–

–

No

−3.4

5.5

1.5 (p