Partisanship is why people vote in person in a pandemic
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1.
California Institute of Technology
Abstract
Objective
The choice of voting methods has increasingly become a politicized, partisan issue. We ask: Can a nationalized partisan rhetoric cast doubt on vote-by-mail (VBM) despite years of experience and a raging pandemic?
Method
Using 2020 general election records in Colorado, an established all-mail voting state, we analyze first the general choice of voting methods using supervised machine learning and then the choice to switch to in-person voting despite having used VBM in previous cycles.
Results
The choice of voting modes is mainly habitual; local variations of COVID-19 hardly mattered. Republican partisanship played an important role in predicting “switchers” to in-person voting; the probability was 5.2 percent conditional on being a Republican as opposed to 1.9 percent for a Democrat.
Conclusions
The results suggest that voting in person can be heavily polarized by partisan communication, despite being a health behavior in a pandemic and voters having experience with mail voting.
Copyright and License
© 2024 The Authors. Social Science Quarterly published by Wiley Periodicals LLC on behalf of Southwestern Social Science Association. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Acknowledgement
An earlier version of this article was presented at the Election Sciences, Reform, & Administration Conference 2021 and MPSA 2023. We thank Eric McGhee, Yimeng Li, Yuki Atsusaka, and the conference participants for their comments. The research reported in this article was reviewed by Caltech's IRB and ruled exempt (IR19-0962) as well as from American University (IRB-2021-251). The code used in the analysis is available at https://github.com/sysilviakim/coloradoVotes. The data are available from the Colorado Secretary of State. This work was supported by the Sogang University Research Grant of 2024.
Data Availability
Table 1: Predicting Turnout and Voting Mode, Multinomial Logistic Regression
Table 2: Predicting Switchers, Simple Linear Regression
Figure 1: Variable Importance Plots for Predicting Voting Modes and Switchers, Top 20 Variables
Files
Additional details
- ISSN
- 1540-6237
- Sogang University