Partisan risk tolerance for in‐person voting during the pandemic: Survey evidence from the U.S. 2020 election
Abstract
To what extent will people turn out to vote under high-risk, high-cost circumstances? We examine the impact of risk tolerance on willingness to vote in person in the U.S. 2020 election during the coronavirus disease-2019 (COVID-19) pandemic. Our results are based on pre- and postelection surveys that indicated partisan Republicans were more willing to vote in person than Democrats or Independents, who preferred voting by mail. Using behavioral measures of risk-taking, we find that Republican in-person voting is predicted by greater generalized and pandemic-related risk tolerance compared with Democrats and nonpartisans. To explain risk tolerance, we employ prospect theory to illustrate how Republicans’ fear of electoral loss, alongside conservative elite and media efforts to downplay COVID-19 severity, likely influenced Republican readiness to assume risks of in-person voting during the pandemic. We urge scholars to consider the implications of risk tolerance for models of electoral behavior under high-risk and high-cost voting conditions.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Iyengar and Westwood (2015) use trust and dictator games to show people discriminate against those from other parties more than they do toward people of different races.
“Iyengar and Westwood (2015) use trust and dictator games to show people discriminate against those from other parties more than they do toward people of different races (also see Carlin & Love, 2013, 2018;Whitt et al., 2024).”
From Measuring affective polarization: Matching concepts to measures (2026) · cites this paper as Whitt et al., 2024
Cites
Prospect Theory: An Analysis of Decision under Risk
Kahneman, D., & Tversky, A. (1979) · Econometrica · doi:10.2307/1914185 · Cited by 48,524 · see citing works
Chapter 113 Men, Women and Risk Aversion: Experimental Evidence
Eckel, C. C., & Grossman, P. J. (2008) · Handbook of Experimental Economics Results · doi:10.1016/s1574-0722(07)00113-8 · Cited by 924 · see citing works
Partisanship, health behavior, and policy attitudes in the early stages of the COVID-19 pandemic
Gadarian, S. K., Goodman, S. W., & Pepinsky, T. B. (2021) · PLOS ONE · doi:10.1371/journal.pone.0249596 · Cited by 744 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Measuring affective polarization: Matching concepts to measures
Druckman, J. N., Klar, S., Krupnikov, Y., Levendusky, M., & Ryan, J. B. (2026) · Political Psychology · doi:10.1111/pops.70152 · Cited by 1 · see citing works
- Authors
- Whitt, S., Yanus, A. B., McDonald, B., & Kifer, M.
- Affiliations
- High Point University (US)
- Corresponding author
- Whitt, Sam
- Venue
- International Journal of Public Opinion Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-01-03
- Volume
- 36
- Issue
- 1
- ISSN
- 0954-2892 (ISSN-L)
- 1471-6909
- OpenAlex
- W4390600835
- Cited by
- 3
- References
- 3
- Citations by year
- 2026: 2 · 2025: 1
- FWCI
- 4.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Public Opinion Research (publishedVersion)
- Funders
- High Point University’s Survey Research Center
- Topics
- Social Sciences › Political Science and International Relations › Electoral Systems and Political Participation
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Social Sciences › Sociology and Political Science › Risk Perception and Management
- Keywords
- risk tolerance, in-person voting, mail-in voting, 2020 US presidential election, COVID-19 pandemic, Republican voters, prospect theory
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review