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Partisanship is why people vote in person in a pandemic

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  • Seo‐Young Silvia Kim
  • Akhil Bandreddi
  • R. Michael Alvarez

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.

Suggested Citation

  • Seo‐Young Silvia Kim & Akhil Bandreddi & R. Michael Alvarez, 2024. "Partisanship is why people vote in person in a pandemic," Social Science Quarterly, Southwestern Social Science Association, vol. 105(4), pages 1042-1060, July.
  • Handle: RePEc:bla:socsci:v:105:y:2024:i:4:p:1042-1060
    DOI: 10.1111/ssqu.13380
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    References listed on IDEAS

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    1. Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
    2. King, Gary & Zeng, Langche, 2001. "Logistic Regression in Rare Events Data," Political Analysis, Cambridge University Press, vol. 9(2), pages 137-163, January.
    3. Wang, Yu, 2019. "Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment," Political Analysis, Cambridge University Press, vol. 27(1), pages 107-110, January.
    4. Allcott, Hunt & Boxell, Levi & Conway, Jacob & Gentzkow, Matthew & Thaler, Michael & Yang, David, 2020. "Polarization and public health: Partisan differences in social distancing during the coronavirus pandemic," Journal of Public Economics, Elsevier, vol. 191(C).
    5. Sean Richey, 2008. "Voting by Mail: Turnout and Institutional Reform in Oregon," Social Science Quarterly, Southwestern Social Science Association, vol. 89(4), pages 902-915, December.
    6. Kousser, Thad & Mullin, Megan, 2007. "Does Voting by Mail Increase Participation? Using Matching to Analyze a Natural Experiment," Political Analysis, Cambridge University Press, vol. 15(4), pages 428-445.
    7. Mark Owens, 2021. "Changes in attitudes, nothing remains quite the same: Absentee voting and public health," Social Science Quarterly, Southwestern Social Science Association, vol. 102(4), pages 1349-1360, July.
    8. Walsh, Katherine Cramer, 2012. "Putting Inequality in Its Place: Rural Consciousness and the Power of Perspective," American Political Science Review, Cambridge University Press, vol. 106(3), pages 517-532, August.
    9. Seo‐young Silvia Kim & R. Michael Alvarez & Christina M. Ramirez, 2020. "Who Voted in 2016? Using Fuzzy Forests to Understand Voter Turnout," Social Science Quarterly, Southwestern Social Science Association, vol. 101(2), pages 978-988, March.
    10. Robert M. Stein & Greg Vonnahme, 2012. "When, Where, and How We Vote: Does it Matter?," Social Science Quarterly, Southwestern Social Science Association, vol. 93(3), pages 692-712, September.
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