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Nail in the Coffin or Lifeline? Evaluating the Electoral Impact of COVID-19 on President Trump in the 2020 Election

October 2022
Political Behavior 46(2)

DOI:10.1007/s11109-022-09826-x

Authors:

Carlos Algara

Claremont Graduate University

Isaac Hale

University of California, Davis

Show all 5 authorsHide

From the onset of the first confirmed case of COVID-19 in January 2020 to Election Day in November, the United States experienced over 9,400,000 cases and 232,000 deaths. This crisis largely defined the campaign between former Vice President Joe Biden and President Donald Trump, centering on the Trump administration's efforts in mitigating the number of cases and deaths. While conventional wisdom suggested that Trump and his party would lose support due to the severity of COVID-19 across the country, such an effect is hotly debated empirically and theoretically. In this research, we evaluate the extent to which the severity of the COVID-19 pandemic influenced support for President Trump in the 2020 election. Across differing modeling strategies and a variety of data sources, we find evidence that President Trump gained support in counties with higher COVID-19 deaths. We provide an explanation for this finding by showing that voters concerned about the economic impacts of pandemic-related restrictions on activity were more likely to support Trump and that local COVID-19 severity was predictive of these economic concerns. While COVID-19 likely contributed to Trump's loss in 2020, our analysis demonstrates that he gained support among voters in localities worst affected by the pandemic. Supplementary information: The online version contains supplementary material available at 10.1007/s11109-022-09826-x.

Δ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Delta $$\end{document} Change in county-level support for president trump during the 2020 election

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COVID-19 severity across U.S. counties

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County COVID-19 severity & change in GOP presidential electoral support. Estimates articulating the relationship between county COVID-19 severity and change in electoral support for President Trump in Fig. 3 are derived from full models specified with demographic and partisan control covariates. Coefficients significant at the 95% confidence level labelled

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Relationship between public health concern & political support for president Trump. The discrete marginal effects articulated in Fig. 4 specified with 90% and 95% confidence intervals from district-clustered robust standard errors. Darker shaded point estimates significant at ρ<0.10\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rho < 0.10$$\end{document}. Models control for local COVID-19 context, education, race, gender, ideology, economic evaluations, COVID-19 infection proximity, and age. Δ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Delta $$\end{document} change in electoral choice models also control for change in partisanship and 2016 Republican presidential vote. Fig. 4 articulates N = 8 unified baseline additive models (one for each panel wave-outcome variable interest articulated in the panel) 8 unified interactive models (one for each panel wave-outcome variable interest articulated in the panel) for a total of N = 16 models. Heterogeneous effects of attitudes across partisanship estimated from multiplicative model interacting context and partisanship. All models fitted with wave-specific survey weights

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Relationship Between Local Severity Context & COVID-19 Attitudes. The minimum-maximum first difference marginal effects articulated in Fig. 5 specified with 90% and 95% confidence intervals from district-clustered robust standard errors. Darker shaded point estimates significant at ρ<0.10\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rho < 0.10$$\end{document}. Models control for education, race, gender, ideology, economic evaluations, COVID-19 infection proximity, and age. Figure articulates N = 4 unified baseline additive models (two per panel wave x two outcome variables of interest) 4 unified interactive models (two per panel wave x two outcome variables of interest) for a total of N = 8 models. Heterogeneous effects of local context on attitudes estimated from multiplicative model interacting context and partisanship. All models fitted with wave-specific survey weights

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Vol.:(0123456789)

Political Behavior (2024) 46:277–305

https://doi.org/10.1007/s11109-022-09826-x

1 3

ORIGINAL PAPER

Nail intheCoﬃn orLifeline? Evaluating theElectoral

Impact ofCOVID‑19 onPresident Trump inthe2020

Election

CarlosAlgara2 · SharifAmlani1· SamuelCollitt1· IsaacHale3·

SaraKazemian1

Accepted: 14 September 2022 / Published online: 23 October 2022

2022

Abstract

From the onset of the ﬁrst conﬁrmed case of COVID-19 in January 2020 to Election

Day in November, the United States experienced over 9,400,000 cases and 232,000

deaths. This crisis largely deﬁned the campaign between former Vice President Joe

Biden and President Donald Trump, centering on the Trump administration′s eﬀorts

in mitigating the number of cases and deaths. While conventional wisdom suggested

that Trump and his party would lose support due to the severity of COVID-19 across

the country, such an eﬀect is hotly debated empirically and theoretically. In this

research, we evaluate the extent to which the severity of the COVID-19 pandemic

inﬂuenced support for President Trump in the 2020 election. Across diﬀering mod-

eling strategies and a variety of data sources, we ﬁnd evidence that President Trump

gained support in counties with higher COVID-19 deaths. We provide an explana-

tion for this ﬁnding by showing that voters concerned about the economic impacts

of pandemic-related restrictions on activity were more likely to support Trump and

that local COVID-19 severity was predictive of these economic concerns. While

COVID-19 likely contributed to Trump’s loss in 2020, our analysis demonstrates

that he gained support among voters in localities worst aﬀected by the pandemic.

Keywords COVID-19 pandemic· 2020 US presidential election· Donald Trump·

Rally-’round-the-ﬂag· Retrospective voting

COVID‑19 intheContext ofthe2020 U.S. Election

Conventional wisdom holds that the COVID-19 pandemic directly and seri-

ously damaged Donald Trump’s reelection eﬀort in 2020. The depth of the cri-

sis during 2020 is hard to overstate: from the onset of the ﬁrst conﬁrmed case of

* Carlos Algara

carlos.algara@cgu.edu

Extended author information available on the last page of the article

Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Exploiting a Major Blunder to Study Policy Accountability: Trump and His Covid Stance in 2020

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POLIT BEHAV

Most theories of democracy assume that voters punish politicians who pursue unpopular policies. However, empirical evidence supporting this claim remains elusive, partly because politicians strategically avoid adopting unpopular stances. The 2020 US presidential election offers a unique opportunity to investigate this phenomenon. Incumbent President Donald Trump publicly embraced unpopular positions on Covid-19, arguably the campaign’s most critical issue. Using panel survey data, we find many Trump supporters disagreed with his Covid-19 stances and preferred more aggressive government action, yet most remained unaware that his policies conflicted with their views even by the campaign’s end. Among those who recognized this contradiction, few defected; instead, many followed Trump, adjusting their own views to match his positions. Although our statistical power to detect voter defections is limited—precisely because learning about his stance was uncommon—Trump’s unpopular positions may still have been decisive given the narrow election margin. Broadly, voters’ failure to perceive or acknowledge conflicts between their policy views and those of favored candidates constrains their ability to hold politicians accountable, highlighting a critical challenge for democratic accountability.

The COVID effect: an empirical analysis of the pandemic and the 2020 U.S. presidential election

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J Econ Finance

The impact of the COVID pandemic on the 2020 election outcome is analyzed using Iowa Electronic Market data, measures of socially and economically driven market volatility, a measure of COVID severity, and selected election-related events. Building on research regarding two previous U.S. presidential elections, we find that the pandemic helped the incumbent in two ways. The largest impact supporting the incumbent came from the apparent medical severity. A secondary impact came from social and economic volatility with the surprising finding that both risks helped the incumbent relative to the challenger. However, these impacts were not adequate to overcome the relatively large advantage of the challenger.

Regional and Temporal Patterns of Partisan Polarization during the COVID-19 Pandemic in the United States and Canada

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Public health measures were among the most polarizing topics debated online during the COVID-19 pandemic. Much of the discussion surrounded specific events, such as when and which particular interventions came into practise. In this work, we develop and apply an approach to measure subnational and event-driven variation of partisan polarization and explore how these dynamics varied both across and within countries. We apply our measure to a dataset of over 50 million tweets posted during late 2020, a salient period of polarizing discourse in the early phase of the pandemic. In particular, we examine regional variations in both the United States and Canada, focusing on three specific health interventions: lockdowns, masks, and vaccines. We find that more politically conservative regions had higher levels of partisan polarization in both countries, especially in the US where a strong negative correlation exists between regional vaccination rates and degree of polarization in vaccine related discussions. We then analyze the timing, context, and profile of spikes in polarization, linking them to specific events discussed on social media across different regions in both countries. These typically last only a few days in duration, suggesting that online discussions reflect and could even drive changes in public opinion, which in the context of pandemic response impacts public health outcomes across different regions and over time.

Cannabis And College Students: Self-Perceived Mental Health Prior to, During, and After the Pandemic

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Introduction: College students demonstrated changes in levels of mental wellbeing as they and the world experienced new levels of stress and anxiety due to the COVID-19 pandemic. As access to healthcare became limited, students turned to alternative methods of coping, which included cannabis use. Objective: To determine if an association between cannabis use and self-perceived mental wellbeing during the pandemic among college students exists. Method: A paired samples t-Test was used to compare self-reported mental wellbeing at different times during the pandemic, a one-way ANOVA to compare self-reported mental health between respondents' cannabis use status, and a Tukey-Kramer post-hoc analysis was used to determine between group significance. All data collected were from participants at a single time point (retrospective self-report during April 2022). Results: Of 103 self-reported college students, the most significant differences in mental wellbeing were reported prior to and during the pandemic. Consistent significant differences were observed between each of the college student groups derived from those students who entirely avoided cannabis use or cessation of use (highest rating), p = .018, as compared to those who initiated cannabis use prior to and during the pandemic (lowest rating) p = .045. Post pandemic mental wellbeing demonstrated a higher level of mental wellbeing among those who had some exposure to cannabis compared to those who avoided cannabis entirely. Conclusions: It cannot be concluded that mental wellbeing was lower due to cannabis use. However, it is possible those with lower self-perceived mental wellbeing turned to cannabis use.

Disease, Scapegoating, and Social Contexts: Examining Social Contexts of the Support for Racist Naming of COVID-19 on Twitter

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J HEALTH SOC BEHAV

Yun Lu

In early 2020, when COVID-19 began to spread in the United States, many Twitter users called it the “Chinese virus,” blaming racial outgroups for the pandemic. I collected tweets containing the “Chinese virus” derivatives posted from March to August 2020 by users within the United States and created a data set with 141,290 tweets published by 50,695 users. I calculated the ratio of users who supported the racist naming of COVID-19 per county and merged Twitter data with the county-level census. Multilevel regression models show that counties with higher COVID-19 mortality or infection rates have more support for the racist naming. Second, the mortality and infection rates effects are stronger in counties with faster minority growth. Moreover, it is mainly in poor counties that minority growth enlarges the effects of infection and mortality rates. These findings relate to the theories on disease-induced xenophobia and the debate between conflict and contact theories.

The Impact of Experiences with COVID-19 on the 2020 Presidential Election and Support for Health Reform

Article

Feb 2025
PUBLIC OPIN QUART

Health shocks may drive the public to support policies and candidates that protect health and well-being. Did the COVID-19 pandemic, as one such shock, shift preferences for health reform in the United States? Using nationally representative surveys of over 70,000 US adults between 2019 and 2020, we find that experiences with COVID-19—measured at both the individual and community levels—increased support for Medicare for All by multiple percentage points. Consistent with partisan entrenchment on health issues, independents and weak partisans drove the association at the individual level; these subgroup differences were not observed for community-level experiences. To reduce concerns about confounding, we use data from multiple points in time to establish the expected temporal relationship between experiences with COVID-19 and support for health reform. Finally, consistent with the importance of health issues in the 2020 presidential race, we find that changes in support for health reform were mirrored by a comparable shift in support away from the incumbent, President Trump, in the weeks leading up to the election. Even if short lived, these shifts may have influenced both the discourse and outcome of the election.

This Crisis Is Different: Weakened Economic Accountability During the COVID‐19 Pandemic

Article

Jan 2025

Matthew Singer

Classic economic voting theories suggest Donald Trump would be held accountable for the recession during the first year of the COVID‐19 pandemic. I argue, however, that traditional economic voting patterns were less important in 2020 than they otherwise would have been due to the pandemic. Three factors potentially reduced the economy's effect: the complicated and multifaceted nature of this crisis made it difficult to attribute responsibility to the president, the economy's salience fell as the public focused on other issues, and cash transfers cushioned households facing economic uncertainty. I test this expectation in three ways, and all show weak economic voting in 2020. President Trump was generally not held accountable for the personal economic dislocations such as job losses or income reductions individuals endured during the pandemic. The individual‐level connection between sociotropic evaluations and presidential approval weakened in 2020. Finally, President Trump's aggregate approval rating reflected consumer confidence before the pandemic but not during it. The electorate does not blindly hold the incumbent accountable for economic outcomes.

سيميائية صور محاولة اغتيال دونالد ترامب خلال الدعاية الانتخابية للرئاسة الأمريكية 2024: “دراسة تحليلية للصور المنشورة على المواقع الرسمية للقنوات التليفزيونية الدولية"

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إلهام سيد

Taking Stock and Looking Forward to the Future of Pathogen Politics in Light of New Insights and Recommendations: COVID-19 Threat Was Meaningfully Associated With Support for Liberal Policies in the United States

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Pers Soc Psychol Bull

Michael Edem Fiagbenu

Infectious disease outbreaks are expected to predict support for conservative policies. However, earlier studies (January–June, 2020) reached conflicting findings regarding the association between COVID-19 threat and policy preferences in the United States. We revisit this issue by analyzing five nationally representative surveys conducted during the relatively severe periods of the pandemic (August 2020–December, 2020; total N = 82,753). Using Bayesian inference, we find strong evidence that subjective (e.g., fear of infection and pandemic outrage) but not objective (e.g., local cases and deaths) threat predicted support for liberal policies (e.g., immigration and universal health care). Meta-analyses revealed that the estimates depend on the type of subjective (.05 ≥ r ≤ .60) or objective (.00 ≥ r ≤ .14) COVID-19 threat. We propose an emotion-mediated dual-process model of pathogen management suggesting that infectious disease outbreaks activate both avoidance and caregiving motives that translate, respectively, into support for right-wing and left-wing policies.

Race, Partisanship, and Democratic Politics: The Role of Racial Attitudes in Motivating White Americans’ Electoral Participation

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Aug 2023

While there is considerable research on the role racial attitudes play in shaping white political preferences, relatively little is known about how racial attitudes influence white participation in democratic politics. We present a model examining the relationship between racial attitudes and political participation in the 2016, 2018, and 2020 U.S. national elections. Using a variety of measures of political participation, our analysis presents a clear finding: the direction of the relationship between latent conservative racial attitudes and political participation is asymmetrical among partisan sub-groups, with conservative racial attitudes motivating participation among white Republicans and, to a greater degree, depressing participation among white Democrats. This finding has stark implications for how racialized appeals are likely to be deployed in an era of increasing affective partisan polarization.

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Objective As the coronavirus pandemic raged throughout 2020, political leaders faced a difficult choice: Should strict social distancing guidelines be maintained until the threat posed by COVID-19 was diminished enough for citizens to return to their regular activities? Or was the economic disruption caused by the pandemic something that was, according to President Trump, "worse than the problem itself"? Methods We analyze data from a 2020 survey of registered voters. Results Democrats were more likely than Republicans to resolve the tradeoff in favor of maintaining social distancing over rebuilding the economy. More importantly, we find that when faced with this moral dilemma (measured by one's choice between a utilitarian vs. a deontological approach when confronted with a real-life "trolley problem"), many citizens from both sides of the partisan aisle were of two minds on the subject. Conclusion Americans are ambivalent about the appropriate government response to COVID-19.

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An important body of literature shows that citizens evaluate elected officials based on their past performance. In the aftermath of the 2020 presidential election, the conventional wisdom in both media and academic discourse was that Donald Trump would have been a two-term president absent an unprecedented, global force majeure. In this research note, we address a simple question: did exposure to COVID-19 impact vote choice in the 2020 presidential election? Using data from the Cooperative Election Study, we find that Trump’s vote share decreased because of COVID-19. However, there is no evidence suggesting that Joe Biden loses the election when no voter reports exposure to coronavirus cases and deaths. These negligible effects are found at both the national and state levels, and are robust to an exhaustive set of confounders across model specifications.

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PLOS ONE

Objective To study the U.S. public’s health behaviors, attitudes, and policy opinions about COVID-19 in the earliest weeks of the national health crisis (March 20–23, 2020). Method We designed and fielded an original representative survey of 3,000 American adults between March 20–23, 2020 to collect data on a battery of 38 health-related behaviors, government policy preferences on COVID-19 response and worries about the pandemic. We test for partisan differences COVID-19 related policy attitudes and behaviors, measured in three different ways: party affiliation, intended 2020 Presidential vote, and self-placed ideological positioning. Our multivariate approach adjusts for a wide range of individual demographic and geographic characteristics that might confound the relationship between partisanship and health behaviors, attitudes, and preferences. Results We find that partisanship—measured as party identification, support for President Trump, or left-right ideological positioning—explains differences in Americans across a wide range of health behaviors and policy preferences. We find no consistent evidence that controlling for individual news consumption, the local policy environment, and local pandemic-related deaths erases the observed partisan differences in health behaviors, beliefs, and attitudes. In further analyses, we use a LASSO regression approach to select predictors, and find that a partisanship indicator is the most commonly selected predictor across the 38 dependent variables that we study. Conclusion Our analysis of individual self-reported behavior, attitudes, and policy preferences in response to COVID-19 reveals that partisanship played a central role in shaping individual responses in the earliest months of the COVID-19 pandemic. These results indicate that partisan differences in responding to a national public health emergency were entrenched from the earliest days of the pandemic.

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Spatial Analysis for the Social Sciences

Book

Nov 2015

David Darmofal

Many theories in the social sciences predict spatial dependence or the similarity of behaviors at neighboring locations. Spatial Analysis for the Social Sciences demonstrates how researchers can diagnose and model this spatial dependence and draw more valid inferences as a result. The book is structured around the well-known Galton's problem and presents a step-by-step guide to the application of spatial analysis. The book examines a variety of spatial diagnostics and models through a series of applied examples drawn from the social sciences. These include spatial lag models that capture behavioral diffusion between actors, spatial error models that account for spatial dependence in errors, and models that incorporate spatial heterogeneity in the effects of covariates. Spatial Analysis for the Social Sciences also examines advanced spatial models for time-series cross-sectional data, categorical and limited dependent variables, count data, and survival data.

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The goal of this paper is to study the electoral impact of crisis management policies. With this aim, we exploit a natural experiment during the COVID-19 pandemic in France to evaluate the effect of the lockdown on voting behaviour. In particular, the country has been divided in two areas, red and green, subject to a ‘hard’ and a ‘soft’ lockdown, respectively. To measure voting behaviour, before and after the policy, we rely on the 2020 French municipal elections: the first round took place before the introduction of the restrictions, while the second round was delayed after the end of the lockdown. We estimate a spatial regression-discontinuity-design model comparing electoral outcomes around the border of red and green areas both in the second round and between the two electoral rounds. The main results suggest that lockdown regulations significantly affected voting outcomes. First, in localities under a harder lockdown, the incumbent’s vote share is higher. Second, voter turnout is larger where more stringent restrictions are adopted. These results suggest that lockdown policy mobilizes citizens and leads them to rally around the incumbent politicians.

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Promoting Higher Social Distancing and Stay-at-Home Decisions During COVID-19: the Underlying Conflict Between Public Health and the Economy

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SAFETY SCI

Social distancing and stay-at-home orders were implemented as a quick response to the public health crisis created by COVID-19. However, these measures led to competing concerns for public health versus the wellbeing of the economy during the pandemic. This drove polarized views and attitudes towards these measures in the US that threatened their effectiveness in controlling the spread of infections. Our study addresses this point by investigating uptake of messaging treatments that highlight the health risks of COVID-19. We also investigate how priming economic risk of COVID-19 affects responsiveness to the health information messaging. A sample of 1,200 US respondents were randomly assigned to a control and four messaging treatments that included information about risks of COVID-19 on own health, public health, the economy, and combination of public health and the economy, respectively. Our results indicate a significant difference in messaging uptake based on political partisanship. Individuals identifying as Democrats increased their social distancing and stay-at-home decisions in response to all information treatments, contrary to Republicans who showed no significant change in their behavior. Using a latent class analysis model, we classify individuals into three main types (dismissive, amenable, and conscious) that differ in their perceptions of the risks associated with COVID-19. We show that only amenable individuals, who account for approximately 34% of the sample, respond significantly to the messaging treatments.