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![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](publication/364656316/figure/fig1/AS:11431281222953596@1707448491965/Ddocumentclass12ptminimal-usepackageamsmath-usepackagewasysym.png)
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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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 numb...
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Background: Studies suggest vaccine hesitancy is an increasingly significant phenomenon in Brazil and other countries. Moreover, political ideologies have emerged as an influencing factor for vaccine hesitancy during the COVID-19 pandemic.
Methods: In this study, we use information from publicly available databases to investigate the association be...
Citations
... No conclusion is made concerning whether this effect cost Trump the election. Algara et al. (2022) find, perhaps counterintuitively, that Trump gained support in counties with higher rates of COVID deaths. The authors believe this result shows that in these counties, the voters were concerned their locale would suffer from a greater economic loss from COVID and that this concern translated into support for Trump as the candidate who was more likely to keep markets open and the economy growing. ...
... The authors believe this result shows that in these counties, the voters were concerned their locale would suffer from a greater economic loss from COVID and that this concern translated into support for Trump as the candidate who was more likely to keep markets open and the economy growing. Baccini et al. (2021) use COVID exposure (not deaths) to present results that seem to contradict Algara et al. (2022). Baccini, Brodeur, and Weymouth (2021) find that more COVID cases in a county hurt Trump's support. ...
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.
... 7a may correspond to partisan differences in public support (or discontent) and discourse surrounding COVID-19 measures. In the days leading up to the 2020 Presidential Election on November 3rd, a pillar of President Trump's campaign messaging on the pandemic characterized lockdowns as tyranny and economic repression [38]. For example, on November 1 st , 2020, the date of the second-largest peak, Trump made a highly controversial claim by stating that the election was a choice between implementing deadly lockdown measures supported by Biden or an efficient end to the COVID-19 crisis with a safe vaccine [39]. ...
... There will be NO LOCKDOWNS. The great American Comeback is underway!!!" [38]. Next, contentious debates related to masks were found coincident with peaks in polarization, as shown in fig. ...
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.
... According to sources that include quantitative (Gaviria-Mendoza et al., 2022;Makowska et al., 2020) and qualitative studies (Kaggwa et al., 2021), self-medication became a major factor in the immediate response of the general public to the pandemic lockdown. The lack of coherent information from leadership (Algara et al., 2022) led to many people around the world turning to alternative sources for information about selftreatment. It has been found that people turned to social media for information about self-treating for COVID-19, and gravitated towards things such as over-the-counter pain medications, as well as cannabis for self-treatment of other chronic neurological and self-perceived mental health conditions, while hospitals and clinics focused on providing treatment and resources exclusively to those in immediate need (Brenneke et al., 2022). ...
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.
... I use mortality and infection rates to measure local COVID-19 prevalence. Past works have used infection and mortality rates to measure local prevalence, with the former referring to the number of COVID-19 infections and the latter to the number of deaths that have occurred due to the coronavirus (e.g., Algara et al. 2022;Baccini et al. 2021). The infection rate may be less accurate than the mortality rate, as some people who contracted the disease were not diagnosed and recorded. ...
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.
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.
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.
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.
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.
Context:
As COVID-19 vaccines were rolled out in early 2011, governments at all levels in the US faced significant difficulty in consistently and efficiently administering injections in the face of vaccination resistance among a public increasingly political polarized on vaccination preferences prior to the beginning of the mass vaccinations.
Methods:
Using an original conjoint experiment fielded to a nationally representative sample prior to the mass proliferation of COVID-19 vaccines, we examine how different incentives (e.g., employer mandates, state-organized or health care provider-organized vaccination clinics, or financial incentives) affect the public's preference to get vaccinated. We also test how financial incentive preferences correlate with self-reported vaccination intention using observational data from the Kaiser Family Foundation June 2021 Health Tracking Poll.
Findings:
We find financial incentives positively influence vaccine preferences among the mass public and all partisan groups, including Republicans initially "unlikely" to be vaccinated. Using the observational data, we replicate our experimental findings showing positive financial incentive attitudes positively correlate with self-reported vaccination disclosures.
Conclusions:
Our results provide support for direct financial incentives, rather than other incentives, as being a valuable tool for policymakers tasked with alleviating vaccination resistance among a US mass public increasingly polarized along partisan lines.
