How did absentee voting affect the 2020 U.S. election?
Ontology highlight
ABSTRACT:
SUBMITTER: Yoder J
PROVIDER: S-EPMC8694609 | biostudies-literature | 2021 Dec
REPOSITORIES: biostudies-literature
Publications
How did absentee voting affect the 2020 U.S. election?
Yoder Jesse J Handan-Nader Cassandra C Myers Andrew A Nowacki Tobias T Thompson Daniel M DM Wu Jennifer A JA Yorgason Chenoa C Hall Andrew B AB
Science advances 20211222 52
The 2020 U.S. election saw a record turnout, saw a huge increase in absentee voting, and brought unified national Democratic control—yet these facts alone do not imply that vote-by-mail increased turnout or benefited Democrats. Using new microdata on millions of individual voters and aggregated turnout data across all 50 states, this paper offers a causal analysis of the impact of absentee vote-by-mail during the COVID-19 (coronavirus disease 2019) pandemic. Focusing on natural experiments in Te ...[more]
Similar Datasets
Project description:IntroductionIn the 2016 U.S. Presidential election, voters in communities with recent stagnation or decline in life expectancy were more likely to vote for the Republican candidate than in prior Presidential elections. We aimed to assess the association between change in life expectancy and voting patterns in the 2020 Presidential election.MethodsWith data on county-level life expectancy from the Institute for Health Metrics and Evaluation and voting data from a GitHub repository of results scraped from news outlets, we used weighted multivariable linear regression to estimate the association between the change in life expectancy from 1980 to 2014 and the proportion of votes for the Republican candidate and change in the proportion of votes cast for the Republican candidate in the 2020 Presidential election.ResultsAmong 3110 U.S counties and Washington, D.C., change in life expectancy at the county level was negatively associated with Republican share of the vote in the 2020 Presidential election (parameter estimate -7.2, 95% confidence interval, -7.8 to -6.6). With the inclusion of state, sociodemographic, and economic variables in the model, the association was attenuated (parameter estimate -0.8; 95% CI, -1.5 to -0.2). County-level change in life expectancy was positively associated with change in Republican vote share 0.29 percentage points (95% CI, 0.23 to 0.36). The association was attenuated when state, sociodemographic, and economic variables were added (parameter estimate 0.24; 95% CI, 0.15 to 0.33).ConclusionCounties with a less positive trajectory in life expectancy were more likely to vote for the Republican candidate in the 2020 U.S. Presidential election, but the Republican candidate's share improved in some counties that experienced marked gains in life expectancy. Associations were moderated by demographic, social and economic factors.
| S-EPMC8209240 | biostudies-literature
Project description:ImportanceIn the U.S. presidential election of 2016, substantial shift in voting patterns occurred relative to previous elections. Although this shift has been associated with both education and race, the extent to which this shift was related to public health status is unclear.ObjectiveTo determine the extent to which county community health was associated with changes in voting between the presidential elections of 2016 and 2012.DesignEcological study with principal component analysis (PCA) using principal axis method to extract the components, then generalized linear regression.SettingGeneral community.ParticipantsAll counties in the United States.ExposuresPhysically unhealthy days, mentally unhealthy days, percent food insecure, teen birth rate, primary care physician visit rate, age-adjusted mortality rate, violent crime rate, average health care costs, percent diabetic, and percent overweight or obese.Main outcomeThe percentage of Donald Trump votes in 2016 minus percentage of Mitt Romney votes in 2012 ("net voting shift").ResultsComplete public health data was available for 3,009 counties which were included in the analysis. The mean net voting shift was 5.4% (+/- 5.8%). Of these 3,009 counties, 2,641 (87.8%) had positive net voting shift (shifted towards Trump) and 368 counties (12.2%) had negative net voting shift (shifted away from Trump). The first principal component ("unhealthy score") accounted for 68% of the total variance in the data. The unhealthy score included all health variables except primary care physician rate, violent crime rate, and health care costs. The mean unhealthy score for counties was 0.39 (SD 0.16). Higher normalized unhealthy score was associated with positive net voting shift (22.1% shift per unit unhealthy, p < 0.0001). This association was stronger in states that switched Electoral College votes from 2012 to 2016 than in other states (5.9% per unit unhealthy, p <0.0001).Conclusions and relevanceSubstantial association exists between a shift toward voting for Donald Trump in 2016 relative to Mitt Romney in 2012 and measures of poor public health. Although these results do not demonstrate causality, these results suggest a possible role for health status in political choices.
| S-EPMC5624580 | biostudies-literature
Project description:About one third of American voters cast a vote more "against" than "for" a candidate in the 2020 Presidential election. This pattern, designated by negative voting, has been initially understood by rational choice scholarship as a product of cognitive dissonance and/or retrospective evaluations. This article revisits this concept through the affective polarization framework in the light of the rise of political sectarianism in American society. Based on an original CAWI survey fielded after the 2020 election, our regression analysis demonstrates that the predicted probability of casting a negative vote significantly increases among individuals for whom out-candidate hate outweighs in-candidate love. Negative voting is less prevalent among partisans as their higher levels of in-group affection can offset out-group contempt. By asserting the enduring relevance of negative voting in American presidential elections, we aim at stimulating further research and discussion of its implications for democratic representation.
| S-EPMC9028101 | biostudies-literature
Project description:The 2020 U.S. Presidential Election required voters to not only form opinions of leading candidates, Donald Trump and Joe Biden, but also to make judgments about the integrity of the election itself and what—if anything—to do about it. However, partisan motivated reasoning theory (Leeper and Slothuus, Political Psychology, 35(Suppl 1): 129–156; Lodge and Taber, The rationalizing voter, Cambridge University Press, 2013) suggests judgments are often strongly influenced toward affectively desirable conclusions. Before, during, and after election projections were announced, partisan supporters of Trump and Biden rated: judgments about voter fraud and foreign interference, their acceptance of the results, and their support for recourse against the outcome (e.g., legal challenges, legislative overhauls, violence). Before the election, partisans were mildly concerned about election integrity but willing to accept the outcome without recourse. However, during vote counting, and especially after Biden was projected to be the winner, partisans dramatically changed their judgments in opposite directions, consistent with the affectively desirable conclusions relevant to each group. Biden supporters affirmed the election’s integrity and accepted the results whereas Trump supporters disputed the integrity, rejected the results, and began to support recourse against the outcome. Data are consistent with partisan motivated reasoning. Discussion highlights the practical implications. Supplementary Information The online version contains supplementary material available at 10.1007/s11031-022-09983-w.
| S-EPMC9513018 | biostudies-literature
Project description:Since it is difficult to determine whether social media content moderators have assessed particular content, it is hard to evaluate the consistency of their decisions within platforms. We study a dataset of 1,035 posts on Facebook and Twitter to investigate this question. The posts in our sample made 78 misleading claims related to the U.S. 2020 presidential election. These posts were identified by the Election Integrity Partnership, a coalition of civil society groups, and sent to the relevant platforms, where employees confirmed receipt. The platforms labeled some (but not all) of these posts as misleading. For 69% of the misleading claims, Facebook consistently labeled each post that included one of those claims-either always or never adding a label. It inconsistently labeled the remaining 31% of misleading claims. The findings for Twitter are nearly identical: 70% of the claims were labeled consistently, and 30% inconsistently. We investigated these inconsistencies and found that based on publicly available information, most of the platforms' decisions were arbitrary. However, in about a third of the cases we found plausible reasons that could explain the inconsistent labeling, although these reasons may not be aligned with the platforms' stated policies. Our strongest finding is that Twitter was more likely to label posts from verified users, and less likely to label identical content from non-verified users. This study demonstrates how academic-industry collaborations can provide insights into typically opaque content moderation practices.
| S-EPMC10650990 | biostudies-literature
Project description:Ballots are the core records of elections. Electronic records of actual ballots cast (cast vote records) are available to the public in some jurisdictions. However, they have been released in a variety of formats and have not been independently evaluated. Here we introduce a database of cast vote records from the 2020 U.S. general election. We downloaded publicly available unstandardized cast vote records, standardized them into a multi-state database, and extensively compared their totals to certified election results. Our release includes vote records for President, Governor, U.S. Senate and House, and state upper and lower chambers, covering 42.7 million voters in 20 states who voted for more than 2,200 candidates. This database serves as a uniquely granular administrative dataset for studying voting behavior and election administration. Using this data, we show that in battleground states, 1.9 percent of solid Republicans (as defined by their congressional and state legislative voting) in our database split their ticket for Joe Biden, while 1.2 percent of solid Democrats split their ticket for Donald Trump.
| S-EPMC11604945 | biostudies-literature
Project description:When explaining why an event occurred, people intuitively highlight some causes while ignoring others. How do people decide which causes to select? Models of causal judgment have been evaluated in simple and controlled laboratory experiments, but they have yet to be tested in a complex real-world setting. Here, we provide such a test, in the context of the 2020 U.S. presidential election. Across tens of thousands of simulations of possible election outcomes, we computed, for each state, an adjusted measure of the correlation between a Biden victory in that state and a Biden election victory. These effect size measures accurately predicted the extent to which U.S. participants (N = 207, preregistered) viewed victory in a given state as having caused Biden to win the presidency. Our findings support the theory that people intuitively select as causes of an outcome the factors with the largest standardized causal effect on that outcome across possible counterfactual worlds.
| S-EPMC10015993 | biostudies-literature
Project description:In this research letter, we examine the impact of municipal budget policy on the percentage of votes for the incumbent majority parties in subsequent elections. We contribute to the academic literature by examining the combined influence of taxes, expenditures and debt. Based on data for Flanders (Belgium) between 1994 and 2012, we find no significant association between these budget variables and the actual election results.
| S-EPMC6715277 | biostudies-literature
Project description: Not available
| S-EPMC8980920 | biostudies-literature
Project description: Not available
| S-EPMC7524541 | biostudies-literature