How did absentee voting affect the 2020 U.S. election?
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ABSTRACT:
SUBMITTER: Yoder J
PROVIDER: S-EPMC8694609 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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.
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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.
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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.
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Project description: Not available
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Project description:Donald Trump's 2016 win despite failing to carry the popular vote has raised concern that 2020 would also see a mismatch between the winner of the popular vote and the winner of the Electoral College. This paper shows how to forecast the electoral vote in 2020 taking into account the unknown popular vote and the configuration of state voting in 2016. We note that 2016 was a statistical outlier. The potential Electoral College bias was slimmer in the past and not always favoring the Republican candidate. We show that in past presidential elections, difference among states in their presidential voting is solely a function of the states' most recent presidential voting (plus new shocks); earlier history does not matter. Based on thousands of simulations, our research suggests that the bias in 2020 probably will favor Trump again but to a lesser degree than in 2016. The range of possible outcomes is sufficiently wide, however, to even include some possibility that Joseph Biden could win in the Electoral College while barely losing the popular vote.
| S-EPMC7668185 | biostudies-literature
Project description:Participants were asked to assess their own personality (i.e. Big Five scales), the personality of politicians shown in brief silent video clips, and the probability that they would vote for these politicians. Response surface analyses (RSA) revealed noteworthy effects of self-ratings and observer-ratings of openness, agreeableness, and emotional stability on voting probability. Furthermore, the participants perceived themselves as being more open, more agreeable, more emotionally stable, and more extraverted than the average politician. The study supports previous findings that first impressions affect decision making on important issues. Results also indicate that when only nonverbal information is available people prefer political candidates they perceive as having personality traits they value in themselves.
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Project description:This article reports new empirical evidence on probabilistic polling, which asks persons to state in percent-chance terms the likelihood that they will vote and for whom. Before the 2008 presidential election, seven waves of probabilistic questions were administered biweekly to participants in the American Life Panel (ALP). Actual voting behavior was reported after the election. We find that responses to the verbal and probabilistic questions are well-aligned ordinally. Moreover, the probabilistic responses predict voting behavior beyond what is possible using verbal responses alone. The probabilistic responses have more predictive power in early August, and the verbal responses have more power in late October. However, throughout the sample period, one can predict voting behavior better using both types of responses than either one alone. Studying the longitudinal pattern of responses, we segment respondents into those who are consistently pro-Obama, consistently anti-Obama, and undecided/vacillators. Membership in the consistently pro- or anti-Obama group is an almost perfect predictor of actual voting behavior, while the undecided/vacillators group has more nuanced voting behavior. We find that treating the ALP as a panel improves predictive power: current and previous polling responses together provide more predictive power than do current responses alone.
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Project description:Although heightened anxiety and health behavior use (i.e., masking, hand washing) may be viewed as an adaptive response to the coronavirus (COVID-19) pandemic, it is unclear how the politicization of the pandemic has influenced the trajectory of such responses. Accordingly, the present study examined differences between those that identify as more conservative or liberal in the trajectory of anxiety and health behaviors during the pandemic. This study also examines shifts in this trajectory before and after the presidential election. As part of a larger study, participants (N = 374) completed a symptom survey starting on May 27, 2020 every 2 weeks for a total of 15 timepoints over 30 weeks. The findings showed that more conservative participants reported lower levels of COVID-19 anxiety and less health behavior use compared to more liberal participants. In fact, anxiety levels increased slightly for more liberal participants and decreased slightly for more conservative participants during the pre-election time frame. Health behavior use also decreased more rapidly for conservative participants than for liberal participants during the pre-election time frame. However, COVID-19 anxiety and health behavior use rose sharply and similarly for both liberal and conservative individuals after the election. Importantly, these patterns were independent of state level variability in COVID-19 positivity and death rates. Subsequent analysis also revealed significant relations between COVID-19 anxiety and health behavior use that was slightly stronger among conservatives. Implications of these findings for navigating the influence of political ideology on anxiety-related responses during a public health emergency like the COVID-19 pandemic are discussed.
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Project description:We use 19 billion likes on the posts of top 2000 U.S. fan pages on Facebook from 2015 to 2016 to measure the dynamic ideological positions for politicians, news outlets, and users at the national and state levels. We then use these measures to derive support rates for 2016 presidential candidates in all 50 states, to predict the election, and to compare them with state-level polls and actual vote shares. We find that: (1) Assuming that users vote for candidates closer to their own ideological positions, support rates calculated using Facebook predict that Trump will win the electoral college vote while Clinton will win the popular vote. (2) State-level Facebook support rates track state-level polling averages and pass the cointegration test, showing two time series share similar trends. (3) Compared with actual vote shares, polls generally have smaller margin of errors, but polls also often overestimate Clinton's support in right-leaning states. Overall, we provide a method to forecast elections at low cost, in real time, and based on passively revealed preference and little researcher discretion.
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