Dataset Information

Voting contagion: Modeling and analysis of a century of U.S. presidential elections.

ABSTRACT: Social influence plays an important role in human behavior and decisions. Sources of influence can be divided as external, which are independent of social context, or as originating from peers, such as family and friends. An important question is how to disentangle the social contagion by peers from external influences. While a variety of experimental and observational studies provided insight into this problem, identifying the extent of contagion based on large-scale observational data with an unknown network structure remains largely unexplored. By bridging the gap between the large-scale complex systems perspective of collective human dynamics and the detailed approach of social sciences, we present a parsimonious model of social influence, and apply it to a central topic in political science-elections and voting behavior. We provide an analytical expression of the county vote-share distribution, which is in excellent agreement with almost a century of observed U.S. presidential election data. Analyzing the social influence topography over this period reveals an abrupt phase transition from low to high levels of social contagion, and robust differences among regions. These results suggest that social contagion effects are becoming more instrumental in shaping large-scale collective political behavior, with implications on democratic electoral processes and policies.

SUBMITTER: Braha D

PROVIDER: S-EPMC5436881 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Voting contagion: Modeling and analysis of a century of U.S. presidential elections.

Braha Dan D de Aguiar Marcus A M MAM

PloS one 20170518 5

Social influence plays an important role in human behavior and decisions. Sources of influence can be divided as external, which are independent of social context, or as originating from peers, such as family and friends. An important question is how to disentangle the social contagion by peers from external influences. While a variety of experimental and observational studies provided insight into this problem, identifying the extent of contagion based on large-scale observational data with an ...[more]

PMID: 28542409

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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.

Project description:BACKGROUND:The outcome of the 2016 presidential election is commonly attributed to socioeconomic and ethnic/racial issues, but health issues, including "deaths of despair," may also have contributed. OBJECTIVE:To assess whether changes in age-adjusted death rates were independently associated with changes in presidential election voting in 2016 vs. 2008. DESIGN:We used publicly available data in each of 3112 US counties to correlate changes in a county's presidential voting in 2016 compared with 2008 with recent changes in its age-adjusted death rate, after controlling for population and rural-urban status, median age, race/ethnicity, income, education, unemployment rate, and health insurance rate. DESIGN SETTING:Cross-sectional analysis of county-specific data. SETTING/PARTICIPANTS:All 3112 US counties. MAIN MEASURES:The independent correlation of a county's change in age-adjusted death rate between 2000 and 2015 with its net percentage Republican gain or loss in the presidential election of 2016 vs. 2008. KEY RESULTS:In 2016, President Trump increased the Republican presidential vote percentage in 83.8% of counties compared with Senator McCain in 2008. Counties with an increased Republican vote percentage in 2016 vs. 2008 had a 15% higher 2015 age-adjusted death rate than counties with an increased Democratic vote percentage. Since 2000, overall death rates declined by less than half as much, and death rates from drugs, alcohol, and suicide increased 2.5 times as much in counties with Republican gains compared with counties with Democratic gains. In multivariable analyses, Republican net presidential gain in 2016 vs. 2008 was independently correlated with slower reductions in a county's age-adjusted death rate. Although correlation cannot infer causality, modest reductions in death rates might theoretically have shifted Pennsylvania, Michigan, and Wisconsin to Secretary Clinton. CONCLUSIONS:Less of a reduction in age-adjusted death rates was an independent correlate of an increased Republican percentage vote in 2016 vs. 2008. Death rates may be markers of dissatisfactions and fears that influenced the 2016 Presidential election outcomes.

Dataset Information

Voting contagion: Modeling and analysis of a century of U.S. presidential elections.

Publications

Voting contagion: Modeling and analysis of a century of U.S. presidential elections.

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