Income Inequality, Social Mobility, and Electoral Participation in the US Counties: Revisiting the Inequality-Participation Nexus

Previous research has provided contested hypotheses about the impact of income inequality on electoral participation. This study reexamines the debate between conflict and relative power theories by focusing on a largely ignored factor: social mobility. We argue that social mobility conditions the inequality-participation nexus by alleviating the frustration, class conflict, and efficacy gaps between the rich and the poor that the prevailing theories assume income inequality to create. By utilizing the Cooperative Congressional Election Survey, we test this argument focusing on US counties. Our analysis confirms that the effects of income inequality on citizens’ likelihood of voting vary depending on mobility, suggesting that social mobility provides a crucial context in which income inequality can play out in substantially different ways. This article implies that more scholarly endeavors should be made to clarify the multifaceted structure of inequality for improving our understanding of the relationship between economic and political inequality.

Does violence within a non-violent social movement help or hurt the movement? Evidence from the 2020 BlackLivesMatter Protests

The murder of George Floyd ignited one of the largest mass mobilizations in US history, including both non-violent and violent BlackLivesMatter protests in the summer of 2020. Many have since asked: did the violence within the largely non-violent movement help or hurt its goals? To answer this question, we used real-world data (ACLED, 2020) about the location of all BlackLivesMatter protests during the summer of 2020 to identify US counties that featured no protests, only nonviolent protests, or both nonviolent and violent protests. We then combined this data with survey data (N = 494, Study 1), data from the Congressional Cooperative Election Study (N = 43,924, Study 2A), and data from Project Implicit (N = 180,480, Study 2B), in order to examine how exposure (i.e. living in a county with) different types of protest affected both support for the key policy goals of the movement and prejudice towards Black Americans. We found that the 2020 BLM protests had no impact on prejudice among either liberals or conservatives. However, they were, even when violent, able to increase support for BlackLivesMatter’s key policy goals among conservatives living in relatively liberal areas. As such, this research suggests that violent, disruptive actions within a broader non-violent movement may affect those likely to be resistant to the movement. We connect these findings to the notion of disruptive action, which explains why these effects do not materialize in reducing prejudice, but in generating support for important policy goals of the movement.

COVID-19 and Historic Influenza Vaccinations in the United States: A Comparative Analysis

The COVID-19 vaccination effort is a monumental global challenge. Recognizing and addressing the causes of vaccine hesitancy will improve vaccine uptake. The primary objective of this study was to compare the COVID-19 vaccination rates in US counties to historical vaccination rates for influenza in persons aged 65 and older. The secondary objective was to identify county-level demographic, socioeconomic, and political factors that influence vaccination rates. County level data were obtained from publicly available databases for comparison and to create predictive models. Overall, in US counties the COVID-19 vaccination rate exceeded influenza vaccination rates amongst those aged 65 or older (69.4.0% vs. 44%, p < 0.0001). 2690 (83.4%) of 3224 counties had vaccinated 50% or more of their 65 and older residents in the first seven months of the COVID-19 vaccination roll out. There were 467 (14.5%) of 3223 counties in which the influenza vaccination rate exceeded the COVID-19 vaccination rate. Most of these counties were in the Southern region, were considered politically “red” and had a significantly higher non-Hispanic Black resident population (14.4% vs. 8.2%, p < 0.0001). Interventions intended to improve uptake should account for nuances in vaccine access, confidence, and consider factual social media messaging, especially in vulnerable counties.

Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network

Prediction of complex epidemiological systems such as COVID-19 is challenging on many grounds. Commonly used compartmental models struggle to handle an epidemiological process that evolves rapidly and is spatially heterogeneous. On the other hand, machine learning methods are limited at the beginning of the pandemics due to small data size for training. We propose a deep learning approach to predict future COVID-19 infection cases and deaths 1 to 4 weeks ahead at the fine granularity of US counties. The multi-variate Long Short-term Memory (LSTM) recurrent neural network is trained on multiple time series samples at the same time, including a mobility series. Results show that adding mobility as a variable and using multiple samples to train the network improve predictive performance both in terms of bias and of variance of the forecasts. We also show that the predicted results have similar accuracy and spatial patterns with a standard ensemble model used as benchmark. The model is attractive in many respects, including the fine geographic granularity of predictions and great predictive performance several weeks ahead. Furthermore, data requirement and computational intensity are reduced by substituting a single model to multiple models folded in an ensemble model.

Change of human mobility during COVID-19: A United States case study

With the onset of COVID-19 and the resulting shelter in place guidelines combined with remote working practices, human mobility in 2020 has been dramatically impacted. Existing studies typically examine whether mobility in specific localities increases or decreases at specific points in time and relate these changes to certain pandemic and policy events. However, a more comprehensive analysis of mobility change over time is needed. In this paper, we study mobility change in the US through a five-step process using mobility footprint data. (Step 1) Propose the Delta Time Spent in Public Places (ΔTSPP) as a measure to quantify daily changes in mobility for each US county from 2019-2020. (Step 2) Conduct Principal Component Analysis (PCA) to reduce the ΔTSPP time series of each county to lower-dimensional latent components of change in mobility. (Step 3) Conduct clustering analysis to find counties that exhibit similar latent components. (Step 4) Investigate local and global spatial autocorrelation for each component. (Step 5) Conduct correlation analysis to investigate how various population characteristics and behavior correlate with mobility patterns. Results show that by describing each county as a linear combination of the three latent components, we can explain 59% of the variation in mobility trends across all US counties. Specifically, change in mobility in 2020 for US counties can be explained as a combination of three latent components: 1) long-term reduction in mobility, 2) no change in mobility, and 3) short-term reduction in mobility. Furthermore, we find that US counties that are geographically close are more likely to exhibit a similar change in mobility. Finally, we observe significant correlations between the three latent components of mobility change and various population characteristics, including political leaning, population, COVID-19 cases and deaths, and unemployment. We find that our analysis provides a comprehensive understanding of mobility change in response to the COVID-19 pandemic.