Numerous polls suggest that COVID-19 is a profoundly partisan issue in the United States. Using the geotracking data of 15 million smartphones per day, we found that US counties that voted for Donald Trump (Republican) over Hillary Clinton (Democrat) in the 2016 presidential election exhibited 14% less physical distancing between March and May 2020. Partisanship was more strongly associated with physical distancing than numerous other factors, including counties' COVID-19 cases, population density, median income, and racial and age demographics. Contrary to our predictions, the observed partisan gap strengthened over time and remained when stay-at-home orders were active. Additionally, county-level consumption of conservative media (Fox News) was related to reduced physical distancing. Finally, the observed partisan differences in distancing were associated with subsequently higher COVID-19 infection and fatality growth rates in pro-Trump counties. Taken together, these data suggest that US citizens' responses to COVID-19 are subject to a deep-and consequential-partisan divide.

Social distancing is currently the single most effective method to reduce the spread of COVID-19. As such, researchers across varying fields are rushing to identify variables that predict social distancing and which interventions can heighten social distancing. Yet, much of this research relies on self-report measures (in part because of social distancing guidelines themselves). In two studies we examine whether self-reported social distancing overlaps with real-world behavior. In Study 1, individuals’ self-reported social distancing predicted decreased movement as quantified by participants’ average daily step-counts (assessed via smartphone pedometers). For every increase of one in self-reported social distancing (z-scored), individuals’ daily steps decreased by approximately 21% (Exp(B) ~ .79). In Study 2, the degree of self-reported social distancing in different U.S. States predicted the degree to which people in those States reduced their overall movement and travel to non-essential retail as assessed by ~17 million smart-phone GPS coordinates (.34 < rs < .57). Collectively, our results indicate that self-report measures of social distancing track actual behavior both at the individual and at the group level.

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Few things bind disparate groups together like a common challenge. Yet, numerous polls suggest that the current COVID-19 pandemic in the U.S. is subject to a partisan divide. Using the geotracking data of 15 million smartphones per day, we show that counties that voted for Donald Trump over Hillary Clinton in 2016 exhibited 14% less physical distancing between March and May, 2020. Partisanship was a stronger predictor of physical distancing than numerous other factors, including counties’ median income, COVID-19 cases, and racial and age make-up. Contrary to our predictions, this finding strengthened over time and remained when stay-at-home orders were active. Additionally, counties’ consumption of conservative media (Fox News) predicted reduced physical distancing. Finally, reduced physical distancing in pro-Trump counties was associated with subsequently higher COVID-19 infection and fatality growth rates. Taken together, these data suggest that U.S. responses towards COVID-19 are subject to a deep partisan divide.

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