RAPID: Understanding increased social bias during the COVID-19 crisis in the United States

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Research has found that disruptive social events can lead to increased social bias toward outgroup members. This research examines this relationship in the context of the COVID-19 crisis in the United States, which has adversely affected the health and economic well-being of millions of Americans. Although numerous incidents of bias directed toward immigrants and people of Asian descent have been reported since the outbreak began, research is needed to understand the extent of this bias and the factors that produce it. This research will address this need, by analyzing both existing as well as new survey data from nationally representative samples of Americans collected throughout much of 2020, as the crisis emerged and continues to evolve. The results will provide insights into how COVID-19 is affecting social attitudes in the United States, and more generally, into the ways that diverse societies respond to large-scale disruptions that threaten their way of life. This research will test the hypothesis that the relationship between COVID-19 risk perceptions and social bias may be less straightforward than existing theory and research suggest, and that this relationship may vary as a function of local threat conditions, type of perceived risk (health vs. economic), and personal characteristics. The research team analyzes public opinion data from leading survey organizations to test this hypothesis. In one component of the research, the team collects new data from a representative sample of the United States public to measure attitudes toward immigration and toward members of different racial and ethnic groups, alongside attitudes about COVID-19. They repeat this survey throughout the spring, summer, and fall of 2020 to study how these attitudes and their relationship may change in real time. In a second component of the research, they analyze existing survey data on COVID-19 attitudes that have been collected since February 2020. By combining new data with existing data, they are building a comprehensive dataset featuring thousands of survey interviews on COVID-19 attitudes and social bias spanning most of 2020. This research will generate robust estimates of COVID-19 attitudes and social bias, and their degree of stability versus change, as the crisis continues to unfold. By revealing where and when social bias is most prevalent, this research will help diverse societies such as the United States protect their residents against negative treatment the next time a similar crisis emerges, as well as during less severe incidents of disruption and insecurity.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.