Despite calls for political consensus, there is growing evidence that the public response to the COVID-19 pandemic has been politicized in the US. We examined the extent to which this polarization exists among the US public across two national studies. In a representative US sample ( N = 699, March 2020) we find that liberals (compared to conservatives) perceive higher risk, place less trust in politicians to handle the pandemic, are more trusting of medical experts such as the WHO, and are more critical of the government response. We replicate these results in a second, pre-registered study ( N = 1000; April 2020), and find that results are similar when considering partisanship, rather than political ideology. In both studies we also find evidence that political polarization extends beyond attitudes, with liberals consistently reporting engaging in a significantly greater number of health protective behaviors (e.g., wearing face masks) than conservatives. We discuss the possible drivers of polarization on COVID-19 attitudes and behaviors, and reiterate the need for fostering bipartisan consensus to effectively address and manage the COVID-19 pandemic.

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Citizens generally try to cooperate with social norms, especially when norm compliance is monitored and publicly disclosed. A recent field experimental study demonstrates that civic appeals that tap into social pressure motivate electoral participation appreciably (Gerber et al., Am Polit Sci Rev 102:33-48, 2008).

If an experimental treatment is experienced by both treated and control group units, tests of hypotheses about causal effects may be difficult to conceptualize, let alone execute. In this article, we show how counterfactual causal models may be written and tested when theories suggest spillover or other network-based interference among experimental units. We show that the “no interference” assumption need not constrain scholars who have interesting questions about interference. We offer researchers the ability to model theories about how treatment given to some units may come to influence outcomes for other units. We further show how to test hypotheses about these causal effects, and we provide tools to enable researchers to assess the operating characteristics of their tests given their own models, designs, test statistics, and data. The conceptual and methodological framework we develop here is particularly applicable to social networks, but may be usefully deployed whenever a researcher wonders about interference between units. Interference between units need not be an untestable assumption; instead, interference is an opportunity to ask meaningful questions about theoretically interesting phenomena.

It is often claimed that conspiracy theories are endorsed with the same level of intensity across the left‐right ideological spectrum. But do liberals and conservatives in the United States embrace conspiratorial thinking to an equivalent degree? There are important historical, philosophical, and scientific reasons dating back to Richard Hofstadter's book The Paranoid Style in American Politics to doubt this claim. In four large studies of U.S. adults (total N = 5049)—including national samples—we investigated the relationship between political ideology, measured in both symbolic and operational terms, and conspiratorial thinking in general. Results reveal that conservatives in the United States were not only more likely than liberals to endorse specific conspiracy theories, but they were also more likely to espouse conspiratorial worldviews in general (r = .27, 95% CI: .24, .30). Importantly, extreme conservatives were significantly more likely to engage in conspiratorial thinking than extreme liberals (Hedges' g = .77, SE = .07, p < .001). The relationship between ideology and conspiratorial thinking was mediated by a strong distrust of officialdom and paranoid ideation, both of which were higher among conservatives, consistent with Hofstadter's account of the paranoid style in American politics.