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Scholars have suggested that White American support for welfare is related to beliefs about the racial composition of welfare recipients. While a host of observational studies lend credence to this view, it has not yet been tested using the tools of randomized inference. In this study, we do this by conducting two incentive-compatible experiments (n = 9,775) in which different participants are randomly given different signals about the share of welfare recipients who identify as Black and White. Our analysis yields four main findings. First, 86% of respondents greatly overestimate the share of welfare recipients who are Black, with the average respondent overestimating this by almost a factor of two. Second, White support for welfare is inversely related to the proportion of welfare recipients who are Black--a causal claim that we establish using treatment assignment as an instrument for beliefs about the racial composition of welfare recipients. Third, just making White participants think about the racial composition of welfare recipients reduces their support for welfare. Fourth, providing White respondents with accurate information about the racial composition of welfare recipients (relative to not receiving any information) does not significantly influence their support for welfare.
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Little is known about how people's beliefs concerning the Coronavirus Disease 2019 (COVID-19) influence their behavior. To shed light on this, we conduct an online experiment (n = 3,610) with US and UK residents. Participants are randomly allocated to a control group or to one of two treatment groups. The treatment groups are shown upperor lower-bound expert estimates of the infectiousness of the virus. We present three main empirical findings. First, individuals dramatically overestimate the dangerousness and infectiousness of COVID-19 relative to expert opinion. Second, providing people with expert information partially corrects their beliefs about the virus. Third, the more infectious people believe that COVID-19 is, the less willing they are to take protective measures, a finding we dub the "fatalism effect". We develop a formal model that can explain the fatalism effect and discuss its implications for optimal policy during the pandemic.
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