Estimating perceptions of the relative COVID risk of different social-distancing behaviors from respondents' pairwise assessments.

How do people compare the effectiveness of different social-distancing behaviors in avoiding the spread of viral infection? During the COVID pandemic, we showed 676 online respondents in the United States, United Kingdom, and Israel 30 pairs of brief videos of acquaintances meeting. We asked respondents to indicate which video from each pair depicted greater risk of COVID infection. Their choices imply that on average, respondents considered talking 14 min longer to be as risky as standing 1 foot closer, being indoors as standing 3 feet closer, being exposed to coughs or sneezes as 3 to 4 ft closer, greeting with a hug as 7 ft closer, and with a handshake as 5 ft closer. Respondents considered properly masking as protecting the wearer and interlocutor equally, removing the mask entirely or only when talking as standing 4 to 5 ft closer but wearing it under the nose as only 1 to 2 ft closer. We provide weaker evidence on beliefs about the interaction effects of different behaviors. In a more limited, ex post analysis, we find little evidence of differences in beliefs across subpopulations.

Conclusions Regarding Cross-Group Differences in Happiness Depend on Difficulty of Reaching Respondents.

A growing literature explores differences in subjective well-being across demographic groups, often relying on surveys with high nonresponse rates. By using the reported number of call attempts made to participants in the University of Michigan's Surveys of Consumers, we show that comparisons among easy-to-reach respondents differ from comparisons among hard-to-reach ones. Notably, easy-to-reach women are happier than easy-to-reach men, but hard-to-reach men are happier than hard-to-reach women, and conclusions of a survey could reverse with more attempted calls. Better alternatives to comparing group sample averages might include putting greater weight on hard-to-reach respondents or even extrapolating trends in responses.

A MODEL OF NONBELIEF IN THE LAW OF LARGE NUMBERS.

People believe that, even in very large samples, proportions of binary signals might depart significantly from the population mean. We model this "non-belief in the Law of Large Numbers" by assuming that a person believes that proportions in any given sample might be determined by a rate different than the true rate. In prediction, a non-believer expects the distribution of signals will have fat tails. In inference, a non-believer remains uncertain and influenced by priors even after observing an arbitrarily large sample. We explore implications for beliefs and behavior in a variety of economic settings.