Thanks to SafeGraph for making their data available to us, as well as other researchers studying the consequences of the Coronavirus epidemic. Thanks to Gianluca Violante and Greg Kaplan for making available their codes. Our measures at the three digit occupation level are available on our websites. The views expressed in this study are those of the author and do not necessarily reflect the position of the Federal Reserve Bank of New York, the Federal Reserve System, or the National Bureau of Economic Research. Replication code is available on the authors' websites. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

Thanks to SafeGraph for making their data available to us, as well as other researchers studying the consequences of the Coronavirus epidemic. Thanks to Gianluca Violante and Greg Kaplan for making available their codes. Our measures at the three digit occupation level are available on our websites. The views expressed in this study are those of the author and do not necessarily reflect the position of the Federal Reserve Bank of New York, the Federal Reserve System, or the National Bureau of Economic Research. Replication code is available on the authors' websites. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

Using data from O ∗ NET, we construct two measures of an occupation’s potential exposure to social distancing measures: (i) the ability to conduct that job from home and (ii) the degree of physical proximity to others the job requires. After validating these measures with comparable measures from ATUS as well as realized work-from-home rates during the pandemic, we employ the measures to study the characteristics of workers in these types of jobs. Our results show that workers in low-work-from-home and high-physical-proximity jobs are more economically vulnerable across various measures constructed from the CPS and PSID: they are less educated, of lower income, have fewer liquid assets relative to income, and are more likely renters. Consistent with the idea that high physical proximity or low work-from-home occupations were more exposed to the Coronavirus shock, we show that the types of workers predicted to be employed in them experienced greater declines in employment during the pandemic. We conclude by comparing the aggregate employment losses in these occupations to their employment losses in the 2008 recession, and find evidence that these occupations were disproportionately exposed to the pandemic shock, and not just comprised of more cyclically sensitive workers. Supplementary Information The online version contains supplementary material available at 10.1007/s10888-021-09487-6.

We investigate the role of information frictions in the US labor market using a new nationally representative panel dataset on individuals' labor market expectations and realizations. We find that expectations about future job offers are, on average, highly predictive of actual outcomes. Despite their predictive power, however, deviations of ex post realizations from ex ante expectations are often sizable. The panel aspect of the data allows us to study how individuals update their labor market expectations in response to such shocks. We find a strong response: an individual who receives a job offer one dollar above her expectation subsequently adjusts her expectations upward by $0.47. The updating patterns we document are, on the whole, inconsistent with Bayesian updating. We embed the empirical evidence on expectations and learning into a model of search on-and off-the job with learning, and show that it is far better able to fit the data on reservation wages relative to a model that assumes complete information. The estimated model indicates that workers would have lower employment transition responses to changes in the value of unemployment through higher unemployment benefits than in a complete information model, suggesting that assuming workers have complete information can bias estimates of the predictions of government interventions. We use the framework to gauge the welfare costs of information frictions which arise because individuals make uninformed job acceptance decisions and find that the costs due to information frictions are sizable, but are largely mitigated by the presence of learning.