Effects of social distancing policy on labor market outcomes.

US workers receive unemployment benefits if they lose their job, but not for reduced working hours. In alignment with the benefits incentives, we find that the labor market responded to COVID-19 and related closure-policies mostly on the extensive (12 pp outright job loss) margin. Exploiting timing variation in state closure-policies, difference-in-differences (DiD) estimates show, between March 12 and April 12, 2020, employment rate fell by 1.7 pp for every 10 extra days of state stay-at-home orders (SAH), with little effect on hours worked/earnings among those employed. Forty percentage of the unemployment was due to a nationwide shock, rest due to social-distancing policies, particularly among "non-essential" workers.

An in-depth examination of requirements for disclosure risk assessment.

The use of formal privacy to protect the confidentiality of responses in the 2020 Decennial Census of Population and Housing has triggered renewed interest and debate over how to measure the disclosure risks and societal benefits of the published data products. We argue that any proposal for quantifying disclosure risk should be based on prespecified, objective criteria. We illustrate this approach to evaluate the absolute disclosure risk framework, the counterfactual framework underlying differential privacy, and prior-to-posterior comparisons. We conclude that satisfying all the desiderata is impossible, but counterfactual comparisons satisfy the most while absolute disclosure risk satisfies the fewest. Furthermore, we explain that many of the criticisms levied against differential privacy would be levied against any technology that is not equivalent to direct, unrestricted access to confidential data. More research is needed, but in the near term, the counterfactual approach appears best-suited for privacy versus utility analysis.

Determinants of Disparities in Early COVID-19 Job Losses.

This study examines the sociodemographic divide in early labor market responses to the U.S. COVID-19 epidemic and associated policies, benchmarked against two previous recessions. Monthly Current Population Survey (CPS) data show greater declines in employment in April and May 2020 (relative to February) for Hispanic individuals, younger workers, and those with a high school diploma or some college. Between April and May, the demographic subgroups considered regained some employment. Reemployment in May was broadly proportional to the employment drop that occurred through April, except for Black individuals, who experienced a smaller rebound. Compared to the 2001 recession and the Great Recession, employment losses in the early COVID-19 recession were smaller for groups with low or high (vs. medium) education. We show that job loss was greater in occupations that require more interpersonal contact and that cannot be performed remotely, and that pre-COVID-19 sorting of workers into occupations and industries along demographic lines can explain a sizable portion of the demographic gaps in new unemployment. For example, while women suffered more job losses than men, their disproportionate pre-epidemic sorting into occupations compatible with remote work shielded them from even larger employment losses. However, substantial gaps in employment losses across groups cannot be explained by socioeconomic differences. We consider policy lessons and future research needs regarding the early labor market implications of the COVID-19 crisis.

Modeling Endogenous Mobility in Earnings Determination.

We evaluate the bias from endogenous job mobility in fixed-effects estimates of worker- and firm-specific earnings heterogeneity using longitudinally linked employer-employee data from the LEHD infrastructure file system of the U.S. Census Bureau. First, we propose two new residual diagnostic tests of the assumption that mobility is exogenous to unmodeled determinants of earnings. Both tests reject exogenous mobility. We relax exogenous mobility by modeling the matched data as an evolving bipartite graph using a Bayesian latent-type framework. Our results suggest that allowing endogenous mobility increases the variation in earnings explained by individual heterogeneity and reduces the proportion due to employer and match effects. To assess external validity, we match our estimates of the wage components to out-of-sample estimates of revenue per worker. The mobility-bias corrected estimates attribute much more of the variation in revenue per worker to variation in match quality and worker quality than the uncorrected estimates.