Mary C. Daly, Shelby R. Buckman, and Lily M. Seitelman
Since COVID-19 hit the United States, more than 20 million American workers have become unemployed and countless others have left the labor force altogether. While the labor market disruptions have affected workers in a wide set of industries and occupations, those without a college degree have experienced the most severe impact. Addressing gaps in educational attainment will be important to creating better economic resiliency for individuals against future shocks.
Shelby R. Buckman, Reuven Glick, Kevin J. Lansing, Nicolas Petrosky-Nadeau, and Lily M. Seitelman
We fit a simple epidemiology model to daily data on the number of currently-infected cases of COVID-19 in China, Italy, the United States, and Brazil. These four countries can be viewed as representing different stages, from late to early, of a COVID-19 epidemic cycle. We solve for a model-implied effective reproduction number Rt each day so that the model closely replicates the daily number of currently infected cases in each country. Using the model-implied time series of Rt, we construct a smoothed version of the in-sample trajectory which is used to project the future evolution of Rt and the out-of-sample number of infected and closed cases (recovered or deceased). For the United States, the number of infected cases is projected to peak around July 19. For Brazil, the number of infected cases is projected to peak around July 24. We show that declines in measures of population mobility tend to precede declines in the model-implied reproduction numbers for each country. This pattern suggests that mandatory and voluntary stay-at-home behavior and social distancing in recent months has helped to reduce the effective reproduction number and reduce the spread of COVID-19.