Sequential Lifting of COVID-19 Interventions with Population Heterogeneity
This paper analyzes a sequential approach to lifting interventions in the COVID-19 pandemic taking heterogeneity in the population into account. The population is heterogeneous in terms of the consequences of infection (need for hospitalization and critical care, and mortality) and in terms of labor force participation. Splitting the population in two groups by age, a less affected younger group that is more likely to work, and a more affected older group less likely to work, and lifting interventions sequentially (for the younger group first and the older group later on) can substantially reduce mortality, demands on the health care system, and the economic cost of interventions.
First version. Preliminary. The author is the William and Sue Gross Professor of Financial Economics at Duke University, an NBER Research Associate, and a CEPR Research Fellow, and is currently on sabbatical leave at Princeton University and NYU; their hospitality is gratefully acknowledged. The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research.