We develop a heterogeneous-agents network-based model to analyze alternative policies during a pandemic outbreak, accounting for health and economic trade-offs within the same empirical framework. We leverage a variety of data sources, including data on individuals’ mobility and encounters across metropolitan areas, health records, and measures of the possibility to be productively working from home. This combination of data sources allows us to build a framework in which the severity of a disease outbreak varies across locations and industries, and across individuals who differ by age, occupation, and preexisting health conditions.

We use this framework to analyze the impact of different social distancing policies in the context of the COVID-19 outbreaks across US metropolitan areas. Our results highlight how outcomes vary across areas in relation to the underlying heterogeneity in population density, social network structures, population health, and employment characteristics. We find that policies by which individuals who can work from home continue to do so, or in which schools and firms alternate schedules across different groups of students and employees, can be effective in limiting the health and healthcare costs of the pandemic outbreak while also reducing employment losses.

More Research From These Scholars

BFI Working Paper Apr 26, 2020

Which Workers Bear the Burden of Social Distancing Policies?

Simon Mongey, Laura Pilossoph, Alex Weinberg
Topics:  COVID-19, Employment & Wages
BFI Working Paper Mar 24, 2020

An SEIR Infectious Disease Model with Testing and Conditional Quarantine

David Berger, Kyle Herkenhoff, Simon Mongey
Topics:  COVID-19
BFI Working Paper Aug 16, 2017

Estimating Equilibrium in Health Insurance Exchanges: Price Competition and Subsidy Design under the ACA

Pietro Tebaldi
Topics:  Industrial Organization, Health care