Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020

https://doi.org/10.1016/j.jpubeco.2020.104311Get rights and content

Abstract

The collapse of economic activity in 2020 from COVID-19 has been immense. An important question is how much of that collapse resulted from government-imposed restrictions versus people voluntarily choosing to stay home to avoid infection. This paper examines the drivers of the economic slowdown using cellular phone records data on customer visits to more than 2.25 million individual businesses across 110 different industries. Comparing consumer behavior over the crisis within the same commuting zones but across state and county boundaries with different policy regimes suggests that legal shutdown orders account for only a modest share of the massive changes to consumer behavior (and that tracking county-level policy conditions is significantly more accurate than using state-level policies alone). While overall consumer traffic fell by 60 percentage points, legal restrictions explain only 7 percentage points of this. Individual choices were far more important and seem tied to fears of infection. Traffic started dropping before the legal orders were in place; was highly influenced by the number of COVID deaths reported in the county; and showed a clear shift by consumers away from busier, more crowded stores toward smaller, less busy stores in the same industry. States that repealed their shutdown orders saw symmetric, modest recoveries in consumer visits, further supporting the small estimated effect of policy. Although the shutdown orders had little aggregate impact, they did have a significant effect in reallocating consumer visits away from “nonessential” to “essential” businesses and from restaurants and bars toward groceries and other food sellers.

Keywords

COVID
Pandemic
Lockdown
Shutdown
Sheltering orders
Shelter in place
Borders
States
Economic activity
Consumer activity
Essential business

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We would like to thank seminar participants at the University of Chicago, the editor and three anonymous referees for their comments and SafeGraph, Inc. for making their data available for academic COVID research. We thank Roxanne Nesbitt and, especially, Nicole Bei Luo for superb research assistance and the Initiative on Global Markets at the University of Chicago for financial assistance.

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