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. 2021 Sep;61(4):696-709.
doi: 10.1111/jors.12539. Epub 2021 Jun 7.

Learning from deregulation: The asymmetric impact of lockdown and reopening on risky behavior during COVID-19

Edward L Glaeser  1   2 , Ginger Z Jin  2   3 , Benjamin T Leyden  4   5 , Michael Luca  2   6
Affiliations

Learning from deregulation: The asymmetric impact of lockdown and reopening on risky behavior during COVID-19

Edward L Glaeser et al. J Reg Sci. 2021 Sep.

Abstract

During the coronavirus disease 2019 (COVID-19) pandemic, states issued and then rescinded stay-at-home orders that restricted mobility. We develop a model of learning by deregulation, which predicts that lifting stay-at-home orders can signal that going out has become safer. Using restaurant activity data, we find that the implementation of stay-at-home orders initially had a limited impact, but that activity rose quickly after states' reopenings. The results suggest that consumers inferred from reopening that it was safer to eat out. The rational, but mistaken inference that occurs in our model may explain why a sharp rise of COVID-19 cases followed reopening in some states.

Keywords: COVID‐19; coronavirus; mobility; public health measures.

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Figures

Figure 1
Figure 1
Nationwide average responses. (a) The average of each measure of mobility and restaurant activity, centered around stay‐at‐home and reopening dates. (b) Yelp Reservations, SafeGraph visits to all places‐of‐interest, and SafeGraph visits to full‐service restaurants for three states. Due to a high degree of day‐of‐week variation, each line presents a trend line of the outcome of interest, calculated using a LOESS seasonal‐trend decomposer [Color figure can be viewed at wileyonlinelibrary.com]
Figure 2
Figure 2
COVID‐19 cases and full‐service restaurant visits after reopening. (a) States are grouped based on whether the number of new cases in the week after reopening is more (“Bad Signal”) or less (“Good Signal”) than the number of new cases in the week prior to before reopening. (b) The same approach, using the change in cases in the week prior to before reopening relative to the change in cases 2 weeks prior to before reopening. Data is relative to the value on each state's reopening. Each line presents a trend line of the outcome of interest, calculated using a LOESS seasonal‐trend decomposer. The following states are excluded from these figures because of insufficient data in the postreopening period: CO, DE, IL, MA, MI, MN, NH, NJ, NY, PA, RI, and WA [Color figure can be viewed at wileyonlinelibrary.com]
See this image and copyright information in PMC

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