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

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          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          During the coronavirus disease 2019 (COVID‐19) pandemic, states issued and then rescinded stay‐at‐home orders that restricted mobility.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.

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          Polarization and Public Health: Partisan Differences in Social Distancing during the Coronavirus Pandemic ☆

          Hunt Allcott, Levi Boxell, Jacob Conway … (2020)
          We study partisan differences in Americans’ response to the COVID-19 pandemic. Political leaders and media outlets on the right and left have sent divergent messages about the severity of the crisis, which could impact the extent to which Republicans and Democrats engage in social distancing and other efforts to reduce disease transmission. We develop a simple model of a pandemic response with heterogeneous agents that clarifies the causes and consequences of heterogeneous responses. We use location data from a large sample of smartphones to show that areas with more Republicans engaged in less social distancing, controlling for other factors including public policies, population density, and local COVID cases and deaths. We then present new survey evidence of significant gaps at the individual level between Republicans and Democrats in self-reported social distancing, beliefs about personal COVID risk, and beliefs about the future severity of the pandemic.
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            Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020 ☆

            Austan Goolsbee, Chad Syverson (2020)
            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.
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              JUE Insight: Measuring movement and social contact with smartphone data: a real-time application to COVID-19 ☆

              Victor Couture, Jonathan Dingel, Allison Green … (2021)
              Tracking human activity in real time and at fine spatial scale is particularly valuable during episodes such as the COVID-19 pandemic. In this paper, we discuss the suitability of smartphone data for quantifying movement and social contact. These data cover broad sections of the US population and exhibit pre-pandemic patterns similar to conventional survey data. We develop and make publicly available a location exposure index that summarizes county-to-county movements and a device exposure index that quantifies social contact within venues. We also investigate the reliability of smartphone movement data during the pandemic.
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                Author and article information

                Contributors
                Michael Luca:
                ORCID: http://orcid.org/0000-0002-0747-7544
                mluca@hbs.edu
                Journal
                Journal ID (nlm-ta): J Reg Sci
                Journal ID (iso-abbrev): J Reg Sci
                Journal ID (doi): 10.1111/(ISSN)1467-9787
                Journal ID (publisher-id): JORS
                Title: Journal of Regional Science
                Publisher: John Wiley and Sons Inc. (Hoboken )
                ISSN (Print): 0022-4146
                ISSN (Electronic): 1467-9787
                Publication date (Electronic): 07 June 2021
                Electronic Location Identifier: 10.1111/jors.12539
                Affiliations
                [ 1 ] Department of Economics Harvard University Cambridge Massachusetts USA
                [ 2 ] NBER Cambridge Massachusetts USA
                [ 3 ] Department of Economics University of Maryland College Park Maryland USA
                [ 4 ] Dyson School of Applied Economics and Management Cornell University Ithaca New York USA
                [ 5 ] CESifo Munich Germany
                [ 6 ] Harvard Business School Harvard University Boston Massachusetts USA
                Author notes
                [*] [* ] Correspondence Michael Luca, Harvard Business School, Harvard University, Soldiers Field, Boston, MA 02163, USA.

                Email: mluca@ 123456hbs.edu

                Author information
                https://orcid.org/0000-0001-7912-3780
                https://orcid.org/0000-0002-7363-2493
                http://orcid.org/0000-0002-0747-7544
                Article
                Publisher ID: JORS12539
                DOI: 10.1111/jors.12539
                PMC ID: 8242873
                PubMed ID: 34226759
                SO-VID: fd41c86d-a2c3-4859-979b-9b5962e6502c
                Copyright © © 2021 The Authors. Journal of Regional Science published by Wiley Periodicals LLC.
                License:

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                Date received : 17 December 2020
                Date accepted : 26 March 2021
                Page count
                Figures: 2, Tables: 3, Pages: 14, Words: 7469
                Funding
                Funded by: Harvard Business School , open-funder-registry 10.13039/100007300;
                Award ID: Research Budget
                Categories
                Subject: Research Article
                Subject: Research Articles
                Custom metadata
                source-schema-version-number 2.0
                edited-state corrected-proof
                details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.2 mode:remove_FC converted:30.06.2021

                Keywords: coronavirus,covid‐19,mobility,public health measures
                Data availability:
                Keywords: coronavirus, covid‐19, mobility, public health measures

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