The DOI matches its Crossref record; 54 of the 55 references it cites were found in a registry or an index.
Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020
Open accessAbstract
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 States that repealed their shutdown orders saw symmetric, modest recoveries in consumer visits, further supporting the small estimated effect of policy.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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“we tend to find relatively small reductions in travel due to SIPOs.”
From Policy evaluation during a pandemic (2023) · cites this paper as Goolsbee and Syverson (2021)
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“First, our results on the effect of SIPOs on travel are more similar to existing estimates (e.g., Goolsbee and Syverson (2021)).”
From Policy evaluation during a pandemic (2023) · cites this paper as Goolsbee and Syverson (2021)
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Cites
COVID-Induced Economic Uncertainty
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The impact of COVID-19 on small business outcomes and expectations
Bartik, A. W., Bertrand, M., Cullen, Z. N., Glaeser, E. L., Luca, M., & Stanton, C. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2006991117 · Cited by 1,614
The effect of large-scale anti-contagion policies on the COVID-19 pandemic
Hsiang, S., Allen, D., Annan-Phan, S., Bell, K., Bolliger, I. W., Chong, T., Druckenmiller, H., Huang, L. Y., Hultgren, A., Krasovich, E., et al. (2020) · Nature · doi:10.1038/s41586-020-2404-8 · Cited by 1,528
Alternative Transformations to Handle Extreme Values of the Dependent Variable
Burbidge, J. B., Magee, L., & Robb, A. L. (1988) · Journal of the American Statistical Association · doi:10.1080/01621459.1988.10478575 · Cited by 1,425
The Cost of the Covid-19 Crisis: Lockdowns, Macroeconomic Expectations, and Consumer Spending
Coibion, O., Gorodnichenko, Y., & Weber, M. (2020) · National Bureau of Economic Research · doi:10.3386/w27141 · Cited by 486
(2020) · Cited by 469
Labor demand in the time of COVID-19: Evidence from vacancy postings and UI claims
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Causal impact of masks, policies, behavior on early COVID-19 pandemic in the U.S
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Research works printed but not linked: 1
- Berry, 2020, Not another COVID paper: the modest effects of shelter-in-place policies, Mimeo
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Metadata
- Authors
- Goolsbee, A., & Syverson, C.
- Affiliations
- University of Chicago (US)
- Corresponding author
- Goolsbee, Austan
- Venue
- Journal of Public Economics
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 193
- Pages
- 104311
- ISSN
- 0047-2727 (ISSN-L)
- PMC
- PMC7687454
- OpenAlex
- W3037490325
- Cited by
- 749
- References
- 55
- Citations by year
- 2026: 35 · 2025: 71 · 2024: 84 · 2023: 117 · 2022: 182 · 2021: 163
- FWCI
- 119.40
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Public Economics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › COVID-19 Pandemic Impacts
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Social Sciences › Sociology and Political Science › COVID-19 Prevention and Impact
- Keywords
- COVID-19 pandemic, economic decline, consumer behavior, fear of infection, business closures, store traffic, essential businesses, COVID-19 mortality, policy effectiveness, restaurant closures, food retail
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