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Cited by 264

The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.

Business disruptions from social distancing

Open access
Koren, Miklós, Pető, Rita·2020·PLOS ONE

Abstract

Social distancing interventions can be effective against epidemics but are potentially detrimental for the economy. Businesses that rely heavily on face-to-face communication or close physical proximity when producing a product or providing a service are particularly vulnerable. There is, however, no systematic evidence about the role of human interactions across different lines of business and about which will be the most limited by social distancing. Here we provide theory-based measures of the reliance of U.S. businesses on human interaction, detailed by industry and geographic location. We find that, before the pandemic hit, 43 million workers worked in occupations that rely heavily on face-to-face communication or require close physical proximity to other workers. Many of these workers lost their jobs since. Consistently with our model, employment losses have been largest in sectors that rely heavily on customer contact and where these contacts dropped the most: retail, hotels and restaurants, arts and entertainment and schools. Our results can help quantify the economic costs of social distancing.

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Crossref · 10.1371/journal.pone.0239113

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Metadata

Authors
Koren, M., & Pető, R.
Venue
PLOS ONE
Published
2020-09-18
Volume
15
Issue
9
Pages
e0239113
ISSN
1932-6203 (ISSN-L)
OpenAlex
W3014364737
Cited by
264
References
30
FWCI
41.80
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Business disruptions from social distancing. https://app.cochat.ai/papers/10.1371/journal.pone.0239113
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Koren, M., & Pető, R. (2020). Business disruptions from social distancing. PLOS ONE. https://doi.org/10.1371/journal.pone.0239113

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.