Business disruptions from social distancing.
Identifieur interne : 000223 ( Main/Exploration ); précédent : 000222; suivant : 000224Business disruptions from social distancing.
Auteurs : Mikl S Koren [Hongrie, Royaume-Uni] ; Rita Pet [Hongrie]Source :
- PloS one [ 1932-6203 ] ; 2020.
Descripteurs français
- KwdFr :
- Betacoronavirus (pathogénicité), Commerce (normes), Commerce (statistiques et données numériques), Commerce (tendances), Emploi (statistiques et données numériques), Emploi (tendances), Emploi (économie), Humains (MeSH), Infections à coronavirus (prévention et contrôle), Infections à coronavirus (transmission), Infections à coronavirus (économie), Infections à coronavirus (épidémiologie), Jeux de données comme sujet (MeSH), Pandémies (prévention et contrôle), Pandémies (économie), Pneumopathie virale (prévention et contrôle), Pneumopathie virale (transmission), Pneumopathie virale (économie), Pneumopathie virale (épidémiologie), Prévention des infections (méthodes), Prévention des infections (normes), Prévention des infections (économie), États-Unis (MeSH).
- MESH :
- méthodes : Prévention des infections.
- normes : Commerce, Prévention des infections.
- pathogénicité : Betacoronavirus.
- prévention et contrôle : Infections à coronavirus, Pandémies, Pneumopathie virale.
- statistiques et données numériques : Commerce, Emploi.
- tendances : Commerce, Emploi.
- économie : Emploi, Infections à coronavirus, Pandémies, Pneumopathie virale, Prévention des infections.
- épidémiologie : Infections à coronavirus, Pneumopathie virale.
- Humains, Jeux de données comme sujet, États-Unis.
- Wicri :
- geographic : États-Unis.
English descriptors
- KwdEn :
- Betacoronavirus (pathogenicity), COVID-19 (MeSH), Commerce (standards), Commerce (statistics & numerical data), Commerce (trends), Coronavirus Infections (economics), Coronavirus Infections (epidemiology), Coronavirus Infections (prevention & control), Coronavirus Infections (transmission), Datasets as Topic (MeSH), Employment (economics), Employment (statistics & numerical data), Employment (trends), Humans (MeSH), Infection Control (economics), Infection Control (methods), Infection Control (standards), Pandemics (economics), Pandemics (prevention & control), Pneumonia, Viral (economics), Pneumonia, Viral (epidemiology), Pneumonia, Viral (prevention & control), Pneumonia, Viral (transmission), SARS-CoV-2 (MeSH), United States (MeSH).
- MESH :
- geographic : United States.
- economics : Coronavirus Infections, Employment, Infection Control, Pandemics, Pneumonia, Viral.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- methods : Infection Control.
- pathogenicity : Betacoronavirus.
- prevention & control : Coronavirus Infections, Pandemics, Pneumonia, Viral.
- standards : Commerce, Infection Control.
- statistics & numerical data : Commerce, Employment.
- transmission : Coronavirus Infections, Pneumonia, Viral.
- trends : Commerce, Employment.
- COVID-19, Datasets as Topic, Humans, SARS-CoV-2.
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.
DOI: 10.1371/journal.pone.0239113
PubMed: 32946463
PubMed Central: PMC7500649
Affiliations:
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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.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32946463</PMID><DateCompleted><Year>2020</Year><Month>11</Month><Day>03</Day></DateCompleted><DateRevised><Year>2020</Year><Month>12</Month><Day>10</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN><JournalIssue CitedMedium="Internet"><Volume>15</Volume><Issue>9</Issue><PubDate><Year>2020</Year></PubDate></JournalIssue><Title>PloS one</Title><ISOAbbreviation>PLoS One</ISOAbbreviation></Journal><ArticleTitle>Business disruptions from social distancing.</ArticleTitle><Pagination><MedlinePgn>e0239113</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0239113</ELocationID><Abstract><AbstractText>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.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Koren</LastName><ForeName>Miklós</ForeName><Initials>M</Initials><Identifier Source="ORCID">0000-0003-4495-7560</Identifier><AffiliationInfo><Affiliation>Central European University, Budapest, Hungary.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Centre for Economic and Regional Studies, Budapest, Hungary.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>CEPR, London, United Kingdom.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Pető</LastName><ForeName>Rita</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>Centre for Economic and Regional Studies, Budapest, Hungary.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>09</Month><Day>18</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS One</MedlineTA><NlmUniqueID>101285081</NlmUniqueID><ISSNLinking>1932-6203</ISSNLinking></MedlineJournalInfo><SupplMeshList><SupplMeshName Type="Disease" UI="C000657245">COVID-19</SupplMeshName><SupplMeshName Type="Organism" UI="C000656484">severe acute respiratory syndrome coronavirus 2</SupplMeshName></SupplMeshList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000073640" MajorTopicYN="N">Betacoronavirus</DescriptorName><QualifierName UI="Q000472" MajorTopicYN="N">pathogenicity</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D000086382" MajorTopicYN="N">COVID-19</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003132" MajorTopicYN="N">Commerce</DescriptorName><QualifierName UI="Q000592" MajorTopicYN="N">standards</QualifierName><QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName><QualifierName UI="Q000639" MajorTopicYN="Y">trends</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus Infections</DescriptorName><QualifierName UI="Q000191" MajorTopicYN="N">economics</QualifierName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName><QualifierName UI="Q000517" MajorTopicYN="Y">prevention & control</QualifierName><QualifierName UI="Q000635" MajorTopicYN="N">transmission</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D066264" MajorTopicYN="N">Datasets as Topic</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004651" MajorTopicYN="N">Employment</DescriptorName><QualifierName UI="Q000191" MajorTopicYN="N">economics</QualifierName><QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName><QualifierName UI="Q000639" MajorTopicYN="Y">trends</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017053" MajorTopicYN="N">Infection Control</DescriptorName><QualifierName UI="Q000191" MajorTopicYN="Y">economics</QualifierName><QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName><QualifierName UI="Q000592" MajorTopicYN="N">standards</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="N">Pandemics</DescriptorName><QualifierName UI="Q000191" MajorTopicYN="N">economics</QualifierName><QualifierName UI="Q000517" MajorTopicYN="Y">prevention & control</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011024" MajorTopicYN="N">Pneumonia, Viral</DescriptorName><QualifierName UI="Q000191" MajorTopicYN="N">economics</QualifierName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName><QualifierName UI="Q000517" 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Sep;189:104235</Citation><ArticleIdList><ArticleId IdType="pubmed">32834177</ArticleId></ArticleIdList></Reference><Reference><Citation>J Travel Med. 2020 Mar 13;27(2):</Citation><ArticleIdList><ArticleId IdType="pubmed">32052841</ArticleId></ArticleIdList></Reference><Reference><Citation>Proc Natl Acad Sci U S A. 2007 May 1;104(18):7588-93</Citation><ArticleIdList><ArticleId IdType="pubmed">17416677</ArticleId></ArticleIdList></Reference><Reference><Citation>JAMA. 2007 Aug 8;298(6):644-54</Citation><ArticleIdList><ArticleId IdType="pubmed">17684187</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>Hongrie</li><li>Royaume-Uni</li></country><region><li>Angleterre</li><li>Grand Londres</li><li>Hongrie centrale</li></region><settlement><li>Budapest</li><li>Londres</li></settlement></list><tree><country name="Hongrie"><region name="Hongrie centrale"><name sortKey="Koren, Mikl S" sort="Koren, Mikl S" uniqKey="Koren M" first="Mikl S" last="Koren">Mikl S Koren</name></region><name sortKey="Koren, Mikl S" sort="Koren, Mikl S" uniqKey="Koren M" first="Mikl S" last="Koren">Mikl S Koren</name><name sortKey="Pet, Rita" sort="Pet, Rita" uniqKey="Pet R" first="Rita" last="Pet">Rita Pet</name></country><country name="Royaume-Uni"><region name="Angleterre"><name sortKey="Koren, Mikl S" sort="Koren, Mikl S" uniqKey="Koren M" first="Mikl S" last="Koren">Mikl S Koren</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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