Were Urban Cowboys Enough to Control COVID-19? Local Shelter-in-Place Orders and Coronavirus Case Growth.
Identifieur interne : 000020 ( Main/Exploration ); précédent : 000019; suivant : 000021Were Urban Cowboys Enough to Control COVID-19? Local Shelter-in-Place Orders and Coronavirus Case Growth.
Auteurs : Dhaval Dave ; Andrew Friedson ; Kyutaro Matsuzawa ; Joseph J. Sabia ; Samuel SaffordSource :
- Journal of urban economics [ 0094-1190 ] ; 2020.
Abstract
One of the most common policy prescriptions to reduce the spread of COVID-19 has been to legally enforce social distancing through shelter-in-place orders (SIPOs). This study examines the role of localized urban SIPO policy in curbing COVID-19 cases. Specifically, we explore (i) the comparative effectiveness of county-level SIPOs in urbanized as compared to non-urbanized areas, (ii) the mechanisms through which SIPO adoption in urban counties yields COVID-related health benefits, and (iii) whether late adoption of a statewide SIPO yields health benefits beyond those achieved from early adopting counties. We exploit the unique laboratory of Texas, a state in which the early adoption of local SIPOs by densely populated counties covered almost two-thirds of the state's population prior to adoption of a statewide SIPO on April 2, 2020. Using an event study framework, we document that countywide SIPO adoption is associated with an 8 percent increase in the percent of residents who remain at home full-time and between a 13 to 19 percent decrease in foot-traffic at venues that may contribute to the spread of COVID-19 such as restaurants, bars, hotels, and entertainment venues. These social distancing effects are largest in urbanized and densely populated counties. Then, we find that in early adopting urban counties, COVID-19 case growth fell by 21 to 26 percentage points two-and-a-half weeks following adoption of a SIPO, a result robust to controls for county-level heterogeneity in COVID-19 outbreak timing, coronavirus testing, the age distribution, and political preferences. We find that approximately 90 percent of the curbed growth in COVID-19 cases in Texas came from the early adoption of SIPOs by urbanized counties, suggesting that the later statewide shelter-in-place mandate yielded relatively few health benefits.
DOI: 10.1016/j.jue.2020.103294
PubMed: 33191960
PubMed Central: PMC7647451
Affiliations:
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Local Shelter-in-Place Orders and Coronavirus Case Growth.</title><author><name sortKey="Dave, Dhaval" sort="Dave, Dhaval" uniqKey="Dave D" first="Dhaval" last="Dave">Dhaval Dave</name><affiliation><nlm:affiliation>Bentley University, IZA & NBER, Center for Health Economics & Policy Studies.</nlm:affiliation><wicri:noCountry code="subField">Center for Health Economics & Policy Studies</wicri:noCountry></affiliation></author><author><name sortKey="Friedson, Andrew" sort="Friedson, Andrew" uniqKey="Friedson A" first="Andrew" last="Friedson">Andrew Friedson</name><affiliation><nlm:affiliation>University of Colorado Denver, Center for Health Economics & Policy Studies.</nlm:affiliation><wicri:noCountry code="subField">Center for Health Economics & Policy Studies</wicri:noCountry></affiliation></author><author><name sortKey="Matsuzawa, Kyutaro" sort="Matsuzawa, Kyutaro" uniqKey="Matsuzawa K" first="Kyutaro" last="Matsuzawa">Kyutaro Matsuzawa</name><affiliation><nlm:affiliation>San Diego State University, Center for Health Economics & Policy Studies.</nlm:affiliation><wicri:noCountry code="subField">Center for Health Economics & Policy Studies</wicri:noCountry></affiliation></author><author><name sortKey="Sabia, Joseph J" sort="Sabia, Joseph J" uniqKey="Sabia J" first="Joseph J" last="Sabia">Joseph J. Sabia</name><affiliation><nlm:affiliation>San Diego State University & IZA, Center for Health Economics & Policy Studies.</nlm:affiliation><wicri:noCountry code="subField">Center for Health Economics & Policy Studies</wicri:noCountry></affiliation></author><author><name sortKey="Safford, Samuel" sort="Safford, Samuel" uniqKey="Safford S" first="Samuel" last="Safford">Samuel Safford</name><affiliation><nlm:affiliation>San Diego State University, Center for Health Economics & Policy Studies.</nlm:affiliation><wicri:noCountry code="subField">Center for Health Economics & Policy Studies</wicri:noCountry></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:33191960</idno><idno type="pmid">33191960</idno><idno type="doi">10.1016/j.jue.2020.103294</idno><idno type="pmc">PMC7647451</idno><idno type="wicri:Area/Main/Corpus">000041</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000041</idno><idno type="wicri:Area/Main/Curation">000041</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">000041</idno><idno type="wicri:Area/Main/Exploration">000041</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Were Urban Cowboys Enough to Control COVID-19? Local Shelter-in-Place Orders and Coronavirus Case Growth.</title><author><name sortKey="Dave, Dhaval" sort="Dave, Dhaval" uniqKey="Dave D" first="Dhaval" last="Dave">Dhaval Dave</name><affiliation><nlm:affiliation>Bentley University, IZA & NBER, Center for Health Economics & Policy Studies.</nlm:affiliation><wicri:noCountry code="subField">Center for Health Economics & Policy Studies</wicri:noCountry></affiliation></author><author><name sortKey="Friedson, Andrew" sort="Friedson, Andrew" uniqKey="Friedson A" first="Andrew" last="Friedson">Andrew Friedson</name><affiliation><nlm:affiliation>University of Colorado Denver, Center for Health Economics & Policy Studies.</nlm:affiliation><wicri:noCountry code="subField">Center for Health Economics & Policy Studies</wicri:noCountry></affiliation></author><author><name sortKey="Matsuzawa, Kyutaro" sort="Matsuzawa, Kyutaro" uniqKey="Matsuzawa K" first="Kyutaro" last="Matsuzawa">Kyutaro Matsuzawa</name><affiliation><nlm:affiliation>San Diego State University, Center for Health Economics & Policy Studies.</nlm:affiliation><wicri:noCountry code="subField">Center for Health Economics & Policy Studies</wicri:noCountry></affiliation></author><author><name sortKey="Sabia, Joseph J" sort="Sabia, Joseph J" uniqKey="Sabia J" first="Joseph J" last="Sabia">Joseph J. Sabia</name><affiliation><nlm:affiliation>San Diego State University & IZA, Center for Health Economics & Policy Studies.</nlm:affiliation><wicri:noCountry code="subField">Center for Health Economics & Policy Studies</wicri:noCountry></affiliation></author><author><name sortKey="Safford, Samuel" sort="Safford, Samuel" uniqKey="Safford S" first="Samuel" last="Safford">Samuel Safford</name><affiliation><nlm:affiliation>San Diego State University, Center for Health Economics & Policy Studies.</nlm:affiliation><wicri:noCountry code="subField">Center for Health Economics & Policy Studies</wicri:noCountry></affiliation></author></analytic><series><title level="j">Journal of urban economics</title><idno type="ISSN">0094-1190</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">One of the most common policy prescriptions to reduce the spread of COVID-19 has been to legally enforce social distancing through shelter-in-place orders (SIPOs). This study examines the role of localized urban SIPO policy in curbing COVID-19 cases. Specifically, we explore (i) the comparative effectiveness of county-level SIPOs in urbanized as compared to non-urbanized areas, (ii) the mechanisms through which SIPO adoption in urban counties yields COVID-related health benefits, and (iii) whether late adoption of a statewide SIPO yields health benefits beyond those achieved from early adopting counties. We exploit the unique laboratory of Texas, a state in which the early adoption of local SIPOs by densely populated counties covered almost two-thirds of the state's population prior to adoption of a statewide SIPO on April 2, 2020. Using an event study framework, we document that countywide SIPO adoption is associated with an 8 percent increase in the percent of residents who remain at home full-time and between a 13 to 19 percent decrease in foot-traffic at venues that may contribute to the spread of COVID-19 such as restaurants, bars, hotels, and entertainment venues. These social distancing effects are largest in urbanized and densely populated counties. Then, we find that in early adopting urban counties, COVID-19 case growth fell by 21 to 26 percentage points two-and-a-half weeks following adoption of a SIPO, a result robust to controls for county-level heterogeneity in COVID-19 outbreak timing, coronavirus testing, the age distribution, and political preferences. We find that approximately 90 percent of the curbed growth in COVID-19 cases in Texas came from the early adoption of SIPOs by urbanized counties, suggesting that the later statewide shelter-in-place mandate yielded relatively few health benefits.</div></front></TEI><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">33191960</PMID><DateRevised><Year>2020</Year><Month>11</Month><Day>17</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0094-1190</ISSN><JournalIssue CitedMedium="Print"><PubDate><Year>2020</Year><Month>Nov</Month><Day>06</Day></PubDate></JournalIssue><Title>Journal of urban economics</Title><ISOAbbreviation>J Urban Econ</ISOAbbreviation></Journal><ArticleTitle>Were Urban Cowboys Enough to Control COVID-19? Local Shelter-in-Place Orders and Coronavirus Case Growth.</ArticleTitle><Pagination><MedlinePgn>103294</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.jue.2020.103294</ELocationID><Abstract><AbstractText>One of the most common policy prescriptions to reduce the spread of COVID-19 has been to legally enforce social distancing through shelter-in-place orders (SIPOs). This study examines the role of localized urban SIPO policy in curbing COVID-19 cases. Specifically, we explore (i) the comparative effectiveness of county-level SIPOs in urbanized as compared to non-urbanized areas, (ii) the mechanisms through which SIPO adoption in urban counties yields COVID-related health benefits, and (iii) whether late adoption of a statewide SIPO yields health benefits beyond those achieved from early adopting counties. We exploit the unique laboratory of Texas, a state in which the early adoption of local SIPOs by densely populated counties covered almost two-thirds of the state's population prior to adoption of a statewide SIPO on April 2, 2020. Using an event study framework, we document that countywide SIPO adoption is associated with an 8 percent increase in the percent of residents who remain at home full-time and between a 13 to 19 percent decrease in foot-traffic at venues that may contribute to the spread of COVID-19 such as restaurants, bars, hotels, and entertainment venues. These social distancing effects are largest in urbanized and densely populated counties. Then, we find that in early adopting urban counties, COVID-19 case growth fell by 21 to 26 percentage points two-and-a-half weeks following adoption of a SIPO, a result robust to controls for county-level heterogeneity in COVID-19 outbreak timing, coronavirus testing, the age distribution, and political preferences. We find that approximately 90 percent of the curbed growth in COVID-19 cases in Texas came from the early adoption of SIPOs by urbanized counties, suggesting that the later statewide shelter-in-place mandate yielded relatively few health benefits.</AbstractText><CopyrightInformation>© 2020 Published by Elsevier Inc.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Dave</LastName><ForeName>Dhaval</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Bentley University, IZA & NBER, Center for Health Economics & Policy Studies.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Friedson</LastName><ForeName>Andrew</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>University of Colorado Denver, Center for Health Economics & Policy Studies.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Matsuzawa</LastName><ForeName>Kyutaro</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>San Diego State University, Center for Health Economics & Policy Studies.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sabia</LastName><ForeName>Joseph J</ForeName><Initials>JJ</Initials><AffiliationInfo><Affiliation>San Diego State University & IZA, Center for Health Economics & Policy Studies.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Safford</LastName><ForeName>Samuel</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>San Diego State University, Center for Health Economics & Policy Studies.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>11</Month><Day>06</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>J Urban Econ</MedlineTA><NlmUniqueID>101084734</NlmUniqueID><ISSNLinking>0094-1190</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">COVID-19</Keyword><Keyword MajorTopicYN="N">coronavirus</Keyword><Keyword MajorTopicYN="N">population density</Keyword><Keyword MajorTopicYN="N">shelter-in-place orders</Keyword><Keyword MajorTopicYN="N">urbanicity</Keyword></KeywordList><CoiStatement>None.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>05</Month><Day>26</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>10</Month><Day>23</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>11</Month><Day>16</Day><Hour>8</Hour><Minute>42</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>11</Month><Day>17</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>11</Month><Day>17</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>aheadofprint</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">33191960</ArticleId><ArticleId IdType="doi">10.1016/j.jue.2020.103294</ArticleId><ArticleId IdType="pii">S0094-1190(20)30065-6</ArticleId><ArticleId IdType="pmc">PMC7647451</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
<ReferenceList><Reference><Citation>JAMA. 2020 Apr 14;323(14):1406-1407</Citation><ArticleIdList><ArticleId IdType="pubmed">32083643</ArticleId></ArticleIdList></Reference><Reference><Citation>N Engl J Med. 2020 Mar 5;382(10):929-936</Citation><ArticleIdList><ArticleId IdType="pubmed">32004427</ArticleId></ArticleIdList></Reference><Reference><Citation>Ann Intern Med. 2020 May 5;172(9):577-582</Citation><ArticleIdList><ArticleId IdType="pubmed">32150748</ArticleId></ArticleIdList></Reference><Reference><Citation>Econ Inq. 2020 Aug 03;:</Citation><ArticleIdList><ArticleId IdType="pubmed">32836519</ArticleId></ArticleIdList></Reference><Reference><Citation>Health Aff (Millwood). 2020 Jul;39(7):1237-1246</Citation><ArticleIdList><ArticleId IdType="pubmed">32407171</ArticleId></ArticleIdList></Reference><Reference><Citation>Lancet Infect Dis. 2020 Apr;20(4):410-411</Citation><ArticleIdList><ArticleId IdType="pubmed">32087116</ArticleId></ArticleIdList></Reference><Reference><Citation>N Engl J Med. 2020 Mar 5;382(10):970-971</Citation><ArticleIdList><ArticleId IdType="pubmed">32003551</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list></list><tree><noCountry><name sortKey="Dave, Dhaval" sort="Dave, Dhaval" uniqKey="Dave D" first="Dhaval" last="Dave">Dhaval Dave</name><name sortKey="Friedson, Andrew" sort="Friedson, Andrew" uniqKey="Friedson A" first="Andrew" last="Friedson">Andrew Friedson</name><name sortKey="Matsuzawa, Kyutaro" sort="Matsuzawa, Kyutaro" uniqKey="Matsuzawa K" first="Kyutaro" last="Matsuzawa">Kyutaro Matsuzawa</name><name sortKey="Sabia, Joseph J" sort="Sabia, Joseph J" uniqKey="Sabia J" first="Joseph J" last="Sabia">Joseph J. Sabia</name><name sortKey="Safford, Samuel" sort="Safford, Samuel" uniqKey="Safford S" first="Samuel" last="Safford">Samuel Safford</name></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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