US Metropolitan Area Resilience: Insights from dynamic spatial panel estimation

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dc.contributor.authorDoran, Justin
dc.contributor.authorFingleton, Bernard
dc.date.accessioned2018-04-19T10:32:08Z
dc.date.available2018-04-19T10:32:08Z
dc.date.issued2017-10-12
dc.date.updated2018-04-17T14:46:47Z
dc.description.abstractIn this paper, we show that the economic crisis commencing in 2007 had different impacts across US Metropolitan Statistical Areas, and seek to understand why differences occurred. The hypothesis of interest is that differences in industrial structure are a cause of variations in response to the crisis. Our approach uses a state-of-the art dynamic spatial panel model to obtain counterfactual predictions of Metropolitan Statistical Area employment levels from 2008 to 2014. The counterfactual employment series are compared with actual employment paths in order to obtain Metropolitan Statistical Area-specific measures of crisis impact, which then are analysed with a view to testing the hypothesis that resilience to the crisis was dependent on Metropolitan Statistical Area industrial structure.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationDoran, J. and Fingleton, B. (2018) 'US Metropolitan Area Resilience: Insights from dynamic spatial panel estimation', Environment and Planning A: Economy and Space, 50(1), pp. 111-132.en
dc.identifier.doi10.1177/0308518x17736067
dc.identifier.endpage132en
dc.identifier.issn0308-518X
dc.identifier.issn1472-3409
dc.identifier.issued1en
dc.identifier.journaltitleEnvironment and Planning Aen
dc.identifier.startpage111en
dc.identifier.urihttps://hdl.handle.net/10468/5825
dc.identifier.volume50en
dc.language.isoenen
dc.publisherSage Publicationsen
dc.rights© The Author(s) 2017. Reprinted by permission of SAGE Publications.en
dc.subjectResistanceen
dc.subjectRecoveryen
dc.subjectResilienceen
dc.subjectVerdoorn’s lawen
dc.subjectPost-Keynesian economicsen
dc.titleUS Metropolitan Area Resilience: Insights from dynamic spatial panel estimationen
dc.typeArticle (peer-reviewed)en
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