Predicting US county economic resilience from industry input-output accounts

Yicheol Han and Stephan Goetz

Applied Economics, 2019, vol. 51, issue 19, 2019-2028

Abstract: Resilience is defined as a system’s ability to initially resist and then recover from a shock. Here we apply this concept to examine the performance of U.S. counties during the Great Recession. The response of local economies to manmade and natural shocks is hypothesized to depend on the centrality of local industries within the economy, or how well connected they are to the other industries. We first calculate a centrality value for each industry using the national Input-Output accounts. We then ‘step down’ these values to the county level using industry employment data. We then test empirically whether local economies containing more centralized industries were more resilient, using a resilience measure that compares the local employment rebound and decline during the Great Recession. Our results suggest that measures of economic centrality adopted from the study of complex networks provide new insights when applied to the fields of regional science and spatial analysis, and economic growth more generally.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (19)

Downloads: (external link)
http://hdl.handle.net/10.1080/00036846.2018.1539806 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:applec:v:51:y:2019:i:19:p:2019-2028

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEC20

DOI: 10.1080/00036846.2018.1539806

Access Statistics for this article

Applied Economics is currently edited by Anita Phillips

More articles in Applied Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().