The Geographic Diversity of U.S. Nonmetropolitan Growth Dynamics: A Geographically Weighted Regression Approach

Mark Partridge, Dan Rickman, Kamar Ali () and Rose Olfert ()
Additional contact information
Kamar Ali: University of Saskatchewan
Rose Olfert: University of Saskatchewan

No 704, Economics Working Paper Series from Oklahoma State University, Department of Economics and Legal Studies in Business

Abstract: Spatial heterogeneity is introduced as an explanation for local-area growth mechanisms, especially employment growth. As these effects are difficult to detect using conventional regression approaches, we use Geographically Weighted Regressions (GWR) for non-metropolitan U.S. counties. We test for geographic heterogeneity in the growth parameters and compare them to global regression estimates. The results indicate significant heterogeneity in the regression coefficients across the country, most notably for amenities and college graduate shares. Using GWR also exposes significant local variations that are masked by global estimates suggesting limitations of a one-size fits all approach to describe growth and to inform public policy.

Keywords: Nonmetropolitan Growth; Geographically Weighted Regression; Economic Geography; Regional and Urban Economics (search for similar items in EconPapers)
JEL-codes: R00 (search for similar items in EconPapers)
Pages: 41 pages
Date: 2007-04
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:okl:wpaper:0704

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