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Spatial Analysis of Rural Economic Development Using a Locally Weighted Regression Model

Published online by Cambridge University Press:  15 September 2016

Seong-Hoon Cho ,
Seung Gyu Kim ,
Christopher D. Clark  and
William M. Park
Seong-Hoon Cho
Affiliation:
Department of Agricultural Economics at the University of Tennessee in Knoxville, Tennessee
Seung Gyu Kim
Affiliation:
Department of Agricultural Economics at the University of Tennessee in Knoxville, Tennessee
Christopher D. Clark
Affiliation:
Department of Agricultural Economics at the University of Tennessee in Knoxville, Tennessee
William M. Park
Affiliation:
Department of Agricultural Economics at the University of Tennessee in Knoxville, Tennessee
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Abstract

This study uses locally weighted regression to identify county-level characteristics that serve as drivers of creative employment throughout the southern United States. We found that higher per capita income, greater infrastructure investments, and the rural nature of a county tended to promote creative employment density, while higher scores on a natural amenity index had the opposite effect. We were also able to identify and map clusters of rural counties where the marginal effects of these variables on creative employment density were greatest. These findings should help rural communities to promote creative employment growth as a means of furthering rural economic development.

Keywords

creative classlocally weighted regressionnatural amenitiesrural economic development
Type
Contributed Papers
Information
Agricultural and Resource Economics Review , Volume 36 , Issue 1 , April 2007 , pp. 24 - 38
Copyright
Copyright © 2007 Northeastern Agricultural and Resource Economics Association 

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