Modeling and Explaining County-level Prosperity in the U.S.

This paper explores the impact of space on prosperity. In order to do this, it develops a spatial model for locating prosperous counties and for identifying factors associated with prosperity. Using principal component analysis, a county-level prosperity index is created that comprises four measures: high school dropouts, housing conditions, unemployment, and poverty rates. Five categories of independent variables—demographic, economic, geographic, agricultural, and human and social capital—are used in the analysis. The spatial autocorrela-tion method has been used to determine the spatial pattern of prosperous counties, and the spatial econometric method has been used to develop a model that explains prosperity. The result shows that more prosperous counties have lower minority populations, more economic opportunities, and higher social and human capital. A policy reformulation is important in addressing the issues of less prosperous counties by creating jobs and enhancing social and human capital at regional levels.


Issue Date:
2014
Publication Type:
Journal Article
PURL Identifier:
http://purl.umn.edu/243970
Published in:
Volume 44
Issue 2
Journal of Regional Analysis and Policy
Page range:
143-156




 Record created 2017-04-01, last modified 2017-08-29

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