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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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References

Anselin, L. 1988. Spatial Econometrics: Methods and Models. Boston, MA: Kluwer Academic Publishers.CrossRefGoogle Scholar
Ashley, C., and Maxwell, S. 2001. “Rethinking Rural Development.” Development Policy Review 19(4): 395–425.Google Scholar
Boarnet, M. G., Chalermpong, S., and Geho, E. 2003. “Specification Issues in Models of Population and Employment Growth.” Unpublished manuscript, Department of Urban and Regional Planning, University of California, Irvine, CA.Google Scholar
Brunsdon, C., Fotheringham, A., and Charlton, M. 1996. “Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity.” Geographical Analysis 28(4): 281–298.Google Scholar
Brunsdon, C., Fotheringham, A., and Charlton, M. 1999. “Some Notes on Parametric Significance Tests for Geographically Weighted Regression.” Journal of Regional Science 39(3): 497–524.Google Scholar
Byrne, J., Shen, B., and Wallace, W. 1998. “The Economics of Sustainable Energy for Rural Development: A Study of Renewable Energy in Rural China.” Energy Policy 26(1): 45–54.Google Scholar
Carlino, G. A., and Mills, E. S. 1987. “The Determinants of County Growth.” Journal of Regional Science 27(1): 39–54.Google ScholarPubMed
Carruthers, J., and Vias, A. 2005. “Urban, Suburban, and Exurban Sprawl in the Rocky Mountain West: Evidence from Regional Adjustment Models.” Journal of Regional Science 45(1): 21–48.Google Scholar
Cleveland, W. S. 1979. “Robust Locally Weighted Regression and Smoothing Scatterplots.” Journal of the American Statistical Association 74(368): 829–836.Google Scholar
Cleveland, W. S., and Devlin, S.J. 1988. “Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting.” Journal of the American Statistical Association 83(403): 596–610.Google Scholar
Congressional Budget Office. 1998. The Economic Effect of Federal Spending on Infrastructure and Other Investment. U. S. Congress, Washington, D. C. Google Scholar
Deller, S. C., Tsai, T. H., Marcouiller, D. W., and English, D.B.K. 2001. “The Role of Amenities and Quality of Life in Rural Economic Growth.” American Journal of Agricultural Economics 83(2): 352–365.Google Scholar
Economic Research Service (ERS). 2004. “Measuring Rurality: 2004 County Typology Codes.” ERS, U. S. Department of Agriculture, Washington, D. C. Google Scholar
Florax, R.J.G.M., and Nijkamp, P. 2003. “Misspecification in Linear Spatial Regression Models.” In Kempf-Leonard, K., ed., Encyclopedia of Social Measurement. San Diego, CA: Academic Press.CrossRefGoogle Scholar
Florida, R. 2002. The Rise of the Creative Class. New York: Basic Books.Google Scholar
Florida, R. 2003. “Cities and the Creative Class.” City & Community 2(1): 3–19.Google Scholar
Fotheringham, A. 2000. “Context-Dependent Spatial Analysis: A Role for GIS?” Geographical Systems 2(1): 71–76.Google Scholar
Fotheringham, A., Brunsdon, C., and Charlton, M. 1998. “Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis.” Environment and Planning A 30(11): 1905–1927.Google Scholar
Fotheringham, A., Brunsdon, C., and Charlton, M. 2002. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. West Sussex, U. K.: John Wiley & Sons Ltd.Google Scholar
Greene, W. H. 1990. Econometric Analysis. New York: Mac-Millan Press.Google Scholar
Huang, Y., and Leung, Y. 2002. “Analyzing Regional Industrialization in Jiangsu Province Using Geographically Weighted Regression.” Journal of Geographical Systems 4(June): 233–249.Google Scholar
Huang, T. L., Orazem, P. F., and Wohlgemuth, D. 2002. “Rural Population Growth, 1950-1990: The Roles of Human Capital, Industry Structure, and Government Policy.” American Journal of Agricultural Economics 84(3): 615–627.Google Scholar
Huffman, W. E., and Lange, M. D. 1989. “Off-Farm Work Decisions of Husbands and Wives: Joint Decision Making.” Review of Economics and Statistics 71(3): 471–480.Google Scholar
Jolliffe, D. 2004. “Rural Poverty at a Glance.” Report No. RDRR 100, Economic Research Service, U. S. Department of Agriculture, Washington, D. C.Google Scholar
Jones, C.I. 1995. “R&D-Based Models of Economic Growth.” Journal of Political Economy 103(4): 759–784.Google Scholar
Kilkenny, M. 1993. “Rural/Urban Effects of Terminating Farm Subsidies.” American Journal of Agricultural Economics 75(4): 968–990.Google Scholar
Kilkenny, M. 1998. “Transport Costs and Rural Development.” Journal of Regional Science 38(2): 293–312.Google Scholar
Leung, Y., Mei, C., and Zhang, W. 2000a. “Statistical Tests for Spatial Nonstationarity Based on the Geographically Weighted Regression Model.” Environment and Planning A 32(1): 9–32.Google Scholar
Leung, Y., Mei, C., and Zhang, W. 2000b. “Testing for Spatial Autocorrelation Among the Residuals of the Geographically Weighted Regression.” Environment and Planning A 32(5): 871–890.Google Scholar
McGranahan, D.A. 1999. “Natural Amenities Drive Population Change.” Report No. AER781, Economic Research Service, U. S. Department of Agriculture, Washington, D. C.Google Scholar
McGranahan, D. A., and Beale, C. L. 2002. “Understanding Rural Population Loss.” Rural America 17(4): 2–11.Google Scholar
Murdoch, J. 2000. “Networks: A New Paradigm of Rural Development.” Journal of Rural Studies 16(4): 407–419.Google Scholar
Paez, A., Uchida, T., and Miyamoto, K. 2002a. “A General Framework for Estimation and Inference of Geographically Weighted Regression Models: 1. Location-Specific Kernel Bandwidths and a Test for Locational Heterogeneity.” Environment and Planning A 34(4): 883–904.Google Scholar
Paez, A., Uchida, T., and Miyamoto, K. 2002b. “A General Framework for Estimation and Inference of Geographically Weighted Regression Models: 2. Spatial Association and Model Specification Tests.” Environment and Planning A 34(5): 883–904.Google Scholar
Renkow, M. 2003. “Employment Growth, Worker Mobility, and Rural Economic Development.” American Journal of Agricultural Economics 8(2): 503–513.Google Scholar
Rogers, C. C. 1999. “Changes in the Older Population and Implication for Rural Areas.” Rural Development Research Report No. 90, Food and Rural Economics Division, Economic Research Service, U. S. Department of Agriculture, Washington, D. C.Google Scholar
Rosenfeld, S. 2004a “Art and Design as Competitive Advantage: The Creative Enterprise Cluster in the Western United States.” European Planning Studies 12(September): 891–904.Google Scholar
Rosenfeld, S. 2004b. “Crafting a New Rural Development Strategy.” Economic Development America (Summer): 11–13. Available online at www.rtsinc.org/publications/EDAsummer’[-]04.pdf.Google Scholar
Rosenfeld, S. 2005. “Art and Design as Economic Development.” In “An Enhanced Quality of Life for Rural Americans: Globalization and Restructuring in Rural America.” Available online at www.ers.usda.gov/Emphases/Rural/GlobalConf/abstracts.htm.Google Scholar
SOC [see Standard Occupational Classification System].Google Scholar
Standard Occupational Classification System. 2000. U. S. Department of Labor, Washington, D. C. Available online at www.bls.gov/soc/.Google Scholar
Tobler, W. R. 1970. “A Computer Movie Simulating Urban Growth in the Detroit Region.” Economic Geography 46(June): 234–240.Google Scholar
Whitener, L.A., and McGranahan, D.A. 2003. “Rural America: Opportunities and Challenges.” Amber Waves (February). Economic Research Service, U. S. Department of Agriculture, Washington, D. C. Google Scholar
Yu, D., and Wu, C. 2004. “Understanding Population Segregation from Landsat ETM+ Imagery: A Geographically Weighted Regression Approach.” GIScience and Remote Sensing 41 (3): 145–164.Google Scholar