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

  • Seong-Hoon Cho (a1), Seung Gyu Kim (a1), Christopher D. Clark (a1) and William M. Park (a1)

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.

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Anselin, L. 1988. Spatial Econometrics: Methods and Models. Boston, MA: Kluwer Academic Publishers.
Ashley, C., and Maxwell, S. 2001. “Rethinking Rural Development.Development Policy Review 19(4): 395425.
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 Unpublished manuscript, Department of Urban and Regional Planning, University of California, Irvine, CA.
Brunsdon, C., Fotheringham, A., and Charlton, M. 1996. “Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity.Geographical Analysis 28(4): 281298.
Brunsdon, C., Fotheringham, A., and Charlton, M. 1999. “Some Notes on Parametric Significance Tests for Geographically Weighted Regression.Journal of Regional Science 39(3): 497524.
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): 4554.
Carlino, G. A., and Mills, E. S. 1987. “The Determinants of County Growth.Journal of Regional Science 27(1): 3954.
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): 2148.
Cleveland, W. S. 1979. “Robust Locally Weighted Regression and Smoothing Scatterplots.Journal of the American Statistical Association 74(368): 829836.
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): 596610.
Congressional Budget Office. 1998. The Economic Effect of Federal Spending on Infrastructure and Other Investment. U. S. Congress, Washington, D. C.
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): 352365.
Economic Research Service (ERS). 2004. “Measuring Rurality: 2004 County Typology Codes.ERS, U. S. Department of Agriculture, Washington, D. C.
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.
Florida, R. 2002. The Rise of the Creative Class. New York: Basic Books.
Florida, R. 2003. “Cities and the Creative Class.City & Community 2(1): 319.
Fotheringham, A. 2000. “Context-Dependent Spatial Analysis: A Role for GIS?Geographical Systems 2(1): 7176.
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): 19051927.
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.
Greene, W. H. 1990. Econometric Analysis. New York: Mac-Millan Press.
Huang, Y., and Leung, Y. 2002. “Analyzing Regional Industrialization in Jiangsu Province Using Geographically Weighted Regression.Journal of Geographical Systems 4(June): 233249.
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): 615627.
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): 471480.
Jolliffe, D. 2004. “Rural Poverty at a Glance.” Report No. RDRR 100, Economic Research Service, U. S. Department of Agriculture, Washington, D. C.
Jones, C.I. 1995. “R&D-Based Models of Economic Growth.Journal of Political Economy 103(4): 759784.
Kilkenny, M. 1993. “Rural/Urban Effects of Terminating Farm Subsidies.American Journal of Agricultural Economics 75(4): 968990.
Kilkenny, M. 1998. “Transport Costs and Rural Development.Journal of Regional Science 38(2): 293312.
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): 932.
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): 871890.
McGranahan, D.A. 1999. “Natural Amenities Drive Population Change.” Report No. AER781, Economic Research Service, U. S. Department of Agriculture, Washington, D. C.
McGranahan, D. A., and Beale, C. L. 2002. “Understanding Rural Population Loss.Rural America 17(4): 211.
Murdoch, J. 2000. “Networks: A New Paradigm of Rural Development.Journal of Rural Studies 16(4): 407419.
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): 883904.
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): 883904.
Renkow, M. 2003. “Employment Growth, Worker Mobility, and Rural Economic Development.American Journal of Agricultural Economics 8(2): 503513.
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.
Rosenfeld, S. 2004aArt and Design as Competitive Advantage: The Creative Enterprise Cluster in the Western United States.” European Planning Studies 12(September): 891904.
Rosenfeld, S. 2004b. “Crafting a New Rural Development Strategy.Economic Development America (Summer): 1113. Available online at www.rtsinc.org/publications/EDAsummer’[-]04.pdf.
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.
SOC [see Standard Occupational Classification System].
Standard Occupational Classification System. 2000. U. S. Department of Labor, Washington, D. C. Available online at www.bls.gov/soc/.
Tobler, W. R. 1970. “A Computer Movie Simulating Urban Growth in the Detroit Region.Economic Geography 46(June): 234240.
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.
Yu, D., and Wu, C. 2004. “Understanding Population Segregation from Landsat ETM+ Imagery: A Geographically Weighted Regression Approach.GIScience and Remote Sensing 41 (3): 145164.

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

  • Seong-Hoon Cho (a1), Seung Gyu Kim (a1), Christopher D. Clark (a1) and William M. Park (a1)

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