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A Note on the Spatial Correlation Structure of County‐Level Growth in the U.S

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  • Christopher H. Wheeler

Abstract

This paper examines the spatial correlation structure of county‐level growth across the contiguous United States. Estimated spatial correlograms using data on four different measures of aggregate economic activity—population, employment, income, and earnings—over the period 1984–1994 indicate that cross‐county interdependence is limited to relatively short ranges of distance. For each of the measures, the average correlation between the growth rates of two counties approaches zero within a range of approximately 200 miles. Moreover, the rate at which correlations decline with distance is not uniform. Inside of roughly 40 miles correlations show only a very slow rate of decline whereas beyond this range they drop off at a substantially higher rate.

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  • Christopher H. Wheeler, 2001. "A Note on the Spatial Correlation Structure of County‐Level Growth in the U.S," Journal of Regional Science, Wiley Blackwell, vol. 41(3), pages 433-449, August.
    • Handle: RePEc:bla:jregsc:v:41:y:2001:i:3:p:433-449
    DOI: 10.1111/0022-4146.00225
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    Cited by:

    1. Krister Sandberg, 2004. "Growth of GRP in Chinese Provinces. A Test for Spatial Spillovers," ERSA conference papers ersa04p596, European Regional Science Association.
    2. Marlon G. Boarnet & Saksith Chalermpong & Elizabeth Geho, 2005. "Specification issues in models of population and employment growth," Papers in Regional Science, Wiley Blackwell, vol. 84(1), pages 21-46, March.
    3. Nica, M., 2010. "Small Business Clusters in Oklahoma: MAR or Jacobs Effects?," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 10(2).
    4. Lukongo, Onyumbe Enumbe & Miller, Thomas, 2017. "Adverse Consequences of the Binding Constitutional Interest Rate Cap in the State of Arkansas," Working Papers 07446, George Mason University, Mercatus Center.
    5. Onyumbe E. Lukongo & Thomas Miller, 2018. "Evaluating the Spatial Consequence of Interest Rate Ceiling Using a Spatial Regime Change Approach," The American Economist, Sage Publications, vol. 63(2), pages 166-186, October.
    6. Paul Voss & David Long & Roger Hammer & Samantha Friedman, 2006. "County child poverty rates in the US: a spatial regression approach," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 25(4), pages 369-391, August.
    7. Deepak Premkumar & Austin Quackenbush & Georgeanne Artz & Peter Orazem, 2013. "If You Build it, Will They Come?: Fiscal Federalism, Local Provision of Public Tourist Amenities, and the Vision Iowa Fund," The Review of Regional Studies, Southern Regional Science Association, vol. 43(2,3), pages 155-173, Winter.
    8. Garmestani, Ahjond S. & Allen, Craig R. & Gallagher, Colin M., 2008. "Power laws, discontinuities and regional city size distributions," Journal of Economic Behavior & Organization, Elsevier, vol. 68(1), pages 209-216, October.
    9. Germán M. Izón & Michael S. Hand & Matias Fontenla & Robert P. Berrens, 2010. "The Economic Value Of Protecting Inventoried Roadless Areas: A Spatial Hedonic Price Study In New Mexico," Contemporary Economic Policy, Western Economic Association International, vol. 28(4), pages 537-553, October.
    10. Dean M Hanink, 2007. "Spatial and Geographical Effects in Regional Multiplier Analysis," Environment and Planning A, , vol. 39(3), pages 748-762, March.
    11. Jeanty, P. Wilner & Ulimwengu, John Mususa, 2011. "Poverty rate and government income transfers: A spatial simultaneous equations approach," IFPRI discussion papers 1076, International Food Policy Research Institute (IFPRI).
    12. Sandberg, Krister, 2004. "Hedonic Prices, Economic Growth, and Spatial Dependence," Umeå Economic Studies 631, Umeå University, Department of Economics.

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