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Accounting for Neighboring Effects in Measures of Spatial Concentration

Author

Listed:
  • Paulo Guimarães

    () (University of South Carolina and CEMPRE)

  • Octávio Figueiredo

    () (CEMPRE and Faculdade de Economia, Universidade do Porto)

  • Douglas Woodward

    () (University of South Carolina)

Abstract

A common problem with spatial economic concentration measures (e.g. Gini, Herfindhal, entropy and Ellison-Glaeser indices) is accounting for the position of regions in space. While they purport to measure spatial clustering, these statistics are confined to calculations within individual areal units. They are insensitive to the proximity of regions - to neighboring effects. Clearly, economic clusters may cross the boundaries of the regions. Yet with current measures, any industrial agglomeration that traverses boundaries will be chopped into two or more pieces. Activity in adjacent spatial units is treated in exactly the same way as activity in far-flung, non-adjacent areas. This paper shows how some popular measures of spatial concentration relying on areal data can be modified to account for neighboring effects and spatial autocorrelation. With a U.S. application, we also show that the new instruments we propose are useful and easy to implement.

Suggested Citation

  • Paulo Guimarães & Octávio Figueiredo & Douglas Woodward, 2009. "Accounting for Neighboring Effects in Measures of Spatial Concentration," FEP Working Papers 353, Universidade do Porto, Faculdade de Economia do Porto.
  • Handle: RePEc:por:fepwps:353
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    Cited by:

    1. Elena Lasarte & Miguel Angel Marquez Paniagua, 2014. "Decomposition of Regional Income Inequality and Neighborhood Component: A Spatial Theil Index," Working Papers. Serie EC 2014-03, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    2. Crafts, Nicholas & Alexander Klein, Alexander, 2017. "A Long-Run Perspective on the Spatial Concentration of Manufacturing Industries in the United States," CAGE Online Working Paper Series 339, Competitive Advantage in the Global Economy (CAGE).
    3. Behrens, Kristian & Bougna, Théophile, 2015. "An anatomy of the geographical concentration of Canadian manufacturing industries," Regional Science and Urban Economics, Elsevier, vol. 51(C), pages 47-69.
    4. Marcon, Eric & Puech, Florence, 2017. "A typology of distance-based measures of spatial concentration," Regional Science and Urban Economics, Elsevier, vol. 62(C), pages 56-67.
    5. Eric Marcon & Florence Puech, 2012. "A typology of distance-based measures of spatial concentration," Working Papers halshs-00679993, HAL.
    6. Marta R. Casanova & Vicente Orts & José M. Albert, 2017. "Sectoral scope and colocalisation of Spanish manufacturing industries," Journal of Geographical Systems, Springer, vol. 19(1), pages 65-92, January.
    7. Register, D. Lane & Lambert, Dayton M. & English, Burton C. & Jensen, Kimberly L. & Menard, R. Jamey & Wilcox, Michael D., 2012. "Geographic Distribution of Renewable Energy Sector Industries: An Analysis Using Recent Developments in Industry Concentration Measurement," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124038, Agricultural and Applied Economics Association.

    More about this item

    Keywords

    Measures of Agglomeration; Spatial Concentration;

    JEL classification:

    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • R39 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Other
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other

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