ABSTRACT A common problem with spatial economic concentration measures based on areal data (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 or to neighboring effects. Clearly, since spillovers do not recognize areal units, economic clusters may cross regional boundaries. 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, nonadjacent areas. This paper shows how popular measures of spatial concentration relying on areal data can be modified
to account for neighboring effects. With a U.S. application, we also demonstrate that the new instruments we propose are easy to implement and can be valuable in regional analysis.
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Document Type: Research Article
University of Porto and Division of Research, Moore School of Business, University of South Carolina, Columbia, SC 29208.
CEF.UP and Faculty of Economics, University of Porto, Rua Dr. Roberto Frias, 4200-464 Porto, Portugal.
Division of Research, Moore School of Business, University of South Carolina, Columbia, SC 29208.
Publication date: 2011-10-01