Accounting for Neighboring Effects in Measures of Spatial Concentration*

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.

Trends and Determinants of the Geographical Distribution of Economic Activities: Evidence From Indonesian Manufacturing

“…Guimarães et al (2011) highlighted the crucial drawbacks of the EG index: it suffers from the modifiable areal unit problem (MAUP) and the checkerboard problem. They argued that the first issue relates to a possible aggregation bias within administrative boundaries or spatial units, while the second arises when ignoring the neighboring effects and treat economic activity in adjacent spatial units in a manner similar to that of activity in the regional center.…”

“…Unlike Kim's (1995) study which makes use of Hoover's localization index, the current study employs the geographical concentration index proposed by Ellison and Glaeser (1997;hereafter, EG index) and an extension of the spatially weighted EG index developed by Guimarães et al (2011;hereafter, EGS index) to measure localization or concentration. The EG index first proposed is the measurement of the geographical concentration of economic activity; it distinguishes between two agglomerative forces namely, natural advantage and spillover while controlling for industrial location.…”

“…Therefore, to account for economic activity in neighboring regions, we adopt the spatially weighted EGS index. This index precludes MAUP and deals with economic clustering that occurs across borders (Guimarães et al, 2011).…”

“…We also introduce the use of weighted Ellison-Glaeser (EG) Index proposed by Guimarães et al (2011) in an empirical modeling of geographical concentration to account for neighboring agglomeration effects. In particular, we evaluate the changes that occurred in tandem with the external shocks of the 1997-1998 Asian financial crisis and the implementation of decentralization policy.…”

“…This study determines the spatial distribution of the economic activities of the Indonesian manufacturing industry by measuring the regional specialization index (RSI), as originally proposed by Krugman (1991b), and the spatial Ellison-Glaeser index is originally developed by Ellison and Glaeser (1997) and extended by Guimarães et al (2011)-to account for neighboring effects. With spatial trends in hand, then we investigate whether economies of scale, resources, international trade activities, labor skills and wage rate can show the changes in geographical concentration.…”