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Spatial Localization in Manufacturing: A Cross-Country Analysis

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This paper employs a homogenous firms database to investigate industry localization in European countries. More specifically, we compare, across industries and countries, the predictions of two of the most popular localization indices, i.e., the Ellison and Glaeser index (Ellison and Glaeser, 1997) and the Duranton and Overman index (Duranton and Overman, 2005). We find that, independently from the index used, localization is a pervasive phenomenon in all countries studied, but the degree of localization is very uneven across industries in each country. Furthermore, we find that the two indices significantly diverge in predicting the intensity of the forces generating localization within each industry. Finally, we perform a crosssectoral analysis of localized industries. We show that, in all countries, localized sectors are mainly “traditional” sectors (like jewelery, wine, and textiles) and sectors where scale economies are important. However, once one controls for countries’ industrial structures science-based sectors turn out to be the most localized ones.

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Paper provided by Observatoire Francais des Conjonctures Economiques (OFCE) in its series Documents de Travail de l'OFCE with number 2009-07.

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Date of creation: 2009
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Handle: RePEc:fce:doctra:0907

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  14. Guy Dumais & Glenn Ellison & Edward L Glaeser, 1998. "Geographic Concentration as a Dynamic Process," Working Papers 98-3, Center for Economic Studies, U.S. Census Bureau.
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  16. Ellison, Glenn & Glaeser, Edward L, 1997. "Geographic Concentration in U.S. Manufacturing Industries: A Dartboard Approach," Journal of Political Economy, University of Chicago Press, vol. 105(5), pages 889-927, October.
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Cited by:
  1. Tobias Scholl & Thomas Brenner, 2011. "Testing for Clustering of Industries - Evidence from micro geographic data," Working Papers on Innovation and Space 2011-02, Philipps University Marburg, Department of Geography.
  2. Tobias Scholl & Thomas Brenner, 2013. "Detecting Spatial Clustering Using a Firm-Level Index," Working Papers on Innovation and Space 2012-02, Philipps University Marburg, Department of Geography.
  3. Tobias Scholl & Thomas Brenner, 2013. "Optimizing Distance-Based Methods for Big Data Analysis," Working Papers on Innovation and Space 2013-09, Philipps University Marburg, Department of Geography.

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