Testing for localization using micro-geographic data

Gilles Duranton and Henry Overman

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: To study the detailed location patterns of industries, and particularly the tendency for industries to cluster relative to overall manufacturing, we develop distance-based tests of localisation. In contrast to previous studies, our approach allows us to assess the statistical significance of departures from randomness. In addition, we treat space as continuous instead of using an arbitrary collection of geographical units. This avoids problems relating to scale and borders. We apply these tests to an exhaustive UK data set. For four-digit industries, we find that (i) 52% of them are localised at a 5% confidence level, (ii) localisation mostly takes place at small scales below 50 kilometres, (iii) the degree of localisation is very skewed, and (iv) industries follow broad sectoral patterns with respect to localisation. Depending on the industry, smaller establishments can be the main drivers of both localisation and dispersion. Three-digit sectors show similar patterns of localisation at small scales as well as a tendency to localise at medium scales.

Keywords: Localisation; clusters; k-density; spatial statistics. JEL classification codes: C19; R12; L70. (search for similar items in EconPapers)
JEL-codes: C19 L70 R12 (search for similar items in EconPapers)
Date: 2005-10
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Citations: View citations in EconPapers (462)

Published in Review of Economic Studies, October, 2005, 72(4), pp. 1077-1106. ISSN: 0034-6527

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Related works:
Journal Article: Testing for Localization Using Micro-Geographic Data (2005)
Working Paper: Testing for Localisation Using Micro-Geographic Data (2003)
Working Paper: Testing for Localisation Using Micro-Geographic Data (2002)
Working Paper: Testing for Localization Using Micro-Geographic Data (2002)
Working Paper: Testing for localisation using micro-geographic data (2002)
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