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A class of spatial econometric methods in the empirical analysis of clusters of firms in the space

Giuseppe Maria Arbia, Giuseppe Espa () and Danny Quah ()

No 705, Department of Economics Working Papers from Department of Economics, University of Trento, Italia

Abstract: In this paper we aim at identifying stylized facts in order to suggest adequate models of spatial co–agglomeration of industries. We describe a class of spatial statistical methods to be used in the empirical analysis of spatial clusters. Compared to previous contributions using point pattern methods, the main innovation of the present paper is to consider clustering for bivariate (rather than univariate) distributions, which allows uncovering co–agglomeration and repulsion phenomena between the different industrial sectors. Furthermore we present the results of an empirical application of such methods to a set of European Patent Office (EPO) data and we produce a series of empirical evidences referred to the the pair–wise intra–sectoral spatial distribution of patents in Italy in the nineties. In this analysis we are able to identify some distinctive joint patterns of location between patents of different sectors and to propose some possible economic interpretations.

Keywords: Agglomeration; Bivariate K–functions; co–agglomeration; Non parametric concentration measures; Spatial clusters; Spatial econometrics (search for similar items in EconPapers)
JEL-codes: C21 D92 L60 O18 R12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-geo and nep-ure
Date: 2007
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