Arbia, Giuseppe, Espa, Giuseppe and Quah, Danny
A class of spatial econometric methods in the empirical analysis of clusters of firms in space.
Empirical economics, 34
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In this paper we aim at identifying stylized facts in order to suggest adequate models for the co-agglomeration of industries in space. We describe a class of spatial statistical methods for the empirical analysis of spatial clusters. The main innovation of the paper consists in considering clustering for bivariate (rather than univariate) distributions. This allows uncovering co-agglomeration and repulsion phenomena between the different sectors. Furthermore we present empirical evidence on the pair-wise intra-sectoral spatial distribution of patents in Italy in 1990s. We identify some distinctive joint patterns of location between different sectors and we propose some possible economic interpretations.
||© 2008 Springer
||agglomeration, Bivariate K–functions, co–agglomeration, non parametric concentration measures, spatial clusters, spatial econometrics
|Library of Congress subject classification:
||H Social Sciences > HB Economic Theory
|Journal of Economic Literature Classification System:
||C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C21 - Cross-Sectional Models; Spatial Models; Treatment Effect Models
D - Microeconomics > D9 - Intertemporal Choice and Growth > D92 - Intertemporal Firm Choice and Growth, Investment, or Financing
L - Industrial Organization > L6 - Industry Studies: Manufacturing > L60 - General
O - Economic Development, Technological Change, and Growth > O1 - Economic Development > O18 - Regional, Urban, and Rural Analyses
R - Urban, Rural, and Regional Economics > R1 - General Regional Economics > R12 - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade
||Departments > Economics
Research centres and groups > LSE IDEAS
Collections > Economists Online
Research centres and groups > Centre for the Study of Human Rights
||07 Feb 2012 11:25
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