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Clusters of firms in an inhomogeneous space: The high-tech industries in Milan

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  • Arbia, G.
  • Espa, G.
  • Giuliani, D.
  • Mazzitelli, A.
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    Abstract

    Why do industrial clusters occur in space? Is it because industries need to stay close together to interact or, conversely, because they concentrate in certain portions of space to exploit favourable conditions like public incentives, proximity to communication networks, to big population concentrations or to reduce transport costs? This is a fundamental question and the attempt to answer to it using empirical data is a challenging statistical task. In economic geography scientists refer to this dichotomy using the two categories of spatial interaction and spatial reaction to common factors. In economics we can refer to a distinction between exogenous causes and endogenous effects. In spatial econometrics and statistics we use the terms of spatial dependence and spatial heterogeneity. A series of recent papers introduced explorative methods to analyse the spatial patterns of firms using micro data and characterizing each firm by its spatial coordinates. In such a setting a spatial distribution of firms is seen as a point pattern and an industrial cluster as the phenomenon of extra-concentration of one industry with respect to the concentration of a benchmarking spatial distribution. Often the benchmarking distribution is that of the whole economy on the ground that exogenous factors affect in the same way all branches. Using such an approach a positive (or negative) spatial dependence between firms is detected when the pattern of a specific sector is more aggregated (or more dispersed) than the one of the whole economy. In this paper we suggest a parametric approach to the analysis of spatial heterogeneity, based on the so-called inhomogeneous K-function (Baddeley et al., 2000). We present an empirical application of the method to the spatial distribution of high-tech industries in Milan (Italy) in 2001. We consider the economic space to be non homogenous, we estimate the pattern of inhomogeneity and we use it to separate spatial heterogeneity from spatial dependence.

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    Bibliographic Info

    Article provided by Elsevier in its journal Economic Modelling.

    Volume (Year): 29 (2012)
    Issue (Month): 1 ()
    Pages: 3-11

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    Handle: RePEc:eee:ecmode:v:29:y:2012:i:1:p:3-11

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    Web page: http://www.elsevier.com/locate/inca/30411

    Related research

    Keywords: Industrial clustering; K-function; Spatial concentration; Spatial dependence; Spatial heterogeneity;

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    References

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    1. Krugman, Paul & Venables, Anthony J., 1996. "Integration, specialization, and adjustment," European Economic Review, Elsevier, vol. 40(3-5), pages 959-967, April.
    2. Gilles Duranton & Henry Overman, 2002. "Testing for Localisation Using Micro-Geographic Data," CEP Discussion Papers dp0540, Centre for Economic Performance, LSE.
    3. A. J. Baddeley, 2000. "Non- and semi-parametric estimation of interaction in inhomogeneous point patterns," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 54(3), pages 329-350.
    4. Eric Marcon & Florence Puech, 2009. "Measures of the Geographic Concentration of Industries: Improving Distance-Based Methods," Working Papers halshs-00372617, HAL.
    5. Edith Gabriel & Peter J. Diggle, 2009. "Second-order analysis of inhomogeneous spatio-temporal point process data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 63(1), pages 43-51.
    6. Martin, Ron, 1999. "The New 'Geographical Turn' in Economics: Some Critical Reflections," Cambridge Journal of Economics, Oxford University Press, vol. 23(1), pages 65-91, January.
    7. Giuseppe Arbia & Giuseppe Espa & Danny Quah, 2008. "A class of spatial econometric methods in the empirical analysis of clusters of firms in the space," Empirical Economics, Springer, vol. 34(1), pages 81-103, February.
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    Cited by:
    1. Roberto Antonietti & Giulio Cainelli & Claudio Lupi, 2012. "Vertical disintegration and spatial co-localization: the case of Kibs in the Metropolitan Region of Milan," Openloc Working Papers 1202, Public policies and local development.

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