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

Author

Listed:
  • Arbia, G.
  • Espa, G.
  • Giuliani, D.
  • Mazzitelli, A.

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.

Suggested Citation

  • Arbia, G. & Espa, G. & Giuliani, D. & Mazzitelli, A., 2012. "Clusters of firms in an inhomogeneous space: The high-tech industries in Milan," Economic Modelling, Elsevier, vol. 29(1), pages 3-11.
  • Handle: RePEc:eee:ecmode:v:29:y:2012:i:1:p:3-11 DOI: 10.1016/j.econmod.2011.01.012
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    References listed on IDEAS

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    1. 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.
    2. Gilles Duranton & Henry G. Overman, 2005. "Testing for Localization Using Micro-Geographic Data," Review of Economic Studies, Oxford University Press, vol. 72(4), pages 1077-1106.
    3. 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.
    4. 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.
    5. Krugman, Paul & Venables, Anthony J., 1996. "Integration, specialization, and adjustment," European Economic Review, Elsevier, vol. 40(3-5), pages 959-967, April.
    6. 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.
    7. Eric Marcon & Florence Puech, 2009. "Measures of the Geographic Concentration of Industries: Improving Distance-Based Methods," Working Papers halshs-00372617, HAL.
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    Citations

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    Cited by:

    1. Antonietti, Roberto & Cainelli, Giulio & Lupi, Claudio, 2013. "Vertical disintegration and spatial co-localization: The case of Kibs in the metropolitan region of Milan," Economics Letters, Elsevier, vol. 118(2), pages 360-363.
    2. Arbia, Giuseppe & Espa, Giuseppe & Giuliani, Diego & Dickson, Maria Michela, 2014. "Spatio-temporal clustering in the pharmaceutical and medical device manufacturing industry: A geographical micro-level analysis," Regional Science and Urban Economics, Elsevier, vol. 49(C), pages 298-304.
    3. Angelo Mazza & Antonio Punzo, 2016. "Spatial attraction in migrants' settlement patterns in the city of Catania," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 35(5), pages 117-138, July.
    4. Lanaspa, Luis & Sanz-Gracia, Fernando & Vera-Cabello, MarĂ­a, 2016. "The (strong) interdependence between intermediate producer services' attributes and manufacturing location," Economic Modelling, Elsevier, vol. 57(C), pages 1-12.
    5. Florent Bonneu & Christine Thomas-Agnan, 2015. "Measuring and Testing Spatial Mass Concentration with Micro-geographic Data," Spatial Economic Analysis, Taylor & Francis Journals, pages 289-316.
    6. Marcon, Eric & Puech, Florence, 2017. "A typology of distance-based measures of spatial concentration," Regional Science and Urban Economics, Elsevier, vol. 62(C), pages 56-67.

    More about this item

    Keywords

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

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C59 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Other
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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