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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.

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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    Cited by:

    1. 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.
    2. Edith Gabriel, 2014. "Estimating Second-Order Characteristics of Inhomogeneous Spatio-Temporal Point Processes," Methodology and Computing in Applied Probability, Springer, vol. 16(2), pages 411-431, June.
    3. Lanaspa, Luis & Sanz-Gracia, Fernando & Vera-Cabello, María, 2016. "The (strong) interdependence between intermediate producer services' attributes and manufacturing location," Economic Modelling, Elsevier, vol. 57(C), pages 1-12.
    4. HAMIDOUCHE M’hamed & BOURCHID ABDELKADER Salim, 2020. "Concentration of Economic Activities in the Kingdom of Saudi Arabia," European Journal of Interdisciplinary Studies, Bucharest Economic Academy, issue 01, March.
    5. Zixin Dou & Yanming Sun & Tao Wang & Huiyin Wan & Shiqi Fan, 2021. "Exploring Regional Advanced Manufacturing and Its Driving Factors: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area," IJERPH, MDPI, vol. 18(11), pages 1-14, May.
    6. Florent Bonneu & Christine Thomas-Agnan, 2015. "Measuring and Testing Spatial Mass Concentration with Micro-geographic Data," Spatial Economic Analysis, Taylor & Francis Journals, vol. 10(3), pages 289-316, September.
    7. 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.
    8. 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.
    9. Hans-Friedrich Eckey & Reinhold Kosfeld & Alexander Werner, 2012. "Bivariate K functions as instruments to analyze inter-industrial concentration processes," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 32(2), pages 133-157, September.
    10. Gabriel Lang & Eric Marcon & Florence Puech, 2020. "Distance-based measures of spatial concentration: introducing a relative density function," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 64(2), pages 243-265, April.
    11. Youwei Tan & Zhihui Gu & Yu Chen & Jiayun Li, 2022. "Industry Linkage and Spatial Co-Evolution Characteristics of Industrial Clusters Based on Natural Semantics—Taking the Electronic Information Industry Cluster in the Pearl River Delta as an Example," Sustainability, MDPI, vol. 14(21), pages 1-14, October.
    12. 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.
    13. Gabriel Lang & Eric Marcon & Florence Puech, 2020. "Distance-based measures of spatial concentration: Introducing a relative density function," Post-Print hal-01082178, HAL.
    14. Shuju Hu & Wei Song & Chenggu Li & Charlie H. Zhang, 2019. "The Evolution of Industrial Agglomerations and Specialization in the Yangtze River Delta from 1990–2018: An Analysis Based on Firm-Level Big Data," Sustainability, MDPI, vol. 11(20), pages 1-21, October.
    15. Fazio, Giorgio & Piacentino, Davide, 2018. "Convergence analysis for hierarchical longitudinal data," Economic Modelling, Elsevier, vol. 73(C), pages 89-99.

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    More about this item

    Keywords

    Industrial clustering; K-function; Spatial concentration; Spatial dependence; Spatial heterogeneity;
    All these keywords.

    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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