IDEAS home Printed from https://ideas.repec.org/a/eee/ecmode/v29y2012i1p3-11.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0264999311000150
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econmod.2011.01.012?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Julian Besag & Peter J. Diggle, 1977. "Simple Monte Carlo Tests for Spatial Pattern," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 26(3), pages 327-333, November.
    2. Gilles Duranton & Henry G. Overman, 2005. "Testing for Localization Using Micro-Geographic Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(4), pages 1077-1106.
    3. Giuseppe Arbia & Giuseppe Espa & Danny Quah, 2009. "A class of spatial econometric methods in the empirical analysis of clusters of firms in the space," Studies in Empirical Economics, in: Giuseppe Arbia & Badi H. Baltagi (ed.), Spatial Econometrics, pages 81-103, Springer.
    4. 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, February.
    5. Martin, Ron, 1999. "The New 'Geographical Turn' in Economics: Some Critical Reflections," Cambridge Journal of Economics, Cambridge Political Economy Society, vol. 23(1), pages 65-91, January.
    6. A. J. Baddeley & J. Møller & R. Waagepetersen, 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, November.
    7. Krugman, Paul & Venables, Anthony J., 1996. "Integration, specialization, and adjustment," European Economic Review, Elsevier, vol. 40(3-5), pages 959-967, April.
    8. Combes, Pierre-Philippe & Overman, Henry G., 2004. "The spatial distribution of economic activities in the European Union," Handbook of Regional and Urban Economics, in: J. V. Henderson & J. F. Thisse (ed.), Handbook of Regional and Urban Economics, edition 1, volume 4, chapter 64, pages 2845-2909, Elsevier.
    9. Jesper Møller & Rasmus P. Waagepetersen, 2007. "Modern Statistics for Spatial Point Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(4), pages 643-684, December.
    10. Eric Marcon & Florence Puech, 2009. "Measures of the Geographic Concentration of Industries: Improving Distance-Based Methods," Working Papers halshs-00372617, HAL.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Éric Marcon & Florence Puech, 2023. "Mapping distributions in non-homogeneous space with distance-based methods [Cartographie des distributions dans un espace non homogène à l'aide de méthodes basées sur la distance]," Post-Print hal-04345149, HAL.
    12. 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.
    13. 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.
    14. Gabriel Lang & Eric Marcon & Florence Puech, 2020. "Distance-based measures of spatial concentration: Introducing a relative density function," Post-Print hal-01082178, HAL.
    15. Lu Cui & Jing Shen & Zhuolin Mai & Chenghui Lin & Shaogu Wang, 2024. "Spatial Distribution and Location Determinants of High-Tech Firms in Shenzhen, a Chinese National Innovative City," Land, MDPI, vol. 13(9), pages 1-18, August.
    16. 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.
    17. Fazio, Giorgio & Piacentino, Davide, 2018. "Convergence analysis for hierarchical longitudinal data," Economic Modelling, Elsevier, vol. 73(C), pages 89-99.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Giuseppe Espa & Giuseppe Arbia & Diego Giuliani, 2013. "Conditional versus unconditional industrial agglomeration: disentangling spatial dependence and spatial heterogeneity in the analysis of ICT firms’ distribution in Milan," Journal of Geographical Systems, Springer, vol. 15(1), pages 31-50, January.
    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. 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.
    4. Giuseppe Arbia & Giuseppe Espa & Diego Giuliani & Maria Michela Dickson, 2017. "Effects of missing data and locational errors on spatial concentration measures based on Ripley’s K-function," Spatial Economic Analysis, Taylor & Francis Journals, vol. 12(2-3), pages 326-346, July.
    5. 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.
    6. 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.
    7. Eric Marcon & Florence Puech, 2012. "A typology of distance-based measures of spatial concentration," Working Papers halshs-00679993, HAL.
    8. Diego Giuliani & Giuseppe Arbia & Giuseppe Espa, 2014. "Weighting Ripley’s K-Function to Account for the Firm Dimension in the Analysis of Spatial Concentration," International Regional Science Review, , vol. 37(3), pages 251-272, July.
    9. Arbia, Giuseppe & Espa, Giuseppe & Giuliani, Diego & Mazzitelli, Andrea, 2010. "Detecting the existence of space-time clustering of firms," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 311-323, September.
    10. Ghorbani, Mohammad & Vafaei, Nafiseh & Dvořák, Jiří & Myllymäki, Mari, 2021. "Testing the first-order separability hypothesis for spatio-temporal point patterns," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    11. Barlet, M. & Briant, A. & Crusson, L., 2013. "Location patterns of service industries in France: A distance-based approach," Regional Science and Urban Economics, Elsevier, vol. 43(2), pages 338-351.
    12. Palan, Nicole & Schmiedeberg, Claudia, 2010. "Structural convergence of European countries," Structural Change and Economic Dynamics, Elsevier, vol. 21(2), pages 85-100, May.
    13. Eric Marcon & Florence Puech, 2009. "Measures of the Geographic Concentration of Industries: Improving Distance-Based Methods," Working Papers halshs-00372617, HAL.
    14. Jesper Møller & Heidi S. Christensen & Francisco Cuevas-Pacheco & Andreas D. Christoffersen, 2021. "Structured Space-Sphere Point Processes and K-Functions," Methodology and Computing in Applied Probability, Springer, vol. 23(2), pages 569-591, June.
    15. Laura de Dominicis & Giuseppe Arbia & Henri L.F. de Groot, 2007. "The Spatial Distribution of Economic Activities in Italy," Tinbergen Institute Discussion Papers 07-094/3, Tinbergen Institute.
    16. Cutrini, Eleonora, 2009. "Using entropy measures to disentangle regional from national localization patterns," Regional Science and Urban Economics, Elsevier, vol. 39(2), pages 243-250, March.
    17. repec:hal:spmain:info:hdl:2441/8001 is not listed on IDEAS
    18. Hiroyasu Inoue & Kentaro Nakajima & Yukiko Umeno Saito, 2019. "Localization of collaborations in knowledge creation," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 62(1), pages 119-140, February.
    19. Maria Tsiapa, 2014. "Industrial Concentration Patterns of the European Union," SCIENZE REGIONALI, FrancoAngeli Editore, vol. 2014(3), pages 5-33.
    20. Juan Tomas Sayago-Gomez & Adam Nowak, 2016. "What is Near and Recent in Crime for a Homeowner? The Cases of Denver and Seattle," Working Papers Working Paper 2016-01, Regional Research Institute, West Virginia University.
    21. Éric Marcon & Florence Puech, 2023. "Mapping distributions in non-homogeneous space with distance-based methods [Cartographie des distributions dans un espace non homogène à l'aide de méthodes basées sur la distance]," Post-Print hal-04345149, HAL.

    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)

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecmode:v:29:y:2012:i:1:p:3-11. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30411 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.