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Concentration, spatial clustering and the size of plants : disentangling the sources of co-location externalities

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  • LAFOURCADE, Miren
  • MION, Giordano

Abstract

Following the model-based approach of Ellison and Glaeser (1997), we develop a framework to test for the link between concentration, spatial clustering and the size of plants. Concentration is an a-spatial concept of variability that can be measured with the standard locational Gini or the more sophisticated Ellison and Glaeser index. By contrast, spatial clustering is directly concerned with distances. Therefore we also use a two-dimensional measure (the Moran index) to identify some specific distance-based patterns. We argue that, in a world where the size of establishments is independent of both concentration and spatial agglomeration, as the standard Dixit-Stiglitz (1977)-Krugman (1980) framework, all the variability in these measures should be accounted for by the variation in the number of plants. Using the Italian 1996 census year data on manufacturing industries, we therefore compare the values and significance of both the EG and Moran indexes computed on an employment and number of plants basis. Our results indicate that, for the majority of Italian manufacturing industries, big plants are much more concentrated than small ones, with size and concentration simultaneously influencing each other. On the other hand, small units are shown to be more spatially correlated, suggesting that different externalities may drive (or may be driven by) concentration and agglomeration patterns according to a size-related basis. These results therefore cast some doubt on the relevance of standard monopolistic frameworks to structurally account for the role of the so-called "pecuniary" externalities compared to more "localized" ones, such as Marshallian, Jacobian or factor endowments based externalities.

Suggested Citation

  • LAFOURCADE, Miren & MION, Giordano, 2003. "Concentration, spatial clustering and the size of plants : disentangling the sources of co-location externalities," LIDAM Discussion Papers CORE 2003091, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • Handle: RePEc:cor:louvco:2003091
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    Cited by:

    1. Javier Changoluisa, 2021. "The early development of new establishments: An evaluation of the role of spatial selection and agglomeration," Jena Economics Research Papers 2021-009, Friedrich-Schiller-University Jena.
    2. Toshihiro Okubo & Vincent Rebeyrol, 2006. "Home Market Effect, regulation costs and heterogeneous firms," Cahiers de la Maison des Sciences Economiques bla06056, Université Panthéon-Sorbonne (Paris 1).
    3. Mukim, Megha, 2011. "Industry and the urge to cluster: a study of the informal sector in India," LSE Research Online Documents on Economics 33592, London School of Economics and Political Science, LSE Library.
    4. R. Paci & S. Usai, 2006. "Agglomeration economies and growth-The case of Italian local labour systems, 1991-2001," Working Paper CRENoS 200612, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    5. Megha Mukim, 2015. "Coagglomeration of formal and informal industry: evidence from India," Journal of Economic Geography, Oxford University Press, vol. 15(2), pages 329-351.
    6. Bjoern Alecke & Gerhard Untiedt, 2006. "Die geografische Konzentration von Industrie und Dienstleistungen in Deutschland. Neue empirische Evidenz mittels des Ellison-Glaeser-Index," Working Papers 2-2006, GEFRA - Gesellschaft fuer Finanz- und Regionalanalysen.
    7. HAEDO , Christian & Mouchart, Michel, 2011. "A Stochastic Independence Approach for different Measures of Global Specialization," LIDAM Discussion Papers ISBA 2011006, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    8. Giordano Mion, 2013. "Input–Output Linkages, Proximity to Final Demand and the Location of Manufacturing Industries," Advances in Spatial Science, in: Riccardo Crescenzi & Marco Percoco (ed.), Geography, Institutions and Regional Economic Performance, edition 127, pages 237-262, Springer.
    9. Francesca Mameli & Alessandra Faggian & Philip McCann, 2008. "Employment Growth in Italian Local Labour Systems: Issues of Model Specification and Sectoral Aggregation," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(3), pages 343-360.
    10. Björn Alecke & Gerhard Untiedt, 2008. "Die räumliche Konzentration von Industrie und Dienstleistungen in Deutschland," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 28(1), pages 61-92, February.
    11. Matouschek, Niko & Robert-Nicoud, Frederic, 2005. "The role of human capital investments in the location decision of firms," Regional Science and Urban Economics, Elsevier, vol. 35(5), pages 570-583, September.
    12. Yuan, Quan & Zhu, Jiren, 2019. "Logistics sprawl in Chinese metropolises: Evidence from Wuhan," Journal of Transport Geography, Elsevier, vol. 74(C), pages 242-252.

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    More about this item

    Keywords

    concentration; spatial correlation; plants’ size; pecuniary externalities;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General
    • R34 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Input Demand Analysis

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