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Do birds of a feather flock together?: Economic linkage and geographic proximity

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  • Jungyul Sohn

Abstract

Spatial association patterns of manufacturing activities are examined in this paper with the corresponding economic linkage patterns. Four specifications are used to measure the spatial association pattern: intraindustry/intracounty (locational Gini), intraindustry/intercounty (Moran’s I), interindustry/ intracounty (correlation coefficient) and interindustry/intercounty (“spatial” correlation coefficient). Two sets of spatial specification were used for the different locational context: unconstrained (full dataset) and constrained (dataset without zeros). The result on the 3,110 US counties for 361 manufacturing sectors revealed that, in the intraindustry context, there is little proof that stronger economic linkage results in and/or from a more concentrated pattern of the industry. However, interindustry economic linkage reflected and/or was reflected from the spatial distribution pattern in a significantly positive way. There was a pattern in the intraindustry model that the industry that showed clustering at a county scale had relatively weaker spatial concentration at a multi-county scale and vice versa. Results of the unconstrained and constrained specification of the data revealed important differences, implying that special care should be taken in the spatial specification to be used and how the results are to be interpreted. However, the relationship between economic linkage and spatial proximity did not substantially change between two cases except Moran’s I that had conflicting signs. Copyright Springer-Verlag 2004

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  • Jungyul Sohn, 2004. "Do birds of a feather flock together?: Economic linkage and geographic proximity," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 38(1), pages 47-73, March.
    • Handle: RePEc:spr:anresc:v:38:y:2004:i:1:p:47-73
    DOI: 10.1007/s00168-003-0145-x
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    Citations

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    Cited by:

    1. J. Knoben, 2009. "Localized inter-organizational linkages, agglomeration effects, and the innovative performance of firms," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 43(3), pages 757-779, September.
    2. Geert Vissers & Ben Dankbaar, 2016. "Spatial Aspects of Interfirm Collaboration: An Exploration of Firm-Level Knowledge Dynamics," Regional Studies, Taylor & Francis Journals, vol. 50(2), pages 260-273, February.
    3. Joris Knoben & L. A. G. (Leon) Oerlemans & R. P. J. H. (Roel) Rutten, 2008. "The Effects of Spatial Mobility on the Performance of Firms," Economic Geography, Taylor & Francis Journals, vol. 84(2), pages 157-183, April.
    4. Arthur Huang & David Levinson, 2011. "Why Retailers Cluster: An Agent Model of Location Choice on Supply Chains," Environment and Planning B, , vol. 38(1), pages 82-94, February.
    5. Domenica Panzera & Alfredo Cartone & Paolo Postiglione, 2022. "New evidence on measuring the geographical concentration of economic activities," Papers in Regional Science, Wiley Blackwell, vol. 101(1), pages 59-79, February.
    6. 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.
    7. J Knoben & AT Arikan & F van Oort & O Raspe, 2016. "Agglomeration and firm performance: One firm’s medicine is another firm’s poison," Environment and Planning A, , vol. 48(1), pages 132-153, January.
    8. Elif Alkay & Geoffrey Hewings, 2012. "The determinants of agglomeration for the manufacturing sector in the Istanbul metropolitan area," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 48(1), pages 225-245, February.

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