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Assessing dynamic externalities from a cluster perspective: the case of the motor metropolis in Japan

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  • Eri Yamada
  • Tetsu Kawakami

Abstract

In this paper, we first apply the methods of exploratory spatial data analysis and investigate the geographical concentration of interrelated growing industries, or “growth clusters,” by using data from the Nagoya metropolitan area in Japan over the period 1986–2006. Second, by applying econometric models, we examine whether and which type of knowledge externalities contribute to region-industry dynamics and to the formation of the detected growth cluster. As a methodological contribution, following Porter’s (Reg Stud 37:549–578, 2003 ) widely recognized cluster concept, we incorporate spatial dependence caused by the geographical proximity between regions and the technological proximity between industries into the empirical models. Combining the information obtained from the ESDA and econometric analysis enables us to assess the role of knowledge externalities for regional growth from a cluster perspective. The empirical results identify the presence of a growth cluster mainly driven by the automobile and associated industries. The core of the cluster is made up of manufacturing industries, but also includes several service sectors. The periphery of the cluster has less variety, mainly including the transportation equipment and electrical machinery industries. We find that intra-industry externalities that diffuse over a broader area within the cluster help the steady growth of the transportation equipment industry. In the core of the cluster, the diversified interrelated structure also contributes to the growth of the auto-related manufacturing sectors. Copyright Springer-Verlag Berlin Heidelberg 2015

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  • Eri Yamada & Tetsu Kawakami, 2015. "Assessing dynamic externalities from a cluster perspective: the case of the motor metropolis in Japan," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 54(1), pages 269-298, January.
  • Handle: RePEc:spr:anresc:v:54:y:2015:i:1:p:269-298
    DOI: 10.1007/s00168-014-0654-9
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    1. Xie, Xiaoyu & Guo, Kaige, 2024. "How does productive service agglomeration promote manufacturing servitisation from an innovation perspective?," International Review of Financial Analysis, Elsevier, vol. 92(C).

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    Keywords

    C31; O18; R11;
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    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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