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Abstract
Yamada E. and Kawakami T. Distribution of industrial growth in Nagoya Metropolitan Area, Japan: an exploratory analysis using geographical and technological proximities, Regional Studies. This study explores the spatial pattern of industry dynamics in Japan's motor metropolis of Nagoya. Exploratory spatial data analysis methodologies that reflect aspects of both geographical and technological proximities within regional industries are proposed and applied to the long-term, sustained manufacturing and service sector growth rates. The descriptive results identify the presence of positive multilayered growth clusters, driven mainly by the automobile and associated industries. These growth clusters differ in sectoral composition and geographical scale; the larger specialized growth cluster of transportation equipment encompasses the smaller one composed of diverse manufacturing and service sectors.
Yamada E. and Kawakami T. 日本名古屋大都会地区的工业成长分佈：运用地理和技术邻近性的探索性分析，区域研究。本研究探讨日本名古屋汽车大都会的产业动态的空间模式。本文提出同时反映一区域工业中的地理和技术邻近性的探索性空间数据分析方法论，并将之应用于长期、持续的製造业及服务业部门成长率。描述性的结果，指认出正向多层级成长集群的存在，并主要是由汽车及相关产业所驱动。这些成长集群在部门组成和地理尺度上有所差异；较大型的专殊化运输设备成长集群，包含了由多样的製造业和服务业部门所组成的较小型成长集群。
Yamada E. et Kawakami T. La distribution de la croissance industrielle sur la zone métropolitaine de Nagoya, au Japon: une analyse exploratoire employant la proximité géographique et technologique, Regional Studies. Cette étude examine la distribution spatiale de la dynamique industrielle de la métropole automobile japonaise à Nagoya. On propose des méthodologies relatives à l'analyse exploratoire des données spatiales qui reflètent des aspects de la proximité géographique et technologique au sein des industries régionales et on les applique aux taux de croissance continus et soutenus des secteurs manufacturiers et des services. Les résultats descriptifs identifient la présence des clusters de croissance positifs à plusieurs niveaux, pilotés dans une large mesure par l'industrie automobile et ses filières. Ces clusters de croissance-là se distinguent en termes de leur structure sectorielle et de leur échelle géographique; dont le cluster de croissance spécialisé plus grand, à savoir le matériel de transport, embrasse le cluster de plus petite taille qui se compose de divers secteurs manufacturiers et des services.
Yamada E. und Kawakami T. Verteilung von Branchenwachstum im Großstadtgebiet Nagoya (Japan): eine exploratorische Analyse unter Verwendung von geografischer und technischer Nähe, Regional Studies. In diesem Beitrag wird das räumliche Muster der Branchendynamik in der japanischen Motormetropole Nagoya untersucht. Wir schlagen Methodologien zur exploratorischen räumlichen Datenanalyse vor, in denen sich Aspekte der geografischen und technischen Nähe innerhalb von regionalen Branchen widerspiegeln, und wenden sie auf die langfristigen und anhaltenden Wachstumsraten im Produktions- und Dienstleistungssektor an. Die deskriptiven Ergebnisse lassen auf das Vorliegen von positiven mehrschichtigen Wachtumsclustern schließen, die vor allem durch die Automobil- und zugehörigen Branchen bedingt sind. Diese Wachtumscluster unterscheiden sich hinsichtlich ihrer sektoriellen Zusammensetzung und ihres geografischen Maßstabs; der größere, spezialisierte Wachtumscluster der Transportmittel umfasst den kleineren Cluster, der aus verschiedenen Produktions- und Dienstleistungssektoren besteht.
Yamada E. y Kawakami T. Distribución del crecimiento industrial en el área metropolitana de Nagoya (Japón): un análisis exploratorio usando las proximidades geográficas y tecnológicas, Regional Studies. En este estudio analizamos el modelo espacial de las dinámicas industriales en la metrópolis automovilística japonesa de Nagoya. Proponemos metodologías de análisis exploratorio de datos espaciales en las que se reflejan las proximidades tanto geográficas como tecnológicas en las industrias regionales, y las aplicamos a las tasas de crecimiento a largo plazo y sostenidas del sector de manufactura y servicios. Los resultados descriptivos identifican la presencia de aglomeraciones de crecimiento positivo multidimensional, impulsado principalmente por las industrias del automóvil y otras asociadas. Estas aglomeraciones de crecimiento difieren en su composición sectorial y escala geográfica; la aglomeración de crecimiento especializada más grande de equipo de transporte abarca a la más pequeña compuesta por diversos sectores de manufactura y servicios.
Acknowledgements
The authors would like to thank the two anonymous referees as well as Jiro Nemoto, Takeo Ihara and Xiao-Ping Zheng for the insightful comments made.
Disclosure statement
No potential conflict of interest was reported by the authors.
Supplemental data
Supplemental data for this article can be accessed at http://10.1080/00343404.2015.1072273
Notes
1. For detailed discussions of the empirical literature on dynamic externalities, see Rosenthal and Strange (Citation2004), De Groot et al. (Citation2009), and Duranton et al. (Citation2010).
2. The exploratory analysis would also provide a complementary input for various detailed qualitative research approaches, including case and comparative studies. In the field of economic geography, for example, despite its accumulated research in Europe and America, there has been little investigation of the spatial structures of ‘Just-in-Time’-based logistics for the distribution of automotive parts in Japan (Kaneko and Nojiri, Citation2008).
3. There are several valid reasons to capture region–industry growth through employment figures. First, employment refers to one of the output-side measures, not to input-side measures such as research and development (R&D) investment or capital. Second, available data for county–industry including service sectors are quite limited. Third, since one of the main goals of cluster-related policies is to encourage job creation, policy-makers are naturally interested in the effects on employment growth.
4. By using a matrix of occupations by sector, Dumais et al. (Citation1997) emphasize the importance of local labour markets for clustering effects. However, only occupational data classified into major categories are available for counties, not figures for each industry.
5. Glaeser (Citation1999) stresses that the primary informational role of cities may not be in creating cutting-edge innovations, but rather in creating everyday learning opportunities.
6. The industry-mix effect represents the positive and negative impacts of the specialization of regional employment in sectors in which the rate of growth at the national level (or the entire area on which the study focuses) is more or less rapid (Dunn, Citation1960; Esteban-Marquillas, Citation1972).
7. The generalized cost consists of vehicle operating cost, toll fare and travel time cost.
8. The number of employees in the Nagoya MA accounts for 74% of those in the Chubu region as of 1986.
9. The Ghosh inverse matrix is defined by:
where B denotes the output coefficient matrix composed of the elements
; and
gives the total outputs in industry i.
10. On the condition for the size of the linkage in equation (7), some alternatives are also considered. However, the final results are not significantly changed with respect to the choice of conditions.
11. The benchmark values of the thresholds are explored by specifying the following first-order spatial autoregressive model so that the estimated value of the maximum likelihood becomes larger, and the test statistic of the estimated parameter is significant:
,
where y, expressed as deviations from the mean, is the vector of the adjusted long-term average growth rates for the regional industries; and is a random component.
12. In this study, ‘growth cluster’ (‘negative growth cluster’) is the term designated for an agglomeration of interrelated industries with relatively substantial positive (negative) adjusted employment growth rates.
13. The permutation is ‘conditional’ in the sense that the evaluated value is maintained constant and the remaining values are randomly permutated.
14. Since geographical distances are measured by transportation time between the municipal offices of counties, it can be problematic to identify the scale of growth clusters by continuous county areas. However, the spatial unit used by the analysis is too small to be given serious consideration in such a measurement problem.
15. Only the values of significant local Moran statistics for the counties considered as the centres of the positive growth clusters (two counties in West Mikawa) are shown in Table A2 because of space constraints. The complete results are available from the authors upon request.