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Regional service clusters and networks. Two approaches to empirical identification and development: the case of logistics in the German port city-states Hamburg and Bremen

机译:区域服务集群和网络。经验识别和发展的两种方法:德国港口城市汉堡和不来梅的物流案例

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This article discusses two approaches to the identification and measurement of regional clusters and its networks in 'cross-sectoral' services which are not available through official industrial statistics. The first approach is a 'secondary-statistical' one consisting of a firm-based blending of two separate official statistical data-sets, industrial and 'functional' (that is, the professions practised within firms). Thus, a service 'cross-sector' is identified across manufacturing and service industries. In the matrices resulting, weights are attached in an expert survey to the numbers of employees to aggregate the 'real' logistics 'cross-sector'. This is applied to the two German port city-states, Hamburg and Bremen. The second approach is 'primary-statistical', based on a small firms survey which generated data on 'functional' supplier relations (the cluster) and on project-based 'strategic' cooperations (the networks within that cluster). This follows a two-stage model of emerging clusters and 'its' networks. This data-set is combined with the firms' affiliations to branches, firm size, age and sales growth classes, in order to connect information with the industry statistics. Also, the net densities and centrality structures are calculated. The combined information provides indications of the relevance of the service cluster and its networks as factors of future regional development. The latter approach is applied to the State of Bremen only. Two results appear to be transferable beyond the German cases: first, the two approaches improve the knowledge about policy-relevant 'cross-sectors', clusters and networks; and second our knowledge about service, namely logistics, clusters and networks (for which port regions are prominent nodes) is improved. Finally, some implications for regional cluster strategies are discussed.
机译:本文讨论了在“跨部门”服务中识别和衡量区域集群及其网络的两种方法,这些方法无法通过官方工业统计数据获得。第一种方法是“二级统计”方法,该方法由基于公司的两种独立的官方统计数据集(工业和“职能”(即公司内部从事的职业))混合而成。因此,跨制造业和服务业确定了服务“跨部门”。在得出的矩阵中,在专家调查中将权重附加到员工人数上,以汇总“实际”物流“跨部门”。这适用于德国的两个港口城市汉堡和不来梅。第二种方法是“主要统计”,基于对小公司的调查,该调查生成了有关“职能”供应商关系(集群)和基于项目的“战略”合作(该集群内的网络)的数据。这遵循了新兴集群及其“其”网络的两阶段模型。该数据集与公司与分支机构的隶属关系,公司规模,年龄和销售增长类别相结合,以便将信息与行业统计数据联系起来。同样,计算净密度和中心结构。合并的信息表明服务集群及其网络作为未来区域发展因素的相关性。后一种方法仅适用于不来梅州。似乎有两个结果可以超越德国的案例:第一,这两种方法提高了与政策相关的“跨部门”,集群和网络的知识;其次,我们对服务的知识,即物流,集群和网络(港口区域是主要节点)得到了改善。最后,讨论了对区域集群战略的一些启示。

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