A Longitudinal Network Analysis of the German Knowledge Economy from 2009 to 2019: Spatio-Temporal Dynamics at the City–Firm Nexus
A Longitudinal Network Analysis of the German Knowledge Economy from 2009 to 2019: Spatio-Temporal Dynamics at the City–Firm Nexus
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DOI：
10.21307/joss-2020-005
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发表时间：
2020-01
影响因子：
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通讯作者：
Silke Zöllner;Stefan Lüthi;A. Thierstein
中科院分区：
文献类型：
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作者：
Silke Zöllner;Stefan Lüthi;A. Thierstein
摘要多地点知识密集型企业跨越其价值链，从而跨越空间位置。全球化的加剧改变了这种知识创造网络的空间结构。纵向社会网络分析允许检测网络中节点和边的布置的时间变化以及由此导致的整体结构的变化。我们使用这种方法来研究德国的时空动态的知识密集型服务企业-先进生产者服务（APS）-在2009年和2019年之间。多区位APS企业被认为是空间结构变化的先锋，因此有助于研究其区位选择行为。一种常见的方法是分析以城市为节点的单模式城际网络。我们采取不同的方法，包括公司的观点。我们的工作直接与原始的数据结构的双模式网络，包括城市和企业作为两个节点集，我们应用随机行为者为导向的网络动态模型。研究结果表明，该区域的时空动态具有集聚经济和网络经济的双重特征。在当地范围内，APS公司继续随着时间的推移，他们的位置扩张，并集中在聚集许多其他APS公司和更大的劳动力可用性。与此同时，他们也会在远离目前所在地的聚集区中选择新的地点。在超局部范围内，网络随着时间的推移而变得越来越密集。2009年对APS公司有吸引力的聚集在2019年变得更具吸引力。我们的分析有助于理解城市和企业之间的互动如何在地方规模上产生经验观察到的超地方规模的网络模式。
Abstract Multi-location knowledge-intensive firms span their value chains and thus their locations across space. Increased globalization alters the spatial configuration of such networks of knowledge creation. Longitudinal social network analysis allows detecting temporal changes in the arrangement of nodes and edges in the network and resulting changes in the overall structure. We use this approach to study for Germany the spatio-temporal dynamics of knowledge-intensive services firms – advanced producer services (APS) – in the years between 2009 and 2019. Multi-location APS firms are considered as vanguard of spatial structural change and thus lending to study their location choice behavior. A common approach is to analyze a one-mode intercity network where cities are the nodes. We take a different approach and include the firms’ perspectives. We work directly with the original data structure of a two-mode network including cities and firms as two node sets and we apply stochastic actor-oriented models for network dynamics. Results show that the spatio-temporal dynamics are characterized by both agglomeration and network economies. On a local scale, APS firms continue their location expansion over time and concentrate in agglomerations where many other APS firms and a greater availability of workforce are present. Simultaneously, they also choose new locations in agglomerations further apart from their present locations. On a supra-local scale, the network grows denser over time. Agglomerations that are attractive for APS firms in 2009 become even more attractive in 2019. Our analysis contributes to an understanding of how interactions amongst cities and firms on a local scale give rise to the empirically observed network patterns on a supra-local scale.