Where do knowledge-intensive firms locate in Germany?-An explanatory framework using exponential random graph modeling.

Mathias Heidinger, Fabian Wenner, Sebastian Sager, Paul Sussmann, Alain Thierstein
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Abstract

This paper analyzes how positional and relational data in 186 regions of Germany influence the location choices of knowledge-based firms. Where firms locate depends on specific local and interconnected resources, which are unevenly distributed in space. This paper presents an innovative way to study such firm location decisions through network analysis that relates exponential random graph modeling (ERGM) to the interlocking network model (INM). By combining attribute and relational data into a comprehensive dataset, we capture both the spatial point characteristics and the relationships between locations. Our approach departs from the general description of individual location decisions in cities and puts extensive networks of knowledge-intensive firms at the center of inquiry. This method can therefore be used to investigate the individual importance of accessibility and supra-local connectivity in firm networks. We use attributional data for transport (rail, air), universities, and population, each on a functional regional level; we use relational data for travel time (rail, road, air) and frequency of relations (rail, air) between two regions. The 186 functional regions are assigned to a three-level grade of urbanization, while knowledge-intensive economic activities are grouped into four knowledge bases. This research is vital to understand further the network structure under which firms choose locations. The results indicate that spatial features, such as the population of or universities in a region, seem to be favorable but also reveal distinct differences, i.e., the proximity to transport infrastructure and different valuations for accessibility for each knowledge base.

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知识密集型企业在德国的位置-使用指数随机图建模的解释性框架。
本文分析了德国186个地区的位置和关系数据如何影响知识型企业的位置选择。企业的位置取决于特定的本地和相互关联的资源,这些资源在空间上分布不均。本文提出了一种创新的方法,通过将指数随机图模型(ERGM)与连锁网络模型(INM)相关联的网络分析来研究此类企业的选址决策。通过将属性数据和关系数据组合成一个综合数据集,我们可以捕捉空间点特征和位置之间的关系。我们的方法偏离了对城市中个人选址决策的一般描述,将知识密集型企业的广泛网络置于调查的中心。因此,该方法可用于研究企业网络中可访问性和超本地连接的个人重要性。我们使用交通(铁路、航空)、大学和人口的归因数据,每个数据都在功能区域层面上;我们使用两个地区之间的旅行时间(铁路、公路、航空)和关系频率(铁路、航空)的关系数据。186个功能区被划分为三级城市化,而知识密集型经济活动被划分为四个知识库。这项研究对于进一步了解企业选址的网络结构至关重要。结果表明,空间特征,如一个地区的人口或大学,似乎是有利的,但也揭示了明显的差异,即与交通基础设施的接近程度和对每个知识库的可访问性的不同评估。
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Aging and regional productivity growth in Germany. COVID-19 and housing prices: evidence from U.S. county-level data. Where do knowledge-intensive firms locate in Germany?-An explanatory framework using exponential random graph modeling. The regional variation of a housing boom. Disparities of land prices in Austria, 2000-2018. Spatial networks and the spread of COVID-19: results and policy implications from Germany.
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