Globalization and Social Dualization, under an Institutional Constraint: The Brussels-Capital Case

Vandermotten, Christian; Roelandts, Marcel

doi:10.2148/benv.32.2.148

Cited by 6 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While focusing on the location patterns of such firms, several studies found different location choices among APS sectors at the local city scale. (e.g., Vandermotten and Roelandts 2006;Bassens et al 2020;Chong and Pan 2020;Waiengnier et al 2020). Waiengnier et al's (2020) study on Brussels, for example, shows that the location preference, and therefore the concentration of firms, depends on the size of the firm and the service they provide.…”

Section: Conceptual Background and Hypothesesmentioning

confidence: 99%

See 1 more Smart Citation

Welche Standorte wählen wissensintensive Unternehmen in Deutschland? – Ein Erklärungsrahmen mit exponential random graph modeling

Heidinger

Wenner

Sager³

et al. 2023

Rev Reg Res

View full text Add to dashboard Cite

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.

show abstract

Section: Conceptual Background and Hypothesesmentioning

confidence: 99%