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

Area of Science:

• Economic Geography
• Network Analysis
• Spatial Economics

Background:

• Firm location decisions are influenced by spatial and relational factors.
• Understanding the network structure of knowledge-intensive firms is crucial.
• Existing models often focus on individual city locations rather than firm networks.

Purpose of the Study:

• To analyze how positional and relational data in German regions affect knowledge-based firm location.
• To develop an innovative network analysis approach combining exponential random graph modeling (ERGM) and interlocking network model (INM).
• To investigate the importance of accessibility and supra-local connectivity in firm networks.

Main Methods:

• Combined attribute data (transport, universities, population) and relational data (travel time, relation frequency).
• Utilized network analysis integrating ERGM and INM for firm location decisions.
• Analyzed 186 functional regions in Germany with varying urbanization levels and four knowledge base types.

Main Results:

• Regional features like population and universities positively influence firm location.
• Proximity to transport infrastructure is a key factor, with varying importance across knowledge bases.
• Distinct differences in location valuation based on accessibility and knowledge base type were observed.

Conclusions:

• The study provides insights into the network structures guiding knowledge-intensive firm location.
• Positional and relational data are essential for understanding complex firm location dynamics.
• Tailored transport accessibility strategies may be needed for different knowledge-based economic activities.