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Modeling Knowledge Networks in Economic Geography: A Discussion of Four Empirical Strategies

Author

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  • Tom Broekel

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  • Pierre-Alexandre Balland
  • Martijn Burger
  • Frank van Oort

Abstract

The importance of network structures for the transmission of knowledge and the diffusion of technological change has been emphasized in economic geography. Since network structures drive the innovative and economic performance of actors in regional contexts, it is crucial to explain how networks form and evolve over time and how they facilitate inter-organizational learning and knowledge transfer. The analysis of relational dependent variables, however, requires specific statistical procedures. In this paper, we discuss four different models that have been used in economic geography to explain the spatial context of network structures and their dynamics. First, we review gravity models and their recent extensions and modifications to deal with the specific characteristics of networked relations. Second, we discuss the quadratic assignment procedure that has been developed in mathematical sociology for diminishing the bias induced by network dependencies. Third, we present exponential random graph models that not only allow dependence between observations, but also model such network dependencies explicitly. Finally, we deal with dynamic networks, by introducing stochastic actor oriented models. Strengths and weaknesses of the different approaches are discussed together with domains of applicability for the analysis of (knowledge) network structures and their dynamics.

Suggested Citation

  • Tom Broekel & Pierre-Alexandre Balland & Martijn Burger & Frank van Oort, 2013. "Modeling Knowledge Networks in Economic Geography: A Discussion of Four Empirical Strategies," Papers in Evolutionary Economic Geography (PEEG) 1325, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Dec 2013.
  • Handle: RePEc:egu:wpaper:1325
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    File URL: http://econ.geo.uu.nl/peeg/peeg1325.pdf
    File Function: Version December 2013
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    References listed on IDEAS

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    3. Jarno Hoekman & Koen Frenken & Frank Oort, 2009. "The geography of collaborative knowledge production in Europe," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 43(3), pages 721-738, September.
    4. Tom Broekel & Matté Hartog, 2013. "Explaining the Structure of Inter-Organizational Networks using Exponential Random Graph Models," Industry and Innovation, Taylor & Francis Journals, vol. 20(3), pages 277-295, April.
    5. Roberto Basile & Roberta Capello & Andrea Caragliu, 2012. "Technological interdependence and regional growth in Europe: Proximity and synergy in knowledge spillovers," Papers in Regional Science, Wiley Blackwell, vol. 91(4), pages 697-722, November.
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    Citations

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    Cited by:

    1. Morescalchi, Andrea & Pammolli, Fabio & Penner, Orion & Petersen, Alexander M. & Riccaboni, Massimo, 2015. "The evolution of networks of innovators within and across borders: Evidence from patent data," Research Policy, Elsevier, vol. 44(3), pages 651-668.
    2. Laurent R. Bergé, 2017. "Network proximity in the geography of research collaboration," Papers in Regional Science, Wiley Blackwell, vol. 96(4), pages 785-815, November.

    More about this item

    Keywords

    Economic geography; knowledge networks; network models; quadratic assignment procedure; gravity model; exponential random graph model; stochastic actor-oriented model;

    JEL classification:

    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

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