Modeling knowledge networks in economic geography: a discussion of four methods

Tom Broekel, Pierre-Alexandre Balland, Martijn Burger, Frank van Oort

    Research output: Contribution to journalArticleAcademicpeer-review

    Abstract

    The importance of network structures for the transmission of knowledge
    and the diffusion of technological change has been recently emphasized in economic
    geography. Since network structures drive the innovative and economic performance
    of actors in regional contexts, it is crucial to explain hownetworks 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 (individual level) 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 approach are discussed together with domains of
    applicability the geography of innovation studies.
    Original languageUndefined/Unknown
    Pages (from-to)423-452
    Number of pages30
    JournalThe annals of regional science
    Volume53
    Issue number2
    DOIs
    Publication statusPublished - 2014

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