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.
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 language | Undefined/Unknown |
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Pages (from-to) | 423-452 |
Number of pages | 30 |
Journal | The annals of regional science |
Volume | 53 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2014 |