Abstract
AbstractThis paper presents a systemic review of the contributions that stochastic actor‐oriented models (SAOMs) and exponential random graph models (ERGMs) have made to the study of industrial clusters and agglomeration processes. Results show that ERGMs and SAOMs are especially popular to study network evolution, proximity dynamics and multiplexity. The paper concludes that although these models have advanced the field by enabling empirical testing of a number of theories, they often operationalize the same theory in completely different ways, making it difficult to draw conclusions that can be generalized beyond the particular case studies on which each paper is based. The paper ends with suggestions of ways to address this problem.
Highlights
Knowledge and information exchange is considered to be among the key drivers of regional and national economic development (Tödtling, Lehner, & Trippl, 2006)
The literature search was conducted in two steps and was based on the specific authors that have been involved in the development of exponential random graph models (ERGMs) and Stochastic actor-oriented models (SAOMs)
The results show some of the versatility of these new statistical tools in operationalizing and empirically testing some of the theoretical concepts related to the network evolution paradigm in economic geography (Glückler, 2007; Powell et al, 2005)
Summary
Knowledge and information exchange is considered to be among the key drivers of regional and national economic development (Tödtling, Lehner, & Trippl, 2006). In general it can be said that the network characteristics of an industrial cluster are of great importance for both the performance of individual organizations within the cluster, as well as the cluster's overall functioning (Belussi & Sammarra, 2010; Breschi & Malerba, 2005; Karlsson, Johansson, & Stough, 2005) In their day-to-day operations, cluster actors engage in a number of different activities such as exchanging financial and material resources, generating and spreading information and collaborating (or competing) with other cluster actors. Stochastic actor-oriented models (SAOMs) and exponential random graph models (ERGMs) are statistical inference models that are among the most popular and theoretically well-developed network models currently in use (Robins, Snijders, Wang, Handcock, & Pattison, 2007; Snijders, 2011) These statistical network models were deemed promising tools to investigate the drivers of economic agglomeration processes within regional studies (Broekel, Balland, Burger, & Van Oort, 2014; Maggioni & Uberti, 2011). Critics of this network modelling approach in economic geography speak of “diminishing returns” because of the repetitive nature of studies that “take the same model and the same methodology to different databases” (Ferru & Rallet, 2016, p.112)
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