Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/232508 
Authors: 
Year of Publication: 
2021
Citation: 
[Journal:] Papers in Regional Science [ISSN:] 1435-5957 [Volume:] 100 [Issue:] 2 [Publisher:] Wiley [Place:] Oxford [Year:] 2021 [Pages:] 379-403
Publisher: 
Wiley, Oxford
Abstract: 
This 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.
Subjects: 
agglomeration
networks
Clusters
ERGM
SAOM
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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