Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/232508 
Autor:innen: 
Erscheinungsjahr: 
2021
Quellenangabe: 
[Journal:] Papers in Regional Science [ISSN:] 1435-5957 [Volume:] 100 [Issue:] 2 [Publisher:] Wiley [Place:] Oxford [Year:] 2021 [Pages:] 379-403
Verlag: 
Wiley, Oxford
Zusammenfassung: 
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.
Schlagwörter: 
agglomeration
networks
Clusters
ERGM
SAOM
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
cc-by Logo
Dokumentart: 
Article
Dokumentversion: 
Published Version

Datei(en):
Datei
Größe





Publikationen in EconStor sind urheberrechtlich geschützt.