Applying social network analysis in economic geography: framing some key analytic issues
Anne L.J. Ter Wal & Ron A. Boschma
Utrecht University, Department of Economic Geography, Faculty of GeoSciences
PO Box 80 115, 3508 TC Utrecht, the Netherlands
Abstract:
Social network analysis attracts increasing attention in economic geography. We claim social network analysis is a promising tool for empirically investigating the structure and evolution of inter-organizational interaction and knowledge flows within and across regions. However, the potential of the application of network methodology to regional issues is far from exhausted. The aim of our paper is twofold. The first objective is to shed light on the untapped potential of social network analysis techniques in economic geography: we set out some theoretical challenges concerning the static and dynamic analysis of networks in geography. Basically, we claim that network analysis has a huge potential to enrich the literature on clusters, regional innovation systems and knowledge spillovers. The second objective is to describe how these challenges can be met through the application of network analysis techniques, using primary (survey) and secondary (patent) data. We argue that the choice between these two types of data has strong implications for the type of research questions that can be dealt with in economic geography, such as the feasibility of dynamic network analysis.
Key words: social network analysis, clusters, regional networks, knowledge spillovers, patent analysis
1 Introduction
Since the last decade, networks have gained a great deal of attention in regional economics and economic geography (Grabher 2006). Only recently, social network analysis techniques have been applied in an effort to examine how the structure of interaction in regions and geographical clusters looks like. More and more researchers get convinced that networks are an appropriate conceptualization of inter-organizational interaction and knowledge flows.
Hence, social network analysis is viewed upon as a promising tool for future directions in regional research. That is to say, now that it is possible to empirically assess the structure of networks, new possibilities have arisen to investigate inter-organizational interactions and their evolution over time in a more quantitative manner .
Virtually all existing empirical studies on networks in clusters (e.g. Morrison 2004; Giuliani and Bell 2005) take a static perspective, depicting the network at a certain point in time. The wider field of network theory, on the other hand, recently experienced an upsurge of interest in the dynamics of networks (e.g. Snijders 2001; Baum et al. 2003). In this dynamic network analysis, concepts like preferential attachment play a key role. However, the application of dynamic network theory to inter-organizational networks (e.g. Orsenigo et al. 1998; Gay and Dousset 2005) still lacks a geographical component (Glückler 2007). Hence, it is especially in the combination of both trends where important theoretical and empirical challenges remain. In this paper, we claim that economic geography could contribute greatly to combining both trends, when taking an evolutionary perspective to networks within and across regions.
The aim of our paper is twofold. The first objective is to shed light on the untapped potential of social network analysis techniques in economic geography. We aim to set out in Section 2 some theoretical challenges concerning the static and dynamic analysis of networks in regional research. Doing so, we claim that especially three types of literature in economic geography can potentially benefit from social network analysis: the cluster literature, the regional innovation system literature, and the literature on agglomeration economies and knowledge spillovers. The second objective of our paper is to describe how these challenges can be assessed through the application of social network analysis techniques, using primary (survey) and secondary (patent) data. We argue in Section 3 that the choice between these two types of data has strong implications for the type of research questions that can be dealt with in economic geography. Section 4 draws some conclusions.
4 Conclusions
Inter-organizational interaction has always played a crucial role in the literature on regional innovation systems and clusters. However, the structure of this interaction has hardly been assessed empirically in more quantitative terms. At the same time, existing empirical studies on clusters and regional innovation systems have been mostly static. Whereas static network studies incorporating social network analysis techniques have emerged in the field of economic geography in the last couple of years, dynamic studies of spatial networks are virtually non-existent. However, both in terms of static and dynamic network research, a lot of challenges remain. These challenges can be organized along three sets of questions that should form the backbone in future regional network research.
The first set of questions refers to how interaction within and across clusters is structured and particularly how this structure has come into being. Further insight is needed how micro-level capabilities and meso-level proximities affect the spatial configuration of interfirm networks. Related is the second set of questions, which concerns the evolution of networks across time and space. To what extent do inter-firm networks evolve along the general principles of preferential attachment and homophily as set out by network theorists? And how do these drivers of network evolution relate to economic geographical factors like geographical proximity, social connectedness and cognitive capabilities? Finally, a third set of questions refers to the effects of networks on performance. At the micro level economic geographers can further contribute to the growing literature on the positive and negative effects of the positions firm occupy in local and non-local networks on their innovative performance. At the macro level, economic geographers can bring in a network approach to the literature on agglomeration economies and cluster development. There is increasing awareness that a dense local system of interactions may lead to cognitive lock-in and economic decline when it is not complemented by a wider network of non-regional linkages.
Network analysis will further contribute to a better understanding of which types of extraregional linkages matter most economically
Social network analysis constitutes an appropriate analytic toolbox for economic geographers to meet these challenges and has created a growing demand for empirical network data. Both empirical network research on the basis of primary data and secondary data can play a central role in meeting these challenges. When carried out thoroughly (i.e., resulting in a very high response rate), primary data network research can generate a detailed network that reveals the real and complete structure of a spatial innovation network. However, due its highly time-intensive nature, it can only be applied to very limited samples. Moreover, the high requirements concerning response – to ensure that one identifies the complete network – makes this methodology unfeasible for large scale empirical work. Since asking organizations for the relationships in the past will not result in reliable information, this methodology is also inappropriate for longitudinal network analysis. Patent-based networks in this case are a better alternative. In these networks, links between firms can be identified back in time through co-patenting and co-inventing. However, this methodology is only appropriate for industries in which intellectual property is generally protected by patents. In addition, it is biased towards explicit and successful forms of inter-firm knowledge exchange.
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