Website Anne ter Wal

Ter Wal, A.L.J. (2009), Cluster emergence and network evolution: a longitudinal analysis of the inventor network in Sophia-Antipolis. Regional Studies, in press. Available on-line as PEEG Working Paper 08.10 (pdf).

It is increasingly acknowledged that clusters do not necessarily exhibit networks of local collective learning. Through a case-study of Sophia-Antipolis this study investigates to what extent networks of collective learning emerged throughout the growth of the business park. A longitudinal analysis of the inventor networks of its two main sectors reveals that a local network of collective learning emerged only in Information Technology and not in Life Sciences. Through the creation of spin-offs and high-tech start-up firms the initial dominance of large multinationals decreased only in Information Technology. This suggests that small firms play an important role in establishing local networks.

Ter Wal, A.L.J. & R.A. Boschma (forthcoming), Co-evolution of firms, industries and networks in space. Regional Studies, in press (pdf).

The cluster literature suffers from a number of shortcomings: (1) by and large, cluster studies do not take into account that firms in a cluster are heterogeneous in terms of capabilities; (2) cluster studies tend to overemphasize the importance of place and geographical proximity and underestimate the role of networks which are, by definition, a-spatial entities; (3) most, if not all cluster studies have a static nature, and do not address questions like the origins and evolution of clusters. Our aim is to overcome these shortcomings and propose a theoretical framework on the evolution of clusters. Bringing together bodies of literature on clusters, industrial dynamics, the evolutionary theory of the firm and network theory, we describe how clusters co-evolve with: (1) the industry they adhere to; (2) the (dynamic) capabilities of the firms they contain; and (3) the industry-wide knowledge network they are part of. Based on this framework, we believe the analysis of cluster evolution provides a promising research agenda in evolutionary economic geography for the years to come.

Ter Wal, A.L.J. & R.A. Boschma (2009), Applying social network analysis in economic geography: framing some key analytic issues. Annals of Regional Science 43 (3), in press (pdf).

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 geograhy, such as the feasibility of dynamic network analysis.

Boschma, R.A. & A.L.J. ter Wal (2007), Knowledge networks and innovative performance in an industrial district: the case of a footwear district in the South of Italy. Industry and Innovation 14 (2), pp. 177-199 (pdf).

The traditional district literature tends to assume that: (1) the competitiveness of firms depends on external sources of knowledge; (2) all firms in a district benefit from knowledge externalities; (3) relying on external knowledge relationships necessarily means these are confined to the district area. Our case study of the Barletta footwear district in the South of Italy suggests otherwise. Based on social network analysis, we demonstrate that the local knowledge network is quite weak and unevenly distributed among the local firms. A strong local network position of a firm tended to increase their innovative performance, and so did their connectivity to extra-local firms. So, it mattered being connected either locally or non-locally: being co-located was surely not enough. Having a high absorptive capacity seemed to raise only indirectly, through non-local relationships, the innovative performance of firms.

Cantner, U., A. Meder & A.L.J. Ter Wal (2008), Innovator networks and regional knowledge base. Jena Economic Research Papers 2008-042.

This paper concerns the regional innovation system approach. It deals with the characteristics of three regional systems, Northern Hesse, Alpes-Maritime and Jena, and focuses on each regional network of innovators. In this context the importance of the size and homogeneity of a regional pool of knowledge spillovers for those networks is analyzed. We find evidence that an increasing regional knowledge base in combination with an increasing homogeneity of this knowledge base enhances the knowledge flows and the incentives for actors to interact with each other.

Cassi, L., A. Morrison & A.L.J. Ter Wal (2009), The evolution of knowledge and trade networks in the global wine sector: a longitudinal study using social network analysis. Papers in Evolutionary Economic Geography 09.09 (pdf).

Throughout the last two decades or so the global pattern of wine production has undergone fundamental changes. New players have emerged and technological and organizational changes have reshaped the way wine is produced and marketed. The aim of this study is to increase our understanding of these processes. We map and compare trade and knowledge networks using social network techniques in order to show how globalization has affected this particular sector, and how the main actors of this industry have responded to these challenges. We are able to give account of the structural changes that have characterised the industry at global level over more than three decades and relate them to the features of the main trade and knowledge blocks.