2.3 Network analysis
Networks provide another unit of analysis. Unlike the competitive nature of industrial
dynamics, network relationships are less competitive and of a more complementary nature. One
important aspect of networks in Evolutionary Economic Geography is that these act as vehicles
for knowledge spillovers. A key research question is then to determine whether knowledge
diffusion and innovation is more a matter of being in the right place, in the right network, or in
both (Boschma and Ter Wal, 2006). Social network analysis provides a rich toolbox for the
analysis of the structure and evolution of networks (Wasserman and Faust, 1994; Carrington et
al., 2005). What is more, there is a lot of interest in theorising about networks and network
formation starting from the pioneering work by Granovetter (1973) and Burt (1982) to more
recent, but already classic contributions of Watts and Strogatz (1998) and Barabasi and Albert
(1999).
In evolutionary economics, interest in networks stems primarily from the increasing
importance of networks among high-technology firms (Hagedoorn, 1993; Powell et al., 1996),
while geographical studies have shown the role of networking in clusters (Uzzi, 1996; Maskell
and Malmberg, 1999). The central question has been whether agents profit from simply being co-
located or whether network relationships are required to carry these knowledge flows. A related
question is whether geographical proximity facilitates the formation of network links. An
innovative study by Breschi and Lissoni (2003) found that, using co-inventor data to indicate
social networks and patent citations to indicate knowledge flows, geographical localisation of
knowledge spillovers can be largely attributed to social networks and labour mobility. This study
shows considerable progress over the study by Jaffe et al. (1993), who treated geographical space
as a black box. The Breschi-Lissoni study suggests that geographical proximity is neither a
necessary nor a sufficient condition for knowledge spillovers to occur. Rather, knowledge
diffuses through social networks, which are dense between proximate actors, but also span across
the globe.
Network analysis between firms in specialised clusters is another field in which social
network analysis can be fruitfully applied. Using survey data, Giuliani (2005) has been able to
map the business and knowledge networks among wine producers in three different clusters. She
found that the distribution of connectivity is much more skewed in knowledge than in business
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