Geography Reference
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less proximate in geographical and non-geographical dimensions. This could be accom-
plished by the formation of new ties that bridge unconnected networks (Burt, 2004;
Glückler, 2007). These ideas call for further rei nements and thorough empirical testing
(Ter Wal and Boschma, 2009b).
5. Conclusion
We have made an attempt to sketch an evolutionary view of the geography of innovation
networks by linking the literatures on proximity and network dynamics. To begin with,
we argued that variety is a key feature of any economy, and knowledge accumulation
at the i rm level is its prime mover. In such an evolutionary framework of heterogene-
ous actors, the replication of knowledge between i rms is considered troublesome unless
there is some degree of proximity between actors on some dimensions: proximity is
required on some (but not necessarily all) dimensions to make i rms connected, and to
enable interactive learning and innovation. Doing so, we have put the proximity concept
into the heart of the theoretical and analytical framework of evolutionary economic
geography.
Such a basic framework also enabled us to connect the proximity concept to the
geography of networks. We made a distinction between i ve forms of proximity. Each
relationship between two heterogeneous actors can be classii ed as being more or less
proximate in all i ve dimensions. The dimensions, analytically dei ned, are orthogonal,
even though many dimensions may often turn out to be correlated. A proximity frame-
work suggests that actors that are proximate in some (if not all) dimensions are more
likely to connect. This approach has led to new insights in the cluster literature, for
instance. Giuliani (2007) has shown that knowledge networks between i rms in a cluster
are not pervasive (as suggested by the cluster literature) but tend to be rather selective,
because these depend on the levels of cognitive proximity between cluster i rms.
While a high degree of proximity is considered a prerequisite to make actors con-
nected, we expect the ef ects of network relations on innovation to be rather ambiguous.
Proximity between actors does not necessarily translate into higher innovative perform-
ance, because excess of proximity may be harmful for interactive learning. We referred
to this as the proximity paradox. One should therefore make a distinction between the
drivers of network formation on the one hand (in which the forms of proximity posi-
tively af ect the establishment of networks), and the ef ects of a network on innovative
performance on the other hand (in which it is uncertain what the ef ects of proximity
on network performance are). Inspired by others, we expect that, for each dimension,
an optimal level of distance exists, at which interactive learning and innovation are
maximized.
We also introduced some propositions about network evolution and the changing role
of proximity during the industry lifecycle. This has led us to conclude that the density of
network relations in geographical clusters is likely to increase over time, despite the fact
that codii cation of knowledge facilitates long-distance networking. A high local density
of network relations may well lie at the root of the problems faced by industrial areas as
a too strong proximity prevents the renewal of a region's industrial base.
We would like to i nish with three research challenges that need to be taken up to build
a true evolutionary perspective on the spatial evolution of innovation networks.
First, a dynamic network approach should assess the relative importance of the
