Geography Reference
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dynamics are path-dependent, and that this path dependence does not simply arise from
the assumption of increasing returns as is the case in NEG models. That is not to say that
increasing returns do not play a role. Rather, if included one should specify both positive
and negative externalities.
In Chapter 24, Giulio Bottazzi and Pietro Dindo provide a i ne example of how mod-
elling may contribute to the further advancement of evolutionary economic geography.
These authors explain how dif erent their evolutionary model of i rm location is from
the neoclassical Krugman model that laid the foundations of the new economic geog-
raphy. They present the outlines of an evolutionary entry-exit model of i rms' location
that describes how the economic landscape evolves over time. As a starting point, they
present a static framework in which a spillover drive scenario leads to agglomeration,
while a market drive scenario may generate either agglomeration or even spatial distribu-
tion. The model further specii es how other variables (like transport costs) may enhance
technological spillovers or market forces and thus agglomerative forces, or not. This
static framework is complemented by an evolutionary entry-exit model, in which hetero-
geneous i rms may change their locational preferences, because of previous decisions of
other i rms. In this dynamic setting, agglomeration is a less likely outcome, and when it
occurs, the agglomeration may not always be stable.
Another promising line of research in evolutionary economic geography is to deter-
mine what kind of agglomeration externalities are needed to promote urban and regional
growth (Feldman and Audretsch, 1999; Glaeser et al., 1992; Henderson et al., 1995;
Jacobs, 1969). Frenken et al. (2007) have gone beyond the dichotomy of Marshall-
Arrow-Romer (MAR)-type versus Jacobs-type externalities by introducing the notion
of 'related variety' type externalities. This means that regions that are endowed with
technologically related sectors might have higher growth rates, because this might af ect
positively the nature and scope of regional knowledge spillovers. That is, the extent to
which the variety of technologies present in a region are related is expected to af ect the
scope for knowledge spillovers, as i rms in dif erent but related activities can proi t more
from mutual spillovers than can i rms in unrelated activities (Boschma and Frenken,
2009b). In other words, related variety performs two tasks at the same time. Some degree
of cognitive proximity (i.e. relatedness between sectors) ensures that ef ective commu-
nication and interactive learning between sectors take place. But also some degree of
cognitive distance (that is, variety between sectors) is needed, to avoid cognitive lock-in
and stimulate novelty (Nooteboom, 2000). Frenken et al. (2007) could demonstrate
empirically for the Netherlands that related variety does indeed have a positive impact
on regional growth. This result has been replicated in studies on other countries (Bishop
and Gripaios, 2009; Boschma and Iammarino, 2009; Essletzbichler, 2007).
The next step to take in these regional growth models is to account for the fact that
new and related variety may also be brought into the region through inter-sectoral link-
ages with other regions. Boschma and Iammarino (2009) have made a i rst attempt to
estimate the ef ects of inter-sectoral learning across regions on regional growth in Italy
by means of trade data. Their analysis suggests that the inl ow of a variety of knowledge
per se did not af ect economic growth of regions in the period 1995-2003. The same
was true when the extra-regional knowledge was similar to the knowledge base of the
region. However, the more related the knowledge base of the region and its import
proi le was, the more it contributed to regional employment growth. This might indicate
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