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exhaustion, lock-in decline or rejuvenation. Of course a measurement methodology
would need to be created to enable obtaining estimates of the seven dimensions in
specific cases and at specific times.
Finally, it is important to recognize that the life-cycle approach developed in this
paper is presented as is not nested in a broader theoretical framework. Mulligan
(2014, personal communication, February 26) suggests that the life-cycle approach
“could benefit by re-examining the literature on city sizes” and that a “cluster could
be viewed as a single unit where the individual firms are all stochastically shifting,
much like a city system where some places are growing and others are falling; so
there is some kind of steady state distribution that perhaps should not be violated by
too much or harm falls to many.” He further notes that Foster and Wild (
1996
)
considered logistic diffusion models and carrying capacities where the survival of
individual firms were examined. Expansion of this type of thinking could lead to
placing the concept of cluster dynamics in a broader theoretical context. For
example, in such a framework events leading to the exhaustion stage might be
viewed as explanations of cases where a lack of rejuvenation leads to a lock-in on
decline. Alternatively, cases where rejuvenation does occur may not in fact be
rejuvenation of the cluster but more of a metamorphosis resulting in a transforma-
tion that leads to a cluster of quite a different nature than the original cluster
(Jackson 2014, personal communication, February 24) such as in the case of
Pittsburgh, PA in the U.S. where rejuvenation of its steel making cluster resulted
in the emergence of a technology services cluster. The observations by Mulligan
and Jackson suggest some exciting new directions that consider cluster dynamics
that go beyond the focus in this paper on the life-cycle model and thus offer a
broader frame of reference for future work.
12.8 Discussion and Application of the Life-Cycle Model
for Policy
A dimensional model has been developed for the assessment of cluster dynamics
and used to examine cluster development stages including policy suggestions for
local regional and national or EU policy makers and practitioners. The paper has
focused on industrial clusters in general and more specifically on innovative
clusters (sometimes called technology clusters). Here some discussion is offered
on how to identify cluster stages and the most productive cases for policy
intervention.
The core industry or industries for innovative clusters are technology intense in
terms of business model and workforce, and sustainability. These are achieved
through continuous renewal and improved technology. This implies a high rate of
entrepreneurship as this is the major vehicle for achieving continuous innovation.
Innovative clusters thrive in regions where knowledge and information production
and spillover is high which in turn are the optimal conditions for entrepreneurship.
Patents and patent applications offer one of several measures that correlate
positively with performance of innovative clusters. Several maps are presented to
