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Wenting, 2007; Buenstorf and Klepper, 2009; Klepper, 2007), which show that spinof s
are often successful because they can exploit knowledge acquired in very successful
parent organisations.
This literature on industrial dynamics focuses on the mechanisms that drive the evo-
lution of a population of i rm-specii c routines in an industry, and which make i tter
routines more dominant in an industry. A key issue concerns the mechanisms through
which successful routines dif use across i rms and cluster spatially when a new indus-
try emerges. As noted above, spinof dynamics and agglomeration economies may act
as vehicles through which knowledge and routines are created and dif used among a
growing population of i rms within a territory. As noted above, spinof dynamics is con-
sidered a driving force behind the spatial formation of an industry, because it dif uses
relevant knowledge from incumbents (parents) to new i rms (spinof s) at the local level.
Moreover, agglomeration ef ects may become manifest, once spatial clustering occurs.
For instance, local knowledge spillovers may become increasingly available, which then
cause a further spatial concentration of the industry. As such, spinof dynamics and
agglomeration economies provide alternative explanations for the spatial clustering
of an industry. However, these may also be complementary, since spinof activity in a
region may strengthen agglomeration forces, which, in turn, may enhance the creation
and survival rate of spinof s (Boschma and Frenken, 2003).
In a study of the long-run evolution of the British automobile industry, Boschma and
Wenting (2007) found that both ef ects play a role, but at dif erent stages of the industry
life- cycle. Spinof companies did not show a higher survival rate during the i rst stage
of the life-cycle, because there is still not much to be learnt from a parent in the same
industry, since a dominant design is still lacking. By contrast, new entrants with working
experience in related industries did well at this stage. Spinof s performed better only in
a later stage of the industry life-cycle, because pre-entry working experience in the same
industry then appeared to be of much higher value. The ef ect of the location on i rm's
survival also dif ered along the industry life-cycle. Start-ups in the British car industry
that were founded in regions with related industries had a higher survival rate during
the i rst stage (see also Buenstorf and Klepper, 2009). While one would expect a positive
ef ect of localisation economies on a i rm's survival, Boschma and Wenting (2007) found
no ef ect in the i rst stage of the industry life-cycle, and even a negative ef ect at a later
stage. The more spatially concentrated the automobile industry became, the harder it
was for new entrants to survive in clusters, probably because of more local competition
(see for similar results Otto and Kohler, 2008; Staber, 2001). This provides an alterna-
tive view on clusters from the more conventional one, in which clusters are supposed to
deliver what they ought to do, that is, bring benei ts to local i rms (see Staber's contribu-
tion in Chapter 10). This is in line with recent studies that show that clusters not only
stimulate entries (being attractors to new i rms) but also enhance exits (clusters being
tough selection environments at the same time) (Wenting, 2008).
This is not to deny that spatial clustering in certain industries may be attributed to
traditional cost factors, as resource intensive industries locate in the vicinity of natural
resources or near transport nodes, and many service sectors locate close to the market.
However, for many industries, there may be no obvious location in which to cluster, as
their main input is knowledge from people (Lambooy, 2002). This knowledge - for the
most part - has to be created alongside the development of the industry itself. The location
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