Skolkovo as a nascent cluster of innovation: formulating a

cluster development framework

Tim P. Jausovec

Stanford University

Addressing the prompt: Identify key frameworks to evaluate initiatives like Skolkovo and

discuss differences in the various approaches. Illustrate your analysis with specifics from

Skolkovo (the Russian context) and also comparative insights from other countries around

the world.

Introduction

Clusters of Innovation (COIs) are hard to define exhaustively. Most literature os-

tensively describes them as geographic concentrations of interconnected organizations, e.g.

suppliers, universities, finance institutions, etc - whereby physical proximity gives rise to

shared advantages through the aggregation of expertise and specialized resources, i.e. ag-

glomeration benefits(Porter, 1990). While useful as a starting point, Porter’s model is

vague, and fails to capture the granularity and diversity of agglomeration benefits.

Engel & Palacio (2009) observe that the model, historically applied, fails to explain

why new and apparently unrelated industries emerge in already existing specialized clus-

ters, e.g. the growth of a new bio-technology industry in Silicon Valley. Firms in a cluster

benefit because of eased access to information and feedback; reduced costs and privileged

access to supply parts, networks and services due to their physical proximity. However,

all of this effects are tied to a specific industry, e.g. information/knowledge, distribution

channels, supply parts, supportive services, etc, in bio-tech industry are significantly dif-

ferent from those in ICT. Nonetheless, some clusters have developed the ability to support

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the continuous emergence of startup high growth entrepreneurial firms almost indepen-

dently of industry alignment’ (Engel & Palacio, 2009, p. 495) In other words they have

developed other agglomeration benefits that are industry-independent, e.g. mobility of re-

sources (money, people, information), alignment of incentives and goals, ‘agglomerating’

new venture creation, experimentation, scaling and failure (Freeman & Engel, 2007). As

an industrial cluster begins to move towards a COI, agglomeration benefits, defined not by

industry specialization, but by the stage of development and innovation begin to dominate.

Roughly speaking, then, agglomeration can be delineated based on industry-specificity, that

is into industry and innovation agglomeration benefits.

What, then, are the causal links between emergence of industry and innovation ag-

glomeration benefits? Does a region, first, have to build strong independent industrial

clusters which, then, interconnect to form a COI? Or, can a large concentration of localized

industry-diverse start-ups reinforce themselves through innovation agglomeration benefits,

first, and gain industry-specific characteristics later, as the number of firms begins to grow;

or, is it possible, to forego industry-specific benefits altogether, forming a “pure” COI?

Answers to this questions can be consolidated into a more holistic cluster development

framework. In order to do so, the first section of this paper takes a historically-descriptive

approach - observing how a specific industrial cluster (ie, Silicon Valley) in the 1960s trans-

formed itself into one of the first and most successful COIs. This points to industry agglom-

eration benefits giving rise to industry-independent network benefits. The second section

expands on this model by searching for commonalities between nascent clusters in Sin-

gapore, Taiwan, Bangalore and Israel. While several additional catalysts stemming from

industry-independent networks are observed, the nascent clusters are similar to 1960’s Sil-

icon Valley in that they again began as very focused industrial clusters which were able

to leverage unexploited demand and only afterwards spawn healthy inter-industry network

effects. In the third section, the proposed cluster development framework is used to evaluate

the Skolkovo initiative.

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Silicon Valley as a cluster of innovation

Silicon Valley as the name suggests began as an industrial cluster focused on inte-

grated circuits and computers. In 1960s it became a premier ground of advancements in

transistor and IC technologies. It saw the rise of several highly successful computer com-

panies, e.g. Fairchild Semiconductor, Hewlett-Packard, Intel, etc; had a constant supply

of tacit knowledge and educated human capital from neighboring universities; and a rising

demand for personal computers. In short, the example of a successful industrial cluster.

As many of the first generation companies grew bigger they were no longer as conducive to

rapid change and re-invention, this lead to several spin-off companies. Spin-offs benefited

from industry-specific agglomeration effects in ways very similar to their parent companies.

But they were also different from them in significant ways, e.g. in the financing structure,

production cycle, and mobility of resources. Due to the success of the spin-offs a whole new

supporting industry tending to their specific needs was created. Moreover, this needs were

no longer industry specific, but process specific, e.g. a bio-tech spin-off requires a very sim-

ilar financing structure to integrated circuits spin-off and running a spin-off (or start-ups)

has a lot of similarities regardless of industry. Thus, a bio-tech start-up now benefited from

the infrastructure and know-how created by a semiconductor industry (Bresnahan et al.,

2002).

This simplified recount of the invention of the innovation agglomeration economy

shows that, historically, a successful industrial-cluster gave rise to a CIO.

Network effects of nascent clusters

However, this was an uncertain and lengthy process where innovation agglomeration

effects first needed to be discovered. The world that nascent clusters are situated in today

is fundamentally different. Could, then, nascent clusters simply implement the innovation

enabling structures that Silicon Valley ‘discovered’ and, thus, skip one evolutionary step,

i.e. be industry-independent from inception?

A quick overview of major innovation centers around the world suggests that a pure

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industrial-cluster still precedes any kind of innovation network benefits. For example, Is-

rael’s focus on security technologies, Singapore’s biomedical sciences and maritime cluster

1, Banglore’s IT, Scandinavian mobile technologies, Taiwan’s semiconductor industry, point

to an industry focus similar to one in 1980s Silicon Valley.

While, the diversity of clusters and conditions into which they’re embedded preclude

strict guidelines regarding ‘correct’ cluster development, strong regularities can be extrap-

olated from the empirical examples presented above. Bresnahan & Gambardella (2004),

distinguish between establishing and growing a cluster, they argue that in a cluster’s early

stage it is important to build a comparative advantage over the existing landscape. Since

developed clusters already benefit from agglomeration economies it is prohibitively difficult

to gain an advantage over them in already explored technologies. So nascent clusters are

forced to generate new valuable technological information (Castells & Hall, 1994, p. 236)

in order to take ‘advantage of technological and market opportunity that haven’t yet been

already exploited.’ (Bresnahan & Gambardella, 2004, p. 11) Usually, this means finding a

niche market or unexplored parts of the value chain. In doing so, nascent clusters often enter

into cooperative relationships with developed ones (eg, Silicon Wadi (Fontenay & Carmel,

2002)) or, more rarely, are driven by indigenous demands created by a new technological

standard (eg, GSM focused Scandinavian clusters) or presence in, otherwise, hard to access

markets.

Moreover, nascent clusters often have limited resources (eg, monetary, space con-

straints...) that limit the number of firms in the cluster. Thus, industry-specialization,

i.e. gaining industry agglomeration benefits, forms a trade-off relationship with innovation

agglomeration benefits - having firms across industries. Therefore, industry-dependance

remains an important evolutionary stage in cluster development, i.e. a strong industrial

cluster is a precursor to COIs.

However, as Engel & Palacio (2009) point-out, these new industrial clusters have

emerged much more rapidly and robustly than Silicon Valley, for example, attracting large

1While two industrial focuses are present in Singapore, they don’t causally interact with each other andare specially distinct. Thus, aren’t considered as an example of a COI.

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concentration of venture capital, and accelerating their growth through interactions with

other clusters. Therefore, while undergoing a similar evolutionary route, they seem to

benefit from their environment more and in different ways than the original clusters.

Concretely, in nascent clusters the linkage between innovation and industrial agglom-

eration benefits isn’t governed by a simple unidirectional causal relationship. In many ways

the two agglomeration effects form a dynamic relationship. One such example is a venture

capital firm forming mutually beneficial linkages with other structures of an industry-specific

cluster - in that sense it is an element in an industrial agglomeration economy. The same

firm, however, also provides capital to a start-up from a different industry - becoming part

of an industry independent network effect. If such a firm succeeds it brings synergies to both

networks it is engaged in, regardless of the network which caused its success. Therefore,

since disparate elements can simultaneously form linkages within industry-independent and

industry-specific networks, the networks themselves become intertwined and gain network

benefits.

More abstractly, a single entity in an innovation ecosystem can play different roles

depending on the context of network that it is viewed in. Moreover, a success of one network

will lead to the success of the entities linked in it, which will have synergies for all the other

networks that this entity is connected with. A very prominent example of such synergies

are argonauts 2 (Saxenian, 2006). Argonauts have formed deep linkages in a specific region

and industry, as they return home they form a become part of a different network and act as

conduits for network benefits by simultaneously being members of two different networks.

Moreover, some of the proximity-induced benefits, e.g. instant and informal dissem-

ination of tacit knowledge, are moving into the virtual sphere. In some sense, the internet

is becoming a substitute, but also an addition to ‘bar chats’ and other forms of resource

mobility.

Therefore, the acceleration and robustness of recent clusters cannot be explained

merely in terms of intra-cluster dynamics, but by the embeddedness of nascent clusters

2A person who received her education or work experience abroad and then returned to her home country

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in a network of other established clusters, mobile knowledge workers and novel ways of

information sharing.

Then, a holistic formulation of cluster development framework could be as follows:

empirical observations of nascent clusters highlight a deep regularity: Industry-independent

inter-cluster relationship that provide nascent clusters with network benefits not experienced

by Silicon Valley in its formative stages. However, such relationships merely act as catalyst

to a reaction which is set into motion by leveraging unexploited demand, often in niche

markets or unexplored parts of the value-chain which can, only, later-on develop features of

a COI.

Evaluating Skolkovo

Skolkovo is a fundamentally different cluster than the ones analyzed above. As previ-

ously noted, successful clusters, normally, begin as industry-specific clusters and, only, later

gain the ability to support innovation across industries. Skolkovo, however, is a nascent

cluster, that wants to grow five ‘priority areas’: energy, IT, biomedical, nuclear and space

(Medvedev, 2011). In light of the above analysis, such a project seems to be highly un-

orthodox.

However, with the ‘priority areas’ acting as independent clusters, Skolkovo can be

seen as a cluster of five-independent industrial-clusters rather than a nascent COI. This

perspective would allow Skolkovo to be consistent with the proposed cluster development

framework, while amending it with an additional network dynamic: strong linkages between

inter-industry nascent clusters. Literature on such linkages is almost non-existent, however,

two conceptual observations can be made:

First, this super-cluster is a hedge against ‘external determinants’. Nascent clusters

bet their success on an emerging technological trend. However, often such trends fail to grab

traction, destroying the cluster in the process. Having multiple clusters each leveraging a

different technology increases the likelihood of, at least, one cluster succeeding. Following

cluster development framework such a successful industrial cluster is then in a phase where

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it can begin supporting other industries, i.e. the other clusters in the super-cluster. Even

if they failed, it will be easier to restart a cluster rather than spawn a completely new

industry.

Second, they can begin enjoying inter-industry agglomeration effects much earlier.

This increases efficiency, since, as discussed above, disparate elements can play multiple

roles within different networks/clusters. In case multiple clusters are successful, the bor-

ders between the clusters will disappear, as process-dependent benefits begin to dominate.

Providing an alternative, potentially much faster route, to a COI.

However, a serious drawback should be considered - the dilution of resources. Each

cluster requires a certain amount of monetary investment, space, promotion, etc. There-

fore, it might be worth asking what are the opportunity costs of spreading an investment

across five clusters. Nonetheless, Russia’s massive oil-rents and the executives commitment

to make Skolkovo a success are big mitigating factors. Additionally, several firms, e.g.

SAP, IBM, Intel, Google, have already committed their own funding and R&D facilities in

Skolkovo.

As to analyzing the successfulness of individual clusters, Watkins (2003) argues that

compared to the macro conditions of the clusters analyzed above, Skolkovo is facing a unique

challenge, stemming from the fact that Finland, Israel, China and India all started with a

relatively underdeveloped enterprise sector and an underdeveloped science and technology

base (S&T) base. While Russia boosts a “a sophisticated science and technology infras-

tructure which, even today, is a world leader in many fields” (Watkins, 2003, p. 3). It is

often implied that this gives Russian ventures a beneficial ‘starting point’.

However, taking a more granular approach, there is little reason why a strong S&T

base would be beneficial to cluster development. Recognize that basic research (or codified

knowledge) doesn’t form a causal relationship with innovation, but merely a correlation.

The reason behind it being that the side-effect of basic research is human capital which strip

down the science and applies it (Pavitt, 1998). Considering the average age of a Russian

PhD-level researcher is 62 years old and aging, in addition to low-mobility of researchers

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and an overall decrease in the amount of researchers(Radosevic, 2003). It paints a stark

contrast to the “sophisticated science and technology infrastructure” suggested by Watkin’s.

Additionally, the believe that S&T is of value to an innovation ecosystem and, thus, must

be preserved despite cost might become a hindering factor, since it creates an institutional

fear of risk and change regarding the sector.

However, considering Skolkovo will employ a very small portion of the general popu-

lations, macro-trends are irrelevant in predicting its success.

Conclusion

As of now, it is hard to say whether Skolkovo is a revolutionary redesign of cluster-

building or a over-ambitious poorly thought through project deemed for failure. As with

most concepts in innovation ecosystems its success and correct characterization will be

highly dependent on execution.

This paper sets up a conceptual framework on which practical guidelines and policy

evaluations can be made. While the development cycle of nascent clusters has already been

studied, the paper suggest that Skolkovo might present a significantly new approach to

cluster development. Specifically, the network effects of inter-industry clusters which are

physically close, i.e. forming a nascent cluster of nascent clusters, are poorly studied. This

paper suggests two ways in which such a super-cluster might conceptual benefit, it sets up

a terminological framework for and calls upon additional studies addressing this concern.

References

Bresnahan, T., & Gambardella, A. (2004). Bulding high-tech clusters: Silicon valley and beyond.

Cambridge University Press.

Bresnahan, T., Gambardella, A., & Saxenian, A. (2002). ”old economy” inputs for ”new economy”

outputs: cluster formation in the new silicon valley. In Industrial dynamics of the new and old

economy - who is embracing whom?

Castells, M., & Hall, P. (1994). Technopoles of the world: The making of twenty-first-century

industrial complexes. Routledge.

9

Engel, J., & Palacio, I. del. (2009). Global networks of clusters of innovation: Accelerating the

innovation process. Business Horizons, 52 , 492-503.

Fontenay, C., & Carmel, E. (2002). Silicon valley and its imitators. In T. Bresnahan, A. Gam-

bardella, & A. Saxenian (Eds.), (chap. Israel’s Silicon Wadi: The forces behind cluster formation).

Routledge.

Freeman, J., & Engel, J. (2007). Models of innovation: Startups and mature corporations. California

Management Review , 50 (1), 94-119.

Medvedev, D. (2011). Mandate of the non-profit organization “fund for development of the center

for elaboration and commercialization of new technologies”. President of the Russian Republic.

Pavitt, K. (1998). The social shaping of the national science base. Research policy , 27 , 793-805.

Porter, M. (1990). The competitive advantage of nations. Free press, New York.

Radosevic, S. (2003). Patterns of preservation, restructuring and survival: science and technology

policy in russia in post-soviet era. Research policy , 32 , 1105-1124.

Saxenian, A. (2006). The new argonauts: Regional advantage in a global economy. Harvard Univer-

sity Press.

Watkins, A. (2003). From knowledge to wealth: Transforming russian science and technology for a

modern knowledge economy. World Bank Policy Research Working Paper , 2974 .