Evolution and Performance of Biotechnology
Regional Systems of Innovation
Jorge Niosi
Department of Management and Technology
Université du Québec à Montréal
J. Niosi May 2009
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Overview
1.0 Background: complexity and evolution
2.0 Thesis: Biotechnology clusters evolve over time, from
knowledge spillovers to knowledge markets
3.0 Research: Evidence from publicly-traded Canadian
biotechnology startups
4.0 Summary and Conclusion
5.0 Needs for further research
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1.0 Background
• RSI: A cluster dedicated to creating inventions, in which there is high
geographic concentration of R&D, complementary inputs.
• Knowledge flows: Enable new technologies to be created, and are of
two kinds:
– Externalities: Usually tacit information which is non-codifiable and difficult to
appropriate. The process of innovation both requires and creates externalities.
– Markets: Usually involves codifiable, appropriable information traded in strategic
factor markets, traded in factor markets (e.g. patent licences). Several
complementary institutions are required in order for markets to function (e.g. courts
enforce property rights).
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1.1 Knowledge Flows: Industry Differences
Industry
Major organis.
within an RSI
Aerospace
A1
A2
Biotechnology
PL
VC
Information
technology
IT f’s
User
User
U
U
CBF
Advanced
materials
VC
Structure of
knowledge
flows
Spillovers
(Network
Externalities)
Markets
Designs,
manufacturing
specs.
Scientific and
experimental
know-how
Shared
platforms,
standards
Widespread
application of
material
Markets for
assemblies,
engineering,
components
Patents, CROs
grants,
equities
Copyrights,
patents,
development
agreements
R&D and
supply
contracts
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1.2 Why such differences?
• Different technologies, different requirements
(technological imperatives)
• Different age, history
• Different degrees of appropriability (property rights)
Could it be that RSI’s are at different stages of
evolution?
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1.3 Complexity
RSI are complex systems characterized by
- dispersed interaction among heterogeneous agents
- no global controller that can exploit all opportunities
-cross-cutting hierarchical organisations with many tangled
interactions
- continual adaptation by learning and evolving agents
- perpetual novelty as new markets, technologies, behaviours and
institutions create new niches in the ecology of the system
- out-of-equilibrium dynamics with either zero or many equilibria
existing and the system unlikely to be near optimum (Arthur et al,
1997)
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1.4 Do firms in clusters perform better?
- Yes, because of local knowledge spillovers,
labour pools, suppliers and clients, and
supporting institutions. All cluster, RSI
theories adopt this position
- No, when agglomeration diseconomies begin
to manifest themselves (i.e. London UK,
Paris or California)
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2.0 Thesis
From Networks to Markets: How RSI’s Evolve
Organizational Networks: Externalities abound
Formal Markets: Value chain emerges
Development of
Property rights
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2.1 Evolution of RSI’s: Supporting Theories
• Population Ecology (Hannan and Freeman): Organizations having
“isomorphic” form survive, others die and disappear.
– Assumes environment is source of evolution, that organizations do not
change.
• Networks, Structural Holes (Burt, Gulati): Idiosyncratic knowledge
allows an agent to broker a new relationship between two other parties.
– Useful in science-based industries because knowledge (e.g., how to solve
a problem) is an important input.
• Evolutionary Economics (Arthur): Path dependencies reinforce the
strengths of some regions, weaken that of others.
What these theories have in common:
Nature and function of exchanges is not fixed but changes
over time.
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2.2 From Spillovers to Markets:
Mechanisms of Evolution
• Early (exploration) stages: spillovers are important
externalities that draw inventors to the cluster.
– Knowledge networks abound: e.g. scientific conferences,
publications, informal meetings
• Later (exploitation) stages: Spillovers may be partially
internalized, benefits appropriated.
– Well-defined factor and output markets are created.
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3.0 Research Hypotheses
• H1: Formal markets appeared progressively in biotechnology.
• H2: Firms in RSIs perform better than firms not in clusters.
• H3: University spin-offs perform better than other types of biotech
firms.
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3.0 Biotechnology
• A set of technologies, not an industry
• Users are mainly in the biopharma, food,
forestry and environment industries
• Yet there are specialised biotechnology
firms conducting R&D on biotechnology
products and processes.
• Universities act as anchor tenants and
attract pools of talent (star scientists) and
spin-off key firms.
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Biotechnology in Canada
• By 2003, there were close to 500 SBFs in
Canada, most of them conducting R&D on
human health products and processes.
Montreal, Toronto and Vancouver are the
main hosts of these SBFs, followed by
Edmonton, Calgary, Quebec City, Ottawa,
and Saskatoon
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Methods and data
•
•
•
•
•
Patent counting
Firm counting
Regional R&D expenditures
Location of key firms
Manufacturing value added, employees, and
deliveries according to region
• Venture capital: regional origins and destination
• University spin-offs and IP management
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3.1 Results
University
$$
University
$$
VC firm
Origins of the system (circa 1980)
Development of the system
(circa 1990)
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3.1 Results:
Progressive organization of venture capital markets (H1)
• Shorter time to IPO:
relative importance
(development time) of
knowledge network
diminishes.
120
100
80
60
Months to IPO
40
20
0
Prior 1991- 19961991 1995 2001
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Canada Biotech patents by CMA
Figure 1 Biotec hnology Patents by CMA, 2000
Saskat oon 1%
Edmonton 6%
0%
Calgary 6%
Quebec City 8%
Toronto 39%
Ott aw a 9%
Vanc ouver 11%
Montreal 20%
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3.1.1 Results:
Property Rights: Transition from Networks to Markets is
Facilitated (H1)
Source: AUTM FY 2000 Licensing Survey
•
Bayh-Dole Act (1989): permits
universities to patent inventions
resulting from federally funded
research (35 U.S.C. 200).
•
Several IDs may be combined into
one patent, vice versa.
•
Health and biotech patents
represent a substantial proportion
(~45%) both by number and
licensing $.
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3.1.2 Results:
Other measures for increased market activity : Licensing
revenues increase (H1)
Source: AUTM FY2000 Licensing Survey
•
Licensing revenues still however
represent less than 5% of university
R&D expenditures
•
Approximately 450 new startups
created in FY 2000 alone
•
4300 licenses executed in 2000;
over 20 000 still active in 2000
•
License income: over 1.26 B$, a
23% increase over the previous
year
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3.2 Results:
Are there advantages (time to IPO) of being located in a
cluster? (H2)
o
uts
ide
clu
ste
r)
s (o
Outside: firms
« leapfrog » to IPO
due to emergence of
markets.
To
ron
t
•
Months to IPO
Pra
i rie
Toronto?: Large
number of older
firms biases sample
upwards.
Mo
ntr
eal
•
90
80
70
60
50
40
30
20
10
0
uve
r
Firms in a cluster
are not necessarily
at an advantage with
respect to going
public.
Va
nco
•
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3.3. Results
Do University Spin-Offs Perform Better? (H3)
University spin-offs
Other firms
9
11
Year founded (average)
1993
1991
Amount in IPO (average)
$12.2 million
$14.6 million
Dimension
Age (average)
Avg. employ. growth (1997-2002)*
Average number of US patents
Median number of US patents
Number of firms
19%
9
4
35
84%
4
1
54
*Growth in the aggregate employment of the firms between 1997 and 2002.
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4.0 Conclusions
• Knowledge flows are appropriated and exploited quicker today, than in
the past (shorter time to IPO) due to the emergence technology
markets.
• RSI provides little advantage today with respect to time to IPO (firms
can obtain strategic inputs in factor markets)
• University spin-offs are generally more productive in terms of patents
(knowledge network effect?) but don’t grow faster. May be their
patents are less valuable
• In Canada, companies located in RSI (CMA) do not perform better,
but companies located in the three largest provinces (B.C., Ontario and
Quebec) definitely do (Niosi and Banik, 2005)
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5.0 Further Research
• Should investigate nature of how collaborative agreements change
stage of technology. Can we associate growth of RSI with proportion
of market transactions to network transactions?
• Do different knowledge flow structures (e.g. hub and spoke of
biotechnology) affect evolution?
• Does the maturity of one technology create path dependencies for
future technologies?
• Is there a parallel evolution of technologies, contracts, institutions?
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End
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Other differences in Growth and
Dynamics of RSIs
Aerospace
Biotechnology
Information
technology
Advanced
materials
Historical accident
in the location of
assembler
(B. Arthur)
High, due to costly
production
facilities and large
labour pool
Close to research
universities
(M. Kenney)
Close to large ICT
companies
(P. Swan)
Close to large
users
(J. Niosi)
High, due to
immobile R&D
institutions and
labour pool
Medium,
because labour
pool is relatively
more mobile
High, due to
costly facilities
and large
requirements
Growth of
cluster (number
of firms)
Addition of new
suppliers
Addition of new
SBFs and
venture caps
Addition of new
spin-offs and
venture caps
Addition of new
spin-offs other
producers
Barriers to entry
of new firms
High
Low
Low
Low
Turnover of
firms
Low
Medium
High
Medium
Initial location of
cluster
Geographical
inertia of agents
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