Transcript Dia 1

Academic knowledge externalities:
spatial proximity and networks
Roderik Ponds, Frank van Oort & Koen Frenken
Background and motivation
• University: a regional booster?
Background and motivation
• University: a regional booster?
• Many studies suggest existence of localised
knowledge externalities (or spillovers) from
academic research
• Importance of scientific research for innovation
differs between industries impact academic
knowledge externalities as well
Pieken
organiseren
zich
Regional Innovation Systems
(in RIS)
Scientific and
technological
knowledge
Academic
institutions
Firms
Non-profit &
Governmental
agencies
Innovation
and
valorisation
Economic
growth
Mechanism of knowledge externalities
Knowledge externalities are as localized as their
mechanism are:
1.Spin-off & start-up dynamics
2.Labour mobility
3.Networks of knowledge exchange
Networks of knowledge exchange
•
Informal knowledge exchange through social
networks, which are mostly localized (Breschi &
Lissoni 2003, 2006)
•
Besides this, formal knowledge exchange through
research collaboration:
•
Strong growth of collaboration in processes of
knowledge creation (see for example WagnerDoebler 2001)
•
University-industry collaboration key feature of
science-based industries (eg. Pavitt 1984,
Cockburn & Hendersson 1998)
Mechanism of knowledge externalities
• University-industry research collaboration not
limited to regional scale (see eg. McKelvey et al.
2003)
• Given the importance of this mechanism in
science-based technologies: network (of research
collaboration) and spatial dimension necessary to
analyze relation between academic knowledge
externalities and regional innovation
Collaboration: a growing phenomenon?
-Share of co-publications over time-
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0
1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Agriculture&Food Chemistry
Analysis, measurement and control techno
Biotechnology
Information Technology
Optics
Organic Fine Chemistry
Semi-conducters
Telecommunications
Research design
•
Knowledge production function approach: regional
innovation is a function of regional private and academic
R&D expenditures
•
Academic R&D can also come from other regions
In two ways:
a. Through localized mechanisms (from nearby
regions)
b. Through networks of research collaboration (from
'connected' regions)
•
Spatial cross-regressive model:
ln Pi,k     ln RDpi,k  2 ln RDui,k  3Wspace (ln RDu j i ,k )  4Wnetwork (ln RDu j i ,k )  
Data
• Focus on science-based technologies (7)
in the Netherlands: 4 physical sciencebased and 3 life-sciences-based industries
• Regional innovation measured by patent
intensity (EPO, 1999-2001)
• Technology specific private and university
R&D (1996-1998)
Biotechnology, 1988-2004
+/- 70%
abroad
Semiconductors, 1988-2004
+/- 80%
abroad
Weight matrices
ln Pi,k     ln RDpi,k  2 ln RDui,k  3Wspace (ln RDu j i ,k )  4Wnetwork (ln RDu j i ,k )  
• Spatial weight matrix: inverse travel time between
regions i and j (cut-off point 90 minutes)
• Network weight matrix: intensity of research
collaboration between university in region i and
firms in region j
Specification of network weight matrix
• Research collaboration measured by copublications between firms and universities in the
relevant scientific fields (1993-1995)
• Relevant scientific fields defined by analysis of
citations of patents per technology to scientific
journals (classified in scientific subfields)
• Assumption: co-publication reflects (formal)
research collaboration and knowledge exchange
between organisations involved.
Specification of network weight matrix
Region 1
Region 2
1
Region 3
Region 4
Specification of network weight matrix
Region 1
Region 2
1
Region 3
Region 4
Sending/
Receiving
1
2
3
4
1
-
0
5
0
2
10
-
0
0
3
20
0
-
0
4
10
0
10
-
Sending/
Receiving
1
2
3
4
1
-
0
1
0
2
1
-
0
0
3
1
0
-
0
4
1/2
0
1/2
-
Number of patents – lifesciences- Negative Binominal regression (robust
standard errors between parentheses)
1
2
3
4
University R&D
0.287**
(0.046)
0.334**
(0.044)
0.313**
(0.043)
0.350**
(0.039)
Private R&D
0.629**
(0.103)
0.559**
(0.097)
0.380**
(0.113)
0.318**
(0.110)
0.677**
(0.157)
W space
W networks
0.642**
(0.153)
0.163**
(0.065)
0.155**
(0.056)
Dummy Agriculture &
food chemistry
-0.182
(0.281)
-0.098
(0.268
-0.187
(0.240)
-0.143
(0.227)
Dummy
Biotechnology
0.206
(0.297)
0.143
(0.265
0.191
(0.261)
0.111
(0.220)
Constant
-0.181
(0.292)
-0.806**
(0.278
0.082
(0.290)
-0.486*
(0.269)
Alpha
0.867**
(0.189)
0.737**
(0.161
0.729**
(0.151)
0.597**
(0.119)
0.506
0.564
0.548
0.606
Cragg & Uhler's R2
Empirical model
• Knowledge production function approach (KPF)
with (column standardized) spatial and relational
weight matrices for academic R&D to explain
regional patent intensity
• Pooled technologies: 3 x 40 observations lifesciences based technologies, 4 x 40 observations
physical science-based technologies
• Technology dummies
Number of patents – physical sciences- Negative Binominal regression (robust standard
errors between parentheses)
1
2
3
4
University R&D
0.234**
(0.068)
0.228**
(0.073)
0.183**
(0.052)
0.158**
(0.055)
Private R&D
0.989**
(0.112)
0.993**
(0.115)
0.645**
(0.111)
0.497**
(0.101)
-0.039
(0.258)
W space
W networks
-0.453
(0.374)
0.188**
(0.030)
0.200**
(0.028)
Dummy
Optics
-2.415**
(0.383)
-2.416**
(0.383)
-1.879**
(0.335)
-2.392**
(0.371)
Dummy
Information
technology
-0.830**
(0.329)
-0.836**
(0.333)
-0.595**
(0.284)
-0.797**
(0.302)
Dummy
semiconductors
-2.106**
(0.340)
-2.103**
(0.337)
-1.871**
(0.295)
-1.895**
(0.290)
Constant
0.431
(0.230)
0.464
(0.338)
0.642**
(0.226)
0.475
(0.325)
Alpha
1.189**
(0.155)
1.187**
(0.156)
0.919**
(0.158)
0.843**
(0.160)
0.697
0.697
0.732
0.743
Cragg & Uhler's R2
Conclusions
• The results suggest the presence of network knowledge
externalities in both life-sciences and physical sciences
based technologies.
• Localized academic knowledge externalities seem to
occur - in both technologies - within the regions where
the university is located, so at a very local scale.
• Interregional localized externalities seem only to take
place within life-sciences based technologies.
Conclusions
• These outcomes suggest that, within the Netherlands,
academic knowledge externalities within science-based
technologies cannot be easily attached to a specific
spatial scale (global-local paradox).
• It seems that policy measures focussing on an increase
of academic knowledge externalities (if necessary at all)
should not be focussed on specific regions. Given the
wide spatial range of these externalities, the national
scale seems more appropriate.