theory and evidence from the Eureka program K Miyagiwa Emory and Kobe and A Sissoko LCU Introduction 1 RJV partners A coordinate research efforts and B share innovation Incentives for ID: 271803
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Slide1
The duration of research joint ventures:theory and evidence from the Eureka program
K.
Miyagiwa
(Emory and Kobe) and
A.
Sissoko
(LCU) Slide2
Introduction - 1RJV = partners (A) coordinate research efforts and (B) share innovation
Incentives for
RJVs
Avoid duplications (Katz 1986)
Internalize technical spillovers (
d’Asprement
and
Jacquemin
1988,
Kamien
et al. 1992,
Miyagiwa
and
Ohno
2002)Slide3
Introduction - 2Instability of RJVs
Lack of monitoring of R&D effort (free-rider problem
)
Solutions to monitoring problems
1. random termination
2. green-porter
3. deadlines (
Miyagiwa
2011)Slide4
Introduction - 3Theory
:
Pre-commitment to the dissolution
of RJV at a pre-set date (duration)
Optimal duration is positively related to innovation valuesSlide5
Introduction - 4Time consistency problemSolution for
RJVs
Private research grants have time limits
Help from government regulations
RJVs
are required to ask for permission from government to be exempted from antitrust laws
U.S. DOC Advanced Technology Program (ATP)
Europe EUREKASlide6
Flow of the presentationTheory Model of optimal RJV durations
Properties of optimal RJV durations
Empirical
Data from Eureka
Main estimation results
Robustness checksSlide7
Part 1: TheoryInfinite horizon, discrete time t
= 1, 2 …
m
firms try to find a new product or technology
Going it alone:
v
: expected value of R&D per firm (
v
≥ 0).Slide8
RJV parametersRJV => share innovation, independent R&D effort
π
= value of innovation per partner
k
= R&D cost (fixed)
q
= (conditional) probability of failure per partner per time
q
m
= (conditional) joint probability of failure for RJVSlide9
RJV without monitoringRJV with an infinite duration
No monitoring and no punishing shirking
V = value of RJV per firm when everyone exerts effort
V = -
k
+ (1 –
q
m
)δπ
+
q
m
δV
V = [-
k
+ (1 – q
m
)δπ]/(1 –
δq
m
)
Assumption 1
: V >
v
(RJV is worthwhile)Slide10
Unstable RJVShirking saves k
but lowers (joint) probability of innovation, yielding to a shirker the payoff
W
d
= (1 – q
m-1
)δπ + q
m-1
δV
Assumption 2
: V – W
d
< 0.
V – W
d
= -
k
+ q
m-1
(1 –
q)δ(π
– V) < 0.Slide11
A one-period RJVAgree to dissolve RJV between t
= 1 and
t
= 2
Equilibrium payoff
R(1) = = -
k
+ (1 –
q
m
)δπ
+
q
m
δv
Shirking yields
R
d
(1)= (1 – q
m-1
)π + q
m-1
δv
R(1) - R
d
(1)= -
k
+ q
m-1
(1 –
q)δ(π
–
v
)Slide12
Prop 1:Given assumption 1 (V > v
) and assumption 2 (V – W
d
< 0), there are ranges of parameters in which R(1) - R
d
(1) ≥ 0.
Compare:
R(1) - R
d
(1)= -
k
+ q
m-1
(1 –
q)δ(π
–
v
) ≥ 0
V – W
d
= -
k
+ q
m-1
(1 –
q)δ(π
– V) < 0Slide13
Extending durationIf prop 1 holds, consider a two-period RJV
R(2) = -
k
+ (1 –
q
m
)δπ
+ q
m
δR(1).
An
n
-period RJV
R(n
) = -
k
+ (1 –
q
m
)δπ
+ q
m
δR(n-1)
Properties of
R(n
)
R(n
) is increasing in
n
.
As
n
goes to infinity,
R(n
) goes to VSlide14
Optimal durationProp 2: If prop 1 holds, there is an optimal duration
n
*
Shirking (at date 1) yields
R
d
(n
)= (1 – q
m-1
)π + q
m-1
δR(n-1)
As
n
goes to infinity,
R
d
(n
) goes to W
d
R(1) - R
d
(1) > 0
As
n
goes to infinity,
R(n
) –
R
d
(n
) goes to V - W
d
< 0, Slide15
Properties of optimal duration (n*)
Prop 3
: An increase in
π
tends to raise
n
*.
Proof:
In
R(n
)
π
appears with positive probability so an increase in
π
raises
R(n
) –
R
d
(n
)= -
k
+ q
m-1
(1 –
q)δ(π
– R(n-1)).Slide16
Properties - 2An increase in the number of partners (m
) has two effects:
reduces
π
(value per member)
raises probability of success
The effect on
R(n
) and hence on
n
* are ambiguous.
Let the data determine the effect.Slide17
Part 2: EmpiricalEuropean Eureka program (1985 –)
Promotes pan-European
RJVs
with subsidies and no-interest loans
Partners are sought from separate
countries
Monitoring
problem exists as R&D conducted in different
countries
RJVs
required to pre-commit to
durations
Time inconsistency problem is resolved.
Ideal for testing the theorySlide18
Data detailswww.eurekanetwork.org
initiation year
duration
costs
types of industries
names, addresses, and nationalities of all partners.
identities and nationalities of RJV initiators.
1,716 Eureka
RJVs
started and completed (1985-2004)
8,520 partners: 4,700 firms and 1,937 other partners (research centers or universities) from the EU-15Slide19
Data summarySlide20
MethodologyEmpirically examine the factors determining the durations of the Eureka projects
Normality test fails
Duration or survival models
Proportional hazards models – death as an event
Hazard decomposes into a baseline hazard h
0
and idiosyncratic characteristics of
RJVs
h
j
(t)= h
0
(t) exp(x
j
β
x
)
.
Slide21
Proportional hazard modelsCox model – no restriction on functional form
Prior info – specific functional form -
Weibull
h
0
(t)
=
p
t
p
-1
exp(β
0
)
p
determines the shape of a baseline hazard
Baseline hazard increasing if and only if
p
> 1
p
= 1 : exponential hazard model
Strategy here
Use
Weibull
– basic model (some ancillary evidence)
Use other models for robustnessSlide22
Hazard ratioHazard ratio = effect of a unit change in the explanatory variableHazard ratio < 1 => explanatory variable has a negative impact on RJV death (increases duration)
Hazard ratio = 0 => explanatory variable has no impactSlide23
Explanatory variablesNo data on innovation valuesRJV cost per partner per month (in million
euros
) = main proxy of innovation values – expected hazard ratio < 1
Number of partners - ?
Initiator dummy – firm initiated – shorter durations
Multi-sector dummy – multi-sector – longer durations
Initiation year dummies
Main industry dummiesSlide24
Table 2: WeibullSlide25
Robustness testingWeibull PH model assumes that all Eureka
RJVs
have a common baseline hazard, which is
Weibull
.
Model
6:
questions the
Weibull
distribution
assumption
Cox (non-parametric) modelSlide26
Table 3Slide27
Robustness checkModel 7
: common hazard
assumption – stratified
Weibull
Stratum 1: small
(2 – 4 partners), 64 %
of the samples
Stratum 2:
medium sized
(5 -
8 partners)
,27.3
%
Stratum
3
large
RJVs
(9 - 196): 8.7 per cent
Results:
large RJV
shape
para
.
p
significant at a
5
% level
No significant difference between the small and the med-size
Close resemblance to VSlide28
Stratified Weibullh
0
(t)= exp(-13.030) (2.974)t
j
1.974
(small)
h
0
(t)= exp(-14.029) (2.974)t
j
1.974
(medium-sized)
h
0
(t)= exp(-13.030) (2.542)
t
j
1.544
(
large)Slide29
Large versus small and med-sizedSlide30
Robustness checksModel 8:
Hidden heterogeneity between data-wise identical
RJVs
frailty
Weibull
test – baseline hazard -
Zh
0
(t
); Z
random
Model 9: make sure that time is not affecting the
rsults
– exponential prop. Hazard modelSlide31
ConclusionsTheoryRJV partners can overcome monitoring problems by committing to dissolve the
RJVs
at a fixed date
Government oversight of
RJVs
help the renegotiation problem
Optimal duration depends positively on innovation values
Ambiguous effect from the number of partnersSlide32
Conclusions - 2Empirical evidenceEureka program – ideal for testing
Proportional
hazards models
RJVs
cost per partner has a positive effect on duration
Number of partners has a positive effect on duration
Firm-initiated
RJVs
have shorter durations
Multi-sector
RJVs
have longer durations