Introduction - 1 RJV = partners (A) coordinate research efforts
and (B) share innovation Incentives for RJVs Avoid duplications
(Katz 1986) Internalize technical spillovers (dAsprement and
Jacquemin 1988, Kamien et al. 1992, Miyagiwa and Ohno 2002)
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Introduction - 2 Instability 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)
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Introduction - 3 Theory: Pre-commitment to the dissolution of
RJV at a pre-set date (duration) Optimal duration is positively
related to innovation values
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Introduction - 4 Time consistency problem Solution 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 EUREKA
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Flow of the presentation Theory Model of optimal RJV durations
Properties of optimal RJV durations Empirical Data from Eureka Main
estimation results Robustness checks
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Part 1: Theory Infinite 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).
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RJV parameters RJV => share innovation, independent R&D
effort = 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 RJV
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RJV without monitoring RJV 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 ) + q m V V = [- k + (1 q m
)]/(1 q m ) Assumption 1: V > v (RJV is worthwhile)
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Unstable RJV Shirking saves k but lowers (joint) probability of
innovation, yielding to a shirker the payoff W d = (1 q m-1 ) + q
m-1 V Assumption 2: V W d < 0. V W d = - k + q m-1 (1 q)( V)
< 0.
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A one-period RJV Agree to dissolve RJV between t = 1 and t = 2
Equilibrium payoff R(1) = = - k + (1 q m ) + q m v Shirking yields
R d (1)= (1 q m-1 ) + q m-1 v R(1) - R d (1)= - k + q m-1 (1 q)(
v)
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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)( v) 0 V W d = - k + q
m-1 (1 q)( V) < 0
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Extending duration If prop 1 holds, consider a two-period RJV
R(2) = - k + (1 q m ) + q m R(1). An n-period RJV R(n) = - k + (1 q
m ) + q m R(n-1) Properties of R(n) R(n) is increasing in n. As n
goes to infinity, R(n) goes to V
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Optimal duration Prop 2: If prop 1 holds, there is an optimal
duration n* Shirking (at date 1) yields R d (n)= (1 q m-1 ) + q m-1
R(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,
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Properties of optimal duration (n*) Prop 3: An increase in
tends to raise n*. Proof: In R(n) appears with positive probability
so an increase in raises R(n) R d (n)= - k + q m-1 (1 q)(
R(n-1)).
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Properties - 2 An increase in the number of partners (m) has
two effects: reduces (value per member) raises probability of
success The effect on R(n) and hence on n* are ambiguous. Let the
data determine the effect.
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Part 2: Empirical European 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 theory
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Data details www.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-15
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Data summary
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Methodology Empirically 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 x ).
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Proportional hazard models Cox model no restriction on
functional form Prior info specific functional form - Weibull h 0
(t) = pt p-1 exp( 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 robustness
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Hazard ratio Hazard ratio = effect of a unit change in the
explanatory variable Hazard ratio explanatory variable has a
negative impact on RJV death (increases duration) Hazard ratio = 0
=> explanatory variable has no impact
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Explanatory variables No data on innovation values RJV 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
dummies
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Table 2: Weibull
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Robustness testing Weibull 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)
model
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Table 3
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Robustness check Model 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 V
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Stratified Weibull h 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)
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Large versus small and med-sized
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Robustness checks Model 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 model
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Conclusions Theory RJV 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 partners
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Conclusions - 2 Empirical evidence Eureka 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
