The duration of research joint ventures: theory and evidence from the Eureka program

1. The duration of research joint ventures:theory and evidence from the Eureka program K. Miyagiwa (Emory and Kobe) and A. Sissoko (LCU)

2. Introduction - 1 • RJV = 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)

3. 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)

4. Introduction - 3 • Theory: • Pre-commitment to the dissolution of RJV at a pre-set date (duration) • Optimal duration is positively related to innovation values

5. 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

6. Flow of the presentation • Theory • Model of optimal RJV durations • Properties of optimal RJV durations • Empirical • Data from Eureka • Main estimation results • Robustness checks

7. 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).

8. RJV parameters • RJV => 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 • qm= (conditional) joint probability of failure for RJV

9. 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 – qm)δπ + qmδV • V = [- k + (1 – qm)δπ]/(1 – δqm ) • Assumption 1: V > v (RJV is worthwhile)

10. Unstable RJV • Shirking saves k but lowers (joint) probability of innovation, yielding to a shirker the payoff Wd = (1 – qm-1)δπ + qm-1δV • Assumption 2: V – Wd < 0. V – Wd = - k + qm-1(1 – q)δ(π – V) < 0.

11. A one-period RJV • Agree to dissolve RJV between t = 1 and t = 2 • Equilibrium payoff R(1) = = - k + (1 – qm)δπ + qmδv • Shirking yields Rd(1)= (1 – qm-1)π + qm-1δv • R(1) - Rd(1)= - k + qm-1(1 – q)δ(π – v)

12. Prop 1: • Given assumption 1 (V > v) and assumption 2 (V – Wd < 0), there are ranges of parameters in which R(1) - Rd(1) ≥ 0. • Compare: • R(1) - Rd(1)= - k + qm-1(1 – q)δ(π – v) ≥ 0 • V – Wd = - k + qm-1(1 – q)δ(π – V) < 0

13. Extending duration • If prop 1 holds, consider a two-period RJV R(2) = - k + (1 – qm)δπ + qmδR(1). • An n-period RJV R(n) = - k + (1 – qm)δπ + qmδR(n-1) • Properties of R(n) • R(n) is increasing in n. • As n goes to infinity, R(n) goes to V

14. Optimal duration • Prop 2: If prop 1 holds, there is an optimal duration n* • Shirking (at date 1) yields Rd(n)= (1 – qm-1)π + qm-1δR(n-1) • As n goes to infinity, Rd(n) goes to Wd • R(1) - Rd(1) > 0 • As n goes to infinity, R(n) – Rd(n) goes to V - W d< 0,

15. 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) – Rd(n)= - k + qm-1(1 – q)δ(π – R(n-1)).

16. Properties - 2 • An 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.

17. 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 • RJVsrequired to pre-commit to durations • Time inconsistency problem is resolved. • Ideal for testing the theory

18. 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

19. Data summary

20. 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 h0 and idiosyncratic characteristics of RJVs hj(t)= h0(t) exp(xj βx).

21. Proportional hazard models • Cox model – no restriction on functional form • Prior info – specific functional form - Weibull • h0(t) = ptp-1 exp(β0) • pdetermines 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

22. Hazard ratio • Hazard ratio = effect of a unit change in the explanatory variable • Hazard ratio < 1 => explanatory variable has a negative impact on RJV death (increases duration) • Hazard ratio = 0 => explanatory variable has no impact

23. 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

24. Table 2: Weibull

25. Robustness testing • Weibull PH model assumes that all Eureka RJVs have a common baseline hazard, which is Weibull. • Model 6: questions the Weibulldistribution assumption • Cox (non-parametric) model

26. Table 3

27. Robustness check • Model7: 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. psignificant at a5% level • No significant difference between the small and the med-size • Close resemblance to V

28. Stratified Weibull • h0(t)= exp(-13.030) (2.974)t j1.974 (small) • h0(t)= exp(-14.029) (2.974)t j1.974 (medium-sized) • h0(t)= exp(-13.030) (2.542)tj1.544(large)

29. Large versus small and med-sized

30. Robustness checks • Model 8: Hidden heterogeneity between data-wise identical RJVs • frailty Weibull test – baseline hazard - Zh0(t); Z random • Model 9: make sure that time is not affecting the rsults – exponential prop. Hazard model

31. 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

32. 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