Harnessing Success: Determinants of University Technology Licensing Performance

1. Harnessing Success: Determinants of University Technology Licensing Performance Sharon Belenzon Nuffield College, Oxford University Mark Schankerman London School of Economics and CEPR Conference on the Economics of Technology Policy Monte Verita, Switzerland June 2007

2. Introduction Universities are an important source of innovation • In the U.S., they account for 50% of basic research. • Increasing use of IP and commercialisation of university innovations. From 1991-2004, number of patent applications from universities rose from 1,584 to 10,517 • During same period, licensing income increased from $218 million to $1.4 billion, which is six percent of federal R&D financing for universities

3. Institutional background to this activity • Bayh-Dole Act, 1980 (U.S.): This gave universities and research institutes ownership of inventions from federally funded R&D, with mandate to undertake technology transfer and to share revenues with university inventors • Bayh-Dole Act did not give birth to technology transfer (Mowery, Nelson and others emphasise this) • Technology licensing occurred before Bayh-Dole, through bilateral arrangements with government agencies. But transactions cost and uncertainty were higher. • Much technology transfer is biomedical-related, which was triggered by the biotechnology revolution rather than Bayh-Dole

4. This paper focuses on the impact of incentives, local development objectives and government constraints on the effectiveness of this technology licensing activity. Of course, this “commercialisation of the universities” has both benefits and costs. We do not make any cost-benefit assessment of the activity itself, or the impact of the Bayh-Dole Act on it.

5. University-Private Sector Science Links Venture Capital “Knowledge spillovers” University Scientists University Technology Licensing Office (TLO) “Market for Technology” Licensed inventions Start-up companies Corporate Research Funding

6. University-Private Sector Science Links Venture Capital “Knowledge spillovers” University Scientists University Technology Licensing Office (TLO) “Market for Technology” Licensed inventions Start-up companies Corporate Research Funding

7. . Lach and Schankerman (2003) showed that royalty incentives to scientists strongly affect licensing outcomes, both through increasing effort and sorting of scientists. But the responsiveness to royalty incentives was stronger in private universities as compared to public ones. And the impact of TLO size is larger in private universities. Why? Gatekeeper Effect: The effectiveness of royalty incentives depends on the effectiveness of the TLO, because TLO’s have monopsony position in the U.S. (“right of first refusal”) If the TLO is ineffective, changing incentives will not have much effect. Thus royalty incentives and TLO effectiveness are complementary in producing better performance.

8. But why should we expect private universities to have more effective TLO’s? Is it ownership per se that matters? Or is it characteristics of TLO’s which ownership affects? This is the starting point for the Belenzon-Schankerman paper. We look inside the “black box” of the TLO.

9. University-Private Sector Science Links Venture Capital “Knowledge spillovers” University Scientists University Technology Licensing Office (TLO) “Market for Technology” Licensed inventions Start-up companies Corporate Research Funding

10. Data Sources • Association of University Technology Managers (AUTM) • Data on licensing revenue, R&D, university and TLO characteristics • 1991-2000 panel data on about 100 U.S. universities • Survey of Technology Licensing Offices (one-time) • On-line survey sent to about 200 U.S./Canadian universities which belong to AUTM. Received 102 responses. After matching to other required data, end up with 86 universities. • No sample selectivity on TLO age, size, private, number of licenses per invention disclosed, or income per license. Only significant correlation is with medical school dummy (positive). • USPTO for patenting information by universities

11. Survey focused on three key areas 1. whether the university TLO uses any form of performance-based pay (“high-powered” incentives): merit pay or bonus pay • No usable information individual vs group basis, or subjective vs objective measures (Baker, Gibbons and Murphy, QJE, 1994) 2. the relative importance the university TLO attaches to different objectives in their licensing activity 3. the severity of formal or informal government constraints on licensing activity

12. Survey Evidence Public Private Significant Univ. Univ. difference? Use of Incentive-pay (% YES) 49 79 Yes Government constraints on: % reporting “important” 1. Choice of license partners 23 0 Yes 2. Setting license contract terms 19 0 Yes 3. License confidentiality 27 0 Yes 4. Use of equity stakes 23 3.5 Yes 5. University liability/indemnification 75 18 Yes 6. Dispute resolution mechanisms 49 3.6 Yes Objectives (% “important”) 1 Number of licenses 97 100 No 2. License income 88 93 No 3 Promoting local/regional dev. 88 57 Yes

13. A Contracting Model of the TLO with Incentivesand Local Development Objectives TLO licenses inventions in national or local market. Unit cost: Unit payoff: (gov’t constraint parameter) = fraction of effort devoted to licensing in the local market Quadratic effort costs: The TLO compensates the worker in two ways: a fixed-wage and a performance-based pay (in the form of a constant fraction of licensing revenues).

14. There is a divergence of interest between the TLO and the worker. The worker cares only about her share of license income net of effort costs. The TLO cares about total license income and license income in local market. Worker:TLO:

15. Suppose the TLO can contract on the worker’s allocation of effort. The first best efforts solves: Effort cost to the worker First-best level of effort:

16. Suppose the TLO cannot contract on the worker’s allocation of effort. Second stage (worker’s choice): Second stage (TLO contract):

17. where Assume there is a fixed cost of adopting high powered incentives, F. TLO adopts high-powered incentives if gains of adoption exceed F: Can show that Prediction 1:Universities that care more about local development objectives, and those that are more constrained, are less likely to adopt incentive pay.

18. where We observe only total licensing income, R. The effect of adopting incentive pay on licensing income is One can show Prediction 2:Universities that care more about local development objectives, and those that are more constrained, generate less licensing income, other things equal. Prediction 3:Universities which use incentive pay generate greater license income per license, other things equal.

19. Empirical Predictions

20. Data Sources • Association of University Technology Managers (AUTM) • Data on licensing revenue, R&D, university and TLO characteristics • 1991-2000 panel data on about 100 U.S. universities • Survey of Technology Licensing Offices (one-time) • On-line survey sent to about 200 U.S./Canadian universities which belong to AUTM. Received 102 responses. After matching to other required data, end up with 86 universities. • No sample selectivity on TLO age, size, private, number of licenses per invention disclosed, or income per license. Only significant correlation is with medical school dummy (positive). • USPTO for patenting information by universities

21. Data description

22. Data description

23. Data description

26. Concern over endogeneity arising from fixed, unobserved heterogeneity (e.g., differences in commercial orientation of universities). Expected bias would be positive on incentives, negative on local development objectives, and ambiguous on constraints. We use Blundell, Griffith, Van Reenen approach: we use average pre-sample patents (1970-1990) and average income per license (1990-1994) as regressors. Other Control variables • Share of faculty in 6 (aggregated from 23) technology areas • High-tech density of university’s location (Milken Index --TechPole) • Private, medical school, Canada and year dummy variables [Note that university quality. faculty size, and size of the TLO should play no direct role here, once we condition on the number of inventions disclosed or the number of licenses. This was confirmed empirically.]

27. Adoption of Performance-Based Pay Regressions include also a medical school dummy and technology area indicators

28. Key Findings: Adoption of Performance Pay • The effect of private ownership is positive and significant -- moving from public to private doubles the probability of using bonus pay (from the mean of 35 to 71 percent) • More “commercial” universities (as implied by their pre-sample number of patents) are more likely to adopt performance pay. Failure to account for this leads to upward bias in the impact of ownership on adoption. • Universities that face more government constraints are less likely to adopt performance-based pay, but it is difficult to disentangle the impact of ownership from local development objectives and government constraints.

29. Estimation Results: licensing income

30. Key Findings: License Income (per active license): • Private ownership has no independent effect on licensing performance, once we control for the adoption of incentive pay (yet, private ownership has a strong positive effect on the adoption of performance-based pay) Thus with appropriate incentives, it may be possible to get private performance from public institutions. • Using performance-based pay is associated with about 30-45% more income per license. As with royalty incentives for research scientists, incentives for TLO licensing activities are also important. • Having strong local development objectives is associated with about 30% less income per license. • Each “important” government constraint is associated with 17% less income per license (average number of constraints reported as “important” is 1.5). These findings are robust to using non-parametric estimation methods.

31. (3) (4) (5) (6) License income: Additional control variables

32. Parametric estimation: licenses executed

33. Key Findings: Number of Licenses Executed (per invention disclosed): • Performance-based pay does not affect the number of licenses executed per invention. With non-parametric estimation, we find a positive effect of about 10%. This is weaker than for license income because numbers are easier to monitor by managers than income per license (“what might have been”). • Having strong local development objectives is associated with 30% more licenses per invention. With non-parametric estimation methods, we find no effect of such objectives. • Having strong government constraints have no significant effect on the number of licenses per invention.

34. Licenses executed: Additional control variables (3) (4) (5) (6)

35. High-tech density matters a lot: Moving university from Iowa City to Chicago is associated with increase of 12% in licenses per invention and in income per license. Thus about 25% more income in total. Moving further to Boston increases total income by another 16%. Indicates either informational or other advantages of being in high tech areas for licensing.

36. The Number and Location of Start-ups

37. Key Findings: Number and Location of Start-ups: • Performance-based pay does not affect the total number of start-up companies, given the number of licenses executed – i.e., it does not affect the licensing mode. • Strong local development objectives do not affect the number of start-ups, given the number of licenses. • Strong local development objectives increase the likelihood of localisation (in-state location), given the number of start-ups. • Strong government constraints reduce the number of start-ups. This is consistent with greater risk and importance of risk-associated constraints. • Private universities are not more likely to license to start-up companies, but when they do, they are less likely to do it locally.

38. Summary of Key Findings • Private universities are much more likely to use high-powered incentives within the TLO. High powered incentives (bonuses) have a powerful effect. • Private ownership does not affect licensing performance, once we control for the adoption of incentive pay. • Strong local development objectives impose an implicit “cost” in terms of reduced license income. Do local knowledge spillovers (or other factors) justify such a local licensing preference? Is this the best way to promote local or regional development? Agglomeration effects do not justify it. However, local knowledge spillovers or possibly local donations to universities might, arguably, justify it. Need studies to check this. 4. Government constraints reduce license income, mainly by affecting the value rather than number of licenses.