Why do university spin-offs attract more venture capitalists?

Abstract

This paper provides empirical evidence that young university spin-offs are more likely to receive venture capital than other technological start-ups. In addition, this fact is explained mainly by the lack of managerial skills among the founders of certain university spin-offs. The data used have been obtained from a questionnaire answered by 64 Spanish technological firms founded between the years 1993 and 2005. Forty of the firms are university spin-offs; the remainder are independent technology-based start-ups. The results support the complementary-assets view that academic entrepreneurs use venture capitalists as a means of gaining access to managerial skills. These results are maintained even when we control for financial constraints, levels of debt and intellectual property protection. Although these latter variables explain why certain high-tech firms are more likely to receive venture capital, we do not find statistical evidence that they explain the differences between university spin-offs and technological start-ups in terms of being backed by venture capitalists. The results therefore suggest that universities and policy makers can stimulate the creation and growth of university spin-offs by facilitating contact and trust between venture capitalists and academic entrepreneurs, mostly with respect to those cases in which there is a severe lack of managerial skills.

Keywords:

• venture capital
• university spin-offs
• technological firms
• managerial skills

Acknowledgements

This work has benefited from comments made by the editors, three blind reviewers and those made at the seminar on ‘The dynamics of science-based entrepreneurship’ organized in April 2008 (Sestri, Italy). This work has received financial support from the SEJ 2007-67895-C04-02 and 04 and benefited from the database created as part of a joint project involving DGPYME and Fundación ICO prepared in association with Vicente Salas and María Victoria Trujillo. We also appreciate their comments.

Notes

 1. See www.surveymonkey.com for more information.

 2. The experts belong to the Spanish institute of small and medium firms (DGPYME). Among them is Maria Callejón, the general director of policies concerning small and medium firms at the Spanish Ministry of Industry and also a full professor at the University of Barcelona and a specialist in these topics.

 3. With the exception of biotechnology, the remainder correspond to the following NACE2008 codes (62, 20, 21 and 72 respectively http://ec.europa.eu/comm/competition/mergers/cases/index/nace_all.html).

 4. This is a public organization depending on the Spanish Ministry of Industry, Tourism and Commerce, that promotes the innovation and technological development of Spanish companies. It grants technological companies their own financial aid and facilitates access to other sources of finance. For more information see www.cdti.es.

 5. We contracted the services of TNS-Demoscopia, a Spanish firm that specializes in surveys.

 6. Note that by definition the total liabilities are equal to total assets, what makes it difficult to introduce both variables to the same estimations. In our case this is solved by using the current employees variable.

 7. All the analyses have been replicated including a dummy variable for these cases, and the results are basically the same. The tests are available from the authors upon request.

 8. We test whether the age of the firm depends on the current stock of formal innovations. The parameter was positive but far from significant. Consequently we cannot reject the null hypothesis that the parameter equals zero.

 9. Note that this variable can be interpreted simply as the innovative output. The limitation would then be that it does not take into account other types of innovation (i.e. informal innovation).

10. These tests are available from the authors upon request.

11. Furthermore, the idea cannot be rejected, at the usual levels of significance, that the differences between the coefficients associated with managerial skills estimated in both Models 2 and 4 are zero. Prob [(USO with lack of managerial skills in Model 2) = (USO with lack of managerial skills in Model 4)] = 0.1669 and 0.1864 in the probit and logit estimates respectively. Prob [(USO with equal managerial skills in Model 2 = USO with equal managerial skills in Model 4)] = 0.4569 and 0.5541 in the probit and logit estimates respectively.

12. Prob [(USO in Model 1) = (USO in Model 3)] = 0.4164 and 0.5653 in the probit and logit estimates respectively.

13. See Lerner (1998, 2002) for a review of such programmes.

14. For example the US SBIR Program (Toole and Czarnitzki 2007) or the Spanish Innova Program (Del Palacio, Solé, and Montiel 2006).