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University spin-offs and the “performance premium”

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Abstract

The creation of spin-off companies is often promoted as a desirable mechanism for transferring knowledge and technologies from research organizations to the private sector for commercialization. In the promotion process, policymakers typically treat these “university” spin-offs like industry start-ups. However, when university spin-offs involve an employment transition by a researcher from the not-for-profit sector, the creation of a university spin-off is likely to impose a higher social cost than the creation of an industry start-up. To offset this higher social cost, university spin-offs must produce a larger stream of social benefits than industry start-ups, a performance premium. This paper outlines the arguments explaining why the social costs of entrepreneurship are likely to be higher for academic entrepreneurs, and empirically investigates the existence of a performance premium using a sample of German start-up companies. We find that university spin-offs exhibit a performance premium of 3.4 % points higher employment growth over industry start-ups. The analysis also shows that the performance premium varies across types of academic entrepreneurs and founders’ academic disciplines.

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Notes

  1. Throughout the paper, we will use “university” as shorthand for all public research organizations (PROs) in the not-for-profit sector.

  2. In our definition, a new company is a university spin-off when it involves an academic entrepreneur. New companies that were formed to commercialize a university technology (e.g., through the technology transfer office) or that received some kind of support from the university do not qualify as university spin-offs under our definition unless they also have an academic entrepreneur in the founding team.

  3. See Salter and Martin (2001) for an overview.

  4. There is a much broader literature studying the influence of patenting and industry sponsorship on academic research (see, for instance, Agrawal and Henderson 2002; Azoulay et al. 2009; Breschi et al. 2007; Czarnitzki et al. 2009, 2012; Rosenberg 1998).

  5. The current body of empirical evidence on changes in research productivity is limited to samples drawn from science and engineering fields. Importantly, the theoretical argument about the potential social costs of university spin-offs is not limited to any particular field of study. For instance, academic researchers in law and social science fields may reduce their contributions to open science when pursuing entrepreneurship. Given the stage of research in the literature, there is no information available that would suggest one field of study is more socially valuable than another.

  6. The stream of benefits that would have been derived from a university researcher’s future contributions to academic research and disclosure is an unobservable counterfactual since the academic entrepreneur cannot be observed as both a full-time university researcher and a spin-off entrepreneur at the same time. This complicates any attempt to directly estimate the necessary size of the performance premium.

  7. See Rothaermel et al. (2007) and Helm and Mauroner (2007) for recent reviews of the literature.

  8. See, e.g., Heckman (1976, 1979), or Verbeek (2012: 248–252) for details of the Heckman selection model.

  9. Recall that we will use the term “university” when referring to any type of science institution. With respect to the German situation, science institutions primarily comprise state-funded universities and other publicly funded research organisations (such as Max Planck Institutes, Fraunhofer Institutes, and governmental laboratories and research centres) as well as a few private universities.

  10. Appendix 2 reports the results of the survival equation estimation. As a robustness check, all the models were re-estimated using sampling weights from the survey. These results are reported in Table 4 in Appendix 3. There are no significant differences between weighted and non-weighted results.

  11. In our empirical analysis, we compared university spin-offs to industry start-ups based on a random sample that was stratified by industry (in particular, knowledge-intensive industries), year of company foundation, and region. Other scholars such as Wennberg et al. (2011) compared university spin-offs to corporate spin-offs. This is a subgroup of industry start-ups that is likely to perform better than average and thereby serves as higher standard of comparison for university spin-offs. For general policy justification, we believe the overall population of industry start-ups (properly stratified) is the relevant control group.

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Acknowledgments

We are grateful to Jürgen Egeln, Sandra Gottschalk, and Alfred Spielkamp for providing access to the survey data, and to Jürgen Moka for extracting information from the Creditreform database. We also thank Helmut Fryges and two anonymous referees for valuable comments.

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Authors and Affiliations

  1. Department of Managerial Economics, Strategy and Innovation, KU Leuven, Naamsestraat 69, 3000, Leuven, Belgium

    Dirk Czarnitzki

  2. Center for R&D Monitoring (ECOOM) at KU Leuven, Leuven, Belgium

    Dirk Czarnitzki

  3. Department of Industrial Economics and International Management, Centre for European Economic Research (ZEW), L 7, 1, 68161, Mannheim, Germany

    Dirk Czarnitzki, Christian Rammer & Andrew A. Toole

  4. U.S. Department of Agriculture, Economic Research Service, 1400 Independence Ave., SW, Mail Stop 1800, Washington, DC, 20250-1800, USA

    Andrew A. Toole

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  1. Dirk Czarnitzki
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Correspondence to Andrew A. Toole.

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Appendices

Appendix 1: Definition of technology sectors

High-tech manufacturing: This sector comprises manufacturing activities characterized by high R&D inputs and includes the following NACE rev. 1.1 codes: 24, 29, 30, 31, 32, 33, 34, 35 (chemicals and pharmaceuticals, machinery and equipment, computer and office machinery, electrical equipment, electronics, medical and measurement instruments, automotive and other vehicles).

Technology-oriented services: This sector covers services that are heavily relying on the use of new technology, particularly information and communication technology, and includes the NACE rev. 1.1 codes: 62.3, 72, 73, 74.2, 74.3, 92.11 (telecommunication, computer services and software, R&D services, engineering, testing, film making).

Knowledge-intensive consulting: This sector represents services that are largely based on high qualified labor while relying less on new technology and includes NACE rev. 1.1 codes: 74.1, 74.4, 74.85.1, 74.85.2, 74.87.2, 74.87.4, (business consulting, advertising, design activities, etc.)

Appendix 2: The survival equation controlling for selection

As described in Sect. 3 of the main text, all regressions results were obtained using sample selection models. In order to model the survival of the firms (as only surviving firms could be included in the surveys), we rely on available data for firms that exited before the survey. Although somewhat limited, we can use information from the Mannheim Foundation Panel (MFP) to model the probability of survival for the new ventures. In particular, we use the founding year, industry, firm location, equity ownership by other firms, real estate property of firm founders, and the level of formal educational attainment.

The industry dummies and foundation cohort dummies are analogous to those included in the growth equation. In addition, we use 13 regional dummies to model survival. The regional dummies are omitted from the growth equations as they always turned out to be insignificant. In the survival equation, they are jointly significant at the 5 % level (the Chi squared test value amounts to 126.64). In the growth equation, we do not include the education-related variables that appear in the selection equation as we have the survey reported data on the education of the academic entrepreneurs and the share of founders with academic degrees. Also, we do not use the real estate variables in the growth equation, but instead include the firm’s credit rating, which is a more general financial performance variable. Part of this decision was based on data limitations. For the non-surviving firms, the rating had too many missing values as it was possibly never constructed for firms that exited soon after foundation (Table 3).

Table 3 Estimates of the survival equation (first stage of the selection model); N = 23,803
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Appendix 3

See Table 4.

Table 4 Start-up employment growth (1996–2000), Heckman selection models using sampling weights
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Czarnitzki, D., Rammer, C. & Toole, A.A. University spin-offs and the “performance premium”. Small Bus Econ 43, 309–326 (2014). https://doi.org/10.1007/s11187-013-9538-0

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