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How does initial public financing influence private incentives for follow-on investment in early-stage technologies?

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Abstract

One common rationale supporting public financing programs for small firms is that initial public investment creates incentives for follow-on private investment. However, there does not appear to be a unified statement in the literature describing how initial public investment creates incentives for follow-on private investment. Focusing on external private investors, we use a two-stage net present value model to identify four effects from initial public investment on the private decision for follow-on investment. Our empirical analysis uses a sample of non-venture backed firms entering the SBIR program to examine how reduced risk, the number of SBIR awards, and size of initial public investment influence the likelihood of follow-on venture capital investment. We find the probability of follow-on venture capital investment is more likely when firms reach Phase II of the program, is less likely as firms win multiple Phase I and Phase II awards, and is more likely as the size of initial public investment in Phase I increases.

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Notes

  1. David et al. (2000) review the literature examining whether publicly financed R&D complements or substitutes for a firm’s own private R&D investment but they do not consider how external financing agents respond.

  2. The results from the theoretical model are not specific to venture capital investors, but apply to all external investors. See de Bettignies and Brander (2007) for an analysis of the entrepreneur’s choice between bank financing and venture capital financing.

  3. Branscomb and Auerswald (2001) include a “financial gap” in their discussion of the challenges to crossing the “Valley of Death” between invention and innovation.

  4. Feldman and Kelly (2003) find a similar effect in their analysis of the Advanced Technology Program. They call this the “halo effect.” Sine et al. (2003) show a “halo effect” from institutional prestige increases the licensing rate of university inventions.

  5. The possibility of the cross-effect being negative is not sensible in this model since initial public investment keeps the project alive. It would indicate that initial investment actually reduces the “effectiveness” of follow-on investment. In that case, no rational investor would provide follow-on financing.

  6. Audretsch et al. (2002) examine the social return to 44 Department of Defense SBIR awards and find the expected social rate of return to be at least 84%. Laidlaw (1998) presents evidence that the Advanced Technology Program awards accelerated private development and commercialization.

  7. See Wallsten (1998) and Audretsch (2003) for additional discussion of the SBIR program.

  8. There are currently eleven federal agencies implementing the SBIR program.

  9. Further detail is available in the US Small Business Administration’s Handbook for SBIR Proposal Preparation, available at http://www.sba.gov/gopher/Innovation-And-Research/SBIR-Pro-Prep/.

  10. The agency dummy variables also account for the situation in which firms win awards from multiple agencies. The US National Institutes of Health is dominant part of the US Department of Health and Human Services.

  11. Data on SBIR applicants are not systematically available. Applicant data would allow more sophisticated statistical methods to be used to account for agency selection into the SBIR program.

  12. See Fried and Hisrich (1994), Kaplan and Stromberg (1999, 2000) for a description of the venture capital screening process.

  13. This sequence assumes all venture capitalists use a homogeneous screening process over time. While not entirely realistic, evidence presented by Fried and Hisrich (1994) suggests it may not be unreasonable. It also assumes the nature of the uncertainties causing venture capital investors to delay investment can be addressed by participating in the SBIR program.

  14. The presence of firms that never wanted VC investment (reason four) in the sample is not a cause for concern since their investor preferences should not be systematically related to their own firm’s success in the SBIR program such as winning a Phase II award.

  15. Observing this in the data depends on efficient information communication to the venture capital community once a Phase I award is obtained. Researchers studying the venture capital decision making process note that some venture capitalists “aggressively seek out deals” (Fried and Hisrich 1994, p. 32). Moreover, the SBA Handbook states, “…some SBIR program managers send abstracts of Phase I awardees to large companies and venture capitalists…” (SBA 2007).

  16. Given that our data are annual, we do not observe the exact dates of regarding when the firms submit their SBIR proposals. Further, to increase the confidence that follow-on venture capital investment is associated with SBIR participation, we restricted the lag between first SBIR award and follow-on VC to be less than nine years. It is also important to drop any SBIR awards won by firms after receiving venture capital since this could reflect the influence of venture capital investors.

  17. While we do not focus on the “downstream” performance of firms receiving follow-on VC investment, Hsu (2006) explored this and found these firms perform better in terms of cooperative commercialization agreements and initial public offerings than SBIR firms without follow-on VC investment.

  18. Of course, interpreting the Phase II dummy in the opposite direction indicates firms which never progress out of Phase I have significantly lower chances for follow-on VC investment. This makes sense because these firms never successfully reduce the technical and/or market uncertainties surrounding their new technologies.

References

  • Archibald, R. B., & Finifter, D. H. (2003). Evaluating the NASA small business innovation research program: Preliminary evidence of a trade-off between commercialization and basic research. Research Policy, 32, 605–919.

    Article  Google Scholar 

  • Arrow, K. J. (1962). Economic welfare and the allocations of resources of invention. In R. R. Nelson (Ed.), The rate and direction of inventive activity: Economic and social factors. Princeton.

  • Audretsch, D. B. (2003). Standing on the shoulders of midgets: The U.S. small business innovation research program (SBIR). Small Business Economics, 20, 129–135.

    Article  Google Scholar 

  • Audretsch, D. B., Link, A. N., & Scott, J. T. (2002). Public/private technology partnerships: Evaluating SBIR-supported research. Research Policy, 31, 145–158.

    Article  Google Scholar 

  • Bhidé, A. V. (2000). The origins and evolution of new business. Oxford: Oxford University Press.

    Google Scholar 

  • Branscomb, L. M., & Auerswald, P. E. (2001). Taking technical risks: How innovators, managers, and investors manage risk in high-tech innovations. Cambridge: MIT Press.

    Google Scholar 

  • David, P. A., Hall, B. H., & Toole, A. A. (2000). Is public R&D a complement or substitute for private R&D? A review of the econometric evidence. Research Policy, 29(4–5), 497–529.

    Article  Google Scholar 

  • de Bettignies, J-E., & Brander, J. A. (2007). Financing entrepreneurship: Bank finance versus venture capital. Journal of Business Venturing, 22, 808–832.

    Article  Google Scholar 

  • Dixit, A. (1992). Investment and hysteresis, Journal of Economic Perspectives, 6(1), 107–132.

    Google Scholar 

  • Dixit, A. K., & Pindyck, R. S. (1994). Investment under uncertainty. Princeton: Princeton University Press.

    Google Scholar 

  • Feldman, M. P., & Kelley, M. R. (2003). Leveraging research and development: Assessing the impact of the U.S. Advanced Technology Program. Small Business Economics, 20, 153–165.

    Article  Google Scholar 

  • Fried, V. H., & Hisrich, R. D. (1994). Toward a model of venture capital investment decision making. Financial Management, 23(3), 28–37.

    Article  Google Scholar 

  • Griliches, Z. (1992). The search for R&D spillovers. Scandinavian Journal of Economics, 94(0), S29–S47.

    Google Scholar 

  • Hall, B. H. (2006). The financing of innovation, forthcoming. In S. Shane (Ed.), Handbook of Technology and Innovation Management. Oxford: Blackwell Publishers, Ltd.

    Google Scholar 

  • Hsu, D. H. (2006). Venture capitalists and cooperative start-up commercialization strategy. Management Science, 52(2), 204–219.

    Article  Google Scholar 

  • Hubbard, R. J. (1998). Capital-market Imperfections and investment. Journal of Economic Literature, 36, 193–225.

    Google Scholar 

  • Kaplan, S. N., & Stromberg, P. (1999). Venture capitalists as principals: Contracting, screening, and monitoring. The American Economic Review Papers and Proceedings, 91(2), 426–430.

    Google Scholar 

  • Kaplan, S. N., & Stromberg, P. (2000). How do venture capitalists choose investments? Working paper, University of Chicago, September 2000.

  • Laidlaw, F. J. (1998). ATP’s impact on accelerating the development and commercialization of advanced technology. Journal of Technology Transfer, 23, 33–41.

    Article  Google Scholar 

  • Lerner, J. (1999). The government as venture capitalist: The long-run impact of the SBIR program. Journal of Business, 72(3), 285–318.

    Article  Google Scholar 

  • National Research Council (2004). An assessment of the small business research program: Project methodology. Committee on capitalizing on science, technology, and innovation. Washington: National Academy Press.

    Google Scholar 

  • Nelson, R. R. (1959). The simple economics of basic scientific research. Journal of Political Economy, 67, 297–306.

    Article  Google Scholar 

  • Pindyck, R. S. (1991). Irreversibility, Uncertainty, and Investment. Journal of Economic Literature, 29(3), 1110–1148.

    Google Scholar 

  • Sine, W. D, Shane, S., Di Gregoria, D. (2003). The halo effect and technology licensing: The influence of institutional prestige on licensing of university inventions, Management Science, 29(4), 478–496.

    Article  Google Scholar 

  • Toole, A. A., Czarnitzki, D. (2007). Biomedical academic entrepreneurship through the SBIR program. Journal of Economic Behavior and Organization, 63, 716–738.

    Article  Google Scholar 

  • US Small Business Administration, Office of Technology (2007). Handbook for SBIR proposal preparation. http://www.sba.gov/gopher/Innovation-And-Research/SBIR-Pro-Prep/. Accessed 6 June 2007.

  • Wallsten, S. (1998). Rethinking the Small Business Innovation Research Program. In L. M. Branscomb & J. H. Keller (Eds.), Investing in innovation. Cambridge: The MIT Press.

    Google Scholar 

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Toole, A.A., Turvey, C. How does initial public financing influence private incentives for follow-on investment in early-stage technologies?. J Technol Transf 34, 43–58 (2009). https://doi.org/10.1007/s10961-007-9074-7

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