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How Venture Capital Creates Externalities in the Bioeconomy: A Geographical Perspective

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Modeling, Dynamics, Optimization and Bioeconomics I

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 73))

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Abstract

A stream of literature has demonstrated that venture capital generates externalities that enhance the knowledge base of a given region and accordingly assist high technology firms to improve their innovative performance. What has gone largely unexamined in this literature is the geographic extent of such externality impact. In this research we address the issue at hand. We do so by analyzing the association between the patenting rate of all life sciences firms that have won Small Business Innovation Research grants from 1983 to 2006 and the venture capital investments that have occurred at increasingly distant spatial units from those firms. Controlling for firm-specific and environmental factors as well as for endogeneity concerns, we document that life sciences firms tend to produce more patents whenever they are situated in very close proximity to where venture capital investments occur. Further, we find that improvements of the patenting rate of focal firms largely emanate from investments that reflect the expertise of venture capitalists on advancing existing prototypes closer to commercialization. We conclude the paper with a discussion on research and policy implications of our findings.

An earlier version of this work was published as “The geographic extent of venture capital externalities on innovation, Kolympiris C. and Kalaitzandonakes N., 2013, Venture Capital: An International Journal of Entrepreneurial Finance 15, 199–236.”

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Notes

  1. 1.

    Using Germany as their case study, Fritsch and Schilder [36] demonstrate that country-specific features can undermine the significance of spatial proximity between venture capitalists and target firms.

  2. 2.

    To illustrate the geographic scope of an MSA, Stuart and Sorenson [90] report that in their study the average area of an MSA was 10,515 square miles.

  3. 3.

    Despite their wide use, patents have certain shortcomings as a measure of innovation. For instance, innovative firms may not patent for strategic reasons [92] or maybe patents relate more to invention rather than to innovation [67]. Albeit a less than perfect proxy for innovation, patents are generally still a reliable measure of innovation [3].

  4. 4.

    The SBIR program is the largest federal program in the US and it provides seed and early stage funding to promising small firms in cutting edge research areas such as life sciences, electronics, materials and energy conversion. Promoting innovation is among the stated goals of the program and it has generally been found successful in achieving that goal [11].

  5. 5.

    Note that the composition of those networks is not necessarily confined only to VCs but it can also include other industry professionals such as lawyers and accountants [75, 103].

  6. 6.

    This is not to say that positive externalities cannot arise from a single VC. But, because externalities typically emanate from the flow of knowledge, networks of VCs are expected to be stronger in that respect.

  7. 7.

    In this section we concentrate on the impact of spatial proximity on post-investment activities; proximity is relevant for pre-investment activities as well but such discussion is beyond our scope.

  8. 8.

    In related empirical studies, that do not focus on the spatial extent of VC relationships but estimate what drives the investment decision of VCs ex ante, Lutz et al. [63], Cumming and Dai [26] and [87] also highlight how significant geographic proximity between VCs and target firms is in shaping that decision.

  9. 9.

    Alternatively we could use a dependent variable that reflects the number of patents per year. Nevertheless, we did not opt for this approach because differential and often unobserved lags in the dates of discovery, patent submission and patent issuance could make the allocation of innovative performance by year an exceedingly difficult task. Relatedly, we performed robustness checks for potential temporal lag effects between the timing of the venture capital investments and the strength of the externality impact. In these tests, the venture capital investments were limited to those that occurred only 1, 2, 3 or 4 years from the birth of the focal LSF. The tests yielded qualitatively similar results to those presented in Figs. 24.5, 24.6, 24.7, 24.8 and 24.9.

  10. 10.

    We use the Negative Binomial maximum likelihood estimator. The Negative Binomial estimator was chosen to address observed overdispersion and to overcome the standard assumption of the Poisson model of equal conditional means and variances, which was not met for the dependent variable in our sample.

  11. 11.

    In fact, Parhankangas [72] specified $100K as the lower bound of that stage. In order to include venture capital disbursement between $25 and $100K in the analysis we included such amounts in the start-up stage. Robustness checks in which these amounts were either included in the seed stage or were excluded from the analysis yielded qualitatively similar results to the results presented here.

  12. 12.

    Note that besides research intensity, we expect the NIH variable to also be capturing the underlying quality and subsequent reputation of the local universities because NIH funds are awarded on a very competitive basis. As we explain in detail in Sect. 24.4, this observation is instrumental for the robustness of the instrumental variable we use in the empirical analysis.

  13. 13.

    Among others, contributions from [19] and [91] take a critical stand towards positive spatial externalities.

  14. 14.

    Examples of private organizations that offer consulting services on securing SBIR grants include Foresight S&T in Rhode Island and the Larta Institute in California and the District of Columbia.

  15. 15.

    Because our sample covers a lengthy period (23 years) in which relevant state characteristics are expected to change, we do not include associated independent variables, whose historical availability is also limited, in the empirical specification. Further, some of these state characteristics may be difficult to observe (e.g. business climate), and hence to approximate with associated variables which adds to our methodological approach.

  16. 16.

    We, however, followed the codification scheme described in Figs. 24.3 and 24.4 because the number of employees is typically reported by firms in discrete categories.

  17. 17.

    There is also a Phase 3 but federal agencies do not provide funds during it.

  18. 18.

    Notice that while direct investments from university endowments to young firms are rare, the local density of young innovative firms may be correlated with university endowments through an alternative process. Typically, the most reputable academic institutions realize the largest endowments. At the same time, these kinds of institutions tend to be research-intensive, which often prompts young innovative firms to locate close to them mainly in order to reap spatial externalities and other sorts of proximity effects. By extension, university endowments maybe correlated with the innovative character of regions that attract VCs. We account for this potential relationship by including the NIH variable in the analysis, which can capture the research intensity and underlying quality/reputation of the local universities as NIH funds are awarded on a very competitive basis.

  19. 19.

    See the correlation table in the Appendix for more details.

  20. 20.

    In the first stage of the instrumental variable approach presented in Figs. 24.10 and 24.11 we construct variables that employ the number of patents that each of the proximate firms that eventually received venture capital investments was granted before such investments took place. To collect the number of patents per firm we searched in the online patent search engine maintained the United States Patent and Trademark Office for patents issued before the first venture capital investment where the focal firm was listed as the applicant/assignee. To ensure that our search was not prone to different name recordings of the same firm we run the searches with different versions of the name of each firm (e.g. instead of inc. we tried inc).

  21. 21.

    To sort out life-sciences grants from the total population of grants from the National Institutes of Health we consulted with life-sciences researchers employed at the authors’ institution. The list was composed of more than 400 terms, including the following: enzyme, peptide, antigen, mutation, clone, immunoassay, coli, hormone, neuron, PCR, cytokines, gene, collagen, bioreactor, elisa, nucleotide, plasmid, biomass, bacillus, bioassay, embryo and genetic.

  22. 22.

    The first year in which the Chronicles of Higher Education report data on university endowments is 1999. Therefore, the average endowment of each state university is calculated with the corresponding value for 1999 as the starting point.

  23. 23.

    The use of narrow units in the analysis offers as an additional methodological advantage in that we escape estimation issues that relate to required spatial corrections in the data when the unit of analysis is administrative units such as states [6].

  24. 24.

    The estimation with standard errors clustered at the state level is carried out with generalized estimating equations which is a method to estimate the standard errors which first estimates the variability within the defined cluster and then sums across all clusters [104].

  25. 25.

    The marginal effects for dummy variables were computed as the change in the expected number of counts (patents) when the value of the variable goes from 0 to 1 and keeping the remaining variables at their mean value.

  26. 26.

    McFadden’s R2 is analogous to the OLS R2 where the log likelihood value for the null model replaces the total sum of squares and the log likelihood value for the unrestricted model replaces the residual sum of squares. An increase in the McFadden statistic indicates better model fit [62].

  27. 27.

    0. 1738 × 12. 876 (the maximum value of the First_01 variable) = 2. 24.

  28. 28.

    Note that in one model with standard errors clustered at the state level, the SBIR_05 estimate is negative and statistically significant. However, the statistical significance is marginal (p-value 0. 095). Accordingly, this estimate should be interpreted with caution.

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Acknowledgements

Research funding provided by the Ewing Marion Kauffman Foundation Strategic Grant #20050176 is gratefully acknowledged.

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Authors and Affiliations

  1. Wageningen University, Hollandseweg 1, 6706 KN Wageningen (Building 201), De Leeuwenborch, Room 5059, Wageningen, The Netherlands

    Christos Kolympiris

  2. Department of Agricultural and Applied Economics, University of Missouri, 125 A Mumford Hall, Columbia, MO, 65211, USA

    Nicholas Kalaitzandonakes

Authors
  1. Christos Kolympiris
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  2. Nicholas Kalaitzandonakes
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Correspondence to Nicholas Kalaitzandonakes .

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Editors and Affiliations

  1. University of Porto, Department of Mathematics, Porto, Portugal

    Alberto Adrego Pinto

  2. Univ. of California, Dept. of Agricultural and Resource Economics, Berkeley, California, USA

    David Zilberman

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Kolympiris, C., Kalaitzandonakes, N. (2014). How Venture Capital Creates Externalities in the Bioeconomy: A Geographical Perspective. In: Pinto, A., Zilberman, D. (eds) Modeling, Dynamics, Optimization and Bioeconomics I. Springer Proceedings in Mathematics & Statistics, vol 73. Springer, Cham. https://doi.org/10.1007/978-3-319-04849-9_24

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