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Multilevel public funding for small business innovation: a review of US state SBIR match programs

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Abstract

US State governments invest in early-stage innovative activity as an economic development strategy. Nevertheless, attention directed at the public sector’s role in this capacity has been placed on federal policy actions overlooking the growing role of states. The primary aims of this paper are two-fold: (1) to articulate the motivations for multilevel public support for small business innovative activity, placing emphasis on state level incentives directed towards entrepreneurial activity; and (2) to empirically evaluate the State Match Phase I (SMP-I) program. The SMP-I program is a diffuse state level policy designed to complement the federal Small Business Innovation Research (SBIR) program by offering noncompetitive matching funds to the state’s successful SBIR Phase I recipients. This offers an opportunity to examine the marginal impact of public R&D given the state intervention. This paper employs a state and year fixed effects model and considers two outcome variables—SBIR Phase II success rates and SBIR Phase I application activity. To account for industrial heterogeneity, the data are stratified by the federal mission agencies. Results from the empirical analysis indicate that the state match increases the Phase II success rates for firms participating in the National Science Foundation SBIR program.

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Notes

  1. Source: http://www.sbir.gov/past-awards; total derived.

  2. As Greenstone et al. (2010) highlight, these spillovers are likely to be local and include “cheaper and faster supply of intermediate goods and services, proximity to workers or consumers, better quality of worker-firm matches in thicker labor markets, lower risk of unemployment […] and knowledge spillovers” (p. 537).

  3. More commonly, private investors tend to provide financial support later in the development cycle when the economic returns are more secure (Lerner 2009).

  4. Mazzucato’s reference to the “State” refers broadly to the government, not to US state governments.

  5. Vannevar Bush wrote this report in 1945. The 1980 reprinted version published by the National Science Foundation is referenced in this article.

  6. Sources: http://www.nsf.gov/statistics/infbrief/nsf14300/ and http://www.nsf.gov/statistics/infbrief/nsf14307/#tab1.

  7. This reference refers to a reprinted version of the Federalist Papers.

  8. The 11 federal agencies that participate in the SBIR/STTR program include: Department of Agriculture, Department of Commerce (National Oceanic and Atmospheric Administration and National Institutes of Standards and Technology), Department of Defense, Department of Education, Department of Energy, Department of Health and Human Services, Department of Homeland Security, Department of Transportation, Environmental Protection Agency, National Aeronautics and Space Administration, and National Science Foundation.

  9. As of 2011, the legislation requires federal agencies with annual extramural R&D budgets in excess of $100 million to set aside 2.5 % of their funds for the program. This has been scheduled to increase incrementally between 0.1 and 0.2 % annually until 2017.

  10. This rule remains prominent for much of the SBIR programs across the agencies. However, in 2012 three mission agencies enacted a Direct to Phase II pilot program, where Phase II applicants were not required to have a Phase I award.

  11. See Table 1 in Lanahan and Feldman (2015) for a comprehensive list of the programs by state.

  12. These services include in-kind funds for proposal preparation, workshops, one-on-one mentoring and consultation support.

  13. Noncompetitive funds are available for all successful Phase I recipients, though it is subject to availability of funds in each fiscal year.

  14. The following states are listed in order of initial program adoption: NY, HI, OK, IN, KS, NJ, KY, NC, IL, MI, NE, CT, MT and VA (Lanahan and Feldman, 2015). See Table 3 in Lanahan and Feldman (2015) for a list of adoption years.

  15. This was confirmed not only in the program solicitations, but also in conversation with a number of program officers responsible for administering the federal SBIR program.

  16. Data on the population of project level state matches is not available; specifically for certain states. Thus to assess the effect of the SMP-I program on a national scale, the author aggregated the data to the state level.

  17. As is emphasized at the annual AAAS Science and Technology Forums, this is subject to frequent fluctuations (e.g. Koizumi 2008). Federal mission agencies with annual extramural R&D budgets in excess of $100 million were required to set aside 2.5 % of their funds for the program in 2011.

  18. “The effective alignment of the program with widely varying mission objectives, needs, and modes of operation is a central challenge for an award program that involves a large number of departments and agencies. The SBIR program has been adapted effectively by the management of the individual departments, services, and agencies, albeit with significant differences in mode of operation reflecting their distinct missions and operational cultures” (Wessner 2008; p. 5).

  19. Refer to Table 3 in Lanahan and Feldman (2015) for policy years.

  20. This variable is included to effectively control for state capacity in the SBIR program. This is likely endogenous with the outcome variables, thus the author is aware that this coefficients for this term will offer insight on the association of this variable with the outcomes. This variable is included to control for endogenous variation that might bias the effect of the SMP-I variable on the two outcome measures.

  21. \( Y_{it} = \beta_{0} + \beta_{1} SMP I_{it} + \beta_{2} SBIR_{it} + \beta_{3} R\& D_{it} + \beta_{4} High\,Tech\,Capacity_{it} + \beta_{5} Y_{it - 1} + \varepsilon_{it} \) (Pooled OLS).

  22. The descriptive statistics for the aggregate measure of Phase II success rates reports greater within state variation (0.1392 SD) than between state variation (0.0602 SD). This trend holds when stratified by mission agency as well.

  23. The DoD has actually received criticism for this concentration of funding (Wessner 2008).

  24. The author was successful in securing project level matches for a handful of the states with the policy; however, some states were unwilling or unable to share this detailed information due to data restrictions or incomplete administrative records. Thus in the effort to offer a broad overview of the SMP-I program, the policy variable is coded as a binary indicator.

  25. TechNet and the SBA’s sbir.gov are the two central repositories for SBIR award data. TechNet was the primary SBIR data source prior to the roll out of the sbir.gov website in 2011. Several diagnostic tests were run comparing the two data sources. The sbir.gov data source is more complete than TechNet; however, TechNet matches Phase II to Phase I awards, which is one of the primary outcome measures of interest in this analysis. TechNet provides data up through 2013; however, complete data is available through 2008. While the sbir.gov data is more complete, determining the Phase II Success Rate would require matching over 19,000 Phase II awards to Phase I awards based on the string variable—Proposal Title. Given limited resources, the author relied on the TechNet database, which matches the awards; however, the data is only complete up through 2008.

  26. The program was initially authorized in 1982 mandating federal agencies with extramural budgets in excess of $100 million to set aside 0.2%. Over the first six years of the program, this increased to 1.25 %. In 1992, the program was reauthorized and the set-aside rate increased to 2.5 %. In 2000, the program was reauthorized a second time resulting in an increase in the size of the Phase I and Phase II awards. The most recent reauthorization in 2011 extended the program to 2017.

  27. Policy activity for the most recent four adoptions falls outside the timeframe of interest; complete data on the key dependent variables are only available through 2011. Moreover, the most recent reauthorization was 2011, prior to these state adoptions.

  28. This paper presents the level ratio of the SBIR location quotient in addition to the quartile rankings of the SBIR location quotient. The former is included as a baseline and is interacted with the primary policy variable of interest. The latter is included to illustrate how the state’s relatively ranking varies with respect to the leading group of states.

  29. This test holds if the null hypothesis that the population moment conditions are correct is not rejected.

  30. The policy variable is significant for the NASA stratification in the pooled OLS estimation with the set of quartile rankings of the SBIR LQ (Table 7 Column 1).

  31. Results are available upon request from the author.

  32. Results are available upon request from the author.

  33. Interestingly, this indicates that the AB model might be appropriate for this specification. The results for the AB for the models with Phase I applications were not included given the lack of theoretical motivation—as discussed in Sect. 4.1.1. Nevertheless, based off these results, there appears to be evidence that lagged application activity is positively associated with subsequent application activity. Although this is not the focus of this particular study, this warrants additional research. Attention, moreover, should be directed to SBIR mills. While research on SBIR mills are lacking, there is a general consensus that these recipients are associated with excessive Phase I award rates that never commercialize. Among the sample of firms that are classified as SBIR mills, this suggests that Phase I success breeds Phase I success rather than applying to Phase II. The author would like to thank one of the anonymous reviewers from earlier revisions of this paper for pointing this out.

  34. Research has found that the nature of innovative activity varies across industry, thus it is useful to account for these differences. These differences are particularly evident when comparing software and biotech industries, for example (Graham et al. 2009).

  35. This was derived as follows based on the marginal effects reported from the coefficients on the state policy variable—SMP-I: 0.40 * 1.194 = 0.4776; 0.40 * 1.238 = 0.4952.

  36. DoE (LQ), NASA (LQ) and NSF (LQ) report the location quotient functional form of grant and contract activity for states by federal mission agency, respectively. The US average serves as the reference category. See the notes in Table 10 for more discussion.

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Acknowledgments

This research was funded in part by the Ewing Marion Kauffman Foundation and the UNC Graduate School. The origins of this line of work builds upon Maryann P. Feldman’s NSF Grant (0947814): State Science Policies: Modeling Their Origins, Nature, Fit, and Effects on Local Universities. The contents of this publication are solely the responsibility of the author. This research is part of the author’s dissertation project, The Multilevel Innovation Policy Mix: State SBIR Matching Programs. I would like to thank my advisor, Maryann Feldman, for her thoughtful comments and guidance on this research project. Additionally, I would like to thank Alexandra Graddy-Reed, Jeffrey Schroeder, Daniel Smith and four anonymous reviewers for reviewing earlier versions of this paper. Jeremy Moulton and Jade Marcus Jenkins both offered valuable feedback on the empirical analysis.

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    Lauren Lanahan

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Appendix: Arellano bond ex-post specification tests

Appendix: Arellano bond ex-post specification tests

Table 11 reports the results from the test of whether \( \Delta \varepsilon_{it} \) are correlated with \( \Delta \varepsilon_{i,t - k} \) for k ≥ 2. This is caluated based on the correlation of fitted residuals \( \Delta \hat{\varepsilon }_{it} \). To pass the test, we reject at order 1, but not at higher levels (Cameron and Trivedi 2009: p. 300). The results hold for the NASA estimation and are weakly supported for the NSF estimation. The results do not hold for the DoE estimation (See Table 11).

Table 11 Serial correlation
Full size table

Table 12 reports the tests for overidentifying restrictions. The null hypothesis states that the population moment conditions are correct (Cameron and Trivedi 2009: p. 301). In each case, we fail to reject that the population moment conditions are correct, and therefore pass the test (See Table 12).

Table 12 Overidentifying restrictions
Full size table

Taken together, the NASA estimation passes the post-estimation tests; the NSF estimation weakly passes the post-estimation tests; and the DoE estimation does not pass the test. All results are reported; however, caution is taken when interpreting the results for the NSF and DoE AB estimations.

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Lanahan, L. Multilevel public funding for small business innovation: a review of US state SBIR match programs. J Technol Transf 41, 220–249 (2016). https://doi.org/10.1007/s10961-015-9407-x

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