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Helping the Little Guy: the impact of government awards on small technology firms

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Abstract

The Small Business Innovation Research (SBIR) program provides federally funded research awards to companies with 500 or fewer employees. We explore the differential effects of the National Aeronautics and Space Administration SBIR program on firms of various sizes on their future patenting activity. Using propensity score matching, we construct comparable samples of selected and non-selected Phase II SBIR applicants by firm size. We then estimate the effect of selection for the matched sample on the probability of forward patent activity and conditional on any forward patenting, the count of patents within three years of the proposal. While firms with fewer than 10 employees, are least likely to patent, their probability of patenting is positively affected by receiving a Phase II award. We find sparse evidence of corresponding increase for larger firms. Nor do we find any evidence that a Phase II award impacts the conditional number of forward patents in the three years following the award. These data suggest that the Phase II award serves to advance the smallest teams "over the hump" to creating a potential source of competitive advantage.

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Notes

  1. NASA SBIR/STTR Participation Guide: https://sbir.gsfc.nasa.gov/sites/default/files/012-19-001-010.pdf. (reference last accessed on October 18th, 2019).

  2. The cost information contained in this document is of a budgetary and planning nature and is intended for informational purposes only. It does not constitute a commitment on the part of JPL and/or Caltech.

  3. NASA SBIR/STTR Participation Guide: https://sbir.gsfc.nasa.gov/sites/default/files/012-19-001-010.pdf (accessed on October 18th, 2019).

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Acknowledgements

We are grateful to the National Science Foundation, Jet Propulsion Laboratory, California Institute of Technology, and the National Aeronautics and Space Administration for the funding and support. We appreciate the help of Saarthak Khanna, Anusha Ramakrishnan, and Dalia Yadegar. We also thank seminar and conference participants at the NASA Jet Propulsion Laboratory, 2018 Industry Studies Association Conference, 2018 North American Summer Meeting of the Econometric Society, United States Patent and Trademark Office (Washington, D.C. Headquarters and Silicon Valley Office), Department of Engineering Management and Systems Engineering (George Washington University), 2018 West Coast Research Symposium, and 2019 Atlanta Conference on Science and Innovation Policy for their comments. Finally, we thank the entßire USC Viterbi Management of INnovation, Entrepreneurial Research, and Venture Analysis (MINERVA) laboratory team for their comments.

Funding

NSF awards 1444080 and 1740721, and Jet Propulsion Laboratory sub-award 1550874. The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004).

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Correspondence to Andrea Belz.

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Conflict of interest

Andrea Belz served previously as co-Principal Investigator and Research PI on the awards acknowledged herein. She currently serves as Division Director of Industrial Innovation and Partnerships at the National Science Foundation in which the NSF SBIR/STTR programs reside. Her research group obtained these data prior to her selection as Division Director. To manage the potential conflicts of interest she has resigned from all roles associated with the NSF awards that funded this research and is recused from all matters related to the awards named herein.

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Appendix

Appendix

See Tables 7, 8, 9, 10, 11 and Fig. 1.

Table 7 Propensity score logistic regression estimations
Table 8 Comparison of covariate distributions post-matching
Table 9 Alternate estimation of propensity scores via nearest neighbor matching
Table 10 Estimation results on sub-sample omitting prior phase II awardees
Table 11 Estimation results on sub-sample omitting firms with prior patents
Fig. 1
figure 1figure 1

Common Support Graphs. Note: The red bars above the line are observations treated (selected) and matched (on-support); green above the line observations are treated and unmatched (off-support); blue below the line observations are matched untreated (non-selected) observations. Firms are off support (unmatched) if their pre-selection characteristics conducive to selection are too high (low) to be matched to a non-selected (selected) counterpart (Color figure online)

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Giga, A., Graddy-Reed, A., Belz, A. et al. Helping the Little Guy: the impact of government awards on small technology firms. J Technol Transf 47, 846–871 (2022). https://doi.org/10.1007/s10961-021-09859-0

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  • DOI: https://doi.org/10.1007/s10961-021-09859-0

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