Title:
Public financing of risky early-stage technology
Public financing of risky early-stage technology
| dc.contributor.advisor | Cozzens, Susan E. | |
| dc.contributor.author | Galope, Reynold | en_US |
| dc.contributor.committeeMember | Hicks, Diana | |
| dc.contributor.committeeMember | Lewis, Gregory | |
| dc.contributor.committeeMember | Melkers, Julia | |
| dc.contributor.committeeMember | Thursby, Jerry | |
| dc.contributor.department | Public Policy | en_US |
| dc.date.accessioned | 2013-01-17T21:19:41Z | |
| dc.date.available | 2013-01-17T21:19:41Z | |
| dc.date.issued | 2012-08-24 | en_US |
| dc.description.abstract | This dissertation examines the role of public investments in inducing small firms to develop risky, early-stage technologies. It contributes to expanding our understanding of the consequences of research, innovation, and entrepreneurship policies and programs by investigating in more depth the effect of the Small Business Innovation Research (SBIR) program on the innovation effort, ability to attract external capital, and other metrics of post-entry performance of small business start-ups using a new sample and estimation approach. Unlike prior R&D subsidy studies that concentrated almost exclusively on European countries, this dissertation focused on small business start-ups in the United States using a new scientific survey of new firms. It integrated the Kauffman Firm Survey (KFS) from the Ewing Marion Kauffman Foundation with the SBIR recipient dataset from the U.S. Small Business Administration (SBA) and used advances in statistical matching to achieve better comparability between the treated and control groups of small business start-ups. The integrated KFS-SBA dataset, which contains both recipient and non-recipient small firms, and statistical matching allowed us to empirically construct the counterfactual outcomes of SBIR recipients. This dissertation balanced the pre-treatment characteristics of SBIR recipients and non-recipients through propensity score matching (PSM). It constructed the comparison sample by identifying non-recipients with nearly identical propensity scores as those of SBIR recipients. Consistent with the propensity score theorem, observations with the same distribution of propensity scores have the same distribution of observable characteristics. PSM made the comparison and treatment samples homogenous except in SBIR program exposure, making the fundamental assumption of ignorability of treatment assignment more plausible. Using the realized outcomes of observationally similar non-recipient start-ups as the counterfactual outcomes of SBIR recipients, we found empirical evidence of the input additionality effect of the SBIR program. Had they not applied for and granted SBIR R&D subsidies, recipient start-ups would have spent only $185,000 in R&D, but with SBIR their R&D effort was significantly increased to $663,000, on average. The treatment effects analyses also found a significant positive effect of SBIR on innovation propensity and employment. However, it appears that public co-financing of commercial R&D has crowded-out privately financed R&D of small business start-ups in the United States. A dollar of SBIR subsidy decreased firm-financed R&D by about $0.16. Contrary to prior SBIR studies, we did not find any significant "halo effect" or "certification effect" of receiving an SBIR award on attracting external capital. However, we discovered a different certification effect of the SBIR program: SBIR grantees are more likely to attract external patents. This finding also confirms that innovation requires a portfolio of internal and external knowledge assets as theorized by David Teece and his colleagues. This dissertation's empirical results may be relevant to the Small Business Administration, SBIR participating agencies, the U.S. Congress, other federal, state and local policymakers, small high-tech start-ups, and scholars in the field of science, technology, and innovation policy. | en_US |
| dc.description.degree | PhD | en_US |
| dc.identifier.uri | http://hdl.handle.net/1853/45801 | |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.subject | Early-stage technology | en_US |
| dc.subject | Public financing | en_US |
| dc.subject | High-tech start-up | en_US |
| dc.subject | Policy evaluation | en_US |
| dc.subject | Research and development (R&D) | en_US |
| dc.subject | Propensity score matching (PSM) | en_US |
| dc.subject | Small business innovation research (SBIR) | en_US |
| dc.subject | Innovation policy | en_US |
| dc.subject.lcsh | Public investments | |
| dc.subject.lcsh | Expenditures, Public | |
| dc.subject.lcsh | Small business Technological innovations | |
| dc.subject.lcsh | Technological innovations | |
| dc.title | Public financing of risky early-stage technology | en_US |
| dc.type | Text | |
| dc.type.genre | Dissertation | |
| dspace.entity.type | Publication | |
| local.contributor.advisor | Cozzens, Susan E. | |
| local.contributor.corporatename | School of Public Policy | |
| local.contributor.corporatename | Ivan Allen College of Liberal Arts | |
| relation.isAdvisorOfPublication | 90848cac-8057-4090-afd9-0032a1945d98 | |
| relation.isOrgUnitOfPublication | a3789037-aec2-41bb-9888-1a95104b7f8c | |
| relation.isOrgUnitOfPublication | b1049ff1-5166-442c-9e14-ad804b064e38 |
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