Abstract
Scholarship studying the effect of public funding on private innovation is split. Research either argues for a complementary effect, in which firms benefit from non-dilutive capital and unique technical support, or for a substitutionary effect, in which public funding comes with bureaucratic red tape, restricting innovation. We investigate these differences by developing a framework to compare private (corporate) and public (state) logics, and draw on that framework to evaluate the interaction of public funding and private innovation. Utilizing a dataset of over three million patents, we then assess the outcomes of public-private institutional interactions, introducing a novel machine learning propensity score matching method to tractably evaluate the full population of U.S. patent data. We find that institutional interactions can increase both private innovation impact and generality.
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