Conference Agenda

Investors increasingly allocate capital outside of public equity markets through private equity investments. We evaluate capital allocation efficiency in the two markets and find that public markets allocate financial capital substantially more efficiently. This efficiency gap is concentrated in late-stage private deals, which generate significantly less revenue growth and fewer patents than comparable deals in public or early-stage private markets. We attribute the higher efficiency in public markets to superior information efficiency and governance mechanisms. Our study highlights the essential role of public markets in improving capital allocation and the implications of private markets' expansion into late-stage financing.

We investigate open-source innovation by public firms and the private value it generates for these firms. Unlike patents, which grant inventors exclusive rights to their inventions, open-source innovations can be used by anyone. Nevertheless, using an extensive dataset of public-firm activity on GitHub, we find that firms with open-source projects represent 68% of the U.S. stock market across 86% of industries. We estimate the private value of all projects in our sample to be nearly $25 billion, with the average project generating $842,000. We find that projects with fully permissive licenses are generally less valuable and firms facing higher competition tend to generate less private value from their projects. We also find that the degree to which a project complements commercial products is not a primary driver of private value. Finally, open-source value is associated with a firm's substantial growth in terms of sales, profits, employment, and patenting, yet it also induces creative destruction. These results contribute to our understanding of the private value generated by innovation in the absence of legal excludability.

While patents grant exclusive rights to inventions, many remain underutilized or non-commercialized. Understanding the extent to which a firm’s patent portfolio contributes to new product development may provide significant implications for future firm performance. We propose a machine learning approach to measure a firm's patent utilization rate, based on textual similarity between patent filings and new product descriptions. At the new product announcement event level, we show that new products supported by more patents yield higher announcement returns. At the firm level, patent utilization rate is positively associated with future new product development, market share growth, profitability, and firm valuation. The effects are predominantly driven by the utilization of high-value patents, and are more pronounced for firms in competitive product markets. We address endogeneity concerns using R&D tax credits as instruments and demonstrate robust findings across various tests. Our findings highlight the strategic importance of integrating patents into the product development pipeline.