Start-ups as Buyers in Technology Markets: Evidence from the Bio-pharmaceutical Industry

My dissertation studies start-ups’ strategies in the market for technologies, with a focus on their role as technology buyers, in the context of the bio-pharmaceutical industry. The first chapter clarifies when and what start-ups are likely to buy. Building on the classic assumption that start-ups are most advantaged in developing early-stage technologies and drawing on research examining M resource reconfigurations, I argue that start-ups will have opportunities to buy from sellers that experienced recent M events because M resource reconfigurations release early-stage technologies that would otherwise be further developed and sold to incumbents when late-staged. To test the predictions, I exploit a novel data set involving 97,202 U.S. bio-pharmaceutical patents, and adopt a quasi-experimental approach, using M that unexpectedly failed to go through. The results show that sellers’ M events increase the probability that a patent will be bought by a start-up by around 50%, a finding that is driven primarily by an increase in start-ups’ acquisitions of early-stage technologies. The second chapter studies the possible impact of venture capital (VC) backing for start-ups’ probability of buying external technologies. An analysis of nearly 3,137 U.S. bio-pharmaceutical start-ups founded between 1996 and 2012 suggests that start-ups are about 2% more likely to acquire external technologies after they receive VC funding. To address the concern that a firm’s VC backing status might not be random, I construct an instrumental variable, capturing exogenous variation in VC funding at the MSA level. IV regression results reveal that VC backing status causes over 30% increase in start-ups’ probability of acquiring external technologies. The third chapter examines a puzzle in the literature explaining technology transaction timing: why some quality signals that reduce information asymmetries promote early licensing (e.g., licensors’ alliance experiences), whereas others do not (e.g., patents). By building a formal model and exploiting a novel data set comprising 124 U.S. exclusive licensing agreements announced between 2009 and 2015, I show that patents, in addition to reducing information asymmetry, induce licensors’ over-optimism regarding buyer competition, which delays licensing timing. Overall, my dissertation contributes to a more comprehensive and nuanced understanding of the market for technologies.Ph.D