Essays on Economic Growth and Innovation

Abstract

A foundational observation by Robert Solow holds that long-run economic growth is primarily driven by the innovation and adoption of new technologies (Solow, 1957). This set of essays provides new theory and evidence to explain how firms choose which technologies to innovate and adopt. A point of emphasis, particularly in the first two chapters, is that complementarities across firms play an important role in determining the rate and direction of technological change. These complementarities arise as firms build shared knowledge by innovating (Chapter 1) and from joint consumption of new products (Chapter 2). They provide a new channel through which market structure and property rights affect long-run technological change.

Chapter 1. The first chapter is motivated by the observation that the direction of innovation shapes both current technologies and future innovation opportunities, as firms acquire expertise and create public knowledge through discovery. But how do firms choose which technologies to develop? Do they ever fail to exploit new technological paradigms? I build a new model of innovation and firm dynamics to study a novel link between market structure, the direction of innovation, and economic growth: Expertise in a current technology gives incumbents a comparative advantage at innovating it relative to entrants, who instead favor a new technology with higher growth potential. Each firm’s innovation decisions influence others through knowledge spillovers, so the initial market structure can affect the long-run direction of innovation. Concentrating R&D resources in a small number of firms allows faster accumulation of expertise, raising growth when all firms innovate the same technology. But it can lower growth when firms face a technology choice, amplifying the influence of incumbents and potentially delaying or preventing the emergence of the new technology. I provide empirical evidence for the theory using data on firm patenting and R&D expenditures. I also show that it explains the historical development of mRNA vaccines, and I explore its implications for the highly concentrated innovation of artificial intelligence.

Chapter 2. In the second chapter, joint with Rebekah Dix, we observe that innovations often combine several components to achieve outcomes greater than the “sum of the parts.” We argue that such combination innovations can introduce an understudied inefficiency—a positive market expansion externality that benefits the owners of the components. We demonstrate the importance of this externality in the market for pharmaceutical cancer treatments, where drug combination therapies have proven highly effective. Using data on clinical trial investments, we document several facts consistent with inefficiently low private innovation: firms are less likely than publicly funded researchers to trial combinations, firms are less likely to trial combinations including other firms’ drugs than those including their own drugs, and firms often wait to trial combinations including other firms’ drugs until those drugs experience generic entry. Using microdata on drug prices and utilization, we quantify the externalities that arise from new combinations and find that the market expansion externality often dominates the standard negative business stealing externality, suggesting too little innovation in combination therapies. As a result, firms may have incentives to free ride off others’ innovation, which we analyze with a dynamic structural model of innovation decisions. We use the model to design cost-effective policies that advance combination innovation. Redirecting publicly funded innovation toward combinations with high predicted market expansion or consumer surplus spillovers minimizes crowd out of private investments, increasing the rate of combination innovation and total welfare while remaining budget neutral.

Chapter 3. The final chapter, joint with Daron Acemoglu, considers incentives to adopt transformative technologies that promise to accelerate productivity growth across many sectors but also present new risks from potential misuse. We develop a multi-sector technology adoption model to study the optimal regulation of transformative technologies when society can learn about these risks over time. Socially optimal adoption is gradual and typically convex. If social damages are large and proportional to the new technology’s productivity, a higher growth rate paradoxically leads to slower optimal adoption. Equilibrium adoption is inefficient when firms do not internalize all social damages, and sector-independent regulation is helpful but generally not sufficient to restore optimality.