Bernando Ribeiro from Princeton University will present "Embracing the Future or Building on the Past? Growth with New and Old Technologies"

Abstract: Is growth driven by the emergence of new paradigms or mostly through the perfection of existing technologies? And is the allocation of research effort between emerging technologies versus established ones efficient? To study these questions, I propose a new semi-endogenous growth model that incorporates technology vintages and the endogenous evolution of multiple technological paradigms through directed innovation. Despite the fact that technologies continuously emerge, making the state space unbounded, the model is remarkably tractable, allowing me to provide a comprehensive characterization of both the balanced growth equilibrium and the transitional dynamics. From a positive perspective, the model can rationalize two distinct empirical patterns of innovation over time and across technologies. Using two centuries of U.S. patent data, I first document that the age profile of patents has a pronounced hump-shape: the majority of contemporary patents are built upon technologies that are between 50 and 100 years old. Second, this age profile has remained remarkably stable throughout the past century. From a normative standpoint, the theory underscores a misallocation of research effort induced by the tendency among profit-maximizing firms to overinvest in further developing mature technologies. This fundamental inefficiency yields a suboptimally slow development of emerging technologies near the technological frontier. An estimated version of my model implies that transitioning from a laissez-faire equilibrium to the efficient allocation would increase the average growth rate of the economy from an annual 2% to 2.18% over the course of a century. These results shed new light on policy discussions concerning the prioritization of emerging technologies versus established ones. For instance, they provide a rationale for public policy to support investments in cutting-edge technologies, such as quantum computing or metabolic engineering.