Conference Agenda

This paper presents a theoretical analysis of how innovation in the form of public payment infrastructures, accessible to all banks with equal efficacy, affects competition in the banking sector. We develop a model of two-bank competition in which one bank’s deposit offers greater payment convenience than its rival’s. In equilibrium, the bank with the convenience advantage captures a larger share of both the deposit market and—via a novel channel involving interest on central bank reserves—the lending market. We show that innovations enabling a uniform level of payment convenience for all consumers help level the playing field between large and small banks. In contrast, paying interest on balances of alternative media of exchange tends

to reinforce the advantages of larger banks. Our theoretical findings are supported by recent empirical evidence from payment innovations in Brazil, China, and the United States.

Network effects in payments technologies drive concentration, but interventions to increase choice risk market fragmentation. We study whether interoperability can resolve this dilemma, using data from India’s Unified Payments Interface and a major pre-existing digital wallet. When these platforms became interoperable, overall digital payment usage rose. This increase was driven by regions that were more fragmented across payment platforms ex ante, consistent with a model in which interoperability expands effective network size without requiring consolidation on a single platform. Model-based estimates imply that interoperability raised total digital payment usage by over 50 percent in the year after integration.

This paper studies how artificial intelligence (AI) affects the finance labor market when humans and AI perform different tasks in investment projects, and workers earn agency rents that grow with project size. We identify two key effects of AI improvement: A free-riding effect raises worker rents by increasing the probability of successful investment when the worker shirks; A capital reallocation effect shifts investment toward workers with higher or lower rents, depending on which tasks AI improves. The model generates predictions that would not be obtained with a frictionless labor market. In particular, AI can increase labor demand and workers' rents even when substituting for workers.