Abstract: We document key properties of crypto monetary policies based on more than 2,000 tokens: (1) Money growth rates decline with age and stabilize at 0.1% per month on average, with younger cohorts converging faster to the long-run growth rate; (2) Long-run money growth rates and convergence speeds are positively correlated in the cross-section; (3) Tokens held heavily by retail investors have relatively low long-run money growth rates and convergence speeds. We present a dynamic model to determine the optimal token issuance and fee policies for issuers. Committing to low future money growth and fees increases profits, and the degree of commitment matters for the existence of equilibria. A Ramsey issuer who maximizes profits, after the initial period, makes choices that maximize the utility value of all tokens. We present a model with probabilistic commitment and show that issuers with high commitment choose low long-run money growth rates and fee ratios, and they reduce them slowly.
Pascal Paul, Federal Reserve Bank of San Francisco
Mauricio Ulate, Federal Reserve Bank of San Francisco
Cynthia Wu, University of Notre Dame
Abstract: We develop a quantitative New Keynesian DSGE model to study the introduction of a central bank digital currency (CBDC): government-backed digital money available to retail consumers. At the heart of our model are monopolistic banks with market power in deposit and loan markets. When a CBDC is introduced, households benefit from an expansion of liquidity services and higher deposit rates as bank deposit market power is curtailed. However, deposits also flow out of the banking system and bank lending contracts. We assess this welfare trade-off for a wide range of economies that differ in their level of interest rates. We find substantial welfare gains from introducing a CBDC with an optimal interest rate that can be approximated by a simple rule of thumb: the maximum between 0% and the policy rate minus 1%.
Joseph Abadi, Federal Reserve Bank of Philadelphia
Markus Brunnermeier, Princeton University
Abstract: We develop a model to compare the governance of traditional shareholder-owned platforms to that of platforms that issue tokens. A traditional shareholder governance structure leads a platform to extract rents from its users. A platform that issues tokens for its services can mitigate this rent extraction, as rent extraction lowers the platform owners’ token seigniorage revenues. However, this mitigation from issuing “service tokens” is effective only if the platform can commit itself not to dilute the “service token” subsequently. Issuing “hybrid tokens” that bundle claims on the platform’s services and its profits enhances efficiency even absent ex-ante commitment power. Finally, giving users the right to vote on platform policies, by contrast, redistributes surplus but does not necessarily enhance efficiency.
Linda Schilling, Washington University in St. Louis
Abstract: We provide an economic analysis of how the observation of data by multiple parties (redundancies)
affect private and socially optimal investment in data security. Our use case is the current banking system where payment transaction data is observed by at least the sending and the receiving institution. Redundancies cause free-riding, and under investment relative to the social optimum which increases the chance of cyber attacks. We show that an optimally protected third party, e.g. a regulator that observes all payment data, can increase overall data security beyond the level provided by the social planner even though all private entities shirk upon its entry. This holds because the information environment that results from providing a third redundancy is inherently different than the information environment the social planner is faced with.
