Working papers

Monetary Misperceptions: Optimal Monetary Policy under Incomplete Information

Working paper: SSRN

Abstract: Inflation targeting is strictly suboptimal when economic actors have incomplete information about the state of the economy. Nominal income targeting is approximately optimal, and exactly optimal under certain parameterizations. We derive this result in a “Lucas islands” monetary misperceptions model built from, unlike prior work, explicit microfoundations. Agents have knowledge of local productivity and money supply conditions, but must perform a signal extraction problem each period to estimate the aggregate productivity shock and the aggregate money supply shock. Without full information, agents cannot perfectly distinguish between relative price shocks and aggregate shocks, causing monetary policy to affect the real economy. Since the model is built from agents optimizing from first principles, we are able to take a second-order welfare approximation and ask what monetary policy rule is optimal. In contrast to sticky price or sticky information models, inflation and price level targeting are always suboptimal, as price level variation provides useful information to agents. Under log utility, nominal income targeting is optimal.


Central Banks and Food Banks: What can food banks teach us about optimal monetary policy under incomplete information?

Working paper available upon request

Abstract: Inflation targeting is suboptimal when agents in the macroeconomy face incomplete information, while nominal income targeting is welfare-maximizing. This is demonstrated formally, and also intuitively through discussion of a scrip economy designed to allocate donations among American food banks which operate under incomplete information.


Works in progress

Toward an understanding of the economics of apologies: evidence from a large-scale natural field experiment

[with Ben Ho, John A. List, and Ian Muir]

Working paper available upon request

Abstract: We combine theory with a nationwide field experiment involving 1.5 million Uber ridesharing consumers whose trips took significantly longer than expected to deepen our understanding of the economics of apologies. Several insights emerge. First, apologies are not a panacea: the efficacy of an apology -- and whether it may backfire -- depend on how and when the apology is made. Second, across all treatments, money speaks louder than words -- we find consistent evidence that the best form of apology is to include a coupon for a future trip. Third, in some cases sending an apology is worse than sending nothing at all, particularly for repeated apologies. Finally, apology efficacy critically depends on the familiarity of the customer with the firm's product and non-monotonically on the severity of the bad experience. For firms, our data suggest that caveat venditor should be the rule when considering apologies.