To sell public or private goods.

Traditional analysis takes the public or private nature of goods as given. However, technological advances, particularly related to digital goods such as non-fungible tokens, increasingly make rivalry a choice variable of the designer. This paper addresses the question of when a profit-maximizing seller prefers to provide an asset as a private good or as a public good. While the public good is subject to a free-rider problem, a profit-maximizing seller or designer faces a nontrivial quantity-exclusivity tradeoff, and so profits from collecting small payments from multiple agents can exceed the large payment from a single agent. We provide conditions under which the profit from the public good exceeds that from a private good. If the cost of production is sufficiently, but not excessively, large, then production is profitable only for the public good. Moreover, if the lower bound of the support of the buyers' value distribution is positive, then the profit from the public good is unbounded in the number of buyers, whereas the profit from selling the private good is never more than the upper bound of the support minus the cost. As the variance of the agents' distribution becomes smaller, public goods eventually outperform private goods, reflecting intuition based on complete information models, in which public goods always outperform private goods in terms of revenue.

Road to recovery: Managing an epidemic.

Without widespread immunization, the road to recovery from the current COVID-19 lockdowns will optimally follow a path that finds the difficult balance between the social and economic benefits of liberty and the toll from the disease. We provide an approach that combines epidemiology and economic models, taking as given that the maximum capacity of the healthcare system imposes a constraint that must not be exceeded. Treating the transmission rate as a decreasing function of the severity of the lockdown, we first determine the minimal lockdown that satisfies this constraint using an epidemiology model with a homogeneous population to predict future demand for healthcare. Allowing for a heterogeneous population, we then derive the optimal lockdown policy under the assumption of homogeneous mixing and show that it is characterized by a bang-bang solution. Possibilities such as the capacity of the healthcare system increasing or a vaccine arriving at some point in the future do not substantively impact the dynamically optimal policy until such an event actually occurs.