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We study the optimal lockdown policy for a planner who wants to control the fatalities of a pandemic while minimizing the output costs of the lockdown. We use the SIR epidemiology model and a linear economy to formalize the planner's dynamic control problem. The optimal policy depends on the fraction of infected and susceptible in the population. We parametrize the model using data on the COVID19 pandemic and the economic breadth of the lockdown. The quantitative analysis identifies the features that shape the intensity and duration of the optimal lockdown policy. Our baseline parametrization is conditional on a 1% of infected agents at the outbreak, no cure for the disease, and the possibility of testing. The optimal policy prescribes a severe lockdown beginning two weeks after the outbreak, covers 60% of the population after a month, and is gradually withdrawn covering 20% of the population after 3 months. The intensity of the lockdown depends on the gradient of the fatality rate as a function of the infected, and on the assumed value of a statistical life. The absence of testing increases the economic costs of the lockdown, and shortens the duration of the optimal lockdown which ends more abruptly. Welfare under the optimal policy with testing is higher, equivalent to a one-time payment of 2% of GDP.

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We use three quasi-natural experiments in Mexico and one in Panama to estimate the effects of having the option to pay with cash on Uber rides. The ability to pay in cash affects the demand for rides, which is reflected in large changes in the total number of trips, fares, miles, and number of users after Uber introduced cash payments, particularly in lower-income city blocks. On the other hand, the effects on prices, estimated times of arrival, and competitor pricing are negligible, consistent with the supply of trips being very elastic. Although cash payments naturally increase the fraction of users that pay exclusively with cash, more than half of the users have access to both cards and cash, and alternate between payment methods. We find evidence consistent with cash and card payments being imperfectly substitutable at both the intensive and extensive margins, which magnifies the impact of policies that restrict the availability of payment methods.

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We estimate the private benefits for Uber riders from using alternative payment methods. We focus on Mexico where riders have the option to use cash or credit cards to pay for rides. We use three field experiments involving approximately 400,000 riders to estimate the loss of private benefits for riders if a ban on cash payments is implemented. We find that Uber riders, using cash as means of payment either sometimes or exclusively, suffer an average loss of approximately 50% of their expenditures on trips paid in cash before the ban.

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This paper develops a dynamic model of technology adoption featuring strategic complementarities: the benefits of usage increase with the number of adopters. We study the diffusion of new means of payments, where such complementarities are pervasive. We show that complementarities give rise to multiple equilibria, suboptimal allocations, and study the planner's problem. The model generates gradualism in adoption, as individuals optimally wait for others to adopt before doing so. We apply the theory to the adoption of SINPE, an electronic peer-to-peer (P2P) payment app developed by the Central Bank of Costa Rica. Transaction-level data on the use of SINPE and several administrative data sets on the network structure allow us to exploit plausibly exogenous variation and to document sizable complementarities. A calibrated version of the model shows that the optimal subsidy pushes the economy to universal adoption.