We study how supply disruptions interact with monopoly pricing, inventory management, and consumer stockpiling in a continuous-time model. Preemption incentives—consumers prefer to stock up before a price hike while the firm prefers to hike before consumers stock up—lead to an equilibrium with gradual stockpiling and endogenous uncertainty over the timing of a price hike, which can trigger a run at the disruption onset. Consumer storage introduces welfare losses from randomized pricing, but can also strengthen the firm’s incentive to hold buffer stock. Rationing, price controls, and reserve mandates can each improve welfare, but only strategic government reserves can implement the social optimum.

We develop a model where some investors are uncertain whether others are trading on informative signals or noise. Uncertainty about others leads to a nonlinear price that reacts asymmetrically to news. We incorporate this uncertainty into a dynamic setting where traders gradually learn about others and show that it generates empirically relevant return dynamics: expected returns are stochastic but predictable, and volatility exhibits clustering and the “leverage” effect. The model nests both the rational expectations (RE) and differences of opinions (DO) approaches and highlights a link between disagreement about fundamentals and uncertainty about other traders.

We study a bargaining model in which a buyer makes frequent offers to a privately informed seller, while gradually learning about the seller’s type from “news.” We show that the buyer’s ability to leverage this information to extract more surplus from the seller is remarkably limited. In fact, the buyer gains nothing from the ability to negotiate a better price despite the fact that a negotiation must take place in equilibrium. During the negotiation, the buyer engages in a form of costly “experimentation” by making offers that are sure to earn her negative payoffs if accepted, but speed up learning and improve her continuation payoff if rejected. We investigate the effects of market power by comparing our results to a setting with competitive buyers. Both efficiency and the seller’s payoff can decrease by introducing competition among buyers.

We study the optimal incentive scheme for a multistage project in which the agent privately observes intermediate progress. The optimal contract involves a soft deadline wherein the principal guarantees funding up to a certain date—if the agent reports progress at that date, then the principal gives him a relatively short hard deadline to complete the project—if progress is not reported at that date, then a probationary phase begins in which the project is randomly terminated at a constant rate until progress is reported. We explore several variants of the model with implications for optimal project design. In particular, we show that the principal benefits by imposing a small cost on the agent for submitting a progress report or by making the first stage of the project somewhat “harder” than the second.

We investigate the effect of scrutiny (e.g., credit ratings, analyst reports, or mandatory disclosures) on the security design problem of a privately informed issuer. We show that scrutiny has important implications for both the form of security designed and the amount of inefficient retention of cash flows. The model predicts that issuers will design informationally sensitive securities (i.e., levered equity) when scrutiny is sufficiently intense. Otherwise, issuers opt for a standard debt contract. Scrutiny increases efficiency by decreasing issuers’ reliance on retention to signal quality and, perhaps counterintuitively, decrease price informativeness.

A new form of secured lending using “digital collateral” has recently emerged, most prominently in low- and middle-income countries. Digital collateral relies on lockout technology, which allows the lender to temporarily disable the flow value of the collateral to the borrower without physically repossessing it. We explore this new form of credit in a model and a field experiment using school-fee loans digitally secured with a solar home system. Securing a loan with digital collateral drastically reduced default rates (by 19 percentage points) and increased the lender’s rate of return (by 49 percentage points). Using a variant of the Karlan and Zinman (2009) methodology, we decompose the total effect on repayment and find that roughly two-thirds is attributable to moral hazard, and one-third to adverse selection. In addition, access to digitally secured school-fee loans significantly increased school enrollment and school-related expenditures without detrimental effects on households’ balance sheets.