Increasingly detailed consumer information makes sophisticated price discrimination possible. At fine levels of aggregation, demand may not obey standard regularity conditions. We propose a new randomized sales mechanism for such environments. Bidders can "buy-it-now" at a posted price, or "take-a-chance" in an auction where the top d > 1 bidders are equally likely to win. The randomized allocation incentivizes high valuation bidders to buy-it-now. We analyze equilibrium behavior, and apply our analysis to advertiser bidding data from Microsoft Advertising Exchange. In counterfactual simulations, our mechanism increases revenue by 4.4% and consumer surplus by 14.5% compared to an optimal second-price auction.

We develop a decentralized Bayesian model of college admissions with two ranked colleges, heterogeneous students and two realistic match frictions: students find it costly to apply to college, and college evaluations of their applications are uncertain. Students thus face a portfolio choice problem in their application decision, while colleges choose admissions standards that act like market-clearing prices. Enrollment at each college is affected by the standards at the other college through student portfolio reallocation. In equilibrium, student-college sorting may fail: weaker students sometimes apply more aggressively, and the weaker college might impose higher standards. Applying our framework, we analyze affirmative action, showing how it induces minority applicants to construct their application portfolios as if they were majority students of higher caliber.

In public procurement, social welfare often depends on how quickly the good is delivered. A leading example is highway construction, where slow completion inflicts a negative externality on commuters. In response, highway departments award some contracts using scoring auctions, which give contractors explicit incentives for accelerated delivery. We characterize efficient design of these mechanisms. We then gather an extensive data set of highway projects awarded by the California Department of Transportation between 2003 and 2008. By comparing otherwise similar contracts, we show that where the scoring design was used, contracts were completed 30–40% faster and the welfare gains to commuters exceeded the increase in procurement costs. Using a structural model that endogenizes participation and bidding, we estimate that the counterfactual welfare gain from switching all contracts from the standard design to the efficient A+B design is nearly 22% of the total contract value ($1.14 billion).

Deregulation of the frequency response market in the UK allowed electricity firms to compete on price in an otherwise stable environment. We provide an analysis of the evolution of the deregulated market from the date it started. Initial activity was volatile, with some firms exploring different prices, while others made few price changes. This was followed by a period in which prices fell and the variance in the cross sec- tional distribution of bids declined markedly. By the end of our study price changes had became relatively rare and small, consistent with convergence to a static Nash equilibrium. We examine how well models of learning do in predicting play during the period prior to convergence but after the initial volatility. Models where perceptions of competitors’ play depend on past play suggest that firms’ weight recent play dispro- portionately. We also find evidence of statistical learning about the underlying demand parameters conditional on competitors’ play. A model that combines these two features fits quite well: it is able to explain 37% of the share-weighted variation in prices, even though none of the model parameters are chosen to fit the pricing behavior.