Since Akerlof (1970), economists have understood the adverse selection problem that information asymmetries can create in used goods markets. The remarkable growth in online used goods auctions thus poses a puzzle. Part of the solution is that sellers voluntarily disclose their private information on the auction web page. This defines a precise contract -- to deliver the car shown for the closing price -- which helps protect the buyer from adverse selection. I test this theory using data from eBay Motors, finding that online disclosures are important price determinants, and that disclosure costs impact both the level of disclosure and prices. (JEL D44, D82, L81)

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).

We develop and estimate a model of consumer search with spatial learning. Consumers make inferences from previously searched objects to unsearched objects that are nearby in attribute space, generating path dependence in search sequences. The estimated model rationalizes patterns in data on online consumer search paths: search tends to converge to the chosen product in attribute space, and consumers take larger steps away from rarely purchased products. Eliminating spatial learning reduces consumer welfare by 12%: cross‐product inferences allow consumers to locate better products in a shorter time. Spatial learning has important implications for product recommendations on retail platforms. We show that consumer welfare can be reduced by unrepresentative product recommendations and that consumer‐optimal product recommendations depend on both consumer learning and competition between platforms.

We document the evolution of the new market for frequency response within the UK electricity system over a six-year period. Firms competed in price while facing considerable initial uncertainty about demand and rival behavior. We show that prices stabilized over time, converging to a rest point that is consistent with equilibrium play. We draw on models of fictitious play and adaptive learning to analyze how this convergence occurs and show that these models predict behavior better than an equilibrium model prior to convergence. (JEL D22, D83, L13, L94, L98)