We study the implications of aggregating consumers’ purchase histories into scores that proxy for unobserved willingness to pay. A long-lived consumer interacts with a sequence of firms. Each firm relies on the consumer’s current score–a linear aggregate of noisy purchase signals—to learn about her preferences and to set prices. If the consumer is strategic, she reduces her demand to manipulate her score, which reduces the average equilibrium price. Firms in turn prefer scores that overweigh past signals relative to applying Bayes’ rule with disaggregated data, as this mitigates the ratchet effect and maximizes the firms’ ability to price discriminate. Consumers with high average willingness to pay benefit from data collection, because the gains from low average prices dominate the losses from price discrimination. Finally, hidden scores—those only observed by the firms—reduce demand sensitivity, increase average prices, and reduce consumer surplus, sometimes below the naive-consumer level.

This paper examines moral hazard in teams over time. Agents are collectively engaged in a project whose duration and outcome are uncertain, and their individual efforts are unobserved. Free-riding leads not only to a reduction in effort, but also to procrastination. Collaboration among agents dwindles over time, but does not cease as long as the project has not succeeded. In addition, the delay until the project succeeds, if it ever does, increases with the number of agents. We show why deadlines, but not necessarily better monitoring, help to mitigate moral hazard. (JEL D81, D82, D83)

A monopolist platform uses data to match heterogeneous consumers with multiproduct sellers. The consumers can purchase the products on the platform or search off the platform. The platform sells targeted ads to sellers that recommend their products to consumers and reveals information to consumers about their match values. The revenue-optimal mechanism is a managed advertising campaign that matches products and preferences efficiently. In equilibrium, sellers offer higher qualities at lower unit prices on than off platform. The platform exploits its information advantage to increase its bargaining power vis-à-vis the sellers. Finally, privacy-respecting data-governance rules can lead to welfare gains for consumers. (JEL D11, D42, D44, D82, D83, M37)

An organization must select among competing projects that differ in their payoff consequences for its members. Each agent chooses a project and exerts effort affecting its completion time. When one or more projects are complete, the agents select which one to adopt. The selection rule for multiple projects that maximizes ex post welfare leads to inefficiently high polarization; rules that favor later proposals improve upon ex post optimal selections. The optimal degree of favoritism increases in the cost of effort and discount rate. This trade-off informs the design of process rules in standard-setting organizations and helps explain their performance. (JEL C78, D23, D71, D72, D83, L15)

We consider learning and signalling in a dynamic Cournot oligopoly where firms have private information about their production costs and only observe the market price, which is subject to unobservable demand shocks. An equilibrium is Markov if play depends on the history only through the firms’ beliefs about costs and calendar time. We characterize symmetric linear Markov equilibria as solutions to a boundary value problem. In every such equilibrium, given a long enough horizon, play converges to the static complete information outcome for the realized costs, but each firm only learns its competitors’ average cost. The weights assigned to costs and beliefs under the equilibrium strategies are non-monotone over time. We explain this by decomposing incentives into signalling and learning, and discuss implications for prices, quantities, and welfare.

We present a model of digital advertising with three key features: (1) advertisers can reach consumers on and off a platform, (2) additional data enhances the value of advertiser–consumer matches, and (3) the allocation of advertisements follows an auction-like mechanism. We contrast data-augmented auctions, which leverage the platform’s data advantage to improve match quality, with managed-campaign mechanisms that automate match formation and price-setting. The platform-optimal mechanism is a managed campaign that conditions the on-platform prices for sponsored products on the off-platform prices set by all advertisers. This mechanism yields the efficient on-platform allocation but inefficiently high off-platform product prices. It attains the vertical integration profit for the platform and the advertisers, and it increases off-platform product prices while decreasing consumer surplus, relative to data-augmented auctions.

A data buyer faces a decision problem under uncertainty. He can augment his initial private information with supplemental data from a data seller. His willingness to pay for supplemental data is determined by the quality of his initial private information. The data seller optimally offers a menu of statistical experiments. We establish the properties that any revenue-maximizing menu of experiments must satisfy. Every experiment is a non-dispersed stochastic matrix, and every menu contains a fully informative experiment. In the cases of binary states and actions, or binary types, we provide an explicit construction of the optimal menu of experiments. (JEL D42, D81, D82, D83)