This paper studies the effects of the EU's General Data Protection Regulation (GDPR) on the ability of firms to collect consumer data, identify consumers over time, accrue revenue via online advertising, and predict their behavior. We utilize a novel dataset that spans many different firms in the online travel industry that allows us to observe consumer visits, purchases, advertisements, and the output of a commercially used algorithm that predicts consumer behavior. We make use of a difference-in-differences analysis that exploits the geographic reach of GDPR. We find a 12.5% drop in observed consumers as a result of GDPR, but at the same time that remaining set of consumers is more persistently identifiable. We provide suggestive evidence that this is driven by a selected set of consumers who substitute from pre-existing privacy means towards those offered as part of GDPR. This substitution affects the ability to predict consumer behavior and preferences as well as improves the ability of advertisers to measure the effectiveness of advertising. In sum, this differential use of privacy tools leads to an increase in the average value of remaining consumers to advertisers, offsetting some of the losses from consumers that opt-out. Our results highlight the externalities that consumer privacy decisions have both on other consumers and for firms.

We study the optimal design of a queueing system when agents' arrival and servicing are governed by a general Markov process. The designer of the system chooses entry and exit rules for agents, their service priority---or queueing discipline---as well as their information, while ensuring that agents have incentives to follow the designer's recommendations not only to join the queue but more importantly to stay in the queue. Under a mild condition, the optimal mechanism has a cutoff structure---agents are induced to enter up to a certain queue length and no agents are to exit the queue once they enter the queue; the agents on the queue are served according to a first-come-first-served (FCFS) rule; and they are given no information throughout the process beyond the recommendations they receive from the designer. FCFS is also necessary for optimality in a rich domain. We identify a novel role for queueing disciplines in regulating agents' beliefs, and their dynamic incentives, thus uncovering a hitherto unrecognized virtue of FCFS in this regard.

We study robustly-optimal mechanisms for selling multiple items. The seller maximizes revenue against a worst-case distribution of a buyer's valuations within a set of distributions, called an "ambiguity" set. We identify the exact forms of robustly-optimal selling mechanisms and the worst-case distributions when the ambiguity set satisfies a variety of moment conditions on the values of subsets of goods. We also identify general properties of the ambiguity set that lead to the robust optimality of partial bundling which includes separate sales and pure bundling as special cases.