A concern central to the economics of privacy is that firms may use consumer data to price discriminate. A common response is that consumers should have control over their data and the ability to choose how firms access it. Since firms draw inferences based on both the data seen as well as the consumer's disclosure choices, the strategic implications of this proposal are unclear. We investigate whether such measures improve consumer welfare in monopolistic and competitive environments. We find that consumer control can guarantee gains for every consumer type relative to both perfect price discrimination and no personalized pricing. This result is driven by two ideas. First, consumers can use disclosure to amplify competition between firms. Second, consumers can share information that induces a seller---even a monopolist---to make price concessions. Furthermore, whether consumer control improves consumer surplus depends on both the technology of disclosure and the competitiveness of the marketplace. In a competitive market, simple disclosure technologies such as "track / do-not-track'' suffice for guaranteeing gains in consumer welfare. However, in a monopolistic market, welfare gains require richer forms of disclosure technology whereby consumers can decide how much information they would like to convey.

Most U.S. government spending on highways and bridges is done through “scaling” procurement auctions, in which private construction firms submit unit price bids for each piece of material required to complete a project. Using data on bridge maintenance projects undertaken by the Massachusetts Department of Transportation (MassDOT), we present evidence that firm bidding behavior in this context is consistent with optimal skewing under risk aversion: firms limit their risk exposure by placing lower unit bids on items with greater uncertainty. We estimate the amount of uncertainty in each auction, and the distribution of bidders' private costs and risk aversion. Simulating equilibrium item‐level bids under counterfactual settings, we estimate the fraction of project spending that is due to risk and evaluate auction mechanisms under consideration by policymakers. We find that scaling auctions provide substantial savings relative to lump sum auctions and show how our framework can be used to evaluate alternative auction designs.

Economists routinely make functional form assumptions on demand curves to derive welfare conclusions. How sensitive are these conclusions to such assumptions? In this paper, we develop robustness measures that quantify the extent to which the true demand curve must deviate from common functional form assumptions in order to overturn a welfare conclusion. We parametrize this variability in terms of the gradient and curvature of the demand curve. By leveraging tools from information design, we show that our measures are easy to compute. Our measures are also flexible and easy to use, as we illustrate through empirical applications.