We study dealers’ bidding behavior in the Bank of England’s quantitative easing (QE) reverse auctions. Using a granular dataset on both accepted and rejected offers together with an equilibrium model of bidding behavior, we estimate dealers’ valuations of securities offered to the Bank of England. We also recover the rents accruing to dealers from participating in the auctions as opposed to liquidating gilts in the secondary market, thereby possibly causing prices to change. These rents or so-called ”liquidity benefits” are largest in the early phases of QE implemented during the Global Financial Crisis, suggesting that QE may be particularly effective in restoring smooth market functioning when market participants are facing large liquidity shocks. Finally, we document that dealers’ valuations vary significantly with the amount of interest rate risk acquired in the secondary gilt market before the auction and with dealers’ regulatory capital.

We recover valuations of time using detailed data from a large ride‐hail platform, where drivers bid on trips and consumers choose between a set of rides with different prices and wait times. Leveraging a consumer panel, we estimate demand as a function of both prices and wait times and use the resulting estimates to recover heterogeneity in the value of time across consumers. We study the welfare implications of personalized pricing and its effect on the platform, drivers, and consumers. Taking into account drivers' optimal reaction to the platform's pricing policy, personalized pricing lowers consumer surplus by 2.5% and increases overall surplus by 5.2%. Like the platform, drivers benefit from personalized pricing. By conditioning prices on drivers' wait times and not on consumers' data, the platform can capture a significant portion of the profits garnered from personalized pricing, and simultaneously benefit consumers.