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 waiting times. Leveraging a consumer panel, we estimate demand as a function of both prices and waiting times and use the resulting estimates to recover heterogeneity in the value of time at the individual level. We study the welfare implications of platform pricing policies that take advantage of this heterogeneity. In particular, we compare the consumers’, drivers’, and platform’s welfare under different forms of price discrimination. Taking into account drivers’ optimal reaction to the platform’s pricing policy, total surplus falls by 6% under personalized pricing relative to the current mechanism. However, total surplus grows by 33% compared to the case in which the platform does not incorporate consumer information into its pricing.
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