The ValUE oF TIME: A High-Frequency Revealed Preference ANALYSIS

(with Nick Buchholz, Jakub Kastl, Filip Matejka, and Tobias Salz)

We use detailed consumer choice data from a large European ride-hailing application to estimate consumer valuations of time. This application offers a unique mechanism that allows drivers to bid on trips and consumers to choose between a set of characteristics of a ride, most importantly, price and waiting time. We leverage rich variation in bids and customer choices to directly measure consumer willingness-to-pay for time savings through revealed preferences. Our estimates provide value-of-time measures that are both time and location dependent. Consumers respond substantially to changes in both price and waiting time, however price elasticities are about three times higher on average than waiting-time elasticities. We then pose a model to microfound the value of time. In the model, the consumer chooses where to spend time as a function of their valuation for different places. The model allows us to decompose the value of time into individual-specific heterogeneity, place-specific heterogeneity, and an interaction between the two. This interaction allows us to measure consumers' location-specific complementarities.