Pricing and Efficiency in a Decentralized Ride-Hailing Platform

Abstract

Asymmetric information about market participants’ valuations and costs plays a crucial role in the efficiency of a platform’s design. Using novel data from a ride-hailing platform called inDriver, I examine whether decentralizing the pricing mechanism improves market efficiency. Unlike its competitors, inDriver requires riders to offer a price for their requested trips, and allows drivers to either agree to the offer, ask for a higher price, or ignore the request. Under this mechanism, a rider with a high willingness to pay for a trip can offer a higher price to increase her chances of being matched. At the same time, under decentralized pricing riders might not truthfully reveal their valuations, which can result in lower average prices on the platform. To understand welfare implications of decentralized pricing for riders and drivers, I develop an equilibrium model of a decentralized ride-hailing market and estimate its parameters using user-level data on the universe of ride requests in a single city. I then use the obtained estimates to compare welfare under a decentralized mechanism to an alternative mechanism in which prices are chosen by the platform. I find that decentralized pricing significantly improves efficiency in the studied market.