The Efficiency of A Dynamic Decentralized Two-sided Matching Market

This paper studies a decentralized dynamic matching market by using data from a Chinese ride-sharing platform to estimate a model of search and matching between drivers and passengers. We measure passenger valuations of trips, driver preferences, distributions of planned search length, and waiting costs. To estimate driver preferences, we develop a new inequality approach based on an incomplete dynamic model. We assess whether centralized algorithms that require different information sets can improve efficiency, and we show that information on agent preferences and planned search length can enable the platform to increase revenue and the total surplus of drivers and passengers.