The Efficiency of a Dynamic Decentralized Two-sided Matching Market

This paper studies a decentralized dynamic matching market by using data from a large ride-sharing platform to estimate a model of search and matching between drivers and passengers. The authors measure the preferences for trips and waiting costs of passengers and drivers. The authors assess whether and to what extent centralized algorithms that require different information sets can improve efficiency, and the authors show that information on agent preferences and search lengths are particularly important for the platform to implement algorithms that increase revenue and the total surplus of drivers and passengers.