The Design of Centralized Matching Systems on Two-Sided Platforms: Evidence from the Ride-Hailing Market

Designing centralized matching systems on two-sided platforms entails a tradeoff between matching efficiency and agent autonomy. We investigate this tradeoff in the ride-hailing market by comparing two prevalent centralized dispatch systems: a driver-accept system, where drivers decide whether to accept e-hail requests, and an auto-accept system, where drivers are automatically assigned to these requests. We develop a dynamic structural model of a two-sided market where strategic drivers optimize acceptance and relocation decisions over a shift, and riders choose between transportation modes. We apply this model to Singapore’s taxi market, where a leading operator used a driver-accept dispatch system for e-hail trips. We develop an iterative algorithm to solve for the market equilibrium as a fixed point of a nested loop. Our counterfactual analyses reveal that in the short-term equilibrium with fixed demand, switching to an auto-accept system increases driver earnings through higher vehicle utilization, despite the loss of private information caused by removing driver choice. In the long-term equilibrium, drivers’ earnings increase further due to market expansion, although benefits are unevenly distributed between e-hail and street-hail services. Consumer surplus increases in both scenarios. Our findings highlight that centralized assignment can enhance overall welfare by mitigating search frictions, even when agents value autonomy.

History: Tat Chan served as the senior editor for this article.

Funding: This work was supported by the National Natural Science Foundation of China [Grant 72502200], the Hong Kong Research Grants Council’s General Research Fund [Grant 17500625], the Hong Kong University Seed Fund for Collaborative Research 2022/23 [Grant 2207101494-109000437], the UCL School of Management Research Funding, and the Singapore Ministry of Education Social Science Research Thematic Grant [Grant MOE2016-SSRTG-059].

Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mksc.2023.0561.