Efficiency vs. Fairness: Analyzing Asymmetric Policy Trade-offs in Subsidizing Ride-Hailing Trips

Abstract

By providing convenient on-demand mobility, ride-hailing services from Transportation Network Companies (TNCs) allow authorities to supplement public transport in areas with limited transit access. This paper examines local government’s subsidizing strategies on ride-hailing trips under different policy goals. We develop a spatial equilibrium model that captures the interactions between commuters, drivers , and TNCs, which is further incorporated into two optimization problems that distribute subsidies to maximize system efficiency and fairness, respectively. The model is calibrated and solved using a real-world dataset from New York City through a fixedpoint algorithm and Alternating Direction Method of Multipliers (ADMM). Our results show that fairness-driven policies prioritize subsidizing inaccessible OD pairs, whereas efficiency-driven policies are largely determined by the utility differences between competing modes. The results demonstrate an asymmetric trade-off: prioritizing fairness incurs a smaller efficiency penalty (1.0%) than the fairness loss resulting from efficiency optimization (1.6%).