Contract Design and Driver Allocation Under Information Asymmetry in Ride‐Hailing Platforms

The equitability of ride‐hailing platforms needs to be studied from the perspective of drivers, particularly in terms of risk exposure, job security, and access to livable wages. The conflicts emerge between platforms and drivers, when platforms make policy decisions while drivers rarely intervene. Disparities in drivers’ income, supply and demand distribution, traffic conditions, and information asymmetry further exacerbate this problem. To resolve and ensure improved working contracts for gig‐economy workers, a socioeconomic solution is required. This study applies principal‐agent theory to model the platform–driver relationship over three nested decision horizons: per‐ride, weekly, and yearly. Utility functions are developed from the revenues earned and costs incurred by both parties, and closed‐form participation thresholds for the platform commission rate are derived and numerically illustrated. Three contract structures, commission based, subscription based, and fixed income, are formally compared, and their risk‐allocation and self‐selection properties under driver heterogeneity are characterized. Passengers and regulators enter the model in reduced form through a commission‐sensitive ride‐volume function and policy constraints, respectively. The analysis yields implementable guidance for tiered commission design, surge‐based incentives, and transparency‐oriented regulation.