Compensation guarantees in crowdsourced delivery: Impact on platform and driver welfare

Crowdsourced delivery and other sharing economy platforms attract freelance workers by offering them flexibility in scheduling their own work hours. Those platforms, however, have been criticized for the lack of protection they offer workers. Since workers are treated as independent contractors, they do not receive minimum wage and other protection measures under labor law. In this paper, we examine the integration of driver compensation guarantees in a platform's dynamic matching decisions. We study the problem of designing dynamic matching policies that guarantee a particular level of compensation for active workers over a time period, while maintaining work hour flexibility. We model three types of policies, that are either wage-based or utilization-based. We propose an MDP model to capture the dynamic and stochastic nature of the problem, then design a value function approximation algorithm to efficiently solve the large-scale MDP model. Extensive computational testing is conducted to assess the performance of the proposed solution methodology and the compensation guarantees, using synthetic and real-world datasets. Our findings suggest that the utilization policy results in the highest earning for drivers, though at the expense of longer empty miles from drivers' origins to the pickup locations of matched orders. On the other hand, the effective wage policy leads to shorter average distance to pickup, but slightly lower earning to drivers. Both policies result in only a slight decrease in platform profit as compared to the base case, and exhibit lower dispersion in the distribution of driver earning while active. In contrast, the nominal wage policy shows a comparable trend to the base-case policy in terms of average driver earnings, suggesting minimal benefits for drivers.