Threshold-based incentives for ride-sourcing drivers: Implications on supply management and welfare effects

Ride-sourcing companies have been widely using threshold-based incentive programs to encour-age drivers to extend their work hours. In such programs, a driver receives a certain amount of monetary reward if she completes a given supply task within a predetermined time window. However, despite the popularity of these incentives, little is known about how drivers respond to them in practice, and currently, there are no means to comprehensively evaluate and optimize their designs. To fill the void, we develop a dynamic discrete choice model that formulates ride -sourcing drivers' working decisions influenced by threshold-based incentives and then calibrate it using real-world data from a ride-sourcing company. Our results provide fresh insights into the market and welfare effects of the threshold-based incentive and its various designs. It is found that the threshold-based incentive could increase welfare significantly for full-time drivers but marginally for part-time drivers. In contrast, involving part-time drivers in the incentive programs generally can yield higher profits for the platform, while incentivizing full-time drivers is mostly unprofitable. On incentive design, the incentive threshold and reward must be closely paired for different driver groups to avoid inferior consequences for the platform and drivers. In addition, switching from threshold-based incentives to direct wage increments may benefit both full-time drivers and the ride-sourcing company.

Automated summary

This study develops a dynamic discrete choice model to examine the influence of threshold-based incentive programs on ride-sourcing drivers and calibrates the model using real-world data. The findings reveal the effects of threshold-based incentives on market and welfare and the different performances for different driver groups.