Understanding Inequalities in Ride-Hailing Services Through Simulations
Abstract
Despite the potential of ride-hailing services to democratize the labor market, they are often accused of fostering unfair working conditions and low wages. This paper investigates the effect of algorithm design decisions on wage inequality in ride-hailing platforms. We create a simplified city environment where taxis serve passengers to emulate a working week in a worker’s life. Our simulation approach overcomes the difficulties stemming from both the complexity of transportation systems and the lack of data and algorithmic transparency. We calibrate the model based on empirical data, including conditions about locations of drivers and passengers, traffic, the layout of the city, and the algorithm that matches requests with drivers. Our results show that small changes in the system parameters can cause large deviations in the income distributions of drivers, leading to an unpredictable system that often distributes vastly different incomes to identically performing drivers. As suggested by recent studies about feedback loops in algorithmic systems, these short-term income differences may result in enforced and long-term wage gaps.
