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.
