  Despite the potential of online sharing economy platforms such as Uber, Lyft,
or Foodora to democratize the labor market, these services are often accused of
fostering unfair working conditions and low wages. These problems have been
recognized by researchers and regulators but the size and complexity of these
socio-technical systems, combined with the lack of transparency about
algorithmic practices, makes it difficult to understand system dynamics and
large-scale behavior. This paper combines approaches from complex systems and
algorithmic fairness to investigate the effect of algorithm design decisions on
wage inequality in ride-hailing markets. We first present a computational model
that includes conditions about locations of drivers and passengers, traffic,
the layout of the city, and the algorithm that matches requests with drivers.
We calibrate the model with parameters derived from empirical data. Our
simulations show that small changes in the system parameters can cause large
deviations in the income distributions of drivers, leading to a highly
unpredictable system which often distributes vastly different incomes to
identically performing drivers. As suggested by recent studies about feedback
loops in algorithmic systems, these initial income differences can result in
enforced and long-term wage gaps.
