# Ride-share matching algorithms generate income inequality

## TL;DR

This paper investigates how ride-share matching algorithms influence income inequality among drivers, revealing that small system changes can cause large disparities and long-term wage gaps.

## Contribution

It introduces a computational model combining complex systems and fairness to analyze the impact of algorithmic decisions on driver income distribution.

## Key findings

- Small parameter changes lead to large income disparities
- Income differences can become long-term and self-reinforcing
- System behavior is highly unpredictable and sensitive to initial conditions

## Abstract

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.