Uncovering Disparities in Rideshare Drivers’ Earning and Work Patterns: A Case Study of Chicago

Abstract

Ride-sharing services are reshaping urban mobility but raising concerns around fairness, safety, and driver working conditions. This mixed-methods study combines large-scale analysis of Chicago’s Trip Network Provider data (2018–2023) with qualitative interviews and reconstructed driver work patterns. Quantitative results reveal temporal and spatial disparities in driver earnings, while qualitative findings explain how drivers’ decision-making—centered on fare rates, pickup distance, and follow-up trip potential—interacts with platform algorithms to reinforce these inequities. We further uncover a growing power imbalance, with declining driver earnings despite rising rider costs, and safety risks tied to governance practices. Finally, we highlight critical data gaps that obscure driver experiences and constrain mobility research. Our findings call for transparency in platform governance and more worker-centered approaches to the design of ride-sharing systems.