Uncovering Disparities in Rideshare Drivers Earning and Work Patterns: A Case Study of Chicago
Hy Dang, Yuwen Lu, Jason Spicer, Tamara Kay, Di Yang, Yang Yang, Jay Brockman, Meng Jiang, Toby Jia-Jun Li
TL;DR
Uncovering Disparities in Rideshare Drivers Earning and Work Patterns uses Chicago's public Trip Network Provider data (2018–2023) to analyze temporal and spatial inequities in earnings. It introduces a trip-based driver assignment algorithm to infer plausible driver work patterns from anonymized trips and applies clustering to reveal distinct driver groups. The study finds a nominal earnings surge in early 2021 followed by fluctuations and a decline in real earnings, with Central and Airport regions commanding higher trip costs than peripheral areas and new low-income groups emerging in 2023. These results highlight the need for transparent pricing and algorithmic accountability to promote equitable outcomes in urban ridesourcing.
Abstract
Ride-sharing services are revolutionizing urban mobility while simultaneously raising significant concerns regarding fairness and driver equity. This study employs Chicago Trip Network Provider dataset to investigate disparities in ride-sharing earnings between 2018 and 2023. Our analysis reveals marked temporal shifts, including an earnings surge in early 2021 followed by fluctuations and a decline in inflation-adjusted income, as well as pronounced spatial disparities, with drivers in Central and airport regions earning substantially more than those in peripheral areas. Recognizing the limitations of trip-level data, we introduce a novel trip-driver assignment algorithm to reconstruct plausible daily work patterns, uncovering distinct driver clusters with varied earning profiles. Notably, drivers operating during late-evening and overnight hours secure higher per-trip and hourly rates, while emerging groups in low-demand regions face significant earnings deficits. Our findings call for more transparent pricing models and a re-examination of platform design to promote equitable driver outcomes.
