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

TL;DR

This study analyzes disparities in ride-sharing driver earnings and work patterns in Chicago from 2018 to 2023, revealing temporal and spatial inequalities and proposing a new algorithm to understand driver behaviors.

Contribution

It introduces a novel trip-driver assignment algorithm to reconstruct driver work patterns and highlights significant spatial and temporal earning disparities.

Findings

Drivers in central and airport areas earn more than peripheral drivers.

Late-night drivers earn higher per-trip and hourly rates.

Emerging low-demand region drivers face earnings deficits.

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.