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

2025-02-13

AI Summary

This study investigates spatiotemporal inequities in earnings and work patterns among ride-hailing drivers using Chicago trip-level data (2018–2023). To reconstruct individual driver work trajectories, we propose a novel trip-to-driver daily assignment algorithm. Integrating inflation-adjusted earnings, spatiotemporal clustering, and counterfactual modeling, we identify four distinct driver cohorts. Results reveal: (1) real hourly wages exhibit a statistically significant downward trend; (2) nighttime drivers earn 37% more per hour than the citywide average; (3) drivers operating primarily in peripheral neighborhoods earn 42% less annually than those in the central business district and face structural income deficits; and (4) airports and the downtown core consistently command wage premiums. These findings provide empirical grounding and methodological support for platform policy interventions—including pricing transparency, time-differentiated subsidies, and spatially targeted equity measures—to mitigate labor market disparities in platform-based transportation.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Crowd Sourcing and Human Computation
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Analyzes ride-sharing earnings disparities in Chicago
• Identifies spatial and temporal income variations among drivers
• Proposes new algorithm to reconstruct driver work patterns

Innovation

• Novel trip-driver assignment algorithm
• Reconstructs daily work patterns
• Identifies distinct driver earnings clusters