Worker Welfare Under Algorithmic Wage Discrimination

• In Event: Ensuring Access to Quality Jobs: Reducing Employment Gaps and Improving Supports

Abstract

This paper examines the welfare consequences of algorithmic wage discrimination in the gig economy and its subsequent policy implications. Algorithmic wage discrimination is defined as the practice of paying workers different hourly wages for similar work, based on their individual preferences. Advances in technology and artificial intelligence have enabled firms to utilize granular behavioral data to automate decisions related to hiring, termination, and compensation, often in ways that remain opaque to workers. Leveraging novel data on Uber drivers, this study provides the first empirical evidence of algorithmic wage discrimination and analyzes the digitized pay policy in the gig economy. To further analyze its impact, the paper develops a game-theoretic model of dynamic learning by firms, demonstrating the consequences of this practice on worker welfare. The findings highlight key policy interventions that could mitigate these challenges and improve outcomes for gig workers.

Author

• JYOTSANA KALA, University of California, Irvine
  Presenting Author