Algorithmic exploitation in the gig economy: A living wage analysis of ride-hailing platforms in Lagos and Accra

Abstract

This research elucidates the systemic mechanisms of algorithmic exploitation within the ride-hailing economies of Africa, contesting the prevalent notion that digital labor platforms enhance worker empowerment in Lagos and Accra. Through a mixed-methods approach that includes the analysis of 15,000 ride records, 87 in-depth driver interviews, and localized cost structures, this study illustrates how platform algorithms contribute to wage reduction, shift operational risks, and perpetuate inequality under the guise of technological advancement. The findings indicate that drivers earn 34% less than living wage benchmarks, despite working 14-hour days. Additionally, they incur hidden costs, including $1,200 per year in vehicle depreciation and 14% of their earnings on mobile data—expenses that platforms intentionally shift to workers. An original Algorithmic Exploitation Index (AEI) was developed to measure these dynamics, indicating that an artificial oversupply of labor reduces fares by 7.4% for every 10% increase in drivers, while embedded biases result in a 31% disadvantage for female drivers in ride allocation. This research integrates labor economics and critical platform studies to propose a framework for analyzing digital exploitation in the Global South, characterized by significant informality and insufficient regulation that facilitate corporate extraction. We propose policy measures, including AEI transparency mandates, cost-adjusted fare floors, and digital collective bargaining to equilibrate power in platform labor markets. This study extends beyond ride-hailing to explore intersectional vulnerabilities in gig work, focusing on algorithmic discrimination and the long-term health impacts of precarious platform employment. The future of equitable employment in a progressively digital urban economy depends on aligning technological advancements with essential labor rights.