Stochastic wage suppression on gig platforms and how to organize against it

2026-04-17

AI Summary

This study addresses how digital gig platforms exploit labor surplus and workers’ uncertainty about task compensation to complete tasks at minimal cost through dynamic pricing. Introducing the notion of coverage targets into a stochastic bidding procurement model, this work integrates stochastic processes, game theory, and synthetic data experiments to demonstrate that even small, strategically coordinated worker coalitions can substantially enhance collective bargaining power. Theoretically, it is shown that without intervention, platforms can complete tasks in $O(M)$ time with total expenditure as low as $O(\log M / M)$; however, targeted collective action by workers elevates platform costs to $\Theta(M)$. Experimental results confirm that this mechanism consistently improves labor welfare across diverse market conditions.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• 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

Digital labor platforms are increasingly used to procure human input, ranging from annotating data and red-teaming AI models, to ride-sharing and food delivery. A central concern in such markets is the ability of platforms to suppress wages by exploiting the abundance of low-cost labor. To study this exploitation pattern, we introduce a novel posted-price procurement model with coverage objectives. A platform seeks to complete M tasks by posting prices to sequentially arriving workers, each of whom accepts a task if it exceeds their private cost. First, we show that under natural assumptions on the workers' estimated cost, there exists a simple pricing strategy for the platform to cover all M tasks with wait time O(M), while paying only a O(log(M)/M) fraction of the total cost of labor. This result highlights how platforms can exploit workers' uncertainty about the cost of labor to effectively suppress wages. Then, we study collective action as a lever to increase wages and promote welfare in digital labor markets. In particular, we show how a small coalition of targeted low-cost workers who commit to a price floor forces the platform's total spending from logarithmic to linear in M. In contrast, a randomly sampled coalition of equal size remains largely ineffective. We complement our theory with synthetic experiments, showcasing the benefits of collective action across different market regimes.

Problem

• wage suppression
• gig platforms
• digital labor markets
• collective action
• posted-price procurement

Innovation

• posted-price procurement
• wage suppression
• collective action
• digital labor platforms
• coverage objectives