Stochastic wage suppression on gig platforms and how to organize against it
Ana-Andreea Stoica, Celestine Mendler-Duenner, Moritz Hardt

TL;DR
This paper models how digital labor platforms suppress wages through strategic pricing and demonstrates that collective action by workers can effectively counteract this exploitation, improving wages and welfare.
Contribution
It introduces a novel procurement model revealing wage suppression tactics and analyzes how targeted worker coalitions can increase wages in digital labor markets.
Findings
Platforms can exploit wage uncertainty to suppress wages effectively.
Targeted coalitions of low-cost workers can significantly increase platform spending.
Random coalitions are largely ineffective in raising wages.
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…
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