Algorithmic wage setting on online labor platforms

Abstract

Online digital labor platforms are increasingly important as tools for matching employers and workers. They reduce labor market frictions and provide the opportunity for remote work, but concerns have been raised about whether workers receive adequate incomes. We study how the use of machine-learning based algorithms for the determination of wage offers affects workers’ wages on online labor platforms by setting up a simulation framework and analyzing outcomes under different assumptions about platform and algorithm design. Firms use reinforcement-learning to update posted wages on the platform, and heterogeneous workers send applications based on the posted information. We show that, if firms use a deep Q-network (DQN), a state-of-the-art machine learning algorithm, collusive wages emerge when the number of competitors is small, and no experience replay is used. Our findings are robust across many features of the model, including the design of the online labor platform.

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DOI

10.1016/j.labeco.2026.102945

Publisher page

https://www.sciencedirect.com/science/article/pii/S0927537126000977?via%3Dihub

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