Stochastic wage suppression on gig platforms and how to organize against it
- URL: http://arxiv.org/abs/2604.15962v1
- Date: Fri, 17 Apr 2026 11:28:38 GMT
- Title: Stochastic wage suppression on gig platforms and how to organize against it
- Abstract summary: We study the ability of platforms to suppress wages by exploiting the abundance of low-cost labor.<n>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.<n>We show how a randomly sampled coalition of equal size remains largely ineffective.
- Score: 31.2768807087302
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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.
Related papers
- Human Capital, AI, and Labor Commoditization [0.0]
We find that in more AI-exposed job categories, the importance of human capital declines and the importance of price rises, suggesting a commoditization effect of AI on labor.<n>Our results have implications for the design of online labor markets, workers' incentives to invest in human capital, and labor welfare.
arXiv Detail & Related papers (2026-06-20T04:42:33Z) - Vesta: A Generalist Embodied Reasoning Model [149.6473783208929]
We present Vesta, a unified embodied generalist that consolidates capabilities into a single foundation model.<n>Our approach combines a diverse and massive curated corpus designed to induce spatial grounding and a simple multimodal memory harness.<n>Our work thus demonstrates that a single generalist is a feasible, scalable, and arguably preferable alternative to combining specialists.
arXiv Detail & Related papers (2026-06-18T20:01:32Z) - Escaping the Diversity Trap in Robotic Manipulation via Anchor-Centric Adaptation [50.23374353859762]
"Maximizing coverage" by collecting diverse, single-shot demonstrations can be self-defeating due to non-vanishing estimation noise.<n>We formalize this phenomenon as a Coverage--Density Trade-off.<n>We propose Anchor-Centric Adaptation (ACA), a two-stage framework that first stabilizes a policy skeleton through repeated demonstrations at core anchors, then expands coverage to high-risk boundaries via teacher-forced error mining and constrained residual updates.
arXiv Detail & Related papers (2026-05-08T07:35:24Z) - Who Prices Cognitive Labor in the Age of Agents? Compute-Anchored Wages [2.629665653283008]
A natural intuition about the economics of AI agents is that, because agents can be replicated at very low marginal cost, agent labor may be supplied highly elastically.<n>We argue this framing is wrong in mechanism but partially correct in conclusion, and that the correction matters for both theory and policy.
arXiv Detail & Related papers (2026-05-07T01:03:29Z) - Can Online GenAI Discussion Serve as Bellwether for Labor Market Shifts? [62.386835769570006]
This paper examines whether online discussions about Large Language Models can function as early indicators of labor market shifts.<n>We employ four distinct analytical approaches to identify the domains and timeframes in which public discourse serves as a leading signal for employment changes.<n>Our findings reveal that discussion intensity predicts employment changes 1-7 months in advance across multiple indicators, including job postings, net hiring rates, tenure patterns, and unemployment duration.
arXiv Detail & Related papers (2025-11-20T04:18:25Z) - Estimating Wage Disparities Using Foundation Models [20.740346109417143]
We develop methods for fine-tuning foundation models to perform estimation problems.<n>To demonstrate our ideas, we study gender wage decomposition.<n>We use a custom-built foundation model to decompose the gender wage gap.
arXiv Detail & Related papers (2024-09-15T23:22:21Z) - Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning [54.40927310957792]
We introduce a novel concept of personalized expert demonstrations, tailored for each individual agent or, more broadly, each individual type of agent within a heterogeneous team.<n>These demonstrations solely pertain to single-agent behaviors and how each agent can achieve personal goals without encompassing any cooperative elements.<n>We propose an approach that selectively utilizes personalized expert demonstrations as guidance and allows agents to learn to cooperate.
arXiv Detail & Related papers (2024-03-13T20:11:20Z) - Beyond Labeling Oracles: What does it mean to steal ML models? [52.63413852460003]
Model extraction attacks are designed to steal trained models with only query access.
We investigate factors influencing the success of model extraction attacks.
Our findings urge the community to redefine the adversarial goals of ME attacks.
arXiv Detail & Related papers (2023-10-03T11:10:21Z) - Incentivizing Massive Unknown Workers for Budget-Limited Crowdsensing:
From Off-Line and On-Line Perspectives [31.24314338983544]
We propose an off-line Context-Aware CMAB-based Incentive (CACI) mechanism.
We also extend the idea to the on-line setting where unknown workers may join in or depart from the systems.
arXiv Detail & Related papers (2023-09-21T14:30:42Z) - Fairness for Workers Who Pull the Arms: An Index Based Policy for
Allocation of Restless Bandit Tasks [30.323831598899773]
We consider a novel RMAB setting, called multi-worker restless bandits (MWRMAB) with heterogeneous workers.
The goal is to plan an intervention schedule that maximizes the expected reward while satisfying budget constraints on each worker.
Our contributions are two-fold: (1) we provide a multi-worker extension of the Whittle index to tackle heterogeneous costs and per-worker budget and (2) we develop an index-based scheduling policy to achieve fairness.
arXiv Detail & Related papers (2023-03-01T19:59:42Z) - Online Auction-Based Incentive Mechanism Design for Horizontal Federated
Learning with Budget Constraint [9.503584357135832]
Federated learning makes it possible for all parties with data isolation to train the model collaboratively and efficiently.
To obtain a high-quality model, an incentive mechanism is necessary to motivate more high-quality workers with data and computing power.
We propose a reverse auction-based online incentive mechanism for horizontal federated learning with budget constraint.
arXiv Detail & Related papers (2022-01-22T13:37:52Z) - Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal
Federated Learning with Reputation and Contribution Measurement [9.503584357135832]
Federated learning trains models across devices with distributed data, while protecting the privacy and obtaining a model similar to that of centralized ML.
We design an auction-based incentive mechanism for horizontal federated learning with reputation and contribution measurement.
arXiv Detail & Related papers (2022-01-07T11:44:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.