Searcharxiv⌕ Search

arXiv · 2609.33367

Is AI Widening the Wage Gap? A Hybrid Agentic Simulation for Labor Equity

Abstract

Artificial intelligence (AI) is reshaping labor markets, yet its effects on wage distribution and the underlying mechanisms remain insufficiently understood. Conventional analytical approaches are limited in their ability to directly examine the dynamic evolution of worker behavior and income distribution under sustained AI shocks and counterfactual policy scenarios. To address this limitation, we present a hybrid agentic framework that aims to challenges of scalability of rule-based models and the limited explainability in LLM-agentic frameworks. Using this framework and sociodemographic data from China, we simulate changes in wage distribution under repeated AI shocks. The results show that both the average-wage ratio between workers in the top and bottom income deciles (T10/B10) and the Gini coefficient increase persistently, suggesting that AI shocks widen the wage gap and exacerbate income inequality. This pattern of a widening wage gap remains robust across alternative large language model decision engines and 30 Monte Carlo simulations. We further conduct counterfactual policy experiments. The results show that education subsidies targeted at low-income workers increase both the number of skill-upgrading attempts and the number of successful upgrades, with particularly pronounced improvements in the upskilling success probability of workers in the bottom income decile. These effects enable the policy to partially mitigate wage inequality. The proposed framework provides an interpretable simulation approach for examining the effects and mechanisms of AI shocks on wage distribution. It also offers policymakers a complementary analytical tool for evaluating policy interventions.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zhongbo Hu, Zonghang Wu, Georgina Curto, Aocheng Tang, Yilei Shao. 2026-09-27. Is AI Widening the Wage Gap? A Hybrid Agentic Simulation for Labor Equity. https://arxiv.org/abs/2609.33367

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Meme Template Identification in the Wild: Comparing Methods for Semi-Open-Set Recognition

Image-with-text memes are a dominant form of online communication, and much of their spread happens through meme templates which are recurring visual formats that users adapt with new text or imagery. Most prior work scores memes individually for engagement or harmful content, an approach that is structurally blind to templates. Templates might amplify these patterns and enable coordinated harassment, which becomes visible only when memes are grouped by shared template. We formalize meme template identification as a semi-open-set recognition problem, requiring methods to both classify known templates and reject template-free memes and non-meme content. We introduce an evaluation framework spanning a controlled setting (1,704 ImgFlip templates) and a heterogeneous real-world social media sample, comparing supervised CNN- and distance-based methods, unsupervised density-based clustering, a novel fused SigLIP2+DINOv2 representation, and a retrieval-augmented LLM pipeline. In real-world social media sources dedicated to meme sharing, we find that only 21% of images use a known template, while 58% are template-free and 21% are not memes at all; identification performance also drops sharply from the controlled setting (best MCC 0.974 to 0.684). The loss comes mainly from deciding whether an image is a template-based meme at all, rather than identifying the template. Our dataset of 1.1M soft-labeled social media images, together with code for reproducibility, is available upon request.

cs.CY↗

(Mis-)Informed Consent: Predatory Apps and the Exploitation of Populations with Limited Literacy

Among populations with limited literacy in emerging digital markets, the adoption of mobile phones, combined with comprehension barriers and poor cybersecurity hygiene, has created hidden privacy risks. This paper examines how informed consent is often abused by predatory financial applications, leading to financial scams that disproportionately affect users with low literacy. We focus on predatory loan, gambling, and trading apps, analyzing a dataset of 50 Google Play Store apps to measure how many omit or obfuscate critical privacy disclosures. We also evaluate comprehension gaps among users with low literacy via a targeted user study and assess whether Large Language Model (LLM)-generated summaries, translations, and visual cues can improve consent clarity. Our findings show that 85% of study participants did not understand basic app permissions, underscoring the urgent need for stronger regulatory oversight and scalable LLM-driven privacy-literacy tools.

cs.CY↗

Validated Behavioral Hypotheses as a Lens for Evaluating Participant Simulation

We propose using validated behavioral hypotheses as a lens for evaluating LLM agents as simulated human participants. This approach makes behavioral agreement in participant simulation measurable and decomposable, revealing which human effects agents reproduce, where they diverge, and how agent design changes that agreement. To operationalize this idea, we build HumanStudy-Bench, an open benchmarking platform that reconstructs experimental protocols from published human studies and administers these protocols to agents serving as silicon participants. The benchmark compares population-level effects derived from agent responses with the corresponding published human findings using two metrics: the Probability Alignment Score (PAS) for inferential agreement and the Effect Consistency Score (ECS) for effect-magnitude agreement. We apply HumanStudy-Bench across 12 human studies, evaluating 10 models under four agent designs, with 6,588 simulated participants per agent configuration. By making published human studies and their validated behavioral hypotheses reusable for evaluating simulated participants, we hope to make the behavioral assumptions underlying LLM-based social simulations and their sensitivity to agent design explicit and empirically testable.

cs.CY↗