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Is AI Widening the Wage Gap? A Hybrid Agentic Simulation for Labor Equity — arXiv:2609.33367

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.33367
License (as archived): CC0-1.0
Archived: 2026-10-07T22:13:37.948Z
SHA-256 of the archived text: 633d70ffa6b0be95ec94690392f4e868dacbb64843e6d4f537bb1f4340102fa1
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How to cite

Is AI Widening the Wage Gap? A Hybrid Agentic Simulation for Labor Equity — arXiv:2609.33367 — arXiv (en), https://arxiv.org/abs/2609.33367 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bd2ede98d556 (Metraipolis archive dc_bd2ede98d556, sha256 633d70ffa6b0be95ec94690392f4e868dacbb64843e6d4f537bb1f4340102fa1)

BibTeX

@misc{dcbd2ede98d556,
  title = {Is AI Widening the Wage Gap? A Hybrid Agentic Simulation for Labor Equity — arXiv:2609.33367},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.33367},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bd2ede98d556; sha256 633d70ffa6b0be95ec94690392f4e868dacbb64843e6d4f537bb1f4340102fa1},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.33367. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: governance digitale e beni comuni (retro-catalogo arXiv: annunci del 2026-09-29 in cs.CY).
Categorie arXiv: Computers and Society (cs.CY).
Pagina del lavoro: https://arxiv.org/abs/2609.33367 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T21:55:04.227Z dalla pagina https://arxiv.org/catchup/cs.CY/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).