发表机构
Shanghai AI-Finance School, East China Normal University; United Nations University Institute in Macau（上海人工智能金融学院，华东师范大学; 联合国大学澳门研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出混合智能体框架，基于中国数据模拟AI冲击，发现其持续扩大工资差距，而针对低收入者的教育补贴可部分缓解不平等。
AI 中文摘要
人工智能（AI）正在重塑劳动力市场，但其对工资分配的影响及潜在机制仍未被充分理解。传统分析方法在直接考察持续AI冲击和反事实政策情景下工人行为与收入分配的动态演变方面能力有限。为解决这一局限，我们提出了一种混合智能体框架，旨在应对基于规则模型的可扩展性挑战以及LLM智能体框架的可解释性有限问题。利用该框架及中国的社会人口统计数据，我们模拟了在反复AI冲击下工资分配的变化。结果显示，收入最高十分位与最低十分位工人之间的平均工资比（T10/B10）以及基尼系数均持续上升，表明AI冲击扩大了工资差距并加剧了收入不平等。这一工资差距扩大的模式在替代性大语言模型决策引擎和30次蒙特卡洛模拟中均保持稳健。我们进一步进行了反事实政策实验。结果表明，针对低收入工人的教育补贴既增加了技能升级尝试的次数，也增加了成功升级的次数，其中对最低收入十分位工人技能升级成功概率的改善尤为显著。这些效应使该政策能够部分缓解工资不平等。所提出的框架为考察AI冲击对工资分配的影响及机制提供了一种可解释的模拟方法，也为政策制定者评估政策干预提供了补充性分析工具。
英文摘要
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.
Comments12 pages,
