AI increases wage gaps but targeted education can reduce inequality
Is AI Widening the Wage Gap? A Hybrid Agentic Simulation for Labor Equity
Computers and Society
Summary
The paper explores how AI affects wages and income equality. The authors find that AI tends to increase the gap between high and low earners. They use computer simulations with Chinese data to study this over time. The study also shows that education subsidies aimed at low-income workers can help reduce this wage gap by helping those workers improve their skills. This approach offers a way to understand how AI changes wages and helps test policies to make wages fairer.
What this means in practice
- •For labor economists: Use the hybrid agentic simulation to analyze how AI-driven labor market changes affect wage inequality over time.
- •For policy analysts: Evaluate targeted education subsidy policies to mitigate wage inequality in AI-impacted labor markets using the presented simulation framework.
Authors
Zhongbo Hu, Zonghang Wu, Georgina Curto, Aocheng Tang, Yilei Shao
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.