AI reduces skill gaps but increases inequality by concentrating valuable assets.
When AI Levels the Playing Field: Skill Homogenization, Asset Concentration, and Two Regimes of Inequality
- 构建任务型模型，分析教育、筛选与企业异质性对不平等的影响。
- 发现两种不平等状态：技术结构与制度决定边界，资产集中是关键变量。
- 提出可检验的预测，需细粒度面板数据验证，现有数据无法测试。
生成式AI压缩了同一任务内的技能差异，同时将经济价值转向集中的互补性资产，形成一种表观悖论：虽使个体表现趋于均等，却可能加剧总体不平等。本文在内生教育、雇主筛选和异质企业框架下，建立任务型模型，揭示两种不平等状态的分界条件，其取决于AI技术结构（专有型与通用型）及劳动力市场制度（租金共享弹性、资产集中度）。通过模拟矩法匹配六个实证目标，校准模型量级；敏感性分解表明，五个非ΔGini矩识别机制速率，但总体符号由m₆和ξ决定，而AI技术结构（η₁与η₀）独立穿越分界线。核心贡献在于机制揭示而非结论定性。基于美国劳工统计局职业就业与工资统计（BLS OEWS，2019–2023）的岗位层面回归显示，此类数据无法检验模型的任务层级预测。真正可检验的预测需要尚未大规模存在的同岗位、同任务纵向数据。
原文摘要 · Abstract (English)
Generative AI compresses within-task skill differences while shifting economic value toward concentrated complementary assets, creating an apparent paradox: the technology that equalizes individual performance may widen aggregate inequality. We formalize this tension in a task-based model with endogenous education, employer screening, and heterogeneous firms. The model yields two regimes whose boundary depends on AI's technology structure (proprietary vs. commodity) and labor market institutions (rent-sharing elasticity, asset concentration). A scenario analysis via Method of Simulated Moments, matching six empirical targets, disciplines the model's quantitative magnitudes; a sensitivity decomposition reveals that the five non-$Δ$Gini moments identify mechanism rates but not the aggregate sign, which at the calibrated parameters is pinned by $m_6$ and $ξ$, while AI's technology structure ($η_1$ vs. $η_0$) independently crosses the boundary. The contribution is the mechanism -- not a verdict on the sign. Occupation-level regressions using BLS OEWS data (2019--2023) illustrate why such data cannot test the model's task-level predictions. The predictions are testable with within-occupation, within-task panel data that do not yet exist at scale.
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