Assortative Learning
研究了技能工人因企业类型不同而生产率不同，且学习速率差异影响工资路径，发现严格超模性下总是存在正向分类匹配，即使低类型企业学习更快也成立。
Because of sorting, more skilled workers are more productive in higher‐type firms. They also learn at different rates about their productivity and therefore expect different wage paths across firms. We show that under strict supermodularity, there is always positive assortative matching: differential learning is always dominated by the impact of productivity. Surprisingly, this holds even if learning is faster in the low‐type firm. The key assumption driving this result is that this is a pure Bayesian learning model. The model provides realistic predictions about wage variance, turnover and the wage distribution that are in line with recent work that estimates the value of learning from co‐workers.