Misallocation and Growth
研究企业和工人异质性下在职学习驱动增长的模型，发现更高效匹配会加速长期增长、加剧不平等并减少人力资本分布变动，且增长与不平等在向平衡增长路径过渡时同步变化。
This paper models growth via on-the-job learning when firms and workers are heterogeneous. It is an overlapping generations model in which young agents match with the old. More efficient assignments lead to faster long-run growth, more inequality, and less turnover in the distribution of human capital. Constant-growth paths are characterized for general functional forms and then, for the Cobb-Douglas case, the transition dynamics are solved analytically when the skill of the young is log-normally distributed and the initial human capital of the old generation is also log-normal. Growth and inequality move together on the transition to the balanced growth path.