Learning, Career Paths, and the Distribution of Wages
构建了一个理论模型，解释人们如何在生产层级中通过随机学习向上晋升，并利用美国1990-2010年数据验证了工资不平等加剧可由技术复杂性和盈利性相对于人口知识分布的变化来解释。
We develop a theory of career paths and earnings where agents organize in production hierarchies. Agents climb these hierarchies as they learn stochastically from others. Earnings grow as agents acquire knowledge and occupy positions with more subordinates. We contrast these and other implications with US census data for the period 1990 to 2010, matching the Lorenz curve of earnings and the observed mean experience-earnings profiles. We show the increase in wage inequality over this period can be rationalized with a shift in the level of the complexity and profitability of technologies relative to the distribution of knowledge in the population.