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Learning, Career Paths, and the Distribution of Wages

机译:学习,职业道路和工资分配

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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.
机译:我们建立了职业路径和收入的理论,代理商在生产层次中进行组织。代理从其他人那里随机学习时会爬上这些层次结构。随着代理商获取知识并在下属中占有更多职位,收入会增加。我们将这些和其他含义与1990年至2010年期间的美国人口普查数据进行对比,匹配收入的洛伦兹曲线和观察到的平均经验-收入曲线。我们表明,在这一时期内,随着技术的复杂性和获利能力相对于人口知识分布的变化,可以合理化工资差距的增加。

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