Tasks, Automation, and the Rise in U.S. Wage Inequality
中文摘要
我们记录了在过去四十年中，美国工资结构变动的50%至70%可由在经历快速自动化的行业中专业化于常规任务的工人群体的相对工资下降所解释。我们发展了一个概念框架，其中跨行业的任务被分配给不同类型的劳动和资本。自动化技术扩展了由资本执行的任务集，将某些工人群体从其具有比较优势的工作中替代出去。该框架产生了一个简单方程，将一个人口群体的工资变动与其所经历的任务替代联系起来。我们报告了支持这一关系的稳健证据，并表明纳入任务替代的回归模型解释了1980年至2016年间教育工资差异的很大一部分变动。当我们控制市场势力变动、去工会化以及其他与自动化无关的资本深化和技术形式时，工资变动与任务替代之间的负向关系不受影响。我们还提出了一种评估自动化全面一般均衡效应的方法，该方法纳入了行业构成的引致变动以及跨不同群体任务再分配的连锁效应。我们的定量评估解释了工资不平等的重大变动如何能与适度的生产率增长相伴而生。
Abstract
We document that between 50% and 70% of changes in the U.S. wage structure over the last four decades are accounted for by relative wage declines of worker groups specialized in routine tasks in industries experiencing rapid automation. We develop a conceptual framework where tasks across industries are allocated to different types of labor and capital. Automation technologies expand the set of tasks performed by capital, displacing certain worker groups from jobs for which they have comparative advantage. This framework yields a simple equation linking wage changes of a demographic group to the task displacement it experiences. We report robust evidence in favor of this relationship and show that regression models incorporating task displacement explain much of the changes in education wage differentials between 1980 and 2016. The negative relationship between wage changes and task displacement is unaffected when we control for changes in market power, deunionization, and other forms of capital deepening and technology unrelated to automation. We also propose a methodology for evaluating the full general equilibrium effects of automation, which incorporate induced changes in industry composition and ripple effects due to task reallocation across different groups. Our quantitative evaluation explains how major changes in wage inequality can go hand‐in‐hand with modest productivity gains.