Tasks, Automation, and the Rise in US Wage Inequality | 2021
Joint with Daron Acemoglu
Econometrica
We document that between 50% and 70% of changes in the US wage structure
over the last four decades are accounted for by the relative wage declines
of worker groups specialized in routine tasks in industries experiencing
rapid automation. We develop a conceptual framework where tasks across a
number of 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 employment opportunities 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 differentials between 1980 and 2016. Our
task displacement variable captures the effects of automation technologies
(and to a lesser degree offshoring) rather than those of rising market
power, markups or deunionization, which themselves do not appear to play a
major role in US wage inequality. We also propose a methodology for
evaluating the full general equilibrium effects of task displacement (which
include induced changes in industry composition and ripple effects as tasks are reallocated across different groups). Our quantitative
evaluation based on this methodology explains how major changes in wage
inequality can go hand-in-hand with modest productivity gains.