Research Details

Abstract

We develop a measure of workers' technology exposure that relies only on textual descriptions of patent documents and the tasks performed by workers in an occupation. Our measure appears to identify a combination of labor-saving innovations but also technologies that may require skills that incumbent workers lack. Using a panel of administrative data, we examine how subsequent worker earnings relate to workers' technology exposure. We find that workers at both the bottom but also the top of the earnings distribution are displaced. Our interpretation is that low-paid workers are displaced as their tasks are automated while the highest-paid workers face lower earnings growth as some of their skills become obsolete. Our calibrated model fits these facts and emphasizes the importance of movements in skill quantities, not just skill prices, for the link between technology and inequality.

Type

Working Paper

Author(s)

Bryan Seegmiller, Leonid Kogan, Dimitris Papanikolaou, Lawrence Schmidt

Date Published

2023

Citations

Seegmiller, Bryan, Leonid Kogan, Dimitris Papanikolaou, and Lawrence Schmidt. 2023. Technology and Labor Displacement: Evidence from Linking Patents with Worker-Level Data.