Technology and Labor Displacement: Evidence from Linking Patents with Worker-Level Data

95 Pages Posted: 6 May 2020 Last revised: 27 Nov 2023

See all articles by Leonid Kogan

Leonid Kogan

Massachusetts Institute of Technology (MIT) - Sloan School of Management; National Bureau of Economic Research (NBER)

Dimitris Papanikolaou

Northwestern University - Kellogg School of Management - Department of Finance; National Bureau of Economic Research (NBER)

Lawrence Schmidt

MIT Sloan School of Management

Bryan Seegmiller

Northwestern University - Kellogg School of Management

Multiple version iconThere are 2 versions of this paper

Date Written: November 1, 2023

Abstract

We develop measures of labor-saving and labor-augmenting technology exposure using textual analysis of patents and job tasks. Using US administrative data, we show that both measures negatively predict earnings growth of individual incumbent workers. While labor-saving technologies predict significant earnings declines and higher likelihood of job loss for all workers, labor-augmenting technologies primarily predict losses for older or highly-paid workers. However, we find a positive effect of labor-augmenting technologies on total worker compensation and employment. A model featuring automation and vintage-specific human capital quantitatively matches these patterns. We extend our analysis to predict the effect of AI on wages.

Suggested Citation

Kogan, Leonid and Papanikolaou, Dimitris and Schmidt, Lawrence and Seegmiller, Bryan, Technology and Labor Displacement: Evidence from Linking Patents with Worker-Level Data (November 1, 2023). Available at SSRN: https://ssrn.com/abstract=3585676 or http://dx.doi.org/10.2139/ssrn.3585676

Leonid Kogan

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

100 Main Street
E62-636
Cambridge, MA 02142
United States
617-253-2289 (Phone)
617-258-6855 (Fax)

HOME PAGE: http://web.mit.edu/lkogan2/www/

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Dimitris Papanikolaou (Contact Author)

Northwestern University - Kellogg School of Management - Department of Finance ( email )

Evanston, IL 60208
United States

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Lawrence Schmidt

MIT Sloan School of Management ( email )

77 Massachusetts Avenue
Cambridge, MA 02139-4307
United States

HOME PAGE: http://https://sites.google.com/site/lawrencedwschmidt/home

Bryan Seegmiller

Northwestern University - Kellogg School of Management ( email )

2001 Sheridan Road
Evanston, IL 60208
United States

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
800
Abstract Views
3,249
Rank
55,239
PlumX Metrics