Technology, Vintage-Specific Human Capital, and Labor Displacement: Evidence from Linking Patents with Occupations

62 Pages Posted: 13 Dec 2021 Last revised: 31 Mar 2025

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

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

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Date Written: December 2021

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.

Suggested Citation

Kogan, Leonid and Papanikolaou, Dimitris and Schmidt, Lawrence and Seegmiller, Bryan, Technology, Vintage-Specific Human Capital, and Labor Displacement: Evidence from Linking Patents with Occupations (December 2021). NBER Working Paper No. w29552, Available at SSRN: https://ssrn.com/abstract=3983906

Leonid Kogan (Contact Author)

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

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Dimitris Papanikolaou

Kellogg School of Management - Department of Finance ( email )

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Lawrence Schmidt

MIT Sloan School of Management ( email )

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Bryan Seegmiller

Northwestern University - Kellogg School of Management ( email )

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