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

99 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

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 exposure to labor-saving technologies negatively affects the earnings of exposed workers. This negative effect is pervasive across both blue-and white-collar workers and across workers of different ages or earnings relative to their peers. In contrast, labor-augmenting technologies have a heterogeneous impact on exposed workers. While the wage bill paid to affected groups rises, this increase is driven primarily by an increase in employment, while earnings rise for new entrants but decline for incumbent workers. This decline is primarily present among white-collar, older, and higher-paid workers, highlighting the importance of vintage-specific human capital. Last, we find positive spillovers of both types of innovation at the industry level, benefiting other workers in the same industry who are not directly exposed to these innovations.

Keywords: technological innovation, labor displacement

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 )

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Dimitris Papanikolaou (Contact Author)

Kellogg School of Management - Department of Finance ( email )

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National Bureau of Economic Research (NBER) ( 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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United States

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