New Digital Technologies and Heterogeneous Employment and Wage Dynamics in the United States: Evidence from Individual-Level Data

49 Pages Posted: 21 May 2019

See all articles by Frank M. Fossen

Frank M. Fossen

University of Nevada, Reno; IZA Institute of Labor Economics

Alina Sorgner

University of Jena

Abstract

We investigate heterogeneous effects of new digital technologies on the individual-level employment- and wage dynamics in the U.S. labor market in the period from 2011-2018. We employ three measures that reflect different aspects of impacts of new digital technologies on occupations. The first measure, as developed by Frey and Osborne (2017), assesses the computerization risk of occupations, the second measure, developed by Felten et al. (2018), provides an estimate of recent advances in artificial intelligence (AI), and the third measure assesses the suitability of occupations for machine learning (Brynjolfsson et al., 2018), which is a subfield of AI. Our empirical analysis is based on large representative panel data, the matched monthly Current Population Survey (CPS) and its Annual Social and Economic Supplement (ASEC). The results suggest that the effects of new digital technologies on employment stability and wage growth are already observable at the individual level. High computerization risk is associated with a high likelihood of switching one's occupation or becoming non-employed, as well as a decrease in wage growth. However, advances in AI are likely to improve an individual's job stability and wage growth. We further document that the effects are heterogeneous. In particular, individuals with high levels of formal education and older workers are most affected by new digital technologies.

Keywords: digitalization, artificial intelligence, machine learning, employment stability, unemployment, wage dynamics

JEL Classification: J22, J23, O33

Suggested Citation

Fossen, Frank M. and Sorgner, Alina, New Digital Technologies and Heterogeneous Employment and Wage Dynamics in the United States: Evidence from Individual-Level Data. IZA Discussion Paper No. 12242, Available at SSRN: https://ssrn.com/abstract=3390231

Frank M. Fossen (Contact Author)

University of Nevada, Reno ( email )

1664 N. Virginia Street
Reno, NV 89557-0030
United States

HOME PAGE: http://business.unr.edu/faculty/ffossen/

IZA Institute of Labor Economics

P.O. Box 7240
Bonn, D-53072
Germany

HOME PAGE: http://www.iza.org/en/webcontent/personnel/photos/index_html?key=2906

Alina Sorgner

University of Jena ( email )

F├╝rstengraben 1
Jena, 07743
Germany

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