Who is Afraid of Machines?

68 Pages Posted: 29 Jul 2019

See all articles by Sotiris Blanas

Sotiris Blanas

National Bank of Belgium

Gino Gancia

Queen Mary University of London; Universitat Pompeu Fabra - Centre de Recerca en Economia Internacional (CREI)

Sang Yoon (Tim) Lee

Queen Mary University of London

Date Written: June 2019

Abstract

We study how various types of machines, namely, information and communication technologies, software, and especially industrial robots, affect the demand for workers of different education, age, and gender. We do so by exploiting differences in the composition of workers across countries, industries and time. Our dataset comprises 10 high-income countries and 30 industries, which span roughly their entire economies, with annual observations over the period 1982-2005. The results suggest that software and robots reduced the demand for low and medium-skill workers, the young, and women - especially in manufacturing industries; but raised the demand for high-skill workers, older workers and men - especially in service industries. These findings are consistent with the hypothesis that automation technologies, contrary to other types of capital, replace humans performing routine tasks. We also find evidence for some types of workers, especially women, having shifted away from such tasks.

Keywords: automation, employment, labor demand, Labor Income Share, robots

JEL Classification: J21, J23, O33

Suggested Citation

Blanas, Sotiris and Gancia, Gino and Lee, Sang Yoon (Tim), Who is Afraid of Machines? (June 2019). Available at SSRN: https://ssrn.com/abstract=3428322

Sotiris Blanas (Contact Author)

National Bank of Belgium ( email )

Brussels, B-1000
Belgium

Gino Gancia

Queen Mary University of London ( email )

Mile End Road
London, London E1 4NS
United Kingdom

Universitat Pompeu Fabra - Centre de Recerca en Economia Internacional (CREI) ( email )

Ramon Trias Fargas, 25-27
Barcelona, 08005
Spain

Sang Yoon (Tim) Lee

Queen Mary University of London ( email )

Mile End Road
London, London E1 4NS
United Kingdom

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