Who is afraid of machines?

Sotiris Blanas, Gino Gancia and Sang Yoon (Tim) Lee

Economic Policy, 2019, vol. 34, issue 100, 627-690

Abstract: SUMMARYWe 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 data set 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: J21; J23; O33; Sotiris Blanas; Gino Gancia and Sang Yoon (Tim) Lee (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (47)

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Related works:
Working Paper: Who Is Afraid of Machines? (2019)
Working Paper: Who Is Afraid of Machines? (2019)
Working Paper: Who Is Afraid of Machines? (2019)
Working Paper: Who Is afraid of machines? (2019)
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