Who Is Afraid of Machines?

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

No 13802, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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; robots; Employment; Labor demand; Labor income share (search for similar items in EconPapers)
JEL-codes: J21 J23 O33 (search for similar items in EconPapers)
Date: 2019-06
New Economics Papers: this item is included in nep-ict, nep-lma and nep-tid
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Citations: View citations in EconPapers (53)

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Journal Article: 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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