Who is afraid of machines? - Sotiris Blanas; Gino Alessandro Gancia; Sang Yoon Lee (Queen Mary University of London, School of Economics and Finance, 2019)
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: J21; J23; O33; 330; Automation; Robots; Employment; Labor Demand; Labor Income Share