Working Paper: CEPR ID: DP13802
Authors: Sotiris Blanas; Gino Gancia; Sang Yoon Tim Lee
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
JEL Codes: J21; J23; O33
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| software and robots (C88) | demand for low and medium-skill workers (J29) |
| software and robots (C88) | demand for high-skill workers (J24) |
| industrial robots (L63) | employment of low-skill workers (J68) |
| industrial robots (L63) | income shares of high and medium-skill workers (D33) |
| industrial robots (L63) | income shares of older workers (J26) |
| industrial robots (L63) | income shares of men (D33) |