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Title: Who Is Afraid of Machines?
Citation Type: Miscellaneous
Publication Year: 2018
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Abstract: We study how machines, embodied in various forms of capital such as ICT capital, software and 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, spanning roughly the entire economy, with annual observations over the period 1982-2005. We find that industries with faster capital growth reduced their demand for middle-educated workers and males, and also some evidence that their demand shifted in favor of middle-age workers. We investigate the robustness of the results across alternative identification strategies, various proxies for the use of machines, and different time periods, including an alternative sample from 2008 to 2015. Our evidence is consistent with the hypothesis that machines lower the demand for workers performing routine tasks, especially in routine-manual occupations. We also find evidence that at least some types of workers have shifted away from such tasks. JEL Classification: J21, J23, O33
Url: http://www.crei.cat/wp-content/uploads/2018/10/Gancia_BGL_EP_20180928_final.pdf
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Authors: Blanas, Sotiris; Gancia, Gino; Lee, Sang Yoon Tim
Publisher: Queen Mary University of London
Data Collections: IPUMS USA
Topics: Labor Force and Occupational Structure, Other
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