Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/211408
Authors: 
Albuquerque, Pedro H.
Saavedra, Cayan Atreio Portela Bárcena
de Morais, Rafael Lima
Alves, Patrick Franco
Peng, Yaohao
Year of Publication: 
2019
Series/Report no.: 
Texto para Discussão 2457
Abstract (Translated): 
This work aimed to reproduce the methodology of Carl Benedikt Frey and Michael Osborne of 2017 for estimating the automation probabilities of occupations in Brazil. These estimates are potentially important for professionals and policymakers because they can guide the career of a worker, as well as define priority courses that educational institutions should offer in order to maximize employment opportunities in the country. We consulted the opinion of 69 scholars and professionals that are experts in machine learning to ground the estimation the automation probability of Brazilian occupations. The findings indicate that a large part of the occupations can be automated in the next years. In addition, it can be seen that these professions with a higher risk of automation show a trend of growth over time, which may result in a high level of unemployment in the coming years if professionals and the government do not prepare for this scenario.
Subjects: 
automation
labor market
artificial intelligence
natural language processing
technical expertise
text mining
JEL: 
J24
J64
Q55
N36
J21
Document Type: 
Working Paper

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