Journal
TECHNOLOGY IN SOCIETY
Volume 67, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.techsoc.2021.101722
Keywords
Automation; Machine learning; Public sector
Categories
Funding
- National School of Public Administration -ENAP [284/2019/CGDAD/DPP/ENAP]
- Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior -Brasil (CAPES) [001]
- Federal District Research Foundation -FAP-DF [04/2017]
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This study explores the impact of automation on public sector employment in Brazil, estimating that approximately 20% of public sector employees are in jobs with a high potential for automation. Government occupations with lower education and salary levels are most susceptible to future automation.
What is the impact of automation on public sector employment? Using machine learning and natural language processing algorithms, this study estimates which occupations and agencies of the Brazilian Federal Government are most susceptible to automation. We contribute to the literature by introducing Bartik Occupational Tasks (BOT), an objective method used to estimate automation susceptibility that avoids subjective or ad hoc classifications. We show that approximately 20% of Brazilian public sector employees work in jobs with a high potential of automation in the coming decades. Government occupations with lower schooling and lower salary levels are most susceptible to future automation.
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