Automation in the future of public sector employment: the case of Brazilian Federal Government
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Organización Internacional del Trabajo (OIT)
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| Auteurs principaux: | , , |
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| Format: | Artículo científico |
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International Labour Organization
2021
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| _version_ | 1874525218740895744 |
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| author | Willian Boschetti Adamczyk Leonardo Monasterio Adelar Fochezatto |
| author_facet | Willian Boschetti Adamczyk Leonardo Monasterio Adelar Fochezatto Willian Boschetti Adamczyk Leonardo Monasterio Adelar Fochezatto |
| contents | Automation in the future of public sector employment: the case of Brazilian Federal Government Willian Boschetti Adamczyk Leonardo Monasterio Adelar Fochezatto 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. 10.1016/j.techsoc.2021.101722 DOI https://doi.org/10.1016/j.techsoc.2021.101722 publication.journalArticle |
| format | Artículo científico |
| id | ilo_995269773102676 |
| institution | Organización Internacional del Trabajo (OIT) |
| publishDate | 2021 |
| publisher | International Labour Organization |
| spellingShingle | Automation in the future of public sector employment: the case of Brazilian Federal Government Willian Boschetti Adamczyk Leonardo Monasterio Adelar Fochezatto Automation in the future of public sector employment: the case of Brazilian Federal Government Willian Boschetti Adamczyk Leonardo Monasterio Adelar Fochezatto 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. 10.1016/j.techsoc.2021.101722 DOI https://doi.org/10.1016/j.techsoc.2021.101722 publication.journalArticle |
| title | Automation in the future of public sector employment: the case of Brazilian Federal Government |
| url | https://researchrepository.ilo.org/esploro/outputs/journalArticle/Automation-in-the-future-of-public/995269773102676 |