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Automation in the future of public sector employment: the case of Brazilian Federal Government

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  • Adamczyk, Willian Boschetti
  • Monasterio, Leonardo
  • Fochezatto, Adelar

Abstract

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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  • Adamczyk, Willian Boschetti & Monasterio, Leonardo & Fochezatto, Adelar, 2021. "Automation in the future of public sector employment: the case of Brazilian Federal Government," Technology in Society, Elsevier, vol. 67(C).
    • Handle: RePEc:eee:teinso:v:67:y:2021:i:c:s0160791x21001974
    DOI: 10.1016/j.techsoc.2021.101722
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    Cited by:

    1. Ramos, Minerva E. & Garza-Rodríguez, Jorge & Gibaja-Romero, Damian E., 2022. "Automation of employment in the presence of industry 4.0: The case of Mexico," Technology in Society, Elsevier, vol. 68(C).
    2. Pablo Casas & José L. Torres, 2024. "Government size and automation," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 31(3), pages 780-807, June.
    3. David, Sofia & Zinica, Daniel & Bărbuță-Mișu, Nicoleta & Savga, Larisa & Virlanuta, Florina-Oana, 2024. "Public administration managers' and employees' perceptions of adaptability to change under “the future of work” paradigm," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    4. Filippi, Emilia & Bannò, Mariasole & Trento, Sandro, 2023. "Automation technologies and their impact on employment: A review, synthesis and future research agenda," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    5. Su, Chi-Wei & Yuan, Xi & Umar, Muhammad & Lobonţ, Oana-Ramona, 2022. "Does technological innovation bring destruction or creation to the labor market?," Technology in Society, Elsevier, vol. 68(C).

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    More about this item

    Keywords

    Automation; Machine learning; Public sector;
    All these keywords.

    JEL classification:

    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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