Analysis of automation and retraining opportunities for the Brazilian federal public service
DOI:
https://doi.org/10.59490/dgo.2025.1031Keywords:
automation, work, retraining, skills, BrazilAbstract
Context: The speed of technological advancement has raised concerns about the impact of automation on work. Previous studies have analyzed this impact on the private sector, but there is little research focused on the Brazilian public sector, which faces additional challenges in dealing with this issue. Objective: To estimate the impact of automation on intermediate-level federal public positions and indicate professional retraining paths for civil servants, in the face of technological changes. Methodology: A 4-step approach was used, involving (1) mapping of automation technologies, (2) extraction and review of the job duties of 142 intermediate-level public positions from a government document with the support of AI models, (3) assessment of the importance level and horizon of automation of each job duty by GPT-4 Turbo and (4) suggestion of professional retraining courses for each position. Results: A considerable potential for automation of the analysed positions was identified, with 72% of the job duties with different automation levels in an immediate or short-term impact horizon. However, also considering the frequency and importance of each job duty, a considerable part of the positions had an impact between 0.10-0.28 points on a scale of 0 to 1. The technologies with the most occurrences in the analysis of automation impact were Machine Learning, Smart Sensors and Process Digitization Systems. In the end, 236 professional retraining courses were suggested to better prepare employees for possible technological impacts.
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Adamczyk, W. B., Monasterio, L., & Fochezatto, A. (2021). Automation in the future of public sector employment:The case of Brazilian Federal Government. Technology in Society, 67, 101722. https://doi.org/10.1016/j.techsoc.2021.101722
Arntz, M., Gregory, T., & Zierahn, U. (2016). The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis (OECD Social, Employment and Migration Working Papers No. 189; Issue 189). OECD. https://doi.org/10.1787/5jlz9h56dvq7-en
Arntz, M., Gregory, T., & Zierahn, U. (2017). Revisiting the risk of automation. Economics Letters, 159, 157–160. https://doi.org/10.1016/j.econlet.2017.07.001
Autor, D. H. (2015). Why Are There Still So Many Jobs? The History and Future of Workplace Automation. Journal of Economic Perspectives, 29(3), Article 3. https://doi.org/10.1257/jep.29.3.3
Autor, D., & Salomons, A. (2018). Is Automation Labor Share-Displacing? Productivity Growth, Employment, and the Labor Share. Brookings Papers on Economic Activity, 1–87.
Banco Interamericano de Desenvolvimento. (2020). Transformação digital e emprego público: O futuro do trabalho do governo. Miguel Porrúa, Mariano Lafuente, Edgardo Mosqueira, Benjamin Roseth, Angela María Reyes. [link]
Brookfield Institute. (2016). The talented Mr. Robot: The impact of automation on Canada’s workforce. Brookfield Institute.
Chui, M., Hazan, E., Roberts, R., Singla, A., & Smaje, K. (2023). The economic potential of generative AI.
Chui, M., Manyika, J., & Miremadi, M. (2015). Four fundamentals of workplace automation. McKinsey Quarterly, 9.
Comin, D., & Hobijn, B. (2010). An Exploration of Technology Diffusion. American Economic Review, 100(5), Article 5. https://doi.org/10.1257/aer.100.5.2031
De Oliveira Teixeira, K., De Guimarães E Souza, G. J., & Schneid Tessmann, M. (2022). The future of work in the Brazilian Federal District’s executive branch. Revista Do Serviço Público, 73(1), 9–31. https://doi.org/10.21874/rsp.v73.i1.6641
Deloitte. (2015a). From Brawns to Brains: The Impact of Technology on Jobs in the UK. Deloitte.
Deloitte. (2015b). Man and Machine: Robots on the rise? The impact of automation on the Swiss job market (p. 8). Deloitte.
Eisfeldt, A. L., Schubert, G., & Zhang, M. B. (2023). Generative AI and Firm Values (SSRN Scholarly Paper No. 4436627). https://doi.org/10.2139/ssrn.4436627
Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models (No. arXiv:2303.10130). arXiv. https://doi.org/10.48550/arXiv.2303.10130
Frey, C. B., & Osborne, M. A. (2017a). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280. https://doi.org/10.1016/j.techfore.2016.08.019
Frey, C. B., & Osborne, M. A. (2017b). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280. https://doi.org/10.1016/j.techfore.2016.08.019
Gmyrek, P., Berg, J., & David, B. (2023). Generative AI and Jobs: A global analysis of potential effects on job quantity and quality. https://doi.org/10.54394/FHEM8239
Kubota, L. C., & Maciente, A. N. (2019). PROPENSÃO À AUTOMAÇÃO DAS TAREFAS OCUPACIONAIS NO BRASIL. Radar, 61(Dez.).
Lima, Y., Barbosa, C. E., Dos Santos, H. S., & De Souza, J. M. (2021). Understanding Technological Unemployment: A Review of Causes, Consequences, and Solutions. Societies, 11(2), 50. https://doi.org/10.3390/soc11020050
Lima, Y., Strauch, J. C. M., Esteves, M. G. P., de Souza, J. M., Chaves, M. B., & Gomes, D. T. (2021). Exploring the future impact of automation in Brazil. Employee Relations. https://doi.org/10.1108/ER-08-2020-0364
McKinsey Global Institute. (2017a). A future that works: Automation, employment, and productivity. MGI.
McKinsey Global Institute. (2017b). Jobs lost, jobs gained: Workplace transitions in a time of automation. MGI.
Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence.
Reuters. (2023, December 19). ChatGPT sets record for fastest-growing user base—Analyst note. [link]
Santos, I., Monroy, S., & Moreno, M. (2015). Technological Change and Labor Market Disruptions: Evidence from the developing world. IZA.
World Bank Group. (2016). World Development Report 2016 (p. 359). World Bank Group.
World Economic Forum. (2018). The Future of Jobs Report 2018. WEF.
World Economic Forum. (2020). Future of Jobs Report 2020. WEF. [link]
World Economic Forum. (2023). Future of Jobs Report 2023. WEF. [link]
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