The future of work in the Brazilian Federal District’s executive branch
Keywords:
future of work, automation, machine learning, public sector, Federal District's GovernmentAbstract
In this article, we address the theme ‘Future of Work’ with focus on the Brazilian Federal District’s public sector. Brazil is a federative republic and the federated entity in question has the eighth largest GDP among the 27 Brazilian federated entities (26 states and the Federal District). Since it is the seat of the federal executive branch, the Federal District has the highest GDP per capita, almost twice that of the runner-up, thus justifying the importance of analyzing its governmental structure. To do so, we reproduced the method of Kubota and Maciente (2019) to estimate the tendency to automate occupations, employing the occupation database of the Federal District’s executive civil servants. The results showed that jobs that require lower levels of education and involve more routine tasks are more prone to automation. Finally, based on our results, we performed a descriptive statistical analysis of schooling, age, and remuneration of public positions in the Federal District.
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ACEMOGLU, D.; RESTREPO, P. (2018). "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment." American Economic Review, 108 (6): 1488-1542.
ADAMCZYK, B. W.; MONASTERIO, L.; FOCHEZATTO, A. (2020). Impacto da Automação no Futuro do Emprego do Setor Público: uma aplicação ao Executivo Federal brasileiro. IPEA Working Papers, 3456.
AGRAWAL, A., GANS, J e GOLDFARB A. (2019). "Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction." Journal of Economic Perspectives, 33 (2): 31-50.
ALBUQUERQUE, P. H. M., SAAVEDRA, C.A.P.B., MORAIS, R.L., ALVES, P.F. e YAOHAO, P. (2019). “Na era das máquinas, o emprego é de quem? Estimação da probabilidade de automação de ocupações no Brasil.” Brasília: Ipea, Texto para Discussão, n. 2457.
ARBACHE, J. (2012) “Is Brazilian manufacturing losing its drive?” Mimeografado. Disponível em: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2150684 Brasília: UnB, acesso em 4/2020.
ARNTZ, M., T. Gregory e U. Zierahn (2016), "O Risco de Automação para Empregos nos Países da OCDE: Uma Análise Comparativa", Documentos de Trabalho da OCDE sobre Social, Emprego e Migração, Nº. 189, OECD Publishing, Paris, https://doi.org/10.1787/5jlz9h56dvq7-en, acesso em 7/2020.
BESSEN, J. E. (2015). Learning by Doing: The Real Connection Between Innovation, Wages, and Wealth, Yale University Press.
BRYNJOLFSSON, E., MITCHELL, T. e ROCK, D. (2018). "What Can Machines Learn, and What Does It Mean for Occupations and the Economy?" AEA Papers and Proceedings, 108: 43-47.
BRYNJOLFSSON, E.; MCAFEE, A. (2014). “The second machine age: work, progress, and prosperity in a time of brilliant technologies.” New York: W.W. Norton and Company, 2014.
CAMERON, E.; HAWKSW (2018) “AI will create as many Jobs as id displaces by boosting economic growth” https://www.pwc.co.uk/press-room/press-releases/AI-will-create-as-many-jobs-as-it-displaces-by-boosting-economic-growth.html, acesso em 7/2020.
CODEPLAN http://infodf.codeplan.df.gov.br/?page_id=2254
DATHEIN, R. (2003). “Inovação e Revoluções Industriais: uma apresentação das mudanças tecnológicas determinantes nos séculos XVIII e XIX.” Publicações DECON Textos Didáticos 02/2003. DECON/UFRGS, Porto Alegre. http://www.ufrgs.br/decon/
DAVID, H. (2015). “Why Are There Still So Many Jobs? The History and Future of Workplace Automation”, Journal of Economic Perspectives—Volume 29, Number 3—Pages 3–30
DVORKIN, M. (2016). Jobs involving routine tasks aren’t growing. Saint Louis; On the Economy
Blog.
FREEMAN, R., GANGULI, I.; HANDEL, M. (2020). “Within-Occupation Changes Dominate Changes in What Workers Do: A Shift-Share Decomposition, 2005–2015”, AEA Papers and Proceedings, 110: 394–399, https://doi.org/10.1257/pandp.20201005.
FREY, C. B.; OSBORNE, M. A. (2017). “The future of employment: how susceptible are jobs to computerisation? Technological Forecasting and Social Change”.
HIDALGO, C.; HAUSMANN, R. (2009). “The building blocks of economic complexity”. Proceedings of the National Academy of Sciences of the United States of America, vol. 106, n. 26, p. 10570–10575.
HOBSBAWM, E. A era das revoluções. 1789-1848.
KAUFMANN, A.; TÖDTLING, F. (2000). “Systems of innovation in traditional industrial regions: the case of Styria in a comparative perspective” Regional Studies, v. 34, n. 1, p. 29-40.
KEYNES, J. M. (1971). A tract on monetary reform. London: Macmillan. (The Collected Writings of John Maynard Keynes, v. IV).
KOVACS, I. (2015). “Os avanços tecnológicos e o futuro do trabalho: debates recentes”. See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/304040159.
KUBOTA, C. L.; MACIENTE, N. A. (2019). Propensão à automação das tarefas ocupacionais no Brasil, disponível em: https://www.ipea.gov.br/portal/images/stories/PDFs/radar/191213_radar_61_art4.pdf, acesso em 4/2020.
MANSFIELD, E. (1988). “The speed and cost of industrial innovation in Japan and the United States: external vs. internal technology”. Management Science, v. 34, n. 10, p. 1157-1168.
MANYIKA, J., CHUI, M., MIREMADI, M., BUGHIN, J., GEORGE, K., WILLMOTT, P.; DEWHURST, M. (2017). A Future That Works: Automation, Employment and Productivity. Chicago: McKinsey Global Institute.
POULIAKAS, K. (2018). “Determinants of Automation Risk in the EU Labour Market: A Skills-Needs Approach”, Cedefop, University of Aberdeen Business School and IZA.
PRETTNER, K.; STRULIK. “Innovation, automation, and inequality: Policy challenges in the race against the machine”. Journal of Monetary Economics, https://doi.org/10.1016/j.jmoneco.2019.10.012, acesso em 7/2020.
RIFKIN, J. (2012). The Third Industrial Revolution.
ROBERTS, C.; LAWRENCE, M.; KING, L. (2017). “Managing automation: Employment, inequality and ethics in the digital age”. IPPR. http://www.ippr.org/publications/managing-automation, acesso em 5/2020.
SCHWAB, K.; DAVIS, N. (2018). Aplicando a Quarta Revolução Industrial.
SILVA, M., MAINARDES, E., RAPOSO, M.; SOUSA, G. (2011) “Determinantes Internos e Externos da Capacidade Inovadora das Empresas de Serviços Portuguesas: Modelo Logit”.
SORGNER, A. (2017). Jobs at Risk! Effects of Automation of Jobs on Occupational Mobility. http://hdl.handle.net/10419/168088, acesso em 5/2020.
TEPASSÊ, A.; FERNANDES, R. (2018). O futuro do trabalho no comércio.
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