INSS Social Security Expenditures and Fiscal Sustainability: An Application of the SARIMA Model

Authors

  • Victor Flávio Pereira Dornelos State University of Rio de Janeiro (UERJ)
  • Elena Soihet Federal Rural University of Rio de Janeiro
  • Julio Cezar Russo Pinto da Silva Federal Rural University of Rio de Janeiro

Keywords:

Social Security, INSS, Time Series, SARIMA Modeling

Abstract

The Brazilian social security system for private sector workers is organized under the General Social Security Regime (RGPS), administered by the National Institute of Social Security (INSS). In the context of accelerated demographic transition, pension expenditures have increased, posing challenges to the actuarial sustainability and financial balance of the system. This article analyzes the behavior of the time series of INSS expenditures and proposes a forecasting model based on the SARIMA methodology, contributing to the debate on the dynamics of Brazilian social security. The study adopts a quantitative approach, using secondary data from the Social Security Information Technology Company (Dataprev) and econometric modeling techniques. The results indicate that pension expenditures are expected to grow above the inflation target in the coming years and are influenced by complex macroeconomic and institutional factors, which limits the performance of purely autoregressive models and suggests the need for complementary approaches.

Author Biographies

Victor Flávio Pereira Dornelos, State University of Rio de Janeiro (UERJ)

Bachelor’s degree in Economics from the Federal Rural University of Rio de Janeiro (UFRRJ), completed in 2025. Worked as an intern at the Brazilian Development Bank (BNDES) between 2024 and 2025, with a focus on health, supplementary pension systems, applied economics, and data science. Currently a master’s student in the Graduate Program in Economic Sciences (PPGCE) at the State University of Rio de Janeiro (UERJ), admitted in 2025. Research interests include Macroeconometrics, Social Security, Econometrics, and quantitative methods applied to economics.

Elena Soihet, Federal Rural University of Rio de Janeiro

PhD in Economics from the Institute of Economics at the Federal University of Rio de Janeiro (2007). Associate Professor IV at the Department of Economics of the Multidisciplinary Institute of the Federal Rural University of Rio de Janeiro (IM/UFRRJ). Completed a postdoctoral fellowship at the Federal University of Rio Grande do Sul (2014) and was a visiting researcher at the Land Department of the University of Cambridge (2013). Works in the fields of Macroeconomics, Monetary Economics, and Financial Economics. Conducts research in Monetary and Financial Economics, with emphasis on the financial system, banking spreads, and the cost of credit, as well as in Public Sector Economics, with a focus on pension reform and fiscal sustainability.

Julio Cezar Russo Pinto da Silva, Federal Rural University of Rio de Janeiro

PhD in Finance and Investment Analysis from the Department of Industrial Engineering at the Pontifical Catholic University of Rio de Janeiro (2017). Associate Professor II at the Multidisciplinary Institute, Department of Economics, of the Federal Rural University of Rio de Janeiro (IM/UFRRJ). Works in the fields of Finance and Investment Analysis, Econometrics, and Mechanism Design. Conducts research in Financial Econometrics, Population Aging Estimates and Pension Impacts, Mechanism Design, and Incentive Compatibility in Public-Private Partnership (PPP) contracts.

References

AFONSO, José Roberto; ARAÚJO, Eliane Cristina; FAJARDO, Bernardo Guelber. The role of fiscal and monetary policies in the Brazilian economy: understanding recent institutional reforms and economic changes. The Quarterly Review of Economics and Finance, v. 62, p. 41–55, 2016. DOI: 10.1016/j.qref.2016.07.005

AKAIKE, Hirotugu. A new look at the statistical model identification. IEEE Transactions on Automatic Control, v. 19, n. 6, p. 716–723, 1974.

BRASIL. Constituição da República Federativa do Brasil de 1988. Disponível em: https://www.planalto.gov.br/ccivil_03/constituicao/constituicao.htm. Acesso em: 28 ago. 2024.

BRASIL. Emenda Constitucional nº 103, de 12 de novembro de 2019. Altera o sistema de previdência social e estabelece regras de transição e disposições transitórias. Diário Oficial da União, Brasília, DF, 13 nov. 2019.

BOX, George E. P.; JENKINS, Gwilym M. Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day, 1976.

BUENO, Rodrigo de Losso da Silveira. Econometria de séries temporais. 2. ed. rev. e atual. São Paulo: Cengage Learning, 2011.

CRESWELL, John W. Projeto de pesquisa: métodos qualitativo, quantitativo e misto. 5. ed. Porto Alegre: Penso, 2018

Empresa de Tecnologia e Informações da Previdência. Infologo AEPS. Disponível em: https://www3.Dataprev.gov.br/infologo/inicio.htm. Acesso em: 10 jan. 2025.

GIAMBIAGI, Fabio. A previdência social no Brasil: tendências e desafios. Rio de Janeiro: Banco Nacional de Desenvolvimento Econômico e Social, 2025. 53 p. (Textos para discussão; 164).

GIL, Antonio Carlos. Métodos e técnicas de pesquisa social. 7. ed. São Paulo: Atlas, 2019.

HYNDMAN, Rob J.; ATHANASOPOULOS, George. Forecasting: Principles and Practice. 3. ed. Melbourne: OTexts, 2021. Disponível em: https://otexts.com/fpp3/. Acesso em: 6 out. 2025.

INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATÍSTICA. Projeção da População do Brasil e das Unidades da Federação, 2024. Rio de Janeiro: IBGE, 2024. Disponível em: https://www.ibge.gov.br/estatisticas/sociais/populacao/9109-projecao-da-populacao.html. Acesso em: 6 out. 2025.

LÜTKEPOHL, Helmut. New Introduction to Multiple Time Series Analysis. Berlin: Springer, 2005.

MORETTIN, Pedro A.; TOLOI, Clélia M. C. Análise de séries temporais. 2. ed. São Paulo: Edgard Blucher, 2006.

SCHWARZ, Gideon. Estimating the dimension of a model. The Annals of Statistics, v. 6, n. 2, p. 461–464, 1978.

SECRETARIA DE REGIME GERAL DE PREVIDÊNCIA SOCIAL. Projeções Financeiras e Atuariais para o RGPS. Brasília: Ministério da Previdência Social, 2023.

SHUMWAY, Robert H.; STOFFER, David S. Time Series Analysis and Its Applications: With R Examples. 3. ed. New York: Springer, 2017.

SIDONE, Otávio José Guerci; AFONSO, Luís Eduardo. Economia da previdência: teoria, desenho e avaliação. Colaboração de Ana Cristina de Souza Queiroz e Emmilly Elizabeth César Félix. São Paulo: Faculdade de Economia, Administração, Contabilidade e Atuária da Universidade de São Paulo – FEA/USP, 2025. 501 p. ISBN 978-85-61522-07-0. DOI: 10.11606/9788561522070

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Published

29-05-2026

How to Cite

Pereira Dornelos, V. F., Soihet, E., & Russo Pinto da Silva, J. C. (2026). INSS Social Security Expenditures and Fiscal Sustainability: An Application of the SARIMA Model. CADERNOS DE FINANÇAS PÚBLICAS, 26(02). Retrieved from https://publicacoes.tesouro.gov.br/index.php/cadernos/article/view/301

Issue

Vol. 26 No. 02 (2026): CADERNOS DE FINANÇAS PÚBLICAS

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Artigos

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