Determinants of Performance in the Public Schools of the State of Rio de Janeiro, 2023

João Vitor Gomes Rodrigues ORCID ,  Rui Alexandre Castanho ORCID ,  Rodrigo Franklin Frogeri ORCID ,  Fabricio Pelloso Piurcosky ORCID
    Received: 10 February 2026; Revised: 13 March 2026; Accepted: 27 March 2026; Published: 13 August 2026

    Abstract

    This study investigates how socioeconomic, demographic, and institutional factors are associated with the performance of 180 public school proxies in Rio de Janeiro on the 2023 National High School Exam (ENEM), utilizing official Anísio Teixeira National Institute for Educational Studies and Research (INEP) microdata. The analysis is based on the application of multiple linear regression models with robust HC3 standard errors and unsupervised clustering techniques. To mitigate multicollinearity issues between parental education and household income, a Socioeconomic Status Index (SSI) was constructed via Principal Component Analysis (PCA), explaining 92.19% of the joint variance of these variables. Regression results identified three consistent determinants: the SSI showed a direct positive association with scores; racial composition, where a higher proportion of non-white students correlated with lower performance; and administrative dependency, with federal schools significantly outperforming state schools. Complementarily, K-means cluster analysis revealed four distinct institutional profiles, showing a gap of approximately 70 points between performance extremes. Robustness tests, including log-linear regression and bootstrap validation with 1,000 resamples, confirmed the stability of the estimates. The findings indicate that the performance of public schools in Rio de Janeiro reflects not only structural socioeconomic inequalities but also a marked heterogeneity across educational networks and racial profiles. It is concluded that overcoming these bottlenecks requires focused, evidence-based educational policies capable of addressing the territorial and institutional disparities that persist in the state’s public education system.

    Keywords

    References

    1. 1. Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira. Microdata from the 2023 ENEM Exam; INEP: Brasília, Brazil, 2024. (in Portuguese)
    2. 2. Soares, J.F.; Collares, A.C.M. Family resources and cognitive performance by primary school students in Brazil. Dados 2006, 49, 615–650.
    3. 3. Coleman, J.S.; Campbell, E.Q.; Hobson, C.J.; et al. Equality of Educational Opportunity; U.S. Government Printing Office: Washington, DC, USA, 1966.
    4. 4. Becker, G.S. Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education; National Bureau of Economic Research: New York, NY, USA, 1964.
    5. 5. Bourdieu, P. The forms of capital. In Handbook of Theory and Research for the Sociology of Education; Richardson, J., Ed.; Greenwood Press: New York, NY, USA, 1986; pp. 241–258.
    6. 6. Soares, J.F.; Alves, M.T.G. Racial inequalities in the Brazilian basic education system. Educ. Pesqui. 2003, 29, 147–165.
    7. 7. Franco, C.; Ortigão, I.; Alves, F.; et al. Quality and equity in education: Reconsidering the meaning of intra-school factors. Rev. Bras. Educ. 2007, 12, 5–26. (in Portuguese)
    8. 8. Melo, A.M.; Soares, J.F.; Xavier, F.P. Factors associated with ENEM performance: A multilevel analysis. Rev. Bras. Estud. Popul. 2021, 38, e0199.
    9. 9. Jolliffe, I.T.; Cadima, J. Principal component analysis: A review and recent developments. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2016, 374, 20150202. DOI: https://doi.org/10.1098/rsta.2015.0202
    10. 10. Baker, R.S.; Inventado, P. Educational data mining and learning analytics. In Learning Analytics; Springer: New York, NY, USA, 2014.
    11. 11. Romero, C.; Ventura, S. Educational data mining and learning analytics: An updated survey. WIREs Data Min. Knowl. Discov. 2020, 10.
    12. 12. OECD. PISA 2022 Results; OECD Publishing: Paris, France, 2023.
    13. 13. Bartlett, M.S. A note on the multiplying factors for various χ² approximations. J. R. Stat. Soc. Ser. B Methodol. 1954, 16, 296–298.
    14. 14. MacKinnon, J.G.; White, H. Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties. J. Econom. 1985, 29, 305–325.
    15. 15. Jarque, C.M.; Bera, A.K. Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Econ. Lett. 1980, 6, 255–259.
    16. 16. Breusch, T.S.; Pagan, A.R. A simple test for heteroscedasticity and random coefficient variation. Econometrica 1979, 47, 1287–1294.
    17. 17. Cook, R.D. Detection of influential observation in linear regression. Technometrics 1977, 19, 15–18.
    18. 18. MacQueen, J. Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA, USA, 21 June–18 July 1965, 27 December 1965–7 January 1966; pp. 281–297.
    19. 19. Rousseeuw, P.J. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 1987, 20, 53–65.
    20. 20. Calinski, T.; Harabasz, J. A dendrite method for cluster analysis. Commun. Stat. 1974, 3, 1–27.
    21. 21. Hubert, L.; Arabie, P. Comparing partitions. J. Classif. 1985, 2, 193–218.
    22. 22. Shapiro, S.S.; Wilk, M.B. An analysis of variance test for normality (complete samples). Biometrika 1965, 52, 591–611.
    23. 23. Ben-Hur, A.; Elisseeff, A.; Guyon, I. A stability-based method for discovering structure in clustered data. In Proceedings of the Pacific Symposium on Biocomputing, Hawaii, HI, USA, 3–7 January 2002; pp. 6–17.
    24. 24. IBGE. Educational indicators advance in 2024, but school delay increases. Available online: https://agenciadenoticias.ibge.gov.br/agencia-noticias/2012-agencia-de-noticias/noticias/43699-indicadores-educacionais-avancam-em-2024-mas-atraso-escolar-aumenta (accessed on 3 February 2026). (in Portuguese)
    25. 25. INEP. Brazil reached the highest percentage of full-time students in high school in 2025. Available online: https://www.gov.br/inep/pt-br/centrais-de-conteudo/noticias/censo-escolar/brasil-atingiu-maior-percentual-de-estudantes-em-tempo-integral (accessed on 3 February 2026). (in Portuguese)
    26. 26. Barbosa, J.A.F.; Cordeiro, R.L.F. Exploratory Analysis of Microdata from the National High School Exam-Enem: Performance and Specificities of the Participants. J. Inf. Data Manag. 2025, 16.
    27. 27. Fitzgerald, A.; Avirmed, T.; Battulga, N. Exploring the factors informing educational inequality in higher education: A systematic literature review. Perspect. Policy Pract. High. Educ. 2025, 29, 199–209. DOI: https://doi.org/10.1080/13603108.2024.2381121
    28. 28. Moraes, C.P.; Peres, R.T. Reflections on performance diferences at ENEM: A socio-economic and schoolar analysis of Southeast Brazil. J. Pol. Educ-s 2022, 16, e85377.
    29. 29. Girão, J.E.d.O. Impacts of the pandemic and socioeconomic inequalities on the ENEM exam in Ceará (2019–2023). UFC Inst. Repos. 2023.
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