Socioeconomic Constraints, Fiscal Resource Allocation, and Secondary School Academic Performance in Nigeria: An Econometric Analysis and I (2) Cointegration Approach (2000–2025)
Authors
Directorate of Academic Planning (Nigeria)
Educational Management, Lagos State University (Nigeria)
Department of Economics, Topmost College of Education (Nigeria)
Article Information
DOI: 10.47772/IJRISS.2026.100700610
Subject Category: Education
Volume/Issue: 10/7 | Page No: 8933-8943
Publication Timeline
Submitted: 2026-07-26
Accepted: 2026-07-31
Published: 2026-08-08
Abstract
This study examines the dynamic empirical relationship between fiscal resource allocations, secondary school academic performance (NECO SSCE benchmark), socioeconomic constraints, and out-of-school populations in Nigeria over a 26-year period (2000–2025). Employing annual time-series data, the preliminary descriptive statistics reveal significant variability across key indicators, with out-of-school children averaging 13.73 million (SD=3.30 million), fiscal education allocations averaging N586.13 billion (SD=759.98 billion), and academic pass rates (≥5 credits including English and Mathematics) averaging 44.32% (SD=21.91%). Pairwise correlation analysis indicates strong positive associations between education budget allocations and out-of-school children (r=0.8469, p<0.01), alongside moderate correlations with academic performance (r=0.4489, p<0.05), highlighting systemic structural bottlenecks. Standard Dickey-Fuller and Zivot-Andrews unit root tests with structural breaks confirm that key series exhibit second-order non-stationarity (I(2)) driven by regime shifts (notably in 2012, 2017, and 2022), rendering standard Autoregressive Distributed Lag (ARDL) bounds testing invalid.
To resolve this, the study implements the Juselius-Johansen I(2) Cointegration procedure [cite: 1]. The joint trace rank test identifies a single common I(2) stochastic trend with cointegrating rank r=1 and I(1) rank s=1. The normalized long-run polynomial cointegrating vector confirms that higher fiscal allocations (β₂=+0.00396) and improved academic performance (β₃=+0.00972) exert a statistically significant stabilizing effect on long-run educational retention [cite: 1]. Short-run dynamics modeled via a second-differenced Vector Error Correction Model (I(2)-VECM) demonstrate strong overall explanatory power across all equations (R²=84.42% for out-of-school acceleration; R²=70.59% for budgetary acceleration; R²=68.05% for pass rate acceleration). The polynomial speed-of-adjustment coefficient (α₁=−0.2051) establishes that 20.51% of short-run acceleration disequilibria is corrected annually toward long-run equilibrium. Lagged out-of-school growth displays significant negative self-correction (γ=−2.2319, p<0.001), while budgetary growth impacts out-of-school acceleration with a lag (γ=+0.0151, p<0.10), underscoring operational implementation lags [cite: 1]. Post-estimation diagnostic suite tests confirm that the estimated system is robust, non-spurious, and free from specification bias. Policy recommendations emphasize transitioning to a Medium-Term Expenditure Framework (MTEF), institutionalizing an automated Education Stabilization Fund, and linking spending expansions to quality benchmarks.
Keywords
I(2) Cointegration, Juselius-Johansen Framework, I(2)-VECM, NECO SSCE Performance, Out-of-School Children, Fiscal Allocation, Socioeconomic Determinants, Nigeria.
Downloads
References
1. Adeyemo, A., & Kelani, T. (2025). Socioeconomic drivers of educational outcomes in West Africa. Journal of African Educational Research, 18(1), 45–62. [Google Scholar] [Crossref]
2. Aluko, O. (2020). Household poverty, adult literacy, and secondary school performance in Nigeria. Nigerian Journal of Social and Economic Studies, 32(3), 112–129. [Google Scholar] [Crossref]
3. Becker, G. S. (1964). Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education. National Bureau of Economic Research (NBER), New York. [Google Scholar] [Crossref]
4. Chima, E., & Yusuf, A. (2023). Public expenditure on education and academic achievement in Nigerian secondary schools. Journal of Educational Economics, 15(2), 112–128. [Google Scholar] [Crossref]
5. Clemente, J., Montañés, A., & Reyes, M. (1998). Testing for a unit root in variables with a double change in the mean. Economics Letters, 59(2), 175–182. [Google Scholar] [Crossref]
6. Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, S95–S120. [Google Scholar] [Crossref]
7. Development Research and Projects Centre [DRPC]. (2026). Regional disparities in Nigerian education outcomes report. Abuja, Nigeria. [Google Scholar] [Crossref]
8. Johansen, S. (1995). Likelihood-Based Inference in Cointegrated Vector Autoregressive Models. Oxford University Press, Oxford. [Google Scholar] [Crossref]
9. Johansen, S., & Juselius, K. (1995). Identification of the long-run and the short-run structure: An application to the ISLM model. Journal of Econometrics, 69(1), 159–201. [Google Scholar] [Crossref]
10. Juselius, K. (2006). The Cointegrated VAR Model: Methodology and Applications. Oxford University Press, Oxford. [Google Scholar] [Crossref]
11. Lee, J., & Strazicich, M. C. (2003). Minimum Lagrange multiplier unit root test with two structural breaks. The Review of Economics and Statistics, 85(4), 1082–1089. [Google Scholar] [Crossref]
12. Lucas, R. E. (1988). On the mechanics of economic development. Journal of Monetary Economics, 22(1), 3–42. [Google Scholar] [Crossref]
13. Oluwatobi, S. O., & Ogunrinola, I. O. (2011). Government spending on human capital development and economic growth in Nigeria. International Journal of Business and Social Science, 2(8), 114–118. [Google Scholar] [Crossref]
14. Ononye, C., & Obiakor, R. (2020). Evaluating high-stakes secondary school examinations in Nigeria. African Journal of Educational Assessment, 12(1), 88–104. [Google Scholar] [Crossref]
15. Onuigbo, E. (2021). Human capital development and long-term economic growth trajectories. Journal of Macroeconomic Policy, 29(4), 201–218. [Google Scholar] [Crossref]
16. Otuoku, E., et al. (2026). National Examination Council outcomes and tertiary education transition in Nigeria. West African Educational Review, 34(2), 145–168. [Google Scholar] [Crossref]
17. Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326. [Google Scholar] [Crossref]
18. Romer, P. M. (1990). Endogenous technological change. Journal of Political Economy, 98(5, Part 2), S71–S102. [Google Scholar] [Crossref]
19. Schultz, T. W. (1961). Investment in human capital. The American Economic Review, 51(1), 1–17. [Google Scholar] [Crossref]
20. Segun, O., & Campbell, O. (2013). Human capital indicators and academic performance in Nigerian secondary schools. Educational Finance and Policy Review, 19(2), 77–95. [Google Scholar] [Crossref]
21. Urhie, E. S. (2014). Public education expenditure and academic performance in Nigeria. Crime, Change and Development, 8(1), 45–58. [Google Scholar] [Crossref]
22. Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 10(3), 251–270. [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
- Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
- “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
- Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
- Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study