An Econometric and Distributional Analysis of Determinants of Educational Delay Inequality

Authors

  • Gaurav Singh Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India
  • Vinod Kumar Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India
  • Arun Kumar Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India

DOI:

https://doi.org/10.13052/jrss0974-8024.19215

Keywords:

Educational Delay, Educational Inequality, Delay Index, Gini Coefficient, Lorenz Curve, Higher Education Policy

Abstract

Educational delay is an underexplored dimension of educational inequality, reflecting disparities in the timing and efficiency of academic progression. Using a dataset of 4,244 student observations from a Portuguese higher education institution, this study constructs a normalized Educational Delay Index and assesses its distribution through the Gini coefficient (G=0.608) and Lorenz curve. A heteroskedasticity-robust (HC3) Ordinary Least Squares model shows that tuition fee compliance, scholarship holding, gender, and age at enrolment are the leading determinants of delay, with financial variables dominating both the regression and the inequality decomposition. Quantile regression further reveals that financial constraints intensify at higher levels of delay. The findings offer policy-relevant insights for strengthening financial support systems in higher education.

Downloads

Download data is not yet available.

Author Biographies

Gaurav Singh, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India

Gaurav Singh is a Research Scholar in the Department of Mathematics, Statistics, and Computer Science at G.B. Pant University of Agriculture and Technology, Pantnagar. He is doing a Ph.D. in Agricultural Statistics from the Department of Mathematics, Statistics and Computer Science at G.B. Pant University of Agriculture and Technology, Pantnagar. His research focuses on Applied Statistics and Agricultural Statistics. He has published extensively in reputed journals and serves as a reviewer for several esteemed academic publications. He completed his B.Sc. from VCSGUUHF, Bharsar and M.Sc. from GBPUA&T, Pantnagar. Alongside his academic responsibilities, he remains actively engaged in research, contributing to advancements in his field.

Vinod Kumar, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India

Vinod Kumar is an esteemed Professor in the Department of Mathematics, Statistics, and Computer Science at G.B. Pant University of Agriculture and Technology, Pantnagar. With a distinguished career in academia, he has also held various administrative positions at the university. His research interests include Applied Statistics, Life Testing, Reliability and Bayesian Inference. He has published numerous research papers in reputed journals and actively contributes as a reviewer for many prestigious publications. Additionally, he serves as an Editor-in-Chief for Journal of Reliability and Statistical Studies further demonstrating his dedication to the advancement of statistical research.

Arun Kumar, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India

Arun Kumar received the Ph.D. degree in Mathematics from Gurukul Kangri University, Haridwar, India, in 1997. His current research interests include Artificial Intelligence and Machine Learning, Fuzzy Multi-Criteria Decision Making (MCDM) and Multi-Objective Optimization. He has actively contributed to these research areas through scholarly publications in reputed international journals. Dr. Kumar also serves as a reviewer for several leading SCI-indexed journals, including Applied Soft Computing, Engineering Applications of Artificial Intelligence, IEEE Transactions on Fuzzy Systems and other high-impact international journals. His current research focuses on developing intelligent, uncertainty-aware, and optimization-driven methodologies for solving complex real-world decision-making problems.

References

Becker, G. S. (1964). Human Capital. University of Chicago Press.

Becker, G. S., and Tomes, N. (1986). Human capital and the rise and fall of families. Journal of Labour Economics, 4(3, Part 2), S1–S39.

Bettinger, E. (2004). How financial aid affects persistence. In College Choices: The Economics of Where to Go, When to Go, and How to Pay for It (pp. 207–238). University of Chicago Press.

Bound, J., Lovenheim, M. F., and Turner, S. (2010). Why have college completion rates declined? An analysis of changing student preparation and collegiate resources. American Economic Journal: Applied Economics, 2(3), 129–157. https://doi.org/10.1257/app.2.3.129.

Breen, R., and Jonsson, J. O. (2005). Inequality of opportunity in comparative perspective: Recent research on educational attainment and social mobility. Annual Review of Sociology, 31, 223–243.

Chetty, R., Hendren, N., Kline, P., and Saez, E. (2014). Where is the land of opportunity? The geography of intergenerational mobility in the United States. The Quarterly Journal of Economics, 129(4), 1553–1623.

Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, S95–S120.

Corak, M. (2013). Income inequality, equality of opportunity, and intergenerational mobility. Journal of Economic Perspectives, 27(3), 79–102.

Cowell, F. (2011). Measuring Inequality. Oxford University Press.

Dynarski, S. M. (2003). Does aid matter? Measuring the effect of student aid on college attendance and completion. American Economic Review, 93(1), 279–288. https://doi.org/10.1257/000282803321455287.

Ferrari, S., and Cribari-Neto, F. (2004). Beta regression for modelling rates and proportions. Journal of Applied Statistics, 31(7), 799–815.

Kane, T. J. (2003). A quasi-experimental estimate of the impact of financial aid on college-going. NBER Working Paper No. 9703. https://doi.org/10.3386/w9703.

Koenker, R. (2005). Quantile Regression. Econometric Society Monographs, Cambridge University Press.

Koenker, R., and Bassett, G. (1978). Regression quantiles. Econometrica, 46(1), 33–50. https://doi.org/10.2307/1913643.

Korkmaz, M. Ç., Chesneau, C., and Korkmaz, Z. S. (2022). The unit folded normal distribution: A new unit probability distribution with the estimation procedures, quantile regression modeling and educational attainment applications. Journal of Reliability and Statistical Studies, 15(1), 261–298.

Lesner, R. V., Damm, A. P., Bertelsen, P., and Pedersen, M. U. (2022). The effect of school-year employment on cognitive skills, risky behavior, and educational achievement. Economics of Education Review, 88, 102241. https://doi.org/10.1016/j.econedurev.2022.102241.

Long, J. S. (1997). Regression Models for Categorical and Limited Dependent Variables. Sage Publications.

Noor-Ul-Amin, M., Asghar, S. U. D., Sanaullah, A., and Shehzad, M. A. (2018). Redescending M-estimator for robust regression. Journal of Reliability and Statistical Studies, 11(2), 69–80.

Oreopoulos, P., and Petronijevic, U. (2013). Making college worth it: A review of research on the returns to higher education. The Future of Children, 23(1), 41–65.

Shorrocks, A. F. (1982). Inequality decomposition by factor components. Econometrica, 50(1), 193–211. https://doi.org/10.2307/1912537.

Tavares, P. A. (2015). The impact of school management practices on educational performance: Evidence from public schools in São Paulo. Economics of Education Review, 48, 1–15. https://doi.org/10.1016/j.econedurev.2015.05.002.

Downloads

Published

2026-09-15

How to Cite

Singh, G., Kumar, V., & Kumar, A. (2026). An Econometric and Distributional Analysis of Determinants of Educational Delay Inequality. Journal of Reliability and Statistical Studies, 19(02), 615–634. https://doi.org/10.13052/jrss0974-8024.19215

Issue

Section

Articles