The worrying trend of non-performing loans in higher education

Jose  Joy Thoppan; Vijay  Victor; Robert  Jeyakumar Nathan; Maria  Fekete-Farkas

doi:10.23762/FSO_VOL7_NO3_5

Published : 2019-09-30

Vol. 7 No. 3 (2019)

The worrying trend of non-performing loans in higher education

Jose Joy Thoppan

https://orcid.org/0000-0002-1015-0251

Vijay Victor

https://orcid.org/0000-0002-0885-5578

Robert Jeyakumar Nathan

https://orcid.org/0000-0002-0897-0015

Maria Fekete-Farkas

https://orcid.org/0000-0002-6058-009X

DOI: https://doi.org/10.23762/FSO_VOL7_NO3_5

Abstract

The rate of education loans is on the rise in both developed and developing countries, whereby an increasing number of middle and upper middle-income families are resorting to bank loans to send their children to pursue higher education. The accessibility of education is among the Sustainable Development Goals (SDG) of the United Nations, listed as SDG Goal 4. Accordingly, economies around the world are promoting higher education, in spite of the high costs. Hence a bank loan becomes an inevitable part of the equation for most individuals who wish to pursue higher education. However, the rising crisis of non-performing education loans is a worrying international trend. Non-performing education loans are categorised as Non-Performing Assets (NPA) in the Indian Banking System. This paper attempts to study the root cause of this rising crisis and subsequently develop a model utilising the variables from an education loan application form, which can be used to predict potential defaults on higher education loans. Further, the study also attempts to explore whether the institutions which offer higher education have any significant impact on a loan becoming non-performing. Statistical tools including the T test, Chi-square test and Linear Discriminant Function were used to analyse the primary data gathered from banks. The results from the study imply that the annual income and net worth of the loan applicant’s parents exhibits a significant relationship with default and non-default on educational loans. This result is connected in turn with the quality of education the candidates receive and the employability of the candidates which various educational institutions produce. Based on the significant findings, the study proposes recommendations which includes advice to banks on being vigilant as regards the repayment ability of the applicant based on certain profiling of the individual, as well as the ability of the education institution and its reputation which affects the employability of the students.

Keywords:

Education loans, Defaults, NPA, Linear Discriminant Analysis

Details

References

Statistics

Authors

Download files

PDF

Citation rules

Joy Thoppan, J., Victor , V. ., Jeyakumar Nathan , R. ., & Fekete-Farkas, M. . (2019). The worrying trend of non-performing loans in higher education. Forum Scientiae Oeconomia, 7(3), 63–76. https://doi.org/10.23762/FSO_VOL7_NO3_5

Altmetric indicators

Cited by / Share

Licence

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

References

Agarwal, P. (2006), Higher education in India: The need for change, working paper no. 180, available at: http://icrier.org/pdf/ ICRIER_WP180__Higher_Education_in_ India_.pdf (accessed 10 May 2019).

Bandyopadhyay, A. (2016), Studying borrower level risk characteristics of education loan in India, IIMB Management Review, 28(3): 126-135. https://doi.org/10.1016/j.iimb. 2016.06.001

Barr, N., Chapman, B., Dearden, L., Dynarski, S. (2019), The US college loans system: Lessons from Australia and England, Economics of Education Review, 71: 32-48. https://doi.org/10.1016/j.econedurev.2018.07.007

Callender, C., Mason, G. (2017), Does student loan debt deter higher education participation? New evidence from England, The Annals of the American Academy of Political and Social Science, 671(1): 20-48. https://doi.org/10.1177/0002716217696041

Britton, J., van der Erve, L., Higgins, T. (2019), Income contingent student loan design: Lessons from around the world, Economics of Education Review, 71: 65-82. https://doi.org/10.1016/j.econedurev.2018.06.001

Erenguc, S.S., Koehler, G.J. (1990), Survey of mathematical programming models and experimental results for linear discriminant analysis, Managerial and Decision Economics, 11(4): 215-225. https://doi.org/10.1002/mde.4090110403

Glen, J.J. (2005), Mathematical programming models for piecewise-linear discriminant analysis, Journal of the Operational Research Society, 56(3): 331- -341.https://doi.org/10.1057/palgrave.jors.2601818

Gross, J.P., Cekic, O., Hossler, D., Hillman, N. (2009), What matters in student loan default: a review of the research literature, Journal of Student Financial Aid, 39(1): 19-29.

Ionescu, A.F. (2008), The federal student loan program: Quantitative implications for college enrolment and default rates, Economics Faculty Working Papers, Paper 4, available at: http://commons.colgate.edu/econ_facschol/4. (accessed 10 May 2019).

Ismail, S., Serguieva, A., Singh, S. (2011), Integrative model of students’ attitude to educational loan repayment: A structural modelling approach, Journal of International Education in Business, 4(2): 125- -140. https://doi.org/10.1108/1836326111 1189522

Kaur, J., Arora, S. (2019), Indian students’ attitude toward educational debt: Scale development and validation, Quality Assurance in Education. DOI: 10.1108/QAE-12-2018-0131 (in press).

Karaçor, Z., Güvenek, B., Ekinci, E., Konya, S. (2018), Panel estimation for the relationship between education expenditure and economic growth for OECD countries, Forum Scientiae Oeconomia, 6(2): 7-20. DOI: 10.23762/FSO_VOL6NO2_18_1

Kliestikova, J., Misankova, M. (2016), European insolvency law harmonisation in terms of global challenges, proceedings from 16th International Scientific Conference on Globalization and its Socio-Economic Consequences (pp. 914-921), 5-6 October 2016, Rajecke Teplice, Slovakia.

Krawczyk, E. (2014), Instruments for higher education adjustment to local labor market needs, Polish Journal of Management Studies, 1(9): 104-114.

Kumar, K., Bhattacharya, S. (2006), Artificial neural network vs linear discriminant analysis in credit ratings forecast: A comparative study of prediction performances, Review of Accounting and Finance, 5(3): 216-227. DOI: 10.1108/14 757700610686426

Mankiw, N.G., Romer, D., Weil, D.N. (1992), A contribution to the empirics of economic growth, The Quarterly Journal of Economics, 107(2): 407-437. DOI: 10.2307/2118477

Mika, S., Ratsch, G., Weston, J., Scholkopf, B., Mullers, K.R. (1999), Fisher discriminant analysis with kernels, in: Neural networks for signal processing IX (pp. 41-48), Proceedings of the 1999 IEEE Signal Processing Society Workshop.

Nathan, R.J., Tan, G.S.T., Shawkataly, O. (2013), Universities at the crossroad: Industry or society driven? Australian Universities’ Review, 55 (2): 110-116.

Onen, D. (2013), Managing the student loans schemes in Africa: Lessons for young loan schemes, International Journal of Education and Research, 2(12): 271-284.

Panjali, N., Kasilingam, R. (2014), Study on education loans default of repayment, ANVESHA – The Journal of Management, 7(4): 28-34.

Pakurár, M., Haddad, H., Popp, J., Khan, T., Oláh, J. (2019a), Supply chain integration, organizational performance and balanced scorecard: An empirical study of the banking sector in Jordan, Journal of International Studies, 12(2): 129-146. DOI:10.14254/2071-8330.2019/12-2/8

Pakurár, M., Haddad, H., Nagy, J., Popp, J., Oláh, J. (2019b), The service quality dimensions that affect customer satisfaction in the Jordanian banking sector, Sustainability 2019, 11, 1113/. DOI: 10.3390/su11041113

Patra, S., Ray, T., Chaudhuri, A.R. (2017), Impact of education loans on higher education: The Indian experience, available at: https://www.isid.ac.in/~epu/ acegd2017/papers/SenjutiPatra.pdf (accessed 20 May 2019).

Plaček, M., Ochrana, F., Půček, M. (2015), Benchmarking in Czech higher education: The case of schools of economics, Journal of Higher Education Policy and Management, 37(4):374-384. DOI: 10.10 80/1360080X.2015.1056601

Rani, P.G. (2018), Financing higher education and education loans in India: The stylized facts, University News, 56: 28.

Rašticová, M., Hazuchová, N., Bédiová, M., Mikušová, J. (2019), Older workers economic activity and the health status – the implication of age management, Polish Journal of Management Studies, 19(1): 322-337. DOI: 10.17512/pjms.2019.19.1.25

Reserve Bank of India (2018), Report on trend and progress of banking in India 2017-18, available at: https://rbidocs.rbi.org.in/rdocs/Publications/PDFs/ 0RTP2018_FE9E97E7 AF7024A4B94 321734CD76DD4F.PDF (accessed 18 March 2019).

Sadaf, R., Oláh, J., Popp, J., Máté, D., (2018), An investigation of the influence of the worldwide governance and competitiveness on accounting fraud cases: A cross-country perspective, Sustainability 2018, 10(3), 588. https://doi.org/10.3390/su10030588

Schüller, D., Pekárek, J., Rašticová, M. (2015), Managerial decisions on optimal The worrying trend of non-performing loans in higher education 75 number of demand segments, in: Conference Proceedings from SGEM 2015: Sociology and Healthcare (pp. 545-552), Sofia: STEF92 Technology Ltd.

Sharma, R.N., Sharma, R.K. (2006), Problems of education in India, New Delhi: Atlantic Publishers & Dist.

Shen, H., Ziderman, A. (2009), Student loans repayment and recovery: International comparisons, Higher Education, 57(3): 315-333. DOI: 10.1007/s10734-008-9146-0

Shu, X., Lu, H. (2014), Linear discriminant analysis with spectral regularization, Applied Intelligence 40(4): 724-731. https://doi.org/10.1007/s10489-013-0485-x

Swicegood, P., Clark, J.A. (2001), Off site monitoring systems for predicting bank underperformance, International Journal of Intelligent Systems in Accounting, Finance and Management, 10(3): 169-186. https://doi.org/10.1002/isaf.201

Ślusarczyk, B., Herbuś, A. (2014), Higher education as a crucial factor of staff development, Polish Journal of Management Studies, 10(2): 216-224.

Štefko, R., Frankovský, M., Bačík, R. (2007), Regional university marketing in underdeveloped regions, Contemporary Economics, 1(3): 71-81.

Thiagarajan, S., Ayyappan, S., Ramachandran, A. (2011), Credit risk determinants of public and private sector banks in India, European Journal of Economics, Finance and Administrative Sciences, 34: 147-154.

Tilak, J. B. (2018), Private higher education in India, in: Education and development in India, Singapore: Palgrave Macmillan.

West, A., Roberts, J., Lewis, J., Noden, P. (2015), Paying for higher education in England: Funding policy and families, British Journal of Educational Studies, 63(1): 23-45. https://doi.org/10.1080/00071005.2014.990353

Ziderman, A. (2013), Increasing access to higher education through student loans, CESifo DICE Report, 11(2): 11-18.

Zvarikova, K., Majerova, J. (2013), Financial literacy in the Slovak Republic, Procedia – Social and Behavioral Sciences, 110: 1106-1115. https://doi.org/10.1016/j.sbspro.2013.12.957

Jose Joy Thoppan

jose.joy@saintgits.org
https://orcid.org/0000-0002-1015-0251

Affiliation:

Saintgits Institute of Management, Kerala India

Bio Statement (e.g., department and rank)

Dr. Jose Joy Thoppan is an associate professor at Saintgits Institute of Management, Kerala, India. He is the Centre Co-ordinator for the Saintgits Centre for Applied Finance (SCAF) and the Saintgits-Bloomberg Finance Lab. He has work experience in the industry spanning around seven years and teaching experience of eight years at the MBA level.

Vijay Victor

Victor.Vijay@phd.uni-szie.hu
https://orcid.org/0000-0002-0885-5578

Affiliation:

Szent Istvan University, Godollo Hungary

Bio Statement (e.g., department and rank)

Vijay Victor is currently a research scholar at the Doctoral School of Management and Business Administration, Szent Istvan University, Hungary. He is an assistant professor at Saintgits Institute of Management, Kerala, India. His areas of interest are pricing strategies in e-commerce, behavioural economics and econometrics

Robert Jeyakumar Nathan

robert.jeyakumar@mmu.edu.my
https://orcid.org/0000-0002-0897-0015

Affiliation:

Multimedia University, Melaka Malaysia

Bio Statement (e.g., department and rank)

Dr. Robert Jeyakumar Nathan is the Head of Marketing at the Faculty of Business, Multimedia University, Malaysia. Prior to joining academia, he was a Systems Analyst with Siemens Semiconductor AG. He works with semiconductor Big Data analytics and trains engineers to solve human problems. He is a design thinking trainer certified by Stanford University Design School and is passionate about empowering organisations to strategically manage innovations with design thinking. His research interests include Marketing, Electronic Commerce, Innovation and Leadership.

Maria Fekete-Farkas

Farkasne.Fekete.Maria@gtk.szie.hu
https://orcid.org/0000-0002-6058-009X

Affiliation:

Szent Istvan University, Godollo Hungary

Bio Statement (e.g., department and rank)

Dr. Maria Fekete Farkas is a professor and head of the Department for Microeconomics at the Faculty of Economics and Social Sciences, Szent Istvan University, Hungary. She is supervisor and leader of English language program for the Doctoral School of Management and Business Administration. Her research areas are sustainable development, Industry 4.0, new market structures and pricing, economics of natural resources, economic, social and environment aspects of climate change, land use and renewable energy. She is a member of the organising committees of several international conferences, and serves certain international journals as a member of the editorial board, reviewer and author.