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Journal articles on the topic 'Student Loan Default'

Author: Grafiati

Published: 11 September 2021

Last updated: 17 July 2025

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1

Greene, Laura L. "An Economic Analysis of Student Loan Default." Educational Evaluation and Policy Analysis 11, no. 1 (1989): 61–68. http://dx.doi.org/10.3102/01623737011001061.

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An existing model of student loan default uses discriminant function analysis to identify the characteristics of borrowers who repay their loans and those who default. This paper uses data on National Direct Student Loan borrowers at the University of North Carolina at Greensboro to confirm the results of a previous paper’s discriminant function analysis and to present an alternative method of analysis, the Tobit technique. An advantage of Tobit is that it uses information not only from the categorical default/no default decision but also from the magnitude of the default.

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Kelchen, Robert, and Amy Y. Li. "Institutional Accountability: A Comparison of the Predictors of Student Loan Repayment and Default Rates." ANNALS of the American Academy of Political and Social Science 671, no. 1 (2017): 202–23. http://dx.doi.org/10.1177/0002716217701681.

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The federal government holds colleges accountable if too many of their students default on loan repayment, but the default measure traditionally used captures only a fraction of students who are struggling to repay their loans. The 2015 College Scorecard dataset introduced a new loan repayment metric, showing that the percentage of students who have not reduced the principal balance of their loans by at least $1 over a given period of time far outstrips the traditional loan default measure. Using a sample of 3,595 colleges, we test the extent to which student demographics, institutional characteristics, and state-level economic factors are associated with repayment rates and default rates. We also examine whether factors associated with loan repayment rates change between one and seven years after students begin repayment. We find that characteristics traditionally associated with economic disadvantage, including being a first-generation college student or a member of an underrepresented minority group, tend to be associated with lower loan repayment rates, as does attendance at for-profit colleges. These factors are just as or more strongly associated with longer-term repayment rates compared to shorter-term repayment rates.

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Darolia, Rajeev. "Messengers of Bad News or Bad Apples? Student Debt and College Accountability." Education Finance and Policy 10, no. 2 (2015): 277–99. http://dx.doi.org/10.1162/edfp_a_00161.

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Student loan debt and defaults have been steadily rising, igniting public worry about the associated public and private risks. This has led to controversial regulatory attempts to curb defaults by holding colleges, particularly those in the for-profit sector, increasingly accountable for the student loan repayment behavior of their students. Such efforts endeavor to protect taxpayers against the misuse of public money used to encourage college enrollment and to safeguard students against potentially risky human capital investments. Recent policy proposals penalize colleges for students’ poor repayment performance, raising questions about institutions’ power to influence this behavior. Many of the schools at risk of not meeting student loan default measures also disproportionately enroll low-income, nontraditional, and financially independent students. Policy makers therefore face the challenge of promoting the efficient use of public funds and protecting students while also encouraging access to higher education.

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Shahdad, Moe. "The Rise Of The Student As A Consumer Of Education." Contemporary Issues in Education Research (CIER) 7, no. 1 (2013): 19. http://dx.doi.org/10.19030/cier.v7i1.8307.

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Data from the US Department of Education (2012) indicates that college education has become an expensive investment to the extent that an increasing number of students cannot pay for it, as they default on their loans. Student loan default rate have increased from 4.6% in 2005 to 9.1% in 2010.

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5

Beyer, Harald, Justine Hastings, Christopher Neilson, and Seth Zimmerman. "Connecting Student Loans to Labor Market Outcomes: Policy Lessons from Chile." American Economic Review 105, no. 5 (2015): 508–13. http://dx.doi.org/10.1257/aer.p20151026.

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Rising student loan default rates and protests over debt suggest that many students make college enrollment and financing choices they regret. Policymakers have considered tying the availability of federally subsidized loans at degree programs to financial outcomes for past students. This paper considers the implementation of such a policy in Chile. We describe how loan repayment varied by degree type at baseline, the design of the loan reform, and how earnings-based loan caps change availability of loans and incentives for students and higher education institutions. We discuss the challenges facing policymakers seeking to link loan availability to earnings outcomes.

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Et. al., Khusboo Srivastava,. "Theoretical Framework Over Vivid Facets on Student’s Intention To Payback Education Loan in India." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (2021): 1895–900. http://dx.doi.org/10.17762/turcomat.v12i5.2269.

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Education loan is a magical solution to address the education financing crises. It bridges the gap between accessibility and sustainability but incidents of defaults in the repayment of education loan are on rise. The Present study covers various theories has been thoroughly analysed to understand the factors which influence the intention of student to repay the education loan. A comprehensive theoretical framework has been designed by reviewing various literatures on student intention. This structural framework encompasses - Attitude to repay or default of education loan, Integrity, Parental influence, awareness of loan agreement, Willingness to invest in educational plans, Student’s characteristics, Financial ability to pay and student’s priority from Theory of reasoned action, Theory of Planned behaviour, Theory of human capital and Theory of ability to pay. In this research study, all efforts have been made to determine the possible linkage between factors that influence student’s intention to pay or default the education loan.

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7

Burrell, Darrell N., Jorja B. Wright, Mindy Perot, et al. "Financial Management Education Courses as Social Societal Learning Tools at Minority-Serving Colleges and Universities." International Journal of Public Sociology and Sociotherapy 1, no. 1 (2021): 39–57. http://dx.doi.org/10.4018/ijpss.2021010104.

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A 2016 article in the Nation outlined that in the United States the average Black family would need 228 years to build comparable wealth to the average white family. Today, achieving the dream of higher education has posed many threats to the Hispanic and African American communities. In order to achieve the dream, many minority students receive student loans to fund their higher education pursuits with hopes that future employment will afford repayment. However, most do not realize the risks. Student loan debt is a severe and mounting problem in the United States. In the United States, seven million student loan borrowers are now in default. Forty-five percent of college students obtain some sort of credit card debt. Lack of knowledge on financial literacy and student loan debt management is a serious issue. This article presents findings that support making financial literacy courses mandatory for college students just like Mathematics and English courses, especially at minority-serving colleges and universities.

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8

Darolia, Rajeev, and Dubravka Ritter. "Strategic Default Among Private Student Loan Debtors: Evidence from Bankruptcy Reform." Education Finance and Policy 15, no. 3 (2020): 487–517. http://dx.doi.org/10.1162/edfp_a_00285.

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Bankruptcy reform in 2005 restricted debtors’ ability to discharge private student loan debt. The reform was motivated by the perceived incentive of some borrowers to file for bankruptcy under Chapter 7 even if they had, or expected to have, sufficient income to service their debt. Using a nationally representative sample of millions of anonymized credit bureau files, we examine whether private student loan borrowers distinctly adjusted their Chapter 7 bankruptcy filing behavior after the reform. We do not find evidence to indicate that the moral hazard associated with dischargeability appreciably affected the behavior of private student loan debtors prior to the policy. Thus, our findings do not provide empirical support to the theoretical concerns about pervasive strategic default that inspired lawmakers to make private student loan debt largely nondischargeable.

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9

Volkwein, J. Fredericks, and Bruce P. Szelest. "Individual and campus characteristics associated with student loan default." Research in Higher Education 36, no. 1 (1995): 41–72. http://dx.doi.org/10.1007/bf02207766.

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10

Cellini, Stephanie R., Rajeev Darolia, and Lesley J. Turner. "Where Do Students Go When For-Profit Colleges Lose Federal Aid?" American Economic Journal: Economic Policy 12, no. 2 (2020): 46–83. http://dx.doi.org/10.1257/pol.20180265.

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We examine the effects of federal sanctions imposed on for-profit institutions in the 1990s. Using county-level variation in the timing and magnitude of sanctions linked to student loan default rates, we estimate that sanctioned for-profits experience a 68 percent decrease in annual enrollment following sanction receipt. Enrollment losses due to for-profit sanctions are 60–70 percent offset by increased enrollment within local community colleges, where students are less likely to default on federal student loans. Conversely, for-profit sanctions decrease enrollment in local unsanctioned for-profit competitors, likely due to improved information about local options and reputational spillovers. Overall, market enrollment declines by 2 percent. (JEL H52, I21, I22, I23, I28)

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O Dobson, Genevieve. "Industry Analysis: The Student Loan Industry and History of Policy and Changes." Muma Business Review 3 (2019): 165–75. http://dx.doi.org/10.28945/4395.

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From the beginning of their inception, Student Loans have been a powerful and many times necessary tool to allow middle and low-income families to send their children to college despite the increasingly higher costs (Collier, D. A., & Herman, R., 2016). These loans are highly regulated by the government under different policies that have changed over time based on the needs of the borrowers and government officials involved. The concern, however, is that the debt is becoming harder for some borrowers to pay causing defaults and other household burdens (Archuleta, Dale and Spann 2013). There is significant importance to understanding the history of the student loan industry to learn the best way to manage student loan debt. There is much debate about which options are the most advantageous and the government has gone to great trouble of adding the new Income Driven Repayment programs to help assist in repayment. These programs came about due to the vast number of borrowers defaulting on their student loans because of a limited number of options. Unfortunately, Student Loan debt is the only debt that has extremely strict rules all but excluding bankruptcy causing some borrowers to default (Mohr, 2017). Consolidation options for borrowers also changed FFEL programs ended in 2007 taking away borrower benefits like 1% rate reductions after so many timely payments. Income Driven Repayment has been filling the gap and assisting in those who cannot afford a standard payment to make lower payments but also shown in Figure 1, only 28% of borrowers are taking advantage of the options. It is suggested the low participation is due to a lack of knowledge about the programs (Collier, D. A., & Herman, R., 2016). My analysis is to shed light on the history of the industry, the current policies in place and how regulations and changes to regulations affect borrowers and the overall U.S. debt load. If we can take a good look at how some regulations have been ineffectively curtailing the debt boom we may be able to find more effective ways to manage student loan debt. I will be also discussing seven key effects causing the higher debt and default rates within the industry: • The monopoly of having only The Department of Education in charge of disbursing and servicing Federal loan debt leaves no competitive advantages for borrowers • The ineffective results of the Consumer Financial Protection Bureau which was once under Dodd-Frank and was created to help alleviate decisive practices against consumers including student loan borrowers • The continued lawsuits against government hired servicing companies who are supposed to have the best interest of the borrower but rather have allegedly mislead borrowers purposely into higher payments and more debt. “The CFPB alleges that, among other allegations, Navient [one of the leading student loan servicing companies] ‘systematically and illegally [failed] borrowers at every stage of repayment’…” (Friedman, 2018) • The lack of information provided to borrowers to make an informed decision about their options • The higher Federal interest rates on student loan which do not coincide with the Libor or Prime rates that continue to be at a considerably lower rate than student loans • The drastic increases in tuition costs at both public, private and for-profit school Before tackling the problem, it is vital to understand the history of the problem and how we got here. There are many changes we can see that have occurred throughout history that started off with the hopes of bridging the gap between high income families and low-income families being able to get a higher education. From what can be seen the government shifted from grants and scholarships which provided for the social good of our society to a loan dominant scheme with huge reliance’s on student loans for an education (Collier, D. A., & Herman, R., 2016).

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12

Hillman, Nicholas W. "College on Credit: A Multilevel Analysis of Student Loan Default." Review of Higher Education 37, no. 2 (2014): 169–95. http://dx.doi.org/10.1353/rhe.2014.0011.

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13

Conzelmann, Johnathan G., T. Austin Lacy, and Nichole D. Smith. "Another Day Another Dollar Metric? An Event History Analysis of Student Loan Repayment." Education Finance and Policy 14, no. 4 (2019): 627–51. http://dx.doi.org/10.1162/edfp_a_00264.

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Students increasingly borrow money to attend postsecondary education. Yet, little attention in the literature has been paid to the repayment of student loans, aside from failure indicators like default. This study examines a nationally representative sample of bachelor's degree recipients and the time it takes to reach different repayment thresholds on federal student loans. Motivated by a new federal emphasis on student loan repayment rates, we use event history analysis to show that bachelor's degree recipients have little trouble paying down $1 of their principal balance upon entering repayment. After just six months, 65 percent of this student borrower population had already done so, often paying back much more than $1. However, borrowers enrolled in federal Income-Driven Repayment (IDR) plans took much longer to pay down any loan balance, and many never did in the time we observed their repayment. The consistently negative relationship we find between IDR and paying down loan balances highlights a tension between repayment rate policies and the push for IDR reform of the U.S. repayment system. We discuss the policy implications of these findings and offer an alternative repayment metric to accommodate the contrasting goals and structures of standard repayment schedules versus income-contingent repayment policies.

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14

Mbah, Ruth Endam. "Expanding the Theoretical Framework in Student Debt Research by Connecting Public Policy Theories to the Student Debt Literature." Advances in Social Sciences Research Journal 8, no. 11 (2021): 211–19. http://dx.doi.org/10.14738/assrj.811.11196.

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Current changes in the economic atmosphere have severely impacted the higher education sector worldwide. Policymakers worldwide are facing the challenge of adjusting tuition and financial aid programs in response to these changing economic times. The shift from federal grants to loans has caused student loans to be a popular means of funding higher education for most low and medium-income families. A result of this, is the increase in student loan default as most college students graduate with unmanageable debts, thus, a rising concern for policymakers. The purpose of this paper is to link four public policy theories (Social Contract Theory, Utilitarian Theory, Theory of Neoliberalism, and Three-Policy Stream Theory) to student loan literature. This is to expand the limited database of public policy theories in student loan debt literature. This theoretical linkage points out the role of policymakers in (1) ensuring the security of lives and the preservation of the property of those who voted them into power (Social Contract Theory); (2) establishing educational policies that ensure the ‘the greatest happiness of the greatest number’ (Utilitarian Theory); (3) upholding social welfare or a ‘welfare state’ through fiscal and monetary policies to ensure high employment rates for graduates, low inflation and the provision of public goods (Theory of Neoliberalism), and (4) the risk of undermining the growing power of an informal interest group that is made up of millennials saddled with student loan debt (Three-Policy Stream). These theories reiterate the principal role of policymakers in enhancing human capital through affordable education.

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Volkwein, J. Fredericks, Bruce P. Szelest, Alberto F. Cabrera, and Michelle R. Napierski-Prancl. "Factors Associated with Student Loan Default among Different Racial and Ethnic Groups." Journal of Higher Education 69, no. 2 (1998): 206. http://dx.doi.org/10.2307/2649206.

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Volkwein, J. Fredericks, Bruce P. Szelest, Alberto F. Cabrera, and Michelle R. Napierski-Prancl. "Factors Associated with Student Loan Default among Different Racial and Ethnic Groups." Journal of Higher Education 69, no. 2 (1998): 206–37. http://dx.doi.org/10.1080/00221546.1998.11775133.

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17

Ionescu, Felicia. "The Federal Student Loan Program: Quantitative implications for college enrollment and default rates." Review of Economic Dynamics 12, no. 1 (2009): 205–31. http://dx.doi.org/10.1016/j.red.2008.09.004.

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18

Looney, Adam, and Constantine Yannelis. "How useful are default rates? Borrowers with large balances and student loan repayment." Economics of Education Review 71 (August 2019): 135–45. http://dx.doi.org/10.1016/j.econedurev.2018.10.004.

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19

Wilms, Wellford W., Richard W. Moore, and Roger E. Bolus. "Whose Fault is Default? A Study of the Impact of Student Characteristics and Institutional Practices on Guaranteed Student Loan Default Rates in California." Educational Evaluation and Policy Analysis 9, no. 1 (1987): 41–54. http://dx.doi.org/10.3102/01623737009001041.

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20

Barr, Andrew, Kelli A. Bird, and Benjamin L. Castleman. "The effect of reduced student loan borrowing on academic performance and default: Evidence from a loan counseling experiment." Journal of Public Economics 202 (October 2021): 104493. http://dx.doi.org/10.1016/j.jpubeco.2021.104493.

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21

Hou, Yujiao, Xiaofeng Ma, Guang Mei, Ning Wang, and Weisheng Xu. "A Trial of Student Self-Sponsored Peer-to-Peer Lending Based on Credit Evaluation Using Big Data Analysis." Computational Intelligence and Neuroscience 2019 (April 16, 2019): 1–11. http://dx.doi.org/10.1155/2019/9898251.

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There is still no effective approach to overcome the problem of credit evaluation for Chinese students. In absence of a reliable credit evaluation system for students, the university students have to only apply through online peer-to-peer (P2P) loan platforms because Chinese financial institutions typically reject students’ loan applications. Lack of students’ financial records hinders financial institutes and banks to routinely evaluate the students’ credit status and assign loans to them. Hence, this paper attempted to benefit from university students’ diversified daily behavior data, and logistic regression (LR) and gradient boosting decision tree (GBDT) algorithms were also used to develop robust credit evaluation models for university students, in which the validation of the proposed models was assessed by a real-time P2P lending platform. In this study, the students’ overdue behavior in returning books to university library was used as an index. With training 17838 samples, the proposed models performed well, while GBDT-based model outperformed in identification of “bad borrowers.” Based on the proposed models, a self-sponsored peer-to-peer loan platform was established and developed in a Chinese university for ten months, and the achieved findings demonstrated that adopting such credit evaluation models can effectively reduce the default ratio.

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Choi, Jae-Seok, Jun-Tae Han, Myeon-Jung Kim, and Jina Jeong. "Developing the high risk group predictive model for student direct loan default using data mining." Journal of the Korean Data and Information Science Society 26, no. 6 (2015): 1417–26. http://dx.doi.org/10.7465/jkdi.2015.26.6.1417.

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23

Cox, James C., Daniel Kreisman, and Susan Dynarski. "Designed to fail: Effects of the default option and information complexity on student loan repayment." Journal of Public Economics 192 (December 2020): 104298. http://dx.doi.org/10.1016/j.jpubeco.2020.104298.

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Kramer, Dennis A., Christina Lamb, and Lindsay C. Page. "The effects of default choice on student loan borrowing: Experimental evidence from a public research university." Journal of Economic Behavior & Organization 189 (September 2021): 470–89. http://dx.doi.org/10.1016/j.jebo.2021.04.023.

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Rasyadan, Resa, and Buddi Wibowo. "Analysis of Failure to Pay Education Fee Receivables at XYZ Institution." Syntax Literate ; Jurnal Ilmiah Indonesia 9, no. 8 (2024): 4536–48. http://dx.doi.org/10.36418/syntax-literate.v9i8.16227.

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The XYZ Institution/Agency, as a governmental institution, has the obligation to resolve debts resulting from financial losses. This study aims to analyze the influence of demographic, financing, and educational institution characteristics on the default of educational debt/student loan (State Compensation Claim for Breach of Service Agreement) at the XYZ Agency. To achieve this objective, the study utilizes data on debtors from 1983 to 2022 at the XYZ Agency and employs binary logistic regression analysis. The results of this study indicate that age and receivable value significantly affect the quality of the debtor's receivables. Specifically, debtors who are over 26 years old and have receivable values above IDR 200,000,000 are more likely to default. In contrast, gender and campus status do not significantly impact the default rates. These findings are expected to provide insights for the XYZ Agency in improving the management of state debts, particularly educational debts, and contribute to the development of policies related to debt management in the public sector. This study also provides a comprehensive overview of the variables influencing the default of educational debts at the XYZ Agency, with a focus on the State Compensation Claim (TGR) process at the XYZ Agency.

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Charles, Kayla D., Shannon Sheaff, Jann Woods, and Lisa Downey. "Decreasing Your Student Loan Cohort Default Rate: Leading a College-Wide Change Initiative at Mohave Community College." Community College Journal of Research and Practice 40, no. 7 (2016): 597–606. http://dx.doi.org/10.1080/10668926.2015.1125814.

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Amos Abidemi Ogunola and Adriana N Dugbartey. "AI-powered financial tools for student debt management in the U.S.: Enhancing financial literacy and economic stability." World Journal of Advanced Research and Reviews 24, no. 2 (2024): 868–91. http://dx.doi.org/10.30574/wjarr.2024.24.2.3441.

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The increasing burden of student debt in the United States has become a significant economic challenge, affecting millions of borrowers and influencing broader economic trends. With over $1.7 trillion in student loan debt, many borrowers struggle with understanding complex repayment plans, managing interest rates, and avoiding default. This paper explores the potential of AI-powered financial tools in transforming student debt management and enhancing financial literacy. It examines how artificial intelligence (AI) can provide personalized solutions to borrowers, helping them navigate loan repayment options, optimize debt management strategies, and improve their financial decision-making. By analysing the key AI tools currently in use—such as budgeting apps, refinancing platforms, and debt consolidation services—this paper demonstrates how AI can simplify and automate the often overwhelming process of student debt management. Furthermore, the paper discusses the role of AI in improving financial literacy, particularly by offering borrowers real-time insights into their financial status and providing customized advice. This integration of AI with financial education helps to build a more financially literate population, better equipped to manage student debt and achieve long-term economic stability. The paper also addresses ethical considerations, such as data privacy and the risk of bias in AI algorithms, and proposes strategies for mitigating these challenges. By focusing on AI-driven innovations, the paper argues that these tools have the potential to revolutionize the way student debt is managed in the U.S., ultimately enhancing economic mobility and reducing the long-term financial strain on borrowers.

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Amos, Abidemi Ogunola, and N. Dugbartey Adriana. "AI-powered financial tools for student debt management in the U.S.: Enhancing financial literacy and economic stability." World Journal of Advanced Research and Reviews 24, no. 2 (2024): 868–91. https://doi.org/10.5281/zenodo.15089473.

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The increasing burden of student debt in the United States has become a significant economic challenge, affecting millions of borrowers and influencing broader economic trends. With over $1.7 trillion in student loan debt, many borrowers struggle with understanding complex repayment plans, managing interest rates, and avoiding default. This paper explores the potential of AI-powered financial tools in transforming student debt management and enhancing financial literacy. It examines how artificial intelligence (AI) can provide personalized solutions to borrowers, helping them navigate loan repayment options, optimize debt management strategies, and improve their financial decision-making. By analysing the key AI tools currently in use—such as budgeting apps, refinancing platforms, and debt consolidation services—this paper demonstrates how AI can simplify and automate the often overwhelming process of student debt management. Furthermore, the paper discusses the role of AI in improving financial literacy, particularly by offering borrowers real-time insights into their financial status and providing customized advice. This integration of AI with financial education helps to build a more financially literate population, better equipped to manage student debt and achieve long-term economic stability. The paper also addresses ethical considerations, such as data privacy and the risk of bias in AI algorithms, and proposes strategies for mitigating these challenges. By focusing on AI-driven innovations, the paper argues that these tools have the potential to revolutionize the way student debt is managed in the U.S., ultimately enhancing economic mobility and reducing the long-term financial strain on borrowers.

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Wright, Laura, David Walters, and David Zarifa. "Government Student Loan Default: Differences between Graduates of the Liberal Arts and Applied Fields in Canadian Colleges and Universities." Canadian Review of Sociology/Revue canadienne de sociologie 50, no. 1 (2013): 89–115. http://dx.doi.org/10.1111/cars.12004.

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Chan, Vivian. "Efficacy and Impact of Key Performance Indicators as Perceived by Key Informants in Ontario Universities." Canadian Journal of Higher Education 45, no. 4 (2015): 440–56. http://dx.doi.org/10.47678/cjhe.v45i4.184917.

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The Ontario Ministry of Education and Training’s Task Force on University Accountability first proposed key performance indicators (KPIs) for colleges and universities in Ontario in the early 1990s. The three main KPIs for Ontario universities are the rates of (1) graduation, (2) employment, and (3) Ontario Student Assistance Program loan default. This exploratory and descriptive study examined the perceptions of 12 key informants from 11 participating universities about the efficacy and effectiveness of these KPIs. The results of this study demonstrate that a clear majority of participants believe these KPIs are not having the intended impact. This paper analyzes the evidence and makes recommendations designed to foster efficient collaboration between stakeholders; it also asks all parties to clarify their goals, agreed expectations, and requirements, in order to develop effective measures of institutional performance and accountability and address the political needs of the government, the universities, and the public.

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31

Flint, Thomas A. "Predicting Student Loan Defaults." Journal of Higher Education 68, no. 3 (1997): 322. http://dx.doi.org/10.2307/2960044.

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Flint, Thomas A. "Predicting Student Loan Defaults." Journal of Higher Education 68, no. 3 (1997): 322–54. http://dx.doi.org/10.1080/00221546.1997.11778986.

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Zakaria, Nor Balkish, Muhammad Rasyid, Norazida Mohamed, Dalila Daud, and Aida Maria Ismail. "Study Loan Defaults Among Tertiary Graduates." International Journal of Financial Research 11, no. 3 (2020): 125. http://dx.doi.org/10.5430/ijfr.v11n3p125.

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Educational Loan is an alternative of financial aids that is provided by the study funding agencies of Malaysia to reduce the economic burden of students in order to finance their studies at tertiary education level. Despite the increasing number of students who obtaining these educational loans, the issue of default in loan repayment among borrowers merely needs research attention. Thus this study aims to investigate the factors of study loan default among Malaysia tertiary graduates. Among the factors examined are family income, employment status and loan amount while respondents’ age, gender, marital status and education level are controlled. Questionnaires were distributed to 430 Universiti Teknologi MARA, Johor, Malaysia graduated respondents via online and 209 were returned The result of this study revealed that the employment status, loan amount and education level are significant to study loan default among Malaysian tertiary graduates. The results could serve some highlights to any study financing agencies to further understand their borrower intuitions in paying back loans they owed.

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Yang, Po. "Default in the student loans." Frontiers of Education in China 9, no. 2 (2014): 283–86. http://dx.doi.org/10.1007/bf03397020.

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35

Brady, Samantha, Julie Miller, Alexa Balmuth, Lisa D'Ambrosio, and Joseph Coughlin. "RETIRING AND CAREGIVING IN THE AGE OF STUDENT LOANS: THE IMPACT OF STUDENT DEBT ON RETIREMENT AND LONGEVITY PLANNING." Innovation in Aging 3, Supplement_1 (2019): S42. http://dx.doi.org/10.1093/geroni/igz038.163.

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Abstract Saving for retirement and the ability to provide care for a loved one can be dramatically affected by student loan debt. Currently, approximately 44 million people of all ages in the United States carry the weight of over 1.4 trillion dollars of student loan debt. Student loan borrowers of all ages may experience lower financial preparedness for retirement as well as decreased ability to provide care for family members, including aging parents. While older adults hold a relatively small proportion of student loans, they are the fastest growing subset of student loan borrowers and have disproportionately high rates of student loan defaults. As a result of their defaults, the Social Security retirement benefits of Americans ages 65 and older experienced a 500% increase in offsets over the last decade. This presentation will spotlight an MIT AgeLab mixed methods study about how student loan borrowers between the ages of 51 and 75 experience student loans within family systems and perceive and prioritize longevity planning in light of their student loans. Data collected for this study include focus groups and a large national survey. Preliminary findings suggest that for older borrowers, student loans are generally one of several financial constraints that can inform spending and saving decisions. For most, student loan payments are regarded as stunting overall retirement savings while the minority regard the two separately. Older borrowers also tend to have increased financial and familial responsibilities, including caring for aging parents, that compete for borrowers’ limited financial and temporal resources.

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Mueller, Holger M., and Constantine Yannelis. "The rise in student loan defaults." Journal of Financial Economics 131, no. 1 (2019): 1–19. http://dx.doi.org/10.1016/j.jfineco.2018.07.013.

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Looney, Adam, and Constantine Yannelis. "What Went Wrong with Federal Student Loans?" Journal of Economic Perspectives 38, no. 3 (2024): 209–36. http://dx.doi.org/10.1257/jep.38.3.209.

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At a time when the returns to college and graduate school are at historic highs, why do so many students struggle with their student loans? The increase in aggregate student debt and the struggles of today’s student loan borrowers can be traced to changes in federal policies intended to broaden access to federal aid and educational opportunities, and which increased enrollment and borrowing in higher-risk circumstances. Starting in the late 1990s, policymakers weakened regulations that had constrained institutions from enrolling aid-dependent students. This led to rising enrollment of relatively disadvantaged students, but primarily at poor-performing, low-value institutions whose students systematically failed to complete a degree, struggled to repay their loans, defaulted at high rates, and foundered in the job market. As these new borrowers experienced similarly poor outcomes, their loans piled up, loan performance deteriorated, and with it the finances of the federal program. The crisis illustrates the important role that educational institutions play in access to postsecondary education and student outcomes, and difficulty of using broadly-available loans to subsidize investments in education when there is so much heterogeneity in outcomes across institutions and programs and in the ability to repay of students.

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Avery, Christopher. "Review of Books on Student Loans." Journal of Economic Literature 57, no. 2 (2019): 403–33. http://dx.doi.org/10.1257/jel.20181469.

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This essay reviews three recent books on the causes and consequences of student debt. In addition to increases in college tuition and fees, supply and resource constraints both contribute to the growing phenomenon of default: degree completion rates are relatively low, especially at two-year colleges. Default rates actually decrease with the amount of debt incurred, as students who incur more debt are more likely to complete degrees that bolster their earning power. These books suggest some promising policy options, but there are no quick fixes given that the net cost of attending both private and public colleges continues to rise. (JEL D14, I22, I23, I28)

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Herbst, Daniel. "The Impact of Income-Driven Repayment on Student Borrower Outcomes." American Economic Journal: Applied Economics 15, no. 1 (2023): 1–25. http://dx.doi.org/10.1257/app.20200362.

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In the United States, most student loans follow a fixed payment schedule that falls early in borrowers’ careers. This structure provides no insurance against earnings risk and may increase student loan defaults. Income-driven repayment (IDR) plans are designed to help distressed student borrowers by lowering their monthly payments to a share of income. Using random variation in a loan servicer’s automatic dialing system, I find that IDR reduces delinquencies by 22 percentage points and decreases outstanding balances within eight months of take-up. I find suggestive long-run impacts on borrower credit scores, mortgage-holding rates, and other measures of financial health. (JEL G23, G51, H52, I22)

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Radnitz, Cynthia, Katharine L. Loeb, Kathleen L. Keller, et al. "Effect of default menus on food selection and consumption in a college dining hall simulation study." Public Health Nutrition 21, no. 7 (2018): 1359–69. http://dx.doi.org/10.1017/s1368980017004220.

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AbstractObjectiveTo test an obesity prevention strategy derived from behavioural economics (optimal defaults plus delay), focused on changing the college dining hall service method.DesignAfter a uniform pre-load, participants attended an experimental lunch in groups randomized to one of three conditions: a nutrient-dense, lower-fat/energy lunch as an optimal default (OD); a less-nutrient-dense, higher-fat/energy lunch as a suboptimal default (SD); or a free array (FA) lunch. In the OD condition, students were presented a menu depicting healthier vegetarian and omnivore foods as default, with opt-out alternatives (SD menu) available on request with a 15 min wait. In the SD condition, the same menu format was used with the positioning of food items switched. In the FA condition, all choices were presented in uniform fonts and were available immediately.SettingPrivate rooms designed to provide a small version of a college dining hall, on two campuses of a Northeastern US university.SubjectsFirst-year college students (n129).ResultsThere was a significant main effect for condition on percentage of optimal choices selected, with 94 % of food choices in the OD condition optimal, 47 % in the FA condition optimal and none in the SD condition optimal. Similarly, energy intake for those in the SD condition significantly exceeded that in the FA condition, which exceeded that in the OD condition.ConclusionsPresenting menu items as optimal defaults with a delay had a significant impact on choice and consumption, suggesting that further research into its long-term applicability is warranted.

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Merisotis, Jamie P. "Forum: Student Loan Defaults Shared Responsibility Could Be Cure." Change: The Magazine of Higher Learning 20, no. 1 (1988): 6–49. http://dx.doi.org/10.1080/00091383.1988.10570173.

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Sungmin Han. "Student Academic Performance, Dropout Decisions and Loan Defaults: Evidence from the Government College Loan Program." KDI Journal of Economic Policy 38, no. 1 (2016): 71–91. http://dx.doi.org/10.23895/kdijep.2016.38.1.71.

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Nyathira Kamau, Pauline. "Modelling Factors Affecting Probability of Loan Default: A Quantitative Analysis of the Kenyan Students' Loan." International Journal of Statistical Distributions and Applications 4, no. 1 (2018): 29. http://dx.doi.org/10.11648/j.ijsd.20180401.14.

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Singhal, Dr Vikas, and Prashant Tiwari. "Predicting Loan Repayment: A Machine Learning Approach." International Research Journal of Computer Science 12, no. 04 (2025): 125–30. https://doi.org/10.26562/irjcs.2025.v1204.02.

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In order to reduce the risk of loan payment default, banks must assess and forecast the loaners' capacity to repay. As a result, a system has been established by the banks to handle the loan application according to the loaner's circumstances, including work status, credit history, etc. However, some loaners, including students or those without credit histories, may not be able to be evaluated for repayment capacity using the present rating approach. We trained a variety of machine learning models on the Home Credit Default Risk Kaggle dataset and assessed the significance of each feature in order to accurately determine the repayment capacity of every group of individuals. Next, using the features relevance score as a guide, we examine and choose the most recognizable characteristics to forecast the loaner's capacity for payback.

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Ionescu, Felicia, and Nicole B. Simpson. "Default Risk and Private Student Loans : Implications for Higher Education Policies." Finance and Economics Discussion Series 2014, no. 66r (2014): 1–59. http://dx.doi.org/10.17016/feds.2014.66r.

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Ionescu, Felicia, and Nicole Simpson. "Default risk and private student loans: Implications for higher education policies." Journal of Economic Dynamics and Control 64 (March 2016): 119–47. http://dx.doi.org/10.1016/j.jedc.2015.12.003.

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Schwartz, Saul. "The Dark Side of Student Loans: Debt Burden, Default, and Bankruptcy." Osgoode Hall Law Journal 37, no. 1 (1999): 307–38. http://dx.doi.org/10.60082/2817-5069.1539.

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Grubb, W. Norton. "The Long-Run Effects of Proprietary Schools on Wages and Earnings: Implications for Federal Policy." Educational Evaluation and Policy Analysis 15, no. 1 (1993): 17–33. http://dx.doi.org/10.3102/01623737015001017.

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Although proprietary schools serve large (and increasing) numbers of students, including many low-income students, the large amounts of student aid going to them, the high default rates of their students, and news about fraud and abuse suggest substantial problems. Unfortunately, information about proprietaries is sparse. This article summarizes what is known from several national data sets, particularly about the amounts of aid allocated to proprietary school students, student composition, completion and noncompletion rates, and effects on wages and earnings. The findings provide little support for proprietary schools on average, though the averages may mask substantial benefits for some schools. One implication is that the assumptions necessary for student loans are not met, and even grants to students in proprietary schools may not be warranted. Another is that requiring better information about the effects of proprietary schools would help in regulating such schools and would improve student choices.

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Dynarski, Mark. "Who defaults on student loans? Findings from the National Postsecondary Student Aid Study." Economics of Education Review 13, no. 1 (1994): 55–68. http://dx.doi.org/10.1016/0272-7757(94)90023-x.

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Knapp, Laura Greene, and Terry G. Seaks. "An Analysis of the Probability of Default on Federally Guranteed Student Loans." Review of Economics and Statistics 74, no. 3 (1992): 404. http://dx.doi.org/10.2307/2109484.

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