Return to search

The Effectiveness of the Student Loan Safety Net| An Evaluation of Income-Driven Loan Repayment

<p> One in five federal student loan borrowers today is enrolled in income-driven loan repayment (IDR), a set of safety net programs in which loan payment amounts are tied to borrowers&rsquo; incomes. Policymakers across the political spectrum support expanding IDR as a way of reducing loan default and encouraging borrowers to work in public service careers, though there is little evidence of the effects of IDR on borrowers&rsquo; outcomes. Through the lenses of human capital theory and risk aversion theory, this study investigates two key gaps in our knowledge about IDR in comparison to other repayment plans: whether IDR borrowers&rsquo; make different career choices and whether IDR borrowers are more successful at paying their loans. Using the National Center for Education Statistics&rsquo; Baccalaureate and Beyond dataset (B&amp;B:08/12), I weighted borrowers by their propensity to enroll in IDR, based on family, institutional, communities, and political and economic characteristics. This analysis found that IDR borrowers are statistically significantly more likely to be women, of Hispanic origin, and from low-income households. IDR borrowers are no more likely to pursue public service careers four years after graduation, but they are substantially more likely to be in repayment as opposed to being in default, forbearance, or any other loan status. The total estimated costs of loan repayment are more varied for IDR borrowers than for borrowers in other plans. Borrowers of color in IDR are likely to pay more than White borrowers in IDR. I conclude with a discussion of the implications for policy, research, and practice, including how policy makers and researchers should interpret the tradeoffs in these results.</p><p>

Identiferoai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:10785890
Date28 April 2018
CreatorsPeek, Audrey
PublisherThe George Washington University
Source SetsProQuest.com
LanguageEnglish
Detected LanguageEnglish
Typethesis

Page generated in 0.002 seconds