![]() ![]() Institution sector and race/ethnicity are clearly important correlates of student loan default. What accounts for patterns of student loan default by sector and race? For-profit entrants are nearly four times as likely to experience a default compared to public two-year entrants (47 percent versus 13 percent), while black non-Hispanic entrants are more than three times as likely as white non-Hispanic entrants to experience a default (38 percent versus 12 percent). The figures also highlight the stark disparities in default by sector and race/ethnicity. 2 The figures show that 17 percent of all entrants (28 percent of undergraduate borrowers) experienced a default within 12 years of entry. Figure 2 provides the same information, but limited to undergraduate borrowers only. Where possible, I have validated my calculations using the restricted data against publicly available measures.įigure 1 below summarizes previously reported rates at which student experience a default within 12 years of entry, by sector and by race for the BPS-2004 cohort. While some of the statistics reported below are publicly accessible from the National Center for Education Statistics (NCES) using the online Power Stats tool, I have computed others using the individual-level data which can only be obtained via a restricted-use data license. 1 Respondents were re-surveyed in 20, and the NSLDS data are available through 2015, enabling certain outcomes to be measured up to 12 years after initial college entry. I focus on the BPS 2003-04 survey sample, which is nationally representative of college entrants who enrolled for the first time in 2003-04. Department of Education in October 2017, linking survey and administrative data from the Beginning Postsecondary Student (BPS) surveys to administrative data on debt and defaults from the National Student Loan Data System (NSLDS). ![]() This report utilizes data released by the U.S. ![]() Similarly, defaulters from for-profit institutions were more likely to consolidate and less likely to rehabilitate a defaulted loan than defaulters from public two-year institutions. ![]() While there is no black-white difference in resolution rates conditional on default, white defaulters are more likely to rehabilitate defaulted loans while black defaulters are more likely to consolidate. At least 14 percent of defaulted borrowers managed to emerge from default and re-enroll in school. The better we can understand what drives these stark gaps, the better policymakers can target their efforts to reduce defaults.Īn additional analysis of what happens post-default shows that more than half of all defaulters (54 percent) were able to successfully resolve at least one of their defaulted loans via rehabilitation, consolidation, paying in full, or having a loan discharged. Differences in loan counseling or loan servicing might also play a role. The adjustments are only as good as the measures included, and better data on earnings, employment, and other post-college circumstances might explain more of the gap. Entering a for-profit is associated with a 10-point higher rate of default even after accounting for everything else in the model.Īdjusted and unadjusted gaps both provide important information one is not more “correct” than the other. Somewhat surprisingly, the gap across sectors is not fully explained by differences in attainment, or by measures of employment and earnings. Similarly, differences in student and family background characteristics can account for slightly less than half of the gap in default rates between for-profit borrowers and public two-year college borrowers (reducing it from 25 to 14 percentage points). But even accounting for differences in degree attainment, college GPA, and post-college income and employment cannot fully explain the black-white difference in default rates, which remains large and statistically significant at 11 percentage points in the most complete model. I find that differences in student and family background characteristics, including measures of family income and wealth, can account for about half of the black-white gap in default (reducing it from 28 to 14 percentage points). ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |