what is the single biggest factor contributing to student loan defaults?

Executive summary

In a previous Bear witness Speaks report, I described the high rates at which student loan borrowers default on their repayment within 12 years of initial higher entry, often on relatively small-scale amounts of debt. Ane of the most hit patterns emerging from that report and other prior work is how dramatically default rates vary by establishment sector and by race/ethnicity: black, non-Hispanic entrants and for-profit entrants experience default at much higher rates than other students. In this written report, I employ the aforementioned source of data to examine whether these disparities in default rates can be explained by other factors. I also examine what happens after a default, and whether this also varies past race or institution sector.

I detect that differences in student and family unit background characteristics, including measures of family unit income and wealth, can account for about one-half of the black-white gap in default (reducing it from 28 to fourteen percentage points). But even accounting for differences in degree attainment, college GPA, and mail-college income and employment cannot fully explain the black-white deviation in default rates, which remains large and statistically significant at xi percentage points in the most complete model.

Similarly, differences in student and family background characteristics tin can account for slightly less than half of the gap in default rates between for-profit borrowers and public two-year higher borrowers (reducing it from 25 to 14 percentage points). Somewhat surprisingly, the gap across sectors is not fully explained by differences in attainment, or by measures of employment and earnings. Entering a for-turn a profit is associated with a 10-point college rate of default even after accounting for everything else in the model.

Adjusted and unadjusted gaps both provide important information; one is not more "right" than the other. The adjustments are only every bit good equally the measures included, and better data on earnings, employment, and other post-college circumstances might explain more of the gap. Differences in loan counseling or loan servicing might likewise play a role. The improve we can understand what drives these stark gaps, the better policymakers tin can target their efforts to reduce defaults.

An additional analysis of what happens post-default shows that more than than half of all defaulters (54 percent) were able to successfully resolve at least one of their defaulted loans via rehabilitation, consolidation, paying in total, or having a loan discharged. At least 14 percent of defaulted borrowers managed to emerge from default and re-enroll in school. While at that place is no black-white difference in resolution rates provisional on default, white defaulters are more likely to rehabilitate defaulted loans while black defaulters are more likely to consolidate. Similarly, defaulters from for-profit institutions were more likely to consolidate and less likely to rehabilitate a defaulted loan than defaulters from public 2-year institutions.

background and information

This report utilizes data released by the U.Due south. Department of Education in October 2017, linking survey and administrative data from the Beginning Postsecondary Educatee (BPS) surveys to authoritative data on debt and defaults from the National Student Loan Data Arrangement (NSLDS). I focus on the BPS 2003-04 survey sample, which is nationally representative of college entrants who enrolled for the starting time time in 2003-04.¹ Respondents were re-surveyed in 2006 and 2009, and the NSLDS data are available through 2015, enabling sure outcomes to be measured upward to 12 years afterward initial higher entry. While some of the statistics reported below are publicly accessible from the National Center for Teaching Statistics (NCES) using the online Power Stats tool, I have computed others using the private-level data which tin only be obtained via a restricted-utilize data license. Where possible, I have validated my calculations using the restricted data against publicly available measures.

Figure 1 below summarizes previously reported rates at which pupil experience a default inside 12 years of entry, past sector and by race for the BPS-2004 accomplice. Effigy 2 provides the same information, but limited to undergraduate borrowers only.² The figures testify that 17 percent of all entrants (28 percent of undergraduate borrowers) experienced a default within 12 years of entry. The figures as well highlight the stark disparities in default by sector and race/ethnicity. For-profit entrants are nearly four times as probable to experience a default compared to public two-year entrants (47 percent versus thirteen pct), while black non-Hispanic entrants are more than than 3 times as probable equally white non-Hispanic entrants to experience a default (38 percent versus 12 per centum).

What accounts for patterns of student loan default past sector and race?

Institution sector and race/ethnicity are clearly of import correlates of educatee loan default. But to what extent might these differences be explained by other student characteristics? And since these two factors are clearly not determinative, what other characteristics or experiences might help explain patterns of default, fifty-fifty for students inside a given sector or of a given race/ethnicity? The goal of the analyses conducted below is not to attempt to identify "causal impacts" of given factors on default, but rather to better understand the constellation of factors that can or cannot explain the stark gaps beyond race and sector. For example, if racial or sectoral gaps could be explained fully past differences in degree attainment, policy attention might be all-time directed toward what happens during higher than what happens subsequently.

In gild for a given factor to explain these gaps, two things must be true: the factor must be associated with likelihood of default, and the prevalence of the factor must differ beyond groups. Prior work has identified a range of factors predicting default, many of which are not terribly surprising. In addition to institutional sector and race, students' age and gender, parental income and education, degree attainment, prior credit scores, and labor market outcomes are all related to default.ⁱⁱⁱ

1 well-documented result that many exercise find surprising is that the amount of debt students hold is if anything inversely related to default rates—that is, those with more debt are significantly less likely to default.⁴ This pattern is driven by the fact that students with larger balances besides tend to have much higher levels of attainment and earnings.^v  After controlling for attainment, prior work has constitute that the inverse human relationship goes abroad, but the remaining correlation between debt size and default is still minor and but weakly positive.⁶

Deming, Goldin, and Katz (2012) perform a like analysis of sectoral gaps in three-year cohort default rates using institution-level data, and find that the gap between for-profits and other sectors cannot be explained by differences in educatee composition and other institution-level characteristics.⁷ The new linkage of the student-level BPS data with the NLSDS provides the opportunity to examine the drivers of default for a relatively recent college entry cohort, over an extended period of time, and with the ability to consider an unusually rich fix of survey and authoritative variables as potential explanatory factors. Using the same information employed here, Kelchen (2018) finds that racial gaps in default cannot exist fully explained past other factors, though I will include a more than comprehensive set of measures.⁸

In club to understand what is driving sectoral and racial gaps in default rates, I first run a regression predicting the likelihood of ever experiencing a default within 12 years as a function of the richest fix of predictors bachelor.⁹ I limit the sample to students who ever borrowed for undergraduate pedagogy. The full fix of predictors included, along with their relationship to the likelihood of default, can be found in Appendix Table A1. In brief, the analysis includes:

• Student and family unit background characteristics. These characteristics, measured in the first yr of enrollment, include race/ethnicity, gender, age and age-squared, whether the educatee was classified as dependent, EFC (this is a summary mensurate of financial need driven primarily by family income)¹⁰, whether or not parents endemic a home, parents' highest level of pedagogy, whether parents provided financial support, SAT scores or equivalent when available, and an indicator for whether or not the student had a credit card in the first year of college.
• Undergraduate borrowing. The regression includes the total corporeality borrowed for undergraduate didactics, also as this amount squared to let for the relationship to be not-linear.
• Institution sector and selectivity. The regression includes indicators for whether the first institution was for-profit, public 4-year, and individual not-for-profit institutions, with public two-year entrants every bit the reference group. Four year institutions are additionally distinguished past level of selectivity.
• Higher performance and attainment. The regression includes indicators for the highest level of attainment at the fourth dimension of the six-year follow-up survey (2009), including whether the respondent was still enrolled, and with BA/BS attainment as the reference group. I also include final known GPA as of the 6 twelvemonth follow-up survey (this variable is primarily derived from student transcripts, not self-reports).^xi
• Measures of employment, earnings, and debt-to-income ratios. The regression includes self-reported employment and earnings (for those not however enrolled) at the time of the 6-year follow up (2009), as well as measures of monthly loan repayment amounts, and debt-to-income ratios. Unfortunately, the data do non include measures of employment or earnings across 2009.

Even every bit measures of correlation rather than causation, individual coefficients from these regressions should exist interpreted cautiously, because some factors in the model are closely related to each other. When this happens, the model cannot ever distinguish which of the related factors is driving the clan.

The results ostend previously established patterns by race, institution sector, and attainment, as well as by measures of financial need (EFC), only besides add some new details. For those with SAT or Deed score data, scores are not significantly related to default holding all else constant, just last known college GPA is, with each GPA point associated with an eight-percentage-point lower rate of default. Proxies for parental wealth—including parental homeownership, parental didactics, and how much financial help parents provided to students while enrolled—are significantly negatively related to likelihood of default, even after controlling for everything else in the model. For instance, students whose parents owned their habitation at higher entry are 3 per centum points less likely to experience a default property all else abiding.

Finally, the total model indicates default is still significantly negatively correlated with undergraduate borrowing and default (with an additional $10,000 of debt associated with a 4-indicate lower rate of default), even after controlling for other factors including attainment.¹² Notwithstanding, default is significantly positively correlated with debt-to-income ratios, highlighting the role of capacity to repay: a 10 point increase in this ratio associated with a 2-points college rate of default.^xiii One surprising effect is that being employed in 2009 is positively associated with defaulting inside 12 years. This could be because those not employed in 2009 are more likely to acquire further instruction and have less time in repayment.

Can these factors explain institutional and racial/ethnic gaps in pupil loan default?

I adjacent examine the extent to which the dramatic disparities in default rates past sector and race tin can be explained by differences in student/family background, amounts borrowed, college achievement and attainment, and post-college earnings and employment. To do this, I run a series of regressions like to higher up, only adding predictors step-by-pace in groups. For case, to examine disparities in default by sector, I first run a probit regression including merely a set of indicators for institution blazon. The resulting coefficients describe the unadjusted differences in default rates past sector, as compared with the default rate in the reference grouping (in this case public two-year institutions). I then add additional predictors in the groups described above and evaluate how much the coefficients on the sector indicators change.

The results for establishment sector are summarized in Effigy 3 (full regression results are available in Appendix Table A2). The beginning set up of columns shows the unadjusted gaps in default rates for undergraduate borrowers from each sector, equally compared with the charge per unit for borrowers who entered public ii-twelvemonth colleges (26 percent). The second set up of columns shows how the gaps alter after adding educatee and family background characteristics. Interestingly, while iv-twelvemonth college borrowers have lower unadjusted default rates than public two-year college borrowers, this advantage is completely eliminated after accounting for differences in educatee and family background across sectors. The for-profit disadvantage shrinks, but at 14 percentage points still remains large and statistically pregnant.

Calculation additional controls for amounts borrowed, attainment, and GPA does little to farther explicate the for-turn a profit disadvantage.^xiv The richest model, including controls for employment in 2009 and debt-to-income ratios, shrinks the gap modestly to xi percent points, simply if for-profit entrants accept lower employment and earnings than other borrowers with similar characteristics, this could well be a consequence of for-profit enrollment rather than a mitigating explanatory gene.

In Figure 4, I repeat the same exercise to examine racial disparities. The starting time prepare of columns shows the differences in default rates by race/ethnicity, every bit compared with the charge per unit for white non-Hispanic borrowers (21 pct).¹⁵ The 2d column accounts for boosted student and family background measures that may differ by race. Adding these measures explains almost half of the black-white gap and more than than 80 percent of the Hispanic-white gap, but none of the white-Asian gap. Accounting for differences in amounts borrowed has little additional effect. Accounting for sector, selectivity, attainment, and GPA reduces the measured black-white gap a bit further. Interestingly, accounting for chore status and debt-to-income ratios hardly changes the black-white gap at all after everything else is included. The richest model still leaves a large, statistically pregnant 11 percentage point black-white gap in likelihood of default, while the adjusted gap between white borrowers and those of Asian or Pacific Islander descent is ix pct points.

Some important caveats are required for interpretation. Get-go, considering many predictors are correlated with each other, the order in which predictors are added matters. Attainment and earnings may accept relatively little additional explanatory power, not because they don't matter, simply simply considering their issue has already been captured by other variables. In fact, in results non shown, I find that differences in sector, selectivity, and attainment, if added on their ain, can explain almost half the blackness-white gap.¹⁶  2nd, predictive models are only as adept as the measures that are included, and additional or more precise measures might reduce gaps further.¹⁷ The 2009 measures of employment and income, in particular, are less than ideal because they are self-reported at a time when many in the sample have not all the same entered repayment, and many are all the same enrolled in school.¹⁸

Finally, while the adapted and unadjusted gaps presented here provide distinct information, i is non necessarily more correct or more useful than the other. For example, even if the blackness-white gap in default could be fully explained past family income and wealth, this would non make it any less problematic for black borrowers who cannot modify their family background. Moreover, borrowing, degree attainment and earnings are themselves potential functions of race and/or institution sector. To the extent that controlling for these factors reduces the gap in default, information technology simply shifts the question to why there are gaps in these predictors.

What happens to defaulters subsequently a default?

The high rates of default among blackness borrowers and those attending for-turn a profit colleges is cause for business concern due to the potential financial ramifications of default. When a pupil loan enters default, the unabridged residue becomes immediately due, and borrowers lose access to options that might otherwise have applied, such as deferment and forbearance.¹⁹ If the borrower does not make arrangements with their servicer to go out of default, the loan may become to collections. Fees of upwardly to 25% of the balance due may exist added as a effect.^xx Defaulting on a student loan can also lower credit scores, making it harder to access credit or even to rent an apartment in the future. In some states, default can lead to revocation of professional licenses, and credit histories may be evaluated as part of employment applications, making it harder to detect or keep a job. Also, students cannot receive any additional federal student aid while they are in default, making it more than hard to return to school.

Notwithstanding, default is a status, not a permanent characteristic, and many students who experience a default exercise somewhen sally from information technology. In fact, more than than one-half of those of those who ever defaulted (54 percent) were able to resolve at least i of those defaults by the end of the 12-year follow up, and at to the lowest degree xiv percent returned to school after a default.²¹ There are 4 means to get out of default: rehabilitation, consolidation, paying in full, or having a loan discharged.

Rehabilitation offers the advantage of having the default removed from the borrower's credit tape, but information technology requires successfully making nine payments over 10 months, and can just exist used one time. Consolidating defaulted loans into a new loan can get a borrower out of default more than apace and may be the only feasible choice for those with multiple defaulted loans, but the default remains on the credit record for up to vii years.

Figure 5 shows the percentage of defaulted students who were ever able to successfully resolve a defaulted loan past the finish of the 12-year follow up, every bit well as the per centum ever emerging from default via one of these pathways, by race/ethnicity. Though black borrowers have a much college rate of default in the outset place, blackness and white defaulters emerge from default at similar rates, while Hispanic defaulters were slightly more likely to resolve a default.²² At the end of the follow-upward period, about 54 pct of white defaulters had resolved at least one defaulted loan, compared to 53 percentage of black defaulters.

Black and white defaulters differ, even so, in how they emerge from default: black defaulters are more likely to go out of default via consolidation (23 versus fifteen percent), while white defaulters are more likely to rehabilitate (32 versus 26 percent) or pay in total (34 versus 30 percent).²³ Since rehabilitation tin can only be used once, I also examine patterns of resolution for the start defaulted loan (not shown), and notice that the same general pattern holds.

Figure 6 shows the aforementioned statistics for defaulters by first establishment sector. Defaulters from individual institutions—whether for-profit or not-for-turn a profit—were more probable to resolve a default than defaulters from public institutions. These defaulters were too more than likely than those from public institutions to resolve via a consolidation. Again, this pattern too holds if I examine only the first defaulted loan.

Future work could apply methods similar to those used above in order to meliorate understand the predictors and consequences of consolidation versus rehabilitation amongst defaulted borrowers. Preliminary analysis (not shown) bespeak that defaulters that resolve their first defaulted loan via consolidation have larger total balances at the fourth dimension of default than those who rehabilitate ($19,185 versus $17, 124), are more than probable to have experienced multiple instances of default (56 pct versus 41 percent), and more likely to receive federal student aid postal service-default (26 per centum versus fourteen percent).²⁴ While the interpretation of these findings is not fully articulate, it is consistent with consolidation being the more than highly-seasoned option for defaulted borrowers with multiple defaulted loans, and too for defaulters who seek to re-enroll in college (since consolidation can happen more than quickly than rehabilitation).

Take-away findings and implications

A number of cardinal findings emerge from this assay. First, about one-half of the full blackness-white gap in default rates, and only under half of the gap between for-profits and public two-year colleges, can exist explained past student and family groundwork including measures of parental wealth and back up. Second, calculation boosted controls reduces both gaps farther; yet even controlling for degree attainment, GPA, and measures of 2009 employment, earnings, and debt-to-income ratios cannot fully explain either gap. Finally, more than half of defaulted borrowers are able to resolve at to the lowest degree one of their defaulted loans within the 12-yr follow-up window, with black defaulters and those from private institutions more than likely than other groups to resolve via consolidation.

Adapted and unadjusted gaps both provide important data; one is not more than "correct" than the other. The adjustments are simply as skilful as the measures included, and because some of the predictors are correlated with each other, the order in which groups of predictors are added tin affair. For example, differences in college sector, selectivity, and attainment explain more than of the black-white gap in default when these predictors are added prior to adding student/family background characteristics.

What could explicate the remaining gaps in default? Ameliorate measures of income and other mail service-college fiscal factors would further explain the gap, as might more information about the timing of when students left school and when they entered repayment. Some of the remaining gap may relate to the quality of loan exit counseling or loan servicing, which could vary by race or sector. Indeed, other enquiry has found significant variation in repayment outcomes across the private loan servicing agents that communicate with borrowers.²⁵

This written report also shows that more than half of defaulted borrowers are able to resolve at least one of their defaulted loans, though resolution does not necessarily erase the consequences of default. Conditional on experiencing a default, the likelihood of resolution does non vary past race, but those who attended private institutions (whether for-profit or non-for-profit) are more probable to resolve a defaulted loan. The pathway to resolution varies both past race and sector: compared with other students, consolidation is more than mutual for blackness defaulters and those from private institutions.

A concluding caveat is that this report has focused on default rather than repayment. Merely because a student is not in default, does not necessarily mean they are paying downwards their loan. And while defaults may exist of greatest consequence to borrowers, repayment rates are a legitimate business concern for policymakers and taxpayers. A similar analysis of predictors of successful repayment would further enrich our agreement of pupil loan outcomes. Qualitative research to illuminate how students transition from school into repayment, and and so oftentimes into default and and so dorsum out again, would also be very valuable. The better nosotros can understand what drives these patterns, the better policymakers can target their efforts to improve student loan outcomes.

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The author did non receive any financial back up from any firm or person for this article or from whatsoever firm or person with a financial or political interest in this article. She is currently not an officeholder, director, or board member of any system with an interest in this article.

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Source: https://www.brookings.edu/research/what-accounts-for-gaps-in-student-loan-default-and-what-happens-after/

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