Abstract
This paper examines the institutional determinants of federal loan status for a recent cohort of college students. We first set out how institutions influence loan accumulations and repayment rates, with particular focus on for-profit colleges. We then test a set of hypotheses about loan status and repayment using national data on loans, defaults, and repayments merged with college-level data. For all measures of loan status there are significant raw gaps between for-profit colleges and public and not-for-profit colleges. After controlling for student characteristics, measures of college quality, and college practices, large gaps in loan balance per student remain: students in for-profit colleges, especially the 2-year colleges, borrow approximately four times as much as they would have at a 2-year public college. For a student attending the ‘average’ college, their repayment rate is predicted to be 5 [9] percentage points lower if that college is for-profit compared to public [non-profit]. Repayment rates are also lower for colleges with higher proportions of minority students and with lower graduation rates; contrary to some claims, single-program institutions appear to have higher repayment rates.
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Notes
These subsequent studies broadly corroborate the student-level influences on default (e.g. minority status and male), as well as affirming a link between student GPA and default.
One specific issue which has received attention is the extent to which institutions are differentially effective with respect to minority students. However, Flint (1997) found that, within racial groups, there are no significant differences between proprietary schools and 2-year colleges.
Similarly, in an analysis of students at 26 colleges in Pennsylvania, Knapp and Seaks (1992) concluded that no institutional factors influenced student default rates.
It is possible to explain this finding using an economic model where decisions about debt accumulation and the subsequent earnings are perfectly aligned, such that the only reason for student default is an exogenous, unpredictable shock to the students’ labor market outcomes.
They were also motivated more toward model specifications that yielded higher proportions of correct predictions, rather the search for policy-relevant institutional or organizational variables.
Other examples are the collinearity between college size as measured by FTEs and public 4-year institutional types and the clustering of single-CIP institutions in <2- and 2-year institutions outside the public sector.
As well as raising caution on inference from the published models, such data loss also led Volkwein and Szelest (1995) to omit a number of institutional variables. Similarly, the Baccalaureate and beyond 1993–1994 dataset samples 1,243 colleges but only 209 remain after sample restrictions are applied in Thomas’s (2000) analysis. Two of these restrictions—on particular subjects of study and availability of SAT data—are also likely to reduce the representation of private colleges.
Thus, our investigations belatedly follows the suggestion of Volkwein et al. (1998) that “Future policy studies should investigate the likelihood of significantly different patterns and causes of default among borrowers from various institution types” (p. 230).
We note here that the proposal was based on program codes based on the NCES Classification of Instructional Programs (CIP) codes. The unit of analysis was not the college or the student, but the program. For repayment rates, programs would qualify for title IV HEA funds if students repay their federal loans at a rate of ≥45 % (total amount of loans divided by the original outstanding balance in the prior four fiscal years); programs may be ineligible if students repay their federal loans at a rate of ≤35 %. For debt-to-income ratios, programs would qualify for title IV HEA funds if the completers have annual debt service payments of either ≤8 % of average annual earnings or ≤20 % of discretionary income; programs may be ineligible if the completers have annual debt service payments of either >12 % of average annual earnings or >30 % of discretionary income. These threshold rates are based on Baum and Schwartz (2006). For clarification, we note here that analysis of repayment and default rates represents only a partial investigation of possible changes to institutional eligibility for federal aid. Eligibility was proposed to also depend on debt-to-income ratios.
We appreciate this suggestion from one of the reviewers.
U.S. Department of Education, National Center for Education Statistics, 2003–2004 and 2007–2008 National Postsecondary Student Aid Study (NPSAS:04 and NPSAS:08). Table 342.
Alternatively, for-profit colleges may raise additional revenues from instruction or other services. This seems unlikely as the option to secure additional revenues is also available for other types of college.
These input measures may capture the aptitude of the student body. They include whether the college provides adult basic education programs or remedial programs, as well as proxies for family background or income status that appear correlated with aptitude (such as race and gender).
No clear effect of institutional size is anticipated. Larger colleges may have economies of scale in services that might reduce debt accumulation. Alternatively, college size may be associated with open enrollment policies that are weakly tied to ability to pay. But these services and enrollment policies are included directly. Nevertheless, we test directly for scale because of the recent focus on small or single-CIP colleges as institutions with relatively poor loan status.
Throughout, we control for state fixed effects to capture state-level policies. However, the results are unchanged if these state effects are excluded from the models.
Matches were performed using the OPEID 6 identification. Failed matches were less than 4 % of sample.
We appreciate a reviewer’s comment that this ‘loan balance per FTE’ should be interpreted carefully. FTEs are not ‘yields’ of graduates and students do not accumulate debt to the same extent each year. The FTE measure reflects the general scale of the debt at that college and so of college output with respect to federal subventions. This measure is strongly correlated with the ‘number of students entering repayment’ (pairwise correlation, 0.88).
The cohort default rate is calculated differently for institutions with less than 30 borrowers entering repayment during a given fiscal year, based on an average rate formula.
The default rate also includes borrowers who meet other specified conditions, such as payment by an independent agency (e.g. employer, school owner) to prevent the borrower defaulting.
Students eligible for public service loan forgiveness are counted as paying down their loans (excluding loans in deferment due to further education or military service).
The pairwise correlation between the repayment rate and the default rate is −0.51.
This issue is less salient for this research than for prior studies that have looked at college-level determinants of student debt using individual data (or indeed studies of college-level determinants of any student outcomes). College-level loan status information is based on the students’ time at each specific college—it is aggregated by college and not by person.
We note that the default rate is considerably below the rate in the NPSAS SLRS sample of 17–21 % (Dynarski 1994; Volkwein et al. 1998). Our default rate differs substantially from the rates derived from student-level research for potentially three reasons. First, student-level rates are measured at a later point in the students’ life. Second, and related, student-level rates are ‘event’ rates, i.e. whether the student ever defaulted over the given time period; they are not status rates, i.e. the student is in default at that time point. Finally, as noted above, the student-level data has high attrition rates. These are possible reasons for the differences, although the preferred definition of the default rate merits further study (especially given the paucity of research in this field). We do note that our measure accords with the federal definition and therefore has direct implications for federal government accounts.
Three measures of careers services are available in IPEDS. But almost all colleges report one of these services. Therefore, they are collapsed into an aggregate dummy variable for when the college provides all three services. Our results are not sensitive to this aggregation (details from author).
We also estimated this model with total loan balances as the dependent variable. As noted above, total loan balances reflect the scale of the institution—both the number of year cohorts and the size of each cohort. Even in this model, the for-profit sector has higher federal loans.
It may also be appropriate to look at the relationship between the total loan balance of the college and its default and repayment rates. When we estimate model (3) of Table 4 using total loan balance instead of loan balance per FTE, we find stronger negative relationships for repayment rates. That is, higher loan balances are associated with lower repayment rates (statistically significant at 5 % level). But the relationship between total loan balances and default is not statistically significant (details available from the author).
We experimented with the reading scores and concatenating the ACT and SAT score measures. The conclusions are invariant to these experiments (details from author).
It is also possible that this variable is collinear with other covariates.
Earlier studies have suggested that the loan system may have disparate impacts on minority students. In separate analysis we investigate whether college sectors are differentially effective in helping minority students. When we estimate model (3), split by sector, we find mixed results such that there is no clear grounds for claims that one sector is differentially more effective for minority student groups (details available from the author).
Alternate specifications including only single-CIP colleges yield equivalent results.
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Belfield, C.R. Student Loans and Repayment Rates: The Role of For-profit Colleges. Res High Educ 54, 1–29 (2013). https://doi.org/10.1007/s11162-012-9268-1
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DOI: https://doi.org/10.1007/s11162-012-9268-1