Category: student loans

  • Universities that expand access have graduates who take longer to repay their loans

    Universities that expand access have graduates who take longer to repay their loans

    I’ll admit that the Neil O’Brien-powered analysis of graduate repayments in The Times recently annoyed me a little.

    There’s nothing worse than somebody attempting to answer a fascinating question with inappropriate data (and if you want to read how bad it is I did a quick piece at the time). But it occurred to me that there is a way to address the issue of whether graduate repayments of student loans do see meaningful differences by provider, and think about what may be causing this phenomenon.

    What I present here is the kind of thing that you could probably refine a little if you were, say, shadow education minister and had access to some numerate researchers to support you. I want to be clear up top is that, with public data and a cavalier use of averages and medians, this can only be described as indicative and should be used appropriately and with care (yes, this means you Neil).

    My findings

    There is a difference in full time undergraduate loan repayment rates over the first five years after graduation by provider in England when you look at the cohort that graduated in 2016-17 (the most recent cohort for which public data over five years is available).

    This has a notable and visible relationship with the proportion of former students in that cohort from POLAR4 quintile 1 (from areas in the lowest 20 per cent of areas).

    Though it is not possible to draw a direct conclusion, it appears that subject of study and gender will also have an impact on repayments.

    There is also a relationship between the average amount borrowed per student and the proportion of the cohort at a provider from POLAR4 Q1.

    The combination of higher average borrowing and lower average earnings makes remaining loan balances (before interest) after five years look worse in providers with a higher proportion of students from disadvantaged backgrounds..

    On the face of it, these are not new findings. We know that pre-application background has an impact on post-graduation success – it is a phenomenon that has been documented numerous times, and the main basis for complaints about the use of progression data as a proxy for the quality of education available at a provider. Likewise, we know that salary differences by gender and by industry (which has a close but not direct link to subject of study).

    Methodology

    The Longitudinal Educational Outcomes dataset currently offers a choice of three cohorts where median salaries are available one, three, and five years after graduation. I’ve chosen to look at the most recent available cohort, which graduated in 2016-17.

    Thinking about the five years between graduation and the last available data point, I’ve assumed that median salaries for year 2 are the same as year 1, and that salaries for year 4 are the same as year 3. I can then take 9 per cent of earnings above the relevant threshold as the average repayment – taking two year ones, two year threes, and a year five gives me an average total repayment over five years.

    The relevant threshold is whatever the Department for Education says was the repayment threshold for Plan 1 (all these loans would have been linked to to Plan 1 repayments) for the year in question.

    How much do students borrow? There is a variation by provider – here we turn to the Student Loans Company 2016 cycle release of Support for Students in Higher Education (England). This provides details of all the full time undergraduate fee and maintenance loans provided to students that year by provider – we can divide the total value of loans by the total number of students to get the average loan amount per student. There’s two problems with this – I want to look at a single cohort, and this gives me an average for all students at the provider that year. In the interests of speed I’ve just multiplied this average by three (for a three year full time undergraduate course) and assumed the year of study differentials net out somehow. It’s not ideal, but there’s not really another straightforward way of doing it.

    We’ve not plotted all of the available data – the focus is on English providers, specifically English higher education institutions (filtering out smaller providers where averages are less reliably). And we don’t show the University of Plymouth (yet), there is a problem with the SLC data somewhere.

    Data

    This first visualisation gives you a choice of X and Y axis as follows:

    • POLAR % – the proportion of students in the cohort from POLAR4 Q1
    • Three year borrowing – the average total borrowing per student, assuming a three year course
    • Repayment 5YAG – the average total amount repaid, five years after graduation
    • Balance 5YAG – the average amount borrowed minus the average total repayments over five years

    You can highlight providers of interest using the highlighter box – the size of the blobs represents the size of the cohort.

    [Full screen]

    Of course, we don’t get data on student borrowing by provider and subject – but we can still calculate repayments on that basis. Here’s a look at average repayments over five years by CAH2 subject (box on the top right to choose) – I’ve plotted against the proportion of the cohort from POLAR4 Q1 because that curve is impressively persistent.

    [Full screen]

    For all of the reasons – and short cuts! – above I want to emphasise again that this is indicative data – there are loads of assumptions here. I’m comfortable with this analysis being used to talk about general trends, but you should not use this for any form of regulation or parliamentary question.

    The question it prompts, for me, is whether it is fair to assume that providers with a bigger proportion of non-traditional students will be less effective at teaching. Graduate outcome measures may offer some clues, but there are a lot of caveats to any analysis that relies solely on that aspect.

    Source link

  • Second-generation student borrowers | SRHE Blog

    Second-generation student borrowers | SRHE Blog

    by Ariane de Gayardon

    Since the 1980s, massification, policy shifts, and changing ideas about who benefits from higher education have led to the expansion of national student loan schemes globally. For instance, student loans were introduced in England in 1990 and generalized in 1998. Australia introduced income-contingent student loans in the late 1980s. While federal student loans were introduced in the US in 1958, their number and the amount of individual student loan debt ramped up in the 1990s.

    A lot of academic research has analysed this trend, evaluating the effect of student loans on access, retention, success, the student experience, and even graduate outcomes. Yet, this research is based on the choices and experiences of first-generation student borrowers and might not apply to current and future students.

    First-generation borrowers enter higher education with parents who have either not been to higher education, or who have a tertiary degree that pre-dates the expansion of student loans. The parents of first-generation borrowers therefore did not take up loans to pay for their higher education and had no associated repayment burden in adulthood. Any cost associated with these parents’ studies will likely have been shouldered by their families or through grants.

    Second-generation borrowers are the offspring of first-generation borrowers. Their parents took out student loans to pay for their own higher education. The choices made by second-generation borrowers when it comes to higher education and its funding could significantly differ from first-generation borrowers, because they are impacted by their parents’ own experience with student loans.

    Parents and parental experience indeed play an important role in children’s higher education choices and financial decisions. On the one hand, parents can provide financial or in-kind support for higher education. This is most evident in the design of student funding policies which often integrate parental income and financial contributions. In many countries, eligibility for financial aid is means-tested and based on family income (Williams & Usher, 2022). Examples include the US where an Expected Family Contribution is calculated upon assessment of financial need, or Germany where the financial aid system is based on a legal obligation for parents to contribute to their children’s study costs. Indeed, evidence shows that parents do contribute to students’ income. In Europe, family contributions make up nearly half of students’ income (Hauschildt et al, 2018). But the role of parents also extends to decisions about student loans: parents tend to try and shield their children from student debt, helping them financially when possible or encouraging cost-saving behaviour (West et al, 2015).

    On the other hand, parents transmit financial values to their children, which might play a role in their higher education decisions. Family financial socialization theory states that children learn their financial attitudes and behaviour from their parents, through direct teaching and via family interactions and relationships (Gudmunson & Danes, 2011). Studies indeed show the intergenerational transmission of social norms and economic preferences (Maccoby, 1992), including attitudes towards general debt (Almenberg et al, 2021). Continuity of financial values over generations has been observed in the specific case of higher education. Parents who received parental financial support for their own studies are more likely to contribute toward their children’s studies (Steelman & Powell, 1991). For some students, negative parental experiences with general debt can lead to extreme student debt aversion (Zerquera et al,2016).

    As countries globally rely increasingly on student loans to fund higher education, many more students will become second-generation borrowers. Because their parents had to repay their own student debt, the family’s financial assets may be depleted, potentially leading to reduced levels of parental financial support for higher education. This is likely to be even worse for students whose parents are still repaying their loans. In addition, parental experiences of student debt could influence the advice they give their children with regard to higher education financial decisions. As a result, this new generation of student borrowers will face challenges that their predecessors did not, fuelled by the transmitted experience of student loans from their parents (Figure 1).

    Figure 1 – Parental influence on second-generation borrowers

    As the share of second-generation borrowers in the student body increases, the need to understand the decision-making process of these students when it comes to (financial) higher education choices is essential. Although the challenges faced by borrowers will emerge at different times and with varying intensity across countries — depending in part on loan repayment formats — we have an opportunity now to be ahead of the curve. By researching this new generation of student borrowers and their parents, we can better assess their financial dilemmas and the support they need, providing further evidence to design future-proof equitable student funding policies.

    Ariane de Gayardon is Assistant Professor of Higher Education at the Center for Higher Education Policy Studies (CHEPS) based at the University of Twente in the Netherlands.

    Author: SRHE News Blog

    An international learned society, concerned with supporting research and researchers into Higher Education

    Source link

  • Student Debt by Ethnicity | HESA

    Student Debt by Ethnicity | HESA

    Hi all. Just a quick one today, this time on some data I recently got from StatsCan.

    We know a fair a bit about student debt in Canada, especially with respect to distribution by gender, type of institution, province, etc. (Chapter 6 of The State of Postsecondary Education in Canada is just chock full of this kind of data if you’re minded to take a deeper dive). But to my knowledge no one has ever pulled and published the data on debt by ethnicity, even though this data has been collected for quite some time through the National Graduates Survey (NGS). So I ordered the data, and here’s what I discovered.

    Figure 1 shows incidence of borrowing for the graduating class of 2020, combined for all graduates of universities and graduates, for the eight largest ethnicities covered by the NGS (and before anyone asks, “indigeneity” is not considered an ethnicity so anyone indicating an indigenous ethnicity is unfortunately excluded from this data… there’s more below on the challenges of getting additional data). And the picture it shows is…a bit complex.

    Figure 1: Incidence of Borrowing, College and University Graduates Combined, Class of 2020

    If you just look at the data on government loan programs (the orange bars), we see that only Arab students have borrowing rates in excess of 1 in 2. But for certain ethnicities, the borrowing rate is much lower. For Latin American and Chinese students, the borrowing rate is below 1 in 3, and among South Asian students the borrowing rate is barely 1 in 5. Evidence of big differences in attitudes towards borrowing!

    Except…well when you add in borrowing from private sources (e.g. from banks and family) so as to take a look at overall rates of borrowing incidence, the differences in borrowing rates are a lot narrower. Briefly, Asian and Latin American students borrow a lot more money from private sources (mainly family) than do Arab students, whites, and Blacks. These probably come with slightly easier repayment terms, but it’s hard to know for sure. An area almost certainly worthy of further research.

    There is a similarly nuanced picture when we look at median levels of indebtedness among graduates who had debt. This is shown below in Figure 2.

    Figure 2: Median Borrowing, College and University Graduates Combined, Class of 2020

    Now, there isn’t a huge amount of difference in exiting debt levels by ethnicity: the gap is only about $6,000 between the lowest total debt levels (Filipinos) and the highest (Chinese). But part of the problem here is that we can’t distinguish the reason for the various debt levels. Based on what we know about ethnic patterns of postsecondary education, we can probably guess that Filipino students have low debt levels not because they are especially wealthy and can afford to go to post-secondary without financial assistance. But rather because they are more likely to go to college and this spend less time, on average, in school paying fees and accumulating debt. Similarly, Chinese students don’t have the highest debt because they have low incomes; they have higher debt because they are the ethnic group the most likely to attend university and spend more time paying (higher) fees.

    (Could we get the data separately for universities and colleges to clear up the confound? Yes, we could. But it cost me $3K just to get this data. Drilling down a level adds costs, as would getting data based on indigenous identity, and this is a free email, and so for the moment what we have above will have to do. If anyone wants to pitch in a couple of grand to do more drilling-down, let me know and I would be happy to coordinate some data liberation).

    It is also possible to use NGS data to look at post-graduate income by debt. I obtained the data by in fairly large ranges (e.g. $0-20K, $20-60K, etc.), but it’s possible on the basis of that to estimate roughly what median incomes are (put it this way: the exact numbers are not exactly right, but the ordinal rank of income of the various ethnicities are probably accurate). My estimations of median 2023 income of 2020 graduates—which includes those graduates who are not in the labour market full-time, if you’re wondering why the numbers look a little low—are shown below in Figure 3.

    Figure 3: Estimate Median 2023 Income, College and University Graduates Combined, Class of 2020

    Are there differences in income here? Yes, but they aren’t huge. Most ethnic groups have median post-graduate incomes between $44 and $46,000. The two lowest-earning groups (Latin Americans and Filipinos) re both disproportionately enrolled in community colleges, which is part of what is going on in this data (if you want disaggregated data, see above).

    Now, the data from the previous graphs can be combined to look at debt-to-income ratios, both for students with debt, and all students (that is, including those that do not borrow). This is shown below in Figure 4.

    Figure 4: Estimated Median 2023 Debt-to-Income Ratios, College and University Graduates Combined, Class of 2020

    If you’re just dividing indebtedness by income (the blue bars), you get a picture that looks a lot like Figure 2 in debt, because differences in income are pretty small. But if you are looking at debt-to-income ratios across all students (including those that do not borrow) you get a very different picture because as we saw in Figure 1, there are some pretty significant differences in overall borrowing rates. So, for instance, Chinese students go from having the worst debt-to-income ratio on one measure to being middle of the pack on another because they have relatively low incidence of borrowing; similarly, students of Latin American origin go from being middle-of-the-pack to nearly the lowest debt-to-income ratios because they are a lot less likely to borrow than others. Black students end up having among the highest debt-to-income ratios not because they earn significantly less than other graduates, but because both the incidence and amount of their borrowing is relatively high.

    But I think the story to go with here is that while there are differences between ethnic groups in terms of borrowing, debt, and repayment ratios, and that it’s worth trying to do something to narrow them, the difference in these rates is not enormous. Overall, it appears that as a country we are achieving reasonably good things here, with the caveat that if this data were disaggregated by university/ college, the story might not be quite as promising.

    And so ends the first-ever analysis of student debt and repayment by ethnic background. Hope you found it moderately enlightening.

    Source link

  • Public Service Loan Forgiveness: Help Employees Achieve Their Financial Goals

    Public Service Loan Forgiveness: Help Employees Achieve Their Financial Goals

    by Julie Burrell | September 17, 2024

    The Public Service Loan Forgiveness (PSLF) program can offer significant financial relief to higher ed employees, but many don’t know they qualify for this benefit. PSLF is open to most full-time higher ed employees of nonprofit colleges and universities who have direct federal student loans.

    HR can spread the word to current employees and use loan forgiveness as part of a retention and recruitment strategy. The average amount of individual loan forgiveness under the PSLF is $70,000, which makes the PSLF an especially attractive benefit to potential employees.

    Here’s what you need to know about who qualifies for PSLF, how to offer a free webinar on PSLF to your employees, and what steps you can take to ensure eligible employees enroll.

    What is PSLF?

    Public Service Loan Forgiveness forgives the balance of direct federal student loans after 120 qualifying payments made by the borrower if they work for a qualifying employer (after October 1, 2007) and are under a qualifying repayment plan. It’s intended to reward and incentivize public service, like teaching, nonprofit work and work in the public sector. PSLF eligibility isn’t about what job an employee does or what their job description is; it’s about where they work.

    Who qualifies for PSLF?

    Full-time employees of a nonprofit organization or a federal, state, tribal, or local government are eligible. Full-time work is defined as 30 hours or more per week. That means most full-time higher ed employees are eligible for PSLF, including those who may work part time at your institution but are also employed at other qualifying jobs (as is the case with many adjuncts). But the PSLF only applies to direct federal student loans. Borrowers with other federal student loans may be able to consolidate them into a direct federal student loan.

    How do I ensure my institution counts as an eligible employer?

    Use the PSLF Help Tool, which will search the federal employer database. The help tool is also useful to recommend to employees since it’s a step-by-step guide through the enrollment process.

    Six Tips for Getting the Word Out

    1. Partner with Public Service Promise, a nonprofit, nonpartisan organization that offers free webinars led by experts.
    2. Encourage HR staff to apply for PSLF. With firsthand experience, you and your team will be able to speak knowledgeably about the process.
    3. Publicize PSLF as a benefit to your employees, especially those who may not know they can take advantage of this program, including adjuncts and non-exempt and part-time employees.
    4. Include information about PSLF on your benefits websites or portal.
    5. Consider appointing a knowledgeable point person on campus, like a financial aid officer, to help answer employee questions.
    6. Involve non-exempt, adjunct and part-time employees in outreach campaigns. Employees can meet the 30 hours per week requirement with more than one job. So if they have multiple jobs at multiple qualifying employers, employees can add those hours up. And the PSLF instructions include how to calculate hours worked by adjunct faculty. Payments do not need to be consecutive, so even adjuncts without summer appointments can still take advantage of PSLF and start to chip away at the 120 payments.



    Source link