An Analysis of Student Loan Defaults Michael Gehm St. Norbert College Abstract Student loan defaults have been coming down in recent years, but there is still an issue when it comes those who do default on loans. There are three main types of institution in which a college graduate may attend a higher level of education: public universities, private not-for-profit colleges, and finally proprietary or for-profit. All of these institutions have experiences drops in student loan default over the past five years credited to President Obama’s income based repayment plans. Using a sample of data obtained from the Institution for Education, I test which types of schools have a higher chance of defaulting. My results indicate that if the school has a graduate program they are less likely to have high student default rates, that private schools tend to default much less than public universities, and finally that for-profit schools have a higher chance of defaulting on student loans than both public and private institutions. Note: In the paper when I refer to private colleges, I am referring to private not-for-profit as opposed to private proprietary schools. Often, I refer to proprietary schools as for-profit institutions.
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An Analysis of Student Loan Defaults
Michael Gehm
St. Norbert College
Abstract
Student loan defaults have been coming down in recent years, but there is still an issue when it comes those who do default on loans. There are three main types of institution in which a college graduate may attend a higher level of education: public universities, private not-for-profit colleges, and finally proprietary or for-profit. All of these institutions have experiences
drops in student loan default over the past five years credited to President Obama’s income based repayment plans. Using a sample of data obtained
from the Institution for Education, I test which types of schools have a higher chance of defaulting. My results indicate that if the school has a
graduate program they are less likely to have high student default rates, that private schools tend to default much less than public universities, and finally that for-profit schools have a higher chance of defaulting on student
loans than both public and private institutions.
Note: In the paper when I refer to private colleges, I am referring to private not-for-profit as opposed to private proprietary schools. Often, I refer to
proprietary schools as for-profit institutions.
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I. Introduction
One of the biggest choices a student makes in their life is what college
they go to. This choice will ultimately affect what they do in their life, who
they meet, and what they choose to major in. However, college institutions
keep rising in price as time goes on and if the student makes the wrong
choices, they may find themselves defaulting on their student loans. There
are several things that could happen when a default occurs: the IRS may
start withholding any tax refund to pay off the loan, the U.S. Government
may start garnishing the borrower’s paycheck, federal benefits may be
withheld, or they could just sue you. Going further, in most cases, the
borrower still has to pay off student loans after filing for bankruptcy unless
they can prove undue hardship that the loan would cost. This seems harsh
as most of the time, student loans are one of the biggest loans taken out in
the borrower’s name.
It is important to distinguish the different types of colleges when
addressing the issue of student loan defaults. When most people say they
are going to college, they are referring to one of three types: public
university, private university, or a proprietary college. A public university is
a college that is subsidized through taxes or other government funding.
These include popular institutions such as UW-Madison, UW-La Crosse, etc.
These colleges tend to have the lowest rates and so appeal to the mass
public. The next type of college is a private university. Private Universities
do not receive government money and are funding through donations and
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tuition collected from students. These colleges have different types that
include Liberal Arts Colleges, Fine Arts Colleges, and may be affiliated with
specific religious denominations. For example, St. Norbert College is a
Liberal Arts College that has an identity of a Norbertine College, which
stems from Catholicism. The final college that was mentioned was the
proprietary college or for-profit. These colleges are owned by private, profit
seeking businesses and try to profit off of post-secondary education.
Proprietary colleges tend to have the highest student default rate and
usually are more expensive than a comparable public school that would
offer the same degree.
These different institutions are distinctly different in some way that
leads us to question: how do these schools compare when looking at the
student loan default rate? Many parents encourage their children to obtain
the higher average earning the power that one can have with a college
degree of some sort. However, the degree is only helpful if the student is
able to manage the debt that they take on from the college they choose to
attend. Using statistical analysis on a random sample of schools, I try to
clarify which type of schools have the lowest default rate using a variety of
macroeconomic variables that pertain to the student loan default rates. In
addition to that, I also address the question: do colleges with graduate
programs tend to default more or less than colleges that only offer
undergraduate courses?
II. Literature Review
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There is literature that suggests that type of college institution
(public, private not-for-profit, and proprietary) does not matter and looks
more at the background of the students who default on student loans.
Research by Volkwein, Szelest, Cabrera, and Napierski-Prancl (1998) find
evidence to support that background information affects the amount of
student default rates. This study looked at a number of factors impacting
student loan rates, but finds that for-profit colleges tend to have a default
rate that is not much higher than accredited two year institutions. Using
micro level data, the research suggests that the gain in earnings from
attending institutions actually tends to “offset” the additional debt taken on.
More important, the authors find that other factors, such as married or
single, dependent children or not, and completion of degree are important
to whether the student will default on their loans or not. There is also
support that college GPA is a good indicator of student default rates, but
completion of degree is more important than the grades earned. These two
variables are tied together in the fact that a student who has a lower GPA is
less likely to complete his/her degree and is therefore less likely get
increased earnings for debt taken on. Overall, the authors find less support
for institution type and more support for background factors.
However, recent findings also indicate that employers may not prefer
for-profit graduates more than high school graduates or other associate
degree holders. Darolia, Koedel, Martorell, Wilson, and Perez-Arce (2014)
sent out 9000 fake resumes to employers looking to hire workers. The
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authors sent resumes in three categories: high school graduates, college
coursework but no formal credential, a non-academic vocation degree, or an
associate degree either from a community college or a for -profit college.
These resumes were posted in seven major U.S. cities. Results from this
experiment indicate that employers show no preference to for-profit
colleges against community colleges despite the higher tuition at for-profit
colleges. This could be one explanation to account for the fact that default
rates at for-profit colleges are much higher than community colleges. When
the students have to pay a much higher tuition than a community college
and are not able to find a job that uses their new degree to produce higher
income, the default rate will intuitively be higher.
In a more recent study, new empirical evidence offers similar findings.
Findings by Yannelis and Looney (2015) show that there may be a shift in
the borrowers who default on loans. Yannelis and Looney suggest that there
is a “non-traditional borrower” that comes from lower income families,
attended less successful schools, and may not be employed once they are
done with school. Specifically, these borrowers tend to attend for-profit
universities that also tend to be much more expensive than traditional
public universities. A student that does not have better job opportunities out
of school will have a more difficult time paying off large loans that college
may require leading to more defaults. Using a decomposition model,
Yannelis and Looney (2015) show that indeed a higher number of borrowers
are attending for-profit schools thus increasing the number loan defaults.
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Similar to the study done by Volkwein et. al. (2015), the shift in increased
student default loans show that it is the “non-traditional borrowers” who
default while the “traditional borrowers” seem to be defaulting at around
the same amount as before the recession. Combining the result of this
empirical study with the study conducted by Darolia, Koedel, Martorell,
Wilson, and Perez-Arce offers support for the same conclusion that for-profit
colleges do not necessarily lead to better job opportunities despite higher
tuition regarding for-profit colleges.
This paper does a very good job of identifying that most of the loan
defaults are coming from for-profit schools; however they leave out an
important variable – the private schools. I believe that since private schools
have a higher tuition rate on average than a public school that they also
need to be looked at for a higher possible default rate. To be exact, private
not-for-profit colleges had an average tuition of $39,173 while public
universities had an average tuition of $15,022 for the 2012-2013 school
year (via nces.ed.gov/fastfacts). These private institutions do tend to offer
more scholarships and discounts to students, which is why looking at tuition
may be not be the most accurate measure that there is to determine student
default rates. For this, I suggest looking at average debt for different types
of institutions to account for scholarships and monetary gifts given by
foundations or private schools. This is a large difference and actually
surpasses the amount that for-profit schools cost ($23,158). The two
previous literatures that suggest that the rate is more closely related to
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demographics use micro level data to identify issues and one suggests that
in fact for-profit colleges may not be hired any more than someone with an
associate’s degree who attended a community college. The question I want
to look at is macro level trends at all indicators of student loan default. For
example, Yannelis and Looney (2015) were able to look and individual
borrowers and identify family income, age, dropout, etc. This is good for
identifying risk in individual borrowers, but looking that the whole
institution of student defaults requires more macro level data such as
average income per capita and how that changes in that may be impacting
the overall change in student default loans.
III. Model
For my model, I feel that there are a few characteristics that would
help explain student loan defaults with these cohort rates. So I believe that:
StudentLoanDefaults = f (Student Debt, Income, Unemployment, For-profit,
Graduate, Private)
Clearly an increase in tuition should have a positive impact on student
loan defaults. The higher the tuition is in each institution, in theory the
higher the expected student loan default rate will be. Each year, college
tuition increases should induce a higher number of student loans into the
population, including some that will not be repaid. However, college tuition
is not necessarily the best indicator as to what students have to repay. So I
have chosen to use student debt coming out of college and this is reflected
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by institution lagged one year as the students would not enter repayment
until the following fiscal year. For instance, a graduate may graduate in
May and they will enter repayment in November. However, under the
Institute of Education’s fiscal year, this falls in the next year as their fiscal
year ends in September.
Income should be negatively associated with student loan defaults. As
average income after college rises, the amount of defaults should go down.
If the students cannot find higher paying jobs after college, it can be
assumed that this would cause an increase in the amount of defaults.
Unemployment should be positively associated with student loan defaults as
well. If there is a higher unemployment rate at any given time, students will
not be able to find the necessary jobs to pay off loans and thus will have a
higher rate of defaulting on their loans. With a lower unemployment rate,
we would expect to see fewer defaults on loans.
My next variables in the model are dummy variables and have to do
with institution type. Previous research looked at for-profit, graduate, and
two year schools as dummy variables (Yannelis & Looney, 2015). I plan to
use all of these as well and am looking to see if there is a statistical
difference between the for-profit schools and not-for-profit schools (both
public and private), looking to see if there is a difference between schools
that offer a graduate program as opposed to schools that do not offer any
graduate program, and finally looking for a difference in student default
rates between public and private institutions (both not-for-profit). These
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new variables will help rank which types of institutions default the most and
which default the least based on the sample used. The for-profit dummy
variable should be positively correlated with student default rates. These
are often some of the highest tuition rates and based on previous work by
Yannelis and Looney (2015), I would expect the “non-traditional” borrowers
to default more than the “traditional” borrowers. Public universities are
typically the least expensive and have traditional borrowers, so a’priori I
expect that private schools will end up having a slightly higher default rate
than public schools. This means that private schools will be positively
correlated with student loan default rates. Graduate schools are the most
expensive; however the increase in earnings by going to these schools
should offset the increased debt taken on by the student. So I expect this to
have a negative relationship with student default rates. In addition to
running this regression on fiscal year data, I will also be running it on year
to year changes to see if there is a change over the years that may indicate
a shift in student defaults. I found the yearly change percentage for each of
my quantitative data sets (student default rates, income, unemployment,
and student debt) and ran more regressions to see if there was a change in
time for the significance of the variables. I expect that over time, the income
variable will become more significant due to the surge of borrowers using
President Obama’s income based repayment plan that would result in less
defaults. I split up the data into three panels: FY2010 - FY2011 changes,
FY2011 - FY2012 changes, as well as a cumulative change from FY2010 –
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FY2012. I will be using OLS to model this relationship. These will be
reflected in a regression that uses the same model but in a separate table.