Three Essays regarding Student Loans
by
Thomas O’Malley Jr., B.S., M.S.
A Dissertation
In
PERSONAL FINANCIAL PLANNING
Submitted to the Graduate Faculty
of Texas Tech University in
Partial Fulfillment of
the Requirement for
the Degree of
DOCTOR OF PHILOSOPHY
Approved
Sandra Huston
Chair of Committee
Dorothy Durband
Michael Finke
Ralph Ferguson
Eleanor von Ende
Dominick Casadonte
Interim Dean of the Graduate School
August, 2013
Copyright 2013, Thomas O’Malley
Texas Tech University, Thomas O’Malley, August 2013
ACKNOWLEDGMENTS
I would like to thank each of my dissertation committee members for their time
and efforts. I am particularly indebted to my committee chair, Dr. Sandra Huston. The
questions, suggestions and corrections have all been of immeasurable benefit. It has
enabled me to grow as a researcher and a writer.
I am grateful to my fellow Ph.D. students. The journey would not have been as
memorable without each of you. The friendships that have developed are perhaps the
best part of this whole process.
I want acknowledge the special role that my family has played during the
dissertation process. Numerous sacrifices have been made by Kristi, Megan and Joe
during these four years. Your love, understanding and support have made this possible.
ii
Texas Tech University, Thomas O’Malley, August 2013
TABLE OF CONTENTS
ACKNOWLEDGEMENTS
ii
ABSTRACT
iv
LIST OF TABLES
vi
LIST OF FIGURES
vii
I. INTRODUCTION
1
II. THE NET WORTH IMPLICATIONS OF STUDENT LOANS
9
III. STUDENT LOAN DEBT AND PERSONAL BANKRUPTCY
54
IV. FOR-PROFIT HIGHER EDUCATION AND WAGES
88
V. CONCLUSION
130
iii
Texas Tech University, Thomas O’Malley, August 2013
ABSTRACT
Debt is now a ubiquitous part of higher education. The proportion of college
students leaving school with student loans has steadily increased over the last two
decades. Average debt loads have also trended higher. Student loans are notably
pervasive for those who attend for-profit schools. For-profit education has grown rapidly
over the last decade and fo-profit students are the more indebted than their peers who
attend non-profit institutions.
This three essay dissertation identifies the financial impact of education debt and
attending proprietary for-profit schools. The first essay investigates whether student loan
use affects a household’s long-term wealth accumulation. The next essay looks at
whether student debt leads to financial distress for some borrowers. The third essay
explores the wage rate and earnings outcomes of attending a proprietary school.
These three essays add to the body of knowledge of wealth creation, bankruptcy
and wage determination. The first study finds student debt is negatively associated with
net worth. ‘Some college’ and associate's degrees confer wealth benefits but there are no
benefits if student loans are used. Among those with at least a bachelor's degree, there
are large wealth gaps between those who have used loans and those who have not. The
next study finds a positive relation between student debt and bankruptcy. Results suggest
that education debt, credit card debt, and adverse events are all complementary
explanations for bankruptcy. The third essay finds that an associate's degree from a forprofit institution has no discernible value. For-profit bachelor's and master's degrees lead
iv
Texas Tech University, Thomas O’Malley, August 2013
to higher wage rates and earnings but the estimated benefits are smaller than the benefits
associated with obtaining an equivalent degree from a traditional non-profit school.
v
Texas Tech University, Thomas O’Malley, August 2013
LIST OF TABLES
2.1
Descriptive Statistics of the Full Sample
33
2.2
Descriptive Statistics: Net Worth by Completion Status
34
2.3
Descriptive Statistics: Net Worth by Degree
34
2.4
OLS and Quantile Results
37
A2.1
Bivariate Correlation Matrix
48
A2.2
Quantile Regression Results
53
3.1
Type of Bankruptcy Declared
70
3.2
Descriptive Statistics: Bankruptcy Filers and Non-Filers
75
3.3
Logistic Regression – Likelihood of Declaring Bankruptcy
77
A3.1
Bivariate Correlation Matrix
81
4.1
Percentage of For-Profit Institutional Revenue Received under Title IV
91
4.2
Descriptive Statistics of the Sample in 2010
109
4.3
OLS Regression of Ln Wage Rate
111
4.4
OLS Regression of Earnings (conditioned on employment)
113
4.5
OLS Regression of Earnings (all)
115
A4.1
Bivariate Correlation Matrix
125
vi
Texas Tech University, Thomas O’Malley, August 2013
LIST OF FIGURES
2.1
2011 Median Weekly Earnings by Degree
11
2.2
2011 Unemployment Rate by Degree
11
2.3
Ability Deciles by Education
12
2.4
Human Capital and Financial Capital through the Lifecycle
18
2.5
The College Decision and Net Worth.
21
3.1
U.S. Personal Bankruptcy Filings (1985-2012)
55
3.2
College Dropout Proportion of Non-Payments and Payments
Behind Schedule
59
3.3
Median Loan-to-Income Ratios
59
3.4
Non-Mortgage Debt Balances (2003-2012)
65
3.5
The College Decision, Adverse Events, Debt and the
Likelihood of Bankruptcy.
67
Two Year Default Rates among Four Year Institutions
95
4.1
vii
Texas Tech University, Thomas O’Malley, August 2013
CHAPTER I
Introduction
Life-cycle theory describes the ideal household savings and consumption patterns
that should occur for people to maximize economic utility throughout life. The theory is
clearly normative and based on the idea that people derive satisfaction from consumption.
Satisfaction increases with consumption but it increases at decreasing rates. Given these
assumptions, individuals should save, invest and consume in such ways as to achieve the
highest, smoothest level of consumption throughout the years of their lives.
Financial planners utilize life-cycle theory and engage in life-cycle planning when
they identify the long-term goals of clients. Planners have long understood that income
must be transferred from the productive period of one’s life into retirement. Asset
management and retirement planning are traditional financial planning functions that use
life-cycle theory. To the extent planners address life, disability, and long-term care
needs, the profession ensures that clients are protected from falls in consumption.
Planning professionals routinely help clients preserve retirement income through
appropriate asset management techniques, sensible withdrawal strategies and
annuitization planning.
Financial planning professionals can also help life-cycle planning by helping
clients manage their human capital. Human capital is the sum of skills, knowledge, and
attributes one can use in the labor market to produce economic value. It is one’s earnings
potential and is the largest asset most households own (Campbell, 2006). The value of
1
Texas Tech University, Thomas O’Malley, August 2013
one’s human capital can be estimated as the present value of all future labor income.
Small improvements in client human capital can lead to dramatic improvements in
lifetime income and economic well-being.
A household’s ‘total’ portfolio consists of financial capital and human capital.
Human capital and financial capital interact throughout the lifecycle. Early in a person’s
life, human capital is large and financial capital is small. People join the labor force and
earn money for their labor. Some of these earnings are then saved and invested. In this
way, human capital is converted to financial capital.
The risk and return characteristics of financial and human capital each need to be
evaluated to manage the total portfolio. Human capital is often viewed as being like a
bond. For most workers, labor income is generally low risk but each profession is
unique. After the properties of human capital are identified, planners can then manage
financial capital to build a total portfolio that is suitable to the client’s risk tolerance and
time horizon. Because human capital typically dwarfs financial capital for young
households and can be treated as a bond, a diversified portfolio may require a heavy
exposure to equities (Bodie, Merton, & Samuelson, 1992).
Financial planners are in a unique position to help individuals manage human
capital. Planners can help clients negotiate a raise or compete for a promotion. Many
planners engage in education planning and some have expertise in business succession
planning. Forward thinking planners understand that the management of human capital
can yield tremendous value for clients. The potential benefits dwarf the marginal
2
Texas Tech University, Thomas O’Malley, August 2013
improvements that could be obtained tweaking portfolios or encouraging higher savings
rates (Veres, 2011).
The most important human capital decision many households face is the college
decision. College attendance is often characterized as a human capital investment since
education determines a person’s earnings capacity. Household education is highly
predictive of future earnings. The skills and knowledge acquired through education are
critical to labor market success.
A college education is a speculative investment however. ’Non-completion’ risk
is high. Almost half of all college students failing to graduate (Castex, 2010). For those
who do graduate, earnings risk is substantial. The payouts to a college education vary
greatly. College graduates experience higher earnings on average but there is substantial
variation in outcomes. Many graduates fail to obtain the earnings that they expected
when they made the enrollment decision.
College investments may be even more speculative now that student loans are
now a ubiquitous part of the college experience. In recent years tuition has soared
beyond the ability to pay for college. Over the last 40 years, household income has
increased by a factor of 6.5 but in-state tuition has swelled 15 times and 24 times for outof-state students (Schumpeter, 2010). More than two-thirds of today’s graduates use
student loans and the average debt load now exceeds $25,000 (Reed, 2011).
Debt financing may magnify risks inherent to human capital investments. Student
loan repayments constrain budgets and shape household decisions. Researchers have
long been concerned that excessive debt loads may unduly influence career choices. For
3
Texas Tech University, Thomas O’Malley, August 2013
example, lawyers may avoid low-paying public service careers and young doctors may
pass up specialist careers that require additional education (Chambers, 1992; Kornhauser
& Revesz, 1995; Spar, Pryor, & Simon, 1993; Rosenthal, Marquette & Diamond, 1996).
Research also investigates whether student debt effects enrollment in professional or
postgraduate degree programs (Weiler, 1991; Fox, 1992; Monks, 2001).
Student debt may affect other household choices. Shand (2008) considers the role
that debt plays in three post-college decisions affecting most graduates. She finds that
educational debt discourages homeownership, defers marriage and delays fertility
decisions. Hyounjin (2010) studies the transition time from school to work and finds
debt induces new graduates to join the labor force quickly.
The stakes are higher than ever and many people considering college need
professional advice. Financial planners frequently help clients with education planning
but a more comprehensive approach to college planning is needed. College payouts and
risks need systematic evaluation. Planners have the expertise to assist clients with
investment decisions, but to comprehensively plan they need more information. This
work attempts to provide this information and fill in some of the holes in the research
literature. Insights gathered here can be used by financial planners to assist households
make wise human capital decisions.
Description of Studies
Chapter Two investigates the extent to which student debt affects household
wealth accumulation. This study uses the National Longitudinal Survey of Youth 1979
(NLSY79) and looks at the long term net worth consequences of using student loans.
4
Texas Tech University, Thomas O’Malley, August 2013
College is expensive and outcomes are uncertain. College credentials often lead to higher
earnings. However, many indebted students fail to graduate and many graduates fail to
find higher paying jobs. Loans repayments are mandatory and systematic reductions in
wealth may occur. For those who take out student loans and then drop out of college,
poor financial outcomes may result. Wealth accumulation may also suffer for those who
graduate but then are unable to secure higher-paying employment. Wealth affects
households over a lifetime and therefore many individuals may experience a lower
standard of living as a result of their education choices.
Chapter Three examines whether education debt affects personal bankruptcy.
Accumulated education debt now surpasses total credit card debt and may now serve as a
major catalyst for financial distress. Debt financing enables a large swath of society to
obtain a college degree and higher income. Without student loans it is likely that fewer
people would attend and graduate from college. While many clearly benefit from using
loans, the payouts to college are stochastic. Poor earnings outcomes combined with
having to make required loan repayments may lead to financial distress and personal
bankruptcy. An asset pricing model is used to capture the essential principles of the
college investment decision including costs, uncertain educational outcomes and
stochastic payouts.
Chapter Four explores the wage rate and earnings consequences of attending a
private for-profit college. For-profit colleges are the fastest growing segment of higher
education. Evidence indicates that students of these proprietary schools have higher
tuition, more student debt, lower graduation rates and higher loan default rates relative to
5
Texas Tech University, Thomas O’Malley, August 2013
those who attend traditional non-profit schools. Existing research looks at the effects that
these institutions have on earnings but the scope of these studies is quite narrow. Few
data sets are useful to measure for-profit effects. This study uses the NLSY97 which
follows a young cohort of respondents, many of whom have attended for-profit schools.
We control for human capital factors, family background, cognitive ability and
personality and estimate wage and earnings effects at the associate's, bachelor's and
master's degree level.
6
Texas Tech University, Thomas O’Malley, August 2013
References
Bodie, Z., Merton, R. C. & Samuelson, W. (1992). Labor supply flexibility and portfolio
choice in a life cycle model. Journal of Economic Dynamics and Control, 16,
427-449.
Campbell, J. Y. (2006). Household finance. Journal of Finance, 61, 1553–1604.
Castex, G. (2010). College risk and return. Working paper. University of Rochester.
Chambers, D. L. (1992). The burdens of educational loans: The impacts of debt on job
Choice and standards of living for students at nine American law schools.
Journal of Legal Education, 42, 188- 192.
Fox, M. (1992). Student debt and enrollment in graduate and professional school.
Applied Economics, 24, 669-677.
Hyounjin, Y. (2010). Three essays in consumer finance: debt stress, payments, and
student loans. Ohio State University Ph.D. thesis.
Kornhauser, L. & Revesz, R. L. (1995). Legal education and entry into the legal
profession: The role of race, gender, and educational debt. New York University
Law Review, 70, 829-945.
Reed, M. (2011). Student debt and the class of 2010. The Institute for College Access &
Success. Retrieved from http://projectonstudentdebt.org/pub_home.php.
Rosenthal, M., Marquette, P., & Diamond, J. (1996). Trends along the debt-income axis:
Implications for medical students' selections of family practice careers. Academic
Medicine, 71(6), 675-677.
7
Texas Tech University, Thomas O’Malley, August 2013
Schumpeter, J. (2010, Sept 2). Declining by degree: Will Americas universities go the
way of its car companies? The Economist. Retrieved from
http://www.economist.com/node/16941775
Shand, J. M. (2008). The impact of early-life debt on household formation: An empirical
investigation of homeownership, marriage and fertility (Unpublished doctoral
dissertation). Ohio State University, Columbus Ohio.
Spar, I. L., Pryor, K. C. & Simon, W. (1993). Effect of debt level on the residency
preferences of graduating medical students. Academic Medicine, 68, 570-572.
Veres, B. (2011). The four-factor service model. Inside Information, 21(4), 1-20.
8
Texas Tech University, Thomas O’Malley, August 2013
CHAPTER II
THE NET WORTH IMPLICATIONS OF STUDENT LOANS
Abstract
Attending college is beneficial for many people but it is inherently risky. Investment risk
increases when student loans are used. This study assesses this risk by measuring the
wealth implications of using education debt. The long-term effect of student debt on
household wealth is analyzed using the National Longitudinal Survey of Youth
(NLSY79). Controlling for education, cognitive ability, and parental resources, we find
student debt is negatively associated with net worth. OLS estimates indicate that ‘some
college’ and associate's degrees confer wealth benefits but there are no benefits if student
loans are used. Among those with at least a bachelor's degree, there are large wealth gaps
between those who have used loans and those who have not. These gaps are even larger
when median wealth is investigated.
Introduction
Human capital is the stock of knowledge, skills and other attributes that enables a
person to produce goods or services that have economic value. It is a person’s earning
potential. Human capital is increasingly thought of as an asset and is commonly
estimated as the discounted present value of all future labor income. Human capital is the
largest asset most individuals own, particularly young households.
Many households enhance their earnings capacity by pursuing additional
education. The decision to attend college is perhaps the common of these human capital
investments. College graduates are rewarded by the labor market. On average, a college
degree delivers 1.5 to 1.7 times more lifetime wages (Restuccia & Urrutia, 2004). The
returns to a college education compare favorably to the returns of other investments.
9
Texas Tech University, Thomas O’Malley, August 2013
Most econometric studies estimate the return to a college education at between 10 and 15
percent (Goldin & Katz, 2008).
While a consensus has formed that going to college is profitable, college is a
risky, speculative investment. Many college students fail to graduate. Since 1960, the
average dropout rate has fluctuated between 47 to 55 percent (Castex, 2010). A large
proportion of the workforce has attempted college but failed to graduate. Nearly 30
percent of all 25-29 year olds are college dropouts (Aud, et. al., 2010). Among
graduates, there is considerable employment uncertainty. Labor market outcomes vary
through time and depend on the educational institution attended, the field of study
undertaken and a host of other factors. Many college graduates work in low-paying
positions. More than a third of college graduates now work at jobs that do not require a
college degree (Vedder, Denhart, Denhart, Matgouranis, & Robe, 2010).
The financial benefits of attending college but failing to graduate are positive but
modest. College dropouts have higher median wages than high school graduates but the
difference is small (Figure 2.1). Workers with ‘some college’ earn approximately 13%
more than those with a high school diploma. This set of workers also has slightly lower
unemployment rates. In 2011, those with ‘some college’ had a 7% lower unemployment
rate than those without college experience (Figure 2.2).
10
Texas Tech University, Thomas O’Malley, August 2013
1800
1600
1665
1400
1200
1551
1263
1000
1053
800
600
400
638
719
768
451
200
0
Source: Bureau of Labor Statistics, Current Population Survey
Figure 2.1-2011 Median Weekly Earnings ($) by Degree
16
14
12
14.1
10
8
6
4
9.4
8.7
6.8
4.9
2
3.6
2.4
0
Source: Bureau of Labor Statistics, Current Population Survey
Figure 2.2 -2011 Unemployment Rate (%) by Degree
11
2.5
Texas Tech University, Thomas O’Malley, August 2013
Straight comparisons between college dropouts and high school graduates may be
misleading. Non-random selection into college may bias these conclusions and lead to an
overstatement of the economic benefits of attending college. Those who choose to attend
college probably have more earnings potential than those who do not choose college. For
example, college dropouts exhibit higher average cognitive ability than high school
graduates who do not attend college (Figure 2.3). IQ and 2008 wages have a .21
correlation (author’s calculation using 2008 NLSY79 data). College dropouts may also
display other behavioral qualities associated with successful outcomes.
180
160
140
120
100
80
60
40
20
0
1
2
3
4
5
High School Only, n=1037
6
7
8
9
Some College, n=740
Figure 2.3 - Ability Deciles by Education (2006 NLSY97)
12
10
Texas Tech University, Thomas O’Malley, August 2013
Comparing the those with 'some college’ to those with no college experience
requires further clarification. The population of ‘high school diploma’ holders often
includes those with a General Equivalency Diploma (GED). While the Department of
Education and the Bureau of Labor Statistics treat GEDs and traditional high school
diplomas equivalently, the labor market does not. GED holders are economically
indistinguishable from high school dropouts (Cameron & Heckman, 1993). After
controlling for this and other factors, the wage premium associated with attending college
may shrink, or vanish altogether. This would be consistent with common experience that
employers hire to a specific credential and ‘some college’ experience is rarely, if ever,
sought after.
The high costs of attending college compound graduation and completion risks.
Costs have risen dramatically in recent decades. Inflation-adjusted tuition has increased
359 percent for public four-year schools and 296 percent for private four-year schools
over the last three decades (College Board, 2010). Meanwhile, the ability to pay for
college has not kept pace with these tuition increases. Over the last 40 years, household
income has increased by a factor of 6.5 but in-state tuition for state schools has grown 15
times and for out-of-state students 24 times (Schumpeter, 2010).
The availability of scholarships and grants has not kept pace with these cost
increases. As a consequence, students have increasingly turned to student loans. From
1999 to 2007, the average loan amount has increased 31 to 50 percent after inflation,
depending on the type of institution (College Board, 2010). More than two-thirds of
13
Texas Tech University, Thomas O’Malley, August 2013
college students now rely on debt financing and the average graduate now leaves college
with more than $25,000 in debt (Reed, 2011).
Federal loans dominate the education debt market. Few private lenders are
willing to operate in this market since risk is high. Outcomes are highly uncertain and
college students lack the assets that could be used to collateralize loans. Given the
limited private market for human capital investments, the federal government has
intervened in the financing of education (Carneiro & Heckman, 2002). The most
significant change occurred with passage of the Higher Education Act of 1965, creating
the forerunner to today’s Pell Grant and Stafford Loan Program.
Federal loans are widely available but may be exceedingly burdensome for many
borrowers. These loans are not subject to standard consumer protections and cannot be
discharged in bankruptcy. Loan recipients are provided an interest-free six month grace
period after leaving school. Repayment begins after this grace period regardless of
whether the loan recipient successfully graduates or finds suitable employment. If the
loan recipient is unable to repay and loan default occurs, this is reported to credit bureaus.
For many, this increases the cost of borrowing and restricts employment opportunities.
In addition, the federal government has the authority to garnish wages and to seize tax
refunds and Social Security benefits in order to recover payments.
Research is needed to examine the financial implications of using student loans.
It is clear that student loans constrain the budgets of many borrowers. This may induce
many borrowers to make choices they would not otherwise make. An extensive body of
work investigates the returns to a college education but research fails to consider how
14
Texas Tech University, Thomas O’Malley, August 2013
student loans affect these investment returns. A few studies look at the immediate impact
of using student loans but fail to investigate the long-term financial implications
associated with education debt. This paper attempts to fill the void and looks at the net
worth implications of using student loans.
Literature Review
Policy experts have long been concerned that large student loan balances may
have an undue influence on the employment decisions borrowers make. Relevant
research provides mixed results and is narrowly focused however. The most exhaustive
of these studies consider decisions made by law and medical school graduates
(Kornhauser & Reves, 1995; Spar, Pryor & Simon, 1993; Kassebaum, Szenas &
Schuchert, 1996; Rosenthal, Marquette & Diamond, 1996; Woodworth, Chang &
Helmer, 2000).
If debt indeed influences career decisions, it may because indebted individuals
face higher interest rates. Those with more debt may attempt to front-load their earnings
profiles. Minicozzi (2005) finds that educational debt is positively associated with initial
wages but negatively related to subsequent wage growth. Education loans may also lead
to shorter transition times from school to work (Hyounjin, 2010).
Student debt may influence advanced degree decisions. Some studies find a
negative relation between debt and graduate school enrollment. Fox (1992) identifies a
negative effect of debt on women’s enrollment rates into master's programs, but no effect
for men. Heller (2001) finds that while undergraduate borrowing is negatively related to
15
Texas Tech University, Thomas O’Malley, August 2013
graduate school enrollment, other characteristics are more influential. Kantrowitz (2010)
finds that debt plays a substantial role; graduates of bachelor's degree programs who do
not have education debt are 70 percent more likely to pursue an advanced degree.
Education loans may negatively affect home ownership. If college loan
repayments discourage home purchases, there may be broad implications for wealth
accumulation. Housing wealth is the dominant asset class of the middle class (Campbell,
2006). Home ownership not only provides long-term service flows but can also serve as
collateral for borrowing and the hedging of consumption shocks. There is evidence that,
given its illiquid nature, housing may also serve as an effective commitment device for
hyperbolic households (Angeletos, Laibson, Repetto, Tobacman & Weinberg, 2001).
Evidence suggests that student loan repayments do deter home purchases. Results
from the 2002 National Student Loan Survey (NSLS) reveal that over half of loan
recipients feel burdened by their loans. Thirty-five percent of high-income recipients and
forty-five percent of low-income recipients indicate that loans delay home ownership
(Baum & O’Malley, 2003). Chiteji (2006) finds that credit card and other noncollateralized debt decreases homeownership 4.2 percent per $10,000 debt. Shand (2008)
uses the 2004 Survey of Consumer Finances (SCF) and finds that homeownership rates
decrease by 1.0 percent for each $10,000 in education debt.
Education debt may delay household formation. Becker (1973) first modeled the
economic factors relevant to the marriage decision. Unmarried men and women compete
in a marriage market, evaluating the economic possibilities of potential spouses. All
16
Texas Tech University, Thomas O’Malley, August 2013
things equal, debt may signal a lower quality marriage offer. Shand (2008) presents
evidence that education debt indeed discourages marriage.
The risk of using education debt may deter risk-adverse college students from
enrolling in college. Many academically talented people bypass college despite the
potential for a lifetime of significantly higher earnings. Among those in the highest
quartile of cognitive ability, nearly one in six does not attend college (Chen, 2001). On
the other hand, many students that pursue a college education clearly do not have the
potential to succeed. Among those that attempt college, half fail to graduate (Castex,
2010).
Studies investigate whether student debt affects wealth accumulation. This work
addresses this gap in the literature and looks at the relation between student loans use and
net worth. Net worth is a useful benchmark to measure welfare losses and gains because
it comprehensively affects all households. It protects individuals from drops in
consumption and provides insurance against labor market uncertainty. Wealth
determines access to housing and schooling and is often used to start a business. It is
used to fund retirement and residual wealth can be transferred through inheritance and
bequest.
Theoretical Framework
The decision to attend college is an investment decision. Consumer debt simply
transfers consumption from one period to another, but student debt is intended to increase
a person’s earnings and consumption throughout life. For this reason, many view
17
Texas Tech University, Thomas O’Malley, August 2013
education debt as investment debt. Investment costs are paid early and uncertain benefits
are received later. Direct costs associated with attending college include tuition and fees.
College students also pay indirect, or opportunity costs. They forego wages and job
experience. Student debt is used to pay direct costs and to a lesser degree can be used to
pay other costs.
At the heart of the college decision is the process that transforms human capital to
financial capital. There is a natural interaction between the two that occurs over the
lifecycle (Figure 2.5). Human capital is large early in life and financial capital is small,
or even nonexistent. People join the labor force, earn money for their labor and then save
some of their earnings.
$2,000,000
$1,800,000
$1,600,000
$1,400,000
$1,200,000
$1,000,000
human capital
$800,000
financial capital
$600,000
$400,000
$200,000
$0
20
40
60
80
Age
Figure 2.4 - Human Capital and Financial Capital through the Lifecycle
18
Texas Tech University, Thomas O’Malley, August 2013
People invest in their education to enhance their earnings potential. Increasing
one’s productive capacity leads to higher earnings and the accumulation of financial
capital. Attending college may prove to be a bad investment however if productive
capacity in not improved. If college provides no earnings advantage and education debt
is acquired, financial loss may be experienced.
Internal rate of return estimations account for these costs and benefits and are
frequently used by economists to evaluate schooling decisions. Tuition costs and forgone
wages are used to capture investment costs. Historical wage information is used to assess
college investment payouts. Additional human capital investments are calculated to be
beneficial if the rate of return exceeds the opportunity cost of funds.
Prospective students should consider costs and benefits when making the college
decision. At a minimum, a simple analysis should consider: 1) direct costs of attending
college such as tuition, books and fees 2) wages foregone during the educational
experience and 3) wage premium earned with a diploma. A standard internal rate of
return calculation captures these tradeoffs (Carnoy & Marenbach, 1975):
(1)
where Yt is the average wage difference in period t between workers with
bachelor's degrees and workers with high school diplomas. The variable Ct captures
direct and indirect college costs. It includes tuition, fees, other direct costs and foregone
wages. If we make a simplifying assumption that only full-time schooling or full-time
work is possible, Yt is equal to zero while the individual is in school and C t is equal to
19
Texas Tech University, Thomas O’Malley, August 2013
zero after the individual leaves college. T is the number of periods in the individual’s
working life and r is the internal rate of return to a college education.
A more complete analysis accounts for non-completion. Equation 2 is a more
general form of the two-period model found in Castex’s (2010):
(2)
where YtG corresponds to the average wages difference between college graduates
and high school graduates. Similarly, YtD is the average wage difference between college
dropouts and high school graduates. The number of periods the individual attends school
is represented by x. The variable ps is the probability that the individual will graduate
from college. This specification recognizes completion risk, employment risk and cost
uncertainty where Ct, YtG, YtD and ps are all stochastically assigned.
Analysis of the college investment decision and student loan use is better suited to
a focus on net worth rather than on earnings. First, households with student debt pay
higher interest charges on other debt, all things equal. Higher borrowing costs may
reduce household net worth but will not affect earnings. Second, if student debt induces
individuals to frontload earnings as postulated by Minicozzi (2005), this may negatively
affect lifetime wealth accumulation. Lastly, Chiteji (2006) and Shand (2008) find student
debt retards homeownership rates. Since housing is the dominant asset class of the
middle class, lower rates of homeownership may negatively impact wealth accumulation
but not earnings.
20
Texas Tech University, Thomas O’Malley, August 2013
The starting point to analyze the college decision is a model of the investment
decision. The decision is modeled as an event tree. Figure 2.4 illustrates an event tree
for the typical person finishing high school and now considering whether to attend
college. Bypassing college allows one to avoid college costs, join the labor force
immediately and receive low average wages. Net worth is stochastically assigned from
distribution 3. The prospective student understands that not all college students
successfully complete the program of study. The odds of success, Ps, are estimated. For
simplicity, investment costs are proportional to the time spent in college. Costs include
tuition and fees, other direct costs and foregone wages. Net worth payouts depend on
graduation status. College dropouts are randomly assigned net worth from distribution 2
and college graduates receive payouts from distribution 1.
Figure 2.5 - The College Decision and Net Worth.
21
Texas Tech University, Thomas O’Malley, August 2013
This model of the college investment decision identifies the relevant economic
factors. A prospective college student chooses a distribution of lifetime wealth.
Individuals consider the ‘most likely’ scenario for each distribution as well as ‘optimistic’
and ‘pessimistic’ possibilities. They evaluate the relevant costs, assess the probability of
success and understand that investment payouts are random variables.
This paper estimates and compares net worth for those who have used education
loans and those who have not. We consider differences in mean and median wealth to
determine if some households experience financial loss. The variables of interest are
student loan use by education degree. Equation 3 provides the conceptual framework.
2008 Net Worth = f (student loans, degree, ability, family, personal traits,
asset ownership, household composition, race, region)
(3)
Controls are used to obtain unbiased estimates and to isolate degree and student
loan effects on net worth. We include variables for individual ability, family resources,
personal traits, asset ownership, household composition, race and region. A short
description of each of these dimensions follows.
Ability
Some research finds no meaningful relation between IQ and wealth accumulation
(Zagorsky, 2007; Hartog & Oosterbeek, 1998). However, other indications point to a
positive relation. From the NLSY79 sample used in this paper, there is a .278 correlation
22
Texas Tech University, Thomas O’Malley, August 2013
between the Armed Services Vocational Aptitude Battery (ASVAB) and 2008 net worth.
The ASVAB is a commonly used proxy for IQ and used here to measure cognitive
ability.
Family Background
Four family background variables are used to control for wealth differences that
result from family and upbringing. People are provided financial, informational and
educational resources from families. The factors included here are: inheritances, family
income, parental education, and number of siblings.
Direct financial transfers commonly occur in the form of an inheritance.
Lifecycle theory and the permanent income hypothesis each propose that households
have a high propensity to save one-time wealth transfers (Brumburg & Modigliani, 1954;
Friedman, 1963). Empirical evidence supports this notion that transfers increase
household net worth (Kotlikoff, 1988). Family income and parental education are also
included. Studies have long confirmed a trans-generational persistence in income and
education. Parents transmit to their children educational and labor market success.
Lastly, a variable measuring the number of siblings is included. Family resources are
diluted in large families. Children from large families tend to underperform academically
and occupationally (Downey, 1995).
Personal traits – time preference and risk tolerance
Risk tolerance refers to a person’s willingness to take financial risks. Over long
periods of time, a higher tolerance for risk appears to help households amass financial
23
Texas Tech University, Thomas O’Malley, August 2013
assets. Risk tolerant households are more willing to accept variation in financial returns
and are more likely to invest in equities and other assets that have high growth potential.
Financial risk tolerance and wealth accumulation are related (Hariharan, Chapman &
Domian, 2000). As stock ownership increases, retirement wealth increases (Yuh, Hanna
& Montalto, 1998).
Time preference measures the degree to which a person prefers current
consumption to future consumption. Households that are present-oriented get satisfaction
from consuming now. Some people exhibit hyperbolic discounting and demand
instantaneous gratification but are patient over the long-term. This creates a self-control
problem (Angeletos et al., 2001). On the other hand, some households are futureoriented. These are ‘planners’ who are more willing to defer consumption in order to
achieve higher levels of future consumption. Planners tend to acquire wealth. Lusardi
and Mitchell (2007) for example find that baby boomers who spend time thinking about
retirement are significantly more wealthy.
Asset ownership – housing and private business ownership
Housing is the principal asset of the middle class (Campbell, 2006). It provides
long-term service flows, can be used for collateralize loans and can hedge the risk of
consumption shocks. Housing is illiquid investment that may serve as an effective
commitment device for hyperbolic households (Angeletos et al., 2001). It serves as an
effective savings program for households most in need of discipline.
Private business ownership is included since it is the dominant asset class among
the wealthy. Entrepreneurs and the self-employed tend acquire more wealth (Wolff,
24
Texas Tech University, Thomas O’Malley, August 2013
1998). Business owners represent less than ten percent of the population but own nearly
forty percent of household wealth (Gentry & Hubbard, 2004).
Household composition
Wealth accumulation is also related to sex, marital status and family size. The
gender pay gap has narrowed significantly in recent decades but it has not disappeared
entirely. Explanations for the pay gap include discrimination, occupational choices and
division of labor theories (Blau & Kahn, 2000). If women’s wages are lower on average,
it is plausible that there will be less wealth accumulation for women. Single people have
less net worth than married couples (Zagorsky, 1999). Lastly, children require significant
investments of time, money and other resources. Larger size families have less average
wealth (Wolff, 1998).
Race
There is a persistent wealth gap between whites and blacks. Several explanations
for wealth underperformance by black Americans have been presented. Blacks earn
more as they complete more education but still earn substantially less than whites at
similar educational levels. Black Americans suffer from income stagnation and are not
rewarded as generously as whites for labor market experience. Asset composition
explains some of the wealth gap. Black households have a much larger proportion of net
worth tied up in functional assets and less in income-producing and other financial assets
(Oliver & Shapiro, 1995).
Region
25
Texas Tech University, Thomas O’Malley, August 2013
Wages and asset prices vary significantly by region. Regional controls are
utilized to account for differences in cost of living factors.
Problem Statement
Existing research fails to determine whether student loan use affects wealth
accumulation. This research tests whether the debt financing of college affects net worth.
Wealth effects are investigated for each level of college education. Both mean and
median wealth are considered.
Data
In order to test the conceptual framework found in Equation 3, data is needed to
measure student loan use, degree accomplishment and the eight other concepts used as
controls. The data are from the National Longitudinal Survey of Youth (NLSY79). The
NLSY79 provides a rich dataset of financial information, employment history, personal
traits, family background and household characteristics. The NLSY79 is sponsored by
the Department of Labor’s Bureau of Labor Statistics. It is a panel data set that captures
the responses of a national sample of 12,686 men and women. The respondents were
born in the years 1957-1964 and were first interviewed in 1979. Follow-up interviews
were conducted annually from 1979 through 1994 and biennially thereafter.
Sample
Responses are limited to those observations that have valid responses to the 2008
net worth questions. Data are censored to those respondents who report having a high
school or higher diploma as their highest degree. Only traditional diplomas are
26
Texas Tech University, Thomas O’Malley, August 2013
considered; survey responses from GED holders are omitted. Respondents who have
obtained an associate's degree, a bachelor's degree or a master's degree are included but
those reporting ‘other’ as their highest degree are excluded. After the data is restricted in
this manner, the initial sample includes 5,505 observations.
Method
The research question is : Does student loan usage impact wealth accumulation? Mean
and median wealth are analyzed. Two regressions are conducted. First, an ordinary least
squares (OLS) regression approach is used to analyze mean wealth. Net worth is the
dependent variable. The extreme 5 percent portions of the net worth distribution are recoded at the respective 5th and 95th percentile values. Net worth data is highly skewed at
the extremes. This re-coding minimizes the influence of these extreme values and
ensures closer adherence to the linearity assumption needed for OLS analysis. Many
studies employ log transformation but that approach is not suitable here since almost 7
percent of the useable sample has a negative net worth and another 4.76 percent reports
zero net worth.
The second analysis utilizes quantile regression. Whereas OLS regression models
the conditional mean of the dependent variable distribution, quantile regression can be
used to estimate the conditional median of the same distribution. Modeling the
conditional median of the distribution is more robust to outliers. Household financial
statistics frequently use the median rather than the mean. For example, median home
values and median income are commonly used to convey information about typical home
27
Texas Tech University, Thomas O’Malley, August 2013
values and income. Here we use quantile regression to estimate the .5 quantile, or
median, of the dependent variable distribution.
Dependent Variable
The dependent variable for this study is net worth. Net worth is a NLSY
summary variable that totalizes the responses to multiple asset and debt questions. The
2008 wave of the NLSY79 captures 2007 financial information. Survey administrators
top-code the highest 2 percent of all values. In the 2008 survey the top-code threshold is
$3,448,187.
The OLS regression uses a ‘winsorizing’ approach to minimize the effects of
outliers. Given the skewed nature of net worth data, the top and bottom 5 percent of net
worth values are re-coded at the 5th and 95th percentiles. The second analysis uses
quantile regression that models the .5 quantile, or median, conditional distribution. Net
worth values are not transformed here.
Independent Variables
The first eight variables are the variables of interest. These variables capture
student loan use at four college degree levels. Student loan questions were first
introduced in the 1984 survey and were included in each subsequent survey
administration. These variables categorize responses by the highest degree obtained.
Research has long identified a “sheepskin effect”. Returns to education studies find that
while wages are positively correlated with years of education, individuals with degrees
receive additional wage premiums. Diploma holders receive wage premiums that cannot
be explained by the number of school years completed (Belman & Haywood, 1991). The
28
Texas Tech University, Thomas O’Malley, August 2013
coding of associate's, bachelor's and postgraduate degrees is straight forward. The ‘some
college’ category is more involved. It captures those who have a high school degree but
report 13 or more years of education. Also, individuals who report 12 years of education
but also separately report college loans are coded as having ‘some college’.
The return to schooling and wealth accumulation literature highlights the need for
multiple controls. The empirical model tested here includes seven sets of control
variables: cognitive ability, family background, personal traits, asset ownership,
household composition, race and region.
Ability
Each NLSY participant was administered the Armed Forces Qualifying Test
(AFQT) in 1981. AFQT has been widely used as proxy for IQ. The original scores were
biased at test administration in favor of older respondents. AFQT scores were adjusted
by the NLSY staff in 2006 to account for this. Responses are reported in decile format.
Family background
Four family variables are used to control for wealth differences resulting from
financial, informational and educational factors available from family. Direct financial
transfers in the form of inheritances are first included. Inheritance questions are first
asked in 1988 and are used in all follow-up interviews. Figures are inflated using the
annual CPI and summed to arrive at a single value measured in 2008 dollars. Family
income from the initial administration of the NLSY79 is included. Parental education is
also included. The highest number of years of education achieved by either parent is
used. Lastly, a variable measuring the number of siblings is included.
29
Texas Tech University, Thomas O’Malley, August 2013
Personal traits
Two personal variables are included: financial risk tolerance and time preference.
Higher financial risk tolerance is associated with wealth accumulation. The NLSY
includes income gamble questions. This variable is coded categorically with possible
values of one, two, three or four. Time preference indicates a person’s preference for
current consumption relative to future consumption. Consumers with high time
preference obtain more utility from current consumption than future consumption and
tend to acquire less wealth. One year time preference is used. It is coded as a dummy
variable with low time preference equal to one.
Asset ownership
Two dummy variables measuring asset ownership are included. Homeownership
is the dominant asset classes for the middle class. Respondents reporting owning their
home in 2008 are coded as homeowners. The second variable captures self-employed
status. Wealthy households are disproportionately self-employed private business
owners.
Household composition, race & region
Dummy variables are included for gender and marital status. A third household
variable is used to measure the number of children in the household. Three dummy
variables are created to measure ‘black’, ‘Hispanic’ and ‘other’ races. White race is the
excluded category. Finally, the NLSY includes four regions of residence. Three dummy
variables are included in this model: ‘northeast’, ‘northcentral’, and ‘south’. ‘West’ is the
excluded reference.
30
Texas Tech University, Thomas O’Malley, August 2013
Results
Descriptive Results
Mean sample values for each variable are provided in Table 2.1. Median values
are provided for the financial variables. Separate statistics are provided for the full
sample, high school graduates, college less-than-bachelor's, and college bachelor's-andhigher sub-samples. The less-than-bachelor's category includes those with some college
experience or an associate's degree. The bachelor's-and-higher category consists of those
who have a bachelor's or master's degree. Mean and median net worth are higher among
'less-than-bachelor's college' than among high school graduates who do not attempt
college. The difference in net worth between less-than-bachelor's college and bachelor'sand-higher is much larger.
A large proportion of those who have gone to college have used student loans.
Fewer respondents in the less-than-bachelor's category use loans than those with
bachelor's-and-higher status. Fifty-eight percent of the first category have used student
debt. Forty-two percent of the bachelor's-and-higher group have used student loans.
Cognitive ability is positively associated with educational level. Ability is highest
among those with a bachelor's or higher level of education. Among these three education
groups, the average IQ proxy are 39th, 49th, and 68th percentiles.
Family characteristics include inheritances, parental education, family income,
and number of siblings. Less-than-bachelor's and high school graduates have similar
family statistics. Those with bachelor's or higher education however come from higher
income families and have more educated parents. Family income is 34 percent higher for
31
Texas Tech University, Thomas O’Malley, August 2013
these respondents than for the other two educational groups. Less-than-bachelor's parents
average 12.0 years of education and bachelor's-and-higher parents average 13.32 years of
education. Lastly, individuals with a bachelor's or higher education come from families
with fewer siblings to share resources with.
Personal traits consist of risk tolerance and time preference. Risk tolerance is
measured on a four point scale. Risk tolerance is highest among the most educated,
averaging a 1.94 score, compared to 1.88 for those in the less-than-bachelor's category.
Time preference is measured as either high or low. There are slight differences between
the two college groups. Fifty-four percent of the bachelor's-and-higher respondents have
low time preference but 48 percent of less-than-bachelor's households have low time
preference.
Prior work identifies homeownership and self-employment as significant
predictors of household wealth. Relative to high school graduates, less-than-bachelor's
respondents are about 6 percent more likely to be homeowners, but bachelor's-and-higher
households are 27 percent more likely to be a homeowner. Respondents from each of the
two college educational levels are nearly 20 percent more likely than high school
graduates to be self-employed.
Household composition variables include the number of children in the
household, sex and marital status. Household size and female representation are
equivalent between the two college educational groups. Bachelor's-and-higher
individuals are 26 percent more likely to be married than those with a less-than-bachelor's
education however.
32
Texas Tech University, Thomas O’Malley, August 2013
Three dummy variables are used to identify the race of the respondents. Black
and Hispanic representation is lowest among those that acquire a bachelor's degree or
more education. Three dummy variables are used to identify regional differences. No
significant regional differences in education can be viewed in Table 2.1.
Table 2.1: Descriptive Statistics of the Full Sample
All
High School
College
Less-thanbachelor's
College
Bachelor's-andhigher
mean (median)
mean (median)
mean (median)
mean (median)
301,469 (125,500)
195,178 (73,068)
student loan use
0.31
0.00
0.42
0.58
IQ decile
5.20
3.86
4.93
6.79
net worth
family inheritances
parent schooling
30,150
(0)
17,630
(0)
218,216 (88,470) 469,161 (241,750)
20,590
(0)
49,740 (1,830)
12.13
11.07
12.00
13.32
19,347 (17,000)
17,153 (15,000)
17,201 (15,000)
23,107 (20,000)
number of siblings
3.61
3.96
3.68
3.20
risk tolerance
1.91
1.91
1.88
1.94
time preference - low
0.50
0.47
0.48
0.54
homeowner
0.73
0.65
0.69
0.83
self employed
0.12
0.11
0.13
0.13
number of children
1.29
1.29
1.32
1.28
age
56.49
56.46
56.48
56.53
female
0.52
0.48
0.55
0.56
married
0.59
0.54
0.53
0.67
black
0.27
0.29
0.34
0.20
Hispanic
0.05
0.05
0.06
0.03
other race
0.05
0.05
0.06
0.05
northeast
0.16
0.15
0.15
0.17
northcentral
0.23
0.24
0.21
0.23
south
0.38
0.38
0.38
0.37
n=5505
n=2175
n=1304
n=2026
family income
33
Texas Tech University, Thomas O’Malley, August 2013
The next tables illustrate the dispersion in wealth. As conceptualized in Figure
2.5, a prospective college student essentially chooses a distribution of lifetime wealth.
The data in Table 2.2 are estimates of the central tendency and dispersion of the
respective wealth distributions. Estimates are also provided by college degree in Table
2.3.
Table 2.2: Descriptive Statistics: Net Worth by Completion Status
Pessimistic
Most Likely
Optimistic
n
Mean - 1 stan dev
Mean
Mean + 1 stan dev
College Bachelor's-and-higher
2,026
-247,676
469,161
1,185,998
College Less-than-bachelor's
1,304
-227,144
218,215
663,574
High School
2,175
-237,527
195,178
627,883
Table 2.3: Descriptive Statistics: Net Worth by Degree
n
Pessimistic
Most Likely
Optimistic
Mean - 1 stan dev
Mean
Mean + 1 stan dev
Associate's
606
-215,383
275,317
766,017
Bachelor's
1,037
-214,309
533,412
1,281,133
383
-257,088
601,905
1,460,898
Master's
34
Texas Tech University, Thomas O’Malley, August 2013
OLS Regression Results
A multivariate OLS analysis is conducted to estimate the determinants of net
worth. Net worth is regressed on the 27 variables specified by the empirical model.
Results are found in Table 2.4. Eighteen variables are statistically significant at the five
percent level. The model explains a significant portion of variation in household net
worth. The adjusted R2 is .3505.
The effects of debt by educational level are estimated. The bachelor's-and-higher
and less-than-bachelor's categories are broken up into four categories: 'some college',
associate's, bachelor's and postgraduate levels. The reference category includes nonGED high school graduates who never attempted college. Some college is estimated to
result in $28,252 more net worth for those who have not used student loans. In contrast,
we do not find a wealth benefit for those who have some college experience and have
used student loans. An associate's degree is estimated to result in $54,387 more net
worth for those who did not use debt financing. We find no wealth benefit to an
associate's degree if student loans were used to finance education.
There are large increases in household wealth associated with the acquisition of a
bachelor's or postgraduate degree. Individuals who have a bachelor's degree and did not
use student loans have an estimated net worth $158,223 higher than that of non-GED
high school graduates. For households with a postgraduate degree and no student loan
use, the benefits are slightly higher at $163,672.
The wealth benefits are much smaller for people who financed their degrees with
student loans. The estimated net worth is $43,000 less for those who used loans to get
35
Texas Tech University, Thomas O’Malley, August 2013
their bachelor's degree. For those with a postgraduate degree, the wealth difference is
approximately $56,000.
Cognitive ability is positively related to wealth accumulation. A one decile
increase in IQ is estimated to increase net worth by $5,059. All family factors except
parental education are statistically significance. A $1,000 of inherited wealth is estimated
to increase net worth by $305. Parental income is positively related to household wealth.
A $1,000 increase in parental income (’79 USD) is estimated to increase household
wealth of the respondent child by $1,950. Lastly, the number of siblings is inversely
related to wealth accumulation. Each additional sibling is estimated to reduce net worth
by $4,967.
The estimated effects of the ‘personal traits’ vary. Time preference is related to
wealth accumulation. Households with low time preference are estimated to have
$39,026 more wealth than those with high time preference. As specified here, risk
tolerance is not related to wealth.
The two modeled asset ownership factors are related to household wealth.
Homeownership is the most influential factor. It is estimated to increase net worth by
$185,524. To a lesser degree, self-employment is also positively related to wealth
accumulation.
Marriage results in significant wealth accumulation. Married respondents are
estimated to have more than $93,190 additional wealth. As expected, age is positively
related to wealth accumulation. Black households are estimated to have less aveage
wealth.
36
Texas Tech University, Thomas O’Malley, August 2013
Table 2.4: OLS and Quantile Results
The dependent variable in both regressions is 2007 net worth. The dependent variable in the OLS
regression is winsorized, but there is no transformation of the dependent variable in the quantile
regression. Asterisks correspond to 5, 1 and .1 percent significance levels.
OLS Regression
Full Model
Estimate
SE
Quantile Regression (q = .5)
Full Model
Estimate
SE
-336664
99080
-168376 51918
28252
-2508
54387
11598
158223
115106
163672
107282
12290
13649
16853
18613
16706
14001
28602
18803
*
IQ decile
5059
1885
inherited wealth
yrs schooling - parents
1979 family income
305
2690
1950
number siblings
risk tolerance
time preference
Intercept
(reference: high school)
some college - no loans
some college - loans
associate's
- no loans
associate's
- loans
bachelor's
- no loans
bachelor's
- loans
postgraduate - no loans
postgraduate - loans
homeowner
self-employed
number children
age
female
married
(reference: white)
race - black
race - Hispanic
race - other
(reference: west)
region - northeast
region - northcentral
region - south
Observations used
10870
-6828
20049
7127
144262
78284
190806
65268
7005
6188
10743
8433
24749
14015
43996
28374
**
2575
1039
29
1462
325
***
***
472
1784
1127
55
864
288
***
*
***
-4967
1702
**
-2327
867
**
290
39026
3292
7679
***
-2385
18422
1704
4502
***
185524
28464
-6570
5262
-10016
93190
9912
11657
3987
1716
7897
9048
***
*
124973
14423
-526
2407
-10778
62192
5061
9493
2453
879
4652
5798
-43972
2456
21562
10639
19119
18039
***
-8947
18013
7632
4835
7160
8541
-6411
-55505
-45382
12552
11505
10572
9966
-9739
-10663
7153
7155
6264
4,254
37
**
***
***
***
***
**
***
***
***
4,254
***
***
***
*
*
***
**
*
***
*
Texas Tech University, Thomas O’Malley, August 2013
Quantile Regression Results
Median net worth is regressed on the same 27 variables used in the previous OLS
regression of mean net worth. Estimates are provided on the right side of Table 2.4.
Appendix 2.2 provides regression estimates for other quantile slices of the net worth
distribution. Several variables either gain or lose significance when transitioning from
OLS to quantile regression techniques.
Differences surface between the quantile regression estimates and the OLS
estimates. First, regressing median wealth results in no statistically significant benefit to
obtaining ‘some college’ or an associate's degree. In other words, there appears to be no
financial benefit to a college education if one does not obtain a bachelor's degree or
higher credential.
The wealth gap expands between those who have used loans and those who have
not at the bachelor's and postgraduate degree levels. Bachelor's degree recipients who
have not used loans are estimated to have roughly $43,000 more average financial
wealth. This difference in wealth expands to nearly $66,000 when estimating median
wealth. The same pattern develops when looking at effects at the postgraduate level.
OLS estimates identify that those with postgraduate degrees and no college loans have
$56,000 higher net worth than their peers who used college loans. The wealth gap
expands to an astounding $125,000 when looking at median wealth.
38
Texas Tech University, Thomas O’Malley, August 2013
Discussion
A college education is a risky, speculative investment. Non-completion risk is
large. Many people who attempt college fail to graduate. For those who graduate from
college, there is substantial employment uncertainty. There are no guarantees that
employers will value the skills acquired in college. Income payouts vary significantly
and depend on the field of study, the academic credential, the quality of the educational
institution, labor market conditions, individual attributes and a host of other factors.
Nearly one-third of college graduates work jobs that do not require a college degree.
Education debt adds an additional layer of risk. Without using debt, the college
decision can be high-stakes. With debt, the potential for poor outcomes are magnified
even more. The financial implications to making an inappropriate choice can be severe
and long lasting. Students have increasingly turned to student loans to finance their
education as tuition growth has outpaced wages. Most education debt is in the form of
public, federally subsidized loans. Ironically, safeguards are limited. These loans are not
subject to usury laws and other consumer protections. This debt cannot be discharged in
bankruptcy and the federal government has the authority to garnish wages and to seize
tax refunds or Social Security benefits to recover payment. Students must repay these
loans regardless of whether the student graduates or finds suitable employment. Many
loan recipients are not able to keep up and end up defaulting on their loans. Loans that
are in default are reported to the credit bureaus. This negatively affects student credit
scores, increases the costs of financial transactions and may restrict employment
opportunities.
39
Texas Tech University, Thomas O’Malley, August 2013
Wise decisions about using education debt require informed consumers. Costs,
benefits and risks need appraisal. In addition to understanding the vagaries of student
loans, potential students need to conduct a realistic evaluation of their academic abilities
and interests. They need to understand that completion is uncertain and should engage in
a sober analysis of their odds of successfully completing a program of study. The
potential earning benefits associated with completing a specific degree need to be
estimated. In short, prospective students should have knowledge of the higher education
landscape, future labor market demand and landscape and the intricacies of education
loans.
Many students appear to be informed when the enrollment and student loan
decisions are made. Their approach to these decisions approximates the normatively
optimal process found in the Figure 2.4. These individuals are more likely to have the
resources to make a rational decision. Higher cognitive ability aids in making a good
decision and is related to completing a four year degree. Sophisticated consumers are
more likely to come from families with educational, informational and financial resources
Other college students may lack the ability to make an informed choice. These
consumers face bounded rationality and simultaneously lack a support structure that
could help them make a good decision. Individuals that attempt college but fail to obtain
a bachelor's degree tend to have meager family resources relative to their peers who go
one to get a bachelor's or master's degree. It is likely that many naively believe college
even utilizing college loans to be a riskless investment. Many student clearly fail to grasp
the risk of non-completion, the employment uncertainty and the potential pitfalls of
40
Texas Tech University, Thomas O’Malley, August 2013
financial leverage. Many students who enroll in college fail to fully anticipate the
downside potential of student loans.
Conclusion
This work is the first to investigate whether student loan use affects wealth
accumulation. Net worth is a useful measure of wealth since it comprehensively affects
all households. Wealth provides households the ability to smooth consumption and avoid
consumption drops which result in welfare losses (Brumberg and Modigliani, 1954). It
determines access to housing, schooling and business opportunities. Wealth provides
insurance against labor market uncertainty and protects households from consumptions
shocks.
OLS and quantile regression techniques are used to determine how student loan
use affects wealth accumulation. Quantile regression is useful here because financial
data is highly skewed. These techniques are more robust to outliers than OLS methods.
Modeling median net worth moderates the influence of outliers and better represents the
typical financial impact of loan use.
OLS estimates indicate that ‘some college’ and associate's degrees provide
modest wealth benefits for those who have not used college loans. These wealth
advantages disappear for those who use student debt. Median wealth analysis suggests
that acquiring ‘some college’ or an associate's degree is not financially beneficial
regardless of whether student loans are used.
Regression of mean and median wealth each suggest that a bachelor's or higher
degree is well worth the investment. Estimates are consistently large and positive for
41
Texas Tech University, Thomas O’Malley, August 2013
those who complete a bachelor's or higher degree. Student loan use negatively affects
wealth accumulation though. There are large wealth gaps between those who use loans
and those who do not. Nonetheless, using debt may be a desirable path if loans are the
only means by which one is able to acquire a bachelor's or postgraduate degree.
42
Texas Tech University, Thomas O’Malley, August 2013
References
Angeletos, G. M., Laibson, D., Repetto, A., Tobacman, J., & Weinberg, S. (2001). The
hyperbolic consumption model: Calibration, simulation, and empirical evaluation.
Journal of Economic Perspectives, 15(3), 37-68.
Aud, S., Hussar, W., Planty, M., Snyder, T., Bianco, K., Fox, M. & Drake, L. (2010).
The Condition of Education 2010 (NCES 2010-028). National Center for
Education Statistics, Institute of Education Sciences, U.S. Department of
Education. Washington, DC.
Baum, S., & O’Malley, M. (2003). College on credit: How borrowers perceive their
education debt. Results of the 2002 National Student Loan Survey. Final Report.
Nellie Mae.
Becker, G. S. (1973). A theory of marriage: Part I. Journal of Political Economy, 81(4),
831-846.
Blau, F. D. & Kahn, L. M. (2000). Gender differences in pay. The Journal of Economic
Perspectives, 14(4), 75-99.
Belman, D., & Heywood, J. (1991). Sheepskin effects in the return to education. Review
of Economics and Statistics, 73, 720-724.
Brumberg, R., & Modigliani, F. (1954). Utility analysis and the consumption function:
An interpretation of cross-section data. In K. Kurihara (Ed.), Post Keynesian
Economics. New Brunswick: Rutgers University Press.
Cameron, S. V., & Heckman, J. J. (1993). The nonequivalence of high school
equivalents. Journal of Labor Economics, 11(1), 1–47.
43
Texas Tech University, Thomas O’Malley, August 2013
Campbell, J. Y. (2006). Household finance. Journal of Finance, 61, 1553–1604.
Carneiro, P. & Heckman, J. J. (2002). The evidence on credit constraints in postsecondary schooling. The Economic Journal, 112, 705-734.
Carnoy, M., & Marenbach D. (1975). The return to schooling in the United States, 1939-69. The Journal of Human Resources, 10(3), 312-331.
Castex, G. (2010). College risk and return. Working paper. University of Rochester.
Chen, S. H. (2001). Is investing in college education risky? State University of New
York at Albany, Department of Education Discussion Papers: No. 01-09.
Chiteji, N.S. (2006). To have and to hold: An analysis of young adult debt. Network on
Transitions to Adulthood Research Network Working Paper.
College Board (2010). Trends in College Pricing 2010.
Downey, D. B. (1995). When bigger Is not better: Family size, parental resources, and
children’s educational performance. American Sociological Review,60, 746–61.
Fox, M. (1992). Student debt and enrollment in graduate and professional school.
Applied Economics, 24, 669-677.
Friedman, M. (1963). Windfalls, the horizon and related concepts in the permanent
income hypothesis. Measurement in Economics, 3-28.
Gentry, W. & Hubbard, G. (2004). Entrepeneurship and household saving. Advances in
Economic Analysis and Policy, 4(1), Article 8.
Goldin, C., & Katz, L. (2008). The race between education and technology. Harvard
University Press, Cambridge, MA.
44
Texas Tech University, Thomas O’Malley, August 2013
Hariharan, G., Chapman, K., & Domian, D. (2000). Risk tolerance and asset allocation
for investors nearing retirement. Financial Services Review, 9(2), 159-170.
Hartog, J. & Oosterbeek, H. (1998). Health, wealth and happiness: Why pursue a higher
education? Economics of Education Review, 17, 245-56.
Heller, D. E. (2001). Debts and Decisions: Student Loans and Their Relationship to
Graduate School and Career Choice. Lumina Foundation for Education, New
Agenda Series, Indianapolis, IN.
Hyounjin, Y. (2010). Three essays in consumer finance: debt stress, payments, and
student loans. Ohio State University Ph.D. thesis.
Kantrowitz, M. (2010). Undergraduate debt causes ‘pipeline leakage’ from undergraduate
school to graduate and professional school. FinAid Student Aid Policy Analysis.
Retrieved from http://www.finaid.org/educators/20101122gradpipelineleakage.pdf
Kassebaum, D. G., Szenas, P. L., & Schuchert, M. K. (1996). Determinants of the
generalist career intentions of 1995 graduating medical students. Academic
Medicine, 71(2), 198-209.
Kornhauser, L., & Revesz, R. L. (1995). Legal education and entry into the legal
profession: The role of race, gender, and educational debt. New York University
Law Review, 70, 829-945.
Kotlikoff, L. J. (1988). Intergenerational transfers and savings. The Journal of Economic
Perspectives, 2(2), 41-58.
45
Texas Tech University, Thomas O’Malley, August 2013
Lusardi, A., & Mitchell, O. S. (2007). Baby Boomer retirement security: The roles of
planning, financial literacy, and housing wealth. Journal of Monetary Economics,
54(1), 205-224.
Minicozzi, A. (2005). The short term effect of educational debt on job decisions.
Economics of Education Review, 24(4), 417-430.
Monks, J. (2001). Loan burdens and educational outcomes. Economics of Education
Review, 20, 545-550.
Oliver M. L. & Shapiro, T. M. (1995). Black Wealth, White Wealth. New York:
Routledge.
Reed, M. (2011). Student debt and the class of 2010. The Institute for College Access
& Success. Retrieved from http://projectonstudentdebt.org/files/pub/classof2010.pdf
Restuccia, D. & Urrutia, C. (2004). Intergenerational persistence of earnings: The role of
early and college education. American Economic Review, 94(4), 1354-1378.
Rosenthal, M., Marquette, P., & Diamond, J. (1996). Trends along the debt-income axis:
Implications for medical students' selections of family practice careers.
Academic Medicine, 71(6), 675-677.
Schumpeter, J. (2010, September 2). Declining by degree: Will America’s universities
go the way of its car companies? The Economist. Retrieved from
http://www.economist.com/node/16941775
Shand, J. M. (2008). The impact of early-life debt on household formation: An empirical
investigation of homeownership, marriage and fertility. Ohio State University
46
Texas Tech University, Thomas O’Malley, August 2013
Ph.D. thesis.
Spar, I. L., Pryor, K. C., & Simon, W. (1993). Effect of debt level on the residency
preferences of graduating medical students. Academic Medicine, 68, 570-572.
Vedder, R., Denhart, C., Denhart, M., Matgouranis, C. & Robe, J. (2010). From Wall
Street to Wal-Mart: why college graduates are not getting good jobs. Policy
paper. The Center for College Affordability and Productivity. Retrieved from:
http://www.centerforcollegeaffordability.org/uploads/From_Wall_Street_to_Wal-Mart.pdf
Veres, B. (2011). The four-factor service model. Inside Information, 21(4), 1-20.
Weiler, W. C. (1991). The effect of undergraduate student loans on the decision to pursue
post baccalaureate study. Educational Evaluation and Policy Analysis, 13, 212–
220.
Wolff, E. N. (1998). Recent trends in the size distribution of household wealth. The
Journal of Economic Perspectives, 12(3), 131-150.
Woodworth, P. A., Chang F. C., & Helmer, S. D. (2000). Debt and other influences on
career choices among surgical and primary care residents in a community-based
hospital system. The American Journal of Surgery, 180, 570-576.
Yuh, Y., Hanna, S., & Montalto, C. (1998). Mean and pessimistic projections of
retirement adequacy. Financial Services Review, 7(3), 175-193.
Zagorsky, J. (1999). Young baby boomers' wealth. Review of Income & Wealth, 45(2),
135-156.
Zagorsky, J. L. (2007). Do you have to be smart to be rich? The impact of IQ on wealth,
income and financial distress. Intelligence, 35(5), 489-501.
47
Texas Tech University, Thomas O’Malley, August 2013
Appendix
Bachelor's –
Loans
Bachelor's No Loans
Associate's No Loans
Associate's –
Loans
Some College –
No Loans
Some College Loans
Appendix 2.1 Bivariate Correlation Matrix (Page 1 of 5)
IQ decile
-0.05 *** -0.02
0.01
0.01
0.20 ***
0.27 ***
Family inheritances
-0.01
-0.04 **
0.00
-0.02
0.09 ***
0.03 *
Parent schooling
-0.01
-0.02
-0.02
-0.03 *
0.17 ***
0.18 ***
Family income
-0.04 **
-0.08 *** -0.01
-0.03 *
0.16 ***
0.11 ***
-0.08 ***
-0.07 ***
Some college - Loans
Some college - No Loans
Associate's -
Loans
Associate's -
No Loans
Bachelor's -
Loans
Bachelor's -
No Loans
Postgraduate - Loans
Postgraduate - No Loans
Number of siblings
0.01
0.01
0.02
0.02
Risk tolerance
-0.02
0.01
-0.01
-0.02
0.00
0.01
Time preference (low)
-0.02
0.00
-0.01
-0.02
0.01
0.05 ***
Homeowner
-0.02
-0.02
0.11 ***
0.11 ***
Self employed
-0.01
0.01
-0.01
0.00
0.04 **
0.03 *
Number of children
0.00
0.00
0.00
0.02
0.01
-0.01
Age
0.04 **
0.03 *
-0.03
Female
0.02
Married
-0.06 *** 0.03 *
0.02
0.02
-0.02
0.05 *** 0.01
-0.01
-0.06 *** 0.02
-0.02
0.09 ***
0.07 ***
Black
0.05 *** 0.08 *** -0.01
0.00
-0.06 ***
-0.05 ***
Hispanic
0.04 **
0.02
0.01
0.01
-0.03 *
-0.03 *
Other
0.02
0.01
0.01
0.02
0.00
-0.01
Northeast
-0.02
0.00
0.01
-0.02
0.02
0.01
Northcentral
-0.05 *** 0.02
-0.04 ** 0.01
-0.01
0.02
0.00
-0.02
South
-0.03 *
-0.07 *** 0.03 *
0.02
-0.01
0.02
*p<.05, **p<.01, ***p<.001
48
0.01
Texas Tech University, Thomas O’Malley, August 2013
Some College - Loans
-0.05 *** -0.01
Some College - No Loans
-0.02
-0.01
-0.04 *** -0.02
Family income
Parent schooling
Family inheritances
IQ decile
Postgraduate –
No Loans
Postgraduate Loans
Appendix 2.1 Bivariate Correlation Matrix (Page 2 of 5)
-0.04 **
-0.08 ***
Associate's -
Loans
0.01
0.00
-0.02
-0.01
Associate's -
No Loans
0.01
-0.02
-0.03 *
-0.03 *
Bachelor's -
Loans
0.20 *** 0.09 *** 0.17 *** 0.16 ***
Bachelor's -
No Loans
0.27 *** 0.03 *
0.18 *** 0.11 ***
Postgraduate - Loans
0.15 *** 0.07 *** 0.12 *** 0.13 ***
Postgraduate - No Loans
0.21 *** 0.04 **
0.14 *** 0.08 ***
IQ decile
0.15 *** 0.21 *** 1.00
Family inheritances
0.07 *** 0.04 **
Parent schooling
0.12 *** 0.14 *** 0.43 *** 0.12 *** 1.00
Family income
0.13 *** 0.08 *** 0.37 *** 0.18 *** 0.40 *** 1.00
Number of siblings
0.13 *** 0.43 *** 0.37 ***
0.13 *** 1.00
0.12 *** 0.18 ***
0.40 ***
-0.06 *** -0.09 *** -0.28 *** -0.09 *** -0.32 *** -0.19 ***
Risk tolerance
0.00
Time preference (low)
0.08 *** 0.02
Homeowner
0.08 *** 0.07 *** 0.28 *** 0.08 *** 0.13 *** 0.18 ***
Self employed
0.02
-0.05 *** -0.01
0.10 *** 0.04 **
-0.01
0.00
Number of children
0.01
-0.02
0.03
Age
0.04 **
0.00
-0.02
Female
0.03 *
0.02
-0.05 *** 0.01
Married
0.07 *** 0.03 *
-0.03
-0.05 **
0.05 *** 0.08 ***
0.07 *** 0.08 *** 0.08 *** 0.08 ***
0.01
0.03 *
-0.03 *
0.00
-0.03 *
0.01
0.01
-0.05 ***
0.21 *** 0.05 *** 0.08 *** 0.14 ***
Black
-0.07 *** -0.05 *** -0.41 *** -0.09 *** -0.12 *** -0.28 ***
Hispanic
-0.03
-0.02
-0.10 *** -0.04 **
-0.20 *** -0.08 ***
-0.03 *
-0.04 **
-0.09 *** -0.04 **
Other
0.00
Northeast
0.02
0.02
0.07 *** 0.04 **
0.04 **
-0.02
0.01
0.09 *** 0.01
0.08 *** 0.12 ***
0.00
-0.02
Northcentral
South
*p<.05, **p<.01, ***p<.001
49
-0.02
-0.16 *** -0.04 **
0.05 ***
-0.05 *** -0.12 ***
Texas Tech University, Thomas O’Malley, August 2013
Some College - Loans
0.01
-0.02
-0.02
Some College - No Loans
0.01
0.01
0.00
Associate's -
Loans
0.02
-0.01
-0.01
Associate's -
No Loans
0.02
-0.02
-0.02
Bachelor's -
Loans
-0.08 *** 0.00
0.01
Bachelor's -
No Loans
-0.02
Number of children
Self employed
Homeowner
Time preference
(low)
Risk tolerance
Number of siblings
Appendix 2.1 Bivariate Correlation Matrix (Page 3 of 5)
-0.01
0.00
-0.07 *** 0.01
0.00
0.03 *
-0.02
-0.01
0.00
0.00
0.02
0.11 *** 0.04 **
0.01
-0.07 *** 0.01
0.05 *** 0.11 *** 0.03 *
Postgraduate - Loans
-0.06 *** 0.00
0.08 *** 0.08 *** -0.01
Postgraduate - No Loans
-0.09 *** 0.02
0.02
IQ decile
-0.28 *** -0.05 *** 0.10 *** 0.28 *** 0.07 *** 0.03
Family inheritances
-0.09 *** -0.01
0.04 **
Parent schooling
-0.32 *** -0.03
0.05 *** 0.13 *** 0.08 *** -0.03 *
Family income
-0.19 *** -0.05 **
0.08 *** 0.18 *** 0.08 *** 0.01
0.07 *** 0.00
0.01
-0.02
0.08 *** 0.08 *** 0.01
Number of siblings
1.00
0.05 **
Risk tolerance
0.05 **
1.00
0.01
Time preference (low)
-0.05 *** 0.01
1.00
Homeowner
-0.10 *** -0.11 *** 0.08 *** 1.00
Self employed
-0.02
-0.05 *** -0.10 *** -0.02
-0.11 *** 0.03 *
0.08 *** 0.00
0.08 ***
-0.04 **
0.00
0.05 *** 0.19
0.00
0.05 *** 1.00
-0.01
Number of children
0.08 *** -0.04 **
0.00
0.19 *** -0.01
1.00
Age
0.05 *** -0.01
0.03 *
0.04 **
0.02
Female
0.02
Married
0.03 *
-0.01
-0.04 **
-0.02
0.02
0.00
-0.11 *** 0.16 ***
-0.06 *** -0.05 *** 0.05 *** 0.44 *** 0.05 *** 0.30 ***
Black
0.23 *** 0.06 *** -0.08 *** -0.25 *** -0.06 *** -0.06 ***
Hispanic
0.09 *** 0.01
Other
Northeast
0.04 **
-0.02
-0.02
-0.02
0.03
0.02
-0.03 *
-0.01
-0.05 *** -0.01
0.02
-0.03 **
-0.01
Northcentral
0.00
-0.03
South
0.05 *** 0.00
*p<.05, **p<.01, ***p<.001
50
0.04 *
0.06 *** -0.01
-0.05 *** -0.02
-0.03 *
0.04 **
0.04 **
-0.01
0.06 ***
-0.06 ***
Texas Tech University, Thomas O’Malley, August 2013
Some College - Loans
Some College - No Loans
Associate's -
Loans
Associate's -
No Loans
Bachelor's -
Loans
Bachelor's -
No Loans
0.04 **
0.02
-0.06 *** 0.02
0.03 *
-0.02
0.03 *
-0.03
0.02
-0.03 *
0.05 *** 0.04 **
-0.06 *** 0.08 *** 0.02
0.01
0.02
-0.01
0.01
0.01
0.00
0.01
0.02
0.01
0.09 *** -0.06 *** -0.03 *
0.00
-0.01
0.07 *** -0.05 *** -0.03 *
-0.01
0.04 **
0.03 *
0.07 *** -0.07 *** -0.03
Postgraduate - No Loans
0.00
0.02
0.03 *
-0.02
0.02
0.05 *** -0.02
Postgraduate - Loans
IQ decile
Other race
Hispanic
Black
Married
Female
Age
Appendix 2.1 Bivariate Correlation Matrix (Page 4 of 5)
-0.05 *** -0.02
0.00
-0.03 *
-0.05 *** 0.21 *** -0.41 *** -0.10 *** -0.04 **
Family inheritances
0.03 *
Parent schooling
0.00
-0.03 *
Family income
0.01
-0.05 *** 0.14 *** -0.28 *** -0.08 *** -0.04 **
Number of siblings
0.05 *** 0.02
Risk tolerance
-0.02
0.08 *** -0.12 *** -0.20 *** -0.09 ***
-0.06 *** 0.23 *** 0.09 *** 0.04 **
-0.05 *** 0.06 *** 0.01
0.03
0.03 *
-0.02
0.05 *** -0.08 *** -0.02
0.02
Homeowner
0.04 **
0.02
0.44 *** -0.25 *** -0.02
Self employed
0.00
Number of children
0.02
0.16 *** 0.30 *** -0.06 *** 0.04 **
0.04 **
Age
1.00
0.02
Female
0.02
1.00
0.02
-0.04 **
0.05 *** -0.09 *** -0.04 **
Time preference (low)
Married
-0.01
0.01
-0.11 *** 0.05 *** -0.06 *** -0.02
-0.04 **
0.02
-0.04 **
1.00
-0.03 *
-0.01
-0.01
-0.01
0.00
0.01
-0.01
0.02
-0.24 *** -0.02
0.00
Black
-0.01
0.01
Hispanic
-0.01
-0.01
-0.02
Other
0.00
0.02
0.00
-0.10 *** -0.04 **
1.00
Northeast
0.03 *
0.00
0.01
-0.07 *** -0.04 **
0.03 *
Northcentral
0.00
0.01
0.02
-0.07 *** -0.09 *** -0.07 ***
South
-0.03 *
0.04 **
*p<.05, **p<.01, ***p<.001
51
-0.24 *** 1.00
-0.13 *** -0.10 ***
-0.13 *** 1.00
-0.06 *** 0.27 *** -0.03 *
-0.04 **
-0.04 **
Texas Tech University, Thomas O’Malley, August 2013
Some College - Loans
Some College - No Loans
Associate's -
Loans
Associate's -
No Loans
Bachelor's Bachelor's -
-0.02
0.00
0.01
-0.05 ***
0.02
-0.04 **
South
Northcentral
Northeast
Appendix 2.1 Bivariate Correlation Matrix (Page 5 of 5)
0.02
-0.01
0.02
-0.02
0.01
0.01
Loans
0.02
-0.01
0.00
No Loans
0.01
0.02
-0.02
Postgraduate - Loans
0.02
-0.02
0.00
Postgraduate - No Loans
0.02
0.01
-0.02
IQ decile
0.07 ***
0.09 ***
-0.16 ***
Family inheritances
0.04 **
0.01
-0.04 **
Parent schooling
0.04 **
0.08 ***
-0.05 ***
Family income
0.05 ***
0.12 ***
-0.12 ***
Number of siblings
-0.05 ***
Risk tolerance
-0.01
Time preference (low)
0.02
Homeowner
-0.03 *
Self employed
-0.01
Number of children
-0.01
0.00
-0.03
0.05 ***
0.00
0.04 *
-0.05 ***
0.06 ***
-0.02
-0.01
-0.03 *
0.06 ***
-0.06 ***
-0.03 *
Age
0.03 *
0.00
Female
0.00
0.01
0.04 **
0.02
-0.06 ***
Married
0.01
Black
-0.07 ***
-0.07 ***
Hispanic
-0.04 **
-0.09 ***
-0.03 *
0.03 *
-0.07 ***
-0.04 **
1.00
-0.24 ***
-0.34 ***
Other
Northeast
Northcentral
-0.24 ***
South
-0.34 ***
*p<.05, **p<.01, ***p<.001
52
1.00
-0.42 ***
0.27 ***
-0.42 ***
1.00
Texas Tech University, Thomas O’Malley, August 2013
Appendix 2.2: Quantile Regression Results
The dependent variable is not transformed. Data is censored to those respondents
who have a high school diploma or higher degree who also report net worth information.
Estimates that have .05 level or higher statistical significance are shaded.
.10 quantile .25 quantile .5 quantile .75 quantile .90 quantile
Predicted NW value
33,221
82,558
181,980
351,915
653,522
Estimate
-22059
Estimate
-126886
Estimate
-168376
Estimate
-162051
Estimate
-154584
1607
7196
10870
9417
31701
Some College - No Loans
-9953
-7593
-6828
-21392
-22291
Associate's -
Loans
10118
15444
20049
35941
45767
Associate's -
No Loans
-5531
6203
7127
-6632
17357
Bachelor's -
Loans
30962
94524
144262
236066
418104
Bachelor's -
No Loans
-8717
13581
78284
205019
356631
Postgraduate - Loans
28087
104609
190806
341397
753438
Postgraduate - No Loans
-2455
34066
65268
194933
347829
1146
1754
2575
3454
11645
67
190
472
770
1994
Parent schooling
759
950
1784
1258
4353
Family income
372
708
1127
1845
4034
Number of siblings
-1292
-1460
-2327
-4313
-4914
Risk tolerance
-1284
-1886
-2385
93
-784
6789
10530
18422
23308
38288
Homeowner
48840
77290
124973
198328
282991
Self employed
-1755
2244
14423
15735
122477
Number of children
-4013
-3683
-526
-1823
-4211
-64
1634
2407
3149
4197
Female
1480
324
-10778
-10307
-19855
Married
15434
31094
62192
115408
162401
Black
-4679
-5135
-8947
-15110
-25931
Hispanic
6809
6701
18013
-2860
4552
Other
1987
2705
7632
25144
37524
Northeast
4773
9081
9966
-5260
-99454
Northcentral
-6613
-8902
-9739
-26302
-135734
South
-4384
-6009
-10663
-25791
-114536
Intercept
Some College - Loans
IQ decile
Family inheritances
Time preference (low)
Age
Observations used
4,254
4,254
53
4,254
4,254
4,254
Texas Tech University, Thomas O’Malley, August 2013
CHAPTER III
STUDENT DEBT AND PERSONAL BANKRUPTCY
Abstract
The effect of student debt on bankruptcy is analyzed using the National Longitudinal
Survey of Youth (NLSY79). This study investigates potential links between student debt
and personal bankruptcy. We find a relation between education debt and bankruptcy.
Relative to high school graduates, college dropouts and associate's degree holders are
more likely to file. Households subject to divorce, job loss and health shocks are
significantly more likely to seek bankruptcy protection. Results suggest that debt, human
capital and adverse event hypotheses may serve as complementary explanations for the
bankruptcy decision.
Introduction
Personal bankruptcy has increased dramatically in recent decades. Bankruptcy
filings increased 4.75 times between the years 1985 and 2005. Bankruptcies peaked in
2005, totaling more than 1.6 million filings (U.S. Department of Commerce, 2012
Statistical Abstract, Table 776). Filings dropped after the passage of the 2005
Bankruptcy Abuse Prevention and Consumer Protection Act (BAPCPA). Congress
passed this legislation to make straight bankruptcy less desirable, increasing filing costs
and requiring debtors to repay debts using post-bankruptcy income. Bankruptcy is still
relatively common though, reaching more than a million filings in several recent years
(Figure 3.1).
Substantial costs accompany personal bankruptcy. Court fees range from $246 to
$1046 depending on the type of filing (US Courts, 2011). Legal advice can cost several
thousand dollars. The non-financial costs can be considerable, including information and
54
Texas Tech University, Thomas O’Malley, August 2013
psychic costs. Filers must acquire knowledge about the bankruptcy process and
procedures. The emotional toll resulting from bankruptcy can be significant.
1,400,000
1,200,000
1,000,000
800,000
Chapter
7
600,000
400,000
Chapter
13
200,000
0
1985
1990
1995
2000
2005
2010
sources: The 2011 Statistical Abstract: http://www.census.gov/compendia/statab/
US Courts: http://www.uscourts.gov/Statistics/BankruptcyStatistics.aspx
Figure 3.1 - U.S. Personal Bankruptcy Filings (1985-2012)
The dramatic increase in household bankruptcies spurred academic interest
starting in the early 1990s. Household debt grew rapidly during this period and
researchers began to question whether higher bankruptcy rates were caused by expanding
credit availability. Mismatches between unsecured credit and income may offer the best
55
Texas Tech University, Thomas O’Malley, August 2013
explanation for the household bankruptcy decision. Several influential papers suggest
that the increase in the bankruptcy rate is primarily the result of higher levels of credit
card debt (Ellis, 1998; Domowitz & Sartain, 1999).
This expansion in credit card debt that occurred in the 1980s and 1990s
foreshadowed an explosion in student debt in the 2000s. For several decades, tuition
growth has outpaced general inflation and wages. Education grants peaked in the 1990s.
Since then college students have increasingly relied on student loans to finance their
education. Loan balances expanded rapidly, impacting the balance sheets of college
graduates and dropouts alike.
The economic and social benefits of a college education all well-documented.
Real wages for high school graduates have fallen over the last two decades, but the wages
for those with a college diploma has held stable. College graduates tend to have fewer
layoffs and work-related disabilities and lower unemployment rates. They have more
labor flexibility, are able to work longer careers, and tend to have higher work
satisfaction. Education is also positively correlated with savings rates, voting, good
health, life expectancies, law-abidingness, marital stability and success of children
(Becker, 2007).
Attending college involves risk though. Half of those who attend college fail to
graduate (Restuccia & Urrutia, 2004). College dropouts receive modest wages. Even for
college graduates, higher salaries are not certain. Many college graduates are employed
in occupations that require a college diploma but pay meager salaries. Graduates
commonly work in entry level jobs that do not require advanced education. More than a
56
Texas Tech University, Thomas O’Malley, August 2013
third of college graduates now work at jobs that do not require a college degree (Vedder,
Denhart, Denhart, Matgouranis, & Robe, 2010).
The debt financing of college and federal loans in particular may make the college
decision even more risky. Government-backed student loans may negatively affect the
credit score of borrowers while still enrolled in college. After leaving school, borrowers
are then required to repay the loan regardless of whether the individual is able to graduate
or find employment. If loan default occurs, this is reported to the credit bureaus and
remains on the loan recipient’s credit report for seven years. Default proceedings can
result in additional collection costs. Further, the government has the authority to garnish
wages and seize tax refunds and Social Security benefits in order to recover payments.
Also, unlike other loans, student loans cannot be discharged in bankruptcy.
There are already indications that many borrowers struggle to pay these loans off.
Among class of 2005 graduates, more than forty percent of borrowers have become
delinquent on one or more loans within a five year repayment period (Cunningham &
Kienzl, 2011). Loans are in default status if the borrower fails to make a payment in 270360 days and fails to make any arrangement with the lender for a forbearance or a
deferment. The ‘official’ default rate provided by the Department of Education measures
the proportion of federal loans resulting in defaults in the first two years of repayment.
The Higher Education Opportunity Act of 2008 recently extended this to three years.
According to unpublished data acquired by The Chronicle of Higher Education, only a
slice of defaults are captured by the 'official' rate (Field, 2010). Default rates double in
the years following the ‘official’ two or three year window. Fifteen years into repayment,
57
Texas Tech University, Thomas O’Malley, August 2013
20 percent of government loans are in default and among borrowers attending community
college and for-profit institutions, long-term default rates exceed 30 and 40 percent,
respectively (Field, 2010).
Repayment risk may be higher for those borrowers who fail to graduate. There is
evidence that college dropouts have difficulty paying off student loans relative to college
graduates. Approximately 30 percent of outstanding loans are owned by college dropouts
(Figure 3.2). Dropouts disproportionately report loans in ‘non-payment’ or ‘behind
schedule’. College dropouts appear to wrestle with repayment if the age of the loan is
considered. Among new loans, debt-to-income ratios are smaller for college dropouts
than for graduates. This pattern then reverses though. Debt ratios for college dropouts
get worse with time. In contrast, college graduates appear to pay off their loan balances
over time (Figure 3.3).
58
Texas Tech University, Thomas O’Malley, August 2013
60.00%
50.00%
40.00%
30.00%
% Loans
% Non-Payment
20.00%
% Pmts Behind Sched
10.00%
0.00%
2004
2007
source: Survey of Consumer Finances (SCF)
Figure 3.2 - College Dropout Proportion of Non-payments and
Payments Behind Schedule
0.30
0.25
0.20
0.15
0.10
Loans <= 5 yrs
0.05
Loans > 5 yrs
0.00
2004
2007
2004
Dropout
2007
Graduate
source: Survey of Consumer Finances (SCF)
Figure 3.3 Median Loan-to-Income Ratios
59
Texas Tech University, Thomas O’Malley, August 2013
Literature Review
The empirical research regarding personal bankruptcy is extensive. However, few
studies consider whether education debt contributes to financial distress. No study looks
directly at the role played by student debt and at the same time considers the role played
by educational attainment. Insight here may be critical in evaluating the appropriate uses
of education debt.
Personal Bankruptcy
The bankruptcy system exists to help households struggling with high levels of
debt. Bankruptcy law provides an avenue by which individuals can eliminate some or all
of their personal debts. Households mired in debt are provided a new financial
beginning. Bankruptcy law originates as federal law. There are two primary methods by
which a household can file for bankruptcy: Chapter 7 or Chapter 13.
A Chapter 7 filing permits a household to completely discharge all unsecured
debts, such as credit card debt, medical bills and installment loans. This is commonly
referred to as a ‘straight’ bankruptcy. Filers are not required to use future earnings to
repay discharged debts, but they are obligated to surrender assets that exceed statespecific exemption levels. These exemptions generally shield retirement assets, provide a
limited homestead exemption, and allow exemptions for automobiles and certain personal
belongings (Gropp, Scholz & White, 1997). Non-exempt assets are then sold and the
proceeds are distributed to creditors. Some types of debt cannot be discharged, including
alimony, child support, income tax liabilities, and government-supported educational
60
Texas Tech University, Thomas O’Malley, August 2013
loans. Chapter 7 filers are not allowed to file again for bankruptcy for eight years. About
two-thirds of filing occurs under these rules.
A reorganization of debts occurs under Chapter 13 of the bankruptcy code.
Debtors with regular income may discharge debts after they pay off a portion of their
debts. The debtor maintains ownership of his or her assets but must propose a repayment
plan that is acceptable to the court. Repayment plans typically span three to five years.
There is no partial discharge of debts if the filer does not fulfill the repayment plan.
Approximately two-thirds of Chapter 13 filers do not successfully execute the repayment
plan and most of these filings then transition to Chapter 7.
Potential Explanations for Bankruptcy
One set of hypotheses links financial distress to financial ‘uncertainty.’
Unanticipated income and expense shocks lead to bankruptcy. Households exposed to
various ‘adverse events’ are more likely to suffer financial distress. Job loss, divorce and
unpaid medical bills are the prominent events that have been studied. Two-thirds of
bankruptcies stem from job loss (Sullivan, Warren & Westbrook, 2000). Bankruptcy is
more likely if the head of household had been recently divorced (White, 2007). The
increase in the bankruptcy rate corresponds with an increase in the proportion of
households that lack health insurance (Warren & Tyagi, 2003).
Bankruptcy may be the product of strategic choice. States vary in the amount of
personal assets that may be legally sheltered from bankruptcy. The benefit of filing
increases as the exemption level increases. Opportunistic households consider the
generosity of state bankruptcy law and are more likely to file when the financial benefit is
61
Texas Tech University, Thomas O’Malley, August 2013
high. State exemption levels and filing rates are positively related (White, 1987). A
$1,000 increase in the financial benefit of bankruptcy increases the probability of filing
by 7 percent (Fay, Hurst & White, 2002).
A reduction in the stigma associated with bankruptcy has also been studied.
Stigma is the emotional penalty imposed by oneself or other persons in response to filing.
As bankruptcy activity increases, filing for bankruptcy may become socially acceptable.
Buckley and Brinig (1998) find that changes in social norms help explain the tripling of
the bankruptcy rate from 1984 to 1991. As local filing rates increase, consumers are
more likely to file for bankruptcy (Fay et al., 2002). Gross and Souleles (2002) find that
increasing bankruptcy rates among credit card holders are consistent with reductions in
stigma.
These three hypotheses have difficulty explaining the increase in filing rates that
started in the 1980s however. ‘Adverse events’ are positively related to individual
bankruptcy decisions in cross-sectional analyses but they fail to explain the time-series
increase. Divorce rates actually declined during the 1980s and 1990s when bankruptcy
rates tripled (White, 2009). Job loss and health shock explanations are also contentious.
The national unemployment rate fell from 7.2% in 1985 to 5.1% in 2005, although there
were significant fluctuations during this twenty year period (U.S. Department of Labor,
Current Population Survey, Household Data Annual Averages, Table 1). Uninsured
health care costs grew during this period, but only slightly as a percent of median
household income (White, 2009). The ‘reductions in stigma’ hypothesis is equally
controversial. There is scant direct evidence that social stigma declined during this
62
Texas Tech University, Thomas O’Malley, August 2013
period. Stigma may have actually increased as bankruptcy information has become
increasingly accessible online and available to the public (Sullivan, Warren &
Westbrook, 2006). The ‘strategic choice’ alternative also seems an implausible
explanation. There is no evidence that state exemption levels have systematically
increased during this period.
Debt and Bankruptcy
The dramatic rise in personal bankruptcy coincided with an increase in credit
availability. Credit cards were introduced in the mid 1960s. Initial card adoption rates
were modest. Credit was extended to only the most creditworthy individuals as state
usury laws limited the maximum interest rates that could be charged. Restrictions were
then lifted in the 1980s and credit card lending spread quickly through society. The
proportion of consumers with bank-type credit cards increased from 16 percent in 1970 to
67 percent by 1995 and while only 6 percent of households carried a balance in 1970, 37
percent carried a balance by 1995 (Durkin, 2000).
Changes in the credit market offer the best explanation for the increase in
personal bankruptcy. Deregulation of consumer interest rates led to lower underwriting
standards, expanded credit availability and more personal bankruptcy (Ellis, 1998).
Innovations in the credit market reduced transactions costs and lowered the cost of
bankruptcy (Livshits, MacGee & Tertilt, 2010). At least 10 percent of the increase in
bankruptcy rates is explained by the expansion of credit that followed banking
deregulation in the 1980s and 1990s (Dick & Lehnert, 2010). An increase in the credit
63
Texas Tech University, Thomas O’Malley, August 2013
card debt from the population average to bankruptcy filer average increases the
conditional probability of filing six times (Domowitz & Sartain, 1999).
The expansion in credit foreshadowed an increase in student loans. While credit
card debt expanded rapidly in the 1980s and 1990s, educational debt ballooned in the
2000s. Over the last twenty years, financial aid packages slowly morphed from being
primarily need-based grants to being mostly loans. Two-thirds of college graduates now
finish school with accumulated debt, compared to less than half in 1993 (Lewin, 2011).
In 2003 cumulative student debt was approximately 253 billion dollars, or 10.5 percent of
all non-mortgage consumer debt. By the end of 2012, total debt has risen to 966 billion
dollars, or 29.2 percent of all non-mortgage debt (FRBNY, 2012). Figure 3.4 documents
the non-mortgage debt trends.
Student debt may be as risky as credit card debt. Student loan underwriting is
limited or non-existent. Risk factors known to be associated with successful loan
repayment are not considered during the loan origination process. This alone may
explain why the bulk of student loans are in default, deferment or forbearance. Only 40
percent of student debt is in active repayment (Pilon, 2010). The student loan sector also
has a clientele that may be more predisposed to financial distress. Education debt is
concentrated among young adults who lack experience making financial decisions. This
leads young households to make poor financial decisions relative to those made by older,
more experienced consumers (Agarwal, Driscoll, Gabaix & Laibson, 2007).
64
Texas Tech University, Thomas O’Malley, August 2013
1.0
Trillions of Dollars
0.8
HE Revolving
Auto Loan
0.6
Credit Card
Student Loan
Other
0.4
0.2
03:Q3 04:Q3 05:Q3 06:Q3 07:Q3 08:Q3 09:Q3 10:Q3 11:Q3 12:Q3
source: FRBNY, Quarterly Report on Household Debt and Credit, Feb 2013
Figure 3.4 - Non-mortgage Debt Balances (2003-2012)
Few bankruptcy studies look at the role that student loans play in bankruptcy.
The effects of education debt may vary by educational attainment. There are no studies
testing whether bankruptcy is more likely among debtors who are college dropouts
relative to those who acquired a bachelor's degree. Domowitz and Sartain (1999) identify
and compare the determinants of Chapter 7 and Chapter 13 filings and find that relative
to income, higher student debt promotes higher Chapter 13 filings. The authors also find
that Chapter 13 filers have three times the amount of education debt as Chapter 7 filers.
This is consistent with the legal restrictions preventing student debt from being
discharged in Chapter 7. Bankruptcy filers are more likely to report having “some
college” but less likely to have obtained a college or advanced degree (Sullivan et al.,
2000). While filers are about half as likely as non-filers to hold a bachelor's degree, they
65
Texas Tech University, Thomas O’Malley, August 2013
are 25 percent more likely to be a high school graduate and 35 percent more likely to
have attained ‘some college’ (Wang, 2007).
Theoretical Framework
All else equal, student debt should be related to bankruptcy. However, college
graduates tend to acquire more student debt and have better employment outcomes.
Given this, student loan and education effects need to be tested jointly. To our
knowledge, prior research does not take this approach. We hypothesize that student debt
and bankruptcy are positively related for those who do not obtain a bachelor's degree.
After controlling credit card debt, adverse events, cognitive ability, family resources and
other household characteristics, we expect student debt to be positively related to
personal bankruptcy.
The bankruptcy decision can be modeled as a function of education and debt.
Figure 3.5 presents an event tree that illustrates bankruptcy as a function of the college
decision, student debt, and adverse events. Prospective college students anticipate that
graduation is not certain. They also know that if they decide to use college loans,
repayment must be made after leaving school irrespective of completion or employment.
Adverse events, such as divorce, layoff, and health shocks, also lead to more
bankruptcies. Debt results in more bankruptcy filings, all else equal.
66
Texas Tech University, Thomas O’Malley, August 2013
Figure 3.5 - The College Decision, Adverse Events, Debt and the
Likelihood of Bankruptcy.
A two period model is developed in which the likelihood of a household declaring
bankruptcy during period t is a function of modeled characteristics in the prior period, t-1.
The predictor variable of primary interest simultaneously captures both educational
attainment and student loan usage. This two period model enables to test whether ‘noncompletion’ of a four year degree and loan usage leads to highest risk of bankruptcy.
Credit card debt and adverse events are included in the model. A vector of family
resource and household variables is also included. Equation 1 provides the conceptual
framework.
(1)
)
67
Texas Tech University, Thomas O’Malley, August 2013
This model controls for the effects derived from ‘adverse events’ and ‘credit card’
hypotheses. It also controls for cognitive ability. Higher cognitive function is associated
with patient, wealth-accumulating behavior such as participation in the financial markets
(Benjamin & Shapiro, 2005; Christelis, Japelli & Padula, 2010). Family factors that may
be relevant to the bankruptcy decision are also included. Households are independent
economic units but they often receive financial, educational and informational resources
from parents. The availability of these resources may mitigate financial distress.
Household factors may influence financial distress as wealth accumulation varies
with marital status, family size and sex. Women tend to earn less than men.
Explanations for a gender pay gap include discrimination, occupational choices and
division of labor theories (Blau & Kahn, 2000). If women’s wages are lower, more
bankruptcy may occur among women. Unmarried people and larger families have less
wealth (Zagorsky, 1999; Wolff, 1998). These households may be more likely to
experience financial distress.
The racial composition of the households needs to be considered as a potential
determinant of financial distress. A wealth gap between whites and blacks persists.
Several explanations for wealth underperformance by black Americans have been
studied. Blacks earn more as they complete more education but earn substantially less
than whites at similar educational levels. Black Americans suffer from income
stagnation and are not rewarded as generously as whites for labor market experience.
Asset composition explains some of the wealth gap with blacks having a much larger
68
Texas Tech University, Thomas O’Malley, August 2013
proportion of net worth tied up in functional assets and less in income-producing and
other financial assets (Oliver & Shapiro, 1995).
Data
Data used to test the conceptual framework in Equation 1 come from the National
Longitudinal Survey of Youth (NLSY79). The NLSY is arguably the best panel dataset
available to investigate personal bankruptcy. The NLSY79 is conducted for the
Department of Labor’s Bureau of Labor Statistics and provides a rich dataset of financial
information, employment history, personal traits, family background and demographics.
It is a panel dataset that captures the responses of a nationally representative sample of
12,686 men and women. The respondents were born in the years 1957-1964 and were
first interviewed in 1979. Follow-up interviews were conducted annually through 1994
and biennially thereafter.
Recent administrations of the NLSY79 make testing of a bankruptcy hypothesis
possible. The 2004 and 2008 waves include bankruptcy modules that capture
information about the respondent’s bankruptcy experience. These survey administrations
are also the first to have student loan and credit card balance summary information. This
dataset also allows us to control for adverse events, including divorce, job loss and health
shocks.
The bankruptcy modules provide detailed data regarding the survey respondent's
bankruptcy experience. Respondents are asked if they ever declared bankruptcy. Nearly
14 percent of 2004 respondents indicate having ever declared bankruptcy. By 2008, 16
percent of respondents report filing at some point in their lives. If bankruptcy was ever
69
Texas Tech University, Thomas O’Malley, August 2013
filed, the respondent is then asked additional questions including the date and type of
bankruptcy. Bankruptcy filing dates range from 1979 to 2008. Bankruptcy types include
‘Chapter 7’, ‘Chapter 11’, ‘Chapter 12’, ‘Chapter 13’ or ‘Other’. This investigation uses
only non-business filings and combines Chapter 7 and 13 bankruptcies. Table 3.1
provides details.
Table 3.1 - Type of Bankruptcy Declared
2004 2008
Chapter 7 - Straight Bankruptcy
63% 60%
Chapter 11 - Business Reorganization
4%
6%
Chapter 12 - Family Farmer Reorganization
0%
0%
Chapter 13 - Personal Debt Reorganization
33% 33%
Other
0%
1%
Note: These calculations are taken from the responses
to the 2004 and 2008 administrations of the NLSY97 and
include bankruptcies covering the years 1979-2004 and 19792008, respectively.
Method
Binary logistic regression is used to test Equation 1. Using data from the 2004
and 2008 waves of the NLSY79, the following model is estimated:
70
Texas Tech University, Thomas O’Malley, August 2013
Bankruptcy. The dependent variable for this study is bankruptcy. It is a dichotomous
variable coded as one if the respondent declared bankruptcy in the either the 2004-2005
or 2008-2009 timeframe. We investigate bankruptcy in these two periods because credit
card and student loans information is provided only in 2004 and 2008. Chapter 7 and
Chapter 13 non-business filings are pooled together. This is common in the literature and
is done because there are limited bankruptcy observations.
Student Loan Use and Educational Attainment. Educational attainment and student loan
use is merged into four dummy variables. These variables differentiate between those
who complete a bachelor's degree or higher and those who attend college but do not
obtain a four year degree. These variables also identify those who have outstanding
student loan balances. This approach is used because we hypothesize that education
debtors who have less than a bachelor's degree are more susceptible to bankruptcy.
Those who have a high school degree or less education are the reference group.
Credit. Credit has been demonstrated to play an important role in the bankruptcy
decision. The 2004 wave of the NLSY79 is the first time detailed credit card account
questions are asked. An imputed variable is used that sums up credit balances from
multiple individual accounts. A positive balance is reported by 7611 respondents in
2003. The top one percent is coded at the 99th percentile to address the influence of
extreme outliers. Balances are in thousands of dollars.
Adverse Events. The adverse events hypothesis posits that job losses, health problems
and divorce lead to personal bankruptcy. Recent research suggests that a period of
observation longer than 1 year should be used (Keyes, 2009). Three dummy variables are
71
Texas Tech University, Thomas O’Malley, August 2013
used to capture the presence of these events in the two years leading up to the 2004-2005
and 2008-2009 bankruptcy observation periods. The job losses considered here are
layoffs. Breaks in employment caused by layoff tend to be unanticipated and more likely
to lead to financial distress. Potential job loss responses that are excluded include: ‘quit
job’, ‘going to school’, ‘in the Armed Forces’, or ‘did not want to work’. A health shock
is considered if it affects the respondent’s ability to work. Breaks in employment that
result from health problems are coded as a health shock. Lastly, divorce is also
considered an ‘adverse event’ if it occurs in the two years leading up to bankruptcy.
Family. Households often receive support from individuals outside the household.
Availability of these resources may impact the bankruptcy decision. Parents often
provide support to children well after the children have left home. Four variables are
used to account for these family factors. The first variable measures the receipt of
inheritances. Respondents are first asked to provide inheritance information in the 1986
administration of the NLSY79. These and subsequent responses are then inflated and
summed in 2004 dollars. Next, the level of formal education attained by the parents is
included. Higher human capital reflects an ability to generate lifetime income and assets
which can potentially be shared with children. This variable measures the highest
number of years of education completed by the respondent’s mother or father. The third
variable is parental income. This is created using average income information from the
1979-1982 waves of the NLSY. The last family variable is the number of siblings. More
siblings in a household imply fewer resources that can be transferred or shared.
72
Texas Tech University, Thomas O’Malley, August 2013
Household. The sex of the respondent is included as dichotomous variable. ‘Female’ is
included and ‘male’ is excluded as the reference group. The marital status of the
respondent is coded as one for those whose more recent marital status was ‘married’.
The number of children in the respondent’s household is included.
Race. Two dummy variables are used to identify the respondent’s race. One identifies
those who are black and one is used for ‘Hispanic’.
BAPCPA. A dichotomous variable is used to control for the 2005 Bankruptcy Abuse
Prevention and Consumer Protection Act. The intent of the legislation was to make it
more difficult to file for bankruptcy and filings dropped significantly after 2005 (Figure
3.1).
Results
Descriptive Statistics of the Sample
A descriptive summary of the NLSY79 sample used in this study is provided in
Table 3.2. Statistics are provided for bankruptcy filers and non-filers to identify any
unique characteristics of the filing population. Filers are twice as likely to have an
unpaid, outstanding education loan. While six percent of non-filers have an active loan,
twelve percent of bankruptcy filers do. Household that seek bankruptcy protection tend
to have less education. Filers are more likely to have ‘some college’ or an associate's
degree but less likely to have a bachelor's degree or higher credential. Consistent with the
credit card hypothesis, sample statistics suggest that bankrupt households have higher
levels of unsecured debt relative to their income. These households have an average
credit card balance-to-income ratio that is more than two and a half times higher than that
73
Texas Tech University, Thomas O’Malley, August 2013
for non-filers. Those who have experienced bankruptcy have slightly lower cognitive
ability, on average. Bankruptcy is also associated with lower levels of family resources.
There are small differences in household composition between filers and non-filers.
Filers are disproportionately female and married and tend to have more children in the
household. Lastly, blacks are somewhat overrepresented among filers but Hispanics are
underrepresented.
74
Texas Tech University, Thomas O’Malley, August 2013
Table 3.2: Descriptive Statistics - Bankruptcy Filers and Non-Filers
Filers
Student Loans
Existing student loans
12.1%
Education
Some college
19.4%
Associate's
14.4%
Bachelor's
11.8%
Master's
2.3%
PHD
0.0%
Debt Load
Credit card balance-to-income ratio
0.47
Adverse Events
Job loss
4.7%
Health shock
6.5%
Divorce
4.2%
Ability
IQ decile
4.16
Family Resources
Inheritances '79-'04 (thousands '04 $s)
13.33
Parent's schooling (number of years)
11.19
Parent's income (thousands of '79 $s)
9.97
Number of siblings
4.04
Household
Female
56.3%
Married
57.2%
Number of children in household
1.28
Race
Hispanic
14.1%
Black
35.2%
notes: adverse events occur in the 2 year period prior to the '04-06
and '08-10 observation windows.
75
Non-Filers
6.1%
14.3%
10.8%
15.4%
5.6%
0.4%
0.18
1.8%
3.1%
1.5%
4.59
16.41
11.63
11.36
3.88
50.5%
54.9%
1.16
19.5%
31.1%
Texas Tech University, Thomas O’Malley, August 2013
Regression Analysis
A logistic regression is used to estimate the likelihood of declaring bankruptcy.
Coefficient estimates and standard errors are provided in Table 3.3. Odds ratios are
provided for those variables that meet the p=.10 threshold of statistical significance.
Results indicate that student loans contribute to bankruptcy at all educational
levels. The effects are most pronounced for those who enroll in college but fail to
complete a four-year degree. The 'college dropouts & associate's' households who do not
have student debt are 35 percent more likely to file for bankruptcy. Households with the
same level of education but who have active student loans are 116 percent more likely to
file. Those with a bachelor's or higher degree who have unpaid student loans are no less
likely to file bankruptcy than the reference group of households with a high school or less
education. In contrast, those with a bachelor's or higher education without debt are onethird less likely to declare bankruptcy.
Credit card debt is positively related to bankruptcy. Like student loans and other
revolving credit, credit cards are unsecured. Large balances relative to income lead to
financial distress. All else equal, a one unit increase in the credit card debt-to-income
ratio is estimated to result in a 21 percent higher likelihood of declaring bankruptcy.
Divorce, job loss, and health shocks are all tested. These adverse events are all
statistically significant. Individuals who experience layoff are 188 percent more likely to
declare bankruptcy. Households that suffer a health shock severe enough to affect
employment are 69 percent more likely to file. Divorce is also linked financial distress.
Recently divorced individuals are 165 percent more likely to seek bankruptcy protection.
76
Texas Tech University, Thomas O’Malley, August 2013
Table 3.3 - Logistic Regression - Likelihood of Declaring Bankruptcy
Variable
Educational level and unpaid student loans
(reference: high school degree or less)
College dropouts & associate's - loans
College dropouts & associate's
Bachelor's & higher credentials - loans
Bachelor's & higher credentials
Debt Load
Credit card balance-to-income ratio
Adverse Events
Job loss
Health shock
Divorce
Ability
IQ decile
Family Resources
Inheritances '79-'04 (thousands '04 $s)
Parent's schooling (number of years)
Parent's income (thousands of '79 $s)
Number of siblings
Household
Female
Married
Number of children in household
Race
Hispanic
Black
BAPCPA legislation
Post BAPCPA
Odds
Ratio
Estimate
S.E.
0.771
0.301
0.416
-0.404
0.294
0.160
0.318
0.243
2.162 ***
1.351 *
0.193
0.043
1.213 ***
1.059
0.525
0.975
0.291
0.269
0.347
2.882 ***
1.691 *
2.651 ***
-0.044
0.032
0.0002
-0.074
-0.003
-0.025
0.001
0.025
0.007
0.027
0.112
-0.008
0.101
0.132
0.143
0.055
-0.709
-0.022
0.220
0.169
0.492 ***
-0.761
0.136
0.467 ***
0.668 *
0.929 ***
1.106 *
note 1: all adverse events occur in the 2 years prior to '04-'05 or '08-'09
note 2: asterisks denote 10, 5, and 1 percentile level of significance
Regression controls are used for family resources, household composition, race
and the Bankruptcy Abuse Prevention and Consumer Protection Act (BAPCPA). Only
77
Texas Tech University, Thomas O’Malley, August 2013
one of the four ‘family resource’ factors is found to be significant. Parental schooling is
negatively related to bankruptcy. Each year of completed parental schooling is estimated
to result in a 7 percent reduction in the likelihood of filing for bankruptcy. Gender and
marital status is unrelated to bankruptcy. However, family size is related to distress.
Each additional child in the household is estimated to lead to ten percent higher
likelihood of filing for Chapter 7 or Chapter 13 protection. Black households are not
more likely to file. Relative to the non-black, non-Hispanic reference group, Hispanic
households are 51 percent less likely to seek bankruptcy protection, all else equal.
Conclusion
The risk associated with attending college has increased as the costs of higher
education have grown rapidly for decades. Higher education is increasingly selffinanced. While federal and state governments continue to spend billions in support of
college education, students increasingly bear the costs of these human capital
investments. Grants and other government subsidies have not kept pace with tuition
increases and student loans have filled the funding gap. A large majority of college
students now rely on college loans to finance their education. The typical student debt
load has grown each year and is now at a record high.
Student loan repayments constrain budgets and shape household decisions.
Researchers have long been concerned that excessive debt loads may excessively
influence the career choices of new graduates. Debt may induce households to front-load
their earnings and sacrifice long-term wage growth opportunities (Minicozzi, 2005).
78
Texas Tech University, Thomas O’Malley, August 2013
Student debt may negatively affect enrollment in professional and postgraduate degree
programs. Educational debt may discourage homeownership, defer marriage and affect
fertility decisions (Shand, 2008). Evidence indicates that many former students struggle
with loan repayments. Loan default rates appear to be high and getting worse. For some,
student debt may lead to extreme financial distress and personal bankruptcy.
The effects of education debt should vary with the level of education. However,
no studies test whether this is the case. An asset pricing model is used to evaluate
whether student loan usage leads to bankruptcy. It accounts for potential differences
resulting from educational completion and considers the costs, uncertain outcomes, and
stochastic payouts associated with higher education.
Results indicate that education debt compels many households to seek bankruptcy
protection. Bankruptcy risk is particularly high for those who acquire education debt but
fail to graduate with a bachelor's degree. This population is more than twice as likely to
declare bankruptcy relative to those who never attended college.
Student debt explanations for bankruptcy complement the adverse events and
credit card debt hypotheses. Households with leveraged balance sheets are vulnerable to
distress. Credit card debt and unsecured education loans expose consumers to elevated
risk. Unanticipated events then push many of these households to the brink of financial
distress. Risk is particularly high for those who take on debt but then fail to graduate
with a bachelor's degree.
Financial planners are uniquely capable of managing human capital investments.
Planners can use the findings here which suggest the college decision needs to be viewed
79
Texas Tech University, Thomas O’Malley, August 2013
as a risky investment. For some households, education debt leads to financial distress
and even bankruptcy. Given the risk, it is critical to make an informed decision. A wise
college decision can only be made after weighing the costs, benefits and risks.
80
Texas Tech University, Thomas O’Malley, August 2013
Appendix
Non-completers - loans
Job loss
CC balance-toincome ratio
Completers - no
loans
Completers loans
Non-completers no loans
Non-completers loans
Appendix 3.1 Bivariate Correlation Matrix (Page 1 of 3)
0.13 *** 0.02 **
Non-completers - no loans
-0.04 *** -0.02 *
Completers - loans
0.16 *** -0.01 *
Completers - no loans
-0.06 *** -0.04 ***
CC balance-to-income ratio 0.13 *** -0.04 *** 0.16 *** -0.06 ***
0.01
Job loss
0.02 ** -0.02 *
-0.01 *
-0.04 *** 0.01
Health shock
0.00
0.00
-0.01
-0.05 *** 0.03 *** -0.02 *
Divorce
0.00
0.01 *
IQ decile
0.01
0.06 *** 0.08 *** 0.49 *** -0.02 ** -0.01
Inheritances
Parent's schooling
Parent's income
-0.01
0.00
-0.01
0.00
0.00
0.01
-0.01
0.00
0.09 *** -0.01
0.04 *** 0.06 *** 0.34 *** -0.01
0.02 **
0.01
-0.03 *** -0.03 *** -0.18 *** 0.02 **
Number of children
0.00
Female
0.02 *** 0.05 *** 0.02 **
0.01
0.01
0.00
Hispanic
0.00
0.16 *** -0.02 ** -0.01
Black
0.06 *** 0.04 *** 0.03 *** -0.12 *** 0.02 **
BAPCPA
0.00
0.04 *** -0.02 ** -0.10 *** -0.02 *
0.00
0.02 *
0.02 *** 0.07 *** -0.04 ***
-0.01
81
-0.02 ***
0.10 *** -0.02 ** -0.02 **
Married
-0.01
-0.01
0.04 *** 0.21 *** -0.04 *** -0.01
Number of siblings
0.00
0.00
0.01
-0.02 **
0.00
0.04 *** 0.00
Texas Tech University, Thomas O’Malley, August 2013
0.00
0.00
0.01
Non-completers - no loans
0.00
0.01 *
0.06 *** 0.00
0.04 *** 0.02 ***
0.00
0.08 *** 0.01
0.06 *** 0.04 ***
-0.01
Completers - no loans
-0.05 *** -0.01
0.00
Parent's income
Non-completers - loans
Completers - loans
-0.01
Parent's schooling
Inheritances
IQ decile
Divorce
Health shock
Appendix 3.1 Bivariate Correlation Matrix (Page 2 of 3)
-0.01
0.49 *** 0.09 *** 0.34 *** 0.21 ***
CC balance-to-income ratio 0.03 *** 0.00
-0.02 ** -0.01
-0.01
Job loss
-0.01
-0.02 *** -0.01
-0.02 *
Health shock
0.00
-0.01 *
-0.01
-0.04 *** -0.01
-0.01 *
IQ decile
-0.04 *** 0.00
Inheritances
-0.01
Parent's schooling
-0.03 *** 0.00
0.46 *** 0.09 ***
Parent's income
-0.03 *** 0.01
0.28 *** 0.07 *** 0.30 ***
0.01
Number of children
-0.01 *
Female
-0.01
Hispanic
-0.02 *
0.00
0.01
0.10 *** 0.46 *** 0.28 ***
0.00
0.10 ***
-0.01
0.09 *** 0.07 ***
0.30 ***
-0.31 *** -0.07 *** -0.34 *** -0.17 ***
-0.05 *** 0.12 *** 0.03 *** 0.03 *** 0.07 ***
0.03 *** -0.01 *
Married
0.00
-0.03 *** -0.03 ***
Divorce
Number of siblings
0.00
-0.04 ***
-0.02 *** 0.02 ** -0.01 ** -0.09 ***
-0.12 *** 0.23 *** 0.04 *** 0.10 *** 0.11 ***
0.01
-0.16 *** -0.04 *** -0.35 *** -0.05 ***
Black
0.02 ** -0.01
-0.39 *** -0.06 *** -0.07 *** -0.15 ***
BAPCPA
0.00
0.00
0.00
82
0.02 **
0.00
0.00
Texas Tech University, Thomas O’Malley, August 2013
Non-completers - loans
0.01
0.00
BAPCPA
Black
Hispanic
Married
Female
Number of
children
Number of
siblings
Appendix 3.1 Bivariate Correlation Matrix (Page 3 of 3)
0.02 *** -0.01 *
0.00
Non-completers - no loans -0.03 *** 0.00
0.05 *** 0.01
0.04 *** 0.04 *** -0.01
Completers - loans
-0.03 *** 0.01
0.02 **
Completers - no loans
-0.18 *** 0.10 *** 0.02 *** 0.16 *** -0.10 *** -0.12 *** 0.01
CC balance-to-income ratio 0.02 ** -0.02 **
0.00
-0.02 **
0.07 *** -0.02 ** -0.02 *
0.06 *** 0.00
0.03 *** 0.00
Job loss
0.02 *
-0.02 ** -0.04 *** -0.01
-0.02 **
0.00
0.04 ***
0.00
Health shock
0.01
-0.01 *
-0.02 *
0.02 **
0.00
0.03 *** -0.01
Divorce
-0.01
IQ decile
-0.31 *** 0.12 *** -0.02 *** 0.23 *** -0.16 *** -0.39 *** 0.00
Inheritances
-0.07 *** 0.03 *** 0.02 **
Parent's schooling
-0.34 *** 0.03 *** -0.01 **
Parent's income
-0.17 *** 0.07 *** -0.09 *** 0.11 *** -0.05 *** -0.15 *** 0.00
Number of siblings
0.01
Number of children
0.01
Female
0.02 **
Married
-0.05 *** -0.01 *
-0.12 *** 0.01
0.02 **
-0.01
0.00
0.04 *** -0.04 *** -0.06 *** 0.02 **
0.10 *** -0.35 *** -0.07 *** 0.00
0.02 ** -0.08 *** 0.14 *** 0.21 *** 0.00
0.10 *** 0.37 *** 0.07 *** -0.10 *** -0.10 ***
0.10 ***
-0.02 ** 0.00
-0.01 *
0.00
-0.08 *** 0.37 *** -0.02 **
-0.01
Hispanic
0.14 *** 0.07 *** 0.00
-0.01
Black
0.21 *** -0.10 *** -0.01 *
-0.25 *** -0.28 ***
BAPCPA
0.00
-0.02 **
-0.10 *** 0.00
83
0.00
-0.25 *** -0.02 **
-0.28 *** 0.00
0.00
0.00
Texas Tech University, Thomas O’Malley, August 2013
References
Agarwal, S., Driscoll, J. C., Gabaix, X. & Laibson, D. (2007). The age of reason:
Financial decisions over the lifecycle, NBER Working Paper n. 13191.
Becker, G. S. (2007). Health as human capital: synthesis and extensions. Oxford
Economic Papers, 59(3), 379-410.
Benjamin, D. J. & Shapiro, J. M. (2005). Who is “behavioral”? Cognitive
ability and anomalous preferences, unpublished paper, Harvard University and
University of Chicago.
Buckley, F. H. & Brinig, M. F. (1998). The bankruptcy puzzle. The Journal of Legal
Studies, 27,187-207.
Christelis, D., Jappelli, T. & Padula, M. (2010). Cognitive abilities and portfolio choice.
European Economic Review, 18–38.
Cunningham, A. F. & Kienzl, G. S. (2011). Delinquency: the untold story of student loan
borrowing. Institute for Higher Education Policy, March 2011.
Dick, A. A. & Lehnert, A. (2010). Personal bankruptcy and credit market competition.
The Journal of Finance, 65, 655-686.
Domowitz, I. & Sartain, R. L. (1999). Determinants of the consumer bankruptcy
decision. The Journal of Finance, 54(1), 403-420.
Durkin, T. A. (2000). Credit cards: Use and consumer attitudes, 1970 –2000. Federal
Reserve Bulletin, September 2000, pp. 623–34.
84
Texas Tech University, Thomas O’Malley, August 2013
Ellis, D. (1998). The effect of consumer interest rate deregulation on credit card
volumes, charge-offs, and the personal bankruptcy rate. Federal Deposit
Insurance Company, Bank Trends, No. 98-05.
Fay, S., Hurst, E. & White, M. J. (2002). The household bankruptcy decision. American
Economic Review, 92, 706–718.
Federal Reserve Bank of New York (FRBNY). August 2012. Quarterly Report on
Household Debt and Credit. Retrieved from:
http://www.newyorkfed.org/research/national_economy/householdcredit/DistrictReport_Q22012.pdf
Field, K. (2010, July 11). Government Vastly Undercounts Defaults. The Chronicle of
Higher Education. Retrieved from:
http://chronicle.com/article/Many-More-Students-Are/66223/
Fisher, J., Filer, L. & Lyons, A. (2004). Is the bankruptcy flag binding? Access to credit
markets for post-bankruptcy households. American Law & Economics
Association Annual Meetings Paper 28.
Gropp, R., Scholz, J. K. & White, M. J. (1997). Personal bankruptcy and credit supply
and demand. Quarterly Journal of Economics, 112(1), 217-251.
Gross, D. B. & Souleles, N. S. (2002). An empirical analysis of personal bankruptcy and
delinquency. Review of Financial Studies, 15(1), 319-347.
Han, S. & Li, G. (2009). Household borrowing after personal bankruptcy. Journal of
Money, Credit and Banking, 43(2-3), 491-517.
Keyes, B. (2009). Three essays on labor and credit markets. Unpublished doctoral
dissertation, University of Michigan, Ann Arbor, MI.
85
Texas Tech University, Thomas O’Malley, August 2013
Lewin, T. (2011, April 11). Burden of College Loans on Graduates Grows. The New
York Times. Retrieved from:
http://www.nytimes.com/2011/04/12/education/12college.html?_r=1&emc=eta1
Livshits, I., MacGee, J. & Tertilt, M. (2010). Accounting for the rise in consumer
bankruptcies. American Economic Journal: Macroeconomics, 2(2), 165-193.
Pilon, M. (2010, February 13). The $555,000 Student-Loan Burden. The Wall Street
Journal. Retrieved from
http://online.wsj.com/article/NA_WSJ_PUB:SB10001424052748703389004575033063806327030.html
Restuccia, D. & Urrutia, C. (2004). Intergenerational persistence of earnings: The role of
early and college education. American Economic Review, 94(5), 1354-1378.
Sullivan, T. A., Warren, E. & Westbrook, L. (2000). The Fragile Middle Class:
Americans in Debt. New Haven: Yale University Press.
Sullivan, T. A., Warren, E. & Westbrook, L. (2006). Less stigma or more financial
distress: An empirical analysis of the extraordinary increase in bankruptcy filings.
Stanford Law Review, 59, 213-256.
U.S. Courts website. Bankruptcy Filing Fees - effective November 1, 2011. Retrieved
from:
http://www.uscourts.gov/FederalCourts/Bankruptcy/BankruptcyResources/BankruptcyFilingFees.aspx
U.S. Department of Commerce, The 2012 Statistical Abstract, The National Data Book,
Table 776. Bankruptcy Petitions Filed and Pending by Type and Chapter: 1990 to
2010. Retrieved from:
http://www.census.gov/compendia/statab/cats/business_enterprise.html
86
Texas Tech University, Thomas O’Malley, August 2013
U.S. Department of Labor, Bureau of Labor Statistics, Current Population Survey,
Household Data Annual Averages, Table 1. Employment status of the civilian
noninstitutional population, 1941 to date. Retrieved from:
http://www.bls.gov/cps/tables.htm
Vedder, R., Denhart, C., Denhart, M., Matgouranis, C. & Robe, J. (2010). From Wall
Street to Wal-Mart: why college graduates are not getting good jobs. Policy
paper. The Center for College Affordability and Productivity. Retrieved from:
http://www.centerforcollegeaffordability.org/uploads/From_Wall_Street_to_Wal-Mart.pdf
Wang, F. Q. (2007). A second life or same old story once again? A study on households’
post-bankruptcy credit card borrowing. Working paper.
Warren, E. & Tyagi, A. W. (2003). The Two-Income Trap: Why Middle-Class Mothers
and Fathers are Going Broke. New York: Basic Books.
White, M. J. (1987). Personal bankruptcy under the 1978 Bankruptcy Code: An economic
analysis. Indiana Law Journal, 63(1), 1–53.
White, M. J. (2007). Bankruptcy reform and credit cards. Journal of Economic
Perspectives, 21(4),175-199.
White, M. J. (2009). Bankruptcy: Past puzzles, recent reforms, and the mortgage crisis.
American Law and Economics Review, 11(1), 1-23.
87
Texas Tech University, Thomas O’Malley, August 2013
CHAPTER IV
FOR-PROFIT HIGHER EDUCATION AND WAGES
Abstract
Private for-profit colleges are the fastest growing segment of higher education. This
paper uses the 1997 National Longitudinal Survey of Youth (NLSY97) to identify
whether for-profit institutions affect wage rates and earnings. We control for human
capital, family background and cognitive ability and find that an associate's degree from a
for-profit institution has no discernible value. For-profit bachelor's and master's degrees
lead to higher wage rates and earnings but the benefits are small relative to what is
obtained graduating from a traditional non-profit school.
Introduction
For-profit institutions have grown dramatically in recent years and become a
prominent element of the higher education landscape. The for-profit sector now
represents 10 percent of the 18 million undergraduate students in the United States
(National Center for Education Statistics [NCES], The Condition of Education 2012,
Indicator 36). In 2010, the for-profit industry conferred 19 percent of all associate’s
degrees, 6 percent of all bachelor's degrees, and 10 percent of all master's degrees
(NCES, The Condition of Education 2012, Indicator 46). Proprietary schools also
dominate the non-degree granting segment of higher education. Most students who are
enrolled in certificate programs are in for-profit schools.
The for-profit sector has rich institutional variety. Numerous technical and
vocational schools provide non-degree and certificate career training such as dog
grooming, automotive repair and cosmetology. The for-profit sector is also made up of
accredited colleges and universities that compete with traditional private and public
88
Texas Tech University, Thomas O’Malley, August 2013
nonprofit schools. These institutions confer associate's, bachelor's and even doctoral
degrees. Most of these schools offer a traditional classroom learning experience but
many specialize offering online distance education. Typical fields of study include
business, healthcare and computers.
The for-profit industry provides training and education to a diverse non-traditional
clientele. Proprietary schools enroll large numbers of older students. Eighteen percent of
for-profit students are 25 years old and older (Deming, Goldin & Katz, 2012). Many forprofit students work full time. The sector educates a disproportionately large share of
poor households, racial minorities, and single parents.
Institutional size varies significantly. The largest for-profit schools are enormous.
Three of the four largest universities by enrollment are for-profit schools (Department of
Education, Digest of Education Statistics, 2011, Table 249). The most influential of
these firms are large publicly-traded corporations (Bennett, Lucchesi, & Vedder, 2010).
At the other end of the spectrum are numerous small providers. These schools have few
enrollments and modest growth prospects. Some of these firms are sole proprietor
businesses. These schools represent the typical proprietary institution. Median
enrollment among these educational providers is less than 200 (Deming et al., 2012).
For-profit institutions have unique qualities. First, for-profit schools almost
always provide an education tailored to a specific trade or vocation. They generally do
not prepare students for follow-on education. Most do not offer a liberal arts or general
education program. In contrast, public community colleges have many students who
transfer to 4-year schools (Cellini, 2009). Second, for-profits tend to offer very
89
Texas Tech University, Thomas O’Malley, August 2013
structured programs. Curriculum is centrally planned and students are usually given few
choices for elective coursework (Hentschke, 2011). Third, virtually all for-profit
programs have open admission policies. Prospective students only need a high school
diploma or GED to enroll. Fourth, for-profit institutions rely heavily on the government
funding. While the industry does not get direct government appropriations, virtually all
degree-granting for-profit schools receive federal Title IV funding (Deming et al., 2012).
Taxpayer funding has fueled the growth in for-profit education. The lion’s share
of for-profit college funding comes from federal grants and loans to students received
through programs authorized under Title IV of the Higher Education Act. These
programs include subsidized and unsubsidized Stafford loans, Pell grants, Parent PLUS
loans, Academic Competitiveness Grants (ACG), and National Science and Mathematics
Access to Retain Talent (SMART). Campus-based aid is also provided and includes
Perkins loans, Supplemental Educational Opportunity Grants, and federal work-study.
More than $32 billion in Title IV funding went to 2,042 for-profit institutions in the
2010-2011 academic year. In 2011 Title IV funding represented 78.8 percent of forprofit funding (Department of Education, FSADC, Proprietary School 90/10 Revenue
Percentages). Among the largest for-profit colleges, the reliance on Title IV funding is
higher. The ten largest colleges account for 40 percent of all Title IV expenditures
received by the for-profit sector and average 80.6 percent of their total revenue from
these sources (Table 4.1).
These figures actually understate the true reliance of these programs on federal
support. Legislation limits to 90 percent the proportion of revenue that these institutions
90
Texas Tech University, Thomas O’Malley, August 2013
can receive under federal Title IV loans and grants. At least 10 percent of the firm’s
annual revenue is supposed to come from private sources. A commonly exploited
loophole allows proprietary schools to exclude GI Bill and veterans assistance
educational benefits from this 90 percent restriction. In effect, these benefits are
classified as private funds. As a consequence, it is certain that many for-profit schools
obtain more than 90 percent of revenue from government sources.
Table 4.1: Percentage of For-Profit Institutional Revenue Received under Title IV
Note - Current systems do not capture actual Title
Top 10 - Proprietary School 90/10 Revenue
IV revenues and other revenues reported by the
Percentages from Financial Statements
institutions that support the calculated
with Fiscal Year End Dates Between 7/1/2010 and 6/30/2011
Source: eZ-Audit as of 6/11/12
* Source eZ-Audit as of 7/27/12
percentage.
Total Funding Received
under Title IV of the HEA by Award Year
School Name
Fiscal Year
Ending Date
90/10 Revenue
percentage
2009-2010
7/1-6/30
2010-2011
7/1-6/30
University of Phoenix
DeVry University
Ashford University
Kaplan University
ITT Technical Institute
Strayer University
Walden University
Argosy University
Capella University
Grand Canyon University
08/31/2010
06/30/2011
12/31/2010
12/31/2010
12/31/2010
12/31/2010
12/31/2010
06/30/2011
12/31/2010
12/31/2010
85.39
81.49
85.03
88.70
60.94
77.62
76.44
88.10
78.20
84.86
$ 5,422,549,464
1,138,888,776
782,173,025
1,130,672,953
158,823,870
785,146,421
650,671,867
549,111,029
522,311,240
480,098,726
$ 5,036,832,694
1,274,413,234
1,145,093,687
1,108,303,044
918,613,948
847,108,349
744,911,672
658,638,305
609,875,012
564,187,328
Source: Department of Education, Federal Student Aid Center, 2011
The fastest growing firms are large ‘chain’ schools that have extensive online
course offerings. Before 1998 these schools were prohibited from having more than half
of their enrollments online to remain Title IV-eligible. In an effort to encourage distance
91
Texas Tech University, Thomas O’Malley, August 2013
education, the Higher Education Act of 1998 authorized the Department of Education to
grant waivers to these requirements. In years that followed, waivers were granted and
online education expanded rapidly. The University of Phoenix, Online Campus
illustrates this growth, growing from 14,783 students in 2000 to 307,965 in 2010
(Department of Education, Digest of Education Statistics, 2011, Table 250).
Scrutiny of proprietary education has increased in recent years. Research has
focused on the efficacy of these institutions. Some findings suggest that for-profit
students fail to benefit from their educational experience. Most for-profit outcomes are
poor relative to the outcomes achieved by traditional nonprofit institutions.
For-profit institutional outcomes
Graduation rates are low. Among those enrolled in proprietary bachelor's degree
programs, few ever complete their program of study. The 2004 cohort six-year
graduation rate for those enrolled in private for-profit colleges is estimated at 28 percent,
but it is 65 percent for private nonprofit students and 56 percent for public students (U.S
Department of Education, NCES, The Condition of Education 2012, Indicator 45). The
large differences can in part be attributed to low admission standards for proprietary
schools. At open admission public 4-year institutions the graduation rate drops to 29
percent and at open admission private non-profit schools it is 36 percent (U.S Department
of Education, NCES, The Condition of Education 2012, Indicator 36). For-profit
students seem to do better in shorter programs though. Students in certificate and
associate's degree programs have higher completion rates than their peers going through
community college (Deming et al., 2012).
92
Texas Tech University, Thomas O’Malley, August 2013
For-profit students use education loans more than not-for-profit students. Among
first-time, full-time students attending a for-profit 4-year institution, 86 percent used
loans in the 2009-2010 academic year. This compares to 63 percent and 50 percent,
respectively, for private nonprofit students and public nonprofit students. A similar
pattern is seen for those enrolled in 2-year institutions. While 24 percent of students at
public institutions and 59 percent at private nonprofits use loans, 78 percent of those
enrolled in private for-profits used loans (U.S Department of Education, National Center
for Education Statistics [NCES], The Condition of Education 2012, Indicator 41).
Average debt loads are higher for proprietary students. The average amount of
student debt acquired by full-time students in 4-year institutions is highest among those
enrolled in for-profit colleges. In the 2009-2010 academic year, for-profit students
averaged $9,641 of education debt – versus $7,466 for those attending private nonprofits
and $6,063 for those attending public institutions. Among 2-year institutions, student
debt averaged $8,035 for those attending for-profit institutions - compared to $6,078 for
private nonprofit students and $4,627 for public students (U.S Department of Education,
National Center for Education Statistics [NCES], The Condition of Education 2012,
Indicator 41).
For-profit students acquire more debt even after accounting for family income and
dependency status. Baum (2011) analyzes institutional differences and finds that median
debt loads for financially dependent students are highest for those attending for-profit
schools. This occurs at all levels of family income. Among financially independent
students, debt loads are also highest among those enrolled in for-profit institutions
93
Texas Tech University, Thomas O’Malley, August 2013
(Baum, 2011). For-profit students have higher debt loads even after controlling for a
large and robust set of observable features (Deming et al., 2012).
Many indebted students default on their loans. Student debt affects college
graduates and dropouts alike. Repayment typically begins six months after the borrower
either graduates or drops out of college. The two-year “cohort default rate” is measured
directly from data provided by Title IV-participating schools. It measures the percentage
of student borrowers who enter repayment on a federal student loan during a particular
fiscal year and then default prior to the end of the next fiscal year.
Students attending for-profit colleges default on their federal student loans more
frequently than their peers at other types of institutions. In 2009, the official default rate
for students who were enrolled in four-year for-profit schools was 15.4 percent. By
contrast, the official rate was 5.2 percent for public four-year colleges and 4.5 percent for
private four-year not-for-profits (Figure 4.1). Among 2 year schools, for-profit default
rates are highest. The 2009 for-profit default rate was 14.8 percent, compared to the
public and private not-for-profit default rates of 11.9 and 10.0 percent, respectively.
Default rates may be higher than suggested by the official default rate. Steinerman,
Volshteyn, and McGarrett (2011) estimate that the 2008 three year cohort default rate
was 24.9 percent at for-profit schools, but only 10.8 percent for public institutions and 7.6
percent for private non-profits.
94
Texas Tech University, Thomas O’Malley, August 2013
0.16
0.14
0.12
0.1
0.08
Public Non-Profit
Private Non-Profit
0.06
Private For-Profit
0.04
0.02
0
2007
2008
2009
Source: National Student Loan Data System (NSLDS)
http://www2.ed.gov/offices/OSFAP/defaultmanagement/instrates.html
Figure 4.1 - Two Year Default Rates among Four Year Institutions
For-profit students are more likely to be unemployed or ‘idle’. These students are
more likely to experience a period of long-term unemployment. These students are also
more likely to be neither working or enrolled in school, or ‘idle’, six years after the initial
enrollment in college (Deming et al., 2012).
Institutional effects on earnings
Supporters of for-profit colleges commonly argue that these institutions provide
valuable training for a segment of society that is underserved by traditional colleges and
universities. Some argue that these schools are only able to generate profits if they
95
Texas Tech University, Thomas O’Malley, August 2013
provide a service that is valued by their students (Hentschke, 2011). Critics of
proprietary schools question the service provided by these schools and claim that these
students are left with few marketable skills and large debt loads. Some accuse the forprofits of being diploma mills or being more concerned with making a profit than
ensuring their students benefit from their education.
One way to assess the quality of an education is to evaluate labor market returns.
Volumes of research estimate labor market returns to education but very few studies
investigate for-profit education. The growth of proprietary education is a recent
development and a lack of data has limited study.
A better understanding of earnings effects should help prospective students make
informed decisions. Insight could be used to guide public policy, particularly in the
allocation and administration of Title IV resources. Lastly, knowledge of the true costs
and benefits of for-profit education will help financial planners, guidance counselors,
college advisors and anyone advising students and parents of students.
Literature Review
The existing work estimating labor market returns to education is voluminous.
Sophisticated modeling has been undertaken and numerous empirical studies have been
conducted. Card (1999) provides a thorough review. Few studies identify the labor
market outcomes of for-profit education and only a few estimate wage or earnings
effects.
96
Texas Tech University, Thomas O’Malley, August 2013
Wilms (1975) compares the labor market outcomes of proprietary students to
public students. This study utilizes a random sample of 2,270 former students of 29
proprietary schools and 21 public schools. The outcomes of six large, fast-growth
occupational fields are evaluated: accountancy, programming, electronics, dental
assistant, secretarial, cosmetology. T-test comparisons of weekly earnings are conducted
with controls for occupation and several other factors. Wilms finds that within these six
occupations proprietary education confers no earnings advantage relative to public
school.
Lyke, Gabe and Aleman (1991) use the High School and Beyond national
longitudinal survey of class of 1980 high school graduates. Nearly six years after
graduation, the graduates of for-profit institutions are identified. Their earnings are
compared to the earnings of community colleges and 4-year institution graduates. OLS
estimates of weekly and hourly earnings are calculated. For-profit college graduates
experience higher earnings than those who do not attempt college. Controls are limited
and selection bias is substantial.
Grubb (1993) conducts OLS regressions of wage and earnings for a cohort 14
years after completing high school. The National Longitudinal Study of the High School
Class of 1972 (NLSY-72) is used and a small sample of respondents who have
proprietary credentials are analyzed. Compared to non-profit schools, proprietary schools
do not provide an earnings premium. Analysis is limited to those who have achieved a
certificate or an associate's degree.
97
Texas Tech University, Thomas O’Malley, August 2013
Chung (2009) looks for evidence of wage effects using the National Education
Longitudinal Study which spans the years 1988 to 2000. This study provides some
evidence that for-profit certificate training and associate's degree education leads to
higher wages than what is achieved with a high school diploma. The estimated effects
are larger among women. However, the author does not find statistically significant
differences between the for-profits and not-for-profits. Analysis is again limited to
students who acquire a certificate or an associate's degree. Again, these findings cannot
be generalized to a bachelor's or advanced degrees.
Cellini and Chaudhary (2011) provide the most robust estimates of for-profit
earnings effects. The authors use the National Longitudinal Survey of Youth (NLSY97)
and analyze two year programs only. Proprietary and public community college students
experience similar earnings gains – approximately 8 percent per year of schooling. The
authors use fixed effects estimation techniques. Earnings are measured before, during
and after the individual’s education. This strategy accounts for unobservable traits that
often afflict earnings and wage studies. However, estimates are biased against public
community colleges. The analysis omits students who transfer to four-year institutions.
Estimates of public 2 year gains are likely to be understated since a large proportion of
community college students leave school to enroll in a four-year degree,
Summary
A large body of work estimates labor market returns to education. Only a few
attempt to identify for-profit labor market outcomes. Of these studies, the sample size
98
Texas Tech University, Thomas O’Malley, August 2013
tends to be small and the controls limited. None investigate effects at the bachelor's or
master's degree level.
Theoretical Framework
Jacob Mincer’s “human capital earnings function” is one of the most extensively
used models in economics. It serves as the analytical foundation for modern labor
economics and links the natural logarithm of earnings to a worker’s education and labor
market experience (Mincer, 1974). Mincer’s earnings equation specifies earnings as a
linear function of schooling years, S, and a quadratic function of work experience, X.
(1)
Natural logarithm functions have long been used in econometric models. Human
capital investments have a multiplicative earnings effect and are generally decided in
regard to a rate of return instead of in reference to absolute returns (Mincer, 1958).
Chung (2009) is one of a handful of studies that estimate for-profit earnings
effects. A variant of the Mincer earnings model is used to explain earnings variation
among graduates of for-profit and traditional nonprofit institutions: This model serves as
a useful starting point for this investigation.
(2)
99
Texas Tech University, Thomas O’Malley, August 2013
where ln earni is the natural log of an individual’s earnings. CREDi is a vector of dummy
variables measuring the highest college credential. These variables code the following:
no credential, a certificate, a for-profit or non-profit associate's degree or a bachelor's
degree. The reference group is those who have never enrolled in college. The minorityi
dummy variable denotes non-Asian minorities. HSi is series of dummy variables
capturing high school credentials including either no high school credential, GED,
traditional high school diploma. CENi are dummies that use the individual’s census
region to control for an individual location.
Chung develops a richer model to address individual heterogeneity and selection
bias using a “selection on observables” approach. Family characteristics and academic
preparedness are now included in equation 3. Controlling for these observables has been
found to substantially moderate biases stemming from non-random schooling choices
(Card, 1999). The expanded model is:
(3)
FAMi is a vector of two dummy variables. The first measures the family income.
The second variable captures the educational attainment of the respondent’s mother
including: less than high school, some college without a degree and bachelor's degree or
higher. The reference category is high school diploma. FAMi is intended to control for
100
Texas Tech University, Thomas O’Malley, August 2013
differences resulting from financial, informational and educational resources available to
the individual. ACADi codes standardized math and reading test scores. It serves as a
proxy for ability and is included to measure academic preparedness. The model found in
equation 3 is a good starting point to test for-profit institutional effects on earnings.
Several modifications to Chung (2009) are undertaken. First, the variable of
interest needs to be expanded significantly since we want to evaluate for-profit institution
effects beyond those at the associate’s degree level. In this regard, EDUCATIONi needs
to be a more exhaustive vector of dummy variables measuring the respondent’s formal
education. The dummies should be mutually exclusive and denote the following
categories: traditional high school diploma, some college with no postsecondary
credential, associate's degree, bachelor's degree or master's degree. The four collegiate
variables need to identify whether the respondent attended a for-profit institution or a
non-profit institution.
A conceptual model should include experience and tenure. Experience and tenure
are human capital variables commonly used in empirical studies to proxy for human
capital acquired through on-the-job training. Experience reflects general on-the-job
training and tenure captures on-the-job training specific to the firm. Many studies lack
work history data and use age or some other proxy. The use of experience is consistent
with the standard life-cycle model of human capital and is a parsimonious way to
simultaneously model the age-earnings profile and the different slopes associated with
different education groups (Lemieux, 2006).
101
Texas Tech University, Thomas O’Malley, August 2013
Experience and tenure are expressed as quadratic functions to approximate the
well-documented concave relation between these variables and earnings. Some skills are
perishable and human capital depreciates. Also, as the expected number of working
years shrinks, there is less time to recoup one’s investment and less incentive to invest in
human capital. For these reasons, earnings increase with experience and tenure but at
decreasing rates. Equation 4 incorporates these improvements:
(4)
In this paper, we examine labor market outcomes of proprietary education. The
earnings of respondents who have attended for-profit colleges are estimated and
compared to the earnings of respondents who have attended other types of institutions.
The conceptual model used is found in Equation 4. We test for effects on earnings at the
associate's, bachelor's and master's degree levels and obtain OLS estimates for three
dependent variables: natural log of the wage rate, earnings conditional on employment
and earnings irrespective of employment.
Data
Some longitudinal data sets with individual-level data are not useful to investigate
for-profit education. The growth in for-profit education is a recent development and
102
Texas Tech University, Thomas O’Malley, August 2013
many of the data sets do not sufficiently identify the type of institution that respondents
attended. We use the National Longitudinal Survey of Youth (NLSY97) which provides
information sufficient to overcome these limitations. This dataset has good information
to measure earnings and wage rates as well as the five concepts hypothesized to affect
wages: education, experience and tenure, ability, family, and personality.
The NLSY97 provides a rich dataset of schooling, work history, financial
information, family background and personal traits. The NLSY97 is conducted for the
Department of Labor’s Bureau of Labor Statistics. It captures the responses of a
nationally representative sample of 8,984 men and women who were age 12 to 18 when
first interviewed in 1997. This survey follows respondents every year and captures
changes in household education, employment, finances and other related topics. We use
data available through the 2010 survey administration.
We exclude from analysis responses from those who are high school dropouts,
have GEDs or professional degrees (JD, DDS, MD) or PhD. Considered responses are
from high school graduates, those with some college but no college credential and those
who have either an associate's, bachelor's or master's degree. Respondents who attend
college are classified as attending either a for-profit or non-profit institution. As a result
of these actions, the initial sample is reduced to 4,191 responses.
Method
For-profit educational outcomes are measured by estimating earnings and wage
rates. Comparisons are then made to earnings and wage estimates associated with
103
Texas Tech University, Thomas O’Malley, August 2013
traditional, non-profit schools. Ordinary least squares (OLS) regression analysis is used
to test the conceptual model in Equation 4. Regression is conducted three times. Each
regression is carried out with a different dependent variable. These three dependent
variables are regressed on a simplified and an expanded Equation 4 model. The
simplified model omits the ABILITYi, and FAMILYi, constructs.
Dependent Variable
The first dependent variable used is the natural logarithm of the 2009 wage rate.
Hourly monetary compensation is calculated by the NLSY survey administrators. It
factors in the reported pay, the rate of pay time unit and the hours worked. There are
extreme high and low values because the NLSY97 administrators do not modify or edit
the data. The smallest observed wage rate is $.03 per hour and the largest is $6,000 per
hour. Research indicates that log wage closely approximates a log-linear function using
standard human capital specifications for wages above the minimum wage (Fortin &
Lemieux, 1998). Given these realities, we restrict our analysis to observations at or
above the minimum wage rate. Extreme high observations are also considered; the top
two percent of the wage distribution are omitted. The cutoffs for the wage distribution
are $7.25 per hour and $52.50 per hour.
The second dependent variable considered is 2009 labor earnings. This variable
is conditioned on the respondent engaged in paid employment. It includes income
received from wages, salary, commissions and tips from all jobs. The top two percent of
values within the dataset are truncated by the NLSY97 staff at $130,254. These topcoded values are omitted from the regression analysis.
104
Texas Tech University, Thomas O’Malley, August 2013
The third dependent variable also captures 2009 labor earnings. However, this
variable is not conditional on employment. As a result, there are a significant number of
‘zero’ income observations reported here. The top two percent of truncated values are
again omitted from analysis.
The responses used in all three regressions are additionally censored in order to
estimate the true benefit of a for-profit educational experience. We restrict the analysis to
respondents with a high school diploma, an associate's, bachelor's or master's degree. We
omit responses from those who are high school dropouts and from those with
professional degrees and PhDs. We exclude responses from those who are full-time
college students, self-employed, government employees or incarcerated.
Independent Variables
Education level and type of institution. The level of education and the type of
institution attended are combined into dummy variables. These are the variables of
interest for this study. The levels of education coded include ‘high school’, ‘some
college’, ‘associate's degree’, ‘bachelor's degree’, and ‘master's degree’. The coding of
these variables is based on the highest degree obtained through 2008. Coding of the
‘some college’ variable also utilizes highest grade attended information.
The type of institution is either a ‘for-profit’ or a ‘not-for-profit’. The ‘not-forprofit’ label identifies respondents who attended either a public or a private not-for-profit
college. In total, eight dummy variables are used which are combinations of the four
levels of college education and the two types of institution. The omitted reference
category is a high school diploma.
Experience and Tenure. Experience and tenure
105
Texas Tech University, Thomas O’Malley, August 2013
represent traditional human capital factors. Experience represents general workplace
skills acquired through on-the-job training. Tenure is similar to experience but represents
firm-specific training. Financial returns to experience and tenure increase, but at
decreasing rates. The concave profile here is modeled using quadratic terms. Experience
is coded in whole year increments for each year that the respondent has positive labor
market earnings. Tenure is coded in whole year increments for each consecutive year the
respondent worked for the same firm.
Ability. Cognitive skills are closely related to economic decisions. Low levels of
cognitive ability are associated with poor outcomes. Cognitive ability is negatively
related to discounting anomalies and other biases that lead to poor economic choices
(Benjamin, Brown & Shapiro, 2006). This study controls for cognitive ability using the
Armed Services Vocational Aptitude Battery (ASVAB) math/verbal score. The ASVAB
is a widely used proxy for cognitive ability. It was administered to 7,093 of the 8,984
NLSY97 participants during the initial survey. The National Longitudinal Survey (NLS)
Program staff computes a percentile score and uses a scoring methodology that accounts
for age differences when the test was taken. Scores are transformed into deciles for this
study.
Family. Families provide resources and have considerable influence on the
educational and financial outcomes of children. Resources can be informational,
educational and financial and can affect enrollment and success in college. Two series of
dummy variables are included in recognition of this. The first set of variables measures
family income as reported in the initial 1997 survey. Parent income is documented for
106
Texas Tech University, Thomas O’Malley, August 2013
6,405 of the original sample of 8,984 respondents. Responses are categorized into
quartiles. A fifth category is included for missing responses. The first income quartile
serves as the reference category.
Another set of dummy variables measures the highest educational credential
obtained by the respondent’s mother. Education is coded into five mutually exclusive
groups: less-than-high-school, high school diploma, associate's degree, bachelor's degree
and graduate degree. Missing information is coded in a sixth category. The ‘high
school’ category is excluded to serve as the reference category.
Race and sex. Dummy variables are used for race. ‘Hispanic’ and ‘Black’ are
coded. The reference group includes those whose race is non-black and non-Hispanic. A
‘female’ dummy variable is also created recognizing a documented gender gap in wages.
Results
Descriptive Statistics of the Sample.
Descriptive statistics are provided in Table 4.2. Data is displayed by institutional
type. Data availability varies with each of the three dependent variables. Descriptive
statistics for the dependent variable with the largest sample are provided. A correlation
matrix is presented in the Appendix.
There are large differences between those who have attended for-profit schools
and those who went to traditional non-profit institutions. Mean income is more than 40
percent higher for those going to traditional schools. Individuals who went to traditional
schools are more likely to complete a four-year or advanced degree. Few for-profit
107
Texas Tech University, Thomas O’Malley, August 2013
students complete a bachelor's or master's degree. More than 48 percent of the non-profit
sample has a four-year or higher credential, but only 6 percent of the for-profit sample
does. Average cognitive ability is noticeably lower among the for-profit sample. More
than two deciles separate the typical for-profit and typical non-profit student.
For-profit students come from families with limited resources. Few of these
respondents come from families in the top quartile of income. Top quartile
representation among for-profit students is only one-third of that seen among non-profit
students. The level of parental education is meager for those who went to for-profit
schools. More than a quarter of traditional school students have a mother with a
bachelor's degree or more education. Less than ten percent of for-profit students have a
mother with this level of education.
Differences by race and gender exist. Relative to traditional schools, for-profit
institutions enroll a larger share of minorities and women.
108
Texas Tech University, Thomas O’Malley, August 2013
Table 4.2 Descriptive Statistics of the Sample in 2010
For profit
n = 129
21,308
0.698
0.24
0.054
0.008
5.946
45.977
2.256
12.643
3.684
0.124
0.225
0.256
0.093
0.302
Non-profit
n = 1694
30,811
0.395
0.12
0.417
0.068
6.482
50.786
2.74
15.681
5.768
0.123
0.166
0.226
0.271
0.215
0.147
0.62
0.093
0.085
0.008
0.047
0.24
0.287
0.178
0.318
0.217
0.574
0.088
0.474
0.129
0.178
0.093
0.038
0.194
0.165
0.249
0.367
0.227
0.515
Variable
Income
Job_Income_10
Some College
Associate's
Education
Bachelor's
Master's
Experience (yrs)
Experience sq
Experience
Tenure (yrs)
Tenure sq (yrs2)
Ability
ASVAB (decile)
Quartile 1
Quartile 2
Family Income
Quartile 3
Quartile 4
Missing
Less-than-High
school
High School
Associate's
Mother's Education
Bachelor's
Graduate
Missing
Black
Race
Hispanic
Midwest
Region
South
West
Gender
Female
Notes: "Non-profit" schools include both private and public 4-yr and
less-than-4-yr colleges. Excluded from the total sample are respondents
with credentials beyond a master's degree and respondents who are full
time students, self-employed, government employees or are incarcerated.
109
Texas Tech University, Thomas O’Malley, August 2013
Regression Analysis.
Three ordinary-least-squares (OLS) regressions are conducted to estimate
institutional effects on wages and earnings. All three regressions use the same conceptual
model. The dependent variable is the only thing that changes between the three
regressions. For each of the three regressions, estimates are calculated for a simple and
an expanded model. The simple model includes standard human capital wage
determination factors with race, gender, and regional controls. The expanded model tests
the full conceptual model found in Equation 4.
Regression 1: Wage Rate
Results from the first regression shed some light on the financial benefit of
attending a private for-profit institution. Table 4.3 has the results of an OLS regression
conducted with wage rate response variable. A substantial portion of wage variance is
explained by this model. The adjusted R2 for the simple model is 31.12 percent and
33.35 percent for the expanded model.
There are wage rate benefits associated with a for-profit bachelor's or master's
degree. These graduates have higher wage rates compared to the wages earned by high
school graduates. The estimated for-profit wage rate benefits are smaller than the
benefits associated with attending a traditional private or public non-profit institution.
The small sample size limits the statistical power of this analysis and precludes us from
definitively concluding that for-profit wage rates are lower.
110
Texas Tech University, Thomas O’Malley, August 2013
Table 4.3: OLS Regression of Ln Wage Rate
Region Race
(Ref:
(Ref: NE) White)
Ability & Family
(Ref: Quartile 1 and high school)
Experience
& Tenure
Education
(Ref: High School)
Variables
Estimate
For-profit
0.0607
Non-profit
0.0584
For-profit
Associate's Degree
SE
Estimate
SE
0.0379
0.0345
0.0421
***
0.0179
0.0128
0.0207
0.0755
*
0.0454
0.0233
0.0509
Non-profit
For-profit
0.1971
0.2194
***
***
0.0284
0.0771
0.1694
0.2172
***
**
0.0316
0.0869
Bachelor's Degree
Non-profit
For-profit
0.3496
0.3999
***
***
0.0186
0.1163
0.2677
0.3099
***
**
0.0243
0.1300
Master's Degree
Experience (yrs)
Non-profit
0.5301
0.0538
***
***
0.0353
0.0086
0.4273
0.0577
***
***
0.0413
0.0097
*
0.0007
-0.0015
**
0.0007
0.0477
***
0.0065
0.0472
***
0.0071
-0.0034
***
0.0007
-0.0034
0.0129
***
***
0.0008
0.0034
Quartile 2
0.0559
**
0.0257
Quartile 3
0.0589
**
0.0264
Quartile 4
0.1470
***
0.0279
Missing
0.1019
***
0.0250
Some College
Experience sq (yrs2)
Tenure (yrs)
2
Tenure sq (yrs )
ASVAB (decile)
-0.0012
Less-than-high school
-0.0277
0.0247
Associate's
-0.0209
0.0248
Bachelor's
-0.0063
0.0233
Graduate
0.0358
0.0320
Missing
Black
-0.0754
*
0.0343
0.0209
Hispanic
Midwest
South
-0.0204
-0.0744
-0.0515
***
*
0.0216
0.0239
0.0220
West
0.0348
-0.1225
Female
Observations:
Adjusted R2:
***
0.0177
0.0406
-0.0372
***
***
0.0182
0.0216
0.0198
0.0208
-0.0732
-0.0390
0.0223
0.0136
0.0353
-0.1171
***
3022
0.3112
***
2508
0.3335
0.0247
0.0149
*p<.10, **p<.05, ***p<.01
Notes: "Non-profit" schools include both private and public 4-yr and less-than-4-yr colleges.
Excluded from the total sample are respondents with credentials beyond a master's degree and
respondents who are full time students, self-employed, government employees or are incarcerated.
Wage rates are excluded if below minimum wage or if in the top two percent of the distribution.
111
Texas Tech University, Thomas O’Malley, August 2013
Stark differences emerge in regard to associate's degree wage estimates. There is
a noticeable and statistically significant benefit to acquiring an associate's degree from a
traditional non-profit college. In contrast, we find no evidence that a for-profit associate's
degree results in higher wages.
Regressions 2 and 3: Earnings
Effects on earnings are also estimated with two earnings specifications. Table 4.4
displays the results of the first of these. This first regression estimates 2009 earnings,
conditional on respondent employment. The expanded model explains much of the
earnings variance. The adjusted R2 is 33.16 percent.
The second earnings regression estimates earnings irrespective of labor market
participation. Responses are not restricted to positive labor income and zero earnings
responses are utilized. As a consequence, this regression has a twenty percent larger
sample than the first earnings regression. The adjusted R2 is 47.47 percent. These results
are found in Table 4.5.
Earnings regression results are similar to the wage rate results. The wage rate and
earnings regressions each indicate that for-profit associate's degrees do not result in
financial benefits for their recipients. In contrast, an associate's degree from a traditional
non-profit school is estimated to result in either $6,984 or $5,847 more annual earnings
than what is earned with a high school diploma.
112
Texas Tech University, Thomas O’Malley, August 2013
Table 4.4: OLS Regression of Earnings (conditioned on employment)
For-profit
Estim
ate
1454
Non-profit
2070
For-profit
2392
Associate's Degree
Non-profit
For-profit
7778
7762
Bachelor's Degree
Non-profit
Region Race
(Ref:
(Ref: NE) White)
Ability & Family
(Ref: Quartile 1 and high school)
Experience
& Tenure
Education
(Ref: High School)
Variable
Some College
Master's Degree
Experience (yrs)
2
Tenure sq (yrs )
Estimate
SE
1688
720
1887
785
450
897
1971
1530
2164
***
**
1196
3226
6984
7824
***
**
1311
3633
13944
***
805
11541
***
1042
For-profit
14789
***
4643
11884
**
5425
Non-profit
17561
2988
***
***
1486
428
14933
3063
***
***
1743
481
-82
***
31
-95
***
34
3323
***
281
3300
***
305
-252
***
30
-246
***
32
454
***
146
Experience sq (yrs2)
Tenure (yrs)
SE
***
ASVAB (decile)
Quartile 2
1667
Quartile 3
2396
**
1127
Quartile 4
5025
***
1191
Missing
4115
***
1089
Less-than-high school
1111
-1727
1090
Associate's
545
1075
Bachelor's
-1417
995
Graduate
-365
1374
Missing
Black
775
952
-818
1540
902
793
938
869
970
589
665
-2134
-325
1243
-6531
938
1022
957
1066
643
Hispanic
Midwest
South
West
Female
Observations:
Adjusted R2:
-2124
-772
-3110
-1122
1006
-6394
***
***
***
2794
0.319
**
***
2335
0.3316
*p<.10, **p<.05, ***p<.01
Notes: "Non-profit" schools include both private and public 4-yr and less-than-4-yr colleges.
Excluded from the total sample are respondents with credentials beyond a master's degree and
respondents who are full time students, self-employed, government employees or are incarcerated.
Earnings are conditioned on employment and the top two percent are top-coded and excluded.
113
Texas Tech University, Thomas O’Malley, August 2013
For-profit master's degrees result in higher income. The income gains are smaller
than what results from traditional degrees though. A bachelor's and master's degree from
a proprietary school is estimated to result in $7,824 and $11,844 more earnings (Table
4.4). While these benefits are economically significant, they are smaller than the
earnings benefits associated with comparable non-profit degrees. As identified in Table
4.4, a traditional bachelor's degree is estimated to yield an additional $3,717 per year in
earnings. A traditional master's degree is also more valuable, estimated to bring $3,049
more income.
The third regression is conducted using a less restrictive measure of earnings.
The earnings considered in this last regression are irrespective of employment and
include ‘zero’ responses. Prior research indicates that for-profit graduates have higher
idleness and unemployment rates (Baum, 2011; Deming et al., 2012). If this is found
here, we would expect larger earnings differences between for-profits and nonprofits.
Table 4.5 lists these estimates and once again there is no evidence that a for-profit
associate's degree has value. Proprietary bachelor's and master's degrees provide income
benefits relative to what can be earned with a high school level education. The earnings
gap between a for-profit and non-profit degree expands however. A bachelor's degree
from a traditional nonprofit school is now estimated to bring $4,202 more earnings than a
comparable for-profit degree. This earnings differential expands to $5,606 for master's
degrees.
114
Texas Tech University, Thomas O’Malley, August 2013
Table 4.5: OLS Regression of Earnings (all)
Variable
Race
Region
(Ref:
(Ref: NE) White)
Ability & Family
(Ref: Quartile 1 and high school)
Experience
& Tenure
Education
(Ref: High School)
For-profit
Some College
Estimate
646
SE
1441
Estimate
82
SE
1619
Non-profit
1400
**
678
259
790
For-profit
3560
*
1848
3127
2061
Associate's Degree
Non-profit
For-profit
6175
7449
***
**
1087
3067
5847
7018
***
**
1216
3450
Bachelor's Degree
Non-profit
For-profit
13328
13314
***
***
734
4408
11220
9712
***
*
963
5121
Master's Degree
Experience (yrs)
Non-profit
17593
3097
***
***
1415
290
15318
3151
***
***
1655
331
-70
***
23
-82
***
26
5188
***
258
5173
***
284
-405
***
28
-399
463
***
***
31
135
Experience sq (yrs2)
Tenure (yrs)
2
Tenure sq (yrs )
ASVAB (decile)
Quartile 2
1183
978
Quartile 3
1174
1006
Quartile 4
2966
***
1068
Missing
2382
**
953
Less-than-high school
-1533
945
Associate's
620
990
Bachelor's
-1203
930
Graduate
-116
1291
675
919
-694
1310
806
710
840
767
866
524
380
-1423
618
1767
-6372
855
932
860
967
583
Missing
Black
Hispanic
Midwest
South
West
Female
Observations:
Adjusted R2:
-1968
-974
-2342
-60
1413
-6009
***
***
***
3467
0.476
*
***
2849
0.4747
*p<.10, **p<.05, ***p<.01
Notes: "Non-profit" schools include both private and public 4-yr and less-than-4-yr colleges.
Excluded from the total sample are respondents with credentials beyond a master's degree and
respondents who are full time students, self-employed, government employees or are incarcerated.
Earnings are not conditioned on employment and the top two percent are top-coded and excluded.
115
Texas Tech University, Thomas O’Malley, August 2013
Discussion
If the higher education market was perfectly competitive, price differences would
fully capture differences in quality. Ideally, consumers would have full information and
be able to assess quality and price differences. They would be able to compare
alternatives and engage in cost-benefit analysis. Under ideal competitive conditions, forprofit institutions would attract new students only if their programs were competitive.
Consumers have difficulty assessing program quality. Individuals do not
purchase education frequently and there are few repeat purchases. It is difficult to
acquire education from one provider, try it out, and then purchase education from another
provider if expectations are not met (Baum, 2011). The quality of an educational
program is uncertain and prospective students undoubtedly experience difficulty making
effective comparisons. It is hard to measure quality before, during and even after the
educational experience. Under the best circumstances, quality can only be measured well
after one has invested significant time and money.
Proprietary schools maintain that they aid underserved populations, but critics
contend that the for-profit industry targets consumers who tend to make poor financial
decisions. For-profits firms use government funding to heavily market to naïve
households. These consumers have meager resources and limited experience making
risky financial decisions and are swayed by this marketing. Naïve consumers tend to be
poor, have less education and are vulnerable to financial exploitation by sophisticated
firms (Campbell, 2006; Gabaix & Laibson, 2006).
116
Texas Tech University, Thomas O’Malley, August 2013
Perhaps the most common complaint is that for-profit schools exaggerate the
benefits of their programs. All too often, for-profit schools sell the dream of making high
salaries but do not provide information that useful to make an informed decision.
Students are not counseled about actual job prospects or the risks associated with taking
on debt. It is no surprise then that many for-profit students are left with questionable
credentials and a limited ability to pay back student loans (Baum, 2011).
These firms profit from information asymmetry. Full disclosure of price and
quality information is not provided. For-profit firms expend substantial sums on sales
and marketing to recruit new students. Advertising represents more than 11 percent of
revenue among large for-profit chain schools and the typical for-profit spends more than
$4,300 per student on sales and marketing (Steinerman et al., 2011).
Some proprietary schools go beyond aggressive marketing. Many engage in
predatory recruiting, deceptive statements and fraudulent activities. Undercover GAO
testing finds that many college representatives exaggerate potential salaries after
graduation and use high pressure sales tactics. Firms often mislead students about
graduation rates, tuition costs and graduation rates (GAO, 2010).
Private for-profit institutions have goals and incentives that are different from
those of traditional schools. These firms have obligations to their shareholders to seek
profitable operations. This is accomplished by maximizing revenue and minimizing
costs. For-profits want to enroll as many students as possible. In contrast, traditional
colleges are driven more by increasing the prestige of the school rather than costeffectiveness or expanding volume. Success is not defined in terms of revenue growth.
117
Texas Tech University, Thomas O’Malley, August 2013
Rather, traditional colleges and universities are more likely to seek the highest quality
student possible (Hentschke, 2010). Traditional universities and elite schools in
particular savor high rates of rejection, not acceptance.
Few higher education institutions, whether for-profit or traditional, have a direct
financial stake in the employment outcomes of their students. As a result, these
institutions may be indifferent to the well-being of their students. The potential for
indifference is moderated in traditional institutions that seek the best qualified student
possible. Traditional institutions often carefully select their students in order to enhance
institutional prestige. Higher quality students experience better labor market outcomes
irrespective of their educational experience. Conversely, for-profit schools which are
almost universally non-selective will attract students with traits the labor market does not
value as highly, on average.
Conclusion
This paper takes a big step in evaluating the quality of a private for-profit college
education. Prior research fails to assess the value of for-profit bachelor's and master's
degrees. Using OLS techniques, we regress log wage rate and annual earnings on
variables that measure the highest degree attained and the type of institution attended. A
rich set of covariates measuring human capital, cognitive ability, family background and
personality are used as controls.
Some limitations need to be stated. First, the NLSY97 is longitudinal sample that
has young cohort of respondents. The oldest respondents are just thirty years old at the
118
Texas Tech University, Thomas O’Malley, August 2013
time of the 2010 survey administration. Many respondents are still in school. The time
horizon is simply not long enough to observe the full earning’s potential of the worker.
Given this, wage and earnings estimates by institutional type may not fully reflect what
will occur over the lifecycle.
Another limitation relates to the youthful nature of the sample. For-profit
institutions cater to older, working non-traditional students. Whereas only 31 percent of
four-year public enrollments consist of individuals 25 years old and older, nearly 65
percent of proprietary students are in this age group (Deming et al., 2012). Thus, the
NLSY97 may not be representative of the for-profit student population.
We find no evidence that a for-profit associate's degree is beneficial in terms of
earnings or wages. In contrast, an associate's degree from a traditional private or public
non-profit school is estimated to provide economic benefits. These results are interesting
in light of other studies. Previous work fails to identify a difference in earnings at this
level (Chung, 2009; Cellini and Chaudhary, 2011).
This study is the first to estimate for-profit wage benefits at the bachelor's and
master's degree level. Testing our conceptual model, we find proprietary bachelor's and
master's degrees yield higher wages and earnings than a high school diploma. However,
the wage and earnings premiums are smaller than what is found with comparable nonprofit degrees. These for-profit wage and earnings estimates are likely overstated relative
to the estimates for non-profit schools. Estimates are not risk-adjusted. For-profit
schools have much lower completion rates (Baum, 2011). Our estimates measure the
wage and earnings benefits for those that actually complete a credential.
119
Texas Tech University, Thomas O’Malley, August 2013
We cannot state definitively that traditional colleges and universities outperform
the for-profits at the bachelor's and higher level. The sample here is prohibitively small
to uncover any potential wage or earnings gap. We cannot reject the hypothesis that
students in for-profit and traditional non-profit schools earn similar wages and earnings.
Better data is needed.
For-profit schools need to generate a wage premium in order to justify higher
costs and higher risk. Proprietary education is more expensive, on average. ‘Net tuition
minus grants’ is much higher for the for-profits than for either two-year or four-year nonprofit institutions (Deming et al., 2012). Absent a labor market benefit justifying these
higher costs, the question at hand is: why would anyone attend a proprietary school if it
were possible to attend a less expensive community college or four-year state school?
This work is an introduction toward understanding the value of a for-profit
credential. More research using other data sets is needed to measure for-profit student
outcomes. Future work could look at different institutional characteristics, such as
enrollment, executive compensation, online course presence or sales and advertising.
Another promising area of investigation should be to look for wage and earnings effects
by occupation.
120
Texas Tech University, Thomas O’Malley, August 2013
References
Baum, S. (2011, June 7). Drowning in debt: Financial outcomes of students at for-profit
colleges [Testimony to the Senate Health, Education, Labor and Pension
Committee]. Washington, DC.
Benjamin D. J., Brown S. A. & Shapiro J. M. (2006). Who is ‘behavioral’? Cognitive
ability and anomalous preferences. Unpublished manuscript. Ithaca, NY: Cornell
University.
Bennett, D. L., Lucchesi, A. R.,& Vedder, R. K. (2010). For-profit higher education:
Growth, innovation, and regulation (Policy Paper). Washington, DC: Center for
College Affordability and Productivity. Retrieved from
http://heartland.org/sites/all/modules/custom/heartland_migration/files/pdfs/29010.pdf
Campbell, J. (2006). Household finance, Journal of Finance. 61, 1553-1604.
Card. D. (1999). Chapter 30 The causal effect of education on earnings. Handbook of
Labor Economics, 3(Part I), 1801-1863.
Cellini, S. R. (2009). Crowded colleges and college crowd-out: The impact of public
subsidies on the two-year college market. American Economic Journal: Economic
Policy, 1(2), 1-30.
Cellini, S. R., & Chaudhary, L. (2011). The labor market returns to a private two-year
college education. Working paper. Washington, DC: George Washington
University.
Chung, A. S. (2009). Effects of for-profit training on earnings. Manuscript, University of
Michigan.
121
Texas Tech University, Thomas O’Malley, August 2013
Deming, D. J., Goldin, C. & Katz, L. F. (2012). The for-profit postsecondary school
sector: Nimble critters or agile predators? Journal of Economic Perspectives,
American Economic Association, 26(1), 139-64.
Fortin, N. M. & Lemieux, T. (1998). Rank regressions, wage distributions, and the
gender gap. Journal of Human Resources 33, 610-643.
Gabaix, X. & Laibson, D. (2006). Shrouded attributes, consumer myopia, and
information suppression in competitive markets. The Quarterly Journal of
Economics, 121(2): 505‐40.
(GAO) United States Government Accountability Office. (2010). For profit colleges:
Undercover testing finds colleges encouraged fraud and engaged in deceptive and
questionable marketing practices. Washington, D.C. Retrieved from
http://www.gao.gov/products/GAO-10-948T.
Hentschke, G. C. For-profit sector innovations in business models and organizational
cultures. In B. Wildavsky, A. P. Kelly and K. Carey, eds., Reinventing higher
education, pp. 159-196, Cambridge, Harvard Education Press, 2011.
Lemieux, T. (2006). ‘The Mincer equation thirty years after Schooling, Experience, and
Earnings’, in S. Grossbard-Shechtman (ed.), Jacob Mincer, A Pioneer of Modern
Labor Economics. New York: Springer, 127-148.
Mincer, J. (1958). Investment in human capital and personal income distribution.
Journal of Political Economy, 66(4): 281-302.
Mincer, J. (1974). Schooling, experience, and earnings. New York: Columbia
University Press and NBER.
122
Texas Tech University, Thomas O’Malley, August 2013
Steinerman, A., Volshteyn, J., & McGarrett, M. (2011). Education services data book.
J.P. Morgan, North American Equity Research, Business and Education Services.
(September) Retrieved from
U.S. Department of Education. NCES. (2012). The Condition of Education 2012.
Retrieved from http://nces.ed.gov/programs/coe/
U.S. Department of Education, NCES, Integrated Postsecondary Education Data Systems
[IPEDS], (2010). IPEDS, College & Career Tables Library. Table 38. Degrees
conferred at Title IV public institutions, by level of degree and field of study:
United States, academic year 2009-10. Table 39. Degrees conferred at Title IV
private nonprofit institutions, by level of degree and field of study: United States,
academic year 2009-10. Table 40. Degrees conferred at Title IV private for-profit
institutions, by level of degree and field of study: United States, academic year
2009-10. Retrieved from http://nces.ed.gov/ipeds/
U.S. Department of Education, Federal Student Aid Data Center. (2011). Proprietary
school 90/10 revenue percentages, 2011 report. Retrieved from
http://federalstudentaid.ed.gov/datacenter/proprietary.html
U.S. Department of Education, NCES. (2012). Digest of Education Statistics, 2011.
Table 249. Enrollment of the 120 largest degree-granting college and university
campuses, by selected characteristics and institution: Fall 2010. Retrieved from
http://nces.ed.gov/programs/digest/d11/tables_3.asp
U.S. Department of Education, NCES. (2012). Digest of Education Statistics, 2011.
Table 250. Selected statistics for degree-granting institutions enrolling more than
15,000 students in 2010: Selected years, 1990 through 2009-10. Retrieved from
http://nces.ed.gov/programs/digest/d11/tables_3.asp
123
Texas Tech University, Thomas O’Malley, August 2013
U.S. Department of Education, NCES. (2012). Digest of Education Statistics, 2011.
Figure 49-2: Two-year student loan cohort default rates at degree-granting
institutions, by level and control of institution: Fiscal years 2006-08. Retrieved
from http://nces.ed.gov/programs/digest/d11/tables_3.asp
124
Texas Tech University, Thomas O’Malley, August 2013
Appendix
Some college - for profit
1.00
***
-0.11
***
-0.03
*
-0.05
***
-0.02
Some college - non-profit
-0.11
***
1.00
***
-0.09
***
-0.17
***
-0.05
Associate's - for profit
-0.03
*
-0.09
***
1.00
***
-0.04
**
Associate's - non-profit
-0.05
***
-0.17
***
-0.04
**
1.00
***
Bachelor's - for profit
-0.02
-0.05
***
-0.01
Bachelor's - non-profit
-0.11
-0.34
***
-0.08
Master's - for profit
-0.01
-0.03
**
-0.01
Master's - non-profit
-0.04
-0.12
***
-0.03
-0.05
Experience
-0.02
0.05
***
0.02
0.08
Experience squared
-0.02
0.05
***
0.02
Tenure
-0.02
-0.02
Tenure squared
-0.01
-0.01
IQ decile
-0.08
Family income Q2
-0.01
0.01
0.01
Family income Q3
0.01
0.01
Family income Q4
-0.05
Family income missing
0.01
-0.02
Mother -<than high school
0.00
0.03
Mother - associate's
0.01
0.00
Mother - bachelor's
-0.05
***
-0.06
Mother - graduate
-0.05
***
-0.05
Mother - missing
-0.01
0.01
Black
0.02
0.06
Hispanic
0.03
0.08
Midwest
-0.01
0.01
0.00
South
-0.01
0.03
-0.04
West
-0.02
0.04
Female
0.05
***
**
***
***
**
***
-0.09
-0.04
-0.01
-0.11
***
-0.34
***
-0.01
-0.08
***
-0.02
-0.15
***
***
1.00
***
-0.05
***
-0.05
***
1.00
***
0.00
-0.03
**
***
-0.02
-0.11
***
***
0.02
0.04
***
0.07
***
0.02
0.00
-0.01
0.07
***
0.03
*
0.05
-0.02
0.08
***
0.03
*
0.01
0.05
***
0.00
0.44
***
-0.01
0.02
-0.08
***
0.01
0.00
-0.02
0.04
**
-0.01
0.02
0.00
0.22
***
0.01
-0.01
0.01
-0.01
0.01
-0.06
***
-0.01
-0.15
***
-0.01
0.04
**
0.00
0.07
***
***
-0.01
0.01
0.00
0.21
***
***
-0.01
-0.03
**
-0.01
0.20
***
0.01
-0.04
**
-0.01
-0.07
***
***
0.01
0.00
-0.01
-0.11
***
***
0.03
***
**
**
**
-0.03
-0.02
Bachelor's
- non-profit
Bachelor's
- for profit
Associate's
- non-profit
Associate's
- for profit
Some college
- non-profit
Some college
- for profit
Appendix 4.1 Bivariate Correlation Matrix (Page 1 of 5)
***
-0.15
***
-0.02
*
*
0.04
**
-0.12
***
-0.01
-0.03
*
0.04
**
0.01
0.00
-0.07
***
0.02
0.01
0.01
0.01
0.00
0.03
0.00
0.07
*p<.10, **p<.05, ***p<.01
125
*
**
-0.03
**
***
*
***
Texas Tech University, Thomas O’Malley, August 2013
Some college - for profit
-0.01
Some college - non-profit
-0.03
Associate's - for profit
-0.04
**
-0.02
-0.12
***
0.05
-0.01
-0.03
*
0.02
Associate's - non-profit
-0.02
-0.05
***
0.08
Bachelor's - for profit
0.00
Bachelor's - non-profit
-0.03
**
-0.11
Master's - for profit
1.00
***
-0.01
Master's - non-profit
-0.01
Experience
0.04
Experience squared
0.03
Tenure
**
-0.02
-0.02
***
***
0.02
***
0.02
***
0.05
0.07
***
Tenure squared
Tenure
Experience
squared
Experience
Master's
- non-profit
Master's
- for profit
Appendix 4.1 Bivariate Correlation Matrix (Page 2 of 5)
-0.02
-0.01
-0.02
-0.01
-0.01
-0.02
0.07
***
0.08
***
*
0.02
0.03
*
0.03
0.04
***
0.00
0.05
***
0.01
0.04
**
0.03
**
0.01
0.01
1.00
***
0.04
***
0.03
**
0.05
***
0.03
**
0.04
***
1.00
***
0.96
***
0.41
***
0.35
***
**
0.03
**
0.96
***
1.00
***
0.39
***
0.37
***
0.01
0.05
***
0.41
***
0.39
***
1.00
***
0.93
***
Tenure squared
0.01
0.03
0.35
***
0.37
***
0.93
***
1.00
***
IQ decile
0.02
0.21
***
0.27
***
0.22
***
0.12
***
0.08
***
Family income Q2
0.01
-0.04
***
0.00
Family income Q3
0.01
-0.02
Family income Q4
0.01
0.08
Family income missing
0.00
Mother -<than high school
0.01
-0.02
-0.01
0.09
***
0.08
***
0.06
***
0.06
***
***
0.10
***
0.09
***
0.07
***
0.05
***
0.05
***
-0.03
**
-0.03
*
-0.02
-0.02
-0.06
***
-0.06
***
-0.05
***
-0.05
Mother - associate's
0.01
0.01
0.05
***
0.04
**
-0.01
Mother - bachelor's
0.02
0.08
***
0.04
**
0.02
Mother - graduate
-0.01
0.15
***
0.03
*
0.02
Mother - missing
-0.02
-0.03
*
-0.19
***
-0.16
***
-0.05
***
-0.04
**
Black
0.04
-0.05
***
-0.21
***
-0.19
***
-0.05
***
-0.03
**
Hispanic
0.00
-0.05
***
-0.02
-0.03
*
-0.03
*
Midwest
-0.01
0.01
South
0.02
-0.04
West
0.01
-0.03
Female
0.01
**
***
0.03
0.03
-0.02
***
**
-0.01
*
0.00
-0.02
-0.03
0.02
-0.01
0.11
***
0.12
***
0.07
***
0.07
***
***
-0.10
***
-0.10
***
-0.06
***
-0.05
***
*
0.03
*
0.02
-0.13
*p<.10, **p<.05, ***p<.01
126
-0.12
*
-0.02
-0.02
-0.05
-0.04
***
Texas Tech University, Thomas O’Malley, August 2013
Mother -<than
high school
Family income
missing
Family income
Q4
Family income
Q3
IQ decile
Family income
Q2
Appendix 4.1 Bivariate Correlation Matrix (Page 3 of 5)
Some college - for profit
-0.08
***
-0.01
0.01
-0.05
***
0.01
0.00
Some college - non-profit
-0.09
***
0.01
0.01
-0.04
**
-0.02
0.03
Associate's - for profit
-0.03
*
0.01
0.01
-0.01
0.01
0.01
Associate's - non-profit
0.05
***
-0.01
0.00
0.02
-0.01
-0.06
Bachelor's - for profit
0.00
0.02
-0.02
0.00
0.01
-0.01
Bachelor's - non-profit
0.44
-0.01
-0.15
Master's - for profit
0.02
0.00
-0.02
Master's - non-profit
0.21
***
-0.04
Experience
0.27
***
0.00
0.09
Experience squared
0.22
***
0.01
0.08
Tenure
0.12
***
-0.02
Tenure squared
0.08
***
-0.01
IQ decile
1.00
***
-0.08
Family income Q2
-0.08
***
Family income Q3
0.10
Family income Q4
***
-0.08
***
0.01
0.04
**
0.22
***
**
***
***
0.01
0.01
-0.02
0.08
***
0.05
***
-0.06
***
***
0.10
***
-0.03
**
-0.06
***
***
0.09
***
-0.03
*
-0.05
***
0.06
***
0.07
***
-0.02
-0.05
***
0.06
***
0.05
***
-0.02
-0.03
**
***
0.10
***
0.27
***
-0.07
***
-0.24
***
1.00
***
-0.23
***
-0.24
***
-0.28
***
0.04
***
***
-0.23
***
1.00
***
-0.24
***
-0.28
***
-0.11
***
0.27
***
-0.24
***
-0.24
***
1.00
***
-0.29
***
-0.16
***
Family income missing
-0.07
***
-0.28
***
-0.28
***
-0.29
***
1.00
***
0.05
***
Mother -<than high school
-0.24
***
0.04
***
-0.11
***
-0.16
***
0.05
***
1.00
***
Mother - associate's
0.08
***
0.01
0.04
***
0.05
***
-0.05
***
-0.13
***
Mother - bachelor's
0.25
***
-0.05
***
0.02
0.18
***
-0.03
*
-0.16
***
Mother - graduate
0.22
***
-0.09
***
-0.03
0.17
***
0.02
-0.10
***
Mother - missing
-0.13
***
-0.03
*
-0.02
-0.04
**
0.04
***
-0.11
***
Black
-0.33
***
0.01
Hispanic
-0.17
***
0.04
Midwest
0.10
***
-0.01
South
-0.14
***
0.04
West
-0.01
Female
0.02
***
***
**
**
*
-0.08
***
-0.17
***
0.08
***
-0.02
-0.07
***
-0.14
***
0.05
***
0.34
***
0.05
***
0.08
***
-0.06
***
-0.08
***
-0.03
*
-0.07
***
-0.01
0.02
0.01
0.09
***
-0.01
0.03
***
-0.01
-0.01
0.02
0.01
-0.02
-0.01
*p<.10, **p<.05, ***p<.01
127
*
Texas Tech University, Thomas O’Malley, August 2013
Some college - for profit
0.01
-0.05
***
-0.05
***
-0.01
0.02
Some college - non-profit
0.00
-0.06
***
-0.05
***
0.01
0.06
Associate's - for profit
-0.01
-0.01
-0.01
0.01
0.01
Associate's - non-profit
0.04
0.01
-0.03
Bachelor's - for profit
0.00
0.00
-0.01
Bachelor's - non-profit
0.07
Master's - for profit
0.01
0.02
Master's - non-profit
0.01
0.08
***
0.15
***
-0.03
Experience
0.05
***
0.04
**
0.03
*
Experience squared
0.04
**
0.02
Tenure
-0.01
0.03
Tenure squared
-0.01
0.02
IQ decile
0.08
Family income Q2
0.01
Family income Q3
0.04
***
0.02
Family income Q4
0.05
***
0.18
Family income missing
-0.05
***
Mother -<than high school
-0.13
***
Mother - associate's
1.00
Mother - bachelor's
**
***
***
0.21
***
0.20
**
**
-0.01
***
-0.01
*
-0.04
-0.07
***
0.03
**
0.08
***
0.03
*
0.00
-0.03
**
-0.01
0.04
**
***
***
-0.11
***
-0.12
0.04
**
0.00
*
-0.05
***
-0.05
-0.02
***
-0.19
***
-0.21
***
-0.02
0.02
-0.16
***
-0.19
***
-0.02
0.00
-0.05
***
-0.05
***
-0.03
*
-0.01
-0.04
**
-0.03
**
-0.03
*
***
-0.17
***
0.04
**
0.25
***
0.22
***
-0.13
***
-0.33
-0.05
***
-0.09
***
-0.03
*
0.01
-0.03
*
-0.02
***
0.17
***
-0.04
-0.03
*
0.02
-0.16
***
-0.10
***
***
-0.13
***
-0.08
-0.13
***
1.00
***
Mother - graduate
-0.08
***
-0.10
Mother - missing
-0.09
***
Black
0.00
Hispanic
-0.05
Midwest
0.02
South
-0.04
**
West
0.03
*
0.03
Female
0.02
***
-0.03
***
Hispanic
Black
Mother
- missing
Mother
- graduate
Mother
- bachelor's
Mother
- associate's
Appendix 4.1 Bivariate Correlation Matrix (Page 4 of 5)
-0.08
***
-0.07
***
**
-0.17
***
-0.14
***
0.04
***
0.08
***
0.05
***
-0.11
***
-0.02
0.34
***
***
-0.09
***
0.00
-0.05
***
-0.10
***
-0.10
***
-0.07
***
-0.12
***
***
1.00
***
-0.07
***
-0.06
***
-0.10
***
-0.10
***
-0.07
***
1.00
***
0.06
***
0.01
-0.07
***
-0.06
***
0.06
***
1.00
***
-0.28
***
-0.12
***
-0.10
***
0.01
-0.28
***
1.00
***
0.00
0.04
**
0.01
-0.08
***
-0.16
***
-0.01
-0.04
**
-0.01
0.27
***
-0.08
***
-0.02
-0.02
-0.20
***
0.30
***
-0.02
-0.02
0.01
***
*
*p<.10, **p<.05, ***p<.01
128
*
0.00
Texas Tech University, Thomas O’Malley, August 2013
Female
West
South
Midwest
Appendix 4.1 Bivariate Correlation Matrix (Page 5 of 5)
Some college - for profit
-0.01
-0.01
-0.02
Some college - non-profit
0.01
0.03
Associate's - for profit
0.00
-0.04
Associate's - non-profit
-0.01
Bachelor's - for profit
-0.03
Bachelor's - non-profit
0.04
Master's - for profit
-0.01
0.02
Master's - non-profit
0.01
-0.04
***
-0.03
*
0.03
**
Experience
0.11
***
-0.10
***
0.03
*
-0.13
***
Experience squared
0.12
***
-0.10
***
0.02
-0.12
***
Tenure
0.07
***
-0.06
***
-0.02
-0.05
***
Tenure squared
0.07
***
-0.05
***
-0.02
-0.04
***
IQ decile
0.10
***
-0.14
***
-0.01
0.02
Family income Q2
-0.01
0.04
**
-0.01
0.01
Family income Q3
0.05
***
-0.03
*
-0.01
-0.02
Family income Q4
0.08
***
-0.07
***
0.02
-0.01
Family income missing
-0.06
***
-0.01
0.01
Mother -<than high school
-0.08
***
0.02
0.09
***
0.03
Mother - associate's
0.02
-0.04
0.03
*
0.02
Mother - bachelor's
0.00
-0.01
0.03
*
-0.03
Mother - graduate
0.04
Mother - missing
0.01
Black
-0.08
***
0.27
***
-0.20
***
0.00
Hispanic
-0.16
***
-0.08
***
0.30
***
0.01
Midwest
1.00
***
-0.42
***
-0.28
***
-0.01
South
-0.42
***
1.00
***
-0.42
***
0.00
West
-0.28
***
-0.42
***
1.00
***
0.02
Female
-0.01
***
0.00
0.04
**
0.00
0.01
0.01
0.03
*
0.00
0.01
0.00
**
-0.07
0.01
0.07
0.01
0.01
-0.04
***
**
**
-0.01
*p<.10, **p<.05, ***p<.01
129
*
***
-0.01
-0.02
-0.02
-0.02
-0.02
0.02
***
-0.01
0.02
**
**
0.05
1.00
**
**
*
Texas Tech University, Thomas O’Malley, August 2013
CHAPTER V
CONCLUSION
Existing research suggests that student loans play increasingly important role in
the financial lives of households (e.g. Shand, 2008; Hyounjin, 2010; Minicozzi 2005).
For-profit students are particularly affected by loans and have high median debt loads
(Baum, 2011). The three essays that make up this dissertation further explore the extent
to which student loans affect household finances. These studies use the 1979 and 1997
versions of the National Longitudinal Survey of Youth (NLSY79 and NLSY97) and
contribute to the existing body of work.
Understanding the financial effects of student loans and for-profit education is
needed to guide public policy. Enormous financial resources are spent on higher
education and the administration of Title IV resources affects thousands of institutions
and millions of households. Insight regarding education debt should be particularly
helpful to prospective students who more than ever bear the financial risk of the college
decision. The financial planning profession has long had expertise in the management of
financial investments. The profession is uniquely qualified to assist households make
decisions about human capital investments.
Essay One
This first essay investigates the extent to which student debt affects household
wealth accumulation. This study uses the NLSY79 and looks at the long term net worth
consequences of using student loans. College is expensive, many people fail to graduate,
130
Texas Tech University, Thomas O’Malley, August 2013
and future income for those that do graduate is far from certain. Loan repayment is
required of borrowers regardless of graduation or employment status. As a consequence,
systematic reductions in wealth may occur for many who use debt to finance their
education.
Several studies investigate student loan effects but this is the first to find a link
between student loans and wealth accumulation. Mere college attendance is not
associated with higher net worth. On the other hand, acquiring a bachelor's or higher
degree is well worth the investment for the typical household. Student loan use is
negatively related to wealth accumulation. This persists for years after the initial college
decision. College dropouts who rely heavily on debt financing may experience lower
lifetime utility.
Essay Two
The second essay examines whether education debt is a determinant of personal
bankruptcy. While many people certainly benefit from loans, the benefits of attending
college vary widely. Financial leverage magnifies risk. For some, the outcome of using
student debt may be financial distress and personal bankruptcy. An asset pricing model
is used to evaluate whether student loans elevate bankruptcy risks. This approach
captures the essential principles of the college investment decision, including the costs,
uncertain outcomes, and stochastic payouts associated with higher education.
Education debt appears to compel many households to seek bankruptcy
protection. These households struggle with loan repayments. These struggles are more
common among those that fail to graduate with a bachelor's degree. ‘Non-completers’
131
Texas Tech University, Thomas O’Malley, August 2013
are almost twice as likely to declare bankruptcy. Failing to complete a four year degree
program while taking on debt may result in poor financial health.
Essay Three
The third essay explores financial outcomes associated with attending private forprofit colleges. The growth of proprietary education is a recent development and a lack
of data has limited academic inquiry. There are indications that for-profit education fails
to measure up to traditional schooling. People who attend for-profit schools pay higher
tuition, have lower graduation rates, more accumulated debt and more loan defaults. Forprofit school advocates maintain that simple comparisons are not appropriate since this
sector educates more non-traditional and disadvantaged students.
One way to assess the quality of proprietary education is to evaluate labor market
returns. A large body of work estimates returns to education but only a few attempt to
identify for-profit labor market outcomes. The sample size tends to be small in these
studies and the controls used are limited. The existing research only investigates effects
at the associate's degree level. This study fills in these gaps and uses the NLSY97, which
follows a young cohort of respondents who are more likely to have attended for-profit
schools. Controls are used for human capital, family background, cognitive ability and
personality. We find no labor market benefit to a for-profit associate's degree. For-profit
bachelor's and master's degrees lead to higher wages but non-profit degrees are more
valuable.
132
Texas Tech University, Thomas O’Malley, August 2013
References
Baum, S. (2011, June 7). Drowning in debt: Financial outcomes of students at for-profit
colleges [Testimony to the Senate Health, Education, Labor and Pension
Committee]. Washington, DC.
Hyounjin, Y. (2010). Three essays in consumer finance: debt stress, payments, and
student loans. Ohio State University Ph.D. thesis.
Minicozzi, A. (2005). The short term effect of educational debt on job decisions.
Economics of Education Review, 24(4), 417-430.
Shand, J. M. (2008). The impact of early-life debt on household formation: An empirical
investigation of homeownership, marriage and fertility. Ohio State University
Ph.D. thesis.
133