Parental Borrowing for Dependent
Children’s Higher Education
Kyung-Wook Cha
Sungshin Women’s University
Robert O. Weagley
University of Missouri-Columbia
Laura Reynolds
University of Alabama
ABSTRACT: Using the 1992–1993 Baccalaureate and Beyond Longitudinal Study,
with the 1997 follow-up, the parental decision to borrow and, for borrowers, the level of
borrowing for dependent children’s college education was analyzed. Parents with
smaller household size and those being college graduates borrowed greater amounts.
White parents borrowed greater amounts than their non-White counterparts. The age of
the student, dependent students’ income and parents’ cash and savings each had a
significant negative impact on the amount parents borrowed, while home equity was a
significant positive factor. Greater college costs significantly increased parents’ decision
to borrow, as well as the borrowed amount. Greater amounts of grants significantly
reduced the amount borrowed.
KEY WORDS: borrowing; college; Heckman; parental investment.
An increasing challenge for individuals and families in the United
States is the expense of a higher education. Clearly, attainment of a
college education is desirable given its high positive correlation with
lifetime earnings. As of 1998, bachelor’s degree recipients earned, on
average, 81% more than those with only a high school diploma. Over a
lifetime, the summed differences in potential earnings between the
high school graduate and the bachelor’s degree recipient exceeds
$1,000,000 (The College Board, 2000a).
Kyung Wook Cha, Sungshin Women’s University, 913 Soojung Hall, Dongseon-dong,
Seongbuk-gu, Seoul, Korea; e-mail: kwcha@sungshin.ac.kr.
Robert O. Weagley, University of Missouri-Columbia, 240 Stanley Hall, Columbia, MO
65211; e-mail: WeagleyR@missouri.edu.
Laura Reynolds, University of Alabama, Box 870158, Tuscaloosa, AL 35487; e-mail:
laura@bama.ua.edu.
Journal of Family and Economic Issues, Vol. 26(3), Fall 2005 2005 Springer Science+Business Media, Inc.
DOI: 10.1007/s10834-005-5900-y
299
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Journal of Family and Economic Issues
The benefits of greater lifetime earnings are partially offset by the
expense of higher education. During the 2000–2001 academic year,
average charges for undergraduate tuition, fees, and room and board
were estimated to be $8,470 at public four-year institutions, and
$22,541 at private four-year institutions. Moreover, these costs have
been rising at a rate faster than the rate of increase in federal grant
aid, the rate of increase in household incomes, and the rate of increase
in overall price levels. Between 1980–1981 and 1999–2000, tuition for
public four-year colleges increased 114%, and tuition for equivalent
private colleges increased 118%. Over this same time period, median
incomes for families whose householders were between the ages of 45
and 54 rose only 20% (The College Board, 2000a). Today, many public
institutions of higher education are raising fees to cope with budgetary
shortfalls as a result of the current recession. Such actions exacerbate
the existing problem.
Most parents consider the funding of their children’s college
education as one of their most important family financial goals. In
1999, about 60% of parents whose children were in grades 6–12 had
started saving money or making other financial plans for postsecondary education (NCES, 2000). Using the 1992 Survey of Consumer
Finances, Lee (1997) indicated that paying for a college education
has traditionally been seen as primarily a family obligation, being
met through some combination of current earnings, savings, and
borrowing.
Governments at both the federal and state level, as well as higher
education institutions, have roles in the drama of higher education
financing. In the 1999–2000 academic year, the federal government
provided over 70% of all direct aid to postsecondary students, and
almost 60% of this aid was in the form of loans (The College Board,
2000b). Federal grants have been declining relative to borrowing
which, when combined with rising college costs and stagnant family
incomes, means that families must borrow more heavily in order to
pay for college costs. Between 1989–1990 and 1999–2000, loan aid has
increased by 125% in constant dollars, while grant aid has increased
by only 55% (The College Board, 2000b). Parental loans, funded by
federal and state governments, as well as private sources, have also
increased.
Without a doubt, the student’s decision to go to college may hinge on
the student’s parents being willing to borrow money to underwrite the
children’s human capital investment. While many parents help defray
the expense of their children’s higher education, the parents’ choice of
funding sources and the amount of their contributions will vary
Kyung-Wook Cha, Robert O. Weagley, and Laura Reynolds
301
according to parental income, the type and cost of institution attended,
and other family characteristics (Churaman, 1992a; Miller & Hexter,
1985). Previous studies have shown the impact of student borrowing
through the effects loans have on educational decisions, such as
access, college choice, and persistence (Campaigne & Hossler, 1998;
Cuccaro-Alamin & Choy, 1998; St. John & Noell, 1989). Little
research, however, has been devoted to the factors that influence
parental borrowing for children’s college education.
This study is an extension of our previous study (Cha & Weagley,
2002) where we examined the total demand for borrowing from all
sources. In the current work, we focus on the identification of factors
important to parents’ borrowing to support their children’s college
educations. A two-stage estimation model is used to examine both the
decision to borrow, as well as how much they borrow, once they decide
to borrow.
Review of Literature
The literature review focuses on parental borrowing for their
dependent children’s college education in the context of socioeconomic
factors. First, parental investment in children’s higher education is
discussed, followed by a presentation of the trends, impacts and types
of borrowing chosen by parents.
Parental Investment in Children
Parental investment in children is typically viewed in the context of
human capital theory. Human capital theory views higher education
as a form of investment in the acquisition of productive abilities, skills,
and knowledge of individuals or of society as a whole. Investments in
children are based on a rational calculation of potential financial
returns (e.g., increased earnings of a child resulting from increased
education) against college costs. Human capital theory assumes
rational parental choice, where the parents are willing to expend
money (costs) towards the education of their children up to the point
where the marginal benefit, both financial and non-financial, equals
the marginal cost. Resources are then allocated in ways that maximize
future payoffs, in the context of current assets and each child’s
endowment of natural human capital (Becker, 1964; Becker & Tomes,
1986; Steelman & Powell, 1991).
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Journal of Family and Economic Issues
Human capital theory asserts that changes in prices (e.g., tuition
and fees) or subsidies (e.g., grants or loans) alter the costs of college
and lead students to reassess the net benefit from an investment in
higher education. The value of human capital is typically expressed
in terms of the measurable component, the income that individuals
receive in return for their productive contributions (Ehrenberg &
Smith, 1991). Sewell and Hauser (1976) focused on college education to underscore the important role of schooling, both directly and
indirectly, in socioeconomic achievement. They stated that a college
education allows the most direct access to occupations of higher
social standing and to a level of living commensurate with greater
earnings. Many studies have found that the demand for college
attendance is negatively related to college costs (Clotfelter, Ehrenberg, Getz, & Siegfried, 1991; Ehrenberg & Smith, 1991). Leslie
and Brinkman (1988) examined the variation in the effect tuition
had on different income levels and, importantly, found that
low-income families were the most responsive to changes in the cost
of tuition.
Churaman (1992b) used the National Postsecondary Student Aid
Study of 1987 to examine parental contributions to their children’s
college education, both in terms of the type of financial transfers from
parent to student and the timing of such transfers. She found that 75%
of parents reported making contributions and parental contributions
were the largest single source of funds for student college expenses.
She also found that 23% of the parents used only current income, 10%
used only accumulated savings, and 3% used only borrowed money to
fund their contribution. The remaining 64% of parents used some
combination of sources.
Catsiapis (1980) investigated the probability of parental contribution to children’s postsecondary education using the National Longitudinal Study of the High School Class of 1972. The study found that
the probability of parental contributions was positively related to
tuition costs and parental income, while negatively related to financial
aid, the number of children in the family, and being male compared to
being female. Steelman and Powell (1991), using the 1980 Parent
Survey of the High School and Beyond, found that family income was a
positive factor and the number of younger siblings a negative factor in
the likelihood of parental support.
Steelman and Powell (1993) investigated racial and ethnic differences in parental attitudes toward funding college education costs, as
well as actual investments in their children’s education. From the
National Educational Longitudinal Study of 1988, they found that
Kyung-Wook Cha, Robert O. Weagley, and Laura Reynolds
303
minority parents were more likely to believe that the responsibility
for their children’s education rested upon themselves and upon the
government, whereas White parents were more likely to believe that
their children should share the responsibility for funding their education.
Parental choice of funding is related to the family’s resource
constraints, the projected amount of financial aid, and other demographic and family characteristics. Miller and Hexter (1985) indicate that middle-income families need to combine available
resources, as well as obtain outside assistance, in order to meet
college expenses. They suggested that middle-income families
consider a financing pattern that mixes grants, loans, and student
employment in order to fill the gap between the available resources
and the full cost of attendance. Chen and Hanna (1996) emphasize
that financing a contribution toward children’s college costs should
be viewed in the context of a comprehensive financial plan. The
family’s values, goals, and short-term needs should be considered,
along with the goal of financing a college education, as borrowing
for a child’s college education may cause a repayment burden. Also,
if those parents who did not save enough money for college
expenses exhaust most of their financial resources to pay for college,
they will have little left for financing their retirement goals (Loewel,
1991).
Borrowing for College Education
The most prominent trend in student aid has been the growing
reliance on borrowing for higher education. Hartman (1971) explained
that student loans are the primary means of providing a general
subsidy to encourage investments in higher education. Moreover,
loans for low-income students, as compared to grants, potentially alter
the distribution of college attendance across socioeconomic classes.
Traditionally, borrowing has been made possible by a combination of
support from the federal government, state governments, and private
sources. In the 1999–2000 academic year, total awarded aid was $68
billion, with the federal government providing over 70% of all direct
aid to postsecondary students or their parents. Approximately 52% of
the total aid ($35 billion) was in the form of federal loans (The College
Board, 2000b). State and private loan programs for students and
parents began to grow in the 1980s as college prices outpaced inflation
and federal aid failed to cover the difference.
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Journal of Family and Economic Issues
Federal, private, and college-sponsored loan options are available to
parents. Originally, the goal of the parental borrowing program was to
extend more credit opportunities to the parents of dependent students,
in order to contribute to their children’s education (Miller, 1986). The
federal Parent Loans to Undergraduate Students (PLUS) is the largest
source of parent loans and is designed to help parents of undergraduate students meet the costs associated with their education. The
annual amount available on a federal PLUS loan is the total cost of
education minus any other financial aid received. In 1999–2000, after
adjusting for inflation, borrowing through the federal PLUS program
rose 6% over the preceding year, with an average loan amount of
$6,769 (The College Board, 2000b).
A number of financial institutions offer private education loans for
parents, although these loans usually carry a higher interest rate than
PLUS loans. Moreover, a small number of colleges offer their own
loans to parents, usually at a lower rate than the federal PLUS loan.
Most of the private loan plans are directed toward parents rather than
students because private lenders do not consider a student’s potential
future earnings as adequate security. Moreover, most students are
reluctant to take out an expensive, unsecured bank loan (Margolin,
1989).
Berkner (1998) examined the role of educational loans, in the
context of the total price of attendance and undergraduates’ family
income over the 1995–1996 academic year. The study found that
families were more likely to take out loans when college costs were
high and less likely to borrow when their family incomes were high.
Choy (2000) focused on low-income undergraduates, defined as
those whose family income was below 125% of the federally established poverty level for their family size, and examined how they
paid for college in 1995–96. The study reported that approximately
86% of the low-income students, attending full-time, full-year,
received some financial aid. Most (81%) received grants, averaging
$3,900, while 51% borrowed with an average loan of $4,700.
Low-income students who received aid received coverage for about
50% of their college budget, while about 32% of the aid was in the
form of loans.
To summarize, previous studies have examined and analyzed
parental contribution in financing their children’s higher education.
The socioeconomic conditions associated with a borrower, such as
income, economic background, and family characteristics, all have an
effect on whether or not to borrow and on how much is borrowed.
These results are the backdrop for this empirical study.
Kyung-Wook Cha, Robert O. Weagley, and Laura Reynolds
305
Theoretical Framework
Becker and Tomes (1979) provide a theoretical foundation for
parental investment in their children’s human capital. Their theory of
inequality and intergenerational mobility assume that each family
maximizes a utility function spanning several generations. The parents are assumed to be concerned about their children’s future
economic well being and choose to finance a portion of the expenditures
associated with their children’s education to maximize that well-being
(Becker & Tomes, 1979, 1986; Catsiapis & Robinson, 1981). In this
desire, the parents are additionally constrained by supply-side factors
that might limit access to the resources of financial institutions.
Typical supply-side constraints that could exist are credit restrictions: character, capacity, collateral, and conditions. Measures of these
factors are not available in the data. To the extent the lender relies on
non-manipulative factors to restrict access to credit (such as racial or
gender discrimination), these will be employed, if necessary, in the
discussion of the results. Other factors, such as income, also have dual
interpretations. Greater income could reduce demand to borrow as the
resources exist to pay for schooling. On the other hand, greater income
increases the willingness of lenders to lend to households, thus
increasing the amount borrowed.
The utility of parents is assumed to depend on their own
consumption, Ct, on the number of children, n, and per capita income,
Yt+1, of those children in the next generation. The exposition may be
simplified by setting n = 1 for each set of parents and assuming the
Cobb--Douglas form for the utility function.
a
Ytþ1
:
Ut ¼ C1a
t
ð1Þ
The income of the child depends on the stock of human capital the
child would have in period t + 1, called his endowment of human
capital, Ht+1, plus the increase in his human capital resulting from the
expenditure of transfers from parents or other sources. Assume that
the market rate of return per unit of human capital in t + 1 is unity,
then the income of the child is given by:
Ytþ1 ¼ Htþ1 þ ð1 þ rÞEt þ ð1 þ rÞGt ;
ð2Þ
where, Et is dollar amounts directly received from parental sources,
Gt is the dollar amount received from other sources including public
and private subsidy, and r is the rate of return one obtains by
converting dollars into human capital.
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Journal of Family and Economic Issues
The income of the parents, Yt, may be spent either on their own
consumption or transferred to their children.
Yt ¼ Ct þ Et :
ð3Þ
Substituting Et from (2) we obtain:
Ct þ
Ytþ1
Htþ1
¼ Yt þ
þ Gt ¼ St ;
ð1 þ rÞ
ð1 þ rÞ
ð4Þ
where, St is the present value of the family income flow over the two
generations, called the family income by Becker and Tomes (1979).
Parents maximize their utility with respect to Ct and Yt+1 subject to
their family income constraint (Equation (4)) and potential supply-side
lender constraints. If they correctly anticipate both the endowment
and market opportunities of their children, the equilibrium conditions
are given by Equation (4) and
@U . @U
1 a Ytþ1
¼
¼ 1 þ r:
ð5Þ
@Ct @Ytþ1
a
Ct
This marginal rate of substitution given by Equation (5) assumes that
the rate of return is independent of the amount invested in children
and that parents can consume more than their own income by creating
a debt to be repaid by their children.
Given Ht+1, Gt, and r, the parents are assumed to choose Ct and Et so
as to maximize their utility (Equation (1)) subject to their family
income constraint (Equation (4)). The results of this maximization
yield the following demand function for Et.
Et ¼ aYt ½ð1 aÞ=ð1 þ rÞHtþ1 ð1 aÞGt :
ð6Þ
Thus, the contribution of the parent to the child’s human capital
should be positively related to the income of the parent and negatively
related to the transfers the child receives from other sources and to the
child’s own endowment.
Empirical Model and Hypotheses
The empirical model of parental borrowing for a child’s college
education (Bi) can be a function of elements of a household’s total
resource vector, as well as the price of a college education and other
family characteristics to control for preferences.
Kyung-Wook Cha, Robert O. Weagley, and Laura Reynolds
307
Bi ¼ b0 þ Rji bj þ EYi by þ Pki bk þ Zli bl þ ei ;
ð7Þ
where Bi is i’s parents’ borrowing for 1992--93 college costs in dollar
amount; Rji is a (J · 1) vector of household i’s resources; EYi is the
estimated expected income of student i; Pki is a (K · 1) vector of the
price of college attendance; Zli is a (L · 1) vector of characteristics of
household i; and the error term is denoted i.
The dependent variable (Bi) has zero for a significant fraction of the
observations because many parents do not borrow for their child’s
education. In situations like this, ordinary least squares regression is
inappropriate since it would lead to biased and inconsistent estimates
(Greene, 2000). In order to model such a distribution, an empirical
method is needed to account for the probability of non-occurrence of
the event. The Tobit model has been traditionally used when the data
censors at zero. In the Tobit model, however, the same set of variables,
with the same coefficients, is held to determine both the probability of
truncation and the expected value of the realized dependent variable,
conditional on its having been observed (Breen, 1996).
The double-hurdle model, proposed by Cragg (1971), features two
separate stochastic processes, under the assumption that the two
stages are independent of each other. Heckman’s (1979) sampleselection model extends Cragg’s model by relaxing the assumption
that the two stages are independent. The logic of the Heckman sample-selection model is appropriate when individuals must pass a
separate hurdle before they are observed to have a positive level of
demand; in this case, the decision to borrow and the level of borrowing.
In this study, a two-stage approach is specified as follows:
0
Decision to borrow equation: Pi ¼ Xpi
a þ ui
Pi ¼ 0
if
Pi 0
Pi ¼ 1
if
Pi > 0
ð8Þ
0
Level of borrowing equation: Bi ¼ Xbi
b þ ei
Bi ¼ Bi
if
Bi not observed if
Pi ¼ 1
Pi ¼ 0
ð9Þ
0
0
and Xbi
are vectors of the independent variables that
where Xpi
influence the decision to borrow and the level of borrowing at observation i, a and b are vectors of the unknown parameters, and ui and
i are error terms. We observe Pi, a dichotomous variable, which is a
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Journal of Family and Economic Issues
realization of an unobserved (or latent) variable, Pi , having a normally distributed and independent error, u, with mean zero and variance r2. For values of Pi = 1, we observe Bi, which is the observed
realization of a second latent variable, Bi , which has a normally distributed, independent error, e, with mean zero and variance r2. The
two error terms across the two equations are assumed to be correlated in the Heckman sample-selection model. The two-stage approach starts with a Probit analysis to measure the decision by
parents to borrow (Pi = 1) or not borrow (Pi = 0), and then estimate
the actual amount borrowed, Bi, in truncated regression analysis
(Abdel-Ghany & Silver, 1998; Breen, 1996; Jones, 1989).
The following hypotheses are posited based on the theoretical
framework and previous research.
Taking other socioeconomic variables into account,
1. A household’s resources (income and assets) have a negative effect on both the parental decision to borrow and the level of
borrowing.
2. A dependent student’s expected future income has a positive
effect on the amount parents borrowed for the student’s college
education.
3. Total college costs are positively related to both the parental
decision to borrow and the level of borrowing.
4. Total amount of grants received is positively related to the decision to borrow, but negatively related to the amount borrowed.
Parents with younger dependent students, larger household size, and
those who had greater levels of education are more likely to decide to
borrow, and if they decide to borrow, to borrow a greater amount.
Methods
Data and Sample
This study used data collected through the 1992–1993 Baccalaureate and
Beyond Longitudinal Study (B&B: 93) to examine the factors important to
the parental decision to borrow and the amount borrowed for their children’s college education. Additionally, this study used the 1997 follow-up
survey (B&B: 93/97) to estimate student expected income that was used as
an independent variable.
Kyung-Wook Cha, Robert O. Weagley, and Laura Reynolds
309
The B&B study, developed by the National Center for Education Statistics,
U.S. Department of Education, tracks the experiences of a cohort of college
graduates who received their bachelor’s degrees during the 1992–1993
academic year and were first interviewed as part of the 1992–93 National
Postsecondary Student Aid Study (NPSAS: 1993). The B&B is an appropriate
data set to use to allow an understanding of students’ educational investment
decisions. While these data provide information concerning the education and
work experiences of baccalaureate recipients, as well as information regarding
the methods used to finance their undergraduate education, it does not include
information about those students that began their college experience yet failed
to persist to graduation. While knowledge of the educational financing decisions of these parents is clearly of interest, nothing is known of them with the
current data. The B&B: 93 included the student interview as well as the
parent interview. Parental interviews were designed to gather data concerning the effects of postsecondary education on family finances. Parents were
asked to describe their personal finances, financial support they had given to
the children, and any money they had borrowed to provide financial aid to the
sampled student. When the parents were not married to each other, the parent
who tended to provide the most financial support was primarily asked. If both
parents contributed equally, the questions were asked to each parent individually (U.S. Department of Education, 1999).
The sample drawn for this work are parents of dependent children who
received bachelor’s degrees in the 1992–1993 academic year and did not enroll
for additional postsecondary education by 1997. First, the decision to remove
those who sought post-secondary education was made to focus the analysis on
those that borrowed money to complete their final year of their baccalaureate
degree. While omitting those that enrolled in further introduces some bias, in
which two facts led to their exclusion: (1) further enrollment would affect the
timing of repayment and, hence, it’s present value to the borrower and (2)
post-graduate education would change the timing of their work force entry and
systematically change their future expected income growth due to experience.
We, thus, viewed their inclusion as introducing more bias to the results than
their omission. To the extent the terminal-baccalaureate degree student is
systematically unique, say they earn less money while in school, the coefficient
on that variable would be biased downward.
Second, the sample was restricted to parents of dependent students because only parents of dependent students are eligible for federal PLUS, the
largest source of parental loans. In determining the need for federal
financial aid, students are considered either dependent on their parents or
independent and self-supporting. For the purpose of determining financial
aid eligibility, undergraduates who are 24 years or younger are assumed to
be financially dependent unless they are married, have dependents of their
own, or are veterans or orphans. Undergraduates 24 years or older are
considered financially independent for purposes of determining their eligibility for financial aid, regardless of their parents’ incomes and assets and
whether or not their parents provide them with any financial assistance
(U.S. Department of Education, 2000). Using these criteria may possibly
misclassify someone who is actually an independent student as a dependent
student, or vice versa. This study, however, followed these criteria as they
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Journal of Family and Economic Issues
were utilized by the U.S. Department of Education to determine federal
financial aid eligibility. The resulting sample was 2,561 parents of dependent students who graduated in 1993.
Variables
In the decision to borrow equation, the dependent variable was dichotomous. The parents who borrowed for college costs were coded as 1, while
non-borrowers were coded as 0. In the level of borrowing equation, if the
parents decided to borrow, the dependent variable was the total amount of
all loans parents took out to pay for their children’s 1992–1993 college
costs. The sources of parental borrowing include federal Parent Loans to
Undergraduate Students (PLUS), state-sponsored parent loans, schoolsponsored parent loans, signature loans, home equity loans, personal lines
of credit used for college expenses, loans against a life insurance policy,
commercial loans, loans from a non-profit underwriter, Family Education
Loans from Sallie Mae, loans against a retirement fund, loans from a former spouse, relatives, or friends, or any other type of loan to the parents in
support of their child’s college education.
Independent variables were selected to account for variation in economic
and student characteristics, and include a vector of current resources, a
measure of expected future income of the student, a vector of the price of
college attendance, and a vector of the family characteristics. The vector of the
current resources included parents’ total income, students’ own income, home
equity, family business or farm equity, parents’ cash and savings, and student’s cash and savings. Home equity and family business or farm equity are
tangible assets that proxy wealth. These variables were estimated by the value
of the parent’s home and business or farm equity. Dependent students’ asset
holdings were not considered because too few of the dependent students had
their own home, family business, or farm equity.
The student’s estimated expected income was generated by regressing the
1996 annual employment income (obtained from the 1997 B&B) of the student
against undergraduate major, GPA, institution type, region of institution, age,
and race. This estimate provided the instrument to control for income expectations of the student. Table 1 reports the results of regression analysis to
estimate students’ future income.
The vector of the price of college attendance is composed of two variables: total
college costs and total amount of grants received. Total costs were the sum of
tuition, fees, and other direct costs of attendance, such as the amount of spending
for books, supplies, and equipment. The total amount of grants received was the
sum of federal, state, institutional, and other grant amounts.
The vector of family characteristics included students’ age, gender, race,
parents’ education, and household size. Continuous variables were used for
respondent’s age as of receipt of a bachelor’s degree in the 1992–93 academic
year, and household size. Dummy variables were used to control for gender (1
if male, 0 if female), race (1 if White, 0 if non-White), and parental education (1
if college graduate or over, 0 if high school or less). The variable of parents’
education measured the highest level of education completed by either parent.
Kyung-Wook Cha, Robert O. Weagley, and Laura Reynolds
311
TABLE 1
Results of Regression Analysis for Student’s Expected Incomea
Annual income
coefficient (Std. error)
Variable (reference group in the parenthesis)
Constant
24,882.702*** (2,061.159)
Undergraduate major_(business, management)
Education
Engineering
Health professions
Public affairs/social services
Biological sciences
Math. and other sciences
Social science
History
Humanities
Psychology
Other
)11,639.585*** (1,058.025)
7,493.408*** (1,267.724)
1,894.636 (1,147.202)
)6,792.609*** (1,659.891)
)8,019.658*** (1,989.371)
2,657.653 (1,413.892)
)1,372.312 (1,144.295)
)13,115.155*** (2,528.571)
)10,406.579*** (1,139.543)
)13,805.467*** (1,862.514)
)5,160.045*** (907.383)
GPA
Normalized GPA on 4.0
773.459 (588.299)
Type of Institution_(Public)
Private, not-for-profit
Private, for-profit
)114.075 (660.464)
)4,289.159* (2,378.398)
Region of Institution (Southeast)
New England
Mid East
Great Lakes
Plains
Southwest
Rocky Mountains
Far West
Outlying areas
4,407.066*** (1,300.903)
705.314 (956.378)
1,892.679* (898.057)
)1,854.427 (1,075.447)
1,720.507 (1,014.307)
)3,230.872* (1,628.506)
2,406.779* (1,111.632)
)15,194.241*** (3,973.508)
Age
Age when received BA
274.626*** (44.720)
Race_(non-White)
White
342.178 (917.637)
a
The analyses were weighted by the adjusted weight (see note 1).
*p < 05, **p < 01, ***p < 001.
Results and Discussions
Borrower/Non-borrower Comparisons
In the sample, only 6.83% of parents borrowed money for their
dependent children’s 1992–93 college costs, with an average loan
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Journal of Family and Economic Issues
amount of $9,894. Table 2 presents descriptive statistics for the
independent variables that were included in the model to specify the
decision to borrow and the borrowing amount equations. For each
variable, the sample average or frequencies were computed over each
subset of 175 borrowers and 2,386 non-borrowers. The significant
differences in the two samples determined by either t-test of mean
differences or chi-square tests of association were indicated by bold,
italic print.
The parents who borrowed money for their children’s college education had significantly greater home equity ($68,844) than their
TABLE 2
Descriptive Statistics of Two Sub Samplesa
Mean (Std. Dev.)/frequencies (percent)
Borrower
(n = 175)
Non-borrower
(n = 2,386)
$64,959.60 (38,507.10)
$3,998.40 (3,737.20)
$68,844.20 (76,996.80)b
$13,833.00 (44,144.50)
$10,282.10 (47,746.80)
$1,469.90 (2,629.50)
$31,047.70 (5,882.40)
–
$63,056.00 (62,491.70)
$4,257.30 (4,049.70)
$48,132.70 (95,835.90)
$10,277.00 (69,840.00)
$6,347.50 (37,767.80)
$2,028.90 (6,829.00)
$31,157.80 (5,882.40)
$14,406.70 (7,923.30)
$11,618.60 (7,161.00)
Total grant
Total amount of all grants
$1,465.90 (2,931.40)
$1,222.90 (2,799.80)
Age
Age when received BA
21.97 (0.78)
22.17 (0.87)
75 (42.9 %)
100 (57.1 %)
1,068 (44.8 %)
1,318 (55.2 %)
Race
White (n = 2,294)
Non-White (n = 267)
Household size
159 (90.9 %)
16 (9.1 %)
3.62 (1.13) (18.9 %)
2,135 (89.5 %)
251 (10.5 %)
3.88 (1.25)
Parents’ Education
High school or less
College graduate or over
33 (81.1 %)
142
733 (30.7 %)
1,653 (69.3 %)
Variables
Total amount borrowed
Parents’ income
Students’ income
Home equity
Family business or farm equity
Parents’ cash and savings
Students’ cash and savings
Student’s expected income
Total costs
Tuition, fees and
non-tuition costs
Gender
Male (n = 1,143)
a
The analyses were weighted by the adjusted weighti.
Statistically significant differences at the .05 level are indicated in bold and italic print.
b
Kyung-Wook Cha, Robert O. Weagley, and Laura Reynolds
313
non-borrower counterparts ($48,133), although parents’ income did
not have a significant difference between borrowers and non-borrowers. Students whose parents borrowed money for college education
faced significantly greater tuition, fees, and other educational
expenses ($14,407) than those whose parents did not borrow ($11,619).
In the sample, the dependent students whose parents were borrowers
were significantly younger (21.97 years) than the students whose
parents were non-borrowers (22.17 years). Regarding household size,
those whose parents did not borrow had a significantly larger household size (3.88) than those whose parents borrowed (3.62). As for the
parents’ highest level of education, approximately 81.1% of borrowers
had at least a college degree, while 69.3% of non-borrowers had at
least a college degree. This difference was found to be statistically
significant.
Decision to Borrow and the Level of Borrowing Equations
Table 3 shows the results of the Heckman sample-selection model
on parental borrowing for 1992–1993 college costs. Among variables
included in the vector of the current resources, the dependent
student’s income, the parents’ home equity, and parental cash and
savings were significant factors to the level of borrowing. Once the
parents had decided to borrow money for their children’s college
education, they increased the borrowing amount as their children’s
own income decreased, although the dependent student’s income was
not a significant factor in the decision to borrow equation. Previous
studies have confirmed the negative relationship between higher
education debt and family income (Berkner, 1998; Grubb & Tuma,
1991). In this case, the parents appear to substitute their debt for the
current income of their children. Home equity was a significant
positive factor in the level of parental borrowing. Parents who decided
to borrow for their children’s college costs were found to borrow a
greater amount, as the value of their home equity increased. One
would expect home equity to be a preferred source of credit for this
purpose. The tax deductibility of mortgage interest dramatically lowered the costs of this source of financing during the time period in
question. Importantly, the value of home equity as a preferred means
of higher education financing is highlighted. Parental cash and savings had a significant negative impact on the amount borrowed for
their children’s college education. As cash and savings increased,
parents were found to borrow less for their children’s college education. Here, the importance of planning for college outlays, as indicated
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Journal of Family and Economic Issues
TABLE 3
Results of Sample-Selection Model for Parental Borrowinga
Variables
Constant
Parents’ income
Students’ income
Home equity
Family business or farm equity
Parents’ cash, savings
Students’ cash, savings
Student’s expected income
Total costs of college Attendance
Total grants received
Student’s age
Student’s gender (female) male
Race (Non-White) White
Household size
Probit: decision to
borrow (n = 2,561)
0.893
(1.271)
)0.692E-6
(0.839E-6)
)0.420E-5
(0.107E-4)
0.579E-6
(0.388E-6)
0.192E-6
(0.567E-6)
0.810E-6
(0.899E-6)
)0.132E-4
(0.120E-4)
)0.368E-5
(0.714E-5)
0.215E-4***
(0.601E-5)
)0.599E-5
(0.150E-4)
)0.103
(0.055)
0.396E-3
(0.085)
)0.045
(0.130)
)0.115**
(0.036)
Lambda (k)b
Log likelihood
)556.391***
Truncated regression:
level of borrowing
(n = 175)
114,372.2**
(27,939.4)
)0.019
(0.030)
)1.376**
(0.496)
0.107**
(0.033)
)0.029
(0.024)
)0.043
(0.023)
)0.043
(0.359)
)0.052
(0.172)
0.636**
(0.240)
)1.034*
(0.466)
)6,799.5**
(2,524.2)
)1,165.1
(1,879.5)
15,291.3**
(5,812.0)
1,268.9
(933.5)
3,827.5
(2,788.7)
47,310.1*
(23,312.5)
)1,888.613***
a
The analyses were weighted by the adjusted weight.
For a technical explanation of Lambda (k)3, see note 2.
*p < .05, **p < .01, ***p < .001.
b
by greater liquid assets, can be seen to offset the need of the family to
borrow for college expenses. Surprisingly, a student’s expected future
income was not significantly related to their parents’ borrowing.
It is of interest to note that parental income was not significant to
the decision to borrow, or to the amount borrowed. One might consider
Kyung-Wook Cha, Robert O. Weagley, and Laura Reynolds
315
it likely for a higher income household to be less likely to borrow yet, if
they were to borrow, to borrow less. On the other hand, the greater
capacity for repayment may induce the lender to consider them a
lower risk and be willing to lend more. Taken together, it is not surprising that the result on the amount-borrowed equation is not
significantly different from zero.
As hypothesized, total costs of tuition, fees, and other educational
expenses had a significant positive effect on both the decision to
borrow and the level of borrowing equations. The greater the costs of
college attendance, the greater the probability that parents borrowed
for college costs and, if they did, they borrowed more. Empirical
studies have shown that students were significantly more likely to
take out loans when they faced higher tuition and other educational
expenses (Choy & Geis, 1997; Cuccaro-Alamin & Choy, 1998; Grubb &
Tuma, 1991). We found that greater costs have a similar effect on
parental borrowing. As the cost of college attendance continues to
increase at rates greater than changes in the general price level, it can
be anticipated that more and more parents will increase their liabilities as a means of financing their children’s education. The other
indicator of the price of college attendance, the total amount of grants
received, was found to be a significant negative factor to the amount of
parental borrowing, but it was not a significant factor in the decision
to borrow. Once parents decided to borrow for their children’s college
costs, borrowing amounts decreased as the amount of grants students
received increased. Generally, it is expected that the demand for
educational loans will be high when alternative sources of financial aid
are limited. Thus, the amount of grant received was expected to have a
negative effect on the level of borrowing, as grants can reduce the
outlays required for attendance. Policy makers need to be aware of
this relationship, as grants are often used to target specific populations, defined by merit, need, or other factors.
Hypotheses regarding family characteristics received limited
support. As was found in previous empirical studies, age was found to
be a significant negative factor to both the decision to borrow and the
amount borrowed (Grubb & Tuma, 1991). When their children were
younger, parents were more likely to decide to borrow to pay college
costs, and if they borrowed, they borrowed more than the parents of
older students. Once the parents decided to borrow for college costs,
White parents borrowed more than did non-White parents. This result
for parents was found to be different from the results of St. John and
Noell’s (1989) study that demonstrated Black and Hispanic students
as being more likely than White students to receive loans, as well as
316
Journal of Family and Economic Issues
other financial aid. This apparent preference for borrowing to increase
investments in children calls for greater research to increase our
understanding of the family and possible differences in subjective
rates of time preference by racial groups. Alternatively, while illegal,
supply-side factors may be indicated where White households’ are able
to borrow more than non-White households. Household size was found
to have a significant negative impact on the decision of parental borrowing, although it was not a significant factor in the amount
borrowed. When family size increased, parents were less likely to
borrow for college costs. As family size increases, the amount of
resources available to each family member declines and, accordingly,
parents take less responsibility for college expenditures (Steelman &
Powell, 1991). On the other hand, lenders may see borrowers with
more dependents to be greater risks, ceteris paribus, and thus be less
willing to give loans to those families. The parents’ highest level of
education was a significant positive factor to the decision to borrow
equation. The parents who had at least a college degree were more
likely than those with a high school diploma, or less, to borrow money
for their children’s college education. However, the education level
was not a significant factor to the amount borrowed. Steelman and
Powell (1991) indicated that parents with a higher education may
place a higher premium on parental assistance than their less
well-educated peers, as many of them have received help from their
parents when they pursued their educational goals.
Conclusions and Implications
By identifying the extent of human capital investment and intergenerational support, the findings of this study provide information
about which factors influence the parental decision to borrow and the
level of borrowing for their children’s college education. The results of
this study underscore using a Heckman sample-selection model to
examine higher education debt, as the results show several factors
with differential effects on each equation. Thus, the two-stage
estimators provided more information than the Tobit model would
have provided.
Financial aid administrators and public policymakers can use these
results as necessary information when developing effective financial
aid programs, and in targeting loans and grants to undergraduate
students and their families. For example, students from large families
are much less likely to use parental support through borrowing than
Kyung-Wook Cha, Robert O. Weagley, and Laura Reynolds
317
those from smaller families. This might induce financial aid offices to
inform such students of other sources of financing.
The question of whether parents have increased their responsibilities to finance their children’s higher education is partially answered
by the fact that the rate of increase in parental loans has been lower
than the rate of increase in student loans. Perhaps, there is a shift in
parental responsibility toward their children’s human capital investments. These results would suggest that parents with greater education are more likely to borrow. More research on how to develop this
value in parents with lesser education could prove valuable to today’s
college students that face ever-increasing fees. Moreover, federal aid
programs should share in future increases in costs, to help relieve
additional future burdens, as well as alleviate present burdens. As an
example, maximum Pell Grants could be inflated to more closely match
changes in the cost of tuition at public institutions of higher education.
We, as a citizenry, have been less committed to the concept of
human capital beyond the level of high school, while there are clear
positive externalities to having an educated population that warrant
greater public investment in higher education. The returns to higher
education, however, seem considerably more private than those
associated with lower grade levels. As such, we are comfortable
privatizing the costs through loan programs. Besides earning more
money following their education, indicating greater productivity,
they participate more in our democracy and instill values related to
human capital enhancement within future generations that are
clearly public goods. Public higher education, in particular, should be
accessible to all, regardless of economic means, as it benefits all.
Governments must continually evaluate the importance of higher
education to the productivity of our society.
This study is subject to several limitations. To analyze parental
borrowing for children’s college education, it would be of interest to
know specific sources of funds that parents have used through all the
student’s undergraduate years. The B&B data, however, did not provide either separate sources or the summed amount that parents
borrowed through all the undergraduate years. Thus, this study focused on the total amount of parental borrowing solely for the 1992–93
academic year. Along those same lines, the sample of this study was
composed of parents whose dependent children received bachelor’s
degrees in the 1992–93 academic year. In other words, this study
examined the borrowing behaviors of parents of graduating seniors.
Thus, this study could not examine those who began college but did
not finish, as these data were not present in the sample. Greater
318
Journal of Family and Economic Issues
variation in the sample of students studied could potentially result in
greater understanding of the decision to borrow for one’s progeny.
Moreover, future research should consider the way parents and student fund college expenses throughout the education years. Borrowing
patterns can be expected to be much different at different stages of the
college experience. One can easily conjecture that the closer one is to
the goal of a college degree, the greater the likelihood of borrowing.
Conversely, lesser academic success or degree uncertainty could retard borrowing, as the returns on the investment would have greater
variance.
Future research can analyze whether socioeconomic factors have
differential effects between student and parental borrowing. In
addition, the factors that affect the repayment of loans following a
child’s or a student’s graduation remains a topic for future research.
Importantly, trends leading to greater borrowing do not seem to be
changing. Greater fees and tuition, stagnant asset markets, and
income growth that lags the increase in the price of college all point
to an increase in borrowing as a source of funding for human
capital investments. How this is distributed across society remains a
topic of social discussion and political debate.
Notes
1. This study employed the B&B panel weight to compensate for an unequal
probability of selection and to adjust for non-response. The panel weight was
calculated by a non-response adjustment to the baseline weight that was
computed by area, institution, and student level components. Using this raw
panel weight, however, inflated the sample size and therefore, the estimated
standard errors were dramatically decreased. To preserve the effective
sample size, while adjusting for the unequal probability of selection, an adjusted
weight was
by dividing the raw panel weight by its mean:
P calculated
wi w where w ¼ wi n. By using this adjusted weight, the estimates of the
means and standard errors can be considered correct (Thomas & Heck,
2001).
2. The level of borrowing equation (Equation (9)) is estimated for the cases in
which the amount of borrowing was greater than zero. Thus, this study needs
to estimate only the expected value of Bi, conditional on Pi = 1. It can be
written as:
0
b þ Eðei jPi ¼ 1Þ
EðBi jP ¼ 1; Xbi Þ ¼ Xbi
0
0
b þ Eðei jui > Xpi
aÞ:
¼ Xbi
Recall that Bi will exceed zero only when the condition on i is met from this equation.
So, instead of having the expression E(i) in a normal OLS model, this study has the
0
aÞ. Since it is assumed that the unconditional
conditional expectation Eðei jui > Xpi
expectation of i is zero, this conditional expectation will be non-zero. Using the
Kyung-Wook Cha, Robert O. Weagley, and Laura Reynolds
319
required standard results for the expectations of a truncated, normal random variable, it can be written:
0
aÞ ¼ qre ru
Eðei jui > Xpi
/
;
U
0
where Ui is the standard normal distribution function evaluated at Xpi
a r. / is the
corresponding standard normal density function evaluated
at the same point. The
ratio of the density to the distribution function ð/i Ui Þ is known as the inverse Mill’s
ratio, or the hazard rate, and is usually symbolized by ki (Breen, 1996; Greene, 2000;
Heckman, 1979). Therefore, the truncated equation is expressed as:
0
0
b þ Eðei jui > Xpi
aÞ
EðBi jP ¼ 1; Xbi Þ ¼ Xbi
0
b þ rue ki :
¼ Xbi
Consequently, this study estimated Ui and /i to calculate estimate ki, the inverse
Mill’s ratio, and finally regressed the non-zero Bi values on ki as well as a vector of
independent variables Xbi to obtain consistent and unbiased estimates of the coefficients of b and rue.
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