THE EFFECT OF RISING STUDENT LOAN DEBT ON MORTGAGE INTEREST RATES AND DEBT

A Thesis submitted to the Faculty of the

Graduate School of Arts and Sciences of Georgetown University

in partial fulfillment of the requirements for the degree of Master of Public Policy

in Public Policy

By

Michele R. Scarbrough, B.A.

Washington, DC April 13, 2012

     

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THE EFFECT OF RISING STUDENT LOAN DEBT ON MORTGAGE INTEREST RATES AND DEBT

Michele R. Scarbrough, B.A.

Thesis Advisor: Matthew H. Fleming, Ph.D.

ABSTRACT

Student loan debt (SLD) enables the pursuit of higher education, as it allows borrowers to

fund more education (or more expensive education) than they otherwise could consume. But SLD

also may carry with it a cost, namely higher debt levels that can crowd out spending later in life,

including on housing. To explore the impact of SLD on such purchasing behavior, this paper studies

the effect of cumulative SLD on mortgage debt amounts and interest rates, and how that effect has

changed over the past 15 years utilizing data provided by the Federal Reserve’s Survey of Consumer

Finances. It does so by employing an ordinary least squares regression analysis with household

demographic and finance controls to isolate the effect SLD has on mortgage interest rates and

mortgage debt. This analysis proves useful to policymakers because there has been little empirical

analysis studying the impact of SLD on households’ ability to participate in the economy in the form

of mortgage loans. It suggests that SLD has the propensity to impact long-term financial decision-

making at the household level and has the potential to alter mortgage markets. My results indicate

that SLD holders pay a premium on their mortgage interest rates and tend to obtain smaller

mortgage loans.

     

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ACKNOWLEDGEMENTS

A special thanks goes to a few close GPPI classmates, Leigh Szubrowski, Sarah Puritz, and Laura Cordero-Alessandri, who provided much-needed support and served as sounding boards throughout this process. Also, I thank my husband Tim Maher, who always offered a critical eye and a listening ear, complete with unwavering encouragement. Finally, I wouldn’t have been able to begin my research without the insights of Mark Kantrowitz and Sandy Baum, whose studies into the impact of student loan debt inspired and informed my work. Many thanks-

     

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TABLE OF CONTENTS

List of Figures and Tables………………………………………………………………… v

I. Introduction………………………………………………………………………………..1

II. Background………………………………………………………………………………...3

III. Literature Review……………..…………………………………………………………….8

IV. Conceptual Framework/Hypothesis………………………………………………………15

V. Data & Methods..……..…………………………………………………………………...17

VI. Results & Analysis…..…………………………………………………………………….22

VII. Discussion and Recommendations………………………………………………………..32

VIII. Works Cited.…….…………………………………………………………………...……37

     

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LIST OF FIGURES

Figure 1: Relationship Between SLD and Mortgage Amounts/ Interest Rates………..…………...16

Figure 2: Mean and Median SLD Trends.…………………………………………………………21

LIST OF TABLES

Table 1: Select Variables Used in Explanatory Model from the SCF 1992-2007…………………...18

Table 2: Descriptive Statistics of Mortgage Interest Rate …………………………………………19

Table 3: Descriptive Statistics of Mortgage Debt Total …………………………………………...19

Table 4: Descriptive Statistics of Household Financial Characteristics…………………………….20

Table 5: Trends in Percentile Changes of Cumulative SLD ……………………………………… 21

Table 6: OLS Regression Results with Fixed Year Effects of Mortgage Interest Rates…………….24 Table 7: Predicted Effect of SLD on Mortgage Interest Rates…………………………………… 26 Table 8: OLS Regression Results with Fixed Year Effects of Mortgage Debt Total…...…….…….28 Table 9: Predicted Effect of SLD on Mortgage Debt Amounts………………………………........30 Table 10: Results of t-tests Comparing Early Years and Later Years of Households with SLD……31

     

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INTRODUCTION

Increased availability of student loans has changed the consumption of higher

education in the United States, affording students of all backgrounds greater access to college and

graduate school. Over time the importance of a college education has grown considerably, and with

that growth has come increases in cost of tuition and student loan debt (SLD). Cumulative growth

in student loans has increased 511 percent since 1999, with $90 billion in outstanding student loans

in 1999 compared to about $550 billion in the second quarter of 2011 (Indiviglio, 2011). Tuition has

grown at an annual average of more than double the rate of inflation—close to eight percent

compared to about three percent since 1981 (Kantrowitz, 2011). This continual tuition increase puts

more pressure on student loan borrowers, and will likely yield an increase in the number of

borrowers and the total amount of loans accrued. SLD has already outpaced credit card debt for the

first time and was set to reach the $1 trillion mark by the end of 2011, which economists warn could

hinder spending and investment among a generation of Americans (Kantrowitz, 2010).

Meanwhile, SLD is often considered “good debt”—that investing in one’s education is

worthwhile (Lewin, 2011; Miller, 2011). Investing in human capital seems inherently positive, but

might there be microeconomic implications for a generation of college graduates burdened with

large amounts of SLD? According to Mark Kantrowitz, the founder of Finaid.org and an expert on

college finances, there are signs that the mere presence of SLD deters people from going to graduate

school. There is also research suggesting that SLD levels help determine which careers are sought

out post-college and which ones are avoided (Rothstein and Rouse, 2007).

A report from the Federal Reserve Bank of New York suggests that while American

household debts have decreased on mortgages, cars and credit cards, SLD has increased 25 percent

over the past three years. The US Department of Education also indicated that default rates for

     

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student loans increased from seven percent in 2008 to almost nine percent in 2009 (WSJ Editorial

Board, 2011).

My research tests the effect of cumulative SLD on both mortgage debt amounts and

mortgage interest rates among a random sample of households with certain criteria to see how that

effect has changed over the past fifteen years. It contributes to the policy conversation in three ways:

First, my analysis evaluates the impact of rising SLD on long-term microeconomic

indicators, mortgage debt amounts and interest rates. These mortgage characteristics are good

microeconomic indicators because they impact long-term household financial decisions. These

indicators could link SLD to long-term negative economic effects due to mortgage loans’ typically

lengthy terms of thirty years, and its relationship with additional household spending over the years.

Secondly, wage levels of college graduates have not increased at a fast enough rate to offset

the amount of SLD accrued by the average student today (Mishel, 2011). If maintaining personal

financial stability requires SLD to fall within a 10 to 15 percent debt to income ratio of starting

salaries (as suggested by Baum & Schwartz, 2006), with 2008 average undergraduate debt estimated

at $23,000 and median starting salaries estimated at $40,000 per year--- recent graduates could be

facing serious long-term financial hardship (NCES, 2011). There are potential policy and economic

implications of allowing this trend to continue.

Third, while certain sources in the literature suggest pending financial trouble for recent

graduates, findings have yet to be empirically confirmed. If SLD impacts long-term consumer

behavior in the form of mortgages, it could also have an impact on the US economy. There is a gap

in the knowledge of how the increase in SLD has impacted these borrowers and how that impact

has changed over time.

In this paper, I seek to contribute to the understanding of how SLD effects mortgages over

time. I analyze data from nationally representative samples to examine the extent to which student

     

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loan debt is an underlying cause of variation in mortgage interest rates and loan totals. Based on the

assumption that SLD is good debt, I test to see if it does, in fact, yield negative results on mortgage

interest rates and debt totals. I will test this hypothesis using data from the Federal Reserve’s Survey

of Consumer Finances.

If an empirical relationship between SLD and mortgage rates and debt exists, then the

increase in SLD levels has the potential to adversely impact the mortgage market. By increasing

mortgage interest rates and decreasing mortgage loan amounts for households with SLD, the SLD

“bubble” could have a wider effect on the US economy. An understanding of the results of this

analysis and the effect SLD has on this subpopulation is relevant to policy makers and can inform

critical decisions regarding student loan financing and a possible SLD bubble they could face in the

coming years.

BACKGROUND

A Brief History of Student Lending

The Higher Education Act of 1965 was born under the Administration of Lyndon B.

Johnson, which sought to reduce financial barriers and overcome inequalities of opportunity among

potential college students. This Act created the Guaranteed Student Loan Program, called the

Federal Family Education Loan (FFEL)- later renamed the Stafford Loan Program- that for several

years remained the primary vehicle for federal student aid to postsecondary students. These loans

were primarily need-based, which were intended as a complement to federal grants and a last resort

for college students seeking to finance their education1 (Roots, 2000).

Although the Higher Education Act increased access to student loans for the poorest                                                                                                                1 The Higher Education Act restricted access to student loans to families with incomes of less than $25,000 per year.

     

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Americans, it did little to enable the average American to obtain a college education. When President

Carter took office, he spearheaded the Middle Income Student Assistance Act, which removed the

provision from the Higher Education Act limiting federal student loans to those with incomes under

$25,000, ensuring increased opportunities for a financially diverse American public.

With the promise of federal student loan funding, states began to decrease funding for

higher education and colleges began to charge higher tuition. Federal expenditures for student loans

more than doubled between 1978 and 1981; students with loans increased from 15 percent to 33

percent in two years (Roots, 2000).

This surge in student borrowing eventually prompted a federal direct lending program,

started by the Bush Administration and taken over by the Clinton Administration in 1993, claiming

to be less costly and simpler to administer than guaranteeing private loans. It proved successful and

was widely accepted by financial aid administrators in colleges nationwide. It also provided

repayment plans based on 20 percent of discretionary income.

However, the private student-lending industry would not gain from the additional

competition, and became a fierce opponent of its diffusion. Facing increasing pressure, Congress

passed a law prohibiting the Department of Education from encouraging or requiring colleges’

participation in the direct loan program. University financial aid offices took deals with lenders who

provided monetary incentives from banks profiting from the guarantee system--- leading students

away from government loans with lower interest rates and negotiable payback plans. As such,

college financial aid offices’ participation in the federal direct loan program declined. The late 1990s

also saw more stringent repayment enforcement, where Social Security payments could be docked to

repay educational loans. An amendment was also passed that struck the requirement allowing

education loans be discharged after seven years in repayment (Kantrowitz, 2012).

Another concession made to private student loan lenders was the passage of the

     

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Bankruptcy Abuse Prevention and Consumer Protection Act of 2005, which includes private

student loans as one of the ten debts that cannot be forgiven through bankruptcy (Siegel, 2007).

This law made taking on SLD even riskier for students, as private student loans were now legally

considered non-dischargeable debt.

However in 2008, with widespread credit market disruptions, private lenders were less able

to participate in the student loan guarantee program and many discontinued involvement. This gap

was filled, for the most part, by the federal government’s direct loan program, and the government’s

share of the total loan volume began to increase later that year. In his FY 2010 budget, President

Obama requested the elimination of the FFEL program, and instead required that all federal student

loans be made available under the direct loan program. In January 2010, Congress passed and

President Obama signed into law a bill that eliminated the FFEL program for all new loans made as

of July 1, 2010 (New America Foundation, 2011).

Federal Direct Lending created safer, more affordable and accessible loans for students of all

backgrounds, offering subsidized interest rates and interest payments while a student is enrolled

half-time and deferral of repayment while enrolled at least half-time in school. Over time, subsidized

Stafford loans increased from about $15 billion in 1990 to $20 billion in 2000, reaching $35 billion in

2009 (Avery, 2012). With college tuition increasing at current levels, however, many students are

forced to max out federal loans and obtain additional private loans just to cover costs.

Student Loan Debt is Different

Student loans enable people of various economic backgrounds to go to a college of their

choosing; federal student loans are also practically guaranteed to all who apply. Students must fill out

the Free Application for Federal Student Aid (FAFSA) form online. This application qualifies

students for federal student aid programs, including the direct loan and Pell grant programs.

     

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Eligibility for federal loans is restricted to US citizens and permanent residents with high school

degrees who have demonstrated some kind of unmet financial need. The FAFSA calculates the cost

of attendance of the school minus any grant aid minus the “Expected Family Contribution” (which

is a combination of assets and income) (Avery, 2012). Obtaining a student loan does not require

extensive credit checks; students do not have to have a job. Students do not even have to choose a

particularly lucrative major against which to hedge their SLD.

In order to obtain a mortgage or a credit card, however, individuals must have good credit---

something also absent from student loan applications. As a matter of fact, after the passage of the

Credit Card Accountability Responsibility Disclosure Act of 2009, individuals can no longer open a

credit card account until they reach 21 years of age, unless there is proof of employment or a

cosigner. Credit cards, which often carry debt limit ceilings, are now harder to obtain than student

loans.

Even in 2008, with the collapse of the financial sector and subsequent credit crunch, over

2,000 private lenders still existed and offered a variety of student loan options. The Department of

Education also contacted college financial aid offices and found no instance in which an eligible

student was unable to get a federal loan. In short, unlike other financial products student loans were

plentiful and readily available.

The federal government currently offers an unsubsidized loan with a fixed interest rate of 6.8

percent and subsidized loans available at a fixed rate of 3.4 percent. These loans also come with

lower fees and flexible repayment terms, where students can file for economic hardship and receive

deferments for extended periods of time. While there are limits to the amount students are allowed

to accrue per year, it ranges between $3,500 as a dependent freshman to $12,500 for an independent

senior to $20,500 for graduate students (The New School, 2011). Private lenders, on the other hand,

tend to require good credit scores or cosigners, and come with variable interest rates ranging from

     

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six to 14 percent. In 2008, the average age of college graduates is about 22, and the average amount

of debt they leave with is $23,000, with an additional $30,000 accumulated for master’s degree

students (Kantrowitz, 2012). College graduates’ investment in their education is not a tangible capital

benefit that can be sold. Graduates cannot easily get rid of SLD, nor can they discharge it in

bankruptcy—like houses and mortgage loan debt.

Today’s Student Loan Debt Outlook

According to the Department of Education, SLD has increased sevenfold from 1999 to

2008, and outstanding SLD has risen from $100 billion to about $805 billion. The unemployment

rate for 20 to 24-year-olds is nearly 15 percent, higher than the October 2011 national

unemployment rate of 9.1 percent. Student loan delinquencies have risen from 6.5 per cent in 2003

to 11.2 percent in June 2011, nearly as high as the 12.2 per cent rate on credit cards (Kennard &

Bond, 2011). A 2011 report by Moody’s Analytics stated, “Unlike other segments of the consumer

credit economy, student loans have not demonstrated much improvement in performance despite

some improvement in the broader economy.” Tuition has even outpaced costs of energy, health

care, and housing costs in the U.S (Deritis, 2011). Additionally, as many state budgets have been cut,

several public universities have been forced to raise tuition rates and cut grants—causing an even

larger share of education expenses to fall to students.

According to the National Association of Realtors’ 2010 report, the average age of first time

homebuyers in the US remains high at 34 years old with an average home sales price of $168,000. As

tuition and SLD continues to increase, there is concern that college graduates might not be

financially prepared to buy a home as early as 34 years of age. People aged 25 to 34 made up 27

percent of all homebuyers in 2011, the lowest share in the past decade and six percentage points

below their share in 2001, according to the National Association of Realtors. In 2006, 67 percent of

     

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college seniors planned to move home due to effects of the recession; in 2010 that percentage

jumped up to 85 percent of graduates (Dickler, 2010). “Students coming out of college are burdened

with more debt than traditionally they have been, and they are also coming into an economy that is

underperforming previous recoveries,” says Rick Palacios, a senior research analyst at John Burns

Real Estate Consulting in Irvine, California. He also says first-time buyers are key to a housing

recovery because they allow current owners to move into larger, pricier homes. Only nine percent of

29 to 34 year olds got a first-time mortgage from 2009 to 2001, compared with 17 percent ten years

earlier (Willis, 2012).

Due to increased tuition and SLD, as well as frozen wages for the average graduate, it is time

to evaluate existing practices and policies surrounding the potentially unsustainable levels of debt

that are being accumulated and what economic implications this debt has for households.

LITERATURE REVIEW

The permanent income hypothesis suggests that people will spend money at a level

consistent with their expected long-term permanent income (Friedman, 1957). In essence, it implies

that younger individuals will consume at higher levels than their current incomes justify, with the

expectation of increased income in the future. This hypothesis is relevant to the study of SLD, as a

stated goal of the student lending programs is enabling students to finance their education in

anticipation of better jobs with higher future income. Therefore, SLD should not affect

consumption for recent graduates. Recent studies, however, seem to indicate otherwise.

According to a report by College Board, there is a correlation between higher levels of

education and higher earnings across all ethnic groups and gender. The income gap between high

school graduates and college graduates has increased significantly over time; average full-time

     

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workers with a bachelor’s degree earned 62 percent more than high school graduates. People with 4-

year degrees are also less likely to be unemployed or living in poverty.

Regarding student debt, a majority of those in repayment report that the benefits of

educational opportunities made possible through student loans were well worth the problems

associated with paying them off. There are indications, however, that negative attitudes toward

education debt are increasing over time, especially for low-income families (Baum & O’Malley,

2002). Since 2000, college graduates in the US have seen real, inflation-adjusted wages deteriorate,

making their futures more precarious (Shierholz, 2011).

Future Decision-Making with Education Debt

When analyzing the economic impact of SLD on a population, it is important to consider

how the presence of SLD alters decision-making. Graduates with debt were more likely to enter

careers in industries such as consulting or banking, rather than government, nonprofit or education

sectors. After gains in the 1980s and 90s, hourly wages for newly college-educated men in 2000 were

$22.75, but that dropped by almost $1 to $21.77 by 2010. For women, hourly wages fell from

$19.38 to $18.43 over the same time period (Shierholz, 2011). These economic realities also push

graduates to jobs with higher projected future incomes.

A paper by Rothstein and Rouse (2007) suggests, through a natural experiment, that the

existence of SLD shifts the types of jobs taken by recent college graduates. In the early 2000s, a

highly selective university, which they refer to as “Anonymous University” phased in a “no-loans”

policy where student financial aid was replaced with grants. They compared the treatment group of

those students given the grants to students attending the university before and after the program

that did not receive the treatment of no-strings-attached education grants. The authors employed a

difference-in-differences estimator combined with control function and instrumental variable

methods.

     

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Their model proposes that in the presence of debt aversion or credit constraints, debt will

lead students to substitute toward high-salaried jobs. Aid recipients shifted out of industries with

high average salaries and into lower-salaried industries, while there was little to no change in the

types of jobs taken by students not given aid. They found that an extra $10,000 in SLD reduces the

likelihood that an individual will take a job in lower-salary industries like education, government and

nonprofits by about five to six percentage points. Their findings suggest that in order for graduates

with debt to afford other things, like a home, they would need to take higher-paying jobs. It hints

that considering SLD as ‘good debt’ might be an oversimplification, as graduates take jobs they

know will help them pay down that debt and have the income to be able to participate in the

economy in other ways.

Another publication by Kantrowitz (2011), examines the role of SLD in choosing to attend

graduate and professional school. He utilized data compiled by the National Center for Education

Statistics (NCES), their National Postsecondary Student Aid Survey (NPSAS), along with the

Datalab function provided by the NCES to perform a multivariate regression analysis.2 He found

that varying amounts of debt do not have much of an impact on attending graduate school, but the

mere presence of SLD does. From there he concluded that bachelor’s degree recipients with no

undergraduate SLD are 70 percent more likely to enroll in graduate or professional school than

students who graduate with some debt. There are also claims that each additional $10,000 in SLD

accounts for a seven-percentage point or more decrease in the likelihood of being married (Gicheva,

2011). If the presence of SLD deters people from attending graduate school or getting married,

there could also be similar implications for buying a home and taking on mortgage loan debt.

                                                                                                               2 The NPSAS data is restricted. As such, the NCES has provided a way for researchers to create charts, tables, and regressions without accessing the raw data itself with Datalab’s PowerStats function. (http://nces.ed.gov/surveys/npsas/)

     

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What Constitutes Unsustainable Debt Levels?

Guidance set by the US Department of Housing and Urban Development states that rent or

mortgage payments should not constitute more than around 28 percent of a household’s gross

income, or the household could be faced with financial hardship.3 In the student-lending world,

however, such financial hardship calculations are not independently considered outside of mortgage

debt. Therefore, researchers have relied upon a generic eight percent rule regarding acceptable SLD

levels.4 Baum and Schwartz (2006) attempt to fill that gap with their study on defining benchmarks

for manageable SLD on its own, as opposed to being a function of mortgage debt. They found that

graduates with less than seven percent rarely expressed concern, but when the debt-service ratio was

between 7 to 11 percent of income borrowers began expressing discomfort. More than 17 percent

of income spent on SLD created a significantly higher burden for borrowers (Baum & Schwartz,

2006).

The authors utilized an OLS multivariate regression to examine the correlation between the

burden index obtained by the NSLS and the SLD-service ratios. They found that there is a strong

positive relationship between the burden index and the debt-service ratio. As the debt-service ratio

increases, the burden of repayment increases as well but at a decreasing rate. They note that while

there is no ideal SLD payment-income ratio, they do conclude that this ratio should never exceed 18

to 20 percent and if debt levels continue to rise rapidly, as they currently are, increasing numbers of

students could face serious financial difficulty (Baum & Schwartz, 2006).

However, others suggest that declaring a particular amount of debt as “too high” is overly

simplistic. The author states that total SLD does not matter as much as people think; it is the terms

of repayment, such as the interest rate, amortization period, as well as the borrower’s potential

                                                                                                               3 Guidelines set by Fannie Mae range from 33-41% of income. 4 The Baum and Schwartz piece notes several instances where studies defaulted to the 8% standard.  

     

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future income (Usher, 2005). In Sweden, for example, students graduate with much more debt than

in the US, but they are given ample time to repay their debt and with very low interest rates. In a

recent paper by Christopher Avery and Sarah Turner, they boldly claim that suggesting “borrowing

is “too high” across the board can—with the possible exception of for-profit colleges—clearly be

rejected…[and] media coverage proclaiming a “student loan bubble”…runs the risk of inhibiting

sound and rational use of credit markets to finance worthwhile investments in collegiate attainment”

(2012).

Other Consumer Debt

In 2007, the collective debt of US households was $13.8 trillion, or ninety-eight percent of

GDP, which amounts to 138 percent of disposable income (New America Foundation, 2009). In

real dollars, the median value of debt held by American families has increased from $24,000 in 1989

to $67,300 in 2007. The average American family in 2007 devoted 18.6 percent of the family’s

income to debt payments (SCF Chartbook, 2007). Over 75 percent of households have one or more

credit cards, with median balances rising 25 percent from 2004 to 2007. Of the 73 percent of

households with credit cards in 2007, 60.3 percent carried balances.

Debt service burdens seem to be associated with higher delinquencies on consumer loans.

An analysis studying the debt service burden indicates that lagged debt service burden is a

statistically significant predictor of current delinquencies, which suggests that it is a good metric to

use as an indicator of household distress. However, the author’s results demonstrate that the debt

service burned does not have a significant effect on spending. He insists that the results simply

indicate that there is no direct and consistent short-term impact of debt service burdens on spending

that is discernable using simple regression models (Maki, 2001).

King (1994) studies the impact of an outside economic shock to a household’s expected

future income. His analysis suggests that this shock causes households to cut spending to reduce

     

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their debts, which leads to an eventual fall in overall national economic output. High debt burdens

transform an initial shock into something of much greater impact than if original household debt

burdens had been lower. If households are focused on paying off large SLD burdens now, they will

likely not be participating in the housing market.

Carroll and Dunn (1997) looked at the relationship between household debt growth and

consumption, finding that credit growth is positively related to future consumption. Their model

focused on loosening down-payment constraints, which lead to an increase in debt loads. Though

households with higher debt loads became more sensitive to higher levels of consumption relative to

unemployment expectations. They interpreted those results to mean that higher debt amounts in the

1980s were a symptom of financial deregulation, and made households more sensitive to changes in

future expectations. These publications reflect similarities in the current state of the economy, that

less job availability, frozen wages, and higher debt loads from student loans may make many younger

people averse to accruing more debt or in this case, obtaining a mortgage.

A paper by Ekici and Dunn (2006), further segmented this finding, by investigating the

relationship between credit card debt and consumption using household level data, employing

lagged values of debt as explanatory variables for consumption growth. Utilizing an OLS model,

they found a negative relationship between lagged debt and consumption growth, where a one

thousand dollar increase in credit card debt results in a decrease in quarterly consumption growth of

almost two percent, further solidifying my model’s theoretical construct.

Research by Becker and Shabani (2010) analyzed the effect of debt on the household

portfolio, more specifically the effect of mortgage debt on investment decisions using the portfolio

choice model. They used data from the Survey of Consumer Finances from 1989 to 2004 and found

that households with mortgage debt are ten percent less likely to own stocks and 37 percent less

likely to own bonds compared to similar households with no mortgage debt. As long as the

     

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household has outstanding mortgage debt, it prefers to repay that debt rather than invest in stocks

or bonds that offer a lower rate of return. Another publication reveals that high debt to service

ratios alone do not indicate higher sensitivity of consumption to a change in income, but that the

debt to service ratio may help identify borrowing constrained households (Johnson & Li 2007). If

the presence of mortgage debt leads households to be investment averse, perhaps the same is true of

households with SLD who could invest in a home mortgage.

In the 1980s single house mortgage rates were employed, instead of risk-based pricing that

became the norm in the early 1990s. Risk-based pricing is used to determine the kind of mortgage

interest rate afforded a household, where lenders charge borrowers a specific rate based on credit

risk (Edelberg, 2003). Edelberg suggests that past credit history helps inform the mortgage interest

rates given to borrowers, as risk premiums for mortgages have risen over time by a significant

amount. SLD could add a risk premium to mortgage interest rates obtained by SLD holders.

Another study primarily analyzing credit cards shows that higher interest rates also lead to

substantially less borrowing, reveling that people are sensitive to interest rates. This detail becomes

more apparent when dealing with young people and newer accounts (Gross & Souleles, 2001).

Other publications focused on the impact of various payment tool uses, such as debit cards and their

impact on household debt. The authors found that households are more likely to use debit cards

when there is not a revolving debt tendency, concluding that debit card usage discourages the

accumulation of household debt (Lee, Abdul-Rahman & Kim, 2007). In general, it seems that

households are relatively debt-averse when their current debt loads are high, especially when

employment levels and economic conditions are unfavorable.

There is a substantial body of literature on consumer debt and its implications, but the

research lacks a clear understanding of how SLD impacts household finances and perhaps the

greater US economy at large. My analysis will contribute to this field with the addition of SLD to

     

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existing research on consumer household debt.

CONCEPTUAL FRAMEWORK AND HYPOTHESES

This research seeks to answer the following question: Does SLD have an effect on mortgage

interest rates and debt totals? The conceptual framework guiding this analysis draws from existing

literature and employs an ordinary least squares multivariate regression model. It accounts for

changes over the past fifteen years and incorporates household-level and economic-year controls. I

developed my conceptual framework based on extant literature, adopted from a combination of

Kantrowitz, Becker and Shobani, and Gicheva’s models. Kantrowitz focuses on how the presence of

SLD impacts, controlling for other debts, the decision to go to graduate school; while Gicheva looks

at the impact of SLD on family formation with an OLS specification. Becker and Shobani, on the

other hand, direct their analysis to the relationship between mortgage debt and investing in stocks

and bonds. These publications include household demographic controls as well. A blend of these

models, including control variables and econometric specifications, helped inform my model design.

I employ a conceptual framework that bases the relationship between SLD and mortgage metrics on

the assumption that SLD holders have become more debt averse, and I utilize controls accounting

for other consumer debt and demographic characteristics (See Figure 1 for a summary of those

relationships).

     

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Figure 1: Relationship Between SLD and Mortgage Amounts/Interest Rates5

The mean cumulative SLD accrued per household varies from year to year, as do mortgage

rates and loans. This paper examines the extent to which the variation in mortgage rates and

mortgage debt is explained by SLD and how it has changed from 1992 to 2007. In previous studies,

researchers have found a negative relationship between investing and debt levels, and I anticipate a

similar result in my analysis on mortgage rates. The literature reviewed indicates that the mere

presence of SLD lessens the likelihood of an individual to accrue more debt as it pertains to

additional schooling, and I predict a comparable relationship with mortgage loan totals. Thus, my

hypotheses for this study are as follows:

H0: SLD, after controlling for financial and demographic characteristics of a household,

has no association with mortgage rates or mortgage debt totals.

HA: SLD, after controlling for financial and demographic characteristics of a household

has a positive relationship with mortgage rates.

HB: SLD, after controlling for financial and demographic characteristics of a household,

has a negative relationship with total mortgage loan debt.

                                                                                                               5 This framework shows the intended model construction, with mortgage interest rates as a proxy for long-term microeconomic effects. “Other debt” will include credit cards, automobile and other consumer loans, like title or payday loans. The double-sided arrows account for possible endogeneity.

Mortgages  Income  

Marital  Status  

Age  

Other  Debts  Student  Loan  

Debt  

Education  Level  

Race  

Employment  Status  

     

  17  

DATA AND METHODS

My sole data source is the Survey of Consumer Finances (SCF), which is conducted on a

triennial basis by the Board of Governors of the Federal Reserve System in cooperation with the

Statistics of Income Division of the Internal Revenue Service. It is a dual-frame, cross-sectional

survey in which two-thirds of the respondents comprise a representative sample of US households.6

The unit of analysis for which the data was compiled is on a household level, or the “primary

economic unit” (PEU). The PEU consists of an economically dominant single individual or a

couple who is financially independent. The survey respondent is typically male and the elder member

of the household. The SCF was conducted primarily by phone and obtained over 4,000 responses

per survey per year.

I pooled the data from 1992 to 2007 and employed time year dummy variabes in order to be

able to control for year-to-year differences in economic performance, federal fund rates, etc. in my

final regression analyses. When pooled, my data contained 52,258 households. After eliminating

households without mortgage interest rates or mortgage debt, my sample for the regressions

decreased to 24,730 and 24,826 households respectively.

The sample I examine includes household-level economic indicators such as pre-tax income,

credit card debt totals, number of credit cards, automobile loans, and includes demographic

controls, like age, race and gender (See Table 1 for a comprehensive list of variables included in my

explanatory model). The subsample I utilized for my analysis includes only those aged 20 to 45 years

of age. After running a descriptive statistics analysis, I discovered that a majority of SLD lies within

that age range, at just over 60 percent of all student loans. Marginal increases in cumulative SLD for

                                                                                                               6 The remainder of the sample is an oversampling of wealthy households.

     

  18  

households over the age of 45 were very small, at less than one percent per additional year of age.

There is still a robust sample after removing people older than 20 and under 45 years of age.

Table 1: Select Variables Used in Explanatory Model from the SCF 1992-2007

Variable Name Variable Code Variable Measure Descr ipt ion Economic Indicators: Mortgage Interest Rates mortrt Dependent Continuous, Rate* Total Mortgage Amount mortamt Dependent Continuous, Total amount of

mortgage debt in Dollars Has SLD hasSLD Independent Binary, Yes = 1; Does not have SLD

= 0 SLD Total sldtotal Independent Continuous, Total amount of SLD in

Dollars Number of Credit Cards numbercc Independent Continuous, Number of credit cards

held by household Total Credit Card Debt cctotal Independent Continuous, Total amount of credit

card debt in Dollars Total Car Loan Debt totalcarloans Independent Continuous, Total amount of

Automobile debt in Dollars Total Other Debt otherloantotal Independent Continuous, Total amount of

Consumer debt in Dollars Employment Status employed Independent Binary, Employed = 1; Not

Employed = 0 Pre-tax Income pretaxincome Independent Continuous, Reported in Dollars per

Year Demographic Indicators: Age age Independent Continuous, Age in Years (20-45) Race racedum Independent Binary, White = 1; Other = 0 Marital Status married Independent Binary, Married = 1; Not

Married = 0 Education Level collegedegree Independent Binary, Obtained College Degree =

1; Less than College = 0 *In the SCF, mortgage interest rates are reported as multiplied by 100.

I chose mortgage loan totals and mortgage interest rates as my dependent variables to

represent proxies for long-term microeconomic impact because they have lengthy terms and

typically represent a household’s largest liability. Households’ abilities to consume are constrained by

     

  19  

bills, and as mortgages tend to be Americans’ largest and longest-term bill, it is a good indicator for

long-term economic impacts among this population.

As mentioned (and shown in Table 2), the average mortgage rate across all households

surveyed from 1992 to 2007 is 8.79%. With a minimum rate of one and a maximum rate of 33, there

is suffieicnet variation within this variable to explore utilizing a regression analysis.

Table 2: Descriptive Statistics of Mortgage Interest Rate

N Mean SD Minimum Maximum 21,440 878.68 381.04 100 3300

*Results Reported in constant 2007 dollars.

The average mortgage loan debt across households in this study is $178,506 (See Table 3).

Here there is also considerable variation across mortgage loan debt totals in order to analyze with a

regression analysis.

Table 3: Descriptive Statistics of Mortgage Debt Total

N Mean SD Minimum Maximum 21,530 $178,506 $242,908.40 $671.44 $3,780,000

*Results Reported in constant 2007 dollars.

In the absence of credit scores, I focused household-level economic indicators on other

forms of consumer debt, or installment debt that typically has negative effects on a credit rating and

therefore mortgage interest rates and mortgage loan amounts. I did this in order to control for

variation in the mortgage interest rate and total amount of mortgage loan taken out. The mean

income of the typical household in this study is $52,992. The average household has about 2 credit

cards, $5,143 in credit card debt, around $13,000 in car loan debt, and a smaller population in this

sample have about $18,000 in other consumer loan debt (See Table 4 for specific figures).

     

  20  

Table 4: Descriptive Statistics of Household Financial Characteristics

Financial Characteristic N Mean SD Minimum Maximum Pre-tax Income 37,623 $52,991.80 $384,772 $0 $29,650,980

Total Credit Card Debt 23,859 $5,143.05 $8,400.87 $0 $143,000 Number of Credit Cards 46,400 1.69 1.85 0 12 Total Car Loan Debt 18,367 $12,682.43 $11,200.06 $0 $145,902.60

Total Other Debt 7,701 $17,978.24 $147,333.70 $0 $3,819,927

My analysis also controlled for various demographic characteristics, which were best

represented by creating binary variables. These descriptive statistics suggest that in an average

household, based on this sample of data:

• 83.3% of households are employed

• 54.3% are married

• 73.3% are white (includes Caucasians, Middle Easterners, and Arabs)

• 78.8% of survey respondents are male

• 40.9% have college degrees (level of degree left unspecified)

Regarding my independent variable of interest, the SCF gives each household the ability to

report on up to six student loans. In order to capture the cumulative effect of SLD on a household’s

finances, I combined the six loans for each household into one variable, in addition to including a

binary variable for having SLD versus not having any SLD. I employed the same method for

examining cumulative mortgage loan total, as well as credit card debt, automobile loans, and other

loan debt and combined all loans reported into one variable figure.

Table 5 illustrates trends in percentile changes of SLD for every three years from 1992 to

2007. With the exception of 2004, the data reveals that SLD in each percentile has steadily increased

over the years. From 1995 to 2007, SLD for the 25th percentile of borrowers has almost doubled;

from 1992 to 2007 in the 90th percentile SLD has almost tripled.

     

  21  

Table 5: Trends in Percentile Changes of Cumulative SLD

Years 25th 50th 75th 90th 1992 $3,695 $7,389 $14,779 $25,123 1995 $3,492 $7,654 $16,115 $26,858 1998 $5,724 $12,720 $25,441 $44,521 2001 $6,088 $14,049 $28,098 $50,343 2004 $5,488 $13,172 $27,441 $52,686 2007 $10,000 $18,000 $34,350 $64,500

*Results Reported in constant 2007 dollars.

Mean and median SLD per household has also sharply increased over the past fifteen years

(See Figure 2). The totals for the year 2004 continue to defy present trends with a slight dip in both

mean and median figures. These numbers were determined by focusing on those households who

reported having SLD, as opposed to including households without SLD.

Figure 2: Mean and Median SLD Trend

*Results Reported in constant 2007 dollars.

$0    

$5,000    

$10,000    

$15,000    

$20,000    

$25,000    

$30,000    

1992   1995   1998   2001   2004   2007  

Years  of  SCF  

Student Loan Debt Trends: Mean & Median

     

  22  

RESULTS

By conducting an ordinary least squares multivariate regression analysis, I was able to test my

hypotheses and understand the different elements that play a role in the relationship between

mortgage interest rates and mortgage loan debt and SLD. The variables of interest are hasSLD and

sldtotal, which account for households with SLD and SLD totals for each year of the SCF’s

completion. Utilizing a pooled model incorporating 1992 through 2007 allows me to calculate

standardized coefficients that are comparable over the years to determine the effect SLD has had on

mortgage rates and debt totals. The models for these analyses are as follows:

Mortrt(1) = βO + β1sldtotal + β2hasSLD + β3sex + β4pretaxincome + β5otherloantotal + β6carloantotal + β7age + β8cctotal + β9numbercc + β10collegedegree + β11marriedt + β12employed + β13race + ϵ

Mortrt(2) = βO + β1sldtotal + β2hasSLD + β3sex + β4pretaxincome + β5otherloantotal + β6carloantotal + β7age + β8cctotal + β9numbercc + β10collegedegree + β11married + β12employed + β13race + time year dummy vars + ϵ

Mortamt(3) = βO + β1sldtotal + β2hasSLD + β3sex + β4pretaxincome + β5otherloantotal + β6carloantotal + β7age + β8cctotal + β9numbercc + β10collegedegree + β11married + β12employed + β13race + ϵ

Mortamt(4) = βO + β1sldtotal + β2hasSLD + β3sex + β4pretaxincome + β5otherloantotal + β6carloantotal + β7age + β8cctotal + β9numbercc + β10collegedegree + β11married + β12employed + β13race + time year dummy vars + ϵ

Mortgage interest rate and mortgage debt amount are the dependent variables for two

separate analyses in this study. The models attempt to explain the variation in mortgage interest rates

and mortgage debt obtained by households with SLD. Mortgage rate, in this analysis, is defined as

the rate with which mortgage loans are being repaid. It includes up to three mortgage rates, for

multiple mortgages compiled into one variable. Mortgage debt amounts as the second model’s

     

  23  

dependent variable allow for up to three reported mortgage loan totals, which are combined for a

cumulative mortgage debt total. All monetary figures are reported in constant 2007 dollars.

Model 1: Mortgage Interest Rates and Student Loan Debt

Before including time-year dummy variables in my analysis, it was important to determine

whether or not there was a relationship between having SLD and mortgage interest rates within the

pooled data in general.

     

  24  

Table 6: OLS Regression Results with Fixed Year Effects of Mortgage Interest Rates

Model 1: Basic Model 2: Full Model with Year Fixed Effects

Has SLD 57.20*** (9.18)

56.90*** (8.94)

SLD Total -0.0010*** (0)

-0.00009 (0)

Age 2.20*** (0.46)

2.60*** (0.45)

Marital Status 24.76*** (7.72)

-1.76 (7.37)

Sex 15.31 (9.89)

19.93** (9.45)

Race 20.33*** (6.89)

-5.94 (6.58)

College Education -72.10*** (5.50)

-82.43*** (5.18)

Employment Status 16.93 (10.69)

32.84*** (10.36)

Pre-tax Income -0.00002*** (0)

-0.00002*** (0)

Credit Card Debt Total 0.0008** (0)

0.0026*** (0)

Number of Credit Cards -5.61*** (1.53)

-4.67*** (1.45)

Automobile Loan Total -0.0006*** (0)

0.0005** (0)

Other Consumer Loan Total .00004*** (0)

.00002 (0)

1995 Year Dummy ------ -110.68*** (8.22)

1998 Year Dummy ------ -124.59*** (8.54)

2001 Year Dummy ------ -157.50*** (10.26)

2004 Year Dummy ------ -351.42*** (9.08)

2007 Year Dummy ------ -331.61*** (7.95)

Constant 776.43*** (20.85)

937.37*** (21.17)

Observations 21,440 21,440 R-Squared 0.015 0.114

Robust standard errors in parentheses. ***p<.01 **p<.05 *p<.1

     

  25  

In Table 6 Model 1, the coefficient estimate of having SLD is statistically significant at the

one percent level, as is the SLD total variable’s coefficient. All the variables are statistically

significant except for sex and employment status. I am particularly surprised by the coefficient on

employment status, as most of the literature on mortgage rates and the ability to obtain a mortgage

in general, require the borrower be employed.

This analysis reported a very low R-squared value of 1.5 percent, indicating that little of the

variation in mortgage interest rates are accounted for by the current structure of my model. The low

R-squared suggests that there is little practical value of the relationship between having SLD and

SLD totals absent other control variables.

Model 2: Full Model with Time Year Fixed Effects-Mortgage Interest Rates

In order to account for year-to-year differences over time, like fluctuations in the federal

funds rate, unemployment rates and various other economy-wide differences between the years, my

analysis employed the use of time-year dummy variables to control for those unique and systematic

characteristics. After employing these controls, having SLD has a similar effect on mortgage interest

rates as the results identified in Model 1. The coefficients are of similar magnitude and sign,

although the addition of time-year dummies decreased the magnitude by about .3. They both,

however, maintain the same level of statistical significance with a p-value of 1 percent. SLD total

also lost its statistical significance and decreased in magnitude with the inclusion of time-year

dummies, but both remain negative. The model itself is also statistically significant. However, it is

important to note that in tests of joint significance the variable has SLD and SLD total were

consistently jointly statistically significant at the 1 percent level throughout this analysis. Similar to

findings by Mark Kantrowitz, the presence of SLD seems to have a greater impact and magnitude

than the total SLD variable.

     

  26  

Of the independent variables, the household finance coefficients (credit card debt total,

number of credit cards, automobile loan total, pre-tax income) remain statistically significant for

both models, with the exception of other consumer loan totals and employment status, which switch

from statistical significance to not being statistically significant, respectively, with the addition of the

year dummies. Household characteristics of marital status, sex and race all changed in statistical

significance from Model 1 to Model 2.

Model 2 also indicates an increase in R-squared, revealing that the addition of time-year

dummies increased the amount of variation explained by the model estimation by 11.4 percent.

While that is a noteworthy increase, there is still little applicability of this model in determining

variation in mortgage interest rates. There are also a multitude of factors on a lending institution’s

behalf that go into the size of a mortgage interest rate offered to a potential homebuyer.

Table 7: Predicted Effect of SLD on Mortgage Interest Rates

25th-75th SLD Percent i l e Range Marginal Rate Increase 1992 $3,695 - $14,779 56.57 -- 55.57 1995 $3,492 - $16,115 56.59 – 55.45 1998 $5,724 - $25,441 56.38 – 54.61 2001 $6,088 - $28,098 56.35 – 54.37 2004 $5,488 - $27,441 56.41 – 54.43 2007 $10,000 - $34,350 56.00 – 53.81

*Mortgage interest rates multiplied by 100 in the SCF. *Reported in constant 2007 dollars.

Given the outcome of the f test of joint significance, predicted values must be calculated in

order to be able to interpret the results from the coefficients produced by the full model ordinary

least squares regression results. I utilized the following formula to calculate the predicted range of

mortgage interest rates for each year of the SCF’s completion and the corresponding ranges of SLD:

Marginal Rate Increase = hasSLD Coefficient + Student Loan Debt Amount * sldtotal Coefficient

     

  27  

Table 7 indicates that the more SLD a household has, the smaller their corresponding

mortgage interest rate premium will be added to their mortgage interest rate. I assumed that as SLD

increased, the corresponding additional interest rate would increase, but this show the opposite

effect. However, it still suggests that there is a premium added onto mortgage interest rates for

borrowers with SLD. Given the statistical significance and magnitude of my variables of interest,

particularly hasSLD, my alternative hypothesis that there is a positive association between SLD and

mortgage interest rates (though at a decreasing rate) is supported.

Model 3: Total Mortgage Loan Debt and Student Loan Debt

To determine SLD’s full impact on mortgages for SLD holders, I included an analysis of

SLD’s effect on mortgage loan totals. For this second phase of my analysis, I employed an additional

ordinary least squares regression analysis utilizing Stata software to test my hypothesis about the

relationship between SLD and mortgage loan totals.

     

  28  

Table 8: OLS Regression Results with Fixed Year Effects of Mortgage Debt Totals

Model 3: Basic Model 4: Full Model with Year Fixed Effects

Has SLD -58,354.07*** (3,981.29)

-56,167.21*** (3,756.99)

SLD Total .9409*** (0.213)

0.6585*** (0.202)

Age 3,426.13*** (236.48)

3,240.06*** (235.23)

Marital Status 31,600.60*** (4,327.02)

35,659.24*** (4,226.29)

Sex 44,923.25*** (4,412.91)

41,101.35*** (4,385.14)

Race -1,385.92 (3,780.77)

3,728.87 (3,862.73)

College Education 95,396.60*** (3,345.57)

93,288.68*** (3,276.90)

Employment Status 13,670.71*** (4,315.12)

11,220.92*** (4,263.08)

Pre-tax Income 0.071*** (0.02)

0.0693*** (0.019)

Credit Card Debt Total 0.5922 (0.436)

0.2681 (0.442)

Number of Credit Cards 9,837.79*** (1,039.29)

9,846.33*** (1,032.56)

Automobile Loan Total -0.4418** (0.146)

-0.5786*** (0.147)

Other Consumer Loan Total

0.1661*** (0.024)

0.1705*** (0.023)

1995 Year Dummy ------ -19,193.21*** (4,449.62)

1998 Year Dummy ------ 7,402.18 (5,078.09)

2001 Year Dummy ------ -3,707.17 (5,105.25)

2004 Year Dummy ------ 10,265.21** (5,162.95)

2007 Year Dummy ------ 85,377.75*** (6,617.45)

Constant -88,847.14*** (10,718.80)

-94,502.73*** (10,989.80)

Observations 21,530 21,530 R-Squared 0.114 0.134

Robust standard errors in parentheses. ***p<.01 **p<.05 *p<.1

     

  29  

The coefficient on having SLD and SLD total are both statistically significant at the 1

percent level in Model 3 and the model is also statistically significant. All of the variables employed

in this model are statistically significant at the one and five percent level, except for race and credit

card debt total, which are not statistically significant at any level. Based on evidence provided in the

literature review, the variable for credit card debt defies predictions with its small magnitude and

lack of statistical significance. The R-squared indicate that 11.4 percent of the variation in mortgage

loan totals is explained by the empirical model.

Model 4: Full Model with Time Year Fixed Effects-Mortgage Debt Totals

This model contains all the variables of interest for this part of the analysis, is statistically

significant and explains slightly more of the variation in mortgage debt totals (R-squared = .134)

than Model 3. The variables for having SLD and SLD total are both statistically significant and

maintain the same sign as in Model 3. They are also both jointly statistically significant at the highest

level (p<.01). With the addition of year fixed effects, the magnitude of the variable hasSLD

decreased by about 2,000.

While most of the controls continued to support the theoretical construct of the model,

credit card debt total continues to fail to support the expected results, its magnitude remaining small

and the coefficient not statistically significant. Race and credit card debt total remained not

statistically significant in both Model 3 and 4

Again, however, the R-squared results indicate that there is little practical significance of the

relationship between having SLD and mortgage debt accrued by households with an R-squared of

.134. With individual or household-level data, it is commonplace to have R-squared values that are

lower. This smaller value is due to the many factors involved in household decision-making. There

     

  30  

are innumerable factors that go into a household’s decision to buy a smaller home or take on smaller

mortgage loan, which may not be accounted for in my model specification.

Table 9: Predicted Effect of SLD on Mortgage Debt Amounts

25th-75th SLD Percent i l e Range

Corresponding Mortgage Amounts

1992 $3,695 - $14,779 -$53,734- -46,435 1995 $3,492 - $16,115 -$53,868- -45,555 1998 $5,724 - $25,441 -$52,398 - -39,414 2001 $6,088 - $28,098 -$52,158 - -37,665 2004 $5,488 - $27,441 -$52,553 - -38,097 2007 $10,000 - $34,350 -$49,582 - -33,548

In Model 4, the variables hasSLD and sldtotal are jointly statistically significant and therefore

must be interpreted utilizing a predicted means equation-- similar to the one from the previous

formula for mortgage interest rates:

Marginal Mortgage Debt Decrease = hasSLD Coefficient + Student Loan Debt Amount * sldtotal Coefficient

Table 9 indicates that households with less loan debt generally obtain smaller mortgage loans;

as increases in SLD lessen the negative impact in the amount of mortgage debt obtained. This trend

could be attributed to households who obtain more education and therefore more SLD, who receive

graduate degrees or become doctors or lawyers with higher incomes who are able to take on more

mortgage loan debt. These figures also indicate that SLD is becoming less and less of a negative

factor in how much mortgage loan debt is obtained. Falling within the 25th percentile of SLD

amounts in 2007 contributes to a smaller average mortgage loan of about $50,000, whereas in 1992 a

paltry $6,088 of SLD meant household would have a mortgage loan that averaged to be about

$54,000 less. Perhaps, as SLD has increased over the years mortgage lenders and credit scoring

agencies have become more accustomed to its presence and factor it into households’ “ability to

     

  31  

pay” as a positive. While I anticipated the opposite result: the more SLD a household maintained the

smaller mortgage could be obtained, it is still clear that the presence of SLD (and not necessarily its

magnitude) negatively impacts the size of mortgage loans, which supports my hypothesis. However,

perhaps as mentioned in the literature review, SLD is becoming “good debt” or an indicator for

future ability to pay a mortgage loan.

Additional Results

Throughout the research process, several media outlets have expressed that the amount of

SLD taken on by households has changed or increased to the point where it has reached a turning

point or crossed the Rubicon (Brown, 2012; Cunningham, 2011; Deritis, 2011; Indiviglio, 2011;

Kennard, 2011; Shierholz, 2011; WSJ Editorial Board, 2011). I decided to test that notion to see if

something is different about SLD in more recent years compared to earlier years. My hypothesis,

therefore, is presented as thus:

H0: μearly /later years = μall other years

Ha: μearly/later years ≠ μall other years

Table 10: Results of t-tests Comparing Early Years and Later Years of Households with SLD

Obs. Mean Std. Err. Std. Dev. t-statistic df Early Years (1992-1995)

3,032 13,308.79 346.68 19,089.43

All other Years

5,797 23,112.41 356.25 27,123.93

17.74***

8,827

Later Years (2004-2007)

3,072 25,265.53 522.51 28,960.68

All other Years

5,757 16,800.27 292.79 22,215.52

-15.29***

8,827

*t-test conducted utilizing a significance level of .05. (***p<.01) Results reported in constant 2007 dollars.

     

  32  

To determine if something was different, or if something had changed over the years that

made SLD a unique issue, an independent-sample t-test was conducted to compare SLD mean levels

for households falling in the early years and later years of this study. There was a significant

difference in the SLD totals in the early years (m= 13,309, sd= 19,089) and all other years (m=

23,112, sd=27,124); t(8,827)=17.74, p<.01. This portion of the analysis also tested the later years to

establish if its SLD mean was different from the other years in the SCF survey. There was also a

significant difference in SLD totals for the later years (m=25,266, sd=28,961); t(8,827)=-15.29,

p<.01. These results imply that there are significant differences between mean SLD levels among the

early years and later years of the survey’s household data, which could have lasting policy

implications for future generations if increases SLD continues on its current path.

DISCUSSION AND RECOMMENDATIONS

My findings suggest that the effect of SLD on mortgage interest rates and mortgage loan

totals, while statistically significant, are so small as to be inapplicable in a practical or public policy

sense. However, the study of the relationship between SLD and other economic factors is a

relatively new research topic and this analysis lays a foundation upon which further research can be

built.

Analysis and Data Limitations

This analysis presented many limitations that could have contributed to the small R-squared

for the regressions. As stated earlier in this analysis, there are many factors that contribute to a

household’s decision to buy a home and therefore take on mortgage debt. There are also many

     

  33  

factors that a lending institution has to consider when determining the size of the mortgage interest

rate given to a potential homebuyer. Also, utilizing mortgage debt totals and interest rates as a proxy

for consumption may be changing with newer generations, and could therefore alter mortgage

metrics over time even with a steady increase in SLD. As I pointed out earlier in the literature review

section, buying a home is trending downward, where less young people are getting mortgages (Willis

2012 & Dickler, 2010). Fed Chairman Bernanke at a homebuilders’ conference in February said,

“First-time home buyers are typically an important source of incremental housing demand, so their

smaller presence in the market affects house prices and construction quite broadly” (Willis 2012).

There were also limitations with the dataset chosen, where wealthy households were

overrepresented and SLD metrics are not as thoroughly reported on, for example, defaults, interest

rates, and deferments were absent from this survey. There was also no indication of whether or not

the SLD reported was for a child in the household or was for the head of the household or some

combination of household members’ debt, or whether it was for undergraduate or graduate school.

These factors might have made a difference in my analysis, as people with graduate degrees and

graduate school debt have different careers at different income levels. In short, the unit of analysis

added some issues to my study because there was no way to identify individual-level characteristics.

There could also be limitations in my assumptions--- that owning a home is an inherently

good thing, both for the economy at large and for the household unit. I also assume that owning a

home is something people in general want and strive to achieve, but this may not be the case and it

may have nothing to do with the economy or debt. Consumers’ preferences could be changing,

where renting and flexibility is preferred to owning and remaining immobile. Additionally, I presume

that homebuyers received less in mortgage loans due to their debt levels, and not due to their own

personal preference.

     

  34  

Policy Implications and Recommendations

The purpose of this study was to understand the extent to which there is empirical validity to

the hypothesis that SLD has an effect on mortgage interest rates and mortgage debt levels.

According to my analysis, SLD holders are burdened with higher interest rates and are forced to pay

more for a home or unable to afford a larger or nicer home. While my analysis shows there is a

statistically significant relationship between having SLD and these mortgage metrics, too little of the

variation in interest rates and debt totals is explained by my model. My belief prior to conducting

this analysis was that as SLD has risen over the years, the implications for potential homebuyers

were increasingly negative. However, while there are still ramifications for SLD holders, trends from

my analysis indicate that adverse implications are lessening over the years, as opposed to becoming a

greater burden. As cited in a piece by the New York Federal Reserve, 65.2 percent of people under

40 years old have outstanding SLD, meaning that $580 billion of the total $870 billion in SLD in the

US is owned by people younger than 40 (Brown, 2012). If a quarter of the population receives

undergraduate degrees (most of whom walk away with SLD) that is a substantial enough sample of

the population to where there could be microeconomic consumption repercussions.

Households with today’s levels of SLD will likely be constrained by their debt burden,

whether they are forced to take higher-paying jobs or forced to live at home with their parents, the

bottom line is with these higher debt loads they will be unable to consume at similar levels to the

generation before them. The United States is a consumption-based economy that depends on

continual growth, especially as it pertains to the housing market. We witnessed housing experts state

prior to the financial collapse that the housing bubble would never burst, but it did eventually

happen. Is there a looming SLD bubble that will also burst? Would that further hinder and

potentially decrease housing sales and growth? There will be consequences to the debt burdens

     

  35  

faced by these households, but only time will tell.

And what if these households cannot buy? Perhaps, they will rent. Rent in most cities is on

the rise, even with studies showing that purchasing a home is a better and more cost effective

investment (Trulia Trends Team, 2012). If this trend continues, subsidizing homeownership may not

be the most cogent way to spend taxpayer dollars. Furthermore, perhaps a more useful initiative to

spur economic growth would be additional college education subsidies.

Americans and Congress should reprioritize; get the nation back on track and able to

compete with countries with stronger federal education programs. I believe a focus on student loan

subsidies or increased grant funding, as opposed to continual support of the federal mortgage

interest rate subsidy, would do just that. While both federal and state governments do subsidize

college education already, these subsidies have weakened over the years, with grants steadily

disappearing and tuition continuing to rise. Students are left to bear the brunt of cost increases. We

only have a finite amount of resources and so perhaps we should incentivize investments in human

capital more than physical capital. This kind of investment could continue to contribute to the

national economy for many years to come. The American Dream of homeownership appears to be

changing, but the government’s policymaking surrounding housing subsidization has not changed to

reflect that notion.

Policymakers could also consider dropping SLD from bankruptcy exemption; there are other

less favorably viewed forms of debt that are currently eligible for discharge—like gambling or credit

card debt. Why should SLD be excluded? As another option, they could reinstate policies similar to

that of 1990 where debt holders could discharge SLD in bankruptcy after seven years of repayment.

Policymakers could also reevaluate inclusion of private student loans, and allow those loans to be

dischargeable, as private student loans are often incurred under much less favorable terms than

     

  36  

federal loans. Congress could also make permanent the 3.4 percent interest rate (down from 6.8

percent) for Stafford Loans that are set to expire in the summer of 2012, or could expand that rate

across all federal loans. At a minimum, these policy alternatives should be on the table and better

options should be available to borrowers who are the most burdened. National policies should be

focused on the struggling borrowers’ needs and not the whims of powerful lobbies and banks.

My analysis results indicate that SLD does, in fact, impact consumption as it pertains to

housing, where SLD holders pay a premium on their mortgage interest rates and tend to obtain

smaller mortgage loans, and I am confident that additional studies would yield similar results with

other forms of household spending. As SLD increases, without additional wages to offset the

burden, graduates are likely to continue to be financially constrained which could adversely impact

the greater US economy. With additional research and further examination of these effects, a more

nuanced understanding of the relationship between SLD and consumption patters will become clear

and will help guide policy makers going forward. My hope is that further research will lead to more

informed and better policy discussions that in turn will ease the burden of SLD on America’s

graduates.

     

  37  

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