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Does everyone accept a free lunch? Decision making under (almost) zero cost borrowing
Michael Insler
U. S. Naval Academy, Department of Economics, 589 McNair Rd, Annapolis, MD 21402, USA, Email:
[email protected] , Phone: 00-1-410-293-6881, Fax: 00-1-410-293-6899
James Compton
United States Navy, 185 Black River Rd, Long Valley, NJ 07853, USA, Email:
[email protected], Phone: 00-1-908-418-5828, Fax: 00-1-410-293-6899
Pamela Schmitt
U. S. Naval Academy, Department of Economics, 589 McNair Rd, Annapolis, MD 21402, USA, Email:
[email protected] , Phone: 00-1-410-293-6888, Fax: 00-1-410-293-6899
Running Head: Decision making under (almost) zero cost borrowing
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Abstract:
Purpose –
This chapter examines the debt-aversion of a group of college students who have the opportunity to take
out a sizable, low-interest, non-credit dependent loan. If the loan is simply invested in low-risk assets, it
would effectively yield a free lunch in net interest earnings.
Methodology –
The research uses survey data to examine demographic, socio-economic, personality traits, and other
characteristics of those willing and unwilling to accept the loan offer, as well as their intentions of early
repayment.
Findings –
Individuals willing to accept the loan tend to have prior debt, longer planning horizons, come from
middle-income families, and may have higher cognitive ability. Anticipated early repayment of the loan is
more likely among those with prior investments, no prior debt, from STEM majors, with upper income
parents, and those who expect to buy a home soon.
Research limitations –
We find no consistent relationships between debt aversion and intellectual ability or gender, but this
finding may be hampered by our small sample of female loan-rejecters. Our limited sample size also
precludes examining interactions between the dimensions of personality types.
Practical implications -
We suggest consideration of policies to encourage “smart” borrowing, focusing on the financially
disadvantaged, particularly for education loans.
Originality – This study examines a uniquely occurring natural experiment regarding the opportunity to
accept a non-credit dependent loan. Our results describe the behavior of young adults, an infrequently
studied yet important segment of the population, especially in the context of borrowing behavior.
JEL classification: C83; D03; D14; G11
Classification: Research paper
Keywords: Survey Methods, Behavioral Economics, Personal Finance, Investment Decisions
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Introduction
In their junior year, students at the United States Naval Academy (USNA) may accept a sizable
loan (up to $36,000) at interest rates at or below market rates of return on even very conservative
investments – that is, they are effectively offered a ‘free lunch’ in potential net interest.1 The federal
government does not subsidize these loans; the Navy Federal Credit Union (NFCU) and the United
Services Automobile Association (USAA) privately offer them in order to establish a relationship with
future officers who may then bank with them for many years. Since all graduates enter the armed services,
their future employment and salary schedules are stable and nearly identical, making the decision to
accept the loan very low risk (all must serve at least five years as junior officers in the United States Navy
or Marine Corps). This paper examines the characteristics of those willing and unwilling to take the loan,
characterizing the latter as debt averse. For those that accept the loan, we also study a weaker indicator of
debt aversion: whether they anticipate repaying it early. We explore how these two decisions relate to
individual and family demographics, socio-economic characteristics, personality traits (as measured by
the Myers-Briggs Type Indicator), cognitive ability (as measured by the Cognitive Reflection Test), and
intellectual ability (as measured by SAT scores and grade point average). We view this opportunity as a
uniquely occurring natural experiment, as all students receive the same loan offer regardless of credit
scores and personal income; in fact, students do not even apply for the loan – lenders directly advertise it
to them.
Despite the clear benefits associated with the loan opportunity, we observe that 10 percent of the
students do not take it; we conjecture that this is primarily due to debt aversion. It is important to
understand debt aversion if we hope to design policies and loan opportunities for groups that may have
difficulty obtaining credit due to socio-economic characteristics. Recent research has noted that even
1 For example, the United Services Automobile Association offers a $36,000 loan at 0.75% annual percentage rate.
According to the U.S. Treasury in February 2013, the annual yield on a 5-year security was roughly 0.9%; investing
$36,000 at 0.9% for five years, compounded annually, would return $37,649. But in that time, cumulative interest
payments for the 0.75% USAA loan would only total about $670. Thus students can profit nearly $1000, virtually
risk-free. Therefore, the decision to reject the loan is akin to refusing a free lunch.
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though credit has loosened since the recession of 2008-2009, young adults have continued to avoid taking
on debt (Fry, 2013). While this may be partially due to demographic factors—young adults are marrying
later, staying in school longer, and paying down education loan debt instead of taking out mortgages—
evidence also suggests an uptick in a “general aversion” to debt (Fry, 2013). It is important to understand
such a motive, as it has important implications for the fledgling housing market recovery, future
education prospects and costs, and smaller ticket purchases such as vehicles and electronics.
Unlike previous studies of high-interest credit card debt or education loans, we isolate debt
aversion from all credit history related factors as well as motives to invest in human capital. Johnes
(1994), Gayle (1996), Hayhoe (2002), and Lyons (2004) focus on credit card debt, while Olson and
Rosenfeld (1984), Johnes (1994) and Gayle (1996), Eckel, Johnson, and Montmarquette (2005) and Eckel
et al. (2007) examine education loans. In our study, the loan is similar to a credit card in that it can be
used for any purpose, but it is also similar to an education loan, as it has an extremely low interest rate.
Loan recipients may smooth their consumption (Hayhoe, 2002; Lyons, 2004); in fact, anticipating the
loan opportunity at the beginning of their junior year, students may take on additional debt in their first
two years to invest (Compton, Insler, and Schmitt, 2013) or to absorb personal or family-related financial
shocks (Wilson et al., 2007).
Our study utilizes survey data from a sample that is relatively homogenous—college students at
USNA—which helps to isolate the characteristics associated with debt aversion (captured by the decision
to take out the loan and the anticipation of early repayment) in a real, high-stakes setting. Despite the
similarities of the subjects, we find substantial variation in the choice to take out the loan and to opt for
early repayment; approximately 10 percent of students reject the loan and 56 percent of loan-takers
anticipate early repayment, and we find that these differences are correlated with a wide range of factors.2
The individual-specific data contains demographic information, including gender, grade point average
(GPA), SAT scores, major, and Myers-Briggs Type Indicator (MBTI); cognitive ability, as measured by
2 We define students anticipating early repayment as those who report that they expect to repay the loan no later than
four years after accepting it. Standard repayment plans require students to complete repayment six years after
accepting the loan.
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Frederick’s (2005) Cognitive Reflection Test3 (CRT, hereafter); self-reported socio-economic
characteristics, such as family education levels and income; self-reported information on sources of
financial advice; and future expectations about starting a family and purchasing a home.
Previous literature has explored how demographics and socio-economic characteristics relate to
debt aversion via credit cards and education loans. Johnes (1994) finds that women are less likely to take
a subsidized education loan. Xiao, Noring, and Anderson (1995) see no connection between credit card
debt and major or gender. Gayle (1996) finds that married students with credit card debt are more likely
to take a student loan and that family income factors are not significant predictors of loan acceptance.
Constanides, Donaldson, Mehra (2002) also found that young adults typically do not hold debt. Lyons
(2004) finds that financially at-risk students are more likely to misuse credit cards; many such students
include minorities and females. Callender and Jackson (2005) determine that prospective students in the
United Kingdom from lower income families are more debt averse. Controlling for credit score, credit
history, income, employment status, and homeownership, Ravina (2012) finds that personal
characteristics significantly affect the likelihood of receiving a loan. Specifically, physically attractive
borrowers, whites, and females are more likely to receive a loan at more favorable terms. Due to the
nature of our subjects’ loan offer, we are able to test and verify many of these findings in a more
controlled environment.
Fewer papers examine loan opportunities in true experimental settings. For example, Wilson et al.
(2010) have subjects participate in a payday loan. Similar to our survey, these loans require no formal
credit check. However, in contrast to ours, their annual percentage rate exceeds 300 percent. In the
3 The CRT consists of three basic questions: (1) A bat and a ball cost $1.10 in total. The bat costs $1.00 more than
the ball. How much does the ball cost? ___cents; (2) If it takes 5 machines 5 minutes to make 5 widgets, how long
would it take 100 machines to make 100 widgets? ____minutes; and (3) In a lake, there is a patch of lily pads. Every
day, the patch doubles in size. If it takes 48 days for the patch to cover the entire lake, how long would it take for the
patch to cover half of the lake? ___days. The intention of the CRT is to test two decision making characteristics:
time preference and risk preference. The “intuitive” answers to these three questions are generally incorrect, and
people who answer them incorrectly tend to believe they are easier than those who answer correctly. Frederick
(2005) finds the correlation of CRT score to SAT, ACT, and other tests to be weak. He hypothesizes that, more than
any of the tests the CRT is compared to, it measures the need for resisting the first “intuitive” answer and instead
solving for the correct solution to a problem and therefore it functions as a test of cognitive ability.
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laboratory, Wilson et al. find that payday loans help recipients deal with financial shocks and aid financial
survival, but the researchers do not consider demographic information. Eckel, Johnson, and
Montmarquette (2005) use experimental subjects from the working poor in Canada to examine high
stakes4 decisions on whether subjects choose a smaller sum of money immediately or a larger sum later
(ranging from one day to one year). They examine discount rates and focus on the differences between
short-horizon and long-horizon investment decisions. They find that age and gender impact patience and
that those with low incomes are less patient, while women and older people tend to be more patient.
Using the same dataset, Eckel et al. (2007) examine the relationship of education grants and loans to
human capital investments. They focus on how individual characteristics (demographics and socio-
economics), risk and time preferences, and debt aversion impact individuals’ decisions to take out loans
and subsequently enroll in school or training programs. They find that individuals are less likely to accept
a loan if the price of the loan increases, if age and wealth levels increase, and if subjects are risk averse.
Our study integrates compelling aspects of both experimental and non-experimental
investigations. We collect a wide variety of individual-specific information and study how it relates to
subjects’ decisions regarding a purely exogenous loan offer. Linear and non-linear regression methods
reveal that, holding all observable factors constant, debt aversion is not consistently associated with
gender or intellectual ability. Those willing to accept the loan often have prior debt. Socio-economic
background also matters; those with greater family income are more willing to take the loan (except for
the wealthiest group). We also uncover some characteristics that explain a weaker brand of debt aversion:
respondents’ reported intention to repay the loan early. Women tend to prefer early repayment of the loan,
as do individuals with low GPA, STEM5 majors (relative to economics majors), students from the
wealthiest families, loan-takers with no prior debt, and “thinkers” (T), based on MBTI score.6
4 In their experiments, earnings averaged $130 and ranged from $0 to $600.
5 Refers to disciplines in the fields of Science, Technology, Engineering, and Mathematics.
6 Section 2.C contains details on MBTI.
7
Although we did not conduct a standard risk assessment procedure, such as in Holt and Laury
(2002),7 we use Frederick (2005)’s Cognitive Refection Test (CRT) as a measure of patience rather than
risk preference. Dohmen et al. (2007) find that risk aversion coincides with lower cognitive ability. We
consider CRT as a proxy for risk aversion, but do not find it to be significantly correlated to loan-related
decisions. However, signs of CRT’s coefficient estimates associate it with higher probability of taking the
loan and lower probability of an intention to repay the loan early, coinciding with the work of Dohmen et
al. (2007).
The paper proceeds as follows. Section 2 discusses the data. Section 3 presents the methods and
results. Section 4 concludes.
The Data
The survey was designed for two main purposes. The first, reported here, was to examine a
unique opportunity (similar to a high stakes experiment) in which college students have the choice to take
out a large, virtually risk-free loan that is not credit-dependent. The opportunity is particularly unique in
the context of the previously discovered connections between debt aversion and age, marital status, and
personal income—all of which are automatically constant in our sample—establishing a much more
controlled natural experiment. The second purpose, reported in a companion paper (Compton, Insler, and
Schmitt, 2013), is to examine subjects’ loan allocation decisions.
Loan Details
Naval Academy students entering their junior year may accept a “Career Starter Loan.” NFCU
and USAA offered this loan, for the class of 2013, in slightly different forms (Table 1 compares the terms
of the banks’ loan offers). The loans are not government subsidized; NFCU and USAA provide them
7 Because students at the United States Naval Academy are paid for their service while students, they cannot receive
payments for participation in experiments. Because of this, we decided not to use the risk assessment tool as shown
in Holt and Laury (2002). In almost every study using Holt and Laury (2002), actual payments are used as an
incentive; Jacobson and Petrie (2009) however, do not pay all subjects and they find no difference if payoffs are real
or hypothetical. Camerer and Hogarth (1999) find ambiguity. To avoid ambiguity, acknowledging the fact we
cannot pay subjects, we focus on the CRT and patience rather than Holt and Laury (2002)’s risk aversion assessment.
8
privately, as they hope to build and maintain banking relationships with newly minted Naval and Marine
Corps officers and their families.
After accepting the loan, recipients must direct-deposit their military paychecks at their lender’s
bank, which precludes taking both loans and ensures (for the bank) at least a minimal financial
relationship. Repayment begins three months after graduation. If a student or officer separates from the
Naval Academy or the Navy/Marine Corps before repaying the loan, the loan rate reverts to the prevailing
signature loan rate at the time. Unlike in Ravina (2012), the ability to (and likelihood of) default on the
loan is nearly negligible because banks automatically deduct payments from recipients’ paychecks and
because all recipients possess excellent job security and stable salaries.
Survey Details
The survey questionnaire began with the three CRT questions (Frederick, 2005), which we
aggregate into an integer from 0 to 3, counting the number of questions answered correctly. A higher
CRT score tends to indicate higher cognitive ability (Dohmen et al., 2007). Students then self-assessed
their financial literacy (“subjectively,” on a scale of 1 to 10) and provided details on their prior assets,
prior debt, and which lender (USAA or NFCU) they chose. Students next answered various questions
about their specific usage of the Career Starter Loan,8 planned timing of repayment, sources of financial
advice, and expectations regarding possible earnings (or losses) on loan-funded investments. Students
also answered an “objective” financial literacy question proposed by Pingue (2011),9 as well as a number
of socio-economic questions about their family background (education levels for each parent, family
income, and financial support given/received to/from parents). Finally, respondents reported future plans
8 See Compton, Insler, and Schmitt (2013).
9 The “objective” and “subjective” financial literacy questions have a moderate positive correlation of 0.27. The
question from Pingue (2011) is: “Which of the following best describes the relationship between the interest rate
charged to a person for a loan and that person’s risk of nonpayment of the loan?” Answer options: (a) Lower interest
rates are charged on loans with a lower risk of nonpayment; (b) Higher interest rates are charged on loans with a
lower risk of nonpayment; (c) Lower interest rates are charged on loans with a higher risk of nonpayment; (d) I
don’t know.
9
including their expected tenure in the military and the timing of their intentions to purchase a home and
have children.
In addition to the online, self-reported questionnaire, the Office of Institutional Research at
USNA merged information for each respondent on gender, major, home state, verbal SAT, math SAT,
grade point average (GPA), MBTI, and class year. The external data helped to minimize reporting errors
and survey fatigue. The office also coded the survey instrument, collected the data, and randomly
distributed two $100 gift cards to incentivize survey completion. In this way, the researchers were
minimally associated with data collection and were at no point involved with identified data.
We administered the survey online to students in the classes of 2013 and 2014. In total, 609
students completed the survey. Our response rate was quite high (28 percent), given that the total number
of students in those classes in the fall of 2012 was 2,182. To construct our final sample, we discard
respondents for whom we could detect critical reporting errors or omissions: We drop four respondents
who did not specify whether they took the loan and one respondent whose answers were blatantly
incorrect. We drop 49 individuals who did not indicate whether they held any prior investments or debt as
well as 15 individuals who did not report their choice of the NFCU or the USAA loan option. We also
omit two individuals who did not report their repayment timing, five who did not report their gain or loss
expectations, 26 who did not respond to the financial literacy questions, and 11 respondents who did not
report on transfers to or from their family.10
Lastly, we discard information from four severe outliers, with
respect to their finances.11
This leaves us with 492 usable observations. Although the number of discarded
10
The reader should bear in mind that the accounting of discarded observations is reported sequentially. We have
recounted the omitted responses in the same order as the survey’s questions. Thus, recalling that the survey was
administered online, the drops predominantly represent submissions of incomplete surveys at various stages of
survey completion. 11
One student reported a transfer to his family far greater than the size of the loan; one student reported debt
holdings far greater than the size of the loan; two students reported prior investments well over $100,000.
10
observations may seem relatively large, our final sample passes a number of simple robustness checks for
sample selection bias.12
Myers-Briggs Type Indicator
Before starting their freshman year,13
students complete the Myers-Briggs Type Indicator (MBTI)
exam. Unlike in Yang, Coble, and Hudson (2009), students do not associate MBTI exam responses with
their loan acceptance decision.
The MBTI captures how individuals process information and behave. It is based Carl G. Jung’s
(1923) theory of psychological types, which argues that certain psychological preferences prefer to
perform certain tasks. The MBTI employs four dichotomous dimensions that identify personality types.
Individuals may have a preference for either extraversion (E) or introversion (I) in orientation, a
preference for sensing (S) or intuition (N) in perception, a preference for thinking (T) or feeling (F) in
judgment, and a preference for judgment (J) or perception (P) in attitude.14
Financial behavior literature ties preferences for extraversion (E), intuition (N), thinking (T), and
perception (P) to risk-seeking choices (Filbeck, Hatfield and Horvath, 2005; Li and Liu, 2008; Su-li and
Ke-fan, 2010). Unlike these studies, we find only a limited connection between personality type and the
decision to accept the loan; those with a preference for thinking (T) are more likely to accept the loan, but
they often plan to repay their loans early. As suggested by Myers et al. (1998), this could be because
thinking (T) types act “by linking ideas together through logical connections” (p. 24), suggesting that
such individuals may be forward looking, perhaps in hopes of establishing good credit.
12
We observe the official data (Class year, gender, MBTI, GPA, SAT, major, home state) from the Office of
Institutional Research, even for “discarded respondents” who did not submit complete surveys. We performed t-tests
for equal means (and proportions where appropriate) between the truncated and un-truncated samples. Even at the
10 percent significance level, we could not reject mean-equality for class year, gender, MBTI, GPA, or SAT. For the
same set of variables, we could not reject variance-equality when performing F-tests for equal variances between the
two samples. We did not check for sample consistency on major or home state due to high dimensionality of these
variables. 13
Freshmen participate in “plebe summer” for approximately two months prior to the start of academic year in
August. During this time, students receive the MBTI and other class placement exams. 14
For more detailed information on the MBTI see Myers et al. (1998).
11
Results
Table 2A presents descriptive statistics, grouped by loan-takers versus loan-rejecters and Table
2B presents descriptive statistics for loan-specific variables for the loan-takers only. Sample standard
deviations account for the known, finite size of the population. At first glance, this simple cut of the data
shows some discrepancies between the two groups amongst point estimates of gender (28 percent of loan-
rejecters are women, while only 21 percent of loan-takers are women), verbal SAT, family income
(particularly for the highest income group), family transfers, and debt holdings. However, given the small
sample of loan-rejecters, confidence intervals for population means from each subgroup generally overlap.
To better understand such patterns, we next condition on all observable individual-specific information to
identify the factors that most strongly relate to loan choices. The following two subsections investigate
respondents’ loan acceptance decisions and respondents’ anticipated early repayment of the loan,
respectively.
Decision to Take the Loan
The loan acceptance decision is a binary response, which we model via linear probability (estimated
by OLS) and a probit model (estimated by maximum likelihood). Table 3 contains coefficient estimates
and standard errors, which account for the finite, known population size. In all four specifications, the
dependent variable is equal to one if a respondent accepted the loan and zero otherwise. The first two
columns display probit and linear probability model (LPM) results, respectively, utilizing the full set of
controls available in our data. The third and fourth columns present analogous models using a smaller set
of covariates. The results do not substantially change as we eliminate less important control variables.
The sign and significance of key variables is consistent across probit and linear probability models; thus
we focus our discussion on the easier-to-interpret LPM results.
We find that verbal SAT score matters significantly: On average, a 100 point increase in verbal
SAT score is associated with a smaller probability of taking the loan by 5 percentage points. Estimates of
an alternate measure of intellectual ability, GPA, are positive but insignificant. Cognitive ability, captured
by CRT score is also positive but insignificant. The female dummy variable is negative and insignificant.
12
Academic major is not a significant predictor of the loan-acceptance decision. These results qualitatively
agree with previous work (Johnes, 1994; Xiao, Noring, and Anderson, 1995; Dohmen et al., 2007), but
some inferences are not strong. We may simply not have enough observations of loan-rejecters (47 obs.)
to tease out weaker relationships. For CRT, at least, in comparing the two probit models, t-ratios increase
notably as degrees of freedom increase, suggesting that additional data collection may strengthen these
findings. The only MBTI coefficient that is statistically significant is for “thinkers” (T), who we associate
with having a greater probability of taking the loan. Models incorporating various interactions of MBTI
categories yielded no interesting results, likely due to too-high dimensionality relative to the small
number of loan-rejecters.
Students from upper-middle income families ($100,000 to $120,000 annually) are almost 7
percentage points more likely to take the loan than students from the highest income families (over
$120,000 annually). Estimates for lower income groups are positive but insignificant, in reference to the
highest income group. These findings are contrary to Callendar and Johnson (2005) but in line with Eckel
et al. (2007). Holding prior debt is associated with a larger chance of taking the loan by 6 percentage
points (similar to Gayle, 1996), and reporting no transfer to one’s family adds 26 percentage points to the
predicted probability of loan acceptance. Students who expect to have children soon are more likely to
accept the loan, holding all other controls constant, compared to students with either long-term or
uncertain planning horizons.
It is important to note that we cannot infer causal relationships amongst variables in these
specifications. On the one hand, there is not likely to be severe simultaneity bias in this setting: It is
difficult to imagine how the loan-acceptance decision itself could subsequently influence demographic
traits, family background variables, or measured cognition.15
But on the other hand, we do not claim that
all selection is explained by the set of observable controls. It is very likely that unexplained factors, such
as unseen financial advice, extenuating life circumstances, or non-classical measurement errors, may be
15
One exception is the case of planning horizon variables. A $36,000 windfall in one’s junior or senior year of
college may certainly influence his or her plans to purchase a home, remain in the armed services, or to have
children.
13
correlated with both the loan decision and the explanatory variables. Thus our inferences reflect
correlational relationships, with an underlying conjecture that some may have causal foundations.
Anticipation of Early Repayment
Table 4 presents estimations of probit and linear probability models for which the outcome is a
binary variable equal to one if a subject anticipated early repayment of the loan and zero otherwise.16
The
models in the third and fourth columns employ a smaller set of covariates. As before, standard error
estimates account for the finite, known population size. The sample includes only the 445 students who
decided to take out the loan. It is important to clarify that the estimation results in this subsection pertain
only to the subset of students who accept the loan. We can envision a two-stage econometric specification
that sequentially models the students’ two decisions: First, they choose whether to take out the loan, and
second, they report their repayment-timing intentions. Due to data limitations—predominantly low
sample size of loan-rejecters—we have been unable to obtain viable results from such a model.
From the second column of Table 4, we find the predicted probability of anticipating early
repayment to be greater, on average, by 12.6 percentage points for women, 12.8 percentage points for
STEM majors (versus economics majors), 10.1 percentage points for those with a preference for thinking
(T), and 8 percentage points for those who held investments prior to the loan (although only significant in
the probit model). The predicted probability is lower by 7.2 percentage points per one-point increase in
student GPA (although only significant in the probit model), and it is lower by 13.5 percentage points for
those uncertain about a future home purchase. Family income dummies are negative, but only one is
statistically significant; there is evidence that students from lower income backgrounds are less likely to
intend to repay the loan early.
As stated earlier, we view the expectation of early repayment as a weaker form of debt aversion,
as compared to the decision to reject the loan altogether. In this regard, the results in Table 4 are generally
16
We consider loan-takers to anticipate early repayment if they report plans to repay the loan in four years or fewer,
versus the standard six year plan. Ten percent of loan-takers report plans to repay within two years; 46 percent
anticipate repayment in two to four years.
14
analogous to the results in Table 3. In Table 4, lower family income relates to standard loan repayment
plans (less debt aversion), while in Table 3, lower family income predicts a greater likelihood of loan
acceptance (also indicative of less debt aversion). In Table 3, cognitive ability (as measured by CRT score)
is not significantly related to loan-acceptance, but its coefficient estimate’s positive sign suggests less
debt aversion. In Table 4, we again do not capture a significant relationship for CRT score, but a negative
slope estimate implies a potential alignment with less debt aversion. We see similarly consistent
relationships—with some statistically significant and some not—between the two tables for gender,
college major (STEM versus economics), prior debt holdings, and planning horizon variables (at least
when comparing family planning and house-purchase planning). The only statistically significant
inconsistencies between the two tables’ predictions of debt aversion come from estimates for Myers-
Briggs thinking (T) types17
and intellectual ability.18
As mentioned in Section 2.C, we conjecture that
since thinking (T) types are particularly strong at logically connecting ideas (Myers et al., 1998), such
individuals may be forward looking, perhaps in hopes to establish good credit. Since the literature does
not take a stand on intellectual ability, we leave this factor as open to further investigation. It is also true
that the results in Table 4 reflect debt aversion specifically for the subsample of students who are already
revealed to be sufficiently “debt-loving” to have taken out the loan in the first place. This pre-condition
may also contribute to such discrepancies.
As before, we cannot infer causal relationships between regressors and loan-repayment
anticipations. Although it is indeed possible that, for instance, an orientation towards thinking (T) directly
affects one’s repayment plans, it is also likely that other unobserved traits correlated with personality type,
such as specific family circumstances, contribute to the effect. Simultaneity bias is another likely culprit
in these models; for instance, repayment plans may impact the choice of USAA versus NFCU loans
because of their differences in timing and terms.
17
Table 3 associates thinkers (T) with the less debt averse choice, while Table 4 associates them with the more debt
averse option. 18
In Table 3, verbal SAT scores reveal that more intellectually able subjects are, on average, more debt averse,
while in Table 4, we see that individuals with greater GPA’s tend to be less debt averse.
15
Conclusion
Despite the challenges posed by causal inference, we establish several compelling links between
debt aversion and observable characteristics. In this paper, we exploit a natural experiment—USNA
students’ Career Starter Loans—to verify and strengthen knowledge of debt aversion. Furthermore, our
results directly describe the behavior of young adults, an infrequently studied yet important segment of
the population, especially in the context of borrowing behavior. To summarize, we find limited evidence
of debt aversion in women, but this finding may be hampered by our small sample of female loan-
rejecters. Measures of intellectual ability do not have consistent relationships with debt aversion; a
superior verbal SAT score predicts a lower likelihood of taking the loan, but GPA portends greater
chances of early repayment. Thinking (T) types are similarly conflicted. We find that subjects from
upper-middle income families, who do not contribute money to their family, who hold prior debt, and
who expect children earlier are more likely to accept the loan. Anticipated early repayment of the loan is
more likely among those with prior investments, no prior debt, from STEM majors, with upper income
parents, and those who expect to buy a home soon. As there is not a statistically significant relationship
between CRT score and the probability of taking the loan, cognitive ability and debt aversion may not be
related; coefficient estimates’ signs provide weak evidence that they are negatively linked.
To summarize our main results, we have framed the loan-acceptance decision—or rather, loan-
rejection—as “debt aversion,” and we build on previous work that investigates related factors. Along with
studies such as Johnes (1994), Gayle (1996), Eckel, Johnson and Montmarquette (2005) and Eckel et al.
(2007), we find links between individuals coming from “better off” backgrounds (i.e. from wealthier roots,
no prior debt, higher cognitive ability) and choosing optimally, which we view as taking the free lunch.
Why are individuals with less ability or from less fortunate backgrounds more debt averse? Such students
may see no credible way to commit to not transferring some of the principal to family, thus accepting the
additional repayment burden that would accompany such a choice. Therefore, they would have a greater
propensity to reject the loan altogether. Interestingly, financial literacy and advice variables are not
16
connected to debt aversion in our results.19
Could educational interventions help potential borrowers make
more informed decisions? We suggest consideration of policies to encourage “smart” borrowing, focusing
on the financially disadvantaged, particularly for education loans.
Acknowledgements
The authors would like to thank Lou Cox for her expertise in programming the survey. We would also
like to thank Kurtis Swope, participants at the 2012 Southern Economic Association Meetings in New
Orleans, the co-editor, and an anonymous referee for helpful comments. IRB human subject’s approval
received September 12, 2012 under HRPP Approval #USNA.2012.0004-AM03-EP7-A.
19
Compton, Insler, and Schmitt (2013) determine that these variables matter much more as they relate to investment
behavior, once students have made the decision to accept the loan.
17
References
Camerer C.F., & Hogarth R. M. (1999). The effect of financial incentives in experiments: a review and
capital-labor-production framework. Journal of Risk and Uncertainty, 19(1-3), 7-42.
Callender, C., & Jackson, J. (2005). Does the Fear of Debt Deter Students from Higher Education?
Journal of Social Policy, 34, 509-540.
Compton, J., Insler, M., & Schmitt, P. (2013). “Open line of credit:” Under no borrowing constraints,
how do young adults invest? Unpublished manuscript.
Constantinides, G., Donaldson, J., & Mehra, R. (2002). Junior can't borrow: A new perspective on the
equity premium puzzle. Quarterly Journal of Economics 117, 269-296.
Dohmen, Thomas, Falk, A., Huffman, D., & Sunde, U. (2007). Are risk aversion and impatience related
to cognitive ability?, IZA Discussion Papers, No. 2735, urn:nbn:de:101:1-20080402396 ,
http://hdl.handle.net/10419/34592
Doran, J.S., Peterson, D.R., & Wright C. (2010). Confidence, opinions and market efficiency, and
investment behavior of finance professors. Journal of Financial Markets, 13, 174-195.
Eckel, C., Johnson, C., & Montmarquette, C. (2005). Saving Decisions of the Working Poor: Short- and
Long-Term Horizons. Field Experiments in Economics: Research in Experimental Economics, 10, 219-
260.
Eckel, C., Johnson, C., Montmarquette, C., & Rojas, C. (2007). Debt Aversion and the Demand for Loans
for Postsecondary Education. Public Finance Review, 35(2), 232-262.
Filbeck, G., Hatfield, P., & Horvath, P. (2005). Risk Aversion and Personality Type. The Journal of
Behavioral Finance, 6(4), 170-180.
Frederick, S. (2006). Cognitive Reflection and Decision Making. Journal of Economic Perspectives,
19(4), 25-42.
Friend, I., & Blume, M. E. (1975). The Demand for Risky Assets. American Economic Review, 65(5),
900-922.
Fry, R. (2013). Young Adults After the Recession: Fewer Homes, Fewer Cars, Less Debt. Pew Research
Center, Technical Report.
Gayle, V. (1996). The Determinants of Student Loan Take-up in the United Kingdom: Another Gaze.
Applied Economics Letters, 3, 25-27.
Hayhoe, C. (2002). Comparison of Affective Credit Attitude Scores and Credit Use of College Students at
Two Points in Time. Journal of Family and Consumer Sciences, 94, 71-77.
Holt, C., & Laury, S. (2002). Risk aversion and incentive effects. American Economic Review, 95(5),
1644-1655.
18
Jacobson, S., & Petrie, R. (2009). What do inconsistent choices over risk tell us? Journal of Risk
Uncertainty, 38, 143-158.
Johnes, G. (1994). The determinants of student loan take-up in the United Kingdom. Applied Economics,
34(10), 999-1005
Jung, C. G. (1923). Psychological types. Princeton, NJ: Princeton University Press.
Li, S., & Liu, C. (2008). Individual differences in a switch from risk-averse preferences for gains to risk-
seeking preferences for losses: can personality variables predict the risk preferences? Journal of Risk
Research, 11(5), 367-686.
Lyons, A. C. (2004). A Profile of Financially At-Risk College Students. The Journal of Consumer Affairs,
38(1), 56-80.
Myers, I., McCaulley, M., Quenk, N., & Hammer, A. (1998). MBTI Manual: A Guide to the Development
and Use of the Myers Briggs Type Indicator. Palo Alto, CA: Consulting Psychologists Press.
Pålsson, A. (1996). Does the degree of relative risk aversion vary with household characteristics? Journal
of Economic Psychology, 17, 771-787.
Pingue, L. Can you pass a high school financial literacy test? March 9, 2011. Reuters.
Ravina, E. (2012). Love & Loans: The Effect of Beauty and Personal Characteristics in Credit Markets,
Columbia University Working Paper.
Su-li, Z., & Ke-fan X. (2010). Research on the entrepreneurial team members’ personality traits influence
on group risk decision-making. 2010 International conference on Management Science & Engineering
(17th). November 24 – 26, 2010, 937-942.
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6328181
Wilson, B., Findlay, D., Meehan J. W., Wellford, C., & Schurter, K. (2010). An experimental analysis of
the demand for payday loans. The B.E. Journal of Economic Analysis & Policy, 10(1) (Topics), Article 93.
Xiao, J., Noring, F., & Anderson, J. (1995). College Students’ Attitudes Toward Credit Cards. Journal of
Consumer Studies and Home Economics, 19, 155-174.
19
Table 1: Career Starter Loan Details:
Lender: NFCU USAA
Availability: August of junior year October-November of junior
year
Principal Amount: $32,000 $36,000
Rate: 1.25% APR 0.75% APR
Monthly Repayment: $564.97 $619.80
Total Repayment: $33,898.20 $37,636.20
Time Window: Closes at graduation Closes one year after
commissioning
20
Table 2A: Descriptive Statistics of loan-takers and loan-rejecters
Took loan Did not take loan
Mean Std. Dev. Mean Std. Dev.
Seniors in College* 0.45 0.50 0.28 0.45
Female* 0.21 0.41 0.28 0.45
SAT (verbal) 648 77 676 73
SAT (math) 679 79 688 70
GPA 3.10 0.57 3.13 0.59
Major: Economics* 0.15 0.35 0.13 0.34
Major: STEM* 0.64 0.48 0.64 0.49
Major: Other* 0.21 0.41 0.23 0.43
MBTI: Extravert (vs. introvert)* 0.53 0.50 0.53 0.50
MBTI: Sensing (vs. intuition)* 0.71 0.45 0.70 0.46
MBTI: Thinking (vs. feeling)* 0.81 0.39 0.74 0.44
MBTI: Judging (vs. perceiving)* 0.64 0.48 0.62 0.49
Mom's Educ.: Less than Bach. Deg.* 0.29 0.46 0.17 0.38
Mom's Educ.: Bach. Deg.* 0.44 0.50 0.49 0.51
Mom's Educ.: Graduate Deg.* 0.26 0.44 0.34 0.48
Dad's Educ.: Less than Bach. Deg.* 0.26 0.44 0.13 0.34
Dad's Educ.: Bach. Deg.* 0.36 0.48 0.45 0.50
Dad's Educ.: Graduate Deg.* 0.37 0.48 0.40 0.50
Family Income: $40,000 or less* 0.08 0.27 0.06 0.25
Family Income: $40,001 to $60,000* 0.11 0.31 0.06 0.25
Family Income: $60,001 to $80,000* 0.11 0.31 0.06 0.25
Family Income: $80,001 to $100,000* 0.14 0.35 0.15 0.36
Family Income: $100,001 to $120,000* 0.13 0.34 0.09 0.28
Family Income: $120,001 or more* 0.28 0.45 0.40 0.50
Family Income: No response* 0.16 0.37 0.17 0.38
Annual transfer from family ($)** 1,500 1,830 2,243 3,696
Annual transfer to family ($)** 1,305 3,364 634 1,245
Self-reported fin. literacy (1-10 scale) 5.90 1.80 5.70 2.30
Fin. literacy question (interest/risk)* 0.84 0.37 0.77 0.43
Held investments prior to loan offer* 0.29 0.45 0.28 0.45
Held debt prior to loan offer* 0.10 0.30 0.02 0.15
Service selection: Restricted line* 0.03 0.17 0.09 0.28
Service selection: Nuke* 0.16 0.37 0.17 0.38
Service selection: Medical Corps* 0.01 0.12 0.04 0.20
Service selection: Naval Aviation* 0.32 0.47 0.26 0.44
Service selection: Special Forces* 0.05 0.22 0.09 0.28
Service selection: Unsure* 0.05 0.22 0.11 0.31
Service selection: Surface Warfare* 0.11 0.32 0.04 0.20
Service selection: Marine Corps* 0.26 0.44 0.21 0.41
Expected years of service (unsure)* 0.28 0.45 0.21 0.41
Expected years of service** 12.3 8.2 13.0 8.8
Expected years to house purchase (unsure)* 0.30 0.46 0.45 0.50
Expected years to house purchase** 4.4 2.4 4.5 2.5
Expected years to having kids (unsure)* 0.25 0.43 0.30 0.46
Expected years to having kids** 6.7 2.5 7.8 2.4
Number of Observations: 445 47
*Binary variable: denotes sample proportion. **Statistics are conditional on observations reporting positive values
21
Table 2B: Descriptive Statistics expectations for loan-takers
Mean Std. Dev.
USAA loan (alternative: NFCU loan)*
0.87 0.33
Plans to repay in 0-2 years*
0.10 0.30
Plans to repay in 2-4 years*
0.46 0.50
Plans to repay in 4-6 years*
0.44 0.50
Advice: Financial advisor*
0.51 0.50
Advice: Friends/family*
0.51 0.50
Advice: News sources*
0.13 0.33
Advice: Personal research*
0.41 0.49
Advice: Other*
0.06 0.23
Expects to gain*
0.62 0.49
Expects to lose*
0.06 0.23
Expects to break even*
0.31 0.46
Expected annual gain (%)**
7.6 4.7
Expected annual loss (%)**
7.0 4.9
Number of Observations:
445
*Binary variable: denotes sample proportion. **Statistics are conditional on observations reporting positive values
22
Table 3: Probability of taking the Loan (N = 492) Dependent Variable: “Took loan” Probit (all controls) LPM (all controls) Probit (subset) LPM (subset)
Senior in College 0.407** 0.0360 0.344** 0.0406*
(0.155) (0.0239) (0.148) (0.0222)
Female -0.142 -0.0345 -0.190 -0.0332
(0.228) (0.0373) (0.196) (0.0331)
SAT (verbal) -0.00347*** -0.000473** -0.00321*** -0.000493***
(0.00134) (0.000200) (0.00130) (0.000199)
SAT (math) -0.00112 -0.000131 -0.00161 -0.000171
(0.00138) (0.000200) (0.00130) (0.000200)
GPA 0.315* 0.0393 0.235 0.0317
(0.175) (0.0265) (0.157) (0.0261)
CRT 0.0439 0.000132 0.0929 0.00689
(0.0870) (0.0111) (0.08170) (0.0117)
Major: STEM (reference: Economics) -0.243 -0.0101
(0.255) (0.0331) Major: Other (reference: Economics) -0.00887 -0.00500
(0.280) (0.0410)
MBTI: Extravert (vs. introvert) -0.101 -0.00807 -0.142 -0.0158
(0.157) (0.0228) (0.154) (0.0234)
MBTI: Sensing (vs. intuition) 0.0440 0.00783 -0.0636 -0.000913
(0.163) (0.0247) (0.165) (0.0254)
MBTI: Thinking (vs. feeling) 0.344* 0.0485 0.378* 0.0541
(0.204) (0.0334) (0.194) (0.0334)
MBTI: Judging (vs. perceiving) 0.102 0.0251 0.137 0.0241
(0.156) (0.0241) (0.151) (0.0242)
Mom's Educ.: Less than Bach. Deg. 0.187 0.0144
(reference: Bach. Deg.) (0.209) (0.0270) Mom's Educ.: Graduate Deg. -0.0788 -0.0129
(reference: Bach. Deg.) (0.189) (0.0313)
Dad's Educ.: Less than Bach. Deg. 0.366 0.0306 (reference: Bach. Deg.) (0.247) (0.0319)
Dad's Educ.: Graduate Deg. 0.393** 0.0425
(reference: Bach. Deg.) (0.173) (0.0284) Family Income: $40,000 or less 0.628* 0.0659 0.405 0.0493
(reference: > $120,000) (0.344) (0.0491) (0.294) (0.0463)
Family Income: $40,001 to $60,000 0.469 0.0490 0.374 0.0523 (reference: > $120,000) (0.304) (0.0395) (0.267) (0.0368)
Family Income: $60,001 to $80,000 0.410 0.0599 0.257 0.0444
(reference: > $120,000) (0.329) (0.0436) (0.276) (0.0375)
Family Income: $80,001 to $100,000 0.569** 0.0541 0.414* 0.0511
(reference: > $120,000) (0.233) (0.0381) (0.222) (0.0396) Family Income: $100,001 to $120,000 0.666** 0.0697* 0.558** 0.0694*
(reference: > $120,000) (0.271) (0.0369) (0.252) (0.0362)
Family Income: No response 0.362 0.0426 0.233 0.0332 (reference: > $120,000) (0.253) (0.0405) (0.226) (0.0401)
No transfer from family -0.449 -0.0705 -0.681 -0.0888
(0.729) (0.0984) (0.690) (0.101)
ln(Annual transfer from family ($)) -0.0124 -0.00474 -0.0582 -0.00786
(0.103) (0.0142) (0.0965) (0.0146)
No transfer to family 1.945** 0.221* 1.864*** 0.256**
(0.822) (0.132) (0.762) (0.128)
ln(Annual transfer to family ($)) 0.286** 0.0317 0.283** 0.0360*
(0.137) (0.0198) (0.126) (0.0188)
Self-reported fin. literacy (1-10 scale) 0.0298 0.00253 -0.00309 0.000268
(0.0576) (0.00846) (0.0540) (0.00827)
Fin. literacy question (interest/risk) 0.351* 0.0518 0.416** 0.0629*
(0.199) (0.0353) (0.198) (0.0366)
Held investments prior to loan offer -0.169 -0.00413 -0.0917 -0.00149
(0.180) (0.0263) (0.175) (0.0263)
Held debt prior to loan offer 0.884*** 0.0623*** 0.887*** 0.0611**
(0.334) (0.0228) (0.327) (0.0217)
Service selection: Restricted line -1.043** -0.171* (reference: Surface Warfare) (0.452) (0.0892)
Service selection: Nuke -0.542 -0.0477
(reference: Surface Warfare) (0.378) (0.0455) Service selection: Medical Corps -0.816 -0.123
(reference: Surface Warfare) (0.567) (0.140)
Service selection: Naval Aviation -0.283 -0.0184 (reference: Surface Warfare) (0.340) (0.0401)
23
Service selection: Special Forces -0.349 -0.0448
(reference: Surface Warfare) (0.429) (0.0669) Service selection: Unsure -0.694* -0.107
(reference: Surface Warfare) (0.393) (0.0670)
Service selection: Marine Corps -0.424 -0.0324 (reference: Surface Warfare) (0.365) (0.0424)
Expected years of service (unsure) 0.258 0.0318
(0.222) (0.0342) Expected years of service -0.0117 -0.00171
(0.00987) (0.00183)
Expected years to house purchase (unsure) -0.323 -0.0528 -0.427 -0.0611
(0.274) (0.0425) (0.259) (0.0410)
Expected years to house purchase 0.0376 0.00385 0.00626 0.00156
(0.0424) (0.00576) (0.0403) (0.00590)
Expected years to having kids (unsure) -0.582* -0.0743 -0.564* -0.0682
(0.349) (0.0476) (0.329) (0.0461)
Expected years to having kids -0.0964*** -0.0134** -0.0944*** -0.0129**
(0.0365) (0.00567) (0.0350) (0.00571)
Constant 1.066 0.919*** 1.858 0.953***
(1.381) (0.204) (1.328) (0.202)
* p<.10, ** p<.05, *** p<.01
24
Table 4: Probability that "Plans early repayment" of Loan (N = 445, only using those that took the loan).
Dependent Variable: "Repay soon" Probit (full) LPM (full)
Probit
(smaller) LPM (smaller)
Senior in College 0.460*** 0.160*** 0.376*** 0.134***
(0.124) (0.0430) (0.121) (0.0429)
Female 0.352** 0.126** 0.243 0.0879
(0.166) (0.0561) (0.156) (0.0539)
SAT (verbal) 0.00161 0.000505 0.00142 0.000451
(0.000999) (0.000347) (0.000967) (0.000344)
SAT (math) -0.000292 -0.000123 -0.000129 -0.0000781
(0.00110) (0.000382) (0.00107) (0.000381)
GPA -0.224* -0.0721 -0.214* -0.0711
(0.135) (0.0466) (0.126) (0.0441)
CRT -0.0561 -0.0207 -0.0398 -0.0141
(0.0588) (0.0207) (0.0583) (0.0209)
Major: STEM (reference: Economics) 0.334* 0.128* 0.396** 0.151**
(0.180) (0.0651) (0.175) (0.0638)
Major: Other (reference: Economics) 0.136 0.0566 0.259 0.0974
(0.215) (0.0771) (0.209) (0.0766)
MBTI: Extravert (vs. introvert) -0.142 -0.0513 -0.130 -0.0487
(0.117) (0.0408) (0.115) (0.0411)
MBTI: Sensing (vs. intuition) -0.0680 -0.0228 -0.0748 -0.0242
(0.133) (0.0475) (0.129) (0.0469)
MBTI: Thinking (vs. feeling) 0.272* 0.101* 0.261* 0.0943*
(0.152) (0.0520) (0.150) (0.0527)
MBTI: Judging (vs. perceiving) -0.0373 -0.0139 -0.0273 -0.00802
(0.124) (0.0432) (0.121) (0.0434)
Mom's Educ.: Less than Bach. Deg. -0.202 -0.0648
(reference: Bach. Deg.) (0.152) (0.0535)
Mom's Educ.: Graduate Deg. -0.305** -0.110**
(reference: Bach. Deg.) (0.140) (0.0494)
Dad's Educ.: Less than Bach. Deg. 0.266 0.0923
(reference: Bach. Deg.) (0.166) (0.0574)
Dad's Educ.: Graduate Deg. -0.0649 -0.0197
(reference: Bach. Deg.) (0.141) (0.0499)
Family Income: $40,000 or less -0.00850 0.00289 0.112 0.0377
(reference: > $120,000) (0.261) (0.0899) (0.254) (0.0874)
Family Income: $40,001 to $60,000 -0.725*** -0.246*** -0.545** -0.194**
(reference: > $120,000) (0.237) (0.0820) (0.221) (0.0783)
Family Income: $60,001 to $80,000 -0.152 -0.0503 0.0100 0.00240
(reference: > $120,000) (0.226) (0.0774) (0.209) (0.0748)
Family Income: $80,001 to $100,000 -0.173 -0.0606 -0.125 -0.0466
(reference: > $120,000) (0.193) (0.0687) (0.186) (0.0667)
Family Income: $101,001 to $120,000 -0.171 -0.0581 -0.153 -0.0582
(reference: > $120,000) (0.188) (0.0658) (0.186) (0.0674)
Family Income: No response -0.268 -0.0892 -0.186 -0.0676
(reference: > $120,000) (0.194) (0.0698) (0.189) (0.0699)
No transfer from family 0.924* 0.326* 0.825* 0.292*
(0.487) (0.176) (0.471) (0.171)
ln(Annual transfer from family ($)) 0.0862 0.0316 0.0670 0.0241
(0.0703) (0.0253) (0.0678) (0.0247)
No transfer to family -0.275 -0.139 -0.201 -0.109
(0.598) (0.209) (0.583) (0.205)
ln(Annual transfer to family ($)) -0.0167 -0.0130 0.0000672 -0.00599
(0.0929) (0.0323) (0.0903) (0.0314)
Self-reported fin. literacy (1-10 scale) -0.0340 -0.00993 -0.0211 -0.00682
(0.0388) (0.0135) (0.0382) (0.0136)
Fin. literacy question (interest/risk) 0.141 0.0435 0.191 0.0657
(0.170) (0.0595) (0.165) (0.0585)
Held investments prior to loan offer 0.238* 0.0805 0.181 0.0663
(0.140) (0.0493) (0.137) (0.0493)
25
Held debt prior to loan offer -0.623*** -0.202*** -0.580*** -0.196***
(0.193) (0.0641) (0.187) (0.0632)
Service selection: Restricted line 0.140 0.0617
(reference: Surface Warfare) (0.381) (0.132)
Service selection: Nuke 0.451* 0.151*
(reference: Surface Warfare) (0.251) (0.0864)
Service selection: Medical Corps 0.121 0.0416
(reference: Surface Warfare) (0.491) (0.171)
Service selection: Naval Aviation 0.161 0.0517
(reference: Surface Warfare) (0.221) (0.0783)
Service selection: Special Forces 0.626* 0.222*
(reference: Surface Warfare) (0.334) (0.115)
Service selection: Unsure 0.178 0.0504
(reference: Surface Warfare) (0.317) (0.113)
Service selection: Marine Corps 0.523** 0.173**
(reference: Surface Warfare) (0.235) (0.0830)
Expected years of service (unsure) -0.126 -0.0383 -0.0972 -0.0320
(0.171) (0.0596) (0.167) (0.0594)
Expected years of service -0.0156* -0.00540* -0.0165** -0.00570*
(0.00885) (0.00305) (0.00836) (0.00296)
Expected years to house purchase (unsure) -0.370* -0.135* -0.358* -0.132*
(0.205) (0.0719) (0.198) (0.0702)
Expected years to house purchase -0.0322 -0.0118 -0.0365 -0.0136
(0.0314) (0.0113) (0.0309) (0.0112)
Expected years to having kids (unsure) -0.0469 -0.0141 -0.0552 -0.0180
(0.244) (0.0852) (0.238) (0.0848)
Expected years to having kids -0.0138 -0.00464 -0.00432 -0.00131
(0.0286) (0.0100) (0.0273) (0.00980)
USAA loan (reference: NFCU loan) -0.0971 -0.0289 -0.108 -0.0349
(0.180) (0.0639) (0.175) (0.0627)
Advice: Financial advisor -0.0897 -0.0329 -0.0927 -0.0349
(0.124) (0.0431) (0.122) (0.0432)
Advice: Friends/family 0.0621 0.0185 0.0406 0.0122
(0.123) (0.0428) (0.119) (0.0424)
Advice: News sources -0.0195 -0.0116 0.00270 0.000166
(0.179) (0.0638) (0.180) (0.0655)
Advice: Personal research 0.0784 0.0308 0.0367 0.0160
(0.138) (0.0490) (0.134) (0.0485)
Advice: Other -0.400 -0.131 -0.349 -0.120
(0.256) (0.0882) (0.251) (0.0883)
Expects to gain (reference: break-even) 0.104 0.0311 0.0366 0.0108
(0.184) (0.0642) (0.183) (0.0652)
Expects to lose (reference: break-even) 0.252 0.0438 0.255 0.0620
(0.483) (0.145) (0.448) (0.142)
Expected annual gain (% - if expects gain) -0.0189 -0.00615 -0.0144 -0.00501
(0.0155) (0.00555) (0.0154) (0.00559)
Expected annual loss (% - if expects loss) 0.00933 0.00693 0.00732 0.00452
(0.0568) (0.0169) (0.0519) (0.0160)
Constant -0.351 0.428 -0.338 0.445
(1.068) (0.377) (1.018) (0.363)
* p<.10, ** p<.05, *** p<.01
