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Understanding how Information Affects Loan Aversion: A Randomized Control Trial of Providing Federal Loan Information to High School Seniors

Brent J. Evans & Angela Boatman

Draft Version October 28, 2016, Please Do Not Cite Without Permission from the Authors

Abstract

We employ a clustered randomized control trial in six diverse high schools in Jefferson County, Louisville, Kentucky to identify the effect of providing loan and repayment information to high school seniors on their borrowing attitudes and perceptions. The information treatment is watching a five-minute video during class that explains the features of federal student loans and the advantages of income based repayment. The control condition is watching a five-minute video that explains how to read a financial aid award letter, theoretically neutral on student loans. Students’ attitudes toward loans are then captured via a ten-minute paper survey. Randomization was conducted at the classroom level within each high school such that an equal number of classrooms (approximately 650 seniors) received the treatment and control conditions. The results indicate that loan information reduces loan aversion on both our general measure of borrowing attitudes and on our specific measure of borrowing for education, although the magnitude of the effect is larger on the latter outcome. We also test whether this treatment effect varies across levels of risk aversion and find some evidence of a stronger effect on less risk averse students. These results suggest providing information on income based repayment options at the time of the borrowing decision can improve college access to low- and middle-income populations who are averse to borrowing.

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Introduction

Federal student loans are a major policy mechanism by which the government relieves

credit constraints for low- and middle-income students so that they can invest in postsecondary

education without otherwise having the financial resources to enroll. Relying on student loans to

finance postsecondary education is pervasive in the U.S., with 68 percent of public and private

nonprofit college graduates in 2015 having an average student loan debt of $30,100 (TICAS,

2016). Borrowers can repay student loans over multiple decades such that 21 percent of all

American households have some form of student loan debt today (Pew Charitable Trusts, 2015).

Despite the prevalence of student loans, there are many potential college students who

prefer to avoid borrowing to finance education. We describe these people as loan (or debt)

averse. An aversion to borrowing for education is prevalent across students seeking and enrolled

in higher education both in the United States (Burdman, 2005; Cunningham & Santiago, 2008;

Goldrick-Rab & Kelchen, 2013) and internationally (Caetano, Palacios, & Patrinos, 2011;

Callender & Jackson, 2005; Palameta & Voyer, 2010). Our own prior work demonstrates that

more than 20 percent of high school seniors do not believe it is ok to borrow money for

education, and more than 40 percent avoid hypothetical financial aid packages that incorporate

student loans (Boatman, Evans, & Soliz, 2015).

When potential college students are unwilling to make use of student loans out of an

aversion to borrowing, they may underinvest in human capital development by delaying or

foregoing postsecondary enrollment altogether. This problem is exacerbated by the increasing

reliance on student loans as the primary financing mechanism of rapidly increasing tuition costs

in higher education. As the cost of college continues to rise, students must weigh the cost of

borrowing money for their education with the potential returns on that investment.

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Although there is evidence that loan aversion is common among current and prospective

college students, there is scant literature on why students avoid borrowing. Evidence suggests

that the framing and labeling of educational financing options affects students’ acceptance of

loans in violation of rational economic behavior suggesting that at least a portion of loan

aversion is due to irrationality (Caetano, Palacios, & Patrinos, 2011; Evans, Boatman, & Soliz,

2016). Another explanation is that students may lack important information such as the benefits

associated with investing in higher education. Average returns to higher education are high

(College Board 2013; Kane & Rouse 1995; Oreopoulos & Petronijevice, 2013), so students

should be willing to borrow because the returns easily outweigh the average student loan burden

(Avery & Turner, 2012). Perhaps if students had more accurate information on potential returns

to college, they would make a different investment decision, although a potential college student

may still rationally assess that his or her individual returns will be too small to justify the

expense.

Potential college students may also lack information on financial aid generally and

federal student loans specifically. Research demonstrates that financial aid information is related

to both loan aversion (Boatman & Evans, 2016) and college enrollment (Bettinger, Long,

Oreopoulos, & Sanbonmatsu 2012; Castleman & Page 2014; Ekstrom 1992; Oreopoulos & Dunn

2013). Providing information on the availability and benefits of federal loans may change

students’ attitudes towards borrowing, although we are unaware of any previous test of this

hypothesis in the literature.

Finally, students may also avoid borrowing due to the risk of not being able to repay the

loan balance after leaving college. The positive returns associated with postsecondary education

predominately accrue from completing a degree, yet a substantial portion of students who enroll,

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fail to graduate. Although the data are somewhat dated, Gladieux and Perna (2005) document

that 20 percent of borrowers who take on student loan debt fail to earn a degree thereby limiting

their ability to repay their loans and increasing the probability of defaulting on their loan

repayments. Even for degree earners, labor market outcomes are variable. For example, entering

the labor market during a recession depresses employment and earnings for an extended period

of time (Kahn 2010; Oreopoulos, von Wachter, & Heisz 2006).

Highly risk averse consumers may be rationality deterred from borrowing for higher

education due to these sources of risk; however, there is a federal student loan policy mechanism

which mitigates these possible negative outcomes: income-based repayment. Forms of income-

based repayment reduce the risk of poor financial outcomes after leaving higher education by

basing repayment amounts on earnings rather than the standard ten year repayment plan monthly

payments. 1 This highly reduces the risk of defaulting on federal student loans and the

subsequent outcomes of default such as wage garnishment and reduced credit scores. In theory,

having knowledge about income-based repayment should reduce loan aversion for risk averse

students. Unfortunately, few students take advantage of this option when entering repayment. In

2014 only 20% of borrowers entering repayment enrolled in income-based plans (College Board,

2015). Furthermore, the timing of providing information about income-based repayment is

important. Currently, students typically learn about repayment options only after making the

decision to borrow through loan entrance counseling. Providing this information sooner, such as

during the senior year of high school may reduce loan aversion and encourage greater investment

in human capital.

1 Federal student loan borrowers are currently repaying loans from several different income-based repayment programs (Income-Contingent Repayment, Income-Based Repayment, Pay As You Earn Repayment, and Revised Pay As You Earn Repayment). We use “income-based repayment” as a broad term referring to all of these types of income-driven repayment plans.

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In this paper, we employ a blocked, clustered randomized control trial to test whether

providing information on federal student loans and income-based repayment to high school

seniors affects their levels of stated loan aversion. We test two hypotheses that have heretofore

been unexamined: first, that providing information on federal student loans, specifically

information on income-based repayment, reduces high school seniors’ aversion toward

borrowing money generally and for college specifically, and second, that risk averse individuals

have a larger change in attitudes due to the reduction in risk that income-based repayment

provides. We also assess whether there is any interaction between the observed, economically

irrational labeling effects and our information treatment.

We find evidence that the information intervention does reduce loan aversion on our

general and education specific loan averse measures. This finding suggests policy interventions

designed to provide earlier information about repayment plans may affect students’ decisions to

enter college and improve investments in human capital.

Literature Review

Loan aversion, as it applies to postsecondary education, is generally defined as “an

unwillingness to take a loan to pay for college, even when that loan would likely offer a positive

long-term return” (Cunningham & Santiago, 2008, p. 10). Researchers have previously examined

race, age, and other demographic characteristics to better understand people who may be averse

to borrowing money for education. For example, Hispanic students are commonly cited as being

less willing to take out student loans (Boatman, Evans, & Soliz 2016; Burdman 2005; Paulsen &

St. John 2002; Santiago & Cunningham 2005). Similarly, low-income students can be reluctant

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to borrow for education (Baum & Schwartz 2013; Callender & Jackson 2005; McDonough &

Calderone 2006; Mortenson 1988).

Some part of loan aversion may be explained by risk aversion. There is inherent risk in

making investment decisions, both generally and specifically in human capital investment

decisions. There are two major components of risk in realizing the average returns to

postsecondary education: completion of the degree and labor market outcomes. If a potential

borrower assesses a high likelihood of failing to earn a degree, he may reasonably be averse to

borrowing. There is also risk in poor labor market outcomes even for degree earners. Rarely, if

ever, are the outcomes of such investments certain, leading people to have to accept some degree

of risk in all borrowing decisions. However, strong aversion to risk may deter investment.

Researchers have found individuals with higher risk tolerance are more likely to take out loans to

finance their education (Oosterbeek & Van Den Broek, 2009; Ortiz-Nunez, 2014). This suggests

that attitudes about risk are related to willingness to borrow, and that reducing the risk associated

with borrowing money may lead to a greater willingness to borrow money for education.

While it is not possible to entirely eliminate the risk associated with taking out loans, the

structure of income-based repayment programs is designed to reduce this risk. Because the

repayment options are directly linked to the borrower’s income, there is reduced risk of not being

able to make these payments due to job loss or lower-than-expected earnings. Income-based

repayment programs directly reduce the risk of defaulting on federal student loans, which in turn

reduces the risk of subsequent negative outcomes, such as wage garnishment and reduced credit

scores. This reduction in risk may, in turn, lead to reduced levels of loan aversion more

generally, at least for the part of loan aversion that is explained by risk aversion. However,

income based repayment programs will only be effective in reducing loan aversion if people are

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aware of their existence prior to making a borrowing decision. If potential borrowers knew their

likelihood of default was dramatically reduced as a result of income based repayment, they may

be more willing to borrow to finance their college education.

There is evidence from prior literature that suggests that information can directly impact

individual’s financial aid decisions. Bettinger, Long, Oreopoulos, & Sanbonmatsu (2012) find

that providing assistance and information to students on the FAFSA increased the likelihood of

college attendance, persistence, and aid receipt. Castleman & Page (2016) further investigate the

impact of a personalized text messaging intervention designed to encourage college freshmen to

refile their FAFSA and find large and positive effects among freshmen at community colleges.

Student receiving financial aid information from texts were almost 14 percentage points more

likely to remain continuously enrolled through the spring of sophomore year. These studies

suggest that information can influence behavior. In this paper we hypothesize that reduction in

risk through information provided to students on income based repayment programs should

reduce loan aversion, and we experimentally test to see if this is the case.

Experimental Design & Data

To collect data on a diverse sample of high school seniors, we partnered with Jefferson

County Public Schools in Louisville, KY. Over the course of three days in January 2016, the

research team visited senior classrooms in six schools in the district to administer the information

intervention and survey instrument. The sample roughly reflects the demographics of those high

schools, which are racially and economically diverse (Appendix Tables 1 and 2).

The intervention was comprised of a treatment and control video which students watched

in the classroom during the school day. Each video was approximately five minutes in length and

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consisted of a narrated Prezi presentation providing information on topics related to financial aid

and college enrollment. The treatment video provided specific information on federal student

loans and income-based repayment. The control video presented information on reading financial

aid award letters. Transcripts of both the treatment and control video are provided in the

appendix.

Immediately following the video, we administered an anonymous survey to students. The

survey took approximately ten minutes to complete and captured demographic information

including their aspirations for higher education, assessed their risk aversion, and measured their

level of loan aversion. To measure whether respondents were attentive to the video, the survey

also included questions on students’ knowledge on federal student loans and financial aid award

letters. Loan aversion was measured three ways. First, we gathered their general borrowing

attitudes using a strongly disagree to strongly agree five point Likert scale on three statements:

you should always save up first before buying something, owing money is basically wrong, and

there is no excuse for borrowing money. We recode each student into having a binary outcome

for each of the three statements equal to one if they answered agree or strongly agree to each

statement. Given the increasing severity of these statements, we also used Guttman scaling to

create a loan aversion scale out of these three binary measures. This scale ranges from zero for

no loan aversion to three for highly loan averse. To focus on attitudes specifically targeting

borrowing for education, we also asked “Do you believe it is ok to borrow for education?”

Students who did not answer yes, are considered loan averse for education.

Finally, we pose a hypothetical situation in which students decide to finance a one year

degree program through either an income-based repayment loan or an income share agreement.

We randomly label half of these questions to identify the financial contracts as loans or income

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share agreements and measure loan aversion in the aggregate for each classroom based on

whether that labeling reduces the proportion who chose the loan. This final measure assess

whether loan aversion is caused by economically irrational behavior based on the labeling of

financial options. All three measures have all been previously used by us and others to assess

loan aversion (Boatman, Evans, & Soliz; 2016, Boatman & Evans; 2016; Callender & Jackson,

2005, Caetano, Palacios, & Patrinos, 2011; Mortensen; 1988).

We employed a blocked, cluster randomized control trial. Because providing individual

videos in a classroom was logistically prohibitive, randomization was conducted at the classroom

level. To ensure treatment and control classrooms existed in each school, we blocked by the six

high schools. Because we controlled the distribution of each treatment or control video,

compliance with treatment assignment at the classroom level was 100 percent. We have no direct

measure of individual compliance (i.e. actually paying attention to the video), but we provide

evidence below that the videos were effective in providing information to students on their

targeted topics, suggesting that a substantial portion of students did learn something by watching

them.

Table 1 provides summary statistics for the analytic sample (excluding those missing all

measures of loan aversion or at least one of our covariates, n = 82) divided between the treatment

and control conditions. The final column of the table provides a p-value assessing balance across

treatment assignment on individual covariates accounting for the blocked, clustered nature of the

experimental design following Hansen and Bowers (2008). We see even balance on the

covariates with the exception of small size racial groups and one level of expected education. An

omnibus test fails to reject the null hypothesis that the covariates are jointly balanced. In general,

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students have very high educational aspirations, are economically diverse with just under half

being eligible for free or reduced price lunch, and are racially diverse with one third non-white.

We estimate treatment effects using a multilevel model approach to account for the

student level data with classroom level treatment. While employing school fixed effects to

account for the blocking structure, we use a random intercept model at the classroom level.

Covariates are included to improve precision. We estimate linear probability models for all

binary outcomes to ease interpretation.

Results

In order to test whether students were attentive to the treatment and control videos, we

begin by examining the treatment effect on knowledge about federal student loans (treatment

video) and knowledge about financial aid award letters (control video) which we captured on the

survey instrument. Table 2 reports unadjusted treatment and control proportions for correctly

answering a true/false/yes/no question about each topic. Both of the award letter knowledge

questions were specifically covered in the control video but not in the treatment video. Two of

the three federal student loan knowledge questions (the federal government offers student loans

and income-based repayment) were explicitly covered in the treatment video but none of the

three were covered in the control video. The final column of Table 2 provides treatment effect

estimates of the effect of being presented the treatment video on knowledge of all five questions

using our preferred multilevel model with blocking variables and covariates to estimate a

treatment effect while accounting for the blocked, cluster RCT design.

The treatment video improves awareness of federal student loans by nearly 7 percentage

points, and awareness of income-based repayment by almost 30 percentage points. In contrast, it

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has no effect on the loan knowledge question about subsidized interest, which was not covered in

the video. There are strong negative effects on the financial aid award letter knowledge as those

topics were covered exclusively by the control video. Treatment students were between 26 and

33 percentage points less likely to answer those questions correctly. Collectively, these findings

affirm that students were attentive to the videos and learned new information by watching the

videos.

Having established a strong treatment effect on knowledge, we estimate the treatment

effect on two measures of loan aversion in Table 3 using the same multilevel model specification

with full controls. The first outcome measure is our loan aversion scale comprised of three

questions about general borrowing attitudes not specific to borrowing for education. The second

outcome measures loan aversion for education specifically. We find the treatment video reduces

loan aversion on both measures relative to the control video at the p<0.10 significance level. The

point estimate on the loan aversion scale is approximately 0.10, corresponding to a 0.14 effect

size. This is driven by the “owing money is basically wrong” question, the question

corresponding to the middle level of severity in the Guttman scale. The treatment video also

causes a nearly 5 percentage point reduction on the loan aversion for education measure,

corresponding to a 30 percent reduction from control baseline levels, and an effect size of 0.40.

Providing information on the availability of federal student loans and income-based repayment

reduces loan aversion for education dramatically and general loan averse attitudes slightly.

Appendix Table 3 provides these treatment effect estimates across different estimation

strategies where we account for clustering without a mixed level model but through clustered

standard errors at the classroom level. It also provides models that do not include the covariates.

Results on the loan aversion scale are not robust to the exclusion of covariates; however, relative

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to our preferred model the treatment effect on education specific loan aversion is larger in

magnitude without the covariates and more highly significant in all other models.

It appears clear that providing information on the availability of federal student loans and

income-based repayment reduces loan averse attitudes. The question becomes what the

mechanism is by which this information reduces loan aversion. We next turn to examining the

various mechanisms through which this information intervention may be reducing loan aversion.

We first explore the irrational behavior explanation by examining whether the information

intervention affects whether the labeling of a financial contract to finance education as a loan or

income share agreement changes students’ preferences. Table 4 demonstrates that when students

are randomly provided labels on the financial contract, they are much more likely to choose the

income share agreement and avoid the loan option in line with prior research ((Caetano, Palacios,

& Patrinos, 2011; Evans, Boatman, & Soliz, 2016). However, there is no differential effect of the

loan labeling between the information treatment and control students. The levels of loan aversion

based on labeling are the same regardless of whether students received information on award

letters or federal loans and income-based repayment suggesting that the information is not

working through a change in irrational behavior.

We next test the theory that knowledge of income-based repayment, which reduces risk,

is the pathway through which the information intervention reduces loan aversion. We expect to

see a greater reduction in loan aversion among the most risk averse students. To accomplish this

test, we rely on two measures of risk aversion assessed in our survey instrument taken from

Eckel and Grossman (2008) and Holt and Laury (2002). In the first measure (coin flip lotteries),

we asked students to choose one of six different fifty-fifty lotteries of varying expected payoffs

and variance of payoffs. The possible payoffs, expected payoffs, and risk (the standard deviation

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of the expected payoff) for each lottery are provided in Table 5 along with the proportion of

treatment and control students who chose each lottery. Lottery 1 is the most risky, and only risk

seeking people should choose it, while lottery 6 has no risk. We group responses into three

groups, the most risk averse (lotteries 5 and 6), somewhat risk averse (lotteries 3 and 4) and least

risk averse or risk seeking (lotteries 1 and 2). We then test the heterogeneity of the treatment

effect across these three groups. Table 6 displays the estimates on the interaction terms between

the treatment and two different levels of risk aversion (the omitted category is lotteries 1 and 2,

risk seeking and the least risk averse). We see no evidence of heterogeneous effects on either the

loan aversion scale or the loan aversion for education outcomes across risk aversion levels by

this measure.

Our second measure of risk aversion (differential odds lotteries) is a series of ten lotteries

with two choices in each lottery. The student chooses either Choice A or Choice B for each

lottery. The payoffs within each choice are consistent across lotteries, but the probabilities of

each payoff differ as shown in Table 7. Across lotteries 1 through 10, the expected payoff of

each choice rises as the risk falls, but due to the differential amounts of the payoff, Choice B

should become more desirable as the probability of the receiving the higher amount increases.

The final column expresses the expected payoff difference in choosing A over B for each lottery.

A risk neutral person who will only be concerned with the expectation of the payoff would

choose Choice A for the first four lotteries and then switch to Choice B for the remaining six.

Risk seeking people will switch earlier, and risk averse people will switch later. This measure

provides greater precision than the previous risk aversion measure, but it comes at the cost of

being more difficult to understand. The consequence of the more confusing nature of this risk

aversion measure is that 228 students in our sample made illogical choices by switching between

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Choice A and B more than once and must be excluded from the analysis on top of those that did

not complete the question at all. From these sets of responses, we create four binary measures of

loan aversion: risk seeking (Choice A 0-3 times), risk neutral (Choice A 4 times), risk averse

(Choice A 5-6 times), and highly risk averse (Choice A 7-9 times).

Table 8 displays the heterogeneous treatment effect estimates using this measure of risk

aversion. Risk seeking remains the omitted category. We see some evidence that the treatment

information raises the level of risk aversion for risk neutral students on the loan aversion scale

and for risk averse students on the loan aversion for education measure. These results are

surprising given that we expected information about income-based repayment would reduce loan

aversion for the most risk averse students.

Discussion & Conclusion

The results from this experiment suggest that providing information to prospective

college students on the features of the federal loan system and the benefits of income-based

repayment reduces levels of loan aversion by as much as 30 percent. We also test whether our

information intervention works through several mechanisms that have been hypothesized to

drive loan aversion. We neither find evidence that the information reduces the irrational labeling

effect of loan aversion, nor do we find evidence that it is more effective for risk averse students

relative to risk seeking students. The combination of these results suggests that the information

and the timing of the information being provided in high school reduces loan aversion through

some other unknown mechanism.

This study demonstrates that information about income-based repayment programs, such

as the federal government’s Pay As You Earn program, may help to reduce levels of loan

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aversion among potential borrowers. Pay As You Earn, and other similar income-based loan

repayment programs allow borrowers to set their monthly student loan payments at 10 percent of

their discretionary income for up to 20 years, at which point any remaining loan balance is

forgiven. Linking monthly payment directly to income reduces the risk of default, which, in

turn, is helpful in reducing loan aversion. Although we find no differential reduction in loan

aversion for risk averse students, making these income-based repayment programs more widely

known and available would likely reduce loan aversion and increase college going. In prior

research on this topic, we suggest several ways to increase the take-up of income-based

repayment options, such as making this the default option for all student loans and reducing the

federal student loan system to only two options: an income-based repayment option and a 10-

year fixed repayment option (Boatman, Evans, & Soliz, 2014). Efforts to continue to publicize

the existence of these repayment programs and their benefits to borrowers could go a long way in

reducing loan aversion among potential college students.

Similarly, these results support the notion that information can be a powerful tool in

shaping students’ attitudes. Simply being provided with information on the federal student loan

system and the benefits of income-based repayment leads students to report being more open to

the idea of borrowing money for college. This finding raises important policy questions

regarding when and how information should be delivered in order to maximize its effect on

student attitudes and perceptions. Our experiment took place during the senior year of high

school, shortly before students were expected to have submitted their FAFSA forms. If

information is a tool in changing prospective students’ attitudes, policies should be designed with

timing and delivery of information in mind. For example, mandatory loan counseling

highlighting the benefits of repayment options may be best covered as students complete their

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FAFSA forms, as opposed to during their college exit counseling, when they are typically told of

such benefits.

Limitations

We are not able to distinguish between the effect of different components of information

provided by the treatment video. It may be the case that simply telling students about loans in

general leads to respondents expressing more acceptance of borrowing money for college. It

may not be that the information is specific to income-based repayment, but information of any

kind about student loans would reduce loan aversion on some level. This study is not able to

tease out the effects of information about loans generally versus information about income-based

repayment, as our treatment condition included references to both. Additionally, our measures of

risk aversion may lack reliability and validity because they ask students to respond to

hypothetical lotteries. It is possible they would make more accurate decisions or fewer errors if

the lotteries actually paid real money.

Finally, although changing attitudes about loan aversion is valuable, policymakers are

concerned with actual college enrollment decisions. Currently we are not able to observe the

correlation between students’ attitudes and their actual enrollment and borrowing behavior.

Future work should target this connection.

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Table 1: Sample Summary Statistics & Balance Check for the Analytic Sample

Treatment: Loan Video

Control: Aid Letter

Video

p-value of difference

Female 0.5015 0.4956 0.427 Senior 0.9628 0.9764 0.489 GPA 2.7554

(1.3940) 2.8260

(1.4399) 0.895

American Indian 0.0217 0.0383 0.252 Asian 0.0433 0.0147 0.036** Black 0.2724 0.2566 0.507 Hispanic 0.0248 0.0649 0.046** Multi-racial 0.0650 0.0560 0.432 Pacific Islander 0.0031 0.0059 0.317 White 0.6718 0.6844 0.795 Asian Indian 0.0031 0.0059 0.317 Middle Eastern 0.0062 0.0059 0.702 Race Missing 0.0031 0.0029 0.083* Free/Reduced Lunch 0.4582 0.4808 0.542 Citizen 0.9598 0.9617 0.407 Parent Attended College 0.7492 0.6637 0.213 Parent Graduated College 0.5728 0.5015 0.434 Expect to not graduate high school 0.0093 0.0177 0.157 Expect to get high school diploma 0.0588 0.0590 0.185 Expect to get some college, no degree 0.0031 0.0147 0.157 Expect to get associate’s degree or certificate 0.0836 0.1475 0.243

Expect to get bachelor’s degree 0.3839 0.3186 0.057* Expect to get graduate degree 0.4613 0.4425 0.448 N 323 339 662 Notes: * p < 0.10, ** p < 0.05, *** p < 0.01. Standard deviations are reported in parentheses for the non-binary variable. The final column is the p-value from a balance check clustered at the classroom-level, 𝑥𝑥2 = 15, p = 0.451).

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Table 2: Summary Statistics and Treatment Effect on Federal Student Loan and Award Letter Knowledge

Treatment: Loan Video

Control: Aid Letter

Video

Treatment Effect

Loan Knowledge The federal government offers student loans to help pay for college. 0.9130 0.8437 0.0668**

(0.0265) The government pays the interest on some types of federal student loans. 0.1273 0.1563 -0.0076

(0.0281) There is a student loan repayment option which allows me to repay my student loans based on how much money I make in my job after college. 0.7719 0.4749 0.2958***

(0.0369) Financial Aid Knowledge

A college’s Cost of Attendance includes living expenses. 0.3500 0.6657 -0.3257*** (0.0373)

Your award letter only reports the amount of grant and scholarship aid; it does not report student loans. 0.0963 0.3481 -0.2594***

(0.0315) N 323 339 662 Note: * p < 0.10, ** p < 0.05, *** p < 0.01. Treatment effect estimates derived from multilevel models accounting for blocking and clustering with standard errors in parentheses.

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Table 3: Treatment Effect on Loan Aversion Measures

Control Mean Treatment Effect

General Loan Aversion Scale 1.2418 -0.0978* (0.0534)

You should always save up first before buying something. 0.9021 -0.0064

(0.0233) Owing money is basically wrong. 0.2404 -0.0940***

(0.0313) There is no excuse for borrowing money. 0.0655 0.0046

(0.0214) Loan Averse for Education 0.1632 -0.0487*

(0.0282) Note: Control sample size is 339, total sample size is 662; * p < 0.10, ** p < 0.05, *** p < 0.01. Treatment effect estimates derived from multilevel models accounting for blocking and clustering with standard errors in parentheses.

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Table 4: Treatment Effect on Loan Labeling Explanation for Loan Aversion

Treatment Effect

Labeling Treatment 0.1605*** (0.0404)

Information Treatment 0.0396 (0.0494)

Treatment Interaction -0.0785 (0.0704)

N 645 Note: * p < 0.10, ** p < 0.05, *** p < 0.01. The outcome is a binary measure of choosing the income share agreement financial contract.

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Table 5: Coin Flip Lotteries Payoffs, Risk, and Subject Selection

Payoff (A) Payoff (B) Expected Payoff Risk a Treatment: Loan Video

Control: Aid Letter Video

Lottery 1 $16 $128 $72 $56 0.1223 0.1051 Lottery 2 $24 $120 $72 $48 0.0734 0.0871 Lottery 3 $30 $102 $66 $36 0.0979 0.0901 Lottery 4 $36 $84 $60 $24 0.0948 0.0841 Lottery 5 $42 $66 $54 $12 0.2324 0.2042 Lottery 6 $48 $48 $48 $0 0.3792 0.4294 a The standard deviation of the payoff

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Table 6: Heterogeneity of Treatment Effect across Levels of Risk Aversion for Coin Flip Lotteries

Loan Aversion Scale Loan Averse for Education

Treatment -0.1194 -0.0185 (0.1189) (0.0620) Somewhat Risk Averse 0.0910 0.0505 (0.1238) (0.0644) Highly Risk Averse 0.0169 -0.0227 (0.0968) (0.0499) Somewhat Risk Averse * Treatment 0.0522 -0.0528 (0.1703) (0.0891) Highly Risk Averse * Treatment 0.0467 -0.0191 (0.1364) (0.0712) N 608 607

Note: * p < 0.10, ** p < 0.05, *** p < 0.01. Regressions include the full set of controls.

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Table 7: Differential Odds Lotteries Payoffs and Risk

Choice A (Payoff: $32 / $40)

Choice B (Payoff: $2 / $77) Expected

Payoff Difference Expected

Payoff Risk a Expected Payoff Risk a

Lottery 1 (Odds: 90/10) $32.80 $7.20 $9.50 $67.50 $23.30 Lottery 2 (Odds 80/20) $33.60 $6.40 $17.00 $60.00 $16.60 Lottery 3 (Odds 70/30) $34.40 $5.60 $24.50 $52.50 $9.90 Lottery 4 (Odds 60/40) $35.20 $4.80 $32.00 $45.00 $3.20 Lottery 5 (Odds 50/50) $36.00 $4.00 $39.50 $37.50 -$3.50 Lottery 6 (Odds 40/60) $36.80 $3.20 $47.00 $30.00 -$10.20 Lottery 7 (Odds 30/70) $37.60 $2.40 $54.50 $22.50 -$16.90 Lottery 8 (Odds 20/80) $38.40 $1.60 $62.00 $15.00 -$23.60 Lottery 9 (Odds 10/90) $39.20 $0.80 $69.50 $7.50 -$30.30 Lottery 10 (Odds 0/100) $40.00 $0.00 $77.00 $0.00 -$37.00 a The standard deviation of the payoff

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Table 8: Heterogeneity of Treatment Effect across Levels of Risk Aversion for Differential Odds Lotteries

Loan Aversion Scale Loan Averse for Education

Treatment -0.1883* -0.1818*** (0.1126) (0.0622) Risk Neutral -0.2328 0.0268 (0.1634) (0.0901) Risk Averse 0.0859 -0.1836*** (0.1175) (0.0646) Highly Risk Averse 0.0106 -0.0691 (0.1138) (0.0627) Risk Neutral * Treatment 0.3695* -0.0026 (0.2206) (0.1219) Risk Averse * Treatment -0.0907 0.2007** (0.1625) (0.0896) Highly Risk Averse * Treatment 0.0540 0.1409 (0.1572) (0.0871) N 380 382

Note: * p < 0.10, ** p < 0.05, *** p < 0.01. Regressions include the full set of controls.

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Appendix

Appendix Table 1: Louisville Schools Demographic Sample Statistics

n %

Female %

Black %

Hispanic %

White

% Free/ Reduced Lunch

Atherton High School 120 44.17 3.25 5.00 64.17 57.63 Butler Traditional High School 173 56.65 31.21 2.89 64.16 55.56 Eastern High 197 49.74 21.83 3.05 75.63 24.23 Fairdale High School MCA 22 45.45 13.64 9.10 72.73 50.00 Pleasure Ridge Park High 64 55.56 20.31 4.69 73.44 46.03 Southern High School 166 37.58 31.33 8.43 52.41 65.83 Weighted Average 742 48.10 22.76 4.85 65.63 48.89

Appendix Table 2: Louisville Schools Demographic Population Statistics

N %

Female %

Black %

Hispanic %

White

% Free/ Reduced Lunch

Atherton High School 1,261 57.10 19.51 5.63 68.04 34.73 Butler Traditional High School 1,688 51.90 32.88 2.61 60.01 46.56 Eastern High 2,040 46.67 23.63 4.56 66.96 26.81 Fairdale High School MCA 1,084 41.97 21.86 12.18 61.9 71.86 Pleasure Ridge Park High 1.837 49.86 27.87 2.99 65.54 57.16 Southern High School 1.129 41.90 31.44 9.12 56.95 65.72 Weighted Average 6,076 49.45 25.03 5.60 64.35 41.99 Source: 2013-14 Public Elementary/Secondary School Universe Survey, Common Core of Data (CCD), National Center for Education Statistics, U.S. Department of Education,

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Appendix Table 3: Treatment Effect Estimates on Loan Aversion across Various Estimation Strategies

Model 1: Simple Regression

Model 2: Clustered SE

Model 3: Clustered SE w/ Covariates

Model 4: Mixed Model

Model 5: Mixed w/ Covariates

Loan Aversion Scale

-0.0320 (0.0520)

-0.0320 (0.0471)

-0.0978* (0.0497)

-0.0320 (0.0513)

-0.0978* (0.0534)

Loan Averse for Education

-0.0598** (0.0273)

-0.0598** (0.0233)

-0.0487** (0.0238)

-0.0598** (0.0265)

-0.0487* (0.0282)

Note: * p < 0.10, ** p < 0.05, *** p < 0.01 Sample size is 662.

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Treatment Transcript for Federal Student Loans and Repayment Video

Slide 1 AB: Hi, I’m Angela Boatman BE: and I’m Brent Evans. We are two professors of education from Vanderbilt University in Nashville, TN, and we want to briefly teach you about student loans and repayment when borrowing for college. Slide 2 AB: In the next few minutes, we are going to answer 4 questions about student loans and loan repayment. Slide 3 AB: The first question is, What is a student loan? Slide 4 BE: A student loan is money you borrow to help you pay for college. Unlike a scholarship or a grant, you have to pay it back after you leave college. You have to pay back more than you originally borrow because of interest. You want a low interest rate because that means you have to pay back less. AB: To give an example, if you borrow $2,000 to help finance college at a 5% interest rate, you would have to pay back over $2,440 after your four years of college. If you borrowed that same $2,000 at 8% interest, you would have to pay back $2,750. Slide 5/6 BE: The second question is, Who provides student loans? Slide 7 AB: There are generally two sources of student loans: the federal government and private banks. The government loans require you to file a FAFSA (the federal government’s financial aid application), but they do not require a credit check. They have a fixed interest rate, and you can be eligible for repaying the loan based on how much money you make (an advantage that we will describe in more detail later). BE: In contrast, loans from private banks usually have more restrictions. You often have to pass a credit check, and a cosigner such as a parent may be required. The interest rates on private loans may vary over time, and the loans are generally more expensive than government loans. You also are not eligible to pay back loans based on your income, a significant disadvantage of private loans. We are generally going to be talking about government loans for the rest of the video. Slide 8/9 AB: You might now be asking, Is there a risk to taking out loans? Slide 10 BE: Generally, yes there is some risk in borrowing student loans. If you fail to make monthly payments after you leave college, you could go into default. AB: Why might you not be able to make your regular monthly payments?

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BE: Well, there are a number of reasons. Some students don’t finish their degree, and don’t get as high paying a job as they hoped. Even if you do graduate, you may not earn as much as you thought you would. In you are unlucky, you could lose your job or have unexpected expenses like medical bills. Slide 11 AB: If you do default on your student loans, there are several, potentially severe, consequences. It will harm your credit score making it more difficult to get a loan in the future say for a house or a car. Collection agencies may contact you asking you to pay. You will lose eligibility for other forms of student financial aid. In extreme cases, the government or bank may even take money out of your paycheck in a process called wage garnishment. Slide 12/13 BE: So after you borrow, how do you repay your loan so that you don’t fall into default? Slide 14 AB: If you have a government loan, you select a repayment plan when you leave college. There are generally two types of repayment plans: standard repayment and income-based repayment, called Pay as you Earn. We will tell you about each. Slide 15 BE: Under the standard repayment plan, you pay a fixed amount every month. The payments are calculated so that you pay off the total loan after 10 years. The payments will not change over time, but if your financial situation changes, you may not be able to make payments on the loan which could risk you falling into default. Slide 16 AB: Under Pay as you Earn, you still make monthly payments, but the amount you pay varies depending on your annual income. If you earn less, you pay less each month. You make payments until the loan is paid off up to a maximum of 20 years. This repayment plan directly reduces the risk of default because your payment becomes smaller if you earn less money. BE: Let’s consider an example of a student repaying her loans under both repayment plans. AB: This student borrowed $7,300 in federal loans per year for four years to pay for her bachelor’s degree. The interest rate is 6.8%. She is now working full time and earning $35,000 per year. BE: This graph shows her monthly payments under both repayment plans during her first year in the job. Under the standard repayment plan, she will pay $340 per month. Under Pay as you Earn, she will only pay $150 per month saving her nearly $200 per month. AB: One important difference between the standard repayment and Pay as you Earn plans is the length of time you have to pay back your loan. The standard repayment is paid back over ten years and the Pay as you Earn over 20 years. This is why your monthly payments are lower under Pay as you Earn. Slide 17 BE: Although you may make loan payments over a longer period of time, Pay as you Earn has some notable advantages. It reduces risk if you get into financial hard times. It also allows you

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the flexibility to take a lower paying job since your monthly loan payments will be smaller. If you haven’t paid off the loan after 20 years, the remaining balance is often forgiven. The monthly payment will never be more than under the standard repayment plan. Slide 18 AB: It is always better to finance your education with grants and scholarships rather than borrowing loans. However, many students find borrowing a viable option to help finance college expenses. If you do borrow, Pay as you Earn plans provide a low-risk way to borrow money from the government to help pay for college. Thanks for listening! Ask your guidance counselor if you have other questions!

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Control Transcript for Understanding a College Financial Aid Award Letter Video

Slide 1 AB: Hi, I’m Angela Boatman BE: and I’m Brent Evans. We are two professors of education from Vanderbilt University in Nashville, TN, and we want to briefly talk with you today about financial aid and how to understand the financial aid award letter you will receive from the colleges where you are accepted. Slide 2 AB: The first question a lot of students ask is “How much financial aid can I receive?” Who determines this and how is it calculated? BE: The amount of money you receive in financial aid for college is determined by the following equation: Cost minus EFC equals Need. We will break down each part of this equation so we can understand It better. Slide 3 AB: The first is Cost of Attending the College, which is the total amount it will cost to go to college each year. Slide 4 AB: This cost is set by the college and typically incudes: Tuition & fees, Rent, the cost of living in a dorm or apartment, Personal costs (medical, toiletries, clothing, laundry), Transportation to and from school, and Books and supplies. Slide 5/6 BE: From the cost of attending the college, we subtract your EFC, or your Expected Family Contribution. Slide 7 BE: Your Expected Family Contribution is the money your family can contribute to your college education. The amount is calculated from your answers on the FAFSA and is based on the number of people in your household, your family's income, your income, the number of children enrolled in college, and other savings your family has. AB: So, my expected family contribution is the money I need to provide to pay for college. Slide 8/9 AB: The Cost of Attendance minus your Expected Family Contribution is the amount of financial need you have for college. Slide 10 BE: So, if I have financial need, how do I get the money to pay for college? AB: If you have financial need, you will be offered several different ways to pay for college. The first are grants, which is money you do not need to pay back. Grants are given to students based on financial need. The second are scholarships, which is another form of money that you

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do not need to pay back. Scholarships are given to students based on merit or performance (like good grades, community service, etc. ). Work-study is a federal program that provides students with part-time employment to help meet their financial needs and give the student work experience. Student loans are financial aid that must be repaid. Most loans that are awarded based on financial need are low-interest loans sponsored by the federal government. There are other student loans that are not sponsored by the federal government and usually have higher fees and/or interest rates. BE: Got it. So, if I have financial need, I will offered some combination of grants, scholarships, work study, or loans. Slide 11 AB: And these different forms of aid will be outlined in the award letter you receive from the college. This letter will tell you how much of each type of financial aid you are being offered. BE: Exactly, and because each college has its own packaging policy, the types of financial aid offered to you will probably vary from college to college. Slide 12 BE: So let’s look at a sample financial aid award letter to see how this looks in the real world. This letter is from a community college and it outlines the total cost of attendance, the different types of financial aid being offered to you, and the net cost, which is the total cost after all of the aid is applied. Slide 13 AB: The letter is structured into different sections corresponding to costs and aid. Let’s look at the costs first. You see on the left the cost of tuition, just over $3,000 per year. On the right, you see the other costs such as room and board, books, and transportation that are estimated by the college. When added to the tuition costs, this makes the total cost of attendance for this college $10,603 next year. Slide 14 BE: This section of the letter breaks out the financial aid offer for each of the two semesters in the academic year and separates out the grant aid (which is free money that you do not have to pay back) from the self-help aid (things like loans and work study). AB: So you can see there are two types of gift aid, an institutional grant from the community college and the Pell Grant which comes from the federal government. In total you would receive $3,600 in grant aid to attend this college next year. The self-help section shows two types of federal student loans totaling $9,100. You may note that there is no work study offered in this letter. Slide 15 AB: The section at the bottom does some addition and subtraction for you. It takes the total cost of attending this college and subtracts off the sources of aid. The Net Total Costs after Gift Aid (the middle line) shows how much money you would have to pay to attend the college for a year after taking the grants. The final line (Net Total Costs after Gift Aid and Self-Help Options) tells you how much you still have to pay after accounting for grants and loans.

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BE: You’ll note the remaining cost is $1,503 after applying all sources of financial aid, and this corresponds with the $1,500 the student is expected to pay from the EFC. Keep in mind that not all financial aid award letters are the same, but they should all include the same basic components. You will be given the option to accept or decline each grant, loan, or work study aid offered to you if you decide to attend that college. Slide 16 BE: In summary, colleges will send you a financial aid award letter after you are admitted. You should read over the award letters closely to understand the different types of financial aid that are offered. AB: Talk to your guidance counselor or financial aid officer at the college for more information. Thanks for listening!

Appendix: Survey questions measuring financial aid knowledge

Survey questions measuring income based repayment