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FRAMING AND LABELING EFFECTS IN BORROWING 1

Framing and Labeling Effects in Preferences for Borrowing for College:

An Experimental Analysis

Brent J. Evans

Angela Boatman

Adela Soliz

Peabody College, Vanderbilt University

230 Appleton Place, PMB 414, Nashville, TN 37203

Author’s Note: All correspondence concerning this article should be addressed to Brent J.

Evans, Peabody College, Vanderbilt University, 230 Appleton Place, PMB 414, Nashville, TN

37203. E-Mail address: [email protected]. Phone Number: 615.322.6491.

mailto:[email protected]


Abstract

Evidence from behavioral economics suggests that framing and labeling affect financial

decisions. Through a randomized control trial of over six thousand high school seniors,

community college students, and adults without a college degree, we identify the existence of

both framing and labeling effects in respondents’ preferences for borrowing for postsecondary

education. How financially equivalent contracts are framed alters the preferences of high school

and community college students. Furthermore, simply labeling a contract a “loan” reduces the

likelihood of selecting that option by 8-11 percentage points among those samples. These effects

are more pronounced among Black high school respondents and Hispanic high school and

community college respondents who are both twice as likely as white respondents to avoid the

loan option when it is labeled. Finally, we provide suggestive evidence that this labeling effect is

driven by more risk averse respondents.

JEL Classification: I22

Keywords: Educational economics; State and federal aid; Student financial aid; Student loans




Section 1. Introduction

Despite large individual returns to higher education (Carnevale, Rose, & Cheah, 2011;

Kane and Rouse, 1995; Pew Research Center, 2014), only 66 percent of recent high school

graduates were enrolled in college in 2013 (National Center for Education Statistics (NCES),

2015). One of the leading explanations for low levels of enrollment is the rising cost of college.

Over the last ten years, college tuition and fees have risen 3.4 percent annually above the rate of

inflation, and total average charges are near $20,000 for in-state public four year colleges and

over $43,000 at four-year private colleges (College Board, 2015a). Net price after accounting for

grant aid is, on average, over $7,000 at public two-year colleges and over $14,000 at public four-

year colleges for in-state students (College Board, 2015a). For low- and middle-income students

and their families, these expenses serve as a barrier to college entry and success, often requiring

additional financial resources in the form of student loans.

Federal student loans are the primary policy mechanism enabling credit constrained

students to pursue higher education. Forty-seven percent of first-time, full-time undergraduate

students receive a government student loan to help finance their college education (NCES,

2016). Federal student loans are the single largest source of financial aid for undergraduate

students at $62.1 billion in 2015, far surpassing expenditures on Pell Grants, the next largest

source of financial aid at $30.3 billion (College Board, 2015b).

Despite the fact that almost half of college matriculants make use of student loans to

finance higher education, there is concern that many students are loan averse; they believe


investing in higher education is worthwhile but are unwilling to finance that investment through

borrowing (Cunningham & Santiago, 2008; Palameta & Voyer, 2010). Prior work has identified

that a substantial subset of potential college students are averse to taking out student loans, with

20-40 percent of high school seniors reporting an aversion to borrowing for college, depending

on the measure (Boatman, Evans, & Soliz, 2017). Credit constrained low- and middle-income

students who are reluctant to borrow money may instead choose to work more while enrolled,

enroll part time, or not enroll at all, decisions which may reduce the likelihood of degree

attainment and result in an under-investment in human capital. Given the widespread reliance on

student loans to finance higher education, the tremendous public investment in student loans, and

the concern that loan averse students are under-investing in human capital, it is important to

better understand the student borrowing decision.

While economic analyses of the borrowing decision exist (for example the effects of

grant aid on amount borrowed (Marx & Turner, 2017) and the extent to which borrowing

decisions are determined by the expected returns to education (Avery & Turner, 2012)), there has

been less attention to behavioral economic considerations of how the borrowing decision is

presented to potential borrowers. A long line of literature in behavioral economics has

demonstrated that the way in which options are framed can affect decisions in an array of areas

such as health care and investments (Banks et al., 1995; Epley, Mak, & Idson, 2006; Johnson et

al., 1993; Keller, Lipkus, & Rimer, 2003; Mullainathan, Schwartzstein, & Shleifer, 2008;

Tversky & Kahneman, 1981). Similar behavioral effects may also influence the borrowing

decision. How a financial contract is described (framing) or whether the words used to identify

the contract include “loan” (labeling) might alter a prospective borrower’s decision to borrow. If

students differentially respond to financial aid offers due to framing and/or labeling effects, it


may be possible to redesign student loan programs to alleviate loan aversion and increase human

capital investment.

This paper uses a randomized control trial to assess whether the stated preferences about

borrowing decisions for education are subject to framing and labeling effects. We ask three

specific research questions: 1) Is there evidence of framing effects in the decision to borrow for

postsecondary education?; 2) Does labeling a financing option as a “loan” change the likelihood

of selecting that option?; 3) Are there heterogeneous effects of labeling by race and level of risk

aversion? Answers to these questions provide important insights into the borrowing decision and

potential causes of loan aversion.

This study replicates and extends a study originally conducted in three Latin American

countries by Caetano, Palacios, and Patrinos (2011), hereafter referred to as CPP. Their study

measured potential framing effects by asking respondents, in a hypothetical situation, to choose

between two financially-equivalent contracts to finance their education, one framed as a loan and

another framed as an income share agreement (ISA). An ISA, also known as a human capital

contract, is a type of financial contract that serves as an alternative to a traditional student loan.

ISAs provide students with upfront money to finance higher education in exchange for receiving

a percentage of the student’s future income after leaving college. The CPP study also measured

the extent to which loan aversion was due to the way in which the financing options were

labeled. They found evidence that respondents prefer the ISA framing and that labeling the

contract a “loan” reduced student preference of the loan framing by approximately 8-12

percentage points.

Our replication and extension of CPP offers three main advantages over this prior work.

First, the original study took place in Chile, Colombia, and Mexico. The context for financing


higher education is quite different in the United States, in which the cost of higher education is

much higher, and students rely more heavily on loans. For example, the cost of one year of

higher education in Colombia is approximately $3,000. Furthermore, the small group studied in

the Latin American paper (767 respondents) were students who had already applied for

educational financing through a company that offers ISAs. The applicants likely had a more

sophisticated level of understanding of their education financing options, calling into question

the external validity of the findings when applied to broader populations of potential students in

higher education.

Second, we conduct this experiment among three distinct samples of respondents: high

school seniors, community college students, and adults aged 20-39 without a college degree who

are not enrolled in college. Previous work has demonstrated that loan aversion may be more

widespread among people who do not apply to college than those that do (Boatman, Evans, &

Soliz, 2017). Comparing the difference in framing and labeling effects across different

populations provides insight into the borrowing decision for traditional and non-traditional

students, while comparing populations that have and have not pursued a college degree allows

for much broader external validity than the prior study. Furthermore, our experiment has higher

sample sizes in all three populations than the original study.

Finally, the diverse populations obtained across our three populations allow for an

analysis of the potentially heterogeneous effects of the labeling experiment on respondents.

Specifically, we examine differential effects across race and elicited risk preferences. Previous

studies have argued that Hispanic students may be more loan averse because they are less likely

to borrow for college (Cunningham & Santiago, 2008; ECMC Group Foundation, 2003;

Hillman, 2015), and there is some quantitative evidence demonstrating that variation in loan


aversion exists across race (Boatman, Evans, & Soliz, 2017). Our current analysis extends the

prior literature by providing evidence of loan aversion differences across race with a higher level

of internal validity given the randomized nature of the analysis.

Prior work has also hypothesized that risk may play a role in reduced borrowing (Avery

& Turner, 2012; Boatman, Evans, & Soliz, 2014; Caetano, Palacios, & Patrinos, 2011). High

levels of risk aversion may deter potential students from borrowing due to the risk of poor

financial outcomes in the labor market limiting a borrower’s ability to repay the loan. There is

little empirical evidence of such an effect on borrowing preferences, and, as such, we examine

whether any observed framing and labeling effects found in our sample are related to risk

aversion. It is possible that if respondents associate risk with the word “loan,” we might see

differential effects.

We find evidence of both framing and labeling effects, in part consistent with the

findings of CPP. In the test for framing effects, respondents are asked to choose which of two

contracts they prefer, one framed as a loan and one framed as an ISA but without the use of the

words “loan” or “income share agreement” in either. In this unlabeled, framing test, we find that

both groups prefer a loan framing. These results contrast with CPP who find their sample prefers

the ISA framing. In the test for labeling effects, we find labeling a borrowing option as a “loan”

reduces the likelihood of selecting that option by 8-11 percentage points for high school and

community college students. These results align closely with the findings of CPP. We also

observe large effects by race. The effects of labeling a borrowing option as a “loan” for Black

and Hispanic high school students as well as Hispanic community college respondents are more

than twice as strong as those for white respondents. Finally, there is suggestive evidence that the

labeling effect is magnified among risk averse respondents.


The paper is organized as follows. Section 2 summarizes the prior literature on loan

aversion and on framing and labeling in economics with an application to financing education

decisions. Section 3 describes the sample, data, experimental design, and analysis. We present

the results in Section 4 and discuss the results and implications for policy and practice in Section

5.

Section 2. Prior Literature

2.1 Loan Aversion

Loan aversion is a widespread phenomenon which can affect credit constrained students’

human capital investment decisions. Evidence of loan aversion has been found in Canada

(Palameta & Voyer, 2010); Latin America (Caetano, Palacios, & Patrinos, 2011); the United

Kingdom (Callendar & Jackson, 2005); and in the United States among prospective college

students (Boatman, Evans, & Soliz, 2017), undergraduates (Boatman, Evans, & Soliz, 2017;

Goldrick-Rab & Kelchen, 2013), and graduate students (Field, 2009). Although common, loan

aversion does not affect all students equally with evidence of differential loan aversion by

gender, race, and socioeconomic status (Boatman, Evans, & Soliz, 2017; Callendar & Jackson,

2005; Cunningham & Santiago; ECMC Group Foundation, 2003; Hillman, 2015).

There are several potential causes of loan aversion. It could rationally be due to risk

aversion. In the face of uncertainty about their returns to education, students may decide to avoid

borrowing to eliminate the risk of default and damaging their credit. Evidence from other areas

of economic analysis supports this conjecture. Higher levels of risk aversion are related to

reduced activity in investment markets (Fellner & Maciejovsky, 2007; Keller & Siegrist, 2006),

reduced recreational gambling (Warneryd, 1996) and reduced cooperation in the prisoner’s


dilemma (Sabater-Grande & Georgantzis, 2002). Risk aversion clearly alters behavior, and it is

important to test its relationship with borrowing preferences.

A few scholars have hypothesized that loan aversion may be caused by cultural factors

that teach negative attitudes about debt (Cunningham & Santiago, 2008; ECMC Group

Foundation, 2003; Hillman, 2015). Finally, loan aversion may be explained by behavioral effects

such as framing and labeling that affect students’ attitudes about borrowing. More evidence is

required to distinguish between these causes, and our analysis is an attempt to experimentally

assess whether framing and labeling effects are a root cause of loan aversion.

2.2 Framing and Labeling Effects in Financing Education

Seemingly unimportant aspects of the way choices are presented can have large effects on

consumers’ decisions (Banks et al., 1995; Johnson et al., 1993; Keller, Lipkus, & Rimer, 2003;

Epley et al., 2006; Mullainathan, Schwartzstein, & Shleifer, 2008; Tversky & Kahneman, 1981).

One broad example of these framing effects is the gain-framed versus loss-framed asymmetry

observed in a number of contexts such as the decision to obtain a mammogram (Banks et al., 1995)

or in spending a tax refund (Epley et al., 2006). Another general example is the asymmetry between

delaying or speeding up consumption in which the amount required to delay a reward is several

times larger than subjects are willing to pay to speed up consumption of that reward (Loewenstein

1988; Loewenstein and Prelec, 1992). These are violations of the standard economic model in

which consumers should be indifferent between financially equivalent choices.

These behavioral effects also exist in decisions related to attending and financing higher

education. Monks (2009) provides evidence that students differentially respond to how the net-

price of their college education is framed. Students are more likely to enroll at a specific institution


if net-price has been reduced by receiving a scholarship than if an equivalent net-price is framed

as having a lower sticker price without a scholarship.

A stark example of framing effects in financing postsecondary education is provided by

Field (2009). Prospective New York University law students interested in public service careers

were randomly assigned two different types of financially equivalent contracts to finance their

legal studies. One contract was framed as a standard loan that would be forgiven if the student

pursued public service while the other was framed as a grant that would transform into a loan that

must be paid back if they did not take a public service job. Prospective students who received the

contract framed as a grant were twice as likely to enroll and 36 percent more likely to enter public

interest law than students who were offered the contract framed as a loan. These findings suggest

that a portion of law students are loan averse when an alternative financially equivalent contract is

available; however, it is unclear if these findings extend beyond highly motivated students who

applied to a single law school.

Palameta and Voyer (2010) provide non-experimental evidence of framing effects in

decisions related to financing postsecondary education from Canada. When Canadian high school

students were asked to choose between cash and various financial aid packages that combined

grants and loans, many students accepted packages with grants but rejected the same level of grants

when an optional loan was added to the package.

Following from these framing effects, the actual words used to attach a label to financial

options can affect decisions (see, for example, Cooper, Gulen, & Rau (2005) on the effects of

changing the name of a mutual fund). Although there is less empirical research on the effects of

labeling in financing postsecondary education, Avery and Hoxby (2004) provide evidence that

students respond differentially to a named scholarship relative to an unnamed grant of equivalent


monetary value. Simply attaching a naming label does not change the underlying financial

situation but does alter students’ perceptions, and in some cases, their behaviors.

Our study builds off the framing and labeling effects identified in students’ borrowing

preferences by CPP in Latin America. In order to identify framing effects, they described two

financially equivalent contracts and asked respondents to choose one. One was framed as an

income based repayment loan and the other was framed as an income share agreement. Their

results suggest that framing a financial contract as an income sharing agreement increases take-up

over a more traditional loan framing. A major caveat of their findings, however, is that each

participant had previously expressed interest in income share agreements and had already applied

for higher education financing.

CPP also disentangle the components of loan aversion caused by labeling effects by

experimentally manipulating whether respondents saw the “loan” and “income share agreement”

labels attached to the two financial options. They observed a reduction in respondents choosing

the loan option of 8-12 percentage points, depending on the specification, when the options were

labeled, suggesting that loan aversion is mostly the result of labeling effects. In other words,

people avoid the term “loan.” As described above, our study replicates and extends CPP to

advance our understanding of behavioral effects in the preferences for financing postsecondary

education.

Section 3. Data and Methods

3.1 Survey and Experimental Design

Most studies of loan aversion have relied on surveys to measure student attitudes about

borrowing or to measure student responses to hypothetical borrowing situations. Our study


follows this tradition by asking students to respond to a hypothetical question about how they

would prefer to finance their education. In order to assess framing and labeling effects on

borrowing preferences, we designed and fielded a survey that asked respondents basic

demographic questions as well as a hypothetical question about which of two financially

equivalent contracts they would prefer in order to finance a $10,000 one-year education program.

The framing and labeling effects are measured from responses to this specific question taken

from CPP:

Suppose you need $10,000 to finance a one-year education program. In one year you will

join the work force. How do you prefer to finance your education? (Choose one.)

60 monthly payments of $200. If in any month your income is below $2,000, then

you only have to pay 10 percent of your income that month.

60 monthly payments equal to 10 percent of your income. If in any month your

income is larger than $2,000, then you only have to pay $200 in that month.

The two choices are financially equivalent; the monthly payment will be the same

regardless of the choice. However, the two choices vary in their framing. The first choice is

framed as an income-based repayment loan in which there is a set amount that must be paid each

month. If one’s monthly earnings are too low to pay that established amount, then the payment

will be reduced to 10 percent of that month’s income. The second choice is framed as an income

share agreement in which there is no fixed monthly payment amount. Instead, the monthly

payment is ten percent of monthly income with a maximum payment amount of $200 in any one

month.

In the absence of behavioral effects respondents should be indifferent between the two

choices because the two contracts result in the same amount paid in any month. This indifference


should be reflected in a 50-50 split in choices between these two options. If the distribution of

respondents deviates from an even division in the preference for one option over the other, it

suggests that framing effects are altering preferences.

In order to experimentally assess the impact of labeling on borrowing preferences, we

randomly assigned half of respondents to receive the same survey question, but with a simple

change in the wording of the two response choices. The treatment group received a survey in

which the first option was explicitly labeled a “loan” and the second option was explicitly

labeled an “income share agreement” as seen below.

Loan: 60 monthly payments of $200. If in any month your income is below $2,000,

then you only have to pay 10 percent of your income that month.

Income Share Agreement: 60 monthly payments equal to 10 percent of your income.

If in any month your income is larger than $2,000, then you only have to pay $200 in

that month.

To ensure an equal distribution of treatment and control in each institution (high school

or college), we blocked by high school or college for the high school and community college

samples. By comparing the choices of respondents who randomly received the labels (the

treatment group) from students who randomly did not receive the “loan” label (the comparison

group), we can determine whether labeling may be driving loan aversion. Although this

methodology cannot identify whether a single person is loan averse because of labeling, it can

identify the existence and extent of the labeling phenomenon in populations of respondents.

In order to allow for subgroup analyses, the survey also collected data on demographic

characteristics, educational goals, and whether or not community college students had borrowed


for college. We also measured risk aversion for a randomly chosen half of each population.1 We

use Eckel and Grossman’s (2008) measure for assessing risk tolerance which asks respondents to

choose their most preferred lottery out of six 50-50 chance lotteries. The lotteries vary in

expectation and risk (standard deviation of expected payoff) such that we can rank respondents

from most risk seeking to most risk averse. Palmeta and Voyer (2010) use a similar measure to

assess risk in their loan aversion study.

In order to improve the validity of our survey, we solicited advice from several experts in

survey design and postsecondary education financing. We pilot tested the survey with a class of

high school seniors, and conducted a focus group at the end of the pilot to gather information on

the timing and areas of confusion caused by any of the questions. The surveys were edited and

provided to expert colleagues for a second round of feedback and revisions.

3.2 Sample

To extend generalizability and investigate the effects of framing and labeling in different

populations, we collected survey data from three distinct populations: high school seniors,

community college students, and adults aged 20-39 without a college degree not currently

enrolled in higher education. Including high school seniors enables us to draw conclusions about

how the effects of framing and labeling on loan aversion might be shaped prior to making the

decision to borrow and enroll in higher education. Community college students have already

made the decision to enroll and borrow for college, but they nonetheless constitute a diverse and

important population of students in higher education. Adults who have not attended college

provide an important perspective into the attitudes about borrowing money among a group with

1 The survey used two forms to reduce the time burden on completion, and the risk measure was only included on one form of the survey. Survey forms were randomly assigned to respondents.


potentially more experience in the credit and labor markets and may also enter higher education

in the future.

For high school seniors, we aimed to obtain a random sample of diverse high schools

across multiple states and survey the entire senior class. Our sampling frame is comprised of all

public high schools in Texas, Kentucky, Tennessee, and Massachusetts that had at least 500 total

students with at least 10 percent of the student body being White, 10 percent Black, 10 percent

Hispanic, and 10 percent low-income, as defined by eligibility for free or reduced price lunch.

Stratifying by state, we randomly ordered eligible high schools and contacted them, in order,

through the guidance office and the principal’s office. We stopped after receiving five positive

responses for high schools in that state or exhausting all of the available high schools. For high

schools that agreed to participate, we traveled to the schools and administered the survey to all

seniors present on the day of administration during the spring semester of the 2014-15 school

year, capturing at least 80 percent of the seniors in every school. Treatment and control surveys

were randomly sorted for each school in advance and handed out in this random order to students

in each classroom. We ultimately surveyed 1,657 high school students in eight schools with no

missing data for the variables we rely on in the analysis.2

The community college sample was selected to supplement the high school analysis. We

administered our survey in one college in Illinois, one in Tennessee, one in Michigan, and two in

Texas. Survey administration took place through an electronic survey emailed to all enrolled

students during the summer and fall of 2015. Randomization occurred once a student agreed to

respond to the survey and began engaging with the instrument. Due to the online nature of the

survey, we received a five percent response rate to the survey of community college students.

2 Results are robust to the inclusion of all respondents and ignoring the covariates for which we have missing values.


This response rate still yielded surveys from 3,770 community college students. This low

response rate may pose a concern for external validity as respondents are unlikely to be

representative of the full student body at these community colleges. We note that nearly three

quarters are women, although we observe substantial diversity across race and income,

suggesting we captured a reasonable cross section of these diverse student bodies. The response

rate does not, however, pose a threat to the internal validity of our experimental analysis because

random treatment assignment occurred only after each respondent agreed to participate;

therefore, treatment assignment can have no bearing on likelihood of response.

For the adult sample, we hired a survey firm, Qualtrics, to find respondents to complete

the online version of our survey. The survey firm relied on marketing email lists to identify and

obtain survey results for approximately 200 adults without a college degree, aged between 20

and 39, and not currently enrolled in higher education in each of four racial categories: White,

Black, Hispanic, and Asian. Similar to the community college survey, randomization occurred

once a respondent began the survey. The 843 adult respondents represent a 12 percent response

rate from the full set of adults contacted. These respondents are admittedly not a random sample

of adults who fit the age and education criteria; however, it is still valuable to compare responses

of adults to those of high school seniors and community college students.

Table 1 presents descriptive statistics for the treatment and comparison groups in each of

the three samples. The samples are racially and socioeconomically diverse. Both the community

college and adult samples have substantially more women than men. This can be explained in

part by larger numbers of women enrolling in college in the community college sample, and the

large female portion of adult respondents likely reflects the marketing lists relied upon by the


Table 1: Descriptive Statistics High School Community College Adults Not Enrolled in College Treatment Comparison p Treatment Comparison p Treatment Comparison p

Female 0.53 0.52 0.67 0.75 0.70 0.06 0.74 0.75 0.61White 0.34 0.37 0.47 0.45 0.44 0.65 0.30 0.26 0.24Black 0.20 0.21 0.90 0.10 0.10 0.76 0.21 0.24 0.32Hispanic 0.26 0.25 0.83 0.29 0.29 0.53 0.18 0.20 0.50Asian 0.03 0.02 0.36 0.05 0.05 0.38 0.19 0.19 0.96Multi-race 0.15 0.14 0.99 0.06 0.07 0.43 0.12 0.11 0.60Age 18.38 18.34 0.26 26.64 26.01 0.01 29.58 29.50 0.83High School Senior 0.98 0.97 0.05 FRPL 0.47 0.47 0.93 Has Pell 0.17 0.17 0.95 0.04 0.06 0.41Has Public Assistance 0.52 0.52 0.53 0.39 0.41 0.62Education goal: some college 0.02 0.01 0.09 0.02 0.02 0.29 0.21 0.29 0.01Education goal: Associate's degree 0.12 0.14 0.42 0.39 0.39 0.96 0.27 0.27 0.83Education goal: Bachelor's degree 0.32 0.32 0.75 0.60 0.60 0.82 0.30 0.31 0.78Education goal: Graduate school 0.50 0.49 0.70 0.21 0.20 0.58 0.14 0.10 0.06At least 1 parent attended college 0.65 0.63 0.52 0.58 0.61 0.11 0.43 0.41 0.54At least 1 parent graduated from college 0.52 0.48 0.31 0.39 0.42 0.02 0.30 0.30 0.96U.S. citizen 0.94 0.94 0.94 0.92 0.91 0.54 0.95 0.91 0.02

N 825 832 1,876 1,894 444 399 Notes: This tables uses the sample excluding respondents with missing values on any of the covariates. Treatment and comparison means are unconditional. P-values come from regressions of each individual variable on a binary variable for treatment status with high school and college fixed effects to account for blocking by school and college for the high school and community college samples.


survey firm. The p-values in the third column test whether there is any statistically significant

difference between treatment and comparison groups on each variable in each sample. With only

three variables being statistically significantly different at the 5 percent significance level, across

the fifty-four tests, we assert that the treatment and comparison groups are well balanced on

observable characteristics.

3.3 Methods of Analysis

The randomized control trial focuses on randomly assigning a label, but we can also

measure framing effects non-experimentally by examining preferences between the financial

contracts for students in the comparison group who did not observe a label. In order to address

whether there is evidence that framing affects the borrowing decision, we rely on examining the

preferences between the two methods of financing a one-year education program for $10,000.

We focus only on preferences of the comparison group, the respondents that did not see the

“loan” and “income share agreement” labels, to avoid the potentially confounding effects of the

labeling experiment. Because the two options are financially equivalent contracts, we expect

students to be indifferent between them, resulting in an even 50-50 split. If there are no framing

effects, half of the respondents would choose the ISA framing and the other half would choose

the loan framing. A chi-squared goodness of fit test formally assesses whether the empirical

distribution of responses differs from the expected distribution.

We then turn to assessing the extent of the labeling effect by comparing the rate of

choosing the income share agreement option between the treatment and comparison groups in

the experiment. If a labeling effect exists, implying respondents are averse to the loan label, the

treatment group should select the income share agreement framing at higher rates than the

comparison group simply because of the label. We formally test this difference using a linear


probability model regression separately for each of the three samples. The regression analysis

allows for the inclusion of covariates and institution (high school or college) fixed effects. We

estimate the following model,

𝐼𝑆𝐴𝑖𝑗 = 𝛽0 + 𝛽1(𝐿𝑎𝑏𝑒𝑙)𝑖𝑗 + 𝑋𝑖𝑗 + 𝛾𝑗 + 𝜖𝑖𝑗 (1)

in which “ISA” is an indicator variable for whether respondent i in school or college j (adults are

not indexed by j) chose the income share agreement option. “Label” is an indicator variable for

respondents who received the treatment survey question with “loan” and “income share

agreement” labels. Xij is a vector of respondent-level covariates including gender, race, age,

educational aspirations, parental education, citizenship status, and proxies for income. Being

eligible for free or reduced price lunch is the income proxy for high school students, and having

received a Pell Grant, SNAP, TANF, or WIC in the last two years serves as the proxy for low-

income in the community college and adult samples. High school or community college fixed

effects are included as . Fixed effects are not included in the models for the adult sample. 𝛾𝑗

Standard errors are clustered by institution in the high school and community college samples.

The coefficient of interest is as it estimates the change in probability of a respondent selecting 𝛽1

the income share agreement due to labeling.

Section 4. Results

We first examine whether the comparison group respondents selected the ISA and loan

framing equally. Theory suggests the comparison groups should be evenly distributed between

the two framings because they are financially equivalent. Table 2 reports the observed percent of

the comparison group choosing the ISA framing and the chi-squared goodness of fit test statistic

comparing the observed percent to the expected even split for all three samples. For both the


Table 2: Chi-squared test of Framing Effect: Choosing the ISA Financing Option

Notes: The observed percent reports the percent of the comparison group that chose the income share agreement framing. The chi-squared statistic is the one sample goodness of fit statistic comparing the expected equal distribution to the observed distribution.

high school and community college samples, the observed percent is less than 50% (43.39% and

43.24% respectively), implying that respondents preferred the loan framing to that of the ISA.

The adult respondents show equal preference for the two framings. The p-value in the last row of

the table demonstrates that these differences are highly statistically significant for high school

and community college respondents, indicating that framing effects for borrowing preferences in

higher education are prevalent in these two populations.

Table 3 presents the regression results from our experimental analysis of the labeling

effect. For each sample, we report the treatment effect with and without covariates. Given the

balance of covariates observed in Table 1, it is unsurprising that the results are similar across

both models, but the inclusion of covariates slightly increase precision for two of the samples.

High school and community college respondents exhibit a large labeling effect. High school

seniors were 11-12 percentage points more likely to choose the ISA option when the options

were labeled “loan” and “income share agreement.” Community college students were over 8

percentage points more likely to choose the ISA option over the loan option once they were both

labeled. The magnitude of these effects is quite large relative to the control means with high

school and community college students increasing their likelihood of preferring the ISA contract

by 26% and 19% respectively. In contrast, adults exhibit no sign of the labeling effect with the

High School Community College AdultsExpected Percent 50.00 50.00 50.00Observed Percent 43.39 43.24 49.87Chi-squared 14.54 34.60 0.00p-value 0.0001 <0.0001 0.9601


Table 3: Treatment Effect Estimates of the Labeling Effect

High School Sample Community College Sample Adult Sample

Treatment 0.1170*** 0.1146*** 0.0802** 0.0835** 0.0103 0.0140(0.0262) (0.0236) (0.0240) (0.0223) (0.0345) (0.0351)

Covariates Yes Yes YesSchool Fixed Effects Yes Yes Yes YesComparison Mean 0.4339 0.4339 0.4324 0.4324 0.4987 0.4987

Observations 1,657 1,657 3,770 3,770 843 843R-squared 0.0206 0.0345 0.0080 0.0221 0.0001 0.0209

Notes: *p<0.10, **p<0.05, ***p<0.01. The outcome is choosing the income share agreement financial contract over the income based repayment loan financial contract. Standard errors are clustered at the school or college level for the high school and community college samples respectively. Covariates include gender, race, age, educational aspirations, parental education, citizenship status, and proxies for income.


Table 4: Heterogeneity of Framing and Labeling Effects by RaceFraming Effect

High School Community College White Black Hispanic Asian White Black Hispanic Asian

Expected Percent 50.00 50.00 50.00 50.00 50.00 50.00 50.00 50.00Observed Percent 44.66 39.08 36.41 47.06 41.90 48.19 41.42 53.35Chi-squared 3.52 8.30 15.22 0.06 22.02 0.25 16.12 0.70p-value 0.0605 0.0040 <0.0001 0.8084 <0.0001 0.6144 0.0001 0.4042

Labeling EffectHigh School Community College

White Black Hispanic Asian White Black Hispanic AsianTreatment 0.0734 0.2132*** 0.1961*** -0.0170 0.0577** 0.0693 0.1350*** -0.0343

(0.0446) (0.0479) (0.0549) (0.2062) (0.0248) (0.0387) (0.0270) (0.0463)

Control Mean 0.4466 0.3908 0.3641 0.4706 0.4190 0.4819 0.4142 0.5435

Observations 592 343 419 43 1,684 386 1,085 185R-squared 0.0419 0.0879 0.0848 0.3546 0.0313 0.0518 0.0367 0.1136

Notes: For the framing effect, the observed percent reports the percent of the comparison group that chose the income share agreement framing. The chi-squared statistic is the one sample goodness of fit statistic comparing the expected equal distribution to the observed distribution. The outcome for the labeling effect is choosing the income share agreement financial contract over the income based repayment loan financial contract. The labeling effect coefficients are denoted with stars to report statistical significance: *p<0.10, **p<0.05, ***p<0.01. All regressions include covariates for gender, race, age, educational aspirations, parental education, citizenship status, and proxies for income. The High School and Community College regressions include fixed effects for high school and community college respectively. Standard errors are clustered at the school or college level for the high school and community college samples respectively.


labels as observed by an insignificant one percentage point change in the likelihood of selecting

the income share agreement. We interpret these results as a clear sign of loan aversion caused by

labeling for high school and community college students.

We answer our third research question by examining these treatment effects by race and

risk aversion within the high school and community college samples. We exclude the adult

sample due to the lack of observable treatment effect. Table 4 provides framing and labeling

effect estimates for White, Black, Hispanic, and Asian subgroups. We observe that the framing

effects are driven by Black and Hispanic respondents among high school seniors, and by White

and Hispanic respondents among community college students. In both groups, Hispanic

respondents are the most likely to prefer the loan framing. We observe similar results for the

labeling effect. Within the high school sample, Black and Hispanic students are approximately

twenty percentage points more likely to preference the ISA contract and reject the loan contract

when labeled. This corresponds to a 53-55 percent increase in the likelihood of avoiding the loan

option relative to the comparison means for these groups, approximately double the labeling

effect observed in the full sample. There is no significant difference for White and Asian

respondents. Among community college students, both White and Hispanic respondents exhibit

statistically significant effects of roughly 6 and 13 percentage points respectively. Although the

coefficient for Black students is higher than for white, the smaller sample size reduces precision.

Again, we observe the largest effect among Hispanic respondents.

Finally, we assess heterogeneity of the framing and labeling effects across levels of risk

aversion for the high school and community college populations in Table 5. An economically

rationally respondent should recognize that there is no differential risk associated with either

contract and indeed that the overall risk is zero. The monthly payment is identical under either


Table 5: Heterogeneity of Framing and Labeling Effects by Risk AversionRisk Seeking ―――――――――――――――――――――――――――> Risk Averse

1 2 3 4 5 6Framing Effects – High School

Expected Percent 50.00 50.00 50.00 50.00 50.00 50.00Observed Percent 36.21 42.86 50.00 51.61 40.95 44.62Chi-squared 4.41 0.86 0.00 0.03 3.44 1.51p-value 0.0356 0.3545 1.0000 0.8575 0.0637 0.2195

Labeling Effects – High SchoolTreatment 0.1841 0.1497 -0.0519 -0.3762 0.0489 0.1646**

(0.0903) (0.0954) (0.0425) (.01768) (0.0538) (0.0419)Control Mean 0.3621 0.4286 0.5000 0.5161 0.4095 0. 4462Observations 97 78 75 64 196 241R-squared 0.2586 0.4544 0.3323 0.4542 0.1189 0.1606

Framing Effects – Community CollegeExpected Percent 50.00 50.00 50.00 50.00 50.00 50.00Observed Percent 46.98 40.28 43.94 38.52 41.92 47.22Chi-squared 0.54 2.72 1.94 6.43 4.37 1.00p-value 0.4609 0.0990 0.1637 0.0112 0.0367 0.3173

Labeling Effects – Community CollegeTreatment -0.0178 0.1300 0.0445 0.1665* 0.1760*** 0.0282

(0.0545) (0.0693) (0.0590) (0.0851) (0.0357) (0.0458)Control Mean 0.4698 0.4028 0.4394 0.3852 0.4192 0.4722Observations 295 144 258 220 304 620R-squared 0.1287 0.2347 0.0903 0.1007 0.1312 0.0232

Notes: Numbers 1 through 6 correspond to the choice from six 50-50 lotteries of varying monetary amounts (1: $16/$128, 2: $24/$120, 3: $30/$102, 4: $36/$84, 5: $42/$66, $48/$48). For the framing effect, the observed percent reports the percent of the comparison group that chose the income share agreement framing. The chi-squared statistic is the one sample goodness of fit statistic comparing the expected equal distribution to the observed distribution. The outcome for the labeling effect is choosing the income share agreement financial contract over the income based repayment loan financial contract. The labeling effect coefficients are denoted with stars to report statistical significance: *p<0.10, **p<0.05, ***p<0.01. All regressions include covariates for gender, race, age, educational aspirations, parental education, citizenship status, and proxies for income. The High School and Community College regressions include fixed effects for high school and community college respectively. Standard errors are clustered at the school or college level for the high school and community college samples respectively.


scenario, and in the case of extremely poor labor market outcomes in which they have no

earnings, the monthly payment falls to zero. However, the presence of framing and labeling

effects suggest respondents do not act rationally. Given prior research identifying effects of risk

aversion on financial behaviors, we examine whether risk aversion plays a role in the observed

framing and labeling effects. It is plausible that more risk averse respondents seek to avoid loans

suggesting they are more likely to avoid the loan framing and are more sensitive to the loan

label.

For the framing effects, we observe a confusing pattern of results.3 For high school

seniors, the strongest framing effects are observed for the most risk seeking individuals and the

second most risk averse individuals. For the community college sample, we observe the strongest

framing effects among the middle groups, neither the most risk seeking nor the most risk averse.

This pattern of results suggests to us that framing effects are not driven by risk aversion in any

systematic way.

We observe somewhat more consistent evidence that labeling effects are related to risk

aversion as the strongest results are driven by the most risk averse high school students and the

more risk averse community college students (although not the most severely risk averse

community college students). Given the small sample sizes for some risk groups and the lack of

real stakes associated selecting the risk lotteries, we take these results as suggestive evidence that

loan aversion caused by labeling may be concentrated among more risk averse individuals. This

is worthy of further study.

Section 5. Discussion and Conclusion

3 The sample size is notably smaller for this analysis because only half of the respondents were asked to complete our risk aversion measure, and there are missing values due to a subset of respondents skipping this question.


As higher education becomes more expensive, there is an increasing reliance on student

loans to finance the rising costs, but there is concern that a subset of prospective enrollees

underinvest in higher education because they avoid student loans. Through a survey of student

borrowing preferences and a randomized control trial of labeling we demonstrate that high

school and community college students change their opinions about borrowing depending on

how the borrowing options are framed and labeled. Labeling the financial options causes an 8-12

percentage point movement towards preferring the income share agreement, which corresponds

to a 19-26 percent change from the comparison mean. The magnitude of the labeling findings are

remarkably similar to the results found in the three Latin American countries from the prior

study, CPP, which found an 11-12 percentage point effect of the label on reducing the preference

for a loan among high school seniors. However, the results of the framing analysis are opposite

of CPP who found that respondents preferred the ISA framing. In our analysis, more students

prefer the framing of a loan over the framing of an income share agreement when the options are

not labeled. This difference could be driven by the fact that the sample in CPP had all expressed

in interest in ISAs and applied for higher education financing. We hypothesize that the unlabeled

preference for loans is due to familiarity of that framing relative to the ISA framing, with which

students likely have less exposure. Our findings are more closely aligned with Delisle (2017)

who find 42% of respondents in a nationally representative survey believe ISAs are a good

alternative to student loans.

We interpret the findings of the labeling experiment to imply a substantial amount of loan

aversion exists and is due to using the word “loan” to describe the borrowing options. It appears

both current and potential college students may not borrow for higher education simply because

of the term used to describe the financial contract. That the effect is larger for high school


students relative to students already in higher education suggests that loan aversion may deter

some portion of high school students from enrolling. We also examined whether the effects

varied across community college students who did and did not borrow to finance their

postsecondary education, but we found no observable difference.

Interestingly, adults in their 20’s and 30’s without a college degree who are not enrolled

in higher education do not exhibit any framing or labeling effects. It is possible adults have more

experience in credit markets and are therefore less effected by behavioral aspects of borrowing

preferences. Ample evidence exists that adults are susceptible to framing effects in general

financial decisions, but perhaps framing has a more limited impact on borrowing decisions. This

hypothesis seems worthy of future examination.

The heterogeneity of effects across race demonstrates that the reluctance to borrow to

finance higher education is likely much greater for racial minorities, especially Hispanic

students. Hispanic respondents consistently exhibited large framing and labeling effects in both

the high school and community college samples, with the labeling effects being more than

double those of White respondents. Our experimental results confirm the concerns expressed but

not documented in the previous literature that loan aversion seems more prevalent among

Hispanic populations (Cunningham & Santiago, 2008; ECMC Group Foundation, 2003;

Hillman, 2015). It is possible the previously hypothesized cultural differences in Hispanic

consumers’ views of debt are driving the observed loan aversion we identify. It is also possible

they are more susceptible to behavioral effects generally that leads to greater loan aversion.

Our analysis of how risk aversion is related to the observed framing and labeling effects

provide suggestive evidence that the labeling effect is greater among risk averse consumers. We

hypothesize that risk averse respondents are generally more likely to avoid loans and therefore


are driven away when the financial contract is labeled as such. This occurs despite the fact that

both financial options are income based which dramatically reduces the risk to the borrower.

These results suggest the loan aversion may be a larger obstacle for risk averse consumers in

situations when risk is not reduced by income based repayment, such as the standard based

repayment system common for federal student loans.

It is possible alternative explanations exist for both the framing and labeling findings.

The framing effect could be caused by a propensity to select the first option due to the primacy

effect of the option order (Krosnick & Alwin, 1987). As the loan framing was the first option on

the survey, some portion of the framing effect could be due to the order of options. Although we

cannot rule out this possibility, we believe the effect is minimal due to only having two choices

(previous studies have demonstrated the effect when there are many more than two choices).

Moreover, we do not observe the effect among adults. Due to the randomized control trial nature

of the labeling test, this primacy effect cannot be used to explain the observed labeling effect;

however, it is possible the labeling effect is due to a preference for the income share agreement

as opposed to an avoidance of loans. We believe this interpretation is unlikely given the

unfamiliarity respondents are likely to have with ISAs. A final limitation of our analysis,

consistent with CPP, is that we mainly observe preferences for borrowing. Future work should

target connecting the framing and labeling effects with borrowing behaviors.

The results have implications for both policy and practice. High school college

counselors and higher education financial aid administrators must be aware of how students

interpret financial aid packages. Institutions of higher education could be more intentional about

how their aid offers are presented, which might change student behaviors. Related to federal

policy, attention to the framing and labeling of financial aid materials on government websites,


on the Free Application for Federal Student Aid (FAFSA), and during loan entrance counseling

may have a sizable effect on the decision to borrow and on college enrollment and success rates.

Removing the term “loan” from most explanations of borrowing may increase human capital

investment.

Specific to ISAs, both states and institutions of higher education have explored using

income share agreements in lieu of traditional loans, and serious policy proposals have examined

the costs and benefits of such policies (Boatman, Evans, & Soliz, 2014; Marcus, 2016; Palacios,

DeSorrento, & Kelly, 2014; Vedder, 2015, March 12; Douglas-Gabrielle, 2015, November 27).

As part of his campaign during the 2016 presidential election, Senator Marco Rubio proposed

making use of ISAs as part of an education policy agenda focused on reducing student debt.

Moreover, in the fall of 2016 Purdue University introduced their “Back a Boiler” program which

offers students the option of financing their higher education with an ISA. The critical difference

between income share agreements and income based repayment loans is that ISAs remove both

the loan principal and the interest rate of a loan. The results of this paper demonstrate that efforts

to implement ISAs may have large implications for the uptake of financial contracts to finance

higher education and affect college enrollment.

Acknowledgements

Funding: This research was supported by the Lumina Foundation.


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Highlights:

Aversion to borrowing to finance postsecondary education may lead to underinvestment

in human capital.

We identify the existence of behavioral framing and labeling effects in the preference for

borrowing to finance postsecondary education that may explain a portion of loan

aversion.

Through a randomized control trial, we demonstrate calling a financial contract a “loan”

substantially reduces a respondent’s preference for that contract.

The effects are more pronounced among students of color.

framing and labeling effects in preferences for borrowing ... · the paper is&organized as...

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