Personal Finance Education Mandates and Student Loan Repayment - Daniel Mangrum

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Personal Finance Education Mandates and Student Loan Repayment

                                            Daniel Mangrum∗
                                              August 4, 2019
                                      Newest version can be found here.

          I estimate how mandates to teach personal finance education during high school affect fed-
      eral student loan repayment outcomes after college. To overcome a lack of quality borrower
      level data, I use the College Scorecard database to estimate how university level student loan
      repayment metrics change due to increased exposure to personal finance education mandates.
      Exposure to mandates is driven by two sources of variation: changes in state mandate status
      and migration of high school students to colleges. I find that cohorts more exposed to personal
      finance education mandates have better student loan repayment rates and this effect is largest
      for first generation and low income students at public universities. I find no evidence that low
      income and first generation students bound by a state mandate reduce student loan borrowing
      and mandated students are no more financially literate than non-mandated students. However,
      I present evidence that students bound by personal finance mandates are better able to answer
      questions pertaining to institutional details of the federal loan system.

Keywords: financial literacy, student loans, consumer finance, high school curriculum, postsecondary finance.
JEL: D14, I22, I28, I26, J24.

   ∗ I am grateful to Andrea Moro, Kitt Carpenter, Brent Evans, Andrew Dustan, Carly Urban, Andrew Goodman-

Bacon, Michelle Marcus, Paul Niekamp, Andrew Hussey, and Katharine Shester for comments as well as seminar
participants at the Vanderbilt Empirical Applied Microeconomics Workgroup, Missouri Valley Economic Association
Annual Conference, Southern Economic Association Annual Conference, Association for Education Finance and Policy
Annual Conference, and the Washington Center for Equitable Growth. I am also thankful for support from the Kirk
Dornbush Dissertation Fellowship. Department of Economics, Vanderbilt University, VU Station B #351819, 2301
Vanderbilt Place, Nashville, TN 37235;

1     Introduction

    In 2016, nearly 70% of high school graduates enrolled in college the semester following high

school graduation.1 During this time, students make a number of consequential decisions regarding

the financing of higher education with a rather limited information set regarding personal finances.

This deficiency in personal finance education is evidenced by a recent study that finds only 27%

of young adults can correctly answer questions regarding risk diversification, inflation, and interest

rates (Lusardi, Mitchell, and Curto, 2010). One intervention state policymakers are increasingly

implementing to improve financial literacy for young people is the addition of personal finance

coursework into state high school graduation standards. Prior to 2000, only three states required

students to complete personal finance coursework as a requirement for high school graduation, but

as of 2014 23 states mandated personal finance education (Stoddard and Urban, 2019). While the

initial motivation for the mandates is to increase general financial literacy, many personal finance

courses also require students to learn to navigate the federal financial aid system and to research

potential career paths and funding sources for postsecondary education. Since many of these courses

coincide with the timing of the federal financial aid application process, requiring personal finance

education can operate as a just-in-time information intervention to improve postsecondary finance


    In this paper, I estimate the impact of personal finance education on federal student loan repay-

ment. To overcome a lack of detailed borrower level data, I examine loan repayment outcomes at the

university level using the College Scorecard database to proxy borrower level impacts. I exploit the

state composition of university students combined with the state rollout of personal finance man-

dates to construct a university-by-year measure of exposure to personal finance education mandates.

I argue that this relationship is causal due to the plausibly exogenous nature of the identifying vari-

ation. As states adopt course mandates, universities are differentially impacted relative to the share

of students that populate the university from adopting states that were bound by a state mandate.

As a result, universities in states with no mandates are still partially affected by adopting states and

some universities in adopting states are less affected by own state adoption due to larger shares of

out-of-state students.

    I find that when a public university’s student body becomes increasingly bound by personal


finance education mandates there is a corresponding improvement in the fraction of students suc-

cessfully paying down students loans. For the high school graduating classes between 2001 and 2008,

personal finance mandates improved the percentage of students successfully paying down loans by

around 5% for first generation and low income students. For the average sized public university

cohort, this effect translates to 27 additional first generation and 34 additional low income students

paying down balances.2 On the other hand, I find little evidence for a corresponding reduction in

defaults which is the most adverse repayment outcome.

    The conclusions from this paper are consistent with two key facts established in the previous

literature. First, consistent with the findings in Brown et al. (2016), I find evidence that mandates

become more effective as they mature. Using a flexible event study specification, I find that the

improvements in student loan repayment induced by personal finance coursework grow for subsequent

cohorts following the adoption of a state mandate. Hence, the estimates presented in this paper are

likely to understate the improvements for loan outcomes in the near future. Second, the effect of

the mandates on student loan repayment is largest for more vulnerable populations (Stoddard and

Urban, 2019). This is also consistent with studies that show increasing graduation requirements

in mathematics have larger impacts for underrepresented minority students (Brown et al., 2016;

Goodman, 2019). I find that the effects of mandates are strongest for students whose families earn

less than $30,000 per year and for students whose parents did not have a college degree.

    I explore several mechanisms by which personal finance education may positively influence fed-

eral student loan repayment. First, I test whether students bound by these mandates are better

able to correctly answer financial literacy questions. Across three surveys, I find little evidence of

improvements in financial literacy for those that were bound by the mandates shown to improve

student loan outcomes. I also explore whether personal finance courses help students navigate the

federal financial aid system. I find that students bound by a later set of mandates were more knowl-

edgeable about the federal financial aid system. Third, I test whether improvements in student loan

repayment are due to decreases in average loan balances. I find no changes to loan balances for the

first generation and low income students who had better repayment rates, however there is evidence

that high income students may be reducing borrowing as a result of mandate exposure. Lastly, I test

     2 The average repayment cohort for a public university contains 1,205 low income students and 1,124 first generation

students. Thus a 2.8 percentage point and 2.4 percentage point improvement in the repayment rate for low income and
first generation students respectively translates to 34 more low income students and 27 more first generation students
successfully paying down balances. Due to the possibility of a student being both low income and first generation,
these estimates likely double count some students.

whether improvements in student loan repayment are due to an increased probability of completing

a bachelor’s degree. I find no evidence that students bound by personal finance mandates were any

more likely to earn a bachelor’s degree.

   While it could be the case that financial literacy is improved in the short term after taking a

required course, students bound by personal finance mandates are no better at answering financial

literacy questions years after graduating high school. There is also no evidence that the improvements

in repayment rates for low income and first generation students are due to reduced borrowing or

an increase in the probability of completion. Rather, the results suggest that the personal finance

education mandates I study in this paper act as a just-in-time intervention where students learn to

navigate the federal financial aid system at the same time they are applying for federal aid. Thus

it may be the case that the improvements in student loan repayment achieved through mandated

personal finance education could be matched or surpassed through the simplification of the federal

student aid system at a lower cost.

   Due to federal subsidies, the improvements in student loan repayment are not only privately

beneficial to borrowers but also publicly beneficial for taxpayers. From a private perspective, students

may gain a better understanding of the costs of postsecondary education and the relative costs and

benefits of the various funding sources available. This information may lead students to make

better informed human capital investments and avoid adverse loan outcomes. From a public finance

perspective, recent legislative changes have added more protections for student loan borrowers in the

form of income-driven repayment and loan forgiveness. These protections increasingly put taxpayers

at risk of subsidizing student loans that are eventually forgiven or never repaid. Hence, improvements

in student loan outcomes as a result of personal finance education may also reduce the burden of


   The remainder of the paper is organized as follows: Section 2 reviews background details of

the federal financial aid system and summarizes the previous studies discussing personal finance

education. Section 4 details the data used in the analysis. Section 5 discusses the empirical strategies

used in the analysis. Section 6 presents the empirical results. Section 7 tests the mechanism through

which personal finance mandates might improve student loan repayment. Lastly, Section 8 concludes

the paper.

2     Background and Literature

2.1    Federal Financial Aid

    Modern federal financial aid in the United States began with the Higher Education Act of 1965

(HEA). Since the initial passage, the Act has been reauthorized eight times with each reauthorization

amending and enacting various policy changes. The HEA has provided financial aid to postsecondary

students since its inception and has been one of the main drivers behind increasing access to higher

education to low income students who face credit constraints (Long, 2007). Although the Department

of Education also disperses funds in the form of work-study programs and need-based grants, the

largest outlays from the Department of Education are in the form of federal student loans. The

two most common types of federal loans are Stafford subsidized and unsubsidized loans. Subsidized

loans are need-based loans made to borrowers where interest does not accrue while the student is in

school but are subject to annual and lifetime maximums for each borrower. Unsubsidized loans are

also subject to annual and lifetime limits but are not need-based and interest on these loans begins

accruing immediately after dispersement.

    In order to qualify for any federal loans, students must complete a Free Application for Federal

Student Aid (FAFSA) which collects details about students and their families including income

and asset information. Loans are limited in accordance with the Cost of Attendance (COA) of

the university and other financial aid received. The federal government also offers Federal Perkins

Loans, Parent PLUS loans, and Grad PLUS loans. Perkins loans are distributed directly to students

to supplement students’ needs that may exceed the limits of Stafford loans. Parent PLUS loans

are available to parents of students in order to help with the child’s educational expenses. Grad

PLUS loans are available to graduate students and have larger annual and lifetime maximums than

Stafford loans for undergraduates. This paper focuses on Stafford loans since these are the most

common loans used by undergraduate students.

    Researchers have long studied the impact of federal financial aid on a host of educational, house-

hold, and labor market outcomes. The literature has largely concluded that access to financial aid

increases access to higher education for low income students (Dynarski, 2003). Recent evidence also

suggests that increasing student loan borrowing also causes higher grades and more completed cred-

its for community college students (Marx and Turner, 2019). Despite the benefits, recent research

has been critical of the burdensome bureaucracy and complicated process for applying and receiving

financial aid (Dynarski and Scott-Clayton, 2006; Bettinger et al., 2009; Novak and McKinney, 2011;

Dynarski and Scott-Clayton, 2013). Castleman, Schwartz, and Baum (2015) summarizes several

recent studies that test interventions designed to improve the decision making process in investing

in higher education. Critics argue that the overly complicated federal student aid process tends to

reduce the receipt of aid for students that would otherwise be eligible (Kofoed, 2017). In particular,

low income and first generation students are less likely to have family support structures who are

familiar with the federal financial aid process and are more likely to have trouble navigating the

federal financial aid system during the college application process.

2.2    Personal Finance Mandates

   While many of the interventions discussed in Castleman, Schwartz, and Baum (2015) are small-

scale, one large scale intervention that might improve student loan repayment is mandating personal

finance education during high school. State policymakers often adjust the state standards and

graduation requirements for high school students in order to increase academic rigor or implement

policy agendas. Between 1957 and 1985, states increasingly began requiring high schools to teach

students consumer education and personal finance during high school. Despite the mandates, there is

evidence these mandates were either poorly implemented or students did not recall learning material

(Bernheim, Garrett, and Maki, 2001). As a result, studies investigating this wave of consumer and

personal finance coursework fail to find improvements in credit behavior as a result of the mandates.

While the authors find a correlation between taking a required course and improved credit behavior,

the result is not robust to the inclusion of controls and state and year fixed effects. In fact, using the

Consumer Credit Panel, the 2000 U.S. Census, and the Survey of Income and Program Participation,

any significant improvement in credit behavior can be ruled out even out to 14 years after a mandate

is adopted (Cole, Paulson, and Shastry, 2016).

   On the other hand, studies that focus on a more recent wave of personal finance standards find

more promising results (Brown et al., 2016; Urban et al., 2018; Stoddard and Urban, 2019). This

more recent wave began with students graduating high school in 1993 and continue to affect high

school students today. Between 2000 and 2014, the number of states requiring personal finance

education in order to graduate high school grew from three to 23 (Stoddard and Urban, 2019).

These mandates might be more effective due to a trend in states explicitly codifying standards

for the coursework and prescribing particular exercises and activities students should complete.

In addition, many states implementing personal finance mandates also require students to learn

about federal student loans and directly teach how to complete the FAFSA. Of the 23 states with

mandated personal finance education, the standards for seven states directly mention student loans

or borrowing for postsecondary education. For example, Arkansas state standards require students to

compare and contrast various sources of credit including credit cards, car loans, and student loans.3

Utah’s standards require students to practice applying for federal aid by using the FAFSA4caster

from the U.S. Department of Education.4 Tennessee’s state standards require students to research

both positive and negative aspects of borrowing federal student loans.5 Oregon’s state standards

require students to research the costs and benefits of using loans to finance higher education.6 While

the primary goal of mandating personal finance courses is likely not to increase the take-up of federal

student aid, the increased focus on postsecondary finance suggests at least one mechanism by which

these mandates might improve student loan outcomes for borrowers.

   Brown et al. (2016) is the first study to investigate the more recent changes in economics, mathe-

matics, and personal finance requirements as collected by the National Council for Economic Educa-

tion. This paper tests for improvements in early adulthood credit health. The results confirm findings

in the previous literature that increasing math requirements increases asset levels and incomes for

young adults (Goodman, 2019). In contrast to the initial wave of personal finance mandates, the

mandates adopted after 1990 are shown to reduce the amount of delinquent debt. The paper also

presents evidence that students bound by these mandates increase student borrowing. Further, the

estimated impacts of these mandates are found to be larger with more mature mandates. This result

suggests either implementation lags on the part of schools or improvements in teaching efficiency

over time.

   This paper is most closely related to Stoddard and Urban (2019) who studies how the post-1990

mandates affected the receipt of federal student aid for first year college students. The authors

create a novel dataset that tracks state standards for personal finance coursework across all fifty

states using the first cohort bound by a personal finance mandate rather than the legislative year as

in Brown et al. (2016). This more precise definition of treated cohorts should reduce attenuation bias

due to misspecified treatment status. Stoddard and Urban (2019) uses several waves of the National



Postsecondary Study of Student Aid (NPSAS) survey to examine how students bound by personal

finance mandates fared compared to unbound students in financing postsecondary education. The

authors find that college freshmen bound by these mandates are more likely to complete the FAFSA,

more likely to borrow from federal sources, borrow fewer private loans, and are less likely to carry a

credit card balance. SU also finds that the impact on extensive borrowing of federal loan dollars and

the lower likelihood of credit card borrowing is larger for low income students. Further, low income

students bound by personal finance mandates are also less likely to work while enrolled in college.

    This paper extends the literature in three distinct dimensions. First, while the previous literature

focused on the impact of personal finance education on financial aid in the first year of college, to my

knowledge this is the first paper to analyze the effect on loan usage in subsequent years and on federal

student loan repayment after college. Second, I estimate the impact of personal finance mandates on

two different measures of repayment progress that vary in sensitivity. The first measure is a rather

adverse and rare outcome that is difficult to affect, however the second measures whether the loan

principle is declining and is thus a more sensitive measure of repayment progress. Third, I use three

data sources to test the effect of mandates on financial literacy to better understand the mechanisms

behind the mandates’ effect on repayment. Lastly, I employ a novel identification strategy that uses

university level benchmarks from the College Scorecard database to proxy borrower level changes

in student loan outcomes. This identification strategy helps to overcome the lack of quality micro

level data on student loan repayment that currently plagues the literature.

3     Mechanisms

    Several mechanisms might explain how personal finance education mandates can affect student

loan repayment. Since the aim of many of the state personal finance education mandates is an

improvement in general financial literacy, this will be the first tested mechanism. Evidence from

previous work suggests that students bound by a mandate have higher credit scores and fewer delin-

quent accounts than those not bound by mandates (Brown et al., 2016; Urban et al., 2018). While

this is consistent with improvements in financial literacy, it is possible for the mandates to improve

credits scores and reduce delinquency without improvements in financial literacy. Additionally, it

can also be the case that improvements in financial literacy are realized during high school but

depreciate quickly after. Evidence from Harvey (2019) suggests that individuals bound by a select

group of more rigorous mandates are more likely to correctly answer financial literacy questions. In

Section 7, I extend this work by employing three data sources which ask various financial literacy

questions to test whether students bound by personal finance education mandates are more likely

to correctly answer these questions.

   Alternatively, it may be the case that requiring students to take these courses increases knowledge

of the federal financial aid system. In addition to changes to general financial literacy, I also test

whether students bound by personal finance education mandates are more knowledgeable about

the federal financial aid system. As discussed previously, simplifications to federal student aid and

various information initiatives during the aid process have shown to improve student outcomes

(Dynarski and Scott-Clayton, 2006; Castleman, Schwartz, and Baum, 2015; Kofoed, 2017). Since

many state standards directly include postsecondary finance topics, mandating this coursework may

teach students how to better navigate the federal aid system which may improve student loan


   Additionally, requiring students to take personal finance classes might change vital postsecondary

financing decisions which can affect borrowing habits and subsequent repayment outcomes. Stoddard

and Urban (2019) shows that students bound by personal finance mandates are more likely to borrow

from federal sources, however, increasing the use of federal aid may have ambiguous effects on

financial health. On one hand, it is possible that increasing borrowing could be optimal for a student

as she can afford to work fewer hours and focus more on academics.7 In this case, an individual

could rationally substitute away from future consumption and toward current consumption while

increasing permanent income with better academic performance. On the other hand, SU shows

that mandated students are also more likely to have grant aid which might reduce borrowing. If

so, the result is a more manageable student loan portfolio to repay. While it is not feasible to test

whether a student loan portfolio is better or worse in overall quality, I test whether the average

original principal changes as a result of mandates. If principals decrease as a result of mandates,

improvements in student loan repayment are likely to be the result of more manageable portfolios.

However, if principals increase while student loan repayment is simultaneously improved, it is unlikely

that these improvements stem from more manageable portfolios since debt levels are rising.

   Lastly, personal finance education may lead to a higher probability of degree receipt. If students

bound by these mandates find better matches for postsecondary education or if increased borrow-

   7 SU   find that low income students bound by mandates are more likely to reduce extensive labor supply.

ing reduces financial frictions and improves grades, affected students could have a higher completion

probability and thus higher labor market earnings. A higher completion rate would also increase stu-

dent loan debt on average (more years in school) while improving student loan repayment, however,

evidence in the literature suggests that personal finance requirements do not change the selection

into whether to attend college or the type of college that students choose to attend (Stoddard and

Urban, 2019). Regardless, I test whether students bound by personal finance education mandates

are more likely to receive a college degree. Since student loan default rates are much higher for

non-completers, an increase in degree receipt should also improve student loan repayment (Hillman,


4     Data

    The primary source of data used in this paper is the College Scorecard database. The Col-

lege Scorecard was developed during the Obama Administration and debuted in 2015 as a website

tool for potential college students to compare universities along important dimensions to provide

more information to students selecting colleges. The College Scorecard website provides information

on financial aid, admissions, student body, graduation rates, costs, earnings, and academic pro-

grams.8 The Department of Education provides access to the university level data dating back to

the 1996-1997 academic year and updates the data annually. The data are sourced via self-reports

by universities, various federal data sources, and administrative data on students receiving financial

aid. The data used in this paper are largely computed using the administrative National Student

Loan Data System (NSLDS) which contains records on the universe of federal aid recipients. The

College Scorecard uses the administrative data from NSLDS to construct the university-by-year

benchmarks on student loan repayment metrics.

    I restrict the sample to four-year baccalaureate universities since four-year universities are largely

populated by first-time degree seeking recent high school graduates.9 I remove any university with

repayment outcomes that are aggregated across multiple branch campuses.10 In addition, non-Title

IV schools do not receive federal student aid dollars and thus do not have student loan repayment
    9 In 2016, 45% of recent high school graduates enrolled in 4-year colleges while 23.7% enrolled in 2-year schools.
   10 This restriction is necessary due to the nature of the identification strategy discussed in the next section. When a

university system aggregates outcome measures across multiple branches, the identifying variation on the right-hand-
side of the estimating equation is aggregated at a smaller granularity than the outcome measure on the left-hand-side.

outcomes and are thus also not considered. In order to construct a balanced panel, I remove

universities that either enter or exit the sample during the sample window. This can occur from a

university opening or closing or a university opting into or losing access to federal aid.11 Table A.10

details the change in sample size as a result of each of the restrictions. The resulting sample contains

1,379 universities across 50 states and the District of Columbia of which 448 are public universities

and 931 are private universities.

    The main outcomes from the College Scorecard are the one year repayment rate and the two

year cohort default rate. Upon exiting college, all borrowers of federal student loans are granted a

six month grace period before entering repayment. One year after entering repayment, borrowers

fit into one of four mutually exclusive bins as depicted in Figure 1.12 If the student is making

payments on her loan and the balance is declining, this student fits in bin A. If the student is

making payments toward her loan but the payment is not sufficient to cover accruing interest (i.e.

negative amortization), the student fits in bin B. If the student has been granted forbearance or

deferment of payments (and thus no payments are required) and the balance is not declining, the

student fits in bin C. If the student has not made any payments for 270 days, the student enters

default and fits in bin D.13

                          Figure 1: Repayment Status Bins for Repayment Cohort

                 A                            B                             C                           D
        Making payments &          Making payments & not            In forbearance or
                                                                                                   In default
        paying down balance         paying down balance                 deferment

                                              A                                      D
                      Repayment Rate =      A+B+C                Default Rate =   A+B+C+D

    The one year repayment rate is defined as the “fraction of repayment cohort who are not in

   11 This restriction helps to alleviate the concern of selection into the sample. Universities may lose access to federal

aid as a result of poor student loan repayment or choose to begin accepting federal aid as a result of unobservables
that change over time. Looney, Yannelis et al. (2019) notes that the majority of the variation in cohort defaults over
time stems from entry into and out of the student loan market.
   12 The College Scorecard database also includes the repayment rate for 3, 5, and 7 years, however the data begins

for all variables for FY 2006 and thus these variables have very small windows of data availability.
   13 Default for students loans is atypical compared to other consumer debt. Upon default, there is no repossession

of assets since the loans are unsecured. Rather, the federal government levies fines and allows loan services to garnish
wages and tax refunds to collect outstanding debts.

default, and with loan balances that have declined one year since entering repayment.”14 Hence, the

one year repayment rate can be calculated by A/(A+B +C) at the end of the first year after entering

repayment. On the other hand, the two year cohort default rate is defined as the “percentage of

a school’s borrowers who enter repayment ... and default or meet other specified conditions prior

to the end of the first following fiscal year.” Hence, the cohort default rate can be calculated by

D/(A + B + C + D) at the end of the second year after entering repayment. Those students in bin B

(making payments but facing negative amortization) and in bin C (not required to make payments

and facing negative amortization) both count against the repayment rate but are not in default. This

makes the repayment rate a more sensitive measure of loan repayment health that does not require

total default.

    In addition to the outcomes for the full repayment cohort, the repayment rate is also reported

for various subsamples of the student body.15 Evidence from the previous literature suggests that

vulnerable populations tend to be more impacted by course mandates (Stoddard and Urban, 2019;

Goodman, 2019). To test this finding, I estimate the impact of personal finance mandates on the full

repayment cohort, for first generation students, and for students by household income bins. First

generation students are defined as students whose parents had not earned a college degree when

the student entered college. Students are split into three household income categories: low income

households of $30,000 or less upon entering college, middle income households of $30,000 to $75,000,

and high income households above $75,000.16 The repayment rate is first reported in the College

Scorecard beginning with the 2007-2008 academic year which includes students entering repayment

in the 2006 fiscal year. The most recent data in the Scorecard covers students that entered repayment

in the 2013 fiscal year. Hence, the current analysis covers eight cohorts entering repayment between

FY2006 and FY2013.17

    Although default is a relatively rare occurrence for borrowers (historically the two year default

rate is around 4.1%), there is significant heterogeneity across universities in default rates. In order

to reduce abuse, a university is removed from eligibility for future federal financial aid if the cohort

default rate rises above 40% in a single fiscal year or has three consecutive fiscal years of cohort

default rates above 30%. This regulation incentivizes a university to prevent the students from
  14 Definitions for each measure come from the College Scorecard Data Dictionary.
  15 The  two year cohort default rate is only available for the full repayment cohort.
   16 The College Scorecard uses nominal dollars to determine these bins. As a result, students with similar household

incomes in real terms might be shifted into higher income bins over time due to inflation.
   17 For repayment cohort counts smaller than 30 students, the data is suppressed and thus these small cells are

omitted from the analysis.

entering default. Beginning in FY2009, the Department of Education began grading universities on

the three-year cohort default rate instead of the previous two year cohort default rate. This change

was a concerted effort to hold universities accountable for borrowers beyond two years after entering

repayment. The College Scorecard continued to include the two year cohort default rate through

FY2011 but deferred to only posting the three-year cohort default in subsequent years. Due to the

change in the cohort default metric, I use the two year cohort default rate in the available years

between FY1995 and FY2011.

    Table 1 reports summary statistics for the sample of universities for the main outcome variables. I

present the means and standard deviations weighted by the number of borrowers used in constructing

each university outcome. The weighted moments are presented to be representative of the population

of student loan borrowers.

                                        Table 1: Descriptive Statistics

                                   All Universities     Public Universities      Private Universities
                                       (n=1,379)              (n=448)                  (n=931)

             Variable               Mean        SD       Mean        SD           Mean        SD

             Default Rate            .041     (.032)      .046      (.030)         .035      (.034)
             Repayment Rate
               Overall               .593     (.170)      .593      (.149)         .593      (.199)
               First Generation      .537     (.165)      .551      (.146)         .510      (.187)
               Low Income            .458     (.170)      .477      (.150)         .421      (.194)
               Middle Income         .630     (.148)      .630      (.136)         .630      (.165)
               High Income           .749     (.112)      .730      (.107)         .777      (.115)
Notes: The table above presents means and standard deviations for the main outcome variables for the sample of
four-year universities and separately for public and private universities. Means and standard deviations are weighted
by the number of students used to compute each outcome to be representative of the population of student
borrowers. Default rate is the two year cohort default rate from FY1995 to FY2013. In addition, the one year
repayment rate is reported for the full repayment cohort and separately for first generation students (students whose
parents did not have a college degree) and for students by household income bins. Low income is defined as students
with family income less than 30,000 upon entering college. Middle income is defined as between 30,000 and 75,000
and high income is defined as above 75,000.

    To construct the measure of university exposure to high school personal finance education man-

dates, I use data from the Integrated Postsecondary Education Data System (IPEDS) which includes

biannual counts of the incoming cohort of students by previous state of residence for each university.

Between 1986 and 1994, these data were collected every two years from each university and after

1994, universities could voluntarily provide these data to IPEDS in odd years but were required to

submit information in even numbered years. The student body count is collected for all first-time

degree seeking students but is also reported for only students who graduated from high school within

the previous 12 months before entering college. Since the focus of this paper is the immediate tran-

sition from high school to college, I focus on the latter measure including students entering college

directly after high school.18 I replace missing student counts in odd years with linearly interpolated

values from neighboring even years.

   Lastly, I use the national rollout of personal finance education mandates since 1990 from Stoddard

and Urban (2019) which is detailed in Table 2. This definition for mandates is an improvement over

the previous iteration in Brown et al. (2016) because the effective year is defined by the first high

school graduating class that is bound by a personal finance mandate rather than the legislative year

in which the standards changed. This improvement in identifying affected cohorts should reduce

attenuation bias due to misclassification of treatment.

                  Table 2: Implementation of Personal Finance Mandates Since 1990

      State              Coursework                                           First Graduating Cohort Bound

      New Hampshire      Incorporated (Economics)                                           1993
      New York           Incorporated (Economics)                                           1996
      Michigan           Incorporated (Career Skills)                                       1998
      Wyoming            Incorporated (Social Studies)                                      2002
      Arkansas           Incorporated (Economics)                                           2005
      Louisiana          Incorporated (Free Enterprise)                                     2005
      Arizona            Incorporated (Economics)                                           2005
      South Dakota       0.5 Credit (Economics or Personal Finance)                         2006
      Texas              Incorporated (Economics)                                           2007
      Idaho              Incorporated (Economics)                                           2007
      North Carolina     Incorporated (Economics)                                           2007
      Georgia            Incorporated (Economics)                                           2007
      Utah               0.5 Credit                                                         2008
      Colorado           Incorporated (Economics, Math)                                     2009
      South Carolina     Incorporated (Math, ELA, Social Studies)                           2009
      Missouri           0.5 Credit                                                         2010
      Tennessee          0.5 Credit                                                         2011
      Iowa               Incorporated (21st Century Skills)                                 2011
      New Jersey         2.5 Credits (Economics or Personal Finance)                        2011
      Kansas             Incorporated (Economics)                                           2012
      Oregon             Incorporated (Social Studies)                                      2013
      Florida            Incorporated (Economics)                                           2014
      Virginia           0.5 Credit                                                         2014

      Notes: Data are from Stoddard and Urban (2019). States denoted as Incorporated require personal finance
      coursework in the required course denoted in parenthesis. States with listed credit requirement require the
      denoted number of credits in a standalone required personal finance course. Arizona removed their mandate and
      deferred to districts for the class of 2013 and beyond. States with a choice of Economics or Personal Finance
      have personal finance course standards in the economics course.

   18 The median university in the sample averages 90% of its incoming student cohort composed of students who

entered college within a year of high school graduation.

5     Empirical Strategy

    In order to estimate how personal finance education impacts student loan repayment outcomes,

I begin by quantifying the degree to which each university cohort is affected by the adoption of

various state personal finance education mandates. The national rollout of course mandates impacts

a university in two ways. The first is through own-state adoption of course mandates. When a state

decides to change course standards for high school graduation, all future high school students within

the state are affected by this change. When these students graduate high school and proceed to

college, the universities within the state are then populated by these affected students. Hence, own-

state adoption will have the largest impact on universities with a higher share of in-state students.

The second source of variation comes from students in adopting states choosing to attend universities

in another state. The across state spillovers of mandated students to universities in unmandated

states allows from the identification of the impact of personal finance mandates without the concern

of endogeneity of state adoption of mandates.

    I combine these two sources of variation to create a continuous measure of university exposure

to personal finance education mandates. I merge the IPEDS previous state of residence data with

the state-by-graduation year panel of personal finance mandates from Stoddard and Urban (2019)

to compute the fraction of each university’s entering cohort that graduated from a state in a year

in which a personal finance mandate was binding. The measure pctBoundist is constructed for each

university i located in state s for incoming cohort t by interacting the state-by-year mandate status

of state j for cohort t (PFjt ) with the number of students attending university i in cohort t from

state j (enrollijt ) and dividing by the total incoming cohort count from all 50 states and D.C. for

cohort t:

                                                      PFjt × enrollijt
                                pctBoundist =         51
                                                                         .                        (1)

    Figure 2 shows an example of how pctBoundist evolves over time for a select group of Tennessee

universities. The first graduating class bound by Tennessee’s personal finance mandate was the

class of 2011. The University of Tennessee Knoxville is the flagship university in the state and as

a result has a large share of its students from Tennessee. Consequently, when PFTN,t turns on

in 2011, pctBoundist for UT Knoxville grows from almost zero almost one and the university is

overwhelming populated by students who were required to take personal finance. However, not all

public universities are overwhelmingly populated by in-state students. Tennessee State University

historically recruits around 60% of each incoming cohort from Tennessee while around 40% of the

cohort comes from other states. As a result, TSU is affected by personal finance mandates adopted

by other states prior to the adoption of Tennessee’s mandates. Vanderbilt University is a private

university in Tennessee which also receives students from a large number of states and thus receives

multiple shocks to pctBoundist in accordance with PFjt for states that historically populate the

university. When Tennessee adopts a personal finance mandate, Vanderbilt experiences only a 20

percentage point change in pctBoundist . While this heterogeneous student body is typical of many

private universities, it is not always the case. Despite similar private status, Christian Brothers

University receives around 80% of its students from Tennessee and thus its exposure to Tennessee’s

mandate resembles UT Knoxville to a greater degree than its private counterpart Vanderbilt.

             Figure 2: Examples of Within State Variation in pctBound from Tennessee

                        University of Tennessee Knoxville                        Tennessee State University







                       1995   2000    2005       2010   2015             1995       2000       2005     2010    2015

                              Vanderbilt University                             Christian Brothers University







                       1995    2000   2005       2010   2015             1995       2000       2005     2010    2015

                                             % Bound by Mandate                            % In-state
Notes: Each panel above plots pctBoundist as constructed in Equation (3) for each university over time along with
the percentage of in-state students in the incoming cohort. Data on previous state of residence come from IPEDS.
In-state percentage is constructed by dividing the number of students in each entering cohort from Tennessee by
the total number of students in each entering cohort. State of residence data that are not submitted to IPEDS in
odd years are interpolated linearly from neighboring even years. The state of Tennessee adopted a personal finance
mandate that was first binding for the class of 2011 as shown by the shaded region in each plot.

   One issue that can arise when leveraging the state adoption of a policy to estimate the impact of

that policy is the possibility that policy adoption is endogenous. If states choose to adopt personal

finance education mandates in response to lower than average levels of financial literacy in the state,

it is unlikely that never adopting states are a suitable comparison to states that choose to adopt.

Hence, any strategy that compares adopting states before and after adoption to never adopting

states would not uncover the casual effect of the policy due to the endogenous adoption. However,

the across state migration of high school students to out-of-state colleges allows for a channel of

policy variation in which students from adopting states can influence outcomes at universities in

non-adopting states and vice versa. An example of this across state spillover is shown in Figure 3.

Each panel in Figure 3 shows the state level equivalent of pctBoundist with the student bodies of all

universities within a state combined. When Georgia adopted a mandate binding for the class of 2007,

Alabama universities experience a corresponding increase in pctBoundist and there are subsequent

increases at Alabama universities when Tennessee and Florida adopt. This is also apparent when

New Jersey adopts a mandate for the 2011 graduating class and as a result Pennsylvania becomes

impacted by New Jersey’s mandate.

                          Figure 3: Examples of Across State Spillovers in pctBound

                                     Georgia                                                    New Jersey







                       1995   2000     2005         2010         2015             1995   2000      2005      2010         2015

                                     Alabama                                                Pennsylvania





                                                            FL (2014)
                                                    TN (2011)                                                NJ & TN (2011)
                                        GA (2007)



                       1995   2000     2005         2010         2015             1995   2000      2005      2010         2015

                                                            % Bound by Mandate
Notes: Each panel above shows the state level equivalent of pctBoundist where all universities within a state are
aggregated. Data on previous state of residence come from IPEDS. Georgia’s mandate was first binding for the class
of 2007 and New Jersey’s mandate was first binding for the class of 2011. Neither Alabama nor Pennsylvania adopt
mandates during the sample period but Alabama is affected by Georgia’s adoption through students from Georgia
high schools attending Alabama universities and again by Tennessee and Florida’s adoption. Similarly, Pennsylvania
does not adopt a mandate but is affected by New Jersey high school students attending Pennsylvania universities.

I exploit this unique source of exogenous variation in personal finance education to estimate

the impact of changes in pctBoundist on student loan repayment outcomes for a university using a

dose response model. Derived from the difference-in-differences framework, a dose response model

compares observed outcomes between more treated units and less treated units rather than between

fully treated and fully untreated units. To illustrate the benefits of the dose response model, consider

the case where a difference-in-differences strategy is used and treatment status is defined by whether

the state a university is located in has a binding mandate. Under this assumption, universities in

states with no mandate would be considered untreated when in reality, universities in non-adopting

states can be affected by mandates from other states. Conversely, universities in treated states would

be considered fully treated, however we know this is not the case for universities who receive a share

of students from states that do not adopt mandates. Using this dose response model, the degree

to which a university is treated is directly measured by pctBoundist which varies between zero and


    The main specification for the dose response model is:

                        yis,t+k = γpctBoundist + β1 X1,it + β2 X2,st + δi + δt + εist ,                               (2)

where yis,t+k is an outcome for university i located in state s for high school cohort t and k is the

number of periods between cohort t entering college and outcome y being observed. The coefficient

of interest is γ which I interpret as the estimated causal impact of increasing the percentage of

students required to take personal finance coursework from 0% to 100%.

    A vector of incoming cohort level controls are included in X1,it . I use high school graduation

state j by high school graduation year t variables, x1,jt , combined with previous state of residence

data, enrollijt , for university i from state j in year t to construct an incoming university cohort

measure for each variable in X1,it in a similar manner to Equation (3):

                                                             xjt × enrollijt
                                             X1,it =         51
                                                                               ,                                      (3)

    This vector includes state level measures of high school staffing that include counts of support
   19 Not all states have high school graduation standards set at the state level. For states with no state standards, the

mean value across all states is used and a binary variable is included denoting local control of high school graduation

staff, guidance staff, and teachers along with total credit hours, English, math, social studies, and

science credit hours required for high school graduation.19 In addition, I include state of university

level controls in X2,st which includes binary variables for whether the state offered a state merit aid

scholarship and unemployment rates for periods t through t + k. Lastly, university and high school

graduation cohort fixed effects are included and standard errors are clustered at the state level to

allow for correlation in the error term, εist , between universities in the same state s.

    I match high school graduating cohorts to university repayment cohorts by assuming that stu-

dents enter repayment after their fourth year of college. Under this assumption, a student graduating

high school in year t will enter repayment in fiscal year t + 5 and first enter the College Scorecard

database in year t + 6. However, the four-year college completion rate for the sample is roughly

35% so students bound by personal finance mandates can enter repayment earlier than fiscal year

t + 5 if they leave school prior to graduation. In this case, it is possible for the mandates to impact

outcomes prior to period t + 6. To alleviate this concern, I test the sensitivity of this assumption in

Appendix A.1.

    In addition to the dose response model, I also estimate an event study specification to test

whether the impact on repayment outcomes grows as mandates mature as has been found in the

literature (Brown et al., 2016). An event study specification flexibly estimates a separate parameter

for each time period relative to an event.20 However, in order to define the time periods relative to

the event, a singular event must be prescribed for each “treated” unit from the continuous measure

of treatment, pctBoundist . For universities that receive a large percentage of the student body from

within the state, the idea of the event is straightforward: the first year in-state students are bound

by a mandate. However, not every university experiences a singular event in which the student

body moves from largely untreated to largely treated.21 Figure 4 plots the distributions for public

and private universities of the change in pctBoundist in the first year of a binding mandate for

universities in a state that adopts a mandate. For public universities, the overwhelming majority

of universities in adopting states see a change in pctBoundist of 80 percentage points or larger. For

private universities, this distribution is not as tight. While the majority of private universities in

the sample exhibit changes of 50 percentage points or more in the first year of mandate adoption,

there are some private universities who are not as affected when the state adopts a mandate.

  20 One of these time periods is normalized to zero and all other parameters are estimated relative to the normalized

  21 Vanderbilt University in Figure 2 is a good example of this type of university.

Figure 4: Distribution of the Change in pctBoundist in the First Treated Year

                         0              0.25                 0.5             0.75         1
                                         pctBoundtreatyear - pctBoundtreatyear-1

                                                    Public             Private

Notes: The above figure plots a kernel density of ∆pctBoundis,t ≡ pctBoundis,t − pctBoundis,t−1 for the year in
which the university’s state adopted a mandate. The density is plotted separately for private and public universities
and includes only those universities in a state that ever adopted a mandate. The bandwidth for both plots is set to

    For the event study specification, I define an event as a year-over-year change in pctBoundist of

50 percentage points or larger:

                             eventist = 1 · {pctBoundist − pctBoundis,t−1 ≥ 0.5}                                  (4)

I choose the 50 percentage point threshold to ensure a university can only experience one event due

to state adoption of mandates.22 Table 3 lists the number of universities experiencing an event by

this definition in each academic-year along with the states adopting in each year. Between 1996

and 2014, 522 of the 1,379 universities experience an academic-year in which the adoption of at

least one mandate changes pctBoundist by at least 50 percentage points. Although there are a few

earlier adopting states, most of these events occur for the high school graduating cohorts of 2005

and later. As a result, there is more outcome data for the periods prior to an event than for the

periods after an event. In addition, due to right censoring of the data, the post-adoption parameters

will be disproportionately estimated by the early adopting events. The estimating equation for the

event study is identical to Equation (2) aside from the event study parameters:23

  22 Appendix   A.3.3 tests the sensitivity of the results to the threshold selection.
  23 In addition, any observations representing time periods relative to an event before -6 are binned into one binary
variable and any observations representing time periods relative to an event after +6 are binned into another binary
variable. These parameters are suppressed from the results.

                     X                         6
         yis,t+k =          γj eventis,t+j +         γj eventis,t+j + β1 X1,it + β2 X2,st + δi + δt + εist .    (5)
                     j=−6                      j=0

                                     Table 3: Events per Academic Year

 Academic Year              University Events            Adopting States
 1996 - 1997                88                           New York
 1997 - 1998                0
 1998 - 1999                34                           Michigan
 1999 - 2000                0
 2000 - 2001                0
 2001 - 2002                0
 2002 - 2003                1                            Wyoming
 2003 - 2004                0
 2004 - 2005                0
 2005 - 2006                42                           Arkansas, Arizona, Louisiana
 2006 - 2007                10                           South Dakota
 2007 - 2008                127                          Georgia, Idaho, North Carolina, Texas
 2008 - 2009                6                            Utah
 2009 - 2010                38                           Colorado, South Carolina
 2010 - 2011                31                           Missouri
 2011 - 2012                66                           Iowa, New Jersey, Tennessee
 2012 - 2013                12                           Kansas
 2013 - 2014                13                           Oregon
 2014 - 2015                54                           Florida, Virginia
 Total Events               522                          22

 Notes: The table shows how many universities experience an event in each academic year where an event is defined as
 a year-over-year change in pctBoundist of 50 percentage points or larger. In addition, the last column summarizes the
 states that adopt a personal finance mandate in each academic year. Bolded academic years denote data availability
 for the one year repayment rate data in the College Scorecard and hence the events for which both pre-event and
 post-event data is available. Events induced by New Hampshire’s 1993 mandate occur before the sample period for
 outcome data.

   One additional component to consider in the interpretation of the event study coefficients is

the relative contribution to each event study parameter of each state’s mandate for the one year

repayment rate. Due to the relatively short window of available data for the one year repayment

rate, each adopting state does not contribute to each event study parameter. This is more clearly

shown in Figure 5 which shows the identifying variation in state mandate adoption across the event

study parameters. The pre-event periods are identified by most of the adopting states, however the

parameters representing the 4th, 5th, and 6th cohorts after the event are only identified by changes

in mandate status for high school graduates of Michigan, New York, and Wyoming. If these states

are particularly effective in their teaching of personal finance, it might be unreasonable to expect

later adopting states to have similar treatment effects as these earlier adopting states. However,

Urban et al. (2018) classifies state personal finance mandates into either rigorous or non-rigorous

depending on the state standards, teacher training and funding, and implementation. They note

that many of the more recent adopting states implement mandates that are more rigorous than the

early adopting states. As a result, if these earlier adopting states are less effective, the parameters 4,

5, 6 which are entirely identified by early adopting states are likely to be smaller than the parameters

for 0, 1, 2, and 3 which are identified by more recently adopting states.

                   Figure 5: State Mandate Contribution to Event Study Parameters

              VA (2014)
               FL (2014)
              OR (2013)
               KS (2012)
               NJ (2011)
               IA (2011)
              TN (2011)
              MO (2010)
              CO (2009)
               SC (2009)
              UT (2008)
               ID (2007)
              TX (2007)
              GA (2007)
              NC (2007)
              SD (2006)
              AZ (2005)
              LA (2005)
              AR (2005)
              WY (2002)
              MI (1998)
              NY (1996)
                             -6    -5    -4    -3   -2    -1     0    1     2     3     4     5    6

                                               Event Study Parameters
Notes: The above graph shows the range of event study parameters that each state’s mandate adoption contributes
to in the one year repayment rate analysis. States adopting for the class of 2009 and later do not contribute to the
post-event parameters while states adopting prior to the class of 2002 do not contribute to the pre-event parameters.
States adopting between 2001 and 2008 contribute to both the pre-event and post-event parameters.

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