Did the PLUS Loans Policy Change Decrease College Enrollment for Black Students?
Tolani Britton
Harvard University
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Abstract
In October 2011, the Obama administration tightened the credit requirements for government
backed Parent Loans for Undergraduate Students (PLUS) college loans by redefining what
constitutes adverse credit history. PLUS loans are the only federally sponsored loans that allow
families to borrow up to the full cost of attendance minus financial aid for any institution and are
generally taken out by lower and middle-income families. Given the recentness of the change,
there have been few studies on the effect of this policy change on college enrollment. However,
in light of the increasing use of PLUS loans and the rising cost of college, it warrants study. In
fiscal year 2013, the federal government provided $133 million dollars in direct loan
commitments of which $18.6 million was in PLUS loans (USDOE, 2013). Using the Integrated
Postsecondary Education Data System (IPEDS), I use a difference in difference strategy to look
at enrollment in colleges with more than 50% black students and then at enrollment in
Historically Black Colleges and Universities (HBCUs). I find that in the year following the loan
policy, enrollment in HBCUs declined by approximately 10% but there was no significant
decline in enrollment at colleges with a majority of Black students when compared to other nonprofit colleges.
Keywords: Loan policy, Black college enrollment
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Introduction
A little over a year after the passage of the Healthcare and Reconciliation Act, and with no
real publicity around the changes, the Obama administration tightened the credit requirements
for government backed Parent Loans for Undergraduate Students (PLUS) college loans by
redefining what constitutes adverse credit history in October 2011. The government adopted this
policy in order to decrease the amount of debt that families take on (Avery & Turner, 2012).
However, this policy has had unintended consequences. For example, Historically Black
Colleges and Universities (HBCUs) claim to have been particularly hard hit financially by this
policy change. Dr. William Harvey, president of Hampton University, believes that this change
has led to an approximate financial loss of $168 million dollars for HBCUs (Young, 2013).
More importantly, parents who previously qualified for loans are no longer eligible, which has
implications for students’ ability to enroll in and complete college. Many students and families
who borrow using these loans come from lower and middle- income families and Black families
(National Center for Education Statistics, 2007).
Given the recentness of the change, there have been few studies on the effect of this policy
change on college enrollment. However, given the increasing use of PLUS loans and the rising
cost of college, it warrants study. In fiscal year 2013, the federal government provided $133
million dollars in direct loan commitments of which $18.6 million was in PLUS loans (USDOE,
2013). In 2011-12, PLUS loans constituted $10.4 billion of $70 billion in total federal loans
disbursals (College Board, 2011). PLUS loans are the only federally sponsored loans that allow
families to borrow up to the full cost of attendance minus financial aid for any institution. Access
to financial aid is one of the determinants of successful college access and completion for lower
and middle-income students and Black students (Dynarski, 1999; Choy, 2002).
In this paper, I will explore how the decrease in access to PLUS loans might have changed
student enrollment patterns for Black students because Black students take out federal loans at
higher rates than their white counterparts (45.6% versus 35%) (NCES, 2013). They are also more
sensitive to changes in price and financial aid, even when controlling for income (Heller, 1997).
In order to answer this question, using the Integrated Postsecondary Education Data System
(IPEDS), I employ a difference in difference strategy to look at 1) schools with a majority of
black students and 2) HBCUs. I find that in the year following the loan policy, enrollment in
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HBCUs declined by approximately 10% when compared to other non-profit colleges but there
was no significant decline for schools that enrolled majority Black students when compared to
other non-profit institutions.
In order to explore the previously stated question, I first look at the literature on borrowing
for college with particular emphasis on the ways in which students finance higher education
through federal programs. I then propose research questions, sampling, methodology, and results.
I conclude with implications of the work.
Literature Review
A brief history of federal financial aid and PLUS loans
Over the past 150 years, the federal government has provided support to institutions and
individuals in order to expand access to higher education. Federal support began with the
founding of public “land-grant” universities and state colleges. The purpose of these institutions
was to expand access to higher education for students coming form a broader geographical area
and socio-economic class (Lucas, 2006). The Higher Education Act (HEA) of 1965, twelve years
after Brown vs. Board of Education, provided broader support to institutions and extended
financial assistance specifically to low and middle- income students attending four-year
institutions. President Lyndon Johnson (1965) declared “For the college years we will provide
scholarships to high school students of the greatest promise and the greatest need and we will
guarantee low-interest loans to students continuing their college studies.” This act established
Stafford loans with subsidized interest rates and deferred interest payments. HEA also introduced
college work-study and guaranteed student loans.
The 1980 reauthorization of the HEA instituted PLUS loans, whereby parents could borrow
up to $3,000 per year for their dependents’ college expenses (Smole, 2013). While these loans
had preset interest rates, they were not subsidized and did not have deferred payments on interest
or principal (Keppel, 1987). In 1992, the PLUS program was renamed the Federal PLUS loan
program. The Higher Education Amendments of 1992 also mandated decreases in the maximum
interest rate on PLUS loans and added additional (origination) fees to PLUS loans. Further, from
a prior maximum limit of $4000 yearly, the program now covered up to the cost of attendance
(COA), which meant any costs not covered by other grants and loans. This change represented a
definitive move towards parental responsibility in college financing, by creating a means
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whereby families could borrow the entire amount of tuition and fees for any college. In theory,
this policy change allowed any student to enroll in any institution. This policy also clearly
signaled that while the federal government would facilitate access to higher education, it would
not pay for it.
In March of 2010, President Obama touted the passage of the Health Care and
Reconciliation Act as “one of the most significant investments in education since the GI Bill”, in
part due to its focus on expansion of access to higher education. In terms of its impact on higher
education financing, this congressional law ended the policy of providing student loans through
private banks. The United States government now would act as a direct lender to all students
seeking federal loans, under the expanded William D Ford Direct Loan Program. According to
the White House’s web site, this change would create a $68 billion dollars savings to the
government over 11 years. This projected savings would be used to finance programs designed
to make college more affordable, thereby increasing access to higher education.
There are four programs covered under the William D. Ford Direct Loan Program, also
known as Title IV loans because their predeccesors were instituted under Title IV of the HEA:
Subsidized Stafford Loans, Unsubsidized Stafford Loans, PLUS loans and Consolidation Loans.
Subsidized loans are for students with demonstrated financial need and are interest free until a
period of time after graduation. Unsubsidized loans are not based on financial need and accrue
interest immediately. Consolidation loans allow students to combine federal loans into one
amount if the borrower meets eligibility criteria. The Stafford loan limits borrowing to $23,000
in subsidized loans with deferred interest and $31,000 in unsubsidized over four years for
dependent students and a maximum of $57,500 for independent students in 2013. However,
tuition and fees can be over $50,000 a year. Thus, of the federal loan programs, only PLUS loans
can cover any costs not included in a financial aid package. On average, in 2011, families
borrowed approximately $12,000 in PLUS loans (Wang, 2012). The availability of PLUS loans
means that students can attend any institution if their parents qualify for and are willing to take
out loans.
Role of federal financial aid in providing access to college
A college education represents a costly investment and many students borrow in order to
pay for it. The average price in 2011-2012 for a private bachelor degree granting institution was
$25,838 according to the College Board. Full time students received an average of $15,530 in
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grant aid from institutions and federal sources to defray costs, which lowered the net price or
amount paid after taking into account aid (Baum & Ma, 2011). Despite financial aid, college still
represents a significant investment, particularly as students attending four-year institutions took
on average debt of $25,250 for the four years in 2009-10 (Reed & Cochrane, 2012).
Undergraduate students in four-year non-profit (54%) institutions had one of the highest rates of
borrowing in 2007-08 (Wei et al., 2010). Given the increasing costs of college, federal aid
policies have real impact on students’ abilities, particularly those that are resource constrained, to
access tertiary, or higher, education.
Students and families take out loans at differential rates based on race and income.
According to King (1999), 54% of Black students take out loans as opposed to 36% of White
students, mostly due to lesser familial contributions. Students who receive familial contributions
during their first year of college are more likely to graduate (Kim, 2007). However, Black
students have the lowest probability of receiving family contributions, when compared to White
and Asian students (Elliot & Griedline, 2013). These higher loan rates and lower familial
contributions might contribute to lower graduation rates for Black students. Black students have
45% graduation rates in 6 years as compared to the national average of 65% (NCES, 2011).
Increasing access for low-income students and students from underrepresented groups in
higher education through the use of federal college grants and loans answers questions of equity
whilst addressing market failure. As previously mentioned, student of color and low-income
students’ graduation rates from college are lower than that of their white and middle class
counterparts. This gap has widened over the past thirty years from a 31% point to a 45% point
difference (Bailey & Dynarski, 2011). The relatively low numbers of Black, Latino, and lowincome students enrolled in four-year colleges represents a sub optimal situation from an
economic perspective since students from these groups derive the most benefit from attending
(Baum, Ma & Payea, 2010). Low-income and Black students who should be in school based on
their aptitude and interest are priced out of attending four-year institutions without financial aid.
Aid can redress the failure of the market by providing the funding to enroll the marginal student,
a young person who might not attend without additional financial incentives (Ehrenberg &
Smith, 2000).
The form of aid provided by the federal government has shifted over time, which has
implications for access. In the 1970’s, the US government provided about an equal proportion of
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grants and loans to qualified students (Archibald, 2002). However, since 2005, approximately
18% of aid comes in the form of grants and 80% in loans (Federal Student Aid, 2014). This
general trend has implications for equity. Low and middle-income students and Black students
are more likely to receive grants and government loans than higher income students (Santiago &
Cunningham, 2005). In 2008, 92% full time full year Black students received government grants
and loans in contrast to 80% for all undergraduates (NCES, 2009). Since the proportion of grants
to loans has been decreasing over the past twenty years, this means that the debt loads that
students are taking on has increased. Further, rather than taking on debt, they might simply be
less likely to enroll in college (Bailey & Dynarski, 2011).
This disadvantage in financing and ultimately accessing higher education has
implications for lifetime income and outcomes. On average, the median income for a high school
graduate is $26,800 less than that of a person with a Bachelor degree and the gap between those
with and without the degree has widened over time (Baum, Ma & Payea, 2010). Brinkman
(2000) finds that individual rates of returns on education compare favorably with those derived
from other assets, although returns vary widely based on field of study, degree level, gender, and
race.
Empirical Strategy
Natural experiment: Policy change to PLUS loans
Despite some clear benefits, parent PLUS loans are also problematic. The aforementioned
flexibility in lending allows families to take on debt that they might struggle to repay. The
existence of the PLUS program creates a situation where families may take on onerous amounts
of debt with no guaranteed return on the investment in the form of a student graduating and
acquiring a job that will allow them to repay the loan. Numerous media outlets have questioned
the use and outcomes of such loans (Young, 2012; Wang, 2012). Thus, while PLUS loans may
increase access to higher education, they might create an unsustainable longer-term debt burden.
In response to the recent recession and broad societal anxieties about families acquiring
debt that they cannot afford to repay, the current administration changed the application of
existing laws governing credit for PLUS loans. Generally, parents must undergo a credit check in
order to get a PLUS loan. However, in October 2011, the federal government made the
requirements for these loans more stringent. In the past, an adverse credit history was defined as
having a major negative credit event such as a bankruptcy or a current delinquency. Now, more
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minor events such as accounts charged off due to lack of payment and accounts in collection
within the past five years without repayment count against families. The policy changes were
enacted in December 2011, during the middle of the academic year. As a result of these changes,
denial rates for PLUS loans increased by ten percentage points overall (USDOE, 2013).
Anecdotal evidence suggests that these changes have had adverse effects on families and
institutions. Parents who previously qualified for loans received mid-year denials, which affected
students’ abilities to finish the school year (Nelson, 2012). One sector of schools that have been
particularly hard hit is HBCU’s. Hampton University president, Dr. William Harvey estimates
that close to half of parents who formerly received PLUS loans have been rejected under the new
standards (Young, 2013). Morehouse and Howard both had their credit ratings downgraded,
which they partially attribute to the drop in enrollment from students who could no longer afford
to attend, though it is clear that both institutions had other financial issues as well (Agyeman–
Fisher, 2013).
Research Questions
Given the recentness of the change in policy with regards to acquiring a federal PLUS loan
for college, a limited body of academic literature treating the impact of the federal policy change
exists. Moving from anecdote to analysis, I examine if the tightening of credit standards has
decreased (1) enrollment for black students in majority Black institutions and (2) in HBCUs, in
the fall of 2012. I analyze schools with a large population of Black students because changes in
enrollment for Black students will be easier to capture in schools where Black students are not a
small minority of the population. Based on the recent change in federal policy, which make it
more difficult to receive a parent PLUS loan to cover tuition and fees for college, my research
questions are:
RQ1: Did the enrollment decline in schools that had greater than 50% enrollment of Black
students after the loan policy change in 2011 when compared to other Title IV schools?
RQ2: Did undergraduate enrollment in HBCUs decrease in Fall 2012 in response to changes in
the availability of credit through PLUS loans when compared to other Title IV schools?
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Sample and Data
My sample is undergraduate not for profit institutions. In order to explore institutional
patterns, I use the panel data set, Integrated Postsecondary Education Data System (IPEDS),
which is collected by the National Center for Education Statistics. IPEDS collects information
from approximately 7,500 institutions of higher education each year. Schools that receive or
have applied for Title IV federal financial aid must report their data. The survey is administered
by web three times a year. The available data includes enrollments, program completions,
graduation rates, faculty and staff, finances, institutional prices, and student financial aid (NCES,
2014). IPEDS receives information from a range of schools from public and private to
proprietary. Further, they classify schools according to the type of college as defined by the
National Association for College Admission Counseling – liberal arts college, HBCU, research
university, community college, etc. Although I have data covering the years 2006-2012, some
institutions are missing data for one of the years in question. This is not an extensive problem, as
schools have on average 6.2 years of data. Given that I explore college enrollments, IPEDS is an
ideal data set because it provides multiple measures for enrollment. First, it shows fall enrollment
by gender, race, and full-time or part-time status. It also collects information on the unduplicated
12-month head count of students.
In order to narrow my sample for this analysis, I exclude for-profit institutions. Although
they have primarily Black and Latino students, in this analysis, I wanted to explore how not-forprofit schools were affected by the tightening of loan standards because these types of schools,
on average, have better outcomes for students, including higher graduation rates and smaller debt
loads for graduates (Avery & Turner, 2012). I also exclude service academies, schools that offer
degrees lower than an associate’s and schools in outlying United States territories. Altogether,
this reduces my sample by approximately half. My total sample is approximately 3300 schools
after these restrictions.
Model
I exploit an arguably exogenous source of variation, which stems from the 2011 HEA
policy change. Because the policy was a change in how an existing policy was applied as
opposed to a change in a law, I posit that the event was unexpected. Given that families expected
to be able to continue using PLUS loans based on being past recipients, but were suddenly
10
denied access to this source of funding, I explore whether the probability of undergraduate
college enrollment in the following fall decreased for underrepresented students in response to
unexpectedly stringent loan policies. I argue that this shock was unexpected, which means that
families would not have been able to foresee and prepare for this event. In my threats to validity
section, I will discuss some of the ways in which I verified that the shock was unanticipated.
From the institutional perspective, I answer the question: did enrollment decrease at schools with
majority Black populations in the fall of 2012? In order to do this, I look at schools where at least
50% of the population is Black and compare their enrollment to other Title IV schools (Table 1).
Because there is a large concentration of Black students at HBCUs and HBCUs have been
particularly vocal about the negative effects of the policy, I will also compare the enrollment at
HBCU’s after the change to all other Title IV institutions and to colleges in the southeast region
because the majority of HBCUs are located in the south to answer my second research question.
Table 1: Summary statistics for all schools and schools with a high proportion of Black students before and after
policy change
Variable
Open Admission
HBCU
Net Price
Private
Four year
Title IV
Size: < 1k
Size:1k to 5k
Size:5k to 10k
Size:10k to 20k
Size: >20k
Applications
Admission
Offer Remed.
Land Grant
Black ft enroll
Black pt enroll
Total ft ug enroll
N
2010
2012
All
> 50% Black All
> 50% Black Dif in dif
0.44
0.57
0.44
0.53
0.03
0.03
0.33
0.03
0.39
-0.07
18435.66
14274.24 20286.65
16234.18 -108.95
0.46
0.52
0.47
0.57
-0.04
0.60
0.53
0.62
0.61
-0.06
0.97
0.77
0.98
0.85
-0.07
0.40
0.31
0.41
0.39
-0.06
0.16
0.12
0.15
0.13
-0.02
0.11
0.03
0.11
0.03
0.00
0.25
0.31
0.26
0.42
-0.10
0.07
0.01
0.07
0.00
0.00
4660.45
3818.77
5014.85
3887.07
286.11
2639.73
1809.35
2788.67
1981.99
-23.70
0.78
0.82
0.77
0.78
0.03
0.03
0.06
0.03
0.06
0.00
385.81
1519.50
376.11
1397.64
112.16
278.92
619.60
289.04
595.66
34.06
3170.40
1902.47
3121.51
1731.64
121.94
3310
270
3237
229
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My identification strategy is a difference in difference. The primary assumption for the
difference in difference model is that the trends in enrollment for the two groups being compared
were parallel prior to the change (Wooldridge, 2010). My data covering the period prior (20062011) shows that the trends appear to be parallel after 2008 and before the policy change (Graph
1 and Graph 2). For the first question, I fit a set of models that estimate whether schools that
have more than 50% black students enrolled saw changes in enrollment when compared with
other Title IV schools as my first difference. For the second question, I fit a model that estimates
enrollment trends at HBCU’s as opposed to all non-profit Title IV schools in the country and
also enrollment at HBCUs as compared to only schools in the southeast. My second difference
for both models is before the policy change and after the policy change.
Graph 1: Total undergraduate enrollment for all schools and those with >50% Black students 2006-2012
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Graph 2: Total undergraduate enrollment for all non-profit colleges and HBCUs 2006-2012
My models are:
Log yit = β0it + β1it Afterchange + β2it hipropblack+ β3it Afterchange*hipropblack + β4it X +εit
(1)
Log yit = β0it + β1it Afterchange + β2it HBCU+ β3it Afterchange*HBCU + β4it X +εit (2)
In my basic model (1), my outcome variable measures the number of total entering students at
the undergraduate level at the beginning of the academic year (IPEDS, 2014). I take the log of
this variable in order to measure percentage change of enrollment. My primary predictors are:
the binary variable for after loan change, where the value 0 is for any time before or during
October 2011 and 1 is for post October 2011, the binary variable for the whether a school has
more than 50% Black students at a given institution and an interaction term between the high
proportion black and the after loan change variable. The X represents a set of covariates that are
correlated with college enrollment. In order to answer my second research question about how
the policy change affected enrollment for HBCU’s, I replace the high proportion of black
students variable with the binary variable for HBCU (2), which takes on value 1 if a school is an
HBCU and 0 if it is not. The interaction term gives the effect of the loan change on schools with
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a high proportion of Black students enrolled as undergraduates after the change in credit policy,
when compared to enrollment in other institutions. If the coefficient is negative and statistically
significant, then it offers evidence that undergraduate enrollment for Black students or
attendance at HBCU’s were negatively affected by the change. The i subscript refers to the
institution and the t to the year.
The X includes dummy variables for public and private colleges and for four- year
institutions and two- year institutions. It also accounts for the selectivity of the school using
dummies calibrated to the Barron’s system (where non- competitive =1 and most selective =6)
and the size of the institution. Epsilon represents the error term. Given that I have data from
multiple institutions over the years from 2006 – 2012, I cluster the standard errors by institution
to account for the fact that observations over time from the same institution are not independent.
I also add state fixed effects as changes in state funding and policies are highly relevant to
college enrollment.
Table 2: Summary statistics for all schools and HBCUs before and after policy change
2010
2012
Difference in
Variable
All schools
HBCU
All schools
HBCU
difference
Undergrad Enrollment
1666.06
887.53
1638.63
804.66
55.43
Open Admission
0.44
0.34
0.44
0.33
0.00
HBCU
0.03
1.00
0.03
1.00
0.00
Public
0.53
0.52
0.53
0.51
0.00
Four year
0.60
0.88
0.62
0.88
0.02
Title IV
0.97
1.00
0.98
1.00
0.01
Size: < 1k
0.25
0.23
0.26
0.23
0.01
Size:1k to 5k
0.40
0.54
0.41
0.55
0.00
Size:5k to 10k
0.16
0.19
0.15
0.18
0.00
Size:10k to 20k
0.11
0.04
0.11
0.04
0.00
Size: >20k
0.07
0.00
0.07
0.00
0.00
Applications
4660.45
4635.83
5014.85
4739.41
250.82
Admission
2639.73
2109.86
2788.67
2281.88
-23.08
% Offer Remed. Courses
0.78
0.91
0.77
0.88
0.02
Land Grant
0.03
0.20
0.03
0.20
0.00
Tuition
11317.86
7443.57
12568.36
8088.68
605.39
Average net price
18435.66 13677.76
20286.65
15808.24
-279.49
Black ft enroll
385.81
2092.36
376.11
1928.59
154.07
Black pt enroll
278.92
292.43
289.04
287.51
15.05
N
3310
95
3237
96
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I use two different comparison groups for HBCUs to answer my second research
question. First, I compare their enrollment to that of all other non-profit institutions in the
country (Table 2). Then I compare HBCUs only to non-profit institutions in the Southeast region
of the U.S. where most HBCUs are located. Neither of these comparisons provides the ideal
comparison group for HBCUs in light of the unique historical context and student population at
HBCU. I will further discuss some of the issues around finding a comparison group in the
discussion.
In addition to fitting the OLS models with clustered standard errors (1), I also fit random
effects specifications, under the assumption that some of my variables will not vary much over
time within institutions but be quite different between institutions (Wooldridge, 2010). My panel
variable is the identification number for each school and my time variable is the academic year.
By using a random effects model, I avoid the problem of serial correlation within observations
for a particular institution. However, the random effects model also has strong assumptions –
namely that the within school correlation of the data is the same across institutions and that the
included regressors are independent of my error term. This model gives me more precise
standard errors if these assumptions are true. The random effects model also allows me to use the
variation between and within cases. I carried out a Haussman test to ensure that I could use a
random effects rather than a fixed effects model. My p-value for the Hausman was p=.94. My
results are similar for OLS, fixed effects, and random effects specifications, although the point
estimates for the random effects model are smaller than those for both the fixed effects and OLS.
Results
I commence with a naïve model where I have a variable for after the policy shift, a
variable for time that removes secular trends, a variable for the whether a school has a majority
of Black students and an interaction term between the after change variable and if a school has a
majority of Black students to capture differential effects on enrollment for Black students
following the change. However, this model has an R2=.01 because I have captured very little of
the variation in enrollment with the included variables. Given that the sector and level of
institution are highly correlated with enrollment, my next model includes controls for whether
the institution is a two-year or a four-year and under public or private control. This model is
slightly more comprehensive but does not account for many of the factors related to why
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students choose to attend institutions. My third model includes the above controls and also adds
fixed effects for size of the college. My final model, which is the most comprehensive, controls
for the state where the campus is located, the size of the institution, and the Barron’s competition
ranking, as well as covariates for control (public or private) and level (2 or 4-year). In the
discussion below, I interpret the results from the fourth and final model because it has an R2 of
approximately .8 for the OLS model, which indicates that my model explains much of the
variation in enrollment. More importantly, it controls for many of the factors related to college
enrollment.
Using a random effects model with my outcome variable as log of total entering
undergraduate enrollment and all of the controls and fixed effects (Table 4), I find that my
regressor of interest, which is the interaction between after the policy change and a high
proportion of Black students, is not statistically significant once I exclude HBCUs from the
analysis. Thus, I do not find any decrease in enrollment for colleges with majority Black students
when compared to other Title IV schools following the change of policy in 2011. In the OLS
model (Table 6), the regressor is statistically significant but the OLS estimates are the least
reliable because of the potential for omitted variable bias.
For my second research question regarding enrollment in HBCUs, the interaction
between HBCU and the variable for after loan change is negative and statistically significant in
all specifications and models. In the random effects specification that controls for region, size of
the institution, the competitive ranking by Barrons, and the other covariates enumerated above
that influence enrollment (Table 7), I find that the year following the change in the policy
HBCU’s saw a decrease of approximately 12.96% in enrollment when compared to other nonprofit institutions. The OLS regression for HBCU (Table 9) using clustered standard errors
returns similar results, although with slightly larger coefficients (~16%).
I also compare enrollment at HBCU to enrollment at schools in the Southeast, as the
majority of HBCU’s are in this region. When comparing the enrollment of HBCUs and other
schools in the southeast region, in the random effects models with all controls (Table 8), I find a
statistically significant decrease of 14.2% in enrollment for HBCU after the change. The results
for the OLS specifications are also similar. While neither of the comparison groups (all schools
and schools in the South East) are perfect comparison groups for HBCUs, the difference in
difference in the values pre and post treatment are not significant for many of the variables
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(Table 2). The statistically significant differences are in the price variables and the number of
black students.
More importantly, when looking at the distribution of log undergraduate enrollment for
all schools, the trends are similar for all non-profit colleges and for HBCUs over the time period
from 2008-2011 (Graph 2). This suggests that the data does not violate the key assumption for a
difference in difference – that trends had a similar slope in the years prior to the change. As
expected, in 2012, log enrollment for HBCU shows a marked decline when compared with
previous years.
Sensitivity Analysis and Robustness Checks
In order to address some of the pre-treatment differences between HBCUs and other nonprofit schools in the Southeast, I also fit models in which I limited analysis to 4- year institutions,
as 87% of HBCU’s are four-year colleges. In comparing log of enrollment in all bachelor
degree-granting schools in the South East and HBCU’s, I find similar results, a statistically
significant decrease in enrolment by 10% for HBCUs.
I also change the year to make sure that I am not picking up on another trend. Given that
the change occurred in 2011, I would not expect to see a significant change in enrollment in
2010. If enrollment has statistically significant changes in the year prior to the policy change,
this is an indication that I have incorrectly specified my model. When the year is 2010, the
change in enrollment for HBCU’s is not statistically significant. Thus, the results from my model
seem robust to a number of different specifications and comparison groups.
In addition to testing whether the change in loan policy changed enrollment for Black
students and for students in HBCU’s. I also looked at other groups that might be highly affected
by changes. Specifically, I carried out similar analysis at schools where more than 50% of their
students receive federal student loans and at schools where more than 50% of the student body
receives federal grant aid. The reason that I chose to look at these other groups was because,
similarly to HBCU’s, they have over half of theirs students getting federal grants and loans to
finance their college education. If a large percentage of students are getting grants based on
being below a certain income threshold or taking out loans, then I posit that many of the families
might also be taking out PLUS loans and would thus be affected by the policy. I did not find
significant effects. These findings are consistent with the findings for my first research question.
17
The policy change did not seem to have a significant effect on enrollment for the groups of
students who are most likely to take out federal loans.
Discussion
A limitation of this study is that only one year of data following the event is available.
However, future years might not reflect the full effect of the policy, in the same way that the year
2012 saw effects, given that the administration reacted to the disapprobation towards the policy
by the HBCUs and other schools serving low income students late in September of 2012
(Stratford, 2013). At the time, the Secretary of Education, Arne Duncan, met with school leaders
and urged schools to have parents who had been rejected for PLUS loans reapply. He also stated
that 98% of parents who appealed had subsequently been accepted for loans. However, we do
not have any sense of how many parents reapplied and were accepted.
One of the greatest problems with natural experiments comes from the possibility that the
credit requirement change was not a sudden disruption. If families anticipated the event, they
might have been more likely to take out PLUS loans before the change. However, there was not
a sudden increase in PLUS loans prior to the change. There is an increase in the number of loans
in 2010. This is due to the fact that the federal government now funded all PLUS loans as
opposed to the loans that had hitherto been lent by private institutions, as previously mentioned.
What is apparent in the graphs is the drop in the number of PLUS loans originated in fall of 2012
(Graph 3). We do not see the same pattern with the subsidized loans (Graph 4).
Graph 3: Number of Federal PLUS loans originated 2006-2013
18
Graph 4: Number of Federal subsidized loans originated 2006-2013
Another indication that the policy change was unanticipated comes from the reaction
within the higher education community following the change. Numerous media outlets in higher
education, such as US World and News and Inside Higher Education, expressed surprise at the
new policy and implied that the government had attempted to carry out this policy surreptitiously
(Nelson, 2013; Equal Justice Works, 2013). If journalists who report on higher education were
unaware of the change, it is unlikely that families with college-aged students had knowledge of
the tightening of credit standards for the PLUS loans. Another indication of the lack of advance
notice comes from the dearth of articles on the change at the time of the change. All of the
currently available articles on the policy change were written in the years following 2011.
While parents might have had access to other sources of credit, it is highly improbable
that borrowers who were rejected by the federal government due to discrepancies on their credit
report were able to borrow the amounts provided by PLUS loans (up to the cost of attendance
after subtracting offered aid) from the private credit market. Given that credit markets
discriminate against Blacks and in light of the evidence suggesting that the Great Recession
disproportionately negatively affected both the wealth and assets of Black and Latino families,
the ability of families to borrow from other sources would be minimal (Shapiro, 2004;
McKernan et al, 2013). Further, in 2012-2013, private and employer loans made up only 5% of
19
total aid to undergraduates when compared to federal loans which comprise 37% of borrowing
(Graph 5).
Graph 5: Percentage and amount of student aid from different sources in 2013.
Source: College Board. (2013). Trends in Student Aid.
In 2012, a number of changes to legislation in student financial aid passed but none of the
changes should have unduly influenced enrollment rates in fall of 2012. In particular, eligibility
for Pell grants changed from nine to six full-time years for all students, including those currently
enrolled in the program. Family income eligibility criteria also shifted slightly. Student loans,
which had hitherto been exempt from interest for six months following graduation, would now
accrue interest immediately.
However, none of these changes should have dramatically
decreased college enrollment in fall of 2012 for Black students, although the 6-year graduation
rate for Black students is approximately 45% (NCES, 2012).
Policy Implications
I find it somewhat puzzling that the HBCUs saw a significant decrease in enrollment but
enrollment in schools with majority Back students did decrease when compared to other
institutions, particularly in the southeast where the proportion of Black students in non-profits on
average is 26.5%, which is almost double the national average of 13.5% according to the IPEDS
data. Given my results, I have two theories. Black students were either substituting away from
HBCUs because of their unfavorable financial packages or not enrolling. I find the first theory
more credible because the data does not indicate a drop in enrollment in schools with a majority
20
of Black students. However, given that enrollment did not increase, even in the South East, and
enrollment in HBCUs decreased, there were likely some students who did not enroll. In order to
test whether students were substituting into other schools, I fit a model where I have three way
interactions between the post policy change variable, public schools and private institutions, and
four year and two year institutions as my independent variables and log enrollment as the
dependent variable. I do not find significant effects on enrollment for any of the interaction
terms. I posit that I do not see increases in enrolment at two and four year institutions because
HBCUs represent a relatively small share of students from Title IV institutions. Thus
substitutions away from HBCUs and towards other non-profits would not have a significant
effect on enrollment.
Negative financial shocks to an underrepresented population in college even over short
periods of time can have longer-term impacts on enrollment and completion. Although there is
debate about whether the income of graduates from HBCUs is less than that of their peers at
other non-profit institutions, it is clear that HBCUs serve an underrepresented population in
higher education (Fryer & Greenstone, 2010; Wood, 2012). Black and Latino students who either
do not enroll in college after high school or take a semester off once they do enroll are less likely
to graduate (Woosley, 2003; Adelman, 2006). Given that negative financial shocks
disproportionately affect Black families, transparency and information in polices around
financing for college are an imperative. Policies that are transparent and well advertised give
institutions and families time to plan and consider the best option for financing college.
Secondly, policies must take into account their impact on the marginal student, who
might not attend college if they do not have access to adequate financial aid. There are already
“high compliance costs” in applying for financial aid for families because it is expensive to learn
about the system, collect the required documents, and complete financial aid forms such as the
FAFSA (Dynarski & Scott-Clayton, 2006). Further, Black and Latino families have fewer assets
and income with which to contribute to college education (Shapiro, 2004). A decrease in access
to governmental loans designed to cover any unmet need has far reaching consequences for
college access. The college financing process currently favors middle and upper class families
who have extensive information and financial resources to help their students’ access and
successfully complete a college education (Long, 2009). Although the context and scope of the
21
federal grant and loan program instituted by the Higher Education Act has expanded, the initial
charge to correct a market failure by providing college financing for needy students still remains.
Access to a college education serves as one of the primary predictors of income. Given that
these gaps in college financing exist between middle class and lower income families and Black
and White families and these gaps persist generationally, if closing the college degree attainment
gap remains a goal, the federal government must move towards polices that do not privilege the
middle class at the expense of the low-income student and student of color. Rather than
expanding only programs for middle class families such as tax credits, federal grant and loan
programs must either keep pace with rising tuition or better target the marginal student. Access
to loans should not be lessened, particularly for families that demonstrate an ability to service the
loans. There exists a balance between not saddling families with debt that they cannot repay and
providing access to loans for education.
In 2013, 75% of financial aid to students came from state and federal programs (College
Board, 2013). Although media reports have focused on the impact of recent changes in PLUS
loans on HBCUs, which serve a historically underrepresented population in higher education,
Black students, this policy might also have affected other underrepresented groups in higher
education, notably Latino students in Hispanic Serving Institutions (HSI). If students who are
already disadvantaged in the college process by lesser familial assets face an inability to fund
college, this policy will have long- term implications on equity and access in college entrance
and graduation. Although, PLUS loans clearly increase the debt loads of families, they also serve
as one of the few means of fully financing a college education at any institution.
22
References
Adelman, C. (2006). The Toolbox Revisited: Paths to Degree Completion From High School
Through College. US Department of Education.
Agyeman-Fisher, A. (November 13, 2013). With GOP Cuts And Plus Loan Eligibility Changes,
Obama Administration Looks To Get HBCUs Back On Track.
http://newsone.com/2728754/plus-loan-hbcu-pell-grants/
Archibald, R.B. (2002). Redesigning the financial aid system. Baltimore, Maryland: The Johns
Hopkins University Press.
Avery, C., & Turner, S. (2012). Student Loans: Do College Students Borrow Too Much—Or Not
Enough? The Journal of Economic Perspectives, 26(1), 165-192.
Baum, S., Ma, J., & Payea, K. (2010). Education Pays, 2010: The Benefits of Higher Education
for Individuals and Society. Trends in Higher Education Series. College Board Advocacy &
Policy Center.
Baum, S., & Ma, J. (2012). Trends in College Pricing, 2012. Trends in Higher Education Series.
College Board Advocacy & Policy Center.
Baum, S., McPherson, M., & Steele, P. (2008). The effectiveness of student aid policies: What
the research tells us.
Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How much should we trust differences-indifferences estimates? The Quarterly Journal of Economics, 119(1), 249-275.
Bettinger, E. (2004). How financial aid affects persistence. In College choices: The economics of
where to go, when to go, and how to pay for it (pp. 207-238). University of Chicago Press.
Bettinger, E. P., & Long, B. T. (2010). Does cheaper mean better? The impact of using adjunct
instructors on student outcomes. The Review of Economics and Statistics, 92(3), 598-613.
Boren, S. (1987). The Higher Education Amendments of 1986 (PL 99-498): A Summary of
Provisions. 87-187 EPW. Library of Congress, Washington, D.C.: Congressional
Research Service.
Boushey, H. (2009). The Debt Explosion Among College Graduates.
Brinkman, P. T. (2000). The economics of higher education: Focus on cost. New Directions for
Institutional Research, 2000(106), 5-17.
Capt, R. (2013). Analysis of the Higher Education Act Reauthorizations: Financial Aid
Influencing College Access and Choice. EDITORIAL BOARD (2013), 2.
Policy
23
Carneiro, P., & Heckman, J. J. (2002). The Evidence on Credit Constraints in Post‐Secondary
Schooling. The Economic Journal, 112(482), 705-734.
Cha, K. W., Weagley, R. O., & Reynolds, L. (2005). Parental borrowing for dependent
children’s higher education. Journal of Family and Economic Issues, 26(3), 299-321.
Choy, S. P. (1999). College Access. National Center for Education Statistics, 1(2), 74.
Congress. (1958). National Defense Education Act.
College Board. (2013). Trends in Student Aid.
Deming, D. J., Goldin, C., & Katz, L. F. (2011). The For-Profit Postsecondary School Sector:
Nimble Critters or Agile Predators? (No. w17710). National Bureau of Economic Research.
Dobbie, W., Fryer, R. G., & Fryer Jr, G. (2011). Are high-quality schools enough to increase
achievement among the poor? Evidence from the Harlem Children's Zone. American
Economic Journal: Applied Economics, 3(3), 158-187.
Dynarski, M. (1994). Who defaults on student loans? Findings from the national
postsecondary student aid study. Economics of Education Review, 13(1), 55-68.
Dynarski, S. M. (1999). Does aid matter? Measuring the effect of student aid on college
attendance and completion (No. w7422). National bureau of economic research.
Dynarski, S. M., & Scott-Clayton, J. E. (2006). The cost of complexity in federal student aid:
Lessons from optimal tax theory and behavioral economics (No. w12227). National Bureau
of Economic Research.
Ehrenberg, R. G., & Smith, R. S. (1993). Modern labor economics: Theory and public policy.
Baltimore, Md.
Elliott, W., & Friedline, T. (2012). “You Pay Your Share, We’ll Pay Our Share”: The College
Cost Burden and the Role of Race, Income, and College Assets. Economics of Education
Review.
Elliott, W., & Nam, I. (2012). Direct effects of assets and savings on the college progress of
black young adults. Educational Evaluation and Policy Analysis, 34(1), 89-108.
Freeman, K. (1997). Increasing African Americans' participation in higher education: African
American high-school students' perspectives. Journal of Higher Education, 68(5), 523-550
Fryer Jr, R. G., & Greenstone, M. (2010). The changing consequences of attending historically
black colleges and universities. American economic journal: Applied economics, 116-148.
24
Gladieux, L. (1995). Federal student aid policy: A history and an assessment. NOTE 128p.
AVAILABLE FROM Superintendent of Documents, US Government Printing, 43.
Heller, D. E. (2008). The impact of student loans on college access. The effectiveness of student
aid policies: What the research tells us, 39-68.
Heller, D. E. (1997). Student price response in higher education: An update to Leslie and
Brinkman. Journal of higher education, 624-659.
Hill, C. B., Winston, G. C., & Boyd, S. A. (2005). Affordability Family Incomes and Net Prices
at Highly Selective Private Colleges and Universities. Journal of Human Resources, 40(4),
769-790.
Johnson, L. (1965). "Annual Message to the Congress on the State of the Union," January 4,
1965. Online by Gerhard Peters and John T. Woolley, The American Presidency Project.
Retrieved from: http://www.presidency.ucsb.edu/ws/?pid=26907.
Keppel, F. (1987). The Higher Education Acts Contrasted, 1965-1986: Has Federal Policy Come
of Age? Harvard Educational Review 57, no. 1 (1987): 49-68
Kim, D. (2007). The effect of loans on students' degree attainment: Differences by student and
institutional characteristics. Harvard Educational Review, 77(1), 64-100.
King, Jacqueline. (2001). Crisis or convenience: Why are students borrowing more? In
Jacqueline E. King (Ed.), Financing a college education—How it works, how it’s changing.
Phoenix, AZ: American Council on Education/ Oryx Press ED 427.
Lucas, Christopher J. (2006). American higher education: A history, (New York: Palgrave).
Lyall, K. C., & Sell, K. R. (2006). The true genius of America at risk: Are we losing our public
universities to de facto privatization? Praeger Publishers
Nelson, L. (2012). Cracking down on PLUS loans. Inside Higher Ed. Retrieved from:
http://www.insidehighered.com/news/2012/10/12/standards-tightening-federal-plus-loans.
National Center for Education Statistics. (2014). Integrated Postsecondary Education Data
System. Retrieved from:http://nces.ed.gov/ipeds/about/
McKernan, S. M., Ratcliffe, C., Steuerle, E., & Zhang, S. (2013). Less than equal: Racial
disparities in wealth accumulation. Washington, DC: The Urban Institute.
PLUS Loans, Federal Student Aid. accessed March 4, 2013,
http://studentaid.ed.gov/types/loans/plus.
Price, D. V. (2004). Borrowing inequality: Race, class, and student loans. Boulder, CO: Lynne
Rienner Pub.
25
Reed, M., & Cochrane, D. (2012). Student Debt and the Class of 2011. Project on Student Debt.
Rothstein, J., & Rouse, C. E. (2011). Constrained after college: Student loans and early-career
occupational choices. Journal of Public Economics, 95(1), 149-163.
Samuel, B., & Hoover, R. (2010). Loan Debt: Reflections of African American College
Graduates. Student Access, Finance, and Success in Higher Education, 73.
Santiago, D. A., & Cunningham, A. F. (2005). How Latino Students Pay for College: Patterns of
Financial Aid in 2003-04. Excelencia in Education.
Silliman, B. R. (2011). College Tuition Tax Credits: An Examination of the Impact of the
American Opportunity, HOPE, and Lifetime Learning Tax Credits Since 1998. Proceedings
of American Society of Business and Behavioral Sciences, 18, 279-288.
Shapiro, T. M. (2004). The hidden cost of being African American: How wealth perpetuates
inequality. Oxford University Press.
Smole, D. (2013). Federal student loans made under the federal family education loan program
and the William T. Ford direct loan program: Terms and conditions for borrowers.
Congressional Research Service Report R-40122, Library of Congress, Washington, D.C.:
Congressional Research Service.
St. John, E. P., Andrieu, S., Oescher, J., & Starkey, J. (1994). The influence of student aid on
within-year persistence by traditional college-age students in four-year college. Research in
Higher Education, 35(4), 445–480.
Tinto, V. (2006). Research and practice of student retention: what next? Journal of College
Student Retention: Research, Theory and Practice, 8(1), 1-19.
U.S. Department of Education. National Center for Education Statistics. (2012). Digest of
Education Statistics, 2011. Washington, DC: Author.
U.S. Department of Education, National Center for Education Statistics, Integrated
Postsecondary Education Data System (IPEDS), Fall 2001 and Spring 2007 through Spring
2012, Graduation Rates component. (This table was prepared November 2012.)
U.S. Department of Education, National Center for Education Statistics, 2007-08 National
Postsecondary Student Aid Study (NPSAS:08). (This table was prepared July 2013.)
U.S. Department of Education. Student Loans Overview: Fiscal Year 2014 Budget Proposal.
(2013), S-22.
U.S. Department of Veteran’s Affairs, G.I Bill. (2013). History of the G.I. Bill. Washington, DC:
Author.
26
Wang, M. (2012). The Parent Loan Trap.
http://chronicle.com/article/The-Parent-Plus-Trap/134844/
Wei, C. C., & Berkner, L. (2008). Trends in Undergraduate Borrowing II: Federal Student Loans
in 1995-96, 1999-2000, and 2003-04. Postsecondary Education Descriptive Analysis Report.
NCES 2008-179. National Center for Education Statistics.
Wood, J. L. (2012). LABOR MARKET OUTCOMES FOR HBCU GRADUATE STUDENTS.
Black Graduate Education at Historically Black Colleges and Universities: Trends,
Experiences, and Outcomes.
Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT press.
Woosley, S. (2003). Stop-out or drop-out? An examination of college withdrawals and reenrollments. Journal of College Student Retention: Research, Theory and Practice, 5(3),
293-303.
Young, R (Executive Producer). (2011, March 7). Historically Black College Lose Millions after
Federal Loan Change. New York City: National Public Radio.
Zhan, M., & Sherraden, M. (2011). Assets and liabilities, race/ethnicity, and children's college
education. Children and Youth Services Review, 33(11), 2168-2175.
27
Table 3: Random effects regression of total population in college as compared to those with more than 50% Black
students (including HBCU) Dependent Variable: Log of total undergraduate enrollment (lnugentern)
(1)
AfterChange
>50% Bl
>50% Bl *
AfterChange
Size FE
State FE
_cons
N
(2)
(3)
-0.0819**
(0.0062)
0.0193**
(0.0047)
-0.0747*
-0.0820**
(0.0062)
0.0195**
(0.0047)
-0.0741*
-0.0528**
(0.0064)
0.0180**
(0.0049)
-0.0742*
(0.0338)
(0.0337)
(0.0336)
x
-74.6721**
(3.5249)
20,704
-72.5811**
(3.5174)
20,704
-45.6565**
(3.5526)
20,704
(4)
-0.0389**
(0.0074)
-0.0006**
(0.0057)
-0.1218*
(0.0460)
x
x
-30.8061**
(4.4926)
11,760
* p<0.05; ** p<0.01
Notes: The first column is the naïve model without any covariates. The second column adds controls for level of institution
(2 yr or 4 yr) and public or private control. The third model accounts for the level and control and adds size fixed effects.
The fourth model has all of the controls listed above, fixed effects for size, state, and Barron’s competition ranking (where
non- competitive =1 and most selective =6). The analysis includes HBCU’s. Standard errors are clustered at the level of the
institution.
p<0.05; ** p<0.01
28
Table 4: Random effects regression of total population in college and those with more than 50% Black students
(excluding HBCU) Dependent Variable: Log of total undergraduate enrollment (lnugentern)
(1)
AfterChange
>50% Bl
>50% Bl *
AfterChange
Size FE
State FE
_cons
N
(2)
(3)
-0.0844**
(0.0062)
0.0194**
(0.0047)
-0.0301
-0.0843**
(0.0062)
0.0196**
(0.0047)
-0.0323
-0.0548**
(0.0065)
0.0176**
(0.0049)
-0.0216
(0.0555)
(0.0553)
(0.0531)
x
-76.2128**
(3.5914)
20,039
-74.0872**
(3.5845)
20,039
-46.9118**
(3.6281)
20,039
(4)
-0.0424**
(0.0075)
-0.0004
(0.0058)
-0.0778
(0.0815)
x
x
-33.0620**
(4.6110)
11,328
Notes: The first column is the naïve model without any covariates. The second column adds controls for level of institution
(2 yr or 4 yr) and public or private control. The third model accounts for the level and control and adds size fixed effects.
The fourth model has all of the controls listed above, fixed effects for size, state, and Barron’s competition ranking (where
non- competitive =1 and most selective =6). The analysis excludes HBCU’s. Standard errors are clustered at the level of the
institution.
p<0.05; ** p<0.01
29
Table 5: OLS regression of total population in college and those with more than 50% Black students (including HBCU)
Dependent Variable: Log of total undergraduate enrollment (lnugentern)
(1)
AfterChange
>50% Bl
>50% Bl *
AfterChange
Size FE
State FE
_cons
R2
N
(2)
(3)
-0.0978**
(0.0121)
0.0626*
(0.0266)
-0.5914**
-0.0855**
(0.0103)
0.0430*
(0.0207)
-0.4921**
-0.0399**
(0.0089)
0.0521**
(0.0123)
-0.0630
(0.0919)
(0.0703)
(0.0506)
x
-135.1784**
(7.5575)
0.01
20,704
-113.3869**
(6.2662)
0.36
20,704
-43.6885**
(5.3320)
0.80
20,704
(4)
-0.0305**
(0.0108)
0.0575**
(0.0133)
-0.1680**
(0.0567)
x
x
-43.6885**
(5.3320)
0.81
11,760
Notes: The first column is the naïve model without any covariates. The second column adds controls for level of institution
(2 yr or 4 yr) and public or private control. The third model accounts for the level and control and adds size fixed effects.
The fourth model has all of the controls listed above, fixed effects for size, state, and Barron’s competition ranking (where
non- competitive =1 and most selective =6). The analysis includes HBCU’s. Standard errors are clustered at the level of the
institution.
p<0.05; ** p<0.01
30
Table 6: OLS regression of total population in college and those with more than 50% Black students with clustered
standard errors (excluding HBCU) Dependent Variable: Log of total undergraduate enrollment (lnugentern)
AfterChange
>50% Bl
>50% Bl *
AfterChange
Size FE
State FE
_cons
R2
N
(1)
(2)
-0.1040**
(0.0123)
0.0857**
(0.0266)
-0.8439**
-0.0900**
(0.0104)
0.0686**
(0.0202)
-0.6453**
-0.0422**
(0.0090)
0.0238*
(0.0113)
-0.1857*
-0.0352**
(0.0109)
0.0504*
(0.0132)
-0.2931*
(0.1825)
(0.1317)
(0.0857)
x
(0.1291)
x
x
-36.9657**
(6.8053)
0.82
11,328
-139.0266**
(7.7739)
0.01
20,039
-115.7910**
(6.4290)
0.36
20,039
(3)
-44.5700**
(5.4559)
0.80
20,039
(4)
Notes: The first column is the naïve model without any covariates. The second column adds controls for level of institution
(2 yr or 4 yr) and public or private control. The third model accounts for the level and control and adds size fixed effects.
The fourth model has all of the controls listed above, fixed effects for size, state, and Barron’s competition ranking (where
non- competitive =1 and most selective =6). The analysis excludes HBCU’s. Standard errors are clustered at the level of the
institution.
p<0.05; ** p<0.01
31
Table 7: Random effects regression of total population in college for HBCU with clustered standard errors:
Dependent Variable: Log of total undergraduate enrollment (lnugentern)
(1)
AfterChange
hbcu
AfterChange xhbcu
Size FE
State FE
_cons
N
(2)
(3)
-0.0867**
(0.0062)
-0.0076
(0.0927)
-0.1040**
(0.0385)
-0.0868**
(0.0062)
-0.0180
(0.0775)
-0.1014**
(0.0385)
-0.0568**
(0.0065)
0.1371*
(0.0552)
-0.1147**
(0.0400)
x
-74.4680**
(3.5217)
20,704
-74.2295**
(3.5052)
20,704
-46.6557**
(3.5485)
20,704
(4)
-0.0395**
(0.0075)
-0.0184*
(0.0636)
-0.1296**
(0.0521)
x
x
-31.9586**
(4.4909)
11,760
Notes: The first column is the naïve model without any covariates. The second column adds controls for level of institution
(2 yr or 4 yr) and public or private control. The third model accounts for the level and control and adds size fixed effects.
The fourth model has all of the controls listed above, fixed effects for size, state, and Barron’s competition ranking (where
non- competitive =1 and most selective =6). Standard errors are clustered at the level of the institution.
p<0.05; ** p<0.01
32
Table 8: Random effects regression of population in southeast in college compared to HBCU with clustered standard
errors: Dependent Variable: Log of total undergraduate enrollment (lnugentern)
(1)
AfterChange
hbcu
AfterChange
xhbcu
Size FE
State FE
_cons
N
(2)
(3)
-0.0717**
(0.0127)
0.0295
(0.1097)
-0.1484**
-0.0736**
(0.0126)
0.2293*
(0.0934)
-0.1483**
-0.0407**
(0.0131)
0.2675**
(0.0655)
-0.1555**
(0.0501)
(0.0501)
(0.0517)
x
-88.4947**
(7.4484)
5,356
-90.1575**
(7.1434)
5,356
-56.9044**
(7.1921)
5,356
(4)
-0.0372**
(0.0133)
0.0526
(0.0691)
-0.1420**
(0.0654)
x
x
-37.0911**
(7.5821)
2,907
Notes: The first column is the naïve model without any covariates. The second column adds controls for level of institution
(2 yr or 4 yr) and public or private control. The third model accounts for the level and control and adds size fixed effects.
The fourth model has all of the controls listed above, fixed effects for size, state, and Barron’s competition ranking (where
non- competitive =1 and most selective =6). Standard errors are clustered at the level of the institution.
p<0.05; ** p<0.01
33
Table 9: OLS linear regression of total population in college for HBCU with clustered standard errors:
Dependent Variable: Log of total undergraduate enrollment (lnugentern)
(1)
AfterChange
hbcu
AfterChange xhbcu
Size FE
State FE
_cons
R2
N
(2)
(3)
-0.1380**
(0.0110)
-0.1030
(0.0879)
-0.1833**
(0.0464)
-0.1169**
(0.0094)
-0.1897*
(0.0796)
-0.1509**
(0.0401)
-0.0490**
(0.0088)
0.2576**
(0.0524)
-0.1628**
(0.0498)
x
-134.6924**
(7.5716)
0.01
20,704
-114.9174**
(6.2824)
0.36
20,704
-43.4866**
(5.3340)
0.80
20,704
(4)
-0.0446**
(0.0107)
0.0767
(0.0510)
-0.1619**
(0.0570)
x
x
-31.1435**
(6.6131)
0.81
11,760
Notes: The first column is the naïve model without any covariates. The second column adds controls for level of institution
(2 yr or 4 yr) and public or private control. The third model accounts for the level and control and adds size fixed effects.
The fourth model has all of the controls listed above, fixed effects for size, state, and Barron’s competition ranking (where
non- competitive =1 and most selective =6). Standard errors are clustered at the level of the institution.
p<0.05; ** p<0.01
34
Table 10: OLS linear regression of population in southeast in college compared to HBCU with clustered standard errors:
Dependent Variable: Log of total undergraduate enrollment (lnugentern)
(1)
AfterChange
hbcu
AfterChange
xhbcu
Size FE
State FE
_cons
R2
N
(2)
(3)
-0.1042**
(0.0173)
-0.0807
(0.1032)
-0.2400**
-0.0985**
(0.0164)
-0.0330
(0.0922)
-0.2005**
-0.0210
(0.0159)
0.2620**
(0.0552)
-0.1919**
(0.0614)
(0.0526)
(0.0616)
x
-138.2127**
(13.7661)
0.01
5,356
-129.6033**
(13.0700)
0.37
5,356
-43.3739**
(10.3759)
0.77
5,356
(4)
-0.0217
(0.0181)
0.0516
(0.0431)
-0.1700**
(0.0703)
x
x
-25.5970**
(11.9413)
0.83
2,907
Notes: The first column is the naïve model without any covariates. The second column adds controls for level of institution
(2 yr or 4 yr) and public or private control. The third model accounts for the level and control and adds size fixed effects.
The fourth model has all of the controls listed above, fixed effects for size, state, and Barron’s competition ranking (where
non- competitive =1 and most selective =6). Standard errors are clustered at the level of the institution.
p<0.05; ** p<0.01
35
Appendix 1: Regional divisions used by the United States Census Bureau
• Region 1: Northeast
Division 1 (New England) Maine, New Hampshire, Vermont, Massachusetts, Rhode Island, Connecticut
Division 2 (Mid-Atlantic) New York, Pennsylvania, New Jersey
• Region 2: Midwest
Division 3 (East North Central) Wisconsin, Michigan, Illinois, Indiana, Ohio
Division 4 (West North Central) Missouri, North Dakota, South Dakota, Nebraska, Kansas, Minnesota, Iowa
• Region 3: South
Division 5 (South Atlantic) Delaware, Maryland, District of Columbia, Virginia, West Virginia, North Carolina, South
Carolina, Georgia, Florida
Division 6 (East South Central) Kentucky, Tennessee, Mississippi, Alabama
Division 7 (West South Central) Oklahoma, Texas, Arkansas, Louisiana
• Region 4: West
Division 8 (Mountain) Idaho, Montana, Wyoming, Nevada, Utah, Colorado, Arizona, New Mexico
Division 9 (Pacific) Alaska, Washington, Oregon, California, Hawaii