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Ozan Jaquette
Bradley R. Curs
Julie R. Posselt
Tuition Rich, Mission Poor:
Nonresident Enrollment Growth and the
Socioeconomic and Racial Composition
of Public Research Universities
Many public research universities fail to enroll a critical mass of low-income and underrepresented minority (URM) students. Though founded with a commitment to access,
public research universities face pressure to increase tuition revenue and to recruit highachieving students. These pressures create an incentive to recruit nonresident students,
who tend to pay more tuition and score higher on admissions exams, but who also tend to
be richer and are less likely be Black or Latino. This paper examines whether the growing share of nonresident students was associated with a declining share of low-income
and URM students at public research universities. Institution-level panel models revealed
that growth in the proportion of nonresident students was associated with a decline in the
proportion of low-income students. This negative relationship was stronger at prestigious
universities and at universities in high-poverty states. Growth in the proportion of nonresident students was also associated with a decline in the proportion of URM students. This
negative relationship was stronger at prestigious universities, universities in states with
large minority populations, and universities in states with affirmative action bans. These
findings yield insights about the changing character of public research universities and
have implications for the campus climate experienced by low-income and URM students.
Keywords: enrollment management, access, organizational behavior, public universities
Ozan Jaquette is Assistant Professor in the Department of Educational Policy Studies
and Practice in the College of Education at the University of Arizona; ozanj@email.
arizona.edu. Bradley R. Curs is Associate Professor in the Department of Educational
Leadership and Policy Analysis and the Truman School of Public Affairs at the University of Missouri; [email protected]. Julie R. Posselt is Assistant Professor in the
Center for the Study of Higher and Postsecondary Education in the School of Education
at the University of Michigan; [email protected].
The Journal of Higher Education, Vol. 87, No. 5 (September/October 2016)
Copyright © 2016 by The Ohio State University
636 The Journal of Higher Education
In the late 1800’s, University of Michigan President James Angell
committed to providing “an uncommon education for the common
man” (as cited in Rudolph, 1962, p. 279). By 2014, however, mean
family income at the University of Michigan approached $200,000,
which president emeritus James Duderstadt (forthcoming) described
as, “more characteristic of society’s ‘1%’ than ‘the common man’”
(p. 273). Michigan is not unique among public research universities
in increasing enrollment from wealthy students. Data from the 2004
high school senior class reveal that students from the lower half of
the socioeconomic distribution accounted for only 23% of enrollment
at selective public universities nationally, and that Black and Latino
students accounted for only 5.4% and 6.6% of enrollment, respectively
(Bastedo & Jaquette, 2011; Posselt, Jaquette, Bielby, & Bastedo, 2012).
Statistics like these are consistent with the Education Trust’s argument
that public research universities have abandoned their “proud tradition
serving as an engine of social mobility” (Gerald & Haycock, 2006, p.
3). Instead, they have become “engines of inequality,” growing “disproportionately whiter and richer” even while “the number of lowincome and minority high school graduates in their states grow” (p. 3).
The failure of many public research universities to achieve a critical mass of low-income and underrepresented minority (URM) students
is due, in part, to their historic goal of a commitment to access facing
competition from contemporary goals. Concern with institutional rankings creates pressure for universities to recruit students with academic
profiles valued by ranking methodologies (Brewer, Gates, & Goldman,
2002). Scholarship on academic capitalism (e.g., Slaughter & Leslie,
1997) shows that public universities increasingly engage in entrepreneurial revenue generating behaviors to compensate for declining state
appropriations. Though universities have sought revenues from research
funding (Slaughter & Leslie, 1997; Slaughter & Rhoades, 2004) and
donations and investments (Cheslock & Gianneschi, 2008), tuition revenue has been the dominant source of revenue growth for most institutions (Jaquette & Curs, 2015).
As public research universities become more tuition reliant the
incentive to increase nonresident enrollment also increases. Growth in
tuition revenue from resident students may not fully offset declines in
state financial support because most public universities lack unilateral
authority over resident tuition pricing (Zinth & Smith, 2012). They have
more authority over nonresident tuition, which is typically more than
twice the price of resident tuition. Table 1, which draws from data in
the 2011–2012 National Postsecondary Student Aid Study, shows that
nonresident students at public research universities generate much more
37.6%
Percent Pell
27.9%
28.6%
BA degree
Graduate degree
1,170
730
600
117,332
3.3%
0.8%
8.9%
12.4%
7.3%
67.3%
35.2%
31.0%
33.8%
33.1%
101,435
1,131
7,982
3,073
11,054
Resident
580
94,922
3.7%
0.9%
17.9%
8.9%
4.3%
64.3%
50.2%
29.2%
20.5%
11.1%
119,061
1,176
23,303
7,568
30,872
Nonresident
High Prestigeb
570
136,299
4.0%
0.6%
7.1%
14.0%
17.9%
56.4%
23.0%
25.3%
51.7%
41.4%
81,382
1,024
5,440
1,374
6,813
Resident
150
34,945
5.9%
1.3%
8.2%
8.3%
8.0%
68.5%
38.1%
28.7%
33.2%
22.4%
106,423
1,079
13,614
4,874
18,488
Nonresident
Low Prestigec
a
Notes. Authors’ calculations based on 2011−12 NPSAS survey, 95 percent confidence intervals available upon request; public research-extensive universities as defined
by the 2000 Carnegie classification; b public research-extensive universities categorized in tiers 1 & 2 of USNWR National University Rankings; c public research-extensive
universities categorized in tiers 3 & 4 of USNWR National University Rankings; d 2012 CPI; e ACT scores converted to SAT scale; f Includes Native American, Alaskan Native, Native Hawaiian and other Pacific Islander; gunweighted sample sizes rounded to the nearest 10 in accordance with NCES restricted data license rules.
Unweighted sample sizeg
129,867
4.3%
253,631
3.7%
Weighted sample size
Other
1.0%
0.7%
8.7%
15.3%
Nativef
13.3%
Hispanic
5.3%
65.4%
7.9%
13.0%
Black
47.0%
29.1%
23.9%
14.2%
115,772
Asian
61.4%
White
Race
43.5%
No BA degree
Parental education
90,886
Family incomed
1,150
20,696
1,074
6,616
SAT scoree
Net tuition revenue
2,160
d
6,843
8,775
27,539
Nonresident
Institutional financial aidd
Resident
Sticker priced
Tuition
All Public Researcha
TABLE 1
Mean Characteristics of Resident and Nonresident Students Attending Public Research-Extensive Universities, 2011−12 NPSAS
638 The Journal of Higher Education
tuition revenue per student than resident students. Furthermore, universities seeking to strengthen their academic profile have an incentive
to increase nonresident enrollment because nonresident students tend
to have higher admission exam scores than resident students. However, Table 1 also indicates that nonresident students are more likely to
come from high-income families and are less likely identify as Black or
Latino.
On average, nonresident freshman enrollment increased from 20.7%
of total freshman enrollment at public research universities in 2002–03
to 24.7% in 2012–13 (authors’ calculations based on IPEDS data). Popular media coverage on public research universities has drawn attention
to the shift towards nonresident enrollment (e.g., Jaschik, 2009; Keller,
2009) and the low representation of low-income and URM students
(e.g., Leonhardt, 2014), but prior research has not examined whether
nonresident enrollment growth may be exacerbating access inequalities.
This paper examines whether growth in the share of nonresident
students has been associated with declines in the share of low-income
students and declines in the share of URM students at public research
universities from 2002 to 2013. Fixed effect panel models revealed that
growth in the proportion of nonresident students was associated with a
decline in the proportion of low-income students. This negative relationship was stronger at more prestigious universities and at universities
in states with high poverty rates. Growth in the proportion of nonresident students was also associated with a decline in the proportion of
URM students. This negative relationship was stronger at prestigious
universities, at universities in states with large URM populations, and at
universities in states with affirmative action bans.
Given prior research on the negative effects of racial and socioeconomic isolation on student experiences (e.g., Bowman, 2013; Gurin,
1999; Park, Denson, & Bowman, 2013), these findings have implications for campus climate and for student development outcomes. As
changes in student body composition are an important indicator of
changing institutional priorities, these findings also contribute to scholarship on the changing character of public research universities (e.g.,
Priest & St. John, 2006; Rhoades, 2006). Shifting institutional enrollments towards other states’ residents over their own may fundamentally
compromise the access mission of public research universities. In their
rush to become preferred destinations for affluent nonresident students,
flagship state institutions increasingly ignore the needs and changing
demographics of their own state. As such, they risk becoming culturally
impoverished places where low-income and underrepresented minority
students face unwelcoming, isolating environments.
Tuition Rich, Mission Poor 639
Literature Review
We situate this research within the literature on enrollment management, with a focus on changing admissions and institutional financial
aid practices that affect opportunities for low-income and URM students
at selective institutions. The goal of enrollment management is to enroll
and retain a student population deemed desirable by the institution
(Hossler & Bean, 1990). Enrollment managers exert control over student body composition by integrating the activities of functional offices
related to recruitment, with a particular emphasis on admissions, financial aid, and marketing (Kraatz, Ventresca, & Deng, 2010).
Admissions policies and practices have shaped access and enrollment for low-income, Black, and Latino students in important ways.
The decline of affirmative action in public universities started in 1995
(Grodsky & Kalogrides, 2008) and, since then, eight states have formally banned race-conscious admissions (Garces, 2014). Policy evaluations have found that these bans have negatively affected URM student
enrollment at selective public universities (e.g., Backes, 2012; Hinrichs,
2012). Meanwhile, admissions criteria at selective institutions have
increasingly undermined access for these underrepresented groups.
Average entrance exam scores have been lower among low-income,
Black, and Latino students (Alon & Tienda, 2007). However, the influence of entrance exam scores on admissions decisions has increased
over time (Bastedo & Jaquette, 2011), a pattern due in part to the methodologies of college ranking systems placing heavy weight on standardized test scores. The increasing institutional focus on rankings, and thus
on entrance exam scores, makes it difficult for public research universities to increase enrollment from low-income and underrepresented
racial minority (URM) students, particularly in state policy environments hostile to affirmative action (Alon & Tienda, 2007).
Prior research has also shown that access for low-income and URM
students has been affected by institutional financial aid policies (e.g.,
Hillman, 2013; Melguizo & Chung, 2012). Historically, the primary
purpose of institutional financial aid was to increase access for lowincome students (Ehrenberg, 2000; McPherson & Schapiro, 1998).
However, as institutions have become more concerned about rankings,
enrollment managers have shifted the focus of institutional financial
aid policy from helping low-income students afford the cost of attendance to attracting high achieving students. Doyle (2010) showed that
institutional expenditures on merit-based aid increased over time relative to institutional financial aid expenditure on need-based aid. Ehrenberg, Zhang, and Levin (2006) found that growth in the number of
640 The Journal of Higher Education
high-achieving students receiving large institutional financial aid awards
was associated with a decline in the share of low-income students who
enrolled. Melguizo and Chung (2012) found that statewide affirmative
action bans undermined the ability for selective public universities to
offer competitive institutional financial aid packages to high-achieving
minority students.
Another trend in institutional financial aid policy is that declines
in state support have compelled public universities to reorient institutional financial aid policy to the goal of maximizing tuition revenue.
A burgeoning enrollment management literature examines how public
universities have utilized institutional financial aid policy to this end
(e.g., Bosshardt, Lichtenstein, Palumbo, & Zaporowski, 2010; Hillman,
2012).
A major strategy for increasing tuition revenue has been growing nonresident student enrollment (DesJardins, 2001). Supply-side research
has found that public universities increase nonresident enrollment (but
not resident enrollment) following declines in the state appropriations
(Jaquette & Curs, 2015). This finding suggests that institutions view
nonresident tuition revenue as a substitute for state appropriations.1
Demand-side research has found that nonresident students are attracted
to academic quality (Adkisson & Peach, 2008; Zhang, 2007), consumption amenities such as collegiate athletics (Mixon & Hsing, 1994), and
geographically desirable locations (Cooke & Boyle, 2011). Compared
to resident students, nonresident students have been relatively insensitive to sticker price (Adkisson & Peach, 2008; Zhang, 2007). However,
nonresident students have been sensitive to institutional financial aid
offers (Curs & Singell, 2010), so many public research universities have
developed institutional financial aid policies that target nonresident students (DesJardins, 2001; Leeds & DesJardins, 2015). Furthermore, nonresident students have had higher mean standardized test scores (Table
1), implying that nonresident enrollment growth also contributes to academic profile goals.
Nonresident students also tend to have higher household incomes
than resident students and are less likely to come from underrepresented racial/ethnic groups (Table 1). These patterns beg the question
of how well institutions are integrating their efforts to enroll nonresidents with their goal of encouraging access for low-income and underrepresented minority students. Scholars of access to higher education
have not examined whether the growing share of nonresident students
in public research universities is associated with a shrinking share of
students from these underrepresented backgrounds. We addressed this
question directly, and by filling this research gap, developed insight into
Tuition Rich, Mission Poor 641
the tension between commitments to access and the pursuits of revenue
and prestige.
Conceptual Framework
Rational Actor Theory and the Iron Triangle of
Enrollment Management
Our conceptual framework begins with rational actor theory. The theory assumes that actors (e.g., people, organizations) attempt to maximize utility amid an unlimited set of goals, constraints to the realization
of those goals (e.g., limited resources, regulations), and idiosyncratic
preferences about which goals are most important (Paulsen & Toutkoushian, 2006). Organizational theorists often criticize the application of
rational actor theory to organizational behavior because sub-units within
organizations may disagree about goals (Scott & Davis, 2006). However, undergraduate enrollment management policy is largely controlled
by central administration (Kraatz et al., 2010), suggesting its appropriateness as a foundation for understanding undergraduate enrollment
priorities.
Moreover, rational actor theory is the basis for the “iron triangle” of
enrollment management (Cheslock & Kroc, 2012; DesJardins & Bell,
2006), which states that universities pursue the broad enrollment goals
of access, academic profile, and revenue (Cheslock & Kroc, 2012).
More specifically, they strive to maximize enrollments from low-income
and racial/ethnic minority students (i.e., access), high-achieving students (i.e., academic profile), and students who contribute to tuition
revenue goals. Because resources are scarce, the image of the iron triangle highlights that pursuit of one enrollment goal often involves tradeoffs with other goals. According to Cheslock and Kroc (2012), “most
enrollment management policies . . . do not advance all three objectives; instead they lead to gains in some areas and declines in others”
(p. 221). These tradeoffs are not accidents. Rather, they are the result of
conscious decisions institutions make in an effort to maximize organizational goals.
Enrollment Management Goals and Behaviors Associated with
Nonresident Students
Enrollment Management Goals. Before presenting hypotheses, we
describe how nonresident enrollment growth fits within the larger
enrollment management goals of academic profile and revenue generation. As discussed in the literature review, prior research suggests that
642 The Journal of Higher Education
public universities have responded to long-term state budget cuts by
deemphasizing the public-good goal of access in favor of the privategood goals of revenue generation and academic prestige (Doyle, 2010;
Jaquette & Curs, 2015). Therefore, we argue that growth in the share of
nonresident students, who pay more tuition and have higher mean test
scores, is one manifestation of institutions prioritizing the revenue and
academic profile corners of the iron triangle. Attracting high-achieving
students requires substantial expenditures on institutional financial aid,
instruction, facilities, etc. (Jacob, McCall, & Stange, 2013; Winston,
1999). As resources are scarce, institutional preferences for academic
profile and tuition revenue may shift financial resources and organizational energy away from the recruitment of underrepresented student
populations.
Enrollment Management Behaviors. Nonresident enrollment growth is
likely to be accompanied by a constellation of enrollment management
behaviors oriented towards the broader goals of revenue generation and/
or academic profile. These behaviors fall into the categories of recruiting, admissions, and budget allocations, including institutional financial
aid and campus amenities. First, rather than recruiting heavily in lowincome high schools with large populations of URM students, institutions often focus recruiting efforts on affluent high schools, both within
and outside of their own state, with large populations of students who
are high-achieving and/or willing to pay high tuition prices (Stevens,
2007). Second, nonresident enrollment growth may be associated with
admissions offices placing more weight on standardized test scores,
increasing the number of slots for nonresident students, or lowering
admissions standards for nonresident applicants (Groen & White, 2004;
Zhang, 2007).
Third, prioritizing academic profile and revenue generation may be
associated with shifts in budget allocations. With respect to institutional financial aid allocations, institutions increase academic profile
by expanding merit-based financial aid (Doyle, 2010). They may also
increase net-tuition revenue by creating institutional financial aid packages that target nonresident students with modest academic achievement
(Curs, 2015; Leeds & DesJardins, 2015) or by cutting need-based aid
when Pell grant availability rises (Turner, 2014). Recognizing affluent
students’ preferences for “consumption amenities” (Jacob et al., 2013),
public universities attempting to recruit an affluent student body may
also increase spending on activities and facilities such as “big time” college athletics and luxury dorms. For example, four public research universities recently built pools with a “lazy river” (Rubin, 2014). These
shifts in resource allocation may negatively affect the access corner
Tuition Rich, Mission Poor 643
of the triangle because they may be associated with a reduction in student-services oriented to the needs of low-income and first-generation
students (e.g., summer bridge programs, tutoring) (Webber, 2012). Having described the enrollment management goals and behaviors associated with nonresident enrollment growth, we now present testable
hypotheses.
Hypotheses
H1: Growth in the proportion of nonresident students is associated with
a decline in the proportion of low-income students. We hypothesize a
negative relationship between the shares of nonresident and low-income
enrollment for two reasons. First, high-income households are better
able to afford nonresident tuition than low-income households. Table 1
shows that nonresident freshmen have higher average household income
and are less likely to be Pell recipients than resident freshmen. Therefore, growth in the share of nonresident students, who tend to be affluent, is likely to be associated with a decline in the share of low-income
students. Second, growth in the share of nonresident students is likely to
be associated with a broader set of enrollment management behaviors,
described previously, aimed at revenue generation and academic profile
(e.g., recruiting in affluent communities, emphasizing standardized test
scores, spending on luxury facilities). These behaviors may be contradictory to the goal of increasing access for low-income students.
A counter-argument is that tuition revenue from nonresident students
could be allocated towards the recruitment of low-income students.
However, recent scholarship does not support this argument, suggesting
instead that public universities have shifted from a focus on need-based
to merit-based aid (Priest & St. John, 2006).
H2: Growth in the proportion of nonresident students is associated with
a decline in the proportion of URM students. Descriptive data in Table 1
shows that nonresident students at public research universities are less
likely to be Black, Latino, and Native American and more likely to be
White and Asian than resident students, suggesting a negative relationship between the share of nonresident students and the share of URM
students. A partial explanation for these descriptive statistics involves
the intersection of income and race. White and Asian households have
significantly higher average household income than other groups and,
thus, may be better able to afford nonresident tuition (U.S. Census
Bureau, 2012). Additionally, growth in the share of nonresident students may also be associated with a broader set of enrollment management behaviors that are oriented towards growing academic profile and
tuition revenue rather than towards increasing access for URM students.
644 The Journal of Higher Education
Moderating Factors
Prestige. We expect that the negative relationships hypothesized in H1
and H2 is stronger at prestigious universities than at nonprestigious universities. Our rationale is as follows: (a) Prestigious public research universities enjoy strong student demand from high-achieving nonresident
applicants (Adkisson & Peach, 2008; Zhang, 2007); (b) Admissions
offices at prestigious universities emphasize SAT/ACT scores (Alon,
2009); and (c) Low-income and URM students on average have lower
scores than high-income, Asian American, and White students (Bastedo
& Jaquette, 2011; Posselt et al., 2012). Therefore, high-achieving nonresident applicants to prestigious universities may crowd out opportunities for low-income and URM applicants.
State Demographics. We also expect state demographic factors to
moderate the hypothesized relationships. With respect to H1 (lowincome students), universities in states with a high poverty rate had a
higher share of low-income students than those in states with low poverty rates (Haycock, Mary, & Engle, 2010). Therefore, we expect that
the negative relationship between the share of nonresident students and
the share of low-income students will be stronger in states with higher
poverty rates because the number of low-income students crowded out
is likely to be higher.
Similarly, with respect to H2 (URM students), universities in states
with large URM populations have a higher proportion of URM students
than institutions in predominantly White states (Haycock et al., 2010).
Therefore, we expect that the negative relationship between the share
of nonresident students and the share of URM students will be stronger in states with relatively large URM populations. By contrast, at universities in predominantly White states, nonresident students are more
likely to be underrepresented students of color than resident students,
and these universities may explicitly recruit nonresident URM students
as a means of increasing minority enrollment.
Statewide Affirmative Action Bans. From a “crowd-out” perspective,
one might expect the relationship between the share of nonresident
students and share of URM students to be weaker at institutions with
affirmative action bans because these institutions have fewer URM students to crowd out in the first place. However, we expect the negative
relationship between the share of nonresident students and the share of
URM students to be stronger at institutions with affirmative bans. Our
rationale is that affirmative action is a tool that mitigates the negative
consequences to access of pursuing academic profile. It enables institutions to pursue academic profile while simultaneously increasing their
Tuition Rich, Mission Poor 645
enrollment of URM students. Affirmative action bans eliminate this
tool, compelling admissions decision-makers to prioritize applicants
who exemplify conventional academic achievement (e.g., high SAT
scores).
Universities in states with affirmative action bans may feel pressure
to reject URM applicants with lower standardized test scores when the
volume of applications from high-scoring nonresident students grows.
Furthermore, because prestigious public universities already struggle to
enroll URM students, and because nonresident applicants to these institutions tend to have particularly high academic achievement, we expect
that the negative relationship between the share of nonresident students
and the share of URM students will be particularly strong at prestigious
public research universities in states with affirmative action bans.
Methodology, Data, and Analysis Sample
Empirical Strategy
We tested the previously described hypotheses using the following
linear regression model:
Yit = βNonresidentit + Xstγ + δt + δi + εit
(1)
Where, Yit measures enrollment from underrepresented students as a
percentage of total enrollment at institution i in time t. Nonresidentit is
a measure of the percentage of nonresident to total enrollment and β is
the associated regression coefficient, Xst is a matrix of state-time-varying covariates, δt represents a year-specific fixed effect, δi represents an
institution-specific fixed effect, and εit represents an idiosyncratic institution- and time-variant error term. In all models, standard errors are
clustered at the state level.
An important modeling issue is the feasibility and the desirability
of isolating the causal relationship between Nonresidentit and Yit. If we
consider causal effects to be what happens under experimental conditions (Cameron & Trivedi, 2005), then estimating the true causal effect
of Nonresidentit on Yit would require randomly assigning public research
universities to different proportions of nonresident students. Such an
experiment is infeasible. Additionally, the causal interpretation of the
coefficient on Nonresidentit would be the effect of pursuing nonresident enrollment growth, holding all other institutional enrollment management behaviors constant. Unfortunately, time-varying measures of
646 The Journal of Higher Education
key enrollment management behaviors are unavailable (e.g., focusing
recruitment efforts on affluent high schools).
More importantly, our conceptual framework suggests that specific
enrollment management actions are not adopted in isolation, but rather
as bundles of actions collectively aimed at achieving broader enrollment
goals. Growth in the share of nonresident students is likely to be accompanied by several enrollment management behaviors (e.g., expenditure
on merit, construction of luxury dorms) oriented towards academic
profile and/or revenue growth, implying that the idea of increasing
recruitment of nonresident students while holding other enrollment
management behaviors constant is an unrealistic model of university
behavior.
Therefore, our estimation strategy examined the relationship between
share of nonresident students and share of underrepresented students
while allowing enrollment management behaviors to vary simultaneously. We controlled for state-level factors that affect enrollment of
underrepresented populations (e.g., state economic and demographic
characteristics) but did not control for institution-level factors. All models estimated institution-level fixed effects, which eliminate all institution-varying time-invariant factors. Thus, estimates of the relationship
between Nonresidentit on Yit were based solely on variation over time
within institutions rather than cross-sectional variation between institutions. Models included indicators for academic year to control for
national time trends affecting Yit, such as changes in the overall generosity of federal financial aid programs. Therefore, β̂ should be interpreted
as the relationship between within-university changes in the share of
nonresident enrollment, and within-university changes in the share of
underrepresented populations, after controlling for national time trends,
and time-varying state-level factors. As a sensitivity analysis, we
attempted to approach an estimate of the causal effect of Nonresidentit by controlling for time-varying institution-level covariates that affect
the enrollment of underrepresented students and have a relationship
with Nonresidentit.
Data, Analysis Period and Sample
We created an institution-level panel analysis dataset to test our
hypotheses, incorporating institution-level data from the Integrated
Postsecondary Education Data System (IPEDS) and state-level data
from several sources. Appendix A provides variable definitions and data
sources. The analysis period consisted of the 11-year period from 2003
to 2013, with each year referring to the ending year of an academic year
(e.g., 2003 refers to the 2002–03 academic year). The starting point of
Tuition Rich, Mission Poor 647
the analysis period was limited by the measures of institutional selectivity used in sensitivity analyses and the end point was limited by the
most recent available data from IPEDS.
The analysis sample began with the 101 undergraduate-serving,
public universities in the 50 U.S. states defined as research-extensive
by the 2000 Carnegie Classification. Following Rizzo and Ehrenberg
(2004), we added one institution to the analysis sample for each of the
four states that did not have a research-extensive institution in order to
best represent the flagship public university mission. Specifically, based
upon indicators of selectivity, we added the University of Alaska-Fairbanks, Montana State University, University of North Dakota, and the
University of South Dakota.
Variables
With the exception of variables measured as a percentage (e.g., percentage of low-income freshmen) or as indicator variables (e.g. affirmative action ban in place), all variables have been logged, reducing
estimator sensitivity due to large variations in institutional size.
Dependent Variables. We analyzed two dependent variables: lowincome full-time freshman enrollment as a percentage of total full-time
freshman enrollment; and URM full-time freshman enrollment as a
percentage of total full-time freshman enrollment. We focused on fulltime freshman enrollment, rather than total undergraduate enrollment
because IPEDS measured the independent variable of interest (i.e. percentage nonresident enrollment) and one of the dependent variables (i.e.
percentage low-income students) only for full-time freshmen.
Measure of Low-Income Enrollment. The best available institutionlevel indicator of the enrollment of low-income students was the proportion of Pell grant recipients. Pell grant recipients are a proxy for
low-income students because Pell is a means-tested program. Over 75%
of all dependent Pell recipients had household incomes of less than
$40,000 in 2011 (U.S. Department of Education, 2012). Because the
IPEDS Student Financial Aid (SFA) survey component did not begin
collecting data on the number of Pell recipients until 2008, we defined
the percentage of low-income students as the percentage of full-time
freshmen receiving any federal student grant aid (excluding the G.I
Bill). In 2012, the correlation between the percentage of Pell recipients
and the percentage of federal grant recipients was 0.99. Therefore, our
measure of the percentage of students receiving federal grant aid is a
strong proxy for the percentage of full-time freshmen receiving Pell
grants.
648 The Journal of Higher Education
Measure of URM Enrollment. Using the IPEDS Fall Enrollment survey, we defined the proportion of URM freshmen as the total number
of fulltime freshmen who identified as Black, Hispanic, Native American or Alaskan Native, or multi-race, divided by the total number of
full-time freshmen. Given diversity in postsecondary enrollment among
Asian students, we would prefer to categorize “Native Hawaiian or
other Pacific Islander” as URM. However, IPEDS did not begin disaggregating this group from “Asian” until 2009, when new race/ethnicity
reporting standards were phased in. These new race/ethnicity standards
also created a new multi-race racial category. Our decision to categorize
multi-race students as underrepresented introduces measurement error
in that in that some students who would have self-reported as White or
Asian in previous years may have reported as multi-race once this category was created. To control for changing definitions, we included an
indicator variable for whether institutions are using the new or old race/
ethnicity categories.
Measure of Nonresident Enrollment. The independent variable of interest was defined as the percentage of full-time freshmen paying nonresident tuition, based on data from the IPEDS Student Financial Aid
survey component. Observations that reported more than zero full-time
freshmen in which tuition residency status was unknown were dropped
from the analysis sample because variation in the share of nonresident
enrollment for these observations was substantially affected by the number of students paying unknown tuition rate.
Control Variables. In addition to fixed effects for institutions and for
years, all models also included time-varying state-level demographic
and economic factors that may affect enrollment by low-income, URM,
and nonresident students. The timing of these covariates was contemporaneous to reflect the state-level conditions facing potential freshmen at
fall enrollment.
We included several state-level indicators of economic health because
student demand may be affected by state economic conditions. Specifically, we included annual unadjusted unemployment rate, per capita
income, total state tax revenues, poverty rate, and a housing price index.
Prior research shows that both state appropriations to higher education
institutions and state financial aid positively affects resident enrollment
and reduces out-migration (e.g., Toutkoushian & Hillman, 2012; Zhang
& Ness, 2010). Therefore, we controlled for annual state appropriations to higher education institutions and state expenditure on need- and
merit-based financial aid. Finally, because state politics affect access
to public universities (Dar, 2012), we include measures of the political
Tuition Rich, Mission Poor 649
party control of the state legislature, governor’s office, and whether a
single party controlled both the legislature and governor’s office.
State-level demographic trends affect low-income and minority student enrollment at public universities and may be correlated with the
share of nonresident enrollment. Thus, we controlled for state population by race/ethnicity and age groupings. Specifically, we included both
the total population and the percentage of the population from URM
backgrounds (defined similarly to the URM dependent variable) for
each of the following age ranges: less than 5-years-old; 5–17; 18–24;
25–44; 45–64; and 65 and over. Second, we included a time-varying
indicator of state affirmative action bans, which likely affected minority
enrollments at selective institutions (Backes, 2012).
Limitations
Before presenting results, we acknowledge limitations of our empirical approach. First, our models estimated correlational relationships
rather than causal relationships. Second, due to limitations of IPEDS
data, our independent and dependent variables were based on full-time
freshman enrollment. Our dependent variables do not capture transfer
students who, in comparison to fulltime freshmen, are more likely to be
low-income and to identify with racial/ethnic groups we categorized as
URM. Thus, our findings only generalize to changes in full-time freshman enrollment. Finally, because our categorizations of race and ethnicity were subject to the available data, Native Hawaiian and Pacific
Islander students were not categorized as underrepresented minority
students.
Results
Descriptive Statistics
The potential sample for our analysis was 1,155 total observations,
based upon 105 public research extensive universities over a period of
11 years. We removed 121 observations where a nonzero percentage of
the students within an institution had unknown residency status. Therefore, the final analysis sample included 105 public four-year research
universities and 1,033 institution-year observations.
Table 2 presents descriptive statistics of the dependent variables and
the independent variable of interest for the analytic sample. On average,
over the entire analysis period, federal grant recipients (i.e., our proxy
for low-income students) represented 25.2% of total freshman enrollment, URM enrollment represented 18.3% of total freshman enrollment,
Panel C: US News tiers 3 and 4
Percentage of federal grant recipients
Percentage of URM enrollment
Percentage of nonresident enrollment
Panel B: US News tiers 1 and 2
Percentage of federal grant recipients
Percentage of URM enrollment
Percentage of nonresident enrollment
Panel A: Full sample
Percentage of federal grant recipients
Percentage of URM enrollment
Percentage of nonresident enrollment
53
52
105
Number of
Institutions
TABLE 2
Descriptive Statistics for the Analytic Sample, 2003−2013
511
522
1,033
Number of
Observations
29.47
22.14
18.96
20.99
14.55
24.10
25.18
18.30
21.56
Mean
10.54
15.49
14.10
8.44
7.63
15.59
10.43
12.74
15.09
Standard
Deviation
5.42
1.85
1.23
6.64
2.56
1.78
5.42
1.85
1.23
Minimum
61.89
84.01
65.98
59.07
48.48
77.13
61.89
84.01
77.13
Maximum
Tuition Rich, Mission Poor 651
and nonresident enrollment represented 21.6% of total freshman enrollment. Highly ranked institutions enrolled larger shares of nonresident
students and lower shares of federal grant recipients and URM students.
Figures 1 through 3 present changes across time for the dependent
and independent variables for the full sample and subsamples by U.S.
News and World Report tiers. The proportion of federal grant recipients
increased dramatically in 2009, most likely due to expansion of the Pell
grant program by the Obama administration. The percentage of URM
students increased over time, consistent with overall population trends
in the United States, and increased following changes in IPEDS race/
ethnicity categories.
On average, nonresident freshman enrollment increased from 20.7%
of total freshman enrollment in 2003 to 24.7% in 2013; however, the
magnitude of this increase differed substantially across institutions. Figure 4 shows the distribution in the percentage point change in nonresident enrollment from 2003 to 2013. For example, IPEDS reports that
the percentage of nonresident freshmen at the University of California
0
Percentage of Freshman Enrollment
10
20
30
40
Full Sample
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
academic year, e.g. 1995=1994-95
Federal grant recipients
Nonresident
URM
F igure 1. Percentages of Federal Grant Recipients, Underrepresented Minority
Students, Nonresident Enrollment across Time: Full Sample
0
Percentage of Freshman Enrollment
10
20
30
40
US News Tiers 1 & 2
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
academic year, e.g. 1995=1994-95
Federal grant recipients
Nonresident
URM
F igure 2. Percentages of Federal Grant Recipients, Underrepresented Minority Students, Nonresident Enrollment across Time: USNWR Tiers 1 & 2
0
Percentage of Freshman Enrollment
10
20
30
40
US News Tiers 3 & 4
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
academic year, e.g. 1995=1994-95
Federal grant recipients
Nonresident
URM
F igure 3. Percentages of Federal Grant Recipients, Underrepresented Minority Students, Nonresident Enrollment across Time: USNWR Tiers 3 & 4
0
.02
Density
.04
.06
Tuition Rich, Mission Poor 653
-10
0
10
20
Change in the percentage of nonresident freshman enrollment
30
F igure 4. Histogram of Percentage Point Changes in Nonresident Enrollment,
2003–2013
Los Angeles was 7.7% in 2003 and 28.5% in 2013, a 20.8 percentage
point increase. After rank ordering institutions by this change, the 25th
and 75th percentile institutions experienced percentage point changes in
nonresident enrollment of 1.0 and 10.2, respectively.
Hypothesis 1: Relationship Between Nonresident and
Low-Income Enrollment
Table 3 presents the estimated relationship between the percentage
of nonresident freshmen and the percentage of federal grant recipients.
Results are presented for the full sample of public research extensive
universities, the subsample of 52 institutions categorized as Tier 1 and
2 by the 2000 U.S. News and World Report (USNWR) rankings, and
the subsample of 53 institutions categorized as Tier 3 and 4 by the 2000
USNWR rankings.
Consistent with H1, within-institution increases in the percentage
of nonresident enrollment were negatively related to the percentage of
students who received federal grants. Each percentage point increase in
654 The Journal of Higher Education
TABLE 3
The Relationship between the Percentages of Nonresident and Low-Income Enrollment
Y: The Percentage of Federal Grant Recipients
Full sample
USNWR 1&2
USNWR 3&4
Percentage of nonresident students
−0.168**
(0.0722)
−0.271**
(0.101)
−0.00371
(0.0501)
State-level control variables
Institution-level control variables
Yes
No
Yes
No
Yes
No
Observations
R-squared
1,033
0.537
522
0.571
511
0.606
Panel A: Base model
Panel B: State population in poverty as a moderator
Percentage of nonresident students
0.403***
(0.125)
0.0710
(0.149)
0.589***
(0.121)
Percentage of nonresident students *
Percentage of state population in poverty
−0.0421***
(0.00849)
−0.0240**
(0.00938)
−0.0468***
(0.00881)
State-level control variables
Institution-level control variables
Yes
No
Yes
No
Yes
No
Observations
R-squared
Number of Institutions
1,033
0.571
105
522
0.586
52
511
0.630
53
Note. Institution-clustered robust standard errors in parentheses.
*p < 0.10. **p < 0.05. ***p < 0.01.
nonresident freshman enrollment (e.g. nonresident freshmen going from
20% of the freshman class to 21% of the freshman class) was associated with a 0.168 percentage point decrease in the percentage of federal
grant recipients, after controlling for state-level covariates.
Although statistically significant, the practical significance of this
relationship was modest for a typical university in our sample. To
place this relationship into context, the mean public research university increased their percentage of nonresident enrollment from 20.4%
to 24.7% from 2003 to 2013. The model predicts that a 4.3 percentage
point increase in the percentage of nonresident enrollment was associated with a 0.7 (i.e. 4.3 * 0.168) percentage point decrease in the percentage of low-income students. However, the practical significance of
this relationship was not trivial for institutions that experienced large
increases in the percentage of nonresident freshmen. For example, the
institution at the 75th percentile for nonresident enrollment growth from
Tuition Rich, Mission Poor 655
2003–2013 experienced a 10.2 percentage point increase in nonresident
enrollment. Assuming a linear relationship, our models indicate that this
change would be associated with a 1.7 (i.e. 10.2 * 0.168) percentage
point decrease in the percentage of low-income students. For such institutions, low-income students decreased from 25% of the freshman class
to 23.3% of the freshman class.
As predicted by the conceptual framework, the magnitude of the
negative relationship was stronger at the more prestigious universities.
For the sample of highly ranked institutions (i.e., USNWR tier 1 and 2)
the coefficient estimate for the percentage of nonresident students was
–0.271, while the coefficient estimate for tier 3 and 4 institutions was
–0.004 and was not significantly different than 0.
Statewide Poverty Rate as a Moderating Factor. We hypothesized that
the negative relationship between nonresident enrollment and lowincome enrollment was stronger in states with higher poverty rates,
since the potential for crowd-out is greater in states with more lowincome households. We tested this hypothesis by including in the model
an interaction between the percentage of nonresident enrollment and the
state-level poverty rate. Table 3, panel B presents the results.
The negative association between the percentages of nonresident
and low-income student enrollment increased as state-level poverty
rate increased. For example, at the minimum state-level poverty rate of
5.4%, results suggest that a one-percentage point increase in nonresident enrollment was associated with a 0.18 percentage point increase in
federal grant recipients. In contrast, at the maximum state-level poverty
rate of 23.1%, the same increase in nonresident enrollment was associated with a 0.57 percentage point decrease in federal grant recipients.
These estimates signified a stronger moderating effect of state poverty
at less prestigious public research universities, as indicated by a larger
spread of estimated relationships across low and high poverty states.
H2: Relationship between Nonresident and Underrepresented
Minority Student Enrollment
Table 4 presents model estimates for the relationship of the percentage of nonresident freshmen to the percentage of underrepresented
minority freshmen. Consistent with H2, we found each percentage point
increase in the percentage of nonresident enrollments was associated
with a 0.082 percentage point decrease in URM enrollment. This relationship, which was significant only at the 0.10 level, was weaker than
the relationship with low-income enrollment. Our model indicated that
the mean 2003–2013 increase in the percentage of nonresident enrollment (4.3 percentage points) was associated with a 0.35 (i.e., 4.3 *
TABLE 4
The Relationship between the Percentages of Nonresident and Underrepresented Minority Enrollment
Y: The percentage of URM students
Full Sample
USNWR 1&2
USNWR 3&4
−0.0815*
(0.0431)
−0.115*
(0.0639)
−0.0181
(0.0464)
Panel A: Base model
Percentage of nonresident students
State-level control variables
Institution-level control variables
Yes
No
Yes
No
Yes
No
Observations
R-squared
1,033
0.625
522
0.750
511
0.613
Percentage of nonresident students
0.0805
(0.0650)
0.0167
(0.0862)
0.121
(0.0888)
Percentage of nonresident students*
Percentage of 18−24 URM population
−0.00585***
(0.00216)
−0.00383
(0.00232)
−0.00671*
(0.00385)
Panel B: State URM population as a moderator
State-level control variables
Institution-level control variables
Yes
No
Yes
No
Yes
No
Observations
R-squared
Number of Institutions
1,033
0.634
105
522
0.756
52
511
0.618
53
Percentage of nonresident students
−0.0498
(0.0386)
−0.0516
(0.0462)
−0.0181
(0.0451)
Percentage of nonresident students *
State affirmative action ban
−0.181***
(0.0675)
−0.231***
(0.0787)
−0.000184
(0.218)
Panel C: State affirmative action bans as a moderator
State-level control variables
Institution-level control variables
Yes
No
Yes
No
Yes
No
Observations
R-squared
Number of Institutions
1,033
0.633
105
522
0.764
52
511
0.613
53
Note. Institution-clustered robust standard errors in parentheses.
*p < 0.10. **p < 0.05. ***p < 0.01.
&4
1*
5)
84
Tuition Rich, Mission Poor 657
0.082) percentage point decrease in the percentage of URM enrollment.
The practical significance of these results is modest even for institutions
experiencing large growth in the percentage of nonresident enrollment.
Even for the institution at the 75th percentile for nonresident enrollment
growth, our models estimated only a 0.84 percentage point decline in
URM enrollment. As with low-income students, the magnitude of the
negative relationship between nonresident enrollment and URM enrollment was stronger at highly ranked institutions than lower ranked ones.
State Demographics as a Moderating Factor. Our conceptual framework predicted that the negative relationship between nonresident students and URM students would be stronger in states with relatively
large underrepresented minority populations. We tested this relationship by modeling an interaction between the percentage of nonresident
enrollment and the percentage of the state-level college-aged population
(18–24 years old) who identify with one of the racial/ethnic categories
that are underrepresented. Table 4, panel B presents the results. As the
percent of young adults in a state who are URM increased, the negative relationship between the percentage of nonresident enrollment and
the percentage of URM enrollment became stronger. For example, at
the minimum value of state-level college-aged URM population (3.7%),
a one percentage point increase in nonresident enrollment was associated with a 0.06 percentage point increase in the percentage of URM
enrollment. In contrast, at the maximum value of state-level collegeaged URM population (66.8%), the same increase in nonresident enrollment would be associated with a 0.31 percentage point decrease in the
percentage of URM enrollment.
Statewide Affirmative Action Bans as a Moderating Factor. Our conceptual framework also suggested that nonresident and underrepresented
student enrollment would have a stronger negative relationship at universities in states with statewide affirmative action bans. We tested this
relationship by modeling an interaction between the percentage of nonresident enrollment and a time-varying indicator of whether the institution was located in a state that banned affirmative action.
Model results, presented in panel C of Table 4, show the negative
relationship between the proportion of nonresident students and the proportion of URM students was stronger in states with affirmative action
bans. To put the results in context, the average public research university in a state without an affirmative action ban increased their percentage of nonresident enrollment from 23.0% to 27.1% between 2003 and
2013, which was associated with a 0.20 (= 4.1*0.0498) percentage point
decrease in the share of underrepresented minorities enrolled. By contrast, the share of nonresident enrollment increased from 9.7% to 16.5%
658 The Journal of Higher Education
between 2003 and 2013 in states with an affirmative action ban, and
this was associated with a 1.57 (= 6.8*(0.0498+0.181)) percentage point
decrease in URM enrollment.
Finally, consistent with our conceptual framework, the negative relationship between shares of nonresident and URM students was particularly strong at prestigious public research universities in states with
affirmative action bans. Among Tier 1 and 2 institutions, a one percentage point increase in nonresident enrollment was associated with
a 0.28 percentage point decrease in states with affirmative action bans
compared to a 0.05 percentage point decrease in states without affirmative action bans. For prestigious institutions in affirmative action ban
states, nonresident enrollment rose from 11.6% to 19.7% across the
sample period, and was associated with a 2.3 percentage point decrease
in the share of Black, Latino, Native American/ Alaskan Native, Native
Hawaiian/Pacific Islander, and multiracial students (e.g., the percentage
fulltime freshmen who are URM decreasing from 20% to 17.7%).
Sensitivity Analyses
We conducted several robustness checks to understand how sensitive
our results were to alternative model specifications and imperfections in
the data. Complete results are omitted for space considerations but are
available upon request.
Institution-Level Covariates. Our main models excluded institutionlevel variables because we wanted to allow institutional enrollment
management behaviors to vary. As a sensitivity analysis, we attempted
to remove potential confounding factors in the estimation of β by controlling for time-varying institution-level factors that plausibly affect Yit
and have a relationship with Nonresidentit.
The sensitivity analyses controlled for in-state and nonresident tuition
price, average institutional, state, and federal grant awards, undergraduate and graduate FTE enrollment, three measures of admissions
selectivity (i.e., percent admitted and the 25th and 75th percentile SAT/
ACT scores of enrolled freshmen), and seven measures of expenditure
per student by expenditure category (i.e., instruction, student services,
academic support, institutional support, auxiliaries, research, public
service). All institution-level controls for institutional desirability and
institutional size were lagged one year for two reasons. First, freshmen matriculation decisions are more likely to be affected by prior-year
quality measures than current-year measures. Second, many quality
measures incorporate student enrollments; thus, current-year measures
would imply a more mechanical relationship between the dependent and
independent variables than is present in reality.
Tuition Rich, Mission Poor 659
Table 5 presents the sensitivity analyses for both dependent variables.
The sample size was reduced compared to the previous models due
to missing composite SAT scores for a small number of observations.
Missing observations for covariates were imputed using the withininstitution average of nonmissing observations from the previous year
and the subsequent year (1.9% of observations in the analysis sample).
Estimated coefficients were robust to inclusion of institution-level
covariates. In general, the inclusion of institution-level covariates was
associated with a slight increase in the magnitude of the coefficients and
a decrease in standard errors due to increased precision.
Changing Race/Ethnicity Definitions. IPEDS phased-in new race/ethnicity categories starting in 2009. We estimated each of the models with
only the observations that used the old race/ethnicity definitions. For
the model examining H2, the point estimate for nonresident enrollment
was smaller using this restricted sample. However, the 95% confidence
intervals overlapped, indicating that the estimates were robust to the
change in how race/ethnicity was categorized.
TABLE 5
Sensitivity of Empirical Models to the Inclusion of Institution-Level Control Variables
Full Sample
USNWR 1&2
USNWR 3&4
−0.282***
(0.100)
−0.0336
(0.0537)
Panel A: Y: The percentage of federal grant recipients
Percentage of nonresident students
−0.185***
(0.0704)
State-level control variables
Institution-level control variables
Yes
Yes
Yes
Yes
Yes
Yes
Observations
R-squared
Number of Institutions
1,018
0.575
104
522
0.606
52
496
0.661
52
−0.0844**
(0.0323)
−0.135**
(0.0538)
−0.0425
(0.0352)
Panel B: Y: The percentage of URM students
Percentage of nonresident students
State-level control variables
Institution-level control variables
Yes
Yes
Yes
Yes
Yes
Yes
Observations
R-squared
Number of Institutions
1,018
0.693
104
522
0.786
52
496
0.715
52
Note. Institution-clustered robust standard errors in parentheses.
*p < 0.10. **p < 0.05. ***p < 0.01.
660 The Journal of Higher Education
Unknown Residency. Finally, IPEDS reported a nonzero percentage
of students with tuition residency status (e.g., resident vs. nonresident)
unknown for 121 institution-year observations. In all models presented
previously, we dropped observations with a nonzero percentage of
unknown residency due to the likelihood of systematic measurement
error bias in our estimated coefficients. As tests of robustness, we ran
two alternative specifications: one including all observations and including a control variable for the percentage of students whose residency
was unknown, and a similar model which only included observations
in which less than 10% of the students had unknown residency. Results
were robust across all dependent variables, with alternative estimates
well within 95% confidence intervals.
Discussion
Public research universities concerned about increasing tuition revenue and strengthening their overall academic profile have a clear incentive to recruit nonresident students. Our conceptual framework argued
that growth in the share of nonresident students may have a direct negative relationship with the share of underrepresented students, and may
be associated with other enrollment management behaviors that do not
prioritize access for underrepresented students. Therefore, our modeling goal was to estimate descriptive relationships rather than the causal
effect of an increase in the share of nonresident students.
We hypothesized that growth in the share of nonresident students was
associated with declines in the share of low-income students and we
expected that this negative relationship would be stronger at prestigious
universities and universities in states with high poverty rates. Model
results supported these hypotheses. If we envision the student body as a
pie chart, with various student populations representing slices, the slice
of low-income students narrows as the slice of out-of-state students
grows, with a greater shift at highly ranked institutions and at institutions in high-poverty states. We also hypothesized that growth in the
share of nonresident students was associated with declines in the share
of URM students, and we expected that this negative relationship would
be stronger at prestigious universities, universities in states with large
URM populations, and universities in states with affirmative action
bans. These hypotheses were supported as well, though model results
for URM students were weaker than model results for low-income
students.
The relationships identified through our analyses were somewhat
weaker from a practical significance perspective than from a statistical
Tuition Rich, Mission Poor 661
standpoint. However, at minimum, modest growth in the share of nonresident students was associated with stagnation in the share of lowincome and underrepresented racial/ethnic minority students—two
groups that most institutions claim they strive to recruit. Furthermore,
many institutions experienced not modest, but substantial, growth in the
share of nonresident students. For these institutions, our models suggest that nonresident enrollment growth was associated with practically
significant declines in the share of low-income students. The negative
relationship between nonresident enrollment and representation from
underrepresented students of color was strongest at prestigious universities and at universities in states with affirmative action bans, the very
institutions struggling most with racial inequality.
Finally, the significant interaction effects on state-level population and poverty measures suggest that, for institutions in states with
high poverty rates and large URM populations, nonresident enrollment
growth is associated with a student body composition that becomes
increasingly estranged from the changing racial and socioeconomic
demographics of the state. Interestingly, simple descriptive statistics
from Figure 1 show that the share of Pell recipients and URM students
increased over time. It is likely that these enrollment trends are largely
due to broader demographic and economic trends and due to increased
Pell Generosity under the Obama Administration. Our models suggest
that the growth in the share of low-income and URM students evident
in Figure 1 would be stronger if public research universities restrained
from growing the share of nonresident students.
These results have implications for research about student learning in,
access to, and the character of public research universities. We discuss
each of these in turn, along with areas of future research. First, class
isolation (Oldfield, 2007) and racial isolation (e.g., Fries-Britt & Turner,
2001; Smith, Allen, & Danley, 2007) impede college student development, and are associated with negative perceptions of campus climate
(Jayakumar, 2008). Therefore, while prioritizing nonresident enrollment is potentially rational from the standpoints of prestige and revenue, campus leaders should acknowledge that it may compromise the
goal of creating healthy learning environments by inhibiting the educational benefits of diversity on campus and in the classroom. To better
understand the consequences of nonresident enrollment on the everyday lives and learning of students from different backgrounds, future
research could examine the experiences of low-income and URM students at public flagship institutions with low and high shares of nonresident students.
662 The Journal of Higher Education
Second, our study can inform future research by showing that nonresident enrollment growth may have negative consequences for access to
selective public universities. Most analyses of access to public research
universities have not considered the effect of nonresident enrollment
growth. Our findings suggest that future research on access should utilize nonresident enrollment as an explanatory variable. For example,
whereas this study focused on the proportion of underrepresented students, state policymakers may be more interested in knowing whether
growth in the number of nonresident students affect the number of
low-income, underrepresented minority, and resident students enrolled.
Future research should also analyze change over time in the amount of
institutional financial aid public universities allocate to resident versus
nonresident students, and should update Groen and White’s (2004) analyses of admissions preferences for resident versus nonresident students.
Finally, our study contributes to scholarship on the changing character of public research universities. The privatization literature argues
that public universities respond to state cuts by becoming more like private universities, which provide education as a private good to paying
customers (e.g., Morphew & Eckel, 2009; Priest & St. John, 2006). In
a related vein, the academic capitalism literature shows that neo-liberal
policy regimes encourage market-like behaviors and a profound shift in
organizational mission from a focus on contributing to society to a focus
on organizational self-interest (Slaughter & Leslie, 1997; Slaughter &
Rhoades, 2004). Both literatures assert that entrepreneurial behaviors
by public universities have negative consequences for access. However,
few studies have assessed these claims empirically. Our findings—that
increasing the share of nonresident students is associated with racial and
socioeconomic isolation—can serve as a mirror that enables campus
leaders and enrollment managers to reflect on the consequences of their
actions and priorities.
Our study also shows that analyses of enrollment management can
yield important insights about the changing values of public universities. The intellectual foundation of enrollment management is standard economic theory (DesJardins & Bell, 2006), which examines the
resource allocation decisions of actors who have limited resources but
unlimited wants. According to the iron triangle of enrollment management, institutions want to pursue access, academic profile, and revenue simultaneously, but resource scarcity forces them to concentrate
resources on the enrollment goals deemed most important. Thus, by
analyzing what kinds of students institutions are pursuing, scholars
can show how the values of public universities are changing. The shift
towards nonresident students suggests that public research universities
Tuition Rich, Mission Poor 663
have increased the value they place on students who pay high tuition
and have high test scores. This shift is indicative of a deeper change in
organizational values, away from the public good emphasis on access
and towards the self-interested emphases of academic profile and revenue generation. As scholars, campus leaders, or policymakers, we must
ask ourselves, whether these are the values we want our flagship public
institutions to promote?
Conclusion
The changing student composition of research universities is more
than an outcome of neutral organizational efforts aimed at financial survival or of striving for academic excellence, because institutions’ apparently “rational” behaviors have disparate impact across race and class.
Public research universities have increasingly relied upon nonresident
students in recent decades, and in many cases have broadened their
notion of the “public” from state taxpayers and their families to national
and international stakeholders. Yet in their eagerness today to connect
with the public beyond the state’s borders, they may be losing sight of
the public at their doorstep. Reversing this trend will require collective commitment to the democratic focus of public higher education,
including renewed financial support by state governments, rejection of
the narrow metrics for student and institutional quality that U.S. News
and World Report promotes, and a redoubling of attention by public university leaders to the needs of their states and the communities within
them.
Notes
We thank John Cheslock and three anonymous reviewers from the American Educational Research Journal for excellent feedback that identified flaws in an earlier version
of this manuscript. We thank two anonymous reviewers from The Journal of Higher
Education for thoughtful suggestions that strengthened this manuscript. We also thank
University of Arizona PhD student, Edna Parra for creating descriptive statistics from
the National Postsecondary Student Aid Study. Any remaining errors are our own.
1 For example, Colorado’s Taxpayer Bill of Rights (TABOR) decreased state appropriations and limited resident tuition increases (Hillman, Tandberg, & Gross, 2014), creating a financial incentive for public universities to increase nonresident enrollment.
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/01/04/71/10471.pdf
Total state revenue from all forms of taxes
Percent of population in poverty
Total state support for higher education (includes Federal Stimulus funds during 2008−2012)
Total state expenditure on non-needs based financial aid
Total state expenditure on needs based financial aid
0/1 indicator of state affirmative action ban or court case that struck down
affirmative action for at least one institution in the state; equals 1 in first academic year where freshman enrollments affected by the ban
0/1 whether the state governor is a Democrat
Total state tax revenues a
Poverty rate
State appropriations to higher education a
State expenditure on needs-based aid
Affirmative Action ban
Democrat state governor
State expenditure on merit-based aid
Unemployment rate
Fannie Mae Quarterly Housing Pricing Index, not seasonally adjusted, 1st
quarter values
Annual state level unadjusted unemployment rates
Per capita personal income
Number of freshman paying nonresident tuition divided by total number of
full-time freshman
Number of freshman receiving federal grants (of any kind) divided by total
number of full-time freshman
URM freshman divided by total freshman. Race categories counted as URM:
Hispanic; Black; Native American and Alaskan Native; and multi-race; “race
unknown” distributed across other race categories.
Data Definition and Notes
Housing price index
State-level covariates
Per capita income a
Independent variable
Percent of freshman paying nonresident tuition
Percent of underrepresented minority (URM) freshman
Dependent variables
Percent of freshman receiving federal grants
Variable
APPENDIX A
Variables, Variable Definitions, and Data Sources
U. of Kentucky Center for Poverty
Research
National Association of State Student
Grant and Aid Programs
National Association of State Student
Grant and Aid Programs
Garces (2014), Hinrichs (2012)
Southern Regional Education Board
U.S. Census
U.S. Census – State Government Finances
Bureau Labor Statistics
Fannie Mae
Bureau of Economic Analysis
IPEDS Student Financial Aid
IPEDS Fall Enrollment
IPEDS Student Financial Aid
Data Sources
Graduate FTE enrollments
Undergraduate FTE enrollments
Average Federal grant award
Average state grant award
Average institutional grant award
Nonresident tuition price a
Resident tuition price a
Institution-level covariates
New IPEDS race categories
State URM population rate:
State population by age
Average Federal grant received for full-time freshman receiving Federal grant
aid
Undergraduate full-time equivalent enrollments based on instructional activity
(credit hours and contact hours). Undergraduate credit hours and contact hours
converted to FTE enrollments based on standard NCES formula
Graduate full-time equivalent enrollments based on instructional activity
(credit hours and contact hours). Graduate credit hours converted to FTE
enrollments based on standard NCES formula
0/1 indicator of whether institution used new race categories (phased in beginning in 2008−09 and required by all institutions in 2010−11)
Required tuition and fees for full-time full-year student paying resident tuition
rate
Required tuition and fees for full-time full-year student paying nonresident
tuition rate
Average institutional grant received for full-time freshman receiving institutional grant aid
Average state grant received for full-time freshman receiving state grant aid
0/1 whether the Democratic party controls both the legislature and the governor’s office
0/1 whether the Republican party controls both the legislature and the governor’s office
Total state-level population for each of the following age categories (separate
variables): less than 5-years-old; 5−17; 18−24; 25−44; 45−64; and 65 and over
Age categories: 12−17; 18−24; and 25−44.
State-level percentage of population from URM backgrounds by the following
age groups (separate variables: less than 5-years-old; 5−17; 18−24; 25−44;
45−64; and 65 and over. URM populations defined by the following race/
ethnicity categories: Black non-Hispanic; Asian Pacific Islander and Native
American; and Hispanic of any race.
Democratic party control
Republican party control
Fraction of a state’s legislature which is democratic
Data Definition and Notes
Democrat representation in legislature
Variable
APPENDIX A (continued)
Variables, Variable Definitions, and Data Sources
(continued)
IPEDS 12-month Enrollments
IPEDS 12-month Enrollments
IPEDS Student Financial Aid
IPEDS Student Financial Aid
IPEDS Student Financial Aid
IPEDS Institutional Characteristics
IPEDS Institutional Characteristics
IPEDS Fall Enrollments
U. of Kentucky Center for Poverty
Research
U. of Kentucky Center for Poverty
Research
U. of Kentucky Center for Poverty
Research
U.S. Census Bureau
Data Sources
IPEDS Finance
IPEDS Finance
Total institutional expenditure on research per credit hour.
Total institutional expenditure on public service per credit hour.
Total institutional expenditure on academic support per credit hour.
Research expenditure per credit hour a,b
Public service expenditure per credit hour a,b
a,b
Total institutional expenditure on instruction per credit hour.
IPEDS Finance
Total institutional expenditure on auxiliary enterprises per credit hour.
a 2012 dollars based upon CPI adjustment; b Measure excludes expenditure on (a) operations & maintenance and (b) interest on debt to maintain consistency over time. These items were categorized as a separate
expenditure category prior to 2007−08.
Auxiliary expenditure per credit hour
IPEDS Finance
a,b
Total institutional expenditure on institutional support per credit hour.
Institutional support expenditure per credit hour a,b
IPEDS Finance
Total institutional expenditure on student services per credit hour.
Student service expenditure per credit hour a,b
Academic support expenditure per credit hour
IPEDS Finance
IPEDS Finance
IPEDS Institutional Characteristics
IPEDS Institutional Characteristics
Instructional expenditure per credit hour a,b
Percent of applicants admitted
75th percentile SAT/ACT scores of freshmen
IPEDS Institutional Characteristics
Data Sources
25th percentile SAT scores of enrolled freshman; ACT scores converted to SAT
scores
75th percentile SAT scores of enrolled freshman; ACT scores converted to SAT
scores
Total number of applicants admitted divided by total number of applicants
Data Definition and Notes
25th percentile SAT/ACT scores of freshmen
Variable
APPENDIX A (continued)
Variables, Variable Definitions, and Data Sources
APPENDIX B
Complete Results for Base Models
% Federal Grant Recipients
The percentage of URM students
Percentage of nonresident students
−0.168**
(0.0722)
−0.185***
(0.0704)
−0.0815*
(0.0431)
−0.0844**
(0.0323)
Per capita income
−0.00653
(0.129)
0.0809
(0.115)
0.118
(0.0832)
0.163**
(0.0699)
Housing price index
−0.0912***
(0.0306)
−0.0977***
(0.0298)
−0.0325*
(0.0180)
−0.0375**
(0.0171)
Unemployment rate
0.000848
(0.00338)
0.00166
(0.00324)
0.00296
(0.00234)
0.00297
(0.00214)
Total state tax revenues
−0.00845
(0.0229)
−0.0217
(0.0230)
−0.00726
(0.0160)
−0.0229
(0.0162)
Poverty rate
0.000312
(0.00114)
−0.000917
(0.00103)
0.00135
(0.000910)
0.000322
(0.000762)
State appropriations to higher education
0.0182
(0.0333)
0.0305
(0.0334)
0.0189
(0.0205)
0.0239
(0.0190)
State expenditure on merit-based aid
−0.000253
(0.000751)
0.000217
(0.000713)
2.08e−05
(0.000477)
0.000562
(0.000380)
State expenditure on needs-based aid
−0.000874
(0.00200)
−0.000637
(0.00201)
−0.00150
(0.00107)
−0.00132
(0.00106)
Affirmative Action ban
0.00916
(0.0177)
0.0109
(0.0155)
−0.0185*
(0.0105)
−0.0202**
(0.00813)
Democrat state governor
−0.00606
(0.00605)
−0.00513
(0.00549)
0.00611
(0.00548)
0.00936*
(0.00484)
Democrat representation in legislature
0.000308
(0.000441)
0.000255
(0.000445)
0.00104*
(0.000544)
0.00104**
(0.000469)
Democratic party control
0.0109*
(0.00596)
0.0102*
(0.00597)
−0.000639
(0.00424)
−0.00260
(0.00381)
Republican party control
−0.0109
(0.00794)
−0.00684
(0.00741)
0.00589
(0.00539)
0.00741
(0.00452)
State population: under 5
0.116
(0.181)
0.0148
(0.179)
0.0119
(0.136)
−0.0557
(0.104)
State population: 5−11
0.0667
(0.187)
−0.0649
(0.190)
0.0449
(0.114)
−0.0156
(0.115)
State population: 12−17
0.225
(0.163)
0.193
(0.154)
−0.0729
(0.108)
−0.0991
(0.102)
State population: 18−24
−0.253*
(0.129)
−0.226**
(0.111)
−0.0401
(0.107)
−0.0116
(0.0979)
State population: 25−44
−0.317
(0.284)
0.0557
(0.279)
0.114
(0.233)
0.373*
(0.224)
State population: 45−64
−0.294
(0.288)
−0.386
(0.252)
−0.499
(0.301)
−0.469*
(0.241)
State population: over 65
0.231
(0.170)
0.249*
(0.150)
0.201
(0.165)
0.190
(0.132)
(continued)
APPENDIX B (continued)
Complete Results for Base Models
% Federal Grant Recipients
The percentage of URM students
State URM population rate: under 5
−0.00565
(0.00468)
−0.000909
(0.00470)
−0.00806**
(0.00381)
−0.00510
(0.00340)
State URM population rate: 5−11
−0.00843
(0.00740)
−0.00995
(0.00698)
−0.00622
(0.00511)
−0.00743
(0.00464)
State URM population rate: 12−17
0.00133
(0.00698)
−0.00150
(0.00645)
0.0111***
(0.00423)
0.00701*
(0.00382)
State URM population rate: 18−24
0.00476
(0.00580)
0.00461
(0.00514)
0.00162
(0.00539)
0.00307
(0.00474)
State URM population rate: 25−44
0.00450
(0.0115)
0.00581
(0.0106)
0.00636
(0.0112)
0.0116
(0.0106)
State URM population rate: 45−64
0.00873
(0.0101)
0.00500
(0.00911)
0.00358
(0.00868)
−0.00436
(0.00968)
State URM population rate: over 65
0.00510
(0.00928)
0.00195
(0.00788)
0.00293
(0.00864)
0.00145
(0.00664)
New IPEDS race categories
0.0106
(0.00701)
0.00676
(0.00750)
0.0161***
(0.00417)
0.0168***
(0.00429)
Resident tuition price
0.0938***
(0.0275)
0.0441*
(0.0260)
Nonresident tuition price
−0.0497**
(0.0220)
0.00375
(0.0194)
Average institutional grant award
0.00866
(0.00787)
0.00797*
(0.00462)
Average state grant award
−0.00962
−0.00602
(0.00584)
(0.00466)
Average Federal grant award
0.0281
(0.0212)
0.00164
(0.00641)
Undergraduate FTE enrollments
−0.00497
(0.0319)
−0.00330
(0.0249)
Graduate FTE enrollments
−0.00498
(0.0101)
−0.00229
(0.00619)
25th percentile SAT/ACT scores of freshmen
−0.336***
(0.113)
−0.321***
(0.0645)
75th percentile SAT/ACT scores of freshmen
0.112
(0.141)
−0.201*
(0.109)
Percent of applicants admitted
−0.0143
(0.0234)
−0.0279
(0.0169)
Instructional expenditure per credit hour
0.0613**
(0.0260)
0.0267
(0.0249)
Research expenditure per credit hour
0.0144
(0.00990)
0.00855
(0.00870)
Public service expenditure per credit hour
−0.00160
(0.00848)
−0.00322
(0.00637)
APPENDIX B (continued)
Complete Results for Base Models
% Federal Grant Recipients
The percentage of URM students
Academic support expenditure per credit hour
−0.0234*
(0.0140)
−0.0194**
(0.00921)
Student service expenditure per credit hour
−0.0217
(0.0144)
0.0160
(0.0124)
Institutional support expenditure per credit hour
−0.0302***
(0.0108)
−0.0214***
(0.00778)
Auxiliary expenditure per credit hour
−0.00658
(0.00925)
−0.00462
(0.00645)
AY 2003−2004
0.0163
(0.0106)
0.0213**
(0.00975)
0.0190*
(0.0100)
0.0193**
(0.00874)
AY 2004−2005
0.0288
(0.0212)
0.0361*
(0.0190)
0.0333*
(0.0187)
0.0350**
(0.0151)
AY 2005−2006
0.0264
(0.0312)
0.0371
(0.0281)
0.0475*
(0.0279)
0.0501**
(0.0227)
AY 2006−2007
0.0259
(0.0386)
0.0387
(0.0349)
0.0525
(0.0354)
0.0555*
(0.0287)
AY 2007−2008
0.0336
(0.0462)
0.0475
(0.0411)
0.0594
(0.0413)
0.0636*
(0.0336)
AY 2008−2009
0.0303
(0.0539)
0.0427
(0.0471)
0.0524
(0.0449)
0.0550
(0.0359)
AY 2009−2010
0.0742
(0.0622)
0.0863
(0.0539)
0.0566
(0.0482)
0.0589
(0.0391)
AY 2010−2011
0.102
(0.0658)
0.116**
(0.0566)
0.0801
(0.0548)
0.0838*
(0.0441)
AY 2011−2012
0.107
(0.0682)
0.120**
(0.0579)
0.0947
(0.0610)
0.0988**
(0.0490)
AY 2012−2013
0.0366
(0.176)
−0.0412
(0.171)
0.0395
(0.101)
−0.0621
(0.0964)
1,033
0.537
105
1,018
0.576
104
1,033
0.625
105
1,018
0.693
104
Observations
R-squared
Number of institutions
Note. Institution-clustered robust standard errors in parentheses.
*p < 0.10. **p < 0.05. ***p < 0.01.