Pricing the Flagships:
The Politics of Tuition Setting at
Public Research Universities*
Michael K. McLendon
Southern Methodist University
James C. Hearn
University of Georgia
and
Robert G. Hammond
North Carolina State University
Date: June 15, 2013
* Michael McLendon is Associate Professor of Higher Education Policy and Leadership
and the Associate Dean at the Simmons School of Education and Human Development,
Southern Methodist University. James Hearn is Professor of Higher Education at the
Institute of Higher Education, University of Georgia. Robert Hammond is Assistant
Professor in the Department of Economics at North Carolina State University. Please
address correspondence to Michael McLendon, Department of Education Policy &
Leadership, Simmons School of Education and Human Development, Southern
Methodist University, 3101 University Blvd, Ste. 247, Box 382, Dallas, TX 75205.
Phone: 214-768-4632. E-mail: [email protected].
We are indebted to William Berry, Peverill Squire, and Carl Klarner for sharing select
data with us. We also thank Will Doyle for his comments on previous drafts of the
manuscript. We bear all responsibility for errors.
Pricing the Flagships:
The Politics of Tuition Setting at Public Research Universities
Abstract
The dramatic recent rises in public-college tuition levels have been much discussed but rarely
examined systematically. What political, socioeconomic, and structural factors might drive one
state to adopt rates far higher or lower than others? For example, why might tuition levels in
South Carolina institutions be twice as high as levels in North Carolina’s nationally regarded
university system? This analysis models tuition setting with panel data for 162 public research
universities across 49 states over the period 1984 to 2006. Results of the fixed-effects regression
analyses suggest that population and postsecondary-enrollment patterns, proximal economic
conditions, state appropriations and aid policies, and university governance arrangements each
affect tuition levels in largely expected ways. Importantly, however, the results also indicate an
important role for political factors. Notably, the analysis reveals that, in the context of a variety
of controls for confounding factors, higher levels of minority representation in state legislatures
has a quantitatively and statistically significant effect on tuition at public flagship universities.
However, the effect is not homogenous across minority groups: increased African American and
female representation in state legislatures is associated with lower tuition, while increased Latino
representation is associated with higher tuition. These results highlight the power of multiple
forces in determining postsecondary tuition levels.
Keywords: Public policy; state politics; higher education; public universities; public finance
1
In the 1950’s, most public institutions in the United States charged only nominal or no
tuition. After several decades of modest rises, those rates began to rise significantly in the
1980s, claiming a progressively larger proportion of disposable income (NCES 2003; 2004;
Garrett and Poole 2006). Long gone is the era of tuition-free public education in California and
at institutions like the City College of New York. Interestingly, the popular press has focused its
attention almost entirely on rises in the private sector, where charges at some elite institutions are
moving well beyond $40,000 a year.1 While tuition rises have been greater in dollar terms in the
private sector, they have been greater in percentage terms in the public institutions (NCES 2003;
2004; College Board 2005).
Most recently, the U.S. Secretary of Education Margaret Spellings has added her voice to
those concerned over rises in college tuition. In an interview focused on her recent national
Commission on the Future of Higher Education, Spellings defended potentially aggressive
federal action regarding tuition, asking “Why should [higher education] be up 375% over the
period from 1982 to 2005, but medical care, which people are in uproar about, is up 223%? I
think that [college costs] are outpacing every other indicator.”2
There are, of course, striking state-to-state variations in pricing and price rises. In
Pennsylvania, in 2005-2006, average tuition and fee charges for in-state students in public fouryear institutions were $8,410, while in Florida the comparable charge was only $3,100 (College
Board 2005).3 Even neighbors vary remarkably: South Carolina charged $6,910 in that year,
while North Carolina charged only $3,440 (ibid.). Overall, among full-time students enrolled in
public four-year institutions in 2005-2006, 36% attended schools charging over $6,000 a year in
tuition and fees, while 4% attended schools charging less than $3,000 a year (ibid.). Yearly
tuition rises vary widely as well. Nationally, one out of every seventeen full-time students in
2
public four-year institutions experienced an annual tuition rise of 15% or more in the 2005-2006
academic year, but one out of every seven students experienced a rise of less than 3% (ibid.).
What is behind these notable differences? Why, for example, would a state set its
flagship institution’s tuition at a level more than double that of its peer institutions in other
states? Doing so represents a market statement but what, precisely, is being said? Such moves
arguably improve efficiency by doing away with low-tuition’s de facto subsidies to well-to-do
students. Is the state arguing for privatization of the marketplace for higher education, by
forcing its major institution to compete with less of a price advantage over comparable private
institutions? Or are the state and its institutions simply forced by financial exigencies to raise
tuition, regardless of the philosophical or competitive consequences? To be sure, there are no
single, simple answers to be found to such questions.
Many analysts have observed that tuition-setting in public institutions is closely tied to
the health of other institutional revenues, particularly appropriations to public higher education.
Yet, while appropriations are clearly a major factor in pricing public institutions, there are other
factors in tuition variations. Prior research points to a constellation of socio-demographic,
economic, and organizational forces as the primary drivers of tuition in public universities,
including state wealth, unemployment levels, student-aid policies, institutional endowment
levels, and governance arrangements (e.g., Ehrenberg 2000; Hearn, Griswold, and Marine 1996;
Kane 1999; Lowry 2001a; Paulsen 2000; Rizzo and Ehrenberg 2004; Toutkoushian and Hollis
1998). There are significant limitations in the extant literature, however.
Importantly, most efforts to model the influences on tuition setting in public universities
have tended to overlook the broader political climates of the states. Until very recently, students
of higher education policy largely have overlooked political science as a framework for
3
organizing research. This is vexing for at least two reasons. First, failure to account for the
political context in which policies arise is a missed opportunity, since the American states
provide one of the world’s most attractive venues in which to study comparatively the formation
of public policy, including public policies in the arena of higher education. Second, public
universities, as state agencies, are embedded within decidedly political environments, and these
environments can hold important implications for the choices those agencies make. Indeed,
recent longitudinal research has pointed to the apparent influence of a variety of political factors
on postsecondary policymaking.
Nicholson-Crotty and Meier (2003), for example, examined contrasting hypotheses
regarding whether centralized governing boards are more or less insulated from political
influence. While their findings did not definitively answer the question, the analysis revealed
several significant relationships between indicators of educational structure and indicators of
political influence. That finding is echoed in work by McLendon, Deaton, and Hearn (2007),
who found that state efforts to reform higher-education governance structures was associated in
predictable ways with the degree of instability in state political institutions, including changes in
party control of legislatures, shifting legislative party strength, and turnover in gubernatorial
leadership. Other recent empirical work also points to political conditions within states as
important drivers of state policy activity in the higher education domain (e.g., Archibald and
Feldman 2006; Hearn, McLendon, and Mokher in press; Lowry 2007; McLendon, Heller, and
Young 2005; Tandberg 2008).
Several recent studies have investigated connections between political factors and highereducation financing.
Rizzo (2004) has found that attempts by institutions and systems to
diversify revenues may penalize them in legislative decision making, creating a dynamic in
4
which success in one domain (diversifying financing) leads to diminution in another domain (the
political arena).
McLendon, Hearn, and Deaton (2006) also found evidence of political
tradeoffs: in their analysis, each political party’s legislative strength was associated with states
initiating distinctive performance-accountability programs. Specifically, Republican legislative
strength was associated with states initiating performance-funding policies tying institutional
appropriations directly to chosen accountability measures, while Democratic legislative strength
was associated with performance budgeting, a less direct approach to tying funding to
performance. Most directly relevant for the present project is recent work by Lowry (2001a)
suggesting that centralized structures that are more open to political, as opposed to academic,
influences tend to be associated with higher tuition rates in public institutions. Conversely,
institutions whose governance arrangements allow more autonomous decision making tend to
charge higher tuition rates. Thus, the more politicized a governance arrangement, the lower a
state’s tuition will tend to be.
Unfortunately, the number of analyses incorporating political factors like these is small.
The existing literature also suffers from two other limitations. First, while some research finds
that the manner in which public universities are governed seems to influence variation in tuition
levels (e.g., Hearn, Griswold, and Marine 1996), these past studies have tended to use single,
unidimensional governance measures (e.g., an indicator of the power of the main state
coordinating or governmental board). Such factors as the number of governing boards in a state
and the level of reliance on trustee elections, for example, seem equally relevant but have been
understudied empirically (Lowry 2001a; White 2004). Second, the panel datasets created to
analyze tuition setting in public higher education often rely on relatively brief time series
5
(usually five to fifteen years) with which to draw inferences about causal relationships (e.g.,
White 2004).
As a consequence of these limitations, our understanding of the factors propelling
changes in public institutions’ tuition charges remains underdeveloped both conceptually and
empirically. Increasing knowledge of those factors will ideally not only add to the growing
theoretical literature on public choice in education but also spur more informed public decisions
regarding the structures, processes, and outcomes of effective postsecondary education
policymaking. Toward that end, this manuscript reports an analysis of the factors associated
with tuition charges at U.S. Flagship universities over a more than 20-year period, from 19842006. We develop and test a theoretical framework for explaining variation in tuition charges
and discuss several of the novel findings that our analysis yields.
Conceptual Framework
We develop a new explanation for tuition setting in public higher education that
incorporates theory and research distilled from the political-science literature on descriptive
representation in state legislatures. We also develop a number of alternative hypotheses drawn
from the literatures on comparative state politics and on postsecondary finance and governance.
Unlike much of the extant literature, our research examines the phenomenon of public university
tuition setting through a political-science lens because these entities, as publicly funded and
governed organizations, are subject presumably to many of the same pressures as other public
agencies operating at the subnational level in the United States.
Descriptive representation, also known as symbolic representation, refers to the degree
of similarity in background (e.g., race, gender, religion) between elected officials and their
6
constituents (Canon 2002; Pitkin 1967). This form of representation in democratic society often
is contrasted with that of substantive representation, which refers to the interests elected officials
serve; i.e., what officials actually do as opposed to their physical characteristics. The distinction
is important inasmuch as it informs a more general theoretical question: whether descriptive
representation enhances substantive representation or, stated differently, whether and to what
extent a legislator’s physical characteristics influence the legislator’s policy preferences and
behaviors (Pitkin 1967). Some scholars have argued that descriptive representation can promote
substantive representation by setting the legislative agenda, by determining the nature of
deliberation, by preferencing certain groups or interests in the political process, and by placing in
office representatives who may be more likely to share the preferences of the groups.4
Scholars of American politics and policy have developed several distinct models with
which to explain minority influence in representative bodies. Our approach aligns closely with
the so-called “presence” model, a basic model that assumes that minority representatives act as
stronger advocates for the minority constituents with whom they share unique experiences and
backgrounds than do non-minority legislators. In effect, this model “predicts that the process of
adding minority representatives fosters governmental responsiveness to minority groups by
increasing the level of advocacy for their interests” (Preuhs 2006, p. 586). Notable empirical
support for the presence model can be found in studies of local school boards (see, for example,
Meier et al. 2005). In the context of state legislatures, the evidence is less decided. On one
hand, a few analysts have concluded that greater African American representation affects neither
perceptions of general influence on legislative decisions nor increases legislative responsiveness
to African American interests (e.g., Critzer 1998). Yet, a number of recent studies persuasively
argue the converse (e.g., Barrett 1995; Bratton and Haynie 1999; Burns et al. 2001; Canon 1999;
7
2002; Haynie 2001; Mansbridge 1999; Moncrief et. al 1996; Owens 2005; Preuhs 2006; Squire
and Hamm 2005; Swers 1998, 2002; Whitby 1997). Owens (2005), for example, examined the
relationship between increased African American representation in state legislatures and state
policy outputs as measured by spending priorities within budgets over a 24-year period. His
analysis appeared to demonstrate that increased African American representation had resulted in
state legislatures giving greater priority to policy areas conventionally thought to be important to
African American elected officials. The author concludes that descriptive representation can
result in increased substantive representation in state legislatures. On balance, the evidence
seems to suggest that, at least on some issues, increased minority representation in legislatures
leads to outcomes that increasingly favor those minority groups.5
On the strength of this evidence, one might expect that increased descriptive
representation (based on race) in state legislatures may result in better policy outcomes for
members of the particular racial group. This leads us to our central study hypothesis: because
African Americans and other lower-income groups traditionally have been perceived to benefit
from lower tuition charges, universities located in states with higher percentages of African
American legislators will be more likely to charge lower tuition at public research universities.
While the concept of descriptive representation is central to our theorizing, clearly tuition
setting by public research universities is likely to be subject to a complex constellation of forces,
necessitating consideration of a variety of prospective influences. Drawing on the literature on
postsecondary finance and governance and on theory and research in the subfield of
comparative-state politics, we distil six additional sets of explanations that we believe might
account for variation in tuition charges across state and institutional contexts and over time: (1)
economic and fiscal conditions of states; (2) demographic and postsecondary enrollment
8
patterns; (3) characteristics of state political systems; (4) postsecondary governance patterns; (5)
regional influences; and, (6) various aid policies at the state, federal, and institutional levels.
The first explanation points to the role of economic and fiscal conditions of the states as
likely influences on tuition setting at public research universities. For example, we posit that
public universities in states with higher unemployment rates will charge higher tuition because
the opportunity cost of enrolling in postsecondary education in those states is lower, relatively
speaking. Because higher education often is considered to be a normal good, we hypothesize
also that demand should be greater in states with higher per capita income (Lowry 2001a; Rizzo
and Ehrenberg 2004).
Second, we believe that demographic and postsecondary enrollment patterns of states
also will influence variation in tuition setting in public higher education. For example, we
believe that public universities located in states with a higher proportion of residents aged 18-24
will charge lower tuition because fostering training and skill development among new laborforce entrants is central to a state’s future economic development, and thus a public good
meriting substantial state subsidy in the form of lower tuitions. One might also argue that
providing visibly affordable access is a major concern of this large constituency of prospective
voters, although low voting rates in the young-adult population may belie that claim.
Turning to the postsecondary landscape, we hypothesize that institutions enrolling higher
levels of nonresident (out-of-state) undergraduate enrollments will charge higher in-state tuition
because these institutions will tend to have the valued marketplace appeal of regional or national
stature as magnets for diverse groups of students. We also would suggest that such institutions
may be pressed to charge higher in-state tuitions to offset the costs of providing education to
non-taxpaying students. That is, while a geographically diverse student body arguably improves
9
educational quality, very few institutions or states would attract many out-of-state students while
charging full- or near-full-cost tuition. Charging out-of-state students lower than full-cost tuition
represents a subsidy from state taxpayers that may be defensibly offset by charging more to the
in-state beneficiaries of this geographic diversity.
We also hypothesize that institutions in states with a higher share of postsecondary
enrollment in private colleges will charge higher tuition because public and private schools are
substitutes, permitting public institutions to raise prices closer to those of their competitors.
Analogously, public universities in states with a higher share of total postsecondary enrollment
in two-year schools will tend to charge lower tuition because a competitor is charging lower
prices (Hearn, Griswold, and Marine 1996; Rizzo and Ehrenberg 2004).
Interstate variation in political systems constitutes our third class of explanatory factors.
Aside from the impact of a legislature’s racial composition, other political conditions of the
states may also influence tuition setting. For example, a number of studies recently have found
evidence that, on some issues at least, the two major political parties appear to have different
policy preference vis-à-vis higher education (Archibald and Feldman 2004; Knott and Payne
2004; McLendon, Heller, and Young 2005; McLendon, Hearn, and Deaton 2006; McLendon,
Deaton, and Hearn 2007; Nicholson-Crotty & Meier 2003; Rizzo 2004). Building on this prior
work, we hypothesize that both Republican legislative strength and Republican gubernatorial
control will be associated with higher tuition charges at public universities. Given the different
preferences of Republicans and Democrats on state governmental spending and on public
subsidy of social services (including education), we believe it is reasonable to presume that
Republicans might be less averse to higher tuition charges in postsecondary education.
10
Conversely, we hold that higher levels of political liberalism among citizens will lead to
lower tuition charges.6 Political ideology may be understood as a coherent and consistent set of
orientations or attitudes toward politics, while citizen ideology refers to the mean position on a
liberal-conservative continuum of the electorate in a state (Berry et al. 1998). We build on
several previous studies (e.g., Hossler et al. 1997; Nicholson-Crotty and Meier 2003) in
reasoning that states with more liberal citizenries – ones that are more prone to support greater
public subsidy of education – will be associated with lower tuition charges.
We also posit that universities located in states with institutionally strong governors will
charge lower tuition.7 Governors everywhere exert considerable sway over executive-branch
decisions and over policy outcomes generally, although the precise extent of their influence
varies from one state to the next, depending in part on their institutional powers (Beyle 2003). In
some states governors wield strong influence over policy through, for example, the line-item
veto, broad appointment powers, and tenure potential.
Elsewhere, governors hold fewer
instruments of policy control, thus restraining their influence (Barrilleaux and Bernick 2003;
Dometrius 1987; Beyle 2003). While little is known about the policy influence of governors in
higher education, per se, abundant anecdotal evidence points to a history of governors in many
states “jawboning” flagship universities into limiting desired tuition increases. Hence, we posit
stronger governors associated with lower tuition rises.
A fourth category of possible influence on tuition setting in public higher education
involves postsecondary governance structures. A substantial body of literature seems to indicate
that the manner in which a state governs its postsecondary system can influence policy outcomes
at both the state and campus levels (Hearn & Griswold 1994; Knott and Payne 2004; Lowry
2001a; McLendon 2003; McLendon, Hearn, and Deaton 2006; Nicholson-Crotty, and Meier
11
2003; Volkwein 1986; Zumeta 1996).
We focus on at least three particular governance
dimensions: the number of separate university governing boards in a state, popular election of
trustees, and the presence of a weak coordinating board.8
For all three relationships, we build on Lowry’s (2001a, pp. 846-848) reasoning
regarding the preferences of legislators and state government executives, on one hand, and the
preferences of public university administrators, on the other hand.
We believe it is not
unreasonable to presume that state officials act as supervisors of higher education and, thus, as
ones who are most directly accountable to voters, they will act in concert with the preferences of
the public, favoring lower university prices. Public university administrators and faculty, by
contrast, will prefer higher tuition prices. First, we hypothesize that institutions located in states
with larger numbers of separately governed boards will charge higher tuition because the costs
for elected officials in monitoring institutional behavior in such states are higher, permitting
institutions to more effectively evade external oversight and to pursue their own interests, which
manifest in higher tuition revenue. Second, because the popular election of trustees may provide
an accountability mechanism that more effectively conveys public preferences to university
leaders, we believe that universities located in states where trustees are popularly elected will
charge lower tuition. Finally, we posit that universities located in states with weak coordinating
boards will charge higher tuition because the absence of strong state-level control will permit
universities to more actively pursue their own preferences, including, as noted, the preference for
higher institutional revenues via tuition.
Our fifth category points to regional influences on tuition setting in public research
universities. Namely, we believe that competitive pressures will permit institutions to respond to
12
higher regional tuition by themselves charging higher tuition (Greene 1994; Hearn, Griswold,
and Marine 1996; Rizzo and Ehrenberg 2004).
Finally, we posit that tuition will be influenced by the prior policy choices that states
have made in the areas of appropriations and student-aid funding (Bennett 1987; Hauptman &
Krop 1998; Hearn, Griswold, and Marine 1996; McPherson & Schapiro 1991; Rizzo and
Ehrenberg 2004). We examine three specific relationships. At the state level, we examine the
impact on tuition changes of state appropriations, positing that higher appropriations levels will
likely be negatively associated with tuition because they are alternate sources of income for the
institution. The presence of a merit-based student financial aid program in a state, however, will
likely be positively associated with tuition, as institutions seek to “capture” increases in
government aid to students by raising tuition.9 We believe institutional endowment will be
positively associated with tuition because endowment may serve as a proxy for reputation.10
Methodology
The purpose of our study was to examine the influence of a variety of sociodemographic, economic, organizational, political, and policy conditions on tuition setting in U.S.
public research universities.
Because our interest was in examining the behavior of these
institutions across states and over time, our investigation demanded a dataset that could
accommodate both the spatial and temporal dimensions of tuition setting.
We therefore
developed a longitudinal dataset that incorporated annual indicators of the conditions we
hypothesized would influence tuition levels over the period, 1984 to 2006. In the remainder of
this section, we describe our data sources and our chief estimation strategies.
13
Sample and Data
The sample for our study includes 162 public research universities representing 49 states.
We excluded Nebraska from our analyses because its nonpartisan, unicameral legislature
precludes our testing several of the study’s core hypotheses, notably ones relating to the political
climates of the states.
Our sample includes every Carnegie-classified Research I and II
University in the U.S.; a handful of Doctoral I and II universities are included for the three states
that lack a Research I or II institution (e.g., North and South Dakota and Maine). As noted, these
data span the period 1984 to 2006. After omitting observations with missing values for any of
the variables of interest, our sample included 3,726 institution-year observations.
We assembled data from a variety of reliable secondary sources. The majority of our
institution level data, including tuition, enrollment, appropriations, and endowment data, were
taken
from
the
Integrated
Postsecondary
Education
Data
System
Surveys
(http://nces.ed.gov/ipeds). Institution level data on the share of first-time freshmen that are
nonresident, non-foreign students were also taken from IPEDS. These data are available for
1986, 1988, 1992, 1994, 1996, 1998, 2000, 2002, 2004, and 2006, only. Rather than employing
casewise deletion, we used multiple imputation techniques to construct the data for other years.11
We also used imputation to overcome 141 missing observations for our endowment data.
State level data on the share of first-time freshmen enrolled in two-year and private
schools were also collected using IPEDS. These and all enrollment figures used were in terms of
full-time equivalent (“FTE”) students. We derived state population demographics from the
Census Bureau (http://www.census.gov/popest/datasets.html). Unemployment data are from the
Bureau of Labor Statistics (http://www.bls.gov/data/home.htm) and state per capita income data
are from the Bureau of Economic Analysis (http://www.bea.gov/bea/regional/spi).
14
We purchased data on African American legislators from the Joint Center for Political
and Economic Studies (http://www.jointcenter.org/DB/index.htm). We obtained most of the data
on the study’s other political variables from several widely used and publicly available data
sources. Our measure of Republican legislative strength indicates the proportion of major party
legislators across both chambers of a state’s legislature that is Republican. Data for years 19842000 came from the datasets developed by Carl Klarner for the State Policy and Politics
Quarterly Data Resource (http://www.unl.edu/SPPQ/journal_ datasets/klarner.html).
Klarner
provided data for 2001-2006 directly to us. We used these same sources for data on Republican
control of governors’ offices.
The variable, “Gubernatorial power,” measures the degree of a governor’s institutional
powers. This variable is a metric combining scores on six individual indices of gubernatorial
power, including the governor’s tenure potential, appointment power, budget power, veto power,
extent to which the governor’s party also control the legislature, and whether the state provides
for separately elected executive branch officials. Data for this variable came from Beyle’s ratings
of gubernatorial power for the years, 1980, 1988, 1994, 1998, and 2001. These data can be found
at http://www.unc.edu/~beyle/gubnewpwr.html.
The variable, “citizen ideology,” refers to the mean position on a liberal-conservative
continuum of the electorate in a state (Berry, et. al 1998). Our measure relies on the index
developed by Berry et al., which assigns ideology scores for all states for all years between 1960
and 2006. Data on these state scores are available at the Inter-university Consortium for Political
and Social Research (http://www.icpsr.umich.edu).
Our analysis included several measures of public university governance, namely the
percent of an institution’s trustees that are popularly elected, the type of statewide governance
15
arrangement, and the number of governing boards in a state. Data on all of these measures were
derived by the authors from multiple editions of McGuinness’ (1985, 1988, 1994, 1997, 2001)
Postsecondary Structures Handbook.
The final specification in our analysis used monetary figures (tuition, per capita income,
tax revenue, appropriations, and endowment) deflated using GDP implicit price deflator. The
function form of our dependent variable is the logarithm of tuition.
Estimation Strategy
Our analytic approach involved use of fixed-effects regression with the institution-year as
our unit of observation.
By using a panel-data model, we argue that universities are
heterogeneous in their pricing behavior in ways that are not easily captured using available data.
Unobservables (i.e., characteristics that are observed by the market participants but not by the
analyst) clearly matter when students choose the university they will attend and thus when tuition
levels are set.
Factors such as the location of the university, variety within the course
curriculum, and the availability of extra-curricular activities may influence demand for an
institution and the tuition charged. Although these intangible dimensions introduce a universityspecific effect that presents problems within an OLS framework, a fixed-effect regression can
directly address this omitted variable problem. Such an approach allows one to assay the
relationship between the regressors of interest and tuition charges while controlling for certain
missing factors (Wooldridge 2002).
The results of both the Breusch-Pagan test and the Hausman test indicated the suitability
of a fixed-effects model.12
Various tests also revealed the presence of heteroskedasticity and
16
autocorrelation, which we addressed via use of Driscoll-Kraay standard errors with an error
structure that is heteroskedastic, correlated over time, and correlated between universities.
Our model specification is as follows:
Yit   0   1 X P ,it   2 X A,it   3 X G ,it   4 X E ,it   5Tt  ci  u it ,
t=1984, …, 2006.
i is the panel variable, the university. t refers to the Fall of the academic year of the observation
in question. X P is a vector of political factors hypothesized to influence university pricing; X A
is a vector of state government choice variables, including appropriations and student aid; X G is
a vector of higher education governance characteristics; and, X E is a vector of state economic
factors. T is a vector of year dummy variables, omitting the dummy for 1984. Including T in
the model allows us to control for unobservable time-specific effects that would otherwise be
problematic (Wooldridge 2002). ci is a university-specific unobservable random variable that is
assumed to be constant across time.
The idiosyncratic error term, uit , varies across both
institutions and time. The fixed effects model allows for any arbitrary correlation between c and
all independent variables ( X  ( X P , X A , X G , X E ) and T ) by demeaning each (Yit , X it ) and
thereby removing the unobserved ci term, allowing direct estimation of the  vector (  =
(  P ,  A ,  G ,  E ).13
Findings and Implications
Our analysis yielded a variety of distinctively new insights into the factors that influence
tuition-setting at public research universities in the United States. Overall, our model performed
well, indicating numerous relationships that are robustly significant at the p <.01 level. Many of
our findings, described below, fit well within the existing literatures in higher education and
economics. Yet, controlling for these factors, we also find evidence that certain characteristics
17
of state political systems also seem to account for changes in tuition levels at public universities
over the past two decades. These “politics”-related findings add to the field’s understanding of
tuition-setting phenomena in distinctively new ways.
Our first set of explanatory variables, new to this literature for the most part, involves
various political drivers of tuition setting. We examined five characteristics of state political
systems that we believed might account for variation in tuition changes across states over the
past 20 years.
Our analysis reveals strong support for most of the relationships that we
hypothesized. First, with respect to our focal hypothesis, we find that minority representation in
state legislatures has a quantitatively and statistically significant effect on tuition at public
flagship universities. However, the effect is not homogenous across minority groups: increased
African American and female representation in state legislatures suppresses tuition, while
increased Latino representation is associated with higher tuition. Our analysis does not permit us
to establish the precise chain of causal influence. Yet, the findings provide added, intriguing
evidence indicating that minority representation in state legislatures may influence postsecondary
policy in the states in ways that have rarely before been systematically examined (Hawes and
Hicklin 2006; Hicklin and Hawes 2003). The findings also raise interesting questions that merit
further systematic attention. For example, specifically how do minority legislators insinuate
their preferences into decision-making by public universities relative to tuition policy? To what
extent is the influence direct (e.g., conveying preferences to members of state or campus
governing boards), as opposed to indirect?
The differential effects of African American legislators and female legislators on one
hand and Latino legislators on the other is an interesting findings that is a particularly fertile
ground for future research. Evidence from other policy arenas (e.g., Clarke et al. (2006))
18
suggests that Latino representatives may also hold distinctive policy preferences, particularly on
social policy. Further, more research is needed particularly in light of evidence by authors who
argue that Latinos are more ideologically moderate than African American (Clarke et al. 2006).
How might such differences account for the policy choices that states make in higher education?
Continuing with political influences, we find that the degree of ideological liberalism of a
state also appears to suppress tuition growth over time. We interpret this result as suggesting
that more liberal citizenries may prefer policies – including tuition policy in public higher
education – that are viewed as encouraging greater equity and broader access to publicly
provided services. Conversely, we find higher tuition associated with Republican legislative
strength and with the institutional powers of governors (i.e., stronger governors associated with
higher tuition). The former finding contributes to the growing body of literature on the impacts
of partisan legislative strength and control on higher education policy, indicating that
partisanship appears to influence the public choices that states make for higher education, at least
in some areas (Archibald and Feldman 2006; McLendon, Hearn, and Deaton 2006; NicholsonCrotty and Meier 2003; Knott and Payne 2004).
We found no statistical evidence that
Republican control of the governorship influences tuition growth in public research universities.
We also investigated relationships between various state economic and demographic
factors and university pricing. For the large part, our results tend to confirm the work of Rizzo
and Ehrenberg (2004) and others, who find strong connections empirically between tuition levels
and unemployment rates (higher rates equate with higher tuition charges), tax revenue (the
higher the revenue, the lower the tuition), and the share of the state population between the ages
of 18-24 (higher share equates with lower tuition). Our results yielded no statistically significant
relationship, however, between per capita income and tuition charges.
19
Next, we hypothesized that certain state policy choices in the areas of appropriations and
student financial aid would play an important role in tuition-setting but found evidence of a
statistically significant relationship only for appropriations: as state appropriations increase,
tuition increases decelerate. In contrast, aid (specifically the availability of HOPE-style meritbased aid) has a statistically insignificant effect on tuition, suggesting that we do not find a
strong tuition-aid link as implied the so-called “Bennett Hypothesis,” which argues that
universities may seek to “capture” increases in government aid to students by raising tuition
(Bennett 1987; Hauptman & Krop 1998; McPherson & Schapiro 1991).
At the institutional level, we find a positive relationship between institutional endowment
and tuition rates. As noted, one potential explanation for the co-movement of endowment and
tuition is the premium paid to universities with better reputations, those very ones likely to have
greater endowment revenues.
With respect to institutional governance, we explored several different potential sources
of influence on university tuition setting. Our three governance variables produced effects that
varied in their statistical power and in the degree to which they supported our hypotheses. First,
we find a negative relationship between the number of separately governed university boards in a
state and university pricing (i.e., more boards lead to lower tuition), a finding at odds with
Lowry’s (2001a) and our initial supposition. Second, our findings indicate that the popular
election of public university trustees is associated with lower tuition.
This relationship is
particularly interesting because it seems to provide direct evidence of an electoral link between
citizens and the decisions made by public university governing boards – a relationship long
maintained by advocates of trustee elections, but one for which there was little supporting
empirical evidence. Third, the presence of a weak board is not associated with tuition in a
20
statistically meaningful way, despite the common intuition that universities with more autonomy
will more readily pursue their own self-interest by charging higher prices.
We find several notable relationships between tuition and higher-education enrollment
patterns. For example, universities that draw a higher percentage of their student body from outof-state charge higher tuition. This finding could reflect a combination of a number of forces at
work, including the influence of a reputation-premium for universities popular with out-of-state
students and/or a rational response by universities to increases in the demand for their services.
Further, the share of a university’s student body enrolled as graduate students is unrelated to
tuition charges. Next, we find that variation in the two-year colleges’ share of total higher
education enrollments is unrelated to university pricing, while private-colleges’ share of total
higher education enrollments is associated with higher tuition charges but the effect (while
meaningfully large) is statistically insignificant. We believe the latter result may represent a
substitution effect: schools competing with (typically high-priced) private colleges are, as a
result, able to increase their price. The lack of significance for the two-year percentage could
indicate a lower degree of substitutability between two-year colleges and the public universities
studied here. Another substitution result can be found involving regional tuition: schools located
in regions with other expensive schools charge higher tuition, ceteris paribus.
We now turn to a discussion of the size of the effects noted in the previous section.14
Table 4 reports the effect sizes (i.e., marginal effects) in two forms, the predicted change in
tuition, ceteris paribus, for the year 2006: (1) from a one unit increase in an explanatory variable
and (2) from a one standard deviation increase in an explanatory variable. The latter approach to
discussing marginal effects allows for easier comparison across variables. It may be helpful to
recall from Table 1 that the average level of tuition for 2006 was $5,415.94. Because the
21
previous literature has not included several of the political variables that we study, the following
discussion of marginal effects is confined to the political variables that are our main focus.
We first note that the relative size of the political impacts we discovered points to a
serious omission in the previous literature studying tuition charges. We find the composition of
a state’s legislature to be a robust and meaningful predictor of tuition charges across our sample
period. Specifically, increasing the proportion of African Americans in a state’s legislature by
one standard deviation is associated with $180 lower tuition charges, while a one standard
deviation increase in female legislators is associated with $80 lower tuition charges. In contrast,
a one standard deviation increase in Latino legislators is associated with $240 higher tuition
charges. The statistical robustness and quantitative size of these effects suggest meaningful
effects of minority representation that are not homogenous, just as these minority groups
themselves are not homogenous. Clarke et al. (2006) provide a framework for future research
aimed at deepening our understanding of how the presence of a diverse set of minority
representatives from a diverse set of minority groups might affect the policy choices that states
make in higher education.
The partisan complexion of state government is another area where the present analysis
has yielded new insights. For example, a one standard deviation increase in the proportion of the
legislature that is Republican leads to tuition increases in excess of $120. Moving from a
Democratic to a Republican governor, on the other hand, has a smaller effect on tuition that is
statistically insignificant. Further, an increase in the degree of political liberalism of a state by
one standard deviation reduces tuition by $115, while an increase in the institutional powers of a
governor by one standard deviation is associated with a $145 increase in tuition.
22
Conclusion
Tuition increases in public research universities represent one of the most prominent and
controversial public policy questions confronting U.S. higher education today (Mumper &
Freeman 2005). Most explanations of the phenomenon point conventionally to a complex
interplay of demographic, economic, and industry-specific factors. Our analysis of university
pricing over a period of nearly 20 years provides added evidence of the importance of these
forces, including population and postsecondary-enrollment patterns, proximal economic
conditions, state appropriations and aid policies, and university governance arrangements.
Yet, our longitudinal analysis points also to newly identified sources of political
influence in shaping tuition setting. Notably, we find strong empirical evidence that minority
representation in state legislatures influences university pricing. Although routinely explored in
many other policy domains, this relationship has rarely before been studied by higher-education
scholars. The finding that descriptive representation (i.e. the racial/ethnic representativeness of
legislatures) can shape the substantive policy choices of governments and their agents (i.e.,
tuition choices of public universities) both substantiates established lines of theory and research
on postsecondary policy outcomes in the American states and opens several new lines of inquiry.
On one hand, our findings provide added evidence in support of recent empirical work linking
certain characteristics of state political systems with the policy choices of states – state political
climates, it would appear, help shape state policy outputs in ways that can be systematically
operationalized and assayed (Archibald and Feldman 2006; Knott and Payne 2004; McLendon,
Hearn, and Deaton 2006; McLendon, Deaton, and Hearn 2007; Nicholson-Crotty and Meier
2003). On the other hand, though, our work suggests the need for future research focusing on
patterns of representation in state legislatures. How state legislative institutions “look” may hold
23
important implications for how the institutions behave – and how the institutions influence their
agents (i.e., public universities) to behave – in the arena of postsecondary policy. Our analysis,
therefore, provides a modest contribution to an emerging research agenda focused at the
intersection of political representation and state postsecondary policy outcomes.
24
TABLE 1. Summary Statistics for the Dependent Variable: In-State Tuition and Fees
Annual
Unweighted Weighted
Standard
Growth
Year Average
Average
Deviation
Median
Minimum
Maximum
Rate
$1,905.19
$783.98
$1,790.07
$582.37
$6,929.26
1984 $1,808.56
$1,924.71
$683.18
$1,876.23
$573.77
$4,217.21
2.83%
1985 $1,859.78
$2,199.87
$745.21
$2,042.81
$927.72
$4,701.77
14.55%
1986 $2,130.29
$2,282.95
$773.31
$2,116.93
$967.27
$4,869.13
3.64%
1987 $2,207.90
$2,376.06
$825.91
$2,130.30
$935.35
$5,144.42
3.10%
1988 $2,276.41
$2,476.01
$850.51
$2,306.60
$975.08
$5,201.30
5.71%
1989 $2,406.40
$2,535.17
$915.81
$2,342.19
$938.84
$5,625.68
2.61%
1990 $2,469.29
$2,748.71
$1,027.93
$2,631.35
$1,044.49
$6,290.61
9.11%
1991 $2,694.15
$2,892.31
$2,943.00
$1,097.23
$2,768.43
$1,021.01
$7,137.84
7.36%
1992
$3,112.07
$1,153.73
$2,869.43
$997.96
$7,241.46
5.86%
1993 $3,061.72
$3,236.09
$1,200.87
$2,991.41
$1,336.16
$7,369.95
4.18%
1994 $3,189.67
$3,331.82
$1,196.70
$3,085.57
$1,515.64
$7,501.12
3.21%
1995 $3,292.10
$3,437.98
$1,202.57
$3,148.55
$1,611.03
$8,232.05
3.14%
1996 $3,395.33
$3,534.46
$1,188.91
$3,193.47
$1,695.78
$7,912.94
2.64%
1997 $3,484.98
$3,639.05
$1,209.16
$3,304.59
$1,756.99
$8,176.48
3.05%
1998 $3,591.19
$3,703.68
$1,195.82
$3,503.70
$1,814.69
$8,219.23
1.97%
1999 $3,661.98
$3,811.06
$1,226.56
$3,544.50
$1,776.00
$8,288.00
3.04%
2000 $3,773.36
$3,995.41
$1,334.03
$3,633.82
$1,734.39
$9,941.48
5.04%
2001 $3,963.58
$4,303.69
$1,480.24
$3,905.46
$1,794.84
$11,199.03 7.08%
2002 $4,244.01
$4,793.49
$1,670.54
$4,376.67
$2,212.30
$13,033.20 11.78%
2003 $4,743.83
$5,178.92
$1,921.30
$4,769.25
$2,260.15
$17,944.20 7.76%
2004 $5,111.74
$5,349.52
$1,964.77
$4,912.67
$2,306.38
$19,009.28 2.70%
2005 $5,249.72
$5,514.87
$2,042.55
$4,995.43
$2,281.52
$19,710.02 3.17%
2006 $5,415.94
Notes: All monetary data are in constant 2000 dollars, deflating using a GDP Implicit Price
Deflator. “Weighted Average” refers to the average tuition and fees weighted by FTE. Annual
Growth Rate is the compound annual growth rate in a given year from the previous year.
25
TABLE 2. Summary Statistics for Control Variables, 1984 and 2006
Variable
Population Share 18-24
Unemployment Rate
Personal Income
Tax Revenue
% Nonresident
% FTE in Private
% FTE in Two-Year
% Republican Legislators
Presence of Republican Governor
Political Liberalism
Governor Power
% African American Legislators
% Women Legislators
% Latino Legislators
Number of Separate Boards
% Trustees Elected
Presence of Weak Board
Regional Tuition
State Appropriations per FTE
Presence of Merit Aid
Endowment per FTE
% Graduate Students
1984
Average
(SD)
12.51%
(0.60%)
7.62%
(1.96%)
$19,699.45
($2,615.32)
$1,206.12
($451.27)
14.87%
(13.01%)
19.59%
(11.17%)
34.04%
(13.17%)
35.48%
(17.92%)
30.25%
(46.08%)
44.32%
(13.43%)
3.72
(0.74)
6.49%
(4.13%)
11.53%
(5.45%)
2.34%
(5.56%)
5.67
(5.02)
5.12%
(20.75%)
43.83%
(49.77%)
$1,749.24
($351.69)
$10,492.65
($9,307.33)
61.11%
(48.90%)
$4,531.91
($26,084.50)
18.14%
(8.99%)
26
2006
Average
(SD)
9.89%
(0.68%)
4.63%
(1.00%)
$30,215.16
($4,082.40)
$1,956.90
($440.08)
17.63%
(14.55%)
22.97%
(11.49%)
34.67%
(11.21%)
51.46%
(13.28%)
60.49%
(49.04%)
52.14%
(12.86%)
3.45
(0.37)
10.36%
(7.22%)
22.06%
(6.69%)
5.56%
(8.80%)
5.90
(4.56)
4.27%
(19.47%)
29.01%
(45.52%)
$5,274.39
($1,115.56)
$11,263.30
($16,194.62)
74.69%
(43.61%)
$32,985.56
($159,805.70)
19.83%
(8.55%)
Annual
Growth
Rate
-1.06%
-2.23%
1.96%
2.22%
0.86%
0.73%
0.08%
1.70%
3.20%
0.74%
-0.35%
2.15%
2.99%
4.00%
0.18%
-0.83%
-1.86%
5.14%
0.32%
0.92%
9.44%
0.41%
Notes: All monetary data are in constant 2000 dollars, deflating using a GDP Implicit Price Deflator. Data in
second and third column for % Nonresidents refers to 1986, the earliest year for which we have non-imputed
1/22
X

data. Annual Growth Rate refers to the compound annual growth rate from 1984 to 2006  2006 
 X 1984 
except in the case of % Nonresidents, where the growth rate is over 20 years.
27
1,
TABLE 3. Regression Results for In-State Tuition and Fees at Flagship Universities
Dependent Variable: Log(In-State Tuition and Fees)
Population Share 18-24
Coefficient
-1.657
(0.351)***
Unemployment Rate
3.070
(0.170)***
Log(Personal Income)
-0.095
(0.176)
Log(Tax Revenue)
-0.202
(0.042)***
Log(% Nonresident)
0.012
(0.003)***
% FTE in Private
0.178
(0.179)
% FTE in Two-Year
0.021
(0.043)
% Republican Legislators
0.254
(0.035)***
Presence of Republican Governor
0.006
(0.006)
Political Liberalism
-0.273
(0.054)***
Governor Power
0.086
(0.024)***
% African American Legislators
-0.891
(0.238)***
% Women Legislators
-0.308
(0.110)***
% Latino Legislators
0.999
(0.236)***
Number of Separate Boards
-0.031
(0.005)***
% Trustees Elected
-0.217
(0.027)***
Presence of Weak Board
-0.000
(0.014)
Log(Regional Tuition)
0.507
(0.032)***
Log(State Appropriations per FTE)
-0.027
(0.017)*
Presence of Merit Aid
0.005
(0.014)
Log(Endowment per FTE)
0.003
(0.001)***
% Graduate Students
0.111
(0.071)
Constant
6.148
(1.877)***
Observations
3726
R-squared
0.890
Notes: * Significant at 5%; ** significant at 1%; *** significant at .1%. Year fixed effects are
included in the regression; their coefficients are suppressed. Standard errors are in parentheses.
28
TABLE 4. Marginal Effects for In-State Tuition and Fees at Flagship Universities
Dependent Variable: In-State Tuition and Fees
Population Share 18-24
Unemployment Rate
Log(Personal Income)
Log(Tax Revenue)
Log(% Nonresident)
% FTE in Private
% FTE in Two-Year
% Republican Legislators
Presence of Republican Governor
Political Liberalism
Governor Power
% African American Legislators
% Women Legislators
% Latino Legislators
Number of Separate Boards
% Trustees Elected
Presence of Weak Board
Log(Regional Tuition)
Log(State Appropriations per FTE)
Presence of Merit Aid
Log(Endowment per FTE)
% Graduate Students
Marginal Effect
Discrete
Change
-$55.40
(11.73)
$102.67
(5.68)
-$0.01
(0.02)
-$0.43
(0.09)
$2.49
(0.57)
$5.97
(5.99)
$0.70
(1.43)
$8.49
(1.17)
$21.55
(19.62)
-$9.14
(1.79)
$287.92
(81.09)
-$29.82
(7.95)
-$10.32
(3.68)
$33.40
(7.88)
-$102.26
(15.42)
-$7.24
(0.90)
-$1.09
(51.21)
$0.52
(0.03)
-$0.01
(0.00)
$18.62
(50.43)
$0.00
(0.00)
$3.73
(2.38)
Notes: Standard errors are in parentheses.
29
Stnd. Dev.
Change
-$60.92
(12.90)
$166.65
(9.22)
-$59.51
(110.32)
-$176.31
(36.83)
$0.35
(0.08)
$65.53
(65.84)
$8.91
(18.35)
$127.21
(17.50)
$10.74
(9.76)
-$116.16
(22.80)
$144.96
(40.83)
-$181.75
(48.43)
-$78.23
(27.91)
$243.21
(57.35)
-$498.44
(75.14)
-$146.18
(18.17)
-$0.53
(24.87)
$651.82
(40.57)
-$113.38
(68.23)
$8.58
(23.22)
$44.62
(15.88)
$32.81
(20.97)
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Notes
1
For example, Columbia charges $47,229 per year in undergraduate tuition and fees (Farrell,
2006).
2
Chronicle of Higher Education (2006b).
3
Dollar figures are for published rates, weighted by enrollment in different sectors.
4
Others argue that, at least in principle, white and male officials can represent racial minorities
and women just as well as more descriptively representative officials can (Swain 1993;
Kymlicka 1995; Young 1997). As Young (1997, 354) points out, “having such a relation of
identity or similarity with constituents says nothing about what the representative does.” In this
view, the electoral incentives of elected officials to be accountable to their constituents obtain
regardless of the correspondence of officials' race or gender with their constituents' race or
gender.
5
Notably, decades of accumulated evidence also provides support for the proposition that
African Americans and whites hold different political preferences. Kinder and Sanders (1996),
in fact, summarize more than 30 years of opinion surveys by concluding that “the racial divide”
in opinion is “a divide without peer” (p. 27). African Americans and whites differ remarkably in
their views on policies that are explicitly race-related like affirmative action, preferential hiring,
and equal employment policies. They disagree substantially (if somewhat less so) on policies
such as federal funding for education, welfare spending, law enforcement, health care, social
security, and other social welfare policies (e.g., Kinder and Sanders 1996; Canon 1999; Kinder
and Winter 2001).
6
Numerous empirical studies find Democratic Party strength linked with higher levels of state
taxation, higher overall spending, and higher spending on certain education and welfare
35
programs (McLendon and Hearn 2007) – the very policy preferences historically permitting
generous subsidies of public higher education and associated low levels of tuition. Given
Republican preferences (generally) for private investment in educational goods and services, we
believe we are likely to find higher tuition levels associated with higher levels of Republican
compositional strength in legislatures.
7
The conventional measure for governors’ institutional powers is the index updated periodically
by Beyle (2003). Beyle’s institutional-powers variable is a metric combining scores on six
individual indices: governor’s tenure potential, appointment power, budget power, veto power,
extent to which the governor’s party also control the legislature, and whether the state provides
for separately elected executive branch officials.
8
By “weak,” we mean a coordinating board lacking authority to approve institutional budgets.
9
This is known as the so-called “Bennett hypothesis.” In 1987, then secretary of education
William Bennett declared, in a New York Times editorial, “If anything, increases in financial aid
in recent years have enabled colleges and universities to blithely raise their tuitions, confident
that Federal loan subsidies would help cushion the increase.”
10
Alternatively, a reasonable case could be made for a negative relationship if institutions used
endowment as an alternate source of income.
11
We used the Stata program uvis (univariate imputation sampling) to impute missing values for
our indicator of non-resident status according to the following algorithm: (a) predict the fitted
values at the nonmissing observations of “non-resident” using the results from a regression of the
nonmissing values of “non-resident” on the corresponding X vector; (b) randomly draw a value
from the posterior distribution of the residual standard deviation; (c) randomly draw a value from
the posterior distribution of beta, allowing for uncertainty in beta via the draw of the standard
36
deviation; (d) use the drawn value of beta to predict the fitted values at the missing observations
of the indicator of non-resident status; (e) for each observation with a missing value for “nonresident,” impute this value using the nonmissing observation whose prediction on observed data
is closest to the prediction for the missing observation in question. For further details, including
extensive discussion of the advantages of this technique over casewise deletion, see Roderick,
Little and Rubin (2002).
12
A Chi-squared statistic of 11,319.80 (p-value=0.000) for the Breusch-Pagan test indicates a
rejection of the OLS model in favor of one accounting for unobservable university-specific
effects. A Chi-squared statistic of 35.41 (p-value=0.026) for the Hausman test indicates that the
assumptions of the random effects model are not appropriate with our data and suggests use of
the fixed effects model.
13
We build on the work of several leading analysts in treating state appropriations as exogenous
(Lowry 2001a; 2001b; Rizzo and Ehrenberg 2004). In particular, see Lowry (2001a) on this
question. Also, we use a dummy variable to indicate whether any merit aid was available in a
given institution-year, rather than relying on level of aid, which is likely to be endogenous. The
dummy is exogenous, we believe, because the existence of a program is likely the result of
influences outside the model explaining tuition (e.g., see Rizzo and Ehrenberg 2004).
14
It should be stressed however that a framework such as ours is best suited to uncover the
direction and strength of the relationships at hand. Measurement issues, potential endogeneity,
and our use of imperfect proxies led us to place less weight on the exact marginal impacts
predicted by the model. Nevertheless, the richness of our empirical approach suggests that there
is value in accessing the size of the impacts we find for our explanatory variables.
37