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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: mmclendon@smu.edu.

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.

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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.

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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 four-year 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

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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

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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 higher-education 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

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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

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(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 1984-2006. 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

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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;

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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

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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 labor-force 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

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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.

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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

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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

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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 socio-demographic, 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.

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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).

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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 1984-2000 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

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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) PostsecondaryStructures 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 university-specific 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

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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:

, 5 , 4 , 3 , 2 , 1 0 Pit Ait Git Eit t i it it X X X X T c u Y         t=1984, …, 2006.

i is the panel variable, the university. t refers to the Fall of the academic year of the observation in question. XP is a vector of political factors hypothesized to influence university pricing;XA is a vector of state government choice variables, including appropriations and student aid; XG is

a vector of higher education governance characteristics; and, XE 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 (XP,XA,XG,XE) and T) by demeaning each (Yit,Xit) 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

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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))

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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; Nicholson-Crotty 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.

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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 merit-based 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

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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 out-of-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

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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.

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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

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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.

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TABLE 1. Summary Statistics for the Dependent Variable: In-State Tuition and Fees Year Unweighted Average Weighted Average Standard

Deviation Median Minimum Maximum

Annual Growth Rate 1984 $1,808.56 $1,905.19 $783.98 $1,790.07 $582.37 $6,929.26 1985 $1,859.78 $1,924.71 $683.18 $1,876.23 $573.77 $4,217.21 2.83% 1986 $2,130.29 $2,199.87 $745.21 $2,042.81 $927.72 $4,701.77 14.55% 1987 $2,207.90 $2,282.95 $773.31 $2,116.93 $967.27 $4,869.13 3.64% 1988 $2,276.41 $2,376.06 $825.91 $2,130.30 $935.35 $5,144.42 3.10% 1989 $2,406.40 $2,476.01 $850.51 $2,306.60 $975.08 $5,201.30 5.71% 1990 $2,469.29 $2,535.17 $915.81 $2,342.19 $938.84 $5,625.68 2.61% 1991 $2,694.15 $2,748.71 $1,027.93 $2,631.35 $1,044.49 $6,290.61 9.11% 1992 $2,892.31 $2,943.00 $1,097.23 $2,768.43 $1,021.01 $7,137.84 7.36% 1993 $3,061.72 $3,112.07 $1,153.73 $2,869.43 $997.96 $7,241.46 5.86% 1994 $3,189.67 $3,236.09 $1,200.87 $2,991.41 $1,336.16 $7,369.95 4.18% 1995 $3,292.10 $3,331.82 $1,196.70 $3,085.57 $1,515.64 $7,501.12 3.21% 1996 $3,395.33 $3,437.98 $1,202.57 $3,148.55 $1,611.03 $8,232.05 3.14% 1997 $3,484.98 $3,534.46 $1,188.91 $3,193.47 $1,695.78 $7,912.94 2.64% 1998 $3,591.19 $3,639.05 $1,209.16 $3,304.59 $1,756.99 $8,176.48 3.05% 1999 $3,661.98 $3,703.68 $1,195.82 $3,503.70 $1,814.69 $8,219.23 1.97% 2000 $3,773.36 $3,811.06 $1,226.56 $3,544.50 $1,776.00 $8,288.00 3.04% 2001 $3,963.58 $3,995.41 $1,334.03 $3,633.82 $1,734.39 $9,941.48 5.04% 2002 $4,244.01 $4,303.69 $1,480.24 $3,905.46 $1,794.84 $11,199.03 7.08% 2003 $4,743.83 $4,793.49 $1,670.54 $4,376.67 $2,212.30 $13,033.20 11.78% 2004 $5,111.74 $5,178.92 $1,921.30 $4,769.25 $2,260.15 $17,944.20 7.76% 2005 $5,249.72 $5,349.52 $1,964.77 $4,912.67 $2,306.38 $19,009.28 2.70% 2006 $5,415.94 $5,514.87 $2,042.55 $4,995.43 $2,281.52 $19,710.02 3.17% 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.

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TABLE 2. Summary Statistics for Control Variables, 1984 and 2006 1984 2006 Annual Growth Rate Variable Average (SD) Average (SD) Population Share 18-24 12.51% 9.89% -1.06% (0.60%) (0.68%) Unemployment Rate 7.62% 4.63% -2.23% (1.96%) (1.00%) Personal Income $19,699.45 $30,215.16 1.96% ($2,615.32) ($4,082.40) Tax Revenue $1,206.12 $1,956.90 2.22% ($451.27) ($440.08) % Nonresident 14.87% 17.63% 0.86% (13.01%) (14.55%) % FTE in Private 19.59% 22.97% 0.73% (11.17%) (11.49%) % FTE in Two-Year 34.04% 34.67% 0.08% (13.17%) (11.21%) % Republican Legislators 35.48% 51.46% 1.70% (17.92%) (13.28%)

Presence of Republican Governor 30.25% 60.49% 3.20% (46.08%) (49.04%)

Political Liberalism 44.32% 52.14% 0.74%

(13.43%) (12.86%)

Governor Power 3.72 3.45 -0.35%

(0.74) (0.37) % African American Legislators 6.49% 10.36% 2.15%

(4.13%) (7.22%)

% Women Legislators 11.53% 22.06% 2.99%

(5.45%) (6.69%)

% Latino Legislators 2.34% 5.56% 4.00%

(5.56%) (8.80%)

Number of Separate Boards 5.67 5.90 0.18%

(5.02) (4.56)

% Trustees Elected 5.12% 4.27% -0.83%

(20.75%) (19.47%)

Presence of Weak Board 43.83% 29.01% -1.86% (49.77%) (45.52%)

Regional Tuition $1,749.24 $5,274.39 5.14% ($351.69) ($1,115.56)

State Appropriations per FTE $10,492.65 $11,263.30 0.32% ($9,307.33) ($16,194.62)

Presence of Merit Aid 61.11% 74.69% 0.92%

(48.90%) (43.61%) Endowment per FTE $4,531.91 $32,985.56 9.44%

($26,084.50) ($159,805.70)

% Graduate Students 18.14% 19.83% 0.41%

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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 data. Annual Growth Rate refers to the compound annual growth rate from 1984 to 2006

1/ 22 2006 1984 1 X X        ,

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TABLE 3. Regression Results for In-State Tuition and Fees at Flagship Universities Dependent Variable: Log(In-State Tuition and Fees) Coefficient

Population Share 18-24 -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

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TABLE 4. Marginal Effects for In-State Tuition and Fees at Flagship Universities

Marginal Effect

Dependent Variable: In-State Tuition and Fees

Discrete Change Stnd. Dev. Change Population Share 18-24 -$55.40 -$60.92 (11.73) (12.90) Unemployment Rate $102.67 $166.65 (5.68) (9.22) Log(Personal Income) -$0.01 -$59.51 (0.02) (110.32) Log(Tax Revenue) -$0.43 -$176.31 (0.09) (36.83) Log(% Nonresident) $2.49 $0.35 (0.57) (0.08) % FTE in Private $5.97 $65.53 (5.99) (65.84) % FTE in Two-Year $0.70 $8.91 (1.43) (18.35) % Republican Legislators $8.49 $127.21 (1.17) (17.50) Presence of Republican Governor $21.55 $10.74

(19.62) (9.76)

Political Liberalism -$9.14 -$116.16

(1.79) (22.80)

Governor Power $287.92 $144.96

(81.09) (40.83) % African American Legislators -$29.82 -$181.75

(7.95) (48.43)

% Women Legislators -$10.32 -$78.23

(3.68) (27.91)

% Latino Legislators $33.40 $243.21

(7.88) (57.35) Number of Separate Boards -$102.26 -$498.44

(15.42) (75.14)

% Trustees Elected -$7.24 -$146.18

(0.90) (18.17) Presence of Weak Board -$1.09 -$0.53

(51.21) (24.87)

Log(Regional Tuition) $0.52 $651.82

(0.03) (40.57) Log(State Appropriations per FTE) -$0.01 -$113.38

(0.00) (68.23)

Presence of Merit Aid $18.62 $8.58

(50.43) (23.22)

Log(Endowment per FTE) $0.00 $44.62

(0.00) (15.88)

% Graduate Students $3.73 $32.81

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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

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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

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