Creating the Out-of-State University: Do Public Universities Increase ...

Res High Educ (2015) 56:535–565 DOI 10.1007/s11162-015-9362-2

Creating the Out-of-State University: Do Public Universities Increase Nonresident Freshman Enrollment in Response to Declining State Appropriations? Ozan Jaquette • Bradley R. Curs

Received: 19 May 2014 / Published online: 23 January 2015 Springer Science+Business Media New York 2015

Abstract This study investigates whether public universities respond to declines in state appropriations by increasing nonresident freshman enrollment. State higher education appropriations declined substantially during the 2000s, compelling public universities to become more dependent on net-tuition revenue. State policy controls often limit the growth of resident tuition price. Therefore, public universities have an incentive to grow nonresident enrollment in order to grow tuition revenue. Drawing on resource dependence theory, we hypothesize that public universities respond to declines in state appropriations by growing nonresident freshman enrollment. Furthermore, we hypothesize that this response will be strongest at research universities because research universities enjoy strong demand from prospective nonresident students. We tested these hypotheses using a sample of all US public baccalaureate granting institutions and an analysis period spanning the 2002–2003 to 2012–2013 academic years. Fixed effects panel models revealed a strong negative relationship between state appropriations and nonresident freshman enrollment. This negative relationship was stronger at research universities than master’s or baccalaureate institutions. These results provide empirical support for assertions by scholars that state disinvestment in public higher education compels public universities to behave like private universities by focusing on attracting paying customers. Keywords State appropriations Nonresident enrollment Higher education finance Public universities Organizational behavior Tuition revenue

O. Jaquette (&) Educational Policy Studies & Practice, University of Arizona, College of Education, 1430 E. Second Street, Room 327A, Tucson, AZ 85721, USA e-mail: [email protected] B. R. Curs Educational Leadership and Policy Analysis, University of Missouri, College of Education, 202 Hill Hall, Columbia, MO 65211, USA e-mail: [email protected]

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Across the US states are disinvesting in public universities. Total state appropriations across all public baccalaureate granting institutions declined from $54.5 billion in 2001–2002 to $45 billion by 2011–2012 (authors’ calculations based on IPEDS data).1 Public universities have responded to declines in state appropriations by seeking alternative revenue sources. Prior research highlights institutional efforts to increase revenue from research (Slaughter and Leslie 1997; Slaughter and Rhoades 2004), donations and investments (Cheslock and Gianneschi 2008), and auxiliary enterprises (Barringer 2013). However, tuition revenue has been the dominant source of revenue growth. Total net-tuition revenue across all US public universities increased from $35 billion in 2003–2004 to $56 billion in 2011–2012. For the median public university, net-tuition revenue increased from $31 million in 2003–2004 (23 % of total revenue) to $42 million in 2011–2012 (28 % of total revenue).2 This paper investigates the research question: do public universities respond to declines in state appropriations by increasing nonresident freshman enrollment? Public universities in most states lack the unilateral authority to increase resident tuition price (Zinth and Smith 2012), limiting the ability of public universities to compensate for declines in state appropriations by increasing tuition revenue from resident students. In contrast, public universities have autonomy over nonresident tuition price, which is typically two to three times greater than resident tuition price (NCES 2014, Table 330.20). Therefore, institutions have a financial incentive to increase nonresident enrollment when state appropriations decline. Several news articles report that public universities—especially public research universities—are growing nonresident enrollment to substitute for recent declines in state appropriations (e.g., Hoover and Keller 2011; Jaschik 2009). At public universities defined as ‘‘doctoral/research-extensive’’ by the 2000 Carnegie Classification, mean nonresident freshman enrollment increased from 747 in 2000–2001 to 1,169 in 2012–2013 (a 56 % increase), compared to an increase of 2,981 to 3,346 for resident freshman (a 12 % increase) (authors’ calculations based on IPEDS). However, prior research by Rizzo and Ehrenberg (2004) on the relationship between state appropriations and nonresident enrollment predate the dramatic decline in state appropriations over the last decade. Drawing on resource dependence theory (Pfeffer and Salancik 1978), we hypothesize that public universities responded to declining state appropriations by growing nonresident freshman enrollment. Furthermore, we hypothesize that this response was stronger for research universities compared to master’s and baccalaureate universities because research universities enjoy stronger demand from prospective nonresident freshmen. We tested these hypotheses using data on US public baccalaureate granting institutions with an analysis period of the 2002-03 to 2012-13 academic years. Fixed effects panel models revealed a strong negative relationship between state appropriations and freshman nonresident enrollment. This negative relationship was stronger at research universities when compared to master’s or bachelor’s universities. These findings have important implications for scholarship on the privatization of public higher education. State policymakers cut higher education funding more than other budget categories during bad economic times because policymakers believe that universities can compensate for state cuts by growing tuition revenue (Delaney and Doyle 2007, 2011; Hovey 1999). The privatization literature argues that state disinvestment in public higher education compels public universities to behave like private universities, by focusing on attracting paying customers rather than focusing on public-good goals associated with the state (e.g., access for state residents) (e.g., Ehrenberg 2006a; Morphew and Eckel 2009; Priest and St. John 2006). 1

All reported monetary values have been adjusted to constant 2012 dollars.

2

Total revenue is defined as the sum of total operating and total non-operating revenue.

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Scholarship on organizational behavior can contribute to policy debates about state higher education funding by developing a corpus of research that assesses the consequences of declines in state appropriations. The present study shows that cuts in state appropriations are associated with growth in nonresident freshman enrollment but no change in resident freshman enrollment. These findings suggest that the proportion of nonresident students grow when state appropriations decline. In turn, the findings from this study have implications for institutional policy concerns about access at public universities. Scholars argue that social and racial stratification in access to flagship public universities is a growing problem (Gerald and Haycock 2006; Haycock et al. 2010). Jaquette et al. (2014) found that growth in the proportion of nonresident freshman was associated with a declining share of low-income and underrepresented minority students at public research universities. Therefore, analyzing the relationship between state appropriations and nonresident freshman enrollment may identify a mechanism through which declining state appropriations has negative consequences for the racial and socioeconomic climate experienced by low-income and underrepresented minority students at public research universities.

Literature Review State Appropriations and Revenue-Seeking Behaviors We review empirical literature on the relationship between state appropriations and revenue generating behaviors because we conceive of nonresident enrollment growth as a potential means of revenue generation. Figure 1 provides context for our literature review by showing median revenues by revenue category in the 2003–2004 and 2011–2012 academic years for public baccalaureate granting institutions (as defined by the 2000 Carnegie Classification). Results are shown separately for doctoral/research universitiesextensive (herein research-extensive), doctoral research universities-intensive (herein research-intensive), master’s colleges and universities (herein master’s), and baccalaureate colleges (including baccalaureate/associate’s colleges). Figure 1 shows that state appropriations were lower in 2011–2012 than 2003–2004 across all four institutional types. State higher education appropriations grow when state economies are strong and decline during recessions (Adams 1977; Clotfelter 1976; Delaney and Doyle 2011; Kane et al. 2003; Toutkoushian and Hollis 1998; Weerts and Ronca 2012), often more so than other state budget items (Delaney and Doyle 2011). State ‘‘tax revolt’’ legislation negatively affects state appropriations by shrinking the pie of state tax revenues (Archibald and Feldman 2006). Holding the pie of state tax revenue constant, state higher education appropriations are negatively affected by state spending on ‘‘mandatory’’ budget categories (e.g., Medicaid, K-12 funding, corrections, etc.) (Kane et al. 2003; Okunade 2004), and Republican state legislatures and governors (McLendon et al. 2009; Nicholson-Crotty and Meier 2003), amongst other factors (for a full review see Tandberg and Griffith 2013). Given these findings, it is unsurprising that the past decade—one characterized by protracted economic recessions, increased spending on mandatory programs, and strong anti-tax sentiment—has been associated with declining state appropriations to public universities. Scholarship on university revenue-seeking responses to declines in state appropriations is often motivated by resource dependence theory (e.g., Slaughter and Leslie 1997). Resource dependence theory argues that resource diversification (e.g., seeking alternative revenues) is a common organizational response to declines in an important resource.

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Fig. 1 Median revenues for public universities by 2000 Carnegie Classification ($millions, 2012 CPI). Note: Authors’ calculations based on IPEDS Finance data for public institutions using GASP reporting standards; Excludes six public institutions that report IPEDS Finance data using the Financial Accounting Standards Board (FASB) standards: Pennsylvania State University; University of Delaware; University of Pittsburgh; Temple University; Pennsylvania College of Technology; Lincoln University of Pennsylvania. However, these institutions are included in panel regression models. State g&c: state non-operating grants (e.g., state financial aid to students); and state operating grants and contract (e.g., state research grants). Net-tuition: Net-tuition revenue, excludes discounts and allowances from institutional and government (e.g., Pell, state financial aid) sources. Federal (all): federal appropriations; federal non-operating grants (e.g., Pell); and federal operating grants (e.g., federal research grants). Aux, hosp: auxiliary revenues excluding discounts and allowances; independent operations; and hospital revenues Priv, gift, invest: private gifts; investment income; capital grants & gifts; additions to permanent endowments; and private and local government grants and contracts (cannot separate private and local g&c in 2004). Local, oth: local appropriations; local non-operating grants; capital appropriations from governmental sources; other operating revenues; other non-operating revenues; other revenues and additions

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Slaughter and Leslie (1997) and Slaughter and Rhoades 2004) found that public universities responded to declining state appropriations by attempting to grow research revenues. In particular, public universities shifted the emphasis of research from basic to applied science. However, only research universities were successful in generating substantial revenues from research. Cheslock and Gianneschi (2008) tested the hypothesis that declines in state appropriations would be associated with subsequent growth in revenues from voluntary support (donations and investments). Their findings did not support this hypothesis, and even provided evidence that state appropriations and voluntary support were positively correlated. However, they found that reliance on voluntary support exacerbates inequality across public universities because voluntary support revenues are positively related to selectivity. Consistent with Cheslock and Gianneschi (2008), Fig. 1 shows that median revenues from private grants, gifts, and investments at researchextensive universities increased from $74 million in 2003–2004 to $103 million in 2011–2012, but that other institutions did not generate significant revenues from these sources. Descriptive analyses have documented the transformation from reliance on state appropriations to reliance on tuition revenue (e.g., Desrochers and Wellman 2011; Ehrenberg 2012; Kirshstein 2013). For example, Barringer (2013) showed a sharp decline in the number of public universities relying predominantly on state funds from 1980 to 2000 and a sharp increase in the number of public universities relying heavily on tuition revenue. Figure 1 reveals that this trend continued during the 2003–2004 to 2011–2012 period. Tuition revenue was the largest source of revenue growth for research-extensive, researchintensive, master’s, and baccalaureate institutions. While research on efforts to increase revenue from research and voluntary support have made valuable contributions (e.g., Cheslock and Gianneschi 2008; Slaughter and Leslie 1997), organizational behaviors designed to increase tuition revenue remain understudied. This deficiency in the literature is particularly important because tuition revenue is the largest source of revenue growth for most public universities. One topic that has been addressed is the relationship between state appropriations and resident tuition price. Koshal and Koshal (2000) and Rizzo and Ehrenberg (2004) found a strong negative relationship between state appropriations and resident tuition price, while Hossler et al. (1997) found an insignificant relationship. However, state policies limit growth in resident tuition price (Zinth and Smith 2012). Therefore, public universities have a financial incentive to respond to declines in state appropriations by increasing nonresident enrollment. To our knowledge, Rizzo and Ehrenberg (2004) remains the only empirical analysis of the effect of state appropriations on nonresident enrollment. Rizzo and Ehrenberg (2004) used an analysis sample of 91 ‘‘flagship’’ public universities and an analysis period of 1979–1998. They examined the effect of state appropriations on four outcome variables. Fixed effects, two-stage least squares estimators showed that state appropriations had (a) a positive-significant relationship with need-based grant aid, (b) a negative, significant relationship with resident-tuition, (c) no relationship with nonresident tuition, and (d) no relationship with the ratio of nonresident to resident freshman enrollment. We argue that the effect of state appropriations on nonresident enrollment merits additional analysis because Rizzo and Ehrenberg (2004) analyzed the subset of ‘‘flagship’’ public universities rather than the population of public universities. Second, state appropriations have declined dramatically since 1998, the final year of Rizzo and Ehrenberg’s (2004) analysis period. Determinants of Nonresident Enrollment at Public Universities The second part of our literature review focuses on the determinants of nonresident enrollment, the dependent variable of interest in the present study. Literature on the

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determinants of nonresident enrollment falls under a broader literature on interstate migration. The terminology of the migration literature refers to the out-migration of state residents from an origin state to a different destination state and the in-migration of nonresident students to an institution located in a different state than the students’ state of origin. We divide our review of the migration literature into (a) factors that affect demand by nonresident students and (b) factors that affect institutional willingness to supply enrollment seats to resident versus nonresident students. Demand by Nonresident Students Several studies find a positive relationship between measures of institutional quality (e.g., academic profile, rankings, expenditure per student) and nonresident enrollment demand (Adkisson and Peach 2008; Baryla and Dotterweich 2001; Mak and Moncur 2003; Zhang 2007). Work by Caroline Hoxby provides intuition for this result. Hoxby (1997, 2009) described the transformation of US higher education from a system of local autarkies, where students attended the institution closest to home, to a nationally integrated system where the ‘‘highest quality’’ students increasingly attend the ‘‘highest quality’’ institutions, regardless of geographic proximity. Prior research finds that nonresident student demand decreases as distance between states increases (Cooke and Boyle 2011; Morgan 1983). However, Long (2004b) found that the importance of distance in student college choice decisions decreased from 1972 to 1992 while the importance of measures of institutional academic quality increased. Aside from academic quality, nonresident students are drawn to quality of life amenities, including strong collegiate athletics (Mixon and Hsing 1994) and desirable natural resources (e.g., topography, climate) (Cooke and Boyle 2011). Other studies have examined the relationship between nonresident enrollment demand and the components of net price, which include tuition and grant aid. Institution-level analyses of in-migration have found that nonresident students are relatively insensitive to growth in the sticker price of nonresident tuition (Adkisson and Peach 2008; Baryla and Dotterweich 2001; Dotterweich and Baryla 2005; Mixon and Hsing 1994; Zhang 2007). This is consistent with the finding that nonresident students tend to be relatively wealthy and unconcerned about costs (Mak and Moncur 2003; Tuckman 1970). Zhang (2007) found that nonresident enrollment at public doctoral and research universities were less sensitive to sticker price increases when compared to nonresident enrollment at master’s universities. However, student level studies have found that nonresident students are relatively more sensitive to net-tuition price when compared to resident students (Curs and Singell 2002, 2010). Additionally, several student-level studies have found that nonresident enrollment decisions are highly sensitive to institutional aid offers (Abraham and Clark 2006; Curs and Singell 2010; DesJardins 2001). Therefore, institutional aid is an important component of enrollment management strategies to increase nonresident enrollment. Research also shows that student out-migration from an origin state to a different destination state is sensitive to the relative prices of institutions within a student’s home state. Cooke and Boyle (2011) found that out-migration rates were higher in states with relatively high resident tuition. State merit aid programs are motivated, in part, by the policy goal of reducing out-migration by high academic ability high school students (Zhang and Ness 2010). A robust literature finds that state merit aid programs reduce outmigration by reducing the costs of attending an in-state institution relative to an out-ofstate institution (e.g., Cornwell et al. 2006; Mak and Moncur 2003; Orsuwan and Heck 2009; Toutkoushian and Hillman 2012; Zhang et al. 2013; Zhang and Ness 2010).

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Institutional Willingness to Supply Seats to Nonresident Students While many studies have analyzed enrollment demand by non-resident students, there have been few supply-side analyses of institutional preferences for resident and nonresident students. Groen and White (2004) analyzed whether universities prefer resident or nonresident applicants, using cohorts of college freshman from 1976 and 1989. Whereas private universities treated resident and nonresident applicants equally, public universities set lower admissions standards for resident applicants even though nonresident students paid higher tuition (Groen and White 2004). The authors argued that public universities set lower admissions standards for residents because state policymakers influence the behavior of public universities and state policymakers desire access for state residents. Using an analysis period of 1986–2007, Winters (2012) found that growth in the population of college-age state residents crowded out nonresident enrollment at public universities. These results imply that public universities prefer enrolling state residents and grow nonresident enrollment when excess capacity exists. Our review of studies on institutional preferences for resident versus nonresident students reveals an important research gap. Extant studies argue that desire by state policymakers to increase access for state residents, coupled with institutional reliance on state funding, compel public universities to prefer resident applicants. However, Rizzo and Ehrenberg (2004) and Groen and White (2004) analyzed an older period when state appropriations were relatively high. Winters (2012) analyzed a more recent period but did not analyze the effect of state appropriations. Figure 1 shows a sharp decline in state appropriations from 2003–2004 to 2011–2012, suggesting that state control over public university admissions preferences may have waned. Unfortunately, extant research has not examined whether public universities responded to recent declines in state appropriations by increasing nonresident enrollment.

Conceptual Framework Following prior research, we draw from resource dependence theory (Pfeffer and Salancik 1978) to develop a model of organizational decision-making that explains the relationship between state appropriations and institutional willingness to supply seats to nonresident students. Pfeffer and Salancik (1978) argued that survival, stability, and autonomy are the primary goals of organizations. Stability and survival depend on a predictable flow of resources from the external environment (Parsons 1956). Organizations can be controlled by external resource providers when that particular resource provided is important for organizational survival—measured as the proportion of total inputs or outputs accounted for by that exchange—and when that resource cannot be easily obtained from other sources (Emerson 1962; Pfeffer and Salancik 1974). Resource dependence theory identifies several strategies organizations may adopt to overcome the problem of reliance on an uncertain or declining resource (Davis and Cobb 2009). For example, organizations adopting a compliance strategy acquiesce to additional performance demands by the resource provider. Organizations adopting a cooptation strategy attempt to commit external resource providers to the goals of the organization by inviting them to participate in organizational activities (e.g., advisory panels). When strategies such as compliance and cooptation fail or when the performance demands of resource providers become too onerous, organizations often engage in resource diversification to reduce reliance on a particular resource provider. Pfeffer and Salancik (1978)

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stated that resource diversification is often associated with mission drift because organizations ‘‘redefine their stated goals to fit new contingencies in the environment… [permitting] the organization to take on new tasks or activities, lessening dependence on old environments and activities’’ (p. 131). We apply resource dependence theory to the present study. Historically, state governments provided the majority of financial resources to public universities (Heller 2001), suggesting that the behavior of public universities was largely oriented to public-good goals defined by the state (e.g., access for state residents, economic development) (Labaree 1997). However, state appropriations per full-time equivalent (FTE) student began to decline in the late 1970s (Kane et al. 2003) and the volatility of state support for higher education increased from 1985 to 2004 (Delaney and Doyle 2011). Finally, state higher education appropriations have declined sharply over the past decade, especially since the most recent recession (Hurlburt and Kirshstein 2012). Resource dependence theory suggests that public universities would respond to initial declines or uncertainty in state appropriations by attempting to demonstrate the value public universities provide to the state. For example, Covaleski and Dirsmith (1988) found the University of Wisconsin System and its constituent campuses responded to state appropriations declines in the 1980s by highlighting its contribution to state economic growth. Resource dependence theory suggests that revenue diversification is the most likely organizational response to prolonged declines in state appropriations. Consistent with this idea, extant research asserts that public universities have responded to declines in state appropriations by attempting to increase revenues from research and voluntary support (Cheslock and Gianneschi 2008; Slaughter and Leslie 1997). However, Fig. 1 shows that tuition revenue has been the dominant source of revenue growth for public universities over the last decade. Descriptive statistics for 2011–2012 college freshmen from the National Postsecondary Student Aid Survey (NPSAS) reveal that public universities derive more net-tuition revenue from each nonresident freshman than each resident freshman. For resident freshmen attending public baccalaureate granting institutions, mean sticker price (tuition and fees) was $7,360 and mean institutional aid was $877, implying mean net-tuition revenue of $6,483 per student (authors’ calculations, 2012 CPI). For nonresident freshmen, mean sticker price was $19,344 and mean institutional aid was $2,365, implying mean net-tuition revenue of $16,979. At research-extensive public universities, each resident student generated mean net-tuition revenue of $7,396 and each nonresident student generated mean net-tuition revenue of $19,606. Although public universities may desire nonresident students for many reasons (e.g., declines in college-age population, nonresident applicants having higher academic profile), descriptive statistics from NPSAS suggest that public universities have a financial incentive to compensate for declines in state appropriations through growth in nonresident enrollment. Therefore, our first hypothesis states that public universities respond to declines in state appropriations by increasing nonresident enrollment. We focus on nonresident freshman enrollment because public universities can exert more control over changes in their freshman cohort than changes in their sophomore, junior, and senior cohorts. H1 State appropriations have a negative relationship with nonresident freshman enrollment. Resource dependence theory suggests that all organizations desire diverse revenue streams but revenue opportunities available to some organizations may be unavailable to others (Pfeffer and Salancik 1978). Applying this idea, many public universities have a revenue incentive to increase nonresident enrollment when state appropriations decline, but

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only universities with sufficient demand from nonresident students can realize this desire. Previously reviewed empirical research suggests that nonresident students have high demand for high-quality public universities and low demand for low-quality public institutions (e.g., Adkisson and Peach 2008; Baryla and Dotterweich 2001; Zhang 2007). In particular, Zhang (2007) found that nonresident enrollment at master’s universities were more sensitive to tuition price than nonresident enrollment at research universities, presumably because research universities enjoyed higher student demand. Using the 2000 Carnegie Classification to define institutional types, we hypothesize: H2 The negative relationship between state appropriations and nonresident freshman enrollment is stronger for research-extensive and research-intensive universities than master’s and baccalaureate institutions.3 Because of data limitations our analyses cannot assess the mechanisms that explain how public universities went about increasing nonresident enrollment. However, a brief discussion of institutional behaviors that link increased desire for nonresident students to actual nonresident enrollment growth helps develop the rationales for what covariates to include and what time-lag assumptions to use in the statistical models. Drawing from the literatures on college-choice (e.g., Hossler et al. 1989, 1999; Hossler and Gallagher 1987; Perna 2006) and enrollment management (e.g., Cheslock and Kroc 2012; Hossler and Bean 1990), institutions increase enrollment from desired student populations by engaging in enrollment management strategies that target particular populations in the ‘‘search’’ and ‘‘choice’’ phases of the college choice process. During the search phase (11th and 12th grade for traditional students), public universities can influence nonresident application decisions through advertising, recruitment events, campus visits, etc. (Hossler et al. 1999). During the choice phase (12th grade for traditional students), public universities can increase nonresident enrollment by lowering admissions standards for nonresident applicants (Zhang 2007). Additionally, enrollment managers can use institutional aid offers to increase the probability of enrollment by admitted nonresident applicants (Ehrenberg and Sherman 1984). Prior research suggests that enrollment decisions by nonresident applicants are sensitive to institutional aid offers (Curs and Singell 2010) and that institutions use increasingly sophisticated modelling techniques to determine the institutional aid offer for each student (Cheslock and Kroc 2012; DesJardins 2001).

Methodology To test our research hypotheses we utilized the following institution-level panel model: Yit ¼ bXi;t1 þ Wi;t1 c þ Vs;t1 h þ dt þ ai þ eit

ð1Þ

where, subscript i represents institutions and subscript t represents time, in years. Yit , nonresident freshman, is the number of nonresident freshman who enroll in institution i at time t. Xi;t1 represents state appropriations lagged 1 year relative to nonresident freshman 3 We acknowledge that H2 could utilize a different construct to classify institutions (e.g., US News and World Report, Barron’s, average SAT/ACT score, athletic conference, etc.). However, we believe that the 2000 Carnegie Classification does a reasonable job of (a) capturing institutional characteristics associated with nonresident enrollment demand (e.g., academic profile, expenditure per student, college athletics) and (b) defining the overall analysis sample while creating sub-groups of institutions (e.g. research-extensive versus master’s) with sufficient sample size for analyses.

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enrollment. b is the coefficient of interest, representing the effect of state appropriations on nonresident freshman enrollment. Wi;t1 is a matrix of institution- and time-varying covariates lagged 1 year. Vs;t1 is a matrix of state- and time-varying covariates lagged 1 year, where subscript s represent the state which institution i is located. dt represents year-varying, institution-invariant effects. ai represents institution-varying, time-invariant omitted variables. eit represents institution-varying, time varying omitted variables. The rationale for time-lag decisions is explained below. The goal of our empirical methodology is to estimate b, the causal effect of state appropriations on nonresident freshman enrollment. Ideally, institutions would be randomly assigned alternative levels of state appropriations and the average treatment effect could be estimated due to the experimental source of exogenous variation. Unfortunately, state appropriations are not randomly assigned. An estimate of b is likely to be a biased measure of the true causal effect if omitted variables that affect nonresident freshman enrollment have a systematic relationship with state appropriations. The following two paragraphs describe our estimation strategy to minimize the potential biases associated with the estimation of Eq. 1 when state appropriations may be endogenous. Panel models must satisfy two assumptions in order to interpret b as a causal effect (Cameron and Trivedi 2005). First, the ‘‘random effects’’ assumption states that there is no relationship between Xi;t1 and the panel-varying, time-invariant error component, ai , after controlling for covariates. The random effects assumption is implausible in most empirical contexts (Cheslock and Rios-Aguilar 2011). For the present study, it is likely that unobserved institution-varying, time-invariant variation affects nonresident freshman enrollment and is correlated with state appropriations. Therefore, we used a fixed effects ‘‘within’’ estimator, which satisfies the random effects assumption by eliminating all unobserved institutionvarying, time-invariant variation, ai .4 Note that when using the fixed effects estimator, b^ is calculated solely from variation over time within institutions because the fixed effects estimator eliminates all cross-sectional variation between institutions. Second, the ‘‘strict exogeneity’’ assumption states that there is no relationship between institution-varying, time-varying omitted variables, eit , and the independent variable of interest, Xi;t1 , in any time period, t ¼ 1; . . .; T, after controlling for covariates. We attempted to satisfy this assumption, first, by including institution-invariant, time-varying fixed effects (i.e., time dummies) to control for national time trends. Second, we attempted to include all time-varying panel-varying, Wi;t1 , and state-varying, Vs;t1 , covariates that plausibly affect Yit and could have a relationship with Xi;t1 . These covariates are described below. Despite these efforts, our results should not be interpreted as causal effects because it is unlikely to eliminate all sources of omitted variable through the inclusion of control variables (Cameron and Trivedi 2005). All models estimated cluster robust standard errors, clustered at the state-level, to relax assumptions about heteroskedasticity and serial correlation between institutions within states. Our models specified a 1-year lag between state appropriations and nonresident freshman enrollment. We use the example of state appropriations from a single year, the 2011–2012 academic year, to explain the logic of the 1-year time lag that we applied to the entire analysis period. We assume that state appropriations for 2011–2012, running August 2011 through July 2012, were finalized by June 2011 because all but four states have fiscal 4

For each dependent variable analyzed, Hausman tests of whether a random effects estimator would be consistent were rejected in all cases (p \ 0.01).

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years ending in June (National Conference of State Legislatures 2012). Drawing from resource dependence theory, we argue that declines in 2011–2012 state appropriations (known in June 2011) caused institutions to desire more nonresident students. Based on the college choice and enrollment management literatures, we assume that institutions attempted to increase nonresident freshman enrollment through the use of enrollment management strategies (e.g., marketing, lowering admissions standards, increasing institutional aid) that targeted the late-search and choice decisions of out-of-state high school seniors who would be college freshman in 2012–2013. Therefore, our justification for a applying a 1-year lag to state appropriations is as follows: we posit that declines in 2011–2012 state appropriations were associated with increased recruitment efforts targeted at out-of-state high-school seniors in 2011–2012, which were associated with growth in 2012–2013 nonresident freshman enrollment. Data and Variables Data, Analysis Period, and Analysis Sample Data This research utilizes time-varying institution-level and time-varying state-level variables. The appendix provides definitions and data sources for all variables. Institution-level variables were constructed from the Integrated Postsecondary Education Data System (IPEDS). State-level variables were drawn from several sources, identified in the Appendix. Analysis Period The analysis period was determined by the availability of institution-level covariates. The analysis period was the 11-year period from 2002–03 to 2012–13 (herein 2003 to 2013). This period was selected because IPEDS measures of admissions competitiveness (e.g., SAT/ACT scores and percent admitted) were unavailable prior to the 2001–02 academic year and because we applied a 1-year lag to all covariates. Analysis Sample The purpose of this study was to analyze the relationship between state appropriations and nonresident freshman enrollment at public universities. Therefore, the population of interest was all public, non-military, baccalaureate-granting institutions in the US that received state appropriations and enrolled freshman. Our analysis sample was not a random sample, but rather the set of all institutions that met these criteria and reported sufficient IPEDS data to be included in analyses. We defined public baccalaureate-granting (N = 500) institutions using the following categories of the 2000 Carnegie Classification (Carnegie Foundation 2001): research universities-extensive; research universities-intensive; master’s universities I; master’s universities II; baccalaureate colleges-liberal arts; baccalaureate colleges-general; baccalaureate/associate’s colleges. Of these 500 institutions, we excluded the following institutions for failing to meet the population criteria: public universities in Colorado (N = 11) because Colorado stopped providing state appropriations in 2006 (Hillman et al. 2014) and The University of the District of Columbia (N = 1) because the institution did not receive substantial state

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appropriations for several years in the analysis period; military institutions (N = 2; e.g., Virginia Military Institute); and institutions that did not enroll freshman (e.g., institutions that only enrolled graduate students or upperclassmen) at any point during the analysis period (N = 13). Therefore, 473 institutions satisfied the population criteria, implying a potential sample size of 5,203 institution-year observations (473 institutions multiplied by 11 years). Institution-year observations were dropped from the analysis sample due to missing data for the dependent variable, the independent variable, or covariates. Table 1 shows how the sample size changed from the potential analysis sample of 473 institutions and 5,203 institution-year observations to the actual analysis sample. Briefly, 33 institutions had missing data in all years, reducing the analysis sample to 440 institutions and reducing the potential number of institution-year observations to 4,860 (440 institutions multiplied by 11 years). 411 of these 4,860 institution-year observations contained missing data. Therefore, our analysis sample was an unbalanced panel of 440 institutions and 4,429 institutionyear observations. The reasons for missing data are explained below in the variables section. Variables All measures except percentage measures (e.g., state unemployment rate) and dummy indicators (e.g. Democratic Governor) were logged, which reduces estimator sensitivity due to differences in institutional size and allows the primary coefficient of interest to be interpreted as an elasticity (e.g., a 1 % change in state appropriations is associated with a b^ percent change in nonresident freshman enrollment) (Cameron and Trivedi 2005). Missing institution-year observations for the dependent variable and the key independent variable were not imputed. For covariates, missing institution-year observations (year t) were imputed using the average of the within-panel 1-year lag (year t - 1) and lead (year t ? 1) Table 1 Analysis of missing data Dependent variable

Primary models

Sensitivity analyses

IPEDS Fall Enrollment

IPEDS Student Financial Aid

Institutions

Institution-year observations (2003–2013)

Institutions

Institution-year observations (2003–2012)

Public BA granting (2000 Carnegie)

500

5,500

500

5,000

Does not meet population criteria

-27

Population of interest

473

Missing dependent variablea Greater than 0 unknown residencyb Missing ACT/SAT scoresc

-33

-27 5,203

473

4,730

-343

-2

0

-476

-426

-33

-374

Missing percent applicants ad

-3

-1

Missing Institutional grants

-2

-1

Actual analysis sample

440

4,429

440

3,876

a

The IPEDS Residence and Migration sub-component of the IPEDS Fall Enrollment survey is mandatory for odd academic years (e.g., the 2012–2013 academic year) and voluntary in even academic years

b

Institution-year observations reporting more than zero students paying an unknown tuition rate were excluded from analyses

c

SAT/ACT scores of enrolled freshman were not reported to IPEDS for institutions that do not require SAT/ACT scores

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observations. For the analysis sample, 169 observations (3.8 % of observations in the analysis sample) had at least one imputed value. Dependent Variable The measure of nonresident freshman enrollment was based on the Residence and Migration sub-component of the IPEDS Fall Enrollment (EF) survey. These data identify the number of first-time freshman from each state, from each US territory, and the number of first-time freshman migrating from a foreign country. Nonresident freshman enrollment, Yit , was defined as the number of freshman that migrated to institution i from a different state or from a US territory or foreign country in year t. Note that Yit measured headcounts because the Residence and Migration data do not differentiate full-time and part-time students. Completing the Residence and Migration survey sub-component is mandatory for odd academic years (e.g., the 2012–2013 academic year) and voluntary in even academic years. Non-missing institution-year observations from voluntary years (e.g., 2011–2012) were included in the analysis sample. Results (available upon request) were robust to alternative model specifications which dropped all observations from non-mandatory years. Independent Variable The independent variable is revenue from state appropriations. The IPEDS Finance Survey component for public institutions using Government Accounting Standards Board (GASB) 34/35 standards defines state appropriations (variable f1b11) as ‘‘amounts received by the institution through acts of a state legislative body, except grants and contracts and capital appropriations. Funds reported in this category are for meeting current operating expenses, not for specific projects or programs’’ (NCES 2013a, p. 24). Covariates We attempted to satisfy the strict exogeneity assumption without strictly exogenous variation by including a rich set of institution-varying, time-varying covariates that plausibly satisfied both conditions of omitted variable bias: (a) the covariate has a causal effect on nonresident enrollment, and (b) the covariate has systematic relationship (e.g., correlation) with lagged state appropriations. Drawing from empirical literature on nonresident enrollment, these covariates were categorized as factors that affect (a) nonresident enrollment demand by students or (b) institutional willingness to supply seats to nonresident students. However, some covariates may fit both categories. All nonresident enrollment demand covariates were lagged 1 year because we assume that enrollment decisions by an incoming freshman cohort are affected by prior year institutional characteristics. All institutional supply covariates were lagged 1 year because we assume that institutional willingness to supply seats to nonresident freshman are affected by prior-year events. Determinants of Nonresident Enrollment Demand Drawing from literature on nonresident student demand (e.g., Cooke and Boyle 2011; Zhang 2007), we attempted to include all covariates (e.g., academic profile, institutional aid) that plausibly (a) make public universities more/less attractive to prospective nonresident freshmen and (b) have a systematic relationship with state appropriations to institutions. Prior research shows that institutional quality has a positive effect on nonresident enrollment demand (e.g., Zhang 2007). Measures of institutional quality may also have a

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systematic relationship with state appropriations. Empirical literature often defines institutional quality in terms of the academic profile of enrolled students and the institutional resources provided to students (e.g., Bound et al. 2010; Bowen 1980; Winston 1999). We included the following measures of an institution’s academic profile: percent of applicants admitted; the 25th percentile SAT score of enrolled freshmen (where ACT scores were converted to SAT scores) and the 75th percentile SAT scores of enrolled freshmen. IPEDS requires institutions to report SAT/ACT scores of enrolled freshman only if SAT or ACT scores are required for admission and 60 % or more of freshman submit test scores (NCES 2013b). Table 1 shows that missing SAT/ACT scores caused 33 institutions and 426 institution-year observations to be dropped from the analysis sample. Nonresident students may also be attracted to institutions with strong financial resources. We controlled for financial resources using seven institutional expenditure measures: instruction; student services; academic support; institutional support; research; public service; and auxiliary enterprises.5 Our main models did not control for institutional revenue measures because expenditures provide a better indicator of resources devoted to students and because ranking systems used by prospective students (e.g., US News and World Report) typically define institutional resources in terms of expenditures. However, revenue covariates were included in sensitivity analyses. Prior research finds net-tuition price affects nonresident enrollment demand (e.g., Curs 2010; DesJardins 2001) and these net price measures may have a systematic relationship with state appropriations. Sticker price and grant aid to students are the major components of net-tuition price. We included the following measures of sticker price: tuition and required fees for full-time full-year resident students; and tuition and required fees for fulltime full-year nonresident students. We included the following measures of average grant aid per full-time freshman student: federal grant aid; state grant aid; and institutional grant aid. Prior research finds that nonresident students are attracted to states with strong economies (e.g., Cooke and Boyle 2011) and state economic factors may have a systematic relationship with state appropriations. We included the following state-level economy measures: unemployment rate; per capita income; poverty rate; housing price index; and total tax revenues. Institutional Willingness to Supply Seats to Nonresident Students To isolate the effect of state appropriations on institutional desire for nonresident students, we controlled for factors—aside from state appropriations—that (a) make nonresident students more/less attractive to public universities and (b) may have a systematic relationship with state appropriations to institutions. Total institutional enrollment size may positively affect nonresident enrollment and may have a systematic relationship with state appropriations. We included two measures of enrollment size: FTE undergraduate enrollment; and FTE graduate enrollment. We created these measures by converting data on 12-month instructional activity (credit hours and contact hours) to FTE enrollments based on based on formulas described in NCES (2013c). Prior studies find that students in states with state merit-aid programs are less likely to migrate to a different state (e.g., Orsuwan and Heck 2009; Zhang and Ness 2010). We

5

Expenditure measures were defined as total institutional expenditures rather than expenditure per full-time equivalent student as the models directly controlled for institutional enrollment size.

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argue that state merit aid programs decrease institutional preferences for nonresident students because state financial aid programs increase the academic profile (e.g., Cornwell et al. 2006) and purchasing power (e.g., Long 2004a) of resident students. These factors increase the attractiveness of resident students relative to nonresident students. Therefore, we controlled for state expenditure on need-based financial aid and merit-based financial aid. Changes in the state-level college-age population affect institutional willingness to enroll nonresident students (DesJardins 2001; Winters 2012) and may have a systematic relationship with state appropriations. We included state-level population size covariates for each permutation of age group (12–17, 18–24, 25–44) and race/ethnicity (White non-Hispanic; Black non-Hispanic; Asian Pacific Islander and Native American; and Hispanic of any race). We included the measure of 12–17 year olds because DesJardins (2001) stated that future projections of the state-college age population affect present recruitment efforts for nonresident students. We excluded the 0–11 and 45? age groups because these age groups are unlikely to affect nonresident freshman enrollment. We included separate population measures by race because college attendance rates differ by race (Posselt et al. 2012). Finally, it is plausible that state political factors affect institutional desire for nonresident students. For example, institutions in heavily Republican states may increase nonresident enrollment under the assumption that state higher education appropriations will continue to decline in future years. Furthermore, prior research finds a systematic relationship between state political factors and state higher education appropriations (Tandberg and Griffith 2013). We included the following two state political measures: an indicator for having a Democrat governor; and the percentage of Democrats in state legislatures. Limitations This paper has several limitations. We attempted to measure the causal effect of state appropriations on nonresident freshman enrollment but our estimates cannot be considered causal effects due to likely violations of the strict exogeneity assumption. It is preferable to satisfy the strict exogeneity assumption by isolating exogenous time-varying variation in the independent variable of interest. An earlier version of this paper attempted to isolate exogenous variation in state appropriations by using state-level Medicaid recipients and state-level prison population as instruments for institution-level state appropriations. Unfortunately, these models performed inconsistently on standard diagnostic tests of instrumental variable model assumptions (e.g., Baum 2009; Berry 2011). Therefore, this manuscript attempted to satisfy the strict exogeneity assumption through the inclusion of time-varying covariates. However, we were unable to control for all factors that affect nonresident freshman enrollment. For example, models did not include time-varying measures of college athletics and natural resource amenities (e.g., climate) or tuition reciprocity agreements. However, institutional fixed effects likely account for substantial variation in these variables. Additionally, we could not locate time-varying data on formal or informal nonresident enrollment caps for each state. The omission of this state policy attribute likely biases our results towards zero, as institutions in states where caps exist would be less likely to increase nonresident enrollment as state appropriations decline. Finally, it is important to note that our analysis sample is not a random sample from a population but an approximation of the population of interest. We acknowledge that institutions and institution-year observations excluded from our analyses were not missing at random. However, our analyses were limited to what data were available in IPEDS.

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Results Descriptive Statistics Figure 2 presents changes in the median nonresident freshman enrollment, resident freshman enrollment, and state appropriations (lagged 1 year) by institutional type for the sample period of 2003–2013.6 In general, both resident and nonresident freshman enrollment steadily increase across the sample period, while state appropriations were more volatile. Through visual inspection, nonresident freshman enrollment increased across the time period at the highest rate for research-extensive institutions (Panel A), followed by research-intensive institutions (Panel B). Visually, a negative relationship between lagged state appropriations and nonresident freshman enrollment appeared within the research-extensive institutions while baccalaureate institutions (Panel D) appeared to have a positive relationship between appropriations and nonresident freshman enrollment.

2005

2007

2009

2011

Nonresident freshman State appropriations (right axis)

2003

Resident freshman

2009

2011

2013

academic year, e.g. 1995=1994-95 Nonresident freshman State appropriations (right axis)

2009

2011

Nonresident freshman State appropriations (right axis)

2013

Resident freshman

Resident freshman

Enrollment

100 200 300 400 500

45 50 60 55 Appropriations (millions)

1500 1000

Enrollment

500

2007

2007

Panel D: Baccalaureate

0

2005

2005

academic year, e.g. 1995=1994-95

Panel C: Master’s

2003

80 85 90 95 100 105 Appropriations (millions)

Enrollment

0

2013

academic year, e.g. 1995=1994-95

19 20 21 23 22 Appropriations (millions)

2003

500 1000 1500 2000

Panel B: Research-Intensive 220 240 260 280 300 Appropriations (millions)

3000 2000 1000

Enrollment

4000

Panel A: Research-Extensive

2003

2005

2007

2009

2011

2013

academic year, e.g. 1995=1994-95 Nonresident freshman State appropriations (right axis)

Resident freshman

Fig. 2 Change in the median nonresident enrollment, resident enrollment, and state appropriations lagged 1 year ($ millions, 2012 CPI) across public institution types 6

Data points for Figure A only include years where the residency component of the IPEDS Fall Enrollment survey was mandatory.

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Nonresident Freshman Enrollment Table 2 presents the results of the estimation of Eq. 1, the relationship between state appropriations and nonresident freshman enrollment, using the IPEDS Fall Enrollment data. For the overall sample (Column 1), we found that a 1 % increase in state appropriations is associated with a 0.27 % decrease in nonresident freshman enrollment. This relationship was significant at an alpha-level of 0.01. When we relaxed the assumption that the relationship was the same across institutional type, important differences emerged. Column 2 of Table 2 presents an analysis when we altered Eq. 1 by interacting state appropriations with a dummy indicator of whether an institution is a master’s or baccalaureate institution compared to the reference group of research universities (research-extensive and -intensive). In general, we found that the relationship between state appropriations and nonresident freshman enrollment was larger Table 2 The relationship between state appropriations and nonresident freshman enrollment Dependent variable: nonresident freshman enrollment (IPEDS Fall Enrollment)

State appropriations (lagged)

(1)

(2)

(3)

-0.274*** (0.0848)

-0.456*** (0.145)

-0.499*** (0.166)

Reference category: research (extensive/intensive) State appropriations 9 master’s/baccalaureate

0.250* (0.146)

Reference category: research-extensive State appropriations 9 research-intensive

0.133 (0.146)

State appropriations 9 master’s

0.291* (0.173)

State appropriations 9 baccalaureate

0.301 (0.222)

Lagged institutional control variables included?a

Yes

Yes

Yes

Lagged state control variables included?b

Yes

Yes

Yes

Institution fixed effects included?

Yes

Yes

Yes

Year fixed effects included?

Yes

Yes

Yes

Observations

4,429

4,429

4,429

R-squared (within)

0.121

0.124

0.124

Number of institutions

440

440

440

Cluster-robust (state-level) standard errors in parentheses; *** p \ 0.01; ** p \ 0.05; * p \ 0.1; all variables (except 0/1 indicators and those in percentage terms) have been logged a

List of lagged institution-level controls: undergraduate FTE enrollments; graduate FTE enrollments; 25th percentile SAT/ACT scores of enrolled freshmen; 75th percentile SAT/ACT scores of enrolled freshmen; percent of applicants admitted; instructional expenditure; research expenditure; public service expenditure; academic support expenditure; student service expenditure; institutional support expenditure; auxiliary enterprise expenditure; resident tuition price; nonresident tuition price; avg. federal grant aid for fulltime freshmen; avg. state grant aid for fulltime freshmen; avg. institutional grant aid for fulltime freshmen

b

List of lagged state-level controls: per capita income; housing price index; unemployment rate; poverty rate; total state tax revenues; Democrat state governor; Democrat representation in legislature; expenditure on state merit-based financial aid; expenditure on state need-based financial aid; state population by age (12–17; 18–24; 25–44) and race/ethnicity (White non-Hispanic; Black non-Hispanic; Asian Pacific Islander and Native American; and Hispanic of any race)

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(p \ 0.1) at research universities than non-research universities. Specifically, a 1 % increase in state appropriations was associated with a 0.46 % decrease in nonresident freshman enrollment for research-extensive and research-intensive public institutions and a 0.21 % decrease at master’s and baccalaureate public institutions. When the relationship was allowed to vary by a finer set of institutional types, we found a similar pattern. Column 3 of Table 2 presents an analysis where we interacted state appropriations with a set of dummy indicators for whether the institution was researchintensive, master’s, and baccalaureate, with research-extensive institutions as the reference group. Specifically, a 1 % increase in state appropriations was associated with a 0.50 % decrease in nonresident freshman enrollment at research-extensive institutions, a 0.37 % decrease at research-intensive institutions (not statistically different from research-extensive institutions), a 0.21 % decrease at master’s institutions (statistically different from research-extensive institutions, p \ 0.1), and a 0.20 % decrease at baccalaureate institutions (not statistically different from research-extensive institutions). Resident Freshman Enrollment As a comparison to the findings for nonresident freshman enrollment, we estimated Eq. 1 with resident freshman enrollment as the dependent variable. This comparison provides context to better understand if the growth in nonresident freshman enrollment was part of a general institutional response to grow enrollment from both resident and nonresident freshman when state appropriations declined. The measure of resident freshman enrollment was defined as the number of freshman that enrolled at institution i from institution i’s state in year t, based on the Residence and Migration sub-component of the IPEDS Fall Enrollment (EF) survey. When creating our covariate set for nonresident models, we specifically included possible correlates with resident freshman enrollment (e.g. resident tuition price), thus the covariate sets are identical for our nonresident and resident models. Table 3 presents the results. The findings indicate that state appropriations and resident freshman enrollment were unrelated. Furthermore, we found no significant differences across institutional type for this relationship. The lack of a relationship found between state appropriations and resident freshman enrollment suggests that public universities seek nonresident students as state appropriations decline, as opposed to simply pursuing overall enrollment increases. This is consistent with our conceptual framework, which argues that public institutions seek nonresident enrollment for revenue generating purposes. Sensitivity Analyses Alternative Measure of Nonresident Freshman Enrollment To test the sensitivity of our findings across an alternative definition of nonresident students, we constructed a second measure of nonresident freshman enrollment. This measure was based on the IPEDS Student Financial Aid (SFA) survey component, which collects annual data on the number of full-time freshman paying (a) in-district, (b) resident, (c) nonresident tuition rates. We defined nonresident freshman enrollment as the number of full-time freshman paying the nonresident tuition rate. Note that nonresident freshman paying resident tuition (e.g., because of tuition reciprocity agreements between states) were categorized as paying resident tuition. This measure of nonresident freshman enrollment is conceptually attractive because our conceptual framework argues that public universities desire nonresident students that pay nonresident tuition rates.

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Table 3 The relationship between state appropriations and resident freshman enrollment Dependent variable: resident freshman enrollment (IPEDS Fall Enrollment)


(1)

(2)

(3)

0.0191 (0.0251)

0.0452 (0.0489)

0.0394 (0.0543)


-0.0358 (0.0712)


0.0168 (0.0645)


-0.0609 (0.0746)


0.138 (0.114)


Yes

Yes

Yes


Yes

Yes

Yes


Yes

Yes

Yes


Yes

Yes

Yes

Observations

4,429

4,429

4,429

R-squared (within)

0.228

0.228

0.232


440

440

440



b


Unfortunately, 10.1 % of institution-year observations report more than zero students with ‘‘unknown’’ tuition-residency status. The number of students paying nonresident tuition at a given institution tends to decline when the number students paying unknown tuition rate increases. We dropped institution-year observations with greater than zero percent unknown tuition rate because variation in nonresident freshman enrollment for these observations is largely determined by the number of students paying unknown tuition rate. The potential for systematic measurement error bias in this measure of nonresident freshman enrollment is the primary reason why we are presenting this measure as a sensitivity test and not as the primary measure. Table 4 presents the findings when Eq. 1 is estimated with the SFA measure of nonresident freshman enrollment. The analysis period for the models presented in Table 4 was 2002–2003 to 2011–2012 because 2012–2013 SFA data were unavailable when we

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Table 4 The relationship between state appropriations and nonresident freshman enrollment Dependent variable: nonresident fulltime freshman enrollment (IPEDS Student Financial Aid)


(1)

(2)

(3)

-0.337*** (0.102)

-0.392** (0.155)

-0.413** (0.172)


0.0754 (0.192)


0.0684 (0.216)


0.0931 (0.223)


0.115 (0.218)


Yes

Yes

Yes


Yes

Yes

Yes


Yes

Yes

Yes


Yes

Yes

Yes

Observations

3,876

3,876

3,876

R-squared (within)

0.050

0.050

0.050


440

440

440



b


conducted our analyses. However, in all other ways the model specifications for models presented in Table 4 mirrored those from Table 2. For the overall sample, we found that a 1 % increase in state appropriations was associated with a 0.34 % decrease in the number of freshman paying nonresident tuition. Although the point estimate was larger for the SFA measure of nonresident freshman enrollment when compared to the EF measure (Table 2), the estimates were not significantly different from each other. When the association between state appropriations and nonresident freshman enrollment was allowed to vary by institutional type, differences between the SFA and EF definitions emerged. Specifically, we found no differences across institutional type when using the SFA data which is in contrast to the differences we found in the EF models. Three possible factors may explain different findings for the EF and SFA interaction models: first, the SFA models have fewer observations due to observations with unknown

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residency status being dropped from models; second, the EF models included the 2012–2013 academic year but the SFA models did not; and, third, the EF and SFA models utilized different measures of residency, one based on state of origin and the other based on paying resident versus nonresident tuition rate. Twice Lagged State Appropriations Our preferred specifications modeled a 1-year lag between state appropriations and nonresident freshman enrollment. These models assumed that institutional efforts to increase nonresident freshman enrollment in response to state appropriations focused on the late search phase (first-half of 12th grade) and the choice phase (12th grade) as described by Hossler et al. (1999). As a sensitivity check, we estimated models using a 2-year lag between state appropriations and nonresident freshman enrollment. These models assumed that institutional efforts to increase nonresident enrollment in response to state appropriations focused on the 11th grade search phase. Table 5 presents the findings when Eq. 1 is estimated with state appropriations lagged 2 years. In general, the general direction of the relationship between state appropriations and nonresident freshman enrollment was similar, but the magnitudes of the estimates are about half the size when compared to models with a single lag of state appropriations. No significant differences are found between institutional types, although the point estimates follow the same general pattern with more research oriented public institutions having the largest relationship. Additional Sensitivity Analyses We conducted additional robustness checks to test the sensitivity of our analyses to alternative model specifications. Complete results are available upon requests of the authors, but omitted due to space considerations. First, we estimated models with state-byyear fixed effects in place of state time-varying covariates to better control for possible differential trends across states. In general, point estimates from this robustness check remained within the 95 % confidence intervals of those presented in Tables 2 and 4, although standard errors increased as expected, given the loss of degrees of freedom. Second, point estimates were robust to alternative model specifications which included institutional revenues covariates in place of, and in conjunction with, institutional expenditures.

Discussion and Conclusions Summary Since the early 1990’s state funding for higher education has stagnated and become increasingly uncertain. The last decade has witnessed dramatic volatility and dramatic decline in state appropriations, especially since the 2007–2008 academic year. Resource dependence theory predicts that public universities would respond to initial uncertainty and decline in state funding by trying to mend relationships with state policymakers. However, administrators now view continually dwindling state appropriations as given (Ehrenberg 2006b) and resource dependence theory predicts that this realization will compel public

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Table 5 The relationship between state appropriations (lagged 2 years) and nonresident freshman enrollment Dependent variable: Nonresident freshman enrollment (IPEDS Fall Enrollment)

State appropriations (lagged twice)

(1)

(2)

(3)

-0.126** (0.0625)

-0.225* (0.118)

-0.243* (0.139)


0.132 (0.152)


0.0488 (0.164)


0.198 (0.183)


-0.0729 (0.173)


Yes

Yes


Yes

Yes

Yes


Yes

Yes

Yes


Yes

Yes

Yes

Observations

4,010

4,010

4,010

R-squared (within)

0.109

0.110

0.111


440

440

440

Yes

Cluster-robust (state-level) standard errors in parentheses; *** p \ 0.01; ** p \ 0.05, * p \ 0.1; all variables (except 0/1 indicators and those in percentage terms) have been logged a


b


universities to seek new revenue sources. Descriptive statistics from NPSAS data show that nonresident students represent an attractive revenue source because nonresident tuition price is higher than resident tuition price, even after accounting for institutional aid. This paper examined whether public universities increased nonresident freshman enrollment in response to declines in state appropriations using an analysis period of 2002–2003 to 2012–2013. Consistent with H1, we found that a 1 % decline in state appropriations (lagged 1 year) was associated with a .27 % increase in nonresident freshman enrollment for the sample of all public baccalaureate granting institutions. Consistent with H2, we found this relationship was stronger for research universities. Specifically, a 1 % decline in state appropriations was associated with a 0.46 % increase in

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nonresident freshman enrollment at research universities (extensive and intensive) and a 0.50 % increase in nonresident freshman enrollment at research-extensive universities. From the perspective of practical significance, our models indicated a strong negative relationship between state appropriations and nonresident freshman enrollment. Rizzo and Ehrenberg (2004), by contrast, did not find a significant relationship between state appropriations and nonresident enrollment. One potential nexplanation for the difference in findings is that Rizzo and Ehrenberg (2004) analyzed the ratio of nonresident to resident enrollment whereas we analyzed nonresident and resident enrollment separately. A more likely explanation is that Rizzo and Ehrenberg (2004) analyzed the years 1979–1998, a period of relatively generous state funding when state appropriations were beginning to become uncertain. By contrast, we analyzed the years 2002–2003 to 2012–2013, a period of high volatility and decline in state appropriations. Implications for Policy and Practice The relationship between state appropriations and nonresident enrollment growth may have important implications for institutional policymakers concerned about access. Jaquette et al. (2014) found that nonresident freshman enrollment growth negatively affected the proportion of low-income and underrepresented minority freshman at public research universities. Prior research suggests that class isolation (Oldfield 2007; Waldorf 2013) and racial isolation (e.g., Fries-Britt and Turner 2001; Hurtado and Ruiz 2012; Smith et al. 2007) negatively affects student development outcomes for low-income and underrepresented minority students and is associated with negative perceptions of campus climate. Therefore, the growth in the proportion of nonresident students that is associated with declining state appropriations may contribute to an unhealthy learning environment for low-income and underrepresented minority students at public research universities. The implications of this study for state policymakers are unclear. Although state higher education policy goals differ, common goals include access for state residents, minimizing ‘‘brain drain’’ of high achieving state residents, and economic growth (Heller 2001, 2002). Our model results imply that cuts in state appropriations compel public universities to increase nonresident freshman enrollment relative to resident freshman enrollment. However, we cannot state that this growth in nonresident freshman enrollment necessarily contradicts state interests. Groen and White (2004) found that the effect of attending college in a particular state on the probability of remaining in the state after graduation is about the same for resident and nonresident students. Therefore, nonresident enrollment growth may positively affect state economic development to the extent that nonresident students work in the same state they receive their bachelor’s degree (Bound et al. 2004). A finding that nonresident enrollment growth crowds out enrollment opportunities for resident students would be inconsistent with state goals. However, our analyses did not examine the effect of nonresident enrollment on resident enrollment. Implications for Scholarship and Theory This study makes contributions to scholarship on the changing behavior of public universities. First, we contribute to the growing empirical literature on academic capitalism. Seminal work by Slaughter and Leslie (1997) defined academic capitalism as ‘‘marketlike efforts to secure external moneys’’ (p. 8). Scholarship on academic capitalism argues that engaging in market-like efforts (e.g., corporate partnerships, technology transfer) affects organizational culture, making the culture of universities more like the culture of for-profit

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corporations (Slaughter and Rhoades 2004). Slaughter and Leslie (1997) found that, following passage of the Bayh Dole Act, public universities responded to declines in state appropriations by attempting to commercialize and monetize research. Similarly, we found that public universities responded to declines in state appropriations by growing nonresident freshman enrollment, which we conceive as an effort to monetize students. Second, we contribute to a growing body of evidence that showing that university revenue seeking behaviors are associated with a strong Matthew Effect (Cheslock and Gianneschi 2008; Slaughter and Leslie 1997). Cheslock and Gianneschi’s (2008) showed that only flagship research universities could generate substantial revenues from voluntary support. Therefore, increasing reliance on voluntary support increases the differences between ‘‘have’’ and ‘‘have-not’’ universities. Similarly, our results suggest that relying on nonresident enrollment growth to compensate for declines in state appropriations also increases the difference between the haves and the have-nots. Many public universities may desire tuition revenue from nonresident students. However, descriptive statistics suggest that only research universities are capable of generating substantial nonresident enrollment. Further, our results suggest that research universities increased nonresident freshman enrollment at a faster rate than non-research universities following declines in state appropriations. Our study also contributes to scholarship on privatization. Whereas declines in state support are not a prerequisite condition for academic capitalism (e.g., private universities engage in academic capitalism), the term ‘‘privatization’’ tends to refer to growth in market-like behaviors by public universities caused by declines in state support (Priest and St. John 2006). Privatization can be understood through resource dependence theory. Resource dependence theory argues that organizations serve the interests of dominant resource providers. Therefore, organizations often respond to resource declines from particular resource providers by reorienting their mission towards the demands of new providers (Pfeffer and Salancik 1978). This logic suggests that public universities will respond to long-term declines in state funding by transforming the organizational mission, away from public good goals associated the state and towards the goal of providing value to paying customers (Ehrenberg 2006a; Morphew and Eckel 2009; Priest and St. John 2006). Scholars of privatization argue that states should increase funding for higher education because declines in state funding have important negative effects. However, aside from Slaughter and Leslie (1997), few scholarly contributions within the privatization literature have empirically demonstrated the consequences of declining state support. The present study contributes empirical evidence to the privatization literature by showing that public universities respond to declines in state appropriations by increasing the number of nonresident students (i.e., paying customers). Public universities may treat state budget cuts as the breaking of an implicit contract between the university and the state, thereby entitling the university to transition from an exclusive relationship with the state to an open relationship that crosses state boundaries. Future research on the privatization of public universities should continue to explore the effects of state funding on efforts to recruit resident versus nonresident students. For example, future research should examine the effect of state appropriations on nonresident tuition price and the amount of institutional aid awarded to resident versus nonresident students. More importantly, future research should help make the case for increased state higher education funding investigating whether declines in state support cause public universities to engage in behaviors that have negative consequences for outcomes valued by state policymakers. For example, future research should assess how nonresident

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enrollment growth at flagship public universities affects the sorting of resident students. For example, does non-resident enrollment growth crowd out opportunities for resident students at public research universities or do universities use tuition revenue from nonresident students to increase access for resident students? Similarly, future research should assess whether nonresident enrollment growth in particular majors crowds out opportunities for resident students in those majors. For example, does the growth of nonresident students in STEM majors at public research universities negatively affect access to STEM majors for resident students? Acknowledgments We would like to thank two anonymous reviews for thoughtful suggestions that strengthened the manuscript. We also thank two University of Arizona PhD students, Edna Parra for creating NPSAS descriptive statistics and Andrew Blatter for editorial assistance. Any remaining errors are our own.

Appendix See Table 6.

Table 6 Variables, variable definitions, and data sources (2012 CPI for monetary variables) Variable

Additional data definitions and notes

Data sources

Number of nonresident freshman (headcount)

Number of nonresident freshman based on place of origin; mandatory in odd academic years (e.g., 2012–2013), voluntary in even academic years (e.g., 2011–2012)

IPEDS Fall Enrollment (EF)

Number of resident freshman (headcount)

Number of resident freshman based on place of origin; mandatory in odd academic years (e.g., 2012–2013), voluntary in even academic years (e.g., 2011–2012)

IPEDS Fall Enrollment (EF)

Dependent variables

IPEDS Student Financial Aid (SFA)

Number of fulltime freshman paying nonresident tuition Independent variable ‘‘Amounts received by the institution through acts of a state legislative body, except grants and contracts and capital appropriations. Funds reported in this category are for meeting current operating expenses, not for specific projects or programs’’ (NCES 2013a, p. 24)

IPEDS Finance

Undergraduate FTE enrollments

Undergraduate full-time equivalent enrollments based on instructional activity (credit hours and contact hours). Undergraduate credit hours and contact hours converted to FTE enrollments based on formulas described in NCES (2013c)

IPEDS 12-month Enrollments

Graduate FTE enrollments

Graduate full-time equivalent enrollments based on instructional activity (credit hours and contact hours). Graduate credit hours converted to FTE enrollments based on formulas described in NCES (2013c)

IPEDS 12-month Enrollments

State appropriations

Institution-level covariates Institutional size

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Table 6 continued Variable


Data sources

25th percentile SAT/ACT scores of enrolled freshmen

25th percentile SAT scores of enrolled freshman; ACT scores converted to SAT scores

IPEDS Institutional Characteristics

75th percentile SAT/ACT scores of enrolled freshmen

75th percentile SAT scores of enrolled freshman; ACT scores converted to SAT scores


Percent of applicants admitted

Total number of applicants admitted divided by total number of applicants


Academic profile

Institutional resources (expenditures) Instructional expenditure

Total institutional expenditure on instruction. Measure excludes expenditure on (a) operations & maintenance and (b) interest on debt to maintain consistency over time. These items were categorized as a separate expenditure category prior to 2007–2008

IPEDS Finance

Research expenditure

Total institutional expenditure on research The measure excludes expenditure on (a) operations & maintenance and (b) interest on debt to maintain consistency over time.

IPEDS Finance

Public service expenditure

Total institutional expenditure on public service The measure excludes expenditure on (a) operations & maintenance and (b) interest on debt to maintain consistency over time

IPEDS Finance

Academic support expenditure

Total institutional expenditure on academic support. The measure excludes expenditure on (a) operations & maintenance and (b) interest on debt to maintain consistency over time

IPEDS Finance

Student service expenditure

Total institutional expenditure on student services. The measure excludes expenditure on (a) operations & maintenance and (b) interest on debt to maintain consistency over time

IPEDS Finance

Institutional support expenditure

Total institutional expenditure on institutional support. The measure excludes expenditure on (a) operations & maintenance and (b) interest on debt to maintain consistency over time

IPEDS Finance

Auxiliary expenditure

Total institutional expenditure on auxiliary enterprises. The measure excludes expenditure on (a) operations & maintenance and (b) interest on debt to maintain consistency over time

IPEDS Finance

Institutional resources (revenues, used in sensitivity analyses) State grants and contracts

State operating grants and contracts; state nonoperating grants

IPEDS Finance

Federal revenues (all)

Federal appropriations; federal operating grants & contracts; federal non-operating grants

IPEDS Finance

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Data sources

Private grants, private gifts, and investments

Private gifts; investment income; capital grants & gifts; additions to permanent endowments; and private and local government grants and contracts. Note: cannot separate private and local grants & contracts in some years during analysis period

IPEDS Finance

Auxiliary, hospital, independent operations

Auxiliary revenues (excludes discounts and allowances); independent operations; and hospital revenues

IPEDS Finance

Local government and other revenues

Local appropriations; local non-operating grants; capital appropriations from govt. sources; other operating revenues; other nonoperating revenues; other revenues and additions

IPEDS Finance

Net-tuition price: sticker price and grant aid Resident tuition price

Required tuition and fees for full-time fullyear student paying resident tuition rate


Nonresident tuition price

Required tuition and fees for full-time fullyear student paying nonresident tuition rate


Average federal grant aid received by full-time freshmen

Total federal grant aid received by full-time freshman divided by the number of full-time freshman

IPEDS SFA

Average state grant aid received by full-time freshmen

Total state grant aid received by full-time freshman divided by the number of full-time freshman

IPEDS SFA

Average institutional grant aid received by full-time freshmen

Total institutional grant aid received by fulltime freshman divided by the number of fulltime freshman

IPEDS SFA

State-level covariates State economy and poverty Per capita income

Bureau of Economic Analysis

Housing price index

Fannie Mae Quarterly Housing Pricing Index, not seasonally adjusted, 1st quarter values

Fannie Mae

Unemployment rate

Annual state-level unadjusted unemployment rates

Bureau Labor Statistics

Poverty rate

U. of Kentucky Center for Poverty Research

Total state tax revenues

US Census

State politics Democrat state governor

0/1 whether the state governor is a Democrat


Democrat representation in legislature

Fraction of a state’s legislature which is democratic


State financial aid to students Expenditure on state merit-based financial aid programs

National Association of State Student Grant and Aid Programs

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Data sources National Association of State Student Grant and Aid Programs

Expenditure on state need-based financial aid programs Population by age and race/ethnicity State population by age and race

Separate variables for each combination of age and race/ethnicity categories. Age categories: 12–17; 18–24; and 25–44. Race/ethnicity categories: White non-Hispanic; Black nonHispanic; Asian Pacific Islander and Native American; and Hispanic of any race

US Census Bureau

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