Creating the out-of-state university: Do public universities respond to declining state appropriations by increasing nonresident freshman enrollment?

Transcription

1 Creating the out-of-state university: Do public universities respond to declining state appropriations by increasing nonresident freshman enrollment? Ozan Jaquette Assistant Professor Educational Policy Studies & Practice University of Arizona, College of Education 1430 E. Second Street, Room 327A Tucson, AZ Fax: Bradley R. Curs, Ph.D. Associate Professor Educational Leadership and Policy Analysis University of Missouri, College of Education 202 Hill Hall Columbia MO, (573) Fax: (573) This version: November 14, 2014(under review) ABSTRACT: This study investigates whether public universities respond to declines in state appropriations by increasing nonresident freshman enrollment. State appropriations to public universities declined substantially during the 2000's, leading public universities to become more dependent on net tuition revenue. State policy controls often limits the growth of resident tuition price. Therefore, public universities may have a financial incentive to grow nonresident enrollments as a revenue generating strategy. Drawing on resource dependence theory, we hypothesize that public universities respond to declines in state appropriations by growing freshman nonresident enrollment. Furthermore, we hypothesize that this response will be strongest at research universities as research universities enjoy strong demand from prospective nonresident students. We tested these hypotheses using a sample of all U.S. public baccalaureate granting institutions and an analysis period spanning the to 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 1

2 Creating the out-of-state university: Do public universities respond to declining state appropriations by increasing nonresident freshman enrollment? The states are disinvesting in public universities. Total state appropriations across all public baccalaureate granting institutions declined from $54.5 billion in to $45 billion by (authors calculations based on IPEDS data). 1 Public universities have responded to declines in state appropriations, in part, by seeking alternative revenue sources. Prior research highlights institutional efforts to increase revenue from research (Slaughter & Leslie, 1997; Slaughter & Rhoades, 2004), donations and investments (Cheslock & Gianneschi, 2008), and auxiliary enterprises (Barringer, 2013). However, tuition revenue has remained the dominant source of revenue growth for public universities. Total net tuition revenue across all U.S. public universities increased from $35 billion in to $56 billion in For the median public university, net tuition revenue increased from $31 million in (23% of total revenue) to $42 million in (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 & 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 ). Therefore, institutions have a financial incentive to increase nonresident enrollment when state appropriations decline. 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. 2

3 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 & Keller, 2011; Jaschik, 2009). Mean nonresident freshman enrollment at public doctoral/research-extensive universities (2000 Carnegie Classification) increased from 747 in to 1,169 in (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, extant research on the relationship between state appropriations and nonresident enrollment predate the decline in state appropriations over the last decade. Drawing on resource dependence theory (Pfeffer & Salancik, 1978), we hypothesize that public universities respond to a decline in state appropriations by growing nonresident freshman enrollment. Furthermore, we hypothesize that this response will be 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 U.S. public baccalaureate granting institutions with an analysis period of the to 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 & Doyle, 2007, 2011; Hovey, 1999). The privatization literature argues that state disinvestment in public higher education 3

4 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 & Eckel, 2009; Priest & St. John, 2006). 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 & Haycock, 2006; Haycock, Mary, & Engle, 2010). Jaquette, Curs, and Posselt (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 4

5 Before reviewing literature on state appropriations and university revenue-seeking behaviors, we briefly review research on the determinants of state higher education appropriations (Tandberg & Griffith, 2013). State appropriations grow when state economic growth is strong and decline when it is weak (Adams, 1977; Clotfelter, 1976; Delaney & Doyle, 2011; Kane, Orszag, & Gunter, 2003; Robert K. Toutkoushian & Hollis, 1998; Weerts & Ronca, 2012), often more so than other state budget items (Delaney & Doyle, 2011). State tax revolt legislation negatively affects state appropriations by shrinking the pie of state tax revenue that can be allocated to higher education (Archibald & Feldman, 2006). Holding the pie of state tax revenue constant, state higher education appropriations are negatively affected by spending on mandatory state budget categories (e.g., Medicaid, K-12 funding, highways, corrections, etc.)(kane, et al., 2003; Okunade, 2004), republican state legislatures and governors, (McLendon, Hearn, & Mokher, 2009; Nicholson-Crotty & Meier, 2003), strong non-higher education interest groups (Tandberg, 2010), and the presence of a large private higher education market (Goldin & Katz, 1998). 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. Figure 1 provides context for our review of the literature on revenue seeking behaviors by universities. It shows median revenues by revenue category in the and academic years for public baccalaureate granting institutions (as defined by the 2000 Carnegie Classification). Results are shown separately for doctoral/research universities-extensive (herein research-extensive), doctoral research universities-intensive (herein research-intensive), master s colleges and universities (herein master s), and baccalaureate colleges (including 5

6 baccalaureate/associate s colleges). Median state appropriations were lower in than across all four institutional types. Scholarship on the relationship between state appropriations and revenue generation (e.g., Cheslock & Gianneschi, 2008; Slaughter & Leslie, 1997) is often motivated by resource dependence theory. Resource dependence theory argues that resource diversification (e.g., seeking alternative revenues) is a common organizational response to declines in an important resource. Slaughter and colleagues (1997; 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). This hypothesis was not supported. However, the authors 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), Figure 1 shows that median revenues from private grants, gifts, and investments at research-extensive universities increased from $74 million in to $103 million in , 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 & 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 6

7 in the number of public universities relying heavily on tuition revenue. Figure 1 reveals that this trend continued during the to period. Tuition revenue was the largest source of revenue growth for research-extensive, research-intensive, master s, and baccalaureate institutions. While research on efforts to increase revenue from research and voluntary support have made valuable contributions (e.g., Cheslock & Gianneschi, 2008; Slaughter & 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 and colleagues (1997) found an insignificant relationship. However, state policies limit growth in resident tuition price (Zinth & 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 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 7

8 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 analyzes the determinants of nonresident enrollment, the dependent variable of interest in the present study. Literature on the 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 & Peach, 2008; Baryla & Dotterweich, 2001; Mak & Moncur, 2003; Zhang, 2007). Work by Caroline Hoxby provides intuition for this result. Hoxby (1997, 2009) described transformation of U.S. 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 & Boyle, 2011; Morgan, 1983). However, Long 8

9 (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 & Hsing, 1994) and desirable natural resources (e.g., topography, climate) (Cooke & 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 enrollment are relatively insensitive to growth in the sticker price of nonresident tuition (Adkisson & Peach, 2008; Baryla & Dotterweich, 2001; Dotterweich & Baryla, 2005; Mixon & Hsing, 1994; Zhang, 2007). This is consistent with the finding that nonresident students tend to be relatively wealthy and unconcerned about costs (Mak & 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 when compared to resident students (Curs, 2010; Curs & Singell, 2010). Additionally, several student-level studies have found that nonresident enrollment decisions are highly sensitive to institutional aid offers (Abraham & Clark, 2006; Curs & Singell, 2010; DesJardins, 2001). Therefore, institutional aid is an important component of enrollment management strategies to increase nonresident enrollment. Research 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 9

10 tuition. State merit aid programs are motivated, in part, by the policy goal of reducing outmigration by high academic ability high school students (Zhang & Ness, 2010). A robust literature finds that state merit aid programs reduce out-migration by reducing the costs of attending an in-state institution relative to an out-of-state institution (e.g., Cornwell, Mustard, & Sridhar, 2006; Mak & Moncur, 2003; Orsuwan & Heck, 2009; R. K. Toutkoushian & Hillman, 2012; Zhang, Hu, & Sensenig, 2013; Zhang & Ness, 2010). Institutional willingness to supply seats to nonresident students. While many studies have analyzed enrollment demand by non-resident students, fewer supply-side studies have analyzed institutional preferences for resident and nonresident students. Groen and White (2004) analyzed whether universities prefer resident or nonresident applicants, using cohorts of college freshman in 1976 and The authors found that private universities treated resident and nonresident applicants equally. Public universities set lower admissions standards for resident applicants even though nonresident students paid higher tuition. 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 to 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 10

11 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 to , suggesting that state control over public university admissions preferences has 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 (Cheslock & Gianneschi, 2008; Slaughter, 1997), we draw from resource dependence theory (Pfeffer & 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 & Salancik, 1974). Resource dependence theory identifies several strategies organizations may adopt to overcome the problem of reliance on an uncertain or declining resource (Davis & 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 11

12 participate in organizational activities (e.g., advisory panels). When strategies such as compliance and cooptation fail or when performance demands become too onerous, organizations often engage in resource diversification to reduce reliance on a particular resource provider. Pfeffer and Salancik (1978) 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 & Doyle, 2011). Finally, state higher education appropriations have declined sharply over the past decade, especially since the most recent recession (Hurlburt & 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 12

13 asserts that public universities have responded to declines in state appropriations by attempting to increase revenues from research and voluntary support (Cheslock & Gianneschi, 2008; Slaughter & Leslie, 1997). However, Figure 1 shows that tuition revenue has been the dominant source of revenue growth for public universities over the last decade. Descriptive statistics for 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 researchextensive 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 statistics 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. Our analyses 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 13

14 Resource dependence theory states that all organizations desire diverse revenue streams but revenue opportunities available to some organizations may be unavailable to others (Pfeffer & Salancik, 1978). Applying this idea, many public universities experience a revenue incentive to increase nonresident enrollment when state appropriations decline, but 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 & Peach, 2008; Baryla & 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. We acknowledge that H2 could utilize a different construct to classify institutions (e.g., U.S. 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. 14

15 Our statistical models 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 in the models. Drawing from the literatures on college-choice (e.g., D. Hossler, Braxton, & Coopersmith, 1989; D. Hossler & Gallagher, 1987; Don Hossler, Schmit, & Vesper, 1999; Perna, 2006) and enrollment management (e.g., Cheslock & Kroc, 2012; Donald Hossler & 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 (11 th and 12 th grade for traditional students) (Don Hossler, et al., 1999), public universities can influence nonresident application decisions through advertising, recruitment events, campus visits, etc. (Donald Hossler & Bean, 1990). During the choice phase (12 th 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 & Sherman, 1984). Prior research suggests that enrollment decisions by nonresident applicants are sensitive to institutional aid offers (Curs & Singell, 2010) and that institutions use increasingly sophisticated modelling techniques to determine the institutional aid offer for each student (Cheslock & Kroc, 2012; DesJardins, 2001). Methodology To test our research hypotheses we utilized the following institution-level panel model: 15

16 Y it = βx i,t 1 + W i,t 1 γ + V s,t 1 θ + δ t + a i + ε it (1) Where, subscript i represents institutions and subscript t represents time, in years. Y it, nonresident freshman, is the number of nonresident freshman who enroll in institution i at time t. X i,t 1 represents state appropriations lagged one year relative to nonresident freshman enrollment. β is the coefficient of interest, representing the effect of state appropriations on nonresident freshman enrollment. W i,t 1 is a matrix of institution- and time-varying covariates lagged one year. V st is a matrix of state- and time-varying covariates lagged one year, where subscript s represent the state which institution i is located. δ t represents year-varying, institution-invariant effects. a i represents institution-varying, time-invariant omitted variables. ε it 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 β, 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 to institutions were not randomly assigned. An estimate of β 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 Equation 1 when state appropriations may be endogenous. Panel models must satisfy two assumptions in order to interpret β as a causal effect (Cameron & Trivedi, 2005). First, the random effects assumption states that there is no relationship between X i,t 1 and the panel-varying, time-invariant error component, a i, after 16

17 controlling for covariates. The random effects assumption is implausible in most empirical contexts (Cheslock & 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 institution-varying, time-invariant variation, a i. 3 Note that when using the fixed effects estimator, β 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, ε it, and the independent variable of interest, X i,t 1, in any time period, t = 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, W i,t 1, and state-varying, V s,t 1, covariates that plausibly affect Y it and could have a relationship with X i,t 1. These covariates are described below. Despite these efforts, our results should not be interpreted as fully causal effects because it is unlikely to eliminate all sources of omitted variable through the inclusion of control variables (Cameron & Trivedi, 2005). All models estimate 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 one-year lag between state appropriations and nonresident freshman enrollment. We use the example of state appropriations from a single year, the For each dependent variable analyzed, Hausman tests of whether a random effects estimator would be consistent were rejected in all cases (p<.01). 17

18 12 academic year, to explain the logic of the one-year time lag that we applied to the entire analysis period. We assume that state appropriations for , running August 2011 through July 2012, were finalized by June 2011 because all but four states have fiscal years ending in June (National Conference of State Legislatures, 2012). Drawing from resource dependence theory, we argue that declines in 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 behaviors (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 Therefore, our justification for a applying a one-year lag to state appropriations is as follows: we posit that declines in state appropriations were associated with increased recruitment efforts targeted at out-of-state high-school seniors in , which were associated with growth in 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. Appendix A 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 Appendix A. Analysis period. The analysis period was determined by the availability of institutionlevel covariates. The analysis period was the 11-year period from to (herein 18

19 2002 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 academic year and because we applied a one-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 U.S. 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 collegesliberal 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, Tandberg, & Gross, 2014) and The University of the District of Columbia (N=1) because the institution did not receive substantial state 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). Institutionyear observations were dropped from the analysis sample due to missing data for the dependent 19

20 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 institutionyear observations contained missing data. Therefore, our analysis sample was an unbalanced panel of 440 institutions and 4,429 institution-year 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 results to be interpreted as elasticities (i.e., percent change in Y associated with a 1% change X) (Cameron & Trivedi, 2005). Missing institutionyear 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 one-year lag (year t-1) and lead (year t+1) observations. 169 observations (3.8 percent 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 U.S. territory, and the number of first-time freshman migrating from a foreign country. Nonresident freshman enrollment, y it, was defined as the number of freshman that migrated to institution i from a different state or from a U.S. territory or foreign country in year t. Note that y it measured 20

21 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 academic year) and voluntary in even academic years. Nonmissing institution-year observations from voluntary years (e.g., ) 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 one year because we assume that enrollment decisions by an incoming freshman cohort are affected by prior year institutional characteristics. All institutional supply covariates were 21

22 lagged one 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 & 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 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, Lovenheim, & Turner, 2010; Bowen, 1980; Winston, 1999). We included the following measures of an institution s academic profile: percent of applicants admitted; 25 th percentile SAT score of enrolled freshmen (where ACT scores converted to SAT scores); and 75 th 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 percent 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 22

23 enterprises. 4 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 in 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 full-time 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 & 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) 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 4 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. 23

24 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 & Heck, 2009; Zhang & Ness, 2010). We argue that institutions in states with 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 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, Jaquette, Bielby, & Bastedo, 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 24

25 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 & 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 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 (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 important state policy attribute likely biases our results towards 25

26 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 necessarily limited to what data were available in IPEDS. Results Descriptive Statistics Figure 2 presents changes in the median nonresident freshman enrollment, resident freshman enrollment, and lagged state appropriations by institutional type for the sample period of 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 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. Nonresident Freshman Enrollment Table 2 presents the results of the estimation of Equation 1, the relationship between state appropriations and nonresident freshman enrollment, using the IPEDS Fall Enrollment data. For 5 Data points for Figure A only include years where the residency component of the IPEDS Fall Enrollment survey was mandatory. 26