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The Heterogeneous Non-resident Student Body: Measuring the Effect of Out-Of-State Students’ Home-State Wealth on Tuition and Fee Price Variations

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Abstract

More than 40 years of research has found a positive relationship between increases in the proportion of non-resident students enrolling in an institution and increases in the tuition prices this institution charges to these same students. Notably, this line of research has consistently treated this non-resident student body as if they constitute a homogeneous group in terms of their socioeconomic well-being, when in reality these students come from states with differing levels of socioeconomic prosperity. Notably, given that tuition and fee charges to non-resident students are market-based, institutions charge what out-of-state students are willing to pay. Under this rationale, it follows that the wealthier the student body of an institution is, the more institutions will be able to charge them in terms of tuition and fees for their education. The purpose of this study is twofold. First, it offers a method to measure the level of wealth of the non-resident student body enrolling at an institution considering the level of wealth of these students’ home states, therefore creating a measure of heterogeneity of the non-resident student body. The second purpose is to evaluate whether this measure of heterogeneity is associated with larger increases in the net tuition and fee prices charged to these students compared to the increases related to the homogeneous structure that ignores these students’ home-state wealth. This twofold purpose was addressed utilizing a dataset built from regional, state, and institutional information of 1743 public and private not-for-profit 4-year institutions across the contiguous United States. Since all the outcome variables were found to be spatially dependent, spatial econometrics techniques were employed for model estimation. Results corroborated that treating non-resident students as a homogeneous structure rendered downwardly biased estimates of institutions’ abilities and/or decisions to set higher or lower tuition and fee prices compared to the estimates obtained using the heterogeneous structure. Considering current general disinvestment of states in higher education, the analysis of factors driving non-resident tuition and fee price-setting has become especially relevant for public policy officials and decision-makers at both the institution- and state-levels. Accordingly, this study examines a critical issue in the finance of higher education—the setting of institutional tuition and fees for non-resident students.

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Fig. 1

Source: IPEDS 2000–2010, code available upon request

Fig. 2
Fig. 3

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Notes

  1. SHEEO is a nonprofit, nationwide association of the chief executive officers serving state-wide coordinating, policy, and governing boards of postsecondary education. The reports cited are based on state-level data from official entities such as Councils on Postsecondary Education, Postsecondary Education Commissions, Board of Governors, Board of Regents, Higher Education Coordinating Boards, and University Systems. The purpose of these reports is to examine the philosophies, policies, and procedures that influence decision-making with regard to public college and university tuition, student fees, and student financial aid programs.

  2. The color saturation in the figure indicates a higher concentration of the distribution of this proportion by institution Carnegie type and sector. Based on the panel structure of the data represented in this figure, each plot contains two regression lines that aimed to capture trends. The red regression line is the estimated linear change of this proportion and its quadratic term as a function of time. The quadratic term was included to capture fluctuations that would diverge from linearity. Similarly, the yellow line is a local regression that aims to overtly capture unconditional distributions at each given year over the entire 11-year observation points. It can be noted that both lines tend to converge indicating that panel data models to be employed in future studies would be well-served by relying on models that assume linearity of tuition variation.

  3. Every year, some institutions participating in state and regional tuition reduction agreements are willing to forego the full non-resident tuition fee price. Typically, students are eligible to pay 150 % of the in-state tuition if their desired program is not offered in the students’ states of residence. It is also worth noting that admission and final tuition price decisions are made at the discretion of the receiving campus and the campus may exercise its right to limit participation or set specific admission requirements as can be seen in the official reports provided by these entities—see Midwestern Higher Education Compact (2014), Southern Regional Education Board (2014), New England Board of Higher Education (2012), and Western Interstate Commission for Higher Education (2014). Considering declines in state support, it seems unlikely that campuses are willing to issue non-resident fee waivers en masse, reserving such waivers for students bringing unique talents that may be beneficial to the institutions above and beyond the foregone non-resident tuition fees.

  4. There is a caveat to the previous statement: IPEDS provides codes in the state of origin column that need to be carefully examined to avoid double counting the number of non-resident students enrolled at a given institution. For example, the code 89 measures the total of students coming from outlying areas, code 99 accounts for all first-time degree/certificate seeking undergraduates of a given IHE i , and, more importantly, code 58 is the total number of students coming from the contiguous U.S. The code that IPEDS uses to report international students (under a student visa) is 90. These students—although they can technically be classified as non-resident students—will not be considered in the computations of the heterogeneous non-resident student body as IPEDS does not provide specific countries of origin. As such, it is impossible to assign any measures of wealth to these students.

  5. Nine colleges and universities were dropped from the models as they did not charge tuition and fees. Six of these IHEs are military or marine institutions, and the other are religious.

  6. These states are: NJ, NY, NC, OH, PA, and the District of Columbia.

  7. See Carnegie Foundation for the Advancement of Teaching (2013) for the relationship between doctoral/research classification and institutional selectivity indicators.

  8. The \(Z_{I}\) score for the statistic is computed as: \(Z_{I} = I - E[I]/\sqrt {V[I]}\), where \(E\left[ I \right] = - \frac{1}{n - 1},\) and \(V\left[ I \right] = E\left[ {I^{2} } \right] - E[I]^{2}\).

  9. For a detailed explanation of the process to deal with spatial dependence of the error terms in SAR, see Bivand et al. (2008, pp. 277–278).

  10. The variance of Moran’s I along with its estimated and expected values rendered the standard deviation as follows \((I - E\left[ I \right])/\sqrt {\text{var}[I]}\) as shown by Bivand et al. (2008, p. 260).

  11. The models fitted in this study show results in real dollars. Models examining the relationships between tuitions using log specifications rendered similar inferences and are available upon request. Results are provided in real dollars to provide more meaningful comparisons of changes in the magnitude of the coefficients.

  12. Consistent with the context of the study, the t-ratio of this difference that resulted in significant differences was obtained using a sandwich-based robust standard error estimator that assumes equal variances within groups, but unequal variances across group populations.

  13. This information is provided by the following state/regional agreements’ official websites and reports: Midwestern Higher Education Compact (2014); New England Board of Higher Education (2012); Southern Regional Education Board (2014); Western Interstate Commission for Higher Education (2014).

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Acknowledgments

I am grateful to Dr. Timothy R. Cain (Institute of Higher Education, University of Georgia) and the two anonimous reviewers for their valuable comments and suggestions. Similarly, I want to thank graduate students and colleagues (Lucia Brajkovic, Jeffrey Harding, Jason Lee, Robert Stollberg, and Melissa Whatley) for their truly important help with the English language.

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    Manuel S. González Canché

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González Canché, M.S. The Heterogeneous Non-resident Student Body: Measuring the Effect of Out-Of-State Students’ Home-State Wealth on Tuition and Fee Price Variations. Res High Educ 58, 141–183 (2017). https://doi.org/10.1007/s11162-016-9422-2

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