AN ANALYSIS OF HIGHER EDUCATION FACILITY EXPANSION

Journal of Business and Educational Leadership

Volume 8, No. 1; Fall 2018

J. David Chapman, Stuart T. MacDonald, Allen G. Arnold, Ryan S. Chapman

ABSTRACT

America’s colleges and universities have expanded campus facilities by renovating and increasing square footage. This is in contrast to general construction activity during the same time period. This quantitative study investigates the relationship between university and college campus facility square footage per Full Time Equivalent (FTE) and university enrollments, institution endowments, and tuition and fees. Dummy variables were created for Carnegie classification and whether the college or university was private or public. Literature documents concern that these increased and upgraded facilities may become overbuilt and thus become liabilities to the institutions. Square footage data gathered over a five-year period from college and university administrators were regressed against enrollment, endowment, tuition, and fees for the same time period (2002-2007). Results show a relationship between university square footage per FTE and endowments per FTE and tuition. The relationship between enrollment and square footage per FTE indicates that total square footage increases with enrollment, however at a lower rate than enrollment. This indicates that administrators may act rationally using this empirical data as suggested in teleological theory. However, the results also show that this theory cannot explain all the increases in campus square footage. It leaves room for such theories as arms race theory and public choice theory. This study adds to the body of knowledge regarding the motivation of administrators to increase campus facility square footage and creates a predictor model for administrators to compare institutions. Keywords: Higher Education Expansion, Arms Race, Public Choice Theory, Teleological Theory

INTRODUCTION

America’s colleges and universities have expanded campus facilities by renovating and increasing square footage. Approximately $846.2 billion in new construction was recorded at a seasonally-adjusted annual rate as of February 2010, according to the U.S. Bureau of the Census. This was down from the 2006 yearly peak of $1.16 trillion (United States Bureau of the Census, 2010). Bucking this downward Journal of Business and Educational Leadership 67 trend in commercial and residential construction and considered by many economists as the bright spot in the construction industry, higher education construction enjoyed an increase in both the number of projects and the dollar amount per project, and was second only to health care in terms of construction and real estate development activity from 1994 to 2011 (Abramson, 2007; Baker, 2009; Haughey, 2010). This quantitative study investigates the relationship between university and college campus facility square footage per Full Time Equivalent (FTE) and university enrollments, institution endowments, and tuition and fees.

LITERATURE REVIEW

Theory This study discovered significant relationships between empirical data, such as endowments and tuition, to changes in college and university campus facility square footage. Higher education administrators acting within the framework of teleological theory would only expand college or university campuses when required to meet the goals or achieve the missions of the institution. In the teleological construct, administrators should expand campus facilities only when relying on empirical data from research based on enrollment, endowments, and tuition. Teleological theory ignores or downplays the possibility that individuals within the organization might act from alternative motives conflicting with those of the organization. The results of this study show that empirical data, such as enrollment, endowment and tuition are being considered; however, increases in campus square footage that cannot be attributed to this empirical data, also appear to take place. This is exemplified by the lower than expected R² results. This study adds to the body of knowledge of college and university campus facility expansion by revealing that although a significant amount of the increase in square footage can be accounted for by careful evaluation of empirical data, other motivations may exist as well. These include the concept of the positional arms race and public choice theory. The low R² numbers in several models indicate that at least some of the changes in square footage is unexplained by the variables regressed. Coase (1960) demonstrated effectively that in the absence of any distorting influences, such as imperfect information or perverse incentives, a rational actor will choose the efficient outcome such as that seen in this study and in teleological theory. Economists develop theories to explain and predict how changes in situations affect economic behavior. There are obvious risks in applying these theories to elucidate the change in square footage of campus facilities. De Alessi (1983) posits that the relationship asserted by neoclassical economic theory predicts behavior, considering idealized variables under theoretical conditions. This theoretical construct highlights the importance of considering applicable theories and alternative hypotheses that affect relationships to real world phenomena Chapman, MacDonald, Arnold and Chapman 68 (Milgrom & Roberts, 1992; Nagel, 1963). In the vernacular of economic theory, consideration must be given for friction, distorting influences, or externalities that might cause otherwise rational actors to make choices that deviate from theoretical expectations. Some economists refer to the actions taken that are counterproductive or inefficient as market failures (Viscusi, Vernon, & Harrington, 2000). Although not considered a formal theory, the concept known as a positional arms race may account for the distorting influences attributed to market failures. Frank (1999) documents recent competition for students among higher education institutions, forcing these institutions into what he refers to as an “arms race” (p. 9) for the biggest and best facilities. A classic example of an arms race is the race for naval supremacy between the United Kingdom and the German empire prior to the First World War. In explaining this arms race, Massie (1991) details how both Germany and the United Kingdom expended significant amounts of their national treasure over a 20 year period to build two fleets that never met in the decisive battle naval theorists had predicted. The result of the First World War would have probably been the same if both nations refrained from engaging in the arms race. Similarly, the competition between universities appears to have characteristics of an arms race, whereby too many of the scarce educational resources available to higher education institutions are consumed in a pointless competition for status contributing to unnecessarily increased costs (Hirsch, 1976; Winston, 2000; Zemsky, Wegner, & Massy, 2005). This competition is partially fueled by the growing importance of academic ranking. Students are increasingly concerned with the rankings published in the U.S. News & World Report’s annual college ranking issue (Ehrenberg, 2001). A testament to this fact is that this issue is the magazine’s leading seller, and university applicant pools swing sharply in response to changes and fluctuations in the rankings. Investments in facility square footage and renovation, made by America’s colleges and universities to compete for the best and brightest students, may be mutually offsetting just as the arms races of competing nations to obtain the most powerful weaponry (Frank, 1999; Hirsch, 1976). In the end, gains are minimized and expenditures are substantial in paying for the added facility square footage and upgrades. Given the propensity of actors in organizations to operate contrary to the principles described in neoclassical theory and their tendency to be drawn into unproductive positional arms race in higher education, public choice theory is subsequently considered to elucidate decision-makers’ motivation and pursuit of facility campus expansion. The public choice theoretical perspective argues that many of the expenditures made to expand campus facilities are wasteful. In their seminal work, Buchanan and Tullock (1962) posited that economic theory could be used to understand government institutions, political actors, and non-profit organizations. They contend that the principle of rational maximization could be applied to Journal of Business and Educational Leadership 69 governmental and bureaucratic behaviors, however one should not expect bureaucrats to take actions that would further the mission of the organization over their own personal well-being. Analysis of self-serving behavior by administrators was further expanded by Jensen (2000), who argued that to view an organization as a rationally maximizing entity is erroneous. Organizational entities are typically composed of self-satisfying rent-seeking actors. This composition of individuals leads to a further issue, as expounded by Milgrom and Roberts (1992), who illustrate how information asymmetries make the costs of monitoring so expensive that it is economically impractical for any board or other supervisors to ever truly eliminate self-regarding behavior in organizational management. Organizational theorists note that physical expansion and growth give the appearance of competence to those administering the growth of the organization (Kaufman, 1973; Marris, 1964; Penrose, 1959; Perrow, 1979; Whetten, 1980). Expansion also gives university administrators the opportunity to dispense favors and expend significant resources in the local community, thereby enhancing their own status. These conditions would potentially influence a self-interested administrator to be biased toward expansion, even if it were not economically preferable (Cyert & March, 1963). The result is an inefficient production of a bureau’s services compounded by potentially perverse motivations in bureaucrat compensation (Downs, 1967; Mueller, 2003). Warren (1975) found that leadership in private industry is normally able to claim a share of savings and profits generated by an increase in efficiency, however, public bureaucrats’ salaries are either unrelated or indirectly and perhaps inversely related to improved efficiency. Without financial incentives in place for the higher education administrator, a host of self-serving behaviors may manifest, including salary inflation, power seeking, public reputation seeking, patronage, and favor dispensation in the community (Niskanen, 1971). Public choice theory paints a clear path and incentive for the bureaucrat to maximize power and utility by increasing budgets and over expanding the campus facilities. With their seminal work Buchanan and Tullock (1962) revolutionized political economy doctrine theory by demonstrating that economic analysis could be used to explain the behavior of government institutions, political actors, and bureaucracies. Just as Jensen (2000) opened the black box called the firm and found individual self-regarding rational actors behaving in their own self-interest, the public choice economist opens the black box called the bureaucracy and finds it filled with rational self-regarding maximizing actors. Applying this concept to higher education, Massey (2001) referred to a situation he calls resource diversion where people follow their own interests at the expense of the organization at every opportunity. Thus, in lieu of using the type of marginal-cost, marginal-benefit analysis, or empirical data, such as enrollment, endowment, and tuition described in teleological theory, the individual bureaucrat may act so as to maximize their personal utility rather than the public’s benefit. In a worst-case scenario, a selfmaximizing administrator in a university system could seek to gain control of a Chapman, MacDonald, Arnold and Chapman 70 program simply to maximize the budget and incentivize over-expansion of campus facilities.

RESEARCH METHODOLOGY

Research Variables: Enrollment, Endowments, and Tuition Enrollment. Measurement: Integrated Postsecondary Education Data System (IPEDS). The U.S Department of Education fulfills a congressional mandate through the National Center for Education Statistics (NCES) to collect, analyze, and report enrollment data from America’s higher education institutions. Much of these NCES data is based on findings from the Integrated Postsecondary Education Data System (IPEDS). National Participation in IPEDS is a requirement for colleges and universities that receive Title IV federal student financial aid programs, such as Pell Grants or Stafford Loans. The proportion of higher education enrollment at four-year public and private universities declined as compared to the higher education industry as a whole. IPEDs data reveals the declining market share at not-for-profit, four-year public and private universities. Both public and private four-year not-for-profit universities lost approximately 10% in market share during the period addressed. The market share loss was tolerable, however, because it came at a time when the entire market grew significantly, from 5.9 million in 1965 to 15.9 million in 2001. Every sector grew substantially: public four-year universities by 113%, private four-year institutions by 82%, and two-year public schools by 366% (United States Government Accountability Office, 2007). Simply put, loss of market share was easier to tolerate in a rapidly growing market. The danger was that institutions losing market share while enrollment was growing might fail to recognize that the shift in students’ preferences away from their institutions could be destructive to these institutions. The Western Interstate Commission for Higher Education (WICHE) projects that the total number of high school graduates in 2022 will be roughly 1% larger than in 2009, but the overall figure masks dramatic changes in high school demographics. Caucasians, who currently attend college in higher numbers, are projected to decline by 14.6%, while Hispanics, who currently attend college in significantly low percentages, will increase by 62.5%. Enrollment in K-12 schools in the United States reached 55.3 million in 2006, and began a declining trend for the first time in 20 years. These data suggest that postsecondary enrollment will decline dramatically if historic university attendance patterns remain unchanged (National Center for Education Statistics, 2006). If higher education is unsuccessful at increasing enrollment patterns of Hispanics, as well as Caucasians and African-Americans, the years described by the commission could witness a declining market for higher education. The institutions that have market shares reduced may well see absolute declines in enrollments (Western Interstate Commission for Higher Education, 2008). Buildings and infrastructure built without consideration to the declining enrollment possibilities could become a Journal of Business and Educational Leadership 71 significant liability to American higher education. Reduction in the number of high school graduates and the demographic makeup of those graduates would be prudent considerations when expanding campus facilities. The literature points to another complication that suggests higher education administration should go beyond looking at the numbers enrolled and look to the types of enrollment. Commercial real estate leaders are currently worried that technology might be a formidable competitor and impair its future economic viability. The concern stems from a fear that businesses operating in brick and mortar buildings would be able to utilize technology to operate virtually, or without physical places, leaving empty retail, industrial, and office space. A comparable situation may be present in higher education. The possibility exists that higher education enrollment could continue to increase, but less square footage of campus facilities could be needed to accommodate the increase. This dichotomy could be caused by the emergence of students’ preference for institutions offering on-line learning (Porter, 2001). The potential shift to on-line learning initiatives may have a substantive effect on the demand for higher education campus facilities. Ambient Insight Research (AIR) released a market forecast predicting that 25 million post-secondary students in the United States will take classes online by 2015. The predicted number of students who take classes exclusively on physical campuses will go from 14.4 million in 2010 to just 4.1 million five years later (Ambient Insight Research, 2011). While the exact numbers of students who attend classes physically on American college and university campuses may certainly be debated, the trend for a growing percentage of students using online learning in lieu of attending classes on physical campuses is nearly certain (Allen & Seaman, 2010). Although there is limited agreement among experts that online learning will strategically change the current higher education landscape, there is very little literature predicting or discussing the impact on higher education campus facilities. Meyer (2008) suggests that the capital for the creation of the online learning curriculum could come by capitalizing on cost-efficiencies of online learning. In a concept called capital-for-capital substitution, many institutions count on online learning to use existing buildings more efficiently and save classroom space; some institutions are even eliminating the physical building altogether and saving 15% of the cost of traditional courses (Campbell, Bourne, Mosterman, Nahvi, Brodersen, & Danwant, 2004; Farmer, 1998; Meyer, 2006; Milam, 2000). Endowment. The size of an institution’s endowment is often now integral to the evaluation of the financial health of the institution by bond underwriters and stakeholders. Along with the amplified dependence on the incomes from endowments comes increased pressure on college and university administrations for higher expected performance of returns on the invested assets. Data from the NACUBO-Commonfund Study indicates that the financial performance of Chapman, MacDonald, Arnold and Chapman 72 endowments may have a significant relationship to the economy, and, specifically to indexes such as the S&P 500 in which at least some of these assets are invested. Endowments of universities not only gain attention from underwriters and stakeholders but also from the U.S. Congress, industry, media, and general society as a whole. The U.S. Senate Finance Committee held hearings in 2006 and 2007 evaluating how college and universities use their 501(C)(3) status and the ability of donors to deduct gifts to educational institutions (United States Senate Committee on Finance, 2006). Industry publications and popular press such as The Chronicle of Higher Education and The New York Times discuss university endowment investments, tuition in relation to endowments, the growing wealth gap between institutions of higher education, and scrutiny over the endowment-toexpense ratio of universities. The endowment-to-expense ratio compares the endowment to an institution’s actual costs and is subjective with some analysts considering more than a 2:1 ratio as evidence of an excessive endowment. Still others suggest that under certain circumstances, an endowment exceeding a ratio of 5:1 would be considered justifiable (Schneider, 2006). There is evidence suggesting that Congress may consider establishing tax-deductibility criteria based on endowment-to-expense ratios (Waldeck, 2009). No matter what ratio is utilized to justify the amount of endowment held by a university, and whether the longterm increases are from increased giving or increased market returns, it is apparent that administrators will be under increasing pressure to spend those revenues and could justify campus facility expansion projects to artificially and strategically fall into a beneficial endowment-to-expense ratio (Waldeck, 2009). Tuition. Current trends in higher education tuition. Considering the importance of a college education to the success of individuals in the United States (Baum & Payea, 2005; Baum & Ma, 2007; Black & Smith, 2004; Card, 2002; Johnstone, 1999; Monks, 2000; United States Government Accountability Office, 2007) and the significance of the degreed individual to society (Colby, Ehrlich, Beaumont, & Stephens, 2003; Torney-Purta, 2002) the issue of college affordability is paramount. College affordability is a complex issue and cannot be captured by simply analyzing tuition and fee increases; however, there is a substantive value in considering trends and issues surrounding tuition. Tuition and fees constitute 67% of the total budget for full-time students enrolled in four-year private colleges and universities and 36% of the budget for in-state residential public students. Data from the period 1981 to 2012 indicates a robust increase in tuition as well as fees in all but two-year public colleges (The College Board, 2006). In recent decades the cost of a college education continued to increase at twice the rate of general inflation (United State Department of Education, n.d.). This occurred in spite of the efforts of business professionals, scholars, and politicians who offered prescriptions to mitigate the increases (Ehrenberg, 2004; Ehrenberg, 2001). As tuition increased, federal and state financing of student funding diminished causing students to become more reliant on student loans (The Journal of Business and Educational Leadership 73 College Board, 2006) and creating concern about unmanageable debt burdens (Harrast, 2004; King & Bannon, 2002). Likewise, the federal government decreased block grant funding for higher education and emphasized programs that require repayment from the student. Because of this shift to a more studentresponsible system and continuing increases in the cost of education, few students were able to pay for college without some form of financial aid. In the 2007-08 school year, over 65% of all four-year undergraduate students graduating with a bachelor’s degree started their careers with education-related debt, and the average debt among graduating seniors was $23,186 (The College Board, 2008). THE MODEL Six separate models were developed to consider the relationship between enrollment, endowments, and tuition to college and university facility square footage. Two regressions were performed for each model. The first regression utilized core educational square footage and the second regression in each model used the square footage of the entire campus. The models were developed in a process of improving goodness of fit. To capture the influence of both undergraduate and graduate tuition and fees, a mathematical formula was used to weight these variables to deal with multicollinearity. The natural log of the enrollment variable was added in order to obtain a better fit. Results of statistical significance were recorded and best model used for the predictor model. The institutional support variable indicated whether a college or university was private or public. This variable showed positive, statistical significance across all six models for total campus square footage, as well as for core educational square footage. The enrollment variable showed inconsistent results depending upon which model was regressed. Tuition and fees showed significant consistency across models, especially once the weighting technique was employed. Endowment proved to be another variable with consistency across all six models. The Carnegie variable, indicating whether or not an institution was a research university, did not show significant consistency across models. Tests for heteroscedasticity were accomplished using the Cook-Weisbert test. Tests for multicollinearity were accomplished using the variance inflations factor command in STATA. Model specification was checked with the use of the ovtest command performing the Ramsey regression specification error test (RESET) for omitted variables. Finally, scatterplots were generated to analyze the relationships between the variables, specifically looking for outliers. The developed models utilized a regression equation to analyze the relationship between college and university square footage, where: Yi = b0 + b1Ug&GrENRi + b2Wt(Ug&GrTNi & Ug&GrFei) + B3EDMi + b4DP + b5DC+ ei. The variables utilized in the equation for model one are defined below. Chapman, MacDonald, Arnold and Chapman 74 UgENRi – This variable is undergraduate enrollment and is measured in full-time equivalent undergraduate students per year. GrENRi – This variable is graduate enrollment and is measured in full-time equivalent graduate students per year. UgTNi – This variable is undergraduate tuition and is measured in dollars per year. GrTNi – This variable is graduate tuition and is measured in dollars per year. UgFei – This variable is undergraduate student fees measured in dollars per year. GrFei – This variable is graduate student fees measured in dollars per year. EDMi – This variable is university endowment per FTE and is measured in dollars. DP – This variable is whether the university is private or public (institutional control). DC – This variable is whether the university is considered a research university according to Carnegie classification. Wt(UgGrTNi) – This variable is the weighted average of undergraduate tuition and graduate tuition and is measured in dollars per year. Wt(UgGrFei) – This variable is the weighted average of undergraduate fees and graduate fees and is measured in dollars per year. Wt(Ug&GrTNi & Ug&GrFei)– This variable is the weighted average of undergraduate tuition, graduate tuition, undergraduate fees and graduate fees measured in dollars per year. Ug&GrENRi – This variable is the undergraduate and graduate enrollment added together to give total enrollment measured in full-time equivalent undergraduate students per year. The Model as a Predictor. The mean values of each regressed variable and the corresponding coefficient value were entered into an Excel spreadsheet. Equations were then generated to compute the predicted value using the following equation: y = 𝛽₀ + β₁X₁ + β₂X₂ + …….. β₃X₃ , where 𝛽₀ is the intercept, β₁,β₂, & βᵢ are the variables, and X₁,X₂, & X₃ are means of those variables. Table 4.10 shows the resulting square footage predictor for each model. This predictor model was used to develop “what if” scenarios with the variables to further confirm the validity of the models. For example, based on the mean values reported in the study, and based on model 4, the predicted value at the means for core educational square footage was 110.69 square feet per FTE. That means that the model predicted that an institution with average levels of each variable (enrollment, endowment, and tuition) could be expected to have 110.69 square feet of space per student. If the same observations were used per model, relatively consistent results would be expected across models. Although there were obvious variations reported in Table 4.10 based on the specifics of each model, the values were not outside of the expected variance confirming, with reasonable certainty, that the models did not contain data entry-type errors. The predictor model is also used to draw conclusions pertaining to the variable sensitivity of the models. Summary of Model Results. As expected, the different models employed in this study provided differing results in statistical significance of the variables. The institutional support variable, indicating whether a college or university was Journal of Business and Educational Leadership 75 private or public, showed positive, statistical significance across all six models. The enrollment variable showed inconsistent results depending upon which model was regressed. Tuition and fees showed significant consistency across models, especially once the weighting technique was employed. Endowment proved to be another variable with consistency across all six models. The Carnegie variable, indicating whether or not an institution was a research university, did not show significant consistency across models. INTERPRETATION OF THE RESULTS Enrollment. Enrollment proved to be an interesting variable. Enrollment showed inconsistent results depending upon which model was used, but tended to be negative when statistically significant. The base model, model one, shown in Table 4.2 and 4.3, produced significant results for both undergraduate and graduate enrollments. The undergraduate enrollment variable was negative and the graduate enrollment variable was positive. The variable also produced inflated VIF scores indicating issues with multicollinearity. This was not unexpected, and to correct the issue the natural logs were taken of undergraduate and graduate enrollment and the regressions were run. Eventually undergraduate and graduate enrollments were added together and the log of total enrollments was used. According to the diagnostic tests, adding the variables and taking the natural log produced the most reliable variable reducing the VIF from 17.4 to 1.92. The results of adding the undergraduate and graduate enrollments together and using the natural log of the total enrollment was negative when significant. This regression result is confirmed in the predictor model indicating that square footage, while increasing with enrollment, does not do so at the same rate. As shown in figure 5.0, at enrollment levels of 5,046 students there are 150.34 square feet per student. At 20,182 students enrolled, each student has 141.26 square feet which is a total increase over previous square footage. Although the ratio is lower per student, the total square footage is increased significantly. This is not too surprising, since total square footage includes athletic facilities, wellness centers, and other square footage that appear to be less dependent upon how many students are actually on campus. This was more surprising in the core educational square footage, which includes classrooms and laboratories. Logically, student enrollment increases more quickly than campus facility square footage increased. To a point, administrators have the ability to hold more sections of classes and add more students to existing classrooms in lieu of adding additional space. In this context, the enrollment variable tended to support the arm race research (Ehrenberg, 2001; Frank, 2008; Frank & Cook, 1995; Hirsch, 1976; Sedlacek & Clark, 2003; Winston, 2000), indicating that some campus expansion was due to competitive pressure and not necessary to accommodate growing enrollments. The lack of consistent statistical significance in enrollment as a variable in the regression equation was also supported with the predictor model. To analyze the Chapman, MacDonald, Arnold and Chapman 76 sensitivity of the variables, the mean total enrollment variable of 10,091 students was changed by 50%, 75%, 150%, and 200% of the mean. As figure 5.0 represents, campus square footage tended to decrease in the predictor as the enrollment variable was increased. This result indicated that campus square footage was not too sensitive to increases in enrollment and was negative based on the models in this study. Endowments. The fact that the estimated coefficient for endowments is statistically significantly different from zero supports much of the literature in Chapter Two. Conti-Brown (2011) analyzed higher education institution endowments and documented a cultural theory that the university President’s legacy is a strong consideration to how endowment proceeds are invested and spent. The correlation between endowments and campus square footage gives support for the edifice complex concept, indicating that donors might prefer to donate money for buildings with naming rights (Bassett, 1983; King, 2005). Administrators understand that naming rights to buildings allow donors to leave lasting legacies. Also documented in the literature review was the pressure administrators feel to spend the endowment proceeds to achieve a beneficial endowment-to-expense ratio. The conjecture that administrators spend endowment proceeds on campus facility expansion projects to fall strategically into a beneficial endowment-to-expense ratio was consistent with the findings in this study. Each of the considerations addressed in this paragraph are developed more fully in the implication sections of this chapter. The fact that endowments were significant and highly correlated to the square footage of American colleges and universities as a variable in the regression equation was also supported with the predictor model. Figure 5.1 graphically illustrates the sensitivity of square footage as endowments per student were reduced by 50%, dropping the square footage per student to 140.82 from the mean of 147.32 and to 160.31 square feet per student when the endowment was doubled. This result indicated that campus square footage was sensitive to increases in endowments, supporting the results of the regression analysis for the variable of endowments. Tuition. Like enrollment, tuition provided opportunities to improve goodness of fit in alternative models. In the base model tuitions appeared to have a strong correlation to square footage. However, testing for heteroscedasticity suggested that applying weighted averages to the variables might capture the influence of the variables while dealing with reliability issues. Adding both undergraduate and graduate fees to undergraduate and graduate tuition, and appropriately weighting the variables, appeared to be the best-fit model. Results in Table 4.2 and 4.3 showed that models five and six, where weighted average techniques were applied, produced statistically significant t scores in both total campus square footage and core educational square footage. Journal of Business and Educational Leadership 77 Based on these results it is reasonable to conclude that higher tuition and fees at the sample institutions provided more square footage in both categories. Not evident in the results of this research study, however, is whether increased tuitions are a result of changes in campus square footage or the cause of changes in campus square footage. The cost of a college degree is increasing at twice the rate of general inflation (United States Department of Education, n.d.). As these costs increase there has been a significant decrease in federal and state funding and more reliance on the student to fund the education with student loans (The College Board, 2006). Chapter Two documents the impact that student choice plays for campus facilities. The statistically significant results indicating a high correlation of square footage and tuition and fees in the regression equation was also supported with the predictor model. Figure 5.2 graphically illustrates the sensitivity of square footage as tuition and fees were reduced by 50%, dropping the square footage per student to 136.57 from the mean of 147.32 and to 168.81 square feet per student when the tuition and fees variable was doubled. This result indicated that campus square footage was sensitive to increases in the weighted tuition and fees variable. This supported the results of the regression equation, showing a correlation between tuition and square footage on college and university campuses. Students want new and expanded facilities with state-of-the-art amenities (Ehrenberg, 2001; Frank, 2007; Hill, 2004; Reeves La Roche, Flanigan, & Copeland, 2010). What was also not evident, either from the results of this study or the literature, is whether students fully understand that the costs of these amenities are being shifted to them and less on the federal and state funding sources. Fees. Student fees in both graduate and undergraduate programs were separated from tuition in models one and two. This provided statistically significant t score results, however, as with tuition there was suspicion that the results might have heteroscedasticity issues. Because of reliability issues in the diagnostics, the fee variables were weighted and added to tuition. The lack of correlation in some models could be explained in the nature of fees charged to the student. Many student fees are specifically designated to an organization or activity on campus. Programs and activities are highly dependent on these fees to function and are not easily diverted to building projects unless designated as such. Fees were then added to tuition in models five and six shown in Table 4.2 and 4.3. The new variable containing the weighted average of tuition and fees provided statistically significant results. Institutional Control. The institutional control variable, indicating whether a university is private or public, provided the most consistent results of all variables and was positive and statistically significant in every model, whether regressed against total campus square footage or core educational square footage. This indicated that public colleges and universities in the sample had more square Chapman, MacDonald, Arnold and Chapman 78 footage per student than private colleges and universities. This difference between square footage in public universities and private universities may be explained in part by public universities typically offering more majors and programs, and some of these majors and programs requiring lab space which significantly increases square footage per student. Carnegie Classification. The variable indicating Carnegie classification was used to specify whether or not the institution was a research institution. In the core square footage regression models two, three, five, and six, Carnegie classification was positive and highly significant indicating the amount of core educational square footage was correlated to whether or not the college or university was a “research” institution as defined by Carnegie classification. Interestingly, the same cannot be said for total square footage of the entire campus. In the total square footage regression, the research variable was only significant in two out of the six models, indicating a lack of correlation with the research variable in those models. This was predictable considering that research universities would likely need additional square footage for laboratories and other research related activities. The entire square footage of the university would thus be less impacted by whether or not the institution was a research university. Because low R² values in the study indicate that the variables used in this study do not entirely explain the square footage decisions of college and university campus facilities, other motivations should be considered. For example, the findings documented by Frank (2008), positing that colleges and universities are locked in a positional arms race forcing administrators to expand campus facilities to compete, should be considered. The results also leave plenty of room for a more cynical elucidation explained by Buchanan and Tullock (1962) as public choice theory. Public choice theory postulates that the bureaucrat personally maximizes power and utility by increasing budgets and over-expanding campus facilities. Any research in the area of square footage expansion would be remiss without acknowledging these plausible alternative theories, however they are beyond the scope of this research paper.

SUMMARY AND CONCLUSION

Enrollments at American colleges and universities are projected to decrease significantly beginning in 2014. The enrollment decline is calculated based on the end of the echo boom generational surge (Bare, 1997; Kennedy, 2011; Roach, 2008). This situation, coupled with growing online enrollment, exacerbates waning facility usage on campuses nationwide. Surplus college and university facilities may become liabilities if administrators miscalculate square footage requirements (Daigneau, 1994). Consequently, to minimize risk, administrators who make decisions regarding campus square footage should do so based on empirical data and strategic planning models. Journal of Business and Educational Leadership 79 This research explored the relationships between facilities square footage and the variables of enrollment, endowment, and tuition. The results indicated a strong correlation between endowments, tuitions, whether a university is classified as a research institution, whether the institution is public or private, and square footage of the campus facilities. The results may accordingly be useful for efforts to minimize risk. A counterintuitive finding was the lack of correlation between enrollment and campus square footage. Although the results demonstrated correlation between the other variables and campus square footage, the results left ample space for alternative theories. Teleological theory as an explanation—based on empirical data such as enrollment, endowment, and tuition—did not fully explain square footage decisions. Therefore, alternative theories such as the arms race concept and Public Choice Theory should be considered. Although the empirical data did not fully explain decisions regarding college and university campus facility square footage, the research revealed the existence of key relationships. 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