Unemployment Rate and Tuition as Enrollment Predictors

An ARIMA (2, 1, 0) time-series model was created to determine whether unemployment rate and tuition predict total credit hour enrollment at Monroe County Community College over a span of 32 years. The model captured the Fall-to-Fall and Winter-to-Winter credit hour correlation and enrollment’s positive linear trend; the autocorrelation parameter at Lag 2 was statistically significant. Using enrollment data to predict itself, 88.4% of the variation in enrollment was accounted for by the model. Adding unemployment rate increased the Stationary R-squared to .90, whereas tuition had no statistically significant relationship with enrollment. Both models were an excellent fit for the data. The benefits of using ARIMA for time-series analysis and enrollment forecasts for the upcoming semesters are included. ENROLLMENT PREDICTION 3 Unemployment Rate and Tuition as Enrollment Predictors Given states’ declining contribution to higher education subsidization (Grapevine, 2012) and thus greater reliance on tuition revenue (Delta Project, 2010), institutions are increasingly relying on enrollment projections to set tuition rates. The aim of college financial offices is to cover budgetary expenses without raising tuition to the point at which it deters students from enrolling. The present study examines the impact of cost per credit hour and local unemployment rate on community college enrollment. To investigate the relationship between enrollment and tuition, previous studies have typically used the Student Price Response Coefficient (SPRC), the percentage change in enrollment given a fixed tuition increase (typically $100 or $1000) per year, or tuition elasticity of demand, the percentage change in enrollment given a percentage change in tuition. Tuition is considered elastic when demand for higher education varies as a function of a tuition adjustment, and inelastic when enrollment is not impacted by tuition modification. The enrollment numbers of public two-year institutions are generally more sensitive to tuition changes relative to fouryear institutions given that they have a higher proportion of low-income and older students (Hearn, 1988; Leslie and Brinkman, 1987). For instance, upon incorporating data from nearly every state and subsequently controlling for unemployment rate and need-based grant spending per state, Kane (1995) found that a $1000 tuition increase (1991 dollars) led to a 3.5% decrease in enrollment at community colleges and a 1.4% decline at four-year institutions. Regarding community colleges specifically, and controlling for the same aforementioned variables, a less substantial increase of $100 (1993 dollars) resulted in a SPRC of -0.36, meaning enrollment decreased about one-third of one percent (Heller, 1996, as cited in Heller, 1997). Similarly, Rouse (1994) found a tuition increase of 8% led to a 0.9% decrease in enrollment and Shires ENROLLMENT PREDICTION 4 (1995) reported the demand price elasticity of California community colleges as -0.15. While studies have generally produced a slight inverse relationship between tuition increase and enrollment, as evidenced in Heller’s (1997) meta-analysis as well as the aforementioned studies, others have found either the opposite effect or no effect (Craft, Baker, Myers, & Harraf, 2012; Shin and Milton, 2008). Several studies have examined the relationship between unemployment and enrollment, with most demonstrating no relationship between the variables (Craft et al., 2012; Hemelt & Marcotte, 2011; Stanley & French, 2009). Kane (1995) however found that two-year public college enrollment was positively related to unemployment (as unemployment went up, enrollment went up), whereas four-year institutions’ enrollment was inversely related (as unemployment went up, enrollment went down). Gallet’s (2007) meta-analysis of tuition elasticity indicates that a majority (72.5%) of tuition analyses use ordinary least squares (OLS). This method is problematic in that OLS assumes homoscedasticity and lack of autocorrelation in the residuals. College enrollment has been increasing over time, a factor that OLS is not equipped to handle with its minimization of the sum of squared errors (Tabachnick & Fidell, 2007). Regardless of the procedure used, analyses have tended to cover the short-run (91%), and correction for autocorrelation (28%), heteroscedasticity (7%), and multicollinearity (11%) has been rare (Gallet, 2007). In studies that include adjustments for autocorrelation and heteroscedasticity, enrollment is less affected by tuition increases (Gallet, 2007). Autoregressive Integrated Moving Average (ARIMA) time-series analysis is an ideal way to examine enrollment patterns over time, test whether variables serve as predictors, and forecast future enrollment, while avoiding the previously mentioned statistical blunders. The selected ENROLLMENT PREDICTION 5 model (p, d, q) characterizes patterns in the data: p = auto-regression, d = integrated (linear or quadratic trend), and q = moving average (random shocks). Integers in the model convey different meanings, with 0 indicating the element does not exist in the model. For instance, an integer in d indicates that the mean and variance over time are not constant and need to be made stationary through differencing or transformation before proceeding.