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This study utilizes an extensive panel data set spanning 15 years (2004–2018) and 752 public community colleges to investigate operating costs and persistent cost efficiency at public community colleges in the United States. We employ a generalized true random effects (GTRE) regression model that takes into account spatial correlation of costs among community colleges, to estimate cost efficiency via stochastic frontier analysis (SFA). The results reveal a positive relationship between operating costs and associate degrees and certificates as well both human (part- and full-time faculty) and financial resources (local, state and federal funding and tuition revenue), controlling for other variables. Furthermore, with an average persistent (long-term) efficiency of 87%, few institutions are relatively cost inefficient. Moving forward, campus leaders and policy makers alike may consider yearly data and efficiency calculations to develop strategic plans and funding alternatives. With 40 percent of first-time students transferring at least once within six years and over half of those students transfer from a community college, future research may study cost efficiency of community colleges while accounting for student transfers as an output. Supplementary Information The online version contains supplementary material available at 10.1007/s11162-021-09634-y.

Using an output distance function as an analytic framework, stochastic frontier analysis (SFA), and a generalized true random effects (GTRE) model, this study examines the financial context of bachelor’s degree production efficiency among public master’s colleges and universities (MCUs) in the United States. Employing a GTRE model, degree production inefficiency is decomposed into transient (short-run) and persistent (long-run) components. This investigation finds that bachelor’s degree production is positively and non-linearly related to doctoral degree production. Persistent efficiency is positively related to tuition revenue, state appropriations, and Pell grant revenue and negatively related to federal grant and contract revenue. This study finds that bachelor’s degree production efficiency scores that take into account the financial context of public MCUs should be considered as “pure” efficiency scores, which differ from the “technical” efficiency scores that don’t adjust for the financial context. Using efficiency scores, this research allows for the ranking of public MCUs, which may be used to further identify best management practices.

There are varied sources of data available to higher education analysts and researchers at the international, national, state, and institutional levels. These data are provided by international organizations, the federal government, regional compacts, and independent organizations. Most of these data are available to the public without restrictions. Many higher education analysts and researchers have used these data to examine numerous topics.

This chapter discusses asking the right policy questions. It points out how the nature of those questions and answers are shaped by the policy context. With the most appropriate methodological tools, policy analysts should be prepared to address follow-up questions. These include “what” and “how” questions. The chapter discusses how academic researchers have to simultaneously use rigorous methods and provide results of their research that is of use to policymakers and the general public.

This chapter introduces intermediate statistical techniques, which include pooled ordinary least squares (OLS), fixed-effects, and random-effects regression. This chapter demonstrates how we can use these statistical techniques to analyze panel data. It shows how various tests can be conducted to determine the appropriate method that should be employed in correlational studies. The chapter also introduces how multivariate regression can be modified to infer causal effects by including difference-in-differences estimators.

This chapter discusses and demonstrates how we can present analysis for presentation to higher education policymakers. The chapter details how to present descriptive statistics in a user-friendly Microsoft Word document format. The chapter also shows how we can use choropleth maps to illustrate data spatially. The chapter also demonstrates how graphs and tables of regression results and marginal effects are created.

This chapter discusses the use of descriptive statistics and graphs to present to policymakers and conduct exploratory data analysis (EDA). Descriptive statistics, that include measures of central tendency and dispersion, are discussed and demonstrated using real data. The utilization of graphs, which includes histograms, box charts, and scatter plots, are also presented.

This chapter discusses and demonstrates the importance of getting to know the data that we use to conduct higher education policy analysis and evaluation. More specifically, this chapter addresses the need to know the structure of datasets. The identification and exploration of missing data are also discussed in this chapter.

This chapter continues our discussion of advanced statistical techniques. More specifically, this chapter introduces and demonstrates the use of advanced statistical techniques that enable higher education policy analysts to exploit the use of emerging macro panel data. These statistical techniques include heterogeneous coefficient regression (HCR) with dynamic coefficient common correlated estimation (DCCE) and mean group (MG) estimators. Using these techniques, analysts can distinguish between: (1) short-run and long-run relationships and; (2) overall average beta coefficients and unit-specific beta coefficients.

Despite occupying a central position in contemporary U.S. higher education discourse, empirical research on administrative costs is limited. The purpose of this study was to extend existing researc...

This study examines costs of public master's colleges and universities in the United States by employing panel data on 248 institutions spanning fiscal years 2004–2012. Our analyses estimates a fle...

The current study examines costs, measured by educational and general (EG (2) there are economies of scale in undergraduate education and diseconomies of scale in graduate education; (3) relatively few institutions are cost inefficient; and (4) cost inefficiency tends to be long-term and persistent rather than short-term and residual. This research also identifies public master’s institutions that are the most cost efficient. Our inquiry has implications for future research as it points towards specific institutions, which may be engaged in effective practices to keep costs low, for possible follow-up case studies. Going forward, the techniques used in this study could be applied to examine economies of scale and scope as well as cost efficiency among other types of higher education institutions, such as public or private research universities, baccalaureate institutions, and community colleges.

The current study examines how nonresident tuition among public research universities has converged toward a national average over the 1987–2006 time period in the USA. Using dynamic fixed-effect panel modeling estimated via GMM (and instrumental variables fixed-effect model to account for endogeneity), we inquire (1) how do competitive market forces and state higher education governance structures influence nonresident tuition rates at public universities and (2) to what extent is conditional convergence of nonresident tuition rates at public universities occurring. We find that over the past 20 years, there has been a rapid convergence in nonresident tuition at public research universities to a national average, shaped by external market forces such as the demand for education and price competition among neighboring states, tuition for residents, the distribution of higher education appropriations internal to states, and the state higher education governance structures. Nonresident tuition has increased at a faster pace in states with low nonresident tuition as compared to states with high nonresident tuition. Our results have implications for higher education policy guidance as they reveal how a tendency toward “privatization” in certain aspects and segments of public higher education has been constrained in states in which higher education is more regulated via consolidated governing boards.

Production frontier analysis and the technical efficiency of production are conceptual tools that can provide insight into the production efficiency of colleges and universities. This chapter provides an overview of the theoretical and conceptual underpinnings of stochastic frontier analysis (SFA) and its application to degree production efficiency in higher education. The theoretical background, conceptual basis, statistical properties, and application of different types of SFA models that are used to generate measures of production efficiency are discussed. True fixed-effects (TFE), true random-effects (TRE), random parameter (RP), and latent class (LC) are among the SFA models introduced and discussed. Using cross-sectional and panel data and various models, this chapter demonstrates how SFA can be employed to examine bachelor’s degree productivity of master’s comprehensive universities. Differences in estimates of the technical efficiency of degree production across TFE, TRE, RP, and LC models of SFA, with different distributional assumptions of technical efficiency are discussed. The chapter also provides an example of the utility of a SFA model and how it is used to rank institutions based on their technical efficiency of degree production. It concludes with recommendations for future applications of SFA models in higher education.

This study shows that financial aspects of state higher education policies, particularly tuition, have an impact on the level of enrollment of adult undergraduates within a state. This study also demonstrates how stochastic frontier analysis (SFA) can be utilized to examine the potential maximum enrollment of adult learners in postsecondary education and generate state-level scores that can be utilized to rank states according to the difference between the potential maximum and actual enrollment of adults in higher education.

Using stochastic frontier analysis and dynamic fixed-effects panel modeling, this study examines how changes in the x-inefficiency of bachelor's degree production are influenced by changes in state higher education policy. The findings from this research show that increases in need-based state financial aid help to mitigate the convergence among states in x-inefficiency with regard to bachelor's degree production.

This study examines how the production of bachelor’s degrees is influenced by selected financial aspects of state higher education policy. This research uses data from 49 states covering a period of 13 years, integrates concepts from the higher education production function as well as the principal-agent model of the state, and employs dynamic fixed-effects panel modeling techniques.

Most research in the area of higher education is plagued by the problem of endogeneity or self-selection bias. Unlike ordinary least squares (OLS) regression, propensity score matching addresses the issue of self-selection bias and allows for a decomposition of treatment effects on outcomes. Using panel data from a national survey of bachelor’s degree recipients, this approach is illustrated via an analysis of the effect of receiving a master’s degree, in various program areas, on wage earning outcomes. The results of this study reveal that substantial self-selection bias is undetected when using OLS regression techniques. This article also shows that, unlike OLS regression, propensity score matching allows for estimates of the average treatment effect, average treatment on the treated effect, and the average treatment on the untreated effect on student outcomes such as wage earnings.

Using national survey data, multilevel modeling techniques, and descriptive statistics, this study makes an effort to understand the influence of the financial context of institutions on the chance of college completion for low socioeconomic status (SES) students at four-year colleges and universities. This research shows that college completion is positively associated with an institution’s tuition revenue as a percent of total revenue and educational and general expenditures per full-time equivalent student. This study also documents that, compared to high SES students, low SES students are disproportionately enrolled in institutions with lower levels of financial resources and higher dependence on tuition as a source of total revenue. The results of this research have implications for policy with regard to addressing the chances of college completion for low SES students.

Using national data from various sources, this study investigates the influence of financial aspects of state higher education policy on college completion. It found that college completion is positively associated with total state grants as a percentage of appropriations of state tax funds for operating expenses of higher education and state need-based grant dollars per individual in the traditional college-age (18–24) population. The study's results have implications for policies which attempt to link institutional eligibility for federal financial aid programs to graduation rates and for state higher education finance strategies that would improve the chance for college completion.

As the 1990s came to close and the twenty-first century began, college student retention and graduation rates became an increasingly important issue. Policy makers at the federal, state, and campus level continue to focus on retention and graduation rates even though more remains to be learned about how institutions contribute to college student persistence. Examining the financial context in which institutions operate may help us to understand further how institutions affect student persistence. This study is unique in that it uses resource dependency theory to frame an examination of the influence of the financial context of institutions on student persistence at four-year colleges and universities. This research reveals that, after taking student predictors into account, the percent of an institution's revenue derived from tuition positively influences college student persistence while the percent of an institution's expenditures on administration negatively influences it. Student Persistence and the Financial Context of Institutions In an effort to link budget allocations to institutional accountability, a majority of states use graduation or retention rates as one of several indicators of performance for higher education institutions and performance funding or budgeting (Burke & Minassians, 2001). Policy makers at the federal level are considering policy options, associated with the 2003 reauthorization of the Higher Education Act, which include linking institutional eligibility for federal student financial aid programs to institutional graduation rates (Burd, 2003). While policy makers are making an effort to link public funding to institutional persistence rates, the relative importance of public funds as a source of institutional revenue has actually declined (NCES, 2001). Between 1981 and 2000, state appropriations, as a percent of all funds to degree-granting public higher education institutions, fell from 44.0% to 32.3% (NCES, 2003). Over the same period, the percent of revenue derived from tuition increased from 12.9% to 18.5%, while the percent of revenue derived from federal grants and contracts rose from 8.8% to 9.4% (NCES, 2003). This shift in funding sources for degree-granting public higher education institutions occurred as changes in institutional expenditure patterns took place. Between 1981 and 2000, the percent of total education and general expenditures on instruction fell from 35.1% to 31.5%, while the share of expenditures on administration rose from 8.4% to 9.0% (NCES, 2003). Some researchers (e.g., Leslie & Slaughter, 1997) observed that colleges and universities have taken more market-based approaches to increasing their share of revenues from such sources as tuition and competitive grants and contracts, while others (e.g., Francis & Hampton, 1999) showed that research universities have also adjusted their internal allocation of financial resources in response to the relative decline in the importance of public funds as a source of revenues. According to an American Council on Education (ACE) survey (Anderson, 1985), as state appropriations decline and higher education institutions become more dependent on tuition as a source of revenue, colleges and universities tend to increasingly focus on retaining students. Turner (in press) contends there is a need to understand how the financial environment of colleges and universities influence student persistence. Although past research (e.g., Cabrera, Nora, & Casteneda, 1992; Paulsen & St. John, 2002; Perna, 1998) has addressed the relationship between student-level financial variables and student persistence, the influence of the financial context of institutions on persistence has not been systematically explored. Most of the research that addresses the role of finances in persistence, using either single-institution (e.g., Cabrera, Nora, & Casteneda, 1992) or multi-institution data (e. …

Although comparable percentages of African American and White high school sophomores expect to finish college (Nettles & Perna, 1997), smaller shares of African Americans and Hispanics than of Whites actually enroll. Only 39% of African American and 32% of Hispanic high school graduates between the ages of 18 and 24 were enrolled in college in 1999, compared with 45% of Whites (National Center for Education Statistics [NCES], 2001). College preparation programs (also known as early intervention programs and pre-collegiate outreach programs) are an increasingly common approach to raising the college enrollment rates of African Americans, Hispanics, and other groups of students who are underrepresented in higher education. Although the federal government has been involved with college preparation programs since the establishment of the TRIO programs in the 1960s, the federal government extended its role in 1998 with the establishment of GEAR-UP (Gaining Early Awareness and Readiness through Undergraduate Preparation). State governments, not-for-profit organizations, and individual colleges and universities also sponsor college preparation programs (Fenske, Geranios, Keller, & Moore, 1997). These programs are designed to promote educational attainment among disadvantaged groups of students by developing the skills, knowledge, confidence, aspirations, and preparation that are needed to enroll in and graduate from college. Administrators of college preparation programs, as well as researchers and policy analysts, generally believe that "parental involvement" is a component of "successful" programs (Swail & Perna, 2000; Tierney, 2002). A 1999 survey by the College Board revealed that more than two thirds (70%) of college preparation programs that target historically underrepresented minority groups report having a parental involvement component; for one third of all programs, parents of participating students are required to participate (Perna, 2002). Despite this high self-reported prevalence, however, some research (Tierney, 2002) suggests that parents are only superficially involved, likely because these programs often lack the time, funding, staffing, and other resources that are required for more substantial involvement. The stated commitment of most college preparation programs to involve parents reflects an assumption that parental involvement promotes the college enrollment of underrepresented groups of students. Researchers have found that parental involvement is associated with a greater likelihood of aspiring to attend college and actually enrolling (Cabrera & La Nasa, 2000; Horn, 1998; Hossler, Braxton & Coopersmith, 1989; Hossler, Schmit & Vesper, 1999; Perna, 2000), as well as with higher grades (Lee, 1993; Muller, 1993; Zick, Bryant, & Osterbacka, 2001), higher eighth grade mathematics and reading achievement (Lee, 1993; Sui-Chu & Willms, 1996), lower rates of behavioral problems (Lee, 1993; Zick, Bryant, & Osterbacka, 2001), and lower likelihood of high school dropout and truancy (McNeal, 1999). Nonetheless, research on the relationship between parental involvement and college enrollment is limited in several ways. When included in quantitative analyses, parental involvement is generally operationalized using just one indicator such as a composite of the frequency of discussions between the parent and child about school-related activities (e.g., Horn, 1998; Perna, 2000), rather than a multidimensional construct as recommended by some researchers (e.g., Sui-Chu & Willms, 1996). The role of parental involvement in college enrollment is also generally conceptualized as an individual-level characteristic with no attention to the structural constraints that influence involvement or the types of resources that may be accessed through such involvement (Horvat, 2001). Moreover, although some researchers (Perna, 2000; St. John, 1991) have shown that the college enrollment process varies across racial/ethnic groups, other researchers (Dika & Singh, 2002; Mattingly, Prislin, McKenzie, Rodriguez, & Kayzar, 2002; McNeal, 1999; Tierney & Auerbach, in press) have concluded that little is known about the extent to which the relationship between parental involvement and college enrollment varies by race/ethnicity. …

Using constructs from Bean's (1990) [In: D. Hossler and J. P. Bean (Eds.), The strategic management of college enrollments. San Francisco:Jossey-Bass] student attrition model and the Berger and Milem (2000) [In: J. C. Smart (Ed.), Higher education: Handbook of theory and research (Vol. XV, pp. 268–338). New York: Agathon Press] college impact model, national survey data, and multilevel modeling techniques, this study examines the extent to which persistence is influenced by the institutional context. The analytic sample is limited to first-time, full-time, degree-seeking undergraduates attending 4-year colleges and universities nationwide. This study finds that selectivity, as measured by the average student academic ability at an institution, has a contextual effect on college student persistence that reflects a positive increment to the chance of persistence that accrues to a student beyond student-level predictors of persistence. Although the implications of the findings are mainly for theory and methods, policy implications are discussed.

This study uses multilevel modeling to examine, after taking student-level predictors of enrollment into account, the effects of state public policies on the type of institution high school graduates attend. Four types of state policies (direct appropriations, tuition, financial aid, and K-12 academic preparation) influence the type of college high school graduates select. Observed socioeconomic status differences in college enrollment patterns are not explained by the student- and state-level variables in the model.

This paper utilizes a nationally representative sample to explore heterogeneity in salary outcomes among master’s degree recipients by program area. The approach taken in this paper includes a difference-in-differences (DID) propensity score (PS) matching technique that takes into account selection on observable and unobservable variables in different program areas. DID-PS matching reveals an average treatment on the treated (ATT) effect of master’s degree completion in the areas of STEM and business on salary outcomes. This study finds no evidence of an ATT effect of receiving a master’s degree in other program areas.