This study investigates the application of the three-dimensional New Public Management (NPM) framework to the public higher education setting to examine the relationship between one of the NPM dimensions, privatization, and cost management at 159 public research institutions in the United States between 2005 and 2015. We develop four statistical measures to quantify privatization and find privatization is linked to lower costs as institutions increase the share of revenue from auxiliary enterprises and tuition and fees, but higher costs when increasing out-of-state first-year enrolment. This raises management questions about public higher education cost efficiency, access and DEI for in-state students.

College students who work would seem to be making time trade-offs with respect to the number of hours devoted to working versus studying. Guiding Framework draws on concepts from the labor economics and college retention literature to examine students' decisions with regard to working while in college as well as how work influences the chance of college completion and salaries. Degree completion and labor supply decisions by college students may be considered singularly as well as jointly. This chapter combines concepts from human capital theory and the student retention literature to examine how working while enrolled in college influences both the chance of bachelor's degree completion and salary outcomes in the labor market. Because most traditional college-age students work part time, substitution effects are likely to dominate over income effects and result in an increase in the number of hours worked.

This study examines the relationship between cost efficiency and privatization at 163 public research institutions in the United States between 2005 and 2015. We employ a spatial autoregressive (SAR) random-effects model and stochastic frontier analysis (SFA) to estimate the relationship between costs and four privatization variables: auxiliary enterprises as a percentage of total revenue, tuition and fees as a percentage of total revenue, private grants/contracts as a percentage of total revenue, and out-of-state first year enrollment. Results showed cost inefficiency at public research universities increased between 2005 and 2015, even as reliance on private sources of revenue increased. Public research universities exhibit 28.5% overall cost inefficiency over the time period studied, 85.6% of which is short-run cost inefficiency. This suggests that most of the cost inefficiency varies across years and may be the result of challenges that institutional leaders face adapting to short-term fluctuations in market-oriented sources of revenue. The results also show a nonlinear relationship between cost inefficiency and three of the privatization variables. Given the expectation of little to no increase in state support for public research universities, this study has implications for policy, institutional management, and future research.

We investigate operating costs at 682 public community colleges in the United States over a 15-year period (2004–2018). The results reveal that costs are spatially correlated across neighboring institutions, indicating the need for a spatial analysis. An institution’s actions are associated with changes in costs for that particular institution (direct-effects), but neighboring institutions’ actions also impact that institution (indirect/neighbor-effect) via labor market channels and local market conditions. The current research found that the neighbor-effects are economically significant at 9

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.

This chapter provides a discussion and demonstration of creating datasets. The management of Excel and Stata datasets is also presented. These datasets include primary and secondary data. While this chapter discusses and demonstrates how to create datasets based on primary data, it focuses on the creation and management of the datasets based on secondary data.

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 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 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 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.

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.

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.

This study examines the relationship between cost efficiency and privatization at 163 public research institutions in the United States between 2005 and 2015.We employ a spatial autoregressive (SAR) random-effects model and stochastic frontier analysis (SFA) to estimate the relationship between costs and four privatization variables: auxiliary enterprises as a percentage of total revenue, tuition and fees as a percentage of total revenue, private grants and contracts as a percentage of total revenue, and out-of-state freshmen enrollment.Public research universities exhibit 28.5% overall cost inefficiency over the time period studied, 85.6% of which is short-run cost inefficiency.The results further show a nonlinear relationship between cost inefficiency and three of the privatization variables.With the expectation of little to no increase in state support to public research universities, this study has implications for campus management and future research.

New Directions for Institutional ResearchVolume 2018, Issue 180 p. 5-10 Editors' Notes Spatial Research in Higher Education: Expanding Understanding and Identifying Inequities Cecilia Rios-Aguilar, Cecilia Rios-Aguilar UCLA's Graduate School of Education and Information Studies (GSEIS)Search for more papers by this authorMarvin A. Titus, Marvin A. Titus University of Maryland, College ParkSearch for more papers by this author Cecilia Rios-Aguilar, Cecilia Rios-Aguilar UCLA's Graduate School of Education and Information Studies (GSEIS)Search for more papers by this authorMarvin A. Titus, Marvin A. Titus University of Maryland, College ParkSearch for more papers by this author First published: 17 May 2019 https://doi.org/10.1002/ir.20283Citations: 3Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume2018, Issue180Special Issue: Spatial Thinking and Analysis in Higher Education ResearchWinter 2018Pages 5-10 RelatedInformation

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 flexible fixed cost quadratic function that also accounts for spatial interdependency to empirically investigate the economies of scale and scope with regards to undergraduate enrollment, graduate enrollment, and research at public master's institutions. Economies of scale exist for undergraduate and graduate education at mean, below and above it, but not for research. Ray economies of scale are present around and below mean. Economies of scope both at the individual and global level are found at current mean, below, and above output levels.

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 research on administrative spending in higher education by empirically examining whether recently shifting to research university status in the Carnegie Classification influences administrative costs. Informing the analysis was a theoretical framework consisting of neo-institutional theory and the revenue theory of costs. The study examined 164 public research universities between 2004 and 2012 using a a pooled regression model with Driscoll-Kraay standard errors and a first-order autoregressive (AR1) lag. Results showed that shifting to research university status had a significant, positive influence on administrative spending at public research universities. Nevertheless, the influence of reclassification on administrative spending dissipated over time and to the point where the difference was no longer statistically significant. Importantly, results also showed that state appropriations and tuition revenue were positively associated with administrative spending, while enrollment was negatively associated with administrative spending. These results have important implications related to understanding and managing administrative spending among U.S. public research universities