Examining degree production and financial context at public master’s colleges and universities in the United States: a distance function approach

Abstract

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.

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References

  1. Agasisti, T., & Belfield, C. (2017a). Efficiency in the community college sector: Stochastic frontier analysis. Tertiary Education and Management, 23(3), 237–259.

    Article  Google Scholar 

  2. Agasisti, T., & Belfield, C. (2017b). Efficiency in the community college sector: Stochastic frontier analysis. Tertiary Education and Management, 23(3), 237–259.

    Article  Google Scholar 

  3. Agasisti, T., & Haelermans, C. (2016). Comparing efficiency of public universities among European countries: Different incentives lead to different performances. Higher Education Quarterly, 70(1), 81–104.

    Article  Google Scholar 

  4. Badunenko, O., & Kumbhakar, S. C. (2016). When, where and how to estimate persistent and transient efficiency in stochastic frontier panel data models. European Journal of Operational Research, 255(1), 272–287. https://doi.org/10.1016/j.ejor.2016.04.049.

    Article  Google Scholar 

  5. Badunenko, O., & Kumbhakar, S. C. (2017). Economies of scale, technical change and persistent and time-varying cost efficiency in Indian banking: Do ownership, regulation and heterogeneity matter? European Journal of Operational Research, 260(2), 789–803.

    Article  Google Scholar 

  6. Battese, G. E., & Coelli, T. J. (1992). Frontier production functions, technical efficiency and panel data: With application to paddy farmers in India. In International applications of productivity and efficiency analysis (pp. 149–165). Retrieved from http://link.springer.com/chapter/10.1007/978-94-017-1923-0_10. Accessed 22 Sept 2017.

  7. Battese, G. E., & Coelli, T. J. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics, 20(2), 325–332.

    Article  Google Scholar 

  8. Das, M., & Das, S. (2014). Technical efficiency of higher education institutions: A study of affiliated degree colleges of Barak Valley in Assam, India. 3(1), 66–76.

  9. Debreu, G. (1951). The coefficient of resource utilization. Econometrica, 19(3), 273–292. https://doi.org/10.2307/1906814.

    Article  Google Scholar 

  10. Doyle, W. (2015). Efficiency in degree production among public comprehensive universities. The University next Door: What Is a Comprehensive University, Who Does It Educate, and Can It Survive, 93–120.

  11. Färe, R., Grosskopf, S., & Lovell, C. A. (1994). Production frontiers. Cambridge [England]: Cambridge University Press.

    Google Scholar 

  12. Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253–290.

    Article  Google Scholar 

  13. Farsi, M., Filippini, M., & Greene, W. (2006). Application of panel data models in benchmarking analysis of the electricity distribution sector. Annals of Public and Cooperative Economics, 77(3), 271–290.

    Article  Google Scholar 

  14. Fieger, P., Villano, R., & Cooksey, R. (2016). Efficiency of Australian technical and further education providers. International Journal of Training Research, 14(1), 62–75.

    Article  Google Scholar 

  15. Filippini, M., & Greene, W. (2016). Persistent and transient productive inefficiency: A maximum simulated likelihood approach. Journal of Productivity Analysis, 45(2), 187–196. https://doi.org/10.1007/s11123-015-0446-y.

    Article  Google Scholar 

  16. Filippini, M., Geissmann, T., & Greene, W. H. (2018). Persistent and transient cost efficiency—An application to the Swiss hydropower sector. Journal of Productivity Analysis, 49(1), 65–77.

    Article  Google Scholar 

  17. Gralka, S. (2018). Persistent inefficiency in the higher education sector: Evidence from Germany. Education Economics, 0(0), 1–20. https://doi.org/10.1080/09645292.2017.1420754.

    Article  Google Scholar 

  18. Greene, W. (2005). Fixed and random effects in stochastic frontier models. Journal of Productivity Analysis, 23(1), 7–32.

  19. Grosskopf, S., Hayes, K. J., Taylor, L. L., & Weber, W. L. (1999). Anticipating the Consequences of School Reform: A New Use of DEA. Management Science, 45(4), 608–620.

  20. Harnisch, T. L., & Lebioda, K. (2016). Top 10 higher education state policy issues for 2016 (p. 7). Retrieved from AASCU website: http://www.aascu.org/policy/publications/policy-matters/TopTen2016.pdf. Accessed 13 Aug 2017.

  21. Hillman, N. W., Tandberg, D. A., & Gross, J. P. K. (2014). Performance funding in higher education: Do Financial incentives impact college completions? The Journal of Higher Education, 85(6), 826–857. https://doi.org/10.1353/jhe.2014.0031.

    Article  Google Scholar 

  22. Hillman, N. W., Hicklin Fryar, A., & Crespín-Trujillo, V. (2017). Evaluating the impact of performance funding in Ohio and Tennessee. American Educational Research Journal, 55, 144–170. https://doi.org/10.3102/0002831217732951.

    Article  Google Scholar 

  23. Hopkins, D. S., & Massy, W. F. (1981). Planning models for colleges and universities. Stanford: Stanford University Press.

    Google Scholar 

  24. Horne, J., & Hu, B. (2008). Estimation of cost efficiency of Australian universities. Mathematics and Computers in Simulation, 78(2), 266–275. https://doi.org/10.1016/j.matcom.2008.01.018.

    Article  Google Scholar 

  25. Huang, C. J., & Liu, J.-T. (1994). Estimation of a non-neutral stochastic frontier production function. Journal of Productivity Analysis, 5(2), 171–180. https://doi.org/10.1007/BF01073853.

    Article  Google Scholar 

  26. Jaquette, O., & Parra, E. E. (2014). Using IPEDS for panel analyses: Core concepts, data challenges, and empirical applications. In Higher education: Handbook of theory and research (Vol. 29, pp. 467–533). Retrieved from http://link.springer.com/chapter/10.1007/978-94-017-8005-6_11

  27. Johnes, J. (2014). Efficiency and mergers in English higher education 1996/97 to 2008/9: Parametric and non-parametric estimation of the multi-input multi-output distance function: Efficiency and mergers in English higher education 1996/97 to 2008/9. The Manchester School, 82(4), 465–487. https://doi.org/10.1111/manc.12030.

    Article  Google Scholar 

  28. Johnes, G., & Johnes, J. (2016). Costs, efficiency, and economies of scale and scope in the English higher education sector. Oxford Review of Economic Policy, 32(4), 596–614.

    Article  Google Scholar 

  29. Jondrow, J., Lovell, C. K., Materov, I. S., & Schmidt, P. (1982). On the estimation of technical inefficiency in the stochastic frontier production function model. Journal of Econometrics, 19(2–3), 233–238.

    Article  Google Scholar 

  30. Kadlec, A., & Shelton, S. (2015). Outcomes-based funding and stakeholder engagement. Lumina Issue Papers. Retrieved from https://www.luminafoundation.org/files/resources/kadlec-shelton-ofb-full.pdf. Accessed 24 Sept 2017.

  31. Kassiola, J. J. (2007). The erroneous accusation of research “Mission creep” at Master’s institutions: Why teaching in the 21st century must be research-based. College Teaching, 55(4), 139–144. https://doi.org/10.3200/CTCH.55.4.139-144.

    Article  Google Scholar 

  32. Kelly, P. J., & Jones, D. P. (2007). A new look at the institutional component of higher education finance: A guide for evaluating performance relative to financial resources. Retrieved from https://eric.ed.gov/?id=ED512623. Accessed 24 Sept 2017.

  33. Kim, M. M., & Ko, J. (2015). The impacts of state control policies on college tuition increase. Educational Policy, 29(5), 815–838.

    Article  Google Scholar 

  34. Kumbhakar, S. C. (1990). Production frontiers, panel data, and time-varying technical inefficiency. Journal of Econometrics, 46(1–2), 201–211.

    Article  Google Scholar 

  35. Kumbhakar, S. C., & Heshmati, A. (1995). Efficiency measurement in Swedish dairy farms: An application of rotating panel data, 1976–88. American Journal of Agricultural Economics, 77(3), 660–674.

    Article  Google Scholar 

  36. Kumbhakar, S. C., Ghosh, S., & McGuckin, J. T. (1991). A generalized production frontier approach for estimating determinants of inefficiency in US dairy farms. Journal of Business & Economic Statistics, 9(3), 279–286.

    Google Scholar 

  37. Kumbhakar, S. C., Lien, G., & Hardaker, J. B. (2014). Technical efficiency in competing panel data models: A study of Norwegian grain farming. Journal of Productivity Analysis, 41(2), 321–337. https://doi.org/10.1007/s11123-012-0303-1.

    Article  Google Scholar 

  38. Last, A.-K., & Wetzel, H. (2010). The efficiency of German public theaters: A stochastic frontier analysis approach. Journal of Cultural Economics, 34(2), 89–110. https://doi.org/10.1007/s10824-009-9111-5.

    Article  Google Scholar 

  39. Laureti, T., Secondi, L., & Biggeri, L. (2014). Measuring the efficiency of teaching activities in Italian universities: An information theoretic approach. Economics of Education Review, 42, 147–164. https://doi.org/10.1016/j.econedurev.2014.07.001.

    Article  Google Scholar 

  40. McLendon, M. K., & Hearn, J. C. (2013). The resurgent interest in performance-based funding for higher education. Academe, 99(6), 25–30.

    Google Scholar 

  41. National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. (2016). Digest of Education Statistics, 2015 (No. NCES 2016-014; p. 1042). Washington, DC.

  42. Rutherford, A., & Rabovsky, T. (2014). Evaluating impacts of performance funding policies on student outcomes in higher education. The Annals of the American Academy of Political and Social Science, 655(1), 185–208. https://doi.org/10.1177/0002716214541048.

    Article  Google Scholar 

  43. Shephard, R. (1970). Theory of cost and production functions. Princeton: Princeton University Press.

    Google Scholar 

  44. Siegel, D., Wright, M., Chapple, W., & Lockett, A. (2008). Assessing the relative performance of university technology transfer in the us and Uk: A stochastic distance function approach. Economics of Innovation and New Technology, 17(7–8), 717–729. https://doi.org/10.1080/10438590701785769.

    Article  Google Scholar 

  45. Stevens, P. A. (2005). A stochastic frontier analysis of English and welsh universities. Education Economics, 13(4), 355–374.

    Article  Google Scholar 

  46. Tandberg, D. A., & Hillman, N. W. (2014). State higher education performance funding: Data, outcomes, and policy implications. Journal of Education Finance, 39(3), 222–243.

    Google Scholar 

  47. Thanassoulis, E. (2001). Introduction to the theory and application of data envelopment analysis. Retrieved from http://link.springer.com/content/pdf/10.1007/978-1-4615-1407-7.pdf. Accessed 21 Sept 2017.

  48. Titus, M. A. (2006). Understanding the influence of the financial context of institutions on student persistence at four-year colleges and universities. The Journal of Higher Education, 77(2), 353–375.

    Article  Google Scholar 

  49. Titus, M. A. (2009a). Bachelor’s degree productivity X-inefficiency: The role of state higher education policy. Journal of College Student Retention: Research, Theory & Practice, 11(1), 7–32.

    Article  Google Scholar 

  50. Titus, M. A. (2009b). The production of bachelor’s degrees and financial aspects of state higher education policy: A dynamic analysis. The Journal of Higher Education, 80(4), 439–468.

    Article  Google Scholar 

  51. Titus, M. A., & Eagan, K. (2016). Examining production efficiency in higher education: The utility of stochastic frontier analysis. In M. Paulsen (Ed.), Higher education: Handbook of theory and research (Vol. 31, pp. 441–512) Retrieved from http://link.springer.com/chapter/10.1007/978-3-319-26829-3_9. Accessed 17 Sept 2017.

    Google Scholar 

  52. Titus, M. A., Vamosiu, A., & McClure, K. R. (2017). Are public Master’s institutions cost efficient? A stochastic frontier and spatial analysis. Research in Higher Education, 58(5), 469–496.

    Article  Google Scholar 

  53. Tsionas, E. G., & Kumbhakar, S. C. (2014). Firm heterogeneity, persistent and transient technical inefficiency: A generalized true random-effects model: Firm heterogeneity, persistent and transient technical inefficiency. Journal of Applied Econometrics, 29(1), 110–132. https://doi.org/10.1002/jae.2300.

    Article  Google Scholar 

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Correspondence to Marvin A. Titus.

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Titus, M.A. Examining degree production and financial context at public master’s colleges and universities in the United States: a distance function approach. Tert Educ Manag 26, 215–231 (2020). https://doi.org/10.1007/s11233-019-09049-6

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Keywords

  • Public master’s colleges and universities
  • Degree production
  • Output distance function
  • Stochastic frontier analysis
  • Generalized true random effects (GTRE) model
  • Transient (short-run) efficiency
  • Persistent (long-run) efficiency