The technical efficiency performance of the higher education systems based on data envelopment analysis with an illustration for the Spanish case

  • Manuel Salas-VelascoEmail author
Original Article


It is important for policymakers and managers of higher education institutions knowing how well their universities are operating. This article aimed to show that data envelopment analysis (DEA) can be an excellent benchmarking instrument in higher education. First, by using several inputs and outputs at the institutional level, DEA can identify technically efficient institutions that may work as a benchmark in the sector becoming a reliable tool for ranking universities. Second, a bootstrapped–truncated regression allows us to understand the factors affecting technical efficiency of the institutions under evaluation. The case of Spanish public universities is taken as an example to verify the usefulness of the proposed methods. Our empirical strategy was based on a two-stage procedure to evaluate their internal efficiency in the provision of teaching and research. In the first stage, we estimated a technical efficiency score for each university. The average efficiency among Spanish universities was about 92%. In the second stage, we regressed the efficiency scores against a set of covariates to investigate their association with the level of university (in)efficiency. We found that universities with a higher percentage of grantees tend to be less inefficient, and a higher percentage of academics with tenure enhances the productive efficiency of the Spanish higher education sector. Finally, we computed Spearman’s rank correlations between DEA efficiency scores and the classification of Spanish institutions in university rankings such as the SCImago and Shanghai rankings. The results revealed that the ranking positions given by DEA scores to Spanish universities matched their positions in recognized rankings.


Higher education policy Data envelopment analysis Benchmarking Bootstrapped–truncated regression University rankings 

JEL Classification

D22 I23 C50 



  1. Abbott, M., & Doucouliagos, C. (2003). The efficiency of Australian universities: A data envelopment analysis. Economics of Education Review, 22(1), 89–97.CrossRefGoogle Scholar
  2. Agasisti, T., & Dal Bianco, A. (2009). Reforming the university sector: Effects on teaching efficiency—Evidence from Italy. Higher Education, 57(4), 477–498.CrossRefGoogle Scholar
  3. Agasisti, T., & Gralka, S. (2019). The transient and persistent efficiency of Italian and German universities: A stochastic frontier analysis. Applied Economics, 51(46), 5012–5030.CrossRefGoogle Scholar
  4. Agasisti, T., & Pérez-Esparrells, C. (2010). Comparing efficiency in a cross-country perspective: The case of Italian and Spanish state universities. Higher Education, 59(1), 85–103.CrossRefGoogle Scholar
  5. Agasisti, T., & Wolszczak-Derlacz, J. (2014). Exploring universities’ efficiency differentials between countries in a multi-year perspective: An application of bootstrap DEA and Malmquist index to Italy and Poland, 2001–2011 (IRLE Working Paper No. 113-14). Retrieved from
  6. Agasisti, T., & Wolszczak-Derlacz, J. (2015). Exploring efficiency differentials between Italian and Polish universities, 2001–2011. Science and Public Policy, 43(1), 128–142.CrossRefGoogle Scholar
  7. Aigner, D., Lovell, C. A. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21–37.CrossRefGoogle Scholar
  8. Angulo-Meza, L., & Lins, M. P. E. (2002). Review of methods for increasing discrimination in data envelopment analysis. Annals of Operations Research, 116(1–4), 225–242.CrossRefGoogle Scholar
  9. Badunenko, O., & Mozharovskyi, P. (2016). Nonparametric frontier analysis using Stata. The Stata Journal, 16(3), 550–589.CrossRefGoogle Scholar
  10. Ball, R., & Halwachi, J. (1987). Performance indicators in higher education. Higher Education, 16(4), 393–405.CrossRefGoogle Scholar
  11. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30, 1078–1092.CrossRefGoogle Scholar
  12. Banker, T., & Natarajan, R. (2008). Evaluating contextual variables affecting productivity using data envelopment analysis. Operations Research, 56(1), 48–58.CrossRefGoogle Scholar
  13. Billaut, J. C., Bouyssou, D., & Vincke, P. (2009). Should you believe in the Shanghai ranking? An MCDM view. Scientometrics, 84(1), 237–263.CrossRefGoogle Scholar
  14. Bougnol, M. L., & Dulá, J. H. (2006). Validating DEA as a ranking tool: An application of DEA to assess performance in higher education. Annals of Operations Research, 145(1), 339–365.CrossRefGoogle Scholar
  15. Cave, M. (1997). The use of performance indicators in higher education: A critical analysis of developing practice. London: Kingsley.Google Scholar
  16. Chang, Y. T., Lee, S., & Park, H. K. (2017). Efficiency analysis of major cruise lines. Tourism Management, 58, 78–88.CrossRefGoogle Scholar
  17. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444.CrossRefGoogle Scholar
  18. Coelli, T. J., Rao, D. S. P., O’Donnell, C. J., & Battese, G. E. (2005). An introduction to efficiency and productivity analysis (2nd ed.). Dordrecht: Springer.Google Scholar
  19. Cohn, E., Rhine, S., & Santos, M. (1989). Institutions of higher education as multi-product firms: Economies of scale and scope. Review of Economics and Statistics, 71(2), 284–290.CrossRefGoogle Scholar
  20. Davoodi, A., & Rezai, H. Z. (2012). Common set of weights in data envelopment analysis: A linear programming problem. Central European Journal of Operations Research, 20(2), 355–365.CrossRefGoogle Scholar
  21. De Mesnard, L. (2012). On some flaws of university rankings: The example of the SCImago report. Journal of Socio-Economics, 41(5), 495–499.CrossRefGoogle Scholar
  22. Docampo, D. (2011). On using the Shanghai ranking to assess the research performance of university systems. Scientometrics, 86(1), 77–92.CrossRefGoogle Scholar
  23. Dynarski, S. M. (2003). Does aid matter? Measuring the effect of student aid on college attendance and completion. American Economic Review, 93(1), 279–288.CrossRefGoogle Scholar
  24. Emrouznejad, A., Parker, B. R., & Tavares, G. (2008). Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA. Journal of Socio-Economics Planning Science, 42(3), 151–157.CrossRefGoogle Scholar
  25. Färe, R., Grosskopf, S., Norris, M., & Zhongyang, Z. (1994). Productivity growth, technical progress and efficiency change in industrialized countries. American Economic Review, 84(1), 66–83.Google Scholar
  26. Färe, R., & Primont, D. (1995). Multi-output production and duality: Theory and applications. Boston: Kluwer.CrossRefGoogle Scholar
  27. Farrell, M. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society (Series A), 120, 253–281.CrossRefGoogle Scholar
  28. Flegg, T., Allen, D., Field, K., & Thurlow, T. W. (2004). Measuring the efficiency of British universities: A multi-period data envelopment analysis. Education Economics, 12(3), 231–249.CrossRefGoogle Scholar
  29. Florian, R. V. (2007). Irreproducibility of the results of the Shanghai academic ranking of world universities. Scientometrics, 72(1), 25–32.CrossRefGoogle Scholar
  30. Fried, H. O., Lovell, C. A. K., & Schmidt, S. S. (Eds.). (2008). The measurement of productive efficiency and productivity growth. Oxford: Oxford University Press.Google Scholar
  31. Gillen, D., & Lall, A. (1997). Developing measures of airport productivity and performance: An application of data envelopment analysis. Transportation Research Part E: Logistics and Transportation Review, 33(4), 261–273.CrossRefGoogle Scholar
  32. Gralka, S., Wohlrabe, K., & Bornmann, L. (2019). How to measure research efficiency in higher education? Research grants vs. publication output. Journal of Higher Education Policy and Management, 41(3), 322–341.CrossRefGoogle Scholar
  33. Gulbrandsen, M., & Slipersæer, S. (2007). The third mission and the entrepreneurial university model. In A. Bonaccorsi & C. Daraio (Eds.), Universities and strategic knowledge creation. Specialization and performance in Europe (pp. 112–143). Cheltenham: Edward Elgar.Google Scholar
  34. Halkos, G., Tzeremes, N. G., & Kourtzidis, S. A. (2012). Measuring public owned university departments’ efficiency: A bootstrapped DEA approach. Journal of Economics and Econometrics, 55(2), 1–24.Google Scholar
  35. Hazelkorn, E. (2007). The impact of league tables and ranking systems on higher education decision making. Higher Education Management and Policy, 19(2), 1–24.CrossRefGoogle Scholar
  36. Hazelkorn, E. (2015a). Rankings and the reshaping of higher education: The battle for world-class excellence (2nd ed.). London: Palgrave Macmillan.CrossRefGoogle Scholar
  37. Hazelkorn, E. (2015b). Globalization, internationalization and rankings. International Higher Education, 53, 8–10.CrossRefGoogle Scholar
  38. Johnes, J. (2006). Data envelopment analysis and its application to the measurement of efficiency in higher education. Economics of Education Review, 25(3), 273–288.CrossRefGoogle Scholar
  39. Johnes, J. (2014). Efficiency and mergers in English higher education 1996/1997 to 2008/2009: Parametric and non-parametric estimation of the multi-input multi-output distance function. Manchester School, 82(4), 465–487.CrossRefGoogle Scholar
  40. Johnes, J. (2016). Performance indicators and rankings in higher education. In R. Barnett, P. Temple & P. Scott (Eds.), Valuing higher education: An appreciation of the work of Gareth Williams (Ch. 4). London: UCL IOE Press.Google Scholar
  41. Johnes, J. (2018). University rankings: What do they really show? Scientometrics, 115(1), 585–606.CrossRefGoogle Scholar
  42. Johnes, J., & Taylor, J. (1990). Performance indicators in higher education. Buckingham: Society for Research into Higher Education and Open University Press.Google Scholar
  43. Jöns, H., & Hoyler, M. (2013). Global geographies of higher education: The perspective of world university rankings. Geoforum, 46, 45–59.CrossRefGoogle Scholar
  44. Kao, C., & Hung, H. T. (2008). Efficiency analysis of university departments: An empirical study. Omega, 36(4), 653–664.CrossRefGoogle Scholar
  45. Krahn, H., & Bowlby, J. W. (1997). Good teaching and satisfied university graduates. Canadian Journal of Higher Education, 27(2/3), 157–179.Google Scholar
  46. Lassibille, G., & Navarro-Gómez, M. L. (2011). How long does it take to earn a higher education degree in Spain? Research in Higher Education, 52(1), 63–80.CrossRefGoogle Scholar
  47. Liu, J. S., Lu, L. Y. Y., Lu, W. M., & Lin, B. J. Y. (2013). A survey of DEA applications. Omega, 41, 893–902.CrossRefGoogle Scholar
  48. McMillan, M. L., & Datta, D. (1998). The relative efficiencies of Canadian universities: A DEA perspective. Canadian Public Policy/Analyse de Politiques, 24(4), 485–511.CrossRefGoogle Scholar
  49. Meeusen, W., & van den Broeck, J. (1977). Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review, 18(2), 435–444.CrossRefGoogle Scholar
  50. Ng, S. W. (2011). Can Hong Kong export its higher education services to the Asian markets? Educational Research for Policy and Practice, 10(2), 115–131.CrossRefGoogle Scholar
  51. Nunamaker, T. R. (1985). Using data envelopment analysis to measure the efficiency of non-profit organizations: A critical evaluation. Managerial and Decision Economics, 6, 50–58.CrossRefGoogle Scholar
  52. OECD. (2017). Benchmarking higher education system performance: Conceptual framework and data. Enhancing higher education system performance. Paris: OECD.Google Scholar
  53. Palomares-Montero, D., & García-Aracil, A. (2011). What are the key indicators for evaluating the activities of universities? Research Evaluation, 20(5), 353–363.CrossRefGoogle Scholar
  54. Propper, C., & Wilson, D. (2003). The use and usefulness of performance measures in the public sector. Oxford Review of Economic Policy, 19(2), 250–267.CrossRefGoogle Scholar
  55. Salas-Velasco, M. (2019). Can educational laws improve efficiency in education production? Assessing students’ academic performance at Spanish public universities, 2008–2014. Higher Education, 77(6), 1103–1123.CrossRefGoogle Scholar
  56. Santelices, M. V., Catalán, X., Kruger, D., & Horn, C. (2016). Determinants of persistence and the role of financial aid: Lessons from Chile. Higher Education, 71(3), 323–342.CrossRefGoogle Scholar
  57. Seiford, L. M. (1997). A bibliography for data envelopment analysis (1978–1996). Annals of Operations Research, 73, 393–438.CrossRefGoogle Scholar
  58. Sheil, T. (2016). Managing expectations. An Australian perspective on the impact and challenges of adopting a university rankings narrative. In M. Yudkevich, P. G. Altbach, & L. E. Rumbley (Eds.), The global academic rankings game. Changing institutional policy, practice, and academic life (1st ed.). London: Routledge.Google Scholar
  59. Shen, W. F., Zhang, D. Q., Liu, W. B., & Yang, G. L. (2016). Increasing discrimination of DEA evaluation by utilizing distances to anti-efficient frontiers. Computers & Operations Research, 75, 163–173.CrossRefGoogle Scholar
  60. Shin, J. C., & Toutkoushian, R. K. (2011). The past, present, and future of university rankings. In J. C. Shin, R. K. Toutkoushian, & U. Teichler (Eds.), University rankings: Theoretical basis, methodology and impacts on global higher education (pp. 1–18). Dordrecht: Springer.CrossRefGoogle Scholar
  61. Simar, L., & Wilson, P. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136(1), 31–64.CrossRefGoogle Scholar
  62. Tauchmann, H. (2016). SIMARWILSON: Stata module to perform Simar & Wilson efficiency analysis. Statistical Software Components. Retrieved from
  63. Taylor, P., & Braddock, R. (2007). International university ranking systems and the idea of university excellence. Journal of Higher Education Policy and Management, 29(3), 245–260.CrossRefGoogle Scholar
  64. Thanassoulis, E., De Witte, K., Johnes, J., Johnes, G., Karagiannis, G., & Portela, C. S. (2016). Applications of data envelopment analysis in education. In J. Zhu (Ed.), Data envelopment analysis: A handbook of empirical studies and applications (pp. 367–438). New York: Springer.CrossRefGoogle Scholar
  65. Titus, M. A., & Eagan, K. (2016). Examining production efficiency in higher education: The utility of stochastic frontier analysis. In M. B. Paulsen (Ed.), Higher education: Handbook of theory and research (Vol. 31, pp. 441–512). New York: Springer.CrossRefGoogle Scholar
  66. Tyagi, P., Yadav, S. P., & Singh, S. P. (2009). Relative performance of academic departments using DEA with sensitivity analysis. Evaluation and Program Planning, 32(2), 168–177.CrossRefGoogle Scholar
  67. Wolszczak-Derlacz, J., & Parteka, A. (2011). Efficiency of European public higher education institutions: A two-stage multicountry approach. Scientometrics, 89(3), 887–917.CrossRefGoogle Scholar
  68. Xavier, C. A., & Alsagoff, L. (2013). Constructing “world-class” as “global:” A case study of the National University of Singapore. Educational Research for Policy and Practice, 12(3), 225–238.CrossRefGoogle Scholar
  69. Yang, R. (2003). Globalization and higher education development: A critical analysis. International Review of Education, 49(3/4), 269–291.CrossRefGoogle Scholar
  70. Zhu, J. (Ed.). (2016). Data envelopment analysis: A handbook of empirical studies and applications. New York: Springer.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Applied EconomicsUniversity of GranadaGranadaSpain

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