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Higher Education

, Volume 77, Issue 6, pp 1103–1123 | Cite as

Can educational laws improve efficiency in education production? Assessing students’ academic performance at Spanish public universities, 2008–2014

  • Manuel Salas-VelascoEmail author
Article

Abstract

The information on academic performance rates—what percentage of the enrolled credits a student can pass in one academic year—showed traditionally a relatively low students’ academic performance at Spanish public universities. However, over the period 2008–2014, the academic productivity of undergraduate students at public higher education institutions improved considerably. In this period, Spanish universities experienced changes related to the structuring of the educational curriculum—the homogenization of undergraduate university degrees—and the policy of tuition fees. In relation to the latter, the entry into force of the Royal Decree-Law 14/2012 (the so-called Decreto Wert) allowed universities a considerable increase in tuition fees. Using data for Spanish public universities for the academic years 2008/2009 and 2013/2014, this paper studied to what extent this educational law contributed to the improvement of the academic performance of undergraduate students. Using a stochastic frontier analysis for panel data, this paper showed that the increase in undergraduate tuition fees (first enrolment) acted as a catalyst in reducing the inefficiencies of the Spanish public university system.

Keywords

Spanish public universities Academic performance Productive efficiency Stochastic frontier analysis Tuition fees 

JEL codes

I21 C10 

Notes

Acknowledgements

The author would like to acknowledge the useful comments given to him by three anonymous referees and the help also received by the editor

References

  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., & Bolli, T. (2013). The impact of the Bologna reform on the productivity of Swiss universities. Higher Education Quarterly, 67(4), 374–397.CrossRefGoogle Scholar
  3. 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
  4. Agasisti, T., & Johnes, G. (2015). Efficiency, costs, rankings and heterogeneity: the case of US higher education. Studies in Higher Education, 40(1), 60–82.CrossRefGoogle Scholar
  5. 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
  6. Agasisti, T., & Wolszczak-Derlacz, J. (2015). Exploring efficiency differentials between Italian and Polish universities, 2001-11. Science and Public Policy, 43(1), 128–142.CrossRefGoogle Scholar
  7. Aghion, P., Dewatripont, M., Hoxby, C., & Mas-Colell, A. (2010). The governance and performance of universities: evidence from Europe and the US. Economic Policy, 25(61), 7–59.CrossRefGoogle Scholar
  8. 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
  9. Altbach, P. G. (2015). What counts for academic productivity in research universities? International Higher Education, 79, 6–7.CrossRefGoogle Scholar
  10. Atkinson, S. E., & Cornwell, C. (1994). Estimation of output and input technical efficiency using a flexible functional form and panel data. International Economic Review, 35(1), 245–255.CrossRefGoogle Scholar
  11. Bachan, R. (2017). Grade inflation in UK higher education. Studies in Higher Education, 42(8), 1580–1600.CrossRefGoogle Scholar
  12. Barr, N. (2004). Higher education funding. Oxford Review of Economic Policy, 20(2), 264–283.CrossRefGoogle Scholar
  13. Battese, G. E., & Coelli, T. J. (1992). Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India. Journal of Productivity Analysis, 3, 153–169.CrossRefGoogle Scholar
  14. 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.CrossRefGoogle Scholar
  15. Belfield, C. R. (2012). Measuring efficiency in the Community College sector (Community College Research Center WP No. 43).Google Scholar
  16. Belotti, F., Daidone, S., Ilardi, G., & Atella, V. (2013). Stochastic frontier analysis using Stata. The Stata Journal, 13(4), 719–758.CrossRefGoogle Scholar
  17. Beneito, P., Boscá, J. E., & Ferri, J. (2016). Tuition fees and student effort at university (FEDEA Policy Papers No. 2016–23).Google Scholar
  18. Berbegal-Mirabent, J., Lafuente, E., & Solé, F. (2013). The pursuit of knowledge transfer activities: an efficiency analysis of Spanish universities. Journal of Business Research, 66(10), 2051–2059.CrossRefGoogle Scholar
  19. Brennan, R. (Ed.). (2006). Educational measurement (4th ed.). Westport: American Council on Education, Praeger.Google Scholar
  20. Caldera, A., & Debande, O. (2010). Performance of Spanish universities in technology transfer: an empirical analysis. Research Policy, 39(9), 1160–1173.CrossRefGoogle Scholar
  21. Christensen, T. (2011). University governance reforms: potential problems of more autonomy? Higher Education, 62(4), 503–517.CrossRefGoogle Scholar
  22. Coelli, T. J., Rao, D. S. P., O’Donnell, C. J., & Battese, G. E. (2005). An introduction to efficiency and productivity analysis (2nd ed.). New York: Springer Science & Business Media.Google Scholar
  23. Cooper, W. W., Seiford, L. M., & Tone, K. (2000). Data envelopment analysis: a comprehensive text with models, applications, references and DEA-Solver Software. Boston: Kluwer Academic Publishers.Google Scholar
  24. Daraio, C., Bonaccorsi, A., & Simar, L. (2015). Efficiency and economies of scale and specialization in European universities: a directional distance approach. Journal of Informetrics, 9(3), 430–448.CrossRefGoogle Scholar
  25. De la Torre, E. M., Agasisti, T., & Perez-Esparrells, C. (2017a). The relevance of knowledge transfer for universities’ efficiency scores: an empirical approximation on the Spanish public higher education system. Research Evaluation, 26(3), 211–229.CrossRefGoogle Scholar
  26. De la Torre, E. M., Gómez-Sancho, J. M., & Perez-Esparrells, C. (2017b). Comparing university performance by legal status: a Malmquist-type index approach for the case of the Spanish higher education system. Tertiary Education and Management, 23(3), 206–221.CrossRefGoogle Scholar
  27. De Paola, M. (2009). Does teacher quality affect student performance? Evidence from an Italian university. Bulletin of Economic Research, 61(4), 353–377.CrossRefGoogle Scholar
  28. De Witte, K., & López-Torres, L. (2017). Efficiency in education: a review of literature and a way forward. Journal of the Operational Research Society, 68(4), 339–363.CrossRefGoogle Scholar
  29. 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
  30. Enders, J., De Boer, H., & Weyer, E. (2013). Regulatory autonomy and performance: the reform of higher education re-visited. Higher Education, 65(1), 5–23.CrossRefGoogle Scholar
  31. Escardíbul-Ferrá, J. O., Perez-Esparrells, C., De La Torre-García, E., & Morales-Sequera, S. (2017). Tuition fees in Spanish public universities: a regional convergence analysis. Estudios sobre Educación, 32, 197–221.CrossRefGoogle Scholar
  32. Farrell, M. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society (Series A), 120, 253–281.CrossRefGoogle Scholar
  33. Fernandez-Sainz, A., García-Merino, J. D., & Urionabarrenetxea, S. (2016). Has the Bologna process been worthwhile? An analysis of the Learning Society-Adapted Outcome Index through quantile regression. Studies in Higher Education, 41(9), 1579–1594.CrossRefGoogle Scholar
  34. Flacher, D., Harari-Kermadec, H., & Moulin, L. (2013). Financing higher education: a contributory scheme (Institut d’Economia de Barcelona Document de Treball No. 34).Google Scholar
  35. Gaultney, J. F. (2010). The prevalence of sleep disorders in college students: impact on academic performance. Journal of American College Health, 59(2), 91–97.CrossRefGoogle Scholar
  36. Gómez-Sancho, J. M., & Mancebón-Torrubia, M. J. (2012). La evaluación de la eficiencia de las universidades públicas españolas: En busca de una evaluación neutral entre áreas de conocimiento. Presupuesto y Gasto Público, 67, 43–70.Google Scholar
  37. Gralka, S. (2018). Persistent inefficiency in the higher education sector: evidence from Germany. Education Economics, 26(4), 373–392.CrossRefGoogle Scholar
  38. Greene, W. H. (1980). On the estimation of a flexible frontier production model. Journal of Econometrics, 13(1), 101–115.CrossRefGoogle Scholar
  39. Greene, W. (2005a). Reconsidering heterogeneity in panel data estimators of the stochastic frontier model. Journal of Econometrics, 126(2), 269–303.CrossRefGoogle Scholar
  40. Greene, W. (2005b). Fixed and random effects in stochastic frontier models. Journal of Productivity Analysis, 23(1), 7–32.CrossRefGoogle Scholar
  41. Greene, W. H. (2008). The econometric approach to efficiency analysis. In H. O. Fried, C. A. K. Lovell, & S. S. Schmidt (Eds.), The measurement of productive efficiency and productivity growth (pp. 92–250). Oxford: Oxford University Press.CrossRefGoogle Scholar
  42. Hadri, K. (1999). Estimation of a doubly heteroscedastic stochastic frontier cost function. Journal of Business & Economic Statistics, 17(3), 359–363.Google Scholar
  43. Hadri, K., Guermat, C., & Whittaker, J. (2003). Estimation of technical inefficiency effects using panel data and doubly heteroscedastic stochastic production frontiers. Empirical Economics, 28(1), 203–222.CrossRefGoogle Scholar
  44. 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
  45. Hernández-Armenteros, J., & Pérez-García, J. A. (2015). The Spanish university in figures. Year 2013, and academic year 2013/2014. Madrid: CRUE Weblink: http://www.crue.org/SitePages/La-Universidad-Espa%C3%B1ola-en-Cifras.aspx.Google Scholar
  46. Hsiao, C. (2007). Panel data analysis: advantages and challenges. Test, 16(1), 1–22.CrossRefGoogle Scholar
  47. Hysenbegasi, A., Hass, S. L., & Rowland, C. R. (2005). The impact of depression on the academic productivity of university students. Journal of Mental Health Policy and Economics, 8(3), 145–151.Google Scholar
  48. Johnes, J. (1990). Determinants of student wastage in higher education. Studies in Higher Education, 15(1), 87–99.CrossRefGoogle Scholar
  49. 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
  50. 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. The Manchester School, 82(4), 465–487.CrossRefGoogle Scholar
  51. Johnes, G., & Johnes, J. (2009). Higher education institutions' costs and efficiency: taking the decomposition a further step. Economics of Education Review, 28(1), 107–113.CrossRefGoogle Scholar
  52. Johnes, G., & Salas-Velasco, M. (2007). The determinants of costs and efficiencies where producers are heterogeneous: the case of Spanish universities. Economics Bulletin, 4(15), 1–9.Google Scholar
  53. Johnes, G., & Schwarzenberger, A. (2011). Differences in cost structure and the evaluation of efficiency: the case of German universities. Education Economics, 19(5), 497–499.CrossRefGoogle Scholar
  54. Johnes, G., & Tone, K. (2017). The efficiency of higher education institutions in England revisited: comparing alternative measures. Tertiary Education and Management, 23(3), 1–15.Google Scholar
  55. Johnstone, D. B. (2004). The economics and politics of cost sharing in higher education: comparative perspectives. Economics of Education Review, 23(4), 403–410.CrossRefGoogle Scholar
  56. Johnstone, D. B. (2006). Financing higher education: cost-sharing in international perspective. Rotterdam: Sense.Google Scholar
  57. Kolevzon, M. S. (1981). Grade inflation in higher education: a comparative study. Research in Higher Education, 15(3), 195–212.CrossRefGoogle Scholar
  58. Kumbhakar, S. C. (1990). Production frontiers, panel data and time-varying technical inefficiency. Journal of Econometrics, 46, 201–211.CrossRefGoogle Scholar
  59. Kumbhakar, S. C., & Lovell, C. A. K. (2000). Stochastic frontier analysis. New York: Cambridge University Press.CrossRefGoogle Scholar
  60. 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.CrossRefGoogle Scholar
  61. Kumbhakar, S. C., Wang, H. J., & Horncastle, A. P. (2015). A practitioner’s guide to stochastic frontier analysis using Stata. New York: Cambridge University Press.CrossRefGoogle Scholar
  62. 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
  63. Lovell, C. A. K. (1993). Production frontiers and productive efficiency. In H. O. Fried, C. A. K. Lovell, & S. S. Schmidt (Eds.), The measurement of productive efficiency: techniques and applications (pp. 3–67). Oxford: Oxford University Press.Google Scholar
  64. McKenzie, K., & Schweitzer, R. (2001). Who succeeds at university? Factors predicting academic performance in first year Australian university students. Higher Education Research & Development, 20(1), 21–33.CrossRefGoogle Scholar
  65. MECD. (2014). Datos básicos del sistema universitario español. Curso 2013–2014. Madrid: Ministerio de Educación, Cultura y Deporte.Google Scholar
  66. 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
  67. OECD. (2001). Measuring productivity: OECD manual. Paris: OECD.CrossRefGoogle Scholar
  68. OECD. (2008). Tertiary education for the knowledge society. Paris: OECD.CrossRefGoogle Scholar
  69. OECD. (2010). Education at a glance 2010: OECD indicators. Paris: OECD.CrossRefGoogle Scholar
  70. OECD. (2014). Education at a glance 2014: OECD indicators. Paris: OECD.CrossRefGoogle Scholar
  71. Proto, E., Sgroi, D., & Oswald, A. J. (2010). Are happiness and productivity lower among university students with newly-divorced parents? An experimental approach (IZA Discussion Paper No. 4755).Google Scholar
  72. Remler, D. K., & Pema, E. (2009). Why do institutions of higher education reward research while selling education? (NBER WP No. 14974).Google Scholar
  73. Sanchez-Serra, D., & Marconi, G. (2018). Increasing international students’ tuition fees: the two sides of the coin. International Higher Education, 92, 13–14.CrossRefGoogle Scholar
  74. 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
  75. Schmidt, P. (1985). Frontier production functions. Econometric Reviews, 4(2), 289–328.CrossRefGoogle Scholar
  76. Seiford, L. M. (1997). A bibliography for data envelopment analysis (1978-1996). Annals of Operations Research, 73, 393–438.CrossRefGoogle Scholar
  77. Shettle, C., Roey, S., Mordica, J., Perkins, R., Nord, C., Teodorovic, J., Brown, J., Lyons, M., Averett, C., & Kastberg, D. (2007). The nation’s report card: America’s high school graduates (NCES 2007-467). Washington, DC: U.S. Government Printing Office.Google Scholar
  78. Stevenson, R. E. (1980). Likelihood functions for generalized stochastic frontier estimation. Journal of Econometrics, 13(1), 57–66.CrossRefGoogle Scholar
  79. 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). Springer.Google Scholar
  80. 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.CrossRefGoogle Scholar
  81. 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
  82. Westrick, P. A., Le, H., Robbins, S. B., Radunzel, J. M., & Schmidt, F. L. (2015). College performance and retention: a meta-analysis of the predictive validities of ACT scores, high school grades, and SES. Educational Assessment, 20(1), 23–45.CrossRefGoogle Scholar
  83. Win, R., & Miller, P. W. (2005). The effects of individual and school factors on university students' academic performance. Australian Economic Review, 38(1), 1–18.CrossRefGoogle Scholar
  84. 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
  85. Worthington, A. (2001). An empirical survey of frontier efficiency measurement techniques in education. Education Economics, 9(3), 245–268.CrossRefGoogle Scholar
  86. Zwick, R. (2006). Higher education admission testing. In R. Brennan (Ed.), Educational measurement (4th ed., pp. 647–679). Westport: Praeger.Google Scholar

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Authors and Affiliations

  1. 1.Department of Applied Economics, Business SchoolUniversity of GranadaGranadaSpain

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