¿Cómo influye una situación de crisis económica en la eficiencia en costes de las universidades públicas? Un análisis de convergencia en el Sistema Universitario Español (2008-2019)
How does a situation of economic crisis influence the cost efficiency of public universities? A convergence analysis in the Spanish University System (2008-2019)
Resumen
Cada vez se exige más que, a la hora de desarrollar las funciones docente, investigadora y social, los gestores de las universidades públicas administren los recursos de una manera eficaz y eficiente para conseguir recortar los gastos, sin eliminar servicios ni perjudicar la calidad. En este marco, los dos objetivos del trabajo son: primero, estimar la eficiencia en costes en el Sistema Universitario Público Español (SUPE) durante el periodo 2008-2019, comparando la situación durante y tras la crisis económico-financiera global del 2008; y, segundo, estudiar la convergencia de la eficiencia en costes de las universidades –a lo largo de dichos periodos, dentro del sector y hacia las mejores prácticas–, así como su posible dependencia de la situación de crisis. Para ello se aplica, por un lado, un Análisis Envolvente de Datos (DEA) de panel de datos condicional y, por otro lado, diferentes modelos de regresión para determinar las convergencias , y . Nuestros hallazgos muestran una mejora de la eficiencia en costes media entre 2008 y 2019, poniendo de manifiesto una reducción de los gastos de las universidades para lograr un nivel dado de outputs. Además, a lo largo de ese periodo, las instituciones que en 2008 gestionaron peor sus fondos mejoraron más su eficiencia en costes que las que inicialmente se comportaron mejor, disminuyendo también la dispersión dentro del SUPE al final del mismo. También se produjo una intensa convergencia hacia las mejores prácticas del sector. Cuando se distingue entre sub-periodos, frente a la etapa de crisis (2008-2013), las universidades públicas españolas, por término medio, tomaron mejores decisiones de reducción de costes a la hora de prestar sus servicios durante el sub-periodo postcrisis (2014-2019), produciéndose, además, un mayor acercamiento institucional tanto a lo largo del tiempo como hacia la media del sector y las mejores prácticas.
Descargas
Citas
Agasisti, T. (2016). Cost structure, productivity and efficiency of the Italian public higher education industry 2001–2011. International Review of Applied Economics, 30(1), 48-68. http://dx.doi.org/10.1080/02692171.2015.1070130
Agasisti, T., & Bertoletti, A. (2022). Higher education and economic growth: A longitudinal study of European regions 2000–2017. Socio-Economic Planning Sciences, 81, 100940 https://doi.org/10.1016/j.seps.2020.100940
Agasisti, T., & Dal Bianco, A. (2006). Data envelopment analysis to the Italian university system: theoretical issues and policy implications. International Journal of Business Performance Management, 8(4), 344-367. https://doi.org/10.1504/IJBPM.2006.009613
Agasisti, T., & Dal Bianco, A. (2009a). Reforming the university sector: effects on teaching efficiency – evidence from Italy. Higher Education, 57, 477-498. https://doi.org/10.1007/s10734-008-9157-x
Agasisti, T., & Dal Bianco, A. (2009b). Measuring efficiency of Higher Education Institutions. International Journal of Management and Decision Making, 10, 443-465. https://doi.org/10.1504/IJMDM.2009.026687
Agasisti, T., Egorov, A., & Serebrennikov, P. (2023). Universities’ efficiency and the socioeconomic characteristics of their environment—Evidence from an empirical analysis. Socio-Economic Planning Sciences, 85, 101445. https://doi.org/10.1016/j.seps.2022.101445
Agasisti, T., Egorov, A., Zinchenko, D., & Leshukov, O. (2021). Efficiency of regional Higher Education systems and regional economic short-run growth: empirical evidence from Russia. Industry and Innovation, 28(4), 507-534 https://doi.org/10.1080/13662716.2020.1738914
Agasisti, T., & Johnes, G. (2010). Heterogeneity and the evaluation of efficiency: The case of Italian universities. Applied Economics, 42(11), 1365-1375. https://doi.org/10.1080/00036840701721463
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. https://doi.org/10.1007/s10734-009-9235-8
Agasisti, T., & Salerno, C. (2007). Assessing the Cost Efficiency of Italian Universities. Education Economics, 15(4), 455-471. https://doi.org/10.1080/09645290701273491
Aparicio, J., López-Torres, L., & Santín, D. (2018). Economic crisis and public education. A productivity analysis using a Hicks-Moorsteen index. Economic Modelling, 71, 34-44. https://doi.org/10.1016/j.econmod.2017.11.017
Aragon, Y., Daouia, A., & Thomas-Agnan, C. (2005). Nonparametric frontier estimation: a conditional quantile-based approach. Econometric Theory, 21(2), 358-389. https://doi.org/10.1017/S0266466605050206
Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277-297. https://doi.org/10.2307/2297968
Athanassapoulos A.D., & Shale. E. (1997). Assessing the comparative efficiency of Higher Education Institutions in the UK by means of Data Envelopment Analysis. Education Economics, 5(2), 117-134. https://doi.org/10.1080/09645299700000011
Barro, R.J., & Sala-i-Martin, X. (1991). Convergence across states and regions. Brookings Papers on Economic Activity, 22(1), 107-182. https://doi.org/10.2307/2534639
Barro, R.J., & Sala-i-Martin, X. (1992). Convergence. Journal of Political Economy, 100(2), 223-251.
Barro, R.J., & Sala-i-Martin, X. (2004). Economic Growth. 2nd edition. The MIT Press.
Berbegal-Mirabent, J. (2018). The influence of regulatory frameworks on research and knowledge transfer outputs: an efficiency analysis of Spanish public universities. Journal of Engineering and Technology Management, 47, 68-80. https://doi.org/10.1016/j.jengtecman.2018.01.003
Bogetoft, P., & Otto, L. (2011). International Series in Operations Research & Management Science: Benchmarking with DEA, SFA, and R. Springer.
Bogetoft, P., & Otto, L. (2022). Benchmarking with DEA and SFA. R package version 0.31.
Bonaccorsi, A., Daraio, C., & Simar, L. (2006). Advanced indicators of productivity of universities. An application of robust nonparametric methods to Italian data. Scientometrics, 66(2), 389-410. https://doi.org/10.1007/s11192-006-0028-x
Carrington, R., O’Donnell, C., & Prasada Rao, D.S. (2018). Australian university productivity growth and public funding revisited. Studies in Higher Education, 43(8), 1417-1438. https://doi.org/10.1080/03075079.2016.1259306
Casu, B., & Girardone, C. (2010). Integration and efficiency convergence in EU banking markets. Omega, 38(5), 260-267. https://doi:10.1016/j.omega.2009.08.004
Cattaneo, M., Horta, H., Malighetti, P., Meoli, M., & Paleari, S. (2019). Universities’ attractiveness to students: The Darwinism effect. Higher Education Quarterly, 73(1), 85-99. https://doi.org/10.1111/hequ.12187
Cazals, C., Florens, J.P., & Simar, L. (2002). Nonparametric frontier estimation: a robust approach. Journal of Econometrics, 106(1), 1-25. https://doi.org/10.1016/S0304-4076(01)00080-X
Clarke, M., Drennan, J., Hyde, A., & Politis, Y. (2018). The impact of austerity on Irish Higher Education faculty. Higher Education, 75(6), 1047-1060. https://doi.org/10.1007/s10734-017-0184-3
Crespo, J., Peiró-Palomino, J., & Tortosa-Ausina, E. (2022). Does university performance have an economic payoff for home regions? Evidence for the Spanish provinces. Industry and Innovation, 29(4), 564-596. https://doi.org/10.1080/13662716.2021.1990019
Daraio, C., & Simar, L. (2007). Conditional nonparametric frontier models for convex and nonconvex technologies: a unifying approach. Journal of Productivity Analysis, 28(1-2), 13-32. https://doi.org/10.1007/s11123-007-0049-3
Daraio, C., Kerstens, K., Nepomuceno, T., & Sickles. R. (2020). Empirical surveys of frontier applications: a meta-review. International Transactions in Operational Research, 27(2), 709-738. https://doi.org/10.1111/itor.12649
Degl’Innocenti, M., Kourtzidis, S.A., Sevic, Z., & Tzeremes, N.G. (2017). Bank productivity growth and convergence in the European Union during the financial crisis. Journal of Banking and Finance, 75, 184-199. http://dx.doi.org/10.1016/j.jbankfin.2016.11.016
El Gibari, S., Gómez Núñez, T., & Ruiz, F. (2022). Combining reference point based composite indicators with data envelopment analysis: Application to the assessment of universities. Scientometrics, 127(1), 4363-4395. https://doi.org/10.1007/s11192-022-04436-0
European University Association (2014). Public Funding Observatory. European University Association.
Fernández-Santos, Y., Campillo-Martínez, A., & Fernández-Fernández, J.M. (2013). Evaluación de la eficiencia y el cambio de productividad en el sistema universitario público español tras la implantación de la LOU. Hacienda Pública Española / Review of Public Economics, 205(2), 71-98. https://hpe-rpe.org/published-articles/#16-81-wpfd-205-2-2013
Guccio, C., Martorana, M.F., & Mazza, I. (2016a). Efficiency assessment and convergence in teaching and research in Italian public universities. Scientometrics, 107(3), 1063-1094. https://doi.org/10.1007/s11192-016-1903-8
Guccio, C., Martorana, M.F., & Monaco, L. (2016b). Evaluating the impact of the Bologna Process on the efficiency convergence of Italian universities: a non-parametric frontier approach. Journal of Productivity Analysis, 45(3), 275-298. https://doi.org/10.1007/s11123-015-0459-6
Harlemans, C., & De Witte, K. (2012). The role of innovations in secondary school performance – Evidence from a conditional efficiency model. European Journal of Operational Research, 223(2), 541-549. http://dx.doi.org/10.1016/j.ejor.2012.06.030
Hayfield, T., & Racine, J.S. (2008). Nonparametric Econometrics: The np Package. Journal of Statistical Software, 27(5), 1-32. https://doi:10.18637/jss.v027.i05
Izzeldin, M., Johnes, J., Ongena, S., Pappas, V., & Tsionas, M. (2021). Efficiency convergence in Islamic and conventional banks. Journal of International Financial Markets, Institutions and Money, 70, 101279. https://doi.org/10.1016/j.intfin.2020.101279
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. http://economicsbulletin.vanderbilt.edu/2007/volume4/EB-07D20004A.pdf
Johnes, J. (2014). Efficiency and mergers in English higher education 1996/97 to 2008/09. Parametric and Non-Parametric Estimation of the Multi-Input Multi-Output Distance Function. Manchester School, 82(4), 465-487. https://ssrn.com/abstract=2546740
Kempkes, G., & Pohl, C. (2010). The efficiency of German universities-some evidence from nonparametric and parametric methods. Applied Economics, 42(16), 2063-2079. https://doi.org/10.1080/00036840701765361
Labra, R., & Torrecillas, C. (2018). Estimating dynamic panel data. A practical approach to perform long panels. Revista Colombiana de Estadística, 41(1), 31-52. http://dx.doi.org/10.15446/rce.v41n1.61885
López-Torres, L., & Prior, D. (2022). Long-term efficiency of public service provision in a context of budget restrictions. An application to the education sector. Socio-Economic Planning Sciences, 81, 100946. https://doi.org/10.1016/j.seps.2020.100946
Lu, W.M. (2012). Intellectual capital and university performance in Taiwan. Economic Modelling, 29(4), 1081-1089. https://doi.org/10.1016/j.econmod.2012.03.021
Martínez-Campillo, A., & Fernández-Santos, Y. (2020). The impact of the economic crisis on the (in)efficiency of public Higher Education Institutions in Southern Europe: the case of Spanish universities. Socio-Economic Planning Sciences, 71, 100771. https://doi.org/10.1016/j.seps.2019.100771
McMillan, M.L., & Chan, W.H. (2006). University efficiency: A comparison and consolidation of results from Stochastic and Non-stochastic Methods. Education Economics, 14(1), 1-30. https://doi.org/10.1080/09645290500481857
Mergoni, A. (2022). rcDEA: Robust and Conditional Data Envelopment Analysis (DEA). R package version 1.0.
Papadimitriou, M., & Johnes, J. (2018). Does merging improve efficiency? A study of English universities. Studies in Higher Education, 44(8), 1454-1474. https://doi.org/10.1080/03075079.2018.1450851
Pérez-Esparrells, C. (2022). La financiación universitaria alternativa. Nueva Revista de Política, Cultura y Arte, 179, 202-204.
Pérez-López, G., Prior, D., & Zafra-Gómez, J.L. (2018). Temporal scale efficiency in DEA panel data estimations. An application to the solid waste disposal service in Spain. Omega, 76, 18-27. https://doi.org/10.1016/j.omega.2017.03.005
Powers, J.B., & McDougall, P.P. (2005). University start-up formation and technology licensing with firms that go public: A resource-based view of academic entrepreneurship. Journal of Business Venturing, 20(3), 291-311. https://doi.org/10.1016/j.jbusvent.2003.12.008
R Development Core Team (2023). R: A Language and Environment for Statistical Computing, version 4.2.3. R Foundation for Statistical Computing. http://www.R-project.org
Sala-i-Martin, X. (1996). Regional cohesion: evidence and theories of regional growth and convergence. European Economic Review, 40(6), 1325-1352. https://doi.org/10.1016/0014-2921(95)00029-1
Simar, L. (2003). Detecting outliers in frontier models: a simple approach. Journal of Productivity Analysis, 20(3), 391-424. https://doi.org/10.1023/A:1027308001925
Simar, L., & Wilson, P.W. (2008). Statistical inference in non-parametric frontier models: recent developments and perspectives. En H. O. Fried, C. A. Knox Lovell, & S. S. Schmidt (Eds.), The Measurement of Productive Efficiency Change, Chapter 4, pp.421-521. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195183528.003.0004
Surroca, J., Prior, D., & Tribó Giné, J.A. (2016). Using panel data DEA to measure CEOs' focus of attention: An application to the study of cognitive group membership and performance. Strategic Management Journal, 37(2), 370-388. https://doi.org/10.1002/smj.2350
Taylor, B., & Harris, G. (2004). Relative efficiency among South African universities: A data envelopment analysis. Higher Education, 47(1), 73-89. https://doi.org/10.1023/B:HIGH.0000009805.98400.4d
Thanassoulis, E., Kortelainen, M., Johnes, G., & Johnes, J. (2011). Cost and efficiency of Higher Education institutions in England: a DEA analysis. Journal of the Operational Research Society, 62(7), 1282-1297. https://doi.org/10.1057/jors.2010.68
Witte, J., Huisman, J., & Purser, L. (2009). European Higher education Reforms in the context of the Bologna process: How did we get here, where are we and where are we going? En OECD (Ed.) Higher Education to 2030 (Globalisation), vol. 2, pp.205-229. OECD Publishers.
Wolszczak-Derlacz, J. (2017). An evaluation and explanation of (in)efficiency in Higher Education Institutions in Europe and the U.S. with the application of two-stage semi-parametric DEA. Research Policy, 46(9), 1595-1605. https://doi.org/10.1016/j.respol.2017.07.010
Derechos de autor 2025 Revista de Contabilidad - Spanish Accounting Review

Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Las obras que se publican en esta revista están sujetas a los siguientes términos:
1. Ediciones de la Universidad de Murcia (EDITUM) y ASEPUC conservan los derechos patrimoniales (copyright) de las obras publicadas, y favorece y permite la reutilización de las mismas bajo la licencia de uso indicada en el punto 2.
2. Las obras se publican en la edición electrónica de la revista bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional. Permite copiar, distribuir e incluir el artículo en un trabajo colectivo (por ejemplo, una antología), siempre y cuando no exista una finalidad comercial, no se altere ni modifique el artículo y se cite apropiadamente el trabajo original. Esta revista no tiene tarifa por la publicación Open Access. ASEPUC y EDITUM financian los costes de producción y publicación de los manuscritos.
3. Condiciones de auto-archivo. Se permite y se anima a los autores a difundir electrónicamente la versión publicada de sus obras, ya que favorece su circulación y difusión y con ello un posible aumento en su citación y alcance entre la comunidad académica.