An efficiency analysis of higher education institutions in China from a regional perspective considering the external environmental impact

  • Jie Wu
  • Ganggang Zhang
  • Qingyuan Zhu
  • Zhixiang ZhouEmail author


Higher education plays a significant role in economic growth and social development. However, the uneven development of higher education in China has become an important factor restricting its overall progress. Traditional data envelopment analysis (DEA) models used by previous studies are deterministic and susceptible to the impacts of measurement errors and the omission of unobserved but potentially relevant variables, which we referred to as environmental variables latter. To address both of these drawbacks, we develop and implement a three-stage DEA model to examine the efficiency of China’s mainland 31 provinces’ Higher Education Institutions (HEIs) in 2016, which fills the gap in the efficiency evaluation of HEIs in all provinces of China. The “real” efficiency about management performance of each province’s HEIs is obtained and decomposed after the impacts of environmental variables and random errors are eliminated. Lastly, relevant policy suggestions are given on how to improve the efficiency of each province’s HEIs.


Higher education institutions Three-stage DEA Efficiency China 



Funding was provided by National Natural Science Foundation of China (Grant Nos. 71571173, 71701059, 71904084). Natural Science Foundation for Jiangsu Institutions (No. BK20190427), Social Science Foundation of Jiangsu Institutions (No. 19GLC017), the Fundamental Research Funds for the Central Universities (Nos.1Z2019HGTB0095, No. XAB19005).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  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. Aigner, D., Lovell, C. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics,6(1), 21–37.MathSciNetCrossRefGoogle Scholar
  3. Anderson, T. R., Daim, T. U., & Lavoie, F. F. (2007). Measuring the efficiency of university technology transfer. Technovation,27(5), 306–318.CrossRefGoogle Scholar
  4. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science,30(9), 1078–1092.CrossRefGoogle Scholar
  5. Banker, R. D., & Natarajan, R. (2008). Evaluating contextual variables affecting productivity using data envelopment analysis. Operations Research,56(1), 48–58.MathSciNetCrossRefGoogle Scholar
  6. Charnes, A., & Cooper, W. W. (1984). Preface to topics in data envelopment analysis. Annals of Operations Research,2(1), 59–94.CrossRefGoogle Scholar
  7. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring efficiency of decision-making units. European Journal of Operational Research,2, 429–444.MathSciNetCrossRefGoogle Scholar
  8. Chen, Y., Liu, B., Shen, Y., & Wang, X. (2016). The energy efficiency of China’s regional construction industry based on the three-stage DEA model and the DEA-DA model. KSCE Journal of Civil Engineering,20(1), 34–47.CrossRefGoogle Scholar
  9. Cheng, Z., & Sun, G. (2008). A Study on the Efficiency of Chinese Universities. China Economic Quarterly,7(3), 1079–1104.Google Scholar
  10. Cooper, W. W., Seiford, L. M., & Zhu, J. (2004). Data envelopment analysis. Handbook on data envelopment analysis. International Series in Operations Research & Management Science,71, 1–39.CrossRefGoogle Scholar
  11. Fare, R., Färe, R., Fèare, R., Grosskopf, S., & Lovell, C. K. (1994). Production frontiers. Cambridge: Cambridge University Press.Google Scholar
  12. Feng, Y. J., Lu, H., & Bi, K. (2004). An AHP/DEA method for measurement of the efficiency of R&D management activities in universities. International Transactions in Operational Research,11(2), 181–191.CrossRefGoogle Scholar
  13. Fried, H. O., Lovell, C. K., Schmidt, S. S., & Yaisawarng, S. (2002). Accounting for environmental effects and statistical noise in data envelopment analysis. Journal of Productivity Analysis,17(1–2), 157–174.CrossRefGoogle Scholar
  14. Fuentes, R., Fuster, B., & Lillo-Bañuls, A. (2016). A three-stage DEA model to evaluate learning-teaching technical efficiency: Key performance indicators and contextual variables. Expert Systems with Applications,48, 89–99.CrossRefGoogle Scholar
  15. Izadi, H., Johnes, G., Oskrochi, R., & Crouchley, R. (2002). Stochastic frontier estimation of a CES cost function: The case of higher education in Britain. Economics of Education Review,21(1), 63–71.CrossRefGoogle Scholar
  16. Johnes, G., Johnes, J., Thanassoulis, E., Lenton, P., & Emrouznejad, A. (2005). An exploratory analysis of the cost structure of higher education in England. London: UK Department for Education and Skills.Google Scholar
  17. Johnes, J. (2006a). Data envelopment analysis and its application to the measurement of efficiency in higher education. Economics of Education Review,25(3), 273–288.CrossRefGoogle Scholar
  18. Johnes, J. (2006b). Measuring efficiency: A comparison of multilevel modelling and data envelopment analysis in the context of higher education. Bulletin of Economic Research,58(2), 75–104.MathSciNetCrossRefGoogle Scholar
  19. Johnes, J., & Taylor, J. (1990). Performance Indicators in Higher Education: UK Universities (Society for Research into Higher Education). Buckingham: Open University Press.Google Scholar
  20. 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.MathSciNetCrossRefGoogle Scholar
  21. Kao, C., & Hung, H. T. (2008). Efficiency analysis of university departments: An empirical study. Omega,36(4), 653–664.CrossRefGoogle Scholar
  22. Kempkes, G., & Pohl, C. (2010). The efficiency of German universities–some evidence from nonparametric and parametric methods. Applied Economics,42(16), 2063–2079.CrossRefGoogle Scholar
  23. Li, K., & Lin, B. (2016). Impact of energy conservation policies on the green productivity in China’s manufacturing sector: Evidence from a three-stage DEA model. Applied Energy,168, 351–363.CrossRefGoogle Scholar
  24. Meeusen, W., & van Den Broeck, J. (1977). Efficiency estimation from Cobb–Douglas production functions with composed error. International Economic Review,18, 435–444.CrossRefGoogle Scholar
  25. Nazarko, J., & Šaparauskas, J. (2014). Application of DEA method in efficiency evaluation of public higher education institutions. Technological and Economic Development of Economy,20(1), 25–44.CrossRefGoogle Scholar
  26. Ng, Y. C., & Li, S. K. (2009). Efficiency and productivity growth in Chinese universities during the post-reform period. China Economic Review,20(2), 183–192.CrossRefGoogle Scholar
  27. Shyu, J., & Chiang, T. (2012). Measuring the true managerial efficiency of bank branches in Taiwan: A three-stage DEA analysis. Expert Systems with Applications,39(13), 11494–11502.CrossRefGoogle Scholar
  28. Simar, L., & Wilson, P. W. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics,136(1), 31–64.MathSciNetCrossRefGoogle Scholar
  29. Stevens, P. A. (2005). A stochastic frontier analysis of English and Welsh universities. Education Economics,13(4), 355–374.CrossRefGoogle Scholar
  30. Wang, X., & Hu, H. (2017). Sustainable evaluation of social science research in higher education institutions based on data envelopment analysis. Sustainability,9(4), 644.CrossRefGoogle Scholar
  31. Witte, K. D., & 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
  32. Wolszczak-Derlacz, J., & Parteka, A. (2011). Efficiency of European public higher education institutions: A two-stage multicountry approach. Scientometrics,89(3), 887.CrossRefGoogle Scholar
  33. Zeng, S., Hu, M., & Su, B. (2016). Research on investment efficiency and policy recommendations for the culture industry of China based on a three-stage DEA. Sustainability,8(4), 324.CrossRefGoogle Scholar
  34. Zhang, J., Liu, Y., Chang, Y., & Zhang, L. (2017). Industrial eco-efficiency in China: A provincial quantification using three-stage data envelopment analysis. Journal of Cleaner Production,143, 238–249.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2019

Authors and Affiliations

  • Jie Wu
    • 1
  • Ganggang Zhang
    • 1
  • Qingyuan Zhu
    • 2
    • 3
  • Zhixiang Zhou
    • 4
    Email author
  1. 1.School of ManagementUniversity of Science and Technology of ChinaHefeiPeople’s Republic of China
  2. 2.College of Economics and ManagementNanjing University of Aeronautics and AstronauticsNanjingPeople’s Republic of China
  3. 3.Research Centre for Soft Energy ScienceNanjing University of Aeronautics and AstronauticsNanjingPeople’s Republic of China
  4. 4.School of EconomicsHefei University of TechnologyHefeiPeople’s Republic of China

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