Evaluation of Educational Resource Efficiency Based on the Data Envelopment Analysis Model and Malmquist Index
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Abstract
This article aimed to apply the DEA-BCC model and the DEA-Malmquist index to evaluate the efficiency of educational resource allocation in provincial universities. Secondary data were obtained from 40 provincial colleges and universities in Sichuan Province from 2017 to 2022. Factors such as human, physical, and financial capital were used as input indicators, while educational outcomes, scientific research, and social services served as output indicators. The DEA-BCC model shows that the average value of comprehensive technical efficiency was 0.911, with an average pure technical efficiency of 0.978 and an average scale efficiency of 0.929, indicating scale efficiency as a constraining factor. According to the DEA-Malmquist index model results, the total factor productivity has an average value of 0.707. This suggests a 29.30% decrease in efficiency over these 6 years. The average value of the technical efficiency index is 0.985, which decreases by 0.074. The average value of the technological progress efficiency index was 0.718, indicating that the sample of provincial universities did not experience high technological progress efficiency. Technological regression during this period primarily influenced the changes in total factor productivity. The research findings indicate technical efficiency issues in utilizing educational resources at provincial universities. The results of this study are anticipated to offer valuable insights and benefits to key stakeholders in the education sector. By presenting a robust tool for assessing the efficiency of educational resource allocation, the study underscores the critical role of technical and scale efficiencies in enhancing effectiveness. Through a comprehensive analysis of educational resource inputs and outputs, this paper identifies the key factors influencing efficiency, providing invaluable data support to decision-makers and managers in the education sector.
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References
Agasisti, T., & Dal, B. 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., Munda, G., & Hippe, R. (2019). Measuring the efficiency of European education systems by combining data envelopment analysis and multiple-criteria evaluation. Journal of Productivity Analysis, 51(2-3), 105-124. https://doi.org/10.1007/s11123-019-00549-6
Alizadeh, R., Gharizadeh Beiragh, R., Soltanisehat, L., Soltanzadeh, E., & Lund, P. D. (2020). Performance evaluation of complex electricity generation systems: A dynamic network-based data envelopment analysis approach. Energy Economics, 91. https://doi.org/10.1016/j.eneco.2020.104894
Altbach, P. G. (2015). The international imperative in higher education. International Higher Education, (38), 2-4.
Bornmann, L., Gralka, S., Anegón, F. D. M., & Wohlrabe, K. (2023). Efficiency of universities and research-focused institutions worldwide: The introduction of a new input indicator reflecting institutional staff numbers. Journal of Informetric, 17(2). https://doi.org/10.1016/j.joi.2023.101400
Chen, Y., Jiang, Y., Zheng, A., Yue, Y., & Hu, Z. H. (2023). What research should vocational education colleges conduct? an empirical study using data envelopment analysis. Sustainability (Switzerland), 15(12). https://doi.org/10.3390/su15129220
China Ministry of Education Quality Assessment Center. (2022). Educational data. Ministry of Education of the People’s Republic of China. https://eva.heec.edu.cn/
Cui, X. H., Wang, C. J., & Zeng, B. (2019). A research on the sci-tech finance support efficiency of technology transfer in Yangtze River economic belt based on DEA-Tobit. Statistics & Information Forum, 19(6), 2062-2065.
Department of Education and Science. (2022). Budget and final accounts disclosure. Sichuan Provincial Department of Finance. https://czt.sc.gov.cn/scczt/c102360/jump.shtml
Department of Financial Affairs. (2022). Funding statistics. Ministry of Education of the People's Republic of China. http://www.moe.gov.cn/srcsite/A05/s3040/202312/t20231202_1092896.html
Department of Financial Management. (2022). Budget and final accounts disclosure. Sichuan Provincial Department of Education. https://edu.sc.gov.cn/scedu/c100573/list_2.shtml
Ding, T., Yang, J., Wu, H., Wen, Y., Tan, C., & Liang, L. (2021). Research performance evaluation of Chinese university: A non-homogeneous network DEA approach. Journal of Management Science and Engineering, 6(4), 467-481. https://doi.org/10.1016/j.jmse.2020.10.003
Ding, T., Zhang, Y., Zhang, D., & Li, F. (2023). Performance evaluation of Chinese research universities: A parallel interactive network DEA approach with shared and fixed sum inputs. Socio-Economic Planning Sciences, 87. https://doi.org/10.1016/j.seps.2023.101582
Duan, S. X. (2019). Measuring university efficiency: An application of data envelopment analysis and strategic group analysis to Australian universities. Benchmarking, 26(4), 1161-1173. https://doi.org/10.1108/BIJ-10-2017-0274
Egorov, A., & Serebrennikov, P. (2023). Measuring the efficiency of universities: what is inside the black box?. Journal of Higher Education Policy and Management. https://doi.org/10.1080/1360080X.2023.2209379
Fadda, N., Marinò, L., Pischedda, G., & Ezza, A. (2022). The effect of performance-oriented funding in higher education: Evidence from the staff recruitment budget in Italian higher education. Higher Education, 83(5), 1003-1019. https://doi.org/10.1007/s10734-021-00725-4
Färe, R., Grosskopf, S., Lindgren, B., & Roos, P. (1992). Productivity changes in Swedish pharmacies 1980-1989: A non-parametric Malmquist approach. Journal of Productivity Analysis, 3(1-2), 85-101.
Haddad, M. Z., Heong, Y. M., Razzaq, A. R. B. A., & Kiong, T. T. (2021). Exploring the innovative methods for evaluating educational efficiency. In Paper presented at the 2021 International Conference on Decision Aid Sciences and Application, DASA 2021
He, T. (2022). A Multiobjective Allocation method for high-quality higher education resources based on cellular genetic algorithm. Mathematical Problems in Engineering, 2022. https://doi.org/10.1155/2022/4322078
Holmlund, H., McNally, S., & Viarengo, M. (2010). Does money matter for schools?. Economics of Education Review, 29(6), 1154-1164. https://doi.org/10.1016/j.econedurev.2010.06.008.
Jin, Z., Li, H., & Wang, H. (2019). Evaluation of input and output efficiency in higher education using DEA. Frontiers of Business Research in China, 13(1), 11.
Kamarudin, N., Rahmat, A., Yusop, N. M., & Januri, S. S. (2023). Measuring efficiency of faculties in a public university using hierarchical network data envelopment analysis. In Paper presented at the AIP Conference Proceedings.
Labanino, R., Pablo, L., & Armin, W. (2022). Measuring financial, personnel and material resources for education for sustainable development in the German school system: A proposal for the German educational monitoring and reporting. Journal of Education for
Sustainable Development, 16(1-2), 42-60. https://doi.org/10.1177/09734082221117603
Lee, B. L., & Worthington, A. C. (2016). A network DEA quantity and quality-orientated production model: An application to Australian university research services. Omega (United Kingdom), 60, 26-33. https://doi.org/10.1016/j.omega.2015.05.014
Lepori, B., Usher, J., & Montauti, M. (2013). Budgetary allocation and organizational characteristics of higher education institutions: A review of existing studies and a framework for future research. Higher Education, 65(1), 59-78. https://doi.org/10.1007/s10734-012-9581-9
Li, X., & Qian, X. (2020). Efficiency evaluation of higher education resource allocation in China: Based on the meta-frontier analysis. Frontiers of Business Research in China, 14(1), 1-23.
Marginson, S. (2011). Higher education and public good. Higher Education Quarterly, 65(4), 411-433.
Munoz, D. A. (2016). Assessing the research efficiency of higher education institutions in Chile: A data envelopment analysis approach. International Journal of Educational Management, 30(6), 809-825. https://doi.org/10.1108/IJEM-03-2015-0022
Ngobeni, V., Aye, G. C., & Breitenbach, M. C. (2023). Estimating wasted financial and human resources in the South African public school system - a data envelopment analysis. African Journal of Business & Economic Research, 18(1), 203-223. https://doi.org/10.31920/1750-4562/2023/v18n1a10
Olariu, G. V., & Brad, S. (2022). Preventive risk management of resource allocation in Romanian Higher education by assessing relative performance of study programs with DEA method. Sustainability (Switzerland), 14(19). https://doi.org/10.3390/su141912527
Qiu, M. (2022). balanced allocation of educational resources based on parallel genetic algorithm. Mathematical Problems in Engineering, 2022. https://doi.org/10.1155/2022/7517267
Wang, H. L., & Zhu, X. L. (2022). Empirical study on the efficiency of higher education resource allocation - based on DEA method in 31 regions nationwide. Journal of Linyi University, 44(01), 1-16. https://doi.org/10.13950/j.cnki.jlu.2022.01.001%WCNKI.
Wei, B., & Jun, B. C. (2022). Research on the efficiency of higher education resource allocation in China: Based on the DEA-Malmquist Model. Journal of Changzhou Institute of Technology, 35(6), 77-84.
Welch, A. R., & Pavel, M. D. (2017). Universities, the emerging development landscape, and the rise of regionalism. World Development, 99, 27-36.
Wohlrabe, K., Anegon, F. M., & Bornmann, L. (2019). How efficiently do elite US Universities produce highly cited papers?. Publications, 7(1). https://doi.org/10.3390/publications7010004
Xiong, X., Yang, G. L., Zhou, D. Q., & Wang, Z. L. (2022). How to allocate multi-period research resources? Centralized resource allocation for public universities in China using a parallel DEA-based approach. Socio-Economic Planning Sciences, 82. https://doi.org/10.1016/j.seps.2022.101317
Yang, G. L., Fukuyama, H., & Song, Y. Y. (2018). Measuring the inefficiency of Chinese research universities based on a two-stage network DEA model. Journal of Informetrics, 12(1), 10-30. https://doi.org/10.1016/j.joi.2017.11.002
Zhang, Q., Kang, N., & Barnes, R. (2016). A systematic literature review of funding for higher education institutions in developed countries. Frontiers of Education in China, 11(4), 519-542. https://doi.org/10.1007/BF03397139
Zhou, D., & Tian, G. (2020). Efficiency of higher education resources allocation in China: Evidence from DEA. Frontiers of Business Research in China, 14(1), 3-22.