Allocation Efficiency of Higher Education Resources in China

Authors

  • Dalai Ma Chongqing University of Technology
  • Xuefeng Li Yunnan Normal University

DOI:

https://doi.org/10.3991/ijet.v16i11.23315

Abstract


This paper uses the super-efficiency data envelopment analysis (DEA) model to measure the higher education resource allocation efficiency (HERAE) of 30 provinces from China 2005-2018, and analyzes the regional difference and dynamic evolution law of the HERAE with Theil index and kernel density estimation, respectively. The results show that: The HERAEs of most provinces are DEA effective, but the HERAEs of a few provinces are DEA ineffective, calling for further improvement to the allocation of higher education resources in these places. There was a certain difference in the HERAE trend between eastern, central, and western regions. In the sample period, eastern region had higher HERAE than central and western regions. With the elapse of time, the internal gap of HERAE decreased to different degrees in the three regions. Eastern region had the largest gap, followed in turn by central and western regions. In addition, China’s HERAEs were polarized in time. With the passage of time, the polarization of regional HERAEs slowly weakened.

Author Biographies

Dalai Ma, Chongqing University of Technology

Dalai Ma, PhD in Applied Economics, was graduated from School of Economics and Business Administration, Chongqing University, and works as associate professor at School of Management, Chongqing University of Technology. He is a seasoned researcher of the evaluation of educational development efficiency.

Xuefeng Li, Yunnan Normal University

Xuefeng Li, Master of Education, was graduated from School of Public Affairs, Chongqing University, and works as a lecturer at Faculty of Education, Yunnan Normal University. He is a seasoned researcher of the evaluation of educational development efficiency

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Published

2021-06-04

How to Cite

Ma, D., & Li, X. (2021). Allocation Efficiency of Higher Education Resources in China. International Journal of Emerging Technologies in Learning (iJET), 16(11), pp. 59–71. https://doi.org/10.3991/ijet.v16i11.23315

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Papers