Abstract
In recent years, although the total funding for higher education by the Chinese government has been increasing year by year, there are still some problems, such as the unreasonable allocation of regional resources and poor funding efficiency. Therefore, it is necessary to evaluate the performance management and analyze government funding in higher education (GFHE). Based on the data envelopment analysis (DEA) model, this paper evaluates the performance of GFHE in 29 provinces in eastern, central, and western areas of China. An empirical analysis is conducted on the influencing factors using the panel Tobit regression model. The results show that from 2008 to 2020, GFHE performance in China is generally high, but offers a “W-shaped” fluctuation rising state. There are significant differences in the performance of different areas, and the scale level of GFHE in the three areas is not wholly consistent with the performance level. In further studies, the performance level of the 29 provinces is divided into three degrees, which are distributed in all three areas. The study also found that the influencing factors of GFHE performance in central, eastern, and western China are also different, and analyzed the positive and negative effects of influencing factors in each area. Finally, the study tests the theoretical hypothesis, and the results are robust.
Highlights
Government funding in higher education is governments’ funding at all levels in higher education institutions
Based on the data envelopment analysis (DEA) model, this paper evaluates the performance of government funding in higher education (GFHE) in 29 provinces in eastern, central, and western areas of China
This paper aims to analyze regional differences in the scale and performance of GFHE in China, as well as the factors affecting the performance level of GFHE in eastern, central, and western China
Summary
Government funding in higher education (hereinafter referred to as GFHE) is governments’ funding at all levels in higher education institutions. Using geographic image environment for the development of higher ed- analysis and micro spatial simulation methods, ucation in Australia and the changes in the pro- Kavroudakis (2013) and others analyzed the soportion of government monetary funds in the cial equity and spatial imbalance related to highsources of higher education funds in recent years, er education investment They studied the impact emphasizing the importance of establishing per- of students’ enrollment opportunities, economic formance and market-oriented university system development, and geographical factors on the alin Australia. Tomchuk et al (2021) revealed coordination relationship between higher edthe formation of the higher education index sys- ucation and economic development levels in 31 tem from the perspective of improving the higher provinces and cities (districts) in China in 2005 education management system and encouraging and 2015 by establishing the coordination degree efficient management talents according to the model. Predicted the medium and long-term development of China’s higher education scale. Zhang and Wang (2014) analyzed the impact of fund-
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