Government spending on local higher education institutions (LHEIs) in China: analysing the determinants of general appropriations and their contributions

ABSTRACT

Government spending on higher education is a perennial research theme, but empirical evidence from China is relatively rare. Based on panel data of local higher education institutions in 31 Chinese provinces from 2003 to 2016, this paper investigates the determinants of general appropriations and quantifies their relative contributions in consideration of socioeconomic, political, and higher education variables. The results of two-way fixed effect modelling reveal that province GDP per capita, government size, percent of education spending, total enrolment, and tuition fees are five major determinants. Further, the results of Shapley decomposition show that these five determinants jointly contribute to 56.793% of total R². Drawing on Chinese practices of higher education expansion in the last two decades, this paper concludes with policy implications for sustaining higher education funding.

KEYWORDS:

• Government spending on higher education
• general appropriations
• China
• two-way fixed effect model
• Shapley decomposition

Acknowledgements

The authors acknowledge the valuable comments and suggestions from two anonymous referees.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Notably, all results in this section are based on Equation (1) without a time lag. For results with a 1-, 2-, or 3-year lag for independent variables, please see the auxiliary results in the Appendix.

2 Launched in 1995, ‘Project 211’ is the first national plan for higher education; it aims to enhance the teaching and research capacities of a group of designated universities. A total of 116 HEIs were entitled as 211 universities. However, this project was formally ended in 2016 because the new project ‘Double First Class University Plan’ was implemented.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China: [grant number 71704115, 71874015].