Feasibility Analysis of Levying Education Tax Based on Property in China——Gray System Prediction Model Using Shandong Province Data
Humanities and Social Sciences
Volume 6, Issue 5, September 2018, Pages: 173-181
Received: Nov. 13, 2018; Published: Dec. 12, 2018
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Authors
Xiao Feng, School of Government, Central University of Finance and Economics, Beijing, P. R. China
Ke Gao, PBC School of Finance, Tsinghua University, Beijing, P. R. China; Development Research Center of Shandong Provincial People’s Government, Jinan, P. R. China
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Abstract
Education is the cornerstone of a country's development and the mainstay of national competitiveness. A high-quality education system is not only the need for the construction of an innovative country, but also the foundation of the great rejuvenation of the Chinese nation. Since the 21st century, the growth of China's fiscal education funds has accelerated year by year. In 2012, the goal of fiscal education funds accounting for 4% of GDP was finally completed. However, this has not been able to meet the needs of the entire society for the explosive growth of education, far less than the world average of 4.4% over the same period. After the 19th National Congress of the Communist Party of China, China will make major changes in the field of education and taxation, China will give priority to the development of education, and expand the scope of property tax collection. With all those changes, China will have a chance to build its own education tax system. This paper proposes to construct a properties based education tax to replace the current education surcharge policy, and through the establishment of a gray model, analyze the data of Shandong Province in recent years, measure the expected income of education tax, and provide data supportfor the construction of education tax system.
Keywords
Fiscal Education Funding, Education Tax, Education Surcharge, Gray System
To cite this article
Xiao Feng, Ke Gao, Feasibility Analysis of Levying Education Tax Based on Property in China——Gray System Prediction Model Using Shandong Province Data, Humanities and Social Sciences. Vol. 6, No. 5, 2018, pp. 173-181. doi: 10.11648/j.hss.20180605.15
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