Comparative study on economic contribution rate of education of China and foreign countries based on soft computing method |
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| Authors: | Han Sun Haixiang Guo Jinglu Hu Kejun Zhu |
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| Affiliation: | 1. School of Economics and Management, China University of Geosciences, Wuhan, 430074, P.R. China;2. Key Laboratory of Tectonics and Petroleum Resources, China University of Geosciences, Wuhan, 430074, P.R. China;3. Graduate School of Information, Production and Systems, Waseda University, Hibikino 2-7, Wakamatsu-ku, Kitakyushu-shi, Fukuoka, 808-0135, Japan;1. Department of Land and Real Estate Management, School of Public Administration and Policy, Renmin University of China, Beijing 100872, PR China;2. Department of Land Economy, University of Cambridge, Cambridge CB3 9EP, UK;3. Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, PR China;1. Department of Public Policy, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China;2. School of Urban-Rural Planning & Management, China Insitute of Regulation Research, Zhejiang University of Finance &Economics, Hangzhou, China;3. School of Civil Engineering and Built Environment, Queensland University of Technology, Brisbane, Australia;1. School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510006, China;2. SYSU-CMU Shunde International Joint Research Institute, Shunde, Foshan 528300, China;3. Key Laboratory of Autonomous Systems and Networked Control, Ministry of Education, Guangzhou 510640, China;1. School of Geography and Oceanographic Science, Research Centre of Human Geography, Nanjing University, Nanjing 210023, China;2. Bartlett School of Planning, University College London, London WC1H 0QB, UK;3. School of the Environment, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia;1. Doctor of School of Architecture of Southeast University, Department of Urban Planning and Design of Nanjing University, No. 22 Hankou Road., Gulou District, Nanjing 210093, China;2. School of Architecture of Southeast University, Key Laboratory of Urban and Architectural Heritage Conservation of Ministry of Education, Nanjing Sipailou 2, 210096, China |
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| Abstract: | Economic contribution rate of education (ECRE) is the key factor of education economics. This article selected China, South Korea, United States and other countries for a total of 15 samples, and put the data of the same period under the framework of soft computing, to simulate the production chain of “education–potential human capital–actual human capital–economic growth”. The basic idea is: Firstly, 15 countries are softly categorized according to the level of science and technology (S&T) progress. Secondly, potential human capital and actual human capital establish the internal correlation (fuzzy mapping) in the same classification, and we conceptualize actual human capital as one production factor, joined with the other two production factors, fixed asset and land, to set up the fuzzy mapping to economic growth., and then calculate economic contribution rate of education of China and foreign by two fuzzy mapping of them. Thirdly, this paper analyzes the present state and differences in the development of education between China and foreign according to different ECRE, and offers proposals for promoting the education in China. |
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