# American Institute of Mathematical Sciences

## The spatial heterogeneity of China's higher education development to promote economic growth since the reform and opening up with in the context of supply-side reform

 1 College of Accounting and Finance, Jiangxi University of Engineering, Xinyu 338000, China 2 School of Marxism, Hainan University, Haikou 570228, China

*Corresponding author: Hongping Yan

Received  May 2019 Revised  May 2019 Published  February 2020

In the past 39 years after China adopted the opening up and reform policy, Chinese education developed rapidly and achieved the world-famous results, and thence the one whether the education development promotes the economic growth becomes the issue concerned by the academic circles. In this paper, the empirical analysis was made for the spatial heterogeneity of Chinese higher education for the economic growth contribution at the different stages. According to the analysis results therefrom, the regions in which the higher education contributed too much to the local economy growth were Shanghai, Beijing and Zhejiang; the regions in which the higher education contributed too less to the local economy growth were Heilongjiang and Tibet; the regions in which the higher education made a greatly varying contribution to the local economy growth were Sichuan and Tibet; the regions in which the higher education made a less varying contribution to the local economy growth were Guangdong and Yunnan. Meanwhile, the higher education of 31 regions in China, during the period from the "ninth 5-year plan" to the "twelfth 5-year plan", showed an outstanding self-correlation for the overall space of contribution rate to economy growth, and most of them were featured in the partial spatial agglomeration, and few of them were featured in the partial spatial heterogeneity. In the process of the current education supply-side structural reform, it is proposed to enlarge the premium educational resource supply, optimize the education resource allocation, and boost better the regional coordinated development of the real economy.

Citation: Manwen Tian, Shurong Yan, Lina Wang, Weihong Li, Hongping Yan. The spatial heterogeneity of China's higher education development to promote economic growth since the reform and opening up with in the context of supply-side reform. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2020270
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##### References:
 [1] S. Aidara, Anticipated backward doubly stochastic differential equations with non-liphschitz coefficients, Mathematics and Nonlinear Sciences, 4 (2019), 9-19.  doi: 10.2478/AMNS.2019.1.00002.  Google Scholar [2] Y. K. Al-Yousif, Education expenditure and economic growth: Some empirical evidence from the gcc countries, Journal of Developing Areas, 42 (2008), 69-80.   Google Scholar [3] D. H. Autor, L. F. Katz and M. S. Kearney, The polarization of the U.S. labor market, American Economic Review, 96 (2006), 189-194.  doi: 10.3386/w11986.  Google Scholar [4] J. Brenner, Life-cycle variations in the association between current and lifetime earnings: Evidence for german natives and guest workers, Labour Economics, 17 (2010), 392-406.  doi: 10.1016/j.labeco.2009.03.006.  Google Scholar [5] M. Browning, G. Mette and S. Leth-Petersen, Housing wealth and consumption: A micro panel study, The Economic Journal, 123 (2013), 401-428.  doi: 10.1111/ecoj.12017.  Google Scholar [6] A. Chandra, Does government expenditure on education promote economic growth? An econometric analysis, University Library of Munich, Germany. Google Scholar [7] C. Colombier, Does the composition of public expenditure affect economic growth? Evidence from the swiss case, Applied Economics Letters, 18 (2011), 1583-1589.  doi: 10.1080/13504851.2011.554361.  Google Scholar [8] M. Corak, Income inequality, equality of opportunity, and intergenerational mobility, Iza Discussion Papers, 27 (2013), 79-102.  doi: 10.1257/jep.27.3.79.  Google Scholar [9] W. Gao and W. F. Wang, New isolated toughness condition for fractional $(g, f, n)$ - critical graph, Colloquium Mathematicum, 147 (2017), 55-65.  doi: 10.4064/cm6713-8-2016.  Google Scholar [10] W. Gao, L. L. Zhu, Y. Guo and K. Y. Wang, Ontology learning algorithm for similarity measuring and ontology mapping using linear programming, Journal of Intelligent and Fuzzy Systems, 33 (2017), 3153-3163.  doi: 10.3233/JIFS-169367.  Google Scholar [11] J. H. Goldthorpe, The role of education in intergenerational social mobility: Problems from empirical research in sociology and some the oretical pointers from economics, Rationality and Society, 26 (2014), 265-289.  doi: 10.1177/1043463113519068.  Google Scholar [12] G. Kraaykamp, J. Tolsma and M. H. J. Wolbers, Educational expansion and field of study: Trends in the intergenerational transmission of educational inequality in the netherlands, British Journal of Sociology of Education, 34 (2013), 888-906.   Google Scholar [13] Z. F. Liu, J. Feng and J. Wang, Effects of the sharing economy on sequential innovation products, Complexity, 2019 (2019), 3089641. doi: 10.1155/2019/3089641.  Google Scholar [14] A. H. Malumfashi, Education expenditure and economic growth in nigeria: Co-integration and correction technique, Journal of Research in Commerce, Economics and Management, 2 (2012), 34-37.   Google Scholar [15] S. Paul, D. Jana, S. Mondal and P. Bhattacharya, Optimal harvesting of two species mutualism model with interval parameters, Journal of Intelligent and Fuzzy Systems, 33 (2017), 1991-2005.  doi: 10.3233/JIFS-161186.  Google Scholar [16] H. Song and X. Guo, Comparison of the hard and soft computing methods of the contribution rate of education to economic growth, Education and Economy, 33 (2017), 83-88.   Google Scholar [17] F. Torche, Is a college degree still the great equalizer? Intergenerational mobility across levels of schooling in the united states1, American Journal of Sociology, 117 (2011), 763-807.   Google Scholar [18] K. Vajravelu, R. Li, M. Dewasurendra, J. Benarroch, N. Ossi, Y. Zhang, M. Sammarco and K. V. Prasad, Effects of second-order slip and drag reduction in boundary layer flows, Applied Mathematics and Nonlinear Sciences, 3 (2018), 291-302.  doi: 10.21042/AMNS.2018.1.00022.  Google Scholar [19] S.-Y. Wang, Property rights and intra-household bargaining, Journal of Development Economics, 107 (2014), 192-201.  doi: 10.3386/w19427.  Google Scholar [20] M. Ye, X. Zheng and B. Wang, Econometric analysis of the contribution of education to economic growth, The Journal of Quantitative and Technical Economics, 20 (2003), 89-92.   Google Scholar
Distribution of culture degree of employees of Fujian Province 2006–2010 (Unit: %)
 Year Uneducated Primary school Junior high school Senior high school Junior college Undergraduate Postgraduate 2006 1.4 22.9 52.3 13.8 6.0 3.3 0.28 2010 0.7 17.5 53.9 14.3 7.6 5.5 0.47
 Year Uneducated Primary school Junior high school Senior high school Junior college Undergraduate Postgraduate 2006 1.4 22.9 52.3 13.8 6.0 3.3 0.28 2010 0.7 17.5 53.9 14.3 7.6 5.5 0.47
Distribution of per capita education years of employees of Fujian Province 2006 and 2010 (Unit: Year)
 Year Primary school Junior high school Senior high school Junior college Undergraduate Postgraduate Education years 2006 5.92 2.27 0.70 0.18 0.14 0.01 9.22 2010 5.96 2.45 0.84 0.23 0.24 0.01 9.73
 Year Primary school Junior high school Senior high school Junior college Undergraduate Postgraduate Education years 2006 5.92 2.27 0.70 0.18 0.14 0.01 9.22 2010 5.96 2.45 0.84 0.23 0.24 0.01 9.73
Annual average growth rate of education comprehensive index during 2006–2010
 Year Education comprehensive index Education comprehensive index after excluding higher education Percentage of higher education to the annual average growth rate of education comprehensive index 2006 10.29 9.63 --- 2010 11.03 10.07 --- Annual average growth rate 1.75% 1.14% 35%
 Year Education comprehensive index Education comprehensive index after excluding higher education Percentage of higher education to the annual average growth rate of education comprehensive index 2006 10.29 9.63 --- 2010 11.03 10.07 --- Annual average growth rate 1.75% 1.14% 35%
Contribution rate of higher education to economic growth (Unit: %)
 Region Ninth 5-year plan Tenth 5-year plan Eleventh 5-year plan Twelfth 5-year plan Nationwide 3.79 1.20 3.59 7.74 Beijing 3.28 11.88 3.95 18.37 Tianjin 1.49 3.92 3.12 7.89 Hebei 0.94 0.82 3.54 7.18 Shanxi 3.04 1.04 2.65 6.31 Inner Mongolia 2.83 0.21 3.40 5.69 Liaoning $-1.31$ 2.92 3.21 3.34 Jilin 1.81 $-0.07$ 3.34 4.72 Heilongjiang 0.48 0.84 2.81 1.78 Shanghai 7.07 7.55 1.30 13.69 Jiangsu 3.92 2.00 2.97 6.29 Zhejiang 0.93 4.38 2.01 12.42 Anhui 3.80 $-0.59$ 3.77 4.06 Fujian 3.78 0.06 3.00 7.68 Jiangxi 6.26 0.42 0.45 3.64 Shandong 5.09 $-0.52$ 3.56 9.34 Henan 5.14 $-0.74$ 2.46 5.59 Hubei 1.83 2.53 0.62 6.07 Hunan 3.97 0.81 1.57 7.33 Guangdong 0.82 1.29 3.04 4.97 Guangxi 3.80 1.39 2.04 2.82 Hainan 3.74 0.44 2.58 6.48 Chongqing 2.95 1.26 4.33 2.90 Sichuan 5.45 $-1.50$ 3.16 3.29 Guizhou 3.53 $-1.47$ 4.38 2.24 Yunnan 2.34 1.41 3.45 3.86 Tibet $-1.03$ 0.47 13.84 $-5.15$ Shaanxi 2.81 1.46 2.12 9.04 Gansu 4.22 $-0.85$ 5.56 6.05 Qinghai 1.29 3.89 3.65 3.43 Ningxia 4.02 1.72 3.12 3.13 Xinjiang 4.89 0.89 2.04 3.17
 Region Ninth 5-year plan Tenth 5-year plan Eleventh 5-year plan Twelfth 5-year plan Nationwide 3.79 1.20 3.59 7.74 Beijing 3.28 11.88 3.95 18.37 Tianjin 1.49 3.92 3.12 7.89 Hebei 0.94 0.82 3.54 7.18 Shanxi 3.04 1.04 2.65 6.31 Inner Mongolia 2.83 0.21 3.40 5.69 Liaoning $-1.31$ 2.92 3.21 3.34 Jilin 1.81 $-0.07$ 3.34 4.72 Heilongjiang 0.48 0.84 2.81 1.78 Shanghai 7.07 7.55 1.30 13.69 Jiangsu 3.92 2.00 2.97 6.29 Zhejiang 0.93 4.38 2.01 12.42 Anhui 3.80 $-0.59$ 3.77 4.06 Fujian 3.78 0.06 3.00 7.68 Jiangxi 6.26 0.42 0.45 3.64 Shandong 5.09 $-0.52$ 3.56 9.34 Henan 5.14 $-0.74$ 2.46 5.59 Hubei 1.83 2.53 0.62 6.07 Hunan 3.97 0.81 1.57 7.33 Guangdong 0.82 1.29 3.04 4.97 Guangxi 3.80 1.39 2.04 2.82 Hainan 3.74 0.44 2.58 6.48 Chongqing 2.95 1.26 4.33 2.90 Sichuan 5.45 $-1.50$ 3.16 3.29 Guizhou 3.53 $-1.47$ 4.38 2.24 Yunnan 2.34 1.41 3.45 3.86 Tibet $-1.03$ 0.47 13.84 $-5.15$ Shaanxi 2.81 1.46 2.12 9.04 Gansu 4.22 $-0.85$ 5.56 6.05 Qinghai 1.29 3.89 3.65 3.43 Ningxia 4.02 1.72 3.12 3.13 Xinjiang 4.89 0.89 2.04 3.17
Moran's I values of contribution rate of Chinese higher education to economic growth
 Period Moran's I value P value Ninth 5-year plan $-0.04638$ 0.455 Tenth 5-year plan 0.17527 0.028 Eleventh 5-year plan 0.01142 0.283 Twelfth 5-year plan 0.28134 0.002
 Period Moran's I value P value Ninth 5-year plan $-0.04638$ 0.455 Tenth 5-year plan 0.17527 0.028 Eleventh 5-year plan 0.01142 0.283 Twelfth 5-year plan 0.28134 0.002
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