Research Center for Science Technology and Society of Fuzhou University, International Studies and Trade, Changle Fuzhou , China

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1 2017 3rd Annual International Conference on Modern Education and Social Science (MESS 2017) ISBN: An Analysis of the Correlation Between the Scale of Higher Education and Economic Growth in Fujian Jian-Ping HUANG a,*, Xiao-Fang QIU b, Jie ZHU c, Li-Hua HUANG d, Feng LIN e, Hang-Zhou ZHONG f Research Center for Science Technology and Society of Fuzhou University, International Studies and Trade, Changle Fuzhou , China a cbg1567@163.com * Jian-ping HUANG Keywords: Higher education, Economic development, Relevance, Cointegration test. Abstract. Education and economy are the two main themes of social development. This article makes a statistical analysis of the two indicators of the number of college students and the actual GDP in Fujian Province in Next, we analyze the time series relationship between higher education and economic growth in Fujian province. The results show that there is a strong correlation between the two. Under the certain conditions, there is a one-way causal relationship between the two. Introduction Higher education is an important part of the education system, which is closely linked with economic development.[1]through this study, we can understand the relationship between higher education and economic development in Fujian from theory and practice as soon as possible. This study will help to promote the rational allocation of higher education resources in Fujian, and provide reference and basis for the development of Fujian province's economic development and higher education. Index Selection The parameters to measure the development level of higher education are "the number of students in school", "the number of graduates", "the expenditure of education in the region" and so on. The parameters to measure the level of economic development are "gross domestic product", "per capita GDP", "total retail sales of social consumer goods" and so on. Referring to the existing research results and analysis of the research ideas, this paper selects the "college students" and "GDP" these two parameters to study the correlation between the higher education and economy in Fujian province. The time range is , the original data of this paper comes from the statistical yearbook of Fujian province in the year of Correlation Analysis Correlation Coefficient Table 1 is the result of the correlation coefficient analysis. From the above table, we can see that the Pearson correlation coefficient is 0.976, which the correlation coefficient between the number of students in Colleges and the actual GDP in Fujian province is about

2 Table 1. Pearson correlation coefficient. Correlations(**. Correlation is significant at the 0.01 level (2-tailed) The Number of Students(ten thausands people) The Actual GDP (a hundreds millions yuan) The Number of Students(ten thousands people The Actual GDP (a hundreds millions yuan) Pearson Correlation ** Sig. (2-tailed) 0 Sum of Squares and Cross-products Covariance N Pearson Correlation 0.976** 1 Sig. (2-tailed) 0 Sum of Squares and Cross-products Covariance N Cointegration Test logy said the logarithm of actual GDP in Fujian. logx said the logarithm of the number of students in Colleges in Fujian. dlogy said the first difference of actual GDP in Fujian Province. dlogx said the first difference of the number of college students in Fujian Province. It can be seen from the table 2 that the ADF statistics of logx and logy are less than the critical values at the significant level, and the results show that the logx and logy are not stable, while the dlogy and dlogx are opposite. logy and logy may have long-term stable equilibrium relationship, which can be further analyzed on the basis of cointegration test. Table 2. Unit root test. ADF Critical Value Variables Statistics 1% 5% 10% Conclusion logx instability logy instability dlogx stability dlogy stability E-G Test The first step is to estimate the equation with ordinary least squares method and calculate the unbalanced error,as shown in Table 3. Table 3. Variables Coeffici ent Std. Error t-statistic Prob. C LOGX R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat Prob(F-statistic)

3 We establish the regression equation between logx and logy : log y log x (0.1332) (0.500) 2 2 R R RSS The and are the regression coefficient T statistical test. R2 is the regression 2 goodness of fit. R is adjusted goodness of fit. The second step is to test the stability of residual et. If et is a stable sequence of I (0), logy and logx are considered to have a cointegration relationship. Through the et unit root test, the ADF test results are shown in table 4: Table 4. variable ADF critical value conclusion statistics 1% 5% 10% residual stability As can be seen from table 3, the ADF statistic of the residual sequences are greater than the three critical values, so we think that the residual sequences are stationary. Therefore, we can draw a conclusion that there is a co integration relationship between economic development and higher education in Fujian province. Johansen Test In the co integration test, the Johansen test is used to ensure the results. Table 5. Hypothesized Trace 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * At most E Table 5 is the result of Johansen test. The T value of "no co integration relationship" is , which is greater than the critical value, and the P value is less than Therefore, we reject the original hypothesis. The T value of at least one co integration relationship is , which is greater than the critical value, and the P value is greater than So we accept the original hypothesis that there is a cointegration relationship between logy and logx. Granger Test Results The co integration relationship between variables indicates that there is a long-run equilibrium relationship between the two variables. If you want to know whether there is a causal relationship between the two variables, we use the Granger test to determine. Table 6. Null Hypothesis: Obs F-Statistic Prob. LOGY does not Granger Cause LOGX LOGX does not Granger Cause LOGY Table 6 is the result of the 1 order Granger test, we can see that the P value of both is greater than So we accept the original hypothesis, and that there is no Grange causal relationship between logy and logx. 232

4 Table 7. Null Hypothesis: Obs F-Statistic Prob. LOGY does not Granger Cause LOGX LOGX does not Granger Cause LOGY Table 7 is the result of the Granger test of the third order. The P values of both are greater than 0.05, so the original hypothesis is accepted. We think that there is no Granger causality between logy and logx. Table 8. Null Hypothesis: Obs F-Statistic Prob. LOGY does not Granger Cause LOGX LOGX does not Granger Cause LOGY Table 8 is the result of the Granger test of the 11 order, the original assumption that "logy is not the Granger cause of logx" P value is greater than 0.05, so accept the original hypothesis, and we believe that logy and logx does not exist Grange causality. The original hypothesis that "logx is not the Granger cause of logy" is less than 0.05 P, so the original hypothesis is rejected, and we believe that logy and logx have Granger causality. y = x x Regression analysis results Seen from the Grainger causality test, the development of higher education and economic development in Fujian province is not directly reciprocal causation. Under certain conditions and conditions, the two have a one-way causal relationship. You can try to infer the change of another variable from the change of a variable. Using a Variety of Models to Fit Because it is impossible to determine which model is closer to the sample data, we choose a variety of models, and the results of parameter estimation with SPSS are shown in Table 8.It can be concluded from the table that the cubic (three curve) model has the largest coefficient, so the cubic (the three curve) is the best model. Table 9. Multiple model fitting. Model name R R Square Adjusted Whether the R Square significance test Liner No Quadratic Yes Logarithmic Yes Cubic Yes Power Yes Logistic Yes Growth Yes Cubic Curve Estimation Table 10 is the coefficient of cubic (the three curve), and the P value of each coefficient has significant statistical significance at the significant level of 5%. Therefore, the fitting equation can be obtained: x 3 233

5 Table 10. Standardiz ed Unstandardized Coefficient Coefficients s t Sig. Std. B Error Beta The Number of Students(ten thousands people) The Number of Students(ten thousands people) ** The Number of Students(ten thousands people) ** (Constant) Figure 1. The Curve Fitting Result. Figure 1 is a graph of the fitted curve and the original observations. As can be seen from the figure, the fitting results are relatively good. Conclusions and Suggestions Conclusions According to the analysis, the conclusions are as follows: firstly, there is a strong correlation between higher education and economy in Fujian province. Second, there is a long-term equilibrium relationship between the level of development of higher education and economic development in Fujian. Third, under certain conditions, the development of higher education in Fujian and the economic development of a one-way causal relationship. Fourth, we can try to infer the change of the economic development of Fujian province from the change of the development of higher education in Fujian province. Suggestions First, it is necessary to further promote the discipline and economic structure of Fujian colleges and universities.[2]according to the "big marine economy and the construction of cultural province" deployment, the higher education in Fujian province should pay close attention to the new direction of economic and social development, and the specialty setting should be based on the adjustment of industrial structure and the development of economy in Fujian province. Second, we should further promote the level of higher education and the technical structure in Fujian. We should vigorously develop the special education to solve the problem of the shortage of low level talents. We should steadily develop undergraduate education, and strive to build new and high science and technology disciplines, applied disciplines, training applied talents. We should actively develop graduate education, improve the operating mechanism of graduate education, 234

6 actively explore and carry out the reform of postgraduate education mode, and cultivate innovative talents. Third, it is very important to further promote the structure of Fujian higher education to adapt to the regional economy. We should give full play to the exemplary role of Central Universities and provincial universities to promote the development of higher education in Fujian province.[3]it is necessary to construct the regional higher education network in Fujian Province, to exert the function of the popularization of the network system, and to change the regional economic forms. We should give full play to the coordination of the government, combined with the local economic characteristics, and actively guide the school enterprise cooperation, and actively integrate into the "government - Enterprise - University" interactive model, and gradually narrow the differences between regions. References [1] Cui Yuping, Zhang Hong.Quantitative Evaluation of the Coordinated Development of Higher Education in China [J]. Modern University Education, 2015(5): 84-91(in Chinese). [2] Shen Yunci."Tao" and "Way" of Teaching Service Oriented University" [J]. Journal of Higher Education, 2014(3): 40-44(in Chinese). [3] Dong Liping. Transformation and Development of Local Colleges and Universities [J]. Educational Research, 2014(8): 67-74(in Chinese). 235