This thesis is to study spatial effect of education output on economic growth through the use of spatial measurement technique. According to the study: there’s the presence of spatial spillover effects in human capital, economic growth, and others; in previous years, human capital depended on mainly the employers with junior school education or below; in recent years, with the reduction of employers with junior school education or below, employers with regular higher education can best promote the economic growth. However, it’s very difficult for human capital with vocational education to promote economic growth, especially in recent years. Therefore, from the perspective of long-term economic growth, China should focus on culturing professional talents and put more resources into the development of vocational education while developing the higher education.
A large number of China’s labor forces in recent years are mainly provided by two education systems of regular higher education, vocational and technical education. The human capital as an important element affects economic growth, and the different education levels of labor forces will also affect the economic growth. Beneficiaries of higher education have stronger adaption to new technologies and ability of creation, the economic growth rate of the economies with rapid development of vocational education is lower than that of the economies taking the priority of developing the higher education. In recent years, China has been in short supply of all kinds of the skilled personnel, and some people call such phenomenon as “shortage of skilled workers.” The experts assert that if the shortage situation of the skilled personnel is left unresolved, it will inevitably become the bottleneck affecting sustained and healthy development of China’s economy. With China’s great success in the 43rd World Skills Competition: 5 gold medals, 6 silver models, 3 bronze medals and 12 prizes for excellence, the national authorities have given a high priority to the skills education. According to the Vice Minister of the Ministry of Education Lu Xin, the Ministry of Education will complete transformation of more than 600 local undergraduate colleges to be application technology and vocational education types. This thesis used the panel data of 31 provinces during 2006-2014, and spatial measurement technology to understand the spatial effect of different educational outputs on economic growth. The educational output studied in this thesis refers to the number of graduates provided by different types of education.
Cobb-Douglas production function has been the most common production function in the literature of analyzing economic growth, namely:
Wherein, Yit represents economic output, Kit represents physical capital investment, Lit represents investment in human capital,
Select the logarithm from the above model to obtain the following model:
The author in this thesis adopts the classic Cobb-Douglas production function and adds the innovation efficiency of the first stage for the spatial econometric analysis. Taking into consideration of the meaning of each index of the production function and taking a reference to the indicator selection conducted by Fan Gang (2011) [
In practical application study, the spatial autocorrelation index of Moran’s I is often used to test whether there’s the spatial correlation, and the corresponding calculation formula is shown as follows:
Wherein,
ber of regions;
In the equation,
Mroan’s I index [
According to the calculation results of Moran’s I index, the normal distribution assumption can be used to test whether there’s the presence of spatial autocorrelation in N regions, and the corresponding standardized form is as follows:
The expectation value and variance of the normal distribution Moran’s I index can be calculated according to the distribution of spatial data:
In the equation,
sum of row i and column j in spatial weight value matrix [
Formula (2) and (3) can be used to test whether there’s the presence of the spatial autocorrelation in N regions. If Z value of normal statistics of Mroan’s I index is greater than the critical value 1.96 of the normal distribution function at the 0.05 level, indicating that there’s a significant positive correlation in the spatial distribution, the positive spatial correlation represents that similar characteristic value of neighboring regions appears the cluster trend.
Spatial lag model focuses on discussing whether variables have the phenomenon of diffusion (spillover effects) in an area, and the corresponding expression is [
In the equation, Y is the dependent variable; X is exogenous independent variable matrix;
Parameter
Mathematical expression of the spatial error model is:
In the equation,
Parameter
The measurement method of principle of direct effect and indirect effect is conducted as follows by taking SLM model as an example [
First, SLM model in Equation (4) is rewritten as follows:
Then the total effect is Sr(W), the average total effect is
The partial derivation of xir is conducted according to the Equation (8) to obtain:
represents the impact of changes of an independent variable of the region j on the dependent variable itself of the region, and this is direct effect [
The partial derivation of xir is conducted according to the Equation (8) to obtain:
represents the changes of an independent variable of the region j will potentially affect the dependent variable of region j, and this is indirect effect.
Thus the expression of the average direct effect, average total effect and average indirect effect required in the study is shown as follows:
In order to make a dynamic comparison and study of the effect of employees of various education systems on economic growth, the author in this thesis respectively studies panel data samples of three time periods of 2006-2010, 2011-2014, and 2006-2014. First, Mroan’s I is used to make the correlation test of various educational outputs and economic growths year by year, to obtain:
Seen from Mroan’s I index (
It can be seen from Mroan’s I scatter diagram of GDP (
Unlike the ordinary panel regression, the parameter estimation of spatial panel model is usually conducted by using maximum likelihood and moment estimation method. SLM and SEM model described in previous section are used to make SLM and SEM fitting of panel data of the three time periods respectively. Finally is to select the one with better fitting effects for analysis, the selection is made mainly based on checking which model has larger log-likelihood, and better variable significance, the models of various time periods obtained through fitting are better SEM models. The estimation results of various models calculated by using R language are shown
Mroan’s I | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 |
---|---|---|---|---|---|---|---|---|---|
GDP | 0.208*** | 0.199*** | 0.2*** | 0.201*** | 0.209*** | 0.217*** | 0.219*** | 0.219*** | 0.22*** |
L1 | 0.0941** | 0.0901** | 0.0884** | 0.0873** | 0.103*** | 0.101*** | 0.113*** | 0.139*** | 0.161*** |
L2 | 0.0362** | 0.0381** | 0.0645** | 0.0673** | 0.183*** | 0.166*** | 0.191*** | 0.224*** | 0.239*** |
L3 | 0.932** | 0.956** | 0.108** | 0.113*** | 0.145*** | 0.187*** | 0.202*** | 0.233*** | 0.237*** |
Note: “*”, “**”, “***” represent to pass the significance test at respective level of 10%, 5% and 1%.
as follows, and P values of parameters are included in parentheses.
It can be seen from estimation results of time period during 2006-2010 (
It can be known from the estimation results of the latest time period during 2011-2014 (
Parameter | Random effect | Fixed space | Fixed time | Fixed space and time |
---|---|---|---|---|
C | 1.6635*** | 3.8166*** | −0.0707 | 5.0677*** |
(2.68E−15) | (2.20E−16) | (0.6658) | (1.39E−12) | |
λ | 1.9286** | 0.4690*** | 0.2526* | −0.1914 |
(0.0055) | (7.65E−08) | (0.0157) | (0.1201) | |
lnK | 0.3989*** | 0.4061*** | 0.7940*** | 0.2756*** |
(2.20E−16) | (2.20E−16) | (2.20E−16) | (3.61E−08) | |
lnL1 | 0.5484*** | 0.1791* | 0.2182*** | 0.1641* |
(2.20E−16) | (0.01982) | (1.31E−09) | (0.0281) | |
lnL2 | 0.1060** | 0.0126** | 0.0823*** | 0.0207** |
(0.0157) | (0.0451) | (0.0001) | (0.0330) | |
lnL3 | 0.1132*** | 0.0241 | 0.1755*** | 0.0183 |
(0.0002) | (0.4224) | (7.71E−10) | (0.50766) | |
Hausman test chisq = 41.897, p-value = 6.18e−08 |
Note: “*”, “**”, “***” represent to pass the significance test at respective level of 10%, 5% and 1%.
technical education in promoting the economic growth is also changed from positive to negative, which is also consistent with the actual situation of China’s “shortage of skilled workers” in recent years. Since most of educated children of this generation are from the only one child families, those families attach great importance to higher education of their children, and they are not too willing to send their children to vocational and technical schools, coupled with enlarged enrollment policy in regular higher colleges and universities, and therefore, regular higher education has played the strongest role in promoting economic growth. However, there’s no substantial contribution according to the coefficient, substantially GDP is increased by more than 20% per increase of 1% labors with regular higher education.
Parameter | Random effect | Fixed space | Fixed time | Fixed space and time |
---|---|---|---|---|
C | 3.4584*** | 3.9933*** | 0.0141 | 4.6854*** |
(2.20E−16) | (2.20E−16) | (0.9175) | (2.20E−16) | |
λ | 1.9286** | 0.0106 | −0.0170 | 0.0053 |
(0.0055) | (0.4030) | (0.2658) | (0.7568) | |
lnK | 0.4538*** | 0.4583*** | 0.7539*** | 0.3875*** |
(0.0000) | (0.0000) | (0.0000) | (0.0000) | |
lnL1 | −0.1661*** | −0.0677* | −0.2164*** | −0.0729* |
(0.0000) | (0.0253) | (0.0000) | (0.0194) | |
lnL2 | −0.0262 | −0.0207 | −0.0967*** | −0.0208 |
(0.8298) | (0.6295) | (0.0092) | (0.3573) | |
lnL3 | 0.2618*** | 0.2377** | 0.2244*** | 0.2256 |
(0.0039) | (0.0623) | (0.0000) | (0.1734) | |
Hausman test chisq = 41.897, p-value = 6.18e−08 |
Note: “*”, “**”, “***” represent to pass the significance test at respective level of 10%, 5% and 1%.
Seen from 2006-2014 (
It can be seen from Hausman test results of estimation tables of the three time periods above that P value of the test is far less than 0.01, indicating that null hypothesis of random selection effect can be rejected, and three models are with better fixed effects. According to the foregoing presentation on principle of the spatial effects, the tables of direct and indirect effects of models with better fixed effects obtained through calculation are shown as follows.
It can be seen from spatial effect of various variables during 2006-2010 (
It can be known from spatial effect of variables during 2011-2014 (
It can be seen from spatial effect of various variables during 2006-2010 (
Parameter | Random effect | Fixed space | Fixed time | Fixed space and time |
---|---|---|---|---|
C | 3.4019*** | 4.3274*** | 0.2267 | 5.0219*** |
(2.20E−16) | (2.20E−16) | (0.2844) | (2.20E−16) | |
λ | 1.9286*** | 0.7920*** | 0.1241 | 0.2596* |
(0.0055) | (2.00E−16) | (0.32242) | (0.0258) | |
lnK | 0.5116*** | 0.4579*** | 0.6865 | 0.3833*** |
(2.20E−16) | (2.00E−16) | (2.20E−16) | (2.00E−16) | |
lnL1 | 0.0897*** | 0.0423* | 0.1953*** | 0.0510* |
(0.0002) | (0.0247) | (3.61E−07) | (0.01171) | |
lnL2 | −0.0021 | −0.0131 | −0.0824* | −0.0152 |
(0.9011) | (0.1808) | (0.0160) | (0.1695) | |
lnL3 | 0.0520** | −0.0009 | 0.3038*** | 0.0024 |
(0.0648) | (0.9685) | (2.20E−16) | (0.9162) | |
Hausman test chisq = 41.897, p-value = 6.18e−08 |
Note: “*”, “**”, “***” represent to pass the significance test at respective level of 10%, 5% and 1%.
Fixed space | Fixed time | Fixed time and space | |||||||
---|---|---|---|---|---|---|---|---|---|
Direct effect | Indirect effect | Total effect | Direct effect | Indirect effect | Total effect | Direct effect | Indirect effect | Total effect | |
K | 0.2873 | 0.1237 | 0.4111 | 0.7390 | 2.13E−04 | 0.7392 | 1.0264 | 0.1240 | 1.1503 |
L1 | 0.1231 | 0.0530 | 0.1761 | 0.2350 | 6.76E−05 | 0.2351 | 0.3581 | 0.0531 | 0.4112 |
L2 | 0.0168 | 0.0072 | 0.0240 | 0.1076 | 3.10E−05 | 0.1077 | 0.1244 | 0.0073 | 0.1317 |
L3 | 0.0160 | 0.0069 | 0.0228 | 0.2322 | 6.68E−05 | 0.2322 | 0.2481 | 0.0069 | 0.2551 |
Fixed space | Fixed time | Fixed space and time | |||||||
---|---|---|---|---|---|---|---|---|---|
Direct effect | Indirect effect | Total effect | Direct effect | Indirect effect | Total effect | Direct effect | Indirect effect | Total effect | |
K | 0.3141 | 0.1829 | 0.4970 | 0.6756 | 0.0030 | 0.6786 | 0.3929 | 0.0804 | 0.4733 |
L1 | −0.0143 | −0.0083 | −0.0226 | −0.2067 | −0.0009 | −0.2076 | −0.2210 | −0.0093 | −0.2302 |
L2 | −0.0751 | −0.0438 | −0.1189 | −0.0864 | −0.0004 | −0.0868 | −0.0193 | −0.0039 | −0.0232 |
L3 | 0.0935 | 0.0545 | 0.1479 | 0.3066 | 0.0014 | 0.3080 | 0.0045 | 0.0009 | 0.0054 |
Fixed space | Fixed time | Fixed space and time | |||||||
---|---|---|---|---|---|---|---|---|---|
Direct effect | Indirect effect | Total effect | Direct effect | Indirect effect | Total effect | Direct effect | Indirect effect | Total effect | |
K | 0.3551 | 0.1069 | 0.4620 | 0.7390 | 2.13E−04 | 0.7392 | 1.0942 | 0.1071 | 1.2013 |
L1 | 0.0570 | 0.0171 | 0.0741 | 0.2322 | 6.68E−05 | 0.2322 | 0.2892 | 0.0172 | 0.3064 |
L2 | −0.0284 | −0.0085 | −0.0369 | −0.1076 | −3.10E−05 | −0.1077 | −0.1360 | −0.0086 | −0.1446 |
L3 | 0.0404 | 0.0121 | 0.0525 | 0.2350 | 6.76E−05 | 0.2351 | 0.2754 | 0.0122 | 0.2876 |
below, and the labors with regular higher education are positive, while the employees with vocational and technical education is negative, various human capitals in promoting the economic growth in contiguity region is with time delay, which can’t be ignored.
It could be known through the econometric model analysis of the three time periods of 2006-2010, 2011-2014 and 2006-2014 that because of the high mobility of human resources, the labors of various education outputs of each time period were with the spatial spillover characteristics. Although the spatial direct effect of each time period is greater than the indirect effect, the indirect effect can’t be ignored, and the human capital between contiguity regions will affect economic growth of the region. The proportion of China’s labors with junior school education or below during 2006-2010 was relatively large, which was more obvious in driving the economic growth. The scale of labors with regular higher education at the time period is not too large, the driving effect on economic growth is not obvious enough; the effect of labors with vocational and technical education on economic growth of the time period is positive. However, with the continuous improvement of China’s education level in recent years, and the expansion of higher education scale, there’s a relatively large scale of reduction of labors with junior school education or below, and some part of labors with vocational and technical education is also transformed to the higher education. Therefore, the effect of labors with vocational and technical education on economic growth is transformed from positive to negative, which is actually unreasonable but also not conducive to China’s sustainable development for the long-term. Because first of all the labors with vocational and technical education have strong professional skills, and they play the role that is irreplaceable for labors with regular higher education, especially for manufacturing, construction and other industries focusing more on the operating ability, and practice ability of labors. Secondly, the labors require more time for achieving the higher education, and they also consume more social resources, and therefore the efficiency of educational output is not high. In addition, many labors with vocational and technical education do not require the senior school education, they not only learn the during the required courses of senior high school during the school education period, and also learn a highly professional vocational skill, so as to effectively participate into the corresponding social work after graduation. Finally, the current society has a very large demand for skilled personnel with the vocational and technical education, and with China’s great success in the 43th WorldSkills, China begins to attach great importance to vocational and technical education.
Therefore, the followed education for China’s students with junior school education or above should:
First, China should pay more attention to vocational and technical education, and focus on the labors with vocational and technical education, improving their social status, broadening their development prospects. In the process of developing the higher education, China should apply some certain resources to the development of the vocational and technical education, so as to cultivate more professional and technical personnel, skilled personnel, and good players of the WorldSkills, thereby improving China’s international status in terms of skills.
Second, China should not neglect the spatial indirect effect while paying attention to the spatial direct effect, and China should strengthen human capital flow, narrowing the gap between human capital quality and quantity between regions. Cities below first tier should pay more attention to the indirect effect of such space, introducing the appropriate talent introduction policies to attract and retain more personnel, so as to narrow the gap between cities in China to ease population pressure in some first-tier cities and population aging issues of some cities, thereby achieving the common development and sustainable development throughout the country.
Wuyuan Sun,Jiayun Ma, (2016) Spatial Effect Research on Educational Output and Economic Growth in China. Open Journal of Statistics,06,387-396. doi: 10.4236/ojs.2016.63034