Hainan’s development level of economics and education captures much attention, which is the youngest province, the largest special economic zone and construction base of international tourism island in China. With the implementation of our country’s policy of industrial structure adjustment, the industrial structure’s optimization and upgrading in economic development also becomes one of the focuses in scholarly research. The thesis combines relevant industrial structure areas in education and economic development. And from the view of financial investment, it firstly studies the education investment’s influences on the industrial structure rationalization in Hainan Province by employing VAR model and its application.
Hainan is the largest special economic zone and construction base of international tourism island in our country, which should make emphasis on its development of industrial structure rationalization. Education is the foundation stone of national revival, and also the motive power and source of economic development. The studies capture much attention in people from every part of society, which research on the education influences of industrial structure rationalization in economic development in Hainan. The thesis combines relevant areas of industrial structure in education and economic development, and makes researches from view of financial investment.
At present, the total education investment can be classified into financial education investment and non-financial education investment. And financial education investment is the most significant investment channel in education investment, which covers the most majority in the total education investment. And the relevant data is easy to get. Therefore, the thesis takes financial education investment as education investment in Hainan, and studies its education investment’s influences on the industrial structure rationalization.
The adjustment and optimization of industrial structure is the core of modern economic development. And the industrial structure rationalization is the significant element of the adjustment and optimization of industrial structure. At present, the main index to measure the industrial structure rationalization is deviational range of industrial structure in academic circles. The computing formula is:
P L = ∑ i = 1 3 | G D P i / G D P L i / L − 1 |
In the formula, PL is deviational range of industrial structure. GDPi and Li are respectively the output value and the employed of industrial i, and GDP and L are respectively the quantity of output value and the employed. G D P i / G D P is the industrial structure, L i / L is the employment structure, the degree of PL and its variation can reflect relevant industry’s industrial structure, the balance of employment structure and the absorption or exclusion of labour force. If it is greater than zero and becomes smaller and smaller gradually, we can be indicated that the relationship between industrial structure and employment structure is not isostatic. The industry has the capacity of absorbing labour and the later will inflow the industry. While with the influx of labour force between industries, the industry structure and employment structure will incline to be balanced. And if PL is smaller than zero, we can be indicated that the employment structure and industry structure are not balanced. The industry has surplus labor and the motivation of going out. Only when PL = 0, industry structure and employment structure couples well and they are totally balanced.
Such research is currently focused on these two ways, namely the evaluation of the industrial structure rationalization measures and its impact on economic growth. Firstly, on the aspect of the evaluation measure of rationalization of industrial structure, Hu Baojian (2010) evaluates the rationalization of the industrial structure of Yan’an based on Shift-Share Model. It is found that economic development is heavily dependent on the secondary industry. Industrial agglomeration should be actively promoted. And the industrial clusters and modern resource-based industry should be developed [
The study of education investment and economic growth in China is relatively more. This paper mainly focuses on the relationship between education investment and economic growth and the contribution of education investment to economic growth. First of all, in the relationship between education investment and economic growth, most scholars believe that education investment has a positive effect on economic growth. Zhang Jin and other scholars (2008) provides an empirical analysis of the relationship between education investment and economic growth in China. The analysis shows that education investment does have a strong impetus [
At present, there are relatively few studies on such issues in China. Through the empirical analysis of relevant data in education investment and industrial structure over the past three decades of reform and opening up in China, Ma Yue (2012) finds that compared with other factors, education investment can not only make the industrial structure tend to rational development, but also can promote the industrial structure advanced [
Through the above literature research, it is found that study of the education investment in Hainan Province and the rationalization of the industrial structure is almost blank. This area is not conducive to the liberal arts in Hainan Province and the development of industrial structure rationalization. On the basis of the above research, this paper will study the contribution degree of education investment to the rationalization of industrial structure in Hainan Province. And relevant policy suggestions for the optimization and upgrading of industrial structure in Hainan Province will be provided.
From
The above data is sorted out from Hainan Statistical Yearbook, China Education Finance Statical Yearbook and the survey data from the Education Department in Hainan Province.
with the speed of 12% every year. And it is inseparable with the rapid development of economy and the government’s high attention to education in Hainan.
The education investment’s proportion in GDP can reflect the government’s emphasis on education.
The proportion of budgetary expenditure on education in financial expenditure can be used to reflect the budget for the development of education in govern-
Data Source: Sorted out from Hainan Statistical Yearbook, China Education Finance Statical Yearbook and the survey data from the Education Department in Hainan Province.
ment financial expenditure. Thus it will reflect the government’s efforts to invest in education.
It can be seen from
With the economic development of Hainan and the construction of international tourism island, Hainan’s industrial structure is also constantly adjusted and optimized. It can be seen from
Since the agriculture develop slowly limited by the natural resources and its own characteristics, and with the influences of industrial structure adjustment and other policies, the output value of primary industry accounted for the
Data source: Hainan Statistical Yearbook 2014.
proportion of GDP shows a trend of rising firstly and then declining. The development of the secondary industry and the tertiary industry begins to be taken seriously. The output value of the secondary industry is increasing continuously, but its proportion of GDP does not change a lot, basically maintaining at about 25%. While its adjustment trend is still relatively significant. The tertiary industry’s role in the economic development of Hainan is always relatively prominent. And the proportion of tertiary industry output value in Hainan’s GDP is relatively high, and the proportion of output value in GDP slowly declines from 45.1% in 1993 to 42.8% in 2009. It starts to rise in 2009 and reaches to 51.9% in 2014, with the speed of increasing 1.5 percentage points every year in those six years. This trend is mainly affected by the construction and development of Hainan international tourism island, the second industry development trend and industrial structure’s optimization and upgrading in Hainan.
It can be seen from
First of all, the structural deviation of the primary industry has always been negative, which indicates that in the primary industry has been a large number of surplus labor, and the industrial structure and employment structure are incompatible. There are two mainly reasons: On one hand, compared with the second and tertiary industries, the labor productivity of primary industry increases slowly. Although its output value is constantly growing over the years,
Data source: Sorted out and calculated from Hainan Statistical Yearbook 2014.
the proportion of output value is constantly decreasing. On the other hand, although the rural surplus labor force constantly flows to the secondary and tertiary industries, in today’s era of knowledge, technology and economy, the rural population’s education level is relatively low, thus there are some difficulties when they transfer to the secondary and tertiary industries.
Secondly, in the past twenty-two years, the structural deviation of the secondary industry has been positive and relatively large, indicating that the employment of the secondary industry is obviously insufficient, and the secondary industry has the ability to absorb the surplus labor force. However, the structural deviation of the secondary industry changes greatly from 1.20 in 1993 to 0.78 in 1997, then rises to 1.75 in 2006 and finally declines from 1.75 in 2006 to 0.98 in 2014. It indicates that Hainan’s industry structural adjustment has a greater impact on the structure of the secondary industry, making the structural deviation index of the secondary industry constantly change. In recent years, the structural deviation of the secondary industry has shown a downward trend.
Finally, the deviation degree of the tertiary industry has been positive and shows a trend of decreasing year by year, dropping from 0.75 in 1993 to 0.11 in 2013 and decreasing about 80%. It indicates during the past two decades, the employment in tertiary industry is not enough. A large number of labors flow into the tertiary industry and it has the ability to absorb the labor force. And it is inseparable with the development of the tertiary industry in Hainan Province, the adjustment of industrial structure policy and the construction of international tourism island.
In order to study the measurement relationship between investment in education and rationalization of industrial structure in Hainan Province, this paper introduces the control variable investment in fixed assets, constructing the vector autoregressive (VAR) model between investment in education, investment in fixed assets and rationalization of industrial structure in Hainan Province, to study their relationship of dynamic changes. The model is constructed as follows:
y t = ∑ i = 1 p Π i y t − i + c + ε t ( t = 1 , 2 , ⋯ , T )
In the above model, yt is the column vector of industrial structure rationalization, education input and fixed asset investment, ∏i is the coefficient matrix, c is the intercept term, εt is the matrix of random error, t is time, i is the lag period, and p is the optimal lag period.
This paper chooses the reduction of industrial structure deviation index in Hainan Province to measure the rationalization of its industrial structure, which is calculated from Hainan Statistical Yearbook. It takes financial education investment in Hainan Province as the education investment. And its data comes from China Education Funds Statistical Yearbook and the survey results of The Education Department of Hainan Province. And it takes the society’s total investment in fixed assets over the years as investment in fixed assets in Hainan Province. The data source is Hainan Statistical Yearbook 2015. Based on the availability of data, the time series data of each variable from 1993 to 2014 are selected as samples. In order to eliminate the impact of inflation, the original data is processed by choosing the price index. As the statistics of fixed asset investment price index in Hainan Province began in 2000, and taking into account the availability of data, so the national fixed asset investment price index in the base period of 1993 is converted. The education investment in 1993 is taken as the base period of the consumer price index (CPI) to reduce. In order to reduce the volatility of data and heteroscedasticity, after rising, the natural logarithm of the data will be calculated. And the processed variables are recorded as LnE and LnK. Since the relative number of industrial structure deviation index has no heteroscedasticity and nonparability phenomena, it is directly recorded as a variable ΔPL.
In order to avoid the “pseudo-regression” or “false regression” phenomena in econometric model analysis caused by the analysis of the unstable time series data, the method of ADF unit root is used to test the stability of each variable.
From
One of the most important problems in VAR model analysis is the choice of model lag order. Therefore, in order to establish a relatively accurate VAR model between the variables in Hainan Province, the lag order p of the VAR model should be determined firstly. In choosing the lag order p, it is necessary to make the lagged period p large enough to show the dynamic characteristics of the constructed model more fully. However, the larger the lag order p means that
Note: The test type (t, c, p) represents the trend t, the constant term c and the hysteresis order p in the ADF test. The choice of the lag order p is determined by the SIC optimal criterion. d represents the first order difference for the variable, and * represents the significant level.
the more parameters to be estimated. The degree of freedom is relatively less. On the other hand, if the selected lag p is too small, the dynamic characteristics of the model can not be well reflected. And the degree of autocorrelation of the error term will be very serious, leading to the non-uniformity of the estimated parameters, and then directly influencing the validity of parameters.
It can be seen from
Through the determination of the stability test and the model lag order P, it can be seen that LnE, ΔPL and LnK time series are the first order single order sequences, namely the first order difference data is stationary. So the relationship of the investment in education, fixed assets investment and industrial structure rationalization in Hainan Province satisfies the requirements for establishing a VAR (3). Eviws9.0 was used to import the variables data of Hainan Province and model estimates. The results are as follows:
D L N E = 1.2391 * D L N E ( − 1 ) − 0.0766 * D L N E ( − 2 ) + 0.1281 * D L N E ( − 3 ) − 1.2518 * D L N K ( − 1 ) + 0.5428 * D L N K ( − 2 ) − 0.2362 * D L N K ( − 3 ) − 0.2617 * Δ P L ( − 1 ) − 0.5619 * Δ P L ( − 2 ) + 0.3228 * Δ P L ( − 3 ) + 0.0933
Δ P L = 1.9571 * D L N E ( − 1 ) − 0.7305 * D L N E ( − 2 ) − 0.9456 * D L N E ( − 3 ) − 3.1154 * D L N K ( − 1 ) + 1.9496 * D L N K ( − 2 ) − 0.3134 * D L N K ( − 3 ) − 0.7855 * Δ P L ( − 1 ) − 0.8695 * Δ P L ( − 2 ) + 1.1149 * Δ P L ( − 3 ) + 0.1581
D L N K = 0.5290 * D L N E ( − 1 ) − 0.0606 * D L N E ( − 2 ) + 0.5444 * D L N E ( − 3 ) + 0.1594 * D L N K ( − 1 ) − 0.2977 * D L N K ( − 2 ) + 0.0625 * D L N K ( − 3 ) − 0.2976 * Δ P L ( − 1 ) + 0.0728 * Δ P L ( − 2 ) − 0.0276 * Δ P L ( − 3 ) + 0.0073
The goodness of the three equations is respectively 0.62%, 0.87% and 0.75%. And most of the t values are about 2. It indicates that the model estimation structure is relatively accurate. After the VAR (3) model is established, the validity of the model is tested to determine whether the model is in a stable state. And only when the model is at a steady state level, it is a valid model. According to
Note: * represents the optimal lag order chosen by each criterion.
The change or impact of an endogenous variable in the vector autoregressive model will not only affect itself, but also influence the change of other endogenous variables through the dynamic structure of the model. And the impulse response function is used to describe the changes trajectory of these effects. In the following section, and by means of impulse response function, Eviews 9 will be applied to study and analyze the influences of a positive impact on the rationalization of industrial structure in Hainan Province, and the impact is produced by investment in education and fixed assets investment in Hainan. The impulse response curve is shown in
It can be clearly seen from the impulse response curve
First of all, from
rationalization of industrial structure changes on their own impact is not sustainable.
Secondly, from
Thirdly, from
In the above section, the paper analyzes the influence of education investment, investments fixed assets on the rationalization of industrial structure in Hainan Province. In order to analyze the degree of influence on the rationalization of industrial structure, in the following section, by means of variance decomposition and from the aspects of its process of industrial structure deviation reduction in Hainan, the contribution of different perturbations to the rationalization of the industrial structure is analyzed.
From
In order to analyze education investment’s influences on the Industrial Structure Rationalization in Hainan Province, this thesis firstly summarizes the relevant concepts and literatures, and then introduces the current situation of education investment, industrial structure and its rationalization in Hainan Province. Secondly, the author employs the VAR model and its application to demonstrate the influences of education investment on the rationalization of industrial structure in Hainan. Through the above research, the basic conclusions can be
drawn.
First of all, the structures of three industries are upgrading. With the economic development, Hainan’s industrial structure is constantly changing. This is mainly manifested as: The proportion of the output value of the primary industry begins to decline, which has declined to 23.4%. The proportion of the secondary industry output value has begun to rise, which basically maintained at 25% or more nearly ten years. The proportion of tertiary industry also begins to improve, and up to 2014, the proportion has reached 51.9%. The proportion of the three industries has changed from the original tertiary, primary, and secondary industry to two the present tertiary, secondary and primary industry. And the industrial structure is constantly upgrading. While compared with the developed countries, the development of the three industries in Hainan is still unbalanced. There is still a lot of room for improvement in the industrial structure.
Secondly, the trend of industrial structure rationalization is more obvious. With the construction of Hainan international tourism island and economic development, the upgrading of the industrial structure has been improved, and the rationalization of industrial structure has also been improved. The degree of deviation between Hainan’s industrial structure and employment structure is constantly decreasing. And the total deviation of the industrial structure from 2.48 in 1993 dropped to 1.59 in 2014, which decreases nearly 36% in recent 22 years. It indicates that Hainan’s industrial structure is constantly upgrading.
Finally, the influences of education investment on the optimization and upgrading of industrial structure is more obvious than that of fixed assets. The human capital formed in education investment can not only continuously increase the proportion of secondary industry and tertiary industry, promoting the industrial structure to the direction of upgrading, and also it will help the flow of labor force among three industries. Therefore, the gap between industrial structure and employment structure will become smaller. And the industrial structure will constantly develop to the direction of rationalization.
In conclusion, this paper analyzes the relationship between investment in education and the rationalization of industrial structure in Hainan Province. And then from theoretical and empirical perspectives, it employs the data samples and empirical analysis tools in recent years to demonstrate that education investment in Hainan Province is conducive to promoting the upgrading of industrial structure. And then appropriate policy recommendations to speed up the optimization and upgrading of industrial structure in Hainan Province will be provided.
This paper does not study the education investment of cities and counties in Hainan Province. And it lacks a comparison of education investment between cities and counties in Hainan Province, which can also be a direction for future research.
Yu, D., Gao, S.S. and Shen, L. (2017) The Empirical Study of the Education Investment’s Influences on the Industrial Structure Rationalization in Hainan Province. Journal of Service Science and Management, 10, 447-463. https://doi.org/10.4236/jssm.2017.105036