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The Empirical Analysis on the Change of Industrial Structure and Economic Growth in China Zhi Dalin Han Jianyu School of economics, Northeast Normal University, P.R.China, 130117 Abstract: Employing dynamic econometrical analysis methods, cointegration test and Granger Causality Test, this paper makes an empirical analysis on the change of industrial structure and economic growth in China from 1952 to 2004. The result shows that there exists Granger causality relation between fluctuation of industrial structure and economic growth in China, on one hand, the perfection of industrial structure will promote economic growth, and on the other hand, economic growth will also accelerate the perfection of industrial structure. In the course of research, it is obtained that although the economic growth in China is extremely fast, the industrial structure does not hold the same space as that of economic development. It is clearly shown that the labor transferring from the Primary industry to the Secondary industry and the Tertiary industry has lagged behind the speed of economic growth, which has confined the realization of modernization in China. Key Words: Industrial Structure, Economic Growth, Cointegration, Granger Causality. 1 Introduction The traditional Neo-classical Growth Theory , from the suppose of absolute competition equilibrium, through marginal analysis, summarize economic growth is due to the three factors: capital accumulation, labor increase and technology, which structural effect is not considered. However, competitive equilibrium has never been realized in the reality. From 1950’s, S.Kuznets, W.W.Rostow, H.B.Chenery and Romer have converted to the structural problem. S.Kuznets[1] and W.W.Rostow[2] holds different opinions on modern economic growth because of the different angles. S.Kuznets focuses on the trend of industrial structure and considers that the increase of total value is more important than structure change, while W.W.Rostow researches the mechanism of industrial structure, considering the increase of total value from the structural angle. The latter economist, such as H.B.Chenery[3], researches economic growth from the structure angle, and states the redistribution of resources among industries can promote the economic growth. H.B.Chenery regards the course of non-equilibrium economic growth as a series changes of national economic structure, and effectively explained the difference of growth rate among different developing countries by adding this slow variable - structure. Romer[4] (2000) thinks, under given capital, labor and technology, different industrial structure will lead to different production rate, so as to bring different contribution on economic growth. Domestic research on the relationship between industrial structure and economic growth started in the middle of 1980’s. Yang Zhi[5] (1985) made macro study on the relation, and pointed out that the revived and discarded industries should be considered with the change of industrial structure. Under market economy, Liu Wei[6] (1996) thinks economic growth, in part is owing to industrialization, so called structure devolution. Guo Kesha[7] (1999) mainly researches in the relationship between optimization of industrial structure and economic growth, pointing out China’s industrial structure has two restraint functions on economic growth, so called structural deviating restraint and structural escalating- slow restraint. Mao Jian[8] (2003), by exploring the industrial structure of some typical countries, summarizes the principle of industrial structural change in the course of economic growth, points out the inconsistence between China’s industrial structure and economic growth and put forward the optimized idea on industrial structure. Furthermore, Hu Xiaopeng[9] (2003) believes there is a cumulative and double-recycling function mechanism between industrial structure and economic growth, and emphasize that change of industrial structure is not only the way of exogenous interference but the media of endogenous function as well, and the modulation of industrial structure is an effective point to realize the qualitative and quantitative enhancement of economic growth. In recent years, domestic researchers also made a lot of empirical study on the relationship between “ ” 723 industrial structure and economic growth. Lu Tie[10] (1999) analyzed the contribution that the change of three industrial structure has functioned on economic growth by resource reallocation effect model, and came to the conclusion the resource reallocation effect of three industrial structure change contributed little on economic growth, only about 3.04%. Liu Wei[11] (2002) made a study on the relationship between industrial structure and economic growth covering all the regions in China from 1992 to 2000 by using the function between different structure and product, concluded that 1% increase of three industries respectively would lead to the increase of GDP by 0.14%, 0.33% and 0.54%, which proves the enhancement of the rate of the Tertiary industry in GDP can lead to faster increase of China’s economy. At the same period, Jiang Zhensheng[12] (2002) made an empirical analysis on the relationship between economic growth and industrial structure change, which shows some exiting economic mechanism makes them keep a long stable interaction relationship. He also proves that the change of China’s industrial structure has an obvious effect on actual economic growth while the total amount of economic growth has less influence on the change of industrial structure by the method of forecasting variance decomposition, which is similar to the result of Granger causality between modulation of industrial structure and economic growth given by Zhu Huiming and Han Yuqi[13] (2003). By the statistic and dynamic analysis, Hu Xiaopeng (2003) quantitatively explored the interrelationship between economic growth rate and the change of industrial structure, and ordered the industry which should be developed first: the development of the Secondary industry is superior to that of the Tertiary industry and much superior to that of the Primary industry. Based on the yearly statistics from 1978 to 2003, Chen Hua[14] (2005) came to the different result from that given by Han Yuqi that the causality between economic growth and industrial structure is not clear, which is caused by the outside power to economic system, such as technology innovation, regulation change and etc. It can be concluded that there still exits debate on the relationship between industrial structure and economic growth, while China’s current situation of industrial structure is still hard to meet the requirement of transferring the economic growth mode, and industrial structure problem (not only industrial structure among three industries, but respective inner structure as well) has been the obstacle of economic sustainable growth in China (Guo Kesha, 1999). Therefore, this paper aims at making the relationship clearer, so as to provide sound empirical basis and possible choice for the improvement of China’s industrial structure and overall economic growth. 2 Methodology Based on the traditional regression analysis methods of analysis to estimate and examine, the relevant variables must be stationary, otherwise false regression occurs. However, in practice most of the economic and financial data are nonstationary, so this paper first carries on the unit root test to the selected variables to determine whether respective time series are stationary. If the original series are nonstationary and same order integration series, cointegrational relation test will be made further, if cointegration relation exists, Granger causality among these variables can be tested. 2.1 Unit root test Unit root test is the standard method to judge the stationarity of time series, altogether including 6 kinds of methods: Dickey-Fuller(DF) Test, Augmented Dickey-Fuller(ADF) Test, Phillips-Perron(PP) Test, KPSS Test, ERS Test and NP Test. ADF employed in this paper is often for two variables. The model for the Augmented Dickey-Fuller test is: ⑴ p ∇yt = γ yt −1 +α +δ t + ∑βi∇yt −i + ut i=1 Where yt is the time series under test , α is constant term, t is time trend, p is the number of lags, and ut is a random error term. The null hypothesis is given by γ = 0 , which means yt has unit root or nonstationary. If ADF statistic result is smaller than Mackinnon critical value and the original hypothesis can be refused, so the series is stationary, otherwise nonstationary . The optimum number of lags is decided by Akaike Information criterion (AIC) and SC. 724 2.2 Cointegration test If there is a stationary linear combination between 2 nonstationary same order integration series, then the combination has no random trend and these 2 series are cointegrated. This linear combination is a cointegration equation that shows a long-term balanced relation between the 2 series. For checking the cointegration relation between variables xt and yt , Engle and Granger put forward 2-step checking way in 1987, namely EG test. This method is for cointegration relation test between the time series of 2 variables. If series of xt and yt are same order integrations, we make regression analysis for one variable to another, and we have: ⑵ yt = α + β xt + ε t ∧ we use ∧ α and β as estimation value of regression coefficient, and model’s residual error ∧ ∧ ∧ estimation value is: ε = yt − α − β xt ∧ If ε ~ I (0) , then xt and yt have cointegration relation between them, otherwise, have not. 2.3 Granger causality test Cointegration relation between variables is the prerequisite of Ganger causality test, but it needs testing that whether a long-term balanced relation between variables can state Granger causality. On condition that two variables xt and yt with past information are contained, if the forecast effect is better than that only the past information of yt is used, that is to say, variable xt is helpful to explain the future change of yt , then we will recognize xt is the Granger cause of yt . Otherwise, xt is not the Granger cause of yt . The model is: p ⑶ q yt = c + ∑ αi yt −i + ∑ β j xt − j + ε t1 i =1 j =1 The null hypothesis is given by β1 = β 2 = ... = β q = 0 , mean that xt is not the Granger cause of yt . If the null hypothesis correct, then: p yt = c + ∑ α i yt −i + ε t 0 i =1 Suppose residual sum of square of Equation ⑶ is ⑷ SSE1 , residual sum of square of Equation ⑷ is SSE0 , then F = ( SSE1 − SSE0 ) q obeys F distribution and degree of freedom is (q, T − p − q − 1) . SSE0 (T − p − q − 1) T is the sample capacity, p and q is the number of lags of y and x , which is decided by AIC. If F statistic value is larger than critical value, original hypothesis can be refused, so xt is the Granger cause of yt , otherwise, original hypothesis is accepted, which means xt is not the Granger cause of yt . 3 Empirical analysis 3.1 Selected variables and the data Variables representing industrial structure change are usually the Primary industry, Secondary industry, Tertiary industry’s output structure, the labor employment structure, the property structure and the technical structure and so on. This paper tends to exert the following indexes to mark industrial structure change : (1) industrial structure modulation coefficient S1 given by Clack, namely, the 725 proportion that the number of Primary industry jobholders accounts for the social employment total number. The smaller the coefficient is, the higher the industrial structure level is. (2) Structure modulation coefficient S2 frequently used by domestic scholars, namely, the proportion that the Primary industry output occupies GDP. The economic growth is indicated by GDP, in order to eliminate the price fluctuation influence, nominal GDP is transformed into actual GDP which is calculated on unchanging price of 1952, through commodity retail price index (1952=100). This paper is based on annual statistics from 1952 to 2004, and all the data are obtained form New China 50 Years statistics compile(1949-1999) and China Statistical Yearbook 2005. 3.2 Analysis process 3.2.1 Unit root test on variables. The change into natural logarithmic of the data does not alter its former cointegration relation, makes it have linear tendency and eliminate the existing heteroskedasticity in the time series. Therefore, time series GDP, S1 and S2 are changed into natural logarithmic respective, which are called LGDP, LS1 and LS2. Employing econometric software, unit root test is carried out on LGDP, LS1 and LS2. The test result is shown in Table 1: 12 0.6 10 0.4 8 6 0.2 4 0.0 2 0 -0.2 -2 -4 -0.4 55 60 65 70 LGDP 75 80 85 LS1 90 95 00 55 LS2 Figure 1 Polygram of LGDP, LS1 and LS2 60 65 70 DLGDP 75 80 85 90 DLS1 00 Figure 2 Polygram of DLGDP, DLS1 and DLS2 Table 1 Unit root test result 1% critical 5% critical 10% critical value value value LGDP -1.451155 -4.1584 -3.5045 -3.1816 LS1 -2.270897 -4.1458 -3.4987 -3.1782 LS2 -2.939683 -4.1459 -3.4987 -3.1782 dLGDP -5.826231 -3.5682 -2.9215 -2.5983 dLS1 -4.797440 -2.6100 -1.9474 -1.6193 dLS2 -5.245383 -2.6090 -1.9473 -1.6192 Note: d reprents first difference of variables; critical value is Mackinnon value. Variable 95 DLS2 ADF value Result Nonstationary Nonstationary Nonstationary Stationary Stationary Stationary Table 1 shows LGDP, LS1 and LS2 are non-stationary, but there first difference are stationary ones, namely, first order integration, which meet the cointegration premise between two variables, so cointegration relation possibly exist among LGDP, LS1 and LS2. 3.2.2 Cointegration test The premise of Granger causality test is that the linear combination of non-stationary series must hold cointegration, so further analysis on the cointegration among LGDP, LS1 and LS2 is necessary. OLS regression is made on LS1 and LS2 respectively by LGDP, estimated residual error series of the model is retained. Furthermore, unit root test is done on residual error series, if the series is stationary, it shows there is cointegration relation, versus not. First, set up the cointegration equation between LGDP and LS1, the unit root test result of residual error series is: 726 Table 2 Cointegration equation and the unit root test result of residual error series LGDP=5.997970-5.449871 LS1 (41.688504) (-16.844918) R-squared 0.847648 Adjusted R-squared 0.844661 F-statistic 283.751267 ADF Test Statistic -2.191998 1% Critical Value -3.562473 5% Critical Value -2.919000 10% Critical Value -2.597019 The test result shows the statistical value of unit root test on residual error, exceeds critical value under 10% significance level, which can not refuse original hypothesis and residual error series is not stationary, so there is no cointegration relation between GDP and S1. Set up the cointegration equation between LGDP and LSD2, the unit root test result of residual error series is: Table 3 Cointegration equation and the unit root test result of residual error series LGDP=4.234404 -3.186992 LS2 (19.386967) (-18.744488) R-squared 0.873247 Adjusted R-squared 0.870762 F-statistic 351.355840 ADF Test Statistic -2.727316 1% Critical Value -2.608092 5% Critical Value -1.947104 10% Critical Value -1.619150 The test result shows the statistical value of unit root test on residual error, smaller than critical value under 1% significance level, which can refuse original hypothesis and residual error series is stationary, so there is cointegration relation between GDP and S2. The cointegration equation shows if S2 changes by 1%, GDP will change by 3.186992% reversely, which can conclude that the change of industrial structure output coefficient greatly affects economic growth. 3.2.3 Granger causality test According to the above analysis, no cointegration relation exists between LGDP and LS1, so there is no Granger causality relation between them. However, cointegration relation exists between LGDP and LS2, so there may be Granger causality relation. The following Table 2 shows the test result through Granger’s theory. Table 4 Granger causality test result of LGDP and LS2 Null Hypothesis F-Statistic Probability Result LS2 can not Granger cause LGDP 16.219746 0.000196 Refuse original hypothesis, LS2 is the Granger cause of LGDP LGDP can not Granger cause LS2 9.5086966 0.003355 Refuse original hypothesis, LGDP is the Granger cause of LS2 From Table 4, it can be concluded that there is inter-Granger causality relation between LS2 and LGDP under 1% significance level, which is different from that given by some scholars who considers industrial structure modulation promotes economic growth, while economic growth does not improve China’s industrial structure modulation, but according to the data, the probability to refuse original hypothesis and make type 1 error is only 0.003355, so there is sound reason to prove LGDP is the cause of LS2. 4 Conclusion and further explanation This paper employs dynamic econometric analysis method (cointegration theory and Granger causality), making an empirical analysis on the relationship between the change of industrial structure and economic growth in China. According to the result, the following conclusions can be obtained: 4.1 Double Granger causality exits between the change of industrial structure and actual economic 727 growth in China First, from the cointegration equation although both China’s economic growth and the change of industrial structure hold instability, they are statistically related in the long run.S2 changes by 1%, and GDP will change by 3.186992% adversely. It shows the ratio structure of the Primary industry changes adversely to actual economic growth and the marginal production of China’s Primary industry is lower than the other industries, so it is important for China’s economic growth to perfect the allocation of labor resource from the Primary industry. Perfection of industrial structure and improvement of structure change have huge potential on the contribution of China’s economic growth. Secondly based on Granger’s theory, the research on the relation between LGDP and LS2 shows inter-Granger’s causality exits between the change of China’s industrial structure and actual economic growth. On one hand, perfection of industrial structure promotes economic growth, on the other hand, economic growth accelerates the escalating of industrial structure, which is consistent with Kuznets and Chenery’s idea that economic structure (mainly refer to industrial structure) changes with economic growth and development which also influences economic growth. All these above also prove the correctness of the paper. Theoretically analyzing, cointegration change also exits between the change of industrial structure and economic growth. Due to the great differences of labor production rate in different industry, the change and modulation of industrial structure is the course of the re-division and re-union of industrial labor production rate, and the shrinkage and enlargement of respective industry is just the course of strengthening specification and division. All these will definitely enhance productivity, so as to strongly pull economic growth. On the other hand, the enlargement of economic amount comes from three industries and also distributes among three industries. Because of the imbalance and instability of the distribution among three industries, the increasing rate is different among three industries, so as to lead to the change of rate of three industries total production amount, which is actually the function mechanism of economic growth on industrial structure. The above conclusions state that the industry policy to modulate industrial structure and control economic growth is positively effective in China, and the modulation of industrial structure should be an effective point for China to realize the qualitative and quantitative improvement of economic growth. The modulation of China’s economic structure and transferring of growing mode set high requirement on improvement of industrial structure. Therefore, it is important to hold latest industry development tendency and promote the perfection of industrial structure, so as to pull economic sustainable growth. 4.2 The employment structure of three industries labor is unreasonable. Labors from the Primary industry is far more than those from the other two and transfer slowly It is easily seen, although the long-term equilibrium and Granger relationship are obtained through the study on the relation between LGDP and LS2, S1 which stands for the change of industrial structure did not pass cointegration test with GDP. Why Granger causality can be obtained from S2, but S1 not? S1 refers to the rate that employees from the Primary industry account for in the social total employees. According to Petty- Clark Theorem, the rate of employment from the Primary industry should be getting lower with economic development. That is to say, with the decreasing of S1, labors will transfer from the Primary industry to the Secondary and Tertiary industry, for benefit from manufacturing is more than that from agriculture, while benefit from commerce is more than that from manufacturing (William Petty). The income difference among different industry leads to the transferring of labors from the lower income industry to higher income industry. The above study shows S1 has no cointegration relation with GDP, so the change of three industries employment structure did not keep the same pace with the change of economic growth, or more specifically speaking, the transferring from the Primary industry to the Secondary and Tertiary industry lagged behind economic development. Current household register system, immature labor market and low labor quality of the Primary industry hinders China’s labor transferring. China ever carried out population policy encouraging birth caused the over accumulation of farmers, which is also an important factor to slow down the labor transferring. This paper emphasizes, the labor transferring from the Primary industry to the other industries is a common principle, but due to the special situation in China, there is still a coordinating course to make labors transfer smoothly. , 728 Because of the slow transferring of the Primary industry, more than 100 million surplus labors backlogged in the country[16], and surplus labor transferring to non-agriculture has revolved into the limit on the realization of China’s modernization. Accordingly, household register system should be reformed to break the obstacle of agriculture labor transfer. Furthermore, the process of urbanization should be accelerated, big cities are under development and reconstructuring, central cities should be focused, and medium and small cities should be developed properly, so as to increase more employment position in the city for surplus labors from country. Moreover, process from country to town should be accelerated, and the division of the labors from the Primary industry to non-agricultural industry in the country should be improved by absorbing agriculture labors in the Primary industry itself and developing the Secondary and Tertiary industry. References [1] S.Kuznets, Chang Xun translated. Economic Growth of Nations: Total Output and Production Structure. Beijing: Commercial Press, 1999:347~401(in Chinese) [2] W•W•Rostow, Guo Xibao and Wang Songmao translated. The Stages of Economic Growth: A Non-communist Manifesto, China Social Science Publishing House, 2001:4~16(in Chinese) [3] H.Chenery, S.Robinson, M.Syrquin. Wu Qi, Wang Songbao translated. Industrialization and Growth: A Comparative Study. Shanghai: Sanlian Press, 1985:23~55(in Chinese) [4] Romer D. Advanced Macroeconomics. Boston: Mc Graw-Hill, 2000:55~87 [5] Yang Zhi. Introduction of Industry Economics. People’s University Publishing House of China, 1985: 56~68(in Chinese) [6] Liu Wei. Study on Industrial Structure in the Industrialization Process. People’s University Publishing House of China, 1995:25~48(in Chinese) [7] Guo Kesha. Quantity or Structure?---- Limitation of Industrial Structure Deviation on China’s Economic Growth and Relative Modulation Methods. Economic Research Journal, 1999, 9 :15~21(in Chinese) [8] Mao Jian. The Improvement of Industrial Structure in Economic Growth. Industrial Economics Research, 2003, (2): 26~36(in Chinese) [9] Hu Xiaopeng. The Study on the Interaction Linkage of China’s Economic Growth and Industry Structure. Industrial Economics Research, 2003, (6): 33~40(in Chinese) [10] Lu Tie, Zhou Shulian. Industrial Structure Grading and Economic Growth Mode Transfer in China. Management World, 1999 1 :113~125(in Chinese) [11] Liu Wei, Li Shaorong. Industrial Structure and Economic Growth. China Industrial Economy, 2002, (5): 14~21(in Chinese) [12] Jiang Zhensheng, Zhou Yingzhang. The Effect of the Adjustment of Industry Structure on the Real Economic Growth in China. Collected Essays on Finance and Economics, 2002, (5): 1~6(in Chinese) [13] Zhu Huiming, Han Yuqi. An Empirical Analysis of the Relationship between the Industrial Structure and Economic Growth. Operations Research and Management Science, 2003, (2): 70~74(in Chinese) [14] Chen Hua. Industrial Structure Change and Economic Growth in China. Statistics and Decision, 2005 3 :70~71(in Chinese) [15] Yi Danhui. Eviews and data analysis. Beijing: China Statistic Publisher, 2002:131~155(in Chinese) [16] Zheng Wanjun, Wang Chenglan. Limitations and Countermeasure of Transferring Surplus Labors from Country in China. Economic Research Guide, 2006,(1):54~56(in Chinese) () ,( ) ,( ) 729