Download The Empirical Analysis on the Change of Industrial Structure and

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
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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.
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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
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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:
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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
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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.
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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.
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