Download A Empirical Analysis on the Relationship Between

A Empirical Analysis on the Relationship Between
Service Industry and Economic Growth
WANG Shuling1, 2, LI Donghui1
1. Hebei University of Economics & Business, Shijiazhuang, P.R.China, 050061
2. School of Management, China University of Mining and Technology, Xuzhou, P.R.China, 221116
[email protected]
Abstract: In order to discuss the relationship between service industry and economic growth in China,
the paper makes an empirical analysis on the relationship between service industry and economic
growth by the means of unit root rest, co-integration test and Granger causality test based on the data
from 1990 to 2008 in China. The results have shown Granger causality and long-term stable equilibrium
relationship between service industry and economic growth. The development of service industry plays
an important role in economic growth. The practicable measures should be taken to accelerate the
development of service industry.
Keywords: service industry, economic growth, co-integration test
1 Introduction
The development level of service industry is an important indicator that measures the overall economic
competitiveness of a nation and region. Modern social services have become a new impetus for regional
economic development. Chinese economy has kept rapid growth since 1990s and Chinese GDP has
broken through 30 trillion RMB by 2008. In the meantime, the service industry has also experienced
rapid development and production in 2008 increased to more than 120,000 RMB, the contribution rate
of China's economic rise year by year, by 2008 over 40%.Therefore, it is very necessary to make
empirical research on the intrinsic relations between the development level of service industry and
economic growth in China. In the related literature from home and abroad, economists have made
researches on the time-series data of certain countries or regions with the approach of ordinary least
squares (OLS), with which however there may be "spurious regression" problem [1]. The paper attempts
to make an empirical analysis on the relationship between service industry and economic growth
according to the data from 1990 to 2008 in China with econometric methodology such as unit root rest,
co-integration test, and Granger causality test etc.
2 Data and Variables
The samples used in the analysis of this paper come from the economic data of China during 1990-2008
calendar years, which are sourced from "China Statistical Yearbook" 2009. In this paper, the services
industry (SVP) is used to reflect the development of service industry conditions and macro-economic
aggregates of gross domestic product (GDP) is used to reflect the situation of economic growth. In
addition, to eliminate influence of price changes on GDP and value of services, the GDP index in the
previous year (which is determined to be 100) is chosen as the raw data from the 1990-2008 calendar
years to calculate the corresponding GDP index (1990 = 100), then the results of GDP index (1990 =
100) are used to multiply the value of GDP in 1990, thus it is possible to know the real gross domestic
product and the actual service output by using the year 1990 as the base period, to be expressed by
RSVP and RGDP respectively.
Since natural logarithm transformation of data does not change the original cointegration relationship,
but can linearize its trend and eliminate time-series heteroscedasticity, natural logarithm transformation
is conducted with RGDP and RSVP, which results are expressed by LRGDP and LRSVP respectively
indicating that real gross domestic product and the actual service output based on the 1990 as the base
period. Specific data are shown in Figure 1.
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LRGDP and LRSVP/Billion
12.5
12.0
11.5
11.0
10.5
10.0
9.5
9.0
8.5
8.0
LRGDP
LRSVP
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
Years
Figure 1 Time-Series Trends for Chinese GDP and Service Industry Output
3 Empirical Analysis
3.1 The Unit Root Test of Time Series
Figure 1 shows very strong upward trend of the two variables, which are non-stationary time series.
Because of non-stationarity of economic variables, there might be "spurious regression" problem for the
equations that is estimated with the ordinary regression method. Therefore, this article first applies unit
root test for LRGDP and LRSVP with ADF as shown in table 1.
、
Table.1 ADF Unit Root Test Results of Time Series Variable LRGDP LRSVP
Variable
ADF Statistic
Test Form(c,t,k)
1% level
5% level
Conclusion
LRGDP
-5.184653
(c,t,1)
-4.616209
-3.710482
non-stationary
LRSVP
31.98056
(0,0,0)
-2.699769
-1.961409
non-stationary
∆LRGDP
-0.346726
(0,0,0)
-2.708094
-1.962813
non-stationary
∆LRSVP
-0.229559
(0,0,0)
-2.708094
-1.962813
non-stationary
∆2LRGDP
-4.239067
(0,0,0)
-2.717511
-1.964418
stationary
∆2LRSVP
-4.644865
(0,0,0)
-2.717511
-1.964418
stationary
Note: (1)All the test results of the above table are obtained by the Eviews 5.0 calculation, the same below; (2) ∆, ∆2
are expressed by the first-order difference and the second-order difference of the variables respectively; (3) The c, t,
k in the testing form represents constant term, trend term and the lags respectively; (4) The selection criteria of lag k
is based on the value of the minimum AIC.
It should be explained that when ADF testing method is used to check whether LRGDP sequence is
stable, the results of Eviews5.0 are as shown in Table 2, where, if by single comparison between ADF
value and critical value, the former is the latter, which seems to indicate that LRGDP sequence is
stable sequence: But if the TREND values and the significance test of t in Table 2 are comprehensively
considered, the coefficient is significantly larger than 0, which indicates a long-term certainty linear
trend for LRGDP, and therefore it can be concluded that LRGDP is non-stationary.
＜
Table.2
ADF Unit Root Test Results of Time Series Variable LRGDP
Null Hypothesis: LRGDP has a unit root
Exogenous: Constant, Linear Trend
Lag Length: 1 (Automatic based on AIC, MAXLAG=3)
t-Statistic
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Prob.*
Augmented Dickey-Fuller test statistic
Test critical values:
-5.184653
1% level
5% level
-4.616209
-3.710482
10% level
-3.297799
0.0036
Variable
Coefficient
Std. Error
t-Statistic
Prob.
LRGDP(-1)
-0.363627
0.070135
-5.184653
0.0002
C
3.608928
0.686463
5.257276
0.0002
@TREND(1990)
0.033154
0.006564
5.050561
0.0002
In conclusion, time-series is stable after the second-order difference at the significance level of 5%, that
is, both LRGDP and LRSVP are I (2) sequence, for which co-integration test can be made.
3.2 Cointegration Test
Although the time series LRGDP and LRSVP are non-stationary, they are both second-order
single-whole sequence, where there might exist a smooth linear combination reflecting the proportional
relation of long-term stability between the variables, namely, the cointegration relationship. In this paper,
EG two-step method is used to make cointegration tests for two time series variables LRGDP and
LRSVP. First, with EG method, the cointegration regression model is estimated as:
LRGDPt = 1.45 + 0.97*LRSVPt+Ut
t=(14.54)
(93.24)
R2=0.99
D.W.=0.22
(1)
In order to determine whether there is cointegration relationship between LRGDP and LRSVP, it is
required to test the smoothness of the residual series of Ut in Equation (1). The method of ADF is used
to make unit root test with the results as shown below:
△U =0.0109-0.0009t-0.3979U
△
△
Ut-1+0.4364 Ut—2
t
t-1+0.2282
t=(0.0324) (0.0323) (0.0008)
(0.2520) (0.0388)
R2=0.853825
D.W.= 2.195146
F=16.06313
(2)
Its ADF test value (-4.569897) is less than 5% critical value (-3.733200), from which it can be seen that
the estimated Ut is smooth (ie, without unit root). Therefore, although LRGDP and LRSVP are not
steady individually, there is cointegration relationship, that is, the long-run equilibrium relationship,
between the two variables. From Equation (1), we can see that there is a higher correlation between
LRGDP and LRSVP. If other conditions remain unchanged, the elasticity of RGDP on the RSVP is 0.97,
that is, every 1% increase in service sector output would promote economic growth 0.97%. It has shown
that the service sector has played a great role on the economic growth.
3.3 The Establishment of Error Correction Model
From the co-integration test, it can be seen that there is a long-run equilibrium relationship between
LRGDP and LRSVP. Of course, in the short term there might be imbalance. Supposing ECM = Ut, we
obtain a error correction model in the manner of Hendry in modeling approach from the general to the
specific with the data of Table 1.
∆LRGDPt = 0.02 + 0.80∆LRGDPt-1+ 0.72∆LRSVPt - 0.74∆LRSVPt-1 - 0.31ECMt-1 +ε
R2=0.92
=0.89 D.W.=2.56
(3)
F=35.34
In Equation (3), ∆ represents the first-difference; ECMt-1 represents a lagged value of the residuals of
Equation (1), as the empirical estimation of equilibrium error term; and ε is a usual error term in nature.
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The short-term dynamic changes of LRGDP and LRSVP are linked with the pre-"balanced" errors of
theirs through equation (3). In this regression, ∆LRSVP is a symbol of short-term interference in LRSVP,
while the error correction term ECMt-1 is a symbol of the adjustment towards long-run equilibrium.
In Equation (3), there is no autocorrelation, and the regression coefficient of the error correction term is
negative, which is consistent with reverse correction mechanism. From the error correction model: we
can see the short-term fluctuations in the service sector will lead to changes in the same direction in
economic growth and in the long term cointegration relationship plays a role in gravity lines that adjusts
the non-equilibrium state back to equilibrium. If economic growth in the current period deviates from
long-run equilibrium value, then to the next phase, 31% of this deviation will be corrected or removed.
3.4 Granger Causality Test
Cointegration results show that there is a long-term stable equilibrium relationship between China's
service industry and economic growth. Whether the relationship constitutes a causal relationship needs
further examination. This paper analyses this issue with the causality testing method put forward by
Granger (1969), which is actually used to test whether lag variables of a variable can be introduced into
the other variables equation. If a variable is subject to the delayed impact of other variables, we can
claim that they have the Granger causality[2].
One important aspect in Granger causality test is the determination of the order of lag. When selecting
the order of lag, on the one hand we want to make it large enough to completely reflect dynamic
characteristics of the model. But on the other hand, the greater the lag is, the more the parameters need
to be estimated, the less degree of freedom the model has. Therefore, usually comprehensive
consideration is required during the choice, which shall include not only a sufficient number of delayed
entries, but also a sufficient number of degrees of freedom[3]. In this paper, the lag order is defined as 2,
and the results of Granger causality test are shown in Table 3 below.
Table 3 Granger Causality Tests of Variables
Null Hypothesis H0
Lags
F-Statistic
Probability
LRSVP does not Granger Cause LRGDP
2
11.0173
0.002
LRGDP does not Granger Cause LRSVP
2
3.26270
0.074
Conclusion
H0 rejected
H0 rejected
From table 3, in the long run, it can be seen that service industry development is the Granger causes of
economic growth (0.2% significance level); and economic growth is also the Granger causes of the
growth of service (7% significant level ).
4. Conclusion
In this paper, econometric analysis is used for the study with the conclusions as follows:
(1) It is proved by unit root tests of non-stationary series that both the time series LRGDP and LRSVP
are all sequences of 2-order single whole, that is, LRGDP ~ I (2), LRSVP ~ I (2). And cointegration
analysis shows that the long-term stable dynamics equilibrium relationship exists between the service
sector and economic growth. Service industry plays an important role in economic growth since every
1% increase in service sector output will promote 0.97% economic growth.
(2) Error correction model (ECM) shows that changes in service sector output have significant positive
effect to GDP over the same period, and about 31% of the gap between gross domestic product of actual
value and the equilibrium value is corrected or removed every year. Gross domestic product is adjusted
to its long-term growth pathways at faster speed after interference.
(3) Granger causality test shows that two-way Granger causality exists between the service sector and
economic growth, services play a clear role in promoting economic growth, and economic growth
further promotes the development of service industries.
At present, China's service industry development is at a critical period, when accelerating the
development of service industries not only can directly pull China's economic growth and meet the
people's growing material and cultural needs, open up employment avenues, and improve social and
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professional level of the service; but also can help promote the development of a market economy,
optimize the allocation of resources, and improve overall economic efficiency and operational quality of
China. It is necessary to adopt practical measures to accelerate the development of services, and
optimize the industrial structure so as to further promote healthy development of service industry as well
as good and fast economic development in China.
References
[1]. Wen-Jen T say, Ching-Fan Chung. The spurious regression of fractionally integrated processes[J].
Journal of Econometrics, 2000, 96(1):155-182.
[2]. J. Roderick Mc Crorie, Marcus J. Chambers. Granger causality and the sampling of economic
processes[J].Journal of Econometrics, 2006, 132(2):311-336.
[3]. Erdal Atukeren. Measuring the strength of co-integration and Granger causality [J]. Applied
Economics, 2006, 37(14):1607-1614.
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