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Empirical Analysis of the Impact of China's Foreign Trade on
Economic Growth in China
WANG Lijuan, XU Ye
Shijiazhuang University of Economics, Shijiazhuang, China, 050031
[email protected]
Abstract: Econometric method is applied in the ADF testing on the stability of relevant economic
variables time series data, the cointegration analysis on the stable variables after adjustment, and the
research on whether equilibrium relation exists in the long run. Analysis is conducted on the
cause-and-effect impact of various economic variables in long run equilibrium and short run fluctuation,
via cointegration equation, error correction model and Granger Causality Test. The result of the research
shows that in the short run, economic growth is inclined to be restrained by needs, while in the long run,
it is determined by the improvement of production effectiveness. Therefore, China should continue to
promote exporting trade to facilitate economic growth, and attach importance to the impact of importing
on economic growth, to polish up the economic effect of importing.
Keywords: Foreign Trade, Economic Growth, Cointegration Analysis, Granger Causality Test
1 Introduction
On the relationship between foreign trade and economic growth, researchers domestic and abroad have
conducted huge amount of studies. Foreign researchers study the relationship between trade and
economic growth from the perspective of developed countries. Their applications require re-examination
on China, a major developing country, as China’s dependence on trade is continuously increasing.
Domestic researchers take few considerations on the stability of economic data when conducting studies,
which impacts on the accuracy of model estimation. Therefore, this paper applies cointegration theory
and Granger Causality Test method, takes into consideration the impact of variables’ short run
fluctuation on variables long run equilibrium, and analyzes the relationship between China’s foreign
trade and economic growth, to further grasp the essence of the economic development mode of China,
and on a certain degree, provide references when making decision on China’s development on foreign
trade.
2 Positive Analysis
2.1 Data Selection and Processing Instructions
This paper conducts analysis on China foreign trade and economic growth with data of 1978-2008. The
data originates from the database of China Bureau of Statistics and China Ministry of Commerce. The
model applies major analytical variables with GDP, EX, IM and TEI. To eliminate the heteroscedasticity
existed in time series, various variables are switched with logarithm. The switched variables are LNGDP,
LNEX, LNIM and LNTEI respectively.
2.2 Positive Analysis
2.2.1 Correlation Analysis
To test the relationship between the variables on a preliminary level, Eviews5.0 is applied in the
calculation of correlation coefficient. The correlation coefficients of the various variables are thus
generated. as shown in table 1.
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Table 1 Correlation Coefficients between LNGDP and LNEX, LNIM, LNTEI
LNTEI
LNGDP
LNEX
LNIM
LNGDP
1.000000
0.995856
0.993824
0.995434
LNEX
0.995856
1.000000
0.997685
0.999269
LNIM
0.993824
0.997685
1.000000
0.999447
LNTEI
0.995434
0.999269
0.999447
1.00000
We can tell from table 1 that the correlation coefficients are extremely high (close to 1). These imply
that evident correlations exist between China GDP and EX, IM and TEI; and evident correlation exists
between IM and EX.
2.2.2 Stationary Analysis
Most of the economic data are non-stable variables, as they have strong change tendency with the
change of time. If regression equation is established on these data, the issue of “Fake Regression” will
emerge. Therefore, this paper applies unit root test to determine whether the variable is stable before
establishing proper regression equation.
Table 2 ADF Unit Root Test Result
Variable
LNGDP
LNGDP
LNEX
LNEX
LNIM
LNIM
LNTEI
LNTEI
△
△
△
△
Test form
ADF value
（C,N,4）
（C,N,3）
（C,N,O）
（C,N,O）
（N,T,1）
（N,N,2）
（C,T,1）
（C,T,1）
-1.33.69
-3.56852
-1.56125
-5.04243
-1.61537
-3.57801
-1.90607
-3.65890
5%TLV
-2.97626
-2.97626
-2.96041
-2.96347
-2.96041
-2.96397
-2.96041
-2.96397
p.s. (C, T, K) represent the constant, time trend and lagging rank figure in the model. N does not include
C and T. refers to rank 1 difference.
△
We can tell from table 2 that although LNGDP, LNEX, LNIM and LNTEI take on non-stable feature,
their rank 1 difference is steady. This implies that they all belong to rank 1 single full series, namely I
(1). Hence, cointegration analysis can be conducted on the difference of the original data.
2.2.3 Cointegration Analysis
To make regression meaning for non-stable series, horizontal series difference can be applies to make
them steady. And then difference series can be used for regression. However, if this method is applied,
the information contained in the original horizontal series will be neglected. And the information is of
crucial importance to the analysis. This paper adopts Johansen’s maximum likelihood estimation method,
to test the cointegraion of various variables. Optimized lagging figure is determined as 2 based on AIC
information optimization standards. Please refer to table 3 for the test result.
Table 3 Johansen Cointegration Analysis table
Cointegration
equation
N=0*
≦
N≦2
N≦3
N 1
eigenvalue
Trace statistic
1%TLV
Prob.
54.68150
35.45817
19.93711
6.634897
0
0.0157
0.1493
0.0975
0.868377
94.7881
0.516328
33.95376
0.269406
12.16330
0.087481
2.746377
p.s. * indicates that the null hypothesis is rejected on 1% evidence level.
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According to the test result, on 1% evidence level, the test result rejects the null hypothesis that the
cointegration equation number N=0. This implies that cointegration exists between the variables. At
least, one cointegration vector exists. That is to say, long run equilibrium cointegration exists in the
system consisted by the variables.
Based on the above cointegration test result, cointegration equation exists in between the various
variables. This suggests that long run stable equilibrium exists among GDP, EX, IM and TEI. Although
the development routes of the various variables turn on relatively big fluctuation, and the routes divert
from equilibrium scale, these diversion are haphazard and temporary. In the long run, the equilibrium
relation is stable. Giving that TEI is composed of IM and EX, and linearity may engender in the
estimation of cointegration, cointegration regression only takes into account the relations between GDP
and IM, EX.
LNGDP = 4.86287 + 0.6705 LNEX + 0.0369 LNIM
（
）（
）（0.213985）
t= 33.76083
4.118307
R =0.992697
DW=0.683621
2
We can tell from the coefficient denotation of various variables that EX and IM are all positive. And this
indicates that the development of EX and IM all have positive impact on GDP in the long run, and the
output elasticity of EX is bigger that IM.
2.2.4 Error Correction Model
The above analysis indicates that long run equilibrium relation exists between China’s economic growth
and the various variables of foreign trade. The time series of these variables present unstable features,
and relatively big fluctuation are engendered in the development process, which may impact on the
equilibrium relation between economic growth and the various variables. In the short run, the
equilibrium relation may lose balance. Error correction model can be applied to analyze the amendment
degree of the short run fluctuation and long run equilibrium diversion of various variables.
∆LNGDP = 0.0932 + 0.1059∆LNEX + 0.0437 ∆LNIM + 0.03317∆LNTEI − 0.2335 Et −1
（
t= 5.0416
R2=0.6532
） （0.6495）
DW=0.7813
（0.1826）
（0.8441）
（-3.8815）
The results show that the short run fluctuation of the various variables has positive impact on economic
growth. As Et-1 coefficient is statistically evident, 23% (0.233576) of the diversion between annual
actual economic growth figure and long run equilibrium is amended.
The above analyzes the correlation between foreign trade and economic growth variables from the
perspective of regression. Nevertheless, the correlation is not necessarily cause-and-effect. To
thoroughly study the relation between import, export trade and economic growth, statistical causality
can be analyzed between the variables.
2.2.5 Granger Causality Test
Cointegration exists between foreign trade and economic growth. Below Granger Causality Test is
applied, to determine whether the expansion of trade brings in economic growth, or economic growth
brings in the expansion of trade. Before applying the Granger Causality Test, the lagging rank figure p
of the series of LNEX, LNIM and LNTEI and LNGDP needs to be set. As the standards stipulate that
the statistical quantity AIC or SC should reach minimum integer, p=5. Please refer to Figure 4 for the
test results.
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Table 4 Granger Causality Test table
F-Statistical
Prob.
Null Hypothesis
Quantity
The reason why LNEX is not LNGDP
3.56486
0.02529
The reason why LNGDP is not LNEX
4.56242
0.01296
The reason why LNIM is not LNGDP
1.60854
0.21786
The reason why LNGDP is not LNIM
2.56240
0.07225
The reason why LNTEI is not LNGDP
2.36252
0.09034
The reason why LNGDP is not LNTEI
3.49278
0.02716
The reason why LNIM is not LNEX
2.62972
0.06708
The reason why LNEX is not LNIM
0.45643
0.80233
p.s. Evident level is 5%. * indicates close to the threshold.
Conclusion
Reject hypothesis
Reject hypothesis
Do not reject hypothesis
Reject hypothesis
Do not reject hypothesis
Reject hypothesis
Reject hypothesis *
Do not reject hypothesis
The results show that, in the long run, export trade is closely related to economic growth. Causality lies
between the two. Meanwhile, import trade’s impact on economic growth is not evident. But the impact
of importing is significant on exporting. In the short run, TEI increase leads to economic growth; in the
long run, economic growth leads to TEI increase.
3 Conclusion
In conclusion, exporting in foreign trade contributes a huge part in China’s economic growth, while
importing has negative impact on the increase of GDP. Although the results of metric testing imply that
importing does not directly have propelling impact on the increase of GDP, importing in fact has
un-neglectable indirect propelling impact on the increase of GDP. Hence, in the future development,
China should not only continue to utilize exporting’s pulling effect on economic growth, but also bring
into play the introduction of advanced production requisites and propelling competition effect by
importing trade. The allocation of production requisites should be optimized, to facilitate the
improvement in production effectiveness. On the aspect of importing trade policy, quality foreign
investment should be brought in, to improve domestic production technology and effectiveness, and the
upgrade of industry structure. In this way, the domestic economy can sustain growth in the long run, on
a fundamental level; and economic growth and foreign trade can have positive inter-impact. However,
on the matter of on what level can economic growth reaches equilibrium, under what circumstances it
diverts, and how to amend it, still require further research.
References
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[2]. Mcnab R M, Moore R E. Trade policy, export expansion, human capital and growth. Journal of
International Trade and Economic Development, 1998(7):237~256.
[3]. Lin Qingquan. Domino Offect Analysis on the J Curve of China Importing and Exporting
Trade,The Journal of Quantitative & Technical Economic. Beijing: Chinese Academy of Social
Sciences, 2007(11):19~25(in Chinese)
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