Download The Influencing Factors of the Growth of China’s Outward Foreign

EASTERN ACADEMIC FORUM
The Influencing Factors of the Growth of China’s Outward Foreign
Direct Investment - Based on Granger Causality Test
LIU Haixiao, WANG Congcong
School of Economics & Management, Yanshan University, P.R.China, 066000
[email protected]
Abstract: Cai Zhibing, Zu Qing (2013) have studied 19 growth factors of China’s outward foreign
direct investment ( OFDI ) based on factor analysis model. And this paper tries to analyze these 19
influencing factors by using Granger causality test. We can draw the following conclusions: GDP ,
import, export, exchange rate, foreign exchange reserve, global foreign direct investment, average wage
and energy demand have an significant impact on the growth of China’s OFDI , while fiscal revenue and
fixed-asset investment ( FAI ) do not.
Keywords: Outward foreign direct investment, Influencing factors, Granger causality test
1 Introduction
In recent years, as China cooperate with overseas in economy frequently, our foreign investment
develop rapidly. During the period of the “11th five-year plan”, foreign direct investment flow grows at
an average annual growth rate of 34.3%. In this context, it is essential for us to study the influencing
factors of China’s OFDI to keep it grow healthy.
Even though there have been so many researches on China’s OFDI , their conclusions may be different
because of different methods and different variables. Cai Zhibing, Zu Qing (2013) selected 19 variables
of 1992-2010: GDP growth rate, foreign exchange reserve growth rate and global FDI growth rate and
soon. Using factor analysis model, they draw the conclusions: the main growth factors of China’s
foreign direct investment, ranked according to their degree of influence, includes the supply factors,
demand factors, externalizing factors and world factors. [1] Many scholars use Granger causality test
which can consider the influencing mechanism to study the influencing factors, but most of them just
discuss one or a few factors lacking systematicness. Huang Jingbo, Zhang Anmin (2009) conducted a
precise cointegration analysis to test the influences of the factors and the result shows that obvious
positive relations exist between export, energy demand, GDP , manufacturing RCA and China's
overseas direct investment. [2] Jiang Xiaojuan (2003) considered export, GDP growth rate and FAI as the
influencing factors. [3] Li Hui (2003)’s research shows that per GDP , export, structure factors, total
global demand and the global trade volume have a significant effect on China’s OFDI. [4]
In view of this, this paper takes into the influencing factors account systematically according to the
conclusions of Cai Zhibing, Zu Qing (2013) first, and then uses Granger causality test to discuss the
influencing mechanism to avoid the disadvantages of factor analysis model which can not reflect the
influencing mechanism of OFDI . We hope that we can compare our conclusions with theirs at last.
Even though there should be many other factors which influence OFDI , this paper did not consider
these variables because of having difficulty quantifying or obtaining the data. These variables include:
comparable advantage of industrial location, China’s institutional factor (economic system, legal system,
etc), non-institutional factor (technical level, labor quantity level, etc) and soon. What we said above is
the limitation of this article.
2 Research Design
2.1 Variable selection and data sources
According to the conclusion of Cai Zhibing, Zu Qing (2013), we can choose influencing factors of
China’s OFDI as this paper’s variables from their 19 variables. Firstly, they believed that GDP growth,
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fiscal revenues growth and the company scale expansion as the supply factors form the most powerful
stimulus to China's OFDI . Corresponding to these factors, we choose four variables: GDP , fiscal
revenues ( FR ), foreign capital inflows ( FDI ) and fixed-asset investment ( FAI ). Secondly, energy
demand as the demand factor is also an variable which can promote China’s OFDI .We choose two
variables: energy demand ( ED ) and average wage ( AW ). Thirdly, we choose exchange rate ( R ),
import ( IM ), export ( EX ) and foreign exchange reserve ( FER ) from externalizing factors. Last, we
select global foreign direct investment ( GFDI ) from world factors. Accordingly, this paper’s variables
are: GDP , FR , FDI , FAI , ED , AW , R , IM , EX , FER , GFDI . All these 11 time series data
will be the influencing factors of China’s OFDI in this paper.
This paper selects the data of 1985-2012 to reflect the influencing factor entirely and objectively. The
main source of the variables is China statistical yearbook complied by national bureau of statistics of
China. The source of FER is SAFE (state administration of foreign exchange). In view of eliminating
the effect of heteroscedasticity, we make logarithmic treatment of original index which can not change
the nature and relationship of the time series. Our variables’ logarithmic form are lnOFDI , lnGDP , lnR ,
lnIM , lnEX , lnFER , lnFDI , lnFR , lnFAI , lnGFDI , lnED and lnAW separately.
2.2 Research method
This paper mainly wants to study the influencing factors and influencing mechanism of OFDI based on
Granger causality test. Before analyzing the data by regression, we have to test data stationarity because
non-stationarity time series data may lead to spurious regression. If the time series data is stationarity,
we can analyze the data by regression. However, most of the economic variables are non-stationarity.
According to the Granger’s theorem, we can know that there must be long-term equilibrium relationship
between the non-stationarity data which can be cointegrated. The common method to test cointegration
are Engel-Granger (EG) and JJ method. This article chooses the EG method which is simple to conduct.
Cointegration sequence doesn’t mean they can not deviate the equilibrium for the time being. And the
long-term stable relationship is maintained under the process of the short-term dynamic adjustment.
There must be Error Collection Model (ECM) between the equilibrium time series and we can build
ECM to illustrate the error collection mechanism. In conclusion, this paper will be arranged as follows:
first of all, we should do the stationary test to the selected variables of this article by using ADF test
method. Secondly, we should estimate whether the variable is cointegration with the depended variable
OFDI using EG method. Thirdly, we can build ECM between the equilibrium time series or between
the non-equilibrium time series by changing their form, such as, first-order difference form. Last, we
will research the influencing factors and mechanism of China’s OFDI by Granger causality test. The
empirical analyzes mentioned above are all operated by Eviwes6.0 program.
3 The Empirical Study on the Influencing Factor of China’s
OFDI
3.1 The stationary test
Adopting ADF stationary test, we can get the following conclusions, as shown in Table 1.
Table 1 ADF test results
Variable
(c,t,p)
ADF test
value
lnOFDI
(c,t,1)
-2.536402
-4.356068
-3.595026
-3.233456
0.3096
Non-stationary
dlnOFDI
(c,0,1)
-5.960089
-3.724070
-2.986225
-2.632604
0.0000
Stationary*
lnGDP
(c,t,1)
-2.746272
-4.356068
-3.595026
-3.233456
0.2279
Non-stationary
1%
5%
10%
P value
Conclusion
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dlnGDP
(c,0,1)
-2.864135
-3.724070
-2.986225
-2.632604
0.0640
Stationary***
lnFAI
(c,t,1)
-3.602328
-4.356068
-3.595026
-3.233456
0.0493
Stationary**
lnR
(c,0,1)
-2.284755
-3.711457
-2.981038
-2.629906
0.1840
Non-stationary
dlnR
(c,t,1)
-4.595623
-4.374307
-3.603202
-3.238054
0.0061
Stationary*
lnEX
(c,0,1)
-1.820946
-3.711457
-2.981038
-2.629906
0.3625
Non-stationary
dlnEX
(c,0,1)
-3.675188
-3.724070
-2.986225
-2.632604
0.0112
Stationary**
lnIM
(c,t,1)
-2.092664
-4.356068
-3.595026
-3.233456
0.5257
Non-stationary
dlnIM
(c,0,1)
-3.370220
-3.724070
-2.986225
-2.632604
0.0221
Stationary**
lnFER
(c,t,1)
-2.157052
-4.356068
-3.595026
-3.233456
0.4921
Non-stationary
dlnFER
(c,0,1)
-3.658743
-3.724070
-2.986225
-2.632604
0.0116
Stationary**
lnFDI
(c,0,1)
-1.773531
-3.711457
-2.981038
-2.629906
0.3845
Non-stationary
dlnFDI
(c,0,1)
-3.297777
-3.724070
-2.986225
-2.632604
0.0259
Stationary**
lnFR
(c,t,1)
-3.260274
0.753688
3.409749
3.458926
0.0952
Stationary***
lnGFDI
(c,t,1)
-3.163179
-4.356068
-3.595026
-3.233456
0.1136
Non-stationary
dlnGFDI
(0,0,1)
-3.735457
-2.660720
-1.955020
-1.609070
0.0006
Stationary*
lnED
(c,t,1)
-2.567987
-4.356068
-3.595026
-3.233456
0.2962
Non-stationary
dlnED
(c,0,1)
-2.710292
-3.724070
-2.986225
-2.632604
0.0864
Stationary**
lnAW
(c,t,1)
-2.196154
-4.356068
-3.595026
-3.233456
0.4719
Non-stationary
dlnAW
(c,0,1)
-2.859450
-3.724070
-2.986225
-2.632604
0.0646
Stationary***
Note: (1) ln* means the Log of the original variable and d means the first difference of the variable. (2) (c,t,p): c
means the intercept; t means the trend; p means the lag item. (3) * means it is stationary under 1% significant level;
** means it is stationary under 5% significant level; *** means it is stationary under 10% significant level.
It can be seen from Table 1, OFDI , GDP , R , IM，EX，FER，FDI，GFDI，ED and AW are
integrated of order one, while FAI and FR are all stationary series. We can do a cointegration test to
these variables which are integrated of order one.
3.2 Cointegration test
We do the cointegration test to the variable with the dependent variable by using EG method. And we
can get the conclusions as follows: R , FDI and OFDI are not cointegration series and it means they
don’t have the long-term equilibrium relationship. However, GDP , EX , IM , FER , GFDI , ED ,
AW and OFDI have the equilibrium relationship.
Owing to space constrains, this paper will discuss the case of the conclusions of OFDI and
GDP briefly. We can get the cointegration regression equation between OFDI and GDP as follows
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(see (1)):
lnOFDI  -8.692571  2.003979 lnGDP
（1）
t （ - 5.859932）（15 .26842）
P （0.0000）（0.0000）
R 2  0.899662
According to the regression results, we can know that GDP has an obvious effect on OFDI
(P=0.0000) and both two variables have long-term equilibrium relationship.89.97% of the total sum of
the squares of deviations can be explained by the sample regression line (R 2=89.97%). Its R2 is high.
3.3 ECM
From the cointegration test above, we can get the conclusions: GDP , EX , IM , FER , GFDI ,
ED , AW and OFDI have the equilibrium relationship. So there must be ECM among these
variables. We take OFDI and GDP as an example. The error collection model between OFDI and
GDP are as follows (see (2)):
dlnOFDI  0.282141 - 0.561978ve cm - 1
（2）
t （2.027018）
（ - 3.335094）
P （0.0535）（0.0027）
R 2  0.307917 DW  2.342070
According to the P value (P=0.0535), we can know the short-term adjustment coefficient is prominent.
56.2% of the deviation between the actual outward foreign direct investment and the long-term
equilibrium value is modified.
3.4 Granger causality test
According to the stationary test and cointegration test, we can research the relationship between the
independent variable and the dependent variable by using the granger causality test. The best lag period
is determined by prominent value of the test results. The granger causality test results are as follows:
Table 2 Granger causality test results
Null hypothesis
Lag period
F value
9.82800
lnGDP → lnOFDI
1
0.37620
lnOFDI → lnGDP
lnEX → lnOFDI
lnOFDI → lnEX
lnIM → lnOFDI
lnOFDI → lnIM
lnFER → lnOFDI
lnOFDI → lnFER
lnFER → lnOFDI
lnOFDI → lnFER
lnGFDI → lnOFDI
lnOFDI → lnGFDI
lnGFDI → lnOFDI
lnOFDI → lnGFDI
1
1
1
3
1
3
375
P value
0.0045
Result
reject
0.5454
accept
9.91497
0.0043
reject
0.08475
0.7735
accept
10.8676
0.0030
reject
0.03002
0.8639
accept
9.03253
0.0061
reject
1.34638
0.2573
accept
2.29859
0.1120
accept
3.58732
0.0342
reject
6.91907
0.0147
reject
1.59657
0.2185
accept
1.66655
0.2098
accept
4.81392
0.0107
reject
EASTERN ACADEMIC FORUM
lnED → lnOFDI
lnOFDI → lnED
lnED → lnOFDI
lnOFDI → lnED
lnAW → lnOFDI
lnOFDI → lnAW
lnFAI → dlnOFDI
dlnOFDI → lnFAI
lnFR → dlnOFDI
dlnOFDI → lnFR
dlnR → dlnOFDI
dlnOFDI → dlnR
dlnFDI → dlnOFDI
dlnOFDI → dlnFDI
1
2
1
1
1
1
1
9.96265
0.0043
reject
0.55136
0.4650
accept
1.83805
0.1838
accept
3.68696
0.0424
reject
13.9510
0.0010
reject
0.41567
0.5252
accept
0.00068
0.9794
accept
0.01537
0.9024
accept
0.01341
0.9088
accept
1.12149
0.3006
accept
3.30732
0.0574
reject
0.03591
0.9648
accept
1.72329
0.2022
accept
0.89559
0.3538
accept
Note: (1) A→B means A is not the granger causality of B. (2) We do the granger causality on the original variable if
the variable is co-integrated with OFDI . (3) Granger causality on FR and FAI , two stationary series with the
growth rate of
OFDI ( dlnOFDI ). (4) R and FDI are not co-integrated with OFDI . We will do the
R ( dlnR ) and the growth rate of FDI ( dlnFDI ) with ( dlnOFDI )
granger causality on the growth rate of
separately.
From the Table 2 above, we can see that GDP , EX , IM , FER , GFDI , ED and AW are all
granger causality of OFDI , while FAI and FR are not granger causality of OFDI . Meanwhile,
dlnR is the granger causality of dlnOFDI , while dlnFDI is not the granger causality of dlnOFDI .
4 Conclusion
In this paper, the analysis results are listed as follows:
(1) In the supply variables, only GDP is the Granger causality of outward foreign direct investment
and there is a long-run equilibrium relationship between GDP and outward foreign direct investment.
There are no granger causation between fiscal revenue, fixed asset investment and the growth rate of
outward foreign direct investment and there is also no granger causation between the growth rate of
attracting foreign investment and the growth rate of outward foreign direct investment. The inspect
results of using different lag period indicate that the increase of GDP can pull the increase of outward
foreign direct investment in both short term and medium-to-long term, but the increase of OFDI can
not pull the increase of GDP . The scale of OFDI is small and OFDI accounts for only 0.94
percent of GDP , so the increase of OFDI in China can not obviously pull the increase of GDP
in short term. It is not balanced between the capital outflows and foreign capital inflows in China and
there are also no granger causality. These inspect results show why China push forward the balanced
development of “bring in” and “go out”. We believe that fiscal revenue and attracting foreign investment
have not an obvious relationship with OFDI , which is different from the result of Cai Zhibing.
(2) In demand variables, energy demand and the average salary level wage which have the long-term
equilibrium relationship with outward foreign direct investment are the Granger causality of the outward
foreign direct investment. From the point of the lag period, the energy demand can have an faster effect
on the outward foreign direct investment than the effect the outward foreign direct investment have on
the energy demand. It verifies that China's outward foreign direct investment is resource-oriented.
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Average wage level affects the outward foreign direct investment in the short or long-term and this
character is complied with the facts that more and more Chinese enterprises transferred to the country
where labor costs are cheaper. While the average wage is rigid which can not be affected by the outward
foreign direct investment. Energy demand and average wage level can affect outward foreign direct
investment, which is the same with the conclusion of Cai Zhibing.
(3) In external variables, import, export and foreign exchange reserves have the long-term equilibrium
relationship with outward foreign direct investment and they are the Granger causality of the outward
foreign direct investment. However, exchange rate and outward foreign direct investment have no
long-term equilibrium relationship. But the growth rate of exchange rate is the Granger causality of the
growth rate of outward foreign direct investment. Outward foreign direct investment is not the Granger
causality of import, export and exchange rate. That means import, export and foreign exchange reserves
in China can cause the increase of the outward foreign direct investment. On the other word, they play
the driving role in the outward foreign direct investment rather than the substituting role. Import, export
and foreign exchange reserves are influencing factors of outward foreign direct investment, which is
consistent with the conclusion of Cai Zhibing.
(4) In the world variables, the global FDI has the long-term equilibrium relationship with the outward
foreign direct investment. And the global FDI is the Granger causality of the outward foreign direct
investment in the short term, while the outward foreign direct investment don’t have an effect on the
global FDI . In the medium to long term, the outward foreign direct investment can faster the scale of
the global FDI . The global FDI reflects the overall economic environment. As China speed up in
stepping into the overall finance market and our enterprise contact with the global enterprise more and
more closely, our outward foreign direct investment will be more and more.
References
[1]. Cai Zhibing, Zu Qing. The Influencing Factors of the Growth of China’s Foreign Direct
Investment--Based on Factor Analysis Model [J]. International Commerce- Journal of International
Business and Economics University, 2012: 86-87 (in Chinese)
[2]. Huang Jingbo, Zhang Anmin. An Empirical Study on the Main Driving Factors of China's
Overseas Direct Investment: An Analysis Based on the Direction of Investment, 1982-2007. [J].
International Economics and Trade Research, 2009: 5-9 (in Chinese)
[3]. Jiang Xiaojuan. Frontier of the theoretical Development of China: International Trade and
Economic Relations [M]. Social Science Academic Press, 2003: 399-402 (in Chinese)
[4]. Li Hui. Economic Growth and China’s Emerging as a Main Source Country of FDI [J]. Economic
Research, 2007: 41-42 (in Chinese)
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