Download The Technical Spillover Effect of the Processing Trade in China

The Technical Spillover Effect of the Processing Trade in China
ZHU Qirong
School of International Trade Shandong University of Economics P.R.China,250014
，
，
Abstract: In this paper, the relationship between China’s technical progress and the technical progress
of its processing trade is empirically analyzed. The results are as follows: A positive relationship
between China’s technical progress rate and that of its processing trade is significant, and there exists a
long term dynamic relationship between the former and the latter. Meanwhile, Granger causality test
shows that the progress of the processing trade is Granger Causality of China’s technical progress. So
the processing trade of China has technical spillover effect.
Key words: processing trade, technical spillover effect, CD production function, technical progress rate
1 Introduction
Studies by Grossmanz and Helpman(1991), Coe and Hoffmaister(1997), Keller(2000), Foster
(2002), Jacob(2005) showed that technology was transferred to the developing countries from the
developed countries through international trade and foreign direct investment. Since China’s reforming
and opening up, its processing trade has developed rapidly. The value of the trade increased from 1.63
billion US dollars in 1980 to 831.86 billion US dollars in 2006. Pan Yue(2003) and Zhang Qian(2004)
considers that as an economic activity which combines international trade with foreign direct investment,
the processing trade provides China with opportunities for capturing the developed countries’
technology, managerial skills, training and so on, which is the so-called “the technical spillover effect.”
Pei Changhong (2005) and Peng Lei (2006) argue that the technical spillover effect taken by the
processing trade is smaller, because most of enterprises that carry on the trade are foreign enterprises
and their inputs of the processing trade industry come from other countries that might not be able to
affect local enterprises in China. These different opinions come from quantitative analysis. In this paper,
we use econometric analysis to study the question.
This paper is structured as follows: Firstly, Cobb-Douglas production function is used to estimate
China’s technical progress rate and the technical progress rate of the processing trade respectively.
Secondly, Cointegration and Granger Casualty test are employed to study the relationship between
China’s technical progress and the technical progress of the processing trade and to identify whether the
processing trade has the technical spillover effect. Finally, the findings of the paper are summarized and
a conclusion is drawn.
2Estimating China’s Technical Progress Rate and the Technical Progress Pate of
the Processing Trade
2.1Model
We use Cobb-Douglas production function to estimate China’s rate of technical progress and its
technical progress rate of processing trade. When only labor and capital input is concerned, Douglas
production function is specified as Equation 1 .
Y = ALα K β
(1)
Where Y represents real GDP, A represents technology, L represents labor input, K represents
real capital input, α is the L elastic of GDP, β is the K elastic of GDP.
From equation (1), we get the generalized Cobb-Douglas production function:
（）
Y = Ae mt Lα K β
(2)
Where m represents the contribution technical progress to GDP, t represents time to allow for
technical change, the meaning of other variables in equation (2) is the same as that in equation (1).
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Differentiating equation (1) totally with respect to time, we obtain equation (3). Setting dt=1, we obtain
equation (4) which is called Sollow model.
dL
dK
dY
(3)
= mdt + α
+ β
L
K
∆Y
∆L
∆K
m =
− α
− β
Y
L
K
Y
（4）
Where m represents the rate of technical progress, ∆Y/Y represents the rate of real GDP growth,
△L/L represents the rate of labor input growth, △K/K represents the rate of real capital input growth, α
is the L elastic of GDP, β is the K elastic of GDP.
2.2Data
In this paper, GDP of China represents output, and its fixed assets investment represents the capital
input, and economic population of China represents the labor input, all of which come from China
Statistics Yearbook. The export value of processing trade of China represents the output of the
processing industry and the import value of processing trade represents the capital input of the industry,
both of which come from China's Foreign Trade Statistics Yearbook. Labor input in the industry comes
from China’s Labor Statistics Yearbook. The annual data mentioned above range from 1985 to 2006.
2.3Estimation Result of China’s technology progress rate and that of the processing trade
By estimating the logarithmic form of equation (1), we get China’s C-D function (shown in
equation (5)) and the C-D function of the processing trade (shown in equation ( 6)) respectively.
(5)
ln Y = − 3 .7516∗ + 0 . 5628 ∗∗∗
ln L + 0 .4372 ∗∗ln K + [ AR (1) = 0 . 5724 ]
( − 1 . 8712 )
2
R =0.9952
(11 . 8620
SE L = 0 . 0474
)
( 3 . 4991
)
( 3 . 3055 ∗∗ )
SE K = 0 .2207
F=13.7041 DW=1.9281
'
L + 0 .5145 ln∗∗∗ K '+ [ AR (1) = 0 .9943 ]
ln Y j = 13 .5239 + 0 .4855 ln
∗∗∗
( 0 .2384 )
R2=0.9995
( 5 .43325
SE L' = 0 . 0803
)
( 6.3572
)
SE K ' = 0 . 0894
(6)
( 4 .7535 ∗∗∗ )
F=12.6271 DW=1.8146
Notes: * represents signification at 10% level.. ** represents signification at 5% level. *** represents at 1% level.
From equation (4), equation (5) and equation (6), we get China’s technical progress rate and the
technical progress rate of processing trade (shown in Table1).
Table1 China’s technical progress rate and the technical progress rate of processing trade from 1985 to 2006
year
1985 1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
lnmS
3.6
6.2
2.8
0.6
3.0
1.9
2.9
-5.4
5.2
2.5
-5.8
lnmJ
5.7
7.8
5.1
6.5
7.4
8.7
10.4
9.7
9.5
9.7
6.2
year
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
lnmS
8.8
1.6
7.8
5.1
0.1
3.2
4.1
2.6
2.9
-0.6
2.5
lnmJ
10.9
11.3
17.9
11.0
10.6
10.2
10.0
15.9
9.5
9.4
9.7
3Relation between China’s Technology Progress and the Processing Trade’s
Technology Progress
3.1Methodology
As most of time series seem to be non-stationary, when they are regressed, there might be
spurious regression that may affect the reliability of econometric analysis. To solve the problem,
Granger (1967) introduced time series analysis that is called co-integration test. Granger causality test
(see Granger1969 and Sims 1972) used in time series analysis to examine the direction of causality
between two economic series has been employed in many econometric studies for the past three decades.
In this paper, we use co-integration and Granger causality test to identify the relationship between
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China’s technical progress rate and the technical progress rate of processing trade. To avoid
autocorrelation of time series, all variables in equations below are transformed into logarithms.
3.2Unit root test
Before conducting cointegrating and Granger causality test, unit roots of a time series should be
examined. Campbell and Perron (1991) provided rules for investigating whether a time series contain
unit roots. The formation is as follows.
（6）
p
∆ y t = α + ∂ t + ω y t −1 + ∑ ri ∆ y t − i + ε t
i =1
△
Where is the first difference operator, yi is random variable, α is constant, t is a time, p is
lagged difference, and the null hypothesis of no co-integration amongst the variable is( H0 ω=0) against
the alternative hypothesis(H1 ω<0). If (H0 ω=0) is accepted, but (H1 ω<0) is rejected, the unit root of
variable {yt} is existent, i.e., yt is non stationary series and vice versa.
：
：
：
：
3.3Cointegrating test
If series xt and yt are non stationary and both of them are integrated with same order, we can use
OLS to estimate equation (7) and then test whether the residual of regression equation (7) is stationary.
If the residual is stationary, there exists a cointegrating relationship between xt and yt, and vice versa.
7
xt = c + β y t + µ t
（）
3.4Granger causality test
Within a bivariant context, the type of Granger causality test states that if a variable X Granger
causes Y, the mean square error (MSE) of a forecast of Y based on the past value of both of X and Y is
lower than that of a forecast that we use only past value of Y. This Granger test is implemented by
running the following regression.
m
（8）
m
∆y t = α 0 + ∑ α i y t − i + ∑ β i x t − i + ε t
i =1
i =1
3.5Results of ADF test
We test unit roots of these series by using Augmented Dickey-Fuller (ADF).Table2 presents all
results of the unit root test of the level series are greater than the critical values. Therefore the null
hypothesis of non-stationary could not be rejected. But after first differencing these variables, the T-bar
test statistics are well less than the corresponding critical values at either 5% or 1% signification level,
which thus indicates that the null of hypothesis of non-stationary should be rejected and the alternative
hypothesis of stationary be accepted. In other words, the variables (lnmS and lnmJ) are integrated of
order one or I(1). i.e., the variables become stationary after being first differenced.
Table 2
variable
lnmS
lnmJ
dlnmS
dlnmJ
ADF statistic
-3.070139
-2.549097
-4.118076
-4.359157
Lags
2
1
2
1
Results of ADF test
1% Critical Value
-3.7856
-3.7497
-3.8067
-3.7497
5% Critical Value
-3.1003
-2.9907
-3.0199
-2.9969
stationary or not
no*
no*
yes
yes
Notes: The optimal lags for conduction ADF test were decided by AIC(Akaike information criteria) . mS and mJ stand for China’s technical
progress rate and the technical progress rate o f the processing trade respectively. dlnmS and dlnmJ stand for the first difference of them.*
represents signification at 1% level.
3.6 Results of cointegration test
Using OLS to estimate the logarithmic form of equation (7), we get the regression equation below.
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（（ ）
（9）
EX P + ( AR 1 = 0.7717)
ln EC = 0.0571+ 0.3539 ln
∗∗∗
（0.1947）
R2 =0.8935
（5.2152 ）
SE exp = 0 . 0417
3.1210∗∗
）
DW=1.9469
Notes: ** represents signification at 5% level, *** represents at1% level.
The regression equation indicates that elasticity of China’s technical progress rate with respect to
the technical progress rate of the processing trade is 0.3539. So the former has significantly positive
effect on the latter. The result (shown in table 3) of stationary testing of residual in equation (9) shows
the residual is a stationary series. So there is a long and dynamic relationship between China’s technical
progress rate and the technical progress rate of the processing trade.
Regression equation
None
Intercept
Trend and intercept
Table 3 Results of stationary of residual test
ADF statistic
ADF(5%)
ADF(1%)
-3.4257
-4.0187
-6.3124
-1.9583
-3.0114
-3.6454
-2.6819
-3.7856
-4.4691
Conclusion
stationary
stationary
stationary
3.7Result of Granger causality test
From the result (presented in table 4), the null hypothesis that “the change of China’s technical
progress rate does not Granger cause the change of the technical progress rate of the processing trade” is
accepted. On the other hand, the null hypothesis that “the change of technical progress rate of the
processing trade does not Granger cause the change of China’s technical progress rate” is rejected at 5%
signification level. Therefore, the change of technical progress rate of the processing trade is Granger
causality of the change of China’s technical progress rate.
Table4
Results of Granger causality test
Null Hypothesis
lags
F-statistic
lnms does not Granger Cause lnmj
3
0.13587
lnmj does not Granger Cause lnms
3
4.73081
Probability
0.87520
0.04014
4Conclusions
Through studying on the relationship between China’s technical progress and technical progress
of its processing trade, we find that a positive relationship between China’s technical progress rate and
the technical progress rate of processing trade is significant and there has a long term dynamic
relationship between the former and the latter. On the other hand, the results of Granger causality test
indicate that the technical progress of processing trade is a cause of China’s technical progress.
Therefore, China’s processing trade has obvious technical spillover effects.
There are still many other questions to be explored on this topic. For example, does the technical
spillover effect come from the processing import or from export? What decides the effect? All of these
questions need to be further studied
References
～
[1]Blomstrm.K. Multinational Corporations and Spillovers .Journal of Economic Surveys,1998 (8): 247 251.
[2]Coe.E and Hoffmaister..R. Trade and technology diffusion in Latin America. Journal of the International Trade
1997(18):177 197.
[3]Foster.E. An Analysis on Industrial Upgrading of China’s Processing Trade. World Investment Report,
2002:1874 1997.
[4]Grossman.J and Helpman.E. Harrison Do Domestic Firms Benefit from Direct Foreign Investment? Journal of
American Economic Review,1991:103 132.
[5]Johansen.B.Statistical Analysis of Cointegration Vectors.Journal of Economic Dynamics and Control
～
～
～
684
～
, 1988(12):231 254.
～
[6]Jacob.S. Technical Capability and Export Succeed in Asia. Routledge, New York,1998:76 95.
[7]Keller.E. The International Segmentation of Production in China. Journal of Development Economics
,2000(17):93-107.
[8]Pan Yue .A Analysis of Effects on the Development of China’s Processing Trade.Journal of Academic
Resarcher,2003(8):78 81.
[9]Pei Changhong. An Analysis on Processing Trade Upgrading in China.Journal of Macro
Economics,2006(1):107 114.
[10]Zhang Jing.The Technical Spillover Effect of Processing Trade. Journal of Technology and
Management,2004(2):60 65.
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～
～
The author can be contacted from e-mail : [email protected]
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