Download The Effects of Technological Spillover through FDI and Import Trade

The Effects of Technological Spillover through FDI and Import Trade
on China’s Innovation
LI Ping
ZHANG Qingchang
School of Economy, Shandong University of Technology, P.R.China, 255049
[email protected]
Abstract Based on the state space equation of time-varying parameter model, this paper analyzes the
effects of different R&D and human capital on China’s innovation, making empirical analyses with
1990—2005 data. The result shows that China’s innovation ability mainly depends on domestic R&D
input, but there is the downtrend in recent years; foreign R&D embodied in FDI stimulates the
innovation of China, and the effect raises slowly recently; import trade blocks the upgrade of innovation,
but the effect of hindrance is descending recently; human capital doesn’t get the lowest threshold which
is needed in host country by technical diffusion through FDI.
Key Words Innovation, Technical Diffusion, Human Capital
1 Introduction
The accelerating of global economic integration trend drives the diffusion of knowledge and
technology, so developing countries not only can depend on the domestic innovation, but also on the
international technical diffusion. FDI and import trade, the way of international technical diffusion,
hasten the technical transfer and diffusion: FDI brings the capital for home country, at the same time
provides the advanced technology and management experience; import trade can buy finished products
which contain high technology, but also imports the intermediate goods to improve the home countries’
technic of product activities (Grossman and Helpman, 1991).
2 Literature Review
The spillover effects through FDI means the unconsciously indirect influence of FDI on the host
country’s economic efficiency and growth (He Jie, 2000). The FDI spillover effect on host country could
be positive or negative. Wang Hongling et al (2006) held with empirical analysis that FDI accelerate the
domestic firms’ R&D. Feinberg and Majumdar (2001) empirically researched the R&D investment of
the multi-national corporations in India and found that the input of foreign investment on R&D sped the
technical innovation in the Indian related industries. The other opinion argues that FDI does not benefit
the host’s technical innovativeness. Aitken and Harrison (1999) analyzed the panel data of Venezuela
firms and found the negative influence of FDI on the domestic firms’ R&D. Romer (1990) argued from
the perspective of human capital allocation that FDI would have negative effect on the host country’s
technical progress. Thus FDI has bidirectional influence on the host country’s technical innovation:
either accelerate or hinder the progress.
Compared with FDI, import trade has obvious positive influence on the host country’s technical
progress. Coe, Helpman and Hoffmaister (1997) analyzes 77 developing countries; Crespo, Martin and
Velazquez (2002) studied 28 OECD countries; Huang Xianhai and Zhang Fan (2004) as well as Fang
Xihua and Bao Qun (2004) empirically research China: All the findings show that import has obvious
technical spillover effect.
With the concern of the domestic and foreign R&D influence on technical innovation, many
economists begin to reflect on the major factors affecting the performance of R&D capital, among
which the human capital is the research focus. Human capital determines the country’s capacity of
creating new suitable techniques and affects the innovation as follows: on one hand, human capital is the
key factor that affects the innovation performance in the developing countries since innovation needs
more capitals and talents compared with re-innovation and developing countries are severely short of
R&D personnel; on the other hand, human capital is the significant factor affecting the technical
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absorbing ability and is important to adopt overseas technical spillover and to accelerate re-innovation.
Xu (2000) found that due to the shortage of human capital in the developing countries, techniques
transmission combined with FDI did not obviously accelerate its productivity growth.
A conclusion can be drawn from the literature review above that the researches to date mainly
focus on the single factor’s effect on the technical innovation no matter from domestic or foreign
perspective, and most are just static analysis. In contrast, this paper has the following breakthrough: (1)
integrating various international technical spillovers and introducing human capital into the model, that
is, analyzing comprehensively China’s innovativeness performance; (2) adopting Time-varying
Parameter Model, for China’s economy structure are gradually changing with economic reforming and
various outside strikes and policy shifts which could not be reflected with the fixed parameter models.
3 Model Formulation and Data
3.1 Time-varing parameter state space model
Using spate space equation constructs time-varying parameter model, and analyses the elasticity
of each R&D inputs.
Measurement equation:
yt = xt β t + zt γ + µt , t = 1, 2, L , T
(1)
β t = ϕβ t −1 + ε t
State equation:
Assumption
 0  σ 2 0 
/
(µt , ε t ) = N    , 
  , t = 1,L , T
 0  0 Q 
(2)
(3)
Where, β t is assumed to move over time as a first-order vector autoregression, means the change
of the effect of variables on dependent variables. zt is matrix of variables whoes coefficients are
constant. and where and are vectors of mean zero, Gaussian disturbances. Note that the unobserved state
vector is assumed to move over time as a first-order vector autoregression.
Depending on the theory of state space model, the function of input-output we construct in the soft
of Eviews is
Measurement equation:
lnInnot = C + α1lnSd t + α 2 ln S f − fdit + α 3 ln S f − imt + ε t
(4)
We use cross term to measure the absorbable capability, so the seconed measurement equation is
lnInnot = C + α1lnSd t + α 2 h ln S f − fdit + α 3 h ln S f −imt + ε t
(5)
State equation:
α t = ϕα t −1 + ϕ t
t = 1, L , 5
(6)
Where Innot is the output term of innovation, lnSd t is the log of domestic R&D capital stock,
S f − fdi t , S f − imt represent the foreign R&D capital stock embodied in inward FDI, import and foreign
patent application respectively, h represents human capital. α t reprents dynamic output elasticity. All
state equations are recursive.
3.2 Mesurement of variables
We measure the domestic R&D capital stock, using perpetual inventory model by Goldsmith
(1951), S d t = (1 − δ ) S d t −1 + RDt . Where δ is the depreciation rate, which was assumed to be 5%
through experimental time–series regression (Coe and Helpman, 1995). RDt is R&D expenditure in
the year t .Domestic R&D stocks in 1985 can be calculated following the procedures suggested by
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d
= RD1985 /( g + δ ) . Where S1985
refers to R&D capital stock in 1985,
is R&D expenditure in 1985. g is the average annual logarithmic growth of R&D expenditure
d
Griliches (1980): S1985
RD1985
in the period of 1985 to 2006.
We use LP approach to calculate foreign R&D stocks embodied in import and inward FDI:
S f − fdi t =
10
FDI jt
∑ GDP
j =1
* S jd t ;
S f − imt =
IM jt
10
∑ GDP
j =1
jt
* S jd t
(7)
jt
Where FDI jt and IM jt represent the real investment and the import flowing to China from
country j in the year t respectively, GDPjt and S jd t represent country j GDP and domestic
R&D stock at time t . We select Chinese top 10 importers from 1985 to 2006, which are Japan, USA,
South Korea, Hong Kong, Germany, Singapore, Australia, France, Italy and Canada, according to
“Chinese Foreign Trade Statistic Yearbook”. Likewise, Chinese top 10 foreign investors, according to
“Chinese Statistic Yearbook”, are: Hong Kong, USA, Japan, Singapore, U.K., Germany, France, Canada,
Australia and Italy.
We use enrolment ratio of higher education to measure the human capital. The reasons are that
high-level talent can contact the foreign high technology frist, and possess high absorbable capability.
3.3 Data
Because of the patent law carried out from 1985, we do regression using the data, 1985-2006, for
the coherence of variables. Domestic R&D expenditure, actually used FDI and total Value of imports
come from “China statistical yearbook”, “China statistical yearbook on science and technology”, “China
trade and foreign economic statistical yearbook”, “China Education Statistical Yearbook”.
Every country and region’s GDP and ppp exchange rate come from “International Monetary Fund,
World Economic Outlook Database, April 2007”, and “OECD, Main Science and Technology Indicators,
2007/2”. The data of R&D stock are calculated through perpetual inventory model, and all the data are
translated into the price in 1985, using ppp exchange rate.
4 Numberical Study
Before regression estimate, we first use Hodrick-Prescott (HP) filter to divide time series into a
trend term and a circle term. The long-term trend component of a series can avoid the disturb brought by
circle component. The theory of HP filter is
Assuming {Yt } is a time series which contains trend and fluctuation,
{Y } represents trend
T
t
{ } represents fluctuation component. So
component, Yt
c
Yt = Yt T + Yt c , t =1,2,…, T
(8)
Technically, the Hodrick-Prescott (HP) filter is a two-sided linear filter that computes the smoothed
T
T
series Yt of Yt by minimizing the variance of Yt around Yt , subject to a penalty that constrains the
T
second difference of Yt . That is, the HP filter chooses to minimize:
T
{
min ∑ (Yt − Yt T ) 2 + λ  (Yt T+1 − Yt T ) − (Yt T − Yt T−1 ) 
t =1
2
}
(9)
Table 1 Model 4
Chow Breakpoint Test: 1992
Model 4
F-statistic
3.534552
Prob. F(5,12)
0.041561
Log likelihood ratio
19.26290
Prob. Chi-Square(5)
0.001892
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Table 2 Model 5
Chow Breakpoint Test: 1992
Model 5
F-statistic
Log likelihood ratio
2.946150
17.51545
Prob. F(5,12)
Prob. Chi-Square(5)
0.058348
0.003568
To see whether there are significant differences in the estimated equations 4 and 5 for each
subsample, we do the the breakpoint Chow test. We choose the breakpint, 1992(see table 1, 2). From the
table 1 and 2, we find a structural change of China economy. So we use time-varing parameter stata
space model to do regression instead of OLS.
SH1
SH1F
SH2
SH2F
0.4
2.5
2
0
1.5
1
-0.4
0.5
-0.8
0
-1.2
1985 1988 1991 1994 1997 2000 2003 2006
Figure1 Compare of elasticity of
1985 1988 1991 1994 1997 2000 2003 2006
Sd t
Figure 2 Compare of elasticity of
S f − fdi t
Graph 1-3 represent the performance of
R&D inputs respectively. SH* is the curve of
elasticity of R&D input-output, and SH*f is the
1
curve, containing the human resource. As a
whole, domestic innovation mainly dependes
0.5
on domestic R&D inputs more and more. After
0
adding the human resource, the elasticity of
-0.5
domestic
R&D
input-output
changes
-1
insignificantly. That shows that our country’s
-1.5
human resource have spaned the threshold of
1985 1988 1991 1994 1997 2000 2003 2006
original innovation. The results show that three
ways of international technology spillover
influnce China innovation differently.
Figure 3 Compare of elasticity of S f − imt
(1) As a whole, the performance of foreign
R&D embodied in FDI is positive significantly. From graph 2, we can see that, before 1994, the
elasticity of foreign R&D embodied in FDI fluctuates significantly. Maybe because chinese outside
condition was bad at that time. After 1994, the elasticity ascended step by step. Li Ping etc. (2007)
confirm the result. However, the promoting effect is limited, strolling at 0.09. Maybe because the effect
of FDI on China innovation mainly appears on the mid-low tech. Appearance design patents contains
little techonology, so it is easy to imitate the appearance design patents in Foreign-Capital Enterprises.
But the influnce carried by appearance design patents after all is samll. Invent patents, almost are
advanced technology contains high tech. Foreign-Capital Enterprises always control the technology
diffusion and spillover through technology-locking strategy. Hence, promoting invent is difficult using
technology spillover embodied in FDI.
In contrary to FDI, as a whole, foreign R&D embodied in import trade reduced the output of China
innovation. The situation is related with structure of import products. The import products mainly were
manufactured products which almost were capital goods and high-tech products. But the competition
and substitution effect of import products would impede the development of related industries, and
infulenced innovation negatively.
(2) After considering the variable of human resource, we find: domestic human capital reduced the
SH3
SH3F
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elasticity of foreign R&D embodied in FDI, even blocked the development of China’s innovation.
China’s human capital haven’t passed the innovation’s threshold. Lai Mingyong, etc. (2003) points out
that the stock of human capital often becomes the bottleneck. Only we have enough human resource,
domestic R&D and FDI can promote China’s innovation.
However, existing human resource increase the elasicity of foreign R&D embodied in import. This
means that China’s human resource promotes R&D spillover, and enhance China’s innovation capability.
But the effect of import trade is limited, and negative. With the enhancement of human resource and
intellectual property protection, they can have more positive influence to China’s innovation.
5. Conclusion
This paper analyses the dynamic effect of domestic and foreign R&D capital and human resource
on China innovation. The following is the result:
(1) Domestic R&D input has been the engine of Chinese innovation. However, we can’t ignore the
effect of international technology diffusion on domestic innovation. The effect of two ways on China
innovation varies significantly. FDI accelerates china innovation more and more. Import trade blocks the
upgrade of innovation.
(2) Human resource as the variable of absorbing promotes China innovation faintly, even impedes
the technology spillover to China of FDI.
References
[1]Grossman, G., and E. Helpman. Quality Ladders in the Theory of Growth. Quarterly Journal of
Economics, 1991,106: 557-586.
[2]S. E. Feinberg, S. K. Majumdar, Technology Spillovers from Foreign Direct Investment in the Indian
Pharmaceutical Industry, Journal of International Business Studies, 2001, 32 (3): 421–437.
[3]Aitken, B. and Harrison, Ann E., Do Domestic Firms Benefit from Foreign Direct Investment?
Evidence from Venezuela. American Economic Review, 1999, 89 (3): 605– 618.
[4]P. M. Romer, Endogenous Technological Change. Journal of Political Economy, 1990, 98(5): 71–102.
[5]Coe, David T., Helpman, Elhanan and Hoffmaister, Alexander W., North-South R&D Spillovers. The
Economic Journal, 1997, 107: 134-149.
[6]Crespo, Jorge, Martin, Carmela and Velázquez, Francisco Javier “International Technology Diffusion
Through Imports and its Impact on Economic Growth,” European Economy Group Working Papers
No.12, 2002.
[7]B. Xu, Multinational Enterprises, Technology Diffusion, and Host Country Productivity Growth.
Journal of Development Economics, 2000, 62(2): 477–493.
[8]Lichtenberg, F., Van Pottelsberghe, B., International R&D Spillovers: A Re-examination, NBER
Working Paper No. 5668, 1996.
[9]He Jie. Farther measure of the spillover effect of Foreign direct investment on China industry. Journal
of World Economy, 2000, 12.
[10]Hong Xianhai, Zhang Yunfan. Analysis of countries’ difference in effect of technology spillover of
China foreign trade. Journal of Economic Survey, 2004,1.
[11]Fang Xihua, Bao Qun. International Technology Spillover:An Empirical Research based on the
Import-oriented Mechanism. Journal of China Soft Science, 2004,7.
[12]Li Ping, Cui Xijun, Liu Jian. Performance Analysis of R&D Capital Input and Output in Chinese
Independent Innovation: with Discussion of the Effects of Human Capital and Intellectual Property
Rights Protection. Journal of Social Sciences in China, 2007,2.
[13]Lai Mingyong, Xu Helian, Baoqun. Import trade and economy growth: theory, model and emperical
study. Shanghai San Lian Press. 2003.
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