Effect of Foreign Direct Investment on Industrial Innovation: An Empirical
Research on the Equipment Manufacturing Industry in China
Xu-sheng Chen, Hong-qi Wang, Yun Zhong
School of Management, Harbin University of Science and Technology, Harbin, China
(xtdllw@163.com)
Abstract - With the advent of globalization, foreign
direct investment (FDI) became an important means for a
host country to obtain advanced technology and
management experience. This paper analyzes the influence
of FDI on industrial innovation in the equipment
manufacturing industry in China. First, the study establishes
a corresponding evaluation index system according to FDI
factors affecting industrial innovation. Second, the paper
demonstrates the performance of the main FDI factors
through scores obtained from super-efficient data
envelopment analysis (DEA), and distinguishes the degree of
the effects of different factors through composite DEA. The
results show that the intensity of market entry of FDI is the
most significant factor in industrial innovation, whereas the
impact of the technology spillover effect of FDI is the least
significant. Finally, the study suggests countermeasures to
improve FDI performance in industrial innovation.
Keywords - Effect, equipment manufacturing industry,
industrial innovation, super efficiency DEA
I. INTRODUCTION
As the largest developing country in the world, in
order to optimize the industrial structure, enhance the
level of the product technology and expand the
international trade cooperation, China needs to have a
leap-forward development in capital, technology and
management, which is difficult to realize by only relying
on the innovations of the domestic enterprises in a short
period of time. Learning and absorbing the foreign
experience and technologies can accelerate the latedevelopment advantage of China.
During 1979-2008, the total amount of FDI in China
reached nearly $900 billion. The foreign investment
changed our market structure. Due to the externality and
spillover effect of technology, FDI can promote
technological innovation by the inter-firm linkages [1].
The advanced technologies and management modes not
only brought the competitive pressures to China’s
enterprises but also became the object to be imitated [2].
The existing literatures mostly study the overall effect
of FDI on innovation [3], however hardly has research with
analyzing the effects of different factors in FDI on
innovation efficiency of Chinese equipment
____________________
Sponsored by National Nature Science Fund Project (70773032),
Humanities and Social Science Research Youth Fund Project of Ministry
of Education in China（10YJC790027）,Chinese Postdoctoral Science
Foundation（20110491099）,Heilongjiang Province Postdoctoral Fund
Project（LBH-Z10112）
manufacturing industry. The research of the influence of
the innovation investment, entry intensity, as well as
scientific and technological activities on innovation can
further define the mechanism of action of FDI in
promoting the industrial innovation and provide the basis
for the formulation of FDI policies.
FDI is widely adopted by the multinational
companies as an important means to expand the global
market and reduce costs. In 2004, the FDI of the U.S.
multinational companies increased by 90% [4]. With the
transmission of information and the reduction of goods
transportation costs, FDI driving force is gradually
shifting to the technical factors. According to the findings
of the U.S. multinational-owned R&D lab, its main
purpose is to obtain the human capital for science and
technology [5]. And the developing countries also have
begun to develop the policies of taking advantage of the
foreign-invested companies to promote the development
of the national high-tech fields [6]. Evidence of Irish also
shows that the number and R&D activities of the foreigninvested enterprises in the high-tech fields have become
the driving force of economic growth [7]. With the
intensifying competition, the multinational companies are
also increasing their R&D investment in the host
countries.
According
to
Kuemmerle’s
detailed
investigation of the labs established by 32 large
multinational companies in five countries, the R&D
investment by the foreign-funded companies has greatly
exceeded that over the past 40 years [8]. As the technology
spillover effect of FDI is becoming increasingly evident,
FDI has become the independent and complementary
innovation system to the national innovation system,
which promotes the relevant researches to attract
increasing attention.
Through the analysis of the panel data of China’s
manufacturing industry during 1998 and 2003, it can be
learnt that the diversity of FDI is conducive to the
technology spillovers [9]. In the manufacturing industry,
market competition, labor mobility and the degree of
openness of the industry are the key factors that affect the
technology spillovers [10]. And the research of FDI in the
developed areas in China shows that high technological
ability can increases the region’s capacity of attract
foreign investments [11]. Owning the advanced technology
and high human capital is an important factor to increase
the FDI spillover effect. FDI always produces the positive
technology spillover effect on the domestic enterprises in
the same industry, especially when these enterprises are in
the same area, but the adverse impact on the domestic
enterprises in other industries [12].
The size of FDI in the host country decides its overall
effectiveness, market influence and ability to avoid the
risk and facilitates the technology spillover effects.
Angelo pointed out according to the empirical studies in
Brazil that the policy makers in emerging markets should
try to stimulate its domestic market to attract foreign
direct investment rather than adjusting the fiscal and
monetary policies [13]. The decisive factor of attracting
FDI in China is the market size, department distribution,
labor, transportation infrastructure, etc. Dang Jun
explained with the application of game model that
China’s implementation of the fair anti-dumping
measures would not be affected by the FDI size in the
long time [14]. In addition, the lower the cultural
differences and efficient government have contributed to
the expansion of FDI scale [15].
II. METHODOLOGY
1) Performance Evaluation Based on SE-DEA:
DEA model have two kinds of forms, one is fractional
programming, and the other is the linear programming.
The two forms are equivalent, the former is obtained by
ratio definition, and the latter is based on a series of
production formula assumption. Based on the formula
assumption of convexity, coning and invalidity, we can
get the following DEA model:
t
n
r 1
j 1
( M 1) min     St   xij  j  0
n
xij0  Si   xij  j  0 i  1, 2,
m
j 1
n
s.t.
 Sr   yrj  j  yrj0
r  1, 2
(1)
t
j 1
j , S , S  0

t

r
j , r , i
According to the discussion about oriented input
DEA model (M1) above, we can see the basic idea of the
DEA is that finding out the effective frontier from the
envelope surface formed by data. (M1) is a constant
return to scale DEA model, when adding the
constraint   j  1 , which based on the formula
assumption of the convexity, coning and invalidity, we
can get the following DEA model (BCC model) which
meet the scale benefits variable. (M1) can be transferred
into an equivalent fractional programming model:
t
（M 2）: max h0   ur yrj0
r 1
m
v x
t 1
t tj0
t
m
s.t.  ut yrj
r 1
v x
 1 j  1,, n
t tj
t 1
(2)
ut , vt   , r , i
Similar to (M1), we can get the DEA model (M3)
oriented output optimization.
t
m
r 1
t 1
( M 3) max     S r    St
n
 yrj  S   yrj  j  0 r  1, 2,

r
0
n
St   xtj  j  xtj0
s.t.
,t
j 1
j  1, 2
(3)
,m
j 1
 j , St , Sr  0
j , r , i
CCR DEA model can not be adopted to distinguish
efficient DMU which value of  is 1. SE-DEA model can
overcome the disadvantages of CCR mode [14], since the
unit’s relative placement regardless of its efficiency can be
calculated, and the model can be expressed as follows:

 
 

min    ( e  T s   e  T s  )  V
D

n


s.t   j X j  S  X 0

j 1
j  j0

n

 jY j  S   Y0


j 1

j  j0


j  0, j  1,2,  n


S   0, S   0

T
 T
e  1,1,  ,1  E m

T
T

e   1,1,  ,1  E n

(4)
 
 
2) Index Comparison Based on Composite DEA:
There are n DMU, they have the types of input m, s types
of output, xi (i  1,，m) is to show the input of i class,
yr (r  1,，m) is to show the output of the r class, said
the target system with D,

D {x1 ,, xm , y1 ,, ys }
Remove the first species with di said output indicator
set. Refers to the use of in di DEA method, find the
effectiveness of various policy unit of coefficient vector
T
obtained  ( Di ) ,  ( Di )  (1 ( Di ),...,  n ( Di )) , (i  1,..., ) , and can
prove that  ( D)   ( Di ) .
For the invalid DMU, the input index which affects
its performances value can be studied, firstly, Invalid
vectors  ( D j 0 ) should be calculated, thus, new vectors
that S i  ( j 0 ( D)   j 0 ( Di )) /  j 0 ( Di ) is defined, For total
influence on invalidation of DUM is computed according
to (5), the index which has bigger influence score regard
as main factor.
 S  (
( D)  j 0( Di )) *100 /  j 0 ( Di )
(5)
15
Manufacture of Chemical Fiber

3) Setting up evaluation indexes: According to the
DEA model, the paper establishes the index system of
inputs and outputs, there is four input indexes, which are
personnel input of FDI on scientific and technical (X1),
organization activities of FDI on science and technology
(X2), intensity of market entry of FDI (X3), and
technology spillover effect of FDI (X4).
The input formulas are expressed as followed.
X1= personnel engaged in S&T activities of joint
ventures (unit: person);
X2= projects for S&T activities of joint ventures (unit:
item);
X3= gross industrial output value of joint ventures/
gross industrial output value;
X4= expenditures for in draught of technology of joint
ventures + expenditures for absorb and digest of joint
ventures + expenditures for inner technology of joint
ventures (unit: 10000 Yuan).
There are two output indexes, which are the ability to
develop new technology (Y1), level of profit of new
product (Y2).
The output formulas are expressed as followed.
Y1= patent applications in industrial enterprises (unit:
item);
Y2= gross industrial output value of new products in
industrial enterprises (unit: 10000 Yuan);
3) Data source: The data of empirical research are
taken from China Statistical Yearbook and China
Statistical Yearbook on Science and Technology (2009);
according to sample requirement of DEA model, 28
industry of manufacturing industry have been selected,
and data of sample is listed in Table I.
16
Manufacture of Rubber
17
Manufacture of Plastic
18
Manufacture of Non-metallic Mineral Products
i
j0
19
Manufacture and Processing of Ferrous Metals
20
Manufacture and Processing of Non-ferrous Metals
21
Manufacture of Metal Products
22
Manufacture of General Purpose Machinery
23
Manufacture of Special Purpose Machinery
24
Manufacture of Transport Equipment
25
Manufacture of Electrical Machinery and Equipment
Manufacture of Communication, Computer, Other
Electronic Equipment
Manufacture of Measuring Instrument, Machinery for
Cultural and Office Work
Manufacture of Artwork, Other Manufacture
26
27
28
Moreover, Other Countries have not proposed the
concept of equipment manufacturing industries except
China. This paper defined former industries which include
DUM21 to DUM27 in Table Ⅰ . Because of the
monopoly position of the Chinese manufacture of
tobacco, FDI is very little in manufacture of tobacco, and
its impact can be ignored, so the samples get rid of
manufacture of tobacco.
III. RESULTS
A. Result of Efficiency by CCR DEA
Scores and rank of efficiency by CCR DEA is listed
in Table II.
TABLE I
TABLE II
DUM OF MANUFACTURING INDUSTRY
DUM
Industry
1
Processing of Food from Agricultural Products
2
Manufacture of Foods
3
Manufacture of Beverage
4
Manufacture of Tobacco
5
9
Manufacture of Textile
Manufacture of Textile Wearing Apparel, Foot ware
and Caps
Manufacture of Leather, Fur, Feather and Its Products
Processing of Timbers, Manufacture of Wood,
Bamboo, Rattan, Palm, Straw
Manufacture of Furniture
10
11
6
7
8
12
13
14
RESULT OF EFFICIENCY BY CCR DEA
DUM
scores
rank
DUM
scores
rank
DUM1
0.3230
26
DUM15
0.6009
19
DUM2
0.4692
21
DUM16
0.2472
27
DUM3
0.3696
25
DUM17
0.7511
16
DUM4
1.0000
1
DUM18
0.6047
18
DUM5
0.4215
22
DUM19
1.0000
1
DUM6
0.3952
24
DUM20
0.9419
9
DUM7
0.8867
11
DUM21
0.8884
10
DUM8
1.0000
1
DUM22
0.8443
12
DUM9
0.2255
28
DUM23
1.0000
1
Manufacture of Paper and Paper Products
DUM10
0.3974
23
DUM24
0.7734
14
Printing, Reproduction of Recording Media
Processing of Petroleum, Coking, Processing of
Nucleus Fuel
Manufacture of Chemical Raw Material and Chemical
Products
Manufacture of Medicines
DUM11
1.0000
1
DUM25
1.0000
1
DUM12
1.0000
1
DUM26
0.7560
15
DUM13
0.6483
17
DUM27
0.8272
13
DUM14
0.4870
20
DUM28
1.0000
1
B. Result of Efficiency by SE-DEA
Scores and rank of efficiency by SE-DEA are listed in
Table III.
DUM27
0.5867
0.8223
0.6522
0.8272
DUM28
0.8077
1.0176
0.9644
1.0176
TABLE V
TABLE III
SUM OF CHANGED RATIO
RESULT OF EFFICIENCY BY SE-DEA
DUM
scores
new rank
DUM
scores
new rank
DUM4
1.4056
4
DUM19
3.6134
1
DUM8
1.3705
5
DUM23
1.0985
7
DUM11
2.1689
2
DUM25
1.3833
6
DUM12
2.0875
3
DUM28
1.0176
8
C. Result of Index Comparison Based on Composite DEA
The efficiency scores of DUM by composite DEA are
listed in Table IV;  ( Di ) represent the efficiency scores of
remove i index. Sum of changed ratio (  S ) is listed in
i
Table V according to (5), which comparison to equipment
manufacturing industry (DUME).
TABLE IV
CHANGED EFFICIENCY SCORES BY COMPOSITE DEA
 j 0 ( Di )
X1
X2
X3
X4
DUM1
0.2894
0.3230
0.2783
0.3168
DUM2
0.3105
0.4692
0.3974
0.4692
DUM3
0.2985
0.3696
0.3206
0.3628
DUM4
1.4056
1.3635
1.0112
1.2571
DUM5
0.4215
0.3941
0.3926
0.3733
DUM6
0.3952
0.3412
0.3952
0.2460
DUM7
0.8258
0.8867
0.8297
0.8785
DUM8
1.3666
1.1201
1.3705
1.1606
DUM9
0.2255
0.1530
0.2255
0.2255
DUM10
0.3974
0.3696
0.3587
0.3260
DUM11
2.1689
2.1689
2.1689
0.8542
DUM12
2.0875
2.0875
2.0875
0.7952
DUM13
0.6172
0.6483
0.4673
0.6483
DUM14
0.4870
0.4870
0.3439
0.4593
DUM15
0.6009
0.4482
0.6009
0.4597
DUM16
0.2221
0.2472
0.2226
0.2431
DUM17
0.7511
0.7511
0.5567
0.3359
DUM18
0.6047
0.4989
0.4872
0.6047
DUM19
3.5133
3.6134
1.6523
3.5130
DUM20
0.9419
0.7467
0.9419
0.9419
DUM21
0.8884
0.8738
0.8254
0.8262
DUM22
0.8443
0.8029
0.5398
0.8443
DUM23
1.0745
1.0985
0.7472
1.0985
DUM24
0.7734
0.7734
0.4598
0.7734
DUM25
1.3833
1.3833
0.6433
1.3783
DUM26
0.7560
0.6872
0.4445
0.7560
S
X1
X2
X3
X4
DUME
43.2275
17.4340
391.1913
7.8939
IV. DISCUSSION
Through the calculation of efficiency value of the
equipment manufacturing industry, the results show that
the efficiency value of manufacture of special purpose
machinery (DUM23) and manufacture of electrical
machinery and equipment (DUM25) all reached 1; while
the efficiency value of the other five industries is between
0.7 and 0.9, and is located in the top fifteen in
manufacturing industry.
As CCR DEA method cannot distinguish the industry
with efficiency DUM which efficiency value is 1, so we
make adjustment for each sample in forefront surface with
SE-DEA according to saving situation of input elements,
and the efficiency value not reaching 1 remains the same
score. After calculation by SE-DEA, the efficiency value
of manufacture of electrical machinery and equipment
marks the highest, namely, 1.3833, followed manufacture
of special purpose machinery whose efficiency value is
1.0985.
Through analyzing and calculating FDI factors which
influence the innovation efficiency of equipment
manufacturing industry, the scores of composite DEA
show that the intensity of market entry pose the most
crucial influence on industrial innovation, and the second
index is personnel input of FDI on scientific and
technical, while the technology spillover effect of FDI
ranks the weakest influence on industrial innovation.
V. CONCLUSION
Based on calculations using super-efficient DEA, the
impact of FDI is found to be different in the equipment
manufacturing industry. The difference of the efficiency
value between the minimum and maximum is 0.6273.
Eight industries have efficiency values reaching 1, the
proportion of which in the total manufacturing industry is
28.57%. The equipment manufacturing industry and other
manufacturing industries account for 7.14% and 21.43%
of the total.
The manufacture of electrical machinery and
equipment has the highest efficiency value because of the
lower input and higher output requirements. The
efficiency value of manufacturing communication,
computer, and other electronic equipment (DUM26) is the
lowest. Although the output indicators of patent
applications and the gross industrial output value of new
products in all manufacturing industries are high, the
corresponding input index in FDI, which includes
personnel input on scientific and technical processes,
organization activities on science and technology,
intensity of market entry, and technology spillover effect,
is also high, resulting in its lowest efficiency value.
According to the efficiency value from the composite
DEA of the equipment manufacturing industry and to the
sum of the change ratio of influential elements, the
intensity of FDI entry into the market has the most
significant effect on industrial innovation. Therefore, the
government should keep an active policy on foreign
inventions and attract capital. At the same time,
enterprises should pay more attention to the quality of
foreign capital and the possibility of spillover effects
brought by foreign capital, while focusing on investment
projects that are scientifically and technologically
innovative, thoughtful of the ecological environment, and
are resource saving.
The technology spillover effect of FDI has the
weakest influence on industrial innovation in the
equipment manufacturing industry. The result indicates
that the capacity of domestic enterprises to receive
advanced technology is weak. Such finding is consistent
with the actual situation in China, wherein foreign
investments limit the spread of high-end equipment in the
country. Enterprises should actively build a market
competition environment to support technology transfer,
lessen their technological and managerial gap with foreign
enterprises, and make effective use of the spillover effect
of R&D investment and knowledge in production.
Personnel input and organization activities relating to
scientific and technical aspects of FDI have relatively
weak influence on the equipment manufacturing industry
partly because intellectual property rights laws in China
are weak, making foreign enterprises reluctant to set up
scientific research institutions in the country. The
innovation ability of domestic enterprises is low, and the
market lacks competition. Hence, joint venture enterprises
need not introduce the most advanced technology.
Clearly, China should strengthen its protection of
intellectual property rights, enhance the independent
innovation ability of its domestic enterprises, and
encourage multinational companies in the country to
increase investments on high technology.
REFERENCES
[1] Marin A, Costa I, “Thinking locally: exploring the
importance of a subsidiary-centered model of FDI-related
spillovers in Brazil”, International Journal of
Technological Learning, Innovation, and Development, vol.
3, no. 1, pp. 87–107, 2010.
[2] Tang Mingfeng, Hussler, Carolin, “Betting on indigenous
innovation or relying on FDI: The Chinese strategy for
catching-up”, Technology in Society, vol. 33, no. 2, pp. 23–
35, 2011.
[3] Lu Yan-Qiu, Wang Xiang-Yang, Zhang Shi-Tong,
“Research on technology spillover of FDI affecting
innovative capacity of enterprises”, Journal of Beijing
Institute of Technology, vol. 16, no.4, pp. 148–152,
December 2007.
[4] Anon, “Growing the global corporation”, Paper Age, vol.
121, no. 4, pp. 20, 2005.
[5] Florida, Richard, “The globalization of R&D: Results of a
survey of foreign-affiliated R& D laboratories in the USA”,
Research Policy, vol. 26, no. 1, pp. 85–103, 1997.
[6] Tidd Joseph, Brocklehurst Michael, “Routes to
technological learning and development: An assessment of
Malaysia’s innovation policy and performance”,
Technological Forecasting and Social Change, vol. 62, no.
3, pp. 239–257, 1999.
[7] Kearns Allan, Ruane Frances, “The tangible contribution of
R and D-spending foreign-owned plants to a host region: A
plant level study of the Irish manufacturing sector (19801996)”, Research Policy, vol. 30, no. 2, pp. 227–244, 2001.
[8] Kuemmerle Walter, “Foreign direct investment in industrial
research in the pharmaceutical and electronics industries results from a survey of multinational firms”, Research
Policy, vol. 28, no. 3, pp. 179–193, 1999.
[9] Zhang Yan, Li Haiyang, Li Yu, Zhou Li-An, “FDI
spillovers in an emerging market: The role of foreign firms
country origin diversity and domestic firms absorptive
capacity”, Strategic Management Journal, vol. 31, no. 9,
pp. 969–989, 2010.
[10] Zhang Lan-Xia, Wang Jing, Yang Hai-Jun, “Empirical
study on FDI's technology spillover effect on equipment
manufacturing industry in Shenyang”, Journal of
Northeastern University, vol. 29, no. 6, pp. 909–912, 2008.
[11] Chen Yufen, Chen Jin, “The impact of FDI on regional
technological capabilities: evidence from China”, Journal
of Knowledge-Based Innovation in China, vol. 1, no. 2, pp.
143–158, 2009.
[12] Tian Xiaowen, Lin Shuanglin, “FDI Technology Spillovers
within and across Industries: Evidence from China”,
Journal of Asia Business Studies, vol. 3, no.2, pp. 231–247,
2009.
[13] Angelo C.F, Eunni R.V, Fouto N.M.M.D, “Determinants of
FDI in emerging markets: evidence from Brazil” ,
International Journal of Commerce & Management, vol.
20, no. 3, pp. 203–216, 2010.
[14] Dang Jun, Feng Zongxian, Lv Han, “The effects of
antidumping measures on the FDI: a pre-marketing
behavior aspect analysis in China”, International Journal of
Organizational Innovation, vol. 2, no. 3, pp.6–24, 2010.
[15] Brouthers, Lance Elio, Gao Yan, Mcnicol Jason Patrick,
“Corruption and market attractiveness influences on
different types of FDI”, Strategic Management Journal,
vol. 29, no. 6, pp. 673–680, 2008.