Influence Factors of Industrial Cluster Development
Hua-ling Lu1, Yan Zhou2, Jian-bo Tang 3
1
Chongqing University of Posts and Telecommunications, Chongqing, China, E-mail: cqluhualing@163.com
2
Chongqing University of Posts and Telecommunications, Chongqing, China, E-mail:zhouyanqjxf@126.com
3
Chongqing University, E-mail: 709179906 @qq.com
Abstract -A substantial proportion of electronic and communication device manufacturing clusters are at the primary stage in the
global. In order to promote the development of this industry and play its improving effect on regional economic growth, the
influence factors of industry cluster development level are studied in this paper. Firstly, 18 impact factors are summarized
according to the actual situation of electronic communication equipment manufacturing industry and correlative literature at
home and abroad which is divided into internal and external factors of an industrial cluster. Secondly, each factor's influences on
the development level of an industry cluster are analyzed based on panel data of the 21 provincial in China from 1997 to 2009.
Seven factors include enterprise scale; employed concentration has significant influence on the industry cluster development level.
Finally, the impacts of key factors on the industry cluster of different development level were analyzed in-depth. The influence of
six factors includes the quantity of patent applications. Number of enterprises in some region which has the overall developmental
level on industrial cluster is greater than those regions which have a high development level.
Keywords
- Industrial Cluster, Development Level, influence factors
I. Introduction
The electronic and communication equipment manufacturing's industrial cluster in each region plays a significant
promoting role for the industrial progress[2][3][4] and the provincial fiscal growth[5][6].Although the researches about
specializes in the factors that influence the industry cluster development level is scarce. Electronic and communication
equipment manufacturing are the leading industry belonging to the high technology industry. So it is meaningful to have the
related research on the factors that impact the high technology industry cluster development level as the theoretical support
mainly[7-12].
Although the electronic and communication equipment manufacturing industry belongs to the high-tech industry, its
innovation investment and various technology trading costs are higher than other industries significantly. So it is very
favorable for improving the development level of this industry cluster to gather these factors in a region. Based on a large
number of literature analyzing high-tech industrial cluster and the influence factors to the development level of industrial
cluster in Potter's diamond model[1], the influence factors on the development level electronic and communication equipment
manufacturing's industrial cluster are concluded in this paper, and these factors are summed up as internal and peripheral
influence factors. The internal factors include: R&D personnel concentration, R&D funds concentration, R&D institution
concentration, level of innovation consciousness, enterprise concentration, enterprise scale, employees concentration, fixed
assets concentration, technical purchase concentration, technology digestion and retrofit concentration; The peripheral
factors include: transportation condition, related industrial concentration, consumption level, system mechanism, number of
people.
II. Model Setting and Variable Declaration
This paper chooses the panel data of the electronic and communication equipment manufacturing industry in
21 provinces of China to carry on the analysis. There are many advantages of the panel date model, such as to
control individual heterogeneity, reduce the multicollinearity between regression variables, improve the
effectiveness of parameter estimation and construct more complex behavior model, etc. Considering the
economic significant of each index, contradistinctive and different variance possibly emerged during econometric
analysis, the natural logarithm is chosen for each variable. According to the above analysis and combine with the
relative economic theory, the econometric model of panel data as follows:
lnY it = α + β1lnX 1it + β2lnX 2it + β3lnX 3it + β4lnX 4it + β5lnX 5it + β6lnX 6it + β7lnX 7it + β8lnX 8it +
β9lnX 9it + β10lnX 10it + β11lnX 11itεit  β12lnX 12it  εit
In the model, the subscript i represent each industry, t represent the year, α represent intercept items,εit is
random perturbation terms, on behalf of other influence not considered. β1——β12 are the regression
coefficient of panel data, the meaning of each X and Y is described in the following table. Y value in Table I is
measured by the location quotient of the total production value in the electronic and communication equipment
manufacturing industry. The location quotient is the quantification of location theory and reflects location theory,
and location theory is a core theory to study the location advantage. It is better to show the development level of
clustering than simply using the concentration of GDP in an area. The explained variable is concluded according
to some papers of empirical research about the influence factors in the development level of an industrial cluster.
Referring to previous research methods, this paper made most external factors as control variable, while the
transport conditions and related industrial concentration within the external factors were made as variable be
explained, not as the one be controlled. It is because that both of them impact the development level of industrial
cluster greatly, even determine whether the industrial cluster can form or not to a great extent. The determination
of related industry is from several industries that have a larger consumption coefficient at the input-output table.
Because the statistics standard in Chinese service industry changed greatly in 2003, so this paper only using the
total number of employees of three kinds of manufacturing to analyze.
Table I
variable definition and calculation
style
name
Explained variable
Industrial agglomeration
Definition and calculation
symbol
The location quotient value of electronic and communication
Y
equipment manufacturing industry in this area *100
The proportion of the R&D personnel of electronic and
R&D personnel
communication equipment manufacturing industry in this region
X1
concentration
to the R&D personnel of this industry all over the world *100
The proportion of the R&D funds of electronic and
R&D funds concentration
communication equipment manufacturing industry in this region
X2
to the R&D funds of this industry all over the world *100
The proportion of the number of R&D institution funds of
R&D institution
electronic and communication equipment manufacturing
concentration
industry in this region to the number of R&D institution of this
X3
Internal factors
plaining variable
industry all over the world *100
to industrial
The proportion of the number of patent applications of
cluster
level of innovation
electronic and communication equipment manufacturing
consciousness
industry in this region to the number of patent applications all
X4
over the world *100
The proportion of the number of the enterprise of electronic and
enterprise concentration
communication equipment manufacturing industry in this region
X5
to the number of the enterprise all over the world *100
The gross output value of electronic and communication
enterprise scale
equipment manufacturing industry in this region / the number of
the enterprise
X6
The proportion of the number of the employees of electronic
employees concentration
and communication equipment manufacturing industry in this
X7
region to the number of the employees all over the world *100
The proportion of the fixed assets of electronic and
fixed assets concentration
communication equipment manufacturing industry in this region
X8
to fixed assets all over the world *100
The proportion of technical purchase funds in domestic and
technical purchase
abroad of electronic and communication equipment
X9
concentration
manufacturing industry in this region represented in the world
The proportion of technology digestion and retrofit funds of
technology digestion and
electronic and communication equipment manufacturing
X10
retrofit concentration
industry in this region represented in the world *100
control variable
External factors
transportation condition
The kilometers of this region / the kilometers of the world
X11
to industrial
related industrial
The proportion of related industrial employees in this region
cluster
concentration
represented in the world *100
Resident consumption level
Resident consumption level in this region
X13
System mechanism
Marketization index
X14
External factors
population
The population in this region / the national population*100
X15
to industrial
The per capita GDP
The per capita GDP of this region
X16
cluster
GDP
The GDP of this region
X17
Educational investment
The proportion of educational investment in this region
concentration
represented the educational investment in the world
X12
X18
Note: the location quotient value = (some industry GDP of a region / total industry GDP of a region) /
(some industry GDP in the nation / all industry GDP in the nation)
III. Sample Selection and Data Sources
the location quotient value of Inner Mongolia, Hainan, Yunnan, Tibet, Gansu, Qinghai, Ningxia, Xinjiang is
basically at the lowest level every year, and it is very difficult to collect the complete data of these areas, so this
paper doesn't analyze these areas, merely choose the 21 provincial level administrative area in the mainland of
China for research. Because the industry clusters are at different stages and the influence of various factors to the
development level of industrial cluster are significantly different, so this article divided the 21 regions into two
categories to analyze in the end, which are the well developed regions and regions that have some development
but still not mature in the electronic and communication equipment manufacturing industry. The first kind
includes Beijing, Tianjin, Shanghai, Jiangsu, Guangdong, Zhejiang and Fujian, the second kind includes Hebei,
Shanxi, Liaoning, Jilin, Heilongjiang, Anhui, Jiangxi, Shandong, Henan, Hubei, Hunan, Guangxi, Chongqing,
Sichuan, Guizhou and Shanxi.
The data in this paper are all from related Yearbook, "China Statistics Yearbook on high technology
industry" 、 "China Statistical Yearbook" 、"China Industrial Economic Statistical Yearbook" 、"Chinese
industry economy yearbook" 、"China Economic Census Yearbook", and "The Index of magnetization of
China".
IV the Empirical Results Analysis
A.The analysis directing as influential factors in the development level of an industry cluster in 21 provincial regions
This paper applies eviews6.0 for regression analysis to the panel data of various factors that influence the
development level of electronic and communication device manufacturing industry cluster. The sample only does
research on data of the 21 provincial regions in China, so the initial estimation is the variable intercept model.
this paper uses constrained F value for judgment. If the result is significant, choose the fixed effect model, or the
mixed model. Then according to Hausman test, if the test result is suggestive, choose the fixed effect model,
otherwise choose the random effect model [13]. According to the two kinds of test results above, we mainly use
the fixed effects model for analysis. At the same time in order to prevent that the unusual selection of methods
will lead to separate errors and to ensure the reliability of the research results in the next step, this paper also
made reference to the random effect consequence. In the fixed effects regression, on the condition that the
number of cross section is greater than the sequence number, weight selection uses the cross-section weights way.
The results show statistics of F are 250.339 and 3101.9201; the significance level value is 0, which mean
that the regression equation has statistical significance. The values of R2 are 0.981 and 0.894 having a better
goodness-of-fit. In addition to the variables of random effects in X 5 and X11 through the significance test, other
results are largely consistent. This paper perform regression with the variables X 1, X2, X4, X6, X7, X11, X12 that
have passed the significance test (control variables, the explained variable are constant), and then eliminate the
explanatory variables that have insignificant effect, concluding the explanatory variables of the flowing models in
the end. In fact, agreed with the variables that testing significance in the fixed effect model. The regression in
these few steps also conducts maximum likelihood ratio and Hausman test at first. The result should establish the
fixed effect model. This paper merely lists the final regression results due to limited space.
This regression equation has statistical significance and a better goodness-of-fit. Under the significant level
of 5%, the coefficients of explanatory variables X1, X2, X4, X6, X7, X11 and X12 are obviously not zero, i.e. the
seven variables have a significant effect on the development level of industrial clusters. The coefficient of X1 is
negative. The coefficients of remaining significant variables are positive. The coefficient of X 7 is maximum,
followed by X6, showing that both of them are the biggest influence level of the advance level of industrial
clusters.
B. Empirical analyses, of influencing factors to the development level of different industrial clusters
In different stages of the development of industrial cluster, each influence factor to the development level of
industry cluster has a unique degree and depth, so the panel data in this area are divided into two kinds of model
for analysis. The first step is also to determine the model panel data through the likelihood ratio test and the
Hausman test. Because the section number of the panel data in regions that have high development level of an
industrial cluster is only seven and less than the sequence number, can't use random effect regression. So can't
carry out Hausman tests. Other test results are to establish the fixed effect model.
Table II
the classification regression results of different development level of industrial clusters
Variable symbol
High development level of industrial
High development level of industrial
clusters
clusters
coefficient
probability
coefficient
probability
C
5.748629
0.0021
4.909172
0.1293
X1
-0.064178
0.1888
-0.048821
0.08
X2
0.125178
0.0035
0.082665
0.0044
X4
0.015974
0.5792
0.069144
0
X5
0.118915
0.465
0.054375
0.5409
X6
0.074132
0.1862
0.318965
0
X7
0.392664
0.004
0.58464
0
X11
0.225212
0.0158
0.257253
0.0981
X12
0.056078
0.0415
0.071437
0.045
X13
-0.173131
0.4042
-0.278766
0.4348
X14
0.010696
0.6123
-0.431943
0.0752
X15
0.043128
0.4893
-0.027698
0.6961
X16
0.054347
0.7857
0.473155
0.1508
X17
-0.439227
0.1837
-0.446865
0.2106
X18
-0.11165
0.5573
0.000117
0.9996
2
2
R 0.966
R 0.951859
Weighted
Adjusted R2 0.956
Adjusted R2 0.940509
statistics
F 98.5454
F 83.86195
Prob(F)
0
D-W 1.375
Prob(F) 0
D-W 1.49677
From the two results in Table II, the regression equations have statistical significance and better
goodness-of-fit. In the models that have a higher development level of industrial cluster, X 2, X7, X11 and X12 pass
the significance test, reflecting that the R&D funds, employees, industry concentration and transport condition
are the key factors influencing the development level of an industrial cluster. In the models that have general
development level of industrial cluster, only X1 and X5 don't pass the significance test, proving that the R&D
funds concentration, the amount of patent applications, the scale of enterprises, annual average number of
employees, transportation conditions and industry concentration are the key factors influencing the development
level of industrial cluster.
Comparing the high development level of industrial cluster with general regression results, except the R&D
funds concentration, the R&D staff input, patent, the number of enterprises, employee concentration, enterprise
scale, transport conditions, coefficient of industrial concentration of related industries are all higher than the
regions of general development level, showing that these factors influence the regions of general development
level of industrial cluster greater. It is consistent with the cyclical theory of an industrial cluster. The more mature
areas, the increase in costs outweigh the increase in benefits because of the excessive enterprise clusters, which
resulting in the decrease of scale returns.
V. Conclusions
In recent years, the cluster areas of electronic and communication equipment manufacturing industry in the
global are in constant adjustment. But its agglomeration effects are in decline. The overall level of clustering is
continuously improving, but the trend of the cluster has slowed down and has declined signs. The electronic and
communication equipment manufacturing industry cluster plays a significant role in promoting the development
itself and the growth of the provincial economy. Essentially the industrial cluster degree has a positive influence
on the industrial output of the condition that the revenue is higher than the cost. And a large number of studies
have shown that the electronic and communication equipment manufacturing industries in the regions of in the
global are in this stage. No matter which stage the industry cluster is in, paying attention to the impact factors to
the development level of industrial clusters cannot only promote the sustainable development of industry clusters,
but also can avoid improving the cluster degree blindly and control unnecessary cost.
From the above empirical results we can see that in the regions of general development level of industrial
clusters, patent application quantity, number of enterprises, employee concentration, enterprise scale, transport
condition and concentration of relevant industries have a greater influence on the development level of industrial
cluster than in the regions of high development level. Among them the enterprise scale and employee
concentration show that much better. The policy that transferring the industry from eastern regions to western
regions carried out in China at present is especially conducive to the development of western regions. Most of the
regions in general and bad cluster degree of electronic and communication equipment manufacturing industry are
in the West. These regions attract more scale enterprises and cultivate enough employees of the required quality
level of the industries and related industries, so the benefits it provides will be more noticeable than the industrial
clusters with high development level. While the innovation capability in the regions of high development level of
industrial clusters is stronger, so we can improve their innovation ability further to increase and maintain the
progress level of industrial clusters. There are positive effects for the development level of industrial clusters in
all regions to improve the traffic condition and the concentration degree of allied industries. Unquestionably it is
closely related to the local economic level.
This paper makes classification analysis on the effects of the advance level of industrial clusters of each
impact factor with the development level of industrial clusters. Nonetheless, the results may be different if the
classification method is changed. For example, dividing into three kinds according to the east, the center and the
west, the results will also be different. At the same time the number of years considered in this paper is limited,
more detailed and more accurate research is hoped for future researchers.
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