AN EMPIRICAL EVALUATION OF THE COMPETITIVENESS
OF THE FINANCIAL SERVICES INDUSTRIAL CLUSTER
Zhang Huiwen, Gu Baoyan
School of Management, University of Shanghai for Science and Technology
516 Jungong Road, 200093 Shanghai, P.R.China
Huang Hai
School of Management, Shanghai Finance University, 201209 Shanghai, P.R.China
Keywords: Financial services industrial cluster, Competitiveness, Evaluation index system, Three domestic economic
rings.
Abstract: The paper mainly intends to establish an evaluation index system of the competitiveness of the industrial
cluster of financial services and an empirical study of the competitiveness of the financial services industrial
cluster of the provinces (cities) within the three domestic economic rings. On the basis of comparing the
differences of the competitiveness of the financial services industrial cluster of the three economic rings,
strategies and suggestions about the further improvement of the competitiveness of the financial services
industrial cluster of the three domestic economic rings are formed.
1 PREFACE
The practice of reform and opening-up proves that
the prompt development of Chinese economy is up
to the driving of some core areas with economic
vitality. For example, the Pearl River Delta Region
having Shenzhen and Guangdong as its core was the
powerful engine of the Chinese economy during the
80s and the middle 90s. Till the middle and late 90s,
the Yangtze River Delta Region, having Shanghai as
its core, have begun to promote the economic
development of Chinese economy. Now the Circum-
Bohai Economic Ring has become the third growth
pole following the Pearl River Delta Region and
Yangtze River Delta Region and attracted wide
domestic and overseas attention. The co-existence of
competition and co-ordination between the three
economic rings and cities within the rings has
surfaced and the competitiveness of the provinces
and cities have become the determinants of the
social development, economic growth and
competitive situation of the regions. As an
important environmental factor of the
competitiveness of provinces, cities, regions, even
nations, financial competitiveness determines the
competency and efficiency of the allocation of
financial resources of various layers. With the
quickening of financial opening-up and degree of
market in finance, the part of the financial services
industrial cluster plays in increasing the
competitiveness of the enterprises, regions and
nations will be more outstanding.
This paper intends to make a quantitative
measurement of and comparison between the
competitiveness of the financial services industrial
cluster of the three economic rings of China and
provinces and cities within the economic rings,
understand the relative status and differences
between the competitiveness of the financial
services industrial cluster of the provinces and cities
within the rings, and the general situation of
development of the three economic rings, and
provides referential basis for the sensible allotment
of regional financial resources, orderly flow and
relevant organs’ scientific decision-making.
212
Baoyan G., Hai H. and Huiwen Z.
AN EMPIRICAL EVALUATION OF THE COMPETITIVENESS OF THE FINANCIAL SERVICES INDUSTRIAL CLUSTER.
DOI: 10.5220/0003269102120217
In Proceedings of the Twelfth International Conference on Informatics and Semiotics in Organisations (ICISO 2010), page
ISBN: 978-989-8425-26-3
Copyright
c
2010 by SCITEPRESS – Science and Technology Publications, Lda. All rights reser ved
2 THE BASIC FRAMEWORK OF
THE EVALUATION INDEX
SYSTEM OF THE
COMPETITIVENESS OF THE
FINANCIAL SERVICES
INDUSTRIAL CLUSTER
Chinese and foreign scholars have different analyses
on the source of the competitive advantages of the
financial services industrial cluster in that these
analyses have different emphases of factors to
consider and have respective characteristics,
advantages and weak points. Because it is hard to
give scientific and reasonable evaluation to the
financial services industrial cluster, Index System,
although widely, is not yet established.
On the basis of close relating to the reality of the
development of the industrial clusters of Chinese
financial services, this paper consults the current
evaluation index systems and evaluation models of
the industrial clusters, takes into account the
characteristics of the financial services industrial
cluster, classifies them according to natures of
various factors, and believes that the evaluation of
competitiveness of the financial services industrial
cluster can be considered from three aspects:
Scale competitiveness of the financial services
industrial cluster: is the apparent index, and is also
the most important indicator to measure the might of
the financial services industrial cluster.
Developmental competitiveness of the
financial services industrial cluster: is the
efficiency index of development of the financial
services industrial cluster and represents the depth
and width of the development of the financial
services industrial cluster.
The environmental competitiveness and the
external environmental factors of financial
services: have the most important influence on the
competitiveness of the industrial cluster.
The final thing is the selection of indexes. This
paper has introduced 20 index variants to reflect the
competitiveness of industrial clusters of financial
services. Please refer to Table 1 for specific index
system.
3 THE MEASUREMENT AND
COMPARISON BETWEEN THE
COMPETITIVENESS OF THE
FINANCIAL SERVICES
INDUSTRIAL CLUSTER OF
THREE ECONOMIC RINGS
3.1 Choosing of Samples and Index
Data
According to the aforementioned evaluation index,
this paper evaluates the competitiveness of the
financial services industrial cluster of the twenty
cities and provinces within the three domestic
economic rings. Among them, the geographical
scope of Circum-bohai Economic Ring contains five
provinces and two cities (Beijing, Tianjin, Hebei,
Liaoning, Shandong, Shanxi, and Inner Mongolia),
the Pan-Yangtze River Delta Region covers four
Table 1: Evaluation index system of the competitiveness
of the financial services industrial cluster.
Categories of Index Items of Index
Scale Competitiveness
(X1)
Personnel of financial
services (X11), Number of
legal persons of financial
institutions (X12), Deposits
at the end of a year (X13),
Loans at the end of a year
(X14), Percentage of
deposits (X15), Percentage
of Loans (X16), Deposit
Balance (X17), Fiscal
Budget Expenditures
(X18), Income of Insurance
Fees (X19)
Developmental
Competitiveness
(X2)
Financial Output Value
(X21), Proportion of
deposits to loans (X22),
Deposit Amount Per Capita
for financial institutions
(X23), Loan Amount per
Capita for financial
institutions (X24),
Financial co-relational
factors (X25),
Securitization Rate of
Capital (X26)
Environmental
Competitiveness
(X3)
Regional General Product
(X31), Formation of Capital
(X32), Investment Amount
of Fixed Assets (X33),
Final Consumption (X34),
Really Used Foreign
Capital (X35)
AN EMPIRICAL EVALUATION OF THE COMPETITIVENESS OF THE FINANCIAL SERVICES INDUSTRIAL
CLUSTER
213
provinces and one city (Shanghai, Jiangsu, Zhejiang,
Anhui, and Jiangxi), Pan-Pearl River Delta Region
used to cover the scope of nine provinces and two
districts including Jiangxi, and the two special
administrative regions of Hong Kong and Macau
according to original “Framework Agreement of the
Regional Co-operation of Pan-Pearl River Delta
Zone”. In view of the fact that Jiangxi province has
been included in the developmental framework of
Pan-Yangtze River Delta Region and the big
differences between the two special administrative
regions of Hong Kong and Macau, and the mainland
provinces, the Pan-Pearl River Delta Region chooses
eight provinces and autonomous regions of Fujian,
Hunan, Guangdong, Guangxi, Hainan, Sichuan,
Guizhou, and Yunnan as research objects. The data
come from the statistical almanacs of the provinces,
Chinese Financial almanacs, statistical almanacs of
Stock Exchanges, Annual reports of Securities
supervisory Committee and Banking Supervisory
Committee. Some data is from various relevant
websites of financial institutions and financial
regulatory organs. All the data was through
calculation and arrangement, and the time of data is
December 29th, 2009.
3.2 The Empirical Analysis on the
Competitiveness of Financial
Industries for Three Economic
Rings
3.2.1 The Process of Empirical Analysis
In this paper, we use principal component and factor
analysis to evaluate the competitiveness of financial
industries for different cities. According to the
requirements of factor analysis, we construct an
evaluation matrix: using the indicated variables and
data of 20 provinces (regions) to establish an
evaluation matrix with 20 rows and 20 columns, and
then analyze the correlation matrix of variables to
validate the significance of factor analysis.
Considering the different variable units and
extremely large variances for some variables, in
order to avoid undue influence on the factor
loadings, we apply non-dimensional treatment to the
raw data, that is, we standardize the raw data and get
Z scores. After the transformation, the mean value is
zero and the standard deviation is 1 (the data and
process omitted).
With SPSS software, we used a quartimax
method to rotate the data 25 times, so that the
elements in each column of the component matrix
can be polarized enough to explain the practical
meaning of the common factors. The number of
extracted factors depends on the common factors’
variance, to the original variables. With a greater
variance contribution, the common factor can
portray the study area’s characteristics with a higher
level of clarity. When the eigenvalues of several
important factors are greater than 1, and their
cumulative variance contribution rate reaches or
exceeds 85%, these factors can represent the original
variables to reflect the overall characteristics of the
study area. By solving the characteristic equation of
the correlation matrix, we obtained 20 unit
eigenvectors. The results showed that three
components could be selected as the principle
components for further analysis. After orthogonal
rotation, the eigenvalues and variance of the three
principle components are shown in Table 2.
Table 2: Total variance explained after rotation.
Component Eigenvalue
% of variance Cumulative %
F1 5.859 39.057 39.057
F2 5.857 39.044 78.101
F3 2.784 17.504 95.605
3.2.2 The Explanation of Principle
Components
According to the rotated component matrix and
component score coefficient matrix, we can analyze
and explain the three principal components.
Principal component 1 has a larger loading in the
variables of year-end balance of deposits, year-end
loan balance, premium income, savings balance,
percentage of deposits, percentage of loans, budget
expenditures, regional GDP, fixed asset investment,
the number of financial institutions, and the number
of financial practitioners. The variables of the
component represent the overall size and
development level of economy and financial
industry, so component 1 can be regarded as the
scale component of the financial industry’s
development. Its contribution rate is 39.057%.
Component 2 has a larger loading in the variables of
financial industry output, per capita deposits for
financial officers, per capital loans for financial
officers, deposit-loan ratio, final consumption,
financial interrelation ratio, and capital securitization
ratio. The variables mainly reflect the depth and
breadth of financial industry’s development, so the
component can be regarded as the efficiency
component of financial industry. Its contribution rate
is 39.044%. Component 3 has a larger loading in the
variables of actual foreign investment and capital
ICISO 2010 - International Conference on Informatics and Semiotics in Organisations
214
Table 3: Competitiveness rankings of financial industry.
Region F1 F2 F3 F Ranking
Shanghai 2.56872 1.18506 1.52383 1.88867 1
Beijing 0.75469 2.26458 1.11495 1.43727 2
Shenzhen 1.05803 1.22925 1.90641 1.28328 3
Guangdong 1.17032 0.85762 0.80739 0.98245 4
Zhejiang 1.04885 1.07945 0.07185 0.88247 5
Jiangsu 1.11583 0.78663 -0.10131 0.75855 6
Tianjin 0.89594 0.27343 -0.47115 0.39142 7
Liaoning 0.37916 -0.00756 -0.25952 0.10429 8
Shandong 0.10938 -0.16659 -0.28557 -0.07563 9
Fujian -0.08294 0.10204 -0.05263 -0.17657 10
Sichuan -0.26171 -0.32813 -0.19326 -0.27630 11
Hebei -0.36543 -0.35482 -0.26224 -0.34220 12
Anhui -0.29976 -0.58798 -0.43536 -0.44229 13
Inner Mongolia -0.55198 -0.47183 -0.39877 -0.49120 14
Shanxi -0.30064 -0.75948 -0.50119 -0.52474 15
Yunnan -0.47501 -0.69985 -0.71032 -0.60991 16
Jiangxi -0.49673 -0.70131 -0.68865 -0.61542 17
Hainan -0.80162 -0.68823 -0.34326 -0.67139 18
Guangxi -0.73296 -0.78345 -0.50951 -0.71267 19
Guizhou -0.74587 -0.95174 -0.74969 -0.83064 20
Table 4: The clustering of financial competitiveness.
First grade Second grade Third grade Fourth grade
Shanghai, Beijing, Shenzhen Guangdong, Zhejiang,
Jiangsu, Tianjin, Liaoning
Shandong, Fujian, Sichuan,
Hebei, Anhui, Inner Mongolia
Shanxi, Yunnan, Jiangxi, Hainan,
Guangxi, Guizhou
formation. It mainly reflects export-oriented
economy’s influence on the competitiveness of
financial industry, so the component can be seen as
the outward component. Its contribution rate is
17.504%.
3.2.3 Component Scores and Ranking
We used regression to get the three component
scores F1, F2, F3, and then compute the weighted
comprehensive component score with the variance
contribution rate as the weight, that is, F = (F1 *
39.057 + F2 * 39.044 + F3 * 17.504)/95.605. The
comprehensive score shows the competitiveness of
respective provinces (regions) in three economic
circles of our country and we can rank them (see
Table 3).
3.2.4 Clustering of the Provinces (Regions)
Based on the preceding component analysis, we get
clustering variables through multiplying the sample
provinces’ component score by their respective
contribution rate, and then we use clustering analysis
to divide up the provinces according to their
competitiveness in financial industry. After
clustering, we divide the provinces with different
competitiveness in financial industry into four
categories (see Table 4).
3.3 Analysis of the Results
The comprehensive ranking and clustering results
reflect the developmental differences between the
financial services industrial cluster of the three
domestic economic rings. It can be clearly found out
from the table that the competitiveness of the
financial services industrial cluster of Shanghai,
Beijing, and Shenzhen is mighty and plays a leading
role respectively in the Pan-Yangtze River Delta
Region having Shanghai as its centre, Circum-bohai
Economic Ring having Beijing as its centre, and
Pan-Pearl River Delta Region having Shenzhen as
its centre. Within the three economic rings, Pan-
Yangtze River Delta Region ranks No. 1 with a
powerful comprehensive competitiveness of the
financial services industrial cluster, and circum-
Bohai Economic Ring ranks No. 2 with an obvious
AN EMPIRICAL EVALUATION OF THE COMPETITIVENESS OF THE FINANCIAL SERVICES INDUSTRIAL
CLUSTER
215
increase of competitiveness, while Pan-Pearl River
Delta Region has a relatively weaker
competitiveness of the financial services industrial
cluster. From the perspective of comprehensive
ranking, the ranking of Shanghai as No.1, Beijing as
No.2, and Shenzhen as No.3 corresponds with our
qualitative analysis of the development of economy
and finance of the three cities. From the perspective
of specific data, the comprehensive score of
Shanghai, Beijing, Shenzhen, Guangdong, Zhejiang,
Jiangsu, Tianjin, and Liaoning are above zero, which
indicates that their performance level is above the
average level within the three economic rings. All
the other provinces, whose comprehensive score are
below zero, fail to reach the average performance
level. Hence the big differences of the
competitiveness of the financial services industrial
cluster between the big three economic rings and
cities within the provinces. Seen from the leading
role of the development of the financial services
industrial cluster played by Shanghai as the core of
the regional development of the Pan-Yangtze River
Delta Region, Shanghai is unique in both the
developmental scale of the financial services
industrial cluster and depth and width of
development of the financial services industrial
cluster, in that it has a balanced development of
banking, securities and insurance, forms a relatively
complete market financial system, is the centre of
the national financial market, hosts a large number
of foreign-funded financial institutions, and has a
leading degree of financial opening-up in China.
Seen from the perspective of the developmental
effect of the financial services industrial cluster of
the central cities of the three economic rings, Beijing
and Tianjin have obvious advantages. Being the
capital, Beijing is the location of many headquarters
of domestic financial institutions and regional
headquarters of international financial institutions so
it has an exclusive advantage of financial
competitiveness of headquarters, which is also able
to connect ideally with the advantages of powerful
international trade and shipping of Tianjin. All that
cannot be emulated by cities and provinces of other
economic rings and is the reason why the
development of the industrial cluster of financial
services of Circum-Bohai Economic Ring can
surpass its previous leaders. Seen from the
perspective of the degree of opening-up of the
development of the industrial cluster of financial
services of the central cities of the three economic
rings, Shenzhen, as the earliest special economic
zone of China, plays the important part of the testing
field of reform and opening-up and enjoys a high
opening-up level of economic and a relatively high
degree of co-operation between Hong Kong,
Shenzhen and Macau because of its vicinity with the
two special administrative zones.
4 CONCLUSIONS AND
SUGGESTIONS
Empirical studies indicate that extremely close
relationship exists between the competitiveness of
the industrial clusters of regional financial services
and comprehensive regional competitiveness,
especially the economic developmental levels.
Gradient differences of modes of economic
development, developmental levels, clustering
abilities of the financial services industrial cluster,
radioactive abilities, spillover effects, financial
market system, financial organization system, and
degree of financial opening-up exist between the
three economic rings. Different judiciary
environments and social credit environments cause
big differences between rings, which generates the
gradient circumstance of the competitiveness of the
financial services industrial cluster among the three
economic rings and provinces and cities within the
rings. That has been proved by the competitiveness
rankings and clustering results of the
competitiveness of the financial services industrial
cluster within the three economic rings and the
clustering results, which basically corresponds with
the regional differences of the economic
development in reality. Thus it can be illustrated that
the competitiveness evaluation index of the financial
services industrial cluster chosen by this paper
closely relates to the fact of development of regional
finance. And on the basis of this evaluation, the
reasons of the differences of regional financial
development are found and suggestions and
strategies to improve the competitiveness of the
financial services industrial cluster are provided.
As for the Pan-Yangtze River Delta Region
having Shanghai as its centre, the source and
improvement of the competitiveness of the industrial
clusters of regional financial services depends on the
strong demand for funds and financial services in the
developmental process of regional economy, which
is the basis of improving the competitiveness of the
industrial clusters of regional financial services.
Simultaneously, the impetus of policy is also an
important factor of development of the industrial
cluster of financial services of the Pan-Yangtze
River Delta Region. Finally, on one hand,
organizational system of financial institutions of the
ICISO 2010 - International Conference on Informatics and Semiotics in Organisations
216
provinces and cities within the region shall be
continuously optimized; on the other hand, efforts
made by the financial institutions themselves of the
provinces and cities within the region shall also be
strengthened. As for the Circum-bohai economic
ring having Beijing and Tianjin as its center, the
main idea to improve the competitiveness of the
financial services industrial cluster shall be the
continuous expansion of financial opening-up and
institutional innovation, the generation of market
environment to construct the financial services
industrial cluster, the promotion of innovation of
financial instruments and financial services, further
formation of the system of financial organization
with apparent clustering effect and the financial
market system involving currency market, credit
market, security market, insurance market promoting
each other, and the exertion of core effects of the
financial services industrial cluster in the functions
of regional development. As for the Pan-Pearl River
Delta Region having Shenzhen and Guangdong as
its centre, improving the competitiveness of the
financial services industrial cluster takes optimizing
regional economic environment, creating conditions
for the co-ordination and development of economy
and finance and forming source and basis for the
competitiveness of the industrial cluster of financial
services of the provinces and cities within the region
through increasing the attraction toward financial
institutions, high quality labor resources and
infrastructure, and sensible and sound regulatory
environment.
The comparative study of the competitiveness of
the financial services industrial cluster of the three
economic rings extends a powerful foundation for
the central bank to stipulate differentiated regional
financial and currency policies: The central bank
may stipulate differentiated indexes of economic
development to reflect the degree of market, degree
of currency, and degree of integration that indicates
economic development of different regions, and
marginal profit rate of the funds and indexes of
returning loans to reflect the abilities of input and
output of funds of different regions to promote the
cross-region flow and operation of funds and
financial institutions, break the regional barriers of
financial resources, mend the segmented situation of
regional financial resources, promote the formation
of the spatial flow and mechanism of allocation of
financial resources to increase the competitive level
of the financial services industrial cluster of the
region as a whole.
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