Technology and Investment, 2013, 4, 1-5
Published Online February 2013 (http://www.SciRP.org/journal/ti)
A Multiple Regression Analysis on Influencing Factors of
Urban Services Growth in China
Yuan Gao, Phd Candidat1, ABDUL Razak bin Chik2
1School of Economics, Finance &Banking, COB, University Utara Malaysia Sintok , Kedah, Malaysia
2College of Economics, HeBei University, 071000, BaoDing, , HeBei, China
Received 2012
ABSTRACT
The indicator of urban success is the success of its urban services. Although much research on services have been made,
there is major gap with regard to the regional services, especially on urban services within a country. As for urban ser-
vices, there are few research on factors influencing urban services and its effect on regional growth. In reaction to this,
the government intend to accelerate the development of urban services and regional economy in the present Twelfth
Five -Year Plan 2011-2015.Thus, the main purpose of this paper is to investigate the factors that influence urban servic-
es growth from demand , supply, institutional environment and spatial agglomeration side. By using cross-section mul-
tiple regression analysis, the study examine the factors influencing urban services growth in China .The model indicated
that except for urbanization, division of labor , other independent variables have contributed positively towards urban
services growth in China.
Keywords: Urban Services; Influencing Factors; Services Growth
1. Introduction
Since reform and opening up, China has made remarka-
ble economic achievements. In the growing process, its
economic structure has also undergone a transformation,
and its industrial structure has been gradually optimized
and deepened. One important manifestation is the status
of services in China's economy has been greatly en-
hanced that their role in promoting economic develop-
ment has been growing(Cheng,2003).Urban are the main
carrier for services’ development（Ji,2004.In 2009, 71%
of value added services in China was created by 285 ci-
ties at prefecture level. (Source of data: China statistical
Yearbook, 2010). Most of the cities have formed a ser-
vice -oriented industrial structure. At present, China has
entered into the post-industrial period, Chinese govern-
ment had already come out with policies to promote ur-
banization process as early as on the Eleventh Five Year
Plan. In the Eleventh National Congress in 2011,Chinese
government stressed once again to accelerate the interac-
tive development of urban services and urban economy
in the Twelfth Five-Year Plan(2011-2015) .The symbol
of urban success is the development level of urban ser-
vices, especially level of knowledge-intensive services.
Urban services are playing an increasingly important role
in the sustained and rapid growth of Chinese economy,
but problems of inadequate total output, inferior internal
structure , apparent regional disparities , have become
major resistance in urban services’ growth. Especially,
the expanding regional gap in services is bound to affect
its sustainable development as well as enlarge the im-
balance in regional economies. In this case, probing the
factors influencing urban services growth have practical
significance for the entire countr y as well as the regional
gro wth.
2. Development of Research Hypotheses
As to factors influencing urban services growth, they will
be classified into demand, supply, the institutional envi-
ronment and services’ spatial concentration side.
2.1. Demand factors and Urban Services Growth
Per capita income and urbanization level are main factors
affecting demand for consumer services. The influence
of Per capita income levels on service is only effective
on consumer services. According to Engel's law and
Maslow's hierarchy of needs theory, human needs are
hierarchical. People will always shift to higher level
needs only after meeting the low-level needs.
Urbanization has brought the concentration of popula-
tion. The concentrated population can generate a huge
mutual demand for services, thereby create necessary
conditions for services’ survival .

Y. GAO ET AL.
Industrialization level, division of labor are main de-
mand factors of producer services’ growth. The urban
with well developed secondary industries is easy to at-
tract the service enterprises to enter, bringing the saving
of transaction costs and the increasing opportunities of
servi ce’s business.
The division and specialization of labor affect servic-
es’ growth in two ways: First, the division of labor be-
tween service and other industries or within services can
increase labor productivity.. Social division of labor
makes a number of industries or sectors differentiated
from other industries or sectors, becoming the indepen-
dent service sectors.
It is, therefore, posited that:
Hypothesis 1: There is a positive relationship be-
tween demand factors and urban ser-
vices growth
Hypothesis 1a: There is a positive relationship be-
tween per capita disposable income
and urban services growth
Hypothesis 1b: There is a positive relationship be-
tween urbanization level and the ur-
ban services growth.
Hypothesis1c: There is a positive relationship between
industrialization and urban services
growth
Hypothesis 1d There is a positive relationship between
division of labor and the urban services
growth .
2.2. Supply Factors and Urban services Growth
According to new-classical economics, which is supply
oriented and emphasizes factor accumulation and tech-
nical progress in economic growth, the amount of physi-
cal capital and labor input in services are directly related
to its output. The higher proportion of physical capital
and labor input in services, the more helpful to promote
services’ growth.
Besid es, the endogenous growth theory, which empha-
sizes the role of human capital accumulation and exter-
nalities, human capital has become an important source
of technological progress and economic growth.
The Structural economists, such as Clark& Fisher [6]
and Chenery [5], found that the economic or industrial
structure was the main force affecting growth.
Then, the hypothesis will be
Hypothesis2: There is a positive relationship between
the supply factors and urban services
growth .
Hypothesis2a: There is a positive relationship between
the input of physical capital and urban
services growth.
Hypothesis2b: There is a positive relationship between
the input of human capital and urban
services growth.
Hypothesis2c: There is a positive relationship between
the input of labor force and urban ser-
vices growth
Hypothesis2d: There is a positive relationship between
the configuration structure of produc-
ing factors and urban services growth.
2.3. The Institutional Environment and Urban
Services Growth
The market-oriented degree have a major impact on the
development of service industry. The higher market de-
gree in local economy, the higher level of its develop-
ment. The development of non-state economy breaks the
monopoly of state-owned economy which helps the
pro-competitive mechanisms play a role.
Political freedom and economic openness has a posi-
tive effect on economic growth [1]. The high openness
level of services in China would promote its growth
through expanded market effect, knowledge and tech-
nology spillovers effect, expanded foreign direct invest-
ment effect, competition and innovation effect. .
The following hypothesis is thus stated
Hypothesis 3: There is a positive relationship between
favorable institutional environment and
urban services growth.
Hypothesis 3a: There is a positive relationship between
economic market-oriented level and
urban services growth;
Hypothesis 3b: There is a positive relationship between
services openness level and urban ser-
vices growth;
2.4. Services’ Spatial Agglomeration and Urban
Services Growth
According to the new geography economics , the spatial
agglomeration of economic activities will be a source of
agglomeration economies that will improve production
efficiency, increase consumer utilities and thus accelerate
economic growth.
Thus,
Hypothesis 4: There is a positive relationship between
services’ spatial agglomeration and urban
services growth.
3. Multiple Regression Analysis
3.1. The Data
This paper selected prefecture-level cities of 30 provinc-
es of china , excepting Tibet ,Hong Kong, Macao and
Taiwan, municipalities. The main method of data collec-
tion is the analysis of documents of official statistics.
Therefore, the main sources of data are secondary data
and the data source is from corresponding year of China
Statistical Yearbook and China Urban Yearbook. Foreign
direct investment data will be from the corresponding
2

Y. GAO ET AL.
year of the "China Foreign Economic and Trade Year-
book".
3.2 Variables
In this study a standard regression method has been con-
ducted in order to test the relationships between influen-
cing factors and urban services growth because all inde-
pendent variables are assumed of equal importance..
Therefore, this study used multiple regression method by
taking the supply, institutional environment and services’
spatial agglomeration into consideration to make a com-
prehensive analysis to test hypotheses 1-4.
Hence, the multiple regression equation is :
lnURBSi=α0+β1lnPIi+β2URBANi+β3lnIND i+β4DISi+
β5lnSKi+β6lnSLi+β7lnSHKi+β8lnSFPi+
β9lnFDIi+β10MARKi+β11USLQi+εi (1)
where
α0: the constant
i: the city i
βk: partial regression coefficients of independent va-
riables
sεi : the random disturbance
URBS= urban services growth level, which is the urban
services output value
PI=per capita disposable income, which is the total dis-
posable income divided by population
URBAN= urbanization level, which is the proportion of
urban non-a gricu lture population to urban total
population
IND=industrialization level, which is the output value of
the secondary industries
DIS= the level of division of labor, which is the
proportion of industrial added value to total
industrial output value
SK=physical capital in urban services, which is the fixed
capital investment in services
SL= labors in urban services, which is measured by the
employees in urban services
HUM = human capital, which is the number of university
students per ten thousand people
SFP =structure of producing factors in services ,which is
the ratio of value added urban services to the
number of employees in service sectors
FDI=the urban openness level, which is the urban foreign
direct investment
MARK= the urban economic market level, which is
the proportion of non-state-owned economies’
output value to GDP
USLQ= urban services’ concentration level, which is the
index of urban services’ location quo ti ent
As for the independent variable of FDI, it will firstly be
converted to the value of the Renminbi using the average
exchange rate of 2011 year. As for the variables of
URBS, IND, FDI ,SK ,PI, they were converted into the
value calculated by the constant 1978 consumer price
index. Then take the natural logarithm of variables with
absolute values to eliminate heteroscedasticity.
3.3. Assumptions Results of Regression
3.3.1. Linearity
An examination of residual scatterplots is employed to
test the assumption of linearity as suggested by
Coakes&Steed(2003). If there is no clear relationship
between the residuals and the predicted values, the
assumption of linearity should be met. By plotting the
stand ardized predicted values (ZPRED ) against the
standardized residuals (ZRESID) , the result of testing
linearity through scatter plot diagrams is shown in Fig-
ure1 , which shows no evidence of nonlinear pattern to
the residuals.
Fi gur e 1 The Scatter Plot for linearity and Homoscedasticity
3.3.2. Normality
The next assumption to be checked is the normality of
the error term with both the normal p-p plot and the
histogram of the distribution of the residuals.
Figure 2 The P-P Plot of Normality Test
3

Y. GAO ET AL.
The cumulative probability plots of residuals (P-P plot)
Is used to judge whether the distribution of variables is
consistent with a specified distribution. if the Standar-
dized residuals are normally distributed, the scatters
should fall on or tightly close to the normal distribution
line .Figure.2 shows that the scatters of the residuals
basically fall straightly on the normal distribution line,
indicating a normal distribution of residual
3.3.3. Homoscedasticity
The residual scatterplots could also be used to test the
assumption of homoscedasticity.If there is no clear
relationship between the residuals and predicted values,
the assumption of homoscedasticity should also be met.
In this study , by plotting the standardized resi-
duals against the predicted values as shown in
figure1 , the researcher found there was no clear
relationship between the residuals and the predicted
values. Therefore, the results suggest that the assumption
of homoscedatisity should be met in this study
3.3.4. Mult icollinea rity
Multicollinearity test is important because if multicolli-
nearity exists between two or more independents va-
riables it can deteriorate the results of multiple regression.
In this study, multicollinearity has been examined be-
tween the independents variables using VIF as shown in
Tab le 1
Table 1. Tolerance Value and the VIF of variables
Variables
Collinearity statistics
Tolerance
VIF
LnSFP
lnIN D
0.257
0.158
3.898
6.333
Ln SL
0.187
5.345
LnUS LQ
Ln SK
0.421
0.193
2.373
5.169
Ln PI
Ln FDI
MARK
0.244
0.343
0.841
4.105
2.914
1.189
D IS 0.887 1.128
URBAN
0.534
1.873
LnHUM
0.314
3.186
The result in Table1 indicates that multicollinearity
does not exist among all independent variables because
the Tolerance values are more than .10 and VIF values
are less than 10. The result suggests that the current study
does not have any problem with multicollinearity and
this allows for standard interpretation of the regression
coefficients.
4. OLS Estimation of the Model
In this model, R² value for the first stage of analysis re-
gression model is 0.967 (refer to Table2), which means
that the influencing factors explain 97 per cent of the
variance in the urban services growth. Standard multiple
regression also provides an adjusted R² value. The ad-
justed R² value in this model was 0.966, indicating a
pretty well fitness of the model.
ANOVA was used to assess the statistical significance
of the result. The result in Table 3 demonstrates that the
null hypothesis that the multiple R in the population is
equal to 0 is rejected since the model of this study is sta-
tistically significant at p= .000).
Tabl e 2 Model Summary
Mod el R R Square
Adjusted
R
Square
Std. Error of
the Estimate D.W
1 .984
a
.967 .966 .17497479 2.011
Tabl e 3 ANOVA
Model
Square
Sum
df
Mean
Squar
e F Sig.
1
Regres-
sion
231.990
11
23.19
9
757.737
.000
a
Residual
7.838
256
.0031
Total
239.828
266
a. Predictors: (Constant), DIS, MARK, LnFDI, USLQ, URBAN, LnHUM, LnSL,
LnSFP, LnPI LnSK, LnIND
b. Dependent Variable: LnURBS
Table 4 coefficients
Variab les
Unstandar-
dized
Coeff i ci e nts
Standardize d
Coeff i ci e nts t Sig.
B
Std.
Error Beta
1
(C)
-0.11
7 0.195 -0.597 0.551
LnSFP
LnIND
0.869
0.406
0.021
0.033
0.433
0.394
41.720
12.358
0.000
0.000
LnSL
0.342
0.029
0.344
11.885
0.000
LnUS LQ
LnSK
0.22
6
0.111
0.017
0.029
0.233
0.110
13.388
3.902
0.0000
0.000
LnPI
LnFD I
MARK
0.110
0.090
0.002
0.023
0.018
0.001
0.109
0.090
0.031
4.776
4.964
2.842
0.000
0.000
0.014
D IS
0.501
0.802
0.007
0.625
0.533
URBAN
0.05
6
0.036 0.024 1.5 63 0.119
LnHUM
0.073
0.065
0.023
1.133
0.258
As can be seen from Table 4, the R² was statistically
significant, with F =757.737 and p < .001, the common
expression of the regression equation is stated as follows:
4

Y. GAO ET AL.
LnURBS=0.11LnPI+0.406LnIND+0.111LnSK+
(4.78***)(12.358**)(3.902***)
0.342LnSL+0.090LnFDI+0.869LnSFP
(11.885***) (4.964***) (41.720*** )
+0.226USLQ+0.002MARK
(13.38***) (2.482**) (2)
Adjusted –R2: 0.966 ; F-statistic:757.73*** ; D.W.: 2.011
The regression coefficients are also tested that they dif-
fer significantly from zero. The results in Table Ⅳ
shows that except for variables of DIS( division of la-
bor) , HUM(human capital),URBAN(urbanization) , the
other independent variables contributed significantly to
the urban services growth in China . The services internal
structure( SFP) has the highest contribution on urban
services growth in china amongst the independents va-
riables. Other variables were also significantly and posi-
tively contributed to the urban services growth arranged
in descending order:, the industrialization level(IND), the
labor in urban services(SL), urban services spatial ag-
glomeration(USLQ ) , the physical capital investement in
urban services(SK) , the per capital income level(PI),
the economic openness level(FDI), the economic market
level(MARK) .Table5 summarizes results of re-
search findings related to the hypothesis1-4.
Table 5 Summarizes of Research Findings
Hypothesis Significant T-value
Assumption
of hypo-
thesis
H1a
H1b
H1c
H1d
H2a
H2b
H2c
H2d
H3a
H3b
H4
Yes
No
Yes
No
Yes
Yes
No
Yes
Yes
Yes
Yes
4.776* **
1.56
12.358 ***
0.625
3.902* **
11.885 ***
1.133
41.720 ***
2.842**
4.984* **
13.388 ***
Supported
Not Sup-
ported
Supported
Not Sup-
ported
Supported
Supported
Not Sup-
ported
Supported
Supported
Supported
Supported
Note : ***、** represent the t-values are statistically significant at 1%, 5%,
level respectively
5. Conclusion
This paper has examined the hypotheses concerning the
relationship between influencing factors and urban ser-
vices growth by multiple regression analysis. It appears
that the internal configuration structure of producing
factors in urban services( SFP) , has the highest con-
tribution on urban services growth in china . Other va-
riables namely the industrialization level(IND), the labor
in urban services(SL), urban services spatial agglomera-
tion(USLQ ) , the physical capital investment in urban
services(SK) , the per capital income level(PI), the eco-
nomic openness level(FDI),the economic market lev-
el(MARK) were also significantly and positively con-
tributed to urban services growth. But, the study did
not find any significant relationship between
urbanization(URBAN), the division of labor (DIS), the
human capital in urban services(hum) and urban services
growth .
6. Acknowledgments
In completing this paper, I would like to express my
deepest appreciation to my supervisor Professor Dr.
ABDUL Razak bin Chik , who has been very patient in
guiding me and supporting from the very beginning of
my thesis. He assisted me immensely in focusing my
thinking and ideas towards the right direction and gave
me his valuable ideas, insights, comments and sugges-
tions towards understanding the empirical predicaments I
have encountered.
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