An Empirical Study of the Influence Factors of Infrastructure Investment on
Promoting Private Investment
--Based on the empirical data of Jiangsu Province from 1990 to 2011
Guo-qing Han 1,Hui Wang 2
1 School of Business, Hohai University, P.R.China, 211100
2 School of Electricity Engineering, Jiangxi Vocational &Technical College of Electricity, P.R.China, 330032
(hanguoqing1983@163.com)
Abstract - The paper analyzes the influence factors of
infrastructure service on promoting private investment by
the method of factor analysis on the data from 1990 to 2011
of Jiangsu Province. The result finds that the main
infrastructure factors on promoting private investment are
per capita road area, total electricity consumption, telephone
penetration, sewage treatment capacity, comprehensive
utilization of industrial solid waste. The level of
infrastructure service on promoting private investment from
2003 to 2011 is higher than that of the period of 1990 to 2002
as cluster analysis. In the end, the paper offers some policy
recommendations.
Keywords - Infrastructure Service, Private Investment,
Factor Analysis, Impact Factors
I.
INTRODUCTION
Infrastructure service provides a large quantity of basic
service for citizens, such as roads, railways, ports, bridges,
airports, water, electricity, gas, posts, telecommunications
and meteorological services, which are needed in
production, living, development and recreational
activities. Because of its basic role and external
characteristics, the infrastructure provides necessary
protection for private enterprises in normal production and
it reduces the information costs and transportation costs of
private enterprise. It needs a long time and huge
investment to establish a perfect infrastructure system
which plays an indispensable role in social development.
The total mileage of highway is 2,886 km at the end of
2005, but it is up to 4,122 km at the end of 2011 in
Jiangsu Province. The highway density of Jiangsu
Province is maximum in our country. The operation
mileage of the railway is more than 2,100 kilometers and
the high-speed rail of Jiangsu section is full opened from
Beijing to Shanghai. The improved infrastructure creates a
favorable environment for private investment. The private
fixed assets investment of Jiangsu Province has reached
1.71982 trillion yuan in 2011, an increase of 25% than the
last year. The proportion of private investment increases
by 2.2 percentage points over the previous year, which has
up to 65.4% in the total social fixed assets investment.
Therefore it is important to arrange the total investment,
The Social Science Project of Ministry of Education(Item No.
10YJA790198)
promote private investment and stimulate economic
development by investigating the specific factor of
infrastructure service on promoting private investment.
Many scholars also study the infrastructure service on
promoting private investment from their own perspective.
Belloc (2006), Martin de Jong(2010), Zhang
Haixing(2001), Li Ronggen(2002), Yang Rong (2010)
and other scholars believe that the investment of positive
fiscal policy is the main focus in the field of the highways,
railways and airports These investments improve the
macro environment of private enterprises and promote
private investment. Yet some other scholars do not
consent to this point, such as Atukeren (2005), Xulia
Gonz'alez (2008), Dai Yuanchen (1999), Liu
Niuxia(2000),Tian Jietang (2002). They believe that the
total wealth of the society is limited, the government
invests heavily in infrastructure service, so the private
investment will be forced to extrusion. Most scholars
study the impact of the infrastructure service on
promoting private investment on the whole, but the
infrastructure is not subdivided. Therefore, this paper
analyzes the influencing factors of infrastructure service
on promoting private investment.
II.
THE SELECTION OF FACTOR INDEX
According to the existing research results of domestic
scholars, the study, taking into account the practical
situation of Jiangsu Province, selects 15 factors on the
basis of the scientific, systematic, comparable and
available principles. These factors are selected from five
areas of transport, energy supply, water and electricity
supply, telecommunications, environmental protection.
Thus we can establish evaluation system of infrastructure
service index. Using factor analysis, we can analyze the
factor of infrastructure service on promoting private
investment and filter out the driving factor(TableⅠ).
III.
A.
EMPIRICAL ANALYSIS
Data Collection
In order to seek the factors of promoting private
investment, the paper selects 15 indicators of the
infrastructure and analyzes the data of Jiangsu Province
from 1990 to 2011 and provides the basis for cluster
analysis.
B. Factor Analysis
Using the method of factor analysis model, the paper
can determine the parameters of the model and explain the
factors on the basis of the results. In order to eliminate the
influence of the dimension, the data is standardized by
SPSS software, and then we can get the factor rotation.
The general model of the factor analysis is as follows:
 X 1  a11 F1  a12 F2  ...  a1n Fn  1 (1)
 X  a F  a F  ...  a F  
 2
21 1
22
2
2n
n
2

...


 X m  a m F1  a m2 F2  ...  amn Fn   m
Among the model,
variable,
(1)
TABLE II.
x1 , x2 , ..., xm are measured
ai j (i  1, 2,..., m; j  1, 2,..., n)
TABLE I.
INFLUENCE FACTORS OF THE INFRASTRUCTURE
SERVICE PROMOTING PRIVATE INVESTMENT INDEX SYSTEM
Layer guide
lines
Influence Factors of the Infrastructure Service On promoting Private Investment
Evaluation System
transport
energy supply
water and
electricity
supply
Telecommunications
Index layer
X1
per capita road area (square meters)
X2
passenger traffic (million)
X3
transport of goods (10,000 tons)
X4
consumption of raw coal(t)
X5
Consumption of saving water
(million cubic meters)
Sewage treatment capacity everyday
(million tons)
compliance rate of wastewater
discharge (%)
telephone penetration rate(the
number of every 1000 people)
the total length of post (km)
green coverage (%)
environmental
protection
Sym
bol
public transport vehicles of every
ten thousand people (unit)
total consumption of
electricity(million kwh)
production capacity (million cubic
meters every day)
comprehensive utilization rate of
Industrial solid waste(%)
comprehensive utilization amount
of Industrial solid waste(10,000
tons)
VARIANCE EXPLAINED
Initial Eigenvalue
is factor loadings;
Fi (i  1, 2,..., m)
is
common
factor, i (i  1, 2,..., m) is special factors. According to
the standard of eigenvalue greater than one, the paper
selects a factor F1(1=13.061). The cumulative variance
contribution rate is up to 87.073%. Therefore we believe
that the common factor is able to explain most of the
original data information with good representation (Table
Ⅱ).
Target
layer
X11(number of every 1,000 people), sewage treatment
capacity everyday X9 (million tons), comprehensive
utilization amount of industrial solid waste X15 (10,000
tons). The five indicators cover transport, energy supply,
water and electricity supply, telecommunications,
environmental protection and so on, it reflects a lot of
content of the infrastructure, so the common factor is
named infrastructure factor.
X6
X7
Component
Total
Variance%
Cumulative%
1
13.061
87.073
87.073
2
0.503
3.355
90.427
3
0.472
3.145
93.573
4
0.38
2.53
96.103
5
0.228
1.521
97.624
6
0.14
0.931
98.555
7
0.097
0.646
99.2
8
0.058
0.389
99.589
9
0.024
0.158
99.747
10
0.015
0.099
99.846
11
0.013
0.084
99.93
12
0.007
0.044
99.975
13
0.002
0.015
99.99
14
0.001
0.008
99.999
Extraction of Square
and Loading
Total
Variance%
Cumulative%
13.061
87.073
87.073
15
0
0.001
100
Extraction Method: Principal Component Analysis.
We can get the factor pattern matrix by SPSS. The
matrix contains the regression coefficients of the equation
which can explain the original variables. The size of
coefficient indicates important degree of variable to factor.
The factor score can be calculated from score coefficient
and standardized value of the corresponding original
variable. The functional equation is as follows:
F1  0.062 X 1  0.076 X 2  0.074 X 3  0.073 X 4  0.074 X 5  0.076 X 6
0.063 X 7  0.069 X 8  0.075 X 9  0.071X 10  0.076 X 11  0.068 X 12
X8
X9
X10
X11
X12
X13
X14
X15
Using the method of variance maximum orthogonal
rotation(Varimax), the paper rotates the factor and we get
the rotated factor loading matrix (Table Ⅲ).
The common factor (F1) has a relatively large loading in
per capita road area X2(square meters), total consumption
of electricity X6(million kwh), telephone penetration rate
0.073 X 13  0.064 X 14  0.075 X 15
(2)
Here we only extract a principal component from the
total variance explained. Therefore, the score of overall
performance is the main component score value F.
After calculating the score of overall performance, the
highest value (1,346,316.82) appeared in 2011, the lowest
value (468,795.87) appeared in 1990. From 1990 to 2011,
the score of overall performance increases on a yearly
basis. The infrastructure service has been greatly
improved with increasing investment in infrastructure,
including transport, energy supply, water supply, postal
and telecommunications, environmental protection.
Because of lack of investment funds at the early stages of
development of private investment, the level of
investment is low in urban facilities, coal, electrical and
water supply, post and telecommunications, landscaping.
After the infrastructure being improved, the influence is
evident in infrastructure service on promoting private
investment. Because of the financial crisis from 2009 to
2010, the investment has reduced greatly in state-owned
enterprises. Although our government launched the four
trillion investment program at the end of 2008, but it did
not make an immediate effect in infrastructure
construction, so the influence has weakened in
infrastructure service on promoting private investment,
the score of overall performance had reduced to
1,244,582.3 and 1,263,728.63 in 2009 and 2010.
TABLE III.
ROTATED FACTOR LOADING MATRIX
Variable
Composition
1
X1
0.062
X2
0.076
X3
0.074
X4
0.073
X5
0.074
X6
0.076
X7
0.063
X8
0.069
X9
0.075
X10
0.071
X11
0.076
X12
0.068
X13
0.073
X14
0.064
X15
0.075
Extraction Method:Principal Component Analysis.
C. Cluster Analysis
Cluster analysis is a basic principle. According to the
intimacy degree of the sample (or target), the first two
highest degree of intimacy classes should be combined.
After considering the degree of intimacy of the combined
classes with other classes, the classes will be combined
again, and we should repeat it until all samples (or target)
merge into one class. The system clustering method is
merged according to the degree of intimacy of the samples
or indicators. There are two methods in measuring the
degree of intimacy, which are distance and similarity
coefficient. Distance is defined that it takes each sample
as a point of “m” variables corresponding to mdimensional space: the closer the distance is, the higher the
degree of intimacy is. If the similarity coefficient is close
to 1 or - 1, the properties of the sample or indicators are
similar. If the similarity coefficient is close to 0, there is no
relation between the samples or indicators. The clustering
analysis is as follows using Euclidean distance by SPSS.
p
(i, j  1, 2,..., n) （2
2
di j 
 (x
t 1
it
 x jt )
）
di j
Among the formula,
expresses Euclidean
xit
distance,
expresses t-dimensional coordinates of i
x
point, jt expresses t-dimensional coordinates of j point,
according to Euclidean distance, the sample can be
divided into two classes from 1990 to 2011.
The analysis results take the period as the first class
from 2003 to 2011. The infrastructure factors accounted
for absolute dominant position, with the development of
the economy, the traffic construction, environmental
protection and communication facilities have been greatly
improved, which reduce the cost of private investment.
However, because of the pressure of population, resources
and environment, the development of private investment is
restrained, especially during the financial crisis in 2008
and 2009, when private investment reduced greatly.
The analysis results take the period as the second
class from 1990 to 2002. In the early 1990s, due to the
poor infrastructure, the total amount of private investment
was low. Yet with the development of urban
transportation, urban greening, energy supply and
environmental protection, it brought private investment
spillover effects. Private capital also increased year by
year, but the overall level of infrastructure was low so that
the influence of infrastructure service was still weak in
promoting private investment.
IV.
CONCLUSIONS AND RECOMMENDATIONS
Using factor analysis method, the paper analyzes the
driving factors of infrastructure service on promoting
private investment in Jiangsu Province, the conclusions
and recommendations are as follows:
A. Five Indicators
The loading of the five indicators is large in per capita
road area, total consumption of electricity, telephone
penetration rate, sewage treatment capacity everyday,
comprehensive utilization amount of industrial solid waste,
which are the main infrastructure factors on promoting
private investment. The result requires us to pay attention
to the five infrastructure factors in the construction of
infrastructure. From the view of the per capita road area
and the telephone penetration rate, it needs us consider per
capita infrastructure and the use of density. We should
invest in the infrastructure according to the people of the
regions and the usage.
B. Composite Score
From the view of the composite score, except
individual years, the score is increasing year by year.
468,795.87 is the lowest in 1990 and 1,346,316.8 is the
highest in 2011.The results indicate that with improved
infrastructure, the role of infrastructure service in
promoting private investment becomes important. If the
transportation and communication are convenient, the
energy efficiency is high and the environmental protection
is perfect, the level of private investment will be enhanced
at the same time. It need us continue to push forward the
construction of infrastructure, and to improve the macroenvironment of private investment. At the same time, the
development of private investment not only depends on
the infrastructure, but also depends on the economic
trends and macroeconomic policies of our country. We
should not, therefore, only emphasize infrastructure
construction, but should also attach importance to the
judgment of macroeconomic situation and the regulation
of economic policies.
C. Cluster Analysis
The cluster analysis treats the period from 2003 to 2011
as the first class. With the development of transportation
construction, network and communication facilities,
environmental protection and pollution treatment in
Jiangsu Province, it greatly stimulates private investment;
The cluster analysis treats the period from 1990 to 2002 as
the second class. Although the pace of infrastructure
development is fast, but the overall level of infrastructure
service is still relatively low. Because of energy shortage,
traffic congestion, environmental degradation, low
utilization of the "three wastes", the degree of the second
class is lower than the first class on promoting private
investment. The levels of infrastructure service are
different between the two periods, therefore the role is
different in promoting private investment. We know that
the construction of infrastructure service is a long-term
project. It will be a long process in promoting the
development of private investment. It also needs longterm vision and development perspective to promote the
construction of infrastructure service, and emphasize the
accumulation of the infrastructure investment, and
promote the development of private investment by the
positive spillover effects.
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