ECONOMIC RESEARCH CENTER DISCUSSION PAPER

June 2004
No.148A Comprehensive Evaluation of and Policy Recommendation
to Foreign Direct Investment Environments in Western China

by
Wu Haiying∗∗

Ningxia Academy of Social Sciences, China, wuhy05@hotmail.com I am
grateful to Professor Kitahara, Professor Hirakawa and staffs at Economic
Research Center, Graduate School of Economics, Nagoya University. I would liketo thank Professor Arayama especially for his helpful comments. Mr. Duan
Qinglin and Mr. Yu Dahai have provided excellent research assistant work.

A Comprehensive Evaluation of and Policy Recommendation to Foreign Direct
Investment Environments in Western China
Wu Haiying
I. Introduction
Ever since its reform and opening-up, the pace of China’s integration into
the economic and financial globalization has been faster and faster. Now China
has become the largest recipient country of foreign direct investment (FDI) among
all developing nations. During 1979—2002, foreign investment in China totaled
US$623.4 billion out of which $446.3billion was FDI. Yet there lies a serious
imbalance as to the actual spread of FDI amongst the country’s different regions.
In 2002, FDI into China was $52.743bil., out of which 86.1% went to the easternregion, 9.5% to the central and the remaining 5.71% to the western region. Thatmeans that per capita FDI in the western region was only $8.30 compared withthe eastern region’s $95.60. Then what are the determinants or factors that
affected the regional distribution of foreign investment in China? What places dothe various provinces in the western region hold in terms of their FDI
environments? What are the disparities and causes? Through its FDI
environment assessment system, this paper conducts a comprehensive evaluationand cluster analysis (CA) of the different investment environments among
China’s different regions by using statistical data and quantitative models.
Literature Review
Academic research in the past 20 or 30 years on the choice of locations offoreign investment has been focused on: 1. increased analysis of location factors ininternational FDI theory to explain the influence of geographical locations on the
choice of FDI recipient countries; 2. site investigation on investors to find out
the decision-making process of their

FDI locations; and 3. quantitative methods

to determine the differences of FDI destination locations or the factors deciding on
the choice thereof.
With the ever-increasing foreign investment into China, research findings on
FDI to China has proliferated in both Chinese and English. Both foreign scholars andChinese scholars working or studying overseas have contributed in English to the field of
research. Due to the growing geographical imbalance, choice of FDI destination locationsin China has become a hot topic of academic research, a major area of which has been to
examine the determinants leading to the choice of FDI locations in the country by relying

1

on the basic principles of modern geographical location theory and by using all kinds of
econometric tools, such as the analysis done by Minghong Lu(1997) on the GDP, labor costand other data from 29 provinces during 1988—1995 in their influence on the FDI
locations in the country. Houkai Wei and Feican He(2002) researched on the same byfurther analyzing the relationships between the choice of locations and different industry
groups, the methodology of entry, differences in economic development stages in China
and the different country origins of FDI.
Kevin Honglin Zhang(2002) is representative of recent research in English in thefield. He used data from 29 provinces during the 1987—1990, ’91—’94 and ’95—’98
periods and analyzed the influence of such factors as market scale, labor cost, labor quality,business concentration, transportation cost, stimulation policy and cultural link on the
choice of FDI destinations. Then he compares the results from his regression analyses of
the three above-mentioned periods with those of the panel estimate from the 12-year
period between 1987—1998. Changhui Zhou, Andrew Delios and Jingyu Yang(2002) useddata from 28 provinces during 1980—1998 for the analysis of Japanese businesses in theirdecision of investment locations. One feature standing out in their research is the use of
the number of businesses and the number of employees as a variable. In their study theaccumulative number of businesses was used to explain the degree of economic
concentration in the variable whereas the same was explained by the development stageof industry in other studies.
Though different variables and data years were used in the above researches, thequantitative methodology remained the same. The main purpose of their research was totry to find out what factors, and to how large a degree, influenced the inflow of FDI into
China or from which country, i.e., the relationship between FDI and certain determining
variables(determinants). Based on the assessment of the FDI environments in western
China, this paper intends to: 1. set up an assessment system of indicators by using theabove-mentioned FDI and its relevant factor analysis methodology to determine thecertain factors most relevant to FDI in China; 2. to ascertain the combined index of FDI in
30 provinces in China in order to discover where western provinces lie in the index; and 3.
to carry out a cluster analysis in the hope of finding out the commonality of the FDI
environments in the 12 western provinces and their disparities with that of their easterncounterparts by objectively analyzing the internal types of the FDI environments in
China’s 30 provinces and regions.

2Ⅱ．An Empirical Assessment of FDI Determinants
2.1 Model specification and variables
Based on calculations, the following model is constructed:
FDIi =α0+αXi+εi
where i =1,…,30; FDIi is the amount of FDI inflow into the provinces in a given
period of time; Xi denotes a set of independent variables that vary acrossprovinces and over time; and ε denotes stochastic disturbance. The variables usedin this analysis are defined below(See Table 1).
FDI: A dependent variable referring to the share of FDI inflow into various
regions, its unit being 100 million yuan(RMB) at the average annual exchange
rate with US dollars listed by the Ministry of Commerce of China.
Following are independent variables:
GDP: Gross Domestic Product, the total amount of production and services of acertain region in a given year, and a substitutive variable of the market volume inthis study, its unit being billions of RMB. Theoretically, its expected impact should
be positive.
LOCA: A dummy variable with the eastern region=3, the central=2, and thewestern=1. Geographically, Guangxi Province belongs to the eastern coastal
region and Inner Mongolia the central, but for the Great Development of Western
China, both Guangxi and Inner Mongolia are considered western province andregion. It should have a positive impact on FDI inflows,
STA: The state-owned industrial output, or ratio of the state-owned andstate-held majority industrial enterprises’ output to the gross industrial output.
Reflecting the degree of maturity of China’s market economy, it should be
negatively related to FDI inflows. Its unit is the percentage (%).
TER: Ratio of the value-added of the tertiary in GDP, mainly indicating the stageof development of finance, transportation, information services, etc. It’s expected
impact should be positive. Its unit is in percentage.
CITY: Urbanization level, representing the ratio of cities with an urban
population of 500,000 out of the total number of cities in a given region.
Theoretically, this variable should be positive on FDI, its unit being in percentage.
POLI: A dummy variable. With reference to Sylvie Démurger’s(2002) approach,this paper measures and tests favorable policy index based on the types of thespecial economic zones established by each province(the weight varies from three
to one and that of non-open regions is null) and the open-door policy(Western3

Development region=0.5). Representing preferential policies, and it should bepositively related to FDI inflows.
WAGE: A dummy variable. It is 1 if the labor cost of the region is higher than thatof the national average, or it will be null. In theory, it is inverse to FDI.
LI: This variable refers to the proportion of light industry above a certain size
relative to the gross industrial output value. With the priority change from heavy
industry to the compensatory development of light industry since China’s reformand opening-up, FDI has been affected by the regional distribution of lightindustry locations in China. Its unit is in percentage.
FTD: Foreign trade dependency degree is equal to the ratio of total imports andexports in GDP. Reflecting the openness of the economic development of theregion concerned, it’s impact should be positive theoretically. Its unit is inpercentage.
FI: The proportion of the amount of foreign enterprises’ imports out of the total
local imports and exports, which reveals the degree to which local governments
control the imports by foreign enterprises. It should be positively related to FDI
inflows, its unit being the percentage.
HC: Illiteracy rate of the population at or over the age of 15 in the region
concerned, which represents the accumulation of the local human resource. It
should be theoretically negative related on FDI inflows, its unit being thepercentage.
We conduct a multi-regression analysis with the comprehensive data of
30 provinces (excluding Tibet) from the various years between 1998--2002 and thecross-sectional data of the year of 2002 respectively.
Time frame for the selection of the data is based on two considerations:
one is that Chongqing, which has attracted quite an enormous amount of FDI inrecent years, began to have its own statistics in 1998, and the other is that the
on-going Great Western Development started in 1999, so statistics from1998—2002 can better reflect the changes that took place after the Development
began.
Model based on the comprehensive data from 1998—2002 is as follows:
Model I: ln(FDI)=a+a1ln(GDP)+a2STA+a3TER+a4LOCA+a5WAGE+ε
Model II: ln(FDI)=a+a1 ln(GDP)+a2LI+a3FTD+a4FI+a5HC+ε
And the model based on the cross-sectional data from 2002 is:4Model III: ln(FDI)=a+a1 ln(GDP)+a2LOCA+a3POLI+a4CITY +ε
2.2 The estimation results
Factors

affecting

FDI

inflows

areestimatedbytheordinary

least-squares(OLS) techniques of the SAS statistic analytic software package.
Through repeated measurements and calculations, 11 factors remarkably
affecting FDI are established. They are lnGDP, TER, CITY, FTD, LI, POLI, LOCA,
FI, STA, WAGE and HC. The results are illustrated in Table 2.
The overall performance of three estimates is satisfactory. Values ofadjusted R2 in the three cases were from 71 percent to 85 percent, indicating astrong explanatory power of the models, and the significance level of F test is
p<0.0001, indicating that the significance of the model regression as a whole ishigh.
The determinant model of FDI in 2002 is purposefully designed as Model
III in order to test the impacts of various factors in pure cross-sectional data, theresults of which denote that factors In (GDP), LOCA, CITY and POLI affect FDI
significantly but both factors of POLI and LOCA are significant at 5 percent.
Specifically, some coefficient estimates in the models appear to be low. But
in effect, when the statistic position of independent variable todependent

variable is horizontal-logarithmic value, the interpretation of coefficient a shouldbe: % y=(100a) x, i.e. the coefficient should be multiplied by 100. For a better
understanding, factors in question are discussed as follows.
1) GDPGDP refers to the economy and market size of a region. In Model I, the impacts of
GDP on FDI is significant and the elastic coefficient is 1.147, denoting that when
GDP between provinces increases at 1%, FDI will correspondingly increase by
1.147%. In addition, we also tested the relationship between FDI and per capita
GDP, but it failed to pass the t test. As other researchers concluded, FDI is mainly
to capture the markets of all provinces, municipalities and autonomous regions
where the average individual consumption level remains low but its total amountis enormous. However, the elasticity estimated by cross-sectional data is far lower
than that by comprehensive data. Provinces with less GDP values in the westernregion do see their FDI inflows affected, but it is relatively more favorable to thoseregions with larger GDP’s such as Sichuan, Shaanxi (Xi’an), Chongqing, etc.
2) LOCA
Three belts of the eastern, central and western regions can fully illustrate thenatural and economic environment variability of China. Regional factors mainly
affecting foreign investment policy-making are transportation costs. Particularly,

5eastern coastal regions are endowed with naturally convenient conditions forexport. Other economic advantages are the closeness between these regions andthe nearby investor countries, the consanguinity with their overseas Chinese
investors, and the geographical factors that offer them superior agriculture,resources, human capital and so on and so forth. The disturbance coefficient of
LOCA on FDI is 0.733, i.e., the regional variation of the eastern, central andwestern regions affects FDI inflow to a certain extent and the regional
disadvantage of western China is unfavorable to attracting FDI inflow.
3) POLI
Establishing regional variations by offering preferential economic policies is
crucial in order to attract foreign investment into China. It is well known that thereform and opening-up of China began with the preferential policies granted toGuangdong and Fujian provinces for their economic and foreign trade activities.
In 1980, four special economic zones(SEZ), typical of which was the Shenzhen
SEZ, were set up as a pilot scheme; in 1984, 14 more eastern coastal cities wereopened up to the outside world. In 1985, the pace of the coastal regions’opening-up hastened. Then in 1988 the whole island of Hainan was established asa SEZ. In 1990, Pudong in Shanghai was opened up for development, followed by
the opening-up of the inland cities along the Yangtze River, land frontier cities
and inland provincial capital cities after 1992. When it comes to the westernregion, its opening-up was by far later than any other region. It has enjoyed no
special preferential policies, either. That is the single serious and institutional
reason as to why there exists this tremendous disparity of the total FDI between
the eastern and western regions. This study shows a significant impact of thepreferential policy factor upon FDI inflow.
4) CITY
Urban structure is an indicator of the urbanization scale and density of a given
province or region. In general, cities in the western region are small in number
and sparsely scattered in scale. Their urban functions are far from perfect and are
inadequate in co-operating with each other. Besides, they are in shortage of lightindustry bases and manpower reserves. In contrast with rural areas, their dyadic
economy is deeply rooted. All of these factors are unfavorable to attracting foreign
investment. The coefficient of the impact of the urban structural index on FDI is0.019, indicating that when the proportion of the number of large- and mediumscale cities with a population of more than 500,000 increases by one percentage
point, FDI will correspondingly go up by 1.9%. The higher the proportion, themore capable it is to attract foreign investment. In addition, we have investigatedon the impact of the urbanization of 2002 on FDI but failed to get significant

6results, which could mean that it is the urban structure but not the urbanizationlevel that affects the FDI more.
5) STAThe ratio of state-owned economy reflects the degree of the market-oriented
reform and the maturity of market economy environment. It represents the
structure of ownership and the structure of competition in the market. In western
China, the state-owned economy still remains a high proportion, while in its
eastern region the non-public ownership in the economy has been growing swiftly
and actively. It is concluded from Model I that when the proportion of state-owned
industrial economy increases one percent, FDI will decrease by 3.5%, whichindicates that foreign investment prefers those regions whose growth rate of themarket economy is higher. Shuyun Chen et al.(1995) calculated that higher
market economy regions mainly lie in the eastern coastal areas whereas thoselower regions are in the west. The stagnation in the development of the market
economy in the western region is a key factor in restricting the FDI inflow.
6) TER
The improvement of the proportion of tertiary industry is a necessity for thenational economic growth to a certain level. The higher the proportion, the higher
the requirements for the division of labor the economic development needs for its
services and infrastructures. During the period of 1978--2002, the ratio of thetertiary industrial structure in the eastern 11 provinces has shifted from
21.5:59.3:19.2 to 10.2:48.9:40.9 while that in the western 12 provinces haschanged from 37.2:43.1:19.7 to 20.1:41.3:38.6. In appearance, the difference
between the eastern and the western regions mainly lies in the development of
the first and second industries. However, the tertiary industry in the westernareas consists of services mostly from the ideological Party and administrative
departments of the government, which controls more and offers less, especially insuch areas as modern service and information industries, thus limiting theamount of FDI inflows. Given that when the TER proportion is increased by one
percent, FDI will increase by 8.3%, TER is the biggest coefficient factor.
7) Wage & HC
Seeking for cheap labor is also an important factor contributing to the FDI inflows.
As the models postulate, when the labor cost in an area is higher than thenational average, FDI decreases by 1.19%; when illiteracy goes up by one percent,
FDI goes down by 7.6%, suggesting that it is necessary for FDI to make a choice
between the decrease of labor cost and increase of human capital. Since theChinese government offered a preferential wage treatment for the workers andstaff members in the border ethnic minority regions and impoverished districts

7

that are at the rudimentary stage of reform, the variation of regional labor cost in
the whole nation is perfectly indistinctive. Thus it is natural for foreign
enterprises to invest in the eastern regions where a large sum of human resources
has been accumulated. With the development of non-public ownership in theeconomy and change in pay policy, additional income beyond wages in the easternregions is increasing by a large extent and the regional labor cost variation has
begun to show up. But in contrast to developed countries, the impact of regional
labor cost in China on FDI is comparatively small. With more and more farmworkers as well as talents from the western region moving to the eastern andsouthern regions in seek of better pay, the impact of WAGE and HC on FDI isgetting weaker and weaker.
8) LI
The reason to design and test the relationship between FI and FDI is because wethink that when it comes to the investment structure, FDI tends to favor realestate and light industrial production and pays more attention to those regionswith light industrial bases when selecting investment locations. In China, easternregions have always been regarded as the major target areas for light industries
to locate while western regions are characteristic of heavy industry sites. To alarge extent, FDI further lowers the position of western regions when it comes tothe locating of light industries. During the period of the centrally planned
economy, China placed its priority on the development of heavy industries. Since
its reform and opening-up, the country has experienced a rapid period of great
development in the production of the means for livelihood. FDI-invested
enterprises and villages and township businesses, especially those in the easternand coastal areas, seized the rare opportunity to quickly develop themselves,
which in turn greatly stimulated FDI. Our models indicate that when the
proportion of light industry increases by one percent, FDI goes up by 3.8%.
9) FTD & FI
Model IV postulates that FDI correspondingly increases by one percent when
trade dependency degree increases by one percent, and 2.1% when the importratio of FDI increases by one percent. It is noted that the import of FDI into China
was initiated from coastal port cities. Thus such provinces and metropolises as
Guangdong, Beijing, Shanghai and Tianjin well-known for their leading positionsin imports and exports have been the earliest and biggest beneficiaries of FDI.
The implementation of the open-door policy and the SEZ policy also originatedfrom coastal port cities with rich international experiences and then slowly
extended to the central and western regions under such circumstances as had
been gradually permitted by administrative decisions. The FTD level is an8

important resource and precious experience in the attraction of FDI. The
development of FDI needs the importation of technology and equipment andlabelling business. Just as the direct correlation of FI to FDI indicates, it is easier
to attract more FDI for regions in which FDI-invested enterprises enjoy a higher
position in the total number of imports.
This research has also measured and tested the factors related to the percapita GDP and the total sum of investment in the nation’s infrastructure. But
these factors were eventually eliminated due to the failure of result confirmations.
The establishment of the assessment system on FDI environments shouldemphasize the comprehensiveness and stability of the indicators. Our analysis
indicators are highly general and representative since they take into
consideration all factors ranging from macro, to location, to policy and to micro
factors.
III. A Comprehensive Evaluation of Foreign Investment Environments by
Principal Componential Analysis
3.1 Calculation of the comprehensive estimation index for foreign investment
environments by Principal Componential Analysis
Following section intends to make a componential analysis of the knumber of affecting factors ascertained through the regressional analysis by using
the SAS statistic analytic software package, to calculate the characteristic value
and vector of the correlation matrix R, contribution ratio and the like, to reduce
the k number of variables to a lesser m number of new variables by a further
selection of factors and reduction of dimensions, and to interpret the economic
significance of the selected principal components.
The analysis is done through the 11 selected interpretative variables oflnGDP, TER, LI, CITY, FTD, POLI, LOCA, FI, N-STA, N-WAGE and N-HC. In
accordance with the comprehensive analysis, three indicators of STA, WAGE andHC are alternated whereby N-STA refers to the proportion of non-state-owned
industrial production value, N-WAGE=-WAGE and N-HC stands for the ratio of
the population with an education beyond the elementary school.
Suppose the previous m as the number of principal components are,respectively:
Y1=f1[ln(GDP), LOCA, CITY …]
Y2=f2[ln(GDP), LOCA, CITY …]…

9Ym=fm[ln(GDP), LOCA, CITY …]
then, when the various interpretative variables of the standardized process of the

ith region are placed in the above model, the values of Y1, Y2, …,Ym will beachieved. When the characteristic values corresponding to the m principal
components are weight-imposed, the comprehensive index Y of foreign investment
environments can be obtained as follows.
Y=b1Y1+ b2Y2+…+bmYm
where b is the characteristic value corresponding to the previous m principal
components. In the operation, the weight is thoroughly determined by the modeland thus the subjective deviation caused by artificially controlled weighting is
avoided.
3.2 The estimation result of the foreign investment environments
We select the 11 factors of ln(GDP), TER, LI, CITY, POLI, LOCA, FI,
N-STA, N-WAGE and N-HC for the componential analysis. As illustrated in Table
3, with the exception of the correlation of TER to FI and

N-STA and LI to N-HC

being weak, the correlation between all the key factors, especially that between
those and GDP, are of medium correlation. There is no factor that can be rejected
due to its extremely low level of correlation(<10%) or extremely highcorrelation(>95%). As shown in Table 4, the representative can be as high as 94%
if m=6 factors is selected. According to Tables 4 and 5, the six principal
components are, respectively, as follows:
Y1=0.2771*In(FDI)+0.2065*TER+0.2200*LI+0.2830*CITY+0.3341*FTD+0.3513*PO
LI+0.3696*LOCA+0.3241*FI+0.3604*N-STA+0.3049*N-WAGE+0.2291*N-HC
Y2=0.3139*In(FDI)+0.5720*TER+0.4028*LI+0.3443*CITY+0.3254*FTD+0.1562*PO
LI+0.0717*LOCA+0.1921*FI+0.2540*N-STA+0.2251*N-WAGE+0.0780*N-HC
Y3=0.3219*In(FDI)+0.0646*TER+0.2811*LI+0.1817*CITY+0.1103*FTD+0.2336*PO
LI+0.1697*LOCA+0.0888*FI+0.1543*N-STA+0.4478*N-WAGE+0.6726*N-HC
Y4=0.2591*In(FDI)+0.1456*TER+0.2629*LI+0.4904*CITY+0.2217*FTD+0.4238*PO
LI+0.1567*LOCA+0.3191*FI+0.2503*N-STA+0.1776*N-WAGE+0.3908*N-HC
Y5=0.4787*In(FDI)+0.0080*TER+0.4136*LI+0.2457*CITY+0.2555*FTD+0.1721*PO
LI+0.2646*LOCA+0.6099*FI+0.0076*N-STA+0.0419*N-WAGE+0.0236*N-HC
Y6=0.3257*In(FDI)+0.3102*TER+0.5950*LI+0.3749*CITY+0.2599*FTD+0.1322*PO
LI+0.0907*LOCA+0.2145*FI+0.1308*N-STA+0.3734*N-WAGE+0.0852*N-HC

10

Y=5.4603 Y1+2.0224 Y2+ 1.2153Y3+ 0.6348Y4+ 0.5614Y5+ 0.4477Y6
If we introduce into the above model the interpretative variables after
standardized treatment from each and every province and autonomous region, we
can obtain the index of the assessment system on the FDI. The result is shown in
Table 6.
(1) The places most favored by FDI are eastern coastal provinces and cities suchas Guangdong, Fujian, Shanghai, Tianjin, Jiangsu, Zhejiang, Beijing, Shandong,
Hainan and Liaoning. In reference to the comments by Minghong Lu on FDI
between 1988--1995, today’s FDI environments have changed. Such provinces as
Jiangsu, Zhejiang, Tianjin and Shandong have advanced quickly. Guangdong has
replaced Fujiang as the most favorable place for FDI. In fact, the position ofGuangdong has also declined a bit, but it’s still ranked number one in terms of thetotal FDI investment.
(2) The most backward places for FDI are Gansu, Qinghai, Ningxia, Xinjiang,
Guizhou, Shaanxi and Inner Mongolia, all located in western China. According to
Minghong Lu, Qinghai, Gansu, Ningxia, Xinjiang and Shaanxi rank No.11, 12, 15,
19 and 21 respectively in the middle echelon on the national scale. In 1988, these
destination provinces and regions accounted for 4.05% of the nation’s total FDI,
which was their highest ever. That share has been dwindling down ever since. It
stood at 0.86% of the national total in 2002, which agrees with our assessment.
(3) Study shows that at present southwestern provinces have better investment
environments than their northwestern counterparts. Guangxi, Chongqing,
Sichuan and Yunnan have progressed to the national average in ranking, which isin line with the actual FDI in the southwestern region. Throughout 1980’s, thesouthwestern region lagged behind the northwestern region in attracting FDI, but
it has surpassed the northwestern region since 1992. In 1994, it even reached3.42% of the national total. According to Lu(1997), the worst FDI environments
went to the southwestern region and such central region provinces as Anhui,
Henan, Hunan, Inner Mongolia and Heilongjiang, etc., but in our analysis, Anhui,
Henan and Hunan’s positions have risen to a much higher level than before.

11IV. A Cluster Analysis of the Regional Model of the Investment Environments andInternal Structures
4.1 Methodology
The cluster analysis is the mathematical method used to classify the
subjects being researched on, the fundamental purpose of which is, firstly, toagglomerate the intended n number of individual candidates into various
categories, and, secondly, to agglomerate the categories that are closest to eachother into a new category of n-1 by stipulating the distance between theindividual candidates or categories, and finally to agglomerate into yet an evennewer category of n-2 by locating the categories that are closest to each other,
until all the individual candidates are integrated into one single category.
With the aid of, again, the SAS statistical analytic software package, we
carry out a systematic cluster analysis of the data after standardized treatment
from all the provinces, municipalities and autonomous regions except Tibet. First,
a mean is obtained of the various indicators from the period of 1998—2002. Thenthe indicators undergo a rudimentary standardization through the use of thenational mean. After that, the indicators that are adjacent to each other inmeaning are combined. Finally, the SAS software package is used forstandardization to make the MEAN=0 and STD=1 so as to carry out the cluster
analysis.
The purpose of such a classification is two-fold, i.e., to establish theregional model of investment environments and to analyze the internal structuresof the investment environment indexes. Specifically, on the one hand, researchers
normally classify various regions into different types based on the data structureand empiricism, which is apt to lead to confusion with large populations of data.
But our research can avoid the deviation caused by the subjective judgment interms of the cluster analysis. Through the division of the regional model, it can
examine the types and differences between and amongst all parts of the nationand is convenient for the direction under categories and the regulation of regional
policies. On the other hand, based on h regional models classified, the researchrespectively summarizes the raw data and figures out the various means of eachprovincial unit. With the comparative study, the internal differences of various
regional models can be seen clearly.
The interpretative variable indexes are divided into seven factors ranging
from market, industry, trade, service, manpower, location and policy. In what
follows, detailed descriptions of these factors are provided.

12

Market factor: X1=InGDP*0.7+N-STA*0.3, where GDP is the market size and STA
the market structure.
Industrial factor: X2=LI, indicating the regional differences of their industrial
structures.
Trade factor: X3=FI*0.7+FTD*0.3, where FI is the impact of FDI on trade and FTD
the degree of foreign trade dependency.
Service factor: X4=TER*0.7+CITY*0.3, where TER represents the development
degree of the tertiary industry and CITY the urban structure.
Human Capital factor: X5=N-HC
Location factor: X6=LOCA
Policy factor: X7=POLI
4. 2

Results after cluster
We adopt the seven factors to undertake a cluster analysis, where X1 isthe market factor, X2 industry factor, X3 trade factor, X4 service factor, X5
manpower factor, X6 location factor and X7 policy factor. Analysis results areshown in Table 7. According to the Table, deviation reaches 63.59% when thewhole country is treated as one category, 17.83% when the whole country as two
categories, 5.12% when treated as four categories. So we suggest that it be divided
into at least two categories. Table 8 indicates that RMSSTD is as high as 1.2148
when the whole country is treated as one single category. It goes down
significantly to 0.9568 when the country is divided into two categories. It goes
down even further to 0.8745 when the country is divided into four categories. It
keeps declining steadily when and after the whole country is divided into six
categories. PST2 declines greatly from one category to two categories and fromfour categories to five categories. PSF reaches a climax at two categories.
According to these indications, we think the country can be divided into two or
five categories (with Guangxi as a single category). For the purpose of research, adivision of four categories (with Guangxi belonging to the 2nd category) seems
most adequate.
As shown in Figure 1, when the whole country is divided into two
categories, there

are

only

the

easterncoastalregionandtheinlandcentral/western region. Following will be the situation when we divide the country
into four categories. We calculated and analysed the internal structures of thefour different categories of regions(see Table 9).
Type One: Guangdong and Fujian, the most favored places by FDI, were also theearliest places to adopt open-door policies. They enjoy China’s most preferential

13policy treatment. From the internal structure, all of the four factors exceed thoseof the country’s average. Their location advantages are obvious and market
conditions good. Their policy and trade advantages are especially outstanding.
The drawback is that their service environment is not as good as that of Type
Two. Between 1997—2002, Guangdong’s ratio of the total national FDI declined
from 26.0% to 21.5%, whereas that of Fujian’s decreased from 9.3% to 7.3%. In
2002, the amount of FDI came to $11.334bil. and $3.838bil. for Guangdong andFujian respectively. Since 1996, their level of FDI has been stagnant mostly due
to the Asian financial crisis and a saturation in domestic economy. A
restructuring is also needed for their industries.
Type Two: Consisting of 10 eastern coastal provinces and cities including Beijing,
Tianjin, Hebei, Liaoning, Shandong, Shanghai, Jiangsu, Zhejiang, Hainan andGuangxi, it is the most vitalizing region to draw FDI. It has the best service andhuman resource environments among all the four categories of regions. Its policy
environment is equivalent to that of Type One at best, but it has superior location
advantages of the eastern coasts and its macro environment is equally sound. It’s
worthwhile to note the rapid growth of FDI in Jiangsu, Shanghai, Zhejiang andShandong. Jiangsu’s FDI surpassed that of Fujian in 1993, thus ranking second
nationally. In 2002, its FDI totalled $10.191bil., not far behind that ofGuangdong’s. It could even take over the number one place of Guangdong’s in acertain number of years. At present, FDI in Shandong and Shanghai has
surpassed that of Fujian’s.
Type Three: Made up of 13 provinces and regions including Hunan, Hubei, Anhui,
Jiangxi, Henan, Chongqing, Sichuan, Shaanxi, Inner Mongolia, Shanxi, Xinjiang,
Jilin and Heilongjiang, it covers the country’s central region and the good areasfrom the western region. Its manpower environment coefficient is slightly higher
than the national average, but all its other environment indicators are inferior to
the national averages. Central region has a location dvantage over the westernregion. Western frontier areas and bigger provinces and regions are better
equipped than other locations from the same western region. From the tendencyof the FDI increasing in the central region, there exists the possibility of the FDI
moving away from concentrating on the eastern region to the central region. Since
the provinces in this category are mainly huge agricultural provinces, their huge
population and market size will lure more FDI. The differences between theinfrastructure and human resources among the major cities in this region and theeastern region are not significant, but the labor and land costs in Type Three

14

areas are cheaper. That would make these areas attractive to the FDI.
Development of Hubei and others as of late is a good example worthy of attention.
Type Four: Comprising of the five inland northwestern and southwestern borderprovinces and regions of Gansu, Qinghai, Ningxia, Guizhou and Yunnan, it is the
major poverty-stricken region of the country (Tibet, which is not included in our
analysis, can be grouped into this category or treated as a separate category of itsown). Its geographical location and transportation conditions are poor and its
market size small. It has too big an emphasis on natural resource exploitation as
its heavy industries. Thus it is lacking in its attraction of FDI. Its macro, location,
micro and policy factors are the worst in the nation. Compared with the six
western areas from Type Three, it is noted that all of the factors in Type Four are
inferior to those of Type Three, indicating that the internal structure of thewestern region in Type Four is disadvantageous in the attraction of FDI.
V. Conclusion and Policy Recommendation Based on the FDI Environment
Research in Western China
Conclusion of the analysis
This paper combined the use of regression analysis, principal
componential analysis and cluster analysis, all of which supported andcomplemented with each other. It not only analyzed the influence of the 11 factors
on the FDI inflow in a quantitative way, but also listed out the order of foreign
investment environments in China. It went on further to group the country’s 30
provinces, municipalities and regions(excluding Tibet) into four different
types(categories) of investment environments and analyzed the differences
between affecting factors from among the four types of investment environments,
especially those between the eastern and western regions.
Specifically, the conclusion of the research is:
1. The discovery that besides such factors as policy, location and labor cost, etc.,
the level of urbanization scale and density, ratio of the light industry output
relative to the total industrial output, ratio of the value-added of the tertiary outof the GDP and the degree of foreign trade dependency all have a significant
impact on the FDI inflows.
2. Through the comprehensive evaluation of the assessment index on the FDI
environments in the 30 provinces, municipalities and regions, it was discovered

15

that: 1). The 11 top-ranking provinces and cities during our assessment period all
happened to be coastal provinces and cities; 2). The ranking of the northwesternregion dropped from the middle echelon during Minghong Lu’s observation
period(based on the data from 1988—1995) to the bottom; and 3). The ranking of
the southwestern region improved from the worst level to a higher position andhas become much better than that of the northwestern region.
3. Discovery from the cluster analysis: 1). The 30 different provinces,
municipalities and regions can be grouped into four types of investment
environments with the western provinces and regions fitting into three different
types, indicating a high level of difference in internal investment environments.
Among the 11 western provinces and regions, the best region, Guangxi, went toType Two and sat shoulder to shoulder with the eastern coastal provinces except
Guangdong and Fujian, which belonged to Type One; the southwestern region
with the exception of Guizhou and Yunnan provinces was all classified into TypeThree; Guizhou and Yunnan together with Gansu, Qinghai and Ningxia were put
into Type Four of the investment environments. 2). Between Type One and Type
Two, the biggest differences lie in the trade, policy and location factors; next comeindustrial structure and market scale. Human resource difference was theminimum between the two types.
Policy recommendation based on the findings:
1. Efforts should be made to enlarge the total economic scale and the market size
in western China. The FDI inflow aims at occupying the local market, which is
influenced by the GDP size. In recent years, the GDP of western provinces andautonomous regions achieved a higher growth than what was in the past, thanks
to the Great Development of the Western Region, which has in turn improved
such investment environment as infrastructure. But the improvement came fromthe government investment with the FDI contributing very little to the GDP of
the region. With the recovery of the national economy, GDP of the eastern region
enjoys a high rate of growth. So it’ll take the western region more time and efforts
to enlarge its economic aggregation and market capacity owing to the lower
development stage, smaller market size and higher freight costs characteristic of
its thinly populated but vast expanse of land size. It’ll be a long-term task toenlarge the economic total and the market capacity of the west. But the westernregion must have a rate of growth faster than that of the eastern and central
regions in order to improve the FDI environment in the west and to increase theaggregation of its economy.

16

2. The regional disadvantage should be counterbalanced with the strategic
opportunity of the on-going industrial restructuring. The geographical andregional disadvantages of the west were always strengthened by the FDI location
imbalance. FDI into China mainly focused on the light industries and the realestate industry, which have a stronger market demand. In addition, investors
have a closer geographical relationship with their eastern region counterparts,
which impacts on the location selection of FDI. But the western region shouldmake full use of the period where there exists a surplus in consumer products andtry hard to attract FDI into developing its tourism industry and catering industry.
It should use its advantage of possessing relatively cheaper labor cost to lureforeign businesses to shift their labor-intensive industries to the western region.
More importantly, market entry restrictions to heavy industries must be loosened
up to attract foreign investments into such areas as energy, exploitation of
petroleum and natural gas and machinery manufacturing. Quicker steps must be
taken to enhance the economic and trade exchanges between western China andthe countries in western Asia, Southeastern Asia, the Middle East and theeastern European countries. Investment in the region should be diversified.
Construction of the Eurasian Continental Railroad should be sped up. Regional
disadvantages can also be counterbalanced through the increased number ofindustries open to the different sources of FDI into the region.
3. It is advisable to carry out the strategy of encouraging FDI through thedevelopment of regional central cities in the west. The economy in the westernregion has a dual nature. Urban composition is made up of small townships. Its
agriculture can’t do without irrigation. Basic infrastructure such as dams andtransportation systems is inadequate. The deficiencies of urban functionality also
restricts the FDI inflow. Therefore, it is not feasible to see an immediate andmassive improvement in the FDI environment in the west. The urbanization
policy, however, of the western region should be to loosen up the regulations ofsmall cities and townships, give emphasis to the construction of capitol cities andmedium and large regional metropolitan centers, transfer its population to large
and medium metropolises and increase the radiation radius of the regional
central cities. The trickle-down development strategy should be replaced withthat of the leadership of regional central metropolises. Its regional geographicaldisadvantages of investment environment should be counterbalanced with the
improvement in the investment environments of the regional metropolitan
centers. Construction of the SEZ applicable to the western region must be17

quickened in order to lay a solid foundation for the environment hardware for FDI
inflows. The strategy of developing a Greater Yinchuan will also help better its
investment environment.
4. More favorable investment policies and economic sovereignty should be given to
the western region. In recent years, the major problem with attracting outside
investment to the western region has been a lack of preferential policy for theregion. What foreign investment truly takes to heart still is preferential
treatment in taxation, land and import and export policies. Since the country’s
opening-up and reform, the western region has never enjoyed any preferential
treatment or foreign trade sovereignty afforded with the coastal SEZ’s. Currently,
fair taxation is being stressed, so again the western region is put at a
disadvantage relative to the eastern region. Thanks to the different starting levels,
the unified national policy scheme, such as the singular market entry
requirement, is restricting the FDI inflow into the west. Tax reduction or waiver
policies to the western region should be granted or extended, differential importand export treatment should be implemented and the quota bidding systemshould be abandoned. The foreign trade and economic sovereignty should be given
to the west. The ethnic autonomous regions in particular should have theflexibility of making their own policies in accordance with the local reality. The
Central Government should provide special and preferential policies for the
specific projects in the west that conform with the industrial development in theregion.
References:
1.

Minghong Lu, (1997), “Geographical Distribution of the FDI and an

Assessment of the Investment Environment in China”. 1997: The
Economic Research, Issue 12
2.

Houkai Wei et al.,(2002), FDI Location Decision-making in China and the

Public Policy. Beijing: The Commercial Press, 2002
3.

Xiaojuan Jiang et Al.,(2002), China’s Foreign Invested Economy. Beijing:
The People’s University Press, 2002

4.

Ding Ding, “An Investigative Analysis on the Soft Environment for

Foreign Investment in China’s Western Region, The Economic Research
Review(http://www.westweekly.net)
5.

Sylvie Démurger et al., (2002),“Geographical Location andPreferential Policies and Their Related Contributions to China’s

18Economic Development”. The Economic Research, Issue 9
6.

L. K. Cheng and Y. K. Kwan, (2002)“What Are the Determinants of the

Location of Foreign Investment? The Chinese Experience”. Journal ofInternational Economies, 51, p.379-400.
7.

J. H. Dunning,(1993), Multinational Enterprises and the Global Economy.
Don Mills, Ontario: Addison-Wesley Publishing Company, 1993

8.

Kevin H. Zhang, (2002),“Why Does China Receive So Much Foreign

Direct Investment?”. China & The World Economy, 3,9.

p.49-58.

Elizabeth Asiedu, (2002), “On the Determinants of Foreign Direct

Investment to Developing Countries: Is Africa Different?”. World
Development, Vol. 30, No. 1, p.107-119.
10. Kevin H. Zhang, (2000), “Why Is US Direct Investment in China So

Small?”. Contemporary Economic Policy, 18(1), p.82-94.
11. Coughlin, C. C., J. V. Terza and V. Arromdee,(1991), “State

Characteristics and the Location of Foreign Direct Investment Within the
United States”. The Review of Economics and Statistics, Vol. 73, Issue 4(Nov.), p.675-83.
12. UNCTAD, World Investment Report 2003, www.unctad.org/fdistatistics

13. Keith Head, John Ries, (1995), “Inter-City Competition for Foreign
Investment: Statis and Dynamic Effects of China’s Incentive Areas ”.
Journal of Urban Economics 40,38-60
14. Robert E. Lipsey,(1999), Working Paper 6876,
http://www.nber.org/papers/w6876
15. Yaohui Zhao, (2001), “Foreign Direct Investment and Relative Wages:

The Case of China”, Chine Economic Review12, 40-57

19

Table 1: Variables of Their Definition
Variables
GDP

Definition
The total amount of the economy and the substitutive
variable of the market size.

Unit Effect
100
M
+
RM
B

LOCA

A dummy variable, the Eastern regions=3, the Central=2,
the Western=1.

STA

The ratio of state-owned and stockholder enterpriseproduction value to the gross industrial production.

%

-

TER

The ratio of the increased value of the tertiary to GDP.

%

+

CITY

The proportion of cities with a population of 500,000 in thecities as a whole.

%

+

POLI

A dummy variable.

+

WAGE

A dummy variable.

-

+

LI

The proportion of light industry over scale in the grossindustrial production.

%

?

FTD

The proportion of the total import-exports in GDP.

%

+

FI

The proportion of the amount of foreign enterprise import in
the total of the local import and export.

%

+

HC

The illiterate proportion of the population at and over theage of 15.

%

-

20

Table 2: Estimates of FDI location Determinants

Variables

ModelⅠ

Constant-7.606﹝-4.17﹞1.147﹝7.09﹞-0.035﹝-3.56﹞0.083﹝3.67﹞-1.194﹝-3.50﹞ln(GDP)
STA
TER
WAGE

ModelⅡ

ModelⅢ-5.440***﹝-5.01﹞
***
0.965
***
﹝6.90﹞***-4.397﹝-3.34﹞0.630﹝3.07﹞

***

***

***
***
0.038
﹝4.67﹞
0.010﹝3.61﹞
0.021﹝4.40﹞-0.076﹝-4.03﹞

LI
FTD
FI
HC
***

***
***
***
***

0.584
**
﹝2.37﹞
0.586
**
POLI
﹝2.07﹞
0.019
***
CITY
﹝2.79﹞
R-Square0.71420.74140.8505
71.97
82.56
35.56
F-value
1998-2002
2002
Date Period 1998-2002
Notes:
1.The asterisks ***,**, and * indicate the levelsof significance at the 1 percent, 5 percent, and 10
percent, respectively.
2.(t-stat )
LOCA

0.733
﹝4.03﹞***

21

Table 3: Correlation Matrix of Variables
lnGDP TER
LI
lnGDP 1.000 -0.122 0.456
TER
1.000 -0.091
LI
1.000
CITY
FTD
POLI
LOCA
FI
N-STA
N-WAGE
N-HC

CITY FTD POLI LOCA FI N-STA N-WAGE N-HC
0.361 0.317 0.457 0.564 0.518 0.668-0.214 0.480
0.681 0.728 0.250 0.337 0.147 0.091-0.570 0.318
0.063 0.196 0.606 0.423 0.385 0.613-0.220 0.038
1.000 0.641 0.239 0.489 0.427 0.391-0.508 0.413
1.000 0.617 0.562 0.324 0.493-0.734 0.423
1.000 0.707 0.628 0.728-0.593 0.321
1.000 0.704 0.693-0.474 0.580
1.000 0.740-0.408 0.376
1.000-0.603 0.253
1.000 -0.055
1.000

22

Table 4: Eigenvalues of the Correlation Matrix
Eigenvalue
1
2
3
4
5
6
7
8
9
10
11

5.46027977
2.02238573
1.21530595
0.63479898
0.56135494
0.44771209
0.21041800
0.15889500
0.12286219
0.09450215
0.07148521

Difference
3.43789404
0.80707978
0.58050697
0.07344405
0.11364284
0.23729410
0.05152300
0.03603281
0.02836004
0.02301694

23

Proportion

Cumulative

0.49640.18390.11050.05770.05100.04070.01910.01440.01120.00860.00650.49640.68020.79070.84840.89950.94020.9593
0.97370.98490.99351.0000

24

lnGDP
TER
LI
CITY
FTD
POL
LOCA
FI
N-STA
N-LAB
N-HUMAN0.27710.20650.22000.28300.33410.35130.36960.32410.3604-0.3049
0.2291Prin1

0.3139-0.57200.4028-0.3443-0.32540.15620.07170.19210.25400.2251-0.0780Prin20.3219-0.0646-0.28110.1817-0.1103-0.23360.16970.0888-0.15430.44780.6726Prin3-0.25910.1456
0.2629-0.4904
0.22170.42380.1567-0.3191-0.25030.17760.3908Prin40.4787-0.00800.4136
0.24570.2555-0.1721-0.2646-0.6099-0.0076
0.0419-0.0236Prin5-0.3257
0.31020.59500.3749-0.2599-0.13220.09070.2145-0.1308
0.3734-0.0852

Prin6

Table 5: Eigenvectors of PCA

0.00820.05090.03360.01230.16970.2588-0.8296
0.39280.00720.13270.2001Prin7
0.2446-0.0732-0.21100.19550.24100.43880.16130.0864-0.4309
0.3873-0.4820Prin8-0.3494-0.1801-0.1250
0.0631
0.4639-0.12470.0557-0.0966
0.58230.4873-0.0788Prin90.32290.46330.0493-0.53270.2541-0.42310.05620.2863-0.00250.1541-0.2060

Prin100.18010.5035-0.2290-0.0292-0.4709
0.3419-0.0783-0.27800.42090.2328-0.0862

Prin11

Table 6: Evaluation Index of FDI Environment
Region

Index

Rank

Guangdong
FujianShanghai
Tianjin
Jiangsu
Zhejiang
Beijing
Shandong
HainanLiaoning
Hebei
Guangxi
Hubei
Anhui
Chongqing
Sichuan
Henan
Hunan
Jilin
Jiangxi
YunnanShanxi
Heilongjiang
Inner Mongolia
Shaanxi
Guizhou
Xinjiang
Ningxia
QinghaiGansu

33.51428.77228.69728.63126.44125.68923.97523.27520.68120.44219.702
19.11317.03116.30015.11615.11515.90715.09315.052
14.13513.83913.272
13.23812.761
12.478
11.12810.521
10.20910.150
9.901

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30

25

Table 7: Eigenvalues of the Covariance Matrix
Eigenvalue
1
2
3
4
5
6

3.81526399
1.07001385
0.44936600
0.30728630
0.21880211
0.13926775

Difference

Proportion Cumulative

2.74525014
0.62064785
0.14207969
0.08848419
0.07953436

0.63590.17830.07490.05120.03650.0232The data have been standardized to mean 0 and variance 1
Root-Mean-Square Total-Sample Standard Deviation = 1
Root-Mean-Square Distance Between Observations = 3.464102

26

0.63590.81420.88910.9403
0.97681.0000Table 8: Cluster History
NCL Clusters Joined FREQ

29
28
27
26
25
24
23
22
21
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1

OB2
OB27
OB16
OB10
OB22
CL27
OB3
OB1
OB4
OB14
CL26
CL25
CL21
OB24
OB12
CL15
OB5
CL16
CL17
OB13
CL22
CL23
CL11
CL9
CL12
CL7
CL6
CL3
CL2

OB9
OB29
OB18
OB15
OB23
OB17
OB20
OB6
OB8
CL24
OB11
OB26
OB7
CL28
CL20
CL18
CL14
OB28
OB30
OB19
CL29
CL19
CL13
CL8
OB25
CL5
OB21
CL10
CL4

2
2
2
2
2
3
2
2
2
4
3
3
3
3
5
8
9
4
4
2
4
5
13
9
5
18
10
12
30

RMS
STD

SPRSQ

RSQ

PSF PST2

Norm
RMS
Dist

0.21270.21880.23110.24520.25900.26340.28560.28700.31200.29700.31470.33070.34920.33950.34170.41580.43140.42700.43800.52470.50120.52030.53230.64700.60860.70250.70180.78571.00000.00160.00170.00180.00210.00230.00290.00280.00280.00340.00430.00480.00520.00510.00630.00700.01810.00960.01090.01140.00950.02160.02770.0461
0.05220.03220.12100.03730.07180.4765

0.998
0.997
0.995
0.993
0.991
0.988
0.985
0.982
0.979
0.974
0.970
0.964
0.959
0.953
0.946
0.928
0.918
0.907
0.896
0.886
0.865
0.837
0.791
0.739
0.707
0.586
0.548
0.477
0.000

22.9 .
23.0 .
22.7 .
22.3 .
21.9 .
20.8 1.6
20.6 .
20.7 .
20.6 .
19.9 1.8
19.4 2.3
19.1 2.3
19.1 1.5
18.9 3.8
18.8 2.3
15.8 4.6
15.9 1.6
16.0 2.7
16.4 2.7
17.3 .
16.8 9.8
16.2 8.6
14.5 7.1
13.6 5.8
15.1 5.1
12.3 11.5
16.4 2.6
25.5 4.4.
25.5

0.21270.21880.23110.24520.25900.27820.28560.28700.31200.32730.34420.36130.36640.38600.39930.47230.48210.49940.5116
0.52470.58730.62300.62920.73680.80780.87450.8876
0.95681.2148

27

28

1.095
1.555
1.148
0.897
0.782

1.042
1.358
1.138
0.935
0.739

X2

0.345

0.653

1.523

2.207

1.182

X3

0.828

0.909

1.213

0.958

0.977

X4

0.919

1.016

1.032

1.019

0.997

X5

0.476

0.806

1.429

1.429

1.035

X6

0.569

0.598

1.417

2.804

1.347

X7

Note: The data is to be standardized treatment from the four different types ofregions.

National
Type One
Type Two
Type Three
Type Four

X1

Table9: Structure of FDI Environment by Region

Figure 1

nan
Yun
i
gha
n
i
Q
a
gxi
Nin
nsu
Ga
ou
izh
u
G
i
anx
Sha
n
hua
Sic
g
qin
ong
h
C
bei
Hu
nan
Hu
nan
He
i
ngx
Jia
hui
lia
An
ngo
o
er M
Inn
g
jian
n
i
X
n
g
Jili
jian
g
n
ilo
He
nxi
Sha
g
d on
g
n
a
Gu
ian
Fuj
n
ina
Ha
g
jian
Zhe
g
don
n
a
Sh
u
ngs
Jia
xi
ang
Gu
bei
He
ai
ngh
a
h
S
njin
Tia
ng
oni
a
i
L
jing
Bei

29