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1,3
Determinants of foreign direct
investment at the regional
level in China
262
Lv Na
University of International Business and Economics, Beijing, China, and
W.S. Lightfoot
International University of Monaco, Monaco
Abstract
Purpose – This paper sets out to analyze the determinants of foreign direct investment (FDI) on both
the country and regional level through the extensive review of past research studies, as well as
through the development of a multiple regression model for identifying key determinants of FDI at the
regional level in China during the critical year of 2002.
Design/methodology/approach – The development of a multiple regression model to identify
statistically significant determinants of FDI by region in China.
Findings – As reforms continue to take place, FDI in China has been unevenly distributed. This
paper examines five potential determinants of FDI in 30 regions (including provinces, centrally
controlled municipalities, and semi-autonomous regions) of China using a regression model. The
specific focus is on 2002, as it is the first full year after China’s accession to the World Trade
Organization, and the first year in which China exceeded the USA in attracting FDI. From this initial
study, one can conclude that the government should consider encouraging capital-intensive FDI
through the further development of a skilled workforce. This means increasing funding for higher
education, and infrastructure, while also encouraging more openness in state-owned enterprises. This
paper sets up further research that may help expose regions with greater potential for FDI, as well as
identifying regions which need to improve certain conditions in order to receive more FDI.
Originality/value – This paper analyzes the determinants of FDI by region in China in 2002. This
year is particularly interesting as it is both the first full year after China’s accession to the World Trade
Organization, as well as the first year in which FDI was greater in China than in the USA While this
research study is only a snap shot of a topic that is of increasing importance to China, it has direct
relevance to the FDI development efforts of the individual regions. This study provides evidence that
GDP that proxies for the market size and potential is shown to be a big attraction for FDI. Labor
quality and the progress of reform or the degree of openness are also important determinants of the
distribution of FDI. There is some mild evidence that high labor cost deters the inflow of FDI and the
level of infrastructure has positive relation to FDI. These results have important implications for both
the central and regional governments as they can be useful in helping the authorities to allocate funds
and resources which will help attract FDI.
Keywords China, Regional development, Direct investment, Determinants
Paper type Research paper
Journal of Technology Management
in China
Vol. 1 No. 3, 2006
pp. 262-278
q Emerald Group Publishing Limited
1746-8779
DOI 10.1108/17468770610704930
Introduction
The study of foreign direct investment (FDI) in China has increased a great deal since
the reforms under Chairman Deng Xiaoping in the late 1970s and early 1980s, with
further acceleration of interest and investment due to their accession to the World
Trade Organization in 2001. As the rate of globalization increases, and the economic
linkage between countries strengthens, FDI is playing an increasingly important role
in the world economy. FDI is widely regarded as an amalgamation of capital,
technology, marketing, and management (Cheng and Kwan, 2000). Many countries
consider the attraction of FDI as a crucial element in their strategy for economic
development. In this paper, we examine if certain variables are significant
determinants of FDI distribution in 30 different regions in China. This is
particularly notable as in 2002, China exceeded the USA in becoming the largest
host of FDI (Lu, 2003a, b, c). Though there are many studies on the effects of FDI on the
country level, relatively few have focused on the regional level. Some authors (Fujita
and Hu, 2001; Cheng and Kwan, 2000; Sun et al., 2002) have focused on the disparity
between coastal and inland provinces, citing historic relationships between Pacific
territories and the coastal regions, export activity, and the amount of privatization of
state owned enterprises (SOE’s).
This study will focus on determining if there is a significant relationship between
foreign direct investment at the regional level and market demand, infrastructure
development and agglomeration, labor quality, labor cost, and the degree of
industrialization and openness[1].
Literature review
As the worlds most populated country, China has attracted a great deal of attention
from a wide range companies seeking to leverage the low relative cost of employing a
Chinese work force, or to gain access to the growing Chinese middle and upper classes.
From the investor’s point of view, picking the right location that provides a competitive
advantage is critical to developing a sustainable business model. The location he or she
would choose as the destination of FDI must ultimately be more profitable to invest in
than in others (Coughlin et al., 1991). Choosing the right location often involves many
different factors.
Hymer (1960) found that American FDI was mainly concentrated in a few industries
and monopolized by several companies. Multinational companies (MNC’s) were the
product of imperfect markets and monopoly advantages where the companies had
the advantage with regards to choosing where to invest. A number of conclusions can
be drawn from Hymer’s analysis that helps frame up this study:
.
First, FDI tends to flow into differentiated markets where a MNC believes they
will have an advantage competitively.
.
Second, companies that are able to make investments overseas all have certain
advantages, such as economies of scale, differentiated products, special skills,
and low-cost production. These companies will make investments in regions that
do not have these advantages.
.
Third, there are many ways in which MNCs can invest overseas in such as
exporting, and licensing, in addition to direct investment. MNCs without local
partners always prefer to choose foreign direct investment.
.
Last but not the least Hymer found that about half of the overseas operating
capital of American firms came from host countries; thus FDI tends to flow into the
countries or regions that have developed financial systems and capital markets.
The case for China as a host for FDI has increased substantially since Hymer first
published his thoughts in 1960. China as a source of low cost labor, and with
Determinants of
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investment
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264
Figure 1.
Flow of FDI from source to
host countries
an attractive, rapidly growing domestic market represents growth for many
companies. The fact that the coastal regions – especially those in close proximity to
Hong Kong, and with historic ties to the West and Taiwan also have sophisticated
financial systems and capital markets supports Hymers conclusions (Figure 1).
Other factors may also contribute to the emergence of China as a leading host for
FDI. In the 1970s and early 1980s, Buckley and Casson (1976) and Rugman (1981) put
forward the concept of internalization. Internalization[2] means the process of
establishing a market inside the company and substituting the internal market for an
external market. The transfer price inside the company enables the internal market to
operate as effectively as the external market. The theory holds the opinion that the
imperfection of external markets compels the company to exchange certain products
inside the company. When this happens across national boundaries, it acts as FDI of
MNCs. Countries that bring about the imperfections in the external markets (such as
high tariff of non-tariff barriers) are where FDI tends to flow. The trend toward
collaboration through joint ventures, partnerships, and other forms of collaborative
relationships may suggest that companies are attempting to create “internal markets”
with a select group of companies. The central and regional governments – through the
degree of openness (i.e. foreign, private ownership of state owned enterprises) may
be significantly responsible for the creation of these “internal markets.”
While low cost, access to the domestic markets, and the creation of internal markets
that simplify the transfer of FDI are often compelling from a country point of view
from a regional perspective, selecting the right location can be a significant challenge.
Dunning (1977) assumed that to make a FDI, companies should have three advantages:
an ownership advantage; a location advantage; and an internalization advantage.
Influenced by industry location theory, Dunning determined the definition of location
advantage as the advantage one location had over another based upon:
.
natural and human resources;
.
the price, quality and productivity of inputs;
.
international transportation and communication cost;
.
investment favor or discrimination;
.
man-made barriers to trade;
.
fundamental facilities;
.
cross-national values, language, culture, commercial practice and politics;
.
research and development;
.
the concentration of production and sales;
.
economic system and political strategy;
.
resource allocation system. (pp. 395-418)
Source Country:
Leading Edge Technique
Intellectual Capital
MNC’s
Host Country:
Low Labor Cost
Domestic Market
The location advantage (or edge) theory is relevant to this study as it holds the opinion
that the reason why an international company makes direct investments in a particular
region is that the company would like to attain certain location advantages which do
not exist in other host countries. Various studies (Table I) have highlighted the factors
that investors care about including: labor cost, market size and market potential, trade
barriers and country risks. Those host countries with lower labor cost, lower transport
cost, greater market potential, trade protectionism, smaller country risk, better
infrastructure and better educated and skilled populations are typically the focus
of FDI.
According to traditional location theory, investment incentives can be mainly
divided into two types: First, those targeted at cost savings, which pursue a production
cost edge in host countries where FDI is mainly focused on producing locally and then
exporting; and second, for those companies who aim to expand their market presence
through increasing their penetration in “local” markets. FDI mainly focuses on local
production and local sale (as opposed to exporting), so this kind of FDI places a high
emphasis on market size, market growth, and consumption ability.
Determinants of
foreign direct
investment
265
Empirical works
Local markets are often considered by country or by region. There is a vast amount of
existing literature focused on the geographical distribution of FDI (Table II). Numerous
studies have been done that identify the determinants of FDI for regions, countries,
regions within countries, as well as FDI flows between countries.
While there is not necessarily a single set of factors that consistently determine FDI,
infrastructure, skilled labor, labor cost/wage, agglomeration, and government support
in terms of openness, and/or attempts at attracting FDI are often cited as having a
significant relationship between FDI and the location choice. And while regions that
receive substantial FDI may benefit, it is ultimately the linkages between education,
government, and industry that perpetuate the development of new activities in the
region, leading to innovation, and further investment (Porter and Stern, 2001).
With the fast development of its economy, China has been of crucial importance to
FDI, thus the study of investments in China has been growing (Table III). Wang and
Swain (1995) examine the determinants of FDI in China, finding that FDI in the
manufacturing sector is positively related to China’s GDP, GDP growth, wages, and
trade barriers, but negatively related to interest rate and exchange rate. Bhagwati and
Srinivasan (1983) conducted a survey to rank provinces of China with the best
investment environment. They identified a number of variables: market size; wage,
Year
2000
2001
2002
Number of agreements
Increase
Number
(percent)
22,347
26,140
34,171
30.68
16.97
30.72
Capital in signed
agreement
Amount
Increase
($ billion)
(percent)
623.80
691.95
827.68
50.17
10.93
16.92
Source: Commercial Ministry Foreign Investment Statistics (2003)
Capital actually utilized
Amount
Increase
($ billion)
(percent)
407.15
468.78
527.43
078
15.14
12.51
Table I.
General situation of
attracting FDI in China
(2000-2002)
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266
Table II.
Selected articles on
determinants of FDI
outside of China
Table III.
Selected articles on
determinants of FDI in
China
Authors and dates
Countries
Scaperlanda and Mauer (1969)
Korbin (1976)
Kravis and Lipesey (1982)
Schollhammer and Nigh (1984)
Bagchi-Sen and Wheeler (1989)
Europe
Europe
USA
USA
USA
Significant determinants
Market capacity
Market capacity
Market demand, market size
GDP, international conflict and cooperation
Per capita retail sales, population growth, population
growth
Coughlin et al. (1991)
USA
Agglomeration, funds to attract FDI, labor market
conditions, per capita income, state land area, taxes,
transportation network, wages
Wheeler and Mody (1992)
USA
Agglomeration, infrastructure, labor cost, market
capacity
Friedman et al. (1996)
USA
Construction cost, funds to attract FDI, market
potential, major port, skilled labor, wage
Braunerhjelm and Svensson (1996) Sweden
Agglomeration, exports, R&D expenditures
Mody and Srinivasan (1998)
USA/Japan Educated work force, good infrastructure, low
country risk, low wage inflation
Fan and Dickie (2000)
Asia
Infrastructure, skilled labor, stable macroeconomic
conditions
Akinlo (2004)
Nigeria
Education
Authors and Dates
Countries
Significant determinants
Bhagwati and Srinivasan (1983)
China
Wang and Swain (1995)
China
Chen (1996)
Wei (1999)
LuMinghong (2000)
Tong and Yueting (2000)
Zhao and Zhu (2000)
China
China
China
China
China
Fujita and Hu (2001)
Sun et al. (2002)
China
China
Education, extent of industrialization, infrastructure,
level of scientific research, living environment,
market size, age
Exchange rate, GDP, GDP growth, interest rate,
trade barriers, wages
Market size
Bribery
Degree of openness, infrastructure, labor quality
Hometown connections of expatriates
Cost factors, infrastructure adequacy, market
potential
Degree of openness (reducing share of SOE’s)
Agglomeration, country risk, degree of openness,
labor cost, labor quality, level of scientific research,
market demand, market size
education, extent of industrialization, infrastructure (transport facilities,
communication facilities, living environment), and the level of scientific research.
Chen (1996) divided China into East, Central and West regions, and found that wage
has no relation with spatial distribution of FDI but, that market size has the most
influence on the Central Area. Wei (1999) found that bribery and other dishonest
behaviors of governors influenced the inflow of FDI. LuMinghong (2000) identified
some additional determinants besides general economic factors such as an
infrastructure factor with energy consumption as proxy; labor quality with the
literacy percentage as proxy; and a system factor with honest government and degree
of openness as proxy. Bhagwati and Srinivasan (1983), and Sun et al. (2002) found a
correlation between investment in scientific research and FDI flows. Tong and Yueting
(2000) considered the hometown connections of overseas Chinese investors as a
determinant of FDI flow into China, noting that Taiwanese investors have often
invested through Hong Kong to limit delays due to government and bureaucracy. Sun
et al. (2002, p. 88) identified possible determinants of FDI flow within China including
market demand and size, agglomeration, labor quality, labor cost, level of scientific
research, degree of openness, country risk.
Determinants of
foreign direct
investment
267
Empirical framework
The location of FDI in China has striking spatial characteristics. The provinces in
China are classified into three regions: Eastern, Central, and Western. FDI is unevenly
distributed across the three. Table IV shows that in 2002, the Eastern region received
more than 86 percent of the total FDI amount while the Central and Western regions
together received less than 14 percent. This is consistent with past trends for FDI in
China and has been cited as one of the reasons that have led to the fast development of
the coastal provinces and the widening gap in terms of economic development between
the coastal and inland provinces. (Cheng and Zhang, 1998) Moreover, the FDI flow into
Eastern China has continued to increase. From 2001, the amount of FDI in the eastern
regions has increased by 1.19 percent while in the central regions, FDI has only risen
by 0.2 percent. In the western regions, FDI actually decreased by 1.39 percent (Lu,
2003d). From these figures it is clear that the location of FDI in China is characterized
by enormous spatial diversity.
There are other two main characteristics of China’s FDI pattern, which may have
some certain links with the spatial pattern. Table IV shows that investment activity
tends to concentrate on secondary industries like utilities, manufacturing, and property
development, while the primary sector attracts only about one fourth of the total FDI.
Table IV also shows that foreign capital flows mainly from Asian countries, with more
than 60 percent of the total foreign capital comes from Hong Kong, Taiwan, Japan,
Korean and other Southeast Asian countries. In terms of the total accumulative amount
of foreign capital that the country or area actually used ($100 million), the top four
sources of FDI were: Hong Kong (2,048.75 mn), the USA (398.89 mn), Japan (363.40 mn),
and Taiwan Province (331.10 mn). (Lu, 2003d) These four sources of FDI contribute
more than two thirds of total FDI; however, Hong Kong’s share of FDI far exceeds
those of other countries and regions (Figure 2). Part of this may be explained to the
pass through investments made by investors from Taiwan and other countries to ease
the flow of capital.
Region
Eastern
Central
Western
New foreign company
Capital in signed agreement
Capital actually utilized
Number (units) Percent Amount ($100 million) Percent Amount ($100 million) Percent
30,001
2,730
1,440
Source: Lu (2003d)
87.80
7.99
4.21
731.78
59.53
35.97
88.41
7.24
4.35
47.29
50.09
20.05
86.70
9.50
3.80
Table IV.
FDI regional distribution
in China in 2002
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Other
30%
Hong King
46%
268
Taiwan
7%
Japan
8%
Figure 2.
Foreign capital used
United States
9%
At the regional level, the data shows that distribution of FDI across China’s
regions is very uneven. In 2002, the top four areas are Guangdong, Jiangsu,
Shandong, and Shanghai, which accounted for 59.28 percent of the total FDI (Figure 3)
(Lu, 2003a, b, c).
The reasons for FDI by region are – as stated previously – varied. In the Eastern
regions, it has been argued that proximity, history, culture, and language all play a
significant role in determining FDI. Others (Table III) have found a wide range of other
significant determinants of FDI. From these past analyses of potential determinants of
FDI in China, five were chosen to determine if there was any significant relationship
between them and FDI across the regions within China (Table V). Table VI reveals the
distributions of FDI according to these determinants (Table VII).
Guangdong
22%
Other Provinces
41%
Jiangsu
20%
Figure 3.
Distribution of FDI across
China
Table V.
Definitions and expected
impacts of independent
variables
Shanghai
8%
Shandong
9%
Market demand and market size
GDP per capita (GDPi)
Agglomeration
Labor quality
Highway and railway per km2 (ROADi)
The total number of primary, secondary schools and
universities (SCHNi)
Wage level of retail price index (WAGEi)
Share of state-owned enterprises in industry output
(SHAREi)
Labor cost
The degree of openness
Average
Max
Min
Median
VARP/deviation
Observation
Regions
1420800.454
9380925.12
15717.6432
345357.7568
6.58094E þ 12
30
FDIRMB
0.995862
3.328468
0.308845
0.752341
0.533238
30
GDP
12307.43
23,959
9,174
11,147
16,571,050
30
WAGE
1.01924699
9.78649652
0.11565894
0.45693793
5.19080721
30
ROAD
(/Km2)
46.43333
93
9
47
506.5479
30
SCHN
(school number)
0.574754064
0.857266728
0.136123791
0.604105024
0.040940919
30
SHARE
(of SOE’s)
Determinants of
foreign direct
investment
269
Table VI.
The distributions of FDI
according to the
determinants
387.56
129.59
Secondary
Tertiary
73.48
24.57
1.95
Percent
Asia
Europe
North America
Free port
Region
2,538,571
447,946
466,367
472,583
2000
2,948,833
418,270
487,452
611,309
2001
3,241,119
370,892
601,190
817,640
2002
25.25
0.01
2.94
45.94
2000
16.16
26.62
4.52
39.93
2001
9.91
211.31
23.33
23.64
2002
62.35
11
11.45
11.61
2000
62.9
8.92
10.4
14.1
2001
Percent
61.45
7.03
11.4
15.5
2002
Notes: Primary industries include mining, agriculture – any industries involved in the conversion of raw materials to sellable items; Secondary
industries include manufacturing; Tertiary refer to the service sector, including distribution and professional services
Source: Commercial Ministry Foreign Investment Statistics (2003)
10.28
Primary
Table VII.
FDI distribution in China
in 2002 by sectora and by
source
Sector
By source
Capital actually utilized ($100 mn) Increasing extent (percent)
270
Capital actually
utilized ($100 mn)
By sector
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.
.
.
.
.
The first determinant is the market size (GDP). It directly affects the expected
revenue of the investment. In fact, one major motivation for FDI is to look for
new markets (Shapiro, 1998). The larger the market size of a particular region,
the more FDI the region should attract given other things remain constant.
Kravis and Lipesey (1982), Blomstrom and Lipsey (1991) and others have
identified market size as having a positive impact on FDI. We use GDP per capita
for demand and size effect.
The second determinant is agglomeration (ROAD) which refers to the
concentration of economic activities that leads to positive externalities and the
economies of scale. Coughlin et al. (1991), Wheeler and Mody (1992) and
Braunerhjelm and Svensson (1996) amongst others found that the level of
agglomeration was positively related to the FDI in a particular country.
This study uses infrastructure quality to capture the agglomeration benefits.
The highway and railway mileage per square kilometer is proxy for the quality
of infrastructure.
The third determinant is labor quality (SCHN). This study used the total number
of the primary, and secondary schools, as well as universities as a proxy for
education and further for labor quality. Mody and Srinivasan (1998),
LuMinghong (2000) and Akinlo (2004), all found that labor quality has a
positive impact on FDI.
The fourth determinant is labor cost (WAGE), as measured by wage. Bhagwati
and Srinivasan (1983), Coughlin et al. (1991), Wang and Swain (1995) all found a
relationship between wage or labor cost, and FDI. However, labor cost may have
a negative correlation. Multinational firms in China tend to hire quality workers
who earn higher wages as a possible reflection of this higher labor quality.
Hence, wages in those provinces that attract more FDI may be higher.
The fifth determinant is the degree of openness and progress of reform (SHARE).
Sun et al. (2002) and Fujita and Hu (2001) found that a significant relationship
between the degree of openness – as defined by the percentage of states owned
enterprises (SOE’s) and FDI. On the one hand, a more open economy means that
foreign investors are more familiar with the host economy and may therefore be
more willing to invest in the country. On the other hand, openness can have a
negative impact on FDI as it may attract more competition, lessening any
competitive advantage a firm may have hoped to realize. We use the share of
state-owned enterprises in all enterprises of a region to measure its degree of
openness and progress of reform.
As noted previously, there are many other commonly used determinants that may have
a correlation with FDI in China – determinants like the number of telephone sets,
number of tourists, level of scientific research, degree of industrialization,
agglomeration of FDI, promotion expenditures for attracting FDI, tax structure, and
the special treatment offered to foreign investors that may have impacts, too. However,
such data is more difficult to obtain on a regional or provincial level in China, which led
to the focus on the five aforementioned determinants.
The typical method of estimating the effect of potential determinants of FDI is to
regress the chosen dependent variable, such as the amount of FDI in a location, on a set
Determinants of
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of independent variables which would possibly affect the profitability of investment.
These variables typically reflect local market potential, and the cost of production and
transport, as well as the general environment faced by the multinational company.
On the basis of the existing statistical analyses of the location of FDI in China, we
postulate that for the amount of FDI in region i:
Y ¼ f ðGDP; ROAD; SCHN; WAGE; SHAREÞ
where GDP, ROAD, SCHN, WAGE, and SHARE represent the variables for market size
and demand, agglomeration/infrastructure, labor quality, wage, degree of openness
and progress of reform, respectively.
Since, our dependent variable is the per capita amount of FDI, we use per capita
GDP to capture the regional market potential or market size. In order to experiment
with an appropriate choice of the infrastructure variable (ROAD), three alternative
proxies were tested:
(1) the total lengths of roads per unit of land mass;
(2) the total lengths of high grade paved roads per unit of land mass; and
(3) the total lengths of railway per unit of land mass.
A region’s labor cost (WAGE) is given by its average labor cost divided by its retail
price index. Our education variable (SCHN) is the total number of primary, secondary
schools, and universities for a given region. All real variables are measured in current
prices. In our sample, a region is either a province, centrally administered municipality,
or an autonomous region.
As the determinants of FDI distribution were examined across regions regardless of
time trends and with known data limitations, this assignment only requires ordinary
least-squares (OLS) estimation. The data used in this study are obtained from China’s
Statistical Yearbook 2003, the China Statistical Yearbook for Regional Economy 2003,
the China Foreign Economic Statistical Yearbook 2003, and the White Books of China’s
Trade and Economics (2003). For this study, two elements of FDI were used: “Signed
Agreement” and “Actually Utilized”. The later one is the actual amount of FDI invested
in the region. Since, GDP and wage are denominated in RMB and the FDI in US dollars,
we converted the FDI into RMB using the average yearly exchange rate in 2002. It is
possible that a high correlation between the various proxies may come out and that the
proxies may overlap with one another, which may lead to serious multicollinearity. In
order to cope with multicollinearity, those highly correlated pairs were excluded.
Specifically, all variables were transformed into the natural logarithm form and were
then stacked up across the 30 regions.
In summary, foreign direct investment at the regional level in China was expected to
be affected by the region’s market demand and market size (GDP), infrastructure
(ROAD), labor quality (SCHN), labor cost (WAGE), and the degree of openness and
progress of reform (SHARE).
A general pooled regression model is used on these variables and is specified as:
InðFDIi Þ ¼ ai þ b1 InðGDPi Þ þ b2 InðROADi Þb3 þ InðSCHNi Þ
þ b4 InðWAGEi Þ þ b5 InðSHAREi Þ þ 1i ði ¼ 1; 2; . . . ; 30Þ
ð1Þ
Where subscript i refers to individual provinces; ai is the intercept; 1 is an error term;
bi (i ¼ 1, 2, 3, 4, 5) are vectors of unknown coefficients to be estimated.
Analysis of results
This section determines how location factors determine flows of FDI. Table VIII
reports the OLS estimation results of equation (1) on the regional level. The model is
regression with a common intercept and the OLS estimates are significant except for
ROAD and SCHN. The Adjusted R 2 value of 0.793 further suggests that the model fits
the data well.
Determinants of
foreign direct
investment
273
Summary of results
This study has found that three of the variables have a statistically significant
relationship with FDI in the 30 different regions studied in China. The relationship
between a regions market demand and market size (GDP) was a significant and
positive factor in attracting FDI in 2002. The Coefficient estimate 2.843 indicates that
foreign direct investment is very responsive to differences in per capita GDP across
provinces. A 1 percent increase in GDP is estimated to lead to a 2.843 percent increase
in FDI. This supports the hypothesis that the market demand and size as well as the
general development level of a region have a positive impact on attracting FDI.
This would seem to suggest that foreign firms may be motivated to invest in China
under an assumption that to do so will allow them to gain access to China’s growing
middle class.
The higher the quality of labor the more attractive a region was to FDI. The results
suggest a positive relation between FDI and the quality of labor, as proxied by the
number of primary, and secondary schools, and universities. A 1 percent increase in
SCHN is estimated to lead to a 0.847 percent increase in FDI. The level of significance
suggests that labor quality is important to FDI consideration. Although China is
making major investments in education, the still relatively low quality of labor in
China may discourage some Western FDI in capital-intensive projects where a skilled
work force is a prerequisite for success.
The final variable that exhibited a significant relationship with FDI was the degree
of openness and level of reform in each region. A 1 percent increase in the share of state
enterprise in industrial output is estimated to lead to a 1.147 percent drop in FDI.
The more ownership governments have in business enterprises, the lower the FDI. The
positive and significant coefficient of SHARE indicates that foreign investors respond
Variable
Coefficient
T-start
GDP
ROAD
SCHN
WAGE
SHARE
2.843
0.064
0.847
20.779
21.147
5.342 * * *
0.356
33.008 * * *
2 0.700
2 2.175 * *
Notes: Adjusted R 2 value ¼ 0.793; constant coefficient ¼ 5.000; number of observations ¼ 30;
* * ¼ 5 percent significance level; * * * ¼ 1 percent significance level
Table VIII.
Determinants of foreign
direct investment
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positively to the economic reform – as determined by a reduction in the level of state
ownership for enterprises in a particular region. The result also gives evidence that
an open economic policy and further economic reforms are crucial in attracting FDI to
China.
The other two variables did not meet the test of statistical significance. As a proxy
of the level of infrastructure, ROAD shows no significance in the regression. Since, this
variable is used to capture the agglomeration effect, the result is not supportive of the
agglomeration argument, i.e. that the level of similar and related businesses in a region
may lead to an increase in FDI. There is some evidence, however, to suggest that the
positive result gives mild evidence that there is a positive relationship between
infrastructure and FDI.
With a t-value of 2 0.700, the impact of labor cost (WAGE) on FDI was also
not statistically significant. The data does suggest, however, that a 1 percent
increase in WAGE is estimated to lead to a 0. 779 percent decrease in FDI. That is
to say, high labor cost deters the inflow of FDI, which is consistent with Coughlin
et al. (1991) and Friedman et al. (1996). This wage elasticity is roughly comparable
to the 2 0.9 estimate by Bartik (1985) who evaluated the wage elasticity of new
branch plants. Many foreign direct investments are encouraged by the enticement
of low-cost labor in China, but the lack of statistical significance indicates that
China attracts foreign investment based on other determinants. Labor cost may not
be a major concern for foreign investors who are motivated by accessing to
China’s domestic market.
A major disadvantage of using the OLS regression is that it cannot resolve or reduce
the magnitude of key econometric problems that often arise in empirical studies,
namely, the unobserved variables that are correlated with explanatory variables. In the
application here, equation (1) does not allow for fixed effects in the cross-section so that
intercepts need to be identical across different provinces. As such, unique but
unobserved factors driving the FDI amount of individual provinces would not be
captured in the respective intercepts in the equation.
Conclusion and recommendations for further research
China’s economic reform has attracted worldwide attention. From the early stage
of pulling in export-oriented industries with cheaper production costs to more
recent investments aiming to tap the huge domestic market, China has gradually
opened up to the rest of the world. While this research study is only a snap shot
of a topic that is of increasing importance to China, it has direct relevance to the
FDI development efforts of the individual regions. This study provides evidence
that GDP that proxies for the market size and potential show up to be a big
attraction for FDI. Labor quality and the progress of reform or the degree of
openness are also important determinants of the distribution of FDI. We have
some mild evidence that high labor cost deters the inflow of FDI and the level of
infrastructure has positive relation to FDI. These results are generally consistent
with Cheng and Kwan (2000), which suggests that they may be applicable over a
longer time period.
The twenty-first century is a century of intellectual competition; thus the human
capital of knowledge, intelligence, and talents will surely be crucial factors relative to
FDI. Past studies (Bhagwati and Srinivasan, 1983; Sun et al., 2002) found a correlation
between the level of scientific research, and FDI. It is clear that there is some movement
in selected regions of China to develop the human capital. The development of human
capital is a key point to both export-oriented FDI and market-seeking FDI. But on the
Chinese mainland, for every 100,000 people, there are only 3,611 who have received a
higher education and 11,146 ones who have received a secondary school education.
There are 85 million illiterate people representing approximately 6.72 percent of the
Chinese population. The relative scarcity of highly qualified laborers in China has
seriously discouraged investment in capital-intensive projects. This is especially
critical when put in the context of the future competitiveness of Chinese based
enterprises. As Porter and Stern (2001, p. 36) emphasized:
. . . long-term competitive advantage relies on being able to avoid imitation by competitors.
Ironically, then, location-based advantages in innovation may prove more sustainable than
simply implementing corporate best practices.
For China to develop a sustainable, nationally competitive advantage, it will have to
move from being a country which attracts FDI based on low costs and growing middle
class, to one which has strategically located agglomerations – this may mean increasing
the support for openness, and FDI in selected regions rather than in all regions.
Limitations
Our study has several limitations that deserve further investigation. First, the
importance of determining factors may change over time. Similarly, the location
determinants of foreign direct investment may differ by industry. Furthermore, due to
data limitation, we were not able to consider the effect of FDI policies, tax incentives,
cumulative FDI, and foreign portfolio investment to FDI in China. Finally, we believe
corruption and the effects of bureaucratic red tape are also important deterring factors
of FDI. A future study would seek to determine if the same relationships exist between
the variables for a longer period of time.
Although there are numerous other topics which are worth researching
further, the current results do carry an important policy implication in that they
highlight that provincial officials have more to do to encourage domestic-market
oriented and capital-intensive FDI in the future. FDI that seeks markets are more
likely to occur if economic reforms go even further and if the government opens
more markets to foreign investors. Furthermore, the government should provide
more financial support for education to enhance the labor quality and to improve
the skill level of laborers.
Notes
1. Openness refers to the amount of state owned enterprises (“SOE”) in a particular region.
2. Internalization refers to a situation where a company creates other companies under one
corporate group umbrella that produces many different components – most of which are
consumed within the group.
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About the authors
Lv Na is a master’s degree student pursuing a degree in Economics at the University of
International Business and Economics. She has translated and edited the book Export Guide for
Automotive Components for UNCTAD in 2005 and has also had a paper titled “Theoretical
Determinants of
foreign direct
investment
277
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analysis to nation-owned company’s regulating in rivalrousness areas” published on
International Business in 2004.
W.S. Lightfoot, PhD, is the Associate Dean of Institutional Advancement and Special
Projects, and Director of the Centre for Entrepreneurship at the International University of
Monaco. He has delivered lectures in the USA, Canada, Greece, China, France, and Monaco. Prior
to becoming a Professor, he spent over 15 years in technical marketing and management for
companies including Schneider Electric (Square D), and Eaton Corporation (Cutler Hammer).
He has also been involved in the early stages of several ventures, including SemiPower Systems,
and Motioninfo.com. W. S. Lightfoot is the corresponding author and can be contacted at:
[email protected]
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