University of Groningen
Faculty of Economics and Business
MSc International Economics and Business
Master Thesis
Foreign Direct Investment and Regional Economic Growth
In China:
A Panel Data Study of 1997-2009
Author: Lin Wang
Student Number: 1928902 Email: L.Wang.12@student.rug.nl Thesis Supervisor: Dr. Dirk Akkermans
Abstract
This paper investigates the relationship between inward FDI and regional economic
growth in China from 1997 to 2009, using a panel data study. Surprisingly, FDI
inflows to the coastal region is significantly yet negatively related to its economic
growth rate, while inward FDI to the central and western regions has an insignificant
impact on their economic growth as we expected. Labor costs suggest a positive effect
on non-coastal regions and a negative effect on their coastal counterpart. In addition,
WTO accession shows a significantly positive relationship with China‟s economic
growth. A second analysis is also provided to find out whether the economic
development in the coastal region can actually affects that in the central and western
regions. The answer is yes, though there is no significant difference of economic
growth between the non-coastal regions.
Contents
Introduction 3
Background 3
Problem Statement 4
Literature Review 6
FDI and Economic Growth 6
Human Capital and Economic Growth 8
Labor Costs and Economic Growth 10
WTO and Economic Growth 11
Methodology 13
Methodology Description 13
Data Measurement 14
Analysis I: Coastal versus Non-coastal 17
Diagnostic Tests I 17
Empirical Results I 23
Analysis II: Coastal to Non-coastal 26
Introduction
Background
Foreign direct investment (FDI) has been one of the most discussed topics over
decades. Under the trend of economic globalization, the relationship between FDI and
economic growth has also been widely studied. While China has emerged as a major
recipient of foreign direct investment, FDI in China is one of the most remarkable outcomes of China‟s Reform and Open Policy, which was adopted in the late 1978. Since 1993, China has already held a position second only to the United States as the
largest host country for FDI (Buckley et al., 2002). Most importantly, FDI has played an important role in contributing to the country‟s economic development and institutional reform (Long, 2005).
As a matter of fact, almost no studies on FDI in China have failed to point out the
highly unbalanced geographical distribution of FDI within the country. The enormous
land area of China is unavoidably associated with tremendous contrasts in conditions
between provinces. Several articles have already pointed out that the degree of
economic development varies substantially across different areas of China, and the
geographic distribution of FDI is characterized by its concentration in coastal areas
(Buckley et al., 2002; Wen, 2005; Zhang, 2006). For instance, in 1996, nearly 90% of
FDI inflows to China were concentrated in coastal provinces, whereas the central
region received only 9% and the west region only 2% of the total FDI inflows
throughout that year (Cheung & Lin, 2004). While an overall positive impact of FDI
on economic growth is supported by empirical literature, China with its large country
and economy size means that this finding may have mixed up different regional
impacts.
In order to shed some light on the nexus of FDI inflows with regional economic
growth in China, this paper is organized as following. The first chapter brings up
methodology are explained in the third chapter. Two analyses are presented in the
following two chapters respectively. And the last one concludes.
Problem Statement
Since the economic reform and opening up to the outside world, China has attracted
increasingly large amount of foreign capital inflows, which can be divided into three
categories: foreign loans, foreign direct investment and other foreign investment. It
has been confirmed that between 1979 and 2000, China‟s actual usage of foreign
capital amount to more than 500 billion dollars, among which over two thirds are in
the form of direct investment (Fung et al., 2002). According to China‟s statistics, most
inward FDI to China have consumed by the industrial sectors in the east coastal
provinces. More strikingly, more than 85 percent of China‟s annual FDI inflows have
been invested in the coastal region. Four provinces, Guangdong, Jiangsu, Shanghai
and Zhejiang, have been recognized as the largest recipients and received more than half of China‟s FDI inflows. While Chinese government advocated development of western China and provided a set of preferential policies to attract FDI to the west in
2000, the increase in share of FDI inflows from 1999 to 2001 in the western region
was still less than 2% (Wen, 2005).
Although a large volume of studies have proved the positive role played by FDI in China‟s economic development, there is still a lack of systematic studies on the impacts of FDI on China‟s economic performance at a sub-national level, as argued
also in Lheem and Guo (2004). This paper aims to study the relationship between
inward FDI and economic growth at the regional level in China for the period of
1997-2009, using recent time-series and provincial-level data. In the mean time, it
also tries to give insights into how the economic growth in different regions can affect
each other. The key issue here is how to explain the unbalanced FDI distribution and
Generally, this study of FDI‟s impacts on China using provincial data is desirable for
a number of reasons. First of all, it contributes to the FDI-growth nexus literature by
using the empirical results at regional level of China. Secondly, we seek to augment
the empirical evidence concerning the economic impacts of inward FDI to different
regions in China using most recent data. Last but not least, an understanding of how
FDI affects different regions is important to the design of policies to attract and
promote foreign capital. This study mainly focuses on the central question: how we
can explain the relationship between regional economic growth and regional FDI
distributions in China. To be specific, the specialized research questions are as
follows:
Is the extent of uneven FDI distribution at the regional level directly related to the regional economic growth in China during 1997-2009?
Is there any difference between the impacts of FDI inflows on economic growth in the coastal region and those in the central and western regions?
Literature Review
FDI and Economic Growth
The impact of foreign direct investment on economic growth is one of the most
controversial topics in the area of economics research. Existing empirical evidence on
the relationship between foreign direct investment inflows and economic growth is
inconclusive, as Wijeweera et al. (2010) argues, despite there are plausible theoretical
grounds for presuming a positive nexus. Mullen and Williams (2005), as well as Choe
(2003) conclude that FDI has a positive effect on economic growth. Carkovic and
Levine (2005) argue FDI does not have any significant impact on economic growth in
the host country, while Mencinger (2003) finds inward FDI is negatively related to
economic growth. Borensztein et al. (1998), Alfaro et al. (2000), and Alfaro et al.
(2008) find that FDI will promote economic growth only when certain economic
conditions are met in the host country, such as a threshold level of human capital.
Markusen and Venables (1999) suggest FDI is likely to be an engine of host countries‟
economic growth, and by its very nature, FDI may bring into host economies special
resources such as management know-how, skilled labor, and access to international
production networks. Moreover, FDI may result in technology transfers and spillover
effects. According to Lheem and Guo (2004), FDI promotes economic growth by
providing external capital, and through growth, spreads the benefits all over the
economy. They also believe inward FDI may enhance capital formation, and also
promote employment augmentation. It is argued that FDI usually brings with it
advanced technology, and better organization management. Hence, virtually, FDI is
one of the most essential engines for economic growth especially in developing
countries.
In fact, most emerging economies are now actively attracting FDI inflows. Emerging
economies have become more receptive to inward FDI, and most of them see FDI
2002). Mencinger (2003) points out the economic benefits of FDI are considered to be
twofold. On the one hand, FDI can help countries if their domestic savings are
insufficient to finance economic expansion; on the other hand, the presence of a
foreign corporate is associated with positive externalities. Zhang (2006) argues that,
in the case of China, perhaps the most prominent contribution of FDI is augmenting China‟s manufacturing exports. Concretely, foreign-invested enterprises not only expand China‟s export volumes, but also upgrade its export structure. He further
argues that, FDI seems to have brought extra gains to China in facilitating its
transition toward a market system which started in the late 1970s. Huang (2009) finds
FDI has a positive and statistically significant impact on economic growth as theory
predicts, using the augmented Solow-Swan model of Mankiw et al. (1992) to analyze
provincial-level data of China over the reform period 1978-2003.
However, due to regional differences, such as social, economic, and institutional
dissimilarities, the relationship between FDI and economic performance is expected
to vary within China, from region to region and even from province to province.
Generally speaking, there are three regions can be categorized, the coastal, the central
and the western region, and each region consists of several provinces. They are
distinguished not only by their geographic features, but also by the level of their
economic development, degree of economic openness, quality of institution, and
factor endowments. Wen (2005) finds that FDI inflows can generate a demonstration
effect in identifying regional market conditions for investment in fixed assets. This
effect on regional exports and regional income growth has varied across the east
coastal, central and western China ever since the second half of 1990s. In Buckley et
al. (2002), the authors argue that host country conditions strongly influence the
growth relationship at both the national and the provincial levels. Their results also
suggest that FDI favors growth in the economically stronger provinces, and the full
benefits of FDI can be realized when competitions of both the foreign and local origin
It is also shown in Fujita and Hu (1999), the income disparity between the coastal and
the interior area has been increasing, with the industrial production showing a strong
agglomeration towards the coastal area. More importantly, globalization (measured by
export and FDI) and economic liberalization had significantly promoted the increase
of regional disparity. As indicated in Graham and Wada (2001), FDI in China has
stimulated much growth in income, which could almost not have been realized in the
absence of this investment. FDI has significantly benefited the coastal region while
most of the rest of China has much less benefits. Zhang (2006) using provincial data
over the period of 1992-2004 shows that FDI seems to promote income growth across
China. Moreover, this positive growth effect seems to rise over time and to be
stronger in the coastal rather than in the inland regions. Thus, according to the above,
we expect to see a positive relationship between FDI and economic growth in east
coastal provinces but not in the central and western regions.
Hypothesis 1: All else being equal, foreign direct investment has a significant positive
impact on economic growth in the east coastal region.
Hypothesis 2: All else being equal, foreign direct investment does not have a
significant impact on economic growth in the central and western regions.
Human Capital and Economic Growth
Many studies have confirmed human capital is one of the primary determinants of
economic growth (Van et al., 2010; Tsai et al., 2010; Noorbakhsh et al., 2001;
Mankiw et al., 1992). Early as in Frank (1960), it is argued that education has made
an increasingly large contribution to output, and investment in human capital is a
major component of economic growth, since education is considered as the main form
of increasing human capital.In Galor and Moav (2004), it is stated that investment in
human capital will eventually replace physical capital as the main engine of economic
growth. Besides, Lee and Lee (1995) show that a higher initial level of human capital
per worker will lead to a higher growth rate of real GDP per worker. With regard to
Wang and Yao (2003) find the accumulation of human capital was quite rapid and it
contributed significantly to the growth and welfare in China.
Actually, several articles have also already pointed out the importance of a
threshold-level human capital as a determinant of inward FDI. Ciruelos and Wang
(2005) suggest a complementarity between FDI inflows and human capital in less
developed countries. They argue that for FDI from developed countries to promote
technology diffusion in less developed countries, a certain threshold of human capital
has to be reached. Borensztein et al. (1998) also find the direct effect of FDI on
growth is negative in countries with low levels of human capital. To be more specific,
only countries with sufficiently high levels of human capital have the ability to exploit
the technological transfers and spillover effects associated with inward FDI. Lheem
and Guo (2004) give an additional argument says, the most important contribution of
FDI is likely to be the fact that the productivity of FDI is higher than those of
domestic investments, while this higher productivity occurs only when the host
country has a minimum threshold stock of human capital.
Broadman and Sun (1997) mention that a host region‟s labor supply influences foreign investors‟ location decisions not only in terms of input costs, but also through the quality of the labor force. They expect locations with highly skilled workers
(measured by education levels) could compete more favourably than their
counterparts in FDI attractiveness. Besides, Noorbakhsh et al. (2001) find out human
capital is a statistically significant determinant of FDI inflows, and its importance has
become increasingly greater over time. More importantly, they argue the geographical
distribution of FDI may be affected by the level of human capital in the host country.
In the case of China, Fleisher et al. (2005) conclude the impacts of human capital may
operate through spillovers from wealthy to poor provinces. Furthermore, in Buckley
(2002), it is shown that for the most economically backward provinces, increases in
investment in human capital make a positive contribution to economic growth. Under
China, has a positive impact on economic growth in the east coastal region which has
comparatively higher level human capital, rather than in the central and western
regions.
Hypothesis 3: All else being equal, the level of human capital, as a determinant of
inward FDI to China, is positively related to economic growth in the coastal region.
Hypothesis 4: All else being equal, the level of human capital, as a determinant of
inward FDI to China, is not significantly related to economic growth in the central and western regions.
Labor Costs and Economic Growth
The level of labor costs has been proved to be an essential factor in affecting the
economic growth of an area. Storper (2010) considers factors such as labor costs,
regulations, and business climates, are explanations for why a city does well or poorly
in its economic development. Besides, Huang (2009) argues differences in labor
markets, industrial structure, and resource endowments, can lead to gaps in regional
per capita income even in equilibrium. In the study of Asian countries by Seguino
(2000), it is also confirmed that low unit labor costs, obtained by offering low wages,
will stimulate investment and exports. The resulting foreign exchange used to
purchase capital and intermediate goods will thus raise productivity and economic
growth rates.
Foreign investors usually aim to take advantage of host countries‟ cheaper factor
inputs. In this regard, labor cost is often considered one of the most important factors
(Broadman & Sun, 1997). Bellak et al. (2008) suggest that higher unit labor costs as
well as higher total labor costs are negatively associated with FDI, by analysing the
determinants of FDI across several Central and Eastern European Countries. Liu and
Pearson (2010) also reveal market size, labor costs, and business ethics are important
factors for promoting FDI to China, using primary data provided by 43 managers of
level is a critical determinant of FDI and high labor costs have significantly negative
effect on FDI, after controlling for labor productivity. Cheng and Kwan (2000) also
argue wage costs have a negative effect on FDI, by estimating the effects of FDI
determinants in China‟s 29 provinces from 1985 to 1995. Especially, Boermans et al.
(2009) use a factor analysis to investigate what drives the uneven regional distribution
of FDI inflows to 31 provinces in China from 1995 to 2006, and find out foreign
investors choose to invest more in provinces with lower labor costs, larger market size,
and better institutions. Hence, while the provinces with higher labor costs compete in
their efforts to attract FDI less favourably, our expectation is that this factor is likely
to generate a negative relationship with provincial inward FDI. Thus, we would
expect labor costs, as a determinant of inward FDI to China, has a positive impact on
economic growth in the central and western regions which have relatively lower labor
costs, but has a negative effect in the east coastal region which has comparatively
higher labor costs.
Hypothesis 5: All else being equal, the level of labor costs, as a determinant of
inward FDI to China, is negatively related to economic growth in the coastal region.
Hypothesis 6: All else being equal, the level of labor costs, as a determinant of
inward FDI to China, is positively related to economic growth in the central and western regions.
WTO and Economic Growth
The significance of entry into WTO is a topic can be barely avoided when discussing
about the relationship between FDI and economic growth in China. After 15 years of
negotiations, China became the 143rd member of the World Trade Organization
(WTO) On December 11, 2001 (IMF, 2002). As addressed in Ding et al. (2009), China‟s entry into WTO represents a „second opening‟ for the country ever since the landmark Reform and Open Policy launched in 1978. They argue that the resulted
rapidly increasing foreign trade has greatly boosted China‟s economic development,
into WTO, as Wang (2003) states, China and its trade partners would both benefit
from further realization of each other‟s comparative advantages in a freer trade
environment, and it is good for bilateral economic growth. Furthermore, Daniel III
(2004) in his study concludes that China‟s entry into WTO has already resulted in a
more efficient economy and an increased rate of economic growth. He considers these
two effects are both resulted from the restructuring of China‟s economy from a closed
central planned economy to a more or less open market economy, followed by the
WTO accession.
In the study of Buthe and Milner (2008), by statistically analyzing 122 developing
countries from 1970 to 2000, they find out joining international trade agreements
allows developing countries to attract more FDI and thus increase economic growth.
Actually, in recent years, China has become one of the most favorite destinations for
global investment because of its preferential foreign investment policies, improving
investment environment, inexpensive labor, increasing purchasing power, and
especially the entry into WTO (Ali & Guo, 2005). Wen (2005) argues that, indeed,
China‟s accession to WTO made it more closely integrated into the world economy
ever since, and it is becoming the largest FDI recipient country. Speaking of the role
WTO plays in the relationship between FDI and economic growth for China, Tuan
and Ng (2004)1 state that FDI-led growth is expected to characterize China‟s future economic growth after the WTO accession, and rapid FDI growth is expected to
continue in the future. It is especially worth mentioned that in Tuan and Ng (2004)2, they conclude further FDI absorption after WTO accession will be followed by
agglomeration economies, which may further strengthen China‟s current biased
regional growth pattern.
Hypothesis 7: All else being equal, China’s accession to the World Trade
Methodology
Methodology Description
Two analyses are performed in this research. At the beginning, multiple regression
models will be run to test the relationship between regional FDI and economic growth
in China respectively in regard to our hypotheses. And the second step, which is also
a highlight of this paper, we add another analysis to find out whether the economic
growth and FDI inflows in the coastal region have any effects on that of the central
and western regions. The regression models are formed as below. These models will
be discussed in detail later in this section.
Analysis I: Coastal versus Non-coastal
GROit=α1+β1FDIit-1+β2HUMit+β3LABit+β4WTO+β5COS+β6(COS*FDIit-1)+εit (1)
GROit=α1+β1FDIit-1+β2HUMit+β3LABit+β4WTO+β5COS+β6(COS*LABit)+εit (1a)
GROit=α1+β1FDIit-1+β2HUMit+β3LABit+β4WTO+β5COS+β6(COS*HUMit)+εit (1b)
Analysis II: Coastal to Non-coastal
GROi2t=α2+γ1GROi1(t-1)+γ2FDIi1(t-1)+γ3HUMi2t+γ4LABi2t+γ5CEN+εit (2)
In the first regression model (1), the impact of inward FDI in province i on its annual
GDP growth at time t is shown in this equation. Dependent variable GROit indicates
the economic growth rate in the host province i at time t, while the independent
variable FDIit-1 in this research represents FDI inflows in the province i at time t-1.
FDIit-1 is taken a one-year lag to correct for the possible endogeneity problem, which
will be discussed in depth in the next chapter. The second and the third independent
variables, HUMit and LABit, are control variables. HUMit stands for the level of human
capital in province i at time t, while LABit stands for labor costs in province i at time t.
There are also two dummy variables included in the equation. WTO equals 1 when
measured after 2001 (from 2002) and 0 before 2001 (including 2001) in the end of
COS is 1 when the province being tested belongs to coastal region, and 0 to other regions. The last independent variable is an interaction term of coastal region dummy
and independent variable FDIit-1. We add this term on the ground that, according to
Brambor et al. (2006), an interaction term should be included in the regression
equation whenever the hypothesis is conditional in nature. Lastly, εit is the error term.
The only thing changed among these three equations is the interaction term.
COS*LABit in regression (1a) is for testing Hypothesis 3&4, the potential different
impacts of labor costs on coastal and non-coastal regions. Similarly, in regression (1b),
COS*HUMit is used to test Hypothesis 5&6, the possible different impacts of human
capital on different region groups.
Next, regression model (2) describes whether the economic growth and FDI inflows
to the coastal region may also influence the situation in the central and western
regions. We take GROj2t as the growth rate of the central and western regions at time t,
while j2 represents provinces in these two regions. By the same token, GROi1(t-1)
measures the growth rate of the coastal region at time t-1, and j1 delegates coastal
region provinces. The second independent variable FDIi1(t-1) represents inward FDI in
the province j1 at time t-1. These two variables are both taken a one-year lag to solve
the potential endogeneity issue. Besides, we also add another two variables HUMi2t
and LABi2t, as control variables. They respectively indicate human capital and labor
costs in the central and western regions. CEN is the central region dummy, which
means a central region province when it equals to 1 and 0 a western region province.
Last but not least, εjt is the error term of equation (2).
Data Measurement
Several relevant literatures that divide all 31 provinces of China into 3 regions
actually include 28 provinces while eliminate Tibet, Chongqing and Hainan (Lee,
1994; Buckley et al., 2002; Tang & Selvanathan, 2005). We tend to be in line with
Cheung and Lin (2004) which involves 30 provinces merely except Tibet. In fact,
note that, Chongqing has been separated from Sichuan province as a centrally
administrated municipality in 1997, thus articles before this year could not possibly
include Chongqing as a research object. While Hainan is a small island apart from the
mainland, most studies try not to include it into any of those three regions. However,
in order to give a whole picture of China‟s economic development, we consider
Hainan still takes an important part which cannot be ignored. Table 1 below describes
how the 30 provinces are classified into 3 regions.
Table 1 Summary of region classification
Region Provinces Number
Coastal Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Guangxi, Hainan
12
Central Shanxi, Inner Mongolia, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, Hunan
9
Western Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu,
Qinghai, Ningxia, Xinjiang
9
All the data needed to run our regression can be found in various issues of China
Statistical Yearbook, from National Bureau of Statistics of China
(http://www.stats.gov.cn/english/statisticaldata/yearlydata/). Growth rates are
calculated as a percentage using regional annual GDP data by province, and corrected
by inflation rates. The annual inflation rates are found in WDI (World Development
Indicators) from World Economic Outlook Database April 2011 of IMF (International
Monetary Fund). In China Statistical Yearbook, regional FDI data is reported
annually as actually used foreign direct investment by each province rather than FDI
inflows to those provinces. We consider this actually used FDI data can be an even
better measurement. Human capital is the number of graduates of higher education
institutions and specialized secondary schools, while labor costs are measured as the
average year wages of staff and workers by region. As for the dummy variable WTO,
we take 2002 as a dividing year, since it is the first full year after China‟s entry to
WTO and the first year in which China exceeded the US in attracting FDI (Na &
Table 2 Summary of data description
Variable Description Source
Growth rate
Calculated from regional annual GDP data as a percentage: [(GDP this year-GDP last year)/GDP last year*100%]-inflation rate this year
China Statistical
Yearbook,1997-2010
Regional FDI
Actually used foreign direct investment by region (USD)
China Statistical
Yearbook,1998-2010
Human capital
Number of graduates of higher education institutions and specialized secondary schools (person)
China Statistical
Yearbook,1998-2010
Labor costs
Average year wage of staff and workers by region (yuan)
China Statistical
Yearbook,1997-2009
WTO 1 represents 2002-2009 and 0 stands for
1997-2001
COS 1 indicates coastal region and 0 otherwise
COS*FDI Interaction term of coastal region dummy and regional FDI
COS*Lab Interaction term of coastal region dummy and labor costs
COS*Hum Interaction term of coastal region dummy and
human capital
Analysis I: Coastal versus Non-coastal
Diagnostic Tests I
Several diagnostic tests are conducted in this section to assure accuracy and reliability of the empirical results. It has to be mentioned that the conclusions we can obtain from diagnostic tests of (1a) and (1b) are the same as we get here for regression (1). Those results are shown in appendices at the end of this paper. Table 3 below shows the descriptive statistics of our research sample. Note that there are two obvious outliers, which are also the minimum and maximum of Growth group, namely 48.99% for Beijing (2001) and -9.96% for Hubei (2001). Therefore, we try to cast back and find out what exactly happened in 2001 has made these two numbers so different from the others. The only relevant information we can think of is that Beijing, as the capital of China, successfully bid for the 2008 Olympic Games on July 13, 2001, and in the end of which China also joined WTO. Both events could have significantly affected the economic growth of Beijing 2001, while the influence of WTO is also what we want to prove in this paper. We are not able to make sure, however, why the economic growth rate of Hubei 2001 can be the only negative one in the whole sample. In order to keep our sample integrated and give a full image of regional FDI‟s impacts on China‟s economy, both outliers are not deleted.
Table 3 Descriptive Statistics
Obs. Minimum Maximum Mean Std. Deviation
Growth 390 -9.9554 48.9926 12.436329 5.0746776
FDI 388 2470000 4.E12 2.88E11 6.963E11
Labor Costs 384 4564 56565 14033.77 8401.958 Human Capital 390 6029 850498 169660.39 162089.639 WTO 390 0 1 0.62 0.487 COS 390 0 1 0.40 0.491 Valid N (listwise) 382 Multicollinearity
Multicollinearity refers to the situation when there are two or more variables in a
sufficiently estimate their separate effects on the dependent variable because the
p-value is higher even if these variables are significant determinants of the dependant
variable (Hill et al., 2007). In order to detect possible multicollinearity, a correlation
matrix of all variables is presented in Table 5. All pairs of correlation we obtained are
relatively low, with the highest 0.620, which thus suggests little collinearity. That is to
say, multicollinearity is not a problem in respect of this research.
Table 5 Correlations Matrix
Growth FDI Labor
Costs Human Capital WTO COS Growth 1 FDI 0.120* 1 Labor Costs 0.334** 0.606** 1 Human Capital -0.023 0.078 0.202** 1 WTO 0.479** 0.324** 0.620** -0.098 1 COS 0.018 0.381** 0.275** 0.096 0.000 1
*. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).
Normality
A histogram of residuals is created by Stata as below. Unbiased coefficient
estimations in a linear regression model should based on the assumption that error
terms and also values of dependent variables are normally distributed (Hill et al.,
2007). The least square estimators should have an approximate normal distribution, if
the Gauss-Markov assumption holds. As we can see from Graph 1, the residuals of
our sample are centred at zero and reasonably bell-shaped, which may suggest a
normal distribution. Thus we further conduct a Jarque-Bera test to formally check the
Graph 1 Histogram of Residuals
Jarque-Bera test is based on two measures, namely skewness and kurtosis (Hill et al.,
2007). It suggests a perfect symmetry of residuals when the skewness is 0, and
indicates a normal distribution when the kurtosis value is 3. The null hypothesis (H0)
of this test suggests the residuals are normally distributed. As we can get from our
results, the value of the calculated Jarque-Bera statistics 3008.6044 is far greater than
the critical value (5.99) of chi-squared distribution with two degrees of freedom
obtained at a 5% significant level. Furthermore, in our case, the estimated values of
skewness and kurtosis are respectively 1.7300 and 16.8773, which do not suggest a
normal distribution. Thus, the null hypothesis should be rejected. While
non-normality of residuals is mainly resulted from issues of heteroskedasticity and
autocorrelation, certain tests are conducted in the following parts to check whether
those problems exist.
Next, Q-Q plots are used to check whether all variables are normality distributed.
From the graphs we may conclude that the dependent variable Growth is nearly
normally distributed, while FDI, Labor Costs and Human Capital can be transformed
to normal distributions. Thus, we use natural logarithm normality transformation
method to treat all these three variables. The Q-Q plot of the dependent variable
Growth is shown in appendices.
Heteroskedasticity
Heteroskedasticity usually occurs when the variances for all observations are not the
same. The assumption for OLS (ordinary least square) estimator suggests a constant
variance for the error term will thus be violated under this circumstance. Although
heteroskedasticity may not bias the coefficient estimates of OLS, it may lead to an
underestimation of the variances and the standard errors of the coefficients
(Wooldridge, 2002). Ignoring heteroskedasticity and using incorrect standard errors
will lead to confidence intervals that are wider than they should be (Hill et al., 2007).
Thus OLS model is no more the best estimator when the problem of
heteroskedasticity appears. To test heteroskedasticity we use a Breusch–Pagan test,
also called a Lagrange Multiplier (LM) test, with the results shown in Table 4. The
null hypothesis of this test assumes a constant variance for the error term. In our case,
as the p-values of all variables are greater than 0.05, the null hypotheses should not be
rejected, which means heteroskedasticity does not occur in our sample. A robustness
test is no longer necessary.
Table 4 Results of Heteroskedasticity
Variable LM value p-value
(ln)FDI_1 0.0602 0.8062 (ln)Labor Costs 0.7421 0.3890 (ln)Human Capital 1.3017 0.2539 WTO 2.7473 0.0974 COS 0.0768 0.7816 COS_(ln)FDI_1 0.0228 0.8799 Autocorrelation
Autocorrelation occurs in the situation where the error terms are correlated.
Autocorrelation problem does not bias the coefficient estimates in an OLS model but
may lead to an underestimation of standard errors (Hill et al., 2007). In order to
examine for autocorrelation in our panel data, we perform a Wooldridge test by Stata
using the xtserial command. As we get from the test result, the p-value 0.0098 is
autocorrelation should be rejected. Thus, there is autocorrelation problem exist in our
sample. We will conduct a simple yet useful method, the xtregar command, to correct
for this problem. This command implements the methods derived in Baltagi and Wu
(1999) to deal with fixed and random effects models with an AR (1) disturbance, and
also can accommodate unbalanced panels whose observations are unequally spaced
over time. Therefore, the original xtreg command will be replaced by xtregar in our
panel data estimation.
Hausman Test
A panel data regression method should be implemented for this research. There are
generally three statistical regression estimators for a panel data analysis, the OLS
estimator, the fixed effects estimator, and the random effects estimator (Hill et al.,
2007). The OLS estimator tends to be inconsistent when one of the problems
(non-normality of residuals, heteroskedasticity, autocorrelation, and multicollinearity)
occurs. Therefore, the OLS estimator seems not to be the most ideal model for our
research, and we will choose between the fixed effects estimator and the random
effects estimator. In general, the fixed effects estimator is always safe to use as it
allows correlation between the explanatory variables and the unobserved components
(Wooldridge, 2002). However, the fixed effects estimator is not capable of estimating
coefficients on time-invariant variables such as the dummy variable COS in our
sample. In contrast to COS, WTO is not strictly time-invariant regarding this research.
In order to statistically decide which model to choose, we conduct a Hausman test.
This test is designed to check for any correlation between the error term and the
regressors within a random effects model, by comparing the coefficient estimates
from the random effects model to those from the fixed effects model (Hill et al., 2007).
The null hypothesis (H0) is organized as there is no correlation between the error term
and any of the explanatory variables. If the null hypothesis is not rejected, then the
random and fixed effects estimates should be similar; whereas a rejection of the null
explanatory variables, thus the random effects estimator is inconsistent (Hill et al.,
2007). In our case, the null hypothesis cannot be rejected due to the insignificant
p-value 0.9923. Therefore, the fixed effects estimator and random effects estimator
are both feasible. Considering of the time-invariant variable in our regression equation,
we finally choose the random effects estimator.
Endogeneity
The problem of endogeneity occurs when one of the independent variables is
correlated with the error term in a regression model. It implies that the regression
coefficient estimates in an OLS model may be biased. Theoretically, endogeneity can
arise as a result of sample selection errors, measurement error in explanatory variables,
omitted variables, and simultaneity (Wooldridge, 2002). In our research, simultaneity
appears to be the main source of endogeneity. Simultaneity is also called as reverse
causality. It indicates that two variables are codetermined, with one affecting the other.
In this paper, the endogeneity problem is caused by a positive reversible causality
between economic growth rate and inward FDI. Not only FDI inflows to a certain
region will accelerate its economic development, but also a host region with higher
economic growth rate tends to attract more inward FDI.
In principle, the method of instrumental variable can be used to solve the problem of
endogeneity. An instrument is a variable which is not included in the regression
equation but is correlated with the endogenous explanatory variables (Hill et al.,
2007). A good instrument should be highly correlated with the explanatory variables
but not with the error term. However, an ideal instrument is hard to find. Thus we use
another simple yet practical method, applying a one-year lag to FDI, to see whether
Empirical Results I
Table 6 (on Page 25) shows the empirical results of the first analysis. Model (1)
represents the results of a general model before adding dummy variables and
interaction terms. It is shown that FDI inflows, after one year, are significantly and
negatively related to the growth rate at a national level. We will further discuss the
reason why this could happen later by Model (3). While Labor Costs is positively
significant at a 1% level, Human Capital is negatively significant at a 1% level in
Model (1). That is to say, a percentage increase in the average wages of staff and
workers can indicate a rise in economic growth rate, while that in the number of
certain graduates can indicate a drop in economic growth rate. The second model
strongly confirms Hypotheses 7. WTO is significant at a 1% level means China‟s entry
to WTO promotes its economic growth at the national level. Besides, Labor Costs is
still significant and positive in Model (2) as in Model (1), whereas FDI_1 and Human
Capital are no longer significant after adding a WTO dummy.
Model (3) is used to test Hypothesis 1&2 regarding the influence of regional inward
FDI. The dummy variable COS shows a significantly positive result, which means
that coastal region has a significantly higher basic economic growth rate than its
non-coastal counterparts do. And the negatively significant interaction term
COS_FDI_1 suggests that inward FDI to the coastal region has a significantly negative effect on the economic growth rate of this region. There is a trick here: the term „economic growth‟ is not completely equal to „economic growth rate‟, however, we use economic growth rate as a measurement of economic growth. A rise or a fall
in economic growth rate can both indicate an (fast or slow) economic growth, unless
the growth rate is negative.
According to our theory and Hypothesis 1, FDI inflows should boost economic
growth in the coastal region. It seems at first sight that this result in Model (3) is the
opposite. However, we shall argue, the more developed a region is, the lower
greater economic growth potential. Ironically, it is true in reality. Thus, we may
conclude that an increase of inward FDI may lower the economic growth rate in the
coastal region but still promote its economic growth. Besides, Hypothesis 2 is
confirmed that FDI does not have a significant impact on the economic growth in the
non-coastal regions, according to the insignificant result of FDI_1 in Model (3).
Although Hypothesis 1 is not entirely confirmed, our assumption of a FDI-led growth
is supported.
Hypothesis 5&6 are confirmed by Model (4), with the interaction term COS_Lab for coastal region is negatively significant at a 1% level and Labor Costs for non-coastal
region is still positively significant. It suggests the relatively higher labor costs of the
coastal region are negatively related to its economic growth, while the comparatively
lower labor costs in the non-coastal regions may have a positive impact on their
economic growth. On the contrary, Hypothesis 3 regarding Human Capital in the
coastal region cannot be proved by Model (5). It is possible because we use the
number of higher education and specialized secondary graduates as a measure of
Human Capital, while the boost of China‟s economy at the beginning mainly is resulted from cheap labors with relatively lower education level. Both the interaction
term COS_Hum for coastal region and Human Capital for the other two regions, are
not significant. However, Hypothesis 4 is confirmed. Besides, WTO dummy is
Table 6 Empirical Results I
Variable (1)General (2)WTO (3)FDI (4)Labor
Costs (5)Human Capital (ln)FDI_1 -0.2702** (0.1271) -0.1975 (0.1275) -0.0714 (0.1351) -0.2315 (0.1280) -0.1933 (0.1293) (ln)Labor Costs 6.1062*** (0.8433) 3.4204*** (1.1370) 3.8333*** (1.1589) 5.2177*** (1.2571) 3.5294*** (1.1629) (ln)Human Capital -0.6401*** (0.2303) -0.2562 (0.2514) -0.2754 (0.2495) -0.2868 (0.2485) -0.1499 (0.2968) WTO --- 3.0207*** (0.8585) 2.8081*** (0.8711) 2.7008*** (0.8686) 2.9141*** (0.8791) COS --- --- 9.4709*** (3.5777) 30.0474*** (9.1845) 3.6370 (5.6020) COS_(ln)FDI_1 --- --- -0.4247*** (0.1503) --- --- COS_(ln)Lab --- --- --- -3.1971*** (0.9617) --- COS_(ln)Hum --- --- --- --- -0.3434 (0.4752) Constant -31.4886*** (6.3190) -14.2405 (7.9760) -20.3305** (8.3058) -29.5946*** (9.1762) -16.3717 (8.4273) Number of Obs. 353 353 353 353 353 R2 0.2066 0.2398 0.2542 0.2581 0.2414 Wald-Statistics 101.18*** 117.36*** 128.11*** 132.16*** 118.04***
Analysis II: Coastal to Non-coastal
Diagnostic Tests II
We are also interested in whether the economic development in the coastal region can
actually affect that in the central and western regions. However, lacking of important
data is a major problem to this research. The amount of FDI which directly comes to
the coastal region that eventually indirectly goes to the other two regions is a number
cannot be exactly calculated at the moment. We are not able to find any this kind of
data from China Statistical Yearbook or the websites of provincial statistics bureaus.
Thus, the already collected data used in Analysis I is also applied to this analysis.
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
Growth 2 234 -9.9554 28.8233 12.363184 5.2142398 Growth 1 156 2.4191 48.9926 12.546046 4.8723460
FDI 1 156 384160000 4.E12 6.11E11 1.009E12
Labor Costs 2 233 4564 30983 12176.49 6243.254 Human Capital 2 234 6029 729119 156960.45 147220.509
CEN 234 0 1 0.50 0.501
Valid N (listwise) 155
Multicollinearity, normality, and heteroskedasticity are also tested, as shown below.
Those variables present little collinearity and all pairs of correlations are relatively
low, with the highest is 0.650. While the histogram of residuals is reasonably
bell-shape, the Jarque-Bera test result of 115.8803 with a p-value of 6.869e-26 (> 0.05)
also suggests a normal distribution. Q-Q plots of all variables have been drawn to
show whether each variable has a normal distribution. Growth 1 and 2 still are
normally distributed separately as when they are together, whereas FDI 1 as well as
Lab 2 and Hum 2 are transformed to normal distributions by natural logarithm. Graphs of Q-Q plots are not attached here. Besides, the results of LM test show that
robustness check is no longer required. A Wooldridge test is also conducted to check
whether there is autocorrelation. The answer is no. And the Hausman test suggests
random effects estimation is feasible. Both tests results are shown in appendices.
Correlations
Growth 2 Growth 1 FDI 1 Labor
Costs 2 Human Capital 2 CEN Growth 2 1 Growth 1 0.174* 1 FDI 1 0.392** 0.137 1 Labor Costs 2 0.490** 0.239** 0.650** 1 Human Capital 2 0.002 0.097 -0.176* 0.194** 1 CEN -0.025 0.032 -0.080 -0.113 -0.055 1
*. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).
Histogram of Residuals
Heteroskedasticity Results
Variable LM value p-value
Empirical Results II
The results are pleasant. Model (1) represents whether the economic growth and FDI
inflows in the coastal region has any effects on that in the central and western regions.
Before adding Labor Costs and Human Capital of non-coastal region as control
variables, we can get from Model (1) that both Growth 1 and FDI 1 with a one-year
lag is positively and significantly related to Growth 2. It suggests inward FDI to the
coastal region after one year indeed promotes the economic growth in the central and
western regions, whereas by what means does this take place is out of the scope of
this paper. In the first two models, Growth 1_1 are both positively and significant
related to Growth 2 at a 5% level, meaning the economic development in the central
and western regions can be driven by that of the coastal region. This result confirms
one of the effects of Reform and Open Policy launched in 1970s: Let parts of our
country and parts of our people get prosperous first, and thus bring along the
development of the rest in times to come.
When we add Labor Costs 2 and Human Capital 2, FDI_1 turns to insignificantly and
negatively related to Growth 2, while Labor Costs 2 of the non-coastal regions are
positively and significantly related to their economic growth at a 1% level. This result
confirms what we get in Model (4) of Empirical Results I. The insignificant Human
Capital 2 also confirms last analysis. In the third model, a central region dummy CEN is added to see whether the impacts within the non-coastal regions, between the
central and the western region, are different. However, this variable is positive but
insignificant. It means that although the central region may have a higher growth rate
than the western region do, this difference is not prominent. Growth 1_1 and Labor
Table 7 Empirical Results II
Variable (1)General (2)Controls (3)CEN
Growth1_1 0.1852** (0.0802) 0.1614** (0.0777) 0.1556** (0.0781) (ln)FDI1_1 0.7535*** (0.1293) -0.0508 (0.2201) -0.0694 (0.2217) (ln)Labor Costs2 --- 9.6999*** (2.1459) 10.0053*** (2.1837) (ln)Human Capital2 --- -0.3317 (0.3735) -0.3222 (0.3742) CEN --- --- 0.5658 (0.7198) Constant -9.2802*** (2.9539) -73.4704*** (16.0544) -76.0978*** (16.4203) Number of Obs. 144 144 144 R2 0.2768 0.3704 0.3732 Wald-Statistics 53.98*** 81.78*** 82.17***
Conclusions
Using a panel data study from 1997 to 2009, this paper investigates the relationship
between inward FDI and regional economic growth in China. Two analyses are
performed in this study. The first analysis, regarding the different impacts of inward
FDI to coastal and non-coastal regions, shows a negative result for the coastal region.
It also suggests FDI inflows insignificantly affect the economic growth in non-coastal
regions. A possible reason for this can be that, studies which found positively
significant results for the coastal region actually used relatively earlier data of China.
On the contrary, we use very recent data from 1997 to 2009.
While inward FDI may contributes the most to the fast economic growth in the east
China and even to the whole country‟s development at the beginning of Reform and
Open Policy, in recent years, as the coastal region economy increasingly approaches
saturation, more FDI inflows may actually slow down its economic growth rate.
Along with the rapid economic growth, land and labor costs also rise faster in the
coastal region than in the other two regions. The increase in production factor prices
will not discourage FDI inflows at the moment, however inward FDI in the coastal
region may not have as big power as it has before. Besides, the surplus labor and
natural resources in the non-coastal regions will be more sufficiently utilized in the
future. To sum up, inward FDI accelerates the economic growth in the central and
western regions is an inevitable trend, regarding China‟s economic development.
In respect that the entry into WTO can be considered as a landmark event contributing to China‟s economic growth, the impact of WTO accession is another focus of this paper. WTO accession shows a significant positive relationship with the economic
growth in China at a national level. After the entry into WTO, FDI remarkably promotes China‟s economic growth. However, we have to mention that this result does not necessary mean WTO is the major factor inducing China‟s growth after 2001.
may also contribute to this result during that period. Moreover, we find a negative
relationship between coastal region‟s labor costs and its economic growth, as well as a
positive relationship between those of the central and western regions. These results
coincide with our theory and hypotheses. However, we could not find any significant
results for the importance of human capital to China during 1997-2009. As we
discussed before, this is mainly because the measurement issue.
The second analysis is to find out whether the economic development in the central
and western regions can actually be driven by that of the coastal region. The answer is
yes. Not only the economic growth in the coastal region will promote that in the
non-coastal regions, but also FDI inflows to the coastal region contribute to the
economic growth in central and western regions. However, the central region dummy
shows an insignificant result, which thus indicates the economic growth in the central
region is not significantly different from that of the western region.
Discussion
Generally, this paper has several contributions to FDI research. First of all, it
contributes to the FDI-growth nexus literature by using very recent data and empirical
results from China. Secondly, an understanding of how FDI affects different regions
in China is also important to the local policy makers and foreign investors. Last but
not least, as far as I am concerned, this is the first academic paper ever investigates
into whether the economic development in the coastal region can actually affects that
in the central and western regions, and even gets pleasant empirical results.
However, this paper also has its limitations. We find that regional FDI inflows to
China does not promote but weaken the economic growth in the coastal region. Due
to this main distinctness of the results between other similar researches and our study,
we consider a dataset of the whole period from 1978 to 2009 can be divided to several
five-year intervals, and it will be better to analyze the real situation in China. The only
variable, Human Capital. We have not found any significant results for the impacts of human capital on China‟s regional economic growth. We argue that it is probably because we use the number of higher education and specialized secondary graduates
as a measure of this variable. Future researches may use measures with lower
education level for the special case of China. Finally, also due to data limitation, the
second analysis uses already collected data for Analysis I. We cannot define by what
means and how the coastal region affects its non-coastal counterparts. If indirect FDI
data from the coastal region to the other two regions can be obtained, it will be a very
Acknowledgement
I would like to express the sincerest gratitude to my thesis supervisor, Dr. Dirk
Akkermans for guiding me during every stage of writing this thesis. He has inspired
me with invaluable ideas and offered me several suggestions with his profound
knowledge in his expertise. His kindness and patience are greatly appreciated. I am also grateful to my methodology supervisor, Prof. Dr. Erik Dietzenbacher, for his
valuable advices and comments on the methodology part as well as throughout this
thesis. Last but not least, I want to thank my family and friends for their unconditional
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Appendices
1. Q-Q plot of Growth
2. Histogram of Residuals (1a)
4. Heteroskedasticity Results (1a)
Variable LM value p-value
(ln)FDI_1 0.0927 0.7608 (ln)Labor Costs 0.6757 0.4111 (ln)Human Capital 1.2799 0.2579 WTO 2.7847 0.0952 COS 0.0645 0.7995 COS_(ln)Lab 0.0908 0.7632 5. Heteroskedasticity Results (1b)
Variable LM value p-value
(ln)FDI_1 0.0301 0.8623 (ln)Labor Costs 0.9564 0.3281 (ln)Human Capital 1.2613 0.2614 WTO 2.7451 0.0976 COS 0.0874 0.7675 COS_(ln)Hum 0.1511 0.6975
6. Wooldridge and Hausman Results
Test (1) (1a) (1b) (2)
Wooldridge 7.652(0.0098) 6.802(0.0142) 6.917(0.0135) 2.461(0.1450)
