Download 4. the empirical evidences

Does Foreign Direct
Investment Crowd out Domestic Investment in China?
Sumei Tang*
School of International Business and Asian Studies,
Griffith University, Nathan, Queensland, AUSTRALIA
April 2005
Abstract This paper investigates the causal link between foreign direct investment (FDI),
domestic investment and economic growth in China at the aggregate level for the period
1988-2003 by using an investment error correction model (ECM) and the innovation
accounting (variance decomposition and impulse response function analysis) techniques.
We find that there is bi-directional causality between domestic investment and economic
growth and only single-directional causality from FDI to domestic investment and
economic growth. There is no evidence to support that FDI crowd out domestic
investment but instead FDI has complimentary effects on domestic investment in China
in the period 1988-2003. FDI has played a great role in China’s economic growth and
development. While FDI has supplementally provided somewhat solutions to the
shortage of capital formation in China’s economic reform era, it has also stimulated
domestic investment through the channel of technology diffusion and the rapid expansion
of the export industries.
* Email: [email protected] I am grateful to Professor G. H. Wan, World Institute for
Development Economic Research, United Nations University, and Professor Saroja Selvanathan, DAFE,
Griffith University, Australia for helpful comments.
1
Does Foreign Direct
Investment Crowd out Domestic Investment in China?
1. INTRODUCTION
China, the one of the world’s fastest growing economies, has attracted a large amount of
FDI over the last two decades and became the largest FDI recipient country in the world
since 2002. The amount of FDI inflows has totalled $487.686 billion US dollars during
the period 1988 - 2003 in China. Does this enormous amount of FDI crowd out domestic
investment in China or whether FDI has substitution or complementary effects on
domestic investment? China is a typical developing country short of capital resources.
FDI has been certainly becoming a great capital flow of choice comparing with portfolio
flows in the wake of the Asian and South American financial crises. It is commonly
believed that FDI not only assist in overcoming a pressing need of capital resources, it
also could contribute to the economic growth and development of the host developing
countries through technology, management skills transfer and spillover effects on
domestic industries, as well as increasing demand for exports. Despite the role of FDI in
the developing economy is highly emphasized and the developing countries aggressively
compete for FDI inflows, the impact of FDI on economic growth is still highly
controversial. de Mello (1999) argues that the impact of FDI on economic growth
depends on the relationship between FDI and domestic investment. This is, the impact of
FDI on economic growth will be positive/negative if FDI has complementary/substitution
effects on domestic investment, respectively.
The impact of FDI on economic growth and development has been discussed extensively
but the direct discussion of the casual link between FDI and domestic investment hence
economic growth is relatively insufficient, particularly in the context of China.
Traditionally, those empirical studies most use either cross-sectional/time series
regressions or panel data framework. However, this study, by applying quarterly time
series data of China from 1988 to 2003, uses an investment error correction model
(ECM) combining with recently developed time series concepts of cointegration,
2
innovation accounting (impulse response function and variance decomposition) and
Grange
causality
testing
procedures
to
investigate:
(1)
whether
FDI
has
complementary/substitution effects on domestic investment in China; (2) the causal
relationships between FDI, domestic investment and economic growth; (3) the role of
FDI in China’s economic growth and development. This study is important. Firstly,
China currently is a largest FDI recipient country in the world, which provides a rich
contest of studying and research. Understanding the dynamics of FDI and its impact on
its economy in China is essential in understanding the dynamics of FDI in the world
economy as a whole. Secondly, China is one of the fastest growing economies in the
developing world, its central and local governments have seen attracting FDI is an
important strategy to overcome capital shortages and to promote economic growth and
development, thus, intensive investment policies such as tax rates deductions and low
regulations were encouraged. Understanding the impacts of FDI on domestic investment
hence economic growth and development in China would provide a great lesson for other
developing countries.
The remained paper is divided into four sections. First, it is a theoretical and empirical
review on previous studies. Second, it overviews FDI inflows, domestic investment and
economic growth in China. Third, attempts to use econometrical analysis to find out
whether FDI has crowded out/in domestic investment in China, the causal links between
FDI, domestic investment and economic growth and the role of FDI in China’s economic
development. The final section presents the conclusion.
2. A REVIEW OF PREVIOUS STUDIES
Theories of FDI, primarily from a manufacturing perspective, study the determinants of
FDI under taken by multinational enterprises (MNEs) outside their national boundaries.
FDI occurs as MNEs possess ownership-specific advantages, market internalization
advantages and location advantages (Dunning, 1981). The ownership-specific advantages
include advanced technologies, management know-how skills, intangible advantages and
transaction cost minimizing advantages etc. The market internalization advantages
explains that firms engage in FDI is to avoid market imperfections such as government
3
interventions on quotas, tariffs and price controls, and seek controls on supplies of inputs
and conditions of sale in a host country. The local advantages describe how the factors of
host nations determine the siting of production of MNEs. The factors include preferential
policies such as tax deduction and low regulation, resource endowments, input prices and
markets etc are crucial elements for attracting FDI inflows.
In the presence of FDI inflows, an open developing economy may achieve economic
growth substantially through technology diffusion (Broensztein et al. 1998) and human
capital development (de Mello, 1999). While FDI by MNEs is a major channel for the
access to advanced technologies by developing countries as most MNEs possess
ownership-specific advantages. FDI plays a vital role in economic growth (Broensztein et
al. 1998, Kim and Seo 2003, Liu et al. 2002 and Shan 2002) and the long-run growth, as
indicated by the new endogenous growth theory, can be achieved because the stock of
FDI increases if the marginal product of capital can be bounded away from the rate of
time preference (de Mello, 1999). In this regard, complementary effects of FDI on
domestic investment are presented in several ways (Noorzoy, 1979). First, FDI in host
country may result in an increase in demand for exports, hence stimulating investment in
the export industries. Second, FDI could be made in high risk areas or establishes new
industries where domestic investment will be limited, therefore, FDI will supplement
domestic capital formation. Third, when FDI occurs in resource industries it is likely to
expand investment in related industries.
The ownership-specific advantages, somehow, also imply that FDI as an aggressive
global strategy by MNEs to advance monopoly power over and above indigenous firms
of the host economy (Hymer, 1960 and Caves, 1971). While the market internalization
and location advantages, for instance, controls on supplies of inputs in the host economy
and tax subsidy benefits provided by the host country, further reinforce the competitive
advantages of MNEs over indigenous firms. Eventually, domestic firms will be forced to
exit their industries so that FDI substitutes for domestic investment. The substitution
effects of FDI may also occur when investment opportunities are limited in a host
economy and MNEs compete for scarce factors in the economy. In addition to an adverse
4
effect of FDI on domestic investment in the same industry, FDI may also disrupt
backward linkages through substitution of imports for domestic commodities (Noorzoy,
1979). However, it is argued that economic growth could still be achieved when
substitution effects of FDI are presented. Because when substitution effects overwhelm
complementary effects, FDI may contribute more to growth than domestic investment in
an economy if repatriation of profit by MNEs can be under control, but predatory
behaviour of MNEs could create a neo-colonial economy (Bornschier and Chase-Dunn
1985, Gowan 1999). However, as an economy, complementary effects of FDI are always
preferred in the context of growth.
Applying province-level panel data set for the years 1986-99 to a regression model,
Braunstein and Epstein (2002) analyzed the long-term effects of FDI on domestic
investment and found that FDI crowded out domestic investment during the late 1980s
and 1990s in China. This was, Braunstein and Epstein argued, contributed by the
decentralization of the FDI bidding process in China. Braunstein and Epstein (2002)
viewed that the social benefits of FDI have been dissipated at least at the provincial level
due to the intensive competition for FDI among the regions in China. The competition, in
turn, forces the regions to lower regulations, taxes, environmental protections, wages and
working conditions in order to attract and retain capital. However, the result produced by
applying a set of the combined provinces panel data to a single regression model is
arguable. First, the panel data is very heterogeneous hence hardly compatible, for
instance, the geographical impact on FDI is prominent and the distribution of FDI in 28
regions and provinces is very unbalanced. Second, the estimation from the single
regression equation is used to interpret some causal long-run relationship may be subject
to a simultaneity bias and failed to consider the feedback among the macroeconomic
variables hence the dynamics within the system. In Contrast to the study of Braunstein
and Epstein, Sun (1998) found a strong significant positive correlation between FDI and
domestic investment in China in the period from 1979 to 1996, but it was argued that the
result of Sun’s study is unconvinced due to the simple regression approach and omitted
important variables (Braunstein and Epstein, 2002). In regard to FDI and domestic
investment in China, Huang (2003) argued that the Chinese investment policies are more
5
‘‘friendly’’ to FIEs than to domestic private firms, as an incentive, Chinese partners are
egger to form a FIE with foreign investors. This type of investment, in fact was invested
by Chinese partners, occurs just for the motivation to qualify for FIE status (Huang,
1998). As a consequence, those types of FIEs not only gain benefits from the preferential
policies and even possess privileges in competing for local scarce resources. Thereby,
FDI crowds out domestic investment (Huang, 1998). However, their study is only a
qualitative analysis lacking of statistical background. In comparison to the above cited
studies, using a more advanced time series analysis approach, a vector auto-regression
(VAR) system, and a set of quarterly time series data from 1986 to 1998, Shan (2002)
examined the inter-relationship among FDI, output growth and other economic variables
in China. Shan found that FDI has a significant impact on the Chinese economy when
FDI shares in China’s output rises. The shortcoming is that Shan ignored the important
issue existing in quarterly data, the seasonality. In addition, the measurement of
economic growth in Shan’ study is not appropriately defined, a better and more accurate
variable reflecting economic growth, e.g. GDP should be considered. Overall, the
empirical studies cited above are only limited in the context of China, thus, the following
section provides some countries’ empirical studies other than China.
Following Caves and Reuber (1971) and Van Loo (1977), Noorzoy (1979) developed an
accelerator-flow of funds model of investment to estimate the effects of FDI
outflows/inflows on domestic investment in Canada for the period 1957-1971. Noorzoy
introduced FDI into the traditional neoclassical investment model as the flow of external
funds. In Noorzoy’s study, the accelerator term was measured by lagged changes in
output (GNP) and the internal funds of investment by firms was given by the sum of
depreciation and undistributed corporate profits. The dependent variable, investment, in
the accelerator-flow of funds model of investment was given by gross capital formation.
Noorzoy found that FDI inflows had complementary effects on domestic investment,
whereas, FDI outflows had negative impact on domestic investment. However, the model
used by Noorzoy is a single regression model which failed to consider the strong causal
links and the feedback among the macroeconomic variables in the system. In contrast to
Noorzoy’s study, applying the bivariate VAR model and the time series concepts of
6
cointegration to a set of time series and panel data, de Mello (1999) analyzed the impact
of FDI on economic growth in 32 host countries and found that FDI is growth-enhancing
via technological upgrading and knowledge spillovers but depends on the degree of
complementarity and substitution between FDI and domestic investment. This study by
de Mello is more theoretical rather than empirical analysis. It is inconvincible that FDIenhancing growth depends on the degree of complementarity and substitution between
FDI and domestic investment. Because, in de Mello’s study, 1) the time series
methodology has hardly ever been utilized to explore dynamic interactions (Kim and Seo
(2003) among economic growth, FDI and domestic investment, 2) only 22 annual
observations were used which might be subject to a small sample bias and (3) the
explanation of complementary and substitution effects of FDI was very unclear.
3. AN OVERVIEW OF THE FDI INFLOWS, DOMESTIC INVESTMENT AND
ECONOMIC GROWTH IN CHINA: 1978 - 2003
Attracting FDI has been a key pillar of China’s “opening up” policies and economic
reforms since 1978. The economic reforms started with the establishment of special
economic zones in selected provinces and regions, while the FDI policies were basically
formed with preferential policies including tax concessions and special privileges for
foreign investors. During the reform period, the Chinese government also has developed
various FDI and ownership legislations, laws, property rights and contract laws to
improve investment condition and business environment in order to attract FDI.
Figure 1 presents domestic investment (DI), GDP and FDI inflows in China from 1978 to
2003. At the initial “opening up” period, FDI inflows were quite low varying between
RMB 0.0468 to RMB 1.2645 billion from 1978 to 1983, but from 1984 till early 1990’s,
FDI increased on an average rate of over 30% per annum. However, the total amount of
FDI was still small and remained as low as RMB 40 billions until 1992†.
†
Calculated from various issues of China Statistical Yearbook.
7
In 1992, the famous “South Tour” by the Chinese leader, Deng XiaoPing, resulted in
further economic reforms in China which led to a new phase of FDI liberalization
policies. As a result, FDI to China entered into a stage of rapid growth and placed China
as the largest FDI hosting country in the developing world since 1993. In 1998, during
the Asian financial crisis period, the Chinese government further liberalized FDI policy.
One such change is the abolishment of the FDI project approval requirement. However,
as the Asian financial crisis was very strong, FDI inflows still decreased slightly in China
during the year of 1999 and 2000. In December 2001, China joined the World Trade
Organization (WTO), which marked the FDI liberalization entering into a new era. In the
same year, China’s FDI inflows (see Figure 1) have increased dramatically from
RMB337.0965 billions in 2000 to RMB 388.0092 billions in 2001 and by 2002 China
became the largest FDI host country in the world attracting RMB436.5538 billions of
FDI.
As can be seen from Figure 1, China’s economic growth has also shown a remarkable
growing strength at an average annual rate of 9 per cent since the economic reform in
1978. China has become one of the fastest growing economies of the world, for which,
many argue, FDI has played an important role in contributing to the growth. For example,
the national total export average annual growth rate from 1978 to 2002 has reached
12.56%, whereas, the share of export by foreign -invested enterprises (FIEs) in the
national total export was almost 50% in 2001. Meanwhile, the domestic investment‡ in
China, in line with the rapid growth in GDP and FDI, has demonstrated a significant
increase with an average rate of 20.11% per annum from 1978 to 2003 as indicated in
Figure 1. The investment system in China is very different from that in the western
economies, which has been continuously following plan-era patterns in the last quarter of
the country. The Chinese National People’s Congress approves annual plans for
investment. The State-Owned-Enterprises (SOE), in general, benefit the most from the
plan while private businesses have been largely excluded from the domestic capital
market.
‡
The domestic investment in this study is the aggregate investment which excludes all types of foreign
investments.
8
Figure1. Trends of Domestic Investment, GDP and FDI in China§
Figure 2 shows the growth rates of FDI and domestic investment in China in the period
from 1978 to 2003. As indicated in Figure 2, in the period 1978 -1983, the amount of FDI
was relatively small but during 1984 -1992, FDI had a large increase. During the two
periods, domestic investment in China had a very similar annual average growth rate of
18.11% and 18.53%**, respectively. From 1993 till the eve of the Asian financial crisis,
FDI showed a sharp rise in China, while the domestic investment also entered into a rapid
growth at an average rate of 23.91% per annum. Domestic investment in China was only
slightly affected during the Asian financial crisis period. Since joining the WTO, FDI in
China has surged to a historical high making China the largest host country of FDI in the
world since 2002, whereas domestic investment also increased dramatically. Overall,
Figures 1 and 2 clearly demonstrate that both FDI and domestic investment have an
upward trend well matching the strong economic growth trend of GDP during the period
of 1978 – 2003.
§
FDI* equals FDI but scaled differently on the right side of Y axis for easier illustration and visual view.
All figures in this paragraph are calculated from various issues of China Statistical Yearbook.
**
9
Figure2. The growth rate of FDI and domestic investment in China
Table 1 presents the ratios of FDI to GDP, domestic investment to GDP and FDI to
domestic investment from 1978 to 2003. As can be seen from Table 1, the relationships
among FDI, domestic investment and economic growth have further confirmed. The
proportions of FDI to GDP (column 2) were quite low and less than 1% from 1978 to
1990, and then it increased gradually but still relatively low at an average rate of 4.18%
from 1991 to 2003. Comparatively, the proportions of domestic investment to GDP
(column 3) were higher than the proportions of FDI to GDP during the same period. In
1978, the proportion of domestic investment to GDP (column 3) was 18.45% and
increased steadily. However, when FDI surged in 1993, the ratio of domestic investment
to GDP also increased to 34.99%, and in 2003 it further increased to 47.56%. However,
the last column of Table 1 indicates that the proportion of FDI to domestic investment
has increased dramatically from 0.07% in 1978 to 1.66% in 1984, and by 1994 it has
reached an all time high of 18.12% but it then gradually decreased to 8.04% in 2003. The
above analysis demonstrates that the dramatic increase in FDI inflows were associated
largely with the rapid rise of domestic investment as well as economic growth during the
period of 1978 to 2002.
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Table1. The Ratios of FDI to GDP, Domestic Investment to GDP and FDI to
Domestic Investment (DI) (percent)
Year
FDI/GDP
DI/GDP
FDI/DI
(1)
(2)
(3)
(4)
1978
0.01
18.45
0.07
1979
0.06
17.32
0.36
1980
0.06
20.16
0.28
1981
0.14
19.02
0.74
1982
0.15
22.10
0.70
1983
0.21
22.98
0.93
1984
0.41
24.57
1.66
1985
0.54
27.35
1.99
1986
0.63
29.24
2.16
1987
0.72
30.18
2.38
1988
0.80
29.33
2.71
1989
0.75
24.36
3.10
1990
0.90
22.82
3.94
1991
1.08
24.40
4.41
1992
2.28
28.57
7.98
1993
4.58
34.99
13.09
1994
6.22
34.34
18.12
1995
5.36
31.17
17.19
1996
5.11
30.36
16.83
1997
5.04
30.32
16.62
1998
4.80
33.31
14.42
1999
4.07
33.81
12.03
2000
3.77
35.12
10.73
2001
4.04
37.79
10.70
2002
4.17
41.00
10.16
2003
3.82
47.56
8.04
Sources: China Statistical
Yearbook various issues.
Figure 3 shows the plots of FDI against domestic investment (DI) and GDP, respectively.
The findings in Figures 1 and 2, as well as Table 1 are also reflected in Figure 3, which
clearly indicates that there is an upward trend and positive relationship between FDI,
domestic investment and GDP. Overall, it appears that FDI inflows to China have had a
11
complementary effect on domestic investment hence spurring economic development and
growth. However, results from a causal investigation on FDI, domestic investment and
economic growth is far from conclusive in drawing an empirical relationship for China as
discussed. Thus, a formal econometric analysis is required which is performed in the
following section.
Figure3. Observations on FDI, domestic investment (DI) and GDP
4. THE EMPIRICAL EVIDENCES
Based on the theoretical argument and empirical study, an investment error correction
model is developed to incorporate the accelerator-flow of funds model of investment into
the error correction model (ECM) form. This model can enable the empirical
investigation to find out the three questions proposed in the first section.
The investment of error correction model
We consider the following three equations as part of the investment error correction
model:
∆FDIt = α1 + αfdi êt-1 +
k
k
k
i 1
i 1
i 1
 α11 (i) ∆FDIt-i +  α12 (i) ∆DIt-i +  α13 (i) ∆GDPt-i + β1Dt + εfdit
(1)
12
∆DIt = α2 + αdi êt-1 +
k
k
k
i 1
i 1
i 1
 α21 (i) ∆FDIt-i +  α22 (i) ∆DIt-i +  α23 (i) ∆GDPt-i + β2Dt + εdit
∆GDPt = α3 + αgdp êt-1 +
k
k
k
i 1
i 1
i 1
(2)
 α31 (i) ∆FDIt-i +  α32 (i) ∆DIt-i +  α33 (i) ∆GDPt-i + β3Dt + εgdpt (3)
Where
FDI =
DI =
GDP =
êt-1 =
Dt =
αi, αij (i) and βi =
εfdit, εdit and εgdpt =
FDI inflows in China
gross capital formation represents domestic investment but
exclude any forms of foreign investment
gross domestic product represents economic growth
the error-correction term;
the centered seasonal dummy variable.
the parameters;
white-noise disturbance terms that may be correlated with
each other;
The investment error correction model used in this study can reflect the lagged changes in
the output (GDP) hence well representing the accelerator term by differencing the
variable. In addition, this model can capture the interactions of all the variables including
the lagged variable itself in the system, which is the advantage over the accelerator-flow
of funds model of investment used by Noorzoy (1979), Van Loo (1977), Caves and
Ruber (1971). Moreover, including both the first differences and the levels of the three
variables in the system will enable the revealing of the short and long run effects of the
all variables. However, a variable for internal fund is not present in the model which is
different from Noorzoy but as same as Van Loo.
Data
The data of the three variables, FDI, DI and GDP are all quarterly time series in terms of
Chinese RMB currency for the period 1988:1 to 2003:4, which are compiled from China
Monthly Statistics (1987:1- 2004:3), Comprehensive Statistical Data and Materials for
50 years of New China and various issues of China Statistical Yearbook all published by
the National Bureau of Statistics (NBS). GDP quarterly time series is specially
constructed on the basis of the monthly gross industrial output (GIO) and the yearly GDP
statistics because of lacking quarterly or monthly GDP statistics for China. It is found
13
that the annual growth pattern of GDP is similar to that of GIO, and following Liu, Song
and Romilly (1997) and Liu, Burridge and Sinclair (2002), the estimated GDP is given
as:
GDPt,q = gt ×GIOt,q
q =1,…, 4
t = 1988, 1989,…, 2003
where gt is the annual GDP/GIO ratio and GIOt,q is the quarterly value of GIO.
Also, a special attention has been given to the seasonality of the three series, FDI, DI and
GDP. China’s central planned economic regime has been a major force in generating a
regular pattern of large seasonal fluctuations (Rawski, 2002) in its economy. It can also
be clearly seen in Figures 3(a), 3(b) and 3(c), the three original time series indeed display
some seasonal patterns. To minimize the effect of seasonal fluctuations, a centered
(orthogonalized) seasonal dummy variable is applied when conducting in cointegration
analysis and model estimation. The reason to use the centered seasonal dummy variable
is that a standard 0-1 seasonal dummy variable will affect both the mean and the trend of
the level series in VAR system but the centered seasonal dummy variable can shift the
mean without contributing to the trend (Johansen, 1995). In addition, given the deficiency
of Chinese official statistics, extra care has been taken to ensure the comparability and
consistency of the data over time.
(a)
14
(b)
(c)
Figure 3 The original series of FDI, DI and GDP in China, 1988 (1) – 2003 (4)
Unit root test
As indicted in Figures 3(a), 3(b) and 3(c), there is a clear upward trend in each of the
variables. In order to ensure all time series in this study are stationary hence avoiding any
potential problem of spurious regression, the augmented Dickey-Fuller (ADF) test is
Table2. ADF test for unit root
1.
ADF test for unit root on the level series
Variables
FDI
DI
GDP
2.
With constant
-1.12(2)
-0.98(2)
-0.87(1)
With trend & constant
No trend & constant
-1.04(1)
-4.40(2)***
-2.29(1)
1.07(2)
0.27(2)
3.46(1)***
ADF test for unit root on the first differenced series
FDI
DI
GDP
-2.29(2)**
-7.43(2)***
-3.55(1)***
-6.45(1)***
-7.45(2)***
-3.69(1)***
- 2.22(2)**
-7.26(2)***
-1.99(1)***
Notes: (1). ** and *** denotes significance at the 5% and 1% levels, respectively.
(2). The figures in the parentheses are the number of lags used.
15
conducted in this study. The ADF tests for unit roots are performed on both levels and
first differences for all three variables in the models of (1) with constant, (2) no trend, (3)
with trend. The results of the ADF tests are reported in Table2. As is apparent from the
table, the null hypothesis of a unit root is not rejected by the level series of FDI in all
three models but rejected by the level series of GDP in the model without constant and
trend, and DI in the model with trend and constant. First differencing of all the three time
series FDI, DI and GDP indicate that they are stationary integrated of order one, I (1), for
all models.
Cointegration test
Based on the results in Table 2, a cointegration test is performed to find out whether there
have been any long-run equilibrium relationships among the three variables of FDI, DI
and GDP. After a careful search and test, a model with 6 lags, constant, drift and centered
seasonal dummy variable was chosen. The result of Joansen cointegration rank test is
summarized in Table 3 and it indicates that there are two presences of cointegrating
vectors at 1% and 5% levels respectively of significance (i.e., the null hypotheses of no
cointegration are rejected for rank of zero and less than or equal to 2). This means that
there exists a long-run stationary relationship among the three variables.
Table3. Johansen co-integration tests
Null (H0)
Alternative (H1)
λmax
Rank = 0
Rank ≤ 1
Rank ≤ 2
r≥1
r≥2
r≥3
30.02**
10.06
5.94*
95% CV
23.78
16.87
3.74
λtrace
46.01**
15.99
5.94*
95% CV
34.55
18.17
3.74
Note: * and ** denote rejection of the null hypothesis at the 5% and 1% significance levels,
respectively.
Model selection test
A model with constant, centered seasonal dummy and 6 lags of length is selected after a
general-to-specific search starting with a system with 4 lags, constant, trend and seasonal
dummies. The model was already shown in the above, the three equations of (1), (2) and
16
(3). The fits of the all three equations are quite high, 76%, 83% and 96% for (1), (2) and
(3), respectively and there are no signs of residual autocorrelation either. The model
diagnostics are summarised in Table 4.
Table 4. Model diagnostics
Adj-R2
∆FDIt
∆DIt
∆GDPt
0.76
0.83
0.96
Autocorrelation test
LM (1), CHISQ (1) = 15.06, P-value = 0.09; LM (6), CHISQ (6) = 15.84, P-value = 0.07
Normality test
CHISQ (6) = 5.55, P-value = 0.0623
Innovation accounting analysis and Granger causality test
Within this near VAR (ECM) system, the accounting innovation (variance decomposition
and impulse response function) technique can be utilised to examine the relationships
among economic variables. In our case, this technique would be a useful tool enabling us
to pick up any complementary or substitution effects of FDI on domestic investment and
simultaneously to reveal the impact on economic growth. The forecast error variance
decomposition allows inference over the proportion of the movements in a time series
due to its own shocks versus shocks to the other variables in the system (Enders, 1995).
The impulse response function analysis is a practical way to visually represent the
behaviour of a time series in response to the various shocks in the system (Enders, 1995).
In other words, the forecast error variance decomposition explains all the forecast error
variance effects on each endogenous variable at short horizons and smaller proportions at
longer horizons, and the impulse response function analysis traces out the time path of
the effects of the various shocks on each endogenous variable to determine how each
endogenous variable responds over time to a shock in that variable and in every other
endogenous variable.
17
Table 5. Decomposition of ten-year forecast error variance (%)
Percent of forecast
error variance in
(Quarters)
FDI
DI
GDP
(1)
(2)
(3)
FDI
1
4
8
12
16
20
24
28
32
36
40
100.0000
90.82013
77.02271
64.17280
56.72252
53.52018
51.69749
49.27031
46.78047
45.03550
43.87666
0.000000
3.468023
5.266766
6.136423
8.290025
9.805131
10.40132
10.38458
10.06185
9.710682
9.375944
0.000000
5.711849
17.71052
29.69078
34.98746
36.67469
37.90118
40.34511
43.15767
45.25382
46.74739
(4)
DI
1
4
8
12
16
20
24
28
32
36
40
0.456898
2.371060
31.67926
49.15293
49.35877
46.67251
46.32242
47.64437
48.79696
49.43196
49.74517
99.54310
79.98403
56.38692
39.86692
39.01755
40.75727
40.29452
39.14215
38.42148
38.01316
37.65754
0.000000
17.64491
11.93382
10.98015
11.62368
12.57022
13.38306
13.21348
12.78156
12.55489
12.59729
GDP
1
4
8
12
16
20
24
28
32
36
40
2.825318
1.884889
31.06297
63.21354
61.82440
56.48597
56.70327
58.09982
59.20184
59.04479
58.87606
37.36442
40.81599
26.99503
15.28816
15.38310
16.15336
16.32276
16.63251
16.79938
16.47414
15.82107
59.81026
57.29912
41.94200
21.49830
22.79250
27.36067
26.97397
25.26767
23.99877
24.48107
25.30287
With a ten-year forecasting horizon used in this study, the variance decompositions in
FDI, DI and GDP of the ECM by using the Choleski decomposition method are reported
in Table 5. As indicated by the result that each time series explains the preponderance of
its own past values. FDI explains 90.82% (column 2) of its forecast error variance at the
4th quarter of the year one, whereas, DI and GDP explain only 3.47% and 5.71% of the
forecast error variance of China’s FDI inflows. DI and GDP explain near 80.00%
18
(column 3) and 57.30% (column 4) of their forecast error variances at the 4th quarter of
the year one, respectively, in contrast with: a). FDI and GDP explain 2.37% and 17.64%
of the forecast error variance of China’s total domestic investment, respectively; b). FDI
and DI explain 1.88% and 40.82% of the forecast error variance of China’s economic
development, respectively.
Importantly, Granger causality tests in Table 6 indicate that: a). the effects of DI and
GDP on FDI are not statistically significant at conventional levels; b). the effects of FDI
and GDP on DI are statistically significant at 1% level; c). the effects of FDI and DI on
GDP are statistically significant at 1% and 5% levels, respectively. Thus, FDI affects DI
and GDP but not the reverse, whereas, the causal links between GDP and DI is bidirectional.
Table 6. Results of Granger causality Test among FDI, DI and GDP in China 1988-2003
Dependent
Wald test statistics
Causality
Variables
______________________________
inference
∆FDIt
∆DIt
∆GDPt
∆FDIt
∆DIt
3.98
24.63**
4.38
FDI  DI
GDP  DI
26.06**
FDI  GDP
DI  GDP
∆GDPt
20.15**
16.20*
Two-way
Links
GDP  DI
Note: * and ** reject null hypothesis at 5% and 1%, respectively.
Given the results of the variance decompositions and Granger causality tests, the impulse
response functions of FDI, DI and GDP to three types of shocks are proceeded. The
illustrations of the response of FDI, DI and GDP to a shock in FDI, DI and GDP,
respectively, are given below:
1. Figure 4 impulse responses to a shock in FDI. The effect of a shock in FDI is to cause
an immediate decrease in DI, shortly, DI begins to increase even surpassing the
19
original level and then declines again. This has been a visible permanent pattern that
DI fluctuates always around its mean level. FDI itself drops rapidly immediately and
around the second quarter of the third year its movement maintains at a new lower
mean long-run level. GDP responses in an immediately increase and converges to its
new higher level permanently by the third quarter of the fourth year. It is interesting
to note that the peak and trough of FDI are almost at opposite positions of DI;
whereas, the peak and trough of GDP occur almost simultaneously with DI.
In general, in the case of China from the period of 1988 to 2003, the result from
Figure 4 evidently indicates that the FDI inflows do not crowd out the domestic
investment in the long-run. In regard to the interesting character that the peak and
trough of FDI inflows always have been at the opposite positions to the total domestic
investment in the short-run, it, perhaps, can be described metaphorically that shortage
of domestic investment in China urges for FDI inflows in the study period. The larger
FDI inflows the greater domestic investment was stimulated. It is true that FDI indeed
has resulted in an increasing demand for exports in China (Zhang and Felmingham,
2001; Liu, Burridge and Sinclair, 2002), consequently, as the market demand
increases, more investment was stimulated in the export industries. In this regard,
FDI has complementary effects in China, which confirms the result from the initial
analysis proposed in the third section and the findings of Sun (1998) and Noorzoy
(1979). The movement of FDI in the Figure 4 also demonstrates that the FDI inflows
in China largely are influenced and determined by its past values. The upward
movement of GDP indicates that FDI has positive impact on China’s economic
development. Overall, Figure 4 provides robust evidence confirming that FDI plays a
crucial role to complement domestic investment in China hence leading economic
growth and development.
2. Figure 5 impulse responses of a shock in DI. In response to a shock in DI, DI itself
immediately drops rapidly and then reverts back but it never reaches its original level.
GDP temporarily decreases and shortly in an expansion position even surpassing its
original level but its mean long-run position is slightly below its original level.
20
Whereas, FDI is not affected much by the shock in DI initially, and it’s long-run
mean level remains around its original level.
It seems that the domestic investment in China during the study period has not had
much influence on the FDI inflows, which has confirmed by the result from the
Granger causality test as well. In contrast, the economic growth seems sensitive to the
shock in DI and both are positively correlated to each other. On the other hand, the
levels of domestic investment in China very much likely depend on its past values.
This is true, especially in a highly planned economy.
3. Figure 6 impulse responses of a shock in GDP. After a shock in GDP, DI begins to
decline continuously reaching the trough point in the 1st quarter of the fourth year.
Also, it is noticeable that the fluctuation pattern of DI is nearly the same as that of the
GDP, the higher level of DI and the greater GDP would be. In response to the shock
in GDP, FDI only slightly declines initially, and then increases converging to a lower
long-run new mean level.
The results show that there is a strong causal link between economic growth and
domestic investment in China, which supports the use of an investment error
correction model for the study period. This also confirms the accelerator principal of
investment, the larger the change in GDP, the greater the desired change in the capital
stock and, therefore, the greater the level of domestic investment. However, GDP
seem to have a slight immediate and medium term impact on the FDI inflows.
5. CONCLUSION
This study has used the ECM techniques of innovation accounting (variance
decomposition and impulse response function analysis) to search for an answer whether
(1) FDI in China crowd out domestic investment or whether FDI has substitution/
complementary effects on domestic investment; (2) the causal links between FDI,
21
domestic investment and economic growth; and (3) the role of FDI in China’s economic
development.
Fig 4. Impulse responses to a shock in FDI
160
120
80
40
0
-40
5
10
15
20
FDI
25
DI
30
35
40
GDP
Fig 5. Impulse responses to a shock in DI
2000
1500
1000
500
0
-500
-1000
-1500
-2000
5
10
15
20
FDI
25
DI
30
35
40
GDP
Fig 6. Impulse responses to a shock in GDP
1200
800
400
0
-400
-800
5
10
15
FDI
20
25
DI
30
35
40
GDP
22
We find that FDI does not crowd out domestic investment but instead it has
complementary effects on domestic investment in China during the period 1988-2003.
Secondly, the causal links between GDP and DI is two-ways, whereas, only a one-way
directional causality runs from FDI to DI and GDP. Overall, FDI indeed has had
significant positive impact on China’s economic growth and development during the
period 1988-2003. FDI not only has supplementally provided somewhat solutions to the
shortage of capital formation in China’s economic reform era, it also has stimulated
domestic investment through the channel of technology diffusion and the rapid expansion
of the export industries.
This study further suggests that China’s economic growth, the determinants of FDI
inflows and the level of domestic investment, somehow, are also explained by other
forces either than variables used in this study. This implies that more empirical research
and studies are required to include variables such as trade, productivity, new technologies
and economic policies. Moreover, further study and research on different types of FDI
and their effects on domestic investment and economic growth in individual industry will
be interesting and useful for FDI host nations and their economic development.
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