Trade, foreign direct investment and economic growth in selected South Asian countries.
Dhakal, Dharmendra ; Pradhan, Gyan ; Upadhyaya, Kamal 等
Abstract
This paper examines the dynamic relationships between foreign
direct investment (FDI), trade and economic growth in India, Pakistan
and Sri Lanka with the help of a VAR model and data covering 1971-2006.
Before estimating the model, the time series properties of the data are
diagnosed. The estimated results indicate that in India, FDI tends to
cause economic growth by improving trade but there is a weak direct
relationship between FDI and GDP. In Pakistan, FDI appears to cause
trade but trade is not a significant factor in economic growth. In Sri
Lanka, very little effect of FDI and trade on GDP is detected.
INTODUCTION
In the past, South Asian countries, like many developing countries,
adopted a policy of either restricting or discouraging foreign direct
investment (FDI) by the multinational corporations (MNCs). The main
reason behind such a policy was the fear that MNCs could influence the
economic policies as well as the politics in host countries. Therefore,
until the early 1980s, most countries in this region regulated FDI by
limiting foreign ownership of local firms, and requiring foreign firms
to use local resources and employment as a precondition. Such inward
looking policies, combined with the policy of discouraging FDI inflows,
contributed to the stagnation of South Asian economies for several
decades. In contrast, during the same period, countries in East and
Southeast Asia saw rapid growth, credited mainly to their open and
liberal foreign trade and FDI inflow policies. By the late 1980s, many
developing countries began to realize the significance of trade and FDI
in economic growth. Consequently, they became more open to international
trade and more receptive to FDI inflows. Developing economies even
encouraged FDI inflows by providing various incentives to MNCs to move
into their countries. In keeping with this trend, South Asian countries
also liberalized their economies hoping to attract more FDI. This
liberalization included less restrictive policies with regard to FDI,
reformed financial systems, industrial policies conducive to private
investment, and other institutional reforms.
There has been tremendous growth in FDI flows around the world in
the past three decades. For instance, FDI flows increased from $53
billion in 1980 to more than $600 billion in 2007. Historically,
developed countries have received the majority of the share of world
FDI. At their peak in 2007, developed economies received 80 per cent of
world FDI. More recently, FDI flows to developed countries have slumped
while those to developing countries have surged. While FDI flows to
developed countries fell by 16 per cent in 2004, they increased to the
Asia and the Pacific region by 55 per cent.
THE FDI, TRADE AND GROWTH NEXUS
FDI is considered to be a growth-enhancing factor for several
reasons. First, FDI serves as an important complement to the local
economy and helps to stimulate growth of output of the host country.
Trevino and Upadhyaya (2003) suggest that the impact of FDI on growth is
expected to be twofold. First, through capital accumulation in the host
country, FDI can be expected to increase economic growth by encouraging
the incorporation of new inputs and foreign technologies in the
production function of the host country (Dunning, 1993; Borensztein et
al., 1998). Second, FDI augments the level of knowledge in the host
country through labor training and skill acquisition (De Mello, 1997).
Capital-market disequilibrium theory suggests that capital in the form
of private investment will flow to those countries where the
risk-adjusted rate of return is the highest. In keeping with the study
by Burnside and Dollar (2000) regarding foreign aid, it has been shown
that in transition economies, FDI tends to flow to those countries that
have pursued market reform (Trevino, Daniels and Arbelaez, 2002).
Capital-market disequilibrium theory suggests that capital in the form
of private investment will flow to those countries where the
risk-adjusted rate of return is the highest.
FDI flows to a host country depend on several factors. One
important factor that helps to attract the inflow of FDI to a host
country is its market size as measured by real GDP. The larger the size
of the market in a country, the more the FDI inflow is likely to be. The
inflow of FDI in turn spurs higher economic growth which attracts more
FDI. Theoretically, the causality may run in both directions. In other
words, the higher the economic growth, the more the inflow of FDI; and
the more the inflow of FDI, the greater the growth rate of the economy.
Openness to international trade, defined as the ratio of the sum of
exports and imports to GDP, is considered to be an important factor that
determines economic growth. Countries that are open to trade can take
advantage of efficiencies based on comparative advantage. This enhances
exports as well as the level of output which leads to higher economic
growth. Further, export expansion allows firms in exporting countries to
take advantage of scale economies. Endogenous growth theory suggests
that export-led economic growth can increase long run growth by allowing
innovation growth in research and development. Dhakal et al. (2007) show
that openness is an important determinant in attracting FDI. According
to their study, multinational corporations prefer to move their
production bases to countries where it is relatively easy to import
intermediate products as well as to distribute (export) output to
foreign markets. This suggests that trade also helps to grow the economy
by attracting more FDI.
The influence of trade and FDI on economic growth has been
discussed widely in the economic literature. Some earlier studies see
exports as a main source of economic growth that helps developing
countries break away from the vicious circle of poverty. In later
studies, exports are seen as an important source of foreign exchange
earnings necessary for developing countries for importing high-tech
machinery that are crucial for competitiveness and economic growth
(McKinnon, 1964). Coe and Helpman (1995) argue that trade improves
domestic productivity from the spillover effects of foreign research and
development. New growth theory also provides a link between trade and
economic growth.
On the other hand, FDI plays an important role in providing much
needed capital and technology to developing countries from
industrialized countries (Saggi, 2002). By extending the hypothesis
advanced by Bhagwati (1973) and Balasubramanyam et al. (1996), some
studies find that growth-enhancing FDI are stronger in countries with
more liberal trade regimes. The interaction of FDI with trade is likely
to have a positive impact on economic growth in two ways. First, liberal
trade policy in host countries attracts higher levels of FDI inflows
because such policies not only allow FDI to take advantages of cheap
labor but also allow a larger market. Second, the neutrality of
incentives associated with exports allows investors to take advantage of
economies of scale and better capacity utilization, making FDI more
productive. Moreover, exports promote technological innovation and
create a favorable environment for technology spillovers from FDI.
Several empirical studies have shown a positive impact of FDI
inflows on economic growth of the host country. For example,
Borensztein, Gregorio and Lee (1998) study the effect of FDI on economic
growth in 69 developing countries over two decades and find that FDI is
an important vehicle for the transfer of technology, contributing more
to growth than domestic investment. Similarly, Bosworth and Collins
(1999) conduct a comprehensive study on FDI, covering 58 developing
countries in Latin America, Asia and Africa during 1978-1995, Their
findings suggest that a one dollar increase in capital inflow (of all
types) is associated with a fifty-cent increase in domestic investment.
In addition, FDI appears to bring about a one-for-one increase in
domestic investment. Thus, FDI appears to have a stronger impact on
domestic investment than do loans or portfolio investment. In a related
study on the effect of FDI on total factor productivity growth, Ericsson
and Irandoust (2000) find that FDI and output are causally related in
the long run in Norway and Sweden.
The above discussion indicates that economic growth, FDI and trade
are closely related with one another. Intuitively, what we see is that
they affect one another but we do not know in what direction the
causality runs. The purpose of this paper is to examine the causal
relationship between real GDP, FDI and trade.
MODEL SPECIFICATION
In order to evaluate the relationship between economic growth,
trade and FDI we consider the following equation:
Y = f (FD, TR) (1)
where Y is real gross domestic product (GDP), FD is real foreign
direct investment and TR is real trade, defined as the sum of exports
and imports. According to the economic literature, there could be a
bi-directional relation between GDP and trade. On the one hand, trade
has a positive impact on GDP because more trade implies more economic
activity that adds to GDP. On the other hand, some view the GDP of
foreign countries as a proxy for the domestic market. That is, a larger
GDP represents bigger purchasing power which results in more trade.
Similarly, FDI is assumed to have a positive relationship with GDP. That
is, FDI is expected to be growth-enhancing (De Mello, 1997). As
mentioned above, FDI acts as an agent of international transfer of
technology (Borensztein et al., 1998; Balasubramanyam et al., 1996).
Foreign firms are considered more productive than domestic firms (De
Gregorio, 1992; Borensztein et al., 1998) and tend to generate highly
beneficial effects on domestic investment. Bosworth and Collins (1999)
report nearly a one-to-one relationship between FDI and domestic
investment.
The above findings suggest that a thorough understanding of the
relationship between economic growth, openness and FDI is necessary to
analyze the sources of the dynamic relationship among the variables
under consideration. Therefore, we develop a trivariate vector
autoregression (VAR) model to examine the possible sources of
association and dynamic linkages between GDP, FDI and trade in India,
Pakistan and Sri Lanka. The logic behind applying VAR modeling is that
we do not have any a priori information regarding the endogeneity and
exogeneity of the variables. Since VAR models do not impose any
restriction and assume that all variables are endogenous (Sims, 1980)
variable, they are better than single equation models. We also conduct
the Granger causality test and variance decomposition to establish a
causal relationship between FDI and economic growth. This method of
analysis allows us to capture the short-run dynamics between the
variables.
We use annual data from 1970 to 2006. All the data have been
obtained from the World Bank's 2008 World Development Indicators
CD-ROM, and are expressed in real terms.
ESTIMATION AND RESULTS
As discussed above, the theoretical relationship between FDI, GDP
and trade may run in one or both directions. Therefore, we specify the
following unrestricted multivariate VAR model. The model consists of FD,
Y and TR and is expressed as follows.
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (2)
where [[beta].sub.0t], [[delta].sub.0t] and [[gamma].sub.0t] are
constant terms, and FD, Y and TR are all expressed in growth form at
time t calculated as the first-difference of the log of the respective
variables. The term at is a vector of innovations that may be
contemporaneously correlated with one another, but are uncorrelated
withtheir own lagged values and uncorrelated with all of the right-hand
side variables, i. e., [[epsilon].sub.t] ~ IN(0, [SIGMA]). Using this
VAR model, we attempt to identify the causal relation (in the Granger
(1969) sense) among the variables.
Since time series data may be non-stationary and may give rise to
spurious associations, it is important to test for the stationarity of
the data series and the possible long-term relationships among the
variables in the system. Therefore, before estimating the VAR model, we
conduct tests to check the stationarity of each variable and the
existence of long-term relationship between the variables.
At the beginning of the empirical analysis, we conduct a unit root
test to examine the time series properties of the three variables using
the Augmented Dickey-Fuller (ADF) and Philips-Perron (PP) tests. The
unit root test results are reported in Table 1. The results show that
most of the variables in the system are nonstationary at the log level.
However, the first-difference of each of the variables is reported
significant. A similar pattern is observed in the case of the PP test
where all variables are found to be integrated of order one or I(1).
Since the variables in the system were all I(1), and may possess
some kind of long-run relationship, we apply the multivariate
cointegration technique developed by Johansen and Juselius (1990) to
test for cointegration among the variables. Table 2 reports the results
of the multivariate cointegration analysis. It provides evidence of not
rejecting the null hypothesis of zero cointegrating vectors at the 1 per
cent level. Therefore, the existence of a long-run relationship does not
find statistical support in all three countries over the period under
examination.
The next logical step involves an estimation of the VAR model as
specified in equation (2). Before estimating the VAR model, we find the
appropriate lag length for each model using the Akaike (1974)
information criterion (AIC). The AIC criterion is defined as:
AIC(k) = Min{In ([sigma] + 2k/T) | k = 0,1, ..., m} (3)
where T is the sample size, and k is the number of parameters. From
the estimated results of AIC, the optimal 1ag lengths are identified as
8 for India and Pakistan, and 5 for Sri Lanka.
Next, we employ the VAR model to analyze the short-run effects
among these macroeconomic variables. As is known, the variance
decompositions (VDCs) show the proportion of forecast error variance for
each variable that is attributable to its own innovations, and the
shocks to the other system variables. The transmission of innovations
among variables may occur via many channels. This helps to explain the
strength of the exogeneity of the variable. The VDCs (1, 5 and 10 years)
are presented in Table 4.
India. As reported in Table 4, in the fifth year 77.7 per cent of
the variability in DFD (first difference of FD) is explained by its own
innovations, while after 10 years only 66.3 per cent of the variability
is explained by its own innovations. Similarly, 20.75 per cent of the
variability is explained by innovations in the DY (first difference of
Y) shock, and 12.95 per cent in the DTR (first difference of TR) shock.
For the DY variable, its own shocks account for only 48.29 per cent
of the forecast variance for the fifth year. After ten years, only 27.42
per cent of the variation is explained by its own variation, 8.6 per
cent by the DFD shock, and almost 64 per cent by the TR shock.
For the DTR variable, its own shocks account for only 56.1 per cent
of the forecast variance for the fifth year. After ten years, it drops
to 52,5 per cent; 34.75 per cent of the variation in DTR is explained by
the variation in DFD, and 12.68 per cent of the variation in DTR comes
from the DY shock.
In the long run, the magnitudes of the explained variability of DFD
remain relatively close to the medium-run period but drop significantly
for DY and DTR. DFD in the medium term is least explained by DTR.
However, in the long run, its explanatory power increases significantly.
We also observe that there is an indirect effect on DY via TR.
Pakistan. The high DTR variability comes from movements in DFD
shocks. In the long run, almost 50 per cent of the variation in DFD
comes from the DY shock. DTR explains only 5 per cent of variation in
DFD both in the medium term and long term. The results also indicate
that about 30 per cent of the variation in DY is explained by DFD, and
DTR only explains about 6 per cent of the variation. Even though the
forecast variance of DTR does not seem to be important in explaining the
variance of the other variables, it explains about 64 per cent of its
own variation. The rest of the variation in DTR is explained by DFD.
Sri Lanka. In the medium term the DFD shock explains 84 per cent of
the variance but in the long term it drops to about 72 per cent. DY and
DTR combine explains about 27% of variation in DFD. Similarly, 86 per
cent of the variation in DYis explained by its own innovation and only
9.45 per cent and 4 per cent are explained by DFD and DTR. DTR explains
about 63 per cent of its variation from its own shock. However, both DFD
and DTR combined explain more than 35 per cent of the variation in DTR.
SUMMARY AND CONCLUSION
During the last decade or so, developing countries in Asia
witnessed strong growth in FDI and trade. India, in particular,
witnessed unprecedented growth in FDI, trade and economic growth. Using
multivariate VAR models, we analyze the dynamic relationships between
FDI, trade and economic growth in India, Pakistan and Sri Lanka using
data covering 1970-2006. To avoid possible spurious results, we test the
time series properties of the data by using ADF and PP tests. We also
conduct cointegration tests to rule out long-term relationships between
the variables. The empirical findings indicate that most of the
variables are integrated of order one. The cointegration tests indicate
no long-run equilibrium relationships among DFD, DY and DTR. The
forecast error variance decomposition analysis reveals information about
the proportion of the movements in sequence due to a variable's
"own" shocks versus shocks from other variables. The variance
decomposition results indicate some very interesting results. In the
case of India, FDI appears to cause economic growth by improving trade
but there is a weaker direct relationship between FDI and GDP. In
Pakistan, FDI causes trade but trade is not a significant factor in
economic growth--only about 6 per cent of the variance in GDP is
explained by trade. Finally, in Sri Lanka, only a small per centage of
the variance in the variables under consideration is explained by
innovations in these variables.
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DHARMENDRA DHAKAL
Tennessee State University, U.S.A.
GYAN PRADHAN
Eastern Kentucky University, U.S.A.
KAMAL UPADHYAYA
University of New Haven, U.S.A.
Table 1
Unit Root Test
ADF PP
First First
Log level Difference Log level Difference
India FD -2.345437 -3.636298 ** -3.202373 -8.276264 *
Y -1.890715 -6.544988 * -1.800454 -7.529541 *
TR 0.026669 -5.299345 * 0.206818 -5.289699 *
Pakistan FD -3.2011070 -13.73008 * -2.478720 -13.73008 *
Y -0.960479 -4.704909 * -1.431875 -4.702186 *
TR -3.588611 ** -4.606025 * -3.599697 ** -4.702186 *
Sri Lanka FD -2.463322 -6.656675 * -2.442728 -6.732651 *
Y -2.753012 -5.116600 * -3.051719 -5.082504 *
TR -3.986823 ** -6.227812 * -4.004919 ** -6.242624 *
All variables are in log form. * and ** indicate significant at
1 and 5 per cent critical levels
Table 2
Johansen's Co-Integration Test (Variables: log FD, log Y,
log TR)
India
Trace
[H.sub.0] Eigen Value Statistic
r [less than or equal to] 0 0.320738 20.43641
r [less than or equal to] 1 0.191732 7.67378
r [less than or equal to] 2 0.019484 0.649307
Pakistan
Trace
[H.sub.0] Eigen Value Statistic
r [less than or equal to] 0 0.499618 35.17973
r [less than or equal to] 1 0.289697 12.33110
r [less than or equal to] 2 0.031112 1.043015
Sri Lanka
[H.sub.0] Trace
Eigen Value Statistic
r [less than or equal to] 0 0.486444 32.54009
r [less than or equal to] 1 0.263468 10.54895
r [less than or equal to] 2 0.013767 0.457462
1% 1%
Critical Max Eigen Critical
[H.sub.0] Value Statistic Value
r [less than or equal to] 0 35.43817 12.76268 25.86121
r [less than or equal to] 1 19.93711 7.024422 18.52001
r [less than or equal to] 2 6.634897 0.649307 6.634897
1% 1%
Critical Max Eigen Critical
[H.sub.0] Value Statistic Value
r [less than or equal to] 0 35.43817 22.84863 25.86121
r [less than or equal to] 1 19.93711 11.28809 18.52001
r [less than or equal to] 2 6.634897 1.043015 6.634897
1% 1%
[H.sub.0] Critical Max Eigen Critical
Value Statistic Value
r [less than or equal to] 0 35.43817 21.99114 25.86121
r [less than or equal to] 1 19.93711 10.09149 18.52001
r [less than or equal to] 2 6.634897 0.457462 6.634897
* indicates rejection of the null hypothesis at 0.01 critical
level. Test statistic indicates no co-integrating equation at
0.01 level
Table 3
Granger Causality/Block Exogeneity Wald Test
Pakistan India Pakistan
Wald Test Chi-sq P-value Chi-sq P-value
DY =/=> DFD 4.183863 0.8402 11.76112 0.1622
DTR =/=> DFD 6.296528 0.6141 26.46637 * 0.0009
DY & DTR =/=> DFD 11.55238 0.7742 39.37982 * 0.0010
DFD =/=> DY 24.13167 * 0.0022 34.40316 * 0.0000
DTR =/=> DY 22.05014 * 0.0048 25.54519 * 0.0013
DFD & DTR =/=> DY 37.28374 * 0.0019 53.01118 * 0.0000
DFD =/=> DTR 13.467292 *** 0.0968 8.498589 0.3863
DY =/=> DTR 18.43635 * 0.6182 6.277244 0.6162
DFD & DY =/=> DTR 22.83660 *** 0.1182 11.73827 0.7618
Pakistan Sri Lanka
Wald Test Chi-sq P-value
DY =/=> DFD 3.117808 0.6818
DTR =/=> DFD 8.174206 0.1469
DY & DTR =/=> DFD 13.73361 0.1855
DFD =/=> DY 3.421579 0.6353
DTR =/=> DY 1.683537 0.8910
DFD & DTR =/=> DY 4.059683 0.9446
DFD =/=> DTR 10,24224 *** 0.0687
DY =/=> DTR 7.048786 0.2170
DFD & DY =/=> DTR 19.83910 ** 0.0308
=/=> indicates does not cause in Granger sense All variables are
in log form. *, ** and *** indicate significant at 1, 5, and 10
per cent levels
Table 4
Variance Decomposition
India Pakistan
Variance Decomposition Variance Decomposition
of DFD of DFD
Period DFD DY DTR DFD DY DTR
1 100.00 0.00 0.00 100.00 0.00 0.00
5 77.71 12.64 9.66 48.84 47.39 3.78
10 66.30 20.75 12.95 46.07 49.14 4.79
Variance Decomposition Variance Decomposition
of DY of DY
period DFD DY DTR DFD DY DTR
1 0.00 100.00 0.00 0.00 100.00 0.00
5 5.03 48.29 46.68 15.69 79.89 4.42
10 8.61 27.42 63.97 28.72 65.41 5.87
Variance Decomposition Variance Decomposition
of DTR of DTR
period DFD DY DTR DFD DY DTR
1 0.00 0.00 100.00 0.00 0.00 100.00
5 38.20 5.68 56.12 36.77 0.29 62.94
10 34.76 12.68 52.57 35.83 0.21 6196
Sri Lanka
Variance Decomposition
of DFD
Period DFD DY DTR
1 100.00 0.00 0.00
5 84.83 3.76 11.41
10 72.36 12.36 15.28
Variance Decomposition
of DY
period DFD DY DTR
1 0.00 100.00 0.00
5 8.01 88.44 3.55
10 9.46 86.53 4.02
Variance Decomposition
of DTR
period DFD DY DTR
1 0.00 0.00 100.00
5 17.03 10.94 72.03
10 17.13 19.61 63.25
