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INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS
DECEMBER 2012
VOL 4, NO 8
EMPIRICAL RELATIONSHIP BETWEEN FOREIGN DIRECT
INVESTMENT AND ECONOMIC OUTPUT IN PAKISTAN.
Sajid Rahman Khattak
Muhammad Ali Jinnah University, Pakistan
Nadeem Iqbal
Faculty of Business Administration
BZU Sub Campus, Dera Ghazi Khan, Pakistan
Muhammad Arif Khattak
Muhammad Ali Jinnah University,
Islamabad,Pakistan
Abdul Qadeer
Muhammad Ali Jinnah University,
Islamabad,Pakistan
Abstract
This paper examines cointegration and causal relationship for both short and long run
between foreign direct investment and economic output in Pakistan from the period 1972 to
2008. For data analysis the study use various econometrics model such as Augmented
Dickey Fuller test, Enger-Granger two step cointegration test, Granger Causality test,
Vector Error Correction Model (VECM). The results indicates that there is a short as well
as long run relationship found between foreign direct investment and gross domestic
product growth rate. The results also indicate that there is Uni-directional relationship (not
Bi-directional) between foreign direct investment and gross domestic product in Pakistan
which means FDI caused economic output in Pakistan.
Keywords: Foreign direct investment, economic output, gross domestic product, ADF,
Enger-Granger two step cointegration, GC, VECM.
1. Introduction
Present day world is considered a global village. Due to technological advancement one can
move around the world within a few hours or days also can handle and manage their
business operations across their country boundaries. Countries become boundary less due to
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fast technological advancement. Such situation motivates investors to increase their
investment in different countries and to reach their products throughout the world.
Multinational companies (MNEs) want to operate in one are more countries for the purpose
to boost their sales as well income. Operating MNEs in the host countries can have several
advantages to the host country as well such as, unemployment rate are decrease, new
technology are come to the country, knowledge and skills sharing between foreign and host
country employees, competition becomes tense because the local firms tries to beat the
market and get competitive advantages which also led to reduce their prices to reasonable
level (compared to monopoly or oligopoly). Foreign direct investment (FDI) is generally
considered to be an instrument of cash and non-cash inflow into the host country from
overseas. According to World Trade Organization (WTO) FDI occurs when an investors
based in one country acquires an assets in another country with this intention to manage that
assets as well is called foreign direct investment. FDI plays an essential role in the economic
development of the countries especially in developing countries like Pakistan. The concept
economic globalization allows investors to produce or sells their products across different
countries; this gives us an imperative ingredient in the form of FDI. Private capital flow in
the form of FDI is one dazzling feature of the present day world. Both developed and
developing countries tries to attract multinational corporations to come and operate in their
country. In the perspective of new theory of economic growth FDI is considered as an
engine of economic growth.
FDI Inflows to South Asian Countries
FDI inflows increased speedily in developing countries especially In South Asia in 1980s.
The drift in FDI inflows is different for each South Asian countries depends on their
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respective government policies. FDI inflows were $0.83 billion in 1980 to 1984, and the
growth was measured 5.34 % annually (Mortaza et al. 2007). According to ABD (2007) FDI
inflows to South Asian countries was about $24.3 billion which is 132.9% higher than 2005.
According to Mortaza et al. (2007) the most favorable destination for FDI is Pakistan and
India followed by Sri Lanka and Bangladesh.
FDI in Pakistan
After its creation in 1947, the average economic growth rate of Pakistan was higher than the
world economic growth rate. Pakistan achieved much admires to its economic progression
in 1960s, because Pakistan was considered a model for economic development in the world.
Unfortunately, in 1990s economy growth becomes slowdown due to hasty economic
policies, economic mismanagement, and a huge amount of public debt. To compete globally
Pakistan changed their economic policy by adopting market based economic policy. The
government provides fair trade policy, fiscal incentives, and tariff facilities to foreign
investors to make attractive investments region in the world. FDI inflow increased
significantly in Pakistan in energy, telecommunication and agriculture sectors.
Decline FDI in Pakistan
Government of Pakistan stated three reasons for the slow performance of the various sectors
these are global financial crisis, security situation and flood situation in Pakistan. The table
and graph shows that FDI level increases in 2001-2002 and 2007-2008 so the above stated
reasons are solely not responsible for decline in FDI. The actual reasons for FDI decline are,
government did not give proper attention to economic growth, energy and gas crisis in
Pakistan, and the government faulty and arrogant approach to international investors led to
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decrease FDI inflow in Pakistan. Table 1 and 2 shows FDI inflows and outflows from 19722008 respectively.
Year
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
Year
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
Table 1
Foreign direct investment, net inflows (% of GDP)
Value
Year
Value
Year
0.06
1988
0.48
2000
0.10
1989
0.52
2001
0.18
1990
0.61
2002
0.30
1991
0.57
2003
1992
2004
0.27
0.69
0.38
1993
0.68
2005
0.21
1994
0.81
2006
0.10
1995
1.19
2007
0.18
1996
1.46
2008
1997
0.42
1.15
0.33
1998
0.81
1999
0.39
0.84
Table 2
Foreign direct investment, net outflows (% of GDP)
Value
Year
Value
1988
0.000
0.033
0.000
1989
0.107
1990
0.000
0.005
0.000
1991
-0.008
1992
0.000
-0.024
1993
0.000
-0.004
0.000
1994
0.002
1995
0.000
0.001
-0.015
1996
0.011
1997
-0.025
-0.039
-0.002
1998
0.080
1999
0.058
0.033
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Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
Value
0.42
0.53
1.14
0.64
1.14
2.01
3.35
3.90
3.32
Value
0.015
0.043
0.039
0.023
0.057
0.040
0.085
0.068
0.030
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Figure 1
Graphical representation of FDI Flow in Pakistan
The above graph show the FDI flow in Pakistan from 2002 to 2011. If we see it FDI inflow
in the start from 2002 to 2005 are increasing with slow growth but in 2007 and 2008 FDI
increased so much and cross the limit of $16 billion, after that the flow start decreasing from
2009 to 2011 to the amount $6 billion. The main reasons of this decreased are investment
environment in the country (which is not so good) and GDP growth rate of the country
(although GDP of the country increasing but the problem of electricity and gas shortage can
decrease the investors’ confidence to invests in Pakistan).
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The remaining paper are divided into four section; section two describe literature review,
section three discuss methodology of the study, section four discuss results of the study, and
section five conclude the paper.
2. Literature Review
Agarwal (2000) investigated the economic impact of FDI in South Asia by understanding
time series, cross section analysis of panel data from five countries including Pakistan,
India, Sri Lanka, Nepal and Bangladesh. The study argues that, FDI impact GDP growth
rate from negative to positive and to strongly positive. There is a negative impact of FDI
inflow on GDP growth rate in 1980, while in early eighties a positive impact were shown,
and strongly positive relations were shown between FDI inflows and GDP growth rate.
Import substitution policies were followed by most South Asian countries which had also
high import tariffs in the 1960s and 1970s. These policies gradually changed with the
passage of time, and mostly the countries adopted market oriented policies. Growth impact
of FDI tends to be greater under in export promotion trade regime compared to an import
substitution regime.
Regale and Chakraborty (2002) investigated long run and short run effects of FDI. They
selected 23 countries for their studies. They found that, there was a long run co integration
relationship exists between FDI and GDP for all 23 countries. Further, they stated that, bidirectional economic causality were exist between FDI and GDP in closed economy
countries for short run, and for long run uni-directional causality exists between FDI and
GDP in closed economy countries.
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Sahoo (2005) explored the impact of FDI on manufacturing sectors in India. The study
found that there is direct impact of FDI inflow in electronics, chemical, and electrical, while
there is indirect impact of FDI inflow in drugs and pharmaceutical sectors. They also argues
that FDI is an important vehicle for transforming technology, skills, knowledge and it have
long run effect on growth.
Wang (2002) explored the relationship between FDI and economic growth and found a
significant relationship between them. Liv and Sincilair (2002) found the long run
relationship between FDI, economic growth, and trade in China. They found that bidirectional causal relationship among FDI growth and export.
Besides there positive relationship some study also found negative relationship between FDI
and GDP as well such as, Levine (2002) studied the impact of FDI and GDP growth rate and
found no significant positive impact between FDI and GDP growth rate. Brecher and
Alejandro (1977) found negative relationship between FDI and GDP growth. They stated
that foreign capital can lower economic growth by earning excessive profits in the country
with the severe trade distortion such as high tariff.
3. Methodology of the study
The study examines cointegration relationship both long run and short run between foreign
direct investment (FDI) and gross domestic product (GDP) in Pakistan. The study use FDI
as an independent variable and GDP as a dependent variable, their log form LFDI and
LGDP also taken. Data were taken from 1972 to 2008 from World Bank website. The study
use Augmented Dickey Fuller (ADF) test, Engle Granger cointegration test, and Granger
causality test to establish cointegration and causal relationship between FDI and GDP.
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3.1 Augmented Dickey Fuller (ADF) test
ADF test is applied to check whether data are stationary at level or at first difference. The
test consist the following two regressions:
Yt = α + xt β + ϵt …………………………………………………………………………………………………1
Where x and y are variables and ϵt is the error term. The test is performed under null of nonstationarity.
ΔYt = βo + β1t + δYt-1 + α∑mi=1 ΔYt-1 + ϵt……………………………………………………………..2
Where Yt indicate variables GDP and FDI, Δ is the changes or difference operator, t = time,
L = lags number, ϵt is error term, βo, β1, δ is constant variance, and ΔYt-1 is the difference
between ΔYt-1 - ΔYt-2. The null hypothesis can be rejected i.e. the series is stationarity if tstat value is negatively less than the critical values at 1%, 5%, and 10% significant level
respectively.
3.2 Engle Granger cointegration test
This test is used to check long run and short run equilibrium relationship between the FDI
and GDP. The following two equations are used for both long run and short run relationship.
For long run relationship
LGDP = α + δLFDIt +
ϵt…………………………………………………………………………3
From the above equation we collected the residual values (ϵt), and then used this data of ϵt
and applying stationarity test to identify integrated order. Variables are cointegrated i.e. long
run relationship exist, if the integrated order of ϵt is less than the integrated order of linear
combination of the variables of I (1).
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For short run relationship
ΔLGDPt = c + ∑pi=1δΔLFDIt-1 + μtECMt-1 + ϵt………………………………………………4
Where ΔLGDPt-1 is the first difference, c is constant coefficient, ΔLFDIt-1 is the first
difference, and ECM is error correction mechanism term.
3.3 Granger Causality test
This test is used to check causal relationship between these two variables GDP and FDI, and
where the relationships between them are uni-directional or bi-directional. According to this
approach, assume Y (GDP) indicates economic growth caused by another variable X (FDI),
and we assumed that Y can be better from the past values of Y and X than from the past
values of Y alone. Through VAR model we test these hypotheses:
LGDP = ∑ni=1 αi LFDIt-1 + ∑ni=1 βj LGDPt-j + ϵ1t………………………………………….5
LFDI = ∑ni=1 λi LFDIt-1 + ∑ni=1 β2j LGDPt-j + ϵ2t………………………………………….6
Where LGDP and LFDI denotes log values of gross domestic product and foreign direct
investment. We assumed that distribution of ϵ1t and ϵ2t are uncorrelated. Equation (5) states
that current LGDP is related to the past values of itself as well as that of FDI and Equation
(6) states that current LFDI is related to the past values of itself as well as that of GDP.
4. Empirical Results
The results show that, ADF statistics values of LGDP in the level with intercept and trend &
intercept (-0.3895 and -1.923 respectively) are greater than the critical values at 1%, 5%,
and 10% significance level. So the null hypothesis cannot be rejected. This means that in the
level LGDP is a unit root problem. Table 3 also demonstrates that, ADF test statistics values
(-8.203 and -8.445) are smaller than the critical values of both intercept and intercept and
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trend at significant levels when LGDP is tested with first difference. The result shows that
LGDP has not any unit root problem so the null hypothesis can be rejected.
On the other hand ADF test statistics LFDI values is higher than the critical values at the
same significance level in both intercept and intercept and intercept and trend. This results
show that null hypothesis cannot be rejected and the series is not stationary. Although at 1 st
difference the values of ADF test statistic is less than critical values.
Table 5 shows that ADF test statistics value is -4.5584, -3.5402 in first stage estimation
which is smaller than critical values at 1%, 5%, and 10% respectively. This results shows
that Residual series is stationary which prove that both FDI and GDP are cointegrated that
there is a long run equilibrium relationship exists between FDI and GDP.
Table 6 shows the results of short run relationship between FDI and GDP. The ECMt-1
values is negative but significant (ECMt-1 used as Res). The results in table 6 show that
25.39% disequilibrium in the short run and deviates with long run each year. The adjusted
coefficient values of ΔLFDIt-1 and t-stat is significant at P- values. These results shows that
there is short run as well as long run relationship exist between FDI and GDP.
Table 7 shows granger causality results and found that F-Stat value is too higher at 5%
significant level which indicates that GDP does not Grander Cause FDI in Pakistan. This
also shows that there is Uni-directional relationship exist between GDP and FDI. This result
is similar with (Lan, 2006; Aqeel and Nishat, 2005; Athukorala, 2003; Sekman, 2007).
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5. Conclusion
There are lot of research exists to find the relationship between foreign direct investment
and economic growth, some study found significant positive relationship while some also
found negative relation as well. The purpose of this study is to investigate the relationship
between foreign direct investment (FDI) and economic growth or gross domestic product
(GDP) in Pakistan from 1972 to 2008. To check this relationship various tests are applied
i.e. ADF test to check the stationarity, VECM, Enger –Granger Causality test. We found
that there is a long run as well as short run causal relationship exists between FDI and GDP.
We also found that there is no granger causality from GDP to FDI, which shows that there is
Uni-directional relationship found between FDI and GDP in Pakistan. It means that there is
a direct relationship of foreign direct investment and economic growth in Pakistan. So it is
good for Pakistani economy to attract more and more FDI to strong their economy. The
government of Pakistan takes necessary actions and changes those rules which create a
problem for foreign investors to invest in Pakistan in such a way that they invest without
any hesitations. This can also lead economy from all sides i.e. knowledge and skills sharing,
management skills, reduce unemployment, increase in per capita income, competitive
environment etc.
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References
[1] Agarwal, Pradeep (2000), Policy Regime and Industrial Competitiveness: A
Comparative Study of South Asia and India, Macmillan, UK.
[2] Alguacil, M. T. and V. Orts (2002), “A Multivariate Cointegrated Model Testing for
Temporal Causality between Exports and Outward Foreign Investment: The Spanish case”,
Applied Economics, 34,119-32.
[3] Alguacil, M. T. A. Cuadros and V. Orts (2002), “Foreign Direct Investment, Exports
and Domestic Performance in Mexico: A Causality Analysis”, Economic Letters, 77, 37176.
[4] Bende-Nabendem, L. Ford, B. Santoso and S. Sen (2003), “The Interaction between
FDI, Output and the Spillover Variables: Cointegration and VAR Analysis for APEC, 19651999”, Applied Economics Letters, 10, 165-72.
[5] Baharumshah, A.Z. and M. A.M Thanoon (2006), “Foreign capital flows and economic
growth in East Asian countries,” China Economic Review, 17, 70-83.
[6] Balasubrament, V. N., M. Salisu and D. Sapsford (1996), “Foreign Direct Investment
and Growth in EP and IS Countries,” Economic Journal, 106: 92-105.
[7] Hsiao, and Hsiao, M. C. W. (2006), “FDI, exports, and GDP in east and southeast AsiaPanel data versus time series causality analysis,” Journal of Asian Economics, 17(2006):
1082-1106.
[8] Jaya Gupta (2007), “Globalization and Indian Economy: Sector-wise Analysis of FDI
inflows.
[9] Jayashree Bose (2007), “FDI inflows in India and China-A Sectoral Experiences,”
ICFAI University Press, Hyderabad.
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[10] Carkovic and Levine (2002), “Does foreign direct investment accelerate economic
growth?’
[11] Chakraborty, C. and P. Basu (2002), “Foreign Direct Investment and growth in India: a
cointegration approach,” Applied Economics, 34, 1061-73.
[12] Zhang, K.H (2005), “How does FDI affect a host country’s export performance? The
case of China,” China Economic Review, 11, 385-396.
[13] Muhammad Shahbaz and Naveed Aamir (2008), “Direct Foreign Investment and
Income Distribution: A Case Study of Pakistan,” International Journal of Finance and
Economics.
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Annexure
Table 3
ADF test for FDI and GDP
Variables
Level/ 1st
difference
Level
-0.389534
Augmented Dickey Fuller Statistics (ADF) test
With Intercept
With trend and Intercept
t-statistics
1%
5%
10%
1%
5%
10%
-3.6268 -2.9458
-2.6115
-1.9193
-4.2349
-3.5403 -3.2024
1st Difference
-8.202848
-3.6329
-2.9484
-2.6128
-8.4363
-4.2436
-3.5442
-3.2046
Level
-0.495709
-3.6267
-2.9458
-2.6115
-3.5228
-4.2349
-3.5403
-3.2024
1st Difference
-8.116388
-3.6329
-2.9484
-2.6128
-7.9342
-4.2436
-3.5442
-3.2046
t-statistics
LGDP
LFDI
Constant (C)
LFDI
R-squared
Adjusted R-squared
S.E of regression
Sum squared resid
Log likelihood
F-statistic
Pro(F-statistic)
Table 4
Long run relationship between FDI and GDP
Dependent Variable: LGDP, Model: Least Squares,
Sample: 1972-2008, Included observation: 37
Coefficient
Std. Error
t-Statistic
8.4325
0.1034
81.5432
0.4160
0.01889
22.0121
0.9335
0.9307
0.21099
1.5581
6.0972
484.5354
0
Mean dependent var
S. D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
Prob.
0
0
10.577
0.8015
-0.2215
-0.1344
-0.1908
1.2481
Table 5
ADF test saved for residual
Series
ADF t statistics
Test Critical Values with Trend & Intercept
1%
5%
10%
Res (saved as residual)
-4.5494
-4.2349
-3.5403
-3.2024
The long run OLS models is LGDP = 8.4325 + 0.4160*LFDI
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Table 6
Short run Relationship between FDI and GDP
Dependent Variable: DLGDP, Model: Least Squares,
Sample: (adjusted) 1972-2008, Included observations: 37 after adjustment
Coefficient
Std. Error
t-Statistic
Constant (C)
0.10307
0.0135
7.5936
DLFDI(-1)
-0.03665
0.0237
-1.5425
RES(-1)
-0.2539
0.0747
-3.3956
Prob.
0
0.1327
0.0018**
R-squared
Adjusted R-squared
S.E of regression
Sum squared resid
0.0937
0.0858
-2.2391
-2.1057
0.2650
0.2190
0.0758
.1839
Mean dependent var
S. D. dependent var
Akaike info criterion
Schwarz criterion
Table 7
Granger Causality test
Null hypothesis
Observations
DLGDP does not Granger Cause with DLFDI
33
DLFDI does not Granger Cause with DLGDP
33
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F-Stat
0.2798
4.6455
Probability
0.8485
0.0099
868