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The Effect of Foreign Direct Investment on Industrial Sector Growth: Evidence from Sri Lanka

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The development of the industrial sector stimulates economic growth and development by reducing poverty and regional disparity, increasing export income, generating quality employment, as well as developing technological capabilities and productive capacities. It has been more than four decades since removing trade-related barriers, and tax incentives liberalized the Sri Lankan economy offered to foreign investors to attract FDI and promote the industrial sector. Hence, the objective of this study is to investigate the relationship between inward FDI and industrial sector performance of Sri Lanka at the aggregate level for the period 1980-2016. We use the Auto Regressive Distributed Lag (ARDL) model to identify the long-run relationship and short-run dynamics of the selected variables. ARDL bounds test verifies the existence of co-integration among the selected variables. The study fails to find a significant relationship between FDI and industrial sector growth of Sri Lanka in the long run as well as in the short run. The attraction of vertically integrated FDI that consists with advanced technology and value-added production is one of the solutions for overcoming the issue of low technology and knowledge of Sri Lankan industrial sector. Sri Lankan FDI strategy associated with industrial sector should consider the pull and push factors related to recipient and source country respectively. To promote the industrial sector via FDI, the government policy should focus on attracting more FDI that could be channeled into those sectors that would contribute to national competitiveness.
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The Effect of Foreign Direct Investment on
Industrial Sector Growth: Evidence from Sri Lanka
NPG Samantha (Corresponding author)
School of Economics
Huazhong University of Science and Technology
P.R. China
E-mail: samanthanpg@yahoo.com
Liu Haiyun
School of Economics
Huazhong University of Science and Technology
P.R. China
Received: June 29, 2018 Accepted: July 31, 2018 Published: August 9, 2018
doi:10.5296/jad.v4i2.13339 URL: https://doi.org/10.5296/jad.v4i2.13339
Abstract
The development of the industrial sector stimulates economic growth and development by
reducing poverty and regional disparity, increasing export income, generating quality
employment, as well as developing technological capabilities and productive capacities. It
has been more than four decades since removing trade-related barriers, and tax incentives
liberalized the Sri Lankan economy offered to foreign investors to attract FDI and promote
the industrial sector. Hence, the objective of this study is to investigate the relationship
between inward FDI and industrial sector performance of Sri Lanka at the aggregate level for
the period 1980-2016. We use the Auto Regressive Distributed Lag (ARDL) model to
identify the long-run relationship and short-run dynamics of the selected variables. ARDL
bounds test verifies the existence of co-integration among the selected variables. The study
fails to find a significant relationship between FDI and industrial sector growth of Sri Lanka
in the long run as well as in the short run. The attraction of vertically integrated FDI that
consists with advanced technology and value-added production is one of the solutions for
overcoming the issue of low technology and knowledge of Sri Lankan industrial sector. Sri
Lankan FDI strategy associated with industrial sector should consider the pull and push
factors related to recipient and source country respectively. To promote the industrial sector
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via FDI, the government policy should focus on attracting more FDI that could be channeled
into those sectors that would contribute to national competitiveness.
Keywords: Foreign direct investment, Industrial sector, Economic growth, Vertical FDI
1. Introduction
Foreign direct investment (FDI) is considered as a vehicle that transfers stable funds,
technology and skills from developed economies to developing economies (Borensztein, De
Gregorio, & Lee, 1998). FDI is essential in attracting more technological innovations, skills,
and spillovers for the development of the industrial sector in developing countries. It will
enhance the industrial base of the nation by increasing resource endowments and changing
the structure of the economy. According to Rodríguez-Clare (1996), the variety of specific
inputs and the relative strength of the competition determine the impact of FDI on the
domestic industrial sector. FDI effect on the industry is twofold: the linkage effect and the
competition effect. Linkage effect on industrial sector of host country occurs when the
production of investing foreign firms depends on intermediate goods produced by local firms.
Because of product competition, foreign firms may force domestic firms to exit the market.
Rapid economic growth in the South East Asian region with higher FDI in industrial sector
witnessed the positive impact of FDI on economic growth. The improvement in the industrial
sector is vital for developing countries like Sri Lanka where labor productivity in the primary
sector is relatively low. Hence, the marginal productivity of labor will increase with the
release of workers from the agricultural sector to the industrial sector.
Sri Lanka is a small open economy in the South Asian Region with US$ 87.2 billion GDP
and 21.44 million populations. Sri Lanka as an independent state since 1948, has achieved
substantial development, despite a recent past marked by nearly thirty years of internal
conflict, which was ended in 2009. The country has shown tremendous progress in poverty
reduction and human development ranking 73 out of 188 countries in the Human
Development Index (HDI). Furthermore, it is in the high human development category and
ranks 4th among 29 developing and emerging Asian countries in the HDI (UNDP, 2016). Sri
Lanka recorded about 5.8 percent annual economic growth since 1990 showing long-term
stable growth. Regarding per capita income, the country marked the highest value in the
south Asian Region and upgraded to the lower middle-income category in 2010. However,
the country is facing macroeconomic challenges at present. The progress recorded in previous
decades is at risk in the medium term due to weak signs of some macroeconomic
fundamentals. Firstly, the low level of government revenues is a limitation in annual budget
implementation that creates a structural budget deficit raising the level of public debt.
Secondly, a sharp depreciation of the national currency and a reduction of foreign exchange
reserves generate external pressure. Despite these economic challenges, the country has to
deal with some social issues such as aging of population and labor market challenges, and
migration of young graduates.
In 1978, Sri Lanka introduced a comprehensive package of trade liberalization as the very
first country in the South Asian region. The prime objective of the policy changes was private
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sector-driven economic growth, FDI promotion, and export lead growth. Since the economic
liberalization, the country implemented numerous economic policies to stimulate industrial
sector performance. The establishment of the Export Development Board in 1978 and export
processing zones, privatization of state industrial undertakings are some examples of such
attempts (CBSL, 2016). The development of the industrial sector will support economic
growth and development by reducing poverty and regional disparity, increasing export
income, generating quality employment, as well as developing technological capabilities and
productive capacities. It has been more than four decades since removing trade-related
barriers, and tax incentives liberalized the Sri Lankan economy offered to foreign investors to
attract FDI and promote the industrial sector. Hence, the objective of this study is to
investigate the relationship between inward FDI and industrial sector performance of Sri
Lanka at the aggregate level for the period 1980-2016. The findings of the study will provide
a direction to government officials and policymakers on how to manage macroeconomic
variables and economic policies to promote the industrial sector through FDI. Further, the
findings could be used as an example for other developing countries with similar economic
characteristics to Sri Lanka.
The rest of the paper will be as follows. The following section discusses the available
literature related to the study. In part three, we develop the methodology and empirical model.
Estimated results and discussion are presented in section four. Section five concludes the
paper.
2. Literature Review
The relationship between FDI and economic growth has extensively been discussed in the
economic literature. It is evidence of the advantage that can be achieved through FDI, which
includes stable funds, advanced technology, and management skills to stimulate economic
growth and development. During the 1990’s, the boom of newly industrialized countries in
South East Asia witnessed rapid economic growth with high inward FDI. The same strategy
followed by the countries like Argentina, Brazil, and Poland. African countries are relatively
shown low economic progress with low inward FDI.
Theoretically, FDI stimulates host country industrial growth by transferring technology and
restructuring industrial sector. Generally, Multinational Co-operations (MNC) or foreign
firms are technologically advanced than local firms when investing in the host country.
Technology transfer takes place when local firms adapt MNC’s or foreign firm’s technology.
The existing competition of the host country may affect in the presence of affiliates of
MNC’s or foreign firms. In such a situation, the industrial structure of the host country may
change or restructure to compete with MNC’s. According to Dunning & Lundan (2008), this
spillover effect arises as a direct consequence of linkages between FDI and host country
economic agents.
In the neoclassical growth model, Solow (1957) emphasized the vital role of technological
progress on economic growth. Furthermore, Solow decomposed economic growth into
growth related different inputs. Based on Solow’s model, Findlay (1978) developed a model
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which shows a positive relationship between FDI and economic growth. Further, he argued
that inflows of FDI increase the rate of technological progress with advanced technology and
methods. Based on endogenous growth models, Blomström and Kokko (2003) paid more
attention to FDI spillover through technology and skills. According to them, to stimulate
economic growth, FDI should increase similarly with educational progress and domestic
investment. Then only employers would invest in technology, method, and knowledge.
Some micro and macro levels studies have been focused on the relationship between FDI and
economic growth. Aitken and Harrison (1999) investigated the effect of FDI on more than
4000 domestic firms in Venezuela during 1976-1989. They have found a positive relationship
between foreign equity participation and productivity in plants less than 50 employees.
Furthermore, they have found an adverse effect of FDI on the productivity of wholly
domestically owned firms. Also, they have suggested that the net impact of foreign
ownership on the economy is quite small. They could not find any spillover effect from
foreign firms to domestic firms. Conducting an influential study, Borensztein et al. (1998)
stated FDI as a vehicle transferring technology from developed to developing countries.
Furthermore, they found that the higher productivity of FDI depends on the level of human
capital of the host county. The absorptive capacity of technology depends on the level of
available technology of the host country. Hence, FDI contributes to economic growth only
when the host country capable of absorbing advanced technology.
By analyzing the impact of FDI on host country economic growth, Agrawal (2015), Agrawal
and Khan (2011), Iamsiraroj and Ulubaşoğlu (2015), Soltani and Ochi (2012) have found
positive significant relationship while Akinlo (2004), Azman-Saini et al. (2010), Masry (2015)
have found weak connection. Furthermore, Herzer et al. (2008) challenged the common
belief of the positive impact of FDI on economic growth. By analyzing data from 28
countries, he could not find any effect of FDI on economic growth in many of the nations.
Despite analyzing the impact of FDI on host country economic growth in aggregate level,
few studies have been focused on identifying the effects of FDI in sector level. In an
influential study, Chakraborty and Nunnenkamp (2008) analyzed the impact of FDI on
economic growth as a whole and sector level for the time series data of India. The study
found a significant causal relationship between FDI stock and manufacturing sector output
and failed to find any causal relationship in the primary sector. Services sector FDI found as a
growth promoter in the manufacturing sector through cross-sector spillovers. They confirmed
the argument that the impact of FDI on economic growth does not depend only on the amount
of FDI but also on its structural composition and type. Masron and Hassan (2016)
investigated the impact of US FDI on the manufacturing sector of Malaysia for the period
from 1999 to 2008. The study could not find positive spillover from FDI inflows to various
industries in the manufacturing industry. By using Cobb-Douglas production function,
Suleman and Amin (2015) found a positive and significant effect of the sectoral foreign direct
investment on the industrial economic growth of Pakistan. Umer and Alam (2013) analyzed
the impact of FDI and trade openness on industrial sector performance of Pakistan under the
VECM framework. The study confirmed the common belief of the positive effects of FDI on
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industrial sector growth. Further, they suggest an increase of FDI and gross capital formation
to stimulate industrial sector growth.
According to the available literature, the impact of FDI on economic growth or industrial
sector performance depends on country-specific characteristics. This study is an attempt to
study the impact of inward FDI on industrial sector performance of Sri Lanka for the period
from 1980 to 2016. The findings of the study would provide policy guidance for the better
performance of the industrial sector of Sri Lanka.
3. Methodology
This section describes the methods used in this paper. Specifically, it covers the theoretical
framework, model specification, estimation strategy, data and description of the variable.
3.1 Theoretical Framework and Model Specification
The conceptual framework of the study begins with the neoclassical growth model. The
neoclassical growth model is considered to be the most vital starting point in growth
economics. This model can describe the growth process by using two equations: the
production equation and the capital accumulation equation. The production equation of the
neoclassical model expresses the current flow of output goods as a function of the existing
stock of capital and labor.
=
 (1)
Where A represents a productivity parameter and is less than one( < 1). Hence,
production encompasses decreasing returns to capital. Capital accumulation depends on
investment and depreciation of capital.
=−
(2)
Where  is the aggregate savings and  is the aggregate depreciation of capital.
Aggregate savings is equal to aggregate investment. According to the neoclassical growth
model, to achieve long-run economic growth, the productivity A also should grow over time.
Solow (1956) refers to this as technological progress, but the concept cannot be explained or
rationalized within the neoclassical growth model. There was a need for a theoretical
framework in which productivity growth is endogenous to analyze policies of growth.
Endogenous growth models overcome this issue by assuming productivity growth to be
endogenous. With the objective of capturing the impact of inward FDI on the output of the
Sri Lankan industrial sector, this study uses an AK type production function.
=
(3)
Where Y, K, and A are the country’s output, capital and productivity parameters at time t,
respectively. To examine the effect of FDI and trade openness on industrial sector growth in
Sri Lanka, we apply a model derived from an endogenous growth model as follows.
= (


) (4)
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By taking logarithms of all the selected variables, we transform a nonlinear function into
a linear function as follows
=+
+
+
+
+ (5)
Where A is the productivity parameter, Yis is the industrial sector value added in time t, Fdi is
the inward foreign direct investment in time t, Gcf is the gross capital formation in time t, Top
is the trade openness in time t, Cpi is the consumer price index in time t and e is the error
term.
Based on the Equation 5, taking technology as constant, the log-linear specification of the
empirical model is developed as below:
=
+
+
+
+
+ (6)
Where is the intercept, and “lnrepresents the natural logarithms. The coefficients of the
selected variables that we are going to examine are indicated by−
.
3.2 Data Sources and Variable Description
We obtained the data used in this study from the World Development Indicators of the World
Bank. The data period covered from 1980 to 2016. We use the value added of the Sri Lankan
industrial sector (Yis) based on the price level of 2010 as the dependent variable. We include
the percentage of inward FDI to GDP as an independent variable into the selected model to
identify the short-run dynamics and long-run relationship between FDI and industrial sector
performance of Sri Lanka. According to the economic literature, the relationship between
FDI and economic growth is ambiguous. Some researchers have found significant positive
association (Agrawal, 2015; Bengoa & Sanchez-Robles, 2003; Fedderke & Romm, 2006;
Hansen & Rand, 2006) between FDI and host country economic growth while some others
have found weak or insignificant relationship (Belloumi, 2014; Haruna Danja, 2012; Masry,
2015). Hence, the sign and the level of significance of the coefficient of FDI with industrial
sector performance have to be tested empirically in the Sri Lankan context.
We use the real value of annual gross capital formation (Gcf) based on the 2010 price level as
a proxy for capital accumulation. The study expects a positive relationship between capital
accumulation and industrial sector performance of Sri Lanka. Trade openness is formulated
as the percentage value of the summation of total trade (Exports + Imports) to the GDP for a
specific period, generally a year. The relationship between trade openness and industrial
growth can be positive or negative depending on country-specific characteristics. For instance,
Sakyi et al. (2015) have found a positive long-run relationship while Umer and Alam (2013)
have found a negative long-run relationship between economic growth and trade openness.
Consumer price index represents the changes of the general price level of the economy over a
period. We use the value of consumer price index taking 2010 as the base year. The
relationship between inflation and economic growth is also a debatable issue. According to
Fischer (1993) inflation is a growth demoting factor which results in reducing investment and
output. The other argument is that one-digit inflation is favorable for economic growth while
two-digit inflation leads towards sluggish growth. By conducting a co-integration analysis for
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selected South Asian countries namely Bangladesh, India, Pakistan, and Sri Lanka, Mallik
and Chowdhury (2001) have found a positive relationship between these two variables.
Hence, we expect a positive coefficient between industrial sector growth and the consumer
price index. Depending on the above discussion, the summary of the anticipated signs of the
factors is ≶0,
>0,
≶0,
> 0.
3.3 Estimation Strategy
3.3.1 Unit Root Test
The objective of undertaking a stationary test is to determine the order of integration of all the
variables under consideration. If the mean and variance of a time series parameter are infinite
and independent of time while its co-variance is finite and independent of time, such a
variable is said to be stationary. In contrast, a variable is supposed to be nonstationary if the
mean and variance of a time series parameter vary over time. Before applying any
co-integration analysis, it is essential to test the stationarity properties of the selected
variables; otherwise, regression analysis will yield spurious results in the presence of
nonstationary data. The Augmented Dickey-Fuller (ADF) test advocated by Dickey and
Fuller (1981) is the most common test to check the order of integration of time series
variables. The following equation (Eq. 7) presents the ADF unit root test.
∆=∅
+∅+
 +∆
 +
(7)
Where is an error term.
3.3.2 ARDL Approach and Co-Integration
In the co-integration analysis, the selection of an appropriate technique depends on the level
of integration of the selected time series variables. If the variables are integrated at different
levels, conventional co-integration tests cannot be applied. However, the Auto Regression
Distributed Lag (ARDL) model advocated by Pesaran, Shin, and Smith ( 2001) overcome this
issue and can be applied to the variables integrated at I(0) and I(1). However, any of the
variables should not be stationary at I(2) to apply the ARDL model. The ARDL equation
used in this study is given below.
∆=
+  +
 +
 +
 +  + ∆
 +
∆
 + ∆
 +
∆
 +∆
 +
(8)
Where is the intercept, is the error term, to represent the long-run elasticities,
and to  are the short-run dynamics. The sign indicates the differenced operator.
The appropriate lag length is determined by using the Akaike Information Criterion (AIC).
The presence of a long-run relationship among the variables is verified using the ARDL
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bound test. The null hypothesis of no long-run relationship of the bound test is (=
=
=
=
=0). In contrast, the alternative hypothesis of the long-run relationship
between the variables of the selected model is ( ≠0,
≠0,
≠0,
≠0,
0). The
acceptance of the alternative hypothesis depends on the critical values of the Pesaran table.
Pesaran et al. (2001) have formulated upper and lower critical values to test the co-integration
among the variables. If the estimated F-statistic of the bound test is higher than the upper
critical value, we can reject the null hypothesis accepting the alternative of co-integration. On
the other hand, the null hypothesis is accepted if the estimated F-statistics value is lower than
the lower critical value. The result is inconclusive if the estimated value of F-statistics
remains between two critical values.
If the modeled variables are co-integrated, then we can explore the short-run relationship by
applying the Error Correction Model (ECM) developed by Engle and Granger (1987). The
ECM version of the ARDL model is specified as follows.
∆= ∆
 + 
∆

+  ∆
 + 
∆

+  ∆
 + + 
(9)
Where Ect and represent error term and speed of adjustment parameter respectively.
4. Results and Discussion
4.1 Unit Root Test
The stationarity results of ADF test are presented in Table 1. Accordingly, only the variable
inward foreign direct investment (lnFdi) is stationary at level. Hence, it can be considered as
an I(0) series. The rest of the variables, real value added in the industrial sector (lnYis), the
real value of gross capital formation (lnGcf), trade openness (lnTop) and consumer price
index (lnCpi) turn into stationary at first difference. Hence, they can be considered as I(1)
series. Since variables are stable at different levels I(0) and I(1), we can apply the ARDL
co-integration model advocated by Pesaran et al. (2001).
4.2 ARDL Regression Analysis
The estimated regression results of the Equation 8 using the ARDL model developed by
Pesaran et al. ( 2001) are presented in Table 2. We follow the Akaike Information Criterion
(AIC) in ARDL regression analysis with the maximum lag length of four for both dependent
and independent variables. Eviews 10 software is used to run ARDL regression. The selected
lag lengths of the variables of the model are (4,0,1,1,0).
The probability of F-statistic is highly significant indicating the overall performance of the
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selected model. Further, the higher value of Durbin-Watson than R squared value is an
indication of free from spurious regression results, serial correlation, and autocorrelation of
the model. The R squared value is also at the desired level confirming the validity of the
model.
Table 1. Results of the ADF test on levels and the first difference
Variables
Level 1st difference
Order of
integration
Constant Constant and
trend Constant Constant and
trend
lnYist
0.3365
(0.9770)
-2.8770
(0.1817)
-4.1859
(0.0024)*
-4.1656
(0.0121)*
I(1)
lnFdit
-3.5246
(0.0131)*
-4.5747
(0.0044)*
I(0)
lnGcft
2.5604
(1.0000)
-1.1059
(0.9128)
-6.0486
(0.0000)*
-5.3615
(0.0006)*
I(1)
lnTopt
-1.5610
(0.4881)
-1.3317
(0.8636)
-5.2275
(0.0001)*
-4.2802
(0.0094)*
I(1)
lnCpit
-2.6505
(0.0926)
-0.6858
(0.9664)
-4.5038
(0.0010)*
-4.9557
(0.0014)*
I(1)
Note. * Stationary status, P values are in parentheses.
Source: Own study.
The regression results in Table 2 show that the coefficients of the variables of gross capital
formation, trade openness and consumer price index are positive and highly significant in
explaining the variance of the industrial sector of Sri Lanka. An increase of 1% of gross
capital formation will lead industrial sector value added by 0.20%. However, inward FDI is
not a significant variable to explain industrial sector performance in Sri Lanka. Inward FDI
has a positive relationship with the industrial sector value added, but it is not statistically
significant.
The existence of co-integration among the selected variables is verified by using the bound
test. Table 3 exhibits the computed F-statistics and critical bounds. The calculated F-statistics
value (7.38) which is higher than the upper critical bound (5.53) confirms the existence of
co-integration among the variables selected in the model rejecting the null hypothesis of no
co-integration (=
=
=
=
=0) at 1% significant level.
Table 4 depicts the long-run relationship and short-run dynamics of the model. In the long run,
only gross capital formation and consumer price index are statistically significant in
explaining the variance of industrial sector value added in Sri Lanka. It realized that the
industrial sector of Sri Lanka more sensitive to local shocks than external shocks. A 1 %
increase in gross capital formation will lead the industrial sector by 0.33% in the long run.
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FDI is found to be an insignificant factor in explaining industrial sector value added in the
long term as well as in short-term for Sri Lanka. The relationship between consumer price
index and industrial sector growth is positive and significant showing that 1% increase in
consumer price index stimulates industrial sector growth by 0.38%. Furthermore, trade
openness is found to be an insignificant factor in explaining the long-run growth of the
industrial sector of Sri Lanka.
Table 2. ARDL regression results
Variable Coefficient t - Statistics Probability
lnYist-1 0.8481 5.7694 0.0000*
lnYist-2 0.0151 0.0839 0.9338
lnYist-3 -0.0312 -0.1735 0.8636
lnYist-4 -0.1688 -1.3502 0.1907
lnFdit 0.0015 0.2152 0.8316
lnGcft 0.2019 5.4198 0.0000*
lnGcft-1 -0.0895 -2.2592 0.0341**
lnTopt 0.1382 3.2145 0.0040*
lnTopt-1 -0.0974 -2.5414 0.0186**
lnCpit 0.1287 3.7121 0.0012*
Constant 4.4954 4.0223 0.0006*
R - squared : 0.96
Adjusted R- squared: 0.96
F - stat : 52.31
Probability of F-stat: 0.0000
Durbin-Watson Stat: 2.1835
Akaike Info Criterion: -5.5337
Schwarz Criterion: -5.0349
Hannan - Quinn Criterion: -5.3659
ARDL(4,0,1,1,0
Note: * and ** denote significant at 1% and 5% level respectively
Source: Own study
Table 3. ARDL Bound test results
Computed F - statistics: 7.3769
Critical value Lower bound value Upper bound value
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1% 4.093 5.532
5% 2.947 4.088
10% 2.46 3.46
Source: Own study
Short-run dynamics of the variables of the model are also presented in Table 4. Gross capital
formation and trade openness are found to be significant variables in explaining industrial
sector growth in the short run. The coefficient of the error term (Ectt-1) is negative and
significant indicating the convergence of short-run shocks to long-run equilibrium. The value
of the speed of adjustment parameter is 33% meaning that approximately three years period is
required to adjust back short-run shocks into long-run equilibrium path.
Table 4. Short run and long-run results
Long run elasticities
Variables Coefficient t- statistics Probability
Constant 13.3479 8.4276 0.0000*
lnFdit 0.0046 0.2112 0.8346
lnGcft 0.3337 5.4806 0.0000*
lnTopt 0.1211 1.4538 0.1601
lnCpit 0.3822 11.1457 0.0000*
Short-run elasticities and error correction model
ΔlnYist-1 0.1849 2.0402 0.0535***
ΔlnYist-2 0.2001 2.0999 0.0474**
ΔlnYist-3 0.1688 2.0724 0.0501***
ΔlnGcft 0.2019 8.4869 0.0000*
ΔlnTopt 0.1382 4.5149 0.0002*
Ect(t-1) -0.3367 -7.3703 0.0000*
R - squared : 0.78
Adjusted R- squared: 0.74
Durbin - Watson Stat: 2.1835
Akaike Info Criterion: -5.8368
Schwarz Criterion: -5.5647
Hannan - Quinn Criterion: -5.7452
ARDL(4,0,1,1,0)
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Source: Own study.
4.3 Diagnostic and Stability Test
Table 5 shows the results of all the diagnostic tests that we conduct to verify the normality of
residuals and stability of the model. The results of the Jarque-Bera statistics confirm the
normal distribution of error term. Furthermore, the results of Table 5 prove that the model is
free from serial correlation, heteroscedasticity, and auto-correlation. Results of Ramsey test
assure the stability of the parameters of the model. Furthermore, we test the stability of the
model by using CUSUM and CUSUM square tests. As shown in Figure 1, both CUSUM and
CUSUMSQ strips lie within the 5% critical range indicating model stability.
4.4 Causality Test
We check short-run causality between the selected variables using the Granger causality test.
Table 6 depicts the results of short-run causality test. Accordingly, bi-directional causality is
found between industrial sector growth and domestic capital formation at 5% significant level.
Unidirectional causality is found from consumer price index to industrial sector growth, FDI,
and gross capital formation. No causality is seen running from FDI to any selected variables.
Table 5. Diagnostic test results
F - Statistics Probability
χ2NORM 0.0850 0.9583
χ2RAMSEY 0.5489 0.4670
χ2ARCH 0.2970 0.5754
χ2Serial Correlation 1.1022 0.1943
Source: Own study.
Figure 1. CUSUM and CUSUMSQ tests
-15
-10
-5
0
5
10
15
96 98 00 02 04 06 08 10 12 14 16
CUSUM 5% Significanc e
-
0.4
-
0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
96 98 00 02 04 06 08 10 12 14 16
CUSUM of S quares 5% Significanc e
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Table 6. Results of Granger Causality test
Variables Yis Fdi Gcf Top Cpi Direction of causality
Yis - 3.0501*** 4.5236** 0.5697 0.1109
Yis → Fdi,
Yis → Gcf
Fdi 0.5056 - 0.5510 2.4087 0.0142 No causality
Gcf 3.1165** 1.3883 - 2.4914 0.7433 Gcf → Yis
Top 1.5343 0.1366 0.4454 - 2.9898*** Top → Cpi,
Cpi 8.0220* 3.2989*** 6.2105** 0.4354 - Cpi → Yis, Cpi → Fdi, Cpi → Gcf
Note: (*), (**) and (***) indicate rejection of no causality at 1%, 5% and 10% significant levels respectively
Source: Own study.
4.5 Discussion
The findings of the empirical study confirm an insignificant relationship between FDI and
industrial sector performance of Sri Lanka for the period from 1980 to 2016. The results of
the study are consistent the argument that the impact of FDI on economic growth depends on
not only the overall size of FDI but also the type and structural composition (Chakraborty &
Nunnenkamp, 2008). Although Sri Lanka changed its economic policies to attract more FDI,
the progress in inward FDI was not at the expected level compared to the neighboring
countries, which had similar economic characteristics at the time of getting independence.
Many developing nations that shared comparable economic characteristics to Sri Lanka
during the 1950s boost their production capabilities in the industrial sector by applying
government lead policies with the objective of expanding manufacturing base exports in the
mid-20th century. It is said that Asian countries followed a “flying geese pattern” lead by
Japan and followed by South Korea, Singapore, Hong Kong, and Taiwan at initially and later
on many other nations like China, Thailand, Malaysia and Indonesia. Latin American
countries such as Chile, Mexico, Brazil, and Peru also followed the same strategy. The
economic and social conditions of all these countries are higher than that of Sri Lanka at
present. The economic structure of these countries first transformed from the agricultural base
to the industrial sector and later on deepened the services sector. Sri Lanka’s growth pattern
was dissimilar from countries that developed through industrialization and has leap-frogged
from an agricultural based economy to a service-based economy (CBSL, 2016). Table 7
shows the changing pattern of sectoral composition of Sri Lanka’s GDP declining agriculture
sector while increasing services sector.
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Table 7. Sectoral composition of GDP – Sri Lanka
Year Agriculture Industry Services
1950 46 20 37
1960 38 17 45
1970 28 24 48
1978 26 24 50
1980 24 24 52
1985 26 26 49
1990 23 27 50
1995 20 30 50
2000 19 28 53
2005 17 27 56
2010 12 29 59
2015 8 27 57
2016 7 27 57
2017 7 27 57
Source: Central Bank of Sri Lanka.
The type of FDI is vital for the development of the industrial sector, especially in the
manufacturing sector that represents a significant share in the industrial sector of Sri Lanka.
Attracting FDI is crucial for the development of the industrial sector to a country like Sri
Lanka where characterized by low domestic savings and high budget deficits. The attraction
of vertically integrated FDI that consists with advanced technology and value-added
production is one of the solutions for overcoming the issue of low technology and knowledge
of Sri Lankan industrial sector. Newly industrial countries in the East and South East Asian
region accelerated their economic growth via attraction of vertically integrated FDI and
entering into global value chains of production. Despite South East Asian countries, Sri
Lanka’s inward FDI consisted with market-seeking investment in the tourism and real estate
sectors in the previous decades (Table 8).
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Table 8. Sectoral composition of Inward FDI – Sri Lanka
Sector
Years
2012 2013 2014 2015 2016
Manufacturing 22.99 25.86 20.66 26.50 30.93
- Textile, Wearing Apparel & Leather 6.48 3.57 5.14 4.68 6.17
- Chemicals, Petroleum, Coal, Rubber& Plastics 4.42 8.22 5.69 7.78 12.43
Agriculture 0.54 0.61 0.35 0.40 0.24
Infrastructure & Services 76.48 73.53 73.55 73.10 68.84
Infrastructure 44.58 56.55 42.22 46.76 42.38
- Housing & Property Development 4.17 15.64 20.99 21.87 9.92
Telephone & Telecommunication Network 18.09 25.86 9.43 14.32 30.41
Services 31.89 16.99 31.33 26.34 26.45
- Hotels & Restaurants 8.77 4.88 4.23 18.76 17.64
Total FDI 1338.16 1391.41 1616.33 969.66 801.00
Source: BOI- Sri Lanka.
Manufacturing sector oriented FDI that can contribute more to export performance was
narrowly concentrated on a few labor-intensive, low-tech industries such as textiles, apparel,
and leather. After the trade liberalization, Sri Lanka’s export composition changed from
primarily agricultural products to more value-added labor-intensive industrial products.
However, the Sri Lankan export basket narrowly concentrated on two traditional products, tea,
and garments, which accounted for more than half the export earnings. The export direction
of Sri Lanka is also suffering from lack of market diversification as only a few markets are
catered to, such as the USA and Europe, which accounted for nearly two-thirds of the total
export earnings (CBSL, 2016).
Sri Lankan FDI strategy associated with industrial sector should consider the pull and push
factors related to recipient and source country respectively. Cross-country empirical studies
have found pull factors such as low-cost labor, competitive exchange rates, low tax regimes,
raw material, access to markets, and the availability of land for setting up plants, etc. FDI
decision of foreign firm depends on the host country’s macroeconomic environment that
facilitates pleasant investment surroundings such as political stability, quality of public
institutions, efficient logistics, ample macroeconomic and legitimate structures, and trade
openness, etc. Global value chains of production in its nature transport product components
from one location to another location depending on the efficiency of production. Hence, the
efficiency of logistics such as access to ports, sea and road transports, customs clearance, low
levels of tariff and non-tariff barriers and warehousing are vital. Especially the FDI
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originating from emerging economies such as China, Russia, Malaysia, and India are more
concern on the quality of infrastructure than did firms of advanced economies in the past
(CBSL, 2016).
Whether there was a spillover effect from foreign firms to local firms in promoting exports in
the Sri Lankan context is questionable though. The internal conflict, from which Sri Lanka
suffered for nearly three-decades, increased the political risk and uncertainty, and
macroeconomic instability. Vertically integrated assembly industries are more sensitive to
political risks than those producing light consumer goods. Hence, the country missed the
opportunity for attracting vertically integrated assembly industries that could have
contributed much to export performance (Athukorala and Jayasuriya, 2004).
5. Conclusion and Recommendation
The prime objective of this study was to examine the effect of FDI on industrial sector
performance of Sri Lanka for the period from 1980 to 2016. In doing so, we applied the
ARDL co-integration technique developed by Pesaran et al. ( 2001). The selected variables
for the model were real value added in the industrial sector, the percentage of FDI to GDP,
trade openness, gross capital formation and consumer price index. Data were extracted from
the World Development Indicators of the World Bank. We used the ADF unit root test to
check the order of integration of the selected variables. Since the variables were stationary at
different levels I(0) and I(1), we applied the ARDL test for co-integration analysis. ARDL
bounds test verified the existence of co-integration among the selected variables rejecting the
null hypothesis of no co-integration (=
=
=
=
=0) at 1% level. In the long
run, only gross capital formation and consumer price index were statistically significant in
explaining the variance of industrial sector value added in Sri Lanka. It realized that the
industrial sector of Sri Lanka more sensitive to local shocks than external shocks. FDI and
trade openness were found to be an insignificant factor in explaining industrial sector value
added in the long run. In the short run, gross capital formation and trade openness were found
to be significant variables in explaining industrial sector growth. The coefficient of the error
term (Ectt-1) was negative and significant indicating the convergence of short-run shocks to
long-run equilibrium. The value of the speed of adjustment parameter was 33% meaning that
approximately three years period would be required to adjust back short-run shocks into
long-run equilibrium path. Results on Sri Lanka are consistent with the cross-country findings
in which the growth effects of FDI depend on several factors such as local skills and
absorptive capacity, linkages between foreign and domestic firms and technological
spillovers, and export orientation.
The attraction of vertically integrated FDI that consists with advanced technology and
value-added production is one of the solutions to overcome the issue of low technology and
knowledge of Sri Lankan industrial sector. Political risks and macroeconomic uncertainty
prevailing in Sri Lanka would hinder the chances of attracting vertically integrated assembly
industries, which can have a significant impact on manufacturing sector growth and export
performance. Sri Lankan FDI strategy associated with industrial sector should consider the
pull and push factors related to recipient and source country respectively. The government
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policy should focus on attracting more FDI that could be channeled into those sectors that
would contribute to national competitiveness to promote the industrial sector via FDI.
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This is the second edition of the celebrated volume by Professor John H. Dunning, first published in 1993, which has now been not only updated but also enriched with the addition of a number of new topics. This addition was not least due to the expertise of the co-author, Sarianna Lundan, in the institutional aspects of international business and the internal governance of transnational corporations (TNCs). It is a comprehensive synthesis of all the theories in International Business based on extremely rich data evaluation in almost all fields of TNC activities and their environment. It is a “creative masterpiece which unbundles the DNA of the field of international business” as described by Alan Rugman in his assessment of this volume.
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I. Introduction, 65. — II. A model of long-run growth, 66. — III. Possible growth patterns, 68. — IV. Examples, 73. — V. Behavior of interest and wage rates, 78. — VI. Extensions, 85. — VII. Qualifications, 91.