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An empirical study of FDI determinants
Beloucif, Ahmed; Islam, Mohammad Shaiful; Boukhobza, Tahar
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Proceedings of the 2020 British Academy of Management Annual Conference
Accepted/In press: 08/06/2020
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Beloucif, A., Islam, M. S., & Boukhobza, T. (Accepted/In press). An empirical study of FDI determinants: a panel
data analysis of South and South-east Asia. In Proceedings of the 2020 British Academy of Management Annual
Conference British Academy of Management.
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Download date: 29 May 2022
An Empirical Study of FDI Determinants:
A Panel Data Analysis of South and South-east Asia
Dr Ahmed Beloucif
School of Business & Creative Industries, University of the West of Scotland, Paisley, UK
Dr Mohammad Shaiful Islam
School of Business & Creative Industries, University of the West of Scotland, Paisley, UK
Dr Tahar Boukhobza
University of Hertfordshire, Hatfield, UK
Abstract
This paper explores and compares the determinants of Foreign Direct Investment (FDI) in
South and South-east Asia over 21 periods ranging from 1996 to 2016. Using panel data
regression with random effects and fixed effects models, the study finds that market size,
labour productivity and infrastructure of the host country exercise a significant influence
upon FDI inflows of both regions. Trade openness, inflation rate, natural resource
endowment and the unemployment rate are more important in South Asia, while the
exchange rate, control of corruption and political stability are more important in South-east
Asia for foreign investors. It appears that foreign investors hold different perceptions of
investment attributes while deciding investment location into these two regions. The results
imply that in seeking to become a potential destination of FDI, policies for both regions
should be devised towards market growth, political stability, and the development of the
quality of infrastructure, human capital and labour productivity.Sound macroeconomic
stability with a flexible and stable exchange rate system is also needed to attract FDI.
Keywords – FDI, Determinants, FDI inflows, Panel Data, South and South-east Asia.
1.INTRODUCTION & CONTEXT
The inflows of foreign direct investment (FDI) are often considered as an essential ingredient
that spurs economic growth by bringing technology, knowledge, capital and jobs, which is
likely to generate a positive impact on the host economy (Cambazoglu and SimayKaraalp,
2014). Therefore, governments of many developing and least developed countries (LDC) are
unequivocally entrusted the private sector and foreign investors to transform their economy
and accelerate economic growth. Consequently, many countries around the world are opening
their economy to foreign investors, restructuring and liberalising their FDI regimes, and
offering several fiscal and non-fiscal incentives to attract the optimal level of FDI.
Alike other countries around the world, South and South-east Asian countries have
recognised that FDI is one of the most significant sources of investment that has the potential
to generate a significant impact on poverty alleviation, foster economic growth, and increase
employment opportunity.Both South Asian and South-east Asian economies have the
absolute advantage to attract global investors over other regions. All countries of these
regions have opened up and formulated policies favourable to foreign investment. Overall,
there has been a growing number of policies restructured and implemented all over South and
South-east Asia. The persistent nature of the reforms pursued by the governments suggest
that these reforms are the part of the long-term strategy of South and South-east Asian
economies to sustain their future as a potential destination of FDI.
Despite the economic reforms pursued by the governments of both regions, South Asia still
lags far behind South-east Asia in terms of their FDI performance (UNCTAD, 2018).
Between 1996 and 2016 (Figure.1), South Asia’s annual average share of world FDI was
1.7%, compared with South-east Asia’s 5.4%. Within this timeframe, South-east Asian
countries hosted nearly three times more FDI, which amounted to US$1337.8 billion
compared to that of South Asia (US$481.5 billion).These differences raise questions for both
policymakers and academia as to what are the significant determinants of FDI inflow in
South Asia and South-east Asia. It is also imperative to know whether there are any
similarities or differences in their FDI determinants. All of these need to be addressed from
both theoretical and empirical perspectives to gain an understanding of the FDI movement
into these regions. In light of these observations, the purpose of this paper is to explore and
compare the determinants of FDI in South Asia and South-east Asia.
US$ Billion
Figure 1: Net FDI in South and South-east Asia
Source: Created from UNCTAD Database
The rest of the paper is organised as follows: the next section reviews the relevant theoretical
literature and hypotheses in terms of the relationship between macroeconomic factors and
FDI inflows. Section 3 outlines the methodology of the study. Section 4 presents and
discusses the results, and Section 5 provides a final summary and conclusion.
2
2.DETERMINANTS AND THEIR IMPACT ON FDI INFLOWS
The importance of and growing interest in FDI has led to develop a number of paradigms.
The formulation of proper theories to explain foreign direct investment was initiated in the
1960s (Rayome and Baker, 1995). Since then, Industrial organisation approach of Hymer
(1960), the Product Life Cycle Theory of Vernon (1966), the Monopolistic power theory of
Kindleberger (1969), Knickerbocker's (1973) theory of oligopolistic competition, the
Internalization theory of Buckley and Casson (1976), and Dunning’s (1977) Eclectic
paradigmhave attempted to explain the existence and the expansion of the international
business activities of multinationals via FDI. Within the mainstream theories, Dunning’s
eclectic paradigm is regarded as one of the most robust and comprehensive theories for
analysing the determinants of FDI. In his eclectic/OLI paradigm, Dunning (1977) claims that
a firm will undertake FDI when all three conditions are satisfied. i.e.: (i) it must possess
ownership advantages (“O”) in order to have a competitive advantage over other firms; (ii) it
must have location-specific advantages (“L”) in host countries for instance, lower labour
costs or transportation costs; (iii) it must be beneficial for the firm to exploit ownership
advantages by internalizing (“I”) its production process abroad rather than licensing or
exporting.FDI theories find out several determinants that could explain FDI flows into a
specific location, linking the macro aspects such as trade openness, market size, infrastructure
quality, natural resource availability, political and macroeconomic stability. We discuss the
macroeconomic influences on FDI in the hypotheses below.
2.1 Market Size
Market size represents the size of the host market (Ibrahim and Hassan, 2013). Tan, Wong
and Goh (2018) argue that the size of the host market is a significant pull factor for attracting
FDI. Similarly, Khamphengvong, Xia and Srithilat (2018) argued that foreign investors
would prefer a large market when investing abroad. Besides, Zheng (2009) claimed that
“Market size directly affects investment return and profits, and a higher market growth
indicates a potential larger market and more promising prospects” (p.268).Therefore, foreign
investors seek to invest in a country with a large market size as it is important for the
exploitation of economies of scale and utilisation of resources (Chakrabarti, 2001).Market
size measured by Gross Domestic Product (GDP) or per-capita GDP has been identified by
vast empirical literature as an important determinant of FDI. Chakrabarti (2001) argues that
“Market size has, by far, been the single most widely accepted as a significant determinant of
FDI flows” (p.96). Tan, Wong and Goh (2018) for ASEAN; Nkoa (2018) for African
countries; Adhikary (2017) for SAARC countries; Aziz and Mishra (2016) for Arab
countries; and Al-Khouri (2015) for MENA countriesfound market size as the significant
determinant of inward FDI.According to Billington (1999), the larger the host market, total
income, and its growth potential, the higher the amount of FDI investment. In light of the
above discussion, we present the following hypothesis:
H1: Market size has a positive impact on FDI inflows.
2.2 Trade Openness
The degree of trade openness reflects the willingness of a country to accept foreign
investment (Aziz and Mishra, 2016). Therefore, the more open economy will attract more
FDI. However, there is mixed evidence regarding the significance of trade openness in
determining FDI (Chakrabarti, 2001). For instance, Tan, Wong and Goh (2018), Aziz and
Mishra (2016) and Gupta and Singh (2016) found a strong positive effect of trade openness
on inward FDI. In contrast, Kumari and Sharma (2017), conclude that trade openness is
statistically insignificant to attract FDI. Contrary to conventional wisdom, Ibrahim and
3
Abdel-Gadir (2015) for Oman, and Bhatt (2008) for ASEAN countries founda negative
impact of trade openness on the inflows of FDI. Therefore, after reviewing the above
empirical studies, it is reasonable to draw the following hypothesis:
H2: Trade openness has a positive impact on FDI inflows
2.3 Labour Productivity
Labour productivity generally reflects the efficiency of labour in an economy. Hoang and Bui
(2015), found that foreign investors are particularly interested in labour productivity rather
than labour cost while investing in the ASEAN region. Studies such as Kalyoncu, Tuluce and
Yaprak (2015) for Turkey; and Villaverde and Maza (2012) for Spain also found a positive
association between inward FDI and labour productivity of the host country. Thus, it is
reasonable to draw the following hypothesis:
H3: Labour productivity has a positive impact on FDI inflows
2.4 Infrastructure
The infrastructure of the host country is often considered as a significant factor to FDI. Prior
empirical studies such as Xaypanya, Rangkakulnuwat and Paweenawat (2015), Bhatt (2008)
and Hoang and Bui (2015) for ASEAN region; Ravinthirakumaran et al. (2015) for Sri
Lanka; and Asiedu (2006) for African countries identified a significantly positive relationship
between inward FDI and infrastructure of the host country.Conversely, Onyeiwu and
Shrestha (2004), andTampakoudis et al. (2017) confirmed that infrastructure is insignificant
for inward FDI.Alam and Shah (2013), concluded that basic infrastructure is sufficient to
attract FDI. However, Kumari and Sharma (2017) revealed a negative impact of
infrastructure on FDI inflows for the panel of 20 developing Asian countries. They conclude
that the impact of infrastructure on FDI can vary from negligible to negative. In light of the
above discussion, we present the following hypothesis:
H4: The infrastructure of the host country has a positive impact on FDI inflows
2.5 Human Capital
Human capital generally reflects the quality of labour of an economy. It is often considered
that countries with high-quality labour can handle contemporary technologies more
efficiently, and in general have higher labour productivity. A well-educated labour force
worked as a positive externality in attracting FDI (Aziz and Mishra, 2016). Seetanah and
Rojid (2011), while investigating the determinants of FDI in Mauritius, found a positive
relationship between FDI and human capital. Further, Hakro and Ghumro (2011), while
investigating the determinants of FDI on Pakistan, exhibited a long-run relationship between
human capital and FDI. Rjoub et al. (2017), Kumari and Sharma (2017), Hoang and Bui
(2015), Yohanna (2013) and Sun, Tong and Yu (2002) also found a significantly positive
association between human capital and inward FDI. Thus, from the above literature, it is
reasonable to draw the following hypothesis:
H5: The stock of human capita has a positive impact on FDI inflows
2.6 Political Stability
Political stability indicates the degree of political risk, institutional quality and the investment
climate of an economy. It decreases the uncertainty about the profitability and cost of the
investment, which adds an important dimension to attract foreign investment (Sun, Tong and
Yu, 2002). Tan, Wong and Goh (2018), while studying the determinants of FDI for ASEAN
member states, explored that political instability possesses a positive long-run relationship
with FDI. Similarly, Leonardo et al. (2018), Rjoub et al. (2017), Zheng (2009), Asiedu
(2006), and Sun, Tong and Yu (2002) found a significant positive relationship between
political stability of the host country and inward FDI.On the other hand, Alam and Shah
4
(2013)found no significant impact of political stability on FDI inflowsfor a panel of ten
OECD countries. Further, political stability has found to be insignificant to inward FDI of
India in the study of Sury (2008). It implies that political stability has minimal impacts on the
decisions of foreign investors. Interestingly, Lucke and Eichler (2016) proved that that
foreign investors prefer to invest in developed countries that are politically unstable
compared to their home country. In light of the above discussion, we present the following
hypothesis:
H6: The relationship between political stability and inward FDI will be positive
2.7 Corruption
Corruption generally creates pressure on and difficulties for MNCs to do business in
countries with weak governance (Desai, Foley, and Hines, 2004). Leonardo et al. (2018)
while examined the perception and policy determinants of FDI to European developing
countries revealed that corruption has a deleterious effect on FDI inflows. Su et al. (2018),
Dauti (2015) and Asiedu (2006) also demonstrated a negative impact of corruption on FDI. In
contrast, Wood et al. (2014) while investigating the determinants of FDI in Africa found no
statistical significance of corruption on inbound FDI. Contrary to conventional wisdom,
Helmy (2013) demonstrated that FDI varies positively with corruption and concludes that
corruption does not hinder inward FDI in the MENA region. Similarly, Lucke and Eichler
(2016) found that foreign investors prefer to invest in developed countries that are more
corrupt compared to the investor’s home country. In light of the above discussion, we present
the following hypothesis:
H7: The relationship between the control of corruption and inward FDI will be positive
2.8 Inflation Rate
Inflation reflects macroeconomic instability. As the unstable macro-economy may create
uncertainty in the investment climate, many empirical studies have found a negative impact
of inflation rate for inward FDI. For instance, Rachdi, Brahim and Guesmi (2016) found that
the inflation rate played a negative role in the inward FDI in emerging countries. Studies such
as Ibrahim and Hassan (2013) for Sudan, Kok and Ersoy (2009) for 24 developing countries,
Asiedu (2002) for 71 developing countries, Demirhan and Masca (2008) for 38 developing
countries, Cevis and Camurdan (2007) for 17 developing and transition economies also
observed a negative impact of inflation rate for inward FDI. On the contrary, Aziz and
Mishra (2016)found that the inflation rate is significant and positively attracting FDI in Arab
countries. Yohanna (2013) for Nigeria and Jadhav (2012) for BRICS countries also found a
positive impact of inflation rate on FDI inflow. Reflecting the above literature, we draw the
following hypothesis:
H8: The relationship between the inflation rate and inward FDI will be negative
2.9 Exchange Rate
A stable exchange rate improves certainty in the local economy, and accordingly increases
investment profitability (Mohammadvandnahidi, Jaberikhosroshahi and Norouzi, 2012). A
depreciation of the host country’s currency tends to reduce the cost of all production inputs
including land, labour and machinery, which eventually raises the profit margin of the
foreign investors and therefore stimulates foreign investment (Ismail, 2009; Boateng et al.,
2015). Regarding the relative impacts of exchange rate on the level of FDI inflows in mixed.
Saleem et al. (2018), Gupta and Singh (2016), Rachdi, Brahim and Guesmi (2016), Ang
(2008) and Ramirez (2006) confirmed that appreciation of the exchange rate decreased the
inflows of FDI. In contrast, Aziz and Mishra (2016), Boateng et al. (2015), and Malefane
(2007) found that an appreciation of host country’s exchange rate surged the inflows of FDI.
5
However, Wood et al. (2014) and Zheng (2009) found no significant effect of exchange rate
on inbound FDI. In light of the above discussion, this study draws the following hypothesis:
H9: The appreciation of the host currency leads to a decrease in FDI inflows
2.10 Unemployment rate
The unemployment rate reflects the labour availability in the host economy. The high rate of
available labour in the host economy will attract more foreign investors. The argument is that
in the case of a high unemployment rate people are valuing their job and work hard for a
lower wage (Boateng et al., 2015). Thus, the higher unemployment rate acts as an incentive
for inward FDI. The positive effect of high unemployment rates on FDI inflow has also been
supported by empirical work of Chidlow et al. (2009) and Nunnenkamp et al. (2007). On the
other hand, Boateng et al. (2015) and Gupta and Singh (2016) have found a negative sign for
unemployment rates symbolising an adverse effect on FDI inflows. In light of the above
discussion, we draw the following hypothesis:
H10: The unemployment rate has a positive impact on FDI inflows
2.11 Natural Resources
Natural resources play a vital role in overall FDI attraction or decisions (Rjoub et al., 2017).
Ibrahim and Abdel-Gadir (2015) found that FDI flows are positively influenced by natural
resources. Similarly, Onyeiwu and Shrestha (2004) concluded that countries with wellendowed natural resources tend to attract more FDI. Asiedu (2006) also confirms that natural
resource endowment is one of the critical drivers of FDI to the African region. In contrast,
Lucke and Eichler (2016) found that the relationship between FDI and natural resources is
negative and significant. From the above discussion, this study draws the following
hypothesis:
H11: Natural resource endowment has a positive impact on FDI inflows
2.12 Tax Rate
High taxation is one of the major constraints to foreign investment (Hess, 2000). Further
poorly implemented and unreliable tax laws indicate an unfriendly business climate, which
hampers the flows of FDI (Onyeiwu and Shrestha, 2004). Wood et al. (2014) confirmed
negative effects of high tax rates in FDI inflows for African countries, and concluded that this
negative correlation indicates that foreign MNCs, at least partially, invest in Africa to save
costs. Koojaroenprasit (2013) and Sury (2008) found that a higher amount of corporate tax
had a decremental effect on the inward FDI. However, Alam and Shah (2013), and Onyeiwu
and Shrestha (2004) found no statistically significant effect of corporate taxes on inward FDI
of the host country. This study draws the following hypothesis to evaluate the impact of the
corporate tax rate in FDI
H12: The relationship between corporate tax rate and FDI inflows will be negative
3. DATA, METHODOLOGICAL FRAMEWORK &MODELLING
3.1 Sources of data and definitions of variables
The analysis in this study covers eight South-east Asian countries and six South Asian
countries over 21 periods ranging from 1996 to 2016. The variables used in this study and
source of data is presented in Table 1. FDI as a percentage of GDP, was applied as the
dependent variable to model the determinants of FDI. Independent variables consist of GDP
per capita as a proxy for market size, trade openness (sum of import and export as % of
GDP), inflation rate and exchange rate as a proxy for the macroeconomic stability, secondary
school enrolment as a proxy for human capital, labour productivity proxies by GDP per
6
person employed; mobile-cellular telephone subscriptions as a proxy of infrastructure,
unemployment rate, corporate tax rate, natural resource endowment, political stability, and
control of corruption. Data for all the variables are obtained from sources such as the World
Bank’s World Development Indicators, the World Bank’s World Governance Indicators, the
United Nations Conference on Trade and Development (UNCTAD), and KPMG databases.
All the tests are performed, and the findingsof this study are based on the data that has been
run by using data analysis and statistical software called STATA.
Table 1: Variables and Data Specifications
The summary statistics and correlation of the variables can be found in Tables 2 and 3
respectively. The table 2 indicates that, over the sample period,South-east Asian countries
displaying on average higher FDI inflows, also having larger market sizes, possess more open
economies, as well as higher exchange rates, human capital, control of corruption, political
stability, labour productivity, natural resources and better infrastructure. South-east Asian
countries also display on average slightly higher unemployment rates (4.39% compared with
4.11% for South Asia). On the other hand, South Asian countries possess a higher inflation
rate as well as higher corporate tax rates.
7
Table 2: Descriptive Statistics
Notes: The 6 South Asian countries in panel A are: Bangladesh, Bhutan, India, Nepal, Pakistan and Sri Lanka. The 8
South-east Asian countries in panel B are: Brunei, Indonesia, Myanmar, Malaysia, Philippines, Singapore, Thailand
and Vietnam. FDIGDP is the FDI as a percentage of GDP, GDPCAP is GDP per capita, OPENNESS counts for
exports plus imports as a percentage of GDP, INFLATION denotes inflation rate, EXCHANGE signifies exchange rate,
HUCAPITAL is school enrolment (secondary, % gross),, PRODUCTIVITY is GDP per person employed,
INFRASTRUCTURE is mobile cellular subscriptions (per 100 people), UNEMPLOYMENT counts for unemployment
rate, TAX is corporate tax rate, NRESOURCE is total natural resources rents (% of GDP), POLSTA signifies political
stability, and CORRUPTION counts for control of corruption.
Table 3 suggest that the highest correlation regarding South Asia exists between
CORRUPTION and POLSTA (0.82), followed by EXCHANGE and NRESOURCE (0.69).
Concerning South-east Asia, the highest correlation exists between POLSTA and
EXCHANGE (0.83), followed byPRODUCTIVITY and GDPCAP (0.64). From the
correlation matrix in table 3, it can be seen that there is the existence of high correlation
among the few independent variables, which may causemulticollinearity problem (Baltagi,
2001).
Table 3: Correlation between Variables
8
The basic problem of multicollinearity makes it very difficult to assess the effect of
independent variables on dependent variables. According to Saunders, Lewis and Thornhill
(2015), if variables are highly correlated then, one of the highly correlated variables must be
removed from the model. This study applied Variance Inflation Factors (VIF) to detect
multicollinearity. Much divergence exists in the literature regarding the acceptable value of
the VIF as the threshold for collinearity. Commonly recommended maximum acceptable
level of VIF in the literature is a value of 10 (Hair et al., 2009); meaning that a VIF equal to
or greater than 10 would suggest the existence of multicollinearity among the variables. The
result of the multicollinearity test in Table 4 indicates that for panel A, POLSTA has the
highest VIF value of 6.62, while for panel B, EXCHANGE has the highest VIF value of 4.56.
VIF values of each variable are lower than recommended value of 10 for both panels. Mean
VIF is 2.80 for the panel of South Asian countries while it is 1.87 for the panel of South-east
Asian countries do not exceed the maximum acceptable value of 10, implying that
multicollinearity problem is not found among the variables. Therefore, none of the variables
needs to be removed from the models.
Table 4: Multicollinearity Test
The unit root is a stochastic trend in the time series that cause unpredictable behaviour and
misleading outcomes (Kennedy, 2003). Therefore, prior to any econometric estimation,
panel-based stationarity tests have been applied in this study to investigate the stationarity of
each variable. Using the Im–Pesaran–Shin (IPS), Levin–Lin–Chu (LLC) and Fisher-type
tests, stationarity test for all variables is reported in Table 5.
Concerning panel unit root tests of South Asia, this study finds that FDIGDP, INFLATION,
EXCHANGE, NRESOURCE, CORRUPTION, POLSTA, UNEMPLOYMENT and TAX are
stationary at level, while GDPCAP, OPENNESS, INFRASTRUCTURE, PRODUCTIVITY
and HUCAPITAL are confirmed to be stationary at I(1). Concerning panel unit root tests of
South-east Asia, this study finds that FDIGDP, INFLATION, EXCHANGE, POLSTA and
UNEMPLOYMENT are stationary at level, whereas NRESOURCE, CORRUPTION, TAX,
INFRASTRUCTURE, PRODUCTIVITY, HUCAPITAL, OPENNESS, GDPCAP are
confirmed to be stationary at I(1).
9
Table5: Panel Unit Root Tests
Significant at *5%, and **10% Level; I(1) indicates integrated at order one
3.2 Model Estimation
In order to evaluate the significant determinants of FDI inflows for the South and South-east
Asia, panel data analysis has been employed. A panel dataset offers several econometric
benefits over traditional time series or cross-section datasets (Hsiao, 1985; Baltagi, 2005). As
described by Ranjan and Agrawal (2011), panel data reduces the risk of obtaining biased
results, improves the problem of multicollinearity and provides a large number of
observations which eventually produce more reliable parameter estimates. The relationship
between the dependent and independent variables in the panel data model is as follows:
FDIGDP = f (market size, trade openness, labour productivity, human capital, level of
infrastructure, inflation rate, exchange rate, political stability, control of corruption, tax rate,
unemployment rate, natural resource endowment)....… (1)
Equation (1) can be changed into a simple linear panel data model form as follows:
FDIGDPi,t = β0+ β1*GDPCAPi,t +β2*OPENNESSi,t +β3*PRODUCTIVITYi,t +
β4*HUCAPITALi,t +β5*INFRASTRUCTUREi,t+β6*EXCHANGEi,t+
β7*INFLATIONi,t+β8*POLSTAi,tβ9*CORRUPTIONi,t+ β10*TAXi,t+
β11*UNEMPLOYMENTi,t +β12*NRESOURCEi,t +εi,t….. (2)
Where irefers to the examined countries, t is the time period from 1996 to 2016, βare the
coefficient, and εis the error term.
Because the examining countries is smaller than the number explanatory variables, therefore,
to uphold the explanatory power of the model, this study introduces different variables
alternatively in different models as applied by Economou et al. (2017), Lucke and Eichler
(2016) and Hong and Bui (2015). According to Helmy (2013), this approach also increases
the explanatory power of the models and the reliability of the results.
10
First of all, this study tests the significance of standard FDI determinants, including market
size, trade openness, exchange rate and inflation rate. After that, the rest of the variables have
been introduced subsequently. The basic model to explore the significant determinants of FDI
for both panels of South Asia and South-east Asia are structured as follows:
FDIGDPit= αi + β1GDPCAPit+ β2OPENNESSit + β3EXCHANGEit+ β4INFLATIONit+ β5Zit+ eit……. (Basic Model)
Here, irefers to the examined countries, t is the time period 1996 to 2016, βare the coefficient,
and eitisthe error term. This study also tests the impact of other Zit variables including
PRODUCTIVITY,
NRESOURCE,
TAX,
HUCAPITAL,
INFRASTRUCTURE,
CORRUPTION, POLSTA and UNEMPLOYMENT alternatively in different models
presented in equation i to viii. Each one of the equations (i - viii) presents an alternative
augmented version of the basic model.
FDIGDPit=
FDIGDPit=
FDIGDPit=
FDIGDPit=
FDIGDPit=
FDIGDPit=
FDIGDPit=
FDIGDPit=
αi + β1GDPCAPit+ β2OPENNESSit + β3EXCHANGEit+ β4INFLATIONit+ β5PRODUCTIVITYit+ eit…….(i)
αi + β1GDPCAPit+ β2OPENNESSit + β3EXCHANGEit+ β4INFLATIONit+ β5NRESOURCEit+ eit…….(ii)
αi + β1GDPCAPit+ β2OPENNESSit + β3EXCHANGEit+ β4INFLATIONit+ β5TAXit+ eit…….(iii)
αi + β1GDPCAPit+ β2OPENNESSit + β3EXCHANGEit+ β4INFLATIONit+ β5HUCAPITALit+ eit…….(iv)
αi + β1GDPCAPit+ β2OPENNESSit + β3EXCHANGEit+ β4INFLATIONit+ β5INFRASTRUCTUREit+ eit…….(v)
αi + β1GDPCAPit+ β2OPENNESSit + β3EXCHANGEit+ β4INFLATIONit+ β5CORRUPTIONit+ eit…….(vi)
αi + β1GDPCAPit+ β2OPENNESSit + β3EXCHANGEit+ β4INFLATIONit+ β5POLSTAit+ eit…….(vii)
αi + β1GDPCAPit+ β2OPENNESSit + β3EXCHANGEit+ β4INFLATIONit+ β5UNEMPLOYMENTit+ eit….(viii)
4 FINDINGS &DISCUSSION
In order to explore the determinants of FDI in the South and South-east Asia, estimates
generated with the random-effects (RE) and the fixed-effects (FE) model, whereas
Hausman’s specification test helped to choose the appropriate panel data model. The
following sections discuss and compare the empirical findings of this study to the existing
literature.
4.1 South Asia
The empirical results for South Asia present in Table 6 show the results of both RE and FE
for every model. In general, the results of the RE and FE are quite similar. However, the
significant difference is that the value of the estimated coefficient is reduced in FE compared
to that of RE. Apart from the basic model, FE is preferred over RE for other models as the
Hausman specification test rejects the null hypothesis. Therefore, for the basic model, results
of the RE is discussed, while for other models results of the FE is discussed.
The coefficient of GDPCAP indicates that the market size of the host country proxied by
GDP per capita is statistically significant in attracting FDI in South Asia. Hence, it can be
argued that market-seeking is an important motive of FDI in South Asian countries. This
finding is in line with many empirical studies such as Aziz and Mishra (2016), Hoang and
Bui (2015), Alam and Shah (2013), Ang (2008), Bhatt (2008), Asiedu (2006) who also found
a positive and significant impact of market size in the FDI inflow of the host country. This
finding supports Dunning's (1993) eclectic paradigm, which states that getting better access
to the host market is one of the primary motives of foreign investors for investing abroad.
The coefficient of OPENNESS indicates that there is a significantand negative
relationship between FDI inflows and trade openness in South Asia. This finding is not
consistent with most of the empirical literature which reported a positive association between
FDI inflows and trade openness (e.g.: Rachdi, Brahim and Guesmi, 2016; Gupta and Singh,
2016; and Asiedu, 2002) but this finding is harmonised with that of Ibrahim and Abdel-Gadir
11
(2015), Koojaroenprasit (2013) and Bhatt (2008) who also found that FDI is negatively
related to the degree of openness.
The coefficient of INFLATION signifies that the relationship between FDI inflows and
the inflation rate is significantand positivein South Asia. The effect of inflation on inward
FDI is also found to be significant by Aziz and Mishra (2016), Gupta and Singh (2016) and
Rachdi, Brahim and Guesmi (2016).
The coefficient of EXCHANGE specifies that there is no significant relationship between
exchange rate and FDI inflows in South Asia. Xaypanya, Rangkakulnuwat and Paweenawat
(2015), Wood et al. (2014) and Zheng (2009) also reported a statistically insignificant
relationship between inward FDI and the exchange rate of the host country.
The coefficient of PRODUCTIVITY in the model (1) indicates that there is a
significantand positive relationship between labour productivity and FDI inflows in
South Asia. Thus, it can be argued that in determining the investment location in South Asia,
foreign investors are highly interested in the labour productivity of the host country. This
finding is consistent with the findings of Hoang and Bui (2015), Kalyoncu, Tuluce and
Yaprak (2015) and Villaverde and Maza (2012) who conclude that foreign investors are
particularly interested in labour productivity.
The coefficient of NRESOURCE in the model (2) indicates that natural resource
endowment is significant and positive to the FDI inflows in South Asia. This relationship
indicates that resource-seeking is an important motive of FDI in South Asian countries. This
finding is consistent with Rjoub et al. (2017), Ibrahim and Abdel-Gadir (2015), Asiedu
(2006) and Onyeiwu and Shrestha (2004).
The coefficient of TAX in the model (3) indicates that the corporatetax rate is negative and
statistically insignificant in attracting FDI inflows in South Asia. This finding is supported
by Onyeiwu and Shrestha (2004), Alam and Shah (2013) and Hunady and Orviska (2014)
who also found no statistically significant effect of corporate taxes on FDI inflow.
The coefficient of HUCAPITAL in the model (4) indicates that human capital is
insignificant in attracting FDI inflows in South Asia, while a positive coefficient signifies
the constructive relationship between human capital and inward FDI. This finding indicates
that human capital in this region is not an essential consideration for the foreign investors.
The coefficient of INFRASTRUCTURE in the model (5) indicates that infrastructure is
positive and significant to the FDI inflow in the context of South Asian countries.
Similarly, Hoang and Bui (2015) and Xaypanya, Rangkakulnuwat and Paweenawat (2015)
for ASEAN countries, Ravinthirakumaran et al. (2015) for Sri Lanka, and Huyen (2015) for
Vietnam observed a significant relationship between FDI inflows and infrastructure
quality.As argued by Hoang and Bui (2015) infrastructure facilities including communication
technology offered by the host country are directly linked to the return on investment; thus,
foreign investors are particularly interested in the infrastructure of the host country.
12
Table 6: Statistical Output- South Asia
The coefficient of CORRUPTION in the model (6) indicates that the control of corruption
is not significant to the FDI inflows in South Asia. Similar to this finding, Wood et al.
(2014), while investigating the determinants of FDI in Africa found no statistical significance
of corruption in FDI inflows, and concludes that corruption does not determine the decisions
of foreign investors.
The coefficient of POLSTA in the model (7) indicates that the political stability of the host
country is insignificant in attracting FDI inflows in South Asia. However, the positive
coefficient of political stability indicates that it will lead to more FDI. This finding is
consistent with the findings of Alam and Shah (2013), Sury (2008) and Bevan and Estrin
(2004) who also found political stability to be an insignificant determinant of FDI inflow in
their respective studies.
The coefficient of UNEMPLOYMENT in the model (8) indicates that the unemployment
rate is positive and significant in attracting FDI inflows in South Asia. This implies that
availability of the labour force is an important consideration for the foreign investor while
selecting FDI destinations in South Asia. This finding is in line with the empirical findings of
Gupta and Singh (2016), Boateng et al. (2015) and Nunnenkamp et al. (2007) who found
unemployment rate to be a significant determinant of FDI inflow.
4.2 South-east Asia
The empirical results for South-east Asia presented in Table 7 shows the results of both RE
and FE for every model. FE is preferred over RE for all models as the Hausman specification
test rejects the null hypothesis. Therefore, the discussion presented in the following sections
reflects the result of the FE for all models.
The coefficient of GDPCAP indicates that the market size of South-east Asia is
statistically significant in attracting FDI. Hence, it can be argued that market-seeking is an
important motive of FDI in South-east Asia. This result is in accordance with the findings of
Tan, Wong and Goh (2018), Hoang and Bui (2015), Xaypanya, Rangkakulnuwat and
13
Paweenawat (2015) and Ismail (2009), who also state that the size of the local market is an
important factor in attracting FDI to South-east Asian countries.
Thecoefficient of OPENNESS is negative and insignificant. This result suggests that trade
openness is not a significant factor in attracting FDI in South-east Asia. This finding is
consistent with the findings of Kumari and Sharma (2017), who for 20 developing countries
from the whole of South, East and South-east Asia found that trade openness, is insignificant
to FDI inflows. Besides, Bhatt (2008) also reports an inverse relationship between FDI and
trade openness of South-east Asia.
The coefficient of INFLATION indicates that the relationship between FDI inflows and
inflation is negative and insignificant in South-east Asia. This finding is also consistent
with the conclusion of Hoang and Bui (2015), who reported that inflation is negative and
statistically insignificant in South-east Asia.
The coefficient of EXCHANGE indicates that there is a significant relationship between
exchange rate and FDI inflows in South-east Asia. This shows that better economic
condition or strong local currency causes inward FDI to surge into South-east Asia. Hadi et
al. (2018), Hoang and Bui (2015), Ismail (2009) and Bhatt (2008) also found a significant
relationship between FDI inflows and exchange rate in South-east Asia.
The coefficient of PRODUCTIVITY in the model (1) indicates that there is a positive and
significant relationship between labour productivity and FDI inflows in South-east Asia.
This finding is consistent with the findings of Hoang and Bui (2015) who state that in
determining the location of investments in the South-east Asian region, foreign investors are
highly interested in the labour productivity of the host country.
The coefficient of NRESOURCE in the model (2) indicates that natural resource
endowment is negative and insignificant to the FDI inflows in South-east Asia. This result
indicates that resource seeking is not an important motive of FDI in South-east Asia. This
finding is in line with the findings of Asiedu (2004), who argued that natural resource
availability does not have a significant impact on FDI.
The coefficient of TAX in the model (3) indicates that the corporatetax rate is statistically
insignificant to attract FDI inflows in South-east Asia. Alam and Shah (2013) and Onyeiwu
and Shrestha (2004) also found no statistically significant relationship between corporate
taxes and FDI inflow. Contrary to this finding, Wood et al. (2014), Koojaroenprasit (2013)
and Ang (2008) reported that corporate taxes have a negative impact on FDI inflow.
The coefficient of HUCAPITAL in the model (4) indicates that human capital is
insignificant in attracting FDI inflows in South-east Asia, while a positive coefficient
indicates a constructive relationship between human capital and inward FDI. This finding
indicates that in determining the investment location in South-east Asia, human capital is not
an important consideration for foreign investors; however, skilled labour of the host country
works as a positive externality to attract FDI inflows. Hoang and Bui (2015) also found a
positive impact of human capital on the inflows of FDI in South-east Asia.
The coefficient of INFRASTRUCTURE in the model (5) indicates that infrastructure is
highly significant to the FDI inflow in the context of South-east Asia. Similarly, Hoang and
Bui (2015) and Xaypanya, Rangkakulnuwat and Paweenawat (2015) observed a significantly
positive relationship between FDI inflows and infrastructure quality for South-east Asia. This
finding indicates that, in determining the location of investments in South-east Asia, foreign
investors are particularly interested in the infrastructure of the host country. Therefore, it can
14
be argued that the policy regarding an improvement in infrastructure facilities can build up
the confidence of foreign investors and then increase FDI inflow into the South-east Asian
region.
The coefficient of CORRUPTION in the model (6) indicates that the control of corruption
is positive and highly significant to the FDI inflows in South-east Asia. The coefficient
value of 1.642 indicates that 1% increase in the control of corruption leads to a 1.642%
increase in FDI inflows. Similar to this finding, Leonardo et al. (2018), Su et al. (2018) and
Dauti (2015) also highlighted the positive impact of the control of corruption on the FDI
inflows.
Table.7: Statistical Output- South-east Asia
The coefficient of POLSTA in the model (7) indicates that the political stability of the host
country is significant in attracting FDI inflows in South-east Asia. The coefficient value of
1.487 indicates a highly significant positive relationship as a 1% increase in the political
stability leads to a 1.487% increase in FDI inflows. Similar to this finding, Tan, Wong and
Goh (2018) also revealed that political stability is an important factor for foreign investors to
decide where to pursue their operations when making an investment decision in the Southeast Asian region. A host country with a high degree of political stability may encourage
foreign investors to invest in the host market because the political stability could maintain the
orderly economic process, which in turn would increase the profitability of the investors.
The coefficient of UNEMPLOYMENT in the model (8) indicates that the unemployment
rate is insignificant in attracting FDI inflows in South-east Asia. The statistically
insignificant relationship between inward FDI and unemployment rate implies that the
availability of the labour is not an important consideration for the foreign investor while
selecting an FDI destination to the South-east Asian countries. Therefore, it can be argued
that foreign investors are particularly interested in labour productivity rather than labour
availability while deciding investment locations in this region. Similar to this, Gupta and
Singh (2016) and Boateng et al. (2015) also found similar result in their respective studies.
15
5. CONCLUSIONS & POLICY IMPLICATIONS
A comparison of the empirical results for South Asia and South-east Asia in Figure 2
indicates that the market size variable is significant and positive for both regions. This
indicates that market seeking is an important motive of the foreign investor investing in these
regions. South Asia and South-east Asia are the two most populous regions in the world.
Although per-capita GDP is still very low in most countries of these regions; however, rapid
economic growth, increasing purchasing power, and long-term market potential have made
both regions attractive to market-seeking FDI. According to the World Investment Report2017, countries with business-friendly environments, a dynamic market size, market position,
and high economic growth continue to attract foreign investors seeking to tap into a major
economy. Therefore, to attract foreign investment, more preferential foreign investment
policies should be offered. More sectors, such as telecommunications, financial and services,
should be opened to FDI. Restriction to various sectors should be lightened to attract further
foreign investment.
Figure 2: Comparison of FDI determinants between South and South-east Asia
The trade openness variable is significant for South Asia but statistically insignificant for
South-east Asia. This may reflect the different strategies pursued by the two regions.
Empirical findings also indicate that human capital is not an important consideration for
foreign investors while investing in either South or South-east Asian countries. This might be
since the majority of human capital in these regions has not reached the minimum threshold
to attract FDI. It is well documented in the literature that a host country needs to have a
certain accumulating level of human capital to enjoy the benefits from FDI spillovers.
Therefore, policymakers of these regions should put the focus on transformational investment
in their people to develop human capital. This will not only upgrade human capital but
promote the country’s competitiveness in the global markets. Besides, the labour productivity
of the host country is a major factor for foreign investors while choosing their FDI location.
Hence policymakers of the respective countries of these regions can increase labour
productivity by improving the quality of education and training, increasing R&D and invest
in new technologies. Such policies not only increase productivity but can directly or
indirectly fuel human capital.
The significance of inflation rate and exchange rate in explaining the distribution of FDI in
South Asia and South-east Asia, indicates that FDI in these regions is much more sensitive to
the macroeconomic stability of the host country. Therefore, it is essential for the host
countries of these regions to maintain macroeconomic stability through monetary policies
while strengthening exchange rate management. Natural resource variable is significant to
South Asia but insignificant to South-east Asia. This might be due to the fact that South
Asian countries are much more enriched with natural resources compared to South-east Asia.
Therefore, unlike South-east Asia, resource seeking is an important motive of the foreign
16
investor investing in South Asia.Foreign investors are not particularly interested in corruption
and the political stability of South Asia, while these factors are highly important to the
investors when making an investment decision in the South-east Asian region. However, it is
necessary for the countries of both regions, to have a low level of corruption and a high
degree of political stability to increase their potential as an investment location.
The infrastructure of the host country is significantly important to foreign investors. This
finding indicates that, in determining the location of investments in both South and Southeast Asia, the foreign investors are concerned about the infrastructure of the host country.
Several countries of these regions are still having difficulties with fragile road conditions,
frequent electricity blackouts, congested roads, and insufficient access to drinking water. A
flimsy infrastructural framework surges the cost of doing business and confines the
attractiveness of the country as an investment location. Therefore, governments of these
regions need to take measures to upgrade infrastructure by increasing government
expenditure on sectors like energy, telecommunication, transport and water.
This study has explored FDI determinants in South and South-east Asia and filled a gap in the
existing literature by providing a comprehensive empirical comparison analysis. The two
panel datasets and appropriate methodology were employed to identify significant
determinants of FDI inflows of these two Asian regions. Although some important findings
are derived from this study, which contribute to the existing knowledge, there are, however,
some limitations to this research. One of the main limitations of this study is the insufficient
data on some potential determinants. Therefore, an extension to this study would be to
consider exploring the significance of some other potential determinants such as labour cost
and interest rate that have overlooked in the statistical analysis of this study.
17
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