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In this increasingly globalized era, foreign direct investments are considered to be one of the most important sources of external financing for all countries. This paper investigates the causal relationship between trade openness and foreign direct investment (FDI) inflows in Romania during the period 1997–2019. Throughout this study, Trade Openness is the main independent variable, and Gross Domestic Product (GDP), Real Effective Exchange Rate (EXR), Inflation (INF), and Education (EDU) act as control variables for investigating the relationships between trade openness (TOP) and FDI inflow in Romania. The Auto Regressive Distributed Lag (ARDL) Bounds test procedure was adopted to achieve the above-mentioned objective. Trade openness has negative and statistically significant long-run and short-run relationships with FDI inflows in Romania throughout the period. Trade openness negatively affects the FDI inflow, which suggest that the higher the level of openness is, the less likely it is that FDI will be attracted in the long run. The result of the Granger causality test indicated that Romania has a unidirectional relationship between trade openness and FDI. It also showed that the direction of causality ran from FDI to trade openness.
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J. Risk Financial Manag. 2021, 14, 90. https://doi.org/10.3390/jrfm14030090 www.mdpi.com/journal/jrfm
Article
Causal Links between Trade Openness and Foreign Direct
Investment in Romania
Malsha Mayoshi Rathnayaka Mudiyanselage, Gheorghe Epuran * and Bianca Tescașiu
Faculty of Economics Sciences and Business Administration, Transilvania University of Brasov,
Universitatii Street, No. 1, Building A, 500068 Brasov, Romania; rathnayaka.mayoshi@unitbv.ro (M.M.R.M.);
bianca.tescasiu@unitbv.ro (B.T.)
* Correspondence: epuran.gheorghe@unitbv.ro
Abstract: In this increasingly globalized era, foreign direct investments are considered to be one of
the most important sources of external financing for all countries. This paper investigates the causal
relationship between trade openness and foreign direct investment (FDI) inflows in Romania dur-
ing the period 1997–2019. Throughout this study, Trade Openness is the main independent varia-
ble, and Gross Domestic Product (GDP), Real Effective Exchange Rate (EXR), Inflation (INF), and
Education (EDU) act as control variables for investigating the relationships between trade open-
ness (TOP) and FDI inflow in Romania. The Auto Regressive Distributed Lag (ARDL) Bounds test
procedure was adopted to achieve the above-mentioned objective. Trade openness has negative
and statistically significant long-run and short-run relationships with FDI inflows in Romania
throughout the period. Trade openness negatively affects the FDI inflow, which suggest that the
higher the level of openness is, the less likely it is that FDI will be attracted in the long run. The
result of the Granger causality test indicated that Romania has a unidirectional relationship be-
tween trade openness and FDI. It also showed that the direction of causality ran from FDI to trade
openness.
Keywords: foreign direct investment; trade openness; ARDL model Romania; panel data analysis
1. Introduction
Foreign direct investment (FDI) inflows play an increasingly strong role in economic
development and progress of countries, and are considered to be one of the major drivers
of globalization. In general, FDI is a crucial component of development of host countries,
and results in capital, external financing, infrastructure, technology, skills and market
access, etc. Most policy makers and economists believe that FDI can positively affect their
countries. In recent years, most emerging and developing countries have implemented
various economic reforms to restructure their economies in order to attract more FDI. In
general, changing global economic situations, policy changes, and political environment
have a crucial impact on foreign direct investment. FDI decisions depend on a variety of
characteristics of the host country, such as market size and potential, exchange rate, trade
openness, political stability or risk, labor costs, trade costs, investment costs, trade deficit,
human capital, tax, inflation, budget deficit, etc.
Many empirical studies have indicated that various aspectssuch as trade openness
and foreign direct investment—might influence a country’s economic development.
There are many definitions concerning trade openness in the literature. Trade openness
is defined as the sum of imports and exports normalized by the gross domestic product.
This is the most common and convenient measurement, and has been used in a variety of
international studies (Adow and Tahmad 2018; Zaman et al. 2018; Ho et al. 2013; Nguyen
and Nguyen 2007).
Citation:
Rathnayaka Mudi-
yanselage
, M.M.; Epuran, G.;
Tescașiu
, B. Causal Links between
Trade Openness and Foreign Direct
Investment in Romania
. J. Risk
Financial Manag.
2021, 14, 90.
https://doi.org/
10.3390/jrfm14030090
Academic Editor
: Shuddhasattwa
Rafiq
Received:
27 January 2021
Accepted:
12 February 2021
Published:
24 February 2021
Publisher’s Note:
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Copyright:
© 2020 by the authors.
Submitted for possible open access
publication
under the terms and
conditions of the Creative Commons
Attribution (CC BY) license
(http://
creativecommons.org/licenses
/by/4.0/).
J. Risk Financial Manag. 2021, 14, 90 2 of 18
Trade openness is useful for observing the exportimport balance of the country,
and is considered to be a key determinant of FDI inflows. Globalization and liberalized
trade policies affect the level of output and economic activity and attract foreign inves-
tors. Therefore, it is important to identify to what degree the trade policies are liberalized.
Many countries have tried to attract more foreign direct investment by making their
economy more open and implementing a variety of progressive policies. The impact of
trade openness on FDI inflow is expected to be mixed. Theoretically, trade openness af-
fects foreign direct investment positively or negatively, depending on the host country’s
trade policies (Liargovas and Skandalis 2012; Ponce 2006). First, the majority of empirical
studies have found a positive relationship between trade openness and FDI inflows, as
demonstrated by the results in studies conducted by Makoni (2018), Sahoo (2006), Janick
and Wunnava (2004), and Zaman et al. (2018). According to these studies, the positive
relationship between trade openness and foreign direct investment indicates that a
country with fewer restrictions on imports and exports has a higher chance of attracting
foreign direct investment. Secondly, some studies have found a negative relationship
between trade openness and FDI inflow (Adow and Tahmad 2018; Cantah et al. 2018;
Khan and Hye 2014). Thirdly, Ho (2013) and Wickramarachchi (2019) found that trade
openness had no significant impact on FDI inflows in BRICS (Brasil, Russia, India, China,
and South Africa) countries.
In line with the theory and earlier empirical papers, we seek to examine the causal
relationship between trade openness and foreign direct investment inflows in Romania
during the period from 1997 to 2019.
At the beginning of this period, between 1990 and 1995, the foreign direct invest-
ment inflows in Romania increased, but, compared to 1994 and 1995, they still remained
low from 1990 to 1993. In 1990, FDI inflows were US$ 0.01 million, increasing, in five
years’ time, to 419 million in 1995. In 1996, there was a slight decline in FDI inflows. After
1996, foreign investments inflow grew, recording higher values of over 1000 million
dollars per year (for instance, they reached US$ 2031 million dollars in 1998). This trend
of FDI growth continued during the period 20022008 (as shown in Figure 1), reaching a
maximum value in 2006, and corresponding to an amount of 13,667 million dollars in
2008, when the global crisis started. The changing global economic situation had a crucial
impact on foreign direct investment, as the global crisis influenced the decision-making
process of investors (Chirilă-Donciu 2013). After the global financial crisis in 2008, FDI
inflows began to decrease until 2011. There was an increase in FDI inflows, reaching an
amount of 3047 million dollars in 2012. However, in comparison with 2008, the level re-
mained low. The latest value of FDI net inflows (BOP, current US$) in Romania was
$6911 million as of 2019.
Figure 1. FDI (foreign direct investment), net inflows (BOPCurrent US$), and FDI inflow (per-
centage of GDP) trends in Romania during the period 1990 to 2019. Source: World Bank, World
Development Indicators (2020).
J. Risk Financial Manag. 2021, 14, 90 3 of 18
When consider FDI inflow as a percentage of GDP, and for that indicator, we pro-
vide data for Romania from 1990 to 2019. The minimum value during that period was
0.137 percent in 1991, and a maximum of 6.377 percent in 2008. The latest value from 2019
is 2.764 percent (see Figure 2).
Figure 2. Trade openness in Romania during the period 1990 to 2019. Source: World Bank, World
Development Indicators (2020).
According to the trade openness data, the maximum trade openness value was 0.86
in 2018, and the minimum trade openness value is 0.39 in 1991. The latest value from 2019
is 0.84. However, trade openness had a dynamic trend during the period.
Based on the empirical and theoretical evidence, this paper investigates the causal
relationship between trade openness and foreign direct investment inflows in the
short-run and long-run using the data over the period of 19972019, and makes some
policy suggestions as to how the government could improve this investment area of the
economy. Throughout this study, Gross Domestic Product (GDP), Real Effective Ex-
change Rate (EXR), Inflation (INF), and Education (EDU) act as the control variables, for
investigating the relationships between trade openness (TOP) and FDI inflow in Roma-
nia.
The paper is organized as follows: In the first part, a review of the literature field
gives empirical evidence of earlier studies on the determinants of foreign direct invest-
ment. Next, the research materials and methodology are presented, respectively: The
functional form of the theoretical model, data, and econometric methodology.
2. Literature Review
The relationship between trade openness and foreign direct investments inflow has
been empirically examined in various regions of the world. Some of the conclusions re-
vealed by the scientific research are useful as theoretical and practical premises of the
current study (Ghosh 2007; Güriş and Gözgör 2015; Koojaroenprasit 2012; Musyoka and
Orcharo 2018; Sjöholm 2016). Regional analysis revealed the fact that a series of statistical
and econometrical models could be used in order to establish possible connections be-
tween the above mentioned variables (Shah 2014; Stone and Jeon 2000; Trinh and Nguyen
2015; Yo et al. 2019).
For instance, a series of studies regarding the influence of trade openness on FDI in a
group of selected Asian countriesIndia, Iran, and Pakistanover the time of 1982 to
2012 were conducted (Zaman et al. 2018; Patsupathi and Sakthi 2019). The fixed effect
and Pooled OLS techniques employed of panel data were used for measuring individual
country effect, group effect, and time effect. The results indicated that exchange rate and
inflation were used as a proxy for macroeconomic stability and GDP per capita variables
had a statistically significant impact on FDI inflow. The authors have found that high
trade openness causes the increase in FDI inflows on both levels—global and national. As
0
0.2
0.4
0.6
0.8
1
Trade Openness
Year
J. Risk Financial Manag. 2021, 14, 90 4 of 18
a conclusion, trade openness would be a better option for sustained foreign direct in-
vestment inflows in long-run.
Alshamsi et al. (2015) examined the impact of inflation rate and GDP per capita on
inward foreign direct investment inflows in United Emirates over the time of 1980 to
2013. They used the ARDL (Auto Regressive Distributed Lag) model, and they found that
GDP per capita had a positive and statistically significant impact on FDI inflows, while
inflation rate did not have the expected sign and it was not statistically significant both in
long-run and short-run. They suggested that more variables in future researches—such
as infrastructure, political stability, country risk, and country opennesswill provide a
better model to examine the impact of inflation rate and GDP per capita on FDI inflows
(Mohamed and Sidiropoulos 2010).
Ho (2013) have examined the relationship between trade openness, market size, and
other fundamentals on FDI in fast emerging six countries including Brazil, China, India,
Russia, South Africa, and Malaysia from 1977 to 2010. The study is based on two econo-
metrics models as macroeconomics factors on FDI inflow and country specific factors on
FDI inflow. Market size (GDP), trade openness, financial development, exchange rate,
interest rate, government consumption, and inflation rate were considered macroeco-
nomic factors with impact on FDI inflows, while the considered country factors were:
Economic freedom, wages, human capital, and infrastructure quality. The results for both
models indicated that market size (GDP), interest rate, literacy rate, economic freedom,
and infrastructure quality had impacts on FDI in the majority of BRICS countries and
Malaysia. GDP had positive and statistically significant impacts on FDI in Russia, China,
and Malaysia (Fazekas 2016). Trade openness had only a statistically significant impact
on FDI in Malaysia, and no impact on other emerging countries (Karimi and Yusop 2009;
Sazali et al. 2018).
Asiamah et al. (2018) employed a Johansen’s approach to co-integration test and
vector autoregressive model to study the determinants of FDI inflow in Ghana for the
period of 19902015. The regression model reported FDI stock as the dependent variable,
and independent variables were: Inflation rate, interest rate, real effective exchange rate
and real gross domestic product rate, electricity production, and telephone usage. The
results indicated that inflation rate, exchange rate, and interest rate had statistically sig-
nificant negative effects on FDI in Ghana, while gross domestic product, electricity pro-
duction, and TU had a positive effect on FDI both in the long-run and short-run. Fur-
thermore, the Granger causality test results indicated that there was a bi-directional
causality between electricity production, telephone usage, and FDI. However, inflation
rate, interest rate, exchange rate, GDP, and FDI has unidirectional causality.
To examine the determinants of foreign direct investment inflow in Sri Lanka, Wi-
jeweera and Mounter (2008) used the vector autoregressive methods (VAR) since its
economic reform (1977). The regression model was built using six variables, namely for-
eign direct investment inflows (FDI) such as dependent variable and market size and
performance (RGDP), an openness indicator (TRADE), a labor cost indicator (WAGE),
the exchange rate (EXR), and the interest rate (IR) as independent variables. The study
found that real GDP used as a market size had a positive and statistically significant
impact on FDI inflows in long-run. Wage rate had a strong negative impact on FDI in-
flows and influenced in a positive way the relationships between host country interest
rates and FDI inflows. Trade openness also shows positive and statistically significant
impact on inward FDI long-run. Finally, the study suggested giving more consideration
on GDP, exchange rates, interest rates, and the level of external trade to attract FDI in-
flows in Sri Lanka, in line with Ravinthirakumaran et al. (2015).
Uduak et al. (2014) examined the determinants of foreign direct investment in Brazil,
Russia, India, China, and South Africa (BRICS) and Mexico, Indonesia, Nigeria, and
Turkey (MINT) using pooled time-series cross sectional analysis and random effect
model over the period 20012011. The analysis considered FDI net inflow as the de-
pendent variable and the considered independent variables were gross domestic prod-
J. Risk Financial Manag. 2021, 14, 90 5 of 18
uct, the share of natural resources in GDP, infrastructure, inflation, trade openness, and
institutional-related indicators. The results showed that market size, infrastructure
availability, and trade openness had significant roles in attracting FDI to BRICS and
MINT, while the roles of availability of natural resources and institutional quality had an
insignificant impact on FDI in long-run.
A study by Demirhan and Masca (2008) examined the determinants of foreign direct
investment (FDI) inflows in 38 developing countries over the period of 20002004 using
panel data analysis. In the model, the dependent variable was FDI net inflows, as a per-
centage of GDP, and the independent variables were: Growth rate of per capita GDP, in-
flation rate, telephone main lines per 1000 people measured in logs, labor cost per worker
in manufacturing industry measured in logs, degree of openness, risk, and corporate top
tax rate. The results indicated that growth rate of per capita, telephone main lines, and
degree of openness had a positive and statistically significant relationship with FDI net
inflows. Inflation rate and tax rate presented negative and statistically significant rela-
tionships with FDI net inflows. However, labor cost has a positive sign and risk has a
negative sign, in line with previous studies. Both variables do not influence FDI net in-
flows, implying that labor cost and risk variable have not been important factors in at-
tracting FDI.
To identify the key determinants of FDI inflows in Sri Lanka during the period from
1970 to 2014, Wickramarachchi (2019) conducted research based on a supply–demand
framework using ARDL method. FDI to private investment acted as the dependent var-
iable, and independent variables were the ratio of FDI flows to developing countries, real
gross domestic product, trade openness, real wage index, and real effective exchange
rate. Political stability is included as a dummy variable and regime changes of FDI policy
captured in three period as 19772000, 20012008, and 20092014. Results indicated that
trade openness and real wage index did not have any significant relationship with FDI
inflow in Sri Lanka. Real gross domestic product was an insignificant factor in deter-
mining FDI inflow in long-run. This result is in line with Athukorala (2003). FDI inflows
to Sri Lanka have been export-oriented type instead of market seeking type, proving the
results. Real effective exchange rate variable had a negative and statistically significant
impact. It indicated that appreciation of the real effective exchange rate reduced the FDI
inflow in Sri Lanka. FDI inflow had a positive and significant impact on the political sta-
bility. All three dummy variables for regime changes of FDI policy are positive and had
significant impacts on FDI inflow, and the results indicated that open economic policies
were successful in attracting FDI compared to closed economic period. Finally, the study
suggested to remove and reduce the existing barriers, policy uncertainties, and ineffi-
ciencies to attract more FDI inflows in Sri Lanka.
A study by Muzurura (2016) examined the determinants of FDI inflow in Zimbabwe
over the period 1980 to 2011. Foreign direct investment acted as an endogenous variable
and the independent variables were external debt, gross fixed capital formation, gross
fixed capital expenditure, gross domestic product, trade openness, and inflation rate. The
results indicated that gross fixed capital formation and trade openness had a positive and
statistically significant relationship with FDI inflow in long-run. Inflation rate was found
to be negative and had a significant impact on FDI inflow. Moreover, the empirical re-
sults did not confirm lagged GDP, external debt, government expenditure, and lagged
exports as major determinants of FDI inflow in short-run, as other studies revealed
(Kandiero and Chitiga 2003; Kariuki 2015).
Quazi (2007) examined the determinants of FDI, the relationship between FDI and
economic freedom, and analyzed the investment climate from foreign firms’ perspective
using panel data from nine countries over 19952004. Lagged changes in FDI, market
size, economic freedom, human capital, infrastructure, trade openness, and return on
investment acted as independent variables in the model. The results indicated that,
among the explanatory variables, incremental lagged changes in FDI, economic freedom,
infrastructure, return on investment, and trade openness turned out highly significant
J. Risk Financial Manag. 2021, 14, 90 6 of 18
with the expected signs. However, market size and human capital variables did not have
a statistically significant impact on FDI.
For analyzing the effect of trade openness of foreign direct investment in African
countries, Makoni (2018) selected nine African countries, over the period 20092016. The
ratio of net FDI inflows to GDP was considered as the dependent variable, and the in-
dependent variables were the log of FDI to GDP and trade openness, real exchange rate,
macroeconomic stability proxy as real economic growth, natural resources endowment,
infrastructure, and capital openness. The study employed various econometric tech-
niques such the pooled OLS, Least Squares Dummy Variable (LSDV), Fixed Effects (FE)
model, Random Effects (RE) model, Generalized Method of Moments (GMM) model, and
the Generalized Least Squares (GLS). According to the results of the random effects
model, foreign direct investment was positively related with trade openness. Real ex-
change rate variables had a positive and statistically significant impact on FDI inflow
while capital openness was positive, but insignificant. The study suggests adopting in-
vestment and macroeconomic policies.
Shah and Khan (2016) assessed the impact of trade liberalization on inward FDIs in
six emerging countries, namely Brazil, China, India, Mexico, the Russian Federation, and
Turkey, for the time period of 1996 to 2014 using the random effects model. The inde-
pendent variablestotal population, GDP per capita, total trade, primary education,
preferential trade agreements, and regional trade agreementsare used as proxies for
market size, development level, openness, human capital, and trade liberalization. As
results, market size, and human capital have a positive and significant impact on FDI in-
flows, while trade and regional trade agreements prove to be insignificant, but preferen-
tial trade agreement positively impacts on FDI inflow.
Trade openness contributes positively to the inflow of FDI in developing economies
in the long-run (Liargovas and Skandalis 2012). They used a sample of 36 developing
economies for the period 19902008. It provided a direct test of causality between FDI
inflows, trade openness, and other key variables in developing regions of the world.
Further, the study indicated that there are some other factors such as political stability,
exchange rate stability, and market size with positive influence to the existence of FDI
inflows.
To examine the macro determinants of FDI inflow to Japan during the period 1989 to
2002 used the panel data analysis method (Kimino et al. 2007). FDI inflows from 17
countries to Japan were considered as the dependent variable, and the independent var-
iables were GDP, export performance of source countries, relative bilateral exchange rate,
borrowing cost differentials, relative labor cost, and country risk rating. According to the
results, the effects of market size, exchange rates, and labor costs factors have a statisti-
cally unimportant effect on FDI to Japan. The export performance of the source country
was found to have a negative impact on FDI.
Sabir et al. (2019) have examined the impact of institutional quality on Foreign Di-
rect Investment inflows using panel data for low, lower middle, upper-middle, and
high-income countries for the sample period of 19962016. The study was based on the
system Generalized Method. Inflation, trade openness, mobile phone subscriptions per
100 people, GDP per capita, and value-added share of agriculture as a percentage of GDP
independent variables acted as control variables to find the impact of institutional quality
on Foreign Direct Investment inflows. The results indicated that institutional quality had
a positive impact on foreign direct investment in all groups of countries. Control of cor-
ruption, government effectiveness, political stability and regulatory quality, rule of law,
and voice and accountability for FDI inflows were higher in developed countries than in
developing countries. GDP per capita, agriculture value-added as a percentage of GDP,
and inflation variables had negative influence on FDI inflows in developed countries,
while GDP per capita, trade openness, agriculture value-added as a percentage of GDP,
and infrastructure had a positive and statistically significant impacts on FDI inflows in
developing countries. At the same time, other studies (Appiah-Kubi et al. 2020) revealed
J. Risk Financial Manag. 2021, 14, 90 7 of 18
that regarding FDI and the companies created in African countries as a result of this kind
of investment, there is a positive connection between the efficiency of corporate admin-
istration and the degree of FDI and a negative connection between the level of FDI and
securities standard regulation.
Mugableha (2014) examined the determinants of foreign direct investment inflows
in Malaysia using an unconditional error correction approach over the period 19772012.
Broadest money supply, consumer price index, exchange rates, gross domestic product,
and trade were considered as the determinants of FDI inflow in the model, ARDL ap-
proach. For the results, exchange rates, gross domestic product, broadest money supply,
and trade had a significant impact on FDI inflows in Malaysia, while consumer price in-
dex had a negative impact on FDI inflow.
A study of Musabeh and Zouaoui (2020) investigated the determinants of FDI in-
flows and impact of FDI-policies adopted by the host countries in North Africa, namely
Algeria, Egypt, Libya, Morocco, and Tunisia over the period 19962013. The independent
variables have been categorized into different classifications as economic variables, in-
stitutional variables, and political variables, with two kinds of investment policies. In-
vestment agreement, investment freedom, market size, trade openness, natural re-
sources, gross fixed capital formation, infrastructure, inflation, exchange rate stability,
corruption perception index, regulation, and Political Constraints Index were the inde-
pendent variables in the model. The results indicated that the trade openness had a posi-
tive and statistically significant relationship with FDI inflows growth. However, the
natural resources and market size variables had a negative and insignificant relationship
with change of FDI inflows in North African countries. Other studies (Appiah-Kubi et al.
2020) revealed the fact that foreign investors should consider not only the elements of
macroeconomic environment, but also the governance systems.
To examine the influential factors on FDI inflow in the South Asian Association for
Regional Cooperation Countries (SAARC) and their impact on economic growth over the
period 1980 to 2018, a study considering a series factors with potential influence on FDI
flow was conducted (Gunawardhana and Damayanthi 2019). GDP per capita, inflation,
money and quasi money (M2), trade openness, current account balance, telephone lines,
and time to export variables were considered as influential factors on FDI inflow. The
results indicated that the market size of a country as per GDP per capita growth, current
account balance, financial deepening (Money and quasi money (M2)) and trade openness
significantly influence FDI flows into the South Asian region. However, the coefficient of
INF variable was positive, but insignificant at any significant level. This indicates that
inflation cannot significantly explain the variation in FDI inflow throughout the years in
the region. Furthermore, infrastructure and other qualitative variables also showed sig-
nificant influence on FDI flows. They found trade and FDI had a bidirectional Granger
causality as the results of the causality for seven countries in the SAARC region.
Hintošová et al. (2018) examined the determinants of foreign direct investment in-
flows into Visegrad countries namely, Poland, Hungary, Czech Republic, and Slovak
Republic, from 1989 to 2016 using OLS and Fixed effect model. Market size, labor cost,
trade openness, economic stability, innovation, and taxation variables were considered as
the independent variables of the model. The results indicated that GDP per capita, infla-
tion rate, unemployment rate, and the innovation variables did not have any significant
relationship to FDI inflows in the case of the Visegrad countries. The level of gross wages
and the share of labor force which achieved at least secondary education variables that
had a positive and statistically significant effect on FDI inflows. Those variables were the
most significant determinants of FDI inflows. Moreover, the results indicated that cor-
porate income tax rate, trade openness, and expenditures on research and development
had negative impact on FDI. This study concluded that the four countries put emphasis
on the investment aid in the form of tax reliefs, or the other investment incentives, rather
than basic macroeconomic variables in the process of FDI attraction.
J. Risk Financial Manag. 2021, 14, 90 8 of 18
Ranjan and Agrawal (2011) used the panel data analysis to examine the determi-
nants of FDI inflow in BRIC countries namely: Brazil, Russia Federation, India, and China
from 1975 to 2009. Market size, economic stability and growth prospects, labor cost, in-
frastructure facilities, trade openness, total labor force, and gross capital formation acted
as the independent variables of the model. The empirical results indicated that market
size, trade openness, labor cost, infrastructure facilities, and macroeconomic stability and
growth prospects were potential determinants of FDI inflow in BRIC. However, gross
capital formation and labor force variables were not statistically significant. It indicated
that gross capital formation and labor cost cannot significantly explain the variation in
FDI inflow throughout the years.
To investigate the determinants of net FDI inflows to Africa over the period
19761996, Anyanwu and Erhijakpor (2004) conducted the research under the topic on
trends and determinants of foreign direct investment in Africa using a pooled regression
approach. The independent variables of the model are: Credit to private sector, export
processing zone, political rights and civil liberties, GDP annual growth rate, inflation
rate, financial deepening (M2/GDP), tax on income profit, international trade, telephone
mainlines per 1000 people, total debt, trade as percentage of GDP, exchange rate volatil-
ity, region (Southern Africa, West Africa, Central Africa, North Africa, and East Africa).
Region is a binary variable representing the various regions of Africa. The results indi-
cated that credit to private sector, export processing zone, and capital gain tax variables
had a negative and statistically significant impact on FDI inflow; GDP annual growth rate
and infrastructure represented by the number of telephones per 1000 people variables
had a positive and statistically significant impact on FDI inflow. However, none of the
other variables, civil and political rights, inflation rate, financial depth, trade tax, debt
service ratio, and exchange rate volatility had significant effect on net FDI inflows to Af-
rica. Finally, they suggest developing infrastructure facilities in African countries to at-
tract more foreign investors.
Seyoum et al. (2014) examined the Granger causality relations between foreign di-
rect investment and trade openness in Sub-Saharan economies using Panel data for 25
sub-Saharan African economies over the period 19772009. The results indicated that a
bidirectional causality relation was identified between trade openness and foreign direct
investment in Sub-Saharan economies. Finally, they suggested that the African countries
should expand their productive capacity to produce and export to promote and attract
FDI.
The causal relationships between FDI and international trade in India and China
(Sharma and Kaur 2013) applied Granger causality tests. Secondary data were applied
from 1976 to 2011 for the study. They found that there was unidirectional causality run-
ning from FDI to imports and FDI to exports, and bidirectional causality existed between
imports and exports in China. The results were different from the results of India,
whereby bidirectional causality existed between FDI and imports; FDI and exports; and
exports and imports. India has shown a dynamic relationship.
To examine the impact of economic and non-economic factors on FDI net inflow in
Sri Lanka, Vijesandiran and Vinayagathasan (2020) conducted a study using ARDL
Bounds test procedure over the period of 19962017. Treasury bill rate, consumer price
index real gross domestic product, exchange rate, corporate tax, labor cost, and trade
openness acted as economic factors, and political instability, the existence of violence or
terrorism, and control of corruption acted as non-economic factors. According to the
long-run equation results that indicated market size proxies as (GDP), depreciation of
domestic currency, interest rate, and wage rate have a positive impact on FDI inflow in
the long-run, whereas inflation rate, corporate tax, trade openness, political instability,
and corruption variables have a negative impact on FDI inflow in the long-run. Accord-
ing to the short-run results, none of the economic and non-economic factors has statisti-
cally significant impact on FDI inflow.
J. Risk Financial Manag. 2021, 14, 90 9 of 18
3. Materials and Methods
This study used annual time series data for the period 1997 to 2019. Data were col-
lected from the World Development Indicator published by the World Bank 2020. This
study adopted the Zaman et al. (2018) theoretical framework to find out the relationship
between the FDI inflow and trade openness. Furthermore, we developed the model
adding an indicator of education in Romania.
The functional form of the theoretical model of this study is drawn as:
FDI = f(LGDP,EXR,TOP,INF,EDU) (1
)
where,
FDI = Per capita Foreign Direct Investment Inflows (Current US$)
LGDP = Log of Gross Domestic Product (Current US$)
EXR = Real Effective Exchange rate Index (2010=100)
TOP = Trade Openness
INF = Inflation, Consumer Prices (annual %)
EDU = Labor force with advanced education (% of total working-age population with
advanced education)
The above functional form can be specified in the following econometric model:
FDIt= β0+ β1LGDP
t+ β2EXRt+ β3 TOP
t +β4 INFt +β5 EDUt +εt
(2)
where, 0 to 5 are the slope coefficients is the white noise error term, and the sub-
script t indicates time.
In order to make the model and variables free from problems associated with time
series data, we used Augmented Dickey-Fuller (ADF) and PP unit root test approaches to
test stationary of the variables. Moreover, diagnostic tests were conducted to check
whether the results are robust. The tests conducted are Breusch-Godfrey Serial Correla-
tion LM Test to detect serial correlation among residuals, Ramsey’s reset test to check
whether the model is specified correctly, test of skewness, and Kurtosis test to check
whether the residuals are normally distributed to detect heteroscedasticity in the model.
The study employs Cumulative sum of recursive residuals (CUSUM) and cumulative
sum of square of recursive residuals (CUSUM of squares) to check the stability of the
model. E-view 10 software was used to analyze the data.
Auto Regressive Distributed Lag (ARDL) co-integration procedure, developed by
Pesaran et al. (2001), was employed to empirically examine Equation (2).
An ARDL representation of Equation (2) is formulated as follows:
FDI = β0+ β1 LFDIt−1 + β2LGDP
t−1 + β3EXRt−1 + β4TOP
t−1 + β5INFt−1 +β6EDUt−1 + � γ1i
q1
i=1 FDIt−i
+� γ2i
q2
i=0 LGDP
t−i +� γ3i
q3
i=0 EXRt−i +� γ4i
q4
i=0 TOP
t−i +� γ5i
q5
i=0 INFt−i
+� γ6i
q6
i=0 EDUt−i + et
(3)
where, Δ denotes the first difference operator, 0 is the drift component, is the usual
white noise error term, 2 β6) correspond to the long-run relationship, the remaining
expressions with the summation sign (1 → 6) represent the short-run dynamic of the
model.
The first step of the estimation bound testing procedure is employed in order to in-
vestigate the existence of long-run relationship the bound tests approach developed by
Pesaran et al. (2001). He has been provided the two sets of critical values in which lower
critical bound assumes that all the variables in the ARDL model are I(0), and the upper
critical bound assumes I(1). If the calculated F-statistic is greater than the appropriate
upper bound critical values, the null hypothesis is rejected, implying co-integration. If
such statistic is below the lower bound, the null cannot be rejected, indicating the lack of
J. Risk Financial Manag. 2021, 14, 90 10 of 18
co-integration. If, however, it lies within the lower and upper bounds, the results are in-
conclusive. After establishing the evidence of the existence of the co-integration between
variables, the lag orders of the variables are chosen by using the appropriate Akaike In-
formation Criteria (AIC).
In the next step of the estimation procedure, we obtain the short run dynamics of
parameters and long run adjustment of the model by estimating the error correction ver-
sion of the ARDL model pertaining to the variables in Equation (4) is as follows:
FDI = α0+� α1i
q1
i=1 FDIt−i +� α2i
q2
i=0 LGDP
t−i +� α3i
q3
i=0 EXRt−i +� α4i
q4
i=0 TOP
t−i +� α5i
q5
i=0 INFt−i
+� α6i
q6
i=0 EDUt−i +γETCt−1 +μt
(4)
where, γ is speed of adjustment coefficient, and μt is pure random error term. However,
in order to estimate the ARDL bound testing technique, first we need to confirm the order
of integration of each series, which can be tested by Augmented Dickey-Fuller (ADF) and
Phillips-Perron (PP) unit root test approaches. Then the optimum lag length that can be
included in the model is selected from the AIC, SC, LR, FPE, and HQIC criterions.
The justification for the important variables used in this study is given below based
on reviewing the existing theoretical and empirical studies:
3.1. Foreign Direct Investment (FDI)
For the purpose of this research, the FDI inflows of Romania were used as the de-
pendent variable. In order to examine the impact those regressors selected for this study
have on the dependent variable (FDI) individually.
FDI (Per capita FDI)= FDI inflows (Current US$)
Total Population
(5)
FDI inflows are measured in current U.S. dollars divided by the host country’s total
population.
3.2. Market Size
Market size and growth is considered as one of the most important determinants of
foreign direct investment. Gross Domestic Product (GDP), GNP, GDP per capita income,
GDP growth and size of the population, etc., variables are generally used to measures
market size. The most of empirical studies are indicated that a large domestic market
tends to attract more FDI, as they pose significant advantages in production and con-
sumption. Investors normally prefer to invest to countries where market size is large
compared to countries with low market size. Market size has a positive impact in di-
recting inward FDI to host countries according to Alshamsi et al. (2015), Ho (2013),
Asiamah et al. (2018), Wijeweera and Mounter (2008), and Zaman et al. (2018), while
Wickramarachchi (2019), Quazi (2007), Muzurura (2016), and Musabeh and Zouaoui
(2020) observed an insignificant effect.
3.3. Exchange Rate
Many empirical studies have highlighted the relationship between exchange rate
and FDI inflows. Exchange rate can affect various ways on the inward foreign direct in-
vestment. Some studies have indicated FDI has a positive relationship with the exchange
rate, some with the negative relationship, while others showed an insignificant relation-
ship. Many empirical studies applied different measures for exchange rate including
nominal, real, volatility, and trade-weighted index. Exchange rate has a negative influ-
ence in directing inward FDI to host countries for the example studies (Asiamah et al.
2018). Exchange rate has a positive and significant influence on FDI inflow, for example,
J. Risk Financial Manag. 2021, 14, 90 11 of 18
Liargovas and Skandalis (2012), Makoni (2018). Kimino et al. (2007) found that there is no
significant impact of exchange rate on FDI inflow.
3.4. Trade Openness
Many countries have tried to attract more foreign direct investment by making their
economy more open and implement a number of progressive policies. The impact of
trade openness on FDI inflow is expected to be mixed. The majority of empirical studies
have found a positive relationship between trade openness and FDI inflows (Makoni
2018; Sahoo 2006; Zaman et al. 2018), while some studies have found a negative rela-
tionship between trade openness and FDI inflow (Adow and Tahmad 2018; Cantah et al.
2018; Khan and Hye 2014). On the other hand, Ho (2013) and Wickramarachchi (2019)
found that there is no significant impact of trade openness on FDI inflows in BRICS
countries.
We formulated the trade openness data from the summation of import and export
and divided it by gross domestic product.
(TOP)Trade Openness =IM +EXP
GDP × 100
(6)
IMRImport Good and Services (Current US$), EXP—Export Good and Services
(Current US$), GDPGross Domestic Product (Current US$).
3.5. Inflation Rate
Inflation rate represents the changes in the general price level. In many empirical
studies, inflation rate is used as a proxy for macroeconomic stability. This has been
widely acknowledged as one of the key influential factors of the flow of foreign direct
investment into the host country. High inflation reduces investment in productive en-
terprises, thus reducing economic growth. Consumer Price Index and Wholesale Price
Index measure inflation rate. The majority of empirical studies found a negative rela-
tionship between inflation rate and FDI inflows (Asiamah et al. 2018; Mugableha 2014;
Quazi 2007), while other studies (Anyanwu and Erhijakpor 2004; Alshamsi et al. 2015;
Hintošová et al. 2018; Gunawardhana and Damayanthi 2019) found that there is no sig-
nificant impact of inflation rate on FDI inflow.
3.6. Education
Foreign investors are more concerned with the quality of the labor force. The quality
of labor force can help to the cost minimization objectives. They are more likely to invest
to locations where there are quality of human capital resources. Labor that is more edu-
cated makes the learning and adoption of new technology easy and faster. Therefore, the
effect of quality of labor on FDI could be positive. Data on labor force with advanced
education (percentage of total working-age population with advanced education) is a
proxy for human capital.
4. Discussion
The first steps of the estimation procedure, employs Augmented Dickey-Fuller
(ADF) and Phillips-Perron (PP) unit root tests to check the stationary. The results of these
tests are presented in Table 1 below.
J. Risk Financial Manag. 2021, 14, 90 12 of 18
Table 1. Results of unit root test.
Variable
ADF Test Statistics
(with Trend and Intercept)
PP Test Statistics
(with Trend and Intercept)
Level
Level
First Dif-
ference
FDI
1.9978
I(1)
2.1558
5.0132 *
I(1)
LGDP
1.5094
I(I)
1.2470
3.0226 ***
I(I)
EXR
2.4195
I(1)
2.4293
6.1709 *
I(1)
TOP
3.8861 **
I(0) I(1)
3.9407 **
6.1917 *
I(0) I(1)
INF
12.6896 *
I(0) I(1)
10.1168 *
21.8679 *
I(0) I(1)
EDU
2.7926
I(1)
2.815735
5.2833 *
I(1)
Note: *, **, *** show significant at 1%, 5% and 10% level respectively. Source: Researcher’s calcula-
tion using E-Views 10. ADF: Augmented Dickey-Fuller; PP: Phillips-Perron.
The results indicated that the null hypothesis of series contains a unit root cannot be
rejected at levels for all variables except TOP and INF in ADF and PP unit root ap-
proaches. Nevertheless, this null hypothesis can be rejected when those variables are
transformed into first difference forms. This reveals that GDP and INF are integrated in
order zero [I (0)], while all other series are integrated in order one [I(1)]. It means the data
are of mixed type of I (0) and I (I) underlying regressors and therefore, the ARDL testing
could be proceeded with. Akaike Information Criteria (AIC) advocated using the ARDL
(1, 1, 1, 1, 0, 1) model for this analysis (Figure 3).
Figure 3. Results of optimum lag length of each variable (AIC). Source: Researcher’s calculation
using E-Views 10.
The diagnostic tests confirm that the models have the desired econometric proper-
ties (see Table 2 below). According to the Lagrange Multiplier test of serial correlation
between the error terms suggests that the residuals are not serially correlated since we
failed to reject the null hypothesis of no serial correlation in the residual, as probability
value is greater than the 5% level of significance. The Jarque-Bera test has indicated that,
the null hypothesis of normally distributed residuals cannot be rejected, as probability
value is higher than 5% level of significance, which means error is normally distributed.
Breusch-Pagan-Godfrey test of heteroscedasticity detected that the disturbance term in
the equation is homoscedastic, as we failed to reject the null hypothesis since the proba-
11.2
11.4
11.6
11.8
12.0
12.2
12.4
12.6
12.8
ARDL(1, 1, 1, 1, 0, 1)
ARDL(1, 1, 1, 1, 0, 0)
ARDL(1, 1, 1, 1, 1, 1)
ARDL(1, 1, 1, 0, 0, 0)
ARDL(1, 1, 1, 1, 1, 0)
ARDL(1, 1, 1, 0, 0, 1)
ARDL(1, 1, 1, 0, 1, 0)
ARDL(1, 1, 1, 0, 1, 1)
ARDL(1, 1, 0, 0, 0, 0)
ARDL(1, 1, 0, 0, 1, 0)
ARDL(1, 1, 0, 0, 0, 1)
ARDL(1, 1, 0, 1, 0, 0)
ARDL(1, 1, 0, 0, 1, 1)
ARDL(1, 1, 0, 1, 1, 0)
ARDL(1, 1, 0, 1, 0, 1)
ARDL(1, 1, 0, 1, 1, 1)
ARDL(1, 0, 0, 0, 0, 0)
ARDL(1, 0, 0, 0, 0, 1)
ARDL(1, 0, 0, 0, 1, 0)
ARDL(1, 0, 0, 1, 0, 0)
Akaike Information Criteria (top 20 models)
J. Risk Financial Manag. 2021, 14, 90 13 of 18
bility value exceed the 5% significance level. Finally, the Ramsey RESET test result con-
firms that there is no specification error in the estimated model.
The model is free from serial correlation and heteroskedasticity. Moreover, the
functional form is correct and stochastic residuals are normally distributed. The esti-
mated model satisfies all indispensable diagnostic tests.
Table 2. Diagnostic test.
Items Test Applied
Probability
Value
Serial correlation
Breusch-Godfrey Serial Correlation LM Test
0.3733
Normality
Normality Test (Jargu- Bera)
0.5958
Heteroscedasticity
Breusch-Pagan-Godfrey
0.9981
Functional Form
Ramsey’s reset test
0.2095
Source: Researcher’s calculation using E-Views 10.
The main characteristic of the model parameters is their sustainability in the long
run. Thus, stability of the model parameters are confirmed by “CUSUM” and “CUSUM
of squares” tests. Parameters stability is identified during all the analyzed periods. Not-
ing that we built a null hypothesis of a model that is not stable, the results of the test are
given below (Figure 4).
Figure 4. The Results of stability test for Auto Regressive Distributed Lag (ARDL). Source: Re-
searcher’s calculation using E-Views 10.
The graphs of CUSUM and CUSUM of squares test confirms that the model is stable
since the residual plot lies between the lower and upper critical bounds at the 5% level of
significance. That is, the selected model has stable parameters which can be used for
long-term forecasts.
In Table 3, calculated F-statistic = 7.1669 is higher than the upper bound critical
value at 5% level of significance (3.38). Since we confirmed the co-integrating relationship
between the variables through the Bounds test, we estimated the long-run relationship
among the variables via the ARDL model. There is strong evidence to support the exist-
ence of a long run association between foreign direct investment inflows and its deter-
minants. Hence, now we estimate the model further in order to confirm whether there
exists long run relationship between the variables under this study.
Table 3. F -Test for the existence of a long-run relationship (F-Bounds test).
F-Bounds Test
Null Hypothesis: No Levels Relationship
Test Statistic
Value
Significant
I(0)
I(1)
F-statistic
7.1669
10%
2.08
3
K
5
5%
2.39
3.38
1%
3.06
4.15
Source: Researcher’s calculation using E-Views 10.
-10.0
-7.5
-5.0
-2.5
0.0
2.5
5.0
7.5
10.0
09 10 11 12 13 14 15 16 17 18 19
CUSUM 5% Signi ficance
-0.4
0.0
0.4
0.8
1.2
1.6
09 10 11 12 13 14 15 16 17 18 19
CUSUM of S quares 5% Significanc e
J. Risk Financial Manag. 2021, 14, 90 14 of 18
The regression results indicate that the R squared value is 0.9383, and an adjusted R2
is 0.8822. This means that 93.83 percent of total variations in FDI inflows to Romania are
explained by changes in GDP growth, Trade Openness, Exchange Rate, and Inflation.
The F-statistic with a p value is 16.74 of 0.000 at 1 percent significance level, which reveals
that all the independent variables were jointly significant in predicting foreign direct in-
vestment inflows to Romania.
According to the results of the long-run, TOP and EDU are the only statistically
significant independent variables in the model. LGDP, EXR, and INF variables are sta-
tistically insignificant, implying that the variables do not affect the dependent variable,
foreign direct investment inflow (FDI) in the long-run in Romania. It indicates that gross
domestic product, real effective exchange rate, and inflation cannot significantly explain
the variation in FDI inflow throughout the years.
TOP with coefficient of 10.3859 has negative and statistically significant impact on
FDI inflow in long-run (Table 4). This result is in line with these empirical studies (Adow
and Tahmad 2018; Cantah et al. 2018; Khan and Hye 2014). Trade openness affects the
FDI inflow negatively as opposite to the theory, which suggests that the higher the level
of openness, the less likely it is to attract FDI in the long-run. Reasons for founding an
unexpected sign between openness and FDI inflow might be that the openness of the
economy of Romania might be inefficient in attracting FDI compared to competing
countries. Data on labor force with advanced education (percentage of total working-age
population with advanced education) is a proxy for human capital. It has a positive and
statistically significant impact on FDI inflow in the long-run. It suggests that laborers that
are more educated can make learning and the adoption of new technology easy and
faster, and can attract more FDI.
Table 4. Long-run coefficient estimates.
Variable
Coefficient
Probability Value
Constant
−7707.323 *
0.0003
LGDP
122.2956
0.1522
EXR
−0.301939
0.9208
TOP
−10.38591 *
0.0010
INF
−3.148495
0.1319
EDU
69.35139 *
0.0001
R
2
0.9383
Adjusted R-squared
0.8822
F-statistics
16.7388 *
Note: Probability values are given in the Table. * imply the rejection of the null hypothesis at 1%,
5%, and 10% levels of significance, respectively. Source: Researcher’s calculation using E-Views 10.
In line with the objective of study, trade openness negatively affects FDI inflow in
the short-run. The coefficient for openness is statistically significant at the level of 1% in
lag 1. This suggests that openness is an important variable in explaining FDI inflow in
Romania. However, trade openness affects the FDI inflow negatively as opposite to the
theory, which suggest that higher the level of openness less likely to attract FDI in the
long-run. Reasons for finding an unexpected sign between openness and FDI inflow
might be openness of the economy of Romania might be inefficient in attracting FDI
compare to competing countries in the world.
LGDP with a coefficient of 1025.851(Table 5) has a positive and statistically signifi-
cant impact on FDI inflow in short-run lag (0). This result is in line with certain empirical
studies (Liargovas and Skandalis 2012; Ho et al. 2013; Asiamah et al. 2018; Wijeweera and
Mounter 2008). Many empirical studies confirm that market size is one of the main ele-
ments of foreign direct investment inflows. In general, a larger market of the host country
attracts more quantum of FDI. However, the impact of LGDP on FDI inflow in lag (1) is
J. Risk Financial Manag. 2021, 14, 90 15 of 18
negative and statistically significant as opposite to the theory. The study found that there
is a statistically significant and positive effect of exchange rate (EXR) variable on FDI in-
flow in lag 1, as expected by the theory and most of the existing empirical studies (Ma-
koni 2018; Liargovas and Skandalis 2012). The result indicates that depreciation in the
host country exchange rate will increase the FDI inflow. The effect shows a theoretically
wrong signal, as inflation affects the FDI positively in the short-run. However, the infla-
tion variable cannot significantly explain the variation in FDI inflow throughout the
years. EDU has a positive and statistically significant impact on FDI inflow in the
shot-run both in lag (0) and in lag (1).
ETC (-1) appears with a negative sign, and it is significant at the significant level 1%,
implying that the whole system can get back to the long-run equilibrium at the speed of
95.26% one period after the exogenous shock.
Table 5. Short-run coefficient estimates and error correction representation.
Dependent Variables: ΔFDI
Lag Order
(0)
(1)
Variables
Coefficient
Prob
Coefficient
Prob
ΔFDI
0.7288
(0.0034) *
ΔLGDP
1025.851
(0.0001) *
743.0723
(0.0034) *
ΔEXR
11.8165
(0.0203) *
16.8160
(0.0003) *
ΔTOP
25.02643
(0.0009) *
2.7752
(0.3718)
ΔINF
0.0075
(0.9956)
ΔEDU
68.9126
(0.0004) *
30.6303
(0.0874) ***
ETC(-1)
0.9526 (0.0232) **
Note: Probability values are given in the parenthesis. *, **, and *** imply the rejection of the null
hypothesis at 1%, 5%, and 10% levels of significance, respectively. Source: Researcher’s calculation
using E-Views 10.
The result of the Granger causality test (Table 6) indicated that Romania has an
unidirectional relationship between trade openness and FDI. It also showed that the di-
rection of causality ran from FDI to trade openness.
Table 6. The result of the Granger causality test.
Null Hypothesis
F-Statistics
Probability
TOP does not Granger Cause FDI
0.11859
0.8889
FDI does not Granger Cause TOP
3.67477
0.0486
Source: Researcher’s calculation using E-Views 10.
5. Conclusions
The results have empirically examined the casual relationship` between trade
openness and foreign direct investment inflows in Romania during the period of 1997 to
2019. The empirical evidence revealed the following findings: Trade openness and edu-
cation variables are the only statistically significant independent variables, and LGDP,
EXR, and INF variables are statistically insignificant, implying that variables do not affect
the dependent variable, foreign direct investment inflow (FDI) in the long-run in Roma-
nia.
Trade openness, which was the main variable, has a negative and statistically sig-
nificant long-run and short-run relationship with FDI inflows in Romania throughout the
period. It indicates that the openness of the economy of Romania might be inefficient in
attracting FDI compared to competing countries. LGDP has a mixed relationship in
short-run. LGDP has a positive and statistically significant impact on FDI inflow in
short-run lag (0). However, the impact of LGDP on FDI inflow in lag (1) is negative and
statistically significant, as opposite to the theory. However, there is a statistically signif-
J. Risk Financial Manag. 2021, 14, 90 16 of 18
icant positive effect of the exchange rate (EXR) variable on FDI inflow in lag 1. It indi-
cated that, real depreciation of domestic currency increases the wealth of foreign inves-
tors relative to that of domestic investors and thereby increases FDI.
Inflation variable has a positive and significant impact on FDI inflows in the
short-run, due to potential endogeneity, as it may be closely related to other policy fac-
tors. However, the inflation variable cannot significantly explain the variation in FDI in-
flow throughout the years. In light of the findings, it is better to promote strong open
trade policies to improve investment climate and level of market size. Furthermore, the
study suggests boosting innovation and keeping political and economic stability, in order
to improve foreign direct investment inflows. In addition, this study has only included
five independent variables. In order to have a more conclusive answer, future research
should include more independent variables, such as wage rate, infrastructure, corporate
tax, and political stability.
Author Contributions: Conceptualization, G.E. and M.M.R.M.; methodology, M.M.R.M.; software,
M.M.R.M.; validation, G.E., M.M.R.M. and B.T.; formal analysis, G.E.; investigation, M.M.R.M.;
resources, B.T.; data curation, M.M.R.M.; writingoriginal draft preparation, M.M.R.M.; writ-
ingreview and editing, B.T.; visualization, M.M.R.M.; supervision, G.E.; project administration,
G.E. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
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... According to economic and investment theories, inflation encourages FDI through local and global shocks and influences other macroeconomic factors [43]. In most empirical investigations [3,7,43,57,69,70] there was a negative association between inflation and FDI inflows, while other studies [71][72][73][74] found no significant impact of inflation rate on FDI inflows. Human capital: Theoretical literature has reflected that human capital in host nations is a determinant of foreign investment in emerging countries. ...
... It confirms that inflation cannot account for major variations in FDI inflows to Sri Lanka throughout the years. This result support to the findings of [71][72][73][74].Surprisingly, the results in our study demonstrate no substantial association between FDI inflows and human capital, despite the fact that the function of human capital in attracting FDI is well recognized in the literature [31,39,50,78,83,86]. Finally, the R squared value associated the selected long run model is 0.871925. ...
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... Namun, tingkat keterbukaan terkadang menjadi tidak memiliki pengaruh terhadap masuknya FDI. Hal ini mungkin bisa terjadi akibat adanya indikator-indikator yang dapat menghambat masuknya FDI seperti kebijakan keterbukaan perdagangan yang tidak dijalankan secara efisien oleh pemerintahan maupun adanya ketidakstabilan ekonomi maupun politik di suatu negara yang nantinya dapat mengacu pada peningkatan risiko (Khan & Hye, 2014;Rathnayaka et al., 2021;Tahmad & Adow, 2018). ...
... Hal tersebut memiliki arti bahwa openness secara signifikan tidak memiliki pengaruh terhadap masuknya foreign direct investment. Seperti penelitianpenelitian sebelumnya yang menyatakan openness terkadang menjadi tidak memiliki pengaruh pengaruh terhadap foreign direct investment dikarenakan ketika suatu negara memiliki aturan yang baik mengenai keterbukaan perdagangan akan tetapi dibarengi dengan ketidakefisiensinya pemerintah dalam menjalankan keterbukaan dapat menyebabkan kurang minatnya investor untuk berinvestasi ke negara yang dituju ditambah lagi seiring dengan tingkat keterbukaan yang tinggi dibarengi dengan ketidakstabilan ekonomi dan politik di suatu negara akan membuat investor khawatir dengan risiko-risiko yang dapat mengganggu kegiatan operasionalnya di negara yang dituju (Khan & Hye, 2014;Rathnayaka et al., 2021;Tahmad & Adow, 2018). ...
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... The coefficient of trade openness is −0.9686, and it is significant at 1%, suggesting that a 1% increase in trade openness can cause FDI to decrease by 0.97% in the long run. Our findings are similar to the conclusion of Mudiyanselage et al. (2021), which also reveals a negative relationship between trade openness and FDI inflows in Romania. However, our estimate is different from most previous findings, such as Zaman et al. (2018), Seyoum et al. (2013), and Asongu et al. (2018). ...
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Foreign direct investment (FDI) plays a vital role in boosting economic growth and providing more job opportunities. Hence, it is imperative to investigate the factors that can spur FDI inflows in the Southeast Asia region (ASEAN) and its three largest trading partners: China, Japan, and South Korea (ASEAN+3). Besides, whether corruption can boost or decrease FDI inflows, and whether larger environmental degradation triggers FDI inflows have been sparsely explored by previous studies. The panel Autoregressive Distributed Lag (ARDL) approach is employed to analyze the period from 1995 to 2020. The results show evidence of the grabbing hand hypothesis in ASEAN+3 as decreasing corruption can positively impact FDI inflows in the long run. However, the results support that increasing environmental degradation has spurred FDI in the region, suggesting reformulating investment promotion policies towards more environmentally friendly ones. These findings are important for policymakers to formulate the right policies for boosting FDI. Punishment for those who act in a corrupt manner may act as a deterrent to would-be offenders. Using more renewable energy could help to reduce environmental degradation and boost FDI simultaneously.
... Furthermore, the empirical results demonstrated that there is not any causal relationship between the aforementioned variables for the remaining countries (Nepal and Pakistan) for the period 1975-2016. Mudiyanselage et al., (2021) found that there is a unidirectional causality from FDI inflows to trade openness in Romania for 1997-2019. Recently, Lee et al., (2021) investigated the nonlinear relationship between trade openness and FDI inflows for Vietnam, 1997-2019. ...
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... These results are in line with Saleem and Shabbir [78] who concluded both short-term and term relationships between FDI and TO in the Asian context. Surprisingly, our results are different than Rathnayaka Mudiyanselage, et al. [79] who concluded that FDI has a negative influence on TO in Romania. The positive association in our results can be aligned with CPEC that is intended for encouraging multinational companies for trade and investment in the regions. ...
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