The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1026-4116.htm
Economic resilience to the FDI
shock during the COVID-19
pandemic: evidence from Asia
Economic
impact on FDI
pre- and postCOVID-19
Youssra Ben Romdhane
Department of Economic, Faculty of Economics and Management of Sfax, LED,
University of Sfax, Sfax, Tunisia
Souhaila Kammoun
Received 5 December 2021
Revised 18 February 2022
Accepted 30 April 2022
Department of Economic, IHEC, CODECI, University of Sfax, Sfax, Tunisia, and
Imen Werghi
Department of Economic, ESC, QuARG, University of Manouba, Tunis, Tunisia
Abstract
Purpose – The purpose of this paper is to study the impact of economic factors on foreign direct investment
(FDI) inflows into Asian region before and after the COVID-19 pandemic.
Design/methodology/approach – The study used the generalized method of moments (GMM) technique to
examine the impact of economic growth, domestic investment and trade openness on FDI in the Asian region, in
two periods from 1996 to 2018 and from 2019 to 2020.
Findings – In the pre-COVID-19 period, the estimated result shows that the economic growth, domestic
investment, imports and exports positively impact FDI. In the post-COVID-19 period, the FDI is influenced by
the strength of the economic characteristics of the region. The main findings indicate that economic growth has
a positive and significant effect on FDI inflows into Asia. The findings also show that the economic resilience to
attract FDI in Asia is significantly affected by economic growth and positively affected by trade openness and
government responses during the pandemic.
Originality/value – The study suggests the Asian governments increasing the domestic investment and
improving the quality of trade openness.
Keywords Resilience, Asia, Foreign direct investment, COVID-19, Pandemic, Macroeconomic aggregates
Paper type Research paper
1. Introduction
Nowadays, investors in all corners of the world face greater challenges than ever before and
the investing landscape has become increasingly uncertain due to economic, political and
pandemic crises (Hasan et al., 2021c). Several empirical studies found out that economic
uncertainties are associated with investment losses and spending changes. In the context of
uncertainty, political and economic instability as well as severe security risks seem to be the
main obstacles to attracting foreign investment (Kammoun et al., 2020; Ben Romdhane Loukil
et al., 2021). In the context of health crises, the examples are numerous. For example, the 1918
pandemic had negative impacts on consumer behavior, savings, long-term human capital and
income and investment (Garrett, 2008). In 2003, the global economic cost of Severe Acute
Respiratory Syndrome (SARS) was of at least about $40 billion (Lee and McKibbin, 2004).
Even in 2014, the longest epidemic, including the Ebola virus that took place in West Africa,
resulted in a negative socioeconomic shock explained by a 1.2% drop in Gross Domestic
Product (GDP) and that most people live below the poverty line at USD 1.25 per day (UNDG,
2015). Indeed, the COVID-19 pandemic started in December 2019 and intensified concerns
about uncertainty, leading to the development of the new Global Pandemic Uncertainty Index
(WPUI) in 2020 (Ahir et al., 2018; WPUI, 2020). This pandemic is among the most severe health
crises in human history. According to the WHO, 4,634 deaths or 4.4 deaths/per million
populations in China, where it faces a further rise in infections as the New Year 2020
Journal of Economic and
Administrative Sciences
© Emerald Publishing Limited
1026-4116
DOI 10.1108/JEAS-12-2021-0250
JEAS
approaches. In India, the situation is gloomy with 145 deaths per million inhabitants; this
country has become the second in the world with more than 10 million confirmed cases at the
end of 2020. The World Health Organization (2020) has shown, as of the date of finalization of
this study, that this health crisis has generated more than 211 million confirmed infected
cases and 4.55 million deaths per worldwide.
The onset of the COVID-19 pandemic has triggered a massive spike in uncertainty (Baker
et al., 2020), and hit some regions especially hard causing an economic downturn or even an
economic collapse and also a large increase in job reallocation (Anayi et al., 2021). In order to
halt the spread of COVID-19, containment measures, including lockdown, business closure
and social distancing, has been implemented in many countries. Certainly, the effects have
been somehow effective in flattening the “pandemic curve” in countries where containment
measures have been implemented faster and in those with stronger health systems. However,
containment measures have also led to uncertainty in economic activities, with social,
economic, financial and political consequences (Brodeur et al., 2020; Fernandes, 2020; Tisdell,
2020). COVID-19-related transportation and travel restrictions directly affect trade in
services, including tourism, foreign direct investment (FDI) due to social distancing
measures, but also investor distrust of the region’s economies (OECD, 2020).
While it is crucial to assess the economic impact of the COVID-19 pandemic, this
assessment remains still difficult due to the tremendous speed with which the crisis erupted
and unfolded. Recently, several empirical studies have been developed to assess the impact of
the COVID-19 pandemic on financial and economic systems. Some studies have focused on
the effect of COVID-19 on financial markets (Alfaro et al., 2020; He et al., 2020; Zhang et al.,
2020; Ali et al., 2020; Ashraf, 2020; Hasan et al., 2021a, b; Hassan et al., 2021; Ftiti et al., 2021,
among others). Other research studies have investigated the impact of COVID-19 on
macroeconomic aggregates such as growth (Susskind and Vines, 2020; Baldwin and
Tomiura, 2020; Gans, 2020) poverty (Binder, 2020) inflation rate and exchange rate (Ozili,
2021; among others). Moreover, the COVID-19 pandemic and the containment measures taken
led to an even greater contraction in FDI in 2020. However, few studies have analyzed the
impact of COVID on FDI (Fang et al., 2021).
To the best of our knowledge, the existing literature has neglected to expose the
relationship between the intensity of the COVID-19 crisis, government responses and the
determinants of FDI together. This issue is particularly important to investigate in a region
containing the most affected countries since the pandemic started in China and are among the
first countries to adopt and implement protection policies. This investigation will provide
new insights on our understanding on how the economic recession influences the future
attraction of FDI in the Asian region. The choice of Asian countries is justified by the fact that
they are characterized by a large market size and low production costs, which makes them
more attractive and promotes large profitable projects.
The overarching purposes of the empirical research are to examine if the cross-border
health risks have disrupted FDI flows to, from and within Asia and to identify the main
economic factors which contribute to Asian countries’ economic resilience to attracting FDI
during COVID. Herein, economic resilience is understood as the ability of a region to minimize
the negative impact of the COVID-19 crisis on economic activities. Understanding what
factors make the Asian region more resilient than another will help us understand how to
make our economies better able to resist the shocks and to recover quickly and return to its
original state.
Whilst there is an important literature linking FDI attractiveness to economic factors for
the case of an Asian country, however, the resilience of FDI to the pandemic shock in the
Asian region has not received sufficient attention. We will fill this gap in the literature and
focus empirically on the association between economic factors and government interventions
to attract FDI and withstand the pandemic shock in the Asian region. The main contribution
of this paper can be described as follows. Firstly, this study follows Ciobanu et al. (2020) and
Jusoh (2020) studies with new contributions to the empirical literature on FDI determinants.
The empirical study aims to determine the main drivers of FDI before COVID by using the
dataset of 24 Asian countries from 1998 to 2018. Based on the existing literature on the
economic determinants of FDI, we use GDP growth, gross fixed capital formation, and import
and export of goods as control variables. Secondly, the paper intends to investigate the
resilience of Asian FDI to the COVID-19 pandemic. To do so, we test the effect of the economic
variables and government actions on FDI after the pandemic. To our knowledge, this study is
the first research to investigate the resilience of Asian FDI to the pandemic through the
analysis of four government responses namely: containment health index, economic support
index, total number of school and work closures among others during the period that runs
from January 1, 2020, to December 2020. In the same line, the study aims to graphically
analyze the impact of government interventions on FDI in the first and second wave of
COVID. As a result, understanding economic resilience of the Asian region to such shocks
becomes a significant policy issue.
The main findings of our paper can be summarized as follows. Although the implications
for the performance of the determinants of FDI are divergent and vary depending on several
factors, our empirical results find that before the pandemic, FDI is driven by three main
factors: economic growth, gross fixed capital formation and trade openness. During the
pandemic period, our findings reveal that not all economic factors have a significant effect on
foreign investors’ decisions in the Asian region. More specifically, we find a positive and
significant relationship between FDI and GDP and a positive relationship between trade
openness and FDI. Thus, GDP and trade openness remain the main determinants of FDI. The
capacity of the Asian region to resist to the pandemic in attracting FDI can be explained by
the economic growth and trade openness. Nevertheless, we find that domestic investment
negatively affects FDI during the COVID-19 pandemic. An understanding of the
determinants of FDI will assist Asian policymakers to formulate and execute policies
measures for attracting FDI to the region.
More interestingly, we provide evidence that government actions including, containment
health index, economic support index, total number of school and work closures among
others have strengthened the economic resilience of these countries to the COVID-19 crisis.
All the more since exporting countries such as Singapore, China and Taiwan have benefited
well from the strong global demand for medical or protective equipment, but also for
electronic products with the development of telecommuting or distance learning. As a result,
we find that Asian FDI to and from the region remain resilient during the pandemic.
The paper is organized as follows: Section 2 reviews the literature on the effects of
uncertainty caused by health pandemics on FDI. Section 3 describes the data and research
methodology. Section 4 presents and discusses the empirical results. Section 5 concludes the
study and sets out the key findings and implications and presents some further research
directions.
2. Review of the literature
There is a growing body of empirical researches that investigate the impact of uncertainty,
political instability and health crisis on investment and economic activity. This section
provides an overview of empirical literature on these issues. This literature can be divided
into three main strands. One strand of literature explores the impact of uncertainty and
political instability on investment. In this field, Hassett et al. (2015) confirmed the negative
relationship between domestic investment, FDI, economic growth and uncertainty. In
addition, Nguyen et al. (2018) found the negative effect of UPR (Economic Policy Uncertainty
Index) on firm performance. They found that firms invest more in countries with lower levels
Economic
impact on FDI
pre- and postCOVID-19
JEAS
of UPE (less uncertainty) than their home countries. These results are confirmed by AlThaqeb and Algharabali (2019). Similarly, Kammoun et al. (2020) confirmed that
macroeconomic instability combined with political instability is a barrier to investment for
14 Middle East and North Africa (MENA) countries over the period 2003–2017. In addition,
Hsieh et al. (2019) find that economic uncertainty from events such as health crises and trade
tensions create shocks in FDI inflows. Nguyen et al. (2019) complemented this literature above
by adding the World Uncertainty Index (WUI) in 23 countries from 2003 to 2013. On the one
hand, they showed the negative effect of domestic uncertainty on FDI inflows and the
existence of a positive effect of global uncertainty on FDI inflows in host countries on the
other hand. Similarly, Avom et al. (2020) studied the effect of global uncertainty on FDI using
a larger dataset of 138 countries from 1996 to 2018. They confirmed that the negative impact
of this uncertainty on FDI was greater in advanced economies than in developing economies.
The second strand of the literature examines the impact of health pandemic on financial
markets. Empirical studies have shown that financial markets have been heavily affected
(Chen et al., 2008; Hsieh, 2013; Chen and Lin, 2018; Del Giudice et al., 2017) due to the
considerable economic costs generated by several epidemics namely SARS in 2002, swine flu
in 2009, Middle East Respiratory Syndrome Coronavirus (MERS-COV) in 2012 and Ebola
Virus Disease (EVD) in 2014–2016, on financial markets have been well documented in
previous studies. Recently, Hasan et al. (2021a) analyzed the safe haven resilience of twelve
assets to the US stock market during the subprime and epidemic crises. The authors found that
silver and the Islamic stock index were safe havens during the 2008 GFC, and that the Islamic
stock index and Tether were safe havens during COVID-19. They concluded that Bitcoin still
exhibits safe haven behavior during the sharp market declines. Using the GARCH model,
Hassan et al. (2021) compared the safe haven properties of various assets against the major
Gulf Cooperation Council stock market indices. They found that sectoral and stock market
indices failed to protect investors most of the time during financial crises and recently during
the COVID-19 pandemic. On the other hand, Rao et al. (2021) examined the effect of the
epidemic crisis on the Indian financial markets using panel data regression of 3,200
observations for daily returns of the Indian market during the containment period. The
authors confirmed the negative effect of COVID-19 on the daily and sectoral returns of the
stock market. They found that the Fast Moving Consumer Goods (FMCG) and Pharma sectors
were the most resilient. In contrast, the banking sector was hit hard by the epidemic crisis.
Hasan et al. (2021b) analyzed the impact of the COVID crisis on conventional and Islamic stock
markets from a global perspective. The authors used multi-time scale techniques on the Dow
Jones and Financial Times Stock Exchange (FTSE) indices during a period between January
21 and November 27, 2020. Their empirical results showed the strong relationship between the
Islamic and conventional markets. Nevertheless, the sharia screening process fails to provide
immunity to the Islamic index market from financial crises.
In addition to political and economic uncertainty in some countries, there is also pandemic
uncertainty that accelerated in 2019 and 2020 due to the COVID. In this context, Hasan et al.
(2021c) explored the impact of the global health crisis on economic activity, market stock and
the energy sector. Using the Structural Vector Autoregression (SVAR) model for data from
January 21, 2020 to February 26, 2021, they found that COVID-19 cases have a significant and
negative impact on all endogenous variables such as the Baltic Drought Index, Morgan
Stanley Capital International (MSCI) World Index, and MSCI World Energy Index. They
concluded that the stock markets were more sensitive to the COVID-19 pandemic than the
real economy. Similarly, Kinateder et al. (2021) analyzed the evolution of financial assets
during the effect of the global financial crisis and the epidemic crisis. The empirical results of
a bivariate generalized autoregressive conditional heteroscedasticity model of dynamic
conditional correlation showed the notable deterioration of the co-relation within the
dominant asset classes in COVID-19 with respect to the global financial crisis, especially
when the Volatility Index (VIX) was at its peak, indicating massive fear among investors.
They concluded that gold and US, UK and German sovereign bonds are a safe option for
investors.
The third strand of the literature focuses on the relationship between uncertainty
pandemic and investment. Avom et al., (2020) introduce the WPUI at the global and national
levels to capture the uncertainty resulting from global pandemics such as SARS, avian
influenza (H5N1), Middle East Respiratory Syndrome (MERS), avian influenza, Ebola and
coronavirus (COVID-19). They found that the level of pandemic uncertainty caused by the
COVID-19 virus is the most severe and worst in the last 25 years. This pandemic has
significantly damaged global investment based on supply and demand shocks. Based on the
quarterly data of OECD countries, BRICS countries and Singapore during the years 2009–
2020, Fang et al. (2021) studied the impact of COVID-19 on the economies. They proved that
the number of new confirmed cases, new deaths and cumulative confirmed cases are found to
have significant negative impacts on FDI. In this same context, Ho and Gan (2021) explored
the impacts of pandemics, including COVID-19 on FDI based on a sample of 142 economies
and during the period from 1996 to 2019. Based on two stages of Generalized Method of
Moments (GMM) estimation of the linear dynamic panel data model, the empirical results
showed that health pandemics have negative effects on FDI. Significantly, the uncertainty
caused by pandemics creates negative shocks on net FDI inflows to Asia–Pacific countries
and emerging economies. Moreover, Demiessie (2020) highlighted the negative shocks of
COVID-19 pandemic uncertainty on investment, employment, prices, import and export in
Ethiopia. In the Turkish context, Fang et al., (2021) use three indices WPUI, WUI and World
Trade Uncertainty Index (WTUI). In the same lines, Ahir et al. (2018) measure the uncertainty
of Turkey’s export markets. The higher level of uncertainty regarding the destinations of
Turkey’s exports leads to weak economic growth in the country. In the Congolese context,
Pinshi (2020) employs WPUI to study the uncertainty shock of COVID-19. The study shows a
strong impact of the uncertainty of the pandemic on aggregate demand, the exchange rate
and trade openness.
Based on these insights, it can be inferred that many determinants have been empirically
tested and proven to have impact on FDI. This study aims to contribute to the existing
literature by establishing a range of empirical measures of resilience. Put briefly, the study
seeks to better understand what factors are contributing to economic resilience across Asian
countries. Even though there are multiple definitions of resilience in the literature, there is still
no standard definition or metric for measuring economic resilience. In this research, we define
economic resilience as the capacity of a country or a region to withstand significant adverse
shocks and recover from shock to a desired growth level (R€ohn et al., 2015; Rose, 2007) by
minimizing the impact on economic activities. The previously discussed determinants are
numerous. Overall, four economic determinants are included in this research: economic
growth, domestic investment and imports and exports. At this end, determinants that
frequently overlapped in previous empirical studies are tested in this research to see whether
those determinants had the same impact before and after COVID-19 pandemic in the case of
Asia region.
3. Data and methodology
As previously stated, the underlying objective of the empirical study is to examine the
economic determinants of FDI for a sample of 18 Asian countries before COVID-19 and how
the Asian region resist to the COVID-19 pandemic. More specifically, the study aims to
analyze the impact of GDP growth, gross fixed capital formation, and import and export of
goods on FDI before and after the COVID-19 pandemic. The economic variables used in this
study were collected from the World Bank. We use the panel estimator of the GMM which will
Economic
impact on FDI
pre- and postCOVID-19
JEAS
allow us to estimate a model with panel data over two periods: the first period from 1996 to
2018 (before Covid-19) and the second period from 2019 to 2020 (during COVID-19).
For the first period: 1996–2018 (before COVID-19), we use the GMM panel estimator
proposed by Blundell and Bond, (1998), which allows us to estimate a model with dynamic
panel data. The convergence of the GMM estimator is conditional on the validity of the
instruments given by the lagged values of the explanatory variables. In general, dynamic
models are treated in first differences by the GMM. In this analytical framework, Anderson
and Hasio (1982) propose using lagged first differences of the endogenous variable as
instruments. Arellano and Bover (1995) add to this list of instruments the lags of the
endogenous variable by showing their orthogonality to the residuals. In order to test the
nature of the association between the variables while avoiding false correlation, the empirical
investigation of this study involves three steps: Firstly, we test the stationarity of the
variables. Secondly, we test the long-run cointegration relationship between the variables in
the next step of the estimation using the cointegration technique developed by Pedroni (2004)
and Pedroni (1999). Finally, we examine the long-run relationship between the variables
under study and explore the causal relationship between the variables by testing Granger
causality in the final step.
For the second period, 2019–2020 (after COVID-19), we use the GMM-GLS estimation
(Generalized Least Squares) to examine the impact of GDP growth, gross fixed capital
formation, import and export of goods on FDI for 18 Asian countries during COVID-19
pandemic. Additionally, we confront our analysis by graphs of the composite index
Coronavirus Government Response Tracker which is developed by the University of Oxford
and based on four response indicators namely: containment health index, economic support
index, total number of school and work closures among others during the period that runs
from January 1, 2020 to December 2020. For some data related to macroeconomic variables,
the comparison was made between the current year and the previous year, while for other
data relations to government responses, the comparison was made on a monthly basis. Due to
the small number of observations and narrow lock-in days during the COVID-19 pandemic,
it’s worth noting that it was impossible to perform robust econometric modeling; therefore,
we have performed a descriptive analysis to seek an answer whether the government
responses play a key role in strengthening Asian countries’ economic resilience to FDI shock
during the COVID-19 pandemic.
The general specification of the model we propose to estimate can be written as follows:
FDIi;t ¼ α0 þ α1 FDIi;t−1 þ α2 GDPit þ α3 GFCFit þ α4 Mit þ α5 Xit þ εi;t
where i: represents the country.
t: the time dimension,
εi;t: the error term of the model.
α0: Constant
Table 1 shows the measures of the variables used in our study. The dependent variable in the
model is net FDI inflows measured as a percentage of GDP. The control variables used in this
study are based on the literature of FDI determinants such as GDP growth, domestic
investment, human capital and trade openness.
To check the integration order and verify the stability of variables, we conducted unit root
tests. Table 2 reports the results of unit root tests for the different series. Recent literature
shows that panel-based unit root tests have higher power than unit root tests based on
individual time series. Based on this alternative approach to panel-based unit root tests, we
use the results of Fisher (1932), we derive tests that combine p-values from individual unit
root tests. This idea has been used in the studies of Maddala and Wu (1999) and Choi (2001).
After normalization of the series, stability test results show that all the variables are
stationary in the Fisher sense. As shown in the table below, the variables series are paused
with a significance level of 1%.
Descriptive statistics for all variables used in the empirical survey are summarized in
Table 3.
As depicted in the table above, descriptive statistical results show that all the variables
in the estimation model have 450 observations over the research period. Moreover,
descriptive statistics on the main variables illustrate the heterogeneity in the sample. As
depicted in Table 3, it’s worth noting that the sample is composed of countries with very
different levels of FDI. In particular, the FDI inflows reached an average of 1.88 and the rate
of FDI varies between 3.81 and 58.51. Besides, domestic investment (GFCF) reached an
average of 4.00 and reached the highest value of 86.21. In terms of trade openness,
statistical results show that imports reached an average of 5.79 and reached the highest
value of 84.74 whereas exports reached an average of 5.18 and the highest value of 85.61.
Finally, statistical results show that the GDP variable has the smallest dispersion for the
panel of countries (15.52).
Abreviation
Variable
Variable measurement
FDI
GDP
GFCF
M
X
FDI inflows
Growth Domestic Product
Domestic investment
Imports
Exports
Foreign Direct Investment net inflows (% of GDP)
GDP growth (annual %)
Gross Fixed Capital Formation (% of GDP)
Sum of imports of goods and services (% of GDP)
Sum of exports of goods and services (% of GDP)
Variables
Method
Statistics p-values
Table 1.
Variables used in the
research model
Integration order
FDI
ADF - Fisher Chi-square
69.1397 (0.0007)***
ADF - Choi Z-stat
2.3538 (0.0093)***
GDP
ADF - Fisher Chi-square
168.3340 (0.0000)***
ADF - Choi Z-stat
8.8331 (0.0000)***
GFCF
ADF - Fisher Chi-square
111.944 (0.0000)***
ADF - Choi Z-stat
5.9489 (0.0000)***
M
ADF - Fisher Chi-square
124.545 (0.0000)***
ADF - Choi Z-stat
6.7122 (0.0000)***
X
ADF - Fisher Chi-square
119.0400 (0.0000)***
ADF - Choi Z-stat
6.6099 (0.0000)***
Note(s): Values in parentheses are p-values; with (***), significant at the 1% level
Mean
Median
Maximum
Minimum
Std. Dev
Observations
Economic
impact on FDI
pre- and postCOVID-19
I(0)
I(0)
I(0)
I(0)
I(0)
I(0)
I(0)
I(0)
I(0)
I(0)
Table 2.
Results of Unit root
tests in ADF panel
FDI
GDP
GFCF
M
X
4.2703
1.8221
58.518
3.8117
7.5968
450
4.6659
4.8350
14.5256
13.1267
3.3447
450
4.002772
3.780081
86.21863
44.02647
10.01563
450
5.79888
4.82582
84.7494
40.6752
10.7793
450
5.1831
4.8283
85.6133
31.8049
9.9355
450
Table 3.
Descriptive statistics
JEAS
4. Empirical results and discussion
Estimation of model (1) by generalized method of moments (GMM) of Arellano and Bover
(1995) for the period 1996–2018 will allow us to determine the main factors driving FDI in the
Asian region. Table 4 presents the empirical results before the outbreak of the COVID-19
pandemic.
As displayed in Table 4, the GMM estimation results are significant and show that the
coefficient on the lagged FDI variable is statistically significant at the 1% level. The
estimation results also show that the coefficients of the variables GDP and Gross Fixed
Capital Formation (GFCF) are statistically significant at the 1% level. The table also shows
that the coefficients of the variables exports and imports are positive and statistically
significant at the 5% level.
Economic growth seems to be one of the main determinants of FDI. More specifically, we
find a positive causal relationship between GDP growth and FDI with a coefficient of 0.24.
This confirms that GDP growth plays an important role in attracting FDI. This result is
confirmed by several empirical studies where economic growth is seen to be one of the major
determinants of FDI (Addison and Heshmati, 2003; Ang, 2008; Blonigen and Piger, 2011;
Hoang and Duong, 2018; Kammoun et al., 2020; Meivitawanli, 2021).
Another distinguishing feature of this research is the emphasis on the role of domestic
investment in attracting foreign investment especially in emerging and low-income
economies. The empirical findings provide evidence that domestic investment is somehow
critical for attracting FDI to host countries, with a positive coefficient on gross fixed capital
formation (0.04). Domestic investment appears as an important determinant of FDI inflows.
This result corroborates the findings of Khadaroo and Seetanah (2009) and Kaur et al. (2016)
who concluded that investment determines FDI inflows only in low-income economies. This
study provides evidence on the linkages between domestic investment and FDI with a view to
shed light on strategies that may help Asian economies to increase private capital inflows. In
this respect, domestic investment is likely to improve a country’s position in the eyes of
foreign investors.
Regarding the impact of trade openness on FDI, we find a positive and significant
relationship between FDI and trade openness namely exports and imports with coefficients of
(0.04) and (0.06). These results are consistent with previous studies indicating that higher
trade openness positively impacted FDI. In this line, Chakrabarti (2001); Campos and
Kinoshita (2003); Ta et al. (2020); Kammoun et al. (2020); Ben Romdhane Loukil et al. (2021) and
Lien (2021); Ho and Gan (2021) found that trade openness is an important determinant of FDI
inflows. Given the economic rise of China and its growing weight in the region, it is important
to note that China’s processing exports were dominated by FDI (Huang, 2003; Sung, 2001).
The result suggests that economies in different regions should review their current trade
agreements and perhaps join economic clusters with developed and developing countries to
recoup FDI inflows (Khadaroo and Seetanah, 2009; Armah and Fosu, 2016; Kaur et al., 2016).
One illustrative example is the case of China. As a result of economic reforms and opening-up
Coeff
T-Stat
FDI(-1)
0.5123
130.9738
GDP
0.2446
11.0209
GFCF
0.0361
6.8096
Table 4.
X
0.0046
2.3428
The determinants of
0.0060
1.9835
FDI before the COVID- M
Note(s): Values in parentheses indicate p-values; (***) indicates significant at the 1% level
19 pandemic
Signif
0.0000***
0.0000***
0.0000***
0.0197**
0.0480**
policy, China’s trade strategy has shifted from import substitution and self-sufficiency to
export promotion (Yao and Zhang, 2001; Zhang et al., 2020). Similarly, Lin and Zhang (2019)
also argued that without the participation of FDI, China would not be able to overcome the
lack of capital, institutional distortions and financial discrimination against private firms.
In the second part, during and after COVID-19 pandemic, the study sets out to understand
what makes the Asian region recover faster from negative economic shocks. In order to
understand what factors are contributing to economic resilience across the region in a shock
context, it would be interesting to answer the following questions: (1) Is the Asian region less
affected by the COVID-19 crisis than others? (2) How does the Asian region recover from this
economic shock? (3) What does a resilient region look like?
To answer the above-mentioned questions, it is necessary to know the characteristics of a
region that lead to economic resilience. As the COVID-19 pandemic spread in early 2020, Asian
countries experienced a double health and economic shock that confronted them with
macroeconomic imbalances. Governments quickly sought to slow down its progression
through interventions such as containment, school and workplace closures and travel
restrictions during the COVID-19 pandemic. Drastic measures were taken, including the use of
military and police forces to impose compulsory containment as the number of deaths increased
in a dangerous way. Nevertheless, these responses have been diverse in Asia and have not all
involved major constraints on individual freedoms, far from it. For clarification purposes, we
take the example of South Korea, which is particularly important because the authorities were
able to avoid widespread containment before the summer of 2020 by using a strategy of
massive testing coupled with the isolation of detected cases. South Korea did not have more
testing capacity than France, but the Korean government reacted quickly after the first case
was detected and was able to mobilize the entire health sector in support of its strategy. The
case of Taiwan is quite similar to that of South Korea. The measures introduced by the
Vietnamese government were more coercive, involving bans on entry into the country or on
travel. However, the strong response of the Vietnamese authorities made it possible to avoid a
nationwide containment. In general, a common feature of East Asian countries is that the use of
strict containment has been localized with better monitoring of patients. As a result, the
economy has been better preserved, without sacrificing health imperatives. Asian countries
have thus overcome the dilemma that has occupied European governments. In what follows, it
should be interesting to highlight the basic factors that influence recovery across the region.
This study proposes to assess the impact of COVID-19 pandemic on the economic activity
of the panel of some Asian countries by using a graphical analysis. We have used the following
indices for the year 2020: Figure 1 shows the sum of the number of death cases (SNDC);
Figure 2 shows the sum of the number of infected cases (SCI): Figure 3 presents the stringency
index (STRI): Figure 4 is related to the workplace closing (WC); Figure 5 shows containment
health index (CH); Figure 6 shows closed public transport (CPT). The figures below present
government responses to the challenges posed by the COVID-19 pandemic in Asia.
From Figure 2, it can be seen that India, China, Pakistan, Brazil and Indonesia have
recorded peaks in the number of deaths. Most interestingly, these countries experienced a
stringency rate of nearly 100, as shown in Figure 7. By way of indication, the Stringency
Index is a composite measure based on nine response indicators including school closures
workplace closures and travel bans, rescaled to a value from zero to 100 (100 5 strictest). It’s
worth noting that even if policies can vary at the subnational level, the Stringency Index
shows the response level of the strictest sub region.
As illustrated in the figure above, most of Asian countries show the greater stringency
index. This is explained by strict governmental responses to rapidly control the spread of the
pandemic. This leads us to further investigate the resilience of this region to pandemic
uncertainty by using the GMM-GLS estimation for the period 2019–2020 to assess the main
Economic
impact on FDI
pre- and postCOVID-19
JEAS
1,200,000,000
1,000,000,000
800,000,000
600,000,000
400,000,000
200,000,000
ei
un
Br
Figure 1.
Sum of the number of
infected cases
Ba
ng
D
H
ar lad
on
eu es
g
ss h
Ko
al
am
ng
SA C
h
R in
,C a
hi
na
I
In nd
do ia
ne
s
Ko Ja ia
re pa
a, n
M Rep
al
a .
M ys
ya ia
nm
a
N
ew Ne r
Ze pal
a
Pa lan
d
Ph kis
ilip tan
Si pn
ng es
a s
Sr po
i L re
a
Th nka
ai
l
Vi and
et
na
m
0
20,000,000
16,000,000
12,000,000
8,000,000
4,000,000
Br
Figure 2.
Sum of the number of
death cases
un Ba
ei
n
D gla
ar
H
eu des
on
ss h
g
Ko
al
am
ng
SA Chi
n
R
,C a
hi
na
In Ind
do ia
ne
si
Ko Ja a
re pa
a, n
R
M ep
al .
ay
M s
ya ia
nm
a
N
ew Ne r
Ze pal
a
Pa land
Ph kist
ilip an
Si pn
ng es
a
Sr po
i L re
a
Th nka
ai
l
Vi and
et
na
m
0
economic determinants of FDI after the COVID pandemic. The results are summarized in
Table 5.
The results presented in Table 5 prove the negative relationship between GFCF and FDI
with a coefficient of (7.3). The decline of investment confirms the effect of the pandemic on
70
Economic
impact on FDI
pre- and postCOVID-19
65
60
55
50
45
40
35
ar
on
Figure 3.
Stringency_index
H
Br
un
ei
D
Ba
ng
la
eu des
ss h
g
Ko
al
am
ng
SA Ch
R ina
,C
hi
na
I
In ndi
do a
ne
si
a
J
Ko a
re pa
a, n
R
M ep
al .
ay
M
ya sia
nm
ar
N
N
ew e
p
Ze al
al
Pa and
k
Ph is
ilip tan
Si pne
ng s
ap
Sr or
iL e
a
Th nka
ai
la
Vi nd
et
na
m
30
2.8
2.4
2.0
1.6
1.2
0.8
0.4
Br
un
B
ei ang
D
la
a
H
re de
on
us sh
g
sa
Ko
la
ng
m
SA Ch
R ina
,C
hi
na
In Indi
do a
ne
si
a
Ko Ja
p
re a
a, n
R
M ep
al .
ay
M
ya sia
nm
ar
N
ew Ne
p
Ze al
al
Pa and
Ph kis
ilip tan
Si pne
ng s
ap
Sr or
iL e
a
Th nka
ai
la
Vi nd
et
na
m
0.0
Asian industry and especially on tangible assets such as machinery, railroads, etc. This is due
to the demobilization of the industry and the loss of the ability to invest, but also to the partial
demobilization of the labor force during 2020 (see Figures 4 and 6), which is a major factor in
explaining the proportional decline in GFCF during the containment period. Specifically, the
Figure 4.
Workplace closures
JEAS
70
65
60
55
50
45
40
Ba
ar
D
ei
on
un
H
Br
Figure 5.
Containment_
health_index
ng
la
eu des
ss h
g
Ko
al
am
ng
SA Ch
R ina
,C
hi
na
I
In ndi
do a
ne
si
a
Ko Ja
re pa
a, n
R
M ep
al .
ay
M
ya sia
nm
ar
N
ew Ne
p
Ze al
al
Pa and
Ph kis
ilip tan
Si pne
ng s
ap
Sr or
iL e
a
Th nka
ai
la
Vi nd
et
na
m
35
1.4
1.2
1.0
0.8
0.6
0.4
0.2
Br
un
Figure 6.
Close public transport
B
ei ang
D
ar lad
H
eu es
on
ss h
g
Ko
al
am
ng
SA Ch
R ina
,C
hi
na
I
In ndi
do a
ne
si
a
J
Ko a
re pa
a, n
R
M ep
al .
ay
M
ya sia
nm
ar
N
N
ew e
p
Ze al
al
a
Pa nd
Ph kis
ilip tan
Si pne
ng s
ap
Sr or
iL e
a
Th nka
ai
la
Vi nd
et
na
m
0.0
economy shifts from goods production to services provision during the COVID-19 period.
Thus, the visibly accelerating pace of technological change has increased the importance of
more intangible forms of GFCF.
Even during COVID-19 pandemic, we find a positive relationship between GDP and FDI,
despite all the constraints in dealing with the pandemic. GDP growth remains a significant
determinant in attracting FDI. This finding is confirmed by several empirical studies before
Economic
impact on FDI
pre- and postCOVID-19
Figure 7.
COVID-19
stringency index
Coeff
T-Stat
α0
1.2154
2.53 Eþ14
FDI(-1)
1.0000
3.03 Eþ15
GDP
3.94E15
3.2274
GFCF
7.30E16
1.5228
M
2.55E16
0.64910
X
6.92E16
1.7359
Note(s): (***) denotes statistical significance at the 1% level
Signif
0.0000***
0.0000***
0.0073***
0.1537
0.5285
0.1081
and after COVID-19 pandemic (Hoang and Duong, 2018; Ho and Gan, 2021).The positive
causal relationship between GPD and FDI shows that the Asian region exhibited resilience to
the pandemic shock. GDP growth has been relatively stable throughout the Covid pandemic.
Among plausible reasons, we cannot exclude that the public policies implemented in response
to the pandemic as well as strong health systems contributed to this difference.
Another finding concerns the positive relationship between trade openness and FDI
during the pandemic period. This result is in line with previous empirical studies that have
found the positive effect of trade openness in attracting FDI (Makoni, 2018; Zaman et al., 2018;
Lee and al., 2021; Ho and Gan, 2021; Lien, 2021). However, other empirical studies found a
negative relationship between trade openness and FDI (Adow and Tahmad, 2018; Cantah
et al., 2018; Rathnayaka Mudiyanselage et al., 2021). Based on these insights, we can say that
foreign investors invest more in Asian countries with large trade openings in order to
promote exports (Kammoun et al., 2020; Ben Romdhane Loukil et al., 2021; Lien, 2021). This
result is also confirmed in Figures 8 and 9 which describes the resilience of exports during the
period 2019–2020 for several Asian countries such as China, India, Japan and the Philippines,
even though other countries such as Nepal, New Zealand and Pakistan experienced a decline
in exports during the pandemic period. Moreover, in November 2020, the world’s largest free
trade agreement (FTA) signed by 15 the Asia–Pacific region that enhances trade and
Table 5.
Determinants of FDI
after the COVID
pandemic
Figure 9.
Imports-exports (2020)
un Ba
ei ng
D lad
H
ar
on
eu esh
g
ss
Ko
al
am
ng
SA Ch
R ina
,C
hi
na
In
In
d
do ia
ne
si
a
Ko Ja
re pa
a, n
R
M ep
al .
a
M ysi
ya a
nm
ar
N
N
ew e
Ze pal
al
Pa and
k
Ph is
ilip tan
p
Si ne
ng ss
a
Sr por
iL e
a
Th nka
ai
la
Vi nd
et
na
m
g
on
H
ng
Ba
eu
ar
D
la
de
sh
s
sa
Ko
la
ng
m
SA Ch
R ina
,C
hi
na
In
In
di
do a
ne
si
a
Ko Ja
p
re
a
a, n
R
M ep
al .
ay
M si
ya a
nm
ar
N
ew Ne
Ze pal
al
Pa and
k
Ph is
ilip tan
p
Si ne
ng ss
a
Sr por
iL e
a
Th nka
ai
la
Vi nd
et
na
m
ei
un
Br
Figure 8.
Imports-exports (2019)
Br
JEAS
20
15
10
5
0
–5
–10
M
M
X
15
10
5
0
–5
–10
X
investment. Thus, the economic resilience to the pandemic crisis can be explained by the
strength of trade openness of the Asian region. This result is also confirmed by the
International Monetary Fund (2020) which found that the Asian region is the only region in
the world that ended the year 2020 with a 0.3% increase in exports. Figures 8 and 9 present
the evolution of trade openness in Asia.
In this context, it’s worth noting that the management of the pandemic limited the extent
of the economic recession in China and other Asian economies, which also allowed imports to
continue. Their imports fell by only 1.3%, compared to 7.6% for Europe and 6.1% for North
America. Containment measures related to the epidemic had severely disrupted
transportation. From the perspective of UNCTAD (2020), the situation in Asia is mixed. It
was remarkably resilient to the shock of the pandemic and was at the forefront of the recovery
of world trade in 2021. Its trade volume remained almost stable in 2020, while world trade fell
by 5.3% and Europe’s by nearly 8%. Moreover, a few countries, particularly China and
Vietnam, host the main Asian final assembly lines and import more from Asia than they
export to Asia. In 2020, the case of Vietnam is striking, with more than 90% of its imports
coming from Asia, and half from the China–South Korea pair, while only 50% of its exports
go to its Asian neighbors (see Figure 9). In sum, some Asian economies should review their
trade agreements and join economic clusters with other countries in order to recover FDI
inflows (Ho and Gan, 2021).
That being said, governments have taken this opportunity to incorporate an active role for
FDI in their pandemic recovery plans. China has begun to do so, introducing stand-alone
measures to support investment through streamlined FDI approval processes. Pakistan has
introduced a new electronic portal to facilitate investment. On the other hand, the Indonesian
government has passed the Omnibus Law to revise tax and labor market laws to stimulate
FDI. Vietnam is also on the same lines with its neighbors where it has expanded the list of
foreign and domestic small and medium-sized enterprises eligible for investment incentives.
In comparison, Myanmar stands out as one of the few countries in the region where the
government has incorporated a direct role for FDI in its comprehensive COVID-19 stimulus
package by including procedures to expedite approvals for labor-intensive and infrastructure
investments. It is clear, however, that all governments will need to refocus their FDI priority
markets and sectors to align with their sustainable development priorities. In practice, this
means identifying FDI projects in key sustainable development sectors such as health,
renewable energy and education. In addition, it is critical that reforms and measures are
implemented now with a view to sustaining them in the post-pandemic period so that the
investment ecosystem can be improved in a sustainable manner.
5. Conclusion and practical implications
The main purpose of the paper was to identify key economic factors that contribute the most
to the economic resilience of the Asian FDI in two periods from 1996 to 2018 and from 2019 to
2020 by using the GMM. We contribute to the empirical literature on FDI by employing panel
data methodologies, by including the COVID-19 pandemic and by analyzing the government
actions to the crisis. In the first part, the paper focused on the main economic determinants of
FDI. The empirical research identifies factors that are important for a region to become more
resilient during a time of crisis. The pre-COVID empirical study found that economic growth,
domestic investment and trade openness are the most important determinants of FDI in
Asian regions. Empirical findings are somewhat consistent with other empirical studies
(Baldwin and Tomiura, 2020; Gans, 2020). In the second part, the paper investigates the
economic resilience to the health crisis. The post-COVID empirical study aimed to advance
our understanding of how countries heavily affected by the crisis can adapt to it. Based on the
main findings, it seems that the Asian region has been able to escape these chaotic
fluctuations and to maintain a profile of a stable region for receiving foreign investment,
which could in the long term help it develop its own. Nevertheless, the findings of this
Economic
impact on FDI
pre- and postCOVID-19
JEAS
empirical investigation contradict the findings of previous studies by Fang et al. (2021) and
Ho and Gan (2021) who confirmed the rigidity of FDI during the pandemic crisis in Asian
countries. Similarly, Demiessie (2020) showed that the African region suffered from the
deterioration of FDI during the containment period. This result can be explained by the
capacity of Asian economy to reduce vulnerabilities and to resist to the COVID-19 pandemic.
The empirical findings highlight the main factors that allowed the region to recover in this
period. Indeed, the collapse of investments in other regions such as Europe or the United
States mechanically places Asia at the center of global flows. Overall, the results provide
useful material to policymakers for designing economic policies with taking advantage of
economic growth and increasing domestic investment and improving trade openness to
effectively attract FDI to the region. Notwithstanding the capacity of economic resilience in
this period, Asian countries still have a relatively high number of barriers to foreign
investment. Asia still has a way to go to reduce these barriers and strengthen the region’s
place in the investment strategies of Asian multinationals. The empirical findings have
important policy implications for economic recovery after the COVID-19 pandemic. In the
long run, fiscal, tax and monetary measures are required to support foreign investors.
Moreover, the pandemic provided a unique moment of reset and an opportunity for
governments to create a sustainable, inclusive, green and resilient recovery pathway.
Therefore, increasing FDI in the green economy can significantly contribute to this by
channeling more investment into sectors such as climate-friendly infrastructure, including
renewable energy and energy efficiency. In addition, this research raises several new issues
that will need to be studied in the future, such as attracting FDI from high-tech industries with
advanced technology transfer, green growth, FDI governance issues, etc.
There are some limitations, however, such as: the limited number of economic factors
included in the analysis to examine the economic resilience of Asian countries and the length
of the panel data for the COVID-19 period. These limitations should give rise to suggestions
for future empirical researches. As a matter of fact, it would be interesting to continue these
empirical studies on the Asian region by including more determinants such as financialmarket development, technological infrastructure and vaccination and to test their impact on
fostering economic recovery and bolstering economic resilience of the region. Additionally,
the empirical literature has neglected the interaction effects between Coronavirus crisis and
government interventions. It would therefore be interesting to examine empirically the
impact of the association between economic factors and government actions on FDI
attractiveness.
References
Addison, T. and Heshmati, A. (2003), “The new global determinants of FDI flows to developing
countries: the importance of ICT and democratization”, Working Paper No. 2003/45, World
Institute for Development Economic Research (UNU-WIDER), pp. 1-31. ISBN 92-9190-477-5.
Adow, A.H. and Tahmad, A.M.I. (2018), “The impact of trade openness on foreign direct investment in
Sudan by sector in the 1990-2017 period: an empirical analysis”, Economic Annals, Vol. 172
No. 7, pp. 14-21, doi: 10.21003/ea.V172-03.
Ahir, H., Bloom, N. and Furceri, D. (2018), “The world uncertainty index”, doi: 10.2139/ssrn.
3275033, pp. 1-31.
Alfaro, L., Chari, A., Greenland, A.N. and Schott, P.K. (2020), “Aggregate and firm-level stock returns
during pandemics, in real time”, Working Paper, No. w26950, National Bureau of Economic
Research, MA, doi: 10.3386/w26950.
Ali, M., Alam, N. and Rizvi, S.A.R. (2020), “Coronavirus (COVID-19)-An epidemic or pandemic for
financial markets”, Journal of Behavioral and Experimental Finance, Vol. 27, 100341, doi: 10.
1016/j.jbef.2020.10034.
Al-Thaqeb, S.A. and Algharabali, B.G. (2019), “Economic policy uncertainty: a literature review”, The
Journal of Economic Asymmetries, Vol. 20, e00133, doi: 10.1016/j.jeca.2019.e00133.
Anayi, L., Barrero, J.M., Bloom, N., Bunn, P., Davis, S., Leather, J., Meyre, B., Oikonomou, M., Mihaylov,
E., Mizen, P. and Thwaites, G. (2021), “Labour market reallocation in the wake of Covid-19”,
VoxEU.org, 13 August, available at: https://voxeu.org/article/labour-market-reallocation-wakecovid-19.
Anderson, T.W. and Hasio, C. (1982), “Formulation and estimation of dynamic models using panel
data”, Journal of Econometrics, Vol. 18 No. 1, pp. 47-82, doi: 10.1016/0304-4076(82)90095-1.
Ang, J.B. (2008), “Determinants of foreign direct investment in Malaysia”, Journal of Policy Modeling,
Vol. 30 No. 1, pp. 185-189, doi: 10.1016/j.jpolmod.2007.06.014.
Arellano, M. and Bover, O. (1995), “Another look at the instrumental variable estimation of errorcomponents models”, Journal of Econometrics, Vol. 68, pp. 29-51, doi: 10.1016/0304-4076(94)
01642-D.
Armah, M. and Fosu, P. (2016), “Infrastructure and foreign direct investment inflows: evidence from
Ghana”, Management and Economic Journal, Vol. 2, pp. 79-93, doi: 10.13140/RG.2.2.19086.59206.
Ashraf, B.D. (2020), “Economic impact of government interventions during the COVID-19 pandemic:
international evidence from financial markets”, Journal of Behavioral and Experimental Finance,
Vol. 27, 100371, doi: 10.1016/j.jbef.2020.100371.
Avom, D., Njangang, H. and Nawo, L. (2020), “World economic policy uncertainty and foreign direct
investment”, Economics Bulletin, Vol. 40 No. 2, pp. 1457-1464, available at: RePEc:ebl:ecbull:eb20-00128.
Baker, S.R., Bloom, N., Davis, S.J. and Terry, S.J. (2020), “COVID-induced economic uncertainty”,
NBER Working Papers No 26983, National Bureau of Economic Research, Inc, doi: 10.3386/
w26983.
Baldwin, R. and Tomiura, E. (2020), “Thinking ahead about the trade impact of COVID-19”, in
Baldwin, R. and Weder di Mauro, B. (Eds), Economics in the Time of COVID-19, CEPR Press
book, p. 59, ISBN 978-1-912179-28-2.
Ben Romdhane Loukil, Y., Kammoun, S. and Ouerghi, I. (2021), “Greenfield FDI determinants in the
MENA region: an empirical analysis using dynamic panel data model”, in Crespo, N. and
Simoes, N. (Eds), Handbook of Research on the Empirical Aspects of Strategic Trade
Negotiations and Management. doi: 10.4018/978-1-7998-7568-0.ch014.
Binder, C. (2020), “Coronavirus fears and macroeconomic expectations”, The Review of Economics and
Statistics, Vol. 102 No. 4, pp. 721-730, available at: RePEc:tpr:restat:v:102:y:2020:i:4:p:721-730.
Blonigen, B.A. and Piger, J. (2011), “Determinants of foreign direct investment”, NBER Working Paper
Series No. 47, doi: 10.3386/w11299.
Brodeur, A., Gray, D.G., Islam, A. and Bhuiyan, S. (2020), “A literature review of the economics of
COVID-19”, IZA, Discussion Paper No. 13411. Germany: Institute of Labor Economics, doi: 10.
2139/ssrn.3636640.
Blundell, R. and Bond, S. (1998), “Initial conditions and moment restrictions in dynamic panel data
models”, Journal of Econometrics, Vol. 87, pp. 115-143.
Campos, N.F. and Kinoshita, Y. (2003), “Why does FDI go where it goes? New evidence from the transition
economies”, IMF Working Paper, No. 03/228. available at: https://ssrn.com/abstract5880925.
Cantah, G.W., Brafu-Insaidoo, G.W., Wiafe, E.A. and Adams, A. (2018), “FDI and trade policy
openness in sub-saharan Africa”, Eastern Economic Journal, Vol. 44 No. 1, pp. 97-116, doi: 10.
1057/eej.2016.9.
Chakrabarti, A. (2001), “The determinants of foreign direct investments: sensitivity analyses of cross
country regressions”, Kyklos, Vol. 54 No. 1, pp. 89-114, doi: 10.1111/1467-6435.00142.
Chen, M.X. and Lin, C. (2018), “Foreign Investment across the Belt and Road: patterns, determinants,
and effects”, Policy Research Working Paper, No. 8607, World Bank, Washington, DC.
Economic
impact on FDI
pre- and postCOVID-19
JEAS
Chen, R., Sharman, R., Chakravarti, N., Rao, H.R. and Upadhyaya, S.J. (2008), “Emergency response
information system interoperability: development of chemical incident response data model”,
Journal of the Association for Information Systems, Vol. 9 No. 3, pp. 200-230.
Choi, I. (2001), “Unit root tests for panel data”, Journal of International Money and Finance, Vol. 20
No. 2, pp. 249-272, doi: 10.1016/S0261-5606(00)00048-6.
Ciobanu, R., Şova, R. and Popa, A.F. (2020), “The impact of FDI over economic growth and how
COVID-19 crisis can impact the CEE economies”, CECCAR Business Review No. 4, pp. 64-72,
doi: 10.37945/cbr.2020.04.08.
Del Giudice, A. and Paltrinieri, A. (2017), “The impact of the Arab Spring and the Ebola outbreak on
African equity mutual fund investor decisions”, Research in International Business and Finance,
Elsevier, Vol. 41 No. C, pp. 600-612, doi: 10.1016/j.ribaf.2017.05.004.
Demiessie, H. (2020), “COVID-19 pandemic uncertainty shock impact on macroeconomic stability in
Ethiopia”, Journal of Advanced Studies in Finance, Vol. 11, pp. 132-158.
Fang, J., Collins, A. and Yao, S. (2021), “On the global COVID-19 pandemic and China’s FDI”, Journal
of Asian Economics, Vol. 74, 101300, doi: 10.1016/j.asieco.2021.101300.
Fernandes, N. (2020), “Economic effects of corona virus outbreak (COVID-19) on the world economy”,
IESE Business School Working Paper No. WP-1240-E, doi: 10.2139/ssrn.3557504.
Fisher, I. (1932), Booms and Depressions: Some First Principles, Adelphi, New York.
Ftiti, Z., Ben Ameur, H. and Louhichi, W. (2021), “Does non-fundamental news related to COVID-19
matter for stock returns? Evidence from Shanghai stock market”, Economic Modelling, Vol. 99,
105484, doi: 10.1016/j.econmod.2021.03.003.
Gans, J. (2020), Economics in the Age of Covid-19, MIT Press, Cambridge, MA, doi: 10.21428/a11c83b7.
c48fa91b.
Garrett, T.A. (2008), “Pandemic economics: the 1918 influenza and its modern-day implications”,
Review-Federal Reserve Bank of St. Louis, Vol. 90 No. 2, pp. 75-93, doi: 10.20955/r.90.74-94.
Hasan, M.B., Hassan, M.K., Rashid, M.M. and Alhenawi, Y. (2021a), “Are safe haven assets really safe
during the 2008 global financial crisis and COVID-19 pandemic?”, Global Finance Journal,
Vol. 50, 100668, doi: 10.1016/j.gfj.2021.100668.
Hasan, M.B., Mahi, M., Hassan, M.K. and Bhuiyan, A.B. (2021b), “Impact of COVID-19 pandemic on
stock markets: conventional vs Islamic indices using Wavelet-based multi-timescales analysis”,
The North American Journal of Economics and Finance, Vol. 58, 101504, doi: 10.1016/j.najef.
2021.101504.
Hasan, M.B., Mahi, M., Sarker, T. and Amin, M.R. (2021c), “Spillovers of the COVID-19 pandemic:
impact on global economic activity, the stock market, and the energy sector”, Journal of Risk
and Financial Management, Vol. 14 No. 5, pp. 1-19, doi: 10.3390/jrfm14050200.
Hassan, M.K., Djajadikerta, H.G., Choudhury, T. and Kamran, M. (2021), “Safe havens in Islamic financial
markets: COVID-19 versus GFC”, Global Finance Journal, 100643, doi: 10.1016/j.gfj.2021.100643.
Hassett, K.A., Veuger, S. and Sullivan, J.W. (2015), “On the Wells report”, AEI Economic Perspectives,
June, available at: RePEc:aei:journl:y:2015:id:845788.
He, Q., Liu, J., Wang, S. and Yu, J. (2020), “The impact of COVID-19 on stock markets”, Economic and
Political Studies, Vol. 8 No. 3, pp. 1-14, doi: 10.1080/20954816.2020.1757570.
Ho, L.T. and Gan, C. (2021), “Foreign direct investment and world pandemic uncertainty index: do
health pandemics matter?”, Journal of Risk Financial Management, Vol. 14 No. 3, pp. 1-15, doi:
10.3390/jrfm14030107.
Hoang, C.Q. and Duong, C.T. (2018), “Analysis of foreign direct investment and economic growth in
Vietnam”, International Journal of Business, Economics and Law, Vol. 15, pp. 19-27.
Hsieh, P. (2013), “Does free trade matter for poverty reduction? The case of ASEAN”, Poverty and the
International Economic Legal System: Duties to the World’s Poor, Research Collection School, pp.
107-120, available at: https://ink.library.smu.edu.sg/sol_research/1142.
Hsieh, H., Boarelli, S. and Vu, T.H. (2019), “The effects of economic policy uncertainty on outward
foreign direct investment”, International Review of Economics and Finance, Vol. 64, pp. 377-392,
doi: 10.1016/j.iref.2019.08.004.
Huang, Y. (2003), “One country, two systems: foreign-invested enterprises and domestic firms in
China”, China Economic Review, Vol. 14 No. 4, pp. 404-416, doi: 10.1016/j.chieco.2003.09.011.
International Monetary Fund (2020), “Global financial stability overview: markets in the time of
COVID-19”, Global Financial Stability Report, ISBN: 9781513529196.
Jusoh, S. (2020), “Malaysia’s direct investment policy post-covid 19: an early analysis of challenges
and the way forward”, IKMAS Working Paper No1.
Kammoun, S., Ben Romdhane, Y. and Fakhfakh, M. (2020), “Effects of economic and political risks on
foreign direct investment: a panel data analysis in MENA region”, in Prabhakar A.C., Kaur, G.
and Erokhin, V. (Eds), Regional Trade and Development Strategies in the Era of Globalization,
pp. 226-240, doi: 10.4018/978-1-7998-1730-7.ch012.
Kaur, M., Khatua, A. and Yadav, S.S. (2016), “Infrastructure development and FDI inflow to
developing economies: evidence from India”, Thunderbird International Business Review,
Vol. 58, pp. 555-563, doi: 10.1002/tie.21784.
Khadaroo, J. and Seetanah, B. (2009), “The role of transport infrastructure in FDI: evidence from
Africa using GMM estimates”, Journal of Transport Economics and Policy (JTEP), Vol. 43 No. 3,
pp. 365-384, available at: RePEc:tpe:jtecpo:v:43:y:2009:i:3:p:365-384.
Kinateder, H., Campbell, R. and Choudhury, T. (2021), “Safe haven in GFC versus COVID-19: 100
turbulent days in the financial markets”, Finance Research Letters, Vol. 43, 101951, doi: 10.1016/
j.frl.2021.101951.
Lee, J. and McKibbin, W.J. (2004), “Estimating the global economic costs of SARS”, in Learning from
SARS: Preparing for the Next Disease Outbreak: Workshop Summary”, National Academies
Press, WA, DC, pp. 92-109, ISBN 0-309-53034-2.
Lien, N.T.K. (2021), “The effect of trade openness on foreign direct investment in Vietnam”, The
Journal of Asian Finance, Economics and Business, Vol. 8 No. 3, pp. 111-118, doi: 10.13106/
JAFEB.2021.VOL8.NO3.0111.
Maddala, G.S. and Wu, S. (1999), “A comparative study of unit root tests with panel data and a new
simple test”, Oxford Bulletin of Economics and Statistics, Vol. 61, pp. 631-652.
Makoni, P.L. (2018), “FDI and trade openness: the case of emerging African economies”, Journal of
Accounting and Management, Vol. 8, No. 2, pp. 141-152, available at: RePEc:pra:mprapa:58074.
Meivitawanli, B. (2021), “Determinants of foreign direct investment: evidence from provincial level
data in Indonesia”, The Journal of Asian Finance, Economics and Business, Vol. 8 No. 5,
pp. 53-60, doi: 10.13106/JAFEB.2021.VOL8.NO5.0053.
Nguyen, Q., Kim, T. and Papanastassiou, M. (2018), “Policy uncertainty, derivatives use, and firm-level
FDI”, Journal of International Business Studies, Vol. 49, pp. 96-126, doi: 10.1057/s41267-0170090-z.
Nguyen, M.T., Bui, T.N. and Nguyen, T.Q. (2019), “Relationships between real estate markets and
economic growth in Vietnam”, Journal of Asian Finance, Economics and Business, Vol. 6 No. 1,
pp. 121-128, doi: 10.13106/jafeb.2019.vol6.no1.121.
OECD (2020). available at: http://www.oecd.org/newsroom/global-economy-faces-gravest-threat-sincethe-crisis-ascoronavirus- spreads.htm.
Ozili, P.K. (2021), “COVID-19 pandemic and economic crisis: the Nigerian experience and structural
causes”, Journal of Economic and Administrative Sciences, Vol. 37 No. 4, pp. 401-418, doi: 10.
1108/JEAS-05-2020-0074.
Pedroni, P. (1999), “Critical values for cointegration tests in heterogenous panels with multiple
regressor”, Oxford Bulletin of Economics and Statistics, Vol. 1 No. 61, pp. 653-670, doi: 10.1111/
1468-0084.0610s1653.
Economic
impact on FDI
pre- and postCOVID-19
JEAS
Pedroni, P. (2004), “Panel cointegration: asymptotic and finite sample properties of pooled time series
tests with an applicaton to the PPP hypothesis”, Econometric Theory, Vol. 20, pp. 597-625, doi:
10.1017/S0266466604203073.
Pinshi, C.P. (2020), “Monetary policy, uncertainty and COVID-19”, Journal of Applied Economic
Sciences, Vol. 3 No. 69, pp. 579-593, doi: 10.14505/jaes.v15.3(69).07.
Rao, P., Goyal, N., Kumar, S., Hassan, M.K. and Shahimi, S. (2021), “Vulnerability of financial markets
in India: the contagious effect of COVID-19”, Research in International Business and Finance,
Vol. 58, 101462, doi: 10.1016/j.ribaf.2021.101462.
Rathnayaka Mudiyanselage, M.M., Epuran, G. and Tescasiu, B. (2021), “Causal links between trade
openness and foreign direct investment in Romania”, Journal of Risk and Financial
Management, Vol. 14 No. 3, pp. 1-18, doi: 10.3390/jrfm14030090.
Rose, A. (2007), “Economic resilience to natural and man-made disasters: multidisciplinary origins and
contextual dimensions”, Environmental Hazards, Vol. 7 No. 4, pp. 383-395, doi: 10.1016/j.envhaz.
2007.10.001.
R€ohn, O., Caldera Sanchez, A., Hermansen, M. and Rasmussen, M. (2015), “Economic resilience: a new
set of vulnerability indicators for OECD countries”, OECD Economics Department Working
Paper No. 1249, OECD Publishing, Paris.
Sung, Y.W. (2001), “Export-oriented foreign direct investment in the People’s Republic of China:
division of value added between source and host economies”, in Cheng, L.K. and Kierzkowski,
H. (Eds), Global Production and Trade in East Asia, Springer, Boston, MA. doi: 10.1007/978-14615-1625-5_10.
Susskind, D. and Vines, D. (2020), “The economics of the COVID-19 pandemic: an assessment”, Oxford
Review of Economic Policy, Vol. 36, pp. 1-13, doi: 10.1093/oxrep/graa036.
Ta, V.L., Le, Q.H., Nguyen, T.L.H., Phan, T.T. and Do, A.D. (2020), “Investigating foreign direct
investment attractive factors of Korean direct investment into Vietnam”, Journal of Asian
Finance, Economics and Business, Vol. 7 No. 6, pp. 117-125, doi: 10.13106/jafeb.2020.vol7.
no6.117.
Tisdell, C.A. (2020), “Economic, social and political issues raised by the COVID-19 pandemic”,
Economic Analysis and Policy, Vol. 68, pp. 17-28, doi: 10.1016/j.eap.2020.08.002.
UNCTAD (2020), “Global trade impact of the coronavirus (COVID-19) epidemic”, Trade and
Development Report Update, ISBN: 978-92-1-005448-5.
UNDG (2015), Socio-Economic Impact of Ebola Virus Disease in West African Countries: a Call for
National and Regional Containment, Recovery and Prevention, UNDG, New York.
World Health Organization (2020), Coronavirus Disease 2019 (COVID-19): Situation Report, World
Health Organization, p. 30, available at: https://apps.who.int/iris/handle/10665/331119.
WPUI(2020), “World pandemic uncertainty index (WPUI): country”, available at: https://
worlduncertaintyindex.com/wp-content/uploads/2020/10/WPUI_Data.xlsx (accessed 8
January 2021).
Yao, Y. and Zhang, Q. (2001), “An analysis of technological efficiency of Chinese industrial firm”,
Economic Research Journal, Vol. 10, pp. 13-19.
Zaman, Q.U., Donghui, Z., Yasin, G., Zaman, S. and Imran, M. (2018), “Trade openness and FDI
inflows: a comparative study of Asian countries”, European Online Journal of Natural and
Social Sciences, Vol. 7, pp. 386-396, ISSN 1805-3602.
Zhang, L. (2017), “The knowledge spillover effects of FDI on the productivity and efficiency of
research activities in China”, China Economic Review, Vol. 42, pp. 1-14, doi: 10.1016/j.chieco.
2016.11.001.
Zhang, D., Hu, M. and Ji, Q. (2020), “Financial markets under the global pandemic of COVID-19”,
Finance Research Letter, Vol. 36, No. 01528, doi: 10.1016/j.frl.2020.101528.
Further reading
Al-Thaqeb, A.S., Algharabali, B.G. and Alabdulghafour, K.T. (2020), “The pandemic and economic
policy uncertainty”, International Journal of Finance and Economics, Vol. 10, pp. 1-11, doi: 10.
1002/ijfe.2298.
Hansen, L.P. (1982), “Large sample properties of generalized method of moments estimators”,
Econometrica, Vol. 50, pp. 1029-1054, available at: RePEc:ecm:emetrp:v:50:y:1982:i:4:p:1029-54.
Corresponding author
youssra Ben Romdhane can be contacted at: youssrabenromdhane776@hotmail.fr
For instructions on how to order reprints of this article, please visit our website:
www.emeraldgrouppublishing.com/licensing/reprints.htm
Or contact us for further details: permissions@emeraldinsight.com
Economic
impact on FDI
pre- and postCOVID-19