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Article

Foreign Direct Investment and World Pandemic Uncertainty Index: Do Health Pandemics Matter?

1
Faculty of Economics and Development Studies, University of Economics, Hue University, Hue 530000, Vietnam
2
Department of Financial and Business Systems, Lincoln University, Christchurch 7647, New Zealand
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2021, 14(3), 107; https://doi.org/10.3390/jrfm14030107
Submission received: 30 January 2021 / Revised: 22 February 2021 / Accepted: 4 March 2021 / Published: 5 March 2021

Abstract

:
This paper explores the impacts of health pandemics on foreign direct investment (FDI) using the new world pandemic uncertainty index (WPUI). We investigate the effects of pandemics, including COVID-19, on FDI based on a sample of 142 economies and sub-samples (incomes and regions) from 1996 to 2019. The two-step system Generalised Method of Moments estimation of linear dynamic panel-data model (DPDGMM) is used in this study. The estimation results are robust with the results of the two-step sequential (two-stage) estimation of linear panel-data models (SELPDM) and the two-step system Generalised Method of Moments estimation (BBGMM). The results show that health pandemics have negative impacts on FDI. Significantly, the uncertainty caused by pandemics creates adverse shocks on FDI net inflows in Asia-Pacific countries and emerging economies.

1. Introduction

Uncertainty from health pandemics has severely impacted economies worldwide. Garrett (2008) discusses the short-term and long-term effects of the 1918 influenza pandemic using evidence from print media in 1918 and research papers such as Brainerd and Siegler (2003) and Almond (2006). The pandemic in 1918 had negative impacts on consumer behaviour, savings, long-term human capital, income, and investment (Garrett 2008). Lee and McKibbin (2004) estimate the global economic costs of Severe Acute Respiratory Syndrome (SARS) in 2003. The authors’ estimation shows that the 2003 SARS’s health and economic cost is about USD 40 billion at least. Lee and McKibbin (2004) emphasise the impacts of SARS on patients and changes of human behaviour in economic activities. The high cost of the SARS shock is associated with the investment losses and changes in spending (Lee and McKibbin 2004).
In 2014, the longest and largest outbreak of Ebola virus disease took place in West Africa (UNDG 2015). According to UNDG (2015), the Ebola pandemic results in a negative social-economic shock in 15 West African economies. A 1.2% loss in the West African region’s GDP due to the Ebola pandemic is a big challenge in recovering the impacted economies where most people live below the poverty line at USD 1.25 per day (UNDG 2015). To contain a pandemic such as the Coronavirus Disease 2019 (COVID-19) pandemic, containment measures including lockdown, business closure, and social distancing are implemented to save lives. However, the containment measures cause uncertainty in economic activities, and result in social, economic, financial and political consequences (Brodeur et al. 2020; Fernandes 2020; Tisdell 2020).
Prior to 2020, no indices have been developed to measure uncertainty caused by pandemics. The development of uncertainty index shows higher concerns about uncertainty worldwide. For example, Baker et al. (2016) first introduced the Economic Policy Uncertainty (EPU) index to measure uncertainty resulted from changes in economic policies for 12 countries in 2016 followed by 26 countries in 2020. However, the EPU index is available for the limited number of countries (mostly advanced economies). In 2018, Ahir et al. developed the World Uncertainty Index (WUI), that measures economic and political uncertainty in general for 143 countries including advanced, emerging, and low-income economies. The COVID-19 pandemic which started in December 2019 accelerated the concerns of uncertainty, which led to the development of the new World Pandemic Uncertainty Index (WPUI) in 2020 (Ahir et al. 2018; WPUI 2020). Separating pandemic uncertainty (WPUI) from aggregate uncertainty (WUI) allows researchers and policy makers to exclusively evaluate the impacts of health pandemics on the economies.
This paper investigates the effects of health pandemic shocks on FDI using the new WPUI index in 142 countries from 1996 to 2019. The estimations are conducted for different sub-samples by regions (Africa, Asia and the Pacific, Europe, Middle East and Central Asia, and Western Hemisphere) and incomes (advanced economies, emerging economies, and low-income economies).
This study follows Nguyen et al. (2019) and Avom et al. (2020) studies with new contributions to the literature. First, to our best knowledge, this is the first study that uses the new WPUI based on the WUI from Ahir et al. (2018) to investigate the impacts of pandemics on FDI. The WUI was used in both Nguyen et al. (2019) and Avom et al. (2020) studies, but the authors did not investigate the effects of pandemics on investment. For example, Avom et al. (2020) use the WUI index to investigate the impact of economic and political uncertainty on FDI regardless of the sources of uncertainty. In our study, instead of using aggregate uncertainty caused by all events, only uncertainty as a direct result of health pandemics (WPUI) is employed to ascertain its impact on FDI. Therefore, evaluating the effect of pandemic uncertainty on FDI inflows separately from aggregate uncertainty will provide important policy implications to economically recover post health pandemics such as the COVID-19. Second, we use a larger panel (142 countries from 1996 to 2019) compared to Nguyen et al. (2019) and Avom et al. (2020) (21 countries from 2003–2013 and 138 countries from 1996–2018, respectively). Third, this paper uses a new estimation technique that is the two-step system Generalised Method of Moments (GMM) estimation of linear dynamic panel-data model introduced by Kripfganz (2019, 2020) or Dynamic Panel-Data GMM (DPDGMM) hereafter. The DPDGMM solves the concerns of incorrect estimates for unbalanced panel data and incorrect degrees of freedom and p-values of the over-identification tests in cases of omitted coefficients (Kripfganz 2020). Our estimation results are robust with the results of the two-step system GMM (Blundell and Bond 1998; Roodman 2009) or Blundell and Bond GMM (BBGMM) and the two-step Sequential (two-stage) Estimation of Linear Panel-data Models (SELPDM) (Kripfganz 2017).
Our findings show that the pandemic uncertainty decreases FDI net inflows worldwide from 1996 to 2019. The significant shocks caused by the pandemic uncertainty on FDI are found in Asia-Pacific countries and emerging economies. The findings suggest that international firms’ behaviour is significantly influenced by pandemic uncertainty, which explains why there is a decline in inward FDI flows into host countries as pandemics occur, especially in emerging economies in Asia-Pacific. The reduction in inward FDI means that host countries may face a higher level of unemployment and an economic contraction. Therefore, this study provides important policy implications to economically recover post the COVID-19 pandemic. For example, in addition to immediate responses to pandemics such as containment measures, emerging countries in Asia and the Pacific should implement fiscal and monetary measures to support foreign investors in the long term. Trade agreements and economic clusters will play important roles in reducing the economic impacts of pandemic uncertainty and economically recovering towards sustainable development.
The paper is organised 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 with the key findings and implications.

2. Literature Review

The relationship between uncertainty and economic behaviour has been documented in the literature. Hassett and Sullivan (2015) review the literature on the impacts of policy uncertainty on governments and firms’ behaviour. The authors focus on the link between investment and uncertainty, and the roles of the EPU index developed by Baker et al. (2016) in explaining economic variables such as domestic investment, FDI, and economic growth. Al-Thaqeb and Algharabali (2019) review the literature on the effects of EPU on firm decisions and financial markets. In terms of the impacts of EPU on FDI, Nguyen et al. (2018) find the negative effect of EPU on firm performance, which explains why firms invest more in countries with lower levels of EPU (less uncertainty) than their home countries. Hsieh et al. (2019) confirm that outward FDI increases after a shock in the home country’s EPU index.
Economic uncertainty from events such as wars, crises, and trade tensions creates shocks in FDI inflows. Nguyen et al. (2019) employ EPU as domestic uncertainty and WUI introduced by Ahir et al. (2018) as world uncertainty to investigate their effects on FDI net inflows in 23 countries from 2003 to 2013. The study shows the negative relationship between domestic uncertainty and FDI inflows, and the positive impact of world uncertainty on FDI inflows into the host countries (Nguyen et al. 2019). Using a larger dataset of 138 countries from 1996 to 2018, Avom et al. (2020) find that world uncertainty (WUI) decreases FDI net inflows in general. The study also shows that the adverse impact of world uncertainty on FDI in emerging and developing economies is greater than in advanced economies (Avom et al. 2020).
Pandemic uncertainty accelerated in 2019 and 2020 because of the COVID-19 pandemic. Ahir et al. (2018) introduce the WPUI index at the global and country levels in 2020 to capture uncertainty as a result of global pandemics such as SARS, Avian flu (H5N1), Swine flu (H1N1), Middle East respiratory syndrome (MERS), Bird flu, Ebola, Coronavirus (COVID-19), and Influenza (H1V1). The higher value of WPUI indicates a higher level of pandemic uncertainty. Figure 1 shows different levels of WPUI corresponding to different pandemics from 1996 to 2020. The pandemic uncertainty level caused by COVID-19 virus is unprecedented and the worst over the last 25 years.
The WPUI index differs from the WUI index in terms of the meaning and theoretical ground. Although both of the indices are constructed for 143 developed and developing countries from 1996, the WUI index measures economic and political uncertainty (Ahir et al. 2018), whereas the WPUI index measures pandemic uncertainty (Ahir et al. 2018; WPUI 2020). The WUI index is constructed based on counting the word “uncertainty” and its variants in the Economist Intelligence Unit (EIU) country reports. Therefore, the WUI index presents economic and political uncertainty or aggregate uncertainty caused by all events such as wars, terrorist attacks, debt and financial crises, trade tensions, health outbreaks, the United States presidential elections, and the Brexit (Ahir et al. 2018). In contrast, the WPUI index reflects the frequencies of the word “uncertainty” relating to only health pandemics in the EIU reports (Ahir et al. 2018; WPUI 2020). In other words, the WPUI index measures pandemic uncertainty or particular uncertainty caused by global pandemics such as SARS, Avian flu, Swine flu, Ebola, and COVID-19.
The 2020 WPUI index contributes to the development of uncertainty index worldwide. The EPU index is first constructed by Baker et al. (2016) to measure concerns about uncertainty due to changes in economic policies. Although the EPU index begins a new era of uncertainty evaluation, it is available for a limited number of countries (26 countries as of 2020). Ahir et al. (2018) develop the WUI index to measure economic and political uncertainty in 2018, and the WPUI index to evaluate pandemic uncertainty in 2020 for 143 countries including advanced, emerging, and low-income economies. The high level of uncertainty caused by the COVID-19 pandemic in December 2019 (see Figure 1) motivates the development of the new WPUI index and suggests an adverse relationship between WPUI and FDI. The development of uncertainty index separating pandemic uncertainty (WPUI) from aggregate uncertainty (WUI) allows researchers and policy makers to exclusively evaluate the impacts of health pandemics on the economies.
Few studies such as Demiessie (2020), Fang et al. (2020), Pinshi (2020) use WPUI to investigate the negative impacts of COVID-19 pandemic uncertainty on economies. However, to our best knowledge, no studies have investigated the impacts of pandemic uncertainty using WPUI on FDI. Demiessie (2020) finds the negative shocks of COVID-19 pandemic uncertainty on investment, employment, prices, import, export in Ethiopia. Fang et al. (2020) use three indices WUI, World Trade Uncertainty Index (WTUI), and WPUI from Ahir et al. (2018) to measure the uncertainty of Turkey’s export markets. The higher level of uncertainty in Turkey’s export destinations leads to the lower level of the country’s economic growth rate (Fang et al. 2020). Pinshi (2020) employs WPUI to investigate the COVID-19 uncertainty shock on the Congolese economy. The study shows a strong impact of the pandemic uncertainty on economic variables such as exchange rate, trade openness, prices, and aggregate demand in Congo.
This paper investigates the effects of health pandemic shocks on FDI using WPUI in 142 countries from 1996 to 2019. The estimations are conducted for different sub-samples by regions (Africa, Asia and the Pacific, Europe, Middle East and Central Asia, and Western Hemisphere) and incomes (advanced economies, emerging economies, and low-income economies). Based on the literature on uncertainty and FDI, we hypothesise that health pandemic uncertainty creates adverse shocks on FDI net inflows. The novelty of our estimations is the use of the WPUI index in the regression model in place of the WUI index. The model with the WUI index used in Avom et al.’s (2020) work shows the impact of economic and political uncertainty on FDI in general regardless of different sources of uncertainty. In contrast, our WPUI index-based model investigates the particular impact of uncertainty caused by health pandemics on FDI. Therefore, the results of our study provide important policy implications to economically recover post health pandemics including the on-going COVID-19.

3. Data and Methodology

This paper uses unbalanced panel data of 142 countries from 1996 to 2019. WPUI is available for 143 countries including Taiwan from 1996 to 2020 (WPUI 2020). However, in terms of other variables (see Table 1), data from the World Bank’s World Development Indicators is insufficient for Taiwan. Therefore, the total of sampled countries is 142 instead of 143 countries. Except for the WPUI and WUI indices, data for the other variables in 2020 are unavailable for our 142 sampled countries as of January 2021. Hence, we use the panel data of 142 countries from 1996 to 2019 to investigate the impact of pandemic uncertainty on FDI inflows. Following the International Monetary Fund (IMF) classification used by Ahir et al. (2018), the sampled countries are grouped into three income groups (advanced, emerging, and low-income) and five regions (Africa, Asia and the Pacific, Europe, Middle East and Central Asia, and Western Hemisphere) (see Table A1 in Appendix A).
The dependent variable is FDI net inflows measured as a percentage of GDP. To measure uncertainty caused by health pandemics, WPUI is used in our study. WUI is used for robustness check in our study. WPUI and WUI are available quarterly from 1996. To obtain annual data for WPUI and WUI, we compute the yearly means for each index.
The control variables used in our study are based on the literature on the determinants of FDI such as GDP growth, domestic investment, human capital, financial development, environment factor, energy security, and trade openness (see Table 1). GDP growth plays an important role in attracting FDI. The positive causal relationship between GDP growth and FDI is confirmed by many studies such as Srinivasan et al. (2010), Blonigen and Piger (2014), and Hoang and Duong (2018). Domestic investment in infrastructure development is vital in attracting FDI into host countries, especially in emerging and low-income economies (Khadaroo and Seetanah 2009; Armah and Fosu 2016; Kaur et al. 2016). Human capital is recognised as one of the important FDI determinants (Kumari 2014; Omri and Kahouli 2014; Kaur et al. 2016). Noorbakhsh et al. (2001) find that human capital is the most important factor in attracting FDI in developing countries. Domestic financial development is a significant factor in increasing host countries’ FDI attractiveness and FDI performance (Hermes and Lensink 2003; Choong 2012; Ayouni and Bardi 2018). Razmi and Behname (2012) and Hasan and Mahvash (2015) find the positive impact of trade openness in attracting FDI inflows.
Environmental and resource factors such as environmental degradation (CO2), energy consumption and energy security exhibit causality relationships with FDI inflows. Dinh and Lin (2014) find the dynamic relationship among CO2 emissions, energy consumption, and FDI. Shahbaz et al. (2015) confirm the bidirectional causality between CO2 emissions and FDI globally. He et al. (2012) find a unidirectional Granger causality from energy consumption to FDI. Sanchez-Martin et al. (2015) conclude that a better energy security strategy positively influences FDI inflows. Nguyen et al. (2019) and Avom et al. (2020) use environmental factor and energy security as control variables to investigate the impact of uncertainty on FDI. Following the Nguyen et al. (2019) and Avom et al.’s (2020) studies, we use CO2 as a proxy of environment factor, and total natural resource rents (percent of GDP) or energy security as a proxy of resource factor in our regression models.
Table A2 in Appendix A presents the data descriptive statistics for the whole sample from 1996 to 2019. The mean of FDI net inflows is 4.15%. Figure A1 and Table A3 in Appendix A show the correlations of the variables. According to Figure A1, Hong Kong, Liberia, the Netherlands, Singapore, and Ireland are the top five countries with the highest levels of FDI net inflows. The figure also shows that Japan, South Korea, Italy, New Zealand, and the United States as the advanced economies have low levels of FDI net inflows. The correlation matrix in Table A3 reports the significant positive correlations between FDI and GDP growth, domestic investment, human capital, financial development, and trade openness. The results suggest that the selection of control variables is consistent with the literature on FDI determinants. All correlation coefficients between the variables are less than 0.7 (see Table A3), which suggests that the variables are not highly correlated.
Figure 1 shows a negative relationship between FDI and WPUI. FDI net inflows declined over the pandemic periods such as 2002–2003 (SARS), 2014–2016 (Ebola), and 2019–2020 (COVID-19) when WPUI reached the higher levels. To investigate the impact of health pandemic shocks on FDI inflows, the following dynamic panel model is used:
F D I i t = α 0 + α 1 F D I i , t 1 + β W P U I i t + γ j X j , i t + ε i t ,
where FDIit is the foreign direct investment net inflows (% of GDP) of country i in year t; WPUI is the world pandemic uncertainty index at the country level; Xj is a vector of control variable j; ε is the error term; and α, β, and γ are the estimated parameters.
Equation (1) is a dynamic model of unbalanced panel data with a lagged dependent variable in a form of an explanatory variable. According to Arellano and Bover (1995) and Blundell and Bond (1998), this type of dynamic model may face endogenous problems, which can be solved by the two-step system GMM. Although the two-step system GMM is improved by Blundell and Bond (1998) (BBGMM) to reduce the bias caused by the fixed effects in short panels, Windmeijer (2005) raises an issue of a bias of uncorrected standard errors. This issue is resolved using the SELPDM (Kripfganz 2017). However, according to Kripfganz (2020), there are several concerns of the estimation results using the BBGMM and SELPDM techniques. For instance, there may be incorrect estimates in unbalanced panel data, which is likely to occur in our study because our data is not balanced. If some coefficients are omitted, degrees of freedom and p-values for the over-identification tests are incorrect (Kripfganz 2020).
Therefore, this study uses the DPDGMM introduced by Kripfganz (2019; 2020) to ensure that our dynamic estimations using the unbalanced and short panel data are not exposed to risks of (i) endogenous problems; (ii) bias caused by uncorrected standard errors or fixed effects in short panels; and (iii) incorrect results of estimators and over-identification tests. Equation (1) is first regressed for the whole sample of 142 countries, then for the sub-samples by income and region. For robustness check, we replace WPUI with WUI and use the SELPDM (Kripfganz 2017) and BBGMM (Blundell and Bond 1998; Roodman 2009) in our study.

4. Results and Discussions

The results of our DPDGMM model with the WPUI are presented in Table 2 and Table 3. The results of robustness tests using the SELPDM and BBGMM models with WUI are presented in Table A4 in Appendix A. All AR(2) and Hansen tests are not statistically significant, which shows that our results are consistent and unbiased (Roodman 2009).
Table 2 shows the significant adverse impact of uncertainty caused by health pandemics on FDI inflows worldwide from 1996 to 2019. The coefficients of WPUI remain negative as shown in columns (1) to (8), and statistically significant in columns (4), (5), (7), and (8) in Table 2. For example, column (8) presents a negative coefficient of WPUI of -0.143, which suggests that a 1 unit increase in world pandemic uncertainty decreases FDI inflows by 14.30%. Therefore, our result shows that the uncertainty from health pandemics adversely impacts the share of FDI inflows. This result is consistent with the Avom et al.’s (2020) finding of the negative effect of world uncertainty on FDI and confirms the adverse impact of uncertainty on firms’ behaviour (Nguyen et al. 2018; Al-Thaqeb and Algharabali 2019; Hsieh et al. 2019).
However, the findings of our results differ from the Avom et al.’s (2020) findings. In our study, the adverse impact of uncertainty on FDI is directly from a single event that is health pandemics. Avom et al. (2020) draw the conclusion on the negative effects of aggregate uncertainty on FDI regardless of the sources of the events. Therefore, investigating the magnitude of the pandemic shocks on FDI inflows (and firms’ behaviour) separately from the aggregate uncertainty will provide important policy implications for governments to recover post health pandemics.
Table 2 shows the decline of FDI inflows is within −14.30% and −5.82% in our sampled countries (see columns (4), (5), (7), and (8)). The decrease in FDI inflows may lead to a high level of unemployment and a downfall in GDP. The correlation between FDI and GDP growth is shown in Table 2 (see the positive significant coefficients of GDP growth). During the COVID-19 pandemic, strong containment measures including mass lockdown, business and school closures, and social distancing were implemented globally. The containment measures led to suspending business activities, job losses and loss of income. For example, OECD (2020b) estimates that the COVID-19 pandemic uncertainty caused a fall of 50% in the world FDI in 2020. The United States’ unemployment rate jumped from 4.4% in March 2020 to 14.7% in April 2020 (OECD 2021). IMF (2021) reports that the world GDP growth dropped from 2.8% in 2019 to −4.4% in 2020. The decline of FDI inflows, high level of unemployment, and economic contraction require immediate and long-term responses from governments to support foreign investors during and after the pandemics.
Table 3 presents the impact of pandemic uncertainty on FDI inflows by sub-samples of income (advanced, emerging, and low-income) and region (Africa, Asia-Pacific, Europe, Middle East and Central Asia, and Western Hemisphere). The results show that the impacts of pandemic uncertainty using WPUI on FDI inflows differ among different income and region sub-samples. The coefficient of WPUI is negative and statistically significant at the 1% level in emerging economies, but insignificant in advanced and low-income economies. If the pandemic uncertainty increases by one unit, it may cause an adverse shock of 51.7% on FDI in emerging countries (see Table 3). The result shows that FDI inflows are very sensitive to the pandemic shocks in the emerging countries compared to the advanced and low-income countries. The different impacts of world uncertainty on FDI are also found in economies at different income levels by Avom et al. (2020). FDI inflows in the advanced economies are less likely influenced by uncertainty than in other economies (Avom et al. 2020). However, our results present the separate shocks of pandemic uncertainty on FDI based on the income sub-samples, whereas the Avom et al.’s (2020) conclusions are based on the aggregate uncertainty caused by all sources of shocks. Our finding suggests that FDI or international firms’ behaviour is more sensitive to the pandemic shocks in emerging countries than in advanced countries. Therefore, policy makers should consider implementing long-term fiscal and monetary measures to support international firms that invest in emerging countries during and post pandemics. Other suggestion includes strengthening the investment environment with investment incentive policies in the long term such as easing the liquidity stress, deferring loan repayments, and using economic recovery tax measures such as lower tax rates for businesses.
In terms of regions, we find the adverse shocks of pandemic uncertainty on FDI inflows in Asia-Pacific countries with a negative and significant coefficient at the 5% level (see Table 3). For the Europe subsample, the results suggest that pandemic uncertainty does not create shocks in FDI net inflows. Our finding is consistent with Jonung and Roeger’s (2006) findings of fewer impacts of pandemics on the European economies. Overall, our findings show the adverse effects of health pandemic shocks on FDI using WPUI in 142 countries from 1996 to 2019, and the pandemic shocks on FDI inflows in emerging economies and Asia-Pacific are severe.
In terms of the control variables, trade openness significantly affects FDI inflows in the 142 sampled countries (see Table 2). Table 3 shows the important role of trade openness in attracting FDI into advanced and emerging economies, Asia-Pacific, Middle East-Central Asia, and Western Hemisphere regions. The result suggests that economies from different regions should review their current trade agreements and perhaps join economic clusters with developed and developing countries to recover FDI inflows. For example, the Regional Comprehensive Economic Partnership (RCEP), the largest free trade agreement (FTA) in the world signed on 15 November 2020 by 15 Asia-Pacific countries (Association of Southeast Asian Nations 2020), will strengthen trade and investment (including FDI) in Asia and the Pacific. In New Zealand, the Trade Recovery Strategy was launched on 8 June 2020 to help the country recover from the impacts of COVID-19 pandemic (New Zealand Ministry of Foreign Affairs and Trade 2021). The most recent achievement of the Trade Recovery Strategy is the upgraded FTA between New Zealand and China signed on 26 January 2021 (Radio New Zealand 2021). The upgraded agreement will encourage trade and investment, and bring many benefits to both countries for the long-term economic recovery. The RCEP and upgraded agreements will also provide a conducive business environment for investors and reduce the impact of pandemic uncertainty on the economy.
We observe that the results of several control variables are not statistically significant in the estimations of all sub-samples (see Table 3). For example, domestic investment determines FDI inflows in only low-income economies and Africa. A one-unit increase in domestic investment will create more than 8.5% and 9.5% increase in FDI in low-income and Africa sub-samples, respectively (see Table 3). This result supports the findings of Khadaroo and Seetanah (2009) and Kaur et al. (2016) of the important role of domestic investment or infrastructure development of host countries in attracting FDI into low-income economies in Africa. Our results in Table 3 also show that foreign investors are more sensitive to financial development and environment factor in Africa than other regions. The probable explanation for this result is that most of countries in Africa are low-income economies with limited development in infrastructure and financial system (Calderón and Servén 2008; Mlachila et al. 2016). Due to digitalisation, international firms have relied on the convenience of fast communication and transportation, and reliable banking service, which are more readily available in emerging and developed countries than low-income economies. Therefore, African governments should consider investing more in quality infrastructure and financial services for sustainable economic development. Infrastructure and financial developments are especially vital to reduce the impact of pandemic uncertainty on African economies.
Regarding energy security, Table 2 shows the negative relationship between energy security and FDI inflows worldwide. This result means foreign investors is more likely to invest in countries that are more independent of natural resource (lower shares of total natural resource rents in GDP). Our finding is consistent with the conclusion of Sanchez-Martin et al. (2015) on the positive impact of good energy security strategy on FDI inflows. However, in our sub-sample estimations, no direct causality from energy security to FDI is found (see Table 3). A possible reason is that international investors is less sensitive to host countries’ energy security strategy within a specific region or income group. In terms of environment factor, we find the effects of CO2 emission on FDI are mixed (see Table 2 and Table 3). This result confirms the indirect relationship between environmental pollutant and FDI in the Dinh and Lin’s (2014) study.
Our results are consistent and unbiased. The number of instruments is less than the number of countries (see Table 2 and Table 3), which does not weaken and bias the Hansen over-identifying restrictions test. The p-values of Hansen test in Table 2 and Table 3 do not reject the validity of instruments used in our estimations. The p-values of AR(2) do not reject the assumption that the error term does not exhibit second-order serial correlation.

5. Conclusions

The impacts of pandemic uncertainty on world economies have been documented in the literature. With the acceleration of uncertainty in 2020/2021 due to the COVID-19 pandemic, this is the first study that investigates the impacts of health pandemics on FDI net inflows using the new pandemic uncertainty measure WPUI in 142 economies from 1996 to 2019. Our findings show that the uncertainty caused by health pandemics leads to a decrease in FDI net inflows worldwide. Using the income and region sub-samples, this paper proves that pandemic uncertainty creates adverse shocks on FDI net inflows in Asia-Pacific countries and emerging economies from 1996 to 2019.
Our findings suggest that pandemic uncertainty highly affects international firms’ behaviour and is associated with a decline of inward FDI flows into the host countries. Furthermore, FDI or international firms’ behaviour is more sensitive to the pandemic shocks in emerging economies and the Asia and the Pacific region than in other economies and regions. The negative impact of pandemic uncertainty may lead to a high level of unemployment and a downfall in GDP. The shocks from health pandemics require urgent actions from governments for economic recovery and sustainable development.
To respond to pandemics, governments across economies and regions need to use extensive fiscal and monetary policies and face the consequences. For instance, tax measures were immediately implemented in most countries to respond to the economic impacts caused by COVID-19 (OECD 2020a). The immediate tax measures consist of reductions in tax rates such as Corporate Income Tax and Value Added Tax, tax waivers, tax reimbursement, and enhanced tax loss provisions (carry-forward or carry-backward). In addition, the expansionary monetary policy was implemented worldwide. Central banks across countries cut monetary policy rates, bought back government bonds, suspended bank dividends to increase the money supply, deferred loan repayments, or suspended loan requirements (OECD 2020a). The collapse of global economic activities and government financing during the COVID-19 pandemic have led to many countries being in debt, recession, and slow recovery until 2024 (IMF 2020). If governments do not take actions early to support international firms, the shocks from pandemic uncertainty on FDI inflows are likely to increase, and economic recovery is unpredictable.
Our study provides important policy implications to economically recover post the COVID-19 pandemic, especially for emerging countries in Asia and the Pacific. Besides immediate responses to pandemics such as containment measures, fiscal and monetary measures to support foreign investors are needed in the long term. For example, economic recovery tax measures such as lower tax rates to support investment should be implemented. Easing the liquidity stress and deferring loan repayments for businesses should be continued. Labour force as a determinant of FDI should be retrained. The investment environment should be strengthened with investment incentive policies via trade and investment agreements. Reviewing and updating current trade agreements will provide a conductive business environment for investors and encourage trade and investment regionally and internationally. Joining economic clusters will also bring benefits to countries for long-term economic recovery post pandemics.

Author Contributions

Both authors contributed equally to this work. Both authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Publicly available datasets were analysed in this study. This data can be found here: https://databank.worldbank.org/source/world-development-indicators (accessed on 8 January 2021); https://worlduncertaintyindex.com/wp-content/uploads/2020/10/WPUI_Data.xlsx (accessed on 8 January 2021); https://worlduncertaintyindex.com/wp-content/uploads/2020/10/WUI_Data.xlsx (accessed on 8 January 2021).

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Figure A1. The correlation between WPUI and FDI. Source: Authors’ calculation.
Figure A1. The correlation between WPUI and FDI. Source: Authors’ calculation.
Jrfm 14 00107 g0a1
Table A1. Country list.
Table A1. Country list.
No.CountryCodeIncomeRegionNo.CountryCodeIncomeRegion
1AfghanistanAFG3MCD72Korea, Rep.KOR1APD
2AngolaAGO2AFR73KuwaitKWT2MCD
3AlbaniaALB2EUR74Lao PDRLAO3APD
4United Arab EmiratesARE2MCD75LebanonLBN2MCD
5ArgentinaARG2WHD76LiberiaLBR3AFR
6ArmeniaARM2MCD77LibyaLBY2MCD
7AustraliaAUS1APD78Sri LankaLKA2APD
8AustriaAUT1EUR79LesothoLSO3AFR
9AzerbaijanAZE2MCD80LithuaniaLTU2EUR
10BurundiBDI3AFR81LatviaLVA1EUR
11BelgiumBEL1EUR82MoroccoMAR2MCD
12BeninBEN3AFR83MoldovaMDA3EUR
13Burkina FasoBFA3AFR84MadagascarMDG3AFR
14BangladeshBGD3APD85MexicoMEX2WHD
15BulgariaBGR2EUR86North MacedoniaMKD2EUR
16Bosnia and HerzegovinaBIH2EUR87MaliMLI3AFR
17BelarusBLR2EUR88MyanmarMMR3APD
18BoliviaBOL3WHD89MongoliaMNG3APD
19BrazilBRA2WHD90MozambiqueMOZ3AFR
20BotswanaBWA2AFR91MauritaniaMRT3MCD
21Central African RepublicCAF3AFR92MalawiMWI3AFR
22CanadaCAN1WHD93MalaysiaMYS2APD
23SwitzerlandCHE1EUR94NamibiaNAM2AFR
24ChileCHL2WHD95NigerNER3AFR
25ChinaCHN2APD96NigeriaNGA3AFR
26Cote d’IvoireCIV3AFR97NicaraguaNIC3WHD
27CameroonCMR3AFR98NetherlandsNLD1EUR
28Congo, Dem. Rep.COD3AFR99NorwayNOR1EUR
29Congo, Rep.COG3AFR100NepalNPL3APD
30ColombiaCOL2WHD101New ZealandNZL1APD
31Costa RicaCRI2WHD102OmanOMN2MCD
32Czech RepublicCZE1EUR103PakistanPAK2MCD
33GermanyDEU1EUR104PanamaPAN2WHD
34DenmarkDNK1EUR105PeruPER2WHD
35Dominican RepublicDOM2WHD106PhilippinesPHL2APD
36AlgeriaDZA2MCD107Papua New GuineaPNG3APD
37EcuadorECU2WHD108PolandPOL2EUR
38Egypt, Arab Rep.EGY2MCD109PortugalPRT1EUR
39EritreaERI3AFR110ParaguayPRY2WHD
40SpainESP1EUR111QatarQAT2MCD
41EthiopiaETH3AFR112RomaniaROU2EUR
42FinlandFIN1EUR113Russian FederationRUS2EUR
43FranceFRA1EUR114RwandaRWA3AFR
44GabonGAB2AFR115Saudi ArabiaSAU2MCD
45United KingdomGBR1EUR116SudanSDN3MCD
46GeorgiaGEO2MCD117SenegalSEN3AFR
47GhanaGHA3AFR118SingaporeSGP1APD
48GuineaGIN3AFR119Sierra LeoneSLE3AFR
49Gambia, TheGMB3AFR120El SalvadorSLV2WHD
50Guinea-BissauGNB3AFR121Slovak RepublicSVK1EUR
51GreeceGRC1EUR122SloveniaSVN1EUR
52GuatemalaGTM2WHD123SwedenSWE1EUR
53Hong Kong SAR, ChinaHKG1APD124ChadTCD3AFR
54HondurasHND3WHD125TogoTGO3AFR
55CroatiaHRV2EUR126ThailandTHA2APD
56HaitiHTI3WHD127TajikistanTJK3MCD
57HungaryHUN2EUR128TurkmenistanTKM2MCD
58IndonesiaIDN2APD129TunisiaTUN2MCD
59IndiaIND2APD130TurkeyTUR2EUR
60IrelandIRL1EUR131TanzaniaTZA3AFR
61Iran, Islamic Rep.IRN2MCD132UgandaUGA3AFR
62IraqIRQ2MCD133UkraineUKR2EUR
63IsraelISR1EUR134UruguayURY2WHD
64ItalyITA1EUR135United StatesUSA1WHD
65JamaicaJAM2WHD136UzbekistanUZB3MCD
66JordanJOR2MCD137Venezuela, RBVEN2WHD
67JapanJPN1APD138VietnamVNM3APD
68KazakhstanKAZ2MCD139Yemen, Rep.YEM3MCD
69KenyaKEN3AFR140South AfricaZAF2AFR
70Kyrgyz RepublicKGZ3MCD141ZambiaZMB3AFR
71CambodiaKHM3APD142ZimbabweZWE3AFR
Source: Ahir et al. (2018). Note: Countries are differentiated based on the IMF classification. Under the income columns, 1 = Advance economies, 2 = Emerging economies, and 3 = Low-income economies. Under the region columns, AFR = Africa, APD = Asia and the Pacific, EUR = Europe, MCD = Middle East and Central Asia, and WHD = Western Hemisphere.
Table A2. Descriptive statistics.
Table A2. Descriptive statistics.
VariableObsMeanStd. Dev.MinMax
FDI33654.15426.9871−40.41103.34
WPUI34080.10641.4173056.47
WUI34080.17010.151301.34
GDP growth33733.99685.2580−62.08123.14
Domestic investment316722.41656.9589−2.4279.46
Human capital232477.176631.85555.28163.93
Financial development274248.649245.81590235.72
Environmental factor29714.50576.46410.0270.04
Energy security32328.670212.1076086.25
Trade openness327580.319948.95150.03442.62
Source: Authors’ calculation.
Table A3. Correlation matrix.
Table A3. Correlation matrix.
VariablesFDIWPUIWUIGDP GrowthDomestic InvestmentHuman CapitalFinancial DevelopmentEnvironmental FactorEnergy SecurityTrade Openness
FDI1.000
WPUI−0.0051.000
(0.796)
WUI−0.057 ***0.057 ***1.000
(0.001)(0.001)
GDP growth0.095 ***0.019−0.105 ***1.000
(0.000)(0.264)(0.000)
Domestic investment0.205 ***−0.005−0.083 ***0.197 ***1.000
(0.000)(0.766)(0.000)(0.000)
Human capital0.113 ***−0.065 ***0.098 ***−0.213 ***0.080 ***1.000
(0.000)(0.002)(0.000)(0.000)(0.000)
Financial development0.127 ***−0.038 **0.055 ***−0.152 ***0.165 ***0.593 ***1.000
(0.000)(0.048)(0.004)(0.000)(0.000)(0.000)
Environmental factor0.019−0.028−0.064 ***−0.0150.134 ***0.504 ***0.391 ***1.000
(0.293)(0.123)(0.001)(0.410)(0.000)(0.000)(0.000)
Energy security0.008−0.002−0.086 ***0.133 ***0.076 ***−0.282 ***−0.326 ***0.207 ***1.000
(0.652)(0.904)(0.000)(0.000)(0.000)(0.000)(0.000)(0.000)
Trade openness0.422 ***0.029 *−0.073 ***0.031 *0.193 ***0.263 ***0.295 ***0.152 ***−0.0081.000
(0.000)(0.095)(0.000)(0.078)(0.000)(0.000)(0.000)(0.000)(0.646)
Source: Authors’ calculation. Note: p-values are in parentheses. *, **, *** are significant levels at 10%, 5%, 1%, respectively.
Table A4. Robustness tests.
Table A4. Robustness tests.
Dependent Variable: FDIWPUIWUI
DPDGMMSELPDMBBGMMDPDGMMSELPDMBBGMM
Lag FDI0.527 ***0.527 ***0.531 ***0.563 ***0.563 ***0.568 ***
(0.0906)(0.0906)(0.0699)(0.0979)(0.0979)(0.0740)
WPUI or WUI−0.143 *−0.143 *−0.144 **0.127 *0.127 *0.113
(0.0751)(0.0751)(0.0673)(0.0688)(0.0688)(0.0703)
GDP growth0.140 **0.140 **0.133 **0.127 **0.127 **0.125 **
(0.0583)(0.0583)(0.0581)(0.0603)(0.0603)(0.0591)
Domestic investment0.05440.05440.05310.03540.03540.0352
(0.0722)(0.0722)(0.0710)(0.0557)(0.0557)(0.0552)
Human capital−0.0618 **−0.0618 **−0.0645 ***−0.0762 **−0.0762 **−0.0783 ***
(0.0305)(0.0305)(0.0242)(0.0322)(0.0322)(0.0268)
Financial development0.005210.005210.004960.01250.01250.0131
(0.0161)(0.0161)(0.0143)(0.0178)(0.0178)(0.0145)
Environmental factor0.1230.1230.1060.2440.2440.237
(0.161)(0.161)(0.159)(0.167)(0.167)(0.170)
Energy security−0.164 ***−0.164 ***−0.164 ***−0.125 **−0.125 **−0.134 **
(0.0598)(0.0598)(0.0616)(0.0593)(0.0593)(0.0602)
Trade openness0.0731 ***0.0731 ***0.0726 ***0.0626 ***0.0626 ***0.0629 ***
(0.0211)(0.0211)(0.0210)(0.0212)(0.0212)(0.0226)
Constant−0.284−0.2840.1041.0181.0181.168
(2.849)(2.849)(2.823)(2.269)(2.269)(2.245)
Observations155115511551155115511551
Number of countries127127127127127127
Number of instruments555555555555
AR(2) (p-value)0.68780.68450.6880.6640.66050.661
Hansen test (p-value)0.13680.13680.1450.30190.30190.333
Source: Authors’ calculation. Note: Standard errors are in parentheses. *, **, *** are significant levels at 10%, 5%, 1%, respectively. Although our sample consists of 142 economies, the estimations is for 127 countries due to the unbalanced panel data.

References

  1. Ahir, Hites, Nicholas Bloom, and Davide Furceri. 2018. Stanford Mimeo. [CrossRef] [Green Version]
  2. Almond, Douglas. 2006. Is the 1918 Influenza Pandemic Over? Long-Term Effects of In Utero Influenza Exposure in the Post-1940 U.S. Population. The Journal of Political Economy 114: 672–712. [Google Scholar] [CrossRef] [Green Version]
  3. Al-Thaqeb, Saud Asaad, and Barrak G. Algharabali. 2019. Economic policy uncertainty: A literature review. Journal of Economic Asymmetries 20: e00133. [Google Scholar] [CrossRef]
  4. Arellano, Manuel, and Olympia Bover. 1995. Another look at the instrumental variable estimation of error-components models. Journal of Econometrics 68: 29–51. [Google Scholar] [CrossRef] [Green Version]
  5. Armah, Mark, and Prince Fosu. 2016. Infrastructure and foreign direct investment inflows: Evidence from Ghana. Management and Economic Journal 2: 79–93. [Google Scholar]
  6. Association of Southeast Asian Nations. 2020. ASEAN Hits Historic Milestone with Signing of RCEP. Available online: https://asean.org/asean-hits-historic-milestone-signing-rcep/ (accessed on 15 January 2021).
  7. Avom, Désiré, Henri Njangang, and Larissa Nawo. 2020. World economic policy uncertainty and foreign direct investment. The Quarterly Journal of Economics 40: 1457–64. [Google Scholar]
  8. Ayouni, Saif E., and Wajdi Bardi. 2018. Financial development and FDI in Tunisia: Non linear relationship. Journal of Economic & Management Perspectives 12: 48–62. [Google Scholar]
  9. Baker, Scott R., Nicholas Bloom, and Steven J. Davis. 2016. Measuring economic policy uncertainty. The Quarterly Journal of Economics 131: 1593–636. [Google Scholar] [CrossRef]
  10. Blonigen, Bruce A., and Jeremy Piger. 2014. Determinants of foreign direct investment. The Canadian Journal of Economics 47: 775–812. [Google Scholar] [CrossRef] [Green Version]
  11. Blundell, Richard, and Stephen Bond. 1998. Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics 87: 115–43. [Google Scholar] [CrossRef] [Green Version]
  12. Brainerd, Elizabeth, and Mark V. Siegler. 2003. The Economic Effect of the 1918 Influenza Epidemic. In Discussion Paper 3791. London: Centre for Economic Policy Research. [Google Scholar]
  13. Brodeur, Abel, David M. Gray, Anik Islam, and Suraiya Bhuiyan. 2020. A Literature Review of the Economics of COVID-19. In IZA Discussion Paper No. 13411. Germany: Institute of Labor Economics. [Google Scholar]
  14. Calderón, César, and Luis Servén. 2008. Infrastructure and Economic Development in Sub-Saharan Africa. In Policy Research Working Paper No. 4712. Washington, DC: World Bank, Available online: https://openknowledge.worldbank.org/handle/10986/6988 (accessed on 22 January 2021).
  15. Choong, Chee-keong. 2012. Does domestic financial development enhance the linkages between foreign direct investment and economic growth? Empirical Economics 42: 819–34. [Google Scholar] [CrossRef]
  16. Demiessie, Habtamu. 2020. COVID-19 pandemic uncertainty shock impact on macroeconomic stability in Ethiopia. Journal of Advanced Studies in Finance 11: 132–58. [Google Scholar] [CrossRef]
  17. Dinh, Hong L., and Shih-Mo Lin. 2014. CO2 emissions, energy consumption, economic growth and FDI in Vietnam. Managing Global Transitions 12: 219–32. [Google Scholar]
  18. Fang, Jianchun, Giray Gozgor, and Sercan Pekel. 2020. Where You Export Matters: Measuring Uncertainty in Turkey’s Export Markets. In CESifo Working Paper Series No. 8404. Munich: Center for Economic Studies and Ifo Institute (CESifo). [Google Scholar]
  19. Fernandes, Nuno. 2020. Economic Effects of Coronavirus Outbreak (COVID-19) on the World Economy. In IESE Business School Working Paper No. WP-1240-E. Barcelona: IESE Business School. [Google Scholar] [CrossRef]
  20. Garrett, Thomas A. 2008. Pandemic economics: The 1918 influenza and its modern-day implications. Review-Federal Reserve Bank of St. Louis 90: 75–93. [Google Scholar] [CrossRef] [Green Version]
  21. Hasan, Farazmand, and Moradi Mahvash. 2015. Determinants of FDI: Does democracy matter? Strategic Management 20: 38–46. [Google Scholar]
  22. Hassett, Kevin A., and Joe Sullivan. 2015. Policy Uncertainty and the Economy: A Review of the Literature. Paper presented at the Exploring the Price of Policy Uncertainty, Washington, DC, UDA, April 7–8. [Google Scholar]
  23. He, Wenfei, Guangkuo Gao, and Yongchong Wang. 2012. The relationship of energy consumption, economic growth and foreign direct investment in Shanghai. Advances in Applied Economics and Finance 3: 507–12. [Google Scholar]
  24. Hermes, Niels, and Robert Lensink. 2003. Foreign direct investment, financial development and economic growth. The Journal of Development Studies 40: 142–63. [Google Scholar] [CrossRef] [Green Version]
  25. Hoang, Chinh Q., and Chi T. Duong. 2018. Analysis of foreign direct investment and economic growth in Vietnam. International Journal of Business, Economics and Law 15: 19–27. [Google Scholar]
  26. Hsieh, Hui-Ching, Sofia Boarelli, and Chi T. H. Vu. 2019. The effects of economic policy uncertainty on outward foreign direct investment. International Review of Economics and Finance 64: 377–92. [Google Scholar] [CrossRef]
  27. International Monetary Fund (IMF). 2020. World Economic Outlook Update, June 2020. Available online: https://www.imf.org/en/Publications/WEO/Issues/2020/06/24/WEOUpdateJune2020 (accessed on 8 July 2020).
  28. International Monetary Fund (IMF). 2021. World Economic Outlook Update, October 2020. Available online: https://www.imf.org/external/datamapper/NGDP_RPCH@WEO/EU/WEOWORLD (accessed on 26 January 2021).
  29. Jonung, Lars, and Werner Roeger. 2006. The macroeconomic effects of a pandemic in Europe-A model-based assessment. In European Economy-Economic Papers 2008–2015 No. 251. Brussels: European Communities. [Google Scholar]
  30. Kaur, Manpreet, Apalak Khatua, and Surendra S. Yadav. 2016. Infrastructure development and FDI inflow to developing economies: Evidence from India. Thunderbird International Business Review 58: 555–63. [Google Scholar] [CrossRef]
  31. Khadaroo, Jameel, and Boopen Seetanah. 2009. The role of transport infrastructure in FDI: Evidence from Africa using GMM estimates. Journal of Transport Economics and Policy 43: 365–84. [Google Scholar]
  32. Kripfganz, Sebastian. 2017. Sequential (two-stage) estimation of linear panel-data models. Paper presented at the German Stata Users’ Group Meetings 2017, Berlin, German, June 23. [Google Scholar]
  33. Kripfganz, Sebastian. 2019. Generalized method of moments estimation of linear dynamic panel data models. Paper presented at the London Stata Conference 2019, London, UK, September 5. [Google Scholar]
  34. Kripfganz, Sebastian. 2020. Generalized method of moments estimation of linear dynamic panel data models. Paper presented at the 2020 US Stata Conference, Virtual, USA, July 31. [Google Scholar]
  35. Kumari, Jyoti. 2014. Foreign Direct Investment and Economic Growth: A Literature Survey. BVIMSR’s Journal of Management Research 6: 118–27. [Google Scholar]
  36. Lee, Jong-Wha, and Warwick J. McKibbin. 2004. Estimating the global economic costs of SARS. In Learning from SARS: Preparing for the Next Disease Outbreak: Workshop Summary. Edited by Stacey Knobler, Adel Mahmoud, Stanley Lemon, Alison Mack, Laura Sivitz and Katherine Oberholtzer. Washington, DC: National Academies Press, pp. 92–109. [Google Scholar]
  37. Mlachila, Montfort, Ahmat Jidoud, Monique Newiak, Bozena Radzewicz-Bak, and Misa Takebe. 2016. Financial Development in Sub-Saharan Africa: Promoting Inclusive and Sustainable Growth. Washington, DC: International Monetary Fund–African Department Paper, Available online: https://www.elibrary.imf.org/doc/IMF087/23663-9781475532401/23663-9781475532401/Other_formats/Source_PDF/23663-9781475536416.pdf (accessed on 22 January 2021).
  38. New Zealand Ministry of Foreign Affairs and Trade. 2021. Trade Recovery Strategy. Available online: https://www.mfat.govt.nz/en/trade/trade-recovery-strategy/trade-recovery-strategy-overview/ (accessed on 26 January 2021).
  39. Nguyen, Quang, Trang Kim, and Marina Papanastassiou. 2018. Policy uncertainty, derivatives use, and firm-level FDI. Journal of International Business Studies 49: 96–126. [Google Scholar] [CrossRef] [Green Version]
  40. Nguyen, Canh P., Binh T. Nguyen, Thanh D. Su, and Christophe Schinckus. 2019. Determinants of foreign direct investment inflows: The role of economic policy uncertainty. International Economics 161: 159–72. [Google Scholar] [CrossRef]
  41. Noorbakhsh, Farhad, Alberto Paloni, and Ali Youssef. 2001. Human capital and FDI inflows to developing countries: New empirical evidence. World Development 29: 1593–610. [Google Scholar] [CrossRef]
  42. Omri, Anis, and Bassem Kahouli. 2014. The nexus among foreign investment, domestic capital and economic growth: Empirical evidence from the MENA region. Research in Economics 68: 257–63. [Google Scholar] [CrossRef] [Green Version]
  43. Organisation for Economic Co-operation and Development (OECD). 2020a. Country Policy Tracker. Available online: https://www.oecd.org/coronavirus/country-policy-tracker/ (accessed on 9 June 2020).
  44. Organisation for Economic Co-operation and Development (OECD). 2020b. FDI in Figures. Available online: https://www.oecd.org/investment/investment-policy/FDI-in-Figures-October-2020.pdf (accessed on 19 January 2021).
  45. Organisation for Economic Co-operation and Development (OECD). 2021. Unemployment Rate (Indicator). Available online: https://doi.org/10.1787/52570002-en (accessed on 26 January 2021).
  46. Pinshi, Christian. 2020. Monetary policy, uncertainty and COVID-19. Journal of Applied Economic Sciences XV: 579–93. [Google Scholar] [CrossRef]
  47. Radio New Zealand. 2021. New Zealand and China Upgrade Free Trade Agreement. Available online: https://www.rnz.co.nz/news/political/435211/new-zealand-and-china-upgrade-free-trade-agreement (accessed on 26 January 2021).
  48. Razmi, Mohammad J., and Mehdi Behname. 2012. FDI determinants and oil effects on foreign direct investment: Evidence from Islamic countries. Advances in Management and Applied Economics 2: 261–70. [Google Scholar]
  49. Roodman, David. 2009. How to do xtabond2: An introduction to difference and system GMM in Stata. The Stata Journal 9: 86–136. [Google Scholar] [CrossRef] [Green Version]
  50. Sánchez-Martín, Miguel E., Gonzalo E. Francés, and Rafael de A. Borda. 2015. Will energy save FDI inflows to Turkey from the cool down of EU accession prospects? A case study of how geo-political alliances and regional networks matter. Turkish Studies 16: 608–38. [Google Scholar] [CrossRef]
  51. Shahbaz, Muhammad, Samia Nasreen, Faisal Abbas, and Omri Anis. 2015. Does foreign direct investment impede environmental quality in high-, middle-, and low-income countries? Energy Economics 51: 275–87. [Google Scholar] [CrossRef]
  52. Srinivasan, Palaniyappan, M. Kalaivani, and Peter Ibrahim. 2010. FDI and Economic Growth in the ASEAN Countries: Evidence from Cointegration Approach and Causality Test. IUP Journal of Management Research 9: 38–63. [Google Scholar]
  53. Tisdell, Clement A. 2020. Economic, social and political issues raised by the COVID-19 pandemic. Economic Analysis and Policy 68: 17–28. [Google Scholar] [CrossRef] [PubMed]
  54. United Nations Development Group (UNDG). 2015. Socio-Economic Impact of Ebola Virus Disease in West African Countries: A Call for National and Regional Containment, Recovery and Prevention. New York: UNDG. [Google Scholar]
  55. Windmeijer, F. 2005. A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of Econometrics 126: 25–51. [Google Scholar] [CrossRef]
  56. World Pandemic Uncertainty Index (WPUI). 2020. World Pandemic Uncertainty Index (WPUI): Country. Available online: https://worlduncertaintyindex.com/wp-content/uploads/2020/10/WPUI_Data.xlsx (accessed on 8 January 2021).
  57. World Uncertainty Index (WUI). 2020. World Uncertainty Index (WUI): Country. Available online: https://worlduncertaintyindex.com/wp-content/uploads/2020/10/WUI_Data.xlsx (accessed on 8 January 2021).
Figure 1. World Pandemic Uncertainty Index and FDI. Source: Authors’ calculation based on Ahir et al. (2018), WPUI (2020), the Organisation for Economic Co-operation and Development (OECD 2020b), and the World Bank’s World Development Indicators (WB-WDI) at https://databank.worldbank.org/source/world-development-indicators (accessed on 8 January 2021). Note: The World Pandemic Uncertainty Index is the aggregate WPUI worldwide as the simple average of 143 countries (Ahir et al. 2018; WPUI 2020). FDI (the right axis) is the world FDI net inflows as a percentage of GDP from the WB-WDI. FDI in 2020 is computed based on data in 2019 using the prediction of a 50% fall by OECD (2020b).
Figure 1. World Pandemic Uncertainty Index and FDI. Source: Authors’ calculation based on Ahir et al. (2018), WPUI (2020), the Organisation for Economic Co-operation and Development (OECD 2020b), and the World Bank’s World Development Indicators (WB-WDI) at https://databank.worldbank.org/source/world-development-indicators (accessed on 8 January 2021). Note: The World Pandemic Uncertainty Index is the aggregate WPUI worldwide as the simple average of 143 countries (Ahir et al. 2018; WPUI 2020). FDI (the right axis) is the world FDI net inflows as a percentage of GDP from the WB-WDI. FDI in 2020 is computed based on data in 2019 using the prediction of a 50% fall by OECD (2020b).
Jrfm 14 00107 g001
Table 1. Variable definition.
Table 1. Variable definition.
VariableDefinition
FDIForeign direct investment net inflows (% of GDP) 1
WPUIWorld Pandemic Uncertainty Index (WPUI) (country level, four-quarter average) 2
WUIWorld Uncertainty Index (WUI) (country level, four-quarter average) 3,4
GDP growthGDP growth (annual %) 1
Domestic investmentGross fixed capital formation (% of GDP) 1
Human capitalSecondary school enrolment (% gross) 1
Financial developmentDomestic credit to private sector (% of GDP) 1
Environmental factorCO2 emission (metric tons per capita) 1
Energy securityTotal natural resource rents (% of GDP) 1
Trade opennessSum of exports and imports of goods and services (% of GDP) 1
Note: 1 Data obtained from the World Bank’s World Development Indicators at https://databank.worldbank.org/source/world-development-indicators (accessed on 8 January 2021); 2 Data obtained from Ahir et al. (2018) and WPUI (2020); 3 Data obtained from Ahir et al. (2018) and WUI (2020); and 4 WUI is used for robustness check.
Table 2. World Pandemic Uncertainty Index and FDI.
Table 2. World Pandemic Uncertainty Index and FDI.
Dependent Variable: FDI(1)(2)(3)(4)(5)(6)(7)(8)
Lag FDI0.490 ***0.483 ***0.423 ***0.594 ***0.560 ***0.587 ***0.598 ***0.527 ***
(0.1010)(0.0788)(0.0753)(0.0871)(0.0951)(0.0814)(0.0858)(0.0906)
WPUI−0.0198−0.0124−0.0173−0.0582 **−0.120 *−0.0899−0.110 **−0.143 *
(0.0358)(0.0364)(0.0385)(0.0284)(0.0682)(0.0566)(0.0520)(0.0751)
GDP growth 0.0863 *0.117 ***0.110 ***0.0899 **0.134 **0.159 ***0.140 **
(0.0441)(0.0392)(0.0412)(0.0451)(0.0556)(0.0609)(0.0583)
Domestic investment 0.133 **0.06850.08910.06810.04250.0544
(0.0534)(0.0640)(0.0667)(0.0727)(0.0738)(0.0722)
Human capital −0.0737 **−0.110 ***−0.0473 **−0.0487 *−0.0618 **
(0.0299)(0.0414)(0.0236)(0.0271)(0.0305)
Financial development 0.01930.0247 *0.01600.00521
(0.0252)(0.0140)(0.0155)(0.0161)
Environmental factor 0.0983−0.02000.123
(0.128)(0.158)(0.161)
Energy security −0.136 **−0.164 ***
(0.0652)(0.0598)
Trade openness 0.0731 ***
(0.0211)
Constant1.801 ***1.464 ***−1.3395.387 *6.650 *1.4063.613−0.284
(0.352)(0.327)(1.158)(2.757)(3.475)(1.772)(2.428)(2.849)
Observations32233216302521111760155115511551
Number of countries142142138132128127127127
Number of instruments1319253137434955
AR(2) (p-value)0.45820.41040.55810.03720.15920.76640.75360.6878
Hansen test (p-value)0.17580.11930.30470.10190.09820.42180.2640.1368
Source: Authors’ calculation. Note: Standard errors are in parentheses. *, **, *** are significant levels at 10%, 5%, 1%, respectively. The numbers of observations and countries in the estimation models are different due to the unbalanced panel data.
Table 3. World Pandemic Uncertainty Index and FDI by income and region.
Table 3. World Pandemic Uncertainty Index and FDI by income and region.
Dependent Variable: FDIAdvancedEmergingLow-IncomeAfricaAsia-PacificEuropeMiddle East
-Central Asia
Western
Hemisphere
Lag FDI0.408 ***0.432 ***0.831 ***0.823 ***0.522 ***0.453 ***0.407 ***0.426 **
(0.0680)(0.120)(0.0542)(0.0307)(0.0678)(0.0666)(0.141)(0.193)
WPUI−2.282−0.517 ***−0.0360−0.319−0.0463 **--0.643
(2.270)(0.135)(0.0345)(0.264)(0.0209) (8.336)
GDP growth0.3500.0695 **−0.01220.03400.191 **0.180−0.01640.0704
(0.227)(0.0325)(0.0493)(0.0566)(0.0887)(0.140)(0.0429)(0.0758)
Domestic investment−0.1240.03970.0865 **0.0953 **0.05590.03190.04680.111
(0.0992)(0.0282)(0.0368)(0.0392)(0.0379)(0.0713)(0.0368)(0.0856)
Human capital0.06000.0272 **−0.0154−0.01410.01920.0368−0.02200.0237 **
(0.0399)(0.0116)(0.0101)(0.0121)(0.0223)(0.0242)(0.0180)(0.0113)
Financial development0.00884−0.004530.002650.0415 ***−0.00104−0.00112−0.01690.00794
(0.0094)(0.0102)(0.0116)(0.0143)(0.0086)(0.0068)(0.0242)(0.0057)
Environmental factor0.207 *−0.02710.313−0.579 ***−0.1210.08630.0657−0.0581
(0.108)(0.0758)(0.339)(0.208)(0.148)(0.146)(0.125)(0.0696)
Energy security−0.158−0.0300−0.00913−0.03200.0167−0.0160−0.0378−0.0183
(0.111)(0.0227)(0.0432)(0.0323)(0.104)(0.0585)(0.0265)(0.0374)
Trade openness0.0535 ***0.0171 **0.008360.01570.0397 ***0.02530.0418 ***0.0283 ***
(0.0096)(0.0081)(0.0097)(0.0159)(0.0112)(0.0183)(0.0117)(0.0057)
Constant−9.092 **−2.215 **−1.137−1.821 **−5.173 **−5.2480.660−4.314 **
(4.583)(1.020)(0.716)(0.846)(2.072)(3.304)(1.562)(1.803)
Observations397658496371205491191293
Number of countries2854453319342021
Number of instruments1818181818171718
AR(2) (p-value)0.43570.34020.61810.69360.2980.74550.60990.5559
Hansen test (p-value)0.77710.26850.18370.3950.52540.63020.29450.516
Source: Authors’ calculation. Note: Standard errors are in parentheses. *, **, *** are significant levels at 10%, 5%, 1%, respectively.
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Ho, L.T.; Gan, C. Foreign Direct Investment and World Pandemic Uncertainty Index: Do Health Pandemics Matter? J. Risk Financial Manag. 2021, 14, 107. https://doi.org/10.3390/jrfm14030107

AMA Style

Ho LT, Gan C. Foreign Direct Investment and World Pandemic Uncertainty Index: Do Health Pandemics Matter? Journal of Risk and Financial Management. 2021; 14(3):107. https://doi.org/10.3390/jrfm14030107

Chicago/Turabian Style

Ho, Linh Tu, and Christopher Gan. 2021. "Foreign Direct Investment and World Pandemic Uncertainty Index: Do Health Pandemics Matter?" Journal of Risk and Financial Management 14, no. 3: 107. https://doi.org/10.3390/jrfm14030107

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