The Effect of Economic Policy Uncertainty on Foreign Direct Investment in the Era of Global Value Chain: Evidence from the Asian Countries

Abstract

The global value chain has promoted foreign direct investments in emerging markets. Not only resources but also public policies can affect the inflows or outflows of foreign direct investments (FDI). This study investigates the effect of economic policy uncertainty on net foreign direct investment inflows in 48 Asian countries. We use the panel dataset from different sources from 1995 to 2020. Our core dependent variable is net foreign direct investment inflows, and the explanatory variable is economic policy uncertainty. The study’s control variables include trade, GDP per capita, GDP growth, population, financial development, inflation, and employment. We use the generalized system method of moment (SYS_GMM). Furthermore, the robustness of our empirical results is checked by using the different proxy variables of policy uncertainty. Our results confirm the negative effect of policy uncertainty on foreign direct investment inflows in 48 Asian countries. Our results show that foreign investment inflows are more sensitive than domestic investment. The influence of domestic and global uncertainty on inward FDI is greater than domestic investment. Furthermore, the interaction effect of financial development (FD) shows that FD does not affect mitigation of the negative impact of global economic policy uncertainty on foreign investment inflow. In contrast, FD mitigates the adverse effects of domestic policy uncertainty on foreign and domestic investment. The findings imply that policies need to be attractive, effective, and transparent to woo FDI to the emerging markets.

1. Introduction

Foreign direct investment (FDI) is the most stable type of foreign capital flow. The global value chain (GVC) has enabled firms to spread their production activities across the globe depending on the availability of competitive resources and competencies [1]. FDI followed the GVC pattern in most destinations with competitive production factors, closeness to the regional/global production network, as well as prospective markets in the region. Traditionally, FDI has long been acknowledged as a source of economic progress through facilitating the spread of new ideas, skills, technology, and management practices in developing economies [1,2,3]. GVC and FDI can provide foreign technology and a source of positive spillover. They have also offered manufacturing procedures and quality control techniques, generating employment opportunities and access to finance, which is not always easily accessible at the local level [4,5,6]. However, during the last two decades, countries worldwide have opened their doors to attract foreign direct investment using various incentives, such as preferential tax and open economy policies [7]. On the supply side of the economy, FDI has three effects on the recipient economies: size effect, composition effect, and technical effect [8,9,10]. The home nation’s input and output determine the FDI’s scale effect on economic activity. The composition effect of FDI is indicated by a structural transition from old to contemporary means of production work, such as pre-stage to take-off stage, which changes the industrial mix of recipient nations. In contrast, FDI’s technical influence is centered on transmitting new ideas, information, and superior technologies. On the consumption side of the economy, FDI impacts income inequality and the social welfare of FDI receiving countries [10,11,12].

The relationship between FDI inflows and economic growth has been studied in theoretical and empirical investigations. In particular, most empirical research investigated the cross-country drivers of FDI and diverse variables that influence investment decisions in foreign countries. Previous findings varied across developed and developing countries. Accordingly, net FDI inflows from developed to developing nations differ from those from developing to developed economies. For instance, geographic proximity and institutional environment, as well as macroeconomic conditions, including market size [13], distance, technological differences, income level, business cost, political stability, and property protection, etc., are the factors affecting investment in a foreign country [14,15]. Remarkably, macroeconomic instability and economic policy uncertainty (EPU) lower domestic and foreign investment inflows [12,15,16,17,18], reduce production [19,20], and decrease the stock of capital [21,22] and real estate prices [23] in the home country. Thus, home and host policy uncertainty become an important source of macroeconomic instability, particularly for emerging and developing economies. Large emerging market economies and developing countries are the major players in the globalized world regarding the rapid pace of economic integration and trade under the global value chain framework. The rapid economic growth, large consumer market, lower wages, and abundant natural resources are attractive to foreign investors in developing economies. However, this research aims to investigate the impact of global and domestic policy uncertainty on the net inflow of foreign direct investment in Asian countries.

Two main factors motivate this research on FDI inflows and policy uncertainty in Asian countries. First, FDI is the most important driving force behind the Asian economic miracles of the last three decades and has provided the capital and expertise to these economies. It contributes to economic growth, creates employment opportunities, and improves productivity and labor force skills through technological transfers. Thus, FDI plays a crucial role today in the global economy and provides additional financial capital to the host countries [2,20]. Second, over the last decades, Asian countries have faced subsequent recessions, such as the Asian financial crisis and global financial crisis, governance issues, and policy uncertainty. Most recently, the COVID-19 pandemic badly hit the economies and induced a large construction and FDI flow slowdown. However, compared to the other developing regions, Asian countries still performed better in their share of global FDI inflows. However, the current pandemic uncertainty, trade tensions between China and the United States, and regional political instability have disrupted global FDI inflows. New and noteworthy investment and trade policies are needed to further encourage and promote FDI inflows in Asian countries. The limited literature highlights the impact of domestic and foreign policy uncertainty on FDI inflows and how the financial development mediates the relationship between domestic and foreign policy uncertainty in Asian countries and FDI inflows. Thus, this study fills the gap by exploring the links between domestic and foreign policy uncertainty in FDI inflows in 48 Asian countries from 2000 to 2020.

Furthermore, we look at the relationship between the macroeconomic determinants of FDI, such as real GDP growth, GDP per capita, trade, inflation rate, population, and employment rate. To measure policy uncertainty, we used the recently developed Economic Policy Uncertainty Index [24,25]. The current study used an alternative method to measure domestic policy uncertainty, such as political risk.

Theoretically, this study sheds light on the institutional theory, which explains the institutional structure and behavior of the investors and the determinants of FDI flows. According to the theory, the countries engaged in international investment and FDI flows are determined by institutional factors, such as business cost, macroeconomic environment, political stability, institutional quality, transparency, and property protection. Thus, our findings contribute to the institutional theory, which explains the constraints to FDI inflows, the role of state interference, institutional support, unpredictability of public policies, and political risk. On the empirical side, this study contributes to the literature in two ways. First, the FDI literature explains the extensive list of cross-border determinants of foreign direct investment [26,27,28,29,30,31]. Among these, the main determinants of FDI are income level, population, institutional quality, GDP growth, liberalization policies, and natural resource endowment. These empirical studies have shown mixed results, and the determinants have varied regarding regions and countries [32]. However, we depart from the current literature in that we focus on policy uncertainty and investigate the impact of policy uncertainty on domestic and foreign investment, particularly on foreign direct investment. We include macroeconomic variables in our models, such as GDP growth, GDP per capita, exchange rate, trade openness, inflation rate, and the unemployment rate, as significant determinants of FDI. Second, our paper also contributes to the recent international investment and macroeconomic literature with regard to the role of financial institutions, focusing on the role of financial development in mitigating the negative effect of domestic and global policy uncertainty.

The rest of the paper is divided into the following sections. The second section offers a survey of the theory and empirical literature. The third section presents the data and methodology of the study, including the theoretical framework, econometric model, sources of data, and definition of variables. The fourth section involves the estimation of the econometric model and empirical findings. The fifth section concludes the overall results and provides some policy implications.

2. Some Stylized Facts from Asia: Inward and Outward FDI Flows

Since the 1990s, the inflow and outflow of foreign direct investment have increased dramatically worldwide, particularly in developing countries. After the 1990s, many developing countries removed restrictions and implemented bilateral investment policies to attract foreign direct investment and trade. This led to rapid FDI, business, and investment expansion in many developing countries, particularly in Asia. According to the World Investment Report (Ref. [33]), the Asian region was the largest recipient of FDI inflows in 2018. As Figure 1 demonstrates, Europe and America were the largest source of both inflows and outflows of FDI in the 1990s. The Asian countries’ shares significantly declined during the 1990s due to the Asian financial crisis (AFC). After the AFC, the Asian countries raised their shares and received large shares of world FDI inflows. At the same time, however, the contribution of both inward and outward FDI to GDP has been increasing in the Asian region over the last two decades; in other regions of the world, it has shown a decreasing trend, as shown in Figure 1.

Regarding the geographical pattern, the inward and outward FDI flows differ among Asian countries. Figure 2 shows both inward and outward FDI in five Asian regions. Among the five Asian regions, southeastern Asia has the largest inward FDI, while eastern Asia is the most significant outer FDI region. This shows that southeastern Asian countries are the most preferred location for foreign investors and a large amount of FDI outflow from eastern Asian countries. However, the COVID-19 pandemic has caused a decline in global FDI flows of 49% in the first quarter of 2020 compared to the previous year. In Asia, FDI inflows dropped from 45% to 35%, and its share of global FDI outflows dropped from 52% to 41% [34]. However, the Asian region has maintained the largest FDI flows. Among the Asian countries, China, Japan, India, Malaysia, and Hong Kong are the countries with the largest FDI flows [35,36].

The Asian developing countries have grown impressively over the last two decades. The Asian countries’ real GDP had expanded four-fold since 1980, but the real per capita income in Asian developing countries remains below the world average. According to the International Monetary Fund [37] economic outlook report, Asia is the fastest growing region in the world, with a GDP growth of 6.4 percent in 2021; Figure 3 shows the regional real GDP growth of five Asian areas. Accordingly, many factors contribute to GDP growth, such as favorable geography and structural characteristic, trade openness, and FDI inflows. Therefore, Asia, particularly the emerging Asian countries in eastern and western Asia, influences the global economy and achieves robust economic growth.

3. Survey of the Literature

3.1. Theory

According to the conventional economic theory, FDI flows in any country have three significant economic, social, and political effects. The economic effects are classified as micro or macro. Some primary macroeconomic implications of FDI are capital provision, output growth, employment level, the balance of payment, wages, and trade flows. In contrast, structural changes at the firm and organizational level and productivity level, local people training, knowledge transfer, and market structure are associated with the microeconomic effects of FDI [38,39,40,41,42]. Despite the significance of FDI in economic growth and development, several economic theories explain the dynamics of international capital flows. Among these internationalization theories, the eclectic ownership, location, and internationalization (OLI) advantage theory, product life cycle theory, institutional theory, and resource-based view (RBV) are famous in the international investment and trade literature. The internationalization theory explains the determinants and motivation of FDI regarding a firm’s efforts. According to the theory, multinational enterprises invest abroad when the cost of internalization is higher than that of external transaction [38,43,44]. This means that firms engage in FDI by exploring the host country’s specific factor advantages instead of relying on local factor endowment in the home market [45]. Furthermore, recent empirical studies extended the internationalization model [44] by expanding FDI in terms of vertical and horizontal FDI and explaining the theory in the context of regionalization and global value chain integration [45,46].

The eclectic ownership, location, and internalization (OLI) paradigm [47,48,49] discusses how businesses compete in international markets by using existing resources, such as ownership advantage (O), location advantage (L), and internalization (I). The ownership advantages are related to the company’s intangible and tangible assets, which may be transferred within transnational companies at a lower cost. Some companies have certain advantages operating in foreign markets, leading to higher profits or lower marginal costs. The location advantage means different countries with some specific characteristics and resources that have comparative advantages compared to host countries with regard to the activities of transnational cooperation—such factors including the transportation cost, manufacturing cost, telecommunication, market size, etc. The social and political factors are also included in location advantage, such as government policies, distance between home and host country, cultural diversity, trade liberalization, and investment reforms.

In addition, Vernon, R. [50] proposed the product cycle theory after World War II, based on the FDI from US-based multinational businesses in Western Europe, with a particular focus on industrial FDI flows. The four stages of the product cycle, such as innovation, growth, maturity, and decline, are explained by this theory. According to the idea, the firm produces the product in the internal market in the first stage of production, and the technology becomes known. In the second stage, the firm starts to export to foreign countries availing the standardization opportunity. In the third stage, the firm finds the countries where cheap inputs are available; they relocate their production to those low-cost countries (LCC) [51,52]. Advanced technology and innovation are constructed specifically for the home market and evolve through successive stages. As a result, many manufacturing activities have been relocated to other industrializing countries and developing regions [53].

The other theories of FDI, such as institutional theory and the dynamic capability view (DCV), explain the organization structure, the behavior of firms, and the determinants of FDI flows. According to the theory, firms engaged in international investment and FDI flows are driven by institutional factors, such as business cost, macroeconomic environment, political stability, institutional quality, transparency, and property protection [54,55]. In contrast, the significant constraints to FDI inflows are institutional constraints, such as state interference, inadequate institutional support, unpredictability of public policies, and political risk.

The initial research review demonstrates that several economic theories explain the causes of international capital mobility. The fundamental goal of these ideas is to explain why a company decides to relocate its business to another country. Regardless of their differing points of view, all of these theories agree that a firm moves abroad to take advantage of the geographic, firm-specific, or market internationalization benefits. These theories also highlight the relevance of domestic government policies in encouraging businesses to invest globally and host government policies in attracting FDI.

3.2. Empirics

The emerging and transitional markets have received a large amount of FDI, which has benefited their economic development and poverty reduction. The potential benefits of knowledge and innovation spillovers to host country industries are critical. Thus, FDI has the potential to generate new production capacity and jobs, as well as infrastructure development, enterprise restructuring, and capital account relief by increasing the host nation’s stock of capital, all of which will result in links to the global marketplace. Intangible assets, such as technology and managerial abilities, are also intended to be transferred to the host country as a source of innovations, organizational systems, and management skills, as a significant boost to economic growth. Several empirical studies examined the central cross-border determinants of FDI inflows. On the other hand, monetary policy uncertainty at the global and domestic levels affects the decision of investors to invest in host countries [56,57,58,59,60,61,62,63].

Julio, B. and Y. Yook [58] examined the effect of government policy uncertainty on cross-border capital flows, mainly focusing on the impact of political uncertainty, i.e., election and election cycle, on the FDI inflows from US companies to foreign affiliates. According to the study, the election cycle substantially impacts FDI more than domestic investment. Furthermore, the authors suggested that there is a possibility that government policies would change, reducing the projected payout to foreign investors and burdening them with extra layers of laws and regulations. Thus, FDI is considered to be more vulnerable to political uncertainty and institutional arrangements.

Hsieh, H.-C., S. Boarelli and T. H. C. Vu [57] analyzed the impact of economic policy uncertainty on foreign direct investment in a panel of twenty US Direct Investment Abroad host countries. The study found a strong causal link between monetary policy uncertainty and US outward FDI. Olanipekun, I. O. and G. Olasehinde-Williams [64] studied the interaction between 14 developing countries’ FDI inflows, FDI outflows, exchange rates, and monetary policy uncertainty from the United States. The finding revealed that the cause and effects of a financial policy varied, i.e., uncertainty from US monetary policy influenced the exchange rate pressure and FDI inflows of only 7 countries out of 14. The authors argued that there are other heterogeneous factors that cause FDI outflows in these countries. Policy uncertainty has serious implication in terms of predictability and, as a consequence, planning and business strategies. This implies that sudden or expected changes in economic policy could disrupt and cause external instability in the wider business eco-system, hampering FDI flows to developing and emerging economies.

Li, F., T. Liang and H. Zhang [59] look at the effect of economic policy uncertainty on cross-border Chinese mergers and acquisitions (M&A) in 21 developing and developed countries from 2001 to 2017. The study found that uncertainty in the economic policy of the home country drives cross-border M&A and uncertainty in the host country, as well as exerting a significant impact on cross-border M&A. Furthermore, the authors argued that the effects of economic policy uncertainty on cross-border M&A differed after and before the global financial crisis [65]. The host country’s monetary policy uncertainty positively correlated with M&A before the financial crisis and significantly negatively correlated after the crisis. In addition, this effect is significant in developed countries.

In contrast, insignificant effects take place in developing countries. Similarly, Zhou, K., S. Kumar, L. Yu and X. Jiang [61] “examined the effect of economic policy uncertainty on the choice of enterprise outward FDI entry mode. The study used the micro-level data of Chinese enterprises from 2000 to 2013”. The results show that economic policy uncertainty has significant impact on the entry mode of enterprises’ OFDI. Significantly, when the tension decreases, outward FDI rapidly increases. Meanwhile, other factors, such as the region, productivity level, factor intensity, financial development, and host country development level, are also important determinants of OFDI.

Choi, S., D. Furceri and C. Yoon [56] examined the effect of domestic policy uncertainty on net foreign direct investment (FDI) inflows in 16 OECD countries. They used the OECD bilateral FDI data and economic policy uncertainty index from 1985 to 2013. The study found that monetary policy uncertainty is a pushing factor of FDI, which reduces the flows of FDI in the source country. In other words, firms like to substitute domestic investment with investment abroad when facing economic policy uncertainty in the home country. Thus, policy uncertainty at home negatively affects FDI inflows. Furthermore, the study explored the role of financial development as a channel through which policy uncertainty affects FDI. The empirical data support the idea that financial deepening can mitigate the negative impact of policy uncertainty on FDI inflows.

Song, Hao, Hao, and Gozgor [56] investigate the nexus between economic policy uncertainty, outward foreign direct investment, and green total factor productivity in China by using firm-level data. The study found a negative relationship between economic policy uncertainty, outward FDI, and green total factor productivity. In addition, outward FDI positively contributes to the green total factor productivity of private and foreign firms in China [66,67].

According to the previous literature assessment, foreign direct investment is gaining increasing attention at the national and international levels. Foreign direct investment (FDI) is a crucial component of economic development in all nations, particularly the emerging ones. Several empirical research works on the link between FDI and monetary policy uncertainty have shown that local and global policy uncertainty has a detrimental impact on the flow of foreign capital, particularly to developing nations. On the other hand, the contribution of FDI to economic development is complicated. From a macro viewpoint, FDI flows are frequently seen as the source of employment, high productivity, competitiveness, and technological spillovers, particularly for the least developed nations [68]. Despite the growing importance of FDI, several emerging economies in Asia and worldwide face global and domestic issues, such as domestic and international economic uncertainty, which negatively affect FDI inflows [69,70]. Previous research, particularly in Asia, has given less attention to the influence of global and domestic economic policy uncertainty on foreign direct investment and domestic investment. Thus, this study fills the gap and investigates the impact of international and domestic economic policy uncertainty on foreign and domestic investment in 48 Asian countries from 2000 to 2020. This research also enables investors to gain insights to improve their understanding and knowledge and a framework for different market and risk analyses to assist them in their investment strategy and choice. It helps policymakers make specific decisions regarding laws and regulations and trade and investment policies to encourage more significant FDI inflows and sustain economic growth in developing countries, particularly in Asia.

Hypothesis 1.

Economic policy uncertainty affects the net inflow of FDI.

Hypothesis 2.

The impact of net inflows of foreign investment is more significant as compared to domestic investment.

Hypothesis 3.

Financial development plays a mediating role between domestic policy uncertainty, foreign direct investment, and domestic investment.

4. Materials and Methods

4.1. Data and Variables

We used several datasets to analyze the “effect of economic policy uncertainty on FDI net inflows”. Data come from different sources, i.e., the World Bank database (WDI), International Financial Statistics (IMF), and UNCATD. “We employ several country-level control variables to capture the macroeconomic environment” at home. The macroeconomic variables include real GDP growth, GDP per capita, trade openness (sum of exports and imports as a percentage of GDP), market size (measured by the total population), inflation rate, unemployment, and financial development. In addition, political risk is used as an indicator to capture the impact of domestic policy uncertainty on FDI and domestic investment. Table 1 represents the sources of data and summary statistics of all variables used in this study.

Interpreted/Dependent Variable: The primary independent variable in our study is the net inflow of foreign direct investment (IFDI) in 48 Asian countries from 2000 to 2020. The data on IFDI (US dollar at the current price of a million) were taken from the United Nations Conference on Trade and Development (UNCTAD) bilateral FDI database on flows and stocks. The dataset contains high and extreme values, i.e., the IFDI data have high positive and negative values. We used the following transformation method to overcome this problem, preserving negative values. This method is suggested by Refs. [57,71].

(1)$\mathrm{l}\mathrm{n}({IFDI}_t+\sqrt{{\left({IFDI}_t\right)}^2+1})$

This “transformation allows the IFDI value to preserve negative values in the dataset while being presented in the logarithmic scale. The same method is used to transform all other variables of” the study.

Explanatory/Independent Variable: Global economic policy uncertainty (GEPU) is our primary independent variable. We downloaded the recently constructed global economic policy uncertainty data from the website (www.policyuncertainty.com (accessed on 15 March 2022)). The data were created by Baker, S. R., N. Bloom and S. J. Davis [24]. They developed the indices related to policy uncertainty for 143 countries based on three main components: (a) newspaper coverage of policy-related uncertainty, (b) several tax code provisions expiring in the future year, and (c) disagreements on economic forecasting. Our analysis used the monthly global economic policy uncertainty index (GEPU) from 2000 to 2020. The GEPU index is constructed based on the monthly data, and we used the average method to convert monthly GEPU data into annual data.

(2)$G{EPU}_y=\frac{\sum _{m=1}^{12}{epu}_m}{12}$

where ${epu}_m$ is the index of global economic policy uncertainty in month m, and ${GEPU}_y$ is the annual data based on the average value of economic policy uncertainty. Furthermore, this study used an alternative measure for domestic policy uncertainty, i.e., political risk (DEPU). The data on PRS were taken from the International Country Risk Guide (ICR). We used two alternative indices to measure DEPU, i.e., political stability, absence of violence, and voice and accountability. Political stability and lack of violence contain four indicators (government stability, internal conflicts, external conflicts, and ethnic conflicts). Similarly, the voice and accountability index contains two measures (military involvement in politics and democratic accountability). High values indicate high political risk, and low values indicate low political risk.

Control Variables: This study used several control variables, which many scholars have previously used in estimating FDI and the economic policy uncertainty model. Among these, real GDP growth, GDP per capita income, trade openness, financial development, inflation rate, population, GDP per capita, and employment are good determinants of FDI. We used GDP growth as a proxy for market size, as suggested by Refs. [72,73,74]. For trade openness, we used the conventional values of the sum of export and imports to GDP. It is expected that a large market size and higher trade liberalization or degree of openness attract more foreign investors [75,76].

Furthermore, the inflation rate is another essential determinant in our econometric model. Inflation rate influences the investment decision, and a stable price level means countries implement more stabilizing policies and programs that help attain higher and steady economic growth in the short and long run. Thus, low price level policies and programs foster an investment climate [58,77]. The employment rate is used as a proxy for labor cost, as suggested by Blanton, R. G., S. L. Blanton and D. Peksen [78].

Interaction Variable: The current study contains two interaction variables. We include the interaction terms of financial development and global economic policy uncertainty index (FD* GEPU) and financial development and domestic monetary policy uncertainty index (FD* DEPU). “Financial development is measured by domestic private credit to GDP ratio, as suggested in the literature [79,80,81,82]”.

4.2. Methodology

To assess the impact of economic policy uncertainty on net inflows of FDI, we estimate the following equation. Our estimation strategy is similar to the one in Refs. [21,28,58]; they evaluate the impact of US presentational election uncertainty on FDI inflows.

(3)${IFDI}_{it}={\varnothing }_i+{\varnothing }_{it}+{\mathsf{\Phi }\mathrm{G}\mathrm{E}\mathrm{P}\mathrm{U}}_t+{\beta X}_{it}+{\epsilon }_{it}$

where ${IFDI}_{it}$ is our main dependent variable of net inflow of foreign direct investment; ${\varnothing }_i$ represents the country’s fixed effects, including a common language, culture, distance, trade agreements, etc., “as well as between-country-level time-invariant factors”; ${\varnothing }_{it}$ is the host country’s time fixed effect, including the macroeconomic shocks, policy changes, and indirect effects of policy uncertainty on IFDI inflows; $X_{it}$ represents the country-level macroeconomic control variables. ${\mathrm{G}\mathrm{E}\mathrm{P}\mathrm{U}}_t$ is our variable of interest, representing the world economic policy uncertainty; $\mathsf{\Phi }$ and $\beta$ define the unknown parameters to be estimated. If the sign of $\mathsf{\Phi }$ is negative, monetary policy uncertainty reduces the net inflows of FDI and vice versa.

The study includes a total of 48 countries from Asia. According to the previous literature on FDI inflows, the determinants of FDI differ from developing to developed countries and expand to developing countries and regions, i.e., north to south and south to north regions [56]. Thus, we further divided our sample countries into five regions for our analysis—for instance, “Central Asia, Eastern Asia, Southeastern Asia, South Asia, and Western Asia”. Furthermore, we include the domestic policy uncertainty variables in our model to check the source of domestic policy uncertainty in IFDI and domestic investment.

(4)${IFDI}_{it}={\varnothing }_i+{\varnothing }_{it}+{\mathsf{\Phi }\mathrm{G}\mathrm{E}\mathrm{P}\mathrm{U}}_t+\psi {DEPU}_{it}+{\beta X}_{it}+{\epsilon }_{it}$

where ${DEPU}_{it}$ represents domestic policy uncertainty, including political risk. We further include the interaction effect of financial development with global economic policy uncertainty and domestic policy uncertainty.

(5)${IFDI}_{it}={\varnothing }_i+{\varnothing }_{it}+{\mathsf{\Phi }\mathrm{E}\mathrm{P}\mathrm{U}}_t+({\psi }_1+{\psi }_2{DEPU)FD}_{it}+({\omega }_1+{\omega }_2{GEPU)FD}_{it}+{\beta X}_{it}+{\epsilon }_{it}$

where $\omega$ and $\psi$ capture the interaction effect of financial development on net FDI inflows. We estimate Equations (3) and (4) by using the system-generalized method of moment proposed by Arellano, M. and O. Bover [83] and Blundell, R. and S. Bond [84]. There are several reasons for using the SYS-GMM model. First, the GMM estimator is widely used to address the endogeneity problem in panel data estimation [83,84]. Secondly, the GMM estimator also avoids simultaneity or reverse causality problems. Third, the GMM estimator provides consistent and unbiased results.

5. Results and Discussion

5.1. Descriptive Statistics and Correlation Analysis

The aggregate descriptive statistics and correlation analysis of the research variables are presented in Table 2 and Table 3. Our sample comprises unbalanced panel data, and some data observations are missing. Before being used in the estimation, all research variables are transformed into logarithmic form. According to the aggregate data, the average inward FDI in 48 Asian countries is 1.458 percent of GDP. The standard deviation is 1.13, with −4.3 and 4.7 being the minimum and maximum values, respectively. Domestic investment has a mean value of 3.86, higher than inward FDI. Inward FDI has a minimum and maximum value of 2.59 and 4.91, respectively. When the mean values of the global and domestic economic policy uncertainty indices are compared, the GEPU index value is greater than the DEPU index value. Due to COVID-19, global uncertainty worldwide is at an all-time high in 2020, and the GEPU will reach its maximum value. The highest DEPU rating was 2.98 in 2015, reported in the Kyrgyz Republic. The average trade-to-GDP ratio is 5.05, with a low of 0.16 and a maximum of 6.7. The average GDP per capita of 48 Asian nations is 8.84, with the highest at 12 and the lowest at 5.1. Vietnam had the lowest GDP per capita among the sample nations in 2014, at 5.199, whereas Oman had the highest GDP per capita in 2006. The average GDP growth in the sample countries is 1.72%, with a high of 4.59% and a low of −4.72%. The inflation and unemployment rates are 2.01 and 4.76 percent, respectively. The highest employment rate is 5.1, whereas the maximum inflation rate is 8.2.

Table 3 shows the pairwise correlation coefficients of variables. According to the results, GEPU (r = −0.104 *) and domestic policy uncertainty (r = 0.059 *) are significantly negatively related to inward FDI. Domestic investment (r = 0.197 *), GDP per capita (r = 0.043 *), GDP growth (r = 0.222 *), and financial development (r = 0.122 *) are significantly negatively related to IFDI. “According to the rule of thumb, if all coefficient values are below 0.7, then they are not considered a serious correlation problem. Thus, our results show that none of the correlation coefficients is higher than the suggested threshold value, indicating the validity of selected variables”.

5.2. Baseline Regression: System GMM Results

Table 4 and Table 5 provide the baseline regression findings for 48 Asian countries. Model (1) depicts the outcomes using global economic policy uncertainty (GEPU) only, whereas model (2) uses the lagged dependent variable and only considers domestic economic policy uncertainty (DEPU). Model (3) depicts the findings of control variables without the study’s main variables. Models (4) and (5) present the results for the whole model, with GEPU and DEPU as the significant variable.

The significant positive coefficients of lagged IFDI and significant negative coefficients of GEPU and DEPU in all specifications confirm policy uncertainty’s negative effect on foreign direct investment inflows in 48 Asian countries. This indicates that both domestic and global economic policy uncertainty are associated with foreign direct investment, but the influence of global uncertainty is more remarkable than domestic economic uncertainty. An increase of 1% in global economic uncertainty leads to a decrease of 18% in inward FDI, while domestic uncertainty decreases inward IFDI flows by 12%, as shown in models 4 and 5.

The coefficient of control variables shows mixed results. In models 3 to 5, the estimated coefficient of trade is statistically significant and varies between 5% and 7%. This indicates a 1% increase in trade openness and inward FDI from 5% to 7%, respectively. Similarly, the coefficient of GDP growth is positive and significant in model 3. The coefficient varies between 0.17 and 0.23, respectively. This implies that a 1% increase in GDP growth leads to a 1.7% to 2.3% increase in inward FDI. The significant and positive coefficients of population and GDP per capita in models 4 and 5 show that the decision to invest in a foreign country increases with a huge population and GDP per capita income. Our empirical results are more consistent with previous studies [85]. Accordingly, a growing economy should provide more profit prospects for foreign investors and boost investor confidence. As a result, as the host nation’s GDP grows, inward FDI will increase in Asian countries.

Furthermore, the negative signs of inflation and unemployment rate show that an increase in the price level leads to a fall in the actual value of earnings by foreign investors. This also reduces foreign investors’ capacity to invest in a host country with high unemployment and inflation rates. The coefficient of financial development (FD) indicates a positive sign, which means that a well-developed financial system supports foreign and domestic investors during economic policy uncertainty. The results are consistent with Ramasamy, B. and M. Yeung [86] and Nkoa, B. E. O. [87], who found a positive effect of financial development on inward FDI.

Table 5 shows the influence of global and domestic economic policy uncertainty on domestic investment in 48 Asian countries. The changes in our dependent variable are the primary difference between Table 3 and Table 4. The empirical findings are consistent with our prior results, indicating that global and domestic policy uncertainty significantly impact domestic investment. The impact of international economic policy uncertainty is more incredible than domestic policy uncertainty, as shown in models 1–2 and baseline models 4–5. Furthermore, our results show that inward FDI is more sensitive to domestic investment. Other coefficients, such as lagged IFDI, trade, GDP growth, GDP per capita, population, and financial development, are significant positive determinants of domestic investment in whole sample countries. In comparison, inflation and employment rate are negative determinants of domestic investment.

5.3. Interaction Effect of Financial Development

Several macroeconomic and international investment literature studies emphasized “the role of financial development concerning domestic and foreign investment and” economic growth. The studies claim that the size and structure of the financial market are associated with the volume of domestic and foreign “investment” [79,80]. A well-developed financial system alleviates investor anxiety toward economic policy uncertainty. Thus, the financial sector contributes to a more favorable investment environment in domestic and foreign markets [20,81]. However, this study introduced two interaction terms, FD*GEPU and FD*DEPU, to examine the influence of financial sector development. The first interaction term concerns the influence of the economic effect on global monetary policy uncertainty concerning FDI inflows [88]. The second interaction term examines the influence of financial development on domestic economic policy uncertainty as it relates to domestic investment.

Table 6 reports the interaction effect of financial development using the system GMM estimation method. Models 1 and 2 show the interaction effect of FD on inward FDI, while models 3 and 4 show the interaction effect of FD on domestic investment. The coefficient of the interaction term is negative but statistically insignificant in model 3. However, it is significant and negative in model 4. This means that FD has no effect on mitigating the negative impact of global economic policy uncertainty on foreign investment. A significant positive coefficient in models 2 and 3 indicates that FD mitigates the adverse effects of domestic policy uncertainty on foreign and domestic investment.

5.4. Robustness Check: Subsample Analysis

In this section, we performed the robustness test to check the consistency of our empirical results. In carrying this out, the whole sample countries are divided into subsamples, such as “Central Asia, Eastern Asia, Southeastern Asia, South Asia, and Western Asia”. Among these, five countries are from central Asia, eight are from eastern Asia, ten are from southeastern Asia, nine are from south Asia, and sixteen are from western Asia. The estimated coefficients of our FDI–policy uncertainty baseline model are shown in Table 7 and Table 8.

For each region in Asia, Table 7 displays the estimated coefficients of the inward FDI–policy uncertainty baseline model. The positive significant lagged IFDI and negative coefficient of global economic policy uncertainty show that policy uncertainty is negatively associated with inward FDI in all specifications. For all regions, the findings are the same as our earlier estimates. This suggests that global economic policy uncertainty has a detrimental impact on foreign direct investment.

In addition, trade, GDP growth, population, financial development, and GDP per capita are the positive determinants of FDI. At the same time, inflation and the employment rate are negative determinants of FDI. However, the impact of GEPU is different, i.e., eastern and south Asia are highly impacted compared to other countries.

Table 8 shows the impact of global economic policy uncertainty on domestic investment across the five regions of Asia. Our results are the same as our previous estimates; the positive and significant coefficient of lagged domestic investment and significant negative coefficient of GEPU show that policy uncertainty negatively affects foreign direct investment across the five regions of Asia.

5.5. Practical Application of the Study

This study adds to the literature by using dynamic panel estimations to examine the impact of domestic and global EPU on FDI inflows in a sample of 48 Asian developing countries during the period 2000–2020. Thus, we examine the role of financial development amid domestic and global economic policy uncertainty across the region. We present remarkable insights. An increase in domestic EPU has a large negative effect on FDI inflows. Similarly, an increase in the global EPU level also has a strong impact on FDI inflows. Financial development is critical to mitigating the harmful effects of economic policy uncertainty. A liberalized and developed financial sector has the capacity to absorb some of the negative impacts coming from economic and financial uncertainty and volatilities. Our findings show that financial development plays a positive role in mitigating the negative effect of domestic and global economic policy uncertainty in Asian countries. Policymakers should consider the role of institutions, which can create a conducive business eco-system and reduce uncertainty in business transactions as a development paradigm with regard to the negative effect of domestic policy and global policy uncertainty. Furthermore, the negative impact of policy uncertainty goes beyond the country of origin, necessitating rigorous monitoring of policy uncertainty both at home and abroad.

6. Conclusions and Policy Recommendations

Several factors, including natural calamities, geo-political instability, trade wars, and changes in geo-economics, have brought the effects of economic policy uncertainty on FDI inflows to the limelight in a new context. Our finding contributes to the growing body of literature examining the relationship between economic policy uncertainty, foreign capital flows, and investor decision in 48 Asian countries from 2000 to 2020. We use the inward FDI and domestic investment data as the primary outcome variables, while global economic policy uncertainty and domestic policy uncertainty data are the primary variable of the study. The FDI and domestic investment data enable us to identify the effect of external and internal uncertainty. We used the generalized moment method, which also produces robust empirical results.

Our findings suggest that increasing policy uncertainty at the global level has a significant negative impact on inward FDI in our sample countries. We use the global economic policy uncertainty (GEPU) index to intensify foreign and domestic investment policy uncertainty. Furthermore, we employed the system GMM method and lagged dependent variable as an instrument to resolve endogeneity and serial correlation in residuals in our baseline findings. We investigate the impact of financial development in explaining how increased global policy uncertainty and domestic uncertainty decrease inward FDI and domestic investment. The results show that FD has no mitigating role in the negative effect of international economic policy uncertainty on inward FDI. In contrast, it has a negative role in mitigating the adverse effects of domestic policy uncertainty.

Domestic policy uncertainty has a negative impact on inward FDI in the whole sample and subsample countries in Asia. In addition, our results show that foreign investment is more sensitive than domestic investment. The impact of domestic and global uncertainty on inward FDI is greater than domestic investment. In addition to these effects, trade, GDP growth, GDP per capita, population, inflation rate, employment rate, and financial development are the main determinants of foreign direct investment across 48 Asian countries.

6.1. Policy Recommendations

Our findings have many policy implications, and we provide the following policy recommendations.

• Despite economic challenges and geo-economic rhetoric, FDI continues to flow to Asian countries. However, policymakers should emphasize the mitigating mechanism of uncertainty by controlling the negative effect of policy uncertainty on FDI, promote FDI inflows, and attract and encourage foreign investors. Geo-political rivalry and decoupling of the global supply chain are further threatening economic uncertainty in the global market. Cooperation among the major economic powers in targeting the policies toward financial stability is needed to create an FDI-friendly business eco-system.

• Decoupling of the global supply chains and geo-economic considerations may shift certain FDI destinations but may not result in any drastic shifts. FDI may leave some markets, such as China, and move to other Asian markets, such as Vietnam, India, Bangladesh, and southeastern Asian markets. Policymakers need to take these shifts into consideration and develop their economic and financial policies to woo a higher share of FDI to their respective economies.

• Our findings imply that a well-developed financial system assists in the management of detrimental effects of economic uncertainty. Policymakers should go above and beyond in their planning to ensure a well-functioning financial system, which reduces the negative impact of uncertainty on FDI. Finally, it is critical to keep a careful eye on policy uncertainties both at home and abroad. Other aspects of the economy, such as trade liberalization, inflation, and domestic employment levels, should be considered by policymakers. Low inflation and unemployment rates and low political risk encourage FDI inflow into Asian countries. The role of institutions has become essential in increasing the attractiveness of a local economy, and the government needs to take proactive steps to encourage FDI inflows.

6.2. Limitation of the Research and Future Direction

Our research has two limitations. First, the findings of this study are generalized and are comparable to Asian developing countries. Second, the sample of countries is limited. Therefore, this study can be extended to increase the sample size of countries for the analysis of high-, low-, and middle-income countries. Economic structure might also be a determining factor in attracting FDI. For example, a manufacturing-focused economy will attract more FDI than a service sector economy in the developing Asian markets. More variables may be analyzed in the future to determine their effect on FDI inflow in high-, middle-, and low-income countries. In addition, with regard to the methodology of the study, machine-learning techniques can improve the quality of the model. Furthermore, the roles of institutions, trade policy, and export and import diversification in economic policy uncertainty can be added for examination.

Author Contributions

Conceptualization, S.B.; Methodology, B.Z. and J.M.; Validation, K.S.; Formal analysis, M.A.K. and K.S.; Data curation, B.Z. and M.A.K.; Writing—original draft, S.B.; Writing—review & editing, J.M. and M.S.A.; Supervision, V.R. and M.S.A.; Funding acquisition, V.R. and M.S.A. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Inward and outward FDI (Source: UNCATD Data (1990–2020)).

Figure 2. Inward and outward FDI inflows in Asian region. Source: UNCATD Data (1990–2020).

Figure 3. Real GDP growth and global economic uncertainty in Asia. Source: UNCATD Data (1990–2020).

Table 1

Definition of variables.

Variable Name	Variable Label	Definition	Source
Inward Foreign Direct Investment	IFDI	Foreign “direct investment, net inflows (% of GDP)”	UNCATD
Domestic Economic Policy Uncertainty	DEPU	Political “Stability and Absence of Violence/Terrorism”	ICR
Global Economic Policy Uncertainty	GEPU	Global “Economic Policy Uncertainty Index: Current Price Adjusted GDP, Index, Annual”	www.policyuncertainty.com (accessed on 15 April 2022)
Domestic Investment	DI	Gross “fixed capital formation (% of GDP)”	WDI
Real GDP Growth	GDPG	GDP growth (annual %)	UNCATD
GDP Per Capita Income	Per. Cap	GDP “per capita (Constant 2015 US$)”	UNCATD
Trade	TR	Trade “(% of GDP)”	UNCATD
Inflation Rate	Inf.	Inflation, “consumer prices (annual %)”	WDI
Employment Rate	EM	Employment “to population ratio, 15+, total (%)”	WDI
Financial Development	FD	Domestic “credit to the private sector by banks (% of GDP)”	WDI
Population	POP	Population, total	WDI

Table 2

Descriptive statistics.

Variable	Obs	Mean	Std. Dev.	Min	Max
Inward FDI	892	1.458	1.136	−4.308	4.475
Domestic Investment	967	3.869	0.296	2.539	4.931
Global EPU	984	5.442	0.421	4.831	6.461
Domestic EPU	960	2.445	0.388	0.881	2.998
Trade	850	5.054	0.772	0.167	6.774
GDP Per Capita	934	8.840	1.649	5.199	12.134
GDP Growth	925	1.724	1.628	−4.724	4.594
Population	1005	16.913	1.590	13.009	19.906
Inflation Rate	880	2.016	1.355	−2.285	8.231
Employment Rate	976	4.761	0.230	4.124	5.168
Financial Development	888	4.241	0.957	0.637	6.329

Table 3

Correlations analysis.

Variables	(1)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)
(1) IFDI	1.000
(3) GEPU	−0.104 *	1.000
(4) DI	0.197 *	0.105 *	1.000
(5) Trade	0.340 *	0.016	0.141 *	1.000
(6) GDP Per Capita	0.043 *	0.217 *	0.130 *	0.233 *	1.000
(7) GDP Growth	0.222 *	−0.294 *	0.165 *	−0.024 *	−0.036 *	1.000
(8) Population	−0.191 *	0.074 *	−0.016	−0.419 *	−0.194 *	0.036 *	1.000
(9) Inflation Rate	−0.036 *	−0.165 *	0.011	−0.145 *	−0.474 *	0.151 *	0.116 *	1.000
(10) EMP R.	−0.070 *	0.010	0.158 *	0.027 *	0.185 *	0.071 *	−0.053 *	−0.109 *	1.000
(11) F Dev.	0.122 *	0.218 *	0.277 *	0.342 *	0.567 *	−0.129 *	0.084 *	−0.463 *	0.167 *	1.000
(12) DEPU	0.059 *	−0.007	−0.026 *	0.053 *	−0.121 *	0.035 *	−0.009	0.094 *	0.035 *	−0.133 *	1.000

* p < 0.1.

Table 4

Impact of economic policy uncertainty on inward FDI (IFD).

	Dependent Variable: Inward FDI (IFD)
	(1)	(2)	(3)	(4)	(5)
Variables	GEPU Only	DEPU Only	Without GEPU	Baseline GEPU	Baseline DEPU
Lagged IFDI	0.326 ***	0.387 ***	0.191 ***	0.138 ***	0.210 ***
	(0.0502)	(0.0492)	(0.0579)	(0.0750)	(0.0785)
Trade			0.591 ***	0.565 ***	0.722 ***
			(0.167)	(0.176)	(0.197)
GDP Growth			0.233 ***	0.171 ***	0.188 ***
			(0.0504)	(0.0619)	(0.0630)
Population			0.0713	0.186 **	0.147 ***
			(0.0783)	(0.0873)	(0.057)
Inflation Rate			−0.0866	−0.220 ***	0.0533
			(0.0645)	(0.0736)	(0.0752)
Employment Rate			−0.115	−0.600 ***	−0.134
			(0.192)	(0.200)	(0.201)
GDP Per Capita			0.01621	0.1906 *	0.194 *
			(0.0739)	(0.0887)	(0.081)
FD			0.1498 ***	0.132 ***	0.116 ***
			(0.042)	(0.074)	(0.051)
GEPU	−0.207 ***			−0.188***
	(0.108)			(0.095)
DEPU		−0.159 ***			−0.129 ***
		(0.075)			(0.049)
Constant	2.221 ***	0.524 **	−0.883	3.507	0.657
	(0.575)	(0.253)	(2.328)	(2.562)	(2.794)
Number of Years	21	21	21	21	21
Countries	48	48	48	48	48
AR (1)	−9.21	−8.02	−7.88	−6.62	−6.31
Prob.	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
AR (2)	0.46	0.83	0.62	0.88	0.69
Prob.	(0.647)	(0.408)	(0.538	(0.377)	(0.489)
Instruments	52	52	66	78	66
Hansen Test	0.179	0.258	0.321	0.349	0.307

Standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

Table 5

Impact of economic policy uncertainty on domestic investment.

	Dependent Variable: Domestic Investment
	(1)	(2)	(3)	(4)	(5)
Variables	GEPU Only	DEPU Only	Without GEPU	Baseline GEPU	Baseline DEPU
Lagged IFDI	0.455 ***	0.416 ***	0.434 ***	0.451 ***	0.435 ***
	(0.0582)	(0.0586)	(0.0549)	(0.0593)	(0.0548)
Trade			0.1280 ***	0.1655 ***	0.0301
			(0.0385)	(0.0376)	(0.0384)
GDP Growth			0.1168 ***	0.13687 ***	0.1068 ***
			(0.0101)	(0.0109)	(0.0101)
Population			0.14778 ***	0.1161	0.15991 **
			(0.0161)	(0.0849)	(0.0842)
Inflation Rate			−0.0732 ***	−0.0709 ***	−0.0735 ***
			(0.0133)	(0.0142)	(0.0133)
Employment Rate			−0.1989**	−0.170 **	−0.0207
			(0.0938)	(0.0987)	(0.0939)
GDP Per Capita			0.04163 ***	0.0341 ***	0.03207 **
			(0.0183)	(0.0194)	(0.0184)
FD			0.0644 **	0.0232	0.0652 **
			(0.0319)	(0.0352)	(0.0315)
GEPU	−0.0850 **			−0.0749 **
	(0.0349)			(0.0373)
DEPU		−0.0396 ***			−0.0286***
		(0.0137)			(0.0140)
Constant	1.645 ***	2.193 ***	1.538 **	−0.170	1.495 **
	(0.272)	(0.249)	(0.602)	(0.662)	(0.610)
Number of Years	21	21	21	21	21
Countries	48	48	48	48	48
AR (1)	−5.04	−5.34	−5.26	−7.08	−5.32
Prob.	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
AR (2)	−1.83	−1.05	−0.93	−0.46	−1.11
Prob.	(0.068)	(0.294)	(0.354)	(0.643)	(0.269)
Instruments	72	72	72	75	75
Hansen Test	0.196	0.328	0.770	0.361	0.301

Standard errors in parentheses, *** p < 0.01, ** p < 0.05.

Table 6

Interaction effect of financial development.

	(1)	(2)	(3)	(4)
Variables	Dependent Variable: Inward FDI		Dependent Variable: Domestic Investment
GEPU	−0.588		0.759 ***
	(1.266)		(0.250)
FD*GEPU	−0.150		−0.147 ***
	(0.269)		(0.0530)
DEPU		−0.241 ***		0.2258 ***
		(0.127)		(0.10549)
FD*DEPU		0.1417**		0.224 ***
		(0.0725)		(0.01270)
Trade	0.550 ***	0.967 ***	0.0862 **	0.00167
	(0.178)	(0.217)	(0.0380)	(0.00702)
GDP Growth	0.174 ***	0.128 **	0.01452 *	0.012502 *
	(0.0622)	(0.0600)	(0.0108)	(0.00186)
Population	0.189 ***	0.145	0.0269 ***	−0.0148 ***
	(0.0874)	(0.0979)	(0.0153)	(0.00260)
Inflation Rate	−0.220 ***	0.00502	−0.0683 ***	−0.01631 **
	(0.0736)	(0.0722)	(0.0141)	(0.00256)
Employment Rate	−0.615 ***	−0.0909	−0.204 ***	−0.0364 ***
	(0.201)	(0.207)	(0.0986)	(0.0171)
GDP Per Capita	0.1908 ***	0.1275 **	0.0333 *	0.01761 **
	(0.0886)	(0.0647)	(0.0195)	(0.00336)
FD	0.665	0.139	0.788 ***	−0.845 ***
	(1.444)	(0.384)	(0.278)	(0.0124)
Lagged IFDI	0.145 **	0.129 *
	(0.0762)	(0.0738)
Lagged Domestic Inv.			0.420 ***	0.0397 ***
			(0.0599)	(0.0111)
Constant	7.382	−0.991	−4.142 ***	3.851 ***
	(7.418)	(2.609)	(1.577)	(0.115)
Number of Years	21	20	21	20
Countries	48	48	48	48
AR (1)	−6.75	−5.72	−5.41	−3.20
Prob.	(0.000)	(0.000)	(0.000)	(0.001)
AR (2)	−0.82	−0.648	−0.89	−1.41
Prob.	(0.413)	(0.473)	(0.374)	(0.159)
Instruments	83	83	72	75
Hansen Test	0.306	0.612	0.827	0.52

Standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

Table 7

Impact of global economic policy uncertainty on inward FDI (IFD).

	(1)	(2)	(3)	(4)	(5)
Variables	Central Asia	Eastern Asia	Southeastern Asia	South Asia	Western Asia
Lagged IFDI	0.573 *	0.0732	0.226 **	0.0406	0.398 ***
	(0.333)	(0.137)	(0.0979)	(0.119)	(0.0810)
GEPU	−1.139 *	−0.268 *	−0.390 *	−0.249 *	−0.393 **
	(0.596)	(0.119)	(0.141)	(0.124)	(0.183)
Trade	2.563 **	0.195 **	1.554 ***	0.260 *	0.253 **
	(1.033)	(0.101)	(0.216)	(0.121)	(0.124)
GDP Growth	0.282 ***	0.246 ***	0.227 **	0.188 *	0.265 ***
	(0.108)	(0.0930)	(0.112)	(0.103)	(0.0708)
Population	0.922	0.448 ***	0.0503	0.2220 ***	0.0951
	(0.654)	(0.150)	(0.119)	(0.104)	(0.109)
Inflation Rate	−0.397 ***	−0.0121	−0.218 *	−0.426 **	−0.1752 ***
	(0.199)	(0.126)	(0.122)	(0.179)	(0.0810)
Employment Rate	−2.264 **	0.121	−1.128 ***	−0.1894 ***	−0.436 **
	(1.080)	(0.599)	(0.437)	(0.618)	(0.223)
FD	1.434 ***	0.609 ***	0.315 **	0.7448 **	0.393 ***
	(0.712)	(0.305)	(0.165)	(0.357)	(0.173)
GDP Per Capita	0.327 ***	0.455 **	0.339 **	0.344 *	0.3152 **
	(0.199)	(0.245)	(0.172)	(0.195)	(0.0930)
Constant	−23.40	9.743 ***	−3.569	−2.619	0.655
	(15.86)	(3.567)	(4.000)	(4.598)	(3.032)
Number of Countries	5	8	10	9	16
Number of Years	21	21	21	21	21
AR (1)	−4.50	−2.49	−1.82	−3.23	−4.12
Prob.	(0.000)	(0.013)	(0.068)	(0.001)	(0.000)
AR (2)	−1.53	−0.47	−1.31	−0.76	0.43
Prob.	(0.127)	(0.639)	(0.189)	(0.448)	(0.666)
Instruments	11	11	21	33	26
Hansen Test	3.08	7.88	1.61	0.112	0.341

Standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

Table 8

Impact of global economic policy uncertainty on domestic investment.

	(1)	(2)	(3)	(4)	(5)
Variables	Central Asia	Eastern Asia	Southeastern Asia	South Asia	Western Asia
Lagged Domestic Inv.	0.882	0.473 ***	0.332 ***	0.243 ***	0.531 ***
	(1.339)	(0.0856)	(0.0834)	(0.0850)	(0.0612)
GEPU	−0.525 ***	−0.2417 ***	−0.2578 ***	−0.2136 ***	−0.1962 ***
	(0.120)	(0.0721)	(0.0574)	(0.0580)	(0.0411)
Trade	0.1914	0.1290 ***	0.150 **	0.1299 *	0.157 **
	(0.791)	(0.0659)	(0.0759)	(0.0727)	(0.0715)
GDP Growth	0.290 ***	0.1353 ***	0.2246 ***	0.1125 ***	0.0384 **
	(0.111)	(0.0304)	(0.0266)	(0.0188)	(0.0171)
Population	0.130	0.0734 **	0.0949 **	−0.0428	0.1334 ***
	(1.741)	(0.0362)	(0.0383)	(0.0314)	(0.0226)
Inflation Rate	−0.157	−0.0848 ***	−0.0715 ***	−0.0250	−0.0972 ***
	(0.215)	(0.0298)	(0.0266)	(0.0331)	(0.0188)
Employment Rate	0.172	0.157	0.232 **	0.1985 **	0.1918 **
	(0.494)	(0.114)	(0.0954)	(0.108)	(0.0841)
FD	0.1811 ***	0.1432 **	0.1734 ***	0.1243 ***	0.9364 ***
	(0.002)	(0.0786)	(0.0500)	(0.0602)	(0.0479)
GDP Per Capita	0.9354 ***	0.6842 *	0.4203 **	0.0536 ***	0.0454 ***
	(0.482)	(0.0345)	(0.0225)	(0.0266)	(0.0199)
Constant	−1.310	1.007	2.816 **	3.244 ***	−0.395
	(45.45)	(0.986)	(1.168)	(1.081)	(0.708)
Number of Years	21	21	21	21	21
Number of Countries	5	8	10	9	16
AR (1)	−2.61	0.013	−3.17	−2.35	−2.74
Prob.	(0.000)	(0.005)	(0.000)	(0.019)	(0.006)
AR (2)	−0.83	−0.63	−0.65	−0.05	−0.62
Prob.	(0.634)	(0.528)	(0.517)	(0.962)	(0.536)
Instruments	11	21	21	33	26
Hansen Test	0.686	0.144	0.455	0.271	0.719

Standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

References

1. Adams, S. Foreign direct investment, domestic investment, and economic growth in sub-saharan africa. J. Policy Model.; 2009; 31, pp. 939-949. [DOI: https://dx.doi.org/10.1016/j.jpolmod.2009.03.003]

2. Liu, H.; Islam, M.A.; Khan, M.A.; Hossain, M.I.; Pervaiz, K. Does financial deepening attract foreign direct investment? Fresh evidence from panel threshold analysis. Res. Int. Bus. Financ.; 2020; 53, 101198. [DOI: https://dx.doi.org/10.1016/j.ribaf.2020.101198]

3. Nistor, P. Fdi implications on brics economy growth. Procedia Econ. Financ.; 2015; 32, pp. 981-985. [DOI: https://dx.doi.org/10.1016/S2212-5671(15)01557-9]

4. Acquah, A.M.; Ibrahim, M. Foreign direct investment, economic growth and financial sector development in africa. J. Sustain. Financ. Invest.; 2020; 10, pp. 315-334. [DOI: https://dx.doi.org/10.1080/20430795.2019.1683504]

5. Osei, M.J.; Kim, J. Foreign direct investment and economic growth: Is more financial development better?. Econ. Model.; 2020; 93, pp. 154-161. [DOI: https://dx.doi.org/10.1016/j.econmod.2020.07.009]

6. Raheem, I.; Oyinlola, M.A. Foreign direct investment-economic growth nexus: The role of the level of financial sector development in africa. J. Econ. Int. Financ.; 2013; 5, pp. 327-337. [DOI: https://dx.doi.org/10.5897/JEIF2013.0542]

7. Paul, J.; Feliciano-Cestero, M.M. Five decades of research on foreign direct investment by mnes: An overview and research agenda. J. Bus. Res.; 2021; 124, pp. 800-812. [DOI: https://dx.doi.org/10.1016/j.jbusres.2020.04.017] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32292218]

8. Yu, C.-M.J. The experience effect and foreign direct investment. Weltwirtschaftliches Arch.; 1990; 126, pp. 561-580. [DOI: https://dx.doi.org/10.1007/BF02709037]

9. Safiullin, R.N.; Afanasyev, A.S.; Reznichenko, V.V. The concept of development of monitoring systems and management of intelligent technical complexes. Записки Гoрнoгo Института; 2019; 237, pp. 322-330. [DOI: https://dx.doi.org/10.31897/pmi.2019.3.322]

10. Vasilyeva, N.V.; Boikov, A.V.; Erokhina,; Trifonov, A.Y. Automated digitization of radial charts. Записки Гoрнoгo Института; 2021; 247, pp. 82-87. [DOI: https://dx.doi.org/10.31897/PMI.2021.1.9]

11. Chintrakarn, P.; Herzer, D.; Nunnenkamp, P. Fdi and income inequality: Evidence from a panel of us states. Econ. Inq.; 2012; 50, pp. 788-801. [DOI: https://dx.doi.org/10.1111/j.1465-7295.2011.00384.x]

12. Wang, E.Z.; Lee, C.C. Foreign direct investment, income inequality and country risk. Int. J. Financ. Econ.; 2021; [DOI: https://dx.doi.org/10.1002/ijfe.2542]

13. Busse, M.; Nunnenkamp, P. Gender disparity in education and the international competition for foreign direct investment. Fem. Econ.; 2009; 15, pp. 61-90. [DOI: https://dx.doi.org/10.1080/13545700802528315]

14. Aizenman, J.; Spiegel, M.M. Institutional efficiency, monitoring costs and the investment share of fdi. Rev. Int. Econ.; 2006; 14, pp. 683-697. [DOI: https://dx.doi.org/10.1111/j.1467-9396.2006.00595.x]

15. Dixit, A. Governance, Development, and Foreign Direct Investment. 2012; Available online: https://hdl.handle.net/1814/19936 (accessed on 15 March 2022).

16. Bonaime, A.; Gulen, H.; Ion, M. Does policy uncertainty affect mergers and acquisitions?. J. Financ. Econ.; 2018; 129, pp. 531-558. [DOI: https://dx.doi.org/10.1016/j.jfineco.2018.05.007]

17. Rosario, S. Impact of economic policy uncertainity on key economic indicators of india. Manag. Dyn.; 2020; 20, pp. 26-38. [DOI: https://dx.doi.org/10.57198/2583-4932.1015]

18. Tajaddini, R.; Gholipour, H.F. Economic policy uncertainty, r&d expenditures and innovation outputs. J. Econ. Stud.; 2020; 48, pp. 413-427.

19. Caggiano, G.; Castelnuovo, E.; Figueres, J.M. Economic policy uncertainty and unemployment in the united states: A nonlinear approach. Econ. Lett.; 2017; 151, pp. 31-34. [DOI: https://dx.doi.org/10.1016/j.econlet.2016.12.002]

20. Karnizova, L.; Li, J.C. Economic policy uncertainty, financial markets and probability of us recessions. Econ. Lett.; 2014; 125, pp. 261-265. [DOI: https://dx.doi.org/10.1016/j.econlet.2014.09.018]

21. Arouri, M.; Estay, C.; Rault, C.; Roubaud, D. Economic policy uncertainty and stock markets: Long-run evidence from the us. Financ. Res. Lett.; 2016; 18, pp. 136-141. [DOI: https://dx.doi.org/10.1016/j.frl.2016.04.011]

22. Pastor, L.; Veronesi, P. Uncertainty about government policy and stock prices. J. Financ.; 2012; 67, pp. 1219-1264. [DOI: https://dx.doi.org/10.1111/j.1540-6261.2012.01746.x]

23. Uddin, M.; Chowdhury, A.; Zafar, S.; Shafique, S.; Liu, J. Institutional determinants of inward fdi: Evidence from pakistan. Int. Bus. Rev.; 2019; 28, pp. 344-358. [DOI: https://dx.doi.org/10.1016/j.ibusrev.2018.10.006]

24. Baker, S.R.; Bloom, N.; Davis, S.J. Measuring economic policy uncertainty. Q. J. Econ.; 2016; 131, pp. 1593-1636. [DOI: https://dx.doi.org/10.1093/qje/qjw024]

25. Li, X.; Wang, J.; Yang, C. Risk prediction in financial management of listed companies based on optimized bp neural network under digital economy. Neural Comput. Appl.; 2022; pp. 1-14. [DOI: https://dx.doi.org/10.1007/s00521-022-07377-0]

26. Ali, S.; Guo, W. Determinants of fdi in china. J. Glob. Bus. Technol.; 2005; 1, pp. 21-33.

27. Gupta, K.; Ahmed, S. Determinants of fdi in south asia: Does corruption matter?. Int. J. Econ. Bus. Res.; 2018; 16, pp. 137-161. [DOI: https://dx.doi.org/10.1504/IJEBR.2018.094009]

28. Khachoo, A.Q.; Khan, M.I. Determinants of FDI inflows to developing countries: A panel data analysis. Int. J. Emerg. Mark.; 2012; 12. [DOI: https://dx.doi.org/10.51240/jibe.2013.2.2]

29. Le, B. New evidence for the determinants of foreign investment in an asian market. Afro-Asian J. Financ. Account.; 2020; 10, pp. 151-167. [DOI: https://dx.doi.org/10.1504/AAJFA.2020.106251]

30. Saini, N.; Singhania, M. Determinants of FDI in developed and developing countries: A quantitative analysis using gmm. J. Econ. Stud.; 2018; 45, pp. 348-382. [DOI: https://dx.doi.org/10.1108/JES-07-2016-0138]

31. Vijayakumar, N.; Sridharan, P.; Rao, K.C.S. Determinants of fdi in brics countries: A panel analysis. Int. J. Bus. Sci. Appl. Manag. (IJBSAM); 2010; 5, pp. 1-13.

32. Liu, X.; Tong, D.; Huang, J.; Zheng, W.; Kong, M.; Zhou, G. What matters in the e-commerce era? Modelling and mapping shop rents in guangzhou, china. Land Use Policy; 2022; 123, 106430. [DOI: https://dx.doi.org/10.1016/j.landusepol.2022.106430]

33. UNCTAD. World Investment Report-Special Economic Zones. 2019; Available online: https://www.un.org/publications (accessed on 15 March 2022).

34. UNCTAD. Investment Policy Responses to the COVID-19 Pandemic Recovery Plan. United Nations Publication. 2020c. Available online: https://unctad.org/webflyer/world-investment-report-2020 (accessed on 15 March 2022).

35. UNCTAD. World Investment Report 2020: International Production Beyond the Pandemic. United Nations Publication. 2020a. Available online: https://unctad.org/webflyer/world-investment-report-2020 (accessed on 15 March 2022).

36. UNCTAD. Global Investment Trends Monitor. October 2020, no. 36. United Nations Publication. 2020b. Available online: https://unctad.org/webflyer/global-investmenttrends-monitor-no-36 (accessed on 15 March 2022).

37. IMF. Regional Economic Outlook. Asia and Pacific: Navigating Waves of New Variants: Pandemic Resurgence Slows the Recovery. International Monetary Fund Publications. 2021; Available online: https://www.imf.org/en/Publications/WEO/Issues/2021/03/23/world-economic-outlook-april-2021 (accessed on 15 March 2022).

38. Buckley, P.J.; Casson, M.C. The internalisation theory of the multinational enterprise: A review of the progress of a research agenda after 30 years. J. Int. Bus. Stud.; 2009; 40, pp. 1563-1580. [DOI: https://dx.doi.org/10.1057/jibs.2009.49]

39. Chan, C.M.; Makino, S.; Isobe, T. Interdependent behavior in foreign direct investment: The multi-level effects of prior entry and prior exit on foreign market entry. J. Int. Bus. Stud.; 2006; 37, pp. 642-665. [DOI: https://dx.doi.org/10.1057/palgrave.jibs.8400216]

40. Fetscherin, M.; Voss, H.; Gugler, P. 30 years of foreign direct investment to china: An interdisciplinary literature review. Int. Bus. Rev.; 2010; 19, pp. 235-246. [DOI: https://dx.doi.org/10.1016/j.ibusrev.2009.12.002]

41. Klier, H.; Schwens, C.; Zapkau, F.B.; Dikova, D. Which resources matter how and where? A meta-analysis on firms’ foreign establishment mode choice. J. Manag. Stud.; 2017; 54, pp. 304-339. [DOI: https://dx.doi.org/10.1111/joms.12220]

42. Paul, J.; Benito, G.R. A review of research on outward foreign direct investment from emerging countries, including china: What do we know, how do we know and where should we be heading?. Asia Pac. Bus. Rev.; 2018; 24, pp. 90-115. [DOI: https://dx.doi.org/10.1080/13602381.2017.1357316]

43. Buckley, P.J.; Casson, M. A long-run theory of the multinational enterprise. The Future of the Multinational Enterprise; Springer: Berlin/Heidelberg, Germany, 1976; pp. 32-65.

44. Carrizosa, M.J.; Stankovic, N.; Vannier, J.-C.; Shklyarskiy, Y.E.; Bardanov, A.I. Multi-terminal dc grid overall control with modular multilevel converters. Записки Гoрнoгo Института; 2020; 243, pp. 357-370.

45. Verbeke, A.; Kano, L. An internalization theory perspective on the global and regional strategies of multinational enterprises. J. World Bus.; 2016; 51, pp. 83-92. [DOI: https://dx.doi.org/10.1016/j.jwb.2015.08.014]

46. Nedosekin, A.O.; Rejshahrit, E.I.; Kozlovskiy, A.N. Strategic approach to assessing economic sustainability objects of mineral resources sector of russia. Записки Гoрнoгo Института; 2019; 237, pp. 354-360. [DOI: https://dx.doi.org/10.31897/pmi.2019.3.354]

47. Dunning, J.H. The eclectic paradigm as an envelope for economic and business theories of mne activity. Int. Bus. Rev.; 2000; 9, pp. 163-190. [DOI: https://dx.doi.org/10.1016/S0969-5931(99)00035-9]

48. Dunning, J.H. The eclectic paradigm of international production: A restatement and some possible extensions. Eclect. Paradig.; 2015; pp. 50-84. [DOI: https://dx.doi.org/10.1007/978-1-137-54471-1_3]

49. Durham, J.B. Absorptive capacity and the effects of foreign direct investment and equity foreign portfolio investment on economic growth. Eur. Econ. Rev.; 2004; 48, pp. 285-306. [DOI: https://dx.doi.org/10.1016/S0014-2921(02)00264-7]

50. Vernon, R. International investment and international trade in the product cycle. International Economic Policies and Their Theoretical Foundations; Elsevier: Amsterdam, The Netherlands, 1992; pp. 415-435.

51. Liu, X.; Kong, M.; Tong, D.; Zeng, X.; Lai, Y. Property rights and adjustment for sustainable development during post-productivist transitions in china. Land Use Policy; 2022; 122, 106379. [DOI: https://dx.doi.org/10.1016/j.landusepol.2022.106379]

52. Fang, Y.-K.; Wang, H.-C.; Fang, P.-H.; Liang, B.; Zheng, K.; Sun, Q.; Li, X.-Q.; Zeng, R.; Wang, A.-J. Life cycle assessment of integrated bioelectrochemical-constructed wetland system: Environmental sustainability and economic feasibility evaluation. Resour. Conserv. Recycl.; 2023; 189, 106740. [DOI: https://dx.doi.org/10.1016/j.resconrec.2022.106740]

53. Contractor, F.J.; Dangol, R.; Nuruzzaman, N.; Raghunath, S. How do country regulations and business environment impact foreign direct investment (fdi) inflows?. Int. Bus. Rev.; 2020; 29, 101640. [DOI: https://dx.doi.org/10.1016/j.ibusrev.2019.101640]

54. Delevic, U.; Heim, I. Institutions in transition: Is the eu integration process relevant for inward fdi in transition european economies?. Eurasian J. Econ. Financ.; 2017; 5, pp. 16-32. [DOI: https://dx.doi.org/10.15604/ejef.2017.05.01.002]

55. Deng, P. Chinese outward direct investment research: Theoretical integration and recommendations. Manag. Organ. Rev.; 2013; 9, pp. 513-539. [DOI: https://dx.doi.org/10.1111/more.12030]

56. Choi, S.; Furceri, D.; Yoon, C. Policy uncertainty and foreign direct investment. Rev. Int. Econ.; 2021; 29, pp. 195-227. [DOI: https://dx.doi.org/10.1111/roie.12495]

57. Hsieh, H.-C.; Boarelli, S.; Vu, T.H.C. The effects of economic policy uncertainty on outward foreign direct investment. Int. Rev. Econ. Financ.; 2019; 64, pp. 377-392. [DOI: https://dx.doi.org/10.1016/j.iref.2019.08.004]

58. Julio, B.; Yook, Y. Policy uncertainty, irreversibility, and cross-border flows of capital. J. Int. Econ.; 2016; 103, pp. 13-26. [DOI: https://dx.doi.org/10.1016/j.jinteco.2016.08.004]

59. Li, F.; Liang, T.; Zhang, H. Does economic policy uncertainty affect cross-border m&as?—A data analysis based on chinese multinational enterprises. Int. Rev. Financ. Anal.; 2021; 73, 101631.

60. Song, Y.; Hao, F.; Hao, X.; Gozgor, G. Economic policy uncertainty, outward foreign direct investments, and green total factor productivity: Evidence from firm-level data in china. Sustainability; 2021; 13, 2339. [DOI: https://dx.doi.org/10.3390/su13042339]

61. Zhou, K.; Kumar, S.; Yu, L.; Jiang, X. The economic policy uncertainty and the choice of entry mode of outward foreign direct investment: Cross-border m&a or greenfield investment. J. Asian Econ.; 2021; 74, 101306.

62. Ignatiev, S.A.; Sudarikov, A.E.; Imashev, A.Z. Modern mathematical forecast methods of maintenance and support conditions for mining tunnel. Записки Гoрнoгo Института; 2019; 238, pp. 371-375. [DOI: https://dx.doi.org/10.31897/pmi.2019.4.371]

63. Litvinenko, V.S.; Tsvetkov, P.S.; Dvoynikov, M.V.; Buslaev, G.V. Barriers to implementation of hydrogen initiatives in the context of global energy sustainable development. Записки Гoрнoгo Института; 2020; 244, pp. 428-438.

64. Olanipekun, I.O.; Olasehinde-Williams, G. Unveiling the causal interactions among the us monetary policy uncertainty, exchange market pressure, and fdi-flows in large emerging market economies. J. Public Aff.; 2021; 22, e2684. [DOI: https://dx.doi.org/10.1002/pa.2684]

65. Cheng, C.; Yang, X.; Jiang, F.; Yang, Z. How to synergize different institutional logics of firms in cross-border acquisitions: A matching theory perspective. Manag. Int. Rev.; 2023; pp. 1-30. [DOI: https://dx.doi.org/10.1007/s11575-023-00502-8]

66. Li, X.; Sun, Y. Application of rbf neural network optimal segmentation algorithm in credit rating. Neural Comput. Appl.; 2021; 33, pp. 8227-8235. [DOI: https://dx.doi.org/10.1007/s00521-020-04958-9]

67. Li, X.; Sun, Y. Stock intelligent investment strategy based on support vector machine parameter optimization algorithm. Neural Comput. Appl.; 2020; 32, pp. 1765-1775. [DOI: https://dx.doi.org/10.1007/s00521-019-04566-2]

68. Iakovleva, E.; Guerra, D.; Tcvetkov, P.; Shklyarskiy, Y. Technical and Economic Analysis of Modernization of Solar Power Plant: A Case Study from the Republic of Cuba. Sustainability; 2022; 14, 822. [DOI: https://dx.doi.org/10.3390/su14020822]

69. Wu, B.; Liu, Z.; Gu, Q.; Tsai, F.-S. Underdog mentality, identity discrimination and access to peer-to-peer lending market: Exploring effects of digital authentication. J. Int. Financ. Mark. Inst. Money; 2023; 83, 101714. [DOI: https://dx.doi.org/10.1016/j.intfin.2022.101714]

70. Hu, F.; Qiu, L.; Zhou, H. Medical device product innovation choices in asia: An empirical analysis based on product space. Front. Public Health; 2022; 10, [DOI: https://dx.doi.org/10.3389/fpubh.2022.871575] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35493362]

71. Busse, M.; Hefeker, C. Political risk, institutions and foreign direct investment. Eur. J. Political Econ.; 2007; 23, pp. 397-415. [DOI: https://dx.doi.org/10.1016/j.ejpoleco.2006.02.003]

72. Bénassy-Quéré, A.; Coupet, M.; Mayer, T. Institutional determinants of foreign direct investment. World Econ.; 2007; 30, pp. 764-782. [DOI: https://dx.doi.org/10.1111/j.1467-9701.2007.01022.x]

73. Cieślik, A. Determinants of foreign direct investment from oecd countries in poland. Eurasian Econ. Rev.; 2020; 10, pp. 9-25. [DOI: https://dx.doi.org/10.1007/s40822-019-00136-y]

74. Jensen, N. Political risk, democratic institutions, and foreign direct investment. J. Politics; 2008; 70, pp. 1040-1052. [DOI: https://dx.doi.org/10.1017/S0022381608081048]

75. Metulini, R.; Riccaboni, M.; Sgrignoli, P.; Zhu, Z. The indirect effects of foreign direct investment on trade: A network perspective. World Econ.; 2017; 40, pp. 2193-2225. [DOI: https://dx.doi.org/10.1111/twec.12504]

76. Wu, Y.; Chen, C. The impact of china’s outward foreign direct investment on trade intensity with belt and road countries. Emerg. Mark. Financ. Trade; 2021; 57, pp. 1773-1792. [DOI: https://dx.doi.org/10.1080/1540496X.2019.1646124]

77. Azam, M.; Khan, M.A.; Iqbal, N. Impact of political risk and uncertainty on fdi in south asia. Transit. Stud. Rev.; 2012; 19, pp. 59-77. [DOI: https://dx.doi.org/10.1007/s11300-012-0230-x]

78. Blanton, R.G.; Blanton, S.L.; Peksen, D. Financial crises and labor: Does tight money loosen labor rights?. World Dev.; 2015; 76, pp. 1-12. [DOI: https://dx.doi.org/10.1016/j.worlddev.2015.06.005]

79. Carrière-Swallow, Y.; Céspedes, L.F. The impact of uncertainty shocks in emerging economies. J. Int. Econ.; 2013; 90, pp. 316-325. [DOI: https://dx.doi.org/10.1016/j.jinteco.2013.03.003]

80. Choi, S.; Furceri, D.; Huang, Y.; Loungani, P. Aggregate uncertainty and sectoral productivity growth: The role of credit constraints. J. Int. Money Financ.; 2018; 88, pp. 314-330. [DOI: https://dx.doi.org/10.1016/j.jimonfin.2017.07.016]

81. Karaman, K.K.; Yıldırım-Karaman, S. How does financial development alter the impact of uncertainty?. J. Bank. Financ.; 2019; 102, pp. 33-42. [DOI: https://dx.doi.org/10.1016/j.jbankfin.2019.03.008]

82. Zhukovskiy, Y.; Tsvetkov, P.; Buldysko, A.; Malkova, Y.; Stoianova, A.; Koshenkova, A. Scenario Modeling of Sustainable Development of Energy Supply in the Arctic. Resources; 2021; 10, 124. [DOI: https://dx.doi.org/10.3390/resources10120124]

83. Arellano, M.; Bover, O. Another look at the instrumental variable estimation of error-components models. J. Econom.; 1995; 68, pp. 29-51. [DOI: https://dx.doi.org/10.1016/0304-4076(94)01642-D]

84. Blundell, R.; Bond, S. Initial conditions and moment restrictions in dynamic panel data models. J. Econom.; 1998; 87, pp. 115-143. [DOI: https://dx.doi.org/10.1016/S0304-4076(98)00009-8]

85. Hui, E.C.; Chan, K.K.K. Foreign direct investment in china’s real estate market. Habitat Int.; 2014; 43, pp. 231-239. [DOI: https://dx.doi.org/10.1016/j.habitatint.2014.04.007]

86. Ramasamy, B.; Yeung, M. The determinants of foreign direct investment in services. World Econ.; 2010; 33, pp. 573-596. [DOI: https://dx.doi.org/10.1111/j.1467-9701.2009.01256.x]

87. Nkoa, B.E.O. Determinants of foreign direct investment in africa: An analysis of the impact of financial development. Econ. Bull.; 2018; 38, pp. 221-233.

88. Buslaev, G.; Tsvetkov, P.; Lavrik, A.; Kunshin, A.; Loseva, E.; Sidorov, D. Ensuring the Sustainability of Arctic Industrial Facilities under Conditions of Global Climate Change. Resources; 2021; 10, 128. [DOI: https://dx.doi.org/10.3390/resources10120128]