Research Article: 2022 Vol: 26 Issue: 2S
Does country risk affect foreign direct investment: Evidence from emerging countries
Anwar Al-Gasaymeh, Applied Science Private University
Hamed Ahmad Almahadin, Applied Science Private University
Najed Alrawashdeh, ISRA University
Haitham Alzoubi, Skyline University College
Marwan Al-ma’aytah, Government Investment Management Company (GIMC)
Citation Information: Al-Gasaymeh, A., Almahadin, H.A., Alrawashdeh, N., Alzoubi, H., & Al-ma’aytah, M. (2022). Does country risk affect foreign direct investment: Evidence from emerging countries. Accounting and Financial Studies Journal, 26(S2), 1-13.
Abstract
The goal of this research is to look at the relationship between country risk and foreign direct inflows in 20 emerging economies from 2010 to 2019. A variety of indicators are used to reflect all aspects of country risk, including political risk, credit ratings, debt indicators, debt in default, short-term financing, financial performance, and forfaiting. Panel data analysis using the FMOLS technique was used for this. The empirical findings reveal that the bulk of country risk indicators negatively impact the foreign direct investment. According to the research, emerging countries' economic crisis has resulted in significant financial concerns. It is undeniable that countries with minimal political risks have more efficient economies. This shows that, in order to attract more international investment, governments should play a key role in decreasing country risk.
Keywords
FMOLS Regression, Country Risk, FDI, Emerging Markets
Introduction
Economic stability and low country risk generate a growing market that attracts investors, and this is seen as a significant means to create new jobs and raise household income. Economic stability is a priority in emerging markets, and various economic components play critical roles in growth and competition, with many components linked to one another, such as Foreign Direct Investment (Moran, 1998). Foreign Direct Investment (FDI) is a term that refers to the transfer of capital between countries as a result of globalization and technological revolution (Musonera, 2008; Oudat et al., 2019). FDI increasingly plays an important role in the development of emerging countries and has emerged as the most important source for improving economic systems and increasing competitiveness, as well as affecting host economies in terms of regional development, economic growth, and long-term development (Iloie, 2015; Li & Resnick, 2003). Furthermore, FDI provides a variety of benefits to economic bodies, such as assisting in the reduction of unemployment by creating new jobs, raising the level of regulation, marketing, and communication, all of which contribute to the economy's strengthening.
The macroeconomics of emerging countries has experienced a period of rapid development since reform and opening up, but since the 2008 financial crisis and political issues in some countries, in the case of a global slowdown and lack of external demand, the economies of countries have grown slowly, entering a soft-landing process and a period of high commercial friction. Traditional trade methods have been difficult to adapt to the global trade pattern, and exports face significant trade barriers (Roberts, 1999). This has had a significant influence on the international trade development of most emerging countries, as well as an immense negative impact on the country's economic efficiency. In this instance, it is vital to examine and discuss the country's risks to foreign direct investment, which are being exacerbated by the present economic slump, in order to discover practical remedies and steps to boost emerging countries' economic and trade development.
Country risk is not a new concept; it has a long and illustrious history. Country risk for foreign funding became an issue for investors many years ago, with the fear of non-performance. Developing countries have invested more time and effort establishing special investment agencies to promote FDI; also, emerging economies are making attempts to attract capital through more global FDI flows than developed economies (Harding & Javorcik, 2011). Emerging countries encourage economic freedom and capital flows, particularly FDI, by limiting the constraints that restrict the transfer of investments and capital, which, according to the pull theory, are linked to host-country economic conditions (Dabla-Norris et al., 2010). Internal determinants of the host nation, as outlined by pull theory, include market size, labor costs, political stability, political risk, credit rating, and the degree of financial liberalization.
Risk in emerging markets, like other economic beings, is the most prevalent stumbling block to foreign investors deciding whether or not to invest, and is mostly decided by uncertainty, as high-risk stifles capital flows. When investing in developing economies, nation risk can account for a considerable portion of the total risk, and state risk is a distinct risk that foreign investors experience when investing in one country versus another (Gelb et al., 2014; Nordahl, 2001). High country risks might incentivize investors, affecting FDI. The importance of country risks to foreign direct investment in developing nations should be investigated further.
Given the foregoing discussion, the current analysis makes a valuable contribution by filling a gap in the literature by giving futuristic empirical evidence on the impact of nation risk characteristics on FDI in Emerging Markets in recent years. As a result, the current research will conduct an empirical analysis utilizing the FMOLS approach to clearly and deeply investigate the influence of nation risk on FDI in emerging nations.
Literature Review
Country risk is not a new concept; it has a long history and is an important consideration for foreign direct investment. For many years, the country's vulnerabilities to international finance have been a source of concern for investors who are concerned about potential non-performance. Expropriation, greater taxes, reduced FDI incentives, and currency inconvertibility could all result from increases in country risk. The effect could also result in the loss of assets and the closure of activities, resulting in lower revenue, management constraints, higher operational costs, and the inability to repatriate cash. Country risk variables influence the profitability and riskiness of FDI by determining future risks from political risk, economic performance, and other future risks from the country's economic environment (Baek & Qian, 2011).
The degree of FDI in a country may vary depending on the country's environment, macroeconomic conditions, or risk level, as well as the country's level of competition. As a result, attention must be paid to FDI determinants such as macroeconomic variables such as GDP, inflation, and country risks. Market conditions and policies in the home nation can have a big impact on cross-border FDI. Some countries may benefit from better regulatory, supervisory, or commercial conditions, allowing country-based firms to operate more efficiently in countries other than their own (Claessens & Van Horen, 2012; Berger, 2003).
The global scope of country risks and financial crises underscores the need for more pragmatic policies to create stability, increase market function, and operate consistently across jurisdictions. In the previous decade, rising countries have seen political upheavals and catastrophes. To address greater systemic risks, such a crisis necessitates to a great extent comprehensive approach to prudential regulation at both the institutional and macroeconomic levels. To avoid further country risk and to boost the economies of emerging countries. This goal would necessitate actions whose target is to strengthen individual institution capital and liquidity requirements, avoiding the accumulation of systemic risk across institutions and the economy over time, and improving national and international resources and financial sector responses to distress (Claessens, 2009).
Butler's political risk model shows how political risk influences investment costs of capital. The end result of changing political risk on the cost of investment capital in the Butler-Joaquin model is determined by the end result of changing political risk on the expected return on investment, as well as the variability of return on investment and return in the market. Any changes in the country's risk environment will have an adverse impact on predicted future cash flows from the investment, raising (lowering) the investment's cost of capital. The impact of changing nation risk is determined by the impact on the expected rate of return, the variance in return on investment, and the market return (Lessard, 1974).
Credit ratings, debt in default, and economic performance are all used to assess country risk. Better positioning and reduced risk are associated with higher country risk levels (Euromoney, 2007; Saini & Bates, 1984). Credit ratings influence asset and bond prices, which can have an impact on economic performance because low valuations force exports and imports to pay higher interest rates (Cantor & Packer, 1996; Reisen & Von Maltzan, 1999). Furthermore, because public debt plays such a large role in the financial system, credit ratings have a detrimental impact on economic performance. Debt in default, on the other hand, is a failure to meet the legal obligations of a loan and is the second determinant of country risk. The performance of a bank is harmed when lenders are unable to appraise the amount of outstanding loans. Economic performance, nevertheless, would have a negative impact on the banking sector because economic performance deteriorates in tandem with the country's enterprises' and investors' debt situations. When a country's economy suffers from poor performance, investors become hesitant to invest, which has a negative impact on the banking sector. Furthermore, bank efficiency will be affected more than cost by the rise of bank lending and the likelihood of indebtedness, as well as credit ratings (Kletzer & Bardhan, 1987). There are no a priori assumptions concerning the impact caused by the country’s risk on banking costs due to a lack of studies.
Inflation and gross domestic product have been incorporated in the macroeconomic conditions. Inflation and foreign direct investment have a favorable association. Devaluation of the national currency and an increase in product prices may result due to higher inflation. The higher the inflation, the higher the costs because inflation can raise the cost of inputs in the manufacturing process (Shen et al., 2008). As a result, a positive inflation coefficient is projected.
Hatice & Zerrin (2016); Samsul (2016); Monireh & Marya (2019); Nicholas, et al., (2020); among others, investigated the interrelation between country risk and foreign direct investment and found a positive relationship. Hatice & Zerrin (2016) look at six political risk factors, including diplomatic stability and lack of violence, freedom of expression and transparency, management effectiveness, regulatory quality, corruption prevention, and the rule of law. Their research looks at the link between political risk and FDI for 91 countries from 2002 to 2012 and includes a range of control factors such as inflation, GDP, exports of goods and services, and population size. In their research, they used the unit root test, F test, Breusch-Pagan Lagrange multiplier test, and likelihood ratio test. Because of the presence of interdivisional correlation, autocorrelation, and heteroscedasticity in the model, the Driscoll-Kraay fixed-effects model was found more suited. The findings also show that when political risk rises and violence is absent, FDI falls, whereas an increase in export and import of products and services causes FDI to rise and work more efficiently. Samsul's (2016) research focuses on the impact of country risk on international trade and whether there is any link between current country risk and international trade. The data clearly reveal that country risk affects the overall operations of international finance, and that country risk tends to be higher, resulting in a lower country rating, and that country risk has negative consequences on international finance. In the other case, when a country's risk is minimal, it receives good ratings and has a favorable impact on international finance. In conclusion, this study demonstrates that high-interest rates, high inflation, and high exchange rate volatility disrupt international trade, limiting import and export between countries, and lowering country risk, which might lead to high ratings, which inhibited global international finance. Monireh & Maryam (2019), on the other hand, look into the link between political risk and FDI using the International Country Risk Guide (ICRG) and 12 political risk indicators. As a result, the findings suggest that there is a strong link between FDI and political risk. The study used time series data from 1985 to 2016 to test for endogeneity and quantify the link between political risk indicators and FDI inflows in Iran using the Wu-Hausman test and the Two-Stage Least Square estimator (2SLS). External conflict, socioeconomic conditions, investment, ethnic tensions, and military are the primary factors of FDI in Iran, according to their results.
Finally, Nicholas, et al., (2020), looks at emerging countries in order to advance the literature, specifically in Lebanon, by looking into 12 International Country Risk Guides (ICGR) and political risk indicators, taking into consideration multicollinearity and categorizing these variables into three groups. The data clearly reveal that "cohesion," "institutional quality," and "governance" are the most essential and relevant factors influencing FDI in Lebanon. Furthermore, this research shows that political risk is a significant determinant of FDI in Lebanon. Generally speaking, investors prefer towards countries with minimal political risk, fewer disputes, and fewer restrictions. As a result, it is critical to boost economic growth and create new job possibilities in order to improve and attract FDI.
This study will attempt to fill the gap in the literature by considering the impact of country risk variables such as political risk, economic performance, credit ratings and debt indicator, debt in default, short term financing, and forfaiting on FDI in emerging markets, based on the above kinds of literature. Furthermore, this study will take into account macroeconomic factors such as GDP and CPI for 20 emerging economies, where there have been few studies on the impact of nation risk on FDI in recent years in emerging markets.
The Methodology
Model Specification
The model specification is reviewed in this part to reflect the hypothesis proposed in the theoretical relationship between political risk and foreign direct investment. As previously stated, the goal of this research is to look at the impact of country risk on foreign direct investment in twenty emerging countries spread across different regions, including Algeria, Bahrain, Ethiopia, Egypt, Jordan, Indonesia, India, Kuwait, Malaysia, Morocco, Nepal, North Korea, Pakistan, Qatar, Saudi Arabia, Thailand, Tunisia, Turkey, and the United Arab Emirates. The functional model is shown below in order to capture the study's purpose:
Foreign direct investment may be influenced by country risk characteristics, according to the above model. As a result, Equation (1)'s functional model should be reorganized as an econometric model as follows:
where FDI is the foreign direct investment indicates the independent variable. The EP, PR, CR, DI, DD, STF, FF, CPI, and GDP are dependent variables, namely economic performance, political risk, credit ratings, debt indicators, debt in default, short term financing, forfaiting, consumer price index (inflation), and gross domestic products, respectively. For the period 2010-2020, the nation risk variables were obtained from the Euromoney database (2020), and the macroeconomics variables were collected from the World Bank (2020). Last but not least, the constant term, long-term coefficients, and error term are represented as α, β, and ε t, respectively.
Theoretical Setting
Focusing on country risk, which includes economic performance, political risk, credit ratings, debt indicators, debt in default, short-term financing, forfaiting, consumer price index (inflation), and gross domestic product. Country risk has a higher value when it comes to better situations and fewer hazards (Euromoney, 2020; Saini & Bates, 1984).
With different countries, corporations, and investors, a country's economic performance deteriorates. For example, if a country performs poorly, investors will become discouraged, reducing FDI (Morrissey & Udomkerdmongkol, 2012; Al-Gasaymeh et al., 2015). In terms of politics, social policy, geography, economic structure, and currency, political dangers exist across nations. To put it another way, changes in government, economic insecurity, and other non-economic variables can render the banking industry useless (Meldrum, 2000; McGowan et al., 2013; Aseel & Al-Gasaymeh, 2018). Credit levels affect asset demand and bond prices, resulting in an economic return since the market is forced to charge higher interest rates during periods of lower value. Furthermore, because of the prominence of public accountability in the financial system, credit ratings have a negative impact on market performance, which can threaten foreign direct investment (Berger, 2003; Cantor & Packer, 1996; Reisen & Von Maltzan, 1999). Unnecessary debt, as well as failure to pay interest or principal on time, are other risk concerns in the country. When a debtor is unable to pay a legal requirement of the debt's needs, a default occurs (Hatice & Zerrin, 2016; Samsul, 2016). Overdue loans, which can be obtained through FDI performance, are not eligible for loan time. Finally, forfaiting indicates the average maximum registration period (maximum registration time or billing time from the issue date to expiration date in the main market, from buy/sell date to expiration date in the secondary market).
The country's risk, on the other hand, may impair economic performance due to heavy debt, poor economic performance, and a bad credit rating (Al-Gasaymeh, 2016). High country risks may deter investors, reducing FDI as a result of lower economic performance. The impact of country risk on economic performance should be investigated further. As a result of a lack of literature on the influence of nation risk on FDI, we will investigate the impact of country risk on FDI. Finally, because there is little research in the literature on the impact of nation risk on FDI, no prior expectation on the effect exists. As a result, a negative coefficient for nation risk variables on FDI is expected.
Inflation and economic growth have been incorporated into the macroeconomic conditions. Inflation is a significant economic element that can affect the macroeconomic situation as well as FDI. The two variables, inflation, and investment have a positive association. Higher inflation may result in the depreciation of the national currency and an increase in product prices. The higher the inflation rate, the higher the costs will be since the input prices in the banking production process will rise. As a result, a positive inflation coefficient is projected. On the other hand, GDP growth may have a beneficial impact on FDI because the higher a country's growth, the larger its FDI return (Al-Gasaymeh, 2018; Al-Gasaymeh, 2016).
Econometric Methodology
The Fully Modified Least Square (FMOLS) technique was used as the main econometric tool to study the impact of country risk indicators on foreign direct investment inflows in the sampled emerging countries (Jawaid et al., 2017). Three different stages were completed as a result of this to complete the essential procedures of this methodology. To begin, five-panel unit root tests were used to identify the order of integration among the studied variables, including Levin-Lin-Chu, Breitung, Im-Pesaran-Shin, Augmented Dickey-Fuller & Phillips–Perron. Second, Kao uses a residual-based panel cointegration approach to assess for the presence of a long-term equilibrium relationship among the variables. This strategy is valid if all variables are in the same order of integration if they are stationary at level [I (0)] or at first difference [I (1)]. This test's null hypothesis is that there is no cointegration between homogeneous and heterogeneous panel data. Long-term, however, the linear combination of two nonstationary series with the same level of integration will result in cointegration. Finally, the long-run coefficients of the functional model of the equation are estimated using Pedroni's (2000) FMOLS technique (2). This methodology has a number of advantages: It allows for serial correlation, which reduces the possibility of multicollinearity among the independent variables; (ii) it effectively captures both endogeneity and cross-sectional heterogeneity by capturing within and between dimensions; and (iii) it provides more reliable estimations for a small sample.
Empirical Results
Stationarity Testing
In general, empirical arguments claim that time series data are nonstationary (have a unit root) if their mean and variance are unstable over time. When nonstationary time series data is transformed to the first difference, the vast majority of them become stationary. As a result, one of the most common pre-testing processes before moving on to regression analysis is checking for the presence of unit roots in panel and time-series data to choose the best estimation method or technique. Levin-Lin-Chu, Breitung, Im-Pesaran-Shin, Augmented Dickey-Fuller & Phillips–Perron are among the five-panel unit root tests used in this work. These tests' statistic values are listed in Table 1 below. All tests universally agreed that the studied variables are nonstationary at level, as shown in this table, but all of them became stationary when converted to the first difference, which is compatible with the empirical arguments provided. The results of unit root tests allow you to move on to the next step of the residual cointegration test.
| Table 1 Unit Root Test |
|||||
|---|---|---|---|---|---|
| Panel (A): Statistic-values of all unit root tests at the level | |||||
| Variable | LLC | B | IPS | ADF | PP |
| FDI | 1.42329 | -1.27403 | -1.09491 | 49.8167 | 119.373*** |
| PR | -3.38618*** | -0.88081 | -0.13208 | 47.8488 | 47.4511 |
| EP | -4.26470*** | 0.76458 | 0.11229 | 39.3970 | 37.6211 |
| CR | 4.23498 | 1.09183 | 0.82042 | 27.5393 | 53.2583* |
| DI | 1.09045 | 3.59307 | 0.84710 | 31.2357 | 36.9730 |
| DD | 2.74788 | 1.99288 | 1.06885 | 27.0379 | 29.0016 |
| F | 5.39018 | 1.35361 | 1.45533 | 20.6611 | 63.4861** |
| STF | 2.22961 | 0.16776 | 0.82680 | 27.3330 | 41.9594 |
| GDP | 0.13089 | 0.27470 | -0.66160 | 54.4335* | 139.807*** |
| CPI | 1.93769 | -1.29011* | 0.75403 | 34.1166 | 64.6593*** |
| Panel (B): Statistic-values of all unit root tests at the first difference | |||||
| Variable | LLC | B | IPS | ADF | PP |
| Δ FDI | -2.17617** | -2.77514*** | -3.92796*** | 90.5360*** | 172.912*** |
| Δ PR | -15.2520*** | -4.17990*** | -2.70680*** | 99.9928*** | 186.125*** |
| Δ EP | -13.2463*** | -1.79554** | -1.96490** | 86.8386*** | 149.183*** |
| Δ CR | -16.9903*** | 1.46301 | -2.00500** | 82.6946*** | 141.608*** |
| Δ DI | -13.3425*** | -3.77224*** | -2.53007*** | 95.5959*** | 175.919*** |
| Δ DD | -8.43547*** | 1.80194 | -0.89425 | 68.9436*** | 94.3127*** |
| Δ F | -12.5888*** | -1.43708* | -1.99691** | 87.7114*** | 201.479*** |
| Δ STF | -23.2709*** | -5.57557*** | -3.68944*** | 121.602*** | 188.307*** |
| Δ GDP | -13.4389*** | -2.64151*** | -2.48056*** | 99.3593*** | 229.273*** |
| Δ CPI | -11.1677*** | -2.92985*** | -3.67537*** | 119.408*** | 155.314*** |
Cointegration Testing
Testing for existing cointegration relationships among the studied variables is a mandatory stage before going to investigate the regression model, as described in the econometric methodology section. When the time series data under consideration are nonstationary (have a unit root), a cointegration test is required to determine whether they have a stable long-run relationship (Christopoulos & Tsionas, 2004). The presence of a cointegration connection indicates that the series move together in the long run, implying that the variables are in a long-term equilibrium relationship. After evaluating the unit root, we used the Kao residual cointegration test to see if there was a long-term equilibrium relationship between the variables in question. The test's empirical results are shown in table 3 below. as shows in Table 2.
| Table 2 Kao Residual Cointegration Testing |
||
|---|---|---|
| t-statistic | P-value | |
| Kao ADF | -2.795472 | 0.0026 |
The absolute t-statistic value of Koa ADF was |2.795472| with a p-value of 0.0026, suggesting that the computed t-statistic is significant at a one-percent significance level. This means that the null hypothesis of no cointegration is invalidated. As a result, the alternative hypothesis of a long-term equilibrium relationship between FDI and country risk indicators is accepted. Finding such a cointegration relationship between the variables so gives strong authorization to proceed with the regression model estimation.
FMOLS Regression Results
The panel FMOLS approach was used to estimate the functional model of equation (1), and the regression results are shown in Table 4. The political risk coefficient is a negative number that is statistically significant at a 1% significance level. With an absolute t-statistic of |2.6904|, the predicted value was -0.0232. This conclusion demonstrates that, across the study period, the political risk proxy has a negative impact on FDI inflows in the sampled emerging nations; that is, as the value of political risk increases, the value of FDI drops.
Economic Performance has a positive coefficient of (0.0553) that is statistically significant at the 1% level. In terms of economics, this suggests that a country's economic success increased the attractiveness of FDI into that country. This will have a favorable impact on the county's multiple dimensions. Reduced unemployment rates and rising income levels are two possible positive outcomes, which would improve residents' well-being. Low economic performance discourages investors, which has a negative impact on banking sectors since it reduces investor deposits. Furthermore, a country's economic performance deteriorates when the debt position of the country, enterprises, and investors worsens.
The empirical result reveals that the predicted credit rating value is negative, with a modest value of -0.0029, and statistically significant at the 1% level, as the absolute t-statistic is |-6.4841|. From a statistical standpoint, the calculated credit rating coefficient implies that credit ratings have had a negative impact on attracting FDI in emerging countries in recent years. This finding could have unfavorable consequences for the economies of these developing countries. The negative impact of credit ratings on FDI can be explained by the increase in public debt and, more recently, the level of military spending in these countries, both of which have made their economies unattractive to foreign investors in recent decades. These countries' credit ratings have been declining over time, sending a negative signal about their attractiveness, resulting in a lack of foreign capital inflows, which is consistent with. Credit ratings influence relative demand for assets and bond prices, which has an impact on bank profitability because low credit ratings require banks to charge higher interest rates. Furthermore, because of the pervasive participation of government debt in the financial system, credit ratings have a negative impact on bank performance, which has a negative impact on FDI (Meldrum, 2000; McGowan et al., 2013; Aseel & Al-Gasaymeh, 2018).
The Debt in Default proxy has a positive coefficient of 0.6441 that is statistically significant at the 1% level, and the t-statistic is very high with a value of 15.1352. The fact that this factor has a positive sign shows that the Debt in Default proxy has a favorable impact on FDI in the emerging sampled nations. This metric measures a country's capacity to meet its debt obligations, including both principle and interest payments, when they become due. The ability of a country to pay its debts sends an important signal to foreign investors about the soundness of its economy, which attracts more foreign investment and boosts economic growth. The Debt in Default proxy has a positive coefficient of 0.6441 that is statistically significant at the 1% level, and the t-statistic is very high with a value of 15.1352. The fact that this factor has a positive sign shows that the Debt in Default proxy has a favorable impact on FDI in the emerging sampled nations. This metric measures a country's capacity to meet its debt obligations, including both principle and interest payments, when they become due (Cantor & Packer, 1996; Reisen & Von Maltzan, 1999). The ability of a country to pay its debts sends an important signal to foreign investors about the soundness of its economy, which attracts more foreign investment and boosts economic growth.
The Forfaiting positive coefficient is statistically significant at the 1% level, with an estimated value of 0.2585 and a t-statistic of 11.2283. This indicator is one of the most important country risk indicators with a significant link to FDI. Forfaiting is a form of available finance resource that allows producers to receive cash right away by selling their receivables through the financial system in that country's intermediary function. To put it another way, forfaiting refers to exporters' ability to sell their receivables through financial intermediary channels, resulting in rapid cash resources and lowering the degree of customer default risk, particularly when selling receivables without recourse. The healthy banking sector of the sampled nations could explain the favorable impact of FDI; their financial systems are bank-based, and the banking sector plays a critical role in these growing economies.
Debt Indicators proxy has an estimated value of -0.2956, which is negative and statistically significant at the 1% level, with an absolute t-statistic of |8.0183|. This outcome is in line with the Credit Risk proxy's findings. FDI inflows are negatively impacted by the debt index of the studied emerging economies. The enormous amount of public debt in these countries could be read in this way. This outcome could also be linked to the political realities in these nations, particularly after the majority of these countries experienced popular upheavals in the last decade. In general, debt has a negative impact on FDI inflows, and a higher debt load limits new private lending as well as FDI inflows. Finally, there is a positive correlation between external debt, economic growth, and investment.
With a value of -0.2956, the coefficients of Bank Financing Access (BFD) proxy record a negative estimator. At a 1% significance level, the absolute t-statistic was |12.8666|, showing that this coefficient was statistically significant. The negative sign of this coefficient implies that the BFD has a negative impact on FDI; as the BFD value rises, FDI falls. as shows in Table 3.
| Table 3 FMOLS Estimation |
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|---|---|---|---|---|
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| PR | -0.0232 | 0.0086 | -2.6904 | 0.0078 |
| EP | 0.0553 | 0.0112 | 4.9595 | 0.0000 |
| CR | -0.0029 | 0.0004 | -6.4841 | 0.0000 |
| DD | 0.6441 | 0.0426 | 15.1352 | 0.0000 |
| F | 0.2585 | 0.0230 | 11.2283 | 0.0000 |
| DI | -0.2956 | 0.0369 | -8.0183 | 0.0000 |
| BFA | -0.2560 | 0.0199 | -12.8666 | 0.0000 |
| GDP | 0.1428 | 0.0099 | 14.3819 | 0.0000 |
| CPI | -0.0181 | 0.0058 | -3.0952 | 0.0023 |
| R-squared | 0.4893 | Mean dependent var | 1.8735 | |
| Adjusted R-squared | 0.43017 | S.D. dependent var | 1.6458 | |
| S.E. of regression | 1.5931 | Sum squared resid | 4.3400 | |
| Long-run variance | 0.0988 | Mean dependent var | ||
The estimated coefficients of the macroeconomic components of GDP and Consumer Price Index (CPI), which were employed as control variables in the functional model of equation (1), were positive and negative, respectively. The estimated GDP coefficient was 0.1428, and the t-statistic value of 14.3819 was statistically significant at a 1% significance level. The positive association between economic growth, as measured by GDP, and FDI inflows can be used to explain this result; as the economy increases, so do FDI inflows. In other words, if a country's economy grows at a rapid pace, it signals that there are several visible investment prospects in that country. This, in turn, attracts more foreign capital inflows to this particular country, resulting in an increase in FDI volume with positive consequences for the entire economy. The calculated coefficient of CPI, on the other hand, was -0.0181 and statistically significant at a threshold of 1% significance since the absolute value of the t-statistic was |-0.0181|. The negative association between inflation and FDI inflows, as assessed by the CPI, is confirmed by this conclusion. Specifically, rising inflation rates in emerging economies have hampered the ability of these countries to attract FDI, which has negative consequences for the entire economy (Bhasin & Garg, 2020).
Conclusion
Emerging countries have spent more time and effort building specific investment agencies to attract FDI. Furthermore, emerging economies are taking greater steps than industrialized ones to attract cash through global FDI flows. Emerging countries encourage capital flows, particularly FDI, by limiting the constraints that prohibit the transfer of investments and money, which, according to the pull hypothesis, are linked to host-country economic conditions. FMOLS is being used in this study to look at the impact of country risk indicators on FDI inflows in the sampled emerging countries. Three different stages were completed as a result of this to complete the required procedures of this methodology. Low country risk, which includes economic performance, political hazards, credit ratings, debt in default, debt indicators, and forfaiting, boosts FDI attractiveness, according to the findings. This shows that, in order to attract FDI, governments should play a key role in decreasing country risk. It is undeniable that countries with minimal political risks have more efficient economies. Higher GDP in emerging markets attracts more foreign direct investment. Higher inflation in emerging countries means less FDI inflows and larger economies suffer from scale economies. As a result, in order to attract FDI, the government should also pay attention to the critical role of country risk in reducing political risk and enhancing political stability. To increase FDI in emerging countries, governments and policymakers in emerging countries must reduce political risk and enhance GDP.
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Received: 20-Nov-2021, Manuscript No. AAFSJ-21-8740; Editor assigned: 23- Nov -2021, PreQC No. AAFSJ-21-8740 (PQ); Reviewed: 08-Dec-2021, QC No. AAFSJ-21-8740; Revised: 20-Dec-2021, Manuscript No. AAFSJ-21-8740 (R); Published: 03-Jan-2022