Does country risk influence foreign direct investment inflows? A case of the Visegrád four

Hassan, Adewale Samuel Article Does country risk influence foreign direct investment inflows? A case of the Visegrád four Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Hassan, Adewale Samuel (2022) : Does country risk influence foreign direct investment inflows? A case of the Visegrád four, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 10, Iss. 9, pp. 1-22, https://doi.org/10.3390/economies10090221 This Version is available at: https://hdl.handle.net/10419/328521 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Economies 10: 221. https://doi.org/10.3390/ economies10090221 Academic Editor: Bruce Morley Received: 5 July 2022 Accepted: 2 September 2022 Published: 9 September 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). economies Article Does Country Risk Influence Foreign Direct Investment Inflows? A Case of the Visegrád Four Adewale Samuel Hassan College of Business and Economics, University of Johannesburg, Auckland Park Kingsway Campus, Auckland Park, Johannesburg P.O. Box 524, South Africa; [email protected] Abstract: The determinants of FDI inflows have been a subject of unremitting debate in the economic literature over the years. However, the role of country risk has received inadequate attention, especially in the context of the Visegrád countries, which comprise the Czech Republic, Hungary, Poland and Slovakia. Hence, this study examined whether country risk matters for FDI inflows into the Visegrád Four for the period 1991–2020. This study accounted for cross-sectional dependency, structural breaks and heterogeneous slopes in the panel of the four countries by employing the dynamic common correlated effect estimator. Additionally, country-wise fully modified least-squares regression was conducted for each country to test the robustness of the estimates. The empirical results revealed that country risk matters for the FDI inflows into the Visegrád countries, as it has a negative effect on the FDI inflows. Furthermore, both the overall panel and country-wise regressions established that economic and political risks are essential determinants of the FDI inflows, as both have a negative relationship with the FDI inflows. However, financial risk had weak and mixed impacts on the FDI inflows in the overall panel and country-wise regressions, respectively. These research outcomes highlight the need for appropriate macroeconomic and government authorities in the Visegrád economies to enhance the market capabilities of their economies by improving and upholding the social, institutional, corporate and macroeconomic structures, and as a way of achieving better country risk attributes. Keywords: FDI inflows; country risk; DCCE; FMOLS; Visegrád Four 1. Introduction The attainment of long-run economic growth remains a fundamental objective of every economy, and a critical vehicle of growth that many countries rely on to achieve this objective is foreign direct investment (FDI). This is due to the unique and essential role of FDI in driving industrialisation and boosting the manufacturing sector, which were identified as principal drivers of growth and development (Akinlo 2004). Furthermore, globalisation has dramatically increased capital flexibility and mobility across the globe, with FDI regarded mainly as the safest and most advantageous form of capital flow. Empirically, studies identified the crucial role that FDI plays in boosting productivity and general macroeconomic performance through the promotion of technology transfer, managerial talent and financial capital, which would otherwise be unavailable or provided only at a much greater cost (Akinlo 2003;Khan 2007;Ugwuegbe et al. 2014). This role of FDI could engender a “spill-over” effect on different aspects of the economy that are not direct beneficiaries of FDI, with a concomitant positive impact on the overall economy (Rappaport 2000). This corroborates the position of the neoclassical and endogenous growth theorists. They stressed the crucial role of innovation, technology transfer, knowledge spill-over, and managerial and technical skills that arise from capital flows in the economic growth process (Grossman and Helpman 1991;Mankiw et al. 1992). Since the early 1990s, when communism and the central planning system crumbled, the four Central European states, Economies 2022,10, 221. https://doi.org/10.3390/economies10090221 https://www.mdpi.com/journal/economies Economies 2022,10, 221 2 of 22 known as the Visegrád Four (V4), which comprise the Czech Republic, Hungary, Poland and Slovakia, have advanced several strategies aimed at enhancing FDI inflow as a way of driving sustained economic growth (Chen et al. 2018;Chidlow et al. 2009;Qi and Li 2017). According to UNCTAD (2007), FDI inflow refers to the capital or finance provided for an enterprise in a host country by a foreign direct investor either directly or through other related enterprises. Many studies investigated the determinants of FDI in the V4, most of which have focused on the two theoretically fundamental factors: the size/growth of the economy and the cost competitiveness (for example, Altomonte 2000;Bobenic Hintosova et al. 2018; Demirhan and Masca 2008;Galego et al. 2004;Gauselmann et al. 2011;Gorbunova et al. 2012;Janicki and Wunnava 2004;Wach and Wojciechowski 2016). Besides the fact that there is not yet a consensus on the shared economic factors that drive FDI in the V4 (Bobenic Hintosova et al. 2018), factors relating to country risk are rarely explored by researchers. This raises the question of whether non-economic considerations are considered by overseas investors. Specifically, this study aimed to determine whether the V4’s extra-economic traits on the major elements of country risk are significant for FDI inflow. According to White and Fan (2006), country risk can be sub-divided into economic, financial, cultural and political risks, while Moosa (2002) refers to it as exposure to a financial loss in international business activities that is brought on by circumstances in a certain nation that are, at least in part, under the government’s authority. Intuitively, country risk should rate high among the most important determinants of FDI considering that how external bodies relate to a country is highly influenced by its economic, financial and political environments. Moreover, investors are generally averse to systematic risks that are mainly external and out of their control. Since all the components of country risk are systematic, foreign investors are bound to be wary of them, which could ultimately influence FDI flows. Root (1987) opined that any foreign investment project must be assessed from the perspectives of its economic, social, political and cultural environments. Therefore, it is not surprising that multinational companies (MNCs) are often more favourably disposed to countries where they may encounter low risk and generate high returns on their investment in making their offshore investment decisions. The focus on the V4 is essential because of certain peculiarities that pertain to this group of countries. First, following their emergence from communism, the V4 were deemed unattractive locations by foreign investors (Gauselmann et al. 2011) and often labelled “catching-up” countries (Tendera-Właszczuk and Szyma´nski 2015). This prompted them to devise various strategies to attract FDI, after which they became prime targets of FDI, especially after exiting the transition recession and acceding to the European Union (EU). Some of the policy measures put in place to drive FDI inflow included lessening the obstacles to FDI (which, according to Koyama and Golub (2006), culminated in maintaining a very low regulatory restrictiveness index relative to the average index in the OECD countries) and developing and deepening financial markets (Vojtovic 2019). As depicted in Figure 1, the country risk scores of the four countries have continually fluctuated since 1995 and have never reached the 80/100 mark (indicating a very low-risk status). Thus, there is a need to investigate how these country risk attributes influence FDI inflow. The 2008–2009 financial crisis, which hit the V4 economies very hard because of their massive exposure to international business cycles, resulted in increased government intervention and measures that could lead to a decline in the share of foreign investment in specific sectors (Hunya 2017;Sallai and Schnyder 2018;Sass 2017). More likely than not, this mixed bag of policy interventions has implications for various components of the country risks of the V4. Yet, to the best of our knowledge, no study has addressed the question of the growth impact of country risk on FDI inflows in the context of the V4 despite the impact that the policy measures and reversals could have on the country risk ratings and the extent to which country risk could impact the investment decision of foreign investors in an economy. Economies 2022,10, 221 3 of 22 Economies2022,10,xFORPEERREVIEW3of22   Figure1.TrendofthecountryriskindexintheVisegrádcountries. Second,theV4havealotofsocialandeconomicinteraction/financialintegrationand sharecommonhistoricalrootsandculturaltraditions,havingemergedfromcommunism, whichheldswayinCentralandEasternEuropeancountries(CEECs)until1989.Conse‐ quently,theprocessofFDIflowstothefourcountrieshasseveralsharedcharacteristics: thefirstyearsofFDIinflowswitnessedapredominanceofbrownfieldinvestments,the followingyearssawmoresignificantemphasisongreenfieldinvestment,theperiodafter theEUaccession(especially,2004–2007)experiencedincreasedanddynamicFDIinflows, adisproportionatelylargepercentageofFDIinflowtotheV4camefromtheEUandFDI inflowtotheV4haswitnessednoticeablestructuralchangestowardstheservicessector overtheyears(Ambroziak2013;Zielińska‐Głębocka2013). DespitethesecommonfeaturesandconsequentinterdependencebetweentheV4 countries,allexistingpanelstudiesonthedeterminantsofFDIintheV4assumedcross‐ sectionalindependenceinthedisturbancesoftheirpanelmodels,therebyfailingtoac‐ countforthelikelycross‐sectionaldependence(CD)betweenthecountries.Eberhardtand Teal(2010)andPesaran(2006)intenselyfaultedthisassumptiononthegroundsthatit couldresultinbiasedestimates,andconsequently,inappropriatepolicyproposals.Tothis end,theypropoundedpanelregressionswithrobuststandarderrorsthatcanaccountfor CDbetweenthecountries.TheneedtoaccountforCDiskeybecauseashocktoanecon‐ omycouldbetransmittedtoothereconomiesthataremacroeconomicallyinterdependent (OlaoyeandAderajo2020;Olaoyeetal.2020).Thisisbecausecommonfeaturesandin‐ terdependencebetweeneconomiescanengenderCDduetoglobalisation(DeHoyosand Sarafidis2006).AsshowninFigure1,beyond1995,thetrendofcountryriskinallfour Visegrádcountriesappearedtomoveinthesamedirectionandaroundsimilarscoresfor eachyear.ThisreflectsatendencyforCDbetweenthefourcountries.Therefore,byusing thextdcce2programsprovidedbyDitzen(2018),whicharedesignedtoproduceestimates forthedynamiccommon‐correlatedeffects(DCCE)estimatorproposedbyChudikand Pesaran(2015),thisworkdepartedfromearlierresearcheffortsbyaccountingforCDand heterogeneousslopesinthepaneloftheV4. Third,whilethepreponderanceoffindingsfromstudiesonthedeterminantsofFDI intheV4hasidentifiedseveralvariablesthatportraythesize/growthandthecostcom‐ petitivenessoftheeconomies,fewotherstudiesstressedthatotherfactorssuchascorrup‐ tion,nationalrisk,reformsinthebankingsector,economicreforms,politicalriskandlib‐ eralisationinfluencetheinflowofFDIintheV4andtheCEECsingeneral(Avioutskiiand Tensaout2016;BevanandEstrin2000;Bradaetal.2006;Cieślik2020;Suetal.2018).Each ofthesefactorsiseitheracomponentofcountryriskorissomewhatconnectedtoit. 0 10 20 30 40 50 60 70 80 90 1985 1990 1995 2000 2005 2010 2015 2020 2025 Countryriskindexscale Year Trendofcountryriskindex CzechRep Hungary Poland Slovakia Figure 1. Trend of the country risk index in the Visegrád countries. Second, the V4 have a lot of social and economic interaction/financial integration and share common historical roots and cultural traditions, having emerged from communism, which held sway in Central and Eastern European countries (CEECs) until 1989. Consequently, the process of FDI flows to the four countries has several shared characteristics: the first years of FDI inflows witnessed a predominance of brownfield investments, the following years saw more significant emphasis on greenfield investment, the period after the EU accession (especially, 2004–2007) experienced increased and dynamic FDI inflows, a disproportionately large percentage of FDI inflow to the V4 came from the EU and FDI inflow to the V4 has witnessed noticeable structural changes towards the services sector over the years (Ambroziak 2013;Zieli´nska-Gł˛ebocka 2013). Despite these common features and consequent interdependence between the V4 countries, all existing panel studies on the determinants of FDI in the V4 assumed cross-sectional independence in the disturbances of their panel models, thereby failing to account for the likely cross-sectional dependence (CD) between the countries. Eberhardt and Teal (2010) and Pesaran (2006) intensely faulted this assumption on the grounds that it could result in biased estimates, and consequently, inappropriate policy proposals. To this end, they propounded panel regressions with robust standard errors that can account for CD between the countries. The need to account for CD is key because a shock to an economy could be transmitted to other economies that are macroeconomically interdependent (Olaoye and Aderajo 2020;Olaoye et al. 2020). This is because common features and interdependence between economies can engender CD due to globalisation (De Hoyos and Sarafidis 2006). As shown in Figure 1, beyond 1995, the trend of country risk in all four Visegrád countries appeared to move in the same direction and around similar scores for each year. This reflects a tendency for CD between the four countries. Therefore, by using the xtdcce2 programs provided by Ditzen (2018), which are designed to produce estimates for the dynamic common-correlated effects (DCCE) estimator proposed by Chudik and Pesaran (2015), this work departed from earlier research efforts by accounting for CD and heterogeneous slopes in the panel of the V4. Third, while the preponderance of findings from studies on the determinants of FDI in the V4 has identified several variables that portray the size/growth and the cost competitiveness of the economies, few other studies stressed that other factors such as corruption, national risk, reforms in the banking sector, economic reforms, political risk and liberalisation influence the inflow of FDI in the V4 and the CEECs in general (Avioutskii and Tensaout 2016;Bevan and Estrin 2000;Brada et al. 2006;Cie´slik 2020;Su et al. 2018). Each of these factors is either a component of country risk or is somewhat connected to it. Economies 2022,10, 221 4 of 22 Meanwhile, it is noteworthy that none of the previous studies on the impact of country risk on FDI inflows (Hammache and Chebini 2017;Khan and Akbar 2013;Nassour et al. 2020; Rodríguez 2016;Salem and Younis 2021;Topal and Gul 2016) focussed on the V4. Thus, the V4 deserves a separate study to determine the impact of country risk and each of its components on the FDI inflows into their economies. This is crucial, as it tends to help the countries set a realistic target of country risk rating, which would potentially increase the V4’s appeal as a preferred FDI location. The remaining segments of this paper focus on the following: Section 2contains a review of both the theoretical and empirical literature. Section 3discusses the methodology, while Section 4discusses the results of the tests and regressions. Section 5concludes the study. 2. Literature Review 2.1. Theoretical Perspective While it is difficult to find a definition of country risk that is generally agreed upon, the definitions and evaluations of the concept in the literature generally point to it as a phenomenon of uncertainties created by financial, economic and political structures (Elleuch et al. 2015;Hoti and McAleer 2002;James 2004;Lee and Naknoi 2014;Moosa 2002; Topal and Gul 2016;White and Fan 2006). This implies that country risk can be broadly classified into economic, financial and political risks. According to Topal and Gul (2016), economic risk refers to unanticipated and unforeseen changes in the economy’s general structure, which could compel adjustments in investors’ projects. Hence, it is measured by variables such as economic growth and GDP per capita because FDI investors always look out for large markets to profit from economies of large-scale production (Anyanwu 2012; Busse and Hefeker 2007;Wach and Wojciechowski 2016). Other variables such as inflation, current account balance and budget deficit are also important indicators of economic risk. Inflation is a crucial measure because galloping inflation could easily erode the real value of the investment, lead to poor returns and aggravate the balance of payment deficits (Arık et al. 2014). Furthermore, a budget deficit could alter the savings–investment balance of an economy in such a way that is deleterious to the current account balance, inflation and international trade (Altunöz 2014). FDI investors usually meticulously monitor developments around these variables to assess the economic risk of their investment. White and Fan (2006) defined financial risk as the increase in a country’s tendency to default on its financial obligation to a foreign body. Therefore, it is measured by variables such as external debt stock, exchange rate stability, current account deficit and foreign exchange earnings. FDI investors are often wary of countries with high and accumulating external debt stock. It could exacerbate the current account deficit and dampen growth (Dey and Tareque 2019;Qureshi and Liaqat 2020), thereby aggravating its financial risk. Exchange rate instability also creates immense uncertainty around investment, as it could depress investment profitability and make forecasts regarding investments complicated (Lee and Naknoi 2014). Foreign exchange earnings are also crucial for moderating both exchange rate volatilities and balance of payments deficits. Poor foreign exchange earnings would therefore increase the financial risk from the perspective of FDI investors. Political risk was also evaluated in the context of FDI for developing countries. It was defined by Haendel (1979) as “the risk or probability of occurrence of some political event(s) that will change the prospects for the probability of a given investment”. Eng et al. (1998) identified the indicators of political risk in the context of FDI to include political willingness/ability to implement structural reforms, cases of arbitrary and changing government regulation, ease/difficulty in repatriating profits by international investors, and how fair and equal the host government treats investors. Other indicators of political risk that are relevant to FDI are bureaucracy, democracy, the rule of law, social compliance and the level of corruption. According to Busse and Hefeker (2007), the deterioration of these indicators could lead to a decline in investors’ profitability. It was also claimed that the Economies 2022,10, 221 5 of 22 poor rating of an economy regarding the indicators could lead to a sharp increase in the cost of production for FDI investors (Elleuch et al. 2015;Khan and Akbar 2013). Against this background, the theoretical framework for this study was founded on an eclectic paradigm theory based on Dunning’s (1979) internalisation theory, otherwise called the OLI model or the OLI framework, as it rests on a three-tiered framework: ownership, location and internalisation (OLI). It is an important framework for evaluating the suitability/profitability or otherwise of prospective FDI projects. The theory holds that for any FDI to benefit the investor, it must possess ownership advantage, locational advantage and internalisation advantage. The concept of locational advantage was extended by Dunning (1998) with the addition of institutional factors to the existing economic factors. He argued that the higher the quality of institutions end economic facilities in an economy, the more attractive the economy to the foreign investors because the investors consider their profitability to be positively related to institutional quality and sound macroeconomic indicators. His position is in line with North (1990) and Lucas (1993), who claim that institutional factors, alongside purely economic factors, are crucial for attracting FDI. Therefore, the motivation for FDI, based on Dunning’s (1998) propositions, comprise market-seeking, resource-seeking, efficiency-seeking, asset-seeking and the quality of institutions of the investment destination. This can be expressed as follows: FDI = f(efficiency-seeking, market-seeking, resource-seeking, asset-seeking, institutions) (1) where FDI is the foreign direct investment. Market-seeking motivation is represented by the market size, which is a key determinant of FDI and is measured by real GDP. Resource-seeking motivation is measured by the availability of natural resources. Efficiencyseeking is denoted by macroeconomic stability, which refers to the country’s economic situation and it is proxied by economic risk and financial risk, as both risks are measured by variables that determine macroeconomic stability. Asset-seeking motivation is measured by the availability of infrastructure. Institutions are proxied by political risk, as indicators of political risk include variables such as the level of corruption, democracy, level of bureaucracy and political willingness/ability to implement structural reforms, which also measure the quality of institutions. Since country risk was classified into economic, financial and political risks, Equation (1) became: FDI = f(country risk, real GDP, natural resources, infrastructure) (2) 2.2. Empirical Literature The attraction of FDI in an economy was identified as an important way of bridging the savings–investment gap, which characterises most developing countries (Sabir and Khan 2018). This is very important for raising capital accumulation, which the traditional neoclassical growth model considers critical to enhancing the per capita income (Koopmans 1965). Therefore, this section is dedicated to the empirical literature on the impact of country risk on FDI inflow. Bevan and Estrin (2000) investigated FDI inflows into transition CEECs by employing a panel dataset. The results from their study established the determinants of FDI as comprising country risk, market size, gravity factors and labour cost. These findings were corroborated by a different study on CEECs by Brada et al. (2006), who concluded that transitional factors such as national risk, privatisation progress, banking sector reforms and trade liberalisation influenced FDI inflows. Similarly, Avioutskii and Tensaout (2016) investigated whether politics influence FDI inflow into CEECs and found that political risk, economic reforms and political liberalisation are critical influencers of FDI inflow. In another study of the factors that affect FDI inflow into the V4 for the post-accession period by Su et al. (2018), results from the generalised ridge regressions employed identified perceived corruption as an influencer of FDI inflows. This finding was supported by another study by Cie´slik and Goczek (2018), who investigated 142 countries for the period 1994– 2014. Estimates from their GMM estimation suggested that corruption in the host country constitutes a drain on its stock of foreign investment. Still, in the V4, Bobenic Hintosova et al. Economies 2022,10, 221 6 of 22 (2018) investigated country-level data for the period 1989–2016 and found that gross wages and an educated labour force positively influence FDI, while trade openness, spending on research and development, and corporate income tax deter FDI. In a related study of five CEECs—the Czech Republic, Hungary, Poland, Romania and Slovakia—Gauselmann et al. (2011) found that access to markets and the price of factors of production mostly affect FDI. Similarly, Wach and Wojciechowski (2016) established that the V4 receive more FDI allocation from EU-15 countries because of each V4 economy’s market potential, as measured using GDP. Using the two-stage least-squares method, the effect of national risk on FDI in Iran was investigated by Rafat and Farahani (2019) for 1985–2016. Their results suggested that indicators of national risk, including religious and ethnic tension, external conflicts, socioeconomic status and military tension, are significant factors that impact the FDI in the economy. Similarly, the impact of economic, political and financial risks was investigated for 10 MENA countries between 2000 and 2017 by Salehnia et al. (2019). The empirical results from their estimation showed that all three types of risk negatively affect the FDI, with the economic risk being the most influential of the three. This result was corroborated by a recent study of a MENA country, namely, Egypt, for the period 2005–2015 by Salem and Younis (2021). They found both economic and political risks as determinants of FDI in the country, with economic risk being the more influential of the pair. It was, however, found that financial risk has no impact on FDI. Meanwhile, in an earlier similar study for MENA countries, Bouyahiaoui and Hammache (2017) identified political risk as the dominant determinant of FDI in the region. An investigation of the impact of political and financial risks on FDI inflows into 90 countries from 1985 to 2007 was conducted by Hayakawa et al. (2013) using the generalised method of moments (GMM) estimator. Their results, which concentrated mainly on developing countries, indicated that of all the estimated components of political risk, the following are closely associated with FDI flows: religious tension, democratic accountability, corruption, ethnic tension, socioeconomic condition, investment profile and government stability. Regarding financial risk components, only exchange rate stability was found to impact the FDI positively, while the remaining components were either insignificant or negative. Sissani and Belkacem (2014) investigated the effect of political and financial risks on FDI in Algeria from 1990–2012; they concluded that political and financial risks are critical to FDI inflows, with financial risk being a strong determinant. In a related study, Krifa-Schneider and Matei (2010) examined the effect of political risk and business climate on FDI in 33 developing and transition economies by using both a fixed-effects model and a GMM estimator over the period 1996–2008. Estimates from their analysis revealed that reducing political risk increases FDI inflow, while the business climate constitutes a key driver of FDI flows. The impact of political risk and economic growth on FDI in South Africa was investigated by Meyer and Habanabakize (2018) for 1995–2016. The findings from their analysis revealed that the impact of political risk on FDI is higher relative to that of GDP. In the same vein, the effect of political risk in Lebanon was investigated for 2008–2018 by Bitar et al. (2020), who reclassified ICRG political risk variables into three components: cohesion, institutional quality and governance. Their results revealed that all three components are significantly associated with FDI inflows into Lebanon. They, therefore, concluded that political stability is a critical determinant of FDI. In a related study, the link between political, economic and financial components of Saudi Arabia’s country risk rating and its stock market movements was examined using the autoregressive distributed lag (ARDL) estimation technique on monthly data between 2005 and 2012 by Almahmoud (2014). Results from the analysis showed that country risk ratings are closely associated with stock market movements in the country, with financial risk exhibiting the most robust sensitivity among the three components. The study concluded that prospective FDI investors should seriously consider financial risk indicators, such as external debt servicing, exchange rate stability and the current account balance, before Economies 2022,10, 221 7 of 22 embarking on any strategic investment in Saudi Arabia. Similarly, Hammoudeh et al. (2011) examined the individual BRICS countries’ country risk ratings related to their respective national stock markets. Their findings expressly pointed to China as being sensitive to all components of country risk. Meanwhile, in developing a behavioural framework for decision-making at the management level, Yasuda and Kotabe (2021) proffered that the mental map of perceived political risk in host nations where MNCs operate and the political risk of the MNCs’ origin country serve as the political risk reference points. Subsequently, their research outcome revealed that if political risks are below (above) their reference points, MNCs perceive them as opportunities (threats) in the host countries. This finding was corroborated by Gonchar and Greve (2022), who alluded to the volatility of FDI in economies with high political risk. By employing the Cox proportional hazard model on Russia’s multinational plant-level data from 2000 to 2016, the authors set out to investigate whether MNCs’ withdrawal decisions are influenced by political risks. The research outcome revealed significant impacts from heightened host-country political risk when the year of arrival was compared with the year of withdrawal. They further established that MNCs are especially sensitive to issues relating to law, order and socioeconomic conditions in Russia, as well as the involvement of the military in domestic politics in the originating country. A synopsis of the findings of extant research is reported in Table 1. Table 1. Studies on the country risk–FDI nexus. Author(s) Data Span Variables Method Country(ies) Findings Salehnia et al. (2019)2000–2019 FDI, ER, FR, PR, GDP, INF, TRADE Fixed-effects model (FE) 10 MENA countries ER, FR and PR affect FDI negatively; ER is most influential Bouyahiaoui and Hammache (2017)2000–2015 FDI, PR variables Qualitative analysis MENA countries Political risk influences FDI Hayakawa et al. (2013)1985–2007 FDI, PR, FR GMM 90 countries PR negatively affects FDI, but FR has no effect Sissani and Belkacem (2014)1990–2012 FDI, PR, FR Multiple regression Algeria PR and FR affect FDI, but FR is stronger Krifa-Schneider and Matei (2010)1996–2008 FDI, business climate, PR, GDP, INF, TRADE FE, GMM 33 developing and transition economies Reducing PR increases FDI; business climate is key for FDI Meyer and Habanabakize (2018) 1995–2016 FDI, PR, GDP ARDL, Granger causality South Africa Impact of PR on FDI is higher than that of GDP Bitar et al. (2020) 2008–2018 FDI, wage rate, INF, TRADE, infrastructure OLS Lebanon There is causality between all PR factors and FDI Almahmoud (2014)2005–2012 All-Share Index, PR, ER, FR ARDL Saudi Arabia CR factors are associated with stock market movements; FR is most sensitive Hammoudeh et al. (2011)1992–2011 Equity return, oil price, ER, FR, PR ARDL BRICS Only the Chinese stock market responds to changes in all the factors; FR is more sensitive than ER and PR Economies 2022,10, 221 8 of 22 Table 1. Cont. Author(s) Data Span Variables Method Country(ies) Findings Bevan and Estrin (2000)1994–1998 FDI, GDP, labour cost, risk, TRADE FE and random effects 14 Central and Eastern European countries (CEECs) Determinants of FDI comprise risk, market size, labour cost and gravity factors Brada et al. (2006) 1990–2002 FDI, GDP, TRADE, POP, INF, number of telephone lines, school enrolment GLS (FGLS) pooled-panel regression Transition economies of Central Europe, the Baltics and the Balkans Transitional factors like national risk, privatisation progress, banking sector reforms and trade liberalisation affect FDI negatively Cie´slik and Goczek (2018)1994–2014 FDI, control of corruption, POP, EXR volatility, GDP GMM 142 countries Corruption reduces stock of FDI Avioutskii and Tensaout (2016)2002–2015 FDI, GDP, PR, corruption index FE, dynamic adjustment model 5 CEECs PR, economic reforms and political liberalisation influence FDI Bobenic Hintosova et al. (2018)1989–2016 FDI, GDP, TRADE, TAX, INF, R&D, EDUC OLS, FE V4 Wages and educated labour force positively affect FDI; TRADE, R&D and TAX negatively affect FDI Gauselmann et al. (2011)2000–2010 FDI, GDP, technology, labour costs Survey analysis 5 CEECs Access to markets and factor price mostly affect FDI Wach and Wojciechowski (2016) 2000–2010 FDI, GDP, labour productivity, access to Baltic Sea, common border Gravity model V4 Each V4 country received FDI from EU countries because of market potential Rafat and Farahani (2019)1985–2016 FDI, PR, TRADE, INF, GDP, EXR Two-stage least-squares (2SLS) Iran National risk variables impact FDI Salem and Younis (2021)2005–2015 FDI, ER, FR, PR, Multiple regression Egypt ER and PR influence FDI; FR has no impact on FDI Yasuda and Kotabe (2021)1992–2007 FDI, PR, MNC attributes, GDP, metal price Zero-inflated negative binomial regression model 444 MNCs from 35 countries; 703 mines from 53 countries MNCs adjudge host countries as investment opportunities (or threats) if their level of risk is lower than (or higher than) that of the origin country Gonchar and Greve (2022)2000–2016 FDI, political risk variables Cox proportional hazard model Russia—MNCs plant-level data MNCs are sensitive to issues of law, order, socioeconomic conditions and military involvement in politics Note: CR—country risk; ER—economic risk; FR—financial risk; PR—political risk. Economies 2022,10, 221 15 of 22 index score (or a 1% decrease in political risk) accounted for a 0.307% increase in the FDI inflows into the V4. This research outcome was in line with a preponderance of extant studies (Avioutskii and Tensaout 2016;Cie´slik and Goczek 2018;Hayakawa et al. 2013; Rafat and Farahani 2019;Salehnia et al. 2019;Bouyahiaoui and Hammache 2017;Su et al. 2018), which established political risk as a major influencer of the FDI inflows. As indicated in Table 1, the average political risk index score for the V4 over the study period was 77.63, while the lowest score was 70.58. Both scores fell within the ICRG score classification of low risk, which implied that all the countries were doing very well in this regard. Therefore, this finding indicated the need for the Visegrád countries to continue to maintain their high political risk rating, which was found to be important for attracting FDI. For the covariates in all four models, the DCCE results demonstrated that real GDP, which represents the market size, was a strong determinant of the FDI inflows, as the coefficient of GDP was positive and strongly significant across the four models. The magnitudes of real GDP in the models suggested that it exerted a strong positive elastic impact on FDI in the V4. This research outcome is consistent with several studies that identified market size as a strong driver of FDI (Demirhan and Masca 2008;Khan and Akbar 2013;Meyer and Habanabakize 2018;Salem and Younis 2021;Wach and Wojciechowski 2016). The coefficient of natural resources was insignificant across the four models. This result suggested that natural resource availability did not influence the inflow of FDI into the V4. For infrastructure, the coefficient was positive and significant throughout. This implied that improvement in the level of infrastructure was associated with an increase in FDI inflow. This result is consistent with the findings of Demirhan and Masca (2008) for 38 developing countries and Gorbunova et al. (2012) for 26 transition countries, which included the V4, who stated that infrastructure in the host country positively influences FDI inflows. The DCCE results further showed that the coefficient of trade openness was positive and significant in all four models, which suggested that increased openness to international trade enhanced the FDI inflows into the V4. This result supports findings by Anyanwu (2012) and Liargovas and Skandalis (2012), who concluded that trade openness is positively linked to FDI. The long-run elasticity results of the V4 panel were already presented and discussed. However, in order to facilitate more robust policy formulation, there is a need to also explore the linkage between the FDI inflow, country risk, real GDP, natural resources, infrastructure and trade openness on a country-wise basis. To this end, FMOLS regressions were conducted for each of the Visegrád countries, and the results are presented in Table 7. The table contains four compartments, with each consisting of an equation with each of the country risk variables. As such, for each of the four countries, models 1–4 represent the results of regressions for the composite country risk, economic risk, financial risk and political risk, respectively, as the country risk variable. The model 1 results revealed that the composite country risk had a negative and significant impact on the FDI inflows into the Visegrád countries. Specifically, a 1% increase in the country risk index score (which implies a decline in country risk) led to an increase in the FDI inflows in the case of the Czech Republic, Hungary, Poland and Slovakia by 0.25%, 0.17%, 0.77% and 0.52%, respectively, though the impact was rather weak with a 10% significance in the case of Hungary. Generally, a healthier country risk rating raises the confidence of overseas investors regarding the safety of their investment in the host country, which, in turn, enhances their tendency to bring in their investment. These results are in line with the findings of Almahmoud (2014) and Sissani and Belkacem (2014) for Saudi Arabia and Algeria, respectively. Concerning the coefficient of the economic risk index, as shown in model 2, it had a significantly positive coefficient, which suggested that the economic risk negatively affected the FDI inflows into the Visegrád countries. In particular, a 1% increase in the economic risk index score (which implies a reduction in economic risk) enhanced the FDI inflows by 0.62%, 0.46%, 0.37% and 0.59% into the Czech Republic, Hungary, Poland and Slovakia, respectively. The strong significance and high coefficients of economic risk Economies 2022,10, 221 16 of 22 in all the countries suggested the cruciality of economic risk in driving the FDI inflows into the V4. Therefore, this research outcome indicated the need for both monetary and fiscal authorities in the Visegrád countries to put appropriate policies in place towards maintaining optimal levels of the inflation rate, GDP growth, budget and current account. Table 7. Results of country-wise FMOLS regressions. Czech Republic Hungary Poland Slovakia Model 1: CRV—Composite Country Risk Index Country risk 0.25 ** (0.02) 0.17 * (0.06) 0.77 *** (0.00) 0.52 ** (0.04) Real GDP 0.11 *** (0.00) 0.52 ** (0.04) 0.20 ** (0.02) 0.19 *** (0.00) Natural Resources 0.91 (0.25) 0.33 (1.02) 0.28 * (0.06) 1.42 (0.17) Infrastructure 0.26 ** (0.01) 0.15 *** (0.00) 0.42 * (0.05) 0.31 *** (0.00) Trade openness 0.03 * (0.08) 0.18 ** (0.01) 0.02 *** (0.00) 0.16 ** (0.02) Model 2: CRV—Economic Risk Index Economic risk 0.62 *** (0.00) 0.46 *** (0.00) 0.37 ** (0.04) 0.59 *** (0.00) Real GDP 0.05 ** (0.01) 0.22 ** (0.03) 0.16 *** (0.00) 0.35 ** (0.01) Natural Resources 0.17 (1.04) 1.38 (1.70) 0.31 * (0.05) 0.62 (0.13) Infrastructure 0.29 * (0.08) 0.19 ** (0.02) 0.33 * (0.07) 0.25 *** (0.00) Trade openness 0.06 *** (0.00) 0.01 *** (0.00) 0.11 ** (0.04) 0.24 ** (0.01) Model 3: CRV—Financial Risk Index Financial risk 0.19 * (0.05) 0.31 *** (0.03) 0.15 * (0.07) 0.36 * (0.06) Real GDP 0.17 *** (0.00) 0.37 ** (0.01) 0.10 *** (0.00) 0.21 ** (0.02) Natural Resources 1.08 (0.13) 0.09 (0.29) 0.22 * (0.05) 0.81 (1.13) Infrastructure 0.03 ** (0.01) 0.26 ** (0.04) 0.41 * (0.08) 0.05 * (0.06) Trade openness 0.15 ** (0.01) 0.21 *** (0.00) 0.30 ** (0.02) 0.04 ** (0.01) Model 4: CRV—Political Risk Index Political risk 0.26 ** (0.02) 0.54 *** (0.00) 0.17 *** (0.00) 0.48 *** (0.00) Real GDP 0.15 *** (0.00) 0.41 ** (0.03) 0.15 * (0.05) 0.32 ** (0.04) Natural Resources 1.33 (0.94) 0.58 (0.43) 0.40 * (0.07) 0.55 (1.83) Infrastructure 0.01 *** (0.00) 0.04 ** (0.04) 0.12 *** (0.00) 0.22 ** (0.03) Trade openness 0.11 *** (0.00) 0.05 ** (0.01) 0.10 *** (0.00) 0.16 *** (0.00) Note: dependent variable—FDI inflows; CRV—country risk variable; all variables are in natural logarithm forms; probability values are in brackets; ***, ** and * denote 1%, 5% and 10% levels of significance, respectively. The estimates of financial risk that are displayed for model 3 show that financial risk had mixed impacts on the FDI inflows across the Visegrád economies. It had a negative and strongly significant impact on the FDI inflows into Hungary. According to the estimates, a 1% increase in the financial risk index score (which suggests a reduction in financial risk) led to an increase in the FDI inflows by 0.31% into Hungary. However, the impacts of financial risk on the FDI inflows into the Czech Republic, Poland and Slovakia were also negative but weak, as the coefficients of the financial risk index score were only significant at the 10% level for these three countries. This mixed country-wise result of the financial risk variable was congruent with the overall panel finding of the weak impact of financial risk on the FDI inflows into the V4. In the case of Hungary with a strong negative impact of financial risk on the FDI inflows, it suggested a consequence of injudicious fiscal policies of the country’s socialist administration in the 2000s, which led to a budget deficit that far exceeded the EU criteria 1 . This led to increased foreign debt at levels far above their V4 counterparts over the years (see Appendix A), which increased the economy’s financial risk and in turn led to fluctuations in the FDI inflows. Therefore, the case of Hungary suggested that for individual V4 countries, financial risk can be a key influencer of FDI inflow, depending on how it is managed. Regarding the coefficient of the political risk index displayed for model 4, it was positive and strongly significant across the four countries. This implied that political risk had a negative effect on the FDI inflow. Particularly, a 1% increase in the political risk index score (which suggests a decrease in political risk) enhanced the FDI inflows by 0.26%, 0.54%, 0.17% and 0.48% into the Czech Republic, Hungary, Poland and Slovakia, respectively. Economies 2022,10, 221 17 of 22 Finally, the estimates of the covariates in all four models for the four countries were generally consistent with those of the overall panel discussed earlier with only a few exceptions. The trio of market size, infrastructure and trade openness bore positive and strongly significant coefficients throughout. This implied that the three variables positively influenced the FDI inflows into each of the Visegrád countries. However, while natural resources were found to be insignificant for the Czech Republic, Hungary and Slovakia, it was weakly significant in the case of Poland in all the models, which implied that natural resource availability somewhat influenced the inflows of FDI to Poland. 6. Conclusions Investigation of FDI determinants has grown significantly over the years due to the vital role that FDI plays in the economic growth process across countries. While several variables were identified as determinants of FDI in different regions of the world, vigorous debate continues as research results have remained predominantly mixed and inconclusive. Meanwhile, studies that considered the peculiarities and heterogeneities of the V4 group in the investigation of country risk as a determinant of FDI inflows are scarce. Hence, this study filled the literature gap by examining whether country risk influenced foreign investors’ decision to invest in the V4. To achieve this objective, this study employed the DCCE estimator, which accounts for CD, structural breaks and heterogenous slopes in panel data estimation. A survey of literature on the subject showed that no previous study accounted for CD despite the increased predominance of globalisation interdependence between countries. To ensure the robustness of results and explore how the coefficients varied across the countries, country-wise FMOLS regressions were also conducted on annual data for the V4 over the period 1991–2020. The empirical results showed that country risk mattered for the FDI inflows, as it negatively impacted the FDI inflows. It was also found that economic and political risks were essential determinants of the FDI inflows, as both had negative effects on the FDI. However, it was found that changes in financial risk had weak and mixed impacts on the FDI inflows in the overall panel and country-wise regressions, respectively. The research outcome also demonstrated that market size, infrastructure and trade openness positively influenced the FDI, while natural resource availability had no impact and a mixed impact in the overall panel and country-wise regressions, respectively. Based on these research outcomes, there is a need for the appropriate macroeconomic and government authorities in the V4 to enhance the market potentials of their economies by improving and upholding the corporate and macroeconomic structures in order to enhance their country risk attributes. Furthermore, appropriate policy should be formulated by the governments in these countries towards increasing the market potential, quality of infrastructure and optimal environment for FDI-inflow-enabling trade. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The author declares no conflict of interest. Economies 2022,10, 221 18 of 22 Appendix A Economies2022,10,xFORPEERREVIEW18of22  TheempiricalresultsshowedthatcountryriskmatteredfortheFDIinflows,asit negativelyimpactedtheFDIinflows.Itwasalsofoundthateconomicandpoliticalrisks wereessentialdeterminantsoftheFDIinflows,asbothhadnegativeeffectsontheFDI. However,itwasfoundthatchangesinfinancialriskhadweakandmixedimpactsonthe FDIinflowsintheoverallpanelandcountry‐wiseregressions,respectively.Theresearch outcomealsodemonstratedthatmarketsize,infrastructureandtradeopennesspositively influencedtheFDI,whilenaturalresourceavailabilityhadnoimpactandamixedimpact intheoverallpanelandcountry‐wiseregressions,respectively. Basedontheseresearchoutcomes,thereisaneedfortheappropriatemacroeconomic andgovernmentauthoritiesintheV4toenhancethemarketpotentialsoftheireconomies byimprovingandupholdingthecorporateandmacroeconomicstructuresinordertoen‐ hancetheircountryriskattributes.Furthermore,appropriatepolicyshouldbeformulated bythegovernmentsinthesecountriestowardsincreasingthemarketpotential,qualityof infrastructureandoptimalenvironmentforFDI‐inflow‐enablingtrade. Funding:Thisresearchreceivednoexternalfunding. InstitutionalReviewBoardStatement:Notapplicable. InformedConsentStatement:Notapplicable. DataAvailabilityStatement:Notapplicable. ConflictsofInterest: Theauthordeclaresnoconflictofinterest. AppendixA  FigureA1.Trendofforeigndebt/GDPintheVisegrádcountries.Source:Muddlingthroughdeficit troubles.https://www.euromoney.com/article/b1320xhhjpfb02/muddling‐through‐deficit‐troubles (accessedon22July2022).  Figure A1. 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