Volume 25 Issue 3 Thematic Issue: Internationalization and Foreign Direct Divestment Flows in Central and Eastern European Economies Article 2 September 2023 Macroeconomic Drivers, Governance, and Foreign Direct Macroeconomic Drivers, Governance, and Foreign Direct Investment in Central and Eastern European Countries (CEECs) Investment in Central and Eastern European Countries (CEECs) Parfait Bihkongnyuy Beri Nkafu Policy Institute, Economic Affairs, Yaoundé, Cameroon and Socio-Economic Research Applications and Projects, Washington, D.C, USA Gabriel Mhonyera University of Johannesburg, College of Business and Economics, Johannesburg, South Africa Follow this and additional works at: https://www.ebrjournal.net/home Part of the International Business Commons, and the International Economics Commons Recommended Citation Recommended Citation Beri, P ., & Mhonyera, G. (2023). Macroeconomic Drivers, Governance, and Foreign Direct Investment in Central and Eastern European Countries (CEECs). Economic and Business Review, 25(3), 131-145. https://doi.org/10.15458/2335-4216.1323 This Original Article is brought to you for free and open access by Economic and Business Review. It has been accepted for inclusion in Economic and Business Review by an authorized editor of Economic and Business Review. ORIGINAL ARTICLE Macroeconomic Drivers, Governance, and Foreign Direct Investment in Central and Eastern European Countries (CEECs) ParfaitBihkongnyuyBeri a,b, * ,GabrielMhonyera c a Nkafu Policy Institute, Economic Affairs, Yaoundé, Cameroon b Socio-Economic Research Applications and Projects, Washington, D.C, USA c University of Johannesburg, College of Business and Economics, Johannesburg, South Africa Abstract Background and objective: The transition to market-oriented economies in CEECs entailed signicant structural economic and institutional reforms. Over the past years, studies have investigated how these reforms affected foreign direct investment (FDI) inows. However, the evidence remains debatable and varies across countries. This study provides new insights by considering the impact of macroeconomic factors, governance, and the moderating effect of governance on the macroeconomic drivers–FDI nexus. Methods: A panel of 12 countries from 1991 to 2020 are analysed within the framework of conventional methods and Seemingly Unrelated Regression (SUR). Results: Results robustly suggest that gross capital formation, macroeconomic stability, and trade openness are signif- icant determinants of FDI at 1%–5% levels. We also observe cross-country differences in FDI performance. Governance does not moderate the relationship in the full sample, but additional results uncover heterogeneous FDI behaviour. Conclusion: In order to attract more FDI in CEECs, policymakers should invigorate domestic macroeconomic policies and trade liberalisation. Contribution: We advance literature by documenting new linkages between macroeconomic drivers, governance, and FDI across CEECs from the lens of SUR, a gap largely ignored by extant studies. Keywords: Transition economies, FDI, Governance, Seemingly Unrelated Regression JEL classication: F21, E02, O43 Introduction W e consider the impact of macroeconomic drivers and governance on foreign direct investment (FDI) in Central and Eastern European Countries (CEECs) 1 from 1996 to 2020. This period succeeded the breakup of the former socialist states in 1992, which paved the way for transitions into market-oriented economies. The transitions were lengthy, gradual and necessitated signicant reforms. New institutions and economic structures were erected or underwent behavioural changes to acclimatise with the neoliberal system (Kolodko, 1999). The study covers the transition period from its early to late years. In the rst phase, CEECs gradually liberalised trade and withdrew the government from many activities to incentivise FDI and accelerate technological and economic development. The second phase was characterised by the development of institutions that underpin market economies, while the last phase consisted of consolidating the systems in place. The global integration of these economies brought many opportunities and challenges. On the one hand, FDI and its trickle-down effects via knowledge trans- fer increased substantially over the following decade, Received 21 July 2022; accepted 22 May 2023. Available online 5 September 2023 * Corresponding author. E-mail addresses: parfaitberi@gmail.com (P . B. Beri), gmhonyera@uj.ac.za (G. Mhonyera). 1 See Table A1 for the geographical classication of European countries. https://doi.org/10.15458/2335-4216.1323 2335-4216/© 2023 School of Economics and Business University of Ljubljana. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/). 132 ECONOMIC AND BUSINESS REVIEW 2023;25:131–145 accelerating development outcomes. On the other, CEECs became increasingly vulnerable to external shocks. Prior to the 2008 nancial crisis, real GDP growth rates and capital inows were already volatile in most CEECs (Jimborean & Kelber, 2017). This was followed by the post-2007 economic downturns, the euro debt crisis in 2011, the COVID-19 pandemic, and recently, the Russo-Ukrainian conict. Economic disruptions pose macroeconomic chal- lenges that could potentially obstruct the sustainable inow of FDI into these economies (Beri et al., 2022; Beri & Nubong, 2023). However, countries with more resilient macroeconomic indicators and institutions can sustain FDI. At this stage, important questions that arise are: (i) What is the impact of macroeconomic drivers on FDI inows? (ii) To what extent does gover- nance inuence FDI? (iii) Does governance moderate the macroeconomic drivers–FDI nexus? Studying this relationship is crucial because FDI provides a vigorous channel for the transfer of tech- nology, creation of jobs, and development of skills and knowledge in workers in host countries. It also bridges the savings–investment gap that percolates into further investment, industrialisation, and eco- nomic growth (Beri & Nubong, 2023; Magbondé & Konté, 2022). Since CEECs have different levels of institutional development (Doro ˙ zy´ nski et al., 2019, 2020), those with better institutions may attract more investments as institutions are known to moderate the macroeconomic drivers–FDI nexus. This explains why FDI inow remains at the centre of policymak- ing and continues to attract rigorous theoretical and empirical scrutiny. According to the Ownership, Internalisation, and Location (OLI) paradigm, multinational companies (MNCs) carefully consider country-specic character- istics and how they rank relative to other potential host countries before investing (Dunning & Lundan, 2008). A review of the paradigm reveals that mar- ket size, infrastructure, trade openness, human and natural resources, appropriate scal and monetary policies, and rm-specic factors are critical for FDI location (Beri & Nubong, 2023; Dunning, 2000). Al- though the inuence of macroeconomic drivers in the spatial distribution of FDI remains debatable and may vary across countries, literature shows that they are indispensable for FDI location in CEECs (Brenton et al., 1999; Carstensen & Toubal, 2004; Jimborean & Kelber, 2017; Lane & Milesi-Ferretti, 2007; Marinova & Marinov, 2017). This theory leads us to the rst hypothesis of the study: H0. Macroeconomic factors and good governance invigo- rate the inow of foreign direct investment Institutional theory has also gained centre stage in recent analyses of FDI. Institutions encompass the informal societal knowledge, norms, and for- mal government regulations that jointly inuence the investment climate (Contractor et al., 2020). Since MNCs operate in dynamic and intricate environ- ments (Doro ˙ zy´ nski et al., 2019), politically stable countries and those with good governance indica- tors provide favourable environments for FDI (Beri & Nubong, 2023; Nielsen et al., 2017; Rjoub et al., 2017). Acemoglu and Robinson (2010) also attributed differ- ences in economic growth to differences in economic institutions, which in turn depended on political insti- tutions in place. Likewise, countries that are devoid of these features but make commitments through investment treaties, members of the World Trade Or- ganisation, and European Union (EU) member states are also perceived credible for FDI inow (Alfaro et al., 2008; Beri & Nubong, 2021). Institutional theory leads us to the following hypothesis: H1. Good governance stimulates the macroeconomic drivers–foreign direct investment nexus Literature on the role of macroeconomic factors and governance in FDI is plentiful. Contractor et al. (2020) found that countries with stronger contract en- forcements and efcient trade regulations attracted more investments. Dang and Nguyen (2021) un- covered that economic growth, quality of economic institutions, and ination played signicant roles in attracting FDI. Doro ˙ zy´ nski et al. (2019, 2020) documented positive relationships between institu- tional environment and FDI. Doytch (2021) uncov- ered that aggregate FDI inows were countercyclical, increasing during economic downturns and reduc- ing during economic booms. Jimborean and Kelber (2017) documented evidence that history of FDI, mar- ket size, openness, and accession to the European Union were signicant determinants of FDI in CEECs. Finally, Mason and Vracheva (2017) found that ina- tion targeting had a positive impact on FDI. Notwithstanding the plethora of potential FDI determinants and the role of cross-country hetero- geneities (Alfaro et al., 2008), the use of mostly aggregative analytical methods for inference in extant research makes it challenging for policymakers to iso- late factors that attract FDI in each country relative to its regional peers. Addressing these issues can ad- vance the frontiers of knowledge and improve policy decisions in CEECs. This paper shows that gross xed capital formation, trade openness, and macroeconomic stability are the most signicant determinants of FDI. Our approach exploits static panel models and the Seemingly ECONOMIC AND BUSINESS REVIEW 2023;25:131–145 133 Unrelated Regression (SUR). A challenge with this strategy is that FDI might be dynamic. Nevertheless, our small sample (12 countries) relative to the time (25 years) does not allow for dynamic modelling, al- though similar studies considered it with a shorter time (Doro ˙ zy´ nski et al., 2020; Jimborean & Kelber, 2017). The study contributes to literature in two ways. First, it complements studies on the impact of macroe- conomic drivers of FDI with more extensive data from 1996 to 2020. While most studies on the determinants of FDI in CEECs employ dynamic panel models, we choose SUR in the second part of the analysis be- cause it controls for aggregative bias and addresses small sample problems and the Nickel bias. Second, previous studies either focused on macroeconomic drivers (Carstensen & Toubal, 2004; Doytch, 2021; Jimborean & Kelber, 2017) or the effect of institutions on FDI (Doro˙ zy´ nski et al., 2019, 2020; Marks-Bielska et al., 2022). We build on these studies and add a layer of originality by examining the moderating role of governance in the macroeconomic drivers– FDI nexus. Our study is closely related to those by Carstensen and Toubal (2004) and Jimborean and Kelber (2017). However, we cover a longer time and also emphasise the heterogeneous behaviour of FDI in CEECs. Section 1 gives a feel of the context by elaborating on the trajectory of FDI and its traditional determinants in CEECs after the collapse of the Soviet Union and Yugoslavia. Section 2 details the variables and analyt- ical procedures. Section 3 presents diagnostics of the data and regression results, while Section 4 concludes the paper. 1 FDI inows and macroeconomic drivers in CEECs: Stylised facts Inward FDI plays a crucial role in the economic growth and development endeavours of CEECs. In a historical context, following the collapse of the Union of Soviet Socialist Republics (USSR) in 1991, the former Soviet CEECs (i.e., Estonia, Latvia, and Lithua- nia) had to undergo signicant political and economic transitions (Hare & Turley, 2013). This entailed the introduction of comprehensive macro-economic sta- bilisation reforms, progression towards a free market economy, and the privatisation of a large part of state-owned enterprises (Carstensen & Toubal, 2004). Similarly, former Yugoslavia, namely Croatia and Slovenia, had to undertake signicant political and economic transformations after the end of the Social- ist Federal Republic of Yugoslavia in 1992 (Horvat, 2015). Again, all CEECs (except Albania) are members of the EU, which maintains rigorous political, eco- nomic, and administrative requirements during the accession process to the bloc. During the pre-2008 Global Financial Crisis (GFC) era after 2003 (see Fig. 1), CEECs attracted large in- ows of FDI primarily incentised by privatisation initiatives and the likelihoods of accession of some of the CEECs into the EU. Hence, the region was mostly effective in attracting FDI relative to other emerging market economies (Castejón & Wörz, 2007). As noted by Damijan and Rojec (2007), the FDI inows into the CEECs have been the central driver of economic restructuring and technology diffusion, eliciting pro- ductivity convergence within the region (Bijsterbosch & Kolasa, 2009). After the GFC, regional FDI in CEECs have been epitomised by an indolent recovery comparative to other emerging market regions. Fig. 1 shows that East Asia and the Pacic region recovered swiftly from the aftermath of the 2008 GFC and has been experiencing a fairly growing trend in FDI since then. From 2010 to 2015, FDI inows were more prominent in the East Asia and Pacic, North America, Latin America, and Northern Europe regions. In contrast, CEECs received signicantly meagre FDI inows. Prior to the COVID-19 pandemic, regional FDI in- ows (except that of the East Asia and Pacic region) largely had declining trajectories. However, while other regions continued to follow a declining path, CEECs began to witness a recovery in 2018, and the trend continued during COVID-19 and beyond. FDI inows in Western Europe, North America, Southern Europe, and Northern Europe generally moved in the same direction as the global trend both in the pre- and post-GFC period. The transitions have also been accompanied by an expansion of FDI inows. CEECs, per se, wit- nessed more inows of FDI from 1993 onwards (see Fig. 2), with Hungary and Poland outperforming many emerging market economies in 1999, perhaps due to the Asian crisis in 1997 (Konings, 2001). The surge in FDI inows in CEEs during this era may be a consequence of a deeper phase of integration of some CEECs into the EU (Brenton & Gros, 1997). Within the CEEC region itself, heterogeneity could be observed with countries exhibiting favourable initial conditions attracting more FDI than riskier and infe- rior performing neighbouring countries (Carstensen & Toubal, 2004). An analysis of FDI crescendos in Fig. 2 reveal that Hungary, Poland, the Czech Republic, and Romania receive the largest shares. The same countries are also the largest CEECs, in terms of economic output as measured by GDP , and the trends in their FDI inows were generally upward before plummeting 134 ECONOMIC AND BUSINESS REVIEW 2023;25:131–145 Fig. 1. Regional classication of FDI inows from 1996 to 2020. Source: World Bank (n.d.-a). Fig. 2. FDI inows in CEECs from 1996 to 2020. Source: World Bank (n.d.-a). during the GFC. Hungary, in particular, experienced a decline from US$75.1 billion in 2008 to US$347.0 million in 2009 and US$20.8 billion in 2010. For other CEECs, both pre- and post-GFC inows of FDI seem mostly subdued. In terms of origin, most of the FDI inows within the region originate from the Organi- sation for Economic Co-operation and Development (OECD) members (see Table A2). Similar to the CEECs and other regional trends, FDI inows in CEECs were already declining before the onset of the COVID-19 pandemic in 2019. In fact, the period between the post-GFC era and pre-COVID- 19 era is characterised by upward and downward swings in FDI inows in the majority of the CEECs. It is during this period that Hungary, again, saw its FDI inows decline from US$69.7 billion in 2016 to US$64.4 billion in 2018 before increasing to US$98.5 billion in 2019 and continuing to recover into the COVID-19 period. While the FDI inows of Poland showed signs of recovery by the end of 2020, the ECONOMIC AND BUSINESS REVIEW 2023;25:131–145 135 Table 1. Overview of the main macroeconomic indicators in selected years. Country GDP growth FDI inows Ination FCF growth Governance EDB score (%) (% of GDP) (%) (%) index 2000 2019 2000 2019 2000 2019 2000 2019 2000 2019 2015 2019 Albania 6.95 2.09 4.11 7.80 5.65 1.26 42.63 3.68 0.62 0.08 58.08 67.75 Bulgaria 4.59 4.04 7.56 3.22 7.38 5.24 16.48 4.53 0.13 0.26 72.46 71.97 Croatia 2.90 3.48 4.65 6.27 4.34 1.92 1.70 9.82 0.17 0.45 71.35 73.62 Czech 4.00 3.03 8.07 4.26 1.84 3.89 7.53 5.95 0.56 0.93 76.11 76.34 Estonia 10.09 4.10 7.32 9.87 3.68 3.19 14.18 6.12 0.90 1.24 80.54 80.62 Hungary 4.48 4.55 5.82 60.24 9.58 4.77 5.99 12.78 0.97 0.47 71.07 73.42 Latvia 5.68 2.48 4.07 3.17 3.62 2.58 22.09 6.93 0.39 0.85 79.13 80.28 Lithuania 3.70 4.57 3.30 6.28 1.30 2.65 6.86 6.64 0.47 0.95 78.99 81.62 Poland 4.56 4.74 5.42 2.82 6.12 3.19 2.19 6.06 0.69 0.64 76.93 76.38 Romania 2.46 4.19 2.78 2.95 43.18 6.80 5.85 12.91 0.18 0.27 72.72 73.33 Slovak 1.17 2.61 7.47 2.17 9.49 2.49 12.23 6.74 0.54 0.64 74.84 75.59 Slovenia 3.67 3.25 0.67 3.97 5.57 2.20 2.38 5.49 0.89 0.99 74.71 76.52 Note: GDPD Gross Domestic Product; FDID Foreign Direct Investment; FCFD Fixed Capital Formation; EDBD Ease of Doing Business. Source: World Bank (n.d.-a, n.d.-b). inows in the rest of the CEECs portrayed signs of dis- tress, and the trend may continue into the future given the negative spill-over effect of the Russo-Ukrainian conict. Table 1 overviews the main macroeconomic indica- tors in the CEECs in selected years. Economic growth averaged 4.52% in 2000 and declined to 3.59% in 2019. CEECs that experienced declining growth include Estonia, Albania, and Latvia. The higher economic growth in these countries during 2000 might have been a corollary of GDP growth from a lower base. FDI inows as a percentage of GDP averaged 5.10% in 2000 and grew to an average of 9.42% in 2019. Hun- gary saw a growth in its FDI from 5.82% in 2000 to 60.24% in 2019, while the Slovak Republic witnessed a decrease from 7.47% to 2.17%. An observation of the ination dynamics shows a decline in the ination average from 8.49% in 2000 to 3.35% in 2019. The most outstanding countries in this regard are Hungary, Romania, and the Slo- vak Republic, whose ination rates declined from 43.18%, 9.58%, and 9.49% in 2000 to 6.8%, 4.77%, and 2.49% in 2019, respectively. The growth in FCF in the CEECs declined from an average of 8.21% in 2000 to 6.69% in 2019. However, the governance index in the CEECs improved from an average of 0.41 in 2000 to 0.63 in 2019. Estonia, in particular, displayed an improvement in general governance with the gover- nance index increasing from 0.90 in 2000 to 1.24 in 2019. The EDB score in the region averaged 73.91 in 2015 and improved to 75.62 in 2019. All the CEECs generally possess business-friendly regulations, with Lithuania holding the highest EDB score of 81.62 in 2019, while Albania has the lowest EDB score of 67.75 during the same year. 2 Estimation procedures and data Our empirical strategy follows a two-stage pro- cess. First, we examine the impact of macroeco- nomic drivers and governance using static panel estimation procedures. In the second stage, we em- ploy a Seemingly Unrelated Regression (SUR) model to account for cross-country differences in FDI in- ow. Drawing from past studies by Alfaro et al. (2008), Beri and Nubong (2023), Beri et al. (2022), Doytch (2021), Marks-Bielska et al. (2022), and Peres et al. (2018), we specify the following semi-log panel model: y i;t DbX i;t Cm i C! t C+ i;t (1) where y i;t is the log of FDI, X i;t is a set of explanatory variables,m i i:i:d(0;s mi ),+ i;t i:i:d(0;s + ), E(m i + i;t )D 0.m i is the country-specic effects and ! t captures the time effects. We analyse and present results for pooled OLS, the within xed effect (FE), the generalised least squares random effect (RE), and the FE and RE mod- els with Driscoll and Kraay robust standard errors. We also analyse the interactive effects of governance and macroeconomic drivers on FDI using equation (2). y i;t DbX i;t GOV i;t Cm i C! t C+ i;t (2) wherebX i;t GOV i;t captures the interaction between macroeconomic drivers and the composite index of governance. Additionally, we employ the SUR or the Zellner (1962) approach to account for heteroscedas- ticity and contemporaneous correlation of residuals in cross-country equations (Dang & Nguyen, 2021; Khan et al., 2014; Kok & Ersoy, 2009). In this case, the model 136 ECONOMIC AND BUSINESS REVIEW 2023;25:131–145 (yD XbC+) takes the following matrix form: yD 2 6 6 6 4 y 1 y 2 . . . y n 3 7 7 7 5 I XD 2 6 6 6 4 X 1 0 0 0 X 2 0 . . . . . . . . . . . . 0 0 X n 3 7 7 7 5 I bD 2 6 6 6 4 b 1 b 2 . . . b n 3 7 7 7 5 I and+D 2 6 6 6 4 + 1 + 2 . . . + n 3 7 7 7 5 (3) where + 1 :::+ n is the error vector. Since E(+ i + 0 j )D s ij I, it implies that E(++ 0 )D P I, where P D 2 6 6 6 4 s 11 s 12 ::: s 1n s 21 s 22 ::: s 2n . . . . . . . . . . . . s n1 s n2 ::: s nn 3 7 7 7 5 and the identity matrix (I) is of order 25 25 Although the OLS method provides consistent pa- rameter estimates, results from SUR are generally more efcient because it accounts for aggregation bias (Zellner, 1962). Nevertheless, OLS produces the same results as SUR when residuals between equations are not correlated, and when the system contains the same explanatory variables (Khan et al., 2014). We recognise that a good history of FDI, GDP , ination, and trade openness can attract future FDI. To avoid issues of endogeneity in our models, we do not use a dynamic model. The following variables are under scrutiny (see summary statistics in Table A3): • Foreign direct investment (FDI it ). FDI inow as a percentage of GDP is our dependent vari- able. Although Beugelsdijk et al. (2010) consider FDI stocks as a more representative measure of MNCs’ activities, Bonnitcha (2017) argued that its quality is usually poor because the methods used in compiling the data are not uniform across countries. The distribution of FDI in CEECs is skewed to the right because some countries at- tract more investments than their counterparts (Skewness, 1.95; Kurtosis, 12.43). Despite this drawback, we consider FDI inows because they are not vulnerable to book value bias. Addition- ally, changes in FDI are not quite apparent with stocks (Jimborean & Kelber, 2017). Using FDI as a percentage of GDP enables us to examine its sensitivity to changes in the business environ- ment. We have collected FDI data from the United Nations Conference on Trade and Development (UNCTAD). • Gross Domestic Product per capita (GDP i;t ). This is a measure of market size widely employed in empirical studies (Gao et al., 2021; Naudé & Krugell, 2007). Most MNCs, especially market- seeking investors, strive to avoid tariff and non- tariff barriers in order to minimise transaction costs. According to the market size hypothe- sis, foreign investors are likely to benet from economies of scale when they locate their busi- nesses in countries with large markets. We expect market size to have a positive effect on FDI (Carstensen & Toubal, 2004), and the effect should be stronger in countries with good governance. GDP per capita data has been gleaned from Eurostat. • Macroeconomic stability (CPI i;t ). Price stability is a crucial macroeconomic policy objective in most economies. Astable macroeconomic environment reduces volatility in returns from FDI. Therefore, countries with a history of low ination and man- ageable scal decits are more credible in the eyes of foreign investors relative to those with high and unpredictable ination rates. One way that governments ensure macroeconomic stability is through ination targeting (Mason & Vracheva, 2017), which has been shown to be associated with lower real exchange rate volatility. This pol- icy strategy was adopted by Armenia, the Czech Republic, Hungary, and Poland after their transi- tion to market economies. Extant studies measure macroeconomic stability with the GDP deator, consumer price index (CPI), or exchange rate (Gao et al., 2021; Peˇ cari´ c et al., 2021). We em- ploy CPI to measure macroeconomic stability and expect a positive association with FDI. We also expect good governance to have a positive mod- ulating effect on the macroeconomic drivers–FDI nexus. • Trade openness (Trade). Trade openness facili- tates the accumulation of physical capital, tech- nology transfer, capacity utilisation as well as opens domestic rms to international competi- tion (Pradhan et al., 2017). Classical economic theories like that of absolute and comparative advantages emphasise the importance of ef- ciency gains from specialisation and free trade (Nath, 2009). In this regard, trade openness en- hances the efcient allocation of resources that percolates into additional investment, productiv- ity, and economic growth. Investors are mostly interested in open economies that promote the free movement of capital. There is no perfect measure of trade openness in economic litera- ture. In this study, however, it refers to exports plus imports as a percentage of GDP . We ex- pect open economies to attract more FDI, with an even more robust effect in countries with good governance. ECONOMIC AND BUSINESS REVIEW 2023;25:131–145 137 • Gross xed capital formation (GFK). FDI is gen- erally expected to move towards countries with more domestic investments. This variable also de- termines the level of infrastructural development in a country. We expect improvements in infras- tructure to be associated with FDI inow, and the effect should be stronger in countries with better governance. We expect a positive relationship be- tween FDI and gross capital formation. • Human capital (School). It measures the qual- ity and price of labour in each country. This variable represents the tertiary school enrolment rate taken as a percentage of gross enrolment. Jimborean and Kelber (2017) argued that FDI in CEECs concentrated in the transport, stor- age and communication, nancial intermedia- tion, business-related services, and information- intensive services sectors that required a highly trained labour force. Although the effect of a highly skilled labour force on FDI could be am- biguous (Doytch, 2021), we expect a positive association with FDI inow. • Governance (GOV). While human capital, phys- ical capital and technology are core economic variables in production, countries with better institutions use their resources more efciently (Acemoglu & Robinson, 2010). Governance insti- tutions set the rules of the game in every society (North, 1990), and as such, play a critical role in cross-country differences on FDI. Good gover- nance is associated with less risk, low transaction costs, a low level of information asymmetry, and high returns, which attract foreign investors (Su et al., 2019). We measure governance with indices from the World Governance Indicators: Voice and Ac- countability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption (Kaufmann et al., 2011, p. 221). The advantage of these indicators is that they enhance the comparisons of governance systems across countries and over time. The indices range from 2.5 to 2.5 and reect weak to strong governance performance. We have employed linear interpo- lation to generate missing observations for the years 1997, 1999, and 2001. Although governance indicators appear to be disparate, they are not strictly independent. For instance, countries characterised by voice and ac- countability are likely to be less corrupt while those that respect the rule of law are more likely to have better regulatory environments. Gover- nance indicators in this study are contemporane- ously correlated (r > :80). In order to minimise the loss of information, we have conated these indicators into a composite index using principal components analysis (PCA). In accordance with the Kaiser rule to retain components with Eigen- values of>1 (EigenvalueD 5.14 in this study), we have recollected the rst component because it explains 85.7% of data variation. The KMO mea- sure of sampling adequacy has been 0:894 > 0:5, justifying the use of PCA. We expect governance and its interactions with macroeconomic drivers to augment the inow of FDI. We have also used polity and democracy scores from the Polity5 project to corroborate results in this study. 3 Results and discussion 3.1 Panel diagnostics In order to choose the most appropriate technique, we have performed several diagnostic tests on the data. Tests for potential multicollinearity indicated high correlations between GDP and human capital (0.6849) as well as GDP and governance (0.6397). However, further analysis using the variance ina- tion factor revealed no evidence of collinearity (VIFD 1.64). Results from the poolability test show that cross sec- tions do not have a common intercept. We have also investigated whether panel FDI regressions are ho- mogenous or heterogeneous (Bersvendsen & Ditzen, 2021; Pesaran & Yamagata, 2008). To this end, we have t two FDI models. In the rst model without the dynamic parameter, we have rejected the null of slope homogeneity at 1% level (DeltaD 2.575, p-valueD 0.010). In the second model that includes the rst lag of FDI and controls for heteroscedasticity with the HAC robust standard errors, we have again re- jected the null of slope homogeneity (DeltaD 2.206, p-valueD 0.027). Therefore, an estimator that allows for heterogeneous slopes, such as the mean group estimator or Seemingly Unrelated Regression, may be apposite for the analysis. Results from the Hausman test show that the RE model is the most appropriate [$ 2 (6)D 1:06, Prob >$ 2 D 0:983]. Pesaran’s test shows evidence of cross-sectional dependence (2.057, p-valueD 0.0397). Based on the modied Wald test for groupwise het- eroscedasticity, we have also found evidence that the variances are non-constant [$ 2 (12)D 2618:15, Prob> $ 2 D 0:0000]. Finally, the Woodridge test for autocor- relation shows no evidence of rst-order autocorrela- tion [F (1, 11)D 2.825, Prob> FD 0.1210]. Table 2 shows that gross domestic product, in- ation, tertiary education enrolment, and gover- nance are stationary at level. Conversely, FDI, trade 138 ECONOMIC AND BUSINESS REVIEW 2023;25:131–145 Table 2. Im-Pesaran-Shin (IPS) unit root test. Variable Statistic Decision (H 0 ) Level D.FD 3.3718 Reject I(1) GDP 2.0406 Reject I(0) CPI 3.3466 Reject I(0) D.Trade 3.2653 Reject I(1) D.GFK 2.9390 Reject I(1) School 2.6093 Reject I(0) GOV (PCA) 5.0355 Reject I(0) Stationary at level, I(0); Stationary at rst difference, I(1). p< 0:01. Table 3. Cointegration test. Statistic P-value Modied Phillips-Perron t 2.3443 0.0095 Phillips-Perron t 6.8934 0.0000 Augmented Dickey-Fuller t 7.2826 0.0000 openness and gross xed capital formation are sta- tionary at rst difference. Finally, we have performed Pedroni’s test for coin- tegration with heterogeneous panels to ascertain the presence of a long-run relationship between the macroeconomic drivers–FDI nexus (Pedroni, 1999). The assumption is that if two or more variables are cointegrated, their residuals will be stationary or I(0). Based on results in Table 3, we can reject the null hy- pothesis and conclude that all panels are cointegrated. The preliminary diagnostics suggest the need to ac- count for heterogeneity, cross-sectional dependence, and heteroscedasticity. Since we have a long panel, the rst conceivable strategy is to follow the nonpara- metric technique by Driscoll and Kraay (1998), whose estimates are based on the asymptotic assumption of large T. The procedure produces standard errors that are robust to heteroscedasticity, autocorrelation, and spatial dependence. We take the rst differ- ence of all nonstationary variables before running the regression. 3.2 Regression results This section presents static regression results, jux- taposed with those from SUR in Table 5. In Table 4, (1) is the Pooled OLS, (2) Fixed Effect (FE), (3) Ran- dom Effect (RE), (4) Driscoll and Kraay FE, and (5) Driscoll and Kraay RE. The results are largely simi- lar, irrespective of the type of estimation. In Table 5, (1)–(12) represent SUR estimates for each country in the system. Table 4 shows that market size (GDP) is only signicant in equation (2) at 0.05 level. Further scrutiny using the SUR model in Table 5 shows that market size is the most signicant determinant of FDI in Hungary, while the effect is negative and signicant in Lithuania, Latvia, and Poland. It is not unusual for studies to uncover a nega- tive and signicant relationship between GDP and FDI (Magbondé & Konté, 2022). The overall implica- tion is that market size plays a largely heterogeneous inuence on FDI, and policymakers must consider cross-country differences when designing policies. While prior studies showed supportive evidence for the market size hypothesis (Carstensen & Toubal, 2004; Doytch, 2021; Jimborean & Kelber, 2017), this is perhaps the rst paper to show cross-country differ- ences in FDI performance. Our second variable was to examine the impact of macroeconomic stability (ination) on FDI. Table 4 Table 4. Pooled OLS, Fixed/Random effects, Driscoll and Kraay (D-K) standard errors results. Variables (1) (2) (3) (4) (5) Pooled OLS FE(within) GLS RE D-K FE D-K RE LGDP 0.014 0.315 0.012 0.315 0.012 (0.032) (0.127) (0.188) (0.488) (0.362) CPI 0.003 0.003 0.003 0.003 0.003 (0.001) (0.001) (0.001) (0.001) (0.001) D.Trade 0.076 0.079 0.076 0.079 0.076 (0.024) (0.023) (0.024) (0.024) (0.024) D.LGFK 2.285 2.032 2.271 2.032 2.271 (0.886) (0.986) (0.911) (0.857) (0.899) School 0.007 0.014 0.006 0.014 0.006 (0.005) (0.009) (0.005) (0.015) (0.015) GOV 0.017 0.212 0.015 0.212 0.015 (0.040) (0.150) (0.054) (0.146) (0.073) Constant 2.524 0.236 2.524 0.236 (0.944) (1.911) (4.218) (3.033) N 288 288 288 288 288 R-squared 0.066 0.069 Groups 12 12 12 12 Robust standard errors in parentheses. p< 0:01, p< 0:05, p< 0:1. ECONOMIC AND BUSINESS REVIEW 2023;25:131–145 139 Table 5. Seemingly unrelated regression results. Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) ALB BGR CZE EST HRV HUN LTU LVA POL ROU SVK SVN GDP 3.743 6.347 5.020 1.772 5.710 17.200 4.324 8.107 8.249 4.221 8.420 4.082 (2.507) (5.492) (4.894) (5.877) (5.221) (8.594) (1.993) (1.706) (3.393) (2.903) (10.267) (3.734) CPI 0.028 0.000 0.345* 0.346 0.252 0.778 0.186 0.078* 0.017 0.017 0.818 0.139 (0.032) (0.003) (0.206) (0.169) (0.172) (0.221) (0.071) (0.043) (0.068) (0.016) (0.328) (0.145) Trade 0.021 0.190 0.045 0.003 0.136 0.043 0.108 0.079 0.068 0.048 0.033 0.004 (0.041) (0.061) (0.041) (0.046) (0.042) (0.055) (0.033) (0.020) (0.075) (0.079) (0.088) (0.041) GFK 2.468 5.408 8.966 6.773 9.792 5.377 2.086 10.125 6.649 3.849 18.210 0.812 (1.861) (2.737) (3.422) (7.948) (3.509) (5.717) (1.438) (1.556) (2.074) (3.310) (8.857) (2.039) School 0.079 0.898 0.033 0.158* 0.080 0.060 0.027 0.057 0.001 0.033 0.337 0.060 (0.038) (0.191) (0.112) (0.095) (0.181) (0.084) (0.046) (0.023) (0.039) (0.063) (0.237) (0.041) GOV 1.559 0.864 0.285 2.957 8.272 4.534 0.277 1.904 0.471 0.316 4.721* 0.391 (0.429) (2.271) (0.432) (0.874) (2.899) (1.513) (0.729) (0.571) (0.365) (1.321) (2.693) (0.748) Constant 10.778 148.22 151.7 165.817 220.06 49.938 11.225 149.644 87.416* 46.669 352.266* 53.101 (26.490) (60.137) (51.618) (154.778) (68.306) (103.288) (27.621) (26.607) (48.354) (70.845) (187.599) (45.979) N 25 25 25 25 25 25 25 25 25 25 25 25 R-sq 0.873 0.714 0.607 0.461 0.475 0.377 0.549 0.721 0.539 0.328 0.354 0.015 F-Statistic 24.63 8.4 5.45 4.96 4.42 3.7 5.8 12.12 3.66 2.16 4.58 1.05 Standard errors in parentheses. p< 0:01, p< 0:05, p< 0:1. Notes: Table 5 presents SUR results. (1) to (12) represent regression output for each CEEC. 140 ECONOMIC AND BUSINESS REVIEW 2023;25:131–145 shows a positive and consistent effect of macroeco- nomic stability on FDI in CEECs across the pooled, FE/RE, and Driscoll-Kraay procedures. Table 5 shows that the positive effect of macroeconomic stability is stronger in the Czech Republic, Hungary, Lithuania, and the Slovak Republic. Low ination is associated with macroeconomic stability and is always viewed favourably by investors because it reduces volatil- ity in returns (Gao et al., 2021; Peˇ cari´ c et al., 2021). Conversely, it seems that ination rates deter FDI from Estonia and Latvia. These countries experienced ination rates of between 1% (min) and 23% (max) over the period considered. Countries with high in- ation are often characterised by macroeconomic instability, which makes investors sceptical. Our re- sults further show how CEECs respond differently to ination. Studies using purchasing power parity as a measure of macroeconomic stability also arrive at similar conclusions, especially for FDI in the service sector (Peˇ cari´ c et al., 2021). Therefore, policymakers should make sure to keep ination at low to moderate levels. The impact of trade openness on FDI is positive and consistent across models (1) to (5) in Table 4. These demonstrate the propelling effect of openness to trade on FDI. Nonetheless, Table 5 shows that the effect is stronger in Bulgaria, Lithuania, and Latvia. Conversely, openness seems to have a reducing effect on FDI in Croatia. These results differ from those by Peˇ cari´ c et al. (2021), who found a negative relation- ship in their analysis of the determinants of sectoral FDI in East European EU economies. Theory gen- erally postulates a positive or negative relationship between trade openness and FDI, depending on the type of ow. For instance, FDI and trade openness complement each other for vertical FDI and substitute each other for horizontal FDI. Therefore, the observed coefcients of trade openness in this study give a sense of the type of investments in CEECs. However, additional studies may be conducted at sectoral levels to ascertain the responsiveness of different types of FDI on trade openness. The effect of human capital is negative and in- signicant, which lends credence to Jimborean and Kelber (2017). Human capital is strongly signicant in Albania (see Table 5). Conversely, human capital is associated with reductions in FDI in Bulgaria, Estonia, and Latvia. The negative effect of human capital could imply an increase in the cost of labour. A more edu- cated labour force is usually more expensive to hire, and in some instances, could retard the inow of FDI. Gross xed capital formation has the largest positive effect on FDI. In economic literature, capital formation is a proxy for the level of infrastructural development (transport, telecommunication, and social). Higher domestic investments indicate more productivity, which sends signals of opportunities for protability to foreign enterprises. Table 5 shows that the effect of GFK is stronger in Bulgaria, Czech Republic, Croatia, Latvia, and Poland. Conversely, GFK is negatively associated with FDI inow in the Slovak Republic. Once again, these results highlight heterogeneity in the performance of macroeconomic drivers, which is consistent with much of the literature (Jimborean & Kelber, 2017; Magbondé & Konté, 2022). We do not nd a signicant effect of governance on FDI. However, the evidence from SUR indicates that governance is associated with FDI in Albania, Hungary, and the Slovak Republic. Conversely, gov- ernance plays a reducing effect on FDI in Estonia, Croatia, and Latvia. After obtaining these results, we employed the polity scores and democracy scores to check for robustness (not presented). The results were largely consistent with those from the governance index. Fig. 3 presents the tted regression model of Driscoll and Kraay standard errors. It shows that, on average, FDI has slid towards a downward trajec- tory in CEECs. Accordingly, only Albania and Estonia have shown signs of recovery. 3.3 The moderating effect of governance on the macroeconomic–FDI nexus The last part of this study examines the impact of the interaction between macroeconomic factors and governance on FDI using the Driscoll and Kraay (1998) model with robust standard errors. (1) to (5) in Table 6 represent the different models estimated. It is worth noting that governance does not moderate the effect of macroeconomic drivers on FDI in all models. In addition, we recollected data on polity and democ- racy from the polity5 project to test for robustness. The results (not presented) were generally consistent with preceding ndings. Although many studies nd that institutions ex- plain cross-country difference in FDI and economic development (Acemoglu & Robinson, 2010; Alfaro et al., 2008; Peres et al., 2018), Moosa (2017) ar- gued such studies are a product of junk-science to justify the dishonest activities of foreign enter- prises. A possible reason for the insignicant coef- cients is that governance and measures of institu- tions scarcely change signicantly over time, which makes it difcult to ascertain their effect on volatile macroeconomic variables like FDI. Doro ˙ zy´ nski et al. (2020); Jimborean and Kelber (2017), and Marks- Bielska et al. (2022) showed that institutions matter for FDI. ECONOMIC AND BUSINESS REVIEW 2023;25:131–145 141 Fig. 3. Fitted FDI in CEECs. Table 6. Moderating effect of governance on macroeconomic drivers. Variables (1) (2) (3) (4) (5) D-K RE D-K RE D-K RE D-K RE D-K RE LGDP 0.007 0.041 0.013 0.012 0.015 (0.379) (0.368) (0.368) (0.367) (0.376) CPI 0.003 0.010 0.003 0.003 0.003 (0.001) (0.045) (0.001) (0.002) (0.001) D.Trade 0.076 0.077 0.083 0.076 0.076 (0.024) (0.026) (0.032) (0.026) (0.024) D.LGFK 2.267 2.320 2.164 2.210 2.254 (0.890) (0.959) (0.923) (0.901) (0.883) School 0.006 0.007 0.006 0.006 0.007 (0.015) (0.015) (0.015) (0.015) (0.015) GOV 0.100 0.011 0.001 0.008 0.076 (0.806) (0.104) (0.076) (0.075) (0.143) LGDP#c.GOV 0.009 (0.085) CPI#GOV 0.004 (0.014) D.Trade#GOV 0.015 (0.013) D.LGFK#GOV 0.125 (0.546) School#GOV 0.001 (0.002) Constant 0.175 0.605 0.225 0.241 0.038 (3.289) (3.078) (3.135) (3.075) (3.227) Observations 288 288 288 288 288 Number of groups 12 12 12 12 12 Robust standard errors in parentheses. p< 0:01, p< 0:05, p< 0:1. 4 Concluding remarks This paper employs data from 1996 to 2020 to ad- dress two important questions: (i) To what extent do macroeconomic drivers and governance inu- ence FDI in CEECs? (ii) Does governance moderate the macroeconomic drivers–FDI nexus? The paper’s main contribution is that it identies and isolates new linkages between macroeconomic factors, gov- ernance, and FDI by using SUR. Disentangling the 142 ECONOMIC AND BUSINESS REVIEW 2023;25:131–145 heterogeneous behaviour of FDI gives policymakers insights on how to incentivise its inows into these economies. We nd that macroeconomic stability, gross xed capital formation, and trade openness are the most signicant determinants of FDI. The results are con- comitant with those obtained by Carstensen and Toubal (2004), Jimborean and Kelber (2017), and Ma- son and Vracheva (2017). The effect of market size is weak, but there is evidence of substantial het- erogeneous responses across countries. We nd no evidence that human capital and governance signif- icantly inuence FDI in the full sample, which fails to support Doro˙ zy´ nski et al. (2019, 2020). Further scrutiny shows that governance is signicant in Alba- nia, Hungary, and the Slovak Republic, while human capital is only signicant in Albania. Fig. A1 (ap- pendix) shows a volatile but downward trajectory in FDI that became conspicuous after the 2011 euro debt crisis. In order to attract more FDI, policymakers should aim to consolidate domestic macroeconomic policies and trade liberalisation. A limitation of the study is that it does not account for structural breaks due to economic crises. The transition into neoliberal economies exposed CEECs to external shocks, which raise several questions. First, what is the effect of in- terconnected crises on FDI? Do institutions in place shield CEECs from these economic shocks? We rec- ommend that future studies examine how these issues affect FDI in CEECs. Funding statement No funding was received for this research project. References Acemoglu, D., & Robinson, J. (2010). The role of institutions in growth and development. 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An efcient method of estimating seemingly unrelated regressions and tests for aggregation bias. Journal of the American Statistical Association, 57(298), 348–368. https:// doi.org/10.1080/01621459.1962.10480664 Appendix Table A1. Geographical classication of European countries. Region Countries Northern Europe Denmark; Faroe Islands; Finland; Greenland; Iceland; Ireland; Norway; Sweden; and United Kingdom. Western Europe Austria; Belgium; France; Germany; Liechtenstein; Luxembourg; Netherlands; and Switzerland. Central and Eastern Europe Albania; Bulgaria; Croatia; Czech Republic; Estonia; Hungary; Latvia; Lithuania; Poland; Romania; Slovak Republic; and Slovenia. Southern Europe Andorra; Bosnia and Herzegovina; Cyprus; Gibraltar; Greece; Italy; Kosovo; Malta; Monaco; Montenegro; North Macedonia; Portugal; San Marino; Serbia; and Spain. Source: One World (2022) and Organisation for Economic Co-operation and Development (2022). 144 ECONOMIC AND BUSINESS REVIEW 2023;25:131–145 Table A2. Top 10 Inward FDI investors in CEECs in 2019 by partner countries (US$ billions). Czech Republic Estonia Hungary Latvia Lithuania Poland Slovak Republic Slovenia Partner US$B Partner US$B Partner US$B Partner US$B Partner US$B Partner US$B Partner US$B Partner US$B Luxembourg 3.80 Sweden 0.65 Netherlands 2.67 Sweden 0.29 Sweden 0.45 Netherlands 6.22 Austria 1.50 Austria 0.40 Netherlands 3.67 Finland 0.34 United States 2.21 Estonia 0.17 Netherlands 0.29 Germany 4.97 Netherlands 0.85 Switzerland 0.19 Germany 3.26 Netherlands 0.15 Canada 1.66 Russia 0.12 Hong Kong 0.26 Luxembourg 3.32 Luxembourg 0.58 Netherlands 0.18 Austria 2.48 United Kingdom 0.13 Germany 1.63 Cyprus 0.12 Estonia 0.25 France 1.91 Germany 0.45 Germany 0.15 Belgium 1.33 Luxembourg 0.09 Austria 1.38 Netherlands 0.11 Germany 0.16 United Kingdom 1.01 South Korea 0.40 Luxembourg 0.11 France 1.24 Cyprus 0.07 Luxembourg 0.93 Luxembourg 0.11 Poland 0.14 Switzerland 0.77 Czech Republic 0.37 Italy 0.10 Switzerland 0.97 Lithuania 0.07 United Kingdom 0.61 Lithuania 0.07 Finland 0.12 Spain 0.69 France 0.19 Croatia 0.08 Cyprus 0.67 Germany 0.06 France 0.46 Denmark 0.05 Cyprus 0.11 Italy 0.66 Belgium 0.18 Hungary 0.07 Italy 0.56 Ukraine 0.06 Jersey 0.44 Germany 0.05 Switzerland 0.09 Cyprus 0.52 Italy 0.18 Sweden 0.06 United Kingdom 0.45 Norway 0.05 Switzerland 0.29 Norway 0.04 Denmark 0.09 Belgium 0.47 United Kingdom 0.11 Serbia 0.06 Source: Organisation for Economic Co-operation and Development (n.d.). ECONOMIC AND BUSINESS REVIEW 2023;25:131–145 145 Table A3. Summary statistics. Variable Mean Std. Dev. Min Max Observations FDI 4.25 3.82 11.62 27.90 300 GDP 19,055.89 9371.61 2717.64 42,847.00 300 CPI 9.34 62.15 1.54 1058.37 300 Trade 109.40 34.61 44.90 190.70 300 GFK 1.97EC10 2.13EC10 7.36EC08 1.06EC11 300 School 54.96 18.44 10.94 94.86 289 GOV 0.08 2.33 6.89 3.89 300 Fig. A1. Predicted patterns of FDI by country.