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The Global Financial Crisis in Transition Economies: The Role of Initial Conditions.


The main objective of this paper is to investigate the effect of the Global Financial Crisis of 2007-2009 on the countries of the former Soviet bloc and to set a new direction for research in this area. The opening of financial markets to foreign capital, financial liberalization, and integration into global markets, while stimulating economic growth, promoted greater dependency on exports and capital inflows, making these countries more vulnerable to external financial shocks. The Mexican crisis of 1994, the East Asian crisis of 1997-1998, the Russian crisis of 1998, and the Argentinean crises of 2000 and 2003 collectively decreased the former soviet bloc region's gross domestic product (GDP) by over $400 billion (European Bank for Reconstruction and Development 2009). Since the inception of the transition, no other event has affected these economies as much as the Global Financial Crisis of 2007-2009. The collapse of the subprime mortgage industry in the U.S. and subsequent severe recession triggered a wave of financial and economic shocks that spread to Europe and to other parts of the world, affecting both developed and emerging markets. The severe slowdown in economic activity in emerging economies was a result of a virtual stop of foreign credit flows and a drastic decline in exports (Reinhart and Rogoff 2009; Ozkan and Unsal 2012).

The impact of the crisis, however, varied widely across the transition area. (1) In the beginning, only the Baltic states and a few Central Eastern European (CEE) countries were affected. Most other transition economies were enjoying a surge in domestic credit (especially in the Caucasus region) and were reaping the benefits of the high volume of capital inflows (European Bank for Reconstruction and Development 2010). By the end of 2008, the whole region was in a sharp downturn. At the height of the crisis, cumulatively during the fourth quarter of 2008 and the first quarter of 2009, output contracted on average by some 6.5% in the transition countries and 12.5% in the Baltic region (European Bank for Reconstruction and Development 2009). Gross fixed capital formation dried up completely in 2008 and became negative in 2009 in all regions except Central Asia. Likewise, credit to the private sector continued to shrink and unemployment soared across the entire region (European Bank for Reconstruction and Development 2010). In 2009, Estonia (already in recession at -3% in 2008) contracted to -10.5%, Lithuania -11.8%, Latvia -13.2%, Russia -7.5%, and Ukraine -10% (European Bank for Reconstruction and Development 2009).

In addition, households in transition economies were more likely to hold their deposits and loans in foreign currency and thus were more exposed to exchange rate risk. The Baltics and Ukraine had the highest foreign currency exposure of household debt, with foreign-currency-denominated loans accounting for over 80% of bank loans to households in Estonia and Latvia (Tiongson et al. 2010). Once the crisis hit, depositors became concerned about the safety of their savings. Given weak and in some cases non-existent bank deposit insurance, depositors began to withdraw their money from the banking system. Additional pressure on the banking system stemmed from the fall in prices of real estate, which often served as collateral for lending. The drastic fall in property prices (that followed the rapid surge prior to the crisis and boosted a pre-crisis GDP in many transition economies) contributed to the severe growth shocks in the region (Stepanyan et al. 2010).

Currencies in most transition economies fell by approximately 20%, which aggravated the debt issues. Following the differences in currency regimes prior to the crisis, exchange rate policies were rather diverse. Bulgaria, Estonia, the Former Yugoslav Republic (FYR) Macedonia, Latvia and Lithuania, maintained their hard pegs, while Armenia, Belarus, Georgia, Kazakhstan and Russia undertook step devaluations (European Bank for Reconstruction and Development 2009). Mongolia, Romania, Serbia and Ukraine allowed managed floats, combined with currency controls in the case of Ukraine (European Bank for Reconstruction and Development 2009). Among Eastern European countries, only Poland and Albania experienced positive economic growth. This was partially due to flexible exchange mechanisms that allowed their currencies to depreciate, encouraging competitiveness. Central Asian countries also maintained a positive rate of economic output, possibly because of a large agricultural sector that circumvented the traditional financial credit system (Shostya 2014).

The recovery was sluggish and uneven. Real GDP continued to contract through 2009 in most countries and into 2010 in Bulgaria, Croatia, and Romania (European Bank for Reconstruction and Development 2010). Early recovery in the former Soviet Union region, especially Central Asia, was mostly driven by net exports fueled by favorable terms of trade for the region's exporters of energy (Russia, Kazakhstan, Azerbaijan, and Turkmenistan), metals (Ukraine, Tajikistan, Uzbekistan, Russia, Armenia, Kazakhstan, and Kyrgyzstan) and cotton (Uzbekistan and Tajikistan). This increase in net exports lowered current account deficits across the region and in some cases even created surpluses. Lithuania, Estonia, Bulgaria and Croatia, on the other hand, were still lagging behind, with -6%. -2%, -4%, and-3% in real GDP growth, respectively (International Monetary Fund 2010). Unemployment also remained high in those countries. In general, Eastern European countries, with the exception of Poland and Slovakia, had a sluggish recovery due to the region's higher reliance on exports to Western Europe. Kyrgyzstan and Ukraine also struggled to emerge from the recession, mostly due to political unrest and uncertainty related to their presidential elections. Government spending increased in the entire region as governments implemented rescue packages, adding up to an accumulation of government debts. Ironically, the impact of the financial crisis streamed through the same transmission channels that previously allowed these countries to build up massive export flows of goods and services and foreign capital that contributed to their economic growth.

It is important to understand the factors explaining the variability in transition economies in response to the crisis. Macroeconomic fundamentals that are commonly used in the literature to explain the sensitivity of advanced countries to various financial shocks may not necessarily have as much explanatory power in the case of transition economies. This is because transition economies' institutional infrastructures were different from those in well-developed market economies (Shostya 2014). A contributing factor to the poor performance during the crisis was political instability, which was an outcome of historical legacy and ethnic heterogeneity. Most of the transition countries still had a number of unresolved issues following the breakup of the Soviet Union and Yugoslavia. The ongoing clash between Russia and the Baltic countries over the Russian-speaking minorities residing in the Baltic region, the war between Russia and Georgia over the breakaway region of South Ossetia, and the tense relationship between Serbia and Montenegro hampered government function in those countries and led to institutional weaknesses that were damaging during the crisis period. Political upheavals in Ukraine and Central Asia lessened the probability of achieving significant engagement of the citizens in critical times or gaining support for governments and the private sectors. Thus many countries lacked the ability to implement adequate policies during the financial crisis, especially the ones pertaining to long-term economic growth goals.

This study attempts to expand the literature in this area by empirically examining the effect of the global financial crisis on 28 transition economies and testing alternative factors that may have played a role in the countries' performances. (2) Special attention was paid to the construction of the Cumulative Crisis Index (CCI), which was calculated for each country.

Brief Literature Review

There is a burgeoning body of literature that explores the transmission mechanisms of the Global Financial Crisis and the variability of its impact on transition economies. Existing studies differed as to the range of the countries investigated, the explanatory variables tested, and the statistical models used. Most of the studies linked the variability in the crisis' impact to the differences in financial vulnerabilities and macroeconomic weaknesses that were in place prior to the crises (Acharya et al. 2009; Aslund 2010; Syllignakis and Kouretas 2011; Connolly 2012; Frankel and Saravelos 2012). In particular, current account deficits, currency mismatches, rapid monetary expansion, and differences in pre-crisis growth rates have been presented in the literature as possible factors behind the variability in the crisis performance of transition economies (Aslund 2010; Claessens et al. 2010; Lane and Milesi-Ferretti 2010; Llaudes et al. 2010; Rose and Spiegel 2012; Bonin et al. 2014). Berkmen et al. (2012) found that countries with more leveraged financial systems, greater credit growth, and larger short-term external debt were affected the most. Frankel and Saravelos (2012) and Llaudes et al. (2010) also discussed the differences in exchange rate mechanism regimes and pre-crisis reserve holdings by the central banks. De Haas and Van Lelyveld (2014) found evidence that some governments in CEE countries were able to use state-owned banks to alleviate the credit crunch when privately-owned banks began to deleverage. In addition, some studies cited the degree of direct exposure to the U.S. financial system (Claessens et al. 2010; Lane and Milesi-Ferretti 2010). Berglof et al. (2010) supported this argument by providing evidence that the differences in the decline in economic activity among emerging European economies could be attributed to the European political and economic integration. The European Bank for Reconstruction and Development (2010) report found that the export product structure played a key role. Exporters of machinery were hit the hardest at the peak of the crisis in winter 2008-2009. This analysis was supported by Gevorkyan (2011) who found that the magnitude of the effect of the crisis on the Commonwealth of Independent States (CIS) differed for net-exporting and net-importing countries due to differences in their external positions.

Most of these studies, however, focused either on emerging economies without an emphasis on transition economies or on a limited sample of the post-Soviet economies, typically some European Union (EU) members, CIS, (3) or CEE members. (4) Few studies offered a comparative analysis of the effect of the crisis on all economies of the former Soviet bloc. For example, Shostya (2014) provided a comprehensive investigation of the 28 transition economies and found that the countries that enjoyed the higher degrees of economic freedom and greater extent of the transition were more vulnerable to the financial crisis. She grouped the transition economies into 12 non-mutually exclusive groups, according to their sovereignty prior to transition, geographic location, former USSR membership, EU membership, and timing of the transition reforms. She concluded that the Baltic region and other EU members (former Soviet countries) were hit the hardest because of their closer links to the U.S. and European financial systems. However, her study was limited as it explored the effect of the crisis on the output gap only.

This study explored the variability in the countries' risk and economic performance during the crisis based on several nontraditional factors that were used in Shostya's (2014) study, namely the duration of the socialist regime and the extent of the transition. Another independent variable, ethnic homogeneity, which has not been used in the literature, has been added as a possible cause of the variability in response to the crisis.

Data and Methodology

Dependent Variables

Two dependent variables were used to measure the impact of the crisis. They were Country Risk in 2009 and the CCI that was constructed using the European Bank for Reconstruction and Development (EBRD) crisis index. To track the severity of the 2007-2009 crisis, the EBRD reported a "crisis index" for each country at three points in time: March 2008, December 2008, and March 2009 (European Bank for Reconstruction and Development 2009,2010). The index ranged from 1 to 4 and is a sum of four indices that measured whether a country experienced the following phenomena:

* Depreciation of the nominal exchange rate in relation to the U.S. dollar (1 if there is a 25% or more depreciation; 0 otherwise)

* A decline in a nominal price of housing since the pre-crisis peak (1 if a decline is 20% or more; 0 otherwise)

* Industrial production decline within the previous six months (1 if the decline lasts for 2 or more months; 0 otherwise)

* Decline in net credit inflows within the previous six months (1 if the decline lasts for 2 or more consecutive months; 0 otherwise).

For consistency reasons, this study followed the EBRD methodology. To capture both the speed and the magnitude of the crisis, all three point values were added together, creating the CCI. Because each point value has a theoretical range of 0-4 (0 is no impact and 4 is the maximum impact), the CCI had a theoretical range from 0 to 12 (0 is no impact and 12 is the maximum impact). The actual calculated CCI values (Table 1) ranged from 2 (best) to 7 (worst). (5) The CCI was the highest in Ukraine, Latvia, Kazakhstan, and Estonia (7) and the lowest is in Uzbekistan, Turkmenistan, Slovenia, Slovakia, Belarus, and Albania (2).

The country risk classifications were calculated by the Organization for Economic Co-operation and Development (OECD) based on the Kneepan Package that assessed country credit risk and classified countries into eight country risk categories, with 0 being the lowest risk and 7 being the highest (Organization for Economic Co-operation and Development 2010). Country risk was composed of transfer and convertibility risk (i.e. the risk a government imposed capital or exchange controls that prevented an entity from converting local currency into foreign currency or transferring funds to creditors located outside the country) and cases of force majeure (e.g. war, revolution, civil disturbance, or natural disaster). Their methodology consisted of the Country Risk Assessment Model (CRAM) which produced a quantitative assessment of country credit risk based on three groups of risk indicators (the payment experience of the participants, the financial situation and the economic situation) and a qualitative assessment of the CRAM results by country risk experts from OECD members (this integrates political risk or other risk factors not taken fully into account by the CRAM). The details of the CRAM are confidential and are not published. The final classification is a consensus decision of the sub-group of country risk experts of the participating export credit agencies. The sub-group of country risk experts meet several times a year. To facilitate open discussions amongst the experts, no written record of the comments and procedure is produced. (6) The list of country risk classifications are published after each meeting.

Independent Variables

A range of variables was employed to capture traditional and alternative transmission mechanisms of the impact of the financial crisis on the transition economies. The first group of variables (control variables) dealt with the sectoral composition of GDP and GDP growth rate during the pre-crisis period. The data came from the World Bank Development Indicators (2006, 2011). The second group (key variables) attempted to capture structural pre-conditions that were related to the extent of the transition, political and economic stability, duration of the socialist regime, and ethnic homogeneity. The variables and the rationale for their inclusion were provided below. 2006 values were used for most explanatory variables to measure pre-crisis conditions. This was done to avoid any earlier effects of the crisis that were transmitted to some countries in 2007. For consistency reasons, all variables (except for years under communism) were annualized.

The variable SERV was service sector as a share of GDP. Empirical evidence showed that at the time of the crisis, a nation's emphasis on a large service sector became a disadvantage (Shostya 2014). Countries that derived a considerable portion of their GDP from tourism (like Bulgaria, Czech Republic, and the Baltic Countries) were affected by a decrease in both domestic and foreign demand (Steiner et al. 2012). In addition, some studies indicated that countries that had more developed financial sectors were affected by the financial crisis via traditional transmission mechanisms (Blanchard et al. 2010; Verick and Islam 2010; Shostya 2014). Therefore, the impact of the crisis on each country's economy was expected to be greater the larger the size of the service sector prior to the crisis.

The variable AGR was agriculture sector as a share of GDP. Studies supported the hypothesis that agriculture could provide a cushion for the fall in GDP (World Bank 1989; Food and Agriculture Organization 2010). An OECD study (Contre and Goldin 1990) applied econometric analysis to show that agriculture served as a "buffer" in Brazil during the same time period when an economic crisis hit the country. Agriculture could cushion the impact of the financial crisis in several ways. First, a significant agricultural sector could provide food security for the rural poor population. In addition, in many of the transition economies (especially Central Asia and the Caucasus) agriculture was mostly small scale and therefore part of the informal or "shadow" economy. Under such conditions, there would be no connection between the financial sector and investment in the agricultural sector. Subsistence farmers were less dependent on bank loans. The 2007-2009 credit crunch, therefore, would have had a smaller impact on the economy that had a large agricultural sector.

The variable GDP06 was included to measure the effect of the initial real GDP growth rate (in 2006) on the effect of the crisis. Studies indicated that higher pre-crisis growth of GDP could aggravate the performance of the emerging economy during the crisis (Lane and Milesi-Ferretti 2010). On the one hand, higher economic growth might signal a stronger catch-up performance of emerging economies. On the other hand, it might be an indicator of the formation of an asset bubble and an unbalanced overheating in a particular market (Aiginger 2011). The impact of the crisis was expected to be greater the higher the real GDP growth in 2006.

The variable 77 was designed to capture the effect of the extent of the transition on the dependent variables. On the onset of the global 2007-2009 crisis, the transition economies varied greatly in terms of the extent of their transition and the degree of economic freedom. Some countries were more successful in abandoning state socialism, while others were sluggish in implementing the main features of the capitalist system. Neoclassical theory suggested that there was a strong relationship between market reforms and economic growth (Williamson 1990; Kornai 1994). However, the experience of the 1990s, especially in the countries affected by the Russian crisis of 1997, led to a re-evaluation of the traditional theoretical framework and a greater emphasis on institutional development (Williamson 2004). The new direction in the economic way of thinking also included the idea that financial liberalization and integration might have significant costs, in terms of encouraging credit booms and overborrowing (European Bank for Reconstruction and Development 2009; Filipovic and Miljkovic 2014). Mathieson and Roldos (2001), for instance, argued that foreign participation might inhibit the growth of domestic financial institutions and exacerbate financial system instability. In fact, some empirical evidence suggested that the extent of the transition from a closed planned economy to the open-market economy was fundamentally important in assessing the impact of the crisis (Myant and Drahokoupil 2012; Filipovic and Miljkovic 2014; Shostya 2014). Thus, the economies that exhibited a higher degree of the transition might have been more vulnerable to the financial crisis.

The transition indicators were developed by the EBRD in the 1994 Transition Report to quantify the country-specific progress in transition (European Bank for Reconstruction and Development 1994). The scores were measured on a scale from 1 to 4, where 1 represented little or no progress in reform and 4 meant that a country had made major advances in transition in the following areas: large-scale privatization, small-scale privatization, governance and enterprise restructuring, price liberalization, trade and foreign exchange system, competition policy, banking reform and interest rate liberalization, securities markets and non-bank financial institutions, and infrastructure (European Bank for Reconstruction and Development 2006a). This study used the scores from 2006, the year before the inception of the financial crisis.

The variable CR06 captured the level of the country risk in 2006, one of the important initial conditions. In most former Soviet bloc countries, the transition from a planned socialist system to a market-based one initially led to an erosion of the standard of living and a corresponding decline in the public sector. This costly and painful process was reflected in social attitudes toward the reforms which affected the economic and political stability of the countries in question. The data to measure country risk in 2006 came from the Organization for Economic Co-operation and Development (2010).

The transition economies had one feature in common. They all had to deal with a burden from the past, namely a rigid state system during a planned socialism stage. However, the degree of rigidity was different in each country or bloc of countries and depended mostly on the length of socialist regime. The length of socialist regime affected the functionality of institutions, both financial and political (Shostya 2014). The financial mechanisms and institutions in the Soviet Union and other Eastern European countries were for the most part uniform once socialism had been established as an economic system. Their fundamental principles were derived from the nature of the system that was state-owned and state-run. In such a system, there were no money and capital markets, no inter-enterprise lending, and no forms of consumer savings other than saving deposits. Therefore, there was no flow of credit between those who had an excess of funds and those who required funds for productive uses. The state was the sole source of credit and nationalized banks were the only financial institutions. Consequently, the price of credit and interest rates did not play the same role in a planned economy as they do in a market economy, where they serve as effective tools in eliminating possible disequilibria (McKinnon 1991). Initially, these basic features of the banking systems were instilled rather homogeneously in all socialist states. With time, however, they underwent a series of alterations according to the various paths that their authorities chose to follow. Countries that joined the Soviet bloc much later, like Czechoslovakia, Poland, and Hungary, were able to initiate more profound changes in their banking sectors and steer toward a more decentralized decision-making structure well before they started transitioning to a free market model (Zwass 1979). These changes created an institutional structure that was potentially more trustworthy and functional during a financial shock.

In comparison, in the countries of the former Soviet Union, where socialism thrived for more than 70 years (with the exception of the Baltic States that joined the USSR right before the WWII), it took years before sound commercial banks that were indirectly controlled by central banks came into existence (Kornai 1994). The variable YEARS was used to capture this effect. It was hypothesized that the longer the country was under the system of planned socialism, the more likely it would have a weak institutional framework and political constraints in a decision-making process to design policies that softened an external shock.

The effectiveness of political and economic institutions and the decision-making process was also determined by cultural and social factors (Stulz and Williamson 2003; Banai 2012; Shostya and Banai 2017). High institutional collectivism societies, in which major decisions are made by groups, might have more robust institutional frameworks and thus might have found it easier to implement risk-mitigating policies during critical times (Banai 2012; Shostya and Banai 2017). High institutional collectivist societies tended to prioritize the group's needs over individual needs. These cultural and social factors were often difficult to assess and quantify. The data were often unreliable and highly qualitative in nature. Ethnic homogeneity was used as a proxy to measure social cohesion. Research demonstrated that societies with more diverse ethnic composition tend to have lower intra-community cohesion (Putnam 2007; Van der Meer and Tolsma 2014; Dinesen and Sonderskov 2018).

There were significant differences among transition economies in terms of ethnic homogeneity. In some Eastern European countries, such as Croatia, the Czech Republic, Hungary, Poland, and Romania, native ethnicity constituted more than 90% of the total population. Other countries were much less homogeneous. The ethnic composition of populations may have affected the likelihood and intensity of internal political conflicts during the crisis times. Countries with a more uniform ethnic composition might have been able to reduce the internal tensions and implement stabilization policies faster. Possibly, there would be more commonality in viewpoints for political and economic changes. Dominant ethnic group as a percentage of the entire population was used to measure the degree of homogeneity. The data came from the World Fact Book, Central Intelligence Agency (2006). Descriptive statistics are presented in Table 2.

The Models

Ordinary least square regression analysis was used to test the hypotheses listed above. Equation (1) estimated the variability in the country risk in 2009 as a function of a number of structural variables and the country risk in 2006. Equation (2) estimated the variability in the CCI as a function of some structural variables that were used in Equation (1), but replaced the country risk variable with those that are more relevant to the output gap, namely GDP in 2006 and Transition Indicators in 2006. Only 27 countries were used to estimate the second model, as the Transition Indicators were not reported for one of the countries (Czech Republic). The equations are as follows:

CRWi = a0 + a1SERVi + a2CR06i + a3ETHNi + a4YEARSi + a5AGRi + [epsilon]i, (1)

CCIi = [beta]0 + [beta]1SERVi + [beta]2GDP06i + [beta]3EETHNi + [beta]4TIi + [beta]5YEARSi + [epsilon]i, (2)

where CR09 is country risk in 2009, CCI is the Cumulative Crisis Index, SERV is service sector as a share of GDP, CR06 is country risk in 2006, GDP06 is the real GDP growth rate in 2006, ETHN is the percentage of dominant ethnic group, YEARS is number of years under planned socialism, AGR is agriculture sector as a share of GDP, TI is the Transition Indicators score, and [epsilon] is an error term.

Testing for multicollinearity did not indicate any serious issues with the models. The individual variable variance inflation factor (VIF) values were below 2.5. Testing for heteroscedasticity (BreuschPagan/Cook-Weisburg test) revealed no problems in the model that estimated the variation in the CCI (P value = 0.12). However, heteroscedasticity was detected in the first regression (P value = 0.005), so the regression was rerun using robust standard errors.

Empirical Results and Discussion

The OLS regression analysis findings are reported in Table 3. These results largely supported the hypothesized models. They suggested that country risk in 2009 was mostly affected by the number of years of central planning and the country risk in 2006. The coefficient on YEARS was +0.045 and was significant at the 5% level (one-tail test) implying that the longer the country was under planned socialism, the higher the country risk in 2009. The coefficient on YEARS was +0.101 in the Equation (2) and was significant at the 1% level, which suggested that the length of the socialist regime also increased the CCI. The number of years of central planning affected the depth and strength of the administrative structure and the size of the private sector. For example, Poland, where the administrative system was in existence for a little more than 40 years, on the onset of transition had the largest private sector (European Bank for Reconstruction and Development 1996). In comparison, most of the former republics of the Soviet Union had very small private sectors, which made it extremely difficult for those regions to introduce profit incentives and efficient management of resources, since those concepts were virtually nonexistent during more than 70 years of socialist rule.

Additionally, the results indicated a strong causal relationship between country risk in 2006 and in 2009 (Equation 1). The coefficient for CR06 is +0.721 and was significant at the 1% level. This implied that the higher the level of country risk in 2006, the higher was the country risk in 2009. These results were consistent with the postulated hypothesis that the impact of the crisis on an economy was positively related to the country's economic and political instability prior to the crisis. While in some countries the transition process was rather smooth and peaceful, in other countries it was associated with social upheaval and unrest. As a result, at the onset of the global 2007-2009 crisis, the transition economies differed in terms of their economic and political stability. Those with more unstable systems were more sensitive to an external shock and thus experienced a more severe impact of the crisis.

The results supported this study's hypothesis and Shostya's (2014) findings that the impact of the crisis was positively related to the extent of the transition from a closed planned economy to the open-market economy. The coefficient on the 77 variable was +1.117 when CCI was used, but it was borderline significant (10% level), potentially implying that the higher the extent of transition in 2006, the greater the impact of the global crisis. Research showed that during the time of the crisis, liberalization of product, factors and capital markets, coupled with an exposure to foreign resources and capital became a serious hindrance for those economies that were closer to the capitalist system (Myant and Drahokoupil 2012; Shostya 2014).

There was also a statistically significant effect of the service sector as a share of GDP and real GDP growth rate in the equation that estimates the variation in CCI. This study's hypothesis of the role of ethnic homogeneity did not seem to be well supported by the empirical results. Although the signs of the coefficients on the ETHN variable ran in expected directions in both specifications, their magnitudes were rather small and only significant in Equation (2). Surprisingly, the coefficient on the AGR variable was positive, but it was rather small in magnitude and statistically insignificant.


This research attempted to add to the existing body of research on the effects of the Global Financial Crisis of 2007-2009 on transition economies. One of the most important questions that the global financial crisis raised among researchers was why the countries' responses were so different. Most scholars agreed that the variability in the effect of the crisis on the real economy (GDP growth, employment, credit flows) must be traced to the difference in economic standing prior to the crisis, but there is much disagreement about which initial conditions matter. Some studies linked the effect of the crisis to the soundness of financial institutions and credit flows, others explored trade linkages and remittances, and still others used pre-existing debt levels as explanatory variables.

Instead of focusing on traditional macroeconomic fundamentals, this study investigated how other initial conditions, such as ethnic homogeneity, the length of the communist regime, and the extent of transition, explained the differences in the responses of the countries of the former Soviet bloc to a financial shock. To measure the effect of the crisis on the 28 countries, this study used two dependent variables: country risk in 2009 (OECD) and CCI, calculated using the EBRD crisis index. The CCI, which measured the magnitude and extent of the impact in 2008-2009, was found to be the highest in Ukraine, Latvia, Kazakhstan and Estonia.

The empirical results indicated that the countries with more favorable initial conditions prior to the crisis (fewer years of central planning prior to the transition inception, lower country risk in 2006, lower real GDP growth rate in 2006) were more likely to exhibit resiliency during the crisis. The countries that had a greater ethnic homogeneity at the onset of the crisis were also more resilient. It was possible that such homogeneity shortened the inside policy lag, the time it takes the policy-makers to evaluate the situation and introduce a policy that would mitigate the spread of the crisis. Perhaps the most important corollary of this regression analysis is that transition came at a cost. Countries that were more successful in building free market economies exhibited a more severe downturn during the 2008-2009 period. Building a market-based system and opening the economies to the turbulence and unpredictability of the outside world made transition economies particularly vulnerable to external shocks. The countries that were more aggressive in their transformation from a planned socialist system to a market system lacked the institutional structure to withstand the financial crash of 2007-2009.

Financial market participants and policy-makers in transition economies needed a good understanding of how institutional infrastructure performs in the face of serious shocks. They had to take into consideration the broader environment, including macroeconomic, regulatory, geopolitical, ethnic, institutional, and historical factors, when they designed the contingency plans and rescue packages. This study explored some of such factors, including ethnic homogeneity and its role in the decision-making process. This, however, was difficult to quantify. There was also a need to include cultural variables as there is a growing body of literature linking economic performance to cultural differences. An inclusion of Hofstede's (1980) cultural dimensions, for example, could improve the models offered in this study. 11293-019-09607-8

Acknowledgements The author would like to express her gratitude to Dr. Joseph Morreale for his thoughtful comments.

Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


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(1) Traditionally, the term "transition economy" refers to the 28 countries of Central and Eastern Europe and the former Soviet Union that had initiated a shift from a centralized planned economy to a decentralized marketbased one. In 2000-2001, the International Monetary Fund (IMF) listed 33 countries as transition economies, including Cambodia, China, India, Laos, and Vietnam (International Monetary Fund 2000,2001). The list was revised later, when ten countries of the former Soviet bloc joined the European Union (8 in 2004 and 2 in 2007) and, therefore, officially completed the transition process (Fabrizio et al. 2010).

(2) This paper excluded Cambodia, China, India, Laos, and Vietnam, which were considered by the IMF to be "transitional economies." At the same time, it included 10 European Union members that officially had finished the transition upon their accession to the Union.

(3) CIS includes 12 out of 15 former Soviet Union Republics -Armenia, Azerbaijan, Belarus, Georgia. Kazakhstan, Kyrgyzstan, Moldova, Russian Federation, Tajikistan, Turkmenistan, Ukraine, and Uzbekistanall but the Baltics (Estonia, Latvia, Lithuania).

(4) CEE includes all the Eastern European countries west of post-WWII border with the former Soviet Union, countries of the former Yugoslavia, and the 3 Baltic states.

(5) The Czech Republic's score was excluded in EBRD's calculations.

(6) See the Organization for Economic Co-operation and Development (2017) paper "Operational Procedures for the Country Risk Experts Group" for a more detailed explanation of confidentiality of the methodological procedure.

Anna Shostya [1] [iD]

Published online: 9 March 2019

[mail] Anna Shostya

[1] Economics Department, Pace University, New York, NY, USA
Table 1 Cumulative crisis index, 2008-2009

Country       CCI

Albania       2
Armenia       4
Azerbaijan      4
Belarus       2
Bosnia-Herzegovina  4
Bulgaria       3
Croatia       4
Czech Republic    N/A
Estonia       7
FYR of Macedonia   3
Georgia       6
Hungary       4
Kazakhstan      7
Kyrgyz Republic   4
Latvia        7
Lithuania      4
Moldova       6
Montenegro      5
Poland        3
Romania       4
Russian Federation  6
Serbia        4
Slovak Republic   2
Slovenia       2
Tajikistan      5
Turkmenistan     2
Ukraine       7
Uzbekistan      2

Sources: Author's calculations based on data from the European
Bank for Reconstruction and Development
(2009, 2010)

Table 2 Summary statistics

Variable              Mean   Standard  Min  Max

Country risk in 2009        4.679  2.294    0   7
Cumulative Crisis Index      4.185  1.711    2   7
Country risk in 2006        4.893  2.149    2   7
Service sector, % of GDP      56.357  11.046   24  75
Transition Indicators       3.082  0.625    1.3  4
GDP growth rate in 2006, %     8.707  5.536    3.1  30.5
Major ethnic group, % of total   78.786  14.229   43  97
Years under communism       55.857  13.077   41  74
Agriculture Sector, % of GDP    11.357  8.376    0   33

Sources: Author's calculations based on data from the Organization
for Economic Co-operation and Development (2010), European Bank
for Reconstruction and Development (2006b, 2009, 2010) and Central
Intelligence Agency (2006)

Table 3 The effect of initial conditions on the Country Risk
and Cumulative Crisis Index

                 (1)     (2)

OECD country risk in 2006     0.721 ***
Real GDP growth rate in 2006         0.095 ***
Service as a share of 2006 GDP  0.041    0.096 **
                 (0.025)   (0.028)
Main ethnic group's share     -0.022   -0.031 *
                 (0.015)   (0.012)
Transition Indicators Score          1.117*
Years under communism       0.045 *   0.101 ***
                 (0.020)   (0.024)
Agriculture share         0.034
Constant             -2.340   -8.690 **
                 (3.162)   (2.765)
R-squared             0.784    0.641
N                 28     27

Sources: Author's calculations based on data from the Organization
for Economic Co-operation and Development (2010), European Bank
for Reconstruction and Development (2006b, 2009, 2010) and Central
Intelligence Agcncy (2006)

*** significance at 1% level, ** significance at 5% level,
* significance at 10% level, (Standard errors in
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Date:Mar 1, 2019
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