The Determinants of the Economic Crisis in Post-Socialist Europe

Abstract

After enjoying a period of sustained economic growth that saw the region converge with EU per capita income levels, the global economic crisis caused post-socialist Europe to suffer a larger decline in output during 2008–2009 than any other region in the world. While there was considerable variation in individual countries' experience of the crisis, this article argues that the severity of the crisis can be explained by three key macro-financial variables. The analysis suggests that alternative explanations focusing on other macroeconomic vulnerabilities, institutional weaknesses or trade vulnerabilities are of little explanatory utility.

Notes

In this article, the term post-socialist Europe refers to those former socialist countries from Eastern Europe and the former Soviet Union which embarked on a process of economic transformation between 1989 and 1991. This region can be disaggregated into three sub-regions. The first—Central Europe and the Baltic sub-region—includes: Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia and Slovenia. The second sub-region—South-Eastern Europe—includes: Albania, Bosnia & Hercegovina, Bulgaria, Croatia, Macedonia (FYR), Montenegro, Romania and Serbia. The third sub-region—labeled Commonwealth of Independent States—consists of: Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Mongolia, Russia, Tajikistan, Turkmenistan, Ukraine and Uzbekistan. In the sections where data are presented, the sub-regions include only those countries for which data are available. However, data availability varies for each of the indicators presented in this article.

A brief perusal of the most recent EBRD Transition Report (2010) suggests that the private sector share of GDP is below 50% in only Belarus, Uzbekistan and Turkmenistan. Even in these countries, the private sector accounts for between a quarter and just under half of all economic activity, a considerable improvement on central planning.

Indeed, East Asian economies tended to accumulate capital surpluses as insurance against the sudden reversal of capital flows experienced during the Asian financial crisis of 1997–1998.

There are a number of different measures available to calculate the effects of the global economic crisis on output. For example, Berglof et al. (2009) use the sum of seasonally adjusted quarter-on-quarter declines in the fourth quarter of 2008 and the first quarter of 2009. While this measure is best for capturing the effects of the crisis in the two quarters that represent the height of the crisis, it can lead to a less accurate description of recessions that stretched out over a longer period of time. For instance, using this measure severely underestimates the impact of the crisis on Estonia (see Table 1). Moreover, this measure also fails to show the extent of the deviation from pre-crisis growth trends caused by the crisis. Because the focus of this article is on why the region has deviated from the course of convergence with EU income levels during the crisis, and also on whether the crisis has resulted in a permanent reduction in the region's capacity to resume the pre-crisis trajectory of convergence, the growth reversal measure is deemed more appropriate. Notwithstanding these methodological differences, the two measures are highly correlated (Pearson's r = 0.87).

The only countries to experience comparable growth reversals were from Africa: Seychelles (−19.1%), Equatorial Guinea (−16.1%) and Angola (−20.7%).

This observation is based on an average of all countries of the region; changes in cross-border lending were much higher than average in some individual countries, such as Russia and Kazakhstan (Berglof et al.2009). Furthermore, while the outflows during the crisis were lower than in other emerging market regions, cross-border inflows of capital have been much slower to return than in other regions (EBRD 2010).

According to BNP Paribas, European banks generally (including the UK and Switzerland) accounted for approximately 75% of all foreign bank claims on developing economies in 2008 ($3.6 trillion out of a total $4.8 trillion of claims) (‘Eurozone: Cold Easterly Wind’, BNP Paribas Research Report, 20 November 2008). Approximately $2.6 trillion of claims were attributable to Eurozone banks, and the other $1.0 trillion to UK, Swiss and Swedish banks. Post-socialist Europe was the area of greatest exposure, accounting for $1.4 trillion of their emerging economy exposure (over 10% of Eurozone GDP). These risks were not spread evenly across the Eurozone, however. Austrian banks' claims on post-socialist European economies, for instance, amounted to approximately 67% of GDP, mostly concentrated in Hungary, Ukraine and Serbia. Elsewhere, domestic bank exposure to post-socialist Europe accounted for around 30% of Swedish GDP, and 20% in Greece and Belgium. As a general rule, the smaller Eurozone countries tended to have greater exposures relative to the size of their economies than the larger economies. Because any reduction in the large stocks of debt across the region is likely to be a long-term process, the exposure of European banks to the post-socialist Europe region is likely to persist for some time.

At the onset of the crisis in 2008, Russia possessed the world's third largest foreign exchange reserves, behind only China and Japan.

The private sector financial balance is the difference between private income and private spending. It can be written as: private sector financial balance = government balance + current account balance.

This is measured as the ratio of medium and high technology goods (as defined by Lall's (2000) classification) in which a country exhibits Revealed Comparative Advantage (RCA) to overall goods; see Balassa (1965).

The ExpY measure of export sophistication is also used to measure the relative sophistication of a country's export basket. This index measures the productivity level associated with a country's export specialisation pattern. ExpY is calculated in two steps. First, using four-digit SITC.4 product classification (which yields more than 1,000 different products), the weighted average of the incomes of the countries exporting each traded product are calculated, where the weights are the RCA of each country in that product group (normalised so that the weights sum up to 1). This gives the income level of that product, which is termed ProdY. Following this, ExpY is then calculated as the weighted average of the ProdY for each country, where the weights are the share of each commodity in that country's total exports. This method is taken from Hausmann et al. (2006).

It should be noted that demand for commodities also rebounded much quicker after reaching a trough in March 2009.

In general, however, most working definitions tend to emphasise the importance of formal structures and the informal rules and norms that structure human conduct. Douglass North's (1990, p. 3) widely used definition of institutions suggests that institutions are ‘the rules of the game in a society or, more formally, are the humanly devised constraints that shape human interaction’ and that they ‘structure incentives in human exchange, whether political, social or economic’. Elsewhere, March and Olsen (1989, p. 22) define institutions more broadly to include ‘beliefs, paradigms, codes, cultures’ so that human behaviour is guided by a logic of appropriateness. All institutions, at the formal, social or personal level, contain a degree of abstraction; they are frameworks for analysing the world in which humans live. This implies that institutions are sometimes difficult to identify observationally. While some components of institutions are readily observable, such as formal rules (constitutions or legal frameworks), other components are almost impossible to observe or measure precisely, such as shared beliefs or conventions.