used to define vulnerability across the domestic and external dimensions―all of which have been well documented in previous studies as heightening a countries’ vulnerability to crises (e.g., Kaminsky and Reinhart, 1999). Also, as noted earlier, the classification is a relative one that is obtained through a arms-length methodology described below. The domestic indicators include financial sector factors as well as public finances; namely, loan-to-deposit ratios as a measure of banks’ balance sheets, private sector credit growth as a measure of economic overheating, and fiscal balances to measure the nature of the saving-investment imbalances. External indicators focus on FX liquidity indicators (short-term debt on a remaining maturity as a share of a country’s foreign exchange reserves), a balance sheet indicator (debt-to-GDP), and the overvaluation of the real effective exchange rate measured by the deviation from its HP filtered series. Across both the domestic and external dimensions the focus is on indicators that can be considered the outcome of initial conditions and economic policies, and the state of global economic conditions.

**Table 2. Change in GDP and Its Components―From Peak to Trough**

||GDP|Domestic<br>Demand|Exports|Imports|
|---|---|---|---|---|
|Latvia|-21.4|-35.3|-13.8|-42.7|
|Estonia|-18.3|-26.5|-18.3|-37.3|
|Ukraine|-15.1|-23.0|-25.6|-38.6|
|Lithuania|-14.8|-25.3|-14.3|-29.4|
|Armenia|-14.2|-16.5|-11.9|-19.4|
|Russia|-7.9|-14.1|-4.7|-30.4|
|Romania|-7.1|-12.6|-5.5|-20.6|
|Moldova|-6.5|-16.4|-7.8|-19.3|
|Hungary|-6.3|-11.5|-9.1|-15.4|
|Croatia|-5.8|-9.3|-16.2|-20.7|
|Bulgaria|-5.0|-15.0|-9.8|-22.3|
|Turkey|-4.7|-7.2|-5.3|-14.3|
|Georgia|-3.9|-11.6|-0.9|-19.4|
|Bosnia & Herzegovina|-3.1|-6.7|-4.0|-11.0|
|FYR Macedonia|-0.8|-2.5|-7.9|-10.5|

Source: IMF WEO Database, October 2010. Note: Albania, Azerbaijan, Belarus, Kazakhstan, and Poland are excluded as they have not suffered a decline in real GDP. Latvia and Estonia are the only two countries that reached their GDP peak in 2007; all others did so during 2008.

Comparing countries across multiple dimensions is never straightforward. Debt-to-GDP ratios might increase, but banking sector developments might be a source of stability; or, on the contrary, add to already weak balance sheet developments. This paper looks at these indicators simultaneously using a methodology known as cluster analysis (CA). The advantage of cluster analysis is that it does not impose ad-hoc thresholds to identify groups in the data. Instead, it lets the data speak for itself. In a nutshell, it minimizes differences within each cluster of data and maximizes the differences across clusters (Everitt, 1993). Specifically, for each country _i_ in the sample and each year _t_ in the period 200008, the six indicators mentioned above are normalized.<sup>5</sup> CA is applied on these normalized indicators and two groups are created: lower vulnerability countries and higher vulnerability countries. The methodology does not fully eliminate the arbitrariness common to the setting of ad-hoc thresholds (e.g. Kaminsky and Reinhart, 1999). In fact, one must still decide into how many groups the data needs to be divided. Nevertheless, it is still considered an arms-length tool to classify data and allows choosing the number of groups in such way that their interpretation becomes tractable.<sup>6</sup> Of course it helps that we

> 5 Given the lack of a theory on how to gauge the relative importance of the six vulnerability indicators, all six indicators enter CA with the same weight.

> 6 This paper also chooses two groups or clusters in both the growth dimension and the vulnerability dimension, which in effects allows a more tractable interpretation of country groups.

3