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This paper investigates the economic and political conditions that are associated to the occurrence of a sovereign debt crisis. We use a new statistical approach (Classification and Regression Tree) that allows us to derive a collection of “rules of thumb” that help identify the typical characteristics of defaulters. We find that not all crises are equal: they differ depending on whether the government faces insolvency, illiquidity, or various macroeconomic risks. We also characterize the set of fundamentals that can be associated with a relatively “risk-free” zone. This classification is important for discussing appropriate policy options to prevent crises and improve response time and prediction.

Following the debt crises of the 1980s, sovereign debt defaults have become more frequent. Episodes of outright default include Russia, Ecuador, and Argentina. In other cases, formal default was avoided via a debt restructuring under a coercive threat of default as in Ukraine, Pakistan, and Uruguay. Default was averted through large-scale IMF financial support in cases such as Mexico, Brazil, and Turkey. While there has been a significant amount of research regarding debt crises in general, and about the policy responses to these defaults, in particular,1 the macroeconomic and structural weaknesses leading to them are still not properly understood: there is little comparative empirical work on the sovereign debt crises of the last decade. Many policymakers and analysts continue to use simple rules of thumb to judge risks and to assess fiscal sustainability (Mody and Saravia, 2003), as well as the soundness of macroeconomic policies. Too often, these rules are not based on a rigorous quantitative analysis, and may miss some core elements that led to these sovereign debt crises. Our aim is to provide answers to the following basic questions. What set of economic and political conditions is empirically associated to the likely occurrence of a sovereign debt crisis? Can one derive thresholds for vulnerability indicators that will effectively signal the risk of a sovereign debt crisis? Part of the motivation for the paper stems from so-called surveillance failures, namely cases where international financial institutions, such as the IMF, as well as rating agencies, private sector agents, and academics, were taken “by surprise” and grossly under-estimated the likelihood of a sovereign default. In the paper, we use a new statistical approach and derive a set of “rules of thumb” that help identify the typical characteristics of defaulters. We find that not all crises are equal: they differ depending on whether the government faced insolvency, illiquidity, or various macroeconomic weaknesses and risks. This classification is crucial for discussing appropriate policy options for preventing crises and responding to them once they occur. For example, it is often argued that solvent but illiquid countries with large amounts of short-term debt may need IMF support to avoid a liquidity run or “roll-off” crisis. Conversely, highly indebted countries may face a debt crisis, unless there is a strong and credible fiscal consolidation. In addition, it is argued that conditionality should set targets indicating that a country's macroeconomic fundamentals are heading towards a relatively “safe” zone. In the paper these concepts of liquidity crisis, insolvency crisis, crisis triggered by weak macro-fundamentals, and relatively “safe zone” are made precise. Unless the diagnosis is correct, it is hard to get the policy cure right. This empirical analysis is based on a dataset containing annual observations for 47 emerging market economies from 1970 to 2002. A country is defined to be in a state of “debt crisis” if it is classified as being in default by Standard & Poor's, or if it receives a large non-concessional IMF loan (where “large” means in excess of 100% of IMF quota). Standard & Poor's rates sovereign issuers in default when a government fails to meet principal or interest payment on an external obligation on due date (including exchange offers, debt equity swaps, and buy back for cash). We employ the Classification and Regression Tree methodology (CART) for classification and prediction.2 CART is a computer-intensive data mining technique that selects explanatory variables, their critical values, and their interactions in order to identify “safe” from “crisis-prone” types. The main conclusions of our empirical analysis are as follows. First, out of 50 candidate variables, 10 predictor variables turn out to be sufficient for classification and prediction: total external debt/GDP ratio; short-term debt reserves ratio; real GDP growth; public external debt/fiscal revenue ratio; CPI inflation; number of years to the next presidential election; U.S. treasury bills rate; external financial requirements (current account balance plus short-term debt as a ratio of foreign reserves); exchange rate overvaluation; and exchange rate volatility. Second, a relatively “safe” country type is described by a handful of economic prerequisites: low total external debt (below 49.7% of GDP); low short-term debt (below 130% of reserves); low public external debt (below 214% of fiscal revenue); and an exchange rate that is not excessively over-appreciated (overvaluation below 48%). Third, three major types of risks are identified: (i) solvency (or debt unsustainability); (ii) illiquidity; and (iii) macro-exchange rate risks. The debt unsustainability risk types are characterized by: external debt in excess of 49.7% of GDP, together with monetary or fiscal imbalances, as well as by large external financing needs that signal illiquidity as an element of debt unsustainability. Liquidity risk types are identified by moderate debt levels, but have short-term debt in excess of 130% of reserves coupled with political uncertainty and tight international capital markets. Macro-exchange rate risk types arise from the combination of low growth and relatively fixed exchange rates. Each of these risk types differ in their likelihood of producing a crisis. Finally, our model has excellent predictive capacity in-sample, while the out-of-sample forecast have both less false alarms and less correct predictions than the Early Warning Signal (EWS) literature. The analysis has one important, albeit simple, implication for sustainability analysis. It shows that unconditional thresholds, for example for debt–output ratios, are of little value per se for assessing the probability of default. One country may be heavily indebted but have a negligible probability of default, while a second may have only moderate values of debt ratios while running a considerable default risk. Why? Because the joint effects of short maturity, political uncertainty, and relatively fixed exchange rates make a liquidity crisis in the latter much more likely than a solvency crisis in the former, particularly if the large external debt burden goes together with monetary stability, a large current account surplus, and sound public finances. The plan of the paper is the following. Section 2 contains a review of the literature. Section 3 describes the dataset. The Classification Tree methodology is reviewed in section 4, and applied to the data in section 5. Section 6 discusses an interpretation of the results. 7 and 8 contain standard regression analysis and a robustness check based on a generalization of the CART procedure. The predictive power of the model is discussed in 9 and 10 performs some “rolling tree” exercises in order to understand if the crises of the nineties are “structurally” different from those in the previous decades. The final section 11 discusses some extensions and summarizes the main conclusions and policy implications.

In this paper we have applied a new statistical methodology to the question of understanding sovereign debt crises, both in terms of fundamentals that lead to a crisis, and of the factors that allow us to predict such crises. This technique allows us to derive endogenously the most important factors associated to vulnerability of sovereign debt, and the thresholds that signal greater risk of a crisis. We find that most debt crises can be classified into three types: i) episodes of insolvency (high debt and high inflation) or debt unsustainability due to high debt and illiquidity; ii) episodes of illiquidity, where near default is driven by large stocks of short-term liabilities relative to foreign reserves; and iii) episodes of macro and exchange rate weaknesses (large overvaluation and negative growth shocks). Conversely, a relatively “risk-free” country type is described by a handful of economic characteristics: low total external debt relative to ability to pay, low short-term debt over foreign reserves, low public external debt over fiscal revenue, and an exchange rate that is not excessively overvalued. Political instability and tight monetary conditions in international financial markets aggravate liquidity problems. The approach suggests that unconditional thresholds—for example, looking at debt to output ratios in isolation—are of little value per se for assessing the probability of default; it is the particular mix of different types of vulnerability that may lead to a sovereign debt crisis. The in-sample predictive power of the tree approach is quite good with very few crises missed; the model predictive accuracy does not suffer when applied to the post-1990 experience. However, despite very few false alarms, the out-of-sample predictive power is not satisfactory: many of the crises of 1990s could not be predicted based on previous decades information, possibly confirming the different nature of the latter crises. The “rolling tree” analysis suggests indeed that the nature of sovereign debt crises changed somewhat in the nineties, with fiscal solvency and political economy issues gaining more relevance, and vulnerability to liquidity shocks and currency over-appreciation becoming larger. Our approach allows to derive rules of thumb which may be useful to predict crises early on (an “early warnings signal” model), and to derive policy adjustment paths, which may reduce the likelihood of a crisis for countries that may be entering in a danger zone. Thus, ideally, this tool can be used for surveillance, crisis prevention, and crisis resolution. A number of possible extensions come to mind, some of which we have tried. First, following the idea that a “history” of past defaults may bear on the credibility of a sovereign and thus affect the crisis probability (as suggested by Reinhart and others, 2003, in their “debt intolerance” hypothesis), we constructed a new variable taking on incremental values each time a new default episode occurred. Our indicator of (bad) “reputation” did not affect the classification tree. Experimenting with contagion effect is also possible. But the use of annual data preclude using same-year information for predicting crises.