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We wish to thank Rex Ghosh, Russell Kincaid, and members of the Policy Review Division at the IMF for numerous discussions held during the preparation of this paper. Comments from participants at two IMF seminars are greatly appreciated. The usual disclaimers apply.
The episodes in the KAC and CG groups might or might not be associated with IMF financing (i.e., the distinction is only in terms of the intensity and persistence of private capital outflows).
Precautionary arrangements are a special case of IMF-supported programs in which the member country does not face an immediate financing need. In such cases, the member accumulates drawing rights and is allowed to draw these resources if needed.
Cottarelli and Giannini (2002) provide a survey of the empirical literature on the catalytic effects of IMF lending.
Many of the econometric challenges in identifying the impact of IMF support are likely to be similar; see Cottarelli and Giannini (2002) for a discussion of the econometric issues involved.
A recent paper by Eichengreen et al. (2005) looks at the effects of IMF support in preventing sudden stops.
Cluster analysis is an iterative process that assigns individual observations into k distinct data clusters. The process begins by guessing the mean of each of the k clusters and assigning each observation to one of these clusters. The means of each cluster are then calculated and, if needed, individual observations are reallocated to the cluster with the closest mean. This iterative process continues until there are no changes in the allocation of individual observations. Different algorithms can be chosen to trigger the iterative process (e.g., medians in lieu of means). The use of alternative algorithms does not affect the paper’s main results.
While cluster analysis can be applied concurrently to several economic indicators, its results might vary depending on the trade-offs that occur among these variables. For example, exchange rates might not change as a result of hefty central bank interventions in the foreign exchange market; still, the market pressure is a clear and present danger. This paper opts for constructing an index of market pressures and applying cluster analysis to this index.
The definition is based on data availability and includes 27 emerging market economies—Algeria, Argentina, Brazil, Bulgaria, Chile, Colombia, Dominican Republic, Ecuador, Hungary, Indonesia, Korea, Malaysia, Mexico, Morocco, Pakistan, Panama, Peru, Philippines, Poland, Russia, South Africa, Thailand, Tunisia, Turkey, Ukraine, Uruguay, and Venezuela.
The most common components used in the construction of these indices are the nominal or real effective exchange rate, foreign exchange reserves, and monetary aggregate ratios.
The interpretation of shifts in the REER assumes no knowledge regarding a country’s equilibrium exchange rate. For example, an appreciation could reveal both a build-up of overvaluation pressures or a reduction in undervaluation relative to a country’s equilibrium rate. Either way, the fact that appreciation occurs means that market pressures are easing.
The duration is measured by the number of months from beginning to end of each market pressure event. As is customary in the literature that identifies events, a “tranquil” period (in this paper 12 months) is needed in between pressure episodes to consider these as different events. Five countries have pressure episodes within less than 12 months of each other: Brazil, October 1997 and August 1998; Mexico, April 1994 and December 1994; Philippines, July 1997 and August 1998; South Africa, July 1997 and July 1998; and Turkey, October 1997 and August 1998. In each case the least severe market pressure was dropped (the first of the identified dates). Also, Ecuador was affected by a protracted period of market pressures that ended with the introduction of a currency board (January 2000); only this last event is kept. Similarly, in Brazil, where the episodes in August 1998 and April 2000 shared some common data for outer quarters, the April 2000 episode was dropped because it was less severe.
Quarterly data for private capital flows is limited to 20 emerging market economies. The World Economic Outlook (WEO) definition of private capital flows is used in the paper, but is applied to quarterly International Financial Statistics (IFS) data.
An alternative definition of step 2 would involve looking at private capital flows plus errors and omissions and applying cluster analysis to this aggregated data series. Such a definition would take into account developments in errors and omissions during KAC episodes. This definition results in a different classification for two market pressure episodes—Mexico 1994 and Venezuela 2003 would be categorized as a KAC—but does not affect the paper’s results.
The discussion is based on the medians for each of the two groups; stylized facts derived using average values are broadly similar to those presented here for median data.
The fiscal balance figures also reflect the strong seasonal pattern of fiscal quarterly data.
An increase in the velocity of money implies that the money growth rate is less than the growth rate in nominal GDP, thus implying a tightening of monetary aggregates.
The AREAER ranks exchange rate regimes from 1 to 8, with 1 being an exchange rate arrangement with no separate legal tender and 8 being an independently floating regime.
Exchange rate overvaluation is defined as the difference between the REER and the trend value of REER, the latter derived by applying the Hodrick-Prescott filter on the REER data.
In Argentina, the July 2001 market pressure event is classified as a KAC (i.e., 2001 Q3 (period t) = 1); hence, in the logit estimation, the dependent variable would be specified as 2001Q2 =1, 2001Q1=1, 2000Q4 =1, and 2000Q3 =1. By contrast, the Argentina 1998 episode has zeros as the dependent variable because the period t episode is in the control group.
The ICRG index prepared by the Political Risk Services Group.
Other international cyclical factors (e.g., U.S. interest rates) were considered, but made the convergence of the maximum likelihood estimation more difficult and, in the end, had no bearing on the results regarding the role of IMF financing.
The sample has only two precautionary arrangements; hence, distinguishing these econometrically from nonprecautionary programs is not possible. Excluding altogether precautionary arrangements from the sample does not affect the results in this paper.
More precisely, the level of IMF financing in period t-1 is calculated as a sum of available IMF resources from t-4 to t-1 as a ratio of short-term debt in t-1; the financing in t-2 is calculated as a sum of available IMF resources from t-5 to t-2 to short-term debt in t-2; and so on for earlier periods.
Growth and inflation performance pre-crisis differ between KAC and CG cases (Figure 2). Adding these variables to the estimation does not alter the thrust of the results in this paper.
The exchange rate overvaluation is excluded from R1 since the regression fails to converge due to its small sample size. It is also dropped from R2 for comparability with R1.
Typically, a dummy variable for the existence of a IMF-supported program is used in the literature. By itself, this does not allow to control for the existence of an “on-track” program. However, this paper finds that an indicator dummy variable constructed based on the existence of an “on-track” IMF-supported program also proves not to be statistically significant.
An alternative specification (not reported) where the IMF financing is defined as the amount of money committed over the life of the IMF-supported program is also not significant.
The model prediction is based on the default cutoff probability of 0.5—a crisis probability greater than 0.5 is classified as a KAC and, if less than 0.5, it is classified as a control group.
The sample is small and, at times, dropping outliers precludes the maximum likelihood from converging. Two approaches were followed in such cases: dropping some regressors (e.g., one or both regional dummies) and setting different cutoff levels for the dfbeta technique. In both cases, once convergence is restored, the results on the role of IMF financing remain unchanged.
While arbitrary, this cutoff is close to what is needed for the logit model not to have type I errors (i.e., an 8 percent cut-off). In addition, it seems sensible for the IMF to target a reduction in crisis probability that has a large margin of success.
Regressions interacting the overvaluation regressor with the exchange rate regime (not reported) show that a pegged exchange rate regime makes the country especially vulnerable. While this underscores the importance of avoiding overvalued fixed exchange rates, it also means that implementing even a relatively modest correction may not be straightforward with potentially significant costs in terms of the credibility of the regime or balance sheet exposures that may arise if the exchange rate overshoots in the process of exiting the regime.
Given that the focus of this paper is on crisis prevention, the logit estimation is based on developments prior to the onset of the market pressures; that is, the estimation is for period t-4 through t-1. Hence, while fundamentals typically deteriorate significantly during the crisis (from period t onwards), these effects are not covered in the econometric estimation in this paper.
For example, the small sample precludes the assessment of nonlinearities associated with threshold effects of high debt levels or of contagion effects across market pressure events.