“In a crisis, you are hostage of your past”
APPENDIX I: Banking Sector Vulnerabilities—Some Partial Evidence
APPENDIX II: A Model for the Duration of Capital Account Crises
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In the process of preparing this paper, we greatly benefited from stimulating discussions with Jorge Marquez-Ruarte, Alan MacArthur, Thanos Arvanitis, Bhaswar Mukhopadhyay, and our colleagues in the Crisis Resolution Issues Division in PDR. We also thank Mark Allen, Matthew Fisher, G. Russell Kincaid, Michael Hadjimichael, Adnan Mazarei, and Atish Ghosh for their many helpful comments and suggestions. Ivetta Hakobyan and Cecilia S. Lon provided excellent research assistance and technical support. All remaining errors are our own.
Ghosh (2006); Calvo, Izquierdo, and Mejia (2004) highlight the importance of balance sheet vulnerabilities; and Frankel and Cavallo (2004) focus on the link between trade openness and the probability of a crisis.
Ghosh et al (2002).
The literature on crisis identification has typically focused on the beginning of crises, with little systematic attention to their end. The methods used in this literature can be broadly divided in two groups: those relying solely on capital flow patterns (in particular sudden stops), and those relying on a broader range of indicators.
Since capital account crises are fundamentally an external financial phenomenon, we focus on external and financial variables to determine their duration. While capital account crises often have various real and structural spillovers (such as output losses, or the need to restructure the domestic banking system), these may be considered consequences of the crisis, which may persist beyond the crisis episode itself.
While changes in financial variables are informative of a crisis, using them to identify its end would risk a premature call when variables stabilize (temporarily) at levels that do not reflect a sustainable equilibrium.
Foreign exchange reserves are net of IMF disbursements. Following Ramakrishnan and Zalduendo (2006), net private capital flows are computed based on the WEO definition of private capital flows applied to quarterly IFS data, excluding FDI but including errors and omissions.
The mean of the index is zero by construction, since each component of the index has zero mean.
The two-quarter exit criterion was discretely overridden in Brazil’s 1998 and Philippines’ 2002 crises, where small but persistent positive index values followed a clear one-quarter drop into the negative territory.
A solution to this problem might have been the use of pre-crisis observations only; lack of sufficiently long pre-crisis series for some countries prevented this correction. In addition, changes in the economy induced by a crisis could cast doubts on the plausibility of an exit criterion relying only on pre-crisis observations.
On the long end, the duration of Thailand’s 1997 crisis was exceptional. Prolonged political uncertainties, mismanagement of the exchange rate, and long and difficult restructuring processes in the corporate and banking sectors resulted in protracted capital outflows and slow rebuilding of reserves. The duration of Argentina’s most recent crisis (14 quarters) also stands out. In this case, key factors were the interplay of deep problems in the external, banking, and government sectors, and the largest, and arguably most complicated, sovereign default in history. On the short end, Turkey’s 1998 bout of sharp capital outflows, which was triggered by the crisis in Russia, was remarkably short lived, owing to the fact that major distress in the balance of payments, the banking sector, or the government’s debt profile could be avoided during this contagion episode. Such deeper problems, however, emerged prominently in Turkey’s subsequent crisis in 2000.
A notable exception is the 1998 Russian crisis, where the IKAC-based duration estimate exceeds the assessment made by IMF staff by 7–8 quarters.
The mean and median durations defined by the alternative measures should be interpreted with caution because statistics are computed on a different number of observations owing to “open ended” cases, where the end point of the indicator is not defined within the sample (for example, when spreads and reserves failed to recover to their pre-crisis levels or when there was no obvious loss of market access). The mean (or median) crisis durations derived from the individual components of the IKAC index do not conform to the mean (or median) of the index because the latter is based on standardized values.
There are some exceptions to this general pattern, Indonesia (1997) being the most prominent one. In Indonesia, output failed to recover until long after the financial crisis had ended, mostly owing to the protracted impact of the crisis and of political uncertainty on domestic consumer and investor confidence. This effect may have been compounded by the absence of a broad-based recovery of non-oil exports.
The data show no clear relationship between the depth of output loss and the duration of crises.
This was most striking in Argentina in 2001, where spreads remained at dramatically higher levels. Brazil (2002), on the other hand, is a notable exception: here, spreads were substantially lower at the end of the crisis, likely reflecting the credibility of the government program.
Uruguay, where capital outflows peaked at 20 percent of GDP, is an outlier in terms of intensity of outflows. The outflows were related to withdrawals of bank deposits by Argentinean residents following the crisis in Argentina.
In fact, consistent with sustained improvements of current account positions, capital inflows to those countries never returned to pre-crisis levels.
In Malaysia, capital outflows even temporarily increased after the crisis, as the funds frozen at the imposition of capital controls were gradually freed.
In only three cases did the external position deteriorate over the course of the crisis, two of which (Philippines 2000 and 2002) were single crisis cases.
The turnaround in Malaysia’s fiscal position (from a sizeable surplus to deficits) was largely the result of policy measures designed to stem the decline in output by aiming for expansionary fiscal policies.
A notable exception is the Brazil crisis in 1998, where a sizeable current account deficit persisted throughout the crisis and reserves did not recover.
To illustrate, Korea and Russia (two triple crises), where reserve accumulations were strongest, ran current account surpluses at the height of their crises of around 10 percent and 20 percent of GDP, respectively banking sector distress. However, data limitations do not allow a systematic test of the impact of a crisis on the banking system (some partial results are presented in Appendix I).
These categories are similar to those that may be used to examine the likelihood of entering into a crisis (see Ramakrisnan and Zalduendo (2006)). However, the considerations relevant to the probability of remaining or exiting a crisis (once the latter occurs) are quite different. See the discussion below.
Structural policies are also likely to be important in determining the duration of a capital account crisis. However, the difficulty of quantifying these policies in a consistent format limits the scope of their inclusion in our econometric analysis.
IMF-supported programs were in place in 15 out of the 18 crises in the sample. There were no programs (at the time) in Malaysia (1997), Philippines (2002), and Turkey (1998).
While the estimates are potentially subject to endogeneity bias (because exiting from crisis and certain policy options may be jointly determined), there is no selection bias since the sample includes only crisis observations, starting from the first quarter in crisis and ending with the exit quarter.
The hazard function determining the baseline probability is assumed to depend on the number of quarters spent in crisis. By construction, the exit probability increases with time if the estimated coefficient on the time variable is positive (i.e., a capital account crisis is treated as a finite event) and decreases if it is negative.
Data availability prevents the inclusion of household, corporate, and banking sector balance sheet indicators.
The AREAER classification ranks the various types of exchange rate regimes in eight categories, with higher values indicating a more flexible regime.
Similar results are obtained when cumulative IMF financing is normalized by the level of countries’ short-term external debt.
The inclusion of the (contemporaneous) IMF variable in the model is likely subject to an endogeneity problem, since the extent of the Fund’s financial involvement is likely to be correlated with the perceived severity of a crisis. To mitigate this problem, this variable is instrumented by the country’s IMF quota, lagged debt-to-GDP ratio, lagged short-term debt-to-reserves ratio, lagged current account balance in percent of GDP, real GDP growth, and a variable capturing the time spent in crisis (log of time in crisis). Because the severity of a crisis is likely to influence the size of the IMF financial package, the value of the IKAC index (lagged two quarters) is also included as an instrument. While the estimation results are robust to the choice of lag, the second lag yields the highest likelihood value.
In the specifications with time dependency, the exchange rate regime has p-values of 0.13 and 0.17 in regressions 2a and 3a, respectively.
By construction, the cumulative IMF financing variable increases with time. Similarly, a shift toward greater exchange rate flexibility (higher indices denote more flexible exchange rate regimes) is also likely to display some correlation with the time variable. These features are likely to introduce multicollinearity in specifications that include the time variable component of the hazard function.
While it is technically difficult to use similar diagnostic tools for the model with no time variable without making arbitrary assumptions on the disbursement profile of IMF financing, the comparable likelihood value for this model also indicates significant explanatory power.
This specification has the lowest Bayesian and Akaike information criteria, suggesting that it strikes the best balance between parsimony and performance among the three alternative specifications.
Given that the dependent variable in the model is a binary indicator of the end of a crisis, a positive coefficient in the estimated regressions associates higher values of an explanatory variable with a greater probability of exiting from crisis.
This result seems to confirm the findings of other studies (see Eichengreen et al (1998)) according to which the chances of a smooth transition to greater exchange rate flexibility are generally not good in a crisis. Indeed, our findings show that the level of a country’s external debt—which is highly sensitive to exchange rate dynamics—is a critical factor influencing the probability of exiting from a crisis.
An alternative interpretation of this term is that it captures measurement errors in recorded regressors or recorded survival times.