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The author would like to thank Peter B. Kenen for his comments and for enormous support, advice, and encouragement on related research done at Princeton; Manmohan Kumar, Andy Berg, Luis Catao, Gabriela Basurto and T.N Srinivasan for numerous helpful suggestions; Ranil Salgado and Luca Ricci for sharing data; and Celia Burns for invaluable assistance with formatting.
This view implies that such crises are not really “contagious” in the epidemiological sense. While they owe to weaknesses in the same fundamentals, and while some have argued that these weaknesses might owe to a common cause, a crisis in one country does not, in this scheme of things, “cause” a crisis in another.
We do not enter into a theoretical discussion of this idea of contagion. It has, however, been formalized (although not in the context of modelling contagion) elsewhere. Shiller (1995) discusses the idea of signals from one financial market influencing events in another. Chari and Jagannathan (1988) present a theoretical model of bank runs where expectational shifts are coordinated by an observable variable-the “length” of the line outside the bank. The idea, here, is that there are other observable variables that could coordinate expectations in currency markets
While this “second-generation” characterization of currency crises is widely accepted there has been recent theoretical work that calls into question the idea of multiple equilibria in currency markets, demonstrating that under imperfect information the equilibrium can be unique. See Morris and Shin (1998).
We also consider similarities between the country for which the observation is being taken and all other countries experiencing crises, in addition to testing for wake-up calls.
Corsetti et. al. (in press) present a theoretical framework in which to view the welfare effects of competitive devaluations.
Closely related to this approach is the concept of “monsoonal effects,” discussed at length in Masson (1998). This line of reasoning holds that contagious currency crises are a set of individual crises, caused by macroeconomic troubles in the countries concerned, which owe their origin to a common cause. This approach has not been explicitly pursued in empirical work on currency crises, although a number of papers have attempted to explain vulnerability to contagious crises in terms of macroeconomic fundamentals.
Following STV, transition economies such as Hungary, Poland and China have been excluded, as have members of the EU such as Greece and Portugal.
See World Bank (1996) for a detailed history and analysis of private capital flows to emerging markets.
The first four variables are the top four in KLR’s ranking of variables on the basis of their accuracy in signaling currency crises in advance. The next three are used by STV/T and BM, among other studies.
In the case of Mexico, for the 1994 Tequila crisis, we use November 1994, rather than December 1994 as the “end of the year.” Also, for ST in the 1998 episode we use the annualized change since the end of the previous crisis period, for those countries that experienced crises in 1997.
Other thresholds have been tested and the results are robust to the choice of threshold.
For the sake of mathematical precision, for exports and stock prices, the Xi denote the negative of those series, since low values of the variables signal crises.
This is because the eighties were a decade characterized by macroeconomic turmoil in many Latin American economies. The nineties are generally seen as a decade significantly distinct and delinked from the 1980’s when speaking of economic circumstances in Latin America.
Although Russia is not part of our sample, we calculate signals for it, for 1998, which are presented along with the signals for other countries in Table 4.
Leaving these observations in raises the R2 substantially. However, since these were the crises that triggered the contagious episodes, whereas the crises that came later happened more or less simultaneously, the crisis index for these data points cannot be explained by the number of other countries in crisis.
Taken alone, this would suggest that the marginal explanatory power of location is modest. Other results, discussed below, indicate otherwise.
Since WC1, CT, and CR are collinear, we cannot any of them as regressors together.
These variables are labeled DM2, DRER, and so on.
By including the economic variables in the regression equation, we have already controlled for common economic weaknesses.
A similar variable, which was a dummy which took the value 1 if a majority of the countries in crisis were in the same region as the country for which the observation was being taken, also turned out to be insignificant.
It should be noted that this index takes into account the fact that currency crises do not have to be manifested in falling exchange rates. Rather, the central bank could be defending a peg under pressure by running down reserves.
A number of different measurement intervals were tried, including the STV/T and BM intervals. The results are robust to the intervals used, and we show the best fit obtained.
The reason for this is straightforward. While the policy instrument the government controls is the nominal exchange rate, the variable the current account responds to is the real exchange rate.
Calvo(1996) has since amended his earlier proposal, suggesting that in comparing countries with significant structural differences, the ratio of M2 to international reserves be adjusted by its first log difference standard deviation. He made this observation in the context of comparing Mexico to Austria. Here, since the countries being compared are rather similar (with the possible exception of Singapore), structurally, in that they are all emerging markets, this adjustment is perhaps less necessary.
The assumption here is that capital controls are not an option.
Bank loans to the private sector are made on the basis of expectations that economic growth will occur at a certain rate. During lending booms, these expectations tend to be unusually optimistic-because banks have excess funds to lend, they are willing to lend to riskier projects. Hence the increased vulnerability of their portfolios to economic contractions.
The former would hurt the banking sector by raising interest rates and reducing growth, at a time when its loan portfolios have an unduly high proportion of loans that are risky, in that they will be repaid only if economic growth is high. This has, of course, to be considered in the context of the currency composition of the liabilities of the banking sector. If a high proportion of liabilities, relative to assets, is denominated in foreign currency, then a devaluation will also hurt banks, by increasing their liabilities by more than their assets. If, however, a large proportion of liabilities is denominated in domestic currency, the devaluation will be a blessing.