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)| false Gavin, Michael, Ricardo Hausmann, Roberto Perotti, and Ernesto Talvi, 1996, “ Managing Fiscal Policy in Latin America and the Caribbean: Volatility, Procyclicality, and Limited Creditworthiness,” Working Paper No. 326 ( Washington: Office of the Chief Economist, Inter-American Development Bank).
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)| false Kletzer, Kenneth, 1997. “ Volatility, External Debt, and Fiscal Risk: Simulations of the Impact of Shocks on Fiscal Adjustment for Thirteen Latin American Countries,” Working Paper No. 358 ( Washington: Office of the Chief Economist, Inter-American Development Bank).
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We thank Tamim Bayoumi, Sandeep Kapur, Carmen Reinhart, and David Robinson for comments on an earlier version of this paper, and Carlos Végh for helpful conversations on the topic. We are also grateful to Gian Maria Milesi-Ferretti for sharing with us his debt data, and to Laura Leon for her assistance with the preparation of this paper. The usual caveats apply.
For instance, in a recent study on debt crises spanning 69 developing countries, Detragiache and Spilimbergo (2001) report pseudo R-squares in the 0.13 to 0.25 range. Using sovereign credit ratings as a catch all indicator and probit regressions to measure its capacity of predicting sovereign defaults, Reinhart (2001) reports even lower pseudo R-squares and mixed results on their statistical significance
As discussed below, one exception is Eaton and Gersovitz’s own empirical estimates presented at the end of their 1981 paper. Specifically, they estimate a disequilibrium model of the supply and demand for foreign loans to sovereigns where the level borrowing is, inter alia, a positive function of the percent variability of export earnings. Their model is not used to estimate default probabilities, however. In an earlier paper, Feder and Just (1977) consider the percent variability in export earnings as a potential determinant of default risk in a logit specification, but drop the variable from their reported estimates on the grounds that it yielded implausible results (Ibid, p. 32).
A formal derivation of the positive relationship between volatility and default risk in a credit-in-advance model of bank lending is provided in Agénor and Aizenman (1998). For the operation of a similar mechanism in a consumption smoothing model of sovereign debt, see Catão (2002).
For empirical evidence on the sizeable fiscal adjustment which are often necessary to ensure debt solvency in countries subject to typically large terms-of-trade shocks, see Kletzer (1997).
One important advantage of restricting our sample to middle- to high-income developing countries - the so-called “emerging markets” – is that data problems are not as severe as faced by other researchers who looked at a broader sample of developing countries. This enables us to consider a wider array of explanatory variables and mitigate estimation biases related to measurement errors in the data.
The other main source of aggregate volatility highlighted in the business cycle literature is technological shocks. This is not considered here both because of the difficulty in measuring such shocks using national data for those emerging market countries and because some of their effects has been shown to be captured by terms of trade variations (Kraay and Ventura, 2001). However, to the extent that technological shocks affects countries asymmetrically and have a significant bearing on macroeconomic volatility that are not captured by any other variable, their impact on sovereign risk should not be dismissed in future research.
This not only minimizes well-known overfiltering problems associated with the first difference operator, but also yields a clear equilibrium interpretation for the coefficient on output. In these calculations, we set the filter’s smoothing parameter lambda to 7, as suggested for annual data (see Pesaran and Pesaran, 1998).
Underlying this measure is the assumption that output and terms of trade (or any other relevant macro variable) affect the numerator and the denominator of this ratio symmetrically so that they cancel themselves out.
The estimates reported are for current output gap, but we have also estimated equation (1) using both one-period lags and an instrumental variable for OGAP (available from the authors upon request). This is not only because there may be lags before changes in the output gap impact on the deficit, so the one-year lagged gap may be the most relevant variable, but also because the estimate on the current gap may suffer from an endogeneity bias, as changes in the fiscal balance at time t will also have an effect on the output gap at time t. The broad conclusions are unchanged to these changes in specification.
Passive accommodation of fiscal deficits can also be another source of monetary policy volatility, particularly in high inflation environment such as those which plagued several emerging markets in the 1970s and 1980s. However, the relationship between fiscal deficits and money growth in the short-run is well known to be weak (Fisher, Sahay, and Végh, 2001).
Several studies have shown that those two instruments have been widely used not only to influence current monetary conditions and aggregate demand, but also to affect the volume and the composition of future capital inflows. See, e.g., de Gregorio, Edward and Valdez, (2000), Rojas-Suarez and Weisbrod (1995) for a discussion on the use of reserve requirements as a monetary policy instrument in Latin America.
The index takes on discrete value ranging from 0 to 4, where 0 stands for no controls, 1 for restrictions on current account transactions, 2 for restrictions on current account and capital transactions, 3 if multiple exchange rates are added on top of those restrictions, and 4 if all those restrictions are added to restrictions on the repatriation of export procedures. For a discussion of the pros and cons of this index, see Leiderman and Razin (1994).
It should be noted that findings regarding the statistical significance of the fiscal balance and the exchange rate misalignment indicator are not consistent across studies. Eichengreen and Portes (1985), for instance, find that changes in the central government fiscal balance help explain defaults, while Cline and Barnes (1997) and Detragiache and Spilimbergo (2001) found that it is not statistically significant at conventional levels.
These events were: Argentina (1982, 2001), Brazil (1983), Chile (1973, 1982), Costa Rica (1981), Ecuador (1982, 1999), Mexico (1982), Panama (1983), Peru (1984), Uruguay (1983), and Venezuela (1983) in Latin America; Philippines (1984) and Pakistan (1998) in Asia; Egypt (1984) and South Africa (1986) in Africa/Middle East; Bulgaria (1990), Poland (1981), Russia (1991, 1998), and Turkey (1978) in Emerging Europe. Because of lack of fiscal data for Bulgaria before 1990, Poland before 1981, and Russia before 1991, the fiscal variable chart is an average of the other 19 countries.
Using the HP-filter to detrend the (CPI-based) real exchange rate indices, we find that the real exchange rate typically appreciates by some 10 percent relative to trend in the four years preceding the default event. In Figure 2, we preferred to plot the raw index rather than the deviations of the HP-trend since the former fares better in the regression analysis, as discussed below.
This is because the logit distribution has a fatter tail than the probit one and thus tends to yield a better fit in this context (see Greene, 2000)
We have also undertaken probit estimations of the same model, which yielded very similar results regarding the relative magnitude and statistical significance of the explanatory variables. However, we have found a slight loss of expiatory power and generally lower coefficients than with the logit specification.
We date the end of debt crises using the periodization proposed in Beim and Calomiris (2001) for the period through 1994 and IMF country desk information for subsequent years. Detragiache and Spilimbergo (2001) base, instead, their periodization on the outstanding arrears of private and public external debt (as published in the IMF’s International Financial Statistics). The main difficulty with this procedure is that the IFS debt arrears series includes all country’s debt, public and private, and not just sovereign debt. As there have been a few situations where sovereign’s access to international capital markets is reestablished even through a segment of the domestic private sector continue accumulate arrears for some time, the two periodizations differ somewhat. Since the main focus of this paper is on sovereign debt, rather than on total external debt, our periodization seems preferrable for present purposes.
Regarding the real exchange rate, we also found it to be significant when deviations from an HP-filtered trend was used. However, the fit of the model in the latter case was significantly poorer so we stuck to the index itself normalized to the same base year (1990=100) for all countries.
The main reason as to why we omitted it in the first place is due the fact that we lack of data for this variable for the outer years in our sample (2000 and 2001). The is also the issue as to why this ratio is endogenous and serially correlated with the other variables, as an increase in the ratio may simply reflect debt servicing problems. See Detragiache and Spilimbergo (2001).
Since our data set spans over 31 years, the choice of a 10-year seems a reasonable compromise between capturing historical differences across countries and allowing for their variation over time. As a robustness check, we also experimented with 5-year rolling standard deviations, which also yielded statistically significant coefficients but a slightly worse fit.