of debt crises, as hypothesized by several commentators after the Asian .... literature uses short-term debt data from the Bank of International Settlement, which.
Short-Term Debt and Crises By Enrica Detragiache and Antonio Spilimbergo*
Abstract After the recent Asian financial crises, many observers blamed external illiquidity for the problems, and called for limits to shortterm exposures by emerging markets. To assess these claims, economists have tested whether illiquidity is correlated with crises. We claim that these tests are misleading, as they fail to take into account that vulnerability itself can force country to borrow at short maturity, as suggested by the theoretical models of Diamond and Rajan (2000) and Jeanne (2001). Using a two-stage procedure, we re-examine the evidence and find that causality runs from vulnerability to short-term debt, and not the other way around. The policy implications are discussed.
Keywords: Debt crises, short-term debt, creditor runs JEL Classification: F34, F32
* International Monetary Fund. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors, and they do not necessarily represent the views of the IMF or its Board of Directors. We wish to thank many of our colleagues at the IMF, Gianluca Femminis, Richard Portes, and participants at the 2001 European Summer Symposium in International Macroeconomics in Israel for very helpful comments on an earlier version of this paper.
2
I. Introduction Financial crises in Mexico and East Asia brought the role of external liquidity to the attention of economists and policy-makers. Before the crises, international investors regarded these economies as successful and poured large amount of capital into them. During the crisis, the reversal in capital flows was sudden and dramatic, out of proportion with observed changes in basic economic conditions (the so-called fundamentals). Many economists noticed that both Mexico and the Asian countries had large short-term external liabilities not matched by foreign assets of similar characteristics, and hypothesized that the lack of external liquidity made these countries particularly vulnerable.1 The policy conclusion from this observation has been to avoid excessive short-term exposure.2 In addition, proposals have been put forward to impose controls on short-term capital, create an international lender of last resort, and introduce bankruptcy procedures for sovereign states (Sachs, 1995; Fischer, 1998; Miller and Zhang, 2000). Models of self-fulfilling creditor runs provide a theoretical underpinning for the proposition that countries with large short-term external debt are more vulnerable to crises.3 To test these theories, some recent econometric studies have examined the 1
See, for instance, Sachs, Tornell, and Velasco (1996) for Mexico, and Chang and Velasco (1998) and Radelet and Sachs (1998) for East Asia. In contrast, Corsetti, Pesenti, and Roubini (1998) have emphasized fundamental economic imbalances as the main source of fragility in East Asia.
2
For instance, the April 27, 1999 communiqué of the Interim Committee of the Board of Governors of the IMF asked the Fund and its member countries to intensify work to “adhere to sound principles of debt management, avoid excessive accumulation of short-term debt and, more generally, maintain an appropriate structure of liabilities; [...]maintain adequate foreign exchange liquidity.”
3
The idea of self-fulfilling creditor runs has been used in several contexts. Calvo (1988) and Alesina, Prati, and Tabellini (1990) applied the idea to domestic nominal government debt, in which “default” takes place through surprise inflation, and expectations of high inflation can become self-fulfilling. Sachs (1984) sketched out an application to sovereign debt. More recently, Cole and Kehoe (1996, 2000) and Detragiache (1996) have studied self-fulfilling creditor runs in full-fledged models of sovereign debt.
3
relationship between short-term debt and crises. Sachs, Tornell, and Velasco (1996) tests if countries affected by the Tequila crisis had a higher share of short-term debt in total capital flows than other countries, and find weak evidence. On the other hand, according to Radelet and Sachs (1998) and Rodrik and Velasco (1999), the ratio of short-term debt to reserves helps predict large reversals of capital flows. However, their findings are only suggestive because they rely on small samples. Using larger samples, Frankel and Rose (1996) and Milesi-Ferretti and Razin (1998) uncover no evidence of a liquidity effect on currency crisis. Finally, Eichengreen and Mody (1998, 1999) find risk spreads on emerging market syndicated loans and bonds to be increasing in the ratio of short-term debt to reserves in the issuing country. This empirical literature treats short-term debt as exogenous, and interprets the correlation with the occurrence of a crisis as a causal one. Yet, if countries with weak economic fundamentals tend to borrow at shorter maturities, causality may go from financial fragility to illiquidity and not the other way around. Indeed, in the theoretical model of Diamond and Rajan (2000) only short-term liabilities can fund increasingly illiquid investment projects when economic conditions deteriorate in countries with inadequate financial infrastructure, such as the Asian crisis countries. In Jeanne (2001), the government of a vulnerable country undertakes fiscal adjustment only if threatened by a large enough withdrawal of external funding; to promote this outcome, creditors must lend short term. If this is the case, restricting access to short-term debt or introducing a lender of last resort may be counterproductive.
Chang and Velasco (1998, 2000) model foreign creditor runs when the borrowers are domestic banks rather than the government.
4
In this paper, we re-examine the empirical relationship between external liquidity and debt crises explicitly taking into account the endogeneity of short-term debt. This framework will therefore allow us to test whether illiquidity has been a systematic cause of debt crises, as hypothesized by several commentators after the Asian crisis and implied by models of self-fulfilling creditor runs. This should help clarify whether calls for curbing external short-term borrowing by emerging economies are justified. We also improve on the existing empirical literature in a number of other dimensions. First, in line with theoretical models we focus on external debt crises rather than currency crises or sudden reversals of capital flows. Second, we use a large data set, consisting of 69 countries over 1971-98. Third, we disentangle the role of the various components of liquidity by entering reserves, short-term debt, and debt service due (on long-term debt) as separate regressors. Fourth, we carry out extensive sensitivity analysis. The paper is organized as follows: section II outlines a testable framework; section III contains an overview of the data, including a description of the debt crisis variable and of the regressors; section IV presents the estimation results and the sensitivity analysis, and section V concludes.
II. Empirical Framework The main equation of our empirical model is the following ``crisis equation’’ P = α1 s + α 2 c + β X + η
(1)
where P is the probability of an external debt crisis, X is a vector of exogenous macro fundamentals and debt characteristics, s is the share of short-term debt in total debt, and c
5
is the sum of principal and interest maturing at t on long term debt (also as a share of total debt).4 The latter variable will be referred to as debt service due. These two variables capture the portion of total external debt that needs to be serviced at t. Under the hypothesis of self-fulfilling creditor runs, these variables should be both significantly positively correlated with the crisis probability. Moreover, α1 and α 2 should have the same value given that from the point of view of the self-fulfilling runs only the remaining life of the debt matters and not the issue dates. Finally, η is a random disturbance term. 5 A second equation explains the share of short-term debt: s = θ Pe + γ X + δ Y + µc + ε ,
(2)
where P e is the expected probability of a debt crisis, and Y is a set of variables that affect the share of short-term debt s without influencing directly the probability of a debt crisis. Under rational expectations P e =P, and equation (2) is reduced to: s = γ ' X + δ 'Y + µ ' c + ε ' .
(3)
Note that equation (3) is a reduced form and the coefficients γ ' , δ ' , and µ ' are not straightforward to interpret. This equation is the first stage in our estimation procedure. In principle, equations (1) and (2) could be estimated as a system. In practice, equation (2) is probably misspecified because there is no definitive model yet explaining maturity
4
To keep the exposition simple, the model is presented in linear terms. In practice, a probit specification will be estimated, but the interpretation of the coefficient is not affected. 5 Most specifications in the literature implicitly impose α1 = α 2 . Foreign exchange reserves could have
both liquidity and non-liquidity effects; so that its coefficient is not necessarily the opposite of α1 . For this reason, the stock of reserves at the end of t-1 is entered as a separate variable in the group of the fundamentals.
6
structure of external debt, and a methodology based on full-information maximum likelihood could lead to inconsistent estimates. Therefore, we opt for the less demanding limited-information maximum likelihood method of instrumental variables. P = α1 s e + α 2 c + β X + η ,
(4)
which is analogous to equation (1), with the exception that we use the predicted value of s from equation (3) rather than the actual value s. If short-term debt increases the probability of a crisis, the coefficients α1 and α 2 should be positive and jointly significant. On the other hand, if the correlation between liquidity variables and crises is driven by causation from the probability of a crisis to debt term structure, the coefficients
α1 and α 2 should not be jointly significant. Section IV tests this implication.
III. Data A. Data sources The explanatory variables are liquidity indicators, variables controlling for the magnitude and structure of external debt, and a set of macroeconomics variables. All debt-related variables (with the exception of debt service due) are lagged one year, since they are end-of-period stocks. Macroeconomic variables are similarly lagged one period to limit simultaneity problems. The data are annual from 1971 through 1998; the sample includes all the countries for which information was available with the exception of those with population of less
7
than one million.6 The external debt variables come from the electronic edition of Global Development Finance of the World Bank (GDF). The baseline sample includes 976 observations for 69 countries. Summary statistics and a correlation matrix for the variables in the sample are in tables A1 and A2 in Appendix I.
B. Definition of debt crisis An observation is classified as a debt crisis if either or both of the following conditions occur: 1) there are arrears of principal or interest on external obligations towards commercial creditors (banks or bondholders) of more than 5 percent of total commercial debt outstanding; 2) there is a rescheduling or debt restructuring agreement with commercial creditors as listed in the GDF. We use the 5 percent minimum threshold to rule out cases in which the share of debt in default is negligible, while the second criterion is to include countries that are not technically in arrears because they reschedule or restructure their obligations before defaulting.7 In addition, arrears or rescheduling of official debt do not count as crisis events given that we are interested in defaults with respect to commercial creditors. Finally, we exclude observations for which commercial
6
Including also small countries increases the sample by 136 observations. Using this larger sample does not change the results. 7 Note that, while we need to normalize the arrears, there are some alternatives as to the possible denominator. GDP is not a good candidate because it is measured with considerable errors in many developing countries and its conversion in dollar terms in complicated. Debt service due avoids the problem of conversion but could give problems of interpretation because a high share of short-term debt increase the debt service coming due. Moreover, payment flows are less stable than debt stock and so are less useful as a normalization variable. As a robustness test, we have experimented with these definition and we have found that liquidity variables were less significant than when using our definition.
8
debt is zero from the sample because they cannot be crisis observations based on our definition.8 A second issue is how to distinguish the beginning of a new crisis from the continuation of the preceding one. In keeping with our crisis definition, we consider an episode concluded when arrears fall below the 5 percent threshold; however, we treat crises beginning within four years since the end of a previous episode as a continuation of the earlier event. In a sensitivity test, we exclude all episodes that follow the first crises, so that each country has at most one crisis. Finally, since we seek to identify the conditions that prompt a crisis rather than the impact of the crisis on macroeconomic developments, we exclude from the sample all observations while the crisis is on going. These criteria identify 55 debt crises in the baseline sample. The episodes are listed in Appendix I, and Figure 1 shows their distribution over time. While events tend to cluster in the early 1980s, when most Latin American countries and several African countries defaulted on their syndicated bank debt following the borrowing boom of the 1970s, there are crises throughout the sample period. Notably, three of the recent Asian crises (Indonesia, Korea, and Thailand) belong to the sample. Our definition of crisis does not capture episodes of external payment difficulties that do not result in arrears or rescheduling, such as the Mexican crisis of 1995, even though they are considered as such in the policy debate. In any case, treating the Mexican episode as a crisis does not change the results.
8
As a robustness test, we have also estimated the baseline regression including these observations and the results do not change.
9
C. Liquidity variables As discussed in the introduction, the existing empirical literature on the role of external liquidity in financial crises usually examines just one measure of liquidity, the ratio of short-term debt (defined on a residual maturity basis) to foreign exchange reserves. We enter short-term debt and reserves as separate regressors, so that it is possible to disentangle the contribution of each component. Furthermore, within shortterm debt we distinguish between debt service due (including principal maturing in the year and interest payments on debt with original maturity of more than one year) and short-term debt (debt with an original maturity of less than one year). Both components affect the amount of funds that the country needs to raise from abroad in a given year but, while debt service due is predetermined, short-term debt is endogenous and jointly determined with the crisis probability. By separating the two components is possible to address the potential simultaneity bias.9 Another reason to separate the two components of liquidity is that statistics on short-term external debt are not very good, while information on debt service payments due on long-term debt is likely to be more accurate. Short-term debt is as reported in the GDF except that arrears on interest are excluded. Debt service due is the sum of interest and principal on commercial debt repaid plus any arrears on either principal or interest. We exclude debt service due to official creditors on the ground that such creditors are unlikely to face coordination problems.
9
Much of the existing literature uses short-term debt data from the Bank of International Settlement, which defines short-term on a residual maturity basis so this distinction is not possible.
10
Summary statistics and correlations for the liquidity variables (as well as for the other variables used in the baseline regression) are reported in Appendix I.10
D. Debt variables The ratio of total debt to GDP measures the relative size of external debt.11 Because the burden of servicing a given amount of debt is likely to depend on the nature of the obligations, we also control for debt characteristics such as the share of debt owed to commercial banks, the share of debt at concessional terms, and the share of debt owed to multilateral creditors. These variables are highly correlated among themselves (see Appendix I), and multicollinearity could lower the significance of each individual variable; nonetheless, we keep them in the regression because we are not particularly interested in their individual impact on the crisis probability. In addition, they are strongly correlated with two of the liquidity variables -short-term debt and debt service due - so omitting them may bias the coefficients of those variables. To control for the interest rate due on debt outstanding, we follow Frankel and Rose (1996) and include a weighted average of interest rates on major international currencies, in which the weights are the shares of total external debt denominated in that currency. Figure 2 illustrates the behavior of share of short-term debt, debt coming due, debt over GDP, and share of reserves over GDP in the years preceding a debt crisis. The
10
The data for reserves comes from IFS statistics line (.1L.D); the data for GDP in dollars come from the World Bank’s Global Development Indicators (code NYGDPMKTPCD). We exclude from the sample three outlier observations for which the reserve GDP ratio is bigger than 80 percent. The data on debt come from the World Bank’s Global Development Finance (1999). 11 The data for debt come from the GDF (code DTDODDECTCD). We exclude from the sample the few observations for which the debt to GDP ratio is implausibly bigger than 1.5.
11
horizontal line represents the average of the variable in non-crisis countries. On average, external debt and the share of short-term debt are higher in crisis countries several years before the crisis occurs and increase sharply as the crisis approaches. Foreign exchange reserves have the opposite behavior, while no clear pattern is observable for debt service coming due. Thus, debt crises in our sample appear to be associated with an increasing share of short-term debt, as pointed out by commentators of the Asian crisis.
E. Macroeconomic characteristics To control for other economic characteristics that are likely to affect the country’s willingness or ability to service external obligations, we use two variables: openness, measured as the sum of exports and imports divided by GDP, and overvaluation of the real exchange rate.12 The latter is the log deviation of the real exchange rate from its moving average in the previous five years. Countries that are more open should be in a better position to service a large external debt through future export revenues, while an overvalued exchange rate is likely to hurt future export performance. In addition, in a willingness to pay framework, more trade openness may make a country more vulnerable to creditor sanctions if it defaults (Bulow and Rogoff, 1988). We have tried several other control variables such as GDP growth, fiscal surplus, inflation, terms of trade volatility, export growth, and the stock of direct foreign investment, but none of them is
12
The real exchange rate is with respect to the U.S. dollar and it is calculated using the GDP deflator from the IMF’s IFS. A CPI-based measure yields similar results but reduces the sample size significantly. The raw data for openness come from the IMF’s IFS (line 70..d, 71..d) and World Bank’ Global Development Indicators (NYGDPMKTPCD).
12
significant.13 Since their inclusion does not change the main results much and reduces sample size substantially, we have excluded these additional regressors from the baseline specification. In a sensitivity test, we estimate a specification including a large number of macroeconomic controls as in Frankel and Rose (1996).
IV. Estimation Results and Sensitivity Tests A. Results from the probit regression Table 1 reports estimation results for the probit specification of equation (1) without controlling for the endogeneity of short-term debt. We correct the standard errors used to compute the z values for country-specific heteroskedasticity. The liquidity variables, short-term debt and debt repayment due, are all highly significant and have the expected signs, and so do reserves and the stock of external debt relative to GDP. Thus, after controlling for the level of debt outstanding, the less liquid is a country the more likely it is to default on its external debt. Both components of liquidity have independent and significant effects; they are also jointly significant. Moreover, a test of equality of the coefficients on short-term debt and debt service coming due does not reject that hypothesis. The probit regression also indicates that countries with a large external debt and a large share of debt towards multilateral lenders are more likely to experience a crisis. The latter result likely reflects the fact that countries experiencing balance of payments problems are more likely to borrow from multilateral lenders. Countries with more overvalued exchange rates and a smaller measure of openness are also more likely to 13
The stock of direct foreign investment is from Lane and Milesi-Ferretti (1999).
13
default, as expected.14 The pseudo-R2 of the regression is 14 percent, indicating that there remains substantial unexplained variation in the default probability. Another way to gauge the performance of the model is to look at its in-sample predictive ability. The convention is to classify an observation as predicting a crisis if the estimated probability exceeds the in-sample frequency of crises. Using this criterion, the model correctly predicts 76 percent of the crises and 67 percent of the non-crisis observations.15
B. Sensitivity Tests Panel regressions using observations from several countries and periods could obviously suffer from sample heterogeneity. Therefore, we perform several tests to control for the most likely sources of heterogeneity. As a first sensitivity exercise, we re-estimate the baseline probit regression excluding one country at a time. The second panel of Table 1 reports the largest and smallest z values obtained in this exercise for each of the regressors. Many results go through even if the smallest z value is considered. In particular, the short-term debt variable is robust to the exclusion of any particular country. The result on debt service coming due, however, is more fragile because its significance decreases greatly excluding a single country.
14
If observations with no commercial long-term debt are included in the sample, the share of short-term debt is not significant; this is probably because countries with no access to long-term private capital also have little short-term debt. 15 Using a logit instead of a probit model yields slightly worse results in terms of model performance, but the debt and liquidity variables remain significant. The two probability models are by-and-large equivalent when the frequency of the event is not too far from 50 percent of the observations, but they can differ in case like ours, when the event is relatively rare.
14
In a second robustness test, we use all the variables used by Frankel and Rose (1996) in their currency crisis regressions. This lowers the sample size substantially, but regression results change little: only openness loses its significance, while all the other control variables do not have any independent effect on the crisis probability. We also try other variations of macroeconomic variables such as deviation of income growth from its moving average with the idea that `surprises’ in income growth with respect to the recent trend could lead to crisis; also in this case, the introduced variables are scarcely significant while our baseline results hold. In conclusion, this confirms that it is difficult to identify a set of macro-variables that are systematically associated with past external debt crises. In additions to these tests, we check the sensitivity of our results to countryspecific and time-specific effects in unreported regressions. Allowing for country random effects has little impact on debt and liquidity variables, while the macroeconomic controls lose significance. Also controlling for yearly dummies does not change significantly the results on liquidity, confirming the strong correlation between debt crises and debt maturity structure. C. Endogeneity of short-term debt As discussed before, if short-term debt is endogenous, the regressions of Table 1 suffer from simultaneity bias and all coefficients are potentially inconsistent. We use instrumental variables to address this simultaneity bias. We start our search for instruments from the recent theories of the maturity structure of external debt. Diamond and Rajan (2000) argue that external private capital
15
flows to emerging markets must be intermediated by the local banking sector, and banks’ external liabilities must be of short maturity to provide appropriate incentives. In this context, it is reasonable to suppose that LDCs at higher level of development, where the financial system is more mature, should have a smaller share of short-term external debt. Concerning sovereign debt, in Jeanne (2001) a government has to issue short-term debt to credibly commit to perform fiscal adjustment ex post. In this type of model, it may also be conjectured that the commitment problem becomes less severe as the country develops, implying a negative correlation between short-term debt and development. These theories, therefore, suggest that GDP per capita, the standard measure of development, might be a good candidate instrument. There is a complication, however: very poor countries that have limited or no access to private capital flows are likely to have little short-term debt, because concessional loans to finance development projects are typically at long maturity. To take this into account, we hypothesize a non-monotonic relationship between the share of short-term debt and GDP per-capita: at lower levels of development, the share of short-term debt is increasing in GDP per capita, as the country gradually gains access to (short-term) private financing. Beyond a certain threshold, most of the financing is private, but as income rises further and the financial system matures the average maturity of the debt lengthens. To test this non-monotonicity, we introduce GDP per capita and its square as instruments in the first stage regression.16 Another candidate instrument is a dummy for the 1990’s, when the new Basle capital adequacy 16
Rodrik and Velasco (2000) present an empirical estimation of the determinants of the share of short-term debt (defined on a residual maturity basis) for a sample of 32 countries. Short-term debt is found to be positively correlated with GDP-per-capita, the ratio of M2 to GDP, and the ratio of debt to GDP. The
16
standards were phased in. As noted by Rodrik and Velasco (2000), the latest Basle capital adequacy standards created a bias towards short-term debt on the part of international banks, as they gave short-term loans a lower risk weight than long-term ones. Aside from the choice of instruments, controlling for the endogeneity of shortterm debt presents other estimation problems. First, the share of short-term debt is bounded between zero and one, so the error terms are not normally distributed. This problem can be easily addressed by transforming the dependent variable using the monotonic logistic transformation y=ln(x/(1+x)). A more serious difficulty is that the computation of the standard errors is not straightforward when the second-stage regression is non-linear. To calculate the appropriate standard errors, we use the two-step methodology proposed by Murphy and Topel (1985) as described in Greene (1997). The first panel of Table 2 reports the results of the first stage regression with the share of short-term debt as dependent variable. The adjusted R-squared in the first-stage regression is 58 percent, indicating that the equation performs surprisingly well. In addition, the coefficient of GDP per capita is positive and significant, while its square is negative and significant, as hypothesized.17 Finally, the test on the over-identifying restriction reject the hypothesis that the instruments are correlated with the error terms in the second stage (Newey, 1985). Thus, the instruments are econometrically valid because
authors interpret the first two correlations as supporting theories in which short-term debt promotes efficient financial intermediation. 17 It is also interesting to note that if we regress the share of short term debt in total commercial debt (rather than total debt) we find that GDP per capita has a negative and significant coefficient, while its square is no longer significant. This supports the view that non-monotonicity is driven by the non-commercial debt.
17
they are highly correlated with the short-term debt but not with the error terms in the second regression.18 The results from the instrumental variable estimation are reported in the second panel of Table 2. In the second stage, while little changes for the other variables, the share of short-term debt loses its significance, suggesting that the effect of this variable on the probability of crisis is likely driven by endogeneity. Furthermore, debt coming due is significant only at the ten percent confidence level.
D. Robustness of the Instrumental Variables Regressions The result that short-term debt share becomes insignificant if we use instrumental variables needs further testing along several dimensions. A negative result with instrumental variables could be driven by the choice of instruments. As robustness test, we experiment with another set of instruments that includes M2 over GDP (as in Rodrik and Velasco, 2000), and the U.S. yield curve, proxying the relative cost of borrowing short- and long-term capital. Panel 3 of Table 2 reports the results. The new set of instruments is jointly significantly correlated with short-term debt and uncorrelated with the error term in the second stage. As before, in the second stage regression short-term debt is no longer significant. As a second robustness test, we repeat the IV regression excluding a country at the time. The first panel of Table 3 reports the highest and the lowest z statistics on the coefficients of the second stage regressions. Even in the most favorable case, the 18
The reported Chi squared is for the null that the error terms from the second stage IV regression are uncorrelated with the instruments.
18
coefficient on short-term debt is not significant.19 As in the straightforward probit regression, the coefficient on debt coming due is very sensitive to the exclusion of a specific country. Finally, the exercise is repeated using all the control variables used by Frankel and Rose (1996). The results, which are reported in panel 1 of Table 3, confirm that short-tem debt is not significant and debt service coming due is weakly significant.
IV. Conclusions In the wake of the Asian crisis, much has been made of external illiquidity as a primary cause of financial fragility, as illiquidity puts countries at the mercy of coordination failures among creditors. More recently, it has been pointed out that countries with low creditworthiness or a weak financial infrastructure may have little alternative to using short-term financing even though that means becoming more vulnerable to crises (Diamond and Rajan, 2000, Jeanne, 2000). In this paper, we have attempted to take into account the possible endogeneity of the share of short-term debt in total debt in examining the empirical relationship between illiquidity and crisis. We have found evidence that, while crises are significantly correlated with liquidity variables, once short-term debt is appropriately instrumented this correlation loses statistical significance. Sensitivity analysis suggests that this finding is robust. These results indicate that, while more-crisis prone countries are more likely to
19
We use the same set of instrument as in the first panel of Table 2 (GDP per-capita and its square, and a dummy for the 1990’s).
19
borrow short-term, a large share of short-term debt has not been a systematic, independent cause of past debt crises. The conclusions of this paper have implications for policy making and future research. They cast doubts on calls to restrict short-term capital flows as a policy to reduce vulnerability to external debt crises. Rather, they suggest that such restrictions may hamper the ability of a country hit by an adverse shock to access external funds, potentially accelerating an external payments crisis. On the other hand, an incipient loss of external liquidity can be a useful warning sign of an impending crisis, so current efforts to improve data collection and monitoring in this area are indeed justified.20 For research, this study indicates that further theoretical research on the determinants of debt maturity is necessary to explain the large cross-country and crosstime variation in the debt maturity structure. As indicated in Table 2, simple panel regressions can explain a relative large portion of the variance in short-term share on total debt.
20
Interestingly, Reinhart (2002) finds that credit rating agencies do not seem to take external liquidity into account when assigning sovereign ratings.
976 0.14
Number of Observations pseudo R2
(55 crises)
-5.09
-2.94 2.21 -1.76
5.23 -0.36 0.52 2.51 1.46
2.62 2.41
z
0.00 0.01 0.26
0.00 0.03 0.08
0.00 0.76 0.62 0.01 0.15
0.01 0.02
P>|z|
0.13
-5.40
-3.50 1.50 -2.35
4.85 -0.86 -0.02 2.12 1.13
2.33 1.61
zmin
0.16
-4.57
-2.74 2.69 -1.40
5.55 0.01 0.85 3.62 1.81
3.12 2.79
zmax 2/
702 0.16
-0.22 9.00 0.67
-0.02 -0.02 0.00 0.16 0.00 -0.50 -0.50 0.03 -0.02
0.11 0.04 0.07 0.16 0.11 0.03
0.18 0.29
dF/dx
z
(44 crises)
-3.66
-0.05 -0.19 -0.02 1.16 -0.16 -1.11 -2.90 2.37 -0.75
4.41 0.66 1.24 3.19 0.60 0.32
2.51 2.20
Notes: z and P>|z| are the test of the underlying coefficient being 0 (standard errors adjusted for clustering on country) 1/ Test of the hypothesis that the coefficients of short-term debt and debt service due are equal. 2/ zmax and zmin of panel regressions excluding one country at the time. 3/ test for the joint significance of shor-term debt and debt coming due
-0.19 9.11 1.26
-0.45 0.03 -0.05
0.09 -0.01 0.02 0.12 0.20
0.14 0.25
Constant Chi2 test on liquidity variables 3/ Chi2(1) 1/
Macroeconomic Variables: FDI Current Account Balance Income Growth Fiscal Surplus Credit Growth OECD growth Reserves Overvaluation Openness
Debt and Debt Characteristics: Total Debt Commercial Share Concessional Share Multilateral Share Interest Rates Variable Share
Liquidity Variables: Short-Term Debt Debt Service Due
dF/dx
TABLE 1. Liquidity and the Probability of Debt Crisis - Probit
20
0.00 0.01 0.41
0.96 0.85 0.99 0.25 0.88 0.27 0.00 0.02 0.45
0.00 0.51 0.22 0.00 0.55 0.75
0.01 0.03
P>|z|
4.31 -0.585 -2.587
Macroeconomic Variables: Reserves Overvaluation Openness
0.58
5.05
-0.45
2.65 -1.73 -5.51
5.55 -9.58 -12.83 0.17 4.8
-4.83
3.42 -2.71 -0.28
0
0.654
0.006 0.084 0
0 0 0 0.865 0
0
0 0.007 0.782
... 976
-0.619
-0.42 0.031 -0.045
0.088 -0.4 -0.03 0.11 0.256
0 0.19
dF/dx
(55 crises)
3.85 2.144
-3.8
-2.74 2.355 -1.652
4.58 -0.89 -0.599 2.24 1.685
0.01 1.629
z Dep. Var.: crisis
0.146 0.342
0
0.006 0.019 0.098
0 0.37 0.549 0.025 0.09
0.99 0.1
P>|z|
Notes: z and P>|z| are the tests of the underlying coefficient being 0. The standard errors adjusted for clustering on country. Notes: The F-test for over-identifying restrictions is for the null that error terms from the IV regression are uncorrelated with the instruments (Newey, 1985).
R2 Number of Observations
Chi2 test on instruments Chi2 test on liquidity variables Chi2 test on over-identifying restrictions
-0.248
2.614 -7.17 -9.068 0.153 17.151
Debt and Debt Characteristics: Total Debt Commercial Share Concessional Share Multilateral Share Interest Rates
Constant
-9.29
0 0 -0.088
Liquidity Variables: Predicted Short-Term Debt Debt Service Due
Instruments GDP per Capita (GDP per Capita)^2 Dummy Variable (after 1989) U.S. yield curve M2/GDP
TABLE 2. Instrumental Variable Estimation Coef. T P>|t| Dep. Var.: Short-term share
21
-3.17
2.81 -1.28
1.73 -2.12 -4.41
-2.55
5.01 -0.54 -0.22 3.09 2.09
0.68 1.91
-3.41
4.18 -1.39 -1.26 1.85 1.33
-0.56 0.72
zmax
702
-0.67
-0.47 -0.30 -0.03 0.00 0.22 0.00 -0.50 0.04 -0.03
0.11 0.04 0.02 0.14 0.14 0.01
0.05 0.25
dF/dx
6.19 3.82 2.99 (44 crises)
-3.10
-2.75 -0.58 -0.23 0.01 1.41 -0.22 -1.03 2.53 -0.72
4.53 0.67 0.33 2.29 0.59 0.09
0.56 1.62
z
0.00 0.15 0.22
0.00
0.01 0.56 0.82 0.99 0.16 0.82 0.30 0.01 0.47
0.00 0.50 0.75 0.02 0.55 0.93
0.58 0.11
P>|z|
Extended set of controls
Notes: z and P>|z| are the test of the underlying coefficient being 0 (standard errors adjusted for clustering on country)
Chi2 on instruments in first regression Chi2 of overidentifying restrictions Chi2 on liquidity variables Number of Observations
Constant
Macroeconomic Variables: Reserves FDI Current Account Balance Income Growth Fiscal Surplus Credit growth OECD rowth Overvaluation Openness
Debt and Debt Characteristics: Total Debt Commercial Share Concessional Share Multilateral Share Interest Rates Variable Share
Liquidity Variables: Short-Term Debt Debt Service Due
zmin
Excluding one country at the time
TABLE 3. Sensitivity Analysis (Instrumental variable regressions)
22
Number of episode in the sample
0
2
4
6
Figure 1: Debt Crises
1974 1976 1980 1982 1984 1986 1988 1990 1992 1996 1998 1978 1994 1972 1975 1997 1973 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995
Debt Crises
23
Average share of short-term debt
Average share of debt over GDP
.35
.4
.45
.5
.55
.14
.15
.16
.17
.18
-6
-4 Years to the crisis
-2
-6
-4 Years to the crisis
-2
0
0
.04
.06
.08
.1
0
.02
.04
.06
.08
.1
-6
-4 Years to the crisis
-2
-6
-4 Years to the crisis
-2
Average share of reserve over gdp in crisis countries
-8
Average share of debt coming due in crisis countries
-8
Figure 2: Crises and Debt Characteristics
Average share of debt over gdp in crisis countries
-8
Average share of short term debt in crisis countries
-8
24
Average share of debt coming due Average share of reserve over GD
0
0
1991 1983 1978 1991 1983 1982 1986 1979 1985 1973 1983 1985 1981 1987 1976 1982 1983 1986
Algeria Argentina Bangladesh Bangladesh Brazil Burkina Faso Burundi Cameroon Cameroon Chile Chile Colombia Costa Rica Cote D'Ivoire Dominican Rep. Dominican Rep. Ecuador Egypt
Table A1: Debt Crises 1984 El Salvador 1995 El Salvador 1987 Ethiopia 1985 Guatemala 1983 Haiti 1976 Honduras 1982 Honduras 1998 Indonesia 1989 Jordan 1990 Kenya 1998 Korea 1990 Lesotho 1980 Madagascar 1982 Malawi 1987 Malawi 1982 Mexico 1985 Morocco 1978 Nicaragua 1984 Niger 1972 Nigeria 1986 Nigeria 1987 Panama 1984 Paraguay 1983 Peru 1984 Philippines 1984 Senegal 1989 Senegal 1972 Sierra Leone 1992 Sri Lanka 1976 Sudan 1998 Thailand 1988 Trinidad & Tobago 1991 Tunisia 1979 Turkey 1984 Venezuela 1975 Zaire 1978 Zambia
Table A1. Episodes of Debt Crisis by Year and Country
Appendix I
25
Number of observations
976 976 976 976 976 976 976 976 976 976 976
Variables
Debt crisis Short-term debt Debt coming Due Reserves Total debt Commershal Share Concessional Share Multilateral Share Interest Rates Overvaluation Openness
0.06 0.15 0.07 0.08 0.41 0.24 0.27 0.18 0.07 -0.04 0.48
Mean
0.23 0.11 0.06 0.08 0.23 0.18 0.24 0.14 0.03 0.32 0.27
Standard Deviation
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 -1.87 0.07
Min
TABLE A2. Summary Statistics of the Sample Variables
26
1.00 0.80 0.47 0.65 1.50 0.84 0.95 0.85 0.16 2.42 1.90
Max
1.00 0.01 0.03 -0.12* 0.12* -0.02 0.02 0.04 0.09* 0.02 -0.06* 1.00 0.09* 0.12* -0.17* 0.16* -0.53* -0.41* 0.03 0.20* -0.01
Short-term Debt
Note: a star means that a variable is significant at 10% or less.
Debt crisis Short term debt Debt service due Reserves Total Debt Commercial share Concessional share Multilateral share Interest rates Overvaluation Openness
Debt crisis
1.00 0.03 -0.11 0.36* -0.49 -0.38 0.05 0.16* 0.15*
Debt service Coming due
1.00 -0.08* 0.07* -0.13* 0.01 -0.12* 0.04 0.55*
Reserves
27
1.00 -0.02 0.05 0.06* 0.06* -0.27 0.20*
Total Debt
1.00 -0.66* -0.45* 0.09* 0.18* 0.09*
Commercial Share
TABLE A3. Correlation Matrix
1.00 0.56* 0.02 -0.20 -0.18
Concessional Share
1.00 -0.02 -0.20* 0.08*
Multilateral Share
1.00 0.17* -0.02
Interest Rates
1.00 0.03
Overvaluation
1.00
Openness
28 References Alesina, Alberto, Alessandro Prati, and Guido Tabellini, 1990, “Public Confidence and Debt Management: A Model and a Case Study of Italy”, in Rudiger Dornbusch and Mario Draghi (Eds.), Public Debt Management: Theory and History, (Cambridge: Cambridge University Press). Bulow, Jeremy, and Kenneth Rogoff, 1988, “A Constant Recontracting Model of Sovereign Debt”, Journal of Political Economy, 97, 155-178. Calvo, Guillermo A., 1988, “Servicing the Public Debt: The Role of Expectations,” American Economic Review, 78, 647-661. Chang, Roberto, and Andres Velasco, 2000, “Banks, Debt Maturity and Financial Crises,” Journal of International Economics, 51(1), 169-194. _____________ , 1999, ΑLiquidity Crises in Emerging Markets: Theory and Policy,” 1999 NBER Macroeconomic Annual (Cambridge, MA: MIT Press) _____________ , 1998, ΑThe Asian Liquidity Crisis,” Working Paper No. 98-11, Federal Reserve Bank of Atlanta. Cole, Harold L., and Timothy J. Kehoe, 1996, “A Self-Fulfilling Model of Mexico’s 19941995 Debt Crisis,” Journal of International Economics, 41, 309-330. _____________ , 2000, “Self-Fulfilling Debt Crises,” Review of Economic Studies, 67, 91116. Corsetti, GianCarlo, Paolo Pesenti, and Nouriel Roubini, 1998a, “What Caused the Asian Currency and Financial Crises? Part I: The Macroeconomic Overview,” NBER Working Paper No. 6833. _____________ , 1998b, “What Caused the Asian Currency and Financial Crises? Part II: The Policy Debate,” NBER Working Paper No. 6834. Detragiache, Enrica,1996, “Rational Liquidity Crises in the Sovereign Debt Market: In Search of a Theory,” IMF Staff Papers, 43, 545-570. Diamond, Douglas W., and Raghuram Rajan, 2000, “Banks, Short-Term Debt and Financial Crises: Theory, Policy Implications, and Applications,” NBER Working Paper No. 7764. Eichengreen, Barry, and Ashoka Mody 1999, “Lending Booms, Reserves, and the Sustainability of Short-Term Debt: Inference from the Pricing of Syndicated Bank Loans,” NBER Working Paper No. 7113.
29 Fischer, Stanley, 1999, “On the Need for an International Lender of Last Resort”, Journal of Economic Perspectives, 13, 85-104. Frankel, Jeffrey A., and Andrew K. Rose, 1996, ΑCurrency Crashes in Emerging Markets: An Empirical Treatment,” Journal of International Economics, 41, 351-366. Greene, William H., 1997, Econometric Analysis. Third edition. Prentice-Hall. Upper Saddle River, New Jersey. Jeanne, Olivier, 2001, “Sovereign Debt Crises and the International Financial Architecture,” unpublished manuscript, IMF. Lane, Philip, and Gian Maria Milesi-Ferretti, 1999, “The External Wealth of Nations. Measures of Foreign Assets and Liabilities for Industrialized and Developing Countries,” IMF Working Paper No. 99/115. Milesi-Ferretti, Gian Maria, and Assaf Razin, 1998, “Current Account Reversal and Currency Crises: Empirical Regularities,” IMF Working Paper 98-89. Miller, Marcus, and Lei Zhang, 2000, “Sovereign Liquidity Crises: The Strategic Case for a Payments Standstill,” Economic Journal, 110, 335-362. Murphy, Kevin H., and Robert H. Topel, 1985, “Estimation and Inference in Two-Step Econometric Models,” Journal of Business & Economic Statistics, 3(4), 370-379. Newey, Whitney K., 1985, “Generalized Method of Moments Specification Testing,” Journal of Econometrics, 29, 229-256. Radelet, Steven, and Jeffrey D. Sachs, 1998, “The East Asian Financial Crisis: Diagnosis, Remedies, Prospects,” Brooking Papers on Economic Activity. Reinhart, Carmen M., 2002, “Default, Currency Crises, and Sovereign Credit Ratings,” NBER Working Paper No. 8738. Rodrik, Dani, and Andres Velasco, 2000, “Short-Term Capital Flows,” in Boris Pleskovic and Joseph E. Stiglitz, Proceedings of the 1999 Annual Bank Conference on Development Economics, (Washington: World Bank). Sachs, Jeffrey D., 1984, “Theoretical Issues in International Borrowing,” Princeton Essays in International Finance No. 54 (Princeton, NJ). Sachs, Jeffrey D., 1995, “Do We Need an International Lender of Last Resort?,” mimeo, Harvard University.
30 Sachs, Jeffrey D., Aaron Tornell, and Andres Velasco, 1996, “Financial Crises in Emerging Markets: The Lessons from 1995,” Brookings Papers on Economic Activity: 1, Brookings Institutions, 147-215.
