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 short-term 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’’

1 2P s c Xα α β 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, 1α and 2α 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:

,es P X Y cθ γ δ µ ε= + + + + (2)

where eP 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 eP =P, and equation (2) is reduced to:

' ' ' 's X Y cγ δ µ 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 1α = 2α . Foreign exchange reserves could have

both liquidity and non-liquidity effects; so that its coefficient is not necessarily the opposite of 1α . 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.

1 2 ,eP s c Xα α β 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 1α and 2α 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

1α and 2α 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 short-

term 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 country-

specific 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 short-

term 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 cross-

time 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.

20

TAB

LE 1

. Liq

uidi

ty a

nd th

e Pr

obab

ility

of

Deb

t Cris

is -

Prob

it

dF/d

xz

P>|z|

zmin

zmax

2/

dF/d

xz

P>|z|

Liq

uidi

ty V

aria

bles

:Sh

ort-T

erm

Deb

t0.

142.

620.

012.

333.

120.

182.

510.

01D

ebt S

ervi

ce D

ue0.

252.

410.

021.

612.

790.

292.

200.

03

Deb

t and

Deb

t Cha

ract

eris

tics:

Tota

l Deb

t0.

095.

230.

004.

855.

550.

114.

410.

00C

omm

erci

al S

hare

-0.0

1-0

.36

0.76

-0.8

60.

010.

040.

660.

51C

once

ssio

nal S

hare

0.02

0.52

0.62

-0.0

20.

850.

071.

240.

22M

ultil

ater

al S

hare

0.12

2.51

0.01

2.12

3.62

0.16

3.19

0.00

Inte

rest

Rat

es0.

201.

460.

151.

131.

810.

110.

600.

55V

aria

ble

Shar

e

0.

030.

320.

75

Mac

roec

onom

ic V

aria

bles

:FD

I

-0

.02

-0.0

50.

96C

urre

nt A

ccou

nt B

alan

ce

-0

.02

-0.1

90.

85In

com

e G

row

th

0.

00-0

.02

0.99

Fisc

al S

urpl

us

0.

161.

160.

25C

redi

t Gro

wth

0.00

-0.1

60.

88O

ECD

gro

wth

-0.5

0-1

.11

0.27

Res

erve

s-0

.45

-2.9

40.

00-3

.50

-2.7

4-0

.50

-2.9

00.

00O

verv

alua

tion

0.03

2.21

0.03

1.50

2.69

0.03

2.37

0.02

Ope

nnes

s-0

.05

-1.7

60.

08-2

.35

-1.4

0-0

.02

-0.7

50.

45

Con

stan

t-0

.19

-5.0

90.

00-5

.40

-4.5

7-0

.22

-3.6

60.

00C

hi2

test

on

liqui

dity

var

iabl

es 3

/9.

110.

01

9.

00

0.

01C

hi2(

1) 1

/1.

260.

26

0.

67

0.

41

Num

ber o

f Obs

erva

tions

976

(55

cris

es)

702

(44

cris

es)

pseu

do R

2

0.14

0.13

0.16

0.16

Not

es: z

and

P>|

z| ar

e th

e te

st o

f the

und

erly

ing

coef

ficie

nt b

eing

0 (

stan

dard

err

ors a

djus

ted

for c

lust

erin

g on

cou

ntry

)1/

Tes

t of t

he h

ypot

hesi

s tha

t the

coe

ffici

ents

of s

hort-

term

deb

t and

deb

t ser

vice

due

are

equ

al.

2/ z

max

and

zm

in o

f pan

el re

gres

sion

s exc

ludi

ng o

ne c

ount

ry a

t the

tim

e.3/

test

for t

he jo

int s

igni

fican

ce o

f sho

r-te

rm d

ebt a

nd d

ebt c

omin

g du

e

21

Coe

f.T

P>|t|

dF/d

xz

P>|z|

Inst

rum

ents

GD

P pe

r Cap

ita0

3.42

0(G

DP

per C

apita

)^2

0-2

.71

0.00

7D

umm

y V

aria

ble

(afte

r 198

9)-0

.088

-0.2

80.

782

U.S

. yie

ld c

urve

M2/

GD

P

Liq

uidi

ty V

aria

bles

:Pr

edic

ted

Shor

t-Ter

m D

ebt

00.

010.

99D

ebt S

ervi

ce D

ue-9

.29

-4.8

30

0.19

1.62

90.

1

Tota

l Deb

t2.

614

5.55

00.

088

4.58

0C

omm

erci

al S

hare

-7.1

7-9

.58

0-0

.4-0

.89

0.37

Con

cess

iona

l Sha

re-9

.068

-12.

830

-0.0

3-0

.599

0.54

9M

ultil

ater

al S

hare

0.15

30.

170.

865

0.11

2.24

0.02

5In

tere

st R

ates

17.1

514.

80

0.25

61.

685

0.09

Res

erve

s4.

312.

650.

006

-0.4

2-2

.74

0.00

6O

verv

alua

tion

-0.5

85-1

.73

0.08

40.

031

2.35

50.

019

Ope

nnes

s-2

.587

-5.5

10

-0.0

45-1

.652

0.09

8

Con

stan

t-0

.248

-0.4

50.

654

-0.6

19-3

.80

5.05

03.

850.

146

2.14

40.

342

R

2

0.58

...

N

umbe

r of O

bser

vatio

ns

976

(55

cris

es)

Not

es: T

he F

-test

for o

ver-i

dent

ifyin

g re

stric

tions

is fo

r the

nul

l tha

t err

or te

rms f

rom

the

IV re

gres

sion

are

unc

orre

late

d w

ith th

e in

stru

men

ts (N

ewey

, 198

5).

Not

es:

z an

d P>

|z| a

re th

e te

sts o

f the

und

erly

ing

coef

ficie

nt b

eing

0.

The

stan

dard

err

ors a

djus

ted

for c

lust

erin

g on

cou

ntry

.

Mac

roec

onom

ic V

aria

bles

:

Chi

2 te

st o

n in

stru

men

ts

Chi

2 te

st o

n liq

uidi

ty v

aria

bles

Chi

2 te

st o

n ov

er-id

entif

ying

rest

rictio

ns

TAB

LE 2

. In

stru

men

tal V

aria

ble

Estim

atio

n

Dep

. Var

.: Sh

ort-t

erm

shar

eD

ep. V

ar.:

cris

is

Deb

t and

Deb

t Cha

ract

eris

tics:

22

TAB

LE 3

. Sen

sitiv

ity A

naly

sis (

Inst

rum

enta

l var

iabl

e re

gres

sion

s)

Excl

udin

g on

e co

untry

Exte

nded

set o

f con

trols

at th

e tim

e

zmin

zmax

dF/d

xz

P>|z|

Liq

uidi

ty V

aria

bles

:Sh

ort-T

erm

Deb

t-0

.56

0.68

0.05

0.56

0.58

Deb

t Ser

vice

Due

0.72

1.91

0.25

1.62

0.11

Deb

t and

Deb

t Cha

ract

eris

tics:

Tota

l Deb

t4.

185.

010.

114.

530.

00C

omm

erci

al S

hare

-1.3

9-0

.54

0.04

0.67

0.50

Con

cess

iona

l Sha

re-1

.26

-0.2

20.

020.

330.

75M

ultil

ater

al S

hare

1.85

3.09

0.14

2.29

0.02

Inte

rest

Rat

es1.

332.

090.

140.

590.

55V

aria

ble

Shar

e0.

010.

090.

93

Mac

roec

onom

ic V

aria

bles

:R

eser

ves

-3.4

1-2

.55

-0.4

7-2

.75

0.01

FD

I-0

.30

-0.5

80.

56 C

urre

nt A

ccou

nt B

alan

ce-0

.03

-0.2

30.

82 I

ncom

e G

row

th0.

000.

010.

99 F

isca

l Sur

plus

0.22

1.41

0.16

Cre

dit g

row

th0.

00-0

.22

0.82

OEC

D ro

wth

-0.5

0-1

.03

0.30

Ove

rval

uatio

n1.

732.

810.

042.

530.

01O

penn

ess

-2.1

2-1

.28

-0.0

3-0

.72

0.47

Con

stan

t-4

.41

-3.1

7-0

.67

-3.1

00.

00

Chi

2 on

inst

rum

ents

in fi

rst r

egre

ssio

n6.

190.

00C

hi2

of o

verid

entif

ying

rest

rictio

ns3.

820.

15C

hi2

on li

quid

ity v

aria

bles

2.99

0.22

Num

ber o

f Obs

erva

tions

702

(44

cris

es)

Not

es: z

and

P>|

z| ar

e th

e te

st o

f the

und

erly

ing

coef

ficie

nt b

eing

0 (

stan

dard

err

ors a

djus

ted

for c

lust

erin

g on

cou

ntry

)

23

Number of episode in the sample

Fig

ure

1: D

ebt C

rise

s

0246

Deb

t Cris

es

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

24

Figu

re 2

: Cris

es a

nd D

ebt C

hara

cter

istic

s

Average share of short-term debt

Ave

rage

sha

re o

f sho

rt te

rm d

ebt i

n cr

isis

cou

ntrie

sY

ears

to th

e cr

isis

-8-6

-4-2

0

.14

.15

.16

.17

.18

Average share of debt coming due

Ave

rage

sha

re o

f deb

t com

ing

due

in c

risis

cou

ntrie

sY

ears

to th

e cr

isis

-8-6

-4-2

0

0

.02

.04

.06

.08.1

Average share of debt over GDP

Ave

rage

sha

re o

f deb

t ove

r gdp

in c

risis

cou

ntrie

sY

ears

to th

e cr

isis

-8-6

-4-2

0

.35.4.45.5.55

Average share of reserve over GD

Ave

rage

sha

re o

f res

erve

ove

r gdp

in c

risis

cou

ntrie

sY

ears

to th

e cr

isis

-8-6

-4-2

0

.04

.06

.08.1

25

App

endi

x I

Ta

ble

A1.

Epi

sode

s of D

ebt C

risis

by

Yea

r and

Cou

ntry

Tabl

e A

1: D

ebt C

rises

1991

Alg

eria

19

84El

Sal

vado

r

1984

N

iger

1983

A

rgen

tina

19

95El

Sal

vado

r19

72

Nig

eria

1978

B

angl

ades

h

1987

Et

hiop

ia19

86

Nig

eria

1991

B

angl

ades

h

1985

Gua

tem

ala

1987

P

anam

a19

83

B

razi

l 19

83

H

aiti

1984

Par

agua

y19

82 B

urki

na F

aso

19

76

Hon

dura

s19

83

Pe

ru19

86

B

urun

di

1982

H

ondu

ras

1984

P

hilip

pine

s19

79

Cam

eroo

n

1998

Ind

ones

ia19

84

Sene

gal

1985

C

amer

oon

19

89

Jor

dan

1989

Se

nega

l19

73

C

hile

19

90

K

enya

1972

S

ierr

a Le

one

1983

Chi

le

1998

Kor

ea19

92

S

ri La

nka

1985

C

olom

bia

19

90

Les

otho

1976

S

udan

1981

C

osta

Ric

a

1980

Mad

agas

car

1998

Tha

iland

1987

Cot

e D

'Ivoi

re

1982

M

alaw

i19

88Tr

inid

ad &

Tob

ago

1976

Dom

inic

an R

ep.

1987

M

alaw

i19

91

Tuni

sia

1982

Dom

inic

an R

ep.

1982

M

exic

o19

79Tu

rkey

1983

Ecu

ador

1985

M

oroc

co19

84

V

enez

uela

1986

Eg

ypt

1978

Nic

arag

ua19

75

Zai

re19

78

Zam

bia

26

TAB

LE A

2. S

umm

ary

Stat

istic

s of t

he S

ampl

e V

aria

bles

Var

iabl

esN

umbe

r of

obse

rvat

ions

Mea

n

Stan

dard

D

evia

tion

Min

Max

Deb

t cris

is97

60.

060.

230.

001.

00Sh

ort-t

erm

deb

t97

60.

150.

110.

000.

80D

ebt c

omin

g D

ue97

60.

070.

060.

000.

47R

eser

ves

976

0.08

0.08

0.00

0.65

Tota

l deb

t97

60.

410.

230.

001.

50C

omm

ersh

al S

hare

976

0.24

0.18

0.00

0.84

Con

cess

iona

l Sha

re97

60.

270.

240.

000.

95M

ultil

ater

al S

hare

976

0.18

0.14

0.00

0.85

Inte

rest

Rat

es97

60.

070.

030.

020.

16O

verv

alua

tion

976

-0.0

40.

32-1

.87

2.42

Ope

nnes

s97

60.

480.

270.

071.

90

27

TAB

LE A

3. C

orre

latio

n M

atrix

Deb

t cris

is

Sh

ort-t

erm

Deb

t ser

vice

Res

erve

sTo

tal D

ebt

Com

mer

cial

Con

cess

iona

lM

ultil

ater

alIn

tere

stO

verv

alua

tion

Ope

nnes

sD

ebt

Com

ing

due

Shar

eSh

are

Shar

eR

ates

Deb

t cris

is

1.

00Sh

ort t

erm

deb

t0.

011.

00D

ebt s

ervi

ce d

ue0.

03 0

.09*

1.

00R

eser

ves

-0.1

2* 0

.12*

0.

031.

00To

tal D

ebt

0.1

2*-0

.17*

-0

.11

-0.0

8*

1.00

Com

mer

cial

shar

e-0

.02

0.1

6*

0.36

* 0

.07*

-0

.02

1.00

Con

cess

iona

l sha

re0.

02-0

.53*

-0

.49

-0.1

3*

0.05

-0.6

6*

1.00

Mul

tilat

eral

shar

e0.

04-0

.41*

-0

.38

0.01

0.06

* -0

.45*

0.

56*

1.00

Inte

rest

rate

s 0

.09*

0.03

0.05

-0.1

2*

0.06

* 0

.09*

0.

02-0

.02

1.00

Ove

rval

uatio

n0.

02 0

.20*

0.

16*

0.04

-0.2

7 0

.18*

-0

.20

-0.2

0*

0.17

*

1.00

Ope

nnes

s -0

.06*

-0.0

10.

15*

0.5

5*

0.20

* 0

.09*

-0

.18

0.0

8*

-0.0

20.

031.

00

Not

e: a

star

mea

ns th

at a

var

iabl

e is

sign

ifica

nt a

t 10%

or l

ess.

28

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