Moral Hazard and International Crisis Lending: A Test
  • 1 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund

Contributor Notes

We test for the existence of a moral hazard effect attributable to official crisis lending by analyzing the evolution of sovereign bond spreads in emerging markets before and after the Russian crisis. The nonbailout of Russia in August 1998 is interpreted as an event that decreased the perceived probability of future crisis lending to emerging markets. In the presence of moral hazard, such an event should raise not only the level of spreads, but also the sensitivity with which spreads reflect fundamentals as well as their cross-country dispersion. We find strong evidence for all three effects.

Abstract

We test for the existence of a moral hazard effect attributable to official crisis lending by analyzing the evolution of sovereign bond spreads in emerging markets before and after the Russian crisis. The nonbailout of Russia in August 1998 is interpreted as an event that decreased the perceived probability of future crisis lending to emerging markets. In the presence of moral hazard, such an event should raise not only the level of spreads, but also the sensitivity with which spreads reflect fundamentals as well as their cross-country dispersion. We find strong evidence for all three effects.

I. Introduction

No subject in the debate on globalization and international institutions suffers from a greater disconnect between policy debate and empirical literature than the “moral hazard” supposedly caused by international official rescues. Ever since the Mexican (1995) bailout, the possibility that large-scale crisis lending might encourage excessive risk taking by investors and imprudent policies in debtor countries has been a constant charge of some IMF critics, and a source of concern to the official com3munity.2 “Limiting moral hazard to the extent possible” has been an objective of IMF policies for some time now, as reflected in attempts to better “involve” the private sector in crisis resolution, and most recently in the proposal to establish a Sovereign Debt Restructuring Mechanism (SDRM) as an alternative approach to resolving debt crises.3 However, no systematic empirical evidence has so far been presented suggesting that moral hazard associated with international crisis lending is, in fact, a problem or has been a problem in the past. Only three studies—by Zhang (1999), Lane and Phillips (2000), and recently Kamin (2002)—study this issue directly, and their conclusions are all negative, either rejecting the presence of moral hazard outright or finding weak and inconsistent effects.4

These negative results are all the more surprising as the literature in this area does not test for moral hazard directly, but instead the much weaker hypothesis that expectations of IMF intervention reduce investor risk, as reflected by emerging market bond spreads. This is a necessary but not sufficient condition for IMF-led crisis loans to cause moral hazard in the sense that they have a palpable impact on investor behavior. It is even further removed from the IMF critics’ claim that official loans cause excessive moral hazard, in the sense that the possible beneficial effects of lending to countries in crises are more than offset by adverse incentive effects. The finding that international crisis lending has no impact on bond spreads implies that official crisis loans have neither adverse incentive effects through reduced investor risk nor beneficial effects in terms of reducing the probability and/or overall economic costs of financial crises, since these should affect a country’s ability to repay investors.

The present paper argues that the literature’s failure to find any clear-cut effect of IMF bailouts on investor risk is mainly a result of the particular methodologies used. We propose an alternative approach, which focuses on an experiment well suited to studying the effects of a shift in expectations of international bailouts—the IMF’s failure to bail out Russia in August of 1998—and presents a range of testable implications which were not exploited in the previous literature. Based on these tests, we find strong evidence for an effect of bailout expectations on investor risk, consistent with the presence of IMF-related moral hazard prior to the 1998 Russian crisis (in many emerging market countries, not just in Russia). This does not contradict a recent paper by Kamin (2002), who applies the tests proposed in this paper to argue that moral hazard after 1998 has not been a serious problem, but it does conflict with the main conclusions of Zhang (1999) and Lane and Phillips (2000).

Zhang (1999) analyzes the longer-term impact of the 1995 Mexican bailout, which constituted the first large-scale crisis loan of the 1990s. His approach is to regress emerging markets bond spreads on a number of macroeconomic fundamentals and a measure of international liquidity (namely, the spread of high-yield US corporate bonds) in a sample that includes observations before and after the bailout. His main result is that a post-Mexico dummy is insignificant and has a positive sign, contrary to what one would expect in the presence of moral hazard. However, this result is based on an event which arguably is not well suited to test for the existence of moral hazard. Widely viewed as the first of a new type of crises, the Mexican crisis probably led to a general reassessment of risks related to emerging market lending, as investors learned that even a country with a recent track record of reform and relatively sound fundamentals was vulnerable to a sudden capital flow reversal.5 Consequently, any reduction in spreads due to moral hazard may have been offset by an increase in the perceived riskiness of emerging market debt. Zhang’s paper also has the problem that it restricts the regression coefficients before and after the Mexican crisis to be the same, notwithstanding the fact that it is precisely through changes in these coefficients that moral hazard, if it existed, should have effects on the level of spreads (see section II.B).

Lane and Phillips (2000) examine the short-term reactions of bond spreads to 22 events that might have changed expectations of future international crisis lending—namely announcements related to the 1995 Mexican bailout, the crises of 1997-98 and the 1998 IMF quota increase. With some exceptions— notably, the 1998 Russian default—these events fail to produce statistically significant reactions of spreads in the expected direction. The problem is that these findings have ambivalent interpretations, as Lane and Phillips themselves point out. Failure to detect a significant reaction of spreads could be due to the fact that changes in bailout expectations have no effect on investor risk, but it could also mean that the event was anticipated. As to the large reaction of (non-Russian) spreads to the Russian default, this could be attributed to a shift in bailout expectations, but it could also just reflect financial market turbulence caused by investor panic and contagion immediately after the default.

To deal with these problems, we adopt the following strategy. Like Zhang (1999), we examine the long run-behavior of emerging market debt spreads in the context of a regression model, controlling for changes in international interest rates as well as most country fundamentals that have been suggested in the literature on bond pricing.6 This helps us disentangle the structural effects of perceptions regarding official lending from short-term changes in spreads attributable to market turbulence. Second, we concentrate on a highly unanticipated event—the August 1998 Russian “nonbailout”—which we argue below is much better suited to test the impact of changing bailout expectations on spreads than the 1995 Mexican crisis.7 Third, we not only examine changes in the levels of spreads after the event, but also changes in the sensitivity with which spreads react to fundamentals8, as well as changes in the cross-country variance of spreads. In the context of a simple model of international lending, these are shown to be testable implications of changes in investor risk attributable to bailout expectations (see section II.B).

Our main result is that the Russian crisis was followed by permanent, significant increases in the levels of spreads in many—but not all—countries studied, in particular in countries with relatively weak fundamentals. This indicates that the Russian non-bailout increased the perceived risk of emerging market debt, particulary for “weak” countries. Moreover, we find a permanent, large, and significant increase in the cross-sectional dispersion of spreads (controlling for fundamentals), indicating that investors paid more attention to differences in country characteristics after the crisis than they had done before. This is strong evidence for a risk-reduction effect of (expected) IMF interventions, which could reflect the presence of moral hazard prior to 1998. However, it is also consistent with the view that the international financial safety net established between 1995 and 1998 made crises less likely or less deep, without necessarily causing moral hazard.9 In that interpretation, spreads rose after 1998 because the perceived curtailing of this safety net made emerging market economies a riskier place, to the detriment of everyone. On the basis of our empirical evidence, one cannot distinguish between these two explanations. Therefore, our results should be interpreted as a confirming a necessary, but not sufficient condition for the presence of moral hazard.

The remainder of the paper is organized as follows. In the context of a simple model of international lending, Section II derives several alternative testable implications of the hypothesis that IMF interventions lower investor risk. Section III discusses the implementation of these tests in the context of an empirical model of spread determination, the validity of the Russian crisis as an “experiment” for our purposes, and our empirical methodology. Section IV presents our results, which are based on two distinct datasets: a dataset of launch bond spreads based on Capital Data’s “Bondware” as well as J.R Morgan’s dataset of secondary market bond spreads contained in the “EMBI Global” Bond Index. Section V interprets the results and concludes.

II. A Simple Model

Suppose one had a clear-cut event affecting the perceived likelihood of future official crisis lending to emerging market economies. Then, it should be possible to use financial market reactions to such an event—such as changes in emerging market bond spreads—to test whether and how this event affects investor risk. Assuming that any such risk reduction is not primarily driven by a reduction in the probability or severity of emerging market crises themselves, this amounts to a test for investor moral hazard, in the sense that in the presence of official intervention investors are more likely to be bailed out when a crisis occurs. In this section, we develop three testable implications of this hypothesis in the context of a simple model of international lending. Methodological issues related to the implementation of these tests—in particular, the selection of a suitable event and the econometric modeling—are left to the next section.

A. Setup

Consider a world where multiple, risk-neutral lenders compete for loans in hard currency to debtor countries. For simplicity, we assume that debtor economies can only be in one of two states: either they suffer from a crisis or they do not. We assume that countries never repudiate their debt, but may default if they suffer a crisis. Then, country i’s probability of default can be decomposed into the probability of a financial crisis in country i, θi and the default probability conditional on a crisis, (1 − λ), where λ denotes the investors’ “recovery rate”, i.e., the probability of being repaid in a crisis. For the time being, this recovery rate is assumed to be identical across countries (this will be relaxed later). In contrast, we allow the probability of a crisis to vary as a function of a vector of observable country-specific fundamentals, xi i.e., θi = θ (xi).

Denoting the exogeneous gross risk-free interest rate as R*, the ex ante gross lending rate is determined such that expected repayment equals the risk-free rate:

Ri=R*1(1λ)θi.

The respective spread over the risk-free rate is then

si=RiR*=R*(1λ)θi1(1λ)θi.(1)

We now introduce the possibility of international crisis lending as in Mexico in 1995, Korea in 1997 or Brazil in 1998 and 2002. Let b denote the perceived probability that a country will receive an international rescue package in the event of a crisis. For now, this probability is assumed to be the same for all countries. In general, the expectation of an international rescue package could affect emerging market spreads through three channels:

  • It might affect observable fundamentals, e.g., through government policies: xi = xi(b). Indirectly, this would also affect the crisis probability.

  • It might directly affect the probability of a financial crisis, conditioning on fundamentals: θ = θ (xi, b). For example, the presence of an international financial “safety net” might reduce the probability of runs on a country’s debt or currency.

  • It might affect the recovery rate in the event of a crisis: λ = λ (b).

“Country moral hazard” usually refers to the first of these effects, i.e., the deterioration of the borrower country’s policies in the face of a financial safety net. “Investor moral hazard” is typically identified with the last effect—an increase in the probability that investors will go scot-free in a crisis. This is the sense in which the term will be used in the discussion that follows, in spite of the fact that “investor moral hazard” really should refer to particular investor actions, such as an increase in risky lending or a reduction in monitoring, rather than an increase in the conditional repayment probability per se. However, in a standard set-up in which unobservable investor actions are explicitly modeled, an increase in the recovery rate would have precisely this effect, since it would insulate investors from the risk of a financial crisis, θ.

In the remainder of the paper we thus speak of investor moral hazard if international crisis lending increases the recovery rate conditioning on a financial crisis, i.e., the following property holds:

λ(b)b>0(2)

At first sight, this condition might appear to be inevitably satisfied. However, it is not true that international rescue packages invariably involve the bailout of private international investors. While the IMF traditionally did not lend to countries that were in default or arrears to their private creditors, it changed its practices in the mid-1980s, and in 1989 formally adopted a policy that explicitly allowed “lending into arrears”. Thus, the extent to which investors make losses during crises that involve IMF intervention will depend on the particular case in question. Beginning with the 1997 Asian crises, the Fund has attempted to build measures into its programs that “involve” the private sector in crisis resolution. In practice, these have ranged from persuading banks to voluntarily extend credit lines to conditioning IMF support on debt or financial sector restructuring measures that involved substantial investor “haircuts”.10 Therefore, the question is whether in light of these policies, a higher probability of international financial rescues is perceived as increasing the probability of being bailed out in case of a financial crisis or not, and how strong this effect is.

In the tests that follow, we focus on investor moral hazard, abstracting from country moral hazard by taking xi as given (in our empirical work, this means controlling for changes in xi). In addition, we will assume that θ does not depend on b, ruling out a direct effect of crisis lending on the probability of crises. As we will see below, this assumption is critical to interpret our tests as tests for moral hazard, as opposed to tests for an investor risk reduction effect that might be driven by the reduction of the likelihood (or severity) of financial crises, rather than an increase in the conditional recovery rate.

The central question is now how to test for investor moral hazard when λ is not directly observable.

B. Testable Implications of “Investor Moral Hazard

Under the assumptions made in the previous subsection, spreads are determined as

si=R*[1λ(b)]θ(xi)1[1λ(b)]θ(xi).(3)

where xi = (xi1,..xij, ..xik). Based on this equation, we can now state three equivalent testable implications of investor moral hazard. For a given set of fundamentals, an increase (decrease) in the perceived likelihood of an international rescue

  1. reduces (increases) the level of spreads for each country,

  2. reduces (increases) the sensitivity of spreads with respect to changes in fundamentals, and

  3. reduces (increases) the spread difference between any pair of countries (with initial spreads “close enough”), translating into a reduction (an increase) in the cross-country variance of spreads.

More formally, the first result can be written as

Proposition 1 Holding constant the set of fundamentals X=(x1,x2,xN), equation (3) implies that λb>0 if and only if sib<0 for any country i.

Proof: see Appendix.

An increase in the probability of rescue packages results in a lower perceived risk associated with international lending, thus reducing country spreads across the board, given fundamentals. Under the stated conditions, this directly provides a test for moral hazard: In the presence of moral hazard, events that increase the perceived probability of international rescue packages should result in lower spreads, when controlling for changes in fundamentals. We will refer to the test based on Proposition 1 as the “level test”.

Assume that all fundamentals are defined such that θi is increasing in all the components of xi (in other words, all fundamentals are expressed as “risk factors”). Then we can state our second result as follows:

Proposition 2 Holding constant the set of fundamentals X=(x1,x2,xN), equation (3) implies that λb>0 if and only if 2sixijb<0 for any country i and any country-specific fundamental xij.

Proof: see Appendix.

From an investor’s standpoint, a higher probability of getting off “scot-free” renders the idiosyncratic characteristics of each country less important, weakening the link between fundamentals and spreads (in the extreme, with λ = 1, all countries would pay the same risk-free interest rate, regardless of their fundamentals). This proposition provides a second test for investor moral hazard: In the presence of moral hazard, events that increase the perceived probability of international rescue packages should reduce the size of the slope coefficients linking country spreads and fundamentals. We will refer to this test as the “slope test”.

Finally, define Δssmsn, m ≠ n, where sm and sn are the interest rate spreads of two countries m and n. If sm and sn are “close enough” in the sense that Δs can be approximated reasonably well by a first-order Taylor expansion, we can prove the following proposition:

Proposition 3 Holding constant the set of fundamentals X=(x1,x2,xN), equation (3) implies that λb>0 if and only if Δsb<0 for any two countries m and n,mn for which we can approximateΔs = smsn by a first-order Taylor expansion.

Proof: see Appendix.

This proposition shows that a higher probability of being bailed out reduces the spread difference between any pair of countries (with initial spreads “close enough”). This means that the higher bailout probability not only lowers the level of the spread as in proposition 1, but that the decrease is more pronounced for countries with higher spreads. Intuitively, as investors pay less attention to differences in fundamentals across countries, the differences between country spreads should also narrow. This further implies that, for any given set of fundamentals, the dispersion of spreads decreases when the probability of being bailed out increases.11 Our test can then be formulated as follows: In the presence of moral hazard, events that increase the perceived probability of international rescue packages should reduce the cross-sectional variance of the spreads. We will refer to this test as the “variance test”.

C. Robustness to Changes in Model Assumptions

If equation (3) holds, the propositions in this section all specify necessary and sufficient conditions for investor moral hazard, thus providing three alternative and equivalent testable implications. However, when the underlying assumptions are relaxed, the three conditions may cease to be sufficient and remain only necessary. At the same time, the equivalence among the three tests may cease to exist.

Consider first what happens if one allows for a beneficial role of international crisis lending in crisis prevention or mitigation. In our set-up this means allowing the probability of a financial crisis, θi, to depend on b, with θ(xi,b)b<0. For example, a crisis-preventing effect may arise if international crisis lending eliminates self-fulfilling debt runs a la Sachs (1984), or provides the domestic authorities with the hard currency necessary to implement domestic financial safety nets and prevent bank runs triggered by shifts in exchange rate expectations (Jeanne and Wyplosz, 2001). One can show that in this case, the “level test” will never be able to distinguish the effects of investor moral hazard from those of a reduction in the crisis probability and that the “slope” and “variance tests” can make this distinction only under conditions that are not necessarily satisfied in practice. The inability to distinguish moral hazard from “true risk reduction” attributable to international crisis lending thus constitutes a fundamental identification problem which we share with the remaining literature in this area, as explained in the introduction.12

Another assumption made in the previous subsection is the invariancc of the recovery rate across countries. This assumption is less critical, and can be relaxed by allowing λ to depend on xi i.e., λ = λ (xi, b).This formulation encompasses two cases that are likely play a role in practice. First, the recovery rate could depend on country fundamentals directly, regardless of expectations of international crisis lending. For example, the efforts that a country makes to repay investors in a crisis (e.g., through fiscal adjustment) is likely to depend on observable fundamentals. Second, the likelihood of international official intervention in a crisis country will generally depend on country characteristics. For example, the international official community might be less prone to extend crisis loans to countries with chronically poor policies, and it may be more prone to extend crisis loans to large countries with systemic impact. In this interpretation, the parameter b would represent a general taste parameter of the IMF and its shareholders—its general propensity to engage in large-scale crisis lending—while the likelihood that a particular country will receive assistance will depend on the interplay of b and xi.

In the Appendix, we show that if λ = λ (xi, b) all propositions go through, provided we impose two weaker conditions instead. The first states that an increase in b affects the expected recovery rate uniformly across countries, the second rules out the (pathological) case where a country with a “much smaller” crisis probability has a higher spread due to a “much smaller” recovery rate. Hence, the general ordering of spreads should depend on θ and should not be reversed by the ordering of the recovery rates.

III. Empirical Methodology

A. Regression-Based Tests for Moral Hazard

We start from a standard model of the determination of bond spreads

sit=xitβ+uit,(4)

where sit denotes the bond spread of country i at time t, and is a 1 × k-vector containing the country’s fundamentals at time t that determine the spreads of sovereign bonds. These fundamentals can be country- and/or time-specific. The term u represents a random error. This equation will be the basis of all our regressions.

Consider now an event that reduces the perceived probability of future bailouts.13 The general estimation procedure will be to estimate a pooled model over the whole period, i.e., before and after the event, without restricting the coefficients of the model to be the same before and after the event. For the ease of exposition, assume that there are only two points in time: “before” the event (t =0) and “after” the event (t = 1).

Then, bond spreads before the event can be described by the model

si0=xi0β0+ui0,(5)

while the model changes to

si1=xi1β1+ui1,(6)

after the event due to a potential structural break. Denoting H0 the null hypothesis that moral hazard is not present, and H1 the presence of moral hazard, the three tests derived in our theoretical framework can be restated as follows in the context of the empirical model:14

1. Under H0 (i.e., no moral hazard), the slopes of the regression equation should be unaffected by an event that reduces expected international crisis lending. Under H1 (i.e., moral hazard), however, we would expect all slopes to increase after the event (in absolute value) because investors bear a larger part of the repayment risk and will price risk factors more than before. This is the test referred to as the slope test in section 2. It can be carried out as a simple t test on the significance of the change of each individual slope.15 In the case of an event that decreases moral hazard, the test can be formulated as follows:

H0:|βk1βk0|=0,k=1,KH1:|βk1βk0|>0,k=1,K

Note that this test refers only to the slopes of the regression and not to the intercept.16

2. Under H0, the level of spreads should not be affected by an event that reduces expected international crisis lending. Under H1, however, the level of spreads should increase for every country, holding fundamentals constant. More formally, the change in the level of spreads can be decomposed into three components:

si1si0=xi1(β1β0)+(xi1xi0)β0+(ui1ui0)=xi0(β1β0)+(xi1xi0)β1+(ui1ui0)(7)

The first term is the change in the level of spreads induced by the change in β, the second term the change in the level of spreads caused by the change in the fundamentals, and the third term reflects the impact of a change in the error term.17 Here, we are only interested in the first term, which captures the effect of a change in the pricing of risks on the level of spreads. Thus, in the case where the event entails a potential decrease in moral hazard, the level test takes the following form:

H0:xit(β1β0)=0H1:xit(β1β0)>0

The test can be carried out as a linear Wald test in which we compare the fitted spreads that result from the models estimated before and after the event.18 Note that the above decomposition and thus the choice of xit is not unique: when controlling for fundamentals, one can either use the fundamentals before or after the event. In fact, this choice can affect the results of the test. Therefore, we present the results for both choices.

3. Under H0, the cross-sectional variance of the spreads should remain unchanged after the event. Under H1, however, the difference in spreads between each pair of countries should increase, which, in turn, implies an increase in the cross-sectional variance of spreads (controlling for changes in fundamentals). More formally, we can write the variance across countries before the event as

Var(s0)=β0Var(X0)β0+σ02(8)

and the variance after the event as

Var(s1)=β1Var(X1)β1+σ12,(9)

where Xt is the N × k-matrix of the fundamentals of all countries at date t and σ02 and σ12 are the variances of the error terms. The change in the variance of spreads can be decomposed into three components:

Var(s1)Var(s0)=[β1Var(X1)β1β0Var(X1)β0]+[β0Var(X1)β0β0Var(X0)β0]+[σ12σ02]=[β1Var(X0)β1β0Var(X0)β0]+[β1Var(X1)β1β1Var(X0)β1]+[σ12σ02](10)

The first term is the change in the variance induced by the change in β, the second term the change in the variance caused by the change in the fundamentals, and the third term reflects the impact of a change in the variance of the error term.19 Again, we are mainly interested in the first term, which captures the effect of a change in the pricing of risks on the variance of spreads. Thus, if the event entails a potential decrease in moral hazard, the variance test takes the following form:20

H0:β1Var(Xt)β1=β0Var(Xt)β0H1:β1Var(Xt)β1>β0Var(Xt)β0

We refer to this test as the variance test. The variance test will be carried out as a nonlinear Wald test (see Appendix for statistical details). Note that the above decomposition is again not unique: the choice of Xi can affect the results of the test, and we report the results for both alternatives.

It is important to clarify the relations between our three tests. If all slopes increase in absolute value, the variance is also going to increase and so are the levels (unless there is a decrease in the intercept strong enough to reverse the effect of the slopes). Thus, there is no point in doing all three tests in this situation. The interesting case is one in which some, but not all slopes show significant increases, while some may even show decreases. In the slope test, this would imply a rejection of H0, which predicted no change in slopes. However, this rejection would not be very convincing if the increase in some slopes were accompanied by decreases in others. Indeed, H1 predicts that all slopes should increase. The question is whether the slope coefficients showing significant increases “outweigh” those showing decreases, so that we can accept the presence of moral hazard with some confidence instead of concluding, for example, that the regression model is misspecified or the experimental event is ambiguous, so that no lessons can be drawn.

How should one decide whether the positive slope movements outweigh the negative ones? One natural way of weighing the slopes is to look at the impact of the change of the slopes on fitted spreads, controlling for fundamentals. This is the logic behind the level test. Unfortunately, the results from this test also are very unlikely to be unambiguous. First, the results from this test may differ across countries and second, the choice of X may affect the test results. Therefore, we also employ the variance test, which allows us to summarize the overall effect of the changing slopes on all countries in a way suggested by our model.

Some caveats remain with respect to the interpretation of the variance test. First, there continues to be an ambiguity with respect to the choice of X. Second, it is important to note that a rejection of the null hypothesis in the variance test does not require all fitted spreads to go up. For the variance test, the direction of the change in fitted spread is irrelevant as long as the spreads move farther apart from each other. Third, our theoretical model predicts that the increase in the variance is driven by an increase in the distance of neighbouring spreads, with the “order” of countries being unchanged. Yet, the order of countries does not enter the variance test. Therefore, the results from the variance test can only be interpreted in combination with the results from the level and slope tests.

B. The Russian Crisis as a Valid Experiment

A critical element of our testing strategy for moral hazard is the choice of an event that constitutes a valid experiment for the purpose of the test. Such an event has to satisfy three conditions:

  1. It has to change investors’ perception of the extent or the character of future international crisis lending.

  2. It has to be unexpected.

  3. It must not lead to a reassessment of risks other than through the expectations of future international rescues.

Arguably, the events following the Russian default in August 1998 satisfy all three conditions reasonably well. The Russian crisis unfolded when the Russian authorities announced a de facto devaluation of the ruble, a unilateral restructuring of ruble-denominated public debt, and a moratorium on foreign debt repayments on August 17, 1998. In our judgement, the poor state of the Russian economy was hardly surprising. In fact, Russia had been downgraded by all three major rating agencies in the first half of 1998, which suggests that investors were well aware of the increasing economic risks.

The real surprise was that the international community did not prevent the default of a country that was widely believed to be “too big and too nuclear to fail”, as witnessed by the enormous build-up of Eurobonds outstanding—from $4.6 billion in March 1998 to $15.9 billion in July 1998—and the oversubscription of all new issues, in spite of worsening fundamentals.21 As a result, the absence of international support during the Russian plight was widely interpreted as a sign of a generally higher reluctance of the international community to support crisis countries, particularly if these countries had not complied with former reform programs. In the words of David Folkerts-Landau, Global Head of Research at Deutsche Bank and former head of capital market studies at the IMF: “The rules of the game have changed… If a country has a significant volume of domestic debt outstanding, if that country is forced into the arms of the IMF… I believe that we should assume from here on that any such program will ask the foreign holders of domestic debt to take a major loss… Clearly, one had the right to be surprised in Russia and face a write-down there.” Similarly, George Soros is quoted to have stated after the Russian crisis that “[Financial markets] … resent any kind of government interference but they hold a belief deep down that if conditions get really rough the authorities will step in. This belief has now been shaken.”22 On this basis, the first two of the above conditions would seem to be satisfied.

The third condition is harder to satisfy. There is at least one interpretation of the events in Russia that has nothing to do with expectations of international bailouts, namely that the crisis “reminded” investors of the risks existing in emerging economies, which led to a general repricing of risks (the “wake-up call” interpretation).23 This argument, which is surely valid in the case of the Mexican and the Asian crises, seems less credible for the Russian crisis. First of all, the two preceding emerging market crises (Tequila, and particularly Asia) should have been sufficient to “wake up” investors. Second, it is not clear that the Russian default, which resulted from an old-fashioned fiscal sustainability problem, contained any information with respect to the risks in other emerging economies.24 We therefore believe that the Russian crisis did not primarily change investors’ evaluation of country risk, but rather their perception about the extent and nature of the international financial safety net.

C. Estimation Strategy

In applying our tests to the Russian crisis, a complication arises from the fact that the Russian crisis was followed by a prolonged period of turbulence in emerging markets. During this high-volatility episode, one cannot reasonably suppose that there was a stable relationship between macroeconomic fundamentals and bond spreads, as is assumed in the static models that are estimated in the literature on emerging market bond spreads.25 Ignoring this problem—i.e., estimating the relationship between spreads and fundamentals before and after the default using a sample that includes the post-default turbulence—will bias our results in the direction of rejecting the null hypothesis, as both levels and the cross-sectional variance of spreads sharply rose in the immediate aftermath of the default, before returning to more normal levels.

There are two alternative ways to deal with this problem. One is to simply exclude the periods immediately following the crisis from our regressions. For example, one could exclude the second half of 1998 and perhaps the first quarter of 1999, until markets calmed down after the Brazilian currency crisis in early 1999. An alternative approach is to have no exclusion period, but estimate the model using a specification and/or estimation procedure which is be able to deal with the presence of financial turbulence in the data. For example, one could use a flexible dynamic specification which allows for several lags in the dependent variable, and/or a GARCH process in the residuals. One could even give up on trying to model the average dynamic of emerging market bond spreads altogether, by including an average index of spreads, such as the EMBI Global (EMBIG), on the right hand side of the regression. This amounts to modeling the cross-sectional deviations of individual country spreads from the EMBIG as opposed to country spreads themselves, If we do this, our “level test” would no longer apply in the form presented above, since the post-crisis model is estimated taking the average increase in spreads as a given. However, our “slope test” and “variance test” would remain valid, and have the same interpretation as before.

The downside of the first approach is that we could bias the results by getting the exclusion period wrong. For example, if our choice of exclusion period is guided by actual crisis events, but market volatility persisted significantly beyond these events, we would have a problem. More generally, we obviously do not want our results to depend on a particular choice of exclusion period. The downside of the second approach is that modeling the extreme swings in spreads witnessed after the Russian crisis requires a lot flexibility. If we use a model that is too restrictive, we might still bias our results in the direction of rejecting the null. In addition, there is the usual trade-off between flexibility (or unbiasedness), and efficiency. We could make our model very flexible by including many dynamic terms and controls, but given that we have a relatively small number of countries and time periods, this might come at the expense of not being able to estimate any parameter with reasonable precision.

Faced with these options and trade-offs, our strategy is as follows. We make the first approach—the one that excludes the period of financial turbulence, i.e., estimates the model using pre- and post- default “tranquil” periods—our primary vehicle. In addition, we use several variants of the second approach to test the robustness of our results. Moreover, we explore whether the results are sensitive to the precise definition of the exclusion period, and in particular whether a larger exclusion window weakens our main finding to any significant degree. This is not the case (see section IV.E).

Using a simple measure of financial turbulence, it is easy to see why the results turn out to be quite insensitive to the choice of the exclusion period. Figure 1 graphs the predicted conditional variance of changes in the EMBIG, using a simple GARCH(1,1) model estimated over the period January 1998 until August 2002, using daily data.26 The main lesson from the Figure is that periods of high market volatility literally stand out; they are easy to identify and to relate to reported events. The figure also shows that by March of 1999, conditional volatility was essentially down to pre-August 1998 levels. So even though conditional volatility is indeed persistent, the persistence does not seem so large as to influence volatility much beyond the crisis events, and whether one ends the exclusion period in February, March, or June of 1999 has no impact on the results.

Figure 1:
Figure 1:

Estimated Conditional Volatility of Changes in the EMBIG Composite Spread, 1998–2002

Citation: IMF Working Papers 2002, 181; 10.5089/9781451859201.001.A001

IV. Empirical Analysis

A. Data

In our analysis, we use two different data sources for bond spreads: launch spreads contained in Capital Data’s “Bondware” dataset and secondary-market spreads included in J.P. Morgan’s Emerging Markets Global Bond Index (EMBI Global). Since both datasets have their strengths and weaknesses, we use both of them in our empirical analysis in order to check the robustness of our results.

The use of the EMBI Global dataset is more straightforward since it is a balanced panel of secondary market spreads. While its predecessors (EMBI, EMBI+) have been used extensively in the academic literature on emerging market bond spreads (Cline and Barnes, 1997, Zhang, 1999, Lane and Phillips, 2000), the much broader—albeit shorter—EMBI Global does not appear to have been used so far. It is made up of US-$ denominated sovereign or “quasi-sovereign”27 bonds that satisfy certain criteria, guaranteeing, e.g., a sufficient liquidity of the bonds. Spreads are available at daily frequency for 21 countries since January 1, 1998.28 The instruments in the index are mainly Brady bonds and Eurobonds, but the index also contains a small number of traded loans as well as local market instruments. The spread of a bond is calculated as the difference between the bond’s yield and the yield of a US government bond with a comparable issue date and maturity. A country’s bond spread is then calculated as a weighted average of the spreads of all bonds, that satisfy the above-mentioned criteria, where the weighting is done according to market capitalization. In the case of Brady bonds, “stripped” spreads are provided.

Capital Data’s “Bondware” dataset contains launch spreads of sovereign and public29 foreign currency bonds of 54 emerging countries. The spread of a bond is calculated as the difference between the bond’s yield and the yield of a government bond of the country issuing the respective currency with a similar issue date and maturity. In contrast to the EMBI Global, the Bondware dataset does not include Brady bonds. Therefore, the two datasets are almost disjoint. The use of the Bondware dataset is more complicated, since it contains primary spreads that are observed only at the time of issue. Thus, this dataset is a highly unbalanced panel, which raises additional econometric problems due to a potential selection bias (see Eichengreen and Mody, 2000). However, “Bondware” has an important advantage over the EMBI Global dataset, namely its much broader coverage of countries. This property is crucial since one of our tests (the variance test) relies on asymptotic results in the cross-sectional dimension. The selection problem can be tackled by estimating a standard Heckman correction model (see below).

On the right hand side of the regressions, we use a rich set of macroeconomic fundamentals that have been compiled from a number of different sources (see Appendix for a complete list of the variables and their sources). In choosing the set of right-hand-side variables, we tried to capture the most important aspects of a country’s macroeconomic performance, using the fundamentals that have been suggested in the literature on bond pricing.30 The economic variables can be grouped into the following categories: Domestic economic condition (real GDP growth, inflation, fiscal balance, domestic credit growth), external sector (current account, external debt), and international interest rates (US ten-year yield and spreads on high-yield U.S. corporate bonds as a liquidity proxy). In addition, we included political variables (political instability and violence), other country characteristics (regional dummies, economic size), and credit ratings.

In the literature on bond pricing, it has been suggested that it is sufficient to include credit ratings to capture the macroeconomic performance of a country (Cantor and Packer, 1996, Kamin and Kleist, 1999). This is contradicted by the fact that one usually finds a large number of significant macroeconomic variables even when ratings are included. Conversely, the inclusion of ratings has been shown to be crucial even when macroeconomic fundamentals are included (Cantor and Packer, 1996, Eichengreen and Mody, 2000). We therefore include both macroeconomic fundamentals and the rating information. We follow Eichengreen and Mody (2000) in including not the ratings themselves, but rather a residual from a regression of the ratings on all included macroeconomic fundamentals. This assumes that the correlation between the included fundamentals and the ratings is entirely due to the fact that the ratings have been calculated on the basis of these fundamentals. The residual impact of the ratings might be due to either other omitted macroeconomic fundamentals that are used in the calculation of ratings or to the ratings themselves.

In the regressions based on the EMBI Global dataset, we use the whole range of right-hand-side variables, while the regressions using the Bondware dataset use a much more parsimonious specification to avoid the exclusion of too many countries from the dataset due to missing data on the right hand side.

B. Spreads Before and After the Russian Crisis: A First Impression

Before we start our formal econometric analysis, it is useful to have a look at the raw bond spread data. Figure 2 shows the evolution of daily bond spreads for the emerging market countries contained in JP Morgan’s EMBI Global index (EMBIG).31 The basic pattern is well-known: in August 1998, virtually all spreads shot up, and their cross-sectional variance widened sharply. By April of 1999, however, most of them—with the exceptions of Russia and Ecuador—seem to have returned to their approximate pre-crisis levels. From Figure 2, it is thus not obvious that the Russian crisis was followed by a permanent increase in the cross-sectional mean and variance of spreads. However, a much clearer impression emerges once Russia and Ecuador (which had idiosyncratic difficulties in 1999 and 2000) are removed from the sample (Figure 3). Now, the cross-sectional variance of spreads appears to be clearly larger in the post-crisis period and so does the average level of spreads.

Figure 2:
Figure 2:

EMBI Global Daily Strip Spreads, 1998–2000, 20 Countries (includes Russia and Ecuador)

(in basis points)

Citation: IMF Working Papers 2002, 181; 10.5089/9781451859201.001.A001

Figure 3:
Figure 3:

EMBI Global Daily Strip Spreads, 1998–2000, 18 countries (excludes Russia and Ecuador)

(in basis points)

Citation: IMF Working Papers 2002, 181; 10.5089/9781451859201.001.A001

These impressions are confirmed by Table 1 (left column), which shows the cross-sectional mean and standard deviation of spreads, based on monthly data, for the pre-crisis, crisis, and post-crisis periods. After the crisis, the mean spread rises by about 100 basis points and the average standard deviation approximately doubles (excluding Russia and Ecuador).

Table 1.

Mean and Cross-Sectional Dispersion of Spreads Before and After the Russian Crisis: Summary Statistics

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Argentina, Bulgaria, Brazil, China, Colombia, Croatia, Korea, Morocco, Mexico, Malaysia, Panama, Peru, the Philippines, Poland, Thailand, Turkey, Venezuela, and South Africa.

Argentina, Brazil, Bulgaria, Chile, China, Colombia, Costa Rica, Croatia, Cyprus, the Czech Republic, E1 Salvador, Estonia, Hong Kong SAR, Hungary, India, Israel, Jamaica, Kazakhstan, Korea, Latvia, Lebanon, Lithuania, Malaysia, Malta, Mexico, Morocco, the Philippines, Poland, Romania, Singapore, the Slovak Republic, Slovenia, South Africa, Taiwan Province of China, Thailand, Tunisia, Turkey, Uruguay, and Venezuela. The Dominican Republic, Ecuador, Egypt, Guatemala, Indonesia, Jordan, Mauritius, Oman, Pakistan, Panama, Peru, and Saudi Arabia did not issue bonds during this period.

Refers to average cross-sectional standard deviation during period.

Refers to standard deviation of all available bonds during period.

The evolution of launch spreads contained in the “Bondware” database is not as easily graphed, since the data consist of single datapoints for each issue, rather than continuous country-specific lines. Moreover, the selection problem makes the raw data more difficult to interpret. For example, the average level of spreads after the Russian crisis is biased downward by the fact that Russia drops out as an issuer. Nevertheless, after excluding Russia from the sample, the raw data confirm the pattern suggested by the EMBIG spreads (right column of Table l).32 In particular, both the cross-sectional average and the cross-sectional standard deviation of spreads remain at substantially higher levels in the post-crisis period than prior to the Russian crisis.

The crucial question is now to what extent these changes are attributable to changes in fundamentals, and whether these changes are statistically significant when controlling for changes in fundamentals.

C. Tests Using Bondware Data

Econometric issues

As Eichengreen and Mody (2000) have pointed out, ordinary-least-squares estimates of the relationship between launch bond spreads and fundamentals suffer from a selection bias: a country’s spread is observed only when the country actually issues a bond. It is very likely that the issue decision depends on factors that influence the level of the spread as well. For instance, we might think that countries with extremely high (latent) spreads are excluded from the market due to adverse selection issues.33 Therefore, the observability of the spreads cannot be considered as “random”, but it depends on the spreads themselves, which has to be taken into account in the econometric analysis.

We follow Eichengreen and Mody (2000) in solving this problem by estimating a standard sample selection model in the spirit of Heckman (1979). Our econometric model thus consists of two equations. The first equation is the spread equation

s˜it=xitβ+uit,(11)

where s˜ denotes the latent spread, which is unobserved. Instead, we do observe the actual spread, s, according to the following observation rule:

sit=s˜itifz˜it>0sit=not observedifz˜it0,(12)

where z˜ is another latent variable, which is also unobserved. The relationship between this latent variable and the observed country characteristics is described by the selection equation

z˜it=witγ+vit.(13)

However, instead of z˜ we observe z and the corresponding observation rule can be written as

zit=1ifz˜it>0zit=0ifz˜it0.(14)

The variable z is a dummy variable indicating whether there was a bond issue in a certain period or not. As usual, we assume that the two errors are jointly normal, with ρ denoting the correlation between u and v. In our case, we would expect ρ to be negative. The matrix Wt=(w1t,w2t,,wNt) includes all variables contained in the matrix Xt and a number of instruments needed for identification. In order to qualify as instruments, these variables must affect the issue decision, but not the level of the spread (unless we want to rely on functional form identification). We use four such variables in our selection equation:

  • Debt issued in the form of bonds in the year preceding the observation divided by the debt stock at the beginning of that period. This variable captures the effect that countries are less likely to issue new bonds if they have issued large amounts of debt in the near past.

  • The number of bond issues in the year preceding the observation, as a proxy for the degree of a country’s issuing activities. A country that issued a large number of bonds in the past year is more likely to issue a bond in the next month than a country that issued only one or two bonds that year.

  • The natural logarithm of per capita GDP in 1993, as a proxy for the economic development of a country. A country with higher per capita GDP typically has a more developed financial sector, increasing the probability of bond issues.

  • A dummy variable that is equal to one for the five countries affected most by the Asian crisis.34 The idea is that the Asian countries might have been excluded from capital markets after the Asian crisis regardless of their fundamentals.

The set of macroeconomic fundamentals in the spread equation was chosen such that we capture the most relevant macroeconomic risk factors, while trying to retain a large number of countries. As mentioned above, the wide coverage of countries is the strength of the Bondware dataset, which is of particular importance for the variance test. Therefore, the right-hand side of the regressions using launch spreads is somewhat more restricted than in the EMBI regressions to avoid an undue reduction in the number of countries included in the estimation. The estimation is done by full maximum likelihood, which is preferable to Heckman’s two-step procedure due to its asymptotic efficiency.

Test results for the Russian crisis

Table 2 contains pre- and post-crisis regression results for the Russian crisis and the results from the slope test described above. We show results for three different specifications, which are inspired by the previous literature relating spreads to fundamentals. Model (1) is a specification similar to the one found in the paper by Eichengreen and Mody (2000).35 Models (2) and (3) are variants of model (1), which drop the variable “External debt/GDP” because it turned out to have the “wrong” sign in model (1). Instead they include additional variables such as inflation, the current account, a measure of political stability (“Political instability and violence”), and a measure of the maturity structure of external debt (“Short-term debt/total debt”). Note that all macroeconomic variables enter the regressions in a way that takes into account reporting lags. This usually means using the first lag rather than the contemporaneous realization. In some cases, we used moving averages to reflect the fact that past trends rather than the latest realization might affect investors; these are denoted as “MA” in the tables. For the reasons outlined above, we excluded the time period between July 1998 and March 1999 from our regressions.

Table 2.

Launch Spread Data: Estimation of Alternative Models Before and After Russian Crisis, and Results for “Slope Test”

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Estimated on pooled sample 1998:01 - 2000:12, excluding 1998:07 - 1999:03, and allowing for different coefficients for pre- and post crisis periods. All estimations use robust standard errors.

1998:01 - 1998:06.

1999:04-2000:12.

p values based on two-sided tests; boldface indicates rejection of equality at the 10 percent level in the direction consistent with H1 (see text).

p value refers to the joint hypothesis that all slopes are the same in the two periods.

Argentina, Brazil, Bulgaria, Chile, China, Colombia, Costa Rica, Croatia, Cyprus, the Czech Republic, the Dominican Republic, Ecuador, Egypt, E1 Salvador, Estonia, Guatemala, Hong Kong SAR, Hungary, India, Indonesia, Israel, Jamaica, Jordan, Kazakhstan, Korea, Latvia, Lebanon, Lithuania, Malaysia, Malta, Mauritius, Mexico, Morocco, Oman, Pakistan, Panama, Peru, the Philippines, Poland, Romania, Saudi Arabia, Singapore, Slovak Republic, Slovenia, South Africa, Taiwan Province of China, Thailand, Tunisia, Turkey, Uruguay, Venezuela.

Reports only coefficients for instruments and correlation coefficient of disturbance terms of the two equations (rho).

The upper panel of the table shows coefficients and p-values for the spread equation, based on regressions which were run on a pooled pre- and post-crisis sample, with all variables in the main equation being interacted with pre-and post-crisis dummies. For each model, the column “test for equality” indicates the p-valucs of the tests whether the coefficients from the pre- and post-crisis samples are significantly different from each other. Rejections at the 10 percent level are typed in boldface if the change is in the direction predicted by H1. The lower panel of the table refers to the estimation results for the selection equation. Only the coefficients of the four variables used for identification are reported, while the other coefficients are suppressed because they are of little interest. The coefficient ρ denotes the estimated correlation of the disturbance terms of the two equations.

Looking first at the selection equation (lower panel), we find that the variables “Number of previous bond issues” and “GDP per capita (1993)” are highly significant and show the expected signs. The maximum-likelihood procedure converged after only 2 or 3 iterations, which supports our identification procedure. However, the coefficient ρ is not significantly different from zero.

This suggests that the selection problem is less severe in this sample than expected. Therefore, we also ran the regressions without a Heckman correction. The results for the spread equation are very similar and are thus not reported.36

The coefficients in the spread equations mostly show the expected signs and are generally highly significant for the period after the crisis, while the same is not true for the period before the crisis. This may be due to the relatively small number of observations in that period.37 The results from the slope test lend some support to the moral-hazard hypothesis, but are not perfectly conclusive. Almost all coefficients change in the direction predicted by H1, but only some of these changes are statistically significant. In particular, the null hypothesis of equal slopes can be rejected at a 10 percent significance level for the rating residual, inflation, and “Political instability and violence”. For GDP growth, the null can be rejected in models (1) and (2), but not in model (3). The null hypothesis cannot be rejected for the current account and the Brady dummy, and the results for the debt variables are difficult to interpret due to their wrong signs.

Another interesting result concerns the coefficient of the US high-yield bond spread, which is positive, but insignificant in the period before the crisis, but becomes negative and highly significant after the crisis. It has often been claimed that the evolution of spreads after the Russian crisis can be explained by a general reluctance of investors to take risks, which would suggest a positive correlation of high-yield bond spreads and emerging market spreads. Our analysis shows, however, that the partial correlation is negative after the crisis, once one controls for other macroeconomic fundamentals. This contradicts the conventional wisdom.

Consider now the level test, which is particularly instructive in view of the somewhat ambiguous results from the slope test (see Table 3). This test tells us whether the overall effect of the changes in coefficients observed in Table 2 is to increase spreads, as one would expect if the driving force behind those changes were moral hazard. We performed the level test for each country, for each month, and for all three models. Table 3 shows the number of significant increases and decreases of fitted spreads for each country and model (out of a potential maximum of 27, which is the number of months in our regression sample).

Table 3.

Launch Spread Data: Summary Results for “Levels Test” (Russia Crisis)

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No. of periods in which fitted spread based on post-crisis model is significantly higher than fitted spread based on pre-crisis model (potential maximum per country: 27, significance level 5%).

No. of periods in which fitted spread based on post-crisis model is significantly lower than fitted spread based on pre-crisis model (potential maximum per country: 27, significance level 5%)

Table 3 contains several noteworthy findings. First, the overall evidence strongly supports the notion that spreads increased significantly after the Russian crisis (controlling for fundamentals) as predicted under the moral hazard hypothesis H1. There are many significant increases in fitted spreads, while there are no significant decreases. Second, this finding docs not apply equally to all countries. Specifically, there exist eight countries for which we do not find a significant increase in any of the three models. Interestingly, five of these are Asian countries which—with the exception of Malaysia—did not directly suffer a crisis during 1997-98. It might well be that these countries experienced an overshooting of their spreads after the Asian crisis erupted. The normalization of spreads after 1998 might mask any moral hazard effect. It should also be noted that the average credit rating of countries without a significant increase (between A, A2 and A-, A3) was well above the one of the remaining countries (BB+, Ba1). The third noteworthy finding concerns the level of the increase in fitted spreads. There is a strong and significant negative correlation (-0.62) between the increase in spreads and the countries’ ratings. In other words, the increase in spreads was higher in countries with worse ratings, which is in line with the moral hazard hypothesis. Summing up, the results from Table 3 are consistent with the moral-hazard interpretation.

Finally, Table 4 presents the results of the variance test, which focuses on the implications of moral hazard on the cross-sectional dispersion of spreads rather than the level of spreads for each country. For each model and each time period, the table compares the variance of fitted spreads using the coefficients from the model estimated on pre-crisis data, with the one based on the model estimated on post-crisis data. The column “test for equality” shows the p-values from the variance test, i.e., it shows whether the two fitted variances are significantly different from each other or not. The results are striking: no matter which period is chosen to calculate the fitted variances, the null hypothesis of equal variances is rejected at high confidence levels. The post-crisis model always significantly overpredicts the pre-crisis variance, while the pre-crisis model always significantly underpredicts the post-crisis variance. This constitutes strong evidence for a stronger differentiation between countries after the Russian crisis, confirming the impression one first obtains on the basis of the raw data. In combination with the results from the level test, these results can be interpreted as strong evidence consistent with the moral-hazard hypothesis. Not only do we find that the cross-sectional variance increases after the event, but we also find that the increase in spreads is strongest for countries with weak fundamentals. Thus, there seems to be a much stronger differentiation between “good” and “bad” countries following the Russian crisis.

Table 4.

Launch Spread Data: Cross-Sectional Variances of Fitted Spreads Before and After Russian Crisis, and Results for “Variance Test”

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Regression coefficients estimated on 1998:01 to 1998:06 data.

Regression coefficients estimated on 1999:04 to 2000:12 data.

p values based on two-sided tests.

Table 5.

EMBIG Data: Estimation of Alternative Models Before and After Russia Crisis, and Results for “Slope Test”

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Estimated on pooled sample 1998:01 -2000:12, excluding 1998:07 - 1999:03, and allowing for different coefficients for pre-and post crisis periods. AH estimations use robust standard errors.

1998:01 - 1998:06.

1999:04 -2000:12.

p values based on two-sided tests; boldface indicates rejection of equality at the 10 percent level in the direction consistent with H1 (see text).

p value refers to the joint hypothesis that all slopes are the same in the two periods.

Full sample of 18 countries: Argentina, Bulgaria, Brazil, China, Colombia, Croatia, Korea, Morocco, Mexico, Malaysia, Panama, Peru, the Philippines, Poland, Thailand, Turkey, Venezuela, and South Africa.

Table 6.

EMBIG Data: Summary Results for “Levels Test”

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No. of periods in which fitted spread based on postcrisis model is significantly higher than fitted spread based on pre-crisis model (potential maximum: 27, significance level 5%).

No. of periods in which fitted spread based on postcrisis model is significantly lower than fitted spread based on pre-crisis model (potential maximum: 27, significance level 5%).

Table 7.

EMBIG Data: Cross-Sectional Variances of Fitted Spreads Before and After Russian Crisis, and Results for “Variance Test”

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Regression coefficients estimated on 1998:01 to 1998:06 data.

Regression coefficients estimated on 1999:04 to 1999:12 data.

p values based on two-sided tests.

Table 8.

Robustness Checks

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Test results for the Mexican and Asian crises

We now discuss the results from applying our test procedures to the Mexican and Asian crises. Appendix Tables 9 to 11 contain the results for the Eichengreen-Mody specification (model (1) in Table 2).38 These results are presented mainly to facilitate a comparison with the existing literature, even though we do not think that these two episodes constitute valid experiments for a test of moral hazard. As in the previous subsection, the crisis period itself is excluded from the regressions.

Table 9.

Launch Spread Data: Estimation Results Before and After Mexican and Asian Crises. and “Slope Test” 1/

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Using model (1) of Table 2 in both cases. Regression (1) is based on the sample 1994:01 - 1997:06, regression (2) on the sample 1996:01 - 1998:06 All estimations use robust standard errors.

1994:01 - 1994:11

1993:06- 1997:06

1996:01 - 1997:06

1998:01 - 1998:06

p values based on two-sided tests.

Sample in model (1): Argentina, Brazil, Chile, China, Colombia, Cyprus, Hong Kong SAR, Hungary, India, Indonesia, Israel, Korea, Malaysia, Malta, Mexico, the Philippines, Singapore, Taiwan Province of China, Thailand, Trinidad and Tobago, Turkey, Uruguay, and Venezuela. Sample in model (2): Argentina, Brazil, Chile, China, Colombia, Cyprus, the Czech Republic, Hong Kong, Hungary, India, Indonesia, Israel, Jordan, Korea, Malaysia, Malta, Mauritius, Mexico, Pakistan, Peru, Philippines, Poland, Romania, Saudi Arabia, Singapore, the Slovak Republic, South Africa, Taiwan, Thailand, Trinidad and Tobago, Tunisia, Turkey, Uruguay, and Venezuela.

Reports only coefficients for instruments and correlation coefficient of disturbance terms of the two equations (rho).

Table 10.

Launch Spread Data; Summary Results for “Levels Test,” Mexican and Asian Crises

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Periods in which fitted spread based on post-crisis model is significantly higher than fitted spread based on pre-crisis model.

Periods in which fitted spread based on post-crisis model is significantly lower than fitted spread based on pre-crisis model.

Estimated average increase or decrease of spreads in basis points for all significant increases/decreases.