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The Costs of Sovereign Default

Author(s):
Eduardo Borensztein, and Ugo Panizza
Published Date:
October 2008
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I. Introduction

There is broad consensus in the economic literature that the presence of costly sovereign defaults is the mechanism that makes sovereign debt possible (Dooley, 2000). In the case of sovereign debt, creditor rights are not as strong as in the case of private debts. If a private firm becomes insolvent, creditors have a welldefined claim on the company’s assets even if they may be insufficient to cover the totality of the debt. These legal rights are necessary for private debts to exist.2 In the case of a sovereign debt, in contrast, the legal recourse available to creditors has limited applicability because many assets are immune from any legal action, and uncertain effectiveness because it is often impossible to enforce any favorable court judgment.3 But the literature sustains that sovereign debt markets are still viable because, if defaults are costly in some way to the borrowing country, there will be an incentive to repay debts, regardless of the effectiveness of legal recourse. It is noteworthy that we use the term default to encompass any situation in which the sovereign does not honor the original terms of the debt contract, including voluntary restructurings where there is a loss of value for the creditors. This is entirely in line with the concept applied by credit rating agencies.

There is much less agreement on what the costs of default actually are, let alone their magnitude. Traditionally, the sovereign debt literature has focused on two mechanisms: reputational costs, which in the extreme could result in absolute exclusion from financial markets, and direct sanctions such as legal attachments of property and international trade sanctions imposed by the countries of residence of creditors. The reputational cost of default has a well-established theoretical and historical tradition, with Eaton and Gersovitz (1981) presenting the canonical, formal model. An influential paper by Bulow and Rogoff (1989), however, casts doubts on the validity of the reputational cost, and points instead to direct sanctions—such as trade embargoes—as the only viable mechanism that makes governments repay their debts. While their argument may not be robust to other model specifications, there is a widespread body of literature based on the sanctions view.4 But there is comparatively little work on assessing the empirical relevance of these mechanisms. An exception is Tomz (2007), who based on an extensive review of historical case studies, finds widespread evidence in favor of the importance of reputation in financial markets, in contrast to the view that seemed to prevail earlier (for example, Lindert and Morton, 1989).5

More recently, recognizing that holders of government debt are not only foreign investors (in fact, perhaps a majority of investors in government bonds are domestic institutions and resident individuals in many cases nowadays) more attention has been paid to the consequences of default for the domestic economy, in particular the banking sector.

This channel is particularly relevant because, in many emerging economies, banks hold significant amounts of government bonds in their portfolios. Thus, a sovereign default would weaken their balance sheets and even create the threat of a bank run. To make matters worse, banking crises are usually resolved through the injection of government “recapitalization” bonds and central bank liquidity. But in a debt crisis, government bonds have questionable value and the domestic currency may not carry much favor with the public either. A corollary of the domestic economic costs of debt crises is that they may also involve a political cost for the authorities. A declining economy and a banking system in crisis do not bode well for the survival in power of the incumbent party and the policymaking authorities. While such linkage has been noted in the case of currency devaluations, for example, it has not been explored in the case of debt defaults.

This paper evaluates empirically each one of the suspected mechanisms through which default costs may affect a sovereign government. It should be recognized at the outset that it is quite difficult to find econometrically sound ways to isolate the costs of default. For instance, while it is easy to find a negative correlation between default and growth, it is much more difficult to test whether this negative correlation is driven by the default episode per se or by a series of other factors that are the cause of both the debt default and an economic recession. Moreover, it is also hard to identify the direction of causality between growth and default.

Thus, this paper has more modest objectives. Rather than attempting to quantify precisely the costs of default on sovereign debt, the objective is to evaluate if there is some empirical basis for—or lack of evidence against—each one of the mechanisms that are believed to be relevant, and perhaps discard those mechanisms that appear to be less consistent with the data. In addition to the traditional reputational and trade sanctions, the paper explores the significance of effects that operate through the domestic banking system and the political costs of default for the government.6

Identifying the channel and magnitude of the costs of sovereign default with some degree of precision would be important for a number of reasons. The “default point” for a sovereign should be the point at which the cost of servicing debt in its full contractual terms is higher than the costs incurred from seeking a restructuring of those terms, when these costs are comprehensively measured. An accurate measure of the default point is necessary, for example, to assess how “safe” a certain level of debt is, namely, how likely it is that an economic shock would trigger a situation of default.7 In fact, it is not possible to compute the probability of default, or to price a sovereign bond without making a judgment about the default point.

From a policy perspective, an understanding of the channels through which default costs apply can help design initiatives to improve the functioning of international financial markets and lower the cost of borrowing for many sovereigns. For example, if the costs of default apply largely through international trade, a more open economy would have a higher default point than a more closed economy, other things equal, and would be less risky for lenders, which would result in lower borrowing costs.

This paper analyzes the incidence of four types of cost that may result from an international sovereign default: reputational costs, international trade exclusion costs, costs to the domestic economy through the financial system, and political costs to the authorities. We find that reputational costs, as reflected in credit ratings and interest rate spreads, are significant but appear to be short-lived; that despite evidence that trade and trade credit are negatively affected by default, controlling for trade credit does not seem to modify the effect of default on trade; that growth in the domestic economy suffers, and more so in cases where the causes for default seem less compelling, although this effect also seems to be short-lived; that default episodes seem to cause banking crises and not vice versa, but that—outside of banking crisis episodes—more credit dependent industries do not suffer more than other industries following a sovereign default; and that the political consequences of a debt crisis are dire for incumbent governments and finance ministers, broadly in line with what happens in currency crises.

We start by briefly describing our data on sovereign defaults and the evolution of default episodes over the last two hundred years (Section II). Next, we look at the relationship between default and GDP growth (Section III), and three possible channels that may lead to costly defaults: the relationship between default and borrowing costs (Section IV), the relationship between default and international trade (Section V), and the relationship between default and banking crises (Section VI). In section VII, we focus on the political cost of default, and in Section VIII we conclude.

II. Two Hundred Years of Sovereign Default

Dating sovereign default episodes and measuring their duration is not a straightforward exercise. Table A1 in the Appendix uses four different sources to classify default episodes over the last two hundred years.8 While there is substantial coincidence between the four sources, the match is far from perfect. There are, for instance, several episodes that are classified as defaults by Standard & Poor’s but not classified as defaults by Beim and Calomiris (2000), and also a few episodes that are classified as defaults by Beim and Calomiris (2000) and not by Standard and Poor’s. There are also differences in the methodology used to measure the length of a default episode. Beim and Calomiris (2000), for instance, find fewer but longer lasting default episodes because they tend to merge into a unique episode defaults that occurred within five years. The methodology used by Detragiache and Spilimbergo (2001), instead, leads to code as defaults several episodes that are not classified as defaults by Standard and Poor’s.9 Largely on the basis of its completeness, the rest of the paper will use Standard and Poor’s classifications as reported in the first four columns of Table A1.

Figure 1 shows the number of default episodes by geographical area for the period from 1824 to 2004. Latin America is the region with the highest number of default episodes at 126, Africa, with 63 episodes, is a distant second. The Latin American “lead” is, however, largely determined by the fact that Latin American countries gained independence and access to international financial markets early in the 19th century, while most African countries continued to be European colonies for another 100 or 150 years Among the developing regions, Asia shows the lowest number of defaults. Table 1 groups the various default episodes by time period and geographical area. Besides reporting the number of episodes, the table also reports the average length of the episodes.

Figure 1:Number of Defaults

(1824-2004)
Table 1.Default Episodes
PeriodAFRICAASIAEAST EUROPELATIN AMERICAWEST EUROPEALL
BondBankAllBondBankAllBondBankAllBondBankAllBondBankAllBondBankAll
N. of Episodes1824-18400000001011401440419019
Average Length0.00.00.00.00.00.052.00.052.021.40.021.424.80.024.823.70.023.7
N. of Episodes1841-1860000000000505101606
Average Length0.00.00.00.00.00.00.00.00.08.00.08.06.00.06.07.70.07.7
N. of Episodes1861-18803031010001401410119019
Average Length10.00.010.05.00.05.00.00.00.014.10.014.12.00.02.012.40.012.4
N. of Episodes1881-19000000002021601610119019
Average Length0.00.00.00.00.00.02.00.02.03.40.03.49.00.09.03.60.03.6
N. of Episodes1901-19205051012021101110120020
Average Length1.60.01.613.00.013.02.50.02.55.30.05.31.00.01.04.30.04.3
N. of Episodes1921-19401014048082002060639039
Average Length3.00.03.07.30.07.312.60.012.69.30.09.36.70.06.79.20.09.2
N. of Episodes1941-1960000101202101000404
Average Length0.00.00.010.00.010.013.50.013.51.00.01.00.00.00.09.50.09.5
N. of Episodes1961-1970101000000101000202
Average Length15.00.015.00.00.00.00.00.00.01.00.01.00.00.00.08.00.08.0
N. of Episodes1971-198005503300007700001515
Average Length0.013.613.60.016.016.00.00.00.00.04.94.90.00.00.00.010.010.0
N. of Episodes1981-1990133340660555242900066874
Average Length2.08.58.40.010.010.00.05.65.63.68.77.80.00.00.03.38.58.1
N. of Episodes1991-2004311141565712538000142640
Average Length2.05.14.41.02.22.01.45.73.91.64.02.50.00.00.01.64.63.5

As noted by Sturzenegger and Zettelmeyer (2006), default episodes tend to happen in clusters and usually follow lending booms. The first cluster of defaults happened in the period that spans from 1824 to 1840 and followed a lending boom driven by the newly acquired independence of most Latin American countries. Out of 19 default episodes recorded during this period, 14 involved Latin American countries. The other 5 default episodes involved Greece, Portugal, and Spain (three episodes). The average length of the default episodes of this period (more than twenty years) suggests difficult restructuring processes.

The following period (1841–1860) was relatively tranquil and comprised only six default episodes. However, a lending boom developed at this time, which soon resulted in a new series of default episodes (see Suter, 2003 and Lindert and Morton, 1989). The period that goes from 1861 to 1920 was characterized by 58 default episodes, including 41 episodes in Latin America and 8 in Africa.10 Resolution of default improved dramatically in speed, with the length of the average default period dropping to less than five years by 1881–1920.

The next wave of defaults was associated with the Great Depression and the Second World War. The 1921–1940 period was punctuated by 39 default episodes. Again, more than half of these defaults happened in Latin American countries and more than one third of them (16 episodes) in Europe. This is the last period in which we observe debt default episodes among Western European countries.

By the end of the War, most developing countries had completely lost access to the international capital market. As a consequence, over the period that goes from 1941 to 1970 we observe very few default episodes (six episodes in total).11 Lending to developing countries restarted timidly in the 1960s, but exploded after the oil shock of 1973 created the need of recycling the earnings of oil-producing countries. One feature that differentiated the lending boom of the 1970s from previous ones is the vehicle used to extend credit to developing countries. While in previous episodes developing countries borrowed by issuing bonds, in the 1970s most of the lending to developing countries took the form of syndicated bank loans. While the lending instrument was different, the fate of the lending boom did not differ, and the tranquil period was soon followed by a chain of defaults. Already in the 1970s, we observe 15 episodes of defaults on syndicated bank loans. The “debt crisis,” however, did not erupt until the Mexican payment suspension of August 1982, which was soon followed by more than 70 default episodes (34 episodes involving African countries and 29 involving Latin American countries).

As in previous cases, credit to developing countries (including to countries that did not experience debt service disruptions) died out in the aftermath of the crisis and did not restart until the end of the restructuring process. The average default lasted approximately 9 years, which suggests that restructuring syndicated bank loans was more cumbersome than restructuring international bonds. Eventually the defaulted bank loans were restructured by issuing new, partly collateralized, bonds that took the name of Brady Bonds (after the name of U.S. Treasury Secretary Nicholas Brady who was main architect of the restructuring process).

The Brady Plan played a key role in creating a bond market for debt issued by emerging market countries and, together with low interest rates in the United States, contributed to a new lending boom to emerging market countries (see Calvo et al, 1993). The defaults that followed this new lending boom are recent history. Over the 1991–2004 period, we observed 40 defaults (14 on bonds and 26 on syndicated bank loans). Most of the syndicated bank loan defaults took place in Africa, where the bond instrument had not become widely used yet, while most of the bond defaults took place among Latin American issuers.

III. Default and GDP Growth

As a first stab at the issue at hand, we examine the effect of default on GDP growth. While this may not distinguish between competing theories of default costs, it can say something about the significance and lag structure of the costs. In addition, we are interested in exploring if the GDP costs are higher for countries that default in circumstances that seem less easily identified as an insolvency problem, what could in principle identify cases of “strategic” default.

In Table 2, following Sturzenegger (2005), we present results from several regressions aimed at estimating the relationship between default and growth. In all regressions we use an unbalanced panel that includes up to 83 countries for the 1972–2000 period, and estimate the following model:

Table 2.Default and Growth, Panel 1972–2000
(1)

GROWTH
(2)

GROWTH
(3)

GROWTH
(4)

GROWTH
INV_GDP1.211

(8.63)***
1.152

(8.08)***
1.205

(8.58)***
1.146

(8.04)***
POP_GR-0.120

(1.22)
-0.119

(1.22)
-0.121

(1.24)
-0.118

(1.20)
GDP_PC70s-0.121

(7.25)***
-0.124

(7.34)***
-0.121

(7.24)***
-0.125

(7.37)***
SEC_ED0.014

(1.62)
0.018

(2.03)**
0.014

(1.63)
0.018

(2.03)**
POP0.004

(6.32)***
0.004

(6.72)***
0.004

(6.30)***
0.004

(6.66)***
GOV_C12.965

(2.91)***
2.974

(2.89)***
2.970

(2.89)***
3.000

(2.89)***
CIV_RIGTH-0.026

(0.37)
-0.033

(0.45)
-0.026

(0.37)
-0.035

(0.49)
DTOT-0.270

(0.22)
-0.111

(0.10)
-0.277

(0.23)
-0.082

(0.07)
OPEN2.149

(3.50)***
2.156

(3.50)***
2.151

(3.49)***
2.146

(3.48)***
SSA-0.859

(2.84)***
-0.832

(2.70)***
-0.839

(2.73)***
-0.788

(2.54)**
LAC-0.399

(1.60)
-0.430

(1.70)*
-0.367

(1.45)
-0.355

(1.39)
TRANS-0.064

(0.10)
-0.266

(0.44)
-0.071

(0.11)
-0.268

(0.44)
BK_CR-1.087

(4.64)***
-1.068

(4.53)***
-1.092

(4.65)***
-1.080

(4.57)***
DEF-1.239

(4.32)***
-1.184

(3.82)***
-1.282

(4.38)***
-1.370

(4.06)***
DEF_B-1.388

(2.11)**
-1.291

(1.93)*
DEF_B10.481

(0.87)
0.916

(1.49)
DEF_B2`0.337

(0.63)
0.495

(0.82)
DEF_B30.994

(1.55)
1.242

(1.90)*
END_DEF-0.665

(1.14)
-1.135

(1.77)*
END_DEF10.002

(0.00)
0.003

(0.01)
END_DEF20.122

(0.22)
-0.384

(0.70)
Constant1.387

(2.16)**
1.474

(2.28)**
1.389

(2.16)**
1.471

(2.28)**
Observations2048198520481985
R-squared0.220.220.220.22
Robust t-statistics in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%
Robust t-statistics in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%

Where GROWTHit is per capita annual real GDP growth in country i and year t, X is a matrix of controls,12 and DEFAULT is a set of dummy variables tracking default episodes. In column 1, the variable DEF takes a value of one each year that a country is in default and zero otherwise. We find that, on average, default is associated with a decrease in growth of 1.2 percentage points per year. This figure is consistent with Sturzenegger’s (2005) finding that default has a negative effect on growth that ranges between 0.5 and 2 percentage points.

We next explore the dynamic structure of the impact of default. In column 2, we augment the regressions with a variable that takes a value of one at the beginning of the default episode (DEF_B) and three lags of this variable (DEF_B1, DEF_B2, and DEF_B3). We find that the impact of default seems to be short-lived. We estimate a large effect in the first year of the default episode (with a drop in growth of 2.6 percentage points), and we find no statistically significant effect of the lagged default variables. This is consistent with results in Levy Yeyati and Panizza (2005), who, using quarterly data, find that crises precede defaults, and that defaults tend to occur at the trough of the recession.

As a check on the validity of the above result, we test whether the estimated negative effect of default is in fact an artifice of the rebound in growth that tends to occur in the post-default years. To control for this possibility, in columns 3 and 4 we augment the regressions with a dummy variable that takes a value of one when a country exits from default (END_DEF) and two lags of this variable.13 We find that these dummy variables are not statistically significant and do not affect the estimated effect of default in the original regressions.

The direction of causality in the relationship between sovereign defaults and growth raises some questions. While the previous regressions suggest a robust association between debt defaults and low growth, they are only indicative of a correlation between the two variables. Debt defaults are usually a consequence of some economic shocks, such as terms of trade shocks, sudden stops, currency crises, etc. that also hurt growth in a direct fashion. While the regressions of Table 2 control for some of these effects (for instance, they control for banking crises) they cannot account for all the variables that jointly affect the probability of a sovereign default and an economic recession. Hence, lower growth might not be the consequence of default per se but of other factors that also affect debt sustainability.

One strategy to get closer to the “true” cost of default is to attempt to decompose the effect of default on economic growth in two parts: the effect owing to all the variables which are themselves determinants of defaults, and the residual effect, which we attribute to the result of the act of default itself. More precisely, the default dummy can be statistically divided into two components:

where pred_defi,t denotes the predicted probability of default obtained by running a logit regression of defaulti,t on a set of standard predictors of default, and vi,t is the error term of the logit model.14

Within this set-up, pred_defi,t captures the predicted effect of default and proxies for the fact that an increase in the probability of default may have a direct effect on growth, while vi,t captures the additional effect following from of the act of default itself. After having estimated the anticipated and unanticipated component of default, we can include these two variables in a set of regressions similar those of Table 2 and gauge their distinct effect on growth. As we predict default using a non-linear model, this strategy is similar but not identical to directly adding to the original growth regression all the variables used to predict default.

Table 3 presents the main results. As the sample of Table 3 is smaller than that of Table 2 (843 versus 2048 observations),15 we start by re-estimating the basic model of Table 2 for the restricted sample and check whether there are any differences in the estimated cost of default and we find that the results are basically unchanged (column 1 Table 3). In particular, we find that the effect of default is a bit smaller but, at 1 percent, still sizable, and it is still highly statistically significant.

Table 3.Default and Growth, Panel 1972–2000
(1)

GROWTH
(2)

GROWTH
(3)

GROWTH
(4)

GROWTH
INV GDP1.607

(5.11)***
1.584

(5.00)***
1.635

(4.58)***
1.584

(5.03)***
POP_GR-0.331

(1.35)
-0.337

(1.37)
-0.319

(1.16)
-0.338

(1.38)
GDP_PC70s-0.259

(1.38)
-0.269

(1.43)
-0.300

(1.53)
-0.275

(1.46)
SEC_ED0.036

(1.56)
0.037

(1.59)
0.039

(1.63)
0.037

(1.60)
POP0.006

(5.36)***
0.006

(5.29)***
0.005

(4.12)***
0.006

(5.25)***
GOV_C13.402

(2.95)***
3.281

(2.76)***
3.084

(2.45)**
3.299

(2.75)***
CIV_RIGTH-0.090

(0.71)
-0.093

(0.73)
-0.050

(0.36)
-0.092

(0.72)
DTOT-2.271

(1.20)
-2.333

(1.23)
-2.133

(1.16)
-2.342

(1.24)
OPEN1.764

(1.52)
1.816

(1.55)
1.677

(1.31)
1.818

(1.55)
SSA-0.542

(1.16)
-0.510

(1.08)
-0.637

(1.23)
-0.520

(1.07)
LAC-0.508

(1.41)
-0.457

(1.26)
-0.381

(1.03)
-0.460

(1.26)
TRANS-2.443

(2.53)**
-2.437

(2.53)**
-2.216

(2.02)**
-2.430

(2.50)**
BK_CR-1.364

(3.81)***
-1.328

(3.73)***
-1.188

(3.36)***
-1.324

(3.71)***
DEF-1.043

(3.15)***
DEF_PR-1.440

(2.30)**
-1.246

(1.89)*
-1.443

(2.29)**
DEF_U-0.930

(2.46)**
-1.037

(2.69)***
-0.930

(2.32)**
DEF_PRB-13.700

(2.13)**
DEF_PRB10.330

(0.07)
DEF_PRB23.506

(0.99)
DEF_PRB3-0.929

(0.24)
DEF_UB0.000

(0.00)
DEF_UB11.098

(1.71)*
DEF_UB20.860

(1.32)
DEF_UB30.898

(1.17)
END_DEF-0.237

(0.36)
END_DEF10.266

(0.49)
END_DEF20.149

(0.24)
Constant2.629

(1.95)*
2.660

(1.97)**
-0.435

(0.30)
2.662

(1.97)**
Observations843843726843
R-squared0.260.260.280.26
Robust t-statistics in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%
Robust t-statistics in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%

The split between anticipated and unanticipated components of default reveals that both variables are statistically significant. The estimate reported in column 2 of the anticipated effect (DEF_PR), at 1.4 percent, is slightly larger than the unanticipated component (DEF_U), which is close to 1.0 percent. This suggests that the default decision itself may involve significant collateral costs for the domestic economy.

In column 3, we estimate the dynamic structure of the anticipated and unanticipated components of default.16 We find that the anticipated default effect (DEF_PRB) is on impact negative, quite large, and statistically significant (we investigated whether the large coefficient of DEF_PRB was due to the presence of outliers but were unable to find evidence in this direction).

In contrast, while we find that the unanticipated component of default is still large and statistically significant, we find no significant negative effect in the first year. In the last column, we augment the regression in column 2 with END_DEF and its two lags and we find that the results are unchanged.17

We note that an alternative interpretation of the effect of the unexpected portion of the default variable is that it captures the cost of “unjustified” defaults, under the assumption that the magnitude of the costs of default to a country depends on whether the default was unavoidable or resulted from a weak willingness to pay. Much of the sovereign debt literature emphasizes the distinction between “ability” to pay and “willingness” to pay. The markets would punish debtors in the latter case, but will be more forgiving in the former case (see Grossman and van Huyck, 1988).18 From this perspective, the specification above can be interpreted as a measure of the degree to which a default was justified by fundamental economic conditions.

We now turn to the investigation of the specific channels through which default may have a negative impact on growth.

IV. Default and Reputation

As argued in section I, whether reputation has a significant effect or not plays a key role on the timing and the circumstances under which a sovereign will initiate a debt restructuring action. Studies that provide empirical evidence in support to the “reputation view” include English (1996) and Tomz (2007). English (1996) focuses on defaults by U.S. states in the 19th century and argues that, since foreign creditors could not impose trade embargoes on the U.S. states, states that paid back their debt did so for reputational reasons alone, and not because of the threat of sanctions. He also shows that debt repudiation did result in exclusion from the capital market, and that states that repaid their debt were able to borrow more than those who did not repay. Tomz (2007) uses the case study method to argue that reputation is the main reason why countries repay their debt. In particular, he deconstructs the conventional argument that in the 1930s Argentina repaid its debt to avoid a trade embargo from the U.K. (Diaz-Alejandro, 1983) and provides evidence suggesting that Argentina repaid its foreign debt in order to strengthen its reputation of good debtor.19

Whatever were the reasons that led Argentina or the U.S. states to repay their debts, there is by now agreement on the fact that default does not lead to a permanent exclusion from the international capital market. In fact, the evidence suggests that, while countries lose access during default, once the restructuring process is fully concluded, financial markets do not discriminate, in terms of access, between defaulters and non-defaulters. External factors and the mood of foreign investors seem to be far more important than default history in determining access to the international capital market. One example of this behavior can be found by observing that in the period that goes from the 1930s to the 1960s all Latin American countries were excluded from the world capital market, and this exclusion reached both countries that defaulted in the 1930s and countries, like the case of Argentina commented above, which had made a successful effort to avoid default. The recent lending booms and default experiences also provide evidence in the same direction. Several countries that had defaulted in the 1980s were able to attract large capital flows in the 1990s and countries that defaulted in the late 1990s regained access to the international capital market almost immediately after their debt restructurings. In fact, Gelos et al. (2004) find that countries that defaulted in the 1980s were able to regain access to international credit in about 4 years.

There is some evidence suggesting that markets also discriminate in terms of cost of credit, in the sense that default history is positively correlated with borrowing costs. What is not clear, however, is whether this effect is long lasting or not. In what follows, we review the existing literature and provide some new evidence.

Studies that measured the impact of default on borrowing costs have focused on both indirect and direct measures. The main indirect measure in this line of work is a country’s credit rating. This is a relevant measure because credit ratings tend to be highly correlated with borrowing costs. Cantor and Packer (1996) were among the first to highlight the link between default history and credit ratings. In their study, they collect data for approximately 50 countries and regress credit ratings in 1995 on a set of eight explanatory variables, and find that this relatively small set of independent variables explains more than 90 percent of the variance in credit ratings.20 They also find that a dummy variable that takes value one for countries that defaulted after 1970 is highly significant and associated with a drop of two notches in a country’s credit rating. Along similar lines, Reinhart et al. (2003) find that a history of default is associated with lower ratings assigned by the Institutional Investor publication.

One important question that the literature does not seem to address is whether default has a long term impact on credit ratings. That is, how long is the markets’ memory? To answer this question, we estimate the following cross country model:

Where RATING measures average credit ratings over the 1999–2002 period, X is a set of explanatory variables also measured over the 1999–2002 period and DEFAULT is the variable measuring previous history of default.21

We measure credit ratings by converting Standard and Poor’s foreign-currency long- term credit ratings into numerical values (20 corresponds to AAA, 19 to AA+, 18 to AA, and so forth, all the way down to selective default rating, SD, which is assigned a value of zero). In selecting the explanatory variables we follow Cantor and Packer (1996) and include the log of GDP per capita (LGDP_PC), GDP growth (GDPGR), the log of inflation (LINF), the central government balance scaled by GDP (CG_BAL takes positive values for fiscal surpluses and negative values for deficits), the external current account balance scaled by GDP (CA_BAL), external debt over exports (EXDEXP), and a dummy variable that takes value one for industrial countries (IND).22 In column 1 of Table 4 we follow Cantor and Packer (1996) and measure the history of default with a dummy variable that takes value one if country i has defaulted over the 1970–2002 period and zero otherwise. Most variables have the expected sign and are statistically significant (the exceptions are GDP growth which has the wrong sign but is not statistically significant and the current account balance which has the expected sign but is not statistically significant). As in Cantor and Packer (1996), we find that this limited set of control variables explains more than 90 percent of the cross-country variance of credit ratings (the R2 of the regression is 0.91). We also find that default history is negatively correlated with credit ratings. In particular, our point estimates indicate that default history leads to a drop in credit rating of 1.7 notches, slightly lower than the estimate of Cantor and Packer (2.5 notches).

Table 4.Default and Credit Ratings, Cross Section Regression, 1999–2002
(1)

RATING
(2)

RATING
(3)

RATING
(4)

RATING
LGDP PC1.627

(4.69)***
1.418

(3.83)***
1.215

(3.20)***
1.366

(3.47)***
GDPGR-1.968

(0.42)
-4.273

(1.06)
-5.324

(1.07)
-4.888

(0.91)
LINF-0.707

(3.48)***
-0.817

(3.88)***
-0.727

(3.04)***
-0.932

(3.65)***
CG_BALW14.131

(2.61)**
6.899

(1.26)
8.079

(1.50)
9.411

(1.20)
CA_GDPW3.011

(0.64)
-2.800

(0.74)
-1.679

(0.40)
-1.697

(0.41)
EXDEXPGDF-0.834

(2.67)***
-0.776

(3.05)***
-0.750

(2.03)**
-0.761

(2.13)**
IND2.549

(2.63)**
2.685

(2.97)***
2.839

(2.96)***
2.847

(2.66)**
DEFAULT-1.669

(3.10)***
-1.486

(2.86)***
-1.855

(3.57)***
DEBT_GDP-0.022

(2.99)***
-0.020

(2.16)**
-0.020

(2.73)***
OR_SIN-1.368

(2.42)**
-1.212

(1.84)*
-1.143

(1.56)
SDTOT-4.102

(0.70)
DEF18000.620

(1.09)
DEF1900_50-0.017

(0.03)
DEF1950_700.426

(0.56)
DEF1970_80-0.043

(0.06)
DEF1980_90-1.049

(1.35)
DEF1990_950.080

(0.08)
DEF1995_02-1.897

(2.79)***
Constant0.394

(0.14)
4.181

(1.28)
6.077

(1.88)*
2.313

(0.73)
Observations68595568
R-squared0.910.940.950.92
Robust t-statistics in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%
Robust t-statistics in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%

In column 2, we add two control variables that have been used in previous studies. The first variable is public debt over GDP (DEBT_GDP) and the second is the index of original sin (OR_SIN), developed by Eichengreen et al. (2005). Both variables have the right sign and are statistically significant. While we lose 16 observations, the results are essentially unchanged. In column 3, we augmented the regression of column 2 with the standard deviation of the terms of trade (SDTOT) of the period 1991–2002. This variable has the right sign but is not statistically significant; the other results do not change. In column 4, we use a specification similar to the one of column 1 but substitute the default dummy with seven dummy variables aimed at tracking default history (DEF1800 takes value 1 for countries that defaulted in the 19th century and zero otherwise; DEF1900_50 takes value 1 for countries that defaulted over the 1900–1950 period; DEF1950_70 takes value 1 if countries that defaulted over the 1950–1970 period; and so forth for the remaining 4 dummies).

The results indicate that defaults episodes do not have a long-term impact on credit ratings. In fact, only defaults in the 1995–2002 period are significantly correlated with credit ratings over the 1999–2002 period.

Next, we look at the direct impact of default on borrowing costs. Empirical studies of the effect of default on borrowing cost can be divided in three groups: (i) papers that do not find any effect of default on borrowing cost; (ii) papers that find a long-lasting but small effect of defaults on borrowing costs; and (iii) papers that find a temporary and rapidly decaying effect of default on borrowing cost.

The first group of papers includes work by Lindert and Morton (1989) and Chowdry (1991) who find that countries that defaulted in the 19th century and in the 1930s did not suffer higher borrowing cost in the 1970s, and more recent work by Ades et al (2000) who find that default history had no significant effect on sovereign spreads in the late 1990s.

The second group of papers includes Eichengreen and Portes (1995) who focus on bonds issued in the 1920s and find that recent defaults were associated with an increase in spreads of approximately 20 basis points but that earlier defaults had no impact on borrowing cost, and Ozler (1993) who focuses on sovereign bank loans extended over the 1968–1981 period and finds a small but statistically significant effect of default in the 1930s. While Ozler’s findings suggest that default history has a long term impact, it is worth noting that her estimates do not seem to cluster the standard errors and, back-of-the-envelope, calculations suggest that clustering would substantially reduce the explanatory power of default in the 1930s. Dell’Ariccia et al. (2002) also find that defaults have a long-lasting effect and show that countries that participated in the Brady exchange suffered higher borrowing costs in the late 1990s. They also show that the effect of the Brady exchange on borrowing costs increased after the Russian crisis of 1998.

The third group of papers includes recent work by Flandreau and Zumer (2004) who focus on the 1880–1914 period and find that default is associated with a jump in spreads of about 90 basis points in the year that follows the end of a default episode but that the effect of default on spreads declines very rapidly over time.

Table 5 reports a set of simple regressions aimed at explaining emerging market sovereign spreads over the 1997–2004 period. We use an unbalanced panel of up to 31 countries to regress the yearly average of EMBI global spreads over a set of standard controls and a set of variables that track default history (in all regressions we drop the observations for countries that are in default in the current year). The controls include the log of GDP per capita (LGDP_PC), the log of inflation (LINF), the fiscal balance scaled by GDP (CG_BAL), the current account balance scaled by GDP (CA_BAL), and the ratio of external debt over exports (EXDEXP). The default variables include a dummy taking a value of one if country’s i last default was in year t-1 (DEF_1YR), a dummy variable taking a value of one if country’s i last default was in year t-2 (DEF_2YRS), a dummy variable taking a value of one if country’s i last default was between year t-3 and year t-5 (DEF3_5YRS), a dummy variable taking a value of one if country’s i last default was between year t-6 and year t-10 (DEF6_10YRS), and a dummy variable taking a value of one if country’s i last default was between year t-11 and year t-25 (DEF11_25YRS).

Table 5.Defaults and Bond Spreads, Panel Regression, 1997–2004
(1)

EMBIG
(2)

EMBIG
(3)

EMBIG
(4)

EMBIG
(5)

EMBIG
(6)

EMBIG
(7)

EMBIG
(8)

EMBIG
LGDP PC-200.578

(4.08)***
-1424.802

(4.97)***
-218.969

(4.70)***
-1237.708

(4.94)***
-216.274

(3.32)***
-1663.319

(5.53)***
-47.260

(0.90)
-1172.255

(3.89)***
LINF46.061

(2.95)***
25.281

(1.55)
55.359

(3.70)***
31.052

(1.96)*
54.589

(2.97)***
33.042

(1.70)*
36.787

(2.15)**
26.325

(1.35)
CG-BALW-446.783

(0.68)
635.532

(0.85)
-718.671

(1.06)
99.209

(0.13)
373.806

(0.59)
237.916

(0.31)
CA-GDPW665.056

(1.73)*
-342.523

(0.81)
794.891

(1.93)*
-454.604

(0.98)
465.100

(1.20)
-757.808

(1.53)
EXDEXPGDF166.770

(5.27)***
207.386

(4.53)***
169.660

(5.71)***
213.708

(4.80)***
192.966

(5.52)***
246.435

(5.13)***
96.262

(3.24)***
189.341

(3.88)***
DEF1YEAR412.863

(3.39)***
307.746

(2.52)**
433.912

(3.95)***
305.783

(2.68)***
389.342

(3.04)***
249.764

(2.04)**
267.770

(2.42)**
249.175

(2.03)**
DEF2YRS246.746

(2.10)**
188.244

(1.63)
267.262

(2.52)**
162.114238.877145.339134.276144.640
(1.49)(1.99)**(1.26)(1.33)(1.25)
DEF3 5YRS122.262

(1.28)
61.572

(0.70)
169.914

(1.92)*
68.725

(0.81)
105.895

(1.07)
14.997

(0.17)
4.983

(0.06)
14.682

(0.16)
DEF6_10YRS112.608

(1.31)
39.982

(0.64)
123.758

(1.53)
45.416

(0.73)
104.661

(1.17)
32.995

(0.53)
14.330

(0.21)
32.061

(0.51)
DEF11_25YRS116.623

(1.25)
123.956

(1.38)
101.621

(1.06)
12.180

(0.17)
RATING_RES-40.583

(2.03)**
-52.359

(2.55)**
RATING-62.546

(4.99)***
-51.225

(2.49)**
Constant1375.104

(3.60)***
11054.177

(4.92)***
1523.830

(4.19)***
9563.936

(4.87)***
1671.025

(3.38)***
13401.740

(5.56)***
1316.136

(3.74)***
9674.835

(4.18)***
Observations150150162162144144144144
Number of cc2929313127272727
R-squared0.560.530.580.58
Region Fixed EffectsYESYESYESYES
Country Fixed EffectsYESYESYESYES
Years Fixed EffectsYESYESYESYESYESYESYESYES
Absolute value of z-statistics in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%
Absolute value of z-statistics in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%

The excluded dummy is the one for countries that defaulted before year t-25 or never defaulted.23

Column 1 uses a random effects model that allows for region fixed effects and year fixed effects. We find that default in year t-1 has a large and statistically significant effect on spreads amounting to 400 basis points. The effect of default the following year is still sizable, 250 basis points, but not statistically significant. Longer-lasting effects are small and not statistically significant. Taken at face value, these results suggest that investors react strongly but have short memory—a result that is consistent with what Flandreau and Zumer (2004) found for the Gold Standard period. Column 2 uses a fixed effect model. As the five default dummies are collinear with the country fixed effects, we drop DEF11_25YRS. Hence, the results for the default dummies should be interpreted as differences with respect to countries that did not default after year t-10. The results are similar to those of the random effect model of column 1. Columns 3 and 4 repeat the models of column 1 and 2 but do not control for CG_BAL and CA_BAL (this allows us to include two extra countries in the sample). The results do not change significantly. In columns 5 to 8, we control for the effect of credit ratings. In columns 5 and 6 we use the residual of a rating regression that includes all the control variables (excluding default history) used in Table 4.24 While we find that ratings have a large and statistically significant effect on spreads (a one notch change in ratings is associated with a jump in spreads of 50 basis points), our finding that default episodes have a short-lived impact on spreads does not change. In columns 7 and 8, we substitute residual ratings with actual ratings and again find similar results.

V. Default and International Trade

While the idea that defaults may lead to some form of trade retaliation has been around for a long time (see, for instance, Diaz Alejandro, 1983), the empirical evidence on a link between default and trade is much more recent. Rose (2005) tests the hypothesis that defaults have a negative effect on trade by including an indicator variable for Paris Club debt renegotiations in a standard gravity trade model that uses bilateral trade data covering 200 countries over the 1948–1997 period. He finds that Paris Club debt renegotiations are associated with a decline in bilateral trade that lasts for 15 years and amounts to approximately eight percent per year. In Borensztein and Panizza (2006), we use industry-level data and find that sovereign defaults are particularly costly for export-oriented industries. However, unlike Rose (2005) we find that the effect of default on exports tends to be short-lived. One question that is not addressed by either Rose (2005) or Borensztein and Panizza (2006) concerns the channel through which default affects trade.

In principle, the reduction in trade following a debt default could come from restrictive measures imposed by the country of residence of the investors. This is the assumption often made by the theoretical debt literature. However, there is little historical record of countries imposing quotas or embargos on a country that falls in default. The current structure of international capital markets, where investors are increasingly anonymous bondholders who may switch from long to short positions in minutes, makes this traditional assumption more implausible nowadays. There is, however, a more likely scenario. The deterioration in the credit quality of exporting firms after the default (that results from the risk of imposition of capital or exchange controls) could make trade credit less available and more expensive. This would, in fact, have consequences similar to those of retaliatory measures. This is the conjecture that we test in this section.25

We study the relationship between default and trade credit using OECD data on net trade credit extended by OECD countries to developing countries and economies in transition. According to the OECD definition, trade credit measures loans for the purpose of trade which are not represented by a negotiable instrument. One problem with the OECD data set is that it only includes loans issued or guaranteed by the official sector and hence it may underestimate total trade credit. With this caveat in mind, we test the trade credit channel using and unbalanced panel to estimate the following equation:

Where NTCi,t is net trade credit scaled by international trade in country i in year t, DEFAULTi,t is a default dummy that takes a value of one if country i is in default in year t, Xi,t is a set of controls (X includes log inflation, log GDP, the change in terms of trade, the change in the real exchange rate, a variable measuring the level of democracy, and lagged trade), and μi is a set of country fixed effects (we also experimented with year fixed effects and our results were unchanged).26 We scale trade credit by trade to implicitly control for the decline in trade associated with defaults. Expressing trade credit as a share of total trade allows an interpretation of the coefficients of the regressions which is similar to the concept of elasticity. For instance, a negative value of α indicates that default episodes lead to a decrease in trade credit greater than the overall decline in trade.27

We start by estimating our baseline model and find that the default dummy has a negative and statistically significant effect on trade credit (column 1 of Table 6). In column 2, we explore the dynamic effect of default by augmenting the model with two dummies that take a value of one in the first and second year of the default episode (DEF_EP take value one in the first year of the default episode and DEF_EP1 is a one-year lag of DEF_EP). We find that the effect of default is smaller in the first year of the default episode (this is probably due to the fact that defaults do not always happen at the beginning of the year) and larger (although the coefficient is not statistically significant) in the second year. In columns 3 and 4, we control for lagged trade and find that including this variable does not affect our baseline estimates.

Table 6.Default and Trade Credit
(1)

NEC
(2)

NEC
(3)

NEC
(4)

NEC
(5)

NEC
(6)

NEC
Estimation method:Fixed EffectsArellano and Bond
DEFAULT-0.800

(5.85)***
-0.800

(5.74)***
-0.800

(5.95)***
-0.900

(5.85)***
-0.134

(4.88)***
0.011

(0.39)
LINF0.000

(0.14)
0.000

(0.21)
0.000

(0.11)
0.000

(0.17)
-0.032

(7.51)***
-0.038

(10.24)***
LGDP0.600

(2.72)***
0.600

(2.72)***
0.400

(1.53)
0.400

(1.55)
0.007

(0.08)
-0.029

(0.34)
DTOT0.000

(0.12)
0.000

(0.03)
0.100

(0.38)
0.100

(0.28)
-0.599

(9.53)***
-0.533

(7.92)***
DRER-0.100

(1.74)*
-0.100

(1.73)*
-0.300

(2.50)**
-0.300

(2.50)**
-0.266

(4.78)***
-0.259

(4.69)***
DEMOC0.000

(1.31)
0.000

(1.42)
0.000

(1.13)
0.000

(1.26)
0.011

(14.99)***
0.012

(17.32)***
DEF_EP0.500

(1.75)*
0.500

(1.76)*
-0.439

(8.10)***
DEF_EP1-0.400

(1.40)
-0.400

(1.35)
-0.449

(6.87)***
TRADE_10.300

(1.31)
0.300

(1.28)
0.337

(7.20)***
0.300

(4.92)***
NEC_17.296

(37.68)***
7.911

(36.16)***
Constant-14.200

(2.70)***
-14.200

(2.70)***
-16.700

(2.94)***
-16.500

(2.92)***
-0.008

(0.80)
0.002

(0.26)
Observations1060106010591059872872
Number of cc999999999696
R-squared0.070.070.070.07
Absolute value of t-statistics in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%
Absolute value of t-statistics in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%

There are at least two problems with the estimations of columns 1 to 4. Fist, they do not allow for persistence in the left-hand side variable. Second, they do not recognize that most variables included in the model are endogenous. Columns 5 and 6 deal with these issues by using the Arellano and Bond (1991) GMM difference estimator which allows to consistently estimate a fixed effect model that includes the lagged dependent variable. Under certain conditions, this class of GMM estimators also allows to deal with endogeneity by instrumenting the explanatory variables with their lagged values. Column 5 replicates the model of column 3 adding the lagged dependent variable and using the Arellano and Bond estimator. We find that the coefficient of the default dummy remains negative and statistically significant but drops from -0.8 to -0.13. Column 6 reproduces the model of column 4 adding the lagged dependent variable and using the Arellano and Bond (1991) estimator. In this case, we find that the effect is negative and large only in the first and second year of the default. This result suggests that default does have a negative effect on trade credit but that this effect is short lived.

To probe the issue further, we run a set of regressions in which we look at whether controlling for trade credit affects the relationship between default and bilateral trade. Formally, we estimate the following gravity model:

Where LTRi,j,t is the log of bilateral trade between country i and country j at time t, μi,j is a country pair fixed effect and Xi,j,t is a set of controls. 28DEF _ NSi,j,t is a dummy variable that takes value one if in year t either country i or country j is in default (as usual, we measure default using Standard and Poor’s data) and the i j pair consists of a developing and industrial country. This strategy, which is similar to the one used by Rose (2005) in his robustness analysis (Table 4c in Roses’s paper), assumes that if there is some retaliation for default that operates through trade credit, this retaliation should mainly affect trade between high-income and low-income countries because the former are the likely creditors. TC _ NSi,j,t measures total trade credit received by the developing country in the pair. In particular, when one of the two countries in the pair is a developing country and the other is an industrial country, TC _ NSi,j,t is set to be equal to the log of the stock of official trade credit received by the developing country in year t, and it takes value 0 if the i, j pair consists of either two industrial countries or two developing countries.

Although trade credit is endogenous with respect to trade, and β should not be given any causal interpretation and only interpreted as the correlation between TC _ NSi,j,t and LTRi,j,t, this exercise is interesting because if it were true that the effect of default operates through trade credit, we should find that controlling for trade credit should reduce the correlation between default and trade.

In column 1 of Table 7, we reproduce the basic result of Rose (2005) and show that defaults are associated with a large and statistically significant decline in bilateral trade flows between advanced and emerging or developing economies. In column 2, we assume that country pairs with large, well established trade relationships should be able to cope better with disruptions arising from default episodes, and control for this possibility by augmenting the regression with a variable that interacts the default dummy with the log of average trade between country i and j (DEF_AVT, where the average is measured using all periods for which data are available). As expected, we find that DEF_AVT has a positive and statistically significant coefficient and that including this variable in the regression increases the point estimates of DEF_NS. In column 3, we estimate the same model of column 2 but restrict the sample to be the same to the one for which we have data on trade credit. Qualitatively, the results are unchanged. In particular, DEF_NS remains negative and statistically significant. Quantitatively, the impact of default is much smaller in the restricted sample.29

Table 7.Default and Trade: Does Trade Credit Matter?
(1)

LTR
(2)

LTR
(3)

LTR
(4)

LTR
(5)

LTR
(6)

LTR
DEF_NS-0.206

(16.46)***
-0.319

(25.21)***
-0.054

(1.68)*
-0.054

(1.66)*
-0.047

(1.47)
-0.104

(3.00)***
LGDP0.315

(40.18)***
0.353

(45.03)***
0.393

(38.75)***
0.393

(38.76)***
0.393

(38.73)***
0.392

(38.39)***
LGDP_PC0.323

(27.51)***
0.262

(22.28)***
0.145

(9.75)***
0.144

(9.70)***
0.145

(9.75)***
0.149

(9.93)***
RTA0.108

(13.28)***
0.104

(12.86)***
0.179

(15.77)***
0.179

(15.76)***
0.179

(15.75)***
0.178

(15.62)***
CURCOL0.332

(3.80)***
0.388

(4.46)***
-0.095

(0.38)
-0.096

(0.38)
-0.095

(0.38)
-0.091

(0.36)
CUSTRICT0.669

(13.39)***
0.665

(13.38)***
0.647

(10.21)***
0.647

(10.21)***
0.647

(10.21)***
0.647

(10.15)***
DEF_AVT0.141

(47.37)***
0.176

(44.87)***
0.176

(44.87)***
0.176

(44.84)***
0.178

(44.88)***
LTC_TOTNS0.073

(4.55)***
LTC_NBNKNS0.055

(3.59)***
LTC_BNKNS0.037

(3.29)***
Constant-10.215

(46.75)***
-11.02

(50.53)***
-11.338

(37.59)***
-11.41

(37.78)***
-11.392

(37.71)***
-11.38

(37.39)***
Observations234457234457151371151371151243147057
Number of pairid121501215011885118851188311687
R-squared0.110.120.080.080.080.08
Absolute value of t-statistics in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%
Absolute value of t-statistics in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%

In column 4, we augment the regression with TC _ NSi,j,t and measure trade credit with the log of the total stock of trade credit to country i (where country i is the developing country in the pair) in year t. As expected, this variable is positive and statistically significant. It is also quantitatively important indicating that the elasticity of trade to trade credit is approximately 7 percent. While this coefficient cannot be interpreted in terms of causality, what is interesting is that controlling for trade credit does not affect the relationship between default and trade. In particular, the coefficients of DEF_NS and DEF_AVT in column 4 are identical to those of column 3. Columns 5 and 6 repeat the experiment by focusing on total non-bank trade credit and total bank trade credit. The results are basically unchanged.

VI. Default and the Domestic Banking System

Sovereign defaults affect not only external creditors but also domestic bondholders. Although data on the breakdown of bondholders by country of residence is scant, some recent default events suggest that domestic residents tend to account for a sizable portion of the holdings, perhaps a majority in some cases. This means that a sovereign default can have serious consequences for the domestic private sector. In particular, when domestic banks hold large amounts of government debt, the domestic financial sector may be put under significant stress by the default (Beim and Calomiris, 2000, Sturzenegger and Zettelmeyer, 2006).

Our strategy is to test if sovereign defaults lead to banking crises or a domestic credit crunch. This may happen for several reasons. First of all, default episodes may cause a collapse in confidence in the domestic financial system and may lead to bank runs, resulting in banking crises or at least a credit crunch. Second, even in the absence of a bank run, default episodes would have a negative effect on banks’ balance sheet, especially if holdings of the defaulted paper are large, and lead banks to adopt more conservative lending strategies. Finally, default episodes are often accompanied by a weakening of creditor rights or at least more uncertainty about them, which, may also have a negative effect on bank lending.

To investigate the possible effect of sovereign defaults on banking crises, we build an index of banking crises using data from Glick and Hutchinson (2001), Caprio and Kingelbiel (2003), and Dell’Ariccia et al. (2005).30 Our data include 149 countries for the 1975–2000 period and a total of 3,874 observations. In this sample, there are 111 banking crises (yielding an unconditional probability of observing a crisis of 2.9 percent) and 85 default episodes (yielding an unconditional probability of observing a default of 2.2 percent). In order to check whether defaults predict currency crisis, we compute the probability of having a banking crisis in year t conditional on having a debt default in year t or year t-1 (this is similar to the test in Kaminsky and Reinhart,1999). The results indicate that the probability of having a banking crisis conditional on default is 14 percent, an 11 percentage point increase with respect to the unconditional probability (Table 8). The statistical significance of the difference between conditional and unconditional probability is quite high.

Table 8.Probabilities of Default and Banking Crisis
Unconditional probability of a banking crisis (111 episodes)2.9
Probability of a banking crisis conditional on a default14.1
P value on a test P(BC/DEF)>P(BC)0.0
Unconditional probability of a sovereign default (85 episodes)2.2
Probability of a default conditional on a banking crisis4.5
P value on a test P(DEF/BC)>P(DEF)0.1

As banking crises tend to involve large fiscal costs, it is also possible that the direction of causality is reversed, namely that banking crises cause default episodes. However, the probability of a default conditional on having a banking crisis is only two percentage points higher than the unconditional probability, and the difference is not statistically significant at conventional confidence levels. These results should be taken with an appropriate degree of caution because we have relatively few cases of “twin” crisis and, as we work with annual data, we lose some precision in the measure of the relative timing of banking crises and default episodes. However, the results suggest that default episodes may increase the probability of a banking crisis much more than the other way round.

To test whether default episodes generate a credit crunch, we use a methodology similar to the one originally developed by Rajan and Zingales (1998), and recently applied by Dell’Ariccia et al. (2005) to investigate the cost of banking crises. The basic idea is to use data at the industry level to test whether defaults have a larger negative impact on sectors that require more external finance.

Following Dell’Ariccia et al. (2005), we pose the following specification:

The dependent variable in (6) measures real value added growth for industry j in country i at time t. The controls comprise a set of country-industry fixed effects (ai,j), a set of country-year fixed effects (bi,t), a set of industry-year fixed effects (cj,t), and the lagged ratio of sector j‘s value added over total manufacturing production (SHVA). Fixed effects control for country-specific, industry-specific, and time-invariant country-industry specific shocks, and hence capture most of the factors that are likely to affect the performance of a given industry and greatly attenuate omitted variable biases. SHVA controls for convergence and mean reversion (possibly due to errors in variables). Our variable of interest is the interaction between a default dummy (DEF) and the index of external financial dependence (EXT) assembled by Rajan and Zingales (1998) and later used by Dell’Ariccia et al. (2005).31

In the above setup, β measures whether value added growth in sectors that require more external financing is affected differentially by default episodes. A negative value of β would provide evidence in support of the hypothesis that default episodes lead to a credit crunch in the banking sector.

The results of estimating this model, reported in Table 9, do not provide much support for the credit crunch hypothesis.32 In column 1, we focus on all the years in which the country is in default (DEF). The coefficient has the wrong (positive) sign, although it is not statistically significant. In column 2, we use three dummy variables taking a value of one in the first, second and third year of a default episode, and find that these variables tend to have the right (negative) sign but that they are never statistically significant (neither individually nor jointly). In columns 3 and 4, we augment the regressions of columns 1 and 2 with the interaction between banking crisis and external dependence (the same variable used by Dell’Ariccia et al., 2005) and find that our results are unchanged. We conclude that, unlike banking crises, defaults do not seem to have a special effect on industries that depend more on external finance.

Table 9.Default and Industry Value-Added Growth
(1)

VAGR
(2)

VAGR
(3)

VAGR
(4)

VAGR
(5)

VAGR
DEF*EXT0.009

(0.74)
0.021

(1.41)
0.009

(0.73)
0.019

(1.30)
DEF_b*EXT-0.027

(1.22)
-0.022

(0.99)
DEF_b1*EXT-0.033

(1.51)
-0.031

(1.42)
DEF_b2*EXT-0.013

(0.60)
-0.013

(0.61)
SHVA-1.251

(15.18)***
-1.25

(15.17)***
-1.253

(15.21)***
-1.252

(15.19)***
-1.253

(15.21)***
BK_CR*EXT-2.277

(2.29)**
-2.164

(2.16)**
-2.282

(2.29)**
Constant0.154

(0.00)
0.152

(0.00)
0.157

(0.00)
0.156

(0.00)
0.3

(0.00)
Observations1587215872158721587215872
R-squared0.460.460.460.460.46
Absolute value of t statistics in parentheses.All regressions exclude top and bottom 5 percent observations in the dependent variable.
Absolute value of t statistics in parentheses.All regressions exclude top and bottom 5 percent observations in the dependent variable.

VII. Political Implications of Default

Sometimes, politicians and bureaucrats seem to go to a great length to postpone what seems to be an unavoidable default. In the case of Argentina, for instance, it is reported that even Wall Street bankers had to work hard to persuade the policymaking authorities to accept reality and initiate a debt restructuring (Blustein, 2005). Why the reluctance? There seems to be evidence that defaults do not bode well for the survival in office of finance ministers and the top executive politicians.

High political costs have two important implications. On the positive side, a high political cost would increase the country’s willingness to pay and hence its level of sustainable debt. On the negative side, politically costly defaults might lead to “gambles for redemption” and possibly amplify the eventual economic costs of default if the gamble does not pay off and results in larger economic costs. Delaying default might be costly for at least three reasons: (i) Non-credible restrictive fiscal policies are ineffective in avoiding default and lead to output contractions; (ii) Delayed defaults may prolong the climate of uncertainty and high interest rates and thus have a negative effect on investment and banks’ balance sheets; (iii) Delayed default may have direct harmful effects on the financial sector.33

This suggests that a politician concerned about his/her political survival faces a tradeoff that is somewhat different from the one affecting the country itself, say, the representative citizen. This contrast can be illustrated in a simple formal framework as follows.34

Assume that a country is entering a period of crisis and the policymaker needs to decide whether to default now or attempt to implement some sort of emergency program with a small chance of success. The social cost of current default is D0. If the measures are successful (with probability Π) there will be no future default (and hence no cost), but if the measures are not successful there will be a delayed default with a cost of D1 (with D1 > D0). Hence, trying to avoid default is optimal if and only if Do > (1–Π)D1. This inequality can be rewritten as

implying that trying to avoid default is socially optimal only if the probability of success is greater than the percent difference between the cost of defaulting today and the cost of defaulting in the future (we assume zero discount rate).

It is now interesting to ask how self-interested politicians can lead to a deviation from the social optimum. Let us assume that the default decision is made by a policymaker who obtains a rent from being in power and that this policymaker knows that in case of default he/she will lose his/her job with probability θ. Let us assume that the policymaker’s objective function is to maximize his/her own utility function, which is given by U = (1−Φ)R + ΦW, where R represents the rents from being in power, W is a measure of social welfare, and Φ (0 ≤ Φ ≤ 1) is the weight that the politician puts on social welfare. In this setup, the politician will decide to attempt to avoid default if:

Where ϑ = (1 −θ). This inequality can be rewritten as:

This inequality implies that politicians who are altruistic (meaning R=0, or equivalently, Φ =1) will just maximize social welfare, which is given by the first term of the right-hand side. The same happens if defaults are not politically costly (that is when θ = 0 and ϑ = 1). However, in the presence of politically costly defaults, politicians who care about their own careers (i.e., politicians with R>0 and Φ <1) will try to delay default even when that is detrimental to social welfare. In fact, the above equation suggests that politicians who do not care about social welfare (Φ =0) will try to postpone default even if the probability of success is zero.

There is no empirical literature on the political costs of default, but there exists a related literature on the political cost of sharp devaluations. In particular, Cooper (1971) was the first to illustrate the political cost of devaluations by showing that devaluations more than double (from 14 to 30 percent) the probability of a political crisis and a government change within the next 12 months. A recent paper by Frankel (2005) updates Cooper’s (1971) data and finds that over the 1971–2003 period devaluations increased the probability of a change in the chief of the executive in the following 12 moths by approximately 45 percent (from 20 to 29 percent).35Frankel (2005) also checked whether devaluations affect the probability of a change of the minister of finance or governor of the central bank (whoever held the position of governor of the IMF) and found that devaluations are associated with a 63 percent increase in the probability of replacement of this official (from 36 to 58 percent).

Applying a similar methodology, we find that defaults have a broadly similar political cost. Table 10 lists all democracies that defaulted over the 1980-2003 period.36 The table also reports the share of votes of the ruling coalition in the elections that preceded and followed the default. Out of 19 countries for which we have data on electoral results before and after defaults, we find that the ruling coalitions lost votes in 18 countries (the exception is Ukraine). We also find that, on average, ruling governments in countries that defaulted observed a 16 percentage point decrease in electoral support, and that in 50 percent of the cases (11 out of 22 episodes) there was a change in the chief of the executive either in the year of the default episode or in the following year. This is more than twice the probability of a change of the chief of the executive in normal times reported by Frankel (2005).

Table 10.Defaults and Elections
Election before defaultElection after defaultChange

in the

chief of

the

executive
Year of

default
YearVotesYearVotesChange in

votes
Argentina2001199937.50200316.90-20.60YES
Bolivia1989198526.42198919.64-6.78YES
Costa Rica1981197839.66198225.79-13.87YES
Costa Rica1983198245.03198641.73-3.30
Dominican Republic1982197837.47198232.85-4.62YES
Ecuador1982197918.2519848.31-9.94
Ecuador1999199818.982002NANAYES
Guatemala1989198523.5619908.48-15.08
Jamaica1981198040.6719830.00-40.67
Jamaica1987198389.86198943.32-46.54
Moldova19981996NA2000NANAYES
Paraguay2003199843.29200323.88-19.41YES
Peru1980198027.7119855.65-22.06YES
Peru1984198027.7119855.65-22.06YES
Trinidad and Tobago19881986NA1992NANA
Ukraine1998199421.55199925.604.05
Uruguay1987198435.39198925.74-9.65
Uruguay1990198933.03199427.18-5.85YES
Uruguay2003199929.3020049.11-20.19
Venezuela1983197839.96198327.85-12.11YES
Venezuela1990198842.23199313.69-28.54
Venezuela1995199313.1819980.00-13.18
The last column of the table lists all the cases in which the chief of the executive changed in the year of the default or in the year after the default.Sources: Inter American Development Bank, Democracies in Development, and International Parliamentary Union, http://www.ipu.org/parline-e/parlinesearch.asp.
The last column of the table lists all the cases in which the chief of the executive changed in the year of the default or in the year after the default.Sources: Inter American Development Bank, Democracies in Development, and International Parliamentary Union, http://www.ipu.org/parline-e/parlinesearch.asp.

We also investigate changes in the top economic officials by looking for changes in the country’s IMF governor (who is typically the finance minister but in some cases the governor of the central bank). The first column of the upper panel of Table 11 shows that in tranquil years there is a 19.4 percent probability of observing a change of the IMF governor, but after a default, the probability jumps to 26 percent (the difference is statistically significant with a p-value of 0.04). Interestingly, defaults on bank loans do not seem to matter (column 2) but bond defaults are particularly perilous for finance ministers. In the latter case, the probability of turnover more than doubles to over 40 percent. To check for the possibility that our results are driven by changes in political and economic institutions, (for example an increase in the ease of government turnovers) we split the sample into two sub-periods. Interestingly, we do not find large differences between the two sub-periods and, if anything, find that defaults seemed to have a higher political cost in the 1980s than in the 1990s. The second panel of the table uses an 18-month window to measure turnover. The results are similar to those of upper panel, but here the impact of bond defaults is even more dramatic, with more than 90 percent of finance ministers losing their job in the 18 months following a default episode (the turnover in tranquil times is 47 percent using this extended window).

Table 11.Default and the Probability of Replacing the Minister of Finance by Type of Default
Probability of replacing the

Minister of Finance
All DefaultsDefaults on

International

Bank Loans
Defaults on

Sovereign

Bonds
All defaults

1977-1989
All defaults

1990-2004
One year later
Tranquil years19.4019.5019.5017.8020.70
After a default25.7024.2040.0024.6028.60
Difference6.404.6020.506.807.90
P value0.040.160.010.070.18
18 months later
Tranquil years47.3047.4047.4043.3050.90
After a default57.7055.6092.3055.1064.40
Difference10.408.2044.8011.8013.50
P value0.010.050.000.010.07
All the p values refer to a 2 tails testSources: IMF Annual Report, various issues, and staff calculations.
All the p values refer to a 2 tails testSources: IMF Annual Report, various issues, and staff calculations.

In Table 12, we divide the sample according to the political regime, between dictatorships and democracies. Somewhat surprisingly, we find that the political cost of defaulting on bank loans is higher in dictatorships but the cost of defaulting on sovereign bonds is higher in democracies. When we pull all defaults together, we find a higher turnover of economic policymakers in dictatorships. This may suggest that dictators find it easier to blame and fire their Minister of Finance. The second panel shows that using 18-month windows does not affect the basic finding described above.

Table 12.Default and the Probability of Replacing the Minister of Finance by Type of Default and Government
Probability of

replacing the

Minister of

Finance
Defaults on

International

Bank Loans
Defaults on

International

Bank Loans
Defaults on

Sovereign

Bonds
Defaults on

Sovereign

Bonds
All DefaultsAll Defaults
DemocraciesDictatorshipsDemocraciesDictatorshipsDemocraciesDictatorships
One year later
Tranquil years21.9017.6021.7018.0021.8017.50
After a default23.3027.0044.4033.3024.7028.70
Difference1.409.4022.7015.302.8011.20
P value0.790.040.020.170.540.01
18 months later
Tranquil years51.1044.9050.6045.5050.8044.80
After a default53.5060.8094.4087.5057.0061.50
Difference2.4015.9043.8042.006.2016.70
P value0.690.000.000.000.290.00
All the p values refer to a 2 tails testSources: IMF Annual Report, various issues, Alvarez, Cheibub, Limongi and Przeworski (1999), ACLP Political and Economic Database, www.ssc.upenn.edu/~cheibub/data/ACLP_Codebook.PDF, and staff calculations.
All the p values refer to a 2 tails testSources: IMF Annual Report, various issues, Alvarez, Cheibub, Limongi and Przeworski (1999), ACLP Political and Economic Database, www.ssc.upenn.edu/~cheibub/data/ACLP_Codebook.PDF, and staff calculations.

VIII. Conclusions

We investigated the empirical basis of the costs of sovereign defaults in its different versions. Our findings suggest that default costs are significant, but short lived. Reputation of sovereign borrowers that fall in default, as measured by credit ratings and spreads, is tainted but only for a short time. While there is some evidence that international trade and trade credit are negatively affected by espisodes of default, we could not trace it to the volume of trade credit, as the default literature suggests. Debt defaults seem to cause banking crises, and not vice versa, but we found weak evidence to suggest the presence of default-driven credit crunches in domestic markets. Finally, defaults seem to shorten the life expectancy of governments and officials in charge of the economy in a significant way.

Our results suggest that default costs remain somewhat vaguely defined, and difficult to quantify. On the positive side, we found a fairly sensible estimate of the effect on credit ratings and bond spreads, and we call attention to the sharp increase in government turnovers following debt crises. On the negative side, our result regarding how international trade credit affect the link between trade and default and our finding that default episodes do not seem to affect bank lending do not seem to be very plausible. Perhaps the most robust and striking finding is that the effect of defaults is short lived, as we almost never can detect effects beyond one or two years.

A relatively unexplored avenue is the decision-making process of which policy makers concerning the timing of defaults (see, however, Alichi, 2008). Defaults tend to be widely anticipated and happen at times when the domestic economic is quite weak. This may happen for two widely different reasons. Self-interested policymakers may try postponing defaults even at increasing economic cost, as the evidence presented in this paper suggests a clearly higher political turnover following a debt default. A different possibility is that policymakers postpone default to ensure that there is broad market consensus that the decision is unavoidable and not strategic. This would be in line with the model in Grossman and Van Huyck (1988) whereby “strategic” defaults are very costly in terms of reputation—and that is why they are never observed in practice—while “unavoidable” defaults carry limited reputation loss in the markets. Hence, choosing the lesser of the two evils, policymakers would postpone the inevitable default decision in order to avoid a higher reputational cost, even at a higher economic cost during the delay.

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APPENDIX
Table A1.Private Lending to Sovereign. Default and Rescheduling
Standard & Poor’s

(1824-2004)
Beim &

Calomiris
Sturzenegger

&

Zettelmeyer

(1874-2003)
Detragiache

&

Spilimbergo

(1973-

1991)
(1800-1992)
Foreign Currency

Bond Debt
Foreign Currency

Bank Debt
REGIONCOUNTRYBeginning

of Period
End

of

Period
Beginning

of Period
End

of

Period
Beginning

of Period
End

of

Period
Beginning

of Period
Beginning

of Period
AfricaAlgeria199119961991
AfricaAngola19852004198819921988
AfricaBurkina Faso198319961982
AfricaBurundi1986
AfricaCameroon1979
AfricaCameroon198520031989199219891985
AfricaCape Verde19811996
AfricaCentral African Rep.1981
AfricaCentral African Rep.19832004
AfricaCongo19832004198619921986
AfricaCongo, Dem. Rep.1961
AfricaCongo, Dem. Rep.197620041976199219761975
AfricaCote d’Ivoire198319981984199219841987
AfricaCote d’Ivoire20002004
AfricaEgypt18761880181618801876
AfricaEgypt1984199219841986
AfricaEthiopia199119991987
AfricaGabon1978
AfricaGabon19861994198619921986
AfricaGabon19992004
AfricaGambia19861990198619881986
AfricaGhana19691974
AfricaGhana1987
AfricaGuinea19861988
AfricaGuinea1991199819851992
AfricaGuinea-Bissau19831996
AfricaKenya199420041990
AfricaLesotho1990
AfricaLiberia18751898187518981874
AfricaLiberia1912
AfricaLiberia19141915
AfricaLiberia19171918
AfricaLiberia19191923191219231912
AfricaLiberia1932193519321935
AfricaLiberia19872004198019921980
AfricaMadagascar198120021981199219811980
AfricaMalawi1982198219881982
AfricaMalawi19881987
AfricaMauritania19921996
AfricaMorocco1903190419031904
AfricaMorocco1983
AfricaMorocco198619901983199019831985
AfricaMozambique1980
AfricaMozambique19831992198419921984
AfricaNiger198319911983199119831984
AfricaNigeria1972
AfricaNigeria19861988
AfricaNigeria1992198219921983199119831986
AfricaNigeria2002
AfricaSao Tomé & Principe19871994
AfricaSenegal198119851981199219811984
AfricaSenegal19901989
AfricaSenegal19921996
AfricaSeychelles20002002
AfricaSierra Leone1972
AfricaSierra Leone19831984
AfricaSierra Leone19861995197719921977
AfricaSouth Africa19851987198519921985
AfricaSouth Africa1989
AfricaSouth Africa1993
AfricaSudan197920041979199219791976
AfricaTanzania19842004198419921984
AfricaTogo19791980
AfricaTogo19821984
AfricaTogo1988
AfricaTogo19911997197919921979
AfricaTunisia1867187018671870
AfricaTunisia1991
AfricaUganda19801993198119921981
AfricaZambia1978
AfricaZambia19831994198319921983
AfricaZimbabwe1965198019651980
AfricaZimbabwe20002004
AsiaBangladesh1978
AsiaBangladesh1991
AsiaChina19211936
AsiaChina1939194919211949
AsiaIndonesia199819991998
AsiaIndonesia2000
AsiaIndonesia2002
AsiaIran197819951992
AsiaIraq1987200419901992
AsiaJapan1942195219421952
AsiaJordan19891993198919921989
AsiaKorea1998
AsiaKorea, Dem. Rep19742004
AsiaMyanmar19972004
AsiaPakistan1999199819991981
AsiaPhilippines198319921983199219831984
AsiaSri Lanka1992
AsiaThailand1998
AsiaTurkey18761881187618811876
AsiaTurkey19151928191519321915
AsiaTurkey19311932
AsiaTurkey19401943194019431940
AsiaTurkey1959
AsiaTurkey1965
AsiaTurkey19781979197819821978
AsiaTurkey1982
AsiaVietnam198519981985199219851984
AsiaYemen19852001
EuropeAlbania1991199519901992
EuropeAustria18021816
EuropeAustria18681870186818701868
EuropeAustria19141915191419151914
EuropeAustria19321933193219521932
EuropeAustria1938
EuropeAustria19401952
EuropeBosnia & Herzegovina19921997
EuropeBulgaria19161920191519201915
EuropeBulgaria1932193219921932
EuropeBulgaria1990199419901992
EuropeCroatia19921996
EuropeCzechoslovakia1938194619381946
EuropeCzechoslovakia1959196019521959
EuropeGermany193219381932
EuropeGermany1939195319321953
EuropeGermany East19491992
EuropeGreece18261878182618781824
EuropeGreece18941897189418971893
EuropeGreece1932196419321964
EuropeHungary1932193719321967
EuropeHungary194119671931
EuropeItaly19401946194019461940
EuropeMacedonia19921997
EuropeMoldova19982002
EuropeMoldova2002
EuropeNetherlands18021814
EuropePoland19361937
EuropePoland19401952193619521936
EuropePoland19811994198119921981
EuropePortugal18371841183418411834
EuropePortugal1850185618501856
EuropePortugal18921901189219011892
EuropeRomania1915
EuropeRomania19331958193319581933
EuropeRomania19811983198219871981
EuropeRomania1986
EuropeRussia/USSR18391839
EuropeRussia/USSR1885
EuropeRussia/USSR1918191719181917
EuropeRussia/USSR1991199719911992
EuropeRussia/USSR199820001998
EuropeSerbia & Montenegro19922004
EuropeSlovenia19921996
EuropeSpain1824183418201820
EuropeSpain183118341831
EuropeSpain1851
EuropeSpain18371867186718721867
EuropeSpain1827188218821882
EuropeUkraine199820001998
EuropeYugoslavia189518951895
EuropeYugoslavia19331950193319601933
EuropeYugoslavia199219831991198319921983
LACAntigua & Barbuda19962004
LACArgentina182818571830
LACArgentina18901893189018931890
LACArgentina19561965
LACArgentina1989198219931982199219821983
LACArgentina20012004200120042001
LACBolivia18751879187518791874
LACBolivia19311948193119571931
LACBolivia19801984198019921980
LACBolivia1989199719861993
LACBrazil18261829182618291826
LACBrazil18981901189819101898
LACBrazil19021910
LACBrazil19141919191419191914
LACBrazil193119331931
LACBrazil1937194319311943
LACBrazil19611964
LACBrazil19831994198319921983
LACChile18261842182618421826
LACChile18801883188018831879
LACChile19311947193119481931
LACChile1965
LACChile197219751973
LACChile19831990198319901983
LACColombia18261845
LACColombia18501861182618611826
LACColombia18731873
LACColombia18801896
LACColombia19001904188019041879
LACColombia193219341900
LACColombia19351944193219441932
LACColombia1985
LACCosta Rica18281840182818401827
LACCosta Rica18741885187418851874
LACCosta Rica18951897
LACCosta Rica19011911189519111895
LACCosta Rica19321952193219531937
LACCosta Rica1962
LACCosta Rica1981
LACCosta Rica198419851983199019811990
LACCuba19331934193319341933
LACCuba196019601963
LACCuba19822004198219921982
LACDominica200320042003
LACDominican Rep.1869
LACDominican Rep.18721888
LACDominican Rep.18921893
LACDominican Rep.1897
LACDominican Rep.18991907187219071899
LACDominican Rep.19311934193119341931
LACDominican Rep.1976
LACDominican Rep.19821994198219921982
LACEcuador18261855183218551832
LACEcuador18681890
LACEcuador18941898186818981868
LACEcuador1906190819061955
LACEcuador190919111911
LACEcuador191419241914
LACEcuador192919541931
LACEcuador198219951982199219821983
LACEcuador199920001999
LACEl Salvador18281860182818601827
LACEl Salvador1898
LACEl Salvador19211922192119221921
LACEl Salvador19321935
LACEl Salvador19381946193219461931
LACEl Salvador1984
LACEl Salvador1995
LACGuatemala18281856182818561828
LACGuatemala18761888187618881876
LACGuatemala18941894
LACGuatemala1899191318941917
LACGuatemala19331936193319361933
LACGuatemala19891985
LACGuyana1979
LACGuyana1982200419821992
LACHaiti198219941983
LACHonduras18281867182818671827
LACHonduras18731925187319251873
LACHonduras1914
LACHonduras1976
LACHonduras198120041981199219811982
LACJamaica1978197919781990
LACJamaica19811985
LACJamaica19871993
LACMexico182818301827
LACMexico18331841
LACMexico1844185018281850
LACMexico18541864
LACMexico18661885185918851867
LACMexico19141922191419221914
LACMexico1928194219281942
LACMexico19821990198219901982
LACNicaragua18281874182818741828
LACNicaragua18941895189418951894
LACNicaragua191119121911
LACNicaragua1915191719111917
LACNicaragua19321937193219371932
LACNicaragua197920041980199219801978
LACPanama19321946193219461932
LACPanama1987199419831996198319921982
LACPanama1987
LACParaguay1827
LACParaguay18741885187418851874
LACParaguay1892189518921895
LACParaguay19201924192019241920
LACParaguay19321944193219441932
LACParaguay198619921986199219861984
LACParaguay20032004
LACPeru18261848182618481826
LACPeru18761889187618891876
LACPeru19311951193119511931
LACPeru19681969
LACPeru1976
LACPeru1978
LACPeru1980
LACPeru19841997197819921978
LACPeru19831983
LACTrin. & Tob.19881989198919891988
LACUruguay18761878187618781876
LACUruguay189118911891
LACUruguay19151921191519211915
LACUruguay19331938193319381933
LACUruguay19831985
LACUruguay1987
Notes to Table A1Standard and Poor’sS&P generally defines sovereign default as the failure to meet a principal or interest payment on the due date (or within the specified grace period) contained in the original terms of the debt issue. In particular, each issuer’s debt is considered in default in any of the following circumstances: (i)For local and foreign currency bonds, notes and bills, when either scheduled debt service is not paid on the due date, or an exchange offer of new debt contains terms less favorable than the original issue. (ii) For central bank currency, when notes are converted into new currency of less than equivalent face value (iii) For bank loans, when either scheduled debt service is not paid on the due date, or a rescheduling of principal and/or interest is agreed to by creditors at less favorable terms then the original loan. Such rescheduling agreements covering short and long term debt are considered defaults even where, for legal or regulatory reasons, creditors deem forced rollover of principal to be voluntary.Beim and CalomirisThis source only includes private lending through bonds, supplier’s credits or banks loans. The dataset does not include every instance of technical default on bond or loan covenants. An extended period (six months or more) was identified where all or part of interest and/or principal payments due were reduced or rescheduled. Some of the defaults and rescheduling involved outright repudiation (a legislative or executive act of government liability), while others were minor and announced ahead of time in a conciliatory fashion by debtor nations. The end of each period of default or rescheduling was recorded when full payments resumed or restructuring was agreed upon. Periods of default or rescheduling within five years of each other were combined. Where a formal repudiation was identified, its date served as the end of the period of default and the repudiation is noted in notes, where no clear repudiation was announced the default was listed as persisting. Voluntary refinancing (Colombia 1985 and Algeria 1992) were not included.Sturzenegger and ZettelmeyerUnless otherwise noted, all defaults are federal or central government defaults. Defaults of US southern states in early 1840s are not shown in the table. Defaults on wars, revolutions, occupations and the collapse of the Soviet Union etc. are excluded, except when they coincide with a cluster. In the event of sequence rescheduling, the year listed refers to the initial default or rescheduling.Detragiache and SpilimbergoAn observation is classified as a debt crises if either or both of the following conditions occur: (i) 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; (ii) there is a rescheduling or debt restructuring agreement with commercial creditors as listed in the GDF
Notes to Table A1Standard and Poor’sS&P generally defines sovereign default as the failure to meet a principal or interest payment on the due date (or within the specified grace period) contained in the original terms of the debt issue. In particular, each issuer’s debt is considered in default in any of the following circumstances: (i)For local and foreign currency bonds, notes and bills, when either scheduled debt service is not paid on the due date, or an exchange offer of new debt contains terms less favorable than the original issue. (ii) For central bank currency, when notes are converted into new currency of less than equivalent face value (iii) For bank loans, when either scheduled debt service is not paid on the due date, or a rescheduling of principal and/or interest is agreed to by creditors at less favorable terms then the original loan. Such rescheduling agreements covering short and long term debt are considered defaults even where, for legal or regulatory reasons, creditors deem forced rollover of principal to be voluntary.Beim and CalomirisThis source only includes private lending through bonds, supplier’s credits or banks loans. The dataset does not include every instance of technical default on bond or loan covenants. An extended period (six months or more) was identified where all or part of interest and/or principal payments due were reduced or rescheduled. Some of the defaults and rescheduling involved outright repudiation (a legislative or executive act of government liability), while others were minor and announced ahead of time in a conciliatory fashion by debtor nations. The end of each period of default or rescheduling was recorded when full payments resumed or restructuring was agreed upon. Periods of default or rescheduling within five years of each other were combined. Where a formal repudiation was identified, its date served as the end of the period of default and the repudiation is noted in notes, where no clear repudiation was announced the default was listed as persisting. Voluntary refinancing (Colombia 1985 and Algeria 1992) were not included.Sturzenegger and ZettelmeyerUnless otherwise noted, all defaults are federal or central government defaults. Defaults of US southern states in early 1840s are not shown in the table. Defaults on wars, revolutions, occupations and the collapse of the Soviet Union etc. are excluded, except when they coincide with a cluster. In the event of sequence rescheduling, the year listed refers to the initial default or rescheduling.Detragiache and SpilimbergoAn observation is classified as a debt crises if either or both of the following conditions occur: (i) 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; (ii) there is a rescheduling or debt restructuring agreement with commercial creditors as listed in the GDF
Table A2.Logit Model for the Probability of Default
(1)

DEF
(2)

DEF_B
Total debt to GDP_1 x Dummy 70s1.973

(0.87)
6.058

(2.89)
Total debt to GDP_1 x Dummy 80s0.081

(0.03)
-5.920

(2.60)
Total debt to GDP_1 x Dummy 90s-0.583

(0.26)
-5.935

(2.75)
Short term debt_1 x Dummy 70s-1.381

(0.33)
0.751

(0.29)
Short term debt_1 x Dummy 80s0.855

(0.20)
-1.448

(0.55)
Short term debt_1 x Dummy 90s1.287

(0.30)
-1.178

(0.43)
Short term interest payments to GDP_1 x Dummy 70s5.552

(2.31)
3.337

(1.95)
Short term interest payments to GDP_1 x Dummy 80s-5.441

(2.25)
-2.693

(1.54)
Short term interest payments to GDP_1 x Dummy 90s-4.418

(1.78)
-1.975

(1.14)
External debt service to reserves_1 x Dummy 70s4.843

(4.42)
5.154

(3.20)
External debt service to reserves_1 x Dummy 80s-1.234

(0.50)
-6.162

(3.52)
External debt service to reserves_1 x Dummy 90s-2.268

(1.22)
-5.778

(3.52)
Current account balance to GDP_1 x Dummy 70s0.409

(0.49)
0.210

(0.20)
Current account balance to GDP_1 x Dummy 80s-0.286

(0.36)
-0.460

(0.40)
Current account balance to GDP_1 x Dummy 90s0.158

(0.19)
0.033

(0.03)
Exports plus imports to GDP_1 x Dummy 70s0.115

(0.32)
-0.267

(0.64)
Exports plus imports to GDP_1 x Dummy 80s-0.396

(1.01)
0.431

(1.02)
Exports plus imports to GDP_1 x Dummy 90s-0.182

(0.48)
0.257

(0.57)
Concessional debt to total debt_1 x Dummy 70s-5.356

(1.85)
-4.456

(1.56)
Concessional debt to total debt_1 x Dummy 80s4.166

(1.39)
3.482

(1.21)
Concessional debt to total debt_1 x Dummy 90s6.343

(2.09)
4.413

(1.39)
US real treasury bill rate_10.184

(2.13)
0.127

(1.29)
Real GDP growth_1-0.063

(3.91)
-0.066

(3.36)
Volatility on inflation_10.093

(3.16)
0.017

(0.76)
Inflation higher than 50 percent_10.164

(0.49)
-0.258

(0.68)
Year of presidential election_1-0.006

(0.03)
-0.553

(1.34)
Civil Liberties Index_1-1.132

(1.22)
-0.028

(0.03)
US real treasury bill rate_1 x Total debt to GDP_1-0.101

(0.76)
-0.187

(1.19)
US real treasury bill rate_1 x Short term debt_10.049

(0.38)
0.276

(1.55)
US real treasury bill rate_1 x Short term interes payments to GDP_10.001

(0.01)
-0.126

(1.62)
US real treasury bill rate_1 x External debt service to reserves_1-0.558

(1.54)
0.192

(1.47)
US real treasury bill rate_1 x Current account balance to GDP_1-0.047

(0.92)
-0.076

(1.12)
Dummy 80s1.868

(0.85)
1.667

(1.29)
Dummy 90s-0.140

(0.06)
0.600

(0.37)
Constant-2.290

(1.05)
-4.532

(3.12)
Observations

R-square
1416

0.313
1416

0.147
Note: The numbers between brackets are t-statistics.
Note: The numbers between brackets are t-statistics.
1

Ugo Panizza is at the Debt and Finance Analysis Unit Division on Globalization and Development Strategies, UNCTAD. Research on this paper was initiated when both authors were at the Research Department of the Inter-American Development Bank. We would like to thank Guillermo Calvo, Jeff Frieden, Eduardo Levy Yeyati, Guido Sandleris, and the participants of seminars at the IADB and the International Economic Association for useful suggestions and comments, and Patricio Valenzuela and Monica Yañez for excellent research assistance.

2

There is ample evidence that protection of creditor rights is positively correlated with the development of the private credit market (La Porta et al., 1998).

3

Some recent litigation strategies against sovereigns in default appear to focus on becoming enough of a nuisance such that sovereigns would acquiesce to an out-of-court settlement, rather than seeking a direct enforcement of property rights. Those strategies, however, can succeed only if the plaintiffs hold a small fraction of the debt.

4

Influential papers that base their results on the assumption that default causes a direct loss of output or trade access—in line with the sanctions view—includes Krugman (1988) and Sachs (1989).

5

For a recent review, see De Paoli, Hoggarth and Saporta (2006).

6

In this paper we do not explore the role of collateral. For a discussion of this issue see Dooley et al. (2007).

7

This is analogous to the evaluation of the probability of default by a private company. Its default point, in theory, is the point at which existing liabilities equal the total market value of its assets, that is, its equity value is zero. See Merton (1974) and Kealhofer (2003). For an application to the sovereign case see Gray et al (2005).

8

The first four columns of the table use data from Standard and Poor’s and include all defaults on sovereign bonds and bank loans. Columns 5 and 6 are from Beim and Calomiris (2000) and also include defaults on suppliers’ credit. Column 7 is from Sturzenegger and Zettelmeyer (2006) and is based on primary data from Beim and Calomiris (2000), and Lindert and Morton (1989). The last column uses data from Detragiache and Spilimbergo (2001). The definitions of default episodes applied by each one of these sources are presented in the appendix.

9

This is the case, for instance, of Nigeria, Zambia, and Sierra Leone in the 1970s; Egypt and El Salvador in the 1980s; and Sri Lanka, Thailand, Korea, and Tunisia in the 1990s.

10

This is also the period in which we observe the first default on bank loans (Russia in 1918).

11

Of these 6 episodes, two were related to World War II (Hungary in 1941 and Japan in 1942), and other two were largely politically motivated defaults by communist countries (Czechoslovakia in 1959 and Cuba in 1960). The remaining two were Costa Rica (1962) and Zimbabwe (1965).

12

Our set of controls includes the investment over GDP ratio (INV_GDP), population growth (POP_GR), GDP per capita in the early 1970s (GDP_PC70s), percentage of the population that completed secondary education (SEC_ED), total population (POP), lagged government consumption over GDP (GOV_C1), an index of civil rights (CIV_RIGHT), the change in terms of trade (DTOT), the degree of openness (OPEN), a dummy variable taking a value of one in presence of a banking crisis (BK_CR), and three regional dummies for Sub-Saharan Africa (SSA), Latin America and Caribbean (LAC), and transition economies (TRANS). Substituting country fixed for the regional dummies does not change the results.

13

That is, if a country was in default from 1982 to 1986, END_DEF takes a value of one in 1987.

14

To predict default we use model similar to that of Manasse et al. (2003). Full regression results are provided in the Table A2 of the Appendix.

15

This is due to the fact that it does not make much sense to estimate the probability of default for industrial countries and, hence, Table 3 only includes developing countries. Furthermore, estimating the probability of default requires variables that are not available for all the countries included in the regressions reported in Table 2.

16

In order to estimate the probability of the beginning of the default episode, we used the logit described in Table A2 of the Appendix but restricted the dependent variable to take value one only in the first year of a default episode.

17

One problem with the regressions of Tables 2 and 3 is that they are based on annual information and hence they cannot capture the precise timing of the default. Levy Yeyati and Panizza (2005) study the impact of default on growth by looking at quarterly data for emerging economies and find that output contractions precede defaults, and that the trough of the contraction coincides with the quarter of default.

18

Alternatively one could try to identify the “avoidable” or unjustified defaults directly, but there are few cases that could clearly be labeled as resulting from lack of willingness to pay. Nearly all unilateral sovereign debt repudiation cases have stemmed from communist revolutions or other radical political postures, and the economic downturns probably resulted more from those political changes than from the debt defaults themselves.

19

Argentine Finance Minister Alberto Hueyo stated: “To honor existing commitments is always highly honorable, but to do it when everyone is failing to and at times of hardship… is a thousand times more valuable.” (quoted from Tomz, 2007).

20

It is remarkable that GDP per capita by itself explains 80 percent of the variance of credit rating, a fact not highlighted in the original paper (thanks to Kevin Cowan for pointing this out).

21

We also estimated the model using average ratings for the 2000-2004 period, and the set of explanatory variables averaged over the 1990-2000 period. The results did not change.

22

Using external debt over GDP yields identical results. Our data for external debt come from the World Bank’s GDF. As this data set only includes data for developing countries, we set EXDEXP equal to zero for industrial countries (therefore EXDEXP can be thought of as the following interaction EE*(1-IND) where EE is a latent variable that contains data on external debt for industrial countries). In all our estimations we drop countries that were in default over the entire 1999–2004 period. The results are robust to keeping these countries in the sample.

23

The results are essentially identical if we add a dummy variable for countries that defaulted between year t-26 and t-50.

24

In the case of column 5 we obtain the residuals by running a random effect model and in the case of column 6 we obtain the residuals by running a fixed effects model.

25

There exists some evidence on the relationship between currency crisis and trade credit. Love and Zaidi (2003) and World Bank (2004) find that, in the case of East Asia, the 1997 crisis had a negative impact on trade credit, albeit smaller than that on total bank lending.

26

In order to make sure that our results are not driven by outliers, we dropped all observations for which the dependent variable had a z-score greater than 5.

27

In particular: α=(CdCTdT)CT (where C is trade credit and T trade, Cd and Td measure the effect of default on trade and trade credit). See Love, Preve, and Sarria-Allende (2005) for a similar interpretation.

28

We use the same set of controls used by Rose (2005) in his fixed effect regressions (log of total GDP, log of GDP per capita, regional trade agreement dummy, colony dummy, and currency union dummy) but also augment the regressions with a variable measuring default interacted with average trade between country i and country j.

29

Running these regressions using imports as the trade measure yields less significant results (not shown here).

30

We code a country-year as a banking crisis if one of the following conditions apply: either Glick and Hutchinson (1999) define the episode as a major banking crisis, or Caprio and Klingebiel (2003) define the episode as a systemic crisis, or the country year is included in the list in Dell’Ariccia et al. (2005).

31

Note that the definition of external finance dependent industries is based on data for advanced economies.

32

We use the same sample restriction used in Dell’Ariccia et al. (2005). In particular, we focus on the 1980-2000 period and restrict the sample to all the countries that observed at least a banking crisis or a default over this period. We drop from the sample the top and bottom 5 percent of observations. The last column of Table 10 uses a specification that is identical to the one used by Dell’Ariccia et al (2005) and obtains results which are similar (although not identical) to those obtained by those authors.

33

This might happen for at least two reasons. Firstly, in the attempt to avoid default, banks might be forced to increase their holdings of government bonds, which later collapse in value, and secondly, the climate of uncertainty and the weakening of the banks’ financial position may trigger a deposit run.

34

This framework is inspired in Sturzenegger and Zettelmeyer (2006), Chapter 11.

35

The impact of the crisis is even higher when the window is restricted to 6 months. In this case the probability of a change in the executive goes from 12 to 23 percent, an increase of nearly 100 percent.

36

The table does not include dictatorships or countries that were transitioning towards democracy at the time of default.

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