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The Costs of Sovereign Default


This paper empirically evaluates four types of costs 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. It finds that the economic costs are generally significant but short-lived, and sometimes do not operate through conventional channels. The political consequences of a debt crisis, by contrast, seem to be particularly dire for incumbent governments and finance ministers, broadly in line with what happens in currency crises.

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Figure 1


  1. In fact, even in private markets debt contracts are not fully enforceable. Djankov and others (2007) show that creditor protection through the legal system is positively correlated with the development of the private credit market.

  2. 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. For a detailed discussion of the law and economics of sovereign debt, see Panizza, Sturzenegger, and Zettelmeyer (forthcoming).

  3. 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).

  4. For recent reviews, see De Paoli, Hoggarth, and Saporta (2006); Hatchondo, Martinez, and Sapriza (2007); and Panizza, Sturzenegger, and Zettelmeyer (forthcoming).

  5. This paper does not explore the role of collateral. For a discussion of this issue see Dooley, Garber, and Folkerts-Landau (2007).

  6. 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 Gapen and others (2005).

  7. 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 Table A1.

  8. 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.

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

  10. Of these six 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).

  11. 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.

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

  13. To predict default we use model similar to that of Manasse, Roubini, and Schimmelpfennig (2003). Full regression results are available upon request.

  14. 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.

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

  16. 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.

  17. 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.

  18. An ever more recent literature focuses on reputation vis-à-vis domestic agents (see Panizza, Sturzenegger, and Zettelmeyer, forthcoming, for a survey).

  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 in Tomz, 2007).

  20. An under-researched topic concerns the relationship between the size of the haircut and the conditions under which defaulters re-access to capital market (Panizza, Sturzenegger, and Zettelmeyer, forthcoming, and Trebesch, 2009, include discussions of this issue).

  21. 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).

  22. We also estimated the model using average ratings for the 2000–04 period, and the set of explanatory variables averaged over the 1990–2000 period. The results did not change.

  23. 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.

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

  25. 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.

  26. See also Martinez and Sandleris (2008).

  27. Kohlscheen and O’Connell (2007) accumulation of international reserves is justified by the fact that, by allowing countries to survive without trade credit, reserves put defaulters in a stronger bargaining position during the renegotiation of defaulted debt.

  28. There exists some evidence on the relationship between currency crisis and trade credit. Love and Zaidi (2004) 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.

  29. 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.

  30. In particular: (where C is trade credit and T trade, C d and T d measure the effect of default on trade and trade credit). See Love, Preve, and Sarria-Allende (2005) for a similar interpretation.

  31. Note that our data for trade credit only cover suppliers of trade credit based in the OECD countries. If a default were to cause a diversion of trade towards non-OECD countries (a fact consistent with Rose's finding), we would be interpreting a change in trade credit pattern as a reduction in credit.

  32. 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.

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

  34. 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, Detragiache, and Rajan (2005).

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

  36. We use the same sample restriction used in Dell’Ariccia, Detragiache, and Rajan (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, Detragiache, and Rajan (2005) and obtains results which are similar (although not identical) to those obtained by those authors.

  37. 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.

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

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

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

  41. This is why we did not attempt to estimate a formal econometric model but just showed that the well-known result that sharp devaluations may be politically costly also applies to episodes of sovereign default.


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Additional information

*Eduardo Borensztein is Regional Economic Advisor for the Southern Cone at the Inter-American Development Bank (IDB). Ugo Panizza is Chief of the Debt and Finance Analysis Unit Division on Globalization and Development Strategies, UN Conference on Trade and Development. Research on this paper was initiated when both authors were with the IDB Research Department. The authors would like to thank two anonymous referees, Bob Flood, Guillermo Calvo, Jeff Frieden, Eduardo Levy Yeyati, Guido Sandleris, and the participants of seminars at the IDB and the International Economic Association for useful suggestions and comments, and Patricio Valenzuela and Monica Yañez for excellent research assistance.


Appendix I

Table A1

Table a1 Private Lending to Sovereign: Default and Rescheduling

Appendix II

Table A2

Table a2 Logit model for the probability of default

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Borensztein, E., Panizza, U. The Costs of Sovereign Default. IMF Econ Rev 56, 683–741 (2009).

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JEL Classifications

  • F34
  • F36
  • H63
  • G15