Linking Bank Crises and Sovereign Defaults: Evidence from Emerging Markets


We analyze the mechanisms through which bank and sovereign distress feed into each other, using a large sample of emerging market economies over three decades. After defining “twin crises” as events where bank crises and sovereign defaults combine, and further distinguishing between those bank crises that end up in sovereign defaults and vice versa, we study what differentiates “single” and “twin” events. Using an event analysis methodology, we document systematic differences between “single” and “twin” crises across various dimensions including the balance sheet interconnection between banks and the sovereign, banking sector characteristics, the state of public finances, the macroeconomic environment and financial openness. The importance of these characteristics in shaping the transmission of stress between banks and sovereigns is confirmed by two alternative discrete-variable multivariate approaches. We also show that “twin” crises themselves are heterogeneous events: taking into account the actual time sequence of crises that compose “twin” episodes is important for understanding their channels of transmission and economic consequences. These findings inform the flourishing theoretical literature on the mechanisms surrounding feedback loops of sovereign and bank stress.

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  1. 1.

    See Mody and Sandri (2012), Acharya et al. (2014), Alter and Beyer (2013), Moody’s (2014), Popov and Van Horen (2013), Ongena et al. (2016) or Jordà et al. (2016) for recent empirical contributions. Relatedly, the theoretical literature is moving beyond modelling the macroeconomic effects of sovereign defaults (Mendoza and Yue 2012 or Arellano 2008) into explaining the role of financial dynamics (Malucci 2015) and banks’ balance sheets (Sosa-Padilla 2012 or Engler and Grosse-Steffen 2016).

  2. 2.

    The “twin crises” literature has mainly focused on the link between bank and balance-of-payments crises (Kaminsky and Reinhart 1999).

  3. 3.

    According to Reinhart and Rogoff (2011), (1) bank crises often lead sovereign crises, (2) external debt surges ahead of bank crises, (3) public debt increases ahead of sovereign crises and (4) short-term debt increases before debt and bank crises.

  4. 4.

    Rosas (2006) finds public bank bailouts more likely in open, rich economies or if turmoil is due to regulatory issues. Instead, electoral limits and central bank independence favor bank closure.

  5. 5.

    The fiscal costs of bank crises are well documented—see Laeven and Valencia (2012b), Feenstra and Taylor (2012), Reinhart and Rogoff (2013) or Arellano and Kocherlakota (2014).

  6. 6.

    The authorities often react to debt problems by coercing local banks to hold sovereign debt (in non-market terms), aggravating the situation in an event of default (Diaz-Cassou et al. 2008).

  7. 7.

    A number of recent papers have provided evidence on the “moral suasion” channel. De Marco and Macchiavelli (2016) present an identification strategy based on political factors, while Ongena et al. (2016) exploit fluctuations in government´s financial needs.

  8. 8.

    They further argue that the composition of fiscal stimulus affects the length of crises. There is a trade-off between boosting aggregate demand (short run) and productivity growth (long run).

  9. 9.

    As noted by Goldstein (2003), debt-to-GDP fails to take into account contingent liabilities.

  10. 10.

    De Paoli et al. (2009) find that two-thirds of sovereign defaults overlap with bank crises, and half with both bank and currency crises.

  11. 11.

    Also, Angeloni and Wolff (2012) assess the impact of sovereign exposures on banks’ performance during the euro area crisis.

  12. 12.

    These papers present a nuanced view of the effects of bond purchases by local banks. Other papers in the literature [see Andritzky (2012) or Asonuma et al. (2015)] show that these purchases can stabilize sovereign bond markets.

  13. 13.

    They find that the probability of a banking crisis conditional on a capital flow bonanza is higher than the unconditional probability in 61% of the countries they cover (for the period 1960–2007).

  14. 14.

    Obstfeld (2011) argues that “gross liabilities, especially those short-term, are what matters”.

  15. 15.

    Our sample contains only countries which experienced at least one crisis during the sample period. We have also performed the analysis with a larger sample, including 15 emerging market countries without any bank or debt crisis in our sample period, and obtained very similar results to the ones presented here.

  16. 16.

    While there are situations in which defaults may either take the form of high inflation episodes or be averted through an IMF intervention, we take a stricter view in this paper and focus on explicit defaults only.

  17. 17.

    There are twin events where both components (banking and debt crises) start the same year. In this case, we use IMF Article IV country reports, financial press and country monographs to understand the sequence of events within a twin episode.

  18. 18.

    In the case of Peru, a sequence of 1976, 1978, 1980, 1983 sovereign defaults and 1983 bank crisis is coded as a 1976 single default and a 1980-1983 twin debt-bank event. In the case of Costa Rica, a sequence of 1981 and 1984 sovereign defaults and 1987 bank crisis is coded as a 1981 single default and a 1984–1987 twin debt-bank crisis. In an earlier version of the paper, we instead code each sequence as one long twin event (i.e., 1976–1983 twin debt-bank crisis in Peru, and 1981–1987 twin debt-bank in Costa Rica) and obtain similar results (see Balteanu and Erce 2014). Finally, following Reinhart and Trebesch (2016b), the defaults experienced by the five former Yugoslavian republics of Serbia, Slovenia, Bosnia and Herzegovina, Croatia and FYR Macedonia in 1992 are not coded as such, given that they were restructurings of debt stocks that each of these countries was allocated at the breakup of the Yugoslavian state.

  19. 19.

    Arguably, a more complete measure of the banking sector´s exposure to the sovereign would also include claims on local government and public companies, which are important indicators of direct and contingent public liabilities. However, we choose to use banks’ claims on central government only due to data availability—data on claims on local/regional government and public companies are noisy and have poor coverage across many countries in our sample. We nevertheless obtain similar results when using a measure of “total claims on government” in an earlier version of this paper (Balteanu and Erce 2014).

  20. 20.

    While Claessens and Van Horen (2015) offer precise estimates of bank foreign assets in a large number of countries, their time series start in 1995 only. In a previous version of the paper (Balteanu and Erce 2014), we limit our sample to more developed emerging markets and use a measure of “total bank assets” instead, with very similar results.

  21. 21.

    The increase in banks’ holdings of public debt can be due to banks’ own decisions on risk taking or retrenching from the private sector (Broner et al. 2014), or to banks being incentivized or forced to sustain the government (Ongena et al. 2016).

  22. 22.

    It may be puzzling that growth rates are not higher than in tranquil times ahead of bank crises that feature a credit boom. Still, our findings fit into an empirical literature which does not converge on a clear pattern for output growth ahead of crises, as discussed by Bordo and Meissner (2016)’s meta-analysis of the literature on “financial and fiscal crises.”

  23. 23.

    This, in turn, may add pressure on the sovereign, given that in emerging markets debt is in a large part denominated in foreign currency.

  24. 24.

    Indeed, the DB group contains a substantial number of hyperinflations. Five out of the 16 DB crises are hyperinflations (identified as such by Hanke and Krus 2013). This implies that 31% of DB episodes are hyperinflations, which contrasts to 5% in BD crises (i.e., only one case out of 19 events), 16% in B events (i.e., eight cases out of 49 events) and 3% in D events (i.e., two cases out of 73 events).

  25. 25.

    As discussed in the previous sections, these features have been well studied in the literature.

  26. 26.

    Using a panel logit model instead delivers similar results (available upon request).

  27. 27.

    Using this methodology, Adams (2006) studies whether R&D spillovers affect the allocation of resources to research.

  28. 28.

    The explanatory variables in the model satisfy: \(E(x_{1i} \varepsilon_{1i} ) = 0\) and \(E(x_{2i} \varepsilon_{2i} ) = 0\).

  29. 29.

    We cannot use nonlinearity as a source of identification as it is done, for instance, in the Heckman model, because if the exclusion restriction fails, the linear system is unidentified.


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We thank M. Bussière, G. Cheng, J. Frost, J. Jimeno, E. Kharroubi, G. Perez-Quirós, R. Portes, P. Rabanal, two anonymous referees and seminar participants at European Stability Mechanism, 2014 Emerging Market Finance Workshop, Bank of Spain, Bank for International Settlements, Workshop for the Sixth High-Level Seminar of the Euro-system and Latin American Central Banks, Tenth Emerging Markets Workshop, CEMLA Meetings, and 2012 European Summer Symposium in International Macroeconomics, for their comments, and K. Siskind for excellent editorial assistance. J. Estefania, L. Fernandez, I. Gramatki, M. Gomez, L. Sanchez and B. Urquizu provided excellent research assistance.

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Correspondence to Irina Balteanu.

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Appendix 1: Crises and Variables

See Tables 1 and 2.

Table 1 “Single” and “twin” crisis events.
Table 2 Variables: definitions and sources

Appendix 2: Fiscal and Output Costs of Banking Crises

See Table 3.

Table 3 Fiscal and output costs of banking crises.

Appendix 3: Figures

Bank (B) Versus Bank-To-Debt (BD) Crises


Debt (D) Versus Debt-To-Bank (DB) Crises


Note: Figures 1–26 plot the evolution of the variables of interest with respect to “non-crisis” times, in a window of −/+ 3 years around the different types of crisis events (where T is the initial year of the crisis). As discussed in Sect. 4, the figures show the economic importance of the coefficients reported in Tables 416 in Appendix 4, obtained by multiplying these coefficients by the median one standard deviation of the non-standardized value of the dependent variable across countries with the same type of crises. The 90% confidence bands are shown in fine lines.

Appendix 4: Econometric Results—Event Analyses

See Tables 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 and 16.

Table 4 Credit from the central bank (% bank domestic assets)
Table 5 Bank domestic assets (% GDP)
Table 6 Claims on government (% bank domestic assets)
Table 7 Credit to the private sector (% GDP)
Table 8 Budget expenditures (% GDP)
Table 9 Budget balance (% GDP)
Table 10 Public debt (% GDP)
Table 11 Real GDP growth (%)
Table 12 Inflation rate (%)
Table 13 Total capital inflows (% GDP)
Table 14 Short-term debt in total foreign debt (%)
Table 15 Real effective exchange rate (index, 2000 = 100)
Table 16 Chinn–Ito index of capital account liberalization (%)

Appendix 5: Econometric Results (II): Multivariate Approaches

See Tables 17 and 18.

Table 17 Multinomial logit regressions
Table 18 Bivariate ordered probit regressions

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Balteanu, I., Erce, A. Linking Bank Crises and Sovereign Defaults: Evidence from Emerging Markets. IMF Econ Rev 66, 617–664 (2018).

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JEL Classification

  • E44
  • F34
  • G01
  • H63