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Anatomy of international banking crises at the onset of the Great Recession

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Abstract

The paper examines a wide range of potential predictors of 25 international banking crises that broke out in 2007–2011 on the basis of cross–sectional logit models and the BCT (binary classification tree) algorithm, a novel technique in assessing the causes of banking crises. The major determinants of the crises arise from an excessive credit depth (measured as private credit to GDP ratio) and illiquidity of the banking sector (credits to deposits ratio). The implementation of explicit deposit insurance schemes is also a pro-crisis factor due to the moral hazard effect they tend to cause. On the contrary, higher values of remittance inflows to GDP decrease the susceptibility to banking crises. These findings are robust under both methodologies. Lower bank concentration, bigger values of cost to income ratios as well as a higher level of economic liberalization and inflation make countries more vulnerable to banking crises, as derived from the logit analysis. The pre-crisis credit depth, credits to deposits ratio, inflation, financial openness and net interest margin are also significant predictors of the crisis costs proxied by the peak ratio of non-performing loans (NPL) relative to gross loans, the increase in public debt to GDP ratio and real output losses.

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Notes

  1. This modeling technique is legitimized by the cross-sectional structure of the data used as well as the focus on a single constellation of banking crises. However, this analysis is static and partial equilibrium by nature. It involves certain simplifications, i.e. the neglect of national monetary policy responses to the banking crises. Therefore, it may downplay the role of important crisis predictors that appear significant in time series framework (VAR/VECM models) for a smaller number of countries. One of such predictors are global liquidity shocks that have had a sizeable impact on macroeconomic and financial variables (interest rates and asset prices, in particular), thus imposing constraints on national monetary policy even in advanced economies. See, for example, Belke et al. (2010, 2014).

  2. This is the year 2007 for all the countries but the USA, the UK (2006) and Nigeria (2008).

  3. The algorithm was implemented using the SALFORD System CART software (http://www.salford-systems.com/products/cart).

  4. See Breiman et al. (1984) for an in-depth technical treatment of the BCT methodology.

  5. To be precise, Manasse et al. (2013) use a modification of the BCT algorithm that appears superior to the baseline BCT and logit regressions in out-of-sample estimation.

  6. The adverse effect of the EDIS may arise from an ex ante insufficient deposit insurance level. Engineer et al. (2013) present a model showing that at non-crisis times low deposit guarantees are beneficial as they are an implicit tax on banks and a deadweight loss for social welfare. These low deposit guarantees, however, cannot prevent deposit withdrawals when a banking crisis starts as depositors transfer their savings to the jurisdictions with a higher level of deposit insurance provision. The ex ante insufficient deposit insurance can prompt beggar-thy-neighbour decisions to ratchet up national deposit guarantees as countries compete to deter deposit outflows. The model describes the situation in the EU before 2007 where most countries (with the exception of France and Italy) set the deposit guarantees as low as €20,000 per depositor. In this regard the increase of the guarantees up to €100,000 as well as the initiative under the EU banking union plan to set up separate deposit insurance funds for big and small banks can remedy the situation.

  7. ANOVA is redundant as reducing the dimension of the data is embedded in the BCT algorithm.

  8. Corresponding figures are not presented here for brevity but available from the author upon request.

  9. See Yohai (1987) for technical details.

  10. The threshold is computed as a partial derivative set to zero \( \frac{\partial (NPL)}{\partial \left( Bank\_ \Pr ivate\_ Credit\_ GDP\right)}=-0.49+0.004* Bank\_ \Pr ivate\_ Credit\_ GDP=0 \).

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Authors and Affiliations

  1. Department of Applied Economics, Moscow State Institute of International Relations (MGIMO-University) 76 Vernadskogo prospect, Moscow, 119454, Russia

    Mikhail Stolbov

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  1. Mikhail Stolbov
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Correspondence to Mikhail Stolbov.

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Table 5 ᅟ
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Stolbov, M. Anatomy of international banking crises at the onset of the Great Recession. Int Econ Econ Policy 12, 553–569 (2015). https://doi.org/10.1007/s10368-014-0293-8

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