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Key Determinants of Non-performing Loans: New Evidence from a Global Sample

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Abstract

Using a novel panel data set we study the macroeconomic determinants of non-performing loans (NPLs) across 75 countries during the past decade. According to our dynamic panel estimates, the following variables are found to significantly affect NPL ratios: real GDP growth, share prices, the exchange rate, and the lending interest rate. In the case of exchange rates, the direction of the effect depends on the extent of foreign exchange lending to unhedged borrowers which is particularly high in countries with pegged or managed exchange rates. In the case of share prices, the impact is found to be larger in countries which have a large stock market relative to GDP. These results are robust to alternative econometric specifications.

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Notes

  1. See for example Committee of European Banking Supervisors (2010); European Banking Authority (2011), Board of Governors of the Federal Reserve System (2009a, b); Bank of England (2008).

  2. While the bank supervisors in many advanced and some emerging economies collect quarterly NPL data such information is usually not publicly available. In addition, quarterly data often have an even shorter time dimension.

  3. A good example of the variety in applied definitions is the NPL definition applied by the Central Bank of Russia which is not comparable with the international practices. Russia’s NPL definition accounts only for due instalments and interest rather than the total amount of the troubled loan. This results in a significant underestimation of the NPLs, which are reported. In order to obtain more realistic figures reflecting the credit quality, we multiplied in our sample officially reported NPLs in Russia roughly by two (based on the long–term ratio of the aggregate NPLs for both definitions).

  4. Despite some information on certain aspects of the NPL definition is available at national level; there are no sources which systemically cover all necessary features to make a clear classification.

  5. An increase in the NEER represents an appreciation of the domestic currency.

  6. We use from the BIS’consolidated banking statistics total international claims of all BIS reporting banks to the respective country, including local lending in foreign currencies and cross-border claims which we assumed to be mainly denominated in foreign currency as in Lane and Shambaugh (2010 )

  7. There are of course other unit root tests that could be applied, but here we follow a standard methodology and apply well established tests in the econometric literature.

  8. One may also use the random effects method in order to deal with the unobserved heterogeneity problem but the additional orthogonality assumption between the unobserved country specifics and the determinants of NPLs may not hold. A Hausman test suggests that there is strong evidence in favour of the fixed effects estimation.

  9. See Roodman (2006)—How to do xtabond2: an introduction to “Difference” and “System” GMM in Stata.

  10. The full set of results, robustness checks, data sets and Stata codes is available upon request. In the result tables we report standard information and statistical tests; it is difficult to report all the information related to our estimations due to space constrains.

  11. Typically, a decline in economic activity tends to affect non-performing loans with a time lag of a few quarters. With annual data, the impact is attributed to the contemporaneous growth rate of real GDP.

  12. While stock market capitalisation in the UK is significantly larger than in Germany which has a more bank-based financial system, both countries have larger stock markets relative to GDP compared to the median of our sample.

  13. The transmission of policy rates on bank lending rates depends on many factors such as the maturity of loans. For the empirical exercise aggregate lending interest rates from the IMF’s International Financial Statistics have been used with the exception of Germany where lending interest rate data refer to mortgage rates for new housing loans as reported by the Deutsche Bundesbank.

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Acknowledgments

The authors would like to thank Elitza Mileva, Jorn Zenhorst, Roland Straub, Philipp Hartmann for useful comments. The views expressed in this paper are those of the authors and do not necessarily reflect those of the institutions the authors are affiliated with or those of the Eurosystem. Financial support from Grant Agency of the Czech Republic GACR 14-02108S is gratefully acknowledged.

Author information

Authors and Affiliations

  1. European Central Bank, Frankfurt, Germany

    Roland Beck

  2. European Insurance and Occupational Pensions Authority (EIOPA), Frankfurt, Germany

    Petr Jakubik

  3. Czech National Bank, Prague, Czech Republic

    Petr Jakubik

  4. Institute of Economic Studies, Charles University in Prague, Prague, Czech Republic

    Petr Jakubik

  5. Deutsche Bank Research, Frankfurt, Germany

    Anamaria Piloiu

  6. Goethe University Frankfurt, Frankfurt, Germany

    Anamaria Piloiu

Authors
  1. Roland Beck
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  2. Petr Jakubik
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  3. Anamaria Piloiu
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Corresponding author

Correspondence to Petr Jakubik.

Appendix

Appendix

Chart 1
figure 1

Non-performing loans to total loans ratio (%) Sources: IMF, World Banks and authors’ calculations

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Chart 2
figure 2

Growth of NPL ratio (%) Sources: IMF, World Banks and authors’ calculations

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Chart 3
figure 3

Growth of NPL ratio and real GDP growth in 2009 Sources: IMF, World Bank, ECB calculation

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Chart 4
figure 4

NPL ratio and real GDP growth in 2009 Sources: IMF, World Bank, ECB calculation

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Chart 5
figure 5

Contribution of independent variables to the growth of NPLs in selected economies Sources: IMF, World Bank and ECB calculations. Notes: All indicators are considered in logarithmic differences. The fitted values of logarithmic difference NPL are computed using Arellano Bond estimates for which the RGDP and NEER were treated as endogenous. For Ukraine, the time series on NPLs is starting in 2005 and for Germany data on NPLs is available until 2009. The contribution of each indicator is computed as the product of its coefficient and the actual value of the variable

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Beck, R., Jakubik, P. & Piloiu, A. Key Determinants of Non-performing Loans: New Evidence from a Global Sample. Open Econ Rev 26, 525–550 (2015). https://doi.org/10.1007/s11079-015-9358-8

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