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Are Weak Banks Leading Credit Booms? Evidence from Emerging Europe

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Abstract

This paper examines the behaviour of weak banks during episodes of brisk loan growth, using bank-level data for central and Eastern Europe and controlling for the feedback effect of credit growth on bank soundness. No evidence is found that rapid loan expansion has weakened banks during the last decade, but over time weak banks seem to have started to expand at least as fast as, and in some markets faster than, sound banks. These findings suggest that during credit booms supervisors need to carefully monitor the soundness of rapidly expanding banks and stand ready to take action to limit the expansion of weak banks.

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Notes

  1. We gratefully acknowledge the cooperation of the central banks of the Czech Republic, Estonia, Lithuania, Poland, the Slovak Republic, and Slovenia in providing breakdowns of bank loan data. We also thank Ugo Panizza for sharing data; Susan Schadler, Juan Jose Fernandez-Ansola, Ashoka Mody, and Poonam Gupta for helping to conceptualise the study; Juan Carlos Flores and David Velazquez-Romero for excellent research assistance; and Jochen Andritzky, Helge Berger, Jörg Decressin, Martin Čihák, Giovanni Dell'Ariccia, Enrica Detragiache, Wim Fonteyne, Gavin Gray, Paul Hilbers, Andy Jobst, Luc Laeven, Ashoka Mody, Ceyla Pazarbasioglu, Vassili Prokopenko, Christoph Rosenberg, Franek Rozwadowski, Piritta Sorsa, Rachel van Elkan, Jan Willem van der Vossen, and Kal Wajid for comments on an earlier draft. We are grateful to Paul Wachtel and an anonymous referee for helpful and insightful comments and to participants in the XIIIth Dubrovnik Economic Conference. The usual caveats apply. This paper should not be reported as representing the views of the IMF. The views expressed in this paper are those of the authors and do not necessarily represent those of the IMF or IMF policy.

  2. The third stream of literature has focused on the role of foreign-owned banks in credit expansion in central and Eastern Europe (see, eg, de Haas and van Lelyveld, 2006). These studies generally do not find any significant differences in the rate of loan growth in foreign- and domestically owned banks, while confirming that foreign-owned banks have a competitive advantage owing to their higher efficiency and liquidity.

  3. Typically, market values of equity are used to calculate the value and volatility of assets. However, these calculations assume that bank stocks are traded in well-functioning and liquid markets. Since these assumptions may not hold for emerging European banks for the period in question, we use a simpler measure of DD based exclusively on balance sheet and income statement data. This measure is sometimes called z-score to differentiate it from the market price-based DD measure.

  4. DD is weakly correlated with contemporaneous measures of return on assets and capital. It is primarily driven by the volatility of returns, which is a proxy for the risks faced by the bank.

  5. The results are robust to alternative calculation methods of return volatility such as computing the standard deviation over 3-year rolling windows.

  6. We consider dummy variables for the share of foreign or public ownership exceeding 50% as part of robustness analysis and controlling for the type of foreign ownership (through wholly owned subsidiaries or partial ownership following takeovers of domestic banks during privatisation) as part of robustness analysis.

  7. The Arellano-Bond (1991) method, which is commonly used for estimating dynamic panel models, does not apply to a simultaneous-equation setting. We use this method on the credit growth equation only, as part of robustness checks.

  8. We use robust standard errors in estimation, which renders similar significance levels as standard errors clustered by country.

  9. For subsample analyses, total bank loan data from Bankscope were supplemented with supervisory data on breakdowns of bank loan portfolios by the currency of loan denomination or indexation and the type of borrower (household or corporate). These additional data were provided by the central banks of the central and Eastern European countries in question (except Hungary and Latvia) for research purposes on the condition of strict confidentiality.

  10. Except for Hungary and Poland, where the coverage measured by the number of banks is slightly lower (64% and 55%, respectively).

  11. For more information on data definitions and sources, see Appendix A.

  12. Note that this approach to calculating DD implies a more sanguine assessment of the risks facing banks than the baseline approach of calculating the volatility of returns for the entire sample period, as the volatility of returns declined in the later part of the sample in part owing to favourable macroeconomic conditions.

  13. The NPL ratio is an imperfect measure of bank soundness: it can be manipulated by the bank, for example, by restructuring and refinancing loans, to disguise poor asset quality (the evergreening problem).

  14. We control for the type of foreign ownership by interacting the continuous foreign ownership variable with a dummy for banks privatized to foreigners. Privatization by selling to foreigners does not have a significant effect on bank soundness over the long run. Even though the coefficient on the interaction term is positive and marginally significant in the earlier period, it becomes negative and insignificant in the later period, suggesting that gains from privatization (at least in terms of enhancing bank soundness) are short-lived.

  15. Results are also robust to excluding Slovenia, the most developed Eastern European economy.

  16. The Bankscope data set for 1995–2002 was provided by Ugo Panizza. These data were used in a study of bank ownership and performance in developing and industrial countries (Micco et al., 2004).

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Correspondence to Natalia T Tamirisa or Deniz O Igan.

Appendix A

Appendix A

Data Sources and Methodology

Macroeconomic data were taken from the February 2006 version of the IMF's International Financial Statistics. Bank-level data were downloaded from the February 2006 version of BankscopeFootnote 16 and cleaned up by carefully matching bank identities and deleting duplicate entries, as well as the entries with possible measurement errors. The Bankscope data set was complemented with confidential supervisory data on the composition of bank loans obtained from the central banks of all CEECs, except Latvia and Hungary, as well as data on bank ownership from various sources, such as Euromoney and banks' websites. Details on the coverage and compatibility of different components of the data set are also presented below. Tables A1 and A2 present the summary statistics for the final dataset. The definitions of variables and units of measurement for bank-level and macroeconomic data are presented in Table A3.

Table a1 Summary statistics
Table a2 Summary statistics by country
Table a3 Variable description

Matching bank identifiers: Bankscope uses a unique identifier for each bank. This identifier remains unchanged when the bank's name changes and sometimes even when the bank is merged with or acquired by another bank. Only if a merger or an acquisition intrinsically changes the bank is a new identifier assigned to the new bank. Data for the banks operating in central and Eastern Europe during 2002–2004 were first downloaded using the February 2006 update of Bankscope. The data were then merged with the historical dataset provided by Ugo Panizza, using the unique identifiers and cross-checking based on the 2002 data.

Avoiding duplications: Bankscope includes both consolidated and unconsolidated balance sheet data. When both are available for the same bank, a different identifier is assigned to each type of data. Moreover, at the time of mergers, the banks involved might stay in the dataset along with the merged entity. To make sure that observations are not duplicated for the same bank, the following procedure was applied to include information from only one of the balance sheets. First, using the ‘rank’ variable in Bankscope, which ranks the banks within a country, nonranked banks were dropped to avoid duplications. However, a second step was necessary to make sure that the duplication was not due to a merger event. If a bank was not ranked but had assets greater than the country average, its history of mergers and acquisitions was examined carefully. Next, the premerger banks were reranked to ensure that they were included in the dataset, and the postmerger banks were deranked to exclude them from the premerger period. Many such banks had both consolidated and unconsolidated balance sheets. To be able to identify individual banks, the unconsolidated data were preserved when both balance sheets were available. If unconsolidated data were unavailable, consolidated data were used to avoid dropping the banks from the sample.

Excluding outliers: To ensure that the analysis is not affected by potential measurement errors and misreporting, about 4% of the observations on the tails of the distributions of the two main variables (bank-level credit growth and DD) were dropped.

Coding ownership: Bankscope does not provide historical information about bank ownership; it provides only the share held by foreign and public investors in the current year. Thanks to extensive work by Micco et al. (2004), the historical ownership data up to 2002 were available for the study. While extending the time coverage to 2004, the most recent ownership information from Bankscope data on central and Eastern European banks was obtained. This information was complemented with information from banks' websites and Bankscope data on parent banks to update ownership information for 2003 and 2004.

Merging in loan breakdowns: The central banks in six of the eight countries included in the study provided bank-by-bank data on the composition of loans, as collected by supervisory authorities. The data covered the period from 1995 to 2005 (except in the Czech Republic, where the coverage was from 2000 to 2005) and broke down total loans into (i) loans to households in local currency, (ii) loans to corporates in local currency, (iii) loans to households in foreign currency, and (iv) loans to corporates in foreign currency. For confidentiality reasons, most countries were unable to disclose the identity of the banks. Banks from the supervisory dataset and from the Bankscope dataset were matched using data on total loans and total assets. To reduce the likelihood of measurement errors and ensure data consistency, dummy variables identifying banks with rapidly growing household and foreign-currency portfolios, rather than actual data on household and foreign-currency loans, were used.

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Tamirisa, N., Igan, D. Are Weak Banks Leading Credit Booms? Evidence from Emerging Europe. Comp Econ Stud 50, 599–619 (2008). https://doi.org/10.1057/ces.2008.35

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