Banking sector concentration, competition and financial stability: the case of the Baltic countries

ABSTRACT

This paper empirically assesses the potential nonlinear relationship between competition and bank risk for a sample of commercial banks in the Baltic countries over the period 2000–2014. Competition is measured by two alternative indexes, the Lerner index and the market share, while we consider the Z-score and loan loss reserves as proxies for bank risk. In line with the theoretical predictions, we find an inverse U-shaped relationship between competition and financial stability. This then means that above a certain threshold, the lack of competition is likely to exacerbate the individual risk-taking behaviour of banks, and could be detrimental to the stability of the banking sector in the Baltic countries. The threshold is around 0.60 for the Lerner index, and close to 50% for market share in terms of assets. The policy implications are that the existence of such a threshold suggests that the future evolution of the structure of the banking industry in these countries is of critical importance. Specifically, this implies that policy-makers should place greater emphasis on mergers and acquisitions to avoid any significant increase of banking sector concentration.

KEYWORDS:

• Bank competition
• banking sector concentration
• market power
• Lerner index
• financial stability
• bank risk-taking
• Baltic countries

Acknowledgments

This paper was written while Yannick Lucotte was a visiting researcher at the Bank of Estonia. He would like to thank the Bank of Estonia for its hospitality and financial support. Juan Carlos acknowledges the financial support from the AEI-MINEIC-FEDER projects ECO2017-85503-R and ECO2017-83255-C3-3-P. We thank the Editor, Richard Connolly, and the anonymous referee for their comments. We also thank Dmitry Kulikov, Aurélien Leroy, Jaanika Meriküll, Tairi Rõõm, and Karsten Staehr for their useful suggestions. The views expressed in this paper are those of the authors and do not necessarily represent the official views of Eesti Pank or the Eurosystem. Any remaining errors are ours.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. See, for instance, Berger et al. (2009), Fungáčová and Weill (2013), Jimenez et al. (2013), Liu et al. (2013), and Fu et al. (2014).

2. See Table A1 in the Appendix for an overview of bank-level analyses on the effect of bank competition on financial stability.

3. Note nonetheless that empirical results obtained by Maudos and de Guevara (2007) for a large sample of European banks do not confirm the quiet life hypothesis. On the contrary, they find a positive relationship between market power and the cost X-efficiency.

4. See Lepetit and Strobel (2013) for a review of different methodologies for computing the Z-score.

5. See Lind and Mehlum (2010) for more details concerning the U-shape test and the computation of the confidence interval. Please see Tables A2, A3 and A4 in the Appendix for more details concerning the control variables.

6. A graphical representation of the marginal effects is displayed in Figure A5 of the Appendix. Please see Figure A1 in the Appendix for a graphical representation of the conditional marginal effects.

7. A robust regression is an alternative approach used when the data contain some outliers or high leverage data points. It is a compromise between excluding these points entirely from the analysis and including all the data points and treating them all equally in the regression. In practice, robust regression works by assigning a weight to each data point. Weighting is done automatically and iteratively using a process called iteratively reweighted least squares. In the first iteration, each point is assigned an equal weight and model coefficients are estimated using ordinary least squares (OLS). At subsequent iterations, weights are recomputed so that points farther from the model predictions in the previous iteration are given a lower weight. The model coefficients are then recomputed using weighted least squares. The process continues until the values of the coefficient estimates converge within a specified tolerance.