Efficiency of the banks: the case of the Visegrad countries

Economic Annals-ХХI: Volume 174, Issue 11-12, Pages: 34-42

Citation information:
Dráb, R., & Kočišová, K. (2018). Efficiency of the banks: the case of the Visegrad countries. Economic Annals-XXI, 174(11-12), 34-42. doi: https://doi.org/10.21003/ea.V174-06


Radovan Dráb
PhD (Economics),
Assistant Professor,
Department of Banking and Investments,
Faculty of Economics,
Technical University of Košice
32 Nemcovej Str., Košice, 04001, Slovak Republic
Radovan.Drab@tuke.sk
ORCID ID: https://orcid.org/0000-0002-6022-5995

Kristína Kočišová
PhD (Economics),
Associate Professor,
Department of Banking and Investments,
Faculty of Economics,
Technical University of Košice
32 Nemcovej Str., Košice, 04001, Slovak Republic
Kristina.Kocisova@tuke.sk
ORCID ID: https://orcid.org/0000-0003-0784-441X

Efficiency of the banks: the case of the Visegrad countries

Abstract. The purpose of the paper is to measure the technical efficiency of domestic commercial banks in the Visegrad countries (V4) by using non-parametric Data Envelopment Analysis (DEA) and estimate the efficiency change in the banking sector. We apply an input-oriented window DEA model with a constant and variable return to scale to investigate the technical efficiency of commercial banks’ deposits to loan the transformation process. The input-oriented model was evaluated using CCR (a measure of the overall technical efficiency) and BCC (a measure of the pure technical efficiency). The model results provide recommendations for managers in managing banks to increase their effectiveness in the analysed group of banks. The analysis is focused on the 2005-2016 period, since the banking went through massive structural and regulatory changes and was affected by the 2008 financial crisis during this period. To obtain the best research results, we considered three sub-periods (2005-2008; 2009-2012; 2013-2016). The growth of the banking market, as well as the development of the economy, has led to changes in the technical efficiency. Therefore, the last part of this paper is focused on the determinants of the efficiency changes relating to individual sub-periods identified by using the radial Malmquist index under the condition of constant return to scale.

The results point to the fact that the positive efficiency change during the 2005-2008 period was primarily due to the innovation and technological growth, while during the 2009-2012 and 2013-2016 periods it was mostly impacted by progress in the efficiency change due to improved operations of management and return to scale effect.

Taking into account the results of the BCC model, which overcome the assumption that banks operate under the condition of their optimal size, we can see that the lea­ding position was reached by the Hungarian banking sector, whose average pure technical efficiency was 78.83% du­ring the whole analysed period. The Czech Republic ranked second, with the average pure technical efficiency equal to 68.63%, the third one was Poland (60.52%), the last one was the Slovak ban­king sector (58.32%).

Data relevant to the years 2018 and 2017 were not available at the time of the analysis, therefore the authors present only a trend of future development. Data provided by the European Central Bank which are partially available for 2017 suggest that the development described by the analysis results with the use of the DEA models covering all the three sub-periods will have an increasing trend with digressive slope.

Keywords: Window Data Envelopment Analysis (DEA); Intermediation Approach; Malmquist Index; Commercial Banks; Banking; Return to Scale Effect; Technical Efficiency; CCR; BCC; Visegrad Countries (V4)

JEL Classification: G21; C14

Acknowledgements: The research behind this paper was supported by the research project VEGA 1/0794/18.

DOI: https://doi.org/10.21003/ea.V174-06

References

  1. Adeleke, O. A., Adeleke, H. M., & Fajobi, D. O. (2017). Productivity Growth, Technical Progress, and Efficiency Change in ECOWAS Agriculture 1971-2009: A Full Cumulative (FC) Extended Malmquist Approach. International Journal of Innovative Research and Advanced Studies, 4(5), 416-423.
    Retrieved from http://www.ijiras.com/2017/Vol_4-Issue_5/paper_80.pdf
  2. Asmild, M., Paradi, J. C., Aggarwall, V., & Schaffnit, C. (2004). Combining DEA Window Analysis with the Malmquist Index Approach in a Study of the Canadian Banking Industry. Journal of Productivity Analysis, 21(1), 67-89.
    doi: https://doi.org/10.1023/B:PROD.0000012453.91326.ec
  3. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in Data Envelopment Analysis. Management Science, 30(9), 1078-1092.
    doi: https://doi.org/10.1287/mnsc.30.9.1078
  4. Battese, G. E., Heshmati, A., & Hjalmarsson, L. (2000). Efficiency of labour use in the Swedish banking industry: a stochastic frontier approach. Empirical Economics, 25(4), 623-640.
    doi: https://doi.org/10.1007/s001810000037
  5. Berg, S. A., Forsund, F. R., & Jansen, E. S. (1992). Malmquist indices of productivity growth during the deregulation of Norwegian banking, 1980-89. The Scandinavian Journal of Economics, 94, 211-228.
    Retrieved from https://ideas.repec.org/a/bla/scandj/v94y1992i0ps211-28.html
  6. Boďa, M., & Zimková, E. (2015). Efficiency in the Slovak banking industry: a comparison of three approaches. Prague Economic Papers, 24(4), 434-451.
    doi: https://doi.org/10.18267/j.pep.546
  7. Casu, B., & Molyneux, Ph. (2003). A comparative study of efficiency in European banking. Applied Economics, 35(17), 1865-1876.
    doi: https://doi.org/10.1080/0003684032000158109
  8. Casu, B., Ferrari, A., Girardone, C., & Wilson, J. O. S. (2016). Integration, productivity and technological spillovers: Evidence for eurozone banking industries. European Journal of Operational Research, 255(3), 971-983.
    doi: https://doi.org/10.1016/j.ejor.2016.06.007
  9. Casu, B., Girardone, C., & Molyneux, Ph. (2004). Productivity change in European banking: a comparison of parametric and non-parametric approaches. Journal of Banking & Finance, 28(10), 2521-2540.
    doi: https://doi.org/10.1016/j.jbankfin.2003.10.014
  10. Černohorská, L., Pilyavskyy, A., & Aaronson, W. (2017). Comparative performance of the Visegrad group banks for the period 2009-2013. E+M Ekonomie a Management, 20(2), 175-187.
    doi: https://doi.org/10.15240/tul/001/2017-2-013
  11. Charnes, A., Clark, T. C., Cooper, W. W., & Golany, B. (1985). A developmental study of data envelopment analysis in measuring the efficiency of maintenance units in U.S. air forces. Annals of Operational Research, 2(1), 95-112.
    doi: https://doi.org/10.1007/BF01874734
  12. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European Journal of Operational Research, 2(6), 429-444.
    doi: https://doi.org/10.1016/0377-2217(78)90138-8
  13. Charnes, A., Cooper, W. W., Lewin, A. Y., & Seiford, L. M. (1995). Data Envelopment Analysis: Theory, Methodology and Applications. New York: Springer-Verla.
  14. Cooper, W. W., Seiford L. M., & Tone, K. (2007). Data Envelopment Analysis: A comprehensive text with models applications. New York: Springer Science.
  15. Cooper, W. W., Seiford, L. M., & Zhu, J. (2011). Handbook on Data Envelopment Analysis. New York: Springer Science.
    doi: https://doi.org/10.1007/978-1-4419-6151-8
  16. Degl’Innocenti, M., Kourtzidis, S. A., Sevic, Z., & Tzeremes, N. G. (2017). Bank productivity growth and convergence in the European Union during the financial crisis. Journal of Banking & Finance, 75, 184-199.
    doi: https://doi.org/10.1016/j.jbankfin.2016.11.016
  17. Färe, R., Grosskopf, S., Lindgren, B., & Roos, P. (1994). Productivity developments in Swedish hospitals: a Malmquist output index approach. In A. Charnes, W. W. Cooper, A. Y. Lewin & L. M. Seiford (Eds). Data envelopment analysis: theory, methodology, and applications (pp. 253-272). Springer.
    doi: https://doi.org/10.1007/978-94-011-0637-5
  18. Jablonský, J. (2012). Multicriteria approaches for ranking of efficient units in DEA models. Central European Journal of Operational Research, 20(3), 435-449.
    doi: https://doi.org/10.1007/s10100-011-0223-6
  19. Kiseľáková, D., & Kiseľák, A. (2013). Analysis of banking business and its impact on financial stability of economies in Euro area. Polish Journal of Management Studies, 8, 121-131.
    Retrieved from http://oaji.net/articles/2014/1384-1416475837.pdf
  20. Kumbhakar, S. C., Lozano-Vivas, A., Lovell, C. A. K., & Hassan, I. (2001). The effects of deregulation on the performance of financial institutions: the case of Spanish savings banks. Journal of Money, Credit and Banking, 33(1), 101-120.
    doi: https://doi.org/10.2307/2673874
  21. Martinez-Miera, D., & Repullo, R. (2010). Does competition reduce the risk of bank failure? The Review of Financial Studies, 23(10), 3638-3664.
    doi: https://doi.org/10.1093/rfs/hhq057
  22. Palečková, I. (2015). Estimation of the efficiency of Slovak commercial banks by the Data Envelopment Analysis. Acta Academica Karvinensia, 1, 130-140.
    Retrieved from http://aak.slu.cz/pdfs/aak/2015/01/11.pdf
  23. Palečková, I. (2017). Efficiency change of banking sectors and banks in the financial conglomerates in Visegrad group countries. Ekonomický časopis, 65(1), 79-92.
    Retrieved from https://www.researchgate.net/publication/316605666_
    Efficiency_Change_of_Banking_Sectors_and_Banks_in_the_Financial_Conglomerates_in_Visegrad_Group_Countries
  24. Pastor, J.-M., Perez, F., & Quesada, J. (1997). Efficiency analysis in banking firms: An international comparison. European Journal of Operational Research, 98(2), 395-407.
    doi: https://doi.org/10.1016/S0377-2217(96)00355-4
  25. Pitoňáková, R. (2015). Determinants of household saving: Evidence from Slovakia. Ekonomický časopis, 63(8), 837-852.
    Retrieved from https://www.researchgate.net/publication/301713519_
    Determinants_of_Household_Saving_Evidence_from_Slovakia
  26. Ponomarenko, V., Kolodiziev, O., & Chmutova, I. (2017). Benchmarking of bank performance using the life cycle concept and the DEA approach. Banks and Bank Systems, 12(3), 74-86.
    doi: https://doi.org/10.21511/bbs.12(3).2017.06
  27. Řepková, I. (2014). Efficiency of the Czech banking sector employing the DEA window analysis approach. Procedia Economics and Finance, 12, 587-596.
    doi: https://doi.org/10.1016/S2212-5671(14)00383-9
  28. Shepherd, R. W. (1953). Cost and Production Functions. Princeton: Princeton University Press.
  29. Sherman, H. D., & Gold, F. (1985). Bank branch operating efficiency: Evaluation with Data Envelopment Analysis. Journal of Banking and Finance, 9(2), 297-315.
    doi: https://doi.org/10.1016/0378-4266(85)90025-1
  30. Toloo, M., Allahyar, M., & Hančlová, J. (2018). A non-radial directional distance method on classifying inputs and outputs in DEA: application to banking industry. Expert Systems with Applications, 92, 495-506.
    doi: https://doi.org/10.1016/j.eswa.2017.09.034
  31. Zimková, E. (2014). Technical efficiency and super-efficiency of the banking sector in Slovakia. Procedia Economics and Finance, 12, 780-787.
    doi: https://doi.org/10.1016/S2212-5671(14)00405-5

Received 8.10.2018