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A bootstrap approach to analyse productivity growth in European banking

  • Theoretical Paper
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Journal of the Operational Research Society

Abstract

This paper analyses productivity growth for European banks over the 1995–2001 period. In contrast to previous literature, our study covers the majority of current European Union (EU) countries—all except Greece and those joining the EU in 2004. We also use resampling methods so as to gain statistical precision, which turns out to be especially important due to the limitations of the database. In order to be consistent, we use additional nonparametric methods to disentangle why productivity differentials might exist. Results show that productivity growth has occurred in most countries, mainly due to improvements in production possibilities. The bootstrap analysis yields further evidence given that for many firms and countries productivity growth or decline is not statistically significant. The two-stage analysis provides some extra insights, suggesting that the relevance of environmental variables found in other studies focusing on efficiency could be lessened when focusing on productivity.

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Notes

  1. This assertion parallels one of the claims by Färe et al (1994b) in their study on the productivity of 17 arbitrarily selected OECD countries.

  2. Note that we do not differentiate between the EU and Europe. Furthermore, the notion EU refers to the EU-15, not the enlarged EU. In our particular setting, we will speak about EU-14 as Greece was not considered.

  3. See, for instance, the article on the history of the MPI by Grosskopf (2003).

  4. The existing literature is already substantial, including studies for many countries such as Australia (Avkiran, 2000), Greece (Noulas, 1997), Japan (Fukuyama and Weber, 2002), South Korea (Gilbert and Wilson, 1998), Italy (Casu and Girardone, 2004), Portugal (Canhoto and Dermine, 2003), Norway (Berg et al, 1992), Spain (Tortosa-Ausina et al, 2008), Turkey (Isik and Hassan, 2003), or the US (Wheelock and Wilson, 2009).

  5. However, these assumptions are far less restrictive than the requirements demanded by parametric methods such as SFA.

  6. The choice of B is limited by the speed of the computer but we need at least 2,000 replications for yielding a reasonable approximation to confidence intervals.

  7. Each value of efficiency is estimated under constant returns to scale because the index only correctly measures the productivity change if the true technology exhibits constant returns to scale everywhere (Grifell-Tatjé and Lovell, 1995).

  8. By reversing t1 and t2 in Equation (13) we obtain

  9. A more exhaustive decomposition of MPI, as in Simar and Wilson (1999b), might imply that although changes in productivity might be significant, the sources of productivity could themselves be nonsignificant.

  10. See Simar and Wilson (1999a) for a comprehensive guideline of the algorithm.

  11. We only illustrate the case of the Malmquist productivity index, but the procedure is identical for each component making up the index.

  12. A11 computations have been performed using codes created by the authors. Specifically, we have applied Matlab codes for the computation of the different components of the Malmquist indices and the efficiency scores, as well as Gauss codes for the bootstrap procedure. Our codes are available upon request.

  13. The DGP is defined by considering i.i.d. (independent and identically distributed) observations within each subgroup, but not necessarily across them.

  14. In order to test significance for the geometric mean of each country we require the empirical distribution of each index, as in Equation (17), but for each country. This is obtained by averaging for each index, the corresponding B bootstrap values of all national banks and afterwards we test for the presence of unity in their percentile confidence interval.

  15. If we had followed the output oriented version of the Malmquist TFP change index, interpretation of results would reverse. This is possible due to the constant returns to scale (CRS) assumption.

  16. Similar possibilities exist for the case of productivity decline.

  17. Following Simar and Wilson (1999b), when averaging bank estimates over time, we also average the corresponding bootstrap values over time to obtain estimates of significance for the complete period.

  18. A previous version of the paper contained tables providing summaries for each particular country. They have been skipped in this version for space reasons but are available from the authors upon request.

  19. Such as the recent takeover of Abbey National by Banco Santander, Spain's largest bank.

  20. We consider six economic neighbourhoods, namely: (i) The Netherlands, Belgium, and Luxembourg; (ii) Sweden, Finland, and Denmark; (iii) UK and Ireland; (iv) Austria and Germany; (v) France and Italy; and (vi) Portugal and Spain. Whereas in most cases they are clearly a reality, in some others they are not so apparent.

  21. We consider three groups: (i) The Netherlands, Belgium, Luxembourg, Italy, France, Germany, UK, Ireland, and Denmark; (ii) Portugal and Spain; and (iii) Sweden, Finland, and Austria. Although the first group contains countries which joined the EU at different points in time, we consider that sufficient time has passed to assume their financial systems are not going to become much more integrated—at least in the short run.

  22. We have confined the analysis to the analysis of Malmquist productivity indices, omitting their decomposition into efficiency and technical change, so as to save space. Results are available upon request.

References

  • Allen F and Santomero AM (2001). What do financial intermediaries do? J Bank Financ 25: 271–294.

    Article  Google Scholar 

  • Avkiran NK (2000). Rising productivity of Australian trading banks under deregulation 1986–1995. J Econ Financ 24: 122–140.

    Article  Google Scholar 

  • Banker RD (1993). Maximum likelihood, consistency and data envelopment analysis: A statistical foundation. Mngt Sci 39: 1265–1273.

    Article  Google Scholar 

  • Bauer PW, Berger AN and Humphrey DB (1993). Efficiency and productivity growth in U.S. banking. In: Fried HO, Lovell CAK and Schmidt SS (eds). The Measurement of Productive Efficiency: Techniques and Applications. Oxford University Press: Oxford, pp. 386–413.

    Google Scholar 

  • Berg SA, Bukh PND and Førsund FR (1995). Banking efficiency in the Nordic countries: A four-country Malmquist index analysis. Working paper. University of Aarhus, Denmark.

  • Berg SA, Førsund FR and Jansen ES (1992). Technical efficiency of Norwegian banks: The nonparametric approach to efficiency measurement. J Prod Anal 2: 127–142.

    Article  Google Scholar 

  • Berger AN and Humphrey DB (1992). Measurement and efficiency issues in commercial banking. In: Griliches Z (ed). Output Measurement in the Service Sectors, NBER Studies in Income and Wealth. The University of Chicago Press: Chicago, pp. 245–300.

    Google Scholar 

  • Canhoto AM and Dermine J (2003). A note on banking efficiency in Portugal, New vs. Old banks. J Bank Financ 27: 2087–2098.

    Article  Google Scholar 

  • Casu B and Girardone C (2004). Financial conglomeration: Efficiency, productivity and strategic drive. Appl Financ Econ 14: 687–696.

    Article  Google Scholar 

  • Casu B, Girardone C and Molyneux P (2004). Productivity change in banking: A comparison of parametric and non-parametric approaches. J Bank Financ 28: 2521–2540.

    Article  Google Scholar 

  • Chaffai ME, Dietsch M and Lozano-Vivas A (2001). Technological and environmental differences in the European banking industries. J Financ Serv Res 19: 147–162.

    Article  Google Scholar 

  • Daraio C and Simar L (2005a). Conditional nonparametric frontier models for convex and non convex technologies: A unifying approach. Discussion Paper DP0502, Institute de Statistique, Université Catholique de Louvain, Louvain-la-Neuve.

  • Daraio C and Simar L (2005b). Introducing environmental variables in nonparametric frontier models: A probabilistic approach. J Prod Anal 24: 93–121.

    Article  Google Scholar 

  • Dietsch M and Lozano-Vivas A (2000). How the environment determines banking efficiency. A comparison between French and Spanish industries. J Bank Financ 24: 985–1004.

    Article  Google Scholar 

  • Efron B (1979). Bootstrap methods: Another look at the jackknife. Ann Stat 7: 1–26.

    Article  Google Scholar 

  • Fan Y and Ullah A (1999). On goodness-of-fit tests for weakly dependent processes using kernel method. J Nonparametr Stat 11: 337–360.

    Article  Google Scholar 

  • Färe R, Grosskopf S and Lovell CAK (1994a). Production Frontiers. Cambridge University Press: Cambridge.

    Google Scholar 

  • Färe R, Grosskopf S, Norris M and Zhang Z (1994b). Productivity growth, technical progress, and efficiency change in industrialized countries. Am Econ Rev 84(1): 66–83.

    Google Scholar 

  • Farrell MJ (1957). The measurement of productive efficiency. J Roy Stat Soc, Ser. A 120: 253–281.

    Article  Google Scholar 

  • Ferrier GD and Hirschberg JD (1997). Bootstrapping confidence intervals for linear programming efficiency scores: With an illustration using Italian banking data. J Prod Anal 8: 19–33.

    Article  Google Scholar 

  • Fukuyama H and Weber WL (2002). Estimating output allocative efficiency and productivity change: Application to Japanese banks. Eur J Opl Res 137: 177–190.

    Article  Google Scholar 

  • Gijbels I, Mammen E, Park B and Simar L (1999). On estimation of monotone and concave frontier functions. J Am Stat Assoc 49: 220–228.

    Article  Google Scholar 

  • Gilbert RA and Wilson PW (1998). Effects of deregulation on the productivity of Korean banks. J Econ Bus 50: 133–155.

    Article  Google Scholar 

  • Gorton G and Rosen R (1995). Corporate control, portfolio choice, and the decline of banking. J Financ 50: 1377–1419.

    Article  Google Scholar 

  • Grifell-Tatjé E and Lovell CAK (1995). A note on the Malmquist productivity index. Econ Lett 47: 169–175.

    Article  Google Scholar 

  • Grifell-Tatjé E and Lovell CAK (1999). Generalized malmquist productivity index. Sociedad de Estadistica e Investigation Operativa 7(1): 81–101.

    Google Scholar 

  • Grosskopf S (1993). Efficiency and productivity. In: Fried HO, Lovell CAK and Schmidt SS (eds). The Measurement of Productive Efficiency: Techniques and Applications, Chapter 4. Oxford University Press: Oxford, pp. 161–193.

    Google Scholar 

  • Grosskopf S (2003). Some remarks on productivity and its decompositions. J Prod Anal 20: 459–474.

    Article  Google Scholar 

  • Harker PT and Zenios SA (2000). What drives the performance of financial institutions? In: Harker PT and Zenios SA (eds). Efficiency of Financial Institutions: Efficiency, Innovation, Regulation, Chapter 1. Cambridge University Press: Cambridge, pp. 3–31.

    Google Scholar 

  • Isik I and Hassan MK (2003). Financial deregulation and total factor productivity change: An empirical study of Turkish commercial banks. J Bank Financ 27: 1455–1485.

    Article  Google Scholar 

  • Kneip A, Park BU and Simar L (1998). A note on the convergence of nonparametric dea estimators for production efficiency scores. Economet Theor 14: 783–793.

    Article  Google Scholar 

  • Kneip A, Simar L and Wilson PW (2003). Asymptotics for DEA estimators in non-parametric frontier models. Discussion Paper 0317, Institut de Statistique, Universite Catholique de Louvain, Louvain-la-Neuve.

  • Korostelev A, Simar L and Tsybakov AB (1995). On estimation of monotone and convex boundaries. Pub Inst Stat Univ Paris XXXIX(1): 3–18.

    Google Scholar 

  • Kumbhakar SC, Lozano-Vivas A, Lovell CAK and Hasan I (2001). The effects of deregulation on the performance of financial institutions: The case of Spanish savings banks. Journal of Money Credit Bank 33(1): 101–120.

    Article  Google Scholar 

  • Li Q (1996). Nonparametric testing of closeness between two unknown distribution functions. Econ Rev 15: 261–274.

    Article  Google Scholar 

  • Noulas AG (1997). Productivity growth in the Hellenic banking industry: State versus private banks. Appl Financ Econ 7: 223–228.

    Article  Google Scholar 

  • Quah DT (1996). Regional convergence clusters across Europe. Eur Econ Rev 40: 951–958.

    Article  Google Scholar 

  • Rogers KE (1998). Nontraditional activities and the efficiency of U.S. commercial banks. J Bank Financ 22: 467–482.

    Article  Google Scholar 

  • Sengupta JK (1982). Efficiency measurement in stochastic input-output systems. Int J Syst Sci 13: 273–287.

    Article  Google Scholar 

  • Silverman BW (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall: London.

    Book  Google Scholar 

  • Simar L (1992). Estimating efficiencies from frontier models with panel data: a comparison of parametric, non-parametric and semi-parametric methods with bootstrapping. J Prod Anal 3: 167–203.

    Article  Google Scholar 

  • Simar L and Steinmann L (2003). On the comparability of efficiency scores in nonparametric frontier models. Discussion Paper 0318 Institut de Statistique, Université Catholique de Louvain, Louvain-la-Neuve.

  • Simar L and Wilson PW (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Mngt Sci 44: 49–61.

    Article  Google Scholar 

  • Simar L and Wilson PW (1999a). Estimating and bootstrapping Malmquist indices. Eur J Opl Res 115: 459–471.

    Article  Google Scholar 

  • Simar L and Wilson PW (1999b). Productivity growth in industrialized countries. Discussion Paper 9836, Institut de Statistique, Université Catholique de Louvain, Louvain-la-Neuve.

  • Simar L and Wilson PW (2007). Estimation and inference in two-stage, semi-parametric models of productive processes. J Econometrics 136(1): 31–64.

    Article  Google Scholar 

  • Simar L and Wilson PW (2008). Statistical inference in nonparametric frontier models: Recent developments and perspectives. In: Fried H, Lovell CAK and Schmidt SS (eds). The Measurement of Productive Efficiency,2nd edn. Oxford University Press: Oxford, pp. 421–521 (Chapter 4).

    Google Scholar 

  • Simar L and Zelenyuk V (2003). Statistical inference for aggregates of Farrell-type efficiencies. Discussion Paper 0324, Institut de Statistique, Université Catholique de Louvain, Louvain-la-Neuve.

  • Stiroh KJ (2000). How did bank holding companies prosper in the 1990s? J Bank Financ 24: 1703–1745.

    Article  Google Scholar 

  • Tortosa-Ausina E, Grifell-Tatjé E, Armero C and Conesa D (2008). Sensitivity analysis of efficiency and Malmquist productivity indices: An application to Spanish savings banks. Eur J Opl Res 184: 1062–1084.

    Article  Google Scholar 

  • Wheelock DC and Wilson PW (1999). Technical progress, inefficiency, and productivity change in US banking, 1984-1993. J Money Credit Bank 31: 212–234.

    Article  Google Scholar 

  • Wheelock DC and Wilson PW (2009). Robust nonparametric estimation of efficiency and technical change in U.S. commercial banking. J Bus Econ Stat 27: 354–368.

    Article  Google Scholar 

Download references

Acknowledgements

All three authors acknowledge the financial support of the Ministerio de Ciencia e Innovacion (SEJ2005-08269/ECON, ECO2008-03813/ECON and ECO2008-05908-C02-01/ECON). Emili Tortosa-Ausina also acknowledges the financial support of the Fundació Caixa Castelló-Bancaixa (P1-1B2008-46) and Generalitat Valenciana (PROME-TEO/2009/066). A previous version of this article circulated as a FUNCAS working paper (207-2005). We also thank an anonymous referee for helpful comments.

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Authors and Affiliations

  1. Universidad de Cantabria, Cantabria, Spain

    C Murillo-Melchor

  2. Universitat de València and Ivie, València, Spain

    J M Pastor

  3. Universitat Jaume I and Ivie, Castelló, Spain

    E Tortosa-Ausina

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  1. C Murillo-Melchor
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  2. J M Pastor
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  3. E Tortosa-Ausina
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Correspondence to E Tortosa-Ausina.

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Murillo-Melchor, C., Pastor, J. & Tortosa-Ausina, E. A bootstrap approach to analyse productivity growth in European banking. J Oper Res Soc 61, 1729–1745 (2010). https://doi.org/10.1057/jors.2009.114

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