Environmental variables and relative efficiency of bank branches: a DEA-bootstrap approach

International Journal of Business Performance Management

Volumn 7, Issue 3, 2005, Pages 228-240

  Barth, Wolfgang ^a   Staat, Matthias ^b  

^a Hochschule der Sparkassen Finanzgruppe   (Germany)

^b UNIVERSITY OF MANNHEIM   (Germany)

Author keywords

adverse selection; bank branches; bootstrap; data envelopment analysis; efficiency; environmental variables; principal agent; yardstick competition

Indexed keywords

EID: 77956388110     PISSN: 13684892     EISSN: 17415039     Source Type: Journal    
DOI: 10.1504/IJBPM.2005.006735     Document Type: Article

References (23)

1. Athanassopoulos, A.D. (2004) ‘Assessing the selling function in retailing: insights from banking, sales forces, restaurants and betting shops’, in W.W. Cooper, L.M. Seiford and J. Zhu (Eds.) Handbook on Data Envelopment Analysis, Kluwer, pp.455–480.
2. Banker, R.D. and Morey, R.C. (1986) ‘Efficiency analysis for exogenously fixed inputs and outputs’, Operations Research, Vol. 34, pp.513–521.
3. Berger, A.N. and Humphrey, D.B. (1997) ‘Efficiency of financial institutions: international survey and directions for future research’, European Journal of Operational Research, Vol. 98, pp.175–221.
4. Bogetoft, P. (1997) ‘DEA-based yardstick competition: the optimality of best practice regulation’, Annals of Operations Research, Vol. 73, pp.277–298.
5. Bogetoft, P. (2000) ‘Activity planning under asymmetric information’, Journal of Productivity Analysis, Vol. 13, pp.7–48.
6. Cooper, W.W., Seiford, L.M. and Tone, K. (2000) Data Envelopment Analysis, Kluwer.
7. Fare, R. and Grosskopf, S. (1996) Intertemporal Production Frontiers: With Dynamic DEA, Kluwer.
8. Farrell, M.J. (1957) ‘The measurement of productive efficiency’, Journal of the Royal Statistical Society Series A, Vol. 125, pp.252–267.
9. Laffont, J-J. and Tirole, J. (1993) A Theory of Incentives in Procurement and Regulation, MIT Press.
10. Paradi, J.C., Vela, S. and Yang, Z. (2004) ‘Assessing bank and bank branch performance: modelling considerations and approaches’, in W.W. Cooper, L.M. Seiford and J. Zhu (Eds.) Handbook on Data Envelopment Analysis, Kluwer, pp.349–400.
11. Simar, L. and Wilson, P.W. (2004) ‘Estimation and inference in two-stage, semi-parametric models of production processes’, Technical Report 0310, IAP Statistics Network.
12. Staat, M. (1999) ‘Treating non-discretionary variables one way or the other: implications for efficiency scores and their interpretation’, in G. Westermann (Ed.) Data Envelopment Analysis in the Service Sector, DUV, pp.23–49.
13. Parameters which are under the control of the management are called discretionary parameters.
14. For a survey on DEA-based yardstick schemes, see Bogetoft (2000).
15. Because the data are confidential we are unable to provide more detailed information.
16. It may seem inconsistent to use one monetary and one nonmonetary figure as input indicators. However, the result of the DEA would not be changed if one multiplied the FTE by a (weighted) wage as the model to be used below is unit invariant (see for example Table 4.4 in Cooper et al., 2000).
17. See the discussion of our results in Section 4.
18. See Section 3.
19. Non-bank customers are charged several euros for using the ATM irrespective of the amount withdrawn. Therefore, frequent use by non-bank customers indicates that many customers of other banks have no convenient alternative to using this ATM.
20. VAB is the code for above average customers.
21. For a more detailed description of the assumptions, see Simar and Wilson (2004).
22. Since we find no significant effects we provide no in-depth discussion of these results.
23. See the discussion in Section 3.1.