Skip to main content
Log in

Combining DEA Window Analysis with the Malmquist Index Approach in a Study of the Canadian Banking Industry

  • Published:
Journal of Productivity Analysis Aims and scope Submit manuscript

Abstract

The banking industry in Canada is essentially an oligopoly with five large participants controlling about 90% of the market. To evaluate the industry's performance over time, we need to deal with the problem of a small number of DMU's compared to the number of relevant inputs and outputs. To overcome this problem we use data envelopment analysis (DEA) window analysis, whereby efficiency scores for the 20 year period 1981–2000 are obtained. To measure productivity changes over time, Malmquist indices can be calculated from DEA scores. Using DEA window analysis scores, however, raise the question of how to define the “same period frontier” in a DEA window analysis. We show that for both the adjacent and the base period Malmquist index and for all suggested definitions of same period frontier, the standard decomposition into frontier shift and catching up effects gives inappropriate results when Malmquist indices are based on DEA window analysis scores.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Ali, A. A. and L. M. Seiford. (1993). “The Mathematical Approach to Efficiency Analysis.” In H. Fried, C. A. K. Lovell and S. Schmidt (eds.), The Measurement of Productive Efficiency: Techniques and Applications. Oxford University Press.

  • Althin, R. (2001). “Measurement of Productivity Changes: Two Malmquist Index Approaches.” Journal of Productivity Analysis 16, 107–128.

    Google Scholar 

  • Banker, R. D. and R. M. Thrall. (1992). “Estimation of Returns to Scale using Data Envelopment Analysis.” European Journal of Operational Research 62, 74–84.

    Google Scholar 

  • Berg, S. A., F. R. Førsund and E. S. Jansen. (1992). “Malmquist Indices of Productivity Growth during the Deregulation of Norwegian Banking, 1980–89.” Scandinavian Journal of Economics (Supplement), 211–228.

    Google Scholar 

  • Berger, A. N. and D. B. Humphrey. (1997). “Efficiency of Financial Institutions: International Survey and Directions for Future Research.” European Journal of Operational Research 98, 175–212.

    Google Scholar 

  • Charnes, A., C. T. Clark, W. W. Cooper and B. Golany. (1985). “A Developmental Study of Data Envelopment Analysis in Measuring the Efficiency of Maintenance Units in the U.S. Air Forces.” Annals of Operations Research 2, 95–112.

    Google Scholar 

  • Charnes, A., W. W. Cooper, A. Y. Lewin and L. M. Seiford (eds.) (1994). Data Envelopment Analysis: Theory, Methodology and Applications. Kluwer Academic Publishers.

  • Charnes, A., W. W. Cooper and L. M. Seiford. (1994). “Extension to DEA Models.” In A. Charnes, W. W. Cooper, A. Y. Lewin and L. M. Seiford (eds.), Data Envelopment Analysis: Theory, Methodology and Applications. Kluwer Academic Publishers.

  • Charnes, A., W. W. Cooper and E. Rhodes. (1978). “Measuring the Efficiency of Decision Making Units.” European Journal of Operational Research 2(6), 429–444.

    Google Scholar 

  • Cook, W. D. and M. Hababou. (2001). “Sales Performance Measurement in Bank Branches.” Omega 29(4), 299–307.

    Google Scholar 

  • Cook, W. D., M. Hababou and H. J. H. Tuenter. (2000). “Multicomponent Efficiency Measurement and Shared Inputs in Data Envelopment Analysis: An Application to Sales and Service Performance in Bank Branches.” Journal of Productivity Analysis 14(3), 209–224.

    Google Scholar 

  • Cooper, W. W., L. M. Seiford and K. Tone. (2000). Data Envelopment Analysis. Kluwer Academic Publishers.

  • Färe, R., S. Grosskopf, B. Lindgren and P. Roos. (1994). “Productivity Developments in Swedish Hospitals. A Malmquist Output Index Approach.” In A. Charnes, W. W. Cooper, A. Y. Lewin and L. M. Seiford (eds.), Data Envelopment Analysis: Theory, Methodology and Applications. Kluwer Academic Publishers.

  • Fischer, I. (1922). The Making of Index Numbers: A Study of Their Varieties, Tests, and Reliability. Boston: Houghton Mifflin Company.

    Google Scholar 

  • Freedman, C. (1998). The Canadian Banking System. Technical Report No. 81, Bank of Canada, Ottawa.

    Google Scholar 

  • Førsund, F. R. (1993). “Productivity Growth in Norwegian Ferries.” In H. Fried, C. A. K. Lovell and S. Schmidt (eds.), The Measurement of Productive Efficiency: Techniques and Applications. Oxford University Press.

  • Goto, M. and M. Tsutsui. (1998). “Comparison of Productive and Cost Efficiencies Among Japanese and U.S. Electric Utilities.” OMEGA: International Journal of Management Science 26(2), 177–194.

    Google Scholar 

  • Grifell-Tatje, E. and C. A. K. Lovell. (1995). “A Note on the Malmquist Productivity Index.” Economics Letters 47, 169–175.

    Google Scholar 

  • Malmquist, S. (1953). “Index Numbers and Indifference Surfaces.” Trabajos de Estadistica 4, 209–242.

    Google Scholar 

  • Parkan, C. (1987). “Measuring the Efficiency of Service Operations: An Application to Bank Branches.” Engineering Costs and Production Economics 12, 237–242.

    Google Scholar 

  • Richardson Greenshields. (1995). The Bank Examiner. Equity Research Report, Sept. 1995.

  • Richardson Greenshields. (1995). Financial Services Monthly Monitor. Equity Research Report, Oct. 1995.

  • Schaffnit, C., D. Rosen and J. C. Paradi. (1997). “Best Practice Analysis of Bank Branches: An Application of DEA in a Large Canadian Bank.” European Journal of Operational Research 98, 269–289.

    Google Scholar 

  • Sueyoshi, T. and S. Aoki. (2001). “A Use of Nonparametric Statistic for DEA Frontier Shift: The Kruskal and Wallis Rank Test.” OMEGA: International Journal of Management Science 29, 1–18.

    Google Scholar 

  • Thompson, R., F. Singleton, R. Thrall and B. Smith. (1986). “Comparative Site Evaluations for Locating a High-Energy Physics Lab in Texas.” Interfaces 16(6), 35–49.

    Google Scholar 

  • Thore, S., G. Kozmetsky and F. Phillips. (1994). “DEA of Financial Statements Data: The U.S. Computer Industry.” The Journal of Productivity Analysis 5, 229–248.

    Google Scholar 

  • Tulkens, H. and P. Vanden Eeckaut. (1995). “Non-Parametric Efficiency, Progress and Regress Measures for Panel Data: Methodological Aspects.” European Journal of Operational Research 80, 474–499.

    Google Scholar 

  • Westergaard, H. (1890). Die Grundzüge der Theorie der Statistik. Jena: Gustav Fischer.

    Google Scholar 

  • Wood Gundy Inc. (1995). The Canadian Chartered Banks, Investment Research Report, Sept. 1995.

  • Yue, P. (1992). “Data Envelopment Analysis and Commercial Bank Performance: A Primer with Applications to Missouri Banks.” Federal Reserve Bank of St. Louis Review, Vol. 74–1.

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Asmild, M., Paradi, J.C., Aggarwall, V. et al. Combining DEA Window Analysis with the Malmquist Index Approach in a Study of the Canadian Banking Industry. Journal of Productivity Analysis 21, 67–89 (2004). https://doi.org/10.1023/B:PROD.0000012453.91326.ec

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/B:PROD.0000012453.91326.ec

Navigation