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Accounting for environmental factors, bias and negative numbers in efficiency estimation: A bootstrapping application to the Hong Kong banking sector


  • Maximilian J. B. Hall

    () (Dept of Economics, Loughborough University)

  • Karligash Kenjegalieva

    () (Dept of Economics, Loughborough University)

  • Richard Simper

    () (Dept of Economics, Loughborough University)


This paper examines the evolution of Hong Kong’s banking industry’s technical efficiency, and its macroeconomic determinants, during the period 2000-2006 through the prism of two alternative approaches to efficiency estimation, namely the intermediation and production approaches. Using a modified (Sharp, Meng and Liu, 2006) slacks-based model (Tone, 2001), and purging the efficiency estimates for random errors (Simar and Zelenyuk, 2007) , we firstly analyse the trends in bank efficiency. We then identify the ‘environmental’ factors that significantly affect the efficiency scores using an adaptation (Kenjegalieva et al. 2009) of the truncated regression approach suggested by Simar and Wilson. 2007). The first part of the analysis reveals that the Hong Kong banking industry suffered a severe downturn in estimated technical efficiency during 2001. It subsequently recovered, posting average efficiency scores of 92 per cent and 85 percent under the intermediation and production approaches respectively by the end of 2006. As for the sub-group analysis, commercial banks are, on average, shown to be the most efficient operators, while the investment bank group are shown to be the least efficient. Finally, with respect to the truncated regression analysis, the results suggest that smaller banks are more efficient than their larger counterparts, although larger banks are still able to enjoy gains from scale economies and benefit from the export of financial services. Moreover, private housing rent and the net export of goods and services are found to be negatively correlated with bank efficiency, while private consumption is shown to be positively correlated.

Suggested Citation

  • Maximilian J. B. Hall & Karligash Kenjegalieva & Richard Simper, 2010. "Accounting for environmental factors, bias and negative numbers in efficiency estimation: A bootstrapping application to the Hong Kong banking sector," Discussion Paper Series 2010_03, Department of Economics, Loughborough University, revised Feb 2010.
  • Handle: RePEc:lbo:lbowps:2010_03

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    References listed on IDEAS

    1. Laeven, Luc & Majnoni, Giovanni, 2003. "Loan loss provisioning and economic slowdowns: too much, too late?," Journal of Financial Intermediation, Elsevier, vol. 12(2), pages 178-197, April.
    2. Drake, Leigh & Hall, Maximilian J.B. & Simper, Richard, 2009. "Bank modelling methodologies: A comparative non-parametric analysis of efficiency in the Japanese banking sector," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 19(1), pages 1-15, February.
    3. Fare, Rolf & Zelenyuk, Valentin, 2003. "On aggregate Farrell efficiencies," European Journal of Operational Research, Elsevier, vol. 146(3), pages 615-620, May.
    4. Manthos D. Delis & Nikolaos I. Papanikolaou, 2009. "Determinants of bank efficiency: evidence from a semi-parametric methodology," Managerial Finance, Emerald Group Publishing, vol. 35(3), pages 260-275, February.
    5. Ana Lozano-Vivas & Jesús Pastor & José Pastor, 2002. "An Efficiency Comparison of European Banking Systems Operating under Different Environmental Conditions," Journal of Productivity Analysis, Springer, vol. 18(1), pages 59-77, July.
    6. Papke, Leslie E & Wooldridge, Jeffrey M, 1996. "Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 619-632, Nov.-Dec..
    7. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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    Cited by:

    1. Portnov, Boris A. & Felsenstein, Daniel, 2010. "On the suitability of income inequality measures for regional analysis: Some evidence from simulation analysis and bootstrapping tests," Socio-Economic Planning Sciences, Elsevier, vol. 44(4), pages 212-219, December.

    More about this item


    Hong Kong Banks; DEA; Slacks; Environmental factors; Negative numbers; Bias.;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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