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Stochastic data envelopment analysis in measuring the efficiency of Taiwan commercial banks

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  • Kao, Chiang
  • Liu, Shiang-Tai

Abstract

Conventional data envelopment analysis (DEA) for measuring the efficiency of a set of decision making units (DMUs) requires the input/output data to be constant. In reality, however, many observations are stochastic in nature; consequently, the resulting efficiencies are stochastic as well. This paper discusses how to obtain the efficiency distribution of each DMU via a simulation technique. The case of Taiwan commercial banks shows that, firstly, the number of replications in simulation analysis has little effect on the estimation of efficiency means, yet 1000 replications are recommended to produce reliable efficiency means and 2000 replications for a good estimation of the efficiency distributions. Secondly, the conventional way of using average data to represent stochastic variables results in efficiency scores which are different from the mean efficiencies of the presumably true efficiency distributions estimated from simulation. Thirdly, the interval-data approach produces true efficiency intervals yet the intervals are too wide to provide valuable information. In conclusion, when multiple observations are available for each DMU, the stochastic-data approach produces more reliable and informative results than the average-data and interval-data approaches do.

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  • Kao, Chiang & Liu, Shiang-Tai, 2009. "Stochastic data envelopment analysis in measuring the efficiency of Taiwan commercial banks," European Journal of Operational Research, Elsevier, vol. 196(1), pages 312-322, July.
  • Handle: RePEc:eee:ejores:v:196:y:2009:i:1:p:312-322
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    Cited by:

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    3. Kao, Ling-Jing & Lu, Chi-Jie & Chiu, Chih-Chou, 2011. "Efficiency measurement using independent component analysis and data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 210(2), pages 310-317, April.
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    7. L-J Kao & C-C Lu & C-C Chiu, 2011. "The training institution efficiency of the semiconductor institute programme in Taiwan—application of spatiotemporal ICA with DEA approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(12), pages 2162-2172, December.
    8. Shiang-Tai Liu, 2018. "A DEA ranking method based on cross-efficiency intervals and signal-to-noise ratio," Annals of Operations Research, Springer, vol. 261(1), pages 207-232, February.
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    13. Quaranta, Anna Grazia & Raffoni, Anna & Visani, Franco, 2018. "A multidimensional approach to measuring bank branch efficiency," European Journal of Operational Research, Elsevier, vol. 266(2), pages 746-760.
    14. Weixin Yang & Lingguang Li, 2017. "Efficiency Evaluation and Policy Analysis of Industrial Wastewater Control in China," Energies, MDPI, Open Access Journal, vol. 10(8), pages 1-18, August.
    15. Jamal Ouenniche & Skarleth Carrales, 2018. "Assessing efficiency profiles of UK commercial banks: a DEA analysis with regression-based feedback," Annals of Operations Research, Springer, vol. 266(1), pages 551-587, July.
    16. Lee, Chia-Yen & Johnson, Andrew L., 2014. "Proactive data envelopment analysis: Effective production and capacity expansion in stochastic environments," European Journal of Operational Research, Elsevier, vol. 232(3), pages 537-548.
    17. Mojtaba Yari & Raheb Bagherpour & Saeed Jamali, 2017. "Development of an evaluation system for blasting patterns to provide efficient production," Journal of Intelligent Manufacturing, Springer, vol. 28(4), pages 975-984, April.
    18. Kao, Chiang & Liu, Shiang-Tai, 2014. "Multi-period efficiency measurement in data envelopment analysis: The case of Taiwanese commercial banks," Omega, Elsevier, vol. 47(C), pages 90-98.
    19. García-Alonso, Carlos R. & Salvador-Carulla, Luis & Fernández-Rodríguez, Vicente, 2015. "Evaluation of system efficiency using the Monte Carlo DEA: The case of small health areasAuthor-Name: Torres-Jiménez, Mercedes," European Journal of Operational Research, Elsevier, vol. 242(2), pages 525-535.
    20. Wai‐Peng Wong & Qiang Deng & Ming-Lang Tseng & Loo‐Hay Lee & Chee‐Wooi Hooy, 2014. "A Stochastic Setting To Bank Financial Performance For Refining Efficiency Estimates," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 21(4), pages 225-245, October.
    21. Kao, Chiang & Liu, Shiang-Tai, 2019. "Stochastic efficiency measures for production units with correlated data," European Journal of Operational Research, Elsevier, vol. 273(1), pages 278-287.
    22. Lau, Kwok Hung, 2013. "Measuring distribution efficiency of a retail network through data envelopment analysis," International Journal of Production Economics, Elsevier, vol. 146(2), pages 598-611.
    23. R G Dyson & E A Shale, 2010. "Data envelopment analysis, operational research and uncertainty," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 25-34, January.
    24. Serhat Yüksel & Shahriyar Mukhtarov & Elvin Mammadov, 2016. "Comparing the Efficiency of Turkish and Azerbaijani Banks: An Application with Data Envelopment Analysis," International Journal of Economics and Financial Issues, Econjournals, vol. 6(3), pages 1059-1067.
    25. Davtalab-Olyaie, Mostafa & Asgharian, Masoud & Nia, Vahid Partovi, 2019. "Stochastic ranking and dominance in DEA," International Journal of Production Economics, Elsevier, vol. 214(C), pages 125-138.

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