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A multi-division efficiency evaluation of U.S. electric power companies using a weighted slacks-based measure

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  • Tsutsui, Miki
  • Goto, Mika

Abstract

Prior to the 1990s, the electric power industry was highly regulated across the world. Under a liberalization policy to open markets and to grow competition commenced in the early 1990s, efficient management has become a necessity for companies in this industry. The current study examines the divisional efficiencies of multi-functional, vertically integrated companies seeking to optimize their overall management efficiency. For this purpose, divisional cost data are used as input into a slacks-based measure (SBM) model. This provides divisional efficiency indices based on slacks, as well as one for the larger firm-level management function. Further, given the important role of cost structure, we introduce a modified SBM, named the weighted SBM (WSBM), which directly incorporates division-specific weights into the objective function. Results reveal that the power generation divisions of the companies studied have significant influence on the overall cost, whereas the impact of the other four divisions - transmission, distribution, sales and general administrative - is limited.

Suggested Citation

  • Tsutsui, Miki & Goto, Mika, 2009. "A multi-division efficiency evaluation of U.S. electric power companies using a weighted slacks-based measure," Socio-Economic Planning Sciences, Elsevier, vol. 43(3), pages 201-208, September.
  • Handle: RePEc:eee:soceps:v:43:y:2009:i:3:p:201-208
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Po-Chi Chen, 2012. "Measurement of technical efficiency in farrow-to-finish swine production using multi-activity network data envelopment analysis: evidence from Taiwan," Journal of Productivity Analysis, Springer, vol. 38(3), pages 319-331, December.
    2. Bi, Gong-Bing & Song, Wen & Zhou, P. & Liang, Liang, 2014. "Does environmental regulation affect energy efficiency in China's thermal power generation? Empirical evidence from a slacks-based DEA model," Energy Policy, Elsevier, vol. 66(C), pages 537-546.
    3. Kao, Chiang, 2014. "Network data envelopment analysis: A review," European Journal of Operational Research, Elsevier, vol. 239(1), pages 1-16.
    4. repec:gam:jsusta:v:9:y:2017:i:5:p:853-:d:99188 is not listed on IDEAS
    5. Bai-Chen, Xie & Ying, Fan & Qian-Qian, Qu, 2012. "Does generation form influence environmental efficiency performance? An analysis of China’s power system," Applied Energy, Elsevier, vol. 96(C), pages 261-271.
    6. Ang, Sheng & Chen, Chien-Ming, 2016. "Pitfalls of decomposition weights in the additive multi-stage DEA model," Omega, Elsevier, vol. 58(C), pages 139-153.
    7. Lin, Tzu-Yu & Chiu, Sheng-Hsiung, 2013. "Using independent component analysis and network DEA to improve bank performance evaluation," Economic Modelling, Elsevier, vol. 32(C), pages 608-616.
    8. repec:eee:energy:v:134:y:2017:i:c:p:392-399 is not listed on IDEAS
    9. Kelly D.T.Trinh & Valentin Zelenyuk, 2015. "Bootstrap-based testing for network DEA: Some Theory and Applications," CEPA Working Papers Series WP052015, School of Economics, University of Queensland, Australia.

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