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Bias and precision in the DEA two-stage method

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  • Darold Barnum
  • John Gleason

Abstract

In Data Envelopment Analysis (DEA), the two-stage method is a popular procedure for accounting for exogenous influences on efficiency. With the conventional two-stage method, a DEA is first conducted using only traditional (endogenous) inputs and outputs. Then, the first-stage DEA scores are regressed on the environmental/contextual (exogenous) inputs of interest. The regression outcomes are used to identify exogenous inputs that influence the first-stage DEA scores to a statistically significant degree, and to adjust DEA scores to account for these influences. Herein, it is demonstrated empirically that the conventional method exhibits substantial bias and low precision, with the degree of bias and precision affected by input variance and correlation. A reverse two-stage procedure that yields estimates without the bias and precision problems that compromise the validity of the conventional method's estimates is suggested.

Suggested Citation

  • Darold Barnum & John Gleason, 2008. "Bias and precision in the DEA two-stage method," Applied Economics, Taylor & Francis Journals, vol. 40(18), pages 2305-2311.
    • Handle: RePEc:taf:applec:v:40:y:2008:i:18:p:2305-2311
    DOI: 10.1080/00036840600949470
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    References listed on IDEAS

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    1. Mei Xue & Patrick T. Harker, 1999. "Overcoming the Inherent Dependency of DEA Efficiency Scores: A Bootstrap Approach," Center for Financial Institutions Working Papers 99-17, Wharton School Center for Financial Institutions, University of Pennsylvania.
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    Cited by:

    1. Welch, Eric & Barnum, Darold, 2009. "Joint environmental and cost efficiency analysis of electricity generation," Ecological Economics, Elsevier, vol. 68(8-9), pages 2336-2343, June.
    2. Saastamoinen, Antti & Bjørndal, Endre & Bjørndal, Mette, 2017. "Specification of merger gains in the Norwegian electricity distribution industry," Energy Policy, Elsevier, vol. 102(C), pages 96-107.
    3. Monchuk, Daniel C. & Chen, Zhuo & Bonaparte, Yosef, 2010. "Explaining production inefficiency in China's agriculture using data envelopment analysis and semi-parametric bootstrapping," China Economic Review, Elsevier, vol. 21(2), pages 346-354, June.
    4. Joseph Paradi & Sandra Vela & Haiyan Zhu, 2010. "Adjusting for cultural differences, a new DEA model applied to a merged bank," Journal of Productivity Analysis, Springer, vol. 33(2), pages 109-123, April.
    5. Cheng, Xiaomei & Bjørndal, Endre & Bjørndal, Mette, 2015. "Optimal Scale in Different Environments – The Case of Norwegian Electricity Distribution Companies," Discussion Papers 2015/22, Norwegian School of Economics, Department of Business and Management Science.
    6. Ørjan Mydland, 2020. "Lost economies of scope and potential merger gains in the Norwegian electricity industry," Empirical Economics, Springer, vol. 58(6), pages 3077-3100, June.
    7. Yao, Xin & Huang, Ruting & Du, Kerui, 2019. "The impacts of market power on power grid efficiency: Evidence from China," China Economic Review, Elsevier, vol. 55(C), pages 99-110.
    8. Saastamoinen, Antti & Bjørndal, Endre & Bjørndal, Mette, 2016. "Specification of merger gains in the Norwegian electricity distribution industry," Discussion Papers 2016/7, Norwegian School of Economics, Department of Business and Management Science.
    9. Cheng, Xiaomei & Bjørndal, Endre & Lien, Gudbrand & Bjørndal, Mette, 2015. "Productivity Development for Norwegian Electricity Distribution Companies 2004-2013," Discussion Papers 2015/27, Norwegian School of Economics, Department of Business and Management Science.

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