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Employing super-efficiency analysis as an alternative to DEA: An application in outpatient substance abuse treatment

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  • Nahra, Tammie A.
  • Mendez, David
  • Alexander, Jeffrey A.

Abstract

A common technique for conducting efficiency analyses consists of a two-stage procedure that combines data envelopment analysis (DEA) with Tobit regression. As the DEA scores are censored at one, this method has the drawback of masking important information at the upper tail of the distribution of scores. In this paper, we present a DEA-based methodology for a two-stage efficiency analysis where the upper bound constraint of one for the efficiency scores is relaxed. This method, super-efficiency DEA, is contrasted with the two-stage approach that employs traditional, bounded DEA scores. We use data from the National Drug Abuse Treatment Survey to examine how the relative efficiency of the treatment units is affected by the organizational structures, operating characteristics and treatment modalities of a nationally representative sample of outpatient substance abuse treatment units. Our results show that the super-efficiency DEA approach offers advantages over the traditional methodology. It is easy to implement, and, for the same sample size provides more information.

Suggested Citation

  • Nahra, Tammie A. & Mendez, David & Alexander, Jeffrey A., 2009. "Employing super-efficiency analysis as an alternative to DEA: An application in outpatient substance abuse treatment," European Journal of Operational Research, Elsevier, vol. 196(3), pages 1097-1106, August.
  • Handle: RePEc:eee:ejores:v:196:y:2009:i:3:p:1097-1106
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    4. Misiunas, Nicholas & Oztekin, Asil & Chen, Yao & Chandra, Kavitha, 2016. "DEANN: A healthcare analytic methodology of data envelopment analysis and artificial neural networks for the prediction of organ recipient functional status," Omega, Elsevier, vol. 58(C), pages 46-54.
    5. Kai Xu & Lawrence Loh & Qiang Chen, 2020. "Sustainable Innovation Governance: An Analysis of Regional Innovation with a Super Efficiency Slack-Based Measure Model," Sustainability, MDPI, vol. 12(7), pages 1-19, April.
    6. Guan, Jiancheng & Chen, Kaihua, 2012. "Modeling the relative efficiency of national innovation systems," Research Policy, Elsevier, vol. 41(1), pages 102-115.
    7. Baldin, Andrea, 2017. "A DEA approach for selecting a bundle of tickets for performing arts events," Journal of Retailing and Consumer Services, Elsevier, vol. 39(C), pages 190-200.
    8. Yiting Xing & Ling Li & Zhuming Bi & Marzena Wilamowska‐Korsak & Li Zhang, 2013. "Operations Research (OR) in Service Industries: A Comprehensive Review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 300-353, May.
    9. Tsolas, Ioannis E., 2014. "Precious metal mutual fund performance appraisal using DEA modeling," Resources Policy, Elsevier, vol. 39(C), pages 54-60.
    10. Fragoudaki, Alexandra & Giokas, Dimitris, 2016. "Airport performance in a tourism receiving country: Evidence from Greece," Journal of Air Transport Management, Elsevier, vol. 52(C), pages 80-89.
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    13. Yang, Wen-Chi & Lee, Yuh-Ming & Hu, Jin-Li, 2016. "Urban sustainability assessment of Taiwan based on data envelopment analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 341-353.
    14. Friesner, Daniel & Mittelhammer, Ron & Rosenman, Robert, 2013. "Inferring the incidence of industry inefficiency from DEA estimates," European Journal of Operational Research, Elsevier, vol. 224(2), pages 414-424.
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