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Weight assurance region in two-stage additive efficiency decomposition DEA model: an application to school data

Author

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  • George E Halkos

    (University of Thessaly, Volos, Greece)

  • Nickolaos G Tzeremes

    (University of Thessaly, Volos, Greece)

  • Stavros A Kourtzidis

    (University of Thessaly, Volos, Greece)

Abstract

In this paper we use the additive efficiency decomposition approach in two-stage data envelopment analysis. Initially, we evaluate the variable returns to scale version and face a structural difficulty of the model. In an extreme case, weights ξ1, which represent the relative importance of the performance of the first and second stages, respectively, become zero for a number of decision making units (DMUs). As a result, individual stage efficiencies for these DMUs are undefined. We propose a weight assurance region model to restrict ξ1, which ensures that both weights are always positive, and therefore individual stage efficiency is always defined. Furthermore, the proposed model is appropriate for policy making in the presence of a priori information about the relative importance of each stage in the overall process. We employ the new model to evaluate the efficiency of secondary education in 65 countries and construct an overall ‘school efficiency’ index. In the first stage we measure the ‘learning environment efficiency’ and in the second we measure the ‘student’s performance efficiency’.

Suggested Citation

  • George E Halkos & Nickolaos G Tzeremes & Stavros A Kourtzidis, 2015. "Weight assurance region in two-stage additive efficiency decomposition DEA model: an application to school data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(4), pages 696-704, April.
  • Handle: RePEc:pal:jorsoc:v:66:y:2015:i:4:p:696-704
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    Citations

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    Cited by:

    1. Aparicio, Juan & López-Torres, Laura & Santín, Daniel, 2018. "Economic crisis and public education. A productivity analysis using a Hicks-Moorsteen index," Economic Modelling, Elsevier, vol. 71(C), pages 34-44.
    2. Shih-Liang Chao & Chun-Wei Lai, 2019. "Comparing the efficiency of alliance members and independent liner carriers: a metafrontier analysis," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 21(2), pages 157-172, June.
    3. Tai-Hsi Wu & Hsiang-Lin Chih & Mei-Chen Lin & Yi Hua Wu, 2020. "A Data Envelopment Analysis-Based Methodology Adopting Assurance Region Approach for Measuring Corporate Social Performance," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 148(3), pages 863-892, April.
    4. Qingxian An & Fanyong Meng & Sheng Ang & Xiaohong Chen, 2018. "A new approach for fair efficiency decomposition in two-stage structure system," Operational Research, Springer, vol. 18(1), pages 257-272, April.
    5. Xifan Chen & Qingyuan Zhu & Chengzhen Xu & Zhiyang Shen & Malin Song, 2024. "Energy and environmental efficiency of China's regional electric power industry by considering renewable energy constraints," Energy & Environment, , vol. 35(2), pages 927-949, March.
    6. Xiao Shi & Yongjun Li & Ali Emrouznejad & Jianhui Xie & Liang Liang, 2017. "Estimation of potential gains from bank mergers: A novel two-stage cost efficiency DEA model," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(9), pages 1045-1055, September.
    7. Henriques, C.O. & Marcenaro-Gutierrez, O.D., 2021. "Efficiency of secondary schools in Portugal: A novel DEA hybrid approach," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    8. Degl'Innocenti, Marta & Kourtzidis, Stavros A. & Sevic, Zeljko & Tzeremes, Nickolaos G., 2017. "Bank productivity growth and convergence in the European Union during the financial crisis," Journal of Banking & Finance, Elsevier, vol. 75(C), pages 184-199.
    9. Georgios Tsaples & Jason Papathanasiou & Andreas C. Georgiou, 2022. "An Exploratory DEA and Machine Learning Framework for the Evaluation and Analysis of Sustainability Composite Indicators in the EU," Mathematics, MDPI, vol. 10(13), pages 1-27, June.
    10. Hashem Omrani & Arash Alizadeh & Ali Emrouznejad & Zeynab Oveysi, 2023. "A novel best‐worst‐method two‐stage data envelopment analysis model considering decision makers' preferences: An application in bank branches evaluation," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 3593-3610, October.
    11. Kuo‐Cheng Kuo & Wen‐Min Lu & Dinh Tam Nguyen & Hsiu Fei Wang, 2020. "The effect of special economic zones on governance performance and their spillover effects in Chinese provinces," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 41(3), pages 446-460, April.
    12. Halkos, George & Petrou, Kleoniki Natalia, 2019. "Treating undesirable outputs in DEA: A critical review," Economic Analysis and Policy, Elsevier, vol. 62(C), pages 97-104.
    13. Degl'Innocenti, Marta & Kourtzidis, Stavros A. & Sevic, Zeljko & Tzeremes, Nickolaos G., 2017. "Investigating bank efficiency in transition economies: A window-based weight assurance region approach," Economic Modelling, Elsevier, vol. 67(C), pages 23-33.
    14. Shabani, Amir & Visani, Franco & Barbieri, Paolo & Dullaert, Wout & Vigo, Daniele, 2019. "Reliable estimation of suppliers’ total cost of ownership: An imprecise data envelopment analysis model with common weights," Omega, Elsevier, vol. 87(C), pages 57-70.

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