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Incorporating heterogeneity in non-parametric models: a methodological comparison

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  • Kristof De Witte
  • Rui Cunha Marques

Abstract

The operational environment can heavily influence the efficiency scores of the evaluated observations. If heterogeneity among entities is neglected, the efficiency evaluation is strongly biased. In this paper, we discuss five methodologies to incorporate heterogeneity in non-parametric frontier models which are robust for outlying observations. In particular, we examine the frontier separation approach, the all-in-one model, the two-stage model, the multi-stage approach and the conditional efficiency measures. We discuss their appropriateness on a simulated and a real-world drinking water data set. Although, the outcomes are closely related on average, the robust conditional efficiency procedure seems to be superior.

Suggested Citation

  • Kristof De Witte & Rui Cunha Marques, 2010. "Incorporating heterogeneity in non-parametric models: a methodological comparison," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 9(2), pages 188-204.
    • Handle: RePEc:ids:ijores:v:9:y:2010:i:2:p:188-204
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    Citations

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    Cited by:

    1. Manuel Salas-Velasco, 2024. "Nonparametric efficiency measurement of undergraduate teaching by university size," Operational Research, Springer, vol. 24(1), pages 1-29, March.
    2. María Molinos-Senante & Alexandros Maziotis, 2019. "Cost Efficiency of English and Welsh Water Companies: a Meta-Stochastic Frontier Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3041-3055, July.
    3. Vilarinho, Hermilio & D’Inverno, Giovanna & Nóvoa, Henriqueta & Camanho, Ana S., 2023. "The measurement of asset management performance of water companies," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    4. Sala-Garrido, Ramon & Mocholi-Arce, Manuel & Maziotis, Alexandros & Molinos-Senante, María, 2023. "The carbon and production performance of water utilities: Evidence from the English and Welsh water industry," Structural Change and Economic Dynamics, Elsevier, vol. 64(C), pages 292-300.
    5. Alda A. Henriques & Milton Fontes & Ana S. Camanho & Giovanna D’Inverno & Pedro Amorim & Jaime Gabriel Silva, 2022. "Performance evaluation of problematic samples: a robust nonparametric approach for wastewater treatment plants," Annals of Operations Research, Springer, vol. 315(1), pages 193-220, August.
    6. Mocholi-Arce, Manuel & Sala-Garrido, Ramon & Molinos-Senante, Maria & Maziotis, Alexandros, 2021. "Performance assessment of water companies: A metafrontier approach accounting for quality of service and group heterogeneities," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    7. Cordero, José Manuel & Alonso-Morán, Edurne & Nuño-Solinis, Roberto & Orueta, Juan F. & Arce, Regina Sauto, 2015. "Efficiency assessment of primary care providers: A conditional nonparametric approach," European Journal of Operational Research, Elsevier, vol. 240(1), pages 235-244.
    8. De Witte, Kristof & Geys, Benny, 2013. "Citizen coproduction and efficient public good provision: Theory and evidence from local public libraries," European Journal of Operational Research, Elsevier, vol. 224(3), pages 592-602.
    9. Vladimír Holý, 2022. "The impact of operating environment on efficiency of public libraries," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(1), pages 395-414, March.
    10. Fusco, Elisa & Vidoli, Francesco & Sahoo, Biresh K., 2018. "Spatial heterogeneity in composite indicator: A methodological proposal," Omega, Elsevier, vol. 77(C), pages 1-14.
    11. De Clercq, Djavan & Wen, Zongguo & Caicedo, Luis & Cao, Xin & Fan, Fei & Xu, Ruifei, 2017. "Application of DEA and statistical inference to model the determinants of biomethane production efficiency: A case study in south China," Applied Energy, Elsevier, vol. 205(C), pages 1231-1243.

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