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Statistical Tests Based on DEA Efficiency Scores

In: Handbook on Data Envelopment Analysis

Author

Listed:
  • Rajiv D. Banker

    (Temple University)

  • Ram Natarajan

    (The University of Texas at Dallas)

Abstract

This chapter is written for analysts and researchers who may use data envelopment analysis (DEA) to statistically evaluate hypotheses about characteristics of production correspondences and factors affecting productivity. Contrary to some characterizations, it is shown that DEA is a full-fledged statistical methodology, based on the characterization of DMU efficiency as a stochastic variable. The DEA estimator of the production frontier has desirable statistical properties, and provides a basis for the construction of a wide range of formal statistical tests (Banker RD Mgmt Sci. 1993;39(10):1265–73). Specific tests described here address issues such as comparisons of efficiency of groups of DMUs, existence of scale economies, existence of allocative inefficiency, separability and substitutability of inputs in production systems, analysis of technical change and productivity change, impact of contextual variables on productivity, and the adequacy of parametric functional forms in estimating monotone and concave production functions.

Suggested Citation

  • Rajiv D. Banker & Ram Natarajan, 2011. "Statistical Tests Based on DEA Efficiency Scores," International Series in Operations Research & Management Science, in: William W. Cooper & Lawrence M. Seiford & Joe Zhu (ed.), Handbook on Data Envelopment Analysis, chapter 0, pages 273-295, Springer.
    • Handle: RePEc:spr:isochp:978-1-4419-6151-8_11
    DOI: 10.1007/978-1-4419-6151-8_11
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    Citations

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    Cited by:

    1. Antonella Basso & Stefania Funari, 2017. "The role of fund size in the performance of mutual funds assessed with DEA models," The European Journal of Finance, Taylor & Francis Journals, vol. 23(6), pages 457-473, May.
    2. Walheer, Barnabé, 2023. "Meta-frontier and technology switchers: A nonparametric approach," European Journal of Operational Research, Elsevier, vol. 305(1), pages 463-474.
    3. Agrell, Per J. & Brea-Solís, Humberto, 2017. "Capturing heterogeneity in electricity distribution operations: A critical review of latent class modelling," Energy Policy, Elsevier, vol. 104(C), pages 361-372.
    4. Karagiannis, Giannis & Ravanos, Panagiotis, 2023. "A composite indicator of social inclusion for EU based on the inverted BoD model," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    5. Panagiotis Ravanos & Giannis Karagiannis, 2022. "In search for the most preferred solution in value efficiency analysis," Journal of Productivity Analysis, Springer, vol. 58(2), pages 203-220, December.
    6. Basso, Antonella & Funari, Stefania, 2014. "Constant and variable returns to scale DEA models for socially responsible investment funds," European Journal of Operational Research, Elsevier, vol. 235(3), pages 775-783.
    7. Sun, Jiasen & Li, Guo & Wang, Zhaohua, 2018. "Optimizing China’s energy consumption structure under energy and carbon constraints," Structural Change and Economic Dynamics, Elsevier, vol. 47(C), pages 57-72.
    8. Liesiö, Juuso & Andelmin, Juho & Salo, Ahti, 2020. "Efficient allocation of resources to a portfolio of decision making units," European Journal of Operational Research, Elsevier, vol. 286(2), pages 619-636.
    9. Simar, Léopold & Wilson, Paul W., 2020. "Technical, allocative and overall efficiency: Estimation and inference," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1164-1176.
    10. Allevi, E. & Basso, A. & Bonenti, F. & Oggioni, G. & Riccardi, R., 2019. "Measuring the environmental performance of green SRI funds: A DEA approach," Energy Economics, Elsevier, vol. 79(C), pages 32-44.
    11. Yang, Min & Li, Yongjun & Chen, Ya & Liang, Liang, 2014. "An equilibrium efficiency frontier data envelopment analysis approach for evaluating decision-making units with fixed-sum outputs," European Journal of Operational Research, Elsevier, vol. 239(2), pages 479-489.
    12. David J. Mayston, 2017. "Data envelopment analysis, endogeneity and the quality frontier for public services," Annals of Operations Research, Springer, vol. 250(1), pages 185-203, March.
    13. Kounetas, Konstantinos & Zervopoulos, Panagiotis D., 2019. "A cross-country evaluation of environmental performance: Is there a convergence-divergence pattern in technology gaps?," European Journal of Operational Research, Elsevier, vol. 273(3), pages 1136-1148.
    14. Zarrin, Mansour & Brunner, Jens O., 2023. "Analyzing the accuracy of variable returns to scale data envelopment analysis models," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1286-1301.
    15. da Silva e Souza, Geraldo & Gomes, Eliane Gonçalves, 2015. "Management of agricultural research centers in Brazil: A DEA application using a dynamic GMM approach," European Journal of Operational Research, Elsevier, vol. 240(3), pages 819-824.
    16. Bigerna, Simona & D'Errico, Maria Chiara & Polinori, Paolo, 2022. "Environmental variables and power firms' productivity: micro panel estimation with time-Invariant variables," MPRA Paper 114157, University Library of Munich, Germany.
    17. Mette Asmild & Dorte Kronborg & Anders Rønn-Nielsen, 2018. "Testing productivity change, frontier shift, and efficiency change," IFRO Working Paper 2018/07, University of Copenhagen, Department of Food and Resource Economics.
    18. Agrell, P & Brea-Solís, H., 2015. "Stationarity of Heterogeneity in Production Technology using Latent Class Modelling," LIDAM Discussion Papers CORE 2015047, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    19. Guido Abate & Ignazio Basile & Pierpaolo Ferrari, 2021. "The level of sustainability and mutual fund performance in Europe: An empirical analysis using ESG ratings," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 28(5), pages 1446-1455, September.

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