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Data Envelopment Analysis: From Foundations to Modern Advancements

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  • Zhichao Wang
  • Valentin Zelenyuk

Abstract

Data envelopment analysis (DEA) is a mainstream method for efficiency and productivity analysis, widely applied in numerous fields, including the healthcare sector, banking, energy generation and distribution, and cross-country economic growth analysis. In this monograph, we aim to provide a compendious overview of DEA. We start with the DEA estimators in various scenarios, such as for estimating technology, cost, revenue, profit functions and related efficiency measures, and its popular variants based on different assumptions about the shape of technology. The statistical properties and extensions on DEA, such as analysis on covariates of efficiency, are also discussed and the practical tips for computations are provided.

Suggested Citation

  • Zhichao Wang & Valentin Zelenyuk, 2024. "Data Envelopment Analysis: From Foundations to Modern Advancements," Foundations and Trends(R) in Econometrics, now publishers, vol. 13(3), pages 170-282, November.
    • Handle: RePEc:now:fnteco:0800000040
    DOI: 10.1561/0800000040
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    References listed on IDEAS

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    1. Wilson, Paul W., 2018. "Dimension reduction in nonparametric models of production," European Journal of Operational Research, Elsevier, vol. 267(1), pages 349-367.
    2. Zelenyuk, Valentin, 2013. "A scale elasticity measure for directional distance function and its dual: Theory and DEA estimation," European Journal of Operational Research, Elsevier, vol. 228(3), pages 592-600.
    3. Valentin Zelenyuk, 2014. "Scale efficiency and homotheticity: equivalence of primal and dual measures," Journal of Productivity Analysis, Springer, vol. 42(1), pages 15-24, August.
    4. Zelenyuk, Valentin & Zhao, Shirong, 2024. "Russell and slack-based measures of efficiency: A unifying framework," European Journal of Operational Research, Elsevier, vol. 318(3), pages 867-876.
    5. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "A survey of data envelopment analysis in energy and environmental studies," European Journal of Operational Research, Elsevier, vol. 189(1), pages 1-18, August.
    6. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    7. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    8. Zelenyuk, Valentin, 2020. "Aggregation of inputs and outputs prior to Data Envelopment Analysis under big data," European Journal of Operational Research, Elsevier, vol. 282(1), pages 172-187.
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