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Scale Elasticity in Non-parametric DEA Approach

In: Data Envelopment Analysis

Author

Listed:
  • Biresh K. Sahoo

    (Xavier Institute of Management)

  • Kaoru Tone

    (National Graduate Institute for Policy Studies)

Abstract

This contribution is an attempt to make an exhaustive critical review of various possible estimation methods of scale economies in a non-parametric data envelopment analysis approach. Three types of technology structure—piecewise linear, piecewise log-linear, and FDH—are found to be adopted for such estimation exercise. These technology structures are built up either in input-output space or in cost-output space. The strengths and weaknesses of the uses of each of these estimation methods are discussed. The issue of which method to use in any empirical application is a matter of an examination of various issues concerning (1) whether factor inputs are indivisible, (2) whether price data are available, and if available, whether they are well measured with certainty, and (3) whether the non-convexities in the underlying production technology are present.

Suggested Citation

  • Biresh K. Sahoo & Kaoru Tone, 2015. "Scale Elasticity in Non-parametric DEA Approach," International Series in Operations Research & Management Science, in: Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 9, pages 269-290, Springer.
    • Handle: RePEc:spr:isochp:978-1-4899-7553-9_9
    DOI: 10.1007/978-1-4899-7553-9_9
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    Citations

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    Cited by:

    1. Podinovski, Victor V., 2016. "Optimal weights in DEA models with weight restrictions," European Journal of Operational Research, Elsevier, vol. 254(3), pages 916-924.
    2. Mehdiloo, Mahmood & Podinovski, Victor V., 2019. "Selective strong and weak disposability in efficiency analysis," European Journal of Operational Research, Elsevier, vol. 276(3), pages 1154-1169.
    3. Ole Bent Olesen & Niels Christian Petersen & Victor V. Podinovski, 2022. "Scale characteristics of variable returns-to-scale production technologies with ratio inputs and outputs," Annals of Operations Research, Springer, vol. 318(1), pages 383-423, November.
    4. Podinovski, Victor V., 2017. "Returns to scale in convex production technologies," European Journal of Operational Research, Elsevier, vol. 258(3), pages 970-982.
    5. Podinovski, Victor V., 2019. "Direct estimation of marginal characteristics of nonparametric production frontiers in the presence of undesirable outputs," European Journal of Operational Research, Elsevier, vol. 279(1), pages 258-276.
    6. Alireza Amirteimoori & Biresh K. Sahoo & Saber Mehdizadeh, 2023. "Data envelopment analysis for scale elasticity measurement in the stochastic case: with an application to Indian banking," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-36, December.
    7. Sahoo, Biresh K & Khoveyni, Mohammad & Eslami, Robabeh & Chaudhury, Pradipta, 2016. "Returns to scale and most productive scale size in DEA with negative data," European Journal of Operational Research, Elsevier, vol. 255(2), pages 545-558.
    8. Barnabé Walheer, 2020. "Output, input, and undesirable output interconnections in data envelopment analysis: convexity and returns-to-scale," Annals of Operations Research, Springer, vol. 284(1), pages 447-467, January.

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