Estimating Returns to Scale in Imprecise Data Envelopment Analysis

Adel Hatami-Marbini (), Zahra Ghelej Beigi (), Jens Leth Hougaard () and Kobra Gholami ()
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Adel Hatami-Marbini: Louvain School of Management, Universite Catholique de Louvain
Zahra Ghelej Beigi: Department of Mathematics, Islamic Azad University
Jens Leth Hougaard: Department of Food and Resource Economics, University of Copenhagen
Kobra Gholami: Department of Science, Islamic Azad University

No 07_2014, MSAP Working Paper Series from University of Copenhagen, Department of Food and Resource Economics

Abstract: The economic concept of Returns-to-Scale (RTS) has been intensively studied in the context of Data Envelopment Analysis (DEA). The conventional DEA models that are used for RTS classification require well-defined and accurate data whereas in reality data are often imprecise, vague, uncertain or incomplete. The purpose of this paper is to estimate RTS of Decision Making Units (DMUs) in Imprecise DEA (IDEA) where the input and output data lie within bounded intervals. In the presence of interval data, we introduce six types of RTS involving increasing, decreasing, constant, non-increasing, non-decreasing and variable RTS. The situation for non-increasing (non-decreasing) RTS is then divided into two partitions; constant or decreasing (constant or increasing) RTS using sensitivity analysis. Additionally, the situation for variable RTS is split into three partitions consisting of constant, decreasing and increasing RTS using sensitivity analysis. Finally, we present the stability region of an observation while preserving its current RTS classification using the optimal values of a set of proposed DEA-based models.

Keywords: Returns-to-scale; Interval data; Data envelopment analysis (search for similar items in EconPapers)
JEL-codes: C61 D24 D80 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2014-11
New Economics Papers: this item is included in nep-eff
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