TY - UNPB

T1 - Estimating returns to scale in imprecise data envelopment analysis

AU - Hatami-Marbini, Adel

AU - Beigi, Zahra Ghelej

AU - Hougaard, Jens Leth

AU - Gholami, Kobra

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

M3 - Working paper

T3 - MSAP Working Paper Series

BT - Estimating returns to scale in imprecise data envelopment analysis

PB - Department of Food and Resource Economics, University of Copenhagen

ER -