A well-defined efficiency measure for dealing with closest targets in DEA

作者：

Highlights：

• An unsolved question in the Data Envelopment Analysis (DEA) literature is whether there exists a well-defined efficiency measure based on closest targets.

• We propose a solution for this question for output-oriented models.

• We show that a new definition of the Russell output measure satisfies an interesting set of properties and, consequently, it is well-defined.

• In order to proof our results we need to work with full dimensional efficient facets (FDEFs).

• Through an empirical example we show that substantial differences between the targets are obtained whether we use the traditional or the new Russell measure.

摘要

Highlights•An unsolved question in the Data Envelopment Analysis (DEA) literature is whether there exists a well-defined efficiency measure based on closest targets.•We propose a solution for this question for output-oriented models.•We show that a new definition of the Russell output measure satisfies an interesting set of properties and, consequently, it is well-defined.•In order to proof our results we need to work with full dimensional efficient facets (FDEFs).•Through an empirical example we show that substantial differences between the targets are obtained whether we use the traditional or the new Russell measure.

论文关键词：Data Envelopment Analysis,Closest targets,Strong monotonicity

论文评审过程：Available online 20 April 2013.

论文官网地址：https://doi.org/10.1016/j.amc.2013.03.042