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A Well-Defined Composite Indicator: An Application to Corporate Social Responsibility

Juan Aparicio (), Magdalena Kapelko () and Juan F. Monge ()
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Juan Aparicio: University Miguel Hernandez of Elche (UMH)
Juan F. Monge: University Miguel Hernandez of Elche (UMH)

Journal of Optimization Theory and Applications, 2020, vol. 186, issue 1, No 15, 299-323

Abstract: Abstract This paper introduces a model to construct composite indicators for performance evaluation of decision making units, which is based upon the determination of the least distance from each assessed unit to a frontier estimated by data envelopment analysis. This generates less demanding targets from a benchmarking point of view. The model also makes it possible to account for the existence of slacks in all the considered dimensions (sub-indicators), playing with the notion of Pareto efficiency. Additionally, our approach satisfies units invariance, translation invariance and strong monotonicity and ensures that the weights used for the aggregation of the sub-indicators are always strictly positive. All previous approaches based on data envelopment analysis have failed to satisfy at least one of these properties. We also implement a new version of the Russell output measure of technical efficiency working with full-dimensional efficient facets. Finally, the new approach is illustrated by an application to the sphere of corporate social responsibility, showing the main empirical implications of the theoretical properties.

Keywords: Data envelopment analysis; Benefit of the Doubt; Least distance; Benchmarking; Strong monotonicity; 90B50; 90C05; 90C11; 91B38 (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10957-020-01701-1

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