Bounded and discrete data and Likert scales in data envelopment analysis: application to regional energy efficiency in China

Ya Chen (), Wade D. Cook (), Juan Du (), Hanhui Hu () and Joe Zhu ()
Additional contact information
Wade D. Cook: York University
Juan Du: Tongji University
Hanhui Hu: Southeast University
Joe Zhu: Nanjing Audit University

Annals of Operations Research, 2017, vol. 255, issue 1, No 18, 347-366

Abstract: Abstract In data envelopment analysis (DEA), it is usually assumed that all data are continuous and not restricted by upper and/or lower bounds. However, there are situations where data are discrete and/or bounded, and where projections arising from DEA models are required to fall within those bounds. Such situations can be found, for example, in cases where percentage data are present and where projected percentages must not exceed the requisite 100 % limit. Other examples include Likert scale data. Using existing integer DEA approaches as a backdrop, the current paper presents models for dealing with bounded and discrete data. Our proposed models address the issue of constraining DEA projections to fall within imposed bounds. It is shown that Likert scale data can be modeled using the proposed approach. The proposed DEA models are used to evaluate the energy efficiency of 29 provinces in China.

Keywords: Data envelopment analysis (DEA); Efficiency; Discrete data; Bounded data; Likert scale; Energy (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (17)

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DOI: 10.1007/s10479-015-1827-3

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