Abstract
This study discusses a guideline on a proper use of Data Envelopment Analysis (DEA) that has been widely used for performance analysis in public and private sectors. The use of DEA is equipped with Strong Complementary Slackness Conditions (SCSCs) in this study, but an application of DEA/SCSCs depends upon its careful use, as summarized in the guideline. The guideline consists of the five suggestions. First, a data set used in the DEA applications should not have a ratio variable (e.g., financial ratios) in an input(s) and/or an output(s). Second, radial DEA models under variable and constant Returns to Scale (RTS) need a special treatment on zero in a data set. Third, the DEA evaluation needs to drop an outlier. Fourth, an imprecise number (e.g., 1/3) may suffer from a round-off error because DEA needs to specify it in a precise expression to operate a computer code. Finally, when a large input or output variable may dominate other variables in DEA computation, it is necessary to normalize the data set or simply to divide each observation by its average. Such a simple treatment produces more reliable DEA results than the one without any data adjustment. This study also discusses how to handle an occurrence of zero in DEA multipliers by applying SCSCs. The DEA/SCSCs can serve for a multiplier restriction approach without any prior information. Thus, the propesed DEA/SCSCs can provide more reliable results than a straight use of DEA.
Highlights
Data Envelopment Analysis (DEA) has been long serving as a methodology to evaluate the performance of organizations in business, economics and other areas
The use of DEA is equipped with Strong Complementary Slackness Conditions (SCSCs) in this study, but an application of DEA/SCSCs depends upon its careful use, as summarized in the guideline
This study provided a set of guidelines for a proper use of DEA and DEA/SCSCs
Summary
Data Envelopment Analysis (DEA) has been long serving as a methodology to evaluate the performance of organizations in business, economics and other areas. A data set whose observation has an outlier(s) and/or an imprecise number (i.e., 2/3) where 2/3 is mathematically precise, but the number becomes imprecise in operating a computer code In these cases, DEA applications need to consider a special treatment for each case. To overcome the problem of zero in multipliers, DEA researchers have long discussed multiplier restriction methods such as assurance region analysis [10] and cone ratio [11]. Such approaches for multiplier restriction are very important in obtaining reliable results and related business/policy implications.
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