Reasons for Selection of DEA

It can be reasonably argued that DEA possesses a certain edge over other commonly used techniques, such as ratio and regression analysis, for performance evaluation of MFIs. In addition to the fact, that a key objective of the current study, related to application of the trade-off approach, necessitate the use of DEA; there are several other desirable characteristics of this technique, that make it particularly suitable for the proposed performance evaluation. A detailed discussion of some of these characteristics is provided hereafter.

Page 43

First of all, an important advantage of the DEA technique is its ability to integrate all potential interrelationships among various factors of production, because it can incorporate multiple inputs and outputs simultaneously in the efficiency analysis (Balkenhol, 2007). Ratio analysis, on the other hand, is unable to capture the multi- dimensional aspects of the production process (Schaffnit et al., 1997). Therefore, a firm showing less than exemplary performance based on individual ratios, may be considered a good performer in a DEA context; where its overall performance is being considered through simultaneous incorporation of all the input and output variables (Thanassoulis et al., 1996). This ability of DEA makes it more suitable than traditional performance ratios, as various MFIs despite using diverse input-output mixes may still be efficient, provided they are using their resources efficiently (Hassan and Sanchez, 2009).

DEA can also help in setting targets because it can simultaneously take into account all the resources being utilized and the outputs being produced, while performing efficiency analysis. Ratio analysis, on the other hand, is unable to identify performance targets, to facilitate efficiency improvement of inefficient units. This is because ratio analysis can only relate one input to a single output at one time. However, ratio analysis is found to be useful in enhancing target setting process, if used in conjunction with DEA (Thanassoulis et al., 1996).

It is observed that DEA and other commonly used statistical techniques including regression and correlation differ in that latter tend to draw inferences from optimizing (i.e. averaging) over all the observations in the data set; whereas former obtains inferences from optimal solutions, obtained for each observation (Charnes et al., 1985). Therefore, DEA makes it possible to compare each DMU’s performance with an ideal,

Page 44

calculated by comparing it to the best performance levels, actually observed for the firms included in the analysis. Such comparison with the best possible performance, based on real time data, is thus considered much more desirable than comparisons with some predetermined performance targets (El-Mahgary and Lahdelma, 1995), or with some statistical averages, which in reality may not be practically applicable to the firms being assessed (Ahn et al., 1988, Avkiran, 2001).

The possibility of using physical rather than monetary measures of inputs and outputs in DEA based analysis, without recourse to costs and prices of these variables, also makes DEA very useful. This is an important quality because inflationary pressures, cost accounting methods and differences in prices across different regions can potentially bias the results, if only monetary measures of these variables are used (Sherman, 1984). Another desirable characteristic of DEA is that it does not require determination of relative importance of different inputs and outputs, as linear programming can be used for determining the weights of all such variables. DEA is also well-known for its ability to offer additional insights that may not be available through other analytical techniques. For example DEA can provide information about reference groups and benchmarks, estimates of amounts and sources of inefficiency (Charnes and Cooper, 1985, Charnes et al., 1985), as well as the measures of possible output augmentation and/or resources conservation (Boussofiane et al., 1991).

The ability of DEA to provide additional information makes it suitable to be used in conjunction with other commonly used statistical techniques. For example, using data obtained from oil and gas industry, Feroz et al. (2003) use statistical testing and demonstrate that Data Envelopment Analysis (DEA) can be used as an auxiliary tool for traditional ratio analysis; for providing supplementary information. There are a number of other studies that have combined DEA with statistical techniques, such as: DEA and

Page 45

regression combination used in the studies conducted by Arnold et al. (1996) and Cooper and Tone (1997), the DEA and canonical correlation analysis method introduced by Friedman and Sinuany-Stern (1997), and the simulation based study by Bardhan et al. (1998) that made use of two stage methodology introduced by Arnold et al. (1996). Thanassoulis et al. (1996) also used DEA and ratio analysis together to compare the two techniques in how well these agreed on relative performance of different DMUs, and on estimated targets for performance improvement. Based upon result of this study, it was concluded that when there is agreement on performance by these two approaches, it is possible to use them in conjunction, to gather useful information, and to facilitate communication of results to non-DEA specialist community. Similar findings were also reported by Gümüş and Çelikkol (2011), who compared these two techniques while using data on a group of non-financial firms. Finally, one of the most important characteristics of the DEA is that, DEA can bypass the problem of explicit specification for relationships or the functional forms, relating different input and output variables (Charnes et al., 1985). This is an important characteristic given the fact that generally, for most studies, and particularly in the case of studies involving socially oriented organizations like MFIs, there is either very little or no knowledge available, for the appropriate functional forms to be employed. Therefore, DEA is considered particularly useful for studies of non-profit and public sector organizations that are involved in producing a number of outputs, not measurable through traditional cost and profit criteria. However the particular usefulness of DEA for non-profit sector does not imply that DEA is not suitable for profit oriented organizations. To the contrary, DEA is found to be equally suitable for analysing for- profit and not-for profit institutions (Sherman, 1984). This characteristic of the DEA

Page 46

methodology is particularly useful for evaluating MFIs, which possess both a profit (financial sustainability) and a non-profit (outreach) orientation.