Some Additional Considerations

This section provides information about a few additional considerations that need to be taken into account during the DEA model development procedure.

4.8.1.

Correlated Variables

The presence of correlated variables is sometimes used as a basis for reducing the total number of variables, by omitting some of the highly correlated variables. The most common objective for such an exercise is to improve the discrimination of the scores obtained through DEA. However, this practice of excluding variables solely on the basis

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of high correlation has been cautioned against (Jenkins and Anderson, 2003). Dyson et al. (2001) have also advised using correlation, only as a test for positive relationship between inputs and outputs; rather than as means of excluding correlated variables from the analysis. For example, in the case of MFIs, loan portfolio and number of loans are bound to have high correlation. Exclusion of either of these two variables can have considerable impact on efficiency scores; unless one is simply a multiple of the other. However, despite high correlation, inclusion of both these variables may be desirable for capturing the depth and breadth of outreach.

4.8.2.

Lending Models

While evaluating and comparing performance of different MFIs, it is advisable to take into account the underlying lending models adopted by these MFIs. For this purpose, inclusion of dummy variables, reflecting such differences, can be an appropriate approach. As discussed earlier, the studies using DEA tend to adopt a two-step methodology for inclusion of dummy variables so that efficiency scores are obtained in the first stage of analysis. These scores are then used through some statistical techniques, such as regression, to further investigate the data set in the second stage of analysis (Cooper and Tone, 1997, Avkiran, 1999). In addition to the lending models, the two stage method can also be used for incorporating any additional considerations, such as environmental characteristic, that cannot be directly included in the first stage of analysis.

4.8.3.

Subsidy Dependence of MFIs

The social mission of MFIs, which focuses on reaching poor people with financial services, has been largely supported by subsidized funds and grants, provided by various donor communities. However, with persistent reduction in subsidized funds and shifting of focus towards utilization of more commercial sources of funds over the last

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few years, there are large scale disparities in funding structures of various categories of MFIs. Consequently, any performance evaluation study of MFIs need to take into account, the extent of selected MFIs’ dependence on subsidized funds and grants, for running their operations. The failure to do so can result in artificial inflation of financial performance of those MFIs that receive considerable amounts of subsidized funds; as opposed to those MFIs that do not receive such subsidies.

The Subsidy Dependence Index (SDI) proposed by Yaron (1999) is used frequently for measuring the impact of subsidies on financial performance of MFIs. However, the use of index measures in DEA based studies has been cautioned against, especially when such measures are mixed with volume measures (Dyson et al., 2001). An alternate approach to incorporate the subsidy dependence of MFIs is to use subsidy adjusted figures for various variables48, selected for inclusion in the DEA models.

4.8.4.

Modelling of Undesirable or Non-Isotonic Variables

The standard DEA models work on the assumption that outputs must be maximized; whereas, inputs need to be minimized. This assumption is known as isotonicity and has been used as a tool to facilitate classification of different variables as inputs or outputs. The condition of isotonic variables implies that a variable is to be classified as input, if lesser amount of that variable is considered desirable. Outputs, on the other hand, include variables, whose maximization or increase is more desirable.

However, there are certain exceptions to this general rule for situations where some non-isotonic variables may be present. For example, bad or doubtful loans are an undesirable output that should not be maximized; as greater production of this variable

48 _{Interested reader is advised to refer to the website managed by PMN for details of how to calculate} amounts of different variables after adjusting for subsidies.

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indicates worsening of performance. To deal with such non-isotonic variables, a number of possible approaches can be adopted that have been suggested in the literature49.

4.8.5.

Heterogeneity Issues and Environmental Variables

Homogeneity is a basic assumption in DEA based studies; whereby, the firms under analysis are considered to be quite similar in terms of technology, environment, available resources, and operations. However, in real life, there are many situations where certain levels of heterogeneity may prevail among the firms being analysed. For the case of MFIs, we can identify two broad categories of heterogeneity issues existing in the environments within which different MFIs work. The first category relates to the use of cross-country data. As discussed in Chapter 2, there are a number of performance evaluation studies that have been conducted by using cross country data on MFIs. Although such data is intended to provide a comparison between performance levels of MFIs from an international perspective, there is a potential threat in the form of heterogeneity; resulting from different economic and regulatory environments prevailing in different countries.

While such heterogeneity is possible to exist more frequently for cross country data, it does not imply that the data from a single country will always be completely homogenous. To the contrary, within the same country, different MFIs may also exhibit a second category of heterogeneity; existing at the institutional level. This second category of heterogeneity can be based on differences in the organizational structures, regulatory authorities, lending models, age, and size of MFIs’ operations. It is therefore advisable to take into account any exogenous factors in efficiency studies that use data

49 _{See for example DYSON, R. G., ALLEN, R., CAMANHO, A. S., PODINOVSKI, V. V., SARRICO,} C. S. & SHALE, E. A. 2001. Pitfalls and protocols in DEA. European Journal of Operational Research,

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from a number of diverse sectors from within individual countries, or from similar sectors of different countries (Benston, 1965, Drake et al., 2006).

To deal with the heterogeneity issues present at the individual MFIs level, a possible solution is to make sub groups of more homogenous MFIs and then compare each MFI with others belonging to the same group (Dyson et al., 2001). The two-stage DEA, discussed earlier, is also an option. Moreover, there are a number of other approaches that have been recommended for dealing with various heterogeneity issues; a good review of which has been provided by Cook and Seiford (2009).

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