Abstract
A three-phased sequential explanatory mixed-methods research framework is designed to conduct the research investigation presented in this book. The first phase of this design, which is quantitative in nature, is presented in this chapter. This quantitative phase is pursued to accomplish the first and second objectives of the study, which aims to identify the determinant and discriminant factors for the sustainability of Indian Microfinance Institutions (MFIs). Identifying these factors is pertinent, as it calls for managerial attention from any Indian MFI aiming to augment its sustainability. Therefore to fulfill these objectives, the author undertakes a quantitative analysis on the data sourced from 50 Indian MFIs. Regression and discriminant analysis techniques are used to conduct this analysis. The results of this analysis depict, Growth Factor, Portfolio Risk Factor, Development Factor and Institutional Factor as the significant factors affecting the Operational Self-sustainability (OSS) of Indian MFIs and Cost Efficiency Factor as the factor discriminating its OSS status. Thus at the end of this chapter, the author narrows done to five significant factors that determine and discriminate the OSS status of Indian MFIs.
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Notes
- 1.
This chapter has material from the article: What discriminates the operational self-sustainability of Indian MFIs: A multiple discriminant analysis inquiry. Marakkath N, Ramanan TR (2012). Paper presented at 3rd international conference on Institutional and Technological Environment for Microfinance (ITEM3) on Cost 650 Management & Social Performance in Microfinance, New Delhi, 4–7 Jan 2012. Organized by Burgundy School of Business, France (Secured Top Paper Presentation Award)
- 2.
Compared to social science research works this is a low adjusted R square value and may apparently give an indication that regression model has less explanatory power. But such low adjusted R square value is typical of most financial research studies. In financial research such low adjusted R square value is regarded quite acceptable, provided the model has overall significance in explaining the variations in the dependent variable (i.e. if the F value of the model is significant). In this study though the adjusted R square value is low, the F value of the model is significant. This denotes that the independent variables used in the model have significant explanatory power. Moreover there are few specific reasons as to why the regression model used in this study has a low R square value. One of the reasons is that this study has deliberately omitted the inclusion of two determinants of sustainability—‘revenue generation factor’ and ‘cost efficiency factor’—as independent variables in the multiple regression model. Operational self-sustainability ratio (OSS ratio), the dependent variable used in the model, is nothing but a ratio of revenue and cost factors. So if we further include revenue and cost factors as independent variables, it will act as exact stand-ins or component factors for the OSS ratio. This will unduly inflate the R square value and violate the statistical principle with which regression works. It will also adversely affect the predictive power of the model (Gujarati and Sangeetha 2007). So, high R square values are a matter of suspicion when it is achieved through the inclusion of exogenous variables that are exact stand-ins or components of dependent variable. So in this study we preferred to ensure the statistical accuracy of the model rather than unduly inflating the R square value. However, the effect of revenue generation factor and cost efficiency factor on OSS ratio was later captured using lagged variables in the discriminant analysis model, without violating the statistical accuracy of the study. Secondly a statistical reason for a low R square is due to the data constraints that this study confronts. The sample size of the study is limited to 50 and the number of independent variables used in the regression model is 11. In regression, the smaller the sample size and the larger the number of independent variables, the lower will be the adjusted R square value. This is so because the formula of adjusted R square is dependent on sample size and number of independent variables.
Adjusted R square = 1−((1−R square)(N−1)/(N−k−1)).
Where N = sample size and k = number of independent variables in the model.
The low adjusted R square value in this study may also be attributed to the fact that the focus of the work is limited to understanding the affect of micro-level factors on the OSS ratio of Indian MFIs. This is so as the interest of the study is to know how well the significant micro-level factors can be managed or controlled by the MFI managers so as to enhance their MFI’s sustainability. But the fact remains that the variations in OSS ratio is also subject to the influence of several macroeconomic factors. The influence of these factors is not accounted in the regression model used in the study as the affect of such uncontrollable factors does not come within the purview of the research objectives. Thus the low adjusted R square value may also be attributed to this selective choice of independent variables made in this study.
- 3.
The null hypothesis of this test is that the dispersion matrices are homogenous. Therefore, if the null hypothesis is accepted, the assumption of homogenous dispersion matrices across the groups of the dependent variable is complied with.
- 4.
Alternatively, the process of classifying new MFIs can also be done using the concept of Mahalanobis distance. A new case (MFI) will have a distance for each of the sustainability group centroids (group means) and therefore can be classified as belonging to the group for which its distance is smallest. If the discriminant score of a new MFI has a standard deviation of more than 1.96 from a group centroid, then it will be regarded to have less than 5Â % chance to belong to that group.
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Marakkath, N. (2014). Quantitative Phase: Identification of Factors Affecting and Discriminating Sustainability. In: Sustainability of Indian Microfinance Institutions. India Studies in Business and Economics. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1629-2_4
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