Measuring the performance of MFIs: An application of factor analysis

The main applications of factor analytic techniques are:

(1) To reduce the number of variables.

(2) To detect structure in the relationships between variables, that is to classify variables.

Therefore, factor analysis is applied as a data reduction or structure detection.

Measuring the performance of microfinance institutions (MFIs) is not a small task. Indeed, looking at the financials of an MFI only gives its performance. As many MFIs primarily exist in order to help the poorest people, one also has to include aspects which influence their performance. Hence, MFIs' performance can be termed multidimensional.

This article which I’m sharing here, talks about how “Factor Analysis” as a statistical tools can offer new insights in the context of MFIs' performance evaluation. Factor analysis is used in a first step to construct performance indices based on several possible associations of variables without posing too many a prior restrictions.

Then the base variables are thus combined to produce different factors, each one representing a distinct dimension of performance. We then use the individual scores attributed to each MFI on each factor as the dependent variables of a simultaneous-equations model and present new evidence on the determinants of MFIs' performance.

Variables can be of different types which, according to me, can be classified as qualitative variables and quantitative as the two broad groups initially.

Qualitative variables like –

(i) Type of investors (are they PE investors or social investors)

ii) Motive of Investors (Highly Profit motive or Social Motive)

iii) Who Is investing

iv) Type of security accepted

v) Purpose of taking loan

etc. are the variables.

Qualitative variables like-

i. Profit on Every account

ii. Interest charged

iii. Amount of loan

Etc

Such Qualitative and Quantitative variables are then grouped into components on the basis of similarities. Such similar groups can be

1. investors 2. Security 3. Margins 4. Safety 5. Liquidity etc.

The correlation & variance within groups and across groups is then taken to know which are the variables are of same features and tackled in a same way. And also which are the most important and most influential ones.

Cluster of groups and their behaviour towards performance of MFIs will help the policymakers to solve the problems related to performance of MFIs.

In fact factor analysis is used for measuring the performances of various financial institutions and also for assessment of risks of banks & institutions.

Ref:- Sylvain Weber, University of Geneva, Department of Economics,Giovanni Ferro Luzzi, University of Geneva.

Submitted By

Akhil Parekh

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