Social Impact Assessment of Microfinance Programmes - weman

Borrowers perceptions about the positive Effect on the Quality of Life are already high as
soon as the first loan is given and continue to rise thereafter – Table A.4.2.20. This trend
is found in most other indicators about perception as well, and most Borrowers believe
that the rise in Income and the improvement of Quality of Life can be sustained over
time.
4.3 Regression Analysis
There are weaknesses in using bivariate analysis, as we do above, since it does not allow
us to examine the nature of the impact, and hence, we use multivariate regression
analysis, which allows us to look at impact controlling for other related variables. These
two sets of analysis also explain why we often get contradictory findings.
The Difference in Differences (DID) impact model estimated for OCT is
Y ij = X ij α + C ij β + M ij γ + T ij δ +v ij
Where Y ij is an outcome on which we measure impact for household i in locality j, X ij is
a vector of household characteristics * , C ij is a dummy equal to 1 for active borrowers and
their matched neighbours and 0 otherwise, M ij is a membership dummy variable equal to
1 if household i self-selects into the credit programme, and 0 otherwise; and T ij is a
variable to capture the treatment effects on households that self selected themselves into
the programme and are already accessing loans. T is also a dummy variable equal to 1 for
active borrowers and 0 otherwise. The coefficient δ on T ij is the main parameter of
interest and measures the average impact of the programme. A positive and significant δ
would indicate that microfinance is having a beneficial effect on the borrowers.
To account for individuals who have been taking loans since a few years, we add a
dummy to our regression. This dummy is for individuals who have taken more than 2
loans, and takes a value of 1 if they are in their third, fourth or fifth cycle and 0
otherwise. There were only 30 individuals in the sample who had taken more than 2
loans.
A Single Difference equation is also estimated to assess impact between active borrowers
and the pipeline clients. The form of the equation is as follows and the variables are
defined as stated above.
Y ij = X ij α + T ij δ +v ij
Unfortunately, we find no significant coefficients for δ in our regressions as reported in
Table 1. The only significant variable was the dummy for loan cycle on food expenditure.
In both single and double difference regressions we found that OPP clients, who had been
borrowing for 3 year or more, were spending more on food expenditure. In the double
* For OPP two household characteristics were included in the regression out of 15 tested through ANOVA.
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