Social Impact Assessment of Microfinance Programmes - weman

Borrowers
Social Empowerment
Score out of 10 Active Borrowers 4.1381 1.58836 -.631 .528
New and Non-
Borrowers
4.2214 1.39406
Note: There are 239 and 271 respondents in each category respectively. t-value greater than 1.6 indicates
the mean difference between two categories is statistically significant. The negative t indicates that
average value of category 2 is greater than the average value of category 1.
8.3 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 Kashf 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 1 , 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.
As Kashf has been around for 10 years, the clients we interviewed were anywhere
between 1 to 7 loan cycles. Therefore to see the impact of continued borrowing we
divided the clients into groups, one was the group of young borrowers (borrowed 3 times
or less) and the other was of old borrowers (borrowed 4 times or more). On each of these
groups we estimated impact separately.
A Single Difference equation is also estimated for to assess impact between active
borrowers and the pipeline clients. This exercise was done for both young and old
borrowers. 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
1 For Kashf nine household characteristics were included in the regression out of 15 tested through
ANOVA.
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