Social Impact Assessment of Microfinance Programmes - weman

Note: There are 160 and 180 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.
5.3 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 impact model estimated for Akhuwat 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 6 , 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. Apart from this a loan cycle dummy has also been
added for those respondents who are in their second loan cycle to see the marginal impact
of microfinance after the first year of borrowing. 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.
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
The results from the estimation of δ are given in Table 5.8. The majority of the results in
our regressions were insignificant. One of the variables in DID estimation on which we
find a significant positive effect is household income for active borrowers (17%, p=0.07).
This result is also validated by single difference estimation (13%, p=0.047) and implies
that active borrowers have higher household income as compared to other respondents.
Another result that is significant in DID estimation is for food expenditure, active
borrowers spend 16% more on food than other categories of respondents (p=0.06).
6 For Akhuwat seven household characteristics were included in the regression out of 15 tested through
ANOVA.
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