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Social Impact Assessment of Microfinance Programmes - weman

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percent for the MF Industry in Pakistan. The return on equity (adjusted) fares quite well<br />

at 13.31 percent as compared to Kashf’s peer group in Pakistan at -497 percent. Yield on<br />

Portfolio is around 38 percent as compared to 14 percent for the industry in Pakistan.<br />

The key to Kashf’s success lies in the fact that it is a dynamic and growing organization.<br />

At Kashf active research and innovation is encouraged at all levels. It is highly<br />

decentralized, which has put loan <strong>of</strong>ficers and the field staff at the fore front <strong>of</strong> all<br />

operations and thus enabled Kashf to expand efficiently. However, the organization needs<br />

to concentrate on maintaining portfolio quality and <strong>of</strong>fer a wider range <strong>of</strong> products and<br />

services to cater to the needs <strong>of</strong> its clientele.<br />

8.2 Survey Results<br />

In this section we present the results from our survey for Kashf. The results are based on<br />

the data collected on the basis <strong>of</strong> the questionnaire – see the Appendix <strong>of</strong> the Report. A<br />

select few <strong>of</strong> the results are presented here in table form, in the main text <strong>of</strong> this Chapter,<br />

while the substantial majority <strong>of</strong> tables are presented in the Appendix <strong>of</strong> the Chapter. The<br />

Appendix to this Chapter contains the largely ‘descriptive’ tables and results, while the<br />

tables which are part <strong>of</strong> the text in this Chapter, are the more ‘analytical’ tables. In the<br />

Appendix to this Chapter, there are far more tables than those on which we <strong>of</strong>fer<br />

comments in the text. Many <strong>of</strong> these tables are simply informative and so we do not<br />

discuss them in the Chapter. They are being provided for the reader’s own interest and<br />

perusal. Only the more interesting, striking or pertinent results and tables from the<br />

Appendix are discussed in the text.<br />

As we show in Chapter 2, the survey was conducted across four types <strong>of</strong> populations for<br />

the purposes <strong>of</strong> the Study. Two <strong>of</strong> the categories are ‘clients’ or ‘borrowers’, while the<br />

other two are ‘non-clients’. In the borrower/client category, there are two types <strong>of</strong> clients,<br />

the ‘Active Borrowers’ and the ‘Pipeline Borrowers’. The former category that <strong>of</strong> ‘Active<br />

Borrower’, is that client who has been in the programme <strong>of</strong> the MFI for longer than ten<br />

months; s/he may have been a client for some years in their nth loan cycle or may have<br />

even been a client in their first year. ‘Pipeline Borrowers’ are classified as those new<br />

clients who had joined the programme <strong>of</strong> the MFI a few months – usually between 1-4<br />

months – <strong>of</strong> the start <strong>of</strong> our survey. There are also two categories <strong>of</strong> ‘Non-Borrowers’,<br />

one which are selected from the neighbourhood <strong>of</strong> the old Active Borrowers, and the<br />

other from the neighbourhood <strong>of</strong> Pipeline Borrowers. Ideally, and in the best case<br />

possible, the Active Borrower and the Pipeline Borrower (and their neighbours) should<br />

have been chosen from ‘old/established’ areas where the MFI has been working for some<br />

years, and ‘new’ areas where they are about to enter an identify and enlist clients.<br />

However, in many cases this was not possible since most MFIs did not have exclusively<br />

‘new’ areas identified, and we were forced to take Active Borrowers and Pipeline<br />

Borrowers, and both sets <strong>of</strong> their neighbours, from the same locality/area. Nevertheless,<br />

this does not undermine our results which are presented in this Section. In some cases we<br />

present results where we compare the Active Borrower with Pipeline Borrowers, and in<br />

some cases we compare both Active Borrowers and Pipeline Borrowers with the two<br />

combined categories <strong>of</strong> neighbours, that <strong>of</strong> Non-Borrowers.<br />

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