1. The Debate on Outreach & Impact:
What do we know and
How do we know it?
Microcredit Summit, Valladolid, Spain
Nov. 14-17, 2011
by David S. Gibbons

2. What do we Know from Research?
• not very much, it must be – as are the effects of
acknowledged at the out- microfinance on
set measures of social well-
• Kathleen Odell summed it being, such as
up in her recent review: education, health and
- microfinance, both women’s
credit and savings, is good empowerment.
for micro-business – results from on-going
-the overall effect on RCTs are eagerly awaited
the incomes and poverty
rates of microfinance
clients is less clear,

3. What do we Know from Practice?
• A Lot: – that amazing repayment
– from its beginning in record of the poor, without
Bangladesh, MF has spread collateral, is the backbone
all over the world, of MF, and the compelling
particularly in the poorer corroborating evidence of
countries, in just 35 years its positive and significant
impact on the incomes and
– demand from the poorest well-being of the poor
remains strong,
everywhere – as the great majority of MF
clients have been poor,
– repayment rates have been living on less than
near perfect and many US$2/per person per day,
MFIs have become they could not, and would
financially-sustainable not have repaid so
faithfully if they had not
benefitted significantly

4. What do the Clients Say?
• When asked how much, if • At CASHPOR India, this
any, your household has question was asked by
benefitted from MF, and ABN AMRO bank in late
given three responses: 2008, of a random sample
none, some or a lot, the of 320 mature clients, i.e.,
overwhelming majority of those who have repaid at
CASHPOR clients tend to least 4 loans, The results
chose a positive rather were:
than a negative response, • None = 8%
and at least a third tend • Some = 59%
say, “a lot”. • A Lot = 33%
• Total = 100%

5. Over-all what do you think about
CASHPOR India?
December 2009 December 2010
• Very Good = 62% • Very Good = 41%
• Good = 36% • Good = 58%
• Not So Good = 1% • Not So Good = 1%
• Bad = 0% • Bad = 0%
• Total = 99% • Total = 100%
• n = 147 • n = 197

6. Client Satisfaction
• again we see that the overwhelming majority of
CASHPOR clients chose a positive rather than
negative response; and sizeable percentages chose
the most positive response
• but what explained the sizeable drop of VG from 62%
to 41% between the two years?
• delays in re-finance, said the clients
• Results show that client satisfaction replies vary with
the quality of service, but remain positive

7. Why hasn’t the Benefit shone through
clearly from the Research?
• Social Impact research is • The wrong question has
difficult and time- been asked: instead of
consuming: attributing “do women with continue
any increase in income or access to MF have higher
social well-being to incomes than similar
microfinance is fraught women without access to
with difficulty due to the MF?”, the question
complexity of social should be: “is continued
phenomena and the borrowing from MFIs
development process, strongly and positively
and it takes a long time to associated statistically
see any effect, as the with higher incomes?”
credit is micro

8. USE of PPI in Monitoring Social
Performance
• the Progress-Out-of-Poverty-Index (PPI) of GFUSA can be used for determining &
monitoring the degree of association between MF and Poverty-Reduction
• the PPI score at the time of entry or when applying for the next loan can be the
base-line data for measuring social performance at T2
• our Internal Audit Department does and annual Social Impact Survey on a random
sample of Mature Clients (5 or more loans), the results of which are given in our
Annual Report. IAD in 2010, “found that 40% of those who had repaid 5 loans
were no longer poor, compared to 48% of those who had repaid 6 to 9 loans and
51% of those who had repaid 10 or more loans.” – Annual Report 2010-11, p.2.
• Of course, we cannot claim that CASHPOR caused the decline of poverty, but we
can say that continued borrowing from CMC is strongly and positively associated
with reduction in poverty.
• That’s good enough for me.

9. PPI is derived from Indian National
Socio-Economic Survey Data
• The Survey data include household expenditure, and other
indicators of level of living, like no. of dependent persons,
principal occupation, source of energy for cooking and
ownership of consumer goods: TV, vehicle, electric fans,
etc.
• By statistical analysis, those questions positively and
strongly correlated with levels of household expenditure
have been identified
• Answers are given a score according to their degree of
statistical relationship with HH exp.
• A total score is calculated for the HH, from 0-100
• It indicates the probability that HH is below a specified
poverty-line

10. Use of PPI in Targeting
• CHI is still the most cost-effective first step, and in
fact it is a component of PPI, with a kaca house
getting 0 pts; and a pucca house = 4 pts
• CASHPOR is now using PPI as the 2nd step in place
of our earlier means test
• We chose the US$2 per person per day poverty-
line, and below 35 pts, to be > 90% sure that the
clients entering are poor, and to give the PPI face
validity so as to give confidence to our staff in
using it

11. PPI Scores & the Probability Poor
PPI Score Indian Nat’l PL <US$1.25 (ppp) <US$2 (ppp)
0-4 77%% 98.5% 100%
5-9 58.5% 92.7% 99.1%
10-14 51.2% 88.1% 97.5%
15-19 35.5% 82% 98.9%
20-24 28.7% 78.7% 98%
25-29 21.3% 65.2% 94.9%
30-34 18.9% 55.8% 93.7%
35-39 14.9% 45.4% 84.7%
40-44 10% 32.9% 77.8%

12. Thank You!