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An operational method for assessing the poverty outreach of development projects
( illustrated with case studies of microfinance institutions in developing countries)
Manohar Sharma
IFPRI
Overview
• Objectives for the development of the method
• Reasons for choice of method• Research approach in developing the
method• How is the index of relative poverty
computed?• Examples of results (India, Nicaragua,
South Africa)• Application potentials of the method
Outreach/Targeting to poor groups
Welfare impact
Financial sustainability of the institution
The critical triangle of institutional development
Source: Zeller, Manfred and Meyer, Richard L. 2000. The critical triangle of micro-finance.Upublished manuscript, IFPRI/Ohio State University.
Objectives of the research project
• It is not an impact evaluation tool • It is not a targeting tool• Develop an operational method that can
be used by donors to assess the poverty level of clients of mfis
• CGAP wanted that the method is: Easy to implement in a relatively short time Not costly Results should be comparable between
different mfis and, if possible, across countries
Principal methods
1. Typical living standard (expenditure) surveys too expensive and complicated
2. Participatory assessment methods not comparable, even between villages
3. Identification of a set of indicators which are used to build an index that is a measure of poverty:
Examples of indices already used: Housing index (for MFI-targeting in South and
Southeast Asia), but poverty has many dimensions (food, asset, social capital)
UN Human Development Index (3 components: income, education, and life expectancy)
But here arbitrary weights need method that objectively sets weights subject to country-specific conditions
Indicator Method - Multi-dimensions of Poverty
How many days in past month not enough to
eat?
Material of walls?
Number of rooms?
Food security Housing condition Assets
Human capital Other
Ownership of bicycle, TV etc
%of adults in household, that can read and
write
Relatives working in
foreign countries?
Poverty
Criteria for selection of indicators
• Nationally valid (can be used in different local contexts, urban vs. Rural)
• Not too sensitive question, can be asked Openly • Practicability (can be observed vs. Require interview)• Ability to discriminate different levels of poverty• Reliability (risks of falsification/error vs. possibility to
verify)• Simplicity (direct answer vs. computed information)• Time (Answer can be elicited quickly)• Universality (can be used in different countries)
Human resources Dwelling
Food security and vulnerability Assets Others
Age and sex of adult household members Level of education of adult household members Occupation of adult household members Number of children below 15 years of age in household Annual clothing/ footwear expenditure for all household members
Number of rooms Type of roofing Type of exterior walls Type of flooring Observed structural condition of dwelling Type of electric connection Type of cooking fuel used Source of drinking water Type of latrine
Number of meals served in last two days Serving frequency (weekly) of three luxury foods Serving frequency (weekly) of one inferior food Hunger episodes in last one month Hunger episodes in last 12 months Frequency of purchase of staple goods Size of stock of local staple in dwelling
Area and value of land owned Number and value of selected livestock resources Value of transportation-related assets Value of electric appliances
Nonclient’s assessment of poverty outreach of MFI
The comparison groups
• Compare level of poverty (or wealth) of RANDOMLY SELECTED new clients
With• Level of poverty (or wealth) of RANDOMLY
SELECTED non-clients in the operational area of the MFI
Principal component analysis
Poverty
Human resources
Dwelling Asset Food Other
Components
Indicators
Computing a poverty index
• Starting with a generic questionnaire, finalize situation specific indicators, questionnaire,
• Collect data on indicators• Construct household-specific poverty indices
as scores from Principal component analysis• • Separate out non-client households, sort, and
form three groups. Group 1= poorest, Group 2= poor, Group 3= less poor.
• Use score range for each group to classify client households.
To which of three poverty tercile groups do client households belong?
Poverty Index Score
Middle 100 non-client households
Bottom 100 non-client households
Top 100 non-client households
Cut-off score
-2.51 -0.70 0.21 3.75
Poorest Poor Less Poor
Poverty Score Index
Client Households
Comparing extent of poverty outreach
across programs and countries.
Measure 1: (percentage of clients belonging to poorest tercile) Higher values show more extensive outreach to the poorest
Measure 2: (percentage of clients belonging to Least poor tercile)Higher values show more outreach to the better-off
Measure 3: indicates whether poorer regions in the countryhave been reached
Case study in India with SHARE
• Provides credit and savings services to targeted poor individuals, mainly rural women in seven districts of Andhra Pradesh in India.
• Women self-selected, then tested for eligibility through questionnaire and interview.
• Has offered services to about 16,000 women with loan sizes in the range of $103 to $308 (year 2000).
• Loans provided without collateral to client groups of five, but require group training and certification.
Results for SHARE in India
58% of SHARE clients in “poorest” tercile, compared to 33% of non-clients
38.5% of SHARE clients in “poor” tercile compared to 33% on non-clients
3.5% of SHARE clients in “less poor” tercile, compared to 33% of non-clients
Poverty group
less poorpoorpoorestP
erc
en
t
70
60
50
40
30
20
10
0
client status
SHARE client
Non-client
Case study in Nicaragua with ACODEP
• ACODEP is the largest micro-finance institution in Nicaragua, serving 12,000 clients.
• Offers range of loan products to individuals for enterprise development ($20 to several thousand loan size).
• Offers savings programs specifically designed for poorer clients.
RESULTS FOR ACODEP IN NICARAGUA
30.9 % of ACODEP clients in “poorest” tercile, compared to 33% of non-clients
37.7 % of ACODEP clients in “poor” tercile compared to 33% on non-clients
31.4% of ACODEP clients in least poor tercile, compared to 33% of non-clients
Poverty group
Less poorPoorPoorest
Proz
ent
40
30
20
10
0
client-non client
non-client
client
Source: Lapenu, Zeller Nicaragua case study
South Africa: Comparison of two credit programs-- Small Enterprise Foundation (SEF)
NON- TARGETED
% client- household
s
% non-client
households Poorest 15 33
Poor 35
33
Less Poor 50 33
TARGETED % client
houshholds % non-client
households Poorest 52 33
Poor 39
33
Less Poor 9 33
Source: Carla Henry, Follow-up case study in South Africa
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