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 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 conditionAssets 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 resourcesDwelling Food security and vulnerabilityAssets 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 DwellingAssetFoodOther 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? PovertyIndexScore Middle 100 non-client households Bottom 100 non-client households Top 100 non-client households Cut-off score PoorestPoorLess Poor Poverty ScoreIndex 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 country have 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

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 Source: Lapenu, Zeller Nicaragua case study

South Africa: Comparison of two credit programs-- Small Enterprise Foundation (SEF) Source: Carla Henry, Follow-up case study in South Africa