HEALTH/NUTRITION/POPULATION (HNP) AND POVERTY SEMINAR REPORT

HEALTH/NUTRITION/POPULATION (HNP) AND POVERTY

SEMINAR REPORT

Topic: Determining Whether HNP Projects Reach the Poor: What Can

We Learn from the Microcredit Movement?

Presenter: Manohar Sharma

Commentators: Adam Wagstaff and Abdo Yazbeck

Date and Time: February 25, 2002, 12:30 – 2:00 PM

The session was the thirty-second in the HNP/Poverty Thematic Group’s seminar

program. Participants included approximately 30 staff members of the World Bank and

other Washington area development agencies. Thematic Group Co-Coordinator Dave

Gwatkin served as moderator.

The purpose of the seminar was to introduce the hnp community to a technique

used by microcredit institutions to determine whether their loans are reaching

disadvantaged population groups, and to assess the applicability of the approach to

assessing how effectively hnp activities are reaching the poor. The presenter, Manohar

Sharma, is a Research Fellow at the International Food Policy Research Institute (IFRPI)

in Washington. He was coordinator of the IFRPI team that developed the technique for

Consultative Group to Assist the Poorest (CGAP), a support body for microcredit

institutions that is located at the World Bank. After Manohar’s presentation of the

technique itself, Adam Wagstaff and Abdo Yazbeck of the World Bank staff opened a

discussion of the technique’s relevance for the hnp sector.

Presentation

Manohar started by explaining the reason for developing the technique, known as

the “CGAP Poverty Assessment Tool.” He explained that microcredit institutions are

committed to reaching two, sometimes conflicting objectives: attaining financial

sustainability, and reaching the poor. Progress toward the first of these objectives can be

and is routinely monitored through the information readily available from the institutions’

financial records. But these records do not contain the information needed to determine

the socioeconomic status of loan recipients relative to that of the population at large. As

a result, the records are inadequate to assess whether the loans are going to poor people,

and a special effort is required to measure progress toward the second objective.

The CGAP poverty assessment tool is a plan for such an effort, in the form of

detailed guidelines for the design, implementation, and analysis of data from a simple

household survey in order to compare the socio-economic status of households that

receive and do not receive loans from microcredit institutions. It is intended to be easy

enough to for non-specialists to use, applicable to a wide range of cultures, inexpensive

(under $10,000 per survey), and rapid (such that results can be available within three

months of a survey’s initiation).

The tool features a questionnaire designed to determine household socioeconomic

status. After considerable thought and experimentation, the tool developers settled upon

a set of 30-35 questions covering five dimensions of socio-economic well-being: human

resources (e.g. education level and type of employment); dwelling characteristics (e.g.

type of roof and/or walls, presence of electricity), household assets or possessions (e.g.

amount of irrigated land owned, presence of possessions like radios and/or fans),

household food security situation (e.g. number of meals per day, consumption of luxury

or inferior foods), and additional dimensions specific to individual countries (e.g.

remittances received from overseas household members). Each country questionnaire

typically employs some 15-20 of these questions, selected on the basis of information

gathered through local focus group surveys and similar methods.

The questionnaire is used in to gather information from 500 randomly-selected

households: 200 households that receive loans from the microcredit institution being

studied, and 300 households in the institution’s service area that do not receive loans.

Once gathered, responses to the different questions are weighted, using weights produced

by applying the statistical technique of principal components analysis to the data set, and

added together in order to produce a wealth score for each household.

The non-client households are then ranked from lowest to highest in terms of the

wealth scores, and are divided into three groups of 100 households each: low, middle,

and high. The wealth scores of the highest and lowest household in each of the three

groups are taken to establish the range for that group; and the proportion of client

households whose wealth scores fall within the range of each non-client group is

calculated. The result is a socioeconomic profile of client households relative to nonclient

households that can be used to assess how well the microcredit institution being

studied is reaching the poor. (For example, a microcredit institution with 50% of its

clients in the lowest population group might be said to reach the poor more effectively

than one with only 25% of its clients in that group.)

After thus describing the poverty assessment tool, Manohar presented some

illustrative results drawn from the several instances where the tool has been applied.

These showed very wide differences in performance. For instance:

— 58% of loans provided through a microcredit program Andhra Pradesh,

India named SHARE went to households in the poorest group, much higher that the

comparable figure for loans made available through ACODEP, one of Nicaragua’s largest

microcredit institutions.

— In South Africa, households in the poorest group received 52% of loans

from two microcredit programs that focused especially on the poor. Only 15% of loans

issued by a third program that did not focus on the poor went to this group.

Commentary

Adam Wagstaff pointed to the basic similarity between the methods used to assess

household socioeconomic status in the poverty assessment tool in the World Bank

hnp/poverty country reports. However, he noted that there were also significant

differences in the particular questions selected for inclusion in the questionnaires and in

the resulting wealth score, and proceeded from this observation to reflect on the criteria

for selecting questions. Although unresolved conceptual questions about what constitutes

wealth prevents any definitive response to what and should not be included, it is still

possible to suggest at least some guidelines. For instance, one would want to add only

those questions that provide additional information – that is, that have been demonstrated

through pilot inquiries to increase the power of the wealth scores to discriminate between

the poor and the better off. Another consideration would be way in which the wealth

scores would be used: for instance, one would wish to avoid introducing circular

reasoning by including educational status in a wealth score used in a study of how

educational status rises with the level of wealth, whereas in studies designed to measure

other things (say, poor-rich differences in access to water or sanitation), inclusion of

education in a wealth score might be appropriate or at least less inappropriate.

Adam concluded by advocating the regular use of some variation of the poverty

assessment tool in monitoring who benefits from Bank-supported hnp projects. This

could be accomplished by earmarking a modest amount of resources for this purpose in

the budget of each Bank loan. The resources could be used to train and empower people

in the countries concerned to carry out such assessments and apply the results to develop

policy.

Abdo Yazbeck praised the poverty assessment tool developers for producing an

approach of clear practical value, which he commended as model for other researchers.

However, he foresaw significant challenges in applying the tool directly to hnp projects

in its present form, because hnp projects lack a readily identifiable client population

comparable to the group of borrowers with whom microcredit institutions retain regular

contact. So at least some modification is likely to be necessary. As an illustration a

modified version of the CGAP approach that might work for hnp projects, Abdo

suggested using a wealth questionnaire for a survey of patients at facilities in a country

where a nationwide household survey including the same wealth questions had recently

been undertaken. This would permit comparison of the socioeconomic status among

facility patients with that of the population at large as measured by the nationwide

survey, enabling one to tell whether the facility had succeeded in reaching the poor. This

approach has been successfully used in India.

Abdo argued against any mandatory earmarking of resources in Bank loans for

this kind of assessment. Instead, he advocated that the Bank actively lead in developing

and disseminating information about equity monitoring methods, but leave it to countries

to take the initiative in deciding to apply them.

Discussion

The ensuring floor discussion covered several issues. Among them were:

— Choice of Questions. A participant asked about the basis for selecting

the particular questions included in the questionnaire over the many alternatives that

might also have been chosen. For example, why were there no questions about health

status in the section on human resources? Manohar replied that the principal reasons for

not including particular questions included political sensitivity, difficulty of

understand ing on the part of respondents, and the production of inconsistent or

implausible results. The participant who had originally raised the question did not find

these sufficient reasons for omitting health questions, despite the well-known doubts

about the accuracy of self-reported health status. She referred to a five-question

instrument developed by the World Health Organization, which made it possible to

surmount this difficulty.

— Reasons for Reaching or not Reaching the Poor. Another participant

noted that the CGAP tool appeared to provide solid data about whether microcredit

institutions were reaching the poor, but produced no information about why the

institutions were or not doing so. This “why” information, he argued, is even more

important than the “whether” data for developing pro-poor approaches. In response, a

representative from CGAP noted that, despite the lack of formal inclusion of “why”

considerations in the tool’s design, it was nonetheless possible to get a pretty clear idea of

why some of the institutions have reached the poor than others. In general, those

institutions shown to have done best in this regard were those known for their

commitment to poverty alleviation and for the use of targeting mechanisms to this end.

The institutions that had done poorly were those which had given higher priority to

financial sustainability.

— Use of Wealth Questions for Targeting Purposes. Two or three

participants wondered whether the same questions appearing in the CGAP tool for

assessing who had received loans after the loans had been made could also be used in

advance for determining who should receive loans. That is, could one administer the

same questionnaire to prospective borrowers, and lend only to those whose wealth score

fell below some predetermined point? Although time limitations prevented a full

discussion of this question, a couple of comments suggested a preference for using some

simpler system for identifying poor individuals; or perhaps better yet, an indirect

approach that would focus on serving everyone in selected poor geographic areas rather

than seeking to find and serve only disadvantaged individuals.

* * * * * * * * * * * * * *

Further information about the CGAP Poverty Assessment Tool is available at:

http://nt1.ids.ac.uk/cgap/poverty/pat.html

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