Many microfinance institutions (MFIs) receive public support. In return for this support, governments and donors demand MFIs not only become financially sustainable but also reach the poor, or even the poorest of the poor. Effective evaluation of the achievement of these objectives requires appraising both the MFI's financial sustainability and the relative poverty of its clients. In recent years, several tools have emerged to assist donors in their assessment of the financial sustainability of MFIs. For example, the Consultative Group to Assist the Poorest (CGAP), which seeks to promote sustainable microfinance institutions for the poor, disseminates a number of tools that allow assessing the financial sustainability and other aspects of institutional performance of MFIs. Currently, no operational tool exists for measuring how well a MFI reaches the poor through its services. In order to gain more transparency on the depth of poverty outreach, CGAP supported research at IFPRI during 1999 and 2000 to design and test a simple, low-cost operational tool to measure the poverty level of MFI clients relative to nonclients. This policy brief summarizes the main features of the tool, how it can be applied, and what type of results can be obtained.Microfinance Evaluation. ,Poverty Research Evaluation. ,Poverty Research Methodology. ,Financial institutions. ,Sustainability. ,Microenterprises Finance. ,

Seller, Manfred

Sharma, Manohar

English

Research Papers in Economics

R R U U R R A A L L     F F I I N N A A N N C C I I A A L L     P P O O L L I I C C I I E E S S    
f f o o r r     F F O O O O D D     S S E E C C U U R R I I T T Y Y     o o f f     t t h h e e     P P O O O O R R  
 
 
IFPRI 
 
 
 
 
 
 
 
RESEARCH 
PROGRAM 
MISSION 
The research 
program titled Rural 
Financial Policies 
for Food Security of 
the Poor seeks to 
identify policies and 
institutional 
arrangements that 
help the poor 
integrate themselves 
into sustainable 
savings and credit 
systems in order to 
increase capacity to 
invest, bear risk, and 
preserve livelihoods.
 
FOCUS 
COUNTRIES 
• Bangladesh 
• Cameroon 
• China 
• Egypt 
• Ghana 
• Madagascar 
• Malawi 
• Nepal 
• Pakistan 
 
ABOUT THE 
AUTHORS 
Manfred Zeller is a 
professor at the 
University of 
Goettingen, 
Germany, and a 
former research 
fellow of the Food 
Consumption and 
Nutrition Division at 
IFPRI. 
Manohar Sharma is 
a postdoctoral 
fellow of the Food 
Consumption and 
Nutrition Division at 
IFPRI. 
POLICY BRIEF No 10   •   JULY 2000 
Many microfinance institutions (MFIs) receive public 
support. In return for this support, governments and 
donors demand MFIs not only become financially 
sustainable but also reach the poor, or even the poorest 
of the poor. Effective evaluation of the achievement of 
these objectives requires appraising both the MFI’s 
financial sustainability and the relative poverty of its 
clients. In recent years, several tools have emerged to 
assist donors in their assessment of the financial 
sustainability of MFIs. For example, the Consultative 
Group to Assist the Poorest (CGAP), which seeks to 
promote sustainable microfinance institutions for the 
poor, disseminates a number of tools that allow 
assessing the financial sustainability and other aspects 
of institutional performance of MFIs. Currently, no 
operational tool exists for measuring how well a MFI 
reaches the poor through its services. In order to gain 
more transparency on the depth of poverty outreach, 
CGAP supported research at IFPRI during 1999 and 
2000 to design and test a simple, low-cost operational 
tool to measure the poverty level of MFI clients 
relative to nonclients. 
This policy brief summarizes the main features of 
the tool, how it can be applied, and what type of results 
can be obtained. Another policy brief informs about 
the results from four test country cases. 
 
 
 
 
 
 
 
 
 
 
 
The poverty assessment (PA) tool developed under 
the collaborative project of IFPRI and CGAP comple-
ments an array of methods already available for assess-
ing various dimensions of the institutional performance 
of MFIs. A widely accepted method in poverty assess-
ment is to conduct a detailed household expenditure 
survey and to use household total expenditure as the 
primary measure to evaluate standard of living of 
households. Although detailed quantitative studies are 
frequently regarded as accurate means of assessing 
poverty levels, the high cost of acquiring and ana-
lyzing such extensive information often precludes the 
methodology from being used for operational appli-
cations. 
Therefore, the new PA tool responds to five specific 
design parameters set to accommodate microfinance 
industry needs: 
§ The tool is operationally straightforward.  
§ The cost of implementing the tool is relatively low.  
§ The timeframe for assessment is short.  
§ The tool specifically addresses poverty measurement 
in developing countries. 
§ The results are readily interpretable and comparable 
across programs and countries. 
 
 
 
 
 
Consideration of the above mentioned design param-
eters led to the adoption of an indicator-based poverty-
assessment method. This involved 
1. identifying a range of indicators that reflect power-
fully on poverty levels and for which credible 
information can be quickly and inexpensively 
obtained; 
2. designing a survey methodology that facilitates the 
collection of information on these indicators from 
households living in the operational area of 
the MFI. 
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE 
2033 K STREET, NW, WASHINGTON, DC 20006-1002 USA 
Web: www.ifpri.org   •   Phone: 1-202-862-5600   •   Fax: 1-202-467-4439   •   Email: ifpri@cgiar.org 
Assessing the Relative Poverty Level in Clients of Microfinance 
Institutions: An Operational Tool 
MANFRED ZELLER AND MANOHAR SHARMA 
Purpose of the Poverty Assessment Tool 
The Tool Uses Indicators for Assessing 
Poverty 
The new tool can measure the poverty level of 
clients of microfinance institutions relative to 
nonclients at low costs. The tool may also be used 
for evaluating the poverty outreach of other types 
of development and safety net programs.  
PAGE 2 
 
Assessing the 
Relative Poverty 
Level of Clients of 
Microfinance 
Institutions: An 
Operational Tool 
 
- - - - - - - - - - - - - - 
 
3. testing these indicators in diverse socioeconomic 
and cultural settings, and identifying those indi-
cators that are strongly related to relative poverty 
levels in each of the four country cases so as to 
reduce the number of questions for future applica-
tions of the tool; and 
4. formulating a single summary index that combined 
information from a range of indicators and that 
could be used to make poverty comparisons be-
tween client and nonclient households. 
 Because of the multifaceted nature of poverty, 
reliance on any one indicator would not be adequate. 
To capture different dimensions of poverty, IFPRI 
used the following classification of indicators in the 
process of developing the generic household-level 
questionnaire: 
1. Indicators expressing the means available to house-
holds to increase their welfare. These reflect the 
earning potential and relate to  
• human capital (family size, education, occupa-
tion, etc.), 
• asset ownership, 
• social capital of household. 
2. Indicators related to the fulfilment of basic needs: 
• health status and access to health services, 
• access to food, shelter and clothing, 
• other dimensions of achievement of welfare 
(security, social status, environment) assess-
ments based on this experience. 
 From an exhaustive list of indicators derived 
through a literature review and expert consultation, the 
IFPRI team initially chose to include a smaller subset 
in a generic questionnaire that then was tested in four 
case study countries with diverse socioeconomic and 
cultural settings. In total, about 300 poverty indicators 
were contained in the questionnaire and tested in the 
four country case studies. 
 
 
 
 The tool prescribes how to collect household data on 
a range of poverty indicators through a standardized 
questionnaire. There are several reasons for identifying 
poverty indicators that are not specific to a particular 
country or region, but rather are general in the sense 
that they can indicate the level of poverty irrespective 
of socioeconomic characteristics of the chosen coun-
tries or regions within countries. First, general indica-
tors allow within-country and across-country compar-
isons. Second, working from a standardized ques-
tionnaire avoids the risks of introducing error and 
eases the complex task of designing appropriate ques-
tions for creating indicators of poverty. However, com-
plete standardization is also impractical, given the 
inherently relative nature of poverty: the “poor” in a 
wealthy country may be considered “nonpoor” in an-
other country. Indicators must be such that they easily 
can be adjusted to different levels of absolute poverty. 
 Relative poverty levels of sampled households are 
measured by these indicators through calculation of a 
household poverty index. This index weighs the 
relative poverty of each household relative to all others 
and provides a ranking score. The mean of the score is 
zero, and its standard deviation is 1. The ranking score 
indicates how each household’s estimated poverty 
level compares with those of all other households sur-
veyed. The higher the score, the relatively less poor the 
household is. 
 To accommodate the needed flexibility in measuring 
poverty to fit each country situation, estimation of the 
poverty index itself demands for a flexible approach. 
In other words, each country requires a different mix 
of indicators to calculate the most meaningful mea-
surement of relative poverty for that country. For this 
reason, the statistical technique of principal compo-
nents analysis was used for determining which indi-
cators contribute the most to creating a poverty index 
for each  individual  country.  Specifically, PC analysis  
 
 
Figure 1. Indicators and underlying components 
 
 
 Comments￿  
 
 
 
 
 
 
 
 
 
 Indicators￿  
 
 
 
 
 
Using Principal Component Analysis to 
Estimate the Poverty Index 
Poverty  Other uncorrelated 
components 
Human  
resource 
indicators 
Housing 
indicators 
Asset 
indicators 
Indicators 
on food 
security 
Other 
indicators  
PAGE 3 
 
Assessing the 
Relative Poverty 
Level of Clients of 
Microfinance 
Institutions: An 
Operational Tool 
 
- - - - - - - - - - - - - - 
 
isolates and measures the poverty component em-
bedded in the various poverty indicators and creates a 
household-specific poverty score or index (Figure 1). 
PC analysis extracts underlying components from a set 
of information provided by summary indicators. In the 
case of this poverty assessment tool, information col-
lected from the questionnaires make up the “indica-
tors” and the underlying component that is isolated and 
measured is “poverty.” 
the “poorest” group (Figure 2). Since there are 300 
nonclients, each group contains 100 households each. 
The cut-off scores for each tercile define the limits of 
each poverty group. Client households are then cate-
gorized into the three groups based on their household 
scores.  
If the pattern of client households’ poverty matches 
that of the nonclient households, client households 
would divide  equally among  the three  poverty group- 
 
Figure 2. Constructing poverty groups 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
To guide evaluators in applying the field research 
tool and to estimate the poverty index, a manual has 
been written that will be published by CGAP (see 
Henry et al. 2000). An evaluation involves conducting 
a household survey with a random sample of 300 non-
client households and 200 client households within the 
operational area of the MFI. On average, conducting 
the interview takes about 15 minutes. 
To use the poverty index for making comparisons, 
the nonclient sample is first sorted in an ascending 
order according to its index score. Once sorted, non-
client households are divided in terciles based on their 
index score: the top third of the nonclient households 
are grouped in the “less poor” group, the middle third 
grouped  in the  “poor” group and  the  bottom third  in  
ings just as the nonclient households, with 33 percent 
falling in each group. Hence any deviation from this 
equal proportion signals a difference between the cli-
ent and the nonclient population. For instance, if 60 
percent of the client households fall into the first ter-
cile or poorest category, the MFI reaches a dispro-
portionate number of very poor clients relative to the 
general population. 
 
 
 
The tool compares distributions of client and non-
client relative poverty levels by creating terciles of 
poverty groups based on poverty scores for nonclient 
households and adds MFI client households to these 
groupings according to their poverty-ranking scores. 
For example, MFIs having a greater share of their 
client households placed in the poorest tercile of the 
general  population  are  credited  with  having  a  more  
The Results of a Poverty Assessment 
Application of the Tool Is Supported by 
a Manual 
Client household with 
scores above 0.21 
Client household with scores 
between -.70 and 0.21 
Client household with 
scores less than -.70 
Lowest  Middle  Higher 
Poverty  Score  Index 
   -2.51  -0.70  0.21  3.75 
Cut-off scores 
   
Bottom 100 
nonclients 
households 
 
   
Middle 100 
nonclients 
households 
 
   
Top 100 
nonclients 
households 
  
ABOUT IFPRI 
 
IFPRI’s mission is 
to identify and 
analyze strategies 
for meeting food 
needs of the 
developing world, 
with particular 
emphasis on low-
income countries 
and the poor. 
 
IFPRI is a member 
of the Consultative 
Group on 
International 
Agricultural 
Research (CGIAR). 
 
- - - - - - - - - - - - - - 
 
Any opinions 
expressed herein are 
those of the 
author(s) and do not 
necessarily reflect 
those of IFPRI. 
 
- - - - - - - - - - - - - - 
 
CONTACT 
Bonnie McClafferty 
Communications 
Specialist 
B.McClafferty@ 
   CGIAR.ORG  
 
favorable poverty outreach profile than MFIs having a 
smaller share assigned to the poorest tercile. Figure 3 
shows the outcome of a recent assessment in one of the 
case-study countries. 
 
 
Figure 3. Results of a recent MFI poverty-
assessment case 
0
10
20
30
40
50
60
70
Poorest Poor Less
Poor
Poverty Group
P
e
r
c
e
n
t
 MFI client 
 Nonclient of MFI 
Client status
 
It is recommended that assessment of the MFI’s 
relative poverty outreach be made not only within the 
operational area of the MFI, but also in the context of 
regional poverty within a country. Recent research 
indicates that MFIs tend to locate their operations in 
above-average regions where transactions costs are 
lower and market volume is higher (Sharma and Zeller 
1999). In addition, to assess the MFI’s poverty out-
reach on an international scale, it is recommended that 
the country’s poverty level be compared with those of 
other developing countries. The tool recommends this 
relative poverty comparison based on the Human 
Development Index (HDI), developed by the United 
Nations Development Program, and published annual-
ly in the Human Development Report. On average, 
households located in countries registering relatively 
low HDI rankings can be seen as relatively poorer than 
households from countries ranked higher in HDI. 
 
 
 
During the second half of 1999, the methodology 
was field tested by IFPRI in collaboration with nation-
al research institutions and counterparts in four case 
studies in Asia, Africa, and Latin America. Overall, 
these tests have shown that the tool is useful and 
sufficiently simple for evaluating the poverty outreach 
of MFIs in an operational context. Moreover, the case 
studies have shown that the four MFIs differ not only 
in terms of poverty outreach, but also in the mission 
they define for themselves, the type of market niche 
they seek for themselves, their preference for a specific 
type of institutional culture, and a host of conditions 
imposed by other external actors at various levels. 
Poverty assessment results have to be interpreted in 
light of these considerations. Ignoring them or pro-
viding incomplete information on institutional details 
fails to tell a complete story. Before recommending the 
use of the poverty assessment tool for widespread use, 
the tool ought to be further tested and eventually im-
proved. For this purpose, a manual will be dissemi-
nated by CGAP that will assist future users in applying 
the tool.￿ 
 
 
 
Henry, C., M. Sharma, C. Lapenu, and M. Zeller. 
2000. Assessing the relative poverty of microfinance 
clients: A CGAP operational tool. Consultative 
Group to Assist the Poorest (CGAP), Washington, 
D.C. (forthcoming). 
Sharma, M., and M. Zeller. 1999. Placement and out-
reach of group-based credit organizations: The cases 
of ASA, BRAC, and PROSHIKA in Bangladesh. 
World Development, Vol. 27(12), pp. 2123-2136, 
December 1999. 
Concluding Remarks 
Selected References 

Assessing the relative poverty level in clients of microfinance institutions

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Assessing the relative poverty level in clients of microfinance institutions

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