the microbanking bulletin - Microfinance Information Exchange
the microbanking bulletin - Microfinance Information Exchange
the microbanking bulletin - Microfinance Information Exchange
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THE<br />
MICROBANKING<br />
BULLETIN<br />
F o c u s o n P r o d u c t i v i t y<br />
ISSUE NO. 6<br />
APRIL 2001<br />
A SEMI-ANNUAL PUBLICATION DEDICATED TO THE FINANCIAL PERFORMANCE OF<br />
ORGANIZATIONS THAT PROVIDE BANKING SERVICES FOR THE POOR
The MicroBanking Standards Project<br />
The MicroBanking Bulletin is one of <strong>the</strong> principal<br />
outputs of <strong>the</strong> MicroBanking Standards Project,<br />
which is funded by <strong>the</strong> Consultative Group to Assist<br />
<strong>the</strong> Poorest (CGAP).<br />
Project Purpose<br />
By collecting financial and portfolio data provided<br />
voluntarily by leading microfinance institutions<br />
(MFIs), organizing <strong>the</strong> data by peer groups, and<br />
reporting this information, this project is building<br />
infrastructure that is critical to <strong>the</strong> development of<br />
<strong>the</strong> industry. The primary purpose of this database<br />
is to help MFI managers and board members<br />
understand <strong>the</strong>ir performance in comparison with<br />
o<strong>the</strong>r MFIs. Secondary objectives include<br />
establishing industry performance standards,<br />
enhancing <strong>the</strong> transparency of financial reporting,<br />
and improving <strong>the</strong> performance of microfinance<br />
institutions.<br />
Project Services<br />
To achieve <strong>the</strong>se objectives, <strong>the</strong> MicroBanking<br />
Standards Project provides three services: 1)<br />
customized financial performance reports; 2) <strong>the</strong><br />
MicroBanking Bulletin; and 3) network services.<br />
MFIs participate in this project on a quid pro quo<br />
basis. They provide us with information about <strong>the</strong>ir<br />
financial and portfolio performance, as well as<br />
details regarding accounting practices, subsidies,<br />
and <strong>the</strong> structure of <strong>the</strong>ir liabilities. Participating<br />
MFIs submit substantiating documentation, such as<br />
audited financial statements, annual reports,<br />
program appraisals, and o<strong>the</strong>r materials that help us<br />
understand <strong>the</strong>ir operations. With this information,<br />
we apply adjustments for inflation, subsidies and<br />
loan loss provisioning to create comparable results.<br />
We do not independently verify <strong>the</strong> information.<br />
Nei<strong>the</strong>r <strong>the</strong> MicroBanking Standards Project nor<br />
CGAP can accept responsibility for <strong>the</strong> validity of <strong>the</strong><br />
information presented or consequences resulting<br />
from its use by third parties.<br />
In return, we prepare a confidential financial<br />
performance report for each participating institution.<br />
These reports, which are <strong>the</strong> primary output of this<br />
project, explain <strong>the</strong> adjustments we made to <strong>the</strong><br />
data, and compare <strong>the</strong> institution’s performance to<br />
its peer group as well as to <strong>the</strong> whole sample of<br />
project participants. These reports are essential<br />
tools for MFI managers and board members to<br />
benchmark <strong>the</strong>ir institution’s performance.<br />
The third core service is to work with national and<br />
regional associations of microfinance institutions to<br />
enhance <strong>the</strong>ir ability to collect and manage<br />
performance indicators. This service is provided in a<br />
variety of different ways, including teaching <strong>the</strong>se<br />
networks to collect, adjust and report data at <strong>the</strong><br />
local level, collecting data on behalf of a network,<br />
and providing customized data analysis to compare<br />
member institutions to external peer groups. This<br />
service to networks allows us to help a wider range<br />
of MFIs to improve <strong>the</strong>ir financial reporting.<br />
New Participants<br />
Organizations that wish to participate in <strong>the</strong><br />
MicroBanking Standards Project, ei<strong>the</strong>r to receive<br />
customized reports or network services, should<br />
contact: mbb@<strong>microbanking</strong>-mbb.org, Tel (202)<br />
659-9802/4, Fax (202) 659-9816. Currently, <strong>the</strong> only<br />
criterion for participation is <strong>the</strong> ability to fulfill fairly<br />
onerous reporting requirements. We reserve <strong>the</strong><br />
right to establish minimum performance criteria for<br />
participation in <strong>the</strong> Bulletin.<br />
Bulletin Submissions<br />
The Bulletin welcomes submissions of articles and<br />
commentaries, particularly regarding analytical work<br />
on <strong>the</strong> financial performance of microfinance<br />
institutions. Submissions may include reviews or<br />
summaries of more extensive work elsewhere.<br />
Articles should not exceed 2,500 words. We also<br />
encourage readers to submit responses to <strong>the</strong><br />
content of this and previous issues of <strong>the</strong> Bulletin.
THE MICROBANKING BULLETIN<br />
FOCUS ON PRODUCTIVITY<br />
ISSUE NO. 6<br />
APRIL 2001<br />
DEDICATED TO THE FINANCIAL PERFORMANCE OF ORGANIZATIONS THAT PROVIDE BANKING<br />
SERVICES FOR THE POOR<br />
EDITORIAL STAFF<br />
Craig F. Churchill<br />
Geetha Nagarajan<br />
Isabelle Barrès<br />
Editor<br />
Associate Editors<br />
EDITORIAL BOARD<br />
Chair:<br />
Robert Peck Christen<br />
Claudio Gonzalez-Vega<br />
Elisabeth Rhyne<br />
Richard Rosenberg<br />
J.D. Von Pischke<br />
Consultative Group to Assist <strong>the</strong> Poorest, The World Bank<br />
The Ohio State University<br />
ACCION International<br />
Consultative Group to Assist <strong>the</strong> Poorest, The World Bank<br />
Frontier Finance International<br />
The MicroBanking Bulletin is funded by <strong>the</strong> Consultative Group to Assist <strong>the</strong> Poorest (CGAP)
CONTENTS<br />
From <strong>the</strong> Editor ............................................................................................................................................................ 1<br />
FEATURE ARTICLES<br />
Designing Financial Incentives to Increase Loan Officer Productivity: Handle With Care! ...................................... 5<br />
Martin Holtmann<br />
We Aren’t Selling Vacuum Cleaners: PRODEM’s Experiences with Staff Incentives............................................ 11<br />
Eduardo Bazoberry<br />
Dropouts and Graduates: Lessons from Bangladesh .............................................................................................. 14<br />
Graham A.N. Wright<br />
Exodus: Why Customers Leave................................................................................................................................ 17<br />
Kim Wilson<br />
Cultivating Client Loyalty: Exit Interviews from Africa and Asia............................................................................... 20<br />
Inez Murray<br />
TALKING ABOUT PERFORMANCE RATIOS<br />
Measuring Client Retention ....................................................................................................................................... 25<br />
Rich Rosenberg<br />
COMMENTARY AND REVIEWS<br />
Book Review: Brand and Gerschick’s Maximizing Efficiency, by Robin Young ..................................................... 27<br />
Book Review: Campion’s Improving Internal Control, by Luis Schunk ................................................................... 29<br />
BULLETIN CASE STUDY<br />
Bosnian MFIs: Performance and Productivity .......................................................................................................... 31<br />
Isabelle Barrès<br />
BULLETIN HIGHLIGHTS AND TABLES<br />
Bulletin Highlights: Productivity Drivers and Trends ................................................................................................ 35<br />
Geetha Nagarajan<br />
An Introduction to <strong>the</strong> Peer Groups and Tables ....................................................................................................... 40<br />
Index of Ratios and Tables........................................................................................................................................ 42<br />
Peer Group Tables..................................................................................................................................................... 44<br />
Additional Analysis Tables......................................................................................................................................... 53<br />
APPENDICES<br />
Appendix I: Notes to Statistical Section .................................................................................................................... 73<br />
Appendix II: Description of Participating MFIs.......................................................................................................... 77
From <strong>the</strong> Editor<br />
An Introduction to Productivity<br />
One of <strong>the</strong> challenges of managing a microfinance<br />
institution is trying to do more with less. To serve<br />
more people with less subsidies. To achieve<br />
greater impact with smaller loans. And, for staff to<br />
manage more clients with fewer arrears. Micro-<br />
Banking Bulletin Issue No. 6 focuses on productivity<br />
by tackling this last example.<br />
Direct and Indirect Influences<br />
Productivity describes how a change in an organization’s<br />
inputs, such as labor, affects its outputs (i.e.<br />
loans). In microfinance, productivity discussions<br />
tend to focus on how staff incentives affect <strong>the</strong><br />
number of clients per loan officer. Besides incentives,<br />
o<strong>the</strong>r inputs such as <strong>the</strong> total remuneration<br />
package, staff training, and <strong>the</strong> use of technology<br />
can positively affect productivity. The challenge is<br />
to estimate how large an effect <strong>the</strong>se inputs might<br />
have so an MFI can determine what investments in<br />
productivity enhancers are worthwhile.<br />
This direct effect between inputs and outputs is a<br />
straightforward way of conceptualizing productivity,<br />
but <strong>the</strong> discussion should not end <strong>the</strong>re. Perhaps<br />
as important is <strong>the</strong> indirect effect caused by an<br />
MFI’s operating conditions. This indirect effect is<br />
harder to quantify, but it has a tremendous influence.<br />
Three sets of conditions are worth mentioning<br />
because of <strong>the</strong>ir powerful effect on productivity.<br />
The first condition is <strong>the</strong> institutional culture. If<br />
employees enjoy <strong>the</strong>ir jobs, <strong>the</strong>ir productivity will<br />
improve. Some organizations forget, however, that<br />
many factors contribute to employee satisfaction<br />
besides one’s salary and benefit package. In fact,<br />
to achieve more with less, MFIs should concentrate<br />
on maximizing employees’ well being at a minimum<br />
cost. It costs almost nothing to give employees<br />
compliments, to acknowledge <strong>the</strong>ir extra efforts,<br />
and to solicit <strong>the</strong>ir suggestions for improvements.<br />
An institutional culture that encourages <strong>the</strong>se types<br />
of interactions—between management and staff,<br />
horizontally between employees and, perhaps most<br />
importantly, between frontline workers and <strong>the</strong><br />
customers—will experience a productivity boost.<br />
Second, <strong>the</strong> level of discipline within <strong>the</strong> organization<br />
dramatically affects productivity. Productive<br />
MFIs are like well-oiled machines. Meetings start<br />
and end on time, and everyone who is supposed to<br />
be in attendance is <strong>the</strong>re. Requests for loan<br />
approvals are accompanied by all <strong>the</strong> required<br />
paperwork. Data are entered into <strong>the</strong> information<br />
system accurately. Disbursements occur on time.<br />
Clients repay on time, and loan officers follow up<br />
immediately with anyone who is late. Only MFIs<br />
that aspire to this standard of zero defects can<br />
achieve high levels of productivity.<br />
The third condition is <strong>the</strong> product-client match.<br />
<strong>Microfinance</strong> institutions that do not provide clients<br />
with appropriate products often experience dropout<br />
rates that undermine productivity. The revolving<br />
door of customer desertion means that many MFIs<br />
are running hard and not getting anywhere. While a<br />
poor product-client match is not <strong>the</strong> sole cause of<br />
desertion, it is <strong>the</strong> desertion driver that MFIs can<br />
most easily influence.<br />
Efforts to reduce desertion by becoming clientfocused,<br />
however, place MFIs in <strong>the</strong> center of <strong>the</strong><br />
great productivity dilemma: <strong>the</strong>y want to provide<br />
flexible services that suit <strong>the</strong> demands of <strong>the</strong><br />
market, yet in doing so <strong>the</strong>y move away from <strong>the</strong><br />
cookie-cutter approach that positively contributed to<br />
productivity in <strong>the</strong> first place. To maximize productivity,<br />
<strong>the</strong> microfinance industry faces <strong>the</strong><br />
oxymoronic challenge of creating “flexible<br />
automation” or “standardized customization”. Some<br />
pundits think that <strong>the</strong> answer lies in <strong>the</strong> use of<br />
technology, such as smart cards, credit scoring and<br />
palm pilots; o<strong>the</strong>rs are skeptical about <strong>the</strong><br />
effectiveness of high-tech solutions in low-tech<br />
environments.<br />
Measuring Productivity<br />
The primary productivity measure used by <strong>the</strong><br />
Bulletin is <strong>the</strong> number of borrowers per staff member.<br />
Productivity is particularly difficult to benchmark<br />
between institutions. Even <strong>the</strong> Bulletin’s peer<br />
group analysis is not sufficiently specialized to<br />
account for <strong>the</strong> causes of vast productivity differences,<br />
such as:<br />
• Lending Methodology: Group-lending has a<br />
natural advantage in generating higher productivity<br />
than individual lending methodologies.<br />
• Menu of Services: The productivity ratio penalizes<br />
financial intermediaries because <strong>the</strong>y have<br />
many employees who are not involved in generating<br />
loans.<br />
• Credit Plus: MFIs that use credit delivery to provide<br />
o<strong>the</strong>r services, such as Credit with Education,<br />
are likely to have lower productivity than<br />
minimalist MFIs, all o<strong>the</strong>r things being equal.<br />
• Loan Term: The length of <strong>the</strong> term is an important<br />
factor because it is easier to manage large<br />
volumes of clients when <strong>the</strong>ir loans are not<br />
renewed frequently, assuming that portfolio<br />
quality is not adversely affected.<br />
MICROBANKING BULLETIN, APRIL 2001 1
• Loan Size: The size of <strong>the</strong> loan also affects productivity<br />
because MFIs need to be more careful<br />
when issuing larger loans.<br />
• Client Market: MFIs serving sparsely populated<br />
areas face a serious productivity challenge.<br />
• Labor Market: Better-educated employees tend<br />
to require less supervision, which raises productivity.<br />
If a tight labor market causes staff<br />
turnover, productivity could be undermined.<br />
• Growth Rate: An MFI in high growth mode will<br />
have lower productivity because new loan<br />
officers will have excess capacity and are<br />
naturally less productive than veterans. In<br />
addition, productivity will suffer if <strong>the</strong> client base<br />
consists of many new clients, since <strong>the</strong>y tend to<br />
require more work than repeat borrowers.<br />
• Age: Continuing with <strong>the</strong> same logic, mature<br />
MFIs tend to have higher rates of productivity<br />
than new organizations.<br />
Because of <strong>the</strong>se (and o<strong>the</strong>r) factors, a comparison<br />
of productivity between institutions needs to be<br />
considered very carefully. Perhaps <strong>the</strong> most useful<br />
productivity benchmark is how an organization compares<br />
to itself over time.<br />
Contents of this Issue<br />
Feature Articles<br />
Although <strong>the</strong>re are numerous strategies to improve<br />
productivity, <strong>the</strong> feature articles in this Bulletin hone<br />
in on two approaches: 1) giving financial incentives<br />
to encourage staff to work harder and smarter (or at<br />
least to reward those that do); and 2) understanding<br />
and reducing customer desertion.<br />
The first two articles provide insights into <strong>the</strong> effects<br />
of staff incentives on productivity; <strong>the</strong> next three<br />
show how productivity is adversely affected by<br />
client desertion. The unifying <strong>the</strong>me of <strong>the</strong>se articles<br />
is that clients and employees behave rationally.<br />
If <strong>the</strong> rewards are right, loan officers can do<br />
more with less. If loan products are well designed,<br />
customers will continue to patronize an MFI.<br />
In “Designing Financial Incentives”, Martin<br />
Holtzman provides a detailed example of an<br />
incentive scheme structure that <strong>the</strong> German<br />
consulting firm IPC has employed successfully in a<br />
variety of different settings. With this flexible model,<br />
MFIs tailor <strong>the</strong> scheme to individual loan officers<br />
and adjust weightings to address different scenarios<br />
at different points in time.<br />
Eduardo Bazoberry counters by describing<br />
PRODEM’s experience with staff incentives. He<br />
contends that individual financial incentives break<br />
down <strong>the</strong> sense of teamwork and commitment to an<br />
MFI’s social mission. He recommends <strong>the</strong> use of<br />
group-based incentives, such as profit sharing and<br />
employee ownership, as well as non-financial<br />
incentives, to motivate staff.<br />
While <strong>the</strong> two authors have different perspectives,<br />
<strong>the</strong>y agree on key aspects:<br />
• If not approached carefully, financial incentives<br />
can do more damage than good.<br />
• The objective of an incentive scheme is to align<br />
<strong>the</strong> goals of <strong>the</strong> employees with those of <strong>the</strong><br />
institution.<br />
• Financial incentives are one piece in a toolkit of<br />
motivational strategies that are needed to<br />
produce optimal levels of staff productivity.<br />
The second set of articles outlines <strong>the</strong> costs (and<br />
benefits) of customer desertion. Based on experience<br />
in Bangladesh, Graham Wright examines <strong>the</strong><br />
inappropriate match between <strong>the</strong> credit product and<br />
clients’ needs. He contends that MFIs in Bangladesh<br />
overemphasize <strong>the</strong> importance of standardized,<br />
low-cost loan products when <strong>the</strong>ir clients want<br />
a broader range of flexible services.<br />
The next two articles, by Kim Wilson and Inez<br />
Murray, highlight <strong>the</strong> value of learning from lost<br />
customers. Through exit interviews, affiliates of<br />
Catholic Relief Services (Bosnia-Herzegovina and<br />
Gaza) and Women’s World Banking (Uganda and<br />
Bangladesh), respectively, learned what was right<br />
and wrong with <strong>the</strong>ir services. Results from this research<br />
enabled <strong>the</strong> MFIs to modify <strong>the</strong>ir products<br />
and delivery mechanisms to become more clientfocused.<br />
These three articles call for a change in <strong>the</strong> service<br />
delivery culture of microfinance institutions. Many<br />
MFIs are supply-driven: <strong>the</strong>y have credit products<br />
that <strong>the</strong>y provide to clients. The authors argue that<br />
MFIs should: 1) embrace a customer service ethic;<br />
2) provide financial products that are demanddriven;<br />
and 3) conduct market research so that <strong>the</strong>y<br />
know what <strong>the</strong>ir customers really want.<br />
Performance Ratios<br />
The first step toward reducing client desertion is<br />
being able to measure it, but <strong>the</strong>re isn’t consensus<br />
on how to do that. Each of <strong>the</strong> articles on customer<br />
retention uses a different formula. In a new section<br />
of <strong>the</strong> Bulletin (“Talking about Performance<br />
Ratios”), Rich Rosenberg reviews five retention<br />
ratios and endorses what he calls <strong>the</strong> Waterfield/<br />
CGAP ratio. Since this is not <strong>the</strong> ratio used by <strong>the</strong><br />
Bulletin, or by any of <strong>the</strong> o<strong>the</strong>r organizations that<br />
defined desertion in this Issue, it is apparent that a<br />
broader discussion is needed on defining and<br />
measuring customer retention.<br />
2 MICROBANKING BULLETIN, APRIL 2001
Commentary and Reviews<br />
We have also added book reviews to <strong>the</strong> Bulletin’s<br />
repertoire. Robin Young (DAI) reviews <strong>the</strong> latest<br />
monograph from ACCION International, Maximizing<br />
Efficiency by Monica Brand and Julie Gerschick.<br />
Luis Schunk, from FEFAD in Albania, employs a<br />
practitioner’s perspective to review <strong>the</strong> Micro-<br />
Finance Network’s first Technical Note, Improving<br />
Internal Control by Anita Campion.<br />
Case Study<br />
As a change of pace, <strong>the</strong> case study section<br />
analyzes eight different programs in one<br />
country—Bosnia-Herzegovina. In keeping with this<br />
Issue’s <strong>the</strong>me, <strong>the</strong> case study by Isabelle Barrès<br />
focuses on <strong>the</strong> productivity and overall performance<br />
of <strong>the</strong>se MFIs operating in a challenging<br />
environment.<br />
Highlights and Tables<br />
The Highlights Section by Geetha Nagarajan uses<br />
<strong>the</strong> Bulletin’s database to analyze productivity<br />
drivers and trends. The results reveal very interesting<br />
differences by region, age of institution, loan<br />
size, and lending methodology that suggest <strong>the</strong><br />
need to think about productivity contextually. The<br />
analysis also examines changes in productivity over<br />
time, which shows that most MFIs are making<br />
steady improvements. Perhaps most importantly,<br />
<strong>the</strong> analysis reveals <strong>the</strong> limitations of productivity as<br />
a useful benchmarking indicator.<br />
In <strong>the</strong> ongoing effort to keep pace with a rapidly<br />
developing field, <strong>the</strong> Bulletin has added three new<br />
productivity ratios: loan officer productivity, staff<br />
allocation, and staff turnover. 1 Besides analyzing<br />
financial performance by peer group, <strong>the</strong> Bulletin<br />
also includes Additional Analysis Tables that slice<br />
up <strong>the</strong> data in different ways, including lending<br />
methodology, age, and institutional type.<br />
Bulletin Participants<br />
This Bulletin contains performance information from<br />
124 organizations that operate in 47 countries. This<br />
represents an increase of 10 MFIs from <strong>the</strong> last<br />
issue—including 8 from Africa. The regional breakdown<br />
looks like this:<br />
• 54 programs from 14 Latin American countries,<br />
(9 from Bolivia and 8 from Ecuador);<br />
• 26 African MFIs from 11 different countries (6<br />
programs in Uganda and 5 in Ghana);<br />
• 24 MFIs from 11 Asian countries (4 each from<br />
<strong>the</strong> Philippines and India);<br />
• 14 Eastern Europe programs from 6 countries<br />
(8 from Bosnia); and<br />
1<br />
For definitions of all ratios used in <strong>the</strong> Bulletin, see page 42.<br />
• 6 MFIs from 5 countries in <strong>the</strong> Middle East and<br />
North Africa (MENA).<br />
Participation in <strong>the</strong> Bulletin is a two-way street. We<br />
prepare a customized report for each MFI, which<br />
compares <strong>the</strong> institution’s performance with its peer<br />
group and with <strong>the</strong> average performance of all<br />
MFIs. This report is a valuable tool for board<br />
members and managing directors to benchmark<br />
<strong>the</strong>ir performance to similar organizations. MFIs<br />
have also found <strong>the</strong>se reports useful in discussions<br />
with regulators and as supplementary documentation<br />
for investors. For more information about<br />
submitting data to <strong>the</strong> Bulletin, contact<br />
mbb@<strong>microbanking</strong>-mbb.org.<br />
Peer Groups<br />
For <strong>the</strong> peer group comparison to be useful, <strong>the</strong><br />
groups have to be fairly homogeneous. With each<br />
Issue, as more MFIs send in <strong>the</strong>ir data, we have<br />
added new peer groups and reorganized o<strong>the</strong>rs.<br />
The three primary criteria we use to define peer<br />
groups are: 1) <strong>the</strong> size of <strong>the</strong> program, 2) its<br />
average loan balance, and 3) its region.<br />
To improve homogeneity, we tweaked <strong>the</strong> peer<br />
group criteria a bit. In Latin America, for example, a<br />
new group was created by applying country income<br />
level as a criterion. We made this adjustment<br />
based on <strong>the</strong> observation that <strong>the</strong> labor and<br />
customer markets are considerably different in<br />
Chile, Argentina and Brazil than in lower income<br />
countries, which creates unique challenges in<br />
operating microfinance institutions.<br />
A peer group that appeared in past issues<br />
(Worldwide High-end) has re-emerged as<br />
Worldwide Small Business. It contains six<br />
institutions with average loan balances that were<br />
too large to justify useful comparisons to MFIs in<br />
<strong>the</strong>ir region. Despite <strong>the</strong> fact that <strong>the</strong>y come from<br />
all corners of <strong>the</strong> globe, <strong>the</strong> data are cohesive.<br />
In Transition<br />
Finally, <strong>the</strong> Bulletin is in transition again.<br />
Calmeadow has been <strong>the</strong> home of <strong>the</strong> Bulletin for<br />
two plus years and <strong>the</strong> past three issues. Due to<br />
reorganization, however, Calmeadow is no longer in<br />
a position to play that role. Consequently, <strong>the</strong><br />
Bulletin is being set up as an independent project<br />
that will be associated with, but separate from,<br />
CGAP. This transition is expected to achieve <strong>the</strong><br />
best of both worlds: a close association with CGAP<br />
should continue to increase <strong>the</strong> outreach of <strong>the</strong><br />
Bulletin; yet a clear separation to maintain and<br />
respect <strong>the</strong> confidentiality of <strong>the</strong> database.<br />
Craig Churchill<br />
MICROBANKING BULLETIN, APRIL 2001 3
FEATURE ARTICLES<br />
FEATURE ARTICLES<br />
Designing Financial Incentives to Increase Loan Officer Productivity:<br />
Handle With Care!<br />
Martin Holtmann<br />
Efficiency in micro and small business lending is<br />
arguably <strong>the</strong> strongest single driver of financial<br />
performance. This realization has increasingly<br />
been reflected in <strong>the</strong> microfinance literature. In<br />
February 2000, <strong>the</strong> MicroBanking Bulletin dedicated<br />
a whole issue to <strong>the</strong> topic of efficiency. More<br />
significantly, during <strong>the</strong> last decade, a number of<br />
lending institutions have made significant<br />
improvements in <strong>the</strong>ir operating efficiency.<br />
One aspect of efficiency is <strong>the</strong> productivity of staff<br />
members. This article takes a closer look at <strong>the</strong><br />
contribution that one specific tool—loan officer<br />
incentive schemes—can make to improving productivity.<br />
It also makes some suggestions for <strong>the</strong><br />
design and implementation of such schemes. As a<br />
reflection of <strong>the</strong> author’s limited experience with<br />
group lending, all examples are from individual loan<br />
programs. However, most of <strong>the</strong> findings of this<br />
article should also be applicable to group lenders.<br />
Why Focus on Loan Officer Productivity?<br />
In microfinance institutions (MFIs), loan officers are<br />
responsible for creating and safeguarding <strong>the</strong><br />
quality of <strong>the</strong> assets (i.e. <strong>the</strong> size and arrears rate of<br />
<strong>the</strong> loan portfolio) as well as for generating <strong>the</strong><br />
income (i.e. interest payments from clients) for <strong>the</strong><br />
institution. In addition, since <strong>the</strong>y are <strong>the</strong> point of<br />
contact with clients, <strong>the</strong> work of <strong>the</strong> loan officers<br />
has an enormous impact on an institution’s<br />
outreach. In a nutshell, <strong>the</strong> loan officers are <strong>the</strong><br />
agents that produce an MFI’s output.<br />
On <strong>the</strong> input side of <strong>the</strong> equation, loan officers<br />
account for a significant share of staff costs, which<br />
in turn accounts for a significant portion (50 to 70<br />
percent) of administrative expenses. Clearly, a<br />
“production factor” that accounts for most of <strong>the</strong><br />
costs and generates almost all of <strong>the</strong> output and<br />
income should be given incentives to become as<br />
productive as possible! Financial incentives can<br />
enhance employee performance and productivity in<br />
microfinance just as in o<strong>the</strong>r industries.<br />
A second argument supporting <strong>the</strong> design and<br />
implementation of loan officer incentives is <strong>the</strong><br />
highly decentralized structure of <strong>the</strong> decision-making<br />
and credit delivery process. In a typical microcredit<br />
operation, loan officers possess vastly more<br />
detailed and accurate information about <strong>the</strong> local<br />
environment and <strong>the</strong> clients than do central<br />
management and <strong>the</strong> owners. In <strong>the</strong> presence of<br />
such “information asymmetries” and high monitoring<br />
costs, managers are well advised to align <strong>the</strong> goals<br />
of <strong>the</strong> institution (which usually include a mix of<br />
outreach and profitability indicators) with those of<br />
<strong>the</strong> agents who actually make <strong>the</strong> vast majority of<br />
operational decisions. Again, well-designed incentives<br />
can be useful in achieving this goal.<br />
Design and Typology of Incentive<br />
Schemes<br />
As <strong>the</strong> basis for designing an incentive scheme, <strong>the</strong><br />
loan officer’s duties must first be clearly defined,<br />
and <strong>the</strong>y should be derived from <strong>the</strong> goals of <strong>the</strong><br />
lending institution. This yields a range of targets<br />
that <strong>the</strong> loan officer is supposed to meet. Typical<br />
examples include:<br />
• Number and volume of loans issued;<br />
• Number and volume of outstanding loans;<br />
• Number of loans to first-time customers; 2<br />
• Quality of <strong>the</strong> loan officer’s portfolio (in <strong>the</strong> form<br />
of <strong>the</strong> lowest feasible portfolio-at-risk rate).<br />
Most incentive schemes consist of some or all of<br />
<strong>the</strong>se variables. The individual components must<br />
be weighted to ensure that <strong>the</strong> goals pursued by<br />
loan officers match <strong>the</strong> institution’s goals as closely<br />
as possible. It is impossible to achieve a perfect<br />
match between <strong>the</strong> goals of loan officers and those<br />
of <strong>the</strong> institution. Also, outreach, credit volume and<br />
portfolio quality cannot be maximized simultaneously.<br />
When putting toge<strong>the</strong>r an incentive package<br />
that gives due consideration to all three factors,<br />
achieving <strong>the</strong> optimal mix of <strong>the</strong> three variables<br />
inevitably involves certain trade-offs.<br />
2<br />
Since lending to existing customers involves less processing<br />
and analytical effort than lending to new customers, experienced<br />
loan officers with a large pool of customers tend to focus on <strong>the</strong>ir<br />
existing clients. This type of behavior is clearly contrary to <strong>the</strong><br />
MFI’s outreach objectives.<br />
MICROBANKING BULLETIN, APRIL 2001 5
FEATURE ARTICLES<br />
While loan officers make <strong>the</strong> most important<br />
contribution to reaching <strong>the</strong> output goals, numerous<br />
factors, such as external economic shocks or<br />
devaluation, are outside <strong>the</strong>ir control. Fur<strong>the</strong>rmore,<br />
loan officers’ basic living expenses must be covered<br />
to ensure that <strong>the</strong>y will be willing to take appropriate<br />
risks in <strong>the</strong> course of <strong>the</strong>ir work. Given <strong>the</strong>se two<br />
points, loan officers should not only receive a performance-related<br />
bonus, but should also receive a<br />
fixed basic salary.<br />
Regarding <strong>the</strong> weighting between bonuses and<br />
salary, <strong>the</strong> general rule is that a bonus of less than<br />
20 percent of total remuneration does not create<br />
significant stimulus to improve performance. Conversely,<br />
a bonus of more than 70 percent of <strong>the</strong><br />
remuneration package will attract loan officers who<br />
are active risk seekers. In practice, <strong>the</strong> share of <strong>the</strong><br />
performance-based bonus in overall compensation<br />
is best if it is between 30 and 50 percent.<br />
Financial incentive schemes vary in complexity.<br />
The simplest form is <strong>the</strong> piece rate system, in which<br />
<strong>the</strong> loan officer receives a set bonus per unit of<br />
output. Multiplying <strong>the</strong> figures for <strong>the</strong> indicators in<br />
question (e.g. number of loans issued to new customers)<br />
by <strong>the</strong> respective set amount yields <strong>the</strong><br />
total bonus. To discourage delinquency, a penalty<br />
can be deducted based on arrears.<br />
Complex bonus systems allow management to set<br />
targets for loan officers in regard to specific variables.<br />
Such systems have <strong>the</strong> advantage of being<br />
oriented to <strong>the</strong> performance values of <strong>the</strong> institution<br />
as a whole, and <strong>the</strong>refore also allow fine-tuning.<br />
Impact of Financial Incentive Systems:<br />
Some Empirical Evidence<br />
There is ample empirical evidence that <strong>the</strong> introduction<br />
of loan officer incentive schemes can make<br />
positive contributions to loan officer efficiency.<br />
Some of <strong>the</strong> most efficient Latin American MFIs<br />
with very high loan officer productivity use financial<br />
incentive systems, including WWB Cali, Financiera<br />
Calpiá, CMAC, Banco ADEMI, and Caja Los Andes.<br />
The BRI Unit Desa, a high productivity lender in<br />
Indonesia, uses a profit bonus system (based on<br />
unit performance) to motivate staff. In addition,<br />
<strong>the</strong>re is a semi-annual contest for cash prizes. 3<br />
3<br />
Based on MicroFinance Training Program, Boulder, Colorado,<br />
course notes by Richard M. Hook. Pure profit-sharing schemes<br />
at <strong>the</strong> loan officer level generally work well in small groups where<br />
potential free riders can be sanctioned. For larger groups, this<br />
self-regulating mechanism usually does not work and<br />
performance should be measured individually.<br />
Loan officer incentive schemes introduced in<br />
several “downscaling” programs in Eastern Europe<br />
typically gave a boost to loan officer productivity.<br />
The performance of loan officers of <strong>the</strong> Russian<br />
State Savings Bank is significantly better in<br />
branches that allowed <strong>the</strong> implementation of an<br />
incentive system than in branches that opted to<br />
maintain a fixed salary. In <strong>the</strong> Siberian city of<br />
Krasnoyarsk, for instance, introduction of incentives<br />
led to positive productivity differentials of more than<br />
30 percent. In Kazakstan, incentives have been<br />
introduced in all <strong>the</strong> banks participating in <strong>the</strong><br />
European Bank for Reconstruction and<br />
Development’s small business program, and <strong>the</strong><br />
effect on productivity has been quite strong.<br />
When a re-designed financial incentive system was<br />
introduced at FEFAD Bank in Albania in early 2000,<br />
loan officer productivity (measured in average<br />
number of loans disbursed per loan officer per<br />
month) increased by more than 100 percent within<br />
a period of five months, while <strong>the</strong> portfolio-at-risk<br />
ratio remained at <strong>the</strong> same low level as before.<br />
Words of Caution…<br />
Design and implementation of loan officer bonus<br />
systems requires careful planning. One obvious<br />
challenge is to ensure that <strong>the</strong> incentives are<br />
properly aligned with <strong>the</strong> goals of <strong>the</strong> organization.<br />
Misalignments can be avoided by testing <strong>the</strong><br />
system during <strong>the</strong> design phase, both through<br />
spreadsheet calculations and a limited field test<br />
(e.g. in a branch office). 4<br />
The effectiveness of incentive systems depends on<br />
<strong>the</strong> cultural environment in which a microfinance<br />
institution operates. Timing is ano<strong>the</strong>r critical issue.<br />
It is useful to phase in an incentive system<br />
gradually. During <strong>the</strong>ir training period, loan officers<br />
need to make mistakes in order to learn from <strong>the</strong>m,<br />
thus <strong>the</strong>y should not be penalized. Later on, as <strong>the</strong><br />
whole organization moves up <strong>the</strong> learning curve<br />
(i.e. average loan officer productivity increases) <strong>the</strong><br />
bonus system can be adjusted. The introduction of<br />
new products also requires changes to <strong>the</strong> bonus<br />
system. Implementation of a bonus system at <strong>the</strong><br />
loan officer level usually generates <strong>the</strong> need for<br />
incentive systems at o<strong>the</strong>r layers of <strong>the</strong> organization<br />
(department heads, branch managers, etc.).<br />
The empirical observation that many systems have<br />
produced completely unwanted effects leads to <strong>the</strong><br />
conclusion that it is better not to have an incentive<br />
system than to have one that is badly designed.<br />
4<br />
Spreadsheet calculations also help to calibrate <strong>the</strong> impact that<br />
<strong>the</strong> system will have on <strong>the</strong> cost of lending operations and to<br />
forecast <strong>the</strong> break-even point.<br />
6 MICROBANKING BULLETIN, APRIL 2001
FEATURE ARTICLES<br />
A bonus system for loan officers is only one part of<br />
a productivity-focused lending technology. O<strong>the</strong>r,<br />
and probably more important elements are: standardization<br />
of products and procedures, decentralization,<br />
effective screening of clients to reduce<br />
dropout rates during analysis, and effective use of<br />
MIS. Fur<strong>the</strong>rmore, compiling and posting individual<br />
and branch rankings creates a healthy spirit of<br />
competition—with <strong>the</strong> corresponding positive<br />
impact on productivity.<br />
What conclusions may be drawn? There is strong<br />
evidence that a well-designed monetary incentive<br />
system for lending staff does indeed boost<br />
productivity. But a bonus system is only part of an<br />
overall culture of high productivity. Every bonus<br />
system must be carefully designed and adapted to<br />
<strong>the</strong> local situation. 5 Do not ever copy someone<br />
else’s system: ra<strong>the</strong>r, design your own! For a<br />
system to be successful, it:<br />
• Needs to incorporate <strong>the</strong> basic goals embedded<br />
in <strong>the</strong> lending policies of <strong>the</strong> organization;<br />
• Must be fair. Loan officers should feel that<br />
better performance is adequately rewarded and<br />
that on average <strong>the</strong> goals set by <strong>the</strong> system are<br />
achievable. This requires continual efforts by<br />
managers to communicate fairness as an overall<br />
objective of <strong>the</strong> system, so that <strong>the</strong> inevitable<br />
adjustments in <strong>the</strong> bonus formula are accepted<br />
by loan officers as routine and not interpreted<br />
as breaches of trust or as a lack of appreciation<br />
for <strong>the</strong>ir efforts;<br />
• Should be transparent so that loan officers can<br />
adjust <strong>the</strong>ir actions according to a few simple<br />
parameters. 6<br />
No incentive system is perfect. All bonus systems<br />
must be regularly reviewed and adapted to <strong>the</strong><br />
changing business environment as well as to <strong>the</strong><br />
maturing of <strong>the</strong> lending organization. Finally, any<br />
system that helps to improve <strong>the</strong> performance and<br />
loyalty of staff deserves to be implemented.<br />
An Illustrative Incentive Scheme<br />
This section describes how an incentive scheme<br />
might work. In this example, loan officers can earn<br />
a monthly bonus up to a maximum of US$400. 7<br />
5<br />
Among <strong>the</strong> many technical issues to be decided are: phasing in<br />
bonuses, <strong>the</strong>ir total value, and frequency of bonus pay. For an<br />
overview of some of <strong>the</strong>se considerations see Christen, Robert<br />
P. (1997). Banking Services for <strong>the</strong> Poor: Managing for Financial<br />
Success. Washington DC: ACCION, pp. 182-189.<br />
6<br />
Transparency can be a problem when implementing bonus<br />
systems in “downscaling” environments in which department<br />
heads and branch managers would prefer to provide bonuses<br />
based on <strong>the</strong>ir own subjective assessments of loan officers’<br />
performance.<br />
The bonus is derived as:<br />
Bonus = L + P + A<br />
Where:<br />
L = Lending (number of loans disbursed to new<br />
and repeat clients)<br />
P = Portfolio (number of loans and balance<br />
outstanding)<br />
A = Arrears (number of loans and portfolio at<br />
risk) 8<br />
The variables create a ratio of actual results to<br />
intended targets. Additionally, weights are applied<br />
to <strong>the</strong> variables, and for each part of <strong>the</strong> formula a<br />
bonus factor is given. The sum of <strong>the</strong> bonus factors<br />
is <strong>the</strong> amount <strong>the</strong> MFI is prepared to pay as a<br />
bonus. It is possible to convert <strong>the</strong> bonus into any<br />
currency. In this illustration, summarized in Figure<br />
1, <strong>the</strong> bonus factor is denominated in US$.<br />
The overall variables are weighted (w 1 through w 7 )<br />
according to <strong>the</strong> importance of <strong>the</strong> respective<br />
performance indicator to <strong>the</strong> MFI and can be<br />
defined for each loan officer individually, depending<br />
on what <strong>the</strong> institution expects <strong>the</strong> loan officer to<br />
achieve during a certain period (usually one month).<br />
Each component of <strong>the</strong> formula is explained below.<br />
Number of Loans Disbursed (L)<br />
L is <strong>the</strong> number of loans disbursed to new and<br />
repeat clients by <strong>the</strong> following formula:<br />
Where:<br />
L = [(n / g) * w 1 + (o / h) * w 2 ] * w 8<br />
n = The number of loans issued to new clients<br />
g = The loan officer’s target for loans issued to<br />
new clients<br />
o = The number of loans issued to repeat<br />
clients<br />
h = The loan officer’s target for loans issued to<br />
repeat clients<br />
w 1 , w 2 = The weight given to <strong>the</strong> number of<br />
loans issued to new and repeat clients,<br />
respectively. w 1 plus w 2 must equal 1. If more<br />
importance is attributed to <strong>the</strong> number of loans<br />
issued to new clients, w 1 should be given a<br />
weight greater than 0.5<br />
7<br />
In <strong>the</strong>ory, a loan officer could earn a bonus higher than US$400<br />
by overshooting <strong>the</strong> targets. Never<strong>the</strong>less, <strong>the</strong>se are set so that<br />
it is hard to surpass <strong>the</strong>m, and readjusted as average<br />
performance improves.<br />
8<br />
In <strong>the</strong> following paragraphs, <strong>the</strong> term “arrears” denotes<br />
“portfolio-at-risk” starting from <strong>the</strong> first day of irregular payment.<br />
MICROBANKING BULLETIN, APRIL 2001 7
FEATURE ARTICLES<br />
Figure 1: An Illustrative Incentive System<br />
Vari<br />
able<br />
g<br />
n<br />
h<br />
o<br />
b<br />
d<br />
a<br />
c<br />
#la<br />
$la<br />
Comments Value Factor Description Value<br />
(L) Target number of disbursed<br />
loans to new customers<br />
(L) Actual number of disbursed<br />
loans to new customers<br />
(L) Target number of disbursed<br />
loans to repeat customers<br />
(L) Actual number of disbursed<br />
loans to repeat customers<br />
(P) Target number of<br />
outstanding loans<br />
(P) Loan officer’s actual number of<br />
outstanding loans<br />
(P) Target outstanding portfolio<br />
(US$)<br />
(P) Loan officer’s actual<br />
outstanding portfolio (US$)<br />
(A) Actual number of loans in<br />
arrears<br />
(A) Actual volume of loan officer’s<br />
loans in arrears (US$)<br />
10 w 1 Weight factor for L<br />
number (new)<br />
0.7 < 1<br />
6 w 2 Weight factor for L<br />
0.3 < 1<br />
number (rep.)<br />
10 w 3 Weight factor for P-<br />
0.3 < 1<br />
volume<br />
4 w 4 Weight factor for P-<br />
0.7 < 1<br />
number<br />
120 w 5 Weight factor for A #la 0.5 < 1<br />
80 w 6 Weight factor for A $la 0.5 < 1<br />
600,000 w 7 Weight factor arrears 5<br />
450,000 w 8 Bonus level factor L ($) 100<br />
3 w 9 Bonus level factor P ($) 100<br />
25,000 w 10 Bonus level factor A ($) 200<br />
w 8 = The bonus level factor for L<br />
In this sample calculation, <strong>the</strong> total monthly bonus<br />
is US$400, which is divided up as follows: w 8 = 100,<br />
w 9 = 100 and w 10 = 200. However, if for a certain<br />
period greater importance is attached to, say, new<br />
loan output ra<strong>the</strong>r than to low arrears, <strong>the</strong>n w 8<br />
should represent a larger portion of <strong>the</strong> bonus than<br />
w 9 or w 10 .<br />
In this example, <strong>the</strong> loan officer issued 6 loans to<br />
new clients, while <strong>the</strong> target was 10, and issued 4<br />
loans to repeat clients, while <strong>the</strong> target was 10. As<br />
a result, <strong>the</strong> bonus amount for L is US$54.<br />
L = [(n / g) * w 1 + (o / h) * w 2 ] * w 8<br />
L = [(6/10) * 0.7 + (4/10) * 0.3] * $100 = $54<br />
Portfolio Outstanding (P)<br />
P stands for portfolio and measures <strong>the</strong> extent to<br />
which a loan officer has met targets for <strong>the</strong> volume<br />
and number of loans outstanding.<br />
P = [(c / a) * w 3 + (d / b) * w 4 ] * w 9<br />
The individual variables are:<br />
c = Loan officer’s total outstanding portfolio<br />
a = Loan officer’s target for outstanding portfolio<br />
d = The actual number of outstanding loans<br />
b = The loan officer’s target for <strong>the</strong> number of<br />
outstanding loans<br />
w 3 , w 4 = The weights represent <strong>the</strong> importance<br />
given to <strong>the</strong> volume and number of loans outstanding,<br />
respectively. w 3 plus w 4 must equal<br />
1. If more importance is attributed to <strong>the</strong> number<br />
of loans than <strong>the</strong>ir size, <strong>the</strong>n w 4 should be<br />
given a weight greater than 0.5<br />
w 9 = The bonus level factor for P<br />
This formula assesses <strong>the</strong> extent to which <strong>the</strong> loan<br />
officer has achieved <strong>the</strong> established targets for<br />
portfolio outstanding. In <strong>the</strong> sample calculation, <strong>the</strong><br />
loan officer had an outstanding portfolio of<br />
US$450,000, or 75 percent of <strong>the</strong> US$600,000<br />
target. In this example, more significance was<br />
given to <strong>the</strong> number of loans issued than <strong>the</strong>ir<br />
value, since w 4 was set at 0.7. It was probably felt<br />
that <strong>the</strong> loan officer needed to increase <strong>the</strong> number<br />
of loans (for instance to increase outreach or<br />
diversification), and hence greater weight was<br />
attached to this target. The target was 120 loans<br />
outstanding, whereas <strong>the</strong> loan officer achieved 80,<br />
or 67 percent of <strong>the</strong> target.<br />
In our example <strong>the</strong> bonus for component P is<br />
calculated as follows:<br />
P = [(c / a) * w 3 + (d / b) * w 4 ] * w 9<br />
P = [($450,000/$600,000)*0.3 + (80/120) * 0.7] *<br />
$100 = $69.17<br />
Arrears (A)<br />
A or arrears is determined by <strong>the</strong> following formula:<br />
A = (w 7 – [(#la / d) * w 5 + ($la / c) * w 6 ]* 100)<br />
(w 7 * w 10 )<br />
8 MICROBANKING BULLETIN, APRIL 2001
FEATURE ARTICLES<br />
This formula calculates <strong>the</strong> degree (by volume and<br />
number) to which a loan officer’s portfolio is delinquent,<br />
and how much <strong>the</strong> bonus is reduced as a<br />
consequence. The components of this formula are:<br />
w 7 = The weight factor for arrears. This can be<br />
any number greater than or equal to 1. The<br />
lower <strong>the</strong> weight factor, <strong>the</strong> greater <strong>the</strong> negative<br />
effect on <strong>the</strong> loan officer’s potential bonus. The<br />
use of this weight factor and <strong>the</strong> fact that <strong>the</strong><br />
arrears component is not deducted from <strong>the</strong><br />
total bonus allows <strong>the</strong> institution to fine-tune <strong>the</strong><br />
impact of arrears targets by changing <strong>the</strong> factors<br />
every month. Thus, a loan officer may get<br />
a substantial bonus for bringing down <strong>the</strong><br />
arrears rate.<br />
#la = The total number of loans in arrears in <strong>the</strong><br />
loan officer’s portfolio<br />
d = Total number of outstanding loans in <strong>the</strong><br />
loan officer’s portfolio (same variable used in<br />
component “P”)<br />
$la = The total balance of outstanding loans in<br />
arrears<br />
c = Loan officer’s total outstanding portfolio<br />
(same variable used in component “P”)<br />
w 5 , w 6 = The weight given to <strong>the</strong> number and<br />
volume of loans in arrears, respectively. w 5<br />
plus w 6 must equal 1. If more importance is<br />
attributed to <strong>the</strong> number of loans in arrears, w 5<br />
should be given a weight greater than 0.5.<br />
w 10 = The bonus level factor for <strong>the</strong> arrears<br />
portion of <strong>the</strong> formula. In our example <strong>the</strong><br />
arrears bonus level accounts for half of <strong>the</strong> total<br />
bonus, i.e. US$200 out of US$400. The loan<br />
officer had 3 arrears cases representing a total<br />
of US$25,000 at <strong>the</strong> end of <strong>the</strong> month.<br />
The bonus for part A is calculated as follows:<br />
A = (w 7 - (#la / d * w 5 + $la / c * w 6 * 100)) / w 7 * w 10<br />
A = (5 – ((3 / 80 * 0.5) + ($25,000 / $450,000 * 0.5)<br />
* 100)) / 5 * 200 = $88.14<br />
Looking at this result and remembering <strong>the</strong> large<br />
portion of <strong>the</strong> maximum total bonus (50 percent)<br />
that <strong>the</strong> loan officer could have earned on arrears, it<br />
appears that <strong>the</strong> loan officer failed to make<br />
significant progress on this objective during <strong>the</strong> past<br />
month.<br />
Totals<br />
Now that all three components of <strong>the</strong> formula have<br />
been calculated, combine <strong>the</strong>m according to <strong>the</strong><br />
formula: Bonus = L + P + A. Thus, <strong>the</strong> resulting<br />
bonus is:<br />
$54 + $69.17 + $88.14 = $211.31<br />
The formula outlined above is only one variant of<br />
<strong>the</strong> multitude of bonus systems employed by MFIs.<br />
Attentive readers will have realized that <strong>the</strong><br />
example and its target values do not originate from<br />
a high-productivity setting, such as urban Latin<br />
America or Indonesia. Indeed, this fictitious case is<br />
typical for Sou<strong>the</strong>astern Europe and <strong>the</strong> Middle<br />
East, which are characterized by low productivity,<br />
high loan officer salaries, and high average loan<br />
sizes. But with a little imagination it is very easy to<br />
adapt <strong>the</strong> formula to o<strong>the</strong>r contexts.<br />
Evaluation<br />
The example above has a couple of shortcomings<br />
and some important strengths. Beginning with <strong>the</strong><br />
advantages, <strong>the</strong> model contains many of <strong>the</strong> basic<br />
variables that make up <strong>the</strong> target functions of<br />
microlending organizations. Secondly, <strong>the</strong> formula<br />
is flexible in that <strong>the</strong> parameters can be adjusted to<br />
reflect different environments as well as different<br />
organizational values (e.g. <strong>the</strong> outreach objective<br />
can be streng<strong>the</strong>ned by assigning a higher weight<br />
factor to <strong>the</strong> number of loans disbursed and outstanding<br />
as opposed to <strong>the</strong> volumes). Thirdly, <strong>the</strong><br />
system is not overly complex and can be understood<br />
both by a keenly analytical loan officer and<br />
reader of this Bulletin. The computation of<br />
individual bonuses is simple enough and does not<br />
require more than a spreadsheet. Fourthly, <strong>the</strong><br />
bonus formula is “linear” ra<strong>the</strong>r than “staged”.<br />
Staged bonus systems often produce unwanted<br />
incentives. 9<br />
One of <strong>the</strong> model’s strengths is also a weakness:<br />
Microlenders have to act in a complex environment,<br />
and this simple formula may not reflect this complexity<br />
of tasks. It is possible to incorporate a<br />
higher degree of complexity in <strong>the</strong> formula by adding<br />
more variables, but this comes at <strong>the</strong> cost of<br />
making <strong>the</strong> system less transparent. Loan officers<br />
will find it harder to keep track of <strong>the</strong> trade-offs and<br />
goal conflicts contained in <strong>the</strong> formula and to adjust<br />
<strong>the</strong>ir actions accordingly. 10<br />
The balancing of trade-offs is particularly relevant in<br />
<strong>the</strong> treatment of delinquency. The age of arrears<br />
has a strong bearing on <strong>the</strong> chances of recovery,<br />
which suggests that shorter-term arrears should be<br />
9<br />
Loan officers will typically adjust <strong>the</strong>ir performance in <strong>the</strong>se<br />
circumstances so that <strong>the</strong>y remain close to a particular “stage” or<br />
limit, e.g. a cap on <strong>the</strong> permitted arrears level. Linear systems,<br />
in contrast, provide a reward or penalty for any change in <strong>the</strong><br />
output or quality variables.<br />
10<br />
Remember that this concern was one of <strong>the</strong> reasons for<br />
introducing an incentive system in <strong>the</strong> first place. An effective<br />
bonus system will induce loan officers to act in <strong>the</strong> interests of<br />
<strong>the</strong> organization without additional supervision. If <strong>the</strong> system<br />
becomes too complex, this cannot be achieved.<br />
MICROBANKING BULLETIN, APRIL 2001 9
FEATURE ARTICLES<br />
subject to a higher penalty. However, it is also<br />
counterproductive to penalize short-term arrears<br />
(say, from 1 to 7 days) too heavily since it would<br />
make loan officers overly risk-averse and have a<br />
negative impact on productivity. This train of<br />
thought could be factored into <strong>the</strong> model by<br />
assigning different weights to different age<br />
categories. Fur<strong>the</strong>rmore, if delinquency is a serious<br />
concern for an institution with a large portfolio, <strong>the</strong><br />
formula could be improved by changing <strong>the</strong> purely<br />
additive calculation contained in <strong>the</strong> example above<br />
into a multiplicative link between outstanding<br />
portfolio and arrears level, <strong>the</strong>reby making it much<br />
more sensitive to portfolio quality.<br />
At a more general level, some readers may take<br />
exception to this article’s focus on financial<br />
incentives. Indeed, <strong>the</strong>re are many o<strong>the</strong>r and<br />
potentially more powerful factors that influence an<br />
individual’s job performance. Opportunities to receive<br />
fur<strong>the</strong>r training, opportunities for advancement,<br />
social status, <strong>the</strong> sense of a common<br />
“mission”, and last but not least, <strong>the</strong> feeling of<br />
contributing to local or national economic and social<br />
development are important components of loan<br />
officers’ job satisfaction and performance.<br />
Never<strong>the</strong>less, loan officers are normally “rational” in<br />
<strong>the</strong> economic sense, and few would deny that<br />
financial incentives do provide an important stimulus.<br />
It is important to inform potential loan officer<br />
candidates during <strong>the</strong> selection phase if a<br />
performance-based incentive system is in place or<br />
is to be introduced. This will help to screen candidates<br />
by ensuring that <strong>the</strong>y are willing to align <strong>the</strong>ir<br />
compensation expectations with <strong>the</strong> MFI’s goals,<br />
and to be compensated on <strong>the</strong> basis of <strong>the</strong>ir<br />
contribution to those goals; it will also help to sort<br />
out risk-averse individuals. Despite <strong>the</strong> general<br />
limitations of monetary incentive systems, <strong>the</strong>y<br />
have proven <strong>the</strong>ir value in practice.<br />
Martin Holtmann is a Managing Director of IPC. He is<br />
based in Moscow, working with several commercial<br />
banks in <strong>the</strong> EBRD Russia Small Business Fund project.<br />
The author would like to thank Andreas Francke, Bryan<br />
Nielsen, and Ralf Niepel, all from IPC, for many useful<br />
inputs and suggestions.<br />
10 MICROBANKING BULLETIN, APRIL 2001
FEATURE ARTICLES<br />
We Aren’t Selling Vacuum Cleaners:<br />
PRODEM’s Experiences with Staff Incentives<br />
Eduardo Bazoberry<br />
Many people in microfinance are familiar with<br />
PRODEM because it was <strong>the</strong> NGO that created<br />
BancoSol, <strong>the</strong> first commercial bank solely dedicated<br />
to microfinance. Since BancoSol’s creation in<br />
1992, PRODEM has quietly gone about doing<br />
something more challenging than establishing a<br />
bank. First, we developed loan products for microentrepreneurs<br />
and small-scale farmers in sparsely<br />
populated rural Bolivia. Then we expanded our<br />
organization while continuously improving our loan<br />
products in response to our customers’ needs. We<br />
survived a weak economy and a crisis in <strong>the</strong><br />
microfinance industry in 1999, and <strong>the</strong>n we created<br />
ano<strong>the</strong>r regulated financial institution known as an<br />
FFP, a private financial fund. PRODEM FFP,<br />
launched in January 2000, now offers a wide range<br />
of financial products including savings accounts,<br />
wire transfers, and leasing.<br />
Many MFIs look to financial incentives as a means<br />
to boost staff productivity. At PRODEM, we pride<br />
ourselves in having created an efficient, productive<br />
and profitable organization with an approach toward<br />
staff incentives that defies <strong>the</strong> conventional wisdom<br />
in <strong>the</strong> microfinance industry.<br />
The Evolution of Incentives at PRODEM<br />
A few months ago, I received an email from a friend<br />
telling me that my institution was one of <strong>the</strong> only<br />
major MFIs in <strong>the</strong> world that did not provide<br />
incentives to loan officers. Hearing this, I explained<br />
that PRODEM did have incentives and that, given<br />
<strong>the</strong> nature of our business, incentives were<br />
necessary, but we implemented our scheme in a<br />
way that did not affect <strong>the</strong> objectivity of <strong>the</strong> loan<br />
officers when <strong>the</strong>y made credit decisions. I proceeded<br />
to explain about <strong>the</strong> circumstances that led<br />
PRODEM to look for better ways to motivate <strong>the</strong><br />
performance and responsibilities of our employees.<br />
During 1993, after looking at <strong>the</strong> different incentives<br />
that MFIs were offering worldwide, we implemented<br />
an incentive system that rewarded loan officers for<br />
accomplishing goals set in <strong>the</strong> incentive program.<br />
These goals included: <strong>the</strong> targeted number of<br />
clients, <strong>the</strong> maximum percentage of loans in<br />
arrears, and <strong>the</strong> average portfolio per loan officer.<br />
In addition, since PRODEM had different types of<br />
branches, we had defined <strong>the</strong> goals in relation to<br />
<strong>the</strong> potential market and <strong>the</strong> location of <strong>the</strong> offices:<br />
in rural areas, at <strong>the</strong> country’s borders, in major<br />
cities, or in secondary cities.<br />
Rosy Preliminary Results<br />
The incentive program worked as we had hoped.<br />
The loan portfolio grew rapidly, <strong>the</strong> portfolio at risk<br />
was under control, <strong>the</strong> number of clients increased<br />
steadily, and profitability improved (see Figure 1 at<br />
<strong>the</strong> end of <strong>the</strong> article). All of our indicators in 1994-<br />
95 suggested that we made a wise decision in<br />
implementing <strong>the</strong> incentive program.<br />
Things Start to Get Sour<br />
By 1996, we sensed something disruptive occurring.<br />
We began to notice a high rate of turnover<br />
among our loan officers, including an increase in<br />
<strong>the</strong> number of staff fired because of corruption or<br />
for constantly breaking <strong>the</strong> methodology and rules<br />
of <strong>the</strong> institution. Obviously, we had not managed<br />
to gain <strong>the</strong> loyalty of <strong>the</strong>se loan officers. Instead,<br />
we had staff members who were mechanically<br />
performing <strong>the</strong>ir functions without a real<br />
responsibility towards <strong>the</strong> institution or our clients.<br />
At <strong>the</strong> same time, some staff members began<br />
demanding larger incentives amounts. They were<br />
under <strong>the</strong> false impression that PRODEM’s good<br />
performance was due solely to <strong>the</strong>ir efforts, without<br />
realizing that everyone was part of one system of<br />
integrated departments, and that o<strong>the</strong>r aspects of<br />
<strong>the</strong> organization were also important for PRODEM’s<br />
performance.<br />
The original scheme awarded a monthly bonus to<br />
individuals who met certain performance standards.<br />
We learned, however, that this type of incentive had<br />
a negative effect on team performance and<br />
encouraged a short-term outlook.<br />
As a result, in 1996, PRODEM changed <strong>the</strong><br />
incentive to an annual bonus awarded for branch<br />
performance. All members of a branch received a<br />
bonus if <strong>the</strong>ir branch met certain performance<br />
targets. The largest bonus was worth an additional<br />
month’s salary.<br />
An annual payment encouraged a long-term<br />
perspective. It corrected <strong>the</strong> “delinquency lag,”<br />
caused by new loans that go into arrears several<br />
months after <strong>the</strong>y were issued. An annual payment<br />
also adjusted for <strong>the</strong> profound seasonal fluctuations<br />
that are common in Bolivian microfinance and it<br />
allowed PRODEM to complete our audit before<br />
issuing bonuses.<br />
MICROBANKING BULLETIN, APRIL 2001 11
FEATURE ARTICLES<br />
This modification was generally successful in motivating<br />
staff and creating teamwork within a branch,<br />
but it still had negative side effects. It discouraged<br />
staff rotation and cooperation between branches. If<br />
employees agreed to transfer to a branch with<br />
problems, <strong>the</strong>y reduced <strong>the</strong>ir chances of obtaining a<br />
bonus. Because some markets were riskier than<br />
o<strong>the</strong>rs, some staff concluded that <strong>the</strong> bonus<br />
involved an element of luck depending on where<br />
one worked. This conclusion generated tension<br />
between those who were perceived to have received<br />
a bonus because <strong>the</strong>y worked in a good<br />
environment and those who failed to earn a bonus<br />
even though <strong>the</strong>y worked extremely hard. In such<br />
cases, <strong>the</strong> incentive system discouraged ra<strong>the</strong>r<br />
than encouraged staff.<br />
Continuous Improvement<br />
This experience motivated management and partners<br />
of PRODEM FFP to pursue a fair incentive<br />
system where clients, investors, and employees<br />
were all winners. We decided to eliminate <strong>the</strong><br />
branch bonus program and instead reward <strong>the</strong><br />
performance of <strong>the</strong> whole institution on an annual<br />
basis. The collective approach reiterates that we<br />
are all in this toge<strong>the</strong>r.<br />
We also designed a pension fund that allows<br />
employees to receive a certain number of PRODEM<br />
FFP shares as part of <strong>the</strong>ir annual benefit package.<br />
Employees will have access to a percentage of<br />
<strong>the</strong>se funds if <strong>the</strong>y leave PRODEM after three or<br />
more years of employment. By offering employees<br />
direct ownership, we can optimize <strong>the</strong>ir long-term<br />
commitment towards PRODEM’s growth and<br />
development, which fulfils <strong>the</strong> number one rule in<br />
designing incentive schemes: aligning <strong>the</strong> risks and<br />
rewards for employees with those of <strong>the</strong> institution.<br />
Non-financial Incentives<br />
This entire discussion about financial incentives,<br />
however, detracts from <strong>the</strong> invaluable non-financial<br />
methods that PRODEM uses to motivate staff to<br />
achieve high levels of performance. The most<br />
important method is <strong>the</strong> institution’s mission. We<br />
hire people who are committed to making a<br />
difference in rural Bolivia by working with lowincome<br />
families and microenterprises. We use our<br />
mission as a motivating tool. Managers regularly<br />
remind <strong>the</strong>ir employees about PRODEM’s critical<br />
contribution to <strong>the</strong> economies of remote communities,<br />
and how integral each staff member’s performance<br />
is to <strong>the</strong> institution’s accomplishments.<br />
PRODEM’s culture directly contributes to <strong>the</strong><br />
performance of all employees. Through <strong>the</strong> orientation<br />
of new staff members, regular training<br />
opportunities and o<strong>the</strong>r communication channels,<br />
PRODEM inculcates employees into a culture of<br />
commitment, trust and excellence that is more<br />
powerful than financial incentives. Granted, an<br />
institution’s culture does not put food on <strong>the</strong><br />
table—that is why it is important to compensate all<br />
employees fairly. But financial incentives cannot<br />
effectively encourage employees to be innovative,<br />
to embrace change, to constantly seek ways of<br />
doing things better, and to not be afraid to learn<br />
from <strong>the</strong>ir mistakes. Only <strong>the</strong> institution’s culture<br />
can accomplish <strong>the</strong>se objectives, which contribute<br />
vitally toward improvements in productivity and<br />
efficiency that must occur for an MFI to remain<br />
competitive and profitable.<br />
To streng<strong>the</strong>n our hand in a competitive market,<br />
PRODEM FFP has developed a complex and<br />
creative matrix of incentives to help employees fulfill<br />
a variety of personal needs ranging from shelter<br />
and security to acceptance and self-fulfillment. The<br />
matrix includes financial as well as non-financial<br />
incentives, such as staff development, job enrichment<br />
and promotional opportunities, extensive<br />
health benefits, achievement awards, and <strong>the</strong><br />
opportunity to take a sabbatical after ten years of<br />
service.<br />
Contrasting Approach<br />
PRODEM’s holistic approach to motivating and<br />
rewarding staff stands in sharp contrast with some<br />
of <strong>the</strong> Bolivian consumer credit providers that have<br />
adversely affected <strong>the</strong> local microfinance industry.<br />
Their primary growth strategy was to give monetary<br />
incentives to loan officers through a variety of<br />
means, including some that are deceitful.<br />
A Bolivian consumer credit company, for example,<br />
was paying (and declaring) <strong>the</strong> equivalent of US$50<br />
per month as average staff salary, while its loan<br />
officers where earning closer to US$900 per month<br />
(including bonuses), or triple <strong>the</strong> industry average.<br />
This slight-of-hand using a dual accounting practice<br />
enabled <strong>the</strong> institution to minimize contributions to<br />
<strong>the</strong> pension fund by paying in 12.5 percent of <strong>the</strong><br />
official salary (of US$50) ra<strong>the</strong>r than <strong>the</strong> actual<br />
amount earned by staff. This arrangement also<br />
reduced <strong>the</strong> costs of firing staff since <strong>the</strong> mandatory<br />
six-month severance package would be based on<br />
<strong>the</strong> official salary.<br />
After two years of bonanza, <strong>the</strong> partners of <strong>the</strong><br />
institution were counting <strong>the</strong>ir losses, partly due to<br />
<strong>the</strong> incentive structure and <strong>the</strong> culture that stemmed<br />
from it. The owners had put a few million dollars of<br />
<strong>the</strong>ir money on <strong>the</strong> line, yet <strong>the</strong>ir biggest accomplishment<br />
was successfully overindebting tens of<br />
thousands of low-income families.<br />
<strong>Microfinance</strong> institutions that promote aggressive<br />
incentive programs, such as this, are going down<br />
12 MICROBANKING BULLETIN, APRIL 2001
FEATURE ARTICLES<br />
<strong>the</strong> wrong path. By relying solely on high return<br />
incentives coupled with low base salaries to<br />
motivate staff, MFIs create an opaque environment<br />
that encourages deception. These organizations<br />
are likely to experience (and are experiencing) <strong>the</strong><br />
following unauthorized activities:<br />
• Frequent rescheduling of loans without much<br />
control<br />
• Loan officers forming ROSCAs to pay for<br />
clients’ arrears, which allows employees to<br />
maintain or increase <strong>the</strong>ir incentive levels<br />
despite worsening portfolio quality<br />
• Creation of “ghost” loans to hide <strong>the</strong> fact that<br />
goals are not met<br />
• Deduction of an arbitrary amount from <strong>the</strong><br />
clients’ loans during disbursement to create a<br />
fund to cover bad loans<br />
• Pressure on loan officers to repay clients’<br />
arrears from <strong>the</strong>ir own salaries<br />
• Utilization of inactive savings accounts to pay<br />
for outstanding debts<br />
In sum, financial incentives need to be used very<br />
carefully. Once you turn on <strong>the</strong> financial incentive<br />
faucet, it is very difficult to turn off again, especially<br />
when incentives represent more than half of a loan<br />
officer’s take home pay.<br />
Incentives need to account for <strong>the</strong> social as well as<br />
<strong>the</strong> commercial mission of microfinance institutions.<br />
Financial incentives must be seen within a bigger<br />
picture of staff motivation that includes a host of<br />
non-financial rewards as well. Finally, incentives<br />
need to be consistent with <strong>the</strong> organization’s<br />
corporate culture. At PRODEM our motto is: “For<br />
me to win, everyone must win.” Consequently,<br />
group-based incentives make more sense for us.<br />
Eduardo Bazoberry (prodebo@ceibo.entelnet.bo) is <strong>the</strong><br />
Managing Director of PRODEM in Bolivia. Parts of this<br />
article were drawn from a forthcoming Calmeadow<br />
publication by Cheryl Frankiewicz entitled, “Building<br />
Institutional Capacity: The Story of PRODEM 1987-2000.”<br />
Figure 1: PRODEM Performance Indicators (1994-1999)<br />
1994 1995 1996 1997 1998 1999*<br />
Number of Borrowers 9,974 18,309 27,486 38,248 47,130 35,924<br />
Total Portfolio (million US$) 2.6 4.5 8.4 18.2 24.2 21.7<br />
Avg. Loan Balance (US$) 261 247 305 477 512 606<br />
Avg. Loan Balance/GNP per capita (%) na 30.9 36.7 49.2 50.7 60.0<br />
Total Staff 129 134 159 237 316 323<br />
Number of Loan Officers 47 81 103 125 164 145<br />
Loan Officers as a % of Total Staff 36 60 64 53 53 45<br />
Staff Turnover (%) 7 13 19 14 14 14<br />
Total Admin. Expenses / Avg. Loan Portfolio (%) 57.5 44.4 32.8 24.2 24.8 25.5<br />
Avg. Staff Salary (multiple of GNP per capita) na 10.2 9.7 8.4 9.4 9.6<br />
Productivity: Clients per Staff Member 77 137 173 161 149 111<br />
Productivity: Clients per Loan Officer 212 226 267 306 287 248<br />
Portfolio Yield (%) 61.5 43.4 55.8 39.1 41.4 37.2<br />
Real Yield (%) 49.6 30.1 37.3 32.0 31.3 34.2<br />
AROA (%) -7.1 -7.6 5.1 9.4 5.0 0.6<br />
AROE (%) -8.0 -9.0 9.6 14.8 9.5 1.4<br />
Operating Self-sufficiency (%) 101 102 178 167 132 105<br />
Financial Self-sufficiency (%) 72 69 119 146 117 102<br />
* The weaker performance in 1999 resulted from two factors. First, <strong>the</strong> entire Bolivian microfinance industry was hit hard by 1)<br />
a weak economy, 2) overindebtedness due to aggressive consumer lenders, and 3) political agitation for debt forgiveness.<br />
Second, PRODEM was consolidating its position in preparation for <strong>the</strong> launch of its FFP.<br />
Source: MicroBanking Bulletin database; Frankiewicz (forthcoming)<br />
MICROBANKING BULLETIN, APRIL 2001 13
FEATURE ARTICLES<br />
Dropouts and Graduates: Lessons from Bangladesh<br />
Graham A.N. Wright<br />
In microfinance, <strong>the</strong> value of retaining clients is<br />
particularly clear. Typically, repeat customers have<br />
a credit history and want to borrow larger loans,<br />
whereas new customers require induction training<br />
and can often weaken <strong>the</strong> solidarity of groups.<br />
MFIs typically break even on a customer only after<br />
<strong>the</strong> fourth or fifth loan (Brand and Gerschick, 2000).<br />
And yet, many MFIs suffer chronic problems with<br />
client desertion.<br />
Client desertion has profound implications for <strong>the</strong><br />
viability of an MFI. High dropouts cost <strong>the</strong> organization<br />
dearly. Groups from which members drop out<br />
are destabilized and must recruit new (less experienced)<br />
members, who qualify for smaller loans thus<br />
reducing <strong>the</strong> overall interest income for <strong>the</strong><br />
institution. The new members have to take a disproportionate<br />
risk and guarantee <strong>the</strong> larger sums<br />
taken by <strong>the</strong>ir fellow group members, adding fur<strong>the</strong>r<br />
stress to <strong>the</strong> group guarantee.<br />
Each dropout is a lost client who underwent<br />
lengthy, expensive training. The replacement members<br />
must ei<strong>the</strong>r receive this training on an individual<br />
basis, or join <strong>the</strong> system without <strong>the</strong> training<br />
that many MFIs regard as critical. The former<br />
option of ad hoc training is extremely inefficient, and<br />
<strong>the</strong> latter—if indeed initial training is so<br />
important—threatens to undermine <strong>the</strong> system. In<br />
<strong>the</strong> face of frequent or multiple dropouts, some of<br />
<strong>the</strong> groups may disintegrate entirely.<br />
Careful analysis of <strong>the</strong> reasons for <strong>the</strong>se dropouts<br />
almost invariably points to inappropriately designed<br />
products that fail to meet <strong>the</strong> needs of <strong>the</strong><br />
customers (see Wright, 2000 and Hulme, 1999).<br />
Much of this problem is driven by <strong>the</strong> attempts to<br />
replicate models and products from foreign environments<br />
without reference to <strong>the</strong> economic or sociocultural<br />
conditions into which <strong>the</strong>y are being<br />
imported. This has been exacerbated by <strong>the</strong> lack of<br />
competition in <strong>the</strong> markets where <strong>the</strong> original<br />
models were developed.<br />
Ironically, many of <strong>the</strong> clients are driven out not only<br />
by <strong>the</strong> inappropriate design of loan products but<br />
also by <strong>the</strong> unwillingness of MFIs to recognize that<br />
(particularly in rural areas) <strong>the</strong>re are seasons when<br />
savings services, not loans, are required. Thus<br />
clients are forced ei<strong>the</strong>r to borrow and try (against<br />
<strong>the</strong> odds) to service <strong>the</strong> loan, or to leave <strong>the</strong> MFI.<br />
All <strong>the</strong> while, <strong>the</strong>ir need for savings is unmet and<br />
ignored.<br />
Dropouts In East Africa<br />
In East Africa, <strong>the</strong> rate of client dropout ranges<br />
between 25 and 60 percent per annum. 11 Clearly this<br />
represents a substantial barrier to achieving selfsufficiency.<br />
When an organization loses over a<br />
quarter of <strong>the</strong> clients every year, it is “running hard to<br />
stand still”. In <strong>the</strong> words of Hulme (1999), “client exit<br />
is a significant problem for MFIs. It increases <strong>the</strong>ir<br />
cost structure, discourages o<strong>the</strong>r clients and reduces<br />
prospects for sustainability.”<br />
Finally, dropouts often leave because <strong>the</strong>y cannot<br />
(or do not want to) manage loan repayments.<br />
These clients stop attending meetings and, freed<br />
from <strong>the</strong> group guarantee and from <strong>the</strong> incentive of<br />
continued access to financial services, are likely to<br />
leave behind an unpaid loan.<br />
High dropout rates often indicate dissatisfaction<br />
with <strong>the</strong> financial services offered by <strong>the</strong> institution.<br />
Members choosing to leave generally do so ei<strong>the</strong>r<br />
because <strong>the</strong> organization is not providing good<br />
enough services to warrant <strong>the</strong> (social and<br />
financial) costs involved, and/or because <strong>the</strong>y have<br />
identified a better alternative.<br />
Members expelled from a microfinance program<br />
(for, of course, not all dropouts are voluntary) are<br />
likely to be indicative of an even more complicated<br />
bundle of factors, including: client selection (or<br />
better said “de-selection”) by fellow members<br />
and/or staff, <strong>the</strong> clients’ inability to pay loans or<br />
even savings, and <strong>the</strong> clients’ unwillingness to<br />
repay loans, which is in part a proxy indicator for<br />
customer dissatisfaction.<br />
Dropouts in Bangladesh<br />
The reasons for desertion are multidimensional.<br />
Indeed, <strong>the</strong> unifying <strong>the</strong>me of <strong>the</strong> studies on <strong>the</strong><br />
subject is that dropout causes are complex. Khan<br />
and Chowdhury (1995) present interesting findings<br />
on <strong>the</strong> high proportion of voluntary dropouts driven<br />
away by <strong>the</strong> inflexibility of BRAC’s system—in<br />
particular its savings facilities (see Figure 1).<br />
11<br />
The desertion rate is <strong>the</strong> number of dropouts in <strong>the</strong> period /<br />
[(number of clients at beginning of <strong>the</strong> period + number of clients<br />
at <strong>the</strong> end of <strong>the</strong> period) / 2]. If <strong>the</strong> period is not a year, <strong>the</strong>n <strong>the</strong><br />
rate is annualized.<br />
14 MICROBANKING BULLETIN, APRIL 2001
FEATURE ARTICLES<br />
Figure 1: Reasons Frequently Cited for<br />
Dropout and Expulsion by Gender<br />
Percentage of dropped out<br />
members mentioned<br />
Reasons for voluntary<br />
dropout<br />
Male Female Total<br />
Group fund is not refunded<br />
Savings not withdrawable<br />
in emergency<br />
O<strong>the</strong>r NGOs provide better<br />
facilities<br />
Failure to repay loan<br />
Family problem<br />
63.2<br />
55.3<br />
36.8<br />
33.6<br />
11.8<br />
70.4<br />
59.2<br />
52.7<br />
38.5<br />
45.0<br />
68.0<br />
57.3<br />
49.8<br />
36.6<br />
29.3<br />
Reasons for expulsion<br />
Failure to repay loan<br />
Irregular attendance in<br />
44.8<br />
17.2<br />
56.1<br />
41.5<br />
59.6<br />
27.3<br />
meeting<br />
Source: Khan and Chowdhury (1995)<br />
An examination of various studies on dropouts in<br />
Bangladesh reveals a common <strong>the</strong>me among <strong>the</strong><br />
75 percent of dropouts who leave voluntarily: dissatisfaction<br />
with <strong>the</strong> available financial services,<br />
and a belief that o<strong>the</strong>r MFIs offer better services<br />
(including crucially, how <strong>the</strong> organization’s staff<br />
behave toward <strong>the</strong>ir clients).<br />
One of <strong>the</strong> key desertion determinants, often lost in<br />
<strong>the</strong> category “failure to repay loan”, is <strong>the</strong> insistence<br />
by field staff that clients take loans. 12 Irrespective<br />
of official policy, <strong>the</strong>re is a clear understanding<br />
among most field staff that <strong>the</strong>y should push out<br />
loans—often with little care for whe<strong>the</strong>r <strong>the</strong> clients<br />
need or can use <strong>the</strong>m. In <strong>the</strong> words of one BRAC<br />
Zonal Manager, “If we do not disburse loans how<br />
can we cover costs?” (personal field notes, 1996).<br />
Similarly, PromPT’s 1996 study of members’<br />
perceptions of Grameen, BRAC, Proshika, ASA and<br />
o<strong>the</strong>r Bangladeshi MFIs (using participatory rural<br />
appraisal and focus groups) found that many<br />
borrowers felt pressurized or “sweet-talked” into<br />
taking loans. Matin (1998) also notes, “MFI lending<br />
technology is insensitive to variations in household<br />
conditions. Most MFIs put all households on a<br />
treadmill of continuously increasing loan size and<br />
insist on a fixed repayment schedule.”<br />
Additional evidence for this pressure is seen in <strong>the</strong><br />
percentage of clients with outstanding loans at any<br />
one time. BURO Tangail offers credit on an entirely<br />
voluntary basis, as and when <strong>the</strong> client wants it,<br />
and (subject to graduated ceilings) however much<br />
<strong>the</strong> client wants. As a result, at any one time only<br />
about half of its clients have a loan outstanding. By<br />
contrast, almost all clients from Grameen, BRAC<br />
and ASA have outstanding loans.<br />
12<br />
There are indications that <strong>the</strong>se practices are now declining.<br />
In <strong>the</strong> extreme case, ASA’s loan policy dictates<br />
when borrowers must take a loan and how big <strong>the</strong><br />
loan must be with no reference to <strong>the</strong> need of <strong>the</strong><br />
client. This policy has lead to a remarkable ability<br />
of clients to manage <strong>the</strong>ir way around <strong>the</strong> system<br />
through on-lending, reciprocal agreements and<br />
cumbersome storage arrangements (Ru<strong>the</strong>rford,<br />
1995). But clearly, managing one’s way around an<br />
inflexible, credit-happy system is not ideal, and so<br />
clients will begin to look at <strong>the</strong> services offered by<br />
o<strong>the</strong>r MFIs.<br />
Clients are now shopping around in search of<br />
flexible, quality financial services. In <strong>the</strong> words of<br />
Khan and Chowdhury (1995), “O<strong>the</strong>r NGOs…working<br />
side by side with BRAC in <strong>the</strong> same areas<br />
provided extra facilities to [village organization]<br />
members. These included: less deductions from<br />
<strong>the</strong> loan, higher loan ceiling, low interest rate, quick<br />
disbursement, etc.” The study revealed that many<br />
dropouts enrolled <strong>the</strong>mselves with o<strong>the</strong>r MFIs for<br />
better terms and opportunities. The microfinance<br />
institution that wants to reduce its level of debilitating<br />
dropout should carefully examine its services<br />
and products, and seek to improve <strong>the</strong>m continuously.<br />
Graduates in Bangladesh<br />
One reason for dropping out, notable by its almost<br />
complete absence from <strong>the</strong> above description, is<br />
client graduation. A few years ago, <strong>the</strong>re was a<br />
belief that credit programs would give such a boost<br />
to <strong>the</strong> income of “beneficiaries” that <strong>the</strong>y would<br />
graduate from poverty. The dynamics of poverty<br />
are such that <strong>the</strong> route out of poverty is nei<strong>the</strong>r<br />
linear nor absolute (Wright, 2000).<br />
There were two schools of thought on graduation.<br />
One held that after a limited number of benign<br />
(subsidized) loan cycles, <strong>the</strong> borrowers’ businesses<br />
would no longer need credit. In retrospect, this was<br />
supreme naiveté, for <strong>the</strong>re is scarcely a firm in <strong>the</strong><br />
world that does not use overdraft facilities to<br />
manage its way through business cycles. And vast<br />
international financial markets have developed<br />
around businesses’ need for capital for expansion.<br />
The o<strong>the</strong>r school, more plausibly, believed that poor<br />
clients could graduate with enough wealth and selfconfidence<br />
to become <strong>the</strong> clients of formal sector<br />
banks. Indeed many MFIs establish self-help<br />
groups, credit unions or village banks and link <strong>the</strong>m<br />
to formal sector financial service institutions. This is<br />
a more viable and desirable option for foreign<br />
NGOs and government projects that do not intend<br />
to establish a permanent banking institution.<br />
But for NGOs seeking to establish permanent MFIs,<br />
<strong>the</strong>se richer potential graduates are <strong>the</strong>ir most valu-<br />
MICROBANKING BULLETIN, APRIL 2001 15
FEATURE ARTICLES<br />
able clients. These clients often take larger loans to<br />
expand <strong>the</strong> working capital of <strong>the</strong>ir businesses or to<br />
finance asset acquisition. The MFI will make most<br />
of its profits on <strong>the</strong>se larger loans since <strong>the</strong> cost of<br />
administering a loan is almost <strong>the</strong> same irrespective<br />
of its size. These long-term customers should also<br />
be better credit risks—although this is subject to<br />
debate. And crucially, <strong>the</strong>se larger-loan clients<br />
allow <strong>the</strong> MFI to finance its smaller loans to poorer<br />
clients. The last thing that an MFI, with its sights<br />
set on financial sustainability, wants to see is <strong>the</strong><br />
graduation of <strong>the</strong>se precious clients. Instead, MFIs<br />
should retain <strong>the</strong>m by seeking to meet <strong>the</strong>ir needs<br />
through a range of client-responsive financial<br />
services.<br />
Conclusions for <strong>the</strong> <strong>Microfinance</strong> Industry<br />
There is compelling evidence, not just from<br />
Bangladesh, to support <strong>the</strong> contention that most<br />
dropouts occur because MFIs do not meet <strong>the</strong><br />
needs of <strong>the</strong>ir market. Dropouts are expensive for<br />
MFIs, in terms of money already invested that is<br />
lost when <strong>the</strong> member leaves, and <strong>the</strong> lost potential<br />
business from that member in <strong>the</strong> future. MFIs<br />
seeking to develop permanent sustainable organizations<br />
should improve <strong>the</strong>ir services to reduce client<br />
dissatisfaction and thus desertion. Such a strategy<br />
is likely to prove cost-effective.<br />
For MFIs committed to creating permanent institutions,<br />
graduating <strong>the</strong> most experienced and affluent<br />
clients into <strong>the</strong> formal banking system is not a<br />
desirable strategy as it implies <strong>the</strong> loss of <strong>the</strong> most<br />
valuable and cost-effective clients. Indeed, MFIs<br />
should tailor <strong>the</strong>ir services to ensure that <strong>the</strong>y retain<br />
<strong>the</strong>se high value customers.<br />
For all <strong>the</strong>se reasons, MFIs should pay (and indeed<br />
are paying) close attention to <strong>the</strong> nature and quality<br />
of <strong>the</strong>ir financial services. The trade-off between<br />
<strong>the</strong> quality of <strong>the</strong> services and cost of providing<br />
<strong>the</strong>m is clear, but finding <strong>the</strong> right balance is<br />
difficult. To date, MFIs in Bangladesh have put too<br />
much emphasis on trying to implement standardized,<br />
inflexible, low-cost, credit-driven systems<br />
when <strong>the</strong>ir clients are asking (and willing to pay) for<br />
a broader range of quality services.<br />
The irony of this situation was that <strong>the</strong> genesis of<br />
microfinance in Bangladesh was originally driven by<br />
an extensive program of careful market and operations<br />
research designed to understand <strong>the</strong> needs<br />
of <strong>the</strong> clients. Professor Yunus’ work with his students<br />
at Chittagong University in <strong>the</strong> village of<br />
Jobra in 1976 was quintessential market research.<br />
It is to <strong>the</strong> fundamentals of market research and<br />
product development that MFIs must return if <strong>the</strong>y<br />
are to retain clients and build sustainable<br />
institutions.<br />
Graham Wright is Programme Director of MicroSave-<br />
Africa, Chair of CGAP’s Savings Mobilisation Working<br />
Group and a Research Associate at <strong>the</strong> Institute of<br />
Development Policy and Management, University of<br />
Manchester, UK. He can be reached at<br />
Graham@MicroSave-Africa.com.<br />
References<br />
Brand, M. and J. Gerschick. (2000). Maximizing Efficiency in<br />
<strong>Microfinance</strong>: The Path to Enhanced Outreach and<br />
Sustainability. Washington: ACCION International.<br />
Hulme, D. (1999). “Client Exits (Drop outs) from East African<br />
Micro-Finance Institutions.” Kampala: MicroSave-Africa.<br />
Khan, Md. K. A. and A.M.R. Chowdhury (1995). “Why VO<br />
Members Drop Out.” Dhaka: BRAC.<br />
Matin, I. (1998). “Informal Credit Transactions of Micro-Credit<br />
Borrowers in Rural Bangladesh.” mimeo, Dhaka.<br />
Mustafa, S, et al. (1996). “Beacon of Hope: An Impact<br />
Assessment Study of BRAC’s Rural Development<br />
Programme.” Dhaka: BRAC.<br />
PromPT (1996). "Financial Services for <strong>the</strong> Rural Poor -<br />
Users' Perspectives." Dhaka: PromPT.<br />
Ru<strong>the</strong>rford, S (1995). ASA: The Biography of an NGO,<br />
Empowerment and Credit in Rural Bangladesh. Dhaka: ASA.<br />
Wright, G.A.N. (2000). “<strong>Microfinance</strong>: The Solution or a<br />
Problem?” in MicroFinance Systems: Designing Quality<br />
Financial Services for <strong>the</strong> Poor. Dhaka: University Press<br />
Limited and London: Zed Books.<br />
16 MICROBANKING BULLETIN, APRIL 2001
FEATURE ARTICLES<br />
Exodus: Why Customers Leave<br />
Kim Wilson<br />
A customer is an asset. It is hard to disagree with<br />
that statement. Yet <strong>the</strong> microfinance industry has<br />
not caught on to a customer’s value. While many<br />
microfinance institutions pay close attention to<br />
minimizing <strong>the</strong> costs of delinquency, <strong>the</strong>y seem to<br />
disregard <strong>the</strong> expense of losing good customers.<br />
This article proposes that <strong>the</strong> microfinance industry<br />
look at deserting customers with <strong>the</strong> same passion<br />
and precision as it examines delinquency. Two<br />
reasons compel us to do so. First, lost customers<br />
place our social agenda in peril. How can <strong>the</strong><br />
empowering benefits of microfinance take place if<br />
our customers flee after one loan cycle? And,<br />
worse, we may be losing our poorest customers,<br />
those whom we most want to serve. Second, lost<br />
customers cost money. They cost additional marketing<br />
dollars to attract and prepare new customers<br />
for loans; and <strong>the</strong>y cost us in lost profits.<br />
An Example of Using Dropouts to Inform<br />
Marketing Decisions<br />
The first step in stemming <strong>the</strong> flow of dropouts is to<br />
understand who is dropping out and why. At Mikra,<br />
a new MFI in Bosnia-Herzegovina, <strong>the</strong> Executive<br />
Director, Sanin Campara, and his staff pay special<br />
attention to data on deserting customers. They use<br />
data to draw conclusions and make changes in <strong>the</strong><br />
program. Campara believes that information supplied<br />
by deserting customers is sometimes far more<br />
valuable than data from existing customers.<br />
Deserting customers have nothing to fear in<br />
answering questions. They have already made <strong>the</strong><br />
decision to leave, so information is likely to be valid,<br />
absent any bias a current customer might have.<br />
Current customers may fear losing services altoge<strong>the</strong>r<br />
if <strong>the</strong>y are honest in answering questions.<br />
Deserting customers can afford to be honest.<br />
Moreover, deserting customers offer a marketsavvy<br />
MFI more than information about <strong>the</strong>mselves.<br />
They may offer clues as to why some customers<br />
may not be interested in <strong>the</strong> MFI in <strong>the</strong> first place,<br />
as well as insights into <strong>the</strong> relative attraction of <strong>the</strong><br />
competition (where applicable).<br />
To understand deserting customers better,<br />
Campara and his team interview every single<br />
dropout. To simplify <strong>the</strong> procedure, <strong>the</strong>y check off<br />
reasons for dropping out on a one-page sheet that<br />
displays <strong>the</strong> most commonly cited answers.<br />
They found that in one town, Kakenj, business<br />
failure caused 30 percent of <strong>the</strong> dropouts. Kakenj<br />
had suffered many economic set backs—<strong>the</strong> closing<br />
of a coal mine, <strong>the</strong>rmo-electric plant, and <strong>the</strong><br />
state-owned salmon processing facilities. Mikra<br />
immediately put this information to use by tailoring<br />
services to <strong>the</strong> local environment. In Kakenj, <strong>the</strong><br />
loan officers started to help village banks become<br />
mindful of potential business problems due to a<br />
failed economy.<br />
In ano<strong>the</strong>r example, 7 percent of <strong>the</strong> dropouts<br />
indicated that <strong>the</strong> loan terms were too short. While<br />
only a small percentage, this figure became alarming<br />
when staff discovered that <strong>the</strong> clients in this<br />
category included some of Mikra’s best customers<br />
who left as <strong>the</strong>y approached <strong>the</strong>ir fourth loan. Mikra<br />
responded by leng<strong>the</strong>ning <strong>the</strong> term in <strong>the</strong> fourth<br />
cycle from six to eight months. Was this <strong>the</strong> right<br />
move? Mikra staff polled two village banks in <strong>the</strong><br />
fourth cycle and learned that 13 percent would have<br />
dropped out if Mikra had not leng<strong>the</strong>ned its loan<br />
term. When asked how many would have dropped<br />
out if competition had offered longer terms, eleven<br />
of twenty-five members in one village bank responded<br />
that <strong>the</strong>y would have defected.<br />
Mikra is now turning its attention to <strong>the</strong> relationship<br />
between delinquency and desertion. Are delinquent<br />
customers dropping out when <strong>the</strong>y would prefer to<br />
stay? Does an increase in tardy payers who are<br />
forced to leave <strong>the</strong> program signal <strong>the</strong> need for a<br />
product that better matches <strong>the</strong>ir cash flow? At<br />
Mikra, approximately 13 percent of <strong>the</strong> dropouts<br />
were delinquent. Staff is fine-tuning <strong>the</strong>ir data<br />
ga<strong>the</strong>ring and database filters to determine who is<br />
both delinquent and dropping out. Do dropout/<br />
delinquent customers have larger loans? Since<br />
larger loan customers are more profitable, changing<br />
<strong>the</strong> loan product to meet <strong>the</strong>ir needs may be worth<br />
<strong>the</strong> effort (if it does not increase <strong>the</strong> organization’s<br />
credit risk). If new customers are deserting, this<br />
suggests <strong>the</strong> need for adjustments to <strong>the</strong> marketing<br />
and screening processes.<br />
Mikra has one of <strong>the</strong> lowest desertion rates of all<br />
CRS-sponsored programs. Over <strong>the</strong> past 30<br />
months, with 2,500 customers, Mikra enjoys an<br />
annualized desertion rate of less than 11 percent. 13<br />
Is this because Mikra’s attention to deserters is<br />
exactly what prevents customers from deserting?<br />
At this early stage, we can only speculate.<br />
13<br />
This is calculated as follows: (number of dropouts last month /<br />
number of clients this month) x 12.<br />
MICROBANKING BULLETIN, APRIL 2001 17
FEATURE ARTICLES<br />
How to Analyze Dropouts<br />
There are many ways to collect and analyze<br />
information on desertions. Each MFI must balance<br />
<strong>the</strong> efficiency of collecting information with <strong>the</strong><br />
quality or depth of <strong>the</strong> information. The downside of<br />
conducting exit interviews with each lost customer<br />
is that it can add significant expenses to <strong>the</strong> lending<br />
process. And if <strong>the</strong> interviewing process becomes<br />
routine, as it often does, <strong>the</strong> value of <strong>the</strong> information<br />
collected is low. Ideally, someone o<strong>the</strong>r than <strong>the</strong><br />
deserter’s loan officer would interview <strong>the</strong> dropout<br />
because <strong>the</strong>re is a chance that <strong>the</strong> loan officer was<br />
<strong>the</strong> problem.<br />
Tim Nourse, CRS advisor in Gaza, believes that a<br />
cost-benefit analysis of exit interviews reveals <strong>the</strong><br />
imperative of interviewing every deserter for three<br />
reasons. First, if <strong>the</strong> organization does not interview<br />
each dropout, it may miss people who would<br />
have indicated dissatisfaction with staff. Second,<br />
staff would ra<strong>the</strong>r not interview dropouts at all.<br />
Such interviews can be tedious and depressing,<br />
motivating staff to keep customers if for no o<strong>the</strong>r<br />
reason than to avoid <strong>the</strong> tiresome task of interviewing.<br />
And third, <strong>the</strong> effort to ask people <strong>the</strong>ir<br />
opinions is often sufficient motivation to encourage<br />
<strong>the</strong>m to return.<br />
Once <strong>the</strong> MFI decides how to ga<strong>the</strong>r information, it<br />
must decide what data to collect. Reasons for<br />
desertion fall into three categories depending on <strong>the</strong><br />
level of control that <strong>the</strong> MFI can exert on <strong>the</strong>m:<br />
• Can Control: Product competitiveness, staff<br />
attitude toward customer, general<br />
dissatisfaction with loan product (terms, rates,<br />
payment schedules);<br />
• Might Control: Family problems, business<br />
problems, and delinquency;<br />
• Cannot Control: Customer unwillingness to<br />
repay own loan, family members stealing or<br />
forbidding loan payments.<br />
Regarding <strong>the</strong> “Might Control” problems, MFIs that<br />
see <strong>the</strong> value in customer retention will create<br />
systems or products that do not force a customer to<br />
dropout because of a family crisis, or will develop<br />
mechanisms for supporting good customers who<br />
experience temporary business problems.<br />
The exit interview form in Box 1, a blend of tools<br />
used in Gaza and Bosnia-Herzegovina, offers an<br />
example of how broad areas of classifying dropouts<br />
are organized to collect data. Initially, an MFI may<br />
have many write-in responses while developing an<br />
efficient tool. As patterns appear, those write-ins<br />
may become pre-written items for staff to check off,<br />
making <strong>the</strong> exit interview faster and data entry<br />
simpler.<br />
Box 1: Sample Exit Interview Form<br />
Customer Name:<br />
Loan Officer:<br />
Village:<br />
Date:<br />
Profile of Lost Customer<br />
Amount of most recent loan:<br />
Amount of savings:<br />
Length of time with MFI:<br />
Length of time in business:<br />
Business Type: Trading food Trading non-food<br />
Fishing/farming Manufacturing Services<br />
Poverty Level: 1 2 3 4 5<br />
Were payments on recent loans delinquent? Y / N<br />
Was customer asked to leave MFI? Y / N<br />
Reasons for Dropping Out and Rejoining<br />
1. If customer is not going to competition, check one<br />
and skip 2:<br />
Unwilling to repay loan (bad customer)<br />
Dissatisfaction with loan amount<br />
…with loan payment schedule and term<br />
…with requirements or restrictions<br />
…with savings product<br />
…with staff<br />
Moving out of town<br />
Problems with business<br />
Family or personal problems<br />
Problems making payments for group<br />
O<strong>the</strong>r___________________<br />
2. If customer is going to competition, <strong>the</strong> most<br />
important reason is that competition has:<br />
Better staff<br />
Better loan product (terms, payments, rate)<br />
Better savings product<br />
Convenience of location<br />
Less restrictions or requirements<br />
O<strong>the</strong>r________________________<br />
3. I would rejoin if___________________________<br />
An Excel database can help analyze data.<br />
Management can filter for different information. For<br />
example, what percent of small-scale producers<br />
drop out because of an inadequate loan product?<br />
Or what percent of customers who have been with<br />
<strong>the</strong> MFI for more than three years leave because of<br />
competition or because <strong>the</strong>y had difficulty meeting<br />
requirements? Do more start-ups drop out in trade<br />
or in production? Are <strong>the</strong> poorest customers dropping<br />
out at a greater rate than <strong>the</strong> less poor?<br />
In Gaza, which enjoys a low dropout rate of 12<br />
percent, Nourse believes that <strong>the</strong> most important<br />
part of ga<strong>the</strong>ring desertion information is training<br />
staff to collect data routinely, with care and<br />
diligence. Since staff members are too willing to<br />
skip this activity in favor of something more enjoyable,<br />
<strong>the</strong> MFI should laud incentives or recognition<br />
on <strong>the</strong> loan officers with best records of collecting<br />
18 MICROBANKING BULLETIN, APRIL 2001
FEATURE ARTICLES<br />
exit interview data and with <strong>the</strong> best suggestions for<br />
improving <strong>the</strong> products and services. He advises<br />
that management must offer regular training for field<br />
staff in both interviewing clients and in correctly<br />
completing an exit interview form.<br />
The effort is worth it. Through exit interviewing,<br />
Nourse learned that dropouts valued <strong>the</strong> business<br />
support of a solidarity group more than <strong>the</strong> repayment<br />
support. The organization has since used this<br />
insight to help market to new customers. The<br />
unearthing of <strong>the</strong>se details points to something<br />
essential. Management must involve itself in interviewing<br />
lost customers. Important information,<br />
whe<strong>the</strong>r statistical or anecdotal, is most useful<br />
when management can interpret it in light of<br />
broader strategic issues. By setting an example,<br />
management also helps establish a culture and<br />
commitment to collecting data from lost customers.<br />
Keeping Customers: Is It Worth It?<br />
The cost of a lost customer is not nominal. A<br />
dropout may cost an MFI as much as a defaulted<br />
loan because <strong>the</strong> organization loses a lifetime of<br />
profits from that customer. Good customers often<br />
leave MFIs because <strong>the</strong>y had bad experiences,<br />
such as having to pay for <strong>the</strong> debts of a group<br />
member. But if <strong>the</strong> MFI had retained that good<br />
customer for five or ten years, <strong>the</strong>n that client might<br />
have generated significant returns to <strong>the</strong> MFI,<br />
directly in <strong>the</strong> form of profits, and indirectly through<br />
referrals.<br />
While MFIs can certainly learn a lot from lost<br />
customers, are <strong>the</strong>y willing to go to great lengths to<br />
keep <strong>the</strong>ir clients? Many organizations resist reengineering<br />
systems to suit customers. Expense,<br />
hassle and inertia stand in <strong>the</strong> way of improving<br />
back-end processes to increase customer retention.<br />
The potential for lost profits may warrant training<br />
staff in tracking dropouts, creating databases, and<br />
building capacity to analyze information. These<br />
“losses” due to dropouts may warrant making<br />
changes to products, marketing strategies, and<br />
information systems.<br />
Mainstream businesses have found that when<br />
desertions are cut in half, profits may increase by<br />
85 percent and beyond. 14 If this holds for MFIs, <strong>the</strong><br />
right product, staff training and MIS designed to<br />
improve satisfaction and retention will yield benefits<br />
above <strong>the</strong> investments made in reducing delinquencies<br />
and defaults.<br />
How can MFIs determine if keeping customers is<br />
worth <strong>the</strong> expense and effort? It is necessary to<br />
conduct a cost-benefit analysis that includes <strong>the</strong><br />
financial benefits of retaining customers. For<br />
example, in assessing whe<strong>the</strong>r an investment in a<br />
line of credit product would be worthwhile, an MFI<br />
has to consider not only whe<strong>the</strong>r that product is<br />
profitable, but also <strong>the</strong> effect that <strong>the</strong> product would<br />
have on customer retention.<br />
Beyond retained profits, customer retention means<br />
that clients will remain with <strong>the</strong> MFI long enough to<br />
extract some social benefits from <strong>the</strong> program. By<br />
remaining with <strong>the</strong> MFI, <strong>the</strong> clients should experience<br />
increased income because <strong>the</strong>y have ongoing<br />
access to financial services. Their continued participation<br />
should also help boost <strong>the</strong>ir confidence and<br />
encourage <strong>the</strong>m to take leadership positions in <strong>the</strong><br />
home and in <strong>the</strong> community.<br />
Where Do We Go From Here?<br />
The microfinance industry needs to make some<br />
radical changes in how it treats its customers. They<br />
are why <strong>the</strong> industry exists. Below are some ideas<br />
of how to improve <strong>the</strong> industry’s awareness of<br />
desertion and ways to improve retention.<br />
• All MFIs should commit to tracking each and<br />
every dropout;<br />
• Management should commit to participating in<br />
exit interviews, to analyzing data, and to<br />
discussing appropriate responses with staff;<br />
• MFIs should reward <strong>the</strong> accurate tracking of<br />
dropouts with <strong>the</strong> same kind of incentives<br />
offered to staff for good delinquency<br />
management;<br />
• The microfinance industry, including The<br />
MicroBanking Bulletin, should standardize <strong>the</strong><br />
definitions of relevant ratios and <strong>the</strong>n begin<br />
tracking desertion rates and <strong>the</strong> net profit per<br />
customer.<br />
By approaching this critical issue collectively, and<br />
taking <strong>the</strong> steady stream of customers that revolve<br />
through our doors seriously, we will improve performance<br />
and restore <strong>the</strong> social benefits inherent in<br />
good microfinance institutions.<br />
Kim Wilson (kwilson@catholicrelief.org) is Senior Advisor<br />
for <strong>Microfinance</strong> at Catholic Relief Services.<br />
14 Reichheld, Frederick F, and W. Earl Sasser, Jr. (1990, Sept.-<br />
Oct.). “Zero Defections: Quality Comes to Services”. Harvard<br />
Business Review, pp. 107-110.<br />
MICROBANKING BULLETIN, APRIL 2001 19
FEATURE ARTICLES<br />
Cultivating Client Loyalty: Exit Interviews from Africa and Asia<br />
Inez Murray<br />
Losing clients is expensive. If an MFI with 30,000<br />
clients loses 20 percent of its customers per<br />
year—that is 6,000 people. If <strong>the</strong> average loan size<br />
is US$150, and in a lifetime a client might borrow<br />
10 loans, <strong>the</strong> MFI is losing up to US$9 million in<br />
future sales. Add to this <strong>the</strong> fact that many MFIs do<br />
not break even until <strong>the</strong>ir fourth or fifth loan 15 and<br />
that <strong>the</strong> client who left probably told nine o<strong>the</strong>r<br />
people about <strong>the</strong>ir negative experience with <strong>the</strong><br />
institution, 16 and it is clear why retaining clients is a<br />
key to profitability.<br />
As <strong>the</strong> microfinance industry becomes more<br />
competitive, clients have more choice regarding<br />
where to shop. If an MFI is to remain competitive, it<br />
must get an accurate read of clients’ satisfaction<br />
and dissatisfaction with its loan products and<br />
service delivery. It must understand why clients<br />
leave, why some of <strong>the</strong>m go to a competitor, and<br />
what kind of product range it should consider<br />
offering to retain clients throughout <strong>the</strong>ir lifecycle.<br />
Customers leave an organization for many reasons,<br />
some of which <strong>the</strong> MFI can mitigate and some that<br />
it cannot. For <strong>the</strong> purpose of analysis, <strong>the</strong> former<br />
are considered internal factors, and <strong>the</strong> latter<br />
external. Internal factors include:<br />
• High prices<br />
• Rigid product design<br />
• Narrow range of products<br />
• High transaction costs<br />
• Insufficient attention to customer service<br />
External factors are exogenous to <strong>the</strong> institution<br />
such as illness, death, family problems, seasonality<br />
seasonal migration patterns, natural disasters,<br />
increasing competition, and economic shocks.<br />
In <strong>the</strong> past couple of years, Women’s World<br />
Banking (WWB), with <strong>the</strong> assistance of Monitor<br />
Group, has conducted intensive research into<br />
customer satisfaction and retention as part of a<br />
strategic positioning service for affiliates operating<br />
in increasingly competitive markets. 17<br />
This paper shares some key findings regarding<br />
client desertion from two affiliates, Shakti Foundation<br />
for Disadvantaged Women in Bangladesh and<br />
<strong>the</strong> Uganda Women’s Finance Trust (UWFT).<br />
Understanding why clients left was one small part of<br />
<strong>the</strong> overall research. This article is a precursor to a<br />
more comprehensive publication on <strong>the</strong> findings of<br />
<strong>the</strong> strategic positioning work completed in Colombia,<br />
Bangladesh, Uganda, and Bosnia-Herzegovina.<br />
The focus on why clients drop out tends to put a<br />
spotlight on areas that an institution may need to<br />
improve. Consequently, WWB thanks both organizations<br />
for <strong>the</strong>ir willingness to share <strong>the</strong> results of<br />
this research as a contribution to <strong>the</strong> field.<br />
This article is divided into three sections: 1)<br />
research methodology, 2) context (description of<br />
lending methodologies, price and service delivery<br />
attributes that are necessary to interpret results),<br />
and 3) findings and implications.<br />
Research Methodology<br />
In both affiliates, two populations were surveyed:<br />
active and former borrowers (dropouts). Trained<br />
college graduates, fluent in local languages,<br />
conducted <strong>the</strong> interviews. Respondents were selected<br />
based on stratified random sampling and a<br />
sufficient number of interviews were conducted to<br />
give a margin of error of +/-4 percent. 18<br />
With <strong>the</strong> sample of dropouts, several sets of<br />
questions were used to identify desertion drivers<br />
including asking clients directly using both openand<br />
close-ended questions. 19 All respondents also<br />
ranked <strong>the</strong>ir satisfaction level with each aspect of<br />
<strong>the</strong> product and service delivery mechanism. In<br />
general, current and former clients expressed high<br />
levels of dissatisfaction with <strong>the</strong> same product and<br />
service delivery attributes that caused client<br />
desertion.<br />
15<br />
Brand, M. and J. Gerschick. (2000). Maximizing Efficiency<br />
in <strong>Microfinance</strong>: The Path to Enhanced Outreach and<br />
Sustainability. Washington: ACCION International.<br />
16<br />
Bhote (1996). Beyond Customer Satisfaction to Customer<br />
Loyalty: The Key To Greater Profitability. American Management<br />
Association, New York, p. 60.<br />
17<br />
The Strategic Positioning Product (SPP) is a technical service<br />
that helps an MFI define or re-define its market position (i.e.,<br />
methodology, product menu, price, customer segments, etc.) to<br />
give it long-term competitive advantage. It is based on work<br />
conducted jointly with Monitor Group, a global family of<br />
professional service firms.<br />
18<br />
Sample size: Bangladesh: survey sample size of current clients<br />
= 515 = +/- 4%; of former clients = 541 = +/- 4%; Uganda:<br />
sample size of current clients size 507 = +/-4%; of former clients<br />
= 500 = +/-4%. Two strata were used to ensure sample<br />
dispersion – branch and loan cycle.<br />
19<br />
With close-ended questions, respondents choose from a list of<br />
options; in open-ended questions, <strong>the</strong> respondents reply with <strong>the</strong><br />
first thought that crosses <strong>the</strong>ir mind.<br />
20 MICROBANKING BULLETIN, APRIL 2001
FEATURE ARTICLES<br />
Context<br />
To interpret <strong>the</strong> results it is necessary to provide<br />
some background on <strong>the</strong> credit methodology, <strong>the</strong><br />
product design, and <strong>the</strong> resulting transaction costs.<br />
The terms and conditions of <strong>the</strong> basic loan product<br />
in both affiliates are summarized in Figure 1.<br />
Group Structure: In Shakti Foundation, <strong>the</strong> Center<br />
consists of six groups of five low-income women for<br />
a maximum center size of 30 persons. Each group<br />
elects a chair and each center elects a leader. In<br />
UWFT, groups may have already existed for o<strong>the</strong>r<br />
purposes (e.g. church-based groups). Groups have<br />
a minimum of five people and no maximum—some<br />
have 60 or more members. When groups form,<br />
<strong>the</strong>y elect a chair, treasurer and a secretary. Unlike<br />
in Bangladesh, <strong>the</strong>re are no sub-groups within a<br />
group.<br />
Group Formation: Each Shakti group undergoes<br />
an 8-week training, at <strong>the</strong> end of which <strong>the</strong>y must<br />
pass an oral test that determines if <strong>the</strong>y understand<br />
<strong>the</strong> rules of <strong>the</strong> institution. After passing <strong>the</strong> test,<br />
clients save for one week before applying for a<br />
loan. UWFT groups are also trained in group roles<br />
and responsibilities and <strong>the</strong> terms and conditions of<br />
borrowing, but no official test is administered. Once<br />
each member has saved 30 percent of <strong>the</strong> principal<br />
of <strong>the</strong> loan, <strong>the</strong> group can submit its application.<br />
Loan Disbursement: Shakti loans are issued to<br />
each individual within a group on a 2-2-1 basis, i.e.,<br />
two group members get loans <strong>the</strong> first week; <strong>the</strong><br />
second two members receive loans a week later<br />
provided <strong>the</strong> first two have paid <strong>the</strong>ir installments;<br />
<strong>the</strong>n <strong>the</strong> last member receives her loan <strong>the</strong><br />
following week. Repayment is made at <strong>the</strong> center<br />
meetings to <strong>the</strong> credit officer.<br />
In UWFT, <strong>the</strong> loan is issued to group leaders who<br />
collect <strong>the</strong> money from <strong>the</strong> branch office on behalf<br />
of <strong>the</strong> group. The group is responsible for ensuring<br />
that each member receives her approved amount.<br />
Clients repay <strong>the</strong>ir loans to group officials who <strong>the</strong>n<br />
submit repayments to <strong>the</strong> branch. The credit officer<br />
is not present at all group meetings.<br />
Dropout Rate<br />
The dropout rate 20 at Shakti Foundation is reported<br />
to be 9 percent in 1999 down from 14 percent in<br />
1998, compared to norms in Bangladesh of 10 to 15<br />
percent. In UWFT, <strong>the</strong> dropout rate is not tracked,<br />
but <strong>the</strong> industry norm of 25 percent or more 21 is a<br />
valid proxy. Seventy percent of dropouts inter-<br />
20<br />
The dropout rate formula is 1 – (number of borrowers (end of<br />
period) – number of new borrowers (for period) / number of<br />
borrowers (beginning of period)).<br />
21<br />
Wright et al. (1999). Drop-outs Amongst Ugandan MFIs.<br />
MicroSave-Africa, p. 1.<br />
viewed in Uganda described <strong>the</strong>mselves as<br />
“resting” and may borrow again 22 .<br />
Figure 1: Terms and Transaction Costs of<br />
Basic Group Loan Product* 23<br />
Shakti Foundation,<br />
Bangladesh<br />
UWFT, Uganda<br />
Target Area<br />
Urban only<br />
Mainly urban and<br />
semi-urban<br />
Credit methodology<br />
Group Lending Grameen/ village<br />
(Grameen)<br />
banking hybrid<br />
Starting loan size Tk. 4,000 ~ US$75 150,000 Shs ~ US$98<br />
Maximum loan size<br />
Avg. outstanding<br />
balance per borrower<br />
Increments<br />
Tk. 10,000 ~<br />
US$189<br />
500,000 Shs<br />
~US$327<br />
US$71 (as of 12/99) US$111 (as of 7/99)<br />
Standardized by<br />
cycle<br />
Based on clients’<br />
repayment capacity<br />
and savings<br />
GNP per capita 24 US$350 US$310<br />
Loan term (average): 50 weeks 16 weeks<br />
Fees and<br />
commissions/<br />
compulsory savings<br />
Group Fund: 5% of<br />
principal 25<br />
Health Fund: Tk. 1<br />
per week 26<br />
MFI Development<br />
Fund: 2% of principal<br />
Commission: 2% of<br />
principal<br />
Compulsory savings:<br />
Tk. 10 per week 27<br />
Compulsory savings:<br />
30% of principal<br />
O<strong>the</strong>r fees: 5,000 Shs<br />
for stationary<br />
Nominal interest rate 1% flat (per month) 2.5% flat (per month)<br />
Effective interest rate<br />
(excl. savings)<br />
21.5 % 75 %<br />
O<strong>the</strong>r requirements:<br />
Collateral N/A Pledge one asset<br />
Guarantors N/A Two guarantors<br />
Group guarantee Must guarantee Must guarantee peers<br />
O<strong>the</strong>r<br />
Group meetings:<br />
peers<br />
N/A<br />
Signature from Local<br />
Councilor<br />
Frequency Weekly Weekly, bimonthly or<br />
monthly<br />
Attendance Compulsory Not compulsory<br />
Average time to form<br />
a group<br />
9 weeks (8 weeks<br />
group formation plus<br />
1 week saving)<br />
Voluntary savings No Yes<br />
12 weeks<br />
22<br />
This question of “resting” was not asked in Shakti because it<br />
was understood that <strong>the</strong> phenomenon is not common in<br />
Bangladesh. However, since so many Ugandans regarded<br />
<strong>the</strong>mselves as “resting” <strong>the</strong> assumption that all clients want or<br />
need to borrow on a continuous basis appears to be false.<br />
23<br />
Terms and conditions approximate industry norms in <strong>the</strong><br />
respective countries and all data reflect institutional policies at<br />
<strong>the</strong> time <strong>the</strong> surveys were administered.<br />
24<br />
As of 1998. World Development Indicators 2000, World Bank.<br />
25<br />
This savings is deducted from <strong>the</strong> loan principal at <strong>the</strong> time of<br />
loan disbursement and can only be withdrawn if client leaves <strong>the</strong><br />
organization after five years.<br />
26<br />
This is a premium for credit life insurance. If a borrower dies,<br />
<strong>the</strong> Health Fund pays a designated surviving family member Tk.<br />
4,000 from which <strong>the</strong> outstanding loan balance is deducted.<br />
27<br />
This consists of Tk. 5 for personal savings accessible when<br />
<strong>the</strong> client leaves; and Tk. 5 for <strong>the</strong> Center Fund, accessible only<br />
if she remains with <strong>the</strong> organization for at least five years.<br />
MICROBANKING BULLETIN, APRIL 2001 21
FEATURE ARTICLES<br />
Industry Dynamics<br />
In a short article it is impossible to do justice to <strong>the</strong><br />
contextual differences between urban Bangladesh<br />
where Shakti operates and <strong>the</strong> semi-urban markets<br />
served by UWFT. What is important to mention is<br />
that both institutions offer loan terms and service<br />
delivery mechanisms that are similar to most<br />
players in <strong>the</strong>ir market and that <strong>the</strong>refore <strong>the</strong> results<br />
of this research are broadly applicable to o<strong>the</strong>r<br />
MFIs.<br />
Findings and Implications<br />
Figures 2 and 3 summarize <strong>the</strong> main reasons why<br />
customers left each program. This section analyzes<br />
<strong>the</strong>se findings and discusses <strong>the</strong>ir implications.<br />
External Reasons<br />
The external factors cited for dropping out highlight<br />
<strong>the</strong> fact that MFIs are serving a precarious market<br />
and many unexpected events can influence a<br />
customer’s demand for financial services.<br />
Business problems: Low profitability and business<br />
failure were important reasons why clients left both<br />
institutions. Clients cited access to business development<br />
services (BDS), particularly marketing support<br />
and skill development, as a huge unmet need.<br />
In both countries, clients must have a business<br />
when <strong>the</strong>y apply for a loan, but not necessarily<br />
before that time. This policy allows <strong>the</strong> MFI to<br />
deepen its outreach, but it could cause an increase<br />
in attrition due to business failure.<br />
Do not need a loan right now: Some clients said<br />
<strong>the</strong>y were not interested in ano<strong>the</strong>r loan at that<br />
time, o<strong>the</strong>rs that <strong>the</strong>y were tired of borrowing or, in<br />
Bangladesh, some clients had found employment.<br />
Moved location: Migration was given a much<br />
higher ranking in Bangladesh than in Uganda,<br />
reflecting <strong>the</strong> more transitory nature of <strong>the</strong> urban<br />
population in Bangladesh. Some clients migrate<br />
temporarily to <strong>the</strong> city due to river erosion or<br />
indebtedness in rural areas, while o<strong>the</strong>rs go back to<br />
rural areas on a seasonal basis to harvest crops.<br />
Illness: Illness, of ei<strong>the</strong>r <strong>the</strong> client or a family<br />
member, is a reason cited in both countries, reflecting<br />
<strong>the</strong> poor conditions in which many clients live<br />
and <strong>the</strong> limited availability of health care for<br />
borrowers and <strong>the</strong>ir families.<br />
O<strong>the</strong>r external reasons include <strong>the</strong>ir husbands<br />
leaving, <strong>the</strong> house or business was robbed, and<br />
famine in one region of Uganda.<br />
In sum, external desertion drivers illustrate <strong>the</strong><br />
vulnerable nature of microfinance clients. These<br />
vulnerabilities, which adversely affect both retention<br />
and repayments, highlight how important it is for<br />
MFIs to address o<strong>the</strong>r needs of <strong>the</strong>ir clients besides<br />
credit. MFIs can ei<strong>the</strong>r develop in-house capacity<br />
or form strategic alliances to provide voluntary<br />
savings, microinsurance and business development<br />
services. Offering access to <strong>the</strong>se valuable wraparound<br />
products could be an important dimension<br />
of any client retention strategy.<br />
Internal Reasons<br />
The internal reasons for leaving fall into three<br />
categories: loan-related, transaction costs, and<br />
customer service. In Bangladesh <strong>the</strong> key internal<br />
causes of desertion were group meetings (frequency,<br />
duration), group guarantee, and loan<br />
related issues (amount, interest rate). In Uganda,<br />
<strong>the</strong> key internal desertion drivers were loan related<br />
issues (term, interest rate, installment amount,<br />
compulsory savings), <strong>the</strong> desire for individual loans,<br />
Figure 2: Reasons for Leaving *<br />
Bangladesh<br />
3%<br />
9%<br />
Uganda<br />
14% 29%<br />
51%<br />
31%<br />
32%<br />
31%<br />
External<br />
Loan Related<br />
Transaction Costs<br />
Customer Service<br />
* In response to <strong>the</strong> open -ended question: “Why did you leave <strong>the</strong> institution? Please tell me <strong>the</strong> most important reason.”<br />
22 MICROBANKING BULLETIN, APRIL 2001
FEATURE ARTICLES<br />
<strong>the</strong> group guarantee, and needing more support<br />
from <strong>the</strong> institution to solve problems.<br />
Loan-Related<br />
In Uganda, issues relating to <strong>the</strong> loan (price, term,<br />
amount, requirements) were stronger desertion<br />
drivers than in Bangladesh. This finding reflects <strong>the</strong><br />
fact that loans in Uganda are more expensive, for<br />
shorter terms, and require several additional guarantees<br />
besides <strong>the</strong> group and compulsory savings.<br />
Interest rate: In Uganda effective interest rates<br />
(taking compulsory savings into account) are four to<br />
five times higher than in Bangladesh. A segment of<br />
UFWT borrowers found that rate to be beyond a<br />
tolerable threshold. While it is important to<br />
acknowledge <strong>the</strong> contextual differences (e.g. lower<br />
population density and higher salaries) that make<br />
<strong>the</strong> cost of doing business substantially higher in<br />
Uganda, <strong>the</strong> challenge is to continuously lower<br />
costs and pass on those savings to clients.<br />
Loan term: Clients in Uganda mentioned that <strong>the</strong><br />
16-week loan term was a reason for dropping out,<br />
whereas <strong>the</strong> 50-week term at Shakti was not an<br />
issue. Since in both countries clients are predominantly<br />
engaged in trading, this finding does not<br />
necessarily reflect a mismatch between business<br />
activity and loan term. Instead, it may be more<br />
useful to look at <strong>the</strong> combination of short terms with<br />
high interest rates that produce prohibitively large<br />
installment sizes for some clients.<br />
Clients in both countries left because <strong>the</strong>y could not<br />
repay <strong>the</strong> loan, but this was a bigger factor in<br />
Uganda. This difference relates to larger installments,<br />
as well as to <strong>the</strong> fact that a higher percentage<br />
of Ugandan clients used <strong>the</strong>ir loan for nonbusiness<br />
purposes such as paying for school fees.<br />
Borrowing requirements: In Uganda, <strong>the</strong> strict<br />
borrowing requirements—including 30 percent<br />
compulsory savings, two co-guarantors and a<br />
pledged asset—was a major desertion driver. In<br />
both Bangladesh and Uganda, and indeed in many<br />
group-lending methodologies, some element of<br />
compulsory savings is required. If an MFI is to<br />
continue to demand compulsory savings <strong>the</strong>n it<br />
should only require a fair minimum, it should offer to<br />
pay reasonable interest on it, and, within limits, it<br />
should provide clients with flexibility in access (e.g.<br />
<strong>the</strong> ability to draw down on some portion of that<br />
money to pay an installment if necessary).<br />
Loan amount: Accessing larger loans is among <strong>the</strong><br />
top five needs that clients express no matter where<br />
<strong>the</strong>y live. Managing <strong>the</strong> tension between <strong>the</strong><br />
demand for larger loans and <strong>the</strong> MFI’s credit risk is<br />
a careful balancing act.<br />
Clients in both programs identified <strong>the</strong> low loan<br />
amount as a reason for leaving. Offering larger<br />
loans and <strong>the</strong>reby retaining growth-oriented clients<br />
is a key to profitability, but it presents a challenge<br />
for group lenders. Many clients are now demanding<br />
individual loans, <strong>the</strong>refore <strong>the</strong> development of this<br />
product is a key retention strategy for group<br />
lenders.<br />
Transaction Costs<br />
Group meetings: The frequency and length of<br />
group meetings was a desertion driver in Shakti<br />
Foundation. Shakti meetings occur every week and<br />
attendance is compulsory, whereas at UWFT,<br />
clients can choose to meet weekly, biweekly or<br />
monthly, and attendance is not enforced.<br />
Figure 3: Top Ten Reasons for Desertion*<br />
Bangladesh<br />
Uganda<br />
1. Loan amount was too small (33%)<br />
2. Too many meetings (28%)<br />
3. The meetings were too long (25%)<br />
4. A member defaulted and I did not want to pay for her<br />
(25%)<br />
5. Loan was too expensive (high interest rates) (22%)<br />
6. The institution does not understand my special needs as<br />
a woman (20%)<br />
7. I do not need a loan right now (18%)<br />
8. I had to go to <strong>the</strong> village (17%)<br />
9. I (or someone in my family) got sick (17%)<br />
10. My business was not profitable (17%)<br />
1. Loan period was too short (65%)<br />
2. Interest on my voluntary savings was too low (64%)<br />
3. Loan was too expensive - high interest rates (57%)<br />
4. Compulsory savings too high (54%)<br />
5. I wanted to borrow as an individual, not as a group (53%)<br />
6. The weekly payment amount was too much (51%)<br />
7. I felt like I was borrowing my own money back (50%)<br />
8. No opportunities to participate in decisions made by <strong>the</strong><br />
institution (46%)<br />
9. A member defaulted; I did not want to pay for her (43%)<br />
10. When a problem arose, not enough support from staff<br />
(43%)<br />
* In response to <strong>the</strong> close-ended question: “Now, I will read you a list of reasons that o<strong>the</strong>r people had for leaving X institution. Please tell<br />
me which ones of <strong>the</strong> following reasons apply to you.” Ranking based on most frequently answered reasons.<br />
MICROBANKING BULLETIN, APRIL 2001 23
FEATURE ARTICLES<br />
The group meeting is <strong>the</strong> nexus around which <strong>the</strong><br />
Grameen model is based and indeed <strong>the</strong> majority of<br />
clients in both Bangladesh and Uganda said <strong>the</strong>y<br />
liked attending meetings. In Bangladesh, clients<br />
indicated that <strong>the</strong>y liked <strong>the</strong> opportunity to socialize,<br />
whereas in Uganda <strong>the</strong> meetings presented an<br />
opportunity to share ideas and learn from each<br />
o<strong>the</strong>r. However, some clients definitely tire of <strong>the</strong><br />
meetings. For some borrowers, <strong>the</strong> opportunity<br />
cost becomes too high; for o<strong>the</strong>rs, changes in<br />
family situations make attendance difficult.<br />
Group guarantee: The ‘peer guarantee’ is an<br />
essential element of most group lending methodologies.<br />
It acts as a collateral substitute, which<br />
enables group lenders to target client segments<br />
below that of individual lenders. However <strong>the</strong><br />
majority of respondents in both countries do not like<br />
it. Not only do <strong>the</strong>y not like paying for o<strong>the</strong>rs, but<br />
also <strong>the</strong>y do not like o<strong>the</strong>rs paying for <strong>the</strong>m.<br />
To reinforce <strong>the</strong> group guarantee, MFIs need to<br />
improve client selection and group formation. It is<br />
important to select only clients who are economically<br />
active, to be forthright about <strong>the</strong> risks associated<br />
with peer guarantees, and to emphasize peer<br />
‘support’ ra<strong>the</strong>r than ‘pressure’ when clients experience<br />
difficulty. Loan officers also need training to<br />
help groups work through solutions to repayment<br />
problems.<br />
Customer Service<br />
In both countries, a small percentage of clients said<br />
that negative interactions with loan officers were <strong>the</strong><br />
reason <strong>the</strong>y dropped out. This appears to be due to<br />
a mixture of factors, including clients not understanding<br />
<strong>the</strong> risks of borrowing and loan officers<br />
being under a lot of strain and being harsh with<br />
clients. Clients can and do generalize about an<br />
entire organization based on one bad interaction.<br />
Loan officers, and indeed all staff, must see clients<br />
as customers who are paying for services. They<br />
must realize that <strong>the</strong>y are managing long-term<br />
relationships, not one-time transactions. Employees<br />
should be given <strong>the</strong> appropriate support from<br />
<strong>the</strong> institution, including skills and time, to deliver a<br />
high quality service.<br />
Conclusion<br />
Since this research was completed, both affiliates<br />
have modified <strong>the</strong>ir loan product and transaction<br />
costs. Shakti Foundation increased loan amounts<br />
and increments. UWFT reduced <strong>the</strong> compulsory<br />
savings requirement, and lowered fees and<br />
commissions. In addition, one half of <strong>the</strong> remaining<br />
fees and commissions were allocated to a loan<br />
insurance fund. Clients were also given <strong>the</strong> option<br />
of increasing loan terms and loan sizes subject to<br />
<strong>the</strong>ir capacity to repay.<br />
The research shows that microfinance clients, like<br />
all consumers, behave rationally. They will stay<br />
with a supplier as long as benefits outweigh costs<br />
and <strong>the</strong>re are no better alternatives. When <strong>the</strong>y<br />
leave, <strong>the</strong>y do so because of product features that<br />
are unsuitable to <strong>the</strong>ir needs and/or service delivery<br />
attributes that are difficult or costly. The research<br />
highlights some key challenges facing MFIs:<br />
• Managing <strong>the</strong> balance between flexible<br />
products and services and <strong>the</strong> need to<br />
standardize procedures to improve efficiency;<br />
• Building a range of products that focuses on<br />
retaining clients over time, such as introducing<br />
individual lending, flexible compulsory savings,<br />
voluntary savings, micro-insurance and access<br />
to BDS;<br />
• Shifting <strong>the</strong> mindset throughout <strong>the</strong> organization<br />
to view <strong>the</strong> client as a customer and to realize<br />
that <strong>the</strong> customer is important.<br />
The research also demonstrates that talking to<br />
former clients can provide an MFI with a wealth of<br />
information about why <strong>the</strong>y became dissatisfied. If<br />
<strong>the</strong> research is designed well, <strong>the</strong> MFI can identify<br />
<strong>the</strong> product or service delivery attributes it should<br />
build on and which features should change to improve<br />
retention. The issue <strong>the</strong>n becomes, which<br />
clients does <strong>the</strong> MFI want to retain? This leads to<br />
<strong>the</strong> next big challenge in microfinance—segmenting<br />
<strong>the</strong> customers.<br />
To segment customers, an MFI should at minimum<br />
track information about <strong>the</strong> borrowing and saving<br />
behavior of individual clients, even within group<br />
lending methodologies. This data can <strong>the</strong>n be<br />
analyzed and fed into complementary market<br />
research activities.<br />
Deepening <strong>the</strong> institution’s understanding of its<br />
client base is an iterative and continuous process.<br />
Every transaction with a client should be viewed as<br />
a learning opportunity. Finding inexpensive ways of<br />
institutionalizing feedback mechanisms from <strong>the</strong><br />
client right up through to <strong>the</strong> board of directors is<br />
critical. This can be achieved, for example, by<br />
holding monthly meetings with field staff and acting<br />
on <strong>the</strong>ir ideas for improvement, and ensuring top<br />
management spends more time talking with current<br />
and former borrowers. Finally, developing a more<br />
refined understanding of client needs and<br />
preferences not only makes good business sense<br />
now, but it will also be a defining source of<br />
competitive advantage in <strong>the</strong> future.<br />
24 MICROBANKING BULLETIN, APRIL 2001
FEATURE ARTICLES<br />
Inez Murray is Organizational Strategy and Effectiveness<br />
Coordinator at WWB. She is responsible for developing<br />
<strong>the</strong> Strategic Positioning Service. Comments on her<br />
article can be sent to imurray@swwb.org.<br />
MICROBANKING BULLETIN, APRIL 2001 25
TALKING ABOUT PERFORMANCE RATIOS<br />
TALKING ABOUT PERFORMANCE RATIOS<br />
Measuring Client Retention<br />
Richard Rosenberg<br />
Most microfinance practitioners are coming to<br />
realize that client dropout has a surprisingly heavy<br />
effect in depressing profitability. 28 As a result, <strong>the</strong>re<br />
is an increasing amount of discussion about how to<br />
measure client desertion (or retention).<br />
Defining Terms<br />
A retention rate (RR) answers <strong>the</strong> question: “When<br />
clients had a chance this period to take out a repeat<br />
loan, what percent actually took <strong>the</strong> loan?” Correspondingly,<br />
a desertion—or dropout—rate (DR)<br />
answers <strong>the</strong> question: “When clients had a chance<br />
this period to take out a follow-on loan, what<br />
percent failed to take <strong>the</strong> loan?” Defined this way,<br />
<strong>the</strong>se two rates are simple complements of each<br />
o<strong>the</strong>r: Both rates are period specific.<br />
“Resting” Clients<br />
Any RR or DR needs to take into account <strong>the</strong> fact<br />
that a client who has paid off a loan without taking a<br />
repeat loan may be leaving <strong>the</strong> program for good,<br />
or simply waiting a while before taking out ano<strong>the</strong>r<br />
loan. No formula can tell us what <strong>the</strong> client will<br />
eventually do. However, a good formula should<br />
give <strong>the</strong> program “credit” when an inactive client<br />
becomes an active borrower again. At times when<br />
<strong>the</strong> number of clients returning to <strong>the</strong> fold is very<br />
large, such a formula will produce negative desertion<br />
rates and, correspondingly, retention rates<br />
above 100 percent, especially when <strong>the</strong> measurement<br />
period is short. Over time, this phenomenon<br />
will smooth out.<br />
The Formulas<br />
The choice of an appropriate formula presents<br />
complex issues. As of yet <strong>the</strong>re is no consensus.<br />
The discussion below represents my tentative<br />
conclusions.<br />
Waterfield/CGAP Formula. The formula that<br />
Chuck Waterfield used in <strong>the</strong> CGAP MIS Handbook<br />
seems simpler and less problematic than o<strong>the</strong>r<br />
formulas. 29 This formula focuses on <strong>the</strong> client’s<br />
main decision point—that is <strong>the</strong> point at which she<br />
has repaid her prior loan and has <strong>the</strong> option to take<br />
out a new one (a “follow-on” loan). At <strong>the</strong> point<br />
where <strong>the</strong> client chooses to take <strong>the</strong> follow-on loan,<br />
she is counted as retained, whe<strong>the</strong>r or not <strong>the</strong>re<br />
was a resting period before <strong>the</strong> follow-on loan.<br />
(1) RR = FL<br />
LP<br />
Where:<br />
FL = <strong>the</strong> number of follow-on loans made<br />
during <strong>the</strong> period<br />
LP = <strong>the</strong> number of loans paid off during <strong>the</strong><br />
period, and<br />
RR = retention rate<br />
Note that this formula produces <strong>the</strong> retention rate<br />
per loan cycle. To estimate how many clients <strong>the</strong><br />
organization is losing (or keeping) per year, it is<br />
necessary to factor in average loan term. 30 Suppose<br />
that <strong>the</strong> RR calculated by this formula is 80<br />
percent. If we run 4 loan cycles per year, <strong>the</strong>n only<br />
about (0.80) 4 = 41 percent of <strong>the</strong> clients active at<br />
<strong>the</strong> beginning of <strong>the</strong> year are still active at <strong>the</strong> end<br />
of <strong>the</strong> year.<br />
If <strong>the</strong> MIS doesn’t directly produce FL or LP, <strong>the</strong><br />
formula can be restated in a way that is more<br />
complex but uses information that may be easier to<br />
produce (see Formula 2).<br />
Mr. Waterfield modestly wrings his hands over <strong>the</strong><br />
fact that his formula can occasionally produce a<br />
retention rate over 100 percent. It’s hard to see<br />
why this bo<strong>the</strong>rs him: as noted above, <strong>the</strong> only way<br />
to keep a retention rate from ever exceeding 100<br />
percent is to ignore resting borrowers.<br />
28<br />
This article, originally prepared for <strong>the</strong> 1999 MicroFinance<br />
Network conference in Bangladesh, draws liberally from<br />
Development Finance Network (DFN) postings by Chuck<br />
Waterfield and Mark Schreiner. So any mistakes here must be<br />
<strong>the</strong>ir fault.<br />
29 The formula in <strong>the</strong> CGAP MIS handbook is a desertion rate. It<br />
is presented here as a retention rate because it is simpler.<br />
30<br />
For more than you wanted to know about estimating average<br />
loan term, see CGAP’s Occasional Paper No. 3, Measuring<br />
<strong>Microfinance</strong> Delinquency: How Ratios Can Be Harmful to Your<br />
Health.<br />
26 MICROBANKING BULLETIN, APRIL 2001
TALKING ABOUT PERFORMANCE RATIOS<br />
(2) RR = (L – NC)<br />
(AC begin + L – AC end )<br />
Where:<br />
L = <strong>the</strong> number of loans disbursed during <strong>the</strong><br />
period<br />
NC = <strong>the</strong> number of new (first time) clients<br />
entering during <strong>the</strong> period<br />
AC begin = <strong>the</strong> number of active clients at <strong>the</strong><br />
beginning of <strong>the</strong> period<br />
AC end = <strong>the</strong> number of active clients at <strong>the</strong><br />
end of <strong>the</strong> period<br />
RR = retention rate<br />
The above formulas do not include <strong>the</strong> effect of<br />
default, because <strong>the</strong> denominator is loans paid off.<br />
Clients who never repay <strong>the</strong>ir last loan are<br />
(presumably) lost to <strong>the</strong> program forever, so some<br />
MFIs will want a desertion formula that takes <strong>the</strong>m<br />
into account. One less-than-perfect way to do this<br />
is shown in Formula 3.<br />
(3) RR = FL<br />
(LP + WO)<br />
Where:<br />
FL = <strong>the</strong> number of follow-on loans made<br />
during <strong>the</strong> period<br />
LP = <strong>the</strong> number of loans paid off during <strong>the</strong><br />
period<br />
WO = <strong>the</strong> number of loans written off during<br />
<strong>the</strong> period (or o<strong>the</strong>rwise classified as<br />
unlikely to be repaid)<br />
RR = retention rate<br />
ACCION Formula. A desertion rate that has been<br />
used by ACCION and o<strong>the</strong>rs (including <strong>the</strong> author<br />
of this note) is shown in Formula 4:<br />
(4) DR = (AC begin + NC - AC end )<br />
AC begin<br />
31<br />
Where:<br />
AC begin = <strong>the</strong> number of active clients at <strong>the</strong><br />
beginning of <strong>the</strong> period<br />
NC = <strong>the</strong> number of new (first time) clients<br />
entering during <strong>the</strong> period<br />
AC end = <strong>the</strong> number of active clients at <strong>the</strong><br />
end of <strong>the</strong> period<br />
DR = desertion rate<br />
Because it includes clients who have not had a<br />
chance to be retained or to desert, it overstates <strong>the</strong><br />
frequency of clients eventually coming back for<br />
ano<strong>the</strong>r loan. (This is how we defined desertion<br />
above. O<strong>the</strong>r definitions are possible.) The longer<br />
<strong>the</strong> initial loan term in relation to <strong>the</strong> reporting<br />
period, <strong>the</strong> more pronounced this effect would be.<br />
Schreiner Formula. Mark Schreiner, a dauntingly<br />
meticulous analyst, uses a variant of Formula 4 that<br />
solves that formula’s problem of being unusable for<br />
start-up operations.<br />
(5) RR = AC end<br />
(AC begin + NC)<br />
Where:<br />
AC begin = number of active clients at <strong>the</strong><br />
beginning of <strong>the</strong> period<br />
NC = <strong>the</strong> number of new (first time) clients<br />
entering during <strong>the</strong> period<br />
AC end = <strong>the</strong> number of active clients at <strong>the</strong><br />
end of <strong>the</strong> period<br />
RR = retention rate<br />
Mark likes this formula because he feels that it uses<br />
information more commonly available to an outside<br />
analyst (like him, for instance). He notes that his<br />
formula, like <strong>the</strong> ACCION formula, does not take<br />
into account <strong>the</strong> fact that some of clients included in<br />
<strong>the</strong> count have not yet had a chance to desert. Just<br />
as in Formula (4), this distortion becomes larger as<br />
<strong>the</strong> average loan term increases in relation to <strong>the</strong><br />
reporting period.<br />
Conclusion<br />
It seems to me that <strong>the</strong> Waterfield/CGAP ratio is<br />
more useful in most situations. I would be grateful<br />
for comments from anyone, especially people who<br />
have on-<strong>the</strong>-ground experience with trying to use<br />
desertion or retention rates.<br />
Send your comments on retention rates to Rich<br />
Rosenberg, a member of <strong>the</strong> Bulletin’s Editorial Board, at<br />
rrosenberg@worldbank.org.<br />
Formula 4 has important weaknesses. It does not<br />
work for a startup program, where AC begin is zero.<br />
31<br />
The MicroBanking Bulletin uses this formula with a slight<br />
modification; borrowers who are returning after a rest period of<br />
more than 2 years are considered new clients (NC).<br />
MICROBANKING BULLETIN, APRIL 2001 27
BULLETIN HIGHLIGHTS AND TABLES<br />
COMMENTARY AND REVIEWS<br />
Maximizing Efficiency: The Path to<br />
Enhanced Outreach and Sustainability<br />
Monica Brand and Julie Gerschick<br />
ACCION Monograph No. 12<br />
Hardcopy US$20.00/Electronic US$13.00<br />
To order: email publications@accion.org or<br />
download from www.accion.org<br />
More, better, faster. These three words have<br />
become <strong>the</strong> mantra of successful businesses during<br />
recent years. Driven by competition, technology<br />
and demanding clients, successful firms constantly<br />
search for ways to provide better products and<br />
services more quickly, and often at lower costs,<br />
while improving <strong>the</strong>ir bottom line. This message<br />
from <strong>the</strong> business world is applied to <strong>the</strong> microfinance<br />
industry in ACCION International's recent<br />
monograph Maximizing Efficiency by Monica Brand<br />
and Julie Gerschick.<br />
In this publication, <strong>the</strong> authors draw on business<br />
literature, empirical data and numerous examples<br />
from microfinance institutions to build <strong>the</strong> case for<br />
efficiency as a bridge between <strong>the</strong> sometimes<br />
competing camps of outreach and sustainability.<br />
The underlying <strong>the</strong>me is that a focus on efficiency<br />
will help institutions reach more clients and attain<br />
higher levels of profitability. Hence, this publication<br />
should be of interest to managers, shareholders,<br />
donors and consultants in <strong>the</strong> microfinance field<br />
who want to lower interest rates, increase outreach<br />
and achieve a healthier bottom line.<br />
The book opens with a concise overview of microfinance<br />
and examines conventional wisdom regarding<br />
<strong>the</strong> correlation between efficiency, outreach and<br />
loan size. Brand and Gerschick cite empirical evidence<br />
showing significant efficiency gains achieved<br />
through growth appear in institutions with up to<br />
10,000 to 12,000 clients.<br />
The authors argue that additional efficiency gains<br />
require more than growth; <strong>the</strong>y require considerable<br />
organizational change. This discussion indicates<br />
that outreach, beyond a certain level, does not<br />
significantly improve efficiency. A fur<strong>the</strong>r examination<br />
of <strong>the</strong> scale required to justify investments in<br />
efficiency enhancements—such as technology,<br />
functional specialization, and product and service<br />
diversification—is warranted given <strong>the</strong> number of<br />
donors supporting <strong>the</strong> proliferation of small microfinance<br />
institutions and <strong>the</strong> entrance of larger<br />
commercial financial institutions. The authors also<br />
argue that small loan size is not an excuse for<br />
inefficient or financially unsustainable organizations<br />
by identifying organizations that have achieved<br />
sustainability while offering very small loans.<br />
Brand and Gerschick demonstrate that sustainability<br />
is not a sufficient measure of financial performance.<br />
Sustainability is often achieved through<br />
high interest rates that mask excessive operating<br />
costs resulting from inefficiencies. They argue that<br />
many indicators that evaluate organizational efficiency<br />
and profitability of microfinance institutions,<br />
such as administrative expenses over average<br />
portfolio and return on assets, do not show <strong>the</strong><br />
whole picture. The authors bring to <strong>the</strong> forefront <strong>the</strong><br />
importance of evaluating <strong>the</strong> relationship between<br />
costs and revenues through <strong>the</strong> analysis of <strong>the</strong><br />
bank efficiency ratio (total pre-tax expenses over<br />
net income) and its reciprocal net profit contribution.<br />
They also delve into <strong>the</strong> complexities of allocating<br />
costs for accurate analysis at different levels, from<br />
<strong>the</strong> entire institution, down to specific client and<br />
product segments.<br />
This monograph presents a myriad of concrete<br />
examples of managing organizational change to<br />
enhance efficiency. The analytical framework is<br />
alignment <strong>the</strong>ory, which <strong>the</strong> authors describe as a<br />
holistic approach to identify multiple causes of<br />
inefficiency by examining strategy, products and<br />
internal systems. The authors cite a number of<br />
areas where managers can enhance efficiency, and<br />
<strong>the</strong>y spice <strong>the</strong> text with examples drawn from microfinance<br />
institutions worldwide, such as:<br />
• Modifying products to reduce collections problems<br />
by customizing repayments to <strong>the</strong> borrowers’<br />
cash flow, offering revolving loans (or lines<br />
of credit) to decrease <strong>the</strong> cost of marketing, and<br />
issuing credit cards to enhance fee generation;<br />
• Increasing staff productivity through incentive<br />
systems, transportation equipment, and establishing<br />
specialized staff positions for routine<br />
administrative functions;<br />
• Improving client retention through pricing incentives<br />
and streamlined repeat loan approvals;<br />
• Pricing to maximize efficiency by differentiating<br />
interest rates and fees to price for risk and<br />
administrative costs;<br />
• Establishing partnerships with business<br />
associations, financial institutions and o<strong>the</strong>rs to<br />
lower <strong>the</strong> cost to attract clients;<br />
28 MICROBANKING BULLETIN, APRIL 2001
COMMENTARY AND REVIEWS<br />
• Standardizing tools and templates for loan<br />
documentation, underwriting criteria and branch<br />
openings;<br />
• Evaluating <strong>the</strong> appropriate level of delinquency<br />
to adjust time spent on selection and<br />
monitoring;<br />
• Utilizing technology to automate processes<br />
such as posting loan payments, generating<br />
collections letters, and establishing call centers<br />
and credit scoring systems.<br />
Three microfinance institutions—ACCION New<br />
York, BancoSol in Bolivia, and MiBanco in<br />
Peru—are showcased as examples of<br />
reengineering initiatives to help achieve higher<br />
levels of efficiency and to maintain (or gain) a<br />
competitive market position. Each case presents<br />
some interesting examples of efficiency<br />
enhancements, from computerizing paperwork to<br />
reducing financial analysis. A follow-up analysis,<br />
applying <strong>the</strong> financial ratios and alignment<br />
framework from <strong>the</strong> first chapters of <strong>the</strong> monograph,<br />
to evaluate <strong>the</strong> efficiencies gained through <strong>the</strong><br />
reengineering process would help to determine if<br />
<strong>the</strong> work achieved its goals.<br />
This monograph is ambitious and dense. However,<br />
<strong>the</strong> overall message is clear and important: microfinance<br />
institutions must improve <strong>the</strong>ir efficiency if<br />
<strong>the</strong>y want to stay in <strong>the</strong> game and fulfill <strong>the</strong>ir<br />
mission. Profit-driven institutions in competitive<br />
environments most likely have incorporated a drive<br />
for efficiency into <strong>the</strong>ir culture and operations, but<br />
this publication will provide ideas for continued<br />
improvements. Donor-dependent organizations<br />
and those operating in less competitive markets are<br />
likely to fall behind <strong>the</strong> efficiency curve and should<br />
glean from this monograph insights into better<br />
performance measurements to come. A more<br />
consistent and extensive application of <strong>the</strong> efficiency<br />
philosophy presented by Brand and Gerschick<br />
would greatly help all microfinance institutions<br />
and clients.<br />
Review prepared by Robin Young of Development<br />
Alternatives, Inc. who presently serves as Deputy Chief of<br />
Party for <strong>the</strong> Rural <strong>Microfinance</strong> Streng<strong>the</strong>ning project in<br />
El Salvador (robin_young@dai.com).<br />
MICROBANKING BULLETIN, APRIL 2001 29
COMMENTARY AND REVIEWS<br />
Improving Internal Control: A Practical<br />
Guide for <strong>Microfinance</strong> Institutions<br />
Anita Campion<br />
MicroFinance Network (with GTZ) Technical Guide<br />
No. 1<br />
Hardcopy US$15.00/Electronic: 25% discount<br />
To order: email mfn@mfnetwork.org or download<br />
from www.mfnetwork.org<br />
In Improving Internal Control, Anita Campion<br />
discusses a topic that is becoming increasingly<br />
important for microfinance institutions: efficient<br />
internal control as an instrument for managing<br />
financial and operational risks—especially <strong>the</strong> risk<br />
of fraud. In her introduction Campion quite rightly<br />
points out that internal control cannot be seen as a<br />
separate function within an institution, but that it<br />
needs to be placed in <strong>the</strong> context of a more broadly<br />
defined risk management framework.<br />
One of <strong>the</strong> book’s main messages, a point that is<br />
repeatedly made explicitly and implicitly, is that well<br />
conceived control policies and procedures, such as<br />
adherence to <strong>the</strong> “second signature” principle or <strong>the</strong><br />
implementation of effective incentive mechanisms<br />
for staff and customers, contribute more to <strong>the</strong> costefficient<br />
reduction of institutional risks than <strong>the</strong><br />
construction of a painstakingly detailed ex-post<br />
control mechanisms. An ounce of prevention is<br />
worth a pound of cure.<br />
None<strong>the</strong>less, fraud cannot be wholly prevented.<br />
Campion describes in detail <strong>the</strong> process of building<br />
an internal control system. Through numerous<br />
practical examples, this document provides a useful<br />
guide to designing an internal control system. The<br />
emphasis is on verifying <strong>the</strong> efficiency of <strong>the</strong><br />
internal control system through ex-post controls<br />
(e.g. <strong>the</strong> internal audit process).<br />
The book is rounded out with a discussion of how<br />
best to institutionalize internal control systems.<br />
Again, <strong>the</strong> emphasis is on ex-post controls, especially<br />
establishing an internal audit department.<br />
Campion points out that <strong>the</strong> scale and scope of <strong>the</strong><br />
operations, as well as <strong>the</strong> regulatory environment,<br />
are major factors that determine <strong>the</strong> optimal<br />
structure and scope of an internal control system.<br />
The main target audiences for this book are<br />
practitioners working at (and <strong>the</strong> members of <strong>the</strong><br />
supervisory bodies of) small and medium-sized<br />
microfinance institutions that (as yet) have a<br />
relatively narrow range of products, i.e. mainly<br />
installment loans and possibly also savings<br />
facilities. For this limited readership, <strong>the</strong> book is<br />
undoubtedly a valuable aid as it draws attention to<br />
key risk management issues, derives from <strong>the</strong>m <strong>the</strong><br />
purposes and benefits of internal controls, and<br />
offers practical suggestions for implementing and<br />
improving efficient internal control systems. In<br />
particular, <strong>the</strong> book will be useful for less experienced<br />
internal auditors and for managers at small<br />
and medium-sized microfinance institutions who<br />
deal with internal audit issues.<br />
However, <strong>the</strong> book deals exclusively with internal<br />
control systems at <strong>the</strong> operational branch level and<br />
<strong>the</strong>refore leaves a number of questions unanswered.<br />
For example, it has little or nothing to say<br />
about managing and controlling operational and<br />
financial risks at <strong>the</strong> level of <strong>the</strong> institution as a<br />
whole—exchange rate risk, interest rate risk and<br />
liquidity risk, for example, which are often monitored<br />
and managed centrally at <strong>the</strong> head office. 32 Nor<br />
does <strong>the</strong> book explain how to control operational<br />
and financial risks associated with a broader range<br />
of products that includes overdrafts, checks, bank<br />
guarantees and documentary payments. Admittedly,<br />
<strong>the</strong> book makes no claim to provide answers<br />
to <strong>the</strong>se questions.<br />
From <strong>the</strong> point of view of a manager of a target<br />
group-oriented commercial bank providing a wide<br />
range of products to a diversified customer base,<br />
one final observation to be made is that <strong>the</strong><br />
principles to be observed when constructing internal<br />
control systems for small microfinance institutions<br />
with NGO status are basically identical to those in<br />
place at large commercial banks. Institutions must<br />
learn to recognize (and accept) <strong>the</strong>ir specific risks,<br />
to quantify <strong>the</strong>m, and to use this information as <strong>the</strong><br />
basis for developing cost-efficient internal control<br />
systems. The real potential of this book is to address<br />
decision-makers at microfinance institutions<br />
who have, knowingly or unknowingly, paid insufficient<br />
attention to this set of issues. It seeks to<br />
open <strong>the</strong>ir eyes to <strong>the</strong> necessity of building internal<br />
control systems and offers suggestions on how to<br />
implement <strong>the</strong>m. One can only hope that <strong>the</strong> decision-makers<br />
are already sufficiently aware of <strong>the</strong><br />
issues to be motivated to actually read this book.<br />
Luis Schunk (schunk@ipcgmbh.com) works for IPC and<br />
is <strong>the</strong> General Manager of FEFAD Bank in Albania.<br />
32<br />
These issues are addressed in a companion publication, “A<br />
Risk Management Framework for <strong>Microfinance</strong> Institutions," by<br />
Janney Carpenter and Lynn Pikholz with Anita Campion,<br />
published by GTZ (www.gtz.de), 2000.<br />
30 MICROBANKING BULLETIN, APRIL 2001
BULLETIN CASE STUDY<br />
BULLETIN CASE STUDY<br />
Bosnian MFIs: Performance, and Productivity<br />
Isabelle Barrès<br />
Bosnia-Herzegovina 33 is a former republic of<br />
Yugoslavia, which was a middle-income country<br />
before its breakup and <strong>the</strong> war. It is now in process<br />
of both post-conflict reconstruction and economic<br />
recovery, and in transition to a market economy.<br />
This has proven difficult, and high unemployment<br />
and limited opportunities in <strong>the</strong> formal sector have<br />
increased <strong>the</strong> demand for microcredit 34 . Due to<br />
<strong>the</strong>se factors, many of <strong>the</strong> poor targeted by MFIs<br />
are “new poor” (i.e. people who lost <strong>the</strong>ir jobs and<br />
assets during <strong>the</strong> war), who were previously<br />
employed in state-owned enterprises with a fairly<br />
stable income and a high level of social security.<br />
Given <strong>the</strong> increased demand for microcredit, <strong>the</strong><br />
government of Bosnia-Herzegovina has been<br />
supporting <strong>the</strong> microfinance industry through a<br />
number of measures: investing government funds in<br />
<strong>the</strong> sector, including microfinance as a key part of<br />
<strong>the</strong>ir development strategy, and adopting legislation<br />
to legalize <strong>the</strong> provision of microcredit by NGOs 35 .<br />
<strong>Microfinance</strong> in <strong>the</strong> country is evolving quickly. As<br />
of March 2000, <strong>the</strong>re were 18 main MFIs in Bosnia-<br />
Herzegovina, down from 34 in 1999. This article<br />
focuses on eight MFIs that represent a range in<br />
terms of both target clientele and performance:<br />
AMK, Bospo, LOK, MEB, Mercy Corps/Partner 36 ,<br />
Mikrofin, Sunrise, and World Vision.<br />
These are relatively new MFIs (average of 2 years<br />
in 1999), averaging just over US$3.3 million in<br />
outstanding portfolio with about 2,500 borrowers.<br />
They use a mix of individual and solidarity group<br />
methodologies, and are all serving broad or highend<br />
clients, with an average loan balance relative to<br />
GNP per capita of 163 percent (or US$1,731). The<br />
latter hides important variations between<br />
33<br />
Bosnia-Herzegovina (BiH) is comprised of: <strong>the</strong> Federation of<br />
BiH and <strong>the</strong> Republika Srpska, governed by different sets of<br />
banking rules.<br />
34<br />
Demand for savings product is considered to be low due to <strong>the</strong><br />
loss of confidence in banks resulting from <strong>the</strong> war.<br />
35<br />
A new law for microcredit organizations was passed in <strong>the</strong><br />
Federation of BiH in July 2000 and in April 2001 in Republika<br />
Srpska. MFIs are now legally able to provide credit to <strong>the</strong>ir<br />
clients.<br />
36<br />
Mercy Corps’s name changed upon registration as a local<br />
microcredit organization under <strong>the</strong> Federation law. Partner's<br />
registration became effective January 1, 2001.<br />
institutions. Indeed, Bospo offers loans that are on<br />
average 39 percent relative to GNP per capita while<br />
MEB targets small business clients, with an<br />
average loan balance of 336 percent relative to<br />
GNP per capita.<br />
All are NGOs except MEB, a full service bank that<br />
offers a wide variety of products, including loans,<br />
savings and payment services. Due to a complex<br />
political environment, only MEB, Mercy<br />
Corps/Partner, and LOK have branch networks that<br />
span <strong>the</strong> whole country.<br />
Issues Facing Bosnian MFIs<br />
Business Environment: MFIs face a challenging<br />
business environment.<br />
Bosnian MFIs and <strong>the</strong>ir clients, as all organizations<br />
in <strong>the</strong> country, face <strong>the</strong> lack of a supportive<br />
business environment. For example, since many<br />
clients are not registered to avoid prohibitive tax<br />
rates on <strong>the</strong>ir small businesses, Bosnian MFIs need<br />
to track <strong>the</strong>ir dual reporting systems (one for <strong>the</strong><br />
financial police 37 , one to track <strong>the</strong> real financial<br />
strength of <strong>the</strong> businesses). This additional task<br />
taken on by <strong>the</strong> MFIs to accommodate clients increases<br />
<strong>the</strong> loan processing time.<br />
Competition: Although still low, competition is on<br />
<strong>the</strong> rise, and MFIs are already trying to adjust to it.<br />
According to LID, a specialized, semi-governmental<br />
microfinance funding and capacity-building body,<br />
competition for microfinance services is low, due to<br />
excess demand and a culture gap between<br />
commercial banking and microfinance. Indeed, <strong>the</strong><br />
microfinance target market in Bosnia-Herzegovina<br />
includes a growing number of high-risk displaced<br />
persons or returnees 38 , whom commercial banks<br />
are reluctant to serve because of <strong>the</strong> risks involved<br />
and <strong>the</strong>ir unwillingness to expand into <strong>the</strong> poorest<br />
regions of <strong>the</strong> country to reach this dispersed<br />
population.<br />
Never<strong>the</strong>less, as young MFIs mature and <strong>the</strong> formal<br />
banking sector stabilizes, competition is expected to<br />
37<br />
A governmental body in charge of conducting financial audits<br />
of institutions and private entities.<br />
38<br />
This refers to persons who were displaced during <strong>the</strong> war and<br />
persons who were able to return to <strong>the</strong>ir homes when it ended.<br />
MICROBANKING BULLETIN, APRIL 2001 31
BULLETIN CASE STUDY<br />
increase. Some MFIs are already beginning to feel<br />
competitive pressure. For example, MEB lost 3<br />
percent of its clients in 6 weeks due to aggressive<br />
campaigns from two Austrian banks that reduced<br />
<strong>the</strong>ir minimum enterprise loan size from DM50,000<br />
to DM25,000 39 , while requiring no collateral, and<br />
offering very competitive interest rates for both<br />
savings and loans. 40 Sunrise is also experiencing<br />
challenges in matching <strong>the</strong> demand for bigger loans<br />
(over DM20,000) with reasonable terms (5 years).<br />
Competition between MFIs and commercial banks<br />
is likely to continue for high-end loans above<br />
DM20,000, and will have <strong>the</strong> most impact on MEB,<br />
which is serving a higher-end clients compared to<br />
<strong>the</strong> NGO MFIs.<br />
Some programs, like Sunrise, fear that MFIs will be<br />
competing on two fronts: with <strong>the</strong> commercial banks<br />
and with <strong>the</strong>ir peers, and are adopting a variety of<br />
measures to address it:<br />
Forming Mergers: Institutions are merging to take<br />
advantage of <strong>the</strong>ir complementary resources and<br />
regional coverage. For example, after merging with<br />
three o<strong>the</strong>r institutions, LOK now has <strong>the</strong> best<br />
infrastructure and branch network. O<strong>the</strong>r examples<br />
include Sunrise, or World Vision, that merged with<br />
an organization in Mostar.<br />
Increasing Product Flexibility: To meet client<br />
needs, MFIs are moving towards a demand-driven<br />
approach for <strong>the</strong> design of <strong>the</strong>ir services. For<br />
example, Mikrofin is increasing <strong>the</strong> flexibility of its<br />
products by introducing bigger ranges per cycle, a<br />
grace period starting with <strong>the</strong> 2 nd cycle, lower<br />
interest rates, and no upper limit for loans in <strong>the</strong>ir<br />
last cycle. Mercy Corps/Partner, on <strong>the</strong> o<strong>the</strong>r hand,<br />
has implemented a program of focus groups to<br />
identify <strong>the</strong>ir client’s needs and adapt <strong>the</strong>ir loan<br />
products accordingly.<br />
Targeting Untapped Markets: MFIs are expanding<br />
<strong>the</strong>ir outreach to less competitive segments of <strong>the</strong><br />
market. For instance, Mercy Corps/Partner targets<br />
lower-end clients and provides loan officers with<br />
incentives for <strong>the</strong> number of first time loans under<br />
DM2,500. In addition, sixty percent of <strong>the</strong>ir clients<br />
are in rural areas. In <strong>the</strong> future, <strong>the</strong>re may be more<br />
opportunities for MFIs to operate in rural areas, as<br />
some industries are expected to fail due to <strong>the</strong><br />
transition from <strong>the</strong> old economic system, which will<br />
increase <strong>the</strong> number of persons turning to selfemployment<br />
in <strong>the</strong>se regions.<br />
Streamlining Procedures: All <strong>the</strong> MFIs studied<br />
were trying to improve <strong>the</strong>ir procedures to be more<br />
responsive to client needs. For instance, Sunrise is<br />
simplifying its complex procedures and reducing <strong>the</strong><br />
number of days to screen clients in order to<br />
increase productivity; Mikrofin has reduced <strong>the</strong> time<br />
for processing loan renewals to two days; MEB is<br />
training loan officers to deliver multiple products to<br />
take advantage of cross-selling opportunities; and<br />
LOK is streamlining <strong>the</strong> disbursement and collection<br />
of its loans. Benefit 41 , following <strong>the</strong> example of ABA<br />
in Egypt, now disburses loans only three times a<br />
month and collects installments only four days a<br />
month to organize <strong>the</strong> workload for <strong>the</strong> head office<br />
and branch staff and improve productivity.<br />
Reducing <strong>the</strong> “Loan Gap”: This gap refers to<br />
clients who require loans from DM10,000 to 30,000<br />
not offered by MFIs or commercial banks. 42<br />
Because <strong>the</strong>se clients are hardly serviced by<br />
anyone, <strong>the</strong>y respond to this challenge by securing<br />
multiple (lower) loans from different MFIs. This<br />
“loan gap” represents an opportunity for MFIs<br />
seeking to increase <strong>the</strong>ir market share. For<br />
example, Mercy Corps/Partner has introduced a<br />
new loan product in 2000 in response to client<br />
demand: loans from DM10,000 to 20,000 for repeat<br />
clients in good standing. While some MFIs, such as<br />
MEB 43 , view this segment as a potential target<br />
market, o<strong>the</strong>r MFIs, such as Bospo, focus on<br />
deepening <strong>the</strong>ir outreach downwards, not upwards.<br />
Staff: There is a high level of competition for<br />
qualified staff.<br />
Bosnia-Herzegovina is experiencing a shortage of<br />
good loan officers, in part due to <strong>the</strong> presence of a<br />
large international community (bilateral and<br />
multilateral agencies) focusing on post-war relief<br />
efforts. It is attracting new university graduates with<br />
very high salaries well above country standards.<br />
Arrears: Arrears are low, but should be carefully<br />
monitored.<br />
Bosnian MFIs have very low arrears rates. This is<br />
due to a combination of factors, including <strong>the</strong> MFIs’<br />
strong focus on delinquency management from <strong>the</strong><br />
very beginning, and <strong>the</strong> credit culture and powerful<br />
sense of honor among <strong>the</strong>ir clients, who are “new”<br />
39<br />
As of December 2000, 1US$ = 2.29DM, IMF Statistics.<br />
40<br />
These banks do require guarantees but no collateral. While<br />
<strong>the</strong> market interest rate for MEB’s clients was 2-3% per month,<br />
Volksbank set its interest rates at 1-5% for SME loans. For<br />
savings, MEB was offering 2–3% vs. 14% at <strong>the</strong> commercial<br />
banks. Because of <strong>the</strong>se interest rate policies, MEB lost 20-30<br />
clients (of a total of 240) during a period of 6 weeks.<br />
41 Benefit does not currently participate in <strong>the</strong> Bulletin, and is not<br />
included in <strong>the</strong> rest of <strong>the</strong> study.<br />
42<br />
Estimates for this gap vary from DM5,000 to 50,000. Even <strong>the</strong><br />
most aggressive commercial banks are starting <strong>the</strong>ir loans at a<br />
minimum of DM20,000.<br />
43<br />
MEB currently has applications pending for loans that fall<br />
within this range.<br />
32 MICROBANKING BULLETIN, APRIL 2001
BULLETIN CASE STUDY<br />
poor, due to <strong>the</strong> post-war conditions. Repaying<br />
loans is an important way for people to regain <strong>the</strong>ir<br />
economic standing, and microfinance services are<br />
highly valued.<br />
Never<strong>the</strong>less, arrears should be carefully monitored,<br />
especially as <strong>the</strong> lack of regulation and increasing<br />
competition motivate clients to take concurrent<br />
loans from different MFIs. MFIs currently<br />
call each o<strong>the</strong>r to share client information because<br />
of <strong>the</strong> difficulty in tracking <strong>the</strong> whereabouts of some<br />
of <strong>the</strong>ir borrowers (i.e. displaced persons). As<br />
competition increases, MFIs may become less<br />
willing to share client information. In addition, with<br />
<strong>the</strong> closure of ZPP, <strong>the</strong> government’s payment<br />
bureau, arrears are expected to increase; all customers<br />
of financial institutions (including MFIs) were<br />
previously required to maintain accounts at ZPP<br />
that were used as collateral for loans.<br />
Performance of Selected Bosnian MFIs<br />
As of 1999, <strong>the</strong> Bosnian programs were profitable<br />
only because of subsidies. After adjusting for<br />
subsidies, <strong>the</strong> financial self-sufficiency (FSS) ratio<br />
averaged 92 percent for <strong>the</strong> eight MFIs in this study<br />
(see Figure 1).<br />
Figure 1: Overall Performance<br />
Bosnian<br />
MFIs<br />
New<br />
MFIs<br />
All<br />
MFIs<br />
Portfolio Yield (%) 33 37 39<br />
Real Yield (%) 18 24 30<br />
Adjusted Return on Assets (%) -3.9 -9.8 -3.5<br />
Adjusted Return on Equity (%) -39.6 -21.5 -5.7<br />
Operational Self-sufficiency (%) 113 93 104<br />
Financial Self-sufficiency (%) 92 76 90<br />
Source: MicroBanking Bulletin database. Data are for Dec. 1999<br />
except for Mercy Corps/Partner (Dec. 2000).<br />
This implies that <strong>the</strong> MFIs were generating income<br />
to cover only 92 percent of <strong>the</strong>ir total expenses.<br />
None<strong>the</strong>less, <strong>the</strong>ir overall performance surpassed<br />
that of all <strong>the</strong> MFIs in <strong>the</strong> Bulletin that fall within<br />
<strong>the</strong>ir age group (operating for less than 3 years).<br />
On average, <strong>the</strong>y showed an adjusted return on<br />
assets of –3.9 percent (compared with –9.8 percent<br />
for all <strong>the</strong> New MFIs in <strong>the</strong> Bulletin), even with lower<br />
yields. These results are explained by <strong>the</strong>ir high<br />
level of efficiency.<br />
Efficiency<br />
As shown in Figure 2, <strong>the</strong> Bosnian MFIs have better<br />
efficiency ratios on average than o<strong>the</strong>r MFIs analyzed<br />
by <strong>the</strong> Bulletin. This is due mostly to a lower<br />
ratio of administrative expenses to average loan<br />
portfolio. The average loan balance as a percentage<br />
of GNP per capita (<strong>the</strong> depth ratio) in Bosnia-<br />
Herzegovina is more than twice that of all New<br />
MFIs in <strong>the</strong> Bulletin, which compensates for a<br />
higher wage structure and lower staff productivity<br />
(with an average of 75 clients per staff vs. 96 for all<br />
New MFIs). These findings hold even after<br />
excluding MEB (<strong>the</strong> only MFI in <strong>the</strong> sample targeted<br />
at small businesses) from <strong>the</strong> Bosnian programs.<br />
Because MEB offers a wide range of financial<br />
products to its clients and targets a higher-end<br />
market, excluding it from <strong>the</strong> sample increases <strong>the</strong><br />
average staff productivity and lowers <strong>the</strong> average<br />
depth ratio for Bosnian MFIs.<br />
Even so, <strong>the</strong> depth ratio is still twice that of all New<br />
MFIs in <strong>the</strong> Bulletin, and three times more in<br />
comparison to all participants. The Bosnian MFIs<br />
would experience much lower efficiency ratios<br />
(relative to loan portfolio) were <strong>the</strong>y not targeting a<br />
higher-end market.<br />
Figure 2: Efficiency Indicators<br />
Bosnian Bosnian<br />
MFIs MFIs*<br />
New<br />
MFIs<br />
All<br />
MFIs<br />
Total Admin Expense / LP (%) 25 26 42 31<br />
Salary Expense / LP (%) 15 16 24 17<br />
Average Salary (multiple of<br />
GNP per capita) 9.3 9.6 7.0 5.8<br />
Staff Productivity 75 81 96 122<br />
Cost per Borrower ($) 333 240 184 137<br />
Depth Ratio (average loan<br />
size/GNP per capita) (%)<br />
163 138 70 45<br />
Source: MicroBanking Bulletin database. Data are for Dec. 1999<br />
except for Mercy Corps/Partner (Dec. 2000).<br />
* Excluding MEB.<br />
The high cost per borrower results from <strong>the</strong> fact that<br />
Bosnian MFIs have fewer borrowers on which to<br />
spread <strong>the</strong> costs (2,486 clients on average<br />
compared to 5,081 for <strong>the</strong> New MFIs in <strong>the</strong><br />
Bulletin).<br />
Productivity<br />
Figure 3 summarizes <strong>the</strong> main productivity indicators.<br />
Although on average staff productivity of<br />
Bosnian MFIs is almost half of that of all MFIs, it is<br />
nearly equivalent when we compare <strong>the</strong>m to<br />
programs that serve similar target markets. Broad<br />
programs (MFIs with average loan balances<br />
between 20 and 150 of GNP per capita) show<br />
productivity levels double that of High-end<br />
programs, which is explained in part by <strong>the</strong> solidarity<br />
group loan methodology used by most programs<br />
serving <strong>the</strong> “Broad” target market.<br />
MICROBANKING BULLETIN, APRIL 2001 33
BULLETIN CASE STUDY<br />
Figure 3: Efficiency and Productivity Indicators for 8 Individual MFIs<br />
Methodology<br />
Clients/<br />
Staff<br />
Clients/<br />
Loan<br />
Officer<br />
Loan<br />
Officer/<br />
Staff (%)<br />
Staff<br />
Turnover<br />
Loan<br />
Portfolio/<br />
Staff ($)<br />
LP/ Loan<br />
Officer ($)<br />
Number of<br />
Borrowers<br />
Total loan<br />
portfolio<br />
($)<br />
Portfolio<br />
at Risk<br />
(%)<br />
AMK Individual 36 76 47 26 62,827 132,636 680 1,193,720 0.0 162<br />
MEB Individual 30 96 31 na 110,348 351,108 2,118 7,724,382 na 336<br />
Sunrise Individual 70 121 58 0 112,173 193,754 1,331 2,131,293 0.0 258<br />
World Vision SG / Ind.** 63 106 60 13 138,761 231,268 1,902 4,162,820 1.2 202<br />
Bosnian (High - end) 50 100 49 13 106,027 227,192 1,508 3,803,054 0.4 239<br />
Bospo Solidarity 156 423 37 11 66,081 179,363 2,961 1,255,540 0.0 39<br />
LOK Ind. / SG 64 121 53 0 87,233 165,283 2,302 3,140,378 0.0 126<br />
Mercy Corps Individual 90 133 67 3 66,797 99,380 5,461 4,074,599 0.1 99<br />
Mikrofin SG. / Ind. 89 156 57 6 80,958 141,677 3,129 2,833,535 0.0 83<br />
Bosnian(Broad) 100 208 54 5 75,267 146,426 3,463 2,826,013 0.0 87<br />
All MFIs (High - end) 48 120 43 7 127,017 300,539 4,373 9,778,077 0.7 310<br />
All MFIs (Broad) 113 252 47 11 70,093 154,481 60,480 24,414,911 2.6 65<br />
All MFIs 122 257 50 11 45,929 101,967 11,398 3,764,997 1.9 45<br />
* GNP per capita = US$1,086 for 1999 and US$760 for 2000, World Bank Statistics.<br />
** SG = Solidarity Groups; Ind. = Individual Loans.<br />
Source: MicroBanking Bulletin database. Data are for December 1999 except for Mercy Corps (December 2000).<br />
Depth<br />
Ratio*<br />
(%)<br />
Despite lower overall productivity for Bosnian MFIs<br />
compared to all MFIs in <strong>the</strong> Bulletin, <strong>the</strong>y have a<br />
higher average loan portfolio per staff, due to higher<br />
average loan sizes. Results also show that Bosnian<br />
MFIs have excellent quality portfolio, with an<br />
average portfolio at risk over 90 days of only 0.2<br />
percent.<br />
The data in Figure 3 also highlight considerable<br />
differences between Bosnian MFIs. Indeed, Bospo,<br />
which uses <strong>the</strong> solidarity group methodology exclusively,<br />
has <strong>the</strong> highest level of staff productivity,<br />
with an average of 423 clients per loan officer.<br />
Mikrofin, with 70 percent solidarity group loans, is<br />
second on <strong>the</strong> list. The MFIs showing <strong>the</strong> lowest<br />
staff productivity offer only individual loans.<br />
Although <strong>the</strong> lending methodology clearly influences<br />
staff productivity, o<strong>the</strong>r factors are also at<br />
play. For example, MFIs that offer a multiple range<br />
of products (i.e., MEB) have lower productivity<br />
levels given <strong>the</strong> burden on field staff who manage<br />
products o<strong>the</strong>r than loans.<br />
Many programs use financial incentives to boost<br />
staff productivity. Given <strong>the</strong> evidence that<br />
differences in staff productivity can arise from<br />
factors that are outside <strong>the</strong> loan officer’s control (i.e.<br />
loan methodology), MFIs should consider a mix of<br />
criteria on which to base incentives. No conclusive<br />
results can yet be drawn from <strong>the</strong> implementation of<br />
incentive plans, but <strong>the</strong>ir effects are worth<br />
monitoring. 44<br />
Conclusion<br />
This article analyzed <strong>the</strong> performance of eight<br />
Bosnian MFIs and <strong>the</strong> environment where <strong>the</strong>y<br />
operate. They are playing a key role in post-war<br />
Bosnia by providing credit to low income<br />
entrepreneurs.<br />
Analysis of <strong>the</strong> financial performance of <strong>the</strong><br />
selected MFIs shows that <strong>the</strong>y are, on average, still<br />
not financially sustainable, and <strong>the</strong>ir staff productivity<br />
is slightly lower than that of all MFIs in <strong>the</strong><br />
Bulletin targeting similar markets. None<strong>the</strong>less,<br />
<strong>the</strong>ir overall performance surpassed that of all MFIs<br />
in <strong>the</strong> Bulletin that fall within <strong>the</strong>ir age group<br />
(operating for less than 3 years).<br />
<strong>Microfinance</strong> in Bosnia-Herzegovina benefits from a<br />
strong credit culture, cooperation between MFIs,<br />
and potential for growth. In <strong>the</strong> future, increased<br />
competition is expected to stimulate efficiency. To<br />
improve outreach while answering <strong>the</strong> challenges of<br />
increased competition and unfavorable business<br />
environment, <strong>the</strong> regulatory framework will need to<br />
be updated.<br />
This case study was prepared by Isabelle Barrès, Bulletin<br />
Editorial Staff, based on a visit conducted in November<br />
2000 to <strong>the</strong> selected organizations, and information<br />
submitted to <strong>the</strong> Bulletin by <strong>the</strong> Bosnian MBB<br />
participants. The MicroBanking Bulletin thanks LID and<br />
Sarah Forster for <strong>the</strong>ir contributions, and all <strong>the</strong><br />
institutions mentioned in this article for <strong>the</strong>ir time and<br />
permission to publish <strong>the</strong>ir financial results.<br />
44<br />
The incentive systems are ei<strong>the</strong>r new (LOK) or were recently<br />
reviewed (MEB and Mercy Corps/Partner). No information was<br />
available for AMK, Bospo, and World Vision.<br />
34 MICROBANKING BULLETIN, APRIL 2001
BULLETIN HIGHLIGHTS AND TABLES<br />
BULLETIN HIGHLIGHTS AND TABLES<br />
Productivity Drivers and Trends<br />
Geetha Nagarajan<br />
Productivity is <strong>the</strong> amount of quality services<br />
delivered by microfinance staff to <strong>the</strong>ir clients. It<br />
quantifies <strong>the</strong> employees’ efforts to deliver an MFI’s<br />
output. By increasing productivity, an MFI can<br />
lower per unit costs, improve efficiency, and ultimately<br />
enhance self-sufficiency.<br />
This Highlights Section, based on <strong>the</strong> information<br />
ga<strong>the</strong>red during <strong>the</strong> past three years from over 100<br />
MFIs around <strong>the</strong> world, examines <strong>the</strong> importance of<br />
productivity on MFI performance.<br />
Measurement of Productivity<br />
Productivity can be measured using several<br />
indicators that capture <strong>the</strong> quantity and quality of<br />
service delivery by MFI staff. Since loans are <strong>the</strong><br />
primary product and source of income for most<br />
MFIs, this analysis is limited to productivity associated<br />
with credit delivery. The Bulletin uses three<br />
main indicators to measure <strong>the</strong> productivity of credit<br />
delivery: staff productivity, loan officer productivity,<br />
and <strong>the</strong> staff allocation ratio.<br />
Of <strong>the</strong>se three, staff productivity is <strong>the</strong> primary<br />
indicator. An MFI’s entire staff is a relevant unit of<br />
service production, so <strong>the</strong> best measure of productivity<br />
collectively accounts for <strong>the</strong> efforts of <strong>the</strong> front<br />
and back offices. The staff productivity indicator<br />
also allows comparison between diverse MFIs that<br />
allocate tasks differently among staff. Some MFIs,<br />
for example, require loan officers to perform multiple<br />
tasks such as collecting repayments, following<br />
up on delinquent loans, member training and<br />
deposit mobilization; whereas o<strong>the</strong>r organizations<br />
enlist administrative or specialized personnel to<br />
fulfill some of <strong>the</strong> credit delivery functions. The staff<br />
productivity indicator is <strong>the</strong>refore more useful when<br />
comparing between less similar MFIs.<br />
As a secondary indicator, <strong>the</strong> number of loans per<br />
loan officer reflects <strong>the</strong> productivity of field staff.<br />
Along <strong>the</strong> same lines, <strong>the</strong> staff allocation ratio (loan<br />
officers to total staff) indicates <strong>the</strong> MFI’s allocation<br />
of resources between staff in <strong>the</strong> field and <strong>the</strong> head<br />
office. These latter indicators are less useful in<br />
comparing financial intermediaries with credit-only<br />
institutions. However, if loan officers are exclusively<br />
responsible for loan activities, <strong>the</strong> two indicators<br />
reflect <strong>the</strong> MFI’s ability to streamline its credit<br />
operations and allocate its resources to <strong>the</strong> core<br />
income-generating activity.<br />
<strong>Microfinance</strong> Productivity: A Snapshot<br />
Based on data provided by <strong>the</strong> 124 Bulletin<br />
participants, this section examines <strong>the</strong> current state<br />
of microfinance productivity (see Figure 1).<br />
Figure 1: MFI Productivity by Peer Groups*<br />
Staff<br />
Productivity<br />
Loan<br />
Officer<br />
Productivity<br />
Staff<br />
Allocation<br />
All MFIs (n = 124) 122 257 0.49<br />
FSS MFIs (n = 64) 133 291 0.51<br />
Non-FSS MFIs (n=60) 132 300 0.48<br />
i. Africa 154 393 0.44<br />
Africa/MENA (n=5) 119 315 0.47<br />
Africa-Medium (n=10) 192 474 0.39<br />
Africa-Small (n=11) 129 285 0.52<br />
ii. Asia 177 306 0.54<br />
Asia Large (n=5) 238 420 0.42<br />
Asia Pacific (n=6) 80 134 0.66<br />
Productivity Indicators Definitions<br />
Staff productivity = Number of active loan clients /<br />
Total number of staff at year end<br />
Loan officer productivity = Number of active loan<br />
clients / Number of loan officers at year end<br />
Staff allocation = Number of loan officers at year end /<br />
Total number of staff year end<br />
Asia South (n=9) 235 452 0.56<br />
iii. Eastern Europe 63 110 0.54<br />
EE Broad (n=7) 79 123 0.59<br />
EE High (n=5) 64 113 0.56<br />
iv. Latin America 125 280 0.46<br />
LA CUs (n=11) 107 -- --<br />
LA Med LI (n=9) 194 417 0.49<br />
LA Small LI (n=7) 140 225 0.58<br />
LA Small UI (n=6) 81 197 0.49<br />
LA Large (n=9) 122 285 0.43<br />
LA Med Broad (n=11) 88 212 0.41<br />
v. O<strong>the</strong>rs<br />
CA/MENA (n=6) 102 174 0.59<br />
WW Small Business (n=7) 37 130 0.32<br />
*Means are calculated by dropping <strong>the</strong> top and bottom percentiles for each group<br />
except for all MFIs where <strong>the</strong> top and bottom deciles are excluded. Composition<br />
of <strong>the</strong> peer groups can be found on page 41. FSS: Financially self-Sufficient; CA<br />
= Central Asia; EE: Eastern Europe; LA = Latin America; MENA = Middle East<br />
and North Africa; LI = Low income; UI = Upper income; WW = Worldwide.<br />
MICROBANKING BULLETIN, APRIL 2001 35
BULLET HIGHLIGHTS AND TABLES<br />
• MFI staff, on average, serviced 122 clients<br />
while loan officers managed an average of 257<br />
borrowers;<br />
• Productivity was similar between <strong>the</strong> MFIs that<br />
are and are not financially self-sufficient. This<br />
indicates that while productivity may affect <strong>the</strong><br />
cost structure and profitability, high productivity<br />
cannot guarantee self-sufficiency;<br />
• Among <strong>the</strong> Bulletin peer groups, staff and loan<br />
officer productivity was <strong>the</strong> highest among <strong>the</strong><br />
MFIs in <strong>the</strong> Asian Large and South Asian peer<br />
groups where <strong>the</strong> population density is high;<br />
• Small business lenders reported <strong>the</strong> lowest<br />
productivity rates. Small business lending may<br />
require a complex financial technology that<br />
limits caseloads for staff;<br />
• Productivity was low in upper-income countries<br />
(two peer groups in Eastern Europe and <strong>the</strong><br />
Latin American upper income group);<br />
• As indicated by <strong>the</strong> staff allocation ratio, loan<br />
officers represented half <strong>the</strong> employees in<br />
MFIs, indicating <strong>the</strong> need for skills o<strong>the</strong>r than<br />
lending for <strong>the</strong> smooth operation of an MFI;<br />
• The proportion of loan officers to total staff was<br />
higher among Asian MFIs (0.54) compared to<br />
Latin American (0.46) and African MFIs (0.44);<br />
• Peer groups with smaller MFIs in Asia, Africa<br />
and Latin America reported higher staff<br />
allocation ratios than peer groups with larger<br />
organizations.<br />
The Effect of Age and Methodology on<br />
Productivity: Rhetoric or Reality?<br />
There are common beliefs in <strong>the</strong> microfinance<br />
industry that: 1) productivity improves with time; and<br />
2) group methodologies have higher productivity<br />
than individual lending. To test <strong>the</strong>se beliefs, <strong>the</strong><br />
Bulletin data were analyzed and <strong>the</strong> results suggest<br />
that <strong>the</strong> rhetoric may indeed be <strong>the</strong> reality, as<br />
shown in Figure 2.<br />
Staff and loan officers at MFIs employing <strong>the</strong> village<br />
bank methodology served significantly more clients<br />
and had a slightly higher staff allocation ratio than<br />
MFIs using an individual lending methodology.<br />
Mature MFIs were considerably more productive<br />
than new and young ones indicating that productivity<br />
improves with time. Higher productivity at<br />
mature MFIs may have resulted from a lower<br />
proportion of new staff members to total employees<br />
and fewer partially occupied loan officers.<br />
Figure 2: Productivity by Methodology and<br />
Age of MFI*<br />
Staff<br />
Productivity<br />
(No.)<br />
Loan Officer<br />
Productivity<br />
(No.)<br />
Staff<br />
Allocation<br />
(%)<br />
I. Methodology<br />
Individual loans (n=54) 102 229 47<br />
Solidarity Groups (n=45) 133 271 50<br />
Village Banks (n=25) 188 408 52<br />
II. Age Cohorts<br />
Mature: > 6 Years (n=65) 158 340 47<br />
Young: 3 to 6 Years (n=29) 126 295 48<br />
New: < 3 Years (n=30) 96 188 51<br />
*Data are calculated by dropping <strong>the</strong> top and bottom percentiles of each<br />
group. n = number of MFIs before dropping observations.<br />
<strong>Information</strong> in Figure 3 shows different patterns<br />
between lending methodologies and <strong>the</strong> age of <strong>the</strong><br />
institutions. Interestingly, on average, productivity<br />
of solidarity group programs does not improve with<br />
age. Older village bank and individual lending programs,<br />
however, demonstrate markedly higher<br />
productivity than <strong>the</strong>ir younger counterparts and<br />
correspondingly higher financial self-sufficiency<br />
(FSS).<br />
Figure 3: Productivity, Efficiency and<br />
Profitability by Age and Methodology*<br />
Staff Admin Exp. /<br />
Productivity Avg. Loan<br />
(No.) Portfolio (%)<br />
FSS<br />
(%)<br />
New Solidarity Group (n=11) 137 55.1 76.1<br />
New Village Bank (n=4) 134 93.1 51.9<br />
New Individual (n=15) 68 24.4 82.9<br />
Young Solidarity (n=10) 140 45.4 79.7<br />
Young Village Bank (n=8) 157 47.7 92.4<br />
Young Individual (n=11) 94 22.1 100.3<br />
Mature Solidarity (n=24) 137 36.7 89.5<br />
Mature Village Bank (n=13) 286 44.1 97.8<br />
Mature Individual (n=28) 146 19.1 113.3<br />
*Data are calculated by dropping <strong>the</strong> top and bottom percentiles. n =<br />
number of MFIs included in each category before dropping observations.<br />
What Drives Productivity?<br />
Increased productivity may be important to reduce<br />
per unit costs and improve self-sufficiency. But<br />
what drives productivity? There are at least four<br />
direct drivers that MFIs can use as levers to<br />
increase productivity: 1) client retention, 2) staff<br />
retention, 3) staff remuneration, and 4) staff<br />
training. 45 The effect of <strong>the</strong>se drivers on<br />
45<br />
Loan terms can also be leng<strong>the</strong>ned to indirectly boost<br />
productivity. Since loans with shorter terms turn over more<br />
frequently, <strong>the</strong>y are more labor-intensive and can undermine<br />
productivity. Assuming that longer terms do not adversely effect<br />
36 MICROBANKING BULLETIN, APRIL 2001
BULLETIN HIGHLIGHTS AND TABLES<br />
productivity and financial performance is discussed<br />
below.<br />
Client Retention<br />
Client retention can dramatically affect productivity.<br />
Productivity gains may result from reduced marketing<br />
and loan sourcing costs, and more efficient processing<br />
of repeat clients. This occurs most effectively<br />
if staff assimilate existing information into <strong>the</strong>ir<br />
decision making process.<br />
Data on client desertion are highly affected by <strong>the</strong><br />
formula used to construct <strong>the</strong> ratio. The Bulletin<br />
measures client desertion as follows:<br />
Client<br />
Desertion =<br />
Active clients beginning of year + New<br />
clients for year* – Active clients end of year<br />
Active clients beginning of year<br />
*New clients = Borrowers with first loan and returnees after 2 years.<br />
Of <strong>the</strong> 124 MFIs that participated in this Issue, 33<br />
MFIs provided information on client desertion.<br />
Figure 4: Client Desertion and Productivity,<br />
by Region and Age<br />
Region<br />
Age Cohorts<br />
A weak inverse relationship emerges between<br />
desertion rates and both productivity and profitability.<br />
Higher levels of client desertion are associated<br />
with lower staff productivity and lower financial<br />
self-sufficiency (FSS).<br />
It is crucial to examine <strong>the</strong> reasons for desertion to<br />
clearly interpret <strong>the</strong> above observations. Fur<strong>the</strong>rmore,<br />
age and methodology may also play a role.<br />
Better understanding on <strong>the</strong> effect of client retention<br />
on productivity and MFI performance may evolve as<br />
<strong>the</strong> MFIs begin to track information on client<br />
desertion systematically.<br />
Staff Retention<br />
Heavy staff turnover, especially of loan officers, can<br />
seriously hurt productivity. Learning occurred in<br />
assessing applicants, and valuable client relationships<br />
built through repeat services in <strong>the</strong> same area<br />
and to same clients, are important factors that<br />
increase productivity.<br />
The Bulletin measures staff turnover as <strong>the</strong> number<br />
of staff members who left <strong>the</strong> MFI during <strong>the</strong> year<br />
relative to average number of staff.<br />
Staff Turnover =<br />
Number of staff who left <strong>the</strong> MFI<br />
Average number of staff<br />
No. MFIs<br />
Reporting<br />
Desertion Rate<br />
(%)<br />
Staff Productivity<br />
(No.)<br />
Admin.<br />
Expenses / Avg.<br />
Loan Portfolio<br />
(%)<br />
All MFIs<br />
Africa<br />
Asia<br />
Latin<br />
America<br />
EE and<br />
MENA<br />
Mature<br />
Young<br />
33 5 8 13 7 17 9 7<br />
New<br />
48 43 29 51 66 37 59 62<br />
123 178 143 123 73 138 105 109<br />
45 94 34 45 26 42 44 53<br />
FSS (%) 94 82 106 94 89 101 91 81<br />
As shown in Figure 4, MFIs in <strong>the</strong> Middle-East and<br />
North Africa (MENA) and Eastern Europe (EE)<br />
experience higher levels of desertion than those in<br />
Asian and African countries. It is not clear why this<br />
is <strong>the</strong> case, but region specific factors such as<br />
migration and level of competition may have<br />
influenced this finding. Desertion rates were high<br />
among young and new MFIs, 59 and 62 percent<br />
respectively, compared to 37 percent for mature<br />
ones. Programs may experience higher desertion<br />
rates where large numbers of clients leave after <strong>the</strong><br />
first and second loan cycles.<br />
portfolio quality (which is a big assumption), an MFI can increase<br />
its productivity by leng<strong>the</strong>ning its average loan term.<br />
The staff turnover rate ranged from 2 to 51 percent<br />
with a mean around 10 percent. As depicted in<br />
Figure 5, although no clear pattern emerges<br />
between staff turnover and productivity, higher<br />
levels of turnover tend to be weakly related to lower<br />
staff productivity.<br />
500<br />
450<br />
400<br />
350<br />
300<br />
250<br />
200<br />
150<br />
100<br />
50<br />
0<br />
Figure 5: Staff Turnover and Productivity<br />
0.02 0.12 0.22 0.32 0.42 0.52<br />
St af f Tu rn ove r<br />
Staff Remuneration<br />
The Bulletin collects aggregated data on staff<br />
remuneration including basic salary, bonuses and<br />
benefits. Remuneration is <strong>the</strong>n expressed in <strong>the</strong><br />
average staff salary indicator, which compares<br />
average staff remuneration to GNP per capita to<br />
account for country specific factors.<br />
MICROBANKING BULLETIN, APRIL 2001 37
BULLET HIGHLIGHTS AND TABLES<br />
It is expected that high wages would positively<br />
affect staff productivity—more inputs (salaries)<br />
should produce greater outputs (loans). But in<br />
microfinance, striking regional differences mean<br />
that you do not necessarily get what you pay for.<br />
Asia has <strong>the</strong> lowest relative remuneration and <strong>the</strong><br />
highest productivity ratio, whereas <strong>the</strong> lowest<br />
productivity comes from <strong>the</strong> region with <strong>the</strong> second<br />
highest wages (Eastern Europe). Although<br />
productivity in Africa is relatively high, salaries are<br />
much higher than anywhere else (relative to GNP<br />
per capita) to <strong>the</strong> point that <strong>the</strong>y were not<br />
adequately covered through interest income. This<br />
partly accounts for that region’s low self-sufficiency<br />
ratio (see Figure 6).<br />
Figure 6: Average Staff Salaries, Productivity<br />
and Self-Sufficiency *<br />
Avg. Staff<br />
Salary /<br />
GNP per<br />
capita<br />
Staff<br />
Productivity<br />
(no.)<br />
FSS<br />
(%)<br />
All MFIs (n=124) 5.8 122 90<br />
Africa (n=26) 12.4 154 83<br />
Asia (n=24) 3.6 177 93<br />
Eastern Europe (n=14) 7.3 63 90<br />
Latin America (n=54) 4.6 125 97<br />
* Data are calculated by dropping <strong>the</strong> top and bottom percentiles for each<br />
region and <strong>the</strong> top and bottom deciles for All MFIs.<br />
Staff Training<br />
The fourth productivity driver is staff training<br />
obtained through apprenticeships and formal<br />
training provided inside and outside of <strong>the</strong> MFI.<br />
One would presume that <strong>the</strong> more an MFI invests in<br />
training (up to a point), <strong>the</strong> more productive its staff<br />
should be. This result should come from <strong>the</strong> direct<br />
investments in skills that enable staff to be more<br />
productive, as well as <strong>the</strong> expectation that training<br />
investments should reduce <strong>the</strong> detrimental effect of<br />
staff turnover on productivity.<br />
About 60 MFIs reported <strong>the</strong> budget spent on<br />
training <strong>the</strong>ir staff. Regional differences, however,<br />
made it difficult to demonstrate a link between<br />
training investments and productivity. On average,<br />
African MFIs spent over 250 percent more than<br />
Latin American MFIs on training, and yet <strong>the</strong>ir<br />
productivity levels were almost <strong>the</strong> same.<br />
Fur<strong>the</strong>rmore, a clear relationship between productivity<br />
and staff training was difficult to establish due<br />
to limited standardization in <strong>the</strong> reporting of <strong>the</strong><br />
training budget. Underestimation is possible since<br />
<strong>the</strong> training budget may not include <strong>the</strong> cost of inhouse<br />
apprenticeships. Similarly, overestimation is<br />
possible if data are contaminated with training<br />
budget for clients.<br />
Productivity Trends<br />
This section provides information on <strong>the</strong> historical<br />
pattern of productivity changes in MFIs and its<br />
effect on institutional performance.<br />
Is <strong>the</strong> Industry Headed in <strong>the</strong> Right Direction?<br />
<strong>Microfinance</strong> institutions are improving <strong>the</strong>ir levels<br />
of productivity and profitability over time, albeit<br />
slowly. Since 1998, <strong>the</strong> Bulletin has accumulated<br />
financial information from 95 MFIs for at least two<br />
consecutive years.<br />
Of <strong>the</strong>se 95 MFIs, 61 percent raised <strong>the</strong>ir financial<br />
self-sufficiency, 73 percent experienced an increase<br />
in staff productivity, and 61 percent showed a<br />
decline in <strong>the</strong>ir administrative costs relative to loan<br />
portfolio. Average change in productivity was about<br />
24 percent.<br />
1<br />
Figure 7: Effect of Positive Change in Productivity on Change in FSS<br />
Change in FSS<br />
0.8<br />
0.6<br />
0.4<br />
R 2 = 0.0268<br />
0.2<br />
0<br />
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br />
Change in Staff Productivity<br />
38 MICROBANKING BULLETIN, APRIL 2001
BULLETIN HIGHLIGHTS AND TABLES<br />
Does Methodology Matter?<br />
As shown in Figure 9, similar levels of productivity<br />
change were observed among MFIs that followed<br />
individual and solidarity group lending methodologies<br />
(27 percent), while village banks showed a<br />
15 percent increase in productivity. It was intriguing<br />
to note a sharper decline in administrative costs<br />
among solidarity group lenders compared to individual<br />
lenders despite similar change in productivity.<br />
The phenomenon is explained in part by <strong>the</strong><br />
loan size increase among group lenders (5.3<br />
percent), while <strong>the</strong> individual lenders experienced a<br />
slight decline in average loan balances (ALB).<br />
Improvement in productivity from one year to <strong>the</strong><br />
next was associated with an increase in profitability.<br />
Figure 7 demonstrates how <strong>the</strong> growth in productivity<br />
had a positive effect on financial selfsufficiency.<br />
On average, a 15 percent increase in<br />
productivity resulted in a 10 percent increase in<br />
FSS. Improvements in FSS appear to peak at a 30<br />
percent improvement in productivity and increase at<br />
a diminishing rate <strong>the</strong>reafter. This weak trend<br />
(R 2 =2.7 percent and <strong>the</strong> correlation coefficient is 28<br />
percent) suggests a limit to increased profitability<br />
due to productivity improvements.<br />
Does Performance Differ by Regions?<br />
There were significant differences in changes<br />
between regions (Figure 8). Average change in<br />
productivity was more prominent in Eastern Europe<br />
and Africa where <strong>the</strong> industry is still developing.<br />
None<strong>the</strong>less, <strong>the</strong> relationship in all <strong>the</strong> regions<br />
between productivity, efficiency, and financial selfsufficiency<br />
followed a common pattern: changes in<br />
administrative cost ratio were indirectly proportional<br />
to changes in productivity while changes in FSS<br />
were directly proportional to changes in productivity.<br />
Figure 8: Number of MFIs with Positive<br />
Change in Performance<br />
(Average percent change in paren<strong>the</strong>ses)<br />
Region<br />
FSS Productivity Efficiency*<br />
Africa (n=19) 14 (38.0) 16 (36.6) 13 (11.6)<br />
Asia (n=20) 13 (17.7) 14 (32.0) 12 (11.9)<br />
E. Europe (n=6) 6 (43.8) 6 (36.8) 6 (18.3)<br />
Latin America<br />
20 (-1.3) 30 (11.6) 24 (0.9)<br />
(n=45)<br />
MENA (n=5) 5 (42.0) 3 (14.4) 3 (7.5)<br />
Total Sample (n=95) 58 (16.3) 69 (23.7) 58 (7.1)<br />
* Efficiency refers to decline in administrative expenses relative to average<br />
loan portfolio.<br />
Effect of Age<br />
While productivity improved with age, as one might<br />
expect, <strong>the</strong> rate of change tends to slow down.<br />
New MFIs showed a 57 percent improvement in<br />
productivity compared to a 20 percent change<br />
among young MFIs and only a 14 percent improvement<br />
for mature institutions (Figure 10).<br />
Figure 10: Percent Change in Performance by<br />
Age of Institution<br />
Age<br />
Cohorts<br />
Staff<br />
Productivity Efficiency*<br />
Avg. Staff<br />
Salary/ GNP<br />
per capita)<br />
Avg.<br />
Loan<br />
Balance<br />
FSS<br />
New 56.7 17.9 31.1 -2.7 57.4<br />
Young 20.0 12.5 -3.6 1.2 13.3<br />
Mature 14.1 0.5 10.3 4.4 5.2<br />
* Efficiency refers to decline in administrative expenses relative to average<br />
loan portfolio.<br />
It is interesting to note that new MFIs experienced a<br />
decline in administrative costs (17.9 percent) despite<br />
an increase in average staff salaries by 31<br />
percent and slight decline in average loan balance.<br />
This may reflect <strong>the</strong> important role of productivity in<br />
reducing administrative costs even if costs are<br />
directly hit by an increase in salaries.<br />
Conclusion<br />
Productivity is interlinked with several o<strong>the</strong>r factors<br />
such as loan size and staff remuneration that affect<br />
<strong>the</strong> cost structure of <strong>the</strong> MFI and its profitability.<br />
Productivity is also affected by regional factors, and<br />
by methodology, size and <strong>the</strong> age of <strong>the</strong> MFIs. The<br />
complex interplay of productivity with o<strong>the</strong>r factors<br />
and <strong>the</strong> dispersed nature of data at <strong>the</strong> Bulletin<br />
have limited clear interpretation of several observations<br />
presented here.<br />
The analyses, never<strong>the</strong>less, show that productivity<br />
is necessary but it is not sufficient to effect MFI<br />
financial performance. Consistent tracking of<br />
pertinent information on productivity drivers is<br />
required for better understanding of productivity.<br />
Fur<strong>the</strong>rmore, it may be relevant for multi-service<br />
and multi-product MFIs to track information by tasks<br />
for appropriate management decisions.<br />
MICROBANKING BULLETIN, APRIL 2001 39
BULLET HIGHLIGHTS AND TABLES<br />
An Introduction to <strong>the</strong> Peer Groups and Tables<br />
Setting up <strong>the</strong> Peer Groups<br />
The MicroBanking Standards Project is designed to<br />
create performance benchmarks against which<br />
managers and directors of microfinance institutions<br />
can compare <strong>the</strong>ir own performance. Since <strong>the</strong><br />
microfinance industry consists of a range of institutions<br />
and operating environments, some with very<br />
different characteristics, an MFI needs to be<br />
compared to similar institutions for <strong>the</strong> reference<br />
points to be useful.<br />
The MicroBanking Bulletin addresses this issue with<br />
its peer group framework. Peer groups are sets of<br />
programs that have similar characteristics—similar<br />
enough that <strong>the</strong>ir managers find utility in comparing<br />
<strong>the</strong>ir results with those of o<strong>the</strong>r organizations in<br />
<strong>the</strong>ir peer group. The Bulletin forms peer groups<br />
based on three main indicators shown in Figure 1:<br />
1) region; 2) scale of operations; and 3) target<br />
market.<br />
In previous issues, <strong>the</strong> same criteria for scale and<br />
target market were applied to all regions. Since<br />
regions demonstrate different growth patterns, however,<br />
we have regionalized <strong>the</strong> scale criterion by<br />
raising <strong>the</strong> bar in some areas and lowering it in<br />
o<strong>the</strong>rs. So now, a program that would be classified<br />
as large in Africa, for example, would be considered<br />
medium in Latin America. We have also added a<br />
new category for target market: Small Business. To<br />
fall into this category, <strong>the</strong> depth indicator (average<br />
loan balance / GNP per capita) needs to exceed<br />
250 percent.<br />
Besides <strong>the</strong>se three primary indicators, <strong>the</strong> Bulletin<br />
also applied two secondary criteria in Latin America<br />
to fur<strong>the</strong>r homogenize <strong>the</strong> peer groups. First, all of<br />
<strong>the</strong> credit unions are grouped toge<strong>the</strong>r. Since<br />
<strong>the</strong>se organizations are savings-driven (unlike most<br />
MFIs, which are credit-driven), <strong>the</strong>y have a unique<br />
cost structure that makes comparison with o<strong>the</strong>r<br />
MFIs less useful.<br />
The o<strong>the</strong>r secondary criterion applied in Latin<br />
America (for institutions that fall in <strong>the</strong> low-end<br />
category) is <strong>the</strong> country income level. The<br />
operating conditions in upper income (UI) countries,<br />
such as Argentina, Brazil and Chile, in terms of<br />
labor markets, levels of productivity, and customer<br />
characteristics, are quite distinct from <strong>the</strong> lower<br />
income (LI) countries in <strong>the</strong> region, and <strong>the</strong> high<br />
number of institutions offering low-end loans<br />
justifies <strong>the</strong> breakdown into multiple peer groups.<br />
Peer Group Composition and Data Quality<br />
The members of each peer group are listed in<br />
Figure 2 on <strong>the</strong> following page, and more detailed<br />
information about each institution can be found in<br />
Appendix II on page 77.<br />
Since <strong>the</strong> Bulletin relies primarily on self-reported<br />
data, we have graded <strong>the</strong> quality of that information<br />
based on <strong>the</strong> degree to which we have independent<br />
verification of its reliability. The data quality grade<br />
is NOT a rating of <strong>the</strong> institution’s performance.<br />
Statistical Issues<br />
In <strong>the</strong> statistical tables that follow, <strong>the</strong> averages for<br />
each peer group are calculated on <strong>the</strong> basis of <strong>the</strong><br />
values between <strong>the</strong> 2 nd and 99 th percentiles, which<br />
usually means that <strong>the</strong> top and bottom values for<br />
each indicator are dropped. For <strong>the</strong> entire sample<br />
of MFIs, <strong>the</strong> top and bottom deciles were excluded.<br />
For more details, see Appendix I on page 73.<br />
Figure 1: Primary Peer Group Criteria<br />
Africa<br />
Africa/ MENA 2<br />
MENA/ Central Asia<br />
Region Scale of Operations 1<br />
Total loan portfolio (US$)<br />
Large: > 5 million<br />
Medium: 900,000 to 5 million<br />
Small: < 900,000<br />
Target Market<br />
Average loan balance /<br />
GNP per capita<br />
Low-end: < 20% OR Avg.<br />
Loan Balance ≤ US$150<br />
Asia<br />
Asia (Pacific)<br />
Asia (South)<br />
Eastern Europe<br />
Latin America<br />
Large: > 8 million<br />
Medium: 1 to 8 million<br />
Small: < 1 million<br />
Large > 10 million,<br />
Medium: 1.5 to 10 million<br />
Small: < 1.5 million<br />
Broad: 20% to 149%<br />
High-end: 150 to 249%<br />
Small Business: ≥ 250%<br />
1<br />
Criteria for classification of scale of operations varies by region. See corresponding group of regions.<br />
2<br />
MENA = Middle East/ North Africa.<br />
40 MICROBANKING BULLETIN, APRIL 2001
BULLETIN HIGHLIGHTS AND TABLES<br />
Figure 2: A Guide to <strong>the</strong> Peer Groups<br />
PEER GROUP N<br />
DATA QUALITY GRADE†<br />
(No. of MFIs with each<br />
grade)<br />
AAA A B<br />
PARTICIPATING INSTITUTIONS *<br />
1. Africa Medium<br />
Size: Medium<br />
Target: Low-end<br />
2. Africa Small<br />
Size: Small<br />
Target: Low-end<br />
3. Africa/ MENA<br />
Size: Large/Medium<br />
Target: Broad<br />
4. Asia (Central) / MENA<br />
Size: Medium/Small<br />
Target: Low-end<br />
5. Asia Large<br />
Size: Large<br />
Target: Low-end/Broad<br />
6. Asia (Pacific)<br />
Size: Medium/Small<br />
Target: Low-end/Broad<br />
7. Asia (South)<br />
Size: Medium/Small<br />
Target: Low-end/Broad<br />
8. Eastern Europe High<br />
Size: All<br />
Target: High-end<br />
9. Eastern Europe Broad<br />
Size: All<br />
Target: Broad<br />
10. LA Large<br />
Size: Large<br />
Target: Broad/High-end<br />
11. LA Medium Broad<br />
Size: Medium<br />
Target: Broad<br />
12. LA Low UI<br />
Size: Medium/Small<br />
Target: Low-end<br />
13. LA Medium Low LI<br />
Size: Medium<br />
Target: Low-end<br />
14. LA Small Low LI<br />
Size: Small<br />
Target: Low-end<br />
15. LA Credit Unions<br />
Size: All<br />
Target: Broad<br />
16. Worldwide Small Business<br />
Size: Large/Medium<br />
Target: Small Business<br />
10 1 7 2 Citi S&L, FINCA Uganda, KWFT, NRB, Pamécas, Pride<br />
Tanzania, Pride Uganda, Pride Vita Guinea, SEF, WAGES<br />
11 3 3 5 ARB, Faulu, FINCA Malawi, FINCA Tanzania, FOCCAS,<br />
MKRB, Piyeli, SAT, SEDA, UWFT, Vital-Finance<br />
5 1 3 1 ABA, Kafo Jiginew, Nyésigiso, PADME, UNRWA<br />
6 1 4 1 Al Amana, Al Majmoua, Constanta, Faten, FINCA Kyrgyzstan,<br />
Microfund for Women<br />
5 1 4 0 ACLEDA, ASA, BAAC, BRAC, BRI<br />
6 1 5 0 CARD Bank, EMT, Hatta Kaksekar, Hublag, RSPI, TSPI<br />
9 5 3 1 AKRSP, Basix, BURO Tangail, CDS, FWWB India,<br />
KASHF, Nirdhan, SEEDS, SHARE<br />
5 0 5 0 AMK, LOK, Moznosti, Sunrise, WVB<br />
7 0 2 5 Bospo, Fundusz Mikro, Inicjatywa Mikro, MC, Mikrofin,<br />
Nachala, NOA<br />
9 2 7 0 Banco ADEMI, BancoSol, Caja de Los Andes, Calpiá, CM<br />
Arequipa, FIE, Finamérica, Mibanco, PRODEM<br />
11 1 7 3 ACODEP, Actuar, ADOPEM, ADRI, BPE, Chispa, FAMA,<br />
Finsol, FONDECO, ProEmpresa, Sartawi<br />
6 2 4 0 Banco do Povo, CEAPE/ PE, Contigo, Emprender,<br />
Portosol, Vivacred<br />
9 1 4 4 CAM, CMM Medellín, Compartamos, Crecer, Enlace,<br />
FINCA Honduras, FMM Popayán, FWWB Cali, ProMujer<br />
7 0 3 4 AGAPE, FED, FINCA Ecuador, FINCA Mexico, FINCA<br />
Nicaragua, FINCA Peru, WR Honduras<br />
11 0 11 0 15 de Abril, 23 de Julio, Acredicom, Chuimequená,<br />
COOSAJO, Ecosaba, Moyután, Oscus, Sagrario,<br />
Tonantel, Tulcán<br />
7 2 5 0 ACEP, Agrocapital, BDB, CERUDEB, FEFAD, MEB, NLC<br />
All MFIs 124 21 77 26<br />
† The MicroBanking Bulletin uses <strong>the</strong> following grading system to classify information received from MFIs:<br />
AAA The information is supported by an in-depth financial analysis conducted by an independent entity in <strong>the</strong> last<br />
three years<br />
A The MBB questionnaire plus audited financial statements, annual reports and o<strong>the</strong>r independent evaluations<br />
B The MBB questionnaire or audited financial statements without additional documentation<br />
Abbreviations: MBB = MicroBanking Bulletin; MENA = Middle East/North Africa; LA = Latin America; UI = Upper Income countries;<br />
LI = Lower Income countries.<br />
* The institutions in italics and bold are new to <strong>the</strong> Bulletin. A short description of all institutions can be found in Appendix II.<br />
MICROBANKING BULLETIN, APRIL 2001 41
BULLETIN HIGHLIGHTS AND TABLES<br />
Index of Ratios and Tables<br />
INDICATORS AND RATIOS DEFINITIONS<br />
OUTREACH AND INSTITUTIONAL INDICATORS<br />
AGE OF INSTITUTION Years functioning as a MFI (years)<br />
NUMBER OF OFFICES Total number of offices (including head office, regional offices, branches, agencies) (number)<br />
NUMBER OF STAFF Total number of employees (number)<br />
NO OF ACTIVE BORROWERS Number of borrowers with loans outstanding (number)<br />
PERCENT WOMEN BORROWERS Total number of active women borrowers / total number of active borrowers (%)<br />
MACROECONOMIC INDICATORS<br />
GNP PER CAPITA (CURRENT PRICES) GNP per capita (US$)<br />
GDP GROWTH RATE Annual average, 1990-1998 (%)<br />
INFLATION RATE Inflation rate (%)<br />
DEPOSIT RATE Deposit rate (%)<br />
FINANCIAL DEEPENING M3 / GDP (%)<br />
PROFITABILITY<br />
ADJUSTED RETURN ON ASSETS (AROA) Adjusted net operating income<br />
Average total assets<br />
ADJUSTED RETURN ON EQUITY (AROE) Adjusted net operating income<br />
Average total equity<br />
OPERATIONAL SELF-SUFFICIENCY (OSS) Operating income<br />
Operating expense<br />
FINANCIAL SELF-SUFFICIENCY (FSS) Adjusted operating income<br />
Adjusted operating expense<br />
PROFIT MARGIN Adjusted net operating income<br />
Adjusted operating income<br />
(%)<br />
(%)<br />
(%)<br />
(%)<br />
(%)<br />
INCOME & EXPENSE<br />
OPERATING INCOME RATIO Adjusted operating income<br />
Average total assets<br />
OPERATING EXPENSE RATIO Adjusted operating expense<br />
Average total assets<br />
NET INTEREST MARGIN RATIO Adjusted net interest margin<br />
Average total assets<br />
PORTFOLIO YIELD Operating income - accrued interest - interest and fee income from investments<br />
Average gross loan portfolio (%)<br />
REAL INTEREST YIELD (Portfolio yield - inflation rate)<br />
(%)<br />
(1+ inflation rate)<br />
TOTAL INTEREST EXPENSE RATIO Adjusted total interest expense<br />
(%)<br />
Average total assets<br />
ADJUSTMENT EXPENSE RATIO Inflation and subsidy adjustment expense<br />
(%)<br />
Average total assets<br />
LOAN LOSS PROVISION EXPENSE RATIO Adjusted loan loss provision expense<br />
(%)<br />
Average total assets<br />
SALARY EXPENSE RATIO Personnel expense + in-kind donations<br />
(%)<br />
Average total assets<br />
OTHER ADMINISTRATIVE EXPENSE RATIO Administrative expense + in-kind donations - personnel expense<br />
(%)<br />
Average total assets<br />
TOTAL ADMINISTRATIVE EXPENSE RATIO Administrative expense + in-kind donations<br />
Average total assets<br />
(%)<br />
(%)<br />
(%)<br />
(%)<br />
EFFICIENCY<br />
TOTAL ADMINISTRATIVE EXPENSE/<br />
LOAN PORTFOLIO<br />
Administrative expense + in-kind donations<br />
Average gross loan portfolio (%)<br />
SALARY EXPENSE/ LOAN PORTFOLIO Personnel expense + in-kind donations<br />
Average gross loan portfolio<br />
OTHER ADMINISTRATIVE EXPENSE /<br />
LOAN PORTFOLIO<br />
Administrative expense + in-kind donations - personnel expense<br />
Average gross loan portfolio (%)<br />
(%)<br />
42 MICROBANKING BULLETIN, APRIL 2001
BULLETIN HIGHLIGHTS AND TABLES<br />
PRODUCTIVITY<br />
AVERAGE SALARY Average personnel expense + in-kind donations<br />
GNP per capita<br />
COST PER BORROWER Administrative expense + in-kind donations<br />
Average number of active borrowers<br />
STAFF PRODUCTIVITY Number of active borrowers<br />
Number of staff<br />
LOAN OFFICER PRODUCTIVITY Number of active borrowers<br />
Number of loan officers<br />
STAFF ALLOCATION RATIO Number loan officers<br />
Number of staff<br />
STAFF TURNOVER Number of staff who left <strong>the</strong> MFI<br />
Average number of staff<br />
(multiple of GNP per capita)<br />
(US$)<br />
(number)<br />
(number)<br />
(%)<br />
(%)<br />
PORTFOLIO<br />
PORTFOLIO AT RISK > 90 DAYS Outstanding balance of loans overdue > 90 days<br />
Total gross loan portfolio<br />
TOTAL GROSS LOAN PORTFOLIO Total gross portfolio outstanding (US$)<br />
AVERAGE LOAN BALANCE Total gross loan portfolio<br />
(US$)<br />
Number of active borrowers<br />
DEPTH Average loan balance<br />
GNP per capita<br />
(%)<br />
(%)<br />
CAPITAL AND LIABILITY STRUCTURE<br />
COMMERCIAL FUNDING LIABILITIES RATIO Borrowings at commercial rates (excludes loans from Head Office and Central bank)<br />
Average gross loan portfolio<br />
CAPITAL / ASSET RATIO Adjusted total equity<br />
Adjusted total assets<br />
(%)<br />
(%)<br />
CLARIFICATION OF TERMS<br />
OPERATING INCOME Interest and fee income from loan portfolio + interest and fee income from<br />
investments + o<strong>the</strong>r income from financial services<br />
OPERATING EXPENSE Administrative expense + total interest expense + loan loss provision expense<br />
ADJUSTED OPERATING INCOME Interest and fee income from loan portfolio + interest and fee income from<br />
investments net of accrued interest + o<strong>the</strong>r income from financial services<br />
ADJUSTED OPERATING EXPENSE Administrative expense, including in-kind donations + adjusted total interest expense<br />
+ adjusted loan loss provision expense<br />
ADMINISTRATIVE EXPENSE Personnel + office supplies + deprecation + rent + utilities + transportation + o<strong>the</strong>r<br />
administrative expenses<br />
PERSONNEL EXPENSE Staff salary + benefits expense<br />
ADJUSTED TOTAL INTEREST EXPENSE Interest and fee expense + exchange rate depreciation expense + o<strong>the</strong>r financial<br />
expense (including inflation expense + subsidy expense)<br />
ADJUSTED NET INTEREST MARGIN Adjusted operating income - total interest expense<br />
NET OPERATING INCOME Operating income - operating expense<br />
ADJUSTED NET OPERATING INCOME Adjusted operating income - adjusted operating expense<br />
ADJUSTED TOTAL EQUITY Total equity, including quasi-equity and adjusted net income<br />
ADJUSTED TOTAL ASSETS Total assets, including loan portfolio and inflation adjustment<br />
TABLES TITLES PAGE<br />
TABLE 1 Institutional Characteristics and Outreach Indicators 44<br />
TABLE 2 Overall Financial Performance and Operating Income 46<br />
TABLE 3 Operating Expenses and Portfolio Management Indicators 48<br />
TABLE 4 Efficiency and Productivity 50<br />
TABLE 5 Macroeconomic Indicators 52<br />
TABLE A Institutional Characteristics and Outreach Indicators 54<br />
TABLE B Profitability and Efficiency Indicators 58<br />
TABLE C Institutional Characteristics and Outreach Indicators for Financially Self-Sufficient<br />
62<br />
MFIs<br />
TABLE D Profitability and Efficiency Indicators for Financially Self-Sufficient MFIs 66<br />
MICROBANKING BULLETIN, APRIL 2001 43
BULLETIN HIGHLIGHTS AND TABLES<br />
TABLE 1. INSTITUTIONAL CHARACTERISTICS AND OUTREACH INDICATORS<br />
PEER GROUP<br />
44 MICROBANKING BULLETIN, APRIL 2001<br />
AGE<br />
(years)<br />
OFFICES<br />
(no.)<br />
TOTAL<br />
STAFF<br />
total number<br />
of employees<br />
(no.)<br />
TOTAL<br />
ASSETS<br />
(US$)<br />
CAPITAL/<br />
ASSET RATIO<br />
adj. total equity /<br />
adj. total assets<br />
(%)<br />
ALL MFIs (n=124) avg 8 13 94 5,512,452 49.5<br />
stdv 4 11 69 5,375,638 23.1<br />
Financially self-Sufficient MFIs (n=64) avg 10* 91* 367 14,498,853* 49.3<br />
stdv 5 304 1,219 27,453,324 24.2<br />
1. Africa – Medium – Low (n=10) avg 6 14 87 2,560,405 49.0<br />
Citi Savings & Loans, FINCA Uganda, KWFT, Nsoatreman Rural Bank, PAMÉCAS, PRIDE stdv 2 10 30 1,187,845 29.7<br />
Tanzania, PRIDE Uganda, PRIDE Vita Guinea, SEF, WAGES<br />
2. Africa – Small – Low (n=11) avg 6 40* 43 997,697* 64.6<br />
Assawinso Rural Bank, Faulu, FINCA Malawi, FINCA Tanzania, FOCCAS, Manya Krobo stdv 6 91 12 300,489 23.0<br />
Rural Bank, Piyeli, SEDA, Sinapi Aba Trust, UWFT, Vital-Finance<br />
3. Africa/MENA – Large/Medium – Broad (n=5) avg 9 23 87 11,458,592 50.0<br />
ABA, Kafo Jiginew, Nyésigiso, PADME, UNRWA stdv 1 25 32 3,693,186 19.7<br />
4. Asia (Central)/MENA – Medium/Small – Low (n=6) avg 4 23 81 2,823,233 96.9*<br />
Al Amana, Al Majmoua, Constanta, Faten, FINCA Kyrgyzstan, stdv 1 17 62 1,463,529 4.0<br />
Microfund for Women<br />
5. Asia – Large – Low/Broad (n=5) avg 20* 700* 8,711* 1,079,413,652* 27.6<br />
ACLEDA, ASA, BAAC, BRAC, BRI stdv 5 168 4,099 1,648,849,794 17.5<br />
6. Asia (Pacific) – Medium/Small – Low/Broad (n=6) avg 11 12 128 2,125,785 52.2<br />
CARD Bank, EMT, Hatta Kaksekar, Hublag, RSPI, TSPI stdv 2 7 66 1,456,672 12.8<br />
7. Asia (South) – Medium/Small – Low/Broad (n=9) avg 8 23 152 3,829,019 50.9<br />
AKRSP, Basix, BURO Tangail, CDS, FWWB India, Kash Foundation, Nirdhan, stdv 5 18 147 2,217,533 26.5<br />
SEEDS, SHARE<br />
8. Eastern Europe – High Size: All (n=5) avg 2* 4 23 2,913,938 42.9<br />
AMK, LOK, Moznosti, Sunrise, WVB stdv 1 2 6 357,400 38.1<br />
9. Eastern Europe – Broad Size: All (n=7) avg 3 9 30 2,585,284 71.4<br />
Bospo, Fundusz Mikro, Inicjatywa Mikro, Mercy Corps, Mikrofin, Nachala, NOA stdv 1 7 19 1,362,189 30.2<br />
10. Latin America – Large – Broad/High (n=9) avg 11 22 245* 34,108,155* 22.3*<br />
Banco Ademi, BancoSol, Caja de Los Andes, Calpiá, CM Arequipa, FIE, Finamérica, stdv 3 13 64 11,038,053 12.6<br />
Mibanco, PRODEM<br />
11. Latin America – Medium – Broad (n=11) avg 9 8 72 4,326,750 41.4<br />
ACODEP, Actuar, ADOPEM, ADRI, Banco de la Pequena Empresa, Chispa, FAMA, stdv 4 5 49 1,601,429 13.2<br />
Finsol, FONDECO, ProEmpresa, Sartawi<br />
12. Latin America – Medium/Small – Low – Upper Income (n=6) avg 3* 5 26 1,974,452 39.6<br />
Banco do Povo de Juiz de Fora, CEAPE/ Pernambuco, Contigo, Emprender, Portsol, stdv 2 1 9 760,126 17.6<br />
Vivacred<br />
13. Latin America – Medium – Low – Lower Income (n=9) avg 10 7 116 5,165,496 62.0<br />
CAM, CMM Medellín, Compartamos, Crecer, Enlace, FINCA Honduras, stdv 3 3 49 2,103,809 18.2<br />
FMM Popayán, FWWB Cali, ProMujer<br />
14. Latin America – Small – Low – Lower Income (n=7) avg 11 7 73 1,424,668 75.7*<br />
AGAPE, FED, FINCA Ecuador, FINCA Mexico, FINCA Nicaragua, FINCA Peru, stdv 5 6 32 877,098 10.9<br />
World Relief Honduras<br />
15. Latin America – Credit Unions – Broad Size: All (n=11) avg 8 3 53 6,153,016 34.4<br />
15 de Abril, 23 de Julio, Acredicom, Chuimequená, COOSAJO, Ecosaba, Moyután, stdv 4 1 11 1,752,518 7.7<br />
Oscus, Sagrario, Tonantel, Tulcán<br />
16. Worldwide – Large/Medium – Small Business (n=7) avg 7 12 134 18,331,933* 30.8<br />
ACEP, Agrocapital, Bank Dagang Bali, CERUDEB, FEFAD, MEB, NLC stdv 4 9 153 8,580,116 18.8<br />
Note: Standard deviations are listed below <strong>the</strong> peer group averages. The averages are calculated on <strong>the</strong> basis of <strong>the</strong> values between <strong>the</strong> ninth and second<br />
deciles for all MFIs and between second and <strong>the</strong> 99th percentiles for each peer group. Group averages different from average for all MFIs at 1 percent<br />
significance level are marked with an asterisk (*). “n” refers to <strong>the</strong> total number of institutions in <strong>the</strong> peer group, before dropping <strong>the</strong> percentiles for averages.<br />
Additional statistical information is available at www.<strong>microbanking</strong>-mbb.org. Abbreviation: MENA=Middle East/North Africa.
BULLETIN HIGHLIGHTS AND TABLES<br />
COMMERCIAL<br />
FUNDING LIABILITIES<br />
RATIO<br />
borrowings at commercial<br />
rates/ average gross loan<br />
portfolio (%)<br />
TOTAL<br />
GROSS LOAN<br />
PORTFOLIO<br />
total gross portfolio<br />
outstanding<br />
(US$)<br />
NUMBER OF<br />
ACTIVE<br />
BORROWERS<br />
borrowers with loans<br />
outstanding<br />
(no.)<br />
AVERAGE<br />
LOAN BALANCE<br />
total gross loan<br />
portfolio / no. active<br />
borrowers (US$)<br />
DEPTH<br />
average loan<br />
balance/ GNP per<br />
capita (%)<br />
PERCENT<br />
WOMEN<br />
BORROWERS<br />
women borrowers /<br />
total borrowers<br />
(%)<br />
PEER GROUP<br />
35.9 3,764,997 11,398 490 44.5 62.4 ALL<br />
36.9 3,540,730 9,023 452 32.4 21.8 MFIs<br />
67.8* 11,113,300* 74,921 686 62.6 60.9 FSS<br />
69.7 22,296,780 341,259 793 73.2 25.5 MFIs<br />
41.8 1,525,339 14,668 123 33.6 79.8 1.<br />
50.7 613,676 7,286 35 14.8 18.1<br />
24.0 512,947* 5,634 92* 31.3 83.8* 2.<br />
47.6 215,068 2,802 29 11.6 18.9<br />
68.1 6,445,652 15,411 492 81.8 35.9 3.<br />
36.5 1,235,647 2,903 237 22.0 20.6<br />
- 1,310,412 8,043 166 11.9 97.5* 4.<br />
- 617,411 6,789 62 3.9 5.0<br />
54.4 352,532,708* 2,046,752* 194 33.6 67.6 5.<br />
68.1 430,311,722 835,243 112 13.3 37.1<br />
37.7 1,509,701 12,974 159 14.3 82.8 6.<br />
9.4 906,275 11,424 24 2.4 10.3<br />
32.5 2,220,962 25,764* 82* 22.0 75.1 7.<br />
33.0 1,487,520 19,443 36 8.6 31.7<br />
1.3 2,698,678 1,377 2,249* 202.8* 38.3 8.<br />
2.2 516,150 504 526 41.8 2.3<br />
6.7 2,352,138 2,652 1,089* 66.2 44.5 9.<br />
15.1 1,253,418 1,916 302 25.3 6.3<br />
91.2* 27,175,166* 29,730* 971* 70.3 50.5 10.<br />
19.7 8,699,651 9,360 279 26.0 8.5<br />
50.9 3,427,876 7,453 609 64.3 46.5 11.<br />
18.5 1,332,599 5,924 414 29.1 16.6<br />
12.1 1,669,824 2,182 789 14.9 - 12.<br />
24.2 880,531 1,207 113 4.0 -<br />
39.8 3,506,001 19,663* 197 12.4* 82.4* 13.<br />
24.4 1,465,732 4,173 93 2.8 16.9<br />
16.5 853,632 8,975 91 6.3* 95.0* 14.<br />
29.4 409,407 3,127 17 3.9 6.6<br />
91.7* 4,105,127 5,122 887* 56.7 40.8* 15.<br />
19.8 1,295,656 1,504 420 26.0 4.9<br />
101.8* 10,322,826* 4,934 2,968* 391.4* 29.0 16.<br />
115.1 2,302,970 3,348 776 175.5 2.8<br />
MICROBANKING BULLETIN, APRIL 2001 45
BULLETIN HIGHLIGHTS AND TABLES<br />
TABLE 2. OVERALL FINANCIAL PERFORMANCE AND OPERATING INCOME<br />
PEER GROUP<br />
ADJUSTED<br />
RETURN ON<br />
ASSETS<br />
adj. net operating<br />
income / average<br />
total assets<br />
(%)<br />
ADJUSTED<br />
RETURN ON<br />
EQUITY<br />
adjusted net<br />
operating<br />
income / average<br />
equity<br />
(%)<br />
OPERATIONAL<br />
SELF-<br />
SUFFICIENCY<br />
operating income /<br />
operating expense<br />
(%)<br />
FINANCIAL<br />
SELF-<br />
SUFFICIENCY<br />
adjusted operating<br />
income / adjusted<br />
operating expense<br />
ALL MFIs (n=124) avg -3.5 -5.7 103.6 90.2<br />
stdv 6.1 14.3 20.9 18.7<br />
Financially self-Sufficient MFIs (n=64) avg 3.0* 8.8* 129.8* 114.3*<br />
stdv 5.3 14.2 36.0 25.8<br />
1. Africa – Medium – Low (n=10) avg -13.5* -23.2* 75.2* 72.7*<br />
Citi Savings & Loans, FINCA Uganda, KWFT, Nsoatreman Rural Bank, PAMÉCAS, PRIDE stdv 10.3 12.4 14.0 12.7<br />
Tanzania, PRIDE Uganda, PRIDE Vita Guinea, SEF, WAGES<br />
2. Africa – Small – Low (n=11) avg -11.4* -8.1 77.5* 69.3*<br />
Assawinso Rural Bank, Faulu, FINCA Malawi, FINCA Tanzania, FOCCAS, Manya Krobo stdv 9.8 25.9 27.6 22.3<br />
Rural Bank, Piyeli, SEDA, Sinapi Aba Trust, UWFT, Vital-Finance<br />
3. Africa/MENA – Large/Medium – Broad (n=5) avg -0.7 -4.4 114.4 101.0<br />
ABA, Kafo Jiginew, Nyésigiso, PADME, UNRWA stdv 2.8 7.4 36.8 23.0<br />
4. Asia (Central)/MENA – Medium/Small – Low (n=6) avg -11.2* -11.9 79.9 70.8<br />
Al Amana, Al Majmoua, Constanta, Faten, FINCA Kyrgyzstan, stdv 10.2 11.2 18.5 18.9<br />
Microfund for Women<br />
5. Asia – Large – Low/Broad (n=5) avg 4.7 13.5 136.8* 121.1*<br />
ACLEDA, ASA, BAAC, BRAC, BRI stdv 3.0 9.5 9.2 12.9<br />
6. Asia (Pacific) – Medium/Small – Low/Broad (n=6) avg 0.7 0.2 111.1 101.8<br />
CARD Bank, EMT, Hatta Kaksekar, Hublag, RSPI, TSPI stdv 3.4 4.8 6.6 9.8<br />
7. Asia (South) – Medium/Small – Low/Broad (n=9) avg -6.8 -12.4 85.6 69.4*<br />
AKRSP, Basix, BURO Tangail, CDS, FWWB India, Kash Foundation, Nirdhan, stdv 3.9 11.8 18.8 19.9<br />
SEEDS, SHARE<br />
8. Eastern Europe – High Size: All (n=5) avg -2.3 -9.0 105.8 90.5<br />
AMK, LOK, Moznosti, Sunrise, WVB stdv 1.4 9.3 19.3 5.6<br />
9. Eastern Europe – Broad Size: All (n=7) avg -3.0 -5.2 106.8 90.1<br />
Bospo, Fundusz Mikro, Inicjatywa Mikro, Mercy Corps, Mikrofin, Nachala, NOA stdv 2.1 4.9 7.3 6.3<br />
10. Latin America – Large – Broad/High (n=9) avg 2.3* 15.3* 110.4 108.9*<br />
Banco Ademi, BancoSol, Caja de Los Andes, Calpiá, CM Arequipa, FIE, Finamérica, stdv 1.3 12.2 5.0 5.3<br />
Mibanco, PRODEM<br />
11. Latin America – Medium – Broad (n=11) avg -1.9 -0.6 106.1 96.5<br />
ACODEP, Actuar, ADOPEM, ADRI, Banco de la Pequena Empresa, Chispa, FAMA, stdv 7.0 16.4 21.4 20.0<br />
Finsol, FONDECO, ProEmpresa, Sartawi<br />
12. Latin America – Medium/Small – Low – Upper Income (n=6) avg -10.6 -44.3* 100.2 81.4<br />
Banco do Povo de Juiz de For a, CEAPE/ Pernambuco, Contigo, Emprender, Portsol, stdv 8.0 51.0 18.0 14.2<br />
Vivacred<br />
13. Latin America – Medium – Low – Lower Income (n=9) avg 4.5* 7.7* 125.2* 111.6*<br />
CAM, CMM Medellín, Compartamos, Crecer, Enlace, FINCA Honduras, stdv 8.2 13.6 27.6 19.0<br />
FMM Popayán, FWWB Cali, ProMujer<br />
14. Latin America – Small – Low – Lower Income (n=7) avg -3.4 -4.6 114.6 93.4<br />
AGAPE, FED, FINCA Ecuador, FINCA Mexico, FINCA Nicaragua, FINCA Peru, stdv 6.9 10.5 8.4 11.4<br />
World Relief Honduras<br />
15. Latin America – Credit Unions – Broad Size: All (n=11) avg -2.6 -5.6 113.2 88.3<br />
15 de Abril, 23 de Julio, Acredicom, Chuimequená, COOSAJO, Ecosaba, Moyután, stdv 4.4 11.7 22.1 21.5<br />
Oscus, Sagrario, Tonantel, Tulcán<br />
16. Worldwide – Large/Medium – Small Business (n=7) avg -2.1 -7.3 104.7 90.4<br />
ACEP, Agrocapital, Bank Dagang Bali, CERUDEB, FEFAD, MEB, NLC stdv 1.7 6.3 6.4 7.0<br />
Note: Standard deviations are listed below <strong>the</strong> peer group averages. The averages are calculated on <strong>the</strong> basis of <strong>the</strong> values between <strong>the</strong> ninth and second deciles<br />
for all MFIs and between second and <strong>the</strong> 99 th percentiles for each peer group. Group averages different from average for all MFIs at 1 percent significance level are<br />
marked with an asterisk (*). “n” refers to <strong>the</strong> total number of institutions in <strong>the</strong> peer group, before dropping <strong>the</strong> percentiles for averages. Additional statistical<br />
information is available at www.<strong>microbanking</strong>-mbb.org. Abbreviation: MENA=Middle East/North Africa.<br />
(%)<br />
46 MICROBANKING BULLETIN, APRIL 2001
BULLETIN HIGHLIGHTS AND TABLES<br />
OPERATING<br />
INCOME RATIO<br />
adjusted operating<br />
income /<br />
average total assets<br />
(%)<br />
PROFIT<br />
MARGIN<br />
adjusted net<br />
operating income /<br />
adjusted<br />
operating income<br />
(%)<br />
NET<br />
INTEREST<br />
MARGIN<br />
adjusted net interest<br />
margin /<br />
average total assets<br />
(%)<br />
PORTFOLIO<br />
YIELD<br />
operating income – accrued<br />
interest – interest and fee<br />
income from investments /<br />
average gross loan portfolio<br />
(%)<br />
REAL<br />
YIELD<br />
(portfolio yield –<br />
inflation rate) /<br />
(1 + inflation rate)<br />
(%)<br />
PEER GROUP<br />
28.3 -16.2 19.8 39.2 30.1 ALL<br />
9.0 26.6 8.2 13.2 12.0 MFIs<br />
33.4* 9.3* 24.7* 45.5* 35.5* FSS<br />
13.0 15.1 11.4 20.1 15.4 MFIs<br />
28.0 -42.1 22.6 42.0 34.5 1.<br />
7.8 29.4 10.2 13.2 12.0<br />
27.4 -57.2* 22.7 54.6* 44.2* 2.<br />
8.5 47.2 8.7 15.8 15.9<br />
13.1* -2.1 9.5 24.6 21.7 3.<br />
1.5 20.5 2.9 1.3 0.7<br />
26.7 -48.0 20.7 44.8 36.8 4.<br />
6.4 34.4 4.7 15.5 3.7<br />
23.6 16.8 12.3 31.7 17.6 5.<br />
4.9 8.9 2.5 13.7 4.9<br />
29.3 1.1 24.1 42.7 36.3 6.<br />
1.6 9.0 2.4 3.6 5.1<br />
15.8* -56.6* 9.6* 22.0* 13.3* 7.<br />
5.4 51.6 5.1 7.8 8.2<br />
26.2 -10.8 17.7 31.8 15.3 8.<br />
3.0 6.8 5.3 3.6 3.1<br />
27.6 -11.4 20.8 31.0 22.7 9.<br />
1.1 7.4 2.7 1.9 5.2<br />
29.1 8.0* 20.2 35.1 29.9 10.<br />
2.2 4.4 2.6 3.0 3.5<br />
36.5* -8.2 25.4 45.9 33.1 11.<br />
7.1 25.5 6.0 8.9 7.3<br />
41.8* -25.7 27.9 52.9 50.2* 12.<br />
5.6 22.1 5.2 5.6 3.2<br />
45.2* 8.4* 30.2* 57.1* 40.3 13.<br />
11.7 13.7 4.4 14.5 5.3<br />
45.0* -8.6 26.6 78.3* 55.3* 14.<br />
3.0 15.4 12.9 13.0 6.4<br />
16.6* -19..4 7.7* 20.3* -9.5* 15.<br />
3.7 28.8 3.7 3.9 32.4<br />
21.0 -11.2 11.4 24.0* 17.0* 16.<br />
3.8 8.7 6.2 3.7 3.7<br />
MICROBANKING BULLETIN, APRIL 2001 47
BULLETIN HIGHLIGHTS AND TABLES<br />
TABLE 3. OPERATING EXPENSES AND PORTFOLIO MANAGEMENT INDICATORS<br />
PEER GROUP<br />
OPERATING<br />
EXPENSE<br />
RATIO<br />
adjusted operating<br />
expense /<br />
average total assets<br />
(%)<br />
TOTAL<br />
INTEREST<br />
EXP. RATIO<br />
adj. total interest<br />
expense /<br />
average total<br />
assets<br />
(%)<br />
ADJUSTMENT<br />
EXPENSE<br />
RATIO<br />
adjustment<br />
expense /<br />
average total<br />
assets<br />
(%)<br />
LOAN LOSS<br />
PROVISION<br />
EXP. RATIO<br />
adj. loan loss<br />
provision expense /<br />
average total<br />
assets<br />
(%)<br />
ALL MFIs (n=124) avg 32.5 3.8 3.5 2.1<br />
stdv 10.9 2.9 2.6 1.5<br />
Financially self-Sufficient MFIs (n=64) avg 30.7 5.1 3.1 1.9<br />
stdv 12.5 4.1 3.1 1.5<br />
1. Africa – Medium – Low (n=10) avg 43.8* 1.7 2.3 1.4<br />
Citi Savings & Loans, FINCA Uganda, KWFT, Nsoatreman Rural Bank, PAMÉCAS, PRIDE stdv 14.9 0.8 1.8 1.1<br />
Tanzania, PRIDE Uganda, PRIDE Vita Guinea, SEF, WAGES<br />
2. Africa – Small – Low (n=11) avg 40.1 1.3* 3.6 1.6<br />
Assawinso Rural Bank, Faulu, FINCA Malawi, FINCA Tanzania, FOCCAS, Manya Krobo stdv 11.8 1.9 2.5 1.1<br />
Rural Bank, Piyeli, SEDA, Sinapi Aba Trust, UWFT, Vital-Finance<br />
3. Africa/MENA – Large/Medium – Broad (n=5) avg 15.7* 1.5 0.6 2.5<br />
ABA, Kafo Jiginew, Nyésigiso, PADME, UNRWA stdv 6.2 1.0 0.9 1.6<br />
4. Asia (Central)/MENA – Medium/Small – Low (n=6) avg 38.0 - 3.6 0.9<br />
Al Amana, Al Majmoua, Constanta, Faten, FINCA Kyrgyzstan, stdv 6.2 - 3.6 0.7<br />
Microfund for Women<br />
5. Asia – Large – Low/Broad (n=5) avg 23.8 4.2 2.3 3.2<br />
ACLEDA, ASA, BAAC, BRAC, BRI stdv 0.5 2.0 1.4 1.5<br />
6. Asia (Pacific) – Medium/Small – Low/Broad (n=6) avg 30.6 3.3 2.4 2.3<br />
CARD Bank, EMT, Hatta Kaksekar, Hublag, RSPI, TSPI stdv 2.4 0.5 0.6 1.2<br />
7. Asia (South) – Medium/Small – Low/Broad (n=9) avg 23.0 2.9 4.1 2.0<br />
AKRSP, Basix, BURO Tangail, CDS, FWWB India, Kash Foundation, Nirdhan, stdv 10.7 2.3 1.9 2.3<br />
SEEDS, SHARE<br />
8. Eastern Europe – High Size: All (n=5) avg 29.5 2.6 4.9 3.1<br />
AMK, LOK, Moznosti, Sunrise, WVB stdv 7.1 1.8 2.9 0.7<br />
9. Eastern Europe – Broad Size: All (n=7) avg 28.6 1.6 5.2 2.8<br />
Bospo, Fundusz Mikro, Inicjatywa Mikro, Mercy Corps, Mikrofin, Nachala, NOA stdv 3.9 1.8 1.4 1.5<br />
10. Latin America – Large – Broad/High (n=9) avg 28.4 8.0* 0.7* 3.5*<br />
Banco Ademi, BancoSol, Caja de Los Andes, Calpiá, CM Arequipa, FIE, Finamérica, stdv 5.1 1.8 0.6 1.2<br />
Mibanco, PRODEM<br />
11. Latin America – Medium – Broad (n=11) avg 37.4 7.2* 3.5 3.4*<br />
ACODEP, Actuar, ADOPEM, ADRI, Banco de la Pequena Empresa, Chispa, FAMA, stdv 7.1 4.4 1.5 2.4<br />
Finsol, FONDECO, ProEmpresa, Sartawi<br />
12. Latin America – Medium/Small – Low – Upper Income (n=6) avg 52.5* 6.5 9.6* 7.2*<br />
Banco do Povo de Juiz de For a, CEAPE/ Pernambuco, Contigo, Emprender, Portsol, stdv 6.4 3.5 3.6 6.7<br />
Vivacred<br />
13. Latin America – Medium – Low – Lower Income (n=9) avg 41.5 5.6 4.3 1.6<br />
CAM, CMM Medellín, Compartamos, Crecer, Enlace, FINCA Honduras, stdv 14.2 4.3 3.5 1.0<br />
FMM Popayán, FWWB Cali, ProMujer<br />
14. Latin America – Small – Low – Lower Income (n=7) avg 55.3* 4.2 10.0* 2.2<br />
AGAPE, FED, FINCA Ecuador, FINCA Mexico, FINCA Nicaragua, FINCA Peru, stdv 13.7 3.3 3.8 0.8<br />
World Relief Honduras<br />
15. Latin America – Credit Unions – Broad Size: All (n=11) avg 19.1* 5.7 3.2 1.0<br />
15 de Abril, 23 de Julio, Acredicom, Chuimequená, COOSAJO, Ecosaba, Moyután, stdv 2.3 3.2 3.5 0.7<br />
Oscus, Sagrario, Tonantel, Tulcán<br />
16. Worldwide – Large/Medium – Small Business (n=7) avg 20.0* 3.4 2.3 1.2<br />
ACEP, Agrocapital, Bank Dagang Bali, CERUDEB, FEFAD, MEB, NLC stdv 6.0 3.3 1.4 0.7<br />
Note: Standard deviations are listed below <strong>the</strong> peer group averages. The averages are calculated on <strong>the</strong> basis of <strong>the</strong> values between <strong>the</strong> ninth and second deciles for<br />
all MFIs and between second and <strong>the</strong> 99 th percentiles for each peer group. Group averages different from average for all MFIs at 1 percent significance level are<br />
marked with an asterisk (*). “n” refers to <strong>the</strong> total number of institutions in <strong>the</strong> peer group, before dropping <strong>the</strong> percentiles for averages. Additional statistical<br />
information is available at www.<strong>microbanking</strong>-mbb.org. Abbreviation: MENA=Middle East/North Africa.<br />
48 MICROBANKING BULLETIN, APRIL 2001
BULLETIN HIGHLIGHTS AND TABLES<br />
SALARY EXPENSE<br />
RATIO<br />
personnel expense + in-kind<br />
donations /<br />
average total assets<br />
(%)<br />
OTHER<br />
ADMINISTRATIVE<br />
EXPENSE RATIO<br />
administrative expense +<br />
in-kind donations –<br />
personnel expense/<br />
average total assets<br />
(%)<br />
TOTAL<br />
ADMINISTRATIVE<br />
EXPENSE RATIO<br />
administrative expense +<br />
in-kind donations/<br />
average total assets<br />
(%)<br />
PORTFOLIO<br />
AT RISK ><br />
90 DAYS<br />
outstanding<br />
balance of loans overdue<br />
90 days /<br />
total gross loan portfolio<br />
(%)<br />
PEER GROUP<br />
11.5 9.1 20.7 1.9 ALL<br />
6.0 3.8 9.2 1.6 MFIs<br />
11.1 8.8 19.9 1.9 FSS<br />
6.5 5.0 10.4 1.5 MFIs<br />
20.6* 14.8* 35.4* 0.8 1.<br />
11.4 6.3 17.0 0.7<br />
17.3* 15.4* 32.5* 2.4 2.<br />
5.6 6.1 11.7 2.4<br />
5.0 3.7* 8.9 4.0 3.<br />
2.3 3.5 3.6 3.3<br />
23.1* 9.1 32.8* 0.1 4.<br />
3.4 1.2 4.8 0.1<br />
5.9 2.3* 8.1* 2.1 5.<br />
3.2 1.6 4.7 1.2<br />
12.0 8.9 20.9 2.7 6.<br />
0.7 1.3 1.9 3.1<br />
7.7 6.3 13.7 1.7 7.<br />
7.3 4.5 11.7 2.5<br />
12.0 7.0 19.5 0.1 8.<br />
3.0 0.6 2.5 0.1<br />
12.0 8.6 20.4 0.9 9.<br />
1.7 1.4 3.3 0.9<br />
8.0 7.2 14.5 2.6 10.<br />
1.8 2.2 2.1 0.7<br />
12.2 11.5 23.5 2.8 11.<br />
4.0 4.1 6.8 1.8<br />
15.5 12.0 27.6 2.7 12.<br />
3.0 1.7 1.6 2.2<br />
14.4 12.6 26.6 1.0 13.<br />
4.9 5.1 9.2 0.9<br />
20.5* 12.8* 32.7* 1.6 14.<br />
5.2 5.5 4.4 0.8<br />
3.0* 5.1* 8.2* - 15.<br />
0.8 0.8 1.3 -<br />
4.3* 5.0* 9.3* 1.0 16.<br />
2.3 3.4 5.4 0.9<br />
MICROBANKING BULLETIN, APRIL 2001 49
BULLETIN HIGHLIGHTS AND TABLES<br />
TABLE 4. EFFICIENCY AND PRODUCTIVIY<br />
PEER GROUP<br />
TOTAL<br />
ADMINISTRATIVE<br />
EXPENSE / LP<br />
administrative<br />
expense + in-kind<br />
donations / average<br />
gross loan portfolio<br />
(%)<br />
SALARY<br />
EXPENSE /<br />
LP<br />
personnel expense<br />
+ in-kind donations<br />
/ average gross<br />
loan portfolio<br />
(%)<br />
OTHER<br />
ADMINISTRATIVE<br />
EXPENSE / LP<br />
administrative expense<br />
+ in-kind donations –<br />
personnel expense/<br />
average gross loan<br />
portfolio (%)<br />
DEPTH<br />
average loan<br />
balance /<br />
GNP per<br />
capita<br />
(%)<br />
ALL MFIs (n=124) avg 31.3 17.3 13.8 44.5<br />
stdv 16.0 10.0 6.8 32.4<br />
Financially self-Sufficient MFIs (n=64) avg 29.2 16.2 12.9 62.6<br />
stdv 17.3 10.1 8.3 73.2<br />
1. Africa – Medium – Low (n=10) avg 56.6* 33.1* 23.6* 33.6<br />
Citi Savings & Loans, FINCA Uganda, KWFT, Nsoatreman Rural Bank, PAMÉCAS, PRIDE stdv 26.9 17.9 9.9 14.8<br />
Tanzania, PRIDE Uganda, PRIDE Vita Guinea, SEF, WAGES<br />
2. Africa – Small – Low (n=11) avg 71.4* 37.6* 33.8* 31.3<br />
Assawinso Rural Bank, Faulu, FINCA Malawi, FINCA Tanzania, FOCCAS, Manya Krobo stdv 22.3 12.1 11.8 11.6<br />
Rural Bank, Piyeli, SEDA, Sinapi Aba Trust, UWFT, Vital-Finance<br />
3. Africa/MENA – Large/Medium – Broad (n=5) avg 17.1 8.8 6.4 81.8<br />
ABA, Kafo Jiginew, Nyésigiso, PADME, UNRWA stdv 6.4 4.0 4.4 22.0<br />
4. Asia (Central)/MENA – Medium/Small – Low (n=6) avg 55.2* 38.7* 16.2 11.9<br />
Al Amana, Al Majmoua, Constanta, Faten, FINCA Kyrgyzstan, stdv 9.4 6.0 3.0 3.9<br />
Microfund for Women<br />
5. Asia – Large – Low/Broad (n=5) avg 12.7 9.1 3.7* 33.6<br />
ACLEDA, ASA, BAAC, BRAC, BRI stdv 3.0 1.5 1.6 13.3<br />
6. Asia (Pacific) – Medium/Small – Low/Broad (n=6) avg 32.6 17.2 14.0 14.3<br />
CARD Bank, EMT, Hatta Kaksekar, Hublag, RSPI, TSPI stdv 2.7 1.4 1.3 2.4<br />
7. Asia (South) – Medium/Small – Low/Broad (n=9) avg 18.8 10.1 9.1 22.0<br />
AKRSP, Basix, BURO Tangail, CDS, FWWB India, Kash Foundation, Nirdhan, stdv 8.2 6.9 2.9 8.6<br />
SEEDS, SHARE<br />
8. Eastern Europe – High Size: All (n=5) avg 23.0 14.1 8.3 202.8*<br />
AMK, LOK, Moznosti, Sunrise, WVB stdv 1.3 2.8 0.7 41.8<br />
9. Eastern Europe – Broad Size: All (n=7) avg 23.7 14.0 9.7 66.2<br />
Bospo, Fundusz Mikro, Inicjatywa Mikro, Mercy Corps, Mikrofin, Nachala, NOA stdv 3.6 2.0 1.7 25.3<br />
10. Latin America – Large – Broad/High (n=9) avg 18.0 9.6 9.1 70.3<br />
Banco Ademi, BancoSol, Caja de Los Andes, Calpiá, CM Arequipa, FIE, Finamérica, stdv 3.6 2.7 3.2 26.0<br />
Mibanco, PRODEM<br />
11. Latin America – Medium – Broad (n=11) avg 31.0 16.0 15.1 64.3<br />
ACODEP, Actuar, ADOPEM, ADRI, Banco de la Pequena Empresa, Chispa, FAMA, stdv 9.6 4.8 5.7 29.1<br />
Finsol, FONDECO, ProEmpresa, Sartawi<br />
12. Latin America – Medium/Small – Low – Upper Income (n=6) avg 36.4 21.2 15.9 14.9<br />
Banco do Povo de Juiz de For a, CEAPE/ Pernambuco, Contigo, Emprender, Portsol, stdv 4.6 1.9 4.0 4.0<br />
Vivacred<br />
13. Latin America – Medium – Low – Lower Income (n=9) avg 39.7 21.6 17.8 12.4*<br />
CAM, CMM Medellín, Compartamos, Crecer, Enlace, FINCA Honduras, stdv 15.8 8.2 6.9 2.8<br />
FMM Popayán, FWWB Cali, ProMujer<br />
14. Latin America – Small – Low – Lower Income (n=7) avg 54.1* 33.6* 24.2* 6.3*<br />
AGAPE, FED, FINCA Ecuador, FINCA Mexico, FINCA Nicaragua, FINCA Peru, stdv 7.4 7.1 9.8 3.9<br />
World Relief Honduras<br />
15. Latin America – Credit Unions – Broad Size: All (n=11) avg 12.2* 4.5* 7.5* 56.7<br />
15 de Abril, 23 de Julio, Acredicom, Chuimequená, COOSAJO, Ecosaba, Moyután, stdv 2.1 1.2 1.5 26.0<br />
Oscus, Sagrario, Tonantel, Tulcán<br />
16. Worldwide – Large/Medium – Small Business (n=7) avg 12.8* 5.7* 7.4 391.4*<br />
ACEP, Agrocapital, Bank Dagang Bali, CERUDEB, FEFAD, MEB, NLC stdv 6.8 2.6 4.2 175.5<br />
Note: Standard deviations are listed below <strong>the</strong> peer group averages. The averages are calculated on <strong>the</strong> basis of <strong>the</strong> values between <strong>the</strong> ninth and second deciles for<br />
all MFIs and between second and <strong>the</strong> 99 th percentiles for each peer group. Group averages different from average for all MFIs at 1 percent significance level are<br />
marked with an asterisk (*). “n” refers to <strong>the</strong> total number of institutions in <strong>the</strong> peer group, before dropping <strong>the</strong> percentiles for averages. Additional statistical<br />
information is available at www.<strong>microbanking</strong>-mbb.org. Abbreviation: MENA=Middle East/North Africa.<br />
50 MICROBANKING BULLETIN, APRIL 2001
BULLETIN HIGHLIGHTS AND TABLES<br />
AVERAGE<br />
SALARY<br />
average personnel<br />
expense + in-kind<br />
donations /<br />
GNP per capita<br />
(multiple of GNP/ capita)<br />
STAFF<br />
PRODUCTIVITY<br />
number active<br />
borrowers /<br />
number of staff<br />
(no.)<br />
LOAN OFFICER<br />
PRODUCTIVITY<br />
number active<br />
borrowers /<br />
number of loan<br />
officers<br />
(no.)<br />
STAFF<br />
ALLOCATION<br />
number of loan<br />
officers /<br />
number of staff<br />
(%)<br />
STAFF<br />
TURNOVER<br />
number staff who<br />
left <strong>the</strong> MFI /<br />
average number<br />
of staff<br />
(%)<br />
COST PER<br />
BORROWER<br />
administrative<br />
expense + in-kind<br />
donations / average<br />
number of active<br />
borrowers (US$)<br />
PEER GROUP<br />
5.8 122 257 49.7 10.8 137 ALL<br />
3.6 49 128 10.5 6.5 171 MFIs<br />
5.9 133 291 51.4 12.7 110 FSS<br />
4.0 72 189 15.3 934 99 MFIs<br />
12.9* 192* 474* 38.9* 5.2 56 1.<br />
7.6 79 292 15.4 3.1 27<br />
11.9* 129 285 51.9 4.6* 62 2.<br />
4.8 41 99 13.1 4.8 30<br />
7.6 119 315 47.2 6.2 40 3.<br />
6.5 95 308 11.5 5.7 8<br />
3.9 102 174 59.2 20.2 76 4.<br />
1.3 10 35 6.8 19.8 41<br />
4.0 238* 420 41.6 5.8 25 5.<br />
1.0 83 84 20.7 1.3 16<br />
2.3 80 134 66.1* 13.8 44 6.<br />
0.6 16 22 11.1 10.1 13<br />
3.4 235* 452* 56.0 9.7 17 7.<br />
1.4 232 494 14.6 5.7 7<br />
10.3 64 113 55.9 6.8 259 8.<br />
0.6 0 8 2.8 6.7 33<br />
5.0 78 123 58.7 7.3 196 9.<br />
2.5 22 28 9.1 2.4 77<br />
7.1 122 285 42.6 13.0 166 10.<br />
2.5 16 58 3.9 4.9 40<br />
6.9 88 212 40.6* 14.2 176 11.<br />
3.9 41 84 7.6 8.2 155<br />
2.1 81 197 48.5 - 241 12.<br />
0.3 30 71 12.7 - 117<br />
3.4 194* 417* 48.5 9.6 56 13.<br />
1.0 52 159 6.6 6.6 9<br />
2.0 140 225 57.1 10.3 52 14.<br />
1.3 29 40 3.6 9.5 15<br />
1.9 107 - - - 105 15.<br />
0.5 8 - - - 34<br />
8.9 37* 130 32.2* 3.3 377* 16.<br />
3.8 21 84 7.8 2.9 272<br />
MICROBANKING BULLETIN, APRIL 2001 51
BULLETIN HIGHLIGHTS AND TABLES<br />
TABLE 5. MACROECONOMIC INDICATORS<br />
GNP PER<br />
CAPITA<br />
GDP GROWTH<br />
RATE, ANNUAL<br />
AVERAGE<br />
1990-98<br />
INFLATION<br />
RATE<br />
DEPOSIT<br />
RATE<br />
FINANCIAL<br />
DEEPENING<br />
(M3 / GDP)<br />
PEER GROUP<br />
(US$) (%) (%) (%) (%)<br />
ALL MFIs (n=124) avg 1,395 3.9 12.4 15.3 35.3<br />
stdv 1,327 2.8 21.9 10.9 19.1<br />
Financially self-Sufficient MFIs (n=64) avg 1,373 3.9 7.5 14.3 35.9<br />
stdv 1,036 2.3 7.9 7.8 17.1<br />
1. Africa – Medium – Low (n=10) avg 660 4.0 5.8 11.2 19.5*<br />
Citi Savings & Loans, FINCA Uganda, KWFT, Nsoatreman Rural Bank, PAMÉCAS, PRIDE stdv 901 2.1 4.3 6.9 9.4<br />
Tanzania, PRIDE Uganda, PRIDE Vita Guinea, SEF, WAGES<br />
2. Africa – Small – Low (n=11) avg 309* 4.8 8.7 13.8 15.8*<br />
Assawinso Rural Bank, Faulu, FINCA Malawi, FINCA Tanzania, FOCCAS, Manya Krobo stdv 74 1.7 6.0 9.5 6.1<br />
Rural Bank, Piyeli, SEDA, Sinapi Aba Trust, UWFT, Vital-Finance<br />
3. Africa/MENA – Large/Medium – Broad (n=5) avg 746 4.0 2.2 5.0 31.6<br />
ABA, Kafo Jiginew, Nyésigiso, PADME, UNRWA stdv 629 0.4 4.6 2.5 33.0<br />
4. Asia (Central)/MENA – Medium/Small – Low (n=6) avg 1,723 -4.6* 7.5 10.2 87.7<br />
Al Amana, Al Majmoua, Constanta, Faten, FINCA Kyrgyzstan, stdv 1,631 10.1 7.4 4.4 22.6<br />
Microfund for Women<br />
5. Asia – Large – Low/Broad (n=5) avg 770 5.7 9.7 12.7 47.3<br />
ACLEDA, ASA, BAAC, BRAC, BRI stdv 798 0.3 6.3 7.4 35.9<br />
6. Asia (Pacific) – Medium/Small – Low/Broad (n=6) avg 837 3.9 5.3 9.2 46.2<br />
CARD Bank, EMT, Hatta Kaksekar, Hublag, RSPI, TSPI stdv 452 0.9 4.6 2.3 27.1<br />
7. Asia (South) – Medium/Small – Low/Broad (n=9) avg 422 5.6 8.0 10.8 46.7<br />
AKRSP, Basix, BURO Tangail, CDS, FWWB India, Kash Foundation, Nirdhan, stdv 162 0.7 2.9 3.6 7.5<br />
SEEDS, SHARE<br />
8. Eastern Europe – High Size: All (n=5) avg 1,127 1.7 11.4 13.5 14.8<br />
AMK, LOK, Moznosti, Sunrise, WVB stdv 91 - 6.6 1.2 -<br />
9. Eastern Europe – Broad Size: All (n=7) avg 2,336 0.8 6.4 10.0 34.4<br />
Bospo, Fundusz Mikro, Inicjatywa Mikro, Mercy Corps, Mikrofin, Nachala, NOA stdv 1,625 3.4 6.9 4.5 1.1<br />
10. Latin America – Large – Broad/High (n=9) avg 1,663 4.8 4.1 14.4 38.1<br />
Banco Ademi, BancoSol, Caja de Los Andes, Calpiá, CM Arequipa, FIE, Finamérica, stdv 671 0.8 3.1 3.4 10.3<br />
Mibanco, PRODEM<br />
11. Latin America – Medium – Broad (n=11) avg 1,373 4.0 8.4 14.7 43.1<br />
ACODEP, Actuar, ADOPEM, ADRI, Banco de la Pequena Empresa, Chispa, FAMA, stdv 883 1.0 3.6 3.9 17.4<br />
Finsol, FONDECO, ProEmpresa, Sartawi<br />
12. Latin America – Medium/Small – Low – Upper Income (n=6) avg 5,257* 4.5 2.2 24.6 32.9<br />
Banco do Povo de Juiz de For a, CEAPE/ Pernambuco, Contigo, Emprender, Portsol, stdv 1,366 1.9 1.7 12.6 5.4<br />
Vivacred<br />
13. Latin America – Medium – Low – Lower Income (n=9) avg 1,931 3.8 13.2 19.7 34.2<br />
CAM, CMM Medellín, Compartamos, Crecer, Enlace, FINCA Honduras, stdv 988 0.8 15.6 12.0 10.9<br />
FMM Popayán, FWWB Cali, ProMujer<br />
14. Latin America – Small – Low – Lower Income (n=7) avg 1,849 3.5 22.6 25.1 36.2<br />
AGAPE, FED, FINCA Ecuador, FINCA Mexico, FINCA Nicaragua, FINCA Peru, stdv 1,170 1.1 20.6 16.9 14.6<br />
World Relief Honduras<br />
15. Latin America – Credit Unions – Broad Size: All (n=11) avg 1,553 3.6 51.4* 26.7* 32.0<br />
15 de Abril, 23 de Julio, Acredicom, Chuimequená, COOSAJO, Ecosaba, Moyután, stdv 31 0.6 54.1 19.1 6.4<br />
Oscus, Sagrario, Tonantel, Tulcán<br />
16. Worldwide – Large/Medium – Small Business (n=7) avg 757 5.5 12.5 15.2 37.8<br />
ACEP, Agrocapital, Bank Dagang Bali, CERUDEB, FEFAD, MEB, NLC stdv 329 2.3 20.4 11.3 19.8<br />
Note: Standard deviations are listed below <strong>the</strong> peer group averages. The averages are calculated on <strong>the</strong> basis of <strong>the</strong> values between <strong>the</strong> ninth and second deciles for<br />
all MFIs and between second and <strong>the</strong> 99 th percentiles for each peer group. Group averages different from average for all MFIs at 1 percent significance level are<br />
marked with an asterisk (*). “n” refers to <strong>the</strong> total number of institutions in <strong>the</strong> peer group, before dropping <strong>the</strong> percentiles for averages. Additional statistical<br />
information is available at www.<strong>microbanking</strong>-mbb.org. Abbreviation: MENA=Middle East/North Africa.<br />
52 MICROBANKING BULLETIN, APRIL 2001
BULLETIN HIGHLIGHTS AND TABLES<br />
Additional Analysis Tables<br />
Tables A and B provide data on selected<br />
performance indicators for groups of institutions<br />
from <strong>the</strong> entire database for this Issue (n=124).<br />
Tables C and D provide information only for <strong>the</strong><br />
financially self-sufficient institutions (n=64). The<br />
following eight characteristics are considered for <strong>the</strong><br />
classification of data:<br />
1) Age: The Bulletin classifies MFIs into three<br />
categories (new, young, and mature) based on<br />
<strong>the</strong> difference between <strong>the</strong> year <strong>the</strong>y started<br />
<strong>the</strong>ir microfinance operations and <strong>the</strong> year for<br />
which <strong>the</strong> institutions have submitted data.<br />
2) Scale of Operations: MFIs are classified as<br />
small, medium and large according to <strong>the</strong> size<br />
of <strong>the</strong>ir loan portfolio to facilitate comparisons of<br />
institutions with similar outreach.<br />
3) Lending Methodology: Performance may vary<br />
by <strong>the</strong> methodology used by <strong>the</strong> institution to<br />
deliver loan products. The Bulletin classifies<br />
MFIs based on <strong>the</strong> primary methodology used,<br />
determined by <strong>the</strong> number of loans outstanding.<br />
4) Level of Financial Intermediation: This<br />
classification is based on <strong>the</strong> ratio of total<br />
voluntary passbook and time deposits to total<br />
assets. It indicates <strong>the</strong> MFI’s ability to mobilize<br />
savings and fund its portfolio through deposits.<br />
5) Target Market: The Bulletin classifies MFIs into<br />
three categories—low-end, broad, and highend—according<br />
to <strong>the</strong> range of clients <strong>the</strong>y<br />
serve based on average loan outstanding per<br />
GNP per capita.<br />
6) Region: Geographic regions—Africa, Asia,<br />
Eastern Europe, and Latin America—are<br />
considered for <strong>the</strong> classification to capture<br />
regional effects. MENA was not included due<br />
to <strong>the</strong> small number of MFIs participating.<br />
7) Charter Type: The charter under which <strong>the</strong><br />
MFIs are registered is used to classify <strong>the</strong> MFIs<br />
into banks, credit unions/ cooperatives, NGOs,<br />
and non-banks.<br />
8) Profit Status: MFIs are classified as for-profit<br />
and non-profit institutions.<br />
The quantitative criteria used to categorize <strong>the</strong>se<br />
characteristics are summarized in <strong>the</strong> table below.<br />
A list of institutions that fall into <strong>the</strong>se categories for<br />
<strong>the</strong> entire sample is located immediately following<br />
Table D. Confidentiality limits <strong>the</strong> publication of<br />
names of financially self-sufficient MFIs included in<br />
<strong>the</strong> database.<br />
These Additional Analysis Tables provide ano<strong>the</strong>r<br />
means of creating performance benchmarks<br />
besides <strong>the</strong> peer groups. Three of <strong>the</strong>se<br />
characteristics—region, scale of operations and<br />
target market—are also factors in determining peer<br />
group composition. The purpose of <strong>the</strong> Additional<br />
Analysis Tables is to look at <strong>the</strong>se characteristics<br />
singularly, ra<strong>the</strong>r than within <strong>the</strong> context of <strong>the</strong> peer<br />
groups. The data are calculated by dropping <strong>the</strong><br />
top and bottom percentile of observations to avoid<br />
<strong>the</strong> effect of outliers.<br />
Age of <strong>the</strong> MFI<br />
Scale of Operations<br />
(Size of gross<br />
outstanding loan<br />
portfolio in US$)<br />
Lending Methodology<br />
Level of Retail Financial<br />
Intermediation<br />
Target Market<br />
New:<br />
Young:<br />
Mature:<br />
Large:<br />
Medium:<br />
Small:<br />
Individual<br />
Solidarity Group:<br />
Village Banking:<br />
Financial Intermediary:<br />
O<strong>the</strong>r:<br />
Low-end:<br />
Broad:<br />
High-end and Small Business:<br />
1 to 2 years<br />
3 to 6 years<br />
over 6 years<br />
Africa and MENA:<br />
Asia (all):<br />
E.Europe and L.America:<br />
Africa and MENA:<br />
Asia:<br />
E.Europe and L.America:<br />
Africa:<br />
Asia:<br />
E.Europe and L.America:<br />
1 borrower<br />
group of 3 to 9 borrowers<br />
groups with ≥ 10 borrowers<br />
> 5 million<br />
> 8 million<br />
> 10 million<br />
900,000 to 5 million<br />
1 to 8 million<br />
1.5 to 10 million<br />
< 900,000<br />
< 1 million<br />
< 1.5 million<br />
passbook and time deposits ≥ 20 % of total assets<br />
passbook and time deposits < 20 % of total assets<br />
depth < 20% OR average loan size < US$150<br />
depth between 20% and 149%<br />
depth ≥ 150%<br />
MICROBANKING BULLETIN, APRIL 2001 53
BULLETIN HIGHLIGHTS AND TABLES<br />
TABLE A: INSTITUTIONAL CHARACTERISTICS AND OUTREACH INDICATORS<br />
CRITERIA<br />
TOTAL<br />
ASSETS<br />
(US$)<br />
CAPITAL /<br />
ASSET<br />
RATIO<br />
adjusted total<br />
equity /<br />
adjusted total<br />
assets (%)<br />
COMMERCIAL<br />
FUNDING LIABILITIES<br />
RATIO<br />
OFFICES<br />
borrowings at commercial<br />
rates / average gross loan<br />
portfolio<br />
(%) (no.)<br />
AGE New (1 – 2 years) avg 2,655,249* 55.2 20.3 9<br />
stdv 1,615,760 29.1 29.9 10<br />
N 28 28 28 27<br />
Young (3 – 6 years) avg 5,441,477 55.3 40.0 34<br />
stdv 5,376,583 30.4 73.4 121<br />
N 26 26 26 26<br />
Mature (> 6 years) avg 62,766,879 45.7 66.0* 94*<br />
stdv 374,922,454 25.8 58.0 334<br />
N 63 63 63 56<br />
SCALE OF Large avg 191,135,173* 32.2* 91.1* 124*<br />
OPERATIONS # stdv 658,265,519 21.1 73.2 254<br />
N 20 20 20 20<br />
Medium avg 4,780,316 49.7 47.6 45<br />
stdv 2,625,596 26.9 47.3 242<br />
N 66 66 66 60<br />
Small avg 1,197,686* 61.2* 21.1 16<br />
stdv 779,360 28.1 33.8 48<br />
N 32 32 32 30<br />
LENDING Individual avg 68,102,103 40.3 71.8* 24<br />
METHOD- (1 borrower) stdv 412,429,864 26.2 71.8 86<br />
OLOGY N 52 52 52 47<br />
Solidarity Groups avg 10,102,851 50.0 37.7 34<br />
(groups of 3 to 9 stdv 19,545,681 25.6 40.3 96<br />
borrowers) N 43 43 43 40<br />
Village Banking avg 2,588,603* 71.1* 18.1 127*<br />
(groups with ≥ 10 stdv 1,912,299 24.0 24.5 406<br />
borrowers) N 23 23 23 23<br />
LEVEL OF Financial Intermediaries avg 121,131,564 20.4* 135.7* 42*<br />
FINANCIAL (passbook and time stdv 550,930,135 14.5 70.9 118<br />
INTER- deposits ≥ 20% of total N 29 29 29 24<br />
MEDIATION assets)<br />
O<strong>the</strong>r avg 5,835,964 59.5* 23.9* 61<br />
(passbook and time stdv 10,349,253 25.7 27.4 238<br />
deposits < 20% of total N 91 91 91 87<br />
assets)<br />
TARGET Low-end avg 4,948,110 59.0 34.4 89*<br />
MARKET (depth < 20% OR avg. stdv 11,581,748 28.6 50.0 297<br />
loan balance < US$ 150) N 57 57 57 55<br />
Broad avg 68,758,671 42.2 61.2* 28<br />
(depth between 20% and stdv 416,494,814 24.7 47.8 88<br />
149%) N 51 51 51 44<br />
High-end and avg 15,975,183* 35.8 57.1 11<br />
Small Business stdv 15,545,156 26.2 91.4 10<br />
(depth ≥ 150%) N 10 10 10 10<br />
Note: Standard deviations are listed below <strong>the</strong> group averages. The averages are calculated on <strong>the</strong> basis of <strong>the</strong> values between second and <strong>the</strong> 99 th<br />
percentiles for each group. Therefore, sample sizes (N) vary across indicators. Group averages different from average for all MFIs at 1 percent significance<br />
level are marked with an asterisk (*). Additional statistical information is available at www.<strong>microbanking</strong>-mbb.org.<br />
# The criteria for classification of scale of operations vary by region. Refer to page 53 for details.<br />
54 MICROBANKING BULLETIN, APRIL 2001
BULLETIN HIGHLIGHTS AND TABLES<br />
TOTAL<br />
STAFF<br />
Total number of<br />
employees<br />
(no.)<br />
TOTAL<br />
GROSS LOAN<br />
PORTFOLIO<br />
total gross portfolio<br />
outstanding<br />
(US$)<br />
PERCENT<br />
WOMEN<br />
BORROWERS<br />
total number of active<br />
women borrowers /<br />
total number of active<br />
borrowers (%)<br />
NUMBER OF<br />
ACTIVE<br />
BORROWERS<br />
number of<br />
borrowers with<br />
loans<br />
outstanding (no.)<br />
AVERAGE<br />
LOAN BALANCE<br />
total gross loan<br />
portfolio / number<br />
of active borrowers<br />
(US$)<br />
DEPTH<br />
average loan<br />
balance /<br />
GNP per capita<br />
(%)<br />
44* 2,039,656* 60.1 5,081* 894* 70.2*<br />
27 1,339,287 26.6 5,950 961 69.3<br />
28 28 23 28 28 28<br />
89 3,452,453 71.4 9.818 554 81.3<br />
72 3,511,633 26.8 6,971 818 145.2<br />
26 26 22 26 26 26<br />
611* 24,643,591 65.2 115,443 552 47.2<br />
2,100 107,735,266 26.7 460,828 686 50.8<br />
56 63 56 63 63 63<br />
1,525* 72,987,141* 50.0 328,895* 1,079* 98.7*<br />
3,374 185,140,979 21.5 788,612 806 91.2<br />
20 20 16 20 20 20<br />
96 3,366,278 64.2 13,741 650 64.6<br />
77 1,665,912 26.4 13,348 868 101.7<br />
61 66 56 66 66 66<br />
44* 669,421* 76.2* 4,797* 338 30.9<br />
27 317,056 25.9 3,776 465 27.6<br />
32 32 29 32 32 32<br />
371 23,341,413 44.4* 55,811 1,206* 103.0*<br />
1,914 116,972,376 14.6 342,044 1,044 122.7<br />
47 52 41 52 52 52<br />
260 7,619,230 74.3* 42,709 328 39.4<br />
783 16,341,007 23.5 163,573 352 31.7<br />
42 43 36 43 43 43<br />
81 1,667,467* 91.3* 14,568 115* 18.0*<br />
54 1,397,743 16.0 11,016 63 13.5<br />
23 23 23 23 23 23<br />
731 41,022,001 45.0* 99,378 879* 77.0*<br />
2,721 156,184,231 11.7 456,954 824 70.1<br />
23 29 23 29 29 29<br />
152 4,359,316 71.9* 25,240 583 52.5<br />
538 8,465,405 27.1 113,421 822 68.9<br />
91 91 79 91 91 91<br />
195 3,509,933 85.9* 35,718 150* 16.8*<br />
675 9,750,386 19.7 142,494 140 10.4<br />
57 57 47 57 57 57<br />
397 24,414,911 50.9* 60,480 797* 64.8*<br />
1,953 118,336,433 19.3 345,018 584 29.3<br />
45 51 47 51 51 51<br />
102 9,778,077* 33.6* 4,373* 2,776* 310.2*<br />
123 8,762,166 3.6 4,426 682 147.6<br />
10 10 7 10 10 10<br />
MICROBANKING BULLETIN, APRIL 2001 55
BULLETIN HIGHLIGHTS AND TABLES<br />
TABLE A: INSTITUTIONAL CHARACTERISTICS AND OUTREACH INDICATORS (continued)<br />
CRITERIA<br />
TOTAL<br />
ASSETS<br />
(US$)<br />
CAPITAL /<br />
ASSET<br />
RATIO<br />
adjusted total<br />
equity /<br />
adjusted total<br />
assets (%)<br />
COMMERCIAL<br />
FUNDING LIABILITIES<br />
RATIO<br />
OFFICES<br />
borrowings at commercial<br />
rates / average gross loan<br />
portfolio<br />
(%) (no.)<br />
REGION ## Africa avg 3,119,574 51.6 53.1 31*<br />
stdv 3,182,947 30.0 74.5 58<br />
N 24 24 24 22<br />
Asia avg 154,874,202* 48.5 44.1 296*<br />
(All) stdv 632,999,291 28.3 58.5 625<br />
N 22 22 22 22<br />
Eastern Europe avg 4,092,029 54.2 8.9* 7<br />
stdv 3,300,650 34.2 14.4 6<br />
N 12 12 12 12<br />
Latin America avg 9,079,001* 44.2 58.0* 10<br />
stdv 11,352,239 22.0 39.7 9<br />
N 52 52 52 45<br />
CHARTER Bank avg 226,598,190* 20.3* 144.5* 54*<br />
TYPE stdv 763,087,300 20.1 102.4 148<br />
N 15 15 15 15<br />
Credit Union/ avg 6,598,534 34.7* 91.3* 15<br />
Cooperative stdv 2,944,242 13.1 27.1 18<br />
N 14 14 14 9<br />
NGO avg 5,763,907 59.5* 23.4* 75<br />
stdv 11,275,071 25.8 27.5 270<br />
N 68 68 68 67<br />
Non-Bank ### avg 7,863,917 46.5 51.3 10<br />
stdv 7,644,069 33.8 46.8 6<br />
N 10 10 10 10<br />
PROFIT Non-Profit avg 5,761,410 58.7* 25.2 71<br />
STATUS stdv 10,732,900 26.2 29.5 257<br />
N 76 76 76 74<br />
Profit avg 125,595,413 32.2* 103.9* 34<br />
stdv 560,535,131 29.7 94.5 109<br />
N 28 28 28 28<br />
Note: Standard deviations are listed below <strong>the</strong> group averages. The averages are calculated on <strong>the</strong> basis of <strong>the</strong> values between second and <strong>the</strong> 99th<br />
percentiles for each group. Therefore, sample sizes (N) vary across indicators. Group averages different from average for all MFIs at 1 percent<br />
significance level are marked with an asterisk (*). Additional statistical information is available at www.<strong>microbanking</strong>-mbb.org.<br />
# The criteria for classification of scale of operations vary by region. Refer to page 53 for details.<br />
## No data were reported for <strong>the</strong> region MENA (Middle East North Africa) due to <strong>the</strong> small size of <strong>the</strong> sample.<br />
### Includes Ltd., financieras, and non-bank financial intermediary (NBFIs).<br />
56 MICROBANKING BULLETIN, APRIL 2001
BULLETIN HIGHLIGHTS AND TABLES<br />
TOTAL<br />
STAFF<br />
Total number of<br />
employees<br />
(no.)<br />
TOTAL<br />
GROSS LOAN<br />
PORTFOLIO<br />
total gross portfolio<br />
outstanding<br />
(US$)<br />
PERCENT<br />
WOMEN<br />
BORROWERS<br />
total number of active<br />
women borrowers /<br />
total number of active<br />
borrowers (%)<br />
NUMBER OF<br />
ACTIVE<br />
BORROWERS<br />
number of<br />
borrowers with<br />
loans<br />
outstanding (no.)<br />
AVERAGE<br />
LOAN BALANCE<br />
total gross loan<br />
portfolio / number<br />
of active borrowers<br />
(US$)<br />
DEPTH<br />
average loan<br />
balance /<br />
GNP per capita<br />
(%)<br />
68 2,119,661 76.0* 11,378 166* 51.7<br />
40 2,611,185 23.6 7,755 165 53.8<br />
24 24 20 24 24 24<br />
1,354* 51,908,531* 74.7 301,190* 299 40.7<br />
3,252 180,641,187 28.8 755,687 675 63.5<br />
22 22 21 22 22 22<br />
32* 3,426,730 40.8* 1,958* 1,975* 146.5*<br />
19 2,351,058 7.0 1,391 1,023 94.8<br />
12 12 11 12 12 12<br />
104 6,866,582 60.7 12,408 695 44.4<br />
81 9,095,332 23.8 11,268 650 40.1<br />
46 52 43 52 52 52<br />
1,112* 73,649,013* 46.0 185,251* 1,131* 106.0*<br />
3,347 215,304,242 20.2 633,497 1,237 112.3<br />
15 15 8 15 15 15<br />
61 4,526,240 40.8* 8,066 831* 67.7*<br />
24 2,428,382 5.9 7,387 486 34.5<br />
8 14 14 14 14 14<br />
170 4,174,406 75.3* 30,103 448 41.6<br />
620 9,260,401 26.3 130,852 684 53.6<br />
68 68 62 68 68 68<br />
91 6,454,998 48.4 8,749 1,093* 52.2<br />
64 7,065,019 20.4 7,423 889 23.6<br />
10 10 10 10 10 10<br />
163 4,198,161 73.8* 28,638 482 46.1<br />
590 8,820,801 26.8 123,821 744 59.3<br />
75 76 67 76 76 76<br />
635 43,096,972* 50.5 103,939 1,048* 106.5*<br />
2,466 158,756,562 19.9 464,813 1,057 145.3<br />
28 28 20 28 28 28<br />
MICROBANKING BULLETIN, APRIL 2001 57
BULLETIN HIGHLIGHTS AND TABLES<br />
TABLE B: PROFITABILITY AND EFFICIENCY INDICATORS<br />
CRITERIA<br />
ADJUSTED<br />
RETURN ON<br />
ASSETS<br />
adjusted net<br />
operating income /<br />
average total<br />
assets<br />
(%)<br />
ADJUSTED<br />
RETURN ON<br />
EQUITY<br />
adjusted net<br />
operating income<br />
/ average total<br />
equity<br />
(%)<br />
OPERATIONAL<br />
SELF-SUFFICIENCY<br />
operating income /<br />
operating expense<br />
(%)<br />
FINANCIAL<br />
SELF-<br />
SUFFICIENCY<br />
adjusted operating<br />
income /<br />
adjusted operating<br />
expense<br />
(%)<br />
PORTFOLIO<br />
YIELD<br />
operating income –<br />
accrued interest –<br />
interest and fee<br />
income from<br />
investments /<br />
average gross loan<br />
portfolio (%)<br />
AGE New (1 - 2 years) avg -9.8* -21.5* 93.0 76.2* 36.6<br />
stdv 10.4 25.7 30.2 21.3 13.6<br />
N 28 28 28 28 28<br />
Young (3 - 6 years) avg -4.0 5.4 101.7 87.7 41.3<br />
stdv 7.1 69.3 29.3 25.7 21.6<br />
N 26 26 26 26 26<br />
Mature (> 6 years) avg -1.0 0.9 115.0* 100.2* 42.6<br />
stdv 8.0 24.1 37.6 28.2 20.2<br />
N 63 63 63 63 63<br />
SCALE OF Large avg 2.3* 7.4* 126.4* 114.3* 33.8<br />
OPERATIONS # stdv 5.6 26.6 37.1 26.3 10.8<br />
N 20 20 20 20 20<br />
Medium avg -3.4 -9.9 106.5 90.6 38.8<br />
stdv 8.8 27.7 35.0 24.6 18.4<br />
N 66 66 66 66 66<br />
Small avg -9.6* 4.1 94.3 78.7* 52.2*<br />
stdv 12.3 114.2 28.4 25.7 21.9<br />
N 32 32 32 32 32<br />
LENDING Individual avg -1.2 -6.7 118.5* 100.7* 33.7<br />
METHOD- (1 borrower) stdv 7.4 49.2 38.7 34.2 15.2<br />
OLOGY N 52 52 52 52 52<br />
Solidarity Groups avg -7.0* -14.3* 93.8* 83.5 40.8<br />
(groups of 3 to 9 stdv 9.5 21.0 25.2 22.9 12.6<br />
borrowers) N 43 43 43 43 43<br />
Village Banking avg -5.5 -5.4 103.0 87.8 57.1*<br />
(groups with ≥ 10 stdv 13.7 17.5 35.1 24.6 26.4<br />
borrowers) N 23 23 23 23 23<br />
LEVEL OF Financial Intermediaries avg -2.2 2.4 103.8 91.7 31.2*<br />
FINANCIAL (passbook and time deposits stdv 5.6 85.0 25.7 23.0 14.6<br />
INTER- ≥ 20% of total assets) N 29 29 29 29 29<br />
MEDIATION<br />
O<strong>the</strong>r avg -4.9 -10.0 107.7 91.8 44.5<br />
(passbook and time deposits stdv 11.6 29.0 39.5 31.7 20.9<br />
< 20% of total assets) N 91 91 91 91 91<br />
TARGET Low-end avg -6.1 -2.5 103.3 87.8 51.6*<br />
MARKET (depth < 20% OR avg. loan stdv 12.6 89.5 36.9 27.7 23.2<br />
Balance < US$150) N 57 57 57 57 57<br />
Broad avg -2.7 -3.1 106.0 92.6 33.2*<br />
(depth between 20% and stdv 6.9 22.2 27.8 24.8 12.0<br />
149%) N 51 51 51 51 51<br />
High-end and avg -0.3 -6.8 117.6 102.1 26.6*<br />
Small Business stdv 4.9 16.5 37.6 32.9 6.1<br />
(depth ≥ 150%) N 10 10 10 10 10<br />
Note: Standard deviations are listed below <strong>the</strong> group averages. The averages are calculated on <strong>the</strong> basis of <strong>the</strong> values between second and <strong>the</strong> 99th percentiles<br />
for each group. Therefore, sample sizes (N) vary across indicators. Group averages different from average for all MFIs at 1 percent significance level are marked<br />
with an asterisk (*). Additional statistical information is available at www.<strong>microbanking</strong>-mbb.org.<br />
# The criteria for classification of scale of operations vary by region. Refer to page 53 for details.<br />
58 MICROBANKING BULLETIN, APRIL 2001
BULLETIN HIGHLIGHTS AND TABLES<br />
PORTFOLIO<br />
AT RISK ><br />
90 DAYS<br />
outstanding<br />
balance of<br />
loans overdue ><br />
90 days / total<br />
gross loan<br />
portfolio<br />
(%)<br />
TOTAL ADMIN.<br />
EXPENSE<br />
RATIO<br />
administrative<br />
expense + inkind<br />
donations<br />
/ total average<br />
assets<br />
(%)<br />
TOTAL ADMIN.<br />
EXPENSE<br />
/ LP<br />
administrative<br />
expense + inkind<br />
donations<br />
/ average loan<br />
portfolio<br />
(%)<br />
SALARY<br />
EXPENSE/<br />
LP<br />
personnel<br />
expense + inkind<br />
donations<br />
average gross<br />
loan portfolio<br />
(%)<br />
DEPTH<br />
average<br />
loan<br />
balance/<br />
GNP<br />
per capita<br />
(%)<br />
AVERAGE<br />
SALARY<br />
average<br />
personnel<br />
expense + inkind<br />
donations /<br />
GNP per capita<br />
(multiple of<br />
GNP/ capita)<br />
STAFF<br />
PRODUCTIVITY<br />
number of active<br />
borrowers /<br />
number of staff<br />
(no.)<br />
LOAN<br />
OFFICER<br />
PRODUCTIVITY<br />
number of active<br />
borrowers /<br />
number of loan<br />
officers<br />
(no.)<br />
COST/<br />
BORROWER<br />
administrative<br />
expense + inkind<br />
donations /<br />
average number<br />
of active<br />
borrowers<br />
(US$)<br />
1.1 25.4* 42.1* 24.0* 70.2 7.0 96* 188* 184<br />
1.7 13.2 32.8 20.4 69.3 5.0 45 109 184<br />
23 28 28 28 28 28 28 26 21<br />
1.7 21.5* 36.5 19.8 81.3 7.5 126 295 116<br />
1.8 10.8 20.2 11.3 145.2 5.3 63 232 115<br />
21 26 26 26 26 26 26 25 26<br />
2.8 19.9* 30.0 16.6 47.2 5.5 158* 340 88<br />
3.1 13.3 20.8 12.9 50.8 4.6 122 299 78<br />
54 63 63 63 63 56 56 55 57<br />
2.2 12.2* 16.7* 9.1* 98.7 6.6 130 285 145<br />
1.1 5.5 8.7 5.1 91.2 4.6 79 125 116<br />
19 20 20 20 20 20 20 19 20<br />
2.0 21.8* 30.9 17.2 64.6 6.5 151 345 122<br />
2.4 13.1 19.2 12.4 101.7 5.2 118 327 164<br />
52 66 66 66 66 61 61 56 55<br />
2.5 28.2* 52.2* 29.3* 30.9 5.7 100 205 108<br />
3.7 12.6 26.9 15.8 27.6 5.3 51 115 112<br />
28 32 32 32 32 32 32 32 30<br />
2.5 14.0* 20.7* 10.3* 103.0 5.1 102 229 191<br />
3.3 7.4 11.8 6.8 122.7 4.1 78 199 193<br />
39 52 52 52 52 47 47 41 48<br />
2.1 25.7* 41.3* 24.5* 39.4 7.5 133 272 88<br />
2.3 11.2 25.1 16.9 31.7 5.9 70 152 66<br />
38 43 43 43 43 42 42 42 35<br />
1.5 32.2* 52.1* 30.0* 18.0 67 188* 408* 49*<br />
1.6 15.5 25.0 13.9 13.5 5.2 134 325 27<br />
22 23 23 23 23 23 23 23 22<br />
2.7 11.3* 21.2* 9.7* 77.0 5.7 127 123* 375*<br />
1.9 4.8 15.8 7.8 70.1 4.5 92 102 311<br />
17 29 29 29 29 23 23 27 18<br />
2.1 25.6* 38.9* 22.4* 52.5 6.6 134 122* 272*<br />
2.8 13.7 25.5 15.7 68.9 5.5 98 139 212<br />
83 91 91 91 91 91 91 79 90<br />
2.2 28.7* 48.0* 27.7* 16.8 5.5 167* 360* 60*<br />
3.1 14.8 27.3 16.9 10.4 5.4 112 304 56<br />
51 57 57 57 57 57 57 57 51<br />
2.6 16.6* 23.5* 12.3* 64.8 6.8 113 252 134<br />
2.2 8.6 13.9 8.5 29.3 4.8 62 176 94<br />
39 51 51 51 51 45 45 39 45<br />
0.7 12.5* 16.6* 9.2* 310.2 9.2* 48* 120* 369*<br />
0.7 5.7 8.2 5.9 147.6 2.8 20 59 204<br />
9 10 10 10 10 10 10 10 9<br />
MICROBANKING BULLETIN, APRIL 2001 59
BULLETIN HIGHLIGHTS AND TABLES<br />
TABLE B: PROFITABILITY AND EFFICIENCY INDICATORS (continued)<br />
CRITERIA<br />
ADJUSTED<br />
RETURN ON<br />
ASSETS<br />
adjusted net<br />
operating income /<br />
average total<br />
assets<br />
(%)<br />
ADJUSTED<br />
RETURN ON<br />
EQUITY<br />
adjusted net<br />
operating income<br />
/ average total<br />
equity<br />
(%)<br />
OPERATIONAL<br />
SELF-SUFFICIENCY<br />
operating income /<br />
operating expense<br />
(%)<br />
FINANCIAL<br />
SELF-<br />
SUFFICIENCY<br />
adjusted operating<br />
income /<br />
adjusted operating<br />
expense<br />
(%)<br />
PORTFOLIO<br />
YIELD<br />
operating income –<br />
accrued interest –<br />
interest and fee<br />
income from<br />
investments /<br />
average gross loan<br />
portfolio (%)<br />
REGION ## Africa avg -9.8* 1.8 88.8* 82.7 45.6<br />
stdv 11.6 73.9 42.7 40.0 18.8<br />
N 24 24 24 24 24<br />
Asia avg -2.6 -3.2 109.8 92.6 33.6<br />
(All) stdv 7.9 25.4 38.5 30.3 15.3<br />
N 22 22 22 22 22<br />
Eastern Europe avg -2.8 -9.7 107.4 90.0 29.5*<br />
stdv 3.1 11.8 12.8 11.4 5.6<br />
N 12 12 12 12 12<br />
Latin America avg -1.6 -7.3 113.8* 97.5 45.8<br />
stdv 8.1 35.9 27.6 23.7 21.4<br />
N 52 52 52 52 52<br />
CHARTER Bank avg -0.7 -9.1 107.6 97.9 41.2<br />
TYPE stdv 6.4 76.7 23.3 22.9 15.5<br />
N 15 15 15 15 15<br />
Credit Union/ avg -1.6 -5.2 109.3 92.5 21.8*<br />
Cooperative stdv 4.4 11.9 24.9 21.4 4.7<br />
N 14 14 14 14 14<br />
NGO avg -5.3 -9.1 106.4 89.0 47.6*<br />
stdv 11.8 23.5 35.9 27.5 22.3<br />
N 68 68 68 68 68<br />
Non-Bank ### avg -4.4 -1.5 96.4 87.3 32.2<br />
stdv 5.6 17.0 16.7 15.3 6.7<br />
N 10 10 10 10 10<br />
PROFIT Non-Profit avg -5.1 -9.3 107.0 90.3 45.4<br />
STATUS stdv 12.0 23.6 38.5 28.2 22.0<br />
N 76 76 76 76 76<br />
Profit avg -2.1 5.3 106.2 96.9 38.2<br />
stdv 6.8 86.3 30.7 27.8 14.4<br />
N 28 28 28 28 28<br />
Note: Standard deviations are listed below <strong>the</strong> group averages. The averages are calculated on <strong>the</strong> basis of <strong>the</strong> values between second and <strong>the</strong> 99th<br />
percentiles for each group. Therefore, sample sizes (N) vary across indicators. Group averages different from average for all MFIs at 1 percent significance<br />
level are marked with an asterisk (*). Additional statistical information is available at www.<strong>microbanking</strong>-mbb.org.<br />
# The criteria for classification of scale of operations vary by region. Refer to page 53 for details.<br />
## No data were reported for <strong>the</strong> region MENA (Middle East North Africa) due to <strong>the</strong> small size of <strong>the</strong> sample.<br />
### Includes Ltd., financieras, and non-bank financial intermediaries (NBFIs).<br />
60 MICROBANKING BULLETIN, APRIL 2001
BULLETIN HIGHLIGHTS AND TABLES<br />
PORTFOLIO<br />
AT RISK ><br />
90 DAYS<br />
outstanding<br />
balance of<br />
loans overdue ><br />
90 days / total<br />
gross loan<br />
portfolio<br />
(%)<br />
TOTAL ADMIN.<br />
EXPENSE<br />
RATIO<br />
administrative<br />
expense + inkind<br />
donations<br />
/ total average<br />
assets<br />
(%)<br />
TOTAL ADMIN.<br />
EXPENSE<br />
/ LP<br />
administrative<br />
expense + inkind<br />
donations<br />
/ average loan<br />
portfolio<br />
(%)<br />
SALARY<br />
EXPENSE/<br />
LP<br />
personnel<br />
expense + inkind<br />
donations<br />
average gross<br />
loan portfolio<br />
(%)<br />
DEPTH<br />
average<br />
loan<br />
balance/<br />
GNP<br />
per capita<br />
(%)<br />
AVERAGE<br />
SALARY<br />
average<br />
personnel<br />
expense + inkind<br />
donations /<br />
GNP per capita<br />
(multiple of<br />
GNP/ capita)<br />
STAFF<br />
PRODUCTIVITY<br />
number of active<br />
borrowers /<br />
number of staff<br />
(no.)<br />
LOAN<br />
OFFICER<br />
PRODUCTIVITY<br />
number of active<br />
borrowers /<br />
number of loan<br />
officers<br />
(no.)<br />
COST/<br />
BORROWER<br />
administrative<br />
expense + inkind<br />
donations /<br />
average number<br />
of active<br />
borrowers<br />
(US$)<br />
2.0 30.3* 57.5* 30.9* 51.7 12.4* 154* 393* 66<br />
2.2 16.5 30.4 17.5 53.8 6.5 83 272 34<br />
22 24 24 24 24 24 24 24 20<br />
2.4 16.0* 23.7 14.3 40.7 3.6* 177* 306 29*<br />
3.0 11.2 16.8 13.0 63.5 2.2 162 315 21<br />
19 22 22 22 22 22 22 22 22<br />
0.6* 19.0* 22.4 12.9 146.5 7.3 63* 110* 317*<br />
0.7 4.5 5.4 4.6 94.8 2.8 23 25 196<br />
12 12 12 12 12 12 12 12 10<br />
2.6 21.1* 29.9 15.8 44.4 4.6 125 280 141<br />
2.6 11.1 16.4 10.3 40.1 3.5 56 139 120<br />
40 52 52 52 52 46 46 41 46<br />
3.0 15.6* 28.5 14.6 106.0 6.0 98 263 227<br />
4.5 7.8 16.5 8.5 112.3 4.4 73 247 277<br />
13 15 15 15 15 15 15 15 15<br />
1.1 9.6* 14.3* 6.0* 67.7 4.2 133 321 103<br />
1.4 3.1 5.2 3.1 34.5 4.2 88 321 45<br />
4 14 14 14 14 8 8 4 13<br />
1.9 26.3* 41.8* 24.0* 41.6 6.5 147 292 96<br />
2.4 13.9 25.9 15.6 53.6 5.5 106 225 98<br />
63 68 68 68 68 68 68 67 61<br />
2.8 17.3* 22.0 12.0 52.2 4.4 90 195 137<br />
1.4 4.5 6.9 4.3 23.6 1.5 31 96 80<br />
10 10 10 10 10 10 10 10 9<br />
2.0 26.0* 40.2* 23.1* 46.1 6.6 150 299 94<br />
2.5 14.5 26.5 16.6 59.3 5.7 111 234 96<br />
69 76 76 76 76 75 75 75 64<br />
2.9 16.6* 26.5 13.8 106.5 6.5 103 277 194<br />
3.4 7.9 15.9 7.7 145.3 4.3 67 250 218<br />
26 28 28 28 28 28 28 28 27<br />
MICROBANKING BULLETIN, APRIL 2001 61
BULLETIN HIGHLIGHTS AND TABLES<br />
TABLE C: INSTITUTIONAL CHARACTERISTICS AND OUTREACH INDICATORS FOR FINANCIALLY<br />
SELF-SUFFICIENT MFIs<br />
CRITERIA<br />
TOTAL<br />
ASSETS<br />
(US$)<br />
CAPITAL /<br />
ASSET<br />
RATIO<br />
adjusted total<br />
equity /<br />
adjusted total<br />
assets (%)<br />
COMMERCIAL<br />
FUNDING LIABILITIES<br />
RATIO<br />
OFFICES<br />
borrowings at commercial<br />
rates / average gross loan<br />
portfolio<br />
(%) (no.)<br />
AGE New (1 - 2 years) avg 3,405,919 57.3 11.2 7<br />
stdv 1,582,222 24.0 18.0 7<br />
N 7 7 7 7<br />
Young (3 - 6 years) avg 6,463,658 58.1 41.5 71*<br />
stdv 5,901,443 22.6 86.6 195<br />
N 10 10 10 10<br />
Mature (> 6 years) avg 18,809,760* 45.4 62.9* 56*<br />
stdv 32,646,028 21.7 57.3 176<br />
N 41 41 41 36<br />
SCALE OF Large avg 44,759,724* 33.9* 82.4* 114*<br />
OPERATIONS # stdv 41,556,930 19.1 65.6 256<br />
N 16 16 16 16<br />
Medium avg 4,823,500 54.0 40.6 13<br />
stdv 1,768,491 24.3 39.6 13<br />
N 30 30 30 26<br />
Small avg 1,040,280* 58.9 24.8 5*<br />
stdv 293,641 15.3 28.4 3<br />
N 12 12 12 11<br />
LENDING Individual avg 12,533,710* 39.2 77.7 10<br />
METHOD- (1 borrower) stdv 14,076,097 19.9 75.7 8<br />
OLOGY N 29 29 29 25<br />
Solidarity Groups avg 18,768,985* 49.2 36.7 63*<br />
(groups of 3 to 9 stdv 28,968,183 20.3 32.4 155<br />
borrowers) N 17 17 17 15<br />
Village Banking avg 2,999,565 72.1* 18.0 61*<br />
(groups with ≥ 10 stdv 1,610,309 17.8 22.8 178<br />
borrowers) N 12 12 12 12<br />
LEVEL OF Financial Intermediaries avg 27,730,881* 19.4* 158.8* 18<br />
FINANCIAL (passbook and time<br />
stdv 27,955,717 10.5 89.5 15<br />
INTER- deposits ≥ 20% of total assets) N 12 12 12 8<br />
MEDIATION<br />
O<strong>the</strong>r avg 8,213,650 57.6 29.4 59*<br />
(passbook and time stdv 13,567,380 20.4 27.2 178<br />
< 20% of total assets) N 48 48 48 46<br />
TARGET Low-end avg 7,632,608 61.2* 36.0 91*<br />
MARKET (depth < 20% OR avg. loan stdv 16,121,525 21.4 57.3 229<br />
balance < US$ 150) N 28 28 28 27<br />
Broad avg 15,465,913* 38.6 69.5* 14<br />
(depth between 20% and stdv 21,292,070 19.0 42.0 15<br />
149%) N 25 25 25 20<br />
High-end and avg 14,479,446* 40.3 21.4 13<br />
Small Business stdv 10,887,845 20.8 21.2 9<br />
(depth ≥ 150%) N 5 5 5 5<br />
Note: Standard deviations are listed below <strong>the</strong> group averages. The averages are calculated on <strong>the</strong> basis of <strong>the</strong> values between second and <strong>the</strong><br />
99th percentiles for each group. Therefore, sample sizes (N) vary across indicators. Group averages different from average for all MFIs at 1<br />
percent significance level are marked with an asterisk (*). Additional statistical information is available at www.<strong>microbanking</strong>-mbb.org.<br />
# The criteria for classification of scale of operations vary by region. Refer to page 53 for details.<br />
62 MICROBANKING BULLETIN, APRIL 2001
BULLETIN HIGHLIGHTS AND TABLES<br />
TOTAL<br />
STAFF<br />
Total number of<br />
employees<br />
(no.)<br />
TOTAL<br />
GROSS LOAN<br />
PORTFOLIO<br />
total gross portfolio<br />
outstanding<br />
(US$)<br />
PERCENT<br />
WOMEN<br />
BORROWERS<br />
total number of active<br />
women borrowers /<br />
total number of active<br />
borrowers (%)<br />
NUMBER OF<br />
ACTIVE<br />
BORROWERS<br />
number of<br />
borrowers with<br />
loans<br />
outstanding (no.)<br />
AVERAGE<br />
LOAN BALANCE<br />
total gross loan<br />
portfolio / number<br />
of active borrowers<br />
(US$)<br />
DEPTH<br />
average loan<br />
balance /<br />
GNP per capita<br />
(%)<br />
44 2,945,845 42.1 3,987 1,031* 79.9*<br />
37 1,248,620 8.5 4,152 569 62.4<br />
7 7 5 7 7 7<br />
126 4,368,732 71.8 11,909 523 93.8*<br />
90 4,314,428 27.9 5,831 871 103.6<br />
10 10 8 10 10 10<br />
511* 14,765,629* 63.6 105,743 566 42.2<br />
1,506 26,649,320 28.2 414,289 638 40.6<br />
36 41 39 41 41 41<br />
1,030* 34,703,379* 50.7 247,973* 935* 95.9*<br />
2,183 34,777,173 23.0 649,553 736 83.5<br />
16 16 14 16 16 16<br />
109 3,519,332 66.0 15,457 545 47.9<br />
72 1,197,937 27.7 13,721 659 58.5<br />
27 30 27 30 30 30<br />
45* 702,118* 67.6 4,624* 354 31.9<br />
27 190,701 28.4 3,679 434 28.7<br />
12 12 11 12 12 12<br />
106 9,226,150* 43.7 10,169 1,122* 96.3<br />
101 10,768,658 15.8 10,192 879 86.2<br />
25 29 26 29 29 29<br />
511 14,744,774* 78.1* 91,019* 305 35.4<br />
1,206 24,400,848 21.2 256,945 313 32.2<br />
17 17 13 17 17 17<br />
99 2,152,440 90.3* 16,690 114* 13.7*<br />
42 1,400,570 17.7 8,151 24 8.3<br />
12 12 12 12 12 12<br />
267* 20,476,770* 39.8* 19,156* 980* 80.6*<br />
180 23,804,140 10.1 20,413 506 43.6<br />
8 12 12 12 12 12<br />
222 6,361,146 70.5 38,879 563 53.6<br />
733 11,155,406 27.5 154,920 770 73.1<br />
48 48 41 48 48 48<br />
313 5,811,051 84.4* 58,889* 135* 13.9*<br />
953 13,605,385 20.6 201,592 84 6.1<br />
28 28 24 28 28 28<br />
150 11,984,081* 48.3* 16,116 832* 69.5*<br />
149 17,442,302 18.5 18,515 424 28.9<br />
21 25 23 25 25 25<br />
91 8,933,026* 32.2* 5,786 2,516* 267.9*<br />
81 4,144,927 3.4 5,071 571 44.0<br />
5 5 5 5 5 5<br />
MICROBANKING BULLETIN, APRIL 2001 63
BULLETIN HIGHLIGHTS AND TABLES<br />
TABLE C: INSTITUTIONAL CHARACTERISTICS AND OUTREACH INDICATORS FOR FINANCIALLY<br />
SELF-SUFFICIENT MFIs (continued)<br />
CRITERIA<br />
TOTAL<br />
ASSETS<br />
(US$)<br />
CAPITAL /<br />
ASSET<br />
RATIO<br />
adjusted total<br />
equity /<br />
adjusted total<br />
assets (%)<br />
COMMERCIAL<br />
FUNDING LIABILITIES<br />
RATIO<br />
OFFICES<br />
borrowings at commercial<br />
rates / average gross loan<br />
portfolio<br />
(%) (no.)<br />
REGION ## Africa avg 5,099,987 49.5 81.5 14<br />
stdv 5,263,973 20.9 135.9 13<br />
N 4 4 4 4<br />
Asia avg 25,625,496* 54.7 22.8 297*<br />
(All) stdv 51,327,787 19.7 16.9 577<br />
N 12 12 12 12<br />
Eastern Europe avg 3,462,697 56.1 12.5 6<br />
stdv 894,196 31.9 18.5 1<br />
N 3 3 3 3<br />
Latin America avg 11,071,252* 45.4 59.6* 11<br />
stdv 11,352,239 22.0 39.7 9<br />
N 35 35 35 29<br />
CHARTER Bank avg 35,929,370* 21.0 166.6 22<br />
TYPE stdv 30,261,580 14.8 116.2 13<br />
N 8 8 8 8<br />
Credit Union/ avg 6,981,940 42.1 88.6* -<br />
Cooperative stdv 4,054,797 14.9 33.7 -<br />
N 5 5 5 -<br />
NGO avg 8,376,486 58.5 26.6 72*<br />
stdv 15,089,775 21.5 25.1 203<br />
N 35 35 35 35<br />
Non-Bank ### avg 11,445,145 27.4 70.3 11<br />
stdv 10,732,928 5.5 27.3 6<br />
N 3 3 3 3<br />
PROFIT Non-Profit avg 7,996,412 59.0 26.3 68*<br />
STATUS stdv 14,398,848 20.6 24.4 195<br />
N 39 39 39 38<br />
Profit avg 26,194,456* 28.1* 120.1* 16<br />
stdv 26,707,461 20.1 104.4 12<br />
N 14 14 14 14<br />
Note: Standard deviations are listed below <strong>the</strong> group averages. The averages are calculated on <strong>the</strong> basis of <strong>the</strong> values between second and <strong>the</strong> 99th<br />
percentiles for each group. Therefore, sample sizes (N) vary across indicators. Group averages different from average for all MFIs at 1 percent<br />
significance level are marked with an asterisk (*). Additional statistical information is available at www.<strong>microbanking</strong>-mbb.org.<br />
# The criteria for classification of scale of operations vary by region. Refer to page 53 for details.<br />
## No data were reported for <strong>the</strong> region MENA (Middle East North Africa) due to <strong>the</strong> small size of <strong>the</strong> sample.<br />
### Includes Ltd., financieras, and non-bank financial intermediary (NBFIs).<br />
64 MICROBANKING BULLETIN, APRIL 2001
BULLETIN HIGHLIGHTS AND TABLES<br />
TOTAL<br />
STAFF<br />
Total number of<br />
employees<br />
(no.)<br />
TOTAL<br />
GROSS LOAN<br />
PORTFOLIO<br />
total gross portfolio<br />
outstanding<br />
(US$)<br />
PERCENT<br />
WOMEN<br />
BORROWERS<br />
total number of active<br />
women borrowers /<br />
total number of active<br />
borrowers (%)<br />
NUMBER OF<br />
ACTIVE<br />
BORROWERS<br />
number of<br />
borrowers with<br />
loans<br />
outstanding (no.)<br />
AVERAGE<br />
LOAN BALANCE<br />
total gross loan<br />
portfolio / number<br />
of active borrowers<br />
(US$)<br />
DEPTH<br />
average loan<br />
balance /<br />
GNP per capita<br />
(%)<br />
68 4,345,934 46.0 10,131 351 109.5*<br />
28 4,793,666 24.1 3,555 359 113.3<br />
4 4 4 4 4 4<br />
1,239* 20,743,904* 76.8 320,267* 148* 30.0<br />
2,512 41,820,342 25.0 743,347 70 26.1<br />
12 12 12 12 12 12<br />
29 3,013,143 42.0 2,121 1,390* 129.5*<br />
7 984,024 7.8 919 697 62.6<br />
3 3 3 3 3 3<br />
125 8,456,682* 65.0 15,234 717 45.3<br />
90 10,550,492 25.6 12,572 737 42.9<br />
30 35 29 35 35 35<br />
326 25,682,534* 46.9 34,389* 527 62.5<br />
142 26,298,497 21.6 21.413 387 56.6<br />
8 8 7 8 8 8<br />
- 4,978,836 36.3* 3,640 1,306* 91.6*<br />
- 3,473,080 1.8 1,752 309 25.8<br />
- 5 5 5 5 5<br />
254 6,421,080 72.6 47,705 490 38.8<br />
857 12,475,421 28.0 181,049 729 55.5<br />
35 35 31 35 35 35<br />
153 10,103,411* 43.0 14.272 958 62.4<br />
83 10,884,914 11.5 11,163 57 29.8<br />
3 3 3 3 3 3<br />
239 6,170,926 72.0 43,667 494 45.4<br />
812 11,917,456 27.8 171,686 722 66.3<br />
39 39 34 39 39 39<br />
233* 19,828,815* 48.0 25,472* 827* 82.3*<br />
165 22,297,199 19.9 21,025 723 70.5<br />
14 14 12 14 14 14<br />
MICROBANKING BULLETIN, APRIL 2001 65
BULLETIN HIGHLIGHTS AND TABLES<br />
TABLE D: PROFITABILITY AND EFFICIENCY INDICATORS FOR FINANCIALLY SELF-SUFFICIENT<br />
MFIs<br />
CRITERIA<br />
ADJUSTED<br />
RETURN ON<br />
ASSETS<br />
adjusted net<br />
operating income /<br />
average total<br />
assets<br />
(%)<br />
ADJUSTED<br />
RETURN ON<br />
EQUITY<br />
adjusted net<br />
operating income<br />
/ average total<br />
equity<br />
(%)<br />
OPERATIONAL<br />
SELF-SUFFICIENCY<br />
operating income /<br />
operating expense<br />
(%)<br />
FINANCIAL<br />
SELF-<br />
SUFFICIENCY<br />
adjusted operating<br />
income /<br />
adjusted operating<br />
expense<br />
(%)<br />
PORTFOLIO<br />
YIELD<br />
operating income –<br />
accrued interest –<br />
interest and fee<br />
income from<br />
investments /<br />
average gross loan<br />
portfolio (%)<br />
AGE New (1 - 2 years) avg 0.6 0.5 119.4 102.8 38.2<br />
stdv 2.1 4.0 13.1 8.3 7.9<br />
N 7 7 7 7 7<br />
Young (3 - 6 years) avg 2.1* 3.7 121.8* 111.1* 52.4*<br />
stdv 4.6 8.5 33.7 22.9 23.7<br />
N 10 10 10 10 10<br />
Mature (> 6 years) avg 3.4* 11.5* 131.6* 115.1* 44.2<br />
stdv 4.9 15.6 34.7 21.4 17.9<br />
N 41 41 41 41 41<br />
SCALE OF Large avg 4.2* 16.5* 134.2* 121.8* 35.0<br />
OPERATIONS # stdv 3.7 16.3 37.0 23.4 11.3<br />
N 16 16 16 16 16<br />
Medium avg 2.8* 4.7* 128.0* 110.4* 45.2<br />
stdv 6.3 11.3 35.2 19.9 17.9<br />
N 30 30 30 30 30<br />
Small avg 1.5* 3.0 121.0* 105.8* 56.8*<br />
stdv 3.4 6.2 16.6 13.6 20.2<br />
N 12 12 12 12 12<br />
LENDING Individual avg 3.9 13.8 136.7 121.9 38.0<br />
METHOD- (1 borrower) stdv 4.1 16.2 39.4 30.9 13.8<br />
OLOGY N 29 29 29 29 29<br />
Solidarity Groups avg 1.1 2.2 114.7 104.9* 41.4<br />
(groups of 3 to 9 borrowers) stdv 3.6 8.7 15.8 13.2 9.9<br />
N 17 17 17 17 17<br />
Village Banking avg 2.7* 3.2 126.8* 105.8* 67.6*<br />
(groups with ≥ 10 stdv 7.1 8.3 29.3 14.4 24.0<br />
borrowers) N 12 12 12 12 12<br />
LEVEL OF Financial Intermediaries avg 3.2* 19.7* 119.1* 114.6* 37.5<br />
FINANCIAL (passbook and time deposits stdv 2.9 20.5 15.5 12.3 15.3<br />
INTER- ≥ 20% of total assets) N 12 12 12 12 12<br />
MEDIATION<br />
O<strong>the</strong>r avg 2.9* 5.5* 131.4* 113.1* 47.1*<br />
(passbook and time deposits stdv 5.7 10.2 37.4 26.5 20.0<br />
< 20% of total assets) N 48 48 48 48 48<br />
TARGET Low-end avg 2.8* 5.2* 128.1* 109.2* 57.1*<br />
MARKET (depth < 20% OR avg. loan stdv 6.6 11.4 33.7 19.7 21.7<br />
Balance < US$150) N 28 28 28 28 28<br />
Broad avg 3.1* 10.7* 124.0* 113.7* 36.4<br />
(depth between 20% stdv 3.1 10.7 17.4 12.5 10.5<br />
and 149%) N 25 25 25 25 25<br />
High-end and avg 2.7 2.9 134.9* 119.7* 29.1<br />
Small Business stdv 5.4 8.7 48.1 40.5 6.5<br />
(depth ≥ 150%) N 5 5 5 5 5<br />
Note: Standard deviations are listed below <strong>the</strong> group averages. The averages are calculated on <strong>the</strong> basis of <strong>the</strong> values between second and <strong>the</strong> 99th<br />
percentiles for each group. Therefore, sample sizes (N) vary across indicators. Group averages different from average for all MFIs at 1 percent significance<br />
level are marked with an asterisk (*). Additional statistical information is available at www.<strong>microbanking</strong>-mbb.org.<br />
# The criteria for classification of scale of operations vary by region. Refer to page 40 for details.<br />
66 MICROBANKING BULLETIN, APRIL 2001
BULLETIN HIGHLIGHTS AND TABLES<br />
PORTFOLIO<br />
AT RISK ><br />
90 DAYS<br />
outstanding<br />
balance of<br />
loans overdue ><br />
90 days / total<br />
gross loan<br />
portfolio<br />
(%)<br />
TOTAL ADMIN.<br />
EXPENSE<br />
RATIO<br />
administrative<br />
expense + inkind<br />
donations<br />
/ total average<br />
assets<br />
(%)<br />
TOTAL ADMIN.<br />
EXPENSE<br />
/ LP<br />
administrative<br />
expense + inkind<br />
donations<br />
/ average loan<br />
portfolio<br />
(%)<br />
SALARY<br />
EXPENSE/<br />
LP<br />
personnel<br />
expense + inkind<br />
donations<br />
average gross<br />
loan portfolio<br />
(%)<br />
DEPTH<br />
average<br />
loan<br />
balance/<br />
GNP<br />
per capita<br />
(%)<br />
AVERAGE<br />
SALARY<br />
average<br />
personnel<br />
expense + inkind<br />
donations /<br />
GNP per capita<br />
(multiple of<br />
GNP/ capita)<br />
STAFF<br />
PRODUCTIVITY<br />
number of active<br />
borrowers /<br />
number of staff<br />
(no.)<br />
LOAN<br />
OFFICER<br />
PRODUCTIVITY<br />
number of active<br />
borrowers /<br />
number of loan<br />
officers<br />
(no.)<br />
COST/<br />
BORROWER<br />
administrative<br />
expense + inkind<br />
donations /<br />
average number<br />
of active<br />
borrowers<br />
(US$)<br />
0.7 21.6* 27.5 14.9 79.9 6.3 83 144 173<br />
1.0 4.3 7.2 3.1 62.4 2.7 26 53 91<br />
7 7 7 7 7 7 7 7 4<br />
1.2 22.1* 37.8 20.8 93.8 8.7 113 283* 101*<br />
1.3 9.6 19.7 10.7 103.6 5.4 55 188 113<br />
8 10 10 10 10 10 10 10 10<br />
2.0 18.8* 26.7 15.1 42.2 4.8 147 313* 91*<br />
1.5 10.9 15.8 10.2 40.6 3.2 77 172 78<br />
35 41 41 41 41 36 36 36 38<br />
2.1 12.8* 17.4* 9.4* 95.9 7.5 129 312* 147*<br />
1.1 4.6 8.5 4.9 83.5 4.7 65 117 115<br />
16 16 16 16 16 16 16 16 16<br />
1.3 20.9* 29.3 16.4 47.9 5.2 139 284* 71*<br />
1.4 9.0 13.5 8.4 58.5 2.8 75 191 45<br />
25 30 30 30 30 27 27 27 23<br />
1.7 25.6* 43.2* 24.3 31.9 3.7 94 174 98*<br />
2.1 9.6 20.3 11.8 28.7 3.5 52 111 85<br />
10 12 12 12 12 12 12 12 12<br />
1.6 13.5 19.6 10.0 96.3 5.7 101 264* 155*<br />
1.3 6.1 11.0 6.0 86.2 4.0 65 193 115<br />
24 29 29 29 29 25 25 25 27<br />
2.0 21.8* 29.4 16.6 35.4 5.7 135 257* 62*<br />
1.7 6.0 9.3 5.4 32.2 3.5 66 109 52<br />
15 17 17 17 17 17 17 17 13<br />
1.1 33.2* 49.8* 30.3* 13.7 5.2 155 314* 49*<br />
1.0 9.7 14.3 8.1 8.3 3.2 31 193 18<br />
12 12 12 12 12 12 12 12 12<br />
1.9 11.7* 21.3 9.9* 80.6 6.3 99 303* 156*<br />
1.0 4.2 16.9 8.0 43.6 3.6 38 133 84<br />
7 12 12 12 12 8 8 8 12<br />
1.7 22.1* 30.9 17.8 53.6 5.5 136 270* 88*<br />
1.6 10.3 16.2 9.9 73.1 3.8 77 176 85<br />
45 48 48 48 48 48 48 48 40<br />
1.6 26.4* 40.6* 23.6* 13.9 4.1 165* 335* 49*<br />
1.7 10.5 17.4 9.8 6.1 2.8 79 216 27<br />
25 28 28 28 28 28 28 28 26<br />
1.8 14.9* 19.5* 9.9* 69.5 6.0 104 247* 125*<br />
1.3 6.6 7.8 4.4 28.9 3.7 41 134 66<br />
20 25 25 25 25 21 21 21 21<br />
1.1 13.4* 16.3 9.3 267.9 11.1* 60* 154 264<br />
0.8 4.7 6.2 3.9 44.0 2.0 15 64 134<br />
5 5 5 5 5 5 5 5 5<br />
MICROBANKING BULLETIN, APRIL 2001 67
BULLETIN HIGHLIGHTS AND TABLES<br />
TABLE D: PROFITABILITY AND EFFICIENCY INDICATORS FOR FINANCIALLY SELF-SUFFICIENT<br />
MFIs (continued)<br />
CRITERIA<br />
ADJUSTED<br />
RETURN ON<br />
ASSETS<br />
adjusted net<br />
operating income /<br />
average total<br />
assets<br />
(%)<br />
ADJUSTED<br />
RETURN ON<br />
EQUITY<br />
adjusted net<br />
operating income<br />
/ average total<br />
equity<br />
(%)<br />
OPERATIONAL<br />
SELF-SUFFICIENCY<br />
operating income /<br />
operating expense<br />
(%)<br />
FINANCIAL<br />
SELF-<br />
SUFFICIENCY<br />
adjusted operating<br />
income /<br />
adjusted operating<br />
expense<br />
(%)<br />
PORTFOLIO<br />
YIELD<br />
operating income –<br />
accrued interest –<br />
interest and fee<br />
income from<br />
investments /<br />
average gross loan<br />
portfolio (%)<br />
REGION ## Africa avg 5.4* 10.9 157.4* 146.4* 51.2<br />
stdv 4.0 9.7 63.2 62.9 20.8<br />
N 4 4 4 4 4<br />
Asia avg 2.8* 6.0* 133.8* 113.5* 38.2<br />
(All) stdv 4.2 10.1 34.4 20.1 11.5<br />
N 12 12 12 12 12<br />
Eastern Europe avg 1.1 -0.1 123.0 105.3 32.1<br />
stdv 2.9 5.3 5.5 12.4 0.4<br />
N 3 3 3 3 3<br />
Latin America avg 2.8* 8.8* 122.7* 110.1* 48.4*<br />
stdv 4.9 12.3 24.7 16.6 18.7<br />
N 35 35 35 35 35<br />
CHARTER Bank avg 3.8* 22.0* 118.6 114.6* 50.1*<br />
TYPE stdv 4.3 25.5 18.7 15.7 13.8<br />
N 8 8 8 8 8<br />
Credit Union/ avg 2.9 8.1 122.5 115.4* 24.0*<br />
Cooperative stdv 2.8 6.0 15.9 13.1 3.3<br />
N 5 5 5 5 5<br />
NGO avg 2.8* 5.1* 130.8* 109.6* 50.2*<br />
stdv 6.0 10.4 30.2 18.3 21.0<br />
N 35 35 35 35 35<br />
Non-Bank ### avg 1.7 11.3 110.4 104.8 34.5<br />
stdv 2.8 12.6 5.0 10.4 5.9<br />
N 3 3 3 3 3<br />
PROFIT Non-Profit avg 2.8* 5.0* 131.8* 110.7* 48.8*<br />
STATUS stdv 6.0 10.4 35.5 20.4 20.6<br />
N 39 39 39 39 39<br />
Profit avg 3.4* 17.9* 122.3* 117.0* 43.5<br />
stdv 3.7 20.5 31.6 24.7 15.7<br />
N 14 14 14 14 14<br />
Note: Standard deviations are listed below <strong>the</strong> group averages. The averages are calculated on <strong>the</strong> basis of <strong>the</strong> values between second and <strong>the</strong> 99th percentiles for<br />
each group. Therefore, sample sizes (N) vary across indicators. Group averages different from average for all MFIs at 1 percent significance level are marked with an<br />
asterisk (*). Additional statistical information is available at www.<strong>microbanking</strong>-mbb.org.<br />
# The criteria for classification of scale of operations vary by region. Refer to page 53 for details<br />
## No data were reported for <strong>the</strong> region MENA (Middle East North Africa) due to <strong>the</strong> small size of <strong>the</strong> sample.<br />
### Includes Ltd., financieras, and non-bank financial intermediary (NBFIs).<br />
68 MICROBANKING BULLETIN, APRIL 2001
BULLETIN HIGHLIGHTS AND TABLES<br />
PORTFOLIO<br />
AT RISK ><br />
90 DAYS<br />
TOTAL ADMIN.<br />
EXPENSE<br />
RATIO<br />
TOTAL ADMIN.<br />
EXPENSE<br />
/ LP<br />
SALARY<br />
EXPENSE/<br />
LP<br />
DEPTH<br />
AVERAGE<br />
SALARY<br />
STAFF<br />
PRODUCTIVITY<br />
LOAN<br />
OFFICER<br />
PRODUCTIVITY<br />
COST/<br />
BORROWER<br />
outstanding<br />
balance of<br />
loans overdue ><br />
90 days / total<br />
gross loan<br />
portfolio<br />
(%)<br />
administrative<br />
expense + inkind<br />
donations<br />
/ total average<br />
assets<br />
(%)<br />
administrative<br />
expense + inkind<br />
donations<br />
/ average loan<br />
portfolio<br />
(%)<br />
personnel<br />
expense + inkind<br />
donations<br />
average gross<br />
loan portfolio<br />
(%)<br />
average<br />
loan<br />
balance/<br />
GNP<br />
per capita<br />
(%)<br />
average<br />
personnel<br />
expense + inkind<br />
donations /<br />
GNP per capita<br />
(multiple of<br />
GNP/ capita)<br />
number of active<br />
borrowers /<br />
number of staff<br />
(no.)<br />
number of active<br />
borrowers /<br />
number of loan<br />
officers<br />
(no.)<br />
administrative<br />
expense + inkind<br />
donations /<br />
average number<br />
of active<br />
borrowers<br />
(US$)<br />
0.8 18.9* 43.5 21.2 109.5 13.0* 132 401* 64*<br />
0.9 10.5 22.7 9.3 113.3 1.8 40 199 38<br />
4 4 4 4 4 4 4 4 4<br />
1.8 16.5* 23.5 13.7 30.0 4.2 149 261* 35*<br />
2.0 7.6 10.5 5.3 26.0 2.5 105 167 21<br />
11 12 12 12 12 12 12 12 12<br />
0.3 17.9* 22.1 12.5 129.5 8.4 74 120 195<br />
0.5 0.5 0.4 0.3 62.6 1.8 14 14 62<br />
3 3 3 3 3 3 3 3 2<br />
1.9 21.5* 30.5 16.8 45.3 4.9 131 287* 129*<br />
1.2 10.8 16.8 11.2 42.9 3.1 54 128 98<br />
29 35 35 35 35 30 30 30 30<br />
1.3 18.2* 32.8 17.3 62.5 7.3 108 315* 150<br />
0.8 5.7 18.0 8.1 56.6 5.3 30 144 117<br />
8 8 8 8 8 8 8 8 8<br />
- 8.2* 11.4* 4.8* 91.6 - - - 146<br />
- 1.2 2.2 1.4 25.8 - - - 30<br />
- 5 5 5 5 - - - 5<br />
1.7 23.3* 32.8 19.0 38.8 4.8 148 279* 79*<br />
1.6 10.6 16.8 10.7 55.5 3.2 80 173 86<br />
33 35 35 35 35 35 35 35 30<br />
2.4 16.2 20.2 11.2 62.4 4.7 90 210 113<br />
0.3 4.1 8.9 3.2 29.8 0.6 32 86 35<br />
3 3 3 3 3 3 3 3 3<br />
1.8 22.8* 32.2 18.6 45.4 5.1 143 274* 79*<br />
1.7 10.6 16.8 10.5 66.3 3.5 79 171 83<br />
39 39 39 39 39 39 39 39 33<br />
1.4 16.3* 26.4 14.0 82.3 7.3 104 305* 139*<br />
0.9 7.0 16.0 7.4 70.5 4.8 41 171 100<br />
14 14 14 14 14 14 14 14 14<br />
MICROBANKING BULLETIN, APRIL 2001 69
BULLETIN HIGHLIGHTS AND TABLES<br />
Composition of Additional Analysis Groupings<br />
AGE #<br />
New<br />
(1 - 2 years)<br />
Young<br />
(3 - 6 years)<br />
Mature<br />
(> 6 years)<br />
23 de Julio<br />
Al Amana<br />
AMK<br />
Banco do Povo<br />
Banco PeqEmpresa<br />
15 de Abril<br />
ACLEDA<br />
Agrocapital<br />
Al Majmoua<br />
ABA<br />
Acep<br />
ACODEP<br />
Acredicom<br />
Actuar<br />
ADOPEM<br />
ADRI<br />
AGAPE<br />
AKRSP<br />
Asawinso RB<br />
Basix<br />
Bospo<br />
Constanta<br />
FINCA Tanzania<br />
Finsol<br />
CEAPE/ Pernamb.<br />
CERUDEB<br />
Citi S&L<br />
Enlace<br />
ASA<br />
BAAC<br />
Banco Ademi<br />
Bancosol<br />
Bank Dagang Bali<br />
BRAC<br />
BRI<br />
BURO Tangail<br />
Caja de Los Andes<br />
Calpia<br />
FOCCAS<br />
Kash Foundation<br />
LOK<br />
Mercy Corps<br />
MEB<br />
Faten<br />
Faulu<br />
FEFAD<br />
FINCA Ecuador<br />
CAM<br />
CARD BANK<br />
CDS<br />
Chispa<br />
Chumiquená<br />
CM Arequipa<br />
CMM Medellín<br />
Compartamos<br />
Contigo<br />
COOSAJO<br />
Microfund for Women<br />
Mikrofin<br />
Moznosti<br />
Nachala<br />
NOA<br />
FINCA Kyrgystan<br />
FINCA Malawi<br />
FINCA Peru<br />
Fundusz Mikro<br />
Crecer<br />
Ecosaba<br />
EMT<br />
FAMA<br />
FED<br />
FIE<br />
Finamérica<br />
FINCA Honduras<br />
FINCA Mexico<br />
FINCA Nicaragua<br />
PAMÉCAS<br />
Portosol<br />
Pride Uganda<br />
Sagrario<br />
SEDA<br />
FONDECO Hattha<br />
Kakserkar<br />
Inicjatywa Mikro<br />
Nirdhan<br />
FINCA Uganda<br />
FMM Popayán<br />
FWWB Cali<br />
FWWB India<br />
Hublag<br />
Kafo Jiginew<br />
KWFT<br />
Mibanco<br />
Manya Krobo RB<br />
Moyután<br />
Sunrise<br />
Tulcán<br />
Vital-Finance<br />
Vivacred<br />
WV Bosnia<br />
Network Leasing<br />
Oscus<br />
PADME<br />
Piyeli<br />
Nsoatreman RB<br />
Nyésigiso<br />
Pride Vita Guinea<br />
PRODEM<br />
ProEmpresa<br />
RSPI<br />
Sartawi<br />
SEEDS<br />
SEF<br />
SHARE<br />
Pride Tanzania<br />
ProMujer<br />
Sinapi Aba Trust<br />
WAGES<br />
Tonantel<br />
TSPI<br />
UNRWA<br />
UWFT<br />
WRHonduras<br />
SCALE OF OPERATIONS ##<br />
Large<br />
Medium<br />
Small<br />
ABA<br />
Acep<br />
ACLEDA<br />
ASA<br />
15 de Abril<br />
23 de Julio<br />
ACODEP<br />
Acredicom<br />
Actuar<br />
ADOPEM<br />
AKRSP<br />
Al Amana<br />
Al Majmoua<br />
Basix<br />
ADRI<br />
AGAPE<br />
AMK<br />
Asawinso RB<br />
Banco do Povo<br />
Agrocapital<br />
BAAC<br />
Bank Dagang Bali<br />
BRAC<br />
BURO Tangail<br />
Banco PeqEmpresa<br />
CAM<br />
CARD BANK<br />
CEAPE/ Pernamb.<br />
Chispa<br />
Chumiquená<br />
CMM Medellín<br />
Crecer<br />
CitiS&L<br />
Bospo<br />
CDS<br />
Constanta<br />
Contigo<br />
Faulu<br />
BRI<br />
Banco Ademi<br />
Bancosol<br />
Caja de Los Andes<br />
Compartamos<br />
Ecosaba<br />
Emprender<br />
EMT<br />
Enlace<br />
FAMA<br />
Faten<br />
FEFAD<br />
FINCA Honduras<br />
FINCA Kyrgystan<br />
FED<br />
FINCA Ecuador<br />
FINCA Malawi<br />
FINCA Mexico<br />
FINCA Nicaragua<br />
Calpia<br />
CERUDEB<br />
CM Arequipa<br />
COOSAJO<br />
FINCA Uganda<br />
FMM Popayán<br />
FONDECO<br />
FWWB India<br />
Finsol<br />
KWFT<br />
Kafo Jiginew<br />
LOK<br />
Mercy Corps MEB<br />
FINCA Peru<br />
FINCA Tanzania<br />
FOCCAS<br />
Hublag<br />
Hattha Kakserkar<br />
FIE<br />
Fundusz Mikro<br />
FWWBCali<br />
Finamérica<br />
Mikrofin<br />
Moznosti<br />
Network Leasing<br />
NOA<br />
Nsoatreman RB<br />
Nyésigiso<br />
Oscus<br />
PADME<br />
PAMÉCAS<br />
Pride Tanzania<br />
Inicjatywa Mikro<br />
Kash Foundation<br />
Microfund for Women<br />
Manya Krobo RB<br />
Moyután<br />
Mibanco<br />
PRODEM<br />
Pride Uganda<br />
Pride Vita Guinea<br />
Portosol<br />
ProEmpresa<br />
ProMujer<br />
SEEDS<br />
SEF<br />
SHARE<br />
Sunrise<br />
Sagrario<br />
Nirdhan<br />
Nachala<br />
Piyeli<br />
RSPI<br />
Sinapi Aba Trust<br />
Sartawi<br />
Tonantel<br />
TSPI<br />
Tulcán<br />
UNRWA<br />
WAGES<br />
WRHonduras<br />
WV Bosnia<br />
SEDA<br />
UWFT<br />
Vital-Finance<br />
Vivacred<br />
LENDING METHODOLOGY<br />
Individual<br />
(1 borrower)<br />
Solidarity<br />
Groups<br />
(groups of 3<br />
to 9<br />
borrowers)<br />
Village<br />
Banking<br />
(groups with ≥<br />
10 borrowers)<br />
15 de Abril<br />
23 de Julio<br />
ABA<br />
Acep<br />
ACODEP<br />
Acredicom<br />
ADRI<br />
Agrocapital<br />
ACLEDA<br />
Actuar<br />
ADOPEM<br />
Al Amana<br />
ASA<br />
Bancosol<br />
Basix<br />
AGAPE<br />
AKRSP<br />
Al Majmoua<br />
CAM<br />
Citi S&L<br />
AMK<br />
Asawinso RB<br />
BAAC<br />
Banco Ademi<br />
Banco do Povo<br />
BanPeqEmpresa<br />
Bank Dagang Bali<br />
BRI<br />
Bospo<br />
BRAC<br />
BURO Tangail<br />
CARD BANK<br />
CEAPE/ Pernamb.<br />
Chispa<br />
Constanta<br />
Compartamos<br />
Crecer<br />
FINCA Ecuador<br />
FINCA Honduras<br />
FINCA Kyrgystan<br />
Caja de Los Andes<br />
Calpia<br />
CDS<br />
CERUDEB<br />
Chumiquená<br />
CM Arequipa<br />
CMM Medellín<br />
COOSAJO<br />
Contigo<br />
EMT<br />
Enlace<br />
FAMA<br />
Faten<br />
Faulu<br />
Finamérica<br />
FINCA Malawi<br />
FINCA Mexico<br />
FINCA Nicaragua<br />
FINCA Peru<br />
FINCA Tanzania<br />
Ecosaba<br />
Emprender<br />
FED<br />
FEFAD<br />
FIE<br />
FMM Popayán<br />
FWWBCali<br />
Hattha Kakserkar<br />
Finsol<br />
Fundusz Mikro<br />
FONDECO<br />
Kash Foundation<br />
KWFT<br />
Mibanco<br />
Mikrofin<br />
FINCA Uganda<br />
FOCCAS<br />
FWWB India<br />
Microfund for Women<br />
ProMujer<br />
Hublag<br />
Inicjatywa Mikro<br />
Kafo Jiginew<br />
LOK<br />
Mercy Corps<br />
MEB<br />
Manya Krobo RB.<br />
Moyután<br />
Nirdhan<br />
Nsoatreman RB<br />
Nyésigiso<br />
PAMÉCAS<br />
Piyeli<br />
Pride Tanzania<br />
Pride Uganda<br />
Sartawi<br />
SEDA<br />
SEEDS<br />
WAGES<br />
WRHonduras<br />
Moznosti<br />
Nachala<br />
Network Leasing<br />
NOA<br />
Oscus<br />
PADME<br />
Portosol<br />
ProEmpresa<br />
Pride Vita Guinea<br />
PRODEM<br />
RSPI<br />
SEF<br />
SHARE<br />
TSPI<br />
UNRWA<br />
Sagrario<br />
Sinapi Aba Trust<br />
Sunrise<br />
Tonantel<br />
Tulcán<br />
Vivacred<br />
UWFT<br />
Vital-Finance<br />
WV Bosnia<br />
# Some institutions did not report <strong>the</strong> information.<br />
## The criteria for classification of scale of operations vary by region. Refer to page 53 for details.<br />
70 MICROBANKING BULLETIN, APRIL 2001
BULLETIN HIGHLIGHTS AND TABLES<br />
Composition of Additional Analysis Groupings, ctd.<br />
LEVEL OF FINANCIAL INTERMEDIATION<br />
Retail Financial 15 de Abril<br />
23 de Julio<br />
Intermediary (passboo<br />
Acredicom<br />
and time deposits ≥ 20%<br />
Assawinso RB<br />
of total assets)<br />
BAAC<br />
Banco Ademi<br />
O<strong>the</strong>r<br />
(passbook and time<br />
deposits < 20% of total<br />
assets)<br />
ABA<br />
Acep<br />
ACLEDA<br />
ACODEP<br />
Actuar<br />
ADOPEM<br />
ADRI<br />
AGAPE<br />
Agrocapital<br />
AKRSP<br />
Al Amana<br />
Al Majmoua<br />
AMK<br />
ASA<br />
Bancosol<br />
Bank Dagang Bali<br />
BRI<br />
Caja de Los Andes<br />
CERUDEB<br />
Chumiquená<br />
Banco do Povo<br />
BanPeqEmpresa<br />
Basix<br />
Bospo<br />
BRAC<br />
BURO Tangail<br />
Calpia<br />
CAM<br />
CARD BANK<br />
CDS<br />
CEAPE/ Pernamb.<br />
Chispa<br />
CMM Medellín<br />
Compartamos<br />
Citi S&L<br />
CM Arequipa<br />
COOSAJO<br />
Ecosaba<br />
Enlace<br />
FEFAD<br />
Constanta<br />
Contigo<br />
Crecer<br />
Emprender<br />
EMT<br />
FAMA<br />
Faten<br />
Faulu<br />
FED<br />
FINCA Ecuador<br />
FINCA Honduras<br />
FINCA Kyrgystan<br />
FINCA Malawi<br />
FINCA Mexico<br />
FIE<br />
Finamérica<br />
Kafo Jiginew<br />
Manya Krobo RB<br />
Moyután<br />
Nsoatreman RB<br />
FINCA Nicaragua<br />
FINCA Peru<br />
FINCA Tanzania<br />
FINCA Uganda<br />
Finsol<br />
Fundusz Mikro<br />
FMM Popayán<br />
FOCCAS<br />
FONDECO<br />
FWWBCali<br />
FWWB India<br />
Hattha Kakserkar<br />
Hublag<br />
Inicjatywa Mikro<br />
Nyésigiso<br />
Oscus<br />
PAMÉCAS<br />
Sagrario<br />
Tonantel<br />
Tulcán<br />
Kash Foundation<br />
KWFT<br />
LOK<br />
Mercy Corps<br />
MEB<br />
Microfund for Women<br />
Mibanco<br />
Mikrofin<br />
Moznosti<br />
Nachala<br />
Nirdhan<br />
Network Leasing<br />
NOA<br />
PADME<br />
UWFT<br />
Piyeli<br />
Portosol<br />
Pride Tanzania<br />
Pride Uganda<br />
Pride Vita Guinea<br />
PRODEM<br />
ProEmpresa<br />
ProMujer<br />
RSPI<br />
Sartawi<br />
Sinapi Aba Trust<br />
SEDA<br />
SEEDS<br />
SEF<br />
SHARE<br />
Sunrise<br />
TSPI<br />
UNRWA<br />
Vital-Finance<br />
Vivacred<br />
WAGES<br />
WRHonduras<br />
WV Bosnia<br />
TARGET MARKET<br />
Low-end<br />
(depth < 20% OR avg.<br />
loan balance<br />
< US$150)<br />
Broad<br />
(depth between 20%<br />
and 149%)<br />
High-end (depth ≥<br />
150%)<br />
Small Business<br />
(depth ≥ 250%)<br />
Actuar<br />
AGAPE<br />
AKRSP<br />
Al Amana<br />
Al Majmoua<br />
Assawinso RB<br />
ASA<br />
BRAC<br />
BURO Tangail<br />
15 de Abril<br />
23 de Julio<br />
ABA<br />
ACLEDA<br />
ACODEP<br />
Acredicom<br />
ADOPEM<br />
ADRI<br />
AMK<br />
Banco Ademi<br />
Acep<br />
Agrocapital<br />
CAM<br />
CARD BANK<br />
CDS<br />
CEAPE/ Pernamb.<br />
CMM Medellín<br />
Compartamos<br />
Constanta<br />
Contigo<br />
Crecer<br />
BAAC<br />
Banco do Povo<br />
Bancosol<br />
BanPeqEmpresa<br />
Basix<br />
Bospo<br />
BRI<br />
Caja de Los Andes<br />
Moznosti<br />
Sunrise<br />
Bank Dagang Bali<br />
CERUDEB<br />
Emprender<br />
EMT<br />
Enlace<br />
Faten<br />
FED<br />
FINCA Ecuador<br />
FINCA Honduras<br />
FINCA Kyrgystan<br />
FINCA Malawi<br />
Calpia<br />
Chispa<br />
Chumiquená<br />
CitiS&L CM<br />
Arequipa<br />
COOSAJO<br />
Ecosaba<br />
FAMA<br />
WV Bosnia<br />
FEFAD<br />
MEB<br />
FINCA Mexico<br />
FINCA Nicaragua<br />
FINCA Peru<br />
FINCA Tanzania<br />
FINCA Uganda<br />
FMM Popayán<br />
FOCCAS<br />
FWWBCali<br />
FWWB India<br />
Faulu<br />
FIE<br />
Finamérica Finsol<br />
Fundusz Mikro<br />
FONDECO<br />
Hattha Kakserkar<br />
Inicjatywa Mikro<br />
Network Leasing<br />
Hublag<br />
Kash Foundation<br />
Microfund for Women<br />
Mibanco<br />
Manya Krobo RB<br />
Nirdhan<br />
Nsoatreman RB<br />
PAMÉCAS<br />
Piyeli<br />
Kafo Jiginew<br />
KWFT<br />
LOK<br />
Mercy Corps<br />
Mikrofin<br />
Moyután<br />
Nachala<br />
NOA<br />
Pride Tanzania<br />
Pride Uganda<br />
ProMujer<br />
RSPI<br />
Sinapi Aba Trust<br />
SEDA<br />
SEEDS<br />
SEF<br />
SHARE<br />
Nyésigiso<br />
Oscus<br />
PADME<br />
Portosol<br />
Pride Vita Guinea<br />
PRODEM<br />
ProEmpresa<br />
Sagrario<br />
TSPI<br />
UWFT<br />
Vivacred<br />
WAGES<br />
WRHonduras<br />
Sartawi<br />
Tonantel<br />
Tulcán<br />
UNRWA<br />
Vital-Finance<br />
REGION<br />
Africa<br />
Asia<br />
(all)<br />
Eastern<br />
Europe<br />
Latin<br />
America<br />
Middle East/<br />
North Africa<br />
Acep<br />
Assawinso RB<br />
CERUDEB<br />
Citi S&L<br />
ACLEDA<br />
AKRSP<br />
ASA<br />
BAAC<br />
AMK<br />
Bospo<br />
15 de Abril<br />
23 de Julio<br />
ACODEP<br />
Acredicom<br />
Actuar<br />
ADOPEM<br />
ADRI<br />
AGAPE<br />
ABA<br />
Al Amana<br />
Faulu<br />
FINCA Malawi<br />
FINCA Tanzania<br />
FINCA Uganda<br />
Basix<br />
Bank Dagang Bali<br />
BRAC<br />
BRI<br />
FEFAD<br />
Fundusz Mikro<br />
Agrocapital<br />
Banco Ademi<br />
Banco do Povo<br />
Bancosol<br />
BanPeqEmpresa<br />
Caja de Los Andes<br />
Calpia<br />
CAM<br />
Al Majmoua<br />
Faten<br />
FOCCAS<br />
Kafo Jiginew<br />
KWFT<br />
Manya Krobo RB<br />
BURO Tangail<br />
CARD BANK<br />
CDS<br />
Constanta<br />
Inicjatywa Mikro<br />
LOK<br />
CEAPE/ Pernambuco<br />
Chispa<br />
Chumiquená<br />
CM Arequipa<br />
CMM Medellín<br />
Compartamos Contigo<br />
COOSAJO<br />
Microfund for Women<br />
UNRWA<br />
Nsoatreman RB<br />
Nyésigiso<br />
PADME<br />
PAMÉCAS<br />
EMT<br />
FINCA Kyrgystan<br />
FWWB India<br />
Hattha Kakserkar<br />
Mercy Corps<br />
MEB<br />
Crecer<br />
Ecosaba<br />
Emprender<br />
Enlace<br />
FAMA<br />
FED<br />
FIE<br />
Finamérica<br />
Piyeli<br />
Pride Tanzania<br />
Pride Uganda<br />
Pride Vita Guinea<br />
Hublag<br />
Kash Foundation<br />
Nirdhan<br />
Network Leasing<br />
Mikrofin<br />
Moznosti<br />
FINCA Ecuador<br />
FINCA Honduras<br />
FINCA Mexico<br />
FINCA Nicaragua<br />
FINCA Peru<br />
Finsol<br />
FMM Popayán<br />
Sinapi Aba Trust<br />
SEDA<br />
SEF<br />
UWFT<br />
RSPI<br />
SEEDS<br />
SHARE<br />
TSPI<br />
Nachala<br />
NOA<br />
FONDECO<br />
FWWBCali<br />
Mibanco<br />
Moyután<br />
Oscus<br />
Portosol<br />
PRODEM<br />
ProEmpresa<br />
Vital-Finance<br />
WAGES<br />
Sunrise<br />
WV Bosnia<br />
ProMujer<br />
Sagrario<br />
Sartawi<br />
Tonantel<br />
Tulcán<br />
Vivacred<br />
WR Honduras<br />
MICROBANKING BULLETIN, APRIL 2001 71
BULLETIN HIGHLIGHTS AND TABLES<br />
Composition of Additional Analysis Groupings, ctd.<br />
CHARTER#<br />
Bank<br />
Credit Union/<br />
Cooperative<br />
NGO<br />
Non-Bank ###<br />
ACLEDA<br />
Asawinso RB<br />
BAAC<br />
15 de Abril<br />
23 de Julio<br />
Acep<br />
ABA<br />
ACODEP<br />
Actuar<br />
ADOPEM<br />
ADRI<br />
AGAPE<br />
AKRSP<br />
AMK<br />
ASA<br />
Agrocapital<br />
Basix<br />
Caja de Los Andes<br />
Calpia<br />
Bank Dagang Bali<br />
BRI<br />
Banco Ademi<br />
Acredicom<br />
Chumiquená<br />
COOSAJO<br />
Al Amana<br />
Al Majmoua<br />
Banco do Povo<br />
Bospo<br />
BRAC<br />
BURO Tangail<br />
CAM<br />
CDS<br />
CEAPE/ Pernamb.<br />
Chispa<br />
Faten<br />
Finamérica<br />
BanPeqEmpresa<br />
Bancosol<br />
CARD BANK<br />
Ecosaba<br />
Kafo Jiginew<br />
Moyután<br />
CMM Medellín<br />
Constanta<br />
Contigo<br />
Crecer<br />
Compartamos<br />
EMT<br />
FAMA<br />
Faulu<br />
FED<br />
FIE<br />
Fundusz Mikro<br />
Inicjatywa Mikro<br />
CERUDEB<br />
CM Arequipa<br />
Enlace<br />
Nachala<br />
Nyésigiso<br />
Oscus<br />
FINCA Ecuador<br />
FINCA Honduras<br />
FINCA Kyrgystan<br />
FINCA Malawi<br />
FINCA Mexico<br />
FINCA Nicaragua<br />
FINCA Peru<br />
FINCA Tanzania<br />
FINCA Uganda<br />
FMM Popayán<br />
Network Leasing<br />
TSPI<br />
FEFAD<br />
MEB<br />
Manya Krobo RB<br />
PAMÉCAS<br />
Sagrario<br />
Tonantel<br />
FOCCAS<br />
FONDECO<br />
FWWBCali<br />
FWWB India<br />
Hublag<br />
Kash Foundation<br />
KWFT<br />
LOK<br />
Microfund for Women<br />
Mikrofin<br />
Mibanco<br />
Nsoatreman RB<br />
Tulcán<br />
Moznosti<br />
Nirdhan<br />
Pride Tanzania<br />
Pride Vita Guinea<br />
PRODEM<br />
Portosol<br />
ProMujer<br />
RSPI<br />
SEDA<br />
SEEDS<br />
SEF<br />
SHARE<br />
Sunrise<br />
Sartawi<br />
UNRWA<br />
UWFT<br />
Vivacred<br />
WAGES<br />
WRHonduras<br />
WV Bosnia<br />
PROFIT STATUS#<br />
Non-profit<br />
Profit<br />
ABA<br />
Acep<br />
ACODEP<br />
Actuar<br />
ADOPEM<br />
ADRI<br />
AGAPE<br />
Agrocapital<br />
AKRSP<br />
Al Amana<br />
Al Majmoua<br />
AMK<br />
ACLEDA<br />
Asawinso RB<br />
BAAC<br />
Banco Ademi<br />
Bancosol<br />
ASA<br />
Banco do Povo<br />
Bospo<br />
BRAC<br />
BURO Tangail<br />
CAM<br />
CDS<br />
CEAPE/ Pernamb.<br />
Chispa<br />
CMM Medellín<br />
Compartamos<br />
Constanta<br />
BanPeqEmpresa<br />
Basix<br />
Bank Dagang Bali<br />
BRI<br />
Caja de Los Andes<br />
Contigo<br />
Crecer<br />
EMT<br />
FAMA<br />
Faten<br />
FED<br />
FIE<br />
FINCA Ecuador<br />
FINCA Honduras<br />
FINCA Kyrgystan<br />
FINCA Malawi<br />
FINCA Mexico<br />
Calpia<br />
CARD BANK<br />
CERUDEB<br />
CitiS&L<br />
CM Arequipa<br />
FINCA Nicaragua<br />
FINCA Peru<br />
FINCA Tanzania<br />
FINCA Uganda<br />
FMM Popayán<br />
FOCCAS<br />
FONDECO<br />
FWWBCali<br />
FWWB India<br />
Hattha Kakserkar<br />
Hublag<br />
Kafo Jiginew<br />
Enlace<br />
Faulu<br />
FEFAD<br />
Finamérica<br />
Fundusz Mikro<br />
Kash Foundation<br />
KWFT<br />
LOK<br />
Microfund for Women<br />
Mikrofin<br />
Moznosti<br />
Nirdhan<br />
NOA<br />
Nyésigiso<br />
PAMÉCAS<br />
Piyeli<br />
Portosol<br />
Inicjatywa Mikro<br />
MEB<br />
Mibanco<br />
Manya Krobo RB.<br />
Nachala<br />
Pride Tanzania<br />
Pride Uganda<br />
Pride Vita Guinea<br />
PRODEM<br />
ProMujer<br />
RSPI<br />
Sartawi<br />
SEDA<br />
SEEDS<br />
SEF<br />
SHARE<br />
Sunrise<br />
Network Leasing<br />
Nsoatreman RB<br />
PADME<br />
ProEmpresa<br />
Sinapi Aba Trust<br />
TSPI<br />
UNRWA<br />
Vivacred<br />
WAGES<br />
WRHonduras<br />
WV Bosnia<br />
# Some institutions did not report <strong>the</strong> information.<br />
### Includes Ltd., financieras, and non-bank financial intermediary (NBFIs).<br />
72 MICROBANKING BULLETIN, APRIL 2001
APPENDICES<br />
APPENDICES<br />
Appendix I: Notes to Statistical Section<br />
The MicroBanking Standards Project, of which The<br />
MicroBanking Bulletin is a major output, is open to<br />
all MFIs that are willing to disclose financial data<br />
that meet a simple quality test. Participating MFIs<br />
typically have three characteristics: 1) <strong>the</strong>y are<br />
willing to be transparent by submitting <strong>the</strong>ir<br />
performance data to an independent agency; 2)<br />
<strong>the</strong>y display a strong social orientation by providing<br />
financial services to low-income persons; and 3)<br />
<strong>the</strong>y are able to answer all <strong>the</strong> questions needed for<br />
our analysis.<br />
The one hundred and twenty four institutions that<br />
provided data for this issue represent a large<br />
proportion of <strong>the</strong> world’s leading microfinance<br />
institutions. They have provided data generally by<br />
completing a detailed questionnaire, supplemented<br />
in most cases by additional information. All<br />
participating MFIs receive a customized report<br />
comparing <strong>the</strong>ir results with those of <strong>the</strong> peer<br />
groups.<br />
Data Quality Issues<br />
The Bulletin classifies information from participating<br />
institutions according to <strong>the</strong> degree to which we<br />
have independent verification of its reliability. AAAgraded<br />
information has been independently<br />
generated through a detailed financial analysis by<br />
an independent third party, such as a CAMEL<br />
evaluation, a CGAP appraisal, or assessments by<br />
reputed rating agencies. A-graded information is<br />
backed by accompanying documentation, such as<br />
audited financial statements, annual reports, and<br />
independent program evaluations that provide a<br />
reasonable degree of confidence for our<br />
adjustments. B-graded information is from MFIs<br />
that have limited <strong>the</strong>mselves to completing our<br />
questionnaire. These grades signify confidence<br />
levels on <strong>the</strong> reliability of <strong>the</strong> information; <strong>the</strong>y are<br />
NOT intended as a rating of <strong>the</strong> financial<br />
performance of <strong>the</strong> MFIs.<br />
The criteria used in constructing <strong>the</strong> Statistical<br />
Tables are important for understanding and<br />
interpreting <strong>the</strong> information presented. Given <strong>the</strong><br />
voluntary nature and origin of <strong>the</strong> data, <strong>the</strong> Bulletin<br />
staff and Editorial Board, and CGAP cannot accept<br />
responsibility for <strong>the</strong> validity of <strong>the</strong> results<br />
presented, or for consequences resulting from <strong>the</strong>ir<br />
use. We employ a system to make tentative<br />
distinctions about <strong>the</strong> quality of data presented to<br />
us and include only information for which we have a<br />
reasonable level of comfort. However, we cannot<br />
exclude <strong>the</strong> possibility of a program<br />
misrepresenting its results.<br />
The most delicate areas of potential distortion are:<br />
(1) unreported subsidies and (2) misrepresented<br />
loan portfolio quality. There can also be<br />
inaccuracies in reporting <strong>the</strong> costs of financial<br />
services in multipurpose institutions that also<br />
provide non-financial services, in part because of<br />
difficulties in assigning overhead costs. These risks<br />
are highest for younger institutions, and for<br />
institutions with a record of optimistic disclosure. If<br />
we have grounds for caution about <strong>the</strong> reliability of<br />
an MFI’s disclosure, we will not include its<br />
information in a peer group unless it has been<br />
externally validated by a third-party.<br />
Adjustments to Financial Data<br />
The Bulletin adjusts <strong>the</strong> financial data it receives to<br />
ensure comparable results. The financial<br />
statements of each organization are converted to<br />
<strong>the</strong> standard chart of accounts used by <strong>the</strong> Bulletin.<br />
This chart of accounts is simpler than that used by<br />
most MFIs, so <strong>the</strong> conversion consists mainly of<br />
consolidation into fewer, more general accounts.<br />
Then three major adjustments are applied to<br />
produce a common treatment for <strong>the</strong> effect of: a)<br />
inflation, b) subsidies, and c) loan loss provisioning<br />
and write-off. In <strong>the</strong> statistical tables <strong>the</strong> reader can<br />
compare adjusted and unadjusted results.<br />
Inflation<br />
The Bulletin reports <strong>the</strong> net effect of inflation by<br />
calculating increases in expenses and incomes due<br />
to inflation. Inflation causes a decrease in <strong>the</strong> real<br />
value of equity. This “cost of funds” is obtained by<br />
multiplying <strong>the</strong> prior year-end equity balance by <strong>the</strong><br />
current-year inflation rate. 15 Fixed asset accounts,<br />
on <strong>the</strong> o<strong>the</strong>r hand, are revalued upward by <strong>the</strong><br />
current year’s inflation rate, which results in inflation<br />
adjustment income, offsetting to some degree <strong>the</strong><br />
15<br />
Inflation data are obtained from line 64x of <strong>the</strong> International<br />
Financial Statistics, International Monetary Fund, various years.<br />
MICROBANKING BULLETIN, APRIL 2001 73
APPENDICES<br />
expense generated by adjusting equity. 16 On <strong>the</strong><br />
balance sheet, this inflation adjustment results in a<br />
reordering of equity accounts: profits are<br />
redistributed between real profit and <strong>the</strong> nominal<br />
profits required to maintain <strong>the</strong> real value of equity.<br />
MFIs that borrow from banks or mobilize savings<br />
have an actual interest expense, which is an<br />
operating cost. In comparison, similar MFIs that<br />
lend only <strong>the</strong>ir equity have no interest expense and<br />
<strong>the</strong>refore have lower operating costs. If an MFI<br />
focuses on sustainability and <strong>the</strong> maintenance of its<br />
capital/asset ratio, it must increase <strong>the</strong> size of its<br />
equity in nominal terms to continue to make <strong>the</strong><br />
same value of loans in real (inflation-adjusted)<br />
terms. Inflation increases <strong>the</strong> cost of tangible items<br />
over time, so that a borrower needs more money to<br />
purchase <strong>the</strong>m. MFIs that want to maintain <strong>the</strong>ir<br />
support to clients must <strong>the</strong>refore offer larger loans.<br />
Employees’ salaries go up with inflation, so <strong>the</strong><br />
average loan balance and portfolio must increase to<br />
compensate, assuming no increase in interest<br />
margin. Therefore, a program that funds its loans<br />
with its equity must maintain <strong>the</strong> real value of that<br />
equity, and pass along <strong>the</strong> cost of doing so to <strong>the</strong><br />
client. This expectation implies MFIs should “pay”<br />
interest rates that include <strong>the</strong> inflation-adjustment<br />
expense as a cost of funds, even if this cost is not<br />
actually paid to anyone outside <strong>the</strong> institution.<br />
Some countries with high or volatile levels of<br />
inflation require businesses to use inflation-based<br />
accounting on <strong>the</strong>ir audited financial statements.<br />
We use this same technique in <strong>the</strong> Bulletin. Of<br />
course, we understand that in countries where high<br />
or volatile inflation is a new experience, MFIs may<br />
find it difficult to pass on <strong>the</strong> full cost of inflation to<br />
clients. We are not recommending policy; ra<strong>the</strong>r,<br />
we are trying to provide a common analytical<br />
framework that compares real financial<br />
performance meaningfully.<br />
Subsidies<br />
We adjust participating organizations’ financial<br />
statements for <strong>the</strong> effect of subsidies by<br />
representing <strong>the</strong> MFI as it would look on an<br />
unsubsidized basis. We do not intend to suggest<br />
whe<strong>the</strong>r MFIs should or should not be subsidized.<br />
Ra<strong>the</strong>r, this adjustment permits <strong>the</strong> Bulletin to see<br />
how each MFI would look without subsidies for<br />
comparative purposes. Most of <strong>the</strong> participating<br />
MFIs indicate a desire to grow beyond <strong>the</strong><br />
limitations imposed by subsidized funding. The<br />
subsidy adjustment permits an MFI to judge<br />
whe<strong>the</strong>r it is on track toward such an outcome. A<br />
16<br />
In fact, an institution that holds fixed assets equal to its equity<br />
avoids <strong>the</strong> cost of inflation that affects MFIs, which hold much of<br />
<strong>the</strong>ir equity in financial form.<br />
focus on sustainable expansion suggests that<br />
subsidies should be used to enhance financial<br />
returns. The subsidy adjustment simply indicates<br />
<strong>the</strong> extent to which <strong>the</strong> subsidy is being passed on<br />
to clients through lower interest rates or whe<strong>the</strong>r it<br />
is building <strong>the</strong> MFI’s capital base for fur<strong>the</strong>r<br />
expansion.<br />
The Bulletin adjusts for three types of subsidies: (1)<br />
a cost-of-funds subsidy from loans at below-market<br />
rates, (2) current-year cash donations to fund<br />
portfolio and cover expenses, and (3) in-kind<br />
subsidies, such as rent-free office space or <strong>the</strong><br />
services of personnel who are not paid by <strong>the</strong> MFI<br />
and thus not reflected on its income statement.<br />
Additionally, for multipurpose institutions, The<br />
MicroBanking Bulletin attempts to isolate <strong>the</strong><br />
performance of <strong>the</strong> financial services program,<br />
removing <strong>the</strong> effect of any cross subsidization.<br />
The cost-of-funds adjustment reflects <strong>the</strong> impact of<br />
soft loans on <strong>the</strong> financial performance of <strong>the</strong><br />
institution. The Bulletin calculates <strong>the</strong> difference<br />
between what <strong>the</strong> MFI actually paid in interest on its<br />
subsidized liabilities and <strong>the</strong> deposit rate for each<br />
country. 17 This difference represents <strong>the</strong> value of<br />
<strong>the</strong> subsidy, which we treat as an additional<br />
financial expense. We apply this subsidy to those<br />
loans to <strong>the</strong> MFI that are priced at less than 75<br />
percent of prevailing market (deposit) rates. The<br />
decreased profit is offset by generating an<br />
“accumulated subsidy adjustment” account on <strong>the</strong><br />
balance sheet.<br />
If <strong>the</strong> MFI passes on <strong>the</strong> interest rate subsidy to its<br />
clients through a lower final rate of interest, this<br />
adjustment may result in an operating loss. If <strong>the</strong><br />
MFI does not pass on this subsidy, but instead uses<br />
it to increase its equity base, <strong>the</strong> adjustment<br />
indicates <strong>the</strong> amount of <strong>the</strong> institution’s profits that<br />
were attributable to <strong>the</strong> subsidy ra<strong>the</strong>r than<br />
operations.<br />
Loan Loss Provisioning<br />
Finally, we apply standardized policies for loan loss<br />
provisioning and write-off. MFIs vary tremendously<br />
in accounting for loan delinquency. Some count <strong>the</strong><br />
entire loan balance as overdue <strong>the</strong> day a payment<br />
is missed. O<strong>the</strong>rs do not consider a loan delinquent<br />
17<br />
Data for shadow interest rates are obtained from line 60l of <strong>the</strong><br />
International Financial Statistics, IMF, various years. The<br />
deposit rate is used because it is a published benchmark in most<br />
countries. Sound arguments can be made for use of different<br />
shadow interest rates. NGOs that wish to borrow from banks<br />
would face interest significantly higher than <strong>the</strong> deposit rate. A<br />
licensed MFI, on <strong>the</strong> o<strong>the</strong>r hand, might mobilize savings at a<br />
lower financial cost than <strong>the</strong> deposit rate, but reserve<br />
requirements and administrative costs would drive up <strong>the</strong> actual<br />
cost of such liabilities.<br />
74 MICROBANKING BULLETIN, APRIL 2001
APPENDICES<br />
until its full term has expired. Some MFIs write off<br />
bad debt within one year of <strong>the</strong> initial delinquency,<br />
while o<strong>the</strong>rs never write off bad loans, thus carrying<br />
forward a hard-core default that <strong>the</strong>y have little<br />
chance of ever recovering.<br />
We classify as “at risk” any loan with a payment<br />
over 90 days late. We provision 50 percent of <strong>the</strong><br />
outstanding balance for loans between 90 and 180<br />
days late, and 100 percent for loans over 180 days<br />
late. Wherever we have adequate information, we<br />
adjust to assure that all loans are fully written off<br />
within one year of <strong>the</strong>ir becoming delinquent.<br />
(Note: We apply <strong>the</strong>se provisioning and write-off<br />
policies for ease of use and uniformity. We do not<br />
recommend that all MFIs use exactly <strong>the</strong> same<br />
policies.) In most cases, <strong>the</strong>se adjustments are not<br />
very precise. Never<strong>the</strong>less, most participating MFIs<br />
have high-quality loan portfolios, so loan loss<br />
provision expense is not an important contributor to<br />
<strong>the</strong>ir overall cost structure. If we felt that a program<br />
did not fairly represent its general level of<br />
delinquency, and we were unable to adjust it<br />
accordingly, we would simply exclude it from <strong>the</strong><br />
peer group.<br />
Financial Statement Adjustments and <strong>the</strong>ir Effects<br />
Adjustment Effect on Financial Statements Type of Institution Most Affected<br />
by Adjustment<br />
Inflation adjustment of equity<br />
Reclassification of certain long term<br />
liabilities into equity, and subsequent<br />
inflation adjustment<br />
Subsidy adjustment: Interest savings<br />
on subsidized liabilities involving at<br />
least a 25 percent discount in relation<br />
to market based loans to <strong>the</strong> same<br />
institution or, in <strong>the</strong> absence of such<br />
loans, <strong>the</strong> deposit rate<br />
Subsidy adjustment: Current-year<br />
cash donations to cover operating<br />
expenses<br />
Subsidy adjustment: In kind donation<br />
of goods or services (e.g., line staff<br />
paid for by technical assistance<br />
providers)<br />
Loan loss provision and write-off<br />
adjustment: Applying policies which<br />
may be more aggressive than <strong>the</strong> MFI<br />
employs on its own books<br />
Increases financial expense accounts<br />
on profit and loss statement, to some<br />
degree offset by inflation income<br />
account for revaluation of fixed assets.<br />
Generates inflation adjustment account<br />
in equity section of balance sheet with<br />
net balance of inflation adjustments.<br />
Decreases concessionary loan account<br />
and increases equity account;<br />
increases inflation adjustment on profit<br />
and loss statement and balance sheet.<br />
Increases financial expense on profit<br />
and loss statement. Increases subsidy<br />
adjustment account on balance sheet.<br />
Reduces operating income on profit and<br />
loss statement (if <strong>the</strong> MFI records<br />
donations as operating income).<br />
Increases subsidy adjustment account<br />
on balance sheet.<br />
Increases expense on profit and loss<br />
statement, increases subsidy<br />
adjustment account on balance sheet.<br />
Increases loan loss provision expense<br />
on profit and loss statement. On<br />
balance sheet, increases in loan loss<br />
reserve and/or write-offs are accounted<br />
for by equal reductions in loan loss<br />
reserve and portfolio.<br />
NGOs funded more by equity than<br />
by liabilities will be hard hit,<br />
especially in high-inflation<br />
countries.<br />
NGOs that have long-term lowinterest<br />
“loans” from international<br />
agencies that function more as<br />
donations than loans.<br />
Banks or NGOs that use large lines<br />
of credit from governments or<br />
international agencies at highly<br />
subsidized rates.<br />
NGOs during <strong>the</strong>ir start-up phase.<br />
This adjustment is relatively less<br />
important for mature institutions<br />
included in this edition.<br />
NGOs during <strong>the</strong>ir start-up phase.<br />
Less important for mature<br />
institutions included in this edition.<br />
MFIs that allow bad loans to<br />
accumulate within <strong>the</strong>ir portfolio.<br />
This common problem tends to<br />
have a limited effect on leading<br />
MFIs because <strong>the</strong>ir loan losses are<br />
low, even after adjustment.<br />
MICROBANKING BULLETIN, APRIL 2001 75
APPENDICES<br />
Statistical Issues<br />
The Bulletin reports <strong>the</strong> means and standard<br />
deviations of <strong>the</strong> performance indicators for each<br />
peer group. At this stage, peer groups are still<br />
small and <strong>the</strong> observations in each peer group<br />
show a high variation. Outliers distort <strong>the</strong> results of<br />
some of <strong>the</strong> peer group averages. Consequently,<br />
<strong>the</strong> reader should be cautious about <strong>the</strong> interpretive<br />
power of <strong>the</strong>se data. Over time, as more MFIs<br />
provide data, we will be in a better position to<br />
generate deeper and more sophisticated types of<br />
analyses of <strong>the</strong> data at our disposal, and will have a<br />
higher degree of comfort with <strong>the</strong> statistical<br />
significance of <strong>the</strong> differences between <strong>the</strong> means<br />
of <strong>the</strong> distinct peer groups.<br />
To ensure that <strong>the</strong> averages reported represent <strong>the</strong><br />
group as accurately as possible, we have excluded<br />
outliers for each of <strong>the</strong> indicators. Statistics for <strong>the</strong><br />
category All MFIs were calculated by deleting<br />
observations in <strong>the</strong> first and last deciles for each<br />
indicator. In o<strong>the</strong>r words, <strong>the</strong> values between <strong>the</strong><br />
11th and 89th percentiles were used for <strong>the</strong><br />
analysis. For <strong>the</strong> FSS sample and peer group<br />
calculations, <strong>the</strong> first and last percentile<br />
observations were excluded for each indicator<br />
except macroeconomic indicators. The averages<br />
are calculated on <strong>the</strong> basis of <strong>the</strong> values between<br />
<strong>the</strong> 2nd and <strong>the</strong> 99th percentiles for each group. In<br />
effect, for each indicator we rank <strong>the</strong> MFIs in <strong>the</strong><br />
group and eliminate <strong>the</strong> top and bottom values. In<br />
most cases, this exclusion eliminates two<br />
observations for each peer group: <strong>the</strong> institution<br />
with <strong>the</strong> highest and <strong>the</strong> lowest value on each<br />
indicator. In cases where indicators contain<br />
observations with tied values for highest and lowest<br />
values, more than two observations are deleted.<br />
This method helps to prevent outliers from<br />
dominating group results, and smoo<strong>the</strong>s <strong>the</strong> data by<br />
minimizing data dispersion. Where <strong>the</strong> sample size<br />
is reduced to n=1, we have not reported <strong>the</strong> result<br />
so as to maintain confidentiality.<br />
We have carried out statistical tests to determine<br />
<strong>the</strong> impact of outliers where <strong>the</strong>y exist, and to<br />
quantify <strong>the</strong> results in terms of how well <strong>the</strong>y<br />
represent <strong>the</strong> peer groups. Where large differences<br />
exist between <strong>the</strong> means of different peer groups or<br />
groups sorted by selection criteria, we have verified<br />
<strong>the</strong>ir statistical significance using t-tests. These<br />
tests compare <strong>the</strong> mean of <strong>the</strong> group to <strong>the</strong> mean of<br />
all MFIs in <strong>the</strong> sample, taking into account factors<br />
like <strong>the</strong> number of observations and <strong>the</strong> dispersion<br />
of <strong>the</strong> sample. The test statistic is <strong>the</strong>n compared<br />
to a standard critical level (using one percent as <strong>the</strong><br />
significance level) to decide whe<strong>the</strong>r <strong>the</strong> difference<br />
between <strong>the</strong> group and <strong>the</strong> sample as a whole is<br />
statistically significant. In o<strong>the</strong>r words, <strong>the</strong>y allow<br />
us to decide whe<strong>the</strong>r <strong>the</strong> difference we see is<br />
robust, by considering it in <strong>the</strong> context of how<br />
cohesive and how large <strong>the</strong> group is.<br />
76 MICROBANKING BULLETIN, APRIL 2001
APPENDICES<br />
Appendix II: Description of Participating MFIs<br />
ACRONYM NAME, LOCATION DATE<br />
15 de Abril Cooperativa 15 de Abril,<br />
Ecuador<br />
23 de Julio Cooperativa 23 de Julio,<br />
Ecuador<br />
ABA<br />
ACEP<br />
ACLEDA<br />
ACODEP<br />
Acredicom<br />
Actuar<br />
ADOPEM<br />
ADRI<br />
AGAPE<br />
Agrocapital<br />
AKRSP<br />
Al Majmoua<br />
Al Amana<br />
Alexandria Business<br />
Association,<br />
Egypt<br />
Agence de Crédit pour<br />
l’Enterprise Privée,<br />
Senegal<br />
Association of Cambodian<br />
Local Economic<br />
Development Agencies,<br />
Cambodia<br />
Asociación de Consultores<br />
para el Desarrollo de la<br />
Pequeña, Mediana y<br />
Microempresa,<br />
Nicaragua<br />
Acredicom,<br />
Guatemala<br />
Corporación Acción por el<br />
Tolima - Actuar<br />
Famiempresas,<br />
Colombia<br />
Asociación Dominicana<br />
para el Desarrollo de la<br />
Mujer,<br />
Dominican Republic<br />
Asociación para el<br />
Desarrollo Rural Integrado,<br />
Costa Rica<br />
Asociación General para<br />
Asesorar Pequeñas<br />
Empresas,<br />
Colombia<br />
Fundación Agrocapital,<br />
Bolivia<br />
Aga Khan Rural Support<br />
Programme,<br />
Pakistan<br />
Lebanese Association for<br />
Development -- Al<br />
Majmoua,<br />
Lebanon<br />
Association Al Amana,<br />
Morocco<br />
DATA<br />
QUALITY<br />
GRADE<br />
DESCRIPTION OF MICROFINANCE PROGRAM<br />
09/00 A 15 de Abril is a credit union in Ecuador that has participated in<br />
WOCCU’s technical assistance program since in 1995. 15 de Abril<br />
offers both credit and voluntary savings services to members.<br />
09/00 A 23 de Julio participates in WOCCU’s technical assistance program in<br />
Ecuador. It is a credit union offering credit and savings services to<br />
members.<br />
12/99 AAA ABA provides credit to small and microenterprises using an<br />
individual lending methodology. It is an NGO founded in 1988 and<br />
based primarily in urban areas. The credit program began in 1990.<br />
12/99 B ACEP began as an NGO in a provincial town in 1987 and has<br />
expanded to operate in o<strong>the</strong>r urban areas in Senegal. It has<br />
converted to a credit union.<br />
12/99 AAA ACLEDA was started in 1993 as an NGO. It provides small and<br />
micro loans to enterprises and trains entrepreneurs in small<br />
business management. Both group and individual loans are made.<br />
12/99 A Founded in 1989, ACODEP serves small and micronterprises<br />
primarily in Managua and o<strong>the</strong>r urban areas of Nicaragua. It is<br />
currently negotiating a voluntary supervision agreement with <strong>the</strong><br />
Superintendent of Banks in Nicaragua.<br />
09/00 A ACREDICOM is a member of <strong>the</strong> FENACOAC credit union system in<br />
Guatemala, and participated in WOCCU’s technical assistance<br />
program. It primarily lends for agriculture and to a lesser extent<br />
microenterprise activities, and mobilizes savings from members.<br />
12/99 B ACTUAR Tolima was founded in 1986. It is an NGO offering loans<br />
to microenterprises in Tolima and surrounding areas, and is affiliated<br />
with ACCION International and Cooperativa Emprender in Colombia.<br />
12/99 A ADOPEM, an affiliate of Women’s World Banking, is an NGO<br />
dedicated to credit for women microentrepreneurs. It has been in<br />
operation since 1982.<br />
12/99 A ADRI is an NGO offering loans to small and microenterprises in<br />
Costa Rica. Founded in 1986, it also offers training and business<br />
development services to its clients.<br />
12/99 A Founded in 1975, AGAPE operates principally in Barranquilla,<br />
offering microcredit through a mixture of methodologies including<br />
village banking, solidarity groups and individual loans. It is an<br />
affiliate of Opportunity International.<br />
12/99 AAA Fundación Agrocapital focuses its services on agriculture and agroindustry,<br />
working mainly in rural and small urban areas of Bolivia. It<br />
is an NGO founded in 1992, and offers a mixture of microloans and<br />
longer term mortgage loans.<br />
12/98 A AKRSP is a multi-service NGO that works in <strong>the</strong> “Roof of <strong>the</strong> World”<br />
region of nor<strong>the</strong>rn Pakistan. Its credit program began in 1983,<br />
offering loans through its network of village organizations.<br />
12/99 A Al Majmoua is a Lebanese NGO, offering village banking-type<br />
services in both urban and rural areas. The program began<br />
operations in 1994 as a project of Save <strong>the</strong> Children. Ownership<br />
was transferred to <strong>the</strong> Lebanese institution in 1998.<br />
06/00 AAA Al Amana offers solidarity group loans through a wide network of<br />
branches in urban areas of Morocco. Founded in 1997, it is an<br />
affiliate of Pride Vita.<br />
MICROBANKING BULLETIN, APRIL 2001 77
APPENDICES<br />
ACRONYM NAME, LOCATION DATE<br />
AMK<br />
ARB<br />
ASA<br />
BAAC<br />
Banco Ademi<br />
Banco do<br />
Povo<br />
BancoSol<br />
Basix<br />
BDB<br />
Bospo<br />
BPE<br />
BRAC<br />
BRI<br />
BURO<br />
Tangail<br />
Caja de Los<br />
Andes<br />
AMK Posusje,<br />
Bosnia and Herzegovina<br />
Asawinso Rural Bank,<br />
Ghana<br />
Association for Social<br />
Advancement,<br />
Bangladesh<br />
Bank for Agriculture and<br />
Agricultural Cooperatives,<br />
Thailand<br />
Banco de Desarrollo<br />
Ademi, S.A.,<br />
Dominican Republic<br />
Banco do Povo de Juiz de<br />
Fora,<br />
Brazil<br />
Banco Solidario, S.A.,<br />
Bolivia<br />
Bharatiya Samruddhi<br />
Finance Ltd.,<br />
India<br />
Bank Dagang Bali,<br />
Indonesia<br />
Bospo,<br />
Bosnia and Herzegovina<br />
Banco de la Pequeña<br />
Empresa, S.A.,<br />
Dominican Republic<br />
Bangladesh Rural<br />
Advancement Committee,<br />
Bangladesh<br />
Bank Rakyat Indonesia,<br />
Unit Desa System,<br />
Indonesia<br />
BURO, Tangail,<br />
Bangladesh<br />
Caja de Ahorros y Créditos<br />
Los Andes,<br />
Bolivia<br />
DATA<br />
QUALITY<br />
GRADE<br />
DESCRIPTION OF MICROFINANCE PROGRAM<br />
12/99 B AMK is a limited liability company founded in 1997 to provide<br />
microcredit to low income self-employed individuals in urban areas.<br />
It is financed by <strong>the</strong> Local Initiatives Department In Bosnia that aims<br />
to improve access to credit to <strong>the</strong> poor to promote economic<br />
reconstruction.<br />
12/99 A The rural bank was started in 1983 and it now provides group and<br />
individual loans, and deposit services to farmers, microentrepreneurs<br />
and civil servants in rural Ghana.<br />
12/99 AAA ASA is an NGO that offers credit services to <strong>the</strong> rural poor in<br />
Bangladesh. The majority of its clients are landless women. It was<br />
founded in 1978 and shifted from an earlier, integrated development<br />
strategy to its current focus on financial services in <strong>the</strong> early 1990s.<br />
It uses a village level group lending methodology.<br />
03/99 A BAAC is a government-owned agricultural bank that lends to small<br />
farmers and farmers’ cooperatives. Founded in 1966, its outreach in<br />
rural areas of Thailand is now estimated to cover more than 80% of<br />
farm families.<br />
12/99 A Banco ADEMI is a formal financial institution, which began<br />
operations in 1998. The bank is <strong>the</strong> successor to <strong>the</strong> NGO, ADEMI,<br />
which was involved in microcredit since 1982.<br />
12/99 AAA Banco do Povo de Juiz de Fora is an NGO operating in Juiz de Fora<br />
in Brazil. It offers individual loans to microentrepreneurs and was<br />
founded in1997. It was formerly known as FAEP.<br />
12/99 A BancoSol is a licensed commercial bank devoted to microfinance,<br />
offering microenterprise credit and passbook savings. Its credit<br />
program focuses on group loans, and it operates primarily in urban<br />
areas of Bolivia. It grew out of <strong>the</strong> NGO PRODEM and was spun off<br />
as a bank in 1992. It is an affiliate of ACCION International.<br />
03/00 AAA BASIX was set up as a non-bank in 1996 to provide financial<br />
services to <strong>the</strong> rural poor, to promote self-employment, and to<br />
provide technical assistance to clients and rural financial institutions.<br />
12/98 AAA Bank Dagang is a private commercial bank that offers savings and<br />
credit facilities to primarily low-income clients in Bali. It was founded<br />
in 1970.<br />
12/99 B BOSPO is a NGO founded in 1995 to provide microcredit to solidarity<br />
groups made of low income women entrepreneurs in secondary<br />
cities of Tuzla. It is financed by <strong>the</strong> Local Initiatives Department in<br />
Bosnia that aims to improve access to credit to <strong>the</strong> poor to promote<br />
economic reconstruction.<br />
12/98 AAA Banco de la Pequeña Empresa was created to serve both<br />
microenterprises and small businesses, and has just completed its<br />
first year of operations. It s a formal financial sector institution and<br />
holds a license to operate as a development bank. It is an affiliate of<br />
ACCION International.<br />
12/99 AAA BRAC is an NGO that started in 1972. It provides both financial and<br />
non-financial services primarily in rural areas. The financial services<br />
include <strong>the</strong> provision of microloans and mobilization of savings.<br />
12/99 AAA BRI is a government-owned bank oriented towards rural areas,<br />
which has operated since 1897. The Unit Desa system is an<br />
extensive network of small banking units, which function as profit<br />
centers and provide individual loans and savings services. The<br />
system has existed in its current form since 1984.<br />
12/99 AAA Flexible voluntary open-savings, microloans and insurance services<br />
are provided by BURO Tangail since 1990. It is an NGO.<br />
12/99 A Caja Los Andes grew out of ProCrédito, an NGO that began lending<br />
operations in 1992. It was converted to a special finance company in<br />
1995. Los Andes operates in urban and some rural areas in Bolivia,<br />
providing individual loans and savings services.<br />
78 MICROBANKING BULLETIN, APRIL 2001
APPENDICES<br />
ACRONYM NAME, LOCATION DATE<br />
Calpiá<br />
CAM<br />
CARD<br />
CDS<br />
Financiera Calpiá, S.A.,<br />
El Salvador<br />
Centro de Apoyo a la<br />
Microempresa,<br />
El Salvador<br />
Center for Agriculture and<br />
Rural Development,<br />
The Philippines<br />
Community Development<br />
Society,<br />
India<br />
DATA<br />
QUALITY<br />
GRADE<br />
DESCRIPTION OF MICROFINANCE PROGRAM<br />
12/98 AAA Financiera Calpiá began as an NGO, AMPES, and was converted<br />
into a finance company in 1995. It offers individual loans to<br />
microenterprises and small businesses and has started to mobilize<br />
savings. It operates mainly in urban areas, although 25% of its<br />
portfolio is now in rural areas.<br />
12/99 B FINCA’s affiliate in El Salvador, <strong>the</strong> CAM was founded in 1990 and is<br />
one of FINCA’s largest affiliates serving over 16,000 clients in all 15<br />
geographic departamentos in El Salvador.<br />
12/99 A CARD started as an NGO in 1986 and is now partially transformed<br />
into a rural bank. It is an affiliate of CASHPOR and Women’s World<br />
Banking. It makes loans and collects deposits.<br />
03/99 A CDS offers microcredit and non-financial services in <strong>the</strong> Nagpur<br />
region of India. It was founded in 1985 and is an affiliate of<br />
Opportunity International.<br />
CEAPE/PE<br />
CERUDEB<br />
Citi S&L<br />
Chispa<br />
Chuimequená<br />
CM Arequipa<br />
CMM/Medellín<br />
Compartamos<br />
Constanta<br />
Contigo<br />
COOSAJO<br />
Crecer<br />
Centro de Apoio aos<br />
Pequeños Empreendimentos<br />
Pernambuco,<br />
Brazil<br />
Centenary Rural<br />
Development Bank,<br />
Uganda<br />
Citi Savings & Loans,<br />
Ghana<br />
Fundación Chispa,<br />
Nicaragua<br />
Cooperativa San Miguel<br />
Chuimequená,<br />
Guatemala<br />
Cajas Municipales de<br />
Arequipa,<br />
Peru<br />
Corporación Mundial de la<br />
Mujer Medellín, Medellín,<br />
Colombia<br />
Asociación Programa<br />
Compartamos, I.A.P.,<br />
Mexico<br />
Constanta,<br />
Georgia<br />
Fundación Contigo,<br />
Chile<br />
Cooperativa San José<br />
Obrero,<br />
Guatemala<br />
Crecer,<br />
Bolivia<br />
12/99 A CEAPE Pernambuco is an urban-based microenterprise credit<br />
program. A member of <strong>the</strong> FENAPE network in Brazil, and of<br />
ACCION International, it was founded in 1992.<br />
12/99 A CERUDEB was founded as a trust company in 1983, and obtained<br />
its banking license in 1992. It received technical assistance from<br />
IPC from 1993-98. CERUDEB provides credit and savings services<br />
in Kampala and Uganda’s district towns.<br />
12/99 B Citi Savings is a private non-bank financial institution that operates in<br />
Greater Accra, Ghana. It lends to rotating savings and credit<br />
associations (susu clubs) and informal savings collectors, and<br />
mobilizes savings from <strong>the</strong> public.<br />
12/99 B Founded in 1991, CHISPA works primarily in urban areas of<br />
Nicaragua. It is affiliated with <strong>the</strong> Mennonite Economic Development<br />
Association (MEDA).<br />
09/00 A San Miguel Chuimequená is a Guatemalan credit union. It is a<br />
member of <strong>the</strong> FENACOAC system and it participates in WOCCU’s<br />
technical assistance program. It offers loans and savings services to<br />
its members.<br />
12/99 A The municipal savings and credit banks of Peru are owned by city<br />
governments. Arequipa is one of <strong>the</strong> largest and most successful<br />
banks of <strong>the</strong> national network, and offers pawn and microenterprise<br />
loans as well as savings products.<br />
12/99 A CMM Medellín is affiliated to <strong>the</strong> Women’s World Banking network,<br />
and operates in Medellín and surrounding areas. It was founded in<br />
1985 and lends to both men and women.<br />
12/99 B Compartamos is <strong>the</strong> lending arm of Gente Nueva, a Mexican NGO<br />
that was founded in 1985. The program uses a village banking<br />
methodology focusing on women, in rural and semi-urban areas of<br />
Mexico. It began lending in 1990.<br />
12/99 A Constanta was established in 1997 with a grant from UNHCR/Save<br />
<strong>the</strong> Children as a local NGO to provide group loans to poor selfemployed<br />
women.<br />
12/99 A CONTIGO began lending operations in 1989, and offers credit<br />
services to microentrepreneurs in communities in <strong>the</strong> south of<br />
Santiago de Chile.<br />
09/00 A San José Obrero is a member of <strong>the</strong> FENACOAC credit union<br />
federation, and participated in WOCCU’s technical assistance<br />
program in Guatemala. It offers loans and savings services to its<br />
members.<br />
12/99 AAA CRECER is an NGO working primarily in rural areas of Bolivia. It<br />
participates in Freedom from Hunger’s “Credit with Education”<br />
program, using a village banking methodology.<br />
MICROBANKING BULLETIN, APRIL 2001 79
APPENDICES<br />
ACRONYM NAME, LOCATION DATE<br />
DATA<br />
QUALITY<br />
GRADE<br />
DESCRIPTION OF MICROFINANCE PROGRAM<br />
Ecosaba<br />
Ecosaba,<br />
Guatemala<br />
Emprender Fundación Emprender,<br />
Argentina<br />
EMT Ennathian Moulethan<br />
Tchonnebat,<br />
Cambodia<br />
Enlace<br />
FAMA<br />
Faten<br />
Faulu<br />
FED<br />
FEFAD<br />
FIE<br />
Finamérica<br />
FINCA EC<br />
FINCA HO<br />
FINCA KY<br />
FINCA MA<br />
FINCA MX<br />
FINCA PE<br />
FINCA NI<br />
FINCA TZ<br />
Programa Enlace, Banco<br />
Solidario,<br />
Ecuador<br />
Fundación de Apoyo a la<br />
Microempresa,<br />
Nicaragua<br />
Palestine for Credit and<br />
Development,<br />
West Bank and Gaza<br />
Food for <strong>the</strong> Hungry<br />
International,<br />
Uganda<br />
Fundación Ecuatoriana de<br />
Desarrollo,<br />
Ecuador<br />
Foundation for Enterprise<br />
Finance and Development,<br />
Albania<br />
FFP - Fomento a<br />
Iniciativas Económicas,<br />
S.A.,<br />
Bolivia<br />
Financiera América, S.A.,<br />
Colombia<br />
FINCA Ecuador,<br />
Ecuador<br />
FINCA Honduras,<br />
Honduras<br />
FINCA Kyrgyzstan,<br />
Kyrgyzstan<br />
FINCA Malawi,<br />
Malawi<br />
FINCA México,<br />
Mexico<br />
FINCA Perú,<br />
Peru<br />
FINCA Nicaragua,<br />
Nicaragua<br />
FINCA Tanzania<br />
Tanzania<br />
09/00 A ECOSABA is a member of <strong>the</strong> FENACOAC credit union federation,<br />
and participated in WOCCU’s technical assistance program in<br />
Guatemala. It offers loans and savings services to its members.<br />
04/00 A Emprender, founded in 1992, is an ACCION affiliate that offers<br />
microenterprise credit in urban areas of Argentina. The majority of its<br />
lending is to solidarity groups.<br />
12/99 A EMT was founded in 1991 as a rural credit project run by <strong>the</strong> French<br />
agency, GRET. It is in <strong>the</strong> process of transformation to an<br />
independent Institution, and operates in rural areas in <strong>the</strong> south of<br />
Cambodia. It offers individual and solidarity group loans.<br />
09/99 B ENLACE is <strong>the</strong> microfinance division of Banco Solidario in Ecuador.<br />
The program was founded in 1995, and Banco Solidario receives<br />
technical assistance from ACCION International. ENLACE offers<br />
both credit and savings services to microentrepreneurs. It also<br />
administers a pawn-lending product.<br />
12/99 A FAMA operates mainly in urban areas of Nicaragua, providing<br />
microenterprise credit. It was founded in 1991 and is affiliated with<br />
ACCION.<br />
12/99 A FATEN was initiated as a Save <strong>the</strong> Children affiliate in 1995 and<br />
spun-off as an independent NGO in 1999. It provides microcredit to<br />
poor women entrepreneurs using group methodology.<br />
12/99 B Founded in 1995 as an affiliate of Food for <strong>the</strong> Hungry International,<br />
FAULU provides group based credit and voluntary deposit services<br />
to small and microentrepreneurs in urban and semi-urban areas.<br />
12/99 A Founded over 30 years ago, FED has an extensive branch network<br />
throughout Ecuador providing individual microloans. It is an affiliate<br />
of ACCION International.<br />
12/99 A Operating mainly in urban areas of Albania, FEFAD offers small<br />
business loans. It was founded in 1995 as an initiative of <strong>the</strong><br />
Albanian and German governments, and receives technical<br />
assistance from IPC.<br />
12/99 A FFP - FIE is a for-profit financial institution offering individual loans to<br />
microenterprises in urban areas of Bolivia. It began lending in 1988<br />
as an NGO, and began operating as a “Private Financial Fund” in<br />
1998 under regulation by <strong>the</strong> Bolivian Superintendency of Banks.<br />
12/99 AAA Finamérica is a regulated finance company operating in Bogotá and<br />
surrounding areas. Its predecessors were <strong>the</strong> NGO Actuar Bogotá,<br />
founded in 1988, <strong>the</strong> NGO Corposol, and <strong>the</strong> financiera Finansol. It<br />
is an affiliate of ACCION International.<br />
12/99 B FINCA Ecuador was founded in 1994 and provides village banking<br />
services to low-income families in three regions of <strong>the</strong> country:<br />
Pichincha, Guayas, and Imbabura.<br />
12/99 B FINCA Honduras is one of <strong>the</strong> largest FINCA affiliates in terms of<br />
portfolio size. It was founded in 1989 and operates in 13 of <strong>the</strong> 18<br />
departamentos of Honduras.<br />
08/00 B Founded in 1995, FINCA Kyrgyzstan is operating in five of <strong>the</strong> six<br />
oblasts of Kyrgyzstan and offers both village banking and individual<br />
loan products to 10,000 clients.<br />
08/99 A FINCA Malawi works with women in <strong>the</strong> country’s sou<strong>the</strong>rn region,<br />
and has been in operation since 1994.<br />
12/99 B FINCA Mexico currently operates village banking groups in <strong>the</strong> state<br />
of Morelos. It was founded in 1989.<br />
12/99 B FINCA Perú is primarily based in rural areas, offering<br />
microenterprise credit to borrowers in Lima, Ayacucho, and<br />
Huancavelica. It was founded in 1993.<br />
06/99 A FINCA’s Nicaraguan affiliate began lending in 1992, and has since<br />
expanded to have branch offices in several urban areas in<br />
Nicaragua.<br />
08/00 B The MFI was formed in 1998 as an affiliate of FINCA International. It<br />
provides loans through village banks.<br />
80 MICROBANKING BULLETIN, APRIL 2001
APPENDICES<br />
ACRONYM NAME, LOCATION DATE<br />
FINCA UG<br />
Finsol<br />
FMM Popayán<br />
FOCCAS<br />
FONDECO<br />
Fundusz<br />
Mikro<br />
FWWB Cali<br />
FWWB India<br />
Hattha<br />
Kaksekar<br />
Hublag<br />
Inicjatywa<br />
Mikro<br />
Kafo Jiginew<br />
KASHF<br />
KWFT<br />
LOK<br />
MC<br />
MEB<br />
Mibanco<br />
FINCA Uganda,<br />
Uganda<br />
Financiera Solidaria S.A.,<br />
Honduras<br />
Fundación Mundo Mujer<br />
Popayán,<br />
Colombia<br />
Foundation for Credit and<br />
Community Assistance,<br />
Uganda<br />
Fondo de Desarrollo<br />
Comunal,<br />
Bolivia<br />
Fundusz Mikro,<br />
Poland<br />
Fundación Women’s World<br />
Banking Cali,<br />
Colombia<br />
Friends of WWB,<br />
India<br />
Hattha Kakesekar,<br />
Cambodia<br />
Hublag Development<br />
Finance Programme,<br />
Philippines<br />
Inicjatywa Mikro,<br />
Poland<br />
Kafo Jiginew,<br />
Mali<br />
Kash Foundation,<br />
Pakistan<br />
Kenya Women Finance<br />
Trust,<br />
Kenya<br />
LOK Sarajevo,<br />
Bosnia and Herzegovina<br />
Mercy Corps,<br />
Bosnia and Herzegovina<br />
Microenterprise Bank,<br />
Bosnia<br />
Banco de la<br />
Microempresa,<br />
Peru<br />
DATA<br />
QUALITY<br />
GRADE<br />
DESCRIPTION OF MICROFINANCE PROGRAM<br />
12/99 AAA One of FINCA’s largest programs, FINCA Uganda has been in<br />
operation since 1992. The program offers village banking services<br />
to over 16,000 women in Kampala, Jinja and Lira.<br />
12/99 B Finsol (ex. FUNADEH) works with small and microenterprises in urban<br />
areas of Honduras. It is an affiliate of ACCION International and was<br />
founded in 1985.<br />
12/99 B FMM Popayán is a Women’s World Banking affiliate working in <strong>the</strong><br />
state of Cauca in Colombia. It began lending to microenterprises in<br />
1985.<br />
12/98 B FOCCAS, an affiliate of Freedom from Hunger, operates a village<br />
banking-style program in Uganda’s district towns and villages. It is<br />
based on a credit with education model.<br />
12/99 A FONDECO is an NGO working primarily in rural areas in Bolivia. It<br />
was founded in 1995.<br />
09/99 A Fundusz Mikro began operations in 1995, and now lends to<br />
microentrepreneurs across Poland through an extensive branch<br />
network. It is a member of <strong>the</strong> MicroFinance Network.<br />
12/99 A FWWB Cali, an affiliate of Women’s World Banking, began lending in<br />
1982. It makes individual loans to urban microenterprises in Cali.<br />
03/00 AAA FWWB India lends to rural women through savings and credit<br />
groups. It was founded in 1982.<br />
06/00 AAA Hattha Kaksekar was founded in 1996. The non-profit Association<br />
offers commercial loans and agricultural credit to entrepreneurs in<br />
urban and rural areas in <strong>the</strong> North-Western and central parts of<br />
Cambodia.<br />
12/98 A The Hublag Development Finance Programme is <strong>the</strong> microlending<br />
arm of <strong>the</strong> Gerry Roxas Foundation. It lends to microenterprises with<br />
both individual and group lending methodologies, and began<br />
operations in 1987.<br />
12/99 A Inicjatywa Mikro lends to microenterprises mainly in urban areas of<br />
Poland. It is affiliated with Opportunity International.<br />
12/99 B Kafo Jiginew is a federation of credit unions operating in rural areas<br />
in <strong>the</strong> south-central region of Mali. It was founded in 1987.<br />
03/00 A KASHF is a NGO founded in 1996 to provide microcredit to low<br />
income women entrepreneurs in rural and urban areas. It is an<br />
affiliate of ASA, Bangladesh.<br />
03/00 B Started as an affiliate of Women’s World Banking in 1992, KWFT<br />
provides loans to women in six regions of Kenya. It has now grown<br />
into <strong>the</strong> largest MFI in Kenya.<br />
12/99 B LOK is a NGO founded in 1997 to provide individual credit to small<br />
entrepreneurs in urban and rural areas. It is financed by <strong>the</strong> Local<br />
Initiatives Department that aims to improve access to credit to <strong>the</strong><br />
poor to promote economic reconstruction.<br />
12/00 B MC is an NGO that started its operation in 1997 and provides<br />
individual credit to microenterprises in war affected areas. Among<br />
o<strong>the</strong>rs, it is also financed by <strong>the</strong> Local Initiatives Department in<br />
Bosnia that aims to improve access to credit to <strong>the</strong> poor to promote<br />
economic reconstruction.<br />
12/99 A The Microenterprise Bank was launched by IPC in 1997 to provide<br />
financial services such as loans, money transfers and deposit<br />
services to micro and small enterprises in Bosnia-Herzegovina.<br />
12/99 A Mibanco is a commercial microfinance bank offering microenterprise<br />
credit in Lima, and is affiliated with ACCION International. Formerly<br />
operated as an NGO under <strong>the</strong> name Acción Comunitaria del Perú,<br />
<strong>the</strong> institution was transformed into a bank in 1998.<br />
MICROBANKING BULLETIN, APRIL 2001 81
APPENDICES<br />
ACRONYM NAME, LOCATION DATE<br />
Microfund<br />
for Women<br />
Mikrofin<br />
Moyután<br />
MKRB<br />
Moznosti<br />
Nachala<br />
NLC<br />
Nirdhan<br />
NOA<br />
NRB<br />
Nyésigiso<br />
Oscus<br />
PADME<br />
PAMÉCAS<br />
Piyeli<br />
Portosol<br />
PRIDE TZ<br />
Microfund for Women,<br />
Jordan<br />
Mikrofin,<br />
Bosnia and Herzegovina<br />
Cooperativa Moyután,<br />
Guatemala<br />
Manya Krobo Rural Bank,<br />
Ghana<br />
Moznosti,<br />
Macedonia<br />
Nachala,<br />
Bulgaria<br />
Network Leasing<br />
Corporation Ltd.,<br />
Pakistan<br />
Nirdhan Utthan,<br />
Nepal<br />
NOA,<br />
Croatia<br />
Nsoatreman Rural Bank,<br />
Ghana<br />
Réseau Nyésigiso,<br />
Mali<br />
Cooperativa Oscus Ltda.,<br />
Ecuador<br />
Association pour la<br />
Promotion et l’Appui au<br />
Développement des<br />
MicroEntreprises,<br />
Benin<br />
Programme d’Appui aux<br />
Mutuelles d’Epargne et de<br />
Crédit au Sénégal,<br />
Senegal<br />
Association Piyeli,<br />
Mali<br />
Portosol,<br />
Brazil<br />
Promotion of Rural<br />
Initiatives and<br />
Development Enterprises,<br />
Tanzania<br />
DATA<br />
QUALITY<br />
GRADE<br />
DESCRIPTION OF MICROFINANCE PROGRAM<br />
12/99 A This former Save <strong>the</strong> Children village banking program in Jordan was<br />
founded in 1994. It focuses primarily on Palestinian women from<br />
squatter communities.<br />
12/99 B MIKROFIN is an affiliate of CARE international and started its<br />
operations in 1997. It provides individual and group loans to<br />
microentrepreneurs in semi-urban areas. It is financed by <strong>the</strong> Local<br />
Initiatives Department.<br />
09/00 A Moyután is a member of <strong>the</strong> FENACOAC credit union federation, and<br />
participated in WOCCU’s technical assistance program in<br />
Guatemala. It offers loans and savings services to its members.<br />
12/99 AAA Started as a rural bank in 1978, MKRB provides group and individual<br />
loans, and deposit services to farmers, micro-entrepreneurs and civil<br />
servants.<br />
12/99 A Moznosti, an affiliate of Opportunity International, began lending in<br />
1996. It operates both in urban and rural areas of Macedonia, and<br />
lends to microenterprises and small businesses.<br />
12/99 B Nachala, an affiliate of Opportunity International, converted into a<br />
cooperative in 1998. It operates both in urban and rural areas and<br />
makes individual loans to microenterprises and small businesses for<br />
working capital.<br />
06/99 A Network Leasing is a private for profit financial company which offers<br />
financial services to microentrepreneurs. It uses leasing, a<br />
methodology considered compatible with Islamic law, which forbids<br />
borrowing on interest.<br />
06/99 AAA Nirdhan is an NGO founded in 1991. It is a Grameen replicate<br />
providing credit and deposit services to <strong>the</strong> poor. Both compulsory<br />
and voluntary deposits services are offered. The NGO has<br />
transformed into Nirdhan Utthan Bank Limited in July 1999. It is a<br />
member of <strong>the</strong> CASHPOR network.<br />
12/98 B NOA, an affiliate of Opportunity International, was started in 1997 to<br />
provide individual and group loans to self employed persons in<br />
agriculture and small businesses.<br />
12/99 A The rural bank was formed in 1984 to provide credit and deposit<br />
services in Brong Ahafo region in Ghana to farmers, microentrepreneurs<br />
and civil servants.<br />
12/99 A Established in 1990 as a credit union, Nyésigiso offers credit and<br />
savings services to both men and women in urban and rural areas of<br />
Mali.<br />
09/00 A Oscus is a credit union in Ecuador, and it participates in WOCCU’s<br />
technical assistance program. Oscus offers both credit and voluntary<br />
savings services to members.<br />
12/99 A PADME is an NGO working in urban and peri-urban areas of Benin.<br />
It offers loans to small and microenterprises, and was founded in<br />
1993.<br />
12/99 A Pamécas was established as a credit union in 1996. It offers a wide<br />
range of savings and credit services, primarily to women, using<br />
individual, solidarity and village banking products in urban and periurban<br />
Senegal. It is a member of <strong>the</strong> Development International<br />
Desjardins network.<br />
12/99 B Piyeli is an Association that was created in 1995. It offers solidarity<br />
group loans to microentrepreneurs in urban and rural areas around<br />
Bamako, as well as voluntary savings.<br />
12/99 AAA Portosol is an NGO operating in Porto Alegre in Brazil. It offers<br />
individual loans to microentrepreneurs and was founded in1996.<br />
12/99 A PRIDE offers microcredit in urban and semi-urban areas of<br />
Tanzania. It was founded in 1993.<br />
82 MICROBANKING BULLETIN, APRIL 2001
APPENDICES<br />
ACRONYM NAME, LOCATION DATE<br />
PRIDE UG<br />
Pride Vita<br />
Guinea<br />
PRODEM<br />
ProEmpresa<br />
ProMujer<br />
RSPI<br />
Sagrario<br />
Sartawi<br />
SAT<br />
SEDA<br />
SEEDS<br />
SEF<br />
SHARE<br />
SUNRISE<br />
Tonantel<br />
TSPI<br />
Tulcán<br />
UNRWA<br />
Promotion of Rural<br />
Initiatives and<br />
Development Enterprises,<br />
Uganda<br />
Pride Finance Guinea,<br />
Republic of Guinea<br />
Fundación para la<br />
Promoción y Desarrollo de<br />
la Microempresa,<br />
Bolivia<br />
EDYPME ProEmpresa,<br />
Peru<br />
ProMujer,<br />
Bolivia<br />
Rangtay Sa Pagrangay<br />
Inc.,<br />
Philippines<br />
Cooperativa El Sagrario,<br />
Ltda.,<br />
Ecuador<br />
Servicio Financiero Rural,<br />
Fundación Sartawi,<br />
Bolivia<br />
Sinapi Aba Trust,<br />
Ghana<br />
Small Enterprise<br />
Development Agency,<br />
Tanzania<br />
Sarvodaya Economic<br />
Enterprises Development<br />
Society,<br />
Sri Lanka<br />
Small Enterprise<br />
Foundation,<br />
South Africa<br />
Society for Helping<br />
Awakening Rural poor<br />
through Education,<br />
India<br />
SUNRISE Sarajevo<br />
Bosnia and Herzegovina<br />
Cooperativa Tonantel,<br />
Guatemala<br />
TSPI Development<br />
Corporation,<br />
Philippines<br />
Cooperativa Tulcán, Ltda.,<br />
Ecuador<br />
United Nations Relief<br />
Works Agency,<br />
Gaza<br />
DATA<br />
QUALITY<br />
GRADE<br />
DESCRIPTION OF MICROFINANCE PROGRAM<br />
12/98 A PRIDE in Uganda was started in 1996. It provides microloans to<br />
borrowers organized as groups in urban and semi-urban areas of<br />
Uganda.<br />
12/98 AAA Pride Vita (or Pride Finance) works primarily in urban and semiurban<br />
areas of Guinea and was founded in 1991.<br />
12/99 B PRODEM began in 1986 as an NGO offering group loans to urban<br />
microenterprises, and was <strong>the</strong> precursor to BancoSol. When its<br />
urban portfolio was passed to BancoSol in 1992, it began to develop<br />
a new clientele in rural areas in Bolivia.<br />
12/99 A ProEmpresa, formerly <strong>the</strong> IDESI network, is now operating as a<br />
formal financial institution in Peru.<br />
12/99 A ProMujer Bolivia was founded in 1991, to provide training and credit<br />
to predominantly women clients.<br />
12/98 A RSPI, an Opportunity International partner, lends primarily to selfhelp<br />
groups in <strong>the</strong> Cordillera and Iloco regions of <strong>the</strong> Philippines.<br />
09/00 A El Sagrario is a credit union in Ecuador, and participates in<br />
WOCCU’s technical assistance program, begun in 1995. It offers<br />
both credit and voluntary savings services to members.<br />
12/98 A Fundación Sartawi offers group credit to producers and o<strong>the</strong>r<br />
microenterprises in rural areas of Bolivia. The credit program has<br />
operated in its current form since 1990.<br />
12/99 A The Sinapi Aba Trust is a member of Opportunity International, and<br />
offers individual and group loans both in rural and urban areas of<br />
Ghana. It was founded in 1995.<br />
09/00 AAA SEDA was started in 1996 as an affiliate of World Vision to provide<br />
financial services to women through village banking methodology in<br />
Tanzania.<br />
03/99 B SEEDS was established in 1987 to provide loans for employment<br />
creation and increasing standard of living, to mobilize deposits<br />
through compulsory and voluntary savings programs and to provide<br />
life and natural disaster insurances.<br />
06/00 A SEF is an NGO working in <strong>the</strong> Nor<strong>the</strong>rn Province of South Africa. It<br />
works with a Grameen methodology to provide loans to rural women,<br />
and was founded in 1991.<br />
03/00 AAA SHARE lends to women in rural areas of Andhra Pradesh in India. It<br />
is a member of <strong>the</strong> CASHPOR network.<br />
12/99 B SUNRISE is a NGO founded in 1997 to provide individual credit to<br />
start-up and established micro enterprises. It is financed by <strong>the</strong><br />
Local Initiatives Department that aims to improve access to credit to<br />
<strong>the</strong> poor to promote economic reconstruction.<br />
09/00 A Tonantel is a member of <strong>the</strong> FENACOAC credit union federation,<br />
and participated in WOCCU’s technical assistance program in<br />
Guatemala. It offers loans and savings services to its members.<br />
06/99 A TSPI operates in urban and semi-urban areas of <strong>the</strong> Philippines,<br />
offering group loans to microenterprises. It was founded in 1981 and<br />
is affiliated to <strong>the</strong> Opportunity Network, <strong>the</strong> MicroFinance Network<br />
and CASHPOR, among o<strong>the</strong>rs.<br />
09/00 A Tulcán is a credit union in Ecuador, and participates in WOCCU’s<br />
technical assistance program, begun in 1995. It offers both credit<br />
and voluntary savings services to members.<br />
12/99 B The Income Generation Program of UNRWA lends to<br />
microenterprises and small businesses in Gaza. It began operations<br />
in 1991.<br />
MICROBANKING BULLETIN, APRIL 2001 83
APPENDICES<br />
ACRONYM NAME, LOCATION DATE<br />
DATA<br />
QUALITY<br />
GRADE<br />
DESCRIPTION OF MICROFINANCE PROGRAM<br />
UWFT<br />
Vital-Finance<br />
Vivacred<br />
WAGES<br />
WR Honduras<br />
WVB<br />
Uganda Women’s Finance<br />
Trust,<br />
Uganda<br />
Vital-Finance,<br />
Benin<br />
Vivacred,<br />
Brazil<br />
Women and Associations<br />
for Gain both Economic<br />
and Social,<br />
Togo<br />
World Relief Honduras,<br />
Honduras<br />
World Vision,<br />
Bosnia<br />
12/99 A Uganda Women’s Finance Trust offers solidarity group and individual<br />
loans to women in Kampala and district towns of Uganda. It is an<br />
affiliate of Women’s World Banking.<br />
06/00 AAA From 1998-2000, Vital-Finance was an NGO, offering individual and<br />
solidarity group loans to small and microentrepreneurs in Benin’s<br />
rural areas. It is now functioning as an Association.<br />
12/99 A Vivacred is an NGO operating in Rio de Janeiro in Brazil. It offers<br />
individual loans to microentrepreneurs, and was founded in 1997.<br />
12/99 A WAGES serves women in Lomé and surrounding areas, working<br />
with borrowers’ associations in a village-banking type methodology.<br />
It was founded in 1994.<br />
09/99 B World Relief, Honduras was founded in 1981 as a NGO. It is part of<br />
COVELO network and network of NGOs FODIPREH. It offers a mix<br />
of individual, solidarity and village banking loan products to women in<br />
urban and semi-urban areas in Honduras.<br />
09/99 A Founded in 1996 as an affiliate of World Vision, <strong>the</strong> NGO provides<br />
individual and group loans to self-employed small and<br />
microentrepreneurs.<br />
84 MICROBANKING BULLETIN, APRIL 2001