the microbanking bulletin - Microfinance Information Exchange

THE 

MICROBANKING 

BULLETIN 

F o c u s o n P r o d u c t i v i t y 

ISSUE NO. 6 

APRIL 2001 

A SEMI-ANNUAL PUBLICATION DEDICATED TO THE FINANCIAL PERFORMANCE OF 

ORGANIZATIONS THAT PROVIDE BANKING SERVICES FOR THE POOR

The MicroBanking Standards Project 

The MicroBanking Bulletin is one of the principal 

outputs of the MicroBanking Standards Project, 

which is funded by the Consultative Group to Assist 

the Poorest (CGAP). 

Project Purpose 

By collecting financial and portfolio data provided 

voluntarily by leading microfinance institutions 

(MFIs), organizing the data by peer groups, and 

reporting this information, this project is building 

infrastructure that is critical to the development of 

the industry. The primary purpose of this database 

is to help MFI managers and board members 

understand their performance in comparison with 

other MFIs. Secondary objectives include 

establishing industry performance standards, 

enhancing the transparency of financial reporting, 

and improving the performance of microfinance 

institutions. 

Project Services 

To achieve these objectives, the MicroBanking 

Standards Project provides three services: 1) 

customized financial performance reports; 2) the 

MicroBanking Bulletin; and 3) network services. 

MFIs participate in this project on a quid pro quo 

basis. They provide us with information about their 

financial and portfolio performance, as well as 

details regarding accounting practices, subsidies, 

and the structure of their liabilities. Participating 

MFIs submit substantiating documentation, such as 

audited financial statements, annual reports, 

program appraisals, and other materials that help us 

understand their operations. With this information, 

we apply adjustments for inflation, subsidies and 

loan loss provisioning to create comparable results. 

We do not independently verify the information. 

Neither the MicroBanking Standards Project nor 

CGAP can accept responsibility for the validity of the 

information presented or consequences resulting 

from its use by third parties. 

In return, we prepare a confidential financial 

performance report for each participating institution. 

These reports, which are the primary output of this 

project, explain the adjustments we made to the 

data, and compare the institution’s performance to 

its peer group as well as to the whole sample of 

project participants. These reports are essential 

tools for MFI managers and board members to 

benchmark their institution’s performance. 

The third core service is to work with national and 

regional associations of microfinance institutions to 

enhance their ability to collect and manage 

performance indicators. This service is provided in a 

variety of different ways, including teaching these 

networks to collect, adjust and report data at the 

local level, collecting data on behalf of a network, 

and providing customized data analysis to compare 

member institutions to external peer groups. This 

service to networks allows us to help a wider range 

of MFIs to improve their financial reporting. 

New Participants 

Organizations that wish to participate in the 

MicroBanking Standards Project, either to receive 

customized reports or network services, should 

contact: mbb@microbanking-mbb.org, Tel (202) 

659-9802/4, Fax (202) 659-9816. Currently, the only 

criterion for participation is the ability to fulfill fairly 

onerous reporting requirements. We reserve the 

right to establish minimum performance criteria for 

participation in the Bulletin. 

Bulletin Submissions 

The Bulletin welcomes submissions of articles and 

commentaries, particularly regarding analytical work 

on the financial performance of microfinance 

institutions. Submissions may include reviews or 

summaries of more extensive work elsewhere. 

Articles should not exceed 2,500 words. We also 

encourage readers to submit responses to the 

content of this and previous issues of the Bulletin.

THE MICROBANKING BULLETIN 

FOCUS ON PRODUCTIVITY 

ISSUE NO. 6 

APRIL 2001 

DEDICATED TO THE FINANCIAL PERFORMANCE OF ORGANIZATIONS THAT PROVIDE BANKING 

SERVICES FOR THE POOR 

EDITORIAL STAFF 

Craig F. Churchill 

Geetha Nagarajan 

Isabelle Barrès 

Editor 

Associate Editors 

EDITORIAL BOARD 

Chair: 

Robert Peck Christen 

Claudio Gonzalez-Vega 

Elisabeth Rhyne 

Richard Rosenberg 

J.D. Von Pischke 

Consultative Group to Assist the Poorest, The World Bank 

The Ohio State University 

ACCION International 

Consultative Group to Assist the Poorest, The World Bank 

Frontier Finance International 

The MicroBanking Bulletin is funded by the Consultative Group to Assist the Poorest (CGAP)

CONTENTS 

From the Editor ............................................................................................................................................................ 1 

FEATURE ARTICLES 

Designing Financial Incentives to Increase Loan Officer Productivity: Handle With Care! ...................................... 5 

Martin Holtmann 

We Aren’t Selling Vacuum Cleaners: PRODEM’s Experiences with Staff Incentives............................................ 11 

Eduardo Bazoberry 

Dropouts and Graduates: Lessons from Bangladesh .............................................................................................. 14 

Graham A.N. Wright 

Exodus: Why Customers Leave................................................................................................................................ 17 

Kim Wilson 

Cultivating Client Loyalty: Exit Interviews from Africa and Asia............................................................................... 20 

Inez Murray 

TALKING ABOUT PERFORMANCE RATIOS 

Measuring Client Retention ....................................................................................................................................... 25 

Rich Rosenberg 

COMMENTARY AND REVIEWS 

Book Review: Brand and Gerschick’s Maximizing Efficiency, by Robin Young ..................................................... 27 

Book Review: Campion’s Improving Internal Control, by Luis Schunk ................................................................... 29 

BULLETIN CASE STUDY 

Bosnian MFIs: Performance and Productivity .......................................................................................................... 31 


BULLETIN HIGHLIGHTS AND TABLES 

Bulletin Highlights: Productivity Drivers and Trends ................................................................................................ 35 


An Introduction to the Peer Groups and Tables ....................................................................................................... 40 

Index of Ratios and Tables........................................................................................................................................ 42 

Peer Group Tables..................................................................................................................................................... 44 

Additional Analysis Tables......................................................................................................................................... 53 

APPENDICES 

Appendix I: Notes to Statistical Section .................................................................................................................... 73 

Appendix II: Description of Participating MFIs.......................................................................................................... 77

From the Editor 

An Introduction to Productivity 

One of the challenges of managing a microfinance 

institution is trying to do more with less. To serve 

more people with less subsidies. To achieve 

greater impact with smaller loans. And, for staff to 

manage more clients with fewer arrears. Micro- 

Banking Bulletin Issue No. 6 focuses on productivity 

by tackling this last example. 

Direct and Indirect Influences 

Productivity describes how a change in an organization’s 

inputs, such as labor, affects its outputs (i.e. 

loans). In microfinance, productivity discussions 

tend to focus on how staff incentives affect the 

number of clients per loan officer. Besides incentives, 

other inputs such as the total remuneration 

package, staff training, and the use of technology 

can positively affect productivity. The challenge is 

to estimate how large an effect these inputs might 

have so an MFI can determine what investments in 

productivity enhancers are worthwhile. 

This direct effect between inputs and outputs is a 

straightforward way of conceptualizing productivity, 

but the discussion should not end there. Perhaps 

as important is the indirect effect caused by an 

MFI’s operating conditions. This indirect effect is 

harder to quantify, but it has a tremendous influence. 

Three sets of conditions are worth mentioning 

because of their powerful effect on productivity. 

The first condition is the institutional culture. If 

employees enjoy their jobs, their productivity will 

improve. Some organizations forget, however, that 

many factors contribute to employee satisfaction 

besides one’s salary and benefit package. In fact, 

to achieve more with less, MFIs should concentrate 

on maximizing employees’ well being at a minimum 

cost. It costs almost nothing to give employees 

compliments, to acknowledge their extra efforts, 

and to solicit their suggestions for improvements. 

An institutional culture that encourages these types 

of interactions—between management and staff, 

horizontally between employees and, perhaps most 

importantly, between frontline workers and the 

customers—will experience a productivity boost. 

Second, the level of discipline within the organization 

dramatically affects productivity. Productive 

MFIs are like well-oiled machines. Meetings start 

and end on time, and everyone who is supposed to 

be in attendance is there. Requests for loan 

approvals are accompanied by all the required 

paperwork. Data are entered into the information 

system accurately. Disbursements occur on time. 

Clients repay on time, and loan officers follow up 

immediately with anyone who is late. Only MFIs 

that aspire to this standard of zero defects can 

achieve high levels of productivity. 

The third condition is the product-client match. 

Microfinance institutions that do not provide clients 

with appropriate products often experience dropout 

rates that undermine productivity. The revolving 

door of customer desertion means that many MFIs 

are running hard and not getting anywhere. While a 

poor product-client match is not the sole cause of 

desertion, it is the desertion driver that MFIs can 

most easily influence. 

Efforts to reduce desertion by becoming clientfocused, 

however, place MFIs in the center of the 

great productivity dilemma: they want to provide 

flexible services that suit the demands of the 

market, yet in doing so they move away from the 

cookie-cutter approach that positively contributed to 

productivity in the first place. To maximize productivity, 

the microfinance industry faces the 

oxymoronic challenge of creating “flexible 

automation” or “standardized customization”. Some 

pundits think that the answer lies in the use of 

technology, such as smart cards, credit scoring and 

palm pilots; others are skeptical about the 

effectiveness of high-tech solutions in low-tech 

environments. 

Measuring Productivity 

The primary productivity measure used by the 

Bulletin is the number of borrowers per staff member. 

Productivity is particularly difficult to benchmark 

between institutions. Even the Bulletin’s peer 

group analysis is not sufficiently specialized to 

account for the causes of vast productivity differences, 

such as: 

• Lending Methodology: Group-lending has a 

natural advantage in generating higher productivity 

than individual lending methodologies. 

• Menu of Services: The productivity ratio penalizes 

financial intermediaries because they have 

many employees who are not involved in generating 

loans. 

• Credit Plus: MFIs that use credit delivery to provide 

other services, such as Credit with Education, 

are likely to have lower productivity than 

minimalist MFIs, all other things being equal. 

• Loan Term: The length of the term is an important 

factor because it is easier to manage large 

volumes of clients when their loans are not 

renewed frequently, assuming that portfolio 

quality is not adversely affected. 

MICROBANKING BULLETIN, APRIL 2001 1

• Loan Size: The size of the loan also affects productivity 

because MFIs need to be more careful 

when issuing larger loans. 

• Client Market: MFIs serving sparsely populated 

areas face a serious productivity challenge. 

• Labor Market: Better-educated employees tend 

to require less supervision, which raises productivity. 

If a tight labor market causes staff 

turnover, productivity could be undermined. 

• Growth Rate: An MFI in high growth mode will 

have lower productivity because new loan 

officers will have excess capacity and are 

naturally less productive than veterans. In 

addition, productivity will suffer if the client base 

consists of many new clients, since they tend to 

require more work than repeat borrowers. 

• Age: Continuing with the same logic, mature 

MFIs tend to have higher rates of productivity 

than new organizations. 

Because of these (and other) factors, a comparison 

of productivity between institutions needs to be 

considered very carefully. Perhaps the most useful 

productivity benchmark is how an organization compares 

to itself over time. 

Contents of this Issue 

Feature Articles 

Although there are numerous strategies to improve 

productivity, the feature articles in this Bulletin hone 

in on two approaches: 1) giving financial incentives 

to encourage staff to work harder and smarter (or at 

least to reward those that do); and 2) understanding 

and reducing customer desertion. 

The first two articles provide insights into the effects 

of staff incentives on productivity; the next three 

show how productivity is adversely affected by 

client desertion. The unifying theme of these articles 

is that clients and employees behave rationally. 

If the rewards are right, loan officers can do 

more with less. If loan products are well designed, 

customers will continue to patronize an MFI. 

In “Designing Financial Incentives”, Martin 

Holtzman provides a detailed example of an 

incentive scheme structure that the German 

consulting firm IPC has employed successfully in a 

variety of different settings. With this flexible model, 

MFIs tailor the scheme to individual loan officers 

and adjust weightings to address different scenarios 

at different points in time. 

Eduardo Bazoberry counters by describing 

PRODEM’s experience with staff incentives. He 

contends that individual financial incentives break 

down the sense of teamwork and commitment to an 

MFI’s social mission. He recommends the use of 

group-based incentives, such as profit sharing and 

employee ownership, as well as non-financial 

incentives, to motivate staff. 

While the two authors have different perspectives, 

they agree on key aspects: 

• If not approached carefully, financial incentives 

can do more damage than good. 

• The objective of an incentive scheme is to align 

the goals of the employees with those of the 

institution. 

• Financial incentives are one piece in a toolkit of 

motivational strategies that are needed to 

produce optimal levels of staff productivity. 

The second set of articles outlines the costs (and 

benefits) of customer desertion. Based on experience 

in Bangladesh, Graham Wright examines the 

inappropriate match between the credit product and 

clients’ needs. He contends that MFIs in Bangladesh 

overemphasize the importance of standardized, 

low-cost loan products when their clients want 

a broader range of flexible services. 

The next two articles, by Kim Wilson and Inez 

Murray, highlight the value of learning from lost 

customers. Through exit interviews, affiliates of 

Catholic Relief Services (Bosnia-Herzegovina and 

Gaza) and Women’s World Banking (Uganda and 

Bangladesh), respectively, learned what was right 

and wrong with their services. Results from this research 

enabled the MFIs to modify their products 

and delivery mechanisms to become more clientfocused. 

These three articles call for a change in the service 

delivery culture of microfinance institutions. Many 

MFIs are supply-driven: they have credit products 

that they provide to clients. The authors argue that 

MFIs should: 1) embrace a customer service ethic; 

2) provide financial products that are demanddriven; 

and 3) conduct market research so that they 

know what their customers really want. 

Performance Ratios 

The first step toward reducing client desertion is 

being able to measure it, but there isn’t consensus 

on how to do that. Each of the articles on customer 

retention uses a different formula. In a new section 

of the Bulletin (“Talking about Performance 

Ratios”), Rich Rosenberg reviews five retention 

ratios and endorses what he calls the Waterfield/ 

CGAP ratio. Since this is not the ratio used by the 

Bulletin, or by any of the other organizations that 

defined desertion in this Issue, it is apparent that a 

broader discussion is needed on defining and 

measuring customer retention. 

2 MICROBANKING BULLETIN, APRIL 2001

Commentary and Reviews 

We have also added book reviews to the Bulletin’s 

repertoire. Robin Young (DAI) reviews the latest 

monograph from ACCION International, Maximizing 

Efficiency by Monica Brand and Julie Gerschick. 

Luis Schunk, from FEFAD in Albania, employs a 

practitioner’s perspective to review the Micro- 

Finance Network’s first Technical Note, Improving 

Internal Control by Anita Campion. 

Case Study 

As a change of pace, the case study section 

analyzes eight different programs in one 

country—Bosnia-Herzegovina. In keeping with this 

Issue’s theme, the case study by Isabelle Barrès 

focuses on the productivity and overall performance 

of these MFIs operating in a challenging 

environment. 

Highlights and Tables 

The Highlights Section by Geetha Nagarajan uses 

the Bulletin’s database to analyze productivity 

drivers and trends. The results reveal very interesting 

differences by region, age of institution, loan 

size, and lending methodology that suggest the 

need to think about productivity contextually. The 

analysis also examines changes in productivity over 

time, which shows that most MFIs are making 

steady improvements. Perhaps most importantly, 

the analysis reveals the limitations of productivity as 

a useful benchmarking indicator. 

In the ongoing effort to keep pace with a rapidly 

developing field, the Bulletin has added three new 

productivity ratios: loan officer productivity, staff 

allocation, and staff turnover. 1 Besides analyzing 

financial performance by peer group, the Bulletin 

also includes Additional Analysis Tables that slice 

up the data in different ways, including lending 

methodology, age, and institutional type. 

Bulletin Participants 

This Bulletin contains performance information from 

124 organizations that operate in 47 countries. This 

represents an increase of 10 MFIs from the last 

issue—including 8 from Africa. The regional breakdown 

looks like this: 

• 54 programs from 14 Latin American countries, 

(9 from Bolivia and 8 from Ecuador); 

• 26 African MFIs from 11 different countries (6 

programs in Uganda and 5 in Ghana); 

• 24 MFIs from 11 Asian countries (4 each from 

the Philippines and India); 

• 14 Eastern Europe programs from 6 countries 

(8 from Bosnia); and 

1 

For definitions of all ratios used in the Bulletin, see page 42. 

• 6 MFIs from 5 countries in the Middle East and 

North Africa (MENA). 

Participation in the Bulletin is a two-way street. We 

prepare a customized report for each MFI, which 

compares the institution’s performance with its peer 

group and with the average performance of all 

MFIs. This report is a valuable tool for board 

members and managing directors to benchmark 

their performance to similar organizations. MFIs 

have also found these reports useful in discussions 

with regulators and as supplementary documentation 

for investors. For more information about 

submitting data to the Bulletin, contact 

mbb@microbanking-mbb.org. 

Peer Groups 

For the peer group comparison to be useful, the 

groups have to be fairly homogeneous. With each 

Issue, as more MFIs send in their data, we have 

added new peer groups and reorganized others. 

The three primary criteria we use to define peer 

groups are: 1) the size of the program, 2) its 

average loan balance, and 3) its region. 

To improve homogeneity, we tweaked the peer 

group criteria a bit. In Latin America, for example, a 

new group was created by applying country income 

level as a criterion. We made this adjustment 

based on the observation that the labor and 

customer markets are considerably different in 

Chile, Argentina and Brazil than in lower income 

countries, which creates unique challenges in 

operating microfinance institutions. 

A peer group that appeared in past issues 

(Worldwide High-end) has re-emerged as 

Worldwide Small Business. It contains six 

institutions with average loan balances that were 

too large to justify useful comparisons to MFIs in 

their region. Despite the fact that they come from 

all corners of the globe, the data are cohesive. 

In Transition 

Finally, the Bulletin is in transition again. 

Calmeadow has been the home of the Bulletin for 

two plus years and the past three issues. Due to 

reorganization, however, Calmeadow is no longer in 

a position to play that role. Consequently, the 

Bulletin is being set up as an independent project 

that will be associated with, but separate from, 

CGAP. This transition is expected to achieve the 

best of both worlds: a close association with CGAP 

should continue to increase the outreach of the 

Bulletin; yet a clear separation to maintain and 

respect the confidentiality of the database. 

Craig Churchill 




Designing Financial Incentives to Increase Loan Officer Productivity: 

Handle With Care! 

Martin Holtmann 

Efficiency in micro and small business lending is 

arguably the strongest single driver of financial 

performance. This realization has increasingly 

been reflected in the microfinance literature. In 

February 2000, the MicroBanking Bulletin dedicated 

a whole issue to the topic of efficiency. More 

significantly, during the last decade, a number of 

lending institutions have made significant 

improvements in their operating efficiency. 

One aspect of efficiency is the productivity of staff 

members. This article takes a closer look at the 

contribution that one specific tool—loan officer 

incentive schemes—can make to improving productivity. 

It also makes some suggestions for the 

design and implementation of such schemes. As a 

reflection of the author’s limited experience with 

group lending, all examples are from individual loan 

programs. However, most of the findings of this 

article should also be applicable to group lenders. 

Why Focus on Loan Officer Productivity? 

In microfinance institutions (MFIs), loan officers are 

responsible for creating and safeguarding the 

quality of the assets (i.e. the size and arrears rate of 

the loan portfolio) as well as for generating the 

income (i.e. interest payments from clients) for the 

institution. In addition, since they are the point of 

contact with clients, the work of the loan officers 

has an enormous impact on an institution’s 

outreach. In a nutshell, the loan officers are the 

agents that produce an MFI’s output. 

On the input side of the equation, loan officers 

account for a significant share of staff costs, which 

in turn accounts for a significant portion (50 to 70 

percent) of administrative expenses. Clearly, a 

“production factor” that accounts for most of the 

costs and generates almost all of the output and 

income should be given incentives to become as 

productive as possible! Financial incentives can 

enhance employee performance and productivity in 

microfinance just as in other industries. 

A second argument supporting the design and 

implementation of loan officer incentives is the 

highly decentralized structure of the decision-making 

and credit delivery process. In a typical microcredit 

operation, loan officers possess vastly more 

detailed and accurate information about the local 

environment and the clients than do central 

management and the owners. In the presence of 

such “information asymmetries” and high monitoring 

costs, managers are well advised to align the goals 

of the institution (which usually include a mix of 

outreach and profitability indicators) with those of 

the agents who actually make the vast majority of 

operational decisions. Again, well-designed incentives 

can be useful in achieving this goal. 

Design and Typology of Incentive 

Schemes 

As the basis for designing an incentive scheme, the 

loan officer’s duties must first be clearly defined, 

and they should be derived from the goals of the 

lending institution. This yields a range of targets 

that the loan officer is supposed to meet. Typical 

examples include: 

• Number and volume of loans issued; 

• Number and volume of outstanding loans; 

• Number of loans to first-time customers; 2 

• Quality of the loan officer’s portfolio (in the form 

of the lowest feasible portfolio-at-risk rate). 

Most incentive schemes consist of some or all of 

these variables. The individual components must 

be weighted to ensure that the goals pursued by 

loan officers match the institution’s goals as closely 

as possible. It is impossible to achieve a perfect 

match between the goals of loan officers and those 

of the institution. Also, outreach, credit volume and 

portfolio quality cannot be maximized simultaneously. 

When putting together an incentive package 

that gives due consideration to all three factors, 

achieving the optimal mix of the three variables 

inevitably involves certain trade-offs. 

2 

Since lending to existing customers involves less processing 

and analytical effort than lending to new customers, experienced 

loan officers with a large pool of customers tend to focus on their 

existing clients. This type of behavior is clearly contrary to the 

MFI’s outreach objectives. 



While loan officers make the most important 

contribution to reaching the output goals, numerous 

factors, such as external economic shocks or 

devaluation, are outside their control. Furthermore, 

loan officers’ basic living expenses must be covered 

to ensure that they will be willing to take appropriate 

risks in the course of their work. Given these two 

points, loan officers should not only receive a performance-related 

bonus, but should also receive a 

fixed basic salary. 

Regarding the weighting between bonuses and 

salary, the general rule is that a bonus of less than 

20 percent of total remuneration does not create 

significant stimulus to improve performance. Conversely, 

a bonus of more than 70 percent of the 

remuneration package will attract loan officers who 

are active risk seekers. In practice, the share of the 

performance-based bonus in overall compensation 

is best if it is between 30 and 50 percent. 

Financial incentive schemes vary in complexity. 

The simplest form is the piece rate system, in which 

the loan officer receives a set bonus per unit of 

output. Multiplying the figures for the indicators in 

question (e.g. number of loans issued to new customers) 

by the respective set amount yields the 

total bonus. To discourage delinquency, a penalty 

can be deducted based on arrears. 

Complex bonus systems allow management to set 

targets for loan officers in regard to specific variables. 

Such systems have the advantage of being 

oriented to the performance values of the institution 

as a whole, and therefore also allow fine-tuning. 

Impact of Financial Incentive Systems: 

Some Empirical Evidence 

There is ample empirical evidence that the introduction 

of loan officer incentive schemes can make 

positive contributions to loan officer efficiency. 

Some of the most efficient Latin American MFIs 

with very high loan officer productivity use financial 

incentive systems, including WWB Cali, Financiera 

Calpiá, CMAC, Banco ADEMI, and Caja Los Andes. 

The BRI Unit Desa, a high productivity lender in 

Indonesia, uses a profit bonus system (based on 

unit performance) to motivate staff. In addition, 

there is a semi-annual contest for cash prizes. 3 

3 

Based on MicroFinance Training Program, Boulder, Colorado, 

course notes by Richard M. Hook. Pure profit-sharing schemes 

at the loan officer level generally work well in small groups where 

potential free riders can be sanctioned. For larger groups, this 

self-regulating mechanism usually does not work and 

performance should be measured individually. 

Loan officer incentive schemes introduced in 

several “downscaling” programs in Eastern Europe 

typically gave a boost to loan officer productivity. 

The performance of loan officers of the Russian 

State Savings Bank is significantly better in 

branches that allowed the implementation of an 

incentive system than in branches that opted to 

maintain a fixed salary. In the Siberian city of 

Krasnoyarsk, for instance, introduction of incentives 

led to positive productivity differentials of more than 

30 percent. In Kazakstan, incentives have been 

introduced in all the banks participating in the 

European Bank for Reconstruction and 

Development’s small business program, and the 

effect on productivity has been quite strong. 

When a re-designed financial incentive system was 

introduced at FEFAD Bank in Albania in early 2000, 

loan officer productivity (measured in average 

number of loans disbursed per loan officer per 

month) increased by more than 100 percent within 

a period of five months, while the portfolio-at-risk 

ratio remained at the same low level as before. 

Words of Caution… 

Design and implementation of loan officer bonus 

systems requires careful planning. One obvious 

challenge is to ensure that the incentives are 

properly aligned with the goals of the organization. 

Misalignments can be avoided by testing the 

system during the design phase, both through 

spreadsheet calculations and a limited field test 

(e.g. in a branch office). 4 

The effectiveness of incentive systems depends on 

the cultural environment in which a microfinance 

institution operates. Timing is another critical issue. 

It is useful to phase in an incentive system 

gradually. During their training period, loan officers 

need to make mistakes in order to learn from them, 

thus they should not be penalized. Later on, as the 

whole organization moves up the learning curve 

(i.e. average loan officer productivity increases) the 

bonus system can be adjusted. The introduction of 

new products also requires changes to the bonus 

system. Implementation of a bonus system at the 

loan officer level usually generates the need for 

incentive systems at other layers of the organization 

(department heads, branch managers, etc.). 

The empirical observation that many systems have 

produced completely unwanted effects leads to the 

conclusion that it is better not to have an incentive 

system than to have one that is badly designed. 

4 

Spreadsheet calculations also help to calibrate the impact that 

the system will have on the cost of lending operations and to 

forecast the break-even point. 



A bonus system for loan officers is only one part of 

a productivity-focused lending technology. Other, 

and probably more important elements are: standardization 

of products and procedures, decentralization, 

effective screening of clients to reduce 

dropout rates during analysis, and effective use of 

MIS. Furthermore, compiling and posting individual 

and branch rankings creates a healthy spirit of 

competition—with the corresponding positive 

impact on productivity. 

What conclusions may be drawn? There is strong 

evidence that a well-designed monetary incentive 

system for lending staff does indeed boost 

productivity. But a bonus system is only part of an 

overall culture of high productivity. Every bonus 

system must be carefully designed and adapted to 

the local situation. 5 Do not ever copy someone 

else’s system: rather, design your own! For a 

system to be successful, it: 

• Needs to incorporate the basic goals embedded 

in the lending policies of the organization; 

• Must be fair. Loan officers should feel that 

better performance is adequately rewarded and 

that on average the goals set by the system are 

achievable. This requires continual efforts by 

managers to communicate fairness as an overall 

objective of the system, so that the inevitable 

adjustments in the bonus formula are accepted 

by loan officers as routine and not interpreted 

as breaches of trust or as a lack of appreciation 

for their efforts; 

• Should be transparent so that loan officers can 

adjust their actions according to a few simple 

parameters. 6 

No incentive system is perfect. All bonus systems 

must be regularly reviewed and adapted to the 

changing business environment as well as to the 

maturing of the lending organization. Finally, any 

system that helps to improve the performance and 

loyalty of staff deserves to be implemented. 

An Illustrative Incentive Scheme 

This section describes how an incentive scheme 

might work. In this example, loan officers can earn 

a monthly bonus up to a maximum of US$400. 7 

5 

Among the many technical issues to be decided are: phasing in 

bonuses, their total value, and frequency of bonus pay. For an 

overview of some of these considerations see Christen, Robert 

P. (1997). Banking Services for the Poor: Managing for Financial 

Success. Washington DC: ACCION, pp. 182-189. 

6 

Transparency can be a problem when implementing bonus 

systems in “downscaling” environments in which department 

heads and branch managers would prefer to provide bonuses 

based on their own subjective assessments of loan officers’ 

performance. 

The bonus is derived as: 

Bonus = L + P + A 

Where: 

L = Lending (number of loans disbursed to new 

and repeat clients) 

P = Portfolio (number of loans and balance 

outstanding) 

A = Arrears (number of loans and portfolio at 

risk) 8 

The variables create a ratio of actual results to 

intended targets. Additionally, weights are applied 

to the variables, and for each part of the formula a 

bonus factor is given. The sum of the bonus factors 

is the amount the MFI is prepared to pay as a 

bonus. It is possible to convert the bonus into any 

currency. In this illustration, summarized in Figure 

1, the bonus factor is denominated in US$. 

The overall variables are weighted (w 1 through w 7 ) 

according to the importance of the respective 

performance indicator to the MFI and can be 

defined for each loan officer individually, depending 

on what the institution expects the loan officer to 

achieve during a certain period (usually one month). 

Each component of the formula is explained below. 

Number of Loans Disbursed (L) 

L is the number of loans disbursed to new and 

repeat clients by the following formula: 

Where: 

L = [(n / g) * w 1 + (o / h) * w 2 ] * w 8 

n = The number of loans issued to new clients 

g = The loan officer’s target for loans issued to 

new clients 

o = The number of loans issued to repeat 

clients 

h = The loan officer’s target for loans issued to 

repeat clients 

w 1 , w 2 = The weight given to the number of 

loans issued to new and repeat clients, 

respectively. w 1 plus w 2 must equal 1. If more 

importance is attributed to the number of loans 

issued to new clients, w 1 should be given a 

weight greater than 0.5 

7 

In theory, a loan officer could earn a bonus higher than US$400 

by overshooting the targets. Nevertheless, these are set so that 

it is hard to surpass them, and readjusted as average 

performance improves. 

8 

In the following paragraphs, the term “arrears” denotes 

“portfolio-at-risk” starting from the first day of irregular payment. 



Figure 1: An Illustrative Incentive System 

Vari 

able 

g 

n 

h 

o 

b 

d 

a 

c 

#la 

$la 

Comments Value Factor Description Value 

(L) Target number of disbursed 

loans to new customers 

(L) Actual number of disbursed 

loans to new customers 

(L) Target number of disbursed 

loans to repeat customers 

(L) Actual number of disbursed 

loans to repeat customers 

(P) Target number of 

outstanding loans 

(P) Loan officer’s actual number of 


(P) Target outstanding portfolio 

(US$) 

(P) Loan officer’s actual 

outstanding portfolio (US$) 

(A) Actual number of loans in 

arrears 

(A) Actual volume of loan officer’s 

loans in arrears (US$) 

10 w 1 Weight factor for L 

number (new) 

0.7 < 1 

6 w 2 Weight factor for L 

0.3 < 1 

number (rep.) 

10 w 3 Weight factor for P- 

0.3 < 1 

volume 

4 w 4 Weight factor for P- 

0.7 < 1 

number 

120 w 5 Weight factor for A #la 0.5 < 1 

80 w 6 Weight factor for A $la 0.5 < 1 

600,000 w 7 Weight factor arrears 5 

450,000 w 8 Bonus level factor L ($) 100 

3 w 9 Bonus level factor P ($) 100 

25,000 w 10 Bonus level factor A ($) 200 

w 8 = The bonus level factor for L 

In this sample calculation, the total monthly bonus 

is US$400, which is divided up as follows: w 8 = 100, 

w 9 = 100 and w 10 = 200. However, if for a certain 

period greater importance is attached to, say, new 

loan output rather than to low arrears, then w 8 

should represent a larger portion of the bonus than 

w 9 or w 10 . 

In this example, the loan officer issued 6 loans to 

new clients, while the target was 10, and issued 4 

loans to repeat clients, while the target was 10. As 

a result, the bonus amount for L is US$54. 

L = [(n / g) * w 1 + (o / h) * w 2 ] * w 8 

L = [(6/10) * 0.7 + (4/10) * 0.3] * $100 = $54 

Portfolio Outstanding (P) 

P stands for portfolio and measures the extent to 

which a loan officer has met targets for the volume 

and number of loans outstanding. 

P = [(c / a) * w 3 + (d / b) * w 4 ] * w 9 

The individual variables are: 

c = Loan officer’s total outstanding portfolio 

a = Loan officer’s target for outstanding portfolio 

d = The actual number of outstanding loans 

b = The loan officer’s target for the number of 


w 3 , w 4 = The weights represent the importance 

given to the volume and number of loans outstanding, 

respectively. w 3 plus w 4 must equal 

1. If more importance is attributed to the number 

of loans than their size, then w 4 should be 

given a weight greater than 0.5 

w 9 = The bonus level factor for P 

This formula assesses the extent to which the loan 

officer has achieved the established targets for 

portfolio outstanding. In the sample calculation, the 

loan officer had an outstanding portfolio of 

US$450,000, or 75 percent of the US$600,000 

target. In this example, more significance was 

given to the number of loans issued than their 

value, since w 4 was set at 0.7. It was probably felt 

that the loan officer needed to increase the number 

of loans (for instance to increase outreach or 

diversification), and hence greater weight was 

attached to this target. The target was 120 loans 

outstanding, whereas the loan officer achieved 80, 

or 67 percent of the target. 

In our example the bonus for component P is 

calculated as follows: 

P = [(c / a) * w 3 + (d / b) * w 4 ] * w 9 

P = [($450,000/$600,000)*0.3 + (80/120) * 0.7] * 

$100 = $69.17 

Arrears (A) 

A or arrears is determined by the following formula: 

A = (w 7 – [(#la / d) * w 5 + ($la / c) * w 6 ]* 100) 

(w 7 * w 10 ) 



This formula calculates the degree (by volume and 

number) to which a loan officer’s portfolio is delinquent, 

and how much the bonus is reduced as a 

consequence. The components of this formula are: 

w 7 = The weight factor for arrears. This can be 

any number greater than or equal to 1. The 

lower the weight factor, the greater the negative 

effect on the loan officer’s potential bonus. The 

use of this weight factor and the fact that the 

arrears component is not deducted from the 

total bonus allows the institution to fine-tune the 

impact of arrears targets by changing the factors 

every month. Thus, a loan officer may get 

a substantial bonus for bringing down the 

arrears rate. 

#la = The total number of loans in arrears in the 

loan officer’s portfolio 

d = Total number of outstanding loans in the 

loan officer’s portfolio (same variable used in 

component “P”) 

$la = The total balance of outstanding loans in 

arrears 

c = Loan officer’s total outstanding portfolio 

(same variable used in component “P”) 

w 5 , w 6 = The weight given to the number and 

volume of loans in arrears, respectively. w 5 

plus w 6 must equal 1. If more importance is 

attributed to the number of loans in arrears, w 5 

should be given a weight greater than 0.5. 

w 10 = The bonus level factor for the arrears 

portion of the formula. In our example the 

arrears bonus level accounts for half of the total 

bonus, i.e. US$200 out of US$400. The loan 

officer had 3 arrears cases representing a total 

of US$25,000 at the end of the month. 

The bonus for part A is calculated as follows: 

A = (w 7 - (#la / d * w 5 + $la / c * w 6 * 100)) / w 7 * w 10 

A = (5 – ((3 / 80 * 0.5) + ($25,000 / $450,000 * 0.5) 

* 100)) / 5 * 200 = $88.14 

Looking at this result and remembering the large 

portion of the maximum total bonus (50 percent) 

that the loan officer could have earned on arrears, it 

appears that the loan officer failed to make 

significant progress on this objective during the past 

month. 

Totals 

Now that all three components of the formula have 

been calculated, combine them according to the 

formula: Bonus = L + P + A. Thus, the resulting 

bonus is: 

$54 + $69.17 + $88.14 = $211.31 

The formula outlined above is only one variant of 

the multitude of bonus systems employed by MFIs. 

Attentive readers will have realized that the 

example and its target values do not originate from 

a high-productivity setting, such as urban Latin 

America or Indonesia. Indeed, this fictitious case is 

typical for Southeastern Europe and the Middle 

East, which are characterized by low productivity, 

high loan officer salaries, and high average loan 

sizes. But with a little imagination it is very easy to 

adapt the formula to other contexts. 

Evaluation 

The example above has a couple of shortcomings 

and some important strengths. Beginning with the 

advantages, the model contains many of the basic 

variables that make up the target functions of 

microlending organizations. Secondly, the formula 

is flexible in that the parameters can be adjusted to 

reflect different environments as well as different 

organizational values (e.g. the outreach objective 

can be strengthened by assigning a higher weight 

factor to the number of loans disbursed and outstanding 

as opposed to the volumes). Thirdly, the 

system is not overly complex and can be understood 

both by a keenly analytical loan officer and 

reader of this Bulletin. The computation of 

individual bonuses is simple enough and does not 

require more than a spreadsheet. Fourthly, the 

bonus formula is “linear” rather than “staged”. 

Staged bonus systems often produce unwanted 

incentives. 9 

One of the model’s strengths is also a weakness: 

Microlenders have to act in a complex environment, 

and this simple formula may not reflect this complexity 

of tasks. It is possible to incorporate a 

higher degree of complexity in the formula by adding 

more variables, but this comes at the cost of 

making the system less transparent. Loan officers 

will find it harder to keep track of the trade-offs and 

goal conflicts contained in the formula and to adjust 

their actions accordingly. 10 

The balancing of trade-offs is particularly relevant in 

the treatment of delinquency. The age of arrears 

has a strong bearing on the chances of recovery, 

which suggests that shorter-term arrears should be 

9 

Loan officers will typically adjust their performance in these 

circumstances so that they remain close to a particular “stage” or 

limit, e.g. a cap on the permitted arrears level. Linear systems, 

in contrast, provide a reward or penalty for any change in the 

output or quality variables. 

10 

Remember that this concern was one of the reasons for 

introducing an incentive system in the first place. An effective 

bonus system will induce loan officers to act in the interests of 

the organization without additional supervision. If the system 

becomes too complex, this cannot be achieved. 



subject to a higher penalty. However, it is also 

counterproductive to penalize short-term arrears 

(say, from 1 to 7 days) too heavily since it would 

make loan officers overly risk-averse and have a 

negative impact on productivity. This train of 

thought could be factored into the model by 

assigning different weights to different age 

categories. Furthermore, if delinquency is a serious 

concern for an institution with a large portfolio, the 

formula could be improved by changing the purely 

additive calculation contained in the example above 

into a multiplicative link between outstanding 

portfolio and arrears level, thereby making it much 

more sensitive to portfolio quality. 

At a more general level, some readers may take 

exception to this article’s focus on financial 

incentives. Indeed, there are many other and 

potentially more powerful factors that influence an 

individual’s job performance. Opportunities to receive 

further training, opportunities for advancement, 

social status, the sense of a common 

“mission”, and last but not least, the feeling of 

contributing to local or national economic and social 

development are important components of loan 

officers’ job satisfaction and performance. 

Nevertheless, loan officers are normally “rational” in 

the economic sense, and few would deny that 

financial incentives do provide an important stimulus. 

It is important to inform potential loan officer 

candidates during the selection phase if a 

performance-based incentive system is in place or 

is to be introduced. This will help to screen candidates 

by ensuring that they are willing to align their 

compensation expectations with the MFI’s goals, 

and to be compensated on the basis of their 

contribution to those goals; it will also help to sort 

out risk-averse individuals. Despite the general 

limitations of monetary incentive systems, they 

have proven their value in practice. 

Martin Holtmann is a Managing Director of IPC. He is 

based in Moscow, working with several commercial 

banks in the EBRD Russia Small Business Fund project. 

The author would like to thank Andreas Francke, Bryan 

Nielsen, and Ralf Niepel, all from IPC, for many useful 

inputs and suggestions. 



We Aren’t Selling Vacuum Cleaners: 

PRODEM’s Experiences with Staff Incentives 

Eduardo Bazoberry 

Many people in microfinance are familiar with 

PRODEM because it was the NGO that created 

BancoSol, the first commercial bank solely dedicated 

to microfinance. Since BancoSol’s creation in 

1992, PRODEM has quietly gone about doing 

something more challenging than establishing a 

bank. First, we developed loan products for microentrepreneurs 

and small-scale farmers in sparsely 

populated rural Bolivia. Then we expanded our 

organization while continuously improving our loan 

products in response to our customers’ needs. We 

survived a weak economy and a crisis in the 

microfinance industry in 1999, and then we created 

another regulated financial institution known as an 

FFP, a private financial fund. PRODEM FFP, 

launched in January 2000, now offers a wide range 

of financial products including savings accounts, 

wire transfers, and leasing. 

Many MFIs look to financial incentives as a means 

to boost staff productivity. At PRODEM, we pride 

ourselves in having created an efficient, productive 

and profitable organization with an approach toward 

staff incentives that defies the conventional wisdom 

in the microfinance industry. 

The Evolution of Incentives at PRODEM 

A few months ago, I received an email from a friend 

telling me that my institution was one of the only 

major MFIs in the world that did not provide 

incentives to loan officers. Hearing this, I explained 

that PRODEM did have incentives and that, given 

the nature of our business, incentives were 

necessary, but we implemented our scheme in a 

way that did not affect the objectivity of the loan 

officers when they made credit decisions. I proceeded 

to explain about the circumstances that led 

PRODEM to look for better ways to motivate the 

performance and responsibilities of our employees. 

During 1993, after looking at the different incentives 

that MFIs were offering worldwide, we implemented 

an incentive system that rewarded loan officers for 

accomplishing goals set in the incentive program. 

These goals included: the targeted number of 

clients, the maximum percentage of loans in 

arrears, and the average portfolio per loan officer. 

In addition, since PRODEM had different types of 

branches, we had defined the goals in relation to 

the potential market and the location of the offices: 

in rural areas, at the country’s borders, in major 

cities, or in secondary cities. 

Rosy Preliminary Results 

The incentive program worked as we had hoped. 

The loan portfolio grew rapidly, the portfolio at risk 

was under control, the number of clients increased 

steadily, and profitability improved (see Figure 1 at 

the end of the article). All of our indicators in 1994- 

95 suggested that we made a wise decision in 

implementing the incentive program. 

Things Start to Get Sour 

By 1996, we sensed something disruptive occurring. 

We began to notice a high rate of turnover 

among our loan officers, including an increase in 

the number of staff fired because of corruption or 

for constantly breaking the methodology and rules 

of the institution. Obviously, we had not managed 

to gain the loyalty of these loan officers. Instead, 

we had staff members who were mechanically 

performing their functions without a real 

responsibility towards the institution or our clients. 

At the same time, some staff members began 

demanding larger incentives amounts. They were 

under the false impression that PRODEM’s good 

performance was due solely to their efforts, without 

realizing that everyone was part of one system of 

integrated departments, and that other aspects of 

the organization were also important for PRODEM’s 

performance. 

The original scheme awarded a monthly bonus to 

individuals who met certain performance standards. 

We learned, however, that this type of incentive had 

a negative effect on team performance and 

encouraged a short-term outlook. 

As a result, in 1996, PRODEM changed the 

incentive to an annual bonus awarded for branch 

performance. All members of a branch received a 

bonus if their branch met certain performance 

targets. The largest bonus was worth an additional 

month’s salary. 

An annual payment encouraged a long-term 

perspective. It corrected the “delinquency lag,” 

caused by new loans that go into arrears several 

months after they were issued. An annual payment 

also adjusted for the profound seasonal fluctuations 

that are common in Bolivian microfinance and it 

allowed PRODEM to complete our audit before 

issuing bonuses. 



This modification was generally successful in motivating 

staff and creating teamwork within a branch, 

but it still had negative side effects. It discouraged 

staff rotation and cooperation between branches. If 

employees agreed to transfer to a branch with 

problems, they reduced their chances of obtaining a 

bonus. Because some markets were riskier than 

others, some staff concluded that the bonus 

involved an element of luck depending on where 

one worked. This conclusion generated tension 

between those who were perceived to have received 

a bonus because they worked in a good 

environment and those who failed to earn a bonus 

even though they worked extremely hard. In such 

cases, the incentive system discouraged rather 

than encouraged staff. 

Continuous Improvement 

This experience motivated management and partners 

of PRODEM FFP to pursue a fair incentive 

system where clients, investors, and employees 

were all winners. We decided to eliminate the 

branch bonus program and instead reward the 

performance of the whole institution on an annual 

basis. The collective approach reiterates that we 

are all in this together. 

We also designed a pension fund that allows 

employees to receive a certain number of PRODEM 

FFP shares as part of their annual benefit package. 

Employees will have access to a percentage of 

these funds if they leave PRODEM after three or 

more years of employment. By offering employees 

direct ownership, we can optimize their long-term 

commitment towards PRODEM’s growth and 

development, which fulfils the number one rule in 

designing incentive schemes: aligning the risks and 

rewards for employees with those of the institution. 

Non-financial Incentives 

This entire discussion about financial incentives, 

however, detracts from the invaluable non-financial 

methods that PRODEM uses to motivate staff to 

achieve high levels of performance. The most 

important method is the institution’s mission. We 

hire people who are committed to making a 

difference in rural Bolivia by working with lowincome 

families and microenterprises. We use our 

mission as a motivating tool. Managers regularly 

remind their employees about PRODEM’s critical 

contribution to the economies of remote communities, 

and how integral each staff member’s performance 

is to the institution’s accomplishments. 

PRODEM’s culture directly contributes to the 

performance of all employees. Through the orientation 

of new staff members, regular training 

opportunities and other communication channels, 

PRODEM inculcates employees into a culture of 

commitment, trust and excellence that is more 

powerful than financial incentives. Granted, an 

institution’s culture does not put food on the 

table—that is why it is important to compensate all 

employees fairly. But financial incentives cannot 

effectively encourage employees to be innovative, 

to embrace change, to constantly seek ways of 

doing things better, and to not be afraid to learn 

from their mistakes. Only the institution’s culture 

can accomplish these objectives, which contribute 

vitally toward improvements in productivity and 

efficiency that must occur for an MFI to remain 

competitive and profitable. 

To strengthen our hand in a competitive market, 

PRODEM FFP has developed a complex and 

creative matrix of incentives to help employees fulfill 

a variety of personal needs ranging from shelter 

and security to acceptance and self-fulfillment. The 

matrix includes financial as well as non-financial 

incentives, such as staff development, job enrichment 

and promotional opportunities, extensive 

health benefits, achievement awards, and the 

opportunity to take a sabbatical after ten years of 

service. 

Contrasting Approach 

PRODEM’s holistic approach to motivating and 

rewarding staff stands in sharp contrast with some 

of the Bolivian consumer credit providers that have 

adversely affected the local microfinance industry. 

Their primary growth strategy was to give monetary 

incentives to loan officers through a variety of 

means, including some that are deceitful. 

A Bolivian consumer credit company, for example, 

was paying (and declaring) the equivalent of US$50 

per month as average staff salary, while its loan 

officers where earning closer to US$900 per month 

(including bonuses), or triple the industry average. 

This slight-of-hand using a dual accounting practice 

enabled the institution to minimize contributions to 

the pension fund by paying in 12.5 percent of the 

official salary (of US$50) rather than the actual 

amount earned by staff. This arrangement also 

reduced the costs of firing staff since the mandatory 

six-month severance package would be based on 

the official salary. 

After two years of bonanza, the partners of the 

institution were counting their losses, partly due to 

the incentive structure and the culture that stemmed 

from it. The owners had put a few million dollars of 

their money on the line, yet their biggest accomplishment 

was successfully overindebting tens of 

thousands of low-income families. 

Microfinance institutions that promote aggressive 

incentive programs, such as this, are going down 



the wrong path. By relying solely on high return 

incentives coupled with low base salaries to 

motivate staff, MFIs create an opaque environment 

that encourages deception. These organizations 

are likely to experience (and are experiencing) the 

following unauthorized activities: 

• Frequent rescheduling of loans without much 

control 

• Loan officers forming ROSCAs to pay for 

clients’ arrears, which allows employees to 

maintain or increase their incentive levels 

despite worsening portfolio quality 

• Creation of “ghost” loans to hide the fact that 

goals are not met 

• Deduction of an arbitrary amount from the 

clients’ loans during disbursement to create a 

fund to cover bad loans 

• Pressure on loan officers to repay clients’ 

arrears from their own salaries 

• Utilization of inactive savings accounts to pay 

for outstanding debts 

In sum, financial incentives need to be used very 

carefully. Once you turn on the financial incentive 

faucet, it is very difficult to turn off again, especially 

when incentives represent more than half of a loan 

officer’s take home pay. 

Incentives need to account for the social as well as 

the commercial mission of microfinance institutions. 

Financial incentives must be seen within a bigger 

picture of staff motivation that includes a host of 

non-financial rewards as well. Finally, incentives 

need to be consistent with the organization’s 

corporate culture. At PRODEM our motto is: “For 

me to win, everyone must win.” Consequently, 

group-based incentives make more sense for us. 

Eduardo Bazoberry (prodebo@ceibo.entelnet.bo) is the 

Managing Director of PRODEM in Bolivia. Parts of this 

article were drawn from a forthcoming Calmeadow 

publication by Cheryl Frankiewicz entitled, “Building 

Institutional Capacity: The Story of PRODEM 1987-2000.” 

Figure 1: PRODEM Performance Indicators (1994-1999) 

1994 1995 1996 1997 1998 1999* 

Number of Borrowers 9,974 18,309 27,486 38,248 47,130 35,924 

Total Portfolio (million US$) 2.6 4.5 8.4 18.2 24.2 21.7 

Avg. Loan Balance (US$) 261 247 305 477 512 606 

Avg. Loan Balance/GNP per capita (%) na 30.9 36.7 49.2 50.7 60.0 

Total Staff 129 134 159 237 316 323 

Number of Loan Officers 47 81 103 125 164 145 

Loan Officers as a % of Total Staff 36 60 64 53 53 45 

Staff Turnover (%) 7 13 19 14 14 14 

Total Admin. Expenses / Avg. Loan Portfolio (%) 57.5 44.4 32.8 24.2 24.8 25.5 

Avg. Staff Salary (multiple of GNP per capita) na 10.2 9.7 8.4 9.4 9.6 

Productivity: Clients per Staff Member 77 137 173 161 149 111 

Productivity: Clients per Loan Officer 212 226 267 306 287 248 

Portfolio Yield (%) 61.5 43.4 55.8 39.1 41.4 37.2 

Real Yield (%) 49.6 30.1 37.3 32.0 31.3 34.2 

AROA (%) -7.1 -7.6 5.1 9.4 5.0 0.6 

AROE (%) -8.0 -9.0 9.6 14.8 9.5 1.4 

Operating Self-sufficiency (%) 101 102 178 167 132 105 

Financial Self-sufficiency (%) 72 69 119 146 117 102 

* The weaker performance in 1999 resulted from two factors. First, the entire Bolivian microfinance industry was hit hard by 1) 

a weak economy, 2) overindebtedness due to aggressive consumer lenders, and 3) political agitation for debt forgiveness. 

Second, PRODEM was consolidating its position in preparation for the launch of its FFP. 

Source: MicroBanking Bulletin database; Frankiewicz (forthcoming) 



Dropouts and Graduates: Lessons from Bangladesh 

Graham A.N. Wright 

In microfinance, the value of retaining clients is 

particularly clear. Typically, repeat customers have 

a credit history and want to borrow larger loans, 

whereas new customers require induction training 

and can often weaken the solidarity of groups. 

MFIs typically break even on a customer only after 

the fourth or fifth loan (Brand and Gerschick, 2000). 

And yet, many MFIs suffer chronic problems with 

client desertion. 

Client desertion has profound implications for the 

viability of an MFI. High dropouts cost the organization 

dearly. Groups from which members drop out 

are destabilized and must recruit new (less experienced) 

members, who qualify for smaller loans thus 

reducing the overall interest income for the 

institution. The new members have to take a disproportionate 

risk and guarantee the larger sums 

taken by their fellow group members, adding further 

stress to the group guarantee. 

Each dropout is a lost client who underwent 

lengthy, expensive training. The replacement members 

must either receive this training on an individual 

basis, or join the system without the training 

that many MFIs regard as critical. The former 

option of ad hoc training is extremely inefficient, and 

the latter—if indeed initial training is so 

important—threatens to undermine the system. In 

the face of frequent or multiple dropouts, some of 

the groups may disintegrate entirely. 

Careful analysis of the reasons for these dropouts 

almost invariably points to inappropriately designed 

products that fail to meet the needs of the 

customers (see Wright, 2000 and Hulme, 1999). 

Much of this problem is driven by the attempts to 

replicate models and products from foreign environments 

without reference to the economic or sociocultural 

conditions into which they are being 

imported. This has been exacerbated by the lack of 

competition in the markets where the original 

models were developed. 

Ironically, many of the clients are driven out not only 

by the inappropriate design of loan products but 

also by the unwillingness of MFIs to recognize that 

(particularly in rural areas) there are seasons when 

savings services, not loans, are required. Thus 

clients are forced either to borrow and try (against 

the odds) to service the loan, or to leave the MFI. 

All the while, their need for savings is unmet and 

ignored. 

Dropouts In East Africa 

In East Africa, the rate of client dropout ranges 

between 25 and 60 percent per annum. 11 Clearly this 

represents a substantial barrier to achieving selfsufficiency. 

When an organization loses over a 

quarter of the clients every year, it is “running hard to 

stand still”. In the words of Hulme (1999), “client exit 

is a significant problem for MFIs. It increases their 

cost structure, discourages other clients and reduces 

prospects for sustainability.” 

Finally, dropouts often leave because they cannot 

(or do not want to) manage loan repayments. 

These clients stop attending meetings and, freed 

from the group guarantee and from the incentive of 

continued access to financial services, are likely to 

leave behind an unpaid loan. 

High dropout rates often indicate dissatisfaction 

with the financial services offered by the institution. 

Members choosing to leave generally do so either 

because the organization is not providing good 

enough services to warrant the (social and 

financial) costs involved, and/or because they have 

identified a better alternative. 

Members expelled from a microfinance program 

(for, of course, not all dropouts are voluntary) are 

likely to be indicative of an even more complicated 

bundle of factors, including: client selection (or 

better said “de-selection”) by fellow members 

and/or staff, the clients’ inability to pay loans or 

even savings, and the clients’ unwillingness to 

repay loans, which is in part a proxy indicator for 

customer dissatisfaction. 

Dropouts in Bangladesh 

The reasons for desertion are multidimensional. 

Indeed, the unifying theme of the studies on the 

subject is that dropout causes are complex. Khan 

and Chowdhury (1995) present interesting findings 

on the high proportion of voluntary dropouts driven 

away by the inflexibility of BRAC’s system—in 

particular its savings facilities (see Figure 1). 

11 

The desertion rate is the number of dropouts in the period / 

[(number of clients at beginning of the period + number of clients 

at the end of the period) / 2]. If the period is not a year, then the 

rate is annualized. 



Figure 1: Reasons Frequently Cited for 

Dropout and Expulsion by Gender 

Percentage of dropped out 

members mentioned 

Reasons for voluntary 

dropout 

Male Female Total 

Group fund is not refunded 

Savings not withdrawable 

in emergency 

Other NGOs provide better 

facilities 

Failure to repay loan 

Family problem 

63.2 

55.3 

36.8 

33.6 

11.8 

70.4 

59.2 

52.7 

38.5 

45.0 

68.0 

57.3 

49.8 

36.6 

29.3 

Reasons for expulsion 

Failure to repay loan 

Irregular attendance in 

44.8 

17.2 

56.1 

41.5 

59.6 

27.3 

meeting 

Source: Khan and Chowdhury (1995) 

An examination of various studies on dropouts in 

Bangladesh reveals a common theme among the 

75 percent of dropouts who leave voluntarily: dissatisfaction 

with the available financial services, 

and a belief that other MFIs offer better services 

(including crucially, how the organization’s staff 

behave toward their clients). 

One of the key desertion determinants, often lost in 

the category “failure to repay loan”, is the insistence 

by field staff that clients take loans. 12 Irrespective 

of official policy, there is a clear understanding 

among most field staff that they should push out 

loans—often with little care for whether the clients 

need or can use them. In the words of one BRAC 

Zonal Manager, “If we do not disburse loans how 

can we cover costs?” (personal field notes, 1996). 

Similarly, PromPT’s 1996 study of members’ 

perceptions of Grameen, BRAC, Proshika, ASA and 

other Bangladeshi MFIs (using participatory rural 

appraisal and focus groups) found that many 

borrowers felt pressurized or “sweet-talked” into 

taking loans. Matin (1998) also notes, “MFI lending 

technology is insensitive to variations in household 

conditions. Most MFIs put all households on a 

treadmill of continuously increasing loan size and 

insist on a fixed repayment schedule.” 

Additional evidence for this pressure is seen in the 

percentage of clients with outstanding loans at any 

one time. BURO Tangail offers credit on an entirely 

voluntary basis, as and when the client wants it, 

and (subject to graduated ceilings) however much 

the client wants. As a result, at any one time only 

about half of its clients have a loan outstanding. By 

contrast, almost all clients from Grameen, BRAC 

and ASA have outstanding loans. 

12 

There are indications that these practices are now declining. 

In the extreme case, ASA’s loan policy dictates 

when borrowers must take a loan and how big the 

loan must be with no reference to the need of the 

client. This policy has lead to a remarkable ability 

of clients to manage their way around the system 

through on-lending, reciprocal agreements and 

cumbersome storage arrangements (Rutherford, 

1995). But clearly, managing one’s way around an 

inflexible, credit-happy system is not ideal, and so 

clients will begin to look at the services offered by 

other MFIs. 

Clients are now shopping around in search of 

flexible, quality financial services. In the words of 

Khan and Chowdhury (1995), “Other NGOs…working 

side by side with BRAC in the same areas 

provided extra facilities to [village organization] 

members. These included: less deductions from 

the loan, higher loan ceiling, low interest rate, quick 

disbursement, etc.” The study revealed that many 

dropouts enrolled themselves with other MFIs for 

better terms and opportunities. The microfinance 

institution that wants to reduce its level of debilitating 

dropout should carefully examine its services 

and products, and seek to improve them continuously. 

Graduates in Bangladesh 

One reason for dropping out, notable by its almost 

complete absence from the above description, is 

client graduation. A few years ago, there was a 

belief that credit programs would give such a boost 

to the income of “beneficiaries” that they would 

graduate from poverty. The dynamics of poverty 

are such that the route out of poverty is neither 

linear nor absolute (Wright, 2000). 

There were two schools of thought on graduation. 

One held that after a limited number of benign 

(subsidized) loan cycles, the borrowers’ businesses 

would no longer need credit. In retrospect, this was 

supreme naiveté, for there is scarcely a firm in the 

world that does not use overdraft facilities to 

manage its way through business cycles. And vast 

international financial markets have developed 

around businesses’ need for capital for expansion. 

The other school, more plausibly, believed that poor 

clients could graduate with enough wealth and selfconfidence 

to become the clients of formal sector 

banks. Indeed many MFIs establish self-help 

groups, credit unions or village banks and link them 

to formal sector financial service institutions. This is 

a more viable and desirable option for foreign 

NGOs and government projects that do not intend 

to establish a permanent banking institution. 

But for NGOs seeking to establish permanent MFIs, 

these richer potential graduates are their most valu- 



able clients. These clients often take larger loans to 

expand the working capital of their businesses or to 

finance asset acquisition. The MFI will make most 

of its profits on these larger loans since the cost of 

administering a loan is almost the same irrespective 

of its size. These long-term customers should also 

be better credit risks—although this is subject to 

debate. And crucially, these larger-loan clients 

allow the MFI to finance its smaller loans to poorer 

clients. The last thing that an MFI, with its sights 

set on financial sustainability, wants to see is the 

graduation of these precious clients. Instead, MFIs 

should retain them by seeking to meet their needs 

through a range of client-responsive financial 

services. 

Conclusions for the Microfinance Industry 

There is compelling evidence, not just from 

Bangladesh, to support the contention that most 

dropouts occur because MFIs do not meet the 

needs of their market. Dropouts are expensive for 

MFIs, in terms of money already invested that is 

lost when the member leaves, and the lost potential 

business from that member in the future. MFIs 

seeking to develop permanent sustainable organizations 

should improve their services to reduce client 

dissatisfaction and thus desertion. Such a strategy 

is likely to prove cost-effective. 

For MFIs committed to creating permanent institutions, 

graduating the most experienced and affluent 

clients into the formal banking system is not a 

desirable strategy as it implies the loss of the most 

valuable and cost-effective clients. Indeed, MFIs 

should tailor their services to ensure that they retain 

these high value customers. 

For all these reasons, MFIs should pay (and indeed 

are paying) close attention to the nature and quality 

of their financial services. The trade-off between 

the quality of the services and cost of providing 

them is clear, but finding the right balance is 

difficult. To date, MFIs in Bangladesh have put too 

much emphasis on trying to implement standardized, 

inflexible, low-cost, credit-driven systems 

when their clients are asking (and willing to pay) for 

a broader range of quality services. 

The irony of this situation was that the genesis of 

microfinance in Bangladesh was originally driven by 

an extensive program of careful market and operations 

research designed to understand the needs 

of the clients. Professor Yunus’ work with his students 

at Chittagong University in the village of 

Jobra in 1976 was quintessential market research. 

It is to the fundamentals of market research and 

product development that MFIs must return if they 

are to retain clients and build sustainable 

institutions. 

Graham Wright is Programme Director of MicroSave- 

Africa, Chair of CGAP’s Savings Mobilisation Working 

Group and a Research Associate at the Institute of 

Development Policy and Management, University of 

Manchester, UK. He can be reached at 

Graham@MicroSave-Africa.com. 

References 

Brand, M. and J. Gerschick. (2000). Maximizing Efficiency in 

Microfinance: The Path to Enhanced Outreach and 

Sustainability. Washington: ACCION International. 

Hulme, D. (1999). “Client Exits (Drop outs) from East African 

Micro-Finance Institutions.” Kampala: MicroSave-Africa. 

Khan, Md. K. A. and A.M.R. Chowdhury (1995). “Why VO 

Members Drop Out.” Dhaka: BRAC. 

Matin, I. (1998). “Informal Credit Transactions of Micro-Credit 

Borrowers in Rural Bangladesh.” mimeo, Dhaka. 

Mustafa, S, et al. (1996). “Beacon of Hope: An Impact 

Assessment Study of BRAC’s Rural Development 

Programme.” Dhaka: BRAC. 

PromPT (1996). "Financial Services for the Rural Poor - 

Users' Perspectives." Dhaka: PromPT. 

Rutherford, S (1995). ASA: The Biography of an NGO, 

Empowerment and Credit in Rural Bangladesh. Dhaka: ASA. 

Wright, G.A.N. (2000). “Microfinance: The Solution or a 

Problem?” in MicroFinance Systems: Designing Quality 

Financial Services for the Poor. Dhaka: University Press 

Limited and London: Zed Books. 



Exodus: Why Customers Leave 

Kim Wilson 

A customer is an asset. It is hard to disagree with 

that statement. Yet the microfinance industry has 

not caught on to a customer’s value. While many 

microfinance institutions pay close attention to 

minimizing the costs of delinquency, they seem to 

disregard the expense of losing good customers. 

This article proposes that the microfinance industry 

look at deserting customers with the same passion 

and precision as it examines delinquency. Two 

reasons compel us to do so. First, lost customers 

place our social agenda in peril. How can the 

empowering benefits of microfinance take place if 

our customers flee after one loan cycle? And, 

worse, we may be losing our poorest customers, 

those whom we most want to serve. Second, lost 

customers cost money. They cost additional marketing 

dollars to attract and prepare new customers 

for loans; and they cost us in lost profits. 

An Example of Using Dropouts to Inform 

Marketing Decisions 

The first step in stemming the flow of dropouts is to 

understand who is dropping out and why. At Mikra, 

a new MFI in Bosnia-Herzegovina, the Executive 

Director, Sanin Campara, and his staff pay special 

attention to data on deserting customers. They use 

data to draw conclusions and make changes in the 

program. Campara believes that information supplied 

by deserting customers is sometimes far more 

valuable than data from existing customers. 

Deserting customers have nothing to fear in 

answering questions. They have already made the 

decision to leave, so information is likely to be valid, 

absent any bias a current customer might have. 

Current customers may fear losing services altogether 

if they are honest in answering questions. 

Deserting customers can afford to be honest. 

Moreover, deserting customers offer a marketsavvy 

MFI more than information about themselves. 

They may offer clues as to why some customers 

may not be interested in the MFI in the first place, 

as well as insights into the relative attraction of the 

competition (where applicable). 

To understand deserting customers better, 

Campara and his team interview every single 

dropout. To simplify the procedure, they check off 

reasons for dropping out on a one-page sheet that 

displays the most commonly cited answers. 

They found that in one town, Kakenj, business 

failure caused 30 percent of the dropouts. Kakenj 

had suffered many economic set backs—the closing 

of a coal mine, thermo-electric plant, and the 

state-owned salmon processing facilities. Mikra 

immediately put this information to use by tailoring 

services to the local environment. In Kakenj, the 

loan officers started to help village banks become 

mindful of potential business problems due to a 

failed economy. 

In another example, 7 percent of the dropouts 

indicated that the loan terms were too short. While 

only a small percentage, this figure became alarming 

when staff discovered that the clients in this 

category included some of Mikra’s best customers 

who left as they approached their fourth loan. Mikra 

responded by lengthening the term in the fourth 

cycle from six to eight months. Was this the right 

move? Mikra staff polled two village banks in the 

fourth cycle and learned that 13 percent would have 

dropped out if Mikra had not lengthened its loan 

term. When asked how many would have dropped 

out if competition had offered longer terms, eleven 

of twenty-five members in one village bank responded 

that they would have defected. 

Mikra is now turning its attention to the relationship 

between delinquency and desertion. Are delinquent 

customers dropping out when they would prefer to 

stay? Does an increase in tardy payers who are 

forced to leave the program signal the need for a 

product that better matches their cash flow? At 

Mikra, approximately 13 percent of the dropouts 

were delinquent. Staff is fine-tuning their data 

gathering and database filters to determine who is 

both delinquent and dropping out. Do dropout/ 

delinquent customers have larger loans? Since 

larger loan customers are more profitable, changing 

the loan product to meet their needs may be worth 

the effort (if it does not increase the organization’s 

credit risk). If new customers are deserting, this 

suggests the need for adjustments to the marketing 

and screening processes. 

Mikra has one of the lowest desertion rates of all 

CRS-sponsored programs. Over the past 30 

months, with 2,500 customers, Mikra enjoys an 

annualized desertion rate of less than 11 percent. 13 

Is this because Mikra’s attention to deserters is 

exactly what prevents customers from deserting? 

At this early stage, we can only speculate. 

13 

This is calculated as follows: (number of dropouts last month / 

number of clients this month) x 12. 



How to Analyze Dropouts 

There are many ways to collect and analyze 

information on desertions. Each MFI must balance 

the efficiency of collecting information with the 

quality or depth of the information. The downside of 

conducting exit interviews with each lost customer 

is that it can add significant expenses to the lending 

process. And if the interviewing process becomes 

routine, as it often does, the value of the information 

collected is low. Ideally, someone other than the 

deserter’s loan officer would interview the dropout 

because there is a chance that the loan officer was 

the problem. 

Tim Nourse, CRS advisor in Gaza, believes that a 

cost-benefit analysis of exit interviews reveals the 

imperative of interviewing every deserter for three 

reasons. First, if the organization does not interview 

each dropout, it may miss people who would 

have indicated dissatisfaction with staff. Second, 

staff would rather not interview dropouts at all. 

Such interviews can be tedious and depressing, 

motivating staff to keep customers if for no other 

reason than to avoid the tiresome task of interviewing. 

And third, the effort to ask people their 

opinions is often sufficient motivation to encourage 

them to return. 

Once the MFI decides how to gather information, it 

must decide what data to collect. Reasons for 

desertion fall into three categories depending on the 

level of control that the MFI can exert on them: 

• Can Control: Product competitiveness, staff 

attitude toward customer, general 

dissatisfaction with loan product (terms, rates, 

payment schedules); 

• Might Control: Family problems, business 

problems, and delinquency; 

• Cannot Control: Customer unwillingness to 

repay own loan, family members stealing or 

forbidding loan payments. 

Regarding the “Might Control” problems, MFIs that 

see the value in customer retention will create 

systems or products that do not force a customer to 

dropout because of a family crisis, or will develop 

mechanisms for supporting good customers who 

experience temporary business problems. 

The exit interview form in Box 1, a blend of tools 

used in Gaza and Bosnia-Herzegovina, offers an 

example of how broad areas of classifying dropouts 

are organized to collect data. Initially, an MFI may 

have many write-in responses while developing an 

efficient tool. As patterns appear, those write-ins 

may become pre-written items for staff to check off, 

making the exit interview faster and data entry 

simpler. 

Box 1: Sample Exit Interview Form 

Customer Name: 

Loan Officer: 

Village: 

Date: 

Profile of Lost Customer 

Amount of most recent loan: 

Amount of savings: 

Length of time with MFI: 

Length of time in business: 

Business Type: Trading food Trading non-food 

Fishing/farming Manufacturing Services 

Poverty Level: 1 2 3 4 5 

Were payments on recent loans delinquent? Y / N 

Was customer asked to leave MFI? Y / N 

Reasons for Dropping Out and Rejoining 

1. If customer is not going to competition, check one 

and skip 2: 

Unwilling to repay loan (bad customer) 

Dissatisfaction with loan amount 

…with loan payment schedule and term 

…with requirements or restrictions 

…with savings product 

…with staff 

Moving out of town 

Problems with business 

Family or personal problems 

Problems making payments for group 

Other___________________ 

2. If customer is going to competition, the most 

important reason is that competition has: 

Better staff 

Better loan product (terms, payments, rate) 

Better savings product 

Convenience of location 

Less restrictions or requirements 

Other________________________ 

3. I would rejoin if___________________________ 

An Excel database can help analyze data. 

Management can filter for different information. For 

example, what percent of small-scale producers 

drop out because of an inadequate loan product? 

Or what percent of customers who have been with 

the MFI for more than three years leave because of 

competition or because they had difficulty meeting 

requirements? Do more start-ups drop out in trade 

or in production? Are the poorest customers dropping 

out at a greater rate than the less poor? 

In Gaza, which enjoys a low dropout rate of 12 

percent, Nourse believes that the most important 

part of gathering desertion information is training 

staff to collect data routinely, with care and 

diligence. Since staff members are too willing to 

skip this activity in favor of something more enjoyable, 

the MFI should laud incentives or recognition 

on the loan officers with best records of collecting 



exit interview data and with the best suggestions for 

improving the products and services. He advises 

that management must offer regular training for field 

staff in both interviewing clients and in correctly 

completing an exit interview form. 

The effort is worth it. Through exit interviewing, 

Nourse learned that dropouts valued the business 

support of a solidarity group more than the repayment 

support. The organization has since used this 

insight to help market to new customers. The 

unearthing of these details points to something 

essential. Management must involve itself in interviewing 

lost customers. Important information, 

whether statistical or anecdotal, is most useful 

when management can interpret it in light of 

broader strategic issues. By setting an example, 

management also helps establish a culture and 

commitment to collecting data from lost customers. 

Keeping Customers: Is It Worth It? 

The cost of a lost customer is not nominal. A 

dropout may cost an MFI as much as a defaulted 

loan because the organization loses a lifetime of 

profits from that customer. Good customers often 

leave MFIs because they had bad experiences, 

such as having to pay for the debts of a group 

member. But if the MFI had retained that good 

customer for five or ten years, then that client might 

have generated significant returns to the MFI, 

directly in the form of profits, and indirectly through 

referrals. 

While MFIs can certainly learn a lot from lost 

customers, are they willing to go to great lengths to 

keep their clients? Many organizations resist reengineering 

systems to suit customers. Expense, 

hassle and inertia stand in the way of improving 

back-end processes to increase customer retention. 

The potential for lost profits may warrant training 

staff in tracking dropouts, creating databases, and 

building capacity to analyze information. These 

“losses” due to dropouts may warrant making 

changes to products, marketing strategies, and 

information systems. 

Mainstream businesses have found that when 

desertions are cut in half, profits may increase by 

85 percent and beyond. 14 If this holds for MFIs, the 

right product, staff training and MIS designed to 

improve satisfaction and retention will yield benefits 

above the investments made in reducing delinquencies 

and defaults. 

How can MFIs determine if keeping customers is 

worth the expense and effort? It is necessary to 

conduct a cost-benefit analysis that includes the 

financial benefits of retaining customers. For 

example, in assessing whether an investment in a 

line of credit product would be worthwhile, an MFI 

has to consider not only whether that product is 

profitable, but also the effect that the product would 

have on customer retention. 

Beyond retained profits, customer retention means 

that clients will remain with the MFI long enough to 

extract some social benefits from the program. By 

remaining with the MFI, the clients should experience 

increased income because they have ongoing 

access to financial services. Their continued participation 

should also help boost their confidence and 

encourage them to take leadership positions in the 

home and in the community. 

Where Do We Go From Here? 

The microfinance industry needs to make some 

radical changes in how it treats its customers. They 

are why the industry exists. Below are some ideas 

of how to improve the industry’s awareness of 

desertion and ways to improve retention. 

• All MFIs should commit to tracking each and 

every dropout; 

• Management should commit to participating in 

exit interviews, to analyzing data, and to 

discussing appropriate responses with staff; 

• MFIs should reward the accurate tracking of 

dropouts with the same kind of incentives 

offered to staff for good delinquency 

management; 

• The microfinance industry, including The 

MicroBanking Bulletin, should standardize the 

definitions of relevant ratios and then begin 

tracking desertion rates and the net profit per 

customer. 

By approaching this critical issue collectively, and 

taking the steady stream of customers that revolve 

through our doors seriously, we will improve performance 

and restore the social benefits inherent in 

good microfinance institutions. 

Kim Wilson (kwilson@catholicrelief.org) is Senior Advisor 

for Microfinance at Catholic Relief Services. 

14 Reichheld, Frederick F, and W. Earl Sasser, Jr. (1990, Sept.- 

Oct.). “Zero Defections: Quality Comes to Services”. Harvard 

Business Review, pp. 107-110. 



Cultivating Client Loyalty: Exit Interviews from Africa and Asia 

Inez Murray 

Losing clients is expensive. If an MFI with 30,000 

clients loses 20 percent of its customers per 

year—that is 6,000 people. If the average loan size 

is US$150, and in a lifetime a client might borrow 

10 loans, the MFI is losing up to US$9 million in 

future sales. Add to this the fact that many MFIs do 

not break even until their fourth or fifth loan 15 and 

that the client who left probably told nine other 

people about their negative experience with the 

institution, 16 and it is clear why retaining clients is a 

key to profitability. 

As the microfinance industry becomes more 

competitive, clients have more choice regarding 

where to shop. If an MFI is to remain competitive, it 

must get an accurate read of clients’ satisfaction 

and dissatisfaction with its loan products and 

service delivery. It must understand why clients 

leave, why some of them go to a competitor, and 

what kind of product range it should consider 

offering to retain clients throughout their lifecycle. 

Customers leave an organization for many reasons, 

some of which the MFI can mitigate and some that 

it cannot. For the purpose of analysis, the former 

are considered internal factors, and the latter 

external. Internal factors include: 

• High prices 

• Rigid product design 

• Narrow range of products 

• High transaction costs 

• Insufficient attention to customer service 

External factors are exogenous to the institution 

such as illness, death, family problems, seasonality 

seasonal migration patterns, natural disasters, 

increasing competition, and economic shocks. 

In the past couple of years, Women’s World 

Banking (WWB), with the assistance of Monitor 

Group, has conducted intensive research into 

customer satisfaction and retention as part of a 

strategic positioning service for affiliates operating 

in increasingly competitive markets. 17 

This paper shares some key findings regarding 

client desertion from two affiliates, Shakti Foundation 

for Disadvantaged Women in Bangladesh and 

the Uganda Women’s Finance Trust (UWFT). 

Understanding why clients left was one small part of 

the overall research. This article is a precursor to a 

more comprehensive publication on the findings of 

the strategic positioning work completed in Colombia, 

Bangladesh, Uganda, and Bosnia-Herzegovina. 

The focus on why clients drop out tends to put a 

spotlight on areas that an institution may need to 

improve. Consequently, WWB thanks both organizations 

for their willingness to share the results of 

this research as a contribution to the field. 

This article is divided into three sections: 1) 

research methodology, 2) context (description of 

lending methodologies, price and service delivery 

attributes that are necessary to interpret results), 

and 3) findings and implications. 

Research Methodology 

In both affiliates, two populations were surveyed: 

active and former borrowers (dropouts). Trained 

college graduates, fluent in local languages, 

conducted the interviews. Respondents were selected 

based on stratified random sampling and a 

sufficient number of interviews were conducted to 

give a margin of error of +/-4 percent. 18 

With the sample of dropouts, several sets of 

questions were used to identify desertion drivers 

including asking clients directly using both openand 

close-ended questions. 19 All respondents also 

ranked their satisfaction level with each aspect of 

the product and service delivery mechanism. In 

general, current and former clients expressed high 

levels of dissatisfaction with the same product and 

service delivery attributes that caused client 

desertion. 

15 

Brand, M. and J. Gerschick. (2000). Maximizing Efficiency 

in Microfinance: The Path to Enhanced Outreach and 

Sustainability. Washington: ACCION International. 

16 

Bhote (1996). Beyond Customer Satisfaction to Customer 

Loyalty: The Key To Greater Profitability. American Management 

Association, New York, p. 60. 

17 

The Strategic Positioning Product (SPP) is a technical service 

that helps an MFI define or re-define its market position (i.e., 

methodology, product menu, price, customer segments, etc.) to 

give it long-term competitive advantage. It is based on work 

conducted jointly with Monitor Group, a global family of 

professional service firms. 

18 

Sample size: Bangladesh: survey sample size of current clients 

= 515 = +/- 4%; of former clients = 541 = +/- 4%; Uganda: 

sample size of current clients size 507 = +/-4%; of former clients 

= 500 = +/-4%. Two strata were used to ensure sample 

dispersion – branch and loan cycle. 

19 

With close-ended questions, respondents choose from a list of 

options; in open-ended questions, the respondents reply with the 

first thought that crosses their mind. 



Context 

To interpret the results it is necessary to provide 

some background on the credit methodology, the 

product design, and the resulting transaction costs. 

The terms and conditions of the basic loan product 

in both affiliates are summarized in Figure 1. 

Group Structure: In Shakti Foundation, the Center 

consists of six groups of five low-income women for 

a maximum center size of 30 persons. Each group 

elects a chair and each center elects a leader. In 

UWFT, groups may have already existed for other 

purposes (e.g. church-based groups). Groups have 

a minimum of five people and no maximum—some 

have 60 or more members. When groups form, 

they elect a chair, treasurer and a secretary. Unlike 

in Bangladesh, there are no sub-groups within a 

group. 

Group Formation: Each Shakti group undergoes 

an 8-week training, at the end of which they must 

pass an oral test that determines if they understand 

the rules of the institution. After passing the test, 

clients save for one week before applying for a 

loan. UWFT groups are also trained in group roles 

and responsibilities and the terms and conditions of 

borrowing, but no official test is administered. Once 

each member has saved 30 percent of the principal 

of the loan, the group can submit its application. 

Loan Disbursement: Shakti loans are issued to 

each individual within a group on a 2-2-1 basis, i.e., 

two group members get loans the first week; the 

second two members receive loans a week later 

provided the first two have paid their installments; 

then the last member receives her loan the 

following week. Repayment is made at the center 

meetings to the credit officer. 

In UWFT, the loan is issued to group leaders who 

collect the money from the branch office on behalf 

of the group. The group is responsible for ensuring 

that each member receives her approved amount. 

Clients repay their loans to group officials who then 

submit repayments to the branch. The credit officer 

is not present at all group meetings. 

Dropout Rate 

The dropout rate 20 at Shakti Foundation is reported 

to be 9 percent in 1999 down from 14 percent in 

1998, compared to norms in Bangladesh of 10 to 15 

percent. In UWFT, the dropout rate is not tracked, 

but the industry norm of 25 percent or more 21 is a 

valid proxy. Seventy percent of dropouts inter- 

20 

The dropout rate formula is 1 – (number of borrowers (end of 

period) – number of new borrowers (for period) / number of 

borrowers (beginning of period)). 

21 

Wright et al. (1999). Drop-outs Amongst Ugandan MFIs. 

MicroSave-Africa, p. 1. 

viewed in Uganda described themselves as 

“resting” and may borrow again 22 . 

Figure 1: Terms and Transaction Costs of 

Basic Group Loan Product* 23 

Shakti Foundation, 

Bangladesh 

UWFT, Uganda 

Target Area 

Urban only 

Mainly urban and 

semi-urban 

Credit methodology 

Group Lending Grameen/ village 

(Grameen) 

banking hybrid 

Starting loan size Tk. 4,000 ~ US$75 150,000 Shs ~ US$98 

Maximum loan size 

Avg. outstanding 

balance per borrower 

Increments 

Tk. 10,000 ~ 

US$189 

500,000 Shs 

~US$327 

US$71 (as of 12/99) US$111 (as of 7/99) 

Standardized by 

cycle 

Based on clients’ 

repayment capacity 

and savings 

GNP per capita 24 US$350 US$310 

Loan term (average): 50 weeks 16 weeks 

Fees and 

commissions/ 

compulsory savings 

Group Fund: 5% of 

principal 25 

Health Fund: Tk. 1 

per week 26 

MFI Development 

Fund: 2% of principal 

Commission: 2% of 

principal 

Compulsory savings: 

Tk. 10 per week 27 

Compulsory savings: 

30% of principal 

Other fees: 5,000 Shs 

for stationary 

Nominal interest rate 1% flat (per month) 2.5% flat (per month) 

Effective interest rate 

(excl. savings) 

21.5 % 75 % 

Other requirements: 

Collateral N/A Pledge one asset 

Guarantors N/A Two guarantors 

Group guarantee Must guarantee Must guarantee peers 

Other 

Group meetings: 

peers 

N/A 

Signature from Local 

Councilor 

Frequency Weekly Weekly, bimonthly or 

monthly 

Attendance Compulsory Not compulsory 

Average time to form 

a group 

9 weeks (8 weeks 

group formation plus 

1 week saving) 

Voluntary savings No Yes 

12 weeks 

22 

This question of “resting” was not asked in Shakti because it 

was understood that the phenomenon is not common in 

Bangladesh. However, since so many Ugandans regarded 

themselves as “resting” the assumption that all clients want or 

need to borrow on a continuous basis appears to be false. 

23 

Terms and conditions approximate industry norms in the 

respective countries and all data reflect institutional policies at 

the time the surveys were administered. 

24 

As of 1998. World Development Indicators 2000, World Bank. 

25 

This savings is deducted from the loan principal at the time of 

loan disbursement and can only be withdrawn if client leaves the 

organization after five years. 

26 

This is a premium for credit life insurance. If a borrower dies, 

the Health Fund pays a designated surviving family member Tk. 

4,000 from which the outstanding loan balance is deducted. 

27 

This consists of Tk. 5 for personal savings accessible when 

the client leaves; and Tk. 5 for the Center Fund, accessible only 

if she remains with the organization for at least five years. 



Industry Dynamics 

In a short article it is impossible to do justice to the 

contextual differences between urban Bangladesh 

where Shakti operates and the semi-urban markets 

served by UWFT. What is important to mention is 

that both institutions offer loan terms and service 

delivery mechanisms that are similar to most 

players in their market and that therefore the results 

of this research are broadly applicable to other 

MFIs. 

Findings and Implications 

Figures 2 and 3 summarize the main reasons why 

customers left each program. This section analyzes 

these findings and discusses their implications. 

External Reasons 

The external factors cited for dropping out highlight 

the fact that MFIs are serving a precarious market 

and many unexpected events can influence a 

customer’s demand for financial services. 

Business problems: Low profitability and business 

failure were important reasons why clients left both 

institutions. Clients cited access to business development 

services (BDS), particularly marketing support 

and skill development, as a huge unmet need. 

In both countries, clients must have a business 

when they apply for a loan, but not necessarily 

before that time. This policy allows the MFI to 

deepen its outreach, but it could cause an increase 

in attrition due to business failure. 

Do not need a loan right now: Some clients said 

they were not interested in another loan at that 

time, others that they were tired of borrowing or, in 

Bangladesh, some clients had found employment. 

Moved location: Migration was given a much 

higher ranking in Bangladesh than in Uganda, 

reflecting the more transitory nature of the urban 

population in Bangladesh. Some clients migrate 

temporarily to the city due to river erosion or 

indebtedness in rural areas, while others go back to 

rural areas on a seasonal basis to harvest crops. 

Illness: Illness, of either the client or a family 

member, is a reason cited in both countries, reflecting 

the poor conditions in which many clients live 

and the limited availability of health care for 

borrowers and their families. 

Other external reasons include their husbands 

leaving, the house or business was robbed, and 

famine in one region of Uganda. 

In sum, external desertion drivers illustrate the 

vulnerable nature of microfinance clients. These 

vulnerabilities, which adversely affect both retention 

and repayments, highlight how important it is for 

MFIs to address other needs of their clients besides 

credit. MFIs can either develop in-house capacity 

or form strategic alliances to provide voluntary 

savings, microinsurance and business development 

services. Offering access to these valuable wraparound 

products could be an important dimension 

of any client retention strategy. 

Internal Reasons 

The internal reasons for leaving fall into three 

categories: loan-related, transaction costs, and 

customer service. In Bangladesh the key internal 

causes of desertion were group meetings (frequency, 

duration), group guarantee, and loan 

related issues (amount, interest rate). In Uganda, 

the key internal desertion drivers were loan related 

issues (term, interest rate, installment amount, 

compulsory savings), the desire for individual loans, 

Figure 2: Reasons for Leaving * 

Bangladesh 

3% 

9% 

Uganda 

14% 29% 

51% 

31% 

32% 

31% 

External 

Loan Related 

Transaction Costs 

Customer Service 

* In response to the open -ended question: “Why did you leave the institution? Please tell me the most important reason.” 



the group guarantee, and needing more support 

from the institution to solve problems. 

Loan-Related 

In Uganda, issues relating to the loan (price, term, 

amount, requirements) were stronger desertion 

drivers than in Bangladesh. This finding reflects the 

fact that loans in Uganda are more expensive, for 

shorter terms, and require several additional guarantees 

besides the group and compulsory savings. 

Interest rate: In Uganda effective interest rates 

(taking compulsory savings into account) are four to 

five times higher than in Bangladesh. A segment of 

UFWT borrowers found that rate to be beyond a 

tolerable threshold. While it is important to 

acknowledge the contextual differences (e.g. lower 

population density and higher salaries) that make 

the cost of doing business substantially higher in 

Uganda, the challenge is to continuously lower 

costs and pass on those savings to clients. 

Loan term: Clients in Uganda mentioned that the 

16-week loan term was a reason for dropping out, 

whereas the 50-week term at Shakti was not an 

issue. Since in both countries clients are predominantly 

engaged in trading, this finding does not 

necessarily reflect a mismatch between business 

activity and loan term. Instead, it may be more 

useful to look at the combination of short terms with 

high interest rates that produce prohibitively large 

installment sizes for some clients. 

Clients in both countries left because they could not 

repay the loan, but this was a bigger factor in 

Uganda. This difference relates to larger installments, 

as well as to the fact that a higher percentage 

of Ugandan clients used their loan for nonbusiness 

purposes such as paying for school fees. 

Borrowing requirements: In Uganda, the strict 

borrowing requirements—including 30 percent 

compulsory savings, two co-guarantors and a 

pledged asset—was a major desertion driver. In 

both Bangladesh and Uganda, and indeed in many 

group-lending methodologies, some element of 

compulsory savings is required. If an MFI is to 

continue to demand compulsory savings then it 

should only require a fair minimum, it should offer to 

pay reasonable interest on it, and, within limits, it 

should provide clients with flexibility in access (e.g. 

the ability to draw down on some portion of that 

money to pay an installment if necessary). 

Loan amount: Accessing larger loans is among the 

top five needs that clients express no matter where 

they live. Managing the tension between the 

demand for larger loans and the MFI’s credit risk is 

a careful balancing act. 

Clients in both programs identified the low loan 

amount as a reason for leaving. Offering larger 

loans and thereby retaining growth-oriented clients 

is a key to profitability, but it presents a challenge 

for group lenders. Many clients are now demanding 

individual loans, therefore the development of this 

product is a key retention strategy for group 

lenders. 

Transaction Costs 

Group meetings: The frequency and length of 

group meetings was a desertion driver in Shakti 

Foundation. Shakti meetings occur every week and 

attendance is compulsory, whereas at UWFT, 

clients can choose to meet weekly, biweekly or 

monthly, and attendance is not enforced. 

Figure 3: Top Ten Reasons for Desertion* 

Bangladesh 

Uganda 

1. Loan amount was too small (33%) 

2. Too many meetings (28%) 

3. The meetings were too long (25%) 

4. A member defaulted and I did not want to pay for her 

(25%) 

5. Loan was too expensive (high interest rates) (22%) 

6. The institution does not understand my special needs as 

a woman (20%) 

7. I do not need a loan right now (18%) 

8. I had to go to the village (17%) 

9. I (or someone in my family) got sick (17%) 

10. My business was not profitable (17%) 

1. Loan period was too short (65%) 

2. Interest on my voluntary savings was too low (64%) 

3. Loan was too expensive - high interest rates (57%) 

4. Compulsory savings too high (54%) 

5. I wanted to borrow as an individual, not as a group (53%) 

6. The weekly payment amount was too much (51%) 

7. I felt like I was borrowing my own money back (50%) 

8. No opportunities to participate in decisions made by the 

institution (46%) 

9. A member defaulted; I did not want to pay for her (43%) 

10. When a problem arose, not enough support from staff 

(43%) 

* In response to the close-ended question: “Now, I will read you a list of reasons that other people had for leaving X institution. Please tell 

me which ones of the following reasons apply to you.” Ranking based on most frequently answered reasons. 



The group meeting is the nexus around which the 

Grameen model is based and indeed the majority of 

clients in both Bangladesh and Uganda said they 

liked attending meetings. In Bangladesh, clients 

indicated that they liked the opportunity to socialize, 

whereas in Uganda the meetings presented an 

opportunity to share ideas and learn from each 

other. However, some clients definitely tire of the 

meetings. For some borrowers, the opportunity 

cost becomes too high; for others, changes in 

family situations make attendance difficult. 

Group guarantee: The ‘peer guarantee’ is an 

essential element of most group lending methodologies. 

It acts as a collateral substitute, which 

enables group lenders to target client segments 

below that of individual lenders. However the 

majority of respondents in both countries do not like 

it. Not only do they not like paying for others, but 

also they do not like others paying for them. 

To reinforce the group guarantee, MFIs need to 

improve client selection and group formation. It is 

important to select only clients who are economically 

active, to be forthright about the risks associated 

with peer guarantees, and to emphasize peer 

‘support’ rather than ‘pressure’ when clients experience 

difficulty. Loan officers also need training to 

help groups work through solutions to repayment 

problems. 

Customer Service 

In both countries, a small percentage of clients said 

that negative interactions with loan officers were the 

reason they dropped out. This appears to be due to 

a mixture of factors, including clients not understanding 

the risks of borrowing and loan officers 

being under a lot of strain and being harsh with 

clients. Clients can and do generalize about an 

entire organization based on one bad interaction. 

Loan officers, and indeed all staff, must see clients 

as customers who are paying for services. They 

must realize that they are managing long-term 

relationships, not one-time transactions. Employees 

should be given the appropriate support from 

the institution, including skills and time, to deliver a 

high quality service. 

Conclusion 

Since this research was completed, both affiliates 

have modified their loan product and transaction 

costs. Shakti Foundation increased loan amounts 

and increments. UWFT reduced the compulsory 

savings requirement, and lowered fees and 

commissions. In addition, one half of the remaining 

fees and commissions were allocated to a loan 

insurance fund. Clients were also given the option 

of increasing loan terms and loan sizes subject to 

their capacity to repay. 

The research shows that microfinance clients, like 

all consumers, behave rationally. They will stay 

with a supplier as long as benefits outweigh costs 

and there are no better alternatives. When they 

leave, they do so because of product features that 

are unsuitable to their needs and/or service delivery 

attributes that are difficult or costly. The research 

highlights some key challenges facing MFIs: 

• Managing the balance between flexible 

products and services and the need to 

standardize procedures to improve efficiency; 

• Building a range of products that focuses on 

retaining clients over time, such as introducing 

individual lending, flexible compulsory savings, 

voluntary savings, micro-insurance and access 

to BDS; 

• Shifting the mindset throughout the organization 

to view the client as a customer and to realize 

that the customer is important. 

The research also demonstrates that talking to 

former clients can provide an MFI with a wealth of 

information about why they became dissatisfied. If 

the research is designed well, the MFI can identify 

the product or service delivery attributes it should 

build on and which features should change to improve 

retention. The issue then becomes, which 

clients does the MFI want to retain? This leads to 

the next big challenge in microfinance—segmenting 

the customers. 

To segment customers, an MFI should at minimum 

track information about the borrowing and saving 

behavior of individual clients, even within group 

lending methodologies. This data can then be 

analyzed and fed into complementary market 

research activities. 

Deepening the institution’s understanding of its 

client base is an iterative and continuous process. 

Every transaction with a client should be viewed as 

a learning opportunity. Finding inexpensive ways of 

institutionalizing feedback mechanisms from the 

client right up through to the board of directors is 

critical. This can be achieved, for example, by 

holding monthly meetings with field staff and acting 

on their ideas for improvement, and ensuring top 

management spends more time talking with current 

and former borrowers. Finally, developing a more 

refined understanding of client needs and 

preferences not only makes good business sense 

now, but it will also be a defining source of 

competitive advantage in the future. 



Inez Murray is Organizational Strategy and Effectiveness 

Coordinator at WWB. She is responsible for developing 

the Strategic Positioning Service. Comments on her 

article can be sent to imurray@swwb.org. 




Measuring Client Retention 

Richard Rosenberg 

Most microfinance practitioners are coming to 

realize that client dropout has a surprisingly heavy 

effect in depressing profitability. 28 As a result, there 

is an increasing amount of discussion about how to 

measure client desertion (or retention). 

Defining Terms 

A retention rate (RR) answers the question: “When 

clients had a chance this period to take out a repeat 

loan, what percent actually took the loan?” Correspondingly, 

a desertion—or dropout—rate (DR) 

answers the question: “When clients had a chance 

this period to take out a follow-on loan, what 

percent failed to take the loan?” Defined this way, 

these two rates are simple complements of each 

other: Both rates are period specific. 

“Resting” Clients 

Any RR or DR needs to take into account the fact 

that a client who has paid off a loan without taking a 

repeat loan may be leaving the program for good, 

or simply waiting a while before taking out another 

loan. No formula can tell us what the client will 

eventually do. However, a good formula should 

give the program “credit” when an inactive client 

becomes an active borrower again. At times when 

the number of clients returning to the fold is very 

large, such a formula will produce negative desertion 

rates and, correspondingly, retention rates 

above 100 percent, especially when the measurement 

period is short. Over time, this phenomenon 

will smooth out. 

The Formulas 

The choice of an appropriate formula presents 

complex issues. As of yet there is no consensus. 

The discussion below represents my tentative 

conclusions. 

Waterfield/CGAP Formula. The formula that 

Chuck Waterfield used in the CGAP MIS Handbook 

seems simpler and less problematic than other 

formulas. 29 This formula focuses on the client’s 

main decision point—that is the point at which she 

has repaid her prior loan and has the option to take 

out a new one (a “follow-on” loan). At the point 

where the client chooses to take the follow-on loan, 

she is counted as retained, whether or not there 

was a resting period before the follow-on loan. 

(1) RR = FL 

LP 

Where: 

FL = the number of follow-on loans made 

during the period 

LP = the number of loans paid off during the 

period, and 

RR = retention rate 

Note that this formula produces the retention rate 

per loan cycle. To estimate how many clients the 

organization is losing (or keeping) per year, it is 

necessary to factor in average loan term. 30 Suppose 

that the RR calculated by this formula is 80 

percent. If we run 4 loan cycles per year, then only 

about (0.80) 4 = 41 percent of the clients active at 

the beginning of the year are still active at the end 

of the year. 

If the MIS doesn’t directly produce FL or LP, the 

formula can be restated in a way that is more 

complex but uses information that may be easier to 

produce (see Formula 2). 

Mr. Waterfield modestly wrings his hands over the 

fact that his formula can occasionally produce a 

retention rate over 100 percent. It’s hard to see 

why this bothers him: as noted above, the only way 

to keep a retention rate from ever exceeding 100 

percent is to ignore resting borrowers. 

28 

This article, originally prepared for the 1999 MicroFinance 

Network conference in Bangladesh, draws liberally from 

Development Finance Network (DFN) postings by Chuck 

Waterfield and Mark Schreiner. So any mistakes here must be 

their fault. 

29 The formula in the CGAP MIS handbook is a desertion rate. It 

is presented here as a retention rate because it is simpler. 

30 

For more than you wanted to know about estimating average 

loan term, see CGAP’s Occasional Paper No. 3, Measuring 

Microfinance Delinquency: How Ratios Can Be Harmful to Your 

Health. 



(2) RR = (L – NC) 

(AC begin + L – AC end ) 

Where: 

L = the number of loans disbursed during the 

period 

NC = the number of new (first time) clients 

entering during the period 

AC begin = the number of active clients at the 

beginning of the period 

AC end = the number of active clients at the 

end of the period 


The above formulas do not include the effect of 

default, because the denominator is loans paid off. 

Clients who never repay their last loan are 

(presumably) lost to the program forever, so some 

MFIs will want a desertion formula that takes them 

into account. One less-than-perfect way to do this 

is shown in Formula 3. 

(3) RR = FL 

(LP + WO) 

Where: 

FL = the number of follow-on loans made 

during the period 

LP = the number of loans paid off during the 

period 

WO = the number of loans written off during 

the period (or otherwise classified as 

unlikely to be repaid) 


ACCION Formula. A desertion rate that has been 

used by ACCION and others (including the author 

of this note) is shown in Formula 4: 

(4) DR = (AC begin + NC - AC end ) 

AC begin 

31 

Where: 

AC begin = the number of active clients at the 






DR = desertion rate 

Because it includes clients who have not had a 

chance to be retained or to desert, it overstates the 

frequency of clients eventually coming back for 

another loan. (This is how we defined desertion 

above. Other definitions are possible.) The longer 

the initial loan term in relation to the reporting 

period, the more pronounced this effect would be. 

Schreiner Formula. Mark Schreiner, a dauntingly 

meticulous analyst, uses a variant of Formula 4 that 

solves that formula’s problem of being unusable for 

start-up operations. 

(5) RR = AC end 

(AC begin + NC) 

Where: 

AC begin = number of active clients at the 







Mark likes this formula because he feels that it uses 

information more commonly available to an outside 

analyst (like him, for instance). He notes that his 

formula, like the ACCION formula, does not take 

into account the fact that some of clients included in 

the count have not yet had a chance to desert. Just 

as in Formula (4), this distortion becomes larger as 

the average loan term increases in relation to the 

reporting period. 

Conclusion 

It seems to me that the Waterfield/CGAP ratio is 

more useful in most situations. I would be grateful 

for comments from anyone, especially people who 

have on-the-ground experience with trying to use 

desertion or retention rates. 

Send your comments on retention rates to Rich 

Rosenberg, a member of the Bulletin’s Editorial Board, at 

rrosenberg@worldbank.org. 

Formula 4 has important weaknesses. It does not 

work for a startup program, where AC begin is zero. 

31 

The MicroBanking Bulletin uses this formula with a slight 

modification; borrowers who are returning after a rest period of 

more than 2 years are considered new clients (NC). 




Maximizing Efficiency: The Path to 

Enhanced Outreach and Sustainability 

Monica Brand and Julie Gerschick 

ACCION Monograph No. 12 

Hardcopy US$20.00/Electronic US$13.00 

To order: email publications@accion.org or 

download from www.accion.org 

More, better, faster. These three words have 

become the mantra of successful businesses during 

recent years. Driven by competition, technology 

and demanding clients, successful firms constantly 

search for ways to provide better products and 

services more quickly, and often at lower costs, 

while improving their bottom line. This message 

from the business world is applied to the microfinance 

industry in ACCION International's recent 

monograph Maximizing Efficiency by Monica Brand 

and Julie Gerschick. 

In this publication, the authors draw on business 

literature, empirical data and numerous examples 

from microfinance institutions to build the case for 

efficiency as a bridge between the sometimes 

competing camps of outreach and sustainability. 

The underlying theme is that a focus on efficiency 

will help institutions reach more clients and attain 

higher levels of profitability. Hence, this publication 

should be of interest to managers, shareholders, 

donors and consultants in the microfinance field 

who want to lower interest rates, increase outreach 

and achieve a healthier bottom line. 

The book opens with a concise overview of microfinance 

and examines conventional wisdom regarding 

the correlation between efficiency, outreach and 

loan size. Brand and Gerschick cite empirical evidence 

showing significant efficiency gains achieved 

through growth appear in institutions with up to 

10,000 to 12,000 clients. 

The authors argue that additional efficiency gains 

require more than growth; they require considerable 

organizational change. This discussion indicates 

that outreach, beyond a certain level, does not 

significantly improve efficiency. A further examination 

of the scale required to justify investments in 

efficiency enhancements—such as technology, 

functional specialization, and product and service 

diversification—is warranted given the number of 

donors supporting the proliferation of small microfinance 

institutions and the entrance of larger 

commercial financial institutions. The authors also 

argue that small loan size is not an excuse for 

inefficient or financially unsustainable organizations 

by identifying organizations that have achieved 

sustainability while offering very small loans. 

Brand and Gerschick demonstrate that sustainability 

is not a sufficient measure of financial performance. 

Sustainability is often achieved through 

high interest rates that mask excessive operating 

costs resulting from inefficiencies. They argue that 

many indicators that evaluate organizational efficiency 

and profitability of microfinance institutions, 

such as administrative expenses over average 

portfolio and return on assets, do not show the 

whole picture. The authors bring to the forefront the 

importance of evaluating the relationship between 

costs and revenues through the analysis of the 

bank efficiency ratio (total pre-tax expenses over 

net income) and its reciprocal net profit contribution. 

They also delve into the complexities of allocating 

costs for accurate analysis at different levels, from 

the entire institution, down to specific client and 

product segments. 

This monograph presents a myriad of concrete 

examples of managing organizational change to 

enhance efficiency. The analytical framework is 

alignment theory, which the authors describe as a 

holistic approach to identify multiple causes of 

inefficiency by examining strategy, products and 

internal systems. The authors cite a number of 

areas where managers can enhance efficiency, and 

they spice the text with examples drawn from microfinance 

institutions worldwide, such as: 

• Modifying products to reduce collections problems 

by customizing repayments to the borrowers’ 

cash flow, offering revolving loans (or lines 

of credit) to decrease the cost of marketing, and 

issuing credit cards to enhance fee generation; 

• Increasing staff productivity through incentive 

systems, transportation equipment, and establishing 

specialized staff positions for routine 

administrative functions; 

• Improving client retention through pricing incentives 

and streamlined repeat loan approvals; 

• Pricing to maximize efficiency by differentiating 

interest rates and fees to price for risk and 

administrative costs; 

• Establishing partnerships with business 

associations, financial institutions and others to 

lower the cost to attract clients; 



• Standardizing tools and templates for loan 

documentation, underwriting criteria and branch 

openings; 

• Evaluating the appropriate level of delinquency 

to adjust time spent on selection and 

monitoring; 

• Utilizing technology to automate processes 

such as posting loan payments, generating 

collections letters, and establishing call centers 

and credit scoring systems. 

Three microfinance institutions—ACCION New 

York, BancoSol in Bolivia, and MiBanco in 

Peru—are showcased as examples of 

reengineering initiatives to help achieve higher 

levels of efficiency and to maintain (or gain) a 

competitive market position. Each case presents 

some interesting examples of efficiency 

enhancements, from computerizing paperwork to 

reducing financial analysis. A follow-up analysis, 

applying the financial ratios and alignment 

framework from the first chapters of the monograph, 

to evaluate the efficiencies gained through the 

reengineering process would help to determine if 

the work achieved its goals. 

This monograph is ambitious and dense. However, 

the overall message is clear and important: microfinance 

institutions must improve their efficiency if 

they want to stay in the game and fulfill their 

mission. Profit-driven institutions in competitive 

environments most likely have incorporated a drive 

for efficiency into their culture and operations, but 

this publication will provide ideas for continued 

improvements. Donor-dependent organizations 

and those operating in less competitive markets are 

likely to fall behind the efficiency curve and should 

glean from this monograph insights into better 

performance measurements to come. A more 

consistent and extensive application of the efficiency 

philosophy presented by Brand and Gerschick 

would greatly help all microfinance institutions 

and clients. 

Review prepared by Robin Young of Development 

Alternatives, Inc. who presently serves as Deputy Chief of 

Party for the Rural Microfinance Strengthening project in 

El Salvador (robin_young@dai.com). 



Improving Internal Control: A Practical 

Guide for Microfinance Institutions 

Anita Campion 

MicroFinance Network (with GTZ) Technical Guide 

No. 1 

Hardcopy US$15.00/Electronic: 25% discount 

To order: email mfn@mfnetwork.org or download 

from www.mfnetwork.org 

In Improving Internal Control, Anita Campion 

discusses a topic that is becoming increasingly 

important for microfinance institutions: efficient 

internal control as an instrument for managing 

financial and operational risks—especially the risk 

of fraud. In her introduction Campion quite rightly 

points out that internal control cannot be seen as a 

separate function within an institution, but that it 

needs to be placed in the context of a more broadly 

defined risk management framework. 

One of the book’s main messages, a point that is 

repeatedly made explicitly and implicitly, is that well 

conceived control policies and procedures, such as 

adherence to the “second signature” principle or the 

implementation of effective incentive mechanisms 

for staff and customers, contribute more to the costefficient 

reduction of institutional risks than the 

construction of a painstakingly detailed ex-post 

control mechanisms. An ounce of prevention is 

worth a pound of cure. 

Nonetheless, fraud cannot be wholly prevented. 

Campion describes in detail the process of building 

an internal control system. Through numerous 

practical examples, this document provides a useful 

guide to designing an internal control system. The 

emphasis is on verifying the efficiency of the 

internal control system through ex-post controls 

(e.g. the internal audit process). 

The book is rounded out with a discussion of how 

best to institutionalize internal control systems. 

Again, the emphasis is on ex-post controls, especially 

establishing an internal audit department. 

Campion points out that the scale and scope of the 

operations, as well as the regulatory environment, 

are major factors that determine the optimal 

structure and scope of an internal control system. 

The main target audiences for this book are 

practitioners working at (and the members of the 

supervisory bodies of) small and medium-sized 

microfinance institutions that (as yet) have a 

relatively narrow range of products, i.e. mainly 

installment loans and possibly also savings 

facilities. For this limited readership, the book is 

undoubtedly a valuable aid as it draws attention to 

key risk management issues, derives from them the 

purposes and benefits of internal controls, and 

offers practical suggestions for implementing and 

improving efficient internal control systems. In 

particular, the book will be useful for less experienced 

internal auditors and for managers at small 

and medium-sized microfinance institutions who 

deal with internal audit issues. 

However, the book deals exclusively with internal 

control systems at the operational branch level and 

therefore leaves a number of questions unanswered. 

For example, it has little or nothing to say 

about managing and controlling operational and 

financial risks at the level of the institution as a 

whole—exchange rate risk, interest rate risk and 

liquidity risk, for example, which are often monitored 

and managed centrally at the head office. 32 Nor 

does the book explain how to control operational 

and financial risks associated with a broader range 

of products that includes overdrafts, checks, bank 

guarantees and documentary payments. Admittedly, 

the book makes no claim to provide answers 

to these questions. 

From the point of view of a manager of a target 

group-oriented commercial bank providing a wide 

range of products to a diversified customer base, 

one final observation to be made is that the 

principles to be observed when constructing internal 

control systems for small microfinance institutions 

with NGO status are basically identical to those in 

place at large commercial banks. Institutions must 

learn to recognize (and accept) their specific risks, 

to quantify them, and to use this information as the 

basis for developing cost-efficient internal control 

systems. The real potential of this book is to address 

decision-makers at microfinance institutions 

who have, knowingly or unknowingly, paid insufficient 

attention to this set of issues. It seeks to 

open their eyes to the necessity of building internal 

control systems and offers suggestions on how to 

implement them. One can only hope that the decision-makers 

are already sufficiently aware of the 

issues to be motivated to actually read this book. 

Luis Schunk (schunk@ipcgmbh.com) works for IPC and 

is the General Manager of FEFAD Bank in Albania. 

32 

These issues are addressed in a companion publication, “A 

Risk Management Framework for Microfinance Institutions," by 

Janney Carpenter and Lynn Pikholz with Anita Campion, 

published by GTZ (www.gtz.de), 2000. 




Bosnian MFIs: Performance, and Productivity 


Bosnia-Herzegovina 33 is a former republic of 

Yugoslavia, which was a middle-income country 

before its breakup and the war. It is now in process 

of both post-conflict reconstruction and economic 

recovery, and in transition to a market economy. 

This has proven difficult, and high unemployment 

and limited opportunities in the formal sector have 

increased the demand for microcredit 34 . Due to 

these factors, many of the poor targeted by MFIs 

are “new poor” (i.e. people who lost their jobs and 

assets during the war), who were previously 

employed in state-owned enterprises with a fairly 

stable income and a high level of social security. 

Given the increased demand for microcredit, the 

government of Bosnia-Herzegovina has been 

supporting the microfinance industry through a 

number of measures: investing government funds in 

the sector, including microfinance as a key part of 

their development strategy, and adopting legislation 

to legalize the provision of microcredit by NGOs 35 . 

Microfinance in the country is evolving quickly. As 

of March 2000, there were 18 main MFIs in Bosnia- 

Herzegovina, down from 34 in 1999. This article 

focuses on eight MFIs that represent a range in 

terms of both target clientele and performance: 

AMK, Bospo, LOK, MEB, Mercy Corps/Partner 36 , 

Mikrofin, Sunrise, and World Vision. 

These are relatively new MFIs (average of 2 years 

in 1999), averaging just over US$3.3 million in 

outstanding portfolio with about 2,500 borrowers. 

They use a mix of individual and solidarity group 

methodologies, and are all serving broad or highend 

clients, with an average loan balance relative to 

GNP per capita of 163 percent (or US$1,731). The 

latter hides important variations between 

33 

Bosnia-Herzegovina (BiH) is comprised of: the Federation of 

BiH and the Republika Srpska, governed by different sets of 

banking rules. 

34 

Demand for savings product is considered to be low due to the 

loss of confidence in banks resulting from the war. 

35 

A new law for microcredit organizations was passed in the 

Federation of BiH in July 2000 and in April 2001 in Republika 

Srpska. MFIs are now legally able to provide credit to their 

clients. 

36 

Mercy Corps’s name changed upon registration as a local 

microcredit organization under the Federation law. Partner's 

registration became effective January 1, 2001. 

institutions. Indeed, Bospo offers loans that are on 

average 39 percent relative to GNP per capita while 

MEB targets small business clients, with an 

average loan balance of 336 percent relative to 

GNP per capita. 

All are NGOs except MEB, a full service bank that 

offers a wide variety of products, including loans, 

savings and payment services. Due to a complex 

political environment, only MEB, Mercy 

Corps/Partner, and LOK have branch networks that 

span the whole country. 

Issues Facing Bosnian MFIs 

Business Environment: MFIs face a challenging 

business environment. 

Bosnian MFIs and their clients, as all organizations 

in the country, face the lack of a supportive 

business environment. For example, since many 

clients are not registered to avoid prohibitive tax 

rates on their small businesses, Bosnian MFIs need 

to track their dual reporting systems (one for the 

financial police 37 , one to track the real financial 

strength of the businesses). This additional task 

taken on by the MFIs to accommodate clients increases 

the loan processing time. 

Competition: Although still low, competition is on 

the rise, and MFIs are already trying to adjust to it. 

According to LID, a specialized, semi-governmental 

microfinance funding and capacity-building body, 

competition for microfinance services is low, due to 

excess demand and a culture gap between 

commercial banking and microfinance. Indeed, the 

microfinance target market in Bosnia-Herzegovina 

includes a growing number of high-risk displaced 

persons or returnees 38 , whom commercial banks 

are reluctant to serve because of the risks involved 

and their unwillingness to expand into the poorest 

regions of the country to reach this dispersed 

population. 

Nevertheless, as young MFIs mature and the formal 

banking sector stabilizes, competition is expected to 

37 

A governmental body in charge of conducting financial audits 

of institutions and private entities. 

38 

This refers to persons who were displaced during the war and 

persons who were able to return to their homes when it ended. 



increase. Some MFIs are already beginning to feel 

competitive pressure. For example, MEB lost 3 

percent of its clients in 6 weeks due to aggressive 

campaigns from two Austrian banks that reduced 

their minimum enterprise loan size from DM50,000 

to DM25,000 39 , while requiring no collateral, and 

offering very competitive interest rates for both 

savings and loans. 40 Sunrise is also experiencing 

challenges in matching the demand for bigger loans 

(over DM20,000) with reasonable terms (5 years). 

Competition between MFIs and commercial banks 

is likely to continue for high-end loans above 

DM20,000, and will have the most impact on MEB, 

which is serving a higher-end clients compared to 

the NGO MFIs. 

Some programs, like Sunrise, fear that MFIs will be 

competing on two fronts: with the commercial banks 

and with their peers, and are adopting a variety of 

measures to address it: 

Forming Mergers: Institutions are merging to take 

advantage of their complementary resources and 

regional coverage. For example, after merging with 

three other institutions, LOK now has the best 

infrastructure and branch network. Other examples 

include Sunrise, or World Vision, that merged with 

an organization in Mostar. 

Increasing Product Flexibility: To meet client 

needs, MFIs are moving towards a demand-driven 

approach for the design of their services. For 

example, Mikrofin is increasing the flexibility of its 

products by introducing bigger ranges per cycle, a 

grace period starting with the 2 nd cycle, lower 

interest rates, and no upper limit for loans in their 

last cycle. Mercy Corps/Partner, on the other hand, 

has implemented a program of focus groups to 

identify their client’s needs and adapt their loan 

products accordingly. 

Targeting Untapped Markets: MFIs are expanding 

their outreach to less competitive segments of the 

market. For instance, Mercy Corps/Partner targets 

lower-end clients and provides loan officers with 

incentives for the number of first time loans under 

DM2,500. In addition, sixty percent of their clients 

are in rural areas. In the future, there may be more 

opportunities for MFIs to operate in rural areas, as 

some industries are expected to fail due to the 

transition from the old economic system, which will 

increase the number of persons turning to selfemployment 

in these regions. 

Streamlining Procedures: All the MFIs studied 

were trying to improve their procedures to be more 

responsive to client needs. For instance, Sunrise is 

simplifying its complex procedures and reducing the 

number of days to screen clients in order to 

increase productivity; Mikrofin has reduced the time 

for processing loan renewals to two days; MEB is 

training loan officers to deliver multiple products to 

take advantage of cross-selling opportunities; and 

LOK is streamlining the disbursement and collection 

of its loans. Benefit 41 , following the example of ABA 

in Egypt, now disburses loans only three times a 

month and collects installments only four days a 

month to organize the workload for the head office 

and branch staff and improve productivity. 

Reducing the “Loan Gap”: This gap refers to 

clients who require loans from DM10,000 to 30,000 

not offered by MFIs or commercial banks. 42 

Because these clients are hardly serviced by 

anyone, they respond to this challenge by securing 

multiple (lower) loans from different MFIs. This 

“loan gap” represents an opportunity for MFIs 

seeking to increase their market share. For 

example, Mercy Corps/Partner has introduced a 

new loan product in 2000 in response to client 

demand: loans from DM10,000 to 20,000 for repeat 

clients in good standing. While some MFIs, such as 

MEB 43 , view this segment as a potential target 

market, other MFIs, such as Bospo, focus on 

deepening their outreach downwards, not upwards. 

Staff: There is a high level of competition for 

qualified staff. 

Bosnia-Herzegovina is experiencing a shortage of 

good loan officers, in part due to the presence of a 

large international community (bilateral and 

multilateral agencies) focusing on post-war relief 

efforts. It is attracting new university graduates with 

very high salaries well above country standards. 

Arrears: Arrears are low, but should be carefully 

monitored. 

Bosnian MFIs have very low arrears rates. This is 

due to a combination of factors, including the MFIs’ 

strong focus on delinquency management from the 

very beginning, and the credit culture and powerful 

sense of honor among their clients, who are “new” 

39 

As of December 2000, 1US$ = 2.29DM, IMF Statistics. 

40 

These banks do require guarantees but no collateral. While 

the market interest rate for MEB’s clients was 2-3% per month, 

Volksbank set its interest rates at 1-5% for SME loans. For 

savings, MEB was offering 2–3% vs. 14% at the commercial 

banks. Because of these interest rate policies, MEB lost 20-30 

clients (of a total of 240) during a period of 6 weeks. 

41 Benefit does not currently participate in the Bulletin, and is not 

included in the rest of the study. 

42 

Estimates for this gap vary from DM5,000 to 50,000. Even the 

most aggressive commercial banks are starting their loans at a 

minimum of DM20,000. 

43 

MEB currently has applications pending for loans that fall 

within this range. 



poor, due to the post-war conditions. Repaying 

loans is an important way for people to regain their 

economic standing, and microfinance services are 

highly valued. 

Nevertheless, arrears should be carefully monitored, 

especially as the lack of regulation and increasing 

competition motivate clients to take concurrent 

loans from different MFIs. MFIs currently 

call each other to share client information because 

of the difficulty in tracking the whereabouts of some 

of their borrowers (i.e. displaced persons). As 

competition increases, MFIs may become less 

willing to share client information. In addition, with 

the closure of ZPP, the government’s payment 

bureau, arrears are expected to increase; all customers 

of financial institutions (including MFIs) were 

previously required to maintain accounts at ZPP 

that were used as collateral for loans. 

Performance of Selected Bosnian MFIs 

As of 1999, the Bosnian programs were profitable 

only because of subsidies. After adjusting for 

subsidies, the financial self-sufficiency (FSS) ratio 

averaged 92 percent for the eight MFIs in this study 

(see Figure 1). 

Figure 1: Overall Performance 

Bosnian 

MFIs 

New 

MFIs 

All 

MFIs 

Portfolio Yield (%) 33 37 39 

Real Yield (%) 18 24 30 

Adjusted Return on Assets (%) -3.9 -9.8 -3.5 

Adjusted Return on Equity (%) -39.6 -21.5 -5.7 

Operational Self-sufficiency (%) 113 93 104 

Financial Self-sufficiency (%) 92 76 90 

Source: MicroBanking Bulletin database. Data are for Dec. 1999 

except for Mercy Corps/Partner (Dec. 2000). 

This implies that the MFIs were generating income 

to cover only 92 percent of their total expenses. 

Nonetheless, their overall performance surpassed 

that of all the MFIs in the Bulletin that fall within 

their age group (operating for less than 3 years). 

On average, they showed an adjusted return on 

assets of –3.9 percent (compared with –9.8 percent 

for all the New MFIs in the Bulletin), even with lower 

yields. These results are explained by their high 

level of efficiency. 

Efficiency 

As shown in Figure 2, the Bosnian MFIs have better 

efficiency ratios on average than other MFIs analyzed 

by the Bulletin. This is due mostly to a lower 

ratio of administrative expenses to average loan 

portfolio. The average loan balance as a percentage 

of GNP per capita (the depth ratio) in Bosnia- 

Herzegovina is more than twice that of all New 

MFIs in the Bulletin, which compensates for a 

higher wage structure and lower staff productivity 

(with an average of 75 clients per staff vs. 96 for all 

New MFIs). These findings hold even after 

excluding MEB (the only MFI in the sample targeted 

at small businesses) from the Bosnian programs. 

Because MEB offers a wide range of financial 

products to its clients and targets a higher-end 

market, excluding it from the sample increases the 

average staff productivity and lowers the average 

depth ratio for Bosnian MFIs. 

Even so, the depth ratio is still twice that of all New 

MFIs in the Bulletin, and three times more in 

comparison to all participants. The Bosnian MFIs 

would experience much lower efficiency ratios 

(relative to loan portfolio) were they not targeting a 

higher-end market. 

Figure 2: Efficiency Indicators 

Bosnian Bosnian 

MFIs MFIs* 

New 

MFIs 

All 

MFIs 

Total Admin Expense / LP (%) 25 26 42 31 

Salary Expense / LP (%) 15 16 24 17 

Average Salary (multiple of 

GNP per capita) 9.3 9.6 7.0 5.8 

Staff Productivity 75 81 96 122 

Cost per Borrower ($) 333 240 184 137 

Depth Ratio (average loan 

size/GNP per capita) (%) 

163 138 70 45 

Source: MicroBanking Bulletin database. Data are for Dec. 1999 

except for Mercy Corps/Partner (Dec. 2000). 

* Excluding MEB. 

The high cost per borrower results from the fact that 

Bosnian MFIs have fewer borrowers on which to 

spread the costs (2,486 clients on average 

compared to 5,081 for the New MFIs in the 

Bulletin). 

Productivity 

Figure 3 summarizes the main productivity indicators. 

Although on average staff productivity of 

Bosnian MFIs is almost half of that of all MFIs, it is 

nearly equivalent when we compare them to 

programs that serve similar target markets. Broad 

programs (MFIs with average loan balances 

between 20 and 150 of GNP per capita) show 

productivity levels double that of High-end 

programs, which is explained in part by the solidarity 

group loan methodology used by most programs 

serving the “Broad” target market. 



Figure 3: Efficiency and Productivity Indicators for 8 Individual MFIs 

Methodology 

Clients/ 

Staff 

Clients/ 

Loan 

Officer 

Loan 

Officer/ 

Staff (%) 

Staff 

Turnover 

Loan 

Portfolio/ 

Staff ($) 

LP/ Loan 

Officer ($) 

Number of 

Borrowers 

Total loan 

portfolio 

($) 

Portfolio 

at Risk 

(%) 

AMK Individual 36 76 47 26 62,827 132,636 680 1,193,720 0.0 162 

MEB Individual 30 96 31 na 110,348 351,108 2,118 7,724,382 na 336 

Sunrise Individual 70 121 58 0 112,173 193,754 1,331 2,131,293 0.0 258 

World Vision SG / Ind.** 63 106 60 13 138,761 231,268 1,902 4,162,820 1.2 202 

Bosnian (High - end) 50 100 49 13 106,027 227,192 1,508 3,803,054 0.4 239 

Bospo Solidarity 156 423 37 11 66,081 179,363 2,961 1,255,540 0.0 39 

LOK Ind. / SG 64 121 53 0 87,233 165,283 2,302 3,140,378 0.0 126 

Mercy Corps Individual 90 133 67 3 66,797 99,380 5,461 4,074,599 0.1 99 

Mikrofin SG. / Ind. 89 156 57 6 80,958 141,677 3,129 2,833,535 0.0 83 

Bosnian(Broad) 100 208 54 5 75,267 146,426 3,463 2,826,013 0.0 87 

All MFIs (High - end) 48 120 43 7 127,017 300,539 4,373 9,778,077 0.7 310 

All MFIs (Broad) 113 252 47 11 70,093 154,481 60,480 24,414,911 2.6 65 

All MFIs 122 257 50 11 45,929 101,967 11,398 3,764,997 1.9 45 

* GNP per capita = US$1,086 for 1999 and US$760 for 2000, World Bank Statistics. 

** SG = Solidarity Groups; Ind. = Individual Loans. 

Source: MicroBanking Bulletin database. Data are for December 1999 except for Mercy Corps (December 2000). 

Depth 

Ratio* 

(%) 

Despite lower overall productivity for Bosnian MFIs 

compared to all MFIs in the Bulletin, they have a 

higher average loan portfolio per staff, due to higher 

average loan sizes. Results also show that Bosnian 

MFIs have excellent quality portfolio, with an 

average portfolio at risk over 90 days of only 0.2 

percent. 

The data in Figure 3 also highlight considerable 

differences between Bosnian MFIs. Indeed, Bospo, 

which uses the solidarity group methodology exclusively, 

has the highest level of staff productivity, 

with an average of 423 clients per loan officer. 

Mikrofin, with 70 percent solidarity group loans, is 

second on the list. The MFIs showing the lowest 

staff productivity offer only individual loans. 

Although the lending methodology clearly influences 

staff productivity, other factors are also at 

play. For example, MFIs that offer a multiple range 

of products (i.e., MEB) have lower productivity 

levels given the burden on field staff who manage 

products other than loans. 

Many programs use financial incentives to boost 

staff productivity. Given the evidence that 

differences in staff productivity can arise from 

factors that are outside the loan officer’s control (i.e. 

loan methodology), MFIs should consider a mix of 

criteria on which to base incentives. No conclusive 

results can yet be drawn from the implementation of 

incentive plans, but their effects are worth 

monitoring. 44 

Conclusion 

This article analyzed the performance of eight 

Bosnian MFIs and the environment where they 

operate. They are playing a key role in post-war 

Bosnia by providing credit to low income 

entrepreneurs. 

Analysis of the financial performance of the 

selected MFIs shows that they are, on average, still 

not financially sustainable, and their staff productivity 

is slightly lower than that of all MFIs in the 

Bulletin targeting similar markets. Nonetheless, 

their overall performance surpassed that of all MFIs 

in the Bulletin that fall within their age group 

(operating for less than 3 years). 

Microfinance in Bosnia-Herzegovina benefits from a 

strong credit culture, cooperation between MFIs, 

and potential for growth. In the future, increased 

competition is expected to stimulate efficiency. To 

improve outreach while answering the challenges of 

increased competition and unfavorable business 

environment, the regulatory framework will need to 

be updated. 

This case study was prepared by Isabelle Barrès, Bulletin 

Editorial Staff, based on a visit conducted in November 

2000 to the selected organizations, and information 

submitted to the Bulletin by the Bosnian MBB 

participants. The MicroBanking Bulletin thanks LID and 

Sarah Forster for their contributions, and all the 

institutions mentioned in this article for their time and 

permission to publish their financial results. 

44 

The incentive systems are either new (LOK) or were recently 

reviewed (MEB and Mercy Corps/Partner). No information was 

available for AMK, Bospo, and World Vision. 




Productivity Drivers and Trends 


Productivity is the amount of quality services 

delivered by microfinance staff to their clients. It 

quantifies the employees’ efforts to deliver an MFI’s 

output. By increasing productivity, an MFI can 

lower per unit costs, improve efficiency, and ultimately 

enhance self-sufficiency. 

This Highlights Section, based on the information 

gathered during the past three years from over 100 

MFIs around the world, examines the importance of 

productivity on MFI performance. 

Measurement of Productivity 

Productivity can be measured using several 

indicators that capture the quantity and quality of 

service delivery by MFI staff. Since loans are the 

primary product and source of income for most 

MFIs, this analysis is limited to productivity associated 

with credit delivery. The Bulletin uses three 

main indicators to measure the productivity of credit 

delivery: staff productivity, loan officer productivity, 

and the staff allocation ratio. 

Of these three, staff productivity is the primary 

indicator. An MFI’s entire staff is a relevant unit of 

service production, so the best measure of productivity 

collectively accounts for the efforts of the front 

and back offices. The staff productivity indicator 

also allows comparison between diverse MFIs that 

allocate tasks differently among staff. Some MFIs, 

for example, require loan officers to perform multiple 

tasks such as collecting repayments, following 

up on delinquent loans, member training and 

deposit mobilization; whereas other organizations 

enlist administrative or specialized personnel to 

fulfill some of the credit delivery functions. The staff 

productivity indicator is therefore more useful when 

comparing between less similar MFIs. 

As a secondary indicator, the number of loans per 

loan officer reflects the productivity of field staff. 

Along the same lines, the staff allocation ratio (loan 

officers to total staff) indicates the MFI’s allocation 

of resources between staff in the field and the head 

office. These latter indicators are less useful in 

comparing financial intermediaries with credit-only 

institutions. However, if loan officers are exclusively 

responsible for loan activities, the two indicators 

reflect the MFI’s ability to streamline its credit 

operations and allocate its resources to the core 

income-generating activity. 

Microfinance Productivity: A Snapshot 

Based on data provided by the 124 Bulletin 

participants, this section examines the current state 

of microfinance productivity (see Figure 1). 

Figure 1: MFI Productivity by Peer Groups* 

Staff 

Productivity 

Loan 

Officer 

Productivity 

Staff 

Allocation 

All MFIs (n = 124) 122 257 0.49 

FSS MFIs (n = 64) 133 291 0.51 

Non-FSS MFIs (n=60) 132 300 0.48 

i. Africa 154 393 0.44 

Africa/MENA (n=5) 119 315 0.47 

Africa-Medium (n=10) 192 474 0.39 

Africa-Small (n=11) 129 285 0.52 

ii. Asia 177 306 0.54 

Asia Large (n=5) 238 420 0.42 

Asia Pacific (n=6) 80 134 0.66 

Productivity Indicators Definitions 

Staff productivity = Number of active loan clients / 

Total number of staff at year end 

Loan officer productivity = Number of active loan 

clients / Number of loan officers at year end 

Staff allocation = Number of loan officers at year end / 

Total number of staff year end 

Asia South (n=9) 235 452 0.56 

iii. Eastern Europe 63 110 0.54 

EE Broad (n=7) 79 123 0.59 

EE High (n=5) 64 113 0.56 

iv. Latin America 125 280 0.46 

LA CUs (n=11) 107 -- -- 

LA Med LI (n=9) 194 417 0.49 

LA Small LI (n=7) 140 225 0.58 

LA Small UI (n=6) 81 197 0.49 

LA Large (n=9) 122 285 0.43 

LA Med Broad (n=11) 88 212 0.41 

v. Others 

CA/MENA (n=6) 102 174 0.59 

WW Small Business (n=7) 37 130 0.32 

*Means are calculated by dropping the top and bottom percentiles for each group 

except for all MFIs where the top and bottom deciles are excluded. Composition 

of the peer groups can be found on page 41. FSS: Financially self-Sufficient; CA 

= Central Asia; EE: Eastern Europe; LA = Latin America; MENA = Middle East 

and North Africa; LI = Low income; UI = Upper income; WW = Worldwide. 


BULLET HIGHLIGHTS AND TABLES 

• MFI staff, on average, serviced 122 clients 

while loan officers managed an average of 257 

borrowers; 

• Productivity was similar between the MFIs that 

are and are not financially self-sufficient. This 

indicates that while productivity may affect the 

cost structure and profitability, high productivity 

cannot guarantee self-sufficiency; 

• Among the Bulletin peer groups, staff and loan 

officer productivity was the highest among the 

MFIs in the Asian Large and South Asian peer 

groups where the population density is high; 

• Small business lenders reported the lowest 

productivity rates. Small business lending may 

require a complex financial technology that 

limits caseloads for staff; 

• Productivity was low in upper-income countries 

(two peer groups in Eastern Europe and the 

Latin American upper income group); 

• As indicated by the staff allocation ratio, loan 

officers represented half the employees in 

MFIs, indicating the need for skills other than 

lending for the smooth operation of an MFI; 

• The proportion of loan officers to total staff was 

higher among Asian MFIs (0.54) compared to 

Latin American (0.46) and African MFIs (0.44); 

• Peer groups with smaller MFIs in Asia, Africa 

and Latin America reported higher staff 

allocation ratios than peer groups with larger 

organizations. 

The Effect of Age and Methodology on 

Productivity: Rhetoric or Reality? 

There are common beliefs in the microfinance 

industry that: 1) productivity improves with time; and 

2) group methodologies have higher productivity 

than individual lending. To test these beliefs, the 

Bulletin data were analyzed and the results suggest 

that the rhetoric may indeed be the reality, as 

shown in Figure 2. 

Staff and loan officers at MFIs employing the village 

bank methodology served significantly more clients 

and had a slightly higher staff allocation ratio than 

MFIs using an individual lending methodology. 

Mature MFIs were considerably more productive 

than new and young ones indicating that productivity 

improves with time. Higher productivity at 

mature MFIs may have resulted from a lower 

proportion of new staff members to total employees 

and fewer partially occupied loan officers. 

Figure 2: Productivity by Methodology and 

Age of MFI* 

Staff 

Productivity 

(No.) 

Loan Officer 

Productivity 

(No.) 

Staff 

Allocation 

(%) 

I. Methodology 

Individual loans (n=54) 102 229 47 

Solidarity Groups (n=45) 133 271 50 

Village Banks (n=25) 188 408 52 

II. Age Cohorts 

Mature: > 6 Years (n=65) 158 340 47 

Young: 3 to 6 Years (n=29) 126 295 48 

New: < 3 Years (n=30) 96 188 51 

*Data are calculated by dropping the top and bottom percentiles of each 

group. n = number of MFIs before dropping observations. 

Information in Figure 3 shows different patterns 

between lending methodologies and the age of the 

institutions. Interestingly, on average, productivity 

of solidarity group programs does not improve with 

age. Older village bank and individual lending programs, 

however, demonstrate markedly higher 

productivity than their younger counterparts and 

correspondingly higher financial self-sufficiency 

(FSS). 

Figure 3: Productivity, Efficiency and 

Profitability by Age and Methodology* 

Staff Admin Exp. / 

Productivity Avg. Loan 

(No.) Portfolio (%) 

FSS 

(%) 

New Solidarity Group (n=11) 137 55.1 76.1 

New Village Bank (n=4) 134 93.1 51.9 

New Individual (n=15) 68 24.4 82.9 

Young Solidarity (n=10) 140 45.4 79.7 

Young Village Bank (n=8) 157 47.7 92.4 

Young Individual (n=11) 94 22.1 100.3 

Mature Solidarity (n=24) 137 36.7 89.5 

Mature Village Bank (n=13) 286 44.1 97.8 

Mature Individual (n=28) 146 19.1 113.3 

*Data are calculated by dropping the top and bottom percentiles. n = 

number of MFIs included in each category before dropping observations. 

What Drives Productivity? 

Increased productivity may be important to reduce 

per unit costs and improve self-sufficiency. But 

what drives productivity? There are at least four 

direct drivers that MFIs can use as levers to 

increase productivity: 1) client retention, 2) staff 

retention, 3) staff remuneration, and 4) staff 

training. 45 The effect of these drivers on 

45 

Loan terms can also be lengthened to indirectly boost 

productivity. Since loans with shorter terms turn over more 

frequently, they are more labor-intensive and can undermine 

productivity. Assuming that longer terms do not adversely effect 



productivity and financial performance is discussed 

below. 

Client Retention 

Client retention can dramatically affect productivity. 

Productivity gains may result from reduced marketing 

and loan sourcing costs, and more efficient processing 

of repeat clients. This occurs most effectively 

if staff assimilate existing information into their 

decision making process. 

Data on client desertion are highly affected by the 

formula used to construct the ratio. The Bulletin 

measures client desertion as follows: 

Client 

Desertion = 

Active clients beginning of year + New 

clients for year* – Active clients end of year 

Active clients beginning of year 

*New clients = Borrowers with first loan and returnees after 2 years. 

Of the 124 MFIs that participated in this Issue, 33 

MFIs provided information on client desertion. 

Figure 4: Client Desertion and Productivity, 

by Region and Age 

Region 

Age Cohorts 

A weak inverse relationship emerges between 

desertion rates and both productivity and profitability. 

Higher levels of client desertion are associated 

with lower staff productivity and lower financial 

self-sufficiency (FSS). 

It is crucial to examine the reasons for desertion to 

clearly interpret the above observations. Furthermore, 

age and methodology may also play a role. 

Better understanding on the effect of client retention 

on productivity and MFI performance may evolve as 

the MFIs begin to track information on client 

desertion systematically. 

Staff Retention 

Heavy staff turnover, especially of loan officers, can 

seriously hurt productivity. Learning occurred in 

assessing applicants, and valuable client relationships 

built through repeat services in the same area 

and to same clients, are important factors that 

increase productivity. 

The Bulletin measures staff turnover as the number 

of staff members who left the MFI during the year 

relative to average number of staff. 

Staff Turnover = 

Number of staff who left the MFI 

Average number of staff 

No. MFIs 

Reporting 

Desertion Rate 

(%) 

Staff Productivity 

(No.) 

Admin. 

Expenses / Avg. 

Loan Portfolio 

(%) 

All MFIs 

Africa 

Asia 

Latin 

America 

EE and 

MENA 

Mature 

Young 

33 5 8 13 7 17 9 7 

New 

48 43 29 51 66 37 59 62 

123 178 143 123 73 138 105 109 

45 94 34 45 26 42 44 53 

FSS (%) 94 82 106 94 89 101 91 81 

As shown in Figure 4, MFIs in the Middle-East and 

North Africa (MENA) and Eastern Europe (EE) 

experience higher levels of desertion than those in 

Asian and African countries. It is not clear why this 

is the case, but region specific factors such as 

migration and level of competition may have 

influenced this finding. Desertion rates were high 

among young and new MFIs, 59 and 62 percent 

respectively, compared to 37 percent for mature 

ones. Programs may experience higher desertion 

rates where large numbers of clients leave after the 

first and second loan cycles. 

portfolio quality (which is a big assumption), an MFI can increase 

its productivity by lengthening its average loan term. 

The staff turnover rate ranged from 2 to 51 percent 

with a mean around 10 percent. As depicted in 

Figure 5, although no clear pattern emerges 

between staff turnover and productivity, higher 

levels of turnover tend to be weakly related to lower 

staff productivity. 

500 

450 

400 

350 

300 

250 

200 

150 

100 

50 

0 

Figure 5: Staff Turnover and Productivity 

0.02 0.12 0.22 0.32 0.42 0.52 

St af f Tu rn ove r 

Staff Remuneration 

The Bulletin collects aggregated data on staff 

remuneration including basic salary, bonuses and 

benefits. Remuneration is then expressed in the 

average staff salary indicator, which compares 

average staff remuneration to GNP per capita to 

account for country specific factors. 



It is expected that high wages would positively 

affect staff productivity—more inputs (salaries) 

should produce greater outputs (loans). But in 

microfinance, striking regional differences mean 

that you do not necessarily get what you pay for. 

Asia has the lowest relative remuneration and the 

highest productivity ratio, whereas the lowest 

productivity comes from the region with the second 

highest wages (Eastern Europe). Although 

productivity in Africa is relatively high, salaries are 

much higher than anywhere else (relative to GNP 

per capita) to the point that they were not 

adequately covered through interest income. This 

partly accounts for that region’s low self-sufficiency 

ratio (see Figure 6). 

Figure 6: Average Staff Salaries, Productivity 

and Self-Sufficiency * 

Avg. Staff 

Salary / 

GNP per 

capita 

Staff 

Productivity 

(no.) 

FSS 

(%) 

All MFIs (n=124) 5.8 122 90 

Africa (n=26) 12.4 154 83 

Asia (n=24) 3.6 177 93 

Eastern Europe (n=14) 7.3 63 90 

Latin America (n=54) 4.6 125 97 

* Data are calculated by dropping the top and bottom percentiles for each 

region and the top and bottom deciles for All MFIs. 

Staff Training 

The fourth productivity driver is staff training 

obtained through apprenticeships and formal 

training provided inside and outside of the MFI. 

One would presume that the more an MFI invests in 

training (up to a point), the more productive its staff 

should be. This result should come from the direct 

investments in skills that enable staff to be more 

productive, as well as the expectation that training 

investments should reduce the detrimental effect of 

staff turnover on productivity. 

About 60 MFIs reported the budget spent on 

training their staff. Regional differences, however, 

made it difficult to demonstrate a link between 

training investments and productivity. On average, 

African MFIs spent over 250 percent more than 

Latin American MFIs on training, and yet their 

productivity levels were almost the same. 

Furthermore, a clear relationship between productivity 

and staff training was difficult to establish due 

to limited standardization in the reporting of the 

training budget. Underestimation is possible since 

the training budget may not include the cost of inhouse 

apprenticeships. Similarly, overestimation is 

possible if data are contaminated with training 

budget for clients. 

Productivity Trends 

This section provides information on the historical 

pattern of productivity changes in MFIs and its 

effect on institutional performance. 

Is the Industry Headed in the Right Direction? 

Microfinance institutions are improving their levels 

of productivity and profitability over time, albeit 

slowly. Since 1998, the Bulletin has accumulated 

financial information from 95 MFIs for at least two 

consecutive years. 

Of these 95 MFIs, 61 percent raised their financial 

self-sufficiency, 73 percent experienced an increase 

in staff productivity, and 61 percent showed a 

decline in their administrative costs relative to loan 

portfolio. Average change in productivity was about 

24 percent. 

1 

Figure 7: Effect of Positive Change in Productivity on Change in FSS 

Change in FSS 

0.8 

0.6 

0.4 

R 2 = 0.0268 

0.2 

0 

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 

Change in Staff Productivity 



Does Methodology Matter? 

As shown in Figure 9, similar levels of productivity 

change were observed among MFIs that followed 

individual and solidarity group lending methodologies 

(27 percent), while village banks showed a 

15 percent increase in productivity. It was intriguing 

to note a sharper decline in administrative costs 

among solidarity group lenders compared to individual 

lenders despite similar change in productivity. 

The phenomenon is explained in part by the 

loan size increase among group lenders (5.3 

percent), while the individual lenders experienced a 

slight decline in average loan balances (ALB). 

Improvement in productivity from one year to the 

next was associated with an increase in profitability. 

Figure 7 demonstrates how the growth in productivity 

had a positive effect on financial selfsufficiency. 

On average, a 15 percent increase in 

productivity resulted in a 10 percent increase in 

FSS. Improvements in FSS appear to peak at a 30 

percent improvement in productivity and increase at 

a diminishing rate thereafter. This weak trend 

(R 2 =2.7 percent and the correlation coefficient is 28 

percent) suggests a limit to increased profitability 

due to productivity improvements. 

Does Performance Differ by Regions? 

There were significant differences in changes 

between regions (Figure 8). Average change in 

productivity was more prominent in Eastern Europe 

and Africa where the industry is still developing. 

Nonetheless, the relationship in all the regions 

between productivity, efficiency, and financial selfsufficiency 

followed a common pattern: changes in 

administrative cost ratio were indirectly proportional 

to changes in productivity while changes in FSS 

were directly proportional to changes in productivity. 

Figure 8: Number of MFIs with Positive 

Change in Performance 

(Average percent change in parentheses) 

Region 

FSS Productivity Efficiency* 

Africa (n=19) 14 (38.0) 16 (36.6) 13 (11.6) 

Asia (n=20) 13 (17.7) 14 (32.0) 12 (11.9) 

E. Europe (n=6) 6 (43.8) 6 (36.8) 6 (18.3) 

Latin America 

20 (-1.3) 30 (11.6) 24 (0.9) 

(n=45) 

MENA (n=5) 5 (42.0) 3 (14.4) 3 (7.5) 

Total Sample (n=95) 58 (16.3) 69 (23.7) 58 (7.1) 

* Efficiency refers to decline in administrative expenses relative to average 

loan portfolio. 

Effect of Age 

While productivity improved with age, as one might 

expect, the rate of change tends to slow down. 

New MFIs showed a 57 percent improvement in 

productivity compared to a 20 percent change 

among young MFIs and only a 14 percent improvement 

for mature institutions (Figure 10). 

Figure 10: Percent Change in Performance by 

Age of Institution 

Age 

Cohorts 

Staff 

Productivity Efficiency* 

Avg. Staff 

Salary/ GNP 

per capita) 

Avg. 

Loan 

Balance 

FSS 

New 56.7 17.9 31.1 -2.7 57.4 

Young 20.0 12.5 -3.6 1.2 13.3 

Mature 14.1 0.5 10.3 4.4 5.2 

* Efficiency refers to decline in administrative expenses relative to average 

loan portfolio. 

It is interesting to note that new MFIs experienced a 

decline in administrative costs (17.9 percent) despite 

an increase in average staff salaries by 31 

percent and slight decline in average loan balance. 

This may reflect the important role of productivity in 

reducing administrative costs even if costs are 

directly hit by an increase in salaries. 

Conclusion 

Productivity is interlinked with several other factors 

such as loan size and staff remuneration that affect 

the cost structure of the MFI and its profitability. 

Productivity is also affected by regional factors, and 

by methodology, size and the age of the MFIs. The 

complex interplay of productivity with other factors 

and the dispersed nature of data at the Bulletin 

have limited clear interpretation of several observations 

presented here. 

The analyses, nevertheless, show that productivity 

is necessary but it is not sufficient to effect MFI 

financial performance. Consistent tracking of 

pertinent information on productivity drivers is 

required for better understanding of productivity. 

Furthermore, it may be relevant for multi-service 

and multi-product MFIs to track information by tasks 

for appropriate management decisions. 



An Introduction to the Peer Groups and Tables 

Setting up the Peer Groups 

The MicroBanking Standards Project is designed to 

create performance benchmarks against which 

managers and directors of microfinance institutions 

can compare their own performance. Since the 

microfinance industry consists of a range of institutions 

and operating environments, some with very 

different characteristics, an MFI needs to be 

compared to similar institutions for the reference 

points to be useful. 

The MicroBanking Bulletin addresses this issue with 

its peer group framework. Peer groups are sets of 

programs that have similar characteristics—similar 

enough that their managers find utility in comparing 

their results with those of other organizations in 

their peer group. The Bulletin forms peer groups 

based on three main indicators shown in Figure 1: 

1) region; 2) scale of operations; and 3) target 

market. 

In previous issues, the same criteria for scale and 

target market were applied to all regions. Since 

regions demonstrate different growth patterns, however, 

we have regionalized the scale criterion by 

raising the bar in some areas and lowering it in 

others. So now, a program that would be classified 

as large in Africa, for example, would be considered 

medium in Latin America. We have also added a 

new category for target market: Small Business. To 

fall into this category, the depth indicator (average 

loan balance / GNP per capita) needs to exceed 

250 percent. 

Besides these three primary indicators, the Bulletin 

also applied two secondary criteria in Latin America 

to further homogenize the peer groups. First, all of 

the credit unions are grouped together. Since 

these organizations are savings-driven (unlike most 

MFIs, which are credit-driven), they have a unique 

cost structure that makes comparison with other 

MFIs less useful. 

The other secondary criterion applied in Latin 

America (for institutions that fall in the low-end 

category) is the country income level. The 

operating conditions in upper income (UI) countries, 

such as Argentina, Brazil and Chile, in terms of 

labor markets, levels of productivity, and customer 

characteristics, are quite distinct from the lower 

income (LI) countries in the region, and the high 

number of institutions offering low-end loans 

justifies the breakdown into multiple peer groups. 

Peer Group Composition and Data Quality 

The members of each peer group are listed in 

Figure 2 on the following page, and more detailed 

information about each institution can be found in 

Appendix II on page 77. 

Since the Bulletin relies primarily on self-reported 

data, we have graded the quality of that information 

based on the degree to which we have independent 

verification of its reliability. The data quality grade 

is NOT a rating of the institution’s performance. 

Statistical Issues 

In the statistical tables that follow, the averages for 

each peer group are calculated on the basis of the 

values between the 2 nd and 99 th percentiles, which 

usually means that the top and bottom values for 

each indicator are dropped. For the entire sample 

of MFIs, the top and bottom deciles were excluded. 

For more details, see Appendix I on page 73. 

Figure 1: Primary Peer Group Criteria 

Africa 

Africa/ MENA 2 

MENA/ Central Asia 

Region Scale of Operations 1 

Total loan portfolio (US$) 

Large: > 5 million 

Medium: 900,000 to 5 million 

Small: < 900,000 

Target Market 

Average loan balance / 

GNP per capita 

Low-end: < 20% OR Avg. 

Loan Balance ≤ US$150 

Asia 

Asia (Pacific) 

Asia (South) 

Eastern Europe 

Latin America 

Large: > 8 million 

Medium: 1 to 8 million 

Small: < 1 million 

Large > 10 million, 

Medium: 1.5 to 10 million 

Small: < 1.5 million 

Broad: 20% to 149% 

High-end: 150 to 249% 

Small Business: ≥ 250% 

1 

Criteria for classification of scale of operations varies by region. See corresponding group of regions. 

2 

MENA = Middle East/ North Africa. 



Figure 2: A Guide to the Peer Groups 

PEER GROUP N 

DATA QUALITY GRADE† 

(No. of MFIs with each 

grade) 

AAA A B 

PARTICIPATING INSTITUTIONS * 

1. Africa Medium 

Size: Medium 

Target: Low-end 

2. Africa Small 

Size: Small 


3. Africa/ MENA 

Size: Large/Medium 

Target: Broad 

4. Asia (Central) / MENA 

Size: Medium/Small 


5. Asia Large 

Size: Large 

Target: Low-end/Broad 

6. Asia (Pacific) 



7. Asia (South) 



8. Eastern Europe High 

Size: All 

Target: High-end 

9. Eastern Europe Broad 

Size: All 

Target: Broad 

10. LA Large 

Size: Large 

Target: Broad/High-end 

11. LA Medium Broad 

Size: Medium 

Target: Broad 

12. LA Low UI 



13. LA Medium Low LI 

Size: Medium 


14. LA Small Low LI 

Size: Small 


15. LA Credit Unions 

Size: All 

Target: Broad 

16. Worldwide Small Business 

Size: Large/Medium 

Target: Small Business 

10 1 7 2 Citi S&L, FINCA Uganda, KWFT, NRB, Pamécas, Pride 

Tanzania, Pride Uganda, Pride Vita Guinea, SEF, WAGES 

11 3 3 5 ARB, Faulu, FINCA Malawi, FINCA Tanzania, FOCCAS, 

MKRB, Piyeli, SAT, SEDA, UWFT, Vital-Finance 

5 1 3 1 ABA, Kafo Jiginew, Nyésigiso, PADME, UNRWA 

6 1 4 1 Al Amana, Al Majmoua, Constanta, Faten, FINCA Kyrgyzstan, 

Microfund for Women 

5 1 4 0 ACLEDA, ASA, BAAC, BRAC, BRI 

6 1 5 0 CARD Bank, EMT, Hatta Kaksekar, Hublag, RSPI, TSPI 

9 5 3 1 AKRSP, Basix, BURO Tangail, CDS, FWWB India, 

KASHF, Nirdhan, SEEDS, SHARE 

5 0 5 0 AMK, LOK, Moznosti, Sunrise, WVB 

7 0 2 5 Bospo, Fundusz Mikro, Inicjatywa Mikro, MC, Mikrofin, 

Nachala, NOA 

9 2 7 0 Banco ADEMI, BancoSol, Caja de Los Andes, Calpiá, CM 

Arequipa, FIE, Finamérica, Mibanco, PRODEM 

11 1 7 3 ACODEP, Actuar, ADOPEM, ADRI, BPE, Chispa, FAMA, 

Finsol, FONDECO, ProEmpresa, Sartawi 

6 2 4 0 Banco do Povo, CEAPE/ PE, Contigo, Emprender, 

Portosol, Vivacred 

9 1 4 4 CAM, CMM Medellín, Compartamos, Crecer, Enlace, 

FINCA Honduras, FMM Popayán, FWWB Cali, ProMujer 

7 0 3 4 AGAPE, FED, FINCA Ecuador, FINCA Mexico, FINCA 

Nicaragua, FINCA Peru, WR Honduras 

11 0 11 0 15 de Abril, 23 de Julio, Acredicom, Chuimequená, 

COOSAJO, Ecosaba, Moyután, Oscus, Sagrario, 

Tonantel, Tulcán 

7 2 5 0 ACEP, Agrocapital, BDB, CERUDEB, FEFAD, MEB, NLC 

All MFIs 124 21 77 26 

† The MicroBanking Bulletin uses the following grading system to classify information received from MFIs: 

AAA The information is supported by an in-depth financial analysis conducted by an independent entity in the last 

three years 

A The MBB questionnaire plus audited financial statements, annual reports and other independent evaluations 

B The MBB questionnaire or audited financial statements without additional documentation 

Abbreviations: MBB = MicroBanking Bulletin; MENA = Middle East/North Africa; LA = Latin America; UI = Upper Income countries; 

LI = Lower Income countries. 

* The institutions in italics and bold are new to the Bulletin. A short description of all institutions can be found in Appendix II. 



Index of Ratios and Tables 

INDICATORS AND RATIOS DEFINITIONS 

OUTREACH AND INSTITUTIONAL INDICATORS 

AGE OF INSTITUTION Years functioning as a MFI (years) 

NUMBER OF OFFICES Total number of offices (including head office, regional offices, branches, agencies) (number) 

NUMBER OF STAFF Total number of employees (number) 

NO OF ACTIVE BORROWERS Number of borrowers with loans outstanding (number) 

PERCENT WOMEN BORROWERS Total number of active women borrowers / total number of active borrowers (%) 

MACROECONOMIC INDICATORS 

GNP PER CAPITA (CURRENT PRICES) GNP per capita (US$) 

GDP GROWTH RATE Annual average, 1990-1998 (%) 

INFLATION RATE Inflation rate (%) 

DEPOSIT RATE Deposit rate (%) 

FINANCIAL DEEPENING M3 / GDP (%) 

PROFITABILITY 

ADJUSTED RETURN ON ASSETS (AROA) Adjusted net operating income 

Average total assets 

ADJUSTED RETURN ON EQUITY (AROE) Adjusted net operating income 

Average total equity 

OPERATIONAL SELF-SUFFICIENCY (OSS) Operating income 

Operating expense 

FINANCIAL SELF-SUFFICIENCY (FSS) Adjusted operating income 

Adjusted operating expense 

PROFIT MARGIN Adjusted net operating income 

Adjusted operating income 

(%) 

(%) 

(%) 

(%) 

(%) 

INCOME & EXPENSE 

OPERATING INCOME RATIO Adjusted operating income 


OPERATING EXPENSE RATIO Adjusted operating expense 


NET INTEREST MARGIN RATIO Adjusted net interest margin 


PORTFOLIO YIELD Operating income - accrued interest - interest and fee income from investments 

Average gross loan portfolio (%) 

REAL INTEREST YIELD (Portfolio yield - inflation rate) 

(%) 

(1+ inflation rate) 

TOTAL INTEREST EXPENSE RATIO Adjusted total interest expense 

(%) 


ADJUSTMENT EXPENSE RATIO Inflation and subsidy adjustment expense 

(%) 


LOAN LOSS PROVISION EXPENSE RATIO Adjusted loan loss provision expense 

(%) 


SALARY EXPENSE RATIO Personnel expense + in-kind donations 

(%) 


OTHER ADMINISTRATIVE EXPENSE RATIO Administrative expense + in-kind donations - personnel expense 

(%) 


TOTAL ADMINISTRATIVE EXPENSE RATIO Administrative expense + in-kind donations 


(%) 

(%) 

(%) 

(%) 

EFFICIENCY 

TOTAL ADMINISTRATIVE EXPENSE/ 

LOAN PORTFOLIO 

Administrative expense + in-kind donations 


SALARY EXPENSE/ LOAN PORTFOLIO Personnel expense + in-kind donations 

Average gross loan portfolio 

OTHER ADMINISTRATIVE EXPENSE / 

LOAN PORTFOLIO 

Administrative expense + in-kind donations - personnel expense 


(%) 



PRODUCTIVITY 

AVERAGE SALARY Average personnel expense + in-kind donations 


COST PER BORROWER Administrative expense + in-kind donations 

Average number of active borrowers 

STAFF PRODUCTIVITY Number of active borrowers 

Number of staff 

LOAN OFFICER PRODUCTIVITY Number of active borrowers 

Number of loan officers 

STAFF ALLOCATION RATIO Number loan officers 

Number of staff 

STAFF TURNOVER Number of staff who left the MFI 

Average number of staff 

(multiple of GNP per capita) 

(US$) 

(number) 

(number) 

(%) 

(%) 

PORTFOLIO 

PORTFOLIO AT RISK > 90 DAYS Outstanding balance of loans overdue > 90 days 

Total gross loan portfolio 

TOTAL GROSS LOAN PORTFOLIO Total gross portfolio outstanding (US$) 

AVERAGE LOAN BALANCE Total gross loan portfolio 

(US$) 

Number of active borrowers 

DEPTH Average loan balance 


(%) 

(%) 

CAPITAL AND LIABILITY STRUCTURE 

COMMERCIAL FUNDING LIABILITIES RATIO Borrowings at commercial rates (excludes loans from Head Office and Central bank) 

Average gross loan portfolio 

CAPITAL / ASSET RATIO Adjusted total equity 

Adjusted total assets 

(%) 

(%) 

CLARIFICATION OF TERMS 

OPERATING INCOME Interest and fee income from loan portfolio + interest and fee income from 

investments + other income from financial services 

OPERATING EXPENSE Administrative expense + total interest expense + loan loss provision expense 

ADJUSTED OPERATING INCOME Interest and fee income from loan portfolio + interest and fee income from 

investments net of accrued interest + other income from financial services 

ADJUSTED OPERATING EXPENSE Administrative expense, including in-kind donations + adjusted total interest expense 

+ adjusted loan loss provision expense 

ADMINISTRATIVE EXPENSE Personnel + office supplies + deprecation + rent + utilities + transportation + other 

administrative expenses 

PERSONNEL EXPENSE Staff salary + benefits expense 

ADJUSTED TOTAL INTEREST EXPENSE Interest and fee expense + exchange rate depreciation expense + other financial 

expense (including inflation expense + subsidy expense) 

ADJUSTED NET INTEREST MARGIN Adjusted operating income - total interest expense 

NET OPERATING INCOME Operating income - operating expense 

ADJUSTED NET OPERATING INCOME Adjusted operating income - adjusted operating expense 

ADJUSTED TOTAL EQUITY Total equity, including quasi-equity and adjusted net income 

ADJUSTED TOTAL ASSETS Total assets, including loan portfolio and inflation adjustment 

TABLES TITLES PAGE 

TABLE 1 Institutional Characteristics and Outreach Indicators 44 

TABLE 2 Overall Financial Performance and Operating Income 46 

TABLE 3 Operating Expenses and Portfolio Management Indicators 48 

TABLE 4 Efficiency and Productivity 50 

TABLE 5 Macroeconomic Indicators 52 

TABLE A Institutional Characteristics and Outreach Indicators 54 

TABLE B Profitability and Efficiency Indicators 58 

TABLE C Institutional Characteristics and Outreach Indicators for Financially Self-Sufficient 

62 

MFIs 

TABLE D Profitability and Efficiency Indicators for Financially Self-Sufficient MFIs 66 



TABLE 1. INSTITUTIONAL CHARACTERISTICS AND OUTREACH INDICATORS 

PEER GROUP 

44 MICROBANKING BULLETIN, APRIL 2001 

AGE 

(years) 

OFFICES 

(no.) 

TOTAL 

STAFF 

total number 

of employees 

(no.) 

TOTAL 

ASSETS 

(US$) 

CAPITAL/ 

ASSET RATIO 

adj. total equity / 

adj. total assets 

(%) 

ALL MFIs (n=124) avg 8 13 94 5,512,452 49.5 

stdv 4 11 69 5,375,638 23.1 

Financially self-Sufficient MFIs (n=64) avg 10* 91* 367 14,498,853* 49.3 

stdv 5 304 1,219 27,453,324 24.2 

1. Africa – Medium – Low (n=10) avg 6 14 87 2,560,405 49.0 

Citi Savings & Loans, FINCA Uganda, KWFT, Nsoatreman Rural Bank, PAMÉCAS, PRIDE stdv 2 10 30 1,187,845 29.7 

Tanzania, PRIDE Uganda, PRIDE Vita Guinea, SEF, WAGES 

2. Africa – Small – Low (n=11) avg 6 40* 43 997,697* 64.6 

Assawinso Rural Bank, Faulu, FINCA Malawi, FINCA Tanzania, FOCCAS, Manya Krobo stdv 6 91 12 300,489 23.0 

Rural Bank, Piyeli, SEDA, Sinapi Aba Trust, UWFT, Vital-Finance 

3. Africa/MENA – Large/Medium – Broad (n=5) avg 9 23 87 11,458,592 50.0 

ABA, Kafo Jiginew, Nyésigiso, PADME, UNRWA stdv 1 25 32 3,693,186 19.7 

4. Asia (Central)/MENA – Medium/Small – Low (n=6) avg 4 23 81 2,823,233 96.9* 

Al Amana, Al Majmoua, Constanta, Faten, FINCA Kyrgyzstan, stdv 1 17 62 1,463,529 4.0 


5. Asia – Large – Low/Broad (n=5) avg 20* 700* 8,711* 1,079,413,652* 27.6 

ACLEDA, ASA, BAAC, BRAC, BRI stdv 5 168 4,099 1,648,849,794 17.5 

6. Asia (Pacific) – Medium/Small – Low/Broad (n=6) avg 11 12 128 2,125,785 52.2 

CARD Bank, EMT, Hatta Kaksekar, Hublag, RSPI, TSPI stdv 2 7 66 1,456,672 12.8 

7. Asia (South) – Medium/Small – Low/Broad (n=9) avg 8 23 152 3,829,019 50.9 

AKRSP, Basix, BURO Tangail, CDS, FWWB India, Kash Foundation, Nirdhan, stdv 5 18 147 2,217,533 26.5 

SEEDS, SHARE 

8. Eastern Europe – High Size: All (n=5) avg 2* 4 23 2,913,938 42.9 

AMK, LOK, Moznosti, Sunrise, WVB stdv 1 2 6 357,400 38.1 

9. Eastern Europe – Broad Size: All (n=7) avg 3 9 30 2,585,284 71.4 

Bospo, Fundusz Mikro, Inicjatywa Mikro, Mercy Corps, Mikrofin, Nachala, NOA stdv 1 7 19 1,362,189 30.2 

10. Latin America – Large – Broad/High (n=9) avg 11 22 245* 34,108,155* 22.3* 

Banco Ademi, BancoSol, Caja de Los Andes, Calpiá, CM Arequipa, FIE, Finamérica, stdv 3 13 64 11,038,053 12.6 

Mibanco, PRODEM 

11. Latin America – Medium – Broad (n=11) avg 9 8 72 4,326,750 41.4 

ACODEP, Actuar, ADOPEM, ADRI, Banco de la Pequena Empresa, Chispa, FAMA, stdv 4 5 49 1,601,429 13.2 


12. Latin America – Medium/Small – Low – Upper Income (n=6) avg 3* 5 26 1,974,452 39.6 

Banco do Povo de Juiz de Fora, CEAPE/ Pernambuco, Contigo, Emprender, Portsol, stdv 2 1 9 760,126 17.6 

Vivacred 

13. Latin America – Medium – Low – Lower Income (n=9) avg 10 7 116 5,165,496 62.0 

CAM, CMM Medellín, Compartamos, Crecer, Enlace, FINCA Honduras, stdv 3 3 49 2,103,809 18.2 

FMM Popayán, FWWB Cali, ProMujer 

14. Latin America – Small – Low – Lower Income (n=7) avg 11 7 73 1,424,668 75.7* 

AGAPE, FED, FINCA Ecuador, FINCA Mexico, FINCA Nicaragua, FINCA Peru, stdv 5 6 32 877,098 10.9 

World Relief Honduras 

15. Latin America – Credit Unions – Broad Size: All (n=11) avg 8 3 53 6,153,016 34.4 

15 de Abril, 23 de Julio, Acredicom, Chuimequená, COOSAJO, Ecosaba, Moyután, stdv 4 1 11 1,752,518 7.7 

Oscus, Sagrario, Tonantel, Tulcán 

16. Worldwide – Large/Medium – Small Business (n=7) avg 7 12 134 18,331,933* 30.8 

ACEP, Agrocapital, Bank Dagang Bali, CERUDEB, FEFAD, MEB, NLC stdv 4 9 153 8,580,116 18.8 

Note: Standard deviations are listed below the peer group averages. The averages are calculated on the basis of the values between the ninth and second 

deciles for all MFIs and between second and the 99th percentiles for each peer group. Group averages different from average for all MFIs at 1 percent 

significance level are marked with an asterisk (*). “n” refers to the total number of institutions in the peer group, before dropping the percentiles for averages. 

Additional statistical information is available at www.microbanking-mbb.org. Abbreviation: MENA=Middle East/North Africa.


COMMERCIAL 

FUNDING LIABILITIES 

RATIO 

borrowings at commercial 

rates/ average gross loan 

portfolio (%) 

TOTAL 

GROSS LOAN 

PORTFOLIO 

total gross portfolio 

outstanding 

(US$) 

NUMBER OF 

ACTIVE 

BORROWERS 

borrowers with loans 

outstanding 

(no.) 

AVERAGE 

LOAN BALANCE 

total gross loan 

portfolio / no. active 

borrowers (US$) 

DEPTH 

average loan 

balance/ GNP per 

capita (%) 

PERCENT 

WOMEN 

BORROWERS 

women borrowers / 

total borrowers 

(%) 

PEER GROUP 

35.9 3,764,997 11,398 490 44.5 62.4 ALL 

36.9 3,540,730 9,023 452 32.4 21.8 MFIs 

67.8* 11,113,300* 74,921 686 62.6 60.9 FSS 

69.7 22,296,780 341,259 793 73.2 25.5 MFIs 

41.8 1,525,339 14,668 123 33.6 79.8 1. 

50.7 613,676 7,286 35 14.8 18.1 

24.0 512,947* 5,634 92* 31.3 83.8* 2. 

47.6 215,068 2,802 29 11.6 18.9 

68.1 6,445,652 15,411 492 81.8 35.9 3. 

36.5 1,235,647 2,903 237 22.0 20.6 

- 1,310,412 8,043 166 11.9 97.5* 4. 

- 617,411 6,789 62 3.9 5.0 

54.4 352,532,708* 2,046,752* 194 33.6 67.6 5. 

68.1 430,311,722 835,243 112 13.3 37.1 

37.7 1,509,701 12,974 159 14.3 82.8 6. 

9.4 906,275 11,424 24 2.4 10.3 

32.5 2,220,962 25,764* 82* 22.0 75.1 7. 

33.0 1,487,520 19,443 36 8.6 31.7 

1.3 2,698,678 1,377 2,249* 202.8* 38.3 8. 

2.2 516,150 504 526 41.8 2.3 

6.7 2,352,138 2,652 1,089* 66.2 44.5 9. 

15.1 1,253,418 1,916 302 25.3 6.3 

91.2* 27,175,166* 29,730* 971* 70.3 50.5 10. 

19.7 8,699,651 9,360 279 26.0 8.5 

50.9 3,427,876 7,453 609 64.3 46.5 11. 

18.5 1,332,599 5,924 414 29.1 16.6 

12.1 1,669,824 2,182 789 14.9 - 12. 

24.2 880,531 1,207 113 4.0 - 

39.8 3,506,001 19,663* 197 12.4* 82.4* 13. 

24.4 1,465,732 4,173 93 2.8 16.9 

16.5 853,632 8,975 91 6.3* 95.0* 14. 

29.4 409,407 3,127 17 3.9 6.6 

91.7* 4,105,127 5,122 887* 56.7 40.8* 15. 

19.8 1,295,656 1,504 420 26.0 4.9 

101.8* 10,322,826* 4,934 2,968* 391.4* 29.0 16. 

115.1 2,302,970 3,348 776 175.5 2.8 



TABLE 2. OVERALL FINANCIAL PERFORMANCE AND OPERATING INCOME 

PEER GROUP 

ADJUSTED 

RETURN ON 

ASSETS 

adj. net operating 

income / average 

total assets 

(%) 

ADJUSTED 

RETURN ON 

EQUITY 

adjusted net 

operating 

income / average 

equity 

(%) 

OPERATIONAL 

SELF- 

SUFFICIENCY 

operating income / 

operating expense 

(%) 

FINANCIAL 

SELF- 

SUFFICIENCY 

adjusted operating 

income / adjusted 


ALL MFIs (n=124) avg -3.5 -5.7 103.6 90.2 

stdv 6.1 14.3 20.9 18.7 

Financially self-Sufficient MFIs (n=64) avg 3.0* 8.8* 129.8* 114.3* 

stdv 5.3 14.2 36.0 25.8 

1. Africa – Medium – Low (n=10) avg -13.5* -23.2* 75.2* 72.7* 

Citi Savings & Loans, FINCA Uganda, KWFT, Nsoatreman Rural Bank, PAMÉCAS, PRIDE stdv 10.3 12.4 14.0 12.7 


2. Africa – Small – Low (n=11) avg -11.4* -8.1 77.5* 69.3* 

Assawinso Rural Bank, Faulu, FINCA Malawi, FINCA Tanzania, FOCCAS, Manya Krobo stdv 9.8 25.9 27.6 22.3 


3. Africa/MENA – Large/Medium – Broad (n=5) avg -0.7 -4.4 114.4 101.0 

ABA, Kafo Jiginew, Nyésigiso, PADME, UNRWA stdv 2.8 7.4 36.8 23.0 

4. Asia (Central)/MENA – Medium/Small – Low (n=6) avg -11.2* -11.9 79.9 70.8 

Al Amana, Al Majmoua, Constanta, Faten, FINCA Kyrgyzstan, stdv 10.2 11.2 18.5 18.9 


5. Asia – Large – Low/Broad (n=5) avg 4.7 13.5 136.8* 121.1* 

ACLEDA, ASA, BAAC, BRAC, BRI stdv 3.0 9.5 9.2 12.9 

6. Asia (Pacific) – Medium/Small – Low/Broad (n=6) avg 0.7 0.2 111.1 101.8 

CARD Bank, EMT, Hatta Kaksekar, Hublag, RSPI, TSPI stdv 3.4 4.8 6.6 9.8 

7. Asia (South) – Medium/Small – Low/Broad (n=9) avg -6.8 -12.4 85.6 69.4* 

AKRSP, Basix, BURO Tangail, CDS, FWWB India, Kash Foundation, Nirdhan, stdv 3.9 11.8 18.8 19.9 

SEEDS, SHARE 

8. Eastern Europe – High Size: All (n=5) avg -2.3 -9.0 105.8 90.5 

AMK, LOK, Moznosti, Sunrise, WVB stdv 1.4 9.3 19.3 5.6 

9. Eastern Europe – Broad Size: All (n=7) avg -3.0 -5.2 106.8 90.1 

Bospo, Fundusz Mikro, Inicjatywa Mikro, Mercy Corps, Mikrofin, Nachala, NOA stdv 2.1 4.9 7.3 6.3 

10. Latin America – Large – Broad/High (n=9) avg 2.3* 15.3* 110.4 108.9* 

Banco Ademi, BancoSol, Caja de Los Andes, Calpiá, CM Arequipa, FIE, Finamérica, stdv 1.3 12.2 5.0 5.3 


11. Latin America – Medium – Broad (n=11) avg -1.9 -0.6 106.1 96.5 

ACODEP, Actuar, ADOPEM, ADRI, Banco de la Pequena Empresa, Chispa, FAMA, stdv 7.0 16.4 21.4 20.0 


12. Latin America – Medium/Small – Low – Upper Income (n=6) avg -10.6 -44.3* 100.2 81.4 

Banco do Povo de Juiz de For a, CEAPE/ Pernambuco, Contigo, Emprender, Portsol, stdv 8.0 51.0 18.0 14.2 

Vivacred 

13. Latin America – Medium – Low – Lower Income (n=9) avg 4.5* 7.7* 125.2* 111.6* 

CAM, CMM Medellín, Compartamos, Crecer, Enlace, FINCA Honduras, stdv 8.2 13.6 27.6 19.0 


14. Latin America – Small – Low – Lower Income (n=7) avg -3.4 -4.6 114.6 93.4 

AGAPE, FED, FINCA Ecuador, FINCA Mexico, FINCA Nicaragua, FINCA Peru, stdv 6.9 10.5 8.4 11.4 


15. Latin America – Credit Unions – Broad Size: All (n=11) avg -2.6 -5.6 113.2 88.3 

15 de Abril, 23 de Julio, Acredicom, Chuimequená, COOSAJO, Ecosaba, Moyután, stdv 4.4 11.7 22.1 21.5 


16. Worldwide – Large/Medium – Small Business (n=7) avg -2.1 -7.3 104.7 90.4 

ACEP, Agrocapital, Bank Dagang Bali, CERUDEB, FEFAD, MEB, NLC stdv 1.7 6.3 6.4 7.0 

Note: Standard deviations are listed below the peer group averages. The averages are calculated on the basis of the values between the ninth and second deciles 

for all MFIs and between second and the 99 th percentiles for each peer group. Group averages different from average for all MFIs at 1 percent significance level are 

marked with an asterisk (*). “n” refers to the total number of institutions in the peer group, before dropping the percentiles for averages. Additional statistical 

information is available at www.microbanking-mbb.org. Abbreviation: MENA=Middle East/North Africa. 

(%) 



OPERATING 

INCOME RATIO 


income / 

average total assets 

(%) 

PROFIT 

MARGIN 

adjusted net 


adjusted 

operating income 

(%) 

NET 

INTEREST 

MARGIN 

adjusted net interest 

margin / 


(%) 

PORTFOLIO 

YIELD 

operating income – accrued 

interest – interest and fee 

income from investments / 

average gross loan portfolio 

(%) 

REAL 

YIELD 

(portfolio yield – 

inflation rate) / 

(1 + inflation rate) 

(%) 

PEER GROUP 

28.3 -16.2 19.8 39.2 30.1 ALL 

9.0 26.6 8.2 13.2 12.0 MFIs 

33.4* 9.3* 24.7* 45.5* 35.5* FSS 

13.0 15.1 11.4 20.1 15.4 MFIs 

28.0 -42.1 22.6 42.0 34.5 1. 

7.8 29.4 10.2 13.2 12.0 

27.4 -57.2* 22.7 54.6* 44.2* 2. 

8.5 47.2 8.7 15.8 15.9 

13.1* -2.1 9.5 24.6 21.7 3. 

1.5 20.5 2.9 1.3 0.7 

26.7 -48.0 20.7 44.8 36.8 4. 

6.4 34.4 4.7 15.5 3.7 

23.6 16.8 12.3 31.7 17.6 5. 

4.9 8.9 2.5 13.7 4.9 

29.3 1.1 24.1 42.7 36.3 6. 

1.6 9.0 2.4 3.6 5.1 

15.8* -56.6* 9.6* 22.0* 13.3* 7. 

5.4 51.6 5.1 7.8 8.2 

26.2 -10.8 17.7 31.8 15.3 8. 

3.0 6.8 5.3 3.6 3.1 

27.6 -11.4 20.8 31.0 22.7 9. 

1.1 7.4 2.7 1.9 5.2 

29.1 8.0* 20.2 35.1 29.9 10. 

2.2 4.4 2.6 3.0 3.5 

36.5* -8.2 25.4 45.9 33.1 11. 

7.1 25.5 6.0 8.9 7.3 

41.8* -25.7 27.9 52.9 50.2* 12. 

5.6 22.1 5.2 5.6 3.2 

45.2* 8.4* 30.2* 57.1* 40.3 13. 

11.7 13.7 4.4 14.5 5.3 

45.0* -8.6 26.6 78.3* 55.3* 14. 

3.0 15.4 12.9 13.0 6.4 

16.6* -19..4 7.7* 20.3* -9.5* 15. 

3.7 28.8 3.7 3.9 32.4 

21.0 -11.2 11.4 24.0* 17.0* 16. 

3.8 8.7 6.2 3.7 3.7 



TABLE 3. OPERATING EXPENSES AND PORTFOLIO MANAGEMENT INDICATORS 

PEER GROUP 

OPERATING 

EXPENSE 

RATIO 


expense / 


(%) 

TOTAL 

INTEREST 

EXP. RATIO 

adj. total interest 

expense / 

average total 

assets 

(%) 

ADJUSTMENT 

EXPENSE 

RATIO 

adjustment 

expense / 

average total 

assets 

(%) 

LOAN LOSS 

PROVISION 

EXP. RATIO 

adj. loan loss 

provision expense / 

average total 

assets 

(%) 

ALL MFIs (n=124) avg 32.5 3.8 3.5 2.1 

stdv 10.9 2.9 2.6 1.5 

Financially self-Sufficient MFIs (n=64) avg 30.7 5.1 3.1 1.9 

stdv 12.5 4.1 3.1 1.5 

1. Africa – Medium – Low (n=10) avg 43.8* 1.7 2.3 1.4 



2. Africa – Small – Low (n=11) avg 40.1 1.3* 3.6 1.6 



3. Africa/MENA – Large/Medium – Broad (n=5) avg 15.7* 1.5 0.6 2.5 


4. Asia (Central)/MENA – Medium/Small – Low (n=6) avg 38.0 - 3.6 0.9 

Al Amana, Al Majmoua, Constanta, Faten, FINCA Kyrgyzstan, stdv 6.2 - 3.6 0.7 


5. Asia – Large – Low/Broad (n=5) avg 23.8 4.2 2.3 3.2 




7. Asia (South) – Medium/Small – Low/Broad (n=9) avg 23.0 2.9 4.1 2.0 


SEEDS, SHARE 

8. Eastern Europe – High Size: All (n=5) avg 29.5 2.6 4.9 3.1 


9. Eastern Europe – Broad Size: All (n=7) avg 28.6 1.6 5.2 2.8 


10. Latin America – Large – Broad/High (n=9) avg 28.4 8.0* 0.7* 3.5* 



11. Latin America – Medium – Broad (n=11) avg 37.4 7.2* 3.5 3.4* 



12. Latin America – Medium/Small – Low – Upper Income (n=6) avg 52.5* 6.5 9.6* 7.2* 


Vivacred 

13. Latin America – Medium – Low – Lower Income (n=9) avg 41.5 5.6 4.3 1.6 



14. Latin America – Small – Low – Lower Income (n=7) avg 55.3* 4.2 10.0* 2.2 



15. Latin America – Credit Unions – Broad Size: All (n=11) avg 19.1* 5.7 3.2 1.0 



16. Worldwide – Large/Medium – Small Business (n=7) avg 20.0* 3.4 2.3 1.2 


Note: Standard deviations are listed below the peer group averages. The averages are calculated on the basis of the values between the ninth and second deciles for 

all MFIs and between second and the 99 th percentiles for each peer group. Group averages different from average for all MFIs at 1 percent significance level are 





SALARY EXPENSE 

RATIO 

personnel expense + in-kind 

donations / 


(%) 

OTHER 

ADMINISTRATIVE 

EXPENSE RATIO 

administrative expense + 

in-kind donations – 

personnel expense/ 


(%) 

TOTAL 


EXPENSE RATIO 

administrative expense + 

in-kind donations/ 


(%) 

PORTFOLIO 

AT RISK > 

90 DAYS 

outstanding 

balance of loans overdue 

90 days / 

total gross loan portfolio 

(%) 

PEER GROUP 

11.5 9.1 20.7 1.9 ALL 

6.0 3.8 9.2 1.6 MFIs 

11.1 8.8 19.9 1.9 FSS 

6.5 5.0 10.4 1.5 MFIs 

20.6* 14.8* 35.4* 0.8 1. 

11.4 6.3 17.0 0.7 

17.3* 15.4* 32.5* 2.4 2. 

5.6 6.1 11.7 2.4 

5.0 3.7* 8.9 4.0 3. 

2.3 3.5 3.6 3.3 

23.1* 9.1 32.8* 0.1 4. 

3.4 1.2 4.8 0.1 

5.9 2.3* 8.1* 2.1 5. 

3.2 1.6 4.7 1.2 

12.0 8.9 20.9 2.7 6. 

0.7 1.3 1.9 3.1 

7.7 6.3 13.7 1.7 7. 

7.3 4.5 11.7 2.5 

12.0 7.0 19.5 0.1 8. 

3.0 0.6 2.5 0.1 

12.0 8.6 20.4 0.9 9. 

1.7 1.4 3.3 0.9 

8.0 7.2 14.5 2.6 10. 

1.8 2.2 2.1 0.7 

12.2 11.5 23.5 2.8 11. 

4.0 4.1 6.8 1.8 

15.5 12.0 27.6 2.7 12. 

3.0 1.7 1.6 2.2 

14.4 12.6 26.6 1.0 13. 

4.9 5.1 9.2 0.9 

20.5* 12.8* 32.7* 1.6 14. 

5.2 5.5 4.4 0.8 

3.0* 5.1* 8.2* - 15. 

0.8 0.8 1.3 - 

4.3* 5.0* 9.3* 1.0 16. 

2.3 3.4 5.4 0.9 



TABLE 4. EFFICIENCY AND PRODUCTIVIY 

PEER GROUP 

TOTAL 


EXPENSE / LP 

administrative 

expense + in-kind 

donations / average 

gross loan portfolio 

(%) 

SALARY 

EXPENSE / 

LP 

personnel expense 

+ in-kind donations 

/ average gross 

loan portfolio 

(%) 

OTHER 


EXPENSE / LP 

administrative expense 

+ in-kind donations – 

personnel expense/ 

average gross loan 

portfolio (%) 

DEPTH 

average loan 

balance / 

GNP per 

capita 

(%) 

ALL MFIs (n=124) avg 31.3 17.3 13.8 44.5 

stdv 16.0 10.0 6.8 32.4 

Financially self-Sufficient MFIs (n=64) avg 29.2 16.2 12.9 62.6 

stdv 17.3 10.1 8.3 73.2 

1. Africa – Medium – Low (n=10) avg 56.6* 33.1* 23.6* 33.6 



2. Africa – Small – Low (n=11) avg 71.4* 37.6* 33.8* 31.3 



3. Africa/MENA – Large/Medium – Broad (n=5) avg 17.1 8.8 6.4 81.8 


4. Asia (Central)/MENA – Medium/Small – Low (n=6) avg 55.2* 38.7* 16.2 11.9 

Al Amana, Al Majmoua, Constanta, Faten, FINCA Kyrgyzstan, stdv 9.4 6.0 3.0 3.9 


5. Asia – Large – Low/Broad (n=5) avg 12.7 9.1 3.7* 33.6 




7. Asia (South) – Medium/Small – Low/Broad (n=9) avg 18.8 10.1 9.1 22.0 


SEEDS, SHARE 

8. Eastern Europe – High Size: All (n=5) avg 23.0 14.1 8.3 202.8* 


9. Eastern Europe – Broad Size: All (n=7) avg 23.7 14.0 9.7 66.2 


10. Latin America – Large – Broad/High (n=9) avg 18.0 9.6 9.1 70.3 



11. Latin America – Medium – Broad (n=11) avg 31.0 16.0 15.1 64.3 



12. Latin America – Medium/Small – Low – Upper Income (n=6) avg 36.4 21.2 15.9 14.9 


Vivacred 

13. Latin America – Medium – Low – Lower Income (n=9) avg 39.7 21.6 17.8 12.4* 



14. Latin America – Small – Low – Lower Income (n=7) avg 54.1* 33.6* 24.2* 6.3* 



15. Latin America – Credit Unions – Broad Size: All (n=11) avg 12.2* 4.5* 7.5* 56.7 



16. Worldwide – Large/Medium – Small Business (n=7) avg 12.8* 5.7* 7.4 391.4* 








AVERAGE 

SALARY 

average personnel 


donations / 


(multiple of GNP/ capita) 

STAFF 

PRODUCTIVITY 

number active 

borrowers / 

number of staff 

(no.) 

LOAN OFFICER 

PRODUCTIVITY 

number active 

borrowers / 

number of loan 

officers 

(no.) 

STAFF 

ALLOCATION 


officers / 


(%) 

STAFF 

TURNOVER 

number staff who 

left the MFI / 

average number 

of staff 

(%) 

COST PER 

BORROWER 



donations / average 

number of active 

borrowers (US$) 

PEER GROUP 

5.8 122 257 49.7 10.8 137 ALL 

3.6 49 128 10.5 6.5 171 MFIs 

5.9 133 291 51.4 12.7 110 FSS 

4.0 72 189 15.3 934 99 MFIs 

12.9* 192* 474* 38.9* 5.2 56 1. 

7.6 79 292 15.4 3.1 27 

11.9* 129 285 51.9 4.6* 62 2. 

4.8 41 99 13.1 4.8 30 

7.6 119 315 47.2 6.2 40 3. 

6.5 95 308 11.5 5.7 8 

3.9 102 174 59.2 20.2 76 4. 

1.3 10 35 6.8 19.8 41 

4.0 238* 420 41.6 5.8 25 5. 

1.0 83 84 20.7 1.3 16 

2.3 80 134 66.1* 13.8 44 6. 

0.6 16 22 11.1 10.1 13 

3.4 235* 452* 56.0 9.7 17 7. 

1.4 232 494 14.6 5.7 7 

10.3 64 113 55.9 6.8 259 8. 

0.6 0 8 2.8 6.7 33 

5.0 78 123 58.7 7.3 196 9. 

2.5 22 28 9.1 2.4 77 

7.1 122 285 42.6 13.0 166 10. 

2.5 16 58 3.9 4.9 40 

6.9 88 212 40.6* 14.2 176 11. 

3.9 41 84 7.6 8.2 155 

2.1 81 197 48.5 - 241 12. 

0.3 30 71 12.7 - 117 

3.4 194* 417* 48.5 9.6 56 13. 

1.0 52 159 6.6 6.6 9 

2.0 140 225 57.1 10.3 52 14. 

1.3 29 40 3.6 9.5 15 

1.9 107 - - - 105 15. 

0.5 8 - - - 34 

8.9 37* 130 32.2* 3.3 377* 16. 

3.8 21 84 7.8 2.9 272 



TABLE 5. MACROECONOMIC INDICATORS 

GNP PER 

CAPITA 

GDP GROWTH 

RATE, ANNUAL 

AVERAGE 

1990-98 

INFLATION 

RATE 

DEPOSIT 

RATE 

FINANCIAL 

DEEPENING 

(M3 / GDP) 

PEER GROUP 

(US$) (%) (%) (%) (%) 

ALL MFIs (n=124) avg 1,395 3.9 12.4 15.3 35.3 

stdv 1,327 2.8 21.9 10.9 19.1 

Financially self-Sufficient MFIs (n=64) avg 1,373 3.9 7.5 14.3 35.9 

stdv 1,036 2.3 7.9 7.8 17.1 

1. Africa – Medium – Low (n=10) avg 660 4.0 5.8 11.2 19.5* 

Citi Savings & Loans, FINCA Uganda, KWFT, Nsoatreman Rural Bank, PAMÉCAS, PRIDE stdv 901 2.1 4.3 6.9 9.4 


2. Africa – Small – Low (n=11) avg 309* 4.8 8.7 13.8 15.8* 

Assawinso Rural Bank, Faulu, FINCA Malawi, FINCA Tanzania, FOCCAS, Manya Krobo stdv 74 1.7 6.0 9.5 6.1 


3. Africa/MENA – Large/Medium – Broad (n=5) avg 746 4.0 2.2 5.0 31.6 

ABA, Kafo Jiginew, Nyésigiso, PADME, UNRWA stdv 629 0.4 4.6 2.5 33.0 

4. Asia (Central)/MENA – Medium/Small – Low (n=6) avg 1,723 -4.6* 7.5 10.2 87.7 

Al Amana, Al Majmoua, Constanta, Faten, FINCA Kyrgyzstan, stdv 1,631 10.1 7.4 4.4 22.6 


5. Asia – Large – Low/Broad (n=5) avg 770 5.7 9.7 12.7 47.3 

ACLEDA, ASA, BAAC, BRAC, BRI stdv 798 0.3 6.3 7.4 35.9 

6. Asia (Pacific) – Medium/Small – Low/Broad (n=6) avg 837 3.9 5.3 9.2 46.2 

CARD Bank, EMT, Hatta Kaksekar, Hublag, RSPI, TSPI stdv 452 0.9 4.6 2.3 27.1 

7. Asia (South) – Medium/Small – Low/Broad (n=9) avg 422 5.6 8.0 10.8 46.7 

AKRSP, Basix, BURO Tangail, CDS, FWWB India, Kash Foundation, Nirdhan, stdv 162 0.7 2.9 3.6 7.5 

SEEDS, SHARE 

8. Eastern Europe – High Size: All (n=5) avg 1,127 1.7 11.4 13.5 14.8 

AMK, LOK, Moznosti, Sunrise, WVB stdv 91 - 6.6 1.2 - 

9. Eastern Europe – Broad Size: All (n=7) avg 2,336 0.8 6.4 10.0 34.4 

Bospo, Fundusz Mikro, Inicjatywa Mikro, Mercy Corps, Mikrofin, Nachala, NOA stdv 1,625 3.4 6.9 4.5 1.1 

10. Latin America – Large – Broad/High (n=9) avg 1,663 4.8 4.1 14.4 38.1 

Banco Ademi, BancoSol, Caja de Los Andes, Calpiá, CM Arequipa, FIE, Finamérica, stdv 671 0.8 3.1 3.4 10.3 


11. Latin America – Medium – Broad (n=11) avg 1,373 4.0 8.4 14.7 43.1 

ACODEP, Actuar, ADOPEM, ADRI, Banco de la Pequena Empresa, Chispa, FAMA, stdv 883 1.0 3.6 3.9 17.4 


12. Latin America – Medium/Small – Low – Upper Income (n=6) avg 5,257* 4.5 2.2 24.6 32.9 

Banco do Povo de Juiz de For a, CEAPE/ Pernambuco, Contigo, Emprender, Portsol, stdv 1,366 1.9 1.7 12.6 5.4 

Vivacred 

13. Latin America – Medium – Low – Lower Income (n=9) avg 1,931 3.8 13.2 19.7 34.2 

CAM, CMM Medellín, Compartamos, Crecer, Enlace, FINCA Honduras, stdv 988 0.8 15.6 12.0 10.9 


14. Latin America – Small – Low – Lower Income (n=7) avg 1,849 3.5 22.6 25.1 36.2 

AGAPE, FED, FINCA Ecuador, FINCA Mexico, FINCA Nicaragua, FINCA Peru, stdv 1,170 1.1 20.6 16.9 14.6 


15. Latin America – Credit Unions – Broad Size: All (n=11) avg 1,553 3.6 51.4* 26.7* 32.0 

15 de Abril, 23 de Julio, Acredicom, Chuimequená, COOSAJO, Ecosaba, Moyután, stdv 31 0.6 54.1 19.1 6.4 


16. Worldwide – Large/Medium – Small Business (n=7) avg 757 5.5 12.5 15.2 37.8 

ACEP, Agrocapital, Bank Dagang Bali, CERUDEB, FEFAD, MEB, NLC stdv 329 2.3 20.4 11.3 19.8 







Additional Analysis Tables 

Tables A and B provide data on selected 

performance indicators for groups of institutions 

from the entire database for this Issue (n=124). 

Tables C and D provide information only for the 

financially self-sufficient institutions (n=64). The 

following eight characteristics are considered for the 

classification of data: 

1) Age: The Bulletin classifies MFIs into three 

categories (new, young, and mature) based on 

the difference between the year they started 

their microfinance operations and the year for 

which the institutions have submitted data. 

2) Scale of Operations: MFIs are classified as 

small, medium and large according to the size 

of their loan portfolio to facilitate comparisons of 

institutions with similar outreach. 

3) Lending Methodology: Performance may vary 

by the methodology used by the institution to 

deliver loan products. The Bulletin classifies 

MFIs based on the primary methodology used, 

determined by the number of loans outstanding. 

4) Level of Financial Intermediation: This 

classification is based on the ratio of total 

voluntary passbook and time deposits to total 

assets. It indicates the MFI’s ability to mobilize 

savings and fund its portfolio through deposits. 

5) Target Market: The Bulletin classifies MFIs into 

three categories—low-end, broad, and highend—according 

to the range of clients they 

serve based on average loan outstanding per 

GNP per capita. 

6) Region: Geographic regions—Africa, Asia, 

Eastern Europe, and Latin America—are 

considered for the classification to capture 

regional effects. MENA was not included due 

to the small number of MFIs participating. 

7) Charter Type: The charter under which the 

MFIs are registered is used to classify the MFIs 

into banks, credit unions/ cooperatives, NGOs, 

and non-banks. 

8) Profit Status: MFIs are classified as for-profit 

and non-profit institutions. 

The quantitative criteria used to categorize these 

characteristics are summarized in the table below. 

A list of institutions that fall into these categories for 

the entire sample is located immediately following 

Table D. Confidentiality limits the publication of 

names of financially self-sufficient MFIs included in 

the database. 

These Additional Analysis Tables provide another 

means of creating performance benchmarks 

besides the peer groups. Three of these 

characteristics—region, scale of operations and 

target market—are also factors in determining peer 

group composition. The purpose of the Additional 

Analysis Tables is to look at these characteristics 

singularly, rather than within the context of the peer 

groups. The data are calculated by dropping the 

top and bottom percentile of observations to avoid 

the effect of outliers. 

Age of the MFI 

Scale of Operations 

(Size of gross 

outstanding loan 

portfolio in US$) 

Lending Methodology 

Level of Retail Financial 

Intermediation 

Target Market 

New: 

Young: 

Mature: 

Large: 

Medium: 

Small: 

Individual 

Solidarity Group: 

Village Banking: 

Financial Intermediary: 

Other: 

Low-end: 

Broad: 

High-end and Small Business: 

1 to 2 years 

3 to 6 years 

over 6 years 

Africa and MENA: 

Asia (all): 

E.Europe and L.America: 

Africa and MENA: 

Asia: 


Africa: 

Asia: 


1 borrower 

group of 3 to 9 borrowers 

groups with ≥ 10 borrowers 

> 5 million 

> 8 million 

> 10 million 

900,000 to 5 million 

1 to 8 million 

1.5 to 10 million 

< 900,000 

< 1 million 

< 1.5 million 

passbook and time deposits ≥ 20 % of total assets 

passbook and time deposits < 20 % of total assets 

depth < 20% OR average loan size < US$150 

depth between 20% and 149% 

depth ≥ 150% 



TABLE A: INSTITUTIONAL CHARACTERISTICS AND OUTREACH INDICATORS 

CRITERIA 

TOTAL 

ASSETS 

(US$) 

CAPITAL / 

ASSET 

RATIO 

adjusted total 

equity / 


assets (%) 

COMMERCIAL 


RATIO 

OFFICES 


rates / average gross loan 

portfolio 

(%) (no.) 

AGE New (1 – 2 years) avg 2,655,249* 55.2 20.3 9 

stdv 1,615,760 29.1 29.9 10 

N 28 28 28 27 

Young (3 – 6 years) avg 5,441,477 55.3 40.0 34 

stdv 5,376,583 30.4 73.4 121 

N 26 26 26 26 

Mature (> 6 years) avg 62,766,879 45.7 66.0* 94* 

stdv 374,922,454 25.8 58.0 334 

N 63 63 63 56 

SCALE OF Large avg 191,135,173* 32.2* 91.1* 124* 

OPERATIONS # stdv 658,265,519 21.1 73.2 254 

N 20 20 20 20 

Medium avg 4,780,316 49.7 47.6 45 

stdv 2,625,596 26.9 47.3 242 

N 66 66 66 60 

Small avg 1,197,686* 61.2* 21.1 16 

stdv 779,360 28.1 33.8 48 

N 32 32 32 30 

LENDING Individual avg 68,102,103 40.3 71.8* 24 

METHOD- (1 borrower) stdv 412,429,864 26.2 71.8 86 

OLOGY N 52 52 52 47 

Solidarity Groups avg 10,102,851 50.0 37.7 34 

(groups of 3 to 9 stdv 19,545,681 25.6 40.3 96 

borrowers) N 43 43 43 40 

Village Banking avg 2,588,603* 71.1* 18.1 127* 

(groups with ≥ 10 stdv 1,912,299 24.0 24.5 406 


LEVEL OF Financial Intermediaries avg 121,131,564 20.4* 135.7* 42* 

FINANCIAL (passbook and time stdv 550,930,135 14.5 70.9 118 

INTER- deposits ≥ 20% of total N 29 29 29 24 

MEDIATION assets) 

Other avg 5,835,964 59.5* 23.9* 61 

(passbook and time stdv 10,349,253 25.7 27.4 238 

deposits < 20% of total N 91 91 91 87 

assets) 

TARGET Low-end avg 4,948,110 59.0 34.4 89* 

MARKET (depth < 20% OR avg. stdv 11,581,748 28.6 50.0 297 

loan balance < US$ 150) N 57 57 57 55 

Broad avg 68,758,671 42.2 61.2* 28 

(depth between 20% and stdv 416,494,814 24.7 47.8 88 

149%) N 51 51 51 44 

High-end and avg 15,975,183* 35.8 57.1 11 

Small Business stdv 15,545,156 26.2 91.4 10 

(depth ≥ 150%) N 10 10 10 10 

Note: Standard deviations are listed below the group averages. The averages are calculated on the basis of the values between second and the 99 th 

percentiles 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 with an asterisk (*). Additional statistical information is available at www.microbanking-mbb.org. 

# The criteria for classification of scale of operations vary by region. Refer to page 53 for details. 



TOTAL 

STAFF 

Total number of 

employees 

(no.) 

TOTAL 

GROSS LOAN 

PORTFOLIO 


outstanding 

(US$) 

PERCENT 

WOMEN 

BORROWERS 

total number of active 



borrowers (%) 

NUMBER OF 

ACTIVE 

BORROWERS 

number of 

borrowers with 

loans 

outstanding (no.) 

AVERAGE 

LOAN BALANCE 


portfolio / number 

of active borrowers 

(US$) 

DEPTH 

average loan 

balance / 


(%) 

44* 2,039,656* 60.1 5,081* 894* 70.2* 

27 1,339,287 26.6 5,950 961 69.3 

28 28 23 28 28 28 

89 3,452,453 71.4 9.818 554 81.3 

72 3,511,633 26.8 6,971 818 145.2 

26 26 22 26 26 26 

611* 24,643,591 65.2 115,443 552 47.2 

2,100 107,735,266 26.7 460,828 686 50.8 

56 63 56 63 63 63 

1,525* 72,987,141* 50.0 328,895* 1,079* 98.7* 

3,374 185,140,979 21.5 788,612 806 91.2 

20 20 16 20 20 20 

96 3,366,278 64.2 13,741 650 64.6 

77 1,665,912 26.4 13,348 868 101.7 

61 66 56 66 66 66 

44* 669,421* 76.2* 4,797* 338 30.9 

27 317,056 25.9 3,776 465 27.6 

32 32 29 32 32 32 

371 23,341,413 44.4* 55,811 1,206* 103.0* 

1,914 116,972,376 14.6 342,044 1,044 122.7 

47 52 41 52 52 52 

260 7,619,230 74.3* 42,709 328 39.4 

783 16,341,007 23.5 163,573 352 31.7 

42 43 36 43 43 43 

81 1,667,467* 91.3* 14,568 115* 18.0* 

54 1,397,743 16.0 11,016 63 13.5 

23 23 23 23 23 23 

731 41,022,001 45.0* 99,378 879* 77.0* 

2,721 156,184,231 11.7 456,954 824 70.1 

23 29 23 29 29 29 

152 4,359,316 71.9* 25,240 583 52.5 

538 8,465,405 27.1 113,421 822 68.9 

91 91 79 91 91 91 

195 3,509,933 85.9* 35,718 150* 16.8* 

675 9,750,386 19.7 142,494 140 10.4 

57 57 47 57 57 57 

397 24,414,911 50.9* 60,480 797* 64.8* 

1,953 118,336,433 19.3 345,018 584 29.3 

45 51 47 51 51 51 

102 9,778,077* 33.6* 4,373* 2,776* 310.2* 

123 8,762,166 3.6 4,426 682 147.6 

10 10 7 10 10 10 



TABLE A: INSTITUTIONAL CHARACTERISTICS AND OUTREACH INDICATORS (continued) 

CRITERIA 

TOTAL 

ASSETS 

(US$) 

CAPITAL / 

ASSET 

RATIO 


equity / 


assets (%) 

COMMERCIAL 


RATIO 

OFFICES 



portfolio 

(%) (no.) 

REGION ## Africa avg 3,119,574 51.6 53.1 31* 

stdv 3,182,947 30.0 74.5 58 

N 24 24 24 22 

Asia avg 154,874,202* 48.5 44.1 296* 

(All) stdv 632,999,291 28.3 58.5 625 

N 22 22 22 22 

Eastern Europe avg 4,092,029 54.2 8.9* 7 

stdv 3,300,650 34.2 14.4 6 

N 12 12 12 12 

Latin America avg 9,079,001* 44.2 58.0* 10 

stdv 11,352,239 22.0 39.7 9 

N 52 52 52 45 

CHARTER Bank avg 226,598,190* 20.3* 144.5* 54* 

TYPE stdv 763,087,300 20.1 102.4 148 

N 15 15 15 15 

Credit Union/ avg 6,598,534 34.7* 91.3* 15 

Cooperative stdv 2,944,242 13.1 27.1 18 

N 14 14 14 9 

NGO avg 5,763,907 59.5* 23.4* 75 

stdv 11,275,071 25.8 27.5 270 

N 68 68 68 67 

Non-Bank ### avg 7,863,917 46.5 51.3 10 

stdv 7,644,069 33.8 46.8 6 

N 10 10 10 10 

PROFIT Non-Profit avg 5,761,410 58.7* 25.2 71 

STATUS stdv 10,732,900 26.2 29.5 257 

N 76 76 76 74 

Profit avg 125,595,413 32.2* 103.9* 34 

stdv 560,535,131 29.7 94.5 109 

N 28 28 28 28 

Note: Standard deviations are listed below the group averages. The averages are calculated on the basis of the values between second and the 99th 

percentiles 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 with an asterisk (*). Additional statistical information is available at www.microbanking-mbb.org. 


## No data were reported for the region MENA (Middle East North Africa) due to the small size of the sample. 

### Includes Ltd., financieras, and non-bank financial intermediary (NBFIs). 



TOTAL 

STAFF 


employees 

(no.) 

TOTAL 

GROSS LOAN 

PORTFOLIO 


outstanding 

(US$) 

PERCENT 

WOMEN 

BORROWERS 




borrowers (%) 

NUMBER OF 

ACTIVE 

BORROWERS 

number of 


loans 


AVERAGE 

LOAN BALANCE 




(US$) 

DEPTH 

average loan 

balance / 


(%) 

68 2,119,661 76.0* 11,378 166* 51.7 

40 2,611,185 23.6 7,755 165 53.8 

24 24 20 24 24 24 

1,354* 51,908,531* 74.7 301,190* 299 40.7 

3,252 180,641,187 28.8 755,687 675 63.5 

22 22 21 22 22 22 

32* 3,426,730 40.8* 1,958* 1,975* 146.5* 

19 2,351,058 7.0 1,391 1,023 94.8 

12 12 11 12 12 12 

104 6,866,582 60.7 12,408 695 44.4 

81 9,095,332 23.8 11,268 650 40.1 

46 52 43 52 52 52 

1,112* 73,649,013* 46.0 185,251* 1,131* 106.0* 

3,347 215,304,242 20.2 633,497 1,237 112.3 

15 15 8 15 15 15 

61 4,526,240 40.8* 8,066 831* 67.7* 

24 2,428,382 5.9 7,387 486 34.5 

8 14 14 14 14 14 

170 4,174,406 75.3* 30,103 448 41.6 

620 9,260,401 26.3 130,852 684 53.6 

68 68 62 68 68 68 

91 6,454,998 48.4 8,749 1,093* 52.2 

64 7,065,019 20.4 7,423 889 23.6 

10 10 10 10 10 10 

163 4,198,161 73.8* 28,638 482 46.1 

590 8,820,801 26.8 123,821 744 59.3 

75 76 67 76 76 76 

635 43,096,972* 50.5 103,939 1,048* 106.5* 

2,466 158,756,562 19.9 464,813 1,057 145.3 

28 28 20 28 28 28 



TABLE B: PROFITABILITY AND EFFICIENCY INDICATORS 

CRITERIA 

ADJUSTED 

RETURN ON 

ASSETS 

adjusted net 


average total 

assets 

(%) 

ADJUSTED 

RETURN ON 

EQUITY 

adjusted net 


/ average total 

equity 

(%) 

OPERATIONAL 

SELF-SUFFICIENCY 



(%) 

FINANCIAL 

SELF- 

SUFFICIENCY 


income / 


expense 

(%) 

PORTFOLIO 

YIELD 

operating income – 

accrued interest – 

interest and fee 

income from 

investments / 


portfolio (%) 

AGE New (1 - 2 years) avg -9.8* -21.5* 93.0 76.2* 36.6 

stdv 10.4 25.7 30.2 21.3 13.6 

N 28 28 28 28 28 

Young (3 - 6 years) avg -4.0 5.4 101.7 87.7 41.3 

stdv 7.1 69.3 29.3 25.7 21.6 

N 26 26 26 26 26 

Mature (> 6 years) avg -1.0 0.9 115.0* 100.2* 42.6 

stdv 8.0 24.1 37.6 28.2 20.2 

N 63 63 63 63 63 

SCALE OF Large avg 2.3* 7.4* 126.4* 114.3* 33.8 

OPERATIONS # stdv 5.6 26.6 37.1 26.3 10.8 

N 20 20 20 20 20 

Medium avg -3.4 -9.9 106.5 90.6 38.8 

stdv 8.8 27.7 35.0 24.6 18.4 

N 66 66 66 66 66 

Small avg -9.6* 4.1 94.3 78.7* 52.2* 

stdv 12.3 114.2 28.4 25.7 21.9 

N 32 32 32 32 32 

LENDING Individual avg -1.2 -6.7 118.5* 100.7* 33.7 

METHOD- (1 borrower) stdv 7.4 49.2 38.7 34.2 15.2 

OLOGY N 52 52 52 52 52 

Solidarity Groups avg -7.0* -14.3* 93.8* 83.5 40.8 

(groups of 3 to 9 stdv 9.5 21.0 25.2 22.9 12.6 

borrowers) N 43 43 43 43 43 

Village Banking avg -5.5 -5.4 103.0 87.8 57.1* 

(groups with ≥ 10 stdv 13.7 17.5 35.1 24.6 26.4 

borrowers) N 23 23 23 23 23 

LEVEL OF Financial Intermediaries avg -2.2 2.4 103.8 91.7 31.2* 

FINANCIAL (passbook and time deposits stdv 5.6 85.0 25.7 23.0 14.6 

INTER- ≥ 20% of total assets) N 29 29 29 29 29 

MEDIATION 

Other avg -4.9 -10.0 107.7 91.8 44.5 

(passbook and time deposits stdv 11.6 29.0 39.5 31.7 20.9 

< 20% of total assets) N 91 91 91 91 91 

TARGET Low-end avg -6.1 -2.5 103.3 87.8 51.6* 

MARKET (depth < 20% OR avg. loan stdv 12.6 89.5 36.9 27.7 23.2 

Balance < US$150) N 57 57 57 57 57 

Broad avg -2.7 -3.1 106.0 92.6 33.2* 

(depth between 20% and stdv 6.9 22.2 27.8 24.8 12.0 

149%) N 51 51 51 51 51 

High-end and avg -0.3 -6.8 117.6 102.1 26.6* 

Small Business stdv 4.9 16.5 37.6 32.9 6.1 

(depth ≥ 150%) N 10 10 10 10 10 

Note: Standard deviations are listed below the group averages. The averages are calculated on the basis of the values between second and the 99th percentiles 

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 

with an asterisk (*). Additional statistical information is available at www.microbanking-mbb.org. 




PORTFOLIO 

AT RISK > 

90 DAYS 

outstanding 

balance of 

loans overdue > 

90 days / total 

gross loan 

portfolio 

(%) 

TOTAL ADMIN. 

EXPENSE 

RATIO 


expense + inkind 

donations 

/ total average 

assets 

(%) 

TOTAL ADMIN. 

EXPENSE 

/ LP 



donations 

/ average loan 

portfolio 

(%) 

SALARY 

EXPENSE/ 

LP 

personnel 


donations 

average gross 


(%) 

DEPTH 

average 

loan 

balance/ 

GNP 

per capita 

(%) 

AVERAGE 

SALARY 

average 

personnel 


donations / 


(multiple of 

GNP/ capita) 

STAFF 

PRODUCTIVITY 


borrowers / 


(no.) 

LOAN 

OFFICER 

PRODUCTIVITY 


borrowers / 


officers 

(no.) 

COST/ 

BORROWER 



donations / 


of active 

borrowers 

(US$) 

1.1 25.4* 42.1* 24.0* 70.2 7.0 96* 188* 184 

1.7 13.2 32.8 20.4 69.3 5.0 45 109 184 

23 28 28 28 28 28 28 26 21 

1.7 21.5* 36.5 19.8 81.3 7.5 126 295 116 

1.8 10.8 20.2 11.3 145.2 5.3 63 232 115 

21 26 26 26 26 26 26 25 26 

2.8 19.9* 30.0 16.6 47.2 5.5 158* 340 88 

3.1 13.3 20.8 12.9 50.8 4.6 122 299 78 

54 63 63 63 63 56 56 55 57 

2.2 12.2* 16.7* 9.1* 98.7 6.6 130 285 145 

1.1 5.5 8.7 5.1 91.2 4.6 79 125 116 

19 20 20 20 20 20 20 19 20 

2.0 21.8* 30.9 17.2 64.6 6.5 151 345 122 

2.4 13.1 19.2 12.4 101.7 5.2 118 327 164 

52 66 66 66 66 61 61 56 55 

2.5 28.2* 52.2* 29.3* 30.9 5.7 100 205 108 

3.7 12.6 26.9 15.8 27.6 5.3 51 115 112 

28 32 32 32 32 32 32 32 30 

2.5 14.0* 20.7* 10.3* 103.0 5.1 102 229 191 

3.3 7.4 11.8 6.8 122.7 4.1 78 199 193 

39 52 52 52 52 47 47 41 48 

2.1 25.7* 41.3* 24.5* 39.4 7.5 133 272 88 

2.3 11.2 25.1 16.9 31.7 5.9 70 152 66 

38 43 43 43 43 42 42 42 35 

1.5 32.2* 52.1* 30.0* 18.0 67 188* 408* 49* 

1.6 15.5 25.0 13.9 13.5 5.2 134 325 27 

22 23 23 23 23 23 23 23 22 

2.7 11.3* 21.2* 9.7* 77.0 5.7 127 123* 375* 

1.9 4.8 15.8 7.8 70.1 4.5 92 102 311 

17 29 29 29 29 23 23 27 18 

2.1 25.6* 38.9* 22.4* 52.5 6.6 134 122* 272* 

2.8 13.7 25.5 15.7 68.9 5.5 98 139 212 

83 91 91 91 91 91 91 79 90 

2.2 28.7* 48.0* 27.7* 16.8 5.5 167* 360* 60* 

3.1 14.8 27.3 16.9 10.4 5.4 112 304 56 

51 57 57 57 57 57 57 57 51 

2.6 16.6* 23.5* 12.3* 64.8 6.8 113 252 134 

2.2 8.6 13.9 8.5 29.3 4.8 62 176 94 

39 51 51 51 51 45 45 39 45 

0.7 12.5* 16.6* 9.2* 310.2 9.2* 48* 120* 369* 

0.7 5.7 8.2 5.9 147.6 2.8 20 59 204 

9 10 10 10 10 10 10 10 9 



TABLE B: PROFITABILITY AND EFFICIENCY INDICATORS (continued) 

CRITERIA 

ADJUSTED 

RETURN ON 

ASSETS 

adjusted net 


average total 

assets 

(%) 

ADJUSTED 

RETURN ON 

EQUITY 

adjusted net 



equity 

(%) 

OPERATIONAL 




(%) 

FINANCIAL 

SELF- 

SUFFICIENCY 


income / 


expense 

(%) 

PORTFOLIO 

YIELD 




income from 

investments / 


portfolio (%) 

REGION ## Africa avg -9.8* 1.8 88.8* 82.7 45.6 

stdv 11.6 73.9 42.7 40.0 18.8 

N 24 24 24 24 24 

Asia avg -2.6 -3.2 109.8 92.6 33.6 

(All) stdv 7.9 25.4 38.5 30.3 15.3 

N 22 22 22 22 22 

Eastern Europe avg -2.8 -9.7 107.4 90.0 29.5* 

stdv 3.1 11.8 12.8 11.4 5.6 

N 12 12 12 12 12 

Latin America avg -1.6 -7.3 113.8* 97.5 45.8 

stdv 8.1 35.9 27.6 23.7 21.4 

N 52 52 52 52 52 

CHARTER Bank avg -0.7 -9.1 107.6 97.9 41.2 

TYPE stdv 6.4 76.7 23.3 22.9 15.5 

N 15 15 15 15 15 

Credit Union/ avg -1.6 -5.2 109.3 92.5 21.8* 

Cooperative stdv 4.4 11.9 24.9 21.4 4.7 

N 14 14 14 14 14 

NGO avg -5.3 -9.1 106.4 89.0 47.6* 

stdv 11.8 23.5 35.9 27.5 22.3 

N 68 68 68 68 68 

Non-Bank ### avg -4.4 -1.5 96.4 87.3 32.2 

stdv 5.6 17.0 16.7 15.3 6.7 

N 10 10 10 10 10 

PROFIT Non-Profit avg -5.1 -9.3 107.0 90.3 45.4 

STATUS stdv 12.0 23.6 38.5 28.2 22.0 

N 76 76 76 76 76 

Profit avg -2.1 5.3 106.2 96.9 38.2 

stdv 6.8 86.3 30.7 27.8 14.4 

N 28 28 28 28 28 






### Includes Ltd., financieras, and non-bank financial intermediaries (NBFIs). 



PORTFOLIO 

AT RISK > 

90 DAYS 

outstanding 

balance of 



gross loan 

portfolio 

(%) 

TOTAL ADMIN. 

EXPENSE 

RATIO 



donations 


assets 

(%) 

TOTAL ADMIN. 

EXPENSE 

/ LP 



donations 


portfolio 

(%) 

SALARY 

EXPENSE/ 

LP 

personnel 


donations 

average gross 


(%) 

DEPTH 

average 

loan 

balance/ 

GNP 

per capita 

(%) 

AVERAGE 

SALARY 

average 

personnel 


donations / 


(multiple of 

GNP/ capita) 

STAFF 

PRODUCTIVITY 


borrowers / 


(no.) 

LOAN 

OFFICER 

PRODUCTIVITY 


borrowers / 


officers 

(no.) 

COST/ 

BORROWER 



donations / 


of active 

borrowers 

(US$) 

2.0 30.3* 57.5* 30.9* 51.7 12.4* 154* 393* 66 

2.2 16.5 30.4 17.5 53.8 6.5 83 272 34 

22 24 24 24 24 24 24 24 20 

2.4 16.0* 23.7 14.3 40.7 3.6* 177* 306 29* 

3.0 11.2 16.8 13.0 63.5 2.2 162 315 21 

19 22 22 22 22 22 22 22 22 

0.6* 19.0* 22.4 12.9 146.5 7.3 63* 110* 317* 

0.7 4.5 5.4 4.6 94.8 2.8 23 25 196 

12 12 12 12 12 12 12 12 10 

2.6 21.1* 29.9 15.8 44.4 4.6 125 280 141 

2.6 11.1 16.4 10.3 40.1 3.5 56 139 120 

40 52 52 52 52 46 46 41 46 

3.0 15.6* 28.5 14.6 106.0 6.0 98 263 227 

4.5 7.8 16.5 8.5 112.3 4.4 73 247 277 

13 15 15 15 15 15 15 15 15 

1.1 9.6* 14.3* 6.0* 67.7 4.2 133 321 103 

1.4 3.1 5.2 3.1 34.5 4.2 88 321 45 

4 14 14 14 14 8 8 4 13 

1.9 26.3* 41.8* 24.0* 41.6 6.5 147 292 96 

2.4 13.9 25.9 15.6 53.6 5.5 106 225 98 

63 68 68 68 68 68 68 67 61 

2.8 17.3* 22.0 12.0 52.2 4.4 90 195 137 

1.4 4.5 6.9 4.3 23.6 1.5 31 96 80 

10 10 10 10 10 10 10 10 9 

2.0 26.0* 40.2* 23.1* 46.1 6.6 150 299 94 

2.5 14.5 26.5 16.6 59.3 5.7 111 234 96 

69 76 76 76 76 75 75 75 64 

2.9 16.6* 26.5 13.8 106.5 6.5 103 277 194 

3.4 7.9 15.9 7.7 145.3 4.3 67 250 218 

26 28 28 28 28 28 28 28 27 



TABLE C: INSTITUTIONAL CHARACTERISTICS AND OUTREACH INDICATORS FOR FINANCIALLY 

SELF-SUFFICIENT MFIs 

CRITERIA 

TOTAL 

ASSETS 

(US$) 

CAPITAL / 

ASSET 

RATIO 


equity / 


assets (%) 

COMMERCIAL 


RATIO 

OFFICES 



portfolio 

(%) (no.) 

AGE New (1 - 2 years) avg 3,405,919 57.3 11.2 7 

stdv 1,582,222 24.0 18.0 7 

N 7 7 7 7 

Young (3 - 6 years) avg 6,463,658 58.1 41.5 71* 

stdv 5,901,443 22.6 86.6 195 

N 10 10 10 10 

Mature (> 6 years) avg 18,809,760* 45.4 62.9* 56* 

stdv 32,646,028 21.7 57.3 176 

N 41 41 41 36 

SCALE OF Large avg 44,759,724* 33.9* 82.4* 114* 

OPERATIONS # stdv 41,556,930 19.1 65.6 256 

N 16 16 16 16 

Medium avg 4,823,500 54.0 40.6 13 

stdv 1,768,491 24.3 39.6 13 

N 30 30 30 26 

Small avg 1,040,280* 58.9 24.8 5* 

stdv 293,641 15.3 28.4 3 

N 12 12 12 11 

LENDING Individual avg 12,533,710* 39.2 77.7 10 

METHOD- (1 borrower) stdv 14,076,097 19.9 75.7 8 

OLOGY N 29 29 29 25 

Solidarity Groups avg 18,768,985* 49.2 36.7 63* 

(groups of 3 to 9 stdv 28,968,183 20.3 32.4 155 


Village Banking avg 2,999,565 72.1* 18.0 61* 

(groups with ≥ 10 stdv 1,610,309 17.8 22.8 178 


LEVEL OF Financial Intermediaries avg 27,730,881* 19.4* 158.8* 18 

FINANCIAL (passbook and time 

stdv 27,955,717 10.5 89.5 15 

INTER- deposits ≥ 20% of total assets) N 12 12 12 8 

MEDIATION 

Other avg 8,213,650 57.6 29.4 59* 

(passbook and time stdv 13,567,380 20.4 27.2 178 

< 20% of total assets) N 48 48 48 46 

TARGET Low-end avg 7,632,608 61.2* 36.0 91* 

MARKET (depth < 20% OR avg. loan stdv 16,121,525 21.4 57.3 229 

balance < US$ 150) N 28 28 28 27 

Broad avg 15,465,913* 38.6 69.5* 14 

(depth between 20% and stdv 21,292,070 19.0 42.0 15 

149%) N 25 25 25 20 

High-end and avg 14,479,446* 40.3 21.4 13 

Small Business stdv 10,887,845 20.8 21.2 9 

(depth ≥ 150%) N 5 5 5 5 

Note: Standard deviations are listed below the group averages. The averages are calculated on the basis of the values between second and the 

99th percentiles 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 with an asterisk (*). Additional statistical information is available at www.microbanking-mbb.org. 




TOTAL 

STAFF 


employees 

(no.) 

TOTAL 

GROSS LOAN 

PORTFOLIO 


outstanding 

(US$) 

PERCENT 

WOMEN 

BORROWERS 




borrowers (%) 

NUMBER OF 

ACTIVE 

BORROWERS 

number of 


loans 


AVERAGE 

LOAN BALANCE 




(US$) 

DEPTH 

average loan 

balance / 


(%) 

44 2,945,845 42.1 3,987 1,031* 79.9* 

37 1,248,620 8.5 4,152 569 62.4 

7 7 5 7 7 7 

126 4,368,732 71.8 11,909 523 93.8* 

90 4,314,428 27.9 5,831 871 103.6 

10 10 8 10 10 10 

511* 14,765,629* 63.6 105,743 566 42.2 

1,506 26,649,320 28.2 414,289 638 40.6 

36 41 39 41 41 41 

1,030* 34,703,379* 50.7 247,973* 935* 95.9* 

2,183 34,777,173 23.0 649,553 736 83.5 

16 16 14 16 16 16 

109 3,519,332 66.0 15,457 545 47.9 

72 1,197,937 27.7 13,721 659 58.5 

27 30 27 30 30 30 

45* 702,118* 67.6 4,624* 354 31.9 

27 190,701 28.4 3,679 434 28.7 

12 12 11 12 12 12 

106 9,226,150* 43.7 10,169 1,122* 96.3 

101 10,768,658 15.8 10,192 879 86.2 

25 29 26 29 29 29 

511 14,744,774* 78.1* 91,019* 305 35.4 

1,206 24,400,848 21.2 256,945 313 32.2 

17 17 13 17 17 17 

99 2,152,440 90.3* 16,690 114* 13.7* 

42 1,400,570 17.7 8,151 24 8.3 

12 12 12 12 12 12 

267* 20,476,770* 39.8* 19,156* 980* 80.6* 

180 23,804,140 10.1 20,413 506 43.6 

8 12 12 12 12 12 

222 6,361,146 70.5 38,879 563 53.6 

733 11,155,406 27.5 154,920 770 73.1 

48 48 41 48 48 48 

313 5,811,051 84.4* 58,889* 135* 13.9* 

953 13,605,385 20.6 201,592 84 6.1 

28 28 24 28 28 28 

150 11,984,081* 48.3* 16,116 832* 69.5* 

149 17,442,302 18.5 18,515 424 28.9 

21 25 23 25 25 25 

91 8,933,026* 32.2* 5,786 2,516* 267.9* 

81 4,144,927 3.4 5,071 571 44.0 

5 5 5 5 5 5 



TABLE C: INSTITUTIONAL CHARACTERISTICS AND OUTREACH INDICATORS FOR FINANCIALLY 

SELF-SUFFICIENT MFIs (continued) 

CRITERIA 

TOTAL 

ASSETS 

(US$) 

CAPITAL / 

ASSET 

RATIO 


equity / 


assets (%) 

COMMERCIAL 


RATIO 

OFFICES 



portfolio 

(%) (no.) 

REGION ## Africa avg 5,099,987 49.5 81.5 14 

stdv 5,263,973 20.9 135.9 13 

N 4 4 4 4 

Asia avg 25,625,496* 54.7 22.8 297* 

(All) stdv 51,327,787 19.7 16.9 577 

N 12 12 12 12 

Eastern Europe avg 3,462,697 56.1 12.5 6 

stdv 894,196 31.9 18.5 1 

N 3 3 3 3 

Latin America avg 11,071,252* 45.4 59.6* 11 

stdv 11,352,239 22.0 39.7 9 

N 35 35 35 29 

CHARTER Bank avg 35,929,370* 21.0 166.6 22 

TYPE stdv 30,261,580 14.8 116.2 13 

N 8 8 8 8 

Credit Union/ avg 6,981,940 42.1 88.6* - 

Cooperative stdv 4,054,797 14.9 33.7 - 

N 5 5 5 - 

NGO avg 8,376,486 58.5 26.6 72* 

stdv 15,089,775 21.5 25.1 203 

N 35 35 35 35 

Non-Bank ### avg 11,445,145 27.4 70.3 11 

stdv 10,732,928 5.5 27.3 6 

N 3 3 3 3 

PROFIT Non-Profit avg 7,996,412 59.0 26.3 68* 

STATUS stdv 14,398,848 20.6 24.4 195 

N 39 39 39 38 

Profit avg 26,194,456* 28.1* 120.1* 16 

stdv 26,707,461 20.1 104.4 12 

N 14 14 14 14 


percentiles 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 with an asterisk (*). Additional statistical information is available at www.microbanking-mbb.org. 






TOTAL 

STAFF 


employees 

(no.) 

TOTAL 

GROSS LOAN 

PORTFOLIO 


outstanding 

(US$) 

PERCENT 

WOMEN 

BORROWERS 




borrowers (%) 

NUMBER OF 

ACTIVE 

BORROWERS 

number of 


loans 


AVERAGE 

LOAN BALANCE 




(US$) 

DEPTH 

average loan 

balance / 


(%) 

68 4,345,934 46.0 10,131 351 109.5* 

28 4,793,666 24.1 3,555 359 113.3 

4 4 4 4 4 4 

1,239* 20,743,904* 76.8 320,267* 148* 30.0 

2,512 41,820,342 25.0 743,347 70 26.1 

12 12 12 12 12 12 

29 3,013,143 42.0 2,121 1,390* 129.5* 

7 984,024 7.8 919 697 62.6 

3 3 3 3 3 3 

125 8,456,682* 65.0 15,234 717 45.3 

90 10,550,492 25.6 12,572 737 42.9 

30 35 29 35 35 35 

326 25,682,534* 46.9 34,389* 527 62.5 

142 26,298,497 21.6 21.413 387 56.6 

8 8 7 8 8 8 

- 4,978,836 36.3* 3,640 1,306* 91.6* 

- 3,473,080 1.8 1,752 309 25.8 

- 5 5 5 5 5 

254 6,421,080 72.6 47,705 490 38.8 

857 12,475,421 28.0 181,049 729 55.5 

35 35 31 35 35 35 

153 10,103,411* 43.0 14.272 958 62.4 

83 10,884,914 11.5 11,163 57 29.8 

3 3 3 3 3 3 

239 6,170,926 72.0 43,667 494 45.4 

812 11,917,456 27.8 171,686 722 66.3 

39 39 34 39 39 39 

233* 19,828,815* 48.0 25,472* 827* 82.3* 

165 22,297,199 19.9 21,025 723 70.5 

14 14 12 14 14 14 



TABLE D: PROFITABILITY AND EFFICIENCY INDICATORS FOR FINANCIALLY SELF-SUFFICIENT 

MFIs 

CRITERIA 

ADJUSTED 

RETURN ON 

ASSETS 

adjusted net 


average total 

assets 

(%) 

ADJUSTED 

RETURN ON 

EQUITY 

adjusted net 



equity 

(%) 

OPERATIONAL 




(%) 

FINANCIAL 

SELF- 

SUFFICIENCY 


income / 


expense 

(%) 

PORTFOLIO 

YIELD 




income from 

investments / 


portfolio (%) 

AGE New (1 - 2 years) avg 0.6 0.5 119.4 102.8 38.2 

stdv 2.1 4.0 13.1 8.3 7.9 

N 7 7 7 7 7 

Young (3 - 6 years) avg 2.1* 3.7 121.8* 111.1* 52.4* 

stdv 4.6 8.5 33.7 22.9 23.7 

N 10 10 10 10 10 

Mature (> 6 years) avg 3.4* 11.5* 131.6* 115.1* 44.2 

stdv 4.9 15.6 34.7 21.4 17.9 

N 41 41 41 41 41 

SCALE OF Large avg 4.2* 16.5* 134.2* 121.8* 35.0 

OPERATIONS # stdv 3.7 16.3 37.0 23.4 11.3 

N 16 16 16 16 16 

Medium avg 2.8* 4.7* 128.0* 110.4* 45.2 

stdv 6.3 11.3 35.2 19.9 17.9 

N 30 30 30 30 30 

Small avg 1.5* 3.0 121.0* 105.8* 56.8* 

stdv 3.4 6.2 16.6 13.6 20.2 

N 12 12 12 12 12 

LENDING Individual avg 3.9 13.8 136.7 121.9 38.0 

METHOD- (1 borrower) stdv 4.1 16.2 39.4 30.9 13.8 

OLOGY N 29 29 29 29 29 

Solidarity Groups avg 1.1 2.2 114.7 104.9* 41.4 

(groups of 3 to 9 borrowers) stdv 3.6 8.7 15.8 13.2 9.9 

N 17 17 17 17 17 

Village Banking avg 2.7* 3.2 126.8* 105.8* 67.6* 

(groups with ≥ 10 stdv 7.1 8.3 29.3 14.4 24.0 

borrowers) N 12 12 12 12 12 

LEVEL OF Financial Intermediaries avg 3.2* 19.7* 119.1* 114.6* 37.5 

FINANCIAL (passbook and time deposits stdv 2.9 20.5 15.5 12.3 15.3 

INTER- ≥ 20% of total assets) N 12 12 12 12 12 

MEDIATION 

Other avg 2.9* 5.5* 131.4* 113.1* 47.1* 

(passbook and time deposits stdv 5.7 10.2 37.4 26.5 20.0 

< 20% of total assets) N 48 48 48 48 48 

TARGET Low-end avg 2.8* 5.2* 128.1* 109.2* 57.1* 

MARKET (depth < 20% OR avg. loan stdv 6.6 11.4 33.7 19.7 21.7 

Balance < US$150) N 28 28 28 28 28 

Broad avg 3.1* 10.7* 124.0* 113.7* 36.4 

(depth between 20% stdv 3.1 10.7 17.4 12.5 10.5 

and 149%) N 25 25 25 25 25 

High-end and avg 2.7 2.9 134.9* 119.7* 29.1 

Small Business stdv 5.4 8.7 48.1 40.5 6.5 

(depth ≥ 150%) N 5 5 5 5 5 







PORTFOLIO 

AT RISK > 

90 DAYS 

outstanding 

balance of 



gross loan 

portfolio 

(%) 

TOTAL ADMIN. 

EXPENSE 

RATIO 



donations 


assets 

(%) 

TOTAL ADMIN. 

EXPENSE 

/ LP 



donations 


portfolio 

(%) 

SALARY 

EXPENSE/ 

LP 

personnel 


donations 

average gross 


(%) 

DEPTH 

average 

loan 

balance/ 

GNP 

per capita 

(%) 

AVERAGE 

SALARY 

average 

personnel 


donations / 


(multiple of 

GNP/ capita) 

STAFF 

PRODUCTIVITY 


borrowers / 


(no.) 

LOAN 

OFFICER 

PRODUCTIVITY 


borrowers / 


officers 

(no.) 

COST/ 

BORROWER 



donations / 


of active 

borrowers 

(US$) 

0.7 21.6* 27.5 14.9 79.9 6.3 83 144 173 

1.0 4.3 7.2 3.1 62.4 2.7 26 53 91 

7 7 7 7 7 7 7 7 4 

1.2 22.1* 37.8 20.8 93.8 8.7 113 283* 101* 

1.3 9.6 19.7 10.7 103.6 5.4 55 188 113 

8 10 10 10 10 10 10 10 10 

2.0 18.8* 26.7 15.1 42.2 4.8 147 313* 91* 

1.5 10.9 15.8 10.2 40.6 3.2 77 172 78 

35 41 41 41 41 36 36 36 38 

2.1 12.8* 17.4* 9.4* 95.9 7.5 129 312* 147* 

1.1 4.6 8.5 4.9 83.5 4.7 65 117 115 

16 16 16 16 16 16 16 16 16 

1.3 20.9* 29.3 16.4 47.9 5.2 139 284* 71* 

1.4 9.0 13.5 8.4 58.5 2.8 75 191 45 

25 30 30 30 30 27 27 27 23 

1.7 25.6* 43.2* 24.3 31.9 3.7 94 174 98* 

2.1 9.6 20.3 11.8 28.7 3.5 52 111 85 

10 12 12 12 12 12 12 12 12 

1.6 13.5 19.6 10.0 96.3 5.7 101 264* 155* 

1.3 6.1 11.0 6.0 86.2 4.0 65 193 115 

24 29 29 29 29 25 25 25 27 

2.0 21.8* 29.4 16.6 35.4 5.7 135 257* 62* 

1.7 6.0 9.3 5.4 32.2 3.5 66 109 52 

15 17 17 17 17 17 17 17 13 

1.1 33.2* 49.8* 30.3* 13.7 5.2 155 314* 49* 

1.0 9.7 14.3 8.1 8.3 3.2 31 193 18 

12 12 12 12 12 12 12 12 12 

1.9 11.7* 21.3 9.9* 80.6 6.3 99 303* 156* 

1.0 4.2 16.9 8.0 43.6 3.6 38 133 84 

7 12 12 12 12 8 8 8 12 

1.7 22.1* 30.9 17.8 53.6 5.5 136 270* 88* 

1.6 10.3 16.2 9.9 73.1 3.8 77 176 85 

45 48 48 48 48 48 48 48 40 

1.6 26.4* 40.6* 23.6* 13.9 4.1 165* 335* 49* 

1.7 10.5 17.4 9.8 6.1 2.8 79 216 27 

25 28 28 28 28 28 28 28 26 

1.8 14.9* 19.5* 9.9* 69.5 6.0 104 247* 125* 

1.3 6.6 7.8 4.4 28.9 3.7 41 134 66 

20 25 25 25 25 21 21 21 21 

1.1 13.4* 16.3 9.3 267.9 11.1* 60* 154 264 

0.8 4.7 6.2 3.9 44.0 2.0 15 64 134 

5 5 5 5 5 5 5 5 5 



TABLE D: PROFITABILITY AND EFFICIENCY INDICATORS FOR FINANCIALLY SELF-SUFFICIENT 

MFIs (continued) 

CRITERIA 

ADJUSTED 

RETURN ON 

ASSETS 

adjusted net 


average total 

assets 

(%) 

ADJUSTED 

RETURN ON 

EQUITY 

adjusted net 



equity 

(%) 

OPERATIONAL 




(%) 

FINANCIAL 

SELF- 

SUFFICIENCY 


income / 


expense 

(%) 

PORTFOLIO 

YIELD 




income from 

investments / 


portfolio (%) 

REGION ## Africa avg 5.4* 10.9 157.4* 146.4* 51.2 

stdv 4.0 9.7 63.2 62.9 20.8 

N 4 4 4 4 4 

Asia avg 2.8* 6.0* 133.8* 113.5* 38.2 

(All) stdv 4.2 10.1 34.4 20.1 11.5 

N 12 12 12 12 12 

Eastern Europe avg 1.1 -0.1 123.0 105.3 32.1 

stdv 2.9 5.3 5.5 12.4 0.4 

N 3 3 3 3 3 

Latin America avg 2.8* 8.8* 122.7* 110.1* 48.4* 

stdv 4.9 12.3 24.7 16.6 18.7 

N 35 35 35 35 35 

CHARTER Bank avg 3.8* 22.0* 118.6 114.6* 50.1* 

TYPE stdv 4.3 25.5 18.7 15.7 13.8 

N 8 8 8 8 8 

Credit Union/ avg 2.9 8.1 122.5 115.4* 24.0* 

Cooperative stdv 2.8 6.0 15.9 13.1 3.3 

N 5 5 5 5 5 

NGO avg 2.8* 5.1* 130.8* 109.6* 50.2* 

stdv 6.0 10.4 30.2 18.3 21.0 

N 35 35 35 35 35 

Non-Bank ### avg 1.7 11.3 110.4 104.8 34.5 

stdv 2.8 12.6 5.0 10.4 5.9 

N 3 3 3 3 3 

PROFIT Non-Profit avg 2.8* 5.0* 131.8* 110.7* 48.8* 

STATUS stdv 6.0 10.4 35.5 20.4 20.6 

N 39 39 39 39 39 

Profit avg 3.4* 17.9* 122.3* 117.0* 43.5 

stdv 3.7 20.5 31.6 24.7 15.7 

N 14 14 14 14 14 

Note: Standard deviations are listed below the group averages. The averages are calculated on the basis of the values between second and the 99th percentiles 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 with an 

asterisk (*). Additional statistical information is available at www.microbanking-mbb.org. 

# The criteria for classification of scale of operations vary by region. Refer to page 53 for details 





PORTFOLIO 

AT RISK > 

90 DAYS 

TOTAL ADMIN. 

EXPENSE 

RATIO 

TOTAL ADMIN. 

EXPENSE 

/ LP 

SALARY 

EXPENSE/ 

LP 

DEPTH 

AVERAGE 

SALARY 

STAFF 

PRODUCTIVITY 

LOAN 

OFFICER 

PRODUCTIVITY 

COST/ 

BORROWER 

outstanding 

balance of 



gross loan 

portfolio 

(%) 



donations 


assets 

(%) 



donations 


portfolio 

(%) 

personnel 


donations 

average gross 


(%) 

average 

loan 

balance/ 

GNP 

per capita 

(%) 

average 

personnel 


donations / 


(multiple of 

GNP/ capita) 


borrowers / 


(no.) 


borrowers / 


officers 

(no.) 



donations / 


of active 

borrowers 

(US$) 

0.8 18.9* 43.5 21.2 109.5 13.0* 132 401* 64* 

0.9 10.5 22.7 9.3 113.3 1.8 40 199 38 

4 4 4 4 4 4 4 4 4 

1.8 16.5* 23.5 13.7 30.0 4.2 149 261* 35* 

2.0 7.6 10.5 5.3 26.0 2.5 105 167 21 

11 12 12 12 12 12 12 12 12 

0.3 17.9* 22.1 12.5 129.5 8.4 74 120 195 

0.5 0.5 0.4 0.3 62.6 1.8 14 14 62 

3 3 3 3 3 3 3 3 2 

1.9 21.5* 30.5 16.8 45.3 4.9 131 287* 129* 

1.2 10.8 16.8 11.2 42.9 3.1 54 128 98 

29 35 35 35 35 30 30 30 30 

1.3 18.2* 32.8 17.3 62.5 7.3 108 315* 150 

0.8 5.7 18.0 8.1 56.6 5.3 30 144 117 

8 8 8 8 8 8 8 8 8 

- 8.2* 11.4* 4.8* 91.6 - - - 146 

- 1.2 2.2 1.4 25.8 - - - 30 

- 5 5 5 5 - - - 5 

1.7 23.3* 32.8 19.0 38.8 4.8 148 279* 79* 

1.6 10.6 16.8 10.7 55.5 3.2 80 173 86 

33 35 35 35 35 35 35 35 30 

2.4 16.2 20.2 11.2 62.4 4.7 90 210 113 

0.3 4.1 8.9 3.2 29.8 0.6 32 86 35 

3 3 3 3 3 3 3 3 3 

1.8 22.8* 32.2 18.6 45.4 5.1 143 274* 79* 

1.7 10.6 16.8 10.5 66.3 3.5 79 171 83 

39 39 39 39 39 39 39 39 33 

1.4 16.3* 26.4 14.0 82.3 7.3 104 305* 139* 

0.9 7.0 16.0 7.4 70.5 4.8 41 171 100 

14 14 14 14 14 14 14 14 14 



Composition of Additional Analysis Groupings 

AGE # 

New 

(1 - 2 years) 

Young 

(3 - 6 years) 

Mature 

(> 6 years) 

23 de Julio 

Al Amana 

AMK 

Banco do Povo 

Banco PeqEmpresa 

15 de Abril 

ACLEDA 

Agrocapital 

Al Majmoua 

ABA 

Acep 

ACODEP 

Acredicom 

Actuar 

ADOPEM 

ADRI 

AGAPE 

AKRSP 

Asawinso RB 

Basix 

Bospo 

Constanta 

FINCA Tanzania 

Finsol 

CEAPE/ Pernamb. 

CERUDEB 

Citi S&L 

Enlace 

ASA 

BAAC 

Banco Ademi 

Bancosol 

Bank Dagang Bali 

BRAC 

BRI 

BURO Tangail 

Caja de Los Andes 

Calpia 

FOCCAS 

Kash Foundation 

LOK 

Mercy Corps 

MEB 

Faten 

Faulu 

FEFAD 

FINCA Ecuador 

CAM 

CARD BANK 

CDS 

Chispa 

Chumiquená 

CM Arequipa 

CMM Medellín 

Compartamos 

Contigo 

COOSAJO 


Mikrofin 

Moznosti 

Nachala 

NOA 

FINCA Kyrgystan 

FINCA Malawi 

FINCA Peru 

Fundusz Mikro 

Crecer 

Ecosaba 

EMT 

FAMA 

FED 

FIE 

Finamérica 

FINCA Honduras 

FINCA Mexico 

FINCA Nicaragua 

PAMÉCAS 

Portosol 

Pride Uganda 

Sagrario 

SEDA 

FONDECO Hattha 

Kakserkar 

Inicjatywa Mikro 

Nirdhan 

FINCA Uganda 

FMM Popayán 

FWWB Cali 

FWWB India 

Hublag 

Kafo Jiginew 

KWFT 

Mibanco 

Manya Krobo RB 

Moyután 

Sunrise 

Tulcán 

Vital-Finance 

Vivacred 

WV Bosnia 

Network Leasing 

Oscus 

PADME 

Piyeli 

Nsoatreman RB 

Nyésigiso 

Pride Vita Guinea 

PRODEM 

ProEmpresa 

RSPI 

Sartawi 

SEEDS 

SEF 

SHARE 

Pride Tanzania 

ProMujer 

Sinapi Aba Trust 

WAGES 

Tonantel 

TSPI 

UNRWA 

UWFT 

WRHonduras 

SCALE OF OPERATIONS ## 

Large 

Medium 

Small 

ABA 

Acep 

ACLEDA 

ASA 

15 de Abril 

23 de Julio 

ACODEP 

Acredicom 

Actuar 

ADOPEM 

AKRSP 

Al Amana 

Al Majmoua 

Basix 

ADRI 

AGAPE 

AMK 

Asawinso RB 

Banco do Povo 

Agrocapital 

BAAC 


BRAC 

BURO Tangail 

Banco PeqEmpresa 

CAM 

CARD BANK 


Chispa 

Chumiquená 

CMM Medellín 

Crecer 

CitiS&L 

Bospo 

CDS 

Constanta 

Contigo 

Faulu 

BRI 

Banco Ademi 

Bancosol 


Compartamos 

Ecosaba 

Emprender 

EMT 

Enlace 

FAMA 

Faten 

FEFAD 



FED 

FINCA Ecuador 

FINCA Malawi 

FINCA Mexico 


Calpia 

CERUDEB 

CM Arequipa 

COOSAJO 

FINCA Uganda 

FMM Popayán 

FONDECO 

FWWB India 

Finsol 

KWFT 

Kafo Jiginew 

LOK 

Mercy Corps MEB 

FINCA Peru 


FOCCAS 

Hublag 

Hattha Kakserkar 

FIE 

Fundusz Mikro 

FWWBCali 

Finamérica 

Mikrofin 

Moznosti 


NOA 

Nsoatreman RB 

Nyésigiso 

Oscus 

PADME 

PAMÉCAS 






Moyután 

Mibanco 

PRODEM 

Pride Uganda 


Portosol 

ProEmpresa 

ProMujer 

SEEDS 

SEF 

SHARE 

Sunrise 

Sagrario 

Nirdhan 

Nachala 

Piyeli 

RSPI 


Sartawi 

Tonantel 

TSPI 

Tulcán 

UNRWA 

WAGES 

WRHonduras 

WV Bosnia 

SEDA 

UWFT 

Vital-Finance 

Vivacred 

LENDING METHODOLOGY 

Individual 

(1 borrower) 

Solidarity 

Groups 

(groups of 3 

to 9 

borrowers) 

Village 

Banking 

(groups with ≥ 

10 borrowers) 

15 de Abril 

23 de Julio 

ABA 

Acep 

ACODEP 

Acredicom 

ADRI 

Agrocapital 

ACLEDA 

Actuar 

ADOPEM 

Al Amana 

ASA 

Bancosol 

Basix 

AGAPE 

AKRSP 

Al Majmoua 

CAM 

Citi S&L 

AMK 

Asawinso RB 

BAAC 

Banco Ademi 

Banco do Povo 

BanPeqEmpresa 


BRI 

Bospo 

BRAC 

BURO Tangail 

CARD BANK 


Chispa 

Constanta 

Compartamos 

Crecer 

FINCA Ecuador 




Calpia 

CDS 

CERUDEB 

Chumiquená 

CM Arequipa 

CMM Medellín 

COOSAJO 

Contigo 

EMT 

Enlace 

FAMA 

Faten 

Faulu 

Finamérica 

FINCA Malawi 

FINCA Mexico 


FINCA Peru 


Ecosaba 

Emprender 

FED 

FEFAD 

FIE 

FMM Popayán 

FWWBCali 


Finsol 

Fundusz Mikro 

FONDECO 


KWFT 

Mibanco 

Mikrofin 

FINCA Uganda 

FOCCAS 

FWWB India 


ProMujer 

Hublag 


Kafo Jiginew 

LOK 

Mercy Corps 

MEB 

Manya Krobo RB. 

Moyután 

Nirdhan 

Nsoatreman RB 

Nyésigiso 

PAMÉCAS 

Piyeli 


Pride Uganda 

Sartawi 

SEDA 

SEEDS 

WAGES 

WRHonduras 

Moznosti 

Nachala 


NOA 

Oscus 

PADME 

Portosol 

ProEmpresa 


PRODEM 

RSPI 

SEF 

SHARE 

TSPI 

UNRWA 

Sagrario 


Sunrise 

Tonantel 

Tulcán 

Vivacred 

UWFT 

Vital-Finance 

WV Bosnia 

# Some institutions did not report the information. 

## The criteria for classification of scale of operations vary by region. Refer to page 53 for details. 



Composition of Additional Analysis Groupings, ctd. 

LEVEL OF FINANCIAL INTERMEDIATION 

Retail Financial 15 de Abril 

23 de Julio 

Intermediary (passboo 

Acredicom 

and time deposits ≥ 20% 

Assawinso RB 

of total assets) 

BAAC 

Banco Ademi 

Other 

(passbook and time 

deposits < 20% of total 

assets) 

ABA 

Acep 

ACLEDA 

ACODEP 

Actuar 

ADOPEM 

ADRI 

AGAPE 

Agrocapital 

AKRSP 

Al Amana 

Al Majmoua 

AMK 

ASA 

Bancosol 


BRI 


CERUDEB 

Chumiquená 

Banco do Povo 

BanPeqEmpresa 

Basix 

Bospo 

BRAC 

BURO Tangail 

Calpia 

CAM 

CARD BANK 

CDS 


Chispa 

CMM Medellín 

Compartamos 

Citi S&L 

CM Arequipa 

COOSAJO 

Ecosaba 

Enlace 

FEFAD 

Constanta 

Contigo 

Crecer 

Emprender 

EMT 

FAMA 

Faten 

Faulu 

FED 

FINCA Ecuador 



FINCA Malawi 

FINCA Mexico 

FIE 

Finamérica 

Kafo Jiginew 


Moyután 

Nsoatreman RB 


FINCA Peru 


FINCA Uganda 

Finsol 

Fundusz Mikro 

FMM Popayán 

FOCCAS 

FONDECO 

FWWBCali 

FWWB India 


Hublag 


Nyésigiso 

Oscus 

PAMÉCAS 

Sagrario 

Tonantel 

Tulcán 


KWFT 

LOK 

Mercy Corps 

MEB 


Mibanco 

Mikrofin 

Moznosti 

Nachala 

Nirdhan 


NOA 

PADME 

UWFT 

Piyeli 

Portosol 


Pride Uganda 


PRODEM 

ProEmpresa 

ProMujer 

RSPI 

Sartawi 


SEDA 

SEEDS 

SEF 

SHARE 

Sunrise 

TSPI 

UNRWA 

Vital-Finance 

Vivacred 

WAGES 

WRHonduras 

WV Bosnia 

TARGET MARKET 

Low-end 

(depth < 20% OR avg. 

loan balance 

< US$150) 

Broad 

(depth between 20% 

and 149%) 

High-end (depth ≥ 

150%) 

Small Business 

(depth ≥ 250%) 

Actuar 

AGAPE 

AKRSP 

Al Amana 

Al Majmoua 

Assawinso RB 

ASA 

BRAC 

BURO Tangail 

15 de Abril 

23 de Julio 

ABA 

ACLEDA 

ACODEP 

Acredicom 

ADOPEM 

ADRI 

AMK 

Banco Ademi 

Acep 

Agrocapital 

CAM 

CARD BANK 

CDS 


CMM Medellín 

Compartamos 

Constanta 

Contigo 

Crecer 

BAAC 

Banco do Povo 

Bancosol 

BanPeqEmpresa 

Basix 

Bospo 

BRI 


Moznosti 

Sunrise 


CERUDEB 

Emprender 

EMT 

Enlace 

Faten 

FED 

FINCA Ecuador 



FINCA Malawi 

Calpia 

Chispa 

Chumiquená 

CitiS&L CM 

Arequipa 

COOSAJO 

Ecosaba 

FAMA 

WV Bosnia 

FEFAD 

MEB 

FINCA Mexico 


FINCA Peru 


FINCA Uganda 

FMM Popayán 

FOCCAS 

FWWBCali 

FWWB India 

Faulu 

FIE 

Finamérica Finsol 

Fundusz Mikro 

FONDECO 




Hublag 



Mibanco 


Nirdhan 

Nsoatreman RB 

PAMÉCAS 

Piyeli 

Kafo Jiginew 

KWFT 

LOK 

Mercy Corps 

Mikrofin 

Moyután 

Nachala 

NOA 


Pride Uganda 

ProMujer 

RSPI 


SEDA 

SEEDS 

SEF 

SHARE 

Nyésigiso 

Oscus 

PADME 

Portosol 


PRODEM 

ProEmpresa 

Sagrario 

TSPI 

UWFT 

Vivacred 

WAGES 

WRHonduras 

Sartawi 

Tonantel 

Tulcán 

UNRWA 

Vital-Finance 

REGION 

Africa 

Asia 

(all) 

Eastern 

Europe 

Latin 

America 

Middle East/ 

North Africa 

Acep 

Assawinso RB 

CERUDEB 

Citi S&L 

ACLEDA 

AKRSP 

ASA 

BAAC 

AMK 

Bospo 

15 de Abril 

23 de Julio 

ACODEP 

Acredicom 

Actuar 

ADOPEM 

ADRI 

AGAPE 

ABA 

Al Amana 

Faulu 

FINCA Malawi 


FINCA Uganda 

Basix 


BRAC 

BRI 

FEFAD 

Fundusz Mikro 

Agrocapital 

Banco Ademi 

Banco do Povo 

Bancosol 

BanPeqEmpresa 


Calpia 

CAM 

Al Majmoua 

Faten 

FOCCAS 

Kafo Jiginew 

KWFT 


BURO Tangail 

CARD BANK 

CDS 

Constanta 


LOK 

CEAPE/ Pernambuco 

Chispa 

Chumiquená 

CM Arequipa 

CMM Medellín 

Compartamos Contigo 

COOSAJO 


UNRWA 

Nsoatreman RB 

Nyésigiso 

PADME 

PAMÉCAS 

EMT 


FWWB India 


Mercy Corps 

MEB 

Crecer 

Ecosaba 

Emprender 

Enlace 

FAMA 

FED 

FIE 

Finamérica 

Piyeli 


Pride Uganda 


Hublag 


Nirdhan 


Mikrofin 

Moznosti 

FINCA Ecuador 


FINCA Mexico 


FINCA Peru 

Finsol 

FMM Popayán 


SEDA 

SEF 

UWFT 

RSPI 

SEEDS 

SHARE 

TSPI 

Nachala 

NOA 

FONDECO 

FWWBCali 

Mibanco 

Moyután 

Oscus 

Portosol 

PRODEM 

ProEmpresa 

Vital-Finance 

WAGES 

Sunrise 

WV Bosnia 

ProMujer 

Sagrario 

Sartawi 

Tonantel 

Tulcán 

Vivacred 

WR Honduras 



Composition of Additional Analysis Groupings, ctd. 

CHARTER# 

Bank 

Credit Union/ 

Cooperative 

NGO 

Non-Bank ### 

ACLEDA 

Asawinso RB 

BAAC 

15 de Abril 

23 de Julio 

Acep 

ABA 

ACODEP 

Actuar 

ADOPEM 

ADRI 

AGAPE 

AKRSP 

AMK 

ASA 

Agrocapital 

Basix 


Calpia 


BRI 

Banco Ademi 

Acredicom 

Chumiquená 

COOSAJO 

Al Amana 

Al Majmoua 

Banco do Povo 

Bospo 

BRAC 

BURO Tangail 

CAM 

CDS 


Chispa 

Faten 

Finamérica 

BanPeqEmpresa 

Bancosol 

CARD BANK 

Ecosaba 

Kafo Jiginew 

Moyután 

CMM Medellín 

Constanta 

Contigo 

Crecer 

Compartamos 

EMT 

FAMA 

Faulu 

FED 

FIE 

Fundusz Mikro 


CERUDEB 

CM Arequipa 

Enlace 

Nachala 

Nyésigiso 

Oscus 

FINCA Ecuador 



FINCA Malawi 

FINCA Mexico 


FINCA Peru 


FINCA Uganda 

FMM Popayán 


TSPI 

FEFAD 

MEB 


PAMÉCAS 

Sagrario 

Tonantel 

FOCCAS 

FONDECO 

FWWBCali 

FWWB India 

Hublag 


KWFT 

LOK 


Mikrofin 

Mibanco 

Nsoatreman RB 

Tulcán 

Moznosti 

Nirdhan 



PRODEM 

Portosol 

ProMujer 

RSPI 

SEDA 

SEEDS 

SEF 

SHARE 

Sunrise 

Sartawi 

UNRWA 

UWFT 

Vivacred 

WAGES 

WRHonduras 

WV Bosnia 

PROFIT STATUS# 

Non-profit 

Profit 

ABA 

Acep 

ACODEP 

Actuar 

ADOPEM 

ADRI 

AGAPE 

Agrocapital 

AKRSP 

Al Amana 

Al Majmoua 

AMK 

ACLEDA 

Asawinso RB 

BAAC 

Banco Ademi 

Bancosol 

ASA 

Banco do Povo 

Bospo 

BRAC 

BURO Tangail 

CAM 

CDS 


Chispa 

CMM Medellín 

Compartamos 

Constanta 

BanPeqEmpresa 

Basix 


BRI 


Contigo 

Crecer 

EMT 

FAMA 

Faten 

FED 

FIE 

FINCA Ecuador 



FINCA Malawi 

FINCA Mexico 

Calpia 

CARD BANK 

CERUDEB 

CitiS&L 

CM Arequipa 


FINCA Peru 


FINCA Uganda 

FMM Popayán 

FOCCAS 

FONDECO 

FWWBCali 

FWWB India 


Hublag 

Kafo Jiginew 

Enlace 

Faulu 

FEFAD 

Finamérica 

Fundusz Mikro 


KWFT 

LOK 


Mikrofin 

Moznosti 

Nirdhan 

NOA 

Nyésigiso 

PAMÉCAS 

Piyeli 

Portosol 


MEB 

Mibanco 

Manya Krobo RB. 

Nachala 


Pride Uganda 


PRODEM 

ProMujer 

RSPI 

Sartawi 

SEDA 

SEEDS 

SEF 

SHARE 

Sunrise 


Nsoatreman RB 

PADME 

ProEmpresa 


TSPI 

UNRWA 

Vivacred 

WAGES 

WRHonduras 

WV Bosnia 

# Some institutions did not report the information. 



APPENDICES 

APPENDICES 

Appendix I: Notes to Statistical Section 

The MicroBanking Standards Project, of which The 

MicroBanking Bulletin is a major output, is open to 

all MFIs that are willing to disclose financial data 

that meet a simple quality test. Participating MFIs 

typically have three characteristics: 1) they are 

willing to be transparent by submitting their 

performance data to an independent agency; 2) 

they display a strong social orientation by providing 

financial services to low-income persons; and 3) 

they are able to answer all the questions needed for 

our analysis. 

The one hundred and twenty four institutions that 

provided data for this issue represent a large 

proportion of the world’s leading microfinance 

institutions. They have provided data generally by 

completing a detailed questionnaire, supplemented 

in most cases by additional information. All 

participating MFIs receive a customized report 

comparing their results with those of the peer 

groups. 

Data Quality Issues 

The Bulletin classifies information from participating 

institutions according to the degree to which we 

have independent verification of its reliability. AAAgraded 

information has been independently 

generated through a detailed financial analysis by 

an independent third party, such as a CAMEL 

evaluation, a CGAP appraisal, or assessments by 

reputed rating agencies. A-graded information is 

backed by accompanying documentation, such as 

audited financial statements, annual reports, and 

independent program evaluations that provide a 

reasonable degree of confidence for our 

adjustments. B-graded information is from MFIs 

that have limited themselves to completing our 

questionnaire. These grades signify confidence 

levels on the reliability of the information; they are 

NOT intended as a rating of the financial 

performance of the MFIs. 

The criteria used in constructing the Statistical 

Tables are important for understanding and 

interpreting the information presented. Given the 

voluntary nature and origin of the data, the Bulletin 

staff and Editorial Board, and CGAP cannot accept 

responsibility for the validity of the results 

presented, or for consequences resulting from their 

use. We employ a system to make tentative 

distinctions about the quality of data presented to 

us and include only information for which we have a 

reasonable level of comfort. However, we cannot 

exclude the possibility of a program 

misrepresenting its results. 

The most delicate areas of potential distortion are: 

(1) unreported subsidies and (2) misrepresented 

loan portfolio quality. There can also be 

inaccuracies in reporting the costs of financial 

services in multipurpose institutions that also 

provide non-financial services, in part because of 

difficulties in assigning overhead costs. These risks 

are highest for younger institutions, and for 

institutions with a record of optimistic disclosure. If 

we have grounds for caution about the reliability of 

an MFI’s disclosure, we will not include its 

information in a peer group unless it has been 

externally validated by a third-party. 

Adjustments to Financial Data 

The Bulletin adjusts the financial data it receives to 

ensure comparable results. The financial 

statements of each organization are converted to 

the standard chart of accounts used by the Bulletin. 

This chart of accounts is simpler than that used by 

most MFIs, so the conversion consists mainly of 

consolidation into fewer, more general accounts. 

Then three major adjustments are applied to 

produce a common treatment for the effect of: a) 

inflation, b) subsidies, and c) loan loss provisioning 

and write-off. In the statistical tables the reader can 

compare adjusted and unadjusted results. 

Inflation 

The Bulletin reports the net effect of inflation by 

calculating increases in expenses and incomes due 

to inflation. Inflation causes a decrease in the real 

value of equity. This “cost of funds” is obtained by 

multiplying the prior year-end equity balance by the 

current-year inflation rate. 15 Fixed asset accounts, 

on the other hand, are revalued upward by the 

current year’s inflation rate, which results in inflation 

adjustment income, offsetting to some degree the 

15 

Inflation data are obtained from line 64x of the International 

Financial Statistics, International Monetary Fund, various years. 


APPENDICES 

expense generated by adjusting equity. 16 On the 

balance sheet, this inflation adjustment results in a 

reordering of equity accounts: profits are 

redistributed between real profit and the nominal 

profits required to maintain the real value of equity. 

MFIs that borrow from banks or mobilize savings 

have an actual interest expense, which is an 

operating cost. In comparison, similar MFIs that 

lend only their equity have no interest expense and 

therefore have lower operating costs. If an MFI 

focuses on sustainability and the maintenance of its 

capital/asset ratio, it must increase the size of its 

equity in nominal terms to continue to make the 

same value of loans in real (inflation-adjusted) 

terms. Inflation increases the cost of tangible items 

over time, so that a borrower needs more money to 

purchase them. MFIs that want to maintain their 

support to clients must therefore offer larger loans. 

Employees’ salaries go up with inflation, so the 

average loan balance and portfolio must increase to 

compensate, assuming no increase in interest 

margin. Therefore, a program that funds its loans 

with its equity must maintain the real value of that 

equity, and pass along the cost of doing so to the 

client. This expectation implies MFIs should “pay” 

interest rates that include the inflation-adjustment 

expense as a cost of funds, even if this cost is not 

actually paid to anyone outside the institution. 

Some countries with high or volatile levels of 

inflation require businesses to use inflation-based 

accounting on their audited financial statements. 

We use this same technique in the Bulletin. Of 

course, we understand that in countries where high 

or volatile inflation is a new experience, MFIs may 

find it difficult to pass on the full cost of inflation to 

clients. We are not recommending policy; rather, 

we are trying to provide a common analytical 

framework that compares real financial 

performance meaningfully. 

Subsidies 

We adjust participating organizations’ financial 

statements for the effect of subsidies by 

representing the MFI as it would look on an 

unsubsidized basis. We do not intend to suggest 

whether MFIs should or should not be subsidized. 

Rather, this adjustment permits the Bulletin to see 

how each MFI would look without subsidies for 

comparative purposes. Most of the participating 

MFIs indicate a desire to grow beyond the 

limitations imposed by subsidized funding. The 

subsidy adjustment permits an MFI to judge 

whether it is on track toward such an outcome. A 

16 

In fact, an institution that holds fixed assets equal to its equity 

avoids the cost of inflation that affects MFIs, which hold much of 

their equity in financial form. 

focus on sustainable expansion suggests that 

subsidies should be used to enhance financial 

returns. The subsidy adjustment simply indicates 

the extent to which the subsidy is being passed on 

to clients through lower interest rates or whether it 

is building the MFI’s capital base for further 

expansion. 

The Bulletin adjusts for three types of subsidies: (1) 

a cost-of-funds subsidy from loans at below-market 

rates, (2) current-year cash donations to fund 

portfolio and cover expenses, and (3) in-kind 

subsidies, such as rent-free office space or the 

services of personnel who are not paid by the MFI 

and thus not reflected on its income statement. 

Additionally, for multipurpose institutions, The 

MicroBanking Bulletin attempts to isolate the 

performance of the financial services program, 

removing the effect of any cross subsidization. 

The cost-of-funds adjustment reflects the impact of 

soft loans on the financial performance of the 

institution. The Bulletin calculates the difference 

between what the MFI actually paid in interest on its 

subsidized liabilities and the deposit rate for each 

country. 17 This difference represents the value of 

the subsidy, which we treat as an additional 

financial expense. We apply this subsidy to those 

loans to the MFI that are priced at less than 75 

percent of prevailing market (deposit) rates. The 

decreased profit is offset by generating an 

“accumulated subsidy adjustment” account on the 

balance sheet. 

If the MFI passes on the interest rate subsidy to its 

clients through a lower final rate of interest, this 

adjustment may result in an operating loss. If the 

MFI does not pass on this subsidy, but instead uses 

it to increase its equity base, the adjustment 

indicates the amount of the institution’s profits that 

were attributable to the subsidy rather than 

operations. 

Loan Loss Provisioning 

Finally, we apply standardized policies for loan loss 

provisioning and write-off. MFIs vary tremendously 

in accounting for loan delinquency. Some count the 

entire loan balance as overdue the day a payment 

is missed. Others do not consider a loan delinquent 

17 

Data for shadow interest rates are obtained from line 60l of the 

International Financial Statistics, IMF, various years. The 

deposit rate is used because it is a published benchmark in most 

countries. Sound arguments can be made for use of different 

shadow interest rates. NGOs that wish to borrow from banks 

would face interest significantly higher than the deposit rate. A 

licensed MFI, on the other hand, might mobilize savings at a 

lower financial cost than the deposit rate, but reserve 

requirements and administrative costs would drive up the actual 

cost of such liabilities. 


APPENDICES 

until its full term has expired. Some MFIs write off 

bad debt within one year of the initial delinquency, 

while others never write off bad loans, thus carrying 

forward a hard-core default that they have little 

chance of ever recovering. 

We classify as “at risk” any loan with a payment 

over 90 days late. We provision 50 percent of the 

outstanding balance for loans between 90 and 180 

days late, and 100 percent for loans over 180 days 

late. Wherever we have adequate information, we 

adjust to assure that all loans are fully written off 

within one year of their becoming delinquent. 

(Note: We apply these provisioning and write-off 

policies for ease of use and uniformity. We do not 

recommend that all MFIs use exactly the same 

policies.) In most cases, these adjustments are not 

very precise. Nevertheless, most participating MFIs 

have high-quality loan portfolios, so loan loss 

provision expense is not an important contributor to 

their overall cost structure. If we felt that a program 

did not fairly represent its general level of 

delinquency, and we were unable to adjust it 

accordingly, we would simply exclude it from the 

peer group. 

Financial Statement Adjustments and their Effects 

Adjustment Effect on Financial Statements Type of Institution Most Affected 

by Adjustment 

Inflation adjustment of equity 

Reclassification of certain long term 

liabilities into equity, and subsequent 

inflation adjustment 

Subsidy adjustment: Interest savings 

on subsidized liabilities involving at 

least a 25 percent discount in relation 

to market based loans to the same 

institution or, in the absence of such 

loans, the deposit rate 

Subsidy adjustment: Current-year 

cash donations to cover operating 

expenses 

Subsidy adjustment: In kind donation 

of goods or services (e.g., line staff 

paid for by technical assistance 

providers) 

Loan loss provision and write-off 

adjustment: Applying policies which 

may be more aggressive than the MFI 

employs on its own books 

Increases financial expense accounts 

on profit and loss statement, to some 

degree offset by inflation income 

account for revaluation of fixed assets. 

Generates inflation adjustment account 

in equity section of balance sheet with 

net balance of inflation adjustments. 

Decreases concessionary loan account 

and increases equity account; 

increases inflation adjustment on profit 

and loss statement and balance sheet. 

Increases financial expense on profit 

and loss statement. Increases subsidy 

adjustment account on balance sheet. 

Reduces operating income on profit and 

loss statement (if the MFI records 

donations as operating income). 

Increases subsidy adjustment account 

on balance sheet. 

Increases expense on profit and loss 

statement, increases subsidy 

adjustment account on balance sheet. 

Increases loan loss provision expense 

on profit and loss statement. On 

balance sheet, increases in loan loss 

reserve and/or write-offs are accounted 

for by equal reductions in loan loss 

reserve and portfolio. 

NGOs funded more by equity than 

by liabilities will be hard hit, 

especially in high-inflation 

countries. 

NGOs that have long-term lowinterest 

“loans” from international 

agencies that function more as 

donations than loans. 

Banks or NGOs that use large lines 

of credit from governments or 

international agencies at highly 

subsidized rates. 

NGOs during their start-up phase. 

This adjustment is relatively less 

important for mature institutions 

included in this edition. 

NGOs during their start-up phase. 

Less important for mature 

institutions included in this edition. 

MFIs that allow bad loans to 

accumulate within their portfolio. 

This common problem tends to 

have a limited effect on leading 

MFIs because their loan losses are 

low, even after adjustment. 


APPENDICES 

Statistical Issues 

The Bulletin reports the means and standard 

deviations of the performance indicators for each 

peer group. At this stage, peer groups are still 

small and the observations in each peer group 

show a high variation. Outliers distort the results of 

some of the peer group averages. Consequently, 

the reader should be cautious about the interpretive 

power of these data. Over time, as more MFIs 

provide data, we will be in a better position to 

generate deeper and more sophisticated types of 

analyses of the data at our disposal, and will have a 

higher degree of comfort with the statistical 

significance of the differences between the means 

of the distinct peer groups. 

To ensure that the averages reported represent the 

group as accurately as possible, we have excluded 

outliers for each of the indicators. Statistics for the 

category All MFIs were calculated by deleting 

observations in the first and last deciles for each 

indicator. In other words, the values between the 

11th and 89th percentiles were used for the 

analysis. For the FSS sample and peer group 

calculations, the first and last percentile 

observations were excluded for each indicator 

except macroeconomic indicators. The averages 

are calculated on the basis of the values between 

the 2nd and the 99th percentiles for each group. In 

effect, for each indicator we rank the MFIs in the 

group and eliminate the top and bottom values. In 

most cases, this exclusion eliminates two 

observations for each peer group: the institution 

with the highest and the lowest value on each 

indicator. In cases where indicators contain 

observations with tied values for highest and lowest 

values, more than two observations are deleted. 

This method helps to prevent outliers from 

dominating group results, and smoothes the data by 

minimizing data dispersion. Where the sample size 

is reduced to n=1, we have not reported the result 

so as to maintain confidentiality. 

We have carried out statistical tests to determine 

the impact of outliers where they exist, and to 

quantify the results in terms of how well they 

represent the peer groups. Where large differences 

exist between the means of different peer groups or 

groups sorted by selection criteria, we have verified 

their statistical significance using t-tests. These 

tests compare the mean of the group to the mean of 

all MFIs in the sample, taking into account factors 

like the number of observations and the dispersion 

of the sample. The test statistic is then compared 

to a standard critical level (using one percent as the 

significance level) to decide whether the difference 

between the group and the sample as a whole is 

statistically significant. In other words, they allow 

us to decide whether the difference we see is 

robust, by considering it in the context of how 

cohesive and how large the group is. 


APPENDICES 

Appendix II: Description of Participating MFIs 

ACRONYM NAME, LOCATION DATE 

15 de Abril Cooperativa 15 de Abril, 

Ecuador 

23 de Julio Cooperativa 23 de Julio, 

Ecuador 

ABA 

ACEP 

ACLEDA 

ACODEP 

Acredicom 

Actuar 

ADOPEM 

ADRI 

AGAPE 

Agrocapital 

AKRSP 

Al Majmoua 

Al Amana 

Alexandria Business 

Association, 

Egypt 

Agence de Crédit pour 

l’Enterprise Privée, 

Senegal 

Association of Cambodian 

Local Economic 

Development Agencies, 

Cambodia 

Asociación de Consultores 

para el Desarrollo de la 

Pequeña, Mediana y 

Microempresa, 

Nicaragua 

Acredicom, 

Guatemala 

Corporación Acción por el 

Tolima - Actuar 

Famiempresas, 

Colombia 

Asociación Dominicana 

para el Desarrollo de la 

Mujer, 

Dominican Republic 

Asociación para el 

Desarrollo Rural Integrado, 

Costa Rica 

Asociación General para 

Asesorar Pequeñas 

Empresas, 

Colombia 

Fundación Agrocapital, 

Bolivia 

Aga Khan Rural Support 

Programme, 

Pakistan 

Lebanese Association for 

Development -- Al 

Majmoua, 

Lebanon 

Association Al Amana, 

Morocco 

DATA 

QUALITY 

GRADE 

DESCRIPTION OF MICROFINANCE PROGRAM 

09/00 A 15 de Abril is a credit union in Ecuador that has participated in 

WOCCU’s technical assistance program since in 1995. 15 de Abril 

offers both credit and voluntary savings services to members. 

09/00 A 23 de Julio participates in WOCCU’s technical assistance program in 

Ecuador. It is a credit union offering credit and savings services to 

members. 

12/99 AAA ABA provides credit to small and microenterprises using an 

individual lending methodology. It is an NGO founded in 1988 and 

based primarily in urban areas. The credit program began in 1990. 

12/99 B ACEP began as an NGO in a provincial town in 1987 and has 

expanded to operate in other urban areas in Senegal. It has 

converted to a credit union. 

12/99 AAA ACLEDA was started in 1993 as an NGO. It provides small and 

micro loans to enterprises and trains entrepreneurs in small 

business management. Both group and individual loans are made. 

12/99 A Founded in 1989, ACODEP serves small and micronterprises 

primarily in Managua and other urban areas of Nicaragua. It is 

currently negotiating a voluntary supervision agreement with the 

Superintendent of Banks in Nicaragua. 

09/00 A ACREDICOM is a member of the FENACOAC credit union system in 

Guatemala, and participated in WOCCU’s technical assistance 

program. It primarily lends for agriculture and to a lesser extent 

microenterprise activities, and mobilizes savings from members. 

12/99 B ACTUAR Tolima was founded in 1986. It is an NGO offering loans 

to microenterprises in Tolima and surrounding areas, and is affiliated 

with ACCION International and Cooperativa Emprender in Colombia. 

12/99 A ADOPEM, an affiliate of Women’s World Banking, is an NGO 

dedicated to credit for women microentrepreneurs. It has been in 

operation since 1982. 

12/99 A ADRI is an NGO offering loans to small and microenterprises in 

Costa Rica. Founded in 1986, it also offers training and business 

development services to its clients. 

12/99 A Founded in 1975, AGAPE operates principally in Barranquilla, 

offering microcredit through a mixture of methodologies including 

village banking, solidarity groups and individual loans. It is an 

affiliate of Opportunity International. 

12/99 AAA Fundación Agrocapital focuses its services on agriculture and agroindustry, 

working mainly in rural and small urban areas of Bolivia. It 

is an NGO founded in 1992, and offers a mixture of microloans and 

longer term mortgage loans. 

12/98 A AKRSP is a multi-service NGO that works in the “Roof of the World” 

region of northern Pakistan. Its credit program began in 1983, 

offering loans through its network of village organizations. 

12/99 A Al Majmoua is a Lebanese NGO, offering village banking-type 

services in both urban and rural areas. The program began 

operations in 1994 as a project of Save the Children. Ownership 

was transferred to the Lebanese institution in 1998. 

06/00 AAA Al Amana offers solidarity group loans through a wide network of 

branches in urban areas of Morocco. Founded in 1997, it is an 

affiliate of Pride Vita. 


APPENDICES 


AMK 

ARB 

ASA 

BAAC 

Banco Ademi 

Banco do 

Povo 

BancoSol 

Basix 

BDB 

Bospo 

BPE 

BRAC 

BRI 

BURO 

Tangail 

Caja de Los 

Andes 

AMK Posusje, 

Bosnia and Herzegovina 

Asawinso Rural Bank, 

Ghana 

Association for Social 

Advancement, 

Bangladesh 

Bank for Agriculture and 

Agricultural Cooperatives, 

Thailand 

Banco de Desarrollo 

Ademi, S.A., 


Banco do Povo de Juiz de 

Fora, 

Brazil 

Banco Solidario, S.A., 

Bolivia 

Bharatiya Samruddhi 

Finance Ltd., 

India 

Bank Dagang Bali, 

Indonesia 

Bospo, 


Banco de la Pequeña 

Empresa, S.A., 


Bangladesh Rural 

Advancement Committee, 

Bangladesh 

Bank Rakyat Indonesia, 

Unit Desa System, 

Indonesia 

BURO, Tangail, 

Bangladesh 

Caja de Ahorros y Créditos 

Los Andes, 

Bolivia 

DATA 

QUALITY 

GRADE 


12/99 B AMK is a limited liability company founded in 1997 to provide 

microcredit to low income self-employed individuals in urban areas. 

It is financed by the Local Initiatives Department In Bosnia that aims 

to improve access to credit to the poor to promote economic 

reconstruction. 

12/99 A The rural bank was started in 1983 and it now provides group and 

individual loans, and deposit services to farmers, microentrepreneurs 

and civil servants in rural Ghana. 

12/99 AAA ASA is an NGO that offers credit services to the rural poor in 

Bangladesh. The majority of its clients are landless women. It was 

founded in 1978 and shifted from an earlier, integrated development 

strategy to its current focus on financial services in the early 1990s. 

It uses a village level group lending methodology. 

03/99 A BAAC is a government-owned agricultural bank that lends to small 

farmers and farmers’ cooperatives. Founded in 1966, its outreach in 

rural areas of Thailand is now estimated to cover more than 80% of 

farm families. 

12/99 A Banco ADEMI is a formal financial institution, which began 

operations in 1998. The bank is the successor to the NGO, ADEMI, 

which was involved in microcredit since 1982. 

12/99 AAA Banco do Povo de Juiz de Fora is an NGO operating in Juiz de Fora 

in Brazil. It offers individual loans to microentrepreneurs and was 

founded in1997. It was formerly known as FAEP. 

12/99 A BancoSol is a licensed commercial bank devoted to microfinance, 

offering microenterprise credit and passbook savings. Its credit 

program focuses on group loans, and it operates primarily in urban 

areas of Bolivia. It grew out of the NGO PRODEM and was spun off 

as a bank in 1992. It is an affiliate of ACCION International. 

03/00 AAA BASIX was set up as a non-bank in 1996 to provide financial 

services to the rural poor, to promote self-employment, and to 

provide technical assistance to clients and rural financial institutions. 

12/98 AAA Bank Dagang is a private commercial bank that offers savings and 

credit facilities to primarily low-income clients in Bali. It was founded 

in 1970. 

12/99 B BOSPO is a NGO founded in 1995 to provide microcredit to solidarity 

groups made of low income women entrepreneurs in secondary 

cities of Tuzla. It is financed by the Local Initiatives Department in 

Bosnia that aims to improve access to credit to the poor to promote 

economic reconstruction. 

12/98 AAA Banco de la Pequeña Empresa was created to serve both 

microenterprises and small businesses, and has just completed its 

first year of operations. It s a formal financial sector institution and 

holds a license to operate as a development bank. It is an affiliate of 

ACCION International. 

12/99 AAA BRAC is an NGO that started in 1972. It provides both financial and 

non-financial services primarily in rural areas. The financial services 

include the provision of microloans and mobilization of savings. 

12/99 AAA BRI is a government-owned bank oriented towards rural areas, 

which has operated since 1897. The Unit Desa system is an 

extensive network of small banking units, which function as profit 

centers and provide individual loans and savings services. The 

system has existed in its current form since 1984. 

12/99 AAA Flexible voluntary open-savings, microloans and insurance services 

are provided by BURO Tangail since 1990. It is an NGO. 

12/99 A Caja Los Andes grew out of ProCrédito, an NGO that began lending 

operations in 1992. It was converted to a special finance company in 

1995. Los Andes operates in urban and some rural areas in Bolivia, 

providing individual loans and savings services. 


APPENDICES 


Calpiá 

CAM 

CARD 

CDS 

Financiera Calpiá, S.A., 

El Salvador 

Centro de Apoyo a la 

Microempresa, 

El Salvador 

Center for Agriculture and 

Rural Development, 

The Philippines 

Community Development 

Society, 

India 

DATA 

QUALITY 

GRADE 


12/98 AAA Financiera Calpiá began as an NGO, AMPES, and was converted 

into a finance company in 1995. It offers individual loans to 

microenterprises and small businesses and has started to mobilize 

savings. It operates mainly in urban areas, although 25% of its 

portfolio is now in rural areas. 

12/99 B FINCA’s affiliate in El Salvador, the CAM was founded in 1990 and is 

one of FINCA’s largest affiliates serving over 16,000 clients in all 15 

geographic departamentos in El Salvador. 

12/99 A CARD started as an NGO in 1986 and is now partially transformed 

into a rural bank. It is an affiliate of CASHPOR and Women’s World 

Banking. It makes loans and collects deposits. 

03/99 A CDS offers microcredit and non-financial services in the Nagpur 

region of India. It was founded in 1985 and is an affiliate of 

Opportunity International. 

CEAPE/PE 

CERUDEB 

Citi S&L 

Chispa 

Chuimequená 

CM Arequipa 

CMM/Medellín 

Compartamos 

Constanta 

Contigo 

COOSAJO 

Crecer 

Centro de Apoio aos 

Pequeños Empreendimentos 

Pernambuco, 

Brazil 

Centenary Rural 

Development Bank, 

Uganda 

Citi Savings & Loans, 

Ghana 

Fundación Chispa, 

Nicaragua 

Cooperativa San Miguel 

Chuimequená, 

Guatemala 

Cajas Municipales de 

Arequipa, 

Peru 

Corporación Mundial de la 

Mujer Medellín, Medellín, 

Colombia 

Asociación Programa 

Compartamos, I.A.P., 

Mexico 

Constanta, 

Georgia 

Fundación Contigo, 

Chile 

Cooperativa San José 

Obrero, 

Guatemala 

Crecer, 

Bolivia 

12/99 A CEAPE Pernambuco is an urban-based microenterprise credit 

program. A member of the FENAPE network in Brazil, and of 

ACCION International, it was founded in 1992. 

12/99 A CERUDEB was founded as a trust company in 1983, and obtained 

its banking license in 1992. It received technical assistance from 

IPC from 1993-98. CERUDEB provides credit and savings services 

in Kampala and Uganda’s district towns. 

12/99 B Citi Savings is a private non-bank financial institution that operates in 

Greater Accra, Ghana. It lends to rotating savings and credit 

associations (susu clubs) and informal savings collectors, and 

mobilizes savings from the public. 

12/99 B Founded in 1991, CHISPA works primarily in urban areas of 

Nicaragua. It is affiliated with the Mennonite Economic Development 

Association (MEDA). 

09/00 A San Miguel Chuimequená is a Guatemalan credit union. It is a 

member of the FENACOAC system and it participates in WOCCU’s 

technical assistance program. It offers loans and savings services to 

its members. 

12/99 A The municipal savings and credit banks of Peru are owned by city 

governments. Arequipa is one of the largest and most successful 

banks of the national network, and offers pawn and microenterprise 

loans as well as savings products. 

12/99 A CMM Medellín is affiliated to the Women’s World Banking network, 

and operates in Medellín and surrounding areas. It was founded in 

1985 and lends to both men and women. 

12/99 B Compartamos is the lending arm of Gente Nueva, a Mexican NGO 

that was founded in 1985. The program uses a village banking 

methodology focusing on women, in rural and semi-urban areas of 

Mexico. It began lending in 1990. 

12/99 A Constanta was established in 1997 with a grant from UNHCR/Save 

the Children as a local NGO to provide group loans to poor selfemployed 

women. 

12/99 A CONTIGO began lending operations in 1989, and offers credit 

services to microentrepreneurs in communities in the south of 

Santiago de Chile. 

09/00 A San José Obrero is a member of the FENACOAC credit union 

federation, and participated in WOCCU’s technical assistance 

program in Guatemala. It offers loans and savings services to its 

members. 

12/99 AAA CRECER is an NGO working primarily in rural areas of Bolivia. It 

participates in Freedom from Hunger’s “Credit with Education” 

program, using a village banking methodology. 


APPENDICES 


DATA 

QUALITY 

GRADE 


Ecosaba 

Ecosaba, 

Guatemala 

Emprender Fundación Emprender, 

Argentina 

EMT Ennathian Moulethan 

Tchonnebat, 

Cambodia 

Enlace 

FAMA 

Faten 

Faulu 

FED 

FEFAD 

FIE 

Finamérica 

FINCA EC 

FINCA HO 

FINCA KY 

FINCA MA 

FINCA MX 

FINCA PE 

FINCA NI 

FINCA TZ 

Programa Enlace, Banco 

Solidario, 

Ecuador 

Fundación de Apoyo a la 

Microempresa, 

Nicaragua 

Palestine for Credit and 

Development, 

West Bank and Gaza 

Food for the Hungry 

International, 

Uganda 

Fundación Ecuatoriana de 

Desarrollo, 

Ecuador 

Foundation for Enterprise 

Finance and Development, 

Albania 

FFP - Fomento a 

Iniciativas Económicas, 

S.A., 

Bolivia 

Financiera América, S.A., 

Colombia 

FINCA Ecuador, 

Ecuador 

FINCA Honduras, 

Honduras 

FINCA Kyrgyzstan, 

Kyrgyzstan 

FINCA Malawi, 

Malawi 

FINCA México, 

Mexico 

FINCA Perú, 

Peru 

FINCA Nicaragua, 

Nicaragua 


Tanzania 

09/00 A ECOSABA is a member of the FENACOAC credit union federation, 

and participated in WOCCU’s technical assistance program in 

Guatemala. It offers loans and savings services to its members. 

04/00 A Emprender, founded in 1992, is an ACCION affiliate that offers 

microenterprise credit in urban areas of Argentina. The majority of its 

lending is to solidarity groups. 

12/99 A EMT was founded in 1991 as a rural credit project run by the French 

agency, GRET. It is in the process of transformation to an 

independent Institution, and operates in rural areas in the south of 

Cambodia. It offers individual and solidarity group loans. 

09/99 B ENLACE is the microfinance division of Banco Solidario in Ecuador. 

The program was founded in 1995, and Banco Solidario receives 

technical assistance from ACCION International. ENLACE offers 

both credit and savings services to microentrepreneurs. It also 

administers a pawn-lending product. 

12/99 A FAMA operates mainly in urban areas of Nicaragua, providing 

microenterprise credit. It was founded in 1991 and is affiliated with 

ACCION. 

12/99 A FATEN was initiated as a Save the Children affiliate in 1995 and 

spun-off as an independent NGO in 1999. It provides microcredit to 

poor women entrepreneurs using group methodology. 

12/99 B Founded in 1995 as an affiliate of Food for the Hungry International, 

FAULU provides group based credit and voluntary deposit services 

to small and microentrepreneurs in urban and semi-urban areas. 

12/99 A Founded over 30 years ago, FED has an extensive branch network 

throughout Ecuador providing individual microloans. It is an affiliate 

of ACCION International. 

12/99 A Operating mainly in urban areas of Albania, FEFAD offers small 

business loans. It was founded in 1995 as an initiative of the 

Albanian and German governments, and receives technical 

assistance from IPC. 

12/99 A FFP - FIE is a for-profit financial institution offering individual loans to 

microenterprises in urban areas of Bolivia. It began lending in 1988 

as an NGO, and began operating as a “Private Financial Fund” in 

1998 under regulation by the Bolivian Superintendency of Banks. 

12/99 AAA Finamérica is a regulated finance company operating in Bogotá and 

surrounding areas. Its predecessors were the NGO Actuar Bogotá, 

founded in 1988, the NGO Corposol, and the financiera Finansol. It 

is an affiliate of ACCION International. 

12/99 B FINCA Ecuador was founded in 1994 and provides village banking 

services to low-income families in three regions of the country: 

Pichincha, Guayas, and Imbabura. 

12/99 B FINCA Honduras is one of the largest FINCA affiliates in terms of 

portfolio size. It was founded in 1989 and operates in 13 of the 18 

departamentos of Honduras. 

08/00 B Founded in 1995, FINCA Kyrgyzstan is operating in five of the six 

oblasts of Kyrgyzstan and offers both village banking and individual 

loan products to 10,000 clients. 

08/99 A FINCA Malawi works with women in the country’s southern region, 

and has been in operation since 1994. 

12/99 B FINCA Mexico currently operates village banking groups in the state 

of Morelos. It was founded in 1989. 

12/99 B FINCA Perú is primarily based in rural areas, offering 

microenterprise credit to borrowers in Lima, Ayacucho, and 

Huancavelica. It was founded in 1993. 

06/99 A FINCA’s Nicaraguan affiliate began lending in 1992, and has since 

expanded to have branch offices in several urban areas in 

Nicaragua. 

08/00 B The MFI was formed in 1998 as an affiliate of FINCA International. It 

provides loans through village banks. 


APPENDICES 


FINCA UG 

Finsol 

FMM Popayán 

FOCCAS 

FONDECO 

Fundusz 

Mikro 

FWWB Cali 

FWWB India 

Hattha 

Kaksekar 

Hublag 

Inicjatywa 

Mikro 

Kafo Jiginew 

KASHF 

KWFT 

LOK 

MC 

MEB 

Mibanco 

FINCA Uganda, 

Uganda 

Financiera Solidaria S.A., 

Honduras 

Fundación Mundo Mujer 

Popayán, 

Colombia 

Foundation for Credit and 

Community Assistance, 

Uganda 

Fondo de Desarrollo 

Comunal, 

Bolivia 

Fundusz Mikro, 

Poland 

Fundación Women’s World 

Banking Cali, 

Colombia 

Friends of WWB, 

India 

Hattha Kakesekar, 

Cambodia 

Hublag Development 

Finance Programme, 

Philippines 

Inicjatywa Mikro, 

Poland 

Kafo Jiginew, 

Mali 

Kash Foundation, 

Pakistan 

Kenya Women Finance 

Trust, 

Kenya 

LOK Sarajevo, 


Mercy Corps, 


Microenterprise Bank, 

Bosnia 

Banco de la 

Microempresa, 

Peru 

DATA 

QUALITY 

GRADE 


12/99 AAA One of FINCA’s largest programs, FINCA Uganda has been in 

operation since 1992. The program offers village banking services 

to over 16,000 women in Kampala, Jinja and Lira. 

12/99 B Finsol (ex. FUNADEH) works with small and microenterprises in urban 

areas of Honduras. It is an affiliate of ACCION International and was 

founded in 1985. 

12/99 B FMM Popayán is a Women’s World Banking affiliate working in the 

state of Cauca in Colombia. It began lending to microenterprises in 

1985. 

12/98 B FOCCAS, an affiliate of Freedom from Hunger, operates a village 

banking-style program in Uganda’s district towns and villages. It is 

based on a credit with education model. 

12/99 A FONDECO is an NGO working primarily in rural areas in Bolivia. It 

was founded in 1995. 

09/99 A Fundusz Mikro began operations in 1995, and now lends to 

microentrepreneurs across Poland through an extensive branch 

network. It is a member of the MicroFinance Network. 

12/99 A FWWB Cali, an affiliate of Women’s World Banking, began lending in 

1982. It makes individual loans to urban microenterprises in Cali. 

03/00 AAA FWWB India lends to rural women through savings and credit 

groups. It was founded in 1982. 

06/00 AAA Hattha Kaksekar was founded in 1996. The non-profit Association 

offers commercial loans and agricultural credit to entrepreneurs in 

urban and rural areas in the North-Western and central parts of 

Cambodia. 

12/98 A The Hublag Development Finance Programme is the microlending 

arm of the Gerry Roxas Foundation. It lends to microenterprises with 

both individual and group lending methodologies, and began 

operations in 1987. 

12/99 A Inicjatywa Mikro lends to microenterprises mainly in urban areas of 

Poland. It is affiliated with Opportunity International. 

12/99 B Kafo Jiginew is a federation of credit unions operating in rural areas 

in the south-central region of Mali. It was founded in 1987. 

03/00 A KASHF is a NGO founded in 1996 to provide microcredit to low 

income women entrepreneurs in rural and urban areas. It is an 

affiliate of ASA, Bangladesh. 

03/00 B Started as an affiliate of Women’s World Banking in 1992, KWFT 

provides loans to women in six regions of Kenya. It has now grown 

into the largest MFI in Kenya. 

12/99 B LOK is a NGO founded in 1997 to provide individual credit to small 

entrepreneurs in urban and rural areas. It is financed by the Local 

Initiatives Department that aims to improve access to credit to the 

poor to promote economic reconstruction. 

12/00 B MC is an NGO that started its operation in 1997 and provides 

individual credit to microenterprises in war affected areas. Among 

others, it is also financed by the Local Initiatives Department in 

Bosnia that aims to improve access to credit to the poor to promote 

economic reconstruction. 

12/99 A The Microenterprise Bank was launched by IPC in 1997 to provide 

financial services such as loans, money transfers and deposit 

services to micro and small enterprises in Bosnia-Herzegovina. 

12/99 A Mibanco is a commercial microfinance bank offering microenterprise 

credit in Lima, and is affiliated with ACCION International. Formerly 

operated as an NGO under the name Acción Comunitaria del Perú, 

the institution was transformed into a bank in 1998. 


APPENDICES 


Microfund 

for Women 

Mikrofin 

Moyután 

MKRB 

Moznosti 

Nachala 

NLC 

Nirdhan 

NOA 

NRB 

Nyésigiso 

Oscus 

PADME 

PAMÉCAS 

Piyeli 

Portosol 

PRIDE TZ 

Microfund for Women, 

Jordan 

Mikrofin, 


Cooperativa Moyután, 

Guatemala 

Manya Krobo Rural Bank, 

Ghana 

Moznosti, 

Macedonia 

Nachala, 

Bulgaria 


Corporation Ltd., 

Pakistan 

Nirdhan Utthan, 

Nepal 

NOA, 

Croatia 

Nsoatreman Rural Bank, 

Ghana 

Réseau Nyésigiso, 

Mali 

Cooperativa Oscus Ltda., 

Ecuador 

Association pour la 

Promotion et l’Appui au 

Développement des 

MicroEntreprises, 

Benin 

Programme d’Appui aux 

Mutuelles d’Epargne et de 

Crédit au Sénégal, 

Senegal 

Association Piyeli, 

Mali 

Portosol, 

Brazil 

Promotion of Rural 

Initiatives and 

Development Enterprises, 

Tanzania 

DATA 

QUALITY 

GRADE 


12/99 A This former Save the Children village banking program in Jordan was 

founded in 1994. It focuses primarily on Palestinian women from 

squatter communities. 

12/99 B MIKROFIN is an affiliate of CARE international and started its 

operations in 1997. It provides individual and group loans to 

microentrepreneurs in semi-urban areas. It is financed by the Local 

Initiatives Department. 

09/00 A Moyután is a member of the FENACOAC credit union federation, and 

participated in WOCCU’s technical assistance program in 


12/99 AAA Started as a rural bank in 1978, MKRB provides group and individual 

loans, and deposit services to farmers, micro-entrepreneurs and civil 

servants. 

12/99 A Moznosti, an affiliate of Opportunity International, began lending in 

1996. It operates both in urban and rural areas of Macedonia, and 

lends to microenterprises and small businesses. 

12/99 B Nachala, an affiliate of Opportunity International, converted into a 

cooperative in 1998. It operates both in urban and rural areas and 

makes individual loans to microenterprises and small businesses for 

working capital. 

06/99 A Network Leasing is a private for profit financial company which offers 

financial services to microentrepreneurs. It uses leasing, a 

methodology considered compatible with Islamic law, which forbids 

borrowing on interest. 

06/99 AAA Nirdhan is an NGO founded in 1991. It is a Grameen replicate 

providing credit and deposit services to the poor. Both compulsory 

and voluntary deposits services are offered. The NGO has 

transformed into Nirdhan Utthan Bank Limited in July 1999. It is a 

member of the CASHPOR network. 

12/98 B NOA, an affiliate of Opportunity International, was started in 1997 to 

provide individual and group loans to self employed persons in 

agriculture and small businesses. 

12/99 A The rural bank was formed in 1984 to provide credit and deposit 

services in Brong Ahafo region in Ghana to farmers, microentrepreneurs 

and civil servants. 

12/99 A Established in 1990 as a credit union, Nyésigiso offers credit and 

savings services to both men and women in urban and rural areas of 

Mali. 

09/00 A Oscus is a credit union in Ecuador, and it participates in WOCCU’s 

technical assistance program. Oscus offers both credit and voluntary 

savings services to members. 

12/99 A PADME is an NGO working in urban and peri-urban areas of Benin. 

It offers loans to small and microenterprises, and was founded in 

1993. 

12/99 A Pamécas was established as a credit union in 1996. It offers a wide 

range of savings and credit services, primarily to women, using 

individual, solidarity and village banking products in urban and periurban 

Senegal. It is a member of the Development International 

Desjardins network. 

12/99 B Piyeli is an Association that was created in 1995. It offers solidarity 

group loans to microentrepreneurs in urban and rural areas around 

Bamako, as well as voluntary savings. 

12/99 AAA Portosol is an NGO operating in Porto Alegre in Brazil. It offers 

individual loans to microentrepreneurs and was founded in1996. 

12/99 A PRIDE offers microcredit in urban and semi-urban areas of 

Tanzania. It was founded in 1993. 


APPENDICES 


PRIDE UG 

Pride Vita 

Guinea 

PRODEM 

ProEmpresa 

ProMujer 

RSPI 

Sagrario 

Sartawi 

SAT 

SEDA 

SEEDS 

SEF 

SHARE 

SUNRISE 

Tonantel 

TSPI 

Tulcán 

UNRWA 

Promotion of Rural 

Initiatives and 

Development Enterprises, 

Uganda 

Pride Finance Guinea, 

Republic of Guinea 

Fundación para la 

Promoción y Desarrollo de 

la Microempresa, 

Bolivia 

EDYPME ProEmpresa, 

Peru 

ProMujer, 

Bolivia 

Rangtay Sa Pagrangay 

Inc., 

Philippines 

Cooperativa El Sagrario, 

Ltda., 

Ecuador 

Servicio Financiero Rural, 

Fundación Sartawi, 

Bolivia 

Sinapi Aba Trust, 

Ghana 

Small Enterprise 

Development Agency, 

Tanzania 

Sarvodaya Economic 

Enterprises Development 

Society, 

Sri Lanka 

Small Enterprise 

Foundation, 

South Africa 

Society for Helping 

Awakening Rural poor 

through Education, 

India 

SUNRISE Sarajevo 


Cooperativa Tonantel, 

Guatemala 

TSPI Development 

Corporation, 

Philippines 

Cooperativa Tulcán, Ltda., 

Ecuador 

United Nations Relief 

Works Agency, 

Gaza 

DATA 

QUALITY 

GRADE 


12/98 A PRIDE in Uganda was started in 1996. It provides microloans to 

borrowers organized as groups in urban and semi-urban areas of 

Uganda. 

12/98 AAA Pride Vita (or Pride Finance) works primarily in urban and semiurban 

areas of Guinea and was founded in 1991. 

12/99 B PRODEM began in 1986 as an NGO offering group loans to urban 

microenterprises, and was the precursor to BancoSol. When its 

urban portfolio was passed to BancoSol in 1992, it began to develop 

a new clientele in rural areas in Bolivia. 

12/99 A ProEmpresa, formerly the IDESI network, is now operating as a 

formal financial institution in Peru. 

12/99 A ProMujer Bolivia was founded in 1991, to provide training and credit 

to predominantly women clients. 

12/98 A RSPI, an Opportunity International partner, lends primarily to selfhelp 

groups in the Cordillera and Iloco regions of the Philippines. 

09/00 A El Sagrario is a credit union in Ecuador, and participates in 

WOCCU’s technical assistance program, begun in 1995. It offers 

both credit and voluntary savings services to members. 

12/98 A Fundación Sartawi offers group credit to producers and other 

microenterprises in rural areas of Bolivia. The credit program has 

operated in its current form since 1990. 

12/99 A The Sinapi Aba Trust is a member of Opportunity International, and 

offers individual and group loans both in rural and urban areas of 

Ghana. It was founded in 1995. 

09/00 AAA SEDA was started in 1996 as an affiliate of World Vision to provide 

financial services to women through village banking methodology in 

Tanzania. 

03/99 B SEEDS was established in 1987 to provide loans for employment 

creation and increasing standard of living, to mobilize deposits 

through compulsory and voluntary savings programs and to provide 

life and natural disaster insurances. 

06/00 A SEF is an NGO working in the Northern Province of South Africa. It 

works with a Grameen methodology to provide loans to rural women, 

and was founded in 1991. 

03/00 AAA SHARE lends to women in rural areas of Andhra Pradesh in India. It 

is a member of the CASHPOR network. 

12/99 B SUNRISE is a NGO founded in 1997 to provide individual credit to 

start-up and established micro enterprises. It is financed by the 

Local Initiatives Department that aims to improve access to credit to 

the poor to promote economic reconstruction. 

09/00 A Tonantel is a member of the FENACOAC credit union federation, 

and participated in WOCCU’s technical assistance program in 


06/99 A TSPI operates in urban and semi-urban areas of the Philippines, 

offering group loans to microenterprises. It was founded in 1981 and 

is affiliated to the Opportunity Network, the MicroFinance Network 

and CASHPOR, among others. 

09/00 A Tulcán is a credit union in Ecuador, and participates in WOCCU’s 

technical assistance program, begun in 1995. It offers both credit 

and voluntary savings services to members. 

12/99 B The Income Generation Program of UNRWA lends to 

microenterprises and small businesses in Gaza. It began operations 

in 1991. 


APPENDICES 


DATA 

QUALITY 

GRADE 


UWFT 

Vital-Finance 

Vivacred 

WAGES 

WR Honduras 

WVB 

Uganda Women’s Finance 

Trust, 

Uganda 

Vital-Finance, 

Benin 

Vivacred, 

Brazil 

Women and Associations 

for Gain both Economic 

and Social, 

Togo 

World Relief Honduras, 

Honduras 

World Vision, 

Bosnia 

12/99 A Uganda Women’s Finance Trust offers solidarity group and individual 

loans to women in Kampala and district towns of Uganda. It is an 

affiliate of Women’s World Banking. 

06/00 AAA From 1998-2000, Vital-Finance was an NGO, offering individual and 

solidarity group loans to small and microentrepreneurs in Benin’s 

rural areas. It is now functioning as an Association. 

12/99 A Vivacred is an NGO operating in Rio de Janeiro in Brazil. It offers 

individual loans to microentrepreneurs, and was founded in 1997. 

12/99 A WAGES serves women in Lomé and surrounding areas, working 

with borrowers’ associations in a village-banking type methodology. 

It was founded in 1994. 

09/99 B World Relief, Honduras was founded in 1981 as a NGO. It is part of 

COVELO network and network of NGOs FODIPREH. It offers a mix 

of individual, solidarity and village banking loan products to women in 

urban and semi-urban areas in Honduras. 

09/99 A Founded in 1996 as an affiliate of World Vision, the NGO provides 

individual and group loans to self-employed small and 

microentrepreneurs.

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