the microbanking bulletin - Microfinance Information Exchange

The MicroBanking Standards ProjectThe MicroBanking Bulletin is one of the principaloutputs of the MicroBanking Standards Project,which is funded by the Consultative Group to Assistthe Poorest (CGAP).Project PurposeBy collecting financial and portfolio data providedvoluntarily by leading microfinance institutions(MFIs), organizing the data by peer groups, andreporting this information, this project is buildinginfrastructure that is critical to the development ofthe industry. The primary purpose of this databaseis to help MFI managers and board membersunderstand their performance in comparison withother MFIs. Secondary objectives includeestablishing industry performance standards,enhancing the transparency of financial reporting,and improving the performance of microfinanceinstitutions.Project ServicesTo achieve these objectives, the MicroBankingStandards Project provides three services: 1)customized financial performance reports; 2) theMicroBanking Bulletin; and 3) network services.MFIs participate in this project on a quid pro quobasis. They provide us with information about theirfinancial and portfolio performance, as well asdetails regarding accounting practices, subsidies,and the structure of their liabilities. ParticipatingMFIs submit substantiating documentation, such asaudited financial statements, annual reports,program appraisals, and other materials that help usunderstand their operations. With this information,we apply adjustments for inflation, subsidies andloan loss provisioning to create comparable results.We do not independently verify the information.Neither the MicroBanking Standards Project norCGAP can accept responsibility for the validity of theinformation presented or consequences resultingfrom its use by third parties.In return, we prepare a confidential financialperformance report for each participating institution.These reports, which are the primary output of thisproject, explain the adjustments we made to thedata, and compare the institution’s performance toits peer group as well as to the whole sample ofproject participants. These reports are essentialtools for MFI managers and board members tobenchmark their institution’s performance.The third core service is to work with national andregional associations of microfinance institutions toenhance their ability to collect and manageperformance indicators. This service is provided in avariety of different ways, including teaching thesenetworks to collect, adjust and report data at thelocal level, collecting data on behalf of a network,and providing customized data analysis to comparemember institutions to external peer groups. Thisservice to networks allows us to help a wider rangeof MFIs to improve their financial reporting.New ParticipantsOrganizations that wish to participate in theMicroBanking Standards Project, either to receivecustomized reports or network services, shouldcontact: mbb@microbanking-mbb.org, Tel (202)659-9802/4, Fax (202) 659-9816. Currently, the onlycriterion for participation is the ability to fulfill fairlyonerous reporting requirements. We reserve theright to establish minimum performance criteria forparticipation in the Bulletin.Bulletin SubmissionsThe Bulletin welcomes submissions of articles andcommentaries, particularly regarding analytical workon the financial performance of microfinanceinstitutions. Submissions may include reviews orsummaries of more extensive work elsewhere.Articles should not exceed 2,500 words. We alsoencourage readers to submit responses to thecontent of this and previous issues of the Bulletin.

THE MICROBANKING BULLETINFOCUS ON PRODUCTIVITYISSUE NO. 6APRIL 2001DEDICATED TO THE FINANCIAL PERFORMANCE OF ORGANIZATIONS THAT PROVIDE BANKINGSERVICES FOR THE POOREDITORIAL STAFFCraig F. ChurchillGeetha NagarajanIsabelle BarrèsEditorAssociate EditorsEDITORIAL BOARDChair:Robert Peck ChristenClaudio Gonzalez-VegaElisabeth RhyneRichard RosenbergJ.D. Von PischkeConsultative Group to Assist the Poorest, The World BankThe Ohio State UniversityACCION InternationalConsultative Group to Assist the Poorest, The World BankFrontier Finance InternationalThe MicroBanking Bulletin is funded by the Consultative Group to Assist the Poorest (CGAP)

CONTENTSFrom the Editor ............................................................................................................................................................ 1FEATURE ARTICLESDesigning Financial Incentives to Increase Loan Officer Productivity: Handle With Care! ...................................... 5Martin HoltmannWe Aren’t Selling Vacuum Cleaners: PRODEM’s Experiences with Staff Incentives............................................ 11Eduardo BazoberryDropouts and Graduates: Lessons from Bangladesh .............................................................................................. 14Graham A.N. WrightExodus: Why Customers Leave................................................................................................................................ 17Kim WilsonCultivating Client Loyalty: Exit Interviews from Africa and Asia............................................................................... 20Inez MurrayTALKING ABOUT PERFORMANCE RATIOSMeasuring Client Retention ....................................................................................................................................... 25Rich RosenbergCOMMENTARY AND REVIEWSBook Review: Brand and Gerschick’s Maximizing Efficiency, by Robin Young ..................................................... 27Book Review: Campion’s Improving Internal Control, by Luis Schunk ................................................................... 29BULLETIN CASE STUDYBosnian MFIs: Performance and Productivity .......................................................................................................... 31Isabelle BarrèsBULLETIN HIGHLIGHTS AND TABLESBulletin Highlights: Productivity Drivers and Trends ................................................................................................ 35Geetha NagarajanAn Introduction to the Peer Groups and Tables ....................................................................................................... 40Index of Ratios and Tables........................................................................................................................................ 42Peer Group Tables..................................................................................................................................................... 44Additional Analysis Tables......................................................................................................................................... 53APPENDICESAppendix I: Notes to Statistical Section .................................................................................................................... 73Appendix II: Description of Participating MFIs.......................................................................................................... 77

From the EditorAn Introduction to ProductivityOne of the challenges of managing a microfinanceinstitution is trying to do more with less. To servemore people with less subsidies. To achievegreater impact with smaller loans. And, for staff tomanage more clients with fewer arrears. Micro-Banking Bulletin Issue No. 6 focuses on productivityby tackling this last example.Direct and Indirect InfluencesProductivity describes how a change in an organization’sinputs, such as labor, affects its outputs (i.e.loans). In microfinance, productivity discussionstend to focus on how staff incentives affect thenumber of clients per loan officer. Besides incentives,other inputs such as the total remunerationpackage, staff training, and the use of technologycan positively affect productivity. The challenge isto estimate how large an effect these inputs mighthave so an MFI can determine what investments inproductivity enhancers are worthwhile.This direct effect between inputs and outputs is astraightforward way of conceptualizing productivity,but the discussion should not end there. Perhapsas important is the indirect effect caused by anMFI’s operating conditions. This indirect effect isharder to quantify, but it has a tremendous influence.Three sets of conditions are worth mentioningbecause of their powerful effect on productivity.The first condition is the institutional culture. Ifemployees enjoy their jobs, their productivity willimprove. Some organizations forget, however, thatmany factors contribute to employee satisfactionbesides one’s salary and benefit package. In fact,to achieve more with less, MFIs should concentrateon maximizing employees’ well being at a minimumcost. It costs almost nothing to give employeescompliments, to acknowledge their extra efforts,and to solicit their suggestions for improvements.An institutional culture that encourages these typesof interactions—between management and staff,horizontally between employees and, perhaps mostimportantly, between frontline workers and thecustomers—will experience a productivity boost.Second, the level of discipline within the organizationdramatically affects productivity. ProductiveMFIs are like well-oiled machines. Meetings startand end on time, and everyone who is supposed tobe in attendance is there. Requests for loanapprovals are accompanied by all the requiredpaperwork. Data are entered into the informationsystem accurately. Disbursements occur on time.Clients repay on time, and loan officers follow upimmediately with anyone who is late. Only MFIsthat aspire to this standard of zero defects canachieve high levels of productivity.The third condition is the product-client match.Microfinance institutions that do not provide clientswith appropriate products often experience dropoutrates that undermine productivity. The revolvingdoor of customer desertion means that many MFIsare running hard and not getting anywhere. While apoor product-client match is not the sole cause ofdesertion, it is the desertion driver that MFIs canmost easily influence.Efforts to reduce desertion by becoming clientfocused,however, place MFIs in the center of thegreat productivity dilemma: they want to provideflexible services that suit the demands of themarket, yet in doing so they move away from thecookie-cutter approach that positively contributed toproductivity in the first place. To maximize productivity,the microfinance industry faces theoxymoronic challenge of creating “flexibleautomation” or “standardized customization”. Somepundits think that the answer lies in the use oftechnology, such as smart cards, credit scoring andpalm pilots; others are skeptical about theeffectiveness of high-tech solutions in low-techenvironments.Measuring ProductivityThe primary productivity measure used by theBulletin is the number of borrowers per staff member.Productivity is particularly difficult to benchmarkbetween institutions. Even the Bulletin’s peergroup analysis is not sufficiently specialized toaccount for the causes of vast productivity differences,such as:• Lending Methodology: Group-lending has anatural advantage in generating higher productivitythan individual lending methodologies.• Menu of Services: The productivity ratio penalizesfinancial intermediaries because they havemany employees who are not involved in generatingloans.• Credit Plus: MFIs that use credit delivery to provideother services, such as Credit with Education,are likely to have lower productivity thanminimalist MFIs, all other things being equal.• Loan Term: The length of the term is an importantfactor because it is easier to manage largevolumes of clients when their loans are notrenewed frequently, assuming that portfolioquality is not adversely affected.MICROBANKING BULLETIN, APRIL 2001 1

• Loan Size: The size of the loan also affects productivitybecause MFIs need to be more carefulwhen issuing larger loans.• Client Market: MFIs serving sparsely populatedareas face a serious productivity challenge.• Labor Market: Better-educated employees tendto require less supervision, which raises productivity.If a tight labor market causes staffturnover, productivity could be undermined.• Growth Rate: An MFI in high growth mode willhave lower productivity because new loanofficers will have excess capacity and arenaturally less productive than veterans. Inaddition, productivity will suffer if the client baseconsists of many new clients, since they tend torequire more work than repeat borrowers.• Age: Continuing with the same logic, matureMFIs tend to have higher rates of productivitythan new organizations.Because of these (and other) factors, a comparisonof productivity between institutions needs to beconsidered very carefully. Perhaps the most usefulproductivity benchmark is how an organization comparesto itself over time.Contents of this IssueFeature ArticlesAlthough there are numerous strategies to improveproductivity, the feature articles in this Bulletin honein on two approaches: 1) giving financial incentivesto encourage staff to work harder and smarter (or atleast to reward those that do); and 2) understandingand reducing customer desertion.The first two articles provide insights into the effectsof staff incentives on productivity; the next threeshow how productivity is adversely affected byclient desertion. The unifying theme of these articlesis that clients and employees behave rationally.If the rewards are right, loan officers can domore with less. If loan products are well designed,customers will continue to patronize an MFI.In “Designing Financial Incentives”, MartinHoltzman provides a detailed example of anincentive scheme structure that the Germanconsulting firm IPC has employed successfully in avariety of different settings. With this flexible model,MFIs tailor the scheme to individual loan officersand adjust weightings to address different scenariosat different points in time.Eduardo Bazoberry counters by describingPRODEM’s experience with staff incentives. Hecontends that individual financial incentives breakdown the sense of teamwork and commitment to anMFI’s social mission. He recommends the use ofgroup-based incentives, such as profit sharing andemployee ownership, as well as non-financialincentives, to motivate staff.While the two authors have different perspectives,they agree on key aspects:• If not approached carefully, financial incentivescan do more damage than good.• The objective of an incentive scheme is to alignthe goals of the employees with those of theinstitution.• Financial incentives are one piece in a toolkit ofmotivational strategies that are needed toproduce optimal levels of staff productivity.The second set of articles outlines the costs (andbenefits) of customer desertion. Based on experiencein Bangladesh, Graham Wright examines theinappropriate match between the credit product andclients’ needs. He contends that MFIs in Bangladeshoveremphasize the importance of standardized,low-cost loan products when their clients wanta broader range of flexible services.The next two articles, by Kim Wilson and InezMurray, highlight the value of learning from lostcustomers. Through exit interviews, affiliates ofCatholic Relief Services (Bosnia-Herzegovina andGaza) and Women’s World Banking (Uganda andBangladesh), respectively, learned what was rightand wrong with their services. Results from this researchenabled the MFIs to modify their productsand delivery mechanisms to become more clientfocused.These three articles call for a change in the servicedelivery culture of microfinance institutions. ManyMFIs are supply-driven: they have credit productsthat they provide to clients. The authors argue thatMFIs should: 1) embrace a customer service ethic;2) provide financial products that are demanddriven;and 3) conduct market research so that theyknow what their customers really want.Performance RatiosThe first step toward reducing client desertion isbeing able to measure it, but there isn’t consensuson how to do that. Each of the articles on customerretention uses a different formula. In a new sectionof the Bulletin (“Talking about PerformanceRatios”), Rich Rosenberg reviews five retentionratios and endorses what he calls the Waterfield/CGAP ratio. Since this is not the ratio used by theBulletin, or by any of the other organizations thatdefined desertion in this Issue, it is apparent that abroader discussion is needed on defining andmeasuring customer retention.2 MICROBANKING BULLETIN, APRIL 2001

Commentary and ReviewsWe have also added book reviews to the Bulletin’srepertoire. Robin Young (DAI) reviews the latestmonograph from ACCION International, MaximizingEfficiency by Monica Brand and Julie Gerschick.Luis Schunk, from FEFAD in Albania, employs apractitioner’s perspective to review the Micro-Finance Network’s first Technical Note, ImprovingInternal Control by Anita Campion.Case StudyAs a change of pace, the case study sectionanalyzes eight different programs in onecountry—Bosnia-Herzegovina. In keeping with thisIssue’s theme, the case study by Isabelle Barrèsfocuses on the productivity and overall performanceof these MFIs operating in a challengingenvironment.Highlights and TablesThe Highlights Section by Geetha Nagarajan usesthe Bulletin’s database to analyze productivitydrivers and trends. The results reveal very interestingdifferences by region, age of institution, loansize, and lending methodology that suggest theneed to think about productivity contextually. Theanalysis also examines changes in productivity overtime, which shows that most MFIs are makingsteady improvements. Perhaps most importantly,the analysis reveals the limitations of productivity asa useful benchmarking indicator.In the ongoing effort to keep pace with a rapidlydeveloping field, the Bulletin has added three newproductivity ratios: loan officer productivity, staffallocation, and staff turnover. 1 Besides analyzingfinancial performance by peer group, the Bulletinalso includes Additional Analysis Tables that sliceup the data in different ways, including lendingmethodology, age, and institutional type.Bulletin ParticipantsThis Bulletin contains performance information from124 organizations that operate in 47 countries. Thisrepresents an increase of 10 MFIs from the lastissue—including 8 from Africa. The regional breakdownlooks like this:• 54 programs from 14 Latin American countries,(9 from Bolivia and 8 from Ecuador);• 26 African MFIs from 11 different countries (6programs in Uganda and 5 in Ghana);• 24 MFIs from 11 Asian countries (4 each fromthe Philippines and India);• 14 Eastern Europe programs from 6 countries(8 from Bosnia); and1For definitions of all ratios used in the Bulletin, see page 42.• 6 MFIs from 5 countries in the Middle East andNorth Africa (MENA).Participation in the Bulletin is a two-way street. Weprepare a customized report for each MFI, whichcompares the institution’s performance with its peergroup and with the average performance of allMFIs. This report is a valuable tool for boardmembers and managing directors to benchmarktheir performance to similar organizations. MFIshave also found these reports useful in discussionswith regulators and as supplementary documentationfor investors. For more information aboutsubmitting data to the Bulletin, contactmbb@microbanking-mbb.org.Peer GroupsFor the peer group comparison to be useful, thegroups have to be fairly homogeneous. With eachIssue, as more MFIs send in their data, we haveadded new peer groups and reorganized others.The three primary criteria we use to define peergroups are: 1) the size of the program, 2) itsaverage loan balance, and 3) its region.To improve homogeneity, we tweaked the peergroup criteria a bit. In Latin America, for example, anew group was created by applying country incomelevel as a criterion. We made this adjustmentbased on the observation that the labor andcustomer markets are considerably different inChile, Argentina and Brazil than in lower incomecountries, which creates unique challenges inoperating microfinance institutions.A peer group that appeared in past issues(Worldwide High-end) has re-emerged asWorldwide Small Business. It contains sixinstitutions with average loan balances that weretoo large to justify useful comparisons to MFIs intheir region. Despite the fact that they come fromall corners of the globe, the data are cohesive.In TransitionFinally, the Bulletin is in transition again.Calmeadow has been the home of the Bulletin fortwo plus years and the past three issues. Due toreorganization, however, Calmeadow is no longer ina position to play that role. Consequently, theBulletin is being set up as an independent projectthat will be associated with, but separate from,CGAP. This transition is expected to achieve thebest of both worlds: a close association with CGAPshould continue to increase the outreach of theBulletin; yet a clear separation to maintain andrespect the confidentiality of the database.Craig ChurchillMICROBANKING BULLETIN, APRIL 2001 3

FEATURE ARTICLESFEATURE ARTICLESDesigning Financial Incentives to Increase Loan Officer Productivity:Handle With Care!Martin HoltmannEfficiency in micro and small business lending isarguably the strongest single driver of financialperformance. This realization has increasinglybeen reflected in the microfinance literature. InFebruary 2000, the MicroBanking Bulletin dedicateda whole issue to the topic of efficiency. Moresignificantly, during the last decade, a number oflending institutions have made significantimprovements in their operating efficiency.One aspect of efficiency is the productivity of staffmembers. This article takes a closer look at thecontribution that one specific tool—loan officerincentive schemes—can make to improving productivity.It also makes some suggestions for thedesign and implementation of such schemes. As areflection of the author’s limited experience withgroup lending, all examples are from individual loanprograms. However, most of the findings of thisarticle should also be applicable to group lenders.Why Focus on Loan Officer Productivity?In microfinance institutions (MFIs), loan officers areresponsible for creating and safeguarding thequality of the assets (i.e. the size and arrears rate ofthe loan portfolio) as well as for generating theincome (i.e. interest payments from clients) for theinstitution. In addition, since they are the point ofcontact with clients, the work of the loan officershas an enormous impact on an institution’soutreach. In a nutshell, the loan officers are theagents that produce an MFI’s output.On the input side of the equation, loan officersaccount for a significant share of staff costs, whichin turn accounts for a significant portion (50 to 70percent) of administrative expenses. Clearly, a“production factor” that accounts for most of thecosts and generates almost all of the output andincome should be given incentives to become asproductive as possible! Financial incentives canenhance employee performance and productivity inmicrofinance just as in other industries.A second argument supporting the design andimplementation of loan officer incentives is thehighly decentralized structure of the decision-makingand credit delivery process. In a typical microcreditoperation, loan officers possess vastly moredetailed and accurate information about the localenvironment and the clients than do centralmanagement and the owners. In the presence ofsuch “information asymmetries” and high monitoringcosts, managers are well advised to align the goalsof the institution (which usually include a mix ofoutreach and profitability indicators) with those ofthe agents who actually make the vast majority ofoperational decisions. Again, well-designed incentivescan be useful in achieving this goal.Design and Typology of IncentiveSchemesAs the basis for designing an incentive scheme, theloan officer’s duties must first be clearly defined,and they should be derived from the goals of thelending institution. This yields a range of targetsthat the loan officer is supposed to meet. Typicalexamples 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 formof the lowest feasible portfolio-at-risk rate).Most incentive schemes consist of some or all ofthese variables. The individual components mustbe weighted to ensure that the goals pursued byloan officers match the institution’s goals as closelyas possible. It is impossible to achieve a perfectmatch between the goals of loan officers and thoseof the institution. Also, outreach, credit volume andportfolio quality cannot be maximized simultaneously.When putting together an incentive packagethat gives due consideration to all three factors,achieving the optimal mix of the three variablesinevitably involves certain trade-offs.2Since lending to existing customers involves less processingand analytical effort than lending to new customers, experiencedloan officers with a large pool of customers tend to focus on theirexisting clients. This type of behavior is clearly contrary to theMFI’s outreach objectives.MICROBANKING BULLETIN, APRIL 2001 5

FEATURE ARTICLESWhile loan officers make the most importantcontribution to reaching the output goals, numerousfactors, such as external economic shocks ordevaluation, are outside their control. Furthermore,loan officers’ basic living expenses must be coveredto ensure that they will be willing to take appropriaterisks in the course of their work. Given these twopoints, loan officers should not only receive a performance-relatedbonus, but should also receive afixed basic salary.Regarding the weighting between bonuses andsalary, the general rule is that a bonus of less than20 percent of total remuneration does not createsignificant stimulus to improve performance. Conversely,a bonus of more than 70 percent of theremuneration package will attract loan officers whoare active risk seekers. In practice, the share of theperformance-based bonus in overall compensationis best if it is between 30 and 50 percent.Financial incentive schemes vary in complexity.The simplest form is the piece rate system, in whichthe loan officer receives a set bonus per unit ofoutput. Multiplying the figures for the indicators inquestion (e.g. number of loans issued to new customers)by the respective set amount yields thetotal bonus. To discourage delinquency, a penaltycan be deducted based on arrears.Complex bonus systems allow management to settargets for loan officers in regard to specific variables.Such systems have the advantage of beingoriented to the performance values of the institutionas a whole, and therefore also allow fine-tuning.Impact of Financial Incentive Systems:Some Empirical EvidenceThere is ample empirical evidence that the introductionof loan officer incentive schemes can makepositive contributions to loan officer efficiency.Some of the most efficient Latin American MFIswith very high loan officer productivity use financialincentive systems, including WWB Cali, FinancieraCalpiá, CMAC, Banco ADEMI, and Caja Los Andes.The BRI Unit Desa, a high productivity lender inIndonesia, uses a profit bonus system (based onunit performance) to motivate staff. In addition,there is a semi-annual contest for cash prizes. 33Based on MicroFinance Training Program, Boulder, Colorado,course notes by Richard M. Hook. Pure profit-sharing schemesat the loan officer level generally work well in small groups wherepotential free riders can be sanctioned. For larger groups, thisself-regulating mechanism usually does not work andperformance should be measured individually.Loan officer incentive schemes introduced inseveral “downscaling” programs in Eastern Europetypically gave a boost to loan officer productivity.The performance of loan officers of the RussianState Savings Bank is significantly better inbranches that allowed the implementation of anincentive system than in branches that opted tomaintain a fixed salary. In the Siberian city ofKrasnoyarsk, for instance, introduction of incentivesled to positive productivity differentials of more than30 percent. In Kazakstan, incentives have beenintroduced in all the banks participating in theEuropean Bank for Reconstruction andDevelopment’s small business program, and theeffect on productivity has been quite strong.When a re-designed financial incentive system wasintroduced at FEFAD Bank in Albania in early 2000,loan officer productivity (measured in averagenumber of loans disbursed per loan officer permonth) increased by more than 100 percent withina period of five months, while the portfolio-at-riskratio remained at the same low level as before.Words of Caution…Design and implementation of loan officer bonussystems requires careful planning. One obviouschallenge is to ensure that the incentives areproperly aligned with the goals of the organization.Misalignments can be avoided by testing thesystem during the design phase, both throughspreadsheet calculations and a limited field test(e.g. in a branch office). 4The effectiveness of incentive systems depends onthe cultural environment in which a microfinanceinstitution operates. Timing is another critical issue.It is useful to phase in an incentive systemgradually. During their training period, loan officersneed to make mistakes in order to learn from them,thus they should not be penalized. Later on, as thewhole organization moves up the learning curve(i.e. average loan officer productivity increases) thebonus system can be adjusted. The introduction ofnew products also requires changes to the bonussystem. Implementation of a bonus system at theloan officer level usually generates the need forincentive systems at other layers of the organization(department heads, branch managers, etc.).The empirical observation that many systems haveproduced completely unwanted effects leads to theconclusion that it is better not to have an incentivesystem than to have one that is badly designed.4Spreadsheet calculations also help to calibrate the impact thatthe system will have on the cost of lending operations and toforecast the break-even point.6 MICROBANKING BULLETIN, APRIL 2001

FEATURE ARTICLESFigure 1: An Illustrative Incentive SystemVariablegnhobdac#la$laComments Value Factor Description Value(L) Target number of disbursedloans to new customers(L) Actual number of disbursedloans to new customers(L) Target number of disbursedloans to repeat customers(L) Actual number of disbursedloans to repeat customers(P) Target number ofoutstanding loans(P) Loan officer’s actual number ofoutstanding loans(P) Target outstanding portfolio(US$)(P) Loan officer’s actualoutstanding portfolio (US$)(A) Actual number of loans inarrears(A) Actual volume of loan officer’sloans in arrears (US$)10 w 1 Weight factor for Lnumber (new)0.7 < 16 w 2 Weight factor for L0.3 < 1number (rep.)10 w 3 Weight factor for P-0.3 < 1volume4 w 4 Weight factor for P-0.7 < 1number120 w 5 Weight factor for A #la 0.5 < 180 w 6 Weight factor for A $la 0.5 < 1600,000 w 7 Weight factor arrears 5450,000 w 8 Bonus level factor L ($) 1003 w 9 Bonus level factor P ($) 10025,000 w 10 Bonus level factor A ($) 200w 8 = The bonus level factor for LIn this sample calculation, the total monthly bonusis US$400, which is divided up as follows: w 8 = 100,w 9 = 100 and w 10 = 200. However, if for a certainperiod greater importance is attached to, say, newloan output rather than to low arrears, then w 8should represent a larger portion of the bonus thanw 9 or w 10 .In this example, the loan officer issued 6 loans tonew clients, while the target was 10, and issued 4loans to repeat clients, while the target was 10. Asa result, the bonus amount for L is US$54.L = [(n / g) * w 1 + (o / h) * w 2 ] * w 8L = [(6/10) * 0.7 + (4/10) * 0.3] * $100 = $54Portfolio Outstanding (P)P stands for portfolio and measures the extent towhich a loan officer has met targets for the volumeand number of loans outstanding.P = [(c / a) * w 3 + (d / b) * w 4 ] * w 9The individual variables are:c = Loan officer’s total outstanding portfolioa = Loan officer’s target for outstanding portfoliod = The actual number of outstanding loansb = The loan officer’s target for the number ofoutstanding loansw 3 , w 4 = The weights represent the importancegiven to the volume and number of loans outstanding,respectively. w 3 plus w 4 must equal1. If more importance is attributed to the numberof loans than their size, then w 4 should begiven a weight greater than 0.5w 9 = The bonus level factor for PThis formula assesses the extent to which the loanofficer has achieved the established targets forportfolio outstanding. In the sample calculation, theloan officer had an outstanding portfolio ofUS$450,000, or 75 percent of the US$600,000target. In this example, more significance wasgiven to the number of loans issued than theirvalue, since w 4 was set at 0.7. It was probably feltthat the loan officer needed to increase the numberof loans (for instance to increase outreach ordiversification), and hence greater weight wasattached to this target. The target was 120 loansoutstanding, whereas the loan officer achieved 80,or 67 percent of the target.In our example the bonus for component P iscalculated as follows:P = [(c / a) * w 3 + (d / b) * w 4 ] * w 9P = [($450,000/$600,000)*0.3 + (80/120) * 0.7] *$100 = $69.17Arrears (A)A or arrears is determined by the following formula:A = (w 7 – [(#la / d) * w 5 + ($la / c) * w 6 ]* 100)(w 7 * w 10 )8 MICROBANKING BULLETIN, APRIL 2001

FEATURE ARTICLESThis formula calculates the degree (by volume andnumber) to which a loan officer’s portfolio is delinquent,and how much the bonus is reduced as aconsequence. The components of this formula are:w 7 = The weight factor for arrears. This can beany number greater than or equal to 1. Thelower the weight factor, the greater the negativeeffect on the loan officer’s potential bonus. Theuse of this weight factor and the fact that thearrears component is not deducted from thetotal bonus allows the institution to fine-tune theimpact of arrears targets by changing the factorsevery month. Thus, a loan officer may geta substantial bonus for bringing down thearrears rate.#la = The total number of loans in arrears in theloan officer’s portfoliod = Total number of outstanding loans in theloan officer’s portfolio (same variable used incomponent “P”)$la = The total balance of outstanding loans inarrearsc = Loan officer’s total outstanding portfolio(same variable used in component “P”)w 5 , w 6 = The weight given to the number andvolume of loans in arrears, respectively. w 5plus w 6 must equal 1. If more importance isattributed to the number of loans in arrears, w 5should be given a weight greater than 0.5.w 10 = The bonus level factor for the arrearsportion of the formula. In our example thearrears bonus level accounts for half of the totalbonus, i.e. US$200 out of US$400. The loanofficer had 3 arrears cases representing a totalof 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 10A = (5 – ((3 / 80 * 0.5) + ($25,000 / $450,000 * 0.5)* 100)) / 5 * 200 = $88.14Looking at this result and remembering the largeportion of the maximum total bonus (50 percent)that the loan officer could have earned on arrears, itappears that the loan officer failed to makesignificant progress on this objective during the pastmonth.TotalsNow that all three components of the formula havebeen calculated, combine them according to theformula: Bonus = L + P + A. Thus, the resultingbonus is:$54 + $69.17 + $88.14 = $211.31The formula outlined above is only one variant ofthe multitude of bonus systems employed by MFIs.Attentive readers will have realized that theexample and its target values do not originate froma high-productivity setting, such as urban LatinAmerica or Indonesia. Indeed, this fictitious case istypical for Southeastern Europe and the MiddleEast, which are characterized by low productivity,high loan officer salaries, and high average loansizes. But with a little imagination it is very easy toadapt the formula to other contexts.EvaluationThe example above has a couple of shortcomingsand some important strengths. Beginning with theadvantages, the model contains many of the basicvariables that make up the target functions ofmicrolending organizations. Secondly, the formulais flexible in that the parameters can be adjusted toreflect different environments as well as differentorganizational values (e.g. the outreach objectivecan be strengthened by assigning a higher weightfactor to the number of loans disbursed and outstandingas opposed to the volumes). Thirdly, thesystem is not overly complex and can be understoodboth by a keenly analytical loan officer andreader of this Bulletin. The computation ofindividual bonuses is simple enough and does notrequire more than a spreadsheet. Fourthly, thebonus formula is “linear” rather than “staged”.Staged bonus systems often produce unwantedincentives. 9One 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 complexityof tasks. It is possible to incorporate ahigher degree of complexity in the formula by addingmore variables, but this comes at the cost ofmaking the system less transparent. Loan officerswill find it harder to keep track of the trade-offs andgoal conflicts contained in the formula and to adjusttheir actions accordingly. 10The balancing of trade-offs is particularly relevant inthe treatment of delinquency. The age of arrearshas a strong bearing on the chances of recovery,which suggests that shorter-term arrears should be9Loan officers will typically adjust their performance in thesecircumstances so that they remain close to a particular “stage” orlimit, e.g. a cap on the permitted arrears level. Linear systems,in contrast, provide a reward or penalty for any change in theoutput or quality variables.10Remember that this concern was one of the reasons forintroducing an incentive system in the first place. An effectivebonus system will induce loan officers to act in the interests ofthe organization without additional supervision. If the systembecomes too complex, this cannot be achieved.MICROBANKING BULLETIN, APRIL 2001 9

FEATURE ARTICLESsubject to a higher penalty. However, it is alsocounterproductive to penalize short-term arrears(say, from 1 to 7 days) too heavily since it wouldmake loan officers overly risk-averse and have anegative impact on productivity. This train ofthought could be factored into the model byassigning different weights to different agecategories. Furthermore, if delinquency is a seriousconcern for an institution with a large portfolio, theformula could be improved by changing the purelyadditive calculation contained in the example aboveinto a multiplicative link between outstandingportfolio and arrears level, thereby making it muchmore sensitive to portfolio quality.At a more general level, some readers may takeexception to this article’s focus on financialincentives. Indeed, there are many other andpotentially more powerful factors that influence anindividual’s job performance. Opportunities to receivefurther training, opportunities for advancement,social status, the sense of a common“mission”, and last but not least, the feeling ofcontributing to local or national economic and socialdevelopment are important components of loanofficers’ job satisfaction and performance.Nevertheless, loan officers are normally “rational” inthe economic sense, and few would deny thatfinancial incentives do provide an important stimulus.It is important to inform potential loan officercandidates during the selection phase if aperformance-based incentive system is in place oris to be introduced. This will help to screen candidatesby ensuring that they are willing to align theircompensation expectations with the MFI’s goals,and to be compensated on the basis of theircontribution to those goals; it will also help to sortout risk-averse individuals. Despite the generallimitations of monetary incentive systems, theyhave proven their value in practice.Martin Holtmann is a Managing Director of IPC. He isbased in Moscow, working with several commercialbanks in the EBRD Russia Small Business Fund project.The author would like to thank Andreas Francke, BryanNielsen, and Ralf Niepel, all from IPC, for many usefulinputs and suggestions.10 MICROBANKING BULLETIN, APRIL 2001

FEATURE ARTICLESWe Aren’t Selling Vacuum Cleaners:PRODEM’s Experiences with Staff IncentivesEduardo BazoberryMany people in microfinance are familiar withPRODEM because it was the NGO that createdBancoSol, the first commercial bank solely dedicatedto microfinance. Since BancoSol’s creation in1992, PRODEM has quietly gone about doingsomething more challenging than establishing abank. First, we developed loan products for microentrepreneursand small-scale farmers in sparselypopulated rural Bolivia. Then we expanded ourorganization while continuously improving our loanproducts in response to our customers’ needs. Wesurvived a weak economy and a crisis in themicrofinance industry in 1999, and then we createdanother regulated financial institution known as anFFP, a private financial fund. PRODEM FFP,launched in January 2000, now offers a wide rangeof financial products including savings accounts,wire transfers, and leasing.Many MFIs look to financial incentives as a meansto boost staff productivity. At PRODEM, we prideourselves in having created an efficient, productiveand profitable organization with an approach towardstaff incentives that defies the conventional wisdomin the microfinance industry.The Evolution of Incentives at PRODEMA few months ago, I received an email from a friendtelling me that my institution was one of the onlymajor MFIs in the world that did not provideincentives to loan officers. Hearing this, I explainedthat PRODEM did have incentives and that, giventhe nature of our business, incentives werenecessary, but we implemented our scheme in away that did not affect the objectivity of the loanofficers when they made credit decisions. I proceededto explain about the circumstances that ledPRODEM to look for better ways to motivate theperformance and responsibilities of our employees.During 1993, after looking at the different incentivesthat MFIs were offering worldwide, we implementedan incentive system that rewarded loan officers foraccomplishing goals set in the incentive program.These goals included: the targeted number ofclients, the maximum percentage of loans inarrears, and the average portfolio per loan officer.In addition, since PRODEM had different types ofbranches, we had defined the goals in relation tothe potential market and the location of the offices:in rural areas, at the country’s borders, in majorcities, or in secondary cities.Rosy Preliminary ResultsThe incentive program worked as we had hoped.The loan portfolio grew rapidly, the portfolio at riskwas under control, the number of clients increasedsteadily, and profitability improved (see Figure 1 atthe end of the article). All of our indicators in 1994-95 suggested that we made a wise decision inimplementing the incentive program.Things Start to Get SourBy 1996, we sensed something disruptive occurring.We began to notice a high rate of turnoveramong our loan officers, including an increase inthe number of staff fired because of corruption orfor constantly breaking the methodology and rulesof the institution. Obviously, we had not managedto gain the loyalty of these loan officers. Instead,we had staff members who were mechanicallyperforming their functions without a realresponsibility towards the institution or our clients.At the same time, some staff members begandemanding larger incentives amounts. They wereunder the false impression that PRODEM’s goodperformance was due solely to their efforts, withoutrealizing that everyone was part of one system ofintegrated departments, and that other aspects ofthe organization were also important for PRODEM’sperformance.The original scheme awarded a monthly bonus toindividuals who met certain performance standards.We learned, however, that this type of incentive hada negative effect on team performance andencouraged a short-term outlook.As a result, in 1996, PRODEM changed theincentive to an annual bonus awarded for branchperformance. All members of a branch received abonus if their branch met certain performancetargets. The largest bonus was worth an additionalmonth’s salary.An annual payment encouraged a long-termperspective. It corrected the “delinquency lag,”caused by new loans that go into arrears severalmonths after they were issued. An annual paymentalso adjusted for the profound seasonal fluctuationsthat are common in Bolivian microfinance and itallowed PRODEM to complete our audit beforeissuing bonuses.MICROBANKING BULLETIN, APRIL 2001 11

FEATURE ARTICLESThis modification was generally successful in motivatingstaff and creating teamwork within a branch,but it still had negative side effects. It discouragedstaff rotation and cooperation between branches. Ifemployees agreed to transfer to a branch withproblems, they reduced their chances of obtaining abonus. Because some markets were riskier thanothers, some staff concluded that the bonusinvolved an element of luck depending on whereone worked. This conclusion generated tensionbetween those who were perceived to have receiveda bonus because they worked in a goodenvironment and those who failed to earn a bonuseven though they worked extremely hard. In suchcases, the incentive system discouraged ratherthan encouraged staff.Continuous ImprovementThis experience motivated management and partnersof PRODEM FFP to pursue a fair incentivesystem where clients, investors, and employeeswere all winners. We decided to eliminate thebranch bonus program and instead reward theperformance of the whole institution on an annualbasis. The collective approach reiterates that weare all in this together.We also designed a pension fund that allowsemployees to receive a certain number of PRODEMFFP shares as part of their annual benefit package.Employees will have access to a percentage ofthese funds if they leave PRODEM after three ormore years of employment. By offering employeesdirect ownership, we can optimize their long-termcommitment towards PRODEM’s growth anddevelopment, which fulfils the number one rule indesigning incentive schemes: aligning the risks andrewards for employees with those of the institution.Non-financial IncentivesThis entire discussion about financial incentives,however, detracts from the invaluable non-financialmethods that PRODEM uses to motivate staff toachieve high levels of performance. The mostimportant method is the institution’s mission. Wehire people who are committed to making adifference in rural Bolivia by working with lowincomefamilies and microenterprises. We use ourmission as a motivating tool. Managers regularlyremind their employees about PRODEM’s criticalcontribution to the economies of remote communities,and how integral each staff member’s performanceis to the institution’s accomplishments.PRODEM’s culture directly contributes to theperformance of all employees. Through the orientationof new staff members, regular trainingopportunities and other communication channels,PRODEM inculcates employees into a culture ofcommitment, trust and excellence that is morepowerful than financial incentives. Granted, aninstitution’s culture does not put food on thetable—that is why it is important to compensate allemployees fairly. But financial incentives cannoteffectively encourage employees to be innovative,to embrace change, to constantly seek ways ofdoing things better, and to not be afraid to learnfrom their mistakes. Only the institution’s culturecan accomplish these objectives, which contributevitally toward improvements in productivity andefficiency that must occur for an MFI to remaincompetitive and profitable.To strengthen our hand in a competitive market,PRODEM FFP has developed a complex andcreative matrix of incentives to help employees fulfilla variety of personal needs ranging from shelterand security to acceptance and self-fulfillment. Thematrix includes financial as well as non-financialincentives, such as staff development, job enrichmentand promotional opportunities, extensivehealth benefits, achievement awards, and theopportunity to take a sabbatical after ten years ofservice.Contrasting ApproachPRODEM’s holistic approach to motivating andrewarding staff stands in sharp contrast with someof the Bolivian consumer credit providers that haveadversely affected the local microfinance industry.Their primary growth strategy was to give monetaryincentives to loan officers through a variety ofmeans, including some that are deceitful.A Bolivian consumer credit company, for example,was paying (and declaring) the equivalent of US$50per month as average staff salary, while its loanofficers where earning closer to US$900 per month(including bonuses), or triple the industry average.This slight-of-hand using a dual accounting practiceenabled the institution to minimize contributions tothe pension fund by paying in 12.5 percent of theofficial salary (of US$50) rather than the actualamount earned by staff. This arrangement alsoreduced the costs of firing staff since the mandatorysix-month severance package would be based onthe official salary.After two years of bonanza, the partners of theinstitution were counting their losses, partly due tothe incentive structure and the culture that stemmedfrom it. The owners had put a few million dollars oftheir money on the line, yet their biggest accomplishmentwas successfully overindebting tens ofthousands of low-income families.Microfinance institutions that promote aggressiveincentive programs, such as this, are going down12 MICROBANKING BULLETIN, APRIL 2001

FEATURE ARTICLESthe wrong path. By relying solely on high returnincentives coupled with low base salaries tomotivate staff, MFIs create an opaque environmentthat encourages deception. These organizationsare likely to experience (and are experiencing) thefollowing unauthorized activities:• Frequent rescheduling of loans without muchcontrol• Loan officers forming ROSCAs to pay forclients’ arrears, which allows employees tomaintain or increase their incentive levelsdespite worsening portfolio quality• Creation of “ghost” loans to hide the fact thatgoals are not met• Deduction of an arbitrary amount from theclients’ loans during disbursement to create afund to cover bad loans• Pressure on loan officers to repay clients’arrears from their own salaries• Utilization of inactive savings accounts to payfor outstanding debtsIn sum, financial incentives need to be used verycarefully. Once you turn on the financial incentivefaucet, it is very difficult to turn off again, especiallywhen incentives represent more than half of a loanofficer’s take home pay.Incentives need to account for the social as well asthe commercial mission of microfinance institutions.Financial incentives must be seen within a biggerpicture of staff motivation that includes a host ofnon-financial rewards as well. Finally, incentivesneed to be consistent with the organization’scorporate culture. At PRODEM our motto is: “Forme to win, everyone must win.” Consequently,group-based incentives make more sense for us.Eduardo Bazoberry (prodebo@ceibo.entelnet.bo) is theManaging Director of PRODEM in Bolivia. Parts of thisarticle were drawn from a forthcoming Calmeadowpublication by Cheryl Frankiewicz entitled, “BuildingInstitutional 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 35,924Total Portfolio (million US$) 2.6 4.5 8.4 18.2 24.2 21.7Avg. Loan Balance (US$) 261 247 305 477 512 606Avg. Loan Balance/GNP per capita (%) na 30.9 36.7 49.2 50.7 60.0Total Staff 129 134 159 237 316 323Number of Loan Officers 47 81 103 125 164 145Loan Officers as a % of Total Staff 36 60 64 53 53 45Staff Turnover (%) 7 13 19 14 14 14Total Admin. Expenses / Avg. Loan Portfolio (%) 57.5 44.4 32.8 24.2 24.8 25.5Avg. Staff Salary (multiple of GNP per capita) na 10.2 9.7 8.4 9.4 9.6Productivity: Clients per Staff Member 77 137 173 161 149 111Productivity: Clients per Loan Officer 212 226 267 306 287 248Portfolio Yield (%) 61.5 43.4 55.8 39.1 41.4 37.2Real Yield (%) 49.6 30.1 37.3 32.0 31.3 34.2AROA (%) -7.1 -7.6 5.1 9.4 5.0 0.6AROE (%) -8.0 -9.0 9.6 14.8 9.5 1.4Operating Self-sufficiency (%) 101 102 178 167 132 105Financial 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)MICROBANKING BULLETIN, APRIL 2001 13

FEATURE ARTICLESDropouts and Graduates: Lessons from BangladeshGraham A.N. WrightIn microfinance, the value of retaining clients isparticularly clear. Typically, repeat customers havea credit history and want to borrow larger loans,whereas new customers require induction trainingand can often weaken the solidarity of groups.MFIs typically break even on a customer only afterthe fourth or fifth loan (Brand and Gerschick, 2000).And yet, many MFIs suffer chronic problems withclient desertion.Client desertion has profound implications for theviability of an MFI. High dropouts cost the organizationdearly. Groups from which members drop outare destabilized and must recruit new (less experienced)members, who qualify for smaller loans thusreducing the overall interest income for theinstitution. The new members have to take a disproportionaterisk and guarantee the larger sumstaken by their fellow group members, adding furtherstress to the group guarantee.Each dropout is a lost client who underwentlengthy, expensive training. The replacement membersmust either receive this training on an individualbasis, or join the system without the trainingthat many MFIs regard as critical. The formeroption of ad hoc training is extremely inefficient, andthe latter—if indeed initial training is soimportant—threatens to undermine the system. Inthe face of frequent or multiple dropouts, some ofthe groups may disintegrate entirely.Careful analysis of the reasons for these dropoutsalmost invariably points to inappropriately designedproducts that fail to meet the needs of thecustomers (see Wright, 2000 and Hulme, 1999).Much of this problem is driven by the attempts toreplicate models and products from foreign environmentswithout reference to the economic or socioculturalconditions into which they are beingimported. This has been exacerbated by the lack ofcompetition in the markets where the originalmodels were developed.Ironically, many of the clients are driven out not onlyby the inappropriate design of loan products butalso by the unwillingness of MFIs to recognize that(particularly in rural areas) there are seasons whensavings services, not loans, are required. Thusclients are forced either to borrow and try (againstthe odds) to service the loan, or to leave the MFI.All the while, their need for savings is unmet andignored.Dropouts In East AfricaIn East Africa, the rate of client dropout rangesbetween 25 and 60 percent per annum. 11 Clearly thisrepresents a substantial barrier to achieving selfsufficiency.When an organization loses over aquarter of the clients every year, it is “running hard tostand still”. In the words of Hulme (1999), “client exitis a significant problem for MFIs. It increases theircost structure, discourages other clients and reducesprospects for sustainability.”Finally, dropouts often leave because they cannot(or do not want to) manage loan repayments.These clients stop attending meetings and, freedfrom the group guarantee and from the incentive ofcontinued access to financial services, are likely toleave behind an unpaid loan.High dropout rates often indicate dissatisfactionwith the financial services offered by the institution.Members choosing to leave generally do so eitherbecause the organization is not providing goodenough services to warrant the (social andfinancial) costs involved, and/or because they haveidentified a better alternative.Members expelled from a microfinance program(for, of course, not all dropouts are voluntary) arelikely to be indicative of an even more complicatedbundle of factors, including: client selection (orbetter said “de-selection”) by fellow membersand/or staff, the clients’ inability to pay loans oreven savings, and the clients’ unwillingness torepay loans, which is in part a proxy indicator forcustomer dissatisfaction.Dropouts in BangladeshThe reasons for desertion are multidimensional.Indeed, the unifying theme of the studies on thesubject is that dropout causes are complex. Khanand Chowdhury (1995) present interesting findingson the high proportion of voluntary dropouts drivenaway by the inflexibility of BRAC’s system—inparticular its savings facilities (see Figure 1).11The desertion rate is the number of dropouts in the period /[(number of clients at beginning of the period + number of clientsat the end of the period) / 2]. If the period is not a year, then therate is annualized.14 MICROBANKING BULLETIN, APRIL 2001

FEATURE ARTICLESFigure 1: Reasons Frequently Cited forDropout and Expulsion by GenderPercentage of dropped outmembers mentionedReasons for voluntarydropoutMale Female TotalGroup fund is not refundedSavings not withdrawablein emergencyOther NGOs provide betterfacilitiesFailure to repay loanFamily problem63.255.336.833.611.870.459.252.738.545.068.057.349.836.629.3Reasons for expulsionFailure to repay loanIrregular attendance in44.817.256.141.559.627.3meetingSource: Khan and Chowdhury (1995)An examination of various studies on dropouts inBangladesh reveals a common theme among the75 percent of dropouts who leave voluntarily: dissatisfactionwith the available financial services,and a belief that other MFIs offer better services(including crucially, how the organization’s staffbehave toward their clients).One of the key desertion determinants, often lost inthe category “failure to repay loan”, is the insistenceby field staff that clients take loans. 12 Irrespectiveof official policy, there is a clear understandingamong most field staff that they should push outloans—often with little care for whether the clientsneed or can use them. In the words of one BRACZonal Manager, “If we do not disburse loans howcan we cover costs?” (personal field notes, 1996).Similarly, PromPT’s 1996 study of members’perceptions of Grameen, BRAC, Proshika, ASA andother Bangladeshi MFIs (using participatory ruralappraisal and focus groups) found that manyborrowers felt pressurized or “sweet-talked” intotaking loans. Matin (1998) also notes, “MFI lendingtechnology is insensitive to variations in householdconditions. Most MFIs put all households on atreadmill of continuously increasing loan size andinsist on a fixed repayment schedule.”Additional evidence for this pressure is seen in thepercentage of clients with outstanding loans at anyone time. BURO Tangail offers credit on an entirelyvoluntary basis, as and when the client wants it,and (subject to graduated ceilings) however muchthe client wants. As a result, at any one time onlyabout half of its clients have a loan outstanding. Bycontrast, almost all clients from Grameen, BRACand ASA have outstanding loans.12There are indications that these practices are now declining.In the extreme case, ASA’s loan policy dictateswhen borrowers must take a loan and how big theloan must be with no reference to the need of theclient. This policy has lead to a remarkable abilityof clients to manage their way around the systemthrough on-lending, reciprocal agreements andcumbersome storage arrangements (Rutherford,1995). But clearly, managing one’s way around aninflexible, credit-happy system is not ideal, and soclients will begin to look at the services offered byother MFIs.Clients are now shopping around in search offlexible, quality financial services. In the words ofKhan and Chowdhury (1995), “Other NGOs…workingside by side with BRAC in the same areasprovided extra facilities to [village organization]members. These included: less deductions fromthe loan, higher loan ceiling, low interest rate, quickdisbursement, etc.” The study revealed that manydropouts enrolled themselves with other MFIs forbetter terms and opportunities. The microfinanceinstitution that wants to reduce its level of debilitatingdropout should carefully examine its servicesand products, and seek to improve them continuously.Graduates in BangladeshOne reason for dropping out, notable by its almostcomplete absence from the above description, isclient graduation. A few years ago, there was abelief that credit programs would give such a boostto the income of “beneficiaries” that they wouldgraduate from poverty. The dynamics of povertyare such that the route out of poverty is neitherlinear 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’ businesseswould no longer need credit. In retrospect, this wassupreme naiveté, for there is scarcely a firm in theworld that does not use overdraft facilities tomanage its way through business cycles. And vastinternational financial markets have developedaround businesses’ need for capital for expansion.The other school, more plausibly, believed that poorclients could graduate with enough wealth and selfconfidenceto become the clients of formal sectorbanks. Indeed many MFIs establish self-helpgroups, credit unions or village banks and link themto formal sector financial service institutions. This isa more viable and desirable option for foreignNGOs and government projects that do not intendto establish a permanent banking institution.But for NGOs seeking to establish permanent MFIs,these richer potential graduates are their most valu-MICROBANKING BULLETIN, APRIL 2001 15

FEATURE ARTICLESable clients. These clients often take larger loans toexpand the working capital of their businesses or tofinance asset acquisition. The MFI will make mostof its profits on these larger loans since the cost ofadministering a loan is almost the same irrespectiveof its size. These long-term customers should alsobe better credit risks—although this is subject todebate. And crucially, these larger-loan clientsallow the MFI to finance its smaller loans to poorerclients. The last thing that an MFI, with its sightsset on financial sustainability, wants to see is thegraduation of these precious clients. Instead, MFIsshould retain them by seeking to meet their needsthrough a range of client-responsive financialservices.Conclusions for the Microfinance IndustryThere is compelling evidence, not just fromBangladesh, to support the contention that mostdropouts occur because MFIs do not meet theneeds of their market. Dropouts are expensive forMFIs, in terms of money already invested that islost when the member leaves, and the lost potentialbusiness from that member in the future. MFIsseeking to develop permanent sustainable organizationsshould improve their services to reduce clientdissatisfaction and thus desertion. Such a strategyis likely to prove cost-effective.For MFIs committed to creating permanent institutions,graduating the most experienced and affluentclients into the formal banking system is not adesirable strategy as it implies the loss of the mostvaluable and cost-effective clients. Indeed, MFIsshould tailor their services to ensure that they retainthese high value customers.For all these reasons, MFIs should pay (and indeedare paying) close attention to the nature and qualityof their financial services. The trade-off betweenthe quality of the services and cost of providingthem is clear, but finding the right balance isdifficult. To date, MFIs in Bangladesh have put toomuch emphasis on trying to implement standardized,inflexible, low-cost, credit-driven systemswhen their clients are asking (and willing to pay) fora broader range of quality services.The irony of this situation was that the genesis ofmicrofinance in Bangladesh was originally driven byan extensive program of careful market and operationsresearch designed to understand the needsof the clients. Professor Yunus’ work with his studentsat Chittagong University in the village ofJobra in 1976 was quintessential market research.It is to the fundamentals of market research andproduct development that MFIs must return if theyare to retain clients and build sustainableinstitutions.Graham Wright is Programme Director of MicroSave-Africa, Chair of CGAP’s Savings Mobilisation WorkingGroup and a Research Associate at the Institute ofDevelopment Policy and Management, University ofManchester, UK. He can be reached atGraham@MicroSave-Africa.com.ReferencesBrand, M. and J. Gerschick. (2000). Maximizing Efficiency inMicrofinance: The Path to Enhanced Outreach andSustainability. Washington: ACCION International.Hulme, D. (1999). “Client Exits (Drop outs) from East AfricanMicro-Finance Institutions.” Kampala: MicroSave-Africa.Khan, Md. K. A. and A.M.R. Chowdhury (1995). “Why VOMembers Drop Out.” Dhaka: BRAC.Matin, I. (1998). “Informal Credit Transactions of Micro-CreditBorrowers in Rural Bangladesh.” mimeo, Dhaka.Mustafa, S, et al. (1996). “Beacon of Hope: An ImpactAssessment Study of BRAC’s Rural DevelopmentProgramme.” 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 aProblem?” in MicroFinance Systems: Designing QualityFinancial Services for the Poor. Dhaka: University PressLimited and London: Zed Books.16 MICROBANKING BULLETIN, APRIL 2001

FEATURE ARTICLESExodus: Why Customers LeaveKim WilsonA customer is an asset. It is hard to disagree withthat statement. Yet the microfinance industry hasnot caught on to a customer’s value. While manymicrofinance institutions pay close attention tominimizing the costs of delinquency, they seem todisregard the expense of losing good customers.This article proposes that the microfinance industrylook at deserting customers with the same passionand precision as it examines delinquency. Tworeasons compel us to do so. First, lost customersplace our social agenda in peril. How can theempowering benefits of microfinance take place ifour customers flee after one loan cycle? And,worse, we may be losing our poorest customers,those whom we most want to serve. Second, lostcustomers cost money. They cost additional marketingdollars to attract and prepare new customersfor loans; and they cost us in lost profits.An Example of Using Dropouts to InformMarketing DecisionsThe first step in stemming the flow of dropouts is tounderstand who is dropping out and why. At Mikra,a new MFI in Bosnia-Herzegovina, the ExecutiveDirector, Sanin Campara, and his staff pay specialattention to data on deserting customers. They usedata to draw conclusions and make changes in theprogram. Campara believes that information suppliedby deserting customers is sometimes far morevaluable than data from existing customers.Deserting customers have nothing to fear inanswering questions. They have already made thedecision to leave, so information is likely to be valid,absent any bias a current customer might have.Current customers may fear losing services altogetherif they are honest in answering questions.Deserting customers can afford to be honest.Moreover, deserting customers offer a marketsavvyMFI more than information about themselves.They may offer clues as to why some customersmay not be interested in the MFI in the first place,as well as insights into the relative attraction of thecompetition (where applicable).To understand deserting customers better,Campara and his team interview every singledropout. To simplify the procedure, they check offreasons for dropping out on a one-page sheet thatdisplays the most commonly cited answers.They found that in one town, Kakenj, businessfailure caused 30 percent of the dropouts. Kakenjhad suffered many economic set backs—the closingof a coal mine, thermo-electric plant, and thestate-owned salmon processing facilities. Mikraimmediately put this information to use by tailoringservices to the local environment. In Kakenj, theloan officers started to help village banks becomemindful of potential business problems due to afailed economy.In another example, 7 percent of the dropoutsindicated that the loan terms were too short. Whileonly a small percentage, this figure became alarmingwhen staff discovered that the clients in thiscategory included some of Mikra’s best customerswho left as they approached their fourth loan. Mikraresponded by lengthening the term in the fourthcycle from six to eight months. Was this the rightmove? Mikra staff polled two village banks in thefourth cycle and learned that 13 percent would havedropped out if Mikra had not lengthened its loanterm. When asked how many would have droppedout if competition had offered longer terms, elevenof twenty-five members in one village bank respondedthat they would have defected.Mikra is now turning its attention to the relationshipbetween delinquency and desertion. Are delinquentcustomers dropping out when they would prefer tostay? Does an increase in tardy payers who areforced to leave the program signal the need for aproduct that better matches their cash flow? AtMikra, approximately 13 percent of the dropoutswere delinquent. Staff is fine-tuning their datagathering and database filters to determine who isboth delinquent and dropping out. Do dropout/delinquent customers have larger loans? Sincelarger loan customers are more profitable, changingthe loan product to meet their needs may be worththe effort (if it does not increase the organization’scredit risk). If new customers are deserting, thissuggests the need for adjustments to the marketingand screening processes.Mikra has one of the lowest desertion rates of allCRS-sponsored programs. Over the past 30months, with 2,500 customers, Mikra enjoys anannualized desertion rate of less than 11 percent. 13Is this because Mikra’s attention to deserters isexactly what prevents customers from deserting?At this early stage, we can only speculate.13This is calculated as follows: (number of dropouts last month /number of clients this month) x 12.MICROBANKING BULLETIN, APRIL 2001 17

FEATURE ARTICLESHow to Analyze DropoutsThere are many ways to collect and analyzeinformation on desertions. Each MFI must balancethe efficiency of collecting information with thequality or depth of the information. The downside ofconducting exit interviews with each lost customeris that it can add significant expenses to the lendingprocess. And if the interviewing process becomesroutine, as it often does, the value of the informationcollected is low. Ideally, someone other than thedeserter’s loan officer would interview the dropoutbecause there is a chance that the loan officer wasthe problem.Tim Nourse, CRS advisor in Gaza, believes that acost-benefit analysis of exit interviews reveals theimperative of interviewing every deserter for threereasons. First, if the organization does not intervieweach dropout, it may miss people who wouldhave 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 otherreason than to avoid the tiresome task of interviewing.And third, the effort to ask people theiropinions is often sufficient motivation to encouragethem to return.Once the MFI decides how to gather information, itmust decide what data to collect. Reasons fordesertion fall into three categories depending on thelevel of control that the MFI can exert on them:• Can Control: Product competitiveness, staffattitude toward customer, generaldissatisfaction with loan product (terms, rates,payment schedules);• Might Control: Family problems, businessproblems, and delinquency;• Cannot Control: Customer unwillingness torepay own loan, family members stealing orforbidding loan payments.Regarding the “Might Control” problems, MFIs thatsee the value in customer retention will createsystems or products that do not force a customer todropout because of a family crisis, or will developmechanisms for supporting good customers whoexperience temporary business problems.The exit interview form in Box 1, a blend of toolsused in Gaza and Bosnia-Herzegovina, offers anexample of how broad areas of classifying dropoutsare organized to collect data. Initially, an MFI mayhave many write-in responses while developing anefficient tool. As patterns appear, those write-insmay become pre-written items for staff to check off,making the exit interview faster and data entrysimpler.Box 1: Sample Exit Interview FormCustomer Name:Loan Officer:Village:Date:Profile of Lost CustomerAmount of most recent loan:Amount of savings:Length of time with MFI:Length of time in business:Business Type: Trading food Trading non-foodFishing/farming Manufacturing ServicesPoverty Level: 1 2 3 4 5Were payments on recent loans delinquent? Y / NWas customer asked to leave MFI? Y / NReasons for Dropping Out and Rejoining1. If customer is not going to competition, check oneand 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 staffMoving out of townProblems with businessFamily or personal problemsProblems making payments for groupOther___________________2. If customer is going to competition, the mostimportant reason is that competition has:Better staffBetter loan product (terms, payments, rate)Better savings productConvenience of locationLess restrictions or requirementsOther________________________3. I would rejoin if___________________________An Excel database can help analyze data.Management can filter for different information. Forexample, what percent of small-scale producersdrop out because of an inadequate loan product?Or what percent of customers who have been withthe MFI for more than three years leave because ofcompetition or because they had difficulty meetingrequirements? Do more start-ups drop out in tradeor in production? Are the poorest customers droppingout at a greater rate than the less poor?In Gaza, which enjoys a low dropout rate of 12percent, Nourse believes that the most importantpart of gathering desertion information is trainingstaff to collect data routinely, with care anddiligence. Since staff members are too willing toskip this activity in favor of something more enjoyable,the MFI should laud incentives or recognitionon the loan officers with best records of collecting18 MICROBANKING BULLETIN, APRIL 2001

FEATURE ARTICLESexit interview data and with the best suggestions forimproving the products and services. He advisesthat management must offer regular training for fieldstaff in both interviewing clients and in correctlycompleting an exit interview form.The effort is worth it. Through exit interviewing,Nourse learned that dropouts valued the businesssupport of a solidarity group more than the repaymentsupport. The organization has since used thisinsight to help market to new customers. Theunearthing of these details points to somethingessential. Management must involve itself in interviewinglost customers. Important information,whether statistical or anecdotal, is most usefulwhen management can interpret it in light ofbroader strategic issues. By setting an example,management also helps establish a culture andcommitment to collecting data from lost customers.Keeping Customers: Is It Worth It?The cost of a lost customer is not nominal. Adropout may cost an MFI as much as a defaultedloan because the organization loses a lifetime ofprofits from that customer. Good customers oftenleave MFIs because they had bad experiences,such as having to pay for the debts of a groupmember. But if the MFI had retained that goodcustomer for five or ten years, then that client mighthave generated significant returns to the MFI,directly in the form of profits, and indirectly throughreferrals.While MFIs can certainly learn a lot from lostcustomers, are they willing to go to great lengths tokeep their clients? Many organizations resist reengineeringsystems to suit customers. Expense,hassle and inertia stand in the way of improvingback-end processes to increase customer retention.The potential for lost profits may warrant trainingstaff in tracking dropouts, creating databases, andbuilding capacity to analyze information. These“losses” due to dropouts may warrant makingchanges to products, marketing strategies, andinformation systems.Mainstream businesses have found that whendesertions are cut in half, profits may increase by85 percent and beyond. 14 If this holds for MFIs, theright product, staff training and MIS designed toimprove satisfaction and retention will yield benefitsabove the investments made in reducing delinquenciesand defaults.How can MFIs determine if keeping customers isworth the expense and effort? It is necessary toconduct a cost-benefit analysis that includes thefinancial benefits of retaining customers. Forexample, in assessing whether an investment in aline of credit product would be worthwhile, an MFIhas to consider not only whether that product isprofitable, but also the effect that the product wouldhave on customer retention.Beyond retained profits, customer retention meansthat clients will remain with the MFI long enough toextract some social benefits from the program. Byremaining with the MFI, the clients should experienceincreased income because they have ongoingaccess to financial services. Their continued participationshould also help boost their confidence andencourage them to take leadership positions in thehome and in the community.Where Do We Go From Here?The microfinance industry needs to make someradical changes in how it treats its customers. Theyare why the industry exists. Below are some ideasof how to improve the industry’s awareness ofdesertion and ways to improve retention.• All MFIs should commit to tracking each andevery dropout;• Management should commit to participating inexit interviews, to analyzing data, and todiscussing appropriate responses with staff;• MFIs should reward the accurate tracking ofdropouts with the same kind of incentivesoffered to staff for good delinquencymanagement;• The microfinance industry, including TheMicroBanking Bulletin, should standardize thedefinitions of relevant ratios and then begintracking desertion rates and the net profit percustomer.By approaching this critical issue collectively, andtaking the steady stream of customers that revolvethrough our doors seriously, we will improve performanceand restore the social benefits inherent ingood microfinance institutions.Kim Wilson (kwilson@catholicrelief.org) is Senior Advisorfor Microfinance at Catholic Relief Services.14 Reichheld, Frederick F, and W. Earl Sasser, Jr. (1990, Sept.-Oct.). “Zero Defections: Quality Comes to Services”. HarvardBusiness Review, pp. 107-110.MICROBANKING BULLETIN, APRIL 2001 19

FEATURE ARTICLESCultivating Client Loyalty: Exit Interviews from Africa and AsiaInez MurrayLosing clients is expensive. If an MFI with 30,000clients loses 20 percent of its customers peryear—that is 6,000 people. If the average loan sizeis US$150, and in a lifetime a client might borrow10 loans, the MFI is losing up to US$9 million infuture sales. Add to this the fact that many MFIs donot break even until their fourth or fifth loan 15 andthat the client who left probably told nine otherpeople about their negative experience with theinstitution, 16 and it is clear why retaining clients is akey to profitability.As the microfinance industry becomes morecompetitive, clients have more choice regardingwhere to shop. If an MFI is to remain competitive, itmust get an accurate read of clients’ satisfactionand dissatisfaction with its loan products andservice delivery. It must understand why clientsleave, why some of them go to a competitor, andwhat kind of product range it should consideroffering to retain clients throughout their lifecycle.Customers leave an organization for many reasons,some of which the MFI can mitigate and some thatit cannot. For the purpose of analysis, the formerare considered internal factors, and the latterexternal. Internal factors include:• High prices• Rigid product design• Narrow range of products• High transaction costs• Insufficient attention to customer serviceExternal factors are exogenous to the institutionsuch as illness, death, family problems, seasonalityseasonal migration patterns, natural disasters,increasing competition, and economic shocks.In the past couple of years, Women’s WorldBanking (WWB), with the assistance of MonitorGroup, has conducted intensive research intocustomer satisfaction and retention as part of astrategic positioning service for affiliates operatingin increasingly competitive markets. 17This paper shares some key findings regardingclient desertion from two affiliates, Shakti Foundationfor Disadvantaged Women in Bangladesh andthe Uganda Women’s Finance Trust (UWFT).Understanding why clients left was one small part ofthe overall research. This article is a precursor to amore comprehensive publication on the findings ofthe strategic positioning work completed in Colombia,Bangladesh, Uganda, and Bosnia-Herzegovina.The focus on why clients drop out tends to put aspotlight on areas that an institution may need toimprove. Consequently, WWB thanks both organizationsfor their willingness to share the results ofthis research as a contribution to the field.This article is divided into three sections: 1)research methodology, 2) context (description oflending methodologies, price and service deliveryattributes that are necessary to interpret results),and 3) findings and implications.Research MethodologyIn both affiliates, two populations were surveyed:active and former borrowers (dropouts). Trainedcollege graduates, fluent in local languages,conducted the interviews. Respondents were selectedbased on stratified random sampling and asufficient number of interviews were conducted togive a margin of error of +/-4 percent. 18With the sample of dropouts, several sets ofquestions were used to identify desertion driversincluding asking clients directly using both openandclose-ended questions. 19 All respondents alsoranked their satisfaction level with each aspect ofthe product and service delivery mechanism. Ingeneral, current and former clients expressed highlevels of dissatisfaction with the same product andservice delivery attributes that caused clientdesertion.15Brand, M. and J. Gerschick. (2000). Maximizing Efficiencyin Microfinance: The Path to Enhanced Outreach andSustainability. Washington: ACCION International.16Bhote (1996). Beyond Customer Satisfaction to CustomerLoyalty: The Key To Greater Profitability. American ManagementAssociation, New York, p. 60.17The Strategic Positioning Product (SPP) is a technical servicethat helps an MFI define or re-define its market position (i.e.,methodology, product menu, price, customer segments, etc.) togive it long-term competitive advantage. It is based on workconducted jointly with Monitor Group, a global family ofprofessional service firms.18Sample 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 sampledispersion – branch and loan cycle.19With close-ended questions, respondents choose from a list ofoptions; in open-ended questions, the respondents reply with thefirst thought that crosses their mind.20 MICROBANKING BULLETIN, APRIL 2001

FEATURE ARTICLESContextTo interpret the results it is necessary to providesome background on the credit methodology, theproduct design, and the resulting transaction costs.The terms and conditions of the basic loan productin both affiliates are summarized in Figure 1.Group Structure: In Shakti Foundation, the Centerconsists of six groups of five low-income women fora maximum center size of 30 persons. Each groupelects a chair and each center elects a leader. InUWFT, groups may have already existed for otherpurposes (e.g. church-based groups). Groups havea minimum of five people and no maximum—somehave 60 or more members. When groups form,they elect a chair, treasurer and a secretary. Unlikein Bangladesh, there are no sub-groups within agroup.Group Formation: Each Shakti group undergoesan 8-week training, at the end of which they mustpass an oral test that determines if they understandthe rules of the institution. After passing the test,clients save for one week before applying for aloan. UWFT groups are also trained in group rolesand responsibilities and the terms and conditions ofborrowing, but no official test is administered. Onceeach member has saved 30 percent of the principalof the loan, the group can submit its application.Loan Disbursement: Shakti loans are issued toeach individual within a group on a 2-2-1 basis, i.e.,two group members get loans the first week; thesecond two members receive loans a week laterprovided the first two have paid their installments;then the last member receives her loan thefollowing week. Repayment is made at the centermeetings to the credit officer.In UWFT, the loan is issued to group leaders whocollect the money from the branch office on behalfof the group. The group is responsible for ensuringthat each member receives her approved amount.Clients repay their loans to group officials who thensubmit repayments to the branch. The credit officeris not present at all group meetings.Dropout RateThe dropout rate 20 at Shakti Foundation is reportedto be 9 percent in 1999 down from 14 percent in1998, compared to norms in Bangladesh of 10 to 15percent. In UWFT, the dropout rate is not tracked,but the industry norm of 25 percent or more 21 is avalid proxy. Seventy percent of dropouts inter-20The dropout rate formula is 1 – (number of borrowers (end ofperiod) – number of new borrowers (for period) / number ofborrowers (beginning of period)).21Wright 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 ofBasic Group Loan Product* 23Shakti Foundation,BangladeshUWFT, UgandaTarget AreaUrban onlyMainly urban andsemi-urbanCredit methodologyGroup Lending Grameen/ village(Grameen)banking hybridStarting loan size Tk. 4,000 ~ US$75 150,000 Shs ~ US$98Maximum loan sizeAvg. outstandingbalance per borrowerIncrementsTk. 10,000 ~US$189500,000 Shs~US$327US$71 (as of 12/99) US$111 (as of 7/99)Standardized bycycleBased on clients’repayment capacityand savingsGNP per capita 24 US$350 US$310Loan term (average): 50 weeks 16 weeksFees andcommissions/compulsory savingsGroup Fund: 5% ofprincipal 25Health Fund: Tk. 1per week 26MFI DevelopmentFund: 2% of principalCommission: 2% ofprincipalCompulsory savings:Tk. 10 per week 27Compulsory savings:30% of principalOther fees: 5,000 Shsfor stationaryNominal 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 assetGuarantors N/A Two guarantorsGroup guarantee Must guarantee Must guarantee peersOtherGroup meetings:peersN/ASignature from LocalCouncilorFrequency Weekly Weekly, bimonthly ormonthlyAttendance Compulsory Not compulsoryAverage time to forma group9 weeks (8 weeksgroup formation plus1 week saving)Voluntary savings No Yes12 weeks22This question of “resting” was not asked in Shakti because itwas understood that the phenomenon is not common inBangladesh. However, since so many Ugandans regardedthemselves as “resting” the assumption that all clients want orneed to borrow on a continuous basis appears to be false.23Terms and conditions approximate industry norms in therespective countries and all data reflect institutional policies atthe time the surveys were administered.24As of 1998. World Development Indicators 2000, World Bank.25This savings is deducted from the loan principal at the time ofloan disbursement and can only be withdrawn if client leaves theorganization after five years.26This 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.27This consists of Tk. 5 for personal savings accessible whenthe client leaves; and Tk. 5 for the Center Fund, accessible onlyif she remains with the organization for at least five years.MICROBANKING BULLETIN, APRIL 2001 21

FEATURE ARTICLESIndustry DynamicsIn a short article it is impossible to do justice to thecontextual differences between urban Bangladeshwhere Shakti operates and the semi-urban marketsserved by UWFT. What is important to mention isthat both institutions offer loan terms and servicedelivery mechanisms that are similar to mostplayers in their market and that therefore the resultsof this research are broadly applicable to otherMFIs.Findings and ImplicationsFigures 2 and 3 summarize the main reasons whycustomers left each program. This section analyzesthese findings and discusses their implications.External ReasonsThe external factors cited for dropping out highlightthe fact that MFIs are serving a precarious marketand many unexpected events can influence acustomer’s demand for financial services.Business problems: Low profitability and businessfailure were important reasons why clients left bothinstitutions. Clients cited access to business developmentservices (BDS), particularly marketing supportand skill development, as a huge unmet need.In both countries, clients must have a businesswhen they apply for a loan, but not necessarilybefore that time. This policy allows the MFI todeepen its outreach, but it could cause an increasein attrition due to business failure.Do not need a loan right now: Some clients saidthey were not interested in another loan at thattime, others that they were tired of borrowing or, inBangladesh, some clients had found employment.Moved location: Migration was given a muchhigher ranking in Bangladesh than in Uganda,reflecting the more transitory nature of the urbanpopulation in Bangladesh. Some clients migratetemporarily to the city due to river erosion orindebtedness in rural areas, while others go back torural areas on a seasonal basis to harvest crops.Illness: Illness, of either the client or a familymember, is a reason cited in both countries, reflectingthe poor conditions in which many clients liveand the limited availability of health care forborrowers and their families.Other external reasons include their husbandsleaving, the house or business was robbed, andfamine in one region of Uganda.In sum, external desertion drivers illustrate thevulnerable nature of microfinance clients. Thesevulnerabilities, which adversely affect both retentionand repayments, highlight how important it is forMFIs to address other needs of their clients besidescredit. MFIs can either develop in-house capacityor form strategic alliances to provide voluntarysavings, microinsurance and business developmentservices. Offering access to these valuable wraparoundproducts could be an important dimensionof any client retention strategy.Internal ReasonsThe internal reasons for leaving fall into threecategories: loan-related, transaction costs, andcustomer service. In Bangladesh the key internalcauses of desertion were group meetings (frequency,duration), group guarantee, and loanrelated issues (amount, interest rate). In Uganda,the key internal desertion drivers were loan relatedissues (term, interest rate, installment amount,compulsory savings), the desire for individual loans,Figure 2: Reasons for Leaving *Bangladesh3%9%Uganda14% 29%51%31%32%31%ExternalLoan RelatedTransaction CostsCustomer Service* In response to the open -ended question: “Why did you leave the institution? Please tell me the most important reason.”22 MICROBANKING BULLETIN, APRIL 2001

FEATURE ARTICLESthe group guarantee, and needing more supportfrom the institution to solve problems.Loan-RelatedIn Uganda, issues relating to the loan (price, term,amount, requirements) were stronger desertiondrivers than in Bangladesh. This finding reflects thefact that loans in Uganda are more expensive, forshorter terms, and require several additional guaranteesbesides the group and compulsory savings.Interest rate: In Uganda effective interest rates(taking compulsory savings into account) are four tofive times higher than in Bangladesh. A segment ofUFWT borrowers found that rate to be beyond atolerable threshold. While it is important toacknowledge the contextual differences (e.g. lowerpopulation density and higher salaries) that makethe cost of doing business substantially higher inUganda, the challenge is to continuously lowercosts and pass on those savings to clients.Loan term: Clients in Uganda mentioned that the16-week loan term was a reason for dropping out,whereas the 50-week term at Shakti was not anissue. Since in both countries clients are predominantlyengaged in trading, this finding does notnecessarily reflect a mismatch between businessactivity and loan term. Instead, it may be moreuseful to look at the combination of short terms withhigh interest rates that produce prohibitively largeinstallment sizes for some clients.Clients in both countries left because they could notrepay the loan, but this was a bigger factor inUganda. This difference relates to larger installments,as well as to the fact that a higher percentageof Ugandan clients used their loan for nonbusinesspurposes such as paying for school fees.Borrowing requirements: In Uganda, the strictborrowing requirements—including 30 percentcompulsory savings, two co-guarantors and apledged asset—was a major desertion driver. Inboth Bangladesh and Uganda, and indeed in manygroup-lending methodologies, some element ofcompulsory savings is required. If an MFI is tocontinue to demand compulsory savings then itshould only require a fair minimum, it should offer topay reasonable interest on it, and, within limits, itshould provide clients with flexibility in access (e.g.the ability to draw down on some portion of thatmoney to pay an installment if necessary).Loan amount: Accessing larger loans is among thetop five needs that clients express no matter wherethey live. Managing the tension between thedemand for larger loans and the MFI’s credit risk isa careful balancing act.Clients in both programs identified the low loanamount as a reason for leaving. Offering largerloans and thereby retaining growth-oriented clientsis a key to profitability, but it presents a challengefor group lenders. Many clients are now demandingindividual loans, therefore the development of thisproduct is a key retention strategy for grouplenders.Transaction CostsGroup meetings: The frequency and length ofgroup meetings was a desertion driver in ShaktiFoundation. Shakti meetings occur every week andattendance is compulsory, whereas at UWFT,clients can choose to meet weekly, biweekly ormonthly, and attendance is not enforced.Figure 3: Top Ten Reasons for Desertion*BangladeshUganda1. 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 asa 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 theinstitution (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 tellme which ones of the following reasons apply to you.” Ranking based on most frequently answered reasons.MICROBANKING BULLETIN, APRIL 2001 23

FEATURE ARTICLESThe group meeting is the nexus around which theGrameen model is based and indeed the majority ofclients in both Bangladesh and Uganda said theyliked attending meetings. In Bangladesh, clientsindicated that they liked the opportunity to socialize,whereas in Uganda the meetings presented anopportunity to share ideas and learn from eachother. However, some clients definitely tire of themeetings. For some borrowers, the opportunitycost becomes too high; for others, changes infamily situations make attendance difficult.Group guarantee: The ‘peer guarantee’ is anessential element of most group lending methodologies.It acts as a collateral substitute, whichenables group lenders to target client segmentsbelow that of individual lenders. However themajority of respondents in both countries do not likeit. Not only do they not like paying for others, butalso they do not like others paying for them.To reinforce the group guarantee, MFIs need toimprove client selection and group formation. It isimportant to select only clients who are economicallyactive, to be forthright about the risks associatedwith peer guarantees, and to emphasize peer‘support’ rather than ‘pressure’ when clients experiencedifficulty. Loan officers also need training tohelp groups work through solutions to repaymentproblems.Customer ServiceIn both countries, a small percentage of clients saidthat negative interactions with loan officers were thereason they dropped out. This appears to be due toa mixture of factors, including clients not understandingthe risks of borrowing and loan officersbeing under a lot of strain and being harsh withclients. Clients can and do generalize about anentire organization based on one bad interaction.Loan officers, and indeed all staff, must see clientsas customers who are paying for services. Theymust realize that they are managing long-termrelationships, not one-time transactions. Employeesshould be given the appropriate support fromthe institution, including skills and time, to deliver ahigh quality service.ConclusionSince this research was completed, both affiliateshave modified their loan product and transactioncosts. Shakti Foundation increased loan amountsand increments. UWFT reduced the compulsorysavings requirement, and lowered fees andcommissions. In addition, one half of the remainingfees and commissions were allocated to a loaninsurance fund. Clients were also given the optionof increasing loan terms and loan sizes subject totheir capacity to repay.The research shows that microfinance clients, likeall consumers, behave rationally. They will staywith a supplier as long as benefits outweigh costsand there are no better alternatives. When theyleave, they do so because of product features thatare unsuitable to their needs and/or service deliveryattributes that are difficult or costly. The researchhighlights some key challenges facing MFIs:• Managing the balance between flexibleproducts and services and the need tostandardize procedures to improve efficiency;• Building a range of products that focuses onretaining clients over time, such as introducingindividual lending, flexible compulsory savings,voluntary savings, micro-insurance and accessto BDS;• Shifting the mindset throughout the organizationto view the client as a customer and to realizethat the customer is important.The research also demonstrates that talking toformer clients can provide an MFI with a wealth ofinformation about why they became dissatisfied. Ifthe research is designed well, the MFI can identifythe product or service delivery attributes it shouldbuild on and which features should change to improveretention. The issue then becomes, whichclients does the MFI want to retain? This leads tothe next big challenge in microfinance—segmentingthe customers.To segment customers, an MFI should at minimumtrack information about the borrowing and savingbehavior of individual clients, even within grouplending methodologies. This data can then beanalyzed and fed into complementary marketresearch activities.Deepening the institution’s understanding of itsclient base is an iterative and continuous process.Every transaction with a client should be viewed asa learning opportunity. Finding inexpensive ways ofinstitutionalizing feedback mechanisms from theclient right up through to the board of directors iscritical. This can be achieved, for example, byholding monthly meetings with field staff and actingon their ideas for improvement, and ensuring topmanagement spends more time talking with currentand former borrowers. Finally, developing a morerefined understanding of client needs andpreferences not only makes good business sensenow, but it will also be a defining source ofcompetitive advantage in the future.24 MICROBANKING BULLETIN, APRIL 2001

FEATURE ARTICLESInez Murray is Organizational Strategy and EffectivenessCoordinator at WWB. She is responsible for developingthe Strategic Positioning Service. Comments on herarticle can be sent to imurray@swwb.org.MICROBANKING BULLETIN, APRIL 2001 25

TALKING ABOUT PERFORMANCE RATIOSTALKING ABOUT PERFORMANCE RATIOSMeasuring Client RetentionRichard RosenbergMost microfinance practitioners are coming torealize that client dropout has a surprisingly heavyeffect in depressing profitability. 28 As a result, thereis an increasing amount of discussion about how tomeasure client desertion (or retention).Defining TermsA retention rate (RR) answers the question: “Whenclients had a chance this period to take out a repeatloan, what percent actually took the loan?” Correspondingly,a desertion—or dropout—rate (DR)answers the question: “When clients had a chancethis period to take out a follow-on loan, whatpercent failed to take the loan?” Defined this way,these two rates are simple complements of eachother: Both rates are period specific.“Resting” ClientsAny RR or DR needs to take into account the factthat a client who has paid off a loan without taking arepeat loan may be leaving the program for good,or simply waiting a while before taking out anotherloan. No formula can tell us what the client willeventually do. However, a good formula shouldgive the program “credit” when an inactive clientbecomes an active borrower again. At times whenthe number of clients returning to the fold is verylarge, such a formula will produce negative desertionrates and, correspondingly, retention ratesabove 100 percent, especially when the measurementperiod is short. Over time, this phenomenonwill smooth out.The FormulasThe choice of an appropriate formula presentscomplex issues. As of yet there is no consensus.The discussion below represents my tentativeconclusions.Waterfield/CGAP Formula. The formula thatChuck Waterfield used in the CGAP MIS Handbookseems simpler and less problematic than otherformulas. 29 This formula focuses on the client’smain decision point—that is the point at which shehas repaid her prior loan and has the option to takeout a new one (a “follow-on” loan). At the pointwhere the client chooses to take the follow-on loan,she is counted as retained, whether or not therewas a resting period before the follow-on loan.(1) RR = FLLPWhere:FL = the number of follow-on loans madeduring the periodLP = the number of loans paid off during theperiod, andRR = retention rateNote that this formula produces the retention rateper loan cycle. To estimate how many clients theorganization is losing (or keeping) per year, it isnecessary to factor in average loan term. 30 Supposethat the RR calculated by this formula is 80percent. If we run 4 loan cycles per year, then onlyabout (0.80) 4 = 41 percent of the clients active atthe beginning of the year are still active at the endof the year.If the MIS doesn’t directly produce FL or LP, theformula can be restated in a way that is morecomplex but uses information that may be easier toproduce (see Formula 2).Mr. Waterfield modestly wrings his hands over thefact that his formula can occasionally produce aretention rate over 100 percent. It’s hard to seewhy this bothers him: as noted above, the only wayto keep a retention rate from ever exceeding 100percent is to ignore resting borrowers.28This article, originally prepared for the 1999 MicroFinanceNetwork conference in Bangladesh, draws liberally fromDevelopment Finance Network (DFN) postings by ChuckWaterfield and Mark Schreiner. So any mistakes here must betheir fault.29 The formula in the CGAP MIS handbook is a desertion rate. Itis presented here as a retention rate because it is simpler.30For more than you wanted to know about estimating averageloan term, see CGAP’s Occasional Paper No. 3, MeasuringMicrofinance Delinquency: How Ratios Can Be Harmful to YourHealth.26 MICROBANKING BULLETIN, APRIL 2001

TALKING ABOUT PERFORMANCE RATIOS(2) RR = (L – NC)(AC begin + L – AC end )Where:L = the number of loans disbursed during theperiodNC = the number of new (first time) clientsentering during the periodAC begin = the number of active clients at thebeginning of the periodAC end = the number of active clients at theend of the periodRR = retention rateThe above formulas do not include the effect ofdefault, because the denominator is loans paid off.Clients who never repay their last loan are(presumably) lost to the program forever, so someMFIs will want a desertion formula that takes theminto account. One less-than-perfect way to do thisis shown in Formula 3.(3) RR = FL(LP + WO)Where:FL = the number of follow-on loans madeduring the periodLP = the number of loans paid off during theperiodWO = the number of loans written off duringthe period (or otherwise classified asunlikely to be repaid)RR = retention rateACCION Formula. A desertion rate that has beenused by ACCION and others (including the authorof this note) is shown in Formula 4:(4) DR = (AC begin + NC - AC end )AC begin31Where:AC begin = the number of active clients at thebeginning of the periodNC = the number of new (first time) clientsentering during the periodAC end = the number of active clients at theend of the periodDR = desertion rateBecause it includes clients who have not had achance to be retained or to desert, it overstates thefrequency of clients eventually coming back foranother loan. (This is how we defined desertionabove. Other definitions are possible.) The longerthe initial loan term in relation to the reportingperiod, the more pronounced this effect would be.Schreiner Formula. Mark Schreiner, a dauntinglymeticulous analyst, uses a variant of Formula 4 thatsolves that formula’s problem of being unusable forstart-up operations.(5) RR = AC end(AC begin + NC)Where:AC begin = number of active clients at thebeginning of the periodNC = the number of new (first time) clientsentering during the periodAC end = the number of active clients at theend of the periodRR = retention rateMark likes this formula because he feels that it usesinformation more commonly available to an outsideanalyst (like him, for instance). He notes that hisformula, like the ACCION formula, does not takeinto account the fact that some of clients included inthe count have not yet had a chance to desert. Justas in Formula (4), this distortion becomes larger asthe average loan term increases in relation to thereporting period.ConclusionIt seems to me that the Waterfield/CGAP ratio ismore useful in most situations. I would be gratefulfor comments from anyone, especially people whohave on-the-ground experience with trying to usedesertion or retention rates.Send your comments on retention rates to RichRosenberg, a member of the Bulletin’s Editorial Board, atrrosenberg@worldbank.org.Formula 4 has important weaknesses. It does notwork for a startup program, where AC begin is zero.31The MicroBanking Bulletin uses this formula with a slightmodification; borrowers who are returning after a rest period ofmore than 2 years are considered new clients (NC).MICROBANKING BULLETIN, APRIL 2001 27

BULLETIN HIGHLIGHTS AND TABLESCOMMENTARY AND REVIEWSMaximizing Efficiency: The Path toEnhanced Outreach and SustainabilityMonica Brand and Julie GerschickACCION Monograph No. 12Hardcopy US$20.00/Electronic US$13.00To order: email publications@accion.org ordownload from www.accion.orgMore, better, faster. These three words havebecome the mantra of successful businesses duringrecent years. Driven by competition, technologyand demanding clients, successful firms constantlysearch for ways to provide better products andservices more quickly, and often at lower costs,while improving their bottom line. This messagefrom the business world is applied to the microfinanceindustry in ACCION International's recentmonograph Maximizing Efficiency by Monica Brandand Julie Gerschick.In this publication, the authors draw on businessliterature, empirical data and numerous examplesfrom microfinance institutions to build the case forefficiency as a bridge between the sometimescompeting camps of outreach and sustainability.The underlying theme is that a focus on efficiencywill help institutions reach more clients and attainhigher levels of profitability. Hence, this publicationshould be of interest to managers, shareholders,donors and consultants in the microfinance fieldwho want to lower interest rates, increase outreachand achieve a healthier bottom line.The book opens with a concise overview of microfinanceand examines conventional wisdom regardingthe correlation between efficiency, outreach andloan size. Brand and Gerschick cite empirical evidenceshowing significant efficiency gains achievedthrough growth appear in institutions with up to10,000 to 12,000 clients.The authors argue that additional efficiency gainsrequire more than growth; they require considerableorganizational change. This discussion indicatesthat outreach, beyond a certain level, does notsignificantly improve efficiency. A further examinationof the scale required to justify investments inefficiency enhancements—such as technology,functional specialization, and product and servicediversification—is warranted given the number ofdonors supporting the proliferation of small microfinanceinstitutions and the entrance of largercommercial financial institutions. The authors alsoargue that small loan size is not an excuse forinefficient or financially unsustainable organizationsby identifying organizations that have achievedsustainability while offering very small loans.Brand and Gerschick demonstrate that sustainabilityis not a sufficient measure of financial performance.Sustainability is often achieved throughhigh interest rates that mask excessive operatingcosts resulting from inefficiencies. They argue thatmany indicators that evaluate organizational efficiencyand profitability of microfinance institutions,such as administrative expenses over averageportfolio and return on assets, do not show thewhole picture. The authors bring to the forefront theimportance of evaluating the relationship betweencosts and revenues through the analysis of thebank efficiency ratio (total pre-tax expenses overnet income) and its reciprocal net profit contribution.They also delve into the complexities of allocatingcosts for accurate analysis at different levels, fromthe entire institution, down to specific client andproduct segments.This monograph presents a myriad of concreteexamples of managing organizational change toenhance efficiency. The analytical framework isalignment theory, which the authors describe as aholistic approach to identify multiple causes ofinefficiency by examining strategy, products andinternal systems. The authors cite a number ofareas where managers can enhance efficiency, andthey spice the text with examples drawn from microfinanceinstitutions worldwide, such as:• Modifying products to reduce collections problemsby customizing repayments to the borrowers’cash flow, offering revolving loans (or linesof credit) to decrease the cost of marketing, andissuing credit cards to enhance fee generation;• Increasing staff productivity through incentivesystems, transportation equipment, and establishingspecialized staff positions for routineadministrative functions;• Improving client retention through pricing incentivesand streamlined repeat loan approvals;• Pricing to maximize efficiency by differentiatinginterest rates and fees to price for risk andadministrative costs;• Establishing partnerships with businessassociations, financial institutions and others tolower the cost to attract clients;28 MICROBANKING BULLETIN, APRIL 2001

COMMENTARY AND REVIEWS• Standardizing tools and templates for loandocumentation, underwriting criteria and branchopenings;• Evaluating the appropriate level of delinquencyto adjust time spent on selection andmonitoring;• Utilizing technology to automate processessuch as posting loan payments, generatingcollections letters, and establishing call centersand credit scoring systems.Three microfinance institutions—ACCION NewYork, BancoSol in Bolivia, and MiBanco inPeru—are showcased as examples ofreengineering initiatives to help achieve higherlevels of efficiency and to maintain (or gain) acompetitive market position. Each case presentssome interesting examples of efficiencyenhancements, from computerizing paperwork toreducing financial analysis. A follow-up analysis,applying the financial ratios and alignmentframework from the first chapters of the monograph,to evaluate the efficiencies gained through thereengineering process would help to determine ifthe work achieved its goals.This monograph is ambitious and dense. However,the overall message is clear and important: microfinanceinstitutions must improve their efficiency ifthey want to stay in the game and fulfill theirmission. Profit-driven institutions in competitiveenvironments most likely have incorporated a drivefor efficiency into their culture and operations, butthis publication will provide ideas for continuedimprovements. Donor-dependent organizationsand those operating in less competitive markets arelikely to fall behind the efficiency curve and shouldglean from this monograph insights into betterperformance measurements to come. A moreconsistent and extensive application of the efficiencyphilosophy presented by Brand and Gerschickwould greatly help all microfinance institutionsand clients.Review prepared by Robin Young of DevelopmentAlternatives, Inc. who presently serves as Deputy Chief ofParty for the Rural Microfinance Strengthening project inEl Salvador (robin_young@dai.com).MICROBANKING BULLETIN, APRIL 2001 29

COMMENTARY AND REVIEWSImproving Internal Control: A PracticalGuide for Microfinance InstitutionsAnita CampionMicroFinance Network (with GTZ) Technical GuideNo. 1Hardcopy US$15.00/Electronic: 25% discountTo order: email mfn@mfnetwork.org or downloadfrom www.mfnetwork.orgIn Improving Internal Control, Anita Campiondiscusses a topic that is becoming increasinglyimportant for microfinance institutions: efficientinternal control as an instrument for managingfinancial and operational risks—especially the riskof fraud. In her introduction Campion quite rightlypoints out that internal control cannot be seen as aseparate function within an institution, but that itneeds to be placed in the context of a more broadlydefined risk management framework.One of the book’s main messages, a point that isrepeatedly made explicitly and implicitly, is that wellconceived control policies and procedures, such asadherence to the “second signature” principle or theimplementation of effective incentive mechanismsfor staff and customers, contribute more to the costefficientreduction of institutional risks than theconstruction of a painstakingly detailed ex-postcontrol mechanisms. An ounce of prevention isworth a pound of cure.Nonetheless, fraud cannot be wholly prevented.Campion describes in detail the process of buildingan internal control system. Through numerouspractical examples, this document provides a usefulguide to designing an internal control system. Theemphasis is on verifying the efficiency of theinternal control system through ex-post controls(e.g. the internal audit process).The book is rounded out with a discussion of howbest to institutionalize internal control systems.Again, the emphasis is on ex-post controls, especiallyestablishing an internal audit department.Campion points out that the scale and scope of theoperations, as well as the regulatory environment,are major factors that determine the optimalstructure and scope of an internal control system.The main target audiences for this book arepractitioners working at (and the members of thesupervisory bodies of) small and medium-sizedmicrofinance institutions that (as yet) have arelatively narrow range of products, i.e. mainlyinstallment loans and possibly also savingsfacilities. For this limited readership, the book isundoubtedly a valuable aid as it draws attention tokey risk management issues, derives from them thepurposes and benefits of internal controls, andoffers practical suggestions for implementing andimproving efficient internal control systems. Inparticular, the book will be useful for less experiencedinternal auditors and for managers at smalland medium-sized microfinance institutions whodeal with internal audit issues.However, the book deals exclusively with internalcontrol systems at the operational branch level andtherefore leaves a number of questions unanswered.For example, it has little or nothing to sayabout managing and controlling operational andfinancial risks at the level of the institution as awhole—exchange rate risk, interest rate risk andliquidity risk, for example, which are often monitoredand managed centrally at the head office. 32 Nordoes the book explain how to control operationaland financial risks associated with a broader rangeof products that includes overdrafts, checks, bankguarantees and documentary payments. Admittedly,the book makes no claim to provide answersto these questions.From the point of view of a manager of a targetgroup-oriented commercial bank providing a widerange of products to a diversified customer base,one final observation to be made is that theprinciples to be observed when constructing internalcontrol systems for small microfinance institutionswith NGO status are basically identical to those inplace at large commercial banks. Institutions mustlearn to recognize (and accept) their specific risks,to quantify them, and to use this information as thebasis for developing cost-efficient internal controlsystems. The real potential of this book is to addressdecision-makers at microfinance institutionswho have, knowingly or unknowingly, paid insufficientattention to this set of issues. It seeks toopen their eyes to the necessity of building internalcontrol systems and offers suggestions on how toimplement them. One can only hope that the decision-makersare already sufficiently aware of theissues to be motivated to actually read this book.Luis Schunk (schunk@ipcgmbh.com) works for IPC andis the General Manager of FEFAD Bank in Albania.32These issues are addressed in a companion publication, “ARisk Management Framework for Microfinance Institutions," byJanney Carpenter and Lynn Pikholz with Anita Campion,published by GTZ (www.gtz.de), 2000.30 MICROBANKING BULLETIN, APRIL 2001

BULLETIN CASE STUDYBULLETIN CASE STUDYBosnian MFIs: Performance, and ProductivityIsabelle BarrèsBosnia-Herzegovina 33 is a former republic ofYugoslavia, which was a middle-income countrybefore its breakup and the war. It is now in processof both post-conflict reconstruction and economicrecovery, and in transition to a market economy.This has proven difficult, and high unemploymentand limited opportunities in the formal sector haveincreased the demand for microcredit 34 . Due tothese factors, many of the poor targeted by MFIsare “new poor” (i.e. people who lost their jobs andassets during the war), who were previouslyemployed in state-owned enterprises with a fairlystable income and a high level of social security.Given the increased demand for microcredit, thegovernment of Bosnia-Herzegovina has beensupporting the microfinance industry through anumber of measures: investing government funds inthe sector, including microfinance as a key part oftheir development strategy, and adopting legislationto legalize the provision of microcredit by NGOs 35 .Microfinance in the country is evolving quickly. Asof March 2000, there were 18 main MFIs in Bosnia-Herzegovina, down from 34 in 1999. This articlefocuses on eight MFIs that represent a range interms 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 yearsin 1999), averaging just over US$3.3 million inoutstanding portfolio with about 2,500 borrowers.They use a mix of individual and solidarity groupmethodologies, and are all serving broad or highendclients, with an average loan balance relative toGNP per capita of 163 percent (or US$1,731). Thelatter hides important variations between33Bosnia-Herzegovina (BiH) is comprised of: the Federation ofBiH and the Republika Srpska, governed by different sets ofbanking rules.34Demand for savings product is considered to be low due to theloss of confidence in banks resulting from the war.35A new law for microcredit organizations was passed in theFederation of BiH in July 2000 and in April 2001 in RepublikaSrpska. MFIs are now legally able to provide credit to theirclients.36Mercy Corps’s name changed upon registration as a localmicrocredit organization under the Federation law. Partner'sregistration became effective January 1, 2001.institutions. Indeed, Bospo offers loans that are onaverage 39 percent relative to GNP per capita whileMEB targets small business clients, with anaverage loan balance of 336 percent relative toGNP per capita.All are NGOs except MEB, a full service bank thatoffers a wide variety of products, including loans,savings and payment services. Due to a complexpolitical environment, only MEB, MercyCorps/Partner, and LOK have branch networks thatspan the whole country.Issues Facing Bosnian MFIsBusiness Environment: MFIs face a challengingbusiness environment.Bosnian MFIs and their clients, as all organizationsin the country, face the lack of a supportivebusiness environment. For example, since manyclients are not registered to avoid prohibitive taxrates on their small businesses, Bosnian MFIs needto track their dual reporting systems (one for thefinancial police 37 , one to track the real financialstrength of the businesses). This additional tasktaken on by the MFIs to accommodate clients increasesthe loan processing time.Competition: Although still low, competition is onthe rise, and MFIs are already trying to adjust to it.According to LID, a specialized, semi-governmentalmicrofinance funding and capacity-building body,competition for microfinance services is low, due toexcess demand and a culture gap betweencommercial banking and microfinance. Indeed, themicrofinance target market in Bosnia-Herzegovinaincludes a growing number of high-risk displacedpersons or returnees 38 , whom commercial banksare reluctant to serve because of the risks involvedand their unwillingness to expand into the poorestregions of the country to reach this dispersedpopulation.Nevertheless, as young MFIs mature and the formalbanking sector stabilizes, competition is expected to37A governmental body in charge of conducting financial auditsof institutions and private entities.38This refers to persons who were displaced during the war andpersons who were able to return to their homes when it ended.MICROBANKING BULLETIN, APRIL 2001 31

BULLETIN CASE STUDYincrease. Some MFIs are already beginning to feelcompetitive pressure. For example, MEB lost 3percent of its clients in 6 weeks due to aggressivecampaigns from two Austrian banks that reducedtheir minimum enterprise loan size from DM50,000to DM25,000 39 , while requiring no collateral, andoffering very competitive interest rates for bothsavings and loans. 40 Sunrise is also experiencingchallenges in matching the demand for bigger loans(over DM20,000) with reasonable terms (5 years).Competition between MFIs and commercial banksis likely to continue for high-end loans aboveDM20,000, and will have the most impact on MEB,which is serving a higher-end clients compared tothe NGO MFIs.Some programs, like Sunrise, fear that MFIs will becompeting on two fronts: with the commercial banksand with their peers, and are adopting a variety ofmeasures to address it:Forming Mergers: Institutions are merging to takeadvantage of their complementary resources andregional coverage. For example, after merging withthree other institutions, LOK now has the bestinfrastructure and branch network. Other examplesinclude Sunrise, or World Vision, that merged withan organization in Mostar.Increasing Product Flexibility: To meet clientneeds, MFIs are moving towards a demand-drivenapproach for the design of their services. Forexample, Mikrofin is increasing the flexibility of itsproducts by introducing bigger ranges per cycle, agrace period starting with the 2 nd cycle, lowerinterest rates, and no upper limit for loans in theirlast cycle. Mercy Corps/Partner, on the other hand,has implemented a program of focus groups toidentify their client’s needs and adapt their loanproducts accordingly.Targeting Untapped Markets: MFIs are expandingtheir outreach to less competitive segments of themarket. For instance, Mercy Corps/Partner targetslower-end clients and provides loan officers withincentives for the number of first time loans underDM2,500. In addition, sixty percent of their clientsare in rural areas. In the future, there may be moreopportunities for MFIs to operate in rural areas, assome industries are expected to fail due to thetransition from the old economic system, which willincrease the number of persons turning to selfemploymentin these regions.Streamlining Procedures: All the MFIs studiedwere trying to improve their procedures to be moreresponsive to client needs. For instance, Sunrise issimplifying its complex procedures and reducing thenumber of days to screen clients in order toincrease productivity; Mikrofin has reduced the timefor processing loan renewals to two days; MEB istraining loan officers to deliver multiple products totake advantage of cross-selling opportunities; andLOK is streamlining the disbursement and collectionof its loans. Benefit 41 , following the example of ABAin Egypt, now disburses loans only three times amonth and collects installments only four days amonth to organize the workload for the head officeand branch staff and improve productivity.Reducing the “Loan Gap”: This gap refers toclients who require loans from DM10,000 to 30,000not offered by MFIs or commercial banks. 42Because these clients are hardly serviced byanyone, they respond to this challenge by securingmultiple (lower) loans from different MFIs. This“loan gap” represents an opportunity for MFIsseeking to increase their market share. Forexample, Mercy Corps/Partner has introduced anew loan product in 2000 in response to clientdemand: loans from DM10,000 to 20,000 for repeatclients in good standing. While some MFIs, such asMEB 43 , view this segment as a potential targetmarket, other MFIs, such as Bospo, focus ondeepening their outreach downwards, not upwards.Staff: There is a high level of competition forqualified staff.Bosnia-Herzegovina is experiencing a shortage ofgood loan officers, in part due to the presence of alarge international community (bilateral andmultilateral agencies) focusing on post-war reliefefforts. It is attracting new university graduates withvery high salaries well above country standards.Arrears: Arrears are low, but should be carefullymonitored.Bosnian MFIs have very low arrears rates. This isdue to a combination of factors, including the MFIs’strong focus on delinquency management from thevery beginning, and the credit culture and powerfulsense of honor among their clients, who are “new”39As of December 2000, 1US$ = 2.29DM, IMF Statistics.40These banks do require guarantees but no collateral. Whilethe market interest rate for MEB’s clients was 2-3% per month,Volksbank set its interest rates at 1-5% for SME loans. Forsavings, MEB was offering 2–3% vs. 14% at the commercialbanks. Because of these interest rate policies, MEB lost 20-30clients (of a total of 240) during a period of 6 weeks.41 Benefit does not currently participate in the Bulletin, and is notincluded in the rest of the study.42Estimates for this gap vary from DM5,000 to 50,000. Even themost aggressive commercial banks are starting their loans at aminimum of DM20,000.43MEB currently has applications pending for loans that fallwithin this range.32 MICROBANKING BULLETIN, APRIL 2001

BULLETIN CASE STUDYpoor, due to the post-war conditions. Repayingloans is an important way for people to regain theireconomic standing, and microfinance services arehighly valued.Nevertheless, arrears should be carefully monitored,especially as the lack of regulation and increasingcompetition motivate clients to take concurrentloans from different MFIs. MFIs currentlycall each other to share client information becauseof the difficulty in tracking the whereabouts of someof their borrowers (i.e. displaced persons). Ascompetition increases, MFIs may become lesswilling to share client information. In addition, withthe closure of ZPP, the government’s paymentbureau, arrears are expected to increase; all customersof financial institutions (including MFIs) werepreviously required to maintain accounts at ZPPthat were used as collateral for loans.Performance of Selected Bosnian MFIsAs of 1999, the Bosnian programs were profitableonly because of subsidies. After adjusting forsubsidies, the financial self-sufficiency (FSS) ratioaveraged 92 percent for the eight MFIs in this study(see Figure 1).Figure 1: Overall PerformanceBosnianMFIsNewMFIsAllMFIsPortfolio Yield (%) 33 37 39Real Yield (%) 18 24 30Adjusted Return on Assets (%) -3.9 -9.8 -3.5Adjusted Return on Equity (%) -39.6 -21.5 -5.7Operational Self-sufficiency (%) 113 93 104Financial Self-sufficiency (%) 92 76 90Source: MicroBanking Bulletin database. Data are for Dec. 1999except for Mercy Corps/Partner (Dec. 2000).This implies that the MFIs were generating incometo cover only 92 percent of their total expenses.Nonetheless, their overall performance surpassedthat of all the MFIs in the Bulletin that fall withintheir age group (operating for less than 3 years).On average, they showed an adjusted return onassets of –3.9 percent (compared with –9.8 percentfor all the New MFIs in the Bulletin), even with loweryields. These results are explained by their highlevel of efficiency.EfficiencyAs shown in Figure 2, the Bosnian MFIs have betterefficiency ratios on average than other MFIs analyzedby the Bulletin. This is due mostly to a lowerratio of administrative expenses to average loanportfolio. The average loan balance as a percentageof GNP per capita (the depth ratio) in Bosnia-Herzegovina is more than twice that of all NewMFIs in the Bulletin, which compensates for ahigher wage structure and lower staff productivity(with an average of 75 clients per staff vs. 96 for allNew MFIs). These findings hold even afterexcluding MEB (the only MFI in the sample targetedat small businesses) from the Bosnian programs.Because MEB offers a wide range of financialproducts to its clients and targets a higher-endmarket, excluding it from the sample increases theaverage staff productivity and lowers the averagedepth ratio for Bosnian MFIs.Even so, the depth ratio is still twice that of all NewMFIs in the Bulletin, and three times more incomparison to all participants. The Bosnian MFIswould experience much lower efficiency ratios(relative to loan portfolio) were they not targeting ahigher-end market.Figure 2: Efficiency IndicatorsBosnian BosnianMFIs MFIs*NewMFIsAllMFIsTotal Admin Expense / LP (%) 25 26 42 31Salary Expense / LP (%) 15 16 24 17Average Salary (multiple ofGNP per capita) 9.3 9.6 7.0 5.8Staff Productivity 75 81 96 122Cost per Borrower ($) 333 240 184 137Depth Ratio (average loansize/GNP per capita) (%)163 138 70 45Source: MicroBanking Bulletin database. Data are for Dec. 1999except for Mercy Corps/Partner (Dec. 2000).* Excluding MEB.The high cost per borrower results from the fact thatBosnian MFIs have fewer borrowers on which tospread the costs (2,486 clients on averagecompared to 5,081 for the New MFIs in theBulletin).ProductivityFigure 3 summarizes the main productivity indicators.Although on average staff productivity ofBosnian MFIs is almost half of that of all MFIs, it isnearly equivalent when we compare them toprograms that serve similar target markets. Broadprograms (MFIs with average loan balancesbetween 20 and 150 of GNP per capita) showproductivity levels double that of High-endprograms, which is explained in part by the solidaritygroup loan methodology used by most programsserving the “Broad” target market.MICROBANKING BULLETIN, APRIL 2001 33

BULLETIN CASE STUDYFigure 3: Efficiency and Productivity Indicators for 8 Individual MFIsMethodologyClients/StaffClients/LoanOfficerLoanOfficer/Staff (%)StaffTurnoverLoanPortfolio/Staff ($)LP/ LoanOfficer ($)Number ofBorrowersTotal loanportfolio($)Portfolioat Risk(%)AMK Individual 36 76 47 26 62,827 132,636 680 1,193,720 0.0 162MEB Individual 30 96 31 na 110,348 351,108 2,118 7,724,382 na 336Sunrise Individual 70 121 58 0 112,173 193,754 1,331 2,131,293 0.0 258World Vision SG / Ind.** 63 106 60 13 138,761 231,268 1,902 4,162,820 1.2 202Bosnian (High - end) 50 100 49 13 106,027 227,192 1,508 3,803,054 0.4 239Bospo Solidarity 156 423 37 11 66,081 179,363 2,961 1,255,540 0.0 39LOK Ind. / SG 64 121 53 0 87,233 165,283 2,302 3,140,378 0.0 126Mercy Corps Individual 90 133 67 3 66,797 99,380 5,461 4,074,599 0.1 99Mikrofin SG. / Ind. 89 156 57 6 80,958 141,677 3,129 2,833,535 0.0 83Bosnian(Broad) 100 208 54 5 75,267 146,426 3,463 2,826,013 0.0 87All MFIs (High - end) 48 120 43 7 127,017 300,539 4,373 9,778,077 0.7 310All MFIs (Broad) 113 252 47 11 70,093 154,481 60,480 24,414,911 2.6 65All 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).DepthRatio*(%)Despite lower overall productivity for Bosnian MFIscompared to all MFIs in the Bulletin, they have ahigher average loan portfolio per staff, due to higheraverage loan sizes. Results also show that BosnianMFIs have excellent quality portfolio, with anaverage portfolio at risk over 90 days of only 0.2percent.The data in Figure 3 also highlight considerabledifferences 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, issecond on the list. The MFIs showing the loweststaff productivity offer only individual loans.Although the lending methodology clearly influencesstaff productivity, other factors are also atplay. For example, MFIs that offer a multiple rangeof products (i.e., MEB) have lower productivitylevels given the burden on field staff who manageproducts other than loans.Many programs use financial incentives to booststaff productivity. Given the evidence thatdifferences in staff productivity can arise fromfactors that are outside the loan officer’s control (i.e.loan methodology), MFIs should consider a mix ofcriteria on which to base incentives. No conclusiveresults can yet be drawn from the implementation ofincentive plans, but their effects are worthmonitoring. 44ConclusionThis article analyzed the performance of eightBosnian MFIs and the environment where theyoperate. They are playing a key role in post-warBosnia by providing credit to low incomeentrepreneurs.Analysis of the financial performance of theselected MFIs shows that they are, on average, stillnot financially sustainable, and their staff productivityis slightly lower than that of all MFIs in theBulletin targeting similar markets. Nonetheless,their overall performance surpassed that of all MFIsin the Bulletin that fall within their age group(operating for less than 3 years).Microfinance in Bosnia-Herzegovina benefits from astrong credit culture, cooperation between MFIs,and potential for growth. In the future, increasedcompetition is expected to stimulate efficiency. Toimprove outreach while answering the challenges ofincreased competition and unfavorable businessenvironment, the regulatory framework will need tobe updated.This case study was prepared by Isabelle Barrès, BulletinEditorial Staff, based on a visit conducted in November2000 to the selected organizations, and informationsubmitted to the Bulletin by the Bosnian MBBparticipants. The MicroBanking Bulletin thanks LID andSarah Forster for their contributions, and all theinstitutions mentioned in this article for their time andpermission to publish their financial results.44The incentive systems are either new (LOK) or were recentlyreviewed (MEB and Mercy Corps/Partner). No information wasavailable for AMK, Bospo, and World Vision.34 MICROBANKING BULLETIN, APRIL 2001

BULLETIN HIGHLIGHTS AND TABLESBULLETIN HIGHLIGHTS AND TABLESProductivity Drivers and TrendsGeetha NagarajanProductivity is the amount of quality servicesdelivered by microfinance staff to their clients. Itquantifies the employees’ efforts to deliver an MFI’soutput. By increasing productivity, an MFI canlower per unit costs, improve efficiency, and ultimatelyenhance self-sufficiency.This Highlights Section, based on the informationgathered during the past three years from over 100MFIs around the world, examines the importance ofproductivity on MFI performance.Measurement of ProductivityProductivity can be measured using severalindicators that capture the quantity and quality ofservice delivery by MFI staff. Since loans are theprimary product and source of income for mostMFIs, this analysis is limited to productivity associatedwith credit delivery. The Bulletin uses threemain indicators to measure the productivity of creditdelivery: staff productivity, loan officer productivity,and the staff allocation ratio.Of these three, staff productivity is the primaryindicator. An MFI’s entire staff is a relevant unit ofservice production, so the best measure of productivitycollectively accounts for the efforts of the frontand back offices. The staff productivity indicatoralso allows comparison between diverse MFIs thatallocate tasks differently among staff. Some MFIs,for example, require loan officers to perform multipletasks such as collecting repayments, followingup on delinquent loans, member training anddeposit mobilization; whereas other organizationsenlist administrative or specialized personnel tofulfill some of the credit delivery functions. The staffproductivity indicator is therefore more useful whencomparing between less similar MFIs.As a secondary indicator, the number of loans perloan officer reflects the productivity of field staff.Along the same lines, the staff allocation ratio (loanofficers to total staff) indicates the MFI’s allocationof resources between staff in the field and the headoffice. These latter indicators are less useful incomparing financial intermediaries with credit-onlyinstitutions. However, if loan officers are exclusivelyresponsible for loan activities, the two indicatorsreflect the MFI’s ability to streamline its creditoperations and allocate its resources to the coreincome-generating activity.Microfinance Productivity: A SnapshotBased on data provided by the 124 Bulletinparticipants, this section examines the current stateof microfinance productivity (see Figure 1).Figure 1: MFI Productivity by Peer Groups*StaffProductivityLoanOfficerProductivityStaffAllocationAll MFIs (n = 124) 122 257 0.49FSS MFIs (n = 64) 133 291 0.51Non-FSS MFIs (n=60) 132 300 0.48i. Africa 154 393 0.44Africa/MENA (n=5) 119 315 0.47Africa-Medium (n=10) 192 474 0.39Africa-Small (n=11) 129 285 0.52ii. Asia 177 306 0.54Asia Large (n=5) 238 420 0.42Asia Pacific (n=6) 80 134 0.66Productivity Indicators DefinitionsStaff productivity = Number of active loan clients /Total number of staff at year endLoan officer productivity = Number of active loanclients / Number of loan officers at year endStaff allocation = Number of loan officers at year end /Total number of staff year endAsia South (n=9) 235 452 0.56iii. Eastern Europe 63 110 0.54EE Broad (n=7) 79 123 0.59EE High (n=5) 64 113 0.56iv. Latin America 125 280 0.46LA CUs (n=11) 107 -- --LA Med LI (n=9) 194 417 0.49LA Small LI (n=7) 140 225 0.58LA Small UI (n=6) 81 197 0.49LA Large (n=9) 122 285 0.43LA Med Broad (n=11) 88 212 0.41v. OthersCA/MENA (n=6) 102 174 0.59WW Small Business (n=7) 37 130 0.32*Means are calculated by dropping the top and bottom percentiles for each groupexcept for all MFIs where the top and bottom deciles are excluded. Compositionof the peer groups can be found on page 41. FSS: Financially self-Sufficient; CA= Central Asia; EE: Eastern Europe; LA = Latin America; MENA = Middle Eastand North Africa; LI = Low income; UI = Upper income; WW = Worldwide.MICROBANKING BULLETIN, APRIL 2001 35

BULLET HIGHLIGHTS AND TABLES• MFI staff, on average, serviced 122 clientswhile loan officers managed an average of 257borrowers;• Productivity was similar between the MFIs thatare and are not financially self-sufficient. Thisindicates that while productivity may affect thecost structure and profitability, high productivitycannot guarantee self-sufficiency;• Among the Bulletin peer groups, staff and loanofficer productivity was the highest among theMFIs in the Asian Large and South Asian peergroups where the population density is high;• Small business lenders reported the lowestproductivity rates. Small business lending mayrequire a complex financial technology thatlimits caseloads for staff;• Productivity was low in upper-income countries(two peer groups in Eastern Europe and theLatin American upper income group);• As indicated by the staff allocation ratio, loanofficers represented half the employees inMFIs, indicating the need for skills other thanlending for the smooth operation of an MFI;• The proportion of loan officers to total staff washigher among Asian MFIs (0.54) compared toLatin American (0.46) and African MFIs (0.44);• Peer groups with smaller MFIs in Asia, Africaand Latin America reported higher staffallocation ratios than peer groups with largerorganizations.The Effect of Age and Methodology onProductivity: Rhetoric or Reality?There are common beliefs in the microfinanceindustry that: 1) productivity improves with time; and2) group methodologies have higher productivitythan individual lending. To test these beliefs, theBulletin data were analyzed and the results suggestthat the rhetoric may indeed be the reality, asshown in Figure 2.Staff and loan officers at MFIs employing the villagebank methodology served significantly more clientsand had a slightly higher staff allocation ratio thanMFIs using an individual lending methodology.Mature MFIs were considerably more productivethan new and young ones indicating that productivityimproves with time. Higher productivity atmature MFIs may have resulted from a lowerproportion of new staff members to total employeesand fewer partially occupied loan officers.Figure 2: Productivity by Methodology andAge of MFI*StaffProductivity(No.)Loan OfficerProductivity(No.)StaffAllocation(%)I. MethodologyIndividual loans (n=54) 102 229 47Solidarity Groups (n=45) 133 271 50Village Banks (n=25) 188 408 52II. Age CohortsMature: > 6 Years (n=65) 158 340 47Young: 3 to 6 Years (n=29) 126 295 48New: < 3 Years (n=30) 96 188 51*Data are calculated by dropping the top and bottom percentiles of eachgroup. n = number of MFIs before dropping observations.Information in Figure 3 shows different patternsbetween lending methodologies and the age of theinstitutions. Interestingly, on average, productivityof solidarity group programs does not improve withage. Older village bank and individual lending programs,however, demonstrate markedly higherproductivity than their younger counterparts andcorrespondingly higher financial self-sufficiency(FSS).Figure 3: Productivity, Efficiency andProfitability by Age and Methodology*Staff Admin Exp. /Productivity Avg. Loan(No.) Portfolio (%)FSS(%)New Solidarity Group (n=11) 137 55.1 76.1New Village Bank (n=4) 134 93.1 51.9New Individual (n=15) 68 24.4 82.9Young Solidarity (n=10) 140 45.4 79.7Young Village Bank (n=8) 157 47.7 92.4Young Individual (n=11) 94 22.1 100.3Mature Solidarity (n=24) 137 36.7 89.5Mature Village Bank (n=13) 286 44.1 97.8Mature 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 reduceper unit costs and improve self-sufficiency. Butwhat drives productivity? There are at least fourdirect drivers that MFIs can use as levers toincrease productivity: 1) client retention, 2) staffretention, 3) staff remuneration, and 4) stafftraining. 45 The effect of these drivers on45Loan terms can also be lengthened to indirectly boostproductivity. Since loans with shorter terms turn over morefrequently, they are more labor-intensive and can undermineproductivity. Assuming that longer terms do not adversely effect36 MICROBANKING BULLETIN, APRIL 2001

BULLETIN HIGHLIGHTS AND TABLESproductivity and financial performance is discussedbelow.Client RetentionClient retention can dramatically affect productivity.Productivity gains may result from reduced marketingand loan sourcing costs, and more efficient processingof repeat clients. This occurs most effectivelyif staff assimilate existing information into theirdecision making process.Data on client desertion are highly affected by theformula used to construct the ratio. The Bulletinmeasures client desertion as follows:ClientDesertion =Active clients beginning of year + Newclients for year* – Active clients end of yearActive clients beginning of year*New clients = Borrowers with first loan and returnees after 2 years.Of the 124 MFIs that participated in this Issue, 33MFIs provided information on client desertion.Figure 4: Client Desertion and Productivity,by Region and AgeRegionAge CohortsA weak inverse relationship emerges betweendesertion rates and both productivity and profitability.Higher levels of client desertion are associatedwith lower staff productivity and lower financialself-sufficiency (FSS).It is crucial to examine the reasons for desertion toclearly interpret the above observations. Furthermore,age and methodology may also play a role.Better understanding on the effect of client retentionon productivity and MFI performance may evolve asthe MFIs begin to track information on clientdesertion systematically.Staff RetentionHeavy staff turnover, especially of loan officers, canseriously hurt productivity. Learning occurred inassessing applicants, and valuable client relationshipsbuilt through repeat services in the same areaand to same clients, are important factors thatincrease productivity.The Bulletin measures staff turnover as the numberof staff members who left the MFI during the yearrelative to average number of staff.Staff Turnover =Number of staff who left the MFIAverage number of staffNo. MFIsReportingDesertion Rate(%)Staff Productivity(No.)Admin.Expenses / Avg.Loan Portfolio(%)All MFIsAfricaAsiaLatinAmericaEE andMENAMatureYoung33 5 8 13 7 17 9 7New48 43 29 51 66 37 59 62123 178 143 123 73 138 105 10945 94 34 45 26 42 44 53FSS (%) 94 82 106 94 89 101 91 81As shown in Figure 4, MFIs in the Middle-East andNorth Africa (MENA) and Eastern Europe (EE)experience higher levels of desertion than those inAsian and African countries. It is not clear why thisis the case, but region specific factors such asmigration and level of competition may haveinfluenced this finding. Desertion rates were highamong young and new MFIs, 59 and 62 percentrespectively, compared to 37 percent for matureones. Programs may experience higher desertionrates where large numbers of clients leave after thefirst and second loan cycles.portfolio quality (which is a big assumption), an MFI can increaseits productivity by lengthening its average loan term.The staff turnover rate ranged from 2 to 51 percentwith a mean around 10 percent. As depicted inFigure 5, although no clear pattern emergesbetween staff turnover and productivity, higherlevels of turnover tend to be weakly related to lowerstaff productivity.500450400350300250200150100500Figure 5: Staff Turnover and Productivity0.02 0.12 0.22 0.32 0.42 0.52St af f Tu rn ove rStaff RemunerationThe Bulletin collects aggregated data on staffremuneration including basic salary, bonuses andbenefits. Remuneration is then expressed in theaverage staff salary indicator, which comparesaverage staff remuneration to GNP per capita toaccount for country specific factors.MICROBANKING BULLETIN, APRIL 2001 37

BULLET HIGHLIGHTS AND TABLESIt is expected that high wages would positivelyaffect staff productivity—more inputs (salaries)should produce greater outputs (loans). But inmicrofinance, striking regional differences meanthat you do not necessarily get what you pay for.Asia has the lowest relative remuneration and thehighest productivity ratio, whereas the lowestproductivity comes from the region with the secondhighest wages (Eastern Europe). Althoughproductivity in Africa is relatively high, salaries aremuch higher than anywhere else (relative to GNPper capita) to the point that they were notadequately covered through interest income. Thispartly accounts for that region’s low self-sufficiencyratio (see Figure 6).Figure 6: Average Staff Salaries, Productivityand Self-Sufficiency *Avg. StaffSalary /GNP percapitaStaffProductivity(no.)FSS(%)All MFIs (n=124) 5.8 122 90Africa (n=26) 12.4 154 83Asia (n=24) 3.6 177 93Eastern Europe (n=14) 7.3 63 90Latin America (n=54) 4.6 125 97* Data are calculated by dropping the top and bottom percentiles for eachregion and the top and bottom deciles for All MFIs.Staff TrainingThe fourth productivity driver is staff trainingobtained through apprenticeships and formaltraining provided inside and outside of the MFI.One would presume that the more an MFI invests intraining (up to a point), the more productive its staffshould be. This result should come from the directinvestments in skills that enable staff to be moreproductive, as well as the expectation that traininginvestments should reduce the detrimental effect ofstaff turnover on productivity.About 60 MFIs reported the budget spent ontraining their staff. Regional differences, however,made it difficult to demonstrate a link betweentraining investments and productivity. On average,African MFIs spent over 250 percent more thanLatin American MFIs on training, and yet theirproductivity levels were almost the same.Furthermore, a clear relationship between productivityand staff training was difficult to establish dueto limited standardization in the reporting of thetraining budget. Underestimation is possible sincethe training budget may not include the cost of inhouseapprenticeships. Similarly, overestimation ispossible if data are contaminated with trainingbudget for clients.Productivity TrendsThis section provides information on the historicalpattern of productivity changes in MFIs and itseffect on institutional performance.Is the Industry Headed in the Right Direction?Microfinance institutions are improving their levelsof productivity and profitability over time, albeitslowly. Since 1998, the Bulletin has accumulatedfinancial information from 95 MFIs for at least twoconsecutive years.Of these 95 MFIs, 61 percent raised their financialself-sufficiency, 73 percent experienced an increasein staff productivity, and 61 percent showed adecline in their administrative costs relative to loanportfolio. Average change in productivity was about24 percent.1Figure 7: Effect of Positive Change in Productivity on Change in FSSChange in FSS0.80.60.4R 2 = 0.02680.200 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1Change in Staff Productivity38 MICROBANKING BULLETIN, APRIL 2001

BULLETIN HIGHLIGHTS AND TABLESDoes Methodology Matter?As shown in Figure 9, similar levels of productivitychange were observed among MFIs that followedindividual and solidarity group lending methodologies(27 percent), while village banks showed a15 percent increase in productivity. It was intriguingto note a sharper decline in administrative costsamong solidarity group lenders compared to individuallenders despite similar change in productivity.The phenomenon is explained in part by theloan size increase among group lenders (5.3percent), while the individual lenders experienced aslight decline in average loan balances (ALB).Improvement in productivity from one year to thenext was associated with an increase in profitability.Figure 7 demonstrates how the growth in productivityhad a positive effect on financial selfsufficiency.On average, a 15 percent increase inproductivity resulted in a 10 percent increase inFSS. Improvements in FSS appear to peak at a 30percent improvement in productivity and increase ata diminishing rate thereafter. This weak trend(R 2 =2.7 percent and the correlation coefficient is 28percent) suggests a limit to increased profitabilitydue to productivity improvements.Does Performance Differ by Regions?There were significant differences in changesbetween regions (Figure 8). Average change inproductivity was more prominent in Eastern Europeand Africa where the industry is still developing.Nonetheless, the relationship in all the regionsbetween productivity, efficiency, and financial selfsufficiencyfollowed a common pattern: changes inadministrative cost ratio were indirectly proportionalto changes in productivity while changes in FSSwere directly proportional to changes in productivity.Figure 8: Number of MFIs with PositiveChange in Performance(Average percent change in parentheses)RegionFSS 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 America20 (-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 averageloan portfolio.Effect of AgeWhile productivity improved with age, as one mightexpect, the rate of change tends to slow down.New MFIs showed a 57 percent improvement inproductivity compared to a 20 percent changeamong young MFIs and only a 14 percent improvementfor mature institutions (Figure 10).Figure 10: Percent Change in Performance byAge of InstitutionAgeCohortsStaffProductivity Efficiency*Avg. StaffSalary/ GNPper capita)Avg.LoanBalanceFSSNew 56.7 17.9 31.1 -2.7 57.4Young 20.0 12.5 -3.6 1.2 13.3Mature 14.1 0.5 10.3 4.4 5.2* Efficiency refers to decline in administrative expenses relative to averageloan portfolio.It is interesting to note that new MFIs experienced adecline in administrative costs (17.9 percent) despitean increase in average staff salaries by 31percent and slight decline in average loan balance.This may reflect the important role of productivity inreducing administrative costs even if costs aredirectly hit by an increase in salaries.ConclusionProductivity is interlinked with several other factorssuch as loan size and staff remuneration that affectthe cost structure of the MFI and its profitability.Productivity is also affected by regional factors, andby methodology, size and the age of the MFIs. Thecomplex interplay of productivity with other factorsand the dispersed nature of data at the Bulletinhave limited clear interpretation of several observationspresented here.The analyses, nevertheless, show that productivityis necessary but it is not sufficient to effect MFIfinancial performance. Consistent tracking ofpertinent information on productivity drivers isrequired for better understanding of productivity.Furthermore, it may be relevant for multi-serviceand multi-product MFIs to track information by tasksfor appropriate management decisions.MICROBANKING BULLETIN, APRIL 2001 39

BULLET HIGHLIGHTS AND TABLESAn Introduction to the Peer Groups and TablesSetting up the Peer GroupsThe MicroBanking Standards Project is designed tocreate performance benchmarks against whichmanagers and directors of microfinance institutionscan compare their own performance. Since themicrofinance industry consists of a range of institutionsand operating environments, some with verydifferent characteristics, an MFI needs to becompared to similar institutions for the referencepoints to be useful.The MicroBanking Bulletin addresses this issue withits peer group framework. Peer groups are sets ofprograms that have similar characteristics—similarenough that their managers find utility in comparingtheir results with those of other organizations intheir peer group. The Bulletin forms peer groupsbased on three main indicators shown in Figure 1:1) region; 2) scale of operations; and 3) targetmarket.In previous issues, the same criteria for scale andtarget market were applied to all regions. Sinceregions demonstrate different growth patterns, however,we have regionalized the scale criterion byraising the bar in some areas and lowering it inothers. So now, a program that would be classifiedas large in Africa, for example, would be consideredmedium in Latin America. We have also added anew category for target market: Small Business. Tofall into this category, the depth indicator (averageloan balance / GNP per capita) needs to exceed250 percent.Besides these three primary indicators, the Bulletinalso applied two secondary criteria in Latin Americato further homogenize the peer groups. First, all ofthe credit unions are grouped together. Sincethese organizations are savings-driven (unlike mostMFIs, which are credit-driven), they have a uniquecost structure that makes comparison with otherMFIs less useful.The other secondary criterion applied in LatinAmerica (for institutions that fall in the low-endcategory) is the country income level. Theoperating conditions in upper income (UI) countries,such as Argentina, Brazil and Chile, in terms oflabor markets, levels of productivity, and customercharacteristics, are quite distinct from the lowerincome (LI) countries in the region, and the highnumber of institutions offering low-end loansjustifies the breakdown into multiple peer groups.Peer Group Composition and Data QualityThe members of each peer group are listed inFigure 2 on the following page, and more detailedinformation about each institution can be found inAppendix II on page 77.Since the Bulletin relies primarily on self-reporteddata, we have graded the quality of that informationbased on the degree to which we have independentverification of its reliability. The data quality gradeis NOT a rating of the institution’s performance.Statistical IssuesIn the statistical tables that follow, the averages foreach peer group are calculated on the basis of thevalues between the 2 nd and 99 th percentiles, whichusually means that the top and bottom values foreach indicator are dropped. For the entire sampleof MFIs, the top and bottom deciles were excluded.For more details, see Appendix I on page 73.Figure 1: Primary Peer Group CriteriaAfricaAfrica/ MENA 2MENA/ Central AsiaRegion Scale of Operations 1Total loan portfolio (US$)Large: > 5 millionMedium: 900,000 to 5 millionSmall: < 900,000Target MarketAverage loan balance /GNP per capitaLow-end: < 20% OR Avg.Loan Balance ≤ US$150AsiaAsia (Pacific)Asia (South)Eastern EuropeLatin AmericaLarge: > 8 millionMedium: 1 to 8 millionSmall: < 1 millionLarge > 10 million,Medium: 1.5 to 10 millionSmall: < 1.5 millionBroad: 20% to 149%High-end: 150 to 249%Small Business: ≥ 250%1Criteria for classification of scale of operations varies by region. See corresponding group of regions.2MENA = Middle East/ North Africa.40 MICROBANKING BULLETIN, APRIL 2001

BULLETIN HIGHLIGHTS AND TABLESFigure 2: A Guide to the Peer GroupsPEER GROUP NDATA QUALITY GRADE†(No. of MFIs with eachgrade)AAA A BPARTICIPATING INSTITUTIONS *1. Africa MediumSize: MediumTarget: Low-end2. Africa SmallSize: SmallTarget: Low-end3. Africa/ MENASize: Large/MediumTarget: Broad4. Asia (Central) / MENASize: Medium/SmallTarget: Low-end5. Asia LargeSize: LargeTarget: Low-end/Broad6. Asia (Pacific)Size: Medium/SmallTarget: Low-end/Broad7. Asia (South)Size: Medium/SmallTarget: Low-end/Broad8. Eastern Europe HighSize: AllTarget: High-end9. Eastern Europe BroadSize: AllTarget: Broad10. LA LargeSize: LargeTarget: Broad/High-end11. LA Medium BroadSize: MediumTarget: Broad12. LA Low UISize: Medium/SmallTarget: Low-end13. LA Medium Low LISize: MediumTarget: Low-end14. LA Small Low LISize: SmallTarget: Low-end15. LA Credit UnionsSize: AllTarget: Broad16. Worldwide Small BusinessSize: Large/MediumTarget: Small Business10 1 7 2 Citi S&L, FINCA Uganda, KWFT, NRB, Pamécas, PrideTanzania, Pride Uganda, Pride Vita Guinea, SEF, WAGES11 3 3 5 ARB, Faulu, FINCA Malawi, FINCA Tanzania, FOCCAS,MKRB, Piyeli, SAT, SEDA, UWFT, Vital-Finance5 1 3 1 ABA, Kafo Jiginew, Nyésigiso, PADME, UNRWA6 1 4 1 Al Amana, Al Majmoua, Constanta, Faten, FINCA Kyrgyzstan,Microfund for Women5 1 4 0 ACLEDA, ASA, BAAC, BRAC, BRI6 1 5 0 CARD Bank, EMT, Hatta Kaksekar, Hublag, RSPI, TSPI9 5 3 1 AKRSP, Basix, BURO Tangail, CDS, FWWB India,KASHF, Nirdhan, SEEDS, SHARE5 0 5 0 AMK, LOK, Moznosti, Sunrise, WVB7 0 2 5 Bospo, Fundusz Mikro, Inicjatywa Mikro, MC, Mikrofin,Nachala, NOA9 2 7 0 Banco ADEMI, BancoSol, Caja de Los Andes, Calpiá, CMArequipa, FIE, Finamérica, Mibanco, PRODEM11 1 7 3 ACODEP, Actuar, ADOPEM, ADRI, BPE, Chispa, FAMA,Finsol, FONDECO, ProEmpresa, Sartawi6 2 4 0 Banco do Povo, CEAPE/ PE, Contigo, Emprender,Portosol, Vivacred9 1 4 4 CAM, CMM Medellín, Compartamos, Crecer, Enlace,FINCA Honduras, FMM Popayán, FWWB Cali, ProMujer7 0 3 4 AGAPE, FED, FINCA Ecuador, FINCA Mexico, FINCANicaragua, FINCA Peru, WR Honduras11 0 11 0 15 de Abril, 23 de Julio, Acredicom, Chuimequená,COOSAJO, Ecosaba, Moyután, Oscus, Sagrario,Tonantel, Tulcán7 2 5 0 ACEP, Agrocapital, BDB, CERUDEB, FEFAD, MEB, NLCAll 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 lastthree yearsA The MBB questionnaire plus audited financial statements, annual reports and other independent evaluationsB The MBB questionnaire or audited financial statements without additional documentationAbbreviations: 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.MICROBANKING BULLETIN, APRIL 2001 41

BULLETIN HIGHLIGHTS AND TABLESIndex of Ratios and TablesINDICATORS AND RATIOS DEFINITIONSOUTREACH AND INSTITUTIONAL INDICATORSAGE 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 INDICATORSGNP 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 (%)PROFITABILITYADJUSTED RETURN ON ASSETS (AROA) Adjusted net operating incomeAverage total assetsADJUSTED RETURN ON EQUITY (AROE) Adjusted net operating incomeAverage total equityOPERATIONAL SELF-SUFFICIENCY (OSS) Operating incomeOperating expenseFINANCIAL SELF-SUFFICIENCY (FSS) Adjusted operating incomeAdjusted operating expensePROFIT MARGIN Adjusted net operating incomeAdjusted operating income(%)(%)(%)(%)(%)INCOME & EXPENSEOPERATING INCOME RATIO Adjusted operating incomeAverage total assetsOPERATING EXPENSE RATIO Adjusted operating expenseAverage total assetsNET INTEREST MARGIN RATIO Adjusted net interest marginAverage total assetsPORTFOLIO YIELD Operating income - accrued interest - interest and fee income from investmentsAverage gross loan portfolio (%)REAL INTEREST YIELD (Portfolio yield - inflation rate)(%)(1+ inflation rate)TOTAL INTEREST EXPENSE RATIO Adjusted total interest expense(%)Average total assetsADJUSTMENT EXPENSE RATIO Inflation and subsidy adjustment expense(%)Average total assetsLOAN LOSS PROVISION EXPENSE RATIO Adjusted loan loss provision expense(%)Average total assetsSALARY EXPENSE RATIO Personnel expense + in-kind donations(%)Average total assetsOTHER ADMINISTRATIVE EXPENSE RATIO Administrative expense + in-kind donations - personnel expense(%)Average total assetsTOTAL ADMINISTRATIVE EXPENSE RATIO Administrative expense + in-kind donationsAverage total assets(%)(%)(%)(%)EFFICIENCYTOTAL ADMINISTRATIVE EXPENSE/LOAN PORTFOLIOAdministrative expense + in-kind donationsAverage gross loan portfolio (%)SALARY EXPENSE/ LOAN PORTFOLIO Personnel expense + in-kind donationsAverage gross loan portfolioOTHER ADMINISTRATIVE EXPENSE /LOAN PORTFOLIOAdministrative expense + in-kind donations - personnel expenseAverage gross loan portfolio (%)(%)42 MICROBANKING BULLETIN, APRIL 2001

BULLETIN HIGHLIGHTS AND TABLESPRODUCTIVITYAVERAGE SALARY Average personnel expense + in-kind donationsGNP per capitaCOST PER BORROWER Administrative expense + in-kind donationsAverage number of active borrowersSTAFF PRODUCTIVITY Number of active borrowersNumber of staffLOAN OFFICER PRODUCTIVITY Number of active borrowersNumber of loan officersSTAFF ALLOCATION RATIO Number loan officersNumber of staffSTAFF TURNOVER Number of staff who left the MFIAverage number of staff(multiple of GNP per capita)(US$)(number)(number)(%)(%)PORTFOLIOPORTFOLIO AT RISK > 90 DAYS Outstanding balance of loans overdue > 90 daysTotal gross loan portfolioTOTAL GROSS LOAN PORTFOLIO Total gross portfolio outstanding (US$)AVERAGE LOAN BALANCE Total gross loan portfolio(US$)Number of active borrowersDEPTH Average loan balanceGNP per capita(%)(%)CAPITAL AND LIABILITY STRUCTURECOMMERCIAL FUNDING LIABILITIES RATIO Borrowings at commercial rates (excludes loans from Head Office and Central bank)Average gross loan portfolioCAPITAL / ASSET RATIO Adjusted total equityAdjusted total assets(%)(%)CLARIFICATION OF TERMSOPERATING INCOME Interest and fee income from loan portfolio + interest and fee income frominvestments + other income from financial servicesOPERATING EXPENSE Administrative expense + total interest expense + loan loss provision expenseADJUSTED OPERATING INCOME Interest and fee income from loan portfolio + interest and fee income frominvestments net of accrued interest + other income from financial servicesADJUSTED OPERATING EXPENSE Administrative expense, including in-kind donations + adjusted total interest expense+ adjusted loan loss provision expenseADMINISTRATIVE EXPENSE Personnel + office supplies + deprecation + rent + utilities + transportation + otheradministrative expensesPERSONNEL EXPENSE Staff salary + benefits expenseADJUSTED TOTAL INTEREST EXPENSE Interest and fee expense + exchange rate depreciation expense + other financialexpense (including inflation expense + subsidy expense)ADJUSTED NET INTEREST MARGIN Adjusted operating income - total interest expenseNET OPERATING INCOME Operating income - operating expenseADJUSTED NET OPERATING INCOME Adjusted operating income - adjusted operating expenseADJUSTED TOTAL EQUITY Total equity, including quasi-equity and adjusted net incomeADJUSTED TOTAL ASSETS Total assets, including loan portfolio and inflation adjustmentTABLES TITLES PAGETABLE 1 Institutional Characteristics and Outreach Indicators 44TABLE 2 Overall Financial Performance and Operating Income 46TABLE 3 Operating Expenses and Portfolio Management Indicators 48TABLE 4 Efficiency and Productivity 50TABLE 5 Macroeconomic Indicators 52TABLE A Institutional Characteristics and Outreach Indicators 54TABLE B Profitability and Efficiency Indicators 58TABLE C Institutional Characteristics and Outreach Indicators for Financially Self-Sufficient62MFIsTABLE D Profitability and Efficiency Indicators for Financially Self-Sufficient MFIs 66MICROBANKING BULLETIN, APRIL 2001 43

BULLETIN HIGHLIGHTS AND TABLESTABLE 1. INSTITUTIONAL CHARACTERISTICS AND OUTREACH INDICATORSPEER GROUP44 MICROBANKING BULLETIN, APRIL 2001AGE(years)OFFICES(no.)TOTALSTAFFtotal numberof employees(no.)TOTALASSETS(US$)CAPITAL/ASSET RATIOadj. total equity /adj. total assets(%)ALL MFIs (n=124) avg 8 13 94 5,512,452 49.5stdv 4 11 69 5,375,638 23.1Financially self-Sufficient MFIs (n=64) avg 10* 91* 367 14,498,853* 49.3stdv 5 304 1,219 27,453,324 24.21. Africa – Medium – Low (n=10) avg 6 14 87 2,560,405 49.0Citi Savings & Loans, FINCA Uganda, KWFT, Nsoatreman Rural Bank, PAMÉCAS, PRIDE stdv 2 10 30 1,187,845 29.7Tanzania, PRIDE Uganda, PRIDE Vita Guinea, SEF, WAGES2. Africa – Small – Low (n=11) avg 6 40* 43 997,697* 64.6Assawinso Rural Bank, Faulu, FINCA Malawi, FINCA Tanzania, FOCCAS, Manya Krobo stdv 6 91 12 300,489 23.0Rural Bank, Piyeli, SEDA, Sinapi Aba Trust, UWFT, Vital-Finance3. Africa/MENA – Large/Medium – Broad (n=5) avg 9 23 87 11,458,592 50.0ABA, Kafo Jiginew, Nyésigiso, PADME, UNRWA stdv 1 25 32 3,693,186 19.74. 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 4.0Microfund for Women5. Asia – Large – Low/Broad (n=5) avg 20* 700* 8,711* 1,079,413,652* 27.6ACLEDA, ASA, BAAC, BRAC, BRI stdv 5 168 4,099 1,648,849,794 17.56. Asia (Pacific) – Medium/Small – Low/Broad (n=6) avg 11 12 128 2,125,785 52.2CARD Bank, EMT, Hatta Kaksekar, Hublag, RSPI, TSPI stdv 2 7 66 1,456,672 12.87. Asia (South) – Medium/Small – Low/Broad (n=9) avg 8 23 152 3,829,019 50.9AKRSP, Basix, BURO Tangail, CDS, FWWB India, Kash Foundation, Nirdhan, stdv 5 18 147 2,217,533 26.5SEEDS, SHARE8. Eastern Europe – High Size: All (n=5) avg 2* 4 23 2,913,938 42.9AMK, LOK, Moznosti, Sunrise, WVB stdv 1 2 6 357,400 38.19. Eastern Europe – Broad Size: All (n=7) avg 3 9 30 2,585,284 71.4Bospo, Fundusz Mikro, Inicjatywa Mikro, Mercy Corps, Mikrofin, Nachala, NOA stdv 1 7 19 1,362,189 30.210. 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 12.6Mibanco, PRODEM11. Latin America – Medium – Broad (n=11) avg 9 8 72 4,326,750 41.4ACODEP, Actuar, ADOPEM, ADRI, Banco de la Pequena Empresa, Chispa, FAMA, stdv 4 5 49 1,601,429 13.2Finsol, FONDECO, ProEmpresa, Sartawi12. Latin America – Medium/Small – Low – Upper Income (n=6) avg 3* 5 26 1,974,452 39.6Banco do Povo de Juiz de Fora, CEAPE/ Pernambuco, Contigo, Emprender, Portsol, stdv 2 1 9 760,126 17.6Vivacred13. Latin America – Medium – Low – Lower Income (n=9) avg 10 7 116 5,165,496 62.0CAM, CMM Medellín, Compartamos, Crecer, Enlace, FINCA Honduras, stdv 3 3 49 2,103,809 18.2FMM Popayán, FWWB Cali, ProMujer14. 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 10.9World Relief Honduras15. Latin America – Credit Unions – Broad Size: All (n=11) avg 8 3 53 6,153,016 34.415 de Abril, 23 de Julio, Acredicom, Chuimequená, COOSAJO, Ecosaba, Moyután, stdv 4 1 11 1,752,518 7.7Oscus, Sagrario, Tonantel, Tulcán16. Worldwide – Large/Medium – Small Business (n=7) avg 7 12 134 18,331,933* 30.8ACEP, Agrocapital, Bank Dagang Bali, CERUDEB, FEFAD, MEB, NLC stdv 4 9 153 8,580,116 18.8Note: Standard deviations are listed below the peer group averages. The averages are calculated on the basis of the values between the ninth and seconddeciles for all MFIs and between second and the 99th percentiles for each peer group. Group averages different from average for all MFIs at 1 percentsignificance 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.

BULLETIN HIGHLIGHTS AND TABLESCOMMERCIALFUNDING LIABILITIESRATIOborrowings at commercialrates/ average gross loanportfolio (%)TOTALGROSS LOANPORTFOLIOtotal gross portfoliooutstanding(US$)NUMBER OFACTIVEBORROWERSborrowers with loansoutstanding(no.)AVERAGELOAN BALANCEtotal gross loanportfolio / no. activeborrowers (US$)DEPTHaverage loanbalance/ GNP percapita (%)PERCENTWOMENBORROWERSwomen borrowers /total borrowers(%)PEER GROUP35.9 3,764,997 11,398 490 44.5 62.4 ALL36.9 3,540,730 9,023 452 32.4 21.8 MFIs67.8* 11,113,300* 74,921 686 62.6 60.9 FSS69.7 22,296,780 341,259 793 73.2 25.5 MFIs41.8 1,525,339 14,668 123 33.6 79.8 1.50.7 613,676 7,286 35 14.8 18.124.0 512,947* 5,634 92* 31.3 83.8* 2.47.6 215,068 2,802 29 11.6 18.968.1 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 5.054.4 352,532,708* 2,046,752* 194 33.6 67.6 5.68.1 430,311,722 835,243 112 13.3 37.137.7 1,509,701 12,974 159 14.3 82.8 6.9.4 906,275 11,424 24 2.4 10.332.5 2,220,962 25,764* 82* 22.0 75.1 7.33.0 1,487,520 19,443 36 8.6 31.71.3 2,698,678 1,377 2,249* 202.8* 38.3 8.2.2 516,150 504 526 41.8 2.36.7 2,352,138 2,652 1,089* 66.2 44.5 9.15.1 1,253,418 1,916 302 25.3 6.391.2* 27,175,166* 29,730* 971* 70.3 50.5 10.19.7 8,699,651 9,360 279 26.0 8.550.9 3,427,876 7,453 609 64.3 46.5 11.18.5 1,332,599 5,924 414 29.1 16.612.1 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 16.916.5 853,632 8,975 91 6.3* 95.0* 14.29.4 409,407 3,127 17 3.9 6.691.7* 4,105,127 5,122 887* 56.7 40.8* 15.19.8 1,295,656 1,504 420 26.0 4.9101.8* 10,322,826* 4,934 2,968* 391.4* 29.0 16.115.1 2,302,970 3,348 776 175.5 2.8MICROBANKING BULLETIN, APRIL 2001 45

BULLETIN HIGHLIGHTS AND TABLESTABLE 2. OVERALL FINANCIAL PERFORMANCE AND OPERATING INCOMEPEER GROUPADJUSTEDRETURN ONASSETSadj. net operatingincome / averagetotal assets(%)ADJUSTEDRETURN ONEQUITYadjusted netoperatingincome / averageequity(%)OPERATIONALSELF-SUFFICIENCYoperating income /operating expense(%)FINANCIALSELF-SUFFICIENCYadjusted operatingincome / adjustedoperating expenseALL MFIs (n=124) avg -3.5 -5.7 103.6 90.2stdv 6.1 14.3 20.9 18.7Financially self-Sufficient MFIs (n=64) avg 3.0* 8.8* 129.8* 114.3*stdv 5.3 14.2 36.0 25.81. 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 12.7Tanzania, PRIDE Uganda, PRIDE Vita Guinea, SEF, WAGES2. 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 22.3Rural Bank, Piyeli, SEDA, Sinapi Aba Trust, UWFT, Vital-Finance3. Africa/MENA – Large/Medium – Broad (n=5) avg -0.7 -4.4 114.4 101.0ABA, Kafo Jiginew, Nyésigiso, PADME, UNRWA stdv 2.8 7.4 36.8 23.04. Asia (Central)/MENA – Medium/Small – Low (n=6) avg -11.2* -11.9 79.9 70.8Al Amana, Al Majmoua, Constanta, Faten, FINCA Kyrgyzstan, stdv 10.2 11.2 18.5 18.9Microfund for Women5. 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 12.96. Asia (Pacific) – Medium/Small – Low/Broad (n=6) avg 0.7 0.2 111.1 101.8CARD Bank, EMT, Hatta Kaksekar, Hublag, RSPI, TSPI stdv 3.4 4.8 6.6 9.87. 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 19.9SEEDS, SHARE8. Eastern Europe – High Size: All (n=5) avg -2.3 -9.0 105.8 90.5AMK, LOK, Moznosti, Sunrise, WVB stdv 1.4 9.3 19.3 5.69. Eastern Europe – Broad Size: All (n=7) avg -3.0 -5.2 106.8 90.1Bospo, Fundusz Mikro, Inicjatywa Mikro, Mercy Corps, Mikrofin, Nachala, NOA stdv 2.1 4.9 7.3 6.310. 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 5.3Mibanco, PRODEM11. Latin America – Medium – Broad (n=11) avg -1.9 -0.6 106.1 96.5ACODEP, Actuar, ADOPEM, ADRI, Banco de la Pequena Empresa, Chispa, FAMA, stdv 7.0 16.4 21.4 20.0Finsol, FONDECO, ProEmpresa, Sartawi12. Latin America – Medium/Small – Low – Upper Income (n=6) avg -10.6 -44.3* 100.2 81.4Banco do Povo de Juiz de For a, CEAPE/ Pernambuco, Contigo, Emprender, Portsol, stdv 8.0 51.0 18.0 14.2Vivacred13. 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 19.0FMM Popayán, FWWB Cali, ProMujer14. Latin America – Small – Low – Lower Income (n=7) avg -3.4 -4.6 114.6 93.4AGAPE, FED, FINCA Ecuador, FINCA Mexico, FINCA Nicaragua, FINCA Peru, stdv 6.9 10.5 8.4 11.4World Relief Honduras15. Latin America – Credit Unions – Broad Size: All (n=11) avg -2.6 -5.6 113.2 88.315 de Abril, 23 de Julio, Acredicom, Chuimequená, COOSAJO, Ecosaba, Moyután, stdv 4.4 11.7 22.1 21.5Oscus, Sagrario, Tonantel, Tulcán16. Worldwide – Large/Medium – Small Business (n=7) avg -2.1 -7.3 104.7 90.4ACEP, Agrocapital, Bank Dagang Bali, CERUDEB, FEFAD, MEB, NLC stdv 1.7 6.3 6.4 7.0Note: Standard deviations are listed below the peer group averages. The averages are calculated on the basis of the values between the ninth and second decilesfor 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 aremarked with an asterisk (*). “n” refers to the total number of institutions in the peer group, before dropping the percentiles for averages. Additional statisticalinformation is available at www.microbanking-mbb.org. Abbreviation: MENA=Middle East/North Africa.(%)46 MICROBANKING BULLETIN, APRIL 2001

BULLETIN HIGHLIGHTS AND TABLESOPERATINGINCOME RATIOadjusted operatingincome /average total assets(%)PROFITMARGINadjusted netoperating income /adjustedoperating income(%)NETINTERESTMARGINadjusted net interestmargin /average total assets(%)PORTFOLIOYIELDoperating income – accruedinterest – interest and feeincome from investments /average gross loan portfolio(%)REALYIELD(portfolio yield –inflation rate) /(1 + inflation rate)(%)PEER GROUP28.3 -16.2 19.8 39.2 30.1 ALL9.0 26.6 8.2 13.2 12.0 MFIs33.4* 9.3* 24.7* 45.5* 35.5* FSS13.0 15.1 11.4 20.1 15.4 MFIs28.0 -42.1 22.6 42.0 34.5 1.7.8 29.4 10.2 13.2 12.027.4 -57.2* 22.7 54.6* 44.2* 2.8.5 47.2 8.7 15.8 15.913.1* -2.1 9.5 24.6 21.7 3.1.5 20.5 2.9 1.3 0.726.7 -48.0 20.7 44.8 36.8 4.6.4 34.4 4.7 15.5 3.723.6 16.8 12.3 31.7 17.6 5.4.9 8.9 2.5 13.7 4.929.3 1.1 24.1 42.7 36.3 6.1.6 9.0 2.4 3.6 5.115.8* -56.6* 9.6* 22.0* 13.3* 7.5.4 51.6 5.1 7.8 8.226.2 -10.8 17.7 31.8 15.3 8.3.0 6.8 5.3 3.6 3.127.6 -11.4 20.8 31.0 22.7 9.1.1 7.4 2.7 1.9 5.229.1 8.0* 20.2 35.1 29.9 10.2.2 4.4 2.6 3.0 3.536.5* -8.2 25.4 45.9 33.1 11.7.1 25.5 6.0 8.9 7.341.8* -25.7 27.9 52.9 50.2* 12.5.6 22.1 5.2 5.6 3.245.2* 8.4* 30.2* 57.1* 40.3 13.11.7 13.7 4.4 14.5 5.345.0* -8.6 26.6 78.3* 55.3* 14.3.0 15.4 12.9 13.0 6.416.6* -19..4 7.7* 20.3* -9.5* 15.3.7 28.8 3.7 3.9 32.421.0 -11.2 11.4 24.0* 17.0* 16.3.8 8.7 6.2 3.7 3.7MICROBANKING BULLETIN, APRIL 2001 47

BULLETIN HIGHLIGHTS AND TABLESTABLE 3. OPERATING EXPENSES AND PORTFOLIO MANAGEMENT INDICATORSPEER GROUPOPERATINGEXPENSERATIOadjusted operatingexpense /average total assets(%)TOTALINTERESTEXP. RATIOadj. total interestexpense /average totalassets(%)ADJUSTMENTEXPENSERATIOadjustmentexpense /average totalassets(%)LOAN LOSSPROVISIONEXP. RATIOadj. loan lossprovision expense /average totalassets(%)ALL MFIs (n=124) avg 32.5 3.8 3.5 2.1stdv 10.9 2.9 2.6 1.5Financially self-Sufficient MFIs (n=64) avg 30.7 5.1 3.1 1.9stdv 12.5 4.1 3.1 1.51. Africa – Medium – Low (n=10) avg 43.8* 1.7 2.3 1.4Citi Savings & Loans, FINCA Uganda, KWFT, Nsoatreman Rural Bank, PAMÉCAS, PRIDE stdv 14.9 0.8 1.8 1.1Tanzania, PRIDE Uganda, PRIDE Vita Guinea, SEF, WAGES2. Africa – Small – Low (n=11) avg 40.1 1.3* 3.6 1.6Assawinso Rural Bank, Faulu, FINCA Malawi, FINCA Tanzania, FOCCAS, Manya Krobo stdv 11.8 1.9 2.5 1.1Rural Bank, Piyeli, SEDA, Sinapi Aba Trust, UWFT, Vital-Finance3. Africa/MENA – Large/Medium – Broad (n=5) avg 15.7* 1.5 0.6 2.5ABA, Kafo Jiginew, Nyésigiso, PADME, UNRWA stdv 6.2 1.0 0.9 1.64. Asia (Central)/MENA – Medium/Small – Low (n=6) avg 38.0 - 3.6 0.9Al Amana, Al Majmoua, Constanta, Faten, FINCA Kyrgyzstan, stdv 6.2 - 3.6 0.7Microfund for Women5. Asia – Large – Low/Broad (n=5) avg 23.8 4.2 2.3 3.2ACLEDA, ASA, BAAC, BRAC, BRI stdv 0.5 2.0 1.4 1.56. Asia (Pacific) – Medium/Small – Low/Broad (n=6) avg 30.6 3.3 2.4 2.3CARD Bank, EMT, Hatta Kaksekar, Hublag, RSPI, TSPI stdv 2.4 0.5 0.6 1.27. Asia (South) – Medium/Small – Low/Broad (n=9) avg 23.0 2.9 4.1 2.0AKRSP, Basix, BURO Tangail, CDS, FWWB India, Kash Foundation, Nirdhan, stdv 10.7 2.3 1.9 2.3SEEDS, SHARE8. Eastern Europe – High Size: All (n=5) avg 29.5 2.6 4.9 3.1AMK, LOK, Moznosti, Sunrise, WVB stdv 7.1 1.8 2.9 0.79. Eastern Europe – Broad Size: All (n=7) avg 28.6 1.6 5.2 2.8Bospo, Fundusz Mikro, Inicjatywa Mikro, Mercy Corps, Mikrofin, Nachala, NOA stdv 3.9 1.8 1.4 1.510. Latin America – Large – Broad/High (n=9) avg 28.4 8.0* 0.7* 3.5*Banco Ademi, BancoSol, Caja de Los Andes, Calpiá, CM Arequipa, FIE, Finamérica, stdv 5.1 1.8 0.6 1.2Mibanco, PRODEM11. Latin America – Medium – Broad (n=11) avg 37.4 7.2* 3.5 3.4*ACODEP, Actuar, ADOPEM, ADRI, Banco de la Pequena Empresa, Chispa, FAMA, stdv 7.1 4.4 1.5 2.4Finsol, FONDECO, ProEmpresa, Sartawi12. Latin America – Medium/Small – Low – Upper Income (n=6) avg 52.5* 6.5 9.6* 7.2*Banco do Povo de Juiz de For a, CEAPE/ Pernambuco, Contigo, Emprender, Portsol, stdv 6.4 3.5 3.6 6.7Vivacred13. Latin America – Medium – Low – Lower Income (n=9) avg 41.5 5.6 4.3 1.6CAM, CMM Medellín, Compartamos, Crecer, Enlace, FINCA Honduras, stdv 14.2 4.3 3.5 1.0FMM Popayán, FWWB Cali, ProMujer14. Latin America – Small – Low – Lower Income (n=7) avg 55.3* 4.2 10.0* 2.2AGAPE, FED, FINCA Ecuador, FINCA Mexico, FINCA Nicaragua, FINCA Peru, stdv 13.7 3.3 3.8 0.8World Relief Honduras15. Latin America – Credit Unions – Broad Size: All (n=11) avg 19.1* 5.7 3.2 1.015 de Abril, 23 de Julio, Acredicom, Chuimequená, COOSAJO, Ecosaba, Moyután, stdv 2.3 3.2 3.5 0.7Oscus, Sagrario, Tonantel, Tulcán16. Worldwide – Large/Medium – Small Business (n=7) avg 20.0* 3.4 2.3 1.2ACEP, Agrocapital, Bank Dagang Bali, CERUDEB, FEFAD, MEB, NLC stdv 6.0 3.3 1.4 0.7Note: 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 forall 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 aremarked with an asterisk (*). “n” refers to the total number of institutions in the peer group, before dropping the percentiles for averages. Additional statisticalinformation is available at www.microbanking-mbb.org. Abbreviation: MENA=Middle East/North Africa.48 MICROBANKING BULLETIN, APRIL 2001

BULLETIN HIGHLIGHTS AND TABLESSALARY EXPENSERATIOpersonnel expense + in-kinddonations /average total assets(%)OTHERADMINISTRATIVEEXPENSE RATIOadministrative expense +in-kind donations –personnel expense/average total assets(%)TOTALADMINISTRATIVEEXPENSE RATIOadministrative expense +in-kind donations/average total assets(%)PORTFOLIOAT RISK >90 DAYSoutstandingbalance of loans overdue90 days /total gross loan portfolio(%)PEER GROUP11.5 9.1 20.7 1.9 ALL6.0 3.8 9.2 1.6 MFIs11.1 8.8 19.9 1.9 FSS6.5 5.0 10.4 1.5 MFIs20.6* 14.8* 35.4* 0.8 1.11.4 6.3 17.0 0.717.3* 15.4* 32.5* 2.4 2.5.6 6.1 11.7 2.45.0 3.7* 8.9 4.0 3.2.3 3.5 3.6 3.323.1* 9.1 32.8* 0.1 4.3.4 1.2 4.8 0.15.9 2.3* 8.1* 2.1 5.3.2 1.6 4.7 1.212.0 8.9 20.9 2.7 6.0.7 1.3 1.9 3.17.7 6.3 13.7 1.7 7.7.3 4.5 11.7 2.512.0 7.0 19.5 0.1 8.3.0 0.6 2.5 0.112.0 8.6 20.4 0.9 9.1.7 1.4 3.3 0.98.0 7.2 14.5 2.6 10.1.8 2.2 2.1 0.712.2 11.5 23.5 2.8 11.4.0 4.1 6.8 1.815.5 12.0 27.6 2.7 12.3.0 1.7 1.6 2.214.4 12.6 26.6 1.0 13.4.9 5.1 9.2 0.920.5* 12.8* 32.7* 1.6 14.5.2 5.5 4.4 0.83.0* 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 0.9MICROBANKING BULLETIN, APRIL 2001 49

BULLETIN HIGHLIGHTS AND TABLESTABLE 4. EFFICIENCY AND PRODUCTIVIYPEER GROUPTOTALADMINISTRATIVEEXPENSE / LPadministrativeexpense + in-kinddonations / averagegross loan portfolio(%)SALARYEXPENSE /LPpersonnel expense+ in-kind donations/ average grossloan portfolio(%)OTHERADMINISTRATIVEEXPENSE / LPadministrative expense+ in-kind donations –personnel expense/average gross loanportfolio (%)DEPTHaverage loanbalance /GNP percapita(%)ALL MFIs (n=124) avg 31.3 17.3 13.8 44.5stdv 16.0 10.0 6.8 32.4Financially self-Sufficient MFIs (n=64) avg 29.2 16.2 12.9 62.6stdv 17.3 10.1 8.3 73.21. Africa – Medium – Low (n=10) avg 56.6* 33.1* 23.6* 33.6Citi Savings & Loans, FINCA Uganda, KWFT, Nsoatreman Rural Bank, PAMÉCAS, PRIDE stdv 26.9 17.9 9.9 14.8Tanzania, PRIDE Uganda, PRIDE Vita Guinea, SEF, WAGES2. Africa – Small – Low (n=11) avg 71.4* 37.6* 33.8* 31.3Assawinso Rural Bank, Faulu, FINCA Malawi, FINCA Tanzania, FOCCAS, Manya Krobo stdv 22.3 12.1 11.8 11.6Rural Bank, Piyeli, SEDA, Sinapi Aba Trust, UWFT, Vital-Finance3. Africa/MENA – Large/Medium – Broad (n=5) avg 17.1 8.8 6.4 81.8ABA, Kafo Jiginew, Nyésigiso, PADME, UNRWA stdv 6.4 4.0 4.4 22.04. Asia (Central)/MENA – Medium/Small – Low (n=6) avg 55.2* 38.7* 16.2 11.9Al Amana, Al Majmoua, Constanta, Faten, FINCA Kyrgyzstan, stdv 9.4 6.0 3.0 3.9Microfund for Women5. Asia – Large – Low/Broad (n=5) avg 12.7 9.1 3.7* 33.6ACLEDA, ASA, BAAC, BRAC, BRI stdv 3.0 1.5 1.6 13.36. Asia (Pacific) – Medium/Small – Low/Broad (n=6) avg 32.6 17.2 14.0 14.3CARD Bank, EMT, Hatta Kaksekar, Hublag, RSPI, TSPI stdv 2.7 1.4 1.3 2.47. Asia (South) – Medium/Small – Low/Broad (n=9) avg 18.8 10.1 9.1 22.0AKRSP, Basix, BURO Tangail, CDS, FWWB India, Kash Foundation, Nirdhan, stdv 8.2 6.9 2.9 8.6SEEDS, SHARE8. Eastern Europe – High Size: All (n=5) avg 23.0 14.1 8.3 202.8*AMK, LOK, Moznosti, Sunrise, WVB stdv 1.3 2.8 0.7 41.89. Eastern Europe – Broad Size: All (n=7) avg 23.7 14.0 9.7 66.2Bospo, Fundusz Mikro, Inicjatywa Mikro, Mercy Corps, Mikrofin, Nachala, NOA stdv 3.6 2.0 1.7 25.310. Latin America – Large – Broad/High (n=9) avg 18.0 9.6 9.1 70.3Banco Ademi, BancoSol, Caja de Los Andes, Calpiá, CM Arequipa, FIE, Finamérica, stdv 3.6 2.7 3.2 26.0Mibanco, PRODEM11. Latin America – Medium – Broad (n=11) avg 31.0 16.0 15.1 64.3ACODEP, Actuar, ADOPEM, ADRI, Banco de la Pequena Empresa, Chispa, FAMA, stdv 9.6 4.8 5.7 29.1Finsol, FONDECO, ProEmpresa, Sartawi12. Latin America – Medium/Small – Low – Upper Income (n=6) avg 36.4 21.2 15.9 14.9Banco do Povo de Juiz de For a, CEAPE/ Pernambuco, Contigo, Emprender, Portsol, stdv 4.6 1.9 4.0 4.0Vivacred13. Latin America – Medium – Low – Lower Income (n=9) avg 39.7 21.6 17.8 12.4*CAM, CMM Medellín, Compartamos, Crecer, Enlace, FINCA Honduras, stdv 15.8 8.2 6.9 2.8FMM Popayán, FWWB Cali, ProMujer14. Latin America – Small – Low – Lower Income (n=7) avg 54.1* 33.6* 24.2* 6.3*AGAPE, FED, FINCA Ecuador, FINCA Mexico, FINCA Nicaragua, FINCA Peru, stdv 7.4 7.1 9.8 3.9World Relief Honduras15. Latin America – Credit Unions – Broad Size: All (n=11) avg 12.2* 4.5* 7.5* 56.715 de Abril, 23 de Julio, Acredicom, Chuimequená, COOSAJO, Ecosaba, Moyután, stdv 2.1 1.2 1.5 26.0Oscus, Sagrario, Tonantel, Tulcán16. Worldwide – Large/Medium – Small Business (n=7) avg 12.8* 5.7* 7.4 391.4*ACEP, Agrocapital, Bank Dagang Bali, CERUDEB, FEFAD, MEB, NLC stdv 6.8 2.6 4.2 175.5Note: 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 forall 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 aremarked with an asterisk (*). “n” refers to the total number of institutions in the peer group, before dropping the percentiles for averages. Additional statisticalinformation is available at www.microbanking-mbb.org. Abbreviation: MENA=Middle East/North Africa.50 MICROBANKING BULLETIN, APRIL 2001

BULLETIN HIGHLIGHTS AND TABLESAVERAGESALARYaverage personnelexpense + in-kinddonations /GNP per capita(multiple of GNP/ capita)STAFFPRODUCTIVITYnumber activeborrowers /number of staff(no.)LOAN OFFICERPRODUCTIVITYnumber activeborrowers /number of loanofficers(no.)STAFFALLOCATIONnumber of loanofficers /number of staff(%)STAFFTURNOVERnumber staff wholeft the MFI /average numberof staff(%)COST PERBORROWERadministrativeexpense + in-kinddonations / averagenumber of activeborrowers (US$)PEER GROUP5.8 122 257 49.7 10.8 137 ALL3.6 49 128 10.5 6.5 171 MFIs5.9 133 291 51.4 12.7 110 FSS4.0 72 189 15.3 934 99 MFIs12.9* 192* 474* 38.9* 5.2 56 1.7.6 79 292 15.4 3.1 2711.9* 129 285 51.9 4.6* 62 2.4.8 41 99 13.1 4.8 307.6 119 315 47.2 6.2 40 3.6.5 95 308 11.5 5.7 83.9 102 174 59.2 20.2 76 4.1.3 10 35 6.8 19.8 414.0 238* 420 41.6 5.8 25 5.1.0 83 84 20.7 1.3 162.3 80 134 66.1* 13.8 44 6.0.6 16 22 11.1 10.1 133.4 235* 452* 56.0 9.7 17 7.1.4 232 494 14.6 5.7 710.3 64 113 55.9 6.8 259 8.0.6 0 8 2.8 6.7 335.0 78 123 58.7 7.3 196 9.2.5 22 28 9.1 2.4 777.1 122 285 42.6 13.0 166 10.2.5 16 58 3.9 4.9 406.9 88 212 40.6* 14.2 176 11.3.9 41 84 7.6 8.2 1552.1 81 197 48.5 - 241 12.0.3 30 71 12.7 - 1173.4 194* 417* 48.5 9.6 56 13.1.0 52 159 6.6 6.6 92.0 140 225 57.1 10.3 52 14.1.3 29 40 3.6 9.5 151.9 107 - - - 105 15.0.5 8 - - - 348.9 37* 130 32.2* 3.3 377* 16.3.8 21 84 7.8 2.9 272MICROBANKING BULLETIN, APRIL 2001 51

BULLETIN HIGHLIGHTS AND TABLESTABLE 5. MACROECONOMIC INDICATORSGNP PERCAPITAGDP GROWTHRATE, ANNUALAVERAGE1990-98INFLATIONRATEDEPOSITRATEFINANCIALDEEPENING(M3 / GDP)PEER GROUP(US$) (%) (%) (%) (%)ALL MFIs (n=124) avg 1,395 3.9 12.4 15.3 35.3stdv 1,327 2.8 21.9 10.9 19.1Financially self-Sufficient MFIs (n=64) avg 1,373 3.9 7.5 14.3 35.9stdv 1,036 2.3 7.9 7.8 17.11. 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 9.4Tanzania, PRIDE Uganda, PRIDE Vita Guinea, SEF, WAGES2. 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 6.1Rural Bank, Piyeli, SEDA, Sinapi Aba Trust, UWFT, Vital-Finance3. Africa/MENA – Large/Medium – Broad (n=5) avg 746 4.0 2.2 5.0 31.6ABA, Kafo Jiginew, Nyésigiso, PADME, UNRWA stdv 629 0.4 4.6 2.5 33.04. Asia (Central)/MENA – Medium/Small – Low (n=6) avg 1,723 -4.6* 7.5 10.2 87.7Al Amana, Al Majmoua, Constanta, Faten, FINCA Kyrgyzstan, stdv 1,631 10.1 7.4 4.4 22.6Microfund for Women5. Asia – Large – Low/Broad (n=5) avg 770 5.7 9.7 12.7 47.3ACLEDA, ASA, BAAC, BRAC, BRI stdv 798 0.3 6.3 7.4 35.96. Asia (Pacific) – Medium/Small – Low/Broad (n=6) avg 837 3.9 5.3 9.2 46.2CARD Bank, EMT, Hatta Kaksekar, Hublag, RSPI, TSPI stdv 452 0.9 4.6 2.3 27.17. Asia (South) – Medium/Small – Low/Broad (n=9) avg 422 5.6 8.0 10.8 46.7AKRSP, Basix, BURO Tangail, CDS, FWWB India, Kash Foundation, Nirdhan, stdv 162 0.7 2.9 3.6 7.5SEEDS, SHARE8. Eastern Europe – High Size: All (n=5) avg 1,127 1.7 11.4 13.5 14.8AMK, 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 34.4Bospo, Fundusz Mikro, Inicjatywa Mikro, Mercy Corps, Mikrofin, Nachala, NOA stdv 1,625 3.4 6.9 4.5 1.110. Latin America – Large – Broad/High (n=9) avg 1,663 4.8 4.1 14.4 38.1Banco Ademi, BancoSol, Caja de Los Andes, Calpiá, CM Arequipa, FIE, Finamérica, stdv 671 0.8 3.1 3.4 10.3Mibanco, PRODEM11. Latin America – Medium – Broad (n=11) avg 1,373 4.0 8.4 14.7 43.1ACODEP, Actuar, ADOPEM, ADRI, Banco de la Pequena Empresa, Chispa, FAMA, stdv 883 1.0 3.6 3.9 17.4Finsol, FONDECO, ProEmpresa, Sartawi12. Latin America – Medium/Small – Low – Upper Income (n=6) avg 5,257* 4.5 2.2 24.6 32.9Banco do Povo de Juiz de For a, CEAPE/ Pernambuco, Contigo, Emprender, Portsol, stdv 1,366 1.9 1.7 12.6 5.4Vivacred13. Latin America – Medium – Low – Lower Income (n=9) avg 1,931 3.8 13.2 19.7 34.2CAM, CMM Medellín, Compartamos, Crecer, Enlace, FINCA Honduras, stdv 988 0.8 15.6 12.0 10.9FMM Popayán, FWWB Cali, ProMujer14. Latin America – Small – Low – Lower Income (n=7) avg 1,849 3.5 22.6 25.1 36.2AGAPE, FED, FINCA Ecuador, FINCA Mexico, FINCA Nicaragua, FINCA Peru, stdv 1,170 1.1 20.6 16.9 14.6World Relief Honduras15. Latin America – Credit Unions – Broad Size: All (n=11) avg 1,553 3.6 51.4* 26.7* 32.015 de Abril, 23 de Julio, Acredicom, Chuimequená, COOSAJO, Ecosaba, Moyután, stdv 31 0.6 54.1 19.1 6.4Oscus, Sagrario, Tonantel, Tulcán16. Worldwide – Large/Medium – Small Business (n=7) avg 757 5.5 12.5 15.2 37.8ACEP, Agrocapital, Bank Dagang Bali, CERUDEB, FEFAD, MEB, NLC stdv 329 2.3 20.4 11.3 19.8Note: 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 forall 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 aremarked with an asterisk (*). “n” refers to the total number of institutions in the peer group, before dropping the percentiles for averages. Additional statisticalinformation is available at www.microbanking-mbb.org. Abbreviation: MENA=Middle East/North Africa.52 MICROBANKING BULLETIN, APRIL 2001

BULLETIN HIGHLIGHTS AND TABLESAdditional Analysis TablesTables A and B provide data on selectedperformance indicators for groups of institutionsfrom the entire database for this Issue (n=124).Tables C and D provide information only for thefinancially self-sufficient institutions (n=64). Thefollowing eight characteristics are considered for theclassification of data:1) Age: The Bulletin classifies MFIs into threecategories (new, young, and mature) based onthe difference between the year they startedtheir microfinance operations and the year forwhich the institutions have submitted data.2) Scale of Operations: MFIs are classified assmall, medium and large according to the sizeof their loan portfolio to facilitate comparisons ofinstitutions with similar outreach.3) Lending Methodology: Performance may varyby the methodology used by the institution todeliver loan products. The Bulletin classifiesMFIs based on the primary methodology used,determined by the number of loans outstanding.4) Level of Financial Intermediation: Thisclassification is based on the ratio of totalvoluntary passbook and time deposits to totalassets. It indicates the MFI’s ability to mobilizesavings and fund its portfolio through deposits.5) Target Market: The Bulletin classifies MFIs intothree categories—low-end, broad, and highend—accordingto the range of clients theyserve based on average loan outstanding perGNP per capita.6) Region: Geographic regions—Africa, Asia,Eastern Europe, and Latin America—areconsidered for the classification to captureregional effects. MENA was not included dueto the small number of MFIs participating.7) Charter Type: The charter under which theMFIs are registered is used to classify the MFIsinto banks, credit unions/ cooperatives, NGOs,and non-banks.8) Profit Status: MFIs are classified as for-profitand non-profit institutions.The quantitative criteria used to categorize thesecharacteristics are summarized in the table below.A list of institutions that fall into these categories forthe entire sample is located immediately followingTable D. Confidentiality limits the publication ofnames of financially self-sufficient MFIs included inthe database.These Additional Analysis Tables provide anothermeans of creating performance benchmarksbesides the peer groups. Three of thesecharacteristics—region, scale of operations andtarget market—are also factors in determining peergroup composition. The purpose of the AdditionalAnalysis Tables is to look at these characteristicssingularly, rather than within the context of the peergroups. The data are calculated by dropping thetop and bottom percentile of observations to avoidthe effect of outliers.Age of the MFIScale of Operations(Size of grossoutstanding loanportfolio in US$)Lending MethodologyLevel of Retail FinancialIntermediationTarget MarketNew:Young:Mature:Large:Medium:Small:IndividualSolidarity Group:Village Banking:Financial Intermediary:Other:Low-end:Broad:High-end and Small Business:1 to 2 years3 to 6 yearsover 6 yearsAfrica and MENA:Asia (all):E.Europe and L.America:Africa and MENA:Asia:E.Europe and L.America:Africa:Asia:E.Europe and L.America:1 borrowergroup of 3 to 9 borrowersgroups with ≥ 10 borrowers> 5 million> 8 million> 10 million900,000 to 5 million1 to 8 million1.5 to 10 million< 900,000< 1 million< 1.5 millionpassbook and time deposits ≥ 20 % of total assetspassbook and time deposits < 20 % of total assetsdepth < 20% OR average loan size < US$150depth between 20% and 149%depth ≥ 150%MICROBANKING BULLETIN, APRIL 2001 53

BULLETIN HIGHLIGHTS AND TABLESTABLE A: INSTITUTIONAL CHARACTERISTICS AND OUTREACH INDICATORSCRITERIATOTALASSETS(US$)CAPITAL /ASSETRATIOadjusted totalequity /adjusted totalassets (%)COMMERCIALFUNDING LIABILITIESRATIOOFFICESborrowings at commercialrates / average gross loanportfolio(%) (no.)AGE New (1 – 2 years) avg 2,655,249* 55.2 20.3 9stdv 1,615,760 29.1 29.9 10N 28 28 28 27Young (3 – 6 years) avg 5,441,477 55.3 40.0 34stdv 5,376,583 30.4 73.4 121N 26 26 26 26Mature (> 6 years) avg 62,766,879 45.7 66.0* 94*stdv 374,922,454 25.8 58.0 334N 63 63 63 56SCALE OF Large avg 191,135,173* 32.2* 91.1* 124*OPERATIONS # stdv 658,265,519 21.1 73.2 254N 20 20 20 20Medium avg 4,780,316 49.7 47.6 45stdv 2,625,596 26.9 47.3 242N 66 66 66 60Small avg 1,197,686* 61.2* 21.1 16stdv 779,360 28.1 33.8 48N 32 32 32 30LENDING Individual avg 68,102,103 40.3 71.8* 24METHOD- (1 borrower) stdv 412,429,864 26.2 71.8 86OLOGY N 52 52 52 47Solidarity Groups avg 10,102,851 50.0 37.7 34(groups of 3 to 9 stdv 19,545,681 25.6 40.3 96borrowers) N 43 43 43 40Village Banking avg 2,588,603* 71.1* 18.1 127*(groups with ≥ 10 stdv 1,912,299 24.0 24.5 406borrowers) N 23 23 23 23LEVEL OF Financial Intermediaries avg 121,131,564 20.4* 135.7* 42*FINANCIAL (passbook and time stdv 550,930,135 14.5 70.9 118INTER- deposits ≥ 20% of total N 29 29 29 24MEDIATION assets)Other avg 5,835,964 59.5* 23.9* 61(passbook and time stdv 10,349,253 25.7 27.4 238deposits < 20% of total N 91 91 91 87assets)TARGET Low-end avg 4,948,110 59.0 34.4 89*MARKET (depth < 20% OR avg. stdv 11,581,748 28.6 50.0 297loan balance < US$ 150) N 57 57 57 55Broad avg 68,758,671 42.2 61.2* 28(depth between 20% and stdv 416,494,814 24.7 47.8 88149%) N 51 51 51 44High-end and avg 15,975,183* 35.8 57.1 11Small Business stdv 15,545,156 26.2 91.4 10(depth ≥ 150%) N 10 10 10 10Note: Standard deviations are listed below the group averages. The averages are calculated on the basis of the values between second and the 99 thpercentiles for each group. Therefore, sample sizes (N) vary across indicators. Group averages different from average for all MFIs at 1 percent significancelevel 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.54 MICROBANKING BULLETIN, APRIL 2001

BULLETIN HIGHLIGHTS AND TABLESTOTALSTAFFTotal number ofemployees(no.)TOTALGROSS LOANPORTFOLIOtotal gross portfoliooutstanding(US$)PERCENTWOMENBORROWERStotal number of activewomen borrowers /total number of activeborrowers (%)NUMBER OFACTIVEBORROWERSnumber ofborrowers withloansoutstanding (no.)AVERAGELOAN BALANCEtotal gross loanportfolio / numberof active borrowers(US$)DEPTHaverage loanbalance /GNP per capita(%)44* 2,039,656* 60.1 5,081* 894* 70.2*27 1,339,287 26.6 5,950 961 69.328 28 23 28 28 2889 3,452,453 71.4 9.818 554 81.372 3,511,633 26.8 6,971 818 145.226 26 22 26 26 26611* 24,643,591 65.2 115,443 552 47.22,100 107,735,266 26.7 460,828 686 50.856 63 56 63 63 631,525* 72,987,141* 50.0 328,895* 1,079* 98.7*3,374 185,140,979 21.5 788,612 806 91.220 20 16 20 20 2096 3,366,278 64.2 13,741 650 64.677 1,665,912 26.4 13,348 868 101.761 66 56 66 66 6644* 669,421* 76.2* 4,797* 338 30.927 317,056 25.9 3,776 465 27.632 32 29 32 32 32371 23,341,413 44.4* 55,811 1,206* 103.0*1,914 116,972,376 14.6 342,044 1,044 122.747 52 41 52 52 52260 7,619,230 74.3* 42,709 328 39.4783 16,341,007 23.5 163,573 352 31.742 43 36 43 43 4381 1,667,467* 91.3* 14,568 115* 18.0*54 1,397,743 16.0 11,016 63 13.523 23 23 23 23 23731 41,022,001 45.0* 99,378 879* 77.0*2,721 156,184,231 11.7 456,954 824 70.123 29 23 29 29 29152 4,359,316 71.9* 25,240 583 52.5538 8,465,405 27.1 113,421 822 68.991 91 79 91 91 91195 3,509,933 85.9* 35,718 150* 16.8*675 9,750,386 19.7 142,494 140 10.457 57 47 57 57 57397 24,414,911 50.9* 60,480 797* 64.8*1,953 118,336,433 19.3 345,018 584 29.345 51 47 51 51 51102 9,778,077* 33.6* 4,373* 2,776* 310.2*123 8,762,166 3.6 4,426 682 147.610 10 7 10 10 10MICROBANKING BULLETIN, APRIL 2001 55

BULLETIN HIGHLIGHTS AND TABLESTABLE A: INSTITUTIONAL CHARACTERISTICS AND OUTREACH INDICATORS (continued)CRITERIATOTALASSETS(US$)CAPITAL /ASSETRATIOadjusted totalequity /adjusted totalassets (%)COMMERCIALFUNDING LIABILITIESRATIOOFFICESborrowings at commercialrates / average gross loanportfolio(%) (no.)REGION ## Africa avg 3,119,574 51.6 53.1 31*stdv 3,182,947 30.0 74.5 58N 24 24 24 22Asia avg 154,874,202* 48.5 44.1 296*(All) stdv 632,999,291 28.3 58.5 625N 22 22 22 22Eastern Europe avg 4,092,029 54.2 8.9* 7stdv 3,300,650 34.2 14.4 6N 12 12 12 12Latin America avg 9,079,001* 44.2 58.0* 10stdv 11,352,239 22.0 39.7 9N 52 52 52 45CHARTER Bank avg 226,598,190* 20.3* 144.5* 54*TYPE stdv 763,087,300 20.1 102.4 148N 15 15 15 15Credit Union/ avg 6,598,534 34.7* 91.3* 15Cooperative stdv 2,944,242 13.1 27.1 18N 14 14 14 9NGO avg 5,763,907 59.5* 23.4* 75stdv 11,275,071 25.8 27.5 270N 68 68 68 67Non-Bank ### avg 7,863,917 46.5 51.3 10stdv 7,644,069 33.8 46.8 6N 10 10 10 10PROFIT Non-Profit avg 5,761,410 58.7* 25.2 71STATUS stdv 10,732,900 26.2 29.5 257N 76 76 76 74Profit avg 125,595,413 32.2* 103.9* 34stdv 560,535,131 29.7 94.5 109N 28 28 28 28Note: Standard deviations are listed below the group averages. The averages are calculated on the basis of the values between second and the 99thpercentiles for each group. Therefore, sample sizes (N) vary across indicators. Group averages different from average for all MFIs at 1 percentsignificance 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.## 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).56 MICROBANKING BULLETIN, APRIL 2001

BULLETIN HIGHLIGHTS AND TABLESTOTALSTAFFTotal number ofemployees(no.)TOTALGROSS LOANPORTFOLIOtotal gross portfoliooutstanding(US$)PERCENTWOMENBORROWERStotal number of activewomen borrowers /total number of activeborrowers (%)NUMBER OFACTIVEBORROWERSnumber ofborrowers withloansoutstanding (no.)AVERAGELOAN BALANCEtotal gross loanportfolio / numberof active borrowers(US$)DEPTHaverage loanbalance /GNP per capita(%)68 2,119,661 76.0* 11,378 166* 51.740 2,611,185 23.6 7,755 165 53.824 24 20 24 24 241,354* 51,908,531* 74.7 301,190* 299 40.73,252 180,641,187 28.8 755,687 675 63.522 22 21 22 22 2232* 3,426,730 40.8* 1,958* 1,975* 146.5*19 2,351,058 7.0 1,391 1,023 94.812 12 11 12 12 12104 6,866,582 60.7 12,408 695 44.481 9,095,332 23.8 11,268 650 40.146 52 43 52 52 521,112* 73,649,013* 46.0 185,251* 1,131* 106.0*3,347 215,304,242 20.2 633,497 1,237 112.315 15 8 15 15 1561 4,526,240 40.8* 8,066 831* 67.7*24 2,428,382 5.9 7,387 486 34.58 14 14 14 14 14170 4,174,406 75.3* 30,103 448 41.6620 9,260,401 26.3 130,852 684 53.668 68 62 68 68 6891 6,454,998 48.4 8,749 1,093* 52.264 7,065,019 20.4 7,423 889 23.610 10 10 10 10 10163 4,198,161 73.8* 28,638 482 46.1590 8,820,801 26.8 123,821 744 59.375 76 67 76 76 76635 43,096,972* 50.5 103,939 1,048* 106.5*2,466 158,756,562 19.9 464,813 1,057 145.328 28 20 28 28 28MICROBANKING BULLETIN, APRIL 2001 57

BULLETIN HIGHLIGHTS AND TABLESTABLE B: PROFITABILITY AND EFFICIENCY INDICATORSCRITERIAADJUSTEDRETURN ONASSETSadjusted netoperating income /average totalassets(%)ADJUSTEDRETURN ONEQUITYadjusted netoperating income/ average totalequity(%)OPERATIONALSELF-SUFFICIENCYoperating income /operating expense(%)FINANCIALSELF-SUFFICIENCYadjusted operatingincome /adjusted operatingexpense(%)PORTFOLIOYIELDoperating income –accrued interest –interest and feeincome frominvestments /average gross loanportfolio (%)AGE New (1 - 2 years) avg -9.8* -21.5* 93.0 76.2* 36.6stdv 10.4 25.7 30.2 21.3 13.6N 28 28 28 28 28Young (3 - 6 years) avg -4.0 5.4 101.7 87.7 41.3stdv 7.1 69.3 29.3 25.7 21.6N 26 26 26 26 26Mature (> 6 years) avg -1.0 0.9 115.0* 100.2* 42.6stdv 8.0 24.1 37.6 28.2 20.2N 63 63 63 63 63SCALE OF Large avg 2.3* 7.4* 126.4* 114.3* 33.8OPERATIONS # stdv 5.6 26.6 37.1 26.3 10.8N 20 20 20 20 20Medium avg -3.4 -9.9 106.5 90.6 38.8stdv 8.8 27.7 35.0 24.6 18.4N 66 66 66 66 66Small avg -9.6* 4.1 94.3 78.7* 52.2*stdv 12.3 114.2 28.4 25.7 21.9N 32 32 32 32 32LENDING Individual avg -1.2 -6.7 118.5* 100.7* 33.7METHOD- (1 borrower) stdv 7.4 49.2 38.7 34.2 15.2OLOGY N 52 52 52 52 52Solidarity 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 12.6borrowers) N 43 43 43 43 43Village Banking avg -5.5 -5.4 103.0 87.8 57.1*(groups with ≥ 10 stdv 13.7 17.5 35.1 24.6 26.4borrowers) N 23 23 23 23 23LEVEL 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 14.6INTER- ≥ 20% of total assets) N 29 29 29 29 29MEDIATIONOther 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 91TARGET 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 23.2Balance < US$150) N 57 57 57 57 57Broad avg -2.7 -3.1 106.0 92.6 33.2*(depth between 20% and stdv 6.9 22.2 27.8 24.8 12.0149%) N 51 51 51 51 51High-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 10Note: Standard deviations are listed below the group averages. The averages are calculated on the basis of the values between second and the 99th percentilesfor each group. Therefore, sample sizes (N) vary across indicators. Group averages different from average for all MFIs at 1 percent significance level are markedwith 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.58 MICROBANKING BULLETIN, APRIL 2001

BULLETIN HIGHLIGHTS AND TABLESPORTFOLIOAT RISK >90 DAYSoutstandingbalance ofloans overdue >90 days / totalgross loanportfolio(%)TOTAL ADMIN.EXPENSERATIOadministrativeexpense + inkinddonations/ total averageassets(%)TOTAL ADMIN.EXPENSE/ LPadministrativeexpense + inkinddonations/ average loanportfolio(%)SALARYEXPENSE/LPpersonnelexpense + inkinddonationsaverage grossloan portfolio(%)DEPTHaverageloanbalance/GNPper capita(%)AVERAGESALARYaveragepersonnelexpense + inkinddonations /GNP per capita(multiple ofGNP/ capita)STAFFPRODUCTIVITYnumber of activeborrowers /number of staff(no.)LOANOFFICERPRODUCTIVITYnumber of activeborrowers /number of loanofficers(no.)COST/BORROWERadministrativeexpense + inkinddonations /average numberof activeborrowers(US$)1.1 25.4* 42.1* 24.0* 70.2 7.0 96* 188* 1841.7 13.2 32.8 20.4 69.3 5.0 45 109 18423 28 28 28 28 28 28 26 211.7 21.5* 36.5 19.8 81.3 7.5 126 295 1161.8 10.8 20.2 11.3 145.2 5.3 63 232 11521 26 26 26 26 26 26 25 262.8 19.9* 30.0 16.6 47.2 5.5 158* 340 883.1 13.3 20.8 12.9 50.8 4.6 122 299 7854 63 63 63 63 56 56 55 572.2 12.2* 16.7* 9.1* 98.7 6.6 130 285 1451.1 5.5 8.7 5.1 91.2 4.6 79 125 11619 20 20 20 20 20 20 19 202.0 21.8* 30.9 17.2 64.6 6.5 151 345 1222.4 13.1 19.2 12.4 101.7 5.2 118 327 16452 66 66 66 66 61 61 56 552.5 28.2* 52.2* 29.3* 30.9 5.7 100 205 1083.7 12.6 26.9 15.8 27.6 5.3 51 115 11228 32 32 32 32 32 32 32 302.5 14.0* 20.7* 10.3* 103.0 5.1 102 229 1913.3 7.4 11.8 6.8 122.7 4.1 78 199 19339 52 52 52 52 47 47 41 482.1 25.7* 41.3* 24.5* 39.4 7.5 133 272 882.3 11.2 25.1 16.9 31.7 5.9 70 152 6638 43 43 43 43 42 42 42 351.5 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 2722 23 23 23 23 23 23 23 222.7 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 31117 29 29 29 29 23 23 27 182.1 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 21283 91 91 91 91 91 91 79 902.2 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 5651 57 57 57 57 57 57 57 512.6 16.6* 23.5* 12.3* 64.8 6.8 113 252 1342.2 8.6 13.9 8.5 29.3 4.8 62 176 9439 51 51 51 51 45 45 39 450.7 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 2049 10 10 10 10 10 10 10 9MICROBANKING BULLETIN, APRIL 2001 59

BULLETIN HIGHLIGHTS AND TABLESTABLE B: PROFITABILITY AND EFFICIENCY INDICATORS (continued)CRITERIAADJUSTEDRETURN ONASSETSadjusted netoperating income /average totalassets(%)ADJUSTEDRETURN ONEQUITYadjusted netoperating income/ average totalequity(%)OPERATIONALSELF-SUFFICIENCYoperating income /operating expense(%)FINANCIALSELF-SUFFICIENCYadjusted operatingincome /adjusted operatingexpense(%)PORTFOLIOYIELDoperating income –accrued interest –interest and feeincome frominvestments /average gross loanportfolio (%)REGION ## Africa avg -9.8* 1.8 88.8* 82.7 45.6stdv 11.6 73.9 42.7 40.0 18.8N 24 24 24 24 24Asia avg -2.6 -3.2 109.8 92.6 33.6(All) stdv 7.9 25.4 38.5 30.3 15.3N 22 22 22 22 22Eastern Europe avg -2.8 -9.7 107.4 90.0 29.5*stdv 3.1 11.8 12.8 11.4 5.6N 12 12 12 12 12Latin America avg -1.6 -7.3 113.8* 97.5 45.8stdv 8.1 35.9 27.6 23.7 21.4N 52 52 52 52 52CHARTER Bank avg -0.7 -9.1 107.6 97.9 41.2TYPE stdv 6.4 76.7 23.3 22.9 15.5N 15 15 15 15 15Credit Union/ avg -1.6 -5.2 109.3 92.5 21.8*Cooperative stdv 4.4 11.9 24.9 21.4 4.7N 14 14 14 14 14NGO avg -5.3 -9.1 106.4 89.0 47.6*stdv 11.8 23.5 35.9 27.5 22.3N 68 68 68 68 68Non-Bank ### avg -4.4 -1.5 96.4 87.3 32.2stdv 5.6 17.0 16.7 15.3 6.7N 10 10 10 10 10PROFIT Non-Profit avg -5.1 -9.3 107.0 90.3 45.4STATUS stdv 12.0 23.6 38.5 28.2 22.0N 76 76 76 76 76Profit avg -2.1 5.3 106.2 96.9 38.2stdv 6.8 86.3 30.7 27.8 14.4N 28 28 28 28 28Note: Standard deviations are listed below the group averages. The averages are calculated on the basis of the values between second and the 99thpercentiles for each group. Therefore, sample sizes (N) vary across indicators. Group averages different from average for all MFIs at 1 percent significancelevel 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.## 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 intermediaries (NBFIs).60 MICROBANKING BULLETIN, APRIL 2001

BULLETIN HIGHLIGHTS AND TABLESPORTFOLIOAT RISK >90 DAYSoutstandingbalance ofloans overdue >90 days / totalgross loanportfolio(%)TOTAL ADMIN.EXPENSERATIOadministrativeexpense + inkinddonations/ total averageassets(%)TOTAL ADMIN.EXPENSE/ LPadministrativeexpense + inkinddonations/ average loanportfolio(%)SALARYEXPENSE/LPpersonnelexpense + inkinddonationsaverage grossloan portfolio(%)DEPTHaverageloanbalance/GNPper capita(%)AVERAGESALARYaveragepersonnelexpense + inkinddonations /GNP per capita(multiple ofGNP/ capita)STAFFPRODUCTIVITYnumber of activeborrowers /number of staff(no.)LOANOFFICERPRODUCTIVITYnumber of activeborrowers /number of loanofficers(no.)COST/BORROWERadministrativeexpense + inkinddonations /average numberof activeborrowers(US$)2.0 30.3* 57.5* 30.9* 51.7 12.4* 154* 393* 662.2 16.5 30.4 17.5 53.8 6.5 83 272 3422 24 24 24 24 24 24 24 202.4 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 2119 22 22 22 22 22 22 22 220.6* 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 19612 12 12 12 12 12 12 12 102.6 21.1* 29.9 15.8 44.4 4.6 125 280 1412.6 11.1 16.4 10.3 40.1 3.5 56 139 12040 52 52 52 52 46 46 41 463.0 15.6* 28.5 14.6 106.0 6.0 98 263 2274.5 7.8 16.5 8.5 112.3 4.4 73 247 27713 15 15 15 15 15 15 15 151.1 9.6* 14.3* 6.0* 67.7 4.2 133 321 1031.4 3.1 5.2 3.1 34.5 4.2 88 321 454 14 14 14 14 8 8 4 131.9 26.3* 41.8* 24.0* 41.6 6.5 147 292 962.4 13.9 25.9 15.6 53.6 5.5 106 225 9863 68 68 68 68 68 68 67 612.8 17.3* 22.0 12.0 52.2 4.4 90 195 1371.4 4.5 6.9 4.3 23.6 1.5 31 96 8010 10 10 10 10 10 10 10 92.0 26.0* 40.2* 23.1* 46.1 6.6 150 299 942.5 14.5 26.5 16.6 59.3 5.7 111 234 9669 76 76 76 76 75 75 75 642.9 16.6* 26.5 13.8 106.5 6.5 103 277 1943.4 7.9 15.9 7.7 145.3 4.3 67 250 21826 28 28 28 28 28 28 28 27MICROBANKING BULLETIN, APRIL 2001 61

BULLETIN HIGHLIGHTS AND TABLESTABLE C: INSTITUTIONAL CHARACTERISTICS AND OUTREACH INDICATORS FOR FINANCIALLYSELF-SUFFICIENT MFIsCRITERIATOTALASSETS(US$)CAPITAL /ASSETRATIOadjusted totalequity /adjusted totalassets (%)COMMERCIALFUNDING LIABILITIESRATIOOFFICESborrowings at commercialrates / average gross loanportfolio(%) (no.)AGE New (1 - 2 years) avg 3,405,919 57.3 11.2 7stdv 1,582,222 24.0 18.0 7N 7 7 7 7Young (3 - 6 years) avg 6,463,658 58.1 41.5 71*stdv 5,901,443 22.6 86.6 195N 10 10 10 10Mature (> 6 years) avg 18,809,760* 45.4 62.9* 56*stdv 32,646,028 21.7 57.3 176N 41 41 41 36SCALE OF Large avg 44,759,724* 33.9* 82.4* 114*OPERATIONS # stdv 41,556,930 19.1 65.6 256N 16 16 16 16Medium avg 4,823,500 54.0 40.6 13stdv 1,768,491 24.3 39.6 13N 30 30 30 26Small avg 1,040,280* 58.9 24.8 5*stdv 293,641 15.3 28.4 3N 12 12 12 11LENDING Individual avg 12,533,710* 39.2 77.7 10METHOD- (1 borrower) stdv 14,076,097 19.9 75.7 8OLOGY N 29 29 29 25Solidarity Groups avg 18,768,985* 49.2 36.7 63*(groups of 3 to 9 stdv 28,968,183 20.3 32.4 155borrowers) N 17 17 17 15Village Banking avg 2,999,565 72.1* 18.0 61*(groups with ≥ 10 stdv 1,610,309 17.8 22.8 178borrowers) N 12 12 12 12LEVEL OF Financial Intermediaries avg 27,730,881* 19.4* 158.8* 18FINANCIAL (passbook and timestdv 27,955,717 10.5 89.5 15INTER- deposits ≥ 20% of total assets) N 12 12 12 8MEDIATIONOther avg 8,213,650 57.6 29.4 59*(passbook and time stdv 13,567,380 20.4 27.2 178< 20% of total assets) N 48 48 48 46TARGET Low-end avg 7,632,608 61.2* 36.0 91*MARKET (depth < 20% OR avg. loan stdv 16,121,525 21.4 57.3 229balance < US$ 150) N 28 28 28 27Broad avg 15,465,913* 38.6 69.5* 14(depth between 20% and stdv 21,292,070 19.0 42.0 15149%) N 25 25 25 20High-end and avg 14,479,446* 40.3 21.4 13Small Business stdv 10,887,845 20.8 21.2 9(depth ≥ 150%) N 5 5 5 5Note: Standard deviations are listed below the group averages. The averages are calculated on the basis of the values between second and the99th percentiles for each group. Therefore, sample sizes (N) vary across indicators. Group averages different from average for all MFIs at 1percent 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.62 MICROBANKING BULLETIN, APRIL 2001

BULLETIN HIGHLIGHTS AND TABLESTOTALSTAFFTotal number ofemployees(no.)TOTALGROSS LOANPORTFOLIOtotal gross portfoliooutstanding(US$)PERCENTWOMENBORROWERStotal number of activewomen borrowers /total number of activeborrowers (%)NUMBER OFACTIVEBORROWERSnumber ofborrowers withloansoutstanding (no.)AVERAGELOAN BALANCEtotal gross loanportfolio / numberof active borrowers(US$)DEPTHaverage loanbalance /GNP per capita(%)44 2,945,845 42.1 3,987 1,031* 79.9*37 1,248,620 8.5 4,152 569 62.47 7 5 7 7 7126 4,368,732 71.8 11,909 523 93.8*90 4,314,428 27.9 5,831 871 103.610 10 8 10 10 10511* 14,765,629* 63.6 105,743 566 42.21,506 26,649,320 28.2 414,289 638 40.636 41 39 41 41 411,030* 34,703,379* 50.7 247,973* 935* 95.9*2,183 34,777,173 23.0 649,553 736 83.516 16 14 16 16 16109 3,519,332 66.0 15,457 545 47.972 1,197,937 27.7 13,721 659 58.527 30 27 30 30 3045* 702,118* 67.6 4,624* 354 31.927 190,701 28.4 3,679 434 28.712 12 11 12 12 12106 9,226,150* 43.7 10,169 1,122* 96.3101 10,768,658 15.8 10,192 879 86.225 29 26 29 29 29511 14,744,774* 78.1* 91,019* 305 35.41,206 24,400,848 21.2 256,945 313 32.217 17 13 17 17 1799 2,152,440 90.3* 16,690 114* 13.7*42 1,400,570 17.7 8,151 24 8.312 12 12 12 12 12267* 20,476,770* 39.8* 19,156* 980* 80.6*180 23,804,140 10.1 20,413 506 43.68 12 12 12 12 12222 6,361,146 70.5 38,879 563 53.6733 11,155,406 27.5 154,920 770 73.148 48 41 48 48 48313 5,811,051 84.4* 58,889* 135* 13.9*953 13,605,385 20.6 201,592 84 6.128 28 24 28 28 28150 11,984,081* 48.3* 16,116 832* 69.5*149 17,442,302 18.5 18,515 424 28.921 25 23 25 25 2591 8,933,026* 32.2* 5,786 2,516* 267.9*81 4,144,927 3.4 5,071 571 44.05 5 5 5 5 5MICROBANKING BULLETIN, APRIL 2001 63

BULLETIN HIGHLIGHTS AND TABLESTABLE C: INSTITUTIONAL CHARACTERISTICS AND OUTREACH INDICATORS FOR FINANCIALLYSELF-SUFFICIENT MFIs (continued)CRITERIATOTALASSETS(US$)CAPITAL /ASSETRATIOadjusted totalequity /adjusted totalassets (%)COMMERCIALFUNDING LIABILITIESRATIOOFFICESborrowings at commercialrates / average gross loanportfolio(%) (no.)REGION ## Africa avg 5,099,987 49.5 81.5 14stdv 5,263,973 20.9 135.9 13N 4 4 4 4Asia avg 25,625,496* 54.7 22.8 297*(All) stdv 51,327,787 19.7 16.9 577N 12 12 12 12Eastern Europe avg 3,462,697 56.1 12.5 6stdv 894,196 31.9 18.5 1N 3 3 3 3Latin America avg 11,071,252* 45.4 59.6* 11stdv 11,352,239 22.0 39.7 9N 35 35 35 29CHARTER Bank avg 35,929,370* 21.0 166.6 22TYPE stdv 30,261,580 14.8 116.2 13N 8 8 8 8Credit 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 203N 35 35 35 35Non-Bank ### avg 11,445,145 27.4 70.3 11stdv 10,732,928 5.5 27.3 6N 3 3 3 3PROFIT Non-Profit avg 7,996,412 59.0 26.3 68*STATUS stdv 14,398,848 20.6 24.4 195N 39 39 39 38Profit avg 26,194,456* 28.1* 120.1* 16stdv 26,707,461 20.1 104.4 12N 14 14 14 14Note: Standard deviations are listed below the group averages. The averages are calculated on the basis of the values between second and the 99thpercentiles for each group. Therefore, sample sizes (N) vary across indicators. Group averages different from average for all MFIs at 1 percentsignificance 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.## 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).64 MICROBANKING BULLETIN, APRIL 2001

BULLETIN HIGHLIGHTS AND TABLESTOTALSTAFFTotal number ofemployees(no.)TOTALGROSS LOANPORTFOLIOtotal gross portfoliooutstanding(US$)PERCENTWOMENBORROWERStotal number of activewomen borrowers /total number of activeborrowers (%)NUMBER OFACTIVEBORROWERSnumber ofborrowers withloansoutstanding (no.)AVERAGELOAN BALANCEtotal gross loanportfolio / numberof active borrowers(US$)DEPTHaverage loanbalance /GNP per capita(%)68 4,345,934 46.0 10,131 351 109.5*28 4,793,666 24.1 3,555 359 113.34 4 4 4 4 41,239* 20,743,904* 76.8 320,267* 148* 30.02,512 41,820,342 25.0 743,347 70 26.112 12 12 12 12 1229 3,013,143 42.0 2,121 1,390* 129.5*7 984,024 7.8 919 697 62.63 3 3 3 3 3125 8,456,682* 65.0 15,234 717 45.390 10,550,492 25.6 12,572 737 42.930 35 29 35 35 35326 25,682,534* 46.9 34,389* 527 62.5142 26,298,497 21.6 21.413 387 56.68 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 5254 6,421,080 72.6 47,705 490 38.8857 12,475,421 28.0 181,049 729 55.535 35 31 35 35 35153 10,103,411* 43.0 14.272 958 62.483 10,884,914 11.5 11,163 57 29.83 3 3 3 3 3239 6,170,926 72.0 43,667 494 45.4812 11,917,456 27.8 171,686 722 66.339 39 34 39 39 39233* 19,828,815* 48.0 25,472* 827* 82.3*165 22,297,199 19.9 21,025 723 70.514 14 12 14 14 14MICROBANKING BULLETIN, APRIL 2001 65

BULLETIN HIGHLIGHTS AND TABLESTABLE D: PROFITABILITY AND EFFICIENCY INDICATORS FOR FINANCIALLY SELF-SUFFICIENTMFIsCRITERIAADJUSTEDRETURN ONASSETSadjusted netoperating income /average totalassets(%)ADJUSTEDRETURN ONEQUITYadjusted netoperating income/ average totalequity(%)OPERATIONALSELF-SUFFICIENCYoperating income /operating expense(%)FINANCIALSELF-SUFFICIENCYadjusted operatingincome /adjusted operatingexpense(%)PORTFOLIOYIELDoperating income –accrued interest –interest and feeincome frominvestments /average gross loanportfolio (%)AGE New (1 - 2 years) avg 0.6 0.5 119.4 102.8 38.2stdv 2.1 4.0 13.1 8.3 7.9N 7 7 7 7 7Young (3 - 6 years) avg 2.1* 3.7 121.8* 111.1* 52.4*stdv 4.6 8.5 33.7 22.9 23.7N 10 10 10 10 10Mature (> 6 years) avg 3.4* 11.5* 131.6* 115.1* 44.2stdv 4.9 15.6 34.7 21.4 17.9N 41 41 41 41 41SCALE OF Large avg 4.2* 16.5* 134.2* 121.8* 35.0OPERATIONS # stdv 3.7 16.3 37.0 23.4 11.3N 16 16 16 16 16Medium avg 2.8* 4.7* 128.0* 110.4* 45.2stdv 6.3 11.3 35.2 19.9 17.9N 30 30 30 30 30Small avg 1.5* 3.0 121.0* 105.8* 56.8*stdv 3.4 6.2 16.6 13.6 20.2N 12 12 12 12 12LENDING Individual avg 3.9 13.8 136.7 121.9 38.0METHOD- (1 borrower) stdv 4.1 16.2 39.4 30.9 13.8OLOGY N 29 29 29 29 29Solidarity 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 9.9N 17 17 17 17 17Village Banking avg 2.7* 3.2 126.8* 105.8* 67.6*(groups with ≥ 10 stdv 7.1 8.3 29.3 14.4 24.0borrowers) N 12 12 12 12 12LEVEL OF Financial Intermediaries avg 3.2* 19.7* 119.1* 114.6* 37.5FINANCIAL (passbook and time deposits stdv 2.9 20.5 15.5 12.3 15.3INTER- ≥ 20% of total assets) N 12 12 12 12 12MEDIATIONOther 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 48TARGET 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 21.7Balance < US$150) N 28 28 28 28 28Broad avg 3.1* 10.7* 124.0* 113.7* 36.4(depth between 20% stdv 3.1 10.7 17.4 12.5 10.5and 149%) N 25 25 25 25 25High-end and avg 2.7 2.9 134.9* 119.7* 29.1Small Business stdv 5.4 8.7 48.1 40.5 6.5(depth ≥ 150%) N 5 5 5 5 5Note: Standard deviations are listed below the group averages. The averages are calculated on the basis of the values between second and the 99thpercentiles for each group. Therefore, sample sizes (N) vary across indicators. Group averages different from average for all MFIs at 1 percent significancelevel 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 40 for details.66 MICROBANKING BULLETIN, APRIL 2001

BULLETIN HIGHLIGHTS AND TABLESPORTFOLIOAT RISK >90 DAYSoutstandingbalance ofloans overdue >90 days / totalgross loanportfolio(%)TOTAL ADMIN.EXPENSERATIOadministrativeexpense + inkinddonations/ total averageassets(%)TOTAL ADMIN.EXPENSE/ LPadministrativeexpense + inkinddonations/ average loanportfolio(%)SALARYEXPENSE/LPpersonnelexpense + inkinddonationsaverage grossloan portfolio(%)DEPTHaverageloanbalance/GNPper capita(%)AVERAGESALARYaveragepersonnelexpense + inkinddonations /GNP per capita(multiple ofGNP/ capita)STAFFPRODUCTIVITYnumber of activeborrowers /number of staff(no.)LOANOFFICERPRODUCTIVITYnumber of activeborrowers /number of loanofficers(no.)COST/BORROWERadministrativeexpense + inkinddonations /average numberof activeborrowers(US$)0.7 21.6* 27.5 14.9 79.9 6.3 83 144 1731.0 4.3 7.2 3.1 62.4 2.7 26 53 917 7 7 7 7 7 7 7 41.2 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 1138 10 10 10 10 10 10 10 102.0 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 7835 41 41 41 41 36 36 36 382.1 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 11516 16 16 16 16 16 16 16 161.3 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 4525 30 30 30 30 27 27 27 231.7 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 8510 12 12 12 12 12 12 12 121.6 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 11524 29 29 29 29 25 25 25 272.0 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 5215 17 17 17 17 17 17 17 131.1 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 1812 12 12 12 12 12 12 12 121.9 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 38 133 847 12 12 12 12 8 8 8 121.7 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 8545 48 48 48 48 48 48 48 401.6 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 2725 28 28 28 28 28 28 28 261.8 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 6620 25 25 25 25 21 21 21 211.1 13.4* 16.3 9.3 267.9 11.1* 60* 154 2640.8 4.7 6.2 3.9 44.0 2.0 15 64 1345 5 5 5 5 5 5 5 5MICROBANKING BULLETIN, APRIL 2001 67

BULLETIN HIGHLIGHTS AND TABLESTABLE D: PROFITABILITY AND EFFICIENCY INDICATORS FOR FINANCIALLY SELF-SUFFICIENTMFIs (continued)CRITERIAADJUSTEDRETURN ONASSETSadjusted netoperating income /average totalassets(%)ADJUSTEDRETURN ONEQUITYadjusted netoperating income/ average totalequity(%)OPERATIONALSELF-SUFFICIENCYoperating income /operating expense(%)FINANCIALSELF-SUFFICIENCYadjusted operatingincome /adjusted operatingexpense(%)PORTFOLIOYIELDoperating income –accrued interest –interest and feeincome frominvestments /average gross loanportfolio (%)REGION ## Africa avg 5.4* 10.9 157.4* 146.4* 51.2stdv 4.0 9.7 63.2 62.9 20.8N 4 4 4 4 4Asia avg 2.8* 6.0* 133.8* 113.5* 38.2(All) stdv 4.2 10.1 34.4 20.1 11.5N 12 12 12 12 12Eastern Europe avg 1.1 -0.1 123.0 105.3 32.1stdv 2.9 5.3 5.5 12.4 0.4N 3 3 3 3 3Latin America avg 2.8* 8.8* 122.7* 110.1* 48.4*stdv 4.9 12.3 24.7 16.6 18.7N 35 35 35 35 35CHARTER Bank avg 3.8* 22.0* 118.6 114.6* 50.1*TYPE stdv 4.3 25.5 18.7 15.7 13.8N 8 8 8 8 8Credit Union/ avg 2.9 8.1 122.5 115.4* 24.0*Cooperative stdv 2.8 6.0 15.9 13.1 3.3N 5 5 5 5 5NGO avg 2.8* 5.1* 130.8* 109.6* 50.2*stdv 6.0 10.4 30.2 18.3 21.0N 35 35 35 35 35Non-Bank ### avg 1.7 11.3 110.4 104.8 34.5stdv 2.8 12.6 5.0 10.4 5.9N 3 3 3 3 3PROFIT Non-Profit avg 2.8* 5.0* 131.8* 110.7* 48.8*STATUS stdv 6.0 10.4 35.5 20.4 20.6N 39 39 39 39 39Profit avg 3.4* 17.9* 122.3* 117.0* 43.5stdv 3.7 20.5 31.6 24.7 15.7N 14 14 14 14 14Note: Standard deviations are listed below the group averages. The averages are calculated on the basis of the values between second and the 99th percentiles foreach group. Therefore, sample sizes (N) vary across indicators. Group averages different from average for all MFIs at 1 percent significance level are marked with anasterisk (*). 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## 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).68 MICROBANKING BULLETIN, APRIL 2001

BULLETIN HIGHLIGHTS AND TABLESPORTFOLIOAT RISK >90 DAYSTOTAL ADMIN.EXPENSERATIOTOTAL ADMIN.EXPENSE/ LPSALARYEXPENSE/LPDEPTHAVERAGESALARYSTAFFPRODUCTIVITYLOANOFFICERPRODUCTIVITYCOST/BORROWERoutstandingbalance ofloans overdue >90 days / totalgross loanportfolio(%)administrativeexpense + inkinddonations/ total averageassets(%)administrativeexpense + inkinddonations/ average loanportfolio(%)personnelexpense + inkinddonationsaverage grossloan portfolio(%)averageloanbalance/GNPper capita(%)averagepersonnelexpense + inkinddonations /GNP per capita(multiple ofGNP/ capita)number of activeborrowers /number of staff(no.)number of activeborrowers /number of loanofficers(no.)administrativeexpense + inkinddonations /average numberof activeborrowers(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 40 199 384 4 4 4 4 4 4 4 41.8 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 2111 12 12 12 12 12 12 12 120.3 17.9* 22.1 12.5 129.5 8.4 74 120 1950.5 0.5 0.4 0.3 62.6 1.8 14 14 623 3 3 3 3 3 3 3 21.9 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 9829 35 35 35 35 30 30 30 301.3 18.2* 32.8 17.3 62.5 7.3 108 315* 1500.8 5.7 18.0 8.1 56.6 5.3 30 144 1178 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 - - - 51.7 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 8633 35 35 35 35 35 35 35 302.4 16.2 20.2 11.2 62.4 4.7 90 210 1130.3 4.1 8.9 3.2 29.8 0.6 32 86 353 3 3 3 3 3 3 3 31.8 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 8339 39 39 39 39 39 39 39 331.4 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 10014 14 14 14 14 14 14 14 14MICROBANKING BULLETIN, APRIL 2001 69

BULLETIN HIGHLIGHTS AND TABLESComposition of Additional Analysis GroupingsAGE #New(1 - 2 years)Young(3 - 6 years)Mature(> 6 years)23 de JulioAl AmanaAMKBanco do PovoBanco PeqEmpresa15 de AbrilACLEDAAgrocapitalAl MajmouaABAAcepACODEPAcredicomActuarADOPEMADRIAGAPEAKRSPAsawinso RBBasixBospoConstantaFINCA TanzaniaFinsolCEAPE/ Pernamb.CERUDEBCiti S&LEnlaceASABAACBanco AdemiBancosolBank Dagang BaliBRACBRIBURO TangailCaja de Los AndesCalpiaFOCCASKash FoundationLOKMercy CorpsMEBFatenFauluFEFADFINCA EcuadorCAMCARD BANKCDSChispaChumiquenáCM ArequipaCMM MedellínCompartamosContigoCOOSAJOMicrofund for WomenMikrofinMoznostiNachalaNOAFINCA KyrgystanFINCA MalawiFINCA PeruFundusz MikroCrecerEcosabaEMTFAMAFEDFIEFinaméricaFINCA HondurasFINCA MexicoFINCA NicaraguaPAMÉCASPortosolPride UgandaSagrarioSEDAFONDECO HatthaKakserkarInicjatywa MikroNirdhanFINCA UgandaFMM PopayánFWWB CaliFWWB IndiaHublagKafo JiginewKWFTMibancoManya Krobo RBMoyutánSunriseTulcánVital-FinanceVivacredWV BosniaNetwork LeasingOscusPADMEPiyeliNsoatreman RBNyésigisoPride Vita GuineaPRODEMProEmpresaRSPISartawiSEEDSSEFSHAREPride TanzaniaProMujerSinapi Aba TrustWAGESTonantelTSPIUNRWAUWFTWRHondurasSCALE OF OPERATIONS ##LargeMediumSmallABAAcepACLEDAASA15 de Abril23 de JulioACODEPAcredicomActuarADOPEMAKRSPAl AmanaAl MajmouaBasixADRIAGAPEAMKAsawinso RBBanco do PovoAgrocapitalBAACBank Dagang BaliBRACBURO TangailBanco PeqEmpresaCAMCARD BANKCEAPE/ Pernamb.ChispaChumiquenáCMM MedellínCrecerCitiS&LBospoCDSConstantaContigoFauluBRIBanco AdemiBancosolCaja de Los AndesCompartamosEcosabaEmprenderEMTEnlaceFAMAFatenFEFADFINCA HondurasFINCA KyrgystanFEDFINCA EcuadorFINCA MalawiFINCA MexicoFINCA NicaraguaCalpiaCERUDEBCM ArequipaCOOSAJOFINCA UgandaFMM PopayánFONDECOFWWB IndiaFinsolKWFTKafo JiginewLOKMercy Corps MEBFINCA PeruFINCA TanzaniaFOCCASHublagHattha KakserkarFIEFundusz MikroFWWBCaliFinaméricaMikrofinMoznostiNetwork LeasingNOANsoatreman RBNyésigisoOscusPADMEPAMÉCASPride TanzaniaInicjatywa MikroKash FoundationMicrofund for WomenManya Krobo RBMoyutánMibancoPRODEMPride UgandaPride Vita GuineaPortosolProEmpresaProMujerSEEDSSEFSHARESunriseSagrarioNirdhanNachalaPiyeliRSPISinapi Aba TrustSartawiTonantelTSPITulcánUNRWAWAGESWRHondurasWV BosniaSEDAUWFTVital-FinanceVivacredLENDING METHODOLOGYIndividual(1 borrower)SolidarityGroups(groups of 3to 9borrowers)VillageBanking(groups with ≥10 borrowers)15 de Abril23 de JulioABAAcepACODEPAcredicomADRIAgrocapitalACLEDAActuarADOPEMAl AmanaASABancosolBasixAGAPEAKRSPAl MajmouaCAMCiti S&LAMKAsawinso RBBAACBanco AdemiBanco do PovoBanPeqEmpresaBank Dagang BaliBRIBospoBRACBURO TangailCARD BANKCEAPE/ Pernamb.ChispaConstantaCompartamosCrecerFINCA EcuadorFINCA HondurasFINCA KyrgystanCaja de Los AndesCalpiaCDSCERUDEBChumiquenáCM ArequipaCMM MedellínCOOSAJOContigoEMTEnlaceFAMAFatenFauluFinaméricaFINCA MalawiFINCA MexicoFINCA NicaraguaFINCA PeruFINCA TanzaniaEcosabaEmprenderFEDFEFADFIEFMM PopayánFWWBCaliHattha KakserkarFinsolFundusz MikroFONDECOKash FoundationKWFTMibancoMikrofinFINCA UgandaFOCCASFWWB IndiaMicrofund for WomenProMujerHublagInicjatywa MikroKafo JiginewLOKMercy CorpsMEBManya Krobo RB.MoyutánNirdhanNsoatreman RBNyésigisoPAMÉCASPiyeliPride TanzaniaPride UgandaSartawiSEDASEEDSWAGESWRHondurasMoznostiNachalaNetwork LeasingNOAOscusPADMEPortosolProEmpresaPride Vita GuineaPRODEMRSPISEFSHARETSPIUNRWASagrarioSinapi Aba TrustSunriseTonantelTulcánVivacredUWFTVital-FinanceWV 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.70 MICROBANKING BULLETIN, APRIL 2001

BULLETIN HIGHLIGHTS AND TABLESComposition of Additional Analysis Groupings, ctd.LEVEL OF FINANCIAL INTERMEDIATIONRetail Financial 15 de Abril23 de JulioIntermediary (passbooAcredicomand time deposits ≥ 20%Assawinso RBof total assets)BAACBanco AdemiOther(passbook and timedeposits < 20% of totalassets)ABAAcepACLEDAACODEPActuarADOPEMADRIAGAPEAgrocapitalAKRSPAl AmanaAl MajmouaAMKASABancosolBank Dagang BaliBRICaja de Los AndesCERUDEBChumiquenáBanco do PovoBanPeqEmpresaBasixBospoBRACBURO TangailCalpiaCAMCARD BANKCDSCEAPE/ Pernamb.ChispaCMM MedellínCompartamosCiti S&LCM ArequipaCOOSAJOEcosabaEnlaceFEFADConstantaContigoCrecerEmprenderEMTFAMAFatenFauluFEDFINCA EcuadorFINCA HondurasFINCA KyrgystanFINCA MalawiFINCA MexicoFIEFinaméricaKafo JiginewManya Krobo RBMoyutánNsoatreman RBFINCA NicaraguaFINCA PeruFINCA TanzaniaFINCA UgandaFinsolFundusz MikroFMM PopayánFOCCASFONDECOFWWBCaliFWWB IndiaHattha KakserkarHublagInicjatywa MikroNyésigisoOscusPAMÉCASSagrarioTonantelTulcánKash FoundationKWFTLOKMercy CorpsMEBMicrofund for WomenMibancoMikrofinMoznostiNachalaNirdhanNetwork LeasingNOAPADMEUWFTPiyeliPortosolPride TanzaniaPride UgandaPride Vita GuineaPRODEMProEmpresaProMujerRSPISartawiSinapi Aba TrustSEDASEEDSSEFSHARESunriseTSPIUNRWAVital-FinanceVivacredWAGESWRHondurasWV BosniaTARGET MARKETLow-end(depth < 20% OR avg.loan balance< US$150)Broad(depth between 20%and 149%)High-end (depth ≥150%)Small Business(depth ≥ 250%)ActuarAGAPEAKRSPAl AmanaAl MajmouaAssawinso RBASABRACBURO Tangail15 de Abril23 de JulioABAACLEDAACODEPAcredicomADOPEMADRIAMKBanco AdemiAcepAgrocapitalCAMCARD BANKCDSCEAPE/ Pernamb.CMM MedellínCompartamosConstantaContigoCrecerBAACBanco do PovoBancosolBanPeqEmpresaBasixBospoBRICaja de Los AndesMoznostiSunriseBank Dagang BaliCERUDEBEmprenderEMTEnlaceFatenFEDFINCA EcuadorFINCA HondurasFINCA KyrgystanFINCA MalawiCalpiaChispaChumiquenáCitiS&L CMArequipaCOOSAJOEcosabaFAMAWV BosniaFEFADMEBFINCA MexicoFINCA NicaraguaFINCA PeruFINCA TanzaniaFINCA UgandaFMM PopayánFOCCASFWWBCaliFWWB IndiaFauluFIEFinamérica FinsolFundusz MikroFONDECOHattha KakserkarInicjatywa MikroNetwork LeasingHublagKash FoundationMicrofund for WomenMibancoManya Krobo RBNirdhanNsoatreman RBPAMÉCASPiyeliKafo JiginewKWFTLOKMercy CorpsMikrofinMoyutánNachalaNOAPride TanzaniaPride UgandaProMujerRSPISinapi Aba TrustSEDASEEDSSEFSHARENyésigisoOscusPADMEPortosolPride Vita GuineaPRODEMProEmpresaSagrarioTSPIUWFTVivacredWAGESWRHondurasSartawiTonantelTulcánUNRWAVital-FinanceREGIONAfricaAsia(all)EasternEuropeLatinAmericaMiddle East/North AfricaAcepAssawinso RBCERUDEBCiti S&LACLEDAAKRSPASABAACAMKBospo15 de Abril23 de JulioACODEPAcredicomActuarADOPEMADRIAGAPEABAAl AmanaFauluFINCA MalawiFINCA TanzaniaFINCA UgandaBasixBank Dagang BaliBRACBRIFEFADFundusz MikroAgrocapitalBanco AdemiBanco do PovoBancosolBanPeqEmpresaCaja de Los AndesCalpiaCAMAl MajmouaFatenFOCCASKafo JiginewKWFTManya Krobo RBBURO TangailCARD BANKCDSConstantaInicjatywa MikroLOKCEAPE/ PernambucoChispaChumiquenáCM ArequipaCMM MedellínCompartamos ContigoCOOSAJOMicrofund for WomenUNRWANsoatreman RBNyésigisoPADMEPAMÉCASEMTFINCA KyrgystanFWWB IndiaHattha KakserkarMercy CorpsMEBCrecerEcosabaEmprenderEnlaceFAMAFEDFIEFinaméricaPiyeliPride TanzaniaPride UgandaPride Vita GuineaHublagKash FoundationNirdhanNetwork LeasingMikrofinMoznostiFINCA EcuadorFINCA HondurasFINCA MexicoFINCA NicaraguaFINCA PeruFinsolFMM PopayánSinapi Aba TrustSEDASEFUWFTRSPISEEDSSHARETSPINachalaNOAFONDECOFWWBCaliMibancoMoyutánOscusPortosolPRODEMProEmpresaVital-FinanceWAGESSunriseWV BosniaProMujerSagrarioSartawiTonantelTulcánVivacredWR HondurasMICROBANKING BULLETIN, APRIL 2001 71

BULLETIN HIGHLIGHTS AND TABLESComposition of Additional Analysis Groupings, ctd.CHARTER#BankCredit Union/CooperativeNGONon-Bank ###ACLEDAAsawinso RBBAAC15 de Abril23 de JulioAcepABAACODEPActuarADOPEMADRIAGAPEAKRSPAMKASAAgrocapitalBasixCaja de Los AndesCalpiaBank Dagang BaliBRIBanco AdemiAcredicomChumiquenáCOOSAJOAl AmanaAl MajmouaBanco do PovoBospoBRACBURO TangailCAMCDSCEAPE/ Pernamb.ChispaFatenFinaméricaBanPeqEmpresaBancosolCARD BANKEcosabaKafo JiginewMoyutánCMM MedellínConstantaContigoCrecerCompartamosEMTFAMAFauluFEDFIEFundusz MikroInicjatywa MikroCERUDEBCM ArequipaEnlaceNachalaNyésigisoOscusFINCA EcuadorFINCA HondurasFINCA KyrgystanFINCA MalawiFINCA MexicoFINCA NicaraguaFINCA PeruFINCA TanzaniaFINCA UgandaFMM PopayánNetwork LeasingTSPIFEFADMEBManya Krobo RBPAMÉCASSagrarioTonantelFOCCASFONDECOFWWBCaliFWWB IndiaHublagKash FoundationKWFTLOKMicrofund for WomenMikrofinMibancoNsoatreman RBTulcánMoznostiNirdhanPride TanzaniaPride Vita GuineaPRODEMPortosolProMujerRSPISEDASEEDSSEFSHARESunriseSartawiUNRWAUWFTVivacredWAGESWRHondurasWV BosniaPROFIT STATUS#Non-profitProfitABAAcepACODEPActuarADOPEMADRIAGAPEAgrocapitalAKRSPAl AmanaAl MajmouaAMKACLEDAAsawinso RBBAACBanco AdemiBancosolASABanco do PovoBospoBRACBURO TangailCAMCDSCEAPE/ Pernamb.ChispaCMM MedellínCompartamosConstantaBanPeqEmpresaBasixBank Dagang BaliBRICaja de Los AndesContigoCrecerEMTFAMAFatenFEDFIEFINCA EcuadorFINCA HondurasFINCA KyrgystanFINCA MalawiFINCA MexicoCalpiaCARD BANKCERUDEBCitiS&LCM ArequipaFINCA NicaraguaFINCA PeruFINCA TanzaniaFINCA UgandaFMM PopayánFOCCASFONDECOFWWBCaliFWWB IndiaHattha KakserkarHublagKafo JiginewEnlaceFauluFEFADFinaméricaFundusz MikroKash FoundationKWFTLOKMicrofund for WomenMikrofinMoznostiNirdhanNOANyésigisoPAMÉCASPiyeliPortosolInicjatywa MikroMEBMibancoManya Krobo RB.NachalaPride TanzaniaPride UgandaPride Vita GuineaPRODEMProMujerRSPISartawiSEDASEEDSSEFSHARESunriseNetwork LeasingNsoatreman RBPADMEProEmpresaSinapi Aba TrustTSPIUNRWAVivacredWAGESWRHondurasWV Bosnia# Some institutions did not report the information.### Includes Ltd., financieras, and non-bank financial intermediary (NBFIs).72 MICROBANKING BULLETIN, APRIL 2001

APPENDICESAPPENDICESAppendix I: Notes to Statistical SectionThe MicroBanking Standards Project, of which TheMicroBanking Bulletin is a major output, is open toall MFIs that are willing to disclose financial datathat meet a simple quality test. Participating MFIstypically have three characteristics: 1) they arewilling to be transparent by submitting theirperformance data to an independent agency; 2)they display a strong social orientation by providingfinancial services to low-income persons; and 3)they are able to answer all the questions needed forour analysis.The one hundred and twenty four institutions thatprovided data for this issue represent a largeproportion of the world’s leading microfinanceinstitutions. They have provided data generally bycompleting a detailed questionnaire, supplementedin most cases by additional information. Allparticipating MFIs receive a customized reportcomparing their results with those of the peergroups.Data Quality IssuesThe Bulletin classifies information from participatinginstitutions according to the degree to which wehave independent verification of its reliability. AAAgradedinformation has been independentlygenerated through a detailed financial analysis byan independent third party, such as a CAMELevaluation, a CGAP appraisal, or assessments byreputed rating agencies. A-graded information isbacked by accompanying documentation, such asaudited financial statements, annual reports, andindependent program evaluations that provide areasonable degree of confidence for ouradjustments. B-graded information is from MFIsthat have limited themselves to completing ourquestionnaire. These grades signify confidencelevels on the reliability of the information; they areNOT intended as a rating of the financialperformance of the MFIs.The criteria used in constructing the StatisticalTables are important for understanding andinterpreting the information presented. Given thevoluntary nature and origin of the data, the Bulletinstaff and Editorial Board, and CGAP cannot acceptresponsibility for the validity of the resultspresented, or for consequences resulting from theiruse. We employ a system to make tentativedistinctions about the quality of data presented tous and include only information for which we have areasonable level of comfort. However, we cannotexclude the possibility of a programmisrepresenting its results.The most delicate areas of potential distortion are:(1) unreported subsidies and (2) misrepresentedloan portfolio quality. There can also beinaccuracies in reporting the costs of financialservices in multipurpose institutions that alsoprovide non-financial services, in part because ofdifficulties in assigning overhead costs. These risksare highest for younger institutions, and forinstitutions with a record of optimistic disclosure. Ifwe have grounds for caution about the reliability ofan MFI’s disclosure, we will not include itsinformation in a peer group unless it has beenexternally validated by a third-party.Adjustments to Financial DataThe Bulletin adjusts the financial data it receives toensure comparable results. The financialstatements of each organization are converted tothe standard chart of accounts used by the Bulletin.This chart of accounts is simpler than that used bymost MFIs, so the conversion consists mainly ofconsolidation into fewer, more general accounts.Then three major adjustments are applied toproduce a common treatment for the effect of: a)inflation, b) subsidies, and c) loan loss provisioningand write-off. In the statistical tables the reader cancompare adjusted and unadjusted results.InflationThe Bulletin reports the net effect of inflation bycalculating increases in expenses and incomes dueto inflation. Inflation causes a decrease in the realvalue of equity. This “cost of funds” is obtained bymultiplying the prior year-end equity balance by thecurrent-year inflation rate. 15 Fixed asset accounts,on the other hand, are revalued upward by thecurrent year’s inflation rate, which results in inflationadjustment income, offsetting to some degree the15Inflation data are obtained from line 64x of the InternationalFinancial Statistics, International Monetary Fund, various years.MICROBANKING BULLETIN, APRIL 2001 73

APPENDICESexpense generated by adjusting equity. 16 On thebalance sheet, this inflation adjustment results in areordering of equity accounts: profits areredistributed between real profit and the nominalprofits required to maintain the real value of equity.MFIs that borrow from banks or mobilize savingshave an actual interest expense, which is anoperating cost. In comparison, similar MFIs thatlend only their equity have no interest expense andtherefore have lower operating costs. If an MFIfocuses on sustainability and the maintenance of itscapital/asset ratio, it must increase the size of itsequity in nominal terms to continue to make thesame value of loans in real (inflation-adjusted)terms. Inflation increases the cost of tangible itemsover time, so that a borrower needs more money topurchase them. MFIs that want to maintain theirsupport to clients must therefore offer larger loans.Employees’ salaries go up with inflation, so theaverage loan balance and portfolio must increase tocompensate, assuming no increase in interestmargin. Therefore, a program that funds its loanswith its equity must maintain the real value of thatequity, and pass along the cost of doing so to theclient. This expectation implies MFIs should “pay”interest rates that include the inflation-adjustmentexpense as a cost of funds, even if this cost is notactually paid to anyone outside the institution.Some countries with high or volatile levels ofinflation require businesses to use inflation-basedaccounting on their audited financial statements.We use this same technique in the Bulletin. Ofcourse, we understand that in countries where highor volatile inflation is a new experience, MFIs mayfind it difficult to pass on the full cost of inflation toclients. We are not recommending policy; rather,we are trying to provide a common analyticalframework that compares real financialperformance meaningfully.SubsidiesWe adjust participating organizations’ financialstatements for the effect of subsidies byrepresenting the MFI as it would look on anunsubsidized basis. We do not intend to suggestwhether MFIs should or should not be subsidized.Rather, this adjustment permits the Bulletin to seehow each MFI would look without subsidies forcomparative purposes. Most of the participatingMFIs indicate a desire to grow beyond thelimitations imposed by subsidized funding. Thesubsidy adjustment permits an MFI to judgewhether it is on track toward such an outcome. A16In fact, an institution that holds fixed assets equal to its equityavoids the cost of inflation that affects MFIs, which hold much oftheir equity in financial form.focus on sustainable expansion suggests thatsubsidies should be used to enhance financialreturns. The subsidy adjustment simply indicatesthe extent to which the subsidy is being passed onto clients through lower interest rates or whether itis building the MFI’s capital base for furtherexpansion.The Bulletin adjusts for three types of subsidies: (1)a cost-of-funds subsidy from loans at below-marketrates, (2) current-year cash donations to fundportfolio and cover expenses, and (3) in-kindsubsidies, such as rent-free office space or theservices of personnel who are not paid by the MFIand thus not reflected on its income statement.Additionally, for multipurpose institutions, TheMicroBanking Bulletin attempts to isolate theperformance of the financial services program,removing the effect of any cross subsidization.The cost-of-funds adjustment reflects the impact ofsoft loans on the financial performance of theinstitution. The Bulletin calculates the differencebetween what the MFI actually paid in interest on itssubsidized liabilities and the deposit rate for eachcountry. 17 This difference represents the value ofthe subsidy, which we treat as an additionalfinancial expense. We apply this subsidy to thoseloans to the MFI that are priced at less than 75percent of prevailing market (deposit) rates. Thedecreased profit is offset by generating an“accumulated subsidy adjustment” account on thebalance sheet.If the MFI passes on the interest rate subsidy to itsclients through a lower final rate of interest, thisadjustment may result in an operating loss. If theMFI does not pass on this subsidy, but instead usesit to increase its equity base, the adjustmentindicates the amount of the institution’s profits thatwere attributable to the subsidy rather thanoperations.Loan Loss ProvisioningFinally, we apply standardized policies for loan lossprovisioning and write-off. MFIs vary tremendouslyin accounting for loan delinquency. Some count theentire loan balance as overdue the day a paymentis missed. Others do not consider a loan delinquent17Data for shadow interest rates are obtained from line 60l of theInternational Financial Statistics, IMF, various years. Thedeposit rate is used because it is a published benchmark in mostcountries. Sound arguments can be made for use of differentshadow interest rates. NGOs that wish to borrow from bankswould face interest significantly higher than the deposit rate. Alicensed MFI, on the other hand, might mobilize savings at alower financial cost than the deposit rate, but reserverequirements and administrative costs would drive up the actualcost of such liabilities.74 MICROBANKING BULLETIN, APRIL 2001

APPENDICESuntil its full term has expired. Some MFIs write offbad debt within one year of the initial delinquency,while others never write off bad loans, thus carryingforward a hard-core default that they have littlechance of ever recovering.We classify as “at risk” any loan with a paymentover 90 days late. We provision 50 percent of theoutstanding balance for loans between 90 and 180days late, and 100 percent for loans over 180 dayslate. Wherever we have adequate information, weadjust to assure that all loans are fully written offwithin one year of their becoming delinquent.(Note: We apply these provisioning and write-offpolicies for ease of use and uniformity. We do notrecommend that all MFIs use exactly the samepolicies.) In most cases, these adjustments are notvery precise. Nevertheless, most participating MFIshave high-quality loan portfolios, so loan lossprovision expense is not an important contributor totheir overall cost structure. If we felt that a programdid not fairly represent its general level ofdelinquency, and we were unable to adjust itaccordingly, we would simply exclude it from thepeer group.Financial Statement Adjustments and their EffectsAdjustment Effect on Financial Statements Type of Institution Most Affectedby AdjustmentInflation adjustment of equityReclassification of certain long termliabilities into equity, and subsequentinflation adjustmentSubsidy adjustment: Interest savingson subsidized liabilities involving atleast a 25 percent discount in relationto market based loans to the sameinstitution or, in the absence of suchloans, the deposit rateSubsidy adjustment: Current-yearcash donations to cover operatingexpensesSubsidy adjustment: In kind donationof goods or services (e.g., line staffpaid for by technical assistanceproviders)Loan loss provision and write-offadjustment: Applying policies whichmay be more aggressive than the MFIemploys on its own booksIncreases financial expense accountson profit and loss statement, to somedegree offset by inflation incomeaccount for revaluation of fixed assets.Generates inflation adjustment accountin equity section of balance sheet withnet balance of inflation adjustments.Decreases concessionary loan accountand increases equity account;increases inflation adjustment on profitand loss statement and balance sheet.Increases financial expense on profitand loss statement. Increases subsidyadjustment account on balance sheet.Reduces operating income on profit andloss statement (if the MFI recordsdonations as operating income).Increases subsidy adjustment accounton balance sheet.Increases expense on profit and lossstatement, increases subsidyadjustment account on balance sheet.Increases loan loss provision expenseon profit and loss statement. Onbalance sheet, increases in loan lossreserve and/or write-offs are accountedfor by equal reductions in loan lossreserve and portfolio.NGOs funded more by equity thanby liabilities will be hard hit,especially in high-inflationcountries.NGOs that have long-term lowinterest“loans” from internationalagencies that function more asdonations than loans.Banks or NGOs that use large linesof credit from governments orinternational agencies at highlysubsidized rates.NGOs during their start-up phase.This adjustment is relatively lessimportant for mature institutionsincluded in this edition.NGOs during their start-up phase.Less important for matureinstitutions included in this edition.MFIs that allow bad loans toaccumulate within their portfolio.This common problem tends tohave a limited effect on leadingMFIs because their loan losses arelow, even after adjustment.MICROBANKING BULLETIN, APRIL 2001 75

APPENDICESStatistical IssuesThe Bulletin reports the means and standarddeviations of the performance indicators for eachpeer group. At this stage, peer groups are stillsmall and the observations in each peer groupshow a high variation. Outliers distort the results ofsome of the peer group averages. Consequently,the reader should be cautious about the interpretivepower of these data. Over time, as more MFIsprovide data, we will be in a better position togenerate deeper and more sophisticated types ofanalyses of the data at our disposal, and will have ahigher degree of comfort with the statisticalsignificance of the differences between the meansof the distinct peer groups.To ensure that the averages reported represent thegroup as accurately as possible, we have excludedoutliers for each of the indicators. Statistics for thecategory All MFIs were calculated by deletingobservations in the first and last deciles for eachindicator. In other words, the values between the11th and 89th percentiles were used for theanalysis. For the FSS sample and peer groupcalculations, the first and last percentileobservations were excluded for each indicatorexcept macroeconomic indicators. The averagesare calculated on the basis of the values betweenthe 2nd and the 99th percentiles for each group. Ineffect, for each indicator we rank the MFIs in thegroup and eliminate the top and bottom values. Inmost cases, this exclusion eliminates twoobservations for each peer group: the institutionwith the highest and the lowest value on eachindicator. In cases where indicators containobservations with tied values for highest and lowestvalues, more than two observations are deleted.This method helps to prevent outliers fromdominating group results, and smoothes the data byminimizing data dispersion. Where the sample sizeis reduced to n=1, we have not reported the resultso as to maintain confidentiality.We have carried out statistical tests to determinethe impact of outliers where they exist, and toquantify the results in terms of how well theyrepresent the peer groups. Where large differencesexist between the means of different peer groups orgroups sorted by selection criteria, we have verifiedtheir statistical significance using t-tests. Thesetests compare the mean of the group to the mean ofall MFIs in the sample, taking into account factorslike the number of observations and the dispersionof the sample. The test statistic is then comparedto a standard critical level (using one percent as thesignificance level) to decide whether the differencebetween the group and the sample as a whole isstatistically significant. In other words, they allowus to decide whether the difference we see isrobust, by considering it in the context of howcohesive and how large the group is.76 MICROBANKING BULLETIN, APRIL 2001

APPENDICESAppendix II: Description of Participating MFIsACRONYM NAME, LOCATION DATE15 de Abril Cooperativa 15 de Abril,Ecuador23 de Julio Cooperativa 23 de Julio,EcuadorABAACEPACLEDAACODEPAcredicomActuarADOPEMADRIAGAPEAgrocapitalAKRSPAl MajmouaAl AmanaAlexandria BusinessAssociation,EgyptAgence de Crédit pourl’Enterprise Privée,SenegalAssociation of CambodianLocal EconomicDevelopment Agencies,CambodiaAsociación de Consultorespara el Desarrollo de laPequeña, Mediana yMicroempresa,NicaraguaAcredicom,GuatemalaCorporación Acción por elTolima - ActuarFamiempresas,ColombiaAsociación Dominicanapara el Desarrollo de laMujer,Dominican RepublicAsociación para elDesarrollo Rural Integrado,Costa RicaAsociación General paraAsesorar PequeñasEmpresas,ColombiaFundación Agrocapital,BoliviaAga Khan Rural SupportProgramme,PakistanLebanese Association forDevelopment -- AlMajmoua,LebanonAssociation Al Amana,MoroccoDATAQUALITYGRADEDESCRIPTION OF MICROFINANCE PROGRAM09/00 A 15 de Abril is a credit union in Ecuador that has participated inWOCCU’s technical assistance program since in 1995. 15 de Abriloffers both credit and voluntary savings services to members.09/00 A 23 de Julio participates in WOCCU’s technical assistance program inEcuador. It is a credit union offering credit and savings services tomembers.12/99 AAA ABA provides credit to small and microenterprises using anindividual lending methodology. It is an NGO founded in 1988 andbased 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 hasexpanded to operate in other urban areas in Senegal. It hasconverted to a credit union.12/99 AAA ACLEDA was started in 1993 as an NGO. It provides small andmicro loans to enterprises and trains entrepreneurs in smallbusiness management. Both group and individual loans are made.12/99 A Founded in 1989, ACODEP serves small and micronterprisesprimarily in Managua and other urban areas of Nicaragua. It iscurrently negotiating a voluntary supervision agreement with theSuperintendent of Banks in Nicaragua.09/00 A ACREDICOM is a member of the FENACOAC credit union system inGuatemala, and participated in WOCCU’s technical assistanceprogram. It primarily lends for agriculture and to a lesser extentmicroenterprise activities, and mobilizes savings from members.12/99 B ACTUAR Tolima was founded in 1986. It is an NGO offering loansto microenterprises in Tolima and surrounding areas, and is affiliatedwith ACCION International and Cooperativa Emprender in Colombia.12/99 A ADOPEM, an affiliate of Women’s World Banking, is an NGOdedicated to credit for women microentrepreneurs. It has been inoperation since 1982.12/99 A ADRI is an NGO offering loans to small and microenterprises inCosta Rica. Founded in 1986, it also offers training and businessdevelopment services to its clients.12/99 A Founded in 1975, AGAPE operates principally in Barranquilla,offering microcredit through a mixture of methodologies includingvillage banking, solidarity groups and individual loans. It is anaffiliate 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. Itis an NGO founded in 1992, and offers a mixture of microloans andlonger 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-typeservices in both urban and rural areas. The program beganoperations in 1994 as a project of Save the Children. Ownershipwas transferred to the Lebanese institution in 1998.06/00 AAA Al Amana offers solidarity group loans through a wide network ofbranches in urban areas of Morocco. Founded in 1997, it is anaffiliate of Pride Vita.MICROBANKING BULLETIN, APRIL 2001 77

APPENDICESACRONYM NAME, LOCATION DATEAMKARBASABAACBanco AdemiBanco doPovoBancoSolBasixBDBBospoBPEBRACBRIBUROTangailCaja de LosAndesAMK Posusje,Bosnia and HerzegovinaAsawinso Rural Bank,GhanaAssociation for SocialAdvancement,BangladeshBank for Agriculture andAgricultural Cooperatives,ThailandBanco de DesarrolloAdemi, S.A.,Dominican RepublicBanco do Povo de Juiz deFora,BrazilBanco Solidario, S.A.,BoliviaBharatiya SamruddhiFinance Ltd.,IndiaBank Dagang Bali,IndonesiaBospo,Bosnia and HerzegovinaBanco de la PequeñaEmpresa, S.A.,Dominican RepublicBangladesh RuralAdvancement Committee,BangladeshBank Rakyat Indonesia,Unit Desa System,IndonesiaBURO, Tangail,BangladeshCaja de Ahorros y CréditosLos Andes,BoliviaDATAQUALITYGRADEDESCRIPTION OF MICROFINANCE PROGRAM12/99 B AMK is a limited liability company founded in 1997 to providemicrocredit to low income self-employed individuals in urban areas.It is financed by the Local Initiatives Department In Bosnia that aimsto improve access to credit to the poor to promote economicreconstruction.12/99 A The rural bank was started in 1983 and it now provides group andindividual loans, and deposit services to farmers, microentrepreneursand civil servants in rural Ghana.12/99 AAA ASA is an NGO that offers credit services to the rural poor inBangladesh. The majority of its clients are landless women. It wasfounded in 1978 and shifted from an earlier, integrated developmentstrategy 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 smallfarmers and farmers’ cooperatives. Founded in 1966, its outreach inrural areas of Thailand is now estimated to cover more than 80% offarm families.12/99 A Banco ADEMI is a formal financial institution, which beganoperations 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 Forain Brazil. It offers individual loans to microentrepreneurs and wasfounded 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 creditprogram focuses on group loans, and it operates primarily in urbanareas of Bolivia. It grew out of the NGO PRODEM and was spun offas 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 financialservices to the rural poor, to promote self-employment, and toprovide technical assistance to clients and rural financial institutions.12/98 AAA Bank Dagang is a private commercial bank that offers savings andcredit facilities to primarily low-income clients in Bali. It was foundedin 1970.12/99 B BOSPO is a NGO founded in 1995 to provide microcredit to solidaritygroups made of low income women entrepreneurs in secondarycities of Tuzla. It is financed by the Local Initiatives Department inBosnia that aims to improve access to credit to the poor to promoteeconomic reconstruction.12/98 AAA Banco de la Pequeña Empresa was created to serve bothmicroenterprises and small businesses, and has just completed itsfirst year of operations. It s a formal financial sector institution andholds a license to operate as a development bank. It is an affiliate ofACCION International.12/99 AAA BRAC is an NGO that started in 1972. It provides both financial andnon-financial services primarily in rural areas. The financial servicesinclude 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 anextensive network of small banking units, which function as profitcenters and provide individual loans and savings services. Thesystem has existed in its current form since 1984.12/99 AAA Flexible voluntary open-savings, microloans and insurance servicesare provided by BURO Tangail since 1990. It is an NGO.12/99 A Caja Los Andes grew out of ProCrédito, an NGO that began lendingoperations in 1992. It was converted to a special finance company in1995. Los Andes operates in urban and some rural areas in Bolivia,providing individual loans and savings services.78 MICROBANKING BULLETIN, APRIL 2001

APPENDICESACRONYM NAME, LOCATION DATECalpiáCAMCARDCDSFinanciera Calpiá, S.A.,El SalvadorCentro de Apoyo a laMicroempresa,El SalvadorCenter for Agriculture andRural Development,The PhilippinesCommunity DevelopmentSociety,IndiaDATAQUALITYGRADEDESCRIPTION OF MICROFINANCE PROGRAM12/98 AAA Financiera Calpiá began as an NGO, AMPES, and was convertedinto a finance company in 1995. It offers individual loans tomicroenterprises and small businesses and has started to mobilizesavings. It operates mainly in urban areas, although 25% of itsportfolio is now in rural areas.12/99 B FINCA’s affiliate in El Salvador, the CAM was founded in 1990 and isone of FINCA’s largest affiliates serving over 16,000 clients in all 15geographic departamentos in El Salvador.12/99 A CARD started as an NGO in 1986 and is now partially transformedinto a rural bank. It is an affiliate of CASHPOR and Women’s WorldBanking. It makes loans and collects deposits.03/99 A CDS offers microcredit and non-financial services in the Nagpurregion of India. It was founded in 1985 and is an affiliate ofOpportunity International.CEAPE/PECERUDEBCiti S&LChispaChuimequenáCM ArequipaCMM/MedellínCompartamosConstantaContigoCOOSAJOCrecerCentro de Apoio aosPequeños EmpreendimentosPernambuco,BrazilCentenary RuralDevelopment Bank,UgandaCiti Savings & Loans,GhanaFundación Chispa,NicaraguaCooperativa San MiguelChuimequená,GuatemalaCajas Municipales deArequipa,PeruCorporación Mundial de laMujer Medellín, Medellín,ColombiaAsociación ProgramaCompartamos, I.A.P.,MexicoConstanta,GeorgiaFundación Contigo,ChileCooperativa San JoséObrero,GuatemalaCrecer,Bolivia12/99 A CEAPE Pernambuco is an urban-based microenterprise creditprogram. A member of the FENAPE network in Brazil, and ofACCION International, it was founded in 1992.12/99 A CERUDEB was founded as a trust company in 1983, and obtainedits banking license in 1992. It received technical assistance fromIPC from 1993-98. CERUDEB provides credit and savings servicesin Kampala and Uganda’s district towns.12/99 B Citi Savings is a private non-bank financial institution that operates inGreater Accra, Ghana. It lends to rotating savings and creditassociations (susu clubs) and informal savings collectors, andmobilizes savings from the public.12/99 B Founded in 1991, CHISPA works primarily in urban areas ofNicaragua. It is affiliated with the Mennonite Economic DevelopmentAssociation (MEDA).09/00 A San Miguel Chuimequená is a Guatemalan credit union. It is amember of the FENACOAC system and it participates in WOCCU’stechnical assistance program. It offers loans and savings services toits members.12/99 A The municipal savings and credit banks of Peru are owned by citygovernments. Arequipa is one of the largest and most successfulbanks of the national network, and offers pawn and microenterpriseloans 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 in1985 and lends to both men and women.12/99 B Compartamos is the lending arm of Gente Nueva, a Mexican NGOthat was founded in 1985. The program uses a village bankingmethodology focusing on women, in rural and semi-urban areas ofMexico. It began lending in 1990.12/99 A Constanta was established in 1997 with a grant from UNHCR/Savethe Children as a local NGO to provide group loans to poor selfemployedwomen.12/99 A CONTIGO began lending operations in 1989, and offers creditservices to microentrepreneurs in communities in the south ofSantiago de Chile.09/00 A San José Obrero is a member of the FENACOAC credit unionfederation, and participated in WOCCU’s technical assistanceprogram in Guatemala. It offers loans and savings services to itsmembers.12/99 AAA CRECER is an NGO working primarily in rural areas of Bolivia. Itparticipates in Freedom from Hunger’s “Credit with Education”program, using a village banking methodology.MICROBANKING BULLETIN, APRIL 2001 79

APPENDICESACRONYM NAME, LOCATION DATEDATAQUALITYGRADEDESCRIPTION OF MICROFINANCE PROGRAMEcosabaEcosaba,GuatemalaEmprender Fundación Emprender,ArgentinaEMT Ennathian MoulethanTchonnebat,CambodiaEnlaceFAMAFatenFauluFEDFEFADFIEFinaméricaFINCA ECFINCA HOFINCA KYFINCA MAFINCA MXFINCA PEFINCA NIFINCA TZPrograma Enlace, BancoSolidario,EcuadorFundación de Apoyo a laMicroempresa,NicaraguaPalestine for Credit andDevelopment,West Bank and GazaFood for the HungryInternational,UgandaFundación Ecuatoriana deDesarrollo,EcuadorFoundation for EnterpriseFinance and Development,AlbaniaFFP - Fomento aIniciativas Económicas,S.A.,BoliviaFinanciera América, S.A.,ColombiaFINCA Ecuador,EcuadorFINCA Honduras,HondurasFINCA Kyrgyzstan,KyrgyzstanFINCA Malawi,MalawiFINCA México,MexicoFINCA Perú,PeruFINCA Nicaragua,NicaraguaFINCA TanzaniaTanzania09/00 A ECOSABA is a member of the FENACOAC credit union federation,and participated in WOCCU’s technical assistance program inGuatemala. It offers loans and savings services to its members.04/00 A Emprender, founded in 1992, is an ACCION affiliate that offersmicroenterprise credit in urban areas of Argentina. The majority of itslending is to solidarity groups.12/99 A EMT was founded in 1991 as a rural credit project run by the Frenchagency, GRET. It is in the process of transformation to anindependent Institution, and operates in rural areas in the south ofCambodia. 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 receivestechnical assistance from ACCION International. ENLACE offersboth credit and savings services to microentrepreneurs. It alsoadministers a pawn-lending product.12/99 A FAMA operates mainly in urban areas of Nicaragua, providingmicroenterprise credit. It was founded in 1991 and is affiliated withACCION.12/99 A FATEN was initiated as a Save the Children affiliate in 1995 andspun-off as an independent NGO in 1999. It provides microcredit topoor 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 servicesto small and microentrepreneurs in urban and semi-urban areas.12/99 A Founded over 30 years ago, FED has an extensive branch networkthroughout Ecuador providing individual microloans. It is an affiliateof ACCION International.12/99 A Operating mainly in urban areas of Albania, FEFAD offers smallbusiness loans. It was founded in 1995 as an initiative of theAlbanian and German governments, and receives technicalassistance from IPC.12/99 A FFP - FIE is a for-profit financial institution offering individual loans tomicroenterprises in urban areas of Bolivia. It began lending in 1988as an NGO, and began operating as a “Private Financial Fund” in1998 under regulation by the Bolivian Superintendency of Banks.12/99 AAA Finamérica is a regulated finance company operating in Bogotá andsurrounding areas. Its predecessors were the NGO Actuar Bogotá,founded in 1988, the NGO Corposol, and the financiera Finansol. Itis an affiliate of ACCION International.12/99 B FINCA Ecuador was founded in 1994 and provides village bankingservices 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 ofportfolio size. It was founded in 1989 and operates in 13 of the 18departamentos of Honduras.08/00 B Founded in 1995, FINCA Kyrgyzstan is operating in five of the sixoblasts of Kyrgyzstan and offers both village banking and individualloan 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 stateof Morelos. It was founded in 1989.12/99 B FINCA Perú is primarily based in rural areas, offeringmicroenterprise credit to borrowers in Lima, Ayacucho, andHuancavelica. It was founded in 1993.06/99 A FINCA’s Nicaraguan affiliate began lending in 1992, and has sinceexpanded to have branch offices in several urban areas inNicaragua.08/00 B The MFI was formed in 1998 as an affiliate of FINCA International. Itprovides loans through village banks.80 MICROBANKING BULLETIN, APRIL 2001

APPENDICESACRONYM NAME, LOCATION DATEFINCA UGFinsolFMM PopayánFOCCASFONDECOFunduszMikroFWWB CaliFWWB IndiaHatthaKaksekarHublagInicjatywaMikroKafo JiginewKASHFKWFTLOKMCMEBMibancoFINCA Uganda,UgandaFinanciera Solidaria S.A.,HondurasFundación Mundo MujerPopayán,ColombiaFoundation for Credit andCommunity Assistance,UgandaFondo de DesarrolloComunal,BoliviaFundusz Mikro,PolandFundación Women’s WorldBanking Cali,ColombiaFriends of WWB,IndiaHattha Kakesekar,CambodiaHublag DevelopmentFinance Programme,PhilippinesInicjatywa Mikro,PolandKafo Jiginew,MaliKash Foundation,PakistanKenya Women FinanceTrust,KenyaLOK Sarajevo,Bosnia and HerzegovinaMercy Corps,Bosnia and HerzegovinaMicroenterprise Bank,BosniaBanco de laMicroempresa,PeruDATAQUALITYGRADEDESCRIPTION OF MICROFINANCE PROGRAM12/99 AAA One of FINCA’s largest programs, FINCA Uganda has been inoperation since 1992. The program offers village banking servicesto over 16,000 women in Kampala, Jinja and Lira.12/99 B Finsol (ex. FUNADEH) works with small and microenterprises in urbanareas of Honduras. It is an affiliate of ACCION International and wasfounded in 1985.12/99 B FMM Popayán is a Women’s World Banking affiliate working in thestate of Cauca in Colombia. It began lending to microenterprises in1985.12/98 B FOCCAS, an affiliate of Freedom from Hunger, operates a villagebanking-style program in Uganda’s district towns and villages. It isbased on a credit with education model.12/99 A FONDECO is an NGO working primarily in rural areas in Bolivia. Itwas founded in 1995.09/99 A Fundusz Mikro began operations in 1995, and now lends tomicroentrepreneurs across Poland through an extensive branchnetwork. It is a member of the MicroFinance Network.12/99 A FWWB Cali, an affiliate of Women’s World Banking, began lending in1982. It makes individual loans to urban microenterprises in Cali.03/00 AAA FWWB India lends to rural women through savings and creditgroups. It was founded in 1982.06/00 AAA Hattha Kaksekar was founded in 1996. The non-profit Associationoffers commercial loans and agricultural credit to entrepreneurs inurban and rural areas in the North-Western and central parts ofCambodia.12/98 A The Hublag Development Finance Programme is the microlendingarm of the Gerry Roxas Foundation. It lends to microenterprises withboth individual and group lending methodologies, and beganoperations in 1987.12/99 A Inicjatywa Mikro lends to microenterprises mainly in urban areas ofPoland. It is affiliated with Opportunity International.12/99 B Kafo Jiginew is a federation of credit unions operating in rural areasin 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 lowincome women entrepreneurs in rural and urban areas. It is anaffiliate of ASA, Bangladesh.03/00 B Started as an affiliate of Women’s World Banking in 1992, KWFTprovides loans to women in six regions of Kenya. It has now growninto the largest MFI in Kenya.12/99 B LOK is a NGO founded in 1997 to provide individual credit to smallentrepreneurs in urban and rural areas. It is financed by the LocalInitiatives Department that aims to improve access to credit to thepoor to promote economic reconstruction.12/00 B MC is an NGO that started its operation in 1997 and providesindividual credit to microenterprises in war affected areas. Amongothers, it is also financed by the Local Initiatives Department inBosnia that aims to improve access to credit to the poor to promoteeconomic reconstruction.12/99 A The Microenterprise Bank was launched by IPC in 1997 to providefinancial services such as loans, money transfers and depositservices to micro and small enterprises in Bosnia-Herzegovina.12/99 A Mibanco is a commercial microfinance bank offering microenterprisecredit in Lima, and is affiliated with ACCION International. Formerlyoperated as an NGO under the name Acción Comunitaria del Perú,the institution was transformed into a bank in 1998.MICROBANKING BULLETIN, APRIL 2001 81

APPENDICESACRONYM NAME, LOCATION DATEMicrofundfor WomenMikrofinMoyutánMKRBMoznostiNachalaNLCNirdhanNOANRBNyésigisoOscusPADMEPAMÉCASPiyeliPortosolPRIDE TZMicrofund for Women,JordanMikrofin,Bosnia and HerzegovinaCooperativa Moyután,GuatemalaManya Krobo Rural Bank,GhanaMoznosti,MacedoniaNachala,BulgariaNetwork LeasingCorporation Ltd.,PakistanNirdhan Utthan,NepalNOA,CroatiaNsoatreman Rural Bank,GhanaRéseau Nyésigiso,MaliCooperativa Oscus Ltda.,EcuadorAssociation pour laPromotion et l’Appui auDéveloppement desMicroEntreprises,BeninProgramme d’Appui auxMutuelles d’Epargne et deCrédit au Sénégal,SenegalAssociation Piyeli,MaliPortosol,BrazilPromotion of RuralInitiatives andDevelopment Enterprises,TanzaniaDATAQUALITYGRADEDESCRIPTION OF MICROFINANCE PROGRAM12/99 A This former Save the Children village banking program in Jordan wasfounded in 1994. It focuses primarily on Palestinian women fromsquatter communities.12/99 B MIKROFIN is an affiliate of CARE international and started itsoperations in 1997. It provides individual and group loans tomicroentrepreneurs in semi-urban areas. It is financed by the LocalInitiatives Department.09/00 A Moyután is a member of the FENACOAC credit union federation, andparticipated in WOCCU’s technical assistance program inGuatemala. It offers loans and savings services to its members.12/99 AAA Started as a rural bank in 1978, MKRB provides group and individualloans, and deposit services to farmers, micro-entrepreneurs and civilservants.12/99 A Moznosti, an affiliate of Opportunity International, began lending in1996. It operates both in urban and rural areas of Macedonia, andlends to microenterprises and small businesses.12/99 B Nachala, an affiliate of Opportunity International, converted into acooperative in 1998. It operates both in urban and rural areas andmakes individual loans to microenterprises and small businesses forworking capital.06/99 A Network Leasing is a private for profit financial company which offersfinancial services to microentrepreneurs. It uses leasing, amethodology considered compatible with Islamic law, which forbidsborrowing on interest.06/99 AAA Nirdhan is an NGO founded in 1991. It is a Grameen replicateproviding credit and deposit services to the poor. Both compulsoryand voluntary deposits services are offered. The NGO hastransformed into Nirdhan Utthan Bank Limited in July 1999. It is amember of the CASHPOR network.12/98 B NOA, an affiliate of Opportunity International, was started in 1997 toprovide individual and group loans to self employed persons inagriculture and small businesses.12/99 A The rural bank was formed in 1984 to provide credit and depositservices in Brong Ahafo region in Ghana to farmers, microentrepreneursand civil servants.12/99 A Established in 1990 as a credit union, Nyésigiso offers credit andsavings services to both men and women in urban and rural areas ofMali.09/00 A Oscus is a credit union in Ecuador, and it participates in WOCCU’stechnical assistance program. Oscus offers both credit and voluntarysavings 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 in1993.12/99 A Pamécas was established as a credit union in 1996. It offers a widerange of savings and credit services, primarily to women, usingindividual, solidarity and village banking products in urban and periurbanSenegal. It is a member of the Development InternationalDesjardins network.12/99 B Piyeli is an Association that was created in 1995. It offers solidaritygroup loans to microentrepreneurs in urban and rural areas aroundBamako, as well as voluntary savings.12/99 AAA Portosol is an NGO operating in Porto Alegre in Brazil. It offersindividual loans to microentrepreneurs and was founded in1996.12/99 A PRIDE offers microcredit in urban and semi-urban areas ofTanzania. It was founded in 1993.82 MICROBANKING BULLETIN, APRIL 2001

APPENDICESACRONYM NAME, LOCATION DATEPRIDE UGPride VitaGuineaPRODEMProEmpresaProMujerRSPISagrarioSartawiSATSEDASEEDSSEFSHARESUNRISETonantelTSPITulcánUNRWAPromotion of RuralInitiatives andDevelopment Enterprises,UgandaPride Finance Guinea,Republic of GuineaFundación para laPromoción y Desarrollo dela Microempresa,BoliviaEDYPME ProEmpresa,PeruProMujer,BoliviaRangtay Sa PagrangayInc.,PhilippinesCooperativa El Sagrario,Ltda.,EcuadorServicio Financiero Rural,Fundación Sartawi,BoliviaSinapi Aba Trust,GhanaSmall EnterpriseDevelopment Agency,TanzaniaSarvodaya EconomicEnterprises DevelopmentSociety,Sri LankaSmall EnterpriseFoundation,South AfricaSociety for HelpingAwakening Rural poorthrough Education,IndiaSUNRISE SarajevoBosnia and HerzegovinaCooperativa Tonantel,GuatemalaTSPI DevelopmentCorporation,PhilippinesCooperativa Tulcán, Ltda.,EcuadorUnited Nations ReliefWorks Agency,GazaDATAQUALITYGRADEDESCRIPTION OF MICROFINANCE PROGRAM12/98 A PRIDE in Uganda was started in 1996. It provides microloans toborrowers organized as groups in urban and semi-urban areas ofUganda.12/98 AAA Pride Vita (or Pride Finance) works primarily in urban and semiurbanareas of Guinea and was founded in 1991.12/99 B PRODEM began in 1986 as an NGO offering group loans to urbanmicroenterprises, and was the precursor to BancoSol. When itsurban portfolio was passed to BancoSol in 1992, it began to developa new clientele in rural areas in Bolivia.12/99 A ProEmpresa, formerly the IDESI network, is now operating as aformal financial institution in Peru.12/99 A ProMujer Bolivia was founded in 1991, to provide training and creditto predominantly women clients.12/98 A RSPI, an Opportunity International partner, lends primarily to selfhelpgroups in the Cordillera and Iloco regions of the Philippines.09/00 A El Sagrario is a credit union in Ecuador, and participates inWOCCU’s technical assistance program, begun in 1995. It offersboth credit and voluntary savings services to members.12/98 A Fundación Sartawi offers group credit to producers and othermicroenterprises in rural areas of Bolivia. The credit program hasoperated in its current form since 1990.12/99 A The Sinapi Aba Trust is a member of Opportunity International, andoffers individual and group loans both in rural and urban areas ofGhana. It was founded in 1995.09/00 AAA SEDA was started in 1996 as an affiliate of World Vision to providefinancial services to women through village banking methodology inTanzania.03/99 B SEEDS was established in 1987 to provide loans for employmentcreation and increasing standard of living, to mobilize depositsthrough compulsory and voluntary savings programs and to providelife and natural disaster insurances.06/00 A SEF is an NGO working in the Northern Province of South Africa. Itworks 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. Itis a member of the CASHPOR network.12/99 B SUNRISE is a NGO founded in 1997 to provide individual credit tostart-up and established micro enterprises. It is financed by theLocal Initiatives Department that aims to improve access to credit tothe 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 inGuatemala. It offers loans and savings services to its members.06/99 A TSPI operates in urban and semi-urban areas of the Philippines,offering group loans to microenterprises. It was founded in 1981 andis affiliated to the Opportunity Network, the MicroFinance Networkand CASHPOR, among others.09/00 A Tulcán is a credit union in Ecuador, and participates in WOCCU’stechnical assistance program, begun in 1995. It offers both creditand voluntary savings services to members.12/99 B The Income Generation Program of UNRWA lends tomicroenterprises and small businesses in Gaza. It began operationsin 1991.MICROBANKING BULLETIN, APRIL 2001 83

APPENDICESACRONYM NAME, LOCATION DATEDATAQUALITYGRADEDESCRIPTION OF MICROFINANCE PROGRAMUWFTVital-FinanceVivacredWAGESWR HondurasWVBUganda Women’s FinanceTrust,UgandaVital-Finance,BeninVivacred,BrazilWomen and Associationsfor Gain both Economicand Social,TogoWorld Relief Honduras,HondurasWorld Vision,Bosnia12/99 A Uganda Women’s Finance Trust offers solidarity group and individualloans to women in Kampala and district towns of Uganda. It is anaffiliate of Women’s World Banking.06/00 AAA From 1998-2000, Vital-Finance was an NGO, offering individual andsolidarity group loans to small and microentrepreneurs in Benin’srural areas. It is now functioning as an Association.12/99 A Vivacred is an NGO operating in Rio de Janeiro in Brazil. It offersindividual loans to microentrepreneurs, and was founded in 1997.12/99 A WAGES serves women in Lomé and surrounding areas, workingwith 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 ofCOVELO network and network of NGOs FODIPREH. It offers a mixof individual, solidarity and village banking loan products to women inurban and semi-urban areas in Honduras.09/99 A Founded in 1996 as an affiliate of World Vision, the NGO providesindividual and group loans to self-employed small andmicroentrepreneurs.84 MICROBANKING BULLETIN, APRIL 2001