THE MICROBANKING BULLETIN No. 20 - Microfinance Information ...

The Premier Source ofIndustry BenchmarksTHEMICROBANKINGBULLETIN No. 20Published byMicrofinance Information eXchangeIssue No. 20September 2010A publication dedicated to the performance of organizations thatprovide banking services for the poor

TheMicroBankingBulletinIssue No. 20September 2010A Publication Dedicated to the Performance ofOrganizations that Provide Banking Services for the PoorCopyright (c) 2009Microfinance Information Exchange, Inc.ISSN 1934–3884. Copyright 2009. All rights reserved. The data in this volume have been carefully compiled and arebelieved to be accurate. Such accuracy is not however guaranteed. Feature articles in MBB are the property of the authorsand permission to reprint or reproduce these should be sought from the authors directly. The publisher regrets it cannotenter into correspondence on this matter. Otherwise, no portion of this publication may be reproduced in any formator by any means including electronically or mechanically, by photocopying, recording or by any information storage orretrieval system, or by any form or manner whatsoever, without prior written consent of the publisher of the publication.Designed by: Macro Graphics Pvt. Ltd., www.macrographics.com

The MicroBanking Bulletin (MBB)The MicroBanking Bulletin is one of the principalpublications of MIX (Microfinance Information Exchange,Inc.). MIX is a non-profit company that works to supportthe growth and development of a healthy microfinancesector. MIX is supported by the Consultative Groupto Assist the Poor (CGAP), Citi Foundation, DeutscheBank Americas Foundation, Omidyar Network, Bill andMelinda Gates Foundation, and others. To learn moreabout MIX, please visit the website at www.themix.org.PurposeBy collecting financial and portfolio data providedvoluntarily by leading microfinance institutions (MFIs),organizing the database by peer groups, and reportingthis information, MIX is building infrastructure that iscritical to the development of the microfinance sector. Theprimary purpose of this database is to help MFI managersand board members understand their performance incomparison to other MFIs. Secondary objectives includeestablishing industry performance standards, enhancingthe transparency of financial reporting, and improvingthe performance of microfinance institutions.Benchmarking ServicesTo achieve these objectives, MIX provides the followingbenchmarking services: 1) the Bulletin’s Tables; 2)customized financial performance reports; and 3)network services.MFIs participate in the MicroBanking Bulletin benchmarksdatabase on a quid pro quo basis. They provide MIXwith information about their financial and portfolioperformance, as well as details regarding accountingpractices, subsidies, and the structure of their liabilities.Participating MFIs must submit substantiatingdocumentation, such as audited financial statements,annual reports, ratings, institutional appraisals, and othermaterials that help us understand their operations. Withthis information, we apply adjustments for inflation,subsidies and loan loss provisioning in order to createcomparable results. Data are presented in the Bulletinanonymously within peer groups. While MIX performsextensive checks on the consistency of data reported,we do not independently verify the information.In return, participating institutions receive acomparative performance report (CPR). Theseindividualized benchmark reports, which are animportant output of the benchmarks database, explainthe adjustments we made to the data, and compare theinstitution’s performance to that of peer institutions.MFI managers and board members use these toolsto understand their institution’s performance in acomparative context.The third core service is to work with networks ofmicrofinance institutions (i.e., affiliate, national, regional),central banks, and researchers in general to enhance theirability to collect and manage performance indicators.MIX provides this service in a variety of ways, including1) training these organizations to collect, adjust andreport data on retail MFIs at the local level and useMIX’s performance monitoring and benchmarkingsoftware, 2) collecting data on behalf of a network,and 3) providing customized data analysis to comparemember institutions to peer groups. This service tonetworks, regulatory agencies, and researchers allowsMIX to reach a wider range of MFIs in order to improvetheir financial reporting.New ParticipantsInstitutions that wish to participate in the Bulletindatabase should contact: info@themix.org, Tel +1 202659 9094, Fax +1 202 659 9095. Currently, the onlycriterion for participation is the ability to fulfill fairlyonerous reporting requirements. MIX reserves theright to establish minimum performance criteria forparticipation in the Bulletin database.SubmissionsThe Bulletin welcomes submissions of articles andcommentaries, particularly regarding analyticalwork on the financial and/or social performance ofmicrofinance institutions. Submissions may includereviews or summaries of more extensive workpublished elsewhere. Articles should not exceed3000 words. To submit an article, please contactDevang Sheth at dsheth@themix.org.DisclaimerNeither MIX nor MIX’s funders accept responsibilityfor the validity of the information presented orconsequences resulting from its use by third parties.

Table of ContentsFeature ArticlesIndian MFIs: Growth for Old and New Institutions Alike.......................................................................... 1Devyani Parameshwar, Neha Aggarwal, Roberto Zanchi, Sagar Siva ShankarMicrofinance Default Rates in Ghana: Evidence from Individual-Liability Credit Contracts ...... 8Gerald Pollio and James ObuobieBulletin TablesIntroduction to the Peer Groups and Tables..............................................................................................15AppendixAppendix: Notes, Adjustments and Statistical Issues..............................................................................17

Letter from the PublisherDear Readers,We are happy to bring you the 20 th Edition of theMicroBanking Bulletin. Included in this issue are aretrospective benchmark data set focused on a detailedreview of performance trends in the microfinanceindustry and articles that address important issues formicrofinance practitioners.For the first time, the benchmarking data set is availablein 6 languages – English, Bahasa Indonesia, Chinese,Spanish, French and Russian. Additionally, you canview each indicator in the benchmarks data set acrossa broad range of metrics that capture the distributionof performance levels: Median, 25 th Percentile, 75 thPercentile, Maximum, Mean, Minimum, and StandardDeviation.This MicroBanking Bulletin issue also features thefollowing articles:• India MFIs: Growth for Old and NewInstitutions Alike: Devyani Parameshwar et alexplore the Indian microfinance market whosestructure and dynamics are vastly differentfrom what they were two years ago. Theindustry emerged from the financial crisis moreconsolidated and dominated by fewer largecommercial players.• Microfinance Default Rates in Ghana: GeraldPollio and James Obuobie present quantitativeevidence on the factors that affect repaymentsamong MFIs in Ghana that use the individualliabilityloan contract.Finally, I want to share with you important changes tothe MicroBanking Bulletin (MBB). Starting with IssueNo. 20, the MicroBanking Bulletin will be deliveredonline only, reflecting a changing readership anduser feedback. The move online will enable additionalchanges to the MBB, most important of which will be aswitch from semi-annual publication to more frequentdelivery. Our commitment to data-driven, high-qualityindustry insight for practitioner use remains strongand we believe that these changes will improve MBB’sability to deliver against this goal.At this time, I would like to acknowledge with deep andsincere thanks the contributions of our entire EditorialBoard. Beginning in September 2010, we will be movingthe editorial review process in-house to accommodatethe structural changes listed above. We look forwardto begin bringing you MBB’s data and articles in a newformat later this year.Sincerely yours,Marten LeijonPublisher, MicroBanking BulletinExecutive Director, MIX

FEATURE ARTICLESMICROBANKING BULLETIN, Issue 20, SeptemBER 2010Indian MFIs: Growth for Old andNew Institutions AlikeDevyani Parameshwar, Neha Aggarwal, Roberto Zanchi,Sagar Siva ShankarIntroductionThe Inverting the Pyramid series was launchedby Intellecap in 2007 as an attempt to capture thegrowth of the microfinance industry in India on anannual basis and track the efforts made, successachieved and challenges that remain. Every year, itmaps the microfinance landscape in India, identifieskey highlights of the year, explores strides madein addressing the huge demand-supply gap thatexists and analyzes the performance of MFIs. Further,it identifies key drivers for future growth andsustainability of this industry, its capital needs and itsrisks and priorities in the short to medium term.The third edition, Indian Microfinance: Comingof Age finds India at the center of global attention,the most closely watched microfinance market inthe world. While its large unbanked population is asignificant contributor to this attention, its fast growth,high investor interest, planned IPOs and continuedstrong operational and financial performance havealso piqued the interest of investors, thought leaders,media and the public alike.This MicroBanking Bulletin issue includes the thirdchapter of Intellecap’s publication, entitled IndianMFIs: Growth for Old and New Institutions Alike.This chapter captures an Indian microfinancemarket whose structure and dynamics are vastlydifferent from what they were two years ago.The industry emerged from the financial crisismore consolidated - the market that was madeof numerous, small and medium sized, non-profitplayers gave way to one dominated by fewer largecommercial players that are successfully attractingequity and debt capital, human resources andclients at a fast rate.The traditional tier classification of MFIs based on theirportfolio size fails to capture the emerging dynamicsand activity in the market, which led Intellecapresearchers to create an alternate classification thataccounts for growth rates. Using this method, theauthors identify three major classes of MFIs: theLeaders, the Moderates and the Young Turks. In chapterthree, the growth and performance of each of thesesegments is examined in greater detail. Towards this,Intellecap uses financial and operational data of asample of 29 MFIs in the country that constitute 80%of the market by portfolio outstanding.(Refer to Page 7 for a full list of abbreviations tobe used in this article)Dramatic Industry EvolutionOver the last two years the organization and dynamicsof Indian microfinance has evolved. The industryemerged from the financial crisis more consolidated;the market that was made-up of numerous, small andmedium sized, non-profit players gave way to onedominated by fewer large commercial players that aresuccessfully attracting equity and debt capital, humanresources and clients at a fast rate. New age MFIs, withprofessional management teams and aggressivegrowth plans, are also managing to attract equityand debt, and successfully weather the crisis. In sharpcontrast, some of the older and smaller players, manyof which failed to attract equity or transform legalstructures before the crisis, are shrinking or slowingtheir growth, sometimes losing human resources andclients to the bigger players. The NBFCs have grown tocapture 81% of the market, attracting unprecedentedinvestor interest and media attention and havecreated a self-regulatory body, MFIN.In the new market scenario, for-profit MFIs canbe categorized into three groups based on theirportfolio growth rate, organizational age andportfolio size: Leaders, Moderates and Young Turks.While the aggregate portfolio of the industry hasgrown by ~103% since 2008, the growth in portfoliosof individual MFIs has been variable. The Leaders grewbetween one and two times the industry average, theModerates grew at a rate below the industry averageand the Young Turks grew more than twice the rate ofthe industry.Microfinance Information eXchange, Inc

FEATURE ARTICLESMICROBANKING BULLETIN, Issue 20, SEpteMBER 2010Figure 1: Sample MFIs by Growth Rates, Age & Portfolio SizeSamhitaSahayataGrowth‘08–’09Not-for-profit MFIsEquitasNBFCsSwadhaarUjjivanIDFMimoSaralaArohan200%AsmithaSpandanaGKBandhan-NBFCSHAREBasixSKSNEED100%AWSSKDRDP1995CashporCresaNote: The size of the circle denotes GLP (figure not to scale)Gram UtthamSahatra UttarayanIIRMVFSBiswaBWDA2005YearBSSESAFMFI ClassLeadersModeratesYoung TurksDescriptionThis group includes SKS, Spandana, SHARE, Bandhan, Asmitha, BASIX and Grameen Koota - thelargest NBFC MFIs in the country, together managing 65% of the industry portfolio. SKS is thelargest and fastest growing MFI, Spandana enjoys high efficiency and robust bottom-lines, SHAREwas the first MFI to acquire an NBFC license, Bandhan has demonstrated scale despite havinghad a non-profit structure for a long period, Asmitha is the youngest of the Leaders, BASIX is thefirst MFI to start as a NBFC and provide integrated livelihood support services to its clients andGrameen Koota has emerged as a strong regional player.This group includes MFIs that have transformed from non-profits to NBFCs and havedemonstrated moderate growth. MFIs such as BISWA, BSS, BWDA, ESAF and VFS are part ofthis group. Many of these MFIs started as NGOs and maintained their non-profit legal statusfor a longer time than the Leaders, but eventually transformed into NBFCs in order to ensuresustainability, wider access to funds and achieve greater outreach.These are high growth young MFIs promoted by teams with prior experience in banking,financial services or microfinance. This group includes MFIs such as Arohan, Equitas, Mimo,Sahayata, Sarala, Swadhaar and Ujjivan. Many of these institutions started their operations inunexplored or underserved geographies (refer Figure 3 in Chapter one), are backed by strongsenior management and governance and are highly capitalized. These MFIs have demonstratedthe robustness of their business model within two to three years of starting operations, and theirgrowth rates have outpaced those of the Leaders primarily because of their small size, althoughUjjivan and Equitas have managed to attain a significant size in a short span of time.The following analysis utilizes data for the 19 NBFC MFIs in the Intellecap sample.Microfinance Information eXchange, Inc

MICROBANKING BULLETIN, Issue 20, SEptEMBER 2010On average, the portfolio of the Young Turks hasgrown three to five times between 2008 and 2009,with Equitas and Sahayata showing extremely highportfolio growth of 13x and 16x respectively. TheLeaders have also exhibited consistent performance,averaging 122% growth, with Bandhan-NBFC showingthe highest growth at 183%, followed by Spandana at156%. Growth rates of the Moderates have been lowerwith a 37% average growth, and only VFS showingclose to 100%.Leaders and Young Turks AttractInvestorsSince 2007, when equity investments in the sectortook off, investors have consistently shown apreference for MFIs that have high growth potential.Thus, the Leaders and the Young Turks dominateinvestor pipelines.Infusion of equity has fuelled the growth of the YoungTurks, which have grown 416% and 627% year onyear in 2008 and 2009 respectively. While the GLP ofthe Leaders is 14.7 times that of the Young Turks, theirequity base in merely 5 times that of the Young Turks,indicating that the Young Turks have been able to instilinvestor confidence and attract equity early in theirlife cycle. These MFIs also dominated the scene withhighest number of equity investments, seven in FY2009 absorbing INR 233 crores (USD 50.6 mn). Whilethe Moderates also received six equity investments, 58the amount was much lower at INR 137 crores(USD 29.8 mn). SKS Microfinance alone raisedINR 366 crores (USD 79.6 mn) pushing the totalof Leaders to three equity investments totalingINR 441 crores (USD 95.8 mn).Decrease in Spread; MFIs Bear Risein Financial CostsThe media has been critical of the microfinanceindustry in India recently: The Wall Street Journal, 59 TheEconomist and The Economic Times have questionedthe industry for its high interest rates, accumulationof profits, and contribution to the over-indebtednessof clients.Intellecap’s analysis shows that these critiques arenot entirely sound. While the incomes of MFIs haveincreased, the growth in income is not proportionalto the rising the cost of debt, indicating that MFIsare absorbing part of the rising costs. The yield for allMFIs in the sample is 29% for 2009, up from 21.5%FEATURE ARTICLESin 2007. 60 Some of the drivers that allow for greateryields are:1. Increasing non-interest income through feesand other credit-related activities such asselling of insurance, remittance services andlivelihood promotion activities2. Recognizing premiums upfront for portfoliobuyouttransactions 613. Levying higher interest rates in newgeographies, while remaining competitive inmature marketsThe cost of borrowing 62 for MFIs has been consistentlygrowing, with a marked increase in 2009 because ofthe global economic slowdown and the liquidity crisis(see Figure 2). The average cost of borrowings for thesample has grown to 12.1% from 10.5% in 2008, anincrease of 15%.Figure 2: Cost of Borrowing9.0%8.9%10.7%6.5%20079.7%8.5%20082009Leaders Moderates Young Turks12.3%12.0%9.9%Until October 2008, both the repo rate 63 and thereverse repo rate 64 were kept high in order to combatinflation, amplifying the liquidity pressure on bankswhich increased their lending rates to MFIs. Althoughthe poor’s demand for credit is fairly elastic, MFIs didnot pass the entire increase in the cost of borrowing totheir clients. This finding has been validated throughinterviews with promoters and management of MFIs.With the easing of monetary policy towards the endof 2008, there has been an easing of liquidity pressuretoo, through a gradual reduction in the cost of debtfor Indian MFIs.Figure 3 shows the ratio of total income of an MFI tofinancial expenses, which appears to be declining forboth Leaders and the Young Turks. The Leaders absorbsome part of the increase in financial expenses anddo not pass the entire cost to the clients. Althoughthe Young Turks are still showing a high ratio, it isMicrofinance Information eXchange, Inc

FEATURE ARTICLESFigure 3: Ratio of Total Income to FinancialExpense6.34MICROBANKING BULLETIN, Issue 20, SEpteMBER 20102. Transaction model: Reducing the time spentin conducting group meetings, increasing thegroup size and maintaining higher borrowerto client ratios contribute to increasing thestaff’s payload3.302.95 2.884.063.413. Urban-Rural composition: Running operationsin urban areas leads to a higher OER, owing tohigher salary and rent costsLeaders20072008Young Turks2009comparable to that of the Leaders in 2007. It is thusexpected that the Young Turks too will follow adeclining trend.Leaders Raise the Bar for OperatingEfficiencyThe Leaders are stable in their Operating Expense Ratio(OER), achieving only minor improvements over threeyears (see Figure 4). However, based on each MFI’soperating model, region of operations and strategythere are variations within the group–Spandana isthe most efficient with an OER of 6%, while BASIX andSKS have very high OERs at 16% and 13% respectively.Factors affecting operating costs of an MFI include:1. Expansion strategy: Aggressively investingin expansion to new geographies versusdeepening engagement with existing clientsand in older geographiesFigure 4: Operating Expense as a % ofAverage GLP63.9%31.8%19.0%10.3%10.7% 10.0% 8.7% 7.8% 11.0%Leaders Moderates Young Turks2007200820094. Investments in infrastructure: higher recurringexpenditure on technology has an impact onthe cost structureThe Young Turks show very high OERs initially owingto heavy investments to fuel their growth, but rapidlyfall as these institutions grow. The Leaders have beenable to reduce their costs by taking advantage ofeconomies of scale. New MFIs typically take 4–5months or longer to operate at capacity.It is also worth noting that reduction in OER isprimarily driven by reductions in non-personnelexpenses. For all NBFCs in the sample, the personnelexpense ratio has increased from 6.1% in 2007 to6.7% in 2009; the non-personnel related operatingexpenses have reduced from 4.7% in 2007 to 3.8%in 2009.Improved Productivity DrivesOperating EfficiencyGreater efficiency also results from improved staffproductivity in terms of both volume and value. TheYoung Turks have shown a marked improvement of115% over two years in their borrowers per personnel(management and field staff ) ratio, which now standsat 246, better than the Moderates at 167 but still lowerthan the Leaders at 304. The loan officer to client ratioin the industry is significantly higher.Equitas, the current market leader in staff productivityhas 484 borrowers per staff member. This is becauseof its innovations in the operating model, basedon standardising and differentiating roles of loandisbursement and loan collection, thus allowingfield staff to handle the higher workload. The Leadersare above the 300 mark with the exception of SKSand BASIX. SKS maintains higher head office staffand technology costs by design to manage theirambitious expansion. BASIX staff members have ahigher workload given their non-traditional modelwhich includes multiple credit plus offerings and doorto door collections.Microfinance Information eXchange, Inc

MICROBANKING BULLETIN, Issue 20, SEptEMBER 2010FEATURE ARTICLESFigure 5: Average Loan OutstandingFigure 6: Return on Assets4,6237,1246,4564,8824,9895,1315,567 6,139-16.3%-10.5%-0.5%Young Turks3,362Moderates2.0%3.1%1.7%Leaders Moderates Young Turks2007 2008 2009200720082009Leaders4.4%2.9%1.7%The portfolio managed per staff also increasedby 93% since 2008 for the Young Turks, reachingINR 15.1 lakhs (USD 32,843) while for the Leaders itis INR 21.6 lakhs (USD 47,006). This improvement isa combined effect of the improvement in personnelproductivity and higher average loan outstandingper borrower, as shown in Figure 5. Moderates toohave shown an improvement, with GLP per staffincreasing by 24% from 2008 to 2009 standing at7.4 lakhs (USD 16,043).The factors contributing to the increase in loan sizesare:Figure 7: Return on Equity28%23% 24%20%14%11%LeadersModerates2007 2008 2009Young Turks-1%-26%-32%1. Graduation of clients in mature markets tohigher loan sizes2. Increased focus of MFIs on urban clients withhigher credit needs than rural clients3. Introduction of individual lending4. Increased ticket size for the first loan to newcustomers by some MFIsIncreasing ProfitabilityAs shown in Figure 6, the Leaders exhibit a consistentlyrising Return on Assets (ROA) - 4.4% in 2009. The YoungTurks on the other hand, while showing explosivegrowth rates, have not all achieved break-even, asthey are heavily investing in their expansion.A similar trend is observed in Return on Equity (RoE)where the Leaders and Young Turks are continuouslyimproving their performance. The Moderateshave shown a decline mainly because many wereundergoing transformation in 2007 and 2008, while in2009, as NBFCs, their equity base is higher than theirprevious donor capital base.The rising profitability of the Leaders is driven bytheir ability to take advantage of economies ofscale, as they are able to better leverage theirinitial investments. These MFIs are also successfullysupplementing their interest income with fee basedrevenue through insurance, managing portfolios,and other product sales. The Young Turks andModerates are expected to follow the same path inthe coming years.Deleveraging Balance SheetsWhile there has been a general fall in debt-equityratios across all MFIs, it has been most pronouncedamong the Moderates because of their transformationfrom unregulated not-for-profit entities to NBFCs withminimum capital adequacy requirements (CAR). TheYoung Turks are the least leveraged MFIs, with Ujjivanand Sahayata at less than 1x. These MFIs have largeequity bases which allow them to be less dependenton commercial debt which is difficult to access forearly stage MFIs. The next step for these MFIs is tostart leveraging their equity, which should not bedifficult given that they now have a proven record andseasoned portfolios.Microfinance Information eXchange, Inc

FEATURE ARTICLESThe debt-equity ratio in the industry is expectedto stabilize at between 4x and 5x, given the morestringent capital adequacy norms that will requireNBFCs to maintain a 15% CAR starting in 2012.Most NBFCs are already working toward complyingwith this stricter requirement, which explains theirreducing debt-equity ratios.Portfolio Quality: Deterioration inPockets, Healthy OverallWhile global PAR > 30 deteriorated from a medianof 2.2% to 4.7% during the first five months of2009, 65 the average PAR > 30 for Indian MFIsstood at

MICROBANKING BULLETIN, Issue 20, SEptEMBER 2010FEATURE ARTICLESList of AbbreviationsBCBLPBISWABSFLBSSBWDACARCAGRCGAPCPCRRDFIECAECBESAFFCRAFDIFMOFWWBFYGDPGLPHNIINRIRDAIPOKYCLACMACSMENAMFIMFINMISMIVBusiness CorrespondentBelow Poverty LineBharat Integrated Social Welfare AgencyBharatiya Samruddhi Financial LimitedBharatha Swamukti SamstheBullock-Cart Workers DevelopmentAssociationCapital Adequacy RatioCompounded Annual Growth RateConsultative Group to Assist the PoorCommercial PaperCash Reserve RatioDevelopment Finance InstitutionEastern Europe and Central AsiaExternal Commercial BorrowingEvangelical Social Action ForumForeign Contribution Regulation ActForeign Direct InvestmentThe Netherlands Development FinanceCompanyFriends of Women’s World BankingFinancial YearGross Domestic ProductGross Loan PortfolioHigh Net-worth IndividualsIndian National RupeeInsurance Regulation DevelopmentAuthorityInitial Public OfferingKnow Your CustomerLatin America and the CaribbeanMutually Aided Cooperative SocietyMiddle East and North AfricaMicrofinance InstitutionMicrofinance Institutions NetworkManagement of Information SystemMicrofinance Investment VehiclesNABARD National Bank for Agriculture and RuralDevelopmentNBFCNCDNFPNGONRIFSOERPACSPANPARPATP/BVPCOPEPLRPSLPTCRBIRMKROARoeRRBSBLPSEBISHGSLRSSISIDBISIPSKSSPVUIDUPUSDVCVFSNon-Banking Financial CompanyNon-Convertible DebenturesNot-for ProfitNon-Government OrganizationNational Rural Financial Inclusion SystemOperating Expense RatioPrimary Agricultural Cooperative SocietiesPermanent Account NumberPortfolio at RiskProfit After TaxPrice to Book ValuePublic Call OfficePrivate EquityPrime Lending RatePriority Sector LendingPass Through CertificateReserve Bank of IndiaRashtriya Mahila KoshReturn on AssetsReturn on equityRegional Rural BanksSelf Help Group-Bank Linkage ProgrammeSecurities and Exchange Board of IndiaSelf Help GroupStatutory Liquidity RatioSmall Scale IndustrySmall Industries Development Bank ofIndiaSystematic Investment PlanSwayam Krishi SangamSpecial Purpose VehicleUnique IdentificationUttar PradeshUnited States DollarVenture CapitalistsVillage Financial ServicesMicrofinance Information eXchange, Inc

FEATURE ARTICLESMICROBANKING BULLETIN, Issue 20, September 2010Microfinance Default Rates in Ghana:Evidence from Individual-LiabilityCredit ContractsGerald Pollio 1 and James ObuobieAbstractIn this paper we present evidence on the factors that affect default probabilities in individual-liability creditcontracts. The data are drawn from a for-profit microfinance lender in Ghana. Our sample consists of nearly1,000 randomly selected loans approved between 2002 and 2007, three quarters of which were repaid; asdefault is relatively rare in microfinance, borrowers who failed to repay their loans were over-sampled. Westudy the impact of demographic, business and loan characteristics on default odds. We find that repaymentis affected mainly by the number of dependents in the household, years in business, use of proceeds, loanstatus, and frequency of loan monitoring. In contrast to other studies, we find no connection between theborrower’s marital status, gender or their savings’ behavior and the likelihood of default.Microfinance, however measured, has increasedrapidly in Ghana since the start of the present decade,growing by 20-30 percent annually. MicrofinanceInstitutions (MFIs) currently provide financialservices to an estimated 15 percent of the country’stotal population compared with 10 percent for thecommercial banking sector.Rural and community banks account for the lion’sshare of MFI activity in Ghana, representing more thanhalf the total number of microfinance borrowers anda similar proportion of the sector’s total loan portfolio(Aryeetey:2008). NGOs, by contrast, are comparativelyunimportant: the average loan size is roughly onethird that provided by rural and community banksand an even smaller fraction (25 percent) of theamount borrowed from savings and loan companies.On the other hand, loan repayment rates, at a reported99 percent, are considerably higher among financialNGOs than among other microfinance providers orgovernment-sponsored lending programs; their loanloss exposure is also relatively modest.Finally, and perhaps not too surprisingly giventheir heavy dependence on donors or officialsources of finance, financial NGOs have the worstrecord of achieving either operational or financialCorresponding author: LSC London, Chaucer House, White Hart Yard,London SE1 1NX, United Kingdom.self-sufficiency, surpassed only by governmentsponsoredprograms. The clear implication here isthat without substantial subsidy, interest rates onloans provided by both NGOs and state-supportedinstitutions would be significantly higher, withan attendant negative impact on repaymentobligations.Ghana’s commercial microfinance operations have,by contrast, broadly achieved a degree of operationalefficiency that compares favorably with mediumsized African financial institutions or worldwide MFIs;even so, in terms of financial sustainability, many stillhave a long way to go. The purpose of this paperis to investigate repayment rates among MFIs thatfollow the individual-liability lending model; sincethis model closely approximates to that pursued bycommercial banks, a close correspondence might beexpected between the two approaches. Our analysis derives from data on loan repaymentsand borrower characteristics provided by ProCredit(Ghana), a local microfinance institution, whichoperates on the basis of individual liability lending.Jha, et al. (2004) and Bank of Ghana (2007) provides an overview ofmicrofinance in Ghana.Owing to space limitations it was not possible to provide the full setof results. Readers interested in obtaining our findings can email thesenior author at the email address given above.Microfinance Information eXchange, Inc

MICROBANKING BULLETIN, Issue 20, September 2010Individuals and micro-entrepreneurs that apply forProCredit loans proceed through three stages prior toobtaining approval.i. Preliminary ScreeningIn this stage, loan applicants make contact withthe institution and are carefully screened andasked to answer specific questions regardingthe status of their business and householdaccounts, in order to establish whether theyqualify under ProCredit‘s eligibility guidelines.ii. Loan Proposal and Credit CommitteeLoan applicants are assigned to specific loanofficers. Applicants undergo a further reviewto verify the information taken at the initialstage, and a visit to the applicant’s businessesand household is arranged. The informationthus developed is organized into a formalloan proposal and presented to the lendinginstitution’s credit committee for approval.The loan amount and tenure are confirmedconditional on the adequacy of the cash flowsgenerated by the borrower’s business, sufficientpersonal collateral and guarantors agreeing toco-sign the loan agreement.iii. Monitoring and RepaymentAfter disbursement, the account officerfrequently visits the borrower’s business toensure that the proceeds are being used forthe specific purpose(s) for which the loanwas granted, and to remind borrowers oftheir next repayment date. Borrowers whomiss payments are pressured at this stage; ifthe arrears continue, legal action is initiatedagainst the borrower and guarantor(s) torecover any amounts owed, but usually afterthe designated collateral has been seized andliquidated.1. Loan Sample DataThe data used here are drawn from ProCredit’slending files. Six of the bank’s twelve branches,including one located outside the national capital,were selected for the study; the remaining brancheswere all established fairly recently and thus haverelatively small loan portfolios.The total sample consists of 960 loans made to localbusinessmen from the database of the institution’s sixbranches, five of which are located in Accra and onein Kumasi. The sample consists of loans granted andFEATURE ARTICLESrepaid (or not) between January 2002 and December2007, and comprise 160 loans from each branch. Thesample thus consists of 720 repaid and 240 defaultedloans, with individual loans in each categorychosen randomly. To obtain a fair representationof the characteristics of defaulted borrowers, wedeliberately over-sampled this category, a decisionmotivated by the low actual default rate. The sampledataset was audited for errors and omissions toensure consistency and uniformity.Twenty-four borrower characteristics were extractedfrom the data and grouped into four main categoriescross-classified by borrower status (Table 1).(1) Individual borrowers’ household characteristics(gender, age, marital status, household income notgenerated from either the business or earnings ofdependants). Dependants consist of the number ofpeople in the household who rely on the businessincome. Households with fewer dependants have asmaller claim on their business income, which shouldserve to reduce the default rate. The borrower’smarital status is also expected to lower the likelihoodof default; working spouses generate an independentincome, thus increasing the financial resourcesavailable to service the loan, in contrast to borrowerswho are single, divorced or widowed, where thereare no supplementary earnings. We also expect theprobability of the loan being repaid to increase ifthe borrower is a woman, in keeping with empiricalevidence to that effect.(2) Savings behavior (default and non-defaultborrowers’ saving behavior during the term of theirrespective loans). Borrowers who save during the termof the loan build up a cash reserve that can be used toservice the debt during periods when the business isfacing liquidity difficulties. The presence of savings isexpected to increase the probability of the loan beingrepaid.(3) Business characteristics: (business type, age ofthe business, location of the business [Branch]).The number of years the borrower has been in thesame business should increase the probability ofthe loan being repaid; there is again ample evidenceshowing that established businesses are less proneto experiencing financial distress than are newlycreated ventures.(4) Loan characteristics (loan amount in Ghana cedis,loan purpose, loan monitoring, collateral type andvalue in Ghana cedis, term of loan, loan status, numberof guarantors [co-signers]). Each loan is unique inMicrofinance Information eXchange, Inc

FEATURE ARTICLESterms of loan amount, tenure, collateral and thenumber of co-signers who act as guarantors for thecredit. A greater number of guarantors and a highcollateral-to-loan ratio should be consistent withlower default risk; so, too, should the intensity withwhich loans are monitored (but see below). Loanstatus indicates whether the borrower had obtainedprior loan(s). Loan tenures are of variable length,though longer maturities appear consistent with alower risk of default; for a given interest rate, longermaturities imply lower periodic installments. Finally,loans used for working capital or stock accumulationappear less risky than then those used for acquisitionof fixed assets.A larger percentage of defaulted borrowers inour sample are single or divorced and younger onaverage, and a relatively larger fraction is madeup of women (48 percent in the defaulted groupcompared with 43 percent among borrowers whorepaid their loans). Defaulted borrowers also havea relatively larger number of dependents thantheir non-defaulted counterparts. When the varioushousehold characteristics are subject to formalstatistical analysis, the only variable shown todiffer significantly is the borrower’s age; defaultedborrowers on average were eight years younger thannon-defaulted borrowers.The majority of borrowers in the default categoryhave less than five years experience running theirbusinesses. Borrowing for the purpose of adding tostock accounted for 50.1 percent of all loans; workingcapital loans or loans to purchase fixed assetsconstitute 17.9 percent and 32 percent, respectively,of the total sample. Statistical analysis confirmsthat number of years in business is an importantdeterminant of default, in contrast to the purpose ofthe loan, though a higher incidence of working capitalloans among repaid loans is marginally significantindicating a favorable impact on the probability ofrepayment.Loans offered fall into two broad categories: thoseabove GHC1,000 are described as loans to small andmedium enterprises (SME) and micro loans, whileloans below GHC1,000 are known as ‘express’ loans.The majority of loans in the sample were expressloans (55.2 percent), with a greater proportion ofdefaulted loans (54.2 percent) falling into the microand SME loan categories; this compares with repaidexpress loans of 57.6 percent. Loan status indicateswhether the borrower is a new client obtaining his/her first loan or is a repeat borrower; 63 percent ofborrowers fall into the former category. Interestingly,MICROBANKING BULLETIN, Issue 20, September 2010the majority of defaulted borrowers were repeat notnew clients, a statistically significant finding.Collateral coverage is measured as the ratio of thecollateral value to the loan amount. For the majorityof clients in the sample (60.5 percent) this ratioexceeded 150 percent; coverage differences arehighly significant. Each loan was guaranteed by atleast one guarantor, who also acted as a co-signer ofthe loan contract; 56.5 percent of borrowers in thetotal sample had their loans guaranteed by at leastone guarantor. Among borrowers that repaid theirloans, nearly one half had more than one guarantor.Loan monitoring is part of the loan cycle: loan officersvisit the residence and business of each borrowerbefore and after loans are made to ensure that theproceeds are used only for the stated purpose andthat the business/project is being run efficiently.Regular visits also serve to strengthen the relationshipwith the borrower, encouraging repayment whilesimultaneously gathering information concerningthe state of the business and household finances, allof which should be consistent with a lower defaultrate. By contrast, more frequent visits could betaken as evidence that borrowers are experiencingrepayment difficulties, higher frequency indicatinggreater severity. The data appear more consistentwith the second interpretation: defaulted loans weremonitored more frequently than repaid loans, whilestatistical analysis confirms that the differences weresignificant.Loan maturities range from 4-12 months, thoughfixed asset loans are sometimes extended for up to18 months. Sector indicates whether the borrowers’main business is in services, trade (buying andselling), or production (manufacturing). The majorityof borrowers operated in the trade sector (58.8percent), with an average loan maturity of up to12 months. Statistical analysis indicates that businesssector does not matter, though the higher incidenceof default among firms operating in the trade sectoris marginally significant. Loan maturities, too, donot appear to be important, with the slightly higherpercentage of shorter maturities among defaultedloans being statistically insignificant. Finally, whilethe data indicate that the percentage of borrowerswho saved over the life of the loan was higheramong repaid than defaulted loans, the differencesare insignificant.Another way of assessing the extent to whichborrower, business and loan characteristics affectrepayment is to present Odds Ratios (OR) as shown10Microfinance Information eXchange, Inc

MICROBANKING BULLETIN, Issue 20, September 2010FEATURE ARTICLESFigure 1: Odds Ratio Classified by Borrower Characteristics1.801.601.401.201.000.800.600.40Series 10.200.00MarriedMore than 40-yearsFemaleMore than 3-dependantsOver 5-years in businessTradeServiceProductionMicro & SME loansOver 12-months loan maturityStockWorking CapitalFixed AssetOver 150% Collater-Loan ratioMore than one guarantorNew loan clientsNo Other Non-Business IncomeNo SavingsNo Monitoringin Figure 1. On this basis, borrowers having more thanthree dependents and operating in the service sectorobtained larger loans with longer maturities, used theproceeds to finance fixed investments, lacked nonbusinessincome, and were at greater risk of default.These findings confirm the bivariate results.2. Multivariate ResultsPair-wise comparisons are illustrative, but fail totake proper account of the interactions that existamong the explanatory variables. Given that themain rationale for this study is to identify andanalyze the factors that influence loan repaymentrates in microfinance institutions, the way forwardis to employ multivariate statistical proceduresbetter able to achieve that objective. The techniquechosen, logistic regression, is perhaps the best ofseveral statistical procedures that can be used whenanalyzing conditional data.The OR is a way of comparing whether the probability of an event isthe same for two groups, and is measured by comparing the ratio ofthe odds of an event occurring (say, default) in one group comparedto the odds of it occurring in another group. An odds ratio of oneimplies that the event is equally likely in both groups. An OR greaterthan one indicates the event is more likely in the first group, while anOR less than one implies the reverse.In the present study, default probability, thedependent variable, is ascribed a value 1 if a givenloan defaulted and 0 otherwise, with default relatedto the various independent variables enumeratedabove. A direct logistic regression was fitted for eachof the independent variables, except for the variousbranches (Tema, Madina, Kaneshie, Tudu, Kokomlemleand Suame) that were used in this study. Theestimation results (not shown) indicate that seven ofthe independent variables are statistically significantat the 5 percent level or higher; other household,business and loan characteristics did not have anysignificant effect on the probability of loan default.1. Other non-business income (OR = 0.5793). A unitincrease in household non-business income leads to areduction in the relative ratio of the default probabilityto repayment by a factor of 0.5793; that is, as thepresence of other income separate from businessincome increases, the rate of credit default declinesby 42 percent. Given that the majority of borrowerswere married, this suggests that in most instancestheir partners either operated income-generatingbusinesses or were working in paid employment. ThisThe zero-order correlation between marital status and non-businessis income is positive and statistically significant.Microfinance Information eXchange, Inc11

FEATURE ARTICLESfinding is consistent with a study undertaken amongborrowers in Caja Los Andes, Bolivia, which indicatesthat borrowers with higher non-business incomeare less likely to default on their loan obligations(Vogelgesang: 2003).2. Loan status (OR = 0.1802). The OR implies an82 percent decline in the default rate among newborrowers compared to repeat borrowers. This mayreflect an incentive effect, with access to future loansdependent upon successful repayment of the currentloan. Knowing this, new borrowers prove themselvesto be a good credit risk, a finding consistent withArmendariz and Morduch (2000), Bolton andSharfstein (1990), and Churchill (1999) amongmicrofinance institutions that employ individualliabilityschemes. The result is also consistent withVogelgesang (2003), who shows that loan repaymentrates among repeat borrowers deteriorate comparedto new borrowers.3. Working capital (OR = 0.5126). Loans used forthe purpose of augmenting working capital reducedefault probabilities by 49 percent; this compareswith an increased default rate of 62 percent for loansused to finance fixed investment [see below (8)].4. Guarantors (OR = 0.3732). A unit increase in thenumber of guarantors produces a decline in thedefault rate by 63 percent. This may be due to socialpressures that guarantors bring to bear on recalcitrantborrowers, and may also be seen as social collateralwith its impact on loan repayment. This result isconsistent with Gine and Karlan (2006), who show ina related study that the use of collateral coupled withsocial pressure among borrowers reduces defaultwhile increasing repayment; it is also consistentwith the findings of a study undertaken in Bolivia(Schreiner: 1999).5. Number of years in business (OR = 0.7176). Asthe number of years a borrower has been in businessincreases, the probability of default declines by28 percent. This confirms that as borrowers gaincommercial experience, the resulting improvedproductivity leads to a significant reduction inthe likelihood of default compared to their lessexperienced counterparts. Alternatively, the effectmay indicate that established businesses, withtheir assured revenues and diversified cash flows,represent better credit risks than younger firms.There is considerable evidence that firms withlong operating histories are less prone to financialdistress than are more recently establishedbusinesses.MICROBANKING BULLETIN, Issue 20, September 20106. Collateral to loan ratio (OR = 0.8437). A unitincrease in the collateral demanded by lenders assecurity for the loan lowers the likelihood of default by16 percent, a finding consistent with (Villas-Boas andSchmidt-Mohr: 1999), who argue that as competitionincreases, so too does the demand for additionalcollateral by MFIs. On the other hand, the variable issignificant at only around the 10 percent level.7. Number of dependants (OR = 1.2234). Foreach additional dependant in the household theprobability of loan default increases by about22 percent. As potential claims against businessincome increase, this is likely to encourage thediversion of resources to direct household purposes(paying school fees, funeral pledges, or other socialcommitments).8. Fixed assets (OR = 1.6180). Loans made for thepurpose of acquiring fixed assets increase thelikelihood of default by 62 percent, a result thatappears to connect with the relatively long gestationbefore fixed investments (machinery, plant andbuilding) generate a satisfactory cash flow. Comparedwith loans used for inventory investment, defaultis reduced by 20 percent though this effect was notstatistically significant.9. Monitoring (OR = 1.4786). Monitoring increasesthe likelihood of default by 48 percent. This maybe due to excessive pressure from the institutions’agents encouraging borrowers to invest in highriskprojects in order to generate higher cash flowsto repay the loan. It may also reflect ‘collusion’between loan officers and borrowers; evidence ofsuch behavior is known, or perhaps it may be due tooutright fraud (Todd: 1996).To test the robustness of the estimated coefficientsand by extension the odds ratios, alternative logisticregressions were run that excluded all of thestatistically insignificant variables. Also, variablesthat were closely correlated with each other werealternated to determine which had the greaterability to classify and predict default. Finally, branchdummies were introduced to control for regional orneighborhood effects.The alternative regressions were consistent witheach other and with those estimated using all ofthe independent variables. Nor can any systematicdifferences be detected in the pattern of branchlending, suggesting that screening and creditprocedures were applied consistently and uniformly.By any of the widely used goodness of fit criteria, the12Microfinance Information eXchange, Inc

MICROBANKING BULLETIN, Issue 20, September 2010results are virtually identical; nor do the alternativespecifications materially alter the percentage ofobservations correctly classified.All in all, we conclude that the probability of defaultincreases with the number of dependents, whetherthe proceeds are used to acquire fixed assets, andthe frequency of monitoring, and decreases withthe availability of non-business income, years inbusiness, the number of guarantors, whether theproceeds were used for working capital purposes,and whether the client is a first time borrower. Theratio of collateral-to-loan value is also associatedwith an increase in the repayment rate, though noneof the estimated coefficients are significant at the0.05 level of higher.ReferencesAryeetey (2008): “From Informal Finance to FormalFinance in Sub-Saharan Africa: Lessons from LinkageEfforts,” Paper presented at the High Level Seminaron African Finance for the 21st Century , IMF andJoint Africa Institute, Tunis, Tunisia (March).Armandariz and Morduch (2000): ”MicrofinanceBeyond Group Lending” Economics of Transition, 8(401-420).Bank of Ghana (2007): “A Note on Microfinance inGhana, Research Department Working Paper WP/BOG-07/01 (August).FEATURE ARTICLESBolton and Sharfstein (1990): “A Theory of PredationBased on Agency Problems in Financial Contracting,”American Economic Review. 80 (93-106)Churchill (1999): Client-focused Lending: The Art ofIndividual Lending, (Calmeadow).Gine and Karlan (2007): Group versus IndividualLiability: A Field Experiment in the Philippines, YaleUniversity Economic Growth Centre Working Paper 940(May).Jha, Negi and Warriar (2004): “Ghana: MicrofinanceInvestment Environment Profile,” unpublished ms.,Princeton University.Schreiner (1999): “Scoring Arrears at a Microlender inBolivia,” Journal of Microfinance 6, (70-86).Todd, H. (1996): Cloning Grameen Bank: Replicatinga Poverty Reduction Model in Indonesia, Nepal andVietnam (Intermediate Technology Publications).Villas-Boas and Schmidt-Mohr (1999): “Oligopolywith Asymmetric Information: Differentiation inCredit Markets,” The Rand Journal of Economics. 30(375–396).Vogelgesang (2003): “Microfinance in Times of Crisis:The Effect of Competition, Rising Indebtedness, andEconomic Crisis on Repayment Behavior” WorldDevelopment. 31 (2085-2114).Microfinance Information eXchange, Inc13

FEATURE ARTICLESMICROBANKING BULLETIN, Issue 20, September 2010Table 1Percentage of Individual, Household, Business and Loan Characteristics of BorrowersEXPLANATORY VARIABLES(Number of observations)MARITAL STATUS1. MARRIED2. SINGLE3. DIVORCE4. WIDOWAGE1. 20–30 YEARS2. 31–40 YEARS3. OVER 40 YEARSGENDER1. FEMALE2. MALENUMBER OF DEPENDANTS1. 0–3 DEPENDANTS2. OVER 3 DEPENDANTSYEARS IN BUSINESS1. 0–5 YEARS2. OVER 5 YEARSBUSINESS SECTOR1. TRADE2. SERVICE3. PRODUCTIONLOAN AMOUNT1. MICRO AND SME LOANS2. EXPRESS LOANSLOAN MATURITY1. 0–12 MONTHS2. OVER 12 MONTHSLOAN PURPOSE1. STOCK2. WORKING CAPITAL3. FIXED ASSETSCOLLATERAL-LOAN RATIO1. 0–150%2. OVER 150%NUMBER OF GUARANTORS1. ONE GUARANTOR2. MORE THAN ONE GUARANTORLOAN STATUS1. NEW CLIENT2. REPEAT CLIENTOTHER NON-BUSINESS INCOME1. YES2. NOSAVINGS ATTITUDE1. YES2. NOLOAN MONITORING1. YES2. NOTOTAL SAMPLE(960)60.21(578)29.27(281)5.73(55)4.79(46)18.75(180)41.46(398)39.79(382)55.31(531)44.69(429)81.77(785)18.23(175)39.27(377)60.73(583)58.65(563)23.64(227)17.71(170)44.79(430)55.21(530)96.25(924)7.75(36)50.10(481)17.92(172)31.98(307)39.48(379)60.52(581)56.46(542)43.54(418)63.67(611)36.35(349)56.67(544)43.33(416)73.02(701)26.98(259)63.85(613)36.15(347)NON-DEFAULT(720)61.81(445)27.78(200)5.28(38)5.14(37)17.22(124)41.94(302)40.84(294)43.47(313)56.53(407)82.64(598)17.36(125)25.97(187)74.03(533)58.75(423)22.92(165)18.33(132)42.36(305)57.64(415)96.53(695)3.47(25)51.39(370)19.30(139)29.31(211)36.25(261)63.75(459)52.08(375)47.72(345)70.28(506)29.72(214)59.86(431)40.14(289)72.22(520)27.78(200)62.08(447)37.92(273)DEFAULTED(240)55.42(133)33.75(81)7.08(17)3.75(9)23.33(56)39.58(95)37.09(89)48.33(116)51.67(124)79.17(190)20.83(50)79.17(190)20.83(50)58.33(140)25.84(62)15.83(38)54.17(130)45.83(110)95.42(229)4.58(11)46.25(111)15.00(36)38.75(93)49.17(118)50.83(122)69.58(167)30.42(73)43.75(105)56.25(135)47.08(113)52.92(127)75.42(181)24.58(59)69.17(181)30.83(74)14Microfinance Information eXchange, Inc

BULLETIN TABLESIntroduction to the Peer Groupsand TablesMICROBANKING BULLETIN, Issue 20, SepteMBER 2010Setting up Peer GroupsThe Bulletin Tables are designed to presentperformance benchmarks against which managersand directors of microfinance institutions can comparetheir institution’s performance with that of similarinstitution. Since the microfinance industry consistsof a range of institutions and operating environments,some with very different characteristics, an MFI shouldbe compared to similar institutions for the referencepoints to be useful.The Bulletin Tables address this issue with a peergroup framework. Peer groups are sets of programsthat have similar characteristics—similar enough thattheir managers find utility in comparing their resultswith those of other organizations in their peer group.The Bulletin Tables present peer groups on two bases:simple and compound peer groups.Simple Peer Groups look at MFIs based on asingle characteristic. This allows users to analyzeperformance based on a common factor, such asage, location or scale of operations. MFIs have beengrouped according to the following ten characteristicsfor this edition of the Bulletin:1) Age: The Bulletin Tables classify MFIs into threecategories (new, young, and mature) based onthe maturity of their microfinance operations.This is calculated as the difference betweenthe year they started their microfinanceoperations and the year of data submitted bythe institutions.2) Charter Type: The charter under which theMFIs are registered is used to classify the MFIsas banks, credit unions/cooperatives, NGOs,and non bank financial institutions.3) Financial Intermediation: This classificationmeasures the extent to which an MFIintermediates between savers and borrowers,funding its assets through mobilized deposits.It is calculated as a percentage of total assetsfunded by voluntary savings.4) Lending Methodology: Performance may varyby the way the institution delivers its loanproducts and serves borrowers. The BulletinTables present MFIs based on the primarymethodology used, determined by the numberand volume of loans outstanding.5) Outreach: Scale of outreach is measured asthe total number of borrowers served.6) Profit Status: According to their registration,MFIs are classified as ‘not for profit’ and ‘forprofit’ institutions.7) Region: MFIs are divided into five maingeographic region: Africa, Asia, Eastern Europeand Central Asia (ECA), Latin America and theCaribbean (LAC) and Middle East and NorthAfrica (MENA).8) Scale: Institutional scale is measured by thesize of an institution’s loan portfolio in USD.The measure of scale is regionalized to reflectdifferences in income levels across regions.9) Sustainability: MFIs are grouped accordingto their level of financial self-sufficiency,representing their ability to cover all costs onan adjusted basis.10) Target Market: The Bulletin Tables classifyMFIs into three categories—low-end, broad,and high-end—according to the averagebalance of loans served. For internationalcomparison, this balance is stated as apercentage of local income levels (GNI percapita).Compound Peer Groups use a more complex set ofvariables to analyze MFI performance. This createsbenchmarks where institutions have a greater numberof similar factors affecting performance.Microfinance Information eXchange, Inc15

BULLETIN TABLESThe Bulletin Tables present compound peer groupsbased on three main factors: (1) Region; (2) Scale; (3)Sustainability.Peer Group CompositionThe quantitative criteria used to categorize these groupsare summarized in the Peer Group Classifications tab ofthe 2006-2008 MFI Trend Lines Benchmarks. The entiresample of institutions that fall into these categoriesis located in the Peer Group Participants tab of the2006-2008 MFI Trend Lines Benchmarks. Confidentialitylimits the publication of names of financially selfsufficientMFIs included in the database.More detailed information about each institution canbe found in the Peer Group Participants tab of the2006-2008 MFI Trend Lines Benchmarks.Data Quality and StatisticalIssuesBecause the Bulletin tables rely primarily onself-reported data, we grade the quality of theMICROBANKING BULLETIN, Issue 20, September 2010information based on the degree to which wehave independent verification of its reliability. Thedata quality grade is not a rating of the institution’sperformance. Additionally, in the statistical tablesincluded in the Benchmarks tab of the 2006-2008MFI Trend Lines Benchmarks, the median values aredisplayed for each indicator. However, you can alsoview the 25th Percentile, 75th Percentile, Maximum,Mean, Minimum, and Standard Deviation valuesfor each indicator by selecting your choice in theupper left-hand corner of the Benchmarks tab in the2006-2008 MFI Trend Lines Benchmarks. For moredetails on both Data Quality and Statistical Issues,see Appendix.View the 2006-2008 MFI Trend LinesBenchmarks. (Note: The 2006-2008 MFI TrendLines Benchmarks are also available in BahasaIndonesia, Chinese, Spanish, French and Russian.To view in another language, select the languageof your choice in the upper left-hand corner ofthe Index tab in the 2006-2008 MFI Trend LinesBenchmarks.)16Microfinance Information eXchange, Inc

APPENDIXAppendix: Notes, Adjustments andStatistical IssuesMICROBANKING BULLETIN, Issue 20, September 2010The MicroBanking Bulletin is open to all MFIs thatare willing to disclose financial data that meeta simple quality test. Participating MFIs typicallyhave three characteristics: 1) they are willing to betransparent by submitting their performance datato an independent agency; 2) they display a strongsocial orientation by providing financial services tolow-income persons; and 3) they are able to answerall the questions needed for our analysis.Data Quality IssuesThe Bulletin has a data quality grade to represent thedegree to which we have independent verification ofan MFI’s data. Three star information (***) has beenindependently generated through a detailed financialanalysis by an independent third party, such as aCAMEL evaluation, a CGAP appraisal, or assessmentsby reputable rating agencies. Two star information(**) is backed by accompanying documentation, suchas audited financial statements, annual reports, andindependent program evaluations that provide areasonable degree of confidence for our adjustments.One star information (*) is from MFIs that have limitedthemselves to completing our questionnaire. Thesegrades signify confidence levels on the reliability ofthe information; and in no way represent a rating ofthe financial performance of the MFIs.The criteria used in constructing the statistical tablesare important for understanding and interpreting theinformation presented. Given the voluntary nature andorigin of the data, the Bulletin staff, Editorial Board andfunders cannot accept responsibility for the validity ofthe results presented, or for consequences resultingfrom their use. The data quality grade makes tentativedistinctions about the quality of data presented tous, and we include only information for which wehave a reasonable level of comfort. However, wecannot exclude the possibility of misrepresented selfreportedresults.There can also be inaccuracies in reporting the costsof financial services in multipurpose institutions thatalso provide non-financial services, in part because ofdifficulties in assigning overhead costs. These risks arehighest for younger institutions, and for institutionswith a record of optimistic statement of results. If wehave grounds for caution about the reliability of anMFI’s disclosure, we will not include its information ina peer group unless it has been externally validatedby a third party in which we have confidence.Adjustments to Financial DataThe Bulletin adjusts the financial data it receives toensure comparable results. The financial statementsof each organization are converted to the standardfinancial statement presentation used by the Bulletin.This presentation can be simpler than that usedby most MFIs; so, the conversion consists mainly ofconsolidation into fewer, more general accounts. Insome instances, and where the original accounts didnot have appropriate disclosure, the Bulletin’s accountsreveal more details on the financial service operationsthan did the originals. After this reclassification,three analytical adjustments are applied to producea common treatment for the effect of: a) inflation, b)subsidies, and c) loan loss provisioning and write-off.In the statistical tables the reader can compare theseadjusted results.InflationThe Bulletin reports the net effect of inflation bycalculating increases in expenses and revenues due toinflation. Inflation decreases the value of net monetaryassets, represented by the Bulletin as the differencebetween equity and fixed assets. This erosion in thevalue of net monetary assets is obtained by multiplyingthe prior year-end equity balance by the current-yearinflation rate. 1 Fixed asset accounts, on the other hand,are revalued upward by the current year’s inflationPotential distortions may arise from: (1) unreportedsubsidies and (2) misrepresented loan portfolio quality.1Inflation data are obtained from line 64x of the InternationalFinancial Statistics, International Monetary Fund, various years.Microfinance Information eXchange, Inc17

Appendixrate, which results in inflation adjustment income,offsetting to some degree the expense generated byadjusting equity. 2 On the balance sheet, this inflationadjustment results in a reordering of equi ty accounts:profits are redistributed between real profit and thenominal profits required to maintain the real valueof equity.MFIs that borrow from banks or mobilize savings havean actual interest expense, which is an operating cost.In comparison, similar MFIs that lend only their equityhave no interest expense and therefore have loweroperating costs. If an MFI focuses on sustainabilityand the maintenance of its capital/asset ratio, itmust increase the size of its equity in nominal termsto continue to make the same value of loans in real(inflation-adjusted) terms. Inflation increases thecost of tangible items over time, so that a borrowerneeds more money to purchase them. MFIs that wantto maintain their support to clients must thereforeoffer larger loans. Employees’ salaries go up withinflation, so the average loan balance and portfoliomust increase to compensate, assuming no increasein interest margin. Therefore, an institution that fundsits loans with its equity must maintain the real value ofthat equity, and pass along the cost of doing so to theclient. This expectation implies MFIs should chargeinterest 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 of inflationrequire businesses to use inflation-based accountingon their audited financial statements. We use a proxyof this same technique in the Bulletin. Of course, weunderstand that in countries where high or volatileinflation is a new experience, MFIs may find it difficultto pass on the full cost of inflation to clients. Theseadjustments not do reflect policy recommendations;rather, they provide a common analytical framework thatcompares real financial performance meaningfully.SubsidiesWe adjust participating institutions’ financialstatements for the effect of subsidies by presentingthem as they would look on an unsubsidized basis.These adjustments do not intend to suggest that MFIsshould or should not be subsidized. Rather, they allowthe Bulletin to see how each MFI would look withoutsubsidies for comparative purposes. Most of theparticipating MFIs indicate a desire to grow beyond2In 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.MICROBANKING BULLETIN, Issue 20, SeptEMBER 2010the limitations imposed by subsidized funding. Thesubsidy adjustment permits an MFI to judge whetherit is on track toward such an outcome. A focus onsustainable expansion suggests that subsidies shouldbe used to defray start-up costs or support innovation.The subsidy adjustment simply indicates the extentto which the subsidy is being passed on to clientsthrough lower interest rates or whether it is buildingthe MFI’s capital base for further expansion.The Bulletin adjusts for three types of subsidies:(1) a cost-of-funds subsidy from loans at belowmarketrates, (2) current-year cash donations to fundportfolio and cover expenses, and (3) in-kind subsidies,such as rent-free office space or the services ofpersonnel who are not paid by the MFI and thus notreflected on its income statement. Additionally, formultipurpose institutions, the MicroBanking Bulletinattempts to isolate the performance of the financialservices program, removing the effect of any crosssubsidization.The cost-of-funds adjustment reflects the impact of softloans on the financial performance of the institution.The Bulletin calculates the difference between whatthe MFI actually paid in interest on its subsidizedliabilities and what it would have paid at market terms. 3This difference represents the value of the subsidy,which we treat as an additional financial expense. Weapply this subsidy adjustment to the average balanceof borrowings carried by the MFI over the year. Thedecreased profit is offset by generating a “cost of fundsadjustment” account on the balance 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 the MFIdoes not pass on this subsidy, but instead uses it toincrease its equity base, the adjustment indicatesthe amount of the institution’s profits that wereattributable to the subsidy rather than operations.Loan Loss ProvisioningFinally, we apply standardized policies for loan lossprovisioning and write-off. MFIs vary tremendously3Data for shadow interest rates are obtained from line 60l ofthe International Financial Statistics, IMF, various years. Thedeposit rate is used because it is a published benchmark inmost countries. Sound arguments can be made for use ofdifferent shadow interest rates. NGOs that wish to borrow frombanks would face interest significantly higher than the depositrate. A licensed MFI, on the other hand, might mobilize savingsat a lower financial cost than the deposit rate, but reserverequirements and administrative costs would drive up theactual cost of such liabilities.18Microfinance Information eXchange, Inc

MICROBANKING BULLETIN, Issue 20, September 2010in accounting for loan delinquency. Some count theentire loan balance as overdue the day a paymentis missed. Others do not consider a loan delinquentuntil 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 defaulted loan that they have little chanceof 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 and180 days late, and 100 percent for loans over180 days late. Some institutions also renegotiate(refinance or reschedule) delinquent loans. As theseloans present a higher probability of default, weprovision all renegotiated balances at 50 percent.AppendixWherever we have adequate information, we adjustto assure that all loans are fully written off withinone year of their becoming delinquent. (Note: Weapply these provisioning and write-off policies forbenchmarking purposes only. We do not recommendthat all MFIs use exactly the same policies.) In mostcases, these adjustments are a rough approximationof risk. They are intended only to create a minimaleven playing field for cross institutional comparisonand benchmarking. Nevertheless, most participatingMFIs have high-quality loan portfolios; so, loan lossprovision expense is not an important contributorto their overall cost structure. If we felt that aprogram did not fairly represent its general levelof delinquency, and we were unable to adjust itaccordingly, we would simply exclude it from thepeer group.Table 1Financial statement adjustments and their effectsAdjustment Effect on Financial Statements Type of Institution MostAffected by AdjustmentInflation adjustment ofequity (minus net fixedassets)Reclassification of certainlong term liabilities intoequity, and subsequentinflation adjustmentCost of funds adjustmentReclassification of donationsbelow net operatingincomeIn-kind subsidy adjustment(e.g., donation ofgoods or services: linestaff paid for by technicalassistance providers)Loan loss provisioningadjustmentWrite-off adjustmentIncreases financial expense accounts on incomestatement, to some degree offset by inflation incomeaccount for revaluation of fixed assets. Generates areserve in the balance sheet’s equity account, reflectingthat portion of the MFI’s retained earnings that hasbeen consumed by the effects of inflation. Decreasesprofitability and “real” retained earnings.Decreases concessional loan account and increases equityaccount; increases inflation adjustment on incomestatement and balance sheet.Increases financial expense on income statement tothe extent that the MFI’s liabilities carry a below-marketrate of interest. Decreases net income and increasessubsidy adjustment account on balance sheet.Reduces net operating income on the income statement.Increases accumulated donations accountunder equity on the balance sheet.Increases administrative expense on income statementto the extent that the MFI is receiving subsidized ordonated goods or services. Decreases net income, increasessubsidy adjustment account on balance sheet.Usually increases loan loss provision expense onincome statement and loan loss reserve on balancesheet.On balance sheet, reduces gross loan portfolio andloan loss reserve by an equal amount, so that neithernet loan portfolio nor total assets is affected.MFIs funded more by equity than byliabilities will be hardest hit, especiallyin high inflation countries.NGOs that have very long-term, verylow-interest “loans” from internationalagencies that function moreas donations than loans, or transformedinstitutions with subordinateddebt.MFIs with heavily subsidized loans(i.e., large lines of credit from governmentsor international agenciesat highly subsidized rates).NGOs during their start-up phase.This adjustment is relatively lessimportant for mature institutions.MFIs using goods or services forwhich they are not paying a marketbasedcost (i.e., MFIs during theirstart-up phase).MFIs that have unrealistic loan lossprovisioning policies.MFIs that leave non-performingloans on their books for over a year.Microfinance Information eXchange, Inc19

ABOUT MIXThe Microfinance Information Exchange, Inc. (MIX) isthe leading provider of data, benchmarks and analysisfor the microfinance industry. Dedicated tostrengthening the microfinance sector by promotingtransparency, MIX provides detailed financial,operational and social performance data onmicrofinance institutions, in addition to generalbusiness information on investors, networks and serviceproviders associated with the industry. MIX does thisthrough a variety of publicly available platforms,including MIX Market (www.mixmarket.org) and theMicroBanking Bulletin.MIX is a non-profit company founded by CGAP (theConsultative Group to Assist the Poor) and sponsoredby CGAP, the Citi Foundation, Deutsche Bank AmericasFoundation, Omidyar Network, IFAD (International Fundfor Agricultural Development), Bill & Melinda GatesFoundation, and others. MIX is a private corporation.Visit www.themix.org for more information.Microfinance Information Exchange, Inc.1901 Pennsylvania Avenue, NW Suite 307Washington, DC 20006Tel: +1 202 659 9094Fax: +1 202 659 9095e-mail: info@themix.orgwebsite: www.themix.org

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